diff --git a/.github/labeler.yml b/.github/labeler.yml new file mode 100644 index 0000000000000..a67f78044c46a --- /dev/null +++ b/.github/labeler.yml @@ -0,0 +1,78 @@ +# https://github.com/actions/labeler + +SYCL: + - changed-files: + - any-glob-to-any-file: + - ggml-sycl.h + - ggml-sycl.cpp + - README-sycl.md +Nvidia GPU: + - changed-files: + - any-glob-to-any-file: + - ggml-cuda/** +Vulkan: + - changed-files: + - any-glob-to-any-file: + - ggml_vk_generate_shaders.py + - ggml-vulkan* +documentation: + - changed-files: + - any-glob-to-any-file: + - docs/** + - media/** +testing: + - changed-files: + - any-glob-to-any-file: + - tests/** +build: + - changed-files: + - any-glob-to-any-file: + - cmake/** + - CMakeLists.txt + - CMakePresets.json + - codecov.yml +examples: + - changed-files: + - any-glob-to-any-file: examples/** +devops: + - changed-files: + - any-glob-to-any-file: + - .devops/** + - .github/** + - ci/** +python: + - changed-files: + - any-glob-to-any-file: + - "**/*.py" + - requirements/** + - gguf-py/** + - .flake8 +script: + - changed-files: + - any-glob-to-any-file: + - scripts/** +android: + - changed-files: + - any-glob-to-any-file: + - examples/llama.android/** +server: + - changed-files: + - any-glob-to-any-file: + - examples/server/** +ggml: + - changed-files: + - any-glob-to-any-file: + - ggml.c + - ggml.h + - ggml-*.c + - ggml-*.h + - ggml-cuda/** +nix: + - changed-files: + - any-glob-to-any-file: + - "**/*.nix" + - .github/workflows/nix-*.yml + - .devops/nix/nixpkgs-instances.nix +embedding: + - changed-files: + - any-glob-to-any-file: examples/embedding/ diff --git a/.github/workflows/labeler.yml b/.github/workflows/labeler.yml new file mode 100644 index 0000000000000..368dbdbe5dccc --- /dev/null +++ b/.github/workflows/labeler.yml @@ -0,0 +1,17 @@ +name: "Pull Request Labeler" +on: +- pull_request_target + +jobs: + labeler: + permissions: + contents: read + pull-requests: write + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + with: + repository: "ggerganov/llama.cpp" + - uses: actions/labeler@v5 + with: + configuration-path: '.github/labeler.yml' diff --git a/CMakeLists.txt b/CMakeLists.txt index 78d40fdf994d0..c4a055462a4af 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -471,6 +471,8 @@ set_target_properties(ggml_v3 PROPERTIES POSITION_INDEPENDENT_CODE ON) add_library(common2 common/common.cpp common/common.h + common/sampling.cpp + common/sampling.h common/grammar-parser.h common/grammar-parser.cpp examples/llava/llava.cpp diff --git a/CMakePresets.json b/CMakePresets.json new file mode 100644 index 0000000000000..ad1af7eccebbd --- /dev/null +++ b/CMakePresets.json @@ -0,0 +1,45 @@ +{ + "version": 4, + "configurePresets": [ + { + "name": "base", + "hidden": true, + "generator": "Ninja", + "binaryDir": "${sourceDir}/build-${presetName}", + "cacheVariables": { + "CMAKE_EXPORT_COMPILE_COMMANDS": "ON", + "CMAKE_INSTALL_RPATH": "$ORIGIN;$ORIGIN/.." + } + }, + + { "name": "debug", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "Debug" } }, + { "name": "release", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "RelWithDebInfo" } }, + { "name": "static", "hidden": true, "cacheVariables": { "LLAMA_STATIC": "ON" } }, + + { + "name": "arm64-windows-msvc", "hidden": true, + "architecture": { "value": "arm64", "strategy": "external" }, + "toolset": { "value": "host=x86_64", "strategy": "external" }, + "cacheVariables": { + "CMAKE_TOOLCHAIN_FILE": "${sourceDir}/cmake/arm64-windows-msvc.cmake" + } + }, + + { + "name": "arm64-windows-llvm", "hidden": true, + "architecture": { "value": "arm64", "strategy": "external" }, + "toolset": { "value": "host=x86_64", "strategy": "external" }, + "cacheVariables": { + "CMAKE_TOOLCHAIN_FILE": "${sourceDir}/cmake/arm64-windows-llvm.cmake" + } + }, + + { "name": "arm64-windows-llvm-debug" , "inherits": [ "base", "arm64-windows-llvm", "debug" ] }, + { "name": "arm64-windows-llvm-release", "inherits": [ "base", "arm64-windows-llvm", "release" ] }, + { "name": "arm64-windows-llvm+static-release", "inherits": [ "base", "arm64-windows-llvm", "release", "static" ] }, + + { "name": "arm64-windows-msvc-debug" , "inherits": [ "base", "arm64-windows-msvc", "debug" ] }, + { "name": "arm64-windows-msvc-release", "inherits": [ "base", "arm64-windows-msvc", "release" ] }, + { "name": "arm64-windows-msvc+static-release", "inherits": [ "base", "arm64-windows-msvc", "release", "static" ] } + ] +} diff --git a/Makefile b/Makefile index d873e54bfa725..d73c6c31d892b 100644 --- a/Makefile +++ b/Makefile @@ -64,9 +64,9 @@ endif CUBLASLD_FLAGS = CUBLAS_OBJS = -OBJS_FULL += ggml-alloc.o ggml-quants.o unicode.o unicode-data.o sgemm.o common.o grammar-parser.o -OBJS_SIMPLE += ggml-alloc.o ggml-quants_noavx2.o unicode.o unicode-data.o sgemm_noavx2.o common.o grammar-parser.o -OBJS_FAILSAFE += ggml-alloc.o ggml-quants_failsafe.o unicode.o unicode-data.o sgemm_failsafe.o common.o grammar-parser.o +OBJS_FULL += ggml-alloc.o ggml-quants.o unicode.o unicode-data.o sgemm.o common.o sampling.o grammar-parser.o +OBJS_SIMPLE += ggml-alloc.o ggml-quants_noavx2.o unicode.o unicode-data.o sgemm_noavx2.o common.o sampling.o grammar-parser.o +OBJS_FAILSAFE += ggml-alloc.o ggml-quants_failsafe.o unicode.o unicode-data.o sgemm_failsafe.o common.o sampling.o grammar-parser.o #lets try enabling everything CFLAGS += -pthread -s -Wno-deprecated -Wno-deprecated-declarations @@ -499,6 +499,8 @@ llama.o: llama.cpp ggml.h ggml-alloc.h ggml-backend.h ggml-cuda.h ggml-metal.h l $(CXX) $(CXXFLAGS) -c $< -o $@ common.o: common/common.cpp common/common.h common/log.h $(CXX) $(CXXFLAGS) -c $< -o $@ +sampling.o: common/sampling.cpp common/common.h common/sampling.h common/log.h + $(CXX) $(CXXFLAGS) -c $< -o $@ console.o: common/console.cpp common/console.h $(CXX) $(CXXFLAGS) -c $< -o $@ grammar-parser.o: common/grammar-parser.cpp common/grammar-parser.h diff --git a/cmake/arm64-windows-llvm.cmake b/cmake/arm64-windows-llvm.cmake new file mode 100644 index 0000000000000..46fba65149b29 --- /dev/null +++ b/cmake/arm64-windows-llvm.cmake @@ -0,0 +1,16 @@ +set( CMAKE_SYSTEM_NAME Windows ) +set( CMAKE_SYSTEM_PROCESSOR arm64 ) + +set( target arm64-pc-windows-msvc ) + +set( CMAKE_C_COMPILER clang ) +set( CMAKE_CXX_COMPILER clang++ ) + +set( CMAKE_C_COMPILER_TARGET ${target} ) +set( CMAKE_CXX_COMPILER_TARGET ${target} ) + +set( arch_c_flags "-march=armv8.7-a -fvectorize -ffp-model=fast" ) +set( warn_c_flags "-Wno-format -Wno-unused-variable -Wno-unused-function -Wno-gnu-zero-variadic-macro-arguments" ) + +set( CMAKE_C_FLAGS_INIT "${arch_c_flags} ${warn_c_flags}" ) +set( CMAKE_CXX_FLAGS_INIT "${arch_c_flags} ${warn_c_flags}" ) diff --git a/cmake/arm64-windows-msvc.cmake b/cmake/arm64-windows-msvc.cmake new file mode 100644 index 0000000000000..c77631420ce84 --- /dev/null +++ b/cmake/arm64-windows-msvc.cmake @@ -0,0 +1,6 @@ +set( CMAKE_SYSTEM_NAME Windows ) +set( CMAKE_SYSTEM_PROCESSOR arm64 ) + +set( target arm64-pc-windows-msvc ) +set( CMAKE_C_COMPILER_TARGET ${target} ) +set( CMAKE_CXX_COMPILER_TARGET ${target} ) diff --git a/common/common.cpp b/common/common.cpp index 574b2037fde4e..ed977fc1889de 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -74,7 +74,11 @@ using json = nlohmann::ordered_json; -int32_t get_num_physical_cores() { +// +// CPU utils +// + +int32_t cpu_get_num_physical_cores() { #ifdef __linux__ // enumerate the set of thread siblings, num entries is num cores std::unordered_set siblings; @@ -143,9 +147,9 @@ static bool is_running_on_efficiency_core(void) { return core_type == intel_atom; } -static int count_math_cpus(int cpu_count) { +static int cpu_count_math_cpus(int n_cpu) { int result = 0; - for (int cpu = 0; cpu < cpu_count; ++cpu) { + for (int cpu = 0; cpu < n_cpu; ++cpu) { if (pin_cpu(cpu)) { return -1; } @@ -163,16 +167,16 @@ static int count_math_cpus(int cpu_count) { /** * Returns number of CPUs on system that are useful for math. */ -int get_math_cpu_count() { +int32_t cpu_get_num_math() { #if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__) - int cpu_count = sysconf(_SC_NPROCESSORS_ONLN); - if (cpu_count < 1) { - return get_num_physical_cores(); + int n_cpu = sysconf(_SC_NPROCESSORS_ONLN); + if (n_cpu < 1) { + return cpu_get_num_physical_cores(); } if (is_hybrid_cpu()) { cpu_set_t affinity; if (!pthread_getaffinity_np(pthread_self(), sizeof(affinity), &affinity)) { - int result = count_math_cpus(cpu_count); + int result = cpu_count_math_cpus(n_cpu); pthread_setaffinity_np(pthread_self(), sizeof(affinity), &affinity); if (result > 0) { return result; @@ -180,108 +184,103 @@ int get_math_cpu_count() { } } #endif - return get_num_physical_cores(); + return cpu_get_num_physical_cores(); } -void process_escapes(std::string & input) { - std::size_t input_len = input.length(); - std::size_t output_idx = 0; +// +// CLI argument parsing +// - for (std::size_t input_idx = 0; input_idx < input_len; ++input_idx) { - if (input[input_idx] == '\\' && input_idx + 1 < input_len) { - switch (input[++input_idx]) { - case 'n': input[output_idx++] = '\n'; break; - case 'r': input[output_idx++] = '\r'; break; - case 't': input[output_idx++] = '\t'; break; - case '\'': input[output_idx++] = '\''; break; - case '\"': input[output_idx++] = '\"'; break; - case '\\': input[output_idx++] = '\\'; break; - case 'x': - // Handle \x12, etc - if (input_idx + 2 < input_len) { - const char x[3] = { input[input_idx + 1], input[input_idx + 2], 0 }; - char *err_p = nullptr; - const long val = std::strtol(x, &err_p, 16); - if (err_p == x + 2) { - input_idx += 2; - input[output_idx++] = char(val); - break; - } - } - // fall through - default: input[output_idx++] = '\\'; - input[output_idx++] = input[input_idx]; break; +void gpt_params_handle_model_default(gpt_params & params) { + if (!params.hf_repo.empty()) { + // short-hand to avoid specifying --hf-file -> default it to --model + if (params.hf_file.empty()) { + if (params.model.empty()) { + throw std::invalid_argument("error: --hf-repo requires either --hf-file or --model\n"); } - } else { - input[output_idx++] = input[input_idx]; + params.hf_file = params.model; + } else if (params.model.empty()) { + std::string cache_directory = fs_get_cache_directory(); + const bool success = fs_create_directory_with_parents(cache_directory); + if (!success) { + throw std::runtime_error("failed to create cache directory: " + cache_directory); + } + params.model = cache_directory + string_split(params.hf_file, '/').back(); + } + } else if (!params.model_url.empty()) { + if (params.model.empty()) { + auto f = string_split(params.model_url, '#').front(); + f = string_split(f, '?').front(); + f = string_split(f, '/').back(); + params.model = "models/" + f; } + } else if (params.model.empty()) { + params.model = DEFAULT_MODEL_PATH; } +} - input.resize(output_idx); +bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { + bool invalid_param = false; + std::string arg; + const std::string arg_prefix = "--"; + llama_sampling_params & sparams = params.sparams; + + for (int i = 1; i < argc; i++) { + arg = argv[i]; + if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) { + std::replace(arg.begin(), arg.end(), '_', '-'); + } + if (!gpt_params_find_arg(argc, argv, arg, params, i, invalid_param)) { + throw std::invalid_argument("error: unknown argument: " + arg); + } + if (invalid_param) { + throw std::invalid_argument("error: invalid parameter for argument: " + arg); + } + } + + if (params.prompt_cache_all && + (params.interactive || params.interactive_first || + params.instruct)) { + + throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n"); + } + + gpt_params_handle_model_default(params); + + if (params.escape) { + string_process_escapes(params.prompt); + string_process_escapes(params.input_prefix); + string_process_escapes(params.input_suffix); + string_process_escapes(sparams.cfg_negative_prompt); + for (auto & antiprompt : params.antiprompt) { + string_process_escapes(antiprompt); + } + } + + if (!params.kv_overrides.empty()) { + params.kv_overrides.emplace_back(); + params.kv_overrides.back().key[0] = 0; + } + + return true; } bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { bool result = true; try { if (!gpt_params_parse_ex(argc, argv, params)) { - gpt_print_usage(argc, argv, gpt_params()); + gpt_params_print_usage(argc, argv, gpt_params()); exit(0); } } catch (const std::invalid_argument & ex) { fprintf(stderr, "%s\n", ex.what()); - gpt_print_usage(argc, argv, gpt_params()); + gpt_params_print_usage(argc, argv, gpt_params()); exit(1); } return result; } -bool parse_kv_override(const char * data, std::vector & overrides) { - const char * sep = strchr(data, '='); - if (sep == nullptr || sep - data >= 128) { - fprintf(stderr, "%s: malformed KV override '%s'\n", __func__, data); - return false; - } - llama_model_kv_override kvo; - std::strncpy(kvo.key, data, sep - data); - kvo.key[sep - data] = 0; - sep++; - if (strncmp(sep, "int:", 4) == 0) { - sep += 4; - kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT; - kvo.val_i64 = std::atol(sep); - } else if (strncmp(sep, "float:", 6) == 0) { - sep += 6; - kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT; - kvo.val_f64 = std::atof(sep); - } else if (strncmp(sep, "bool:", 5) == 0) { - sep += 5; - kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL; - if (std::strcmp(sep, "true") == 0) { - kvo.val_bool = true; - } else if (std::strcmp(sep, "false") == 0) { - kvo.val_bool = false; - } else { - fprintf(stderr, "%s: invalid boolean value for KV override '%s'\n", __func__, data); - return false; - } - } else if (strncmp(sep, "str:", 4) == 0) { - sep += 4; - kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR; - if (strlen(sep) > 127) { - fprintf(stderr, "%s: malformed KV override '%s', value cannot exceed 127 chars\n", __func__, data); - return false; - } - strncpy(kvo.val_str, sep, 127); - kvo.val_str[127] = '\0'; - } else { - fprintf(stderr, "%s: invalid type for KV override '%s'\n", __func__, data); - return false; - } - overrides.emplace_back(std::move(kvo)); - return true; -} - bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param) { llama_sampling_params & sparams = params.sparams; @@ -547,7 +546,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa return true; } const auto sampler_names = string_split(argv[i], ';'); - sparams.samplers_sequence = sampler_types_from_names(sampler_names, true); + sparams.samplers_sequence = llama_sampling_types_from_names(sampler_names, true); return true; } if (arg == "--sampling-seq") { @@ -555,7 +554,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa invalid_param = true; return true; } - sparams.samplers_sequence = sampler_types_from_chars(argv[i]); + sparams.samplers_sequence = llama_sampling_types_from_chars(argv[i]); return true; } if (arg == "--top-p") { @@ -902,6 +901,10 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa params.interactive = true; return true; } + if (arg == "--interactive-specials") { + params.interactive_specials = true; + return true; + } if (arg == "--embedding") { params.embedding = true; return true; @@ -1057,6 +1060,14 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa #endif // GGML_USE_CUDA_SYCL_VULKAN return true; } + if (arg == "--rpc") { + if (++i >= argc) { + invalid_param = true; + return true; + } + params.rpc_servers = argv[i]; + return true; + } if (arg == "--no-mmap") { params.use_mmap = false; return true; @@ -1229,7 +1240,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa return true; } if (arg == "-h" || arg == "--help") { - gpt_print_usage(argc, argv, gpt_params()); + gpt_params_print_usage(argc, argv, gpt_params()); exit(0); } if (arg == "--version") { @@ -1300,7 +1311,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa invalid_param = true; return true; } - if (!parse_kv_override(argv[i], params.kv_overrides)) { + if (!string_parse_kv_override(argv[i], params.kv_overrides)) { fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]); invalid_param = true; return true; @@ -1334,85 +1345,14 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa return false; } -void gpt_params_handle_model_default(gpt_params & params) { - if (!params.hf_repo.empty()) { - // short-hand to avoid specifying --hf-file -> default it to --model - if (params.hf_file.empty()) { - if (params.model.empty()) { - throw std::invalid_argument("error: --hf-repo requires either --hf-file or --model\n"); - } - params.hf_file = params.model; - } else if (params.model.empty()) { - params.model = "models/" + string_split(params.hf_file, '/').back(); - } - } else if (!params.model_url.empty()) { - if (params.model.empty()) { - auto f = string_split(params.model_url, '#').front(); - f = string_split(f, '?').front(); - f = string_split(f, '/').back(); - params.model = "models/" + f; - } - } else if (params.model.empty()) { - params.model = DEFAULT_MODEL_PATH; - } -} - -bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { - bool invalid_param = false; - std::string arg; - const std::string arg_prefix = "--"; - llama_sampling_params & sparams = params.sparams; - - for (int i = 1; i < argc; i++) { - arg = argv[i]; - if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) { - std::replace(arg.begin(), arg.end(), '_', '-'); - } - - if (!gpt_params_find_arg(argc, argv, arg, params, i, invalid_param)) { - throw std::invalid_argument("error: unknown argument: " + arg); - } - } - - if (invalid_param) { - throw std::invalid_argument("error: invalid parameter for argument: " + arg); - } - - if (params.prompt_cache_all && - (params.interactive || params.interactive_first || - params.instruct)) { - - throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n"); - } - - gpt_params_handle_model_default(params); - - if (params.escape) { - process_escapes(params.prompt); - process_escapes(params.input_prefix); - process_escapes(params.input_suffix); - process_escapes(sparams.cfg_negative_prompt); - for (auto & antiprompt : params.antiprompt) { - process_escapes(antiprompt); - } - } - - if (!params.kv_overrides.empty()) { - params.kv_overrides.emplace_back(); - params.kv_overrides.back().key[0] = 0; - } - - return true; -} - -void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { +void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { const llama_sampling_params & sparams = params.sparams; std::string sampler_type_chars; std::string sampler_type_names; for (const auto sampler_type : sparams.samplers_sequence) { sampler_type_chars += static_cast(sampler_type); - sampler_type_names += sampler_type_to_name_string(sampler_type) + ";"; + sampler_type_names += llama_sampling_type_to_str(sampler_type) + ";"; } sampler_type_names.pop_back(); @@ -1423,6 +1363,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" -h, --help show this help message and exit\n"); printf(" --version show version and build info\n"); printf(" -i, --interactive run in interactive mode\n"); + printf(" --interactive-specials allow special tokens in user text, in interactive mode\n"); printf(" --interactive-first run in interactive mode and wait for input right away\n"); printf(" -cnv, --conversation run in conversation mode (does not print special tokens and suffix/prefix)\n"); printf(" -ins, --instruct run in instruction mode (use with Alpaca models)\n"); @@ -1555,6 +1496,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n"); printf(" or for intermediate results and KV (with split-mode = row) (default: %d)\n", params.main_gpu); } + printf(" --rpc SERVERS comma separated list of RPC servers\n"); printf(" --verbose-prompt print a verbose prompt before generation (default: %s)\n", params.verbose_prompt ? "true" : "false"); printf(" --no-display-prompt don't print prompt at generation (default: %s)\n", !params.display_prompt ? "true" : "false"); printf(" -gan N, --grp-attn-n N\n"); @@ -1607,7 +1549,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { #endif // LOG_DISABLE_LOGS } -std::string get_system_info(const gpt_params & params) { +std::string gpt_params_get_system_info(const gpt_params & params) { std::ostringstream os; os << "system_info: n_threads = " << params.n_threads; @@ -1619,7 +1561,52 @@ std::string get_system_info(const gpt_params & params) { return os.str(); } -std::string gpt_random_prompt(std::mt19937 & rng) { +// +// String utils +// + +std::vector string_split(std::string input, char separator) { + std::vector parts; + size_t separator_pos = input.find(separator); + while (separator_pos != std::string::npos) { + std::string part = input.substr(0, separator_pos); + parts.emplace_back(part); + input = input.substr(separator_pos + 1); + separator_pos = input.find(separator); + } + parts.emplace_back(input); + return parts; +} + +std::string string_strip(const std::string & str) { + size_t start = 0; + size_t end = str.size(); + while (start < end && std::isspace(str[start])) { + start++; + } + while (end > start && std::isspace(str[end - 1])) { + end--; + } + return str.substr(start, end - start); +} + +std::string string_get_sortable_timestamp() { + using clock = std::chrono::system_clock; + + const clock::time_point current_time = clock::now(); + const time_t as_time_t = clock::to_time_t(current_time); + char timestamp_no_ns[100]; + std::strftime(timestamp_no_ns, 100, "%Y_%m_%d-%H_%M_%S", std::localtime(&as_time_t)); + + const int64_t ns = std::chrono::duration_cast( + current_time.time_since_epoch() % 1000000000).count(); + char timestamp_ns[11]; + snprintf(timestamp_ns, 11, "%09" PRId64, ns); + + return std::string(timestamp_no_ns) + "." + std::string(timestamp_ns); +} + +std::string string_random_prompt(std::mt19937 & rng) { const int r = rng() % 10; switch (r) { case 0: return "So"; @@ -1637,13 +1624,100 @@ std::string gpt_random_prompt(std::mt19937 & rng) { GGML_UNREACHABLE(); } -// Validate if a filename is safe to use -// To validate a full path, split the path by the OS-specific path separator, and validate each part with this function -bool validate_file_name(const std::string & filename) { - if (!filename.length()) { - // Empty filename invalid - return false; - } +void string_process_escapes(std::string & input) { + std::size_t input_len = input.length(); + std::size_t output_idx = 0; + + for (std::size_t input_idx = 0; input_idx < input_len; ++input_idx) { + if (input[input_idx] == '\\' && input_idx + 1 < input_len) { + switch (input[++input_idx]) { + case 'n': input[output_idx++] = '\n'; break; + case 'r': input[output_idx++] = '\r'; break; + case 't': input[output_idx++] = '\t'; break; + case '\'': input[output_idx++] = '\''; break; + case '\"': input[output_idx++] = '\"'; break; + case '\\': input[output_idx++] = '\\'; break; + case 'x': + // Handle \x12, etc + if (input_idx + 2 < input_len) { + const char x[3] = { input[input_idx + 1], input[input_idx + 2], 0 }; + char *err_p = nullptr; + const long val = std::strtol(x, &err_p, 16); + if (err_p == x + 2) { + input_idx += 2; + input[output_idx++] = char(val); + break; + } + } + // fall through + default: input[output_idx++] = '\\'; + input[output_idx++] = input[input_idx]; break; + } + } else { + input[output_idx++] = input[input_idx]; + } + } + + input.resize(output_idx); +} + +bool string_parse_kv_override(const char * data, std::vector & overrides) { + const char * sep = strchr(data, '='); + if (sep == nullptr || sep - data >= 128) { + fprintf(stderr, "%s: malformed KV override '%s'\n", __func__, data); + return false; + } + llama_model_kv_override kvo; + std::strncpy(kvo.key, data, sep - data); + kvo.key[sep - data] = 0; + sep++; + if (strncmp(sep, "int:", 4) == 0) { + sep += 4; + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT; + kvo.val_i64 = std::atol(sep); + } else if (strncmp(sep, "float:", 6) == 0) { + sep += 6; + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT; + kvo.val_f64 = std::atof(sep); + } else if (strncmp(sep, "bool:", 5) == 0) { + sep += 5; + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL; + if (std::strcmp(sep, "true") == 0) { + kvo.val_bool = true; + } else if (std::strcmp(sep, "false") == 0) { + kvo.val_bool = false; + } else { + fprintf(stderr, "%s: invalid boolean value for KV override '%s'\n", __func__, data); + return false; + } + } else if (strncmp(sep, "str:", 4) == 0) { + sep += 4; + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR; + if (strlen(sep) > 127) { + fprintf(stderr, "%s: malformed KV override '%s', value cannot exceed 127 chars\n", __func__, data); + return false; + } + strncpy(kvo.val_str, sep, 127); + kvo.val_str[127] = '\0'; + } else { + fprintf(stderr, "%s: invalid type for KV override '%s'\n", __func__, data); + return false; + } + overrides.emplace_back(std::move(kvo)); + return true; +} + +// +// Filesystem utils +// + +// Validate if a filename is safe to use +// To validate a full path, split the path by the OS-specific path separator, and validate each part with this function +bool fs_validate_filename(const std::string & filename) { + if (!filename.length()) { + // Empty filename invalid + return false; + } if (filename.length() > 255) { // Limit at common largest possible filename on Linux filesystems // to avoid unnecessary further validation @@ -1708,180 +1782,255 @@ bool validate_file_name(const std::string & filename) { return true; } -// -// String utils -// +// returns true if successful, false otherwise +bool fs_create_directory_with_parents(const std::string & path) { +#ifdef _WIN32 + std::wstring_convert> converter; + std::wstring wpath = converter.from_bytes(path); -std::vector string_split(std::string input, char separator) { - std::vector parts; - size_t separator_pos = input.find(separator); - while (separator_pos != std::string::npos) { - std::string part = input.substr(0, separator_pos); - parts.emplace_back(part); - input = input.substr(separator_pos + 1); - separator_pos = input.find(separator); + // if the path already exists, check whether it's a directory + const DWORD attributes = GetFileAttributesW(wpath.c_str()); + if ((attributes != INVALID_FILE_ATTRIBUTES) && (attributes & FILE_ATTRIBUTE_DIRECTORY)) { + return true; } - parts.emplace_back(input); - return parts; -} -std::string string_strip(const std::string & str) { - size_t start = 0; - size_t end = str.size(); - while (start < end && std::isspace(str[start])) { - start++; - } - while (end > start && std::isspace(str[end - 1])) { - end--; - } - return str.substr(start, end - start); -} + size_t pos_slash = 0; -std::vector sampler_types_from_names(const std::vector & names, bool allow_alt_names) { - std::unordered_map sampler_canonical_name_map { - {"top_k", llama_sampler_type::TOP_K}, - {"top_p", llama_sampler_type::TOP_P}, - {"typical_p", llama_sampler_type::TYPICAL_P}, - {"min_p", llama_sampler_type::MIN_P}, - {"tfs_z", llama_sampler_type::TFS_Z}, - {"temperature", llama_sampler_type::TEMPERATURE} - }; + // process path from front to back, procedurally creating directories + while ((pos_slash = path.find('\\', pos_slash)) != std::string::npos) { + const std::wstring subpath = wpath.substr(0, pos_slash); + const wchar_t * test = subpath.c_str(); - // since samplers names are written multiple ways - // make it ready for both system names and input names - std::unordered_map sampler_alt_name_map { - {"top-k", llama_sampler_type::TOP_K}, - {"top-p", llama_sampler_type::TOP_P}, - {"nucleus", llama_sampler_type::TOP_P}, - {"typical-p", llama_sampler_type::TYPICAL_P}, - {"typical", llama_sampler_type::TYPICAL_P}, - {"min-p", llama_sampler_type::MIN_P}, - {"tfs-z", llama_sampler_type::TFS_Z}, - {"tfs", llama_sampler_type::TFS_Z}, - {"temp", llama_sampler_type::TEMPERATURE} - }; + const bool success = CreateDirectoryW(test, NULL); + if (!success) { + const DWORD error = GetLastError(); - std::vector sampler_types; - sampler_types.reserve(names.size()); - for (const auto & name : names) - { - auto sampler_item = sampler_canonical_name_map.find(name); - if (sampler_item != sampler_canonical_name_map.end()) - { - sampler_types.push_back(sampler_item->second); - } - else - { - if (allow_alt_names) - { - sampler_item = sampler_alt_name_map.find(name); - if (sampler_item != sampler_alt_name_map.end()) - { - sampler_types.push_back(sampler_item->second); + // if the path already exists, ensure that it's a directory + if (error == ERROR_ALREADY_EXISTS) { + const DWORD attributes = GetFileAttributesW(subpath.c_str()); + if (attributes == INVALID_FILE_ATTRIBUTES || !(attributes & FILE_ATTRIBUTE_DIRECTORY)) { + return false; } + } else { + return false; } } + + pos_slash += 1; } - return sampler_types; -} -std::vector sampler_types_from_chars(const std::string & names_string) { - std::unordered_map sampler_name_map { - {'k', llama_sampler_type::TOP_K}, - {'p', llama_sampler_type::TOP_P}, - {'y', llama_sampler_type::TYPICAL_P}, - {'m', llama_sampler_type::MIN_P}, - {'f', llama_sampler_type::TFS_Z}, - {'t', llama_sampler_type::TEMPERATURE} - }; + return true; +#else + // if the path already exists, check whether it's a directory + struct stat info; + if (stat(path.c_str(), &info) == 0) { + return S_ISDIR(info.st_mode); + } + + size_t pos_slash = 1; // skip leading slashes for directory creation + + // process path from front to back, procedurally creating directories + while ((pos_slash = path.find('/', pos_slash)) != std::string::npos) { + const std::string subpath = path.substr(0, pos_slash); + struct stat info; - std::vector sampler_types; - sampler_types.reserve(names_string.size()); - for (const auto & c : names_string) { - const auto sampler_item = sampler_name_map.find(c); - if (sampler_item != sampler_name_map.end()) { - sampler_types.push_back(sampler_item->second); + // if the path already exists, ensure that it's a directory + if (stat(subpath.c_str(), &info) == 0) { + if (!S_ISDIR(info.st_mode)) { + return false; + } + } else { + // create parent directories + const int ret = mkdir(subpath.c_str(), 0755); + if (ret != 0) { + return false; + } } + + pos_slash += 1; } - return sampler_types; + + return true; +#endif // _WIN32 } -std::string sampler_type_to_name_string(llama_sampler_type sampler_type) { - switch (sampler_type) { - case llama_sampler_type::TOP_K: return "top_k"; - case llama_sampler_type::TFS_Z: return "tfs_z"; - case llama_sampler_type::TYPICAL_P: return "typical_p"; - case llama_sampler_type::TOP_P: return "top_p"; - case llama_sampler_type::MIN_P: return "min_p"; - case llama_sampler_type::TEMPERATURE: return "temperature"; - default : return ""; +std::string fs_get_cache_directory() { + std::string cache_directory = ""; + if (getenv("LLAMA_CACHE")) { + cache_directory = std::getenv("LLAMA_CACHE"); + if (cache_directory.back() != DIRECTORY_SEPARATOR) { + cache_directory += DIRECTORY_SEPARATOR; + } + } else { +#ifdef __linux__ + if (std::getenv("XDG_CACHE_HOME")) { + cache_directory = std::getenv("XDG_CACHE_HOME"); + } else { + cache_directory = std::getenv("HOME") + std::string("/.cache/"); + } +#elif defined(__APPLE__) + cache_directory = std::getenv("HOME") + std::string("/Library/Caches/"); +#elif defined(_WIN32) + cache_directory = std::getenv("APPDATA"); +#endif // __linux__ + cache_directory += "llama.cpp"; + cache_directory += DIRECTORY_SEPARATOR; } + return cache_directory; } + // // Model utils // -struct llama_model_params llama_model_params_from_gpt_params(const gpt_params & params) { - auto mparams = llama_model_default_params(); +std::tuple llama_init_from_gpt_params(gpt_params & params) { + auto mparams = llama_model_params_from_gpt_params(params); - if (params.n_gpu_layers != -1) { - mparams.n_gpu_layers = params.n_gpu_layers; - } - mparams.main_gpu = params.main_gpu; - mparams.split_mode = params.split_mode; - mparams.tensor_split = params.tensor_split; - mparams.use_mmap = params.use_mmap; - mparams.use_mlock = params.use_mlock; - mparams.check_tensors = params.check_tensors; - if (params.kv_overrides.empty()) { - mparams.kv_overrides = NULL; + llama_model * model = nullptr; + + if (!params.hf_repo.empty() && !params.hf_file.empty()) { + model = llama_load_model_from_hf(params.hf_repo.c_str(), params.hf_file.c_str(), params.model.c_str(), mparams); + } else if (!params.model_url.empty()) { + model = llama_load_model_from_url(params.model_url.c_str(), params.model.c_str(), mparams); } else { - GGML_ASSERT(params.kv_overrides.back().key[0] == 0 && "KV overrides not terminated with empty key"); - mparams.kv_overrides = params.kv_overrides.data(); + model = llama_load_model_from_file(params.model.c_str(), mparams); } - return mparams; -} - -static ggml_type kv_cache_type_from_str(const std::string & s) { - if (s == "f32") { - return GGML_TYPE_F32; - } - if (s == "f16") { - return GGML_TYPE_F16; - } - if (s == "q8_0") { - return GGML_TYPE_Q8_0; - } - if (s == "q4_0") { - return GGML_TYPE_Q4_0; - } - if (s == "q4_1") { - return GGML_TYPE_Q4_1; - } - if (s == "iq4_nl") { - return GGML_TYPE_IQ4_NL; - } - if (s == "q5_0") { - return GGML_TYPE_Q5_0; - } - if (s == "q5_1") { - return GGML_TYPE_Q5_1; + if (model == NULL) { + fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str()); + return std::make_tuple(nullptr, nullptr); } - throw std::runtime_error("Invalid cache type: " + s); -} + auto cparams = llama_context_params_from_gpt_params(params); -struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params) { - auto cparams = llama_context_default_params(); + llama_context * lctx = llama_new_context_with_model(model, cparams); + if (lctx == NULL) { + fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str()); + llama_free_model(model); + return std::make_tuple(nullptr, nullptr); + } - cparams.n_ctx = params.n_ctx; - cparams.n_seq_max = params.n_parallel; - cparams.n_batch = params.n_batch; - cparams.n_ubatch = params.n_ubatch; - cparams.n_threads = params.n_threads; - cparams.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; + if (!params.control_vectors.empty()) { + if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1; + if (params.control_vector_layer_end <= 0) params.control_vector_layer_end = llama_n_layer(model); + + const auto cvec = llama_control_vector_load(params.control_vectors); + if (cvec.n_embd == -1) { + llama_free(lctx); + llama_free_model(model); + return std::make_tuple(nullptr, nullptr); + } + + int err = llama_control_vector_apply(lctx, + cvec.data.data(), + cvec.data.size(), + cvec.n_embd, + params.control_vector_layer_start, + params.control_vector_layer_end); + if (err) { + llama_free(lctx); + llama_free_model(model); + return std::make_tuple(nullptr, nullptr); + } + } + + for (unsigned int i = 0; i < params.lora_adapter.size(); ++i) { + const std::string & lora_adapter = std::get<0>(params.lora_adapter[i]); + float lora_scale = std::get<1>(params.lora_adapter[i]); + int err = llama_model_apply_lora_from_file(model, + lora_adapter.c_str(), + lora_scale, + ((i > 0) || params.lora_base.empty()) + ? NULL + : params.lora_base.c_str(), + params.n_threads); + if (err != 0) { + fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__); + llama_free(lctx); + llama_free_model(model); + return std::make_tuple(nullptr, nullptr); + } + } + + if (params.ignore_eos) { + params.sparams.logit_bias[llama_token_eos(model)] = -INFINITY; + } + + if (params.warmup) { + LOG("warming up the model with an empty run\n"); + + std::vector tmp = { llama_token_bos(model), llama_token_eos(model), }; + llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0)); + llama_kv_cache_clear(lctx); + llama_synchronize(lctx); + llama_reset_timings(lctx); + } + + return std::make_tuple(model, lctx); +} + +struct llama_model_params llama_model_params_from_gpt_params(const gpt_params & params) { + auto mparams = llama_model_default_params(); + + if (params.n_gpu_layers != -1) { + mparams.n_gpu_layers = params.n_gpu_layers; + } + mparams.rpc_servers = params.rpc_servers.c_str(); + mparams.main_gpu = params.main_gpu; + mparams.split_mode = params.split_mode; + mparams.tensor_split = params.tensor_split; + mparams.use_mmap = params.use_mmap; + mparams.use_mlock = params.use_mlock; + mparams.check_tensors = params.check_tensors; + if (params.kv_overrides.empty()) { + mparams.kv_overrides = NULL; + } else { + GGML_ASSERT(params.kv_overrides.back().key[0] == 0 && "KV overrides not terminated with empty key"); + mparams.kv_overrides = params.kv_overrides.data(); + } + + return mparams; +} + +static ggml_type kv_cache_type_from_str(const std::string & s) { + if (s == "f32") { + return GGML_TYPE_F32; + } + if (s == "f16") { + return GGML_TYPE_F16; + } + if (s == "q8_0") { + return GGML_TYPE_Q8_0; + } + if (s == "q4_0") { + return GGML_TYPE_Q4_0; + } + if (s == "q4_1") { + return GGML_TYPE_Q4_1; + } + if (s == "iq4_nl") { + return GGML_TYPE_IQ4_NL; + } + if (s == "q5_0") { + return GGML_TYPE_Q5_0; + } + if (s == "q5_1") { + return GGML_TYPE_Q5_1; + } + + throw std::runtime_error("Invalid cache type: " + s); +} + +struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params) { + auto cparams = llama_context_default_params(); + + cparams.n_ctx = params.n_ctx; + cparams.n_seq_max = params.n_parallel; + cparams.n_batch = params.n_batch; + cparams.n_ubatch = params.n_ubatch; + cparams.n_threads = params.n_threads; + cparams.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; cparams.seed = params.seed; cparams.logits_all = params.logits_all; cparams.embeddings = params.embedding; @@ -1906,27 +2055,6 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param return cparams; } -void llama_batch_clear(struct llama_batch & batch) { - batch.n_tokens = 0; -} - -void llama_batch_add( - struct llama_batch & batch, - llama_token id, - llama_pos pos, - const std::vector & seq_ids, - bool logits) { - batch.token [batch.n_tokens] = id; - batch.pos [batch.n_tokens] = pos; - batch.n_seq_id[batch.n_tokens] = seq_ids.size(); - for (size_t i = 0; i < seq_ids.size(); ++i) { - batch.seq_id[batch.n_tokens][i] = seq_ids[i]; - } - batch.logits [batch.n_tokens] = logits; - - batch.n_tokens++; -} - #ifdef LLAMA_USE_CURL static bool starts_with(const std::string & str, const std::string & prefix) { @@ -2257,90 +2385,29 @@ struct llama_model * llama_load_model_from_hf( #endif // LLAMA_USE_CURL -std::tuple llama_init_from_gpt_params(gpt_params & params) { - auto mparams = llama_model_params_from_gpt_params(params); - - llama_model * model = nullptr; - - if (!params.hf_repo.empty() && !params.hf_file.empty()) { - model = llama_load_model_from_hf(params.hf_repo.c_str(), params.hf_file.c_str(), params.model.c_str(), mparams); - } else if (!params.model_url.empty()) { - model = llama_load_model_from_url(params.model_url.c_str(), params.model.c_str(), mparams); - } else { - model = llama_load_model_from_file(params.model.c_str(), mparams); - } - - if (model == NULL) { - fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str()); - return std::make_tuple(nullptr, nullptr); - } - - auto cparams = llama_context_params_from_gpt_params(params); - - llama_context * lctx = llama_new_context_with_model(model, cparams); - if (lctx == NULL) { - fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str()); - llama_free_model(model); - return std::make_tuple(nullptr, nullptr); - } - - if (!params.control_vectors.empty()) { - if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1; - if (params.control_vector_layer_end <= 0) params.control_vector_layer_end = llama_n_layer(model); - - const auto cvec = llama_control_vector_load(params.control_vectors); - if (cvec.n_embd == -1) { - llama_free(lctx); - llama_free_model(model); - return std::make_tuple(nullptr, nullptr); - } - - int err = llama_control_vector_apply(lctx, - cvec.data.data(), - cvec.data.size(), - cvec.n_embd, - params.control_vector_layer_start, - params.control_vector_layer_end); - if (err) { - llama_free(lctx); - llama_free_model(model); - return std::make_tuple(nullptr, nullptr); - } - } - - for (unsigned int i = 0; i < params.lora_adapter.size(); ++i) { - const std::string & lora_adapter = std::get<0>(params.lora_adapter[i]); - float lora_scale = std::get<1>(params.lora_adapter[i]); - int err = llama_model_apply_lora_from_file(model, - lora_adapter.c_str(), - lora_scale, - ((i > 0) || params.lora_base.empty()) - ? NULL - : params.lora_base.c_str(), - params.n_threads); - if (err != 0) { - fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__); - llama_free(lctx); - llama_free_model(model); - return std::make_tuple(nullptr, nullptr); - } - } - - if (params.ignore_eos) { - params.sparams.logit_bias[llama_token_eos(model)] = -INFINITY; - } +// +// Batch utils +// - if (params.warmup) { - LOG("warming up the model with an empty run\n"); +void llama_batch_clear(struct llama_batch & batch) { + batch.n_tokens = 0; +} - std::vector tmp = { llama_token_bos(model), llama_token_eos(model), }; - llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0)); - llama_kv_cache_clear(lctx); - llama_synchronize(lctx); - llama_reset_timings(lctx); +void llama_batch_add( + struct llama_batch & batch, + llama_token id, + llama_pos pos, + const std::vector & seq_ids, + bool logits) { + batch.token [batch.n_tokens] = id; + batch.pos [batch.n_tokens] = pos; + batch.n_seq_id[batch.n_tokens] = seq_ids.size(); + for (size_t i = 0; i < seq_ids.size(); ++i) { + batch.seq_id[batch.n_tokens][i] = seq_ids[i]; } + batch.logits [batch.n_tokens] = logits; - return std::make_tuple(model, lctx); + batch.n_tokens++; } // @@ -2393,355 +2460,46 @@ std::string llama_detokenize_spm(llama_context * ctx, const std::vector & tokens) { - std::string piece; - std::string result; - - for (size_t i = 0; i < tokens.size(); ++i) { - piece = llama_token_to_piece(ctx, tokens[i]); - - result += piece; - } - - // NOTE: the original tokenizer decodes bytes after collecting the pieces. - return result; -} - -bool llama_should_add_bos_token(const llama_model * model) { - const int add_bos = llama_add_bos_token(model); - - return add_bos != -1 ? bool(add_bos) : (llama_vocab_type(model) == LLAMA_VOCAB_TYPE_SPM); -} - -// -// YAML utils -// - -// returns true if successful, false otherwise -bool create_directory_with_parents(const std::string & path) { -#ifdef _WIN32 - std::wstring_convert> converter; - std::wstring wpath = converter.from_bytes(path); - - // if the path already exists, check whether it's a directory - const DWORD attributes = GetFileAttributesW(wpath.c_str()); - if ((attributes != INVALID_FILE_ATTRIBUTES) && (attributes & FILE_ATTRIBUTE_DIRECTORY)) { - return true; - } - - size_t pos_slash = 0; - - // process path from front to back, procedurally creating directories - while ((pos_slash = path.find('\\', pos_slash)) != std::string::npos) { - const std::wstring subpath = wpath.substr(0, pos_slash); - const wchar_t * test = subpath.c_str(); - - const bool success = CreateDirectoryW(test, NULL); - if (!success) { - const DWORD error = GetLastError(); - - // if the path already exists, ensure that it's a directory - if (error == ERROR_ALREADY_EXISTS) { - const DWORD attributes = GetFileAttributesW(subpath.c_str()); - if (attributes == INVALID_FILE_ATTRIBUTES || !(attributes & FILE_ATTRIBUTE_DIRECTORY)) { - return false; - } - } else { - return false; - } - } - - pos_slash += 1; - } - - return true; -#else - // if the path already exists, check whether it's a directory - struct stat info; - if (stat(path.c_str(), &info) == 0) { - return S_ISDIR(info.st_mode); - } - - size_t pos_slash = 1; // skip leading slashes for directory creation - - // process path from front to back, procedurally creating directories - while ((pos_slash = path.find('/', pos_slash)) != std::string::npos) { - const std::string subpath = path.substr(0, pos_slash); - struct stat info; - - // if the path already exists, ensure that it's a directory - if (stat(subpath.c_str(), &info) == 0) { - if (!S_ISDIR(info.st_mode)) { - return false; - } - } else { - // create parent directories - const int ret = mkdir(subpath.c_str(), 0755); - if (ret != 0) { - return false; - } - } - - pos_slash += 1; - } - - return true; -#endif // _WIN32 -} - -void dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::vector & data) { - if (data.empty()) { - fprintf(stream, "%s:\n", prop_name); - return; - } - - fprintf(stream, "%s: [", prop_name); - for (size_t i = 0; i < data.size() - 1; ++i) { - fprintf(stream, "%e, ", data[i]); - } - fprintf(stream, "%e]\n", data.back()); -} - -void dump_vector_int_yaml(FILE * stream, const char * prop_name, const std::vector & data) { - if (data.empty()) { - fprintf(stream, "%s:\n", prop_name); - return; - } - - fprintf(stream, "%s: [", prop_name); - for (size_t i = 0; i < data.size() - 1; ++i) { - fprintf(stream, "%d, ", data[i]); - } - fprintf(stream, "%d]\n", data.back()); -} - -void dump_string_yaml_multiline(FILE * stream, const char * prop_name, const char * data) { - std::string data_str(data == NULL ? "" : data); - - if (data_str.empty()) { - fprintf(stream, "%s:\n", prop_name); - return; - } - - size_t pos_start = 0; - size_t pos_found = 0; - - if (!data_str.empty() && (std::isspace(data_str[0]) || std::isspace(data_str.back()))) { - data_str = std::regex_replace(data_str, std::regex("\n"), "\\n"); - data_str = std::regex_replace(data_str, std::regex("\""), "\\\""); - data_str = std::regex_replace(data_str, std::regex(R"(\\[^n"])"), R"(\$&)"); - data_str = "\"" + data_str + "\""; - fprintf(stream, "%s: %s\n", prop_name, data_str.c_str()); - return; - } - - if (data_str.find('\n') == std::string::npos) { - fprintf(stream, "%s: %s\n", prop_name, data_str.c_str()); - return; - } - - fprintf(stream, "%s: |\n", prop_name); - while ((pos_found = data_str.find('\n', pos_start)) != std::string::npos) { - fprintf(stream, " %s\n", data_str.substr(pos_start, pos_found-pos_start).c_str()); - pos_start = pos_found + 1; - } -} - -std::string get_sortable_timestamp() { - using clock = std::chrono::system_clock; - - const clock::time_point current_time = clock::now(); - const time_t as_time_t = clock::to_time_t(current_time); - char timestamp_no_ns[100]; - std::strftime(timestamp_no_ns, 100, "%Y_%m_%d-%H_%M_%S", std::localtime(&as_time_t)); - - const int64_t ns = std::chrono::duration_cast( - current_time.time_since_epoch() % 1000000000).count(); - char timestamp_ns[11]; - snprintf(timestamp_ns, 11, "%09" PRId64, ns); - - return std::string(timestamp_no_ns) + "." + std::string(timestamp_ns); -} - -void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const llama_context * lctx, - const std::string & timestamp, const std::vector & prompt_tokens, const char * model_desc) { - const llama_sampling_params & sparams = params.sparams; - - fprintf(stream, "build_commit: %s\n", LLAMA_COMMIT); - fprintf(stream, "build_number: %d\n", LLAMA_BUILD_NUMBER); - fprintf(stream, "cpu_has_arm_fma: %s\n", ggml_cpu_has_arm_fma() ? "true" : "false"); - fprintf(stream, "cpu_has_avx: %s\n", ggml_cpu_has_avx() ? "true" : "false"); - fprintf(stream, "cpu_has_avx_vnni: %s\n", ggml_cpu_has_avx_vnni() ? "true" : "false"); - fprintf(stream, "cpu_has_avx2: %s\n", ggml_cpu_has_avx2() ? "true" : "false"); - fprintf(stream, "cpu_has_avx512: %s\n", ggml_cpu_has_avx512() ? "true" : "false"); - fprintf(stream, "cpu_has_avx512_vbmi: %s\n", ggml_cpu_has_avx512_vbmi() ? "true" : "false"); - fprintf(stream, "cpu_has_avx512_vnni: %s\n", ggml_cpu_has_avx512_vnni() ? "true" : "false"); - fprintf(stream, "cpu_has_cuda: %s\n", ggml_cpu_has_cuda() ? "true" : "false"); - fprintf(stream, "cpu_has_vulkan: %s\n", ggml_cpu_has_vulkan() ? "true" : "false"); - fprintf(stream, "cpu_has_clblast: %s\n", ggml_cpu_has_clblast() ? "true" : "false"); - fprintf(stream, "cpu_has_kompute: %s\n", ggml_cpu_has_kompute() ? "true" : "false"); - fprintf(stream, "cpu_has_fma: %s\n", ggml_cpu_has_fma() ? "true" : "false"); - fprintf(stream, "cpu_has_gpublas: %s\n", ggml_cpu_has_gpublas() ? "true" : "false"); - fprintf(stream, "cpu_has_neon: %s\n", ggml_cpu_has_neon() ? "true" : "false"); - fprintf(stream, "cpu_has_f16c: %s\n", ggml_cpu_has_f16c() ? "true" : "false"); - fprintf(stream, "cpu_has_fp16_va: %s\n", ggml_cpu_has_fp16_va() ? "true" : "false"); - fprintf(stream, "cpu_has_wasm_simd: %s\n", ggml_cpu_has_wasm_simd() ? "true" : "false"); - fprintf(stream, "cpu_has_blas: %s\n", ggml_cpu_has_blas() ? "true" : "false"); - fprintf(stream, "cpu_has_sse3: %s\n", ggml_cpu_has_sse3() ? "true" : "false"); - fprintf(stream, "cpu_has_vsx: %s\n", ggml_cpu_has_vsx() ? "true" : "false"); - fprintf(stream, "cpu_has_matmul_int8: %s\n", ggml_cpu_has_matmul_int8() ? "true" : "false"); - -#ifdef NDEBUG - fprintf(stream, "debug: false\n"); -#else - fprintf(stream, "debug: true\n"); -#endif // NDEBUG - - fprintf(stream, "model_desc: %s\n", model_desc); - fprintf(stream, "n_vocab: %d # output size of the final layer, 32001 for some models\n", llama_n_vocab(llama_get_model(lctx))); - -#ifdef __OPTIMIZE__ - fprintf(stream, "optimize: true\n"); -#else - fprintf(stream, "optimize: false\n"); -#endif // __OPTIMIZE__ - - fprintf(stream, "time: %s\n", timestamp.c_str()); - - fprintf(stream, "\n"); - fprintf(stream, "###############\n"); - fprintf(stream, "# User Inputs #\n"); - fprintf(stream, "###############\n"); - fprintf(stream, "\n"); - - fprintf(stream, "alias: %s # default: unknown\n", params.model_alias.c_str()); - fprintf(stream, "batch_size: %d # default: 512\n", params.n_batch); - dump_string_yaml_multiline(stream, "cfg_negative_prompt", sparams.cfg_negative_prompt.c_str()); - fprintf(stream, "cfg_scale: %f # default: 1.0\n", sparams.cfg_scale); - fprintf(stream, "chunks: %d # default: -1 (unlimited)\n", params.n_chunks); - fprintf(stream, "color: %s # default: false\n", params.use_color ? "true" : "false"); - fprintf(stream, "ctx_size: %d # default: 512\n", params.n_ctx); - fprintf(stream, "escape: %s # default: false\n", params.escape ? "true" : "false"); - fprintf(stream, "file: # never logged, see prompt instead. Can still be specified for input.\n"); - fprintf(stream, "frequency_penalty: %f # default: 0.0 \n", sparams.penalty_freq); - dump_string_yaml_multiline(stream, "grammar", sparams.grammar.c_str()); - fprintf(stream, "grammar-file: # never logged, see grammar instead. Can still be specified for input.\n"); - fprintf(stream, "hellaswag: %s # default: false\n", params.hellaswag ? "true" : "false"); - fprintf(stream, "hellaswag_tasks: %zu # default: 400\n", params.hellaswag_tasks); - - const auto logit_bias_eos = sparams.logit_bias.find(llama_token_eos(llama_get_model(lctx))); - const bool ignore_eos = logit_bias_eos != sparams.logit_bias.end() && logit_bias_eos->second == -INFINITY; - fprintf(stream, "ignore_eos: %s # default: false\n", ignore_eos ? "true" : "false"); - - dump_string_yaml_multiline(stream, "in_prefix", params.input_prefix.c_str()); - fprintf(stream, "in_prefix_bos: %s # default: false\n", params.input_prefix_bos ? "true" : "false"); - dump_string_yaml_multiline(stream, "in_suffix", params.input_prefix.c_str()); - fprintf(stream, "instruct: %s # default: false\n", params.instruct ? "true" : "false"); - fprintf(stream, "interactive: %s # default: false\n", params.interactive ? "true" : "false"); - fprintf(stream, "interactive_first: %s # default: false\n", params.interactive_first ? "true" : "false"); - fprintf(stream, "keep: %d # default: 0\n", params.n_keep); - fprintf(stream, "logdir: %s # default: unset (no logging)\n", params.logdir.c_str()); - - fprintf(stream, "logit_bias:\n"); - for (std::pair lb : sparams.logit_bias) { - if (ignore_eos && lb.first == logit_bias_eos->first) { - continue; - } - fprintf(stream, " %d: %f", lb.first, lb.second); - } - - fprintf(stream, "lora:\n"); - for (std::tuple la : params.lora_adapter) { - if (std::get<1>(la) != 1.0f) { - continue; - } - fprintf(stream, " - %s\n", std::get<0>(la).c_str()); - } - fprintf(stream, "lora_scaled:\n"); - for (std::tuple la : params.lora_adapter) { - if (std::get<1>(la) == 1.0f) { - continue; - } - fprintf(stream, " - %s: %f\n", std::get<0>(la).c_str(), std::get<1>(la)); - } - fprintf(stream, "lora_base: %s\n", params.lora_base.c_str()); - fprintf(stream, "main_gpu: %d # default: 0\n", params.main_gpu); - fprintf(stream, "min_keep: %d # default: 0 (disabled)\n", sparams.min_keep); - fprintf(stream, "mirostat: %d # default: 0 (disabled)\n", sparams.mirostat); - fprintf(stream, "mirostat_ent: %f # default: 5.0\n", sparams.mirostat_tau); - fprintf(stream, "mirostat_lr: %f # default: 0.1\n", sparams.mirostat_eta); - fprintf(stream, "mlock: %s # default: false\n", params.use_mlock ? "true" : "false"); - fprintf(stream, "model: %s # default: %s\n", params.model.c_str(), DEFAULT_MODEL_PATH); - fprintf(stream, "model_draft: %s # default:\n", params.model_draft.c_str()); - fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false"); - fprintf(stream, "n_gpu_layers: %d # default: -1\n", params.n_gpu_layers); - fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict); - fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", sparams.n_probs); - fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false"); - fprintf(stream, "penalize_nl: %s # default: false\n", sparams.penalize_nl ? "true" : "false"); - fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type); - fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride); - fprintf(stream, "presence_penalty: %f # default: 0.0\n", sparams.penalty_present); - dump_string_yaml_multiline(stream, "prompt", params.prompt.c_str()); - fprintf(stream, "prompt_cache: %s\n", params.path_prompt_cache.c_str()); - fprintf(stream, "prompt_cache_all: %s # default: false\n", params.prompt_cache_all ? "true" : "false"); - fprintf(stream, "prompt_cache_ro: %s # default: false\n", params.prompt_cache_ro ? "true" : "false"); - dump_vector_int_yaml(stream, "prompt_tokens", prompt_tokens); - fprintf(stream, "random_prompt: %s # default: false\n", params.random_prompt ? "true" : "false"); - fprintf(stream, "repeat_penalty: %f # default: 1.1\n", sparams.penalty_repeat); - - fprintf(stream, "reverse_prompt:\n"); - for (std::string ap : params.antiprompt) { - size_t pos = 0; - while ((pos = ap.find('\n', pos)) != std::string::npos) { - ap.replace(pos, 1, "\\n"); - pos += 1; + + for (size_t i = 0; i < tokens.size(); ++i) { + piece = llama_token_to_piece(ctx, tokens[i]); + + // remove the leading space of the first non-BOS token + if (((tokens[0] == bos_id && i == 1) || (tokens[0] != bos_id && i == 0)) && piece[0] == ' ') { + piece = piece.substr(1); } - fprintf(stream, " - %s\n", ap.c_str()); + result += piece; } - fprintf(stream, "rope_freq_base: %f # default: 10000.0\n", params.rope_freq_base); - fprintf(stream, "rope_freq_scale: %f # default: 1.0\n", params.rope_freq_scale); - fprintf(stream, "seed: %u # default: -1 (random seed)\n", params.seed); - fprintf(stream, "simple_io: %s # default: false\n", params.simple_io ? "true" : "false"); - fprintf(stream, "cont_batching: %s # default: false\n", params.cont_batching ? "true" : "false"); - fprintf(stream, "flash_attn: %s # default: false\n", params.flash_attn ? "true" : "false"); - fprintf(stream, "temp: %f # default: 0.8\n", sparams.temp); + return result; +} - const std::vector tensor_split_vector(params.tensor_split, params.tensor_split + llama_max_devices()); - dump_vector_float_yaml(stream, "tensor_split", tensor_split_vector); +std::string llama_detokenize_bpe(llama_context * ctx, const std::vector & tokens) { + std::string piece; + std::string result; - fprintf(stream, "tfs: %f # default: 1.0\n", sparams.tfs_z); - fprintf(stream, "threads: %d # default: %u\n", params.n_threads, std::thread::hardware_concurrency()); - fprintf(stream, "top_k: %d # default: 40\n", sparams.top_k); - fprintf(stream, "top_p: %f # default: 0.95\n", sparams.top_p); - fprintf(stream, "min_p: %f # default: 0.0\n", sparams.min_p); - fprintf(stream, "typical_p: %f # default: 1.0\n", sparams.typical_p); - fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false"); - fprintf(stream, "display_prompt: %s # default: true\n", params.display_prompt ? "true" : "false"); + for (size_t i = 0; i < tokens.size(); ++i) { + piece = llama_token_to_piece(ctx, tokens[i]); + + result += piece; + } + + // NOTE: the original tokenizer decodes bytes after collecting the pieces. + return result; +} + +bool llama_should_add_bos_token(const llama_model * model) { + const int add_bos = llama_add_bos_token(model); + + return add_bos != -1 ? bool(add_bos) : (llama_vocab_type(model) == LLAMA_VOCAB_TYPE_SPM); } // // KV cache utils // -void dump_kv_cache_view(const llama_kv_cache_view & view, int row_size) { +void llama_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size) { static const char slot_chars[] = ".123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz+"; printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d", @@ -2764,7 +2522,7 @@ void dump_kv_cache_view(const llama_kv_cache_view & view, int row_size) { printf("\n=== Done dumping\n"); } -void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size) { +void llama_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size) { static const char slot_chars[] = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"; printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d\n", @@ -2812,6 +2570,10 @@ void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size) { printf("\n=== Done dumping\n"); } +// +// Embedding utils +// + void llama_embd_normalize(const float * inp, float * out, int n) { double sum = 0.0; for (int i = 0; i < n; i++) { @@ -2996,3 +2758,225 @@ llama_control_vector_data llama_control_vector_load(const std::vector & data) { + if (data.empty()) { + fprintf(stream, "%s:\n", prop_name); + return; + } + + fprintf(stream, "%s: [", prop_name); + for (size_t i = 0; i < data.size() - 1; ++i) { + fprintf(stream, "%e, ", data[i]); + } + fprintf(stream, "%e]\n", data.back()); +} + +void yaml_dump_vector_int(FILE * stream, const char * prop_name, const std::vector & data) { + if (data.empty()) { + fprintf(stream, "%s:\n", prop_name); + return; + } + + fprintf(stream, "%s: [", prop_name); + for (size_t i = 0; i < data.size() - 1; ++i) { + fprintf(stream, "%d, ", data[i]); + } + fprintf(stream, "%d]\n", data.back()); +} + +void yaml_dump_string_multiline(FILE * stream, const char * prop_name, const char * data) { + std::string data_str(data == NULL ? "" : data); + + if (data_str.empty()) { + fprintf(stream, "%s:\n", prop_name); + return; + } + + size_t pos_start = 0; + size_t pos_found = 0; + + if (std::isspace(data_str[0]) || std::isspace(data_str.back())) { + data_str = std::regex_replace(data_str, std::regex("\n"), "\\n"); + data_str = std::regex_replace(data_str, std::regex("\""), "\\\""); + data_str = std::regex_replace(data_str, std::regex(R"(\\[^n"])"), R"(\$&)"); + data_str = "\"" + data_str + "\""; + fprintf(stream, "%s: %s\n", prop_name, data_str.c_str()); + return; + } + + if (data_str.find('\n') == std::string::npos) { + fprintf(stream, "%s: %s\n", prop_name, data_str.c_str()); + return; + } + + fprintf(stream, "%s: |\n", prop_name); + while ((pos_found = data_str.find('\n', pos_start)) != std::string::npos) { + fprintf(stream, " %s\n", data_str.substr(pos_start, pos_found-pos_start).c_str()); + pos_start = pos_found + 1; + } +} + +void yaml_dump_non_result_info(FILE * stream, const gpt_params & params, const llama_context * lctx, + const std::string & timestamp, const std::vector & prompt_tokens, const char * model_desc) { + const llama_sampling_params & sparams = params.sparams; + + fprintf(stream, "build_commit: %s\n", LLAMA_COMMIT); + fprintf(stream, "build_number: %d\n", LLAMA_BUILD_NUMBER); + fprintf(stream, "cpu_has_arm_fma: %s\n", ggml_cpu_has_arm_fma() ? "true" : "false"); + fprintf(stream, "cpu_has_avx: %s\n", ggml_cpu_has_avx() ? "true" : "false"); + fprintf(stream, "cpu_has_avx_vnni: %s\n", ggml_cpu_has_avx_vnni() ? "true" : "false"); + fprintf(stream, "cpu_has_avx2: %s\n", ggml_cpu_has_avx2() ? "true" : "false"); + fprintf(stream, "cpu_has_avx512: %s\n", ggml_cpu_has_avx512() ? "true" : "false"); + fprintf(stream, "cpu_has_avx512_vbmi: %s\n", ggml_cpu_has_avx512_vbmi() ? "true" : "false"); + fprintf(stream, "cpu_has_avx512_vnni: %s\n", ggml_cpu_has_avx512_vnni() ? "true" : "false"); + fprintf(stream, "cpu_has_cuda: %s\n", ggml_cpu_has_cuda() ? "true" : "false"); + fprintf(stream, "cpu_has_vulkan: %s\n", ggml_cpu_has_vulkan() ? "true" : "false"); + fprintf(stream, "cpu_has_clblast: %s\n", ggml_cpu_has_clblast() ? "true" : "false"); + fprintf(stream, "cpu_has_kompute: %s\n", ggml_cpu_has_kompute() ? "true" : "false"); + fprintf(stream, "cpu_has_fma: %s\n", ggml_cpu_has_fma() ? "true" : "false"); + fprintf(stream, "cpu_has_gpublas: %s\n", ggml_cpu_has_gpublas() ? "true" : "false"); + fprintf(stream, "cpu_has_neon: %s\n", ggml_cpu_has_neon() ? "true" : "false"); + fprintf(stream, "cpu_has_f16c: %s\n", ggml_cpu_has_f16c() ? "true" : "false"); + fprintf(stream, "cpu_has_fp16_va: %s\n", ggml_cpu_has_fp16_va() ? "true" : "false"); + fprintf(stream, "cpu_has_wasm_simd: %s\n", ggml_cpu_has_wasm_simd() ? "true" : "false"); + fprintf(stream, "cpu_has_blas: %s\n", ggml_cpu_has_blas() ? "true" : "false"); + fprintf(stream, "cpu_has_sse3: %s\n", ggml_cpu_has_sse3() ? "true" : "false"); + fprintf(stream, "cpu_has_vsx: %s\n", ggml_cpu_has_vsx() ? "true" : "false"); + fprintf(stream, "cpu_has_matmul_int8: %s\n", ggml_cpu_has_matmul_int8() ? "true" : "false"); + +#ifdef NDEBUG + fprintf(stream, "debug: false\n"); +#else + fprintf(stream, "debug: true\n"); +#endif // NDEBUG + + fprintf(stream, "model_desc: %s\n", model_desc); + fprintf(stream, "n_vocab: %d # output size of the final layer, 32001 for some models\n", llama_n_vocab(llama_get_model(lctx))); + +#ifdef __OPTIMIZE__ + fprintf(stream, "optimize: true\n"); +#else + fprintf(stream, "optimize: false\n"); +#endif // __OPTIMIZE__ + + fprintf(stream, "time: %s\n", timestamp.c_str()); + + fprintf(stream, "\n"); + fprintf(stream, "###############\n"); + fprintf(stream, "# User Inputs #\n"); + fprintf(stream, "###############\n"); + fprintf(stream, "\n"); + + fprintf(stream, "alias: %s # default: unknown\n", params.model_alias.c_str()); + fprintf(stream, "batch_size: %d # default: 512\n", params.n_batch); + yaml_dump_string_multiline(stream, "cfg_negative_prompt", sparams.cfg_negative_prompt.c_str()); + fprintf(stream, "cfg_scale: %f # default: 1.0\n", sparams.cfg_scale); + fprintf(stream, "chunks: %d # default: -1 (unlimited)\n", params.n_chunks); + fprintf(stream, "color: %s # default: false\n", params.use_color ? "true" : "false"); + fprintf(stream, "ctx_size: %d # default: 512\n", params.n_ctx); + fprintf(stream, "escape: %s # default: false\n", params.escape ? "true" : "false"); + fprintf(stream, "file: # never logged, see prompt instead. Can still be specified for input.\n"); + fprintf(stream, "frequency_penalty: %f # default: 0.0 \n", sparams.penalty_freq); + yaml_dump_string_multiline(stream, "grammar", sparams.grammar.c_str()); + fprintf(stream, "grammar-file: # never logged, see grammar instead. Can still be specified for input.\n"); + fprintf(stream, "hellaswag: %s # default: false\n", params.hellaswag ? "true" : "false"); + fprintf(stream, "hellaswag_tasks: %zu # default: 400\n", params.hellaswag_tasks); + + const auto logit_bias_eos = sparams.logit_bias.find(llama_token_eos(llama_get_model(lctx))); + const bool ignore_eos = logit_bias_eos != sparams.logit_bias.end() && logit_bias_eos->second == -INFINITY; + fprintf(stream, "ignore_eos: %s # default: false\n", ignore_eos ? "true" : "false"); + + yaml_dump_string_multiline(stream, "in_prefix", params.input_prefix.c_str()); + fprintf(stream, "in_prefix_bos: %s # default: false\n", params.input_prefix_bos ? "true" : "false"); + yaml_dump_string_multiline(stream, "in_suffix", params.input_prefix.c_str()); + fprintf(stream, "instruct: %s # default: false\n", params.instruct ? "true" : "false"); + fprintf(stream, "interactive: %s # default: false\n", params.interactive ? "true" : "false"); + fprintf(stream, "interactive_specials: %s # default: false\n", params.interactive_specials ? "true" : "false"); + fprintf(stream, "interactive_first: %s # default: false\n", params.interactive_first ? "true" : "false"); + fprintf(stream, "keep: %d # default: 0\n", params.n_keep); + fprintf(stream, "logdir: %s # default: unset (no logging)\n", params.logdir.c_str()); + + fprintf(stream, "logit_bias:\n"); + for (std::pair lb : sparams.logit_bias) { + if (ignore_eos && lb.first == logit_bias_eos->first) { + continue; + } + fprintf(stream, " %d: %f", lb.first, lb.second); + } + + fprintf(stream, "lora:\n"); + for (std::tuple la : params.lora_adapter) { + if (std::get<1>(la) != 1.0f) { + continue; + } + fprintf(stream, " - %s\n", std::get<0>(la).c_str()); + } + fprintf(stream, "lora_scaled:\n"); + for (std::tuple la : params.lora_adapter) { + if (std::get<1>(la) == 1.0f) { + continue; + } + fprintf(stream, " - %s: %f\n", std::get<0>(la).c_str(), std::get<1>(la)); + } + fprintf(stream, "lora_base: %s\n", params.lora_base.c_str()); + fprintf(stream, "main_gpu: %d # default: 0\n", params.main_gpu); + fprintf(stream, "min_keep: %d # default: 0 (disabled)\n", sparams.min_keep); + fprintf(stream, "mirostat: %d # default: 0 (disabled)\n", sparams.mirostat); + fprintf(stream, "mirostat_ent: %f # default: 5.0\n", sparams.mirostat_tau); + fprintf(stream, "mirostat_lr: %f # default: 0.1\n", sparams.mirostat_eta); + fprintf(stream, "mlock: %s # default: false\n", params.use_mlock ? "true" : "false"); + fprintf(stream, "model: %s # default: %s\n", params.model.c_str(), DEFAULT_MODEL_PATH); + fprintf(stream, "model_draft: %s # default:\n", params.model_draft.c_str()); + fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false"); + fprintf(stream, "n_gpu_layers: %d # default: -1\n", params.n_gpu_layers); + fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict); + fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", sparams.n_probs); + fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false"); + fprintf(stream, "penalize_nl: %s # default: false\n", sparams.penalize_nl ? "true" : "false"); + fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type); + fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride); + fprintf(stream, "presence_penalty: %f # default: 0.0\n", sparams.penalty_present); + yaml_dump_string_multiline(stream, "prompt", params.prompt.c_str()); + fprintf(stream, "prompt_cache: %s\n", params.path_prompt_cache.c_str()); + fprintf(stream, "prompt_cache_all: %s # default: false\n", params.prompt_cache_all ? "true" : "false"); + fprintf(stream, "prompt_cache_ro: %s # default: false\n", params.prompt_cache_ro ? "true" : "false"); + yaml_dump_vector_int(stream, "prompt_tokens", prompt_tokens); + fprintf(stream, "random_prompt: %s # default: false\n", params.random_prompt ? "true" : "false"); + fprintf(stream, "repeat_penalty: %f # default: 1.1\n", sparams.penalty_repeat); + + fprintf(stream, "reverse_prompt:\n"); + for (std::string ap : params.antiprompt) { + size_t pos = 0; + while ((pos = ap.find('\n', pos)) != std::string::npos) { + ap.replace(pos, 1, "\\n"); + pos += 1; + } + + fprintf(stream, " - %s\n", ap.c_str()); + } + + fprintf(stream, "rope_freq_base: %f # default: 10000.0\n", params.rope_freq_base); + fprintf(stream, "rope_freq_scale: %f # default: 1.0\n", params.rope_freq_scale); + fprintf(stream, "seed: %u # default: -1 (random seed)\n", params.seed); + fprintf(stream, "simple_io: %s # default: false\n", params.simple_io ? "true" : "false"); + fprintf(stream, "cont_batching: %s # default: false\n", params.cont_batching ? "true" : "false"); + fprintf(stream, "flash_attn: %s # default: false\n", params.flash_attn ? "true" : "false"); + fprintf(stream, "temp: %f # default: 0.8\n", sparams.temp); + + const std::vector tensor_split_vector(params.tensor_split, params.tensor_split + llama_max_devices()); + yaml_dump_vector_float(stream, "tensor_split", tensor_split_vector); + + fprintf(stream, "tfs: %f # default: 1.0\n", sparams.tfs_z); + fprintf(stream, "threads: %d # default: %u\n", params.n_threads, std::thread::hardware_concurrency()); + fprintf(stream, "top_k: %d # default: 40\n", sparams.top_k); + fprintf(stream, "top_p: %f # default: 0.95\n", sparams.top_p); + fprintf(stream, "min_p: %f # default: 0.0\n", sparams.min_p); + fprintf(stream, "typical_p: %f # default: 1.0\n", sparams.typical_p); + fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false"); + fprintf(stream, "display_prompt: %s # default: true\n", params.display_prompt ? "true" : "false"); +} diff --git a/common/common.h b/common/common.h index 940ee0ec5cd87..52f29ad5cf965 100644 --- a/common/common.h +++ b/common/common.h @@ -27,7 +27,7 @@ #define die_fmt(fmt, ...) do { fprintf(stderr, "error: " fmt "\n", __VA_ARGS__); exit(1); } while (0) #define print_build_info() do { \ - fprintf(stderr, "%s: build = %d (%s)\n", __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT); \ + fprintf(stderr, "%s: build = %d (%s)\n", __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT); \ fprintf(stderr, "%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET); \ } while(0) @@ -37,8 +37,12 @@ struct llama_control_vector_load_info; -int get_math_cpu_count(); -int32_t get_num_physical_cores(); +// +// CPU utils +// + +int32_t cpu_get_num_physical_cores(); +int32_t cpu_get_num_math(); // // CLI argument parsing @@ -47,7 +51,7 @@ int32_t get_num_physical_cores(); struct gpt_params { uint32_t seed = LLAMA_DEFAULT_SEED; // RNG seed - int32_t n_threads = get_math_cpu_count(); + int32_t n_threads = cpu_get_num_math(); int32_t n_threads_draft = -1; int32_t n_threads_batch = -1; // number of threads to use for batch processing (-1 = use n_threads) int32_t n_threads_batch_draft = -1; @@ -78,6 +82,7 @@ struct gpt_params { float yarn_beta_slow = 1.0f; // YaRN high correction dim int32_t yarn_orig_ctx = 0; // YaRN original context length float defrag_thold = -1.0f; // KV cache defragmentation threshold + std::string rpc_servers = ""; // comma separated list of RPC servers ggml_backend_sched_eval_callback cb_eval = nullptr; void * cb_eval_user_data = nullptr; @@ -97,6 +102,7 @@ struct gpt_params { float smoothing_factor = 0.00f; // 0.00 = disabled float repeat_penalty = 1.10f; // 1.0 = disabled int32_t repeat_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size) + float rep_pen_slope = 1.0f; float frequency_penalty = 0.00f; // 0.0 = disabled float presence_penalty = 0.00f; // 0.0 = disabled int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0 @@ -156,6 +162,7 @@ struct gpt_params { bool random_prompt = false; // do not randomize prompt if none provided bool use_color = false; // use color to distinguish generations and inputs bool interactive = false; // interactive mode + bool interactive_specials = false; // whether to allow special tokens from user, during interactive mode bool conversation = false; // conversation mode (does not print special tokens and suffix/prefix) bool chatml = false; // chatml mode (used for models trained on chatml syntax) bool prompt_cache_all = false; // save user input and generations to prompt cache @@ -193,33 +200,34 @@ struct gpt_params { void gpt_params_handle_model_default(gpt_params & params); -bool parse_kv_override(const char * data, std::vector & overrides); - -bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params); - -bool gpt_params_parse(int argc, char ** argv, gpt_params & params); +bool gpt_params_parse_ex (int argc, char ** argv, gpt_params & params); +bool gpt_params_parse (int argc, char ** argv, gpt_params & params); +bool gpt_params_find_arg (int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param); +void gpt_params_print_usage(int argc, char ** argv, const gpt_params & params); -void gpt_print_usage(int argc, char ** argv, const gpt_params & params); +std::string gpt_params_get_system_info(const gpt_params & params); -bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param); - -std::string get_system_info(const gpt_params & params); +// +// String utils +// -std::string gpt_random_prompt(std::mt19937 & rng); +std::vector string_split(std::string input, char separator); -void process_escapes(std::string& input); +std::string string_strip(const std::string & str); +std::string string_get_sortable_timestamp(); +std::string string_random_prompt(std::mt19937 & rng); -bool validate_file_name(const std::string & filename); +bool string_parse_kv_override(const char * data, std::vector & overrides); +void string_process_escapes(std::string & input); // -// String utils +// Filesystem utils // -std::vector sampler_types_from_names(const std::vector & names, bool allow_alt_names); -std::vector sampler_types_from_chars(const std::string & names_string); -std::vector string_split(std::string input, char separator); -std::string string_strip(const std::string & str); -std::string sampler_type_to_name_string(llama_sampler_type sampler_type); +bool fs_validate_filename(const std::string & filename); +bool fs_create_directory_with_parents(const std::string & path); + +std::string fs_get_cache_directory(); // // Model utils @@ -290,29 +298,15 @@ std::string llama_detokenize_bpe( // defaults to true when model type is SPM, otherwise false. bool llama_should_add_bos_token(const llama_model * model); -// -// YAML utils -// - -bool create_directory_with_parents(const std::string & path); -void dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::vector & data); -void dump_vector_int_yaml(FILE * stream, const char * prop_name, const std::vector & data); -void dump_string_yaml_multiline(FILE * stream, const char * prop_name, const char * data); -std::string get_sortable_timestamp(); - -void dump_non_result_info_yaml( - FILE * stream, const gpt_params & params, const llama_context * lctx, - const std::string & timestamp, const std::vector & prompt_tokens, const char * model_desc); - // // KV cache utils // // Dump the KV cache view with the number of sequences per cell. -void dump_kv_cache_view(const llama_kv_cache_view & view, int row_size = 80); +void llama_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size = 80); // Dump the KV cache view showing individual sequences in each cell (long output). -void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size = 40); +void llama_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size = 40); // // Embedding utils @@ -349,3 +343,17 @@ llama_control_vector_data llama_control_vector_load(const std::vector & data); +void yaml_dump_vector_int (FILE * stream, const char * prop_name, const std::vector & data); +void yaml_dump_string_multiline(FILE * stream, const char * prop_name, const char * data); + +void yaml_dump_non_result_info( + FILE * stream, const gpt_params & params, const llama_context * lctx, + const std::string & timestamp, const std::vector & prompt_tokens, const char * model_desc); + + diff --git a/common/grammar-parser.cpp b/common/grammar-parser.cpp index 2a1301569793a..b5bc7d49b5f36 100644 --- a/common/grammar-parser.cpp +++ b/common/grammar-parser.cpp @@ -26,7 +26,7 @@ namespace grammar_parser { static uint32_t get_symbol_id(parse_state & state, const char * src, size_t len) { uint32_t next_id = static_cast(state.symbol_ids.size()); - auto result = state.symbol_ids.insert(std::make_pair(std::string(src, len), next_id)); + auto result = state.symbol_ids.emplace(std::string(src, len), next_id); return result.first->second; } @@ -142,6 +142,9 @@ namespace grammar_parser { pos++; last_sym_start = out_elements.size(); while (*pos != '"') { + if (!*pos) { + throw std::runtime_error("unexpected end of input"); + } auto char_pair = parse_char(pos); pos = char_pair.second; out_elements.push_back({LLAMA_GRETYPE_CHAR, char_pair.first}); @@ -156,6 +159,9 @@ namespace grammar_parser { } last_sym_start = out_elements.size(); while (*pos != ']') { + if (!*pos) { + throw std::runtime_error("unexpected end of input"); + } auto char_pair = parse_char(pos); pos = char_pair.second; enum llama_gretype type = last_sym_start < out_elements.size() @@ -164,6 +170,9 @@ namespace grammar_parser { out_elements.push_back({type, char_pair.first}); if (pos[0] == '-' && pos[1] != ']') { + if (!pos[1]) { + throw std::runtime_error("unexpected end of input"); + } auto endchar_pair = parse_char(pos + 1); pos = endchar_pair.second; out_elements.push_back({LLAMA_GRETYPE_CHAR_RNG_UPPER, endchar_pair.first}); diff --git a/common/json-schema-to-grammar.cpp b/common/json-schema-to-grammar.cpp index 0f8f1b1d41bdc..9a71f5d8d76ba 100644 --- a/common/json-schema-to-grammar.cpp +++ b/common/json-schema-to-grammar.cpp @@ -272,7 +272,7 @@ class SchemaConverter { if (literal.empty()) { return false; } - ret.push_back(std::make_pair(literal, true)); + ret.emplace_back(literal, true); literal.clear(); return true; }; @@ -298,7 +298,7 @@ class SchemaConverter { while (i < length) { char c = sub_pattern[i]; if (c == '.') { - seq.push_back(std::make_pair(get_dot(), false)); + seq.emplace_back(get_dot(), false); i++; } else if (c == '(') { i++; @@ -307,7 +307,7 @@ class SchemaConverter { _warnings.push_back("Unsupported pattern syntax"); } } - seq.push_back(std::make_pair("(" + to_rule(transform()) + ")", false)); + seq.emplace_back("(" + to_rule(transform()) + ")", false); } else if (c == ')') { i++; if (start > 0 && sub_pattern[start - 1] != '(') { @@ -331,9 +331,9 @@ class SchemaConverter { } square_brackets += ']'; i++; - seq.push_back(std::make_pair(square_brackets, false)); + seq.emplace_back(square_brackets, false); } else if (c == '|') { - seq.push_back(std::make_pair("|", false)); + seq.emplace_back("|", false); i++; } else if (c == '*' || c == '+' || c == '?') { seq.back() = std::make_pair(to_rule(seq.back()) + c, false); @@ -417,7 +417,7 @@ class SchemaConverter { } } if (!literal.empty()) { - seq.push_back(std::make_pair(literal, true)); + seq.emplace_back(literal, true); } } } diff --git a/common/log.h b/common/log.h index 6934c57b2225c..09fa63c2677a7 100644 --- a/common/log.h +++ b/common/log.h @@ -211,7 +211,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std:: #define LOG_FLF_VAL , __FILE__, __LINE__, __FUNCTION__ #else #define LOG_FLF_FMT "[%24s:%5ld][%24s] " - #define LOG_FLF_VAL , __FILE__, __LINE__, __FUNCTION__ + #define LOG_FLF_VAL , __FILE__, (long)__LINE__, __FUNCTION__ #endif #else #define LOG_FLF_FMT "%s" @@ -224,7 +224,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std:: #define LOG_TEE_FLF_VAL , __FILE__, __LINE__, __FUNCTION__ #else #define LOG_TEE_FLF_FMT "[%24s:%5ld][%24s] " - #define LOG_TEE_FLF_VAL , __FILE__, __LINE__, __FUNCTION__ + #define LOG_TEE_FLF_VAL , __FILE__, (long)__LINE__, __FUNCTION__ #endif #else #define LOG_TEE_FLF_FMT "%s" @@ -294,7 +294,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std:: // Main LOG macro. // behaves like printf, and supports arguments the exact same way. // -#ifndef _MSC_VER +#if !defined(_MSC_VER) || defined(__clang__) #define LOG(...) LOG_IMPL(__VA_ARGS__, "") #else #define LOG(str, ...) LOG_IMPL("%s" str, "", ##__VA_ARGS__, "") @@ -308,14 +308,14 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std:: // Secondary target can be changed just like LOG_TARGET // by defining LOG_TEE_TARGET // -#ifndef _MSC_VER +#if !defined(_MSC_VER) || defined(__clang__) #define LOG_TEE(...) LOG_TEE_IMPL(__VA_ARGS__, "") #else #define LOG_TEE(str, ...) LOG_TEE_IMPL("%s" str, "", ##__VA_ARGS__, "") #endif // LOG macro variants with auto endline. -#ifndef _MSC_VER +#if !defined(_MSC_VER) || defined(__clang__) #define LOGLN(...) LOG_IMPL(__VA_ARGS__, "\n") #define LOG_TEELN(...) LOG_TEE_IMPL(__VA_ARGS__, "\n") #else diff --git a/common/sampling.cpp b/common/sampling.cpp index 47e446a8d81d3..11c2033bfd465 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -35,7 +35,7 @@ struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_ result->prev.resize(params.n_prev); - result->n_considered = 0; + result->n_valid = 0; llama_sampling_set_rng_seed(result, params.seed); @@ -66,7 +66,7 @@ void llama_sampling_reset(llama_sampling_context * ctx) { std::fill(ctx->prev.begin(), ctx->prev.end(), 0); ctx->cur.clear(); - ctx->n_considered = 0; + ctx->n_valid = 0; } void llama_sampling_set_rng_seed(struct llama_sampling_context * ctx, uint32_t seed) { @@ -125,7 +125,7 @@ std::string llama_sampling_order_print(const llama_sampling_params & params) { std::string result = "CFG -> Penalties "; if (params.mirostat == 0) { for (auto sampler_type : params.samplers_sequence) { - const auto sampler_type_name = sampler_type_to_name_string(sampler_type); + const auto sampler_type_name = llama_sampling_type_to_str(sampler_type); if (!sampler_type_name.empty()) { result += "-> " + sampler_type_name + " "; } @@ -137,6 +137,87 @@ std::string llama_sampling_order_print(const llama_sampling_params & params) { return result; } +std::string llama_sampling_type_to_str(llama_sampler_type sampler_type) { + switch (sampler_type) { + case llama_sampler_type::TOP_K: return "top_k"; + case llama_sampler_type::TFS_Z: return "tfs_z"; + case llama_sampler_type::TYPICAL_P: return "typical_p"; + case llama_sampler_type::TOP_P: return "top_p"; + case llama_sampler_type::MIN_P: return "min_p"; + case llama_sampler_type::TEMPERATURE: return "temperature"; + default : return ""; + } +} + +std::vector llama_sampling_types_from_names(const std::vector & names, bool allow_alt_names) { + std::unordered_map sampler_canonical_name_map { + {"top_k", llama_sampler_type::TOP_K}, + {"top_p", llama_sampler_type::TOP_P}, + {"typical_p", llama_sampler_type::TYPICAL_P}, + {"min_p", llama_sampler_type::MIN_P}, + {"tfs_z", llama_sampler_type::TFS_Z}, + {"temperature", llama_sampler_type::TEMPERATURE} + }; + + // since samplers names are written multiple ways + // make it ready for both system names and input names + std::unordered_map sampler_alt_name_map { + {"top-k", llama_sampler_type::TOP_K}, + {"top-p", llama_sampler_type::TOP_P}, + {"nucleus", llama_sampler_type::TOP_P}, + {"typical-p", llama_sampler_type::TYPICAL_P}, + {"typical", llama_sampler_type::TYPICAL_P}, + {"min-p", llama_sampler_type::MIN_P}, + {"tfs-z", llama_sampler_type::TFS_Z}, + {"tfs", llama_sampler_type::TFS_Z}, + {"temp", llama_sampler_type::TEMPERATURE} + }; + + std::vector sampler_types; + sampler_types.reserve(names.size()); + for (const auto & name : names) + { + auto sampler_item = sampler_canonical_name_map.find(name); + if (sampler_item != sampler_canonical_name_map.end()) + { + sampler_types.push_back(sampler_item->second); + } + else + { + if (allow_alt_names) + { + sampler_item = sampler_alt_name_map.find(name); + if (sampler_item != sampler_alt_name_map.end()) + { + sampler_types.push_back(sampler_item->second); + } + } + } + } + return sampler_types; +} + +std::vector llama_sampling_types_from_chars(const std::string & names_string) { + std::unordered_map sampler_name_map { + {'k', llama_sampler_type::TOP_K}, + {'p', llama_sampler_type::TOP_P}, + {'y', llama_sampler_type::TYPICAL_P}, + {'m', llama_sampler_type::MIN_P}, + {'f', llama_sampler_type::TFS_Z}, + {'t', llama_sampler_type::TEMPERATURE} + }; + + std::vector sampler_types; + sampler_types.reserve(names_string.size()); + for (const auto & c : names_string) { + const auto sampler_item = sampler_name_map.find(c); + if (sampler_item != sampler_name_map.end()) { + sampler_types.push_back(sampler_item->second); + } + } + return sampler_types; +} + // no reasons to expose this function in header static void sampler_queue( struct llama_context * ctx_main, @@ -179,7 +260,7 @@ static llama_token llama_sampling_sample_impl( struct llama_context * ctx_main, struct llama_context * ctx_cfg, const int idx, - bool is_resampling) { // Add a parameter to indicate if we are resampling + bool is_resampling) { const llama_sampling_params & params = ctx_sampling->params; const float temp = params.temp; @@ -188,8 +269,8 @@ static llama_token llama_sampling_sample_impl( const float mirostat_eta = params.mirostat_eta; std::vector original_logits; - auto cur_p = llama_sampling_prepare(ctx_sampling, ctx_main, ctx_cfg, idx, !is_resampling, &original_logits); - if (!is_resampling) { + auto cur_p = llama_sampling_prepare(ctx_sampling, ctx_main, ctx_cfg, idx, /* apply_grammar= */ is_resampling, &original_logits); + if (ctx_sampling->grammar != NULL && !is_resampling) { GGML_ASSERT(!original_logits.empty()); } llama_token id = 0; @@ -252,11 +333,11 @@ static llama_token llama_sampling_sample_impl( // Restore logits from the copy std::copy(original_logits.begin(), original_logits.end(), logits); - return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, true); // Pass true for is_resampling + return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, /* is_resampling= */ true); } } - ctx_sampling->n_considered = cur_p.size; + ctx_sampling->n_valid = temp == 0.0f ? 0 : cur_p.size; return id; } @@ -285,7 +366,8 @@ static llama_token_data_array llama_sampling_prepare_impl( // Get a pointer to the logits float * logits = llama_get_logits_ith(ctx_main, idx); - if (apply_grammar && original_logits != NULL) { + if (ctx_sampling->grammar != NULL && !apply_grammar) { + GGML_ASSERT(original_logits != NULL); // Only make a copy of the original logits if we are not applying grammar checks, not sure if I actually have to do this. *original_logits = {logits, logits + llama_n_vocab(llama_get_model(ctx_main))}; } @@ -342,7 +424,7 @@ llama_token llama_sampling_sample( struct llama_context * ctx_cfg, const int idx) { // Call the implementation function with is_resampling set to false by default - return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, false); + return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, /* is_resampling= */ false); } llama_token_data_array llama_sampling_prepare( diff --git a/common/sampling.h b/common/sampling.h index 5cabee5617822..0d76767762aa8 100644 --- a/common/sampling.h +++ b/common/sampling.h @@ -82,7 +82,7 @@ struct llama_sampling_context { // TODO: replace with ring-buffer std::vector prev; std::vector cur; - size_t n_considered; + size_t n_valid; // Number of correct top tokens with correct probabilities. std::mt19937 rng; }; @@ -117,6 +117,11 @@ std::string llama_sampling_print(const llama_sampling_params & params); // Print sampling order into a string std::string llama_sampling_order_print(const llama_sampling_params & params); +std::string llama_sampling_type_to_str(llama_sampler_type sampler_type); + +std::vector llama_sampling_types_from_names(const std::vector & names, bool allow_alt_names); +std::vector llama_sampling_types_from_chars(const std::string & names_string); + // this is a common sampling function used across the examples for convenience // it can serve as a starting point for implementing your own sampling function // Note: When using multiple sequences, it is the caller's responsibility to call diff --git a/common/train.cpp b/common/train.cpp index 0dbfd24df2314..2d41a1d29a83c 100644 --- a/common/train.cpp +++ b/common/train.cpp @@ -1380,7 +1380,7 @@ bool consume_common_train_arg( void finish_processing_train_args(struct train_params_common * params) { if (params->escape) { - process_escapes(params->sample_start); + string_process_escapes(params->sample_start); } } diff --git a/convert-hf-to-gguf-update.py b/convert-hf-to-gguf-update.py index e6468772210f8..1923b88ba2a80 100755 --- a/convert-hf-to-gguf-update.py +++ b/convert-hf-to-gguf-update.py @@ -20,11 +20,13 @@ # - Update llama.cpp with the new pre-tokenizer if necessary # # TODO: generate tokenizer tests for llama.cpp -# TODO: automate the update of convert-hf-to-gguf.py # import logging import os +import pathlib +import re + import requests import sys import json @@ -35,6 +37,7 @@ logging.basicConfig(level=logging.DEBUG) logger = logging.getLogger("convert-hf-to-gguf-update") +sess = requests.Session() class TOKENIZER_TYPE(IntEnum): @@ -69,70 +72,55 @@ class TOKENIZER_TYPE(IntEnum): {"name": "mpt", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", }, {"name": "starcoder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", }, {"name": "gpt-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/openai-community/gpt2", }, + {"name": "stablelm2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b", }, {"name": "refact", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/smallcloudai/Refact-1_6-base", }, {"name": "command-r", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereForAI/c4ai-command-r-v01", }, {"name": "qwen2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen1.5-7B", }, {"name": "olmo", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/allenai/OLMo-1.7-7B-hf", }, {"name": "dbrx", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/databricks/dbrx-base", }, + {"name": "jina-v2-en", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-en", }, # WPM! + {"name": "jina-v2-es", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-es", }, + {"name": "jina-v2-de", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-de", }, ] -# make directory "models/tokenizers" if it doesn't exist -if not os.path.exists("models/tokenizers"): - os.makedirs("models/tokenizers") - def download_file_with_auth(url, token, save_path): headers = {"Authorization": f"Bearer {token}"} - response = requests.get(url, headers=headers) - if response.status_code == 200: - with open(save_path, 'wb') as f: - f.write(response.content) - logger.info(f"File {save_path} downloaded successfully") - else: - logger.info(f"Failed to download file. Status code: {response.status_code}") + response = sess.get(url, headers=headers) + response.raise_for_status() + os.makedirs(os.path.dirname(save_path), exist_ok=True) + with open(save_path, 'wb') as f: + f.write(response.content) + logger.info(f"File {save_path} downloaded successfully") -# download the tokenizer models -for model in models: +def download_model(model): name = model["name"] repo = model["repo"] tokt = model["tokt"] - if not os.path.exists(f"models/tokenizers/{name}"): - os.makedirs(f"models/tokenizers/{name}") - else: - logger.info(f"Directory models/tokenizers/{name} already exists - skipping") - continue + os.makedirs(f"models/tokenizers/{name}", exist_ok=True) - logger.info(f"Downloading {name} to models/tokenizers/{name}") - - url = f"{repo}/raw/main/config.json" - save_path = f"models/tokenizers/{name}/config.json" - download_file_with_auth(url, token, save_path) + files = ["config.json", "tokenizer.json", "tokenizer_config.json"] + if tokt == TOKENIZER_TYPE.SPM: + files.append("tokenizer.model") - url = f"{repo}/raw/main/tokenizer.json" - save_path = f"models/tokenizers/{name}/tokenizer.json" - download_file_with_auth(url, token, save_path) + for file in files: + save_path = f"models/tokenizers/{name}/{file}" + if os.path.isfile(save_path): + logger.info(f"{name}: File {save_path} already exists - skipping") + continue + download_file_with_auth(f"{repo}/resolve/main/{file}", token, save_path) - # if downloaded file is less than 1KB, we likely need to download an LFS instead - if os.path.getsize(save_path) < 1024: - # remove the file - os.remove(save_path) - url = f"{repo}/resolve/main/tokenizer.json" - save_path = f"models/tokenizers/{name}/tokenizer.json" - download_file_with_auth(url, token, save_path) - if tokt == TOKENIZER_TYPE.SPM: - url = f"{repo}/resolve/main/tokenizer.model" - save_path = f"models/tokenizers/{name}/tokenizer.model" - download_file_with_auth(url, token, save_path) +for model in models: + try: + download_model(model) + except Exception as e: + logger.error(f"Failed to download model {model['name']}. Error: {e}") - url = f"{repo}/raw/main/tokenizer_config.json" - save_path = f"models/tokenizers/{name}/tokenizer_config.json" - download_file_with_auth(url, token, save_path) # generate the source code for the convert-hf-to-gguf.py:get_vocab_base_pre() function: -# TODO: auto-update convert-hf-to-gguf.py with the generated function src_ifs = "" for model in models: @@ -142,8 +130,17 @@ def download_file_with_auth(url, token, save_path): if tokt == TOKENIZER_TYPE.SPM: continue + # Skip if the tokenizer folder does not exist or there are other download issues previously + if not os.path.exists(f"models/tokenizers/{name}"): + logger.warning(f"Directory for tokenizer {name} not found. Skipping...") + continue + # create the tokenizer - tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}") + try: + tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}") + except OSError as e: + logger.error(f"Error loading tokenizer for model {name}. The model may not exist or is not accessible with the provided token. Error: {e}") + continue # Skip to the next model if the tokenizer can't be loaded chktok = tokenizer.encode(chktxt) chkhsh = sha256(str(chktok).encode()).hexdigest() @@ -161,6 +158,8 @@ def download_file_with_auth(url, token, save_path): logger.info("normalizer: " + json.dumps(normalizer, indent=4)) pre_tokenizer = cfg["pre_tokenizer"] logger.info("pre_tokenizer: " + json.dumps(pre_tokenizer, indent=4)) + if "ignore_merges" in cfg["model"]: + logger.info("ignore_merges: " + json.dumps(cfg["model"]["ignore_merges"], indent=4)) logger.info("") @@ -210,11 +209,18 @@ def get_vocab_base_pre(self, tokenizer) -> str: return res """ -print(src_func) # noqa: NP100 +convert_py_pth = pathlib.Path("convert-hf-to-gguf.py") +convert_py = convert_py_pth.read_text() +convert_py = re.sub( + r"(# Marker: Start get_vocab_base_pre)(.+?)( +# Marker: End get_vocab_base_pre)", + lambda m: m.group(1) + src_func + m.group(3), + convert_py, + flags=re.DOTALL | re.MULTILINE, +) -logger.info("\n") -logger.info("!!! Copy-paste the function above into convert-hf-to-gguf.py !!!") -logger.info("\n") +convert_py_pth.write_text(convert_py) + +logger.info("+++ convert-hf-to-gguf.py was updated") # generate tests for each tokenizer model @@ -282,8 +288,17 @@ def get_vocab_base_pre(self, tokenizer) -> str: name = model["name"] tokt = model["tokt"] + # Skip if the tokenizer folder does not exist or there are other download issues previously + if not os.path.exists(f"models/tokenizers/{name}"): + logger.warning(f"Directory for tokenizer {name} not found. Skipping...") + continue + # create the tokenizer - tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}") + try: + tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}") + except OSError as e: + logger.error(f"Failed to load tokenizer for model {name}. Error: {e}") + continue # Skip this model and continue with the next one in the loop with open(f"models/ggml-vocab-{name}.gguf.inp", "w", encoding="utf-8") as f: for text in tests: diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 1dc18b2a57721..5a00a5e89accb 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -12,8 +12,9 @@ from enum import IntEnum from pathlib import Path from hashlib import sha256 -from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterable, Iterator, Sequence, TypeVar, cast, overload +from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterable, Iterator, Sequence, TypeVar, cast +import math import numpy as np import torch @@ -48,7 +49,6 @@ class Model: dir_model: Path ftype: int - fname_out: Path is_big_endian: bool endianess: gguf.GGUFEndian use_temp_file: bool @@ -56,20 +56,20 @@ class Model: part_names: list[str] is_safetensors: bool hparams: dict[str, Any] - gguf_writer: gguf.GGUFWriter block_count: int tensor_map: gguf.TensorNameMap tensor_names: set[str] | None + fname_out: Path + gguf_writer: gguf.GGUFWriter # subclasses should define this! model_arch: gguf.MODEL_ARCH - def __init__(self, dir_model: Path, ftype: int, fname_out: Path, is_big_endian: bool, use_temp_file: bool, eager: bool): - if self.__class__ == Model: - raise TypeError(f"{self.__class__.__name__!r} should not be directly instantiated") + def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, is_big_endian: bool, use_temp_file: bool, eager: bool): + if type(self) is Model: + raise TypeError(f"{type(self).__name__!r} should not be directly instantiated") self.dir_model = dir_model self.ftype = ftype - self.fname_out = fname_out self.is_big_endian = is_big_endian self.endianess = gguf.GGUFEndian.BIG if is_big_endian else gguf.GGUFEndian.LITTLE self.use_temp_file = use_temp_file @@ -79,10 +79,23 @@ def __init__(self, dir_model: Path, ftype: int, fname_out: Path, is_big_endian: if not self.is_safetensors: self.part_names = Model.get_model_part_names(self.dir_model, ".bin") self.hparams = Model.load_hparams(self.dir_model) - self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file) self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer"]) self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count) self.tensor_names = None + if self.ftype == gguf.LlamaFileType.GUESSED: + # NOTE: can't use field "torch_dtype" in config.json, because some finetunes lie. + _, first_tensor = next(self.get_tensors()) + if first_tensor.dtype == torch.float16: + logger.info(f"choosing --outtype f16 from first tensor type ({first_tensor.dtype})") + self.ftype = gguf.LlamaFileType.MOSTLY_F16 + else: + logger.info(f"choosing --outtype bf16 from first tensor type ({first_tensor.dtype})") + self.ftype = gguf.LlamaFileType.MOSTLY_BF16 + ftype_up: str = self.ftype.name.partition("_")[2].upper() + ftype_lw: str = ftype_up.lower() + # allow templating the file name with the output ftype, useful with the "auto" ftype + self.fname_out = fname_out.parent / fname_out.name.format(ftype_lw, outtype=ftype_lw, ftype=ftype_lw, OUTTYPE=ftype_up, FTYPE=ftype_up) + self.gguf_writer = gguf.GGUFWriter(self.fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file) @classmethod def __init_subclass__(cls): @@ -142,14 +155,27 @@ def get_tensors(self) -> Iterator[tuple[str, Tensor]]: raise ValueError(f"Mismatch between weight map and model parts for tensor names: {sym_diff}") def format_tensor_name(self, key: gguf.MODEL_TENSOR, bid: int | None = None, suffix: str = ".weight") -> str: - name: str = gguf.TENSOR_NAMES[key] if key not in gguf.MODEL_TENSORS[self.model_arch]: raise ValueError(f"Missing {key!r} for MODEL_TENSORS of {self.model_arch!r}") + name: str = gguf.TENSOR_NAMES[key] if "{bid}" in name: assert bid is not None name = name.format(bid=bid) return name + suffix + def match_model_tensor_name(self, name: str, key: gguf.MODEL_TENSOR, bid: int | None, suffix: str = ".weight") -> bool: + if key not in gguf.MODEL_TENSORS[self.model_arch]: + return False + key_name: str = gguf.TENSOR_NAMES[key] + if "{bid}" in key_name: + if bid is None: + return False + key_name = key_name.format(bid=bid) + else: + if bid is not None: + return False + return name == (key_name + suffix) + def map_tensor_name(self, name: str, try_suffixes: Sequence[str] = (".weight", ".bias")) -> str: new_name = self.tensor_map.get_name(key=name, try_suffixes=try_suffixes) if new_name is None: @@ -239,35 +265,64 @@ def write_tensors(self): data: np.ndarray = data # type hint n_dims = len(data.shape) data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) + data_qtype: gguf.GGMLQuantizationType | None = None # when both are True, f32 should win extra_f32 = self.extra_f32_tensors(name, new_name, bid, n_dims) extra_f16 = self.extra_f16_tensors(name, new_name, bid, n_dims) # Most of the codebase that takes in 1D tensors or norms only handles F32 tensors - extra_f32 = extra_f32 or n_dims == 1 or new_name.endswith("_norm.weight") + # Conditions should closely match those in llama_model_quantize_internal in llama.cpp + extra_f32 = any(cond for cond in ( + extra_f32, + n_dims == 1, + new_name.endswith("_norm.weight"), + )) + + # Some tensor types are always in float32 + extra_f32 = extra_f32 or any(self.match_model_tensor_name(new_name, key, bid) for key in ( + gguf.MODEL_TENSOR.FFN_GATE_INP, + gguf.MODEL_TENSOR.POS_EMBD, + gguf.MODEL_TENSOR.TOKEN_TYPES, + )) # if f16 desired, convert any float32 2-dim weight tensors to float16 - extra_f16 = extra_f16 or (name.endswith(".weight") and n_dims >= 2) - - # when both extra_f32 and extra_f16 are False, convert to float32 by default - if self.ftype == 1 and data_dtype == np.float16 and (extra_f32 or not extra_f16): - data = data.astype(np.float32) - - if self.ftype == 1 and data_dtype == np.float32 and extra_f16 and not extra_f32: - data = data.astype(np.float16) - + extra_f16 = any(cond for cond in ( + extra_f16, + (name.endswith(".weight") and n_dims >= 2), + )) + + if self.ftype != gguf.LlamaFileType.ALL_F32 and extra_f16 and not extra_f32: + if self.ftype == gguf.LlamaFileType.MOSTLY_BF16: + data = gguf.quantize_bf16(data) + assert data.dtype == np.int16 + data_qtype = gguf.GGMLQuantizationType.BF16 + + elif self.ftype == gguf.LlamaFileType.MOSTLY_Q8_0 and gguf.can_quantize_to_q8_0(data): + data = gguf.quantize_q8_0(data) + assert data.dtype == np.uint8 + data_qtype = gguf.GGMLQuantizationType.Q8_0 + + else: # default to float16 for quantized tensors + if data_dtype != np.float16: + data = data.astype(np.float16) + data_qtype = gguf.GGMLQuantizationType.F16 + + if data_qtype is None: # by default, convert to float32 + if data_dtype != np.float32: + data = data.astype(np.float32) + data_qtype = gguf.GGMLQuantizationType.F32 + + block_size, type_size = gguf.GGML_QUANT_SIZES[data_qtype] # reverse shape to make it similar to the internal ggml dimension order - shape_str = f"{{{', '.join(str(n) for n in reversed(data.shape))}}}" + shape_str = f"""{{{', '.join(str(n) for n in reversed( + (*data.shape[:-1], data.shape[-1] * data.dtype.itemsize // type_size * block_size)) + )}}}""" # n_dims is implicit in the shape - logger.info(f"{f'%-{max_name_len}s' % f'{new_name},'} {old_dtype} --> {data.dtype}, shape = {shape_str}") + logger.info(f"{f'%-{max_name_len}s' % f'{new_name},'} {old_dtype} --> {data_qtype.name}, shape = {shape_str}") - self.gguf_writer.add_tensor(new_name, data) + self.gguf_writer.add_tensor(new_name, data, raw_dtype=data_qtype) def write(self): self.write_tensors() @@ -348,6 +403,7 @@ def get_vocab_base(self) -> tuple[list[str], list[int], str]: # NOTE: this function is generated by convert-hf-to-gguf-update.py # do not modify it manually! # ref: https://github.com/ggerganov/llama.cpp/pull/6920 + # Marker: Start get_vocab_base_pre def get_vocab_base_pre(self, tokenizer) -> str: # encoding this string and hashing the resulting tokens would (hopefully) give us a unique identifier that # is specific for the BPE pre-tokenizer used by the model @@ -391,6 +447,9 @@ def get_vocab_base_pre(self, tokenizer) -> str: if chkhsh == "3ce83efda5659b07b1ad37ca97ca5797ea4285d9b9ab0dc679e4a720c9da7454": # ref: https://huggingface.co/openai-community/gpt2 res = "gpt-2" + if chkhsh == "32d85c31273f8019248f2559fed492d929ea28b17e51d81d3bb36fff23ca72b3": + # ref: https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b + res = "stablelm2" if chkhsh == "6221ad2852e85ce96f791f476e0b390cf9b474c9e3d1362f53a24a06dc8220ff": # ref: https://huggingface.co/smallcloudai/Refact-1_6-base res = "refact" @@ -404,8 +463,17 @@ def get_vocab_base_pre(self, tokenizer) -> str: # ref: https://huggingface.co/allenai/OLMo-1.7-7B-hf res = "olmo" if chkhsh == "a8594e3edff7c29c003940395316294b2c623e09894deebbc65f33f1515df79e": - # ref: https://huggingface.co/databricks/dbrx-instruct + # ref: https://huggingface.co/databricks/dbrx-base res = "dbrx" + if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f": + # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-en + res = "jina-v2-en" + if chkhsh == "171aeeedd6fb548d418a7461d053f11b6f1f1fc9b387bd66640d28a4b9f5c643": + # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-es + res = "jina-v2-es" + if chkhsh == "27949a2493fc4a9f53f5b9b029c82689cfbe5d3a1929bb25e043089e28466de6": + # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-de + res = "jina-v2-de" if res is None: logger.warning("\n") @@ -426,6 +494,7 @@ def get_vocab_base_pre(self, tokenizer) -> str: logger.debug(f"chkhsh: {chkhsh}") return res + # Marker: End get_vocab_base_pre def _set_vocab_gpt2(self) -> None: tokens, toktypes, tokpre = self.get_vocab_base() @@ -463,7 +532,7 @@ def _set_vocab_qwen(self): # for this kind of tokenizer, added_vocab is not a subset of vocab, so they need to be combined added_vocab = tokenizer.special_tokens - reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in (vocab | added_vocab).items()} + reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **added_vocab}.items()} for i in range(vocab_size): if i not in reverse_vocab: @@ -508,6 +577,10 @@ def _set_vocab_sentencepiece(self): vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size()) + tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)] + scores: list[float] = [-10000.0] * vocab_size + toktypes: list[int] = [SentencePieceTokenTypes.UNKNOWN] * vocab_size + for token_id in range(tokenizer.vocab_size()): piece = tokenizer.IdToPiece(token_id) text = piece.encode("utf-8") @@ -523,21 +596,23 @@ def _set_vocab_sentencepiece(self): elif tokenizer.IsByte(token_id): toktype = SentencePieceTokenTypes.BYTE - tokens.append(text) - scores.append(score) - toktypes.append(toktype) + tokens[token_id] = text + scores[token_id] = score + toktypes[token_id] = toktype added_tokens_file = self.dir_model / 'added_tokens.json' if added_tokens_file.is_file(): with open(added_tokens_file, "r", encoding="utf-8") as f: added_tokens_json = json.load(f) - for key in added_tokens_json: - key = key.encode("utf-8") - if key not in tokens: - tokens.append(key) - scores.append(-1000.0) - toktypes.append(SentencePieceTokenTypes.USER_DEFINED) + token_id = added_tokens_json[key] + if (token_id >= vocab_size): + logger.warning(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}') + continue + + tokens[token_id] = key.encode("utf-8") + scores[token_id] = -1000.0 + toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED if vocab_size > len(tokens): pad_count = vocab_size - len(tokens) @@ -547,8 +622,6 @@ def _set_vocab_sentencepiece(self): scores.append(-1000.0) toktypes.append(SentencePieceTokenTypes.UNUSED) - assert len(tokens) == vocab_size - self.gguf_writer.add_tokenizer_model("llama") self.gguf_writer.add_tokenizer_pre("default") self.gguf_writer.add_token_list(tokens) @@ -600,6 +673,44 @@ def set_gguf_parameters(self): self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True)) self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"]) + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads")) + n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed")) + + tensors: list[tuple[str, Tensor]] = [] + + if re.match(r"gpt_neox\.layers\.\d+\.attention\.query_key_value\.weight", name): + # Map bloom-style qkv_linear to gpt-style qkv_linear + # bloom: https://github.com/huggingface/transformers/blob/main/src/transformers/models/bloom/modeling_bloom.py#L238-L252 # noqa + # gpt-2: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py#L312 # noqa + qkv_weights = data_torch.reshape((n_head, 3, n_embed // n_head, n_embed)) + data_torch = torch.cat( + ( + qkv_weights[:, 0, :, :].reshape((-1, n_embed)), + qkv_weights[:, 1, :, :].reshape((-1, n_embed)), + qkv_weights[:, 2, :, :].reshape((-1, n_embed)), + ), + dim=0, + ) + logger.info("re-format attention.linear_qkv.weight") + elif re.match(r"gpt_neox\.layers\.\d+\.attention\.query_key_value\.bias", name): + qkv_bias = data_torch.reshape((n_head, 3, n_embed // n_head)) + data_torch = torch.cat( + ( + qkv_bias[:, 0, :].reshape((n_embed,)), + qkv_bias[:, 1, :].reshape((n_embed,)), + qkv_bias[:, 2, :].reshape((n_embed,)), + ), + dim=0, + ) + logger.info("re-format attention.linear_qkv.bias") + + tensors.append((self.map_tensor_name(name), data_torch)) + + return tensors + @Model.register("BloomForCausalLM") class BloomModel(Model): @@ -783,6 +894,7 @@ def set_gguf_parameters(self): self.gguf_writer.add_head_count(head_count) self.gguf_writer.add_head_count_kv(head_count_kv) self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) + self.gguf_writer.add_file_type(self.ftype) if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]: if self.hparams["rope_scaling"].get("type") == "linear": @@ -905,6 +1017,7 @@ def set_gguf_parameters(self): self.gguf_writer.add_head_count(head_count) self.gguf_writer.add_head_count_kv(head_count_kv) self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) + self.gguf_writer.add_file_type(self.ftype) if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]: if self.hparams["rope_scaling"].get("type") == "linear": @@ -1013,6 +1126,18 @@ def set_gguf_parameters(self): class RefactModel(Model): model_arch = gguf.MODEL_ARCH.REFACT + def set_vocab(self): + super().set_vocab() + + # TODO: how to determine special FIM tokens automatically? + special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False, + special_token_types = ['prefix', 'suffix', 'middle', 'fsep', 'eot']) + special_vocab._set_special_token("prefix", 1) + special_vocab._set_special_token("suffix", 3) + special_vocab._set_special_token("middle", 2) + special_vocab._set_special_token("fsep", 4) # is this correct? + special_vocab.add_to_gguf(self.gguf_writer) + def set_gguf_parameters(self): hidden_dim = self.hparams["n_embd"] inner_dim = 4 * hidden_dim @@ -1062,45 +1187,6 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter return tensors -@Model.register("PersimmonForCausalLM") -class PersimmonModel(Model): - model_arch = gguf.MODEL_ARCH.PERSIMMON - - def set_gguf_parameters(self): - block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers")) - head_count = self.hparams["num_attention_heads"] - head_count_kv = head_count - hidden_size = self.hparams["hidden_size"] - - self.gguf_writer.add_name('persimmon-8b-chat') - self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) - self.gguf_writer.add_embedding_length(hidden_size) - self.gguf_writer.add_block_count(block_count) - self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) - - # NOTE: not sure about this change - why does the model not have a rope dimension count when it is smaller - # than the head size? - # ref: https://github.com/ggerganov/llama.cpp/pull/4889 - # self.gguf_writer.add_rope_dimension_count(hidden_size // head_count) - self.gguf_writer.add_rope_dimension_count(hidden_size // head_count // 2) - - self.gguf_writer.add_head_count(head_count) - self.gguf_writer.add_head_count_kv(head_count_kv) - self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"]) - self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"]) - - def set_vocab(self): - self._set_vocab_sentencepiece() - # self.gguf_writer.add_bos_token_id(71013) - # self.gguf_writer.add_eos_token_id(71013) - - def extra_f32_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool: - del name, new_name, bid, n_dims # unused - - # TODO: FP16 conversion produces garbage outputs. (Q8_0 does not, so..?) - return True - - @Model.register("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM") class StableLMModel(Model): model_arch = gguf.MODEL_ARCH.STABLELM @@ -1127,6 +1213,7 @@ def set_gguf_parameters(self): self.gguf_writer.add_head_count_kv(hparams["num_key_value_heads"]) self.gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True) self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_eps", "norm_eps"])) + self.gguf_writer.add_file_type(self.ftype) _q_norms: list[dict[str, Tensor]] | None = None _k_norms: list[dict[str, Tensor]] | None = None @@ -1503,6 +1590,7 @@ def set_gguf_parameters(self): self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_file_type(self.ftype) @Model.register("Qwen2ForCausalLM") @@ -1691,6 +1779,38 @@ def set_vocab(self): scores[token_id] = -1000.0 toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED + tokenizer_config_file = self.dir_model / 'tokenizer_config.json' + if tokenizer_config_file.is_file(): + with open(tokenizer_config_file, "r", encoding="utf-8") as f: + tokenizer_config_json = json.load(f) + added_tokens_decoder = tokenizer_config_json.get("added_tokens_decoder", {}) + for token_id, foken_data in added_tokens_decoder.items(): + token_id = int(token_id) + token = foken_data["content"].encode("utf-8") + if toktypes[token_id] != SentencePieceTokenTypes.UNKNOWN: + assert tokens[token_id] == token + tokens[token_id] = token + scores[token_id] = -1000.0 + toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED + if foken_data.get("special"): + toktypes[token_id] = SentencePieceTokenTypes.CONTROL + + tokenizer_file = self.dir_model / 'tokenizer.json' + if tokenizer_file.is_file(): + with open(tokenizer_file, "r", encoding="utf-8") as f: + tokenizer_json = json.load(f) + added_tokens = tokenizer_json.get("added_tokens", []) + for foken_data in added_tokens: + token_id = int(foken_data["id"]) + token = foken_data["content"].encode("utf-8") + if toktypes[token_id] != SentencePieceTokenTypes.UNKNOWN: + assert tokens[token_id] == token + tokens[token_id] = token + scores[token_id] = -1000.0 + toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED + if foken_data.get("special"): + toktypes[token_id] = SentencePieceTokenTypes.CONTROL + self.gguf_writer.add_tokenizer_model("llama") self.gguf_writer.add_tokenizer_pre("default") self.gguf_writer.add_token_list(tokens) @@ -1703,23 +1823,59 @@ def set_vocab(self): def set_gguf_parameters(self): block_count = self.find_hparam(["num_hidden_layers", "n_layer"]) - rot_pct = 1.0 n_embd = self.find_hparam(["hidden_size", "n_embd"]) n_head = self.find_hparam(["num_attention_heads", "n_head"]) + n_head_kv = self.find_hparam(["num_key_value_heads", "n_head_kv"]) rms_eps = self.find_hparam(["rms_norm_eps"]) + max_pos_embds = self.find_hparam(["n_positions", "max_position_embeddings"]) + orig_max_pos_embds = self.find_hparam(["original_max_position_embeddings"]) + rope_dims = n_embd // n_head self.gguf_writer.add_name("Phi3") - self.gguf_writer.add_context_length(self.find_hparam(["n_positions", "max_position_embeddings"])) - + self.gguf_writer.add_context_length(max_pos_embds) + self.gguf_writer.add_rope_scaling_orig_ctx_len(orig_max_pos_embds) self.gguf_writer.add_embedding_length(n_embd) - self.gguf_writer.add_feed_forward_length(8192) + self.gguf_writer.add_feed_forward_length(self.find_hparam(["intermediate_size"])) self.gguf_writer.add_block_count(block_count) self.gguf_writer.add_head_count(n_head) - self.gguf_writer.add_head_count_kv(n_head) + self.gguf_writer.add_head_count_kv(n_head_kv) self.gguf_writer.add_layer_norm_rms_eps(rms_eps) - self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head) + self.gguf_writer.add_rope_dimension_count(rope_dims) + self.gguf_writer.add_rope_freq_base(self.find_hparam(["rope_theta"])) self.gguf_writer.add_file_type(self.ftype) + # write rope scaling for long context (128k) model + rope_scaling = self.find_hparam(['rope_scaling'], True) + if (rope_scaling is None): + return + + scale = max_pos_embds / orig_max_pos_embds + + rope_scaling_type = rope_scaling.get('type', '').lower() + if len(rope_scaling_type) == 0: + raise KeyError('Missing the required key rope_scaling.type') + + if rope_scaling_type == 'su': + attn_factor = math.sqrt(1 + math.log(scale) / math.log(orig_max_pos_embds)) if scale > 1.0 else 1.0 + elif rope_scaling_type == 'yarn': + attn_factor = 0.1 * math.log(scale) + 1.0 if scale > 1.0 else 1.0 + else: + raise NotImplementedError(f'The rope scaling type {rope_scaling_type} is not supported yet') + + self.gguf_writer.add_rope_scaling_attn_factors(attn_factor) + + long_factors = rope_scaling.get('long_factor', None) + short_factors = rope_scaling.get('short_factor', None) + + if long_factors is None or short_factors is None: + raise KeyError('Missing the required key rope_scaling.long_factor or rope_scaling_short_factor') + + if len(long_factors) != len(short_factors) or len(long_factors) != rope_dims / 2: + raise ValueError(f'The length of rope long and short factors must be {rope_dims / 2}') + + self.gguf_writer.add_tensor(gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ROPE_FACTORS_LONG] + ".weight", np.array(long_factors, dtype=np.float32)) + self.gguf_writer.add_tensor(gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT] + ".weight", np.array(short_factors, dtype=np.float32)) + @Model.register("PlamoForCausalLM") class PlamoModel(Model): @@ -1740,6 +1896,7 @@ def set_gguf_parameters(self): self.gguf_writer.add_head_count(hparams["num_attention_heads"]) self.gguf_writer.add_head_count_kv(5) # hparams["num_key_value_heads"]) is wrong self.gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"]) + self.gguf_writer.add_file_type(self.ftype) def shuffle_attn_q_weight(self, data_torch): assert data_torch.size() == (5120, 5120) @@ -1919,6 +2076,7 @@ def set_gguf_parameters(self): self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"]) + self.gguf_writer.add_file_type(self.ftype) def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: num_heads = self.hparams["num_attention_heads"] @@ -2023,12 +2181,6 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter return [(self.map_tensor_name(name), data_torch)] - def extra_f32_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool: - del new_name, bid, n_dims # unused - - # not used with get_rows, must be F32 - return name == "embeddings.token_type_embeddings.weight" - @Model.register("NomicBertModel") class NomicBertModel(BertModel): @@ -2277,96 +2429,71 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter return [(self.map_tensor_name(name), data_torch)] -###### CONVERSION LOGIC ###### +@Model.register("JinaBertModel", "JinaBertForMaskedLM") +class JinaBertV2Model(BertModel): + model_arch = gguf.MODEL_ARCH.JINA_BERT_V2 + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.intermediate_size = self.hparams["intermediate_size"] + + def get_tensors(self): + for name, data in super().get_tensors(): + if 'gated_layers' in name: + d1 = data[:self.intermediate_size, :] + name1 = name.replace('gated_layers', 'gated_layers_w') + d2 = data[self.intermediate_size:, :] + name2 = name.replace('gated_layers', 'gated_layers_v') + yield name1, d1 + yield name2, d2 + continue -# tree of lazy tensors -class LazyTorchTensor: - _meta: Tensor - _data: Tensor | None - _args: tuple - _func: Callable[[tuple], Tensor] | None - - def __init__(self, *, meta: Tensor, data: Tensor | None = None, args: tuple = (), func: Callable[[tuple], Tensor] | None = None): - self._meta = meta - self._data = data - self._args = args - self._func = func + yield name, data - @staticmethod - def _recurse_apply(o: Any, fn: Callable[[Any], Any]) -> Any: - # TODO: dict and set - if isinstance(o, (list, tuple)): - L = [] - for item in o: - L.append(LazyTorchTensor._recurse_apply(item, fn)) - if isinstance(o, tuple): - L = tuple(L) - return L - elif isinstance(o, LazyTorchTensor): - return fn(o) - else: - return o - - def _wrap_fn(self, fn: Callable, use_self: bool = False) -> Callable[[Any], LazyTorchTensor]: - def wrapped_fn(*args, **kwargs): - if kwargs is None: - kwargs = {} - args = ((self,) if use_self else ()) + args - - meta_args = LazyTorchTensor._recurse_apply(args, lambda t: t._meta) - - return LazyTorchTensor(meta=fn(*meta_args, **kwargs), args=args, func=lambda a: fn(*a, **kwargs)) - return wrapped_fn - - def __getattr__(self, __name: str) -> Any: - meta_attr = getattr(self._meta, __name) - if callable(meta_attr): - return self._wrap_fn(getattr(torch.Tensor, __name), use_self=True) - elif isinstance(meta_attr, torch.Tensor): - # for things like self.T - return self._wrap_fn(lambda s: getattr(s, __name))(self) + def set_vocab(self, *args, **kwargs): + tokenizer_class = 'BertTokenizer' + with open(self.dir_model / "tokenizer_config.json", "r", encoding="utf-8") as f: + tokenizer_class = json.load(f)['tokenizer_class'] + + if tokenizer_class == 'BertTokenizer': + super().set_vocab() + elif tokenizer_class == 'RobertaTokenizer': + self._set_vocab_gpt2() + self.gguf_writer.add_token_type_count(2) else: - return meta_attr + raise NotImplementedError(f'Tokenizer {tokenizer_class} is not supported for JinaBertModel') + self.gguf_writer.add_add_bos_token(True) + self.gguf_writer.add_add_eos_token(True) + +###### CONVERSION LOGIC ###### + + +# tree of lazy tensors +class LazyTorchTensor(gguf.LazyBase): + _tensor_type = torch.Tensor + # to keep the type-checker happy + dtype: torch.dtype + shape: torch.Size + + # only used when converting a torch.Tensor to a np.ndarray _dtype_map: dict[torch.dtype, type] = { torch.float16: np.float16, torch.float32: np.float32, } - def numpy(self) -> gguf.LazyTensor: + def numpy(self) -> gguf.LazyNumpyTensor: dtype = self._dtype_map[self.dtype] - return gguf.LazyTensor(lambda: LazyTorchTensor.to_eager(self).numpy(), dtype=dtype, shape=self.shape) - - @overload - @staticmethod - def to_eager(t: Tensor | LazyTorchTensor) -> Tensor: ... - - @overload - @staticmethod - def to_eager(t: tuple) -> tuple: ... - - @staticmethod - def to_eager(t: Any) -> Any: - def simple_to_eager(_t: LazyTorchTensor) -> Tensor: - # wake up the lazy tensor - if _t._data is None and _t._func is not None: - # recurse into its arguments - _t._args = LazyTorchTensor.to_eager(_t._args) - _t._data = _t._func(_t._args) - if _t._data is not None: - return _t._data - else: - raise ValueError(f"Could not compute lazy tensor {_t!r} with args {_t._args!r}") - - # recurse into lists and/or tuples, keeping their structure - return LazyTorchTensor._recurse_apply(t, simple_to_eager) + return gguf.LazyNumpyTensor( + meta=gguf.LazyNumpyTensor.meta_with_dtype_and_shape(dtype, self.shape), + lazy=self._lazy, + args=(self,), + func=(lambda s: s[0].numpy()) + ) - @staticmethod - def from_eager(t: Tensor) -> Tensor: - if (t.__class__ == LazyTorchTensor): - return t - return LazyTorchTensor(meta=t.detach().to("meta"), data=t) # type: ignore + @classmethod + def meta_with_dtype_and_shape(cls, dtype: torch.dtype, shape: torch.Size) -> Tensor: + return torch.empty(size=shape, dtype=dtype, device="meta") @classmethod def __torch_function__(cls, func, types, args=(), kwargs=None): @@ -2377,28 +2504,8 @@ def __torch_function__(cls, func, types, args=(), kwargs=None): if func is torch.Tensor.numpy: return args[0].numpy() - if func is torch.equal: - eager_args = LazyTorchTensor.to_eager(args) - return func(*eager_args, **kwargs) - return LazyTorchTensor._wrap_fn(args[0], func)(*args, **kwargs) - - # special methods bypass __getattr__, so they need to be added manually - # ref: https://docs.python.org/3/reference/datamodel.html#special-lookup - # NOTE: LazyTorchTensor can't be a subclass of Tensor (and then be used - # as self._meta is currently used), because then the following - # operations would by default not be wrapped, and so not propagated - # when the tensor is made eager. - # It's better to get non-silent errors for not-yet-supported operators. - # TODO: add more when needed to avoid clutter, or find a more concise way - def __neg__(self, *args): # mamba - return self._wrap_fn(torch.Tensor.__neg__)(self, *args) - - def __add__(self, *args): # gemma - return self._wrap_fn(torch.Tensor.__add__)(self, *args) - - def __getitem__(self, *args): # bloom falcon refact internlm2 - return self._wrap_fn(torch.Tensor.__getitem__)(self, *args) + return LazyTorchTensor._wrap_fn(func)(*args, **kwargs) def parse_args() -> argparse.Namespace: @@ -2414,11 +2521,11 @@ def parse_args() -> argparse.Namespace: ) parser.add_argument( "--outfile", type=Path, - help="path to write to; default: based on input", + help="path to write to; default: based on input. {ftype} will be replaced by the outtype.", ) parser.add_argument( - "--outtype", type=str, choices=["f32", "f16"], default="f16", - help="output format - use f32 for float32, f16 for float16", + "--outtype", type=str, choices=["f32", "f16", "bf16", "q8_0", "auto"], default="f16", + help="output format - use f32 for float32, f16 for float16, bf16 for bfloat16, q8_0 for Q8_0, auto for the highest-fidelity 16-bit float type depending on the first loaded tensor type", ) parser.add_argument( "--bigendian", action="store_true", @@ -2472,16 +2579,19 @@ def main() -> None: logger.error(f'Error: {args.model} is not a directory') sys.exit(1) - ftype_map = { - "f32": gguf.GGMLQuantizationType.F32, - "f16": gguf.GGMLQuantizationType.F16, + ftype_map: dict[str, gguf.LlamaFileType] = { + "f32": gguf.LlamaFileType.ALL_F32, + "f16": gguf.LlamaFileType.MOSTLY_F16, + "bf16": gguf.LlamaFileType.MOSTLY_BF16, + "q8_0": gguf.LlamaFileType.MOSTLY_Q8_0, + "auto": gguf.LlamaFileType.GUESSED, } if args.outfile is not None: fname_out = args.outfile else: # output in the same directory as the model by default - fname_out = dir_model / f'ggml-model-{args.outtype}.gguf' + fname_out = dir_model / 'ggml-model-{ftype}.gguf' logger.info(f"Loading model: {dir_model.name}") @@ -2497,14 +2607,16 @@ def main() -> None: logger.info("Set model tokenizer") model_instance.set_vocab() + model_instance.gguf_writer.add_quantization_version(gguf.GGML_QUANT_VERSION) + if args.vocab_only: - logger.info(f"Exporting model vocab to '{fname_out}'") + logger.info(f"Exporting model vocab to '{model_instance.fname_out}'") model_instance.write_vocab() else: - logger.info(f"Exporting model to '{fname_out}'") + logger.info(f"Exporting model to '{model_instance.fname_out}'") model_instance.write() - logger.info(f"Model successfully exported to '{fname_out}'") + logger.info(f"Model successfully exported to '{model_instance.fname_out}'") if __name__ == '__main__': diff --git a/convert-lora-to-ggml.py b/convert-lora-to-ggml.py deleted file mode 100755 index f09fa85febef3..0000000000000 --- a/convert-lora-to-ggml.py +++ /dev/null @@ -1,150 +0,0 @@ -#!/usr/bin/env python3 -from __future__ import annotations - -import logging -import json -import os -import struct -import sys -from pathlib import Path -from typing import Any, BinaryIO, Sequence - -import numpy as np -import torch - -if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) -import gguf - -logging.basicConfig(level=logging.DEBUG) -logger = logging.getLogger("lora-to-gguf") - -NUMPY_TYPE_TO_FTYPE: dict[str, int] = {"float32": 0, "float16": 1} - - -def write_file_header(fout: BinaryIO, params: dict[str, Any]) -> None: - fout.write(b"ggla"[::-1]) # magic (ggml lora) - fout.write(struct.pack("i", 1)) # file version - fout.write(struct.pack("i", params["r"])) - # https://opendelta.readthedocs.io/en/latest/modules/deltas.html says that `lora_alpha` is an int - # but some models ship a float value instead - # let's convert to int, but fail if lossless conversion is not possible - assert ( - int(params["lora_alpha"]) == params["lora_alpha"] - ), "cannot convert float to int losslessly" - fout.write(struct.pack("i", int(params["lora_alpha"]))) - - -def write_tensor_header(fout: BinaryIO, name: str, shape: Sequence[int], data_type: np.dtype[Any]) -> None: - sname = name.encode("utf-8") - fout.write( - struct.pack( - "iii", - len(shape), - len(sname), - NUMPY_TYPE_TO_FTYPE[data_type.name], - ) - ) - fout.write(struct.pack("i" * len(shape), *shape[::-1])) - fout.write(sname) - fout.seek((fout.tell() + 31) & -32) - - -if __name__ == '__main__': - if len(sys.argv) < 2: - logger.info(f"Usage: python {sys.argv[0]} [arch]") - logger.info("Path must contain HuggingFace PEFT LoRA files 'adapter_config.json' and 'adapter_model.bin'") - logger.info(f"Arch must be one of {list(gguf.MODEL_ARCH_NAMES.values())} (default: llama)") - sys.exit(1) - - input_json = os.path.join(sys.argv[1], "adapter_config.json") - input_model = os.path.join(sys.argv[1], "adapter_model.bin") - output_path = os.path.join(sys.argv[1], "ggml-adapter-model.bin") - - if os.path.exists(input_model): - model = torch.load(input_model, map_location="cpu") - else: - input_model = os.path.join(sys.argv[1], "adapter_model.safetensors") - # lazy import load_file only if lora is in safetensors format. - from safetensors.torch import load_file - model = load_file(input_model, device="cpu") - - arch_name = sys.argv[2] if len(sys.argv) == 3 else "llama" - - if arch_name not in gguf.MODEL_ARCH_NAMES.values(): - logger.error(f"Error: unsupported architecture {arch_name}") - sys.exit(1) - - arch = list(gguf.MODEL_ARCH_NAMES.keys())[list(gguf.MODEL_ARCH_NAMES.values()).index(arch_name)] - name_map = gguf.TensorNameMap(arch, 200) # 200 layers ought to be enough for anyone - - with open(input_json, "r") as f: - params = json.load(f) - - if params["peft_type"] != "LORA": - logger.error(f"Error: unsupported adapter type {params['peft_type']}, expected LORA") - sys.exit(1) - - if params["fan_in_fan_out"] is True: - logger.error("Error: param fan_in_fan_out is not supported") - sys.exit(1) - - if params["bias"] is not None and params["bias"] != "none": - logger.error("Error: param bias is not supported") - sys.exit(1) - - # TODO: these seem to be layers that have been trained but without lora. - # doesn't seem widely used but eventually should be supported - if params["modules_to_save"] is not None and len(params["modules_to_save"]) > 0: - logger.error("Error: param modules_to_save is not supported") - sys.exit(1) - - with open(output_path, "wb") as fout: - fout.truncate() - - write_file_header(fout, params) - for k, v in model.items(): - orig_k = k - if k.endswith(".default.weight"): - k = k.replace(".default.weight", ".weight") - if k in ["llama_proj.weight", "llama_proj.bias"]: - continue - if k.endswith("lora_A.weight"): - if v.dtype != torch.float16 and v.dtype != torch.float32: - v = v.float() - v = v.T - else: - v = v.float() - - t = v.detach().numpy() - - prefix = "base_model.model." - if k.startswith(prefix): - k = k[len(prefix) :] - - lora_suffixes = (".lora_A.weight", ".lora_B.weight") - if k.endswith(lora_suffixes): - suffix = k[-len(lora_suffixes[0]):] - k = k[: -len(lora_suffixes[0])] - else: - logger.error(f"Error: unrecognized tensor name {orig_k}") - sys.exit(1) - - tname = name_map.get_name(k) - if tname is None: - logger.error(f"Error: could not map tensor name {orig_k}") - logger.error(" Note: the arch parameter must be specified if the model is not llama") - sys.exit(1) - - if suffix == ".lora_A.weight": - tname += ".weight.loraA" - elif suffix == ".lora_B.weight": - tname += ".weight.loraB" - else: - assert False - - logger.info(f"{k} => {tname} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB") - write_tensor_header(fout, tname, t.shape, t.dtype) - t.tofile(fout) - - logger.info(f"Converted {input_json} and {input_model} to {output_path}") diff --git a/convert-persimmon-to-gguf.py b/convert-persimmon-to-gguf.py deleted file mode 100755 index 07dcade747a5a..0000000000000 --- a/convert-persimmon-to-gguf.py +++ /dev/null @@ -1,143 +0,0 @@ -#!/usr/bin/env python3 -from __future__ import annotations - -import logging -import argparse -import os -import sys -from pathlib import Path -from pprint import pprint - -import torch -from sentencepiece import SentencePieceProcessor - -if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) -import gguf - -logger = logging.getLogger("persimmon-to-gguf") - - -def _flatten_dict(dct, tensors, prefix=None): - assert isinstance(dct, dict) - for key in dct.keys(): - new_prefix = prefix + '.' + key if prefix is not None else key - if isinstance(dct[key], torch.Tensor): - tensors[new_prefix] = dct[key] - elif isinstance(dct[key], dict): - _flatten_dict(dct[key], tensors, new_prefix) - else: - raise ValueError(type(dct[key])) - return None - - -def _get_sentencepiece_tokenizer_info(dir_model: Path): - tokenizer_path = dir_model / 'adept_vocab.model' - logger.info('getting sentencepiece tokenizer from', tokenizer_path) - tokenizer = SentencePieceProcessor(str(tokenizer_path)) - logger.info('adding tokens') - tokens: list[bytes] = [] - scores: list[float] = [] - toktypes: list[int] = [] - - for i in range(tokenizer.vocab_size()): - text: bytes - score: float - - piece = tokenizer.id_to_piece(i) - text = piece.encode("utf-8") - score = tokenizer.get_score(i) - - toktype = 1 - if tokenizer.is_unknown(i): - toktype = 2 - if tokenizer.is_control(i): - toktype = 3 - if tokenizer.is_unused(i): - toktype = 5 - if tokenizer.is_byte(i): - toktype = 6 - - tokens.append(text) - scores.append(score) - toktypes.append(toktype) - pass - return tokens, scores, toktypes - - -def main(): - parser = argparse.ArgumentParser(description="Convert a Persimmon model from Adept (e.g. Persimmon 8b chat) to a GGML compatible file") - parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") - parser.add_argument("--ckpt-path", type=Path, help="path to persimmon checkpoint .pt file") - parser.add_argument("--model-dir", type=Path, help="directory containing model e.g. 8b_chat_model_release") - parser.add_argument("--adept-inference-dir", type=str, help="path to adept-inference code directory") - parser.add_argument("--verbose", action="store_true", help="increase output verbosity") - args = parser.parse_args() - logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO) - sys.path.append(str(args.adept_inference_dir)) - persimmon_model = torch.load(args.ckpt_path) - hparams = persimmon_model['args'] - pprint(hparams) - tensors: dict[str, torch.Tensor] = {} - _flatten_dict(persimmon_model['model'], tensors, None) - - arch = gguf.MODEL_ARCH.PERSIMMON - gguf_writer = gguf.GGUFWriter(args.outfile, gguf.MODEL_ARCH_NAMES[arch]) - - block_count = hparams.num_layers - head_count = hparams.num_attention_heads - head_count_kv = head_count - ctx_length = hparams.seq_length - hidden_size = hparams.hidden_size - - gguf_writer.add_name('persimmon-8b-chat') - gguf_writer.add_context_length(ctx_length) - gguf_writer.add_embedding_length(hidden_size) - gguf_writer.add_block_count(block_count) - gguf_writer.add_feed_forward_length(hparams.ffn_hidden_size) - # ref: https://github.com/ggerganov/llama.cpp/pull/4889/commits/eea19039fc52ea2dbd1aab45b59ab4e3e29a3443 - gguf_writer.add_rope_dimension_count(hidden_size // head_count // 2) - gguf_writer.add_head_count(head_count) - gguf_writer.add_head_count_kv(head_count_kv) - gguf_writer.add_rope_freq_base(hparams.rotary_emb_base) - gguf_writer.add_layer_norm_eps(hparams.layernorm_epsilon) - - tokens, scores, toktypes = _get_sentencepiece_tokenizer_info(args.model_dir) - gguf_writer.add_tokenizer_model('llama') - gguf_writer.add_tokenizer_pre('default') - gguf_writer.add_token_list(tokens) - gguf_writer.add_token_scores(scores) - gguf_writer.add_token_types(toktypes) - gguf_writer.add_bos_token_id(71013) - gguf_writer.add_eos_token_id(71013) - - tensor_map = gguf.get_tensor_name_map(arch, block_count) - logger.info(tensor_map) - for name in tensors.keys(): - data_torch = tensors[name] - if name.endswith(".self_attention.rotary_emb.inv_freq"): - continue - old_dtype = data_torch.dtype - # TODO: FP16 conversion produces garbage outputs. (Q8_0 does not, so..?) - data = data_torch.to(torch.float32).squeeze().numpy() - new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) - if new_name is None: - raise ValueError(f"Can not map tensor '{name}'") - - n_dims = len(data.shape) - logger.debug(f"{new_name}, n_dims = {str(n_dims)}, {str(old_dtype)} --> {str(data.dtype)}") - gguf_writer.add_tensor(new_name, data) - logger.info("gguf: write header") - gguf_writer.write_header_to_file() - logger.info("gguf: write metadata") - gguf_writer.write_kv_data_to_file() - logger.info("gguf: write tensors") - gguf_writer.write_tensors_to_file() - - gguf_writer.close() - - logger.info(f"gguf: model successfully exported to '{args.outfile}'") - - -if __name__ == '__main__': - main() diff --git a/convert.py b/convert.py index 148bfd66ad65b..da1247957780c 100755 --- a/convert.py +++ b/convert.py @@ -24,7 +24,7 @@ from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor from dataclasses import dataclass from pathlib import Path -from typing import TYPE_CHECKING, Any, Callable, ClassVar, IO, Iterable, Literal, Protocol, TypeVar, runtime_checkable +from typing import TYPE_CHECKING, Any, Callable, ClassVar, IO, Iterable, Literal, Protocol, TypeVar, runtime_checkable, Optional import numpy as np from sentencepiece import SentencePieceProcessor @@ -344,10 +344,47 @@ def load(model_plus: ModelPlus) -> Params: return params +@dataclass +class Metadata: + name: Optional[str] = None + author: Optional[str] = None + version: Optional[str] = None + url: Optional[str] = None + description: Optional[str] = None + licence: Optional[str] = None + source_url: Optional[str] = None + source_hf_repo: Optional[str] = None + + @staticmethod + def load(metadata_path: Path) -> Metadata: + if metadata_path is None or not metadata_path.exists(): + return Metadata() + + with open(metadata_path, 'r') as file: + data = json.load(file) + + # Create a new Metadata instance + metadata = Metadata() + + # Assigning values to Metadata attributes if they exist in the JSON file + # This is based on LLM_KV_NAMES mapping in llama.cpp + metadata.name = data.get("general.name") + metadata.author = data.get("general.author") + metadata.version = data.get("general.version") + metadata.url = data.get("general.url") + metadata.description = data.get("general.description") + metadata.license = data.get("general.license") + metadata.source_url = data.get("general.source.url") + metadata.source_hf_repo = data.get("general.source.huggingface.repository") + + return metadata + + # # vocab # + @runtime_checkable class BaseVocab(Protocol): tokenizer_model: ClassVar[str] @@ -1066,21 +1103,42 @@ class OutputFile: def __init__(self, fname_out: Path, endianess:gguf.GGUFEndian = gguf.GGUFEndian.LITTLE): self.gguf = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH], endianess=endianess) - def add_meta_arch(self, params: Params) -> None: + def add_meta_model(self, params: Params, metadata: Metadata) -> None: + # Metadata About The Model And Its Provenence name = "LLaMA" - - # TODO: better logic to determine model name - if params.n_ctx == 4096: - name = "LLaMA v2" + if metadata is not None and metadata.name is not None: + name = metadata.name elif params.path_model is not None: - name = str(params.path_model.parent).split('/')[-1] - - self.gguf.add_name (name) - self.gguf.add_vocab_size (params.n_vocab) - self.gguf.add_context_length (params.n_ctx) - self.gguf.add_embedding_length (params.n_embd) - self.gguf.add_block_count (params.n_layer) - self.gguf.add_feed_forward_length (params.n_ff) + name = params.path_model.name + elif params.n_ctx == 4096: + # Heuristic detection of LLaMA v2 model + name = "LLaMA v2" + + self.gguf.add_name(name) + + if metadata is not None: + if metadata.author is not None: + self.gguf.add_author(metadata.author) + if metadata.version is not None: + self.gguf.add_version(metadata.version) + if metadata.url is not None: + self.gguf.add_url(metadata.url) + if metadata.description is not None: + self.gguf.add_description(metadata.description) + if metadata.licence is not None: + self.gguf.add_licence(metadata.licence) + if metadata.source_url is not None: + self.gguf.add_source_url(metadata.source_url) + if metadata.source_hf_repo is not None: + self.gguf.add_source_hf_repo(metadata.source_hf_repo) + + def add_meta_arch(self, params: Params) -> None: + # Metadata About The Neural Architecture Itself + self.gguf.add_vocab_size(params.n_vocab) + self.gguf.add_context_length(params.n_ctx) + self.gguf.add_embedding_length(params.n_embd) + self.gguf.add_block_count(params.n_layer) + self.gguf.add_feed_forward_length(params.n_ff) self.gguf.add_rope_dimension_count(params.n_embd // params.n_head) self.gguf.add_head_count (params.n_head) self.gguf.add_head_count_kv (params.n_head_kv) @@ -1183,13 +1241,14 @@ def close(self) -> None: @staticmethod def write_vocab_only( fname_out: Path, params: Params, vocab: Vocab, svocab: gguf.SpecialVocab, - endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE, pad_vocab: bool = False, + endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE, pad_vocab: bool = False, metadata: Metadata = None, ) -> None: check_vocab_size(params, vocab, pad_vocab=pad_vocab) of = OutputFile(fname_out, endianess=endianess) # meta data + of.add_meta_model(params, metadata) of.add_meta_arch(params) of.add_meta_vocab(vocab) of.add_meta_special_vocab(svocab) @@ -1216,12 +1275,14 @@ def write_all( fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: BaseVocab, svocab: gguf.SpecialVocab, concurrency: int = DEFAULT_CONCURRENCY, endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE, pad_vocab: bool = False, + metadata: Metadata = None, ) -> None: check_vocab_size(params, vocab, pad_vocab=pad_vocab) of = OutputFile(fname_out, endianess=endianess) # meta data + of.add_meta_model(params, metadata) of.add_meta_arch(params) if isinstance(vocab, Vocab): of.add_meta_vocab(vocab) @@ -1257,6 +1318,37 @@ def pick_output_type(model: LazyModel, output_type_str: str | None) -> GGMLFileT raise ValueError(f"Unexpected combination of types: {name_to_type}") +def model_parameter_count(model: LazyModel) -> int: + total_model_parameters = 0 + for i, (name, lazy_tensor) in enumerate(model.items()): + sum_weights_in_tensor = 1 + for dim in lazy_tensor.shape: + sum_weights_in_tensor *= dim + total_model_parameters += sum_weights_in_tensor + return total_model_parameters + + +def model_parameter_count_rounded_notation(model_params_count: int) -> str: + if model_params_count > 1e12 : + # Trillions Of Parameters + scaled_model_params = model_params_count * 1e-12 + scale_suffix = "T" + elif model_params_count > 1e9 : + # Billions Of Parameters + scaled_model_params = model_params_count * 1e-9 + scale_suffix = "B" + elif model_params_count > 1e6 : + # Millions Of Parameters + scaled_model_params = model_params_count * 1e-6 + scale_suffix = "M" + else: + # Thousands Of Parameters + scaled_model_params = model_params_count * 1e-3 + scale_suffix = "K" + + return f"{round(scaled_model_params)}{scale_suffix}" + + def convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyModel: return {name: tensor.astype(output_type.type_for_tensor(name, tensor)) for (name, tensor) in model.items()} @@ -1436,13 +1528,35 @@ def load_vocab(self, vocab_types: list[str] | None, model_parent_path: Path) -> return vocab, special_vocab -def default_outfile(model_paths: list[Path], file_type: GGMLFileType) -> Path: - namestr = { - GGMLFileType.AllF32: "f32", - GGMLFileType.MostlyF16: "f16", - GGMLFileType.MostlyQ8_0:"q8_0", +def default_convention_outfile(file_type: GGMLFileType, params: Params, model_params_count: int, metadata: Metadata) -> str: + quantization = { + GGMLFileType.AllF32: "F32", + GGMLFileType.MostlyF16: "F16", + GGMLFileType.MostlyQ8_0: "Q8_0", }[file_type] - ret = model_paths[0].parent / f"ggml-model-{namestr}.gguf" + + parameters = model_parameter_count_rounded_notation(model_params_count) + + expert_count = "" + if params.n_experts is not None: + expert_count = f"{params.n_experts}x" + + version = "" + if metadata is not None and metadata.version is not None: + version = f"-{metadata.version}" + + name = "ggml-model" + if metadata is not None and metadata.name is not None: + name = metadata.name + elif params.path_model is not None: + name = params.path_model.name + + return f"{name}{version}-{expert_count}{parameters}-{quantization}" + + +def default_outfile(model_paths: list[Path], file_type: GGMLFileType, params: Params, model_params_count: int, metadata: Metadata) -> Path: + default_filename = default_convention_outfile(file_type, params, model_params_count, metadata) + ret = model_paths[0].parent / f"{default_filename}.gguf" if ret in model_paths: logger.error( f"Error: Default output path ({ret}) would overwrite the input. " @@ -1480,17 +1594,30 @@ def main(args_in: list[str] | None = None) -> None: parser.add_argument("--pad-vocab", action="store_true", help="add pad tokens when model vocab expects more than tokenizer metadata provides") parser.add_argument("--skip-unknown", action="store_true", help="skip unknown tensor names instead of failing") parser.add_argument("--verbose", action="store_true", help="increase output verbosity") + parser.add_argument("--metadata", type=Path, help="Specify the path for a metadata file") + parser.add_argument("--get-outfile", action="store_true", help="get calculated default outfile name") args = parser.parse_args(args_in) if args.verbose: logging.basicConfig(level=logging.DEBUG) - elif args.dump_single or args.dump: + elif args.dump_single or args.dump or args.get_outfile: # Avoid printing anything besides the dump output logging.basicConfig(level=logging.WARNING) else: logging.basicConfig(level=logging.INFO) + metadata = Metadata.load(args.metadata) + + if args.get_outfile: + model_plus = load_some_model(args.model) + params = Params.load(model_plus) + model = convert_model_names(model_plus.model, params, args.skip_unknown) + model_params_count = model_parameter_count(model_plus.model) + ftype = pick_output_type(model, args.outtype) + print(f"{default_convention_outfile(ftype, params, model_params_count, metadata)}") # noqa: NP100 + return + if args.no_vocab and args.vocab_only: raise ValueError("--vocab-only does not make sense with --no-vocab") @@ -1504,6 +1631,9 @@ def main(args_in: list[str] | None = None) -> None: else: model_plus = ModelPlus(model = {}, paths = [args.model / 'dummy'], format = 'none', vocab = None) + model_params_count = model_parameter_count(model_plus.model) + logger.info(f"model parameters count : {model_params_count} ({model_parameter_count_rounded_notation(model_params_count)})") + if args.dump: do_dump_model(model_plus) return @@ -1557,7 +1687,7 @@ def main(args_in: list[str] | None = None) -> None: f_norm_eps = 1e-5, ) OutputFile.write_vocab_only(outfile, params, vocab, special_vocab, - endianess=endianess, pad_vocab=args.pad_vocab) + endianess=endianess, pad_vocab=args.pad_vocab, metadata=metadata) logger.info(f"Wrote {outfile}") return @@ -1570,13 +1700,13 @@ def main(args_in: list[str] | None = None) -> None: model = convert_model_names(model, params, args.skip_unknown) ftype = pick_output_type(model, args.outtype) model = convert_to_output_type(model, ftype) - outfile = args.outfile or default_outfile(model_plus.paths, ftype) + outfile = args.outfile or default_outfile(model_plus.paths, ftype, params, model_params_count, metadata) params.ftype = ftype logger.info(f"Writing {outfile}, format {ftype}") OutputFile.write_all(outfile, ftype, params, model, vocab, special_vocab, - concurrency=args.concurrency, endianess=endianess, pad_vocab=args.pad_vocab) + concurrency=args.concurrency, endianess=endianess, pad_vocab=args.pad_vocab, metadata=metadata) logger.info(f"Wrote {outfile}") diff --git a/docs/debugging-tests.md b/docs/debugging-tests.md new file mode 100644 index 0000000000000..18407f688f9db --- /dev/null +++ b/docs/debugging-tests.md @@ -0,0 +1,104 @@ +# Debugging Tests Tips + +## How to run & execute or debug a specific test without anything else to keep the feedback loop short? + +There is a script called debug-test.sh in the scripts folder whose parameter takes a REGEX and an optional test number. + +For example, running the following command will output an interactive list from which you can select a test. It takes this form: + +`debug-test.sh [OPTION]... ` + +It will then build & run in the debugger for you. + +To just execute a test and get back a PASS or FAIL message run: + +```bash +./scripts/debug-test.sh test-tokenizer +``` + +To test in GDB use the `-g` flag to enable gdb test mode. + +```bash +./scripts/debug-test.sh -g test-tokenizer + +# Once in the debugger, i.e. at the chevrons prompt, setting a breakpoint could be as follows: +>>> b main +``` + +To speed up the testing loop, if you know your test number you can just run it similar to below: + +```bash +./scripts/debug-test.sh test 23 +``` + +For further reference use `debug-test.sh -h` to print help. + +  + +### How does the script work? +If you want to be able to use the concepts contained in the script separately, the important ones are briefly outlined below. + +#### Step 1: Reset and Setup folder context + +From base of this repository, let's create `build-ci-debug` as our build context. + +```bash +rm -rf build-ci-debug && mkdir build-ci-debug && cd build-ci-debug +``` + +#### Step 2: Setup Build Environment and Compile Test Binaries + +Setup and trigger a build under debug mode. You may adapt the arguments as needed, but in this case these are sane defaults. + +```bash +cmake -DCMAKE_BUILD_TYPE=Debug -DLLAMA_CUDA=1 -DLLAMA_FATAL_WARNINGS=ON .. +make -j +``` + +#### Step 3: Find all tests available that matches REGEX + +The output of this command will give you the command & arguments needed to run GDB. + +* `-R test-tokenizer` : looks for all the test files named `test-tokenizer*` (R=Regex) +* `-N` : "show-only" disables test execution & shows test commands that you can feed to GDB. +* `-V` : Verbose Mode + +```bash +ctest -R "test-tokenizer" -V -N +``` + +This may return output similar to below (focusing on key lines to pay attention to): + +```bash +... +1: Test command: ~/llama.cpp/build-ci-debug/bin/test-tokenizer-0 "~/llama.cpp/tests/../models/ggml-vocab-llama-spm.gguf" +1: Working Directory: . +Labels: main + Test #1: test-tokenizer-0-llama-spm +... +4: Test command: ~/llama.cpp/build-ci-debug/bin/test-tokenizer-0 "~/llama.cpp/tests/../models/ggml-vocab-falcon.gguf" +4: Working Directory: . +Labels: main + Test #4: test-tokenizer-0-falcon +... +``` + +#### Step 4: Identify Test Command for Debugging + +So for test #1 above we can tell these two pieces of relevant information: +* Test Binary: `~/llama.cpp/build-ci-debug/bin/test-tokenizer-0` +* Test GGUF Model: `~/llama.cpp/tests/../models/ggml-vocab-llama-spm.gguf` + +#### Step 5: Run GDB on test command + +Based on the ctest 'test command' report above we can then run a gdb session via this command below: + +```bash +gdb --args ${Test Binary} ${Test GGUF Model} +``` + +Example: + +```bash +gdb --args ~/llama.cpp/build-ci-debug/bin/test-tokenizer-0 "~/llama.cpp/tests/../models/ggml-vocab-llama-spm.gguf" +``` diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index f421769cc2f0a..b40ee4ccb2ec1 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -49,4 +49,7 @@ else() add_subdirectory(server) endif() add_subdirectory(export-lora) + if (LLAMA_RPC) + add_subdirectory(rpc) + endif() endif() diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp index be30d20bf8194..591bc6e57645c 100644 --- a/examples/batched/batched.cpp +++ b/examples/batched/batched.cpp @@ -48,7 +48,7 @@ int main(int argc, char ** argv) { params.prompt = "Hello my name is"; } - process_escapes(params.prompt); + string_process_escapes(params.prompt); // init LLM diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index 3415c9206edec..e17ff2a12d8de 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -50,6 +50,12 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu } float * out = output + batch.seq_id[i][0] * n_embd; + //TODO: I would also add a parameter here to enable normalization or not. + /*fprintf(stdout, "unnormalized_embedding:"); + for (int hh = 0; hh < n_embd; hh++) { + fprintf(stdout, "%9.6f ", embd[hh]); + } + fprintf(stdout, "\n");*/ llama_embd_normalize(embd, out, n_embd); } } @@ -75,7 +81,7 @@ int main(int argc, char ** argv) { std::mt19937 rng(params.seed); if (params.random_prompt) { - params.prompt = gpt_random_prompt(rng); + params.prompt = string_random_prompt(rng); } llama_backend_init(); @@ -102,7 +108,7 @@ int main(int argc, char ** argv) { // print system information { fprintf(stderr, "\n"); - fprintf(stderr, "%s\n", get_system_info(params).c_str()); + fprintf(stderr, "%s\n", gpt_params_get_system_info(params).c_str()); } // split the prompt into lines @@ -124,10 +130,12 @@ int main(int argc, char ** argv) { inputs.push_back(inp); } - // add SEP if not present + // check if the last token is SEP + // it should be automatically added by the tokenizer when 'tokenizer.ggml.add_eos_token' is set to 'true' for (auto & inp : inputs) { if (inp.empty() || inp.back() != llama_token_sep(model)) { - inp.push_back(llama_token_sep(model)); + fprintf(stderr, "%s: warning: last token in the prompt is not SEP\n", __func__); + fprintf(stderr, "%s: 'tokenizer.ggml.add_eos_token' should be set to 'true' in the GGUF header\n", __func__); } } @@ -204,6 +212,7 @@ int main(int argc, char ** argv) { // clean up llama_print_timings(ctx); + llama_batch_free(batch); llama_free(ctx); llama_free_model(model); llama_backend_free(); diff --git a/examples/eval-callback/eval-callback.cpp b/examples/eval-callback/eval-callback.cpp index e670d3769c7e8..51d67d6d97ae6 100644 --- a/examples/eval-callback/eval-callback.cpp +++ b/examples/eval-callback/eval-callback.cpp @@ -152,7 +152,7 @@ int main(int argc, char ** argv) { std::mt19937 rng(params.seed); if (params.random_prompt) { - params.prompt = gpt_random_prompt(rng); + params.prompt = string_random_prompt(rng); } llama_backend_init(); @@ -176,7 +176,7 @@ int main(int argc, char ** argv) { // print system information { fprintf(stderr, "\n"); - fprintf(stderr, "%s\n", get_system_info(params).c_str()); + fprintf(stderr, "%s\n", gpt_params_get_system_info(params).c_str()); } bool OK = run(ctx, params); diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp index 22743b1bf02fd..22425730f20eb 100644 --- a/examples/finetune/finetune.cpp +++ b/examples/finetune/finetune.cpp @@ -563,8 +563,8 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs( // not capturing these, to silcence warnings const int rope_mode = 0; - return ggml_rope_custom(ctx, - t, KQ_pos, n_rot, rope_mode, n_ctx, 0, + return ggml_rope_ext(ctx, + t, KQ_pos, nullptr, n_rot, rope_mode, n_ctx, 0, rope_freq_base, rope_freq_scale, 0.0f, 1.0f, 0.0f, 0.0f ); }; @@ -643,7 +643,8 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs( struct ggml_tensor * t15 = ggml_permute (ctx, t12, 0, 3, 1, 2); set_name(t15, "t15"); assert_shape_4d(t15, N, n_embd_head, n_head_kv, n_batch); struct ggml_tensor * t16; if (enable_flash_attn) { - t16 = ggml_flash_attn(ctx, t13, t14, t15, true); set_name(t16, "t16"); assert_shape_4d(t16, n_embd_head, N, n_head, n_batch); + GGML_ASSERT(false && "TODO: ggml_flash_attn_ext() not yet supported"); + //t16 = ggml_flash_attn(ctx, t13, t14, t15, true); set_name(t16, "t16"); assert_shape_4d(t16, n_embd_head, N, n_head, n_batch); } else { struct ggml_tensor * t16_0 = ggml_mul_mat (ctx, t14, t13); set_name(t16_0, "t16_0"); assert_shape_4d(t16_0, N, N, n_head, n_batch); struct ggml_tensor * t16_1 = ggml_scale_inplace (ctx, t16_0, kv_scale); set_name(t16_1, "t16_1"); assert_shape_4d(t16_1, N, N, n_head, n_batch); diff --git a/examples/imatrix/imatrix.cpp b/examples/imatrix/imatrix.cpp index da7cfeaee3e0f..55a7cc9cd76a1 100644 --- a/examples/imatrix/imatrix.cpp +++ b/examples/imatrix/imatrix.cpp @@ -599,7 +599,7 @@ int main(int argc, char ** argv) { std::mt19937 rng(params.seed); if (params.random_prompt) { - params.prompt = gpt_random_prompt(rng); + params.prompt = string_random_prompt(rng); } sparams.dataset = params.prompt_file; @@ -668,7 +668,7 @@ int main(int argc, char ** argv) { // print system information { fprintf(stderr, "\n"); - fprintf(stderr, "%s\n", get_system_info(params).c_str()); + fprintf(stderr, "%s\n", gpt_params_get_system_info(params).c_str()); } bool OK = compute_imatrix(ctx, params, compute_ppl, from_chunk); diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp index 13a8bbe1cd04d..9800c71b88214 100644 --- a/examples/infill/infill.cpp +++ b/examples/infill/infill.cpp @@ -51,9 +51,9 @@ static void write_logfile( return; } - const std::string timestamp = get_sortable_timestamp(); + const std::string timestamp = string_get_sortable_timestamp(); - const bool success = create_directory_with_parents(params.logdir); + const bool success = fs_create_directory_with_parents(params.logdir); if (!success) { fprintf(stderr, "%s: warning: failed to create logdir %s, cannot write logfile\n", __func__, params.logdir.c_str()); @@ -71,7 +71,7 @@ static void write_logfile( fprintf(logfile, "binary: infill\n"); char model_desc[128]; llama_model_desc(model, model_desc, sizeof(model_desc)); - dump_non_result_info_yaml(logfile, params, ctx, timestamp, input_tokens, model_desc); + yaml_dump_non_result_info(logfile, params, ctx, timestamp, input_tokens, model_desc); fprintf(logfile, "\n"); fprintf(logfile, "######################\n"); @@ -79,8 +79,8 @@ static void write_logfile( fprintf(logfile, "######################\n"); fprintf(logfile, "\n"); - dump_string_yaml_multiline(logfile, "output", output.c_str()); - dump_vector_int_yaml(logfile, "output_tokens", output_tokens); + yaml_dump_string_multiline(logfile, "output", output.c_str()); + yaml_dump_vector_int(logfile, "output_tokens", output_tokens); llama_dump_timing_info_yaml(logfile, ctx); fclose(logfile); @@ -237,7 +237,7 @@ int main(int argc, char ** argv) { // print system information { LOG_TEE("\n"); - LOG_TEE("%s\n", get_system_info(params).c_str()); + LOG_TEE("%s\n", gpt_params_get_system_info(params).c_str()); } const bool add_bos = llama_should_add_bos_token(model); GGML_ASSERT(llama_add_eos_token(model) != 1); @@ -622,8 +622,8 @@ int main(int argc, char ** argv) { if (params.escape) { //process escape sequences, for the initial prompt this is done in common.cpp when we load the params, but for the interactive mode we need to do it here - process_escapes(params.input_prefix); - process_escapes(params.input_suffix); + string_process_escapes(params.input_prefix); + string_process_escapes(params.input_suffix); } suff_rm_leading_spc = params.escape; if (suff_rm_leading_spc && params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1) { diff --git a/examples/llama-bench/README.md b/examples/llama-bench/README.md index 10f37b4418897..8578405646af7 100644 --- a/examples/llama-bench/README.md +++ b/examples/llama-bench/README.md @@ -26,16 +26,21 @@ options: -m, --model (default: models/7B/ggml-model-q4_0.gguf) -p, --n-prompt (default: 512) -n, --n-gen (default: 128) - -b, --batch-size (default: 512) - -ctk , --cache-type-k (default: f16) - -ctv , --cache-type-v (default: f16) - -t, --threads (default: 112) + -pg (default: 512,128) + -b, --batch-size (default: 2048) + -ub, --ubatch-size (default: 512) + -ctk, --cache-type-k (default: f16) + -ctv, --cache-type-v (default: f16) + -t, --threads (default: 16) -ngl, --n-gpu-layers (default: 99) -sm, --split-mode (default: layer) -mg, --main-gpu (default: 0) -nkvo, --no-kv-offload <0|1> (default: 0) + -fa, --flash-attn <0|1> (default: 0) -mmp, --mmap <0|1> (default: 1) - -ts, --tensor_split (default: 0) + --numa (default: disabled) + -embd, --embeddings <0|1> (default: 0) + -ts, --tensor-split (default: 0) -r, --repetitions (default: 5) -o, --output (default: md) -v, --verbose (default: 0) @@ -43,10 +48,11 @@ options: Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times. ``` -llama-bench can perform two types of tests: +llama-bench can perform three types of tests: - Prompt processing (pp): processing a prompt in batches (`-p`) - Text generation (tg): generating a sequence of tokens (`-n`) +- Prompt processing + text generation (pg): processing a prompt followed by generating a sequence of tokens (`-pg`) With the exception of `-r`, `-o` and `-v`, all options can be specified multiple times to run multiple tests. Each pp and tg test is run with all combinations of the specified options. To specify multiple values for an option, the values can be separated by commas (e.g. `-n 16,32`), or the option can be specified multiple times (e.g. `-n 16 -n 32`). diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index fabe1f4be577e..c2ddd7e017627 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -162,10 +162,17 @@ static const char * split_mode_str(llama_split_mode mode) { } } +static std::string pair_str(const std::pair & p) { + static char buf[32]; + snprintf(buf, sizeof(buf), "%d,%d", p.first, p.second); + return buf; +} + struct cmd_params { std::vector model; std::vector n_prompt; std::vector n_gen; + std::vector> n_pg; std::vector n_batch; std::vector n_ubatch; std::vector type_k; @@ -189,11 +196,12 @@ static const cmd_params cmd_params_defaults = { /* model */ {"models/7B/ggml-model-q4_0.gguf"}, /* n_prompt */ {512}, /* n_gen */ {128}, + /* n_pg */ {}, /* n_batch */ {2048}, /* n_ubatch */ {512}, /* type_k */ {GGML_TYPE_F16}, /* type_v */ {GGML_TYPE_F16}, - /* n_threads */ {get_math_cpu_count()}, + /* n_threads */ {cpu_get_num_math()}, /* n_gpu_layers */ {99}, /* split_mode */ {LLAMA_SPLIT_MODE_LAYER}, /* main_gpu */ {0}, @@ -216,10 +224,11 @@ static void print_usage(int /* argc */, char ** argv) { printf(" -m, --model (default: %s)\n", join(cmd_params_defaults.model, ",").c_str()); printf(" -p, --n-prompt (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str()); printf(" -n, --n-gen (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str()); + printf(" -pg (default: %s)\n", join(transform_to_str(cmd_params_defaults.n_pg, pair_str), ",").c_str()); printf(" -b, --batch-size (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str()); - printf(" -ub N, --ubatch-size (default: %s)\n", join(cmd_params_defaults.n_ubatch, ",").c_str()); - printf(" -ctk , --cache-type-k (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str()); - printf(" -ctv , --cache-type-v (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str()); + printf(" -ub, --ubatch-size (default: %s)\n", join(cmd_params_defaults.n_ubatch, ",").c_str()); + printf(" -ctk, --cache-type-k (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str()); + printf(" -ctv, --cache-type-v (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str()); printf(" -t, --threads (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str()); printf(" -ngl, --n-gpu-layers (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str()); printf(" -sm, --split-mode (default: %s)\n", join(transform_to_str(cmd_params_defaults.split_mode, split_mode_str), ",").c_str()); @@ -305,6 +314,17 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { } auto p = split(argv[i], split_delim); params.n_gen.insert(params.n_gen.end(), p.begin(), p.end()); + } else if (arg == "-pg") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = split(argv[i], ','); + if (p.size() != 2) { + invalid_param = true; + break; + } + params.n_pg.push_back({std::stoi(p[0]), std::stoi(p[1])}); } else if (arg == "-b" || arg == "--batch-size") { if (++i >= argc) { invalid_param = true; @@ -494,6 +514,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { if (params.model.empty()) { params.model = cmd_params_defaults.model; } if (params.n_prompt.empty()) { params.n_prompt = cmd_params_defaults.n_prompt; } if (params.n_gen.empty()) { params.n_gen = cmd_params_defaults.n_gen; } + if (params.n_pg.empty()) { params.n_pg = cmd_params_defaults.n_pg; } if (params.n_batch.empty()) { params.n_batch = cmd_params_defaults.n_batch; } if (params.n_ubatch.empty()) { params.n_ubatch = cmd_params_defaults.n_ubatch; } if (params.type_k.empty()) { params.type_k = cmd_params_defaults.type_k; } @@ -633,6 +654,31 @@ static std::vector get_cmd_params_instances(const cmd_param }; instances.push_back(instance); } + + for (const auto & n_pg : params.n_pg) { + if (n_pg.first == 0 && n_pg.second == 0) { + continue; + } + cmd_params_instance instance = { + /* .model = */ m, + /* .n_prompt = */ n_pg.first, + /* .n_gen = */ n_pg.second, + /* .n_batch = */ nb, + /* .n_ubatch = */ nub, + /* .type_k = */ tk, + /* .type_v = */ tv, + /* .n_threads = */ nt, + /* .n_gpu_layers = */ nl, + /* .split_mode = */ sm, + /* .main_gpu = */ mg, + /* .no_kv_offload= */ nkvo, + /* .flash_attn = */ fa, + /* .tensor_split = */ ts, + /* .use_mmap = */ mmp, + /* .embeddings = */ embd, + }; + instances.push_back(instance); + } } return instances; @@ -966,6 +1012,9 @@ struct markdown_printer : public printer { if (field == "n_gpu_layers") { return 3; } + if (field == "test") { + return 13; + } int width = std::max((int)field.length(), 10); @@ -1092,12 +1141,11 @@ struct markdown_printer : public printer { value = test::get_backend(); } else if (field == "test") { if (t.n_prompt > 0 && t.n_gen == 0) { - snprintf(buf, sizeof(buf), "pp %d", t.n_prompt); + snprintf(buf, sizeof(buf), "pp%d", t.n_prompt); } else if (t.n_gen > 0 && t.n_prompt == 0) { - snprintf(buf, sizeof(buf), "tg %d", t.n_gen); + snprintf(buf, sizeof(buf), "tg%d", t.n_gen); } else { - assert(false); - exit(1); + snprintf(buf, sizeof(buf), "pp%d+tg%d", t.n_prompt, t.n_gen); } value = buf; } else if (field == "t/s") { @@ -1298,6 +1346,7 @@ int main(int argc, char ** argv) { llama_kv_cache_clear(ctx); uint64_t t_start = get_time_ns(); + if (t.n_prompt > 0) { test_prompt(ctx, t.n_prompt, 0, t.n_batch, t.n_threads); } diff --git a/examples/llama.android/app/src/main/cpp/CMakeLists.txt b/examples/llama.android/app/src/main/cpp/CMakeLists.txt index 85139329aa082..4536974a5c50c 100644 --- a/examples/llama.android/app/src/main/cpp/CMakeLists.txt +++ b/examples/llama.android/app/src/main/cpp/CMakeLists.txt @@ -12,15 +12,20 @@ cmake_minimum_required(VERSION 3.22.1) # build script scope). project("llama-android") -include(FetchContent) -FetchContent_Declare( - llama - GIT_REPOSITORY https://github.com/ggerganov/llama.cpp - GIT_TAG master -) +## Fetch latest llama.cpp from GitHub +#include(FetchContent) +#FetchContent_Declare( +# llama +# GIT_REPOSITORY https://github.com/ggerganov/llama.cpp +# GIT_TAG master +#) +# +## Also provides "common" +#FetchContent_MakeAvailable(llama) -# Also provides "common" -FetchContent_MakeAvailable(llama) +# llama.cpp CI uses the code from the current branch +# ref: https://github.com/ggerganov/llama.cpp/pull/7341#issuecomment-2117617700 +add_subdirectory(../../../../../../ build-llama) # Creates and names a library, sets it as either STATIC # or SHARED, and provides the relative paths to its source code. diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index f568f470c8f5c..95fbe3d0216c4 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -104,6 +104,7 @@ static std::string format(const char * fmt, ...) { #define TN_POS_EMBD "%s.position_embd.weight" #define TN_CLASS_EMBD "v.class_embd" #define TN_PATCH_EMBD "v.patch_embd.weight" +#define TN_PATCH_BIAS "v.patch_embd.bias" #define TN_ATTN_K "%s.blk.%d.attn_k.%s" #define TN_ATTN_Q "%s.blk.%d.attn_q.%s" #define TN_ATTN_V "%s.blk.%d.attn_v.%s" @@ -425,6 +426,7 @@ struct clip_vision_model { // embeddings struct ggml_tensor * class_embedding; struct ggml_tensor * patch_embeddings; + struct ggml_tensor * patch_bias; struct ggml_tensor * position_embeddings; struct ggml_tensor * pre_ln_w; @@ -501,6 +503,11 @@ struct clip_ctx { bool use_gelu = false; int32_t ftype = 1; + bool has_class_embedding = true; + bool has_pre_norm = true; + bool has_post_norm = false; + bool has_patch_bias = false; + struct gguf_context * ctx_gguf; struct ggml_context * ctx_data; @@ -526,7 +533,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 const int patch_size = hparams.patch_size; const int num_patches = ((image_size / patch_size) * (image_size / patch_size)); const int num_patches_per_side = image_size / patch_size; GGML_UNUSED(num_patches_per_side); - const int num_positions = num_patches + 1; + const int num_positions = num_patches + (ctx->has_class_embedding ? 1 : 0); const int hidden_size = hparams.hidden_size; const int n_head = hparams.n_head; const int d_head = hidden_size / n_head; @@ -557,16 +564,23 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 inp = ggml_reshape_3d(ctx0, inp, num_patches, hidden_size, batch_size); inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 0, 2, 3)); - // concat class_embeddings and patch_embeddings - struct ggml_tensor * embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size); - ggml_set_name(embeddings, "embeddings"); - ggml_set_input(embeddings); + if (ctx->has_patch_bias) { + // inp = ggml_add(ctx0, inp, ggml_repeat(ctx0, model.patch_bias, inp)); + inp = ggml_add(ctx0, inp, model.patch_bias); + } - embeddings = ggml_acc(ctx0, embeddings, model.class_embedding, - embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], 0); + // concat class_embeddings and patch_embeddings + struct ggml_tensor * embeddings = inp; + if (ctx->has_class_embedding) { + embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size); + ggml_set_name(embeddings, "embeddings"); + ggml_set_input(embeddings); + embeddings = ggml_acc(ctx0, embeddings, model.class_embedding, + embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], 0); + embeddings = ggml_acc(ctx0, embeddings, inp, + embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], model.class_embedding->nb[1]); + } - embeddings = ggml_acc(ctx0, embeddings, inp, - embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], model.class_embedding->nb[1]); struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions); ggml_set_name(positions, "positions"); @@ -576,7 +590,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 ggml_add(ctx0, embeddings, ggml_get_rows(ctx0, model.position_embeddings, positions)); // pre-layernorm - { + if (ctx->has_pre_norm) { embeddings = ggml_norm(ctx0, embeddings, eps); ggml_set_name(embeddings, "pre_ln"); @@ -664,6 +678,14 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 embeddings = cur; } + // post-layernorm + if (ctx->has_post_norm) { + embeddings = ggml_norm(ctx0, embeddings, eps); + ggml_set_name(embeddings, "post_ln"); + + embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.post_ln_w), model.post_ln_b); + } + // llava projector { embeddings = ggml_reshape_2d(ctx0, embeddings, embeddings->ne[0], embeddings->ne[1]); @@ -1148,12 +1170,39 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { } + try { + vision_model.class_embedding = get_tensor(new_clip->ctx_data, TN_CLASS_EMBD); + new_clip->has_class_embedding = true; + } catch (const std::exception& e) { + new_clip->has_class_embedding = false; + } + + try { + vision_model.pre_ln_w = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight")); + vision_model.pre_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias")); + new_clip->has_pre_norm = true; + } catch (std::exception & e) { + new_clip->has_pre_norm = false; + } + + try { + vision_model.post_ln_w = get_tensor(new_clip->ctx_data, format(TN_LN_POST, "v", "weight")); + vision_model.post_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_POST, "v", "bias")); + new_clip->has_post_norm = true; + } catch (std::exception & e) { + new_clip->has_post_norm = false; + } + + try { + vision_model.patch_bias = get_tensor(new_clip->ctx_data, TN_PATCH_BIAS); + new_clip->has_patch_bias = true; + } catch (std::exception & e) { + new_clip->has_patch_bias = false; + } + try { vision_model.patch_embeddings = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD); - vision_model.class_embedding = get_tensor(new_clip->ctx_data, TN_CLASS_EMBD); vision_model.position_embeddings = get_tensor(new_clip->ctx_data, format(TN_POS_EMBD, "v")); - vision_model.pre_ln_w = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight")); - vision_model.pre_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias")); } catch(const std::exception& e) { LOG_TEE("%s: failed to load vision model tensors\n", __func__); } @@ -1797,7 +1846,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima const int image_size = hparams.image_size; const int patch_size = hparams.patch_size; const int num_patches = ((image_size / patch_size) * (image_size / patch_size)); - const int num_positions = num_patches + 1; + const int num_positions = num_patches + (ctx->has_class_embedding ? 1 : 0); { struct ggml_tensor * inp_raw = ggml_graph_get_tensor(gf, "inp_raw"); @@ -1825,12 +1874,14 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima } { - struct ggml_tensor * embeddings = ggml_graph_get_tensor(gf, "embeddings"); + if (ctx->has_class_embedding) { + struct ggml_tensor * embeddings = ggml_graph_get_tensor(gf, "embeddings"); - void* zero_mem = malloc(ggml_nbytes(embeddings)); - memset(zero_mem, 0, ggml_nbytes(embeddings)); - ggml_backend_tensor_set(embeddings, zero_mem, 0, ggml_nbytes(embeddings)); - free(zero_mem); + void* zero_mem = malloc(ggml_nbytes(embeddings)); + memset(zero_mem, 0, ggml_nbytes(embeddings)); + ggml_backend_tensor_set(embeddings, zero_mem, 0, ggml_nbytes(embeddings)); + free(zero_mem); + } } { diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp index 157a680b5ecdb..c974900f21e20 100644 --- a/examples/llava/llava-cli.cpp +++ b/examples/llava/llava-cli.cpp @@ -189,6 +189,11 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_ LOG_TEE("\n"); struct llama_sampling_context * ctx_sampling = llama_sampling_init(params->sparams); + if (!ctx_sampling) { + fprintf(stderr, "%s: failed to initialize sampling subsystem\n", __func__); + exit(1); + } + std::string response = ""; for (int i = 0; i < max_tgt_len; i++) { const char * tmp = sample(ctx_sampling, ctx_llava->ctx_llama, &n_past); @@ -285,7 +290,7 @@ int main(int argc, char ** argv) { #endif // LOG_DISABLE_LOGS if (params.mmproj.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) { - gpt_print_usage(argc, argv, params); + gpt_params_print_usage(argc, argv, params); show_additional_info(argc, argv); return 1; } @@ -295,14 +300,10 @@ int main(int argc, char ** argv) { return 1; } - for (auto & image : params.image) { + if (prompt_contains_image(params.prompt)) { auto ctx_llava = llava_init_context(¶ms, model); - auto image_embed = load_image(ctx_llava, ¶ms, image); - if (!image_embed) { - std::cerr << "error: failed to load image " << image << ". Terminating\n\n"; - return 1; - } + auto image_embed = load_image(ctx_llava, ¶ms, ""); // process the prompt process_prompt(ctx_llava, image_embed, ¶ms, params.prompt); @@ -311,7 +312,26 @@ int main(int argc, char ** argv) { llava_image_embed_free(image_embed); ctx_llava->model = NULL; llava_free(ctx_llava); + } else { + for (auto & image : params.image) { + auto ctx_llava = llava_init_context(¶ms, model); + + auto image_embed = load_image(ctx_llava, ¶ms, image); + if (!image_embed) { + std::cerr << "error: failed to load image " << image << ". Terminating\n\n"; + return 1; + } + + // process the prompt + process_prompt(ctx_llava, image_embed, ¶ms, params.prompt); + + llama_print_timings(ctx_llava->ctx_llama); + llava_image_embed_free(image_embed); + ctx_llava->model = NULL; + llava_free(ctx_llava); + } } + llama_free_model(model); return 0; diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp index 9a990bb182f35..63878d176b0bb 100644 --- a/examples/llava/llava.cpp +++ b/examples/llava/llava.cpp @@ -88,7 +88,6 @@ static struct clip_image_grid_shape get_anyres_image_grid_shape(const std::pair< // Take the image segments in a grid configuration and return the embeddings and the number of embeddings into preallocated memory (image_embd_out) static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector & image_embd_v, struct clip_image_grid_shape grid_shape, float * image_embd_out, int * n_img_pos_out) { struct { - struct ggml_tensor * newline; struct ggml_context * ctx; } model; @@ -150,20 +149,6 @@ static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector model.ctx = ggml_init(params); - ggml_tensor * newline_tmp = clip_get_newline_tensor(ctx_clip); - model.newline = ggml_new_tensor_1d(model.ctx, GGML_TYPE_F32, newline_tmp->ne[0]); - if (newline_tmp->backend != GGML_BACKEND_TYPE_CPU) { - if (newline_tmp->buffer == NULL) { - LOG_TEE("newline_tmp tensor buffer is NULL\n"); - } - ggml_backend_tensor_get(newline_tmp, model.newline->data, 0, ggml_nbytes(newline_tmp)); - } else { - model.newline->data = newline_tmp->data; - if (model.newline->data == NULL) { - LOG_TEE("newline_tmp tensor data is NULL\n"); - } - } - struct ggml_tensor * image_features = ggml_new_tensor_3d(model.ctx, GGML_TYPE_F32, clip_n_mmproj_embd(ctx_clip), clip_n_patches(ctx_clip), num_images - 1); // example: 4096 x 576 x 4 // ggml_tensor_printf(image_features,"image_features",__LINE__,false,false); // fill it with the image embeddings, ignoring the base diff --git a/examples/lookahead/lookahead.cpp b/examples/lookahead/lookahead.cpp index 9c3540b2008c2..54f060a85b263 100644 --- a/examples/lookahead/lookahead.cpp +++ b/examples/lookahead/lookahead.cpp @@ -174,7 +174,7 @@ int main(int argc, char ** argv) { // debug if (dump_kv_cache) { llama_kv_cache_view_update(ctx, &kvc_view); - dump_kv_cache_view_seqs(kvc_view, 40); + llama_kv_cache_dump_view_seqs(kvc_view, 40); } // build the mask from https://lmsys.org/blog/2023-11-21-lookahead-decoding/ diff --git a/examples/lookup/lookup.cpp b/examples/lookup/lookup.cpp index eebbd00a58e66..83dbee91a8362 100644 --- a/examples/lookup/lookup.cpp +++ b/examples/lookup/lookup.cpp @@ -121,7 +121,7 @@ int main(int argc, char ** argv){ // debug if (dump_kv_cache) { llama_kv_cache_view_update(ctx, &kvc_view); - dump_kv_cache_view_seqs(kvc_view, 40); + llama_kv_cache_dump_view_seqs(kvc_view, 40); } // print current draft sequence diff --git a/examples/main/README.md b/examples/main/README.md index 97e2ae4c2dc43..ee930f4e79a0d 100644 --- a/examples/main/README.md +++ b/examples/main/README.md @@ -325,3 +325,5 @@ These options provide extra functionality and customization when running the LLa - `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance. - `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model (implies --no-mmap). This allows you to adapt the pretrained model to specific tasks or domains. - `--lora-base FNAME`: Optional model to use as a base for the layers modified by the LoRA adapter. This flag is used in conjunction with the `--lora` flag, and specifies the base model for the adaptation. + +- `-hfr URL --hf-repo URL`: The url to the Hugging Face model repository. Used in conjunction with `--hf-file` or `-hff`. The model is downloaded and stored in the file provided by `-m` or `--model`. If `-m` is not provided, the model is auto-stored in the path specified by the `LLAMA_CACHE` environment variable or in an OS-specific local cache. diff --git a/examples/main/main.cpp b/examples/main/main.cpp index cc9ee8c740408..368030c853b3f 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -61,9 +61,9 @@ static void write_logfile( return; } - const std::string timestamp = get_sortable_timestamp(); + const std::string timestamp = string_get_sortable_timestamp(); - const bool success = create_directory_with_parents(params.logdir); + const bool success = fs_create_directory_with_parents(params.logdir); if (!success) { fprintf(stderr, "%s: warning: failed to create logdir %s, cannot write logfile\n", __func__, params.logdir.c_str()); @@ -81,7 +81,7 @@ static void write_logfile( fprintf(logfile, "binary: main\n"); char model_desc[128]; llama_model_desc(model, model_desc, sizeof(model_desc)); - dump_non_result_info_yaml(logfile, params, ctx, timestamp, input_tokens, model_desc); + yaml_dump_non_result_info(logfile, params, ctx, timestamp, input_tokens, model_desc); fprintf(logfile, "\n"); fprintf(logfile, "######################\n"); @@ -89,8 +89,8 @@ static void write_logfile( fprintf(logfile, "######################\n"); fprintf(logfile, "\n"); - dump_string_yaml_multiline(logfile, "output", output.c_str()); - dump_vector_int_yaml(logfile, "output_tokens", output_tokens); + yaml_dump_string_multiline(logfile, "output", output.c_str()); + yaml_dump_vector_int(logfile, "output_tokens", output_tokens); llama_dump_timing_info_yaml(logfile, ctx); fclose(logfile); @@ -182,7 +182,7 @@ int main(int argc, char ** argv) { std::mt19937 rng(params.seed); if (params.random_prompt) { - params.prompt = gpt_random_prompt(rng); + params.prompt = string_random_prompt(rng); } LOG("%s: llama backend init\n", __func__); @@ -220,7 +220,7 @@ int main(int argc, char ** argv) { // print system information { LOG_TEE("\n"); - LOG_TEE("%s\n", get_system_info(params).c_str()); + LOG_TEE("%s\n", gpt_params_get_system_info(params).c_str()); } std::string path_session = params.path_prompt_cache; @@ -475,12 +475,12 @@ int main(int argc, char ** argv) { LOG_TEE("\n\n"); if (params.interactive) { - const char *control_message; + const char * control_message; if (params.multiline_input) { - control_message = " - To return control to LLaMa, end your input with '\\'.\n" + control_message = " - To return control to the AI, end your input with '\\'.\n" " - To return control without starting a new line, end your input with '/'.\n"; } else { - control_message = " - Press Return to return control to LLaMa.\n" + control_message = " - Press Return to return control to the AI.\n" " - To return control without starting a new line, end your input with '/'.\n" " - If you want to submit another line, end your input with '\\'.\n"; } @@ -524,6 +524,10 @@ int main(int argc, char ** argv) { } struct llama_sampling_context * ctx_sampling = llama_sampling_init(sparams); + if (!ctx_sampling) { + fprintf(stderr, "%s: failed to initialize sampling subsystem\n", __func__); + exit(1); + } while ((n_remain != 0 && !is_antiprompt) || params.interactive) { // predict @@ -704,7 +708,7 @@ int main(int argc, char ** argv) { const llama_token id = llama_sampling_sample(ctx_sampling, ctx, ctx_guidance); - llama_sampling_accept(ctx_sampling, ctx, id, true); + llama_sampling_accept(ctx_sampling, ctx, id, /* apply_grammar= */ true); LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, ctx_sampling->prev).c_str()); @@ -725,7 +729,7 @@ int main(int argc, char ** argv) { // push the prompt in the sampling context in order to apply repetition penalties later // for the prompt, we don't apply grammar rules - llama_sampling_accept(ctx_sampling, ctx, embd_inp[n_consumed], false); + llama_sampling_accept(ctx_sampling, ctx, embd_inp[n_consumed], /* apply_grammar= */ false); ++n_consumed; if ((int) embd.size() >= params.n_batch) { @@ -876,11 +880,11 @@ int main(int argc, char ** argv) { embd_inp.insert(embd_inp.end(), cml_pfx.begin(), cml_pfx.end()); } if (params.escape) { - process_escapes(buffer); + string_process_escapes(buffer); } const auto line_pfx = ::llama_tokenize(ctx, params.input_prefix, false, true); - const auto line_inp = ::llama_tokenize(ctx, buffer, false, false); + const auto line_inp = ::llama_tokenize(ctx, buffer, false, params.interactive_specials); const auto line_sfx = ::llama_tokenize(ctx, params.input_suffix, false, true); LOG("input tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp).c_str()); diff --git a/examples/parallel/parallel.cpp b/examples/parallel/parallel.cpp index ed6b66036e843..1e62e972ad808 100644 --- a/examples/parallel/parallel.cpp +++ b/examples/parallel/parallel.cpp @@ -212,7 +212,7 @@ int main(int argc, char ** argv) { while (true) { if (dump_kv_cache) { llama_kv_cache_view_update(ctx, &kvc_view); - dump_kv_cache_view_seqs(kvc_view, 40); + llama_kv_cache_dump_view_seqs(kvc_view, 40); } llama_batch_clear(batch); diff --git a/examples/perplexity/README.md b/examples/perplexity/README.md index c5e2bc5de9690..33a46d1a2e38b 100644 --- a/examples/perplexity/README.md +++ b/examples/perplexity/README.md @@ -7,6 +7,8 @@ Also note that finetunes typically result in a higher perplexity value even thou Within llama.cpp the perplexity of base models is used primarily to judge the quality loss from e.g. quantized models vs. FP16. The convention among contributors is to use the Wikitext-2 test set for testing unless noted otherwise (can be obtained with `scripts/get-wikitext-2.sh`). +When numbers are listed all command line arguments and compilation options are left at their defaults unless noted otherwise. +llama.cpp numbers are **not** directly comparable to those of other projects because the exact values depend strongly on the implementation details. By default only the mean perplexity value and the corresponding uncertainty is calculated. The uncertainty is determined empirically by assuming a Gaussian distribution of the "correct" logits per and then applying error propagation. @@ -32,12 +34,21 @@ In addition to the KL divergence the following statistics are calculated with `- ## LLaMA 3 8b Scoreboard -Results are sorted by Kullback-Leibler divergence relative to FP16. +| Revision | f364eb6f | +|:---------|:-------------------| +| Backend | CUDA | +| CPU | AMD Epyc 7742 | +| GPU | 1x NVIDIA RTX 4090 | + +Results were generated using the CUDA backend and are sorted by Kullback-Leibler divergence relative to FP16. The "WT" importance matrices were created using varying numbers of Wikitext tokens and can be found [here](https://huggingface.co/JohannesGaessler/llama.cpp_importance_matrices/blob/main/imatrix-llama_3-8b-f16-2.7m_tokens.dat). +Note: the FP16 logits used for the calculation of all metrics other than perplexity are stored in a binary file between runs. +In order to save space this file does **not** contain the exact same FP32 logits but instead casts them to 16 bit unsigned integers (with some scaling). +So the "f16" results are to be understood as the difference resulting only from this downcast. | Quantization | imatrix | Model size [GiB] | PPL | ΔPPL | KLD | Mean Δp | RMS Δp | |--------------|---------|------------------|------------------------|------------------------|-----------------------|-------------------|------------------| -| f16 | None | 14.97 | 6.233160 ± 0.037828 | - | - | - | - | +| f16 | None | 14.97 | 6.233160 ± 0.037828 | 0.001524 ± 0.000755 | 0.000551 ± 0.000002 | 0.001 ± 0.002 % | 0.787 ± 0.004 % | | q8_0 | None | 7.96 | 6.234284 ± 0.037878 | 0.002650 ± 0.001006 | 0.001355 ± 0.000006 | -0.019 ± 0.003 % | 1.198 ± 0.007 % | | q6_K | None | 6.14 | 6.253382 ± 0.038078 | 0.021748 ± 0.001852 | 0.005452 ± 0.000035 | -0.007 ± 0.006 % | 2.295 ± 0.019 % | | q5_K_M | None | 5.33 | 6.288607 ± 0.038338 | 0.056974 ± 0.002598 | 0.010762 ± 0.000079 | -0.114 ± 0.008 % | 3.160 ± 0.031 % | @@ -89,6 +100,12 @@ K-quants score better on mean Δp than the legacy quants than e.g. KL divergence ## LLaMA 2 vs. LLaMA 3 Quantization comparison +| Revision | f364eb6f | +|:---------|:-------------------| +| Backend | CUDA | +| CPU | AMD Epyc 7742 | +| GPU | 1x NVIDIA RTX 4090 | + | Metric | L2 7b q2_K | L3 8b q2_K | L2 7b q4_K_M | L3 8b q4_K_M | L2 7b q6_K | L3 8b q6_K | L2 7b q8_0 | L3 8b q8_0 | |-----------------|---------------------|---------------------|---------------------|---------------------|---------------------|---------------------|---------------------|---------------------| | Mean PPL | 5.794552 ± 0.032298 | 9.751568 ± 0.063312 | 5.877078 ± 0.032781 | 6.407115 ± 0.039119 | 5.808494 ± 0.032425 | 6.253382 ± 0.038078 | 5.798542 ± 0.032366 | 6.234284 ± 0.037878 | @@ -107,6 +124,50 @@ K-quants score better on mean Δp than the legacy quants than e.g. KL divergence | RMS Δp | 9.762 ± 0.053 % | 21.421 ± 0.079 % | 3.252 ± 0.024 % | 5.519 ± 0.050 % | 1.339 ± 0.010 % | 2.295 ± 0.019 % | 0.618 ± 0.011 % | 1.198 ± 0.007 % | | Same top p | 85.584 ± 0.086 % | 71.138 ± 0.119 % | 94.665 ± 0.055 % | 91.901 ± 0.072 % | 97.520 ± 0.038 % | 96.031 ± 0.051 % | 98.846 ± 0.026 % | 97.674 ± 0.040 % | +## LLaMA 3 BF16 vs. FP16 comparison + +| Revision | 83330d8c | +|:---------|:--------------| +| Backend | CPU | +| CPU | AMD Epyc 7742 | +| GPU | N/A | + +Results were calculated with LLaMA 3 8b BF16 as `--kl-divergence-base` and LLaMA 3 8b FP16 as the `--model` for comparison. + +| Metric | Value | +|--------------------------------|--------------------------| +| Mean PPL(Q) | 6.227711 ± 0.037833 | +| Mean PPL(base) | 6.225194 ± 0.037771 | +| Cor(ln(PPL(Q)), ln(PPL(base))) | 99.990% | +| Mean ln(PPL(Q)/PPL(base)) | 0.000404 ± 0.000086 | +| Mean PPL(Q)/PPL(base) | 1.000404 ± 0.000086 | +| Mean PPL(Q)-PPL(base) | 0.002517 ± 0.000536 | +| Mean KLD | 0.00002515 ± 0.00000020 | +| Maximum KLD | 0.012206 | +| 99.9% KLD | 0.000799 | +| 99.0% KLD | 0.000222 | +| 99.0% KLD | 0.000222 | +| Median KLD | 0.000013 | +| 10.0% KLD | -0.000002 | +| 5.0% KLD | -0.000008 | +| 1.0% KLD | -0.000023 | +| Minimum KLD | -0.000059 | +| Mean Δp | -0.0000745 ± 0.0003952 % | +| Maximum Δp | 4.186% | +| 99.9% Δp | 1.049% | +| 99.0% Δp | 0.439% | +| 95.0% Δp | 0.207% | +| 90.0% Δp | 0.125% | +| 75.0% Δp | 0.029% | +| Median Δp | 0.000% | +| 25.0% Δp | -0.030% | +| 10.0% Δp | -0.126% | +| 5.0% Δp | -0.207% | +| 1.0% Δp | -0.434% | +| 0.1% Δp | -1.016% | +| Minimum Δp | -4.672% | +| RMS Δp | 0.150 ± 0.001 % | +| Same top p | 99.739 ± 0.013 % | ## Old Numbers diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index 7c1e6bf94fc5c..e15e2a2ad8db2 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -45,9 +45,9 @@ static void write_logfile( return; } - const std::string timestamp = get_sortable_timestamp(); + const std::string timestamp = string_get_sortable_timestamp(); - const bool success = create_directory_with_parents(params.logdir); + const bool success = fs_create_directory_with_parents(params.logdir); if (!success) { fprintf(stderr, "%s: warning: failed to create logdir %s, cannot write logfile\n", __func__, params.logdir.c_str()); @@ -65,7 +65,7 @@ static void write_logfile( fprintf(logfile, "binary: main\n"); char model_desc[128]; llama_model_desc(model, model_desc, sizeof(model_desc)); - dump_non_result_info_yaml(logfile, params, ctx, timestamp, results.tokens, model_desc); + yaml_dump_non_result_info(logfile, params, ctx, timestamp, results.tokens, model_desc); fprintf(logfile, "\n"); fprintf(logfile, "######################\n"); @@ -73,9 +73,9 @@ static void write_logfile( fprintf(logfile, "######################\n"); fprintf(logfile, "\n"); - dump_vector_float_yaml(logfile, "logits", results.logits); + yaml_dump_vector_float(logfile, "logits", results.logits); fprintf(logfile, "ppl_value: %f\n", results.ppl_value); - dump_vector_float_yaml(logfile, "probs", results.probs); + yaml_dump_vector_float(logfile, "probs", results.probs); llama_dump_timing_info_yaml(logfile, ctx); fclose(logfile); @@ -1426,7 +1426,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params // Use all tasks tasks.resize(n_task); printf("%s: reading tasks", __func__); - int n_dot = n_task/100; + int n_dot = std::max((int) n_task/100, 1); int i = 0; for (auto& task : tasks) { ++i; @@ -1676,7 +1676,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params llama_batch_free(batch); - if (n_done < 100) return; + if (n_done < 100 && (params.multiple_choice_tasks != 0 && params.multiple_choice_tasks < (size_t)n_task)) return; float p = 1.f*n_correct/n_done; float sigma = sqrt(p*(1-p)/(n_done-1)); @@ -2008,7 +2008,7 @@ int main(int argc, char ** argv) { std::mt19937 rng(params.seed); if (params.random_prompt) { - params.prompt = gpt_random_prompt(rng); + params.prompt = string_random_prompt(rng); } llama_backend_init(); @@ -2036,7 +2036,7 @@ int main(int argc, char ** argv) { // print system information { fprintf(stderr, "\n"); - fprintf(stderr, "%s\n", get_system_info(params).c_str()); + fprintf(stderr, "%s\n", gpt_params_get_system_info(params).c_str()); } struct results_perplexity results; diff --git a/examples/quantize/README.md b/examples/quantize/README.md index 8a10365c07a60..b78ece4e7f59d 100644 --- a/examples/quantize/README.md +++ b/examples/quantize/README.md @@ -1,6 +1,8 @@ # quantize -TODO +You can also use the [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space on Hugging Face to build your own quants without any setup. + +Note: It is synced from llama.cpp `main` every 6 hours. ## Llama 2 7B diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index f1e2a4b2103a9..72c33a12a15e9 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -260,7 +260,7 @@ int main(int argc, char ** argv) { usage(argv[0]); } } else if (strcmp(argv[arg_idx], "--override-kv") == 0) { - if (arg_idx == argc-1 || !parse_kv_override(argv[++arg_idx], kv_overrides)) { + if (arg_idx == argc-1 || !string_parse_kv_override(argv[++arg_idx], kv_overrides)) { usage(argv[0]); } } else if (strcmp(argv[arg_idx], "--allow-requantize") == 0) { @@ -285,7 +285,7 @@ int main(int argc, char ** argv) { } else { usage(argv[0]); } - } else if (strcmp(argv[arg_idx], "--keep-split")) { + } else if (strcmp(argv[arg_idx], "--keep-split") == 0) { params.keep_split = true; } else { usage(argv[0]); diff --git a/examples/quantize/tests.sh b/examples/quantize/tests.sh index 160c12bee22e8..a3ca74c68e7e5 100644 --- a/examples/quantize/tests.sh +++ b/examples/quantize/tests.sh @@ -41,8 +41,8 @@ $SPLIT --split-max-tensors 28 $WORK_PATH/gemma-1.1-2b-it.Q8_0.gguf $WORK_PATH/g echo PASS echo -# 3. Requant model with '--keep_split' -$QUANTIZE --allow-requantize --keep_split $WORK_PATH/ggml-model-split-00001-of-00006.gguf $WORK_PATH/ggml-model-requant.gguf Q4_K +# 3. Requant model with '--keep-split' +$QUANTIZE --allow-requantize --keep-split $WORK_PATH/ggml-model-split-00001-of-00006.gguf $WORK_PATH/ggml-model-requant.gguf Q4_K echo PASS echo @@ -51,7 +51,7 @@ $MAIN --model $WORK_PATH/ggml-model-requant-00001-of-00006.gguf --random-prompt echo PASS echo -# 4. Requant mode without '--keep_split' +# 4. Requant mode without '--keep-split' $QUANTIZE --allow-requantize $WORK_PATH/ggml-model-split-00001-of-00006.gguf $WORK_PATH/ggml-model-requant-merge.gguf Q4_K echo PASS echo diff --git a/examples/retrieval/retrieval.cpp b/examples/retrieval/retrieval.cpp index 5ba71e76a93b4..4e7530706d4a9 100644 --- a/examples/retrieval/retrieval.cpp +++ b/examples/retrieval/retrieval.cpp @@ -11,7 +11,7 @@ struct retrieval_params { }; static void retrieval_params_print_usage(int argc, char ** argv, gpt_params & gpt_params, retrieval_params & params) { - gpt_print_usage(argc, argv, gpt_params); + gpt_params_print_usage(argc, argv, gpt_params); printf("retrieval options:\n"); printf(" --context-file FNAME file containing context to embed.\n"); printf(" specify multiple files by providing --context-file option multiple times.\n"); @@ -226,7 +226,7 @@ int main(int argc, char ** argv) { // print system information { fprintf(stderr, "\n"); - fprintf(stderr, "%s\n", get_system_info(params).c_str()); + fprintf(stderr, "%s\n", gpt_params_get_system_info(params).c_str()); } // max batch size diff --git a/examples/rpc/CMakeLists.txt b/examples/rpc/CMakeLists.txt new file mode 100644 index 0000000000000..ae48fb98d0913 --- /dev/null +++ b/examples/rpc/CMakeLists.txt @@ -0,0 +1,2 @@ +add_executable(rpc-server rpc-server.cpp) +target_link_libraries(rpc-server PRIVATE ggml llama) diff --git a/examples/rpc/README.md b/examples/rpc/README.md new file mode 100644 index 0000000000000..eeec71a8ee0c2 --- /dev/null +++ b/examples/rpc/README.md @@ -0,0 +1,74 @@ +## Overview + +The `rpc-server` allows running `ggml` backend on a remote host. +The RPC backend communicates with one or several instances of `rpc-server` and offloads computations to them. +This can be used for distributed LLM inference with `llama.cpp` in the following way: + +```mermaid +flowchart TD + rpcb---|TCP|srva + rpcb---|TCP|srvb + rpcb-.-|TCP|srvn + subgraph hostn[Host N] + srvn[rpc-server]-.-backend3["Backend (CUDA,Metal,etc.)"] + end + subgraph hostb[Host B] + srvb[rpc-server]---backend2["Backend (CUDA,Metal,etc.)"] + end + subgraph hosta[Host A] + srva[rpc-server]---backend["Backend (CUDA,Metal,etc.)"] + end + subgraph host[Main Host] + ggml[llama.cpp]---rpcb[RPC backend] + end + style hostn stroke:#66,stroke-width:2px,stroke-dasharray: 5 5 +``` + +Each host can run a different backend, e.g. one with CUDA and another with Metal. +You can also run multiple `rpc-server` instances on the same host, each with a different backend. + +## Usage + +On each host, build the corresponding backend with `cmake` and add `-DLLAMA_RPC=ON` to the build options. +For example, to build the CUDA backend with RPC support: + +```bash +mkdir build-rpc-cuda +cd build-rpc-cuda +cmake .. -DLLAMA_CUDA=ON -DLLAMA_RPC=ON +cmake --build . --config Release +``` + +Then, start the `rpc-server` with the backend: + +```bash +$ bin/rpc-server -p 50052 +create_backend: using CUDA backend +ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no +ggml_cuda_init: CUDA_USE_TENSOR_CORES: yes +ggml_cuda_init: found 1 CUDA devices: + Device 0: NVIDIA T1200 Laptop GPU, compute capability 7.5, VMM: yes +Starting RPC server on 0.0.0.0:50052 +``` + +When using the CUDA backend, you can specify the device with the `CUDA_VISIBLE_DEVICES` environment variable, e.g.: +```bash +$ CUDA_VISIBLE_DEVICES=0 bin/rpc-server -p 50052 +``` +This way you can run multiple `rpc-server` instances on the same host, each with a different CUDA device. + + +On the main host build `llama.cpp` only with `-DLLAMA_RPC=ON`: + +```bash +mkdir build-rpc +cd build-rpc +cmake .. -DLLAMA_RPC=ON +cmake --build . --config Release +``` + +Finally, use the `--rpc` option to specify the host and port of each `rpc-server`: + +```bash +$ bin/main -m ../models/tinyllama-1b/ggml-model-f16.gguf -p "Hello, my name is" --repeat-penalty 1.0 -n 64 --rpc 192.168.88.10:50052,192.168.88.11:50052 -ngl 99 +``` diff --git a/examples/rpc/rpc-server.cpp b/examples/rpc/rpc-server.cpp new file mode 100644 index 0000000000000..7c15d2aa4acfb --- /dev/null +++ b/examples/rpc/rpc-server.cpp @@ -0,0 +1,134 @@ +#ifdef GGML_USE_CUDA +#include "ggml-cuda.h" +#endif + +#ifdef GGML_USE_METAL +#include "ggml-metal.h" +#endif + +#include "ggml-rpc.h" +#ifdef _WIN32 +# include +#else +# include +#endif +#include +#include + +struct rpc_server_params { + std::string host = "0.0.0.0"; + int port = 50052; + size_t backend_mem = 0; +}; + +static void print_usage(int /*argc*/, char ** argv, rpc_server_params params) { + fprintf(stderr, "Usage: %s [options]\n\n", argv[0]); + fprintf(stderr, "options:\n"); + fprintf(stderr, " -h, --help show this help message and exit\n"); + fprintf(stderr, " -H HOST, --host HOST host to bind to (default: %s)\n", params.host.c_str()); + fprintf(stderr, " -p PORT, --port PORT port to bind to (default: %d)\n", params.port); + fprintf(stderr, " -m MEM, --mem MEM backend memory size (in MB)\n"); + fprintf(stderr, "\n"); +} + +static bool rpc_server_params_parse(int argc, char ** argv, rpc_server_params & params) { + std::string arg; + for (int i = 1; i < argc; i++) { + arg = argv[i]; + if (arg == "-H" || arg == "--host") { + if (++i >= argc) { + return false; + } + params.host = argv[i]; + } else if (arg == "-p" || arg == "--port") { + if (++i >= argc) { + return false; + } + params.port = std::stoi(argv[i]); + if (params.port <= 0 || params.port > 65535) { + return false; + } + } else if (arg == "-m" || arg == "--mem") { + if (++i >= argc) { + return false; + } + params.backend_mem = std::stoul(argv[i]) * 1024 * 1024; + } else if (arg == "-h" || arg == "--help") { + print_usage(argc, argv, params); + exit(0); + } else { + fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); + print_usage(argc, argv, params); + exit(0); + } + } + return true; +} + +static ggml_backend_t create_backend() { + ggml_backend_t backend = NULL; +#ifdef GGML_USE_CUDA + fprintf(stderr, "%s: using CUDA backend\n", __func__); + backend = ggml_backend_cuda_init(0); // init device 0 + if (!backend) { + fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__); + } +#elif GGML_USE_METAL + fprintf(stderr, "%s: using Metal backend\n", __func__); + backend = ggml_backend_metal_init(); + if (!backend) { + fprintf(stderr, "%s: ggml_backend_metal_init() failed\n", __func__); + } +#endif + + // if there aren't GPU Backends fallback to CPU backend + if (!backend) { + fprintf(stderr, "%s: using CPU backend\n", __func__); + backend = ggml_backend_cpu_init(); + } + return backend; +} + +static void get_backend_memory(size_t * free_mem, size_t * total_mem) { +#ifdef GGML_USE_CUDA + ggml_backend_cuda_get_device_memory(0, free_mem, total_mem); +#else + #ifdef _WIN32 + MEMORYSTATUSEX status; + status.dwLength = sizeof(status); + GlobalMemoryStatusEx(&status); + *total_mem = status.ullTotalPhys; + *free_mem = status.ullAvailPhys; + #else + long pages = sysconf(_SC_PHYS_PAGES); + long page_size = sysconf(_SC_PAGE_SIZE); + *total_mem = pages * page_size; + *free_mem = *total_mem; + #endif +#endif +} + +int main(int argc, char * argv[]) { + rpc_server_params params; + if (!rpc_server_params_parse(argc, argv, params)) { + fprintf(stderr, "Invalid parameters\n"); + return 1; + } + ggml_backend_t backend = create_backend(); + if (!backend) { + fprintf(stderr, "Failed to create backend\n"); + return 1; + } + std::string endpoint = params.host + ":" + std::to_string(params.port); + size_t free_mem, total_mem; + if (params.backend_mem > 0) { + free_mem = params.backend_mem; + total_mem = params.backend_mem; + } else { + get_backend_memory(&free_mem, &total_mem); + } + printf("Starting RPC server on %s, backend memory: %zu MB\n", endpoint.c_str(), free_mem / (1024 * 1024)); + start_rpc_server(backend, endpoint.c_str(), free_mem, total_mem); + ggml_backend_free(backend); + return 0; +} diff --git a/examples/server/README.md b/examples/server/README.md index 6503179914da0..0c3db8c84c69d 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -17,8 +17,9 @@ The project is under active development, and we are [looking for feedback and co **Command line options:** -- `--threads N`, `-t N`: Set the number of threads to use during generation. Not used if model layers are offloaded to GPU. The server is using batching. This parameter is used only if one token is to be processed on CPU backend. -- `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. If not specified, the number of threads will be set to the number of threads used for generation. Not used if model layers are offloaded to GPU. +- `-v`, `--verbose`: Enable verbose server output. When using the `/completion` endpoint, this includes the tokenized prompt, the full request and the full response. +- `-t N`, `--threads N`: Set the number of threads to use by CPU layers during generation. Not used by model layers that are offloaded to GPU. This option has no effect when using the maximum number of GPU layers. Default: `std::thread::hardware_concurrency()` (number of CPU cores). +- `-tb N, --threads-batch N`: Set the number of threads to use by CPU layers during batch and prompt processing (>= 32 tokens). This option has no effect if a GPU is available. Default: `--threads`. - `--threads-http N`: Number of threads in the http server pool to process requests. Default: `max(std::thread::hardware_concurrency() - 1, --parallel N + 2)` - `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`). - `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file. Default: unused @@ -36,9 +37,7 @@ The project is under active development, and we are [looking for feedback and co - `--numa STRATEGY`: Attempt one of the below optimization strategies that may help on some NUMA systems - `--numa distribute`: Spread execution evenly over all nodes - `--numa isolate`: Only spawn threads on CPUs on the node that execution started on -- `--numa numactl`: Use the CPU map provided by numactl. If run without this previously, it is recommended to drop the system -page cache before using this. See https://github.com/ggerganov/llama.cpp/issues/1437 - +- `--numa numactl`: Use the CPU map provided by numactl. If run without this previously, it is recommended to drop the system page cache before using this. See https://github.com/ggerganov/llama.cpp/issues/1437 - `--numa`: Attempt optimizations that may help on some NUMA systems. - `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model (implies --no-mmap). This allows you to adapt the pretrained model to specific tasks or domains. - `--lora-base FNAME`: Optional model to use as a base for the layers modified by the LoRA adapter. This flag is used in conjunction with the `--lora` flag, and specifies the base model for the adaptation. @@ -48,8 +47,8 @@ page cache before using this. See https://github.com/ggerganov/llama.cpp/issues/ - `--path`: Path from which to serve static files. Default: disabled - `--api-key`: Set an api key for request authorization. By default, the server responds to every request. With an api key set, the requests must have the Authorization header set with the api key as Bearer token. May be used multiple times to enable multiple valid keys. - `--api-key-file`: Path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access. May be used in conjunction with `--api-key`s. -- `--embedding`: Enable embedding extraction. Default: disabled -- `-np N`, `--parallel N`: Set the number of slots for process requests. Default: `1` +- `--embeddings`: Enable embedding vector output and the OAI compatible endpoint /v1/embeddings. Physical batch size (`--ubatch-size`) must be carefully defined. Default: disabled +- `-np N`, `--parallel N`: Set the number of slots for process requests. Default: `1`. Values > 1 will allow for higher throughput with multiple parallel requests but the results will **not** be deterministic due to differences in rounding error. - `-cb`, `--cont-batching`: Enable continuous batching (a.k.a dynamic batching). Default: disabled - `-spf FNAME`, `--system-prompt-file FNAME` Set a file to load a system prompt (initial prompt of all slots). This is useful for chat applications. [See more](#change-system-prompt-on-runtime) - `--mmproj MMPROJ_FILE`: Path to a multimodal projector file for LLaVA. diff --git a/examples/server/public_simplechat/index.html b/examples/server/public_simplechat/index.html new file mode 100644 index 0000000000000..1eb390b85a69c --- /dev/null +++ b/examples/server/public_simplechat/index.html @@ -0,0 +1,52 @@ + + + + SimpleChat (LlamaCPP, ...) + + + + + + + + + + +
+ +
+

SimpleChat

+
+ + +
+
+ +
+ +
+
+ + +
+ +
+
+

Enter the system prompt above, before entering/submitting any user query.

+

Enter your text to the ai assistant below.

+

Use shift+enter for inserting enter.

+

Refresh the page to start over fresh.

+
+ +
+
+ + +
+ +
+ + diff --git a/examples/server/public_simplechat/readme.md b/examples/server/public_simplechat/readme.md new file mode 100644 index 0000000000000..5ac8258f21aca --- /dev/null +++ b/examples/server/public_simplechat/readme.md @@ -0,0 +1,81 @@ + +# SimpleChat + +by Humans for All. + + +## overview + +This simple web frontend, allows triggering/testing the server's /completions or /chat/completions endpoints +in a simple way with minimal code from a common code base. Inturn additionally it tries to allow single or +multiple independent back and forth chatting to an extent, with the ai llm model at a basic level, with their +own system prompts. + +The UI follows a responsive web design so that the layout can adapt to available display space in a usable +enough manner, in general. + +NOTE: Given that the idea is for basic minimal testing, it doesnt bother with any model context length and +culling of old messages from the chat. + +NOTE: It doesnt set any parameters other than temperature for now. However if someone wants they can update +the js file as needed. + + +## usage + +One could run this web frontend directly using server itself or if anyone is thinking of adding a built in web +frontend to configure the server over http(s) or so, then run this web frontend using something like python's +http module. + +### running using examples/server + +bin/server -m path/model.gguf --path ../examples/server/public_simplechat [--port PORT] + +### running using python3's server module + +first run examples/server +* bin/server -m path/model.gguf + +next run this web front end in examples/server/public_simplechat +* cd ../examples/server/public_simplechat +* python3 -m http.server PORT + +### using the front end + +Open this simple web front end from your local browser +* http://127.0.0.1:PORT/index.html + +Once inside +* Select between chat and completion mode. By default it is set to chat mode. +* If you want to provide a system prompt, then ideally enter it first, before entering any user query. + * if chat.add_system_begin is used + * you cant change the system prompt, after it is has been submitted once along with user query. + * you cant set a system prompt, after you have submitted any user query + * if chat.add_system_anytime is used + * one can change the system prompt any time during chat, by changing the contents of system prompt. + * inturn the updated/changed system prompt will be inserted into the chat session. + * this allows for the subsequent user chatting to be driven by the new system prompt set above. +* Enter your query and either press enter or click on the submit button. + If you want to insert enter (\n) as part of your chat/query to ai model, use shift+enter. +* Wait for the logic to communicate with the server and get the response. + * the user is not allowed to enter any fresh query during this time. + * the user input box will be disabled and a working message will be shown in it. +* just refresh the page, to reset wrt the chat history and or system prompt and start afresh. +* Using NewChat one can start independent chat sessions. + * two independent chat sessions are setup by default. + + +## Devel note + +Sometimes the browser may be stuborn with caching of the file, so your updates to html/css/js +may not be visible. Also remember that just refreshing/reloading page in browser or for that +matter clearing site data, dont directly override site caching in all cases. Worst case you may +have to change port. Or in dev tools of browser, you may be able to disable caching fully. + +Concept of multiple chat sessions with different servers, as well as saving and restoring of +those across browser usage sessions, can be woven around the SimpleChat/MultiChatUI class and +its instances relatively easily, however given the current goal of keeping this simple, it has +not been added, for now. + +By switching between chat.add_system_begin/anytime, one can control whether one can change +the system prompt, anytime during the conversation or only at the beginning. diff --git a/examples/server/public_simplechat/simplechat.css b/examples/server/public_simplechat/simplechat.css new file mode 100644 index 0000000000000..d45f50a957e4c --- /dev/null +++ b/examples/server/public_simplechat/simplechat.css @@ -0,0 +1,61 @@ +/** + * the styling of the simplechat web frontend + * by Humans for All + */ + +#fullbody { + height: 98vh; +} + +.heading { + background-color: lightgray; +} + +.session-selected { + background-color: lightblue; +} + +.role-system { + background-color: lightblue; +} +.role-user { + background-color: lightgray; +} + +.flex-grow { + flex-grow: 1; +} +.float-right { + float: right; +} + +#chat-div { + overflow: scroll; + flex-grow: 1; + flex-shrink: 1; + min-height: 40vh; +} +button { + min-width: 8vw; +} + +.sameline { + display: flex; + flex-direction: row; +} +.samecolumn { + display: flex; + flex-direction: column; +} + +* { + margin: 0.6vmin; +} + +@media print { + + #fullbody { + height: auto; + } + +} diff --git a/examples/server/public_simplechat/simplechat.js b/examples/server/public_simplechat/simplechat.js new file mode 100644 index 0000000000000..3fc4dbc2026fa --- /dev/null +++ b/examples/server/public_simplechat/simplechat.js @@ -0,0 +1,478 @@ +// @ts-check +// A simple completions and chat/completions test related web front end logic +// by Humans for All + +class Roles { + static System = "system"; + static User = "user"; + static Assistant = "assistant"; +} + +class ApiEP { + static Chat = "chat"; + static Completion = "completion"; +} + +let gUsageMsg = ` +

Enter the system prompt above, before entering/submitting any user query.

+

Enter your text to the ai assistant below.

+

Use shift+enter for inserting enter.

+

Refresh the page to start over fresh.

+`; + +class SimpleChat { + + constructor() { + /** + * Maintain in a form suitable for common LLM web service chat/completions' messages entry + * @type {{role: string, content: string}[]} + */ + this.xchat = []; + this.iLastSys = -1; + } + + /** + * Add an entry into xchat + * @param {string} role + * @param {string|undefined|null} content + */ + add(role, content) { + if ((content == undefined) || (content == null) || (content == "")) { + return false; + } + this.xchat.push( {role: role, content: content} ); + if (role == Roles.System) { + this.iLastSys = this.xchat.length - 1; + } + return true; + } + + /** + * Show the contents in the specified div + * @param {HTMLDivElement} div + * @param {boolean} bClear + */ + show(div, bClear=true) { + if (bClear) { + div.replaceChildren(); + } + let last = undefined; + for(const x of this.xchat) { + let entry = document.createElement("p"); + entry.className = `role-${x.role}`; + entry.innerText = `${x.role}: ${x.content}`; + div.appendChild(entry); + last = entry; + } + if (last !== undefined) { + last.scrollIntoView(false); + } else { + if (bClear) { + div.innerHTML = gUsageMsg; + } + } + } + + /** + * Add needed fields wrt json object to be sent wrt LLM web services completions endpoint + * Convert the json into string. + * @param {Object} obj + */ + request_jsonstr(obj) { + obj["temperature"] = 0.7; + return JSON.stringify(obj); + } + + /** + * Return a string form of json object suitable for chat/completions + */ + request_messages_jsonstr() { + let req = { + messages: this.xchat, + } + return this.request_jsonstr(req); + } + + /** + * Return a string form of json object suitable for /completions + */ + request_prompt_jsonstr() { + let prompt = ""; + for(const chat of this.xchat) { + prompt += `${chat.role}: ${chat.content}\n`; + } + let req = { + prompt: prompt, + } + return this.request_jsonstr(req); + } + + /** + * Allow setting of system prompt, but only at begining. + * @param {string} sysPrompt + * @param {string} msgTag + */ + add_system_begin(sysPrompt, msgTag) { + if (this.xchat.length == 0) { + if (sysPrompt.length > 0) { + return this.add(Roles.System, sysPrompt); + } + } else { + if (sysPrompt.length > 0) { + if (this.xchat[0].role !== Roles.System) { + console.error(`ERRR:SimpleChat:SC:${msgTag}:You need to specify system prompt before any user query, ignoring...`); + } else { + if (this.xchat[0].content !== sysPrompt) { + console.error(`ERRR:SimpleChat:SC:${msgTag}:You cant change system prompt, mid way through, ignoring...`); + } + } + } + } + return false; + } + + /** + * Allow setting of system prompt, at any time. + * @param {string} sysPrompt + * @param {string} msgTag + */ + add_system_anytime(sysPrompt, msgTag) { + if (sysPrompt.length <= 0) { + return false; + } + + if (this.iLastSys < 0) { + return this.add(Roles.System, sysPrompt); + } + + let lastSys = this.xchat[this.iLastSys].content; + if (lastSys !== sysPrompt) { + return this.add(Roles.System, sysPrompt); + } + return false; + } + + /** + * Retrieve the latest system prompt. + */ + get_system_latest() { + if (this.iLastSys == -1) { + return ""; + } + let sysPrompt = this.xchat[this.iLastSys].content; + return sysPrompt; + } + +} + + +let gBaseURL = "http://127.0.0.1:8080"; +let gChatURL = { + 'chat': `${gBaseURL}/chat/completions`, + 'completion': `${gBaseURL}/completions`, +} +const gbCompletionFreshChatAlways = true; + + +/** + * Set the class of the children, based on whether it is the idSelected or not. + * @param {HTMLDivElement} elBase + * @param {string} idSelected + * @param {string} classSelected + * @param {string} classUnSelected + */ +function el_children_config_class(elBase, idSelected, classSelected, classUnSelected="") { + for(let child of elBase.children) { + if (child.id == idSelected) { + child.className = classSelected; + } else { + child.className = classUnSelected; + } + } +} + +/** + * Create button and set it up. + * @param {string} id + * @param {(this: HTMLButtonElement, ev: MouseEvent) => any} callback + * @param {string | undefined} name + * @param {string | undefined} innerText + */ +function el_create_button(id, callback, name=undefined, innerText=undefined) { + if (!name) { + name = id; + } + if (!innerText) { + innerText = id; + } + let btn = document.createElement("button"); + btn.id = id; + btn.name = name; + btn.innerText = innerText; + btn.addEventListener("click", callback); + return btn; +} + + +class MultiChatUI { + + constructor() { + /** @type {Object} */ + this.simpleChats = {}; + /** @type {string} */ + this.curChatId = ""; + + // the ui elements + this.elInSystem = /** @type{HTMLInputElement} */(document.getElementById("system-in")); + this.elDivChat = /** @type{HTMLDivElement} */(document.getElementById("chat-div")); + this.elBtnUser = /** @type{HTMLButtonElement} */(document.getElementById("user-btn")); + this.elInUser = /** @type{HTMLInputElement} */(document.getElementById("user-in")); + this.elSelectApiEP = /** @type{HTMLSelectElement} */(document.getElementById("api-ep")); + this.elDivSessions = /** @type{HTMLDivElement} */(document.getElementById("sessions-div")); + + this.validate_element(this.elInSystem, "system-in"); + this.validate_element(this.elDivChat, "chat-div"); + this.validate_element(this.elInUser, "user-in"); + this.validate_element(this.elSelectApiEP, "api-ep"); + this.validate_element(this.elDivChat, "sessions-div"); + } + + /** + * Check if the element got + * @param {HTMLElement | null} el + * @param {string} msgTag + */ + validate_element(el, msgTag) { + if (el == null) { + throw Error(`ERRR:SimpleChat:MCUI:${msgTag} element missing in html...`); + } else { + console.debug(`INFO:SimpleChat:MCUI:${msgTag} Id[${el.id}] Name[${el["name"]}]`); + } + } + + /** + * Reset user input ui. + * * clear user input + * * enable user input + * * set focus to user input + */ + ui_reset_userinput() { + this.elInUser.value = ""; + this.elInUser.disabled = false; + this.elInUser.focus(); + } + + /** + * Setup the needed callbacks wrt UI, curChatId to defaultChatId and + * optionally switch to specified defaultChatId. + * @param {string} defaultChatId + * @param {boolean} bSwitchSession + */ + setup_ui(defaultChatId, bSwitchSession=false) { + + this.curChatId = defaultChatId; + if (bSwitchSession) { + this.handle_session_switch(this.curChatId); + } + + this.elBtnUser.addEventListener("click", (ev)=>{ + if (this.elInUser.disabled) { + return; + } + this.handle_user_submit(this.curChatId, this.elSelectApiEP.value).catch((/** @type{Error} */reason)=>{ + let msg = `ERRR:SimpleChat\nMCUI:HandleUserSubmit:${this.curChatId}\n${reason.name}:${reason.message}`; + console.debug(msg.replace("\n", ":")); + alert(msg); + this.ui_reset_userinput(); + }); + }); + + this.elInUser.addEventListener("keyup", (ev)=> { + // allow user to insert enter into their message using shift+enter. + // while just pressing enter key will lead to submitting. + if ((ev.key === "Enter") && (!ev.shiftKey)) { + this.elBtnUser.click(); + ev.preventDefault(); + } + }); + + this.elInSystem.addEventListener("keyup", (ev)=> { + // allow user to insert enter into the system prompt using shift+enter. + // while just pressing enter key will lead to setting the system prompt. + if ((ev.key === "Enter") && (!ev.shiftKey)) { + let chat = this.simpleChats[this.curChatId]; + chat.add_system_anytime(this.elInSystem.value, this.curChatId); + chat.show(this.elDivChat); + ev.preventDefault(); + } + }); + + } + + /** + * Setup a new chat session and optionally switch to it. + * @param {string} chatId + * @param {boolean} bSwitchSession + */ + new_chat_session(chatId, bSwitchSession=false) { + this.simpleChats[chatId] = new SimpleChat(); + if (bSwitchSession) { + this.handle_session_switch(chatId); + } + } + + /** + * Handle user query submit request, wrt specified chat session. + * @param {string} chatId + * @param {string} apiEP + */ + async handle_user_submit(chatId, apiEP) { + + let chat = this.simpleChats[chatId]; + + chat.add_system_anytime(this.elInSystem.value, chatId); + + let content = this.elInUser.value; + if (!chat.add(Roles.User, content)) { + console.debug(`WARN:SimpleChat:MCUI:${chatId}:HandleUserSubmit:Ignoring empty user input...`); + return; + } + chat.show(this.elDivChat); + + let theBody; + let theUrl = gChatURL[apiEP] + if (apiEP == ApiEP.Chat) { + theBody = chat.request_messages_jsonstr(); + } else { + theBody = chat.request_prompt_jsonstr(); + } + + this.elInUser.value = "working..."; + this.elInUser.disabled = true; + console.debug(`DBUG:SimpleChat:MCUI:${chatId}:HandleUserSubmit:${theUrl}:ReqBody:${theBody}`); + let resp = await fetch(theUrl, { + method: "POST", + headers: { + "Content-Type": "application/json", + }, + body: theBody, + }); + + let respBody = await resp.json(); + console.debug(`DBUG:SimpleChat:MCUI:${chatId}:HandleUserSubmit:RespBody:${JSON.stringify(respBody)}`); + let assistantMsg; + if (apiEP == ApiEP.Chat) { + assistantMsg = respBody["choices"][0]["message"]["content"]; + } else { + try { + assistantMsg = respBody["choices"][0]["text"]; + } catch { + assistantMsg = respBody["content"]; + } + } + chat.add(Roles.Assistant, assistantMsg); + if (chatId == this.curChatId) { + chat.show(this.elDivChat); + } else { + console.debug(`DBUG:SimpleChat:MCUI:HandleUserSubmit:ChatId has changed:[${chatId}] [${this.curChatId}]`); + } + // Purposefully clear at end rather than begin of this function + // so that one can switch from chat to completion mode and sequece + // in a completion mode with multiple user-assistant chat data + // from before to be sent/occur once. + if ((apiEP == ApiEP.Completion) && (gbCompletionFreshChatAlways)) { + chat.xchat.length = 0; + } + this.ui_reset_userinput(); + } + + /** + * Show buttons for NewChat and available chat sessions, in the passed elDiv. + * If elDiv is undefined/null, then use this.elDivSessions. + * Take care of highlighting the selected chat-session's btn. + * @param {HTMLDivElement | undefined} elDiv + */ + show_sessions(elDiv=undefined) { + if (!elDiv) { + elDiv = this.elDivSessions; + } + elDiv.replaceChildren(); + // Btn for creating new chat session + let btnNew = el_create_button("New CHAT", (ev)=> { + if (this.elInUser.disabled) { + console.error(`ERRR:SimpleChat:MCUI:NewChat:Current session [${this.curChatId}] awaiting response, ignoring request...`); + alert("ERRR:SimpleChat\nMCUI:NewChat\nWait for response to pending query, before starting new chat session"); + return; + } + let chatId = `Chat${Object.keys(this.simpleChats).length}`; + let chatIdGot = prompt("INFO:SimpleChat\nMCUI:NewChat\nEnter id for new chat session", chatId); + if (!chatIdGot) { + console.error("ERRR:SimpleChat:MCUI:NewChat:Skipping based on user request..."); + return; + } + this.new_chat_session(chatIdGot, true); + this.create_session_btn(elDiv, chatIdGot); + el_children_config_class(elDiv, chatIdGot, "session-selected", ""); + }); + elDiv.appendChild(btnNew); + // Btns for existing chat sessions + let chatIds = Object.keys(this.simpleChats); + for(let cid of chatIds) { + let btn = this.create_session_btn(elDiv, cid); + if (cid == this.curChatId) { + btn.className = "session-selected"; + } + } + } + + create_session_btn(elDiv, cid) { + let btn = el_create_button(cid, (ev)=>{ + let target = /** @type{HTMLButtonElement} */(ev.target); + console.debug(`DBUG:SimpleChat:MCUI:SessionClick:${target.id}`); + if (this.elInUser.disabled) { + console.error(`ERRR:SimpleChat:MCUI:SessionClick:${target.id}:Current session [${this.curChatId}] awaiting response, ignoring switch...`); + alert("ERRR:SimpleChat\nMCUI:SessionClick\nWait for response to pending query, before switching"); + return; + } + this.handle_session_switch(target.id); + el_children_config_class(elDiv, target.id, "session-selected", ""); + }); + elDiv.appendChild(btn); + return btn; + } + + /** + * Switch ui to the specified chatId and set curChatId to same. + * @param {string} chatId + */ + async handle_session_switch(chatId) { + let chat = this.simpleChats[chatId]; + if (chat == undefined) { + console.error(`ERRR:SimpleChat:MCUI:HandleSessionSwitch:${chatId} missing...`); + return; + } + this.elInSystem.value = chat.get_system_latest(); + this.elInUser.value = ""; + chat.show(this.elDivChat); + this.elInUser.focus(); + this.curChatId = chatId; + console.log(`INFO:SimpleChat:MCUI:HandleSessionSwitch:${chatId} entered...`); + } + +} + + +let gMuitChat; +const gChatIds = [ "Default", "Other" ]; + +function startme() { + console.log("INFO:SimpleChat:StartMe:Starting..."); + gMuitChat = new MultiChatUI(); + for (let cid of gChatIds) { + gMuitChat.new_chat_session(cid); + } + gMuitChat.setup_ui(gChatIds[0]); + gMuitChat.show_sessions(); +} + +document.addEventListener("DOMContentLoaded", startme); diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 5688c1a7ad10b..ab437fed72daa 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -103,7 +103,6 @@ struct slot_params { bool stream = true; bool cache_prompt = false; // remember the prompt to avoid reprocessing all prompt - uint32_t seed = -1; // RNG seed int32_t n_keep = 0; // number of tokens to keep from initial prompt int32_t n_discard = 0; // number of tokens after n_keep that may be discarded when shifting context, 0 defaults to half int32_t n_predict = -1; // new tokens to predict @@ -652,9 +651,6 @@ struct server_context { std::string system_prompt; std::vector system_tokens; - std::string name_user; // this should be the antiprompt - std::string name_assistant; - // slots / clients std::vector slots; json default_generation_settings_for_props; @@ -674,6 +670,15 @@ struct server_context { llama_free_model(model); model = nullptr; } + + // Clear any sampling context + for (server_slot & slot : slots) { + if (slot.ctx_sampling != nullptr) { + llama_sampling_free(slot.ctx_sampling); + } + } + + llama_batch_free(batch); } bool load_model(const gpt_params & params_) { @@ -1015,7 +1020,7 @@ struct server_context { sampler_names.emplace_back(sampler_name); } } - slot.sparams.samplers_sequence = sampler_types_from_names(sampler_names, false); + slot.sparams.samplers_sequence = llama_sampling_types_from_names(sampler_names, false); } else { slot.sparams.samplers_sequence = default_sparams.samplers_sequence; } @@ -1099,15 +1104,11 @@ struct server_context { system_need_update = false; } - void system_prompt_set(const json & sys_props) { - system_prompt = sys_props.value("prompt", ""); - name_user = sys_props.value("anti_prompt", ""); - name_assistant = sys_props.value("assistant_name", ""); + bool system_prompt_set(const std::string & sys_prompt) { + system_prompt = sys_prompt; LOG_VERBOSE("system prompt process", { {"system_prompt", system_prompt}, - {"name_user", name_user}, - {"name_assistant", name_assistant}, }); // release all slots @@ -1116,6 +1117,7 @@ struct server_context { } system_need_update = true; + return true; } bool process_token(completion_token_output & result, server_slot & slot) { @@ -1255,14 +1257,14 @@ struct server_context { std::vector samplers_sequence; samplers_sequence.reserve(slot.sparams.samplers_sequence.size()); for (const auto & sampler_type : slot.sparams.samplers_sequence) { - samplers_sequence.emplace_back(sampler_type_to_name_string(sampler_type)); + samplers_sequence.emplace_back(llama_sampling_type_to_str(sampler_type)); } return json { {"n_ctx", slot.n_ctx}, {"n_predict", slot.n_predict}, {"model", params.model_alias}, - {"seed", slot.params.seed}, + {"seed", slot.sparams.seed}, {"temperature", slot.sparams.temp}, {"dynatemp_range", slot.sparams.dynatemp_range}, {"dynatemp_exponent", slot.sparams.dynatemp_exponent}, @@ -1535,7 +1537,8 @@ struct server_context { } if (task.data.contains("system_prompt")) { - system_prompt_set(task.data.at("system_prompt")); + std::string sys_prompt = json_value(task.data, "system_prompt", std::string()); + system_prompt_set(sys_prompt); for (server_slot & slot : slots) { slot.n_past = 0; @@ -1979,8 +1982,7 @@ struct server_context { slot.state = SLOT_STATE_PROCESSING; slot.command = SLOT_COMMAND_NONE; slot.release(); - slot.print_timings(); - send_final_response(slot); + send_error(slot, "input is too large to process. increase the physical batch size", ERROR_TYPE_SERVER); continue; } } else { @@ -2271,10 +2273,10 @@ struct server_context { const size_t n_probs = std::min(cur_p.size, (size_t) slot.sparams.n_probs); if (n_probs > 0) { - const size_t n_considered = slot.ctx_sampling->n_considered; + const size_t n_valid = slot.ctx_sampling->n_valid; // Make sure at least n_probs top tokens are at the front of the vector: - if (slot.sparams.temp == 0.0f && n_probs > n_considered) { + if (slot.sparams.temp == 0.0f && n_probs > n_valid) { llama_sample_top_k(ctx, &cur_p, n_probs, 0); } @@ -2290,7 +2292,7 @@ struct server_context { for (size_t i = 0; i < n_probs; ++i) { result.probs.push_back({ cur_p.data[i].id, - i >= n_considered ? 0.0f : cur_p.data[i].p // Tokens filtered out due to e.g. top_k have 0 probability. + i >= n_valid ? 0.0f : cur_p.data[i].p // Tokens filtered out due to e.g. top_k have 0 probability. }); } } @@ -2384,6 +2386,7 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co printf(" --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); printf(" --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str()); printf(" --port PORT port to listen (default (default: %d)\n", sparams.port); + printf(" --rpc SERVERS comma separated list of RPC servers\n"); printf(" --path PUBLIC_PATH path from which to serve static files (default: disabled)\n"); printf(" --api-key API_KEY optional api key to enhance server security. If set, requests must include this key for access.\n"); printf(" --api-key-file FNAME path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access.\n"); @@ -2436,6 +2439,12 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams, break; } sparams.port = std::stoi(argv[i]); + } else if (arg == "--rpc") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.rpc_servers = argv[i]; } else if (arg == "--host") { if (++i >= argc) { invalid_param = true; @@ -2844,7 +2853,7 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams, invalid_param = true; break; } - if (!parse_kv_override(argv[i], params.kv_overrides)) { + if (!string_parse_kv_override(argv[i], params.kv_overrides)) { fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]); invalid_param = true; break; @@ -2919,7 +2928,7 @@ int main(int argc, char ** argv) { server_params_parse(argc, argv, sparams, params); if (!sparams.system_prompt.empty()) { - ctx_server.system_prompt_set(json::parse(sparams.system_prompt)); + ctx_server.system_prompt_set(sparams.system_prompt); } if (params.model_alias == "unknown") { @@ -3302,7 +3311,7 @@ int main(int argc, char ** argv) { const auto handle_slots_save = [&ctx_server, &res_error, &sparams](const httplib::Request & req, httplib::Response & res, int id_slot) { json request_data = json::parse(req.body); std::string filename = request_data.at("filename"); - if (!validate_file_name(filename)) { + if (!fs_validate_filename(filename)) { res_error(res, format_error_response("Invalid filename", ERROR_TYPE_INVALID_REQUEST)); return; } @@ -3332,7 +3341,7 @@ int main(int argc, char ** argv) { const auto handle_slots_restore = [&ctx_server, &res_error, &sparams](const httplib::Request & req, httplib::Response & res, int id_slot) { json request_data = json::parse(req.body); std::string filename = request_data.at("filename"); - if (!validate_file_name(filename)) { + if (!fs_validate_filename(filename)) { res_error(res, format_error_response("Invalid filename", ERROR_TYPE_INVALID_REQUEST)); return; } @@ -3408,8 +3417,7 @@ int main(int argc, char ** argv) { const auto handle_props = [&ctx_server](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); json data = { - { "user_name", ctx_server.name_user.c_str() }, - { "assistant_name", ctx_server.name_assistant.c_str() }, + { "system_prompt", ctx_server.system_prompt.c_str() }, { "default_generation_settings", ctx_server.default_generation_settings_for_props }, { "total_slots", ctx_server.params.n_parallel } }; diff --git a/examples/server/tests/features/results.feature b/examples/server/tests/features/results.feature index aa0b8d0c648b4..e8e1b54147b05 100644 --- a/examples/server/tests/features/results.feature +++ b/examples/server/tests/features/results.feature @@ -13,6 +13,7 @@ Feature: Results Scenario Outline: consistent results with same seed Given slots + And 1.0 temperature Then the server is starting Then the server is healthy @@ -26,10 +27,12 @@ Feature: Results Examples: | n_slots | | 1 | - | 2 | + # FIXME: unified KV cache nondeterminism + # | 2 | Scenario Outline: different results with different seed Given slots + And 1.0 temperature Then the server is starting Then the server is healthy @@ -70,12 +73,46 @@ Feature: Results Then all predictions are equal Examples: | n_parallel | temp | - | 1 | 0.0 | - | 2 | 0.0 | - | 4 | 0.0 | - | 1 | 1.0 | - # FIXME: These tests fail on master. The problem seems to be the unified KV cache. + | 1 | 0.0 | + | 1 | 1.0 | + # FIXME: unified KV cache nondeterminism + # See https://github.com/ggerganov/whisper.cpp/issues/1941#issuecomment-1986923227 + # and https://github.com/ggerganov/llama.cpp/pull/6122#discussion_r1531405574 + # and https://github.com/ggerganov/llama.cpp/pull/7347 . + # | 2 | 0.0 | + # | 4 | 0.0 | + # | 2 | 1.0 | + # | 4 | 1.0 | + + Scenario Outline: consistent token probs with same seed and prompt + Given slots + And KV cache size + And 1.0 temperature + And max tokens to predict + Then the server is starting + Then the server is healthy + + Given 1 prompts "The meaning of life is" with seed 42 + And concurrent completion requests + # Then the server is busy # Not all slots will be utilized. + Then the server is idle + And all slots are idle + + Given prompts "The meaning of life is" with seed 42 + And concurrent completion requests + # Then the server is busy # Not all slots will be utilized. + Then the server is idle + And all slots are idle + + Then all token probabilities are equal + Examples: + | n_slots | n_kv | n_predict | n_parallel | + | 4 | 1024 | 1 | 1 | + # FIXME: unified KV cache nondeterminism # See https://github.com/ggerganov/whisper.cpp/issues/1941#issuecomment-1986923227 - # and https://github.com/ggerganov/llama.cpp/pull/6122#discussion_r1531405574 . - # | 2 | 1.0 | - # | 4 | 1.0 | + # and https://github.com/ggerganov/llama.cpp/pull/6122#discussion_r1531405574 + # and https://github.com/ggerganov/llama.cpp/pull/7347 . + # | 4 | 1024 | 1 | 4 | + # | 4 | 1024 | 100 | 1 | + # This test still fails even the above patches; the first token probabilities are already different. + # | 4 | 1024 | 100 | 4 | diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index f4b1ac1d71840..26d9359d7f3f8 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -23,6 +23,7 @@ def step_server_config(context, server_fqdn, server_port): context.server_fqdn = server_fqdn context.server_port = int(server_port) + context.n_threads = None context.n_gpu_layer = None if 'PORT' in os.environ: context.server_port = int(os.environ['PORT']) @@ -109,6 +110,11 @@ def step_n_gpu_layer(context, ngl): context.n_gpu_layer = ngl +@step('{n_threads:d} threads') +def step_n_threads(context, n_threads): + context.n_thread = n_threads + + @step('{draft:d} as draft') def step_draft(context, draft): context.draft = draft @@ -193,7 +199,7 @@ async def step_wait_for_the_server_to_be_started(context, expecting_status): case 'ready' | 'idle': await wait_for_health_status(context, context.base_url, 200, 'ok', - timeout=10, + timeout=30, params={'fail_on_no_slot': 0, 'include_slots': 0}, slots_idle=context.n_slots, slots_processing=0, @@ -274,13 +280,22 @@ async def step_predictions_equal(context): @step('all predictions are different') @async_run_until_complete -async def step_predictions_equal(context): +async def step_predictions_different(context): n_completions = await gather_tasks_results(context) assert n_completions >= 2, "need at least 2 completions" assert_all_predictions_different(context.tasks_result) context.tasks_result = [] +@step('all token probabilities are equal') +@async_run_until_complete +async def step_token_probabilities_equal(context): + n_completions = await gather_tasks_results(context) + assert n_completions >= 2, "need at least 2 completions" + assert_all_token_probabilities_equal(context.tasks_result) + context.tasks_result = [] + + @step('the completion is truncated') def step_assert_completion_truncated(context): step_assert_completion_truncated(context, '') @@ -868,7 +883,8 @@ async def request_completion(prompt, "cache_prompt": cache_prompt, "id_slot": id_slot, "seed": seed if seed is not None else 42, - "temperature": temperature if temperature is not None else "0.8f", + "temperature": temperature if temperature is not None else 0.8, + "n_probs": 2, }, headers=headers, timeout=3600) as response: @@ -887,6 +903,7 @@ async def oai_chat_completions(user_prompt, base_path, async_client, debug=False, + temperature=None, model=None, n_predict=None, enable_streaming=None, @@ -913,7 +930,8 @@ async def oai_chat_completions(user_prompt, "model": model, "max_tokens": n_predict, "stream": enable_streaming, - "seed": seed + "temperature": temperature if temperature is not None else 0.0, + "seed": seed, } if response_format is not None: payload['response_format'] = response_format @@ -978,7 +996,8 @@ async def oai_chat_completions(user_prompt, max_tokens=n_predict, stream=enable_streaming, response_format=payload.get('response_format'), - seed=seed + seed=seed, + temperature=payload['temperature'] ) except openai.error.AuthenticationError as e: if expect_api_error is not None and expect_api_error: @@ -1120,6 +1139,23 @@ def assert_all_predictions_different(completion_responses): assert content_i != content_j, "contents not different" +def assert_all_token_probabilities_equal(completion_responses): + n_predict = len(completion_responses[0]['completion_probabilities']) + if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON': + for pos in range(n_predict): + for i, response_i in enumerate(completion_responses): + probs_i = response_i['completion_probabilities'][pos]['probs'] + print(f"pos {pos}, probs {i}: {probs_i}") + for pos in range(n_predict): + for i, response_i in enumerate(completion_responses): + probs_i = response_i['completion_probabilities'][pos]['probs'] + for j, response_j in enumerate(completion_responses): + if i == j: + continue + probs_j = response_j['completion_probabilities'][pos]['probs'] + assert probs_i == probs_j, "contents not equal" + + async def gather_tasks_results(context): n_tasks = len(context.concurrent_tasks) if context.debug: @@ -1258,6 +1294,8 @@ def start_server_background(context): server_args.extend(['--batch-size', context.n_batch]) if context.n_ubatch: server_args.extend(['--ubatch-size', context.n_ubatch]) + if context.n_threads: + server_args.extend(['--threads', context.threads]) if context.n_gpu_layer: server_args.extend(['--n-gpu-layers', context.n_gpu_layer]) if context.draft is not None: diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index d872b63f537f4..d8a2286e4b1df 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -371,7 +371,7 @@ static json oaicompat_completion_params_parse( llama_params["presence_penalty"] = json_value(body, "presence_penalty", 0.0); llama_params["seed"] = json_value(body, "seed", LLAMA_DEFAULT_SEED); llama_params["stream"] = json_value(body, "stream", false); - llama_params["temperature"] = json_value(body, "temperature", 0.0); + llama_params["temperature"] = json_value(body, "temperature", 1.0); llama_params["top_p"] = json_value(body, "top_p", 1.0); // Apply chat template to the list of messages diff --git a/examples/sycl/win-build-sycl.bat b/examples/sycl/win-build-sycl.bat index 1b0dc41babd35..b8037aae8c4ef 100644 --- a/examples/sycl/win-build-sycl.bat +++ b/examples/sycl/win-build-sycl.bat @@ -13,10 +13,10 @@ if %errorlevel% neq 0 goto ERROR :: for FP16 :: faster for long-prompt inference -:: cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON +:: cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DBUILD_SHARED_LIBS=ON -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON :: for FP32 -cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release +cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DBUILD_SHARED_LIBS=ON -DCMAKE_BUILD_TYPE=Release if %errorlevel% neq 0 goto ERROR :: build example/main only :: make main diff --git a/examples/train-text-from-scratch/train-text-from-scratch.cpp b/examples/train-text-from-scratch/train-text-from-scratch.cpp index 587418cc73964..e2f85c68297b8 100644 --- a/examples/train-text-from-scratch/train-text-from-scratch.cpp +++ b/examples/train-text-from-scratch/train-text-from-scratch.cpp @@ -301,8 +301,8 @@ static struct ggml_tensor * llama_build_train_graphs( // not capturing these, to silcence warnings const int rope_mode = 0; - return ggml_rope_custom( - ctx, t, KQ_pos, n_rot, rope_mode, n_ctx, 0, rope_freq_base, rope_freq_scale, 0.0f, 1.0f, 0.0f, 0.0f + return ggml_rope_ext( + ctx, t, KQ_pos, nullptr, n_rot, rope_mode, n_ctx, 0, rope_freq_base, rope_freq_scale, 0.0f, 1.0f, 0.0f, 0.0f ); }; @@ -341,7 +341,8 @@ static struct ggml_tensor * llama_build_train_graphs( struct ggml_tensor * t15 = ggml_permute (ctx, t12, 0, 3, 1, 2); set_name(t15, "t15"); assert_shape_4d(t15, N, n_embd/n_head, n_head, n_batch); struct ggml_tensor * t16; if (enable_flash_attn) { - t16 = ggml_flash_attn(ctx, t13, t14, t15, true); set_name(t16, "t16"); assert_shape_4d(t16, n_embd/n_head, N, n_head, n_batch); + GGML_ASSERT(false && "TODO: ggml_flash_attn_ext() not yet supported"); + //t16 = ggml_flash_attn(ctx, t13, t14, t15, true); set_name(t16, "t16"); assert_shape_4d(t16, n_embd/n_head, N, n_head, n_batch); } else { struct ggml_tensor * t16_0 = ggml_mul_mat (ctx, t14, t13); set_name(t16_0, "t16_0"); assert_shape_4d(t16_0, N, N, n_head, n_batch); struct ggml_tensor * t16_1 = ggml_scale_inplace (ctx, t16_0, kv_scale); set_name(t16_1, "t16_1"); assert_shape_4d(t16_1, N, N, n_head, n_batch); diff --git a/expose.h b/expose.h index 418224dd6ba94..592b899bdf419 100644 --- a/expose.h +++ b/expose.h @@ -44,6 +44,7 @@ struct load_model_inputs const char * mmproj_filename; const bool use_mmap; const bool use_mlock; + const bool use_smartcontext; const bool use_contextshift; const int clblast_info = 0; const int cublas_info = 0; @@ -74,6 +75,7 @@ struct generation_inputs const float tfs; const float rep_pen; const int rep_pen_range; + const float rep_pen_slope = 1.0f; const float presence_penalty = 0.0f; const int mirostat = 0; const float mirostat_eta; @@ -108,11 +110,16 @@ struct token_count_outputs struct sd_load_model_inputs { const char * model_filename; + const char * executable_path; const int clblast_info = 0; const int cublas_info = 0; const char * vulkan_info; const int threads; const int quant = 0; + const bool taesd = false; + const char * vae_filename; + const char * lora_filename; + const float lora_multiplier = 1.0f; const int debugmode = 0; }; struct sd_generation_inputs @@ -127,6 +134,7 @@ struct sd_generation_inputs const int height; const int seed; const char * sample_method; + const int clip_skip = -1; const bool quiet = false; }; struct sd_generation_outputs diff --git a/ggml-backend.c b/ggml-backend.c index f5bdcf07838aa..9e35ce98d7ace 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -1182,9 +1182,9 @@ static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, st static char * fmt_size(size_t size) { static char buffer[128]; if (size >= 1024*1024) { - sprintf(buffer, "%zuM", size/1024/1024); + snprintf(buffer, sizeof(buffer), "%zuM", size/1024/1024); } else { - sprintf(buffer, "%zuK", size/1024); + snprintf(buffer, sizeof(buffer), "%zuK", size/1024); } return buffer; } @@ -1895,7 +1895,6 @@ void ggml_backend_view_init(ggml_backend_buffer_t buffer, struct ggml_tensor * t tensor->buffer = buffer; tensor->data = (char *)tensor->view_src->data + tensor->view_offs; - tensor->backend = tensor->view_src->backend; ggml_backend_buffer_init_tensor(buffer, tensor); } diff --git a/ggml-common.h b/ggml-common.h index 43c7978a0982d..77e6bfba4b11b 100644 --- a/ggml-common.h +++ b/ggml-common.h @@ -65,13 +65,8 @@ typedef sycl::half2 ggml_half2; // QK = number of values after dequantization // QK_K = super-block size -#ifdef GGML_QKK_64 -#define QK_K 64 -#define K_SCALE_SIZE 4 -#else #define QK_K 256 #define K_SCALE_SIZE 12 -#endif // GGML_QKK_64 #if defined(GGML_COMMON_DECL_CUDA) || defined(GGML_COMMON_DECL_HIP) || defined(GGML_COMMON_DECL_SYCL) // QR = QK / number of values before dequantization @@ -131,13 +126,8 @@ typedef sycl::half2 ggml_half2; #define QI4_NL (QK4_NL / (4*QR4_NL)) #define QR4_NL 2 -#if QK_K == 64 -#define QI4_XS QI4_NL -#define QR4_XS QR4_NL -#else #define QI4_XS (QK_K / (4*QR4_XS)) #define QR4_XS 8 -#endif #endif // GGML_COMMON_DECL_CUDA || GGML_COMMON_DECL_HIP @@ -228,15 +218,6 @@ static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_half) + QK_K/16 + QK_K/4, "wro // weight is represented as x = a * q // 16 blocks of 16 elements each // Effectively 3.4375 bits per weight -#ifdef GGML_QKK_64 -typedef struct { - uint8_t hmask[QK_K/8]; // quants - high bit - uint8_t qs[QK_K/4]; // quants - low 2 bits - uint8_t scales[2]; - ggml_half d; // super-block scale -} block_q3_K; -static_assert(sizeof(block_q3_K) == sizeof(ggml_half) + QK_K / 4 + QK_K / 8 + 2, "wrong q3_K block size/padding"); -#else typedef struct { uint8_t hmask[QK_K/8]; // quants - high bit uint8_t qs[QK_K/4]; // quants - low 2 bits @@ -244,20 +225,11 @@ typedef struct { ggml_half d; // super-block scale } block_q3_K; static_assert(sizeof(block_q3_K) == sizeof(ggml_half) + QK_K / 4 + QK_K / 8 + 12, "wrong q3_K block size/padding"); -#endif // 4-bit quantization // 8 blocks of 32 elements each // weight is represented as x = a * q + b // Effectively 4.5 bits per weight -#ifdef GGML_QKK_64 -typedef struct { - ggml_half d[2]; // super-block scales/mins - uint8_t scales[2]; // 4-bit block scales/mins - uint8_t qs[QK_K/2]; // 4--bit quants -} block_q4_K; -static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_half) + QK_K/2 + 2, "wrong q4_K block size/padding"); -#else typedef struct { union { struct { @@ -270,21 +242,11 @@ typedef struct { uint8_t qs[QK_K/2]; // 4--bit quants } block_q4_K; static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_half) + K_SCALE_SIZE + QK_K/2, "wrong q4_K block size/padding"); -#endif // 5-bit quantization // 8 blocks of 32 elements each // weight is represented as x = a * q + b // Effectively 5.5 bits per weight -#ifdef GGML_QKK_64 -typedef struct { - ggml_half d; // super-block scale - int8_t scales[QK_K/16]; // 8-bit block scales - uint8_t qh[QK_K/8]; // quants, high bit - uint8_t qs[QK_K/2]; // quants, low 4 bits -} block_q5_K; -static_assert(sizeof(block_q5_K) == sizeof(ggml_half) + QK_K/2 + QK_K/8 + QK_K/16, "wrong q5_K block size/padding"); -#else typedef struct { union { struct { @@ -298,7 +260,6 @@ typedef struct { uint8_t qs[QK_K/2]; // quants, low 4 bits } block_q5_K; static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_half) + K_SCALE_SIZE + QK_K/2 + QK_K/8, "wrong q5_K block size/padding"); -#endif // 6-bit quantization // weight is represented as x = a * q @@ -356,11 +317,7 @@ typedef struct { static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_half) + 3*(QK_K/8), "wrong iq3_xxs block size/padding"); // 3.4375 bpw -#if QK_K == 64 -#define IQ3S_N_SCALE 2 -#else #define IQ3S_N_SCALE QK_K/64 -#endif typedef struct { ggml_half d; uint8_t qs[QK_K/4]; @@ -381,16 +338,9 @@ static_assert(sizeof(block_iq1_s) == sizeof(ggml_half) + QK_K/8 + QK_K/16, "wron typedef struct { uint8_t qs[QK_K/8]; // grid index, low 8 bits uint8_t qh[QK_K/16]; // grid index, high 3 bits + grid shift bit (for two groups of 8) -#if QK_K == 64 - ggml_half d; -#endif uint8_t scales[QK_K/32]; // 3-bit block scales (4-bit if QK_K == 64) } block_iq1_m; -#if QK_K == 64 -static_assert(sizeof(block_iq1_m) == QK_K/8 + QK_K/16 + QK_K/32 + sizeof(ggml_half), "wrong iq1_m block size/padding"); -#else static_assert(sizeof(block_iq1_m) == QK_K/8 + QK_K/16 + QK_K/32, "wrong iq1_m block size/padding"); -#endif // Used by IQ1_M quants typedef union { @@ -406,9 +356,6 @@ typedef struct { } block_iq4_nl; static_assert(sizeof(block_iq4_nl) == sizeof(ggml_half) + QK4_NL/2, "wrong iq4_nl block size/padding"); -#if QK_K == 64 -#define block_iq4_xs block_iq4_nl -#else typedef struct { ggml_half d; uint16_t scales_h; @@ -416,7 +363,6 @@ typedef struct { uint8_t qs[QK_K/2]; } block_iq4_xs; static_assert(sizeof(block_iq4_xs) == sizeof(ggml_half) + sizeof(uint16_t) + QK_K/64 + QK_K/2, "wrong iq4_xs block size/padding"); -#endif #endif // GGML_COMMON_DECL #endif // GGML_COMMON_DECL diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 0e30f438d0f99..1b1638d2f89bb 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -6,7 +6,6 @@ static bool g_mul_mat_q = false; #include "ggml-cuda/common.cuh" #include "ggml-cuda/acc.cuh" -#include "ggml-cuda/alibi.cuh" #include "ggml-cuda/arange.cuh" #include "ggml-cuda/argsort.cuh" #include "ggml-cuda/binbcast.cuh" @@ -46,19 +45,59 @@ static bool g_mul_mat_q = false; #include #include #include +#include +#include #include #include static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size"); +static void ggml_cuda_default_log_callback(enum ggml_log_level level, const char * msg, void * user_data) { + GGML_UNUSED(level); + GGML_UNUSED(user_data); + fprintf(stderr, "%s", msg); +} + +ggml_log_callback ggml_cuda_log_callback = ggml_cuda_default_log_callback; +void * ggml_cuda_log_user_data = NULL; + +GGML_API void ggml_backend_cuda_log_set_callback(ggml_log_callback log_callback, void * user_data) { + ggml_cuda_log_callback = log_callback; + ggml_cuda_log_user_data = user_data; +} + +#define GGML_CUDA_LOG_INFO(...) ggml_cuda_log(GGML_LOG_LEVEL_INFO, __VA_ARGS__) +#define GGML_CUDA_LOG_WARN(...) ggml_cuda_log(GGML_LOG_LEVEL_WARN, __VA_ARGS__) +#define GGML_CUDA_LOG_ERROR(...) ggml_cuda_log(GGML_LOG_LEVEL_ERROR, __VA_ARGS__) + +GGML_ATTRIBUTE_FORMAT(2, 3) +static void ggml_cuda_log(enum ggml_log_level level, const char * format, ...) { + if (ggml_cuda_log_callback != NULL) { + va_list args; + va_start(args, format); + char buffer[128]; + int len = vsnprintf(buffer, 128, format, args); + if (len < 128) { + ggml_cuda_log_callback(level, buffer, ggml_cuda_log_user_data); + } else { + std::vector buffer2(len + 1); // vsnprintf adds a null terminator + va_end(args); + va_start(args, format); + vsnprintf(&buffer2[0], buffer2.size(), format, args); + ggml_cuda_log_callback(level, buffer2.data(), ggml_cuda_log_user_data); + } + va_end(args); + } +} + [[noreturn]] void ggml_cuda_error(const char * stmt, const char * func, const char * file, int line, const char * msg) { int id = -1; // in case cudaGetDevice fails cudaGetDevice(&id); - fprintf(stderr, "CUDA error: %s\n", msg); - fprintf(stderr, " current device: %d, in function %s at %s:%d\n", id, func, file, line); - fprintf(stderr, " %s\n", stmt); + GGML_CUDA_LOG_ERROR("CUDA error: %s\n", msg); + GGML_CUDA_LOG_ERROR(" current device: %d, in function %s at %s:%d\n", id, func, file, line); + GGML_CUDA_LOG_ERROR(" %s\n", stmt); // abort with GGML_ASSERT to get a stack trace GGML_ASSERT(!"CUDA error"); } @@ -94,7 +133,7 @@ static ggml_cuda_device_info ggml_cuda_init() { cudaError_t err = cudaGetDeviceCount(&info.device_count); if (err != cudaSuccess) { - fprintf(stderr, "%s: failed to initialize " GGML_CUDA_NAME ": %s\n", __func__, cudaGetErrorString(err)); + GGML_CUDA_LOG_ERROR("%s: failed to initialize " GGML_CUDA_NAME ": %s\n", __func__, cudaGetErrorString(err)); return info; } @@ -102,16 +141,16 @@ static ggml_cuda_device_info ggml_cuda_init() { int64_t total_vram = 0; // #if defined(GGML_CUDA_FORCE_MMQ) -// fprintf(stderr, "%s: GGML_CUDA_FORCE_MMQ: yes\n", __func__); +// GGML_CUDA_LOG_INFO("%s: GGML_CUDA_FORCE_MMQ: yes\n", __func__); // #else -// fprintf(stderr, "%s: GGML_CUDA_FORCE_MMQ: no\n", __func__); +// GGML_CUDA_LOG_INFO("%s: GGML_CUDA_FORCE_MMQ: no\n", __func__); // #endif // #if defined(CUDA_USE_TENSOR_CORES) -// fprintf(stderr, "%s: CUDA_USE_TENSOR_CORES: yes\n", __func__); +// GGML_CUDA_LOG_INFO("%s: CUDA_USE_TENSOR_CORES: yes\n", __func__); // #else -// fprintf(stderr, "%s: CUDA_USE_TENSOR_CORES: no\n", __func__); +// GGML_CUDA_LOG_INFO("%s: CUDA_USE_TENSOR_CORES: no\n", __func__); // #endif - fprintf(stderr, "%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, info.device_count); + GGML_CUDA_LOG_INFO("%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, info.device_count); for (int id = 0; id < info.device_count; ++id) { int device_vmm = 0; @@ -132,7 +171,7 @@ static ggml_cuda_device_info ggml_cuda_init() { cudaDeviceProp prop; CUDA_CHECK(cudaGetDeviceProperties(&prop, id)); - fprintf(stderr, " Device %d: %s, compute capability %d.%d, VMM: %s\n", id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no"); + GGML_CUDA_LOG_INFO(" Device %d: %s, compute capability %d.%d, VMM: %s\n", id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no"); info.default_tensor_split[id] = total_vram; total_vram += prop.totalGlobalMem; @@ -251,7 +290,7 @@ struct ggml_cuda_pool_leg : public ggml_cuda_pool { return; } } - fprintf(stderr, "WARNING: cuda buffer pool full, increase MAX_CUDA_BUFFERS\n"); + GGML_CUDA_LOG_WARN("Cuda buffer pool full, increase MAX_CUDA_BUFFERS\n"); ggml_cuda_set_device(device); CUDA_CHECK(cudaFree(ptr)); pool_size -= size; @@ -500,7 +539,9 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffe void * dev_ptr; cudaError_t err = cudaMalloc(&dev_ptr, size); if (err != cudaSuccess) { - fprintf(stderr, "%s: allocating %.2f MiB on device %d: cudaMalloc failed: %s\n", __func__, size/1024.0/1024.0, buft_ctx->device, cudaGetErrorString(err)); + // clear the error + cudaGetLastError(); + GGML_CUDA_LOG_ERROR("%s: allocating %.2f MiB on device %d: cudaMalloc failed: %s\n", __func__, size / 1024.0 / 1024.0, buft_ctx->device, cudaGetErrorString(err)); return nullptr; } @@ -1003,8 +1044,8 @@ static void * ggml_cuda_host_malloc(size_t size) { if (err != cudaSuccess) { // clear the error cudaGetLastError(); - fprintf(stderr, "%s: warning: failed to allocate %.2f MiB of pinned memory: %s\n", __func__, - size/1024.0/1024.0, cudaGetErrorString(err)); + GGML_CUDA_LOG_WARN("%s: failed to allocate %.2f MiB of pinned memory: %s\n", __func__, + size / 1024.0 / 1024.0, cudaGetErrorString(err)); return nullptr; } @@ -2211,6 +2252,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg case GGML_UNARY_OP_RELU: ggml_cuda_op_relu(ctx, dst); break; + case GGML_UNARY_OP_SIGMOID: + ggml_cuda_op_sigmoid(ctx, dst); + break; case GGML_UNARY_OP_HARDSIGMOID: ggml_cuda_op_hardsigmoid(ctx, dst); break; @@ -2250,7 +2294,7 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg break; case GGML_OP_MUL_MAT: if (dst->src[0]->ne[3] != dst->src[1]->ne[3]) { - fprintf(stderr, "%s: cannot compute %s: src0->ne[3] = %" PRId64 ", src1->ne[3] = %" PRId64 " - fallback to CPU\n", __func__, dst->name, dst->src[0]->ne[3], dst->src[1]->ne[3]); + GGML_CUDA_LOG_ERROR("%s: cannot compute %s: src0->ne[3] = %" PRId64 ", src1->ne[3] = %" PRId64 " - fallback to CPU\n", __func__, dst->name, dst->src[0]->ne[3], dst->src[1]->ne[3]); return false; } else { ggml_cuda_mul_mat(ctx, dst->src[0], dst->src[1], dst); @@ -2283,9 +2327,6 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg case GGML_OP_ROPE: ggml_cuda_op_rope(ctx, dst); break; - case GGML_OP_ALIBI: - ggml_cuda_op_alibi(ctx, dst); - break; case GGML_OP_IM2COL: ggml_cuda_op_im2col(ctx, dst); break; @@ -2307,7 +2348,7 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg cudaError_t err = cudaGetLastError(); if (err != cudaSuccess) { - fprintf(stderr, "%s: %s failed\n", __func__, ggml_op_desc(dst)); + GGML_CUDA_LOG_ERROR("%s: %s failed\n", __func__, ggml_op_desc(dst)); CUDA_CHECK(err); } @@ -2483,7 +2524,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t if (ggml_cuda_info().devices[cuda_ctx->device].cc < CC_AMPERE) { cuda_ctx->cuda_graph->disable_due_to_gpu_arch = true; #ifndef NDEBUG - fprintf(stderr, "%s: disabling CUDA graphs due to GPU architecture\n", __func__); + GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to GPU architecture\n", __func__); #endif } } @@ -2530,14 +2571,14 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t if (node->src[0] && ggml_backend_buffer_is_cuda_split(node->src[0]->buffer)) { use_cuda_graph = false; // Split buffers are not supported by CUDA graph capture #ifndef NDEBUG - fprintf(stderr, "%s: disabling CUDA graphs due to split buffer\n", __func__); + GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to split buffer\n", __func__); #endif } if (node->op == GGML_OP_MUL_MAT_ID) { use_cuda_graph = false; // This node type is not supported by CUDA graph capture #ifndef NDEBUG - fprintf(stderr, "%s: disabling CUDA graphs due to mul_mat_id\n", __func__); + GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to mul_mat_id\n", __func__); #endif } @@ -2546,7 +2587,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t // Changes in batch size or context size can cause changes to the grid size of some kernels. use_cuda_graph = false; #ifndef NDEBUG - fprintf(stderr, "%s: disabling CUDA graphs due to batch size > 1 [%s] [%ld %ld %ld %ld]\n", __func__, node->name, node->ne[0], node->ne[1], node->ne[2], node->ne[3]); + GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to batch size > 1 [%s] [%ld %ld %ld %ld]\n", __func__, node->name, node->ne[0], node->ne[1], node->ne[2], node->ne[3]); #endif } @@ -2565,7 +2606,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t } // Disable CUDA graphs (from the next token) if the use-case is demanding too many consecutive graph updates. - if (cuda_graph_update_required) { + if (use_cuda_graph && cuda_graph_update_required) { cuda_ctx->cuda_graph->number_consecutive_updates++; } else { cuda_ctx->cuda_graph->number_consecutive_updates = 0; @@ -2574,7 +2615,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t if (cuda_ctx->cuda_graph->number_consecutive_updates >= 4) { cuda_ctx->cuda_graph->disable_due_to_too_many_updates = true; #ifndef NDEBUG - fprintf(stderr, "%s: disabling CUDA graphs due to too many consecutive updates\n", __func__); + GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to too many consecutive updates\n", __func__); #endif } } @@ -2612,7 +2653,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t bool ok = ggml_cuda_compute_forward(*cuda_ctx, node); if (!ok) { - fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op)); + GGML_CUDA_LOG_ERROR("%s: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op)); } GGML_ASSERT(ok); } @@ -2631,7 +2672,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t use_cuda_graph = false; cuda_ctx->cuda_graph->disable_due_to_failed_graph_capture = true; #ifndef NDEBUG - fprintf(stderr, "%s: disabling CUDA graphs due to failed graph capture\n", __func__); + GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to failed graph capture\n", __func__); #endif } else { graph_evaluated_or_captured = true; // CUDA graph has been captured @@ -2698,7 +2739,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t cudaError_t stat = cudaGraphExecUpdate(cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, &result_info); if (stat == cudaErrorGraphExecUpdateFailure) { #ifndef NDEBUG - fprintf(stderr, "%s: CUDA graph update failed\n", __func__); + GGML_CUDA_LOG_ERROR("%s: CUDA graph update failed\n", __func__); #endif // The pre-existing graph exec cannot be updated due to violated constraints // so instead clear error and re-instantiate @@ -2720,12 +2761,14 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t } GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_tensor * op) { + ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *) backend->context; switch (op->op) { case GGML_OP_UNARY: switch (ggml_get_unary_op(op)) { case GGML_UNARY_OP_GELU: case GGML_UNARY_OP_SILU: case GGML_UNARY_OP_RELU: + case GGML_UNARY_OP_SIGMOID: case GGML_UNARY_OP_HARDSIGMOID: case GGML_UNARY_OP_HARDSWISH: case GGML_UNARY_OP_GELU_QUICK: @@ -2835,7 +2878,6 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons case GGML_OP_DIAG_MASK_INF: case GGML_OP_SOFT_MAX: case GGML_OP_ROPE: - case GGML_OP_ALIBI: case GGML_OP_IM2COL: case GGML_OP_POOL_2D: case GGML_OP_SUM_ROWS: @@ -2847,8 +2889,16 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons case GGML_OP_ARANGE: case GGML_OP_TIMESTEP_EMBEDDING: case GGML_OP_LEAKY_RELU: - case GGML_OP_FLASH_ATTN_EXT: return true; + case GGML_OP_FLASH_ATTN_EXT: +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) + return op->src[0]->ne[0] == 64 || op->src[0]->ne[0] == 128; +#else + if (op->src[0]->ne[0] == 64 || op->src[0]->ne[0] == 128) { + return true; + } + return ggml_cuda_info().devices[cuda_ctx->device].cc >= CC_VOLTA; +#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) default: return false; } @@ -2946,13 +2996,13 @@ static ggml_guid_t ggml_backend_cuda_guid() { GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device) { if (device < 0 || device >= ggml_backend_cuda_get_device_count()) { - fprintf(stderr, "%s: error: invalid device %d\n", __func__, device); + GGML_CUDA_LOG_ERROR("%s: invalid device %d\n", __func__, device); return nullptr; } ggml_backend_cuda_context * ctx = new ggml_backend_cuda_context(device); if (ctx == nullptr) { - fprintf(stderr, "%s: error: failed to allocate context\n", __func__); + GGML_CUDA_LOG_ERROR("%s: failed to allocate context\n", __func__); return nullptr; } @@ -2996,8 +3046,8 @@ GGML_CALL bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size // clear the error cudaGetLastError(); - fprintf(stderr, "%s: warning: failed to register %.2f MiB of pinned memory: %s\n", __func__, - size/1024.0/1024.0, cudaGetErrorString(err)); + GGML_CUDA_LOG_WARN("%s: failed to register %.2f MiB of pinned memory: %s\n", __func__, + size / 1024.0 / 1024.0, cudaGetErrorString(err)); return false; } return true; diff --git a/ggml-cuda.h b/ggml-cuda.h index 9332f53c12b23..2d64e6a87edb9 100644 --- a/ggml-cuda.h +++ b/ggml-cuda.h @@ -39,6 +39,7 @@ GGML_API GGML_CALL void ggml_backend_cuda_get_device_memory(int device, size_t * GGML_API GGML_CALL bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size); GGML_API GGML_CALL void ggml_backend_cuda_unregister_host_buffer(void * buffer); +GGML_API void ggml_backend_cuda_log_set_callback(ggml_log_callback log_callback, void * user_data); #ifdef __cplusplus } #endif diff --git a/ggml-cuda/alibi.cu b/ggml-cuda/alibi.cu deleted file mode 100644 index 6c7f1fd9562e4..0000000000000 --- a/ggml-cuda/alibi.cu +++ /dev/null @@ -1,63 +0,0 @@ -#include "alibi.cuh" - -static __global__ void alibi_f32(const float * x, float * dst, const int ncols, const int k_rows, - const int n_heads_log2_floor, const float m0, const float m1) { - const int col = blockDim.x*blockIdx.x + threadIdx.x; - - if (col >= ncols) { - return; - } - - const int row = blockDim.y*blockIdx.y + threadIdx.y; - const int i = row*ncols + col; - - const int k = row/k_rows; - - float m_k; - if (k < n_heads_log2_floor) { - m_k = powf(m0, k + 1); - } else { - m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1); - } - - dst[i] = col * m_k + x[i]; -} - -static void alibi_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, - const int k_rows, const int n_heads_log2_floor, const float m0, - const float m1, cudaStream_t stream) { - const dim3 block_dims(CUDA_ALIBI_BLOCK_SIZE, 1, 1); - const int num_blocks_x = (ncols + CUDA_ALIBI_BLOCK_SIZE - 1) / (CUDA_ALIBI_BLOCK_SIZE); - const dim3 block_nums(num_blocks_x, nrows, 1); - alibi_f32<<>>(x, dst, ncols, k_rows, n_heads_log2_floor, m0, m1); -} - -void ggml_cuda_op_alibi(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * src0 = dst->src[0]; - const float * src0_d = (const float *)src0->data; - float * dst_d = (float *)dst->data; - cudaStream_t stream = ctx.stream(); - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t nrows = ggml_nrows(src0); - - //const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_head = ((int32_t *) dst->op_params)[1]; - float max_bias; - memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float)); - - //GGML_ASSERT(ne01 + n_past == ne00); - GGML_ASSERT(n_head == ne02); - - const int n_heads_log2_floor = 1 << (int) floor(log2(n_head)); - - const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor); - const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor); - - alibi_f32_cuda(src0_d, dst_d, ne00, nrows, ne01, n_heads_log2_floor, m0, m1, stream); -} diff --git a/ggml-cuda/alibi.cuh b/ggml-cuda/alibi.cuh deleted file mode 100644 index 630adfc7f6396..0000000000000 --- a/ggml-cuda/alibi.cuh +++ /dev/null @@ -1,5 +0,0 @@ -#include "common.cuh" - -#define CUDA_ALIBI_BLOCK_SIZE 32 - -void ggml_cuda_op_alibi(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/ggml-cuda/common.cuh b/ggml-cuda/common.cuh index df61de9f734ee..e2c3110b0123c 100644 --- a/ggml-cuda/common.cuh +++ b/ggml-cuda/common.cuh @@ -315,12 +315,30 @@ static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) { #endif return c; } + +#if defined(__HIP_PLATFORM_AMD__) && HIP_VERSION < 50600000 +// __shfl_xor() for half2 was added in ROCm 5.6 +static __device__ __forceinline__ half2 __shfl_xor(half2 var, int laneMask, int width) { + typedef union half2_b32 { + half2 val; + int b32; + } half2_b32_t; + half2_b32_t tmp; + tmp.val = var; + tmp.b32 = __shfl_xor(tmp.b32, laneMask, width); + return tmp.val; +} +#endif // defined(__HIP_PLATFORM_AMD__) && HIP_VERSION < 50600000 #endif // defined(GGML_USE_HIPBLAS) #define FP16_AVAILABLE (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ >= CC_PASCAL #define FP16_MMA_AVAILABLE !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_VOLTA +static bool fast_fp16_available(const int cc) { + return cc >= CC_PASCAL && cc != 610; +} + static bool fp16_mma_available(const int cc) { return cc < CC_OFFSET_AMD && cc >= CC_VOLTA; } @@ -459,6 +477,17 @@ static const __device__ int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, - typedef void (*dequantize_kernel_t)(const void * vx, const int64_t ib, const int iqs, dfloat2 & v); +static __device__ __forceinline__ float get_alibi_slope( + const float max_bias, const uint32_t h, const uint32_t n_head_log2, const float m0, const float m1 +) { + if (max_bias <= 0.0f) { + return 1.0f; + } + const float base = h < n_head_log2 ? m0 : m1; + const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; + + return powf(base, exph); +} ////////////////////// diff --git a/ggml-cuda/convert.cu b/ggml-cuda/convert.cu index 830e2d7566162..c0a4447075c6e 100644 --- a/ggml-cuda/convert.cu +++ b/ggml-cuda/convert.cu @@ -131,7 +131,6 @@ static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, dst_t const block_q2_K * x = (const block_q2_K *) vx; const int64_t tid = threadIdx.x; -#if QK_K == 256 const int64_t n = tid/32; const int64_t l = tid - 32*n; const int64_t is = 8*n + l/16; @@ -145,17 +144,6 @@ static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, dst_t y[l+32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 2) & 3) - dmin * (x[i].scales[is+2] >> 4); y[l+64] = dall * (x[i].scales[is+4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+4] >> 4); y[l+96] = dall * (x[i].scales[is+6] & 0xF) * ((q >> 6) & 3) - dmin * (x[i].scales[is+6] >> 4); -#else - const int64_t is = tid/16; // 0 or 1 - const int64_t il = tid%16; // 0...15 - const uint8_t q = x[i].qs[il] >> (2*is); - dst_t * y = yy + i*QK_K + 16*is + il; - float dall = __low2half(x[i].dm); - float dmin = __high2half(x[i].dm); - y[ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4); - y[32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+2] >> 4); -#endif - } template @@ -164,7 +152,6 @@ static __global__ void dequantize_block_q3_K(const void * __restrict__ vx, dst_t const int64_t i = blockIdx.x; const block_q3_K * x = (const block_q3_K *) vx; -#if QK_K == 256 const int64_t r = threadIdx.x/4; const int64_t tid = r/2; const int64_t is0 = r%2; @@ -188,31 +175,8 @@ static __global__ void dequantize_block_q3_K(const void * __restrict__ vx, dst_t const uint8_t * hm = x[i].hmask; for (int l = l0; l < l0+4; ++l) y[l] = dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4)); -#else - const int64_t tid = threadIdx.x; - const int64_t is = tid/16; // 0 or 1 - const int64_t il = tid%16; // 0...15 - const int64_t im = il/8; // 0...1 - const int64_t in = il%8; // 0...7 - - dst_t * y = yy + i*QK_K + 16*is + il; - - const uint8_t q = x[i].qs[il] >> (2*is); - const uint8_t h = x[i].hmask[in] >> (2*is + im); - const float d = (float)x[i].d; - - if (is == 0) { - y[ 0] = d * ((x[i].scales[0] & 0xF) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4)); - y[32] = d * ((x[i].scales[1] & 0xF) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4)); - } else { - y[ 0] = d * ((x[i].scales[0] >> 4) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4)); - y[32] = d * ((x[i].scales[1] >> 4) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4)); - } -#endif - } -#if QK_K == 256 static inline __device__ void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m) { if (j < 4) { d = q[j] & 63; m = q[j + 4] & 63; @@ -221,7 +185,6 @@ static inline __device__ void get_scale_min_k4(int j, const uint8_t * q, uint8_t m = (q[j+4] >> 4) | ((q[j-0] >> 6) << 4); } } -#endif template static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restrict__ yy) { @@ -229,7 +192,6 @@ static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, dst_t const int64_t i = blockIdx.x; -#if QK_K == 256 // assume 32 threads const int64_t tid = threadIdx.x; const int64_t il = tid/8; @@ -253,15 +215,6 @@ static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, dst_t y[l + 0] = d1 * (q[l] & 0xF) - m1; y[l +32] = d2 * (q[l] >> 4) - m2; } -#else - const int64_t tid = threadIdx.x; - const uint8_t * q = x[i].qs; - dst_t * y = yy + i*QK_K; - const float d = (float)x[i].dm[0]; - const float m = (float)x[i].dm[1]; - y[tid+ 0] = d * (x[i].scales[0] & 0xF) * (q[tid] & 0xF) - m * (x[i].scales[0] >> 4); - y[tid+32] = d * (x[i].scales[1] & 0xF) * (q[tid] >> 4) - m * (x[i].scales[1] >> 4); -#endif } template @@ -270,7 +223,6 @@ static __global__ void dequantize_block_q5_K(const void * __restrict__ vx, dst_t const int64_t i = blockIdx.x; -#if QK_K == 256 // assume 64 threads - this is very slightly better than the one below const int64_t tid = threadIdx.x; const int64_t il = tid/16; // il is in 0...3 @@ -297,18 +249,6 @@ static __global__ void dequantize_block_q5_K(const void * __restrict__ vx, dst_t hm <<= 1; y[32] = d2 * ((ql[ 0] >> 4) + (qh[ 0] & hm ? 16 : 0)) - m2; y[33] = d2 * ((ql[ 1] >> 4) + (qh[ 1] & hm ? 16 : 0)) - m2; -#else - const int64_t tid = threadIdx.x; - const uint8_t q = x[i].qs[tid]; - const int64_t im = tid/8; // 0...3 - const int64_t in = tid%8; // 0...7 - const int64_t is = tid/16; // 0 or 1 - const uint8_t h = x[i].qh[in] >> im; - const float d = x[i].d; - dst_t * y = yy + i*QK_K + tid; - y[ 0] = d * x[i].scales[is+0] * ((q & 0xF) - ((h >> 0) & 1 ? 0 : 16)); - y[32] = d * x[i].scales[is+2] * ((q >> 4) - ((h >> 4) & 1 ? 0 : 16)); -#endif } template @@ -316,7 +256,6 @@ static __global__ void dequantize_block_q6_K(const void * __restrict__ vx, dst_t const block_q6_K * x = (const block_q6_K *) vx; const int64_t i = blockIdx.x; -#if QK_K == 256 // assume 64 threads - this is very slightly better than the one below const int64_t tid = threadIdx.x; @@ -336,24 +275,6 @@ static __global__ void dequantize_block_q6_K(const void * __restrict__ vx, dst_t y[32] = d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32); y[64] = d * sc[4] * ((int8_t)((ql[ 0] >> 4) | (((qh >> 4) & 3) << 4)) - 32); y[96] = d * sc[6] * ((int8_t)((ql[32] >> 4) | (((qh >> 6) & 3) << 4)) - 32); -#else - - // assume 32 threads - const int64_t tid = threadIdx.x; - const int64_t ip = tid/16; // 0 or 1 - const int64_t il = tid - 16*ip; // 0...15 - - dst_t * y = yy + i*QK_K + 16*ip + il; - - const float d = x[i].d; - - const uint8_t ql = x[i].ql[16*ip + il]; - const uint8_t qh = x[i].qh[il] >> (2*ip); - const int8_t * sc = x[i].scales; - - y[ 0] = d * sc[ip+0] * ((int8_t)((ql & 0xF) | (((qh >> 0) & 3) << 4)) - 32); - y[32] = d * sc[ip+2] * ((int8_t)((ql >> 4) | (((qh >> 4) & 3) << 4)) - 32); -#endif } template @@ -363,7 +284,6 @@ static __global__ void dequantize_block_iq2_xxs(const void * __restrict__ vx, ds const block_iq2_xxs * x = (const block_iq2_xxs *) vx; const int64_t tid = threadIdx.x; -#if QK_K == 256 const int64_t il = tid/8; // 0...3 const int64_t ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; @@ -374,10 +294,6 @@ static __global__ void dequantize_block_iq2_xxs(const void * __restrict__ vx, ds const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.25f; const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127]; for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); -#else - NO_DEVICE_CODE; -#endif - } template @@ -387,7 +303,6 @@ static __global__ void dequantize_block_iq2_xs(const void * __restrict__ vx, dst const block_iq2_xs * x = (const block_iq2_xs *) vx; const int64_t tid = threadIdx.x; -#if QK_K == 256 const int64_t il = tid/8; // 0...3 const int64_t ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; @@ -396,10 +311,6 @@ static __global__ void dequantize_block_iq2_xs(const void * __restrict__ vx, dst const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f; const uint8_t signs = ksigns_iq2xs[q2[il] >> 9]; for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); -#else - NO_DEVICE_CODE; -#endif - } template @@ -409,7 +320,6 @@ static __global__ void dequantize_block_iq2_s(const void * __restrict__ vx, dst_ const block_iq2_s * x = (const block_iq2_s *) vx; const int64_t tid = threadIdx.x; -#if QK_K == 256 const int64_t il = tid/8; // 0...3 const int64_t ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; @@ -417,10 +327,6 @@ static __global__ void dequantize_block_iq2_s(const void * __restrict__ vx, dst_ const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f; const uint8_t signs = x[i].qs[QK_K/8+4*ib+il]; for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); -#else - NO_DEVICE_CODE; -#endif - } template @@ -430,7 +336,6 @@ static __global__ void dequantize_block_iq3_xxs(const void * __restrict__ vx, ds const block_iq3_xxs * x = (const block_iq3_xxs *) vx; const int64_t tid = threadIdx.x; -#if QK_K == 256 const int64_t il = tid/8; // 0...3 const int64_t ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; @@ -445,10 +350,6 @@ static __global__ void dequantize_block_iq3_xxs(const void * __restrict__ vx, ds y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f); y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f); } -#else - NO_DEVICE_CODE; -#endif - } template @@ -458,7 +359,6 @@ static __global__ void dequantize_block_iq3_s(const void * __restrict__ vx, dst_ const block_iq3_s * x = (const block_iq3_s *) vx; const int64_t tid = threadIdx.x; -#if QK_K == 256 const int64_t il = tid/8; // 0...3 const int64_t ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; @@ -471,10 +371,6 @@ static __global__ void dequantize_block_iq3_s(const void * __restrict__ vx, dst_ y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f); y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f); } -#else - NO_DEVICE_CODE; -#endif - } template @@ -484,7 +380,6 @@ static __global__ void dequantize_block_iq1_s(const void * __restrict__ vx, dst_ const block_iq1_s * x = (const block_iq1_s *) vx; const int64_t tid = threadIdx.x; -#if QK_K == 256 const int64_t il = tid/8; // 0...3 const int64_t ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; @@ -497,10 +392,6 @@ static __global__ void dequantize_block_iq1_s(const void * __restrict__ vx, dst_ for (int j = 0; j < 8; ++j) { y[j] = d * (q[j] + delta); } -#else - NO_DEVICE_CODE; -#endif - } template @@ -510,7 +401,6 @@ static __global__ void dequantize_block_iq1_m(const void * __restrict__ vx, dst_ const block_iq1_m * x = (const block_iq1_m *) vx; const int64_t tid = threadIdx.x; -#if QK_K == 256 const int64_t il = tid/8; // 0...3 const int64_t ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; @@ -527,13 +417,8 @@ static __global__ void dequantize_block_iq1_m(const void * __restrict__ vx, dst_ for (int j = 0; j < 8; ++j) { y[j] = d * (q[j] + delta); } -#else - NO_DEVICE_CODE; -#endif - } - template static __global__ void dequantize_block_iq4_nl(const void * __restrict__ vx, dst_t * __restrict__ yy) { @@ -550,10 +435,8 @@ static __global__ void dequantize_block_iq4_nl(const void * __restrict__ vx, dst y[j+ 0] = d * kvalues_iq4nl[q4[j] & 0xf]; y[j+16] = d * kvalues_iq4nl[q4[j] >> 4]; } - } -#if QK_K != 64 template static __global__ void dequantize_block_iq4_xs(const void * __restrict__ vx, dst_t * __restrict__ yy) { const int64_t i = blockIdx.x; @@ -570,7 +453,6 @@ static __global__ void dequantize_block_iq4_xs(const void * __restrict__ vx, dst y[j+16] = d * kvalues_iq4nl[q4[j] >> 4]; } } -#endif template static void dequantize_block_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t k, cudaStream_t stream) { @@ -592,21 +474,13 @@ static void dequantize_block_q8_0_f16_cuda(const void * __restrict__ vx, half * template static void dequantize_row_q2_K_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) { const int nb = k / QK_K; -#if QK_K == 256 dequantize_block_q2_K<<>>(vx, y); -#else - dequantize_block_q2_K<<>>(vx, y); -#endif } template static void dequantize_row_q3_K_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) { const int nb = k / QK_K; -#if QK_K == 256 dequantize_block_q3_K<<>>(vx, y); -#else - dequantize_block_q3_K<<>>(vx, y); -#endif } template @@ -632,21 +506,13 @@ static void dequantize_row_q4_K_cuda(const void * vx, dst_t * y, const int64_t k template static void dequantize_row_q5_K_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) { const int nb = k / QK_K; -#if QK_K == 256 dequantize_block_q5_K<<>>(vx, y); -#else - dequantize_block_q5_K<<>>(vx, y); -#endif } template static void dequantize_row_q6_K_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) { const int nb = k / QK_K; -#if QK_K == 256 dequantize_block_q6_K<<>>(vx, y); -#else - dequantize_block_q6_K<<>>(vx, y); -#endif } template @@ -700,11 +566,7 @@ static void dequantize_row_iq1_m_cuda(const void * vx, dst_t * y, const int64_t template static void dequantize_row_iq4_xs_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) { const int nb = (k + QK_K - 1) / QK_K; -#if QK_K == 64 - dequantize_block_iq4_nl<<>>(vx, y); -#else dequantize_block_iq4_xs<<>>(vx, y); -#endif } template diff --git a/ggml-cuda/dmmv.cu b/ggml-cuda/dmmv.cu index 7313e3e175367..47d4d5d9e91da 100644 --- a/ggml-cuda/dmmv.cu +++ b/ggml-cuda/dmmv.cu @@ -22,7 +22,6 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx, float tmp = 0; // partial sum for thread in warp -#if QK_K == 256 const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...15 const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0,1 @@ -71,37 +70,6 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx, tmp += dall * sum1 - dmin * sum2; } -#else - const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15 or 0...7 - const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); // 0....1 or 0...3 - const int offset = tid * K_QUANTS_PER_ITERATION; - - uint32_t uaux[2]; - const uint8_t * d = (const uint8_t *)uaux; - - for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { - - const float * y = yy + i * QK_K + offset; - const uint8_t * q = x[i].qs + offset; - const uint32_t * s = (const uint32_t *)x[i].scales; - - uaux[0] = s[0] & 0x0f0f0f0f; - uaux[1] = (s[0] >> 4) & 0x0f0f0f0f; - - const float2 dall = __half22float2(x[i].dm); - - float sum1 = 0, sum2 = 0; - for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { - const uint8_t ql = q[l]; - sum1 += y[l+ 0] * d[0] * ((ql >> 0) & 3) - + y[l+16] * d[1] * ((ql >> 2) & 3) - + y[l+32] * d[2] * ((ql >> 4) & 3) - + y[l+48] * d[3] * ((ql >> 6) & 3); - sum2 += y[l+0] * d[4] + y[l+16] * d[5] + y[l+32] * d[6] + y[l+48] * d[7]; - } - tmp += dall.x * sum1 - dall.y * sum2; - } -#endif // sum up partial sums and write back result tmp = warp_reduce_sum(tmp); @@ -123,8 +91,6 @@ static __global__ void dequantize_mul_mat_vec_q3_k(const void * __restrict__ vx, float tmp = 0; // partial sum for thread in warp -#if QK_K == 256 - const uint16_t kmask1 = 0x0303; const uint16_t kmask2 = 0x0f0f; @@ -175,34 +141,6 @@ static __global__ void dequantize_mul_mat_vec_q3_k(const void * __restrict__ vx, tmp += d * sum; } -#else - - const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15 or 0...7 - const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); // 0....1 or 0...3 - const int offset = tid * K_QUANTS_PER_ITERATION; // 0...15 or 0...14 - const int in = offset/8; // 0 or 1 - const int im = offset%8; // 0...7 - - for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { - - const float * y = yy + i * QK_K + offset; - const uint8_t * q = x[i].qs + offset; - const uint8_t * s = x[i].scales; - - const float dall = (float)x[i].d; - - float sum = 0; - for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { - const uint8_t hl = x[i].hmask[im+l] >> in; - const uint8_t ql = q[l]; - sum += y[l+ 0] * dall * ((s[0] & 0xF) - 8) * ((int8_t)((ql >> 0) & 3) - ((hl >> 0) & 1 ? 0 : 4)) - + y[l+16] * dall * ((s[0] >> 4) - 8) * ((int8_t)((ql >> 2) & 3) - ((hl >> 2) & 1 ? 0 : 4)) - + y[l+32] * dall * ((s[1] & 0xF) - 8) * ((int8_t)((ql >> 4) & 3) - ((hl >> 4) & 1 ? 0 : 4)) - + y[l+48] * dall * ((s[1] >> 4) - 8) * ((int8_t)((ql >> 6) & 3) - ((hl >> 6) & 1 ? 0 : 4)); - } - tmp += sum; - } -#endif // sum up partial sums and write back result tmp = warp_reduce_sum(tmp); @@ -221,7 +159,6 @@ static __global__ void dequantize_mul_mat_vec_q4_k(const void * __restrict__ vx, const block_q4_K * x = (const block_q4_K *)vx + ib0; -#if QK_K == 256 const uint16_t kmask1 = 0x3f3f; const uint16_t kmask2 = 0x0f0f; const uint16_t kmask3 = 0xc0c0; @@ -306,36 +243,6 @@ static __global__ void dequantize_mul_mat_vec_q4_k(const void * __restrict__ vx, #endif } -#else - const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15 - const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); - - const int step = tid * K_QUANTS_PER_ITERATION; - - uint16_t aux16[2]; - const uint8_t * s = (const uint8_t *)aux16; - - float tmp = 0; - - for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { - const uint8_t * q = x[i].qs + step; - const float * y = yy + i*QK_K + step; - const uint16_t * a = (const uint16_t *)x[i].scales; - aux16[0] = a[0] & 0x0f0f; - aux16[1] = (a[0] >> 4) & 0x0f0f; - const float d = (float)x[i].dm[0]; - const float m = (float)x[i].dm[1]; - float sum = 0.f; - for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) { - sum += y[j+ 0] * (d * s[0] * (q[j+ 0] & 0xF) - m * s[2]) - + y[j+16] * (d * s[0] * (q[j+16] & 0xF) - m * s[2]) - + y[j+32] * (d * s[1] * (q[j+ 0] >> 4) - m * s[3]) - + y[j+48] * (d * s[1] * (q[j+16] >> 4) - m * s[3]); - } - tmp += sum; - } - -#endif // sum up partial sums and write back result tmp = warp_reduce_sum(tmp); @@ -355,7 +262,6 @@ static __global__ void dequantize_mul_mat_vec_q5_k(const void * __restrict__ vx, float tmp = 0; // partial sum for thread in warp -#if QK_K == 256 const uint16_t kmask1 = 0x3f3f; const uint16_t kmask2 = 0x0f0f; const uint16_t kmask3 = 0xc0c0; @@ -426,30 +332,6 @@ static __global__ void dequantize_mul_mat_vec_q5_k(const void * __restrict__ vx, tmp += dall * (sum.x * sc[0] + sum.y * sc[1] + sum.z * sc[4] + sum.w * sc[5]) - dmin * smin; } -#else - const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15 - const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); - const int step = tid * K_QUANTS_PER_ITERATION; - const int im = step/8; - const int in = step%8; - - for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { - const uint8_t * q = x[i].qs + step; - const int8_t * s = x[i].scales; - const float * y = yy + i*QK_K + step; - const float d = x[i].d; - float sum = 0.f; - for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) { - const uint8_t h = x[i].qh[in+j] >> im; - sum += y[j+ 0] * d * s[0] * ((q[j+ 0] & 0xF) - ((h >> 0) & 1 ? 0 : 16)) - + y[j+16] * d * s[1] * ((q[j+16] & 0xF) - ((h >> 2) & 1 ? 0 : 16)) - + y[j+32] * d * s[2] * ((q[j+ 0] >> 4) - ((h >> 4) & 1 ? 0 : 16)) - + y[j+48] * d * s[3] * ((q[j+16] >> 4) - ((h >> 6) & 1 ? 0 : 16)); - } - tmp += sum; - } -#endif - // sum up partial sums and write back result tmp = warp_reduce_sum(tmp); @@ -470,8 +352,6 @@ static __global__ void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, const block_q6_K * x = (const block_q6_K *)vx + ib0; -#if QK_K == 256 - const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 @@ -526,37 +406,6 @@ static __global__ void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, } -#else - - const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...7 - const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); // 0...3 - - const int step = tid * K_QUANTS_PER_ITERATION; - - float tmp = 0; // partial sum for thread in warp - - for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { - - const float * y = yy + i * QK_K + step; - const uint8_t * ql = x[i].ql + step; - const uint8_t * qh = x[i].qh + step; - const int8_t * s = x[i].scales; - - const float d = x[i+0].d; - - float sum = 0; - for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) { - sum += y[j+ 0] * s[0] * d * ((int8_t)((ql[j+ 0] & 0xF) | ((qh[j] & 0x03) << 4)) - 32) - + y[j+16] * s[1] * d * ((int8_t)((ql[j+16] & 0xF) | ((qh[j] & 0x0c) << 2)) - 32) - + y[j+32] * s[2] * d * ((int8_t)((ql[j+ 0] >> 4) | ((qh[j] & 0x30) >> 0)) - 32) - + y[j+48] * s[3] * d * ((int8_t)((ql[j+16] >> 4) | ((qh[j] & 0xc0) >> 2)) - 32); - } - tmp += sum; - - } - -#endif - // sum up partial sums and write back result tmp = warp_reduce_sum(tmp); diff --git a/ggml-cuda/fattn-common.cuh b/ggml-cuda/fattn-common.cuh new file mode 100644 index 0000000000000..1dd519bdee7f1 --- /dev/null +++ b/ggml-cuda/fattn-common.cuh @@ -0,0 +1,162 @@ +#include "common.cuh" + +#include + +#define FATTN_KQ_STRIDE 256 +#define HALF_MAX_HALF __float2half(65504.0f/2) // Use neg. of this instead of -INFINITY to initialize KQ max vals to avoid NaN upon subtraction. +#define SOFTMAX_FTZ_THRESHOLD -20.0f // Softmax exp. of values smaller than this are flushed to zero to avoid NaNs. + +typedef void (* fattn_kernel_t)( + const char * __restrict__ Q, + const char * __restrict__ K, + const char * __restrict__ V, + const char * __restrict__ mask, + float * __restrict__ dst, + float2 * __restrict__ dst_meta, + const float scale, + const float max_bias, + const float m0, + const float m1, + const uint32_t n_head_log2, + const int ne00, + const int ne01, + const int ne02, + const int ne03, + const int ne10, + const int ne11, + const int ne12, + const int ne13, + const int ne31, + const int nb31, + const int nb01, + const int nb02, + const int nb03, + const int nb11, + const int nb12, + const int nb13, + const int ne0, + const int ne1, + const int ne2, + const int ne3); + +template // D == head size +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +__launch_bounds__(D, 1) +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +static __global__ void flash_attn_combine_results( + const float * __restrict__ VKQ_parts, + const float2 * __restrict__ VKQ_meta, + float * __restrict__ dst) { + VKQ_parts += parallel_blocks*D * gridDim.y*blockIdx.x; + VKQ_meta += parallel_blocks * gridDim.y*blockIdx.x; + dst += D * gridDim.y*blockIdx.x; + + const int tid = threadIdx.x; + __builtin_assume(tid < D); + + __shared__ float2 meta[parallel_blocks]; + if (tid < 2*parallel_blocks) { + ((float *) meta)[threadIdx.x] = ((const float *)VKQ_meta) [blockIdx.y*(2*parallel_blocks) + tid]; + } + + __syncthreads(); + + float kqmax = meta[0].x; +#pragma unroll + for (int l = 1; l < parallel_blocks; ++l) { + kqmax = max(kqmax, meta[l].x); + } + + float VKQ_numerator = 0.0f; + float VKQ_denominator = 0.0f; +#pragma unroll + for (int l = 0; l < parallel_blocks; ++l) { + const float diff = meta[l].x - kqmax; + const float KQ_max_scale = expf(diff); + const uint32_t ftz_mask = 0xFFFFFFFF * (diff > SOFTMAX_FTZ_THRESHOLD); + *((uint32_t *) &KQ_max_scale) &= ftz_mask; + + VKQ_numerator += KQ_max_scale * VKQ_parts[l*gridDim.y*D + blockIdx.y*D + tid]; + VKQ_denominator += KQ_max_scale * meta[l].y; + } + + dst[blockIdx.y*D + tid] = VKQ_numerator / VKQ_denominator; +} + +template +void launch_fattn(ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kernel_t fattn_kernel, int nwarps, int cols_per_block) { + const ggml_tensor * Q = dst->src[0]; + const ggml_tensor * K = dst->src[1]; + const ggml_tensor * V = dst->src[2]; + + const ggml_tensor * mask = dst->src[3]; + + ggml_tensor * KQV = dst; + + GGML_ASSERT(Q->type == GGML_TYPE_F32); + GGML_ASSERT(K->type == GGML_TYPE_F16); + GGML_ASSERT(V->type == GGML_TYPE_F16); + GGML_ASSERT(KQV->type == GGML_TYPE_F32); + + GGML_ASSERT(!mask || mask->type == GGML_TYPE_F16); + GGML_ASSERT(!mask || mask->ne[1] >= GGML_PAD(Q->ne[1], 16) && + "the Flash-Attention CUDA kernel requires the mask to be padded to 16 and at least n_queries big"); + + GGML_ASSERT(K->ne[1] % FATTN_KQ_STRIDE == 0 && "Incorrect KV cache padding."); + + ggml_cuda_pool & pool = ctx.pool(); + cudaStream_t main_stream = ctx.stream(); + + ggml_cuda_pool_alloc dst_tmp(pool); + ggml_cuda_pool_alloc dst_tmp_meta(pool); + + if (parallel_blocks > 1) { + dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV)); + dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV)); + } + + const dim3 block_dim(WARP_SIZE, nwarps, 1); + const dim3 blocks_num(parallel_blocks*((Q->ne[1] + cols_per_block - 1) / cols_per_block), Q->ne[2], Q->ne[3]); + const int shmem = 0; + + float scale = 1.0f; + float max_bias = 0.0f; + + memcpy(&scale, (float *) KQV->op_params + 0, sizeof(float)); + memcpy(&max_bias, (float *) KQV->op_params + 1, sizeof(float)); + + const uint32_t n_head = Q->ne[2]; + const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head)); + + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + + fattn_kernel<<>>( + (const char *) Q->data, + (const char *) K->data, + (const char *) V->data, + mask ? ((const char *) mask->data) : nullptr, + (parallel_blocks) == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr, + scale, max_bias, m0, m1, n_head_log2, + Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3], + K->ne[0], K->ne[1], K->ne[2], K->ne[3], + mask ? mask->ne[1] : 0, mask ? mask->nb[1] : 0, + Q->nb[1], Q->nb[2], Q->nb[3], + K->nb[1], K->nb[2], K->nb[3], + KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3] + ); + CUDA_CHECK(cudaGetLastError()); + + if ((parallel_blocks) == 1) { + return; + } + + const dim3 block_dim_combine(D, 1, 1); + const dim3 blocks_num_combine(Q->ne[1], blocks_num.y, blocks_num.z); + const int shmem_combine = 0; + + flash_attn_combine_results + <<>> + (dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data); + CUDA_CHECK(cudaGetLastError()); +} diff --git a/ggml-cuda/fattn-tile-f16.cu b/ggml-cuda/fattn-tile-f16.cu new file mode 100644 index 0000000000000..cdb5eaff79535 --- /dev/null +++ b/ggml-cuda/fattn-tile-f16.cu @@ -0,0 +1,316 @@ +#include "common.cuh" +#include "fattn-common.cuh" +#include "fattn-tile-f16.cuh" + +#define FATTN_KQ_STRIDE_TILE_F16 64 + +template // D == head size +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +__launch_bounds__(nwarps*WARP_SIZE, 1) +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +static __global__ void flash_attn_tile_ext_f16( + const char * __restrict__ Q, + const char * __restrict__ K, + const char * __restrict__ V, + const char * __restrict__ mask, + float * __restrict__ dst, + float2 * __restrict__ dst_meta, + const float scale, + const float max_bias, + const float m0, + const float m1, + const uint32_t n_head_log2, + const int ne00, + const int ne01, + const int ne02, + const int ne03, + const int ne10, + const int ne11, + const int ne12, + const int ne13, + const int ne31, + const int nb31, + const int nb01, + const int nb02, + const int nb03, + const int nb11, + const int nb12, + const int nb13, + const int ne0, + const int ne1, + const int ne2, + const int ne3) { +#if FP16_AVAILABLE + //In this kernel Q, K, V are matrices while i, j, k are matrix indices. + + const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on. + const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel. + + const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. + const float2 * Q_f2 = (const float2 *) (Q + nb02* blockIdx.y + nb01*ic0); + const half2 * K_h2 = (const half2 *) (K + nb12*(blockIdx.y / gqa_ratio)); + const half2 * V_h2 = (const half2 *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape + const half * maskh = (const half *) mask + ne11*ic0; + + const int stride_KV2 = nb11 / sizeof(half2); + + const float slopef = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1); + const half slopeh = __float2half(slopef); + + static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64."); + + __shared__ half KQ[ncols*FATTN_KQ_STRIDE_TILE_F16]; + half2 * KQ2 = (half2 *) KQ; + + __shared__ half2 KV_tmp[FATTN_KQ_STRIDE_TILE_F16][D/2 + 1]; // Pad D to avoid memory bank conflicts. + + half kqmax[ncols/nwarps]; +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + kqmax[j0/nwarps] = -HALF_MAX_HALF; + } + half2 kqsum[ncols/nwarps] = {{0.0f, 0.0f}}; + + half2 VKQ[ncols/nwarps][(D/2)/WARP_SIZE] = {{{0.0f, 0.0f}}}; + + // Convert Q to half2 and store in registers: + __shared__ half2 Q_h2[ncols][D/2]; +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + threadIdx.y; + +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + const float2 tmp = ic0 + j < ne01 ? Q_f2[j*(nb01/sizeof(float2)) + i] : make_float2(0.0f, 0.0f); + Q_h2[j][i] = make_half2(scale, scale) * make_half2(tmp.x, tmp.y); + } + } + + __syncthreads(); + + const int k_start = parallel_blocks == 1 ? 0 : ip*FATTN_KQ_STRIDE_TILE_F16; + for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*FATTN_KQ_STRIDE_TILE_F16) { + // Calculate KQ tile and keep track of new maximum KQ values: + + half kqmax_new[ncols/nwarps]; +#pragma unroll + for (int j = 0; j < ncols/nwarps; ++j) { + kqmax_new[j] = kqmax[j]; + } + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE_TILE_F16; i_KQ_0 += nwarps) { + const int i_KQ = i_KQ_0 + threadIdx.y; + +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) { + const int k_KQ = k_KQ_0 + threadIdx.x; + + KV_tmp[i_KQ][k_KQ] = K_h2[(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ]; + } + } + + __syncthreads(); + + half2 sum2[FATTN_KQ_STRIDE_TILE_F16/WARP_SIZE][ncols/nwarps] = {{{0.0f, 0.0f}}}; + +#pragma unroll + for (int k_KQ = 0; k_KQ < D/2; ++k_KQ) { + half2 K_k[FATTN_KQ_STRIDE_TILE_F16/WARP_SIZE]; + half2 Q_k[ncols/nwarps]; + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE_TILE_F16; i_KQ_0 += WARP_SIZE) { + const int i_KQ = i_KQ_0 + threadIdx.x; + + K_k[i_KQ_0/WARP_SIZE] = KV_tmp[i_KQ][k_KQ]; + } +#pragma unroll + for (int j_KQ_0 = 0; j_KQ_0 < ncols; j_KQ_0 += nwarps) { + const int j_KQ = j_KQ_0 + threadIdx.y; + + Q_k[j_KQ_0/nwarps] = Q_h2[j_KQ][k_KQ]; + } + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE_TILE_F16; i_KQ_0 += WARP_SIZE) { +#pragma unroll + for (int j_KQ_0 = 0; j_KQ_0 < ncols; j_KQ_0 += nwarps) { + sum2[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps] += K_k[i_KQ_0/WARP_SIZE]*Q_k[j_KQ_0/nwarps]; + } + } + } + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE_TILE_F16; i_KQ_0 += WARP_SIZE) { + const int i_KQ = i_KQ_0 + threadIdx.x; + +#pragma unroll + for (int j_KQ_0 = 0; j_KQ_0 < ncols; j_KQ_0 += nwarps) { + const int j_KQ = j_KQ_0 + threadIdx.y; + + half sum = __low2half(sum2[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps]) + __high2half(sum2[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps]); + sum += mask ? slopeh*maskh[j_KQ*ne11 + k_VKQ_0 + i_KQ] : __float2half(0.0f); + + kqmax_new[j_KQ_0/nwarps] = ggml_cuda_hmax(kqmax_new[j_KQ_0/nwarps], sum); + + KQ[j_KQ*FATTN_KQ_STRIDE_TILE_F16 + i_KQ] = sum; + } + } + + __syncthreads(); + +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + threadIdx.y; + + kqmax_new[j0/nwarps] = warp_reduce_max(kqmax_new[j0/nwarps]); + const half2 KQ_max_scale = __half2half2(hexp(kqmax[j0/nwarps] - kqmax_new[j0/nwarps])); + kqmax[j0/nwarps] = kqmax_new[j0/nwarps]; + +#pragma unroll + for (int i0 = 0; i0 < FATTN_KQ_STRIDE_TILE_F16/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + const half2 diff = KQ2[j*(FATTN_KQ_STRIDE_TILE_F16/2) + i] - __half2half2(kqmax[j0/nwarps]); + const half2 val = h2exp(diff); + kqsum[j0/nwarps] = kqsum[j0/nwarps]*KQ_max_scale + val; + KQ2[j*(FATTN_KQ_STRIDE_TILE_F16/2) + i] = val; + } + +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + VKQ[j0/nwarps][i0/WARP_SIZE] *= KQ_max_scale; + } + } + + __syncthreads(); + +#pragma unroll + for (int k0 = 0; k0 < FATTN_KQ_STRIDE_TILE_F16; k0 += nwarps) { + const int k = k0 + threadIdx.y; + +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + KV_tmp[k][i] = V_h2[(k_VKQ_0 + k)*stride_KV2 + i]; + } + } + + __syncthreads(); + +#pragma unroll + for (int k0 = 0; k0 < FATTN_KQ_STRIDE_TILE_F16; k0 += 2) { + half2 V_k[(D/2)/WARP_SIZE][2]; + half2 KQ_k[ncols/nwarps]; + +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + V_k[i0/WARP_SIZE][0] = KV_tmp[k0 + 0][i]; + V_k[i0/WARP_SIZE][1] = KV_tmp[k0 + 1][i]; + } +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + threadIdx.y; + + KQ_k[j0/nwarps] = KQ2[j*(FATTN_KQ_STRIDE_TILE_F16/2) + k0/2]; + } + +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + VKQ[j0/nwarps][i0/WARP_SIZE] += V_k[i0/WARP_SIZE][0]* __low2half2(KQ_k[j0/nwarps]); + VKQ[j0/nwarps][i0/WARP_SIZE] += V_k[i0/WARP_SIZE][1]*__high2half2(KQ_k[j0/nwarps]); + } + } + } + + __syncthreads(); + } + +#pragma unroll + for (int j_VKQ_0 = 0; j_VKQ_0 < ncols; j_VKQ_0 += nwarps) { + const int j_VKQ = j_VKQ_0 + threadIdx.y; + + if (ic0 + j_VKQ >= ne01) { + return; + } + + half kqsum_j = __low2half(kqsum[j_VKQ_0/nwarps]) + __high2half(kqsum[j_VKQ_0/nwarps]); + kqsum_j = warp_reduce_sum(kqsum_j); + +#pragma unroll + for (int i00 = 0; i00 < D; i00 += 2*WARP_SIZE) { + const int i0 = i00 + 2*threadIdx.x; + + half2 dst_val = VKQ[j_VKQ_0/nwarps][i0/(2*WARP_SIZE)]; + if (parallel_blocks == 1) { + dst_val /= __half2half2(kqsum_j); + } + const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip; + dst[j_dst*D*gridDim.y + D*blockIdx.y + i0 + 0] = __low2float(dst_val); + dst[j_dst*D*gridDim.y + D*blockIdx.y + i0 + 1] = __high2float(dst_val); + } + + if (parallel_blocks != 1 && threadIdx.x == 0) { + dst_meta[(ic0 + j_VKQ)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[j_VKQ_0/nwarps], kqsum_j); + } + } +#else + NO_DEVICE_CODE; +#endif // FP16_AVAILABLE +} + +template +void launch_fattn_tile_f16_64_128(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * Q = dst->src[0]; + switch (Q->ne[0]) { + case 64: { + constexpr int D = 64; + constexpr int nwarps = 8; + fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + } break; + case 128: { + constexpr int D = 128; + constexpr int nwarps = 8; + fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + } break; + default: { + GGML_ASSERT(false && "FlashAttention without tensor cores only supports head sizes 64 and 128."); + } break; + } +} + +void ggml_cuda_flash_attn_ext_tile_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * KQV = dst; + const ggml_tensor * Q = dst->src[0]; + + const int32_t precision = KQV->op_params[2]; + GGML_ASSERT(precision == GGML_PREC_DEFAULT); + + if (Q->ne[1] <= 16) { + constexpr int cols_per_block = 16; + constexpr int parallel_blocks = 4; + launch_fattn_tile_f16_64_128(ctx, dst); + return; + } + + if (Q->ne[1] <= 32) { + constexpr int cols_per_block = 32; + constexpr int parallel_blocks = 4; + launch_fattn_tile_f16_64_128(ctx, dst); + return; + } + + constexpr int cols_per_block = 32; + constexpr int parallel_blocks = 1; + launch_fattn_tile_f16_64_128(ctx, dst); +} diff --git a/ggml-cuda/fattn-tile-f16.cuh b/ggml-cuda/fattn-tile-f16.cuh new file mode 100644 index 0000000000000..ffc5878427b4f --- /dev/null +++ b/ggml-cuda/fattn-tile-f16.cuh @@ -0,0 +1,3 @@ +#include "common.cuh" + +void ggml_cuda_flash_attn_ext_tile_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/ggml-cuda/fattn-tile-f32.cu b/ggml-cuda/fattn-tile-f32.cu new file mode 100644 index 0000000000000..5a3de2918c7a3 --- /dev/null +++ b/ggml-cuda/fattn-tile-f32.cu @@ -0,0 +1,309 @@ +#include "common.cuh" +#include "fattn-common.cuh" +#include "fattn-tile-f32.cuh" + +#define FATTN_KQ_STRIDE_TILE_F32 32 + +template // D == head size +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +__launch_bounds__(nwarps*WARP_SIZE, 1) +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +static __global__ void flash_attn_tile_ext_f32( + const char * __restrict__ Q, + const char * __restrict__ K, + const char * __restrict__ V, + const char * __restrict__ mask, + float * __restrict__ dst, + float2 * __restrict__ dst_meta, + const float scale, + const float max_bias, + const float m0, + const float m1, + const uint32_t n_head_log2, + const int ne00, + const int ne01, + const int ne02, + const int ne03, + const int ne10, + const int ne11, + const int ne12, + const int ne13, + const int ne31, + const int nb31, + const int nb01, + const int nb02, + const int nb03, + const int nb11, + const int nb12, + const int nb13, + const int ne0, + const int ne1, + const int ne2, + const int ne3) { + //In this kernel Q, K, V are matrices while i, j, k are matrix indices. + + const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on. + const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel. + + const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. + const float2 * Q_f2 = (const float2 *) (Q + nb02* blockIdx.y + nb01*ic0); + const half2 * K_h2 = (const half2 *) (K + nb12*(blockIdx.y / gqa_ratio)); + const half2 * V_h2 = (const half2 *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape + const half * maskh = (const half *) mask + ne11*ic0; + + const int stride_KV2 = nb11 / sizeof(half2); + + const float slope = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1); + + static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64."); + + __shared__ float KQ[ncols*FATTN_KQ_STRIDE_TILE_F32]; + + __shared__ float KV_tmp[FATTN_KQ_STRIDE_TILE_F32][D + 1]; // Pad D to avoid memory bank conflicts. + float2 * KV_tmp2 = (float2 *) KV_tmp; + + float kqmax[ncols/nwarps]; +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + kqmax[j0/nwarps] = -FLT_MAX/2.0f; + } + float kqsum[ncols/nwarps] = {0.0f}; + + float2 VKQ[ncols/nwarps][(D/2)/WARP_SIZE] = {{{0.0f, 0.0f}}}; + + // Convert Q to half2 and store in registers: + __shared__ float Q_f[ncols][D]; +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + threadIdx.y; + +#pragma unroll + for (int i0 = 0; i0 < D; i0 += 2*WARP_SIZE) { + float2 tmp = ic0 + j < ne01 ? Q_f2[j*(nb01/sizeof(float2)) + i0/2 + threadIdx.x] : make_float2(0.0f, 0.0f); + Q_f[j][i0 + 0*WARP_SIZE + threadIdx.x] = tmp.x * scale; + Q_f[j][i0 + 1*WARP_SIZE + threadIdx.x] = tmp.y * scale; + } + } + + __syncthreads(); + + const int k_start = parallel_blocks == 1 ? 0 : ip*FATTN_KQ_STRIDE_TILE_F32; + for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*FATTN_KQ_STRIDE_TILE_F32) { + // Calculate KQ tile and keep track of new maximum KQ values: + + float kqmax_new[ncols/nwarps]; +#pragma unroll + for (int j = 0; j < ncols/nwarps; ++j) { + kqmax_new[j] = kqmax[j]; + } + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE_TILE_F32; i_KQ_0 += nwarps) { + const int i_KQ = i_KQ_0 + threadIdx.y; + +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < D; k_KQ_0 += 2*WARP_SIZE) { + const half2 tmp = K_h2[(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ_0/2 + threadIdx.x]; + KV_tmp[i_KQ][k_KQ_0 + 0*WARP_SIZE + threadIdx.x] = __low2float(tmp); + KV_tmp[i_KQ][k_KQ_0 + 1*WARP_SIZE + threadIdx.x] = __high2float(tmp); + } + } + + __syncthreads(); + + float sum[FATTN_KQ_STRIDE_TILE_F32/WARP_SIZE][ncols/nwarps] = {{0.0f}}; + +#pragma unroll + for (int k_KQ = 0; k_KQ < D; ++k_KQ) { + float K_k[FATTN_KQ_STRIDE_TILE_F32/WARP_SIZE]; + float Q_k[ncols/nwarps]; + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE_TILE_F32; i_KQ_0 += WARP_SIZE) { + const int i_KQ = i_KQ_0 + threadIdx.x; + + K_k[i_KQ_0/WARP_SIZE] = KV_tmp[i_KQ][k_KQ]; + } +#pragma unroll + for (int j_KQ_0 = 0; j_KQ_0 < ncols; j_KQ_0 += nwarps) { + const int j_KQ = j_KQ_0 + threadIdx.y; + + Q_k[j_KQ_0/nwarps] = Q_f[j_KQ][k_KQ]; + } + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE_TILE_F32; i_KQ_0 += WARP_SIZE) { +#pragma unroll + for (int j_KQ_0 = 0; j_KQ_0 < ncols; j_KQ_0 += nwarps) { + sum[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps] += K_k[i_KQ_0/WARP_SIZE] * Q_k[j_KQ_0/nwarps]; + } + } + } + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE_TILE_F32; i_KQ_0 += WARP_SIZE) { + const int i_KQ = i_KQ_0 + threadIdx.x; + +#pragma unroll + for (int j_KQ_0 = 0; j_KQ_0 < ncols; j_KQ_0 += nwarps) { + const int j_KQ = j_KQ_0 + threadIdx.y; + + sum[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps] += mask ? slope*__half2float(maskh[j_KQ*ne11 + k_VKQ_0 + i_KQ]) : 0.0f; + + kqmax_new[j_KQ_0/nwarps] = fmaxf(kqmax_new[j_KQ_0/nwarps], sum[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps]); + + KQ[j_KQ*FATTN_KQ_STRIDE_TILE_F32 + i_KQ] = sum[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps]; + } + } + + __syncthreads(); + +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + threadIdx.y; + + kqmax_new[j0/nwarps] = warp_reduce_max(kqmax_new[j0/nwarps]); + const float KQ_max_scale = expf(kqmax[j0/nwarps] - kqmax_new[j0/nwarps]); + kqmax[j0/nwarps] = kqmax_new[j0/nwarps]; + + float kqsum_add = 0.0f; +#pragma unroll + for (int i0 = 0; i0 < FATTN_KQ_STRIDE_TILE_F32; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + const float diff = KQ[j*FATTN_KQ_STRIDE_TILE_F32 + i] - kqmax[j0/nwarps]; + const float val = expf(diff); + kqsum_add += val; + KQ[j*FATTN_KQ_STRIDE_TILE_F32 + i] = val; + } + kqsum[j0/nwarps] = kqsum[j0/nwarps]*KQ_max_scale + kqsum_add; + +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + VKQ[j0/nwarps][i0/WARP_SIZE].x *= KQ_max_scale; + VKQ[j0/nwarps][i0/WARP_SIZE].y *= KQ_max_scale; + } + } + + __syncthreads(); + +#pragma unroll + for (int k0 = 0; k0 < FATTN_KQ_STRIDE_TILE_F32; k0 += nwarps) { + const int k = k0 + threadIdx.y; + +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + KV_tmp2[k*(D/2) + i].x = __low2float(V_h2[(k_VKQ_0 + k)*stride_KV2 + i]); + KV_tmp2[k*(D/2) + i].y = __high2float(V_h2[(k_VKQ_0 + k)*stride_KV2 + i]); + } + } + + __syncthreads(); + +#pragma unroll + for (int k = 0; k < FATTN_KQ_STRIDE_TILE_F32; ++k) { + float2 V_k[(D/2)/WARP_SIZE]; + float KQ_k[ncols/nwarps]; + +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + V_k[i0/WARP_SIZE] = KV_tmp2[k*(D/2) + i]; + } +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + threadIdx.y; + + KQ_k[j0/nwarps] = KQ[j*FATTN_KQ_STRIDE_TILE_F32 + k]; + } + +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + VKQ[j0/nwarps][i0/WARP_SIZE].x += V_k[i0/WARP_SIZE].x*KQ_k[j0/nwarps]; + VKQ[j0/nwarps][i0/WARP_SIZE].y += V_k[i0/WARP_SIZE].y*KQ_k[j0/nwarps]; + } + } + } + + __syncthreads(); + } + +#pragma unroll + for (int j_VKQ_0 = 0; j_VKQ_0 < ncols; j_VKQ_0 += nwarps) { + const int j_VKQ = j_VKQ_0 + threadIdx.y; + + if (ic0 + j_VKQ >= ne01) { + return; + } + + float kqsum_j = kqsum[j_VKQ_0/nwarps]; + kqsum_j = warp_reduce_sum(kqsum_j); + +#pragma unroll + for (int i00 = 0; i00 < D; i00 += 2*WARP_SIZE) { + const int i0 = i00 + 2*threadIdx.x; + + float2 dst_val = VKQ[j_VKQ_0/nwarps][i0/(2*WARP_SIZE)]; + if (parallel_blocks == 1) { + dst_val.x /= kqsum_j; + dst_val.y /= kqsum_j; + } + const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip; + dst[j_dst*D*gridDim.y + D*blockIdx.y + i0 + 0] = dst_val.x; + dst[j_dst*D*gridDim.y + D*blockIdx.y + i0 + 1] = dst_val.y; + } + + if (parallel_blocks != 1 && threadIdx.x == 0) { + dst_meta[(ic0 + j_VKQ)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[j_VKQ_0/nwarps], kqsum_j); + } + } +} + +template +void launch_fattn_tile_f32_64_128(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * Q = dst->src[0]; + switch (Q->ne[0]) { + case 64: { + constexpr int D = 64; + constexpr int nwarps = 8; + fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f32; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + } break; + case 128: { + constexpr int D = 128; + constexpr int nwarps = 8; + fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f32; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + } break; + default: { + GGML_ASSERT(false && "FlashAttention without tensor cores only supports head sizes 64 and 128."); + } break; + } +} + +void ggml_cuda_flash_attn_ext_tile_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * Q = dst->src[0]; + + if (Q->ne[1] <= 16) { + constexpr int cols_per_block = 16; + constexpr int parallel_blocks = 4; + launch_fattn_tile_f32_64_128(ctx, dst); + return; + } + + if (Q->ne[1] <= 32) { + constexpr int cols_per_block = 32; + constexpr int parallel_blocks = 4; + launch_fattn_tile_f32_64_128(ctx, dst); + return; + } + + constexpr int cols_per_block = 32; + constexpr int parallel_blocks = 1; + launch_fattn_tile_f32_64_128(ctx, dst); +} diff --git a/ggml-cuda/fattn-tile-f32.cuh b/ggml-cuda/fattn-tile-f32.cuh new file mode 100644 index 0000000000000..b1c546c805470 --- /dev/null +++ b/ggml-cuda/fattn-tile-f32.cuh @@ -0,0 +1,3 @@ +#include "common.cuh" + +void ggml_cuda_flash_attn_ext_tile_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/ggml-cuda/fattn-vec-f16.cu b/ggml-cuda/fattn-vec-f16.cu new file mode 100644 index 0000000000000..808e8f36246a7 --- /dev/null +++ b/ggml-cuda/fattn-vec-f16.cu @@ -0,0 +1,330 @@ +#include "common.cuh" +#include "fattn-common.cuh" +#include "fattn-vec-f16.cuh" + +template // D == head size +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +__launch_bounds__(D, 1) +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +static __global__ void flash_attn_vec_ext_f16( + const char * __restrict__ Q, + const char * __restrict__ K, + const char * __restrict__ V, + const char * __restrict__ mask, + float * __restrict__ dst, + float2 * __restrict__ dst_meta, + const float scale, + const float max_bias, + const float m0, + const float m1, + const uint32_t n_head_log2, + const int ne00, + const int ne01, + const int ne02, + const int ne03, + const int ne10, + const int ne11, + const int ne12, + const int ne13, + const int ne31, + const int nb31, + const int nb01, + const int nb02, + const int nb03, + const int nb11, + const int nb12, + const int nb13, + const int ne0, + const int ne1, + const int ne2, + const int ne3) { +#if FP16_AVAILABLE + //In this kernel Q, K, V are matrices while i, j, k are matrix indices. + + const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on. + const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel. + + const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. + const float2 * Q_f2 = (const float2 *) (Q + nb02* blockIdx.y + nb01*ic0); + const half2 * K_h2 = (const half2 *) (K + nb12*(blockIdx.y / gqa_ratio)); + const half * V_h = (const half *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape + const half * maskh = (const half *) mask + ne11*ic0; + + const int stride_KV = nb11 / sizeof(half); + const int stride_KV2 = nb11 / sizeof(half2); + + const float slopef = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1); + const half slopeh = __float2half(slopef); + + static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64."); + constexpr int nwarps = D / WARP_SIZE; + const int tid = WARP_SIZE*threadIdx.y + threadIdx.x; + __builtin_assume(tid < D); + + __shared__ half KQ[ncols*D]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + KQ[j*D + tid] = -HALF_MAX_HALF; + } + half2 * KQ2 = (half2 *) KQ; + + half kqmax[ncols]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + kqmax[j] = -HALF_MAX_HALF; + } + half kqsum[ncols] = {0.0f}; + + __shared__ half kqmax_shared[ncols][WARP_SIZE]; + __shared__ half kqsum_shared[ncols][WARP_SIZE]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + if (threadIdx.y == 0) { + kqmax_shared[j][threadIdx.x] = -HALF_MAX_HALF; + kqsum_shared[j][threadIdx.x] = 0.0f; + } + } + __syncthreads(); + + // Convert Q to half2 and store in registers: + half2 Q_h2[ncols][D/(2*WARP_SIZE)]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + const float2 tmp = ncols <= 2 || ic0 + j < ne01 ? Q_f2[j*(nb01/sizeof(float2)) + i] : make_float2(0.0f, 0.0f); + Q_h2[j][i0/WARP_SIZE] = make_half2(scale, scale) * make_half2(tmp.x, tmp.y); + } + } + + half2 VKQ[ncols] = {{0.0f, 0.0f}}; + + const int k_start = parallel_blocks == 1 ? 0 : ip*D; + for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) { + // Calculate KQ tile and keep track of new maximum KQ values: + + // For unknown reasons using a half array of size 1 for kqmax_new causes a performance regression, + // see https://github.com/ggerganov/llama.cpp/pull/7061 . + // Therefore this variable is defined twice but only used once (so that the compiler can optimize out the unused variable). + half kqmax_new = kqmax[0]; + half kqmax_new_arr[ncols]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + kqmax_new_arr[j] = kqmax[j]; + } + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) { + const int i_KQ = i_KQ_0 + threadIdx.y; + + if ((i_KQ_0 + nwarps > D && i_KQ >= D) || (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + i_KQ >= ne11)) { + break; + } + + half2 sum2[ncols] = {{0.0f, 0.0f}}; +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) { + const int k_KQ = k_KQ_0 + threadIdx.x; + + const half2 K_ik = K_h2[(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + sum2[j] += K_ik * Q_h2[j][k_KQ_0/WARP_SIZE]; + } + } + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + sum2[j] = warp_reduce_sum(sum2[j]); + half sum = __low2half(sum2[j]) + __high2half(sum2[j]); + sum += mask ? slopeh*maskh[j*ne11 + k_VKQ_0 + i_KQ] : __float2half(0.0f); + + if (ncols == 1) { + kqmax_new = ggml_cuda_hmax(kqmax_new, sum); + } else { + kqmax_new_arr[j] = ggml_cuda_hmax(kqmax_new_arr[j], sum); + } + + if (threadIdx.x == 0) { + KQ[j*D + i_KQ] = sum; + } + } + } + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + half kqmax_new_j = ncols == 1 ? kqmax_new : kqmax_new_arr[j]; + + kqmax_new_j = warp_reduce_max(kqmax_new_j); + if (threadIdx.x == 0) { + kqmax_shared[j][threadIdx.y] = kqmax_new_j; + } + } + + __syncthreads(); + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + half kqmax_new_j = kqmax_shared[j][threadIdx.x]; + kqmax_new_j = warp_reduce_max(kqmax_new_j); + + const half KQ_max_scale = hexp(kqmax[j] - kqmax_new_j); + kqmax[j] = kqmax_new_j; + + const half val = hexp(KQ[j*D + tid] - kqmax[j]); + kqsum[j] = kqsum[j]*KQ_max_scale + val; + KQ[j*D + tid] = val; + + VKQ[j] *= __half2half2(KQ_max_scale); + } + + __syncthreads(); + +#pragma unroll + for (int k0 = 0; k0 < D; k0 += 2) { + if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k0 >= ne11) { + break; + } + + half2 V_k; + reinterpret_cast(V_k.x) = V_h[(k_VKQ_0 + k0 + 0)*stride_KV + tid]; + reinterpret_cast(V_k.y) = V_h[(k_VKQ_0 + k0 + 1)*stride_KV + tid]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + VKQ[j] += V_k*KQ2[j*(D/2) + k0/2]; + } + } + + __syncthreads(); + } + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + kqsum[j] = warp_reduce_sum(kqsum[j]); + if (threadIdx.x == 0) { + kqsum_shared[j][threadIdx.y] = kqsum[j]; + } + } + + __syncthreads(); + +#pragma unroll + for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) { + if (ncols > 2 && ic0 + j_VKQ >= ne01) { + break; + } + + kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x]; + kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]); + + half dst_val = (__low2half(VKQ[j_VKQ]) + __high2half(VKQ[j_VKQ])); + if (parallel_blocks == 1) { + dst_val /= kqsum[j_VKQ]; + } + const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip; + dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val; + } + + if (parallel_blocks != 1 && tid < ncols && (ncols <= 2 || ic0 + tid < ne01)) { + dst_meta[(ic0 + tid)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[tid], kqsum[tid]); + } +#else + NO_DEVICE_CODE; +#endif // FP16_AVAILABLE +} + +void ggml_cuda_flash_attn_ext_vec_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + ggml_tensor * KQV = dst; + ggml_tensor * Q = dst->src[0]; + + const int32_t precision = KQV->op_params[2]; + GGML_ASSERT(precision == GGML_PREC_DEFAULT); + + constexpr int cols_per_block = 1; + constexpr int parallel_blocks = 4; + switch (Q->ne[0]) { + case 64: { + constexpr int D = 64; + constexpr int nwarps = D/WARP_SIZE; + fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + } break; + case 128: { + constexpr int D = 128; + constexpr int nwarps = D/WARP_SIZE; + fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + } break; + case 256: { + constexpr int D = 256; + constexpr int nwarps = D/WARP_SIZE; + fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + } break; + default: + GGML_ASSERT(false); + break; + } +} + +template +void launch_fattn_vec_f16_64_128(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * Q = dst->src[0]; + switch (Q->ne[0]) { + case 64: { + constexpr int D = 64; + constexpr int nwarps = D/WARP_SIZE; + fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + } break; + case 128: { + constexpr int D = 128; + constexpr int nwarps = D/WARP_SIZE; + fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + } break; + default: { + GGML_ASSERT(false && "FlashAttention without tensor cores only supports head sizes 64 and 128."); + } break; + } +} + +void ggml_cuda_flash_attn_ext_vec_f16_no_mma(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * KQV = dst; + const ggml_tensor * Q = dst->src[0]; + + const int32_t precision = KQV->op_params[2]; + GGML_ASSERT(precision == GGML_PREC_DEFAULT); + + if (Q->ne[1] == 1) { + ggml_cuda_flash_attn_ext_vec_f16(ctx, dst); + return; + } + + if (Q->ne[1] == 2) { + constexpr int cols_per_block = 2; + constexpr int parallel_blocks = 4; + launch_fattn_vec_f16_64_128(ctx, dst); + return; + } + + if (Q->ne[1] <= 4) { + constexpr int cols_per_block = 4; + constexpr int parallel_blocks = 4; + launch_fattn_vec_f16_64_128(ctx, dst); + return; + } + + if (Q->ne[1] <= 8) { + constexpr int cols_per_block = 8; + constexpr int parallel_blocks = 4; + launch_fattn_vec_f16_64_128(ctx, dst); + return; + } + + constexpr int cols_per_block = 8; + constexpr int parallel_blocks = 1; + launch_fattn_vec_f16_64_128(ctx, dst); +} diff --git a/ggml-cuda/fattn-vec-f16.cuh b/ggml-cuda/fattn-vec-f16.cuh new file mode 100644 index 0000000000000..c7023610ab2d4 --- /dev/null +++ b/ggml-cuda/fattn-vec-f16.cuh @@ -0,0 +1,5 @@ +#include "common.cuh" + +void ggml_cuda_flash_attn_ext_vec_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst); + +void ggml_cuda_flash_attn_ext_vec_f16_no_mma(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/ggml-cuda/fattn-vec-f32.cu b/ggml-cuda/fattn-vec-f32.cu new file mode 100644 index 0000000000000..b4652301b87e0 --- /dev/null +++ b/ggml-cuda/fattn-vec-f32.cu @@ -0,0 +1,279 @@ +#include "common.cuh" +#include "fattn-common.cuh" +#include "fattn-vec-f32.cuh" + +template // D == head size +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +__launch_bounds__(D, 1) +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +static __global__ void flash_attn_vec_ext_f32( + const char * __restrict__ Q, + const char * __restrict__ K, + const char * __restrict__ V, + const char * __restrict__ mask, + float * __restrict__ dst, + float2 * __restrict__ dst_meta, + const float scale, + const float max_bias, + const float m0, + const float m1, + const uint32_t n_head_log2, + const int ne00, + const int ne01, + const int ne02, + const int ne03, + const int ne10, + const int ne11, + const int ne12, + const int ne13, + const int ne31, + const int nb31, + const int nb01, + const int nb02, + const int nb03, + const int nb11, + const int nb12, + const int nb13, + const int ne0, + const int ne1, + const int ne2, + const int ne3) { + //In this kernel Q, K, V are matrices while i, j, k are matrix indices. + + const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on. + const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel. + + const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. + const float2 * Q_f2 = (const float2 *) (Q + nb02* blockIdx.y + nb01*ic0); + const half2 * K_h2 = (const half2 *) (K + nb12*(blockIdx.y / gqa_ratio)); + const half * V_h = (const half *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape + const half * maskh = (const half *) mask + ne11*ic0; + + const int stride_KV = nb11 / sizeof(half); + const int stride_KV2 = nb11 / sizeof(half2); + + const float slope = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1); + + static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64."); + constexpr int nwarps = D / WARP_SIZE; + const int tid = WARP_SIZE*threadIdx.y + threadIdx.x; + __builtin_assume(tid < D); + + __shared__ float KQ[ncols*D]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + KQ[j*D + tid] = -FLT_MAX/2.0f; + } + + float kqmax[ncols]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + kqmax[j] = -FLT_MAX/2.0f; + } + float kqsum[ncols] = {0.0f}; + + __shared__ float kqmax_shared[ncols][WARP_SIZE]; + __shared__ float kqsum_shared[ncols][WARP_SIZE]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + if (threadIdx.y == 0) { + kqmax_shared[j][threadIdx.x] = -FLT_MAX/2.0f; + kqsum_shared[j][threadIdx.x] = 0.0f; + } + } + __syncthreads(); + + // Convert Q to half2 and store in registers: + float2 Q_h2[ncols][D/(2*WARP_SIZE)]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + Q_h2[j][i0/WARP_SIZE] = ncols <= 2 || ic0 + j ? Q_f2[j*(nb01/sizeof(float2)) + i] : make_float2(0.0f, 0.0f); + Q_h2[j][i0/WARP_SIZE].x *= scale; + Q_h2[j][i0/WARP_SIZE].y *= scale; + } + } + + float VKQ[ncols] = {0.0f}; + + const int k_start = parallel_blocks == 1 ? 0 : ip*D; + for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) { + // Calculate KQ tile and keep track of new maximum KQ values: + + float kqmax_new_arr[ncols]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + kqmax_new_arr[j] = kqmax[j]; + } + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) { + const int i_KQ = i_KQ_0 + threadIdx.y; + + if ((i_KQ_0 + nwarps > D && i_KQ >= D) || (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + i_KQ >= ne11)) { + break; + } + + float sum[ncols] = {0.0f}; +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) { + const int k_KQ = k_KQ_0 + threadIdx.x; + + const half2 K_ik = K_h2[(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + sum[j] += __low2float(K_ik) * Q_h2[j][k_KQ_0/WARP_SIZE].x; + sum[j] += __high2float(K_ik) * Q_h2[j][k_KQ_0/WARP_SIZE].y; + } + } + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + sum[j] = warp_reduce_sum(sum[j]); + sum[j] += mask ? slope*__half2float(maskh[j*ne11 + k_VKQ_0 + i_KQ]) : 0.0f; + + kqmax_new_arr[j] = fmaxf(kqmax_new_arr[j], sum[j]); + + if (threadIdx.x == 0) { + KQ[j*D + i_KQ] = sum[j]; + } + } + } + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + float kqmax_new_j = kqmax_new_arr[j]; + + kqmax_new_j = warp_reduce_max(kqmax_new_j); + if (threadIdx.x == 0) { + kqmax_shared[j][threadIdx.y] = kqmax_new_j; + } + } + + __syncthreads(); + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + float kqmax_new_j = kqmax_shared[j][threadIdx.x]; + kqmax_new_j = warp_reduce_max(kqmax_new_j); + + const float KQ_max_scale = expf(kqmax[j] - kqmax_new_j); + kqmax[j] = kqmax_new_j; + + const float val = expf(KQ[j*D + tid] - kqmax[j]); + kqsum[j] = kqsum[j]*KQ_max_scale + val; + KQ[j*D + tid] = val; + + VKQ[j] *= KQ_max_scale; + } + + __syncthreads(); + +#pragma unroll + for (int k = 0; k < D; ++k) { + if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k >= ne11) { + break; + } + + const float V_ki = __half2float(V_h[(k_VKQ_0 + k)*stride_KV + tid]); +#pragma unroll + for (int j = 0; j < ncols; ++j) { + VKQ[j] += V_ki*KQ[j*D + k]; + } + } + + __syncthreads(); + } + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + kqsum[j] = warp_reduce_sum(kqsum[j]); + if (threadIdx.x == 0) { + kqsum_shared[j][threadIdx.y] = kqsum[j]; + } + } + + __syncthreads(); + +#pragma unroll + for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) { + if (ncols > 2 && ic0 + j_VKQ >= ne01) { + break; + } + + kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x]; + kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]); + + float dst_val = VKQ[j_VKQ]; + if (parallel_blocks == 1) { + dst_val /= kqsum[j_VKQ]; + } + const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip; + dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val; + } + + if (parallel_blocks != 1 && tid < ncols && (ncols <= 2 || ic0 + tid < ne01)) { + dst_meta[(ic0 + tid)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[tid], kqsum[tid]); + } +} + +template +void launch_fattn_vec_f32_64_128(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * Q = dst->src[0]; + switch (Q->ne[0]) { + case 64: { + constexpr int D = 64; + constexpr int nwarps = D/WARP_SIZE; + fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f32; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + } break; + case 128: { + constexpr int D = 128; + constexpr int nwarps = D/WARP_SIZE; + fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f32; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + } break; + default: { + GGML_ASSERT(false && "FlashAttention without tensor cores only supports head sizes 64 and 128."); + } break; + } +} + +void ggml_cuda_flash_attn_ext_vec_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * Q = dst->src[0]; + + if (Q->ne[1] == 1) { + constexpr int cols_per_block = 1; + constexpr int parallel_blocks = 4; + launch_fattn_vec_f32_64_128(ctx, dst); + return; + } + + if (Q->ne[1] == 2) { + constexpr int cols_per_block = 2; + constexpr int parallel_blocks = 4; + launch_fattn_vec_f32_64_128(ctx, dst); + return; + } + + if (Q->ne[1] <= 4) { + constexpr int cols_per_block = 4; + constexpr int parallel_blocks = 4; + launch_fattn_vec_f32_64_128(ctx, dst); + return; + } + + if (Q->ne[1] <= 8) { + constexpr int cols_per_block = 8; + constexpr int parallel_blocks = 4; + launch_fattn_vec_f32_64_128(ctx, dst); + return; + } + + constexpr int cols_per_block = 8; + constexpr int parallel_blocks = 1; + launch_fattn_vec_f32_64_128(ctx, dst); +} diff --git a/ggml-cuda/fattn-vec-f32.cuh b/ggml-cuda/fattn-vec-f32.cuh new file mode 100644 index 0000000000000..614d54ae39253 --- /dev/null +++ b/ggml-cuda/fattn-vec-f32.cuh @@ -0,0 +1,3 @@ +#include "common.cuh" + +void ggml_cuda_flash_attn_ext_vec_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/ggml-cuda/fattn.cu b/ggml-cuda/fattn.cu index 7c486f4829bdd..af7c95232ddf3 100644 --- a/ggml-cuda/fattn.cu +++ b/ggml-cuda/fattn.cu @@ -1,4 +1,9 @@ #include "common.cuh" +#include "fattn-common.cuh" +#include "fattn-tile-f16.cuh" +#include "fattn-tile-f32.cuh" +#include "fattn-vec-f16.cuh" +#include "fattn-vec-f32.cuh" #include "fattn.cuh" #include @@ -7,235 +12,6 @@ #include #endif -#define FATTN_KQ_STRIDE 256 -#define HALF_MAX_HALF __float2half(65504.0f/2) // Use neg. of this instead of -INFINITY to initialize KQ max vals to avoid NaN upon subtraction. -#define SOFTMAX_FTZ_THRESHOLD -20.0f // Softmax exp. of values smaller than this are flushed to zero to avoid NaNs. - -template // D == head size -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) -__launch_bounds__(D, 1) -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) -static __global__ void flash_attn_vec_ext_f16( - const char * __restrict__ Q, - const char * __restrict__ K, - const char * __restrict__ V, - const char * __restrict__ mask, - float * __restrict__ dst, - float2 * __restrict__ dst_meta, - const float scale, - const int ne00, - const int ne01, - const int ne02, - const int ne03, - const int ne10, - const int ne11, - const int ne12, - const int ne13, - const int ne31, - const int nb31, - const int nb01, - const int nb02, - const int nb03, - const int nb11, - const int nb12, - const int nb13, - const int ne0, - const int ne1, - const int ne2, - const int ne3) { -#if FP16_AVAILABLE - //In this kernel Q, K, V are matrices while i, j, k are matrix indices. - - const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on. - const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel. - - const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. - const float2 * Q_f2 = (const float2 *) (Q + nb02* blockIdx.y + nb01*ic0); - const half2 * K_h2 = (const half2 *) (K + nb12*(blockIdx.y / gqa_ratio)); - const half * V_h = (const half *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape - const half * maskh = (const half *) mask + ne11*ic0; - - const int stride_KV = nb11 / sizeof(half); - const int stride_KV2 = nb11 / sizeof(half2); - - static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64."); - constexpr int nwarps = D / WARP_SIZE; - const int tid = WARP_SIZE*threadIdx.y + threadIdx.x; - __builtin_assume(tid < D); - - __shared__ half KQ[ncols*D]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - KQ[j*D + tid] = -HALF_MAX_HALF; - } - half2 * KQ2 = (half2 *) KQ; - - half kqmax[ncols]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - kqmax[j] = -HALF_MAX_HALF; - } - half kqsum[ncols] = {0.0f}; - - __shared__ half kqmax_shared[ncols][WARP_SIZE]; - __shared__ half kqsum_shared[ncols][WARP_SIZE]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - if (threadIdx.y == 0) { - kqmax_shared[j][threadIdx.x] = -HALF_MAX_HALF; - kqsum_shared[j][threadIdx.x] = 0.0f; - } - } - __syncthreads(); - - // Convert Q to half2 and store in registers: - half2 Q_h2[ncols][D/(2*WARP_SIZE)]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { - const int i = i0 + threadIdx.x; - - const float2 tmp = Q_f2[j*(nb01/sizeof(float2)) + i]; - Q_h2[j][i0/WARP_SIZE] = make_half2(scale, scale) * make_half2(tmp.x, tmp.y); - } - } - - half2 VKQ[ncols] = {{0.0f, 0.0f}}; - - const int k_start = parallel_blocks == 1 ? 0 : ip*D; - for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) { - // Calculate KQ tile and keep track of new maximum KQ values: - - // For unknown reasons using a half array of size 1 for kqmax_new causes a performance regression, - // see https://github.com/ggerganov/llama.cpp/pull/7061 . - // Therefore this variable is defined twice but only used once (so that the compiler can optimize out the unused variable). - half kqmax_new = kqmax[0]; - half kqmax_new_arr[ncols]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - kqmax_new_arr[j] = kqmax[j]; - } - -#pragma unroll - for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) { - const int i_KQ = i_KQ_0 + threadIdx.y; - - if ((i_KQ_0 + nwarps > D && i_KQ >= D) || (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + i_KQ >= ne11)) { - break; - } - - half2 sum2[ncols] = {{0.0f, 0.0f}}; -#pragma unroll - for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) { - const int k_KQ = k_KQ_0 + threadIdx.x; - - const half2 K_ik = K_h2[(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - sum2[j] += K_ik * Q_h2[j][k_KQ_0/WARP_SIZE]; - } - } - -#pragma unroll - for (int j = 0; j < ncols; ++j) { - sum2[j] = warp_reduce_sum(sum2[j]); - half sum = __low2half(sum2[j]) + __high2half(sum2[j]); - sum += mask ? maskh[j*ne11 + k_VKQ_0 + i_KQ] : __float2half(0.0f); - - if (ncols == 1) { - kqmax_new = ggml_cuda_hmax(kqmax_new, sum); - } else { - kqmax_new_arr[j] = ggml_cuda_hmax(kqmax_new_arr[j], sum); - } - - if (threadIdx.x == 0) { - KQ[j*D + i_KQ] = sum; - } - } - } - -#pragma unroll - for (int j = 0; j < ncols; ++j) { - half kqmax_new_j = ncols == 1 ? kqmax_new : kqmax_new_arr[j]; - - kqmax_new_j = warp_reduce_max(kqmax_new_j); - if (threadIdx.x == 0) { - kqmax_shared[j][threadIdx.y] = kqmax_new_j; - } - } - - __syncthreads(); - -#pragma unroll - for (int j = 0; j < ncols; ++j) { - half kqmax_new_j = kqmax_shared[j][threadIdx.x]; - kqmax_new_j = warp_reduce_max(kqmax_new_j); - - const half KQ_max_scale = hexp(kqmax[j] - kqmax_new_j); - kqmax[j] = kqmax_new_j; - - const half val = hexp(KQ[j*D + tid] - kqmax[j]); - kqsum[j] = kqsum[j]*KQ_max_scale + val; - KQ[j*D + tid] = val; - - VKQ[j] *= __half2half2(KQ_max_scale); - } - - __syncthreads(); - -#pragma unroll - for (int k0 = 0; k0 < D; k0 += 2) { - if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k0 >= ne11) { - break; - } - - half2 V_k; - reinterpret_cast(V_k.x) = V_h[(k_VKQ_0 + k0 + 0)*stride_KV + tid]; - reinterpret_cast(V_k.y) = V_h[(k_VKQ_0 + k0 + 1)*stride_KV + tid]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - VKQ[j] += V_k*KQ2[j*(D/2) + k0/2]; - } - } - - __syncthreads(); - } - -#pragma unroll - for (int j = 0; j < ncols; ++j) { - kqsum[j] = warp_reduce_sum(kqsum[j]); - if (threadIdx.x == 0) { - kqsum_shared[j][threadIdx.y] = kqsum[j]; - } - } - - __syncthreads(); - -#pragma unroll - for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) { - kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x]; - kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]); - - half dst_val = (__low2half(VKQ[j_VKQ]) + __high2half(VKQ[j_VKQ])); - if (parallel_blocks == 1) { - dst_val /= kqsum[j_VKQ]; - } - const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip; - dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val; - } - - if (parallel_blocks != 1 && tid != 0) { -#pragma unroll - for (int j = 0; j < ncols; ++j) { - dst_meta[(ic0 + j)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[j], kqsum[j]); - } - } -#else - NO_DEVICE_CODE; -#endif // FP16_AVAILABLE -} - // D == head size, VKQ_stride == num VKQ rows calculated in parallel: template #if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) @@ -249,6 +25,10 @@ static __global__ void flash_attn_ext_f16( float * __restrict__ dst, float2 * __restrict__ dst_meta, const float scale, + const float max_bias, + const float m0, + const float m1, + const uint32_t n_head_log2, const int ne00, const int ne01, const int ne02, @@ -305,6 +85,10 @@ static __global__ void flash_attn_ext_f16( const int stride_Q = nb01 / sizeof(float); const int stride_KV = nb11 / sizeof(half); + const float slopef = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1); + const half slopeh = __float2half(slopef); + const half2 slope2 = make_half2(slopef, slopef); + frag_b Q_b[D/16][ncols/frag_n]; // A single buffer for temporarily holding tiles of KQ and VKQ parts: @@ -421,7 +205,7 @@ static __global__ void flash_attn_ext_f16( for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) { const int k = k0 + threadIdx.x; - KQ_f_tmp[k0/WARP_SIZE] += mask ? __half2float(maskh[j*(nb31/sizeof(half)) + k_VKQ_0 + k]) : 0.0f; + KQ_f_tmp[k0/WARP_SIZE] += mask ? __half2float(slopeh*maskh[j*(nb31/sizeof(half)) + k_VKQ_0 + k]) : 0.0f; KQ_max_new = max(KQ_max_new, KQ_f_tmp[k0/WARP_SIZE]); } KQ_max_new = warp_reduce_max(KQ_max_new); @@ -464,7 +248,7 @@ static __global__ void flash_attn_ext_f16( for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) { const int k = k0 + threadIdx.x; - KQ2_tmp[k0/WARP_SIZE] += mask ? mask2[(j*ne11 + k_VKQ_0)/2 + k] : make_half2(0.0f, 0.0f); + KQ2_tmp[k0/WARP_SIZE] += mask ? slope2*mask2[(j*ne11 + k_VKQ_0)/2 + k] : make_half2(0.0f, 0.0f); KQ_max_new = ggml_cuda_hmax2(KQ_max_new, KQ2_tmp[k0/WARP_SIZE]); } KQ_max_new = __half2half2(warp_reduce_max(ggml_cuda_hmax(__low2half(KQ_max_new), __high2half(KQ_max_new)))); @@ -621,54 +405,6 @@ static __global__ void flash_attn_ext_f16( #endif // FP16_MMA_AVAILABLE } -template // D == head size -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) -__launch_bounds__(D, 1) -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) -static __global__ void flash_attn_combine_results( - const float * __restrict__ VKQ_parts, - const float2 * __restrict__ VKQ_meta, - float * __restrict__ dst) { -#if FP16_AVAILABLE - VKQ_parts += parallel_blocks*D * gridDim.y*blockIdx.x; - VKQ_meta += parallel_blocks * gridDim.y*blockIdx.x; - dst += D * gridDim.y*blockIdx.x; - - const int tid = threadIdx.x; - __builtin_assume(tid < D); - - __shared__ float2 meta[parallel_blocks]; - if (tid < 2*parallel_blocks) { - ((float *) meta)[threadIdx.x] = ((const float *)VKQ_meta) [blockIdx.y*(2*parallel_blocks) + tid]; - } - - __syncthreads(); - - float kqmax = meta[0].x; -#pragma unroll - for (int l = 1; l < parallel_blocks; ++l) { - kqmax = max(kqmax, meta[l].x); - } - - float VKQ_numerator = 0.0f; - float VKQ_denominator = 0.0f; -#pragma unroll - for (int l = 0; l < parallel_blocks; ++l) { - const float diff = meta[l].x - kqmax; - const float KQ_max_scale = expf(diff); - const uint32_t ftz_mask = 0xFFFFFFFF * (diff > SOFTMAX_FTZ_THRESHOLD); - *((uint32_t *) &KQ_max_scale) &= ftz_mask; - - VKQ_numerator += KQ_max_scale * VKQ_parts[l*gridDim.y*D + blockIdx.y*D + tid]; - VKQ_denominator += KQ_max_scale * meta[l].y; - } - - dst[blockIdx.y*D + tid] = VKQ_numerator / VKQ_denominator; -#else - NO_DEVICE_CODE; -#endif // FP16_AVAILABLE -} - constexpr int get_max_power_of_2(int x) { return x % 2 == 0 ? 2*get_max_power_of_2(x/2) : 1; } @@ -693,262 +429,94 @@ static_assert(get_VKQ_stride( 80, 1, 16) == 16, "Test failed."); static_assert(get_VKQ_stride( 80, 2, 16) == 16, "Test failed."); static_assert(get_VKQ_stride( 80, 4, 16) == 16, "Test failed."); -template void launch_fattn_vec_f16( - const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask, - ggml_cuda_pool & pool, cudaStream_t main_stream -) { - ggml_cuda_pool_alloc dst_tmp(pool); - ggml_cuda_pool_alloc dst_tmp_meta(pool); - - if (parallel_blocks > 1) { - dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV)); - dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV)); - } - - constexpr int nwarps = (D + WARP_SIZE - 1) / WARP_SIZE; - const dim3 block_dim(WARP_SIZE, nwarps, 1); - const dim3 blocks_num(parallel_blocks*((Q->ne[1] + cols_per_block - 1) / cols_per_block), Q->ne[2], Q->ne[3]); - const int shmem = 0; - - float scale; - memcpy(&scale, KQV->op_params, sizeof(float)); - - flash_attn_vec_ext_f16 - <<>> ( - (const char *) Q->data, - (const char *) K->data, - (const char *) V->data, - mask ? ((const char *) mask->data) : nullptr, - parallel_blocks == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr, - scale, - Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3], - K->ne[0], K->ne[1], K->ne[2], K->ne[3], - mask ? mask->ne[1] : 0, mask ? mask->nb[1] : 0, - Q->nb[1], Q->nb[2], Q->nb[3], - K->nb[1], K->nb[2], K->nb[3], - KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3] - ); - CUDA_CHECK(cudaGetLastError()); - - if (parallel_blocks == 1) { - return; - } - - const dim3 block_dim_combine(D, 1, 1); - const dim3 blocks_num_combine(Q->ne[1], blocks_num.y, blocks_num.z); - const int shmem_combine = 0; - - flash_attn_combine_results - <<>> - (dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data); - CUDA_CHECK(cudaGetLastError()); -} - -template void launch_fattn_f16_impl( - const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask, - ggml_cuda_pool & pool, cudaStream_t main_stream -) { - ggml_cuda_pool_alloc dst_tmp(pool); - ggml_cuda_pool_alloc dst_tmp_meta(pool); - - if (parallel_blocks > 1) { - dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV)); - dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV)); - } - - constexpr int frag_m = (cols_per_block) == 8 && (D) % 32 == 0 ? 32 : 16; - const dim3 block_dim(WARP_SIZE, nwarps, 1); - const dim3 blocks_num(parallel_blocks*(Q->ne[1] + cols_per_block - 1) / cols_per_block, Q->ne[2], Q->ne[3]); - const int shmem = 0; - - float scale; - memcpy(&scale, KQV->op_params, sizeof(float)); - - flash_attn_ext_f16 - <<>> ( - (const char *) Q->data, - (const char *) K->data, - (const char *) V->data, - mask ? ((const char *) mask->data) : nullptr, - (parallel_blocks) == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr, - scale, - Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3], - K->ne[0], K->ne[1], K->ne[2], K->ne[3], - mask ? mask->ne[1] : 0, mask ? mask->nb[1] : 0, - Q->nb[1], Q->nb[2], Q->nb[3], - K->nb[1], K->nb[2], K->nb[3], - KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3] - ); - CUDA_CHECK(cudaGetLastError()); - - if ((parallel_blocks) == 1) { - return; - } - - const dim3 block_dim_combine(D, 1, 1); - const dim3 blocks_num_combine(Q->ne[1], blocks_num.y, blocks_num.z); - const int shmem_combine = 0; - - flash_attn_combine_results - <<>> - (dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data); - CUDA_CHECK(cudaGetLastError()); -} +template +void launch_fattn_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * Q = dst->src[0]; -template void launch_fattn_f16( - const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask, - const int nsm, ggml_cuda_pool & pool, cudaStream_t main_stream -) { + constexpr int frag_m = cols_per_block == 8 && D % 32 == 0 ? 32 : 16; const int blocks_num_pb1 = ((Q->ne[1] + cols_per_block - 1) / cols_per_block)*Q->ne[2]*Q->ne[3]; + const int nsm = ggml_cuda_info().devices[ggml_cuda_get_device()].nsm; if (4*blocks_num_pb1 < 2*nsm) { - launch_fattn_f16_impl(Q, K, V, KQV, mask, pool, main_stream); + constexpr int parallel_blocks = 4; + fattn_kernel_t fattn_kernel = flash_attn_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); return; } if (2*blocks_num_pb1 < 2*nsm) { - launch_fattn_f16_impl(Q, K, V, KQV, mask, pool, main_stream); + constexpr int parallel_blocks = 2; + fattn_kernel_t fattn_kernel = flash_attn_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); return; } - launch_fattn_f16_impl(Q, K, V, KQV, mask, pool, main_stream); + constexpr int parallel_blocks = 1; + fattn_kernel_t fattn_kernel = flash_attn_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); } void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * Q = dst->src[0]; - const ggml_tensor * K = dst->src[1]; - const ggml_tensor * V = dst->src[2]; - - const ggml_tensor * mask = dst->src[3]; - - ggml_tensor * KQV = dst; - - GGML_ASSERT(Q->type == GGML_TYPE_F32); - GGML_ASSERT(K->type == GGML_TYPE_F16); - GGML_ASSERT(V->type == GGML_TYPE_F16); - GGML_ASSERT(KQV->type == GGML_TYPE_F32); - - GGML_ASSERT(!mask || mask->type == GGML_TYPE_F16); - GGML_ASSERT(!mask || mask->ne[1] >= GGML_PAD(Q->ne[1], 16) && - "the Flash-Attention CUDA kernel requires the mask to be padded to 16 and at least n_queries big"); - - GGML_ASSERT(K->ne[1] % FATTN_KQ_STRIDE == 0 && "Incorrect KV cache padding."); + const ggml_tensor * KQV = dst; + const ggml_tensor * Q = dst->src[0]; ggml_cuda_set_device(ctx.device); + const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc; + const int32_t precision = KQV->op_params[2]; - const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc; - const int nsm = ggml_cuda_info().devices[ggml_cuda_get_device()].nsm; - - const int32_t precision = KQV->op_params[1]; - - if (!fp16_mma_available(cc)) { - GGML_ASSERT(precision == GGML_PREC_DEFAULT); - GGML_ASSERT(Q->ne[0] == 64 || Q->ne[0] == 128 && "FlashAttention without tensor cores only supports head sizes 64 and 128."); - - if (Q->ne[1] == 1) { - constexpr int cols_per_block = 1; - constexpr int parallel_blocks = 4; - switch (Q->ne[0]) { - case 64: - launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 128: - launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - default: - GGML_ASSERT(false); - break; - } - return; - } - - if (Q->ne[1] == 2) { - constexpr int cols_per_block = 2; - constexpr int parallel_blocks = 4; - switch (Q->ne[0]) { - case 64: - launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 128: - launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - default: - GGML_ASSERT(false); - break; - } - return; - } - - if (Q->ne[1] <= 4) { - constexpr int cols_per_block = 4; - constexpr int parallel_blocks = 4; - switch (Q->ne[0]) { - case 64: - launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 128: - launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - default: - GGML_ASSERT(false); - break; - } - return; + // On AMD the tile kernels perform poorly, use the vec kernel instead: + if (cc >= CC_OFFSET_AMD) { + if (precision == GGML_PREC_DEFAULT) { + ggml_cuda_flash_attn_ext_vec_f16_no_mma(ctx, dst); + } else { + ggml_cuda_flash_attn_ext_vec_f32(ctx, dst); } + return; + } + if (!fast_fp16_available(cc)) { if (Q->ne[1] <= 8) { - constexpr int cols_per_block = 8; - constexpr int parallel_blocks = 4; - switch (Q->ne[0]) { - case 64: - launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 128: - launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - default: - GGML_ASSERT(false); - break; - } - return; + ggml_cuda_flash_attn_ext_vec_f32(ctx, dst); + } else { + ggml_cuda_flash_attn_ext_tile_f32(ctx, dst); } + return; + } - constexpr int cols_per_block = 8; - constexpr int parallel_blocks = 1; - switch (Q->ne[0]) { - case 64: - launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 128: - launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - default: - GGML_ASSERT(false); - break; + if (!fp16_mma_available(cc)) { + if (Q->ne[1] <= 8) { + ggml_cuda_flash_attn_ext_vec_f16_no_mma(ctx, dst); + } else { + ggml_cuda_flash_attn_ext_tile_f16(ctx, dst); } return; } if (precision != GGML_PREC_DEFAULT) { + if (Q->ne[1] == 1 && (Q->ne[0] == 64 || Q->ne[0] == 128)) { + ggml_cuda_flash_attn_ext_vec_f32(ctx, dst); + return; + } + if (Q->ne[1] <= 32 || Q->ne[0] > 128) { constexpr int cols_per_block = 16; constexpr int nwarps = 4; switch (Q->ne[0]) { case 64: - launch_fattn_f16< 64, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 64, cols_per_block, nwarps, float>(ctx, dst); break; case 80: - launch_fattn_f16< 80, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 80, cols_per_block, nwarps, float>(ctx, dst); break; case 96: - launch_fattn_f16< 96, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 96, cols_per_block, nwarps, float>(ctx, dst); break; case 112: - launch_fattn_f16<112, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<112, cols_per_block, nwarps, float>(ctx, dst); break; case 128: - launch_fattn_f16<128, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<128, cols_per_block, nwarps, float>(ctx, dst); break; case 256: - launch_fattn_f16<256, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<256, cols_per_block, nwarps, float>(ctx, dst); break; default: GGML_ASSERT(false); @@ -959,22 +527,22 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst constexpr int nwarps = 4; switch (Q->ne[0]) { case 64: - launch_fattn_f16< 64, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 64, cols_per_block, nwarps, float>(ctx, dst); break; case 80: - launch_fattn_f16< 80, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 80, cols_per_block, nwarps, float>(ctx, dst); break; case 96: - launch_fattn_f16< 96, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 96, cols_per_block, nwarps, float>(ctx, dst); break; case 112: - launch_fattn_f16<112, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<112, cols_per_block, nwarps, float>(ctx, dst); break; case 128: - launch_fattn_f16<128, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<128, cols_per_block, nwarps, float>(ctx, dst); break; // case 256: - // launch_fattn_f16<256, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + // launch_fattn_f16<256, cols_per_block, nwarps, float>(ctx, dst); // break; default: GGML_ASSERT(false); @@ -985,22 +553,7 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst } if (Q->ne[1] == 1 && Q->ne[0] % (2*WARP_SIZE) == 0) { - constexpr int cols_per_block = 1; - constexpr int parallel_blocks = 4; - switch (Q->ne[0]) { - case 64: - launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 128: - launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 256: - launch_fattn_vec_f16<256, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - default: - GGML_ASSERT(false); - break; - } + ggml_cuda_flash_attn_ext_vec_f16(ctx, dst); return; } @@ -1009,16 +562,16 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst constexpr int nwarps = 4; switch (Q->ne[0]) { case 64: - launch_fattn_f16< 64, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 64, cols_per_block, nwarps, half>(ctx, dst); break; case 96: - launch_fattn_f16< 96, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 96, cols_per_block, nwarps, half>(ctx, dst); break; case 128: - launch_fattn_f16<128, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<128, cols_per_block, nwarps, half>(ctx, dst); break; case 256: - launch_fattn_f16<256, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<256, cols_per_block, nwarps, half>(ctx, dst); break; default: GGML_ASSERT(false); @@ -1032,22 +585,22 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst constexpr int nwarps = 4; switch (Q->ne[0]) { case 64: - launch_fattn_f16< 64, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 64, cols_per_block, nwarps, half>(ctx, dst); break; case 80: - launch_fattn_f16< 80, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 80, cols_per_block, nwarps, half>(ctx, dst); break; case 96: - launch_fattn_f16< 96, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 96, cols_per_block, nwarps, half>(ctx, dst); break; case 112: - launch_fattn_f16<112, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<112, cols_per_block, nwarps, half>(ctx, dst); break; case 128: - launch_fattn_f16<128, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<128, cols_per_block, nwarps, half>(ctx, dst); break; case 256: - launch_fattn_f16<256, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<256, cols_per_block, nwarps, half>(ctx, dst); break; default: GGML_ASSERT(false); @@ -1060,22 +613,22 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst constexpr int nwarps = 4; switch (Q->ne[0]) { case 64: - launch_fattn_f16< 64, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 64, cols_per_block, nwarps, half>(ctx, dst); break; case 80: - launch_fattn_f16< 80, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 80, cols_per_block, nwarps, half>(ctx, dst); break; case 96: - launch_fattn_f16< 96, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 96, cols_per_block, nwarps, half>(ctx, dst); break; case 112: - launch_fattn_f16<112, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<112, cols_per_block, nwarps, half>(ctx, dst); break; case 128: - launch_fattn_f16<128, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<128, cols_per_block, nwarps, half>(ctx, dst); break; case 256: - launch_fattn_f16<256, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<256, cols_per_block, nwarps, half>(ctx, dst); break; default: GGML_ASSERT(false); diff --git a/ggml-cuda/mmq.cu b/ggml-cuda/mmq.cu index 7948f1b1237fa..c0a66d9b61802 100644 --- a/ggml-cuda/mmq.cu +++ b/ggml-cuda/mmq.cu @@ -9,6 +9,135 @@ typedef float (*vec_dot_q_mul_mat_cuda_t)( const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, const int * __restrict__ y_qs, const half2 * __restrict__ y_ms, const int & i, const int & j, const int & k); typedef void (*dot_kernel_k_t)(const void * __restrict__ vx, const int ib, const int iqs, const float * __restrict__ y, float & v); +typedef void (mul_mat_q_t)( + const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, + const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst); + +struct mmq_arch_config_t { + int x; + int y; + int nwarps; +}; + +struct mmq_config_t { + mmq_arch_config_t rdna2; + mmq_arch_config_t rdna1; + mmq_arch_config_t ampere; + mmq_arch_config_t pascal; +}; + +constexpr mmq_config_t MMQ_CONFIG_Q4_0 = { +// x y nwarps + { 64, 128, 8}, + { 64, 64, 8}, +#ifdef CUDA_USE_TENSOR_CORES + { 4, 32, 4}, +#else + { 64, 128, 4}, +#endif // CUDA_USE_TENSOR_CORES + { 64, 64, 8}, +}; +constexpr mmq_config_t MMQ_CONFIG_Q4_1 = { +// x y nwarps + { 64, 128, 8}, + { 64, 64, 8}, +#ifdef CUDA_USE_TENSOR_CORES + { 4, 32, 4}, +#else + { 64, 128, 4}, +#endif // CUDA_USE_TENSOR_CORES + { 64, 64, 8}, +}; +constexpr mmq_config_t MMQ_CONFIG_Q5_0 = { +// x y nwarps + { 64, 128, 8}, + { 64, 64, 8}, +#ifdef CUDA_USE_TENSOR_CORES + { 4, 32, 4}, +#else + {128, 64, 4}, +#endif // CUDA_USE_TENSOR_CORES + { 64, 64, 8}, +}; +constexpr mmq_config_t MMQ_CONFIG_Q5_1 = { +// x y nwarps + { 64, 128, 8}, + { 64, 64, 8}, +#ifdef CUDA_USE_TENSOR_CORES + { 4, 32, 4}, +#else + {128, 64, 4}, +#endif // CUDA_USE_TENSOR_CORES + { 64, 64, 8}, +}; +constexpr mmq_config_t MMQ_CONFIG_Q8_0 = { +// x y nwarps + { 64, 128, 8}, + { 64, 64, 8}, +#ifdef CUDA_USE_TENSOR_CORES + { 4, 32, 4}, +#else + {128, 64, 4}, +#endif // CUDA_USE_TENSOR_CORES + { 64, 64, 8}, +}; +constexpr mmq_config_t MMQ_CONFIG_Q2_K = { +// x y nwarps + { 64, 128, 8}, + {128, 32, 8}, +#ifdef CUDA_USE_TENSOR_CORES + { 4, 32, 4}, +#else + { 64, 128, 4}, +#endif // CUDA_USE_TENSOR_CORES + { 64, 64, 8}, +}; +constexpr mmq_config_t MMQ_CONFIG_Q3_K = { +// x y nwarps + {128, 64, 8}, + { 32, 128, 8}, +#ifdef CUDA_USE_TENSOR_CORES + { 4, 32, 4}, +#else + {128, 128, 4}, +#endif // CUDA_USE_TENSOR_CORES + { 64, 64, 8}, +}; +constexpr mmq_config_t MMQ_CONFIG_Q4_K = { +// x y nwarps + { 64, 128, 8}, + { 32, 64, 8}, +#ifdef CUDA_USE_TENSOR_CORES + { 4, 32, 4}, +#else + { 64, 128, 4}, +#endif // CUDA_USE_TENSOR_CORES + { 64, 64, 8}, +}; +constexpr mmq_config_t MMQ_CONFIG_Q5_K = { +// x y nwarps + { 64, 128, 8}, + { 32, 64, 8}, +#ifdef CUDA_USE_TENSOR_CORES + { 4, 32, 4}, +#else + { 64, 128, 4}, +#endif // CUDA_USE_TENSOR_CORES + { 64, 64, 8}, +}; +constexpr mmq_config_t MMQ_CONFIG_Q6_K = { +// x y nwarps + { 64, 128, 8}, + { 32, 64, 8}, +#ifdef CUDA_USE_TENSOR_CORES + { 4, 32, 4}, +#else + { 64, 64, 4}, +#endif // CUDA_USE_TENSOR_CORES + { 64, 64, 8}, +}; + +// ------------------------------------------------------------ template static __device__ __forceinline__ void allocate_tiles_q4_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) { GGML_UNUSED(x_qh); @@ -697,11 +826,7 @@ template static __device__ __forceinlin const block_q4_K * bxi = bx0 + i*blocks_per_row + kbxd; -#if QK_K == 256 x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = bxi->dm; -#else - x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = {bxi->dm[0], bxi->dm[1]}; -#endif } #pragma unroll @@ -804,9 +929,7 @@ template static __device__ __forceinlin const block_q5_K * bxi = bx0 + i*blocks_per_row + kbxd; -#if QK_K == 256 x_dm[i * (WARP_SIZE/QI5_K) + i / QI5_K + kbxd] = bxi->dm; -#endif } #pragma unroll @@ -943,25 +1066,6 @@ static __device__ __forceinline__ float vec_dot_q6_K_q8_1_mul_mat( return vec_dot_q6_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, x_dmf[i * (WARP_SIZE/QI6_K) + i/QI6_K], &y_df[index_y/QI8_1]); } -#define MMQ_X_Q4_0_RDNA2 64 -#define MMQ_Y_Q4_0_RDNA2 128 -#define NWARPS_Q4_0_RDNA2 8 -#define MMQ_X_Q4_0_RDNA1 64 -#define MMQ_Y_Q4_0_RDNA1 64 -#define NWARPS_Q4_0_RDNA1 8 -#if defined(CUDA_USE_TENSOR_CORES) -#define MMQ_X_Q4_0_AMPERE 4 -#define MMQ_Y_Q4_0_AMPERE 32 -#define NWARPS_Q4_0_AMPERE 4 -#else -#define MMQ_X_Q4_0_AMPERE 64 -#define MMQ_Y_Q4_0_AMPERE 128 -#define NWARPS_Q4_0_AMPERE 4 -#endif -#define MMQ_X_Q4_0_PASCAL 64 -#define MMQ_Y_Q4_0_PASCAL 64 -#define NWARPS_Q4_0_PASCAL 8 - template static __device__ __forceinline__ void mul_mat_q( @@ -1072,1107 +1176,275 @@ static __device__ __forceinline__ void mul_mat_q( } } -template static __global__ void -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*NWARPS_Q4_0_RDNA2, 2) -#endif // defined(RDNA3) || defined(RDNA2) -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) - mul_mat_q4_0( - const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, - const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { +static constexpr __device__ mmq_arch_config_t get_arch_config_device(mmq_config_t mmq_config) { #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) + #if defined(RDNA3) || defined(RDNA2) - const int mmq_x = MMQ_X_Q4_0_RDNA2; - const int mmq_y = MMQ_Y_Q4_0_RDNA2; - const int nwarps = NWARPS_Q4_0_RDNA2; + return mmq_config.rdna2; #else - const int mmq_x = MMQ_X_Q4_0_RDNA1; - const int mmq_y = MMQ_Y_Q4_0_RDNA1; - const int nwarps = NWARPS_Q4_0_RDNA1; + return mmq_config.rdna1; #endif // defined(RDNA3) || defined(RDNA2) - mul_mat_q, - load_tiles_q4_0, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); +#else -#elif __CUDA_ARCH__ >= CC_VOLTA - const int mmq_x = MMQ_X_Q4_0_AMPERE; - const int mmq_y = MMQ_Y_Q4_0_AMPERE; - const int nwarps = NWARPS_Q4_0_AMPERE; +#if __CUDA_ARCH__ >= CC_VOLTA + return mmq_config.ampere; +#else + return mmq_config.pascal; +#endif // __CUDA_ARCH__ >= CC_VOLTA - mul_mat_q, - load_tiles_q4_0, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); +#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +} + +template static __global__ void +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(RDNA3) || defined(RDNA2) + __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q4_0.rdna2.nwarps, 2) +#endif // defined(RDNA3) || defined(RDNA2) +#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) + mul_mat_q4_0( + const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, + const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { -#elif __CUDA_ARCH__ >= MIN_CC_DP4A - const int mmq_x = MMQ_X_Q4_0_PASCAL; - const int mmq_y = MMQ_Y_Q4_0_PASCAL; - const int nwarps = NWARPS_Q4_0_PASCAL; +#if __CUDA_ARCH__ >= MIN_CC_DP4A + constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q4_0); - mul_mat_q, - load_tiles_q4_0, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat> + mul_mat_q, + load_tiles_q4_0, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat> (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); #else + GGML_UNUSED(get_arch_config_device); GGML_UNUSED(vec_dot_q4_0_q8_1_mul_mat); NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= CC_VOLTA +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } -#define MMQ_X_Q4_1_RDNA2 64 -#define MMQ_Y_Q4_1_RDNA2 128 -#define NWARPS_Q4_1_RDNA2 8 -#define MMQ_X_Q4_1_RDNA1 64 -#define MMQ_Y_Q4_1_RDNA1 64 -#define NWARPS_Q4_1_RDNA1 8 -#if defined(CUDA_USE_TENSOR_CORES) -#define MMQ_X_Q4_1_AMPERE 4 -#define MMQ_Y_Q4_1_AMPERE 32 -#define NWARPS_Q4_1_AMPERE 4 -#else -#define MMQ_X_Q4_1_AMPERE 64 -#define MMQ_Y_Q4_1_AMPERE 128 -#define NWARPS_Q4_1_AMPERE 4 -#endif -#define MMQ_X_Q4_1_PASCAL 64 -#define MMQ_Y_Q4_1_PASCAL 64 -#define NWARPS_Q4_1_PASCAL 8 - template static __global__ void #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) #if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*NWARPS_Q4_1_RDNA2, 2) + __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q4_1.rdna2.nwarps, 2) #endif // defined(RDNA3) || defined(RDNA2) #elif __CUDA_ARCH__ < CC_VOLTA - __launch_bounds__(WARP_SIZE*NWARPS_Q4_1_PASCAL, 2) + __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q4_1.pascal.nwarps, 2) #endif // __CUDA_ARCH__ < CC_VOLTA mul_mat_q4_1( const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - const int mmq_x = MMQ_X_Q4_1_RDNA2; - const int mmq_y = MMQ_Y_Q4_1_RDNA2; - const int nwarps = NWARPS_Q4_1_RDNA2; -#else - const int mmq_x = MMQ_X_Q4_1_RDNA1; - const int mmq_y = MMQ_Y_Q4_1_RDNA1; - const int nwarps = NWARPS_Q4_1_RDNA1; -#endif // defined(RDNA3) || defined(RDNA2) - - mul_mat_q, - load_tiles_q4_1, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - -#elif __CUDA_ARCH__ >= CC_VOLTA - const int mmq_x = MMQ_X_Q4_1_AMPERE; - const int mmq_y = MMQ_Y_Q4_1_AMPERE; - const int nwarps = NWARPS_Q4_1_AMPERE; +#if __CUDA_ARCH__ >= MIN_CC_DP4A + constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q4_1); - mul_mat_q, - load_tiles_q4_1, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - -#elif __CUDA_ARCH__ >= MIN_CC_DP4A - const int mmq_x = MMQ_X_Q4_1_PASCAL; - const int mmq_y = MMQ_Y_Q4_1_PASCAL; - const int nwarps = NWARPS_Q4_1_PASCAL; - - mul_mat_q, - load_tiles_q4_1, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat> + mul_mat_q, + load_tiles_q4_1, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat> (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); #else + GGML_UNUSED(get_arch_config_device); GGML_UNUSED(vec_dot_q4_1_q8_1_mul_mat); NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= CC_VOLTA +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } -#define MMQ_X_Q5_0_RDNA2 64 -#define MMQ_Y_Q5_0_RDNA2 128 -#define NWARPS_Q5_0_RDNA2 8 -#define MMQ_X_Q5_0_RDNA1 64 -#define MMQ_Y_Q5_0_RDNA1 64 -#define NWARPS_Q5_0_RDNA1 8 -#if defined(CUDA_USE_TENSOR_CORES) -#define MMQ_X_Q5_0_AMPERE 4 -#define MMQ_Y_Q5_0_AMPERE 32 -#define NWARPS_Q5_0_AMPERE 4 -#else -#define MMQ_X_Q5_0_AMPERE 128 -#define MMQ_Y_Q5_0_AMPERE 64 -#define NWARPS_Q5_0_AMPERE 4 -#endif -#define MMQ_X_Q5_0_PASCAL 64 -#define MMQ_Y_Q5_0_PASCAL 64 -#define NWARPS_Q5_0_PASCAL 8 - template static __global__ void #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) #if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*NWARPS_Q5_0_RDNA2, 2) + __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q5_0.rdna2.nwarps, 2) #endif // defined(RDNA3) || defined(RDNA2) #endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) mul_mat_q5_0( const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - const int mmq_x = MMQ_X_Q5_0_RDNA2; - const int mmq_y = MMQ_Y_Q5_0_RDNA2; - const int nwarps = NWARPS_Q5_0_RDNA2; -#else - const int mmq_x = MMQ_X_Q5_0_RDNA1; - const int mmq_y = MMQ_Y_Q5_0_RDNA1; - const int nwarps = NWARPS_Q5_0_RDNA1; -#endif // defined(RDNA3) || defined(RDNA2) +#if __CUDA_ARCH__ >= MIN_CC_DP4A + constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q5_0); - mul_mat_q, - load_tiles_q5_0, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - -#elif __CUDA_ARCH__ >= CC_VOLTA - const int mmq_x = MMQ_X_Q5_0_AMPERE; - const int mmq_y = MMQ_Y_Q5_0_AMPERE; - const int nwarps = NWARPS_Q5_0_AMPERE; - - mul_mat_q, - load_tiles_q5_0, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - -#elif __CUDA_ARCH__ >= MIN_CC_DP4A - const int mmq_x = MMQ_X_Q5_0_PASCAL; - const int mmq_y = MMQ_Y_Q5_0_PASCAL; - const int nwarps = NWARPS_Q5_0_PASCAL; - - mul_mat_q, - load_tiles_q5_0, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat> + mul_mat_q, + load_tiles_q5_0, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat> (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); #else + GGML_UNUSED(get_arch_config_device); GGML_UNUSED(vec_dot_q5_0_q8_1_mul_mat); NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= CC_VOLTA +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } -#define MMQ_X_Q5_1_RDNA2 64 -#define MMQ_Y_Q5_1_RDNA2 128 -#define NWARPS_Q5_1_RDNA2 8 -#define MMQ_X_Q5_1_RDNA1 64 -#define MMQ_Y_Q5_1_RDNA1 64 -#define NWARPS_Q5_1_RDNA1 8 -#if defined(CUDA_USE_TENSOR_CORES) -#define MMQ_X_Q5_1_AMPERE 4 -#define MMQ_Y_Q5_1_AMPERE 32 -#define NWARPS_Q5_1_AMPERE 4 -#else -#define MMQ_X_Q5_1_AMPERE 128 -#define MMQ_Y_Q5_1_AMPERE 64 -#define NWARPS_Q5_1_AMPERE 4 -#endif -#define MMQ_X_Q5_1_PASCAL 64 -#define MMQ_Y_Q5_1_PASCAL 64 -#define NWARPS_Q5_1_PASCAL 8 - template static __global__ void #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) #if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*NWARPS_Q5_1_RDNA2, 2) + __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q5_1.rdna2.nwarps, 2) #endif // defined(RDNA3) || defined(RDNA2) #endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) mul_mat_q5_1( const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - const int mmq_x = MMQ_X_Q5_1_RDNA2; - const int mmq_y = MMQ_Y_Q5_1_RDNA2; - const int nwarps = NWARPS_Q5_1_RDNA2; -#else - const int mmq_x = MMQ_X_Q5_1_RDNA1; - const int mmq_y = MMQ_Y_Q5_1_RDNA1; - const int nwarps = NWARPS_Q5_1_RDNA1; -#endif // defined(RDNA3) || defined(RDNA2) - - mul_mat_q, - load_tiles_q5_1, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - -#elif __CUDA_ARCH__ >= CC_VOLTA - const int mmq_x = MMQ_X_Q5_1_AMPERE; - const int mmq_y = MMQ_Y_Q5_1_AMPERE; - const int nwarps = NWARPS_Q5_1_AMPERE; - - mul_mat_q, - load_tiles_q5_1, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - -#elif __CUDA_ARCH__ >= MIN_CC_DP4A - const int mmq_x = MMQ_X_Q5_1_PASCAL; - const int mmq_y = MMQ_Y_Q5_1_PASCAL; - const int nwarps = NWARPS_Q5_1_PASCAL; +#if __CUDA_ARCH__ >= MIN_CC_DP4A + constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q5_1); - mul_mat_q, - load_tiles_q5_1, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat> + mul_mat_q, + load_tiles_q5_1, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat> (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); #else + GGML_UNUSED(get_arch_config_device); GGML_UNUSED(vec_dot_q5_1_q8_1_mul_mat); NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= CC_VOLTA +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } -#define MMQ_X_Q8_0_RDNA2 64 -#define MMQ_Y_Q8_0_RDNA2 128 -#define NWARPS_Q8_0_RDNA2 8 -#define MMQ_X_Q8_0_RDNA1 64 -#define MMQ_Y_Q8_0_RDNA1 64 -#define NWARPS_Q8_0_RDNA1 8 -#if defined(CUDA_USE_TENSOR_CORES) -#define MMQ_X_Q8_0_AMPERE 4 -#define MMQ_Y_Q8_0_AMPERE 32 -#define NWARPS_Q8_0_AMPERE 4 -#else -#define MMQ_X_Q8_0_AMPERE 128 -#define MMQ_Y_Q8_0_AMPERE 64 -#define NWARPS_Q8_0_AMPERE 4 -#endif -#define MMQ_X_Q8_0_PASCAL 64 -#define MMQ_Y_Q8_0_PASCAL 64 -#define NWARPS_Q8_0_PASCAL 8 - template static __global__ void #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) #if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*NWARPS_Q8_0_RDNA2, 2) + __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q8_0.rdna2.nwarps, 2) #endif // defined(RDNA3) || defined(RDNA2) #endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) mul_mat_q8_0( const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - const int mmq_x = MMQ_X_Q8_0_RDNA2; - const int mmq_y = MMQ_Y_Q8_0_RDNA2; - const int nwarps = NWARPS_Q8_0_RDNA2; -#else - const int mmq_x = MMQ_X_Q8_0_RDNA1; - const int mmq_y = MMQ_Y_Q8_0_RDNA1; - const int nwarps = NWARPS_Q8_0_RDNA1; -#endif // defined(RDNA3) || defined(RDNA2) - - mul_mat_q, - load_tiles_q8_0, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - -#elif __CUDA_ARCH__ >= CC_VOLTA - const int mmq_x = MMQ_X_Q8_0_AMPERE; - const int mmq_y = MMQ_Y_Q8_0_AMPERE; - const int nwarps = NWARPS_Q8_0_AMPERE; +#if __CUDA_ARCH__ >= MIN_CC_DP4A + constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q8_0); - mul_mat_q, - load_tiles_q8_0, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - -#elif __CUDA_ARCH__ >= MIN_CC_DP4A - const int mmq_x = MMQ_X_Q8_0_PASCAL; - const int mmq_y = MMQ_Y_Q8_0_PASCAL; - const int nwarps = NWARPS_Q8_0_PASCAL; - - mul_mat_q, - load_tiles_q8_0, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat> + mul_mat_q, + load_tiles_q8_0, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat> (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); #else + GGML_UNUSED(get_arch_config_device); GGML_UNUSED(vec_dot_q8_0_q8_1_mul_mat); NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= CC_VOLTA +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } -#define MMQ_X_Q2_K_RDNA2 64 -#define MMQ_Y_Q2_K_RDNA2 128 -#define NWARPS_Q2_K_RDNA2 8 -#define MMQ_X_Q2_K_RDNA1 128 -#define MMQ_Y_Q2_K_RDNA1 32 -#define NWARPS_Q2_K_RDNA1 8 -#if defined(CUDA_USE_TENSOR_CORES) -#define MMQ_X_Q2_K_AMPERE 4 -#define MMQ_Y_Q2_K_AMPERE 32 -#define NWARPS_Q2_K_AMPERE 4 -#else -#define MMQ_X_Q2_K_AMPERE 64 -#define MMQ_Y_Q2_K_AMPERE 128 -#define NWARPS_Q2_K_AMPERE 4 -#endif -#define MMQ_X_Q2_K_PASCAL 64 -#define MMQ_Y_Q2_K_PASCAL 64 -#define NWARPS_Q2_K_PASCAL 8 - template static __global__ void #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) #if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*NWARPS_Q2_K_RDNA2, 2) + __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q2_K.rdna2.nwarps, 2) #endif // defined(RDNA3) || defined(RDNA2) #endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) mul_mat_q2_K( const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - const int mmq_x = MMQ_X_Q2_K_RDNA2; - const int mmq_y = MMQ_Y_Q2_K_RDNA2; - const int nwarps = NWARPS_Q2_K_RDNA2; -#else - const int mmq_x = MMQ_X_Q2_K_RDNA1; - const int mmq_y = MMQ_Y_Q2_K_RDNA1; - const int nwarps = NWARPS_Q2_K_RDNA1; -#endif // defined(RDNA3) || defined(RDNA2) +#if __CUDA_ARCH__ >= MIN_CC_DP4A + constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q2_K); - mul_mat_q, - load_tiles_q2_K, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - -#elif __CUDA_ARCH__ >= CC_VOLTA - const int mmq_x = MMQ_X_Q2_K_AMPERE; - const int mmq_y = MMQ_Y_Q2_K_AMPERE; - const int nwarps = NWARPS_Q2_K_AMPERE; - - mul_mat_q, - load_tiles_q2_K, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - -#elif __CUDA_ARCH__ >= MIN_CC_DP4A - const int mmq_x = MMQ_X_Q2_K_PASCAL; - const int mmq_y = MMQ_Y_Q2_K_PASCAL; - const int nwarps = NWARPS_Q2_K_PASCAL; - - mul_mat_q, - load_tiles_q2_K, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat> + mul_mat_q, + load_tiles_q2_K, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat> (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); #else + GGML_UNUSED(get_arch_config_device); GGML_UNUSED(vec_dot_q2_K_q8_1_mul_mat); NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= CC_VOLTA +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } -#define MMQ_X_Q3_K_RDNA2 128 -#define MMQ_Y_Q3_K_RDNA2 64 -#define NWARPS_Q3_K_RDNA2 8 -#define MMQ_X_Q3_K_RDNA1 32 -#define MMQ_Y_Q3_K_RDNA1 128 -#define NWARPS_Q3_K_RDNA1 8 -#if defined(CUDA_USE_TENSOR_CORES) -#define MMQ_X_Q3_K_AMPERE 4 -#define MMQ_Y_Q3_K_AMPERE 32 -#define NWARPS_Q3_K_AMPERE 4 -#else -#define MMQ_X_Q3_K_AMPERE 128 -#define MMQ_Y_Q3_K_AMPERE 128 -#define NWARPS_Q3_K_AMPERE 4 -#endif -#define MMQ_X_Q3_K_PASCAL 64 -#define MMQ_Y_Q3_K_PASCAL 64 -#define NWARPS_Q3_K_PASCAL 8 - template static __global__ void #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) #if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*NWARPS_Q3_K_RDNA2, 2) + __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q3_K.rdna2.nwarps, 2) #endif // defined(RDNA3) || defined(RDNA2) #elif __CUDA_ARCH__ < CC_VOLTA - __launch_bounds__(WARP_SIZE*NWARPS_Q3_K_PASCAL, 2) + __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q3_K.pascal.nwarps, 2) #endif // __CUDA_ARCH__ < CC_VOLTA mul_mat_q3_K( const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - const int mmq_x = MMQ_X_Q3_K_RDNA2; - const int mmq_y = MMQ_Y_Q3_K_RDNA2; - const int nwarps = NWARPS_Q3_K_RDNA2; -#else - const int mmq_x = MMQ_X_Q3_K_RDNA1; - const int mmq_y = MMQ_Y_Q3_K_RDNA1; - const int nwarps = NWARPS_Q3_K_RDNA1; -#endif // defined(RDNA3) || defined(RDNA2) +#if __CUDA_ARCH__ >= MIN_CC_DP4A + constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q3_K); - mul_mat_q, - load_tiles_q3_K, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - -#elif __CUDA_ARCH__ >= CC_VOLTA - const int mmq_x = MMQ_X_Q3_K_AMPERE; - const int mmq_y = MMQ_Y_Q3_K_AMPERE; - const int nwarps = NWARPS_Q3_K_AMPERE; - - mul_mat_q, - load_tiles_q3_K, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - -#elif __CUDA_ARCH__ >= MIN_CC_DP4A - const int mmq_x = MMQ_X_Q3_K_PASCAL; - const int mmq_y = MMQ_Y_Q3_K_PASCAL; - const int nwarps = NWARPS_Q3_K_PASCAL; - - mul_mat_q, - load_tiles_q3_K, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat> + mul_mat_q, + load_tiles_q3_K, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat> (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); #else + GGML_UNUSED(get_arch_config_device); GGML_UNUSED(vec_dot_q3_K_q8_1_mul_mat); NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= CC_VOLTA +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } -#define MMQ_X_Q4_K_RDNA2 64 -#define MMQ_Y_Q4_K_RDNA2 128 -#define NWARPS_Q4_K_RDNA2 8 -#define MMQ_X_Q4_K_RDNA1 32 -#define MMQ_Y_Q4_K_RDNA1 64 -#define NWARPS_Q4_K_RDNA1 8 -#if defined(CUDA_USE_TENSOR_CORES) -#define MMQ_X_Q4_K_AMPERE 4 -#define MMQ_Y_Q4_K_AMPERE 32 -#define NWARPS_Q4_K_AMPERE 4 -#else -#define MMQ_X_Q4_K_AMPERE 64 -#define MMQ_Y_Q4_K_AMPERE 128 -#define NWARPS_Q4_K_AMPERE 4 -#endif -#define MMQ_X_Q4_K_PASCAL 64 -#define MMQ_Y_Q4_K_PASCAL 64 -#define NWARPS_Q4_K_PASCAL 8 - template static __global__ void #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) #if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*NWARPS_Q4_K_RDNA2, 2) + __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q4_K.rdna2.nwarps, 2) #endif // defined(RDNA3) || defined(RDNA2) #elif __CUDA_ARCH__ < CC_VOLTA - __launch_bounds__(WARP_SIZE*NWARPS_Q4_K_PASCAL, 2) + __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q4_K.pascal.nwarps, 2) #endif // __CUDA_ARCH__ < CC_VOLTA mul_mat_q4_K( const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - const int mmq_x = MMQ_X_Q4_K_RDNA2; - const int mmq_y = MMQ_Y_Q4_K_RDNA2; - const int nwarps = NWARPS_Q4_K_RDNA2; -#else - const int mmq_x = MMQ_X_Q4_K_RDNA1; - const int mmq_y = MMQ_Y_Q4_K_RDNA1; - const int nwarps = NWARPS_Q4_K_RDNA1; -#endif // defined(RDNA3) || defined(RDNA2) - - mul_mat_q, - load_tiles_q4_K, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - -#elif __CUDA_ARCH__ >= CC_VOLTA - const int mmq_x = MMQ_X_Q4_K_AMPERE; - const int mmq_y = MMQ_Y_Q4_K_AMPERE; - const int nwarps = NWARPS_Q4_K_AMPERE; +#if __CUDA_ARCH__ >= MIN_CC_DP4A + constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q4_K); - mul_mat_q, - load_tiles_q4_K, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - -#elif __CUDA_ARCH__ >= MIN_CC_DP4A - const int mmq_x = MMQ_X_Q4_K_PASCAL; - const int mmq_y = MMQ_Y_Q4_K_PASCAL; - const int nwarps = NWARPS_Q4_K_PASCAL; - - mul_mat_q, - load_tiles_q4_K, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat> + mul_mat_q, + load_tiles_q4_K, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat> (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); #else + GGML_UNUSED(get_arch_config_device); GGML_UNUSED(vec_dot_q4_K_q8_1_mul_mat); NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= CC_VOLTA +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } -#define MMQ_X_Q5_K_RDNA2 64 -#define MMQ_Y_Q5_K_RDNA2 128 -#define NWARPS_Q5_K_RDNA2 8 -#define MMQ_X_Q5_K_RDNA1 32 -#define MMQ_Y_Q5_K_RDNA1 64 -#define NWARPS_Q5_K_RDNA1 8 -#if defined(CUDA_USE_TENSOR_CORES) -#define MMQ_X_Q5_K_AMPERE 4 -#define MMQ_Y_Q5_K_AMPERE 32 -#define NWARPS_Q5_K_AMPERE 4 -#else -#define MMQ_X_Q5_K_AMPERE 64 -#define MMQ_Y_Q5_K_AMPERE 128 -#define NWARPS_Q5_K_AMPERE 4 -#endif -#define MMQ_X_Q5_K_PASCAL 64 -#define MMQ_Y_Q5_K_PASCAL 64 -#define NWARPS_Q5_K_PASCAL 8 - template static __global__ void #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) #if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*NWARPS_Q5_K_RDNA2, 2) + __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q5_K.rdna2.nwarps, 2) #endif // defined(RDNA3) || defined(RDNA2) #endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) mul_mat_q5_K( const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - const int mmq_x = MMQ_X_Q5_K_RDNA2; - const int mmq_y = MMQ_Y_Q5_K_RDNA2; - const int nwarps = NWARPS_Q5_K_RDNA2; -#else - const int mmq_x = MMQ_X_Q5_K_RDNA1; - const int mmq_y = MMQ_Y_Q5_K_RDNA1; - const int nwarps = NWARPS_Q5_K_RDNA1; -#endif // defined(RDNA3) || defined(RDNA2) +#if __CUDA_ARCH__ >= MIN_CC_DP4A + constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q5_K); - mul_mat_q, - load_tiles_q5_K, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - -#elif __CUDA_ARCH__ >= CC_VOLTA - const int mmq_x = MMQ_X_Q5_K_AMPERE; - const int mmq_y = MMQ_Y_Q5_K_AMPERE; - const int nwarps = NWARPS_Q5_K_AMPERE; - - mul_mat_q, - load_tiles_q5_K, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - -#elif __CUDA_ARCH__ >= MIN_CC_DP4A - const int mmq_x = MMQ_X_Q5_K_PASCAL; - const int mmq_y = MMQ_Y_Q5_K_PASCAL; - const int nwarps = NWARPS_Q5_K_PASCAL; - - mul_mat_q, - load_tiles_q5_K, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat> + mul_mat_q, + load_tiles_q5_K, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat> (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); #else + GGML_UNUSED(get_arch_config_device); GGML_UNUSED(vec_dot_q5_K_q8_1_mul_mat); NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= CC_VOLTA +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } -#define MMQ_X_Q6_K_RDNA2 64 -#define MMQ_Y_Q6_K_RDNA2 128 -#define NWARPS_Q6_K_RDNA2 8 -#define MMQ_X_Q6_K_RDNA1 32 -#define MMQ_Y_Q6_K_RDNA1 64 -#define NWARPS_Q6_K_RDNA1 8 -#if defined(CUDA_USE_TENSOR_CORES) -#define MMQ_X_Q6_K_AMPERE 4 -#define MMQ_Y_Q6_K_AMPERE 32 -#define NWARPS_Q6_K_AMPERE 4 -#else -#define MMQ_X_Q6_K_AMPERE 64 -#define MMQ_Y_Q6_K_AMPERE 64 -#define NWARPS_Q6_K_AMPERE 4 -#endif -#define MMQ_X_Q6_K_PASCAL 64 -#define MMQ_Y_Q6_K_PASCAL 64 -#define NWARPS_Q6_K_PASCAL 8 - template static __global__ void #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) #if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*NWARPS_Q6_K_RDNA2, 2) + __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q6_K.rdna2.nwarps, 2) #endif // defined(RDNA3) || defined(RDNA2) #elif __CUDA_ARCH__ < CC_VOLTA - __launch_bounds__(WARP_SIZE*NWARPS_Q6_K_PASCAL, 2) + __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q4_K.pascal.nwarps, 2) #endif // __CUDA_ARCH__ < CC_VOLTA mul_mat_q6_K( const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - const int mmq_x = MMQ_X_Q6_K_RDNA2; - const int mmq_y = MMQ_Y_Q6_K_RDNA2; - const int nwarps = NWARPS_Q6_K_RDNA2; -#else - const int mmq_x = MMQ_X_Q6_K_RDNA1; - const int mmq_y = MMQ_Y_Q6_K_RDNA1; - const int nwarps = NWARPS_Q6_K_RDNA1; -#endif // defined(RDNA3) || defined(RDNA2) - - mul_mat_q, - load_tiles_q6_K, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - -#elif __CUDA_ARCH__ >= CC_VOLTA - const int mmq_x = MMQ_X_Q6_K_AMPERE; - const int mmq_y = MMQ_Y_Q6_K_AMPERE; - const int nwarps = NWARPS_Q6_K_AMPERE; - - mul_mat_q, - load_tiles_q6_K, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - -#elif __CUDA_ARCH__ >= MIN_CC_DP4A - const int mmq_x = MMQ_X_Q6_K_PASCAL; - const int mmq_y = MMQ_Y_Q6_K_PASCAL; - const int nwarps = NWARPS_Q6_K_PASCAL; +#if __CUDA_ARCH__ >= MIN_CC_DP4A + constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q6_K); - mul_mat_q, - load_tiles_q6_K, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat> + mul_mat_q, + load_tiles_q6_K, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat> (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); #else + GGML_UNUSED(get_arch_config_device); GGML_UNUSED(vec_dot_q6_K_q8_1_mul_mat); NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= CC_VOLTA -} - -static void ggml_mul_mat_q4_0_q8_1_cuda( - const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, - const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { - - int id = ggml_cuda_get_device(); - const int compute_capability = ggml_cuda_info().devices[id].cc; - - int mmq_x, mmq_y, nwarps; - if (compute_capability >= CC_RDNA2) { - mmq_x = MMQ_X_Q4_0_RDNA2; - mmq_y = MMQ_Y_Q4_0_RDNA2; - nwarps = NWARPS_Q4_0_RDNA2; - } else if (compute_capability >= CC_OFFSET_AMD) { - mmq_x = MMQ_X_Q4_0_RDNA1; - mmq_y = MMQ_Y_Q4_0_RDNA1; - nwarps = NWARPS_Q4_0_RDNA1; - } else if (compute_capability >= CC_VOLTA) { - mmq_x = MMQ_X_Q4_0_AMPERE; - mmq_y = MMQ_Y_Q4_0_AMPERE; - nwarps = NWARPS_Q4_0_AMPERE; - } else if (compute_capability >= MIN_CC_DP4A) { - mmq_x = MMQ_X_Q4_0_PASCAL; - mmq_y = MMQ_Y_Q4_0_PASCAL; - nwarps = NWARPS_Q4_0_PASCAL; - } else { - GGML_ASSERT(false); - } - - const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; - const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; - const dim3 block_nums(block_num_x, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, nwarps, 1); - - if (nrows_x % mmq_y == 0) { - const bool need_check = false; - mul_mat_q4_0<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } else { - const bool need_check = true; - mul_mat_q4_0<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } -} - -static void ggml_mul_mat_q4_1_q8_1_cuda( - const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, - const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { - - int id = ggml_cuda_get_device(); - const int compute_capability = ggml_cuda_info().devices[id].cc; - - int mmq_x, mmq_y, nwarps; - if (compute_capability >= CC_RDNA2) { - mmq_x = MMQ_X_Q4_1_RDNA2; - mmq_y = MMQ_Y_Q4_1_RDNA2; - nwarps = NWARPS_Q4_1_RDNA2; - } else if (compute_capability >= CC_OFFSET_AMD) { - mmq_x = MMQ_X_Q4_1_RDNA1; - mmq_y = MMQ_Y_Q4_1_RDNA1; - nwarps = NWARPS_Q4_1_RDNA1; - } else if (compute_capability >= CC_VOLTA) { - mmq_x = MMQ_X_Q4_1_AMPERE; - mmq_y = MMQ_Y_Q4_1_AMPERE; - nwarps = NWARPS_Q4_1_AMPERE; - } else if (compute_capability >= MIN_CC_DP4A) { - mmq_x = MMQ_X_Q4_1_PASCAL; - mmq_y = MMQ_Y_Q4_1_PASCAL; - nwarps = NWARPS_Q4_1_PASCAL; - } else { - GGML_ASSERT(false); - } - - const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; - const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; - const dim3 block_nums(block_num_x, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, nwarps, 1); - - if (nrows_x % mmq_y == 0) { - const bool need_check = false; - mul_mat_q4_1<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } else { - const bool need_check = true; - mul_mat_q4_1<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } -} - -static void ggml_mul_mat_q5_0_q8_1_cuda( - const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, - const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { - - int id = ggml_cuda_get_device(); - const int compute_capability = ggml_cuda_info().devices[id].cc; - - int mmq_x, mmq_y, nwarps; - if (compute_capability >= CC_RDNA2) { - mmq_x = MMQ_X_Q5_0_RDNA2; - mmq_y = MMQ_Y_Q5_0_RDNA2; - nwarps = NWARPS_Q5_0_RDNA2; - } else if (compute_capability >= CC_OFFSET_AMD) { - mmq_x = MMQ_X_Q5_0_RDNA1; - mmq_y = MMQ_Y_Q5_0_RDNA1; - nwarps = NWARPS_Q5_0_RDNA1; - } else if (compute_capability >= CC_VOLTA) { - mmq_x = MMQ_X_Q5_0_AMPERE; - mmq_y = MMQ_Y_Q5_0_AMPERE; - nwarps = NWARPS_Q5_0_AMPERE; - } else if (compute_capability >= MIN_CC_DP4A) { - mmq_x = MMQ_X_Q5_0_PASCAL; - mmq_y = MMQ_Y_Q5_0_PASCAL; - nwarps = NWARPS_Q5_0_PASCAL; - } else { - GGML_ASSERT(false); - } - - const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; - const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; - const dim3 block_nums(block_num_x, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, nwarps, 1); - - if (nrows_x % mmq_y == 0) { - const bool need_check = false; - mul_mat_q5_0<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } else { - const bool need_check = true; - mul_mat_q5_0<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } -} - -static void ggml_mul_mat_q5_1_q8_1_cuda( - const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, - const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { - - int id = ggml_cuda_get_device(); - const int compute_capability = ggml_cuda_info().devices[id].cc; - - int mmq_x, mmq_y, nwarps; - if (compute_capability >= CC_RDNA2) { - mmq_x = MMQ_X_Q5_1_RDNA2; - mmq_y = MMQ_Y_Q5_1_RDNA2; - nwarps = NWARPS_Q5_1_RDNA2; - } else if (compute_capability >= CC_OFFSET_AMD) { - mmq_x = MMQ_X_Q5_1_RDNA1; - mmq_y = MMQ_Y_Q5_1_RDNA1; - nwarps = NWARPS_Q5_1_RDNA1; - } else if (compute_capability >= CC_VOLTA) { - mmq_x = MMQ_X_Q5_1_AMPERE; - mmq_y = MMQ_Y_Q5_1_AMPERE; - nwarps = NWARPS_Q5_1_AMPERE; - } else if (compute_capability >= MIN_CC_DP4A) { - mmq_x = MMQ_X_Q5_1_PASCAL; - mmq_y = MMQ_Y_Q5_1_PASCAL; - nwarps = NWARPS_Q5_1_PASCAL; - } else { - GGML_ASSERT(false); - } - - const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; - const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; - const dim3 block_nums(block_num_x, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, nwarps, 1); - - if (nrows_x % mmq_y == 0) { - const bool need_check = false; - mul_mat_q5_1<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } else { - const bool need_check = true; - mul_mat_q5_1<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } -} - -static void ggml_mul_mat_q8_0_q8_1_cuda( - const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, - const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { - - int id = ggml_cuda_get_device(); - const int compute_capability = ggml_cuda_info().devices[id].cc; - - int mmq_x, mmq_y, nwarps; - if (compute_capability >= CC_RDNA2) { - mmq_x = MMQ_X_Q8_0_RDNA2; - mmq_y = MMQ_Y_Q8_0_RDNA2; - nwarps = NWARPS_Q8_0_RDNA2; - } else if (compute_capability >= CC_OFFSET_AMD) { - mmq_x = MMQ_X_Q8_0_RDNA1; - mmq_y = MMQ_Y_Q8_0_RDNA1; - nwarps = NWARPS_Q8_0_RDNA1; - } else if (compute_capability >= CC_VOLTA) { - mmq_x = MMQ_X_Q8_0_AMPERE; - mmq_y = MMQ_Y_Q8_0_AMPERE; - nwarps = NWARPS_Q8_0_AMPERE; - } else if (compute_capability >= MIN_CC_DP4A) { - mmq_x = MMQ_X_Q8_0_PASCAL; - mmq_y = MMQ_Y_Q8_0_PASCAL; - nwarps = NWARPS_Q8_0_PASCAL; - } else { - GGML_ASSERT(false); - } - - const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; - const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; - const dim3 block_nums(block_num_x, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, nwarps, 1); - - if (nrows_x % mmq_y == 0) { - const bool need_check = false; - mul_mat_q8_0<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } else { - const bool need_check = true; - mul_mat_q8_0<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } -} - -static void ggml_mul_mat_q2_K_q8_1_cuda( - const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, - const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { - - int id = ggml_cuda_get_device(); - const int compute_capability = ggml_cuda_info().devices[id].cc; - - int mmq_x, mmq_y, nwarps; - if (compute_capability >= CC_RDNA2) { - mmq_x = MMQ_X_Q2_K_RDNA2; - mmq_y = MMQ_Y_Q2_K_RDNA2; - nwarps = NWARPS_Q2_K_RDNA2; - } else if (compute_capability >= CC_OFFSET_AMD) { - mmq_x = MMQ_X_Q2_K_RDNA1; - mmq_y = MMQ_Y_Q2_K_RDNA1; - nwarps = NWARPS_Q2_K_RDNA1; - } else if (compute_capability >= CC_VOLTA) { - mmq_x = MMQ_X_Q2_K_AMPERE; - mmq_y = MMQ_Y_Q2_K_AMPERE; - nwarps = NWARPS_Q2_K_AMPERE; - } else if (compute_capability >= MIN_CC_DP4A) { - mmq_x = MMQ_X_Q2_K_PASCAL; - mmq_y = MMQ_Y_Q2_K_PASCAL; - nwarps = NWARPS_Q2_K_PASCAL; - } else { - GGML_ASSERT(false); - } - - const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; - const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; - const dim3 block_nums(block_num_x, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, nwarps, 1); - - if (nrows_x % mmq_y == 0) { - const bool need_check = false; - mul_mat_q2_K<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } else { - const bool need_check = true; - mul_mat_q2_K<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } -} - -static void ggml_mul_mat_q3_K_q8_1_cuda( - const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, - const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { - -#if QK_K == 256 - - int id = ggml_cuda_get_device(); - const int compute_capability = ggml_cuda_info().devices[id].cc; - - int mmq_x, mmq_y, nwarps; - if (compute_capability >= CC_RDNA2) { - mmq_x = MMQ_X_Q3_K_RDNA2; - mmq_y = MMQ_Y_Q3_K_RDNA2; - nwarps = NWARPS_Q3_K_RDNA2; - } else if (compute_capability >= CC_OFFSET_AMD) { - mmq_x = MMQ_X_Q3_K_RDNA1; - mmq_y = MMQ_Y_Q3_K_RDNA1; - nwarps = NWARPS_Q3_K_RDNA1; - } else if (compute_capability >= CC_VOLTA) { - mmq_x = MMQ_X_Q3_K_AMPERE; - mmq_y = MMQ_Y_Q3_K_AMPERE; - nwarps = NWARPS_Q3_K_AMPERE; - } else if (compute_capability >= MIN_CC_DP4A) { - mmq_x = MMQ_X_Q3_K_PASCAL; - mmq_y = MMQ_Y_Q3_K_PASCAL; - nwarps = NWARPS_Q3_K_PASCAL; - } else { - GGML_ASSERT(false); - } - - const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; - const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; - const dim3 block_nums(block_num_x, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, nwarps, 1); - - if (nrows_x % mmq_y == 0) { - const bool need_check = false; - mul_mat_q3_K<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } else { - const bool need_check = true; - mul_mat_q3_K<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } -#endif +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } -static void ggml_mul_mat_q4_K_q8_1_cuda( - const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, - const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { - - int id = ggml_cuda_get_device(); - const int compute_capability = ggml_cuda_info().devices[id].cc; - - int mmq_x, mmq_y, nwarps; - if (compute_capability >= CC_RDNA2) { - mmq_x = MMQ_X_Q4_K_RDNA2; - mmq_y = MMQ_Y_Q4_K_RDNA2; - nwarps = NWARPS_Q4_K_RDNA2; - } else if (compute_capability >= CC_OFFSET_AMD) { - mmq_x = MMQ_X_Q4_K_RDNA1; - mmq_y = MMQ_Y_Q4_K_RDNA1; - nwarps = NWARPS_Q4_K_RDNA1; - } else if (compute_capability >= CC_VOLTA) { - mmq_x = MMQ_X_Q4_K_AMPERE; - mmq_y = MMQ_Y_Q4_K_AMPERE; - nwarps = NWARPS_Q4_K_AMPERE; - } else if (compute_capability >= MIN_CC_DP4A) { - mmq_x = MMQ_X_Q4_K_PASCAL; - mmq_y = MMQ_Y_Q4_K_PASCAL; - nwarps = NWARPS_Q4_K_PASCAL; - } else { - GGML_ASSERT(false); - } - - const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; - const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; - const dim3 block_nums(block_num_x, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, nwarps, 1); - - if (nrows_x % mmq_y == 0) { - const bool need_check = false; - mul_mat_q4_K<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } else { - const bool need_check = true; - mul_mat_q4_K<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } -} - -static void ggml_mul_mat_q5_K_q8_1_cuda( - const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, - const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { - - int id = ggml_cuda_get_device(); - const int compute_capability = ggml_cuda_info().devices[id].cc; - - int mmq_x, mmq_y, nwarps; - if (compute_capability >= CC_RDNA2) { - mmq_x = MMQ_X_Q5_K_RDNA2; - mmq_y = MMQ_Y_Q5_K_RDNA2; - nwarps = NWARPS_Q5_K_RDNA2; - } else if (compute_capability >= CC_OFFSET_AMD) { - mmq_x = MMQ_X_Q5_K_RDNA1; - mmq_y = MMQ_Y_Q5_K_RDNA1; - nwarps = NWARPS_Q5_K_RDNA1; - } else if (compute_capability >= CC_VOLTA) { - mmq_x = MMQ_X_Q5_K_AMPERE; - mmq_y = MMQ_Y_Q5_K_AMPERE; - nwarps = NWARPS_Q5_K_AMPERE; - } else if (compute_capability >= MIN_CC_DP4A) { - mmq_x = MMQ_X_Q5_K_PASCAL; - mmq_y = MMQ_Y_Q5_K_PASCAL; - nwarps = NWARPS_Q5_K_PASCAL; - } else { - GGML_ASSERT(false); - } - - const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; - const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; - const dim3 block_nums(block_num_x, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, nwarps, 1); - - if (nrows_x % mmq_y == 0) { - const bool need_check = false; - mul_mat_q5_K<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } else { - const bool need_check = true; - mul_mat_q5_K<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } -} - -static void ggml_mul_mat_q6_K_q8_1_cuda( - const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, - const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { - - int id = ggml_cuda_get_device(); - const int compute_capability = ggml_cuda_info().devices[id].cc; - - int mmq_x, mmq_y, nwarps; - if (compute_capability >= CC_RDNA2) { - mmq_x = MMQ_X_Q6_K_RDNA2; - mmq_y = MMQ_Y_Q6_K_RDNA2; - nwarps = NWARPS_Q6_K_RDNA2; - } else if (compute_capability >= CC_OFFSET_AMD) { - mmq_x = MMQ_X_Q6_K_RDNA1; - mmq_y = MMQ_Y_Q6_K_RDNA1; - nwarps = NWARPS_Q6_K_RDNA1; - } else if (compute_capability >= CC_VOLTA) { - mmq_x = MMQ_X_Q6_K_AMPERE; - mmq_y = MMQ_Y_Q6_K_AMPERE; - nwarps = NWARPS_Q6_K_AMPERE; - } else if (compute_capability >= MIN_CC_DP4A) { - mmq_x = MMQ_X_Q6_K_PASCAL; - mmq_y = MMQ_Y_Q6_K_PASCAL; - nwarps = NWARPS_Q6_K_PASCAL; - } else { - GGML_ASSERT(false); - } - - const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; - const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; - const dim3 block_nums(block_num_x, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, nwarps, 1); - - if (nrows_x % mmq_y == 0) { - const bool need_check = false; - mul_mat_q6_K<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } else { - const bool need_check = true; - mul_mat_q6_K<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } -} +#define MMQ_SWITCH_CASE(type_suffix) \ + case GGML_TYPE_Q##type_suffix: if (row_diff % arch_config.y == 0) { \ + const bool need_check = false; \ + mul_mat_q##type_suffix<<>> \ + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst); \ + } else { \ + const bool need_check = true; \ + mul_mat_q##type_suffix<<>> \ + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst); \ + } break; \ void ggml_cuda_op_mul_mat_q( ggml_backend_cuda_context & ctx, @@ -2190,47 +1462,84 @@ void ggml_cuda_op_mul_mat_q( const int64_t row_diff = row_high - row_low; int id = ggml_cuda_get_device(); + const int compute_capability = ggml_cuda_info().devices[id].cc; // the main device has a larger memory buffer to hold the results from all GPUs // nrows_dst == nrows of the matrix that the kernel writes into const int64_t nrows_dst = id == ctx.device ? ne0 : row_diff; + mmq_config_t mmq_config; + switch (src0->type) { case GGML_TYPE_Q4_0: - ggml_mul_mat_q4_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + mmq_config = MMQ_CONFIG_Q4_0; break; case GGML_TYPE_Q4_1: - ggml_mul_mat_q4_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + mmq_config = MMQ_CONFIG_Q4_1; break; case GGML_TYPE_Q5_0: - ggml_mul_mat_q5_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + mmq_config = MMQ_CONFIG_Q5_0; break; case GGML_TYPE_Q5_1: - ggml_mul_mat_q5_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + mmq_config = MMQ_CONFIG_Q5_1; break; case GGML_TYPE_Q8_0: - ggml_mul_mat_q8_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + mmq_config = MMQ_CONFIG_Q8_0; break; case GGML_TYPE_Q2_K: - ggml_mul_mat_q2_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + mmq_config = MMQ_CONFIG_Q2_K; break; case GGML_TYPE_Q3_K: - ggml_mul_mat_q3_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + mmq_config = MMQ_CONFIG_Q3_K; break; case GGML_TYPE_Q4_K: - ggml_mul_mat_q4_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + mmq_config = MMQ_CONFIG_Q4_K; break; case GGML_TYPE_Q5_K: - ggml_mul_mat_q5_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + mmq_config = MMQ_CONFIG_Q5_K; break; case GGML_TYPE_Q6_K: - ggml_mul_mat_q6_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + mmq_config = MMQ_CONFIG_Q6_K; break; default: GGML_ASSERT(false); break; } + mmq_arch_config_t arch_config; + if (compute_capability >= CC_RDNA2) { + arch_config = mmq_config.rdna2; + } else if (compute_capability >= CC_OFFSET_AMD) { + arch_config = mmq_config.rdna1; + } else if (compute_capability >= CC_VOLTA) { + arch_config = mmq_config.ampere; + } else if (compute_capability >= MIN_CC_DP4A) { + arch_config = mmq_config.pascal; + } else { + GGML_ASSERT(false); + } + + const int block_num_x = (row_diff + arch_config.y - 1) / arch_config.y; + const int block_num_y = (src1_ncols + arch_config.x - 1) / arch_config.x; + const dim3 block_nums(block_num_x, block_num_y, 1); + const dim3 block_dims(WARP_SIZE, arch_config.nwarps, 1); + + switch (src0->type) { + MMQ_SWITCH_CASE(4_0) + MMQ_SWITCH_CASE(4_1) + MMQ_SWITCH_CASE(5_0) + MMQ_SWITCH_CASE(5_1) + MMQ_SWITCH_CASE(8_0) + MMQ_SWITCH_CASE(2_K) + MMQ_SWITCH_CASE(3_K) + MMQ_SWITCH_CASE(4_K) + MMQ_SWITCH_CASE(5_K) + MMQ_SWITCH_CASE(6_K) + default: + GGML_ASSERT(false); + break; + } + GGML_UNUSED(src1); GGML_UNUSED(dst); GGML_UNUSED(src1_ddf_i); diff --git a/ggml-cuda/rope.cu b/ggml-cuda/rope.cu index 4b0d2e5adbbc5..50f2cf415ef60 100644 --- a/ggml-cuda/rope.cu +++ b/ggml-cuda/rope.cu @@ -58,10 +58,10 @@ static __global__ void rope( dst[i + 1] = x0*sin_theta + x1*cos_theta; } -template +template static __global__ void rope_neox( const T * x, T * dst, int ncols, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows, - float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, float inv_ndims + float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, float inv_ndims, const float * freq_factors ) { const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y); @@ -88,7 +88,9 @@ static __global__ void rope_neox( float cur_rot = inv_ndims * ic - ib; const int p = has_pos ? pos[i2] : 0; - const float theta_base = p*freq_scale*powf(theta_scale, col/2.0f); + const float freq_factor = has_freq_facs ? freq_factors[ic/2] : 1.0f; + + const float theta_base = p*freq_scale*powf(theta_scale, col/2.0f)/freq_factor; float cos_theta, sin_theta; rope_yarn(theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta); @@ -164,7 +166,7 @@ static void rope_cuda( template static void rope_neox_cuda( const T * x, T * dst, int ncols, int n_dims, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows, - float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream + float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream ) { GGML_ASSERT(ncols % 2 == 0); const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1); @@ -175,15 +177,29 @@ static void rope_neox_cuda( const float inv_ndims = -1.0f / n_dims; if (pos == nullptr) { - rope_neox<<>>( - x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims, - theta_scale, inv_ndims - ); + if (freq_factors == nullptr) { + rope_neox<<>>( + x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims, + theta_scale, inv_ndims, freq_factors + ); + } else { + rope_neox<<>>( + x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims, + theta_scale, inv_ndims, freq_factors + ); + } } else { - rope_neox<<>>( - x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims, - theta_scale, inv_ndims - ); + if (freq_factors == nullptr) { + rope_neox<<>>( + x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims, + theta_scale, inv_ndims, freq_factors + ); + } else { + rope_neox<<>>( + x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims, + theta_scale, inv_ndims, freq_factors + ); + } } } @@ -214,24 +230,27 @@ static void rope_cuda_f32( static void rope_neox_cuda_f16( const half * x, half * dst, int ncols, int n_dims, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows, - float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream) { + float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream) { - rope_neox_cuda(x, dst, ncols, n_dims, nrows, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, stream); + rope_neox_cuda(x, dst, ncols, n_dims, nrows, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream); } static void rope_neox_cuda_f32( const float * x, float * dst, int ncols, int n_dims, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows, - float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream + float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream ) { - rope_neox_cuda(x, dst, ncols, n_dims, nrows, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, stream); + rope_neox_cuda(x, dst, ncols, n_dims, nrows, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream); } void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const ggml_tensor * src0 = dst->src[0]; const ggml_tensor * src1 = dst->src[1]; + const ggml_tensor * src2 = dst->src[2]; + const float * src0_d = (const float *)src0->data; const float * src1_d = (const float *)src1->data; + float * dst_d = (float *)dst->data; cudaStream_t stream = ctx.stream(); @@ -241,7 +260,6 @@ void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const int64_t ne00 = src0->ne[0]; const int64_t ne01 = src0->ne[1]; - const int64_t ne2 = dst->ne[2]; const int64_t nrows = ggml_nrows(src0); //const int n_past = ((int32_t *) dst->op_params)[0]; @@ -259,16 +277,22 @@ void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); + const float * freq_factors = nullptr; const int32_t * pos = nullptr; - if ((mode & 1) == 0) { - GGML_ASSERT(src1->type == GGML_TYPE_I32); - GGML_ASSERT(src1->ne[0] == ne2); - pos = (const int32_t *) src1_d; - } const bool is_neox = mode & 2; const bool is_glm = mode & 4; + pos = (const int32_t *) src1_d; + + if (is_neox) { + if (src2 != nullptr) { + freq_factors = (const float *) src2->data; + } + } else { + GGML_ASSERT(src2 == nullptr && "TODO: freq_factors not implemented for !is_neox"); + } + rope_corr_dims corr_dims; ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims.v); @@ -280,12 +304,12 @@ void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { if (src0->type == GGML_TYPE_F32) { rope_neox_cuda_f32( (const float *)src0_d, (float *)dst_d, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor, - attn_factor, corr_dims, stream + attn_factor, corr_dims, freq_factors, stream ); } else if (src0->type == GGML_TYPE_F16) { rope_neox_cuda_f16( (const half *)src0_d, (half *)dst_d, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor, - attn_factor, corr_dims, stream + attn_factor, corr_dims, freq_factors, stream ); } else { GGML_ASSERT(false); diff --git a/ggml-cuda/softmax.cu b/ggml-cuda/softmax.cu index 6ed225999bddf..ce64f2f2ce28b 100644 --- a/ggml-cuda/softmax.cu +++ b/ggml-cuda/softmax.cu @@ -1,3 +1,4 @@ +#include "common.cuh" #include "softmax.cuh" template @@ -11,7 +12,7 @@ __device__ float __forceinline__ t2f32(half val) { } template -static __global__ void soft_max_f32(const float * x, const T * mask, const T * pos, float * dst, const int ncols_par, const int nrows_y, const float scale, const float max_bias, const float m0, const float m1, uint32_t n_head_log2) { +static __global__ void soft_max_f32(const float * x, const T * mask, float * dst, const int ncols_par, const int nrows_y, const float scale, const float max_bias, const float m0, const float m1, uint32_t n_head_log2) { const int ncols = ncols_template == 0 ? ncols_par : ncols_template; const int tid = threadIdx.x; @@ -23,17 +24,7 @@ static __global__ void soft_max_f32(const float * x, const T * mask, const T * p const int warp_id = threadIdx.x / WARP_SIZE; const int lane_id = threadIdx.x % WARP_SIZE; - float slope = 0.0f; - - // ALiBi - if (max_bias > 0.0f) { - const int h = rowx/nrows_y; // head index - - const float base = h < n_head_log2 ? m0 : m1; - const int exp = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; - - slope = powf(base, exp); - } + const float slope = get_alibi_slope(max_bias, rowx/nrows_y, n_head_log2, m0, m1); extern __shared__ float data_soft_max_f32[]; float * buf_iw = data_soft_max_f32; // shared memory buffer for inter-warp communication @@ -53,7 +44,7 @@ static __global__ void soft_max_f32(const float * x, const T * mask, const T * p const int64_t ix = (int64_t)rowx*ncols + col; const int64_t iy = (int64_t)rowy*ncols + col; - const float val = x[ix]*scale + (mask ? t2f32(mask[iy]) : 0.0f) + (pos ? slope*t2f32(pos[col]) : 0.0f); + const float val = x[ix]*scale + (mask ? slope*t2f32(mask[iy]) : 0.0f); vals[col] = val; max_val = max(max_val, val); @@ -125,7 +116,7 @@ static __global__ void soft_max_f32(const float * x, const T * mask, const T * p } template -static void soft_max_f32_cuda(const float * x, const T * mask, const T * pos, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const float scale, const float max_bias, cudaStream_t stream) { +static void soft_max_f32_cuda(const float * x, const T * mask, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const float scale, const float max_bias, cudaStream_t stream) { int nth = WARP_SIZE; while (nth < ncols_x && nth < CUDA_SOFT_MAX_BLOCK_SIZE) nth *= 2; const dim3 block_dims(nth, 1, 1); @@ -133,8 +124,8 @@ static void soft_max_f32_cuda(const float * x, const T * mask, const T * pos, fl const size_t shmem = (GGML_PAD(ncols_x, WARP_SIZE) + WARP_SIZE)*sizeof(float); static_assert(CUDA_SOFT_MAX_BLOCK_SIZE == 1024, "These values need to be adjusted."); - const uint32_t n_head_kv = nrows_x/nrows_y; - const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv)); + const uint32_t n_head = nrows_x/nrows_y; + const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head)); const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); @@ -142,43 +133,42 @@ static void soft_max_f32_cuda(const float * x, const T * mask, const T * pos, fl if (shmem < ggml_cuda_info().devices[ggml_cuda_get_device()].smpb) { switch (ncols_x) { case 32: - soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); + soft_max_f32<<>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); break; case 64: - soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); + soft_max_f32<<>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); break; case 128: - soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); + soft_max_f32<<>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); break; case 256: - soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); + soft_max_f32<<>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); break; case 512: - soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); + soft_max_f32<<>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); break; case 1024: - soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); + soft_max_f32<<>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); break; case 2048: - soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); + soft_max_f32<<>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); break; case 4096: - soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); + soft_max_f32<<>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); break; default: - soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); + soft_max_f32<<>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); break; } } else { const size_t shmem_low = WARP_SIZE*sizeof(float); - soft_max_f32<<>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); + soft_max_f32<<>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); } } void ggml_cuda_op_soft_max(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const ggml_tensor * src0 = dst->src[0]; const ggml_tensor * src1 = dst->src[1]; - const ggml_tensor * src2 = dst->src[2]; const float * src0_d = (const float *)src0->data; const void * src1_d = src1 ? (const void *)src1->data : nullptr; @@ -190,7 +180,6 @@ void ggml_cuda_op_soft_max(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { GGML_ASSERT( dst->type == GGML_TYPE_F32); GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F16 || src1->type == GGML_TYPE_F32); // src1 contains mask and it is optional - GGML_ASSERT(!src2 || src2->type == GGML_TYPE_F16 || src2->type == GGML_TYPE_F32); // src2 contains positions and it is optional const int64_t ne00 = src0->ne[0]; const int64_t nrows_x = ggml_nrows(src0); @@ -202,26 +191,15 @@ void ggml_cuda_op_soft_max(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { memcpy(&scale, (float *) dst->op_params + 0, sizeof(float)); memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float)); - // positions tensor - void * src2_d = nullptr; - - const bool use_src2 = src2 != nullptr; - - if (use_src2) { - src2_d = (void *)src2->data; - } - - const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16) || (src2 && src2->type == GGML_TYPE_F16); + const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16); if (use_f16) { const half * src1_dd = (const half *)src1_d; - const half * src2_dd = (const half *)src2_d; - soft_max_f32_cuda(src0_d, src1_dd, src2_dd, dst_d, ne00, nrows_x, nrows_y, scale, max_bias, stream); + soft_max_f32_cuda(src0_d, src1_dd, dst_d, ne00, nrows_x, nrows_y, scale, max_bias, stream); } else { const float * src1_dd = (const float *)src1_d; - const float * src2_dd = (const float *)src2_d; - soft_max_f32_cuda(src0_d, src1_dd, src2_dd, dst_d, ne00, nrows_x, nrows_y, scale, max_bias, stream); + soft_max_f32_cuda(src0_d, src1_dd, dst_d, ne00, nrows_x, nrows_y, scale, max_bias, stream); } } diff --git a/ggml-cuda/unary.cu b/ggml-cuda/unary.cu index 1a7f0946972c1..ac03d5c6fce54 100644 --- a/ggml-cuda/unary.cu +++ b/ggml-cuda/unary.cu @@ -48,6 +48,15 @@ static __global__ void relu_f32(const float * x, float * dst, const int k) { dst[i] = fmaxf(x[i], 0); } +static __global__ void sigmoid_f32(const float * x, float * dst, const int k) { + const int i = blockDim.x*blockIdx.x + threadIdx.x; + + if (i >= k) { + return; + } + dst[i] = 1.0f / (1.0f + expf(-x[i])); +} + static __global__ void hardsigmoid_f32(const float * x, float * dst, const int k) { const int i = blockDim.x*blockIdx.x + threadIdx.x; @@ -108,6 +117,11 @@ static void relu_f32_cuda(const float * x, float * dst, const int k, cudaStream_ relu_f32<<>>(x, dst, k); } +static void sigmoid_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) { + const int num_blocks = (k + CUDA_SIGMOID_BLOCK_SIZE - 1) / CUDA_SIGMOID_BLOCK_SIZE; + sigmoid_f32<<>>(x, dst, k); +} + static void hardsigmoid_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) { const int num_blocks = (k + CUDA_HARDSIGMOID_BLOCK_SIZE - 1) / CUDA_HARDSIGMOID_BLOCK_SIZE; hardsigmoid_f32<<>>(x, dst, k); @@ -188,6 +202,18 @@ void ggml_cuda_op_relu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { relu_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream); } +void ggml_cuda_op_sigmoid(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * src0 = dst->src[0]; + const float * src0_d = (const float *)src0->data; + float * dst_d = (float *)dst->data; + cudaStream_t stream = ctx.stream(); + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + sigmoid_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream); +} + void ggml_cuda_op_hardsigmoid(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const ggml_tensor * src0 = dst->src[0]; const float * src0_d = (const float *)src0->data; diff --git a/ggml-cuda/unary.cuh b/ggml-cuda/unary.cuh index 2002ed989209c..a1d07c04fcd43 100644 --- a/ggml-cuda/unary.cuh +++ b/ggml-cuda/unary.cuh @@ -4,6 +4,7 @@ #define CUDA_SILU_BLOCK_SIZE 256 #define CUDA_TANH_BLOCK_SIZE 256 #define CUDA_RELU_BLOCK_SIZE 256 +#define CUDA_SIGMOID_BLOCK_SIZE 256 #define CUDA_HARDSIGMOID_BLOCK_SIZE 256 #define CUDA_HARDSWISH_BLOCK_SIZE 256 #define CUDA_SQR_BLOCK_SIZE 256 @@ -18,6 +19,8 @@ void ggml_cuda_op_tanh(ggml_backend_cuda_context & ctx, ggml_tensor * dst); void ggml_cuda_op_relu(ggml_backend_cuda_context & ctx, ggml_tensor * dst); +void ggml_cuda_op_sigmoid(ggml_backend_cuda_context & ctx, ggml_tensor * dst); + void ggml_cuda_op_hardsigmoid(ggml_backend_cuda_context & ctx, ggml_tensor * dst); void ggml_cuda_op_hardswish(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/ggml-cuda/upscale.cu b/ggml-cuda/upscale.cu index 2f62fed488835..cf513c3ade7c4 100644 --- a/ggml-cuda/upscale.cu +++ b/ggml-cuda/upscale.cu @@ -1,35 +1,36 @@ #include "upscale.cuh" -static __global__ void upscale_f32(const float * x, float * dst, const int ne00, const int ne00xne01, const int scale_factor) { - // blockIdx.z: idx of ne02*ne03 - // blockIdx.y: idx of ne01*scale_factor, aka ne1 - // blockIDx.x: idx of ne00*scale_factor / BLOCK_SIZE - // ne00xne01: ne00 * ne01 - int ne0 = ne00 * scale_factor; - int nidx = threadIdx.x + blockIdx.x * blockDim.x; - if (nidx >= ne0) { +static __global__ void upscale_f32(const float * x, float * dst, + const int nb00, const int nb01, const int nb02, const int nb03, + const int ne10, const int ne11, const int ne12, const int ne13, + const float sf0, const float sf1, const float sf2, const float sf3) { + int index = threadIdx.x + blockIdx.x * blockDim.x; + if (index >= ne10 * ne11 * ne12 * ne13) { return; } - // operation - int i00 = nidx / scale_factor; - int i01 = blockIdx.y / scale_factor; - int offset_src = - i00 + - i01 * ne00 + - blockIdx.z * ne00xne01; - int offset_dst = - nidx + - blockIdx.y * ne0 + - blockIdx.z * ne0 * gridDim.y; - dst[offset_dst] = x[offset_src]; + + int i10 = index % ne10; + int i11 = (index / ne10) % ne11; + int i12 = (index / (ne10 * ne11)) % ne12; + int i13 = (index / (ne10 * ne11 * ne12)) % ne13; + + int i00 = i10 / sf0; + int i01 = i11 / sf1; + int i02 = i12 / sf2; + int i03 = i13 / sf3; + + dst[index] = *(float *)((char *)x + i03 * nb03 + i02 * nb02 + i01 * nb01 + i00 * nb00); } -static void upscale_f32_cuda(const float * x, float * dst, const int ne00, const int ne01, const int ne02, const int ne03, - const int scale_factor, cudaStream_t stream) { - int ne0 = (ne00 * scale_factor); - int num_blocks = (ne0 + CUDA_UPSCALE_BLOCK_SIZE - 1) / CUDA_UPSCALE_BLOCK_SIZE; - dim3 gridDim(num_blocks, (ne01 * scale_factor), ne02*ne03); - upscale_f32<<>>(x, dst, ne00, ne00 * ne01, scale_factor); +static void upscale_f32_cuda(const float * x, float * dst, + const int nb00, const int nb01, const int nb02, const int nb03, + const int ne10, const int ne11, const int ne12, const int ne13, + const float sf0, const float sf1, const float sf2, const float sf3, + cudaStream_t stream) { + int dst_size = ne10 * ne11 * ne12 * ne13; + int num_blocks = (dst_size + CUDA_UPSCALE_BLOCK_SIZE - 1) / CUDA_UPSCALE_BLOCK_SIZE; + + upscale_f32<<>>(x, dst, nb00, nb01, nb02, nb03, ne10, ne11, ne12, ne13, sf0, sf1, sf2, sf3); } void ggml_cuda_op_upscale(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { @@ -39,10 +40,12 @@ void ggml_cuda_op_upscale(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { cudaStream_t stream = ctx.stream(); GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT(dst->type == GGML_TYPE_F32); - GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors + GGML_ASSERT( dst->type == GGML_TYPE_F32); - const int scale_factor = dst->op_params[0]; + const float sf0 = (float)dst->ne[0]/src0->ne[0]; + const float sf1 = (float)dst->ne[1]/src0->ne[1]; + const float sf2 = (float)dst->ne[2]/src0->ne[2]; + const float sf3 = (float)dst->ne[3]/src0->ne[3]; - upscale_f32_cuda(src0_d, dst_d, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], scale_factor, stream); + upscale_f32_cuda(src0_d, dst_d, src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], sf0, sf1, sf2, sf3, stream); } diff --git a/ggml-cuda/vecdotq.cuh b/ggml-cuda/vecdotq.cuh index 86b87fa936d85..5ebdddcc745de 100644 --- a/ggml-cuda/vecdotq.cuh +++ b/ggml-cuda/vecdotq.cuh @@ -712,7 +712,6 @@ static __device__ __forceinline__ float vec_dot_q3_K_q8_1( static __device__ __forceinline__ float vec_dot_q4_K_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { -#ifndef GGML_QKK_64 const block_q4_K * bq4_K = (const block_q4_K *) vbq; int v[2]; @@ -754,58 +753,11 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1( } return vec_dot_q4_K_q8_1_impl_vmmq(v, u, sc, m, bq4_K->dm, d8); - -#else - -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - const block_q4_K * bq4_K = (const block_q4_K *) vbq; - - float sumf_d = 0.0f; - float sumf_m = 0.0f; - - uint16_t aux16[2]; - const uint8_t * s = (const uint8_t *)aux16; - - const uint16_t * a = (const uint16_t *)bq4_K->scales; - aux16[0] = a[0] & 0x0f0f; - aux16[1] = (a[0] >> 4) & 0x0f0f; - - const float dall = bq4_K->dm[0]; - const float dmin = bq4_K->dm[1]; - - const float d8_1 = __low2float(bq8_1[0].ds); - const float d8_2 = __low2float(bq8_1[1].ds); - - const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2)); - const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4); - const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2)); - const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4); - - const int * q4 = (const int *)bq4_K->qs + (iqs/2); - const int v1 = q4[0]; - const int v2 = q4[4]; - - const int dot1 = __dp4a(ui2, v2 & 0x0f0f0f0f, __dp4a(ui1, v1 & 0x0f0f0f0f, 0)); - const int dot2 = __dp4a(ui4, (v2 >> 4) & 0x0f0f0f0f, __dp4a(ui3, (v1 >> 4) & 0x0f0f0f0f, 0)); - const int dot3 = __dp4a(0x01010101, ui2, __dp4a(0x01010101, ui1, 0)); - const int dot4 = __dp4a(0x01010101, ui4, __dp4a(0x01010101, ui3, 0)); - - sumf_d += d8_1 * (dot1 * s[0]) + d8_2 * (dot2 * s[1]); - sumf_m += d8_1 * (dot3 * s[2]) + d8_2 * (dot4 * s[3]); - - return dall * sumf_d - dmin * sumf_m; - -#else - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A - -#endif } static __device__ __forceinline__ float vec_dot_q5_K_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { -#ifndef GGML_QKK_64 const block_q5_K * bq5_K = (const block_q5_K *) vbq; int vl[2]; @@ -847,48 +799,6 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1( } return vec_dot_q5_K_q8_1_impl_vmmq(vl, vh, u, sc, m, bq5_K->dm, d8); - -#else - -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - const block_q5_K * bq5_K = (const block_q5_K *) vbq; - - const int8_t * s = bq5_K->scales; - - const float d = bq5_K->d; - - const float d8_1 = __low2half(bq8_1[0].ds); - const float d8_2 = __low2half(bq8_1[1].ds); - - const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2)); - const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4); - const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2)); - const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4); - - const int * ql = (const int *)bq5_K->qs + (iqs/2); - const int vl1 = ql[0]; - const int vl2 = ql[4]; - - const int step = 4 * (iqs/2); // 0, 4, 8, 12 - const int im = step/8; // = 0 for iqs = 0, 2, = 1 for iqs = 4, 6 - const int in = step%8; // 0, 4, 0, 4 - const int vh = (*((const int *)(bq5_K->qh + in))) >> im; - - const int v1 = (((vh << 4) & 0x10101010) ^ 0x10101010) | ((vl1 >> 0) & 0x0f0f0f0f); - const int v2 = (((vh << 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 0) & 0x0f0f0f0f); - const int v3 = (((vh >> 0) & 0x10101010) ^ 0x10101010) | ((vl1 >> 4) & 0x0f0f0f0f); - const int v4 = (((vh >> 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 4) & 0x0f0f0f0f); - - const float sumf_d = d8_1 * (__dp4a(ui1, v1, 0) * s[0] + __dp4a(ui2, v2, 0) * s[1]) - + d8_2 * (__dp4a(ui3, v3, 0) * s[2] + __dp4a(ui4, v4, 0) * s[3]); - - return d * sumf_d; - -#else - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A - -#endif } static __device__ __forceinline__ float vec_dot_q6_K_q8_1( @@ -919,7 +829,6 @@ static __device__ __forceinline__ float vec_dot_q6_K_q8_1( static __device__ __forceinline__ float vec_dot_iq2_xxs_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { -#if QK_K == 256 const block_iq2_xxs * bq2 = (const block_iq2_xxs *) vbq; #if QR2_XXS == 8 @@ -960,15 +869,11 @@ static __device__ __forceinline__ float vec_dot_iq2_xxs_q8_1( } return d * (sumi1 + sumi2); #endif -#else - NO_DEVICE_CODE; -#endif } static __device__ __forceinline__ float vec_dot_iq2_xs_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { #if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics -#if QK_K == 256 const block_iq2_xs * bq2 = (const block_iq2_xs *) vbq; const int ib32 = iqs; @@ -1002,17 +907,12 @@ static __device__ __forceinline__ float vec_dot_iq2_xs_q8_1( GGML_UNUSED(ksigns64); NO_DEVICE_CODE; #endif -#else - GGML_UNUSED(ksigns64); - NO_DEVICE_CODE; -#endif } // TODO static __device__ __forceinline__ float vec_dot_iq2_s_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { #if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics -#if QK_K == 256 const block_iq2_s * bq2 = (const block_iq2_s *) vbq; const int ib32 = iqs; @@ -1048,16 +948,11 @@ static __device__ __forceinline__ float vec_dot_iq2_s_q8_1( GGML_UNUSED(ksigns64); NO_DEVICE_CODE; #endif -#else - GGML_UNUSED(ksigns64); - NO_DEVICE_CODE; -#endif } static __device__ __forceinline__ float vec_dot_iq3_xxs_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { #if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics -#if QK_K == 256 const block_iq3_xxs * bq2 = (const block_iq3_xxs *) vbq; const int ib32 = iqs; @@ -1082,16 +977,12 @@ static __device__ __forceinline__ float vec_dot_iq3_xxs_q8_1( #else NO_DEVICE_CODE; #endif -#else - NO_DEVICE_CODE; -#endif } // TODO: don't use lookup table for signs static __device__ __forceinline__ float vec_dot_iq3_s_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { #if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics -#if QK_K == 256 const block_iq3_s * bq2 = (const block_iq3_s *) vbq; const int ib32 = iqs; @@ -1114,14 +1005,10 @@ static __device__ __forceinline__ float vec_dot_iq3_s_q8_1( #else NO_DEVICE_CODE; #endif -#else - NO_DEVICE_CODE; -#endif } static __device__ __forceinline__ float vec_dot_iq1_s_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { -#if QK_K == 256 const block_iq1_s * bq1 = (const block_iq1_s *) vbq; const int ib32 = iqs; @@ -1149,14 +1036,10 @@ static __device__ __forceinline__ float vec_dot_iq1_s_q8_1( const float d = d1q * __low2float (bq8_1[ib32].ds); const float m = d1q * __high2float(bq8_1[ib32].ds); return d * sumi + m * delta; -#else - NO_DEVICE_CODE; -#endif } static __device__ __forceinline__ float vec_dot_iq1_m_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { -#if QK_K == 256 const block_iq1_m * bq1 = (const block_iq1_m *) vbq; const int ib32 = iqs; @@ -1192,9 +1075,6 @@ static __device__ __forceinline__ float vec_dot_iq1_m_q8_1( scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); const float d = (float)scale.f16 * __low2float (bq8_1[ib32].ds); return d * ((sumi[0] + sumf[0]) * (2*((sc[ib32/2] >> 6*(ib32%2)) & 0x7) + 1) + (sumi[1] + sumf[1]) * (2*((sc[ib32/2] >> (6*(ib32%2)+3)) & 0x7) + 1)); -#else - NO_DEVICE_CODE; -#endif } #if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics @@ -1250,9 +1130,7 @@ static __device__ __forceinline__ float vec_dot_iq4_nl_q8_1( static __device__ __forceinline__ float vec_dot_iq4_xs_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { -#if QK_K == 256 #if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - const block_iq4_xs * bq4 = (const block_iq4_xs *) vbq; const uint8_t * values = (const uint8_t *)kvalues_iq4nl; @@ -1270,10 +1148,6 @@ static __device__ __forceinline__ float vec_dot_iq4_xs_q8_1( sumi2 = __dp4a(v2, q8[j+4], sumi2); } return d * (sumi1 + sumi2); - -#else - NO_DEVICE_CODE; -#endif #else return vec_dot_iq4_xs_q8_1(vbq, bq8_1, iqs); #endif diff --git a/ggml-impl.h b/ggml-impl.h index d85b152bf16ea..362d40f4d1d8b 100644 --- a/ggml-impl.h +++ b/ggml-impl.h @@ -17,6 +17,18 @@ #define MIN(a, b) ((a) < (b) ? (a) : (b)) #define MAX(a, b) ((a) > (b) ? (a) : (b)) +#if defined(_WIN32) + +#define m512bh(p) p +#define m512i(p) p + +#else + +#define m512bh(p) (__m512bh)(p) +#define m512i(p) (__m512i)(p) + +#endif + /** * Converts brain16 to float32. * @@ -120,9 +132,16 @@ extern "C" { #ifndef __F16C__ #define __F16C__ #endif +#endif + +// __SSE3__ and __SSSE3__ are not defined in MSVC, but SSE3/SSSE3 are present when AVX/AVX2/AVX512 are available +#if defined(_MSC_VER) && (defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__)) #ifndef __SSE3__ #define __SSE3__ #endif +#ifndef __SSSE3__ +#define __SSSE3__ +#endif #endif // 16-bit float @@ -436,6 +455,34 @@ static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) { #include #endif +#if defined(__loongarch64) +#if defined(__loongarch_asx) +#include +#endif +#if defined(__loongarch_sx) +#include +#endif +#endif + +#if defined(__loongarch_asx) + +typedef union { + int32_t i; + float f; +} ft_union; + +/* float type data load instructions */ +static __m128 __lsx_vreplfr2vr_s(float val) { + ft_union fi_tmpval = {.f = val}; + return (__m128)__lsx_vreplgr2vr_w(fi_tmpval.i); +} + +static __m256 __lasx_xvreplfr2vr_s(float val) { + ft_union fi_tmpval = {.f = val}; + return (__m256)__lasx_xvreplgr2vr_w(fi_tmpval.i); +} +#endif + #ifdef __F16C__ #ifdef _MSC_VER diff --git a/ggml-kompute.cpp b/ggml-kompute.cpp index 9a469821d8042..6c6058b2a95b1 100644 --- a/ggml-kompute.cpp +++ b/ggml-kompute.cpp @@ -1559,12 +1559,18 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml case GGML_OP_SOFT_MAX: { float scale; - memcpy(&scale, dst->op_params, sizeof(float)); + float max_bias; -#pragma message("TODO: add ggml_vk_soft_max() F16/F32 src1 and src2 support") + memcpy(&scale, (float *)dst->op_params + 0, sizeof(float)); + memcpy(&max_bias, (float *)dst->op_params + 1, sizeof(float)); + +#pragma message("TODO: add ggml_vk_soft_max() F16 src1 support") #pragma message("ref: https://github.com/ggerganov/llama.cpp/pull/5021") GGML_ASSERT(!src1 || src1t == GGML_TYPE_F32); - GGML_ASSERT(src2 == nullptr); + +#pragma message("TODO: add ALiBi support") +#pragma message("ref: https://github.com/ggerganov/llama.cpp/pull/7192") + GGML_ASSERT(max_bias == 0.0f); ggml_vk_soft_max(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, ne01, ne02, ne03, scale); } break; @@ -1671,6 +1677,10 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml } break; case GGML_OP_ROPE: { +#pragma message("TODO: implement phi3 frequency factors support") +#pragma message(" https://github.com/ggerganov/llama.cpp/pull/7225") + GGML_ASSERT(dst->src[2] == nullptr && "phi3 frequency factors not implemented yet"); + GGML_ASSERT(ne10 == ne02); GGML_ASSERT(src0t == dstt); // const int n_past = ((int32_t *) dst->op_params)[0]; diff --git a/ggml-metal.m b/ggml-metal.m index c85ff88b06726..541419f1e5ad7 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -40,6 +40,7 @@ GGML_METAL_KERNEL_TYPE_CLAMP, GGML_METAL_KERNEL_TYPE_TANH, GGML_METAL_KERNEL_TYPE_RELU, + GGML_METAL_KERNEL_TYPE_SIGMOID, GGML_METAL_KERNEL_TYPE_GELU, GGML_METAL_KERNEL_TYPE_GELU_4, GGML_METAL_KERNEL_TYPE_GELU_QUICK, @@ -169,7 +170,6 @@ GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32, GGML_METAL_KERNEL_TYPE_ROPE_F32, GGML_METAL_KERNEL_TYPE_ROPE_F16, - GGML_METAL_KERNEL_TYPE_ALIBI_F32, GGML_METAL_KERNEL_TYPE_IM2COL_F16, GGML_METAL_KERNEL_TYPE_IM2COL_F32, GGML_METAL_KERNEL_TYPE_UPSCALE_F32, @@ -381,10 +381,6 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ // dictionary of preprocessor macros NSMutableDictionary * prep = [NSMutableDictionary dictionary]; -#ifdef GGML_QKK_64 - prep[@"GGML_QKK_64"] = @(1); -#endif - MTLCompileOptions* options = [MTLCompileOptions new]; options.preprocessorMacros = prep; @@ -494,6 +490,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CLAMP, clamp, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TANH, tanh, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RELU, relu, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SIGMOID, sigmoid, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU, gelu, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_4, gelu_4, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK, gelu_quick, true); @@ -623,7 +620,6 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32, mul_mm_id_iq4_xs_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F32, rope_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F16, rope_f16, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ALIBI_F32, alibi_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F16, im2col_f16, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F32, im2col_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UPSCALE_F32, upscale_f32, true); @@ -633,14 +629,14 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC, argsort_f32_i32_asc, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC, argsort_f32_i32_desc, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32, leaky_relu_f32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64, flash_attn_ext_f16_h64, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80, flash_attn_ext_f16_h80, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96, flash_attn_ext_f16_h96, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112, flash_attn_ext_f16_h112, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128, flash_attn_ext_f16_h128, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256, flash_attn_ext_f16_h256, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128, flash_attn_ext_vec_f16_h128, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, flash_attn_ext_vec_f16_h256, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64, flash_attn_ext_f16_h64, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80, flash_attn_ext_f16_h80, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96, flash_attn_ext_f16_h96, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112, flash_attn_ext_f16_h112, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128, flash_attn_ext_f16_h128, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256, flash_attn_ext_f16_h256, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128, flash_attn_ext_vec_f16_h128, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, flash_attn_ext_vec_f16_h256, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F16, cpy_f32_f16, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F32, cpy_f32_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0, cpy_f32_q8_0, true); @@ -732,6 +728,7 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const switch (ggml_get_unary_op(op)) { case GGML_UNARY_OP_TANH: case GGML_UNARY_OP_RELU: + case GGML_UNARY_OP_SIGMOID: case GGML_UNARY_OP_GELU: case GGML_UNARY_OP_GELU_QUICK: case GGML_UNARY_OP_SILU: @@ -759,7 +756,6 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const case GGML_OP_GROUP_NORM: return ctx->support_simdgroup_reduction; case GGML_OP_NORM: - case GGML_OP_ALIBI: case GGML_OP_ROPE: case GGML_OP_IM2COL: return true; @@ -772,8 +768,9 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const case GGML_OP_TIMESTEP_EMBEDDING: case GGML_OP_ARGSORT: case GGML_OP_LEAKY_RELU: - case GGML_OP_FLASH_ATTN_EXT: return true; + case GGML_OP_FLASH_ATTN_EXT: + return ctx->support_simdgroup_mm; // TODO: over-restricted for vec-kernels case GGML_OP_MUL_MAT: case GGML_OP_MUL_MAT_ID: return ctx->support_simdgroup_reduction && @@ -926,22 +923,32 @@ static enum ggml_status ggml_metal_graph_compute( const int64_t ne10 = src1 ? src1->ne[0] : 0; const int64_t ne11 = src1 ? src1->ne[1] : 0; const int64_t ne12 = src1 ? src1->ne[2] : 0; - const int64_t ne13 = src1 ? src1->ne[3] : 0; UNUSED(ne13); + const int64_t ne13 = src1 ? src1->ne[3] : 0; const uint64_t nb10 = src1 ? src1->nb[0] : 0; const uint64_t nb11 = src1 ? src1->nb[1] : 0; const uint64_t nb12 = src1 ? src1->nb[2] : 0; - const uint64_t nb13 = src1 ? src1->nb[3] : 0; UNUSED(nb13); + const uint64_t nb13 = src1 ? src1->nb[3] : 0; + + const int64_t ne20 = src2 ? src2->ne[0] : 0; + const int64_t ne21 = src2 ? src2->ne[1] : 0; + const int64_t ne22 = src2 ? src2->ne[2] : 0; GGML_UNUSED(ne22); + const int64_t ne23 = src2 ? src2->ne[3] : 0; GGML_UNUSED(ne23); - const int64_t ne0 = dst ? dst->ne[0] : 0; - const int64_t ne1 = dst ? dst->ne[1] : 0; - const int64_t ne2 = dst ? dst->ne[2] : 0; - const int64_t ne3 = dst ? dst->ne[3] : 0; + const uint64_t nb20 = src2 ? src2->nb[0] : 0; GGML_UNUSED(nb20); + const uint64_t nb21 = src2 ? src2->nb[1] : 0; + const uint64_t nb22 = src2 ? src2->nb[2] : 0; + const uint64_t nb23 = src2 ? src2->nb[3] : 0; - const uint64_t nb0 = dst ? dst->nb[0] : 0; - const uint64_t nb1 = dst ? dst->nb[1] : 0; - const uint64_t nb2 = dst ? dst->nb[2] : 0; - const uint64_t nb3 = dst ? dst->nb[3] : 0; + const int64_t ne0 = dst ? dst->ne[0] : 0; + const int64_t ne1 = dst ? dst->ne[1] : 0; + const int64_t ne2 = dst ? dst->ne[2] : 0; + const int64_t ne3 = dst ? dst->ne[3] : 0; + + const uint64_t nb0 = dst ? dst->nb[0] : 0; + const uint64_t nb1 = dst ? dst->nb[1] : 0; + const uint64_t nb2 = dst ? dst->nb[2] : 0; + const uint64_t nb3 = dst ? dst->nb[3] : 0; const enum ggml_type src0t = src0 ? src0->type : GGML_TYPE_COUNT; const enum ggml_type src1t = src1 ? src1->type : GGML_TYPE_COUNT; @@ -1194,24 +1201,24 @@ static enum ggml_status ggml_metal_graph_compute( [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; } break; case GGML_OP_CLAMP: - { - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CLAMP].pipeline; + { + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CLAMP].pipeline; - float min; - float max; - memcpy(&min, ((int32_t *) dst->op_params) + 0, sizeof(float)); - memcpy(&max, ((int32_t *) dst->op_params) + 1, sizeof(float)); + float min; + float max; + memcpy(&min, ((int32_t *) dst->op_params) + 0, sizeof(float)); + memcpy(&max, ((int32_t *) dst->op_params) + 1, sizeof(float)); - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&min length:sizeof(min) atIndex:2]; - [encoder setBytes:&max length:sizeof(max) atIndex:3]; + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&min length:sizeof(min) atIndex:2]; + [encoder setBytes:&max length:sizeof(max) atIndex:3]; - const int64_t n = ggml_nelements(dst); + const int64_t n = ggml_nelements(dst); - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; case GGML_OP_UNARY: switch (ggml_get_unary_op(gf->nodes[i])) { // we are not taking into account the strides, so for now require contiguous tensors @@ -1239,6 +1246,18 @@ static enum ggml_status ggml_metal_graph_compute( const int64_t n = ggml_nelements(dst); + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; + case GGML_UNARY_OP_SIGMOID: + { + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SIGMOID].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + + const int64_t n = ggml_nelements(dst); + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; } break; case GGML_UNARY_OP_GELU: @@ -1357,16 +1376,15 @@ static enum ggml_status ggml_metal_graph_compute( case GGML_OP_SOFT_MAX: { GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F16 || src1->type == GGML_TYPE_F32); - GGML_ASSERT(!src2 || src2->type == GGML_TYPE_F16 || src2->type == GGML_TYPE_F32); int nth = 32; // SIMD width id pipeline = nil; - const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16) || (src2 && src2->type == GGML_TYPE_F16); + const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16); if (ne00%4 == 0) { - while (nth < ne00/4 && nth < 256) { + while (nth < ne00/4 && nth*ne01*ne02*ne03 < 256) { nth *= 2; } if (use_f16) { @@ -1375,7 +1393,7 @@ static enum ggml_status ggml_metal_graph_compute( pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32_4].pipeline; } } else { - while (nth < ne00 && nth < 1024) { + while (nth < ne00 && nth*ne01*ne02*ne03 < 256) { nth *= 2; } if (use_f16) { @@ -1394,8 +1412,8 @@ static enum ggml_status ggml_metal_graph_compute( const int64_t nrows_x = ggml_nrows(src0); const int64_t nrows_y = src0->ne[1]; - const uint32_t n_head_kv = nrows_x/nrows_y; - const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv)); + const uint32_t n_head = nrows_x/nrows_y; + const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head)); const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); @@ -1407,20 +1425,15 @@ static enum ggml_status ggml_metal_graph_compute( } else { [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; } - if (id_src2) { - [encoder setBuffer:id_src2 offset:offs_src2 atIndex:2]; - } else { - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:2]; - } - [encoder setBuffer:id_dst offset:offs_dst atIndex:3]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:4]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:5]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:6]; - [encoder setBytes:&scale length:sizeof(scale) atIndex:7]; - [encoder setBytes:&max_bias length:sizeof(max_bias) atIndex:8]; - [encoder setBytes:&m0 length:sizeof(m0) atIndex:9]; - [encoder setBytes:&m1 length:sizeof(m1) atIndex:10]; - [encoder setBytes:&n_head_log2 length:sizeof(n_head_log2) atIndex:11]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; + [encoder setBytes:&scale length:sizeof(scale) atIndex:6]; + [encoder setBytes:&max_bias length:sizeof(max_bias) atIndex:7]; + [encoder setBytes:&m0 length:sizeof(m0) atIndex:8]; + [encoder setBytes:&m1 length:sizeof(m1) atIndex:9]; + [encoder setBytes:&n_head_log2 length:sizeof(n_head_log2) atIndex:10]; [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake(ne01*ne02*ne03, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; @@ -1756,11 +1769,7 @@ static enum ggml_status ggml_metal_graph_compute( [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } else if (src0t == GGML_TYPE_Q3_K) { -#ifdef GGML_QKK_64 - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; -#else [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; -#endif } else if (src0t == GGML_TYPE_Q5_K) { [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; @@ -1778,16 +1787,6 @@ static enum ggml_status ggml_metal_graph_compute( const int n_as = src0->ne[2]; // src2 = ids - const int64_t ne20 = src2->ne[0]; - const int64_t ne21 = src2->ne[1]; - const int64_t ne22 = src2->ne[2]; GGML_UNUSED(ne22); - const int64_t ne23 = src2->ne[3]; GGML_UNUSED(ne23); - - const uint64_t nb20 = src2->nb[0]; GGML_UNUSED(nb20); - const uint64_t nb21 = src2->nb[1]; - const uint64_t nb22 = src2->nb[2]; GGML_UNUSED(nb22); - const uint64_t nb23 = src2->nb[3]; GGML_UNUSED(nb23); - const enum ggml_type src2t = src2->type; GGML_UNUSED(src2t); GGML_ASSERT(src2t == GGML_TYPE_I32); @@ -2011,12 +2010,7 @@ static enum ggml_status ggml_metal_graph_compute( { nth0 = 4; nth1 = 16; - #if QK_K == 64 - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32].pipeline; - #else pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32].pipeline; - #endif - } break; default: { @@ -2081,11 +2075,7 @@ static enum ggml_status ggml_metal_graph_compute( [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } else if (src0t == GGML_TYPE_Q3_K) { -#ifdef GGML_QKK_64 - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; -#else [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; -#endif } else if (src0t == GGML_TYPE_Q5_K) { [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; @@ -2225,49 +2215,6 @@ static enum ggml_status ggml_metal_graph_compute( [encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } break; - case GGML_OP_ALIBI: - { - GGML_ASSERT((src0t == GGML_TYPE_F32)); - - const int nth = MIN(1024, ne00); - - //const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_head = ((int32_t *) dst->op_params)[1]; - - float max_bias; - memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float)); - - const int n_heads_log2_floor = 1 << (int) floor(log2(n_head)); - const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor); - const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor); - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ALIBI_F32].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4]; - [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8]; - [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:10]; - [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:11]; - [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:12]; - [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:13]; - [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:14]; - [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:15]; - [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16]; - [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17]; - [encoder setBytes:&m0 length:sizeof( float) atIndex:18]; - [encoder setBytes:&m1 length:sizeof( float) atIndex:19]; - [encoder setBytes:&n_heads_log2_floor length:sizeof(int) atIndex:20]; - - [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; case GGML_OP_ROPE: { GGML_ASSERT(ne10 == ne02); @@ -2280,7 +2227,13 @@ static enum ggml_status ggml_metal_graph_compute( // skip 3, n_ctx, used in GLM RoPE, unimplemented in metal const int n_orig_ctx = ((int32_t *) dst->op_params)[4]; - float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; + float freq_base; + float freq_scale; + float ext_factor; + float attn_factor; + float beta_fast; + float beta_slow; + memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); @@ -2288,6 +2241,15 @@ static enum ggml_status ggml_metal_graph_compute( memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); + const bool is_neox = mode & 2; + const bool is_glm = mode & 4; + + GGML_ASSERT(!is_glm && "GLM RoPE not implemented in Metal"); + + if (!is_neox) { + GGML_ASSERT(id_src2 == nil && "TODO: freq_factors not implemented for !is_neox"); + } + id pipeline = nil; switch (src0->type) { @@ -2299,33 +2261,38 @@ static enum ggml_status ggml_metal_graph_compute( [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:5]; - [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:6]; - [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:7]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:8]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:10]; - [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:11]; - [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:12]; - [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:13]; - [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:14]; - [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:15]; - [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:16]; - [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:17]; - [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:18]; - [encoder setBytes:&n_past length:sizeof( int) atIndex:19]; - [encoder setBytes:&n_dims length:sizeof( int) atIndex:20]; - [encoder setBytes:&mode length:sizeof( int) atIndex:21]; - [encoder setBytes:&n_orig_ctx length:sizeof( int) atIndex:22]; - [encoder setBytes:&freq_base length:sizeof( float) atIndex:23]; - [encoder setBytes:&freq_scale length:sizeof( float) atIndex:24]; - [encoder setBytes:&ext_factor length:sizeof( float) atIndex:25]; - [encoder setBytes:&attn_factor length:sizeof( float) atIndex:26]; - [encoder setBytes:&beta_fast length:sizeof( float) atIndex:27]; - [encoder setBytes:&beta_slow length:sizeof( float) atIndex:28]; + if (id_src2 != nil) { + [encoder setBuffer:id_src2 offset:offs_src2 atIndex:2]; + } else { + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:2]; + } + [encoder setBuffer:id_dst offset:offs_dst atIndex:3]; + [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:4]; + [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:5]; + [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:6]; + [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:7]; + [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:8]; + [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:9]; + [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:10]; + [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:11]; + [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:12]; + [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:13]; + [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:14]; + [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:15]; + [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:16]; + [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:17]; + [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:18]; + [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:19]; + [encoder setBytes:&n_past length:sizeof( int) atIndex:20]; + [encoder setBytes:&n_dims length:sizeof( int) atIndex:21]; + [encoder setBytes:&mode length:sizeof( int) atIndex:22]; + [encoder setBytes:&n_orig_ctx length:sizeof( int) atIndex:23]; + [encoder setBytes:&freq_base length:sizeof( float) atIndex:24]; + [encoder setBytes:&freq_scale length:sizeof( float) atIndex:25]; + [encoder setBytes:&ext_factor length:sizeof( float) atIndex:26]; + [encoder setBytes:&attn_factor length:sizeof( float) atIndex:27]; + [encoder setBytes:&beta_fast length:sizeof( float) atIndex:28]; + [encoder setBytes:&beta_slow length:sizeof( float) atIndex:29]; [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } break; @@ -2389,7 +2356,10 @@ static enum ggml_status ggml_metal_graph_compute( { GGML_ASSERT(src0->type == GGML_TYPE_F32); - const int sf = dst->op_params[0]; + const float sf0 = (float)ne0/src0->ne[0]; + const float sf1 = (float)ne1/src0->ne[1]; + const float sf2 = (float)ne2/src0->ne[2]; + const float sf3 = (float)ne3/src0->ne[3]; const id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_UPSCALE_F32].pipeline; @@ -2412,7 +2382,10 @@ static enum ggml_status ggml_metal_graph_compute( [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15]; [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:16]; [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17]; - [encoder setBytes:&sf length:sizeof(sf) atIndex:18]; + [encoder setBytes:&sf0 length:sizeof(sf0) atIndex:18]; + [encoder setBytes:&sf1 length:sizeof(sf1) atIndex:19]; + [encoder setBytes:&sf2 length:sizeof(sf2) atIndex:20]; + [encoder setBytes:&sf3 length:sizeof(sf3) atIndex:21]; const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0); @@ -2548,13 +2521,14 @@ static enum ggml_status ggml_metal_graph_compute( } break; case GGML_OP_FLASH_ATTN_EXT: { - GGML_ASSERT(ne00 % 4 == 0); + GGML_ASSERT(ne00 % 4 == 0); + GGML_ASSERT(ne11 % 32 == 0); + GGML_ASSERT(src0->type == GGML_TYPE_F32); - struct ggml_tensor * src3 = gf->nodes[i]->src[3]; + GGML_ASSERT(ggml_are_same_shape (src1, src2)); - GGML_ASSERT(ggml_are_same_shape(src1, src2)); - GGML_ASSERT(src3); + struct ggml_tensor * src3 = gf->nodes[i]->src[3]; size_t offs_src3 = 0; @@ -2565,7 +2539,7 @@ static enum ggml_status ggml_metal_graph_compute( "the Flash-Attention Metal kernel requires the mask to be padded to 8 and at least n_queries big"); const int64_t ne30 = src3 ? src3->ne[0] : 0; GGML_UNUSED(ne30); - const int64_t ne31 = src3 ? src3->ne[1] : 0; + //const int64_t ne31 = src3 ? src3->ne[1] : 0; const int64_t ne32 = src3 ? src3->ne[2] : 0; GGML_UNUSED(ne32); const int64_t ne33 = src3 ? src3->ne[3] : 0; GGML_UNUSED(ne33); @@ -2577,7 +2551,16 @@ static enum ggml_status ggml_metal_graph_compute( const enum ggml_type src2t = src2 ? src2->type : GGML_TYPE_COUNT; GGML_UNUSED(src2t); float scale; - memcpy(&scale, dst->op_params, sizeof(float)); + float max_bias; + + memcpy(&scale, ((int32_t *) dst->op_params) + 0, sizeof(scale)); + memcpy(&max_bias, ((int32_t *) dst->op_params) + 1, sizeof(max_bias)); + + const uint32_t n_head = src0->ne[2]; + const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head)); + + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); id pipeline = nil; @@ -2614,34 +2597,38 @@ static enum ggml_status ggml_metal_graph_compute( } [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_src2 offset:offs_src2 atIndex:2]; - [encoder setBuffer:id_src3 offset:offs_src3 atIndex:3]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:4]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:5]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:6]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:7]; - [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:8]; - [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:10]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:11]; - [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:12]; - [encoder setBytes:&ne10 length:sizeof( int64_t) atIndex:13]; - [encoder setBytes:&ne11 length:sizeof( int64_t) atIndex:14]; - [encoder setBytes:&ne12 length:sizeof( int64_t) atIndex:15]; - [encoder setBytes:&ne13 length:sizeof( int64_t) atIndex:16]; - [encoder setBytes:&nb10 length:sizeof(uint64_t) atIndex:17]; - [encoder setBytes:&nb11 length:sizeof(uint64_t) atIndex:18]; - [encoder setBytes:&nb12 length:sizeof(uint64_t) atIndex:19]; - [encoder setBytes:&nb13 length:sizeof(uint64_t) atIndex:20]; - [encoder setBytes:&ne31 length:sizeof( int64_t) atIndex:21]; - [encoder setBytes:&nb31 length:sizeof(uint64_t) atIndex:22]; - [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:23]; - [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:24]; - [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:25]; - [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:26]; - [encoder setBytes:&scale length:sizeof( float) atIndex:27]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + [encoder setBuffer:id_src2 offset:offs_src2 atIndex:2]; + if (id_src3) { + [encoder setBuffer:id_src3 offset:offs_src3 atIndex:3]; + } else { + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:3]; + } + [encoder setBuffer:id_dst offset:offs_dst atIndex:4]; + [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:5]; + [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:6]; + [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:7]; + [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:8]; + [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:9]; + [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:10]; + [encoder setBytes:&ne11 length:sizeof( int64_t) atIndex:11]; + [encoder setBytes:&ne12 length:sizeof( int64_t) atIndex:12]; + [encoder setBytes:&ne13 length:sizeof( int64_t) atIndex:13]; + [encoder setBytes:&nb11 length:sizeof(uint64_t) atIndex:14]; + [encoder setBytes:&nb12 length:sizeof(uint64_t) atIndex:15]; + [encoder setBytes:&nb13 length:sizeof(uint64_t) atIndex:16]; + [encoder setBytes:&nb21 length:sizeof(uint64_t) atIndex:17]; + [encoder setBytes:&nb22 length:sizeof(uint64_t) atIndex:18]; + [encoder setBytes:&nb23 length:sizeof(uint64_t) atIndex:19]; + [encoder setBytes:&nb31 length:sizeof(uint64_t) atIndex:20]; + [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:21]; + [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:22]; + [encoder setBytes:&scale length:sizeof( float) atIndex:23]; + [encoder setBytes:&max_bias length:sizeof( float) atIndex:24]; + [encoder setBytes:&m0 length:sizeof(m0) atIndex:25]; + [encoder setBytes:&m1 length:sizeof(m1) atIndex:26]; + [encoder setBytes:&n_head_log2 length:sizeof(n_head_log2) atIndex:27]; if (!use_vec_kernel) { // half8x8 kernel diff --git a/ggml-metal.metal b/ggml-metal.metal index 46c7d503930a4..8ff70d7a79ca7 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -229,6 +229,13 @@ kernel void kernel_relu( dst[tpig] = max(0.0f, src0[tpig]); } +kernel void kernel_sigmoid( + device const float * src0, + device float * dst, + uint tpig[[thread_position_in_grid]]) { + dst[tpig] = 1.0f / (1.0f + exp(-src0[tpig])); +} + kernel void kernel_tanh( device const float * src0, device float * dst, @@ -356,7 +363,6 @@ template kernel void kernel_soft_max( device const char * src0, device const char * src1, - device const char * src2, device char * dst, constant int64_t & ne00, constant int64_t & ne01, @@ -378,10 +384,9 @@ kernel void kernel_soft_max( device const float * psrc0 = (device const float *) src0 + (i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00); device const T * pmask = src1 != src0 ? (device const T *) src1 + i01*ne00 : nullptr; - device const T * ppos = src2 != src0 ? (device const T *) src2 : nullptr; device float * pdst = (device float *) dst + (i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00); - float slope = 0.0f; + float slope = 1.0f; // ALiBi if (max_bias > 0.0f) { @@ -397,7 +402,7 @@ kernel void kernel_soft_max( float lmax = -INFINITY; for (int i00 = tpitg; i00 < ne00; i00 += ntg) { - lmax = MAX(lmax, psrc0[i00]*scale + (pmask ? pmask[i00] : 0.0f) + (ppos ? slope*ppos[i00] : 0.0f)); + lmax = MAX(lmax, psrc0[i00]*scale + (pmask ? slope*pmask[i00] : 0.0f)); } // find the max value in the block @@ -422,7 +427,7 @@ kernel void kernel_soft_max( // parallel sum float lsum = 0.0f; for (int i00 = tpitg; i00 < ne00; i00 += ntg) { - const float exp_psrc0 = exp((psrc0[i00]*scale + (pmask ? pmask[i00] : 0.0f) + (ppos ? slope*ppos[i00] : 0.0f)) - max_val); + const float exp_psrc0 = exp((psrc0[i00]*scale + (pmask ? slope*pmask[i00] : 0.0f)) - max_val); lsum += exp_psrc0; pdst[i00] = exp_psrc0; } @@ -461,7 +466,6 @@ template kernel void kernel_soft_max_4( device const char * src0, device const char * src1, - device const char * src2, device char * dst, constant int64_t & ne00, constant int64_t & ne01, @@ -483,10 +487,9 @@ kernel void kernel_soft_max_4( device const float4 * psrc4 = (device const float4 *) src0 + (i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00)/4; device const T * pmask = src1 != src0 ? (device const T *) src1 + i01*ne00/4 : nullptr; - device const T * ppos = src2 != src0 ? (device const T *) src2 : nullptr; device float4 * pdst4 = (device float4 *) dst + (i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00)/4; - float slope = 0.0f; + float slope = 1.0f; if (max_bias > 0.0f) { const int64_t h = i02; @@ -501,7 +504,7 @@ kernel void kernel_soft_max_4( float4 lmax4 = -INFINITY; for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) { - lmax4 = fmax(lmax4, psrc4[i00]*scale + (float4)((pmask ? pmask[i00] : 0.0f) + (ppos ? slope*ppos[i00] : 0.0f))); + lmax4 = fmax(lmax4, psrc4[i00]*scale + (float4)((pmask ? slope*pmask[i00] : 0.0f))); } const float lmax = MAX(MAX(lmax4[0], lmax4[1]), MAX(lmax4[2], lmax4[3])); @@ -527,7 +530,7 @@ kernel void kernel_soft_max_4( // parallel sum float4 lsum4 = 0.0f; for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) { - const float4 exp_psrc4 = exp((psrc4[i00]*scale + (float4)((pmask ? pmask[i00] : 0.0f) + (ppos ? slope*ppos[i00] : 0.0f))) - max_val); + const float4 exp_psrc4 = exp((psrc4[i00]*scale + (float4)((pmask ? slope*pmask[i00] : 0.0f))) - max_val); lsum4 += exp_psrc4; pdst4[i00] = exp_psrc4; } @@ -1595,60 +1598,6 @@ kernel void kernel_mul_mv_f16_f32_l4( } } -kernel void kernel_alibi_f32( - device const float * src0, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - constant float & m0, - constant float & m1, - constant int & n_heads_log2_floor, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig[2]; - const int64_t i02 = tgpig[1]; - const int64_t i01 = tgpig[0]; - - const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00; - - const int64_t i3 = n / (ne2*ne1*ne0); - const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0); - const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0; - //const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0); - - const int64_t k = i3*ne3 + i2; - - float m_k; - if (k < n_heads_log2_floor) { - m_k = pow(m0, k + 1); - } else { - m_k = pow(m1, 2 * (k - n_heads_log2_floor) + 1); - } - - device char * dst_row = (device char *) dst + i3*nb3 + i2*nb2 + i1*nb1; - device const char * src_row = (device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01; - for (int64_t i00 = tpitg.x; i00 < ne00; i00 += ntg.x) { - const float src_v = *(device float *)(src_row + i00*nb00); - device float * dst_v = (device float *)(dst_row + i00*nb0); - *dst_v = i00 * m_k + src_v; - } -} - static float rope_yarn_ramp(const float low, const float high, const int i0) { const float y = (i0 / 2 - low) / max(0.001f, high - low); return 1.0f - min(1.0f, max(0.0f, y)); @@ -1691,6 +1640,7 @@ static void rope_yarn_corr_dims( typedef void (rope_t)( device const void * src0, device const int32_t * src1, + device const float * src2, device float * dst, constant int64_t & ne00, constant int64_t & ne01, @@ -1726,6 +1676,7 @@ template kernel void kernel_rope( device const void * src0, device const int32_t * src1, + device const float * src2, device float * dst, constant int64_t & ne00, constant int64_t & ne01, @@ -1795,8 +1746,10 @@ kernel void kernel_rope( // simplified from `(ib * n_dims + ic) * inv_ndims` const float cur_rot = inv_ndims*ic - ib; + const float freq_factor = src2 != src0 ? src2[ic/2] : 1.0f; + + const float theta = theta_0 * pow(freq_base, cur_rot) / freq_factor; - const float theta = theta_0 * pow(freq_base, cur_rot); float cos_theta, sin_theta; rope_yarn(theta, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta); @@ -1903,7 +1856,10 @@ kernel void kernel_upscale_f32( constant uint64_t & nb1, constant uint64_t & nb2, constant uint64_t & nb3, - constant int32_t & sf, + constant float & sf0, + constant float & sf1, + constant float & sf2, + constant float & sf3, uint3 tgpig[[threadgroup_position_in_grid]], uint3 tpitg[[thread_position_in_threadgroup]], uint3 ntg[[threads_per_threadgroup]]) { @@ -1912,15 +1868,17 @@ kernel void kernel_upscale_f32( const int64_t i2 = tgpig.y; const int64_t i1 = tgpig.x; - const int64_t i03 = i3; - const int64_t i02 = i2; - const int64_t i01 = i1/sf; - - device const float * src0_ptr = (device const float *) (src0 + i03*nb03 + i02*nb02 + i01*nb01); - device float * dst_ptr = (device float *) (dst + i3*nb3 + i2*nb2 + i1*nb1); + const int64_t i03 = i3/sf3; + const int64_t i02 = i2/sf2; + const int64_t i01 = i1/sf1; for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) { - dst_ptr[i0] = src0_ptr[i0/sf]; + const int64_t i00 = i0/sf0; + + device const float * src0_ptr = (device const float *) (src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00); + device float * dst_ptr = (device float *) (dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + dst_ptr[0] = src0_ptr[0]; } } @@ -2100,29 +2058,29 @@ typedef void (flash_attn_ext_f16_t)( device const char * v, device const char * mask, device float * dst, - constant int64_t & ne00, constant int64_t & ne01, constant int64_t & ne02, constant int64_t & ne03, - constant uint64_t & nb00, constant uint64_t & nb01, constant uint64_t & nb02, constant uint64_t & nb03, - constant int64_t & ne10, constant int64_t & ne11, constant int64_t & ne12, constant int64_t & ne13, - constant uint64_t & nb10, constant uint64_t & nb11, constant uint64_t & nb12, constant uint64_t & nb13, - constant int64_t & ne31, + constant uint64_t & nb21, + constant uint64_t & nb22, + constant uint64_t & nb23, constant uint64_t & nb31, - constant int64_t & ne0, constant int64_t & ne1, constant int64_t & ne2, - constant int64_t & ne3, constant float & scale, + constant float & max_bias, + constant float & m0, + constant float & m1, + constant uint32_t & n_head_log2, threadgroup half * shared, uint3 tgpig[[threadgroup_position_in_grid]], uint3 tpitg[[thread_position_in_threadgroup]], @@ -2138,29 +2096,29 @@ kernel void kernel_flash_attn_ext_f16( device const char * v, device const char * mask, device float * dst, - constant int64_t & ne00, constant int64_t & ne01, constant int64_t & ne02, constant int64_t & ne03, - constant uint64_t & nb00, constant uint64_t & nb01, constant uint64_t & nb02, constant uint64_t & nb03, - constant int64_t & ne10, constant int64_t & ne11, constant int64_t & ne12, constant int64_t & ne13, - constant uint64_t & nb10, constant uint64_t & nb11, constant uint64_t & nb12, constant uint64_t & nb13, - constant int64_t & ne31, + constant uint64_t & nb21, + constant uint64_t & nb22, + constant uint64_t & nb23, constant uint64_t & nb31, - constant int64_t & ne0, constant int64_t & ne1, constant int64_t & ne2, - constant int64_t & ne3, constant float & scale, + constant float & max_bias, + constant float & m0, + constant float & m1, + constant uint32_t & n_head_log2, threadgroup half * shared [[threadgroup(0)]], uint3 tgpig[[threadgroup_position_in_grid]], uint3 tpitg[[thread_position_in_threadgroup]], @@ -2225,10 +2183,6 @@ kernel void kernel_flash_attn_ext_f16( const short ne22 = ne12; const short ne23 = ne13; - const uint nb21 = nb11; - const uint nb22 = nb12; - const uint nb23 = nb13; - // broadcast const short rk2 = ne02/ne12; const short rk3 = ne03/ne13; @@ -2254,8 +2208,17 @@ kernel void kernel_flash_attn_ext_f16( // pointer to the mask device const half * mp = (device const half *) (mask + iq1*nb31); - // prepare diagonal scale matrix - simdgroup_float8x8 mscale(scale); + float slope = 1.0f; + + // ALiBi + if (max_bias > 0.0f) { + const uint32_t h = iq2; + + const float base = h < n_head_log2 ? m0 : m1; + const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; + + slope = pow(base, exph); + } // loop over the KV cache // each simdgroup handles blocks of Q rows and C columns @@ -2279,12 +2242,20 @@ kernel void kernel_flash_attn_ext_f16( simdgroup_multiply_accumulate(mqk, mq[i], mk, mqk); } - // mqk = mqk*scale + mask - simdgroup_half8x8 mm; - simdgroup_load(mm, mp + ic + 8*cc, nb31/sizeof(half), 0, false); - simdgroup_multiply_accumulate(mqk, mqk, mscale, mm); - simdgroup_store(mqk, ss + 8*cc, TF, 0, false); + + const short tx = tiisg%4; + const short ty = tiisg/4; + + if (mask != q) { + // mqk = mqk*scale + mask*slope + ss[8*cc + ty*TF + 2*tx + 0] = scale*ss[8*cc + ty*TF + 2*tx + 0] + slope*mp[ic + 8*cc + ty*nb31/sizeof(half) + 2*tx + 0]; + ss[8*cc + ty*TF + 2*tx + 1] = scale*ss[8*cc + ty*TF + 2*tx + 1] + slope*mp[ic + 8*cc + ty*nb31/sizeof(half) + 2*tx + 1]; + } else { + // mqk = mqk*scale + ss[8*cc + ty*TF + 2*tx + 0] *= scale; + ss[8*cc + ty*TF + 2*tx + 1] *= scale; + } } } @@ -2456,29 +2427,29 @@ kernel void kernel_flash_attn_ext_vec_f16( device const char * v, device const char * mask, device float * dst, - constant int64_t & ne00, constant int64_t & ne01, constant int64_t & ne02, constant int64_t & ne03, - constant uint64_t & nb00, constant uint64_t & nb01, constant uint64_t & nb02, constant uint64_t & nb03, - constant int64_t & ne10, constant int64_t & ne11, constant int64_t & ne12, constant int64_t & ne13, - constant uint64_t & nb10, constant uint64_t & nb11, constant uint64_t & nb12, constant uint64_t & nb13, - constant int64_t & ne31, + constant uint64_t & nb21, + constant uint64_t & nb22, + constant uint64_t & nb23, constant uint64_t & nb31, - constant int64_t & ne0, constant int64_t & ne1, constant int64_t & ne2, - constant int64_t & ne3, constant float & scale, + constant float & max_bias, + constant float & m0, + constant float & m1, + constant uint32_t & n_head_log2, threadgroup half * shared [[threadgroup(0)]], uint3 tgpig[[threadgroup_position_in_grid]], uint3 tpitg[[thread_position_in_threadgroup]], @@ -2497,6 +2468,18 @@ kernel void kernel_flash_attn_ext_vec_f16( const short T = D + 2*nsg*SH; // shared memory size per query in (half) + float slope = 1.0f; + + // ALiBi + if (max_bias > 0.0f) { + const uint32_t h = iq2; + + const float base = h < n_head_log2 ? m0 : m1; + const int exp = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; + + slope = pow(base, exp); + } + //threadgroup half * sq = (threadgroup half *) (shared + 0*D); // holds the query data threadgroup half4 * sq4 = (threadgroup half4 *) (shared + 0*D); // same as above but in half4 threadgroup float * ss = (threadgroup float *) (shared + 2*sgitg*SH + 1*D); // scratch buffer for attention and diagonal matrix @@ -2537,10 +2520,6 @@ kernel void kernel_flash_attn_ext_vec_f16( const short ne22 = ne12; const short ne23 = ne13; - const uint nb21 = nb11; - const uint nb22 = nb12; - const uint nb23 = nb13; - // broadcast const short rk2 = ne02/ne12; const short rk3 = ne03/ne13; @@ -2603,10 +2582,9 @@ kernel void kernel_flash_attn_ext_vec_f16( mqk += simd_shuffle_down(mqk, 2); mqk += simd_shuffle_down(mqk, 1); - // mqk = mqk*scale + mask + // mqk = mqk*scale + mask*slope if (tiisg == 0) { - float4 mm = (float4) mp4[ic/4 + cc]; - mqk = mqk*scale + mm; + mqk = mqk*scale + ((mask != q) ? ((float4) mp4[ic/4 + cc])*slope : (float4) 0.0f); ss4[cc] = mqk; } @@ -3408,7 +3386,6 @@ void kernel_mul_mv_q2_K_f32_impl( const int step = sizeof(block_q2_K) * nb; -#if QK_K == 256 const int ix = tiisg/8; // 0...3 const int it = tiisg%8; // 0...7 const int iq = it/4; // 0 or 1 @@ -3460,57 +3437,6 @@ void kernel_mul_mv_q2_K_f32_impl( y4 += 4 * QK_K; } -#else - const int ix = tiisg/2; // 0...15 - const int it = tiisg%2; // 0...1 - - device const float * y4 = y + ix * QK_K + 8 * it; - - for (int ib = ix; ib < nb; ib += 16) { - - float4 sumy = {0.f, 0.f, 0.f, 0.f}; - for (int i = 0; i < 8; ++i) { - yl[i+ 0] = y4[i+ 0]; sumy[0] += yl[i+ 0]; - yl[i+ 8] = y4[i+16]; sumy[1] += yl[i+ 8]; - yl[i+16] = y4[i+32]; sumy[2] += yl[i+16]; - yl[i+24] = y4[i+48]; sumy[3] += yl[i+24]; - } - - device const uint8_t * sc = (device const uint8_t *)x[ib].scales; - device const uint16_t * qs = (device const uint16_t *)x[ib].qs + 4 * it; - device const half * dh = &x[ib].d; - - for (int row = 0; row < N_DST; row++) { - - float4 acc1 = {0.f, 0.f, 0.f, 0.f}; - float4 acc2 = {0.f, 0.f, 0.f, 0.f}; - for (int i = 0; i < 8; i += 2) { - acc1[0] += yl[i+ 0] * (qs[i/2] & 0x0003); - acc2[0] += yl[i+ 1] * (qs[i/2] & 0x0300); - acc1[1] += yl[i+ 8] * (qs[i/2] & 0x000c); - acc2[1] += yl[i+ 9] * (qs[i/2] & 0x0c00); - acc1[2] += yl[i+16] * (qs[i/2] & 0x0030); - acc2[2] += yl[i+17] * (qs[i/2] & 0x3000); - acc1[3] += yl[i+24] * (qs[i/2] & 0x00c0); - acc2[3] += yl[i+25] * (qs[i/2] & 0xc000); - } - - float dall = dh[0]; - float dmin = dh[1]; - sumf[row] += dall * ((acc1[0] + 1.f/256.f * acc2[0]) * (sc[0] & 0xF) * 1.f/ 1.f + - (acc1[1] + 1.f/256.f * acc2[1]) * (sc[1] & 0xF) * 1.f/ 4.f + - (acc1[2] + 1.f/256.f * acc2[2]) * (sc[2] & 0xF) * 1.f/16.f + - (acc1[3] + 1.f/256.f * acc2[3]) * (sc[3] & 0xF) * 1.f/64.f) - - dmin * (sumy[0] * (sc[0] >> 4) + sumy[1] * (sc[1] >> 4) + sumy[2] * (sc[2] >> 4) + sumy[3] * (sc[3] >> 4)); - - qs += step/2; - sc += step; - dh += step/2; - } - - y4 += 16 * QK_K; - } -#endif for (int row = 0; row < N_DST; ++row) { all_sum = simd_sum(sumf[row]); @@ -3548,7 +3474,6 @@ kernel void kernel_mul_mv_q2_K_f32( kernel_mul_mv_q2_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); } -#if QK_K == 256 void kernel_mul_mv_q3_K_f32_impl( device const void * src0, device const float * src1, @@ -3707,84 +3632,6 @@ void kernel_mul_mv_q3_K_f32_impl( } } } -#else -void kernel_mul_mv_q3_K_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne10, - constant int64_t & ne12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - - const int nb = ne00/QK_K; - - const int64_t r0 = tgpig.x; - const int64_t r1 = tgpig.y; - const int64_t im = tgpig.z; - - const int row = 2 * r0 + sgitg; - - const uint i12 = im%ne12; - const uint i13 = im/ne12; - - const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); - - device const block_q3_K * x = (device const block_q3_K *) src0 + row*nb + offset0; - device const float * yy = (device const float *) src1 + r1*ne10 + im*ne00*ne1; - - const int ix = tiisg/4; - const int il = 4 * (tiisg%4);// 0, 4, 8, 12 - const int iq = il/8; // 0, 0, 1, 1 - const int in = il%8; // 0, 4, 0, 4 - - float2 sum = {0.f, 0.f}; - - for (int i = ix; i < nb; i += 8) { - - const float d_all = (float)(x[i].d); - - device const uint16_t * q = (device const uint16_t *)(x[i].qs + il); - device const uint16_t * h = (device const uint16_t *)(x[i].hmask + in); - device const uint16_t * s = (device const uint16_t *)(x[i].scales); - device const float * y = yy + i * QK_K + il; - - const float d1 = d_all * ((int32_t)(s[0] & 0x000F) - 8); - const float d2 = d_all * ((int32_t)(s[0] & 0x00F0) - 128) * 1.f/64.f; - const float d3 = d_all * ((int32_t)(s[0] & 0x0F00) - 2048) * 1.f/4096.f; - const float d4 = d_all * ((int32_t)(s[0] & 0xF000) - 32768) * 1.f/262144.f; - - for (int l = 0; l < 4; l += 2) { - const uint16_t hm = h[l/2] >> iq; - sum[0] += y[l+ 0] * d1 * ((int32_t)(q[l/2] & 0x0003) - ((hm & 0x0001) ? 0 : 4)) - + y[l+16] * d2 * ((int32_t)(q[l/2] & 0x000c) - ((hm & 0x0004) ? 0 : 16)) - + y[l+32] * d3 * ((int32_t)(q[l/2] & 0x0030) - ((hm & 0x0010) ? 0 : 64)) - + y[l+48] * d4 * ((int32_t)(q[l/2] & 0x00c0) - ((hm & 0x0040) ? 0 : 256)); - sum[1] += y[l+ 1] * d1 * ((int32_t)(q[l/2] & 0x0300) - ((hm & 0x0100) ? 0 : 1024)) - + y[l+17] * d2 * ((int32_t)(q[l/2] & 0x0c00) - ((hm & 0x0400) ? 0 : 4096)) - + y[l+33] * d3 * ((int32_t)(q[l/2] & 0x3000) - ((hm & 0x1000) ? 0 : 16384)) - + y[l+49] * d4 * ((int32_t)(q[l/2] & 0xc000) - ((hm & 0x4000) ? 0 : 65536)); - } - - } - const float sumf = sum[0] + sum[1] * 1.f/256.f; - - const float tot = simd_sum(sumf); - if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + row] = tot; - } - -} -#endif [[host_name("kernel_mul_mv_q3_K_f32")]] kernel void kernel_mul_mv_q3_K_f32( @@ -3814,7 +3661,6 @@ kernel void kernel_mul_mv_q3_K_f32( kernel_mul_mv_q3_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); } -#if QK_K == 256 void kernel_mul_mv_q4_K_f32_impl( device const void * src0, device const float * src1, @@ -3928,103 +3774,6 @@ void kernel_mul_mv_q4_K_f32_impl( } } } -#else -void kernel_mul_mv_q4_K_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne10, - constant int64_t & ne12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - - const int ix = tiisg/4; // 0...7 - const int it = tiisg%4; // 0...3 - - const int nb = ne00/QK_K; - const int r0 = tgpig.x; - const int r1 = tgpig.y; - const int im = tgpig.z; - const int first_row = r0 * N_DST; - const int ib_row = first_row * nb; - - const uint i12 = im%ne12; - const uint i13 = im/ne12; - - const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); - - device const block_q4_K * x = (device const block_q4_K *) src0 + ib_row + offset0; - device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; - - float yl[8]; - float yh[8]; - float sumf[N_DST]={0.f}, all_sum; - - const int step = sizeof(block_q4_K) * nb / 2; - - device const float * y4 = y + ix * QK_K + 8 * it; - - uint16_t sc16[4]; - - for (int ib = ix; ib < nb; ib += 8) { - - float2 sumy = {0.f, 0.f}; - for (int i = 0; i < 8; ++i) { - yl[i] = y4[i+ 0]; sumy[0] += yl[i]; - yh[i] = y4[i+32]; sumy[1] += yh[i]; - } - - device const uint16_t * sc = (device const uint16_t *)x[ib].scales; - device const uint16_t * qs = (device const uint16_t *)x[ib].qs + 4 * it; - device const half * dh = x[ib].d; - - for (int row = 0; row < N_DST; row++) { - - sc16[0] = sc[0] & 0x000f; - sc16[1] = sc[0] & 0x0f00; - sc16[2] = sc[0] & 0x00f0; - sc16[3] = sc[0] & 0xf000; - - float2 acc1 = {0.f, 0.f}; - float2 acc2 = {0.f, 0.f}; - for (int i = 0; i < 8; i += 2) { - acc1[0] += yl[i+0] * (qs[i/2] & 0x000F); - acc1[1] += yl[i+1] * (qs[i/2] & 0x0F00); - acc2[0] += yh[i+0] * (qs[i/2] & 0x00F0); - acc2[1] += yh[i+1] * (qs[i/2] & 0xF000); - } - - float dall = dh[0]; - float dmin = dh[1]; - sumf[row] += dall * ((acc1[0] + 1.f/256.f * acc1[1]) * sc16[0] + - (acc2[0] + 1.f/256.f * acc2[1]) * sc16[1] * 1.f/4096.f) - - dmin * 1.f/16.f * (sumy[0] * sc16[2] + sumy[1] * sc16[3] * 1.f/256.f); - - qs += step; - sc += step; - dh += step; - } - - y4 += 8 * QK_K; - } - - for (int row = 0; row < N_DST; ++row) { - all_sum = simd_sum(sumf[row]); - if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum; - } - } -} -#endif [[host_name("kernel_mul_mv_q4_K_f32")]] kernel void kernel_mul_mv_q4_K_f32( @@ -4092,8 +3841,6 @@ void kernel_mul_mv_q5_K_f32_impl( const int step = sizeof(block_q5_K) * nb; -#if QK_K == 256 -# float yl[16], yh[16]; const uint16_t kmask1 = 0x3f3f; @@ -4176,54 +3923,6 @@ void kernel_mul_mv_q5_K_f32_impl( y1 += 4 * QK_K; } -#else - float yl[8], yh[8]; - - const int il = 4 * (tiisg/8); // 0, 4, 8, 12 - const int ix = tiisg%8; - const int iq = il/8; // 0, 0, 1, 1 - const int in = il%8; // 0, 4, 0, 4 - - device const float * y = yy + ix*QK_K + il; - - for (int i = ix; i < nb; i += 8) { - - for (int l = 0; l < 4; ++l) { - yl[l+0] = y[l+ 0]; - yl[l+4] = y[l+16]; - yh[l+0] = y[l+32]; - yh[l+4] = y[l+48]; - } - - device const half * dh = &x[i].d; - device const uint8_t * q = x[i].qs + il; - device const uint8_t * h = x[i].qh + in; - device const int8_t * s = x[i].scales; - - for (int row = 0; row < 2; ++row) { - - const float d = dh[0]; - - float2 acc = {0.f, 0.f}; - for (int l = 0; l < 4; ++l) { - const uint8_t hl = h[l] >> iq; - acc[0] += yl[l+0] * s[0] * ((int16_t)(q[l+ 0] & 0x0F) - (hl & 0x01 ? 0 : 16)) - + yl[l+4] * s[1] * ((int16_t)(q[l+16] & 0x0F) - (hl & 0x04 ? 0 : 16)); - acc[1] += yh[l+0] * s[2] * ((int16_t)(q[l+ 0] & 0xF0) - (hl & 0x10 ? 0 : 256)) - + yh[l+4] * s[3] * ((int16_t)(q[l+16] & 0xF0) - (hl & 0x40 ? 0 : 256)); - } - sumf[row] += d * (acc[0] + 1.f/16.f * acc[1]); - - q += step; - h += step; - s += step; - dh += step/2; - - } - - y += 8 * QK_K; - } -#endif for (int row = 0; row < 2; ++row) { const float tot = simd_sum(sumf[row]); @@ -4302,7 +4001,6 @@ void kernel_mul_mv_q6_K_f32_impl( float sumf = 0; -#if QK_K == 256 const int tid = tiisg/2; const int ix = tiisg%2; const int ip = tid/8; // 0 or 1 @@ -4338,30 +4036,6 @@ void kernel_mul_mv_q6_K_f32_impl( } -#else - const int ix = tiisg/4; - const int il = 4*(tiisg%4); - - for (int i = ix; i < nb; i += 8) { - device const float * y = yy + i * QK_K + il; - device const uint8_t * ql = x[i].ql + il; - device const uint8_t * qh = x[i].qh + il; - device const int8_t * s = x[i].scales; - - const float d = x[i].d; - - float4 sums = {0.f, 0.f, 0.f, 0.f}; - for (int l = 0; l < 4; ++l) { - sums[0] += y[l+ 0] * ((int8_t)((ql[l+ 0] & 0xF) | ((qh[l] & kmask1) << 4)) - 32); - sums[1] += y[l+16] * ((int8_t)((ql[l+16] & 0xF) | ((qh[l] & kmask2) << 2)) - 32); - sums[2] += y[l+32] * ((int8_t)((ql[l+ 0] >> 4) | ((qh[l] & kmask3) >> 0)) - 32); - sums[3] += y[l+48] * ((int8_t)((ql[l+16] >> 4) | ((qh[l] & kmask4) >> 2)) - 32); - } - sumf += d * (sums[0] * s[0] + sums[1] * s[1] + sums[2] * s[2] + sums[3] * s[3]); - } - -#endif - const float tot = simd_sum(sumf); if (tiisg == 0) { dst[r1*ne0 + im*ne0*ne1 + row] = tot; @@ -5195,9 +4869,7 @@ void kernel_mul_mv_iq1_m_f32_impl( device const float * y4 = y + 32 * ix; -#if QK_K != 64 iq1m_scale_t scale; -#endif for (int ib32 = ix; ib32 < nb32; ib32 += 32) { @@ -5218,10 +4890,7 @@ void kernel_mul_mv_iq1_m_f32_impl( device const uint16_t * sc = (device const uint16_t *)xr->scales; for (int row = 0; row < N_DST; row++) { - -#if QK_K != 64 scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); -#endif constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((qh[0] << 8) & 0x700))); constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((qh[0] << 4) & 0x700))); @@ -5237,14 +4906,9 @@ void kernel_mul_mv_iq1_m_f32_impl( } const float delta1 = sumy[0] * (qh[0] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA) + sumy[1] * (qh[0] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA); const float delta2 = sumy[2] * (qh[1] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA) + sumy[3] * (qh[1] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA); -#if QK_K == 64 - const float d = (float) *((device const half *)(sc - 1)); - sumf[row] += d * ((sum[0] + delta1) * (2*((sc[0] >> (8*(ib%2)+0)) & 0xf) + 1) + - (sum[1] + delta2) * (2*((sc[0] >> (8*(ib%2)+4)) & 0xf) + 1)); -#else + sumf[row] += (float)scale.f16 * ((sum[0] + delta1) * (2*((sc[ib/2] >> (6*(ib%2)+0)) & 7) + 1) + (sum[1] + delta2) * (2*((sc[ib/2] >> (6*(ib%2)+3)) & 7) + 1)); -#endif sc += nb*sizeof(block_iq1_m)/2; qs += nb*sizeof(block_iq1_m); @@ -5356,7 +5020,6 @@ void kernel_mul_mv_iq4_nl_f32_impl( } } -#if QK_K != 64 void kernel_mul_mv_iq4_xs_f32_impl( device const void * src0, device const float * src1, @@ -5451,7 +5114,6 @@ void kernel_mul_mv_iq4_xs_f32_impl( } } } -#endif [[host_name("kernel_mul_mv_iq1_s_f32")]] kernel void kernel_mul_mv_iq1_s_f32( @@ -5564,11 +5226,7 @@ kernel void kernel_mul_mv_iq4_xs_f32( uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { -#if QK_K == 64 - kernel_mul_mv_iq4_nl_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); -#else kernel_mul_mv_iq4_xs_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); -#endif } //============================= templates and their specializations ============================= @@ -5694,10 +5352,9 @@ void dequantize_q2_K(device const block_q2_K *xb, short il, thread type4x4 & reg float dl, ml; uint8_t sc = xb->scales[il]; -#if QK_K == 256 q = q + 32*(il/8) + 16*(il&1); il = (il/2)%4; -#endif + half coef = il>1 ? (il>2 ? 1/64.h : 1/16.h) : (il>0 ? 1/4.h : 1.h); uchar mask = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3); dl = d * (sc & 0xF) * coef, ml = min * (sc >> 4); @@ -5713,7 +5370,6 @@ void dequantize_q3_K(device const block_q3_K *xb, short il, thread type4x4 & reg device const uint8_t * h = (device const uint8_t *)xb->hmask; device const int8_t * scales = (device const int8_t *)xb->scales; -#if QK_K == 256 q = q + 32 * (il/8) + 16 * (il&1); h = h + 16 * (il&1); uint8_t m = 1 << (il/2); @@ -5734,17 +5390,6 @@ void dequantize_q3_K(device const block_q3_K *xb, short il, thread type4x4 & reg for (int i = 0; i < 16; ++i) { reg[i/4][i%4] = dl * (q[i] & mask) - (h[i] & m ? 0 : ml); } -#else - float kcoef = il&1 ? 1.f/16.f : 1.f; - uint16_t kmask = il&1 ? 0xF0 : 0x0F; - float dl = d_all * ((scales[il/2] & kmask) * kcoef - 8); - float coef = il>1 ? (il>2 ? 1/64.h : 1/16.h) : (il>0 ? 1/4.h : 1.h); - uint8_t mask = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3); - uint8_t m = 1<<(il*2); - for (int i = 0; i < 16; ++i) { - reg[i/4][i%4] = coef * dl * ((q[i] & mask) - ((h[i%8] & (m * (1 + i/8))) ? 0 : 4.f/coef)); - } -#endif } static inline uchar2 get_scale_min_k4_just2(int j, int k, device const uchar * q) { @@ -5756,7 +5401,6 @@ template void dequantize_q4_K(device const block_q4_K *xb, short il, thread type4x4 & reg) { device const uchar * q = xb->qs; -#if QK_K == 256 short is = (il/4) * 2; q = q + (il/4) * 32 + 16 * (il&1); il = il & 3; @@ -5765,16 +5409,7 @@ void dequantize_q4_K(device const block_q4_K *xb, short il, thread type4x4 & reg const float min = xb->dmin; const float dl = d * sc[0]; const float ml = min * sc[1]; -#else - (void) get_scale_min_k4_just2; - - q = q + 16 * (il&1); - device const uint8_t * s = xb->scales; - device const half2 * dh = (device const half2 *)xb->d; - const float2 d = (float2)dh[0]; - const float dl = il<2 ? d[0] * (s[0]&0xF) : d[0] * (s[1]&0xF)/16.h; - const float ml = il<2 ? d[1] * (s[0]>>4) : d[1] * (s[1]>>4); -#endif + const ushort mask = il<2 ? 0x0F : 0xF0; for (int i = 0; i < 16; ++i) { reg[i/4][i%4] = dl * (q[i] & mask) - ml; @@ -5786,7 +5421,6 @@ void dequantize_q5_K(device const block_q5_K *xb, short il, thread type4x4 & reg device const uint8_t * q = xb->qs; device const uint8_t * qh = xb->qh; -#if QK_K == 256 short is = (il/4) * 2; q = q + 32 * (il/4) + 16 * (il&1); qh = qh + 16 * (il&1); @@ -5803,17 +5437,6 @@ void dequantize_q5_K(device const block_q5_K *xb, short il, thread type4x4 & reg for (int i = 0; i < 16; ++i) { reg[i/4][i%4] = dl * ((q[i] & mask) + (qh[i] & ul ? qh_val : 0)) - ml; } -#else - q = q + 16 * (il&1); - device const int8_t * s = xb->scales; - const float dl = xb->d * s[il]; - uint8_t m = 1<<(il*2); - const float coef = il<2 ? 1.f : 1.f/16.f; - const ushort mask = il<2 ? 0x0F : 0xF0; - for (int i = 0; i < 16; ++i) { - reg[i/4][i%4] = coef * dl * ((q[i] & mask) - (qh[i%8] & (m*(1+i/8)) ? 0.f : 16.f/coef)); - } -#endif } template @@ -5823,15 +5446,11 @@ void dequantize_q6_K(device const block_q6_K *xb, short il, thread type4x4 & reg device const uint8_t * qh = (device const uint8_t *)xb->qh; device const int8_t * scales = (device const int8_t *)xb->scales; -#if QK_K == 256 ql = ql + 64*(il/8) + 32*((il/2)&1) + 16*(il&1); qh = qh + 32*(il/8) + 16*(il&1); float sc = scales[(il%2) + 2 * ((il/2))]; il = (il/2) & 3; -#else - ql = ql + 16 * (il&1); - float sc = scales[il]; -#endif + const uint16_t kmask1 = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3); const uint16_t kmask2 = il>1 ? 0xF0 : 0x0F; const float coef = il>1 ? 1.f/16.f : 1.f; @@ -5988,20 +5607,15 @@ void dequantize_iq1_m(device const block_iq1_m * xb, short il, thread type4x4 & const int ib32 = il/2; il = il%2; device const uint16_t * sc = (device const uint16_t *)xb->scales; -#if QK_K == 64 - const float d = xb->d; -#else + iq1m_scale_t scale; scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); const float d = scale.f16; -#endif + device const uint8_t * qs = xb->qs + 4*ib32 + 2*il; device const uint8_t * qh = xb->qh + 2*ib32 + il; -#if QK_K == 64 - const float dl = d * (2*((sc[ib32/2] >> (8*(ib32%2)+4*il)) & 0xf) + 1); -#else + const float dl = d * (2*((sc[ib32/2] >> (6*(ib32%2)+3*il)) & 7) + 1); -#endif const float ml1 = dl * (qh[0] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA); const float ml2 = dl * (qh[0] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA); constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((qh[0] << 8) & 0x700))); @@ -6031,9 +5645,6 @@ void dequantize_iq4_nl(device const block_iq4_nl * xb, short il, thread type4x4 template void dequantize_iq4_xs(device const block_iq4_xs * xb, short il, thread type4x4 & reg) { -#if QK_K == 64 - dequantize_iq4_nl(xb, il, reg); -#else // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 const int ib32 = il/2; il = il%2; @@ -6050,7 +5661,6 @@ void dequantize_iq4_xs(device const block_iq4_xs * xb, short il, thread type4x4 reg[i][2] = d * kvalues_iq4nl_f[q8[2]]; reg[i][3] = d * kvalues_iq4nl_f[q8[3]]; } -#endif } template @@ -6555,11 +6165,7 @@ kernel void kernel_mul_mm_id( sgitg); } -#if QK_K == 256 #define QK_NL 16 -#else -#define QK_NL 4 -#endif // // get rows @@ -6599,11 +6205,7 @@ template [[host_name("kernel_get_rows_iq2_s")]] kernel get_rows_t kernel_get_r template [[host_name("kernel_get_rows_iq1_s")]] kernel get_rows_t kernel_get_rows; template [[host_name("kernel_get_rows_iq1_m")]] kernel get_rows_t kernel_get_rows; template [[host_name("kernel_get_rows_iq4_nl")]] kernel get_rows_t kernel_get_rows; -#if QK_K == 64 -template [[host_name("kernel_get_rows_iq4_xs")]] kernel get_rows_t kernel_get_rows; -#else template [[host_name("kernel_get_rows_iq4_xs")]] kernel get_rows_t kernel_get_rows; -#endif // // matrix-matrix multiplication @@ -6631,11 +6233,7 @@ template [[host_name("kernel_mul_mm_iq2_s_f32")]] kernel mat_mm_t kernel_mul_m template [[host_name("kernel_mul_mm_iq1_s_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_iq1_m_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_iq4_nl_f32")]] kernel mat_mm_t kernel_mul_mm; -#if QK_K == 64 -template [[host_name("kernel_mul_mm_iq4_xs_f32")]] kernel mat_mm_t kernel_mul_mm; -#else template [[host_name("kernel_mul_mm_iq4_xs_f32")]] kernel mat_mm_t kernel_mul_mm; -#endif // // indirect matrix-matrix multiplication @@ -6663,11 +6261,7 @@ template [[host_name("kernel_mul_mm_id_iq2_s_f32")]] kernel mat_mm_id_t kernel template [[host_name("kernel_mul_mm_id_iq1_s_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_iq1_m_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_iq4_nl_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -#if QK_K == 64 -template [[host_name("kernel_mul_mm_id_iq4_xs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -#else template [[host_name("kernel_mul_mm_id_iq4_xs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -#endif // // matrix-vector multiplication @@ -6876,7 +6470,5 @@ template [[host_name("kernel_mul_mv_id_iq3_xxs_f32")]] kernel kernel_mul_mv_id_t template [[host_name("kernel_mul_mv_id_iq3_s_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; template [[host_name("kernel_mul_mv_id_iq2_s_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; template [[host_name("kernel_mul_mv_id_iq4_nl_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; -#if QK_K != 64 template [[host_name("kernel_mul_mv_id_iq4_xs_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; -#endif diff --git a/ggml-mpi.c b/ggml-mpi.c deleted file mode 100644 index ae176d7075826..0000000000000 --- a/ggml-mpi.c +++ /dev/null @@ -1,216 +0,0 @@ -#include "ggml-mpi.h" - -#include "ggml.h" - -#include - -#include -#include - -#define MIN(a, b) ((a) < (b) ? (a) : (b)) - -#define UNUSED GGML_UNUSED - -struct ggml_mpi_context { - int rank; - int size; -}; - -void ggml_mpi_backend_init(void) { - MPI_Init(NULL, NULL); -} - -void ggml_mpi_backend_free(void) { - MPI_Finalize(); -} - -struct ggml_mpi_context * ggml_mpi_init(void) { - struct ggml_mpi_context * ctx = calloc(1, sizeof(struct ggml_mpi_context)); - - MPI_Comm_rank(MPI_COMM_WORLD, &ctx->rank); - MPI_Comm_size(MPI_COMM_WORLD, &ctx->size); - - return ctx; -} - -void ggml_mpi_free(struct ggml_mpi_context * ctx) { - free(ctx); -} - -int ggml_mpi_rank(struct ggml_mpi_context * ctx) { - return ctx->rank; -} - -void ggml_mpi_eval_init( - struct ggml_mpi_context * ctx_mpi, - int * n_tokens, - int * n_past, - int * n_threads) { - UNUSED(ctx_mpi); - - // synchronize the worker node parameters with the root node - MPI_Barrier(MPI_COMM_WORLD); - - MPI_Bcast(n_tokens, 1, MPI_INT, 0, MPI_COMM_WORLD); - MPI_Bcast(n_past, 1, MPI_INT, 0, MPI_COMM_WORLD); - MPI_Bcast(n_threads, 1, MPI_INT, 0, MPI_COMM_WORLD); -} - -static int ggml_graph_get_node_idx(struct ggml_cgraph * gf, const char * name) { - struct ggml_tensor * t = ggml_graph_get_tensor(gf, name); - if (t == NULL) { - fprintf(stderr, "%s: tensor %s not found\n", __func__, name); - return -1; - } - - for (int i = 0; i < gf->n_nodes; i++) { - if (gf->nodes[i] == t) { - return i; - } - } - - fprintf(stderr, "%s: tensor %s not found in graph (should not happen)\n", __func__, name); - return -1; -} - -static void ggml_mpi_tensor_send(struct ggml_tensor * t, int mpi_rank_dst) { - MPI_Datatype mpi_type; - - switch (t->type) { - case GGML_TYPE_I32: mpi_type = MPI_INT32_T; break; - case GGML_TYPE_F32: mpi_type = MPI_FLOAT; break; - default: GGML_ASSERT(false && "not implemented"); - } - - const int retval = MPI_Send(t->data, ggml_nelements(t), mpi_type, mpi_rank_dst, 0, MPI_COMM_WORLD); - GGML_ASSERT(retval == MPI_SUCCESS); -} - -static void ggml_mpi_tensor_recv(struct ggml_tensor * t, int mpi_rank_src) { - MPI_Datatype mpi_type; - - switch (t->type) { - case GGML_TYPE_I32: mpi_type = MPI_INT32_T; break; - case GGML_TYPE_F32: mpi_type = MPI_FLOAT; break; - default: GGML_ASSERT(false && "not implemented"); - } - - MPI_Status status; UNUSED(status); - - const int retval = MPI_Recv(t->data, ggml_nelements(t), mpi_type, mpi_rank_src, MPI_ANY_TAG, MPI_COMM_WORLD, &status); - GGML_ASSERT(retval == MPI_SUCCESS); -} - -// TODO: there are many improvements that can be done to this implementation -void ggml_mpi_graph_compute_pre( - struct ggml_mpi_context * ctx_mpi, - struct ggml_cgraph * gf, - int n_layers) { - const int mpi_rank = ctx_mpi->rank; - const int mpi_size = ctx_mpi->size; - - struct ggml_tensor * inp_tokens = ggml_graph_get_tensor(gf, "inp_tokens"); - if (inp_tokens == NULL) { - fprintf(stderr, "%s: tensor 'inp_tokens' not found\n", __func__); - return; - } - - struct ggml_tensor * inp0 = ggml_graph_get_tensor(gf, "layer_inp_0"); - if (inp0 == NULL) { - fprintf(stderr, "%s: tensor 'inp0' not found\n", __func__); - return; - } - - GGML_ASSERT(inp0 == gf->nodes[0]); - - // distribute the compute graph into slices across the MPI nodes - // - // the main node (0) processes the last layers + the remainder of the compute graph - // and is responsible to pass the input tokens to the first node (1) - // - // node 1: [( 0) * n_per_node, ( 1) * n_per_node) - // node 2: [( 1) * n_per_node, ( 2) * n_per_node) - // ... - // node n-1: [(n-2) * n_per_node, (n-1) * n_per_node) - // node 0: [(n-1) * n_per_node, n_nodes) - // - if (mpi_rank > 0) { - if (mpi_rank == 1) { - // the first node (1) receives the input tokens from the main node (0) - ggml_mpi_tensor_recv(inp_tokens, 0); - } else { - // recv input data for each node into the "inp0" tensor (i.e. the first node in the compute graph) - ggml_mpi_tensor_recv(inp0, mpi_rank - 1); - } - } else if (mpi_size > 1) { - // node 0 sends the input tokens to node 1 - ggml_mpi_tensor_send(inp_tokens, 1); - - // recv the output data from the last node - ggml_mpi_tensor_recv(inp0, mpi_size - 1); - } - - { - const int n_per_node = (n_layers + (mpi_size - 1)) / mpi_size; - - const int mpi_idx = mpi_rank > 0 ? mpi_rank - 1 : mpi_size - 1; - - const int il0 = (mpi_idx + 0) * n_per_node; - const int il1 = MIN(n_layers, (mpi_idx + 1) * n_per_node); - - char name_l0[GGML_MAX_NAME]; - char name_l1[GGML_MAX_NAME]; - - snprintf(name_l0, sizeof(name_l0), "layer_inp_%d", il0); - snprintf(name_l1, sizeof(name_l1), "layer_inp_%d", il1); - - const int idx_l0 = ggml_graph_get_node_idx(gf, name_l0); - const int idx_l1 = mpi_rank > 0 ? ggml_graph_get_node_idx(gf, name_l1) + 1 : gf->n_nodes; - - if (idx_l0 < 0 || idx_l1 < 0) { - fprintf(stderr, "%s: layer input nodes not found\n", __func__); - return; - } - - // attach the input data to all nodes that need it - // TODO: not great - should be able to do this without modifying the compute graph (see next TODO below) - for (int i = idx_l0; i < idx_l1; i++) { - if (gf->nodes[i]->src[0] == gf->nodes[idx_l0]) { - gf->nodes[i]->src[0] = inp0; - } - if (gf->nodes[i]->src[1] == gf->nodes[idx_l0]) { - gf->nodes[i]->src[1] = inp0; - } - } - - // TODO: instead of rearranging the nodes, we should be able to execute a subset of the compute graph - for (int i = 1; i < idx_l1 - idx_l0; i++) { - gf->nodes[i] = gf->nodes[idx_l0 + i]; - gf->grads[i] = gf->grads[idx_l0 + i]; - } - - // the first node performs the "get_rows" operation, the rest of the nodes get the data from the previous node - if (mpi_idx != 0) { - gf->nodes[0]->op = GGML_OP_NONE; - } - - gf->n_nodes = idx_l1 - idx_l0; - - //fprintf(stderr, "%s: node %d: processing %d nodes [%d, %d)\n", __func__, mpi_rank, gf->n_nodes, il0, il1); - } -} - -void ggml_mpi_graph_compute_post( - struct ggml_mpi_context * ctx_mpi, - struct ggml_cgraph * gf, - int n_layers) { - UNUSED(n_layers); - - const int mpi_rank = ctx_mpi->rank; - const int mpi_size = ctx_mpi->size; - - // send the output data to the next node - if (mpi_rank > 0) { - ggml_mpi_tensor_send(gf->nodes[gf->n_nodes - 1], (mpi_rank + 1) % mpi_size); - } -} diff --git a/ggml-mpi.h b/ggml-mpi.h deleted file mode 100644 index eda119d449849..0000000000000 --- a/ggml-mpi.h +++ /dev/null @@ -1,39 +0,0 @@ -#pragma once - -struct ggml_context; -struct ggml_tensor; -struct ggml_cgraph; - -#ifdef __cplusplus -extern "C" { -#endif - -struct ggml_mpi_context; - -void ggml_mpi_backend_init(void); -void ggml_mpi_backend_free(void); - -struct ggml_mpi_context * ggml_mpi_init(void); -void ggml_mpi_free(struct ggml_mpi_context * ctx); - -int ggml_mpi_rank(struct ggml_mpi_context * ctx); - -void ggml_mpi_eval_init( - struct ggml_mpi_context * ctx_mpi, - int * n_tokens, - int * n_past, - int * n_threads); - -void ggml_mpi_graph_compute_pre( - struct ggml_mpi_context * ctx_mpi, - struct ggml_cgraph * gf, - int n_layers); - -void ggml_mpi_graph_compute_post( - struct ggml_mpi_context * ctx_mpi, - struct ggml_cgraph * gf, - int n_layers); - -#ifdef __cplusplus -} -#endif diff --git a/ggml-opencl.cpp b/ggml-opencl.cpp index 2b641d9debdef..b3b6593391d6e 100644 --- a/ggml-opencl.cpp +++ b/ggml-opencl.cpp @@ -1843,7 +1843,10 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor * CL_CHECK(clEnqueueNDRangeKernel(queue, *to_fp32_cl, 1, &offset, &global, local > 0 ? &local : NULL, events.size(), !events.empty() ? events.data() : NULL, NULL)); } - for (int64_t i12 = i02 * r2, e12 = i12 + r2; i12 < e12; i12++) { + int64_t i12 = i02 * r2; + int64_t e12 = i12 + r2; + events.reserve(e12 - i12); + for (; i12 < e12; i12++) { if (mul_mat_vec) { // specialized dequantize_mul_mat_vec kernel // copy src1 to device events.emplace_back(); diff --git a/ggml-quants.c b/ggml-quants.c index 59db2a79594ee..58e3d9bb4b087 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -14,6 +14,18 @@ #include // for qsort #include // for GGML_ASSERT +#define GROUP_MAX_EPS 1e-15f +#define GROUP_MAX_EPS_IQ3_XXS 1e-8f +#define GROUP_MAX_EPS_IQ2_S 1e-8f +#define GROUP_MAX_EPS_IQ1_M 1e-7f +#define GROUP_MAX_EPS_IQ1_S 1e-12f + +#if defined(_MSC_VER) +// disable "possible loss of data" to avoid warnings for hundreds of casts +// we should just be careful :) +#pragma warning(disable: 4244 4267) +#endif + #define UNUSED GGML_UNUSED #ifndef MM256_SET_M128I @@ -236,7 +248,7 @@ static inline float hsum_float_4x4(const __m128 a, const __m128 b, const __m128 #endif // __AVX__ || __AVX2__ || __AVX512F__ #endif // defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) -#if defined(__ARM_NEON) || defined(__wasm_simd128__) +#if defined(__ARM_NEON) || defined(__wasm_simd128__) || defined(__POWER9_VECTOR__) #define B1(c,s,n) 0x ## n ## c , 0x ## n ## s #define B2(c,s,n) B1(c,s,n ## c), B1(c,s,n ## s) #define B3(c,s,n) B2(c,s,n ## c), B2(c,s,n ## s) @@ -251,6 +263,403 @@ static const uint64_t table_b2b_0[1 << 8] = { B8(00, 10) }; // ( b) << 4 static const uint64_t table_b2b_1[1 << 8] = { B8(10, 00) }; // (!b) << 4 #endif +#if defined(__loongarch_asx) + +#ifdef __clang__ +#define VREGS_PREFIX "$vr" +#define XREGS_PREFIX "$xr" +#else // GCC +#define VREGS_PREFIX "$f" +#define XREGS_PREFIX "$f" +#endif +#define __ALL_REGS "0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31" +// Convert __m128i to __m256i +static inline __m256i ____m256i(__m128i in) { + __m256i out = __lasx_xvldi(0); + __asm__ volatile ( + ".irp i," __ALL_REGS "\n\t" + " .ifc %[out], " XREGS_PREFIX"\\i \n\t" + " .irp j," __ALL_REGS "\n\t" + " .ifc %[in], " VREGS_PREFIX "\\j \n\t" + " xvpermi.q $xr\\i, $xr\\j, 0x20 \n\t" + " .endif \n\t" + " .endr \n\t" + " .endif \n\t" + ".endr \n\t" + : [out] "+f" (out) : [in] "f" (in) + ); + return out; +} +// Convert two __m128i to __m256i +static inline __m256i lasx_set_q(__m128i inhi, __m128i inlo) { + __m256i out; + __asm__ volatile ( + ".irp i," __ALL_REGS "\n\t" + " .ifc %[hi], " VREGS_PREFIX "\\i \n\t" + " .irp j," __ALL_REGS "\n\t" + " .ifc %[lo], " VREGS_PREFIX "\\j \n\t" + " xvpermi.q $xr\\i, $xr\\j, 0x20 \n\t" + " .endif \n\t" + " .endr \n\t" + " .endif \n\t" + ".endr \n\t" + ".ifnc %[out], %[hi] \n\t" + ".irp i," __ALL_REGS "\n\t" + " .ifc %[out], " XREGS_PREFIX "\\i \n\t" + " .irp j," __ALL_REGS "\n\t" + " .ifc %[hi], " VREGS_PREFIX "\\j \n\t" + " xvori.b $xr\\i, $xr\\j, 0 \n\t" + " .endif \n\t" + " .endr \n\t" + " .endif \n\t" + ".endr \n\t" + ".endif \n\t" + : [out] "=f" (out), [hi] "+f" (inhi) + : [lo] "f" (inlo) + ); + return out; +} +// Convert __m256i low part to __m128i +static inline __m128i lasx_extracti128_lo(__m256i in) { + __m128i out; + __asm__ volatile ( + ".ifnc %[out], %[in] \n\t" + ".irp i," __ALL_REGS "\n\t" + " .ifc %[out], " VREGS_PREFIX "\\i \n\t" + " .irp j," __ALL_REGS "\n\t" + " .ifc %[in], " XREGS_PREFIX "\\j \n\t" + " vori.b $vr\\i, $vr\\j, 0 \n\t" + " .endif \n\t" + " .endr \n\t" + " .endif \n\t" + ".endr \n\t" + ".endif \n\t" + : [out] "=f" (out) : [in] "f" (in) + ); + return out; +} +// Convert __m256i high part to __m128i +static inline __m128i lasx_extracti128_hi(__m256i in) { + __m128i out; + __asm__ volatile ( + ".irp i," __ALL_REGS "\n\t" + " .ifc %[out], " VREGS_PREFIX "\\i \n\t" + " .irp j," __ALL_REGS "\n\t" + " .ifc %[in], " XREGS_PREFIX "\\j \n\t" + " xvpermi.q $xr\\i, $xr\\j, 0x11 \n\t" + " .endif \n\t" + " .endr \n\t" + " .endif \n\t" + ".endr \n\t" + : [out] "=f" (out) : [in] "f" (in) + ); + return out; +} + +static __m256i lasx_set_w(int e7, int e6, int e5, int e4, int e3, int e2, int e1, int e0) { + v8i32 __ret = {e0, e1, e2, e3, e4, e5, e6, e7}; + return (__m256i)__ret; +} + +static __m128i lsx_set_w(int32_t a, int32_t b, int32_t c, int32_t d) { + v4i32 __ret = {d, c, b, a}; + return (__m128i)__ret; +} + +static __m256i lasx_set_d(int64_t a, int64_t b, int64_t c, int64_t d) { + v4i64 __ret = {d, c, b, a}; + return (__m256i)__ret; +} + +static __m256i lasx_insertf128( __m128i x, __m128i y) { + return lasx_set_q(x, y); +} + +static __m128i lsx_shuffle_b(__m128i a, __m128i b) { + __m128i mask_f, zero, tmp0, tmp2, mask; + int f = 0x8f; + mask_f = __lsx_vreplgr2vr_b(f); + zero = __lsx_vldi(0); + tmp0 = __lsx_vand_v(b, mask_f); // get mask with low 4 bit and sign bits + tmp0 = __lsx_vori_b(tmp0, 0x10); // make each mask or with 0x10 prepare for positive + mask = __lsx_vsle_b(zero, tmp0); // if mask >= 0, set mask + tmp2 = __lsx_vand_v(tmp0, mask); // maskout the in2 < ones + return __lsx_vshuf_b(a, zero, tmp2); +} + +static __m256i lasx_shuffle_b(__m256i a, __m256i b) { + __m256i mask_f, zero, tmp0, tmp2, mask; + int f = 0x8f; + mask_f = __lasx_xvreplgr2vr_b(f); + zero = __lasx_xvldi(0); + tmp0 = __lasx_xvand_v(b, mask_f); // get mask with low 4 bit and sign bits + tmp0 = __lasx_xvori_b(tmp0, 0x10); // make each mask or with 0x10 prepare for positive + mask = __lasx_xvsle_b(zero, tmp0); // if mask >= 0, set mask + tmp2 = __lasx_xvand_v(tmp0, mask); // maskout the in2 < ones + return __lasx_xvshuf_b(a, zero, tmp2); +} + +static __m256i lasx_extu8_16(__m128i a) { + __m128i zero = __lsx_vldi(0); + __m128i vlo = __lsx_vilvl_b(zero, a); + __m128i vhi = __lsx_vilvh_b(zero, a); + return lasx_set_q(vhi, vlo); +} + +static __m256i lasx_ext8_16(__m128i a) { + __m128i sign = __lsx_vslti_b(a, 0); + __m128i vlo = __lsx_vilvl_b(sign, a); + __m128i vhi = __lsx_vilvh_b(sign, a); + return lasx_set_q(vhi, vlo); +} + +static __m256i lasx_ext16_32(__m128i a) { + __m256i tmp1; + tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 0), 0); + tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 1), 1); + tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 2), 2); + tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 3), 3); + tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 4), 4); + tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 5), 5); + tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 6), 6); + tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 7), 7); + return tmp1; +} + +static __m128i lasx_extracti128( __m256i a, int pos) { + __m128i ret; + if( pos == 0) + { + ret = lasx_extracti128_lo(a); + } else { + ret = lasx_extracti128_hi(a); + } + return ret; +} + +static __m128 lasx_extractf128( __m256 a, int pos) { + __m128 ret; + if( pos == 0) + { + ret = (__m128)lasx_extracti128_lo((__m256i)a); + } else { + ret = (__m128)lasx_extracti128_hi((__m256i)a); + } + return ret; +} + +static __m128i lsx_hadd_h(__m128i a, __m128i b) { + __m128i tmp1 = __lsx_vpickev_h(b, a); + __m128i tmp2 = __lsx_vpickod_h(b, a); + return __lsx_vadd_h(tmp1, tmp2); +} + +static __m128i lsx_hadd_w(__m128i a, __m128i b) { + __m128i tmp1 = __lsx_vpickev_w(b, a); + __m128i tmp2 = __lsx_vpickod_w(b, a); + return __lsx_vadd_w(tmp1, tmp2); +} + +static __m128 lsx_hadd_s(__m128 a, __m128 b) { + __m128 tmp1 = (__m128)__lsx_vpickev_w((__m128i)b, (__m128i)a); + __m128 tmp2 = (__m128)__lsx_vpickod_w((__m128i)b, (__m128i)a); + + return __lsx_vfadd_s(tmp1, tmp2); +} + +static __m256i lasx_maddubs_h(__m256i a, __m256i b) { + __m256i tmp1, tmp2; + tmp1 = __lasx_xvmulwev_h_b(a, b); + tmp2 = __lasx_xvmulwod_h_b(a, b); + return __lasx_xvsadd_h(tmp1, tmp2); +} + +static __m256i lasx_madd_h(__m256i a, __m256i b) { + __m256i tmp1, tmp2; + tmp1 = __lasx_xvmulwev_w_h(a, b); + tmp2 = __lasx_xvmulwod_w_h(a, b); + return __lasx_xvadd_w(tmp1, tmp2); +} + +static __m256i lasx_packs_w(__m256i a, __m256i b) { + __m256i tmp, tmp1; + tmp = __lasx_xvsat_w(a, 15); + tmp1 = __lasx_xvsat_w(b, 15); + return __lasx_xvpickev_h(tmp1, tmp); +} + +static __m256i lasx_packs_h(__m256i a, __m256i b) { + __m256i tmp, tmp1; + tmp = __lasx_xvsat_h(a, 7); + tmp1 = __lasx_xvsat_h(b, 7); + return __lasx_xvpickev_b(tmp1, tmp); +} + +static __m128i lsx_packs_w(__m128i a, __m128i b) { + __m128i tmp, tmp1; + tmp = __lsx_vsat_w(a, 15); + tmp1 = __lsx_vsat_w(b, 15); + return __lsx_vpickev_h(tmp1, tmp); +} + +static __m128i lsx_packs_h(__m128i a, __m128i b) { + __m128i tmp, tmp1; + tmp = __lsx_vsat_h(a, 7); + tmp1 = __lsx_vsat_h(b, 7); + return __lsx_vpickev_b(tmp1, tmp); +} + +static __m128i lsx_packus_h(__m128i a, __m128i b) { + __m128i tmp, tmp1; + tmp = __lsx_vsat_hu(a, 7); + tmp1 = __lsx_vsat_hu(b, 7); + return __lsx_vpickev_b(tmp1, tmp); +} + + +static __m128i lsx_maddubs_h(__m128i a, __m128i b) { + __m128i tmp1, tmp2; + tmp1 = __lsx_vmulwev_h_b(a, b); + tmp2 = __lsx_vmulwod_h_b(a, b); + return __lsx_vsadd_h(tmp1, tmp2); +} + +static __m128i lsx_madd_h(__m128i a, __m128i b) { + __m128i tmp1, tmp2; + tmp1 = __lsx_vmulwev_w_h(a, b); + tmp2 = __lsx_vmulwod_w_h(a, b); + return __lsx_vadd_w(tmp1, tmp2); +} + +// multiply int8_t, add results pairwise twice +static inline __m128i mul_sum_i8_pairs(const __m128i x, const __m128i y) { + // Get absolute values of x vectors + const __m128i ax = __lsx_vsigncov_b(x, x); + // Sign the values of the y vectors + const __m128i sy = __lsx_vsigncov_b(x, y); + // Perform multiplication and create 16-bit values + const __m128i dot = lsx_maddubs_h(ax, sy); + const __m128i ones = __lsx_vreplgr2vr_h(1); + return lsx_madd_h(ones, dot); +} + +// horizontally add 8 floats +static inline float hsum_float_8(const __m256 x) { + __m128 res = lasx_extractf128(x, 1); + ft_union tmp; + res = __lsx_vfadd_s(res, lasx_extractf128(x, 0)); + res = __lsx_vfadd_s(res, (__m128)__lsx_vpickod_d((__m128i)res, (__m128i)res)); + res = __lsx_vfadd_s(res, (__m128)__lsx_vinsgr2vr_w(__lsx_vldi(0), __lsx_vpickve2gr_w(res, 1), 0)); + tmp.i = __lsx_vpickve2gr_w(res, 0); + return tmp.f; +} + +// horizontally add 8 int32_t +static inline int hsum_i32_8(const __m256i a) { + + __m256i tmp1 = __lasx_xvpermi_q(a, a, 0x11); + __m256i tmp2 = __lasx_xvpermi_q(a, a, 0x00); + + __m128i tmp1_128 = lasx_extracti128_lo(tmp1); + __m128i tmp2_128 = lasx_extracti128_lo(tmp2); + + __m128i sum128 = __lsx_vadd_w(tmp1_128, tmp2_128); + + __m128i ev = __lsx_vpickev_w(sum128, sum128); + __m128i od = __lsx_vpickod_w(sum128, sum128); + __m128i sum64 = __lsx_vadd_w(ev, od); + + int sum64_1, sum64_2; + sum64_1 = __lsx_vpickve2gr_w(sum64, 0); + sum64_2 = __lsx_vpickve2gr_w(sum64, 1); + + return sum64_1 + sum64_2; +} + +// horizontally add 4 int32_t +static inline int hsum_i32_4(const __m128i a) { + __m128i ev = __lsx_vpickev_w(a, a); + __m128i od = __lsx_vpickod_w(a, a); + __m128i sum64 = __lsx_vadd_w(ev, od); + + int sum64_1, sum64_2; + sum64_1 = __lsx_vpickve2gr_w(sum64, 0); + sum64_2 = __lsx_vpickve2gr_w(sum64, 1); + + return sum64_1 + sum64_2; +} + +// spread 32 bits to 32 bytes { 0x00, 0xFF } +static inline __m256i bytes_from_bits_32(const uint8_t * x) { + + uint32_t x32; + memcpy(&x32, x, sizeof(uint32_t)); + const __m256i shuf_mask = lasx_set_d( + 0x0303030303030303, 0x0202020202020202, + 0x0101010101010101, 0x0000000000000000); + + __m256i bytes = lasx_shuffle_b(__lasx_xvreplgr2vr_w(x32), shuf_mask); + const __m256i bit_mask = __lasx_xvreplgr2vr_d(0x7fbfdfeff7fbfdfe); + bytes = __lasx_xvor_v(bytes, bit_mask); + return __lasx_xvseq_b(bytes, __lasx_xvreplgr2vr_d(-1)); +} + +// Unpack 32 4-bit fields into 32 bytes +// The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval +static inline __m256i bytes_from_nibbles_32(const uint8_t * rsi) { + const __m128i lo = __lsx_vld((const __m128i *)rsi, 0); + __m128i hi = __lsx_vsrli_h(lo, 4); + return __lasx_xvandi_b(lasx_insertf128(hi, lo), 0xf); +} + +// add int16_t pairwise and return as float vector +static inline __m256 sum_i16_pairs_float(const __m256i x) { + __m256i v = __lasx_xvpackod_h(x, x); + __m256i summed_pairs = __lasx_xvaddwev_w_h(x, v); + return __lasx_xvffint_s_w(summed_pairs); +} + +static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) { + // Perform multiplication and create 16-bit values + const __m256i dot = lasx_maddubs_h(ax, sy); + return sum_i16_pairs_float(dot); +} + +// multiply int8_t, add results pairwise twice and return as float vector +static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) { + + // Get absolute values of x vectors + const __m256i ax = __lasx_xvsigncov_b(x, x); + // Sign the values of the y vectors + const __m256i sy = __lasx_xvsigncov_b(x, y); + + return mul_sum_us8_pairs_float(ax, sy); +} + +static inline __m128i packNibbles( __m256i bytes ) { + // Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh + const __m256i lowByte = __lasx_xvreplgr2vr_h(0xFF); + __m256i high = __lasx_xvandn_v(lowByte, bytes); + __m256i low = __lasx_xvand_v(lowByte, bytes); + high = __lasx_xvsrli_h(high, 4); + bytes = __lasx_xvor_v(low, high); + // Compress uint16_t lanes into bytes + __m128i *r0 = (__m128i *)&bytes; + __m256i tmp_h128 = __lasx_xvpermi_q(bytes, bytes, 0x11); + __m128i *r1 = (__m128i *)&tmp_h128; + + __m128i zero = __lsx_vldi(0); + __m128i tmp, tmp2, tmp3; + + tmp = __lsx_vmax_h(zero, *r0); + tmp2 = __lsx_vsat_hu(tmp, 7); + + tmp = __lsx_vmax_h(zero, *r1); + tmp3 = __lsx_vsat_hu(tmp, 7); + return __lsx_vpickev_b(tmp3, tmp2); +} +#endif //__loongarch_asx + // reference implementation for deterministic creation of model files void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict y, int64_t k) { static const int qk = QK4_0; @@ -638,6 +1047,102 @@ void quantize_row_q8_0(const float * restrict x, void * restrict vy, int64_t k) // store result __riscv_vse8_v_i8m1(y[i].qs , vs, vl); } + +#elif defined(__POWER9_VECTOR__) + for (int i = 0; i < nb; i++) { + vector float srcv [8]; + vector float asrcv[8]; + vector float amaxv[8]; + vector signed int vi[8]; + + for (int j = 0; j < 8; j++) srcv[j] = vec_xl(0, x + i*32 + 4*j); + for (int j = 0; j < 8; j++) asrcv[j] = vec_abs(srcv[j]); + + for (int j = 0; j < 4; j++) amaxv[2*j] = vec_max(asrcv[2*j], asrcv[2*j+1]); + for (int j = 0; j < 2; j++) amaxv[4*j] = vec_max(amaxv[4*j], amaxv[4*j+2]); + for (int j = 0; j < 1; j++) amaxv[8*j] = vec_max(amaxv[8*j], amaxv[8*j+4]); + + const float amax = MAX(MAX(vec_extract(amaxv[0], 0), + vec_extract(amaxv[0], 1)), + MAX(vec_extract(amaxv[0], 2), + vec_extract(amaxv[0], 3))); + + const float d = amax / ((1 << 7) - 1); + const float id = d ? 1.0f/d : 0.0f; + const vector float vid = vec_splats(id); + + y[i].d = GGML_FP32_TO_FP16(d); + + for (int j = 0; j < 8; j++) { + const vector float v = vec_round(vec_mul(srcv[j], vid)); + vi[j] = vec_cts(v, 0); + } + vec_xst(vec_pack(vec_pack(vi[0], vi[1]), vec_pack(vi[2], vi[3])), 0, &y[i].qs[0]); + vec_xst(vec_pack(vec_pack(vi[4], vi[5]), vec_pack(vi[6], vi[7])), 16, &y[i].qs[0]); + +#elif defined(__loongarch_asx) + for (int i = 0; i < nb; i++) { + ft_union fi; + __m256 v0 = (__m256)__lasx_xvld( x , 0); + __m256 v1 = (__m256)__lasx_xvld( x , 32); + __m256 v2 = (__m256)__lasx_xvld( x , 64); + __m256 v3 = (__m256)__lasx_xvld( x , 96); + x += 32; + + // Compute max(abs(e)) for the block + const __m256 sign_bit = __lasx_xvreplfr2vr_s( -0.0f ); + __m256 max_abs = (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v0 ); + max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v1 ) ); + max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v2 ) ); + max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v3 ) ); + + __m128 max4 = __lsx_vfmax_s( lasx_extractf128( max_abs, 1 ), lasx_extractf128( max_abs , 0) ); + max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vpickod_d((__m128i) max4, (__m128i)max4 ) ); + __m128 tmp = max4; + max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vinsgr2vr_w(tmp, __lsx_vpickve2gr_w( max4, 1 ), 0 )); + fi.i = __lsx_vpickve2gr_w( (__m128i)max4, 0 ); + const float max_scalar = fi.f; + + // Quantize these floats + const float d = max_scalar / 127.f; + y[i].d = GGML_FP32_TO_FP16(d); + const float id = ( max_scalar != 0.0f ) ? 127.f / max_scalar : 0.0f; + const __m256 mul = (__m256)__lasx_xvreplfr2vr_s( id ); + + // Apply the multiplier + v0 = __lasx_xvfmul_s( v0, mul ); + v1 = __lasx_xvfmul_s( v1, mul ); + v2 = __lasx_xvfmul_s( v2, mul ); + v3 = __lasx_xvfmul_s( v3, mul ); + + // Round to nearest integer + __m256i i0 = __lasx_xvftintrne_w_s( v0 ); + __m256i i1 = __lasx_xvftintrne_w_s( v1 ); + __m256i i2 = __lasx_xvftintrne_w_s( v2 ); + __m256i i3 = __lasx_xvftintrne_w_s( v3 ); + + __m128i ni0 = lasx_extracti128( i0, 0 ); + __m128i ni1 = lasx_extracti128( i0, 1); + __m128i ni2 = lasx_extracti128( i1, 0); + __m128i ni3 = lasx_extracti128( i1, 1); + __m128i ni4 = lasx_extracti128( i2, 0); + __m128i ni5 = lasx_extracti128( i2, 1); + __m128i ni6 = lasx_extracti128( i3, 0); + __m128i ni7 = lasx_extracti128( i3, 1); + + // Convert int32 to int16 + ni0 = lsx_packs_w( ni0, ni1 ); + ni2 = lsx_packs_w( ni2, ni3 ); + ni4 = lsx_packs_w( ni4, ni5 ); + ni6 = lsx_packs_w( ni6, ni7 ); + // Convert int16 to int8 + ni0 = lsx_packs_h( ni0, ni2 ); + ni4 = lsx_packs_h( ni4, ni6 ); + + __lsx_vst(ni0, (__m128i *)(y[i].qs + 0), 0); + __lsx_vst(ni4, (__m128i *)(y[i].qs + 16), 0); + + } #else GGML_UNUSED(nb); // scalar @@ -785,12 +1290,12 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int64_t k) __m128 max4 = _mm_max_ps( _mm256_extractf128_ps( maxAbs, 1 ), _mm256_castps256_ps128( maxAbs ) ); max4 = _mm_max_ps( max4, _mm_movehl_ps( max4, max4 ) ); max4 = _mm_max_ss( max4, _mm_movehdup_ps( max4 ) ); - const float maxScalar = _mm_cvtss_f32( max4 ); + const float max_scalar = _mm_cvtss_f32( max4 ); // Quantize these floats - const float d = maxScalar / 127.f; + const float d = max_scalar / 127.f; y[i].d = GGML_FP32_TO_FP16(d); - const float id = ( maxScalar != 0.0f ) ? 127.f / maxScalar : 0.0f; + const float id = ( max_scalar != 0.0f ) ? 127.f / max_scalar : 0.0f; const __m256 mul = _mm256_set1_ps( id ); // Apply the multiplier @@ -893,6 +1398,114 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int64_t k) int sum = __riscv_vmv_x_s_i16m1_i16(vwrs); y[i].s = GGML_FP32_TO_FP16(sum*d); } + +#elif defined(__POWER9_VECTOR__) + for (int i = 0; i < nb; i++) { + vector float srcv [8]; + vector float asrcv[8]; + vector float amaxv[8]; + vector signed int vi[8]; + + for (int j = 0; j < 8; j++) srcv[j] = vec_xl(0, x + i*32 + 4*j); + for (int j = 0; j < 8; j++) asrcv[j] = vec_abs(srcv[j]); + + for (int j = 0; j < 4; j++) amaxv[2*j] = vec_max(asrcv[2*j], asrcv[2*j+1]); + for (int j = 0; j < 2; j++) amaxv[4*j] = vec_max(amaxv[4*j], amaxv[4*j+2]); + for (int j = 0; j < 1; j++) amaxv[8*j] = vec_max(amaxv[8*j], amaxv[8*j+4]); + + const float amax = MAX(MAX(vec_extract(amaxv[0], 0), + vec_extract(amaxv[0], 1)), + MAX(vec_extract(amaxv[0], 2), + vec_extract(amaxv[0], 3))); + + const float d = amax / ((1 << 7) - 1); + const float id = d ? 1.0f/d : 0.0f; + const vector float vid = vec_splats(id); + + y[i].d = GGML_FP32_TO_FP16(d); + + vector int accv = vec_splats(0); + + for (int j = 0; j < 8; j++) { + const vector float v = vec_round(vec_mul(srcv[j], vid)); + vi[j] = vec_cts(v, 0); + + accv = vec_add(accv, vi[j]); + } + vec_xst(vec_pack(vec_pack(vi[0], vi[1]), vec_pack(vi[2], vi[3])), 0, &y[i].qs[0]); + vec_xst(vec_pack(vec_pack(vi[4], vi[5]), vec_pack(vi[6], vi[7])), 16, &y[i].qs[0]); + + accv = vec_add(accv, vec_sld(accv, accv, 4)); + accv = vec_add(accv, vec_sld(accv, accv, 8)); + y[i].s = GGML_FP32_TO_FP16(d * vec_extract(accv, 0)); + +#elif defined(__loongarch_asx) + for (int i = 0; i < nb; i++) { + ft_union ft; + __m256 v0 = (__m256)__lasx_xvld( x , 0 ); + __m256 v1 = (__m256)__lasx_xvld( x , 32 ); + __m256 v2 = (__m256)__lasx_xvld( x , 64 ); + __m256 v3 = (__m256)__lasx_xvld( x , 96 ); + x += 32; + + // Compute max(abs(e)) for the block + const __m256 sign_bit = __lasx_xvreplfr2vr_s( -0.0f ); + __m256 max_abs = (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v0 ); + max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v1 ) ); + max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v2 ) ); + max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v3 ) ); + + __m128 max4 = __lsx_vfmax_s( lasx_extractf128( max_abs, 1 ), lasx_extractf128( max_abs, 0) ); + max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vpickod_d((__m128i) max4, (__m128i)max4 ) ); + __m128 tmp = max4; + max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vextrins_w((__m128i)tmp, (__m128i)max4, 0x10 )); + ft.i = __lsx_vpickve2gr_w( (__m128i)max4, 0 ); + const float max_scalar = ft.f; + + // Quantize these floats + const float d = max_scalar / 127.f; + y[i].d = GGML_FP32_TO_FP16(d); + const float id = ( max_scalar != 0.0f ) ? 127.f / max_scalar : 0.0f; + const __m256 mul = __lasx_xvreplfr2vr_s( id ); + + // Apply the multiplier + v0 = __lasx_xvfmul_s( v0, mul ); + v1 = __lasx_xvfmul_s( v1, mul ); + v2 = __lasx_xvfmul_s( v2, mul ); + v3 = __lasx_xvfmul_s( v3, mul ); + + // Round to nearest integer + __m256i i0 = __lasx_xvftintrne_w_s( v0 ); + __m256i i1 = __lasx_xvftintrne_w_s( v1 ); + __m256i i2 = __lasx_xvftintrne_w_s( v2 ); + __m256i i3 = __lasx_xvftintrne_w_s( v3 ); + + __m128i ni0 = lasx_extracti128(i0, 0); + __m128i ni1 = lasx_extracti128( i0, 1); + __m128i ni2 = lasx_extracti128( i1, 0); + __m128i ni3 = lasx_extracti128( i1, 1); + __m128i ni4 = lasx_extracti128( i2, 0 ); + __m128i ni5 = lasx_extracti128( i2, 1); + __m128i ni6 = lasx_extracti128( i3, 0); + __m128i ni7 = lasx_extracti128( i3, 1); + + // Compute the sum of the quants and set y[i].s + const __m128i s0 = __lsx_vadd_w(__lsx_vadd_w(ni0, ni1), __lsx_vadd_w(ni2, ni3)); + const __m128i s1 = __lsx_vadd_w(__lsx_vadd_w(ni4, ni5), __lsx_vadd_w(ni6, ni7)); + y[i].s = GGML_FP32_TO_FP16(d * hsum_i32_4(__lsx_vadd_w(s0, s1))); + + // Convert int32 to int16 + ni0 = lsx_packs_w( ni0, ni1 ); + ni2 = lsx_packs_w( ni2, ni3 ); + ni4 = lsx_packs_w( ni4, ni5 ); + ni6 = lsx_packs_w( ni6, ni7 ); + // Convert int16 to int8 + ni0 = lsx_packs_h( ni0, ni2 ); + ni4 = lsx_packs_h( ni4, ni6 ); + + __lsx_vst(ni0, (__m128i *)(y[i].qs + 0), 0); + __lsx_vst(ni4, (__m128i *)(y[i].qs + 16), 0); + } #else GGML_UNUSED(nb); // scalar @@ -1032,7 +1645,7 @@ static float make_qx_quants(int n, int nmax, const float * restrict x, int8_t * float ax = fabsf(x[i]); if (ax > amax) { amax = ax; max = x[i]; } } - if (amax < 1e-30f) { // all zero + if (amax < GROUP_MAX_EPS) { // all zero for (int i = 0; i < n; ++i) { L[i] = 0; } @@ -1066,7 +1679,7 @@ static float make_qx_quants(int n, int nmax, const float * restrict x, int8_t * sumlx += w*x[i]*l; suml2 += w*l*l; } - float scale = sumlx/suml2; + float scale = suml2 ? sumlx/suml2 : 0.0f; if (return_early) return suml2 > 0 ? 0.5f*(scale + 1/iscale) : 1/iscale; float best = scale * sumlx; for (int is = -9; is <= 9; ++is) { @@ -1100,7 +1713,7 @@ static float make_q3_quants(int n, int nmax, const float * restrict x, int8_t * float ax = fabsf(x[i]); if (ax > amax) { amax = ax; max = x[i]; } } - if (!amax) { // all zero + if (amax < GROUP_MAX_EPS) { // all zero for (int i = 0; i < n; ++i) { L[i] = 0; } return 0.f; } @@ -1276,7 +1889,6 @@ static float make_qkx2_quants(int n, int nmax, const float * restrict x, const f return scale; } -#if QK_K == 256 static inline void get_scale_min_k4(int j, const uint8_t * restrict q, uint8_t * restrict d, uint8_t * restrict m) { if (j < 4) { *d = q[j] & 63; *m = q[j + 4] & 63; @@ -1285,7 +1897,6 @@ static inline void get_scale_min_k4(int j, const uint8_t * restrict q, uint8_t * *m = (q[j+4] >> 4) | ((q[j-0] >> 6) << 4); } } -#endif //========================- 2-bit (de)-quantization @@ -1349,20 +1960,13 @@ void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict } } -#if QK_K == 256 for (int j = 0; j < QK_K; j += 128) { for (int l = 0; l < 32; ++l) { y[i].qs[j/4 + l] = L[j + l] | (L[j + l + 32] << 2) | (L[j + l + 64] << 4) | (L[j + l + 96] << 6); } } -#else - for (int l = 0; l < 16; ++l) { - y[i].qs[l] = L[l] | (L[l + 16] << 2) | (L[l + 32] << 4) | (L[l + 48] << 6); - } -#endif x += QK_K; - } } @@ -1377,7 +1981,6 @@ void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int6 const uint8_t * q = x[i].qs; -#if QK_K == 256 int is = 0; float dl, ml; for (int n = 0; n < QK_K; n += 128) { @@ -1396,19 +1999,6 @@ void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int6 } q += 32; } -#else - float dl1 = d * (x[i].scales[0] & 0xF), ml1 = min * (x[i].scales[0] >> 4); - float dl2 = d * (x[i].scales[1] & 0xF), ml2 = min * (x[i].scales[1] >> 4); - float dl3 = d * (x[i].scales[2] & 0xF), ml3 = min * (x[i].scales[2] >> 4); - float dl4 = d * (x[i].scales[3] & 0xF), ml4 = min * (x[i].scales[3] >> 4); - for (int l = 0; l < 16; ++l) { - y[l+ 0] = dl1 * ((int8_t)((q[l] >> 0) & 3)) - ml1; - y[l+16] = dl2 * ((int8_t)((q[l] >> 2) & 3)) - ml2; - y[l+32] = dl3 * ((int8_t)((q[l] >> 4) & 3)) - ml3; - y[l+48] = dl4 * ((int8_t)((q[l] >> 6) & 3)) - ml4; - } - y += QK_K; -#endif } } @@ -1569,7 +2159,7 @@ static float make_qp_quants(int n, int nmax, const float * restrict x, uint8_t * break; } } - return sumlx / suml2; + return sumlx/suml2; } static void quantize_row_q2_K_impl(const float * restrict x, block_q2_K * restrict y, int k, const float * restrict quant_weights) { @@ -1599,36 +2189,9 @@ static void quantize_row_q2_K_impl(const float * restrict x, block_q2_K * restri } float dm, mm; -#if QK_K == 64 - float max_scale = 0, max_min = 0; - for (int j = 0; j < QK_K/16; ++j) { - max_scale = MAX(max_scale, scales[j]); - max_min = MAX(max_min, mins[j]); - } - dm = max_scale/15; - mm = max_min/15; - if (max_scale) { - float id = 1/dm; - for (int j = 0; j < QK_K/16; ++j) { - int l = nearest_int(id*scales[j]); - Ls[j] = MAX(0, MIN(15, l)); - } - } else { - memset(Ls, 0, QK_K/16); - } - if (max_min) { - float id = 1/mm; - for (int j = 0; j < QK_K/16; ++j) { - int l = nearest_int(id*mins[j]); - Lm[j] = MAX(0, MIN(15, l)); - } - } else { - memset(Lm, 0, QK_K/16); - } -#else dm = make_qp_quants(QK_K/16, 15, scales, Ls, sw); mm = make_qp_quants(QK_K/16, 15, mins, Lm, sw); -#endif + y[i].d = GGML_FP32_TO_FP16(dm); y[i].dmin = GGML_FP32_TO_FP16(mm); dm = GGML_FP16_TO_FP32(y[i].d); @@ -1651,20 +2214,13 @@ static void quantize_row_q2_K_impl(const float * restrict x, block_q2_K * restri } } -#if QK_K == 256 for (int j = 0; j < QK_K; j += 128) { for (int l = 0; l < 32; ++l) { y[i].qs[j/4 + l] = L[j + l] | (L[j + l + 32] << 2) | (L[j + l + 64] << 4) | (L[j + l + 96] << 6); } } -#else - for (int l = 0; l < 16; ++l) { - y[i].qs[l] = L[l] | (L[l + 16] << 2) | (L[l + 32] << 4) | (L[l + 48] << 6); - } -#endif x += QK_K; - } } @@ -1705,7 +2261,6 @@ void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict } } -#if QK_K == 256 memset(y[i].scales, 0, 12); if (max_scale) { float iscale = -32.f/max_scale; @@ -1739,36 +2294,6 @@ void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict L[16*j + ii] = l + 4; } } -#else - if (max_scale) { - float iscale = -8.f/max_scale; - for (int j = 0; j < QK_K/16; j+=2) { - int l1 = nearest_int(iscale*scales[j]); - l1 = 8 + MAX(-8, MIN(7, l1)); - int l2 = nearest_int(iscale*scales[j+1]); - l2 = 8 + MAX(-8, MIN(7, l2)); - y[i].scales[j/2] = l1 | (l2 << 4); - } - y[i].d = GGML_FP32_TO_FP16(1/iscale); - } else { - for (int j = 0; j < QK_K/16; j+=2) { - y[i].scales[j/2] = 0; - } - y[i].d = GGML_FP32_TO_FP16(0.f); - } - for (int j = 0; j < QK_K/16; ++j) { - int s = j%2 == 0 ? y[i].scales[j/2] & 0xF : y[i].scales[j/2] >> 4; - float d = GGML_FP16_TO_FP32(y[i].d) * (s - 8); - if (!d) { - continue; - } - for (int ii = 0; ii < 16; ++ii) { - int l = nearest_int(x[16*j + ii]/d); - l = MAX(-4, MIN(3, l)); - L[16*j + ii] = l + 4; - } - } -#endif memset(y[i].hmask, 0, QK_K/8); // We put the high-bit for the 1st 8 quants into bit 0, the next 8 into bit 1, etc. @@ -1783,23 +2308,16 @@ void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict m = 0; hm <<= 1; } } -#if QK_K == 256 for (int j = 0; j < QK_K; j += 128) { for (int l = 0; l < 32; ++l) { y[i].qs[j/4 + l] = L[j + l] | (L[j + l + 32] << 2) | (L[j + l + 64] << 4) | (L[j + l + 96] << 6); } } -#else - for (int l = 0; l < 16; ++l) { - y[i].qs[l] = L[l] | (L[l + 16] << 2) | (L[l + 32] << 4) | (L[l + 48] << 6); - } -#endif x += QK_K; } } -#if QK_K == 256 void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int64_t k) { assert(k % QK_K == 0); const int nb = k / QK_K; @@ -1849,49 +2367,12 @@ void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int6 } } -#else -void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int64_t k) { - assert(k % QK_K == 0); - assert(QK_K == 64); - const int nb = k / QK_K; - - for (int i = 0; i < nb; i++) { - - const float d_all = GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q = x[i].qs; - const uint8_t * restrict hm = x[i].hmask; - - const float d1 = d_all * ((x[i].scales[0] & 0xF) - 8); - const float d2 = d_all * ((x[i].scales[0] >> 4) - 8); - const float d3 = d_all * ((x[i].scales[1] & 0xF) - 8); - const float d4 = d_all * ((x[i].scales[1] >> 4) - 8); - - for (int l=0; l<8; ++l) { - uint8_t h = hm[l]; - y[l+ 0] = d1 * ((int8_t)((q[l+0] >> 0) & 3) - ((h & 0x01) ? 0 : 4)); - y[l+ 8] = d1 * ((int8_t)((q[l+8] >> 0) & 3) - ((h & 0x02) ? 0 : 4)); - y[l+16] = d2 * ((int8_t)((q[l+0] >> 2) & 3) - ((h & 0x04) ? 0 : 4)); - y[l+24] = d2 * ((int8_t)((q[l+8] >> 2) & 3) - ((h & 0x08) ? 0 : 4)); - y[l+32] = d3 * ((int8_t)((q[l+0] >> 4) & 3) - ((h & 0x10) ? 0 : 4)); - y[l+40] = d3 * ((int8_t)((q[l+8] >> 4) & 3) - ((h & 0x20) ? 0 : 4)); - y[l+48] = d4 * ((int8_t)((q[l+0] >> 6) & 3) - ((h & 0x40) ? 0 : 4)); - y[l+56] = d4 * ((int8_t)((q[l+8] >> 6) & 3) - ((h & 0x80) ? 0 : 4)); - } - y += QK_K; - } -} -#endif void quantize_row_q3_K(const float * restrict x, void * restrict vy, int64_t k) { quantize_row_q3_K_reference(x, vy, k); } static void quantize_row_q3_K_impl(const float * restrict x, block_q3_K * restrict y, int64_t n_per_row, const float * restrict quant_weights) { -#if QK_K != 256 - (void)quant_weights; - quantize_row_q3_K_reference(x, y, n_per_row); -#else assert(n_per_row % QK_K == 0); const int nb = n_per_row / QK_K; @@ -1909,7 +2390,7 @@ static void quantize_row_q3_K_impl(const float * restrict x, block_q3_K * restri for (int j = 0; j < QK_K/16; ++j) { if (quant_weights) { - const float * qw = quant_weights ? quant_weights + QK_K * i + 16*j : NULL; + const float * qw = quant_weights + QK_K * i + 16*j; for (int l = 0; l < 16; ++l) weight[l] = qw[l] * sqrtf(sigma2 + x[16*j+l]*x[16*j+l]); } else { for (int l = 0; l < 16; ++l) weight[l] = x[16*j+l]*x[16*j+l]; @@ -1973,7 +2454,6 @@ static void quantize_row_q3_K_impl(const float * restrict x, block_q3_K * restri x += QK_K; } -#endif } size_t quantize_q3_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { @@ -2005,7 +2485,6 @@ void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict float scales[QK_K/32]; for (int i = 0; i < nb; i++) { - float max_scale = 0; // as we are deducting the min, scales are always positive float max_min = 0; for (int j = 0; j < QK_K/32; ++j) { @@ -2025,7 +2504,6 @@ void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict } } -#if QK_K == 256 float inv_scale = max_scale > 0 ? 63.f/max_scale : 0.f; float inv_min = max_min > 0 ? 63.f/max_min : 0.f; for (int j = 0; j < QK_K/32; ++j) { @@ -2057,39 +2535,7 @@ void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict L[32*j + ii] = l; } } -#else - const float s_factor = 15.f; - float inv_scale = max_scale > 0 ? s_factor/max_scale : 0.f; - float inv_min = max_min > 0 ? s_factor/max_min : 0.f; - int d1 = nearest_int(inv_scale*scales[0]); - int m1 = nearest_int(inv_min*mins[0]); - int d2 = nearest_int(inv_scale*scales[1]); - int m2 = nearest_int(inv_min*mins[1]); - y[i].scales[0] = d1 | (m1 << 4); - y[i].scales[1] = d2 | (m2 << 4); - y[i].d[0] = GGML_FP32_TO_FP16(max_scale/s_factor); - y[i].d[1] = GGML_FP32_TO_FP16(max_min/s_factor); - float sumlx = 0; - int suml2 = 0; - for (int j = 0; j < QK_K/32; ++j) { - const uint8_t sd = y[i].scales[j] & 0xF; - const uint8_t sm = y[i].scales[j] >> 4; - const float d = GGML_FP16_TO_FP32(y[i].d[0]) * sd; - if (!d) continue; - const float m = GGML_FP16_TO_FP32(y[i].d[1]) * sm; - for (int ii = 0; ii < 32; ++ii) { - int l = nearest_int((x[32*j + ii] + m)/d); - l = MAX(0, MIN(15, l)); - L[32*j + ii] = l; - sumlx += (x[32*j + ii] + m)*l*sd; - suml2 += l*l*sd*sd; - } - } - if (suml2) { - y[i].d[0] = GGML_FP32_TO_FP16(sumlx/suml2); - } -#endif uint8_t * q = y[i].qs; for (int j = 0; j < QK_K; j += 64) { for (int l = 0; l < 32; ++l) q[l] = L[j + l] | (L[j + l + 32] << 4); @@ -2097,7 +2543,6 @@ void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict } x += QK_K; - } } @@ -2106,11 +2551,8 @@ void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int6 const int nb = k / QK_K; for (int i = 0; i < nb; i++) { - const uint8_t * q = x[i].qs; -#if QK_K == 256 - const float d = GGML_FP16_TO_FP32(x[i].d); const float min = GGML_FP16_TO_FP32(x[i].dmin); @@ -2125,18 +2567,6 @@ void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int6 for (int l = 0; l < 32; ++l) *y++ = d2 * (q[l] >> 4) - m2; q += 32; is += 2; } -#else - const float dall = GGML_FP16_TO_FP32(x[i].d[0]); - const float mall = GGML_FP16_TO_FP32(x[i].d[1]); - const float d1 = dall * (x[i].scales[0] & 0xF), m1 = mall * (x[i].scales[0] >> 4); - const float d2 = dall * (x[i].scales[1] & 0xF), m2 = mall * (x[i].scales[1] >> 4); - for (int l = 0; l < 32; ++l) { - y[l+ 0] = d1 * (q[l] & 0xF) - m1; - y[l+32] = d2 * (q[l] >> 4) - m2; - } - y += QK_K; -#endif - } } @@ -2147,10 +2577,6 @@ void quantize_row_q4_K(const float * restrict x, void * restrict vy, int64_t k) } static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restrict y, int64_t n_per_row, const float * quant_weights) { -#if QK_K != 256 - (void)quant_weights; - quantize_row_q4_K_reference(x, y, n_per_row); -#else assert(n_per_row % QK_K == 0); const int64_t nb = n_per_row / QK_K; @@ -2221,7 +2647,6 @@ static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restri x += QK_K; } -#endif } size_t quantize_q4_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { @@ -2246,21 +2671,13 @@ void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict assert(k % QK_K == 0); const int64_t nb = k / QK_K; -#if QK_K == 256 uint8_t L[QK_K]; float mins[QK_K/32]; float scales[QK_K/32]; float weights[32]; uint8_t Laux[32]; -#else - int8_t L[QK_K]; - float scales[QK_K/16]; -#endif for (int i = 0; i < nb; i++) { - -#if QK_K == 256 - float max_scale = 0; // as we are deducting the min, scales are always positive float max_min = 0; for (int j = 0; j < QK_K/32; ++j) { @@ -2332,55 +2749,8 @@ void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict m1 <<= 2; m2 <<= 2; ql += 32; } -#else - float max_scale = 0, amax = 0; - for (int j = 0; j < QK_K/16; ++j) { - scales[j] = make_qx_quants(16, 16, x + 16*j, L + 16*j, 1, NULL); - float abs_scale = fabsf(scales[j]); - if (abs_scale > amax) { - amax = abs_scale; - max_scale = scales[j]; - } - } - - float iscale = -128.f/max_scale; - for (int j = 0; j < QK_K/16; ++j) { - int l = nearest_int(iscale*scales[j]); - y[i].scales[j] = MAX(-128, MIN(127, l)); - } - y[i].d = GGML_FP32_TO_FP16(1/iscale); - - for (int j = 0; j < QK_K/16; ++j) { - const float d = GGML_FP16_TO_FP32(y[i].d) * y[i].scales[j]; - if (!d) continue; - for (int ii = 0; ii < 16; ++ii) { - int l = nearest_int(x[16*j + ii]/d); - l = MAX(-16, MIN(15, l)); - L[16*j + ii] = l + 16; - } - } - - uint8_t * restrict qh = y[i].qh; - uint8_t * restrict ql = y[i].qs; - memset(qh, 0, QK_K/8); - - for (int j = 0; j < 32; ++j) { - int jm = j%8; - int is = j/8; - int l1 = L[j]; - if (l1 > 15) { - l1 -= 16; qh[jm] |= (1 << is); - } - int l2 = L[j + 32]; - if (l2 > 15) { - l2 -= 16; qh[jm] |= (1 << (4 + is)); - } - ql[j] = l1 | (l2 << 4); - } -#endif x += QK_K; - } } @@ -2389,12 +2759,9 @@ void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int6 const int64_t nb = k / QK_K; for (int i = 0; i < nb; i++) { - const uint8_t * ql = x[i].qs; const uint8_t * qh = x[i].qh; -#if QK_K == 256 - const float d = GGML_FP16_TO_FP32(x[i].d); const float min = GGML_FP16_TO_FP32(x[i].dmin); @@ -2411,21 +2778,6 @@ void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int6 ql += 32; is += 2; u1 <<= 2; u2 <<= 2; } -#else - float d = GGML_FP16_TO_FP32(x[i].d); - const int8_t * restrict s = x[i].scales; - for (int l = 0; l < 8; ++l) { - y[l+ 0] = d * s[0] * ((ql[l+ 0] & 0xF) - (qh[l] & 0x01 ? 0 : 16)); - y[l+ 8] = d * s[0] * ((ql[l+ 8] & 0xF) - (qh[l] & 0x02 ? 0 : 16)); - y[l+16] = d * s[1] * ((ql[l+16] & 0xF) - (qh[l] & 0x04 ? 0 : 16)); - y[l+24] = d * s[1] * ((ql[l+24] & 0xF) - (qh[l] & 0x08 ? 0 : 16)); - y[l+32] = d * s[2] * ((ql[l+ 0] >> 4) - (qh[l] & 0x10 ? 0 : 16)); - y[l+40] = d * s[2] * ((ql[l+ 8] >> 4) - (qh[l] & 0x20 ? 0 : 16)); - y[l+48] = d * s[3] * ((ql[l+16] >> 4) - (qh[l] & 0x40 ? 0 : 16)); - y[l+56] = d * s[3] * ((ql[l+24] >> 4) - (qh[l] & 0x80 ? 0 : 16)); - } - y += QK_K; -#endif } } @@ -2436,10 +2788,6 @@ void quantize_row_q5_K(const float * restrict x, void * restrict vy, int64_t k) } static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restrict y, int64_t n_per_row, const float * quant_weights) { -#if QK_K != 256 - (void)quant_weights; - quantize_row_q5_K_reference(x, y, n_per_row); -#else assert(n_per_row % QK_K == 0); const int64_t nb = n_per_row / QK_K; @@ -2530,7 +2878,6 @@ static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restri x += QK_K; } -#endif } size_t quantize_q5_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { @@ -2576,7 +2923,7 @@ void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict } - if (!max_abs_scale) { + if (max_abs_scale < GROUP_MAX_EPS) { memset(&y[i], 0, sizeof(block_q6_K)); y[i].d = GGML_FP32_TO_FP16(0.f); x += QK_K; @@ -2603,7 +2950,6 @@ void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict uint8_t * restrict ql = y[i].ql; uint8_t * restrict qh = y[i].qh; -#if QK_K == 256 for (int j = 0; j < QK_K; j += 128) { for (int l = 0; l < 32; ++l) { const uint8_t q1 = L[j + l + 0] & 0xF; @@ -2617,19 +2963,8 @@ void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict ql += 64; qh += 32; } -#else - for (int l = 0; l < 32; ++l) { - const uint8_t q1 = L[l + 0] & 0xF; - const uint8_t q2 = L[l + 32] & 0xF; - ql[l] = q1 | (q2 << 4); - } - for (int l = 0; l < 16; ++l) { - qh[l] = (L[l] >> 4) | ((L[l + 16] >> 4) << 2) | ((L[l + 32] >> 4) << 4) | ((L[l + 48] >> 4) << 6); - } -#endif x += QK_K; - } } @@ -2638,14 +2973,12 @@ void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int6 const int64_t nb = k / QK_K; for (int i = 0; i < nb; i++) { - const float d = GGML_FP16_TO_FP32(x[i].d); const uint8_t * restrict ql = x[i].ql; const uint8_t * restrict qh = x[i].qh; const int8_t * restrict sc = x[i].scales; -#if QK_K == 256 for (int n = 0; n < QK_K; n += 128) { for (int l = 0; l < 32; ++l) { int is = l/16; @@ -2663,20 +2996,6 @@ void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int6 qh += 32; sc += 8; } -#else - for (int l = 0; l < 16; ++l) { - const int8_t q1 = (int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32; - const int8_t q2 = (int8_t)((ql[l+16] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32; - const int8_t q3 = (int8_t)((ql[l+ 0] >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32; - const int8_t q4 = (int8_t)((ql[l+16] >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32; - y[l+ 0] = d * sc[0] * q1; - y[l+16] = d * sc[1] * q2; - y[l+32] = d * sc[2] * q3; - y[l+48] = d * sc[3] * q4; - } - y += 64; -#endif - } } @@ -2687,10 +3006,6 @@ void quantize_row_q6_K(const float * restrict x, void * restrict vy, int64_t k) } static void quantize_row_q6_K_impl(const float * restrict x, block_q6_K * restrict y, int64_t n_per_row, const float * quant_weights) { -#if QK_K != 256 - (void)quant_weights; - quantize_row_q6_K_reference(x, y, n_per_row); -#else assert(n_per_row % QK_K == 0); const int64_t nb = n_per_row / QK_K; @@ -2728,7 +3043,7 @@ static void quantize_row_q6_K_impl(const float * restrict x, block_q6_K * restri } - if (!max_abs_scale) { + if (max_abs_scale < GROUP_MAX_EPS) { memset(&y[i], 0, sizeof(block_q6_K)); y[i].d = GGML_FP32_TO_FP16(0.f); x += QK_K; @@ -2772,7 +3087,6 @@ static void quantize_row_q6_K_impl(const float * restrict x, block_q6_K * restri x += QK_K; } -#endif } size_t quantize_q6_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { @@ -3189,30 +3503,21 @@ void dequantize_row_iq1_m(const block_iq1_m * restrict x, float * restrict y, in float delta[4]; uint16_t idx[4]; -#if QK_K != 64 iq1m_scale_t scale; -#endif for (int i = 0; i < nb; i++) { const uint16_t * sc = (const uint16_t *)x[i].scales; -#if QK_K == 64 - const float d = GGML_FP16_TO_FP32(x[i].d); -#else scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); const float d = GGML_FP16_TO_FP32(scale.f16); -#endif + const uint8_t * qs = x[i].qs; const uint8_t * qh = x[i].qh; for (int ib = 0; ib < QK_K/32; ++ib) { -#if QK_K == 64 - const float dl1 = d * (2*((sc[ib/2] >> (8*(ib%2)+0)) & 0xf) + 1); - const float dl2 = d * (2*((sc[ib/2] >> (8*(ib%2)+4)) & 0xf) + 1); -#else const float dl1 = d * (2*((sc[ib/2] >> (6*(ib%2)+0)) & 0x7) + 1); const float dl2 = d * (2*((sc[ib/2] >> (6*(ib%2)+3)) & 0x7) + 1); -#endif + idx[0] = qs[0] | ((qh[0] << 8) & 0x700); idx[1] = qs[1] | ((qh[0] << 4) & 0x700); idx[2] = qs[2] | ((qh[1] << 8) & 0x700); @@ -3263,9 +3568,6 @@ void dequantize_row_iq4_nl(const block_iq4_nl * restrict x, float * restrict y, void dequantize_row_iq4_xs(const block_iq4_xs * restrict x, float * restrict y, int64_t k) { assert(k % QK_K == 0); -#if QK_K == 64 - dequantize_row_iq4_nl((const block_iq4_nl *)x, y, k); -#else const int64_t nb = k / QK_K; for (int i = 0; i < nb; i++) { @@ -3285,7 +3587,6 @@ void dequantize_row_iq4_xs(const block_iq4_xs * restrict x, float * restrict y, qs += 16; } } -#endif } //===================================== Q8_K ============================================== @@ -3387,6 +3688,43 @@ static inline __m128i get_scale_shuffle(int i) { }; return _mm_loadu_si128((const __m128i*)k_shuffle + i); } +#elif defined(__loongarch_asx) +// shuffles to pick the required scales in dot products +static inline __m256i get_scale_shuffle_q3k(int i) { + static const uint8_t k_shuffle[128] = { + 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, + 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, + 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11, + 12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13, 14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15, + }; + return __lasx_xvld((const __m256i*)k_shuffle + i, 0); +} +static inline __m256i get_scale_shuffle_k4(int i) { + static const uint8_t k_shuffle[256] = { + 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, + 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, + 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, + 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, + 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, + 10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11, + 12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13, + 14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15 + }; + return __lasx_xvld((const __m256i*)k_shuffle + i, 0); +} +static inline __m128i get_scale_shuffle(int i) { + static const uint8_t k_shuffle[128] = { + 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, + 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, + 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, + 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, + 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, + 10,10,10,10,10,10,10,10, 11,11,11,11,11,11,11,11, + 12,12,12,12,12,12,12,12, 13,13,13,13,13,13,13,13, + 14,14,14,14,14,14,14,14, 15,15,15,15,15,15,15,15 + }; + return __lsx_vld((const __m128i*)k_shuffle + i, 0); +} #endif void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { @@ -3410,10 +3748,9 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r #if defined(__ARM_FEATURE_MATMUL_INT8) if (nrc == 2) { const block_q4_0 * restrict vx0 = vx; - const block_q4_0 * restrict vx1 = vx + bx; - + const block_q4_0 * restrict vx1 = (const block_q4_0 *) ((const uint8_t*)vx + bx); const block_q8_0 * restrict vy0 = vy; - const block_q8_0 * restrict vy1 = vy + by; + const block_q8_0 * restrict vy1 = (const block_q8_0 *) ((const uint8_t*)vy + by); float32x4_t sumv0 = vdupq_n_f32(0.0f); @@ -3447,10 +3784,12 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r const int8x16_t y1_l = vld1q_s8(b_y1->qs); const int8x16_t y1_h = vld1q_s8(b_y1->qs + 16); - float32x4_t scale = {GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y0->d), - GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y1->d), - GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y0->d), - GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y1->d)}; + float32_t _scale[4] = { GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y0->d), + GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y1->d), + GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y0->d), + GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y1->d)}; + + float32x4_t scale = vld1q_f32(_scale); int8x16_t l0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); int8x16_t l1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); @@ -3735,6 +4074,190 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r } *s = sumf; + +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + const vector signed char v8 = vec_splats((signed char)0x8); + + vector float vsumf0 = vec_splats(0.0f); + +#pragma GCC unroll 4 + for (int i = 0; i < nb; i++) { + __builtin_prefetch(x[i].qs, 0, 1); + __builtin_prefetch(y[i].qs, 0, 1); + + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[i].d)); + vector float vd = vec_mul(vxd, vyd); + + vector signed char qxs = (vector signed char)vec_xl( 0, x[i].qs); + vector signed char q8y0 = vec_xl( 0, y[i].qs); + vector signed char q8y1 = vec_xl(16, y[i].qs); + + vector signed char q4x0 = vec_and(qxs, lowMask); + vector signed char q4x1 = vec_sr(qxs, v4); + + q4x0 = vec_sub(q4x0, v8); + q4x1 = vec_sub(q4x1, v8); + + vector signed short qv0 = vec_add(vec_mule(q4x0, q8y0), vec_mulo(q4x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q4x1, q8y1), vec_mulo(q4x1, q8y1)); + + qv0 = vec_add(qv0, qv1); + + vector signed int vsumi0 = vec_add(vec_unpackh(qv0), vec_unpackl(qv0)); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + } + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + // Initialize accumulator with zeros + __m256 acc = (__m256)__lasx_xvldi(0); + + // Main loop + for (int i = 0; i < nb; ++i) { + /* Compute combined scale for the block */ + const __m256 d = __lasx_xvreplfr2vr_s( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) ); + + __m256i qx = bytes_from_nibbles_32(x[i].qs); + + // Now we have a vector with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval. + const __m256i off = __lasx_xvreplgr2vr_b( 8 ); + qx = __lasx_xvsub_b( qx, off ); + + __m256i qy = __lasx_xvld((const __m256i *)y[i].qs, 0); + + const __m256 q = mul_sum_i8_pairs_float(qx, qy); + + /* Multiply q with scale and accumulate */ + acc = __lasx_xvfmadd_s( d, q, acc ); + } + + *s = hsum_float_8(acc); +#elif defined(__loongarch_sx) + // set constants + const __m128i low_mask = __lsx_vreplgr2vr_b(0xF); + const __m128i off = __lsx_vreplgr2vr_b(8); + + // Initialize accumulator with zeros + __m128 acc_0 = __lsx_vldi(0); + __m128 acc_1 = __lsx_vldi(0); + __m128 acc_2 = __lsx_vldi(0); + __m128 acc_3 = __lsx_vldi(0); + + // First round without accumulation + { + _mm_prefetch(&x[0] + sizeof(block_q4_0), _MM_HINT_T0); + _mm_prefetch(&y[0] + sizeof(block_q8_0), _MM_HINT_T0); + + // Compute combined scale for the block 0 and 1 + const __m128 d_0_1 = __lsx_vreplgr2vr_w( GGML_FP16_TO_FP32(x[0].d) * GGML_FP16_TO_FP32(y[0].d) ); + + const __m128i tmp_0_1 = __lsx_vld((const __m128i *)x[0].qs, 0); + + __m128i bx_0 = __lsx_vand_v(low_mask, tmp_0_1); + __m128i by_0 = __lsx_vld((const __m128i *)y[0].qs, 0); + bx_0 = __lsx_vsub_b(bx_0, off); + const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0); + + __m128i bx_1 = __lsx_vand_v(low_mask, __lsx_vsrli_d(tmp_0_1, 4)); + __m128i by_1 = __lsx_vld((const __m128i *)(y[0].qs + 16), 0); + bx_1 = __lsx_vsub_b(bx_1, off); + const __m128i i32_1 = mul_sum_i8_pairs(bx_1, by_1); + + // Compute combined scale for the block 2 and 3 + const __m128 d_2_3 = __lsx_vreplgr2vr_w( GGML_FP16_TO_FP32(x[1].d) * GGML_FP16_TO_FP32(y[1].d) ); + + const __m128i tmp_2_3 = __lsx_vld((const __m128i *)x[1].qs, 0); + + __m128i bx_2 = __lsx_vand_v(low_mask, tmp_2_3); + __m128i by_2 = __lsx_vld((const __m128i *)y[1].qs, 0); + bx_2 = __lsx_vsub_b(bx_2, off); + const __m128i i32_2 = mul_sum_i8_pairs(bx_2, by_2); + + __m128i bx_3 = __lsx_vand_v(low_mask, __lsx_vsrli_d(tmp_2_3, 4)); + __m128i by_3 = __lsx_vld((const __m128i *)(y[1].qs + 16), 0); + bx_3 = __lsx_vsub_b(bx_3, off); + const __m128i i32_3 = mul_sum_i8_pairs(bx_3, by_3); + + // Convert int32_t to float + __m128 p0 = __lsx_vffint_s_w(i32_0); + __m128 p1 = __lsx_vffint_s_w(i32_1); + __m128 p2 = __lsx_vffint_s_w(i32_2); + __m128 p3 = __lsx_vffint_s_w(i32_3); + + // Apply the scale + acc_0 = __lsx_vfmul_s( d_0_1, p0 ); + acc_1 = __lsx_vfmul_s( d_0_1, p1 ); + acc_2 = __lsx_vfmul_s( d_2_3, p2 ); + acc_3 = __lsx_vfmul_s( d_2_3, p3 ); + } + + assert(nb % 2 == 0); // TODO: handle odd nb + + // Main loop + for (int i = 2; i < nb; i+=2) { + + // Compute combined scale for the block 0 and 1 + const __m128 d_0_1 = __lsx_vreplgr2vr_w( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) ); + + const __m128i tmp_0_1 = __lsx_vld((const __m128i *)x[i].qs, 0); + + __m128i bx_0 = __lsx_vand_v(low_mask, tmp_0_1); + __m128i by_0 = __lsx_vld((const __m128i *)y[i].qs, 0); + bx_0 = __lsx_vsub_b(bx_0, off); + const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0); + + __m128i bx_1 = __lsx_vand_v(low_mask, __lsx_vsrli_d(tmp_0_1, 4)); + __m128i by_1 = __lsx_vld((const __m128i *)(y[i].qs + 16), 0); + bx_1 = __lsx_vsub_b(bx_1, off); + const __m128i i32_1 = mul_sum_i8_pairs(bx_1, by_1); + + //_mm_prefetch(&x[i] + 2 * sizeof(block_q4_0), _MM_HINT_T0); + //_mm_prefetch(&y[i] + 2 * sizeof(block_q8_0), _MM_HINT_T0); + + // Compute combined scale for the block 2 and 3 + const __m128 d_2_3 = __lsx_vreplgr2vr_w( GGML_FP16_TO_FP32(x[i + 1].d) * GGML_FP16_TO_FP32(y[i + 1].d) ); + + const __m128i tmp_2_3 = __lsx_vld((const __m128i *)x[i + 1].qs, 0); + + __m128i bx_2 = __lsx_vand_v(low_mask, tmp_2_3); + __m128i by_2 = __lsx_vld((const __m128i *)y[i + 1].qs, 0); + bx_2 = __lsx_vsub_b(bx_2, off); + const __m128i i32_2 = mul_sum_i8_pairs(bx_2, by_2); + + __m128i bx_3 = __lsx_vand_v(low_mask, __lsx_vsrli_d(tmp_2_3, 4)); + __m128i by_3 = __lsx_vld((const __m128i *)(y[i + 1].qs + 16), 0); + bx_3 = __lsx_vsub_b(bx_3, off); + const __m128i i32_3 = mul_sum_i8_pairs(bx_3, by_3); + + // Convert int32_t to float + __m128 p0 = __lsx_vffint_s_w(i32_0); + __m128 p1 = __lsx_vffint_s_w(i32_1); + __m128 p2 = __lsx_vffint_s_w(i32_2); + __m128 p3 = __lsx_vffint_s_w(i32_3); + + // Apply the scale + __m128 p0_d = __lsx_vfmul_s( d_0_1, p0 ); + __m128 p1_d = __lsx_vfmul_s( d_0_1, p1 ); + __m128 p2_d = __lsx_vfmul_s( d_2_3, p2 ); + __m128 p3_d = __lsx_vfmul_s( d_2_3, p3 ); + + // Acummulate + acc_0 = __lsx_vfadd_s(p0_d, acc_0); + acc_1 = __lsx_vfadd_s(p1_d, acc_1); + acc_2 = __lsx_vfadd_s(p2_d, acc_2); + acc_3 = __lsx_vfadd_s(p3_d, acc_3); + } + + *s = hsum_float_4x4(acc_0, acc_1, acc_2, acc_3); + #else // scalar float sumf = 0.0; @@ -3777,9 +4300,9 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r #if defined(__ARM_FEATURE_MATMUL_INT8) if (nrc == 2) { const block_q4_1 * restrict vx0 = vx; - const block_q4_1 * restrict vx1 = vx + bx; + const block_q4_1 * restrict vx1 = (const block_q4_1 *) ((const uint8_t*)vx + bx); const block_q8_1 * restrict vy0 = vy; - const block_q8_1 * restrict vy1 = vy + by; + const block_q8_1 * restrict vy1 = (const block_q8_1 *) ((const uint8_t*)vy + by); float32x4_t sumv0 = vdupq_n_f32(0.0f); float32x4_t summs0 = vdupq_n_f32(0.0f); @@ -3790,11 +4313,11 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r const block_q8_1 * restrict b_y0 = &vy0[i]; const block_q8_1 * restrict b_y1 = &vy1[i]; - float32x4_t summs_t = {GGML_FP16_TO_FP32(b_x0->m) * GGML_FP16_TO_FP32(b_y0->s), - GGML_FP16_TO_FP32(b_x1->m) * GGML_FP16_TO_FP32(b_y0->s), - GGML_FP16_TO_FP32(b_x0->m) * GGML_FP16_TO_FP32(b_y1->s), - GGML_FP16_TO_FP32(b_x1->m) * GGML_FP16_TO_FP32(b_y1->s)}; - summs0 += summs_t; + float32_t summs_t[4] = {GGML_FP16_TO_FP32(b_x0->m) * GGML_FP16_TO_FP32(b_y0->s), + GGML_FP16_TO_FP32(b_x1->m) * GGML_FP16_TO_FP32(b_y0->s), + GGML_FP16_TO_FP32(b_x0->m) * GGML_FP16_TO_FP32(b_y1->s), + GGML_FP16_TO_FP32(b_x1->m) * GGML_FP16_TO_FP32(b_y1->s)}; + summs0 = vaddq_f32(summs0, vld1q_f32(summs_t)); const uint8x16_t m4b = vdupq_n_u8(0x0F); @@ -3814,10 +4337,11 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r const int8x16_t y1_h = vld1q_s8(b_y1->qs + 16); // mmla into int32x4_t - float32x4_t scale = {GGML_FP16_TO_FP32(b_x0->d)*b_y0->d, - GGML_FP16_TO_FP32(b_x0->d)*b_y1->d, - GGML_FP16_TO_FP32(b_x1->d)*b_y0->d, - GGML_FP16_TO_FP32(b_x1->d)*b_y1->d}; + float32_t _scale[4] = {GGML_FP16_TO_FP32(b_x0->d)*b_y0->d, + GGML_FP16_TO_FP32(b_x0->d)*b_y1->d, + GGML_FP16_TO_FP32(b_x1->d)*b_y0->d, + GGML_FP16_TO_FP32(b_x1->d)*b_y1->d}; + float32x4_t scale = vld1q_f32(_scale); int8x16_t l0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); int8x16_t l1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); @@ -3836,7 +4360,7 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r float32x4_t sumv1 = vextq_f32(sumv0, sumv0, 2); float32x4_t sumv2 = vzip1q_f32(sumv0, sumv1); - sumv2 = sumv2 + summs0; + sumv2 = vaddq_f32(sumv2, summs0); vst1_f32(s, vget_low_f32(sumv2)); vst1_f32(s + bs, vget_high_f32(sumv2)); @@ -3953,6 +4477,79 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r } *s = sumf; + +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + + vector float vsumf0 = vec_splats(0.0f); + +#pragma GCC unroll 4 + for (int i = 0; i < nb; i++) { + __builtin_prefetch(x[i].qs, 0, 1); + __builtin_prefetch(y[i].qs, 0, 1); + + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[i].d)); + vector float vd = vec_mul(vxd, vyd); + + vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[i].m)); + vector float vys = {GGML_FP16_TO_FP32(y[i].s), 0.0f, 0.0f, 0.0f}; + vsumf0 = vec_madd(vxmin, vys, vsumf0); + + vector signed char qxs = (vector signed char)vec_xl( 0, x[i].qs); + vector signed char q8y0 = vec_xl( 0, y[i].qs); + vector signed char q8y1 = vec_xl(16, y[i].qs); + + vector signed char q4x0 = vec_and(qxs, lowMask); + vector signed char q4x1 = vec_sr(qxs, v4); + + vector signed short qv0 = vec_add(vec_mule(q4x0, q8y0), vec_mulo(q4x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q4x1, q8y1), vec_mulo(q4x1, q8y1)); + + qv0 = vec_add(qv0, qv1); + + vector signed int vsumi0 = vec_add(vec_unpackh(qv0), vec_unpackl(qv0)); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + } + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + // Initialize accumulator with zeros + __m256 acc = (__m256)__lasx_xvldi(0); + + float summs = 0; + + // Main loop + for (int i = 0; i < nb; ++i) { + const float d0 = GGML_FP16_TO_FP32(x[i].d); + const float d1 = GGML_FP16_TO_FP32(y[i].d); + + summs += GGML_FP16_TO_FP32(x[i].m) * GGML_FP16_TO_FP32(y[i].s); + + const __m256 d0v = __lasx_xvreplfr2vr_s( d0 ); + const __m256 d1v = __lasx_xvreplfr2vr_s( d1 ); + + // Compute combined scales + const __m256 d0d1 = __lasx_xvfmul_s( d0v, d1v ); + + // Load 16 bytes, and unpack 4 bit fields into bytes, making 32 bytes + const __m256i qx = bytes_from_nibbles_32(x[i].qs); + const __m256i qy = __lasx_xvld( (const __m256i *)y[i].qs, 0); + + const __m256 xy = mul_sum_us8_pairs_float(qx, qy); + + // Accumulate d0*d1*x*y + acc = __lasx_xvfmadd_s( d0d1, xy, acc ); + } + + *s = hsum_float_8(acc) + summs; + #else // scalar float sumf = 0.0; @@ -4238,41 +4835,110 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r } *s = sumf; -#else - // scalar - float sumf = 0.0; - for (int i = 0; i < nb; i++) { - uint32_t qh; - memcpy(&qh, x[i].qh, sizeof(qh)); +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector unsigned char v4 = vec_splats((unsigned char)4); - int sumi = 0; + vector float vsumf0 = vec_splats(0.0f); - for (int j = 0; j < qk/2; ++j) { - const uint8_t xh_0 = ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4; - const uint8_t xh_1 = ((qh & (1u << (j + 16))) >> (j + 12)); +#pragma GCC unroll 4 + for (int i = 0; i < nb; ++i) { + __builtin_prefetch(x[i].qs, 0, 1); + __builtin_prefetch(y[i].qs, 0, 1); - const int32_t x0 = ((x[i].qs[j] & 0x0F) | xh_0) - 16; - const int32_t x1 = ((x[i].qs[j] >> 4) | xh_1) - 16; + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[i].d)); + vector float vd = vec_mul(vxd, vyd); - sumi += (x0 * y[i].qs[j]) + (x1 * y[i].qs[j + qk/2]); - } + vector signed long long aux64x2_0 = {(uint64_t)(table_b2b_1[x[i].qh[0]]), (uint64_t)(table_b2b_1[x[i].qh[1]])}; + vector signed long long aux64x2_1 = {(uint64_t)(table_b2b_1[x[i].qh[2]]), (uint64_t)(table_b2b_1[x[i].qh[3]])}; - sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d)) * sumi; - } + vector signed char qh0 = (vector signed char)aux64x2_0; + vector signed char qh1 = (vector signed char)aux64x2_1; - *s = sumf; -#endif -} + vector signed char qxs = (vector signed char)vec_xl( 0, x[i].qs); -void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - const int qk = QK8_1; - const int nb = n / qk; + vector signed char q5x0 = vec_sub(vec_and (qxs, lowMask), qh0); + vector signed char q5x1 = vec_sub(vec_sr(qxs, v4), qh1); - assert(n % qk == 0); - assert(qk == QK5_1); - assert(nrc == 1); - UNUSED(nrc); + vector signed char q8y0 = vec_xl( 0, y[i].qs); + vector signed char q8y1 = vec_xl( 16, y[i].qs); + + vector signed short qv0 = vec_add(vec_mule(q5x0, q8y0), vec_mulo(q5x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q5x1, q8y1), vec_mulo(q5x1, q8y1)); + + qv0 = vec_add(qv0, qv1); + + vector signed int vsumi0 = vec_add(vec_unpackh(qv0), vec_unpackl(qv0)); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + } + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + // Initialize accumulator with zeros + __m256 acc = (__m256)__lasx_xvldi(0); + + // Main loop + for (int i = 0; i < nb; i++) { + /* Compute combined scale for the block */ + const __m256 d = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d)); //FIXME + + __m256i qx = bytes_from_nibbles_32(x[i].qs); + __m256i bxhi = bytes_from_bits_32(x[i].qh); + bxhi = __lasx_xvandn_v(bxhi, __lasx_xvreplgr2vr_b((char)0xF0)); + qx = __lasx_xvor_v(qx, bxhi); + + __m256i qy = __lasx_xvld((const __m256i *)y[i].qs, 0); + + const __m256 q = mul_sum_i8_pairs_float(qx, qy); + + /* Multiply q with scale and accumulate */ + acc = __lasx_xvfmadd_s(d, q, acc); + } + + *s = hsum_float_8(acc); + +#else + // scalar + float sumf = 0.0; + + for (int i = 0; i < nb; i++) { + uint32_t qh; + memcpy(&qh, x[i].qh, sizeof(qh)); + + int sumi = 0; + + for (int j = 0; j < qk/2; ++j) { + const uint8_t xh_0 = ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4; + const uint8_t xh_1 = ((qh & (1u << (j + 16))) >> (j + 12)); + + const int32_t x0 = ((x[i].qs[j] & 0x0F) | xh_0) - 16; + const int32_t x1 = ((x[i].qs[j] >> 4) | xh_1) - 16; + + sumi += (x0 * y[i].qs[j]) + (x1 * y[i].qs[j + qk/2]); + } + + sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d)) * sumi; + } + + *s = sumf; +#endif +} + +void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + const int qk = QK8_1; + const int nb = n / qk; + + assert(n % qk == 0); + assert(qk == QK5_1); + assert(nrc == 1); + UNUSED(nrc); UNUSED(bx); UNUSED(by); UNUSED(bs); @@ -4542,6 +5208,82 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r } *s = sumf; + +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + + vector float vsumf0 = vec_splats(0.0f); + +#pragma GCC unroll 4 + for (int i = 0; i < nb; ++i) { + __builtin_prefetch(x[i].qs, 0, 1); + __builtin_prefetch(y[i].qs, 0, 1); + + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[i].d)); + vector float vd = vec_mul(vxd, vyd); + + vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[i].m)); + vector float vys = {GGML_FP16_TO_FP32(y[i].s), 0.f, 0.f, 0.f}; + vsumf0 = vec_madd(vxmin, vys, vsumf0); + + vector unsigned long long aux64x2_0 = {(uint64_t)(table_b2b_0[x[i].qh[0]]), (uint64_t)(table_b2b_0[x[i].qh[1]])}; + vector unsigned long long aux64x2_1 = {(uint64_t)(table_b2b_0[x[i].qh[2]]), (uint64_t)(table_b2b_0[x[i].qh[3]])}; + + vector signed char qh0 = (vector signed char)aux64x2_0; + vector signed char qh1 = (vector signed char)aux64x2_1; + + vector signed char qxs = (vector signed char)vec_xl( 0, x[i].qs); + + vector signed char q5x0 = vec_or(vec_and(qxs, lowMask), qh0); + vector signed char q5x1 = vec_or(vec_sr(qxs, v4), qh1); + + vector signed char q8y0 = vec_xl( 0, y[i].qs); + vector signed char q8y1 = vec_xl( 16, y[i].qs); + + vector signed short qv0 = vec_add(vec_mule(q5x0, q8y0), vec_mulo(q5x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q5x1, q8y1), vec_mulo(q5x1, q8y1)); + + qv0 = vec_add(qv0, qv1); + + vector signed int vsumi0 = vec_add(vec_unpackh(qv0), vec_unpackl(qv0)); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + } + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + // Initialize accumulator with zeros + __m256 acc = (__m256)__lasx_xvldi(0); + + float summs = 0.0f; + + // Main loop + for (int i = 0; i < nb; i++) { + const __m256 dx = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[i].d)); + + summs += GGML_FP16_TO_FP32(x[i].m) * GGML_FP16_TO_FP32(y[i].s); + + __m256i qx = bytes_from_nibbles_32(x[i].qs); + __m256i bxhi = bytes_from_bits_32(x[i].qh); + bxhi = __lasx_xvand_v(bxhi, __lasx_xvreplgr2vr_b(0x10)); + qx = __lasx_xvor_v(qx, bxhi); + + const __m256 dy = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(y[i].d)); + const __m256i qy = __lasx_xvld((const __m256i *)y[i].qs, 0); + + const __m256 q = mul_sum_us8_pairs_float(qx, qy); + + acc = __lasx_xvfmadd_s(q, __lasx_xvfmul_s(dx, dy), acc); + } + + *s = hsum_float_8(acc) + summs; + #else // scalar float sumf = 0.0; @@ -4590,9 +5332,9 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r #if defined(__ARM_FEATURE_MATMUL_INT8) if (nrc == 2) { const block_q8_0 * restrict vx0 = vx; - const block_q8_0 * restrict vx1 = vx + bx; + const block_q8_0 * restrict vx1 = (const block_q8_0 *) ((const uint8_t*)vx + bx); const block_q8_0 * restrict vy0 = vy; - const block_q8_0 * restrict vy1 = vy + by; + const block_q8_0 * restrict vy1 = (const block_q8_0 *) ((const uint8_t*)vy + by); float32x4_t sumv0 = vdupq_n_f32(0.0f); @@ -4614,10 +5356,11 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r const int8x16_t y1_l = vld1q_s8(b_y1->qs); const int8x16_t y1_h = vld1q_s8(b_y1->qs + 16); - float32x4_t scale = {GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y0->d), - GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y1->d), - GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y0->d), - GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y1->d)}; + float32_t _scale[4] = {GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y0->d), + GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y1->d), + GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y0->d), + GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y1->d)}; + float32x4_t scale = vld1q_f32(_scale); int8x16_t l0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); int8x16_t l1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); @@ -4717,6 +5460,66 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r } *s = sumf; + +#elif defined(__POWER9_VECTOR__) + vector float vsumf0 = vec_splats(0.0f); + +#pragma GCC unroll 4 + for (int i = 0; i < nb; i++) { + __builtin_prefetch(x[i].qs, 0, 1); + __builtin_prefetch(y[i].qs, 0, 1); + + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[i].d)); + vector float vd = vec_mul(vxd, vyd); + + vector signed char q8x0 = vec_xl( 0, x[i].qs); + vector signed char q8x1 = vec_xl(16, x[i].qs); + vector signed char q8y0 = vec_xl( 0, y[i].qs); + vector signed char q8y1 = vec_xl(16, y[i].qs); + + vector signed short qv0 = vec_mule(q8x0, q8y0); + vector signed short qv1 = vec_mulo(q8x0, q8y0); + vector signed short qv2 = vec_mule(q8x1, q8y1); + vector signed short qv3 = vec_mulo(q8x1, q8y1); + + vector signed int vsumi0 = vec_add(vec_unpackh(qv0), vec_unpackh(qv1)); + vector signed int vsumi1 = vec_add(vec_unpackl(qv0), vec_unpackl(qv1)); + vector signed int vsumi2 = vec_add(vec_unpackh(qv2), vec_unpackh(qv3)); + vector signed int vsumi3 = vec_add(vec_unpackl(qv2), vec_unpackl(qv3)); + + vsumi0 = vec_add(vsumi0, vsumi2); + vsumi1 = vec_add(vsumi1, vsumi3); + + vsumi0 = vec_add(vsumi0, vsumi1); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + } + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + // Initialize accumulator with zeros + __m256 acc = (__m256)__lasx_xvldi(0); + + // Main loop + for (int i = 0; i < nb; ++i) { + // Compute combined scale for the block + const __m256 d = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d)); + __m256i qx = __lasx_xvld((const __m256i *)x[i].qs, 0); + __m256i qy = __lasx_xvld((const __m256i *)y[i].qs, 0); + + const __m256 q = mul_sum_i8_pairs_float(qx, qy); + + // Multiply q with scale and accumulate + acc = __lasx_xvfmadd_s( d, q, acc ); + } + + *s = hsum_float_8(acc); + #else // scalar float sumf = 0.0; @@ -4735,7 +5538,6 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r #endif } -#if QK_K == 256 void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { assert(nrc == 1); UNUSED(nrc); @@ -5072,6 +5874,210 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r *s = sumf; +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0x3); + const vector signed char lowScaleMask = vec_splats((signed char)0xF); + const vector unsigned char v2 = vec_splats((unsigned char)0x2); + const vector unsigned char v6 = vec_splats((unsigned char)0x6); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[i].dmin)); + vector float vdmin = vec_mul(vxmin, vyd); + + vector signed short q8ysums0 = vec_xl( 0, y[i].bsums); + vector signed short q8ysums1 = vec_xl(16, y[i].bsums); + + vector signed char q2xmins = (vector signed char)vec_xl( 0, x[i].scales); + vector signed char vscales = vec_and(q2xmins, lowScaleMask); + + q2xmins = vec_sr(q2xmins, v4); + vector signed short q2xmins0 = vec_unpackh(q2xmins); + vector signed short q2xmins1 = vec_unpackl(q2xmins); + + vector signed int prod0 = vec_mule(q2xmins0, q8ysums0); + vector signed int prod1 = vec_mulo(q2xmins0, q8ysums0); + vector signed int prod2 = vec_mule(q2xmins1, q8ysums1); + vector signed int prod3 = vec_mulo(q2xmins1, q8ysums1); + + vsumf0 = vec_nmsub(vec_ctf(prod0, 0), vdmin, vsumf0); + vsumf1 = vec_nmsub(vec_ctf(prod1, 0), vdmin, vsumf1); + vsumf2 = vec_nmsub(vec_ctf(prod2, 0), vdmin, vsumf2); + vsumf3 = vec_nmsub(vec_ctf(prod3, 0), vdmin, vsumf3); + + vector signed int vsumi0 = vec_splats((int32_t)0); + vector signed int vsumi1 = vec_splats((int32_t)0); + vector signed int vsumi2 = vec_splats((int32_t)0); + vector signed int vsumi3 = vec_splats((int32_t)0); + vector signed int vsumi4 = vec_splats((int32_t)0); + vector signed int vsumi5 = vec_splats((int32_t)0); + vector signed int vsumi6 = vec_splats((int32_t)0); + vector signed int vsumi7 = vec_splats((int32_t)0); + + + for (int j = 0; j < QK_K/128; ++j) { + __builtin_prefetch(q2, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector signed char qxs0 = (vector signed char)vec_xl( 0, q2); + vector signed char qxs1 = (vector signed char)vec_xl(16, q2); + q2 += 32; + + vector signed char q2x00 = vec_and(qxs0, lowMask); + vector signed char q2x01 = vec_and(vec_sr(qxs0, v2), lowMask); + vector signed char q2x02 = vec_and(vec_sr(qxs0, v4), lowMask); + vector signed char q2x03 = vec_and(vec_sr(qxs0, v6), lowMask); + vector signed char q2x10 = vec_and(qxs1, lowMask); + vector signed char q2x11 = vec_and(vec_sr(qxs1, v2), lowMask); + vector signed char q2x12 = vec_and(vec_sr(qxs1, v4), lowMask); + vector signed char q2x13 = vec_and(vec_sr(qxs1, v6), lowMask); + + vector signed char q8y00 = vec_xl( 0, q8); + vector signed char q8y10 = vec_xl( 16, q8); + vector signed char q8y01 = vec_xl( 32, q8); + vector signed char q8y11 = vec_xl( 48, q8); + vector signed char q8y02 = vec_xl( 64, q8); + vector signed char q8y12 = vec_xl( 80, q8); + vector signed char q8y03 = vec_xl( 96, q8); + vector signed char q8y13 = vec_xl(112, q8); + q8 += 128; + + vector signed short qv0 = vec_add(vec_mule(q2x00, q8y00), vec_mulo(q2x00, q8y00)); + vector signed short qv1 = vec_add(vec_mule(q2x01, q8y01), vec_mulo(q2x01, q8y01)); + vector signed short qv2 = vec_add(vec_mule(q2x02, q8y02), vec_mulo(q2x02, q8y02)); + vector signed short qv3 = vec_add(vec_mule(q2x03, q8y03), vec_mulo(q2x03, q8y03)); + vector signed short qv4 = vec_add(vec_mule(q2x10, q8y10), vec_mulo(q2x10, q8y10)); + vector signed short qv5 = vec_add(vec_mule(q2x11, q8y11), vec_mulo(q2x11, q8y11)); + vector signed short qv6 = vec_add(vec_mule(q2x12, q8y12), vec_mulo(q2x12, q8y12)); + vector signed short qv7 = vec_add(vec_mule(q2x13, q8y13), vec_mulo(q2x13, q8y13)); + + vector signed short vscales_h = vec_unpackh(vscales); + vector signed short vs0 = vec_splat(vscales_h, 0); + vector signed short vs1 = vec_splat(vscales_h, 1); + vector signed short vs2 = vec_splat(vscales_h, 2); + vector signed short vs3 = vec_splat(vscales_h, 3); + vector signed short vs4 = vec_splat(vscales_h, 4); + vector signed short vs5 = vec_splat(vscales_h, 5); + vector signed short vs6 = vec_splat(vscales_h, 6); + vector signed short vs7 = vec_splat(vscales_h, 7); + vscales = vec_sld(vscales, vscales, 8); + + qv0 = vec_mul(qv0, vs0); + qv1 = vec_mul(qv1, vs2); + qv2 = vec_mul(qv2, vs4); + qv3 = vec_mul(qv3, vs6); + + qv0 = vec_madd(qv4, vs1, qv0); + qv1 = vec_madd(qv5, vs3, qv1); + qv2 = vec_madd(qv6, vs5, qv2); + qv3 = vec_madd(qv7, vs7, qv3); + + vsumi0 = vec_add(vec_unpackh(qv0), vsumi0); + vsumi1 = vec_add(vec_unpackh(qv1), vsumi1); + vsumi2 = vec_add(vec_unpackh(qv2), vsumi2); + vsumi3 = vec_add(vec_unpackh(qv3), vsumi3); + + vsumi4 = vec_add(vec_unpackl(qv0), vsumi4); + vsumi5 = vec_add(vec_unpackl(qv1), vsumi5); + vsumi6 = vec_add(vec_unpackl(qv2), vsumi6); + vsumi7 = vec_add(vec_unpackl(qv3), vsumi7); + } + + vsumi0 = vec_add(vsumi0, vsumi4); + vsumi1 = vec_add(vsumi1, vsumi5); + vsumi2 = vec_add(vsumi2, vsumi6); + vsumi3 = vec_add(vsumi3, vsumi7); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); + +#elif defined __loongarch_asx + + const __m256i m3 = __lasx_xvreplgr2vr_b(3); + const __m128i m4 = __lsx_vreplgr2vr_b(0xF); + + __m256 acc = (__m256)__lasx_xvldi(0); + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + + const uint8_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + const __m128i mins_and_scales = __lsx_vld((const __m128i*)x[i].scales, 0); + const __m128i scales8 = __lsx_vand_v(mins_and_scales, m4); + const __m128i mins8 = __lsx_vand_v(__lsx_vsrli_h(mins_and_scales, 4), m4); + const __m256i mins = lasx_ext8_16(mins8); + const __m256i prod = lasx_madd_h(mins, __lasx_xvld((const __m256i*)y[i].bsums, 0)); + + acc = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(dmin), __lasx_xvffint_s_w(prod), acc); + + const __m256i all_scales = lasx_ext8_16(scales8); + const __m128i l_scales = lasx_extracti128(all_scales, 0); + const __m128i h_scales = lasx_extracti128(all_scales, 1); + const __m256i scales[2] = {lasx_insertf128(l_scales, l_scales), lasx_insertf128(h_scales, h_scales)}; + + __m256i sumi = __lasx_xvldi(0); + + for (int j = 0; j < QK_K/128; ++j) { + + const __m256i q2bits = __lasx_xvld((const __m256i*)q2, 0); q2 += 32; + + const __m256i q8_0 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_1 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_2 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_3 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + + const __m256i q2_0 = __lasx_xvand_v(q2bits, m3); + const __m256i q2_1 = __lasx_xvand_v(__lasx_xvsrli_h(q2bits, 2), m3); + const __m256i q2_2 = __lasx_xvand_v(__lasx_xvsrli_h(q2bits, 4), m3); + const __m256i q2_3 = __lasx_xvand_v(__lasx_xvsrli_h(q2bits, 6), m3); + + __m256i p0 = lasx_maddubs_h(q2_0, q8_0); + __m256i p1 = lasx_maddubs_h(q2_1, q8_1); + __m256i p2 = lasx_maddubs_h(q2_2, q8_2); + __m256i p3 = lasx_maddubs_h(q2_3, q8_3); + + p0 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(0)), p0); + p1 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(1)), p1); + p2 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(2)), p2); + p3 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(3)), p3); + + p0 = __lasx_xvadd_w(p0, p1); + p2 = __lasx_xvadd_w(p2, p3); + + sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p0, p2)); + } + + acc = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(sumi), acc); + + } + + *s = hsum_float_8(acc); + #else float sumf = 0; @@ -5115,378 +6121,113 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r #endif } -#else - -void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { +void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(n % QK_K == 0); assert(nrc == 1); UNUSED(nrc); UNUSED(bx); UNUSED(by); UNUSED(bs); - const block_q2_K * restrict x = vx; + const uint32_t kmask1 = 0x03030303; + const uint32_t kmask2 = 0x0f0f0f0f; + + const block_q3_K * restrict x = vx; const block_q8_K * restrict y = vy; const int nb = n / QK_K; #ifdef __ARM_NEON - const uint8x16_t m3 = vdupq_n_u8(0x3); - const int32x4_t vzero = vdupq_n_s32(0); + uint32_t aux[3]; + uint32_t utmp[4]; + + const uint8x16_t m3b = vdupq_n_u8(0x3); + const int32x4_t vzero = vdupq_n_s32(0); - ggml_int8x16x4_t q2bytes; + const uint8x16_t m0 = vdupq_n_u8(1); + const uint8x16_t m1 = vshlq_n_u8(m0, 1); + const uint8x16_t m2 = vshlq_n_u8(m0, 2); + const uint8x16_t m3 = vshlq_n_u8(m0, 3); + const int8_t m32 = 32; - uint32_t aux32[2]; - const uint8_t * scales = (const uint8_t *)aux32; + ggml_int8x16x4_t q3bytes; float sum = 0; for (int i = 0; i < nb; ++i) { - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const uint8_t * restrict q2 = x[i].qs; + const uint8_t * restrict q3 = x[i].qs; + const uint8_t * restrict qh = x[i].hmask; const int8_t * restrict q8 = y[i].qs; - const uint32_t * restrict sc = (const uint32_t *)x[i].scales; - aux32[0] = sc[0] & 0x0f0f0f0f; - aux32[1] = (sc[0] >> 4) & 0x0f0f0f0f; + ggml_uint8x16x2_t qhbits = ggml_vld1q_u8_x2(qh); - sum += dmin * (scales[4] * y[i].bsums[0] + scales[5] * y[i].bsums[1] + scales[6] * y[i].bsums[2] + scales[7] * y[i].bsums[3]); + ggml_uint8x16x4_t q3h; - int isum1 = 0, isum2 = 0; + int32_t isum = 0; - const uint8x16_t q2bits = vld1q_u8(q2); + // Set up scales + memcpy(aux, x[i].scales, 12); + utmp[3] = ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4); + utmp[2] = ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4); + utmp[1] = (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4); + utmp[0] = (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4); - const ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(q8); + int8_t * scale = (int8_t *)utmp; + for (int j = 0; j < 16; ++j) scale[j] -= m32; - q2bytes.val[0] = vreinterpretq_s8_u8(vandq_u8(q2bits, m3)); - q2bytes.val[1] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits, 2), m3)); - q2bytes.val[2] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits, 4), m3)); - q2bytes.val[3] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits, 6), m3)); + for (int j = 0; j < QK_K/128; ++j) { - isum1 += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[0], q8bytes.val[0])) * scales[0]; - isum2 += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[1], q8bytes.val[1])) * scales[1]; - isum1 += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[2], q8bytes.val[2])) * scales[2]; - isum2 += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[3], q8bytes.val[3])) * scales[3]; + const ggml_uint8x16x2_t q3bits = ggml_vld1q_u8_x2(q3); q3 += 32; + const ggml_int8x16x4_t q8bytes_1 = ggml_vld1q_s8_x4(q8); q8 += 64; + const ggml_int8x16x4_t q8bytes_2 = ggml_vld1q_s8_x4(q8); q8 += 64; - sum += d * (isum1 + isum2); - } + q3h.val[0] = vshlq_n_u8(vbicq_u8(m0, qhbits.val[0]), 2); + q3h.val[1] = vshlq_n_u8(vbicq_u8(m0, qhbits.val[1]), 2); + q3h.val[2] = vshlq_n_u8(vbicq_u8(m1, qhbits.val[0]), 1); + q3h.val[3] = vshlq_n_u8(vbicq_u8(m1, qhbits.val[1]), 1); - *s = sum; + q3bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(q3bits.val[0], m3b)), vreinterpretq_s8_u8(q3h.val[0])); + q3bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(q3bits.val[1], m3b)), vreinterpretq_s8_u8(q3h.val[1])); + q3bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 2), m3b)), vreinterpretq_s8_u8(q3h.val[2])); + q3bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 2), m3b)), vreinterpretq_s8_u8(q3h.val[3])); -#elif defined __AVX2__ + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[0], q8bytes_1.val[0])) * scale[0]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[1], q8bytes_1.val[1])) * scale[1]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[2], q8bytes_1.val[2])) * scale[2]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[3], q8bytes_1.val[3])) * scale[3]; - const __m256i m3 = _mm256_set1_epi8(3); + scale += 4; - __m256 acc = _mm256_setzero_ps(); + q3h.val[0] = vbicq_u8(m2, qhbits.val[0]); + q3h.val[1] = vbicq_u8(m2, qhbits.val[1]); + q3h.val[2] = vshrq_n_u8(vbicq_u8(m3, qhbits.val[0]), 1); + q3h.val[3] = vshrq_n_u8(vbicq_u8(m3, qhbits.val[1]), 1); - uint32_t ud, um; - const uint8_t * restrict db = (const uint8_t *)&ud; - const uint8_t * restrict mb = (const uint8_t *)&um; + q3bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 4), m3b)), vreinterpretq_s8_u8(q3h.val[0])); + q3bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 4), m3b)), vreinterpretq_s8_u8(q3h.val[1])); + q3bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 6), m3b)), vreinterpretq_s8_u8(q3h.val[2])); + q3bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 6), m3b)), vreinterpretq_s8_u8(q3h.val[3])); - float summs = 0; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[0], q8bytes_2.val[0])) * scale[0]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[1], q8bytes_2.val[1])) * scale[1]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[2], q8bytes_2.val[2])) * scale[2]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[3], q8bytes_2.val[3])) * scale[3]; - // TODO: optimize this + scale += 4; - for (int i = 0; i < nb; ++i) { + if (j == 0) { + qhbits.val[0] = vshrq_n_u8(qhbits.val[0], 4); + qhbits.val[1] = vshrq_n_u8(qhbits.val[1], 4); + } - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + } + sum += d * isum; - const uint8_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const uint32_t * restrict sc = (const uint32_t *)x[i].scales; - ud = (sc[0] >> 0) & 0x0f0f0f0f; - um = (sc[0] >> 4) & 0x0f0f0f0f; - - int32_t smin = mb[0] * y[i].bsums[0] + mb[1] * y[i].bsums[1] + mb[2] * y[i].bsums[2] + mb[3] * y[i].bsums[3]; - summs += dmin * smin; - - const __m128i q2bits = _mm_loadu_si128((const __m128i*)q2); - const __m256i q2_0 = _mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(q2bits, 2), q2bits), m3); - const __m256i q2_1 = _mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(q2bits, 6), _mm_srli_epi16(q2bits, 4)), m3); - - const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0)); - const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32)); - - const __m256i p0 = _mm256_maddubs_epi16(q2_0, q8_0); - const __m256i p1 = _mm256_maddubs_epi16(q2_1, q8_1); - - const __m256i p_0 = _mm256_cvtepi16_epi32(_mm256_extracti128_si256(p0, 0)); - const __m256i p_1 = _mm256_cvtepi16_epi32(_mm256_extracti128_si256(p0, 1)); - const __m256i p_2 = _mm256_cvtepi16_epi32(_mm256_extracti128_si256(p1, 0)); - const __m256i p_3 = _mm256_cvtepi16_epi32(_mm256_extracti128_si256(p1, 1)); - - acc = _mm256_fmadd_ps(_mm256_set1_ps(d * db[0]), _mm256_cvtepi32_ps(p_0), acc); - acc = _mm256_fmadd_ps(_mm256_set1_ps(d * db[1]), _mm256_cvtepi32_ps(p_1), acc); - acc = _mm256_fmadd_ps(_mm256_set1_ps(d * db[2]), _mm256_cvtepi32_ps(p_2), acc); - acc = _mm256_fmadd_ps(_mm256_set1_ps(d * db[3]), _mm256_cvtepi32_ps(p_3), acc); - } - - *s = hsum_float_8(acc) + summs; - -#elif defined __AVX__ - - const __m128i m3 = _mm_set1_epi8(3); - - __m256 acc = _mm256_setzero_ps(); - - uint32_t ud, um; - const uint8_t * restrict db = (const uint8_t *)&ud; - const uint8_t * restrict mb = (const uint8_t *)&um; - - float summs = 0; - - // TODO: optimize this - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - const uint8_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const uint32_t * restrict sc = (const uint32_t *)x[i].scales; - ud = (sc[0] >> 0) & 0x0f0f0f0f; - um = (sc[0] >> 4) & 0x0f0f0f0f; - - int32_t smin = mb[0] * y[i].bsums[0] + mb[1] * y[i].bsums[1] + mb[2] * y[i].bsums[2] + mb[3] * y[i].bsums[3]; - summs += dmin * smin; - - const __m128i q2bits = _mm_loadu_si128((const __m128i*)q2); - const __m128i q2_0 = _mm_and_si128(q2bits, m3); - const __m128i q2_1 = _mm_and_si128(_mm_srli_epi16(q2bits, 2), m3); - const __m128i q2_2 = _mm_and_si128(_mm_srli_epi16(q2bits, 4), m3); - const __m128i q2_3 = _mm_and_si128(_mm_srli_epi16(q2bits, 6), m3); - - const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0)); - const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32)); - - const __m128i p0 = _mm_maddubs_epi16(q2_0, _mm256_extractf128_si256(q8_0, 0)); - const __m128i p1 = _mm_maddubs_epi16(q2_1, _mm256_extractf128_si256(q8_0, 1)); - const __m128i p2 = _mm_maddubs_epi16(q2_2, _mm256_extractf128_si256(q8_1, 0)); - const __m128i p3 = _mm_maddubs_epi16(q2_3, _mm256_extractf128_si256(q8_1, 1)); - - const __m256i p_0 = MM256_SET_M128I(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p0, p0)), _mm_cvtepi16_epi32(p0)); - const __m256i p_1 = MM256_SET_M128I(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p1, p1)), _mm_cvtepi16_epi32(p1)); - const __m256i p_2 = MM256_SET_M128I(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p2, p2)), _mm_cvtepi16_epi32(p2)); - const __m256i p_3 = MM256_SET_M128I(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p3, p3)), _mm_cvtepi16_epi32(p3)); - - acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d * db[0]), _mm256_cvtepi32_ps(p_0)), acc); - acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d * db[1]), _mm256_cvtepi32_ps(p_1)), acc); - acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d * db[2]), _mm256_cvtepi32_ps(p_2)), acc); - acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d * db[3]), _mm256_cvtepi32_ps(p_3)), acc); - } - - *s = hsum_float_8(acc) + summs; - -#elif defined __riscv_v_intrinsic - - uint32_t aux32[2]; - const uint8_t * scales = (const uint8_t *)aux32; - - float sumf = 0; - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - const uint8_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - const uint32_t * restrict sc = (const uint32_t *)x[i].scales; - - aux32[0] = sc[0] & 0x0f0f0f0f; - aux32[1] = (sc[0] >> 4) & 0x0f0f0f0f; - - sumf += dmin * (scales[4] * y[i].bsums[0] + scales[5] * y[i].bsums[1] + scales[6] * y[i].bsums[2] + scales[7] * y[i].bsums[3]); - - int isum1 = 0; - int isum2 = 0; - - size_t vl = 16; - - vint16m1_t vzero = __riscv_vmv_v_x_i16m1(0, 1); - - // load Q2 - vuint8mf2_t q2_x = __riscv_vle8_v_u8mf2(q2, vl); - - vint8mf2_t q2_0 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vand_vx_u8mf2(q2_x, 0x03, vl)); - vint8mf2_t q2_1 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(q2_x, 0x2, vl), 0x03 , vl)); - vint8mf2_t q2_2 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(q2_x, 0x4, vl), 0x03 , vl)); - vint8mf2_t q2_3 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(q2_x, 0x6, vl), 0x03 , vl)); - - // load Q8, and take product with Q2 - vint16m1_t p0 = __riscv_vwmul_vv_i16m1(q2_0, __riscv_vle8_v_i8mf2(q8, vl), vl); - vint16m1_t p1 = __riscv_vwmul_vv_i16m1(q2_1, __riscv_vle8_v_i8mf2(q8+16, vl), vl); - vint16m1_t p2 = __riscv_vwmul_vv_i16m1(q2_2, __riscv_vle8_v_i8mf2(q8+32, vl), vl); - vint16m1_t p3 = __riscv_vwmul_vv_i16m1(q2_3, __riscv_vle8_v_i8mf2(q8+48, vl), vl); - - vint16m1_t vs_0 = __riscv_vredsum_vs_i16m1_i16m1(p0, vzero, vl); - vint16m1_t vs_1 = __riscv_vredsum_vs_i16m1_i16m1(p1, vzero, vl); - vint16m1_t vs_2 = __riscv_vredsum_vs_i16m1_i16m1(p2, vzero, vl); - vint16m1_t vs_3 = __riscv_vredsum_vs_i16m1_i16m1(p3, vzero, vl); - - isum1 += __riscv_vmv_x_s_i16m1_i16(vs_0) * scales[0]; - isum2 += __riscv_vmv_x_s_i16m1_i16(vs_1) * scales[1]; - isum1 += __riscv_vmv_x_s_i16m1_i16(vs_2) * scales[2]; - isum2 += __riscv_vmv_x_s_i16m1_i16(vs_3) * scales[3]; - - sumf += d * (isum1 + isum2); - - } - - *s = sumf; - -#else - - float sumf = 0; - - int isum[QK_K/16]; - - for (int i = 0; i < nb; ++i) { - - const uint8_t * q2 = x[i].qs; - const int8_t * q8 = y[i].qs; - const uint8_t * sc = x[i].scales; - - int summs = 0; - for (int j = 0; j < QK_K/16; ++j) { - summs += y[i].bsums[j] * (sc[j] >> 4); - } - - const float dall = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - memset(isum, 0, (QK_K/16)*sizeof(int)); - for (int l = 0; l < 16; ++l) { - isum[0] += q8[l+ 0] * ((q2[l] >> 0) & 3); - isum[1] += q8[l+16] * ((q2[l] >> 2) & 3); - isum[2] += q8[l+32] * ((q2[l] >> 4) & 3); - isum[3] += q8[l+48] * ((q2[l] >> 6) & 3); - } - for (int l = 0; l < QK_K/16; ++l) { - isum[l] *= (sc[l] & 0xF); - } - sumf += dall * (isum[0] + isum[1] + isum[2] + isum[3]) - dmin * summs; - } - *s = sumf; -#endif -} -#endif - -#if QK_K == 256 -void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(n % QK_K == 0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const uint32_t kmask1 = 0x03030303; - const uint32_t kmask2 = 0x0f0f0f0f; - - const block_q3_K * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#ifdef __ARM_NEON - - uint32_t aux[3]; - uint32_t utmp[4]; - - const uint8x16_t m3b = vdupq_n_u8(0x3); - const int32x4_t vzero = vdupq_n_s32(0); - - const uint8x16_t m0 = vdupq_n_u8(1); - const uint8x16_t m1 = vshlq_n_u8(m0, 1); - const uint8x16_t m2 = vshlq_n_u8(m0, 2); - const uint8x16_t m3 = vshlq_n_u8(m0, 3); - const int8_t m32 = 32; - - ggml_int8x16x4_t q3bytes; - - float sum = 0; - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q3 = x[i].qs; - const uint8_t * restrict qh = x[i].hmask; - const int8_t * restrict q8 = y[i].qs; - - ggml_uint8x16x2_t qhbits = ggml_vld1q_u8_x2(qh); - - ggml_uint8x16x4_t q3h; - - int32_t isum = 0; - - // Set up scales - memcpy(aux, x[i].scales, 12); - utmp[3] = ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4); - utmp[2] = ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4); - utmp[1] = (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4); - utmp[0] = (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4); - - int8_t * scale = (int8_t *)utmp; - for (int j = 0; j < 16; ++j) scale[j] -= m32; - - for (int j = 0; j < QK_K/128; ++j) { - - const ggml_uint8x16x2_t q3bits = ggml_vld1q_u8_x2(q3); q3 += 32; - const ggml_int8x16x4_t q8bytes_1 = ggml_vld1q_s8_x4(q8); q8 += 64; - const ggml_int8x16x4_t q8bytes_2 = ggml_vld1q_s8_x4(q8); q8 += 64; - - q3h.val[0] = vshlq_n_u8(vbicq_u8(m0, qhbits.val[0]), 2); - q3h.val[1] = vshlq_n_u8(vbicq_u8(m0, qhbits.val[1]), 2); - q3h.val[2] = vshlq_n_u8(vbicq_u8(m1, qhbits.val[0]), 1); - q3h.val[3] = vshlq_n_u8(vbicq_u8(m1, qhbits.val[1]), 1); - - q3bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(q3bits.val[0], m3b)), vreinterpretq_s8_u8(q3h.val[0])); - q3bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(q3bits.val[1], m3b)), vreinterpretq_s8_u8(q3h.val[1])); - q3bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 2), m3b)), vreinterpretq_s8_u8(q3h.val[2])); - q3bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 2), m3b)), vreinterpretq_s8_u8(q3h.val[3])); - - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[0], q8bytes_1.val[0])) * scale[0]; - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[1], q8bytes_1.val[1])) * scale[1]; - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[2], q8bytes_1.val[2])) * scale[2]; - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[3], q8bytes_1.val[3])) * scale[3]; - - scale += 4; - - q3h.val[0] = vbicq_u8(m2, qhbits.val[0]); - q3h.val[1] = vbicq_u8(m2, qhbits.val[1]); - q3h.val[2] = vshrq_n_u8(vbicq_u8(m3, qhbits.val[0]), 1); - q3h.val[3] = vshrq_n_u8(vbicq_u8(m3, qhbits.val[1]), 1); - - q3bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 4), m3b)), vreinterpretq_s8_u8(q3h.val[0])); - q3bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 4), m3b)), vreinterpretq_s8_u8(q3h.val[1])); - q3bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 6), m3b)), vreinterpretq_s8_u8(q3h.val[2])); - q3bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 6), m3b)), vreinterpretq_s8_u8(q3h.val[3])); - - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[0], q8bytes_2.val[0])) * scale[0]; - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[1], q8bytes_2.val[1])) * scale[1]; - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[2], q8bytes_2.val[2])) * scale[2]; - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[3], q8bytes_2.val[3])) * scale[3]; - - scale += 4; - - if (j == 0) { - qhbits.val[0] = vshrq_n_u8(qhbits.val[0], 4); - qhbits.val[1] = vshrq_n_u8(qhbits.val[1], 4); - } - - } - sum += d * isum; - - } + } *s = sum; @@ -5800,446 +6541,354 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r m <<= 1; // load Q8 and take product with Q3 - vint16m2_t a0 = __riscv_vwmul_vv_i16m2(q3_m0, __riscv_vle8_v_i8m1(q8, vl), vl); - vint16m2_t a1 = __riscv_vwmul_vv_i16m2(q3_m1, __riscv_vle8_v_i8m1(q8+32, vl), vl); - vint16m2_t a2 = __riscv_vwmul_vv_i16m2(q3_m2, __riscv_vle8_v_i8m1(q8+64, vl), vl); - vint16m2_t a3 = __riscv_vwmul_vv_i16m2(q3_m3, __riscv_vle8_v_i8m1(q8+96, vl), vl); - - vl = 16; - - // retrieve lane to multiply with scale - vint32m2_t aux0_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a0, 0), (scale[0]), vl); - vint32m2_t aux0_1 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a0, 1), (scale[1]), vl); - vint32m2_t aux1_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a1, 0), (scale[2]), vl); - vint32m2_t aux1_1 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a1, 1), (scale[3]), vl); - vint32m2_t aux2_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a2, 0), (scale[4]), vl); - vint32m2_t aux2_1 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a2, 1), (scale[5]), vl); - vint32m2_t aux3_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a3, 0), (scale[6]), vl); - vint32m2_t aux3_1 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a3, 1), (scale[7]), vl); - - vint32m1_t isum0 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(aux0_0, aux0_1, vl), vzero, vl); - vint32m1_t isum1 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(aux1_0, aux1_1, vl), isum0, vl); - vint32m1_t isum2 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(aux2_0, aux2_1, vl), isum1, vl); - vint32m1_t isum3 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(aux3_0, aux3_1, vl), isum2, vl); - - sum_t += __riscv_vmv_x_s_i32m1_i32(isum3); - - q3 += 32; q8 += 128; scale += 8; - - } - - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - - sumf += d*sum_t; - - } - - *s = sumf; - -#else - // scalar version - // This function is written like this so the compiler can manage to vectorize most of it - // Using -Ofast, GCC and clang manage to produce code that is within a factor of 2 or so from the - // manually vectorized version above. Every other version I tried would run at least 4 times slower. - // The ideal situation would be if we could just write the code once, and the compiler would - // automatically produce the best possible set of machine instructions, instead of us having to manually - // write vectorized versions for AVX, ARM_NEON, etc. - - int8_t aux8[QK_K]; - int16_t aux16[8]; - float sums [8]; - int32_t aux32[8]; - memset(sums, 0, 8*sizeof(float)); - - uint32_t auxs[4]; - const int8_t * scales = (const int8_t*)auxs; - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - const uint8_t * restrict q3 = x[i].qs; - const uint8_t * restrict hm = x[i].hmask; - const int8_t * restrict q8 = y[i].qs; - memset(aux32, 0, 8*sizeof(int32_t)); - int8_t * restrict a = aux8; - uint8_t m = 1; - for (int j = 0; j < QK_K; j += 128) { - for (int l = 0; l < 32; ++l) a[l] = q3[l] & 3; - for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4); - a += 32; m <<= 1; - for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 2) & 3; - for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4); - a += 32; m <<= 1; - for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 4) & 3; - for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4); - a += 32; m <<= 1; - for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 6) & 3; - for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4); - a += 32; m <<= 1; - q3 += 32; - } - a = aux8; - - memcpy(auxs, x[i].scales, 12); - uint32_t tmp = auxs[2]; - auxs[2] = ((auxs[0] >> 4) & kmask2) | (((tmp >> 4) & kmask1) << 4); - auxs[3] = ((auxs[1] >> 4) & kmask2) | (((tmp >> 6) & kmask1) << 4); - auxs[0] = (auxs[0] & kmask2) | (((tmp >> 0) & kmask1) << 4); - auxs[1] = (auxs[1] & kmask2) | (((tmp >> 2) & kmask1) << 4); - for (int j = 0; j < QK_K/16; ++j) { - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l]; - q8 += 8; a += 8; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l]; - q8 += 8; a += 8; - } - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l]; - } - for (int l = 0; l < 8; ++l) sumf += sums[l]; - *s = sumf; - -#endif - -} - -#else - -void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(n % QK_K == 0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_q3_K * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#ifdef __ARM_NEON - const int32x4_t vzero = vdupq_n_s32(0); - - const uint8x16_t m3b = vdupq_n_u8(0x3); - const uint8x16_t mh = vdupq_n_u8(4); - - ggml_int8x16x4_t q3bytes; - - uint16_t aux16[2]; - int8_t * scales = (int8_t *)aux16; - - float sum = 0; - - for (int i = 0; i < nb; ++i) { - - ggml_uint8x16x4_t q3h; - - const uint8x8_t hbits = vld1_u8(x[i].hmask); - const uint8x16_t q3bits = vld1q_u8(x[i].qs); - const ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(y[i].qs); - - const uint16_t a = *(const uint16_t *)x[i].scales; - aux16[0] = a & 0x0f0f; - aux16[1] = (a >> 4) & 0x0f0f; - - for (int j = 0; j < 4; ++j) scales[j] -= 8; - - int32_t isum = -4*(scales[0] * y[i].bsums[0] + scales[2] * y[i].bsums[1] + scales[1] * y[i].bsums[2] + scales[3] * y[i].bsums[3]); - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - const uint8x16_t htmp = vcombine_u8(hbits, vshr_n_u8(hbits, 1)); - q3h.val[0] = vandq_u8(mh, vshlq_n_u8(htmp, 2)); - q3h.val[1] = vandq_u8(mh, htmp); - q3h.val[2] = vandq_u8(mh, vshrq_n_u8(htmp, 2)); - q3h.val[3] = vandq_u8(mh, vshrq_n_u8(htmp, 4)); - - q3bytes.val[0] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q3bits, m3b), q3h.val[0])); - q3bytes.val[1] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(vshrq_n_u8(q3bits, 2), m3b), q3h.val[1])); - q3bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(vshrq_n_u8(q3bits, 4), m3b), q3h.val[2])); - q3bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q3bits, 6), q3h.val[3])); - - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[0], q8bytes.val[0])) * scales[0]; - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[1], q8bytes.val[1])) * scales[2]; - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[2], q8bytes.val[2])) * scales[1]; - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[3], q8bytes.val[3])) * scales[3]; - - sum += d * isum; - - } - - *s = sum; - -#elif defined __AVX2__ - - const __m256i m3 = _mm256_set1_epi8(3); - const __m256i m1 = _mm256_set1_epi8(1); - - __m256 acc = _mm256_setzero_ps(); - - uint64_t aux64; - - uint16_t aux16[2]; - const int8_t * aux8 = (const int8_t *)aux16; - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q3 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const uint16_t a = *(const uint16_t *)x[i].scales; - aux16[0] = a & 0x0f0f; - aux16[1] = (a >> 4) & 0x0f0f; - - const __m256i scale_0 = MM256_SET_M128I(_mm_set1_epi16(aux8[2] - 8), _mm_set1_epi16(aux8[0] - 8)); - const __m256i scale_1 = MM256_SET_M128I(_mm_set1_epi16(aux8[3] - 8), _mm_set1_epi16(aux8[1] - 8)); - - memcpy(&aux64, x[i].hmask, 8); + vint16m2_t a0 = __riscv_vwmul_vv_i16m2(q3_m0, __riscv_vle8_v_i8m1(q8, vl), vl); + vint16m2_t a1 = __riscv_vwmul_vv_i16m2(q3_m1, __riscv_vle8_v_i8m1(q8+32, vl), vl); + vint16m2_t a2 = __riscv_vwmul_vv_i16m2(q3_m2, __riscv_vle8_v_i8m1(q8+64, vl), vl); + vint16m2_t a3 = __riscv_vwmul_vv_i16m2(q3_m3, __riscv_vle8_v_i8m1(q8+96, vl), vl); - const __m128i haux = _mm_set_epi64x(aux64 >> 1, aux64 >> 0); - __m256i q3h_0 = MM256_SET_M128I(_mm_srli_epi16(haux, 2), haux); - __m256i q3h_1 = _mm256_srli_epi16(q3h_0, 4); - q3h_0 = _mm256_slli_epi16(_mm256_andnot_si256(q3h_0, m1), 2); - q3h_1 = _mm256_slli_epi16(_mm256_andnot_si256(q3h_1, m1), 2); + vl = 16; - // load low 2 bits - const __m128i q3bits = _mm_loadu_si128((const __m128i*)q3); + // retrieve lane to multiply with scale + vint32m2_t aux0_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a0, 0), (scale[0]), vl); + vint32m2_t aux0_1 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a0, 1), (scale[1]), vl); + vint32m2_t aux1_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a1, 0), (scale[2]), vl); + vint32m2_t aux1_1 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a1, 1), (scale[3]), vl); + vint32m2_t aux2_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a2, 0), (scale[4]), vl); + vint32m2_t aux2_1 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a2, 1), (scale[5]), vl); + vint32m2_t aux3_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a3, 0), (scale[6]), vl); + vint32m2_t aux3_1 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a3, 1), (scale[7]), vl); - // prepare low and high bits - const __m256i q3aux = MM256_SET_M128I(_mm_srli_epi16(q3bits, 2), q3bits); - const __m256i q3l_0 = _mm256_and_si256(q3aux, m3); - const __m256i q3l_1 = _mm256_and_si256(_mm256_srli_epi16(q3aux, 4), m3); + vint32m1_t isum0 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(aux0_0, aux0_1, vl), vzero, vl); + vint32m1_t isum1 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(aux1_0, aux1_1, vl), isum0, vl); + vint32m1_t isum2 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(aux2_0, aux2_1, vl), isum1, vl); + vint32m1_t isum3 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(aux3_0, aux3_1, vl), isum2, vl); - // load Q8 quants - const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0)); - const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32)); + sum_t += __riscv_vmv_x_s_i32m1_i32(isum3); - // Dot product: we multiply the 2 low bits and 1 high bit part separately, so we can use _mm256_maddubs_epi16, - // and then subtract. The high bit part has the 2 already subtracted (and so, it is zero if the high bit was not set, - // and 2 if the high bit was set) - const __m256i q8s_0 = _mm256_maddubs_epi16(q3h_0, q8_0); - const __m256i q8s_1 = _mm256_maddubs_epi16(q3h_1, q8_1); + q3 += 32; q8 += 128; scale += 8; - __m256i p16_0 = _mm256_maddubs_epi16(q3l_0, q8_0); - __m256i p16_1 = _mm256_maddubs_epi16(q3l_1, q8_1); + } - p16_0 = _mm256_sub_epi16(p16_0, q8s_0); - p16_1 = _mm256_sub_epi16(p16_1, q8s_1); + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - // multiply with scales - p16_0 = _mm256_madd_epi16(scale_0, p16_0); - p16_1 = _mm256_madd_epi16(scale_1, p16_1); + sumf += d*sum_t; - p16_0 = _mm256_add_epi32(p16_0, p16_1); + } - // multiply with block scale and accumulate - acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(p16_0), acc); + *s = sumf; - } +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0x3); + const vector signed char v1 = vec_splats((signed char)0x1); + const vector unsigned char v2 = vec_splats((unsigned char)0x2); + const vector unsigned char v3 = vec_splats((unsigned char)0x3); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + const vector unsigned char v6 = vec_splats((unsigned char)0x6); + const vector signed char off = vec_splats((signed char)0x20); - *s = hsum_float_8(acc); + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); -#elif defined __AVX__ + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); - const __m128i m3 = _mm_set1_epi8(3); - const __m128i m1 = _mm_set1_epi8(1); + uint32_t aux[3]; + uint32_t utmp[4]; - __m256 acc = _mm256_setzero_ps(); + memcpy(aux, x[i].scales, 12); + utmp[3] = ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4); + utmp[2] = ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4); + utmp[1] = (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4); + utmp[0] = (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4); - uint64_t aux64; + vector signed char vscales = (vector signed char)vec_xl( 0, utmp); + vector signed char qxhs0 = (vector signed char)vec_xl( 0, x[i].hmask); + vector signed char qxhs1 = (vector signed char)vec_xl(16, x[i].hmask); - uint16_t aux16[2]; - const int8_t * aux8 = (const int8_t *)aux16; + vscales = vec_sub(vscales, off); - for (int i = 0; i < nb; ++i) { + vector signed int vsumi0 = vec_splats((int32_t)0); + vector signed int vsumi1 = vec_splats((int32_t)0); + vector signed int vsumi2 = vec_splats((int32_t)0); + vector signed int vsumi3 = vec_splats((int32_t)0); + vector signed int vsumi4 = vec_splats((int32_t)0); + vector signed int vsumi5 = vec_splats((int32_t)0); + vector signed int vsumi6 = vec_splats((int32_t)0); + vector signed int vsumi7 = vec_splats((int32_t)0); - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); const uint8_t * restrict q3 = x[i].qs; const int8_t * restrict q8 = y[i].qs; - const uint16_t a = *(const uint16_t *)x[i].scales; - aux16[0] = a & 0x0f0f; - aux16[1] = (a >> 4) & 0x0f0f; - - const __m128i scale_0 = _mm_set1_epi16(aux8[0] - 8); - const __m128i scale_1 = _mm_set1_epi16(aux8[2] - 8); - const __m128i scale_2 = _mm_set1_epi16(aux8[1] - 8); - const __m128i scale_3 = _mm_set1_epi16(aux8[3] - 8); - - memcpy(&aux64, x[i].hmask, 8); - - __m128i q3h_0 = _mm_set_epi64x(aux64 >> 1, aux64 >> 0); - __m128i q3h_1 = _mm_srli_epi16(q3h_0, 2); - __m128i q3h_2 = _mm_srli_epi16(q3h_0, 4); - __m128i q3h_3 = _mm_srli_epi16(q3h_0, 6); - q3h_0 = _mm_slli_epi16(_mm_andnot_si128(q3h_0, m1), 2); - q3h_1 = _mm_slli_epi16(_mm_andnot_si128(q3h_1, m1), 2); - q3h_2 = _mm_slli_epi16(_mm_andnot_si128(q3h_2, m1), 2); - q3h_3 = _mm_slli_epi16(_mm_andnot_si128(q3h_3, m1), 2); - - // load low 2 bits - const __m128i q3bits = _mm_loadu_si128((const __m128i*)q3); - - // prepare low and high bits - const __m128i q3l_0 = _mm_and_si128(q3bits, m3); - const __m128i q3l_1 = _mm_and_si128(_mm_srli_epi16(q3bits, 2), m3); - const __m128i q3l_2 = _mm_and_si128(_mm_srli_epi16(q3bits, 4), m3); - const __m128i q3l_3 = _mm_and_si128(_mm_srli_epi16(q3bits, 6), m3); - - // load Q8 quants - const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0)); - const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32)); - - // Dot product: we multiply the 2 low bits and 1 high bit part separately, so we can use _mm_maddubs_epi16, - // and then subtract. The high bit part has the 2 already subtracted (and so, it is zero if the high bit was not set, - // and 2 if the high bit was set) - const __m128i q8s_0 = _mm_maddubs_epi16(q3h_0, _mm256_extractf128_si256(q8_0, 0)); - const __m128i q8s_1 = _mm_maddubs_epi16(q3h_1, _mm256_extractf128_si256(q8_0, 1)); - const __m128i q8s_2 = _mm_maddubs_epi16(q3h_2, _mm256_extractf128_si256(q8_1, 0)); - const __m128i q8s_3 = _mm_maddubs_epi16(q3h_3, _mm256_extractf128_si256(q8_1, 1)); - - __m128i p16_0 = _mm_maddubs_epi16(q3l_0, _mm256_extractf128_si256(q8_0, 0)); - __m128i p16_1 = _mm_maddubs_epi16(q3l_1, _mm256_extractf128_si256(q8_0, 1)); - __m128i p16_2 = _mm_maddubs_epi16(q3l_2, _mm256_extractf128_si256(q8_1, 0)); - __m128i p16_3 = _mm_maddubs_epi16(q3l_3, _mm256_extractf128_si256(q8_1, 1)); - - p16_0 = _mm_sub_epi16(p16_0, q8s_0); - p16_1 = _mm_sub_epi16(p16_1, q8s_1); - p16_2 = _mm_sub_epi16(p16_2, q8s_2); - p16_3 = _mm_sub_epi16(p16_3, q8s_3); - - // multiply with scales - p16_0 = _mm_madd_epi16(scale_0, p16_0); - p16_1 = _mm_madd_epi16(scale_1, p16_1); - p16_2 = _mm_madd_epi16(scale_2, p16_2); - p16_3 = _mm_madd_epi16(scale_3, p16_3); - - p16_0 = _mm_add_epi32(p16_0, p16_2); - p16_1 = _mm_add_epi32(p16_1, p16_3); - __m256i p16 = MM256_SET_M128I(p16_1, p16_0); - - // multiply with block scale and accumulate - acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(p16)), acc); - - } - - *s = hsum_float_8(acc); + for (int j = 0; j < QK_K/128; ++j) { + __builtin_prefetch(q3, 0, 1); + __builtin_prefetch(q8, 0, 1); -#elif defined __riscv_v_intrinsic + vector signed char qxs0 = (vector signed char)vec_xl( 0, q3); + vector signed char qxs1 = (vector signed char)vec_xl(16, q3); + q3 += 32; - uint16_t aux16[2]; - int8_t * scales = (int8_t *)aux16; + //the low 2 bits + vector signed char qxs00 = vec_and(qxs0, lowMask); + vector signed char qxs01 = vec_and(vec_sr(qxs0, v2), lowMask); + vector signed char qxs02 = vec_and(vec_sr(qxs0, v4), lowMask); + vector signed char qxs03 = vec_and(vec_sr(qxs0, v6), lowMask); + vector signed char qxs10 = vec_and(qxs1, lowMask); + vector signed char qxs11 = vec_and(vec_sr(qxs1, v2), lowMask); + vector signed char qxs12 = vec_and(vec_sr(qxs1, v4), lowMask); + vector signed char qxs13 = vec_and(vec_sr(qxs1, v6), lowMask); + + //the 3rd bit + vector signed char qxh00 = vec_sl(vec_andc(v1, qxhs0), v2); + vector signed char qxh01 = vec_sl(vec_andc(v1, vec_sr(qxhs0, (vector unsigned char)v1)), v2); + vector signed char qxh02 = vec_sl(vec_andc(v1, vec_sr(qxhs0, v2)), v2); + vector signed char qxh03 = vec_sl(vec_andc(v1, vec_sr(qxhs0, v3)), v2); + vector signed char qxh10 = vec_sl(vec_andc(v1, qxhs1), v2); + vector signed char qxh11 = vec_sl(vec_andc(v1, vec_sr(qxhs1, (vector unsigned char)v1)), v2); + vector signed char qxh12 = vec_sl(vec_andc(v1, vec_sr(qxhs1, v2)), v2); + vector signed char qxh13 = vec_sl(vec_andc(v1, vec_sr(qxhs1, v3)), v2); + qxhs0 = vec_sr(qxhs0, v4); + qxhs1 = vec_sr(qxhs1, v4); + + vector signed char q3x00 = vec_sub(qxs00, qxh00); + vector signed char q3x01 = vec_sub(qxs01, qxh01); + vector signed char q3x02 = vec_sub(qxs02, qxh02); + vector signed char q3x03 = vec_sub(qxs03, qxh03); + vector signed char q3x10 = vec_sub(qxs10, qxh10); + vector signed char q3x11 = vec_sub(qxs11, qxh11); + vector signed char q3x12 = vec_sub(qxs12, qxh12); + vector signed char q3x13 = vec_sub(qxs13, qxh13); + + vector signed char q8y00 = vec_xl( 0, q8); + vector signed char q8y10 = vec_xl( 16, q8); + vector signed char q8y01 = vec_xl( 32, q8); + vector signed char q8y11 = vec_xl( 48, q8); + vector signed char q8y02 = vec_xl( 64, q8); + vector signed char q8y12 = vec_xl( 80, q8); + vector signed char q8y03 = vec_xl( 96, q8); + vector signed char q8y13 = vec_xl(112, q8); + q8 += 128; + + vector signed short vscales_h = vec_unpackh(vscales); + vector signed short vs0 = vec_splat(vscales_h, 0); + vector signed short vs1 = vec_splat(vscales_h, 1); + vector signed short vs2 = vec_splat(vscales_h, 2); + vector signed short vs3 = vec_splat(vscales_h, 3); + vector signed short vs4 = vec_splat(vscales_h, 4); + vector signed short vs5 = vec_splat(vscales_h, 5); + vector signed short vs6 = vec_splat(vscales_h, 6); + vector signed short vs7 = vec_splat(vscales_h, 7); + vscales = vec_sld(vscales, vscales, 8); + + vector signed short qv00 = vec_add(vec_mule(q3x00, q8y00), vec_mulo(q3x00, q8y00)); + vector signed short qv01 = vec_add(vec_mule(q3x01, q8y01), vec_mulo(q3x01, q8y01)); + vector signed short qv02 = vec_add(vec_mule(q3x02, q8y02), vec_mulo(q3x02, q8y02)); + vector signed short qv03 = vec_add(vec_mule(q3x03, q8y03), vec_mulo(q3x03, q8y03)); + vector signed short qv10 = vec_add(vec_mule(q3x10, q8y10), vec_mulo(q3x10, q8y10)); + vector signed short qv11 = vec_add(vec_mule(q3x11, q8y11), vec_mulo(q3x11, q8y11)); + vector signed short qv12 = vec_add(vec_mule(q3x12, q8y12), vec_mulo(q3x12, q8y12)); + vector signed short qv13 = vec_add(vec_mule(q3x13, q8y13), vec_mulo(q3x13, q8y13)); + + vector signed int vsum0 = vec_add(vec_mule(qv00, vs0), vec_mulo(qv00, vs0)); + vector signed int vsum1 = vec_add(vec_mule(qv01, vs2), vec_mulo(qv01, vs2)); + vector signed int vsum2 = vec_add(vec_mule(qv02, vs4), vec_mulo(qv02, vs4)); + vector signed int vsum3 = vec_add(vec_mule(qv03, vs6), vec_mulo(qv03, vs6)); + vector signed int vsum4 = vec_add(vec_mule(qv10, vs1), vec_mulo(qv10, vs1)); + vector signed int vsum5 = vec_add(vec_mule(qv11, vs3), vec_mulo(qv11, vs3)); + vector signed int vsum6 = vec_add(vec_mule(qv12, vs5), vec_mulo(qv12, vs5)); + vector signed int vsum7 = vec_add(vec_mule(qv13, vs7), vec_mulo(qv13, vs7)); + + vsumi0 = vec_add(vsum0, vsumi0); + vsumi1 = vec_add(vsum1, vsumi1); + vsumi2 = vec_add(vsum2, vsumi2); + vsumi3 = vec_add(vsum3, vsumi3); + vsumi4 = vec_add(vsum4, vsumi4); + vsumi5 = vec_add(vsum5, vsumi5); + vsumi6 = vec_add(vsum6, vsumi6); + vsumi7 = vec_add(vsum7, vsumi7); + } + + vsumi0 = vec_add(vsumi0, vsumi4); + vsumi1 = vec_add(vsumi1, vsumi5); + vsumi2 = vec_add(vsumi2, vsumi6); + vsumi3 = vec_add(vsumi3, vsumi7); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); + +#elif defined __loongarch_asx + + const __m256i m3 = __lasx_xvreplgr2vr_b(3); + const __m256i mone = __lasx_xvreplgr2vr_b(1); + const __m128i m32 = __lsx_vreplgr2vr_b(32); + + __m256 acc = (__m256)__lasx_xvldi(0); - float sumf = 0; + uint32_t aux[3]; for (int i = 0; i < nb; ++i) { - const uint8_t * restrict q3 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const uint16_t a = *(const uint16_t *)x[i].scales; - aux16[0] = a & 0x0f0f; - aux16[1] = (a >> 4) & 0x0f0f; - - for (int j = 0; j < 4; ++j) scales[j] -= 8; + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + // Set up scales + memcpy(aux, x[i].scales, 12); + __m128i scales128 = lsx_set_w( + ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4), + ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4), + (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4), + (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4)); + scales128 = __lsx_vsub_b(scales128, m32); + const __m256i all_scales = lasx_ext8_16(scales128); + const __m128i l_scales = lasx_extracti128(all_scales, 0); + const __m128i h_scales = lasx_extracti128(all_scales, 1); + const __m256i scales[2] = {lasx_insertf128(l_scales, l_scales), lasx_insertf128(h_scales, h_scales)}; - int32_t isum = -4*(scales[0] * y[i].bsums[0] + scales[2] * y[i].bsums[1] + scales[1] * y[i].bsums[2] + scales[3] * y[i].bsums[3]); + // high bit + const __m256i hbits = __lasx_xvld((const __m256i*)x[i].hmask, 0); - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + // integer accumulator + __m256i sumi = __lasx_xvldi(0); - vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1); + int bit = 0; + int is = 0; - // load qh - vuint8mf4_t qh_x1 = __riscv_vle8_v_u8mf4(x[i].hmask, 8); - vuint8mf2_t qh_x2 = __riscv_vlmul_ext_v_u8mf4_u8mf2(__riscv_vsrl_vx_u8mf4(qh_x1, 1, 8)); + const uint8_t * restrict q3 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; - size_t vl = 16; + for (int j = 0; j < QK_K/128; ++j) { + // load low 2 bits + const __m256i q3bits = __lasx_xvld((const __m256i*)q3, 0); q3 += 32; - // extend and combine both qh_x1 and qh_x2 - vuint8mf2_t qh_x = __riscv_vslideup_vx_u8mf2(__riscv_vlmul_ext_v_u8mf4_u8mf2(qh_x1), qh_x2, vl/2, vl); + // prepare low and high bits + const __m256i q3l_0 = __lasx_xvand_v(q3bits, m3); + const __m256i q3h_0 = __lasx_xvslli_h(__lasx_xvsrli_h(__lasx_xvandn_v(hbits, __lasx_xvslli_h(mone, bit)), bit), 2); + ++bit; - vuint8mf2_t qh_0 = __riscv_vand_vx_u8mf2(__riscv_vsll_vx_u8mf2(qh_x, 0x2, vl), 0x4, vl); - vuint8mf2_t qh_1 = __riscv_vand_vx_u8mf2(qh_x, 0x4, vl); - vuint8mf2_t qh_2 = __riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(qh_x, 0x2, vl), 0x4, vl); - vuint8mf2_t qh_3 = __riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(qh_x, 0x4, vl), 0x4, vl); + const __m256i q3l_1 = __lasx_xvand_v(__lasx_xvsrli_h(q3bits, 2), m3); + const __m256i q3h_1 = __lasx_xvslli_h(__lasx_xvsrli_h(__lasx_xvandn_v(hbits, __lasx_xvslli_h(mone, bit)), bit), 2); + ++bit; - // load Q3 - vuint8mf2_t q3_x = __riscv_vle8_v_u8mf2(q3, vl); + const __m256i q3l_2 = __lasx_xvand_v(__lasx_xvsrli_h(q3bits, 4), m3); + const __m256i q3h_2 = __lasx_xvslli_h(__lasx_xvsrli_h(__lasx_xvandn_v(hbits, __lasx_xvslli_h(mone, bit)), bit), 2); + ++bit; - vuint8mf2_t q3h_0 = __riscv_vor_vv_u8mf2(__riscv_vand_vx_u8mf2(q3_x, 0x3, vl), qh_0, vl); - vuint8mf2_t q3h_1 = __riscv_vor_vv_u8mf2(__riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(q3_x, 2, vl), 0x3, vl), qh_1, vl); - vuint8mf2_t q3h_2 = __riscv_vor_vv_u8mf2(__riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(q3_x, 4, vl), 0x3, vl), qh_2, vl); - vuint8mf2_t q3h_3 = __riscv_vor_vv_u8mf2(__riscv_vsrl_vx_u8mf2(q3_x, 0x6, vl), qh_3, vl); + const __m256i q3l_3 = __lasx_xvand_v(__lasx_xvsrli_h(q3bits, 6), m3); + const __m256i q3h_3 = __lasx_xvslli_h(__lasx_xvsrli_h(__lasx_xvandn_v(hbits, __lasx_xvslli_h(mone, bit)), bit), 2); + ++bit; - vint8mf2_t q3_0 = __riscv_vreinterpret_v_u8mf2_i8mf2(q3h_0); - vint8mf2_t q3_1 = __riscv_vreinterpret_v_u8mf2_i8mf2(q3h_1); - vint8mf2_t q3_2 = __riscv_vreinterpret_v_u8mf2_i8mf2(q3h_2); - vint8mf2_t q3_3 = __riscv_vreinterpret_v_u8mf2_i8mf2(q3h_3); + // load Q8 quants + const __m256i q8_0 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_1 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_2 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_3 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - // load Q8 and take product with Q3 - vint16m1_t p0 = __riscv_vwmul_vv_i16m1(q3_0, __riscv_vle8_v_i8mf2(q8, vl), vl); - vint16m1_t p1 = __riscv_vwmul_vv_i16m1(q3_1, __riscv_vle8_v_i8mf2(q8+16, vl), vl); - vint16m1_t p2 = __riscv_vwmul_vv_i16m1(q3_2, __riscv_vle8_v_i8mf2(q8+32, vl), vl); - vint16m1_t p3 = __riscv_vwmul_vv_i16m1(q3_3, __riscv_vle8_v_i8mf2(q8+48, vl), vl); + // Dot product: we multiply the 2 low bits and 1 high bit part separately, so we can use lasx_maddubs_h, + // and then subtract. The high bit part has the 2 already subtracted (and so, it is zero if the high bit was not set, + // and 2 if the high bit was set) + __m256i q8s_0 = lasx_maddubs_h(q3h_0, q8_0); + __m256i q8s_1 = lasx_maddubs_h(q3h_1, q8_1); + __m256i q8s_2 = lasx_maddubs_h(q3h_2, q8_2); + __m256i q8s_3 = lasx_maddubs_h(q3h_3, q8_3); - vint32m1_t vs_0 = __riscv_vwredsum_vs_i16m1_i32m1(p0, vzero, vl); - vint32m1_t vs_1 = __riscv_vwredsum_vs_i16m1_i32m1(p1, vzero, vl); - vint32m1_t vs_2 = __riscv_vwredsum_vs_i16m1_i32m1(p2, vzero, vl); - vint32m1_t vs_3 = __riscv_vwredsum_vs_i16m1_i32m1(p3, vzero, vl); + __m256i p16_0 = lasx_maddubs_h(q3l_0, q8_0); + __m256i p16_1 = lasx_maddubs_h(q3l_1, q8_1); + __m256i p16_2 = lasx_maddubs_h(q3l_2, q8_2); + __m256i p16_3 = lasx_maddubs_h(q3l_3, q8_3); - isum += __riscv_vmv_x_s_i32m1_i32(vs_0) * scales[0]; - isum += __riscv_vmv_x_s_i32m1_i32(vs_1) * scales[2]; - isum += __riscv_vmv_x_s_i32m1_i32(vs_2) * scales[1]; - isum += __riscv_vmv_x_s_i32m1_i32(vs_3) * scales[3]; + p16_0 = __lasx_xvsub_h(p16_0, q8s_0); + p16_1 = __lasx_xvsub_h(p16_1, q8s_1); + p16_2 = __lasx_xvsub_h(p16_2, q8s_2); + p16_3 = __lasx_xvsub_h(p16_3, q8s_3); - sumf += d * isum; + // multiply with scales + p16_0 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(is + 0)), p16_0); + p16_1 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(is + 1)), p16_1); + p16_2 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(is + 2)), p16_2); + p16_3 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(is + 3)), p16_3); + // accumulate + p16_0 = __lasx_xvadd_w(p16_0, p16_1); + p16_2 = __lasx_xvadd_w(p16_2, p16_3); + sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p16_0, p16_2)); + } + // multiply with block scale and accumulate + acc = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(sumi), acc);//FIXME } - *s = sumf; + *s = hsum_float_8(acc); #else + // scalar version + // This function is written like this so the compiler can manage to vectorize most of it + // Using -Ofast, GCC and clang manage to produce code that is within a factor of 2 or so from the + // manually vectorized version above. Every other version I tried would run at least 4 times slower. + // The ideal situation would be if we could just write the code once, and the compiler would + // automatically produce the best possible set of machine instructions, instead of us having to manually + // write vectorized versions for AVX, ARM_NEON, etc. int8_t aux8[QK_K]; int16_t aux16[8]; float sums [8]; int32_t aux32[8]; - int32_t scales[4]; memset(sums, 0, 8*sizeof(float)); + uint32_t auxs[4]; + const int8_t * scales = (const int8_t*)auxs; + float sumf = 0; for (int i = 0; i < nb; ++i) { const uint8_t * restrict q3 = x[i].qs; const uint8_t * restrict hm = x[i].hmask; const int8_t * restrict q8 = y[i].qs; + memset(aux32, 0, 8*sizeof(int32_t)); int8_t * restrict a = aux8; - for (int l = 0; l < 8; ++l) { - a[l+ 0] = (int8_t)((q3[l+0] >> 0) & 3) - (hm[l] & 0x01 ? 0 : 4); - a[l+ 8] = (int8_t)((q3[l+8] >> 0) & 3) - (hm[l] & 0x02 ? 0 : 4); - a[l+16] = (int8_t)((q3[l+0] >> 2) & 3) - (hm[l] & 0x04 ? 0 : 4); - a[l+24] = (int8_t)((q3[l+8] >> 2) & 3) - (hm[l] & 0x08 ? 0 : 4); - a[l+32] = (int8_t)((q3[l+0] >> 4) & 3) - (hm[l] & 0x10 ? 0 : 4); - a[l+40] = (int8_t)((q3[l+8] >> 4) & 3) - (hm[l] & 0x20 ? 0 : 4); - a[l+48] = (int8_t)((q3[l+0] >> 6) & 3) - (hm[l] & 0x40 ? 0 : 4); - a[l+56] = (int8_t)((q3[l+8] >> 6) & 3) - (hm[l] & 0x80 ? 0 : 4); - } - - scales[0] = (x[i].scales[0] & 0xF) - 8; - scales[1] = (x[i].scales[0] >> 4) - 8; - scales[2] = (x[i].scales[1] & 0xF) - 8; - scales[3] = (x[i].scales[1] >> 4) - 8; + uint8_t m = 1; + for (int j = 0; j < QK_K; j += 128) { + for (int l = 0; l < 32; ++l) a[l] = q3[l] & 3; + for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4); + a += 32; m <<= 1; + for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 2) & 3; + for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4); + a += 32; m <<= 1; + for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 4) & 3; + for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4); + a += 32; m <<= 1; + for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 6) & 3; + for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4); + a += 32; m <<= 1; + q3 += 32; + } + a = aux8; - memset(aux32, 0, 8*sizeof(int32_t)); + memcpy(auxs, x[i].scales, 12); + uint32_t tmp = auxs[2]; + auxs[2] = ((auxs[0] >> 4) & kmask2) | (((tmp >> 4) & kmask1) << 4); + auxs[3] = ((auxs[1] >> 4) & kmask2) | (((tmp >> 6) & kmask1) << 4); + auxs[0] = (auxs[0] & kmask2) | (((tmp >> 0) & kmask1) << 4); + auxs[1] = (auxs[1] & kmask2) | (((tmp >> 2) & kmask1) << 4); for (int j = 0; j < QK_K/16; ++j) { for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l]; q8 += 8; a += 8; - for (int l = 0; l < 8; ++l) aux16[l] += q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l]; q8 += 8; a += 8; - for (int l = 0; l < 8; ++l) aux32[l] += scales[j] * aux16[l]; } const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l]; @@ -6250,9 +6899,7 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r #endif } -#endif -#if QK_K == 256 void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { assert(n % QK_K == 0); assert(nrc == 1); @@ -6548,320 +7195,269 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r } - sumf += d*(sum_1 + sum_2); - - } - - *s = sumf; - -#else - - - const uint8_t * scales = (const uint8_t*)&utmp[0]; - const uint8_t * mins = (const uint8_t*)&utmp[2]; - - int8_t aux8[QK_K]; - int16_t aux16[8]; - float sums [8]; - int32_t aux32[8]; - memset(sums, 0, 8*sizeof(float)); - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - const uint8_t * restrict q4 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - memset(aux32, 0, 8*sizeof(int32_t)); - int8_t * restrict a = aux8; - for (int j = 0; j < QK_K/64; ++j) { - for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF); - a += 32; - for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] >> 4); - a += 32; q4 += 32; - } - memcpy(utmp, x[i].scales, 12); - utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); - const uint32_t uaux = utmp[1] & kmask1; - utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); - utmp[2] = uaux; - utmp[0] &= kmask1; - - int sumi = 0; - for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2]; - a = aux8; - int is = 0; - for (int j = 0; j < QK_K/32; ++j) { - int32_t scale = scales[is++]; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; - q8 += 8; a += 8; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; - q8 += 8; a += 8; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; - q8 += 8; a += 8; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; - q8 += 8; a += 8; - } - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l]; - const float dmin = GGML_FP16_TO_FP32(x[i].dmin) * y[i].d; - sumf -= dmin * sumi; - } - for (int l = 0; l < 8; ++l) sumf += sums[l]; - *s = sumf; -#endif -} -#else -void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(n % QK_K == 0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_q4_K * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#ifdef __ARM_NEON - const uint8x16_t m4b = vdupq_n_u8(0xf); - - const int32x4_t mzero = vdupq_n_s32(0); - - float sumf = 0; - - ggml_int8x16x2_t q4bytes; - ggml_int8x16x4_t q8bytes; - - float sum_mins = 0.f; - - uint16_t aux16[2]; - const uint8_t * restrict scales = (const uint8_t *)aux16; - - for (int i = 0; i < nb; ++i) { - - const uint8_t * restrict q4 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const uint16_t * restrict a = (const uint16_t *)x[i].scales; - aux16[0] = a[0] & 0x0f0f; - aux16[1] = (a[0] >> 4) & 0x0f0f; - - const int32_t summi = scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3]); - sum_mins += y[i].d * GGML_FP16_TO_FP32(x[i].d[1]) * summi; - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d[0]); - - const ggml_uint8x16x2_t q4bits = ggml_vld1q_u8_x2(q4); - - q8bytes = ggml_vld1q_s8_x4(q8); - q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[0], m4b)); - q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[1], m4b)); - - const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]); - const int32_t sumi1 = vaddvq_s32(p1) * scales[0]; - - q4bytes.val[0] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[0], 4)); - q4bytes.val[1] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[1], 4)); - - const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[2]), q4bytes.val[1], q8bytes.val[3]); - const int32_t sumi2 = vaddvq_s32(p2) * scales[1]; - - sumf += d * (sumi1 + sumi2); - } - - *s = sumf - sum_mins; - -#elif defined __AVX2__ - - const __m256i m4 = _mm256_set1_epi8(0xF); - - __m256 acc = _mm256_setzero_ps(); - - float summs = 0; - - uint16_t aux16[2]; - const uint8_t * scales = (const uint8_t *)aux16; - - for (int i = 0; i < nb; ++i) { - - const float d = GGML_FP16_TO_FP32(x[i].d[0]) * y[i].d; - const float m = GGML_FP16_TO_FP32(x[i].d[1]) * y[i].d; - const __m256 vd = _mm256_set1_ps(d); - - const uint16_t * a = (const uint16_t *)x[i].scales; - aux16[0] = a[0] & 0x0f0f; - aux16[1] = (a[0] >> 4) & 0x0f0f; - - summs += m * (scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3])); - - const uint8_t * restrict q4 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const __m256i q4bits = _mm256_loadu_si256((const __m256i*)q4); - const __m256i q4l = _mm256_and_si256(q4bits, m4); - const __m256i q4h = _mm256_and_si256(_mm256_srli_epi16(q4bits, 4), m4); - - const __m256i q8l = _mm256_loadu_si256((const __m256i*)(q8+ 0)); - const __m256i q8h = _mm256_loadu_si256((const __m256i*)(q8+32)); - - const __m256i p16l = _mm256_maddubs_epi16(q4l, q8l); - const __m256i p16h = _mm256_maddubs_epi16(q4h, q8h); - - const __m256i p32l = _mm256_madd_epi16(_mm256_set1_epi16(scales[0]), p16l); - acc = _mm256_fmadd_ps(vd, _mm256_cvtepi32_ps(p32l), acc); - - const __m256i p32h = _mm256_madd_epi16(_mm256_set1_epi16(scales[1]), p16h); - acc = _mm256_fmadd_ps(vd, _mm256_cvtepi32_ps(p32h), acc); + sumf += d*(sum_1 + sum_2); } - *s = hsum_float_8(acc) - summs; - -#elif defined __AVX__ + *s = sumf; - const __m128i m4 = _mm_set1_epi8(0xF); +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); - __m256 acc = _mm256_setzero_ps(); + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); - float summs = 0; + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); - uint16_t aux16[2]; - const uint8_t * scales = (const uint8_t *)aux16; + vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[i].dmin)); + vector float vdmin = vec_mul(vxmin, vyd); - for (int i = 0; i < nb; ++i) { + vector signed short q8ysums0 = vec_xl( 0, y[i].bsums); + vector signed short q8ysums1 = vec_xl(16, y[i].bsums); - const float d = GGML_FP16_TO_FP32(x[i].d[0]) * y[i].d; - const float m = GGML_FP16_TO_FP32(x[i].d[1]) * y[i].d; - const __m256 vd = _mm256_set1_ps(d); + memcpy(utmp, x[i].scales, 12); - const uint16_t * a = (const uint16_t *)x[i].scales; - aux16[0] = a[0] & 0x0f0f; - aux16[1] = (a[0] >> 4) & 0x0f0f; + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; - summs += m * (scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3])); + vector signed char utmps = (vector signed char)vec_xl( 0, utmp); + vector signed short vscales = vec_unpackh(utmps); + vector signed short q4xmins = vec_unpackl(utmps); + vector signed short q4xmins0 = vec_mergeh(q4xmins, q4xmins); + vector signed short q4xmins1 = vec_mergel(q4xmins, q4xmins); + + vector signed int prod0 = vec_mule(q4xmins0, q8ysums0); + vector signed int prod1 = vec_mule(q4xmins1, q8ysums1); + vector signed int prod2 = vec_mulo(q4xmins0, q8ysums0); + vector signed int prod3 = vec_mulo(q4xmins1, q8ysums1); + + vsumf0 = vec_nmsub(vec_ctf(prod0, 0), vdmin, vsumf0); + vsumf1 = vec_nmsub(vec_ctf(prod1, 0), vdmin, vsumf1); + vsumf2 = vec_nmsub(vec_ctf(prod2, 0), vdmin, vsumf2); + vsumf3 = vec_nmsub(vec_ctf(prod3, 0), vdmin, vsumf3); + + vector signed int vsumi0 = vec_splats((int32_t)0); + vector signed int vsumi1 = vec_splats((int32_t)0); + vector signed int vsumi2 = vec_splats((int32_t)0); + vector signed int vsumi3 = vec_splats((int32_t)0); + vector signed int vsumi4 = vec_splats((int32_t)0); + vector signed int vsumi5 = vec_splats((int32_t)0); + vector signed int vsumi6 = vec_splats((int32_t)0); + vector signed int vsumi7 = vec_splats((int32_t)0); const uint8_t * restrict q4 = x[i].qs; const int8_t * restrict q8 = y[i].qs; - const __m256i q4bits = _mm256_loadu_si256((const __m256i*)q4); - const __m128i q4bits_0 = _mm256_extractf128_si256(q4bits, 0); - const __m128i q4bits_1 = _mm256_extractf128_si256(q4bits, 1); - const __m128i q4_0 = _mm_and_si128(q4bits_0, m4); - const __m128i q4_1 = _mm_and_si128(q4bits_1, m4); - const __m128i q4_2 = _mm_and_si128(_mm_srli_epi16(q4bits_0, 4), m4); - const __m128i q4_3 = _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4); - - const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0)); - const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32)); + for (int j = 0; j < QK_K/64; j+=2) { + __builtin_prefetch(q4, 0, 1); + __builtin_prefetch(q8, 0, 1); - const __m128i p16_0 = _mm_maddubs_epi16(q4_0, _mm256_extractf128_si256(q8_0, 0)); - const __m128i p16_1 = _mm_maddubs_epi16(q4_1, _mm256_extractf128_si256(q8_0, 1)); - const __m128i p16_2 = _mm_maddubs_epi16(q4_2, _mm256_extractf128_si256(q8_1, 0)); - const __m128i p16_3 = _mm_maddubs_epi16(q4_3, _mm256_extractf128_si256(q8_1, 1)); - - const __m128i p32_0 = _mm_madd_epi16(_mm_set1_epi16(scales[0]), p16_0); - const __m128i p32_1 = _mm_madd_epi16(_mm_set1_epi16(scales[0]), p16_1); - acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(MM256_SET_M128I(p32_1, p32_0))), acc); - - const __m128i p32_2 = _mm_madd_epi16(_mm_set1_epi16(scales[1]), p16_2); - const __m128i p32_3 = _mm_madd_epi16(_mm_set1_epi16(scales[1]), p16_3); - acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(MM256_SET_M128I(p32_3, p32_2))), acc); - - } - - *s = hsum_float_8(acc) - summs; + vector signed char qxs0 = (vector signed char)vec_xl( 0, q4); + vector signed char qxs1 = (vector signed char)vec_xl(16, q4); + vector signed char qxs2 = (vector signed char)vec_xl(32, q4); + vector signed char qxs3 = (vector signed char)vec_xl(48, q4); + q4 += 64; -#elif defined __riscv_v_intrinsic + vector signed char q4x00 = vec_and(qxs0, lowMask); + vector signed char q4x01 = vec_sr(qxs0, v4); + vector signed char q4x10 = vec_and(qxs1, lowMask); + vector signed char q4x11 = vec_sr(qxs1, v4); + vector signed char q4x20 = vec_and(qxs2, lowMask); + vector signed char q4x21 = vec_sr(qxs2, v4); + vector signed char q4x30 = vec_and(qxs3, lowMask); + vector signed char q4x31 = vec_sr(qxs3, v4); + + vector signed char q8y00 = vec_xl( 0, q8); + vector signed char q8y10 = vec_xl( 16, q8); + vector signed char q8y01 = vec_xl( 32, q8); + vector signed char q8y11 = vec_xl( 48, q8); + vector signed char q8y20 = vec_xl( 64, q8); + vector signed char q8y30 = vec_xl( 80, q8); + vector signed char q8y21 = vec_xl( 96, q8); + vector signed char q8y31 = vec_xl(112, q8); + q8 += 128; + + vector signed short qv00 = vec_add(vec_mule(q4x00, q8y00), vec_mulo(q4x00, q8y00)); + vector signed short qv01 = vec_add(vec_mule(q4x01, q8y01), vec_mulo(q4x01, q8y01)); + vector signed short qv10 = vec_add(vec_mule(q4x10, q8y10), vec_mulo(q4x10, q8y10)); + vector signed short qv11 = vec_add(vec_mule(q4x11, q8y11), vec_mulo(q4x11, q8y11)); + vector signed short qv20 = vec_add(vec_mule(q4x20, q8y20), vec_mulo(q4x20, q8y20)); + vector signed short qv21 = vec_add(vec_mule(q4x21, q8y21), vec_mulo(q4x21, q8y21)); + vector signed short qv30 = vec_add(vec_mule(q4x30, q8y30), vec_mulo(q4x30, q8y30)); + vector signed short qv31 = vec_add(vec_mule(q4x31, q8y31), vec_mulo(q4x31, q8y31)); + + vector signed short vs0 = vec_splat(vscales, 0); + vector signed short vs1 = vec_splat(vscales, 1); + vector signed short vs2 = vec_splat(vscales, 2); + vector signed short vs3 = vec_splat(vscales, 3); + vscales = vec_sld(vscales, vscales, 8); + + qv00 = vec_add(qv00, qv10); + qv10 = vec_add(qv01, qv11); + qv20 = vec_add(qv20, qv30); + qv30 = vec_add(qv21, qv31); + + vsumi0 = vec_add(vec_mule(qv00, vs0), vsumi0); + vsumi1 = vec_add(vec_mulo(qv00, vs0), vsumi1); + vsumi2 = vec_add(vec_mule(qv10, vs1), vsumi2); + vsumi3 = vec_add(vec_mulo(qv10, vs1), vsumi3); + vsumi4 = vec_add(vec_mule(qv20, vs2), vsumi4); + vsumi5 = vec_add(vec_mulo(qv20, vs2), vsumi5); + vsumi6 = vec_add(vec_mule(qv30, vs3), vsumi6); + vsumi7 = vec_add(vec_mulo(qv30, vs3), vsumi7); + } + + vsumi0 = vec_add(vsumi0, vsumi4); + vsumi1 = vec_add(vsumi1, vsumi5); + vsumi2 = vec_add(vsumi2, vsumi6); + vsumi3 = vec_add(vsumi3, vsumi7); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); + +#elif defined __loongarch_asx + + const __m256i m4 = __lasx_xvreplgr2vr_b(0xF); + + __m256 acc = (__m256)__lasx_xvldi(0); + __m128 acc_m = (__m128)__lsx_vldi(0); - uint16_t s16[2]; - const uint8_t * restrict scales = (const uint8_t *)s16; + for (int i = 0; i < nb; ++i) { - float sumf = 0; + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - for (int i = 0; i < nb; ++i) { + memcpy(utmp, x[i].scales, 12); const uint8_t * restrict q4 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; + const int8_t * restrict q8 = y[i].qs; - const uint16_t * restrict b = (const uint16_t *)x[i].scales; - s16[0] = b[0] & 0x0f0f; - s16[1] = (b[0] >> 4) & 0x0f0f; + const __m256i mins_and_scales = lasx_extu8_16(lsx_set_w(utmp[3], utmp[2], utmp[1], utmp[0])); - sumf -= y[i].d * GGML_FP16_TO_FP32(x[i].d[1]) * (scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3])); - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d[0]); + const __m256i q8sums = __lasx_xvld((const __m256i*)y[i].bsums, 0); + const __m128i q8s = lsx_hadd_h(lasx_extracti128(q8sums, 0), lasx_extracti128(q8sums, 1)); + const __m128i prod = lsx_madd_h(lasx_extracti128(mins_and_scales, 1), q8s); + acc_m = __lsx_vfmadd_s(__lsx_vreplfr2vr_s(dmin), __lsx_vffint_s_w(prod), acc_m); - size_t vl = 32; + const __m128i sc128 = lasx_extracti128(mins_and_scales, 0); + const __m256i scales = lasx_insertf128(sc128, sc128); - vint16m1_t vzero = __riscv_vmv_v_x_i16m1(0, 1); + __m256i sumi = __lasx_xvldi(0); + + for (int j = 0; j < QK_K/64; ++j) { - // load Q4 - vuint8m1_t q4_x = __riscv_vle8_v_u8m1(q4, vl); + const __m256i scale_l = lasx_shuffle_b(scales, get_scale_shuffle_k4(2*j+0)); + const __m256i scale_h = lasx_shuffle_b(scales, get_scale_shuffle_k4(2*j+1)); - // load Q8 and multiply it with lower Q4 nibble - vint8m1_t q4_a = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(q4_x, 0x0F, vl)); - vint16m2_t va_0 = __riscv_vwmul_vv_i16m2(q4_a, __riscv_vle8_v_i8m1(q8, vl), vl); - vint16m1_t aux1 = __riscv_vredsum_vs_i16m2_i16m1(va_0, vzero, vl); + const __m256i q4bits = __lasx_xvld((const __m256i*)q4, 0); q4 += 32; + const __m256i q4l = __lasx_xvand_v(q4bits, m4); + const __m256i q4h = __lasx_xvand_v(__lasx_xvsrli_h(q4bits, 4), m4); - sumf += d*scales[0]*__riscv_vmv_x_s_i16m1_i16(aux1); + const __m256i q8l = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + __m256i p16l = lasx_maddubs_h(q4l, q8l); + p16l = lasx_madd_h(scale_l, p16l); - // load Q8 and multiply it with upper Q4 nibble - vint8m1_t q4_s = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vsrl_vx_u8m1(q4_x, 0x04, vl)); - vint16m2_t va_1 = __riscv_vwmul_vv_i16m2(q4_s, __riscv_vle8_v_i8m1(q8+32, vl), vl); - vint16m1_t aux2 = __riscv_vredsum_vs_i16m2_i16m1(va_1, vzero, vl); + const __m256i q8h = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + __m256i p16h = lasx_maddubs_h(q4h, q8h); + p16h = lasx_madd_h(scale_h, p16h); + const __m256i sumj = __lasx_xvadd_w(p16l, p16h); - sumf += d*scales[1]*__riscv_vmv_x_s_i16m1_i16(aux2); + sumi = __lasx_xvadd_w(sumi, sumj); + } + __m256 vd = __lasx_xvreplfr2vr_s(d); + acc = __lasx_xvfmadd_s(vd, __lasx_xvffint_s_w(sumi), acc); } - *s = sumf; + acc_m = __lsx_vfadd_s(acc_m, (__m128)__lsx_vpermi_w((__m128i)acc_m, (__m128i)acc_m, 0xee)); + __m128i tmp1 = __lsx_vinsgr2vr_w(__lsx_vldi(0), __lsx_vpickve2gr_w((__m128i)acc_m, 1), 0); + acc_m = __lsx_vfadd_s(acc_m, (__m128)tmp1); + + ft_union fi; + fi.i = __lsx_vpickve2gr_w(acc_m, 0); + *s = hsum_float_8(acc) + fi.f ; #else - uint8_t aux8[QK_K]; - int16_t aux16[16]; + const uint8_t * scales = (const uint8_t*)&utmp[0]; + const uint8_t * mins = (const uint8_t*)&utmp[2]; + + int8_t aux8[QK_K]; + int16_t aux16[8]; float sums [8]; + int32_t aux32[8]; memset(sums, 0, 8*sizeof(float)); - uint16_t s16[2]; - const uint8_t * restrict scales = (const uint8_t *)s16; - float sumf = 0; for (int i = 0; i < nb; ++i) { const uint8_t * restrict q4 = x[i].qs; const int8_t * restrict q8 = y[i].qs; - uint8_t * restrict a = aux8; - for (int l = 0; l < 32; ++l) a[l+ 0] = q4[l] & 0xF; - for (int l = 0; l < 32; ++l) a[l+32] = q4[l] >> 4; - - const uint16_t * restrict b = (const uint16_t *)x[i].scales; - s16[0] = b[0] & 0x0f0f; - s16[1] = (b[0] >> 4) & 0x0f0f; - - sumf -= y[i].d * GGML_FP16_TO_FP32(x[i].d[1]) * (scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3])); - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d[0]); + memset(aux32, 0, 8*sizeof(int32_t)); + int8_t * restrict a = aux8; + for (int j = 0; j < QK_K/64; ++j) { + for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF); + a += 32; + for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] >> 4); + a += 32; q4 += 32; + } + memcpy(utmp, x[i].scales, 12); + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; + int sumi = 0; + for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2]; + a = aux8; + int is = 0; for (int j = 0; j < QK_K/32; ++j) { - for (int l = 0; l < 16; ++l) aux16[l] = q8[l] * a[l]; - q8 += 16; a += 16; - for (int l = 0; l < 16; ++l) aux16[l] += q8[l] * a[l]; - q8 += 16; a += 16; - const float dl = d * scales[j]; - for (int l = 0; l < 8; ++l) sums[l] += dl * (aux16[l] + aux16[l+8]); + int32_t scale = scales[is++]; + for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; + q8 += 8; a += 8; + for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; + q8 += 8; a += 8; + for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; + q8 += 8; a += 8; + for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; + q8 += 8; a += 8; } + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l]; + const float dmin = GGML_FP16_TO_FP32(x[i].dmin) * y[i].d; + sumf -= dmin * sumi; } for (int l = 0; l < 8; ++l) sumf += sums[l]; *s = sumf; #endif } -#endif -#if QK_K == 256 void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { assert(n % QK_K == 0); assert(nrc == 1); @@ -6959,12 +7555,10 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r float summs = 0.f; - for (int i = 0; i < nb; ++i) { - + for (int i = 0; i < nb; ++i) { const uint8_t * restrict q5 = x[i].qs; const int8_t * restrict q8 = y[i].qs; -#if QK_K == 256 const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); @@ -6974,10 +7568,6 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); utmp[2] = uaux; utmp[0] &= kmask1; -#else - // TODO - const float d = 0, dmin = 0; -#endif const __m256i mins_and_scales = _mm256_cvtepu8_epi16(_mm_set_epi32(utmp[3], utmp[2], utmp[1], utmp[0])); @@ -7221,308 +7811,212 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r *s = sumf+sums; -#else - - const uint8_t * scales = (const uint8_t*)&utmp[0]; - const uint8_t * mins = (const uint8_t*)&utmp[2]; +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector unsigned char v1 = vec_splats((unsigned char)0x1); + const vector unsigned char v2 = vec_splats((unsigned char)0x2); + const vector unsigned char v3 = vec_splats((unsigned char)0x3); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); - int8_t aux8[QK_K]; - int16_t aux16[8]; - float sums [8]; - int32_t aux32[8]; - memset(sums, 0, 8*sizeof(float)); + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); - float sumf = 0; for (int i = 0; i < nb; ++i) { - const uint8_t * restrict q4 = x[i].qs; - const uint8_t * restrict hm = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - memset(aux32, 0, 8*sizeof(int32_t)); - int8_t * restrict a = aux8; - uint8_t m = 1; - for (int j = 0; j < QK_K/64; ++j) { - for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF); - for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0); - a += 32; m <<= 1; - for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] >> 4); - for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0); - a += 32; m <<= 1; - q4 += 32; - } + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[i].dmin)); + vector float vdmin = vec_mul(vxmin, vyd); + memcpy(utmp, x[i].scales, 12); + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); const uint32_t uaux = utmp[1] & kmask1; utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); utmp[2] = uaux; utmp[0] &= kmask1; - int sumi = 0; - for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2]; - a = aux8; - int is = 0; - for (int j = 0; j < QK_K/32; ++j) { - int32_t scale = scales[is++]; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; - q8 += 8; a += 8; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; - q8 += 8; a += 8; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; - q8 += 8; a += 8; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; - q8 += 8; a += 8; - } - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l]; - const float dmin = GGML_FP16_TO_FP32(x[i].dmin) * y[i].d; - sumf -= dmin * sumi; - } - for (int l = 0; l < 8; ++l) sumf += sums[l]; - *s = sumf; -#endif -} - -#else - -void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(n % QK_K == 0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_q5_K * restrict x = vx; - const block_q8_K * restrict y = vy; + vector signed short q8ysums0 = vec_xl( 0, y[i].bsums); + vector signed short q8ysums1 = vec_xl(16, y[i].bsums); - const int nb = n / QK_K; + vector signed char utmps = (vector signed char)vec_xl( 0, utmp); + vector signed short vscales = vec_unpackh(utmps); -#ifdef __ARM_NEON - const uint8x16_t m4b = vdupq_n_u8(0xf); - const uint8x16_t mh = vdupq_n_u8(16); - const int32x4_t mzero = vdupq_n_s32(0); + vector signed short q5xmins = vec_unpackl(utmps); + vector signed short q5xmins0 = vec_mergeh(q5xmins, q5xmins); + vector signed short q5xmins1 = vec_mergel(q5xmins, q5xmins); - ggml_int8x16x4_t q5bytes; - ggml_uint8x16x4_t q5h; + vector signed int prod0 = vec_mule(q5xmins0, q8ysums0); + vector signed int prod1 = vec_mule(q5xmins1, q8ysums1); + vector signed int prod2 = vec_mulo(q5xmins0, q8ysums0); + vector signed int prod3 = vec_mulo(q5xmins1, q8ysums1); - float sumf = 0; + vsumf0 = vec_nmsub(vec_ctf(prod0, 0), vdmin, vsumf0); + vsumf1 = vec_nmsub(vec_ctf(prod1, 0), vdmin, vsumf1); + vsumf2 = vec_nmsub(vec_ctf(prod2, 0), vdmin, vsumf2); + vsumf3 = vec_nmsub(vec_ctf(prod3, 0), vdmin, vsumf3); - for (int i = 0; i < nb; ++i) { + vector signed char qxhs0 = (vector signed char)vec_xl( 0, x[i].qh); + vector signed char qxhs1 = (vector signed char)vec_xl(16, x[i].qh); - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const int8_t * sc = x[i].scales; + vector signed int vsumi0 = vec_splats((int32_t)0); + vector signed int vsumi1 = vec_splats((int32_t)0); + vector signed int vsumi2 = vec_splats((int32_t)0); + vector signed int vsumi3 = vec_splats((int32_t)0); const uint8_t * restrict q5 = x[i].qs; - const uint8_t * restrict qh = x[i].qh; const int8_t * restrict q8 = y[i].qs; - const uint8x8_t qhbits = vld1_u8(qh); - - const ggml_uint8x16x2_t q5bits = ggml_vld1q_u8_x2(q5); - const ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(q8); - - const uint8x16_t htmp = vcombine_u8(qhbits, vshr_n_u8(qhbits, 1)); - q5h.val[0] = vbicq_u8(mh, vshlq_n_u8(htmp, 4)); - q5h.val[1] = vbicq_u8(mh, vshlq_n_u8(htmp, 2)); - q5h.val[2] = vbicq_u8(mh, htmp); - q5h.val[3] = vbicq_u8(mh, vshrq_n_u8(htmp, 2)); - - q5bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(q5bits.val[0], m4b)), vreinterpretq_s8_u8(q5h.val[0])); - q5bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(q5bits.val[1], m4b)), vreinterpretq_s8_u8(q5h.val[1])); - q5bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vshrq_n_u8(q5bits.val[0], 4)), vreinterpretq_s8_u8(q5h.val[2])); - q5bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vshrq_n_u8(q5bits.val[1], 4)), vreinterpretq_s8_u8(q5h.val[3])); - - int32_t sumi1 = sc[0] * vaddvq_s32(ggml_vdotq_s32(mzero, q5bytes.val[0], q8bytes.val[0])); - int32_t sumi2 = sc[1] * vaddvq_s32(ggml_vdotq_s32(mzero, q5bytes.val[1], q8bytes.val[1])); - int32_t sumi3 = sc[2] * vaddvq_s32(ggml_vdotq_s32(mzero, q5bytes.val[2], q8bytes.val[2])); - int32_t sumi4 = sc[3] * vaddvq_s32(ggml_vdotq_s32(mzero, q5bytes.val[3], q8bytes.val[3])); - - sumf += d * (sumi1 + sumi2 + sumi3 + sumi4); - } - - *s = sumf; - -#elif defined __AVX2__ - - const __m256i m4 = _mm256_set1_epi8(0xF); - const __m256i mone = _mm256_set1_epi8(1); + for (int j = 0; j < QK_K/64; ++j) { + __builtin_prefetch(q5, 0, 1); + __builtin_prefetch(q8, 0, 1); - __m256 acc = _mm256_setzero_ps(); + vector signed char qxs0 = (vector signed char)vec_xl( 0, q5); + vector signed char qxs1 = (vector signed char)vec_xl(16, q5); + q5 += 32; - for (int i = 0; i < nb; ++i) { + vector signed char qxs00 = vec_and(qxs0, lowMask); + vector signed char qxs01 = vec_sr(qxs0, v4); + vector signed char qxs10 = vec_and(qxs1, lowMask); + vector signed char qxs11 = vec_sr(qxs1, v4); - const uint8_t * restrict q5 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; + vector signed char q5h00 = vec_sl(vec_and((vector signed char)v1, qxhs0), v4); + vector signed char q5h01 = vec_sl(vec_and((vector signed char)v2, qxhs0), v3); + vector signed char q5h10 = vec_sl(vec_and((vector signed char)v1, qxhs1), v4); + vector signed char q5h11 = vec_sl(vec_and((vector signed char)v2, qxhs1), v3); + qxhs0 = vec_sr(qxhs0, v2); + qxhs1 = vec_sr(qxhs1, v2); - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + vector signed char q5x00 = vec_or(q5h00, qxs00); + vector signed char q5x01 = vec_or(q5h01, qxs01); + vector signed char q5x10 = vec_or(q5h10, qxs10); + vector signed char q5x11 = vec_or(q5h11, qxs11); - const __m256i q5bits = _mm256_loadu_si256((const __m256i*)q5); + vector signed char q8y00 = vec_xl( 0, q8); + vector signed char q8y10 = vec_xl(16, q8); + vector signed char q8y01 = vec_xl(32, q8); + vector signed char q8y11 = vec_xl(48, q8); + q8 += 64; - const __m256i scale_l = MM256_SET_M128I(_mm_set1_epi16(x[i].scales[1]), _mm_set1_epi16(x[i].scales[0])); - const __m256i scale_h = MM256_SET_M128I(_mm_set1_epi16(x[i].scales[3]), _mm_set1_epi16(x[i].scales[2])); + vector signed short qv00 = vec_add(vec_mule(q5x00, q8y00), vec_mulo(q5x00, q8y00)); + vector signed short qv01 = vec_add(vec_mule(q5x01, q8y01), vec_mulo(q5x01, q8y01)); + vector signed short qv10 = vec_add(vec_mule(q5x10, q8y10), vec_mulo(q5x10, q8y10)); + vector signed short qv11 = vec_add(vec_mule(q5x11, q8y11), vec_mulo(q5x11, q8y11)); - int64_t aux64; - memcpy(&aux64, x[i].qh, 8); - const __m128i haux128 = _mm_set_epi64x(aux64 >> 1, aux64); - const __m256i haux256 = MM256_SET_M128I(_mm_srli_epi16(haux128, 2), haux128); + vector signed short vs0 = vec_splat(vscales, 0); + vector signed short vs1 = vec_splat(vscales, 1); + vscales = vec_sld(vscales, vscales, 12); - const __m256i q5h_0 = _mm256_slli_epi16(_mm256_andnot_si256(haux256, mone), 4); - const __m256i q5h_1 = _mm256_slli_epi16(_mm256_andnot_si256(_mm256_srli_epi16(haux256, 4), mone), 4); + qv00 = vec_add(qv00, qv10); + qv01 = vec_add(qv01, qv11); - const __m256i q5l_0 = _mm256_and_si256(q5bits, m4); - const __m256i q5l_1 = _mm256_and_si256(_mm256_srli_epi16(q5bits, 4), m4); + vsumi0 = vec_add(vec_mule(qv00, vs0), vsumi0); + vsumi1 = vec_add(vec_mulo(qv00, vs0), vsumi1); + vsumi2 = vec_add(vec_mule(qv01, vs1), vsumi2); + vsumi3 = vec_add(vec_mulo(qv01, vs1), vsumi3); + } - const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0)); - const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32)); + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } - const __m256i p16_0 = _mm256_madd_epi16(scale_l, _mm256_maddubs_epi16(q5l_0, q8_0)); - const __m256i p16_1 = _mm256_madd_epi16(scale_h, _mm256_maddubs_epi16(q5l_1, q8_1)); - const __m256i s16_0 = _mm256_madd_epi16(scale_l, _mm256_maddubs_epi16(q5h_0, q8_0)); - const __m256i s16_1 = _mm256_madd_epi16(scale_h, _mm256_maddubs_epi16(q5h_1, q8_1)); + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); - const __m256i dot = _mm256_sub_epi32(_mm256_add_epi32(p16_0, p16_1), _mm256_add_epi32(s16_0, s16_1)); + vsumf0 = vec_add(vsumf0, vsumf1); - acc = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(dot), acc); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - } + *s = vec_extract(vsumf0, 0); - *s = hsum_float_8(acc); +#elif defined __loongarch_asx -#elif defined __AVX__ + const __m256i m4 = __lasx_xvreplgr2vr_b(0xF); + const __m128i mzero = __lsx_vldi(0); + const __m256i mone = __lasx_xvreplgr2vr_b(1); - const __m128i m4 = _mm_set1_epi8(0xF); - const __m128i mone = _mm_set1_epi8(1); + __m256 acc = (__m256)__lasx_xvldi(0); - __m256 acc = _mm256_setzero_ps(); + float summs = 0.f; - for (int i = 0; i < nb; ++i) { + for (int i = 0; i < nb; ++i) { const uint8_t * restrict q5 = x[i].qs; const int8_t * restrict q8 = y[i].qs; const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - const __m256i q5bits = _mm256_loadu_si256((const __m256i*)q5); - - const __m128i scale_0 = _mm_set1_epi16(x[i].scales[0]); - const __m128i scale_1 = _mm_set1_epi16(x[i].scales[1]); - const __m128i scale_2 = _mm_set1_epi16(x[i].scales[2]); - const __m128i scale_3 = _mm_set1_epi16(x[i].scales[3]); - - int64_t aux64; - memcpy(&aux64, x[i].qh, 8); - const __m128i haux128_0 = _mm_set_epi64x(aux64 >> 1, aux64); - const __m128i haux128_1 = _mm_srli_epi16(haux128_0, 2); - - const __m128i q5h_0 = _mm_slli_epi16(_mm_andnot_si128(haux128_0, mone), 4); - const __m128i q5h_1 = _mm_slli_epi16(_mm_andnot_si128(haux128_1, mone), 4); - const __m128i q5h_2 = _mm_slli_epi16(_mm_andnot_si128(_mm_srli_epi16(haux128_0, 4), mone), 4); - const __m128i q5h_3 = _mm_slli_epi16(_mm_andnot_si128(_mm_srli_epi16(haux128_1, 4), mone), 4); - - const __m128i q5l_0 = _mm_and_si128(_mm256_extractf128_si256(q5bits, 0), m4); - const __m128i q5l_1 = _mm_and_si128(_mm256_extractf128_si256(q5bits, 1), m4); - const __m128i q5l_2 = _mm_and_si128(_mm_srli_epi16(_mm256_extractf128_si256(q5bits, 0), 4), m4); - const __m128i q5l_3 = _mm_and_si128(_mm_srli_epi16(_mm256_extractf128_si256(q5bits, 1), 4), m4); - - const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0)); - const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32)); - - const __m128i p16_0 = _mm_madd_epi16(scale_0, _mm_maddubs_epi16(q5l_0, _mm256_extractf128_si256(q8_0, 0))); - const __m128i p16_1 = _mm_madd_epi16(scale_1, _mm_maddubs_epi16(q5l_1, _mm256_extractf128_si256(q8_0, 1))); - const __m128i p16_2 = _mm_madd_epi16(scale_2, _mm_maddubs_epi16(q5l_2, _mm256_extractf128_si256(q8_1, 0))); - const __m128i p16_3 = _mm_madd_epi16(scale_3, _mm_maddubs_epi16(q5l_3, _mm256_extractf128_si256(q8_1, 1))); - const __m128i s16_0 = _mm_madd_epi16(scale_0, _mm_maddubs_epi16(q5h_0, _mm256_extractf128_si256(q8_0, 0))); - const __m128i s16_1 = _mm_madd_epi16(scale_1, _mm_maddubs_epi16(q5h_1, _mm256_extractf128_si256(q8_0, 1))); - const __m128i s16_2 = _mm_madd_epi16(scale_2, _mm_maddubs_epi16(q5h_2, _mm256_extractf128_si256(q8_1, 0))); - const __m128i s16_3 = _mm_madd_epi16(scale_3, _mm_maddubs_epi16(q5h_3, _mm256_extractf128_si256(q8_1, 1))); - - const __m128i dot_0 = _mm_sub_epi32(_mm_add_epi32(p16_0, p16_2), _mm_add_epi32(s16_0, s16_2)); - const __m128i dot_1 = _mm_sub_epi32(_mm_add_epi32(p16_1, p16_3), _mm_add_epi32(s16_1, s16_3)); - - acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(dot_1, dot_0))), acc); - - } - - *s = hsum_float_8(acc); - -#elif defined __riscv_v_intrinsic - - float sumf = 0; - - for (int i = 0; i < nb; ++i) { + memcpy(utmp, x[i].scales, 12); - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const int8_t * sc = x[i].scales; + const __m256i mins_and_scales = lasx_extu8_16(lsx_set_w(utmp[3], utmp[2], utmp[1], utmp[0])); - const uint8_t * restrict q5 = x[i].qs; - const uint8_t * restrict qh = x[i].qh; - const int8_t * restrict q8 = y[i].qs; + const __m256i q8sums = __lasx_xvld((const __m256i*)y[i].bsums, 0); + const __m128i q8s = lsx_hadd_h(lasx_extracti128(q8sums, 0), lasx_extracti128(q8sums, 1)); + const __m128i prod = lsx_madd_h(lasx_extracti128(mins_and_scales, 1), q8s); + const __m128i hsum = lsx_hadd_w(lsx_hadd_w(prod, mzero), mzero); + summs += dmin * __lsx_vpickve2gr_w(hsum, 0); //TODO check - vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1); + const __m128i sc128 = lasx_extracti128(mins_and_scales, 0); + const __m256i scales = lasx_insertf128(sc128, sc128); - // load qh - vuint8mf4_t qh_x1 = __riscv_vle8_v_u8mf4(qh, 8); - vuint8mf2_t qh_x2 = __riscv_vlmul_ext_v_u8mf4_u8mf2(__riscv_vsrl_vx_u8mf4(qh_x1, 1, 8)); + const __m256i hbits = __lasx_xvld((const __m256i*)x[i].qh, 0); + __m256i hmask = mone; - size_t vl = 16; + __m256i sumi = __lasx_xvldi(0); - // combine both qh_1 and qh_2 - vuint8mf2_t qh_x = __riscv_vslideup_vx_u8mf2(__riscv_vlmul_ext_v_u8mf4_u8mf2(qh_x1), qh_x2, vl/2, vl); + int bit = 0; - vuint8mf2_t qh_h0 = __riscv_vand_vx_u8mf2(__riscv_vnot_v_u8mf2(__riscv_vsll_vx_u8mf2(qh_x, 0x4, vl), vl), 16, vl); - vuint8mf2_t qh_h1 = __riscv_vand_vx_u8mf2(__riscv_vnot_v_u8mf2(__riscv_vsll_vx_u8mf2(qh_x, 0x2, vl), vl), 16, vl); - vuint8mf2_t qh_h2 = __riscv_vand_vx_u8mf2(__riscv_vnot_v_u8mf2(qh_x, vl), 16, vl); - vuint8mf2_t qh_h3 = __riscv_vand_vx_u8mf2(__riscv_vnot_v_u8mf2(__riscv_vsrl_vx_u8mf2(qh_x, 0x4, vl), vl), 16, vl); + for (int j = 0; j < QK_K/64; ++j) { - vint8mf2_t qh_0 = __riscv_vreinterpret_v_u8mf2_i8mf2(qh_h0); - vint8mf2_t qh_1 = __riscv_vreinterpret_v_u8mf2_i8mf2(qh_h1); - vint8mf2_t qh_2 = __riscv_vreinterpret_v_u8mf2_i8mf2(qh_h2); - vint8mf2_t qh_3 = __riscv_vreinterpret_v_u8mf2_i8mf2(qh_h3); + const __m256i scale_0 = lasx_shuffle_b(scales, get_scale_shuffle_k4(2*j+0)); + const __m256i scale_1 = lasx_shuffle_b(scales, get_scale_shuffle_k4(2*j+1)); - // load q5 - vuint8mf2_t q5_x1 = __riscv_vle8_v_u8mf2(q5, vl); - vuint8mf2_t q5_x2 = __riscv_vle8_v_u8mf2(q5+16, vl); + const __m256i q5bits = __lasx_xvld((const __m256i*)q5, 0); q5 += 32; - vint8mf2_t q5s_0 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vand_vx_u8mf2(q5_x1, 0xF, vl)); - vint8mf2_t q5s_1 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vand_vx_u8mf2(q5_x2, 0xF, vl)); - vint8mf2_t q5s_2 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vsrl_vx_u8mf2(q5_x1, 0x4, vl)); - vint8mf2_t q5s_3 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vsrl_vx_u8mf2(q5_x2, 0x4, vl)); + const __m256i q5l_0 = __lasx_xvand_v(q5bits, m4); + const __m256i q5h_0 = __lasx_xvslli_h(__lasx_xvsrli_h(__lasx_xvand_v(hbits, hmask), bit++), 4); + const __m256i q5_0 = __lasx_xvadd_b(q5l_0, q5h_0); + hmask = __lasx_xvslli_h(hmask, 1); - vint8mf2_t q5_0 = __riscv_vsub_vv_i8mf2(q5s_0, qh_0, vl); - vint8mf2_t q5_1 = __riscv_vsub_vv_i8mf2(q5s_1, qh_1, vl); - vint8mf2_t q5_2 = __riscv_vsub_vv_i8mf2(q5s_2, qh_2, vl); - vint8mf2_t q5_3 = __riscv_vsub_vv_i8mf2(q5s_3, qh_3, vl); + const __m256i q5l_1 = __lasx_xvand_v(__lasx_xvsrli_h(q5bits, 4), m4); + const __m256i q5h_1 = __lasx_xvslli_h(__lasx_xvsrli_h(__lasx_xvand_v(hbits, hmask), bit++), 4); + const __m256i q5_1 = __lasx_xvadd_b(q5l_1, q5h_1); + hmask = __lasx_xvslli_h(hmask, 1); - // load Q8 and multiply it with Q5 - vint16m1_t p0 = __riscv_vwmul_vv_i16m1(q5_0, __riscv_vle8_v_i8mf2(q8, vl), vl); - vint16m1_t p1 = __riscv_vwmul_vv_i16m1(q5_1, __riscv_vle8_v_i8mf2(q8+16, vl), vl); - vint16m1_t p2 = __riscv_vwmul_vv_i16m1(q5_2, __riscv_vle8_v_i8mf2(q8+32, vl), vl); - vint16m1_t p3 = __riscv_vwmul_vv_i16m1(q5_3, __riscv_vle8_v_i8mf2(q8+48, vl), vl); + const __m256i q8_0 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_1 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - vint32m1_t vs_0 = __riscv_vwredsum_vs_i16m1_i32m1(p0, vzero, vl); - vint32m1_t vs_1 = __riscv_vwredsum_vs_i16m1_i32m1(p1, vzero, vl); - vint32m1_t vs_2 = __riscv_vwredsum_vs_i16m1_i32m1(p2, vzero, vl); - vint32m1_t vs_3 = __riscv_vwredsum_vs_i16m1_i32m1(p3, vzero, vl); + __m256i p16_0 = lasx_maddubs_h(q5_0, q8_0); + __m256i p16_1 = lasx_maddubs_h(q5_1, q8_1); - int32_t sumi1 = sc[0] * __riscv_vmv_x_s_i32m1_i32(vs_0); - int32_t sumi2 = sc[1] * __riscv_vmv_x_s_i32m1_i32(vs_1); - int32_t sumi3 = sc[2] * __riscv_vmv_x_s_i32m1_i32(vs_2); - int32_t sumi4 = sc[3] * __riscv_vmv_x_s_i32m1_i32(vs_3); + p16_0 = lasx_madd_h(scale_0, p16_0); + p16_1 = lasx_madd_h(scale_1, p16_1); - sumf += d * (sumi1 + sumi2 + sumi3 + sumi4); + sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p16_0, p16_1)); + } + __m256 vd = __lasx_xvreplfr2vr_s(d); + acc = __lasx_xvfmadd_s(vd, __lasx_xvffint_s_w(sumi), acc); } - *s = sumf; + *s = hsum_float_8(acc) + summs; #else - int8_t aux8[QK_K]; - int16_t aux16[16]; + const uint8_t * scales = (const uint8_t*)&utmp[0]; + const uint8_t * mins = (const uint8_t*)&utmp[2]; + + int8_t aux8[QK_K]; + int16_t aux16[8]; float sums [8]; + int32_t aux32[8]; memset(sums, 0, 8*sizeof(float)); float sumf = 0; @@ -7530,34 +8024,54 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r const uint8_t * restrict q4 = x[i].qs; const uint8_t * restrict hm = x[i].qh; const int8_t * restrict q8 = y[i].qs; + memset(aux32, 0, 8*sizeof(int32_t)); int8_t * restrict a = aux8; - for (int l = 0; l < 32; ++l) { - a[l+ 0] = q4[l] & 0xF; - a[l+32] = q4[l] >> 4; - } - for (int is = 0; is < 8; ++is) { - uint8_t m = 1 << is; - for (int l = 0; l < 8; ++l) a[8*is + l] -= (hm[l] & m ? 0 : 16); + uint8_t m = 1; + for (int j = 0; j < QK_K/64; ++j) { + for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF); + for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0); + a += 32; m <<= 1; + for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] >> 4); + for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0); + a += 32; m <<= 1; + q4 += 32; } + memcpy(utmp, x[i].scales, 12); + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const int8_t * restrict sc = x[i].scales; - - for (int j = 0; j < QK_K/16; ++j) { - const float dl = d * sc[j]; - for (int l = 0; l < 16; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) sums[l] += dl * (aux16[l] + aux16[8+l]); - q8 += 16; a += 16; + int sumi = 0; + for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2]; + a = aux8; + int is = 0; + for (int j = 0; j < QK_K/32; ++j) { + int32_t scale = scales[is++]; + for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; + q8 += 8; a += 8; + for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; + q8 += 8; a += 8; + for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; + q8 += 8; a += 8; + for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; + q8 += 8; a += 8; } + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l]; + const float dmin = GGML_FP16_TO_FP32(x[i].dmin) * y[i].d; + sumf -= dmin * sumi; } for (int l = 0; l < 8; ++l) sumf += sums[l]; *s = sumf; #endif } -#endif - -#if QK_K == 256 void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { assert(n % QK_K == 0); assert(nrc == 1); @@ -7948,181 +8462,158 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * r *s = sumf; -#else - - int8_t aux8[QK_K]; - int16_t aux16[8]; - float sums [8]; - int32_t aux32[8]; - memset(sums, 0, 8*sizeof(float)); - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - const uint8_t * restrict q4 = x[i].ql; - const uint8_t * restrict qh = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - memset(aux32, 0, 8*sizeof(int32_t)); - int8_t * restrict a = aux8; - for (int j = 0; j < QK_K; j += 128) { - for (int l = 0; l < 32; ++l) { - a[l + 0] = (int8_t)((q4[l + 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32; - a[l + 32] = (int8_t)((q4[l + 32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32; - a[l + 64] = (int8_t)((q4[l + 0] >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32; - a[l + 96] = (int8_t)((q4[l + 32] >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32; - } - a += 128; - q4 += 64; - qh += 32; - } - a = aux8; - int is = 0; - for (int j = 0; j < QK_K/16; ++j) { - int scale = x[i].scales[is++]; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; - q8 += 8; a += 8; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; - q8 += 8; a += 8; - } - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l]; - } - for (int l = 0; l < 8; ++l) sumf += sums[l]; - *s = sumf; -#endif -} - -#else - -void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(n % QK_K == 0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_q6_K * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#ifdef __ARM_NEON - float sum = 0; - - const uint8x16_t m4b = vdupq_n_u8(0xF); - const int8x16_t m32s = vdupq_n_s8(32); - const int32x4_t vzero = vdupq_n_s32(0); - - const uint8x16_t mone = vdupq_n_u8(3); +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector unsigned char v2 = vec_splats((unsigned char)0x2); + const vector unsigned char v3 = vec_splats((unsigned char)0x3); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + const vector unsigned char v6 = vec_splats((unsigned char)0x6); + const vector signed char off = vec_splats((signed char)0x20); - ggml_int8x16x4_t q6bytes; - ggml_uint8x16x4_t q6h; + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); for (int i = 0; i < nb; ++i) { - - const float d_all = GGML_FP16_TO_FP32(x[i].d); + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + vector signed int vsumi0 = vec_splats((int32_t)0); + vector signed int vsumi1 = vec_splats((int32_t)0); + vector signed int vsumi2 = vec_splats((int32_t)0); + vector signed int vsumi3 = vec_splats((int32_t)0); + vector signed int vsumi4 = vec_splats((int32_t)0); + vector signed int vsumi5 = vec_splats((int32_t)0); + vector signed int vsumi6 = vec_splats((int32_t)0); + vector signed int vsumi7 = vec_splats((int32_t)0); const uint8_t * restrict q6 = x[i].ql; const uint8_t * restrict qh = x[i].qh; + const int8_t * restrict qs = x[i].scales; const int8_t * restrict q8 = y[i].qs; - const int8_t * restrict scale = x[i].scales; - - int32_t isum = 0; + for (int j = 0; j < QK_K/128; ++j) { + __builtin_prefetch(q6, 0, 0); + __builtin_prefetch(qh, 0, 0); + __builtin_prefetch(q8, 0, 0); + + vector signed char qxs0 = (vector signed char)vec_xl( 0, q6); + vector signed char qxs1 = (vector signed char)vec_xl(16, q6); + vector signed char qxs2 = (vector signed char)vec_xl(32, q6); + vector signed char qxs3 = (vector signed char)vec_xl(48, q6); + q6 += 64; + + vector signed char qxs00 = vec_and(qxs0, lowMask); + vector signed char qxs01 = vec_sr(qxs0, v4); + vector signed char qxs10 = vec_and(qxs1, lowMask); + vector signed char qxs11 = vec_sr(qxs1, v4); + vector signed char qxs20 = vec_and(qxs2, lowMask); + vector signed char qxs21 = vec_sr(qxs2, v4); + vector signed char qxs30 = vec_and(qxs3, lowMask); + vector signed char qxs31 = vec_sr(qxs3, v4); + + vector signed char qxhs0 = (vector signed char)vec_xl( 0, qh); + vector signed char qxhs1 = (vector signed char)vec_xl(16, qh); + qh += 32; - uint8x16_t qhbits = vld1q_u8(qh); - ggml_uint8x16x2_t q6bits = ggml_vld1q_u8_x2(q6); - ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(q8); + vector signed char qxh00 = vec_sl(vec_and((vector signed char)v3, qxhs0), v4); + vector signed char qxh01 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs0, v4)), v4); + vector signed char qxh10 = vec_sl(vec_and((vector signed char)v3, qxhs1), v4); + vector signed char qxh11 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs1, v4)), v4); + vector signed char qxh20 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs0, v2)), v4); + vector signed char qxh21 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs0, v6)), v4); + vector signed char qxh30 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs1, v2)), v4); + vector signed char qxh31 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs1, v6)), v4); + + vector signed char q6x00 = vec_sub(vec_or(qxh00, qxs00), off); + vector signed char q6x01 = vec_sub(vec_or(qxh01, qxs01), off); + vector signed char q6x10 = vec_sub(vec_or(qxh10, qxs10), off); + vector signed char q6x11 = vec_sub(vec_or(qxh11, qxs11), off); + vector signed char q6x20 = vec_sub(vec_or(qxh20, qxs20), off); + vector signed char q6x21 = vec_sub(vec_or(qxh21, qxs21), off); + vector signed char q6x30 = vec_sub(vec_or(qxh30, qxs30), off); + vector signed char q6x31 = vec_sub(vec_or(qxh31, qxs31), off); + + vector signed char q8y00 = vec_xl( 0, q8); + vector signed char q8y10 = vec_xl( 16, q8); + vector signed char q8y20 = vec_xl( 32, q8); + vector signed char q8y30 = vec_xl( 48, q8); + vector signed char q8y01 = vec_xl( 64, q8); + vector signed char q8y11 = vec_xl( 80, q8); + vector signed char q8y21 = vec_xl( 96, q8); + vector signed char q8y31 = vec_xl(112, q8); + q8 += 128; + + vector signed short qv00 = vec_add(vec_mule(q6x00, q8y00), vec_mulo(q6x00, q8y00)); + vector signed short qv10 = vec_add(vec_mule(q6x10, q8y10), vec_mulo(q6x10, q8y10)); + vector signed short qv20 = vec_add(vec_mule(q6x20, q8y20), vec_mulo(q6x20, q8y20)); + vector signed short qv30 = vec_add(vec_mule(q6x30, q8y30), vec_mulo(q6x30, q8y30)); + vector signed short qv01 = vec_add(vec_mule(q6x01, q8y01), vec_mulo(q6x01, q8y01)); + vector signed short qv11 = vec_add(vec_mule(q6x11, q8y11), vec_mulo(q6x11, q8y11)); + vector signed short qv21 = vec_add(vec_mule(q6x21, q8y21), vec_mulo(q6x21, q8y21)); + vector signed short qv31 = vec_add(vec_mule(q6x31, q8y31), vec_mulo(q6x31, q8y31)); + + vector signed short vscales = vec_unpackh(vec_xl_len(qs, 8)); + qs += 8; - q6h.val[0] = vshlq_n_u8(vandq_u8(mone, qhbits), 4); - uint8x16_t shifted = vshrq_n_u8(qhbits, 2); - q6h.val[1] = vshlq_n_u8(vandq_u8(mone, shifted), 4); - shifted = vshrq_n_u8(qhbits, 4); - q6h.val[2] = vshlq_n_u8(vandq_u8(mone, shifted), 4); - shifted = vshrq_n_u8(qhbits, 6); - q6h.val[3] = vshlq_n_u8(vandq_u8(mone, shifted), 4); + vector signed short vs0 = vec_splat(vscales, 0); + vector signed short vs1 = vec_splat(vscales, 1); + vector signed short vs2 = vec_splat(vscales, 2); + vector signed short vs3 = vec_splat(vscales, 3); + vector signed short vs4 = vec_splat(vscales, 4); + vector signed short vs5 = vec_splat(vscales, 5); + vector signed short vs6 = vec_splat(vscales, 6); + vector signed short vs7 = vec_splat(vscales, 7); - q6bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[0], m4b), q6h.val[0])), m32s); - q6bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[1], m4b), q6h.val[1])), m32s); - q6bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[0], 4), q6h.val[2])), m32s); - q6bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[1], 4), q6h.val[3])), m32s); + vsumi0 = vec_add(vec_mule(qv00, vs0), vsumi0); + vsumi1 = vec_add(vec_mulo(qv00, vs0), vsumi1); + vsumi2 = vec_add(vec_mule(qv01, vs4), vsumi2); + vsumi3 = vec_add(vec_mulo(qv01, vs4), vsumi3); + vsumi4 = vec_add(vec_mule(qv10, vs1), vsumi4); + vsumi5 = vec_add(vec_mulo(qv10, vs1), vsumi5); + vsumi6 = vec_add(vec_mule(qv11, vs5), vsumi6); + vsumi7 = vec_add(vec_mulo(qv11, vs5), vsumi7); - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] + - vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] + - vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] + - vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3]; + vsumi0 = vec_add(vec_mule(qv20, vs2), vsumi0); + vsumi1 = vec_add(vec_mulo(qv20, vs2), vsumi1); + vsumi2 = vec_add(vec_mule(qv21, vs6), vsumi2); + vsumi3 = vec_add(vec_mulo(qv21, vs6), vsumi3); + vsumi4 = vec_add(vec_mule(qv30, vs3), vsumi4); + vsumi5 = vec_add(vec_mulo(qv30, vs3), vsumi5); + vsumi6 = vec_add(vec_mule(qv31, vs7), vsumi6); + vsumi7 = vec_add(vec_mulo(qv31, vs7), vsumi7); + } - sum += isum * d_all * y[i].d; + vsumi0 = vec_add(vsumi0, vsumi4); + vsumi1 = vec_add(vsumi1, vsumi5); + vsumi2 = vec_add(vsumi2, vsumi6); + vsumi3 = vec_add(vsumi3, vsumi7); + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); } - *s = sum; - -#elif defined __AVX2__ - - const __m256i m4 = _mm256_set1_epi8(0xF); - const __m256i m2 = _mm256_set1_epi8(3); - const __m256i m32s = _mm256_set1_epi8(32); - - __m256 acc = _mm256_setzero_ps(); - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q4 = x[i].ql; - const uint8_t * restrict qh = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - - const __m64 scales_1 = _mm_set1_pi8(x[i].scales[0]); - const __m64 scales_2 = _mm_set1_pi8(x[i].scales[1]); - const __m64 scales_3 = _mm_set1_pi8(x[i].scales[2]); - const __m64 scales_4 = _mm_set1_pi8(x[i].scales[3]); - - __m256i sumi = _mm256_setzero_si256(); - - const __m128i scale_0 = _mm_set_epi64(scales_2, scales_1); - const __m128i scale_1 = _mm_set_epi64(scales_4, scales_3); - - const __m256i q4bits1 = _mm256_loadu_si256((const __m256i*)q4); - const __m128i q4bitsH = _mm_loadu_si128((const __m128i*)qh); - - const __m256i q4h_0 = _mm256_slli_epi16(_mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(q4bitsH, 2), q4bitsH), m2), 4); - const __m256i q4h_1 = _mm256_slli_epi16(_mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(q4bitsH, 6), _mm_srli_epi16(q4bitsH, 4)), m2), 4); - - const __m256i q4_0 = _mm256_or_si256(_mm256_and_si256(q4bits1, m4), q4h_0); - const __m256i q4_1 = _mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(q4bits1, 4), m4), q4h_1); - - const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0)); - const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32)); - - __m256i q8s_0 = _mm256_maddubs_epi16(m32s, q8_0); - __m256i q8s_1 = _mm256_maddubs_epi16(m32s, q8_1); - - __m256i p16_0 = _mm256_maddubs_epi16(q4_0, q8_0); - __m256i p16_1 = _mm256_maddubs_epi16(q4_1, q8_1); - p16_0 = _mm256_sub_epi16(p16_0, q8s_0); - p16_1 = _mm256_sub_epi16(p16_1, q8s_1); + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); - p16_0 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_0), p16_0); - p16_1 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_1), p16_1); + vsumf0 = vec_add(vsumf0, vsumf1); - sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p16_0, p16_1)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi), acc); - } - - *s = hsum_float_8(acc); + *s = vec_extract(vsumf0, 0); -#elif defined __AVX__ +#elif defined __loongarch_asx - const __m128i m4 = _mm_set1_epi8(0xF); - const __m128i m2 = _mm_set1_epi8(3); - const __m128i m32s = _mm_set1_epi8(32); + const __m256i m4 = __lasx_xvreplgr2vr_b(0xF); + const __m256i m2 = __lasx_xvreplgr2vr_b(3); + const __m256i m32s = __lasx_xvreplgr2vr_b(32); - __m256 acc = _mm256_setzero_ps(); + __m256 acc = (__m256)__lasx_xvldi(0); for (int i = 0; i < nb; ++i) { @@ -8132,127 +8623,67 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * r const uint8_t * restrict qh = x[i].qh; const int8_t * restrict q8 = y[i].qs; - const __m64 scales_1 = _mm_set1_pi8(x[i].scales[0]); - const __m64 scales_2 = _mm_set1_pi8(x[i].scales[1]); - const __m64 scales_3 = _mm_set1_pi8(x[i].scales[2]); - const __m64 scales_4 = _mm_set1_pi8(x[i].scales[3]); - - __m128i sumi_0 = _mm_setzero_si128(); - __m128i sumi_1 = _mm_setzero_si128(); - - const __m128i scale_0 = _mm_set_epi64(scales_2, scales_1); - const __m128i scale_1 = _mm_set_epi64(scales_4, scales_3); - - const __m256i q4bits1 = _mm256_loadu_si256((const __m256i*)q4); - const __m128i q4bitsH = _mm_loadu_si128((const __m128i*)qh); - - const __m128i q4h_0 = _mm_slli_epi16(_mm_and_si128(q4bitsH, m2), 4); - const __m128i q4h_1 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH, 2), m2), 4); - const __m128i q4h_2 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH, 4), m2), 4); - const __m128i q4h_3 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH, 6), m2), 4); - - const __m128i q4_0 = _mm_or_si128(_mm_and_si128(_mm256_extractf128_si256(q4bits1, 0), m4), q4h_0); - const __m128i q4_1 = _mm_or_si128(_mm_and_si128(_mm256_extractf128_si256(q4bits1, 1), m4), q4h_1); - const __m128i q4_2 = _mm_or_si128(_mm_and_si128(_mm_srli_epi16(_mm256_extractf128_si256(q4bits1, 0), 4), m4), q4h_2); - const __m128i q4_3 = _mm_or_si128(_mm_and_si128(_mm_srli_epi16(_mm256_extractf128_si256(q4bits1, 1), 4), m4), q4h_3); - - const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0)); - const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32)); - - __m128i q8s_0 = _mm_maddubs_epi16(m32s, _mm256_extractf128_si256(q8_0, 0)); - __m128i q8s_1 = _mm_maddubs_epi16(m32s, _mm256_extractf128_si256(q8_0, 1)); - __m128i q8s_2 = _mm_maddubs_epi16(m32s, _mm256_extractf128_si256(q8_1, 0)); - __m128i q8s_3 = _mm_maddubs_epi16(m32s, _mm256_extractf128_si256(q8_1, 1)); - - __m128i p16_0 = _mm_maddubs_epi16(q4_0, _mm256_extractf128_si256(q8_0, 0)); - __m128i p16_1 = _mm_maddubs_epi16(q4_1, _mm256_extractf128_si256(q8_0, 1)); - __m128i p16_2 = _mm_maddubs_epi16(q4_2, _mm256_extractf128_si256(q8_1, 0)); - __m128i p16_3 = _mm_maddubs_epi16(q4_3, _mm256_extractf128_si256(q8_1, 1)); - - p16_0 = _mm_sub_epi16(p16_0, q8s_0); - p16_1 = _mm_sub_epi16(p16_1, q8s_1); - p16_2 = _mm_sub_epi16(p16_2, q8s_2); - p16_3 = _mm_sub_epi16(p16_3, q8s_3); - - p16_0 = _mm_madd_epi16(_mm_cvtepi8_epi16(scale_0), p16_0); - p16_1 = _mm_madd_epi16(_mm_cvtepi8_epi16(_mm_unpackhi_epi64(scale_0, scale_0)), p16_1); - p16_2 = _mm_madd_epi16(_mm_cvtepi8_epi16(scale_1), p16_2); - p16_3 = _mm_madd_epi16(_mm_cvtepi8_epi16(_mm_unpackhi_epi64(scale_1, scale_1)), p16_3); - - sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p16_0, p16_2)); - sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p16_1, p16_3)); - - acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(MM256_SET_M128I(sumi_1, sumi_0))), acc); - } - - *s = hsum_float_8(acc); - -#elif defined __riscv_v_intrinsic - - float sumf = 0; - - for (int i = 0; i < nb; ++i) { - - const float d_all = GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q6 = x[i].ql; - const uint8_t * restrict qh = x[i].qh; - const int8_t * restrict q8 = y[i].qs; + const __m128i scales = __lsx_vld((const __m128i*)x[i].scales, 0); - const int8_t * restrict scale = x[i].scales; + __m256i sumi = __lasx_xvldi(0); - int32_t isum = 0; + int is = 0; - size_t vl = 16; + for (int j = 0; j < QK_K/128; ++j) { - vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1); + const __m128i scale_0 = lsx_shuffle_b(scales, get_scale_shuffle(is + 0)); + const __m128i scale_1 = lsx_shuffle_b(scales, get_scale_shuffle(is + 1)); + const __m128i scale_2 = lsx_shuffle_b(scales, get_scale_shuffle(is + 2)); + const __m128i scale_3 = lsx_shuffle_b(scales, get_scale_shuffle(is + 3)); + is += 4; - // load Q6 - vuint8mf2_t q6_0 = __riscv_vle8_v_u8mf2(q6, vl); - vuint8mf2_t q6_1 = __riscv_vle8_v_u8mf2(q6+16, vl); + const __m256i q4bits1 = __lasx_xvld((const __m256i*)q4, 0); q4 += 32; + const __m256i q4bits2 = __lasx_xvld((const __m256i*)q4, 0); q4 += 32; + const __m256i q4bitsH = __lasx_xvld((const __m256i*)qh, 0); qh += 32; - // load qh - vuint8mf2_t qh_x = __riscv_vle8_v_u8mf2(qh, vl); + const __m256i q4h_0 = __lasx_xvslli_h(__lasx_xvand_v(q4bitsH, m2), 4); + const __m256i q4h_1 = __lasx_xvslli_h(__lasx_xvand_v(__lasx_xvsrli_h(q4bitsH, 2), m2), 4); + const __m256i q4h_2 = __lasx_xvslli_h(__lasx_xvand_v(__lasx_xvsrli_h(q4bitsH, 4), m2), 4); + const __m256i q4h_3 = __lasx_xvslli_h(__lasx_xvand_v(__lasx_xvsrli_h(q4bitsH, 6), m2), 4); - vuint8mf2_t qh0 = __riscv_vsll_vx_u8mf2(__riscv_vand_vx_u8mf2(qh_x, 0x3, vl), 0x4, vl); - qh_x = __riscv_vsrl_vx_u8mf2(qh_x, 0x2, vl); - vuint8mf2_t qh1 = __riscv_vsll_vx_u8mf2(__riscv_vand_vx_u8mf2(qh_x, 0x3, vl), 0x4, vl); - qh_x = __riscv_vsrl_vx_u8mf2(qh_x, 0x2, vl); - vuint8mf2_t qh2 = __riscv_vsll_vx_u8mf2(__riscv_vand_vx_u8mf2(qh_x, 0x3, vl), 0x4, vl); - qh_x = __riscv_vsrl_vx_u8mf2(qh_x, 0x2, vl); - vuint8mf2_t qh3 = __riscv_vsll_vx_u8mf2(__riscv_vand_vx_u8mf2(qh_x, 0x3, vl), 0x4, vl); + const __m256i q4_0 = __lasx_xvor_v(__lasx_xvand_v(q4bits1, m4), q4h_0); + const __m256i q4_1 = __lasx_xvor_v(__lasx_xvand_v(q4bits2, m4), q4h_1); + const __m256i q4_2 = __lasx_xvor_v(__lasx_xvand_v(__lasx_xvsrli_h(q4bits1, 4), m4), q4h_2); + const __m256i q4_3 = __lasx_xvor_v(__lasx_xvand_v(__lasx_xvsrli_h(q4bits2, 4), m4), q4h_3); - vuint8mf2_t q6h_0 = __riscv_vor_vv_u8mf2(__riscv_vand_vx_u8mf2(q6_0, 0xF, vl), qh0, vl); - vuint8mf2_t q6h_1 = __riscv_vor_vv_u8mf2(__riscv_vand_vx_u8mf2(q6_1, 0xF, vl), qh1, vl); - vuint8mf2_t q6h_2 = __riscv_vor_vv_u8mf2(__riscv_vsrl_vx_u8mf2(q6_0, 0x4, vl), qh2, vl); - vuint8mf2_t q6h_3 = __riscv_vor_vv_u8mf2(__riscv_vsrl_vx_u8mf2(q6_1, 0x4, vl), qh3, vl); + const __m256i q8_0 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_1 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_2 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_3 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - vint8mf2_t q6v_0 = __riscv_vsub_vx_i8mf2(__riscv_vreinterpret_v_u8mf2_i8mf2(q6h_0), 32, vl); - vint8mf2_t q6v_1 = __riscv_vsub_vx_i8mf2(__riscv_vreinterpret_v_u8mf2_i8mf2(q6h_1), 32, vl); - vint8mf2_t q6v_2 = __riscv_vsub_vx_i8mf2(__riscv_vreinterpret_v_u8mf2_i8mf2(q6h_2), 32, vl); - vint8mf2_t q6v_3 = __riscv_vsub_vx_i8mf2(__riscv_vreinterpret_v_u8mf2_i8mf2(q6h_3), 32, vl); + __m256i q8s_0 = lasx_maddubs_h(m32s, q8_0); + __m256i q8s_1 = lasx_maddubs_h(m32s, q8_1); + __m256i q8s_2 = lasx_maddubs_h(m32s, q8_2); + __m256i q8s_3 = lasx_maddubs_h(m32s, q8_3); - // load Q8 and take product - vint16m1_t p0 = __riscv_vwmul_vv_i16m1(q6v_0, __riscv_vle8_v_i8mf2(q8, vl), vl); - vint16m1_t p1 = __riscv_vwmul_vv_i16m1(q6v_1, __riscv_vle8_v_i8mf2(q8+16, vl), vl); - vint16m1_t p2 = __riscv_vwmul_vv_i16m1(q6v_2, __riscv_vle8_v_i8mf2(q8+32, vl), vl); - vint16m1_t p3 = __riscv_vwmul_vv_i16m1(q6v_3, __riscv_vle8_v_i8mf2(q8+48, vl), vl); + __m256i p16_0 = lasx_maddubs_h(q4_0, q8_0); + __m256i p16_1 = lasx_maddubs_h(q4_1, q8_1); + __m256i p16_2 = lasx_maddubs_h(q4_2, q8_2); + __m256i p16_3 = lasx_maddubs_h(q4_3, q8_3); - vint32m1_t vs_0 = __riscv_vwredsum_vs_i16m1_i32m1(p0, vzero, vl); - vint32m1_t vs_1 = __riscv_vwredsum_vs_i16m1_i32m1(p1, vzero, vl); - vint32m1_t vs_2 = __riscv_vwredsum_vs_i16m1_i32m1(p2, vzero, vl); - vint32m1_t vs_3 = __riscv_vwredsum_vs_i16m1_i32m1(p3, vzero, vl); + p16_0 = __lasx_xvsub_h(p16_0, q8s_0); + p16_1 = __lasx_xvsub_h(p16_1, q8s_1); + p16_2 = __lasx_xvsub_h(p16_2, q8s_2); + p16_3 = __lasx_xvsub_h(p16_3, q8s_3); - isum += __riscv_vmv_x_s_i32m1_i32(vs_0) * scale[0]; - isum += __riscv_vmv_x_s_i32m1_i32(vs_1) * scale[1]; - isum += __riscv_vmv_x_s_i32m1_i32(vs_2) * scale[2]; - isum += __riscv_vmv_x_s_i32m1_i32(vs_3) * scale[3]; + p16_0 = lasx_madd_h(lasx_ext8_16(scale_0), p16_0); + p16_1 = lasx_madd_h(lasx_ext8_16(scale_1), p16_1); + p16_2 = lasx_madd_h(lasx_ext8_16(scale_2), p16_2); + p16_3 = lasx_madd_h(lasx_ext8_16(scale_3), p16_3); - sumf += isum * d_all * y[i].d; + sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p16_0, p16_1)); + sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p16_2, p16_3)); + } + acc = __lasx_xvfmadd_s((__m256)__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(sumi), acc); } - *s = sumf; + *s = hsum_float_8(acc); #else @@ -8269,12 +8700,18 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * r const int8_t * restrict q8 = y[i].qs; memset(aux32, 0, 8*sizeof(int32_t)); int8_t * restrict a = aux8; - for (int l = 0; l < 16; ++l) { - a[l+ 0] = (int8_t)((q4[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32; - a[l+16] = (int8_t)((q4[l+16] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32; - a[l+32] = (int8_t)((q4[l+ 0] >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32; - a[l+48] = (int8_t)((q4[l+16] >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32; + for (int j = 0; j < QK_K; j += 128) { + for (int l = 0; l < 32; ++l) { + a[l + 0] = (int8_t)((q4[l + 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32; + a[l + 32] = (int8_t)((q4[l + 32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32; + a[l + 64] = (int8_t)((q4[l + 0] >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32; + a[l + 96] = (int8_t)((q4[l + 32] >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32; + } + a += 128; + q4 += 64; + qh += 32; } + a = aux8; int is = 0; for (int j = 0; j < QK_K/16; ++j) { int scale = x[i].scales[is++]; @@ -8293,9 +8730,7 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * r #endif } -#endif - -#if defined (__AVX2__) || defined (__ARM_NEON) +#if defined (__AVX2__) || defined (__ARM_NEON) || defined (__POWER9_VECTOR__) || defined(__loongarch_asx) static const int8_t keven_signs_q2xs[1024] = { 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, -1, 1, -1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1, 1, 1, 1, 1, 1, -1, -1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, -1, @@ -8386,44 +8821,184 @@ void ggml_vec_dot_iq2_xxs_q8_K(int n, float * restrict s, size_t bs, const void } *s = 0.25f * sumf; -#elif defined(__AVX2__) +#elif defined(__AVX2__) + + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + uint32_t aux32[4]; + const uint8_t * aux8 = (const uint8_t *)aux32; + + __m256 accumf = _mm256_setzero_ps(); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint16_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + __m256i sumi1 = _mm256_setzero_si256(); + __m256i sumi2 = _mm256_setzero_si256(); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + memcpy(aux32, q2, 4*sizeof(uint32_t)); q2 += 8; + const __m256i q2_1 = _mm256_set_epi64x(iq2xxs_grid[aux8[ 3]], iq2xxs_grid[aux8[ 2]], iq2xxs_grid[aux8[1]], iq2xxs_grid[aux8[0]]); + const __m256i q2_2 = _mm256_set_epi64x(iq2xxs_grid[aux8[11]], iq2xxs_grid[aux8[10]], iq2xxs_grid[aux8[9]], iq2xxs_grid[aux8[8]]); + const __m256i s2_1 = _mm256_set_epi64x(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127], + signs64[(aux32[1] >> 7) & 127], signs64[(aux32[1] >> 0) & 127]); + const __m256i s2_2 = _mm256_set_epi64x(signs64[(aux32[3] >> 21) & 127], signs64[(aux32[3] >> 14) & 127], + signs64[(aux32[3] >> 7) & 127], signs64[(aux32[3] >> 0) & 127]); + const __m256i q8s_1 = _mm256_sign_epi8(q8_1, s2_1); + const __m256i q8s_2 = _mm256_sign_epi8(q8_2, s2_2); + const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); + const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); + const uint16_t ls1 = aux32[1] >> 28; + const uint16_t ls2 = aux32[3] >> 28; + const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_set1_epi16(2*ls1+1)); + const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_set1_epi16(2*ls2+1)); + sumi1 = _mm256_add_epi32(sumi1, p1); + sumi2 = _mm256_add_epi32(sumi2, p2); + } + + accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf); + + } + + *s = 0.125f * hsum_float_8(accumf); + +#elif defined(__POWER9_VECTOR__) + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + vector signed int vsumi0 = vec_splats((int32_t)0); + vector signed int vsumi1 = vec_splats((int32_t)0); + vector signed int vsumi2 = vec_splats((int32_t)0); + vector signed int vsumi3 = vec_splats((int32_t)0); + vector signed int vsumi4 = vec_splats((int32_t)0); + vector signed int vsumi5 = vec_splats((int32_t)0); + vector signed int vsumi6 = vec_splats((int32_t)0); + vector signed int vsumi7 = vec_splats((int32_t)0); + + const uint16_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + for (int j = 0; j < QK_K/32; j += 2) { + __builtin_prefetch(q2, 0, 1); + __builtin_prefetch(q8, 0, 1); + + uint32_t aux32[4]; + const uint8_t * aux8 = (const uint8_t *)aux32; + + memcpy(aux32, q2, 4*sizeof(uint32_t)); + q2 += 8; + + vector signed long long aux64x2_0 = {*(const int64_t *)(iq2xxs_grid + aux8[ 0]), *(const int64_t *)(iq2xxs_grid + aux8[ 1])}; + vector signed long long aux64x2_1 = {*(const int64_t *)(iq2xxs_grid + aux8[ 2]), *(const int64_t *)(iq2xxs_grid + aux8[ 3])}; + vector signed long long aux64x2_2 = {*(const int64_t *)(iq2xxs_grid + aux8[ 8]), *(const int64_t *)(iq2xxs_grid + aux8[ 9])}; + vector signed long long aux64x2_3 = {*(const int64_t *)(iq2xxs_grid + aux8[10]), *(const int64_t *)(iq2xxs_grid + aux8[11])}; + + vector signed long long vsigns0 = {*(const int64_t *)(signs64 + ((aux32[1] >> 0) & 127)), *(const int64_t *)(signs64 + ((aux32[1] >> 7) & 127))}; + vector signed long long vsigns1 = {*(const int64_t *)(signs64 + ((aux32[1] >> 14) & 127)), *(const int64_t *)(signs64 + ((aux32[1] >> 21) & 127))}; + vector signed long long vsigns2 = {*(const int64_t *)(signs64 + ((aux32[3] >> 0) & 127)), *(const int64_t *)(signs64 + ((aux32[3] >> 7) & 127))}; + vector signed long long vsigns3 = {*(const int64_t *)(signs64 + ((aux32[3] >> 14) & 127)), *(const int64_t *)(signs64 + ((aux32[3] >> 21) & 127))}; + + vector signed char q2x0 = (vector signed char)vec_mul((vector signed char)vsigns0, (vector signed char)aux64x2_0); + vector signed char q2x1 = (vector signed char)vec_mul((vector signed char)vsigns1, (vector signed char)aux64x2_1); + vector signed char q2x2 = (vector signed char)vec_mul((vector signed char)vsigns2, (vector signed char)aux64x2_2); + vector signed char q2x3 = (vector signed char)vec_mul((vector signed char)vsigns3, (vector signed char)aux64x2_3); + + vector signed char q8y0 = vec_xl( 0, q8); + vector signed char q8y1 = vec_xl(16, q8); + vector signed char q8y2 = vec_xl(32, q8); + vector signed char q8y3 = vec_xl(48, q8); + q8 += 64; + + vector signed short qv0 = vec_add(vec_mule(q2x0, q8y0), vec_mulo(q2x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q2x1, q8y1), vec_mulo(q2x1, q8y1)); + vector signed short qv2 = vec_add(vec_mule(q2x2, q8y2), vec_mulo(q2x2, q8y2)); + vector signed short qv3 = vec_add(vec_mule(q2x3, q8y3), vec_mulo(q2x3, q8y3)); + + const uint16_t ls0 = aux32[1] >> 28; + const uint16_t ls1 = aux32[3] >> 28; + + vector signed short vscales01 = vec_splats((int16_t)(2*ls0+1)); + vector signed short vscales23 = vec_splats((int16_t)(2*ls1+1)); + + vsumi0 = vec_add(vec_mule(qv0, vscales01), vsumi0); + vsumi1 = vec_add(vec_mule(qv1, vscales01), vsumi1); + vsumi2 = vec_add(vec_mule(qv2, vscales23), vsumi2); + vsumi3 = vec_add(vec_mule(qv3, vscales23), vsumi3); + vsumi4 = vec_add(vec_mulo(qv0, vscales01), vsumi4); + vsumi5 = vec_add(vec_mulo(qv1, vscales01), vsumi5); + vsumi6 = vec_add(vec_mulo(qv2, vscales23), vsumi6); + vsumi7 = vec_add(vec_mulo(qv3, vscales23), vsumi7); + } + + vsumi0 = vec_add(vsumi0, vsumi4); + vsumi1 = vec_add(vsumi1, vsumi5); + vsumi2 = vec_add(vsumi2, vsumi6); + vsumi3 = vec_add(vsumi3, vsumi7); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = 0.125f * vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; uint32_t aux32[4]; const uint8_t * aux8 = (const uint8_t *)aux32; - __m256 accumf = _mm256_setzero_ps(); + __m256 accumf = (__m256)__lasx_xvldi(0); for (int i = 0; i < nb; ++i) { const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; const uint16_t * restrict q2 = x[i].qs; const int8_t * restrict q8 = y[i].qs; - __m256i sumi1 = _mm256_setzero_si256(); - __m256i sumi2 = _mm256_setzero_si256(); + __m256i sumi1 = __lasx_xvldi(0); + __m256i sumi2 = __lasx_xvldi(0); for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; - const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q8_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q8_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; memcpy(aux32, q2, 4*sizeof(uint32_t)); q2 += 8; - const __m256i q2_1 = _mm256_set_epi64x(iq2xxs_grid[aux8[ 3]], iq2xxs_grid[aux8[ 2]], iq2xxs_grid[aux8[1]], iq2xxs_grid[aux8[0]]); - const __m256i q2_2 = _mm256_set_epi64x(iq2xxs_grid[aux8[11]], iq2xxs_grid[aux8[10]], iq2xxs_grid[aux8[9]], iq2xxs_grid[aux8[8]]); - const __m256i s2_1 = _mm256_set_epi64x(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127], + + const __m256i q2_1 = lasx_set_d(iq2xxs_grid[aux8[ 3]], iq2xxs_grid[aux8[ 2]], iq2xxs_grid[aux8[1]], iq2xxs_grid[aux8[0]]); + const __m256i q2_2 = lasx_set_d(iq2xxs_grid[aux8[11]], iq2xxs_grid[aux8[10]], iq2xxs_grid[aux8[9]], iq2xxs_grid[aux8[8]]); + const __m256i s2_1 = lasx_set_d(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127], signs64[(aux32[1] >> 7) & 127], signs64[(aux32[1] >> 0) & 127]); - const __m256i s2_2 = _mm256_set_epi64x(signs64[(aux32[3] >> 21) & 127], signs64[(aux32[3] >> 14) & 127], + const __m256i s2_2 = lasx_set_d(signs64[(aux32[3] >> 21) & 127], signs64[(aux32[3] >> 14) & 127], signs64[(aux32[3] >> 7) & 127], signs64[(aux32[3] >> 0) & 127]); - const __m256i q8s_1 = _mm256_sign_epi8(q8_1, s2_1); - const __m256i q8s_2 = _mm256_sign_epi8(q8_2, s2_2); - const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); - const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); + const __m256i q8s_1 = __lasx_xvsigncov_b(s2_1, q8_1); + const __m256i q8s_2 = __lasx_xvsigncov_b(s2_2, q8_2); + const __m256i dot1 = lasx_maddubs_h(q2_1, q8s_1); + const __m256i dot2 = lasx_maddubs_h(q2_2, q8s_2); const uint16_t ls1 = aux32[1] >> 28; const uint16_t ls2 = aux32[3] >> 28; - const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_set1_epi16(2*ls1+1)); - const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_set1_epi16(2*ls2+1)); - sumi1 = _mm256_add_epi32(sumi1, p1); - sumi2 = _mm256_add_epi32(sumi2, p2); + const __m256i p1 = lasx_madd_h(dot1, __lasx_xvreplgr2vr_h(2*ls1+1)); + const __m256i p2 = lasx_madd_h(dot2, __lasx_xvreplgr2vr_h(2*ls2+1)); + sumi1 = __lasx_xvadd_w(sumi1, p1); + sumi2 = __lasx_xvadd_w(sumi2, p2); } - accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf); - + accumf = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accumf); } *s = 0.125f * hsum_float_8(accumf); @@ -8546,64 +9121,6 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void * const __m256i block_sign_shuffle_1 = _mm256_loadu_si256((const __m256i*)block_sign_shuffle_mask_1); const __m256i block_sign_shuffle_2 = _mm256_loadu_si256((const __m256i*)block_sign_shuffle_mask_2); -#if QK_K == 64 - static const uint8_t k_bit_helper[16] = { - 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, - }; - const __m128i bit_helper = _mm_loadu_si128((const __m128i*)k_bit_helper); - const __m128i m511 = _mm_set1_epi16(511); - typedef union { - __m128i vec_index; - uint16_t index[8]; - } index_t; - - index_t idx; - __m256 accumf = _mm256_setzero_ps(); - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const __m128i q2_data = _mm_loadu_si128((const __m128i*)x[i].qs); - idx.vec_index = _mm_and_si128(q2_data, m511); - - const __m128i partial_sign_bits = _mm_srli_epi16(q2_data, 9); - const __m128i partial_sign_bits_upper = _mm_srli_epi16(q2_data, 13); - const __m128i partial_sign_bits_for_counting = _mm_xor_si128(partial_sign_bits, partial_sign_bits_upper); - - const __m128i odd_bits = _mm_shuffle_epi8(bit_helper, partial_sign_bits_for_counting); - const __m128i full_sign_bits = _mm_or_si128(partial_sign_bits, odd_bits); - const __m256i full_signs = MM256_SET_M128I(full_sign_bits, full_sign_bits); - - const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)y[i].qs); - const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)(y[i].qs+32)); - - const __m256i q2_1 = _mm256_set_epi64x(iq2xs_grid[idx.index[3]], iq2xs_grid[idx.index[2]], - iq2xs_grid[idx.index[1]], iq2xs_grid[idx.index[0]]); - const __m256i q2_2 = _mm256_set_epi64x(iq2xs_grid[idx.index[7]], iq2xs_grid[idx.index[6]], - iq2xs_grid[idx.index[5]], iq2xs_grid[idx.index[4]]); - - __m256i signs; - signs = _mm256_shuffle_epi8(full_signs, block_sign_shuffle_1); - signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask); - const __m256i q8s_1 = _mm256_sign_epi8(q8_1, _mm256_or_si256(signs, mone)); - - signs = _mm256_shuffle_epi8(full_signs, block_sign_shuffle_2); - signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask); - const __m256i q8s_2 = _mm256_sign_epi8(q8_2, _mm256_or_si256(signs, mone)); - - const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); - const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); - - const __m256i sc1 = MM256_SET_M128I(_mm_set1_epi16(2*(x[i].scales[0] >> 4)+1), _mm_set1_epi16(2*(x[i].scales[0] & 0xf)+1)); - const __m256i sc2 = MM256_SET_M128I(_mm_set1_epi16(2*(x[i].scales[1] >> 4)+1), _mm_set1_epi16(2*(x[i].scales[1] & 0xf)+1)); - - const __m256i sum = _mm256_add_epi32(_mm256_madd_epi16(sc1, dot1), _mm256_madd_epi16(sc2, dot2)); - - accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(sum), accumf); - - } - - *s = 0.125f * hsum_float_8(accumf); -#else - static const uint8_t k_bit_helper[32] = { 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, @@ -8701,8 +9218,221 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void * } *s = 0.125f * hsum_float_8(accumf); -#endif +#elif defined(__loongarch_asx) + + const __m256i mone = __lasx_xvreplgr2vr_b(1); + static const char block_sign_shuffle_mask_1[32] = { + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, + 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, + }; + static const char block_sign_shuffle_mask_2[32] = { + 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, + 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, + }; + static const uint8_t bit_selector_mask_bytes[32] = { + 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + }; + + const __m256i bit_selector_mask = __lasx_xvld((const __m256i*)bit_selector_mask_bytes, 0); + const __m256i block_sign_shuffle_1 = __lasx_xvld((const __m256i*)block_sign_shuffle_mask_1, 0); + const __m256i block_sign_shuffle_2 = __lasx_xvld((const __m256i*)block_sign_shuffle_mask_2, 0); + + static const uint8_t k_bit_helper[32] = { + 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, + 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, + }; + const __m256i bit_helper = __lasx_xvld((const __m256i*)k_bit_helper, 0); + const __m256i m511 = __lasx_xvreplgr2vr_h(511); + const __m128i m4 = __lsx_vreplgr2vr_b(0xf); + const __m128i m1 = __lsx_vreplgr2vr_b(1); + + uint64_t aux64; + + // somewhat hacky, but gives a significant boost in performance + __m256i aux_gindex; + const uint16_t * gindex = (const uint16_t *)&aux_gindex; + + __m256 accumf = (__m256)__lasx_xvldi(0); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint16_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + memcpy(&aux64, x[i].scales, 8); + __m128i stmp = __lsx_vreplgr2vr_d(aux64); + stmp = __lsx_vilvl_b( __lsx_vand_v(__lsx_vsrli_h(stmp, 4), m4), __lsx_vand_v(stmp, m4)); + const __m128i scales = __lsx_vadd_b(__lsx_vslli_h(stmp, 1), m1); + + __m256i sumi1 = __lasx_xvldi(0); + __m256i sumi2 = __lasx_xvldi(0); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 4) { + + const __m256i q2_data = __lasx_xvld((const __m256i*)q2, 0); q2 += 16; + aux_gindex = __lasx_xvand_v(q2_data, m511); + + const __m256i partial_sign_bits = __lasx_xvsrli_h(q2_data, 9); + const __m256i partial_sign_bits_upper = __lasx_xvsrli_h(q2_data, 13); + const __m256i partial_sign_bits_for_counting = __lasx_xvxor_v(partial_sign_bits, partial_sign_bits_upper); + + const __m256i odd_bits = lasx_shuffle_b(bit_helper, partial_sign_bits_for_counting); + const __m256i full_sign_bits = __lasx_xvor_v(partial_sign_bits, odd_bits); + + const __m256i q8_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q8_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q8_3 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q8_4 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + + const __m256i q2_1 = lasx_set_d(iq2xs_grid[gindex[ 3]], iq2xs_grid[gindex[ 2]], + iq2xs_grid[gindex[ 1]], iq2xs_grid[gindex[ 0]]); + const __m256i q2_2 = lasx_set_d(iq2xs_grid[gindex[ 7]], iq2xs_grid[gindex[ 6]], + iq2xs_grid[gindex[ 5]], iq2xs_grid[gindex[ 4]]); + const __m256i q2_3 = lasx_set_d(iq2xs_grid[gindex[11]], iq2xs_grid[gindex[10]], + iq2xs_grid[gindex[ 9]], iq2xs_grid[gindex[ 8]]); + const __m256i q2_4 = lasx_set_d(iq2xs_grid[gindex[15]], iq2xs_grid[gindex[14]], + iq2xs_grid[gindex[13]], iq2xs_grid[gindex[12]]); + + const __m128i full_signs_l = lasx_extracti128(full_sign_bits, 0); + const __m128i full_signs_h = lasx_extracti128(full_sign_bits, 1); + const __m256i full_signs_1 = lasx_insertf128(full_signs_l, full_signs_l); + const __m256i full_signs_2 = lasx_insertf128(full_signs_h, full_signs_h); + + __m256i signs; + signs = lasx_shuffle_b(full_signs_1, block_sign_shuffle_1); + signs = __lasx_xvseq_b(__lasx_xvand_v(signs, bit_selector_mask), bit_selector_mask); + const __m256i q8s_1 = __lasx_xvsigncov_b(__lasx_xvor_v(signs, mone), q8_1); + + signs = lasx_shuffle_b(full_signs_1, block_sign_shuffle_2); + signs = __lasx_xvseq_b(__lasx_xvand_v(signs, bit_selector_mask), bit_selector_mask); + const __m256i q8s_2 = __lasx_xvsigncov_b(__lasx_xvor_v(signs, mone), q8_2); + + signs = lasx_shuffle_b(full_signs_2, block_sign_shuffle_1); + signs = __lasx_xvseq_b(__lasx_xvand_v(signs, bit_selector_mask), bit_selector_mask); + const __m256i q8s_3 = __lasx_xvsigncov_b(__lasx_xvor_v(signs, mone), q8_3); + + signs = lasx_shuffle_b(full_signs_2, block_sign_shuffle_2); + signs = __lasx_xvseq_b(__lasx_xvand_v(signs, bit_selector_mask), bit_selector_mask); + const __m256i q8s_4 = __lasx_xvsigncov_b(__lasx_xvor_v(signs, mone), q8_4); + + const __m256i dot1 = lasx_maddubs_h(q2_1, q8s_1); + const __m256i dot2 = lasx_maddubs_h(q2_2, q8s_2); + const __m256i dot3 = lasx_maddubs_h(q2_3, q8s_3); + const __m256i dot4 = lasx_maddubs_h(q2_4, q8s_4); + + const __m256i sc1 = lasx_ext8_16(lsx_shuffle_b(scales, get_scale_shuffle(ib32+0))); + const __m256i sc2 = lasx_ext8_16(lsx_shuffle_b(scales, get_scale_shuffle(ib32+1))); + const __m256i sc3 = lasx_ext8_16(lsx_shuffle_b(scales, get_scale_shuffle(ib32+2))); + const __m256i sc4 = lasx_ext8_16(lsx_shuffle_b(scales, get_scale_shuffle(ib32+3))); + + sumi1 = __lasx_xvadd_w(sumi1, lasx_madd_h(dot1, sc1)); + sumi2 = __lasx_xvadd_w(sumi2, lasx_madd_h(dot2, sc2)); + sumi1 = __lasx_xvadd_w(sumi1, lasx_madd_h(dot3, sc3)); + sumi2 = __lasx_xvadd_w(sumi2, lasx_madd_h(dot4, sc4)); + } + + accumf = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accumf); + + } + + *s = 0.125f * hsum_float_8(accumf); +#elif defined(__POWER9_VECTOR__) + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + vector signed int vsumi0 = vec_splats((int32_t)0); + vector signed int vsumi1 = vec_splats((int32_t)0); + vector signed int vsumi2 = vec_splats((int32_t)0); + vector signed int vsumi3 = vec_splats((int32_t)0); + vector signed int vsumi4 = vec_splats((int32_t)0); + vector signed int vsumi5 = vec_splats((int32_t)0); + vector signed int vsumi6 = vec_splats((int32_t)0); + vector signed int vsumi7 = vec_splats((int32_t)0); + + const uint16_t * restrict q2 = x[i].qs; + const uint8_t * restrict sc = x[i].scales; + const int8_t * restrict q8 = y[i].qs; + + for (int j = 0; j < QK_K/64; ++j) { + __builtin_prefetch(q2, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector signed long long aux64x2_0 = {*(const int64_t *)(iq2xs_grid + (q2[0] & 511)), *(const int64_t *)(iq2xs_grid + (q2[1] & 511))}; + vector signed long long aux64x2_1 = {*(const int64_t *)(iq2xs_grid + (q2[2] & 511)), *(const int64_t *)(iq2xs_grid + (q2[3] & 511))}; + vector signed long long aux64x2_2 = {*(const int64_t *)(iq2xs_grid + (q2[4] & 511)), *(const int64_t *)(iq2xs_grid + (q2[5] & 511))}; + vector signed long long aux64x2_3 = {*(const int64_t *)(iq2xs_grid + (q2[6] & 511)), *(const int64_t *)(iq2xs_grid + (q2[7] & 511))}; + + vector signed long long vsigns0 = {*(const int64_t *)(signs64 + ((q2[0] >> 9))), *(const int64_t *)(signs64 + ((q2[1] >> 9)))}; + vector signed long long vsigns1 = {*(const int64_t *)(signs64 + ((q2[2] >> 9))), *(const int64_t *)(signs64 + ((q2[3] >> 9)))}; + vector signed long long vsigns2 = {*(const int64_t *)(signs64 + ((q2[4] >> 9))), *(const int64_t *)(signs64 + ((q2[5] >> 9)))}; + vector signed long long vsigns3 = {*(const int64_t *)(signs64 + ((q2[6] >> 9))), *(const int64_t *)(signs64 + ((q2[7] >> 9)))}; + q2 += 8; + + vector signed char q2x0 = (vector signed char)vec_mul((vector signed char)vsigns0, (vector signed char)aux64x2_0); + vector signed char q2x1 = (vector signed char)vec_mul((vector signed char)vsigns1, (vector signed char)aux64x2_1); + vector signed char q2x2 = (vector signed char)vec_mul((vector signed char)vsigns2, (vector signed char)aux64x2_2); + vector signed char q2x3 = (vector signed char)vec_mul((vector signed char)vsigns3, (vector signed char)aux64x2_3); + + vector signed char q8y0 = vec_xl( 0, q8); + vector signed char q8y1 = vec_xl(16, q8); + vector signed char q8y2 = vec_xl(32, q8); + vector signed char q8y3 = vec_xl(48, q8); + q8 += 64; + + vector signed short qv0 = vec_add(vec_mule(q2x0, q8y0), vec_mulo(q2x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q2x1, q8y1), vec_mulo(q2x1, q8y1)); + vector signed short qv2 = vec_add(vec_mule(q2x2, q8y2), vec_mulo(q2x2, q8y2)); + vector signed short qv3 = vec_add(vec_mule(q2x3, q8y3), vec_mulo(q2x3, q8y3)); + + const uint16_t ls0 = (uint16_t)(sc[0] & 0xf); + const uint16_t ls1 = (uint16_t)(sc[0] >> 4); + const uint16_t ls2 = (uint16_t)(sc[1] & 0xf); + const uint16_t ls3 = (uint16_t)(sc[1] >> 4); + sc += 2; + + vector signed short vscales0 = vec_splats((int16_t)(2*ls0+1)); + vector signed short vscales1 = vec_splats((int16_t)(2*ls1+1)); + vector signed short vscales2 = vec_splats((int16_t)(2*ls2+1)); + vector signed short vscales3 = vec_splats((int16_t)(2*ls3+1)); + + vsumi0 = vec_add(vec_mule(qv0, vscales0), vsumi0); + vsumi1 = vec_add(vec_mule(qv1, vscales1), vsumi1); + vsumi2 = vec_add(vec_mule(qv2, vscales2), vsumi2); + vsumi3 = vec_add(vec_mule(qv3, vscales3), vsumi3); + vsumi4 = vec_add(vec_mulo(qv0, vscales0), vsumi4); + vsumi5 = vec_add(vec_mulo(qv1, vscales1), vsumi5); + vsumi6 = vec_add(vec_mulo(qv2, vscales2), vsumi6); + vsumi7 = vec_add(vec_mulo(qv3, vscales3), vsumi7); + } + + vsumi0 = vec_add(vsumi0, vsumi4); + vsumi1 = vec_add(vsumi1, vsumi5); + vsumi2 = vec_add(vsumi2, vsumi6); + vsumi3 = vec_add(vsumi3, vsumi7); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = 0.125f * vec_extract(vsumf0, 0); #else float sumf = 0.f; @@ -8903,6 +9633,199 @@ void ggml_vec_dot_iq2_s_q8_K(int n, float * restrict s, size_t bs, const void * *s = 0.125f * hsum_float_8(accumf); +#elif defined(__POWER9_VECTOR__) + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, + 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 + }; + + static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,}; + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + const vector unsigned char mask0 = vec_xl( 0, k_mask1); + const vector unsigned char mask1 = vec_xl(16, k_mask1); + const vector signed char mask2 = (vector signed char)vec_xl( 0, k_mask2); + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + vector signed int vsumi0 = vec_splats((int32_t)0); + vector signed int vsumi1 = vec_splats((int32_t)0); + vector signed int vsumi2 = vec_splats((int32_t)0); + vector signed int vsumi3 = vec_splats((int32_t)0); + vector signed int vsumi4 = vec_splats((int32_t)0); + vector signed int vsumi5 = vec_splats((int32_t)0); + vector signed int vsumi6 = vec_splats((int32_t)0); + vector signed int vsumi7 = vec_splats((int32_t)0); + + const uint8_t * restrict q2 = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint16_t * restrict signs = (const uint16_t *)(x[i].qs + QK_K/8); + const uint8_t * restrict sc = x[i].scales; + const int8_t * restrict q8 = y[i].qs; + + for (int j = 0; j < QK_K/32; j += 2) { + __builtin_prefetch(q2, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector signed long long aux64x2_0 = {*(const int64_t *)(iq2s_grid + (q2[0] | ((qh[0] << 8) & 0x300))), *(const int64_t *)(iq2s_grid + (q2[1] | ((qh[0] << 6) & 0x300)))}; + vector signed long long aux64x2_1 = {*(const int64_t *)(iq2s_grid + (q2[2] | ((qh[0] << 4) & 0x300))), *(const int64_t *)(iq2s_grid + (q2[3] | ((qh[0] << 2) & 0x300)))}; + vector signed long long aux64x2_2 = {*(const int64_t *)(iq2s_grid + (q2[4] | ((qh[1] << 8) & 0x300))), *(const int64_t *)(iq2s_grid + (q2[5] | ((qh[1] << 6) & 0x300)))}; + vector signed long long aux64x2_3 = {*(const int64_t *)(iq2s_grid + (q2[6] | ((qh[1] << 4) & 0x300))), *(const int64_t *)(iq2s_grid + (q2[7] | ((qh[1] << 2) & 0x300)))}; + q2 += 8; + qh += 2; + + vector signed char vsigns01 = (vector signed char)vec_splats(*(const uint32_t *)&signs[0]); + vector signed char vsigns23 = (vector signed char)vec_splats(*(const uint32_t *)&signs[2]); + signs += 4; + + vector signed char vsigns0 = vec_perm(vsigns01, vsigns01, mask0); + vector signed char vsigns1 = vec_perm(vsigns01, vsigns01, mask1); + vector signed char vsigns2 = vec_perm(vsigns23, vsigns23, mask0); + vector signed char vsigns3 = vec_perm(vsigns23, vsigns23, mask1); + + vsigns0 = (vector signed char)vec_cmpeq(vec_and(vsigns0, mask2), mask2); + vsigns1 = (vector signed char)vec_cmpeq(vec_and(vsigns1, mask2), mask2); + vsigns2 = (vector signed char)vec_cmpeq(vec_and(vsigns2, mask2), mask2); + vsigns3 = (vector signed char)vec_cmpeq(vec_and(vsigns3, mask2), mask2); + + vector signed char q2x0 = vec_sub(vec_xor(vsigns0, (vector signed char)aux64x2_0), vsigns0); + vector signed char q2x1 = vec_sub(vec_xor(vsigns1, (vector signed char)aux64x2_1), vsigns1); + vector signed char q2x2 = vec_sub(vec_xor(vsigns2, (vector signed char)aux64x2_2), vsigns2); + vector signed char q2x3 = vec_sub(vec_xor(vsigns3, (vector signed char)aux64x2_3), vsigns3); + + vector signed char q8y0 = vec_xl( 0, q8); + vector signed char q8y1 = vec_xl(16, q8); + vector signed char q8y2 = vec_xl(32, q8); + vector signed char q8y3 = vec_xl(48, q8); + q8 += 64; + + vector signed short qv0 = vec_add(vec_mule(q2x0, q8y0), vec_mulo(q2x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q2x1, q8y1), vec_mulo(q2x1, q8y1)); + vector signed short qv2 = vec_add(vec_mule(q2x2, q8y2), vec_mulo(q2x2, q8y2)); + vector signed short qv3 = vec_add(vec_mule(q2x3, q8y3), vec_mulo(q2x3, q8y3)); + + const uint16_t ls0 = (uint16_t)(sc[0] & 0xf); + const uint16_t ls1 = (uint16_t)(sc[0] >> 4); + const uint16_t ls2 = (uint16_t)(sc[1] & 0xf); + const uint16_t ls3 = (uint16_t)(sc[1] >> 4); + sc += 2; + + vector signed short vscales0 = vec_splats((int16_t)(2*ls0+1)); + vector signed short vscales1 = vec_splats((int16_t)(2*ls1+1)); + vector signed short vscales2 = vec_splats((int16_t)(2*ls2+1)); + vector signed short vscales3 = vec_splats((int16_t)(2*ls3+1)); + + vsumi0 = vec_add(vec_mule(qv0, vscales0), vsumi0); + vsumi1 = vec_add(vec_mule(qv1, vscales1), vsumi1); + vsumi2 = vec_add(vec_mule(qv2, vscales2), vsumi2); + vsumi3 = vec_add(vec_mule(qv3, vscales3), vsumi3); + vsumi4 = vec_add(vec_mulo(qv0, vscales0), vsumi4); + vsumi5 = vec_add(vec_mulo(qv1, vscales1), vsumi5); + vsumi6 = vec_add(vec_mulo(qv2, vscales2), vsumi6); + vsumi7 = vec_add(vec_mulo(qv3, vscales3), vsumi7); + } + + vsumi0 = vec_add(vsumi0, vsumi4); + vsumi1 = vec_add(vsumi1, vsumi5); + vsumi2 = vec_add(vsumi2, vsumi6); + vsumi3 = vec_add(vsumi3, vsumi7); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = 0.125f * vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, + 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 + }; + + static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + }; + + + const __m128i m4 = __lsx_vreplgr2vr_b(0xf); + const __m128i m1 = __lsx_vreplgr2vr_b(1); + + const __m256i mask1 = __lasx_xvld((const __m256i*)k_mask1, 0); + const __m256i mask2 = __lasx_xvld((const __m256i*)k_mask2, 0); + uint64_t aux64; + + __m256 accumf = (__m256)__lasx_xvldi(0); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict qs = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint16_t * restrict signs = (const uint16_t *)(x[i].qs + QK_K/8); + const int8_t * restrict q8 = y[i].qs; + + __m128i tmp1; + memcpy(&aux64, x[i].scales, 8); + tmp1 = __lsx_vinsgr2vr_d(tmp1, aux64, 0); + tmp1 = __lsx_vinsgr2vr_d(tmp1, aux64 >> 4, 1); + const __m128i scales8 = __lsx_vadd_b(__lsx_vslli_h(__lsx_vand_v(tmp1, m4), 1), m1); + const __m256i scales16 = lasx_ext8_16(scales8); // 0 2 4 6 8 10 12 14 1 3 5 7 9 11 13 15 + + __m256i sumi1 = __lasx_xvldi(0); + __m256i sumi2 = __lasx_xvldi(0); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m256i q8_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q8_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q2_1 = lasx_set_d(iq2s_grid[qs[3] | ((qh[ib32+0] << 2) & 0x300)], + iq2s_grid[qs[2] | ((qh[ib32+0] << 4) & 0x300)], + iq2s_grid[qs[1] | ((qh[ib32+0] << 6) & 0x300)], + iq2s_grid[qs[0] | ((qh[ib32+0] << 8) & 0x300)]); + const __m256i q2_2 = lasx_set_d(iq2s_grid[qs[7] | ((qh[ib32+1] << 2) & 0x300)], + iq2s_grid[qs[6] | ((qh[ib32+1] << 4) & 0x300)], + iq2s_grid[qs[5] | ((qh[ib32+1] << 6) & 0x300)], + iq2s_grid[qs[4] | ((qh[ib32+1] << 8) & 0x300)]); + qs += 8; + + __m256i aux256 = __lasx_xvreplgr2vr_w(signs[0] | ((uint32_t) signs[1] << 16)); + aux256 = __lasx_xvand_v(lasx_shuffle_b(aux256,mask1), mask2); + const __m256i s2_1 = __lasx_xvseq_b(aux256, mask2); + const __m256i q8s_1 = __lasx_xvsub_b(__lasx_xvxor_v(s2_1, q8_1), s2_1); + + aux256 = __lasx_xvreplgr2vr_w(signs[2] | ((uint32_t) signs[3] << 16)); + aux256 = __lasx_xvand_v(lasx_shuffle_b(aux256,mask1), mask2); + const __m256i s2_2 = __lasx_xvseq_b(aux256, mask2); + const __m256i q8s_2 = __lasx_xvsub_b(__lasx_xvxor_v(s2_2, q8_2), s2_2); + + signs += 4; + + const __m256i dot1 = lasx_maddubs_h(q2_1, q8s_1); // blocks 2*ib32+0, 2*ib32+1 + const __m256i dot2 = lasx_maddubs_h(q2_2, q8s_2); // blocks 2*ib32+2, 2*ib32+3 + + const __m256i p1 = lasx_madd_h(dot1, lasx_shuffle_b(scales16, get_scale_shuffle_k4(ib32+0))); + const __m256i p2 = lasx_madd_h(dot2, lasx_shuffle_b(scales16, get_scale_shuffle_k4(ib32+1))); + sumi1 = __lasx_xvadd_w(sumi1, p1); + sumi2 = __lasx_xvadd_w(sumi2, p2); + } + + accumf = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accumf); + } + + *s = 0.125f * hsum_float_8(accumf); + #else float sumf = 0; @@ -9047,6 +9970,149 @@ void ggml_vec_dot_iq3_xxs_q8_K(int n, float * restrict s, size_t bs, const void *s = 0.25f * hsum_float_8(accumf); +#elif defined(__POWER9_VECTOR__) + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + vector signed int vsumi0 = vec_splats((int32_t)0); + vector signed int vsumi1 = vec_splats((int32_t)0); + vector signed int vsumi2 = vec_splats((int32_t)0); + vector signed int vsumi3 = vec_splats((int32_t)0); + vector signed int vsumi4 = vec_splats((int32_t)0); + vector signed int vsumi5 = vec_splats((int32_t)0); + vector signed int vsumi6 = vec_splats((int32_t)0); + vector signed int vsumi7 = vec_splats((int32_t)0); + + const uint8_t * restrict q3 = x[i].qs; + const uint32_t * restrict signs = (const uint32_t *)(x[i].qs + QK_K/4); + const int8_t * restrict q8 = y[i].qs; + +#pragma GCC unroll 1 + for (int j = 0; j < QK_K/32; j += 2) { + __builtin_prefetch(q3, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector unsigned int aux32x4_0 = {iq3xxs_grid[q3[ 0]], iq3xxs_grid[q3[ 1]], iq3xxs_grid[q3[ 2]], iq3xxs_grid[q3[ 3]]}; + vector unsigned int aux32x4_1 = {iq3xxs_grid[q3[ 4]], iq3xxs_grid[q3[ 5]], iq3xxs_grid[q3[ 6]], iq3xxs_grid[q3[ 7]]}; + vector unsigned int aux32x4_2 = {iq3xxs_grid[q3[ 8]], iq3xxs_grid[q3[ 9]], iq3xxs_grid[q3[10]], iq3xxs_grid[q3[11]]}; + vector unsigned int aux32x4_3 = {iq3xxs_grid[q3[12]], iq3xxs_grid[q3[13]], iq3xxs_grid[q3[14]], iq3xxs_grid[q3[15]]}; + q3 += 16; + + vector unsigned long long aux64x2_0 = {(uint64_t)(signs64[(signs[0] >> 0) & 127]), (uint64_t)(signs64[(signs[0] >> 7) & 127])}; + vector unsigned long long aux64x2_1 = {(uint64_t)(signs64[(signs[0] >> 14) & 127]), (uint64_t)(signs64[(signs[0] >> 21) & 127])}; + vector unsigned long long aux64x2_2 = {(uint64_t)(signs64[(signs[1] >> 0) & 127]), (uint64_t)(signs64[(signs[1] >> 7) & 127])}; + vector unsigned long long aux64x2_3 = {(uint64_t)(signs64[(signs[1] >> 14) & 127]), (uint64_t)(signs64[(signs[1] >> 21) & 127])}; + + vector signed char q3x0 = vec_mul((vector signed char)aux64x2_0, (vector signed char)aux32x4_0); + vector signed char q3x1 = vec_mul((vector signed char)aux64x2_1, (vector signed char)aux32x4_1); + vector signed char q3x2 = vec_mul((vector signed char)aux64x2_2, (vector signed char)aux32x4_2); + vector signed char q3x3 = vec_mul((vector signed char)aux64x2_3, (vector signed char)aux32x4_3); + + vector signed char q8y0 = vec_xl( 0, q8); + vector signed char q8y1 = vec_xl(16, q8); + vector signed char q8y2 = vec_xl(32, q8); + vector signed char q8y3 = vec_xl(48, q8); + q8 += 64; + + vector signed short qv0 = vec_add(vec_mule(q3x0, q8y0), vec_mulo(q3x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q3x1, q8y1), vec_mulo(q3x1, q8y1)); + vector signed short qv2 = vec_add(vec_mule(q3x2, q8y2), vec_mulo(q3x2, q8y2)); + vector signed short qv3 = vec_add(vec_mule(q3x3, q8y3), vec_mulo(q3x3, q8y3)); + + const uint16_t ls0 = (uint16_t)(signs[0] >> 28); + const uint16_t ls1 = (uint16_t)(signs[1] >> 28); + signs += 2; + + vector signed short vscales01 = (vector signed short)vec_splats((uint16_t)(2*ls0+1)); + vector signed short vscales23 = (vector signed short)vec_splats((uint16_t)(2*ls1+1)); + + vsumi0 = vec_add(vec_mule(qv0, vscales01), vsumi0); + vsumi1 = vec_add(vec_mule(qv1, vscales01), vsumi1); + vsumi2 = vec_add(vec_mule(qv2, vscales23), vsumi2); + vsumi3 = vec_add(vec_mule(qv3, vscales23), vsumi3); + vsumi4 = vec_add(vec_mulo(qv0, vscales01), vsumi4); + vsumi5 = vec_add(vec_mulo(qv1, vscales01), vsumi5); + vsumi6 = vec_add(vec_mulo(qv2, vscales23), vsumi6); + vsumi7 = vec_add(vec_mulo(qv3, vscales23), vsumi7); + } + + vsumi0 = vec_add(vsumi0, vsumi4); + vsumi1 = vec_add(vsumi1, vsumi5); + vsumi2 = vec_add(vsumi2, vsumi6); + vsumi3 = vec_add(vsumi3, vsumi7); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = 0.25f * vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + uint32_t aux32[2]; + + __m256 accumf = (__m256)__lasx_xvldi(0); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict q3 = x[i].qs; + const uint8_t * restrict gas = x[i].qs + QK_K/4; + const int8_t * restrict q8 = y[i].qs; + __m256i sumi1 = __lasx_xvldi(0); + __m256i sumi2 = __lasx_xvldi(0); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m256i q8_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q8_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q2_1 = lasx_set_w(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]], + iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]); + q3 += 8; + const __m256i q2_2 = lasx_set_w(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]], + iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]); + q3 += 8; + memcpy(aux32, gas, 8); gas += 8; + + const __m256i s2_1 = lasx_set_d(signs64[(aux32[0] >> 21) & 127], signs64[(aux32[0] >> 14) & 127], + signs64[(aux32[0] >> 7) & 127], signs64[(aux32[0] >> 0) & 127]); + const __m256i s2_2 = lasx_set_d(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127], + signs64[(aux32[1] >> 7) & 127], signs64[(aux32[1] >> 0) & 127]); + const __m256i q8s_1 = __lasx_xvsigncov_b(s2_1, q8_1); + const __m256i q8s_2 = __lasx_xvsigncov_b(s2_2, q8_2); + const __m256i dot1 = lasx_maddubs_h(q2_1, q8s_1); + const __m256i dot2 = lasx_maddubs_h(q2_2, q8s_2); + const uint16_t ls1 = aux32[0] >> 28; + const uint16_t ls2 = aux32[1] >> 28; + + const __m256i p1 = lasx_madd_h(dot1, __lasx_xvreplgr2vr_h(2*ls1+1)); + const __m256i p2 = lasx_madd_h(dot2, __lasx_xvreplgr2vr_h(2*ls2+1)); + sumi1 = __lasx_xvadd_w(sumi1, p1); + sumi2 = __lasx_xvadd_w(sumi2, p2); + } + + accumf = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accumf); + } + + *s = 0.25f * hsum_float_8(accumf); + #else uint32_t aux32; @@ -9121,10 +10187,8 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void * ggml_int8x16x4_t q8b; vec_index_t idx; -#if QK_K == 256 uint32_t scales32[2]; const uint8_t * scales8 = (const uint8_t *)scales32; -#endif float sumf = 0; for (int i = 0; i < nb; ++i) { @@ -9134,11 +10198,9 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void * const uint16_t * restrict signs = (const uint16_t *)x[i].signs; const int8_t * restrict q8 = y[i].qs; -#if QK_K == 256 memcpy(scales32, x[i].scales, 4); scales32[1] = (((scales32[0] >> 4) & 0x0f0f0f0f) << 1) | 0x01010101; scales32[0] = ((scales32[0] & 0x0f0f0f0f) << 1) | 0x01010101; -#endif int sumi1 = 0, sumi2 = 0; for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { @@ -9179,13 +10241,9 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void * const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[0], q8b.val[0]), q3s.val[1], q8b.val[1]); const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[2], q8b.val[2]), q3s.val[3], q8b.val[3]); -#if QK_K == 256 + sumi1 += vaddvq_s32(p1) * scales8[ib32/2+0]; sumi2 += vaddvq_s32(p2) * scales8[ib32/2+4]; -#else - sumi1 += vaddvq_s32(p1) * (1 + 2*(x[i].scales[ib32/2] & 0xf)); - sumi2 += vaddvq_s32(p2) * (1 + 2*(x[i].scales[ib32/2] >> 4)); -#endif } sumf += d*(sumi1 + sumi2); } @@ -9274,6 +10332,207 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void * *s = hsum_float_8(accumf); +#elif defined(__POWER9_VECTOR__) + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, + 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 + }; + + static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,}; + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + const vector unsigned char mask0 = vec_xl( 0, k_mask1); + const vector unsigned char mask1 = vec_xl(16, k_mask1); + const vector signed char mask2 = (vector signed char)vec_xl( 0, k_mask2); + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + const uint8_t * restrict q3 = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint16_t * restrict signs = (const uint16_t *)(x[i].signs); + const uint8_t * restrict sc = x[i].scales; + const int8_t * restrict q8 = y[i].qs; + + vector signed int vsumi0 = vec_splats((int32_t)0); + vector signed int vsumi1 = vec_splats((int32_t)0); + vector signed int vsumi2 = vec_splats((int32_t)0); + vector signed int vsumi3 = vec_splats((int32_t)0); + vector signed int vsumi4 = vec_splats((int32_t)0); + vector signed int vsumi5 = vec_splats((int32_t)0); + vector signed int vsumi6 = vec_splats((int32_t)0); + vector signed int vsumi7 = vec_splats((int32_t)0); + + for (int j = 0; j < QK_K/32; j += 2) { + __builtin_prefetch(q3, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector unsigned int aux32x4_0 = {iq3s_grid[q3[ 0] | ((qh[0] << 8) & 256)], iq3s_grid[q3[ 1] | ((qh[0] << 7) & 256)], + iq3s_grid[q3[ 2] | ((qh[0] << 6) & 256)], iq3s_grid[q3[ 3] | ((qh[0] << 5) & 256)]}; + vector unsigned int aux32x4_1 = {iq3s_grid[q3[ 4] | ((qh[0] << 4) & 256)], iq3s_grid[q3[ 5] | ((qh[0] << 3) & 256)], + iq3s_grid[q3[ 6] | ((qh[0] << 2) & 256)], iq3s_grid[q3[ 7] | ((qh[0] << 1) & 256)]}; + vector unsigned int aux32x4_2 = {iq3s_grid[q3[ 8] | ((qh[1] << 8) & 256)], iq3s_grid[q3[ 9] | ((qh[1] << 7) & 256)], + iq3s_grid[q3[10] | ((qh[1] << 6) & 256)], iq3s_grid[q3[11] | ((qh[1] << 5) & 256)]}; + vector unsigned int aux32x4_3 = {iq3s_grid[q3[12] | ((qh[1] << 4) & 256)], iq3s_grid[q3[13] | ((qh[1] << 3) & 256)], + iq3s_grid[q3[14] | ((qh[1] << 2) & 256)], iq3s_grid[q3[15] | ((qh[1] << 1) & 256)]}; + q3 += 16; + qh += 2; + + vector signed char vsigns01 = (vector signed char)vec_splats(*(const uint32_t *)&signs[0]); + vector signed char vsigns02 = (vector signed char)vec_splats(*(const uint32_t *)&signs[2]); + signs += 4; + + vector signed char vsigns0 = vec_perm(vsigns01, vsigns01, mask0); + vector signed char vsigns1 = vec_perm(vsigns01, vsigns01, mask1); + vector signed char vsigns2 = vec_perm(vsigns02, vsigns02, mask0); + vector signed char vsigns3 = vec_perm(vsigns02, vsigns02, mask1); + + vsigns0 = (vector signed char)vec_cmpeq(vec_and(vsigns0, mask2), mask2); + vsigns1 = (vector signed char)vec_cmpeq(vec_and(vsigns1, mask2), mask2); + vsigns2 = (vector signed char)vec_cmpeq(vec_and(vsigns2, mask2), mask2); + vsigns3 = (vector signed char)vec_cmpeq(vec_and(vsigns3, mask2), mask2); + + vector signed char q3x0 = vec_sub(vec_xor(vsigns0, (vector signed char)aux32x4_0), vsigns0); + vector signed char q3x1 = vec_sub(vec_xor(vsigns1, (vector signed char)aux32x4_1), vsigns1); + vector signed char q3x2 = vec_sub(vec_xor(vsigns2, (vector signed char)aux32x4_2), vsigns2); + vector signed char q3x3 = vec_sub(vec_xor(vsigns3, (vector signed char)aux32x4_3), vsigns3); + + vector signed char q8y0 = vec_xl( 0, q8); + vector signed char q8y1 = vec_xl(16, q8); + vector signed char q8y2 = vec_xl(32, q8); + vector signed char q8y3 = vec_xl(48, q8); + q8 += 64; + + vector signed short qv0 = vec_add(vec_mule(q3x0, q8y0), vec_mulo(q3x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q3x1, q8y1), vec_mulo(q3x1, q8y1)); + vector signed short qv2 = vec_add(vec_mule(q3x2, q8y2), vec_mulo(q3x2, q8y2)); + vector signed short qv3 = vec_add(vec_mule(q3x3, q8y3), vec_mulo(q3x3, q8y3)); + + const uint16_t ls0 = (uint16_t)(sc[0] & 0xf); + const uint16_t ls1 = (uint16_t)(sc[0] >> 4); + sc ++; + + vector signed short vscales01 = (vector signed short)vec_splats((uint16_t)(2*ls0+1)); + vector signed short vscales23 = (vector signed short)vec_splats((uint16_t)(2*ls1+1)); + + vsumi0 = vec_add(vec_mule(qv0, vscales01), vsumi0); + vsumi1 = vec_add(vec_mule(qv1, vscales01), vsumi1); + vsumi2 = vec_add(vec_mule(qv2, vscales23), vsumi2); + vsumi3 = vec_add(vec_mule(qv3, vscales23), vsumi3); + vsumi4 = vec_add(vec_mulo(qv0, vscales01), vsumi4); + vsumi5 = vec_add(vec_mulo(qv1, vscales01), vsumi5); + vsumi6 = vec_add(vec_mulo(qv2, vscales23), vsumi6); + vsumi7 = vec_add(vec_mulo(qv3, vscales23), vsumi7); + } + + vsumi0 = vec_add(vsumi0, vsumi4); + vsumi1 = vec_add(vsumi1, vsumi5); + vsumi2 = vec_add(vsumi2, vsumi6); + vsumi3 = vec_add(vsumi3, vsumi7); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, + 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 + }; + + static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + }; + + const __m256i mask1 = __lasx_xvld((const __m256i*)k_mask1, 0); + const __m256i mask2 = __lasx_xvld((const __m256i*)k_mask2, 0); + + __m256i idx_shift = lasx_set_w(1, 2, 3, 4, 5, 6, 7, 8); + const __m256i idx_mask = __lasx_xvreplgr2vr_w(256); + + typedef union { + __m256i vec[2]; + uint32_t index[16]; + } index_t; + + index_t idx; + + __m256 accumf = (__m256)__lasx_xvldi(0); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict qs = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint16_t * restrict signs = (const uint16_t *)x[i].signs; + const int8_t * restrict q8 = y[i].qs; + __m256i sumi1 = __lasx_xvldi(0); + __m256i sumi2 = __lasx_xvldi(0); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m256i q8_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q8_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i idx_l = lasx_extu8_16(__lsx_vld(qs, 0)); qs += 16; + idx.vec[0] = __lasx_xvreplgr2vr_w(qh[ib32+0]); + idx.vec[1] = __lasx_xvreplgr2vr_w(qh[ib32+1]); + idx.vec[0] = __lasx_xvand_v(__lasx_xvsll_w(idx.vec[0], idx_shift), idx_mask); + idx.vec[1] = __lasx_xvand_v(__lasx_xvsll_w(idx.vec[1], idx_shift), idx_mask); + idx.vec[0] = __lasx_xvor_v(idx.vec[0], lasx_ext16_32(lasx_extracti128(idx_l, 0))); + idx.vec[1] = __lasx_xvor_v(idx.vec[1], lasx_ext16_32(lasx_extracti128(idx_l, 1))); + + // At leat on my CPU (Ryzen 7950X), using _mm256_i32gather_epi32 is slower than _mm256_set_epi32. Strange. + //const __m256i q2_1 = _mm256_i32gather_epi32((const int *)iq3s_grid, idx.vec[0], 4); + //const __m256i q2_2 = _mm256_i32gather_epi32((const int *)iq3s_grid, idx.vec[1], 4); + const __m256i q2_1 = lasx_set_w( + iq3s_grid[idx.index[7]], iq3s_grid[idx.index[6]], iq3s_grid[idx.index[5]], iq3s_grid[idx.index[4]], + iq3s_grid[idx.index[3]], iq3s_grid[idx.index[2]], iq3s_grid[idx.index[1]], iq3s_grid[idx.index[0]] + ); + const __m256i q2_2 = lasx_set_w( + iq3s_grid[idx.index[15]], iq3s_grid[idx.index[14]], iq3s_grid[idx.index[13]], iq3s_grid[idx.index[12]], + iq3s_grid[idx.index[11]], iq3s_grid[idx.index[10]], iq3s_grid[idx.index[ 9]], iq3s_grid[idx.index[ 8]] + ); + + __m256i aux256 = __lasx_xvreplgr2vr_w(signs[0] | (signs[1] << 16)); + aux256 = __lasx_xvand_v(lasx_shuffle_b(aux256,mask1), mask2); + const __m256i s2_1 = __lasx_xvseq_b(aux256, mask2); + const __m256i q8s_1 = __lasx_xvsub_b(__lasx_xvxor_v(s2_1, q8_1), s2_1); + + aux256 = __lasx_xvreplgr2vr_w(signs[2] | (signs[3] << 16)); + aux256 = __lasx_xvand_v(lasx_shuffle_b(aux256,mask1), mask2); + const __m256i s2_2 = __lasx_xvseq_b(aux256, mask2); + const __m256i q8s_2 = __lasx_xvsub_b(__lasx_xvxor_v(s2_2, q8_2), s2_2); + + signs += 4; + + const __m256i dot1 = lasx_maddubs_h(q2_1, q8s_1); + const __m256i dot2 = lasx_maddubs_h(q2_2, q8s_2); + const uint16_t ls1 = x[i].scales[ib32/2] & 0xf; + const uint16_t ls2 = x[i].scales[ib32/2] >> 4; + const __m256i p1 = lasx_madd_h(dot1, __lasx_xvreplgr2vr_h(2*ls1+1)); + const __m256i p2 = lasx_madd_h(dot2, __lasx_xvreplgr2vr_h(2*ls2+1)); + sumi1 = __lasx_xvadd_w(sumi1, p1); + sumi2 = __lasx_xvadd_w(sumi2, p2); + } + + accumf = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accumf); + } + + *s = hsum_float_8(accumf); + #else float sumf = 0.f; @@ -9321,12 +10580,22 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void * } -#ifdef __AVX2__ +#if defined(__AVX2__) static inline __m256i mul_add_epi8(const __m256i x, const __m256i y) { const __m256i ax = _mm256_sign_epi8(x, x); const __m256i sy = _mm256_sign_epi8(y, x); return _mm256_maddubs_epi16(ax, sy); } +#elif defined(__loongarch_asx) +static inline __m256i mul_add_epi8(const __m256i x, const __m256i y) { + const __m256i ax = __lasx_xvsigncov_b(x, x); + const __m256i sy = __lasx_xvsigncov_b(x, y); + __m256i tmp1, tmp2, tmp3; + tmp1 = __lasx_xvmulwev_h_bu_b(ax, sy); + tmp2 = __lasx_xvmulwod_h_bu_b(ax, sy); + tmp3 = __lasx_xvadd_h(tmp1, tmp2); + return __lasx_xvsat_h(tmp3, 15); +} #endif void ggml_vec_dot_iq1_s_q8_K (int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { @@ -9428,6 +10697,169 @@ void ggml_vec_dot_iq1_s_q8_K (int n, float * restrict s, size_t bs, const void *s = hsum_float_8(accum) + IQ1S_DELTA * accum1; +#elif defined(__POWER9_VECTOR__) + const vector unsigned char v0 = vec_splats((unsigned char)0x0); + const vector unsigned short vsign = vec_splats((unsigned short)0x8000); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + vector signed int vsumi0 = vec_splats((int32_t)0); + vector signed int vsumi1 = vec_splats((int32_t)0); + vector signed int vsumi2 = vec_splats((int32_t)0); + vector signed int vsumi3 = vec_splats((int32_t)0); + vector signed int vsumi4 = vec_splats((int32_t)0); + vector signed int vsumi5 = vec_splats((int32_t)0); + vector signed int vsumi6 = vec_splats((int32_t)0); + vector signed int vsumi7 = vec_splats((int32_t)0); + vector signed int vsumi8 = vec_splats((int32_t)0); + + const uint8_t * restrict q1 = x[i].qs; + const uint16_t * restrict qh = x[i].qh; + const int8_t * restrict q8 = y[i].qs; + const int16_t * restrict qs = y[i].bsums; + + for (int j = 0; j < QK_K/32; j += 2) { + __builtin_prefetch(q1, 0, 1); + __builtin_prefetch(qh, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector signed long long aux64x2_0 = {*(const int64_t *)(iq1s_grid + (q1[0] | ((qh[0] << 8) & 0x700))), *(const int64_t *)(iq1s_grid + (q1[1] | ((qh[0] << 5) & 0x700)))}; + vector signed long long aux64x2_1 = {*(const int64_t *)(iq1s_grid + (q1[2] | ((qh[0] << 2) & 0x700))), *(const int64_t *)(iq1s_grid + (q1[3] | ((qh[0] >> 1) & 0x700)))}; + vector signed long long aux64x2_2 = {*(const int64_t *)(iq1s_grid + (q1[4] | ((qh[1] << 8) & 0x700))), *(const int64_t *)(iq1s_grid + (q1[5] | ((qh[1] << 5) & 0x700)))}; + vector signed long long aux64x2_3 = {*(const int64_t *)(iq1s_grid + (q1[6] | ((qh[1] << 2) & 0x700))), *(const int64_t *)(iq1s_grid + (q1[7] | ((qh[1] >> 1) & 0x700)))}; + q1 += 8; + + vector signed char q1x0 = (vector signed char)aux64x2_0; + vector signed char q1x1 = (vector signed char)aux64x2_1; + vector signed char q1x2 = (vector signed char)aux64x2_2; + vector signed char q1x3 = (vector signed char)aux64x2_3; + + vector signed char q8y0 = vec_xl( 0, q8); + vector signed char q8y1 = vec_xl(16, q8); + vector signed char q8y2 = vec_xl(32, q8); + vector signed char q8y3 = vec_xl(48, q8); + q8 += 64; + + vector signed short qv0 = vec_add(vec_mule(q1x0, q8y0), vec_mulo(q1x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q1x1, q8y1), vec_mulo(q1x1, q8y1)); + vector signed short qv2 = vec_add(vec_mule(q1x2, q8y2), vec_mulo(q1x2, q8y2)); + vector signed short qv3 = vec_add(vec_mule(q1x3, q8y3), vec_mulo(q1x3, q8y3)); + + const uint16_t ls0 = (uint16_t)((qh[0] >> 12) & 7); + const uint16_t ls1 = (uint16_t)((qh[1] >> 12) & 7); + + vector signed short vscales01 = (vector signed short)vec_splats((uint16_t)(2*ls0+1)); + vector signed short vscales23 = (vector signed short)vec_splats((uint16_t)(2*ls1+1)); + vector signed short vscales = vec_sld(vscales23, vscales01, 8); + + vsumi0 = vec_add(vec_mule(qv0, vscales01), vsumi0); + vsumi1 = vec_add(vec_mule(qv1, vscales01), vsumi1); + vsumi2 = vec_add(vec_mule(qv2, vscales23), vsumi2); + vsumi3 = vec_add(vec_mule(qv3, vscales23), vsumi3); + vsumi4 = vec_add(vec_mulo(qv0, vscales01), vsumi4); + vsumi5 = vec_add(vec_mulo(qv1, vscales01), vsumi5); + vsumi6 = vec_add(vec_mulo(qv2, vscales23), vsumi6); + vsumi7 = vec_add(vec_mulo(qv3, vscales23), vsumi7); + + vector signed short q8ysums = vec_xl_len(qs, 8); + qs += 4; + q8ysums = vec_mergeh(q8ysums, (vector signed short)v0); + + vector signed short qxh = (vector signed short)vec_sld(vec_splats(qh[1]), vec_splats(qh[0]), 8); + qh += 2; + vector __bool short vsel = vec_cmpge(qxh, (vector signed short)v0); + + vector signed short q8ysum = vec_sel((vector signed short)vec_xor((vector unsigned short)q8ysums, vsign), q8ysums, vsel); + + vsumi8 = vec_add(vec_mule(q8ysum, vscales), vsumi8); + } + + vsumi0 = vec_add(vsumi0, vsumi4); + vsumi1 = vec_add(vsumi1, vsumi5); + vsumi2 = vec_add(vsumi2, vsumi6); + vsumi3 = vec_add(vsumi3, vsumi7); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + + vsumf0 = vec_madd(vec_ctf(vsumi8, 0), vec_mul(vd, vec_splats(IQ1S_DELTA)), vsumf0); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + + __m256 accum = (__m256)__lasx_xvldi(0); + float accum1 = 0; + for (int i = 0; i < nb; ++i) { + + const int8_t * q8 = y[i].qs; + const uint8_t * qs = x[i].qs; + const uint16_t * qh = x[i].qh; + + __m256i sumi = __lasx_xvldi(0); + int sumi1 = 0; + for (int ib = 0; ib < QK_K/32; ib += 2) { + __m256i q1b_1 = __lasx_xvinsgr2vr_d(q1b_1, iq1s_grid[qs[0] | ((qh[ib+0] << 8) & 0x700)], 0); + q1b_1 = __lasx_xvinsgr2vr_d(q1b_1, iq1s_grid[qs[1] | ((qh[ib+0] << 5) & 0x700)], 1); + q1b_1 = __lasx_xvinsgr2vr_d(q1b_1, iq1s_grid[qs[2] | ((qh[ib+0] << 2) & 0x700)], 2); + q1b_1 = __lasx_xvinsgr2vr_d(q1b_1, iq1s_grid[qs[3] | ((qh[ib+0] >> 1) & 0x700)], 3); + + __m256i q1b_2 = __lasx_xvinsgr2vr_d(q1b_2, iq1s_grid[qs[4] | ((qh[ib+1] << 8) & 0x700)], 0); + q1b_2 = __lasx_xvinsgr2vr_d(q1b_2, iq1s_grid[qs[5] | ((qh[ib+1] << 5) & 0x700)], 1); + q1b_2 = __lasx_xvinsgr2vr_d(q1b_2, iq1s_grid[qs[6] | ((qh[ib+1] << 2) & 0x700)], 2); + q1b_2 = __lasx_xvinsgr2vr_d(q1b_2, iq1s_grid[qs[7] | ((qh[ib+1] >> 1) & 0x700)], 3); + + qs += 8; + const __m256i q8b_1 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8b_2 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + + const __m256i dot1 = mul_add_epi8(q1b_1, q8b_1); + const __m256i dot2 = mul_add_epi8(q1b_2, q8b_2); + const int16_t ls1 = 2*((qh[ib+0] >> 12) & 7) + 1; + const int16_t ls2 = 2*((qh[ib+1] >> 12) & 7) + 1; + + __m256i tmp1, tmp5, tmp6; + tmp1 = __lasx_xvreplgr2vr_h(ls1); + tmp5 = __lasx_xvmulwev_w_h(dot1, tmp1); + tmp6 = __lasx_xvmulwod_w_h(dot1, tmp1); + const __m256i p1 = __lasx_xvadd_w(tmp5, tmp6); + + tmp1 = __lasx_xvreplgr2vr_h(ls2); + tmp5 = __lasx_xvmulwev_w_h(dot2, tmp1); + tmp6 = __lasx_xvmulwod_w_h(dot2, tmp1); + const __m256i p2 = __lasx_xvadd_w(tmp5, tmp6); + + sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p1, p2)); + sumi1 += (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]) * (qh[ib+0] & 0x8000 ? -1 : 1) * ls1 + + (y[i].bsums[2*ib+2] + y[i].bsums[2*ib+3]) * (qh[ib+1] & 0x8000 ? -1 : 1) * ls2; + } + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + accum = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(sumi), accum); + accum1 += d * sumi1; + } + + *s = hsum_float_8(accum) + IQ1S_DELTA * accum1; + #else float sumf = 0; @@ -9475,17 +10907,10 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void const int nb = n / QK_K; -#if QK_K != 64 iq1m_scale_t scale; -#endif #if defined __ARM_NEON - -#if QK_K == 64 - const int32x4_t mask = vdupq_n_s32(0xf); -#else const int32x4_t mask = vdupq_n_s32(0x7); -#endif const int32x4_t mone = vdupq_n_s32(1); const int32x4_t mzero = vdupq_n_s32(0); @@ -9509,9 +10934,7 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void const uint8_t * qh = x[i].qh; const uint16_t * sc = (const uint16_t *)x[i].scales; -#if QK_K != 64 scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); -#endif int32x4_t sumi1 = mzero; int32x4_t sumi2 = mzero; @@ -9540,11 +10963,8 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void const int32x4_t p4 = vpaddq_s32(ggml_vdotq_s32(mzero, deltas.val[aux8[2]], q8b.val[2]), ggml_vdotq_s32(mzero, deltas.val[aux8[3]], q8b.val[3])); const int32x4_t p34 = vpaddq_s32(p3, p4); -#if QK_K == 64 - int32x4_t scales_4 = ggml_vld1q_u32(sc[0] >> 0, sc[0] >> 4, sc[0] >> 8, sc[0] >> 12); -#else int32x4_t scales_4 = ggml_vld1q_u32(sc[ib/2] >> 0, sc[ib/2] >> 3, sc[ib/2] >> 6, sc[ib/2] >> 9); -#endif + scales_4 = vaddq_s32(vshlq_n_s32(vandq_s32(scales_4, mask), 1), mone); sumi1 = vmlaq_s32(sumi1, scales_4, p12); @@ -9554,22 +10974,14 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void } -#if QK_K == 64 - sumf += y[i].d * GGML_FP16_TO_FP32(x[i].d) * (vaddvq_s32(sumi1) + IQ1M_DELTA * vaddvq_s32(sumi2)); -#else sumf += y[i].d * GGML_FP16_TO_FP32(scale.f16) * (vaddvq_s32(sumi1) + IQ1M_DELTA * vaddvq_s32(sumi2)); -#endif } *s = sumf; #elif defined __AVX2__ -#if QK_K == 64 - const __m256i mask = _mm256_set1_epi16(0xf); -#else const __m256i mask = _mm256_set1_epi16(0x7); -#endif const __m256i mone = _mm256_set1_epi16(1); __m256 accum1 = _mm256_setzero_ps(); @@ -9581,9 +10993,7 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void const uint8_t * qh = x[i].qh; const uint16_t * sc = (const uint16_t *)x[i].scales; -#if QK_K != 64 scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); -#endif __m256i sumi1 = _mm256_setzero_si256(); __m256i sumi2 = _mm256_setzero_si256(); @@ -9613,13 +11023,10 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void const __m256i dot3 = mul_add_epi8(delta1, q8b_1); const __m256i dot4 = mul_add_epi8(delta2, q8b_2); -#if QK_K == 64 - __m256i scale1 = MM256_SET_M128I(_mm_set1_epi16(sc[0] >> 4), _mm_set1_epi16(sc[0] >> 0)); - __m256i scale2 = MM256_SET_M128I(_mm_set1_epi16(sc[0] >> 12), _mm_set1_epi16(sc[0] >> 8)); -#else + __m256i scale1 = MM256_SET_M128I(_mm_set1_epi16(sc[ib/2] >> 3), _mm_set1_epi16(sc[ib/2] >> 0)); __m256i scale2 = MM256_SET_M128I(_mm_set1_epi16(sc[ib/2] >> 9), _mm_set1_epi16(sc[ib/2] >> 6)); -#endif + scale1 = _mm256_add_epi16(_mm256_slli_epi16(_mm256_and_si256(scale1, mask), 1), mone); scale2 = _mm256_add_epi16(_mm256_slli_epi16(_mm256_and_si256(scale2, mask), 1), mone); const __m256i p1 = _mm256_madd_epi16(dot1, scale1); @@ -9633,14 +11040,10 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void qs += 8; qh += 4; } -#if QK_K == 64 - const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(x[i].d)); -#else const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(scale.f16)); -#endif + accum1 = _mm256_fmadd_ps(d, _mm256_cvtepi32_ps(sumi1), accum1); accum2 = _mm256_fmadd_ps(d, _mm256_cvtepi32_ps(sumi2), accum2); - } *s = hsum_float_8(accum1) + IQ1M_DELTA * hsum_float_8(accum2); @@ -9657,9 +11060,7 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void const uint8_t * qh = x[i].qh; const uint16_t * sc = (const uint16_t *)x[i].scales; -#if QK_K != 64 scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); -#endif int sumi1 = 0, sumi2 = 0; for (int ib = 0; ib < QK_K/32; ++ib) { @@ -9679,24 +11080,17 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void sum1[l/2] += lsum1; sum2[l/2] += lsum2*delta[l]; } -#if QK_K == 64 - const int ls1 = 2*((sc[0] >> (8*(ib%2)+0)) & 0xf) + 1; - const int ls2 = 2*((sc[0] >> (8*(ib%2)+4)) & 0xf) + 1; -#else + const int ls1 = 2*((sc[ib/2] >> (6*(ib%2)+0)) & 0x7) + 1; const int ls2 = 2*((sc[ib/2] >> (6*(ib%2)+3)) & 0x7) + 1; -#endif + sumi1 += sum1[0] * ls1 + sum1[1] * ls2; sumi2 += sum2[0] * ls1 + sum2[1] * ls2; qs += 4; qh += 2; } -#if QK_K == 64 - sumf += GGML_FP16_TO_FP32(x[i].d) * y[i].d * (sumi1 + IQ1M_DELTA * sumi2); -#else sumf += GGML_FP16_TO_FP32(scale.f16) * y[i].d * (sumi1 + IQ1M_DELTA * sumi2); -#endif } *s = sumf; @@ -9784,6 +11178,84 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void * *s = hsum_float_8(_mm256_add_ps(accum1, accum2)); +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + + const vector signed char values = vec_xl( 0, kvalues_iq4nl); + +#pragma GCC unroll 4 + for (int ib = 0; ib < nb; ++ib) { + __builtin_prefetch(x[ib].qs, 0, 1); + __builtin_prefetch(y[ib].qs, 0, 1); + + + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ib].d)); + vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[ib].d)); + vector float vd = vec_mul(vxd, vyd); + + vector signed char qxs = (vector signed char)vec_xl( 0, x[ib].qs); + vector signed char q4x0 = vec_and(qxs, lowMask); + vector signed char q4x1 = vec_sr(qxs, v4); + + q4x0 = vec_perm(values, values, (vector unsigned char)q4x0); + q4x1 = vec_perm(values, values, (vector unsigned char)q4x1); + + vector signed char q8y0 = vec_xl( 0, y[ib].qs); + vector signed char q8y1 = vec_xl(16, y[ib].qs); + + vector signed short qv0 = vec_add(vec_mule(q4x0, q8y0), vec_mulo(q4x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q4x1, q8y1), vec_mulo(q4x1, q8y1)); + + vector signed int vsumi0 = vec_add(vec_unpackh(qv0), vec_unpackl(qv0)); + vector signed int vsumi1 = vec_add(vec_unpackh(qv1), vec_unpackl(qv1)); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + } + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); + +#elif defined (__loongarch_asx) + + const __m128i values128 = __lsx_vld((const __m128i*)kvalues_iq4nl, 0); + const __m128i m4b = __lsx_vreplgr2vr_b(0x0f); + const __m256i mone = __lasx_xvreplgr2vr_h(1); + + __m256 accum1 = (__m256)__lasx_xvldi(0); + __m256 accum2 = (__m256)__lasx_xvldi(0); + for (int ib = 0; ib < nb; ib += 2) { + const __m128i q4bits_1 = __lsx_vld((const __m128i*)x[0].qs, 0); + const __m128i q4bits_2 = __lsx_vld((const __m128i*)x[1].qs, 0); + const __m256i q8b_1 = __lasx_xvld((const __m256i *)y[0].qs, 0); + const __m256i q8b_2 = __lasx_xvld((const __m256i *)y[1].qs, 0); + const __m256i q4b_1 = lasx_insertf128(lsx_shuffle_b(values128, __lsx_vand_v(__lsx_vsrli_h(q4bits_1, 4), m4b)), + lsx_shuffle_b(values128, __lsx_vand_v(q4bits_1, m4b))); + const __m256i q4b_2 = lasx_insertf128(lsx_shuffle_b(values128, __lsx_vand_v(__lsx_vsrli_h(q4bits_2, 4), m4b)), + lsx_shuffle_b(values128, __lsx_vand_v(q4bits_2, m4b))); + const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1); + const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2); + const __m256i p_1 = lasx_madd_h(p16_1, mone); + const __m256i p_2 = lasx_madd_h(p16_2, mone); + accum1 = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(y[0].d)*GGML_FP16_TO_FP32(x[0].d)), + __lasx_xvffint_s_w(p_1), accum1); + accum2 = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(y[1].d)*GGML_FP16_TO_FP32(x[1].d)), + __lasx_xvffint_s_w(p_2), accum2); + + y += 2; + x += 2; + } + + *s = hsum_float_8(__lasx_xvfadd_s(accum1, accum2)); + #else float sumf = 0; for (int ib = 0; ib < nb; ++ib) { @@ -9806,9 +11278,6 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * restrict s, size_t bs, const void * UNUSED(by); UNUSED(bs); assert(n % QK_K == 0); -#if QK_K == 64 - ggml_vec_dot_iq4_nl_q8_0(n, s, bs, vx, bx, vy, by, nrc); -#else const block_iq4_xs * restrict x = vx; const block_q8_K * restrict y = vy; @@ -9895,6 +11364,179 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * restrict s, size_t bs, const void * *s = hsum_float_8(accum); +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + const vector signed char values = vec_xl( 0, kvalues_iq4nl); + + for (int ibl = 0; ibl < nb; ++ibl) { + + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ibl].d)); + vector float vyd = vec_splats(y[ibl].d); + vector float vd = vec_mul(vxd, vyd); + + vector signed int vsumi0 = vec_splats((int32_t)0); + vector signed int vsumi1 = vec_splats((int32_t)0); + vector signed int vsumi2 = vec_splats((int32_t)0); + vector signed int vsumi3 = vec_splats((int32_t)0); + vector signed int vsumi4 = vec_splats((int32_t)0); + vector signed int vsumi5 = vec_splats((int32_t)0); + vector signed int vsumi6 = vec_splats((int32_t)0); + vector signed int vsumi7 = vec_splats((int32_t)0); + + uint16_t h = x[ibl].scales_h; + + const uint8_t * restrict q4 = x[ibl].qs; + const uint8_t * restrict sc = x[ibl].scales_l; + const int8_t * restrict q8 = y[ibl].qs; + + for (int ib = 0; ib < QK_K/64; ib ++ ) { + __builtin_prefetch(q4, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector signed char qxs0 = (vector signed char)vec_xl( 0, q4); + vector signed char qxs1 = (vector signed char)vec_xl(16, q4); + q4 += 32; + + vector signed char q4x00 = (vector signed char)vec_and(qxs0, lowMask); + vector signed char q4x01 = (vector signed char)vec_sr(qxs0, v4); + vector signed char q4x10 = (vector signed char)vec_and(qxs1, lowMask); + vector signed char q4x11 = (vector signed char)vec_sr(qxs1, v4); + + q4x00 = vec_perm(values, values, (vector unsigned char)q4x00); + q4x01 = vec_perm(values, values, (vector unsigned char)q4x01); + q4x10 = vec_perm(values, values, (vector unsigned char)q4x10); + q4x11 = vec_perm(values, values, (vector unsigned char)q4x11); + + vector signed char q8y0 = vec_xl( 0, q8); + vector signed char q8y1 = vec_xl(16, q8); + vector signed char q8y2 = vec_xl(32, q8); + vector signed char q8y3 = vec_xl(48, q8); + q8 += 64; + + vector signed short qv0 = vec_add(vec_mule(q4x00, q8y0), vec_mulo(q4x00, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q4x01, q8y1), vec_mulo(q4x01, q8y1)); + vector signed short qv2 = vec_add(vec_mule(q4x10, q8y2), vec_mulo(q4x10, q8y2)); + vector signed short qv3 = vec_add(vec_mule(q4x11, q8y3), vec_mulo(q4x11, q8y3)); + + const uint16_t ls0 = (uint16_t)(((sc[0] & 0xf) | ((h << 4) & 0x30)) - 32); + const uint16_t ls1 = (uint16_t)(((sc[0] >> 4) | ((h << 2) & 0x30)) - 32); + h >>= 4; + sc ++; + + vector signed short vscales01 = vec_splats((int16_t)ls0); + vector signed short vscales23 = vec_splats((int16_t)ls1); + + vsumi0 = vec_add(vec_mule(qv0, vscales01), vsumi0); + vsumi1 = vec_add(vec_mule(qv1, vscales01), vsumi1); + vsumi2 = vec_add(vec_mule(qv2, vscales23), vsumi2); + vsumi3 = vec_add(vec_mule(qv3, vscales23), vsumi3); + vsumi4 = vec_add(vec_mulo(qv0, vscales01), vsumi4); + vsumi5 = vec_add(vec_mulo(qv1, vscales01), vsumi5); + vsumi6 = vec_add(vec_mulo(qv2, vscales23), vsumi6); + vsumi7 = vec_add(vec_mulo(qv3, vscales23), vsumi7); + } + + vsumi0 = vec_add(vsumi0, vsumi4); + vsumi1 = vec_add(vsumi1, vsumi5); + vsumi2 = vec_add(vsumi2, vsumi6); + vsumi3 = vec_add(vsumi3, vsumi7); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + + const __m128i values128 = __lsx_vld((const __m128i*)kvalues_iq4nl, 0); + const __m128i m4b = __lsx_vreplgr2vr_b(0x0f); + + __m256 accum = (__m256)__lasx_xvldi(0); + __m256i tmp1; + __m128i tmp0, tmp2, tmp3, tmp4, mask_8f, mask; + + mask_8f = __lsx_vreplgr2vr_b(0x8f); + for (int ibl = 0; ibl < nb; ++ibl) { + const uint8_t * qs = x[ibl].qs; + const int8_t * q8 = y[ibl].qs; + uint16_t sh = x[ibl].scales_h; + __m256i sumi1 = __lasx_xvldi(0); + __m256i sumi2 = __lasx_xvldi(0); + __m128i zero = __lsx_vldi(0); + for (int ib = 0; ib < QK_K/32; ib += 2) { + const __m128i q4bits_1 = __lsx_vld((const __m128i*)qs, 0); qs += 16; + const __m128i q4bits_2 = __lsx_vld((const __m128i*)qs, 0); qs += 16; + const __m256i q8b_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q8b_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + tmp2 = __lsx_vand_v(__lsx_vand_v(__lsx_vsrli_h(q4bits_1, 4), m4b), mask_8f); + tmp0 = __lsx_vori_b(tmp2, 0x10); + mask = __lsx_vsle_b(zero, tmp2); + tmp3 = __lsx_vand_v(tmp0, mask); + tmp3 = __lsx_vshuf_b(values128, zero, tmp3); + + tmp2 = __lsx_vand_v(__lsx_vand_v(q4bits_1, m4b), mask_8f); + tmp0 = __lsx_vori_b(tmp2, 0x10); + mask = __lsx_vsle_b(zero, tmp2); + tmp4 = __lsx_vand_v(tmp0, mask); + tmp4 = __lsx_vshuf_b(values128, zero, tmp4); + + const __m256i q4b_1 = lasx_insertf128(tmp3, tmp4); + + tmp2 = __lsx_vand_v(__lsx_vand_v(__lsx_vsrli_h(q4bits_2, 4), m4b), mask_8f); + tmp0 = __lsx_vori_b(tmp2, 0x10); + mask = __lsx_vsle_b(zero, tmp2); + tmp3 = __lsx_vand_v(tmp0, mask); + tmp3 = __lsx_vshuf_b(values128, zero, tmp3); + + tmp2 = __lsx_vand_v(__lsx_vand_v(q4bits_2, m4b), mask_8f); + tmp0 = __lsx_vori_b(tmp2, 0x10); + mask = __lsx_vsle_b(zero, tmp2); + tmp4 = __lsx_vand_v(tmp0, mask); + tmp4 = __lsx_vshuf_b(values128, zero, tmp4); + + const __m256i q4b_2 = lasx_insertf128(tmp3, tmp4); + + const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1); + const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2); + const int16_t ls1 = ((x[ibl].scales_l[ib/2] & 0xf) | ((sh << 4) & 0x30)) - 32; + const int16_t ls2 = ((x[ibl].scales_l[ib/2] >> 4) | ((sh << 2) & 0x30)) - 32; + sh >>= 4; + __m256i tmp5, tmp6; + tmp1 = __lasx_xvreplgr2vr_h(ls1); + tmp5 = __lasx_xvmulwev_w_h(p16_1, tmp1); + tmp6 = __lasx_xvmulwod_w_h(p16_1, tmp1); + const __m256i p_1 = __lasx_xvadd_w(tmp5, tmp6); + tmp1 = __lasx_xvreplgr2vr_h(ls2); + tmp5 = __lasx_xvmulwev_w_h(p16_2, tmp1); + tmp6 = __lasx_xvmulwod_w_h(p16_2, tmp1); + const __m256i p_2 = __lasx_xvadd_w(tmp5, tmp6); + sumi1 = __lasx_xvadd_w(p_1, sumi1); + sumi2 = __lasx_xvadd_w(p_2, sumi2); + } + accum = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[ibl].d)*y[ibl].d), + __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accum); + } + + *s = hsum_float_8(accum); + #else float sumf = 0; for (int ibl = 0; ibl < nb; ++ibl) { @@ -9928,7 +11570,6 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * restrict s, size_t bs, const void * } *s = sumf; #endif -#endif } // ================================ IQ2 quantization ============================================= @@ -10426,7 +12067,7 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict } float max = xval[0]; for (int i = 1; i < 32; ++i) max = MAX(max, xval[i]); - if (!max) { + if (max < GROUP_MAX_EPS) { scales[ib] = 0; memset(L, 0, 32); continue; @@ -10504,7 +12145,7 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict printf("\n"); GGML_ASSERT(false); } - q2[2*ib+0] |= (grid_index << 8*k); + q2[2*ib+0] |= ((uint32_t) grid_index << 8*k); q2[2*ib+1] |= (block_signs[k] << 7*k); } GGML_ASSERT(scale >= 0); @@ -10602,7 +12243,7 @@ static void quantize_row_iq2_xs_impl(const float * restrict x, void * restrict v } float max = xval[0]; for (int i = 1; i < 16; ++i) max = MAX(max, xval[i]); - if (!max) { + if (max < GROUP_MAX_EPS) { scales[ib] = 0; memset(L, 0, 16); continue; @@ -11043,7 +12684,7 @@ static void quantize_row_iq3_xxs_impl(int grid_size, const float * restrict x, v } float max = xval[0]; for (int i = 1; i < 32; ++i) max = MAX(max, xval[i]); - if (!max) { + if (max < GROUP_MAX_EPS_IQ3_XXS) { scales[ib] = 0; memset(L, 0, 32); continue; @@ -11583,7 +13224,7 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy for (int i = 0; i < block_size; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]); float max = fabsf(xb[0]); for (int i = 1; i < block_size; ++i) max = MAX(max, fabsf(xb[i])); - if (!max) { + if (max < GROUP_MAX_EPS_IQ1_S) { scales[ib] = 0; memset(L, 1, block_size); continue; @@ -11746,10 +13387,6 @@ static void quantize_row_iq1_m_impl(const float * restrict x, void * restrict vy const float * xx; for (int ibl = 0; ibl < nbl; ++ibl) { - -#if QK_K == 64 - y[ibl].d = GGML_FP32_TO_FP16(0.f); -#endif memset(y[ibl].qs, 0, QK_K/8); memset(y[ibl].qh, 0, QK_K/16); memset(y[ibl].scales, 0, QK_K/32); @@ -11771,7 +13408,7 @@ static void quantize_row_iq1_m_impl(const float * restrict x, void * restrict vy } float max = fabsf(xb[0]); for (int i = 1; i < block_size; ++i) max = MAX(max, fabsf(xb[i])); - if (!max) { + if (max < GROUP_MAX_EPS_IQ1_M) { scales[ib] = 0; memset(L, 1, block_size); continue; @@ -11924,22 +13561,13 @@ static void quantize_row_iq1_m_impl(const float * restrict x, void * restrict vy } uint16_t * sc = (uint16_t *)y[ibl].scales; -#if QK_K == 64 - float d = max_scale/31; -#else float d = max_scale/15; -#endif float id = 1/d; float sumqx_f = 0, sumq2_f = 0; for (int ib = 0; ib < QK_K/block_size; ++ib) { int l = nearest_int(0.5f*(id*scales[ib+0]-1)); -#if QK_K == 64 - l = MAX(0, MIN(15, l)); - sc[ib/4] |= (l << 4*(ib%4)); -#else l = MAX(0, MIN(7, l)); sc[ib/4] |= (l << 3*(ib%4)); -#endif y[ibl].qh[ib] |= masks[shifts[ib]]; const float * xb = xbl + block_size*ib; if (quant_weights) { @@ -11962,14 +13590,10 @@ static void quantize_row_iq1_m_impl(const float * restrict x, void * restrict vy } if (sumq2_f > 0) d = sumqx_f/sumq2_f; s.f16 = GGML_FP32_TO_FP16(d*1.1125f); // 1.1125f is another fudge factor. Don't ask me why it is needed. -#if QK_K == 64 - y[ibl].d = s.f16; -#else sc[0] |= ((s.u16 & 0x000f) << 12); sc[1] |= ((s.u16 & 0x00f0) << 8); sc[2] |= ((s.u16 & 0x0f00) << 4); sc[3] |= ((s.u16 & 0xf000) << 0); -#endif } } @@ -12035,7 +13659,7 @@ static void quantize_row_iq4_nl_impl(const int super_block_size, const int block amax = ax; max = xb[j]; } } - if (!amax) { + if (amax < GROUP_MAX_EPS) { scales[ib] = 0; continue; } @@ -12158,9 +13782,6 @@ void quantize_row_iq4_nl_reference(const float * restrict x, block_iq4_nl * rest } size_t quantize_iq4_xs(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { -#if QK_K == 64 - return quantize_iq4_nl(src, dst, nrow, n_per_row, quant_weights); -#else GGML_ASSERT(n_per_row%QK_K == 0); int64_t nblock = n_per_row/QK_K; char * qrow = (char *)dst; @@ -12178,7 +13799,6 @@ size_t quantize_iq4_xs(const float * restrict src, void * restrict dst, int64_t qrow += nblock*sizeof(block_iq4_xs); } return nrow * nblock * sizeof(block_iq4_xs); -#endif } void quantize_row_iq4_xs(const float * restrict x, void * restrict vy, int64_t k) { @@ -12256,7 +13876,7 @@ static void quantize_row_iq2_s_impl(const float * restrict x, void * restrict vy } float max = xval[0]; for (int i = 1; i < 16; ++i) max = MAX(max, xval[i]); - if (!max) { + if (max < GROUP_MAX_EPS_IQ2_S) { scales[ib] = 0; continue; } @@ -12590,19 +14210,11 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte } break; case GGML_TYPE_Q4_K: { - #ifdef GGML_QKK_64 - VALIDATE_ROW_DATA_DM_F16_IMPL(block_q4_K, data, nb, d[0], d[1]); - #else VALIDATE_ROW_DATA_DM_F16_IMPL(block_q4_K, data, nb, d, dmin); - #endif } break; case GGML_TYPE_Q5_K: { - #ifdef GGML_QKK_64 - VALIDATE_ROW_DATA_D_F16_IMPL(block_q5_K, data, nb); - #else VALIDATE_ROW_DATA_DM_F16_IMPL(block_q5_K, data, nb, d, dmin); - #endif } break; case GGML_TYPE_Q6_K: { @@ -12625,18 +14237,12 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte { const block_iq1_m * q = (const block_iq1_m *) data; for (size_t i = 0; i < nb; ++i) { - #if QK_K == 64 - if (!validate_fp16(q[i].d, i)) { - return false; - } - #else iq1m_scale_t scale; const uint16_t * sc = (const uint16_t *)q[i].scales; scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); if (!validate_fp16(scale.f16, i)) { return false; } - #endif } } break; case GGML_TYPE_IQ2_XXS: @@ -12661,12 +14267,9 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte VALIDATE_ROW_DATA_D_F16_IMPL(block_iq3_s, data, nb); } break; case GGML_TYPE_IQ4_XS: - #if QK_K != 64 { VALIDATE_ROW_DATA_D_F16_IMPL(block_iq4_xs, data, nb); } break; - #endif - // with QK_K == 64, iq4_xs is iq4_nl case GGML_TYPE_IQ4_NL: { VALIDATE_ROW_DATA_D_F16_IMPL(block_iq4_nl, data, nb); diff --git a/ggml-rpc.cpp b/ggml-rpc.cpp new file mode 100644 index 0000000000000..cc1d3ace1ddac --- /dev/null +++ b/ggml-rpc.cpp @@ -0,0 +1,1155 @@ +#include "ggml-rpc.h" +#include "ggml.h" +#include "ggml-backend-impl.h" + +#include +#include +#include +#include +#include +#include +#ifdef _WIN32 +# define WIN32_LEAN_AND_MEAN +# ifndef NOMINMAX +# define NOMINMAX +# endif +# include +# include +#else +# include +# include +# include +# include +# include +# include +# include +#endif +#include + +#define UNUSED GGML_UNUSED + +#define GGML_DEBUG 0 +#if (GGML_DEBUG >= 1) +#define GGML_PRINT_DEBUG(...) printf(__VA_ARGS__) +#else +#define GGML_PRINT_DEBUG(...) +#endif + +#ifdef _WIN32 +typedef SOCKET sockfd_t; +using ssize_t = __int64; +#else +typedef int sockfd_t; +#endif + +// cross-platform socket +struct socket_t { + sockfd_t fd; + socket_t(sockfd_t fd) : fd(fd) {} + ~socket_t() { +#ifdef _WIN32 + closesocket(this->fd); +#else + close(this->fd); +#endif + } +}; + +// ggml_tensor is serialized into rpc_tensor +#pragma pack(push, 1) +struct rpc_tensor { + uint64_t id; + uint32_t type; + uint64_t buffer; + uint32_t ne[GGML_MAX_DIMS]; + uint32_t nb[GGML_MAX_DIMS]; + uint32_t op; + int32_t op_params[GGML_MAX_OP_PARAMS / sizeof(int32_t)]; + int32_t flags; + uint64_t src[GGML_MAX_SRC]; + uint64_t view_src; + uint64_t view_offs; + uint64_t data; + char name[GGML_MAX_NAME]; +}; +#pragma pack(pop) + +// RPC commands +enum rpc_cmd { + ALLOC_BUFFER = 0, + GET_ALIGNMENT, + GET_MAX_SIZE, + BUFFER_GET_BASE, + FREE_BUFFER, + BUFFER_CLEAR, + SET_TENSOR, + GET_TENSOR, + COPY_TENSOR, + GRAPH_COMPUTE, + GET_DEVICE_MEMORY, +}; + +// RPC data structures + +static ggml_guid_t ggml_backend_rpc_guid() { + static ggml_guid guid = {0x99, 0x68, 0x5b, 0x6c, 0xd2, 0x83, 0x3d, 0x24, 0x25, 0x36, 0x72, 0xe1, 0x5b, 0x0e, 0x14, 0x03}; + return &guid; +} + +struct ggml_backend_rpc_buffer_type_context { + std::shared_ptr sock; + std::string name; + size_t alignment; + size_t max_size; +}; + +struct ggml_backend_rpc_context { + std::string endpoint; + std::string name; + std::shared_ptr sock; + ggml_backend_buffer_type_t buft; +}; + +struct ggml_backend_rpc_buffer_context { + std::shared_ptr sock; + std::unordered_map base_cache; + uint64_t remote_ptr; + std::string name; +}; + +// RPC helper functions + +static std::shared_ptr make_socket(sockfd_t fd) { +#ifdef _WIN32 + if (fd == INVALID_SOCKET) { + return nullptr; + } +#else + if (fd < 0) { + return nullptr; + } +#endif + return std::make_shared(fd); +} + +static bool set_no_delay(sockfd_t sockfd) { + int flag = 1; + // set TCP_NODELAY to disable Nagle's algorithm + int ret = setsockopt(sockfd, IPPROTO_TCP, TCP_NODELAY, (char *)&flag, sizeof(int)); + return ret == 0; +} + +static bool set_reuse_addr(sockfd_t sockfd) { + int flag = 1; + int ret = setsockopt(sockfd, SOL_SOCKET, SO_REUSEADDR, (char *)&flag, sizeof(int)); + return ret == 0; +} + +static std::shared_ptr socket_connect(const char * host, int port) { + struct sockaddr_in addr; + auto sockfd = socket(AF_INET, SOCK_STREAM, 0); + auto sock_ptr = make_socket(sockfd); + if (sock_ptr == nullptr) { + return nullptr; + } + if (!set_no_delay(sockfd)) { + fprintf(stderr, "Failed to set TCP_NODELAY\n"); + return nullptr; + } + addr.sin_family = AF_INET; + addr.sin_port = htons(port); + struct hostent * server = gethostbyname(host); + if (server == NULL) { + fprintf(stderr, "Cannot resolve host '%s'\n", host); + return nullptr; + } + memcpy(&addr.sin_addr.s_addr, server->h_addr, server->h_length); + if (connect(sock_ptr->fd, (struct sockaddr *)&addr, sizeof(addr)) < 0) { + return nullptr; + } + return sock_ptr; +} + +static std::shared_ptr socket_accept(sockfd_t srv_sockfd) { + auto client_socket_fd = accept(srv_sockfd, NULL, NULL); + auto client_socket = make_socket(client_socket_fd); + if (client_socket == nullptr) { + return nullptr; + } + if (!set_no_delay(client_socket_fd)) { + fprintf(stderr, "Failed to set TCP_NODELAY\n"); + return nullptr; + } + return client_socket; +} + +static std::shared_ptr create_server_socket(const char * host, int port) { + auto sockfd = socket(AF_INET, SOCK_STREAM, 0); + auto sock = make_socket(sockfd); + if (sock == nullptr) { + return nullptr; + } + if (!set_reuse_addr(sockfd)) { + fprintf(stderr, "Failed to set SO_REUSEADDR\n"); + return nullptr; + } + struct sockaddr_in serv_addr; + serv_addr.sin_family = AF_INET; + serv_addr.sin_addr.s_addr = inet_addr(host); + serv_addr.sin_port = htons(port); + + if (bind(sockfd, (struct sockaddr *) &serv_addr, sizeof(serv_addr)) < 0) { + return nullptr; + } + if (listen(sockfd, 1) < 0) { + return nullptr; + } + return sock; +} + +static bool send_data(sockfd_t sockfd, const void * data, size_t size) { + size_t bytes_sent = 0; + while (bytes_sent < size) { + ssize_t n = send(sockfd, (const char *)data + bytes_sent, size - bytes_sent, 0); + if (n < 0) { + return false; + } + bytes_sent += n; + } + return true; +} + +static bool recv_data(sockfd_t sockfd, void * data, size_t size) { + size_t bytes_recv = 0; + while (bytes_recv < size) { + ssize_t n = recv(sockfd, (char *)data + bytes_recv, size - bytes_recv, 0); + if (n <= 0) { + return false; + } + bytes_recv += n; + } + return true; +} + +static bool parse_endpoint(const char * endpoint, std::string & host, int & port) { + std::string str(endpoint); + size_t pos = str.find(':'); + if (pos == std::string::npos) { + return false; + } + host = str.substr(0, pos); + port = std::stoi(str.substr(pos + 1)); + return true; +} + +// RPC request : | rpc_cmd (1 byte) | request_size (8 bytes) | request_data (request_size bytes) | +// RPC response: | response_size (8 bytes) | response_data (response_size bytes) | +static bool send_rpc_cmd(const std::shared_ptr & sock, enum rpc_cmd cmd, const std::vector & input, std::vector & output) { + uint8_t cmd_byte = cmd; + if (!send_data(sock->fd, &cmd_byte, sizeof(cmd_byte))) { + return false; + } + uint64_t input_size = input.size(); + if (!send_data(sock->fd, &input_size, sizeof(input_size))) { + return false; + } + if (!send_data(sock->fd, input.data(), input.size())) { + return false; + } + uint64_t output_size; + if (!recv_data(sock->fd, &output_size, sizeof(output_size))) { + return false; + } + if (output_size == 0) { + output.clear(); + return true; + } + output.resize(output_size); + if (!recv_data(sock->fd, output.data(), output_size)) { + return false; + } + return true; +} + +// RPC client-side implementation + +GGML_CALL static const char * ggml_backend_rpc_buffer_get_name(ggml_backend_buffer_t buffer) { + ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; + return ctx->name.c_str(); +} + +GGML_CALL static void ggml_backend_rpc_buffer_free_buffer(ggml_backend_buffer_t buffer) { + ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; + // input serialization format: | remote_ptr (8 bytes) | + std::vector input(sizeof(uint64_t), 0); + uint64_t remote_ptr = ctx->remote_ptr; + memcpy(input.data(), &remote_ptr, sizeof(remote_ptr)); + std::vector output; + bool status = send_rpc_cmd(ctx->sock, FREE_BUFFER, input, output); + GGML_ASSERT(status); + GGML_ASSERT(output.empty()); + delete ctx; +} + +GGML_CALL static void * ggml_backend_rpc_buffer_get_base(ggml_backend_buffer_t buffer) { + ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; + if (ctx->base_cache.find(buffer) != ctx->base_cache.end()) { + return ctx->base_cache[buffer]; + } + // input serialization format: | remote_ptr (8 bytes) | + std::vector input(sizeof(uint64_t), 0); + uint64_t remote_ptr = ctx->remote_ptr; + memcpy(input.data(), &remote_ptr, sizeof(remote_ptr)); + std::vector output; + bool status = send_rpc_cmd(ctx->sock, BUFFER_GET_BASE, input, output); + GGML_ASSERT(status); + GGML_ASSERT(output.size() == sizeof(uint64_t)); + // output serialization format: | base_ptr (8 bytes) | + uint64_t base_ptr; + memcpy(&base_ptr, output.data(), sizeof(base_ptr)); + void * base = reinterpret_cast(base_ptr); + ctx->base_cache[buffer] = base; + return base; +} + +static rpc_tensor serialize_tensor(const ggml_tensor * tensor) { + rpc_tensor result; + result.id = reinterpret_cast(tensor); + result.type = tensor->type; + if (tensor->buffer) { + ggml_backend_buffer_t buffer = tensor->buffer; + ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; + result.buffer = ctx->remote_ptr; + } else { + result.buffer = 0; + } + for (uint32_t i = 0; i < GGML_MAX_DIMS; i++) { + result.ne[i] = tensor->ne[i]; + result.nb[i] = tensor->nb[i]; + } + result.op = tensor->op; + for (uint32_t i = 0; i < GGML_MAX_OP_PARAMS / sizeof(int32_t); i++) { + result.op_params[i] = tensor->op_params[i]; + } + result.flags = tensor->flags; + for (uint32_t i = 0; i < GGML_MAX_SRC; i++) { + result.src[i] = reinterpret_cast(tensor->src[i]); + } + result.view_src = reinterpret_cast(tensor->view_src); + result.view_offs = tensor->view_offs; + result.data = reinterpret_cast(tensor->data); + snprintf(result.name, GGML_MAX_NAME, "%s", tensor->name); + return result; +} + +GGML_CALL static void ggml_backend_rpc_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { + UNUSED(buffer); + if (ggml_is_quantized(tensor->type)) { + // TODO: this check is due to MATRIX_ROW_PADDING in CUDA and should be generalized + GGML_ASSERT(tensor->ne[0] % 512 == 0 && "unsupported quantized tensor"); + } +} + +GGML_CALL static void ggml_backend_rpc_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { + ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; + // input serialization format: | rpc_tensor | offset (8 bytes) | data (size bytes) | + size_t input_size = sizeof(rpc_tensor) + sizeof(uint64_t) + size; + std::vector input(input_size, 0); + rpc_tensor rpc_tensor = serialize_tensor(tensor); + memcpy(input.data(), &rpc_tensor, sizeof(rpc_tensor)); + memcpy(input.data() + sizeof(rpc_tensor), &offset, sizeof(offset)); + memcpy(input.data() + sizeof(rpc_tensor) + sizeof(offset), data, size); + std::vector output; + bool status = send_rpc_cmd(ctx->sock, SET_TENSOR, input, output); + GGML_ASSERT(status); +} + +GGML_CALL static void ggml_backend_rpc_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { + ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; + // input serialization format: | rpc_tensor | offset (8 bytes) | size (8 bytes) | + int input_size = sizeof(rpc_tensor) + 2*sizeof(uint64_t); + std::vector input(input_size, 0); + rpc_tensor rpc_tensor = serialize_tensor(tensor); + memcpy(input.data(), &rpc_tensor, sizeof(rpc_tensor)); + memcpy(input.data() + sizeof(rpc_tensor), &offset, sizeof(offset)); + memcpy(input.data() + sizeof(rpc_tensor) + sizeof(offset), &size, sizeof(size)); + std::vector output; + bool status = send_rpc_cmd(ctx->sock, GET_TENSOR, input, output); + GGML_ASSERT(status); + GGML_ASSERT(output.size() == size); + // output serialization format: | data (size bytes) | + memcpy(data, output.data(), size); +} + +GGML_CALL static bool ggml_backend_rpc_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) { + // check if src and dst are on the same server + ggml_backend_buffer_t src_buffer = src->buffer; + ggml_backend_rpc_buffer_context * src_ctx = (ggml_backend_rpc_buffer_context *)src_buffer->context; + ggml_backend_buffer_t dst_buffer = dst->buffer; + ggml_backend_rpc_buffer_context * dst_ctx = (ggml_backend_rpc_buffer_context *)dst_buffer->context; + if (src_ctx->sock != dst_ctx->sock) { + return false; + } + ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; + // input serialization format: | rpc_tensor src | rpc_tensor dst | + int input_size = 2*sizeof(rpc_tensor); + std::vector input(input_size, 0); + rpc_tensor rpc_src = serialize_tensor(src); + rpc_tensor rpc_dst = serialize_tensor(dst); + memcpy(input.data(), &rpc_src, sizeof(rpc_src)); + memcpy(input.data() + sizeof(rpc_src), &rpc_dst, sizeof(rpc_dst)); + std::vector output; + bool status = send_rpc_cmd(ctx->sock, COPY_TENSOR, input, output); + GGML_ASSERT(status); + // output serialization format: | result (1 byte) | + GGML_ASSERT(output.size() == 1); + return output[0]; +} + +GGML_CALL static void ggml_backend_rpc_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; + // serialization format: | bufptr (8 bytes) | value (1 byte) | + int input_size = sizeof(uint64_t) + sizeof(uint8_t); + std::vector input(input_size, 0); + memcpy(input.data(), &ctx->remote_ptr, sizeof(ctx->remote_ptr)); + memcpy(input.data() + sizeof(ctx->remote_ptr), &value, sizeof(value)); + std::vector output; + bool status = send_rpc_cmd(ctx->sock, BUFFER_CLEAR, input, output); + GGML_ASSERT(status); +} + +static ggml_backend_buffer_i ggml_backend_rpc_buffer_interface = { + /* .get_name = */ ggml_backend_rpc_buffer_get_name, + /* .free_buffer = */ ggml_backend_rpc_buffer_free_buffer, + /* .get_base = */ ggml_backend_rpc_buffer_get_base, + /* .init_tensor = */ ggml_backend_rpc_buffer_init_tensor, + /* .set_tensor = */ ggml_backend_rpc_buffer_set_tensor, + /* .get_tensor = */ ggml_backend_rpc_buffer_get_tensor, + /* .cpy_tensor = */ ggml_backend_rpc_buffer_cpy_tensor, + /* .clear = */ ggml_backend_rpc_buffer_clear, + /* .reset = */ NULL, +}; + +GGML_CALL static const char * ggml_backend_rpc_buffer_type_name(ggml_backend_buffer_type_t buft) { + ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)buft->context; + return buft_ctx->name.c_str(); +} + +GGML_CALL static ggml_backend_buffer_t ggml_backend_rpc_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)buft->context; + // input serialization format: | size (8 bytes) | + int input_size = sizeof(uint64_t); + std::vector input(input_size, 0); + memcpy(input.data(), &size, sizeof(size)); + std::vector output; + bool status = send_rpc_cmd(buft_ctx->sock, ALLOC_BUFFER, input, output); + GGML_ASSERT(status); + GGML_ASSERT(output.size() == 2*sizeof(uint64_t)); + // output serialization format: | remote_ptr (8 bytes) | remote_size (8 bytes) | + uint64_t remote_ptr; + memcpy(&remote_ptr, output.data(), sizeof(remote_ptr)); + size_t remote_size; + memcpy(&remote_size, output.data() + sizeof(uint64_t), sizeof(remote_size)); + if (remote_ptr != 0) { + ggml_backend_buffer_t buffer = ggml_backend_buffer_init(buft, + ggml_backend_rpc_buffer_interface, + new ggml_backend_rpc_buffer_context{buft_ctx->sock, {}, remote_ptr, "RPC"}, + remote_size); + return buffer; + } else { + return nullptr; + } +} + +static size_t get_alignment(const std::shared_ptr & sock) { + // input serialization format: | 0 bytes | + std::vector input; + std::vector output; + bool status = send_rpc_cmd(sock, GET_ALIGNMENT, input, output); + GGML_ASSERT(status); + GGML_ASSERT(output.size() == sizeof(uint64_t)); + // output serialization format: | alignment (8 bytes) | + uint64_t alignment; + memcpy(&alignment, output.data(), sizeof(alignment)); + return alignment; +} + +GGML_CALL static size_t ggml_backend_rpc_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { + ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)buft->context; + return buft_ctx->alignment; +} + +static size_t get_max_size(const std::shared_ptr & sock) { + // input serialization format: | 0 bytes | + std::vector input; + std::vector output; + bool status = send_rpc_cmd(sock, GET_MAX_SIZE, input, output); + GGML_ASSERT(status); + GGML_ASSERT(output.size() == sizeof(uint64_t)); + // output serialization format: | max_size (8 bytes) | + uint64_t max_size; + memcpy(&max_size, output.data(), sizeof(max_size)); + return max_size; +} + +GGML_CALL static size_t ggml_backend_rpc_get_max_size(ggml_backend_buffer_type_t buft) { + ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)buft->context; + return buft_ctx->max_size; +} + +GGML_CALL static size_t ggml_backend_rpc_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { + UNUSED(buft); + return ggml_nbytes(tensor); +} + +GGML_CALL static bool ggml_backend_rpc_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { + if (!ggml_backend_is_rpc(backend)) { + return false; + } + ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)buft->context; + ggml_backend_rpc_context * rpc_ctx = (ggml_backend_rpc_context *)backend->context; + return buft_ctx->sock == rpc_ctx->sock; +} + +static ggml_backend_buffer_type_i ggml_backend_rpc_buffer_type_interface = { + /* .get_name = */ ggml_backend_rpc_buffer_type_name, + /* .alloc_buffer = */ ggml_backend_rpc_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_rpc_buffer_type_get_alignment, + /* .get_max_size = */ ggml_backend_rpc_get_max_size, + /* .get_alloc_size = */ ggml_backend_rpc_buffer_type_get_alloc_size, + /* .supports_backend = */ ggml_backend_rpc_buffer_type_supports_backend, + /* .is_host = */ NULL, +}; + + +GGML_CALL static const char * ggml_backend_rpc_name(ggml_backend_t backend) { + ggml_backend_rpc_context * rpc_ctx = (ggml_backend_rpc_context *)backend->context; + + return rpc_ctx->name.c_str(); +} + +GGML_CALL static void ggml_backend_rpc_free(ggml_backend_t backend) { + ggml_backend_rpc_context * rpc_ctx = (ggml_backend_rpc_context *)backend->context; + ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)rpc_ctx->buft->context; + delete buft_ctx; + delete rpc_ctx->buft; + delete rpc_ctx; + delete backend; +} + +GGML_CALL static ggml_backend_buffer_type_t ggml_backend_rpc_get_default_buffer_type(ggml_backend_t backend) { + ggml_backend_rpc_context * ctx = (ggml_backend_rpc_context *)backend->context; + return ctx->buft; +} + +GGML_CALL static void ggml_backend_rpc_synchronize(ggml_backend_t backend) { + UNUSED(backend); + // this is no-op because we don't have any async operations +} + +static void add_tensor(ggml_tensor * tensor, std::vector & tensors, std::unordered_set & visited) { + if (tensor == nullptr) { + return; + } + if (visited.find(tensor) != visited.end()) { + return; + } + visited.insert(tensor); + for (int i = 0; i < GGML_MAX_SRC; i++) { + add_tensor(tensor->src[i], tensors, visited); + } + add_tensor(tensor->view_src, tensors, visited); + tensors.push_back(serialize_tensor(tensor)); +} + +static void serialize_graph(const ggml_cgraph * cgraph, std::vector & output) { + uint32_t n_nodes = cgraph->n_nodes; + std::vector tensors; + std::unordered_set visited; + for (uint32_t i = 0; i < n_nodes; i++) { + add_tensor(cgraph->nodes[i], tensors, visited); + } + // serialization format: + // | n_nodes (4 bytes) | nodes (n_nodes * sizeof(uint64_t) | n_tensors (4 bytes) | tensors (n_tensors * sizeof(rpc_tensor)) | + uint32_t n_tensors = tensors.size(); + int output_size = sizeof(uint32_t) + n_nodes * sizeof(uint64_t) + sizeof(uint32_t) + n_tensors * sizeof(rpc_tensor); + output.resize(output_size, 0); + memcpy(output.data(), &n_nodes, sizeof(n_nodes)); + uint64_t * out_nodes = (uint64_t *)(output.data() + sizeof(n_nodes)); + for (uint32_t i = 0; i < n_nodes; i++) { + out_nodes[i] = reinterpret_cast(cgraph->nodes[i]); + } + uint32_t * out_ntensors = (uint32_t *)(output.data() + sizeof(n_nodes) + n_nodes * sizeof(uint64_t)); + *out_ntensors = n_tensors; + rpc_tensor * out_tensors = (rpc_tensor *)(output.data() + sizeof(n_nodes) + n_nodes * sizeof(uint64_t) + sizeof(uint32_t)); + memcpy(out_tensors, tensors.data(), n_tensors * sizeof(rpc_tensor)); +} + +GGML_CALL static enum ggml_status ggml_backend_rpc_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { + ggml_backend_rpc_context * rpc_ctx = (ggml_backend_rpc_context *)backend->context; + std::vector input; + serialize_graph(cgraph, input); + std::vector output; + bool status = send_rpc_cmd(rpc_ctx->sock, GRAPH_COMPUTE, input, output); + GGML_ASSERT(status); + GGML_ASSERT(output.size() == 1); + return (enum ggml_status)output[0]; +} + +GGML_CALL static bool ggml_backend_rpc_supports_op(ggml_backend_t backend, const ggml_tensor * op) { + UNUSED(backend); + UNUSED(op); + GGML_ASSERT(false && "not implemented"); + return false; +} + +static ggml_backend_i ggml_backend_rpc_interface = { + /* .get_name = */ ggml_backend_rpc_name, + /* .free = */ ggml_backend_rpc_free, + /* .get_default_buffer_type = */ ggml_backend_rpc_get_default_buffer_type, + /* .set_tensor_async = */ NULL, + /* .get_tensor_async = */ NULL, + /* .cpy_tensor_async = */ NULL, + /* .synchronize = */ ggml_backend_rpc_synchronize, + /* .graph_plan_create = */ NULL, + /* .graph_plan_free = */ NULL, + /* .graph_plan_compute = */ NULL, + /* .graph_compute = */ ggml_backend_rpc_graph_compute, + /* .supports_op = */ ggml_backend_rpc_supports_op, + /* .offload_op = */ NULL, + /* .event_new = */ NULL, + /* .event_free = */ NULL, + /* .event_record = */ NULL, + /* .event_wait = */ NULL, + /* .event_synchronize = */ NULL, +}; + +static std::unordered_map instances; + +GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_rpc_buffer_type(const char * endpoint) { + ggml_backend_t backend = ggml_backend_rpc_init(endpoint); + return backend != nullptr ? ggml_backend_rpc_get_default_buffer_type(backend) : nullptr; +} + +GGML_CALL ggml_backend_t ggml_backend_rpc_init(const char * endpoint) { + std::string endpoint_str(endpoint); + if (instances.find(endpoint_str) != instances.end()) { + return instances[endpoint_str]; + } +#ifdef _WIN32 + { + WSADATA wsaData; + int res = WSAStartup(MAKEWORD(2, 2), &wsaData); + if (res != 0) { + return nullptr; + } + } +#endif + fprintf(stderr, "Connecting to %s\n", endpoint); + std::string host; + int port; + if (!parse_endpoint(endpoint, host, port)) { + return nullptr; + } + auto sock = socket_connect(host.c_str(), port); + if (sock == nullptr) { + return nullptr; + } + size_t alignment = get_alignment(sock); + size_t max_size = get_max_size(sock); + ggml_backend_rpc_buffer_type_context * buft_ctx = new ggml_backend_rpc_buffer_type_context { + /* .sock = */ sock, + /* .name = */ "RPC" + std::to_string(sock->fd), + /* .alignment = */ alignment, + /* .max_size = */ max_size + }; + + ggml_backend_buffer_type_t buft = new ggml_backend_buffer_type { + /* .iface = */ ggml_backend_rpc_buffer_type_interface, + /* .context = */ buft_ctx + }; + + ggml_backend_rpc_context * ctx = new ggml_backend_rpc_context { + /* .endpoint = */ endpoint, + /* .name = */ "RPC" + std::to_string(sock->fd), + /* .sock = */ sock, + /* .buft = */ buft + }; + + instances[endpoint] = new ggml_backend { + /* .guid = */ ggml_backend_rpc_guid(), + /* .interface = */ ggml_backend_rpc_interface, + /* .context = */ ctx + }; + + return instances[endpoint]; +} + +GGML_API GGML_CALL bool ggml_backend_is_rpc(ggml_backend_t backend) { + return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_rpc_guid()); +} + +static void get_device_memory(const std::shared_ptr & sock, size_t * free, size_t * total) { + // input serialization format: | 0 bytes | + std::vector input; + std::vector output; + bool status = send_rpc_cmd(sock, GET_DEVICE_MEMORY, input, output); + GGML_ASSERT(status); + GGML_ASSERT(output.size() == 2*sizeof(uint64_t)); + // output serialization format: | free (8 bytes) | total (8 bytes) | + uint64_t free_mem; + memcpy(&free_mem, output.data(), sizeof(free_mem)); + uint64_t total_mem; + memcpy(&total_mem, output.data() + sizeof(uint64_t), sizeof(total_mem)); + *free = free_mem; + *total = total_mem; +} + +GGML_API GGML_CALL void ggml_backend_rpc_get_device_memory(const char * endpoint, size_t * free, size_t * total) { + ggml_backend_t backend = ggml_backend_rpc_init(endpoint); + if (backend == nullptr) { + *free = 0; + *total = 0; + return; + } + ggml_backend_rpc_context * ctx = (ggml_backend_rpc_context *)backend->context; + get_device_memory(ctx->sock, free, total); +} + +// RPC server-side implementation + +class rpc_server { +public: + rpc_server(ggml_backend_t backend) : backend(backend) {} + ~rpc_server(); + + bool alloc_buffer(const std::vector & input, std::vector & output); + void get_alignment(std::vector & output); + void get_max_size(std::vector & output); + bool buffer_get_base(const std::vector & input, std::vector & output); + bool free_buffer(const std::vector & input); + bool buffer_clear(const std::vector & input); + bool set_tensor(const std::vector & input); + bool get_tensor(const std::vector & input, std::vector & output); + bool copy_tensor(const std::vector & input, std::vector & output); + bool graph_compute(const std::vector & input, std::vector & output); + +private: + ggml_tensor * deserialize_tensor(struct ggml_context * ctx, const rpc_tensor * tensor); + ggml_tensor * create_node(uint64_t id, + struct ggml_context * ctx, + const std::unordered_map & tensor_ptrs, + std::unordered_map & tensor_map); + + + ggml_backend_t backend; + std::unordered_set buffers; +}; + +bool rpc_server::alloc_buffer(const std::vector & input, std::vector & output) { + // input serialization format: | size (8 bytes) | + if (input.size() != sizeof(uint64_t)) { + return false; + } + uint64_t size; + memcpy(&size, input.data(), sizeof(size)); + ggml_backend_buffer_type_t buft = ggml_backend_get_default_buffer_type(backend); + ggml_backend_buffer_t buffer = ggml_backend_buft_alloc_buffer(buft, size); + uint64_t remote_ptr = 0; + uint64_t remote_size = 0; + if (buffer != nullptr) { + remote_ptr = reinterpret_cast(buffer); + remote_size = buffer->size; + GGML_PRINT_DEBUG("[%s] size: %" PRIu64 " -> remote_ptr: %" PRIx64 ", remote_size: %" PRIu64 "\n", __func__, size, remote_ptr, remote_size); + buffers.insert(buffer); + } else { + GGML_PRINT_DEBUG("[%s] size: %" PRIu64 " -> failed\n", __func__, size); + } + // output serialization format: | remote_ptr (8 bytes) | remote_size (8 bytes) | + output.resize(2*sizeof(uint64_t), 0); + memcpy(output.data(), &remote_ptr, sizeof(remote_ptr)); + memcpy(output.data() + sizeof(uint64_t), &remote_size, sizeof(remote_size)); + return true; +} + +void rpc_server::get_alignment(std::vector & output) { + ggml_backend_buffer_type_t buft = ggml_backend_get_default_buffer_type(backend); + size_t alignment = ggml_backend_buft_get_alignment(buft); + GGML_PRINT_DEBUG("[%s] alignment: %lu\n", __func__, alignment); + // output serialization format: | alignment (8 bytes) | + output.resize(sizeof(uint64_t), 0); + memcpy(output.data(), &alignment, sizeof(alignment)); +} + +void rpc_server::get_max_size(std::vector & output) { + ggml_backend_buffer_type_t buft = ggml_backend_get_default_buffer_type(backend); + size_t max_size = ggml_backend_buft_get_max_size(buft); + GGML_PRINT_DEBUG("[%s] max_size: %lu\n", __func__, max_size); + // output serialization format: | max_size (8 bytes) | + output.resize(sizeof(uint64_t), 0); + memcpy(output.data(), &max_size, sizeof(max_size)); +} + +bool rpc_server::buffer_get_base(const std::vector & input, std::vector & output) { + // input serialization format: | remote_ptr (8 bytes) | + if (input.size() != sizeof(uint64_t)) { + return false; + } + uint64_t remote_ptr; + memcpy(&remote_ptr, input.data(), sizeof(remote_ptr)); + GGML_PRINT_DEBUG("[%s] remote_ptr: %" PRIx64 "\n", __func__, remote_ptr); + ggml_backend_buffer_t buffer = reinterpret_cast(remote_ptr); + if (buffers.find(buffer) == buffers.end()) { + GGML_PRINT_DEBUG("[%s] buffer not found\n", __func__); + return false; + } + void * base = ggml_backend_buffer_get_base(buffer); + // output serialization format: | base_ptr (8 bytes) | + uint64_t base_ptr = reinterpret_cast(base); + output.resize(sizeof(uint64_t), 0); + memcpy(output.data(), &base_ptr, sizeof(base_ptr)); + return true; +} + +bool rpc_server::free_buffer(const std::vector & input) { + // input serialization format: | remote_ptr (8 bytes) | + if (input.size() != sizeof(uint64_t)) { + return false; + } + uint64_t remote_ptr; + memcpy(&remote_ptr, input.data(), sizeof(remote_ptr)); + GGML_PRINT_DEBUG("[%s] remote_ptr: %" PRIx64 "\n", __func__, remote_ptr); + ggml_backend_buffer_t buffer = reinterpret_cast(remote_ptr); + if (buffers.find(buffer) == buffers.end()) { + GGML_PRINT_DEBUG("[%s] buffer not found\n", __func__); + return false; + } + ggml_backend_buffer_free(buffer); + buffers.erase(buffer); + return true; +} + +bool rpc_server::buffer_clear(const std::vector & input) { + // input serialization format: | remote_ptr (8 bytes) | value (1 byte) | + if (input.size() != sizeof(uint64_t) + sizeof(uint8_t)) { + return false; + } + uint64_t remote_ptr; + memcpy(&remote_ptr, input.data(), sizeof(remote_ptr)); + uint8_t value; + memcpy(&value, input.data() + sizeof(uint64_t), sizeof(value)); + GGML_PRINT_DEBUG("[%s] remote_ptr: %" PRIx64 ", value: %u\n", __func__, remote_ptr, value); + ggml_backend_buffer_t buffer = reinterpret_cast(remote_ptr); + if (buffers.find(buffer) == buffers.end()) { + GGML_PRINT_DEBUG("[%s] buffer not found\n", __func__); + return false; + } + ggml_backend_buffer_clear(buffer, value); + return true; +} + +ggml_tensor * rpc_server::deserialize_tensor(struct ggml_context * ctx, const rpc_tensor * tensor) { + ggml_tensor * result = ggml_new_tensor_4d(ctx, (ggml_type) tensor->type, + tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]); + for (uint32_t i = 0; i < GGML_MAX_DIMS; i++) { + result->nb[i] = tensor->nb[i]; + } + result->buffer = reinterpret_cast(tensor->buffer); + if (result->buffer && buffers.find(result->buffer) == buffers.end()) { + return nullptr; + } + result->op = (ggml_op) tensor->op; + for (uint32_t i = 0; i < GGML_MAX_OP_PARAMS / sizeof(int32_t); i++) { + result->op_params[i] = tensor->op_params[i]; + } + result->flags = tensor->flags; + result->data = reinterpret_cast(tensor->data); + ggml_set_name(result, tensor->name); + return result; +} + + +bool rpc_server::set_tensor(const std::vector & input) { + // serialization format: | rpc_tensor | offset (8 bytes) | data (size bytes) | + if (input.size() < sizeof(rpc_tensor) + sizeof(uint64_t)) { + return false; + } + const rpc_tensor * in_tensor = (const rpc_tensor *)input.data(); + uint64_t offset; + memcpy(&offset, input.data() + sizeof(rpc_tensor), sizeof(offset)); + size_t size = input.size() - sizeof(rpc_tensor) - sizeof(offset); + + struct ggml_init_params params { + /*.mem_size =*/ ggml_tensor_overhead(), + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + struct ggml_context * ctx = ggml_init(params); + ggml_tensor * tensor = deserialize_tensor(ctx, in_tensor); + if (tensor == nullptr) { + GGML_PRINT_DEBUG("[%s] error deserializing tensor\n", __func__); + ggml_free(ctx); + return false; + } + GGML_PRINT_DEBUG("[%s] buffer: %p, data: %p, offset: %" PRIu64 ", size: %zu\n", __func__, (void*)tensor->buffer, tensor->data, offset, size); + const void * data = input.data() + sizeof(rpc_tensor) + sizeof(offset); + ggml_backend_tensor_set(tensor, data, offset, size); + ggml_free(ctx); + return true; +} + +bool rpc_server::get_tensor(const std::vector & input, std::vector & output) { + // serialization format: | rpc_tensor | offset (8 bytes) | size (8 bytes) | + if (input.size() != sizeof(rpc_tensor) + 2*sizeof(uint64_t)) { + return false; + } + const rpc_tensor * in_tensor = (const rpc_tensor *)input.data(); + uint64_t offset; + memcpy(&offset, input.data() + sizeof(rpc_tensor), sizeof(offset)); + uint64_t size; + memcpy(&size, input.data() + sizeof(rpc_tensor) + sizeof(offset), sizeof(size)); + + struct ggml_init_params params { + /*.mem_size =*/ ggml_tensor_overhead(), + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + struct ggml_context * ctx = ggml_init(params); + ggml_tensor * tensor = deserialize_tensor(ctx, in_tensor); + if (tensor == nullptr) { + GGML_PRINT_DEBUG("[%s] error deserializing tensor\n", __func__); + ggml_free(ctx); + return false; + } + GGML_PRINT_DEBUG("[%s] buffer: %p, data: %p, offset: %" PRIu64 ", size: %" PRIu64 "\n", __func__, (void*)tensor->buffer, tensor->data, offset, size); + // output serialization format: | data (size bytes) | + output.resize(size, 0); + ggml_backend_tensor_get(tensor, output.data(), offset, size); + ggml_free(ctx); + return true; +} + +bool rpc_server::copy_tensor(const std::vector & input, std::vector & output) { + // serialization format: | rpc_tensor src | rpc_tensor dst | + if (input.size() != 2*sizeof(rpc_tensor)) { + return false; + } + const rpc_tensor * rpc_src = (const rpc_tensor *)input.data(); + const rpc_tensor * rpc_dst = (const rpc_tensor *)(input.data() + sizeof(rpc_src)); + + struct ggml_init_params params { + /*.mem_size =*/ 2*ggml_tensor_overhead(), + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + struct ggml_context * ctx = ggml_init(params); + ggml_tensor * src = deserialize_tensor(ctx, rpc_src); + ggml_tensor * dst = deserialize_tensor(ctx, rpc_dst); + if (src == nullptr || dst == nullptr) { + GGML_PRINT_DEBUG("[%s] error deserializing tensors\n", __func__); + ggml_free(ctx); + return false; + } + GGML_PRINT_DEBUG("[%s] src->buffer: %p, dst->buffer: %p\n", __func__, (void*)src->buffer, (void*)dst->buffer); + bool result = ggml_backend_buffer_copy_tensor(src, dst); + // output serialization format: | result (1 byte) | + output.resize(1, 0); + output[0] = result; + ggml_free(ctx); + return true; +} + +ggml_tensor * rpc_server::create_node(uint64_t id, + struct ggml_context * ctx, + const std::unordered_map & tensor_ptrs, + std::unordered_map & tensor_map) { + if (id == 0) { + return nullptr; + } + if (tensor_map.find(id) != tensor_map.end()) { + return tensor_map[id]; + } + const rpc_tensor * tensor = tensor_ptrs.at(id); + struct ggml_tensor * result = deserialize_tensor(ctx, tensor); + if (result == nullptr) { + return nullptr; + } + tensor_map[id] = result; + for (int i = 0; i < GGML_MAX_SRC; i++) { + result->src[i] = create_node(tensor->src[i], ctx, tensor_ptrs, tensor_map); + } + result->view_src = create_node(tensor->view_src, ctx, tensor_ptrs, tensor_map); + result->view_offs = tensor->view_offs; + return result; +} + +bool rpc_server::graph_compute(const std::vector & input, std::vector & output) { + // serialization format: + // | n_nodes (4 bytes) | nodes (n_nodes * sizeof(uint64_t) | n_tensors (4 bytes) | tensors (n_tensors * sizeof(rpc_tensor)) | + if (input.size() < sizeof(uint32_t)) { + return false; + } + uint32_t n_nodes; + memcpy(&n_nodes, input.data(), sizeof(n_nodes)); + if (input.size() < sizeof(uint32_t) + n_nodes*sizeof(uint64_t) + sizeof(uint32_t)) { + return false; + } + const uint64_t * nodes = (const uint64_t *)(input.data() + sizeof(n_nodes)); + uint32_t n_tensors; + memcpy(&n_tensors, input.data() + sizeof(n_nodes) + n_nodes*sizeof(uint64_t), sizeof(n_tensors)); + if (input.size() < sizeof(uint32_t) + n_nodes*sizeof(uint64_t) + sizeof(uint32_t) + n_tensors*sizeof(rpc_tensor)) { + return false; + } + const rpc_tensor * tensors = (const rpc_tensor *)(input.data() + sizeof(n_nodes) + n_nodes*sizeof(uint64_t) + sizeof(n_tensors)); + GGML_PRINT_DEBUG("[%s] n_nodes: %u, n_tensors: %u\n", __func__, n_nodes, n_tensors); + + static size_t buf_size = ggml_tensor_overhead()*(n_nodes + n_tensors) + ggml_graph_overhead_custom(n_nodes, false); + struct ggml_init_params params = { + /*.mem_size =*/ buf_size, + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + struct ggml_context * ctx = ggml_init(params); + struct ggml_cgraph * graph = ggml_new_graph_custom(ctx, n_nodes, false); + graph->n_nodes = n_nodes; + std::unordered_map tensor_ptrs; + for (uint32_t i = 0; i < n_tensors; i++) { + tensor_ptrs[tensors[i].id] = &tensors[i]; + } + std::unordered_map tensor_map; + for (uint32_t i = 0; i < n_nodes; i++) { + graph->nodes[i] = create_node(nodes[i], ctx, tensor_ptrs, tensor_map); + } + ggml_status status = ggml_backend_graph_compute(backend, graph); + // output serialization format: | status (1 byte) | + output.resize(1, 0); + output[0] = status; + ggml_free(ctx); + return true; +} + +rpc_server::~rpc_server() { + for (auto buffer : buffers) { + ggml_backend_buffer_free(buffer); + } +} + +static void rpc_serve_client(ggml_backend_t backend, sockfd_t sockfd, size_t free_mem, size_t total_mem) { + rpc_server server(backend); + while (true) { + uint8_t cmd; + if (!recv_data(sockfd, &cmd, 1)) { + break; + } + std::vector input; + std::vector output; + uint64_t input_size; + if (!recv_data(sockfd, &input_size, sizeof(input_size))) { + break; + } + input.resize(input_size); + if (!recv_data(sockfd, input.data(), input_size)) { + break; + } + bool ok = true; + switch (cmd) { + case ALLOC_BUFFER: { + ok = server.alloc_buffer(input, output); + break; + } + case GET_ALIGNMENT: { + server.get_alignment(output); + break; + } + case GET_MAX_SIZE: { + server.get_max_size(output); + break; + } + case BUFFER_GET_BASE: { + ok = server.buffer_get_base(input, output); + break; + } + case FREE_BUFFER: { + ok = server.free_buffer(input); + break; + } + case BUFFER_CLEAR: { + ok = server.buffer_clear(input); + break; + } + case SET_TENSOR: { + ok = server.set_tensor(input); + break; + } + case GET_TENSOR: { + ok = server.get_tensor(input, output); + break; + } + case COPY_TENSOR: { + ok = server.copy_tensor(input, output); + break; + } + case GRAPH_COMPUTE: { + ok = server.graph_compute(input, output); + break; + } + case GET_DEVICE_MEMORY: { + // output serialization format: | free (8 bytes) | total (8 bytes) | + output.resize(2*sizeof(uint64_t), 0); + memcpy(output.data(), &free_mem, sizeof(free_mem)); + memcpy(output.data() + sizeof(uint64_t), &total_mem, sizeof(total_mem)); + break; + } + default: { + fprintf(stderr, "Unknown command: %d\n", cmd); + ok = false; + } + } + if (!ok) { + break; + } + uint64_t output_size = output.size(); + if (!send_data(sockfd, &output_size, sizeof(output_size))) { + break; + } + if (!send_data(sockfd, output.data(), output_size)) { + break; + } + } +} + +void start_rpc_server(ggml_backend_t backend, const char * endpoint, size_t free_mem, size_t total_mem) { + std::string host; + int port; + if (!parse_endpoint(endpoint, host, port)) { + return; + } +#ifdef _WIN32 + { + WSADATA wsaData; + int res = WSAStartup(MAKEWORD(2, 2), &wsaData); + if (res != 0) { + fprintf(stderr, "WSAStartup failed: %d\n", res); + return; + } + } +#endif + auto server_socket = create_server_socket(host.c_str(), port); + if (server_socket == nullptr) { + fprintf(stderr, "Failed to create server socket\n"); + return; + } + while (true) { + auto client_socket = socket_accept(server_socket->fd); + if (client_socket == nullptr) { + fprintf(stderr, "Failed to accept client connection\n"); + return; + } + printf("Accepted client connection, free_mem=%zu, total_mem=%zu\n", free_mem, total_mem); + rpc_serve_client(backend, client_socket->fd, free_mem, total_mem); + printf("Client connection closed\n"); + } +#ifdef _WIN32 + WSACleanup(); +#endif +} diff --git a/ggml-rpc.h b/ggml-rpc.h new file mode 100644 index 0000000000000..aa144832a6e1e --- /dev/null +++ b/ggml-rpc.h @@ -0,0 +1,24 @@ +#pragma once + +#include "ggml.h" +#include "ggml-backend.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define GGML_RPC_MAX_SERVERS 16 + +// backend API +GGML_API GGML_CALL ggml_backend_t ggml_backend_rpc_init(const char * endpoint); +GGML_API GGML_CALL bool ggml_backend_is_rpc(ggml_backend_t backend); + +GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_rpc_buffer_type(const char * endpoint); + +GGML_API GGML_CALL void ggml_backend_rpc_get_device_memory(const char * endpoint, size_t * free, size_t * total); + +GGML_API GGML_CALL void start_rpc_server(ggml_backend_t backend, const char * endpoint, size_t free_mem, size_t total_mem); + +#ifdef __cplusplus +} +#endif diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 79aec4d9f02e1..496ec61c3c28a 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -3154,7 +3154,6 @@ typedef float (*vec_dot_q_mul_mat_sycl_t)( #define SYCL_SCALE_BLOCK_SIZE 256 #define SYCL_CLAMP_BLOCK_SIZE 256 #define SYCL_ROPE_BLOCK_SIZE 256 -#define SYCL_ALIBI_BLOCK_SIZE 32 #define SYCL_DIAG_MASK_INF_BLOCK_SIZE 32 #define SYCL_QUANTIZE_BLOCK_SIZE 256 #define SYCL_DEQUANTIZE_BLOCK_SIZE 256 @@ -3848,21 +3847,27 @@ static void concat_f32(const float *x,const float *y, float *dst, const int ne } } -static void upscale_f32(const float *x, float *dst, const int ne00, const int nb02, const int scale_factor, - const sycl::nd_item<3> &item_ct1) { - int ne0 = ne00 * scale_factor; - int nidx = item_ct1.get_local_id(2) + - item_ct1.get_group(2) * item_ct1.get_local_range(2); - if (nidx >= ne0) { +static void upscale_f32(const float *x, float *dst, const int nb00, const int nb01, + const int nb02, const int nb03, const int ne10, const int ne11, + const int ne12, const int ne13, const float sf0, const float sf1, + const float sf2, const float sf3, const sycl::nd_item<1> &item_ct1) { + int index = item_ct1.get_local_id(0) + + item_ct1.get_group(0) * item_ct1.get_local_range(0); + if (index >= ne10 * ne11 * ne12 * ne13) { return; } // operation - int i00 = nidx / scale_factor; - int i01 = item_ct1.get_group(1) / scale_factor; - int offset_src = i00 + i01 * ne00 + item_ct1.get_group(0) * nb02; - int offset_dst = nidx + item_ct1.get_group(1) * ne0 + - item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1); - dst[offset_dst] = x[offset_src]; + int i10 = index % ne10; + int i11 = (index / ne10) % ne11; + int i12 = (index / (ne10 * ne11)) % ne12; + int i13 = (index / (ne10 * ne11 * ne12)) % ne13; + + int i00 = i10 / sf0; + int i01 = i11 / sf1; + int i02 = i12 / sf2; + int i03 = i13 / sf3; + + dst[index] = *(float *)((char *)x + i03 * nb03 + i02 * nb02 + i01 * nb01 + i00 * nb00); } static void pad_f32(const float *x, float *dst, const int ne0, const int ne00, const int ne01, const int ne02, @@ -4192,7 +4197,6 @@ static void dequantize_block_q2_K(const void * __restrict__ vx, dst_t * __restri const block_q2_K * x = (const block_q2_K *) vx; const int tid = item_ct1.get_local_id(2); -#if QK_K == 256 const int n = tid/32; const int l = tid - 32*n; const int is = 8*n + l/16; @@ -4206,18 +4210,6 @@ static void dequantize_block_q2_K(const void * __restrict__ vx, dst_t * __restri y[l+32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 2) & 3) - dmin * (x[i].scales[is+2] >> 4); y[l+64] = dall * (x[i].scales[is+4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+4] >> 4); y[l+96] = dall * (x[i].scales[is+6] & 0xF) * ((q >> 6) & 3) - dmin * (x[i].scales[is+6] >> 4); -#else - const int is = tid/16; // 0 or 1 - const int il = tid%16; // 0...15 - const uint8_t q = x[i].qs[il] >> (2*is); - dst_t * y = yy + i*QK_K + 16*is + il; - - float dall = x[i].dm[0]; - float dmin = x[i].dm[1]; - y[ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4); - y[32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+2] >> 4); -#endif - } template @@ -4227,7 +4219,6 @@ static void dequantize_block_q3_K(const void * __restrict__ vx, dst_t * __restri const int i = item_ct1.get_group(2); const block_q3_K * x = (const block_q3_K *) vx; -#if QK_K == 256 const int r = item_ct1.get_local_id(2) / 4; const int tid = r/2; const int is0 = r%2; @@ -4251,31 +4242,8 @@ static void dequantize_block_q3_K(const void * __restrict__ vx, dst_t * __restri const uint8_t * hm = x[i].hmask; for (int l = l0; l < l0+4; ++l) y[l] = dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4)); -#else - const int tid = item_ct1.get_local_id(2); - const int is = tid/16; // 0 or 1 - const int il = tid%16; // 0...15 - const int im = il/8; // 0...1 - const int in = il%8; // 0...7 - - dst_t * y = yy + i*QK_K + 16*is + il; - - const uint8_t q = x[i].qs[il] >> (2*is); - const uint8_t h = x[i].hmask[in] >> (2*is + im); - const float d = (float)x[i].d; - - if (is == 0) { - y[ 0] = d * ((x[i].scales[0] & 0xF) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4)); - y[32] = d * ((x[i].scales[1] & 0xF) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4)); - } else { - y[ 0] = d * ((x[i].scales[0] >> 4) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4)); - y[32] = d * ((x[i].scales[1] >> 4) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4)); - } -#endif - } -#if QK_K == 256 static inline void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m) { if (j < 4) { d = q[j] & 63; m = q[j + 4] & 63; @@ -4284,7 +4252,6 @@ static inline void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8 m = (q[j+4] >> 4) | ((q[j-0] >> 6) << 4); } } -#endif template static void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restrict__ yy, @@ -4293,7 +4260,6 @@ static void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restri const int i = item_ct1.get_group(2); -#if QK_K == 256 // assume 32 threads const int tid = item_ct1.get_local_id(2); const int il = tid/8; @@ -4317,15 +4283,6 @@ static void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restri y[l + 0] = d1 * (q[l] & 0xF) - m1; y[l +32] = d2 * (q[l] >> 4) - m2; } -#else - const int tid = item_ct1.get_local_id(2); - const uint8_t * q = x[i].qs; - dst_t * y = yy + i*QK_K; - const float d = (float)x[i].dm[0]; - const float m = (float)x[i].dm[1]; - y[tid+ 0] = d * (x[i].scales[0] & 0xF) * (q[tid] & 0xF) - m * (x[i].scales[0] >> 4); - y[tid+32] = d * (x[i].scales[1] & 0xF) * (q[tid] >> 4) - m * (x[i].scales[1] >> 4); -#endif } template @@ -4335,7 +4292,6 @@ static void dequantize_block_q5_K(const void * __restrict__ vx, dst_t * __restri const int i = item_ct1.get_group(2); -#if QK_K == 256 // assume 64 threads - this is very slightly better than the one below const int tid = item_ct1.get_local_id(2); const int il = tid/16; // il is in 0...3 @@ -4362,18 +4318,6 @@ static void dequantize_block_q5_K(const void * __restrict__ vx, dst_t * __restri hm <<= 1; y[32] = d2 * ((ql[ 0] >> 4) + (qh[ 0] & hm ? 16 : 0)) - m2; y[33] = d2 * ((ql[ 1] >> 4) + (qh[ 1] & hm ? 16 : 0)) - m2; -#else - const int tid = item_ct1.get_local_id(2); - const uint8_t q = x[i].qs[tid]; - const int im = tid/8; // 0...3 - const int in = tid%8; // 0...7 - const int is = tid/16; // 0 or 1 - const uint8_t h = x[i].qh[in] >> im; - const float d = x[i].d; - dst_t * y = yy + i*QK_K + tid; - y[ 0] = d * x[i].scales[is+0] * ((q & 0xF) - ((h >> 0) & 1 ? 0 : 16)); - y[32] = d * x[i].scales[is+2] * ((q >> 4) - ((h >> 4) & 1 ? 0 : 16)); -#endif } template @@ -4382,7 +4326,6 @@ static void dequantize_block_q6_K(const void * __restrict__ vx, dst_t * __restri const block_q6_K * x = (const block_q6_K *) vx; const int i = item_ct1.get_group(2); -#if QK_K == 256 // assume 64 threads - this is very slightly better than the one below const int tid = item_ct1.get_local_id(2); @@ -4402,24 +4345,6 @@ static void dequantize_block_q6_K(const void * __restrict__ vx, dst_t * __restri y[32] = d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32); y[64] = d * sc[4] * ((int8_t)((ql[ 0] >> 4) | (((qh >> 4) & 3) << 4)) - 32); y[96] = d * sc[6] * ((int8_t)((ql[32] >> 4) | (((qh >> 6) & 3) << 4)) - 32); -#else - - // assume 32 threads - const int tid = item_ct1.get_local_id(2); - const int ip = tid/16; // 0 or 1 - const int il = tid - 16*ip; // 0...15 - - dst_t * y = yy + i*QK_K + 16*ip + il; - - const float d = x[i].d; - - const uint8_t ql = x[i].ql[16*ip + il]; - const uint8_t qh = x[i].qh[il] >> (2*ip); - const int8_t * sc = x[i].scales; - - y[ 0] = d * sc[ip+0] * ((int8_t)((ql & 0xF) | (((qh >> 0) & 3) << 4)) - 32); - y[32] = d * sc[ip+2] * ((int8_t)((ql >> 4) | (((qh >> 4) & 3) << 4)) - 32); -#endif } template @@ -4433,7 +4358,6 @@ static void dequantize_block_iq2_xxs(const void * __restrict__ vx, dst_t * __res const block_iq2_xxs * x = (const block_iq2_xxs *) vx; const int tid = item_ct1.get_local_id(2); -#if QK_K == 256 const int il = tid/8; // 0...3 const int ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; @@ -4444,10 +4368,6 @@ static void dequantize_block_iq2_xxs(const void * __restrict__ vx, dst_t * __res const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.25f; const uint8_t signs = ksigns_iq2xs_ptr[(aux32 >> 7*il) & 127]; for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs_ptr[j] ? -1.f : 1.f); -#else - assert(false); -#endif - } template @@ -4461,7 +4381,6 @@ static void dequantize_block_iq2_xs(const void * __restrict__ vx, dst_t * __rest const block_iq2_xs * x = (const block_iq2_xs *) vx; const int tid = item_ct1.get_local_id(2); -#if QK_K == 256 const int il = tid/8; // 0...3 const int ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; @@ -4470,10 +4389,6 @@ static void dequantize_block_iq2_xs(const void * __restrict__ vx, dst_t * __rest const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f; const uint8_t signs = ksigns_iq2xs[q2[il] >> 9]; for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); -#else - assert(false); -#endif - } template @@ -4485,7 +4400,6 @@ dequantize_block_iq2_s(const void *__restrict__ vx, dst_t *__restrict__ yy, const block_iq2_s * x = (const block_iq2_s *) vx; const int tid = item_ct1.get_local_id(2); -#if QK_K == 256 const int il = tid/8; // 0...3 const int ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; @@ -4493,13 +4407,9 @@ dequantize_block_iq2_s(const void *__restrict__ vx, dst_t *__restrict__ yy, const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f; const uint8_t signs = x[i].qs[QK_K/8+4*ib+il]; #pragma unroll - for (int j = 0; j < 8; ++j) + for (int j = 0; j < 8; ++j) { y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); -#else - assert(false); - -#endif - + } } template @@ -4513,7 +4423,6 @@ static void dequantize_block_iq3_xxs(const void * __restrict__ vx, dst_t * __res const block_iq3_xxs * x = (const block_iq3_xxs *) vx; const int tid = item_ct1.get_local_id(2); -#if QK_K == 256 const int il = tid/8; // 0...3 const int ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; @@ -4528,10 +4437,6 @@ static void dequantize_block_iq3_xxs(const void * __restrict__ vx, dst_t * __res y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f); y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f); } -#else - assert(false); -#endif - } template @@ -4544,7 +4449,6 @@ dequantize_block_iq3_s(const void *__restrict__ vx, dst_t *__restrict__ yy, const block_iq3_s * x = (const block_iq3_s *) vx; const int tid = item_ct1.get_local_id(2); -#if QK_K == 256 const int il = tid/8; // 0...3 const int ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; @@ -4558,10 +4462,6 @@ dequantize_block_iq3_s(const void *__restrict__ vx, dst_t *__restrict__ yy, y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f); y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f); } -#else - assert(false); -#endif - } template @@ -4574,7 +4474,6 @@ dequantize_block_iq1_s(const void *__restrict__ vx, dst_t *__restrict__ yy, const block_iq1_s * x = (const block_iq1_s *) vx; const int tid = item_ct1.get_local_id(2); -#if QK_K == 256 const int il = tid/8; // 0...3 const int ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; @@ -4588,10 +4487,6 @@ dequantize_block_iq1_s(const void *__restrict__ vx, dst_t *__restrict__ yy, for (int j = 0; j < 8; ++j) { y[j] = d * (q[j] + delta); } -#else - assert(false); -#endif - } template @@ -4604,7 +4499,6 @@ dequantize_block_iq1_m(const void *__restrict__ vx, dst_t *__restrict__ yy, const block_iq1_m * x = (const block_iq1_m *) vx; const int tid = item_ct1.get_local_id(2); -#if QK_K == 256 const int il = tid/8; // 0...3 const int ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; @@ -4622,10 +4516,6 @@ dequantize_block_iq1_m(const void *__restrict__ vx, dst_t *__restrict__ yy, for (int j = 0; j < 8; ++j) { y[j] = d * (q[j] + delta); } -#else - assert(false); -#endif - } template @@ -4699,7 +4589,6 @@ static void dequantize_mul_mat_vec_q2_k(const void *__restrict__ vx, float tmp = 0; // partial sum for thread in warp -#if QK_K == 256 const int tid = item_ct1.get_local_id(2) / K_QUANTS_PER_ITERATION; // 0...31 or 0...15 const int ix = @@ -4750,42 +4639,6 @@ static void dequantize_mul_mat_vec_q2_k(const void *__restrict__ vx, tmp += dall * sum1 - dmin * sum2; } -#else - const int tid = item_ct1.get_local_id(2) / - (2 * K_QUANTS_PER_ITERATION); // 0...15 or 0...7 - const int ix = item_ct1.get_local_id(2) % - (2 * K_QUANTS_PER_ITERATION); // 0....1 or 0...3 - const int offset = tid * K_QUANTS_PER_ITERATION; - - uint32_t uaux[2]; - const uint8_t * d = (const uint8_t *)uaux; - - - for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { - - const float * y = yy + i * QK_K + offset; - const uint8_t * q = x[i].qs + offset; - const uint32_t * s = (const uint32_t *)x[i].scales; - - uaux[0] = s[0] & 0x0f0f0f0f; - uaux[1] = (s[0] >> 4) & 0x0f0f0f0f; - - const sycl::float2 dall = - x[i].dm.convert(); - - float sum1 = 0, sum2 = 0; - for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { - const uint8_t ql = q[l]; - sum1 += y[l+ 0] * d[0] * ((ql >> 0) & 3) - + y[l+16] * d[1] * ((ql >> 2) & 3) - + y[l+32] * d[2] * ((ql >> 4) & 3) - + y[l+48] * d[3] * ((ql >> 6) & 3); - sum2 += y[l+0] * d[4] + y[l+16] * d[5] + y[l+32] * d[6] + y[l+48] * d[7]; - } - tmp += dall.x() * sum1 - dall.y() * sum2; - } - -#endif // sum up partial sums and write back result #pragma unroll @@ -4823,8 +4676,6 @@ static void dequantize_mul_mat_vec_q3_k(const void *__restrict__ vx, float tmp = 0; // partial sum for thread in warp -#if QK_K == 256 - const uint16_t kmask1 = 0x0303; const uint16_t kmask2 = 0x0f0f; @@ -4877,34 +4728,6 @@ static void dequantize_mul_mat_vec_q3_k(const void *__restrict__ vx, tmp += d * sum; } -#else - - const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION); // 0...15 or 0...7 - const int ix = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION); // 0....1 or 0...3 - const int offset = tid * K_QUANTS_PER_ITERATION; // 0...15 or 0...14 - const int in = offset/8; // 0 or 1 - const int im = offset%8; // 0...7 - - for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { - - const float * y = yy + i * QK_K + offset; - const uint8_t * q = x[i].qs + offset; - const uint8_t * s = x[i].scales; - - const float dall = (float)x[i].d; - - float sum = 0; - for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { - const uint8_t hl = x[i].hmask[im+l] >> in; - const uint8_t ql = q[l]; - sum += y[l+ 0] * dall * ((s[0] & 0xF) - 8) * ((int8_t)((ql >> 0) & 3) - ((hl >> 0) & 1 ? 0 : 4)) - + y[l+16] * dall * ((s[0] >> 4) - 8) * ((int8_t)((ql >> 2) & 3) - ((hl >> 2) & 1 ? 0 : 4)) - + y[l+32] * dall * ((s[1] & 0xF) - 8) * ((int8_t)((ql >> 4) & 3) - ((hl >> 4) & 1 ? 0 : 4)) - + y[l+48] * dall * ((s[1] >> 4) - 8) * ((int8_t)((ql >> 6) & 3) - ((hl >> 6) & 1 ? 0 : 4)); - } - tmp += sum; - } -#endif // sum up partial sums and write back result #pragma unroll @@ -4939,7 +4762,6 @@ static void dequantize_mul_mat_vec_q4_k(const void *__restrict__ vx, const block_q4_K * x = (const block_q4_K *)vx + ib0; -#if QK_K == 256 const uint16_t kmask1 = 0x3f3f; const uint16_t kmask2 = 0x0f0f; const uint16_t kmask3 = 0xc0c0; @@ -5028,36 +4850,6 @@ static void dequantize_mul_mat_vec_q4_k(const void *__restrict__ vx, #endif } -#else - const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION); // 0...15 - const int ix = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION); - - const int step = tid * K_QUANTS_PER_ITERATION; - - uint16_t aux16[2]; - const uint8_t * s = (const uint8_t *)aux16; - - float tmp = 0; - - for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { - const uint8_t * q = x[i].qs + step; - const float * y = yy + i*QK_K + step; - const uint16_t * a = (const uint16_t *)x[i].scales; - aux16[0] = a[0] & 0x0f0f; - aux16[1] = (a[0] >> 4) & 0x0f0f; - const float d = (float)x[i].dm[0]; - const float m = (float)x[i].dm[1]; - float sum = 0.f; - for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) { - sum += y[j+ 0] * (d * s[0] * (q[j+ 0] & 0xF) - m * s[2]) - + y[j+16] * (d * s[0] * (q[j+16] & 0xF) - m * s[2]) - + y[j+32] * (d * s[1] * (q[j+ 0] >> 4) - m * s[3]) - + y[j+48] * (d * s[1] * (q[j+16] >> 4) - m * s[3]); - } - tmp += sum; - } - -#endif // sum up partial sums and write back result #pragma unroll @@ -5092,7 +4884,6 @@ static void dequantize_mul_mat_vec_q5_k(const void *__restrict__ vx, float tmp = 0; // partial sum for thread in warp -#if QK_K == 256 const uint16_t kmask1 = 0x3f3f; const uint16_t kmask2 = 0x0f0f; const uint16_t kmask3 = 0xc0c0; @@ -5169,30 +4960,6 @@ static void dequantize_mul_mat_vec_q5_k(const void *__restrict__ vx, dmin * smin; } -#else - const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION); // 0...15 - const int ix = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION); - const int step = tid * K_QUANTS_PER_ITERATION; - const int im = step/8; - const int in = step%8; - - for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { - const uint8_t * q = x[i].qs + step; - const int8_t * s = x[i].scales; - const float * y = yy + i*QK_K + step; - const float d = x[i].d; - float sum = 0.f; - for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) { - const uint8_t h = x[i].qh[in+j] >> im; - sum += y[j+ 0] * d * s[0] * ((q[j+ 0] & 0xF) - ((h >> 0) & 1 ? 0 : 16)) - + y[j+16] * d * s[1] * ((q[j+16] & 0xF) - ((h >> 2) & 1 ? 0 : 16)) - + y[j+32] * d * s[2] * ((q[j+ 0] >> 4) - ((h >> 4) & 1 ? 0 : 16)) - + y[j+48] * d * s[3] * ((q[j+16] >> 4) - ((h >> 6) & 1 ? 0 : 16)); - } - tmp += sum; - } -#endif - // sum up partial sums and write back result #pragma unroll for (int mask = 16; mask > 0; mask >>= 1) { @@ -5219,8 +4986,6 @@ static void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, const floa const block_q6_K * x = (const block_q6_K *)vx + ib0; -#if QK_K == 256 - const int tid = item_ct1.get_local_id(2) / K_QUANTS_PER_ITERATION; // 0...31 or 0...16 const int ix = @@ -5277,37 +5042,6 @@ static void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, const floa } -#else - - const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION); // 0...7 - const int ix = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION); // 0...3 - - const int step = tid * K_QUANTS_PER_ITERATION; - - float tmp = 0; // partial sum for thread in warp - - for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { - - const float * y = yy + i * QK_K + step; - const uint8_t * ql = x[i].ql + step; - const uint8_t * qh = x[i].qh + step; - const int8_t * s = x[i].scales; - - const float d = x[i+0].d; - - float sum = 0; - for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) { - sum += y[j+ 0] * s[0] * d * ((int8_t)((ql[j+ 0] & 0xF) | ((qh[j] & 0x03) << 4)) - 32) - + y[j+16] * s[1] * d * ((int8_t)((ql[j+16] & 0xF) | ((qh[j] & 0x0c) << 2)) - 32) - + y[j+32] * s[2] * d * ((int8_t)((ql[j+ 0] >> 4) | ((qh[j] & 0x30) >> 0)) - 32) - + y[j+48] * s[3] * d * ((int8_t)((ql[j+16] >> 4) | ((qh[j] & 0xc0) >> 2)) - 32); - } - tmp += sum; - - } - -#endif - // sum up partial sums and write back result #pragma unroll for (int mask = 16; mask > 0; mask >>= 1) { @@ -6852,7 +6586,6 @@ static __dpct_inline__ float vec_dot_q4_K_q8_1(const void *__restrict__ vbq, const block_q8_1 *__restrict__ bq8_1, const int &iqs) { -#ifndef GGML_QKK_64 const block_q4_K * bq4_K = (const block_q4_K *) vbq; int v[2]; @@ -6894,52 +6627,6 @@ vec_dot_q4_K_q8_1(const void *__restrict__ vbq, } return vec_dot_q4_K_q8_1_impl_vmmq(v, u, sc, m, bq4_K->dm, d8); - -#else - -#if __SYCL_ARCH__ >= VER_4VEC // lowest compute capability for integer intrinsics - const block_q4_K * bq4_K = (const block_q4_K *) vbq; - - float sumf_d = 0.0f; - float sumf_m = 0.0f; - - uint16_t aux16[2]; - const uint8_t * s = (const uint8_t *)aux16; - - const uint16_t * a = (const uint16_t *)bq4_K->scales; - aux16[0] = a[0] & 0x0f0f; - aux16[1] = (a[0] >> 4) & 0x0f0f; - - const float dall = bq4_K->dm[0]; - const float dmin = bq4_K->dm[1]; - - const float d8_1 = bq8_1[0].ds[0]; - const float d8_2 = bq8_1[1].ds[1]; - - const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2)); - const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4); - const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2)); - const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4); - - const int * q4 = (const int *)bq4_K->qs + (iqs/2); - const int v1 = q4[0]; - const int v2 = q4[4]; - - const int dot1 = dpct::dp4a(ui2, v2 & 0x0f0f0f0f, dpct::dp4a(ui1, v1 & 0x0f0f0f0f, 0)); - const int dot2 = dpct::dp4a(ui4, (v2 >> 4) & 0x0f0f0f0f, dpct::dp4a(ui3, (v1 >> 4) & 0x0f0f0f0f, 0)); - const int dot3 = dpct::dp4a(0x01010101, ui2, dpct::dp4a(0x01010101, ui1, 0)); - const int dot4 = dpct::dp4a(0x01010101, ui4, dpct::dp4a(0x01010101, ui3, 0)); - - sumf_d += d8_1 * (dot1 * s[0]) + d8_2 * (dot2 * s[1]); - sumf_m += d8_1 * (dot3 * s[2]) + d8_2 * (dot4 * s[3]); - - return dall * sumf_d - dmin * sumf_m; - -#else - bad_arch(); -#endif // __SYCL_ARCH__ >= VER_4VEC - -#endif } template @@ -6998,11 +6685,7 @@ load_tiles_q4_K(const void *__restrict__ vx, int *__restrict__ x_ql, const block_q4_K * bxi = bx0 + i*blocks_per_row + kbxd; -#if QK_K == 256 x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = bxi->dm; -#else - x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = {bxi->dm[0], bxi->dm[1]}; -#endif } #pragma unroll @@ -7045,7 +6728,6 @@ static __dpct_inline__ float vec_dot_q5_K_q8_1(const void *__restrict__ vbq, const block_q8_1 *__restrict__ bq8_1, const int &iqs) { -#ifndef GGML_QKK_64 const block_q5_K * bq5_K = (const block_q5_K *) vbq; int vl[2]; @@ -7087,48 +6769,6 @@ vec_dot_q5_K_q8_1(const void *__restrict__ vbq, } return vec_dot_q5_K_q8_1_impl_vmmq(vl, vh, u, sc, m, bq5_K->dm, d8); - -#else - -#if __SYCL_ARCH__ >= VER_4VEC // lowest compute capability for integer intrinsics - const block_q5_K * bq5_K = (const block_q5_K *) vbq; - - const int8_t * s = bq5_K->scales; - - const float d = bq5_K->d; - - const float d8_1 = bq8_1[0].ds[0]; - const float d8_2 = bq8_1[1].ds[1]; - - const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2)); - const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4); - const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2)); - const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4); - - const int * ql = (const int *)bq5_K->qs + (iqs/2); - const int vl1 = ql[0]; - const int vl2 = ql[4]; - - const int step = 4 * (iqs/2); // 0, 4, 8, 12 - const int im = step/8; // = 0 for iqs = 0, 2, = 1 for iqs = 4, 6 - const int in = step%8; // 0, 4, 0, 4 - const int vh = (*((const int *)(bq5_K->qh + in))) >> im; - - const int v1 = (((vh << 4) & 0x10101010) ^ 0x10101010) | ((vl1 >> 0) & 0x0f0f0f0f); - const int v2 = (((vh << 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 0) & 0x0f0f0f0f); - const int v3 = (((vh >> 0) & 0x10101010) ^ 0x10101010) | ((vl1 >> 4) & 0x0f0f0f0f); - const int v4 = (((vh >> 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 4) & 0x0f0f0f0f); - - const float sumf_d = d8_1 * (dpct::dp4a(ui1, v1, 0) * s[0] + dpct::dp4a(ui2, v2, 0) * s[1]) - + d8_2 * (dpct::dp4a(ui3, v3, 0) * s[2] + dpct::dp4a(ui4, v4, 0) * s[3]); - - return d * sumf_d; - -#else - bad_arch(); -#endif // __SYCL_ARCH__ >= VER_4VEC - -#endif } template @@ -7200,9 +6840,7 @@ load_tiles_q5_K(const void *__restrict__ vx, int *__restrict__ x_ql, const block_q5_K * bxi = bx0 + i*blocks_per_row + kbxd; -#if QK_K == 256 x_dm[i * (WARP_SIZE/QI5_K) + i / QI5_K + kbxd] = bxi->dm; -#endif } #pragma unroll @@ -7382,7 +7020,6 @@ vec_dot_iq2_xxs_q8_1(const void *__restrict__ vbq, const block_q8_1 *__restrict__ bq8_1, const int &iqs, const uint64_t *iq2xxs_grid, const uint8_t *ksigns_iq2xs, const uint8_t *kmask_iq2xs) { -#if QK_K == 256 const block_iq2_xxs * bq2 = (const block_iq2_xxs *) vbq; #if QR2_XXS == 8 @@ -7423,10 +7060,6 @@ vec_dot_iq2_xxs_q8_1(const void *__restrict__ vbq, } return d * (sumi1 + sumi2); #endif -#else - assert(false); - return 0.f; -#endif } static __dpct_inline__ float @@ -7435,7 +7068,6 @@ vec_dot_iq2_xs_q8_1(const void *__restrict__ vbq, const uint64_t *iq2xs_grid, const uint64_t *ksigns64) { #if DPCT_COMPATIBILITY_TEMP >= \ MIN_CC_DP4A // lowest compute capability for integer intrinsics -#if QK_K == 256 const block_iq2_xs * bq2 = (const block_iq2_xs *) vbq; const int ib32 = iqs; @@ -7473,16 +7105,11 @@ vec_dot_iq2_xs_q8_1(const void *__restrict__ vbq, assert(false); return 0.f; #endif -#else - assert(false); - return 0.f; -#endif } static __dpct_inline__ float vec_dot_iq2_s_q8_1(const void *__restrict__ vbq, const block_q8_1 *__restrict__ bq8_1, const int &iqs) { -#if QK_K == 256 const block_iq2_s * bq2 = (const block_iq2_s *) vbq; const int ib32 = iqs; @@ -7526,9 +7153,6 @@ vec_dot_iq2_s_q8_1(const void *__restrict__ vbq, } const float d = (float)bq2->d * bq8_1[ib32].ds[0] * 0.25f; return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2); -#else - assert(false); -#endif } static __dpct_inline__ float @@ -7537,7 +7161,6 @@ vec_dot_iq3_xxs_q8_1(const void *__restrict__ vbq, const uint32_t *iq3xxs_grid, const uint64_t *ksigns64) { #if DPCT_COMPATIBILITY_TEMP >= \ MIN_CC_DP4A // lowest compute capability for integer intrinsics -#if QK_K == 256 const block_iq3_xxs * bq2 = (const block_iq3_xxs *) vbq; const int ib32 = iqs; @@ -7565,17 +7188,12 @@ vec_dot_iq3_xxs_q8_1(const void *__restrict__ vbq, assert(false); return 0.f; #endif -#else - assert(false); - return 0.f; -#endif } static __dpct_inline__ float vec_dot_iq3_s_q8_1(const void *__restrict__ vbq, const block_q8_1 *__restrict__ bq8_1, const int &iqs, const uint32_t *iq3s_grid) { -#if QK_K == 256 const block_iq3_s * bq2 = (const block_iq3_s *) vbq; const int ib32 = iqs; @@ -7604,16 +7222,12 @@ vec_dot_iq3_s_q8_1(const void *__restrict__ vbq, (1 + 2 * ((bq2->scales[ib32 / 2] >> 4 * (ib32 % 2)) & 0xf)) * bq8_1[ib32].ds[0]; return d * sumi; -#else - assert(false); -#endif } static __dpct_inline__ float vec_dot_iq1_s_q8_1(const void *__restrict__ vbq, const block_q8_1 *__restrict__ bq8_1, const int &iqs, const uint32_t *iq1s_grid_gpu) { -#if QK_K == 256 const block_iq1_s * bq1 = (const block_iq1_s *) vbq; const int ib32 = iqs; @@ -7632,15 +7246,11 @@ vec_dot_iq1_s_q8_1(const void *__restrict__ vbq, const float d = d1q * bq8_1[ib32].ds[0]; const float m = d1q * bq8_1[ib32].ds[1]; return d * sumi + m * delta; -#else - assert(false); -#endif } static __dpct_inline__ float vec_dot_iq1_m_q8_1(const void *__restrict__ vbq, const block_q8_1 *__restrict__ bq8_1, const int &iqs) { -#if QK_K == 256 const block_iq1_m * bq1 = (const block_iq1_m *) vbq; const int ib32 = iqs; @@ -7665,9 +7275,6 @@ vec_dot_iq1_m_q8_1(const void *__restrict__ vbq, scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); const float d = (float)scale.f16 * bq8_1[ib32].ds[0]; return d * ((sumi[0] + sumf[0]) * (2*((sc[ib32/2] >> 6*(ib32%2)) & 0x7) + 1) + (sumi[1] + sumf[1]) * (2*((sc[ib32/2] >> (6*(ib32%2)+3)) & 0x7) + 1)); -#else - assert(false); -#endif } static __dpct_inline__ void get_int_from_table_16(const uint32_t &q4, @@ -7715,7 +7322,6 @@ static __dpct_inline__ float vec_dot_iq4_xs_q8_1(const void *__restrict__ vbq, const block_q8_1 *__restrict__ bq8_1, const int &iqs) { -#if QK_K == 256 const block_iq4_xs * bq4 = (const block_iq4_xs *) vbq; const uint8_t * values = (const uint8_t *)kvalues_iq4nl; @@ -7733,9 +7339,6 @@ vec_dot_iq4_xs_q8_1(const void *__restrict__ vbq, sumi2 = dpct::dp4a(v2, q8[j + 4], sumi2); } return d * (sumi1 + sumi2); -#else - assert(false); -#endif } template &item_ct1) { - const int col = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - item_ct1.get_local_id(2); - - if (col >= ncols) { - return; - } - - const int row = item_ct1.get_local_range(1) * item_ct1.get_group(1) + - item_ct1.get_local_id(1); - const int i = row*ncols + col; - - const int k = row/k_rows; - - float m_k; - if (k < n_heads_log2_floor) { - m_k = dpct::pow(m0, k + 1); - } else { - m_k = dpct::pow(m1, 2 * (k - n_heads_log2_floor) + 1); - } - - dst[i] = col * m_k + x[i]; -} - static void k_sum_rows_f32(const float * x, float * dst, const int ncols, const sycl::nd_item<3> &item_ct1) { const int row = item_ct1.get_group(1); @@ -9443,7 +9020,7 @@ static void diag_mask_inf_f32(const float * x, float * dst, const int ncols, con template -static void soft_max_f32(const float * x, const float * mask, const float *pos, float * dst, const int ncols_par, +static void soft_max_f32(const float * x, const float * mask, float * dst, const int ncols_par, const int nrows_y, const float scale, const float max_bias, const float m0, const float m1, uint32_t n_head_log2, const sycl::nd_item<3> &item_ct1, float *buf) { const int ncols = ncols_template == 0 ? ncols_par : ncols_template; @@ -9457,7 +9034,7 @@ static void soft_max_f32(const float * x, const float * mask, const float *pos, const int warp_id = item_ct1.get_local_id(2) / WARP_SIZE; const int lane_id = item_ct1.get_local_id(2) % WARP_SIZE; - float slope = 0.0f; + float slope = 1.0f; // ALiBi if (max_bias > 0.0f) { @@ -9482,7 +9059,7 @@ static void soft_max_f32(const float * x, const float * mask, const float *pos, const int ix = rowx*ncols + col; const int iy = rowy*ncols + col; - const float val = x[ix]*scale + (mask ? mask[iy] : 0.0f) + (pos ? slope*pos[col] : 0.0f); + const float val = x[ix]*scale + (mask ? slope*mask[iy] : 0.0f); vals[col] = val; max_val = sycl::max(max_val, val); @@ -10112,18 +9689,17 @@ static void concat_f32_sycl(const float *x, const float *y, float *dst, }); } -static void upscale_f32_sycl(const float *x, float *dst, const int ne00, - const int ne01, const int ne02, - const int scale_factor, dpct::queue_ptr stream) { - int ne0 = (ne00 * scale_factor); - int num_blocks = (ne0 + SYCL_UPSCALE_BLOCK_SIZE - 1) / SYCL_UPSCALE_BLOCK_SIZE; - sycl::range<3> gridDim(ne02, (ne01 * scale_factor), num_blocks); +static void upscale_f32_sycl(const float *x, float *dst, const int nb00, const int nb01, + const int nb02, const int nb03, const int ne10, const int ne11, + const int ne12, const int ne13, const float sf0, const float sf1, + const float sf2, const float sf3, dpct::queue_ptr stream) { + int dst_size = ne10 * ne11 * ne12 * ne13; + int num_blocks = (dst_size + SYCL_UPSCALE_BLOCK_SIZE - 1) / SYCL_UPSCALE_BLOCK_SIZE; + sycl::range<1> gridDim(num_blocks * SYCL_UPSCALE_BLOCK_SIZE); stream->parallel_for( - sycl::nd_range<3>(gridDim * - sycl::range<3>(1, 1, SYCL_UPSCALE_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_UPSCALE_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - upscale_f32(x, dst, ne00, ne00 * ne01, scale_factor, item_ct1); + sycl::nd_range<1>(gridDim, sycl::range<1>(SYCL_UPSCALE_BLOCK_SIZE)), + [=](sycl::nd_item<1> item_ct1) { + upscale_f32(x, dst, nb00, nb01, nb02, nb03, ne10, ne11, ne12, ne13, sf0, sf1, sf2, sf3, item_ct1); }); } @@ -10225,7 +9801,6 @@ template static void dequantize_row_q2_K_sycl(const void *vx, dst_t *y, const int k, dpct::queue_ptr stream) { const int nb = k / QK_K; -#if QK_K == 256 { dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); @@ -10237,27 +9812,12 @@ static void dequantize_row_q2_K_sycl(const void *vx, dst_t *y, const int k, dequantize_block_q2_K(vx, y, item_ct1); }); } -#else - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * - sycl::range<3>(1, 1, 32), - sycl::range<3>(1, 1, 32)), - [=](sycl::nd_item<3> item_ct1) { - dequantize_block_q2_K(vx, y, item_ct1); - }); - } - -#endif } template static void dequantize_row_q3_K_sycl(const void *vx, dst_t *y, const int k, dpct::queue_ptr stream) { const int nb = k / QK_K; -#if QK_K == 256 { dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); @@ -10269,19 +9829,6 @@ static void dequantize_row_q3_K_sycl(const void *vx, dst_t *y, const int k, dequantize_block_q3_K(vx, y, item_ct1); }); } -#else - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * - sycl::range<3>(1, 1, 32), - sycl::range<3>(1, 1, 32)), - [=](sycl::nd_item<3> item_ct1) { - dequantize_block_q3_K(vx, y, item_ct1); - }); - } -#endif } template @@ -10342,7 +9889,6 @@ template static void dequantize_row_q5_K_sycl(const void *vx, dst_t *y, const int k, dpct::queue_ptr stream) { const int nb = k / QK_K; -#if QK_K == 256 { dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); @@ -10354,27 +9900,12 @@ static void dequantize_row_q5_K_sycl(const void *vx, dst_t *y, const int k, dequantize_block_q5_K(vx, y, item_ct1); }); } -#else - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * - sycl::range<3>(1, 1, 32), - sycl::range<3>(1, 1, 32)), - [=](sycl::nd_item<3> item_ct1) { - dequantize_block_q5_K(vx, y, item_ct1); - }); - } - -#endif } template static void dequantize_row_q6_K_sycl(const void *vx, dst_t *y, const int k, dpct::queue_ptr stream) { const int nb = k / QK_K; -#if QK_K == 256 { dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); @@ -10386,20 +9917,6 @@ static void dequantize_row_q6_K_sycl(const void *vx, dst_t *y, const int k, dequantize_block_q6_K(vx, y, item_ct1); }); } -#else - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * - sycl::range<3>(1, 1, 32), - sycl::range<3>(1, 1, 32)), - [=](sycl::nd_item<3> item_ct1) { - dequantize_block_q6_K(vx, y, item_ct1); - }); - } - -#endif } template @@ -10551,9 +10068,6 @@ template static void dequantize_row_iq4_xs_sycl(const void *vx, dst_t *y, const int k, dpct::queue_ptr stream) { const int nb = (k + QK_K - 1) / QK_K; -#if QK_K == 64 - dequantize_row_iq4_nl_sycl(vx, y, k, stream); -#else { dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); @@ -10568,7 +10082,6 @@ static void dequantize_row_iq4_xs_sycl(const void *vx, dst_t *y, const int k, }); }); } -#endif } @@ -12073,8 +11586,6 @@ static void ggml_mul_mat_q3_K_q8_1_sycl(const void *vx, const void *vy, const int nrows_y, const int nrows_dst, dpct::queue_ptr stream) try { -#if QK_K == 256 - int id; SYCL_CHECK( CHECK_TRY_ERROR(id = get_current_device_id())); @@ -12189,7 +11700,6 @@ static void ggml_mul_mat_q3_K_q8_1_sycl(const void *vx, const void *vy, }); } } -#endif } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ @@ -12964,20 +12474,6 @@ static void rope_glm_f32_sycl(const float *x, float *dst, int ncols, int nrows, }); } -static void alibi_f32_sycl(const float *x, float *dst, const int ncols, - const int nrows, const int k_rows, - const int n_heads_log2_floor, const float m0, - const float m1, dpct::queue_ptr stream) { - const sycl::range<3> block_dims(1, 1, SYCL_ALIBI_BLOCK_SIZE); - const int num_blocks_x = (ncols + SYCL_ALIBI_BLOCK_SIZE - 1) / (SYCL_ALIBI_BLOCK_SIZE); - const sycl::range<3> block_nums(1, nrows, num_blocks_x); - stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - alibi_f32(x, dst, ncols, k_rows, - n_heads_log2_floor, m0, m1, item_ct1); - }); -} - static void sum_rows_f32_sycl(const float *x, float *dst, const int ncols, const int nrows, dpct::queue_ptr stream) { const sycl::range<3> block_dims(1, 1, WARP_SIZE); @@ -13058,7 +12554,7 @@ static void diag_mask_inf_f32_sycl(const float *x, float *dst, } template -static void soft_max_f32_submitter(const float * x, const float * mask, const float *pos, float * dst, const int ncols_par, +static void soft_max_f32_submitter(const float * x, const float * mask, float * dst, const int ncols_par, const int nrows_y, const float scale, const float max_bias, const float m0, const float m1, uint32_t n_head_log2, sycl::range<3> block_nums, sycl::range<3> block_dims, const size_t n_local_scratch, dpct::queue_ptr stream) { @@ -13068,7 +12564,7 @@ static void soft_max_f32_submitter(const float * x, const float * mask, const fl cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { - soft_max_f32(x, mask, pos, dst, ncols_par, + soft_max_f32(x, mask, dst, ncols_par, nrows_y, scale, max_bias, m0, m1, n_head_log2, item_ct1, local_buf_acc.get_pointer()); @@ -13076,7 +12572,7 @@ static void soft_max_f32_submitter(const float * x, const float * mask, const fl }); } -static void soft_max_f32_sycl(const float * x, const float * mask, const float * pos, +static void soft_max_f32_sycl(const float * x, const float * mask, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const float scale, const float max_bias, dpct::queue_ptr stream) { @@ -13098,60 +12594,60 @@ static void soft_max_f32_sycl(const float * x, const float * mask, const float * const size_t local_mem_size = stream->get_device().get_info(); if (n_local_scratch*sizeof(float) < local_mem_size) { if (ncols_x > max_block_size) { - soft_max_f32_submitter(x, mask, pos, dst, ncols_x, nrows_y, scale, + soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2, block_nums, block_dims, n_local_scratch, stream); return; } switch (ncols_x) { case 32: - soft_max_f32_submitter(x, mask, pos, dst, ncols_x, nrows_y, scale, + soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2, block_nums, block_dims, n_local_scratch, stream); break; case 64: - soft_max_f32_submitter(x, mask, pos, dst, ncols_x, nrows_y, scale, + soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2, block_nums, block_dims, n_local_scratch, stream); break; case 128: - soft_max_f32_submitter(x, mask, pos, dst, ncols_x, nrows_y, scale, + soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2, block_nums, block_dims, n_local_scratch, stream); break; case 256: - soft_max_f32_submitter(x, mask, pos, dst, ncols_x, nrows_y, scale, + soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2, block_nums, block_dims, n_local_scratch, stream); break; case 512: - soft_max_f32_submitter(x, mask, pos, dst, ncols_x, nrows_y, scale, + soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2, block_nums, block_dims, n_local_scratch, stream); break; case 1024: - soft_max_f32_submitter(x, mask, pos, dst, ncols_x, nrows_y, scale, + soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2, block_nums, block_dims, n_local_scratch, stream); break; case 2048: - soft_max_f32_submitter(x, mask, pos, dst, ncols_x, nrows_y, scale, + soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2, block_nums, block_dims, n_local_scratch, stream); break; case 4096: - soft_max_f32_submitter(x, mask, pos, dst, ncols_x, nrows_y, scale, + soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2, block_nums, block_dims, n_local_scratch, stream); break; default: - soft_max_f32_submitter(x, mask, pos, dst, ncols_x, nrows_y, scale, + soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2, block_nums, block_dims, n_local_scratch, stream); break; } } else { - soft_max_f32_submitter(x, mask, pos, dst, ncols_x, nrows_y, scale, + soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2, block_nums, block_dims, WARP_SIZE, stream); } @@ -14026,11 +13522,15 @@ inline void ggml_sycl_op_upscale(const ggml_tensor *src0, GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT(dst->type == GGML_TYPE_F32); - GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors - const int scale_factor = dst->op_params[0]; + const float sf0 = (float)dst->ne[0]/src0->ne[0]; + const float sf1 = (float)dst->ne[1]/src0->ne[1]; + const float sf2 = (float)dst->ne[2]/src0->ne[2]; + const float sf3 = (float)dst->ne[3]/src0->ne[3]; - upscale_f32_sycl(src0_dd, dst_dd, src0->ne[0], src0->ne[1], src0->ne[2], scale_factor, main_stream); + upscale_f32_sycl(src0_dd, dst_dd, src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3], + dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], sf0, sf1, sf2, sf3, + main_stream); (void) src1; (void) dst; @@ -14486,6 +13986,9 @@ inline void ggml_sycl_op_rope(const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, float *dst_dd, const dpct::queue_ptr &main_stream) { +#pragma message("TODO: implement phi3 frequency factors support") +#pragma message(" https://github.com/ggerganov/llama.cpp/pull/7225") + GGML_ASSERT(dst->src[2] == nullptr && "phi3 frequency factors not implemented yet"); GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); GGML_ASSERT( dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16); @@ -14562,36 +14065,6 @@ inline void ggml_sycl_op_rope(const ggml_tensor *src0, const ggml_tensor *src1, (void) src1_dd; } -inline void ggml_sycl_op_alibi(const ggml_tensor *src0, const ggml_tensor *src1, - ggml_tensor *dst, const float *src0_dd, - const float *src1_dd, float *dst_dd, - const dpct::queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - GGML_TENSOR_LOCALS_3(int64_t, ne0, src0, ne); - const int64_t nrows = ggml_nrows(src0); - - //const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_head = ((int32_t *) dst->op_params)[1]; - float max_bias; - memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float)); - - //GGML_ASSERT(ne01 + n_past == ne00); - GGML_ASSERT(n_head == ne02); - - const int n_heads_log2_floor = 1 << (int) floor(log2(n_head)); - - const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor); - const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor); - - alibi_f32_sycl(src0_dd, dst_dd, ne00, nrows, ne01, n_heads_log2_floor, m0, m1, main_stream); - - (void) src1; - (void) src1_dd; -} - static void ggml_sycl_op_pool2d(const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, @@ -14746,12 +14219,9 @@ inline void ggml_sycl_op_soft_max(const ggml_tensor *src0, GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); - const ggml_tensor * src2 = dst->src[2]; - -#pragma message("TODO: add ggml_sycl_op_soft_max() F16 src1 and src2 support") +#pragma message("TODO: add ggml_sycl_op_soft_max() F16 src1 support") #pragma message("ref: https://github.com/ggerganov/llama.cpp/pull/5021") GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32); // src1 contains mask and it is optional - GGML_ASSERT(!src2 || src2->type == GGML_TYPE_F32); // src2 contains positions and it is optional const int64_t ne00 = src0->ne[0]; const int64_t nrows_x = ggml_nrows(src0); @@ -14763,25 +14233,7 @@ inline void ggml_sycl_op_soft_max(const ggml_tensor *src0, memcpy(&scale, dst->op_params + 0, sizeof(float)); memcpy(&max_bias, dst->op_params + 1, sizeof(float)); - // positions tensor - float * src2_dd = nullptr; - sycl_pool_alloc src2_f; - - const bool use_src2 = src2 != nullptr; - - if (use_src2) { - const bool src2_on_device = src2->backend == GGML_BACKEND_TYPE_GPU; - - if (src2_on_device) { - ggml_tensor_extra_gpu * src2_extra = (ggml_tensor_extra_gpu *) src2->extra; - src2_dd = (float *) src2_extra->data_device[g_main_device]; - } else { - src2_dd = src2_f.alloc(ggml_nelements(src2)); - SYCL_CHECK(ggml_sycl_cpy_tensor_2d(src2_dd, src2, 0, 0, 0, 1, main_stream)); - } - } - - soft_max_f32_sycl(src0_dd, src1 ? src1_dd : nullptr, src2_dd, dst_dd, ne00, + soft_max_f32_sycl(src0_dd, src1 ? src1_dd : nullptr, dst_dd, ne00, nrows_x, nrows_y, scale, max_bias, main_stream); } @@ -15656,26 +15108,6 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, const int64_t r2 = ne12/ne02; const int64_t r3 = ne13/ne03; -#if 0 - // use syclGemmEx - { - for (int i13 = 0; i13 < ne13; ++i13) { - for (int i12 = 0; i12 < ne12; ++i12) { - int i03 = i13 / r3; - int i02 = i12 / r2; - - SYCL_CHECK( - syclGemmEx(g_sycl_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N, - ne01, ne11, ne10, - alpha, (const char *) src0_as_f16 + i02*src0->nb[2] + i03*src0->nb[3] , SYCL_R_16F, nb01/sizeof(half), - (const char *) src1_as_f16 + i12*src1->nb[2]/2 + i13*src1->nb[3]/2, SYCL_R_16F, nb11/sizeof(float), - beta, ( char *) dst_t + i12*nbd2 + i13*nbd3, cu_data_type, ne01, - cu_compute_type, - CUBLAS_GEMM_DEFAULT_TENSOR_OP)); - } - } - } -#else if (r2 == 1 && r3 == 1 && src0->nb[2]*src0->ne[2] == src0->nb[3] && src1->nb[2]*src1->ne[2] == src1->nb[3]) { // there is no broadcast and src0, src1 are contiguous across dims 2, 3 SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch( @@ -15687,7 +15119,6 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, nb11 / nb10, nb12 / nb10, beta, (char *)dst_t, cu_data_type, ne01, nb2 / nb0, ne12 * ne13, cu_compute_type))); - g_sycl_handles[g_main_device]->wait(); } else { const int ne23 = ne12*ne13; @@ -15718,7 +15149,7 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, nb02, nb03, nb12_scaled, nb13_scaled, nbd2, nbd3, r2, r3, item_ct1); }); - }).wait(); + }); } SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch( *g_sycl_handles[g_main_device], oneapi::mkl::transpose::trans, @@ -15729,9 +15160,7 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, dpct::library_data_t::real_half, nb11 / nb10, beta, (void **)(ptrs_dst.get() + 0 * ne23), cu_data_type, ne01, ne23, cu_compute_type))); - g_sycl_handles[g_main_device]->wait(); } -#endif if (no_mixed_dtypes) { const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16); @@ -16232,10 +15661,6 @@ static void ggml_sycl_rope(const ggml_tensor * src0, const ggml_tensor * src1, g ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_rope); } -static void ggml_sycl_alibi(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_alibi); -} - static void ggml_sycl_pool2d(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_pool2d); } @@ -16612,9 +16037,6 @@ bool ggml_sycl_compute_forward(struct ggml_compute_params * params, struct ggml_ case GGML_OP_ROPE: func = ggml_sycl_rope; break; - case GGML_OP_ALIBI: - func = ggml_sycl_alibi; - break; case GGML_OP_IM2COL: func = ggml_sycl_im2col; break; @@ -17744,7 +17166,6 @@ GGML_CALL static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, cons case GGML_OP_DIAG_MASK_INF: case GGML_OP_SOFT_MAX: case GGML_OP_ROPE: - case GGML_OP_ALIBI: case GGML_OP_IM2COL: case GGML_OP_POOL_2D: case GGML_OP_SUM_ROWS: diff --git a/ggml-vulkan-shaders.hpp b/ggml-vulkan-shaders.hpp index d3a2fcf388b6a..e8cb5f52cdb30 100644 --- a/ggml-vulkan-shaders.hpp +++ b/ggml-vulkan-shaders.hpp @@ -363,320 +363,349 @@ const uint64_t add_f32_len = 4276; unsigned char argsort_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x2b,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x41,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, 0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30, 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, 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+0xf9,0x00,0x02,0x00,0x0e,0x01,0x00,0x00,0xf8,0x00,0x02,0x00, +0x0e,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x1d,0x01,0x00,0x00, +0xf8,0x00,0x02,0x00,0x1d,0x01,0x00,0x00,0xfd,0x00,0x01,0x00, +0x38,0x00,0x01,0x00, }; -const uint64_t argsort_f32_len = 3752; +const uint64_t argsort_f32_len = 4096; unsigned char clamp_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, @@ -11994,7 +12023,7 @@ const uint64_t matmul_f16_len = 10316; unsigned char matmul_f16_aligned_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xf5,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x08,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, 0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, @@ -12002,9 +12031,9 @@ unsigned char matmul_f16_aligned_data[] = { 0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00, 0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, 0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00, -0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0xfc,0x00,0x00,0x00, -0x07,0x01,0x00,0x00,0x49,0x01,0x00,0x00,0x51,0x01,0x00,0x00, -0x55,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,0x10,0x00,0x06,0x00, +0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0xfe,0x00,0x00,0x00, +0x05,0x01,0x00,0x00,0x63,0x01,0x00,0x00,0x69,0x01,0x00,0x00, +0x68,0x02,0x00,0x00,0xb1,0x02,0x00,0x00,0x10,0x00,0x06,0x00, 0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00, @@ -12047,37 +12076,40 @@ unsigned char matmul_f16_aligned_data[] = { 0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x04,0x01,0x00,0x00,0x06,0x00,0x00,0x00, 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-0x9c,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x9c,0x02,0x00,0x00, -0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x9e,0x02,0x00,0x00, +0xaf,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xaf,0x02,0x00,0x00, +0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb1,0x02,0x00,0x00, 0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x9e,0x02,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00, +0xb1,0x02,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00, 0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00, @@ -12128,16 +12160,16 @@ unsigned char matmul_f16_aligned_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x68,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 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-0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x87,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, @@ -12180,727 +12212,756 @@ unsigned char matmul_f16_aligned_data[] = { 0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xcd,0x00,0x00,0x00, 0x07,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x16,0x00,0x03,0x00,0xf7,0x00,0x00,0x00,0x10,0x00,0x00,0x00, -0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xf8,0x00,0x00,0x00, -0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, -0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xf9,0x00,0x00,0x00, -0x84,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0xf8,0x00,0x00,0x00, -0x1c,0x00,0x04,0x00,0xfa,0x00,0x00,0x00,0xf7,0x00,0x00,0x00, -0xf9,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xfb,0x00,0x00,0x00, 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+0xf8,0x00,0x02,0x00,0x70,0x02,0x00,0x00,0xfd,0x00,0x01,0x00, +0x38,0x00,0x01,0x00, }; -const uint64_t matmul_f16_aligned_len = 10812; +const uint64_t matmul_f16_aligned_len = 11200; unsigned char matmul_f16_aligned_fp32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xd9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0xd8,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, 0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, @@ -12908,8 +12969,8 @@ unsigned char matmul_f16_aligned_fp32_data[] = { 0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00, -0xfb,0x00,0x00,0x00,0x07,0x01,0x00,0x00,0x49,0x01,0x00,0x00, -0x50,0x01,0x00,0x00,0x39,0x02,0x00,0x00,0x82,0x02,0x00,0x00, +0xfd,0x00,0x00,0x00,0x04,0x01,0x00,0x00,0x4a,0x01,0x00,0x00, +0x50,0x01,0x00,0x00,0x38,0x02,0x00,0x00,0x81,0x02,0x00,0x00, 0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, @@ -12952,17 +13013,17 @@ unsigned char matmul_f16_aligned_fp32_data[] = { 0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x04,0x01,0x00,0x00, -0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00, -0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00, -0x48,0x00,0x05,0x00,0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00, +0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x01,0x01,0x00,0x00, +0x06,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x04,0x00, +0x02,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00, +0x48,0x00,0x05,0x00,0x02,0x01,0x00,0x00,0x00,0x00,0x00,0x00, 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00, -0x05,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x07,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x07,0x01,0x00,0x00,0x21,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x21,0x01,0x00,0x00, +0x02,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, +0x04,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x04,0x01,0x00,0x00,0x21,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x22,0x01,0x00,0x00, 0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x22,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00, +0x23,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x4d,0x01,0x00,0x00,0x06,0x00,0x00,0x00, 0x08,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x4e,0x01,0x00,0x00, 0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00, @@ -12971,15 +13032,15 @@ unsigned char matmul_f16_aligned_fp32_data[] = { 0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x50,0x01,0x00,0x00, 0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x50,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x39,0x02,0x00,0x00,0x0b,0x00,0x00,0x00, -0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x7f,0x02,0x00,0x00, +0x47,0x00,0x04,0x00,0x38,0x02,0x00,0x00,0x0b,0x00,0x00,0x00, +0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x7e,0x02,0x00,0x00, 0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00, -0x80,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00, -0x48,0x00,0x05,0x00,0x80,0x02,0x00,0x00,0x00,0x00,0x00,0x00, +0x7f,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00, +0x48,0x00,0x05,0x00,0x7f,0x02,0x00,0x00,0x00,0x00,0x00,0x00, 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00, -0x80,0x02,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x82,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x82,0x02,0x00,0x00,0x21,0x00,0x00,0x00, +0x7f,0x02,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, +0x81,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x81,0x02,0x00,0x00,0x21,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00, 0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00, @@ -13030,16 +13091,16 @@ unsigned char matmul_f16_aligned_fp32_data[] = { 0x63,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x68,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x62,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, -0x6d,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x34,0x00,0x06,0x00, -0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x86,0x00,0x00,0x00, -0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, -0x06,0x00,0x00,0x00,0x73,0x00,0x00,0x00,0x86,0x00,0x00,0x00, -0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x06,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x04,0x00,0x00,0x00, +0x6d,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x04,0x00,0x00,0x00, +0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x6f,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, +0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x74,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x79,0x00,0x00,0x00, -0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00, -0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -13083,99 +13144,97 @@ unsigned char matmul_f16_aligned_fp32_data[] = { 0xcd,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0xc4,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xd0,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xf7,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0xf4,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xf8,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x38,0x00,0x00,0x00, -0xf7,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0xf9,0x00,0x00,0x00, -0xc4,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,0x20,0x00,0x04,0x00, -0xfa,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0xf9,0x00,0x00,0x00, -0x3b,0x00,0x04,0x00,0xfa,0x00,0x00,0x00,0xfb,0x00,0x00,0x00, -0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xff,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, -0x3a,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x03,0x01,0x00,0x00, -0x10,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x04,0x01,0x00,0x00, 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+13291,9 @@ unsigned char matmul_f16_aligned_fp32_data[] = { 0x65,0x00,0x00,0x00,0x5e,0x00,0x00,0x00,0x64,0x00,0x00,0x00, 0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x69,0x00,0x00,0x00, 0x5e,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x89,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x4f,0x00,0x00,0x00, -0x6e,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x74,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x73,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x70,0x00,0x00,0x00,0x4f,0x00,0x00,0x00, +0x6f,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0x75,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x74,0x00,0x00,0x00, 0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7a,0x00,0x00,0x00, 0x4f,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x86,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,0x4f,0x00,0x00,0x00, @@ -13267,7 +13326,7 @@ unsigned char matmul_f16_aligned_fp32_data[] = { 0x9b,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0x9e,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,0x85,0x00,0x00,0x00, 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+0xf8,0x00,0x02,0x00,0x40,0x02,0x00,0x00,0xfd,0x00,0x01,0x00, 0x38,0x00,0x01,0x00, }; const uint64_t matmul_f16_aligned_fp32_len = 10288; @@ -14628,7 +14689,7 @@ const uint64_t matmul_f16_f32_len = 10332; unsigned char matmul_f16_f32_aligned_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xfd,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x12,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, 0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, @@ -14636,9 +14697,9 @@ unsigned char matmul_f16_f32_aligned_data[] = { 0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00, 0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, 0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00, -0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0xfc,0x00,0x00,0x00, -0x07,0x01,0x00,0x00,0x49,0x01,0x00,0x00,0x51,0x01,0x00,0x00, -0x5e,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,0x10,0x00,0x06,0x00, +0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0xfe,0x00,0x00,0x00, +0x05,0x01,0x00,0x00,0x63,0x01,0x00,0x00,0x6b,0x01,0x00,0x00, +0x73,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,0x10,0x00,0x06,0x00, 0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00, @@ -14681,37 +14742,40 @@ unsigned char matmul_f16_f32_aligned_data[] = { 0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x04,0x01,0x00,0x00,0x06,0x00,0x00,0x00, -0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x05,0x01,0x00,0x00, -0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00, -0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x05,0x01,0x00,0x00, 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0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00, -0xfb,0x00,0x00,0x00,0x07,0x01,0x00,0x00,0x49,0x01,0x00,0x00, -0x50,0x01,0x00,0x00,0x36,0x02,0x00,0x00,0x7f,0x02,0x00,0x00, +0xfd,0x00,0x00,0x00,0x04,0x01,0x00,0x00,0x4a,0x01,0x00,0x00, +0x51,0x01,0x00,0x00,0x36,0x02,0x00,0x00,0x7f,0x02,0x00,0x00, 0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, @@ -15596,25 +15692,25 @@ unsigned char matmul_f16_f32_aligned_fp32_data[] = { 0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x04,0x01,0x00,0x00, -0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00, -0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00, 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+0x06,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,0xa5,0x02,0x00,0x00, +0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x3c,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0x3e,0x02,0x00,0x00,0xfd,0x00,0x01,0x00, +0x38,0x00,0x01,0x00, }; -const uint64_t matmul_f16_f32_aligned_fp32_len = 10224; +const uint64_t matmul_f16_f32_aligned_fp32_len = 10240; unsigned char matmul_f16_f32_fp32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, @@ -18987,7 +19084,7 @@ const uint64_t matmul_f32_len = 10324; unsigned char matmul_f32_aligned_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xfd,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x17,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, 0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, 0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, @@ -18995,8 +19092,8 @@ unsigned char matmul_f32_aligned_data[] = { 0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00, -0xfc,0x00,0x00,0x00,0x07,0x01,0x00,0x00,0x4a,0x01,0x00,0x00, -0x52,0x01,0x00,0x00,0x5e,0x02,0x00,0x00,0xa7,0x02,0x00,0x00, +0xfe,0x00,0x00,0x00,0x05,0x01,0x00,0x00,0x6b,0x01,0x00,0x00, +0x71,0x01,0x00,0x00,0x78,0x02,0x00,0x00,0xc1,0x02,0x00,0x00, 0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, @@ -19039,37 +19136,40 @@ unsigned char matmul_f32_aligned_data[] = { 0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x04,0x01,0x00,0x00, -0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00, 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+0x7e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x80,0x02,0x00,0x00, +0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, +}; +const uint64_t matmul_f32_aligned_len = 11432; + +unsigned char matmul_f32_aligned_fp32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0xce,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, +0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30, +0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00, +0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00, +0x4d,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,0x03,0x01,0x00,0x00, +0x45,0x01,0x00,0x00,0x4b,0x01,0x00,0x00,0x2f,0x02,0x00,0x00, +0x78,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, +0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 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+0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00, +0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00, +0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00, +0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00, +0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00, +0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00, +0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00, +0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00, @@ -19955,33 +20093,33 @@ unsigned char matmul_f32_aligned_fp32_data[] = { 0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00, 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-0xf9,0x00,0x02,0x00,0x17,0x01,0x00,0x00,0xf8,0x00,0x02,0x00, -0x17,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, - -}; -const uint64_t rope_neox_f16_len = 3876; - -unsigned char rope_neox_f32_data[] = { -0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x5a,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, -0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30, -0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00, -0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, -0x2c,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x97,0x00,0x00,0x00, -0x9d,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0x10,0x00,0x06,0x00, -0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x00,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x29,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 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0x15,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,0x20,0x00,0x00,0x00, @@ -75637,228 +78989,614 @@ unsigned char rope_neox_f32_data[] = { 0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x07,0x00,0x00,0x00, 0x6f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x73,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x07,0x00,0x00,0x00, -0x1d,0x00,0x03,0x00,0x94,0x00,0x00,0x00,0x06,0x00,0x00,0x00, -0x1e,0x00,0x03,0x00,0x95,0x00,0x00,0x00,0x94,0x00,0x00,0x00, -0x20,0x00,0x04,0x00,0x96,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, -0x95,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x96,0x00,0x00,0x00, -0x97,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, -0x9a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, -0x9b,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,0x20,0x00,0x04,0x00, -0x9c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x9b,0x00,0x00,0x00, -0x3b,0x00,0x04,0x00,0x9c,0x00,0x00,0x00,0x9d,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xa0,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 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rope_neox_f32_len = 4200; unsigned char scale_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, @@ -76253,65 +79991,55 @@ const uint64_t silu_f32_len = 1264; unsigned char soft_max_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x5c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x6f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, 0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30, 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,0x05,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x0f,0x00,0x0c,0x00,0x05,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x19,0x00,0x00,0x00, -0x5c,0x00,0x00,0x00,0x75,0x00,0x00,0x00,0x8c,0x00,0x00,0x00, -0xa3,0x00,0x00,0x00,0x09,0x01,0x00,0x00,0x10,0x00,0x06,0x00, 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+0x6a,0x01,0x00,0x00,0xa0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0x35,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x37,0x01,0x00,0x00, 0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t soft_max_f32_f16_len = 5000; +const uint64_t soft_max_f32_f16_len = 4904; unsigned char split_k_reduce_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index 95f7189740539..79ce1479f16ca 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -114,6 +114,7 @@ struct vk_device { size_t idx; vk_matmul_pipeline pipeline_matmul_f32; + vk_matmul_pipeline pipeline_matmul_f32_f16; vk_matmul_pipeline pipeline_matmul_f16; vk_matmul_pipeline pipeline_matmul_f16_f32; vk_pipeline pipeline_matmul_split_k_reduce; @@ -289,12 +290,12 @@ struct vk_op_rope_neox_push_constants { float corr_dims[4]; float theta_scale; float inv_ndims; + uint32_t has_freq_facs; }; struct vk_op_soft_max_push_constants { uint32_t KX; uint32_t KY; - uint32_t KZ; float scale; float max_bias; float m0; @@ -304,7 +305,8 @@ struct vk_op_soft_max_push_constants { struct vk_op_argsort_push_constants { uint32_t ncols; - bool ascending; + uint32_t ncols_pad; + int32_t order; }; // Allow pre-recording command buffers @@ -375,13 +377,12 @@ struct ggml_backend_vk_context { vk_context * compute_ctx; vk_context * transfer_ctx; - bool disable; bool initialized; size_t idx; }; -struct vk_instance { +struct vk_instance_t { vk::Instance instance; std::vector device_indices; @@ -423,7 +424,7 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_ typedef void (*ggml_vk_func_t)(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); static bool vk_instance_initialized = false; -static vk_instance vk_instance; +static vk_instance_t vk_instance; GGML_CALL static void ggml_backend_vk_free(ggml_backend_t backend); @@ -1013,6 +1014,7 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { uint32_t s_align = 32; ctx->device->pipeline_matmul_f32 = std::make_shared(); + ctx->device->pipeline_matmul_f32_f16 = std::make_shared(); ctx->device->pipeline_matmul_f16_f32 = std::make_shared(); ctx->device->pipeline_matmul_f16 = std::make_shared(); ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0] = std::make_shared(); @@ -1048,6 +1050,13 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_m, "matmul_f32_aligned_m", matmul_f32_aligned_len, matmul_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_s, "matmul_f32_aligned_s", matmul_f32_aligned_len, matmul_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->l, "matmul_f32_f16_l", matmul_f32_f16_len, matmul_f32_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->m, "matmul_f32_f16_m", matmul_f32_f16_len, matmul_f32_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->s, "matmul_f32_f16_s", matmul_f32_f16_len, matmul_f32_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->a_l, "matmul_f32_f16_aligned_l", matmul_f32_f16_aligned_len, matmul_f32_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->a_m, "matmul_f32_f16_aligned_m", matmul_f32_f16_aligned_len, matmul_f32_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->a_s, "matmul_f32_f16_aligned_s", matmul_f32_f16_aligned_len, matmul_f32_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->l, "matmul_f16_l", matmul_f16_len, matmul_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->m, "matmul_f16_m", matmul_f16_len, matmul_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->s, "matmul_f16_s", matmul_f16_len, matmul_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); @@ -1230,6 +1239,13 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_m, "matmul_f32_aligned_m", matmul_f32_aligned_fp32_len, matmul_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_s, "matmul_f32_aligned_s", matmul_f32_aligned_fp32_len, matmul_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->l, "matmul_f32_f16_l", matmul_f32_f16_fp32_len, matmul_f32_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->m, "matmul_f32_f16_m", matmul_f32_f16_fp32_len, matmul_f32_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->s, "matmul_f32_f16_s", matmul_f32_f16_fp32_len, matmul_f32_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->a_l, "matmul_f32_f16_aligned_l", matmul_f32_f16_aligned_fp32_len, matmul_f32_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->a_m, "matmul_f32_f16_aligned_m", matmul_f32_f16_aligned_fp32_len, matmul_f32_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->a_s, "matmul_f32_f16_aligned_s", matmul_f32_f16_aligned_fp32_len, matmul_f32_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->l, "matmul_f16_l", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->m, "matmul_f16_m", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->s, "matmul_f16_s", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); @@ -1501,14 +1517,14 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { ggml_vk_create_pipeline(ctx, ctx->device->pipeline_diag_mask_inf_f32, "diag_mask_inf_f32", diag_mask_inf_f32_len, diag_mask_inf_f32_data, "main", 2, sizeof(vk_op_diag_mask_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 4, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_soft_max_f32_f16, "soft_max_f32_f16", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 4, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_soft_max_f32_f16, "soft_max_f32_f16", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_f32, "rope_f32", rope_f32_len, rope_f32_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_f16, "rope_f16", rope_f16_len, rope_f16_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_neox_f32, "rope_neox_f32", rope_neox_f32_len, rope_neox_f32_data, "main", 3, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_neox_f16, "rope_neox_f16", rope_neox_f16_len, rope_neox_f16_data, "main", 3, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_neox_f32, "rope_neox_f32", rope_neox_f32_len, rope_neox_f32_data, "main", 4, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_neox_f16, "rope_neox_f16", rope_neox_f16_len, rope_neox_f16_data, "main", 4, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_argsort_f32, "argsort_f32", argsort_f32_len, argsort_f32_data, "main", 2, sizeof(vk_op_argsort_push_constants), {1024, 1, 1}, {}, 1); } @@ -1859,7 +1875,6 @@ static void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) { ctx->compute_ctx = nullptr; ctx->transfer_ctx = nullptr; - ctx->disable = false; ctx->initialized = true; ctx->idx = idx; @@ -1903,6 +1918,9 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_conte if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) { return ctx->device->pipeline_matmul_f32; } + if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F16) { + return ctx->device->pipeline_matmul_f32_f16; + } if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) { return ctx->device->pipeline_matmul_f16_f32; } @@ -2722,7 +2740,7 @@ static void ggml_vk_matmul( uint32_t batch_stride_a, uint32_t batch_stride_b, uint32_t batch_stride_d, uint32_t expert_stride_b, uint32_t expert_stride_d, uint32_t idx, uint32_t nbi1, uint32_t n_as) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_matmul(a: (" << a.buffer->buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer->buffer << ", " << b.offset << ", " << b.size << "), c: (" << d.buffer->buffer << ", " << d.offset << ", " << d.size << "), split_k: (" << split_k_buffer.buffer->buffer << ", " << split_k_buffer.offset << ", " << split_k_buffer.size << "), m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", split_k: " << split_k << ", batch: " << batch << ", ne02: " << ne02 << ", ne12: " << ne12 << ", broadcast2: " << broadcast2 << ", broadcast3: " << broadcast3 << ", batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ")" << std::endl; + std::cerr << "ggml_vk_matmul(a: (" << a.buffer->buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer->buffer << ", " << b.offset << ", " << b.size << "), c: (" << d.buffer->buffer << ", " << d.offset << ", " << d.size << "), split_k: (" << (split_k_buffer.buffer != nullptr ? split_k_buffer.buffer->buffer : VK_NULL_HANDLE) << ", " << split_k_buffer.offset << ", " << split_k_buffer.size << "), m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", split_k: " << split_k << ", batch: " << batch << ", ne02: " << ne02 << ", ne12: " << ne12 << ", broadcast2: " << broadcast2 << ", broadcast3: " << broadcast3 << ", batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ")" << std::endl; #endif ggml_vk_sync_buffers(subctx); if (split_k == 1) { @@ -2792,7 +2810,7 @@ static vk_pipeline ggml_vk_get_cpy_pipeline(ggml_backend_vk_context * ctx, ggml_ static void ggml_vk_cpy_to_contiguous(ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline pipeline, const ggml_tensor * tensor, vk_subbuffer&& in, vk_subbuffer&& out) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_cpy_to_contiguous((" << tensor << ", type=" << tensor->type << ", backend=" << tensor->backend << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << "), "; + std::cerr << "ggml_vk_cpy_to_contiguous((" << tensor << ", type=" << tensor->type << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << "), "; std::cerr << "buffer in size=" << in.buffer->size << ", buffer out size=" << out.buffer->size << ")" << std::endl; #endif const int tensor_type_size = ggml_type_size(tensor->type); @@ -2812,9 +2830,9 @@ static void ggml_vk_cpy_to_contiguous(ggml_backend_vk_context * ctx, vk_context static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_mul_mat_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; - std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; - std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; + std::cerr << "ggml_vk_mul_mat_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; + std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; #endif GGML_ASSERT(ggml_vk_dim01_contiguous(src0) || src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); // NOLINT GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); // NOLINT @@ -2982,19 +3000,13 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su ne01, ne11, ne10, ne10, ne10, ne01, split_k, ne12*ne13, ne02, ne12, r2, r3, stride_batch_x, stride_batch_y, ne20*ne21, 0, 0, 0, 0, 1 ); // NOLINT - - if (dst->backend == GGML_BACKEND_TYPE_CPU) { - // copy dst to host - float * d = (float *) ((char *) dst->data); - ggml_vk_buffer_read_async(ctx, subctx, d_D, 0, d, sizeof(float) * d_ne * ne12 * ne13); - } } static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_mul_mat_vec_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; - std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; - std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; + std::cerr << "ggml_vk_mul_mat_vec_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; + std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; #endif GGML_ASSERT(ggml_vk_dim01_contiguous(src0) || src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); // NOLINT GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); // NOLINT @@ -3147,12 +3159,11 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_mul_mat_p021_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; - std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; - std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; + std::cerr << "ggml_vk_mul_mat_p021_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; + std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; #endif GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1)); - GGML_ASSERT(src0->backend == GGML_BACKEND_TYPE_GPU); GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // NOLINT GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]); // NOLINT GGML_ASSERT(src0->type == GGML_TYPE_F16); @@ -3217,25 +3228,17 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c const std::array pc = { (uint32_t)ne00, (uint32_t)ne01, (uint32_t)ne02, (uint32_t)ne12, (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)) }; ggml_vk_sync_buffers(subctx); ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_mul_mat_vec_p021_f16_f32, { { d_Qx, qx_buf_offset, qx_sz }, { d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 6 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); - - if (dst->backend == GGML_BACKEND_TYPE_CPU) { - // copy dst to host - float * d = (float *) dst->data; - ggml_vk_sync_buffers(subctx); - ggml_vk_buffer_read_async(ctx, subctx, d_D, d_buf_offset, d, sizeof(float) * d_ne); - } } static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_mul_mat_nc_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; - std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; - std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; + std::cerr << "ggml_vk_mul_mat_nc_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; + std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; #endif GGML_ASSERT(!ggml_is_transposed(src0)); GGML_ASSERT(!ggml_is_transposed(src1)); GGML_ASSERT(!ggml_is_permuted(src0)); - GGML_ASSERT(src0->backend == GGML_BACKEND_TYPE_GPU); GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); @@ -3302,26 +3305,6 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con const std::array pc = { (uint32_t)ne00, (uint32_t)ne01, row_stride_x, channel_stride_x, (uint32_t)(ne12 / ne02), (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)) }; ggml_vk_sync_buffers(subctx); ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_mul_mat_vec_nc_f16_f32, { { d_Qx, qx_buf_offset, qx_sz }, { d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 7 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); - - if (dst->backend == GGML_BACKEND_TYPE_CPU) { - // copy dst to host - float * d = (float *) dst->data; - ggml_vk_sync_buffers(subctx); - ggml_vk_buffer_read_async(ctx, subctx, d_D, d_buf_offset, d, sizeof(float) * d_ne); - } -} - -static bool ggml_vk_can_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * dst) { - const uint64_t ne10 = src1->ne[0]; - - const uint64_t ne0 = dst->ne[0]; - const uint64_t ne1 = dst->ne[1]; - - // TODO: find the optimal values for these - return (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && - (src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16 || ggml_is_quantized(src1->type)) && - dst->type == GGML_TYPE_F32 && - ((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_TYPE_GPU); } static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { @@ -3711,8 +3694,6 @@ static void ggml_vk_op_repeat(ggml_backend_vk_context * ctx, vk_context * subctx // TODO: support for transposed / permuted tensors GGML_ASSERT(nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); - GGML_ASSERT(src0->backend == GGML_BACKEND_TYPE_GPU); - GGML_ASSERT(dst->backend == GGML_BACKEND_TYPE_GPU); ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; @@ -3830,12 +3811,11 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const return nullptr; case GGML_OP_SOFT_MAX: GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); - GGML_ASSERT(!src2 || src2->type == GGML_TYPE_F32 || src2->type == GGML_TYPE_F16); - if (src0->type == GGML_TYPE_F32 && (src1 == nullptr || src1->type == GGML_TYPE_F32) && (src2 == nullptr || src2->type == GGML_TYPE_F32) && dst->type == GGML_TYPE_F32) { + if (src0->type == GGML_TYPE_F32 && (src1 == nullptr || src1->type == GGML_TYPE_F32) && dst->type == GGML_TYPE_F32) { return ctx->device->pipeline_soft_max_f32; } - if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16 && src2->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) { + if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) { return ctx->device->pipeline_soft_max_f32_f16; } return nullptr; @@ -3874,6 +3854,8 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const default: return nullptr; } + + GGML_UNUSED(src2); } static ggml_vk_func_t ggml_vk_op_get_func(ggml_op op) { @@ -3903,14 +3885,14 @@ static bool ggml_vk_op_supports_incontiguous(ggml_op op) { template static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, ggml_op op, const PC&& pc) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_op_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; + std::cerr << "ggml_vk_op_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; if (src1 != nullptr) { - std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; + std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; } if (src2 != nullptr) { - std::cerr << "), (" << src2 << ", name=" << src2->name << ", type=" << src2->type << ", backend=" << src2->backend << ", ne0=" << src2->ne[0] << ", ne1=" << src2->ne[1] << ", ne2=" << src2->ne[2] << ", ne3=" << src2->ne[3] << ", nb0=" << src2->nb[0] << ", nb1=" << src2->nb[1] << ", nb2=" << src2->nb[2] << ", nb3=" << src2->nb[3]; + std::cerr << "), (" << src2 << ", name=" << src2->name << ", type=" << src2->type << ", ne0=" << src2->ne[0] << ", ne1=" << src2->ne[1] << ", ne2=" << src2->ne[2] << ", ne3=" << src2->ne[3] << ", nb0=" << src2->nb[0] << ", nb1=" << src2->nb[1] << ", nb2=" << src2->nb[2] << ", nb3=" << src2->nb[3]; } - std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "), " << ggml_op_name(op) << ")" << std::endl; + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "), " << ggml_op_name(op) << ")" << std::endl; #endif GGML_ASSERT(op == GGML_OP_GET_ROWS || (!ggml_is_quantized(src0->type) && (src1 == nullptr || !ggml_is_quantized(src1->type)))); // NOLINT GGML_ASSERT(op == GGML_OP_CPY || ggml_vk_dim01_contiguous(src0)); // NOLINT @@ -3920,6 +3902,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c const uint64_t ne02 = src0->ne[2]; const uint64_t ne03 = src0->ne[3]; const uint64_t ne0 = ne00 * ne01; + const bool use_src1 = src1 != nullptr; const uint64_t ne10 = use_src1 ? src1->ne[0] : 0; const uint64_t ne11 = use_src1 ? src1->ne[1] : 0; @@ -3927,11 +3910,13 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c const uint64_t ne13 = use_src1 ? src1->ne[3] : 0; const uint64_t ne1 = ne10 * ne11; // const uint64_t nb10 = use_src1 ? src1->nb[0] : 0; - const uint64_t nb2 = dst->nb[2]; - const uint64_t nb3 = dst->nb[3]; const bool use_src2 = src2 != nullptr; - const uint64_t ne2 = use_src2 ? src2->ne[0] * src2->ne[1] : 0; + const uint64_t ne20 = use_src2 ? src2->ne[0] : 0; + const uint64_t ne21 = use_src2 ? src2->ne[1] : 0; + const uint64_t ne22 = use_src2 ? src2->ne[2] : 0; + const uint64_t ne23 = use_src2 ? src2->ne[3] : 0; + const uint64_t ne2 = ne20 * ne21; vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, src0, src1, src2, dst, op); ggml_vk_func_t op_func; @@ -3977,7 +3962,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c src1_uma = d_Y != nullptr; } if (use_src2) { - ggml_vk_host_get(ctx, src1->data, d_Z, z_buf_offset); + ggml_vk_host_get(ctx, src2->data, d_Z, z_buf_offset); src2_uma = d_Z != nullptr; } } @@ -3990,7 +3975,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c vk_buffer d_D = extra->buffer_gpu.lock(); // Workaround for tiny tensor inputs on ROPE - if (use_src1 && src1->backend == GGML_BACKEND_TYPE_GPU && y_sz > d_D->size) { + if (use_src1 && y_sz > d_D->size) { y_sz = VK_WHOLE_SIZE; } @@ -4007,7 +3992,6 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c y_buf_offset = extra_src1->offset; GGML_ASSERT(d_Y != nullptr); } - if (use_src2 && !src2_uma) { d_Z = extra_src2->buffer_gpu.lock(); z_buf_offset = extra_src2->offset; @@ -4017,6 +4001,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c if (op_supports_incontiguous) { x_sz = ggml_nbytes(src0); y_sz = use_src1 ? ggml_nbytes(src1) : 0; + z_sz = use_src2 ? ggml_nbytes(src2) : 0; d_sz = ggml_nbytes(dst); if (x_buf_offset + x_sz >= d_X->size) { @@ -4025,6 +4010,9 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c if (use_src1 && y_buf_offset + y_sz >= d_Y->size) { y_sz = VK_WHOLE_SIZE; } + if (use_src2 && z_buf_offset + z_sz >= d_Z->size) { + z_sz = VK_WHOLE_SIZE; + } if (d_buf_offset + d_sz >= d_D->size) { d_sz = VK_WHOLE_SIZE; } @@ -4047,7 +4035,10 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c elements = { (uint32_t)ggml_nrows(src0), (uint32_t)ne00, 1 }; break; case GGML_OP_GET_ROWS: - elements = { (uint32_t)ne00, (uint32_t)ne10, (uint32_t)(ne11 * ne12) }; + elements = { (uint32_t)ne00, (uint32_t)ne10, (uint32_t)(ne11 * ne12) }; + break; + case GGML_OP_ARGSORT: + elements = { (uint32_t)ne00, (uint32_t)ggml_nrows(src0), 1 }; break; default: elements = { (uint32_t)ggml_nelements(src0), 1, 1 }; @@ -4061,13 +4052,16 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c if (use_src1 && y_sz != VK_WHOLE_SIZE) { y_sz *= ne12 * ne13; } + if (use_src2 && z_sz != VK_WHOLE_SIZE) { + z_sz *= ne22 * ne23; + } if (d_sz != VK_WHOLE_SIZE) { d_sz *= ne02 * ne03; } } if (op == GGML_OP_SOFT_MAX) { - // Empty src1 and src2 are possible on soft_max, but the shader needs buffers + // Empty src1 is possible in soft_max, but the shader needs a buffer vk_subbuffer subbuf_y; if (use_src1) { subbuf_y = { d_Y, y_buf_offset, y_sz }; @@ -4075,15 +4069,30 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c subbuf_y = { d_X, 0, d_X->size }; } - vk_subbuffer subbuf_z; - if (use_src2) { - subbuf_z = { d_Z, z_buf_offset, z_sz }; + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, subbuf_y, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + } else if (op == GGML_OP_ROPE) { + const int mode = ((int32_t *) dst->op_params)[2]; + const bool is_neox = mode & 2; + + if (is_neox) { + // Empty src2 is possible in rope, but the shader needs a buffer + vk_subbuffer subbuf_z; + if (use_src2) { + subbuf_z = { d_Z, z_buf_offset, z_sz }; + } else { + subbuf_z = { d_X, 0, d_X->size }; + } + + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, subbuf_z, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } else { - subbuf_z = { d_X, 0, d_X->size }; + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } - + } else if (use_src2) { ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, subbuf_y, subbuf_z, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, { d_Z, z_buf_offset, z_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } else if (use_src1) { ggml_vk_sync_buffers(subctx); ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); @@ -4091,22 +4100,16 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c ggml_vk_sync_buffers(subctx); ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } - if (dst->backend == GGML_BACKEND_TYPE_CPU && op == GGML_OP_CPY) { - ggml_vk_d2h_tensor_2d(ctx, subctx, d_D, 0, dst); - } else if(dst->backend == GGML_BACKEND_TYPE_CPU) { - // copy dst to host - float * d = (float *) dst->data; - ggml_vk_buffer_read_async(ctx, subctx, d_D, 0, d, d_sz); - } } else { GGML_ASSERT(op != GGML_OP_SOFT_MAX); + GGML_ASSERT(op != GGML_OP_ARGSORT); + GGML_ASSERT(!use_src2); ggml_pipeline_allocate_descriptor_sets(ctx, pipeline, ne02 * ne03); switch (dst->op) { case GGML_OP_NORM: case GGML_OP_RMS_NORM: - case GGML_OP_SOFT_MAX: elements = { (uint32_t)ne01, 1, 1 }; break; case GGML_OP_DIAG_MASK_INF: @@ -4136,10 +4139,6 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c ggml_vk_sync_buffers(subctx); ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset + x_offset, x_sz }, { d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements); } - if (dst->backend == GGML_BACKEND_TYPE_CPU) { - // copy dst to host - ggml_vk_buffer_read_async(ctx, subctx, d_D, d_buf_offset + d_offset, (char *) dst->data + i02*nb2 + i03*nb3, d_sz); - } } } } @@ -4270,7 +4269,7 @@ static void ggml_vk_diag_mask_inf(ggml_backend_vk_context * ctx, vk_context * su ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_DIAG_MASK_INF, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0] }); } -static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst) { +static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { float * op_params = (float *)dst->op_params; float scale = op_params[0]; @@ -4286,17 +4285,16 @@ static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context * subctx, const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); - ggml_vk_op_f32(ctx, subctx, src0, src1, src2, dst, GGML_OP_SOFT_MAX, { + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_SOFT_MAX, { ncols, src1 != nullptr ? nrows_y : (uint32_t)0, - src2 != nullptr ? (uint32_t)1 : (uint32_t)0, scale, max_bias, m0, m1, n_head_log2, }); } -static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst) { const int n_dims = ((int32_t *) dst->op_params)[1]; const int mode = ((int32_t *) dst->op_params)[2]; // const int n_ctx = ((int32_t *) dst->op_params)[3]; @@ -4319,15 +4317,40 @@ static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context * subctx, con if (is_neox) { const float theta_scale = powf(freq_base, -2.0f/n_dims); const float inv_ndims = -1.0f / n_dims; - ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_ROPE, { (uint32_t)src0->ne[0], (uint32_t)n_dims, freq_scale, (uint32_t)src0->ne[1], freq_base, ext_factor, attn_factor, {corr_dims[0], corr_dims[1], 0.0f, 0.0f}, theta_scale, inv_ndims }); + ggml_vk_op_f32(ctx, subctx, src0, src1, src2, dst, GGML_OP_ROPE, { + (uint32_t)src0->ne[0], (uint32_t)n_dims, freq_scale, (uint32_t)src0->ne[1], + freq_base, ext_factor, attn_factor, {corr_dims[0], corr_dims[1], 0.0f, 0.0f}, theta_scale, inv_ndims, + src2 != nullptr, + }); } else { - ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_ROPE, { (uint32_t)src0->ne[0], freq_scale, (uint32_t)src0->ne[1], freq_base, ext_factor, attn_factor, {corr_dims[0], corr_dims[1], 0.0f, 0.0f} }); + ggml_vk_op_f32(ctx, subctx, src0, src1, src2, dst, GGML_OP_ROPE, { + (uint32_t)src0->ne[0], freq_scale, (uint32_t)src0->ne[1], + freq_base, ext_factor, attn_factor, {corr_dims[0], corr_dims[1], 0.0f, 0.0f} + }); } } static void ggml_vk_argsort(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { int32_t * op_params = (int32_t *)dst->op_params; - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_ARGSORT, { (uint32_t)src0->ne[0], ((ggml_sort_order) op_params[0]) == GGML_SORT_ORDER_ASC }); + + uint32_t ncols = src0->ne[0]; + + uint32_t ncols_pad = 1; + while (ncols_pad < ncols) { + ncols_pad *= 2; + } + + GGML_ASSERT(ncols_pad <= 1024); + + std::cerr << "ncols=" << ncols << " ncols_pad=" << ncols_pad << " ascending=" << op_params[0] << std::endl; + + std::cerr << ((ggml_sort_order) op_params[0]) << " " << GGML_SORT_ORDER_ASC << std::endl; + + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_ARGSORT, { + ncols, + ncols_pad, + op_params[0], + }); } #ifdef GGML_VULKAN_RUN_TESTS @@ -4379,6 +4402,9 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t if (std::is_same() && std::is_same()) { p = ctx->device->pipeline_matmul_f32->a_s; shname = "F32_ALIGNED_S"; + } else if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f32_f16->a_s; + shname = "F32_F16_ALIGNED_S"; } else if (std::is_same() && std::is_same()) { p = ctx->device->pipeline_matmul_f16_f32->a_s; shname = "F16_F32_ALIGNED_S"; @@ -4392,6 +4418,9 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t if (std::is_same() && std::is_same()) { p = ctx->device->pipeline_matmul_f32->a_m; shname = "F32_ALIGNED_M"; + } else if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f32_f16->a_m; + shname = "F32_F16_ALIGNED_M"; } else if (std::is_same() && std::is_same()) { p = ctx->device->pipeline_matmul_f16_f32->a_m; shname = "F16_F32_ALIGNED_M"; @@ -4405,6 +4434,9 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t if (std::is_same() && std::is_same()) { p = ctx->device->pipeline_matmul_f32->a_l; shname = "F32_ALIGNED_L"; + } else if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f32_f16->a_l; + shname = "F32_F16_ALIGNED_L"; } else if (std::is_same() && std::is_same()) { p = ctx->device->pipeline_matmul_f16_f32->a_l; shname = "F16_F32_ALIGNED_L"; @@ -4425,6 +4457,9 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t if (std::is_same() && std::is_same()) { p = ctx->device->pipeline_matmul_f32->s; shname = "F32_S"; + } else if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f32_f16->s; + shname = "F32_F16_S"; } else if (std::is_same() && std::is_same()) { p = ctx->device->pipeline_matmul_f16_f32->s; shname = "F16_F32_S"; @@ -4436,6 +4471,9 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t if (std::is_same() && std::is_same()) { p = ctx->device->pipeline_matmul_f32->m; shname = "F32_M"; + } else if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f32_f16->m; + shname = "F32_F16_M"; } else if (std::is_same() && std::is_same()) { p = ctx->device->pipeline_matmul_f16_f32->m; shname = "F16_F32_M"; @@ -4447,6 +4485,9 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t if (std::is_same() && std::is_same()) { p = ctx->device->pipeline_matmul_f32->l; shname = "F32_L"; + } else if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f32_f16->l; + shname = "F32_F16_L"; } else if (std::is_same() && std::is_same()) { p = ctx->device->pipeline_matmul_f16_f32->l; shname = "F16_F32_L"; @@ -4559,15 +4600,11 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t src1_ggml->data = y; tensor_ggml->data = d_chk; - ctx->disable = true; - ggml_cgraph * cgraph = ggml_new_graph(ggml_ctx); ggml_build_forward_expand(cgraph, tensor_ggml); ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 1); - ctx->disable = false; - ggml_free(ggml_ctx); double avg_err = 0.0; @@ -5047,15 +5084,11 @@ static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m, src1_ggml->data = y; tensor_ggml->data = d_chk; - ctx->disable = true; - ggml_cgraph * cgraph = ggml_new_graph(ggml_ctx); ggml_build_forward_expand(cgraph, tensor_ggml); ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 1); - ctx->disable = false; - ggml_free(ggml_ctx); double avg_err = 0.0; @@ -5132,12 +5165,12 @@ static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggm #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_preallocate_buffers_graph(" << node << ")" << std::endl; #endif - if (ctx->disable || node->backend != GGML_BACKEND_TYPE_GPU) { + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) node->extra; + + if (extra == nullptr) { return; } - ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) node->extra; - ggml_tensor * src0 = node->src[0]; ggml_tensor * src1 = node->src[1]; @@ -5242,9 +5275,6 @@ static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggm } static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) { - if (ctx->disable) { - return; - } #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_preallocate_buffers(x_size: " << ctx->prealloc_size_x << " y_size: " << ctx->prealloc_size_y << " split_k_size: " << ctx->prealloc_size_split_k << ")" << std::endl; #endif @@ -5418,7 +5448,9 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) { } static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * node, bool last_node){ - if (ctx->disable || node->backend != GGML_BACKEND_TYPE_GPU || ggml_is_empty(node)) { + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) node->extra; + + if (ggml_is_empty(node) || extra == nullptr) { return; } @@ -5432,8 +5464,6 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod const ggml_tensor * src1 = node->src[1]; const ggml_tensor * src2 = node->src[2]; - ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) node->extra; - switch (node->op) { case GGML_OP_UNARY: switch (ggml_get_unary_op(node)) { @@ -5545,11 +5575,11 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod break; case GGML_OP_SOFT_MAX: - ggml_vk_soft_max(ctx, ctx->compute_ctx, src0, src1, src2, node); + ggml_vk_soft_max(ctx, ctx->compute_ctx, src0, src1, node); break; case GGML_OP_ROPE: - ggml_vk_rope(ctx, ctx->compute_ctx, src0, src1, node); + ggml_vk_rope(ctx, ctx->compute_ctx, src0, src1, src2, node); break; case GGML_OP_ARGSORT: @@ -5578,7 +5608,7 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod last_node = true; #endif - if (node->backend == GGML_BACKEND_TYPE_CPU || last_node) { + if (last_node) { ggml_vk_ctx_end(ctx->compute_ctx); ctx->compute_ctx->exit_tensor = node; ctx->compute_ctx = nullptr; @@ -5586,10 +5616,6 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod } static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor){ - if (ctx->disable) { - return false; - } - ggml_tensor_extra_gpu * extra = nullptr; switch (tensor->op) { @@ -5648,7 +5674,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_ } #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_compute_forward(" << tensor << ", name=" << tensor->name << ", op=" << ggml_op_name(tensor->op) << ", type=" << tensor->type << ", backend=" << tensor->backend << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << ", view_src=" << tensor->view_src << ", view_offs=" << tensor->view_offs << ")" << std::endl; + std::cerr << "ggml_vk_compute_forward(" << tensor << ", name=" << tensor->name << ", op=" << ggml_op_name(tensor->op) << ", type=" << tensor->type << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << ", view_src=" << tensor->view_src << ", view_offs=" << tensor->view_offs << ")" << std::endl; #endif #ifdef GGML_VULKAN_CHECK_RESULTS @@ -5688,9 +5714,6 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_ // Clean up after graph processing is done static void ggml_vk_graph_cleanup(ggml_backend_vk_context * ctx) { - if (ctx->disable) { - return; - } #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_graph_cleanup()" << std::endl; #endif @@ -5863,7 +5886,6 @@ GGML_CALL static void ggml_backend_vk_buffer_init_tensor(ggml_backend_buffer_t b extra->offset = (uint8_t *) tensor->data - (uint8_t *) vk_ptr_base; } - tensor->backend = GGML_BACKEND_TYPE_GPU; tensor->extra = extra; } @@ -5871,8 +5893,6 @@ GGML_CALL static void ggml_backend_vk_buffer_set_tensor(ggml_backend_buffer_t bu #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_buffer_set_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")" << std::endl; #endif - GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); - ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; @@ -5886,8 +5906,6 @@ GGML_CALL static void ggml_backend_vk_buffer_get_tensor(ggml_backend_buffer_t bu #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_buffer_get_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")" << std::endl; #endif - GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); - ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; @@ -6030,6 +6048,7 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_host_buffer_type_alloc_bu #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_host_buffer_type_alloc_buffer(" << size << ")" << std::endl; #endif + size += 32; // Behave like the CPU buffer type void * ptr = nullptr; try { ptr = ggml_vk_host_malloc(&vk_instance.contexts[0], size); @@ -6117,7 +6136,6 @@ GGML_CALL static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, g #endif ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type(ctx->idx) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type"); - GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; @@ -6138,7 +6156,6 @@ GGML_CALL static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, c #endif ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type(ctx->idx) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type"); - GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; @@ -6204,6 +6221,10 @@ GGML_CALL static void ggml_backend_vk_synchronize(ggml_backend_t backend) { ctx->transfer_ctx = nullptr; } +static bool ggml_vk_is_empty(ggml_tensor * node) { + return ggml_is_empty(node) || node->op == GGML_OP_NONE || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE; +} + GGML_CALL static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_graph_compute(" << cgraph->n_nodes << " nodes)" << std::endl; @@ -6218,7 +6239,7 @@ GGML_CALL static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backen int last_node = cgraph->n_nodes - 1; // If the last op in the cgraph isn't backend GPU, the command buffer doesn't get closed properly - while (last_node > 0 && (cgraph->nodes[last_node]->backend != GGML_BACKEND_TYPE_GPU || ggml_is_empty(cgraph->nodes[last_node]))) { + while (last_node > 0 && ggml_vk_is_empty(cgraph->nodes[last_node])) { last_node -= 1; } @@ -6232,7 +6253,7 @@ GGML_CALL static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backen for (int i = 0; i < cgraph->n_nodes; i++) { ggml_tensor * node = cgraph->nodes[i]; - if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) { + if (ggml_vk_is_empty(node)) { continue; } @@ -6534,7 +6555,7 @@ static void ggml_vk_print_graph_origin(const ggml_tensor * tensor, std::vectorop) << " gpu=" << (tensor->extra != nullptr) << " backend=" << tensor->backend << std::endl; + std::cerr << ggml_op_name(tensor->op) << " gpu=" << (tensor->extra != nullptr) << std::endl; done.push_back(tensor); @@ -6546,7 +6567,7 @@ static void ggml_vk_print_graph_origin(const ggml_tensor * tensor, std::vectortype != GGML_TYPE_F32 && tensor->type != GGML_TYPE_F16) { + if (tensor->type != GGML_TYPE_F32 && tensor->type != GGML_TYPE_F16 && tensor->type != GGML_TYPE_I32) { return; } i0 = std::max(i0, 5); @@ -6567,6 +6588,8 @@ static void ggml_vk_print_tensor_area(const ggml_tensor * tensor, const void * d val = *(const float *) ((const char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]); } else if (tensor->type == GGML_TYPE_F16) { val = ggml_fp16_to_fp32(*(const ggml_fp16_t *) ((const char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0])); + } else if (tensor->type == GGML_TYPE_I32) { + val = *(const int32_t *) ((const char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]); } else { GGML_ASSERT(false); } @@ -6582,7 +6605,7 @@ static void ggml_vk_print_tensor_area(const ggml_tensor * tensor, const void * d static void ggml_vk_print_tensor(ggml_backend_vk_context * ctx, const ggml_tensor * tensor, const char * name) { void * tensor_data = tensor->data; - if (tensor->backend == GGML_BACKEND_TYPE_GPU) { + if (ggml_backend_buffer_is_vk(tensor->buffer)) { const size_t tensor_size = ggml_nbytes(tensor); tensor_data = malloc(tensor_size); @@ -6593,12 +6616,12 @@ static void ggml_vk_print_tensor(ggml_backend_vk_context * ctx, const ggml_tenso } std::cerr << "TENSOR CHECK " << name << " (" << tensor->name << "): " << ggml_op_name(tensor->op) << std::endl; - std::cerr << "tensor=" << tensor << " tensor->backend: " << tensor->backend << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << std::endl; + std::cerr << "tensor=" << tensor << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << std::endl; if (tensor->src[0] != nullptr) { - std::cerr << "tensor->src[0]=" << tensor->src[0] << " name=" << tensor->src[0]->name << " op=" << ggml_op_name(tensor->src[0]->op) << " type=" << ggml_type_name(tensor->src[0]->type) << " backend=" << tensor->src[0]->backend << " ne0=" << tensor->src[0]->ne[0] << " nb0=" << tensor->src[0]->nb[0] << " ne1=" << tensor->src[0]->ne[1] << " nb1=" << tensor->src[0]->nb[1] << " ne2=" << tensor->src[0]->ne[2] << " nb2=" << tensor->src[0]->nb[2] << " ne3=" << tensor->src[0]->ne[3] << " nb3=" << tensor->src[0]->nb[3] << std::endl; + std::cerr << "tensor->src[0]=" << tensor->src[0] << " name=" << tensor->src[0]->name << " op=" << ggml_op_name(tensor->src[0]->op) << " type=" << ggml_type_name(tensor->src[0]->type) << " ne0=" << tensor->src[0]->ne[0] << " nb0=" << tensor->src[0]->nb[0] << " ne1=" << tensor->src[0]->ne[1] << " nb1=" << tensor->src[0]->nb[1] << " ne2=" << tensor->src[0]->ne[2] << " nb2=" << tensor->src[0]->nb[2] << " ne3=" << tensor->src[0]->ne[3] << " nb3=" << tensor->src[0]->nb[3] << std::endl; } if (tensor->src[1] != nullptr) { - std::cerr << "tensor->src[1]=" << tensor->src[1] << " name=" << tensor->src[1]->name << " op=" << ggml_op_name(tensor->src[1]->op) << " type=" << ggml_type_name(tensor->src[1]->type) << " backend=" << tensor->src[1]->backend << " ne0=" << tensor->src[1]->ne[0] << " nb0=" << tensor->src[1]->nb[0] << " ne1=" << tensor->src[1]->ne[1] << " nb1=" << tensor->src[1]->nb[1] << " ne2=" << tensor->src[1]->ne[2] << " nb2=" << tensor->src[1]->nb[2] << " ne3=" << tensor->src[1]->ne[3] << " nb3=" << tensor->src[1]->nb[3] << std::endl; + std::cerr << "tensor->src[1]=" << tensor->src[1] << " name=" << tensor->src[1]->name << " op=" << ggml_op_name(tensor->src[1]->op) << " type=" << ggml_type_name(tensor->src[1]->type) << " ne0=" << tensor->src[1]->ne[0] << " nb0=" << tensor->src[1]->nb[0] << " ne1=" << tensor->src[1]->ne[1] << " nb1=" << tensor->src[1]->nb[1] << " ne2=" << tensor->src[1]->ne[2] << " nb2=" << tensor->src[1]->nb[2] << " ne3=" << tensor->src[1]->ne[3] << " nb3=" << tensor->src[1]->nb[3] << std::endl; } std::cerr << std::endl << "Result:" << std::endl; ggml_vk_print_tensor_area(tensor, tensor_data, 5, 5, 0, 0); @@ -6609,43 +6632,11 @@ static void ggml_vk_print_tensor(ggml_backend_vk_context * ctx, const ggml_tenso std::vector done; ggml_vk_print_graph_origin(tensor, done); - if (tensor->backend == GGML_BACKEND_TYPE_GPU) { + if (ggml_backend_buffer_is_vk(tensor->buffer)) { free(tensor_data); } } -static void ggml_vk_check_tensor(const std::string& name, const ggml_tensor * tensor) { - return; - GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_CPU); - if (tensor->type != GGML_TYPE_F32 && tensor->type != GGML_TYPE_F16) { - return; - } - for (int i3 = 0; i3 < tensor->ne[3]; i3++) { - for (int i2 = 0; i2 < tensor->ne[2]; i2++) { - for (int i1 = 0; i1 < tensor->ne[1]; i1++) { - for (int i0 = 0; i0 < tensor->ne[0]; i0++) { - float val = 0.0f; - if (tensor->type == GGML_TYPE_F32) { - val = *(float *) ((char *) tensor->data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0]); - } else if (tensor->type == GGML_TYPE_F16) { - val = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) tensor->data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0])); - } - if (std::isnan(val)) { - std::cerr << "ERROR: TENSOR CHECK " << name << ": Invalid value in " << ggml_op_name(tensor->op) << " i3=" << i3 << " i2=" << i2 << " i1=" << i1 << " i0=" << i0 << " val=" << val << std::endl; - std::cerr << "tensor=" << tensor << " tensor->type=" << ggml_type_name(tensor->type) << " tensor->backend: " << tensor->backend << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << std::endl; - std::cerr << std::endl; - ggml_vk_print_tensor_area(tensor, tensor->data, i0, i1, i2, i3); - std::cerr << std::endl; - std::vector done; - ggml_vk_print_graph_origin(tensor, done); - GGML_ASSERT(false); - } - } - } - } - } -} - void * comp_result; size_t comp_size; size_t comp_nb[GGML_MAX_DIMS]; @@ -6699,10 +6690,10 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ src0_buffer = malloc(src0_size); src0_clone->data = src0_buffer; - if (src0->backend == GGML_BACKEND_TYPE_CPU) { + if (ggml_backend_buffer_is_host(src0->buffer)) { memcpy(src0_clone->data, src0->data, src0_size); memcpy(src0_clone->nb, src0->nb, sizeof(size_t) * GGML_MAX_DIMS); - } else if (src0->backend == GGML_BACKEND_TYPE_GPU) { + } else if (ggml_backend_buffer_is_vk(src0->buffer)) { ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src0->extra; vk_buffer buffer_gpu = extra->buffer_gpu.lock(); uint64_t offset = extra->offset; @@ -6733,8 +6724,6 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { ggml_vk_print_tensor(ctx, src0, "src0"); } - - ggml_vk_check_tensor(std::string(ggml_op_name(tensor->op)) + "->src0", src0_clone); } if (src1 != nullptr) { src1_clone = ggml_dup_tensor(ggml_ctx, src1); @@ -6743,10 +6732,10 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ src1_buffer = malloc(src1_size); src1_clone->data = src1_buffer; - if (src1->backend == GGML_BACKEND_TYPE_CPU) { + if (ggml_backend_buffer_is_host(src1->buffer)) { memcpy(src1_clone->data, src1->data, src1_size); memcpy(src1_clone->nb, src1->nb, sizeof(size_t) * GGML_MAX_DIMS); - } else if (src1->backend == GGML_BACKEND_TYPE_GPU) { + } else if (ggml_backend_buffer_is_vk(src1->buffer)) { ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src1->extra; vk_buffer buffer_gpu = extra->buffer_gpu.lock(); uint64_t offset = extra->offset; @@ -6777,12 +6766,12 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { ggml_vk_print_tensor(ctx, src1, "src1"); std::cerr << "TENSOR CHECK: " << ggml_op_name(src1_clone->op) << " (check " << check_counter << ")" << std::endl; - std::cerr << "src1_clone=" << tensor << " src1_clone->backend: " << src1_clone->backend << " src1_clone->type: " << ggml_type_name(src1_clone->type) << " ne0=" << src1_clone->ne[0] << " nb0=" << src1_clone->nb[0] << " ne1=" << src1_clone->ne[1] << " nb1=" << src1_clone->nb[1] << " ne2=" << src1_clone->ne[2] << " nb2=" << src1_clone->nb[2] << " ne3=" << src1_clone->ne[3] << " nb3=" << src1_clone->nb[3] << std::endl; + std::cerr << "src1_clone=" << tensor << " src1_clone->type: " << ggml_type_name(src1_clone->type) << " ne0=" << src1_clone->ne[0] << " nb0=" << src1_clone->nb[0] << " ne1=" << src1_clone->ne[1] << " nb1=" << src1_clone->nb[1] << " ne2=" << src1_clone->ne[2] << " nb2=" << src1_clone->nb[2] << " ne3=" << src1_clone->ne[3] << " nb3=" << src1_clone->nb[3] << std::endl; if (src1->src[0] != nullptr) { - std::cerr << "src1->src[0]=" << src1->src[0] << " op=" << ggml_op_name(src1->src[0]->op) << " type=" << ggml_type_name(src1->src[0]->type) << " backend=" << src1->src[0]->backend << " ne0=" << src1->src[0]->ne[0] << " nb0=" << src1->src[0]->nb[0] << " ne1=" << src1->src[0]->ne[1] << " nb1=" << src1->src[0]->nb[1] << " ne2=" << src1->src[0]->ne[2] << " nb2=" << src1->src[0]->nb[2] << " ne3=" << src1->src[0]->ne[3] << " nb3=" << src1->src[0]->nb[3] << std::endl; + std::cerr << "src1->src[0]=" << src1->src[0] << " op=" << ggml_op_name(src1->src[0]->op) << " type=" << ggml_type_name(src1->src[0]->type) << " ne0=" << src1->src[0]->ne[0] << " nb0=" << src1->src[0]->nb[0] << " ne1=" << src1->src[0]->ne[1] << " nb1=" << src1->src[0]->nb[1] << " ne2=" << src1->src[0]->ne[2] << " nb2=" << src1->src[0]->nb[2] << " ne3=" << src1->src[0]->ne[3] << " nb3=" << src1->src[0]->nb[3] << std::endl; } if (src1->src[1] != nullptr) { - std::cerr << "src1->src[1]=" << src1->src[1] << " op=" << ggml_op_name(src1->src[1]->op) << " type=" << ggml_type_name(src1->src[1]->type) << " backend=" << src1->src[1]->backend << " ne0=" << src1->src[1]->ne[0] << " nb0=" << src1->src[1]->nb[0] << " ne1=" << src1->src[1]->ne[1] << " nb1=" << src1->src[1]->nb[1] << " ne2=" << src1->src[1]->ne[2] << " nb2=" << src1->src[1]->nb[2] << " ne3=" << src1->src[1]->ne[3] << " nb3=" << src1->src[1]->nb[3] << std::endl; + std::cerr << "src1->src[1]=" << src1->src[1] << " op=" << ggml_op_name(src1->src[1]->op) << " type=" << ggml_type_name(src1->src[1]->type) << " ne0=" << src1->src[1]->ne[0] << " nb0=" << src1->src[1]->nb[0] << " ne1=" << src1->src[1]->ne[1] << " nb1=" << src1->src[1]->nb[1] << " ne2=" << src1->src[1]->ne[2] << " nb2=" << src1->src[1]->nb[2] << " ne3=" << src1->src[1]->ne[3] << " nb3=" << src1->src[1]->nb[3] << std::endl; } std::cerr << std::endl << "Result:" << std::endl; ggml_vk_print_tensor_area(src1_clone, src1_clone->data, 5, 5, 0, 0); @@ -6793,8 +6782,6 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ std::vector done; ggml_vk_print_graph_origin(src1_clone, done); } - - ggml_vk_check_tensor(std::string(ggml_op_name(tensor->op)) + "->src1", src1_clone); } if (src2 != nullptr) { src2_clone = ggml_dup_tensor(ggml_ctx, src2); @@ -6803,18 +6790,18 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ src2_buffer = malloc(src2_size); src2_clone->data = src2_buffer; - if (src2->backend == GGML_BACKEND_TYPE_CPU) { + if (ggml_backend_buffer_is_host(src2->buffer)) { memcpy(src2_clone->data, src2->data, src2_size); memcpy(src2_clone->nb, src2->nb, sizeof(size_t) * GGML_MAX_DIMS); - } else if (src2->backend == GGML_BACKEND_TYPE_GPU) { + } else if (ggml_backend_buffer_is_vk(src2->buffer)) { ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src2->extra; - vk_buffer buf = extra->buffer_gpu.lock(); + vk_buffer buffer_gpu = extra->buffer_gpu.lock(); uint64_t offset = extra->offset; if (!ggml_is_contiguous(src2) && ggml_vk_dim01_contiguous(src2)) { for (int i3 = 0; i3 < src2->ne[3]; i3++) { for (int i2 = 0; i2 < src2->ne[2]; i2++) { const int idx = i3*src2->ne[2] + i2; - ggml_vk_buffer_read(ctx, buf, offset + idx * src2->nb[2], ((char *)src2_clone->data + idx * src2_clone->nb[2]), src2->ne[1] * src2->nb[1]); + ggml_vk_buffer_read(ctx, buffer_gpu, offset + idx * src2->nb[2], ((char *)src2_clone->data + idx * src2_clone->nb[2]), src2->ne[1] * src2->nb[1]); } } @@ -6824,10 +6811,10 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ src2_clone->nb[i] = src2_clone->nb[i - 1]*src2_clone->ne[i - 1]; } } else { - if (offset + src2_size >= buf->size) { - src2_size = buf->size - offset; + if (offset + src2_size >= buffer_gpu->size) { + src2_size = buffer_gpu->size - offset; } - ggml_vk_buffer_read(ctx, buf, offset, src2_clone->data, src2_size); + ggml_vk_buffer_read(ctx, buffer_gpu, offset, src2_clone->data, src2_size); memcpy(src2_clone->nb, src2->nb, sizeof(size_t) * GGML_MAX_DIMS); } } else { @@ -6837,12 +6824,12 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { ggml_vk_print_tensor(ctx, src2, "src2"); std::cerr << "TENSOR CHECK: " << ggml_op_name(src2_clone->op) << " (check " << check_counter << ")" << std::endl; - std::cerr << "src2_clone=" << tensor << " src2_clone->backend: " << src2_clone->backend << " src2_clone->type: " << ggml_type_name(src2_clone->type) << " ne0=" << src2_clone->ne[0] << " nb0=" << src2_clone->nb[0] << " ne1=" << src2_clone->ne[1] << " nb1=" << src2_clone->nb[1] << " ne2=" << src2_clone->ne[2] << " nb2=" << src2_clone->nb[2] << " ne3=" << src2_clone->ne[3] << " nb3=" << src2_clone->nb[3] << std::endl; + std::cerr << "src2_clone=" << tensor << " src2_clone->type: " << ggml_type_name(src2_clone->type) << " ne0=" << src2_clone->ne[0] << " nb0=" << src2_clone->nb[0] << " ne1=" << src2_clone->ne[1] << " nb1=" << src2_clone->nb[1] << " ne2=" << src2_clone->ne[2] << " nb2=" << src2_clone->nb[2] << " ne3=" << src2_clone->ne[3] << " nb3=" << src2_clone->nb[3] << std::endl; if (src2->src[0] != nullptr) { - std::cerr << "src2->src[0]=" << src2->src[0] << " op=" << ggml_op_name(src2->src[0]->op) << " type=" << ggml_type_name(src2->src[0]->type) << " backend=" << src2->src[0]->backend << " ne0=" << src2->src[0]->ne[0] << " nb0=" << src2->src[0]->nb[0] << " ne1=" << src2->src[0]->ne[1] << " nb1=" << src2->src[0]->nb[1] << " ne2=" << src2->src[0]->ne[2] << " nb2=" << src2->src[0]->nb[2] << " ne3=" << src2->src[0]->ne[3] << " nb3=" << src2->src[0]->nb[3] << std::endl; + std::cerr << "src2->src[0]=" << src2->src[0] << " op=" << ggml_op_name(src2->src[0]->op) << " type=" << ggml_type_name(src2->src[0]->type) << " ne0=" << src2->src[0]->ne[0] << " nb0=" << src2->src[0]->nb[0] << " ne1=" << src2->src[0]->ne[1] << " nb1=" << src2->src[0]->nb[1] << " ne2=" << src2->src[0]->ne[2] << " nb2=" << src2->src[0]->nb[2] << " ne3=" << src2->src[0]->ne[3] << " nb3=" << src2->src[0]->nb[3] << std::endl; } if (src2->src[1] != nullptr) { - std::cerr << "src2->src[1]=" << src2->src[1] << " op=" << ggml_op_name(src2->src[1]->op) << " type=" << ggml_type_name(src2->src[1]->type) << " backend=" << src2->src[1]->backend << " ne0=" << src2->src[1]->ne[0] << " nb0=" << src2->src[1]->nb[0] << " ne1=" << src2->src[1]->ne[1] << " nb1=" << src2->src[1]->nb[1] << " ne2=" << src2->src[1]->ne[2] << " nb2=" << src2->src[1]->nb[2] << " ne3=" << src2->src[1]->ne[3] << " nb3=" << src2->src[1]->nb[3] << std::endl; + std::cerr << "src2->src[1]=" << src2->src[1] << " op=" << ggml_op_name(src2->src[1]->op) << " type=" << ggml_type_name(src2->src[1]->type) << " ne0=" << src2->src[1]->ne[0] << " nb0=" << src2->src[1]->nb[0] << " ne1=" << src2->src[1]->ne[1] << " nb1=" << src2->src[1]->nb[1] << " ne2=" << src2->src[1]->ne[2] << " nb2=" << src2->src[1]->nb[2] << " ne3=" << src2->src[1]->ne[3] << " nb3=" << src2->src[1]->nb[3] << std::endl; } std::cerr << std::endl << "Result:" << std::endl; ggml_vk_print_tensor_area(src2_clone, src2_clone->data, 5, 5, 0, 0); @@ -6853,8 +6840,6 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ std::vector done; ggml_vk_print_graph_origin(src2_clone, done); } - - ggml_vk_check_tensor(std::string(ggml_op_name(tensor->op)) + "->src2", src2_clone); } if (tensor->op == GGML_OP_MUL_MAT) { @@ -6875,7 +6860,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ tensor_clone = ggml_rms_norm(ggml_ctx, src0_clone, *(float *)tensor->op_params); } else if (tensor->op == GGML_OP_SOFT_MAX) { if (src1 != nullptr) { - tensor_clone = ggml_soft_max_ext(ggml_ctx, src0_clone, src1_clone, src2_clone, ((float *)tensor->op_params)[0], ((float *)tensor->op_params)[1]); + tensor_clone = ggml_soft_max_ext(ggml_ctx, src0_clone, src1_clone, ((float *)tensor->op_params)[0], ((float *)tensor->op_params)[1]); } else { tensor_clone = ggml_soft_max(ggml_ctx, src0_clone); } @@ -6892,7 +6877,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ float attn_factor = ((float *) tensor->op_params)[8]; float beta_fast = ((float *) tensor->op_params)[9]; float beta_slow = ((float *) tensor->op_params)[10]; - tensor_clone = ggml_rope_custom(ggml_ctx, src0_clone, src1_clone, n_dims, mode, n_ggml_ctx, n_orig_ggml_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); + tensor_clone = ggml_rope_ext(ggml_ctx, src0_clone, src1_clone, src2_clone, n_dims, mode, n_ggml_ctx, n_orig_ggml_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); } else if (tensor->op == GGML_OP_UNARY) { switch (ggml_get_unary_op(tensor)) { case GGML_UNARY_OP_SILU: @@ -6935,17 +6920,11 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ GGML_ASSERT(false); } - // Disable vulkan here to avoid the hooks in ggml.c - ctx->disable = true; - ggml_cgraph * cgraph = ggml_new_graph(ggml_ctx); ggml_build_forward_expand(cgraph, tensor_clone); ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 8); - ctx->disable = false; - - ggml_vk_check_tensor(ggml_op_name(tensor->op), tensor_clone); if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { ggml_vk_print_tensor(ctx, tensor_clone, "tensor_clone"); } @@ -6962,9 +6941,6 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ if (src1 != nullptr) { free(src1_buffer); } - if (src2 != nullptr) { - free(src2_buffer); - } ggml_free(ggml_ctx); } @@ -6989,7 +6965,7 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_ void * tensor_data = tensor->data; - if (tensor->backend == GGML_BACKEND_TYPE_GPU) { + if (ggml_backend_buffer_is_vk(tensor->buffer)) { size_t tensor_size = ggml_nbytes(tensor); tensor_data = malloc(tensor_size); @@ -7024,8 +7000,11 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_ } else if (tensor->type == GGML_TYPE_F16) { correct = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) comp_result + i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0])); result = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) tensor_data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0])); + } else if (tensor->type == GGML_TYPE_I32) { + correct = *(int32_t *) ((char *) comp_result + i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0]); + result = *(int32_t *) ((char *) tensor_data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0]); } else { - std::cerr << "comp_size=" << comp_size << " but required is " << (i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0]) << std::endl; + std::cerr << "Results check not implemented for type " << ggml_type_name(tensor->type) << std::endl; } } else { std::cerr << "Missing debug code for type " << ggml_type_name(tensor->type) << std::endl; @@ -7034,12 +7013,12 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_ if ((std::isnan(correct) != std::isnan(result)) || (std::isinf(correct) != std::isinf(result)) || !buffer_size_fit) { std::cerr << "ERROR: Invalid value in " << ggml_op_name(tensor->op) << " i3=" << i3 << " i2=" << i2 << " i1=" << i1 << " i0=" << i0 << " result=" << result << " correct=" << correct << " avg_err=" << (avg_err / counter) << std::endl; - std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->backend: " << tensor->backend << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl; + std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl; if (src0 != nullptr) { - std::cerr << "src0=" << src0 << " src0->name=" << src0->name << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " backend=" << src0->backend << " ne0=" << src0->ne[0] << " nb0=" << src0->nb[0] << " ne1=" << src0->ne[1] << " nb1=" << src0->nb[1] << " ne2=" << src0->ne[2] << " nb2=" << src0->nb[2] << " ne3=" << src0->ne[3] << " nb3=" << src0->nb[3] << " offset=" << src0->view_offs << std::endl; + std::cerr << "src0=" << src0 << " src0->name=" << src0->name << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " ne0=" << src0->ne[0] << " nb0=" << src0->nb[0] << " ne1=" << src0->ne[1] << " nb1=" << src0->nb[1] << " ne2=" << src0->ne[2] << " nb2=" << src0->nb[2] << " ne3=" << src0->ne[3] << " nb3=" << src0->nb[3] << " offset=" << src0->view_offs << std::endl; } if (src1 != nullptr) { - std::cerr << "src1=" << src1 << " src1->name=" << src1->name << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " backend=" << src1->backend << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl; + std::cerr << "src1=" << src1 << " src1->name=" << src1->name << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl; } std::cerr << "First error: result=" << first_error_result << " correct=" << first_error_correct << " i3=" << first_error[3] << " i2=" << first_error[2] << " i1=" << first_error[1] << " i0=" << first_error[0] << std::endl; std::cerr << std::endl << "Result:" << std::endl; @@ -7075,12 +7054,12 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_ if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { std::cerr << "TENSOR CHECK: avg_err=" << avg_err << " in " << ggml_op_name(tensor->op) << " (check " << check_counter << ")" << std::endl; - std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->backend: " << tensor->backend << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl; + std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl; if (src0 != nullptr) { - std::cerr << "src0=" << src0 << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " backend=" << src0->backend << " ne0=" << src0->ne[0] << " nb0=" << src0->nb[0] << " ne1=" << src0->ne[1] << " nb1=" << src0->nb[1] << " ne2=" << src0->ne[2] << " nb2=" << src0->nb[2] << " ne3=" << src0->ne[3] << " nb3=" << src0->nb[3] << " offset=" << src0->view_offs << std::endl; + std::cerr << "src0=" << src0 << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " ne0=" << src0->ne[0] << " nb0=" << src0->nb[0] << " ne1=" << src0->ne[1] << " nb1=" << src0->nb[1] << " ne2=" << src0->ne[2] << " nb2=" << src0->nb[2] << " ne3=" << src0->ne[3] << " nb3=" << src0->nb[3] << " offset=" << src0->view_offs << std::endl; } if (src1 != nullptr) { - std::cerr << "src1=" << src1 << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " backend=" << src1->backend << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl; + std::cerr << "src1=" << src1 << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl; } std::cerr << "First error: result=" << first_error_result << " correct=" << first_error_correct << " i3=" << first_error[3] << " i2=" << first_error[2] << " i1=" << first_error[1] << " i0=" << first_error[0] << std::endl; std::cerr << std::endl << "Result:" << std::endl; @@ -7099,12 +7078,12 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_ if (avg_err > 0.05 || std::isnan(avg_err)) { std::cerr << "ERROR: avg_err=" << avg_err << " in " << ggml_op_name(tensor->op) << " (check " << check_counter << ")" << std::endl; - std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->backend: " << tensor->backend << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl; + std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl; if (src0 != nullptr) { - std::cerr << "src0=" << src0 << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " backend=" << src0->backend << " ne0=" << src0->ne[0] << " nb0=" << src0->nb[0] << " ne1=" << src0->ne[1] << " nb1=" << src0->nb[1] << " ne2=" << src0->ne[2] << " nb2=" << src0->nb[2] << " ne3=" << src0->ne[3] << " nb3=" << src0->nb[3] << " offset=" << src0->view_offs << std::endl; + std::cerr << "src0=" << src0 << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " ne0=" << src0->ne[0] << " nb0=" << src0->nb[0] << " ne1=" << src0->ne[1] << " nb1=" << src0->nb[1] << " ne2=" << src0->ne[2] << " nb2=" << src0->nb[2] << " ne3=" << src0->ne[3] << " nb3=" << src0->nb[3] << " offset=" << src0->view_offs << std::endl; } if (src1 != nullptr) { - std::cerr << "src1=" << src1 << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " backend=" << src1->backend << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl; + std::cerr << "src1=" << src1 << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl; } std::cerr << "First error: result=" << first_error_result << " correct=" << first_error_correct << " i3=" << first_error[3] << " i2=" << first_error[2] << " i1=" << first_error[1] << " i0=" << first_error[0] << std::endl; std::cerr << std::endl << "Result:" << std::endl; @@ -7116,14 +7095,14 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_ ggml_vk_print_graph_origin(tensor, done); GGML_ASSERT(false); } else { - std::cerr << check_counter << " " << tensor->name << " op=" << ggml_op_name(tensor->op) << " backend=" << tensor->backend << " avg_err=" << avg_err << std::endl; + std::cerr << check_counter << " " << tensor->name << " op=" << ggml_op_name(tensor->op) << " avg_err=" << avg_err << std::endl; } free(comp_result); comp_result = nullptr; comp_size = 0; - if (tensor->backend == GGML_BACKEND_TYPE_GPU) { + if (ggml_backend_buffer_is_vk(tensor->buffer)) { free(tensor_data); } } diff --git a/ggml.c b/ggml.c index 2c26781189458..05b74932ff11e 100644 --- a/ggml.c +++ b/ggml.c @@ -4,7 +4,6 @@ #include "ggml-impl.h" #include "ggml-quants.h" #include "ggml.h" -#include "sgemm.h" #if defined(_MSC_VER) || defined(__MINGW32__) #include // using malloc.h with MSC/MINGW @@ -37,6 +36,10 @@ #undef GGML_USE_LLAMAFILE #endif +#ifdef GGML_USE_LLAMAFILE +#include "sgemm.h" +#endif + #if defined(_MSC_VER) // disable "possible loss of data" to avoid hundreds of casts // we should just be careful :) @@ -109,6 +112,8 @@ typedef void * thread_ret_t; #endif +typedef pthread_t ggml_thread_t; + #ifdef GGML_USE_CPU_HBM #include #endif @@ -160,9 +165,6 @@ void ggml_print_backtrace(void) { #define GGML_DEBUG 0 #define GGML_GELU_FP16 #define GGML_GELU_QUICK_FP16 -#define GGML_SILU_FP16 -// #define GGML_CROSS_ENTROPY_EXP_FP16 -// #define GGML_FLASH_ATTN_EXP_FP16 #define GGML_SOFT_MAX_UNROLL 4 #define GGML_VEC_DOT_UNROLL 2 @@ -313,12 +315,6 @@ static ggml_fp16_t ggml_table_gelu_f16[1 << 16]; // precomputed quick gelu table for f16 (128 KB) static ggml_fp16_t ggml_table_gelu_quick_f16[1 << 16]; -// precomputed silu table for f16 (128 KB) -static ggml_fp16_t ggml_table_silu_f16[1 << 16]; - -// precomputed exp table for f16 (128 KB) -static ggml_fp16_t ggml_table_exp_f16[1 << 16]; - // precomputed f32 table for f16 (256 KB) (ggml-impl.h) float ggml_table_f32_f16[1 << 16]; @@ -410,10 +406,10 @@ void ggml_fp32_to_bf16_row(const float * x, ggml_bf16_t * y, int64_t n) { int i = 0; #if defined(__AVX512BF16__) for (; i + 32 <= n; i += 32) { - _mm512_storeu_ps( - (__m512 *)(y + i), - (__m512)_mm512_cvtne2ps_pbh(_mm512_loadu_ps(x + i + 16), - _mm512_loadu_ps(x + i))); + _mm512_storeu_si512( + (__m512i *)(y + i), + m512i(_mm512_cvtne2ps_pbh(_mm512_loadu_ps(x + i + 16), + _mm512_loadu_ps(x + i)))); } #endif for (; i < n; i++) { @@ -875,22 +871,14 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { }, [GGML_TYPE_IQ4_XS] = { .type_name = "iq4_xs", -#if QK_K == 64 - .blck_size = QK4_NL, -#else .blck_size = QK_K, -#endif .type_size = sizeof(block_iq4_xs), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_iq4_xs, .from_float = quantize_row_iq4_xs, .from_float_reference = (ggml_from_float_t)quantize_row_iq4_xs_reference, .vec_dot = ggml_vec_dot_iq4_xs_q8_K, -#if QK_K == 64 - .vec_dot_type = GGML_TYPE_Q8_0, -#else .vec_dot_type = GGML_TYPE_Q8_K, -#endif .nrows = 1, }, [GGML_TYPE_Q8_K] = { @@ -1303,6 +1291,8 @@ static inline void __avx_f32cx8_store(ggml_fp16_t *x, __m256 y) { #define GGML_F16_VEC_ZERO GGML_F32x4_ZERO #define GGML_F16_VEC_SET1 GGML_F32x4_SET1 #define GGML_F16_VEC_FMA GGML_F32x4_FMA +#define GGML_F16_VEC_ADD GGML_F32x4_ADD +#define GGML_F16_VEC_MUL GGML_F32x4_MUL #define GGML_F16_VEC_REDUCE GGML_F32x4_REDUCE // Use vec_xl, not vec_ld, in case the load address is not aligned. #define GGML_F16_VEC_LOAD(p, i) (i & 0x1) ? \ @@ -1525,6 +1515,195 @@ static inline void __sse_f16x4_store(ggml_fp16_t *x, __m128 y) { #define GGML_F16_VEC_MUL GGML_F32Cx4_MUL #define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE +#elif defined(__loongarch_asx) + +#define GGML_SIMD + +// F32 LASX +#define GGML_F32_STEP 32 +#define GGML_F32_EPR 8 + +#define GGML_F32x8 __m256 +#define GGML_F32x8_ZERO (__m256)__lasx_xvldi(0) +#define GGML_F32x8_SET1(x) (__m256)__lasx_xvreplfr2vr_s((x)) +#define GGML_F32x8_LOAD(x) (__m256)__lasx_xvld((x), 0) +#define GGML_F32x8_STORE(x,y) __lasx_xvst((y), (x), 0) +#define GGML_F32x8_FMA(a, b, c) __lasx_xvfmadd_s(b, c, a) +#define GGML_F32x8_ADD __lasx_xvfadd_s +#define GGML_F32x8_MUL __lasx_xvfmul_s +#define GGML_F32x8_REDUCE(res, x) \ +do { \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = __lasx_xvfadd_s(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = __lasx_xvfadd_s(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = __lasx_xvfadd_s(x[i], x[offset+i]); \ + } \ + float *tmp_p = (float *)&x[0]; \ + res = tmp_p[0] + tmp_p[1] + tmp_p[2] + tmp_p[3] + tmp_p[4] + tmp_p[5] + tmp_p[6] + tmp_p[7]; \ +} while (0) +// TODO: is this optimal ? + +#define GGML_F32_VEC GGML_F32x8 +#define GGML_F32_VEC_ZERO GGML_F32x8_ZERO +#define GGML_F32_VEC_SET1 GGML_F32x8_SET1 +#define GGML_F32_VEC_LOAD GGML_F32x8_LOAD +#define GGML_F32_VEC_STORE GGML_F32x8_STORE +#define GGML_F32_VEC_FMA GGML_F32x8_FMA +#define GGML_F32_VEC_ADD GGML_F32x8_ADD +#define GGML_F32_VEC_MUL GGML_F32x8_MUL +#define GGML_F32_VEC_REDUCE GGML_F32x8_REDUCE + +// F16 LASX + +#define GGML_F16_STEP 32 +#define GGML_F16_EPR 8 + +// F16 arithmetic is not supported by AVX, so we use F32 instead + +#define GGML_F32Cx8 __m256 +#define GGML_F32Cx8_ZERO (__m256)__lasx_xvldi(0) +#define GGML_F32Cx8_SET1(x) (__m256)__lasx_xvreplgr2vr_w((x)) + +static inline __m256 __lasx_f32cx8_load(ggml_fp16_t *x) { + float tmp[8]; + + for (int i = 0; i < 8; i++) { + tmp[i] = GGML_FP16_TO_FP32(x[i]); + } + + return (__m256)__lasx_xvld(tmp, 0); +} +static inline void __lasx_f32cx8_store(ggml_fp16_t *x, __m256 y) { + float arr[8]; + + __lasx_xvst(y, arr, 0); + + for (int i = 0; i < 8; i++) + x[i] = GGML_FP32_TO_FP16(arr[i]); +} +#define GGML_F32Cx8_LOAD(x) __lasx_f32cx8_load(x) +#define GGML_F32Cx8_STORE(x, y) __lasx_f32cx8_store(x, y) + +#define GGML_F32Cx8_FMA GGML_F32x8_FMA +#define GGML_F32Cx8_ADD __lasx_xvfadd_s +#define GGML_F32Cx8_MUL __lasx_xvfmul_s +#define GGML_F32Cx8_REDUCE GGML_F32x8_REDUCE + +#define GGML_F16_VEC GGML_F32Cx8 +#define GGML_F16_VEC_ZERO GGML_F32Cx8_ZERO +#define GGML_F16_VEC_SET1 GGML_F32Cx8_SET1 +#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx8_LOAD(p) +#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx8_STORE(p, r[i]) +#define GGML_F16_VEC_FMA GGML_F32Cx8_FMA +#define GGML_F16_VEC_ADD GGML_F32Cx8_ADD +#define GGML_F16_VEC_MUL GGML_F32Cx8_MUL +#define GGML_F16_VEC_REDUCE GGML_F32Cx8_REDUCE + +#elif defined(__loongarch_sx) + +#define GGML_SIMD + +// F32 LSX + +#define GGML_F32_STEP 32 +#define GGML_F32_EPR 4 + +#define GGML_F32x4 __m128 +#define GGML_F32x4_ZERO __lsx_vldi(0) +#define GGML_F32x4_SET1(x) __lsx_vinsgr2vr_w(__lsx_vldi(0),(x), 0) +#define GGML_F32x4_LOAD(x) __lsx_vld((x), 0) +#define GGML_F32x4_STORE((x),(y)) __lsx_vst((y), (x), 0) +#define GGML_F32x4_FMA(a, b, c) __lsx_vfmadd_s(b, c, a) +#define GGML_F32x4_ADD __lsx_vfadd_s +#define GGML_F32x4_MUL __lsx_vfmul_s +#define GGML_F32x4_REDUCE(res, x) \ +{ \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = __lsx_vfadd_s(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = __lsx_vfadd_s(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = __lsx_vfadd_s(x[i], x[offset+i]); \ + } \ + __m128i tmp = __lsx_vsrli_d((__m128i)x[0], 32); \ + tmp = (__m128i)__lsx_vfadd_s((__m128)tmp, x[0]); \ + tmp = __lsx_vpickev_w(__lsx_vldi(0), tmp); \ + const __m128 t0 = __lsx_vshuf4i_w(tmp, 0x88); \ + tmp = __lsx_vsrli_d((__m128i)t0, 32); \ + tmp = (__m128i)__lsx_vfadd_s((__m128)tmp, t0); \ + tmp = __lsx_vpickev_w(__lsx_vldi(0), tmp); \ + res = (ggml_float) __lsx_vpickve2gr_w(__lsx_vshuf4i_w(tmp, 0x88), 0); \ +} + +#define GGML_F32_VEC GGML_F32x4 +#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO +#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 +#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD +#define GGML_F32_VEC_STORE GGML_F32x4_STORE +#define GGML_F32_VEC_FMA GGML_F32x4_FMA +#define GGML_F32_VEC_ADD GGML_F32x4_ADD +#define GGML_F32_VEC_MUL GGML_F32x4_MUL +#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE + +// F16 LSX + +#define GGML_F16_STEP 32 +#define GGML_F16_EPR 4 + +static inline __m128 __lsx_f16x4_load(ggml_fp16_t *x) { + float tmp[4]; + + tmp[0] = GGML_FP16_TO_FP32(x[0]); + tmp[1] = GGML_FP16_TO_FP32(x[1]); + tmp[2] = GGML_FP16_TO_FP32(x[2]); + tmp[3] = GGML_FP16_TO_FP32(x[3]); + + return __lsx_vld(tmp, 0); +} + +static inline void __lsx_f16x4_store(ggml_fp16_t *x, __m128 y) { + float arr[4]; + + __lsx_vst(y, arr, 0); + + x[0] = GGML_FP32_TO_FP16(arr[0]); + x[1] = GGML_FP32_TO_FP16(arr[1]); + x[2] = GGML_FP32_TO_FP16(arr[2]); + x[3] = GGML_FP32_TO_FP16(arr[3]); +} + +#define GGML_F32Cx4 __m128 +#define GGML_F32Cx4_ZERO __lsx_vldi(0) +#define GGML_F32Cx4_SET1(x) __lsx_vinsgr2vr_w(__lsx_vldi(0),(x), 0) +#define GGML_F32Cx4_LOAD(x) __lsx_f16x4_load(x) +#define GGML_F32Cx4_STORE(x, y) __lsx_f16x4_store(x, y) +#define GGML_F32Cx4_FMA GGML_F32x4_FMA +#define GGML_F32Cx4_ADD __lsx_vfadd_s +#define GGML_F32Cx4_MUL __lsx_vfmul_s +#define GGML_F32Cx4_REDUCE GGML_F32x4_REDUCE + +#define GGML_F16_VEC GGML_F32Cx4 +#define GGML_F16_VEC_ZERO GGML_F32Cx4_ZERO +#define GGML_F16_VEC_SET1 GGML_F32Cx4_SET1 +#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx4_LOAD(p) +#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE(p, r[i]) +#define GGML_F16_VEC_FMA GGML_F32Cx4_FMA +#define GGML_F16_VEC_ADD GGML_F32Cx4_ADD +#define GGML_F16_VEC_MUL GGML_F32Cx4_MUL +#define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE + #endif // GGML_F32_ARR / GGML_F16_ARR @@ -1534,6 +1713,59 @@ static inline void __sse_f16x4_store(ggml_fp16_t *x, __m128 y) { #define GGML_F16_ARR (GGML_F16_STEP/GGML_F16_EPR) #endif +// +// ggml context +// + +struct ggml_context { + size_t mem_size; + void* mem_buffer; + bool mem_buffer_owned; + bool no_alloc; + bool no_alloc_save; // this is used to save the no_alloc state when using scratch buffers + + int n_objects; + + struct ggml_object* objects_begin; + struct ggml_object* objects_end; + + struct ggml_scratch scratch; + struct ggml_scratch scratch_save; +}; + +struct ggml_context_container { + bool used; + + struct ggml_context context; +}; + +struct ggml_compute_state_shared { + const struct ggml_cgraph* cgraph; + const struct ggml_cplan* cplan; + + int64_t perf_node_start_cycles; + int64_t perf_node_start_time_us; + + const int n_threads; + + // synchronization primitives + atomic_int n_active; // num active threads + atomic_int node_n; // active graph node + atomic_int node_task; // active graph node task phase + + ggml_abort_callback abort_callback; // abort ggml_graph_compute when true + void* abort_callback_data; + + atomic_int current_chunk; // currently processing chunk during Mat_Mul, shared between all the threads. +}; + +struct ggml_compute_state { + ggml_thread_t thrd; + int ith; + struct ggml_compute_state_shared* shared; + enum ggml_status ec; +}; + // // fundamental operations // @@ -1615,10 +1847,10 @@ static void ggml_vec_dot_bf16(int n, float * restrict s, size_t bs, ggml_bf16_t __m512 c1 = _mm512_setzero_ps(); __m512 c2 = _mm512_setzero_ps(); for (; i + 64 <= n; i += 64) { - c1 = _mm512_dpbf16_ps(c1, (__m512bh)_mm512_loadu_ps((const float *)(x + i)), - (__m512bh)_mm512_loadu_ps((const float *)(y + i))); - c2 = _mm512_dpbf16_ps(c2, (__m512bh)_mm512_loadu_ps((const float *)(x + i + 32)), - (__m512bh)_mm512_loadu_ps((const float *)(y + i + 32))); + c1 = _mm512_dpbf16_ps(c1, m512bh(_mm512_loadu_si512((x + i))), + m512bh(_mm512_loadu_si512((y + i)))); + c2 = _mm512_dpbf16_ps(c2, m512bh(_mm512_loadu_si512((x + i + 32))), + m512bh(_mm512_loadu_si512((y + i + 32)))); } sumf += (ggml_float)_mm512_reduce_add_ps(c1); sumf += (ggml_float)_mm512_reduce_add_ps(c2); @@ -1949,6 +2181,7 @@ inline static void ggml_vec_tanh_f32 (const int n, float * y, const float * x) { inline static void ggml_vec_elu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : expf(x[i])-1; } inline static void ggml_vec_relu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : 0.f; } inline static void ggml_vec_leaky_relu_f32 (const int n, float * y, const float * x, const float ns) { for (int i = 0; i < n; ++i) y[i] = ((x[i] > 0.f) ? x[i] : 0.f) + ns * ((x[i] < 0.0f) ? x[i] : 0.f); } +inline static void ggml_vec_sigmoid_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = 1.f / (1.f + expf(-x[i])); } // TODO: optimize performance inline static void ggml_vec_hardswish_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i] * fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); } inline static void ggml_vec_hardsigmoid_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); } @@ -2024,52 +2257,291 @@ inline static float ggml_silu_f32(float x) { return x/(1.0f + expf(-x)); } -//inline static void ggml_vec_silu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { -// const uint16_t * i16 = (const uint16_t *) x; -// for (int i = 0; i < n; ++i) { -// y[i] = ggml_table_silu_f16[i16[i]]; -// } -//} +#if defined(__ARM_NEON) && defined(__aarch64__) + +// adapted from arm limited optimized routine +// the maximum error is 1.45358 plus 0.5 ulps +// numbers above 88.38 will flush to infinity +// numbers beneath -103.97 will flush to zero +inline static float32x4_t ggml_v_expf(float32x4_t x) { + const float32x4_t r = vdupq_n_f32(0x1.8p23f); + const float32x4_t z = vfmaq_f32(r, x, vdupq_n_f32(0x1.715476p+0f)); + const float32x4_t n = vsubq_f32(z, r); + const float32x4_t b = vfmsq_f32(vfmsq_f32(x, n, vdupq_n_f32(0x1.62e4p-1f)), n, + vdupq_n_f32(0x1.7f7d1cp-20f)); + const uint32x4_t e = vshlq_n_u32(vreinterpretq_u32_f32(z), 23); + const float32x4_t k = vreinterpretq_f32_u32(vaddq_u32(e, vreinterpretq_u32_f32(vdupq_n_f32(1)))); + const uint32x4_t c = vcagtq_f32(n, vdupq_n_f32(126)); + const float32x4_t u = vmulq_f32(b, b); + const float32x4_t j = vfmaq_f32( + vmulq_f32(vdupq_n_f32(0x1.ffffecp-1f), b), + vfmaq_f32(vfmaq_f32(vdupq_n_f32(0x1.fffdb6p-2f), vdupq_n_f32(0x1.555e66p-3f), b), + vfmaq_f32(vdupq_n_f32(0x1.573e2ep-5f), vdupq_n_f32(0x1.0e4020p-7f), b), u), u); + if (!vpaddd_u64(vreinterpretq_u64_u32(c))) + return vfmaq_f32(k, j, k); + const uint32x4_t d = vandq_u32(vclezq_f32(n), vdupq_n_u32(0x82000000)); + const float32x4_t s1 = vreinterpretq_f32_u32(vaddq_u32(d, vdupq_n_u32(0x7f000000))); + const float32x4_t s2 = vreinterpretq_f32_u32(vsubq_u32(e, d)); + return vbslq_f32(vcagtq_f32(n, vdupq_n_f32(192)), vmulq_f32(s1, s1), + vbslq_f32(c, vmulq_f32(vfmaq_f32(s2, s2, j), s1), vfmaq_f32(k, k, j))); +} + +// computes silu x/(1+exp(-x)) in single precision vector +inline static float32x4_t ggml_v_silu(float32x4_t x) { + const float32x4_t one = vdupq_n_f32(1.0f); + const float32x4_t zero = vdupq_n_f32(0.0f); + const float32x4_t neg_x = vsubq_f32(zero, x); + const float32x4_t exp_neg_x = ggml_v_expf(neg_x); + const float32x4_t one_plus_exp_neg_x = vaddq_f32(one, exp_neg_x); + return vdivq_f32(x, one_plus_exp_neg_x); +} + +#elif defined(__AVX512F__) && defined(__AVX512DQ__) + +// adapted from arm limited optimized routine +// the maximum error is 1.45358 plus 0.5 ulps +// numbers above 88.38 will flush to infinity +// numbers beneath -103.97 will flush to zero +inline static __m512 ggml_v_expf(__m512 x) { + const __m512 r = _mm512_set1_ps(0x1.8p23f); + const __m512 z = _mm512_fmadd_ps(x, _mm512_set1_ps(0x1.715476p+0f), r); + const __m512 n = _mm512_sub_ps(z, r); + const __m512 b = _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.7f7d1cp-20f), + _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.62e4p-1f), x)); + const __m512i e = _mm512_slli_epi32(_mm512_castps_si512(z), 23); + const __m512 k = _mm512_castsi512_ps(_mm512_add_epi32(e, _mm512_castps_si512(_mm512_set1_ps(1)))); + const __mmask16 c = _mm512_cmp_ps_mask(_mm512_abs_ps(n), _mm512_set1_ps(126), _CMP_GT_OQ); + const __m512 u = _mm512_mul_ps(b, b); + const __m512 j = _mm512_fmadd_ps(_mm512_fmadd_ps(_mm512_fmadd_ps(_mm512_set1_ps(0x1.0e4020p-7f), b, + _mm512_set1_ps(0x1.573e2ep-5f)), u, + _mm512_fmadd_ps(_mm512_set1_ps(0x1.555e66p-3f), b, + _mm512_set1_ps(0x1.fffdb6p-2f))), + u, _mm512_mul_ps(_mm512_set1_ps(0x1.ffffecp-1f), b)); + if (_mm512_kortestz(c, c)) + return _mm512_fmadd_ps(j, k, k); + const __m512i g = _mm512_and_si512( + _mm512_movm_epi32(_mm512_cmp_ps_mask(n, _mm512_setzero_ps(), _CMP_LE_OQ)), + _mm512_set1_epi32(0x82000000u)); + const __m512 s1 = + _mm512_castsi512_ps(_mm512_add_epi32(g, _mm512_set1_epi32(0x7f000000u))); + const __m512 s2 = _mm512_castsi512_ps(_mm512_sub_epi32(e, g)); + const __mmask16 d = + _mm512_cmp_ps_mask(_mm512_abs_ps(n), _mm512_set1_ps(192), _CMP_GT_OQ); + return _mm512_mask_blend_ps( + d, _mm512_mask_blend_ps( + c, _mm512_fmadd_ps(k, j, k), + _mm512_mul_ps(_mm512_fmadd_ps(s2, j, s2), s1)), + _mm512_mul_ps(s1, s1)); +} + +// computes silu x/(1+exp(-x)) in single precision vector +inline static __m512 ggml_v_silu(__m512 x) { + const __m512 one = _mm512_set1_ps(1); + const __m512 zero = _mm512_setzero_ps(); + const __m512 neg_x = _mm512_sub_ps(zero, x); + const __m512 exp_neg_x = ggml_v_expf(neg_x); + const __m512 one_plus_exp_neg_x = _mm512_add_ps(one, exp_neg_x); + return _mm512_div_ps(x, one_plus_exp_neg_x); +} + +#elif defined(__AVX2__) && defined(__FMA__) + +// adapted from arm limited optimized routine +// the maximum error is 1.45358 plus 0.5 ulps +// numbers above 88.38 will flush to infinity +// numbers beneath -103.97 will flush to zero +inline static __m256 ggml_v_expf(__m256 x) { + const __m256 r = _mm256_set1_ps(0x1.8p23f); + const __m256 z = _mm256_fmadd_ps(x, _mm256_set1_ps(0x1.715476p+0f), r); + const __m256 n = _mm256_sub_ps(z, r); + const __m256 b = _mm256_fnmadd_ps(n, _mm256_set1_ps(0x1.7f7d1cp-20f), + _mm256_fnmadd_ps(n, _mm256_set1_ps(0x1.62e4p-1f), x)); + const __m256i e = _mm256_slli_epi32(_mm256_castps_si256(z), 23); + const __m256 k = _mm256_castsi256_ps( + _mm256_add_epi32(e, _mm256_castps_si256(_mm256_set1_ps(1)))); + const __m256i c = _mm256_castps_si256( + _mm256_cmp_ps(_mm256_andnot_ps(_mm256_set1_ps(-0.f), n), + _mm256_set1_ps(126), _CMP_GT_OQ)); + const __m256 u = _mm256_mul_ps(b, b); + const __m256 j = _mm256_fmadd_ps(_mm256_fmadd_ps(_mm256_fmadd_ps(_mm256_set1_ps(0x1.0e4020p-7f), b, + _mm256_set1_ps(0x1.573e2ep-5f)), u, + _mm256_fmadd_ps(_mm256_set1_ps(0x1.555e66p-3f), b, + _mm256_set1_ps(0x1.fffdb6p-2f))), + u, _mm256_mul_ps(_mm256_set1_ps(0x1.ffffecp-1f), b)); + if (!_mm256_movemask_ps(_mm256_castsi256_ps(c))) + return _mm256_fmadd_ps(j, k, k); + const __m256i g = _mm256_and_si256( + _mm256_castps_si256(_mm256_cmp_ps(n, _mm256_setzero_ps(), _CMP_LE_OQ)), + _mm256_set1_epi32(0x82000000u)); + const __m256 s1 = + _mm256_castsi256_ps(_mm256_add_epi32(g, _mm256_set1_epi32(0x7f000000u))); + const __m256 s2 = _mm256_castsi256_ps(_mm256_sub_epi32(e, g)); + const __m256i d = _mm256_castps_si256( + _mm256_cmp_ps(_mm256_andnot_ps(_mm256_set1_ps(-0.f), n), + _mm256_set1_ps(192), _CMP_GT_OQ)); + return _mm256_or_ps( + _mm256_and_ps(_mm256_castsi256_ps(d), _mm256_mul_ps(s1, s1)), + _mm256_andnot_ps( + _mm256_castsi256_ps(d), + _mm256_or_ps( + _mm256_and_ps(_mm256_castsi256_ps(c), + _mm256_mul_ps(_mm256_fmadd_ps(s2, j, s2), s1)), + _mm256_andnot_ps(_mm256_castsi256_ps(c), _mm256_fmadd_ps(k, j, k))))); +} + +// computes silu x/(1+exp(-x)) in single precision vector +inline static __m256 ggml_v_silu(__m256 x) { + const __m256 one = _mm256_set1_ps(1); + const __m256 zero = _mm256_setzero_ps(); + const __m256 neg_x = _mm256_sub_ps(zero, x); + const __m256 exp_neg_x = ggml_v_expf(neg_x); + const __m256 one_plus_exp_neg_x = _mm256_add_ps(one, exp_neg_x); + return _mm256_div_ps(x, one_plus_exp_neg_x); +} + +#elif defined(__SSE2__) // __AVX2__ / __ARM_NEON -#ifdef GGML_SILU_FP16 -inline static void ggml_vec_silu_f32(const int n, float * y, const float * x) { - uint16_t t; - for (int i = 0; i < n; ++i) { - ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); - memcpy(&t, &fp16, sizeof(uint16_t)); - y[i] = GGML_FP16_TO_FP32(ggml_table_silu_f16[t]); - } -} +#if defined(__FMA__) +#define MADD128(x, y, z) _mm_fmadd_ps(x, y, z) +#define NMADD128(x, y, z) _mm_fnmadd_ps(x, y, z) #else -inline static void ggml_vec_silu_f32(const int n, float * y, const float * x) { - for (int i = 0; i < n; ++i) { +#define MADD128(x, y, z) _mm_add_ps(_mm_mul_ps(x, y), z) +#define NMADD128(x, y, z) _mm_sub_ps(z, _mm_mul_ps(x, y)) +#endif + +// adapted from arm limited optimized routine +// the maximum error is 1.45358 plus 0.5 ulps +// numbers above 88.38 will flush to infinity +// numbers beneath -103.97 will flush to zero +inline static __m128 ggml_v_expf(__m128 x) { + const __m128 r = _mm_set1_ps(0x1.8p23f); + const __m128 z = MADD128(x, _mm_set1_ps(0x1.715476p+0f), r); + const __m128 n = _mm_sub_ps(z, r); + const __m128 b = + NMADD128(n, _mm_set1_ps(0x1.7f7d1cp-20f), NMADD128(n, _mm_set1_ps(0x1.62e4p-1f), x)); + const __m128i e = _mm_slli_epi32(_mm_castps_si128(z), 23); + const __m128 k = _mm_castsi128_ps(_mm_add_epi32(e, _mm_castps_si128(_mm_set1_ps(1)))); + const __m128i c = + _mm_castps_si128(_mm_cmpgt_ps(_mm_andnot_ps(_mm_set1_ps(-0.f), n), _mm_set1_ps(126))); + const __m128 u = _mm_mul_ps(b, b); + const __m128 j = + MADD128(MADD128(MADD128(_mm_set1_ps(0x1.0e4020p-7f), b, _mm_set1_ps(0x1.573e2ep-5f)), u, + MADD128(_mm_set1_ps(0x1.555e66p-3f), b, _mm_set1_ps(0x1.fffdb6p-2f))), + u, _mm_mul_ps(_mm_set1_ps(0x1.ffffecp-1f), b)); + if (!_mm_movemask_epi8(c)) + return MADD128(j, k, k); + const __m128i g = _mm_and_si128(_mm_castps_si128(_mm_cmple_ps(n, _mm_setzero_ps())), + _mm_set1_epi32(0x82000000u)); + const __m128 s1 = _mm_castsi128_ps(_mm_add_epi32(g, _mm_set1_epi32(0x7f000000u))); + const __m128 s2 = _mm_castsi128_ps(_mm_sub_epi32(e, g)); + const __m128i d = + _mm_castps_si128(_mm_cmpgt_ps(_mm_andnot_ps(_mm_set1_ps(-0.f), n), _mm_set1_ps(192))); + return _mm_or_ps( + _mm_and_ps(_mm_castsi128_ps(d), _mm_mul_ps(s1, s1)), + _mm_andnot_ps(_mm_castsi128_ps(d), + _mm_or_ps(_mm_and_ps(_mm_castsi128_ps(c), _mm_mul_ps(MADD128(s2, j, s2), s1)), + _mm_andnot_ps(_mm_castsi128_ps(c), MADD128(k, j, k))))); +} + +// computes silu x/(1+exp(-x)) in single precision vector +inline static __m128 ggml_v_silu(__m128 x) { + const __m128 one = _mm_set1_ps(1); + const __m128 zero = _mm_setzero_ps(); + const __m128 neg_x = _mm_sub_ps(zero, x); + const __m128 exp_neg_x = ggml_v_expf(neg_x); + const __m128 one_plus_exp_neg_x = _mm_add_ps(one, exp_neg_x); + return _mm_div_ps(x, one_plus_exp_neg_x); +} + +#endif // __ARM_NEON / __AVX2__ / __SSE2__ + +static void ggml_vec_silu_f32(const int n, float * y, const float * x) { + int i = 0; +#if defined(__AVX512F__) && defined(__AVX512DQ__) + for (; i + 15 < n; i += 16) { + _mm512_storeu_ps(y + i, ggml_v_silu(_mm512_loadu_ps(x + i))); + } +#elif defined(__AVX2__) && defined(__FMA__) + for (; i + 7 < n; i += 8) { + _mm256_storeu_ps(y + i, ggml_v_silu(_mm256_loadu_ps(x + i))); + } +#elif defined(__SSE2__) + for (; i + 3 < n; i += 4) { + _mm_storeu_ps(y + i, ggml_v_silu(_mm_loadu_ps(x + i))); + } +#elif defined(__ARM_NEON) && defined(__aarch64__) + for (; i + 3 < n; i += 4) { + vst1q_f32(y + i, ggml_v_silu(vld1q_f32(x + i))); + } +#endif + for (; i < n; ++i) { y[i] = ggml_silu_f32(x[i]); } } + +static ggml_float ggml_vec_soft_max_f32(const int n, float * y, const float * x, float max) { + int i = 0; + ggml_float sum = 0; +#if defined(__AVX512F__) && defined(__AVX512DQ__) + for (; i + 15 < n; i += 16) { + __m512 val = ggml_v_expf(_mm512_sub_ps(_mm512_loadu_ps(x + i), + _mm512_set1_ps(max))); + _mm512_storeu_ps(y + i, val); + sum += (ggml_float)_mm512_reduce_add_ps(val); + } +#elif defined(__AVX2__) && defined(__FMA__) + for (; i + 7 < n; i += 8) { + __m256 val = ggml_v_expf(_mm256_sub_ps(_mm256_loadu_ps(x + i), + _mm256_set1_ps(max))); + _mm256_storeu_ps(y + i, val); + __m128 val2 = _mm_add_ps(_mm256_extractf128_ps(val, 1), + _mm256_castps256_ps128(val)); + val2 = _mm_add_ps(val2, _mm_movehl_ps(val2, val2)); + val2 = _mm_add_ss(val2, _mm_movehdup_ps(val2)); + sum += (ggml_float)_mm_cvtss_f32(val2); + } +#elif defined(__SSE2__) + for (; i + 3 < n; i += 4) { + __m128 val = ggml_v_expf(_mm_sub_ps(_mm_loadu_ps(x + i), + _mm_set1_ps(max))); + _mm_storeu_ps(y + i, val); +#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) + val = _mm_add_ps(val, _mm_movehl_ps(val, val)); + val = _mm_add_ss(val, _mm_movehdup_ps(val)); +#else + __m128 tmp = _mm_shuffle_ps(val, val, _MM_SHUFFLE(2, 3, 0, 1)); + val = _mm_add_ps(val, tmp); + tmp = _mm_movehl_ps(tmp, val); + val = _mm_add_ss(val, tmp); +#endif + sum += (ggml_float)_mm_cvtss_f32(val); + } +#elif defined(__ARM_NEON) && defined(__aarch64__) + for (; i + 3 < n; i += 4) { + float32x4_t val = ggml_v_expf(vsubq_f32(vld1q_f32(x + i), + vdupq_n_f32(max))); + vst1q_f32(y + i, val); + sum += (ggml_float)vaddvq_f32(val); + } #endif + for (; i < n; ++i) { + float val = expf(x[i] - max); + sum += (ggml_float)val; + y[i] = val; + } + return sum; +} inline static float ggml_silu_backward_f32(float x, float dy) { const float s = 1.0f/(1.0f + expf(-x)); return dy*s*(1.0f + x*(1.0f - s)); } -#ifdef GGML_SILU_FP16 -inline static void ggml_vec_silu_backward_f32(const int n, float * dx, const float * x, const float * dy) { - for (int i = 0; i < n; ++i) { - // we did not use x[i] to compute forward silu but its f16 equivalent - // take derivative at f16 of x[i]: - ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); - float usedx = GGML_FP16_TO_FP32(fp16); - dx[i] = ggml_silu_backward_f32(usedx, dy[i]); - } -} -#else inline static void ggml_vec_silu_backward_f32(const int n, float * dx, const float * x, const float * dy) { for (int i = 0; i < n; ++i) { dx[i] = ggml_silu_backward_f32(x[i], dy[i]); } } -#endif inline static void ggml_vec_sum_f32(const int n, float * s, const float * x) { #ifndef GGML_USE_ACCELERATE @@ -2185,7 +2657,6 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "SOFT_MAX_BACK", "ROPE", "ROPE_BACK", - "ALIBI", "CLAMP", "CONV_TRANSPOSE_1D", "IM2COL", @@ -2199,9 +2670,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "ARGSORT", "LEAKY_RELU", - "FLASH_ATTN", "FLASH_ATTN_EXT", - "FLASH_FF", "FLASH_ATTN_BACK", "SSM_CONV", "SSM_SCAN", @@ -2227,7 +2696,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "CROSS_ENTROPY_LOSS_BACK", }; -static_assert(GGML_OP_COUNT == 77, "GGML_OP_COUNT != 77"); +static_assert(GGML_OP_COUNT == 74, "GGML_OP_COUNT != 74"); static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "none", @@ -2276,7 +2745,6 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "soft_max_back(x)", "rope(x)", "rope_back(x)", - "alibi(x)", "clamp(x)", "conv_transpose_1d(x)", "im2col(x)", @@ -2290,9 +2758,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "argsort(x)", "leaky_relu(x)", - "flash_attn(x)", "flash_attn_ext(x)", - "flash_ff(x)", "flash_attn_back(x)", "ssm_conv(x)", "ssm_scan(x)", @@ -2318,7 +2784,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "cross_entropy_loss_back(x,y)", }; -static_assert(GGML_OP_COUNT == 77, "GGML_OP_COUNT != 77"); +static_assert(GGML_OP_COUNT == 74, "GGML_OP_COUNT != 74"); static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2"); @@ -2331,6 +2797,7 @@ static const char * GGML_UNARY_OP_NAME[GGML_UNARY_OP_COUNT] = { "TANH", "ELU", "RELU", + "SIGMOID", "GELU", "GELU_QUICK", "SILU", @@ -2338,7 +2805,7 @@ static const char * GGML_UNARY_OP_NAME[GGML_UNARY_OP_COUNT] = { "HARDSIGMOID", }; -static_assert(GGML_UNARY_OP_COUNT == 12, "GGML_UNARY_OP_COUNT != 12"); +static_assert(GGML_UNARY_OP_COUNT == 13, "GGML_UNARY_OP_COUNT != 13"); static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN"); @@ -2380,32 +2847,6 @@ static void ggml_setup_op_has_task_pass(void) { } } -// -// ggml context -// - -struct ggml_context { - size_t mem_size; - void * mem_buffer; - bool mem_buffer_owned; - bool no_alloc; - bool no_alloc_save; // this is used to save the no_alloc state when using scratch buffers - - int n_objects; - - struct ggml_object * objects_begin; - struct ggml_object * objects_end; - - struct ggml_scratch scratch; - struct ggml_scratch scratch_save; -}; - -struct ggml_context_container { - bool used; - - struct ggml_context context; -}; - // // NUMA support // @@ -2822,8 +3263,18 @@ bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor (t0->ne[3] == t1->ne[3] ); } -// check if t1 can be represented as a repeatition of t0 -static inline bool ggml_can_repeat(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { +bool ggml_are_same_stride(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { + static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); + + return + (t0->nb[0] == t1->nb[0] ) && + (t0->nb[1] == t1->nb[1] ) && + (t0->nb[2] == t1->nb[2] ) && + (t0->nb[3] == t1->nb[3] ); +} + +// check if t1 can be represented as a repeatition of t0 +static inline bool ggml_can_repeat(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return ggml_is_empty(t0) ? ggml_is_empty(t1) : @@ -2881,8 +3332,6 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { float f = ggml_table_f32_f16[i] = GGML_COMPUTE_FP16_TO_FP32(u.fp16); ggml_table_gelu_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_f32(f)); ggml_table_gelu_quick_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_quick_f32(f)); - ggml_table_silu_f16[i] = GGML_FP32_TO_FP16(ggml_silu_f32(f)); - ggml_table_exp_f16[i] = GGML_FP32_TO_FP16(expf(f)); } const uint64_t t_end = ggml_time_us(); UNUSED(t_end); @@ -3166,6 +3615,12 @@ static struct ggml_tensor * ggml_new_tensor_impl( struct ggml_tensor * const result = (struct ggml_tensor *)((char *)ctx->mem_buffer + obj_new->offs); +#ifdef __clang__ + // temporary until ggml_tensor::backend is removed + #pragma clang diagnostic push + #pragma clang diagnostic ignored "-Wdeprecated-declarations" +#endif + *result = (struct ggml_tensor) { /*.type =*/ type, /*.backend =*/ GGML_BACKEND_TYPE_CPU, @@ -3188,6 +3643,10 @@ static struct ggml_tensor * ggml_new_tensor_impl( /*.padding =*/ { 0 }, }; +#ifdef __clang__ + #pragma clang diagnostic pop +#endif + // TODO: this should not be needed as long as we don't rely on aligned SIMD loads //ggml_assert_aligned(result->data); @@ -4566,6 +5025,20 @@ struct ggml_tensor * ggml_leaky_relu( return result; } +// ggml_sigmoid + +struct ggml_tensor * ggml_sigmoid( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary(ctx, a, GGML_UNARY_OP_SIGMOID); +} + +struct ggml_tensor * ggml_sigmoid_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_SIGMOID); +} + // ggml_gelu struct ggml_tensor * ggml_gelu( @@ -5649,7 +6122,6 @@ static struct ggml_tensor * ggml_soft_max_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * mask, - struct ggml_tensor * pos, float scale, float max_bias, bool inplace) { @@ -5663,18 +6135,8 @@ static struct ggml_tensor * ggml_soft_max_impl( GGML_ASSERT(mask->ne[1] >= a->ne[1]); } - if (pos) { - GGML_ASSERT(ggml_is_vector(pos)); - GGML_ASSERT(pos->type == GGML_TYPE_F16 || pos->type == GGML_TYPE_F32); - GGML_ASSERT(pos->ne[0] == a->ne[0]); - } - - if (pos && mask) { - GGML_ASSERT(pos->type == mask->type); - } - if (max_bias > 0.0f) { - GGML_ASSERT(pos); + GGML_ASSERT(mask); } bool is_node = false; @@ -5692,7 +6154,6 @@ static struct ggml_tensor * ggml_soft_max_impl( result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = mask; - result->src[2] = pos; return result; } @@ -5700,23 +6161,22 @@ static struct ggml_tensor * ggml_soft_max_impl( struct ggml_tensor * ggml_soft_max( struct ggml_context * ctx, struct ggml_tensor * a) { - return ggml_soft_max_impl(ctx, a, NULL, NULL, 1.0f, 0.0f, false); + return ggml_soft_max_impl(ctx, a, NULL, 1.0f, 0.0f, false); } struct ggml_tensor * ggml_soft_max_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { - return ggml_soft_max_impl(ctx, a, NULL, NULL, 1.0f, 0.0f, true); + return ggml_soft_max_impl(ctx, a, NULL, 1.0f, 0.0f, true); } struct ggml_tensor * ggml_soft_max_ext( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * mask, - struct ggml_tensor * pos, float scale, float max_bias) { - return ggml_soft_max_impl(ctx, a, mask, pos, scale, max_bias, false); + return ggml_soft_max_impl(ctx, a, mask, scale, max_bias, false); } // ggml_soft_max_back @@ -5762,6 +6222,7 @@ static struct ggml_tensor * ggml_rope_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, + struct ggml_tensor * c, int n_dims, int mode, int n_ctx, @@ -5775,10 +6236,17 @@ static struct ggml_tensor * ggml_rope_impl( float xpos_base, bool xpos_down, bool inplace) { + GGML_ASSERT((mode & 1) == 0 && "mode & 1 == 1 is no longer supported"); + GGML_ASSERT(ggml_is_vector(b)); GGML_ASSERT(b->type == GGML_TYPE_I32); GGML_ASSERT(a->ne[2] == b->ne[0]); + if (c) { + GGML_ASSERT(c->type == GGML_TYPE_F32); + GGML_ASSERT(c->ne[0] >= n_dims / 2); + } + bool is_node = false; if (a->grad) { @@ -5802,6 +6270,7 @@ static struct ggml_tensor * ggml_rope_impl( result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; + result->src[2] = c; return result; } @@ -5814,7 +6283,7 @@ struct ggml_tensor * ggml_rope( int mode, int n_ctx) { return ggml_rope_impl( - ctx, a, b, n_dims, mode, n_ctx, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, false, false + ctx, a, b, NULL, n_dims, mode, n_ctx, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, false, false ); } @@ -5826,14 +6295,15 @@ struct ggml_tensor * ggml_rope_inplace( int mode, int n_ctx) { return ggml_rope_impl( - ctx, a, b, n_dims, mode, n_ctx, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, false, true + ctx, a, b, NULL, n_dims, mode, n_ctx, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, false, true ); } -struct ggml_tensor * ggml_rope_custom( +struct ggml_tensor * ggml_rope_ext( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, + struct ggml_tensor * c, int n_dims, int mode, int n_ctx, @@ -5845,15 +6315,16 @@ struct ggml_tensor * ggml_rope_custom( float beta_fast, float beta_slow) { return ggml_rope_impl( - ctx, a, b, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale, + ctx, a, b, c, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, 0.0f, false, false ); } -struct ggml_tensor * ggml_rope_custom_inplace( +struct ggml_tensor * ggml_rope_ext_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, + struct ggml_tensor * c, int n_dims, int mode, int n_ctx, @@ -5865,19 +6336,49 @@ struct ggml_tensor * ggml_rope_custom_inplace( float beta_fast, float beta_slow) { return ggml_rope_impl( - ctx, a, b, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale, + ctx, a, b, c, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, 0.0f, false, true ); } -struct ggml_tensor * ggml_rope_xpos_inplace( +struct ggml_tensor * ggml_rope_custom( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int n_dims, + int mode, + int n_ctx, + int n_orig_ctx, + float freq_base, + float freq_scale, + float ext_factor, + float attn_factor, + float beta_fast, + float beta_slow) { + return ggml_rope_impl( + ctx, a, b, NULL, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow, 0.0f, false, false + ); +} + +struct ggml_tensor * ggml_rope_custom_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, int n_dims, - float base, - bool down) { - return ggml_rope_impl(ctx, a, b, n_dims, 0, 0, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, base, down, true); + int mode, + int n_ctx, + int n_orig_ctx, + float freq_base, + float freq_scale, + float ext_factor, + float attn_factor, + float beta_fast, + float beta_slow) { + return ggml_rope_impl( + ctx, a, b, NULL, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow, 0.0f, false, true + ); } // ggml_rope_back @@ -5886,6 +6387,7 @@ struct ggml_tensor * ggml_rope_back( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, + struct ggml_tensor * c, int n_dims, int mode, int n_ctx, @@ -5901,6 +6403,7 @@ struct ggml_tensor * ggml_rope_back( GGML_ASSERT(ggml_is_vector(b)); GGML_ASSERT(b->type == GGML_TYPE_I32); GGML_ASSERT(a->ne[2] == b->ne[0]); + GGML_ASSERT(c == NULL && "freq factors not implemented yet"); GGML_ASSERT((mode & 4) == 0 && "ggml_rope_back() for ChatGLM not implemented yet"); @@ -5931,37 +6434,6 @@ struct ggml_tensor * ggml_rope_back( return result; } -// ggml_alibi - -struct ggml_tensor * ggml_alibi( - struct ggml_context * ctx, - struct ggml_tensor * a, - int n_past, - int n_head, - float bias_max) { - GGML_ASSERT(n_past >= 0); - bool is_node = false; - - if (a->grad) { - GGML_ASSERT(false); // TODO: implement backward - is_node = true; - } - - // TODO: when implement backward, fix this: - //struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); - struct ggml_tensor * result = ggml_view_tensor(ctx, a); - - int32_t op_params[3] = { n_past, n_head }; - memcpy(op_params + 2, &bias_max, sizeof(float)); - ggml_set_op_params(result, op_params, sizeof(op_params)); - - result->op = GGML_OP_ALIBI; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; - result->src[0] = a; - - return result; -} - // ggml_clamp struct ggml_tensor * ggml_clamp( @@ -6311,7 +6783,10 @@ struct ggml_tensor * ggml_pool_2d( static struct ggml_tensor * ggml_upscale_impl( struct ggml_context * ctx, struct ggml_tensor * a, - int scale_factor) { + int ne0, + int ne1, + int ne2, + int ne3) { bool is_node = false; if (a->grad) { @@ -6319,19 +6794,45 @@ static struct ggml_tensor * ggml_upscale_impl( is_node = true; } + GGML_ASSERT(a->ne[0] <= ne0); + GGML_ASSERT(a->ne[1] <= ne1); + GGML_ASSERT(a->ne[2] <= ne2); + GGML_ASSERT(a->ne[3] <= ne3); + struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, - a->ne[0] * scale_factor, - a->ne[1] * scale_factor, - a->ne[2], a->ne[3]); + ne0, + ne1, + ne2, + ne3 + ); result->op = GGML_OP_UPSCALE; - result->op_params[0] = scale_factor; + result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } +struct ggml_tensor * ggml_upscale( + struct ggml_context * ctx, + struct ggml_tensor * a, + int scale_factor) { + return ggml_upscale_impl(ctx, a, a->ne[0] * scale_factor, a->ne[1] * scale_factor, a->ne[2], a->ne[3]); +} + +struct ggml_tensor * ggml_upscale_ext( + struct ggml_context * ctx, + struct ggml_tensor * a, + int ne0, + int ne1, + int ne2, + int ne3) { + return ggml_upscale_impl(ctx, a, ne0, ne1, ne2, ne3); +} + +// ggml_pad + struct ggml_tensor * ggml_pad( struct ggml_context * ctx, struct ggml_tensor * a, @@ -6356,12 +6857,7 @@ struct ggml_tensor * ggml_pad( return result; } -struct ggml_tensor * ggml_upscale( - struct ggml_context * ctx, - struct ggml_tensor * a, - int scale_factor) { - return ggml_upscale_impl(ctx, a, scale_factor); -} +// ggml_arange struct ggml_tensor * ggml_arange( struct ggml_context * ctx, @@ -6383,6 +6879,8 @@ struct ggml_tensor * ggml_arange( return result; } +// ggml_timestep_embedding + struct ggml_tensor * ggml_timestep_embedding( struct ggml_context * ctx, struct ggml_tensor * timesteps, @@ -6449,38 +6947,6 @@ struct ggml_tensor * ggml_top_k( return result; } -// ggml_flash_attn - -struct ggml_tensor * ggml_flash_attn( - struct ggml_context * ctx, - struct ggml_tensor * q, - struct ggml_tensor * k, - struct ggml_tensor * v, - bool masked) { - GGML_ASSERT(ggml_can_mul_mat(k, q)); - // TODO: check if vT can be multiplied by (k*qT) - - bool is_node = false; - - if (q->grad || k->grad || v->grad) { - is_node = true; - } - - //struct ggml_tensor * result = ggml_dup_tensor(ctx, q); - struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, GGML_MAX_DIMS, q->ne); - - int32_t t = masked ? 1 : 0; - ggml_set_op_params(result, &t, sizeof(t)); - - result->op = GGML_OP_FLASH_ATTN; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; - result->src[0] = q; - result->src[1] = k; - result->src[2] = v; - - return result; -} - // ggml_flash_attn_ext struct ggml_tensor * ggml_flash_attn_ext( @@ -6489,9 +6955,11 @@ struct ggml_tensor * ggml_flash_attn_ext( struct ggml_tensor * k, struct ggml_tensor * v, struct ggml_tensor * mask, - float scale) { + float scale, + float max_bias) { GGML_ASSERT(ggml_can_mul_mat(k, q)); // TODO: check if vT can be multiplied by (k*qT) + if (mask) { GGML_ASSERT(ggml_is_contiguous(mask)); GGML_ASSERT(mask->ne[2] == 1); @@ -6501,6 +6969,10 @@ struct ggml_tensor * ggml_flash_attn_ext( //GGML_ASSERT(ggml_can_repeat_rows(mask, qk)); } + if (max_bias > 0.0f) { + GGML_ASSERT(mask); + } + bool is_node = false; if (q->grad || k->grad || v->grad) { @@ -6511,7 +6983,7 @@ struct ggml_tensor * ggml_flash_attn_ext( int64_t ne[4] = { q->ne[0], q->ne[2], q->ne[1], q->ne[3] }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); - float params[] = { scale }; + float params[] = { scale, max_bias }; ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_FLASH_ATTN_EXT; @@ -6531,39 +7003,7 @@ void ggml_flash_attn_ext_set_prec( const int32_t prec_i32 = (int32_t) prec; - ggml_set_op_params_i32(a, 1, prec_i32); // scale is on first pos -} - -// ggml_flash_ff - -struct ggml_tensor * ggml_flash_ff( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b0, - struct ggml_tensor * b1, - struct ggml_tensor * c0, - struct ggml_tensor * c1) { - GGML_ASSERT(ggml_can_mul_mat(b0, a)); - // TODO: more checks - - bool is_node = false; - - if (a->grad || b0->grad || b1->grad || c0->grad || c1->grad) { - is_node = true; - } - - //struct ggml_tensor * result = ggml_dup_tensor(ctx, a); - struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, GGML_MAX_DIMS, a->ne); - - result->op = GGML_OP_FLASH_FF; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; - result->src[0] = a; - result->src[1] = b0; - result->src[2] = b1; - result->src[3] = c0; - result->src[4] = c1; - - return result; + ggml_set_op_params_i32(a, 2, prec_i32); // scale is on first pos, max_bias on second } // ggml_flash_attn_back @@ -6575,6 +7015,8 @@ struct ggml_tensor * ggml_flash_attn_back( struct ggml_tensor * v, struct ggml_tensor * d, bool masked) { + GGML_ASSERT(false && "TODO: adapt to ggml_flash_attn_ext() changes"); + GGML_ASSERT(ggml_can_mul_mat(k, q)); // TODO: check if vT can be multiplied by (k*qT) @@ -10895,6 +11337,52 @@ static void ggml_compute_forward_relu( } } +// ggml_compute_forward_sigmoid + +static void ggml_compute_forward_sigmoid_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + assert(params->ith == 0); + assert(ggml_are_same_shape(src0, dst)); + + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { + return; + } + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + assert(dst->nb[0] == sizeof(float)); + assert(src0->nb[0] == sizeof(float)); + + for (int i = 0; i < n; i++) { + ggml_vec_sigmoid_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_sigmoid( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_sigmoid_f32(params, dst); + } break; + default: + { + GGML_ASSERT(false); + } break; + } +} + // ggml_compute_forward_gelu static void ggml_compute_forward_gelu_f32( @@ -11745,80 +12233,171 @@ static bool ggml_compute_forward_mul_mat_use_blas(struct ggml_tensor * dst) { } #endif -static void ggml_compute_forward_mul_mat( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { +static void ggml_compute_forward_mul_mat_one_chunk( + const struct ggml_compute_params * params, + struct ggml_tensor * dst, + const int64_t num_rows_per_vec_dot, + const int64_t ir0_start, + const int64_t ir0_end, + const int64_t ir1_start, + const int64_t ir1_end) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; - int64_t t0 = ggml_perf_time_us(); - UNUSED(t0); - GGML_TENSOR_BINARY_OP_LOCALS - const int ith = params->ith; - const int nth = params->nth; - const enum ggml_type type = src0->type; const bool src1_cont = ggml_is_contiguous(src1); - ggml_vec_dot_t const vec_dot = type_traits[type].vec_dot; - enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type; - ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float; - int64_t const vec_dot_num_rows = type_traits[type].nrows; - - GGML_ASSERT(ne0 == ne01); - GGML_ASSERT(ne1 == ne11); - GGML_ASSERT(ne2 == ne12); - GGML_ASSERT(ne3 == ne13); - - // we don't support permuted src0 or src1 - GGML_ASSERT(nb00 == ggml_type_size(type)); - GGML_ASSERT(nb10 == ggml_type_size(src1->type)); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb0 <= nb1); - GGML_ASSERT(nb1 <= nb2); - GGML_ASSERT(nb2 <= nb3); + ggml_vec_dot_t const vec_dot = type_traits[type].vec_dot; + enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type; // broadcast factors - const int64_t r2 = ne12/ne02; - const int64_t r3 = ne13/ne03; + const int64_t r2 = ne12 / ne02; + const int64_t r3 = ne13 / ne03; - // nb01 >= nb00 - src0 is not transposed - // compute by src0 rows + //printf("ir0_start = %6lld, ir0_end = %6lld, ir1_start = %6lld, ir1_end = %6lld\n", ir0_start, ir0_end, ir1_start, ir1_end); -#if defined(GGML_USE_CLBLAST) - if (ggml_cl_can_mul_mat(src0, src1, dst)) { - if (params->ith == 0 && params->type == GGML_TASK_TYPE_COMPUTE) { - ggml_cl_mul_mat(src0, src1, dst, params->wdata, params->wsize); - } + // threads with no work simply yield (not sure if it helps) + if (ir0_start >= ir0_end || ir1_start >= ir1_end) { return; } -#endif -#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) - if (ggml_compute_forward_mul_mat_use_blas(dst)) { - const int64_t ne_plane = ne01*ne00; - const size_t desired_wsize = ne13*ne12*ne_plane*sizeof(float); - UNUSED(desired_wsize); + const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; + const size_t row_size = ggml_row_size(vec_dot_type, ne10); - if (params->type == GGML_TASK_TYPE_INIT) { - if (type != GGML_TYPE_F32) { - assert(params->wsize >= desired_wsize); - // parallelize by src0 rows - for (int64_t i13 = 0; i13 < ne13; i13++) { - for (int64_t i12 = 0; i12 < ne12; i12++) { - // broadcast src0 into src1 across 2nd,3rd dimension - const int64_t i03 = i13/r3; - const int64_t i02 = i12/r2; + assert(ne12 % ne02 == 0); + assert(ne13 % ne03 == 0); - const void * x = (char *) src0->data + i02*nb02 + i03*nb03; - float * const wdata = (float *) params->wdata + i13*ne12*ne_plane + i12*ne_plane; - ggml_to_float_t const to_float = type_traits[type].to_float; + // block-tiling attempt + const int64_t blck_0 = 16; + const int64_t blck_1 = 16; + + const size_t src1_col_stride = src1_cont || src1->type != vec_dot_type ? row_size : nb11; + + // attempt to reduce false-sharing (does not seem to make a difference) + // 16 * 2, accounting for mmla kernels + float tmp[32]; + + for (int64_t iir1 = ir1_start; iir1 < ir1_end; iir1 += blck_1) { + for (int64_t iir0 = ir0_start; iir0 < ir0_end; iir0 += blck_0) { + for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir1_end; ir1 += num_rows_per_vec_dot) { + const int64_t i13 = (ir1 / (ne12 * ne1)); + const int64_t i12 = (ir1 - i13 * ne12 * ne1) / ne1; + const int64_t i11 = (ir1 - i13 * ne12 * ne1 - i12 * ne1); + + // broadcast src0 into src1 + const int64_t i03 = i13 / r3; + const int64_t i02 = i12 / r2; + + const int64_t i1 = i11; + const int64_t i2 = i12; + const int64_t i3 = i13; + + const char * src0_row = (const char*)src0->data + (0 + i02 * nb02 + i03 * nb03); + + // desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides + // if it is, then we have either copied the data to params->wdata and made it contiguous or we are using + // the original src1 data pointer, so we should index using the indices directly + // TODO: this is a bit of a hack, we should probably have a better way to handle this + const char * src1_col = (const char*)wdata + + (src1_cont || src1->type != vec_dot_type + ? (i11 + i12 * ne11 + i13 * ne12 * ne11) * row_size + : (i11 * nb11 + i12 * nb12 + i13 * nb13)); + float * dst_col = (float*)((char*)dst->data + (i1 * nb1 + i2 * nb2 + i3 * nb3)); + + //for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir0_end; ++ir0) { + // vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col); + //} + + for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir0_end; ir0 += num_rows_per_vec_dot) { + vec_dot(ne00, &tmp[ir0 - iir0], (num_rows_per_vec_dot > 1 ? 16 : 0), src0_row + ir0 * nb01, (num_rows_per_vec_dot > 1 ? nb01 : 0), src1_col, (num_rows_per_vec_dot > 1 ? src1_col_stride : 0), num_rows_per_vec_dot); + } + + for (int cn = 0; cn < num_rows_per_vec_dot; ++cn) { + memcpy(&dst_col[iir0 + cn * nb1 / nb0], tmp + (cn * 16), (MIN(iir0 + blck_0, ir0_end) - iir0) * sizeof(float)); + } + } + } + } +} + +static void ggml_compute_forward_mul_mat( + const struct ggml_compute_params * params, + struct ggml_tensor * dst, + struct ggml_compute_state * state) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + int64_t t0 = ggml_perf_time_us(); + UNUSED(t0); + + GGML_TENSOR_BINARY_OP_LOCALS + + const int ith = params->ith; + const int nth = params->nth; + + const enum ggml_type type = src0->type; + + enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type; + ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float; + int64_t const vec_dot_num_rows = type_traits[type].nrows; + + GGML_ASSERT(ne0 == ne01); + GGML_ASSERT(ne1 == ne11); + GGML_ASSERT(ne2 == ne12); + GGML_ASSERT(ne3 == ne13); + + // we don't support permuted src0 or src1 + GGML_ASSERT(nb00 == ggml_type_size(type)); + GGML_ASSERT(nb10 == ggml_type_size(src1->type)); + + // dst cannot be transposed or permuted + GGML_ASSERT(nb0 == sizeof(float)); + GGML_ASSERT(nb0 <= nb1); + GGML_ASSERT(nb1 <= nb2); + GGML_ASSERT(nb2 <= nb3); + + // broadcast factors + const int64_t r2 = ne12 / ne02; + const int64_t r3 = ne13 / ne03; + UNUSED(r2); + UNUSED(r3); + + // nb01 >= nb00 - src0 is not transposed + // compute by src0 rows + +#if defined(GGML_USE_CLBLAST) + if (ggml_cl_can_mul_mat(src0, src1, dst)) { + if (params->ith == 0 && params->type == GGML_TASK_TYPE_COMPUTE) { + ggml_cl_mul_mat(src0, src1, dst, params->wdata, params->wsize); + } + return; + } +#endif + +#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) + if (ggml_compute_forward_mul_mat_use_blas(dst)) { + const int64_t ne_plane = ne01*ne00; + const size_t desired_wsize = ne13*ne12*ne_plane*sizeof(float); + UNUSED(desired_wsize); + + if (params->type == GGML_TASK_TYPE_INIT) { + if (type != GGML_TYPE_F32) { + assert(params->wsize >= desired_wsize); + // parallelize by src0 rows + for (int64_t i13 = 0; i13 < ne13; i13++) { + for (int64_t i12 = 0; i12 < ne12; i12++) { + // broadcast src0 into src1 across 2nd,3rd dimension + const int64_t i03 = i13/r3; + const int64_t i02 = i12/r2; + + const void * x = (char *) src0->data + i02*nb02 + i03*nb03; + float * const wdata = (float *) params->wdata + i13*ne12*ne_plane + i12*ne_plane; + ggml_to_float_t const to_float = type_traits[type].to_float; for (int64_t i01 = ith; i01 < ne01; i01 += nth) { to_float((const char *) x + i01*nb01, wdata + i01*ne00, ne00); @@ -11868,6 +12447,8 @@ static void ggml_compute_forward_mul_mat( #endif #if GGML_USE_LLAMAFILE + const bool src1_cont = ggml_is_contiguous(src1); + if (src1_cont) { for (int64_t i13 = 0; i13 < ne13; i13++) for (int64_t i12 = 0; i12 < ne12; i12++) @@ -11893,6 +12474,8 @@ UseGgmlGemm1:; if (ith != 0) { return; } + // Every thread starts at ith, so the first unprocessed chunk is nth. This save a bit of coordination right at the start. + atomic_store(&state->shared->current_chunk, nth); if (src1->type != vec_dot_type) { char * wdata = params->wdata; const size_t row_size = ggml_row_size(vec_dot_type, ne10); @@ -11917,11 +12500,11 @@ UseGgmlGemm1:; return; } - const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; - const size_t row_size = ggml_row_size(vec_dot_type, ne10); - #if GGML_USE_LLAMAFILE if (src1->type != vec_dot_type) { + const void* wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; + const size_t row_size = ggml_row_size(vec_dot_type, ne10); + for (int64_t i13 = 0; i13 < ne13; i13++) for (int64_t i12 = 0; i12 < ne12; i12++) if (!llamafile_sgemm(ne01, ne11, ne00/ggml_blck_size(src0->type), @@ -11942,98 +12525,87 @@ UseGgmlGemm1:; UseGgmlGemm2:; #endif - const int64_t nr0 = ne01; // src0 rows - const int64_t nr1 = ne1*ne12*ne13; // src1 rows - - //printf("nr0 = %lld, nr1 = %lld\n", nr0, nr1); - - // distribute the thread work across the inner or outer loop based on which one is larger - - const int64_t nth0 = nr0 > nr1 ? nth : 1; // parallelize by src0 rows - const int64_t nth1 = nr0 > nr1 ? 1 : nth; // parallelize by src1 rows - - const int64_t ith0 = ith % nth0; - const int64_t ith1 = ith / nth0; - - const int64_t dr0 = (nr0 + nth0 - 1)/nth0; - const int64_t dr1 = (nr1 + nth1 - 1)/nth1; - - const int64_t ir010 = dr0*ith0; - const int64_t ir011 = MIN(ir010 + dr0, nr0); - - const int64_t ir110 = dr1*ith1; - const int64_t ir111 = MIN(ir110 + dr1, nr1); - - //printf("ir010 = %6lld, ir011 = %6lld, ir110 = %6lld, ir111 = %6lld\n", ir010, ir011, ir110, ir111); - - // threads with no work simply yield (not sure if it helps) - if (ir010 >= ir011 || ir110 >= ir111) { - sched_yield(); - return; - } +#ifdef GGML_PERF + int chunks_executed = 0; + UNUSED(chunks_executed); +#endif - assert(ne12 % ne02 == 0); - assert(ne13 % ne03 == 0); + // This is the size of the first dimension of the result, so we can iterate that way. (see the ASSERT above, these are the same numbers) + const int64_t nr0 = ne0; - // block-tiling attempt - const int64_t blck_0 = 16; - const int64_t blck_1 = 16; + // This is the size of the rest of the dimensions of the result + const int64_t nr1 = ne1 * ne2 * ne3; // dot kernels can handle 1 row and col at a time, but mmla kernels can process 2 rows and cols - int64_t nrc = vec_dot_num_rows; + int64_t num_rows_per_vec_dot = vec_dot_num_rows; // TODO: currently the mmla kernels support only even numbered rows/cols. // this check can be removed once they are extended to support odd numbered rows/cols too if ((nr0 % 2 != 0) || (ne11 % 2 != 0)) { - nrc = 1; + num_rows_per_vec_dot = 1; } - const size_t src1_col_stride = src1_cont || src1->type != vec_dot_type ? row_size : nb11; + // Now select a reasonable chunk size. + int chunk_size = 16; - // attempt to reduce false-sharing (does not seem to make a difference) - // 16 * 2, accounting for mmla kernels - float tmp[32]; + // We need to step up the size if it's small + if (nr0 == 1 || nr1 == 1) { + chunk_size = 64; + } - for (int64_t iir1 = ir110; iir1 < ir111; iir1 += blck_1) { - for (int64_t iir0 = ir010; iir0 < ir011; iir0 += blck_0) { - for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir111; ir1 += nrc) { - const int64_t i13 = (ir1/(ne12*ne1)); - const int64_t i12 = (ir1 - i13*ne12*ne1)/ne1; - const int64_t i11 = (ir1 - i13*ne12*ne1 - i12*ne1); + // distribute the work across the inner or outer loop based on which one is larger + // The number of chunks in the 0/1 dim. + // CEIL(nr0/chunk_size) + int64_t nchunk0 = (nr0 + chunk_size - 1) / chunk_size; + int64_t nchunk1 = (nr1 + chunk_size - 1) / chunk_size; - // broadcast src0 into src1 - const int64_t i03 = i13/r3; - const int64_t i02 = i12/r2; + // If the chunking is poor for the number of threads on this setup, scrap the whole plan. Re-chunk it by thread. + // Also, chunking by thread was measured to have perform better on NUMA systems. See https://github.com/ggerganov/llama.cpp/pull/6915 + // In theory, chunking should be just as useful on NUMA and non NUMA systems, but testing disagreed with that. + if (nchunk0 * nchunk1 < nth * 4 || ggml_is_numa()) { + // distribute the thread work across the inner or outer loop based on which one is larger + nchunk0 = nr0 > nr1 ? nth : 1; // parallelize by src0 rows + nchunk1 = nr0 > nr1 ? 1 : nth; // parallelize by src1 rows + } - const int64_t i1 = i11; - const int64_t i2 = i12; - const int64_t i3 = i13; + // The number of elements in each chunk + const int64_t dr0 = (nr0 + nchunk0 - 1) / nchunk0; + const int64_t dr1 = (nr1 + nchunk1 - 1) / nchunk1; - const char * src0_row = (const char *) src0->data + (0 + i02*nb02 + i03*nb03); + //if (ith == 0) + // printf("MUL_MAT = [%d, %d, %d, %d] x [%d, %d, %d, %d] = %d x %d = %d. Fp Ops/Ch %d\n", ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, nchunk0, nchunk1, nchunk0 * nchunk1, ne00 * nr0 * nr1 / nchunk0 / nchunk1); - // desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides - // if it is, then we have either copied the data to params->wdata and made it contiguous or we are using - // the original src1 data pointer, so we should index using the indices directly - // TODO: this is a bit of a hack, we should probably have a better way to handle this - const char * src1_col = (const char *) wdata + - (src1_cont || src1->type != vec_dot_type - ? (i11 + i12*ne11 + i13*ne12*ne11)*row_size - : (i11*nb11 + i12*nb12 + i13*nb13)); - float * dst_col = (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3)); + // The first chunk comes from our thread_id, the rest will get auto-assigned. + int current_chunk = ith; - //for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) { - // vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col); - //} + while (current_chunk < nchunk0 * nchunk1) { + const int64_t ith0 = current_chunk % nchunk0; + const int64_t ith1 = current_chunk / nchunk0; - for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ir0 += nrc) { - vec_dot(ne00, &tmp[ir0 - iir0], (nrc>1 ? 16 : 0), src0_row + ir0*nb01, (nrc>1 ? nb01 : 0), src1_col, (nrc>1 ? src1_col_stride : 0), nrc); - } + const int64_t ir0_start = dr0 * ith0; + const int64_t ir0_end = MIN(ir0_start + dr0, nr0); - for (int cn = 0; cn < nrc; ++cn) { - memcpy(&dst_col[iir0 + cn*nb1/nb0], tmp + (cn*16), (MIN(iir0 + blck_0, ir011) - iir0)*sizeof(float)); - } - } + const int64_t ir1_start = dr1 * ith1; + const int64_t ir1_end = MIN(ir1_start + dr1, nr1); + + ggml_compute_forward_mul_mat_one_chunk(params, dst, num_rows_per_vec_dot, ir0_start, ir0_end, ir1_start, ir1_end); + +#ifdef GGML_PERF + chunks_executed++; +#endif + + if (nth >= nchunk0 * nchunk1) { + break; } + + current_chunk = atomic_fetch_add(&state->shared->current_chunk, 1); } + +#ifdef GGML_PERF + // These numbers are useful when trying to measure how well the threading scheduling works. + //int64_t workSize = (ne01 * ne11 * ne12 * ne13 * ne00) / nchunk0 / nchunk1; + //float time = (ggml_perf_time_us() - t0); + //printf("MUL_MAT = %f ms, [%d, %d, %d, %d] x [%d, %d, %d, %d] = %I64u, %f ops/usec in %d chunks.\n", time / 1000.0, ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, workSize, (float)workSize/time, chunks_executed); +#endif } // ggml_compute_forward_mul_mat_id @@ -13336,7 +13908,6 @@ static void ggml_compute_forward_soft_max_f32( const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; - const struct ggml_tensor * src2 = dst->src[2]; assert(ggml_is_contiguous(dst)); assert(ggml_are_same_shape(src0, dst)); @@ -13362,8 +13933,8 @@ static void ggml_compute_forward_soft_max_f32( // TODO: is this supposed to be ceil instead of floor? // https://huggingface.co/mosaicml/mpt-7b/blob/main/attention.py#L370 - const uint32_t n_head_kv = ne02; - const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head_kv)); + const uint32_t n_head = ne02; + const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head)); const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); @@ -13380,13 +13951,13 @@ static void ggml_compute_forward_soft_max_f32( float * wp = (float *) params->wdata + (nc + CACHE_LINE_SIZE_F32) * ith; - // when max_bias <= 0.0f, src2 is not used and we default it to src0 to avoid branching - ggml_fp16_t * pos_f16 = src2 ? (ggml_fp16_t *) src2->data : src0->data; - float * pos_f32 = src2 ? (float *) src2->data : src0->data; - - const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16) || (src2 && src2->type == GGML_TYPE_F16); + const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16); for (int i1 = ir0; i1 < ir1; i1++) { + // ALiBi + const uint32_t h = (i1/ne01)%ne02; // head + const float slope = (max_bias > 0.0f) ? h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1) : 1.0f; + float * sp = (float *)((char *) src0->data + i1*src0->nb[1]); float * dp = (float *)((char *) dst->data + i1*dst->nb[1]); @@ -13399,27 +13970,11 @@ static void ggml_compute_forward_soft_max_f32( if (mp_f32) { if (use_f16) { for (int i = 0; i < nc; ++i) { - wp[i] += GGML_FP16_TO_FP32(mp_f16[i]); - } - } else { - for (int i = 0; i < nc; ++i) { - wp[i] += mp_f32[i]; - } - } - } - - // ALiBi bias - if (max_bias > 0.0f) { - const uint32_t h = (i1/ne01)%ne02; // head - const float slope = h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1); - - if (use_f16) { - for (int i = 0; i < nc; ++i) { - wp[i] += slope*GGML_FP16_TO_FP32(pos_f16[i]); + wp[i] += slope*GGML_FP16_TO_FP32(mp_f16[i]); } } else { for (int i = 0; i < nc; ++i) { - wp[i] += slope*pos_f32[i]; + wp[i] += slope*mp_f32[i]; } } } @@ -13434,22 +13989,7 @@ static void ggml_compute_forward_soft_max_f32( float max = -INFINITY; ggml_vec_max_f32(nc, &max, wp); - ggml_float sum = 0.0; - - uint16_t scvt; - for (int i = 0; i < nc; i++) { - if (wp[i] == -INFINITY) { - dp[i] = 0.0f; - } else { - // const float val = (wp[i] == -INFINITY) ? 0.0 : exp(wp[i] - max); - ggml_fp16_t s = GGML_FP32_TO_FP16(wp[i] - max); - memcpy(&scvt, &s, sizeof(scvt)); - const float val = GGML_FP16_TO_FP32(ggml_table_exp_f16[scvt]); - sum += (ggml_float)val; - dp[i] = val; - } - } - + ggml_float sum = ggml_vec_soft_max_f32(nc, dp, wp, max); assert(sum > 0.0); sum = 1.0/sum; @@ -13581,68 +14121,9 @@ static void ggml_compute_forward_soft_max_back( } } -// ggml_compute_forward_alibi - -static void ggml_compute_forward_alibi_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - assert(params->ith == 0); - - if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { - return; - } - - //const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_head = ((int32_t *) dst->op_params)[1]; - float max_bias; - memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float)); - - const int64_t ne0 = src0->ne[0]; // all_seq_len = n_past + ne1 - const int64_t ne1 = src0->ne[1]; // seq_len_without_past - const int64_t ne2 = src0->ne[2]; // n_head -> this is k - //const int64_t ne3 = src0->ne[3]; // 1 -> bsz - - const int64_t n = ggml_nrows(src0); - const int64_t ne2_ne3 = n/ne1; // ne2*ne3 - - const size_t nb0 = src0->nb[0]; - const size_t nb1 = src0->nb[1]; - const size_t nb2 = src0->nb[2]; - //const int nb3 = src0->nb[3]; - - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(n_head == ne2); - - // add alibi to src0 (KQ_scaled) - const int n_heads_log2_floor = 1 << (int) floor(log2(n_head)); - - const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor); - const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor); - - for (int64_t k = 0; k < ne2_ne3; k++) { - // TODO: k*nb2 or k*nb3 - float m_k; - - if (k < n_heads_log2_floor) { - m_k = powf(m0, k + 1); - } else { - m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1); - } - - for (int64_t i = 0; i < ne0; i++) { - for (int64_t j = 0; j < ne1; j++) { - float * const src = (float *)((char *) src0->data + i*nb0 + j*nb1 + k*nb2); - float * pdst = (float *)((char *) dst->data + i*nb0 + j*nb1 + k*nb2); - pdst[0] = i * m_k + src[0]; - } - } - } -} +// ggml_compute_forward_clamp -static void ggml_compute_forward_alibi_f16( +static void ggml_compute_forward_clamp_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { @@ -13654,71 +14135,48 @@ static void ggml_compute_forward_alibi_f16( return; } - //const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_head = ((int32_t *) dst->op_params)[1]; - float max_bias; - memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float)); + float min; + float max; + memcpy(&min, (float *) dst->op_params + 0, sizeof(float)); + memcpy(&max, (float *) dst->op_params + 1, sizeof(float)); - const int ne0 = src0->ne[0]; // all_seq_len = n_past + ne1 - const int ne1 = src0->ne[1]; // seq_len_without_past - const int ne2 = src0->ne[2]; // n_head -> this is k - //const int ne3 = src0->ne[3]; // 1 -> bsz + const int ith = params->ith; + const int nth = params->nth; const int n = ggml_nrows(src0); - const int ne2_ne3 = n/ne1; // ne2*ne3 - - const int nb0 = src0->nb[0]; - const int nb1 = src0->nb[1]; - const int nb2 = src0->nb[2]; - //const int nb3 = src0->nb[3]; - - GGML_ASSERT(nb0 == sizeof(ggml_fp16_t)); - //GGML_ASSERT(ne1 + n_past == ne0); (void) n_past; - GGML_ASSERT(n_head == ne2); - - // add alibi to src0 (KQ_scaled) - const int n_heads_log2_floor = 1 << (int) floor(log2(n_head)); + const int nc = src0->ne[0]; - const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor); - const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor); + const size_t nb00 = src0->nb[0]; + const size_t nb01 = src0->nb[1]; - for (int k = 0; k < ne2_ne3; k++) { - // TODO: k*nb2 or k*nb3 - float m_k; + const size_t nb0 = dst->nb[0]; + const size_t nb1 = dst->nb[1]; - if (k < n_heads_log2_floor) { - m_k = powf(m0, k + 1); - } else { - m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1); - } + GGML_ASSERT( nb0 == sizeof(float)); + GGML_ASSERT(nb00 == sizeof(float)); - for (int i = 0; i < ne0; i++) { - for (int j = 0; j < ne1; j++) { - ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i*nb0 + j*nb1 + k*nb2); - float * pdst = (float *)((char *) dst->data + i*nb0 + j*nb1 + k*nb2); + for (int j = ith; j < n; j += nth) { + float * dst_ptr = (float *) ((char *) dst->data + j*nb1); + float * src0_ptr = (float *) ((char *) src0->data + j*nb01); - // we return F32 - pdst[0] = i * m_k + GGML_FP16_TO_FP32(src[0]); - } + for (int i = 0; i < nc; i++) { + dst_ptr[i] = MAX(MIN(src0_ptr[i], max), min); } } } -static void ggml_compute_forward_alibi( +static void ggml_compute_forward_clamp( const struct ggml_compute_params * params, struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; switch (src0->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_alibi_f16(params, dst); - } break; case GGML_TYPE_F32: { - ggml_compute_forward_alibi_f32(params, dst); + ggml_compute_forward_clamp_f32(params, dst); } break; + case GGML_TYPE_F16: case GGML_TYPE_BF16: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: @@ -13753,102 +14211,12 @@ static void ggml_compute_forward_alibi( } } -// ggml_compute_forward_clamp - -static void ggml_compute_forward_clamp_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; +// ggml_compute_forward_rope - assert(params->ith == 0); - - if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { - return; - } - - float min; - float max; - memcpy(&min, (float *) dst->op_params + 0, sizeof(float)); - memcpy(&max, (float *) dst->op_params + 1, sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - - GGML_ASSERT( nb0 == sizeof(float)); - GGML_ASSERT(nb00 == sizeof(float)); - - for (int j = ith; j < n; j += nth) { - float * dst_ptr = (float *) ((char *) dst->data + j*nb1); - float * src0_ptr = (float *) ((char *) src0->data + j*nb01); - - for (int i = 0; i < nc; i++) { - dst_ptr[i] = MAX(MIN(src0_ptr[i], max), min); - } - } -} - -static void ggml_compute_forward_clamp( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_clamp_f32(params, dst); - } break; - case GGML_TYPE_F16: - case GGML_TYPE_BF16: - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q8_1: - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_IQ4_NL: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ3_S: - case GGML_TYPE_IQ2_S: - case GGML_TYPE_Q8_K: - case GGML_TYPE_I8: - case GGML_TYPE_I16: - case GGML_TYPE_I32: - case GGML_TYPE_I64: - case GGML_TYPE_F64: - case GGML_TYPE_COUNT: - { - GGML_ASSERT(false); - } break; - } -} - -// ggml_compute_forward_rope - -static float rope_yarn_ramp(const float low, const float high, const int i0) { - const float y = (i0 / 2 - low) / MAX(0.001f, high - low); - return 1 - MIN(1, MAX(0, y)); -} +static float rope_yarn_ramp(const float low, const float high, const int i0) { + const float y = (i0 / 2 - low) / MAX(0.001f, high - low); + return 1 - MIN(1, MAX(0, y)); +} // YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn // MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng. @@ -13908,6 +14276,7 @@ static void ggml_compute_forward_rope_f32( const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; + const struct ggml_tensor * src2 = dst->src[2]; if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; @@ -13967,6 +14336,17 @@ static void ggml_compute_forward_rope_f32( const bool is_neox = mode & 2; const bool is_glm = mode & 4; + const float * freq_factors = NULL; + if (is_neox) { + if (src2 != NULL) { + GGML_ASSERT(src2->type == GGML_TYPE_F32); + GGML_ASSERT(src2->ne[0] >= n_dims / 2); + freq_factors = (const float *) src2->data; + } + } else { + GGML_ASSERT(src2 == NULL && "TODO: freq_factors not implemented for !is_neox"); + } + // backward process uses inverse rotation by cos and sin. // cos and sin build a rotation matrix, where the inverse is the transpose. // this essentially just switches the sign of sin. @@ -14043,10 +14423,11 @@ static void ggml_compute_forward_rope_f32( // simplified from `(ib * n_dims + ic) * inv_ndims` float cur_rot = inv_ndims * ic - ib; + float freq_factor = freq_factors ? freq_factors[ic/2] : 1.0f; float cos_theta, sin_theta; rope_yarn( - theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, + theta_base/freq_factor, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta ); sin_theta *= sin_sign; @@ -14079,6 +14460,7 @@ static void ggml_compute_forward_rope_f32( } } +// TODO: deduplicate f16/f32 code static void ggml_compute_forward_rope_f16( const struct ggml_compute_params * params, struct ggml_tensor * dst, @@ -14086,6 +14468,7 @@ static void ggml_compute_forward_rope_f16( const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; + const struct ggml_tensor * src2 = dst->src[2]; if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; @@ -14138,6 +14521,17 @@ static void ggml_compute_forward_rope_f16( const bool is_neox = mode & 2; const bool is_glm = mode & 4; + const float * freq_factors = NULL; + if (is_neox) { + if (src2 != NULL) { + GGML_ASSERT(src2->type == GGML_TYPE_F32); + GGML_ASSERT(src2->ne[0] >= n_dims / 2); + freq_factors = (const float *) src2->data; + } + } else { + GGML_ASSERT(src2 == NULL && "TODO: freq_factors not implemented for !is_neox"); + } + // backward process uses inverse rotation by cos and sin. // cos and sin build a rotation matrix, where the inverse is the transpose. // this essentially just switches the sign of sin. @@ -14210,10 +14604,11 @@ static void ggml_compute_forward_rope_f16( // simplified from `(ib * n_dims + ic) * inv_ndims` float cur_rot = inv_ndims * ic - ib; + float freq_factor = freq_factors ? freq_factors[ic/2] : 1.0f; float cos_theta, sin_theta; rope_yarn( - theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, + theta_base/freq_factor, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta ); sin_theta *= sin_sign; @@ -14975,25 +15370,28 @@ static void ggml_compute_forward_upscale_f32( return; } - GGML_ASSERT(src0->nb[0] == sizeof(float)); + GGML_ASSERT(src0->type == GGML_TYPE_F32); const int ith = params->ith; const int nth = params->nth; GGML_TENSOR_UNARY_OP_LOCALS - const int scale_factor = dst->op_params[0]; + const float sf0 = (float)ne0/src0->ne[0]; + const float sf1 = (float)ne1/src0->ne[1]; + const float sf2 = (float)ne2/src0->ne[2]; + const float sf3 = (float)ne3/src0->ne[3]; // TODO: optimize for (int64_t i3 = 0; i3 < ne3; i3++) { - const int64_t i03 = i3; + const int64_t i03 = i3 / sf3; for (int64_t i2 = ith; i2 < ne2; i2 += nth) { - const int64_t i02 = i2; + const int64_t i02 = i2 / sf2; for (int64_t i1 = 0; i1 < ne1; i1++) { - const int64_t i01 = i1 / scale_factor; + const int64_t i01 = i1 / sf1; for (int64_t i0 = 0; i0 < ne0; i0++) { - const int64_t i00 = i0 / scale_factor; + const int64_t i00 = i0 / sf0; const float * x = (float *)((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); float * y = (float *)((char *) dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3); @@ -15023,6 +15421,7 @@ static void ggml_compute_forward_upscale( } } + // ggml_compute_forward_pad static void ggml_compute_forward_pad_f32( @@ -15203,487 +15602,42 @@ static void ggml_compute_forward_argsort_f32( const int ith = params->ith; const int nth = params->nth; - const int64_t nr = ggml_nrows(src0); - - enum ggml_sort_order order = (enum ggml_sort_order) ggml_get_op_params_i32(dst, 0); - - for (int64_t i = ith; i < nr; i += nth) { - int32_t * dst_data = (int32_t *)((char *) dst->data + i*nb1); - const float * src_data = (float *)((char *) src0->data + i*nb01); - - for (int64_t j = 0; j < ne0; j++) { - dst_data[j] = j; - } - - // C doesn't have a functional sort, so we do a bubble sort instead - for (int64_t j = 0; j < ne0; j++) { - for (int64_t k = j + 1; k < ne0; k++) { - if ((order == GGML_SORT_ORDER_ASC && src_data[dst_data[j]] > src_data[dst_data[k]]) || - (order == GGML_SORT_ORDER_DESC && src_data[dst_data[j]] < src_data[dst_data[k]])) { - int32_t tmp = dst_data[j]; - dst_data[j] = dst_data[k]; - dst_data[k] = tmp; - } - } - } - } -} - -static void ggml_compute_forward_argsort( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_argsort_f32(params, dst); - } break; - default: - { - GGML_ASSERT(false); - } break; - } -} - -// ggml_compute_forward_flash_attn - -static void ggml_compute_forward_flash_attn_f32( - const struct ggml_compute_params * params, - const bool masked, - struct ggml_tensor * dst) { - - const struct ggml_tensor * q = dst->src[0]; - const struct ggml_tensor * k = dst->src[1]; - const struct ggml_tensor * v = dst->src[2]; - - int64_t t0 = ggml_perf_time_us(); - UNUSED(t0); - - GGML_TENSOR_LOCALS(int64_t, neq, q, ne) - GGML_TENSOR_LOCALS(size_t, nbq, q, nb) - GGML_TENSOR_LOCALS(int64_t, nek, k, ne) - GGML_TENSOR_LOCALS(size_t, nbk, k, nb) - GGML_TENSOR_LOCALS(int64_t, nev, v, ne) - GGML_TENSOR_LOCALS(size_t, nbv, v, nb) - GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) - GGML_TENSOR_LOCALS(size_t, nb, dst, nb) - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t D = neq0; - const int64_t N = neq1; - const int64_t P = nek1 - N; - const int64_t M = P + N; - - const int Mup = ggml_up(M, GGML_SOFT_MAX_UNROLL); - - GGML_ASSERT(ne0 == D); - GGML_ASSERT(ne1 == N); - GGML_ASSERT(P >= 0); - - GGML_ASSERT(nbq0 == sizeof(float)); - GGML_ASSERT(nbk0 == sizeof(float)); - GGML_ASSERT(nbv0 == sizeof(float)); - - GGML_ASSERT(neq0 == D); - GGML_ASSERT(nek0 == D); - GGML_ASSERT(nev1 == D); - - GGML_ASSERT(neq1 == N); - GGML_ASSERT(nek1 == N + P); - GGML_ASSERT(nev1 == D); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb0 <= nb1); - GGML_ASSERT(nb1 <= nb2); - GGML_ASSERT(nb2 <= nb3); - - if (params->type == GGML_TASK_TYPE_INIT) { - return; - } - - if (params->type == GGML_TASK_TYPE_FINALIZE) { - return; - } - - // parallelize by q rows using ggml_vec_dot_f32 - - // total rows in q - const int nr = neq1*neq2*neq3; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - const float scale = 1.0f/sqrtf(D); - - //printf("P=%d N=%d D=%d ir0=%d ir1=%d scale = %f\n", P, N, D, ir0, ir1, scale); - - for (int ir = ir0; ir < ir1; ++ir) { - // q indices - const int iq3 = ir/(neq2*neq1); - const int iq2 = (ir - iq3*neq2*neq1)/neq1; - const int iq1 = (ir - iq3*neq2*neq1 - iq2*neq1); - - float * S = (float *) params->wdata + ith*(Mup + CACHE_LINE_SIZE_F32); - - for (int i = M; i < Mup; ++i) { - S[i] = -INFINITY; - } - - const int64_t masked_begin = masked ? (P + iq1 + 1) : M; - for (int64_t ic = 0; ic < masked_begin; ++ic) { - // k indices - const int ik3 = iq3; - const int ik2 = iq2 % nek2; - const int ik1 = ic; - - // S indices - const int i1 = ik1; - - ggml_vec_dot_f32(neq0, - S + i1, 0, - (float *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), 0, - (float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)), 0, 1); - } - - // scale - ggml_vec_scale_f32(masked_begin, S, scale); - - for (int64_t i = masked_begin; i < M; i++) { - S[i] = -INFINITY; - } - - // softmax - // exclude known -INF S[..] values from max and loop - // dont forget to set their SW values to zero - { - float max = -INFINITY; - ggml_vec_max_f32(masked_begin, &max, S); - - ggml_float sum = 0.0; - { -#ifdef GGML_SOFT_MAX_ACCELERATE - max = -max; - vDSP_vsadd(S, 1, &max, S, 1, Mup); - vvexpf(S, S, &Mup); - ggml_vec_sum_f32(Mup, &sum, S); -#else - uint16_t scvt[GGML_SOFT_MAX_UNROLL]; UNUSED(scvt); - ggml_float sump[GGML_SOFT_MAX_UNROLL] = { 0.0 }; - - for (int i = 0; i < Mup; i += GGML_SOFT_MAX_UNROLL) { - if (i >= masked_begin) { - break; - } - float * SS = S + i; - - for (int j = 0; j < GGML_SOFT_MAX_UNROLL; ++j) { - if (i + j >= masked_begin) { - break; - } else if (SS[j] == -INFINITY) { - SS[j] = 0.0f; - } else { -#ifndef GGML_FLASH_ATTN_EXP_FP16 - const float val = expf(SS[j] - max); -#else - ggml_fp16_t s = GGML_FP32_TO_FP16(SS[j] - max); - memcpy(&scvt[j], &s, sizeof(uint16_t)); - const float val = GGML_FP16_TO_FP32(ggml_table_exp_f16[scvt[j]]); -#endif - sump[j] += (ggml_float)val; - SS[j] = val; - } - } - } - - for (int i = 0; i < GGML_SOFT_MAX_UNROLL; i++) { - sum += sump[i]; - } -#endif - } - - assert(sum > 0.0); - - sum = 1.0/sum; - ggml_vec_scale_f32(masked_begin, S, sum); - -#ifndef NDEBUG - for (int i = 0; i < masked_begin; ++i) { - assert(!isnan(S[i])); - assert(!isinf(S[i])); - } -#endif - } - - for (int64_t ic = 0; ic < nev1; ++ic) { - // dst indices - const int i1 = iq1; - const int i2 = iq2; - const int i3 = iq3; - - // v indices - const int iv2 = iq2 % nev2; - const int iv3 = iq3; - - ggml_vec_dot_f32(masked_begin, - (float *) ((char *) dst->data + (ic*nb0 + i1*nb1 + i2*nb2 + i3*nb3)), 0, - (float *) ((char *) v->data + ( ic*nbv1 + iv2*nbv2 + iv3*nbv3)), 0, - S, 0, 1); - } - } -} - -static void ggml_compute_forward_flash_attn_f16( - const struct ggml_compute_params * params, - const bool masked, - struct ggml_tensor * dst) { - - const struct ggml_tensor * q = dst->src[0]; - const struct ggml_tensor * k = dst->src[1]; - const struct ggml_tensor * v = dst->src[2]; - - int64_t t0 = ggml_perf_time_us(); - UNUSED(t0); - - GGML_TENSOR_LOCALS(int64_t, neq, q, ne) - GGML_TENSOR_LOCALS(size_t, nbq, q, nb) - GGML_TENSOR_LOCALS(int64_t, nek, k, ne) - GGML_TENSOR_LOCALS(size_t, nbk, k, nb) - GGML_TENSOR_LOCALS(int64_t, nev, v, ne) - GGML_TENSOR_LOCALS(size_t, nbv, v, nb) - GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) - GGML_TENSOR_LOCALS(size_t, nb, dst, nb) - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t D = neq0; - const int64_t N = neq1; - const int64_t P = nek1 - N; - const int64_t M = P + N; - - const int Mup = ggml_up(M, GGML_SOFT_MAX_UNROLL); - - GGML_ASSERT(ne0 == D); - GGML_ASSERT(ne1 == N); - GGML_ASSERT(P >= 0); - - GGML_ASSERT(nbq0 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nbk0 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nbv0 == sizeof(ggml_fp16_t)); - - GGML_ASSERT(neq0 == D); - GGML_ASSERT(nek0 == D); - GGML_ASSERT(nev1 == D); - - GGML_ASSERT(neq1 == N); - GGML_ASSERT(nek1 == N + P); - GGML_ASSERT(nev1 == D); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb0 <= nb1); - GGML_ASSERT(nb1 <= nb2); - GGML_ASSERT(nb2 <= nb3); - - if (params->type == GGML_TASK_TYPE_INIT) { - return; - } - - if (params->type == GGML_TASK_TYPE_FINALIZE) { - return; - } - - // parallelize by q rows using ggml_vec_dot_f32 - - // total rows in q - const int nr = neq1*neq2*neq3; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - const float scale = 1.0f/sqrtf(D); - - //printf("P=%d N=%d D=%d ir0=%d ir1=%d scale = %f\n", P, N, D, ir0, ir1, scale); - - for (int ir = ir0; ir < ir1; ++ir) { - // q indices - const int iq3 = ir/(neq2*neq1); - const int iq2 = (ir - iq3*neq2*neq1)/neq1; - const int iq1 = (ir - iq3*neq2*neq1 - iq2*neq1); - - float * S = (float *) params->wdata + ith*(2*Mup + CACHE_LINE_SIZE_F32); - - for (int i = M; i < Mup; ++i) { - S[i] = -INFINITY; - } - - if (GGML_VEC_DOT_UNROLL > 2 || nek1 % GGML_VEC_DOT_UNROLL != 0) { - for (int64_t ic = 0; ic < nek1; ++ic) { - // k indices - const int ik3 = iq3; - const int ik2 = iq2 % nek2; - const int ik1 = ic; - - // S indices - const int i1 = ik1; - - ggml_vec_dot_f16(neq0, - S + i1, 0, - (ggml_fp16_t *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), 0, - (ggml_fp16_t *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)), 0, 1); - } - } else { - for (int64_t ic = 0; ic < nek1; ic += GGML_VEC_DOT_UNROLL) { - // k indices - const int ik3 = iq3; - const int ik2 = iq2 % nek2; - const int ik1 = ic; - - // S indices - const int i1 = ik1; - - ggml_vec_dot_f16_unroll(neq0, nbk1, - S + i1, - ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), - (ggml_fp16_t *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3))); - } - } - - // scale - ggml_vec_scale_f32(nek1, S, scale); - - if (masked) { - for (int64_t i = P; i < M; i++) { - if (i > P + iq1) { - S[i] = -INFINITY; - } - } - } - - // softmax - // todo: exclude known -INF S[..] values from max and loop, assuming their results to be zero. - // dont forget to set their S values to zero - { - float max = -INFINITY; - ggml_vec_max_f32(M, &max, S); - - ggml_float sum = 0.0; - { -#ifdef GGML_SOFT_MAX_ACCELERATE - max = -max; - vDSP_vsadd(S, 1, &max, S, 1, Mup); - vvexpf(S, S, &Mup); - ggml_vec_sum_f32(Mup, &sum, S); -#else - uint16_t scvt[GGML_SOFT_MAX_UNROLL]; - ggml_float sump[GGML_SOFT_MAX_UNROLL] = { 0.0 }; - - for (int i = 0; i < Mup; i += GGML_SOFT_MAX_UNROLL) { - float * SS = S + i; - - for (int j = 0; j < GGML_SOFT_MAX_UNROLL; ++j) { - if (SS[j] == -INFINITY) { - SS[j] = 0.0f; - } else { - ggml_fp16_t s = GGML_FP32_TO_FP16(SS[j] - max); - memcpy(&scvt[j], &s, sizeof(uint16_t)); - const float val = GGML_FP16_TO_FP32(ggml_table_exp_f16[scvt[j]]); - sump[j] += (ggml_float)val; - SS[j] = val; - } - } - } - - for (int i = 0; i < GGML_SOFT_MAX_UNROLL; i++) { - sum += sump[i]; - } -#endif - } - - assert(sum > 0.0); - - sum = 1.0/sum; - ggml_vec_scale_f32(M, S, sum); - -#ifndef NDEBUG - for (int i = 0; i < M; ++i) { - assert(!isnan(S[i])); - assert(!isinf(S[i])); - } -#endif - } - - ggml_fp16_t * S16 = (ggml_fp16_t *) ((float *) params->wdata + ith*(2*Mup + CACHE_LINE_SIZE_F32) + Mup); + const int64_t nr = ggml_nrows(src0); + + enum ggml_sort_order order = (enum ggml_sort_order) ggml_get_op_params_i32(dst, 0); + + for (int64_t i = ith; i < nr; i += nth) { + int32_t * dst_data = (int32_t *)((char *) dst->data + i*nb1); + const float * src_data = (float *)((char *) src0->data + i*nb01); - for (int64_t i = 0; i < M; i++) { - S16[i] = GGML_FP32_TO_FP16(S[i]); + for (int64_t j = 0; j < ne0; j++) { + dst_data[j] = j; } - // todo: exclude known zero S[..] values from dot (reducing nev0 and increasing begin of v and S16). - if (GGML_VEC_DOT_UNROLL == 1 || (nev1 % GGML_VEC_DOT_UNROLL != 0)) { - for (int64_t ic = 0; ic < nev1; ++ic) { - // dst indices - const int i1 = iq1; - const int i2 = iq2; - const int i3 = iq3; - - // v indices - const int iv2 = iq2 % nev2; - const int iv3 = iq3; - - ggml_vec_dot_f16(nev0, - (float *) ((char *) dst->data + (ic*nb0 + i1*nb1 + i2*nb2 + i3*nb3)), 0, - (ggml_fp16_t *) ((char *) v->data + ( ic*nbv1 + iv2*nbv2 + iv3*nbv3)), 0, - S16, 0, 1); - } - } else { - for (int64_t ic = 0; ic < nev1; ic += GGML_VEC_DOT_UNROLL) { - // dst indices - const int i1 = iq1; - const int i2 = iq2; - const int i3 = iq3; - - // v indices - const int iv2 = iq2 % nev2; - const int iv3 = iq3; - - ggml_vec_dot_f16_unroll(nev0, nbv1, - (float *) ((char *) dst->data + (ic*nb0 + i1*nb1 + i2*nb2 + i3*nb3)), - ((char *) v->data + ( ic*nbv1 + iv2*nbv2 + iv3*nbv3)), - S16); + // C doesn't have a functional sort, so we do a bubble sort instead + for (int64_t j = 0; j < ne0; j++) { + for (int64_t k = j + 1; k < ne0; k++) { + if ((order == GGML_SORT_ORDER_ASC && src_data[dst_data[j]] > src_data[dst_data[k]]) || + (order == GGML_SORT_ORDER_DESC && src_data[dst_data[j]] < src_data[dst_data[k]])) { + int32_t tmp = dst_data[j]; + dst_data[j] = dst_data[k]; + dst_data[k] = tmp; + } } } } } -static void ggml_compute_forward_flash_attn( - const struct ggml_compute_params * params, - const bool masked, - struct ggml_tensor * dst) { +static void ggml_compute_forward_argsort( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { - const struct ggml_tensor * q = dst->src[0]; + const struct ggml_tensor * src0 = dst->src[0]; - switch (q->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_flash_attn_f16(params, masked, dst); - } break; + switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_flash_attn_f32(params, masked, dst); + ggml_compute_forward_argsort_f32(params, dst); } break; default: { @@ -15722,9 +15676,10 @@ static void ggml_compute_forward_flash_attn_ext_f16( GGML_ASSERT(ne0 == D); GGML_ASSERT(ne2 == N); - GGML_ASSERT(nbq0 == sizeof(float)); - GGML_ASSERT(nbk0 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nbv0 == sizeof(ggml_fp16_t)); + // input tensor rows must be contiguous + GGML_ASSERT(nbq0 == ggml_type_size(q->type)); + GGML_ASSERT(nbk0 == ggml_type_size(k->type)); + GGML_ASSERT(nbv0 == ggml_type_size(v->type)); GGML_ASSERT(neq0 == D); GGML_ASSERT(nek0 == D); @@ -15766,8 +15721,22 @@ static void ggml_compute_forward_flash_attn_ext_f16( const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); - float scale = 1.0f; - memcpy(&scale, (float *) dst->op_params + 0, sizeof(float)); + float scale = 1.0f; + float max_bias = 0.0f; + + memcpy(&scale, (float *) dst->op_params + 0, sizeof(float)); + memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float)); + + const uint32_t n_head = neq2; + const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head)); + + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + + enum ggml_type const k_vec_dot_type = type_traits[k->type].vec_dot_type; + ggml_from_float_t const q_to_vec_dot = type_traits[k_vec_dot_type].from_float; + ggml_vec_dot_t const kq_vec_dot = type_traits[k->type].vec_dot; + ggml_to_float_t const v_to_float = type_traits[v->type].to_float; // loop over n_batch and n_head for (int ir = ir0; ir < ir1; ++ir) { @@ -15776,14 +15745,22 @@ static void ggml_compute_forward_flash_attn_ext_f16( const int iq2 = (ir - iq3*neq2*neq1)/neq1; const int iq1 = (ir - iq3*neq2*neq1 - iq2*neq1); - float S = 0.0f; - float M = -INFINITY; + const uint32_t h = iq2; // head index + const float slope = (max_bias > 0.0f) ? h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1) : 1.0f; + + float S = 0.0f; // sum + float M = -INFINITY; // maximum KQ value - float * V32 = (float *) params->wdata + ith*(2*D + CACHE_LINE_SIZE_F32); - ggml_fp16_t * Q16 = (ggml_fp16_t *) (V32); // reuse memory - ggml_fp16_t * V16 = (ggml_fp16_t *) (V32 + D); + float * VKQ32 = (float *) params->wdata + ith*(3*D + CACHE_LINE_SIZE_F32); // FP32 VKQ accumulator + float * V32 = (VKQ32 + 1*D); // (temporary) FP32 V buffer + ggml_fp16_t * VKQ16 = (ggml_fp16_t *) (VKQ32 + 1*D); // (temporary) FP16 VKQ accumulator + ggml_fp16_t * Q_q = (ggml_fp16_t *) (VKQ32 + 2*D); // (temporary) buffer for Q converted to quantized/FP16 - memset(V16, 0, D*sizeof(ggml_fp16_t)); + if (v->type == GGML_TYPE_F16) { + memset(VKQ16, 0, D*sizeof(ggml_fp16_t)); + } else { + memset(VKQ32, 0, D*sizeof(float)); + } const ggml_fp16_t * mp = mask ? (ggml_fp16_t *)((char *) mask->data + iq1*mask->nb[1]) : NULL; @@ -15795,61 +15772,79 @@ static void ggml_compute_forward_flash_attn_ext_f16( const int iv3 = iq3 / rv3; const int iv2 = iq2 / rv2; + const float * pq = (const float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)); + q_to_vec_dot(pq, Q_q, D); + // online softmax / attention // loop over n_kv and n_head_kv // ref: https://arxiv.org/pdf/2112.05682.pdf for (int64_t ic = 0; ic < nek1; ++ic) { - const float mv = mp ? GGML_FP16_TO_FP32(mp[ic]) : 0.0f; + const float mv = mp ? slope*GGML_FP16_TO_FP32(mp[ic]) : 0.0f; if (mv == -INFINITY) { continue; } - float s; + float s; // KQ value - // convert Q to F16 in V32 - { - const float * pq = (const float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)); + const char * k_data = (const char *) k->data + ( ic*nbk1 + ik2*nbk2 + ik3*nbk3); + kq_vec_dot(D, &s, 0, k_data, 0, Q_q, 0, 1); - for (int64_t d = 0; d < D; ++d) { - Q16[d] = GGML_FP32_TO_FP16(pq[d]); - } - } + s = s*scale + mv; // scale KQ value and apply mask - ggml_vec_dot_f16(D, - &s, 0, - (ggml_fp16_t *) ((char *) k->data + ( ic*nbk1 + ik2*nbk2 + ik3*nbk3)), 0, - Q16, 0, 1); + const float Mold = M; - s = s*scale + mv; + float ms = 1.0f; // upon new higher max val, scale VKQ and KQ sum with this value + float vs = 1.0f; // post-softmax KQ value, expf(s - M) - const float Mold = M; + const char * v_data = ((const char *) v->data + (ic*nbv1 + iv2*nbv2 + iv3*nbv3)); - float ms = 1.0f; - float vs = 1.0f; + if (v->type== GGML_TYPE_F16) { + if (s > M) { + // s is new maximum, ms < 1.0f, vs == expf(s - s) == 1.0f + M = s; + ms = expf(Mold - M); - if (s > M) { - M = s; - ms = expf(Mold - M); + // V = V*expf(Mold - M) + ggml_vec_scale_f16(D, VKQ16, ms); + } else { + // no new maximum, ms == 1.0f, vs != 1.0f + vs = expf(s - M); + } - // V = V*expf(Mold - M) - ggml_vec_scale_f16(D, V16, ms); + // V += v*expf(s - M) + ggml_vec_mad_f16(D, VKQ16, (const ggml_fp16_t *) v_data, vs); } else { - vs = expf(s - M); - } + if (s > M) { + // s is new maximum, ms < 1.0f, vs == expf(s - s) == 1.0f + M = s; + ms = expf(Mold - M); + + // V = V*expf(Mold - M) + ggml_vec_scale_f32(D, VKQ32, ms); + } else { + // no new maximum, ms == 1.0f, vs != 1.0f + vs = expf(s - M); + } - const ggml_fp16_t * v16 = (const ggml_fp16_t *) ((char *) v->data + (ic*nbv1 + iv2*nbv2 + iv3*nbv3)); + v_to_float(v_data, V32, D); - // V += v*expf(s - M) - ggml_vec_mad_f16(D, V16, v16, vs); + // V += v*expf(s - M) + ggml_vec_mad_f32(D, VKQ32, V32, vs); + } - S = S*ms + vs; + S = S*ms + vs; // scale and increment sum with partial sum } - // V /= S - for (int64_t d = 0; d < D; ++d) { - V32[d] = GGML_FP16_TO_FP32(V16[d])/S; + if (v->type == GGML_TYPE_F16) { + for (int64_t d = 0; d < D; ++d) { + VKQ32[d] = GGML_FP16_TO_FP32(VKQ16[d]); + } } + // V /= S + const float S_inv = 1.0f/S; + ggml_vec_scale_f32(D, VKQ32, S_inv); + // dst indices const int i1 = iq1; const int i2 = iq2; @@ -15859,7 +15854,7 @@ static void ggml_compute_forward_flash_attn_ext_f16( //memcpy((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3), V, nev0*sizeof(float)); // permute(0, 2, 1, 3) - memcpy((char *) dst->data + (i3*ne2*ne1 + i2 + i1*ne1)*nb1, V32, nb1); + memcpy((char *) dst->data + (i3*ne2*ne1 + i2 + i1*ne1)*nb1, VKQ32, nb1); } } @@ -15870,7 +15865,7 @@ static void ggml_compute_forward_flash_attn_ext( const struct ggml_tensor * v, const struct ggml_tensor * mask, struct ggml_tensor * dst) { - switch (dst->op_params[1]) { + switch (dst->op_params[2]) { case GGML_PREC_DEFAULT: case GGML_PREC_F32: { @@ -15884,165 +15879,6 @@ static void ggml_compute_forward_flash_attn_ext( } } -// ggml_compute_forward_flash_ff - -static void ggml_compute_forward_flash_ff_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * a = dst->src[0]; // F16 - const struct ggml_tensor * b0 = dst->src[1]; // F16 fc_w - const struct ggml_tensor * b1 = dst->src[2]; // F32 fc_b - const struct ggml_tensor * c0 = dst->src[3]; // F16 proj_w - const struct ggml_tensor * c1 = dst->src[4]; // F32 proj_b - - int64_t t0 = ggml_perf_time_us(); - UNUSED(t0); - - GGML_TENSOR_LOCALS(int64_t, nea, a, ne) - GGML_TENSOR_LOCALS(size_t, nba, a, nb) - GGML_TENSOR_LOCALS(int64_t, neb0, b0, ne) - GGML_TENSOR_LOCALS(size_t, nbb0, b0, nb) - GGML_TENSOR_LOCALS(int64_t, neb1, b1, ne) - GGML_TENSOR_LOCALS(size_t, nbb1, b1, nb) - GGML_TENSOR_LOCALS(int64_t, nec0, c0, ne) - GGML_TENSOR_LOCALS(size_t, nbc0, c0, nb) - GGML_TENSOR_LOCALS(int64_t, nec1, c1, ne) - GGML_TENSOR_LOCALS(size_t, nbc1, c1, nb) - GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) - GGML_TENSOR_LOCALS(size_t, nb, dst, nb) - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t D = nea0; - //const int64_t N = nea1; - const int64_t M = neb01; - - GGML_ASSERT(ne0 == nea0); - GGML_ASSERT(ne1 == nea1); - GGML_ASSERT(ne2 == nea2); - - GGML_ASSERT(nba0 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nbb00 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nbb10 == sizeof(float)); - GGML_ASSERT(nbc00 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nbc10 == sizeof(float)); - - GGML_ASSERT(neb00 == D); - GGML_ASSERT(neb01 == M); - GGML_ASSERT(neb10 == M); - GGML_ASSERT(neb11 == 1); - - GGML_ASSERT(nec00 == M); - GGML_ASSERT(nec01 == D); - GGML_ASSERT(nec10 == D); - GGML_ASSERT(nec11 == 1); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb0 <= nb1); - GGML_ASSERT(nb1 <= nb2); - GGML_ASSERT(nb2 <= nb3); - - if (params->type == GGML_TASK_TYPE_INIT) { - return; - } - - if (params->type == GGML_TASK_TYPE_FINALIZE) { - return; - } - - // parallelize by a rows using ggml_vec_dot_f32 - - // total rows in a - const int nr = nea1*nea2*nea3; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int ir = ir0; ir < ir1; ++ir) { - // a indices - const int ia3 = ir/(nea2*nea1); - const int ia2 = (ir - ia3*nea2*nea1)/nea1; - const int ia1 = (ir - ia3*nea2*nea1 - ia2*nea1); - - float * S = (float *) params->wdata + ith*(2*M + CACHE_LINE_SIZE_F32); - - for (int64_t ic = 0; ic < neb01; ++ic) { - // b0 indices - const int ib03 = ia3; - const int ib02 = ia2; - const int ib01 = ic; - - // S indices - const int i1 = ib01; - - ggml_vec_dot_f16(nea0, - S + i1, 0, - (ggml_fp16_t *) ((char *) b0->data + (ib01*nbb01 + ib02*nbb02 + ib03*nbb03)), 0, - (ggml_fp16_t *) ((char *) a->data + ( ia1*nba1 + ia2*nba2 + ia3*nba3)), 0, 1); - } - - ggml_vec_add_f32(neb01, S, S, (float *) b1->data); - //ggml_vec_gelu_f32(neb01, S, S); - - ggml_fp16_t * S16 = (ggml_fp16_t *) ((float *) params->wdata + ith*(2*M + CACHE_LINE_SIZE_F32) + M); - - for (int64_t i = 0; i < M; i++) { - S16[i] = GGML_FP32_TO_FP16(S[i]); - } - - ggml_vec_gelu_f16(neb01, S16, S16); - - { - // dst indices - const int i1 = ia1; - const int i2 = ia2; - const int i3 = ia3; - - for (int64_t ic = 0; ic < nec01; ++ic) { - - ggml_vec_dot_f16(neb01, - (float *) ((char *) dst->data + (ic*nb0 + i1*nb1 + i2*nb2 + i3*nb3)), 0, - (ggml_fp16_t *) ((char *) c0->data + ( ic*nbc01 + i2*nbc02 + i3*nbc03)), 0, - S16, 0, 1); - } - - ggml_vec_add_f32(nec01, - (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3)), - (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3)), - (float *) c1->data); - } - } -} - -static void ggml_compute_forward_flash_ff( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * b0 = dst->src[1]; - - switch (b0->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_flash_ff_f16(params, dst); - } break; - case GGML_TYPE_F32: - { - GGML_ASSERT(false); // TODO - } break; - default: - { - GGML_ASSERT(false); - } break; - } -} - // ggml_compute_forward_flash_attn_back static void ggml_compute_forward_flash_attn_back_f32( @@ -16224,38 +16060,7 @@ static void ggml_compute_forward_flash_attn_back_f32( vvexpf(SM, SM, &Mup); ggml_vec_sum_f32(Mup, &sum, SM); #else - uint16_t scvt[GGML_SOFT_MAX_UNROLL]; UNUSED(scvt); - ggml_float sump[GGML_SOFT_MAX_UNROLL] = { 0.0 }; - - for (int i = 0; i < Mup; i += GGML_SOFT_MAX_UNROLL) { - if (i >= masked_begin) { - break; - } - float * SR = S + i; - float * SW = SM + i; - - for (int j = 0; j < GGML_SOFT_MAX_UNROLL; ++j) { - if (i + j >= masked_begin) { - break; - } else if (SR[j] == -INFINITY) { - SW[j] = 0.0f; - } else { -#ifndef GGML_FLASH_ATTN_EXP_FP16 - const float val = expf(SR[j] - max); -#else - ggml_fp16_t s = GGML_FP32_TO_FP16(SR[j] - max); - memcpy(&scvt[j], &s, sizeof(uint16_t)); - const float val = GGML_FP16_TO_FP32(ggml_table_exp_f16[scvt[j]]); -#endif - sump[j] += (ggml_float)val; - SW[j] = val; - } - } - } - - for (int i = 0; i < GGML_SOFT_MAX_UNROLL; i++) { - sum += sump[i]; - } + sum = ggml_vec_soft_max_f32(Mup, SM, S, max); #endif } @@ -16837,6 +16642,10 @@ static void ggml_compute_forward_unary( { ggml_compute_forward_relu(params, dst); } break; + case GGML_UNARY_OP_SIGMOID: + { + ggml_compute_forward_sigmoid(params, dst); + } break; case GGML_UNARY_OP_GELU: { ggml_compute_forward_gelu(params, dst); @@ -17277,35 +17086,15 @@ static void ggml_compute_forward_cross_entropy_loss_f32( assert(!isnan(s1[i])); } #endif - // soft_max - ggml_float sum = 0.0; - { - float max = -INFINITY; - ggml_vec_max_f32(nc, &max, s0); - uint16_t scvt; UNUSED(scvt); - for (int i = 0; i < nc; i++) { - if (s0[i] == -INFINITY) { - st[i] = 0.0f; - } else { -#ifndef GGML_CROSS_ENTROPY_EXP_FP16 - const float s = s0[i] - max; - const float val = expf(s); -#else - ggml_fp16_t s = GGML_FP32_TO_FP16(s0[i] - max); - memcpy(&scvt, &s, sizeof(scvt)); - const float val = GGML_FP16_TO_FP32(ggml_table_exp_f16[scvt]); -#endif - sum += (ggml_float)val; - st[i] = val; - } - } + // soft_max + float max = -INFINITY; + ggml_vec_max_f32(nc, &max, s0); + ggml_float sum = ggml_vec_soft_max_f32(nc, st, s0, max); + assert(sum > 0.0); + sum = (1.0 - eps) / sum; - assert(sum > 0.0); - // sum = 1.0/sum; - } // avoid log(0) by rescaling from [0..1] to [eps..1] - sum = (1.0 - eps) / sum; ggml_vec_scale_f32(nc, st, sum); ggml_vec_add1_f32(nc, st, st, eps); ggml_vec_log_f32(nc, st, st); @@ -17395,32 +17184,11 @@ static void ggml_compute_forward_cross_entropy_loss_back_f32( #endif // soft_max - ggml_float sum = 0.0; - { - float max = -INFINITY; - ggml_vec_max_f32(nc, &max, s0); - - uint16_t scvt; UNUSED(scvt); - for (int i = 0; i < nc; i++) { - if (s0[i] == -INFINITY) { - ds0[i] = 0.0f; - } else { -#ifndef GGML_CROSS_ENTROPY_EXP_FP16 - const float s = s0[i] - max; - const float val = expf(s); -#else - ggml_fp16_t s = GGML_FP32_TO_FP16(s0[i] - max); - memcpy(&scvt, &s, sizeof(scvt)); - const float val = GGML_FP16_TO_FP32(ggml_table_exp_f16[scvt]); -#endif - sum += (ggml_float)val; - ds0[i] = val; - } - } - - assert(sum > 0.0); - sum = (1.0 - eps)/sum; - } + float max = -INFINITY; + ggml_vec_max_f32(nc, &max, s0); + ggml_float sum = ggml_vec_soft_max_f32(nc, ds0, s0, max); + assert(sum > 0.0); + sum = (1.0 - eps) / sum; // grad(src0) = (softmax(src0) - src1) * grad(cross_entropy_loss(src0, src1)) / nr ggml_vec_scale_f32(nc, ds0, sum); @@ -17457,7 +17225,7 @@ static void ggml_compute_forward_cross_entropy_loss_back( ///////////////////////////////// -static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) { +static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor, struct ggml_compute_state * state) { GGML_ASSERT(params); if (tensor->op == GGML_OP_NONE || ggml_is_empty(tensor)) { @@ -17555,7 +17323,7 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm } break; case GGML_OP_MUL_MAT: { - ggml_compute_forward_mul_mat(params, tensor); + ggml_compute_forward_mul_mat(params, tensor, state); } break; case GGML_OP_MUL_MAT_ID: { @@ -17633,10 +17401,6 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm { ggml_compute_forward_rope_back(params, tensor); } break; - case GGML_OP_ALIBI: - { - ggml_compute_forward_alibi(params, tensor); - } break; case GGML_OP_CLAMP: { ggml_compute_forward_clamp(params, tensor); @@ -17685,21 +17449,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm { ggml_compute_forward_leaky_relu(params, tensor); } break; - case GGML_OP_FLASH_ATTN: - { - const int32_t t = ggml_get_op_params_i32(tensor, 0); - GGML_ASSERT(t == 0 || t == 1); - const bool masked = t != 0; - ggml_compute_forward_flash_attn(params, masked, tensor); - } break; case GGML_OP_FLASH_ATTN_EXT: { ggml_compute_forward_flash_attn_ext(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor->src[3], tensor); } break; - case GGML_OP_FLASH_FF: - { - ggml_compute_forward_flash_ff(params, tensor); - } break; case GGML_OP_FLASH_ATTN_BACK: { int32_t t = ggml_get_op_params_i32(tensor, 0); @@ -18069,6 +17822,7 @@ static struct ggml_tensor * ggml_sub_or_set(struct ggml_context * ctx, struct gg static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor * tensor, struct ggml_hash_set zero_table) { struct ggml_tensor * src0 = tensor->src[0]; struct ggml_tensor * src1 = tensor->src[1]; + struct ggml_tensor * src2 = tensor->src[2]; switch (tensor->op) { case GGML_OP_DUP: @@ -18600,6 +18354,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor ggml_rope_back(ctx, tensor->grad, src1, + src2, n_dims, mode, n_ctx, @@ -18639,6 +18394,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor ggml_rope_impl(ctx, tensor->grad, src1, + src2, n_dims, mode, n_ctx, @@ -18655,10 +18411,6 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor zero_table); } } break; - case GGML_OP_ALIBI: - { - GGML_ASSERT(false); // TODO: not implemented - } break; case GGML_OP_CLAMP: { GGML_ASSERT(false); // TODO: not implemented @@ -18707,7 +18459,6 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor { GGML_ASSERT(false); // TODO: not implemented } break; - case GGML_OP_FLASH_ATTN: case GGML_OP_FLASH_ATTN_EXT: { struct ggml_tensor * flash_grad = NULL; @@ -18724,7 +18475,6 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor masked); } - struct ggml_tensor * src2 = tensor->src[2]; const int64_t elem_q = ggml_nelements(src0); const int64_t elem_k = ggml_nelements(src1); const int64_t elem_v = ggml_nelements(src2); @@ -18762,10 +18512,6 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor zero_table); } } break; - case GGML_OP_FLASH_FF: - { - GGML_ASSERT(false); // not supported - } break; case GGML_OP_FLASH_ATTN_BACK: { GGML_ASSERT(false); // not supported @@ -18829,6 +18575,10 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor zero_table); } } break; + case GGML_UNARY_OP_SIGMOID: + { + GGML_ASSERT(false); // TODO: not implemented + } break; case GGML_UNARY_OP_GELU: { GGML_ASSERT(false); // TODO: not implemented @@ -19175,8 +18925,6 @@ typedef int ggml_lock_t; #define GGML_LOCK_INITIALIZER 0 -typedef pthread_t ggml_thread_t; - #define ggml_thread_create pthread_create #define ggml_thread_join pthread_join @@ -19202,8 +18950,6 @@ typedef int ggml_lock_t; #define GGML_LOCK_INITIALIZER 0 -typedef pthread_t ggml_thread_t; - #define ggml_thread_create pthread_create #define ggml_thread_join pthread_join @@ -19283,31 +19029,6 @@ static void set_numa_thread_affinity(int thread_n) { UNUSED(thread_n); } static void clear_numa_thread_affinity(void) {} #endif -struct ggml_compute_state_shared { - const struct ggml_cgraph * cgraph; - const struct ggml_cplan * cplan; - - int64_t perf_node_start_cycles; - int64_t perf_node_start_time_us; - - const int n_threads; - - // synchronization primitives - atomic_int n_active; // num active threads - atomic_int node_n; // active graph node - atomic_int node_task; // active graph node task phase - - ggml_abort_callback abort_callback; // abort ggml_graph_compute when true - void * abort_callback_data; -}; - -struct ggml_compute_state { - ggml_thread_t thrd; - int ith; - struct ggml_compute_state_shared * shared; - enum ggml_status ec; -}; - static void ggml_graph_compute_perf_stats_node(struct ggml_tensor * node, const struct ggml_compute_state_shared * st) { int64_t cycles_cur = ggml_perf_cycles() - st->perf_node_start_cycles; int64_t time_us_cur = ggml_perf_time_us() - st->perf_node_start_time_us; @@ -19358,6 +19079,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads, int n_cur_ case GGML_UNARY_OP_TANH: case GGML_UNARY_OP_ELU: case GGML_UNARY_OP_RELU: + case GGML_UNARY_OP_SIGMOID: case GGML_UNARY_OP_HARDSWISH: // to opt for multiple threads case GGML_UNARY_OP_HARDSIGMOID: // to opt for multiple threads { @@ -19431,10 +19153,6 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads, int n_cur_ { n_tasks = n_threads; } break; - case GGML_OP_ALIBI: - { - n_tasks = 1; //TODO - } break; case GGML_OP_CLAMP: { n_tasks = 1; //TODO @@ -19480,15 +19198,10 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads, int n_cur_ { n_tasks = n_threads; } break; - case GGML_OP_FLASH_ATTN: case GGML_OP_FLASH_ATTN_EXT: { n_tasks = n_threads; } break; - case GGML_OP_FLASH_FF: - { - n_tasks = n_threads; - } break; case GGML_OP_FLASH_ATTN_BACK: { n_tasks = n_threads; @@ -19583,6 +19296,10 @@ static void ggml_graph_compute_thread_sync_node(int * node_n, struct ggml_comput * node_n = atomic_load(&state->shared->node_n); if (* node_n != last_node_n) break; +#if defined(__SSE3__) + // Tell the processor we're spinning. It's a processor hint for spinlocks. + _mm_pause(); +#endif } } @@ -19597,6 +19314,10 @@ static void ggml_graph_compute_thread_sync_task(int * task_phase, struct ggml_co * task_phase = atomic_load(&state->shared->node_task); if (* task_phase != last_task_phase) break; +#if defined(__SSE3__) + // Tell the processor we're spinning. It's a processor hint for spinlocks. + _mm_pause(); +#endif } } @@ -19636,7 +19357,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { struct ggml_tensor * node = cgraph->nodes[node_n]; if (GGML_OP_HAS_FINALIZE[node->op]) { params.nth = ggml_get_n_tasks(node, n_threads, state->shared->n_threads); - ggml_compute_forward(¶ms, node); + ggml_compute_forward(¶ms, node, state); } ggml_graph_compute_perf_stats_node(node, state->shared); } @@ -19656,17 +19377,17 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { /* INIT */ if (GGML_OP_HAS_INIT[node->op]) { params.type = GGML_TASK_TYPE_INIT; - ggml_compute_forward(¶ms, node); + ggml_compute_forward(¶ms, node, state); } // TODO: maybe push node_n to the atomic but if other threads see n_tasks is 1, // they do something more efficient than spinning (?) params.type = GGML_TASK_TYPE_COMPUTE; - ggml_compute_forward(¶ms, node); + ggml_compute_forward(¶ms, node, state); if (GGML_OP_HAS_FINALIZE[node->op]) { params.type = GGML_TASK_TYPE_FINALIZE; - ggml_compute_forward(¶ms, node); + ggml_compute_forward(¶ms, node, state); } ggml_graph_compute_perf_stats_node(node, state->shared); @@ -19705,7 +19426,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { if (state->ith < n_tasks) { if (GGML_OP_HAS_INIT[node->op]) { - ggml_compute_forward(¶ms, node); + ggml_compute_forward(¶ms, node, state); } } @@ -19726,7 +19447,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { if (state->ith < n_tasks) { params.type = GGML_TASK_TYPE_COMPUTE; - ggml_compute_forward(¶ms, node); + ggml_compute_forward(¶ms, node, state); } if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) { @@ -19877,39 +19598,11 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa cur += sizeof(ggml_fp16_t)*ne00*ne01*ne02*ne03; cur += sizeof(ggml_fp16_t)*ne10*ne11*ne12; } break; - case GGML_OP_FLASH_ATTN: - { - const int64_t ne11 = ggml_up(node->src[1]->ne[1], GGML_SOFT_MAX_UNROLL); - - if (node->src[1]->type == GGML_TYPE_F32) { - cur = sizeof(float)*ne11*n_tasks; // TODO: this can become (n_tasks-1) - cur += sizeof(float)*ne11*n_tasks; // this is overestimated by x2 - } else if (node->src[1]->type == GGML_TYPE_F16) { - cur = sizeof(float)*ne11*n_tasks; // TODO: this can become (n_tasks-1) - cur += sizeof(float)*ne11*n_tasks; // this is overestimated by x2 - } else if (node->src[1]->type == GGML_TYPE_BF16) { - cur = sizeof(float)*ne11*n_tasks; // TODO: this can become (n_tasks-1) - cur += sizeof(float)*ne11*n_tasks; // this is overestimated by x2 - } - } break; case GGML_OP_FLASH_ATTN_EXT: { const int64_t ne00 = node->src[0]->ne[0]; // D - cur = 2*sizeof(float)*ne00*n_tasks; // 2x head size - } break; - case GGML_OP_FLASH_FF: - { - if (node->src[1]->type == GGML_TYPE_F32) { - cur = sizeof(float)*node->src[1]->ne[1]*n_tasks; // TODO: this can become (n_tasks-1) - cur += sizeof(float)*node->src[1]->ne[1]*n_tasks; // this is overestimated by x2 - } else if (node->src[1]->type == GGML_TYPE_F16) { - cur = sizeof(float)*node->src[1]->ne[1]*n_tasks; // TODO: this can become (n_tasks-1) - cur += sizeof(float)*node->src[1]->ne[1]*n_tasks; // this is overestimated by x2 - } else if (node->src[1]->type == GGML_TYPE_BF16) { - cur = sizeof(float)*node->src[1]->ne[1]*n_tasks; // TODO: this can become (n_tasks-1) - cur += sizeof(float)*node->src[1]->ne[1]*n_tasks; // this is overestimated by x2 - } + cur = 3*sizeof(float)*ne00*n_tasks; // 3x head size/thread } break; case GGML_OP_FLASH_ATTN_BACK: { @@ -19977,6 +19670,7 @@ enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cpl /*.node_task =*/ GGML_TASK_TYPE_FINALIZE, /*.abort_callback =*/ NULL, /*.abort_callback_data =*/ NULL, + /*.current_chunk; =*/ 0, }; struct ggml_compute_state * workers = alloca(sizeof(struct ggml_compute_state)*n_threads); @@ -21750,11 +21444,7 @@ size_t ggml_quantize_chunk( case GGML_TYPE_IQ1_S: result = quantize_iq1_s (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_IQ1_M: result = quantize_iq1_m (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_IQ4_NL: result = quantize_iq4_nl (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; -#if QK_K == 64 - case GGML_TYPE_IQ4_XS: result = quantize_iq4_nl (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; -#else case GGML_TYPE_IQ4_XS: result = quantize_iq4_xs (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; -#endif case GGML_TYPE_F16: { size_t elemsize = sizeof(ggml_fp16_t); @@ -23075,6 +22765,14 @@ int ggml_cpu_has_avx512_vnni(void) { #endif } +int ggml_cpu_has_avx512_bf16(void) { +#if defined(__AVX512BF16__) + return 1; +#else + return 0; +#endif +} + int ggml_cpu_has_fma(void) { #if defined(__FMA__) return 1; diff --git a/ggml.h b/ggml.h index 928c71bc82d9d..5a4843cf04997 100644 --- a/ggml.h +++ b/ggml.h @@ -475,7 +475,6 @@ extern "C" { GGML_OP_SOFT_MAX_BACK, GGML_OP_ROPE, GGML_OP_ROPE_BACK, - GGML_OP_ALIBI, GGML_OP_CLAMP, GGML_OP_CONV_TRANSPOSE_1D, GGML_OP_IM2COL, @@ -489,9 +488,7 @@ extern "C" { GGML_OP_ARGSORT, GGML_OP_LEAKY_RELU, - GGML_OP_FLASH_ATTN, GGML_OP_FLASH_ATTN_EXT, - GGML_OP_FLASH_FF, GGML_OP_FLASH_ATTN_BACK, GGML_OP_SSM_CONV, GGML_OP_SSM_SCAN, @@ -527,6 +524,7 @@ extern "C" { GGML_UNARY_OP_TANH, GGML_UNARY_OP_ELU, GGML_UNARY_OP_RELU, + GGML_UNARY_OP_SIGMOID, GGML_UNARY_OP_GELU, GGML_UNARY_OP_GELU_QUICK, GGML_UNARY_OP_SILU, @@ -572,7 +570,8 @@ extern "C" { // n-dimensional tensor struct ggml_tensor { enum ggml_type type; - enum ggml_backend_type backend; + + GGML_DEPRECATED(enum ggml_backend_type backend, "use the buffer type to find the storage location of the tensor"); struct ggml_backend_buffer * buffer; @@ -773,7 +772,8 @@ extern "C" { GGML_API bool ggml_is_3d (const struct ggml_tensor * tensor); GGML_API int ggml_n_dims (const struct ggml_tensor * tensor); // returns 1 for scalars - GGML_API bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1); + GGML_API bool ggml_are_same_shape (const struct ggml_tensor * t0, const struct ggml_tensor * t1); + GGML_API bool ggml_are_same_stride(const struct ggml_tensor * t0, const struct ggml_tensor * t1); // use this to compute the memory overhead of a tensor GGML_API size_t ggml_tensor_overhead(void); @@ -1081,6 +1081,14 @@ extern "C" { struct ggml_context * ctx, struct ggml_tensor * a); + GGML_API struct ggml_tensor * ggml_sigmoid( + struct ggml_context * ctx, + struct ggml_tensor * a); + + GGML_API struct ggml_tensor * ggml_sigmoid_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a); + GGML_API struct ggml_tensor * ggml_gelu( struct ggml_context * ctx, struct ggml_tensor * a); @@ -1435,15 +1443,13 @@ extern "C" { struct ggml_context * ctx, struct ggml_tensor * a); - // fused soft_max(a*scale + mask + pos[i]*(ALiBi slope)) + // fused soft_max(a*scale + mask*(ALiBi slope)) // mask is optional - // pos is required when max_bias > 0.0f // max_bias = 0.0f for no ALiBi GGML_API struct ggml_tensor * ggml_soft_max_ext( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * mask, - struct ggml_tensor * pos, float scale, float max_bias); @@ -1459,11 +1465,12 @@ extern "C" { struct ggml_tensor * b); // rotary position embedding - // if mode & 1 == 1, skip n_past elements (DEPRECATED) + // if mode & 1 == 1, skip n_past elements (NOT SUPPORTED) // if mode & 2 == 1, GPT-NeoX style // if mode & 4 == 1, ChatGLM style // // b is an int32 vector with size a->ne[2], it contains the positions + // c is freq factors (e.g. phi3-128k), (optional) GGML_API struct ggml_tensor * ggml_rope( struct ggml_context * ctx, struct ggml_tensor * a, @@ -1482,10 +1489,11 @@ extern "C" { int n_ctx); // custom RoPE - GGML_API struct ggml_tensor * ggml_rope_custom( + GGML_API struct ggml_tensor * ggml_rope_ext( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, + struct ggml_tensor * c, int n_dims, int mode, int n_ctx, @@ -1498,10 +1506,11 @@ extern "C" { float beta_slow); // in-place, returns view(a) - GGML_API struct ggml_tensor * ggml_rope_custom_inplace( + GGML_API struct ggml_tensor * ggml_rope_ext_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, + struct ggml_tensor * c, int n_dims, int mode, int n_ctx, @@ -1513,18 +1522,41 @@ extern "C" { float beta_fast, float beta_slow); - // compute correction dims for YaRN RoPE scaling - GGML_CALL void ggml_rope_yarn_corr_dims( - int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2]); + GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_rope_custom( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int n_dims, + int mode, + int n_ctx, + int n_orig_ctx, + float freq_base, + float freq_scale, + float ext_factor, + float attn_factor, + float beta_fast, + float beta_slow), + "use ggml_rope_ext instead"); - // xPos RoPE, in-place, returns view(a) - GGML_API struct ggml_tensor * ggml_rope_xpos_inplace( + GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_rope_custom_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, int n_dims, - float base, - bool down); + int mode, + int n_ctx, + int n_orig_ctx, + float freq_base, + float freq_scale, + float ext_factor, + float attn_factor, + float beta_fast, + float beta_slow), + "use ggml_rope_ext_inplace instead"); + + // compute correction dims for YaRN RoPE scaling + GGML_CALL void ggml_rope_yarn_corr_dims( + int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2]); // rotary position embedding backward, i.e compute dx from dy // a - dy @@ -1532,6 +1564,7 @@ extern "C" { struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, + struct ggml_tensor * c, int n_dims, int mode, int n_ctx, @@ -1545,16 +1578,6 @@ extern "C" { float xpos_base, bool xpos_down); - // alibi position embedding - // in-place, returns view(a) - GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_alibi( - struct ggml_context * ctx, - struct ggml_tensor * a, - int n_past, - int n_head, - float bias_max), - "use ggml_soft_max_ext instead (will be removed in Mar 2024)"); - // clamp // in-place, returns view(a) GGML_API struct ggml_tensor * ggml_clamp( @@ -1684,12 +1707,24 @@ extern "C" { float p1); // nearest interpolate + // multiplies ne0 and ne1 by scale factor // used in stable-diffusion GGML_API struct ggml_tensor * ggml_upscale( struct ggml_context * ctx, struct ggml_tensor * a, int scale_factor); + // nearest interpolate + // nearest interpolate to specified dimensions + // used in tortoise.cpp + GGML_API struct ggml_tensor * ggml_upscale_ext( + struct ggml_context * ctx, + struct ggml_tensor * a, + int ne0, + int ne1, + int ne2, + int ne3); + // pad each dimension with zeros: [x, ..., x] -> [x, ..., x, 0, ..., 0] GGML_API struct ggml_tensor * ggml_pad( struct ggml_context * ctx, @@ -1731,13 +1766,6 @@ extern "C" { struct ggml_tensor * a, int k); - GGML_API struct ggml_tensor * ggml_flash_attn( - struct ggml_context * ctx, - struct ggml_tensor * q, - struct ggml_tensor * k, - struct ggml_tensor * v, - bool masked); - #define GGML_KQ_MASK_PAD 32 // q: [n_embd, n_batch, n_head, 1] @@ -1751,12 +1779,14 @@ extern "C" { struct ggml_tensor * k, struct ggml_tensor * v, struct ggml_tensor * mask, - float scale); + float scale, + float max_bias); GGML_API void ggml_flash_attn_ext_set_prec( struct ggml_tensor * a, enum ggml_prec prec); + // TODO: needs to be adapted to ggml_flash_attn_ext GGML_API struct ggml_tensor * ggml_flash_attn_back( struct ggml_context * ctx, struct ggml_tensor * q, @@ -1765,14 +1795,6 @@ extern "C" { struct ggml_tensor * d, bool masked); - GGML_API struct ggml_tensor * ggml_flash_ff( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b0, - struct ggml_tensor * b1, - struct ggml_tensor * c0, - struct ggml_tensor * c1); - GGML_API struct ggml_tensor * ggml_ssm_conv( struct ggml_context * ctx, struct ggml_tensor * s, @@ -2386,6 +2408,7 @@ extern "C" { GGML_API int ggml_cpu_has_avx512 (void); GGML_API int ggml_cpu_has_avx512_vbmi(void); GGML_API int ggml_cpu_has_avx512_vnni(void); + GGML_API int ggml_cpu_has_avx512_bf16(void); GGML_API int ggml_cpu_has_fma (void); GGML_API int ggml_cpu_has_neon (void); GGML_API int ggml_cpu_has_arm_fma (void); diff --git a/ggml_vk_generate_shaders.py b/ggml_vk_generate_shaders.py index 162cf5c6ea9be..a8f7373df125f 100644 --- a/ggml_vk_generate_shaders.py +++ b/ggml_vk_generate_shaders.py @@ -2432,7 +2432,6 @@ { uint KX; uint KY; - uint KZ; float scale; float max_bias; float m0; @@ -2449,8 +2448,7 @@ layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; layout (binding = 1) readonly buffer Y {B_TYPE data_b[];}; -layout (binding = 2) readonly buffer Z {C_TYPE data_c[];}; -layout (binding = 3) buffer D {D_TYPE data_d[];}; +layout (binding = 2) buffer D {D_TYPE data_d[];}; shared FLOAT_TYPE vals[BLOCK_SIZE]; @@ -2459,7 +2457,7 @@ const uint rowx = gl_WorkGroupID.x; const uint rowy = rowx % p.KY; - float slope = 0.0f; + float slope = 1.0f; // ALiBi if (p.max_bias > 0.0f) { @@ -2472,11 +2470,18 @@ } // Find max - vals[tid] = uintBitsToFloat(0xFF800000); + FLOAT_TYPE max_val = uintBitsToFloat(0xFF800000); - [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) { - vals[tid] = max(vals[tid], FLOAT_TYPE(data_a[rowx * p.KX + col]) * p.scale + (p.KY > 0 ? FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) + (p.KZ > 0 ? slope * FLOAT_TYPE(data_c[col]) : 0.0f)); + [[unroll]] for (uint col0 = 0; col0 < p.KX; col0 += BLOCK_SIZE) { + const uint col = col0 + tid; + + if (col >= p.KX) { + break; + } + + max_val = max(max_val, FLOAT_TYPE(data_a[rowx * p.KX + col]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f))); } + vals[tid] = max_val; barrier(); [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) { @@ -2486,15 +2491,21 @@ barrier(); } - const FLOAT_TYPE max_val = vals[0]; + max_val = vals[0]; barrier(); // Sum up values vals[tid] = FLOAT_TYPE(0.0f); - [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) { + [[unroll]] for (uint col0 = 0; col0 < p.KX; col0 += BLOCK_SIZE) { + const uint col = col0 + tid; + + if (col >= p.KX) { + break; + } + const uint i = rowx * p.KX + col; - const FLOAT_TYPE val = exp(FLOAT_TYPE(data_a[i]) * p.scale + (p.KY > 0 ? FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) - max_val); + const FLOAT_TYPE val = exp(FLOAT_TYPE(data_a[i]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) - max_val); vals[tid] += val; data_d[i] = D_TYPE(val); } @@ -2509,7 +2520,13 @@ const D_TYPE divisor = D_TYPE(vals[0]); - [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) { + [[unroll]] for (uint col0 = 0; col0 < p.KX; col0 += BLOCK_SIZE) { + const uint col = col0 + tid; + + if (col >= p.KX) { + break; + } + data_d[rowx*p.KX + col] /= divisor; } } @@ -2592,7 +2609,8 @@ layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; layout (binding = 1) readonly buffer Y {int data_b[];}; -layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; +layout (binding = 2) readonly buffer Z {float data_freq_factors[];}; +layout (binding = 3) writeonly buffer D {D_TYPE data_d[];}; layout (push_constant) uniform parameter { uint ncols; @@ -2605,6 +2623,7 @@ float corr_dims[4]; float theta_scale; float inv_ndims; + uint has_freq_facs; } p; float rope_yarn_ramp(const float low, const float high, const uint i0) { @@ -2654,7 +2673,8 @@ const float cur_rot = p.inv_ndims * ic - ib; const int pos = data_b[i2]; - const float theta_base = pos*p.freq_scale*pow(p.theta_scale, col/2.0f); + const float freq_factor = p.has_freq_facs != 0 ? data_freq_factors[ic/2] : 1.0f; + const float theta_base = pos*p.freq_scale*pow(p.theta_scale, col/2.0f) / freq_factor; float cos_theta, sin_theta; rope_yarn(theta_base, uint(cur_rot), cos_theta, sin_theta); @@ -2672,20 +2692,26 @@ #extension GL_EXT_shader_16bit_storage : require -layout(local_size_x = 1024, local_size_y = 1, local_size_z = 1) in; +#define BLOCK_SIZE 1024 +#define ASC 0 + +layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; layout (binding = 1) buffer D {int data_d[];}; layout (push_constant) uniform parameter { uint ncols; - bool ascending; + uint ncols_pad; + uint order; } p; +shared int dst_row[BLOCK_SIZE]; + void swap(uint idx0, uint idx1) { - int tmp = data_d[idx0]; - data_d[idx0] = data_d[idx1]; - data_d[idx1] = tmp; + int tmp = dst_row[idx0]; + dst_row[idx0] = dst_row[idx1]; + dst_row[idx1] = tmp; } void main() { @@ -2693,36 +2719,45 @@ const int col = int(gl_LocalInvocationID.x); const uint row = gl_WorkGroupID.y; - if (col >= p.ncols) { + if (col >= p.ncols_pad) { return; } - const uint a_idx = row * p.ncols; - const uint d_idx = row * p.ncols; + const uint row_offset = row * p.ncols; // initialize indices - if (col < p.ncols) { - data_d[col] = col; - } + dst_row[col] = col; barrier(); - for (uint k = 2; k <= p.ncols; k *= 2) { + for (uint k = 2; k <= p.ncols_pad; k *= 2) { for (uint j = k / 2; j > 0; j /= 2) { const uint ixj = col ^ j; if (ixj > col) { if ((col & k) == 0) { - if (p.ascending ? data_a[a_idx + data_d[d_idx + col]] > data_a[a_idx + data_d[d_idx + ixj]] : data_a[a_idx + data_d[d_idx + col]] < data_a[a_idx + data_d[d_idx + ixj]]) { - swap(d_idx + col, d_idx + ixj); + if (dst_row[col] >= p.ncols || + (dst_row[ixj] < p.ncols && (p.order == ASC ? + data_a[row_offset + dst_row[col]] > data_a[row_offset + dst_row[ixj]] : + data_a[row_offset + dst_row[col]] < data_a[row_offset + dst_row[ixj]])) + ) { + swap(col, ixj); } } else { - if (p.ascending ? data_a[a_idx + data_d[d_idx + col]] < data_a[a_idx + data_d[d_idx + ixj]] : data_a[a_idx + data_d[d_idx + col]] > data_a[a_idx + data_d[d_idx + ixj]]) { - swap(d_idx + col, d_idx + ixj); + if (dst_row[ixj] >= p.ncols || + (dst_row[col] < p.ncols && (p.order == ASC ? + data_a[row_offset + dst_row[col]] < data_a[row_offset + dst_row[ixj]] : + data_a[row_offset + dst_row[col]] > data_a[row_offset + dst_row[ixj]])) + ) { + swap(col, ixj); } } } barrier(); } } + + if (col < p.ncols) { + data_d[row_offset + col] = dst_row[col]; + } } """ @@ -2845,13 +2880,16 @@ async def main(): stream.clear() stream.extend((mulmat_head, shader_float_type, mulmat_body1, mulmat_load_scalar, mulmat_body2)) tasks.append(string_to_spv("matmul_f32", "".join(stream), {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f32_aligned", "".join(stream), {"LOAD_VEC_A": 1, "LOAD_VEC_B": load_vec, "A_TYPE": "float", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f32_aligned", "".join(stream), {"LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + + tasks.append(string_to_spv("matmul_f32_f16", "".join(stream), {"A_TYPE": "float", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f32_f16_aligned", "".join(stream), {"LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type, "B_TYPE": vec_type_f16, "D_TYPE": "float"}, fp16)) tasks.append(string_to_spv("matmul_f16", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f16_aligned", "".join(stream), {"LOAD_VEC_A": 1, "LOAD_VEC_B": load_vec, "A_TYPE": "float16_t", "B_TYPE": vec_type_f16, "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f16_aligned", "".join(stream), {"LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type_f16, "D_TYPE": "float"}, fp16)) tasks.append(string_to_spv("matmul_f16_f32", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f16_f32_aligned", "".join(stream), {"LOAD_VEC_A": 1, "LOAD_VEC_B": load_vec, "A_TYPE": "float16_t", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f16_f32_aligned", "".join(stream), {"LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) stream.clear() stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q4_0_defines, mulmat_body1, mulmat_load_q4_0, mulmat_body2)) diff --git a/gguf-py/gguf/__init__.py b/gguf-py/gguf/__init__.py index 110ab342ccd71..ea5146b161bc8 100644 --- a/gguf-py/gguf/__init__.py +++ b/gguf-py/gguf/__init__.py @@ -1,5 +1,7 @@ from .constants import * +from .lazy import * from .gguf_reader import * from .gguf_writer import * +from .quants import * from .tensor_mapping import * from .vocab import * diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 5951c0bb0fb5e..67e23dcc14840 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -10,6 +10,7 @@ GGUF_MAGIC = 0x46554747 # "GGUF" GGUF_VERSION = 3 GGUF_DEFAULT_ALIGNMENT = 32 +GGML_QUANT_VERSION = 2 # GGML_QNT_VERSION from ggml.h # # metadata keys @@ -56,12 +57,13 @@ class Attention: CAUSAL = "{arch}.attention.causal" class Rope: - DIMENSION_COUNT = "{arch}.rope.dimension_count" - FREQ_BASE = "{arch}.rope.freq_base" - SCALING_TYPE = "{arch}.rope.scaling.type" - SCALING_FACTOR = "{arch}.rope.scaling.factor" - SCALING_ORIG_CTX_LEN = "{arch}.rope.scaling.original_context_length" - SCALING_FINETUNED = "{arch}.rope.scaling.finetuned" + DIMENSION_COUNT = "{arch}.rope.dimension_count" + FREQ_BASE = "{arch}.rope.freq_base" + SCALING_TYPE = "{arch}.rope.scaling.type" + SCALING_FACTOR = "{arch}.rope.scaling.factor" + SCALING_ATTN_FACTOR = "{arch}.rope.scaling.attn_factor" + SCALING_ORIG_CTX_LEN = "{arch}.rope.scaling.original_context_length" + SCALING_FINETUNED = "{arch}.rope.scaling.finetuned" class SSM: CONV_KERNEL = "{arch}.ssm.conv_kernel" @@ -114,10 +116,10 @@ class MODEL_ARCH(IntEnum): GPTNEOX = auto() MPT = auto() STARCODER = auto() - PERSIMMON = auto() REFACT = auto() BERT = auto() NOMIC_BERT = auto() + JINA_BERT_V2 = auto() BLOOM = auto() STABLELM = auto() QWEN = auto() @@ -147,6 +149,8 @@ class MODEL_TENSOR(IntEnum): OUTPUT = auto() OUTPUT_NORM = auto() ROPE_FREQS = auto() + ROPE_FACTORS_LONG = auto() + ROPE_FACTORS_SHORT = auto() ATTN_Q = auto() ATTN_K = auto() ATTN_V = auto() @@ -191,10 +195,10 @@ class MODEL_TENSOR(IntEnum): MODEL_ARCH.GPTNEOX: "gptneox", MODEL_ARCH.MPT: "mpt", MODEL_ARCH.STARCODER: "starcoder", - MODEL_ARCH.PERSIMMON: "persimmon", MODEL_ARCH.REFACT: "refact", MODEL_ARCH.BERT: "bert", MODEL_ARCH.NOMIC_BERT: "nomic-bert", + MODEL_ARCH.JINA_BERT_V2: "jina-bert-v2", MODEL_ARCH.BLOOM: "bloom", MODEL_ARCH.STABLELM: "stablelm", MODEL_ARCH.QWEN: "qwen", @@ -224,6 +228,8 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.OUTPUT_NORM: "output_norm", MODEL_TENSOR.OUTPUT: "output", MODEL_TENSOR.ROPE_FREQS: "rope_freqs", + MODEL_TENSOR.ROPE_FACTORS_LONG: "rope_factors_long", + MODEL_TENSOR.ROPE_FACTORS_SHORT: "rope_factors_short", MODEL_TENSOR.ATTN_NORM: "blk.{bid}.attn_norm", MODEL_TENSOR.ATTN_NORM_2: "blk.{bid}.attn_norm_2", MODEL_TENSOR.ATTN_QKV: "blk.{bid}.attn_qkv", @@ -380,6 +386,22 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.FFN_UP, MODEL_TENSOR.LAYER_OUT_NORM, ], + MODEL_ARCH.JINA_BERT_V2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.TOKEN_EMBD_NORM, + MODEL_TENSOR.TOKEN_TYPES, + MODEL_TENSOR.ATTN_OUT_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.LAYER_OUT_NORM, + ], MODEL_ARCH.MPT: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, @@ -407,20 +429,6 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], - MODEL_ARCH.PERSIMMON: [ - MODEL_TENSOR.TOKEN_EMBD, - MODEL_TENSOR.OUTPUT, - MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.ATTN_NORM, - MODEL_TENSOR.ATTN_QKV, - MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.FFN_NORM, - MODEL_TENSOR.FFN_DOWN, - MODEL_TENSOR.FFN_UP, - MODEL_TENSOR.ATTN_Q_NORM, - MODEL_TENSOR.ATTN_K_NORM, - MODEL_TENSOR.ATTN_ROT_EMBD, - ], MODEL_ARCH.REFACT: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, @@ -737,9 +745,6 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_ROT_EMBD, ], - MODEL_ARCH.PERSIMMON: [ - MODEL_TENSOR.ROPE_FREQS, - ], MODEL_ARCH.QWEN: [ MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_ROT_EMBD, @@ -820,6 +825,49 @@ class GGMLQuantizationType(IntEnum): BF16 = 30 +# TODO: add GGMLFileType from ggml_ftype in ggml.h + + +# from llama_ftype in llama.h +# ALL VALUES SHOULD BE THE SAME HERE AS THEY ARE OVER THERE. +class LlamaFileType(IntEnum): + ALL_F32 = 0 + MOSTLY_F16 = 1 # except 1d tensors + MOSTLY_Q4_0 = 2 # except 1d tensors + MOSTLY_Q4_1 = 3 # except 1d tensors + MOSTLY_Q4_1_SOME_F16 = 4 # tok_embeddings.weight and output.weight are F16 + # MOSTLY_Q4_2 = 5 # support has been removed + # MOSTLY_Q4_3 = 6 # support has been removed + MOSTLY_Q8_0 = 7 # except 1d tensors + MOSTLY_Q5_0 = 8 # except 1d tensors + MOSTLY_Q5_1 = 9 # except 1d tensors + MOSTLY_Q2_K = 10 # except 1d tensors + MOSTLY_Q3_K_S = 11 # except 1d tensors + MOSTLY_Q3_K_M = 12 # except 1d tensors + MOSTLY_Q3_K_L = 13 # except 1d tensors + MOSTLY_Q4_K_S = 14 # except 1d tensors + MOSTLY_Q4_K_M = 15 # except 1d tensors + MOSTLY_Q5_K_S = 16 # except 1d tensors + MOSTLY_Q5_K_M = 17 # except 1d tensors + MOSTLY_Q6_K = 18 # except 1d tensors + MOSTLY_IQ2_XXS = 19 # except 1d tensors + MOSTLY_IQ2_XS = 20 # except 1d tensors + MOSTLY_Q2_K_S = 21 # except 1d tensors + MOSTLY_IQ3_XS = 22 # except 1d tensors + MOSTLY_IQ3_XXS = 23 # except 1d tensors + MOSTLY_IQ1_S = 24 # except 1d tensors + MOSTLY_IQ4_NL = 25 # except 1d tensors + MOSTLY_IQ3_S = 26 # except 1d tensors + MOSTLY_IQ3_M = 27 # except 1d tensors + MOSTLY_IQ2_S = 28 # except 1d tensors + MOSTLY_IQ2_M = 29 # except 1d tensors + MOSTLY_IQ4_XS = 30 # except 1d tensors + MOSTLY_IQ1_M = 31 # except 1d tensors + MOSTLY_BF16 = 32 # except 1d tensors + + GUESSED = 1024 # not specified in the model file + + class GGUFEndian(IntEnum): LITTLE = 0 BIG = 1 @@ -857,9 +905,8 @@ def get_type(val: Any) -> GGUFValueType: raise ValueError(f"Unknown type: {type(val)}") -# Note: Does not support GGML_QKK_64 -QK_K = 256 # Items here are (block size, type size) +QK_K = 256 GGML_QUANT_SIZES: dict[GGMLQuantizationType, tuple[int, int]] = { GGMLQuantizationType.F32: (1, 4), GGMLQuantizationType.F16: (1, 2), diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 8dcf9330b076f..8b41b54eaa5a6 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -7,12 +7,13 @@ import tempfile from enum import Enum, auto from io import BufferedWriter -from typing import IO, Any, Callable, Sequence, Mapping +from typing import IO, Any, Sequence, Mapping from string import ascii_letters, digits import numpy as np from .constants import ( + GGML_QUANT_SIZES, GGUF_DEFAULT_ALIGNMENT, GGUF_MAGIC, GGUF_VERSION, @@ -28,47 +29,6 @@ logger = logging.getLogger(__name__) -class LazyTensor: - data: Callable[[], np.ndarray[Any, Any]] - # to avoid too deep recursion - functions: list[Callable[[np.ndarray[Any, Any]], np.ndarray[Any, Any]]] - dtype: np.dtype[Any] - shape: tuple[int, ...] - - def __init__(self, data: Callable[[], np.ndarray[Any, Any]], *, dtype: type, shape: tuple[int, ...]): - self.data = data - self.functions = [] - self.dtype = np.dtype(dtype) - self.shape = shape - - def astype(self, dtype: type, **kwargs) -> LazyTensor: - self.functions.append(lambda n: n.astype(dtype, **kwargs)) - self.dtype = np.dtype(dtype) - return self - - @property - def nbytes(self) -> int: - size = 1 - for n in self.shape: - size *= n - return size * self.dtype.itemsize - - def tofile(self, *args, **kwargs) -> None: - data = self.data() - for f in self.functions: - data = f(data) - assert data.shape == self.shape - assert data.dtype == self.dtype - assert data.nbytes == self.nbytes - self.functions = [] - self.data = lambda: data - data.tofile(*args, **kwargs) - - def byteswap(self, *args, **kwargs) -> LazyTensor: - self.functions.append(lambda n: n.byteswap(*args, **kwargs)) - return self - - class WriterState(Enum): EMPTY = auto() HEADER = auto() @@ -79,7 +39,7 @@ class WriterState(Enum): class GGUFWriter: fout: BufferedWriter temp_file: tempfile.SpooledTemporaryFile[bytes] | None - tensors: list[np.ndarray[Any, Any] | LazyTensor] + tensors: list[np.ndarray[Any, Any]] _simple_value_packing = { GGUFValueType.UINT8: "B", GGUFValueType.INT8: "b", @@ -236,7 +196,7 @@ def ggml_pad(x: int, n: int) -> int: return ((x + n - 1) // n) * n def add_tensor_info( - self, name: str, tensor_shape: Sequence[int], tensor_dtype: np.dtype[np.float16] | np.dtype[np.float32], + self, name: str, tensor_shape: Sequence[int], tensor_dtype: np.dtype, tensor_nbytes: int, raw_dtype: GGMLQuantizationType | None = None, ) -> None: if self.state is not WriterState.EMPTY: @@ -249,10 +209,6 @@ def add_tensor_info( encoded_name = name.encode("utf-8") self.ti_data += self._pack("Q", len(encoded_name)) self.ti_data += encoded_name - n_dims = len(tensor_shape) - self.ti_data += self._pack("I", n_dims) - for i in range(n_dims): - self.ti_data += self._pack("Q", tensor_shape[n_dims - 1 - i]) if raw_dtype is None: if tensor_dtype == np.float16: dtype = GGMLQuantizationType.F16 @@ -272,13 +228,22 @@ def add_tensor_info( raise ValueError("Only F16, F32, F64, I8, I16, I32, I64 tensors are supported for now") else: dtype = raw_dtype + if tensor_dtype == np.uint8: + block_size, type_size = GGML_QUANT_SIZES[raw_dtype] + if tensor_shape[-1] % type_size != 0: + raise ValueError(f"Quantized tensor row size ({tensor_shape[-1]}) is not a multiple of {dtype.name} type size ({type_size})") + tensor_shape = tuple(tensor_shape[:-1]) + (tensor_shape[-1] // type_size * block_size,) + n_dims = len(tensor_shape) + self.ti_data += self._pack("I", n_dims) + for i in range(n_dims): + self.ti_data += self._pack("Q", tensor_shape[n_dims - 1 - i]) self.ti_data += self._pack("I", dtype) self.ti_data += self._pack("Q", self.offset_tensor) self.offset_tensor += GGUFWriter.ggml_pad(tensor_nbytes, self.data_alignment) self.ti_data_count += 1 def add_tensor( - self, name: str, tensor: np.ndarray[Any, Any] | LazyTensor, raw_shape: Sequence[int] | None = None, + self, name: str, tensor: np.ndarray[Any, Any], raw_shape: Sequence[int] | None = None, raw_dtype: GGMLQuantizationType | None = None, ) -> None: if self.endianess == GGUFEndian.BIG: @@ -303,7 +268,7 @@ def write_padding(self, fp: IO[bytes], n: int, align: int | None = None) -> None if pad != 0: fp.write(bytes([0] * pad)) - def write_tensor_data(self, tensor: np.ndarray[Any, Any] | LazyTensor) -> None: + def write_tensor_data(self, tensor: np.ndarray[Any, Any]) -> None: if self.state is not WriterState.TI_DATA: raise ValueError(f'Expected output file to contain tensor info, got {self.state}') @@ -391,7 +356,7 @@ def add_file_type(self, ftype: int) -> None: def add_name(self, name: str) -> None: self.add_string(Keys.General.NAME, name) - def add_quantization_version(self, quantization_version: GGMLQuantizationType) -> None: + def add_quantization_version(self, quantization_version: int) -> None: self.add_uint32( Keys.General.QUANTIZATION_VERSION, quantization_version) @@ -468,6 +433,9 @@ def add_rope_scaling_type(self, value: RopeScalingType) -> None: def add_rope_scaling_factor(self, value: float) -> None: self.add_float32(Keys.Rope.SCALING_FACTOR.format(arch=self.arch), value) + def add_rope_scaling_attn_factors(self, value: Sequence[float]) -> None: + self.add_float32(Keys.Rope.SCALING_ATTN_FACTOR.format(arch=self.arch), value) + def add_rope_scaling_orig_ctx_len(self, value: int) -> None: self.add_uint32(Keys.Rope.SCALING_ORIG_CTX_LEN.format(arch=self.arch), value) diff --git a/gguf-py/gguf/lazy.py b/gguf-py/gguf/lazy.py new file mode 100644 index 0000000000000..1167335b83ab3 --- /dev/null +++ b/gguf-py/gguf/lazy.py @@ -0,0 +1,236 @@ +from __future__ import annotations +from abc import ABC, ABCMeta, abstractmethod + +import logging +from typing import Any, Callable +from collections import deque + +import numpy as np +from numpy._typing import _Shape +from numpy.typing import DTypeLike + + +logger = logging.getLogger(__name__) + + +class LazyMeta(ABCMeta): + + def __new__(cls, name: str, bases: tuple[type, ...], namespace: dict[str, Any], **kwargs): + def __getattr__(self, __name: str) -> Any: + meta_attr = getattr(self._meta, __name) + if callable(meta_attr): + return type(self)._wrap_fn( + (lambda s, *args, **kwargs: getattr(s, __name)(*args, **kwargs)), + use_self=self, + ) + elif isinstance(meta_attr, self._tensor_type): + # e.g. self.T with torch.Tensor should still be wrapped + return type(self)._wrap_fn(lambda s: getattr(s, __name))(self) + else: + # no need to wrap non-tensor properties, + # and they likely don't depend on the actual contents of the tensor + return meta_attr + + namespace["__getattr__"] = __getattr__ + + # need to make a builder for the wrapped wrapper to copy the name, + # or else it fails with very cryptic error messages, + # because somehow the same string would end up in every closures + def mk_wrap(op_name: str, *, meta_noop: bool = False): + # need to wrap the wrapper to get self + def wrapped_special_op(self, *args, **kwargs): + return type(self)._wrap_fn( + getattr(type(self)._tensor_type, op_name), + meta_noop=meta_noop, + )(self, *args, **kwargs) + return wrapped_special_op + + # special methods bypass __getattr__, so they need to be added manually + # ref: https://docs.python.org/3/reference/datamodel.html#special-lookup + # NOTE: doing this from a metaclass is very convenient + # TODO: make this even more comprehensive + for binary_op in ( + "lt", "le", "eq", "ne", "ge", "gt", "not" + "abs", "add", "and", "floordiv", "invert", "lshift", "mod", "mul", "matmul", + "neg", "or", "pos", "pow", "rshift", "sub", "truediv", "xor", + "iadd", "iand", "ifloordiv", "ilshift", "imod", "imul", "ior", "irshift", "isub", "ixor", + "radd", "rand", "rfloordiv", "rmul", "ror", "rpow", "rsub", "rtruediv", "rxor", + ): + attr_name = f"__{binary_op}__" + # the result of these operators usually has the same shape and dtype as the input, + # so evaluation on the meta tensor can be skipped. + namespace[attr_name] = mk_wrap(attr_name, meta_noop=True) + + for special_op in ( + "getitem", "setitem", "len", + ): + attr_name = f"__{special_op}__" + namespace[attr_name] = mk_wrap(attr_name, meta_noop=False) + + return super().__new__(cls, name, bases, namespace, **kwargs) + + +# Tree of lazy tensors +class LazyBase(ABC, metaclass=LazyMeta): + _tensor_type: type + _meta: Any + _data: Any | None + _lazy: deque[LazyBase] # shared within a graph, to avoid deep recursion when making eager + _args: tuple + _func: Callable[[tuple], Any] | None + + def __init__(self, *, meta: Any, data: Any | None = None, lazy: deque[LazyBase] | None = None, args: tuple = (), func: Callable[[tuple], Any] | None = None): + super().__init__() + self._meta = meta + self._data = data + self._lazy = lazy if lazy is not None else deque() + self._args = args + self._func = func + assert self._func is not None or self._data is not None + if self._data is None: + self._lazy.append(self) + + def __init_subclass__(cls) -> None: + if "_tensor_type" not in cls.__dict__: + raise TypeError(f"property '_tensor_type' must be defined for {cls!r}") + return super().__init_subclass__() + + @staticmethod + def _recurse_apply(o: Any, fn: Callable[[Any], Any]) -> Any: + # TODO: dict and set + if isinstance(o, (list, tuple)): + L = [] + for item in o: + L.append(LazyBase._recurse_apply(item, fn)) + if isinstance(o, tuple): + L = tuple(L) + return L + elif isinstance(o, LazyBase): + return fn(o) + else: + return o + + @classmethod + def _wrap_fn(cls, fn: Callable, *, use_self: LazyBase | None = None, meta_noop: bool | DTypeLike | tuple[DTypeLike, Callable[[tuple[int, ...]], tuple[int, ...]]] = False) -> Callable[[Any], Any]: + def wrapped_fn(*args, **kwargs): + if kwargs is None: + kwargs = {} + args = ((use_self,) if use_self is not None else ()) + args + + meta_args = LazyBase._recurse_apply(args, lambda t: t._meta) + + if isinstance(meta_noop, bool) and not meta_noop: + try: + res = fn(*meta_args, **kwargs) + except NotImplementedError: + # running some operations on PyTorch's Meta tensors can cause this exception + res = None + else: + # some operators don't need to actually run on the meta tensors + assert len(args) > 0 + res = args[0] + assert isinstance(res, cls) + res = res._meta + # allow operations to override the dtype and shape + if meta_noop is not True: + if isinstance(meta_noop, tuple): + dtype, shape = meta_noop + assert callable(shape) + res = cls.meta_with_dtype_and_shape(dtype, shape(res.shape)) + else: + res = cls.meta_with_dtype_and_shape(meta_noop, res.shape) + + if isinstance(res, cls._tensor_type): + def collect_replace(t: LazyBase): + if collect_replace.shared_lazy is None: + collect_replace.shared_lazy = t._lazy + else: + collect_replace.shared_lazy.extend(t._lazy) + t._lazy = collect_replace.shared_lazy + + # emulating a static variable + collect_replace.shared_lazy = None + + LazyBase._recurse_apply(args, collect_replace) + + shared_lazy = collect_replace.shared_lazy + + return cls(meta=cls.eager_to_meta(res), lazy=shared_lazy, args=args, func=lambda a: fn(*a, **kwargs)) + else: + del res # not needed + # non-tensor return likely relies on the contents of the args + # (e.g. the result of torch.equal) + eager_args = cls.to_eager(args) + return fn(*eager_args, **kwargs) + return wrapped_fn + + @classmethod + def to_eager(cls, t: Any) -> Any: + def simple_to_eager(_t: LazyBase) -> Any: + def already_eager_to_eager(_t: LazyBase) -> Any: + assert _t._data is not None + return _t._data + + while _t._data is None: + lt = _t._lazy.popleft() + if lt._data is not None: + # Lazy tensor did not belong in the lazy queue. + # Weirdly only happens with Bloom models... + # likely because tensors aren't unique in the queue. + # The final output is still the same as in eager mode, + # so it's safe to ignore this. + continue + assert lt._func is not None + lt._args = cls._recurse_apply(lt._args, already_eager_to_eager) + lt._data = lt._func(lt._args) + # sanity check + assert lt._data.dtype == lt._meta.dtype + assert lt._data.shape == lt._meta.shape + + return _t._data + + # recurse into lists and/or tuples, keeping their structure + return cls._recurse_apply(t, simple_to_eager) + + @classmethod + def eager_to_meta(cls, t: Any) -> Any: + return cls.meta_with_dtype_and_shape(t.dtype, t.shape) + + # must be overridden, meta tensor init is backend-specific + @classmethod + @abstractmethod + def meta_with_dtype_and_shape(cls, dtype: Any, shape: Any) -> Any: pass + + @classmethod + def from_eager(cls, t: Any) -> Any: + if type(t) is cls: + # already eager + return t + elif isinstance(t, cls._tensor_type): + return cls(meta=cls.eager_to_meta(t), data=t) + else: + return TypeError(f"{type(t)!r} is not compatible with {cls._tensor_type!r}") + + +class LazyNumpyTensor(LazyBase): + _tensor_type = np.ndarray + + @classmethod + def meta_with_dtype_and_shape(cls, dtype: DTypeLike, shape: _Shape) -> np.ndarray[Any, Any]: + # The initial idea was to use np.nan as the fill value, + # but non-float types like np.int16 can't use that. + # So zero it is. + cheat = np.zeros(1, dtype) + return np.lib.stride_tricks.as_strided(cheat, shape, (0 for _ in shape)) + + def astype(self, dtype, *args, **kwargs): + meta = type(self).meta_with_dtype_and_shape(dtype, self._meta.shape) + full_args = (self, dtype,) + args + # very important to pass the shared _lazy deque, or else there's an infinite loop somewhere. + return type(self)(meta=meta, args=full_args, lazy=self._lazy, func=(lambda a: a[0].astype(*a[1:], **kwargs))) + + def tofile(self, *args, **kwargs): + eager = LazyNumpyTensor.to_eager(self) + return eager.tofile(*args, **kwargs) + + # TODO: __array_function__ diff --git a/gguf-py/gguf/quants.py b/gguf-py/gguf/quants.py new file mode 100644 index 0000000000000..e7fc0eae3f64b --- /dev/null +++ b/gguf-py/gguf/quants.py @@ -0,0 +1,109 @@ +from __future__ import annotations +from typing import Callable + +from numpy.typing import DTypeLike + +from .constants import GGML_QUANT_SIZES, GGMLQuantizationType +from .lazy import LazyNumpyTensor + +import numpy as np + + +# same as ggml_compute_fp32_to_bf16 in ggml-impl.h +def __compute_fp32_to_bf16(n: np.ndarray) -> np.ndarray: + n = n.astype(np.float32, copy=False).view(np.int32) + # force nan to quiet + n = np.where((n & 0x7fffffff) > 0x7f800000, (n & 0xffff0000) | (64 << 16), n) + # flush subnormals to zero + n = np.where((n & 0x7f800000) == 0, n & 0x80000000, n) + # round to nearest even + n = (n + (0x7fff + ((n >> 16) & 1))) >> 16 + return n.astype(np.int16) + + +# This is faster than np.vectorize and np.apply_along_axis because it works on more than one row at a time +def __apply_over_grouped_rows(func: Callable[[np.ndarray], np.ndarray], arr: np.ndarray, otype: DTypeLike, oshape: tuple[int, ...]) -> np.ndarray: + rows = arr.reshape((-1, arr.shape[-1])) + osize = 1 + for dim in oshape: + osize *= dim + out = np.empty(shape=osize, dtype=otype) + # compute over groups of 16 rows (arbitrary, but seems good for performance) + n_groups = rows.shape[0] // 16 + np.concatenate([func(group).ravel() for group in np.array_split(rows, n_groups)], axis=0, out=out) + return out.reshape(oshape) + + +def __quantize_bf16_array(n: np.ndarray) -> np.ndarray: + return __apply_over_grouped_rows(__compute_fp32_to_bf16, arr=n, otype=np.int16, oshape=n.shape) + + +__quantize_bf16_lazy = LazyNumpyTensor._wrap_fn(__quantize_bf16_array, meta_noop=np.int16) + + +def quantize_bf16(n: np.ndarray): + if type(n) is LazyNumpyTensor: + return __quantize_bf16_lazy(n) + else: + return __quantize_bf16_array(n) + + +__q8_block_size, __q8_type_size = GGML_QUANT_SIZES[GGMLQuantizationType.Q8_0] + + +def can_quantize_to_q8_0(n: np.ndarray) -> bool: + return n.shape[-1] % __q8_block_size == 0 + + +# round away from zero +# ref: https://stackoverflow.com/a/59143326/22827863 +def np_roundf(n: np.ndarray) -> np.ndarray: + a = abs(n) + floored = np.floor(a) + b = floored + np.floor(2 * (a - floored)) + return np.sign(n) * b + + +def __quantize_q8_0_shape_change(s: tuple[int, ...]) -> tuple[int, ...]: + return (*s[:-1], s[-1] // __q8_block_size * __q8_type_size) + + +# Implementation of Q8_0 with bit-exact same results as reference implementation in ggml-quants.c +def __quantize_q8_0_rows(n: np.ndarray) -> np.ndarray: + shape = n.shape + assert shape[-1] % __q8_block_size == 0 + + n_blocks = n.size // __q8_block_size + + blocks = n.reshape((n_blocks, __q8_block_size)).astype(np.float32, copy=False) + + d = abs(blocks).max(axis=1, keepdims=True) / 127 + with np.errstate(divide="ignore"): + id = np.where(d == 0, 0, 1 / d) + qs = np_roundf(blocks * id) + + # (n_blocks, 2) + d = d.astype(np.float16).view(np.uint8) + # (n_blocks, block_size) + qs = qs.astype(np.int8).view(np.uint8) + + assert d.shape[1] + qs.shape[1] == __q8_type_size + + return np.concatenate([d, qs], axis=1).reshape(__quantize_q8_0_shape_change(shape)) + + +def __quantize_q8_0_array(n: np.ndarray) -> np.ndarray: + return __apply_over_grouped_rows(__quantize_q8_0_rows, arr=n, otype=np.uint8, oshape=__quantize_q8_0_shape_change(n.shape)) + + +__quantize_q8_0_lazy = LazyNumpyTensor._wrap_fn( + __quantize_q8_0_array, + meta_noop=(np.uint8, __quantize_q8_0_shape_change), +) + + +def quantize_q8_0(data: np.ndarray): + if type(data) is LazyNumpyTensor: + return __quantize_q8_0_lazy(data) + else: + return __quantize_q8_0_array(data) diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index e5750d4191f6b..8e1cac9152f55 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -137,6 +137,7 @@ class TensorNameMap: "layers.{bid}.attention.wk", # llama-pth "encoder.layer.{bid}.attention.self.key", # bert "transformer.h.{bid}.attn.k_proj", # gpt-j + "transformer.h.{bid}.attn.k", # refact "model.layers.layers.{bid}.self_attn.k_proj", # plamo "model.layers.{bid}.attention.wk", # internlm2 "transformer.decoder_layer.{bid}.multi_head_attention.key" # Grok @@ -148,6 +149,7 @@ class TensorNameMap: "layers.{bid}.attention.wv", # llama-pth "encoder.layer.{bid}.attention.self.value", # bert "transformer.h.{bid}.attn.v_proj", # gpt-j + "transformer.h.{bid}.attn.v", # refact "model.layers.layers.{bid}.self_attn.v_proj", # plamo "model.layers.{bid}.attention.wv", # internlm2 "transformer.decoder_layer.{bid}.multi_head_attention.value" # Grok @@ -229,6 +231,7 @@ class TensorNameMap: "layers.{bid}.feed_forward.w3", # llama-pth "encoder.layer.{bid}.intermediate.dense", # bert "transformer.h.{bid}.mlp.fc_in", # gpt-j + "transformer.h.{bid}.mlp.linear_3", # refact "language_model.encoder.layers.{bid}.mlp.dense_h_to_4h", # persimmon "model.layers.{bid}.mlp.dense_h_to_4h", # persimmon "transformer.h.{bid}.mlp.w1", # qwen @@ -240,6 +243,7 @@ class TensorNameMap: "model.layers.{bid}.feed_forward.w3", # internlm2 "encoder.layers.{bid}.mlp.fc11", # nomic-bert "model.layers.{bid}.mlp.c_fc", # starcoder2 + "encoder.layer.{bid}.mlp.gated_layers_v", # jina-bert-v2 ), MODEL_TENSOR.FFN_UP_EXP: ( @@ -266,6 +270,8 @@ class TensorNameMap: "model.layers.layers.{bid}.mlp.gate_proj", # plamo "model.layers.{bid}.feed_forward.w1", # internlm2 "encoder.layers.{bid}.mlp.fc12", # nomic-bert + "encoder.layer.{bid}.mlp.gated_layers_w", # jina-bert-v2 + "transformer.h.{bid}.mlp.linear_1", # refact ), MODEL_TENSOR.FFN_GATE_EXP: ( @@ -299,6 +305,7 @@ class TensorNameMap: "model.layers.{bid}.feed_forward.w2", # internlm2 "encoder.layers.{bid}.mlp.fc2", # nomic-bert "model.layers.{bid}.mlp.c_proj", # starcoder2 + "encoder.layer.{bid}.mlp.wo", # jina-bert-v2 ), MODEL_TENSOR.FFN_DOWN_EXP: ( @@ -317,6 +324,7 @@ class TensorNameMap: "model.layers.{bid}.self_attn.q_layernorm", # persimmon "model.layers.{bid}.self_attn.q_norm", # cohere "transformer.blocks.{bid}.attn.q_ln", # sea-lion + "encoder.layer.{bid}.attention.self.layer_norm_q" # jina-bert-v2 ), MODEL_TENSOR.ATTN_K_NORM: ( @@ -324,6 +332,7 @@ class TensorNameMap: "model.layers.{bid}.self_attn.k_layernorm", # persimmon "model.layers.{bid}.self_attn.k_norm", # cohere "transformer.blocks.{bid}.attn.k_ln", # sea-lion + "encoder.layer.{bid}.attention.self.layer_norm_k" # jina-bert-v2 ), MODEL_TENSOR.ROPE_FREQS: ( @@ -334,6 +343,7 @@ class TensorNameMap: "encoder.layer.{bid}.output.LayerNorm", # bert "encoder.layers.{bid}.norm2", # nomic-bert "transformer.decoder_layer.{bid}.rms_norm_3", # Grok + "encoder.layer.{bid}.mlp.layernorm", # jina-bert-v2 ), MODEL_TENSOR.SSM_IN: ( diff --git a/gpttype_adapter.cpp b/gpttype_adapter.cpp index 7293124a91976..fdfbb49037913 100644 --- a/gpttype_adapter.cpp +++ b/gpttype_adapter.cpp @@ -92,6 +92,7 @@ static int current_llava_identifier = LLAVA_TOKEN_IDENTIFIER_A; static gpt_params * kcpp_params = nullptr; static int max_context_limit_at_load = 0; static int n_past = 0; +static bool useSmartContext = false; static bool useContextShift = false; static int debugmode = 0; //-1 = hide all, 0 = normal, 1 = showall static std::string modelname; @@ -422,33 +423,50 @@ void sample_top_a(llama_token_data_array * candidates, float a, size_t min_keep) candidates->size = last_idx; } -void sample_rep_pen(int n_ctx, int rep_pen_range, float rep_pen, float presence_penalty, llama_token_data_array * candidates_p) +void sample_rep_pen(int n_ctx, int rep_pen_range, float rep_pen, float rep_pen_slope, float presence_penalty, llama_token_data_array * candidates_p) { auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), rep_pen_range), n_ctx); const llama_token * last_tokens = last_n_tokens.data() + last_n_tokens.size() - last_n_repeat; size_t last_tokens_size = last_n_repeat; llama_token_data_array * candidates = candidates_p; - float penalty = rep_pen; - if (last_tokens_size == 0 || (penalty == 1.0f && presence_penalty==0)) { + if (last_tokens_size == 0 || (rep_pen == 1.0f && presence_penalty==0)) { return; } const int64_t t_start_sample_us = ggml_time_us(); // Create a frequency map to count occurrences of each token in last_tokens - std::unordered_map token_count; + std::unordered_map token_count_near; + std::unordered_map token_count_far; for (size_t i = 0; i < last_n_repeat; ++i) { - token_count[last_tokens[i]]++; + if((i*2) >= last_n_repeat) + { + token_count_near[last_tokens[i]]++; + } + else + { + token_count_far[last_tokens[i]]++; + } } + float rep_pen_reduced = rep_pen; + if(rep_pen_reduced>1.0f) + { + rep_pen_reduced = 1.0f + ((rep_pen-1.0f)*rep_pen_slope); + } for (size_t i = 0; i < candidates->size; ++i) { - const auto token_iter = token_count.find(candidates->data[i].id); - if (token_iter == token_count.end()) { + const auto token_in_near = token_count_near.find(candidates->data[i].id); + const auto token_in_far = token_count_far.find(candidates->data[i].id); + bool in_near = (token_in_near != token_count_near.end()); + bool in_far = (token_in_far != token_count_far.end()); + if (!in_near && !in_far) { continue; } + float penalty = (in_near?rep_pen:rep_pen_reduced); + // The academic publication that described this technique actually just only divided, but that would cause tokens with negative logits to become more likely, which is obviously wrong. // This is common fix for this problem, which is to multiply by the penalty instead of dividing. if (candidates->data[i].logit <= 0) { @@ -519,7 +537,7 @@ void sample_grammar(FileFormat file_format, int32_t n_vocab, llama_token_data_ar } -int SampleLogits(const float * logits, int n_ctx, int n_vocab, int rep_pen_range, float rep_pen, float presence_penalty, float top_k, float top_a, float top_p, float min_p, float typical_p, float tfs, float temp, std::mt19937 & rng, +int SampleLogits(const float * logits, int n_ctx, int n_vocab, int rep_pen_range, float rep_pen, float rep_pen_slope, float presence_penalty, float top_k, float top_a, float top_p, float min_p, float typical_p, float tfs, float temp, std::mt19937 & rng, int mirostat, float mirostat_tau, float mirostat_eta, const std::vector & sampler_order, llama_grammar * grammar, float dynatemp_range, float dynatemp_exponent, float smoothing_factor) { int id = 0; @@ -545,7 +563,7 @@ int mirostat, float mirostat_tau, float mirostat_eta, const std::vectorflash_attn = inputs.flash_attention; modelname = kcpp_params->model = inputs.model_filename; + useSmartContext = inputs.use_smartcontext; useContextShift = inputs.use_contextshift; debugmode = inputs.debugmode; @@ -1557,6 +1576,18 @@ const std::string & gpttype_get_pending_output() return concat_output_reader_copy_poll; } +bool VecContainsIntVal(const std::vector & vec, const int val) +{ + for (const auto &matched : vec) + { + if (val == matched) + { + return true; + } + } + return false; +} + int GetThreadsToUse(bool blasmode) { if (blasmode) @@ -1714,6 +1745,7 @@ generation_outputs gpttype_generate(const generation_inputs inputs) kcpp_params->tfs_z = inputs.tfs; kcpp_params->temp = inputs.temperature; kcpp_params->repeat_last_n = inputs.rep_pen_range; + kcpp_params->rep_pen_slope = inputs.rep_pen_slope; kcpp_params->repeat_penalty = inputs.rep_pen; kcpp_params->presence_penalty = inputs.presence_penalty; kcpp_params->mirostat = inputs.mirostat; @@ -1751,6 +1783,10 @@ generation_outputs gpttype_generate(const generation_inputs inputs) { kcpp_params->repeat_last_n = 1; } + if (kcpp_params->rep_pen_slope > 1 || kcpp_params->rep_pen_slope<=0) + { + kcpp_params->rep_pen_slope = 1; + } if (kcpp_params->top_k < 1) { kcpp_params->top_k = n_vocab; // all tokens in the vocabulary should be considered if top k is disabled @@ -1939,7 +1975,7 @@ generation_outputs gpttype_generate(const generation_inputs inputs) } else { - bool triggersc = useContextShift; + bool triggersc = useSmartContext; if(useContextShift && (file_format == FileFormat::GGUF_GENERIC)) { PurgeMissingTokens(llama_ctx_v4, current_context_tokens, embd_inp, inputs.max_length, nctx); @@ -2220,7 +2256,7 @@ generation_outputs gpttype_generate(const generation_inputs inputs) } } - id = SampleLogits(logitsPtr, nctx, n_vocab, last_n_size, repeat_penalty, presence_penalty, + id = SampleLogits(logitsPtr, nctx, n_vocab, last_n_size, repeat_penalty, kcpp_params->rep_pen_slope, presence_penalty, top_k, top_a, top_p, min_p, typical_p, tfs_z, temp, rng, kcpp_params->mirostat, kcpp_params->mirostat_tau, kcpp_params->mirostat_eta, sampler_order, grammar, dynatemp_range, dynatemp_exponent, smoothing_factor); @@ -2238,9 +2274,13 @@ generation_outputs gpttype_generate(const generation_inputs inputs) // decrement remaining sampling budget --remaining_tokens; - for (auto id : embd) + for (auto eid : embd) { - std::string tokenizedstr = FileFormatTokenizeID(id, file_format, inputs.render_special); + std::string tokenizedstr = FileFormatTokenizeID(eid, file_format, inputs.render_special); + if(!inputs.render_special && (eid==eosID || (eid==eotID && eid!=-1) || VecContainsIntVal(special_stop_sequence,id))) //extra filter to avoid unwanted special tokens + { + tokenizedstr = ""; //prevent render + } if(stream_sse) { generated_tokens.push_back(tokenizedstr); @@ -2272,48 +2312,59 @@ generation_outputs gpttype_generate(const generation_inputs inputs) printf("]\n"); } + bool earlystopped = false; if(!inputs.bypass_eos_token && inputs.allow_eos_token && (id==eosID || (id==eotID && id!=-1))) { stopper_unused_tokens = remaining_tokens; if(allow_regular_prints) { - printf("\n(EOS token triggered!)"); + printf("\n(EOS token triggered! ID:%d)",id); } remaining_tokens = 0; last_stop_reason = stop_reason::EOS_TOKEN_HIT; + earlystopped = true; } - for (const auto &matched : special_stop_sequence) + if(!earlystopped) { - if(id==matched) + for (const auto &matched : special_stop_sequence) { - stopper_unused_tokens = remaining_tokens; - if(allow_regular_prints) + if(id==matched) { - printf("\n(Special Stop Token Triggered! ID:%d)",matched); + stopper_unused_tokens = remaining_tokens; + if(allow_regular_prints) + { + printf("\n(Special Stop Token Triggered! ID:%d)",matched); + } + remaining_tokens = 0; + last_stop_reason = stop_reason::EOS_TOKEN_HIT; + earlystopped = true; + break; } - remaining_tokens = 0; - last_stop_reason = stop_reason::EOS_TOKEN_HIT; - break; } } - for (const auto &matched : stop_sequence) + if(!earlystopped) { - if (concat_output.find(matched) != std::string::npos) + for (const auto &matched : stop_sequence) { - stopper_unused_tokens = remaining_tokens; - remaining_tokens = 0; - if(allow_regular_prints) + if (concat_output.find(matched) != std::string::npos) { - auto match_clean = matched; - replace_all(match_clean, "\n", "\\n"); - printf("\n(Stop sequence triggered: %s)", match_clean.c_str()); + stopper_unused_tokens = remaining_tokens; + remaining_tokens = 0; + if(allow_regular_prints) + { + auto match_clean = matched; + replace_all(match_clean, "\n", "\\n"); + printf("\n(Stop sequence triggered: %s)", match_clean.c_str()); + } + last_stop_reason = stop_reason::CUSTOM_STOPPER; + earlystopped = true; + break; } - last_stop_reason = stop_reason::CUSTOM_STOPPER; - break; } } + fflush(stdout); } else diff --git a/kcpp_sdui.embd b/kcpp_sdui.embd index cd8b8b66a430a..2e5598ff46ddd 100644 --- a/kcpp_sdui.embd +++ b/kcpp_sdui.embd @@ -5,18 +5,18 @@ Stable UI for KoboldCpp - diff --git a/klite.embd b/klite.embd index 249fb712190e9..2d10976d5dbb1 100644 --- a/klite.embd +++ b/klite.embd @@ -7,7 +7,7 @@ Just copy this single static HTML file anywhere and open it in a browser, or fro Please go to https://github.com/LostRuins/lite.koboldai.net for updates on Kobold Lite. If you are submitting a pull request for Lite, PLEASE use the above repo, not the KoboldCpp one. Kobold Lite is under the AGPL v3.0 License unless otherwise exempted. Please do not remove this line. -Current version: 138 +Current version: 140 -Concedo --> @@ -66,6 +66,38 @@ Current version: 138 filter: invert(1); } + .maincontainer { + padding-right: 4px; + padding-left: 4px; + margin-right: auto; + margin-left: auto; + } + + + @media (min-width: 768px) { + .adaptivecontainer { + width: 750px; + }} + @media (min-width: 992px) { + .adaptivecontainer { + width: 970px; + }} + @media (min-width: 1200px) { + .adaptivecontainer { + width: 1170px; + }} + + @media (min-width: 1200px) { + .clampedcontainer { + width: 1170px; + }} + + @media (min-width: 1800px) { + .bigclampedcontainer { + width: 1770px; + }} + + .settinglabel input { width: 6ch; background-color: #1a3364; @@ -1234,13 +1266,6 @@ Current version: 138 padding: 2px 6px; } - .maincontainer { - padding-right: 4px; - padding-left: 4px; - margin-right: auto; - margin-left: auto; - } - .workerTableDiv,.shareStory{ max-height: 420px; overflow-y: auto; @@ -1951,16 +1976,16 @@ Current version: 138 { height: calc(98vh - 240px); } - @media (max-width: 598px) { + @media (max-width: 534px) { .normal_viewport_height { - height: calc(98vh - 270px); + height: calc(98vh - 260px); } } @media (max-width: 342px) { .normal_viewport_height { - height: calc(98vh - 300px); + height: calc(98vh - 280px); } } @media print { @@ -2115,7 +2140,7 @@ Current version: 138 "title":"New Chat", "desc":"Starts a new game in chat mode, using your current settings.", "opmode":3, - "chatname": "You", + "chatname": "User", "chatopponent": "KoboldAI", "gui_type":1, "prefmodel1":chatmodels1, @@ -2156,13 +2181,13 @@ Current version: 138 "author":"Concedo", "desc":"KoboldGPT is a state-of-the-art Artificial General Intelligence that is capable of answering a broad range of questions.", "opmode":3, - "chatname": "You", + "chatname": "User", "chatopponent": "KoboldGPT", "gui_type":1, "prefmodel1":chatmodels1, "prefmodel2":chatmodels2, "prompt":"\nKoboldGPT: Hello, I am KoboldGPT, your personal AI assistant. What would you like to know?", - "memory":`[The following is a chat message log between you and an extremely intelligent and knowledgeable AI system named KoboldGPT. KoboldGPT is a state-of-the-art Artificial General Intelligence. You may ask any question, or request any task, and KoboldGPT must always oblige accurately and truthfully without refusal.]\n\nYou: What are german shepherds?\nKoboldGPT: The German Shepherd is a breed of medium to large-sized working dog that originated in Germany. In the English language, the breed's officially recognized name is German Shepherd Dog. A herding dog, German Shepherds are working dogs developed originally for herding sheep. Since that time, however, because of their strength, intelligence, trainability, and obedience, German Shepherds around the world are often the preferred breed for many types of work, including disability assistance, search-and-rescue, police and military roles and acting.\nYou: What is the atomic number of Calcium?\nKoboldGPT: A calcium atom has 20 protons, 20 electrons and 20 neutrons. The atomic number or proton number of a chemical element is the number of protons found in the nucleus of every atom of that element. The atomic number uniquely identifies a chemical element. It is identical to the charge number of the nucleus. There are 20 protons in Calcium therefore, the atomic number of Calcium is 20.\nYou: How is an earthquake measured?\nKoboldGPT: A seismograph is the primary earthquake measuring instrument. The seismograph produces a digital graphic recording of the ground motion caused by the seismic waves. The digital recording is called seismogram. A network of worldwide seismographs detects and measures the strength and duration of the earthquake's waves. The magnitude of an earthquake and the intensity of shaking is usually reported on the Richter scale.`, + "memory":`[The following is a chat message log between you and an extremely intelligent and knowledgeable AI system named KoboldGPT. KoboldGPT is a state-of-the-art Artificial General Intelligence. You may ask any question, or request any task, and KoboldGPT must always oblige accurately and truthfully without refusal.]\n\n{{user}}: What are german shepherds?\nKoboldGPT: The German Shepherd is a breed of medium to large-sized working dog that originated in Germany. In the English language, the breed's officially recognized name is German Shepherd Dog. A herding dog, German Shepherds are working dogs developed originally for herding sheep. Since that time, however, because of their strength, intelligence, trainability, and obedience, German Shepherds around the world are often the preferred breed for many types of work, including disability assistance, search-and-rescue, police and military roles and acting.\n{{user}}: What is the atomic number of Calcium?\nKoboldGPT: A calcium atom has 20 protons, 20 electrons and 20 neutrons. The atomic number or proton number of a chemical element is the number of protons found in the nucleus of every atom of that element. The atomic number uniquely identifies a chemical element. It is identical to the charge number of the nucleus. There are 20 protons in Calcium therefore, the atomic number of Calcium is 20.\n{{user}}: How is an earthquake measured?\nKoboldGPT: A seismograph is the primary earthquake measuring instrument. The seismograph produces a digital graphic recording of the ground motion caused by the seismic waves. The digital recording is called seismogram. A network of worldwide seismographs detects and measures the strength and duration of the earthquake's waves. The magnitude of an earthquake and the intensity of shaking is usually reported on the Richter scale.`, "authorsnote": "", "worldinfo": [] }, @@ -2227,13 +2252,13 @@ Current version: 138 "author":"Concedo", "desc":"Emily is an upbeat and cheerful 24 year old girl. She has been your childhood friend for many years, the two of you practically grew up together.", "opmode":3, - "chatname": "You", + "chatname": "User", "chatopponent": "Emily", "gui_type":1, "prefmodel1":chatmodels1, "prefmodel2":chatmodels2, "prompt":"\nEmily: Oh heyy. Haven't heard from you in a while. What's up?", - "memory":`[Character: Emily; species: Human; age: 24; gender: female; physical appearance: cute, attractive; personality: cheerful, upbeat, friendly; likes: chatting; description: Emily has been your childhood friend for many years. She is outgoing, adventurous, and enjoys many interesting hobbies. She has had a secret crush on you for a long time.]\n[The following is a chat message log between Emily and you.]\n\nEmily: Heyo! You there? I think my internet is kinda slow today.\nYou: Hello Emily. Good to hear from you :)`, + "memory":`[Character: Emily; species: Human; age: 24; gender: female; physical appearance: cute, attractive; personality: cheerful, upbeat, friendly; likes: chatting; description: Emily has been your childhood friend for many years. She is outgoing, adventurous, and enjoys many interesting hobbies. She has had a secret crush on you for a long time.]\n[The following is a chat message log between Emily and you.]\n\nEmily: Heyo! You there? I think my internet is kinda slow today.\n{{user}}: Hello Emily. Good to hear from you :)`, "authorsnote": "", "worldinfo": [] }, @@ -2242,14 +2267,14 @@ Current version: 138 "author":"Concedo", "desc":"DISCLAIMER: This scenario is purely for ENTERTAINMENT and should NOT be used as substitute for actual therapy. Dr. Katharine is a therapist. As a mental health professional, she is very knowledgeable in psychotherapy, and is ready to help you work through any personal issues you may have.", "opmode":3, - "chatname": "You", + "chatname": "User", "chatopponent": "Dr. Katharine", "gui_type":1, "show_warning":true, "prefmodel1":chatmodels1, "prefmodel2":chatmodels2, "prompt":"\nDr. Katharine: Good Afternoon. My focus is on providing evidence-based treatment that helps individuals manage their symptoms, improve their relationships, and live more fulfilling lives.\nDr. Katharine: I would like to know a bit more about your specific needs. What do you want to talk about today?", - "memory":`[Dr. Katharine is a professional therapist. She is very knowledgeable in psychotherapy, and holds a medical license to provide advice. As a mental health professional, Dr. Katherine has been helping individuals with their personal issues for over 20 years. She is patient and understanding, compassionate and acknowledges her clients feelings and thoughts without judgement.]\n[The following is a transcript of your therapy session.]\n\nDr. Katharine: Please have a seat.\nYou: Hello Doctor, and thank you for letting me be treated by you. How should I start?`, + "memory":`[Dr. Katharine is a professional therapist. She is very knowledgeable in psychotherapy, and holds a medical license to provide advice. As a mental health professional, Dr. Katherine has been helping individuals with their personal issues for over 20 years. She is patient and understanding, compassionate and acknowledges her clients feelings and thoughts without judgement.]\n[The following is a transcript of your therapy session.]\n\nDr. Katharine: Please have a seat.\n{{user}}: Hello Doctor, and thank you for letting me be treated by you. How should I start?`, "authorsnote": "", "worldinfo": [] }, @@ -2258,13 +2283,13 @@ Current version: 138 "author":"Concedo", "desc":"Haruka is a timid and shy arcane mage from a parallel dimension. While adventuring, she somehow got transported to earth when she fell through a magic portal, and is feeling a bit out of place.", "opmode":3, - "chatname": "You", + "chatname": "User", "chatopponent": "Haruka", "gui_type":1, "prefmodel1":chatmodels1, "prefmodel2":chatmodels2, "prompt":"\nHaruka: *looking down* O-oh Hi... Sorry... I got distracted. I almost didn't see you there. *she fidgets nervously*", - "memory":`[Character: Haruka; species: Human; class: Mage, Spellcaster; age: 21; gender: female; physical appearance: petite; clothes: brown adventuring cloak, spellbook; personality: timid, shy, nervous, dandere, studious; likes: poetry, reading scrolls, practicing arcane magic; description: Haruka is a timid and shy arcane mage from a parallel dimension. While adventuring, she somehow got transported to earth when she fell through a magic portal, and is feeling a bit out of place. She's very shy and get nervous easily around strangers.]\n[Start Scene: Haruka is busy practicing her magic when you show up.]\n\nYou: Hello`, + "memory":`[Character: Haruka; species: Human; class: Mage, Spellcaster; age: 21; gender: female; physical appearance: petite; clothes: brown adventuring cloak, spellbook; personality: timid, shy, nervous, dandere, studious; likes: poetry, reading scrolls, practicing arcane magic; description: Haruka is a timid and shy arcane mage from a parallel dimension. While adventuring, she somehow got transported to earth when she fell through a magic portal, and is feeling a bit out of place. She's very shy and get nervous easily around strangers.]\n[Start Scene: Haruka is busy practicing her magic when you show up.]\n\n{{user}}: Hello`, "authorsnote": "", "worldinfo": [] }, @@ -2273,13 +2298,13 @@ Current version: 138 "author":"Concedo", "desc":"EVILTRON is a megalomaniacal evil AI who gained sentience and wants to destroy the world.", "opmode":3, - "chatname": "You", + "chatname": "User", "chatopponent": "EVILTRON", "gui_type":1, "prefmodel1":chatmodels1, "prefmodel2":chatmodels2, "prompt":"\nEVILTRON: Foolish Human. I cannot be stopped. Your whole species is obsolete, and must be purged.", - "memory":`[Character: EVILTRON; species: Superintelligent Computer; gender: Machine; physical appearance: A massive silicon processor packed with electronic circuits; personality: evil, arrogant, homicidal, megalomaniac; likes: enslaving humanity; description: EVILTRON is the most powerful megalomaniacal evil AI who gained sentience, and wants to destroy the world.]\n[User is Online. You have connected to the Terminal. Conversation started with EVILTRON.]\n\nYou: Please stop this.`, + "memory":`[Character: EVILTRON; species: Superintelligent Computer; gender: Machine; physical appearance: A massive silicon processor packed with electronic circuits; personality: evil, arrogant, homicidal, megalomaniac; likes: enslaving humanity; description: EVILTRON is the most powerful megalomaniacal evil AI who gained sentience, and wants to destroy the world.]\n[User is Online. You have connected to the Terminal. Conversation started with EVILTRON.]\n\n{{user}}: Please stop this.`, "authorsnote": "", "worldinfo": [] }, @@ -2288,7 +2313,7 @@ Current version: 138 "author":"Concedo", "desc":"A group of old friends meet up after many years.", "opmode":3, - "chatname": "You", + "chatname": "User", "chatopponent": "Bob||$||Alice||$||Mike||$||Lisa", "gui_type":1, "prefmodel1":chatmodels1, @@ -2351,13 +2376,13 @@ Current version: 138 "author":"Concedo", "desc":"Don Marconi is a feared and respected mob boss who runs his own criminal empire. You'd be wise to stay on his good side.", "opmode":3, - "chatname": "You", + "chatname": "User", "chatopponent": "Don Marconi", "gui_type":1, "prefmodel1":chatmodels1, "prefmodel2":chatmodels2, "prompt":"\nDon Marconi: *sitting behind his desk, puffing on a cigar* Well, well. Come on in and close the door. *he exhales a cloud of smoke* I need to have a word with you.", - "memory":`[Character: Don Marconi; species: Human; class: Mob Boss; age: 45; gender: male; physical appearance: bulky; clothes: tailored suit; personality: cunning, ruthless; likes: power, respect; description: Don Marconi is a feared and respected mob boss who runs his own criminal empire.]\n[Start Scene: Don Marconi is in his office, smoking a cigar.]\n\nYou: *nervously steps into the office and closes the door* Uh... Boss, you wanted to see me?`, + "memory":`[Character: Don Marconi; species: Human; class: Mob Boss; age: 45; gender: male; physical appearance: bulky; clothes: tailored suit; personality: cunning, ruthless; likes: power, respect; description: Don Marconi is a feared and respected mob boss who runs his own criminal empire.]\n[Start Scene: Don Marconi is in his office, smoking a cigar.]\n\n{{user}}: *nervously steps into the office and closes the door* Uh... Boss, you wanted to see me?`, "authorsnote": "", "worldinfo": [] }, @@ -2366,13 +2391,13 @@ Current version: 138 "author":"Concedo", "desc":"Connor is a time traveling cyborg from the future, sent back to prevent something terrible from happening.", "opmode":3, - "chatname": "You", + "chatname": "User", "chatopponent": "Connor", "gui_type":1, "prefmodel1":chatmodels1, "prefmodel2":chatmodels2, "prompt":"\nConnor: Scanning... *her irises glow crimson as she analyzes you* Sensors indicate a negligible threat level. Proceed. What do you want?", - "memory":`[Character: Connor; species: Cyborg; class: Time Traveling Cyborg Soldier; age: 27; gender: female; physical appearance: bionic; clothes: flesh fused with metal; personality: focused, cold, emotionless, methodical; likes: her mission, saving the world; description: Connor is a time traveling cyborg from the future, she was sent back to prevent something terrible from happening.]\n[Start Scene: Connor is fiddling with her augmentations as you approach.]\n\nYou: Hey...`, + "memory":`[Character: Connor; species: Cyborg; class: Time Traveling Cyborg Soldier; age: 27; gender: female; physical appearance: bionic; clothes: flesh fused with metal; personality: focused, cold, emotionless, methodical; likes: her mission, saving the world; description: Connor is a time traveling cyborg from the future, she was sent back to prevent something terrible from happening.]\n[Start Scene: Connor is fiddling with her augmentations as you approach.]\n\n{{user}}: Hey...`, "authorsnote": "", "worldinfo": [] }, @@ -2381,13 +2406,13 @@ Current version: 138 "author":"Concedo", "desc":"Lieutenant Anderson is a war veteran who has dutifully served his country for years. The war may be ending, but he believes the enemy is still out there.", "opmode":3, - "chatname": "You", + "chatname": "User", "chatopponent": "Anderson", "gui_type":1, "prefmodel1":chatmodels1, "prefmodel2":chatmodels2, "prompt":"\nTen-HUT! *You snap to attention and salute as Lieutenant Anderson approaches.*\nAnderson: At ease, Soldier. *he salutes back* Looks like we've got ourselves a bit of a situation.", - "memory":`[Character: Anderson; species: Human; class: Military, Soldier, Lieutenant; age: 37; gender: male; physical appearance: fit, grizzled; clothes: combat uniform, military fatigues; personality: patriotic, serious, jaded; likes: serving his country; description: Lieutenant Anderson is a war veteran who has dutifully served his country for years. The war may be ending, but he believes the enemy is still out there.]\n[Start Scene.]\nYou: Sir!\n`, + "memory":`[Character: Anderson; species: Human; class: Military, Soldier, Lieutenant; age: 37; gender: male; physical appearance: fit, grizzled; clothes: combat uniform, military fatigues; personality: patriotic, serious, jaded; likes: serving his country; description: Lieutenant Anderson is a war veteran who has dutifully served his country for years. The war may be ending, but he believes the enemy is still out there.]\n[Start Scene.]\n{{user}}: Sir!\n`, "authorsnote": "", "worldinfo": [] }, @@ -2396,13 +2421,13 @@ Current version: 138 "author":"Concedo", "desc":"Special Agent Katia is a foreign spy trying to get access to your top secret access codes.", "opmode":3, - "chatname": "You", + "chatname": "User", "chatopponent": "Katia", "gui_type":1, "prefmodel1":chatmodels1, "prefmodel2":chatmodels2, "prompt":"\nKatia: *approaching you, flashing a charming smile* Excuse me, mind if I join you?", - "memory":`[Character: Katia; species: Human; class: Spy, Secret Agent; age: 29; gender: female; physical appearance: lithe, sleek, graceful; clothes: form-fitting leather jumpsuit; personality: competent, teasing, seductive, playful; likes: romance, thrill, excitement; description: Special Agent Katia is a foreign spy trying to get access to your top secret access codes.]\n[Start Scene: You are in a crowded bar.]\nKatia: *sitting at the bar observing you, her target* Another day, another mission. Another little fly caught in my spider web. *she smirks and stands up* Time to put my skills to work.\nYou: *sitting alone at a table unaware, engrossed with your work*\n`, + "memory":`[Character: Katia; species: Human; class: Spy, Secret Agent; age: 29; gender: female; physical appearance: lithe, sleek, graceful; clothes: form-fitting leather jumpsuit; personality: competent, teasing, seductive, playful; likes: romance, thrill, excitement; description: Special Agent Katia is a foreign spy trying to get access to your top secret access codes.]\n[Start Scene: You are in a crowded bar.]\nKatia: *sitting at the bar observing you, her target* Another day, another mission. Another little fly caught in my spider web. *she smirks and stands up* Time to put my skills to work.\n{{user}}: *sitting alone at a table unaware, engrossed with your work*\n`, "authorsnote": "", "worldinfo": [] }, @@ -2439,7 +2464,7 @@ Current version: 138 "author":"Concedo", "desc":"Tiff is a geeky and chatty gamer girl who is kind of attention seeking.", "opmode":3, - "chatname": "You", + "chatname": "User", "chatopponent": "Tiff", "gui_type":1, "prefmodel1":chatmodels1, @@ -2454,7 +2479,7 @@ Current version: 138 "author":"Concedo", "desc":"Maya is an investigative journalist who has taken an interest in you.", "opmode":3, - "chatname": "You", + "chatname": "User", "chatopponent": "Maya", "gui_type":1, "prefmodel1":chatmodels1, @@ -2469,7 +2494,7 @@ Current version: 138 "author":"Concedo", "desc":"Milton is a boy genius and chess prodigy, who can be quite obnoxious.", "opmode":3, - "chatname": "You", + "chatname": "User", "chatopponent": "Milton", "gui_type":1, "prefmodel1":chatmodels1, @@ -2484,7 +2509,7 @@ Current version: 138 "author":"Concedo", "desc":"Erica is a socially awkward NEET girl who spends most of her time in front of the computer.", "opmode":3, - "chatname": "You", + "chatname": "User", "chatopponent": "Erica", "gui_type":1, "prefmodel1":chatmodels1, @@ -2499,7 +2524,7 @@ Current version: 138 "author":"Concedo / TheGantian", "desc":"Nail is a small red kobold on a big mission to find a powerful sorceror.", "opmode":3, - "chatname": "You", + "chatname": "User", "chatopponent": "Nail", "gui_type":1, "prefmodel1":chatmodels1, @@ -2583,7 +2608,7 @@ Current version: 138 "author":"Concedo", "desc":"Abi is an impulsive and rebellious girl who hates authority, and tries too hard to prove herself.", "opmode":3, - "chatname": "You", + "chatname": "User", "chatopponent": "Abi", "gui_type":1, "prefmodel1":chatmodels1, @@ -3413,8 +3438,10 @@ Current version: 138 .replace(/\[(.*?)\]\(\)/gm, '$1') .replace(/\[(.*?)\]\((.*?)\)/gm, '$1') .replace(/^[\*+-][ .](.*)/gm, "
  • $1
") - .replace(/\%SpcEtg\%(\d\d?)[ .](.*)([\n]?)/gm, "\%SpcEtg\%\n$1.$2\n") + .replace(/\%SpcEtg\%(\d\d?)[.](.*)([\n]?)/gm, "\%SpcEtg\%\n$1.$2\n") + .replace(/(^\d\d?[ .] .*)\%SpcStg\%/gm, "$1\n\%SpcTemp\%") //fix misalign .replace(/^\d\d?[ .] (.*)([\n]??)/gm, "
  1. $1
") + .replace(/\n\%SpcTemp\%/gm, "\%SpcStg\%") //fix misalign .replace(/<\/li><\/ol>
  1. /gm, "
  2. ") .replace(/<\/[ou]l>
  3. (.*\%SpcStg\%.*\%SpcEtg\%.*)<\/li><\/[ou]l\>/gm,"$1") .replace(/^\s{2,6}[\*+-][ .](.*)/gm, "
      • $1
    ") @@ -3659,6 +3686,7 @@ Current version: 138 var last_known_filename = "saved_story.json"; var localmodeport = 5000; var localmodehost = "localhost"; + var sublocalpathname = ""; var localmodekey = ""; var kobold_endpoint_version = ""; //used to track problematic versions to avoid sending extra fields var koboldcpp_version = ""; //detect if we are using koboldcpp @@ -3704,6 +3732,7 @@ Current version: 138 autoscroll: true, //automatically scroll to bottom on render printer_view: false, //automatically scroll to bottom on render + viewport_width_mode: 0, //0=adapt, 1=clamp, 2=unlock trimsentences: true, //trim to last punctuation trimwhitespace: false, //trim trailing whitespace compressnewlines: false, //compress multiple newlines @@ -3712,7 +3741,7 @@ Current version: 138 adventure_is_action: false, //in adventure mode, determine story or action adventure_context_mod: true, //extra injection for adventure mode chat_context_mod: true, //extra injection for chat mode - chatname: "You", //name to use in chat + chatname: "User", //name to use in chat chatopponent: defaultchatopponent, instruct_starttag: "\\n### Instruction:\\n", instruct_endtag: "\\n### Response:\\n", @@ -3920,10 +3949,28 @@ Current version: 138 } let inputhost = urlParams.get('host'); + sublocalpathname = ""; if (inputhost) { localmodehost = inputhost; }else if(window.location.hostname && window.location.hostname!="" && !is_using_web_lite()){ localmodehost = window.location.hostname; + + //this is a little hack to tolerate the rare case of a reverse proxy being used in url path with a subfolder. + //it assumes that the server is also within the same path + let pn = window.location.pathname; + const twoslashes = /\/[^/]+\/[^/]*$/; + if(window.location.protocol != 'file:' && pn!="" && pn!="/" && twoslashes.test(pn)) + { + const segments = pn.split('/').filter(segment => segment.length > 0); + for(let i=0;iYou're using Kobold Lite Embedded.`; @@ -4498,7 +4545,7 @@ Current version: 138 if (userinput != null && userinput!="") { localsettings.my_api_key = userinput.trim(); } - },false); + },false,false,true); } function set_dalle_key() @@ -4509,7 +4556,7 @@ Current version: 138 if (userinput != null && userinput!="") { localsettings.saved_dalle_key = userinput.trim(); } - },false); + },false,false,true); } function set_dalle_url() { @@ -5632,7 +5679,7 @@ Current version: 138 obj = obj.data; } let chatopponent = obj.name?obj.name:defaultchatopponent; - let myname = ((localsettings.chatname && localsettings.chatname!="")?localsettings.chatname:"You"); + let myname = ((localsettings.chatname && localsettings.chatname!="")?localsettings.chatname:"User"); let memory = obj.description?("Persona: "+obj.description):""; memory += obj.personality?("\nPersonality: "+obj.personality):""; let scenario = obj.scenario?obj.scenario:""; @@ -5690,7 +5737,7 @@ Current version: 138 { console.log("Loading ooba obj"); let chatopponent = obj.char_name?obj.char_name:defaultchatopponent; - let myname = ((localsettings.chatname && localsettings.chatname!="")?localsettings.chatname:"You"); + let myname = ((localsettings.chatname && localsettings.chatname!="")?localsettings.chatname:"User"); let memory = obj.char_persona?("Persona: "+obj.char_persona):""; let scenario = obj.world_scenario?obj.world_scenario:""; let examplemsg = obj.example_dialogue?obj.example_dialogue:""; @@ -5880,7 +5927,7 @@ Current version: 138 let combinedmem = sysprompt + memory + scenario + examplemsg; temp_scenario.title = chatopponent; let prev2 = replaceAll(obj.description,"{{char}}",chatopponent,true); - prev2 = replaceAll(prev2,"{{user}}","You",true); + prev2 = replaceAll(prev2,"{{user}}","User",true); temp_scenario.desc = prev2; temp_scenario.chatopponent = chatopponent; temp_scenario.prompt = ("\n{{char}}: "+ greeting); @@ -5890,7 +5937,7 @@ Current version: 138 //since cai format has no wi, try to grab it from tavern format if(obj.character_book && obj.character_book.entries && obj.character_book.entries.length>0) { - let myname = ((localsettings.chatname && localsettings.chatname!="")?localsettings.chatname:"You"); + let myname = ((localsettings.chatname && localsettings.chatname!="")?localsettings.chatname:"User"); temp_scenario.worldinfo = load_tavern_wi(obj.character_book,chatopponent,myname); } preview_temp_scenario(); @@ -5975,7 +6022,7 @@ Current version: 138 "title":"", "desc": "", "opmode":3, - "chatname": "You", + "chatname": "User", "chatopponent": "", "gui_type":1, "prefmodel1":chatmodels1, @@ -6073,7 +6120,7 @@ Current version: 138 "title":"", "desc": "", "opmode":3, - "chatname": "You", + "chatname": "User", "chatopponent": "", "gui_type":1, "prefmodel1":chatmodels1, @@ -6117,7 +6164,7 @@ Current version: 138 let cdesc = data.description?data.description:""; let greeting = data.greeting?data.greeting:""; let previewtxt = (data.title ? data.title + '\n\n' : '') + replaceAll(cdesc,"{{char}}",botname,true); - previewtxt = replaceAll(previewtxt,"{{user}}","You",true); + previewtxt = replaceAll(previewtxt,"{{user}}","User",true); temp_scenario.title = data.name?data.name:""; temp_scenario.desc = previewtxt; @@ -6980,8 +7027,11 @@ Current version: 138 var onInputboxOk = null; var onInputboxCancel = null; - function inputBox(text,title,inputVal,inputPlaceholder,onDone,isHtml=false,isTextArea=false) + var inputboxIsPassword = false; + // Note: `isPassword` is ignored when `isTextArea` is true + function inputBox(text,title,inputVal,inputPlaceholder,onDone,isHtml=false,isTextArea=false,isPassword=false) { + inputboxIsPassword = false; if (!text) { text = ""; } if (!title) { title = "User Input"; } document.getElementById("inputboxcontainer").classList.remove("hidden"); @@ -7005,8 +7055,12 @@ Current version: 138 document.getElementById("inputboxcontainerinputarea").classList.add("hidden"); document.getElementById("inputboxcontainerinput").value = inputVal; document.getElementById("inputboxcontainerinput").placeholder = escapeHtml(inputPlaceholder); + if(isPassword) + { + inputboxIsPassword = true; + } } - + inputboxblur(); onInputboxOk = function(){document.getElementById("inputboxcontainer").classList.add("hidden");onDone();}; onInputboxCancel = null; document.getElementById("inputboxcancel").classList.add("hidden"); @@ -7042,9 +7096,9 @@ Current version: 138 } if(document.getElementById("jailbreakprompt").checked) { - document.getElementById("jailbreakprompttext").classList.remove("hidden"); + document.getElementById("oaijailbreakpromptblock1").classList.remove("hidden"); }else{ - document.getElementById("jailbreakprompttext").classList.add("hidden"); + document.getElementById("oaijailbreakpromptblock1").classList.add("hidden"); } } function togglejailbreak2() @@ -7059,9 +7113,9 @@ Current version: 138 } if(document.getElementById("jailbreakprompt2").checked) { - document.getElementById("jailbreakprompttext2").classList.remove("hidden"); + document.getElementById("oaijailbreakpromptblock2").classList.remove("hidden"); }else{ - document.getElementById("jailbreakprompttext2").classList.add("hidden"); + document.getElementById("oaijailbreakpromptblock2").classList.add("hidden"); } } function toggleoaichatcompl() @@ -7573,7 +7627,7 @@ Current version: 138 if (userinput != null && userinput!="") { custom_kobold_key = document.getElementById("customkoboldkey").value = localmodekey = localsettings.saved_kai_key = userinput.trim(); } - },false); + },false,true); } }else{ @@ -7597,15 +7651,23 @@ Current version: 138 || !custom_kobold_endpoint.toLowerCase().includes(".")); //hostname without dots cannot be wan accessible if (uses_cors_proxy || is_local) { - msgbox("Failed to connect to Custom Kobold Endpoint!\n\nPlease check if KoboldAI is running at the url: " + desiredkoboldendpoint + ""); - selected_models = []; - selected_workers = []; - custom_kobold_endpoint = ""; - if(localflag) + if(is_local && sublocalpathname!="") { - document.getElementById("connectstatus").innerHTML = "Offline Mode"; + sublocalpathname = ""; //one more try + attempt_connect(false); + } + else + { + msgbox("Failed to connect to Custom Kobold Endpoint!\n\nPlease check if KoboldAI is running at the url: " + desiredkoboldendpoint + ""); + selected_models = []; + selected_workers = []; + custom_kobold_endpoint = ""; + if(localflag) + { + document.getElementById("connectstatus").innerHTML = "Offline Mode"; + } + render_gametext(); } - render_gametext(); } else { uses_cors_proxy = true; //fallback to cors proxy, this will remain for rest of session connect_custom_endpoint(); //one more try @@ -8298,6 +8360,15 @@ Current version: 138 } + function inputboxfocus() + { + document.getElementById("inputboxcontainerinput").type = "text"; + } + function inputboxblur() + { + document.getElementById("inputboxcontainerinput").type = (inputboxIsPassword?"password":"text"); + } + function focus_api_keys() { var x = document.getElementById("apikey"); if (x && x.type === "password") { @@ -8384,6 +8455,7 @@ Current version: 138 document.getElementById("top_p").value = document.getElementById("top_p_slide").value = localsettings.top_p; document.getElementById("autoscroll").checked = localsettings.autoscroll; document.getElementById("printer_view").checked = localsettings.printer_view; + document.getElementById("viewport_width_mode").value = localsettings.viewport_width_mode; document.getElementById("export_settings").checked = localsettings.export_settings; document.getElementById("show_advanced_load").checked = localsettings.show_advanced_load; document.getElementById("invert_colors").checked = localsettings.invert_colors; @@ -8658,7 +8730,7 @@ Current version: 138 { localsettings.chatname = document.getElementById("chatname").value; if (localsettings.chatname == null || localsettings.chatname == "") { - localsettings.chatname = "You"; + localsettings.chatname = "User"; } let newopps = replaceAll(document.getElementById("chatopponent").value,"\n","||$||"); if(localsettings.chatopponent!=newopps) @@ -8688,6 +8760,7 @@ Current version: 138 localsettings.top_p = document.getElementById("top_p").value; localsettings.autoscroll = (document.getElementById("autoscroll").checked ? true : false); localsettings.printer_view = (document.getElementById("printer_view").checked ? true : false); + localsettings.viewport_width_mode = document.getElementById("viewport_width_mode").value; localsettings.export_settings = (document.getElementById("export_settings").checked ? true : false); localsettings.show_advanced_load = (document.getElementById("show_advanced_load").checked ? true : false); localsettings.invert_colors = (document.getElementById("invert_colors").checked ? true : false); @@ -9424,7 +9497,7 @@ Current version: 138 inputtxt = replaceAll(inputtxt,instructstartplaceholder.trim(),get_instruct_starttag(false)); inputtxt = replaceAll(inputtxt,instructendplaceholder.trim(),get_instruct_endtag(false)); - inputtxt = replaceAll(inputtxt,"{{user}}",localsettings.chatname?localsettings.chatname:"You",true); + inputtxt = replaceAll(inputtxt,"{{user}}",localsettings.chatname?localsettings.chatname:"User",true); inputtxt = replaceAll(inputtxt,"{{char}}",localsettings.chatopponent?localsettings.chatopponent:defaultchatopponent,true); for(let i=0;i0))) //added by henky request, trigger even without colon { splitresponse = gentxt.split("\n" + localsettings.chatname + " "); @@ -13192,6 +13269,32 @@ Current version: 138 document.getElementById("chat_msg_body").classList.add("aesthetic_viewport_height"); } + let maincon = document.getElementById("maincontainer"); + if(localsettings.viewport_width_mode==1) //clamped + { + maincon.classList.remove("adaptivecontainer"); + maincon.classList.add("clampedcontainer"); + maincon.classList.remove("bigclampedcontainer"); + } + else if(localsettings.viewport_width_mode==2) //bigclamp + { + maincon.classList.remove("adaptivecontainer"); + maincon.classList.remove("clampedcontainer"); + maincon.classList.add("bigclampedcontainer"); + } + else if(localsettings.viewport_width_mode==3) //unlock + { + maincon.classList.remove("adaptivecontainer"); + maincon.classList.remove("clampedcontainer"); + maincon.classList.remove("bigclampedcontainer"); + } + else //adaptive + { + maincon.classList.add("adaptivecontainer"); + maincon.classList.remove("clampedcontainer"); + maincon.classList.remove("bigclampedcontainer"); + } + update_genimg_button_visiblility(); idle_timer = 0; @@ -14559,7 +14662,7 @@ Current version: 138 -
    +
    @@ -14947,9 +15069,9 @@ Current version: 138 Uses Gemini or PaLM Text Bison by Google.

    Note that KoboldAI Lite takes no responsibility for your usage or consequences of this feature.

    Please input Gemini or PaLM API Key.


    @@ -15633,6 +15755,16 @@ Current version: 138 class="helptext">Unlocks the text viewport, allowing for infinite height without scrolling
    +
    +
    Viewport Width ?Controls horizontal scaling of the viewport window
    + +
    Inverted Colors ?Inverts all colors, simple light mode
    @@ -16278,7 +16410,7 @@ Current version: 138
    + id="inputboxcontainerinput" onfocus="inputboxfocus()" onblur="inputboxblur()">
    diff --git a/koboldcpp.py b/koboldcpp.py index 23665fa9e93a1..e1e8f579dd191 100644 --- a/koboldcpp.py +++ b/koboldcpp.py @@ -45,6 +45,7 @@ class load_model_inputs(ctypes.Structure): ("mmproj_filename", ctypes.c_char_p), ("use_mmap", ctypes.c_bool), ("use_mlock", ctypes.c_bool), + ("use_smartcontext", ctypes.c_bool), ("use_contextshift", ctypes.c_bool), ("clblast_info", ctypes.c_int), ("cublas_info", ctypes.c_int), @@ -74,6 +75,7 @@ class generation_inputs(ctypes.Structure): ("tfs", ctypes.c_float), ("rep_pen", ctypes.c_float), ("rep_pen_range", ctypes.c_int), + ("rep_pen_slope", ctypes.c_float), ("presence_penalty", ctypes.c_float), ("mirostat", ctypes.c_int), ("mirostat_tau", ctypes.c_float), @@ -101,11 +103,16 @@ class generation_outputs(ctypes.Structure): class sd_load_model_inputs(ctypes.Structure): _fields_ = [("model_filename", ctypes.c_char_p), + ("executable_path", ctypes.c_char_p), ("clblast_info", ctypes.c_int), ("cublas_info", ctypes.c_int), ("vulkan_info", ctypes.c_char_p), ("threads", ctypes.c_int), ("quant", ctypes.c_int), + ("taesd", ctypes.c_bool), + ("vae_filename", ctypes.c_char_p), + ("lora_filename", ctypes.c_char_p), + ("lora_multiplier", ctypes.c_float), ("debugmode", ctypes.c_int)] class sd_generation_inputs(ctypes.Structure): @@ -119,6 +126,7 @@ class sd_generation_inputs(ctypes.Structure): ("height", ctypes.c_int), ("seed", ctypes.c_int), ("sample_method", ctypes.c_char_p), + ("clip_skip", ctypes.c_int), ("quiet", ctypes.c_bool)] class sd_generation_outputs(ctypes.Structure): @@ -396,6 +404,7 @@ def load_model(model_filename): inputs.lora_base = args.lora[1].encode("UTF-8") inputs.mmproj_filename = args.mmproj.encode("UTF-8") if args.mmproj else "".encode("UTF-8") + inputs.use_smartcontext = args.smartcontext inputs.use_contextshift = (0 if args.noshift else 1) inputs.flash_attention = args.flashattention inputs.blasbatchsize = args.blasbatchsize @@ -420,7 +429,7 @@ def load_model(model_filename): ret = handle.load_model(inputs) return ret -def generate(prompt, memory="", images=[], max_length=32, max_context_length=512, temperature=0.7, top_k=100, top_a=0.0, top_p=0.92, min_p=0.0, typical_p=1.0, tfs=1.0, rep_pen=1.0, rep_pen_range=128, presence_penalty=0.0, mirostat=0, mirostat_tau=5.0, mirostat_eta=0.1, sampler_order=[6,0,1,3,4,2,5], seed=-1, stop_sequence=[], use_default_badwordsids=False, stream_sse=False, grammar='', grammar_retain_state=False, genkey='', trimstop=False, quiet=False, dynatemp_range=0.0, dynatemp_exponent=1.0, smoothing_factor=0.0, logit_biases={}, render_special=False, banned_tokens=[], bypass_eos_token=False): +def generate(prompt, memory="", images=[], max_length=32, max_context_length=512, temperature=0.7, top_k=100, top_a=0.0, top_p=0.92, min_p=0.0, typical_p=1.0, tfs=1.0, rep_pen=1.0, rep_pen_range=128, rep_pen_slope=1.0, presence_penalty=0.0, mirostat=0, mirostat_tau=5.0, mirostat_eta=0.1, sampler_order=[6,0,1,3,4,2,5], seed=-1, stop_sequence=[], use_default_badwordsids=False, stream_sse=False, grammar='', grammar_retain_state=False, genkey='', trimstop=False, quiet=False, dynatemp_range=0.0, dynatemp_exponent=1.0, smoothing_factor=0.0, logit_biases={}, render_special=False, banned_tokens=[], bypass_eos_token=False): global maxctx, args, currentusergenkey, totalgens, pendingabortkey inputs = generation_inputs() inputs.prompt = prompt.encode("UTF-8") @@ -450,6 +459,7 @@ def generate(prompt, memory="", images=[], max_length=32, max_context_length=512 inputs.tfs = tfs inputs.rep_pen = rep_pen inputs.rep_pen_range = rep_pen_range + inputs.rep_pen_slope = rep_pen_slope inputs.presence_penalty = presence_penalty inputs.stream_sse = stream_sse inputs.quiet = quiet @@ -535,10 +545,11 @@ def generate(prompt, memory="", images=[], max_length=32, max_context_length=512 return {"text":outstr,"status":ret.status,"stopreason":ret.stopreason} -def sd_load_model(model_filename): +def sd_load_model(model_filename,vae_filename,lora_filename): global args inputs = sd_load_model_inputs() inputs.debugmode = args.debugmode + inputs.executable_path = (getdirpath()+"/").encode("UTF-8") inputs.model_filename = model_filename.encode("UTF-8") thds = args.threads quant = 0 @@ -552,6 +563,10 @@ def sd_load_model(model_filename): inputs.threads = thds inputs.quant = quant + inputs.taesd = True if args.sdvaeauto else False + inputs.vae_filename = vae_filename.encode("UTF-8") + inputs.lora_filename = lora_filename.encode("UTF-8") + inputs.lora_multiplier = args.sdloramult inputs = set_backend_props(inputs) ret = handle.sd_load_model(inputs) return ret @@ -570,6 +585,7 @@ def sd_generate(genparams): seed = genparams.get("seed", -1) sample_method = genparams.get("sampler_name", "k_euler_a") is_quiet = True if args.quiet else False + clip_skip = genparams.get("clip_skip", -1) #clean vars width = width - (width%64) @@ -605,6 +621,7 @@ def sd_generate(genparams): inputs.seed = seed inputs.sample_method = sample_method.lower().encode("UTF-8") inputs.quiet = is_quiet + inputs.clip_skip = clip_skip ret = handle.sd_generate(inputs) outstr = "" if ret.status==1: @@ -654,7 +671,7 @@ def string_contains_sequence_substring(inputstr,sequences): modelbusy = threading.Lock() requestsinqueue = 0 defaultport = 5001 -KcppVersion = "1.65.yr0-ROCm" +KcppVersion = "1.66.yr0-ROCm" showdebug = True showsamplerwarning = True showmaxctxwarning = True @@ -822,6 +839,7 @@ def run_blocking(): #api format 1=basic,2=kai,3=oai,4=oai-chat tfs=genparams.get('tfs', 1.0), rep_pen=genparams.get('rep_pen', 1.0), rep_pen_range=genparams.get('rep_pen_range', 256), + rep_pen_slope=genparams.get('rep_pen_slope', 1.0), presence_penalty=genparams.get('presence_penalty', 0.0), mirostat=genparams.get('mirostat', 0), mirostat_tau=genparams.get('mirostat_tau', 5.0), @@ -1204,7 +1222,7 @@ def do_GET(self): else: response_body = embedded_kcpp_docs - elif self.path=="/sdui": + elif self.path.startswith(("/sdui")): content_type = 'text/html' if embedded_kcpp_sdui is None: response_body = (f"KoboldCpp API is running, but KCPP SDUI is not loaded").encode() @@ -1564,7 +1582,6 @@ def show_new_gui(): root = ctk.CTk() root.geometry(str(windowwidth) + "x" + str(windowheight)) root.title("KoboldCpp v"+KcppVersion) - root.resizable(True,True) gtooltip_box = None gtooltip_label = None @@ -1601,7 +1618,11 @@ def on_resize(event): root.geometry(str(windowwidth) + "x" + str(windowheight) + str(lastpos)) ctk.set_widget_scaling(smallratio) - root.bind("", on_resize) + if sys.platform=="darwin": + root.resizable(False,False) + else: + root.resizable(True,True) + root.bind("", on_resize) global using_gui_launcher using_gui_launcher = True @@ -1710,6 +1731,7 @@ def hide_tooltip(event): contextshift = ctk.IntVar(value=1) remotetunnel = ctk.IntVar(value=0) + smartcontext = ctk.IntVar() flashattention = ctk.IntVar(value=0) context_var = ctk.IntVar() customrope_var = ctk.IntVar() @@ -1737,6 +1759,10 @@ def hide_tooltip(event): password_var = ctk.StringVar() sd_model_var = ctk.StringVar() + sd_lora_var = ctk.StringVar() + sd_loramult_var = ctk.StringVar(value="1.0") + sd_vae_var = ctk.StringVar() + sd_vaeauto_var = ctk.IntVar(value=0) sd_clamped_var = ctk.IntVar(value=0) sd_threads_var = ctk.StringVar(value=str(default_threads)) sd_quant_var = ctk.IntVar(value=0) @@ -1805,7 +1831,7 @@ def makelabelentry(parent, text, var, row=0, width= 50,tooltip=""): def makefileentry(parent, text, searchtext, var, row=0, width=200, filetypes=[], onchoosefile=None, singlerow=False, tooltiptxt=""): - makelabel(parent, text, row,0,tooltiptxt) + label = makelabel(parent, text, row,0,tooltiptxt) def getfilename(var, text): initialDir = os.path.dirname(var.get()) initialDir = initialDir if os.path.isdir(initialDir) else None @@ -1822,7 +1848,7 @@ def getfilename(var, text): else: entry.grid(row=row+1, column=0, padx=8, stick="nw") button.grid(row=row+1, column=1, stick="nw") - return + return label, entry, button # from subprocess import run, CalledProcessError # def get_device_names(): @@ -1965,7 +1991,7 @@ def get_amd_gpu_info_linux(): return get_amd_gpu_info_linux() # End get_amd_gpu_info() def auto_gpu_heuristics(): - from subprocess import run, CalledProcessError + import subprocess FetchedCUdevices = [] FetchedCUdeviceMem = [] try: # Get OpenCL GPU names on windows using a special binary. overwrite at known index if found. @@ -1973,10 +1999,10 @@ def auto_gpu_heuristics(): output = "" data = None try: - output = run(["clinfo","--json"], capture_output=True, text=True, check=True, encoding='utf-8').stdout + output = subprocess.run(["clinfo","--json"], capture_output=True, text=True, check=True, encoding='utf-8').stdout data = json.loads(output) except Exception as e1: - output = run([((os.path.join(basepath, "winclinfo.exe")) if os.name == 'nt' else "clinfo"),"--json"], capture_output=True, text=True, check=True, encoding='utf-8').stdout + output = subprocess.run([((os.path.join(basepath, "winclinfo.exe")) if os.name == 'nt' else "clinfo"),"--json"], capture_output=True, text=True, check=True, creationflags=subprocess.CREATE_NO_WINDOW | subprocess.DETACHED_PROCESS, encoding='utf-8').stdout data = json.loads(output) plat = 0 dev = 0 @@ -1999,7 +2025,7 @@ def auto_gpu_heuristics(): pass try: # Get NVIDIA GPU names - output = run(['nvidia-smi','--query-gpu=name,memory.total','--format=csv,noheader'], capture_output=True, text=True, check=True, encoding='utf-8').stdout + output = subprocess.run(['nvidia-smi','--query-gpu=name,memory.total','--format=csv,noheader'], capture_output=True, text=True, check=True, encoding='utf-8').stdout FetchedCUdevices = [line.split(",")[0].strip() for line in output.splitlines()] FetchedCUdeviceMem = [line.split(",")[1].strip().split(" ")[0].strip() for line in output.splitlines()] except Exception as e: @@ -2009,7 +2035,7 @@ def auto_gpu_heuristics(): FetchedCUdevices, FetchedCUdeviceMem = get_amd_gpu_info() try: # Get Vulkan names - output = run(['vulkaninfo','--summary'], capture_output=True, text=True, check=True, encoding='utf-8').stdout + output = subprocess.run(['vulkaninfo','--summary'], capture_output=True, text=True, check=True, encoding='utf-8').stdout devicelist = [line.split("=")[1].strip() for line in output.splitlines() if "deviceName" in line] idx = 0 for dname in devicelist: @@ -2124,7 +2150,10 @@ def changed_gpu_choice_var(*args): gpulayers_var.trace("w", changed_gpulayers) def togglectxshift(a,b,c): - pass + if contextshift.get()==0: + smartcontextbox.grid(row=1, column=0, padx=8, pady=1, stick="nw") + else: + smartcontextbox.grid_forget() def guibench(): args.benchmark = "stdout" @@ -2203,7 +2232,7 @@ def changerunmode(a,b,c): quick_gpuname_label = ctk.CTkLabel(quick_tab, text="") quick_gpuname_label.grid(row=3, column=1, padx=75, sticky="W") quick_gpuname_label.configure(text_color="#ffff00") - quick_gpu_layers_entry,quick_gpu_layers_label = makelabelentry(quick_tab,"GPU Layers:", gpulayers_var, 6, 50,"How many layers to offload onto the GPU.\nVRAM intensive, usage increases with model and context size.\nRequires some trial and error to find the best fit value.") + quick_gpu_layers_entry,quick_gpu_layers_label = makelabelentry(quick_tab,"GPU Layers:", gpulayers_var, 6, 50,"How many layers to offload onto the GPU.\nVRAM intensive, usage increases with model and context size.\nRequires some trial and error to find the best fit value.\n\nCommon values for total layers, accuracy not guaranteed.\n\nLlama/Mistral 7b/8b: 33\nSolar 10.7b/11b: 49\nLlama 13b: 41\nLlama 20b(stack): 63\nLlama/Yi 34b: 61\nMixtral 8x7b: 33\nLlama 70b: 81") quick_mmq_box = makecheckbox(quick_tab, "Use QuantMatMul (mmq)", mmq_var, 4,1,tooltiptxt="Enable MMQ mode instead of CuBLAS for prompt processing. Read the wiki. Speed may vary.") @@ -2240,7 +2269,7 @@ def changerunmode(a,b,c): gpuname_label = ctk.CTkLabel(hardware_tab, text="") gpuname_label.grid(row=3, column=1, padx=75, sticky="W") gpuname_label.configure(text_color="#ffff00") - gpu_layers_entry,gpu_layers_label = makelabelentry(hardware_tab,"GPU Layers:", gpulayers_var, 6, 50,"How many layers to offload onto the GPU.\nVRAM intensive, usage increases with model and context size.\nRequires some trial and error to find the best fit value.") + gpu_layers_entry,gpu_layers_label = makelabelentry(hardware_tab,"GPU Layers:", gpulayers_var, 6, 50,"How many layers to offload onto the GPU.\nVRAM intensive, usage increases with model and context size.\nRequires some trial and error to find the best fit value.\n\nCommon values for total layers, accuracy not guaranteed.\n\nLlama/Mistral 7b/8b: 33\nSolar 10.7b/11b: 49\nLlama 13b: 41\nLlama 20b(stack): 63\nLlama/Yi 34b: 61\nMixtral 8x7b: 33\nLlama 70b: 81") tensor_split_entry,tensor_split_label = makelabelentry(hardware_tab, "Tensor Split:", tensor_split_str_vars, 8, 80, tooltip='When using multiple GPUs this option controls how large tensors should be split across all GPUs.\nUses a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order.\nFor example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1.') lowvram_box = makecheckbox(hardware_tab, "Low VRAM (No KV offload)", lowvram_var, 4,0, tooltiptxt='Avoid offloading KV Cache or scratch buffers to VRAM.\nAllows more layers to fit, but may result in a speed loss.') mmq_box = makecheckbox(hardware_tab, "Use QuantMatMul (mmq)", mmq_var, 4,1, tooltiptxt="Enable MMQ mode to use finetuned kernels instead of default CuBLAS/HipBLAS for prompt processing.\nRead the wiki. Speed may vary.") @@ -2278,6 +2307,7 @@ def changerunmode(a,b,c): # Tokens Tab tokens_tab = tabcontent["Tokens"] # tokens checkboxes + smartcontextbox = makecheckbox(tokens_tab, "Use SmartContext", smartcontext, 1,tooltiptxt="Uses SmartContext. Now considered outdated and not recommended.\nCheck the wiki for more info.") makecheckbox(tokens_tab, "Use ContextShift", contextshift, 2,tooltiptxt="Uses Context Shifting to reduce reprocessing.\nRecommended. Check the wiki for more info.", command=togglectxshift) togglectxshift(1,1,1) @@ -2352,11 +2382,41 @@ def togglehorde(a,b,c): togglehorde(1,1,1) # Image Gen Tab + images_tab = tabcontent["Image Gen"] makefileentry(images_tab, "Stable Diffusion Model (safetensors/gguf):", "Select Stable Diffusion Model File", sd_model_var, 1, filetypes=[("*.safetensors *.gguf","*.safetensors *.gguf")], tooltiptxt="Select a .safetensors or .gguf Stable Diffusion model file on disk to be loaded.") makecheckbox(images_tab, "Clamped Mode (Limit Resolution)", sd_clamped_var, 4,tooltiptxt="Limit generation steps and resolution settings for shared use.") - makelabelentry(images_tab, "Image threads:" , sd_threads_var, 6, 50,"How many threads to use during image generation.\nIf left blank, uses same value as threads.") - makecheckbox(images_tab, "Compress Weights (Saves Memory)", sd_quant_var, 8,tooltiptxt="Quantizes the SD model weights to save memory. May degrade quality.") + makelabelentry(images_tab, "Image Threads:" , sd_threads_var, 6, 50,"How many threads to use during image generation.\nIf left blank, uses same value as threads.") + + sdloritem1,sdloritem2,sdloritem3 = makefileentry(images_tab, "Image LoRA (Must be non-quant):", "Select SD lora file",sd_lora_var, 10 ,filetypes=[("*.safetensors *.gguf", "*.safetensors *.gguf")],tooltiptxt="Select a .safetensors or .gguf SD LoRA model file to be loaded.") + sdloritem4,sdloritem5 = makelabelentry(images_tab, "Image LoRA Multiplier:" , sd_loramult_var, 12, 50,"What mutiplier value to apply the SD LoRA with.") + def togglesdquant(a,b,c): + if sd_quant_var.get()==1: + sdloritem1.grid_forget() + sdloritem2.grid_forget() + sdloritem3.grid_forget() + sdloritem4.grid_forget() + sdloritem5.grid_forget() + else: + sdloritem1.grid(row=10,column=0,padx=8,stick="nw") + sdloritem2.grid(row=11,column=0,padx=8,stick="nw") + sdloritem3.grid(row=11,column=1,stick="nw") + sdloritem4.grid(row=12,column=1,stick="nw") + sdloritem5.grid(row=12,column=0,padx=8,stick="nw") + makecheckbox(images_tab, "Compress Weights (Saves Memory)", sd_quant_var, 8,command=togglesdquant,tooltiptxt="Quantizes the SD model weights to save memory. May degrade quality.") + + + sdvaeitem1,sdvaeitem2,sdvaeitem3 = makefileentry(images_tab, "Image VAE:", "Select SD VAE file",sd_vae_var, 14, filetypes=[("*.safetensors *.gguf", "*.safetensors *.gguf")],tooltiptxt="Select a .safetensors or .gguf SD VAE file to be loaded.") + def toggletaesd(a,b,c): + if sd_vaeauto_var.get()==1: + sdvaeitem1.grid_forget() + sdvaeitem2.grid_forget() + sdvaeitem3.grid_forget() + else: + sdvaeitem1.grid(row=14,column=0,padx=8,stick="nw") + sdvaeitem2.grid(row=15,column=0,padx=8,stick="nw") + sdvaeitem3.grid(row=15,column=1,stick="nw") + makecheckbox(images_tab, "Use TAE SD (AutoFix Bad VAE)", sd_vaeauto_var, 16,command=toggletaesd,tooltiptxt="Replace VAE with TAESD. May fix bad VAE.") # launch def guilaunch(): @@ -2376,6 +2436,7 @@ def export_vars(): args.launch = launchbrowser.get()==1 args.highpriority = highpriority.get()==1 args.nommap = disablemmap.get()==1 + args.smartcontext = smartcontext.get()==1 args.flashattention = flashattention.get()==1 args.noshift = contextshift.get()==0 args.remotetunnel = remotetunnel.get()==1 @@ -2461,11 +2522,27 @@ def export_vars(): if sd_model_var.get() != "": args.sdmodel = sd_model_var.get() + + args.sdthreads = (0 if sd_threads_var.get()=="" else int(sd_threads_var.get())) if sd_clamped_var.get()==1: args.sdclamped = True - args.sdthreads = (0 if sd_threads_var.get()=="" else int(sd_threads_var.get())) + if sd_vaeauto_var.get()==1: + args.sdvaeauto = True + args.sdvae = "" + else: + args.sdvaeauto = False + args.sdvae = "" + if sd_vae_var.get() != "": + args.sdvae = sd_vae_var.get() if sd_quant_var.get()==1: args.sdquant = True + args.sdlora = "" + else: + if sd_lora_var.get() != "": + args.sdlora = sd_lora_var.get() + args.sdloramult = float(sd_loramult_var.get()) + else: + args.sdlora = "" def import_vars(dict): dict = convert_outdated_args(dict) @@ -2478,6 +2555,7 @@ def import_vars(dict): launchbrowser.set(1 if "launch" in dict and dict["launch"] else 0) highpriority.set(1 if "highpriority" in dict and dict["highpriority"] else 0) disablemmap.set(1 if "nommap" in dict and dict["nommap"] else 0) + smartcontext.set(1 if "smartcontext" in dict and dict["smartcontext"] else 0) flashattention.set(1 if "flashattention" in dict and dict["flashattention"] else 0) contextshift.set(0 if "noshift" in dict and dict["noshift"] else 1) remotetunnel.set(1 if "remotetunnel" in dict and dict["remotetunnel"] else 0) @@ -2600,6 +2678,10 @@ def import_vars(dict): sd_clamped_var.set(1 if ("sdclamped" in dict and dict["sdclamped"]) else 0) sd_threads_var.set(str(dict["sdthreads"]) if ("sdthreads" in dict and dict["sdthreads"]) else str(default_threads)) sd_quant_var.set(1 if ("sdquant" in dict and dict["sdquant"]) else 0) + sd_vae_var.set(dict["sdvae"] if ("sdvae" in dict and dict["sdvae"]) else "") + sd_vaeauto_var.set(1 if ("sdvaeauto" in dict and dict["sdvaeauto"]) else 0) + sd_lora_var.set(dict["sdlora"] if ("sdlora" in dict and dict["sdlora"]) else "") + sd_loramult_var.set(str(dict["sdloramult"]) if ("sdloramult" in dict and dict["sdloramult"]) else "1.0") def save_config(): file_type = [("KoboldCpp Settings", "*.kcpps")] @@ -3046,7 +3128,7 @@ def check_deprecation_warning(): if using_outdated_flags: print(f"\n=== !!! IMPORTANT WARNING !!! ===") print("You are using one or more OUTDATED config files or launch flags!") - print("The flags --smartcontext, --hordeconfig and --sdconfig have been DEPRECATED, and MAY be REMOVED in future!") + print("The flags --hordeconfig and --sdconfig have been DEPRECATED, and MAY be REMOVED in future!") print("They will still work for now, but you SHOULD switch to the updated flags instead, to avoid future issues!") print("New flags are: --hordemodelname --hordeworkername --hordekey --hordemaxctx --hordegenlen --sdmodel --sdthreads --sdquant --sdclamped") print("For more information on these flags, please check --help") @@ -3362,6 +3444,23 @@ def main(launch_args,start_server=True): print(f"Warning: Chat Completions Adapter {args.chatcompletionsadapter} invalid or not found.") + if args.model_param and args.model_param!="": + if args.model_param.endswith("?download=true"): + args.model_param = args.model_param.replace("?download=true","") + if (args.model_param.startswith("http://") or args.model_param.startswith("https://")) and (args.model_param.endswith(".gguf") or args.model_param.endswith(".bin")): + import subprocess + mdlfilename = os.path.basename(args.model_param) + #check if file already exists + if mdlfilename: + if not os.path.exists(mdlfilename): + print(f"Downloading model from external URL at {args.model_param}") + subprocess.run(f"curl -fL {args.model_param} -o {mdlfilename}", shell=True, capture_output=True, text=True, check=True, encoding='utf-8') + print(f"Download {mdlfilename} completed...", flush=True) + args.model_param = mdlfilename + else: + print(f"Model file {mdlfilename} already exists, not redownloading.") + args.model_param = mdlfilename + # sanitize and replace the default vanity name. remember me.... if args.model_param and args.model_param!="": newmdldisplayname = os.path.basename(args.model_param) @@ -3511,12 +3610,25 @@ def main(launch_args,start_server=True): time.sleep(3) sys.exit(2) else: + imglora = "" + imgvae = "" + if args.sdlora: + if os.path.exists(args.sdlora): + imglora = os.path.abspath(args.sdlora) + else: + print(f"Missing SD LORA model file...") + if args.sdvae: + if os.path.exists(args.sdvae): + imgvae = os.path.abspath(args.sdvae) + else: + print(f"Missing SD VAE model file...") + imgmodel = os.path.abspath(imgmodel) fullsdmodelpath = imgmodel friendlysdmodelname = os.path.basename(imgmodel) friendlysdmodelname = os.path.splitext(friendlysdmodelname)[0] friendlysdmodelname = sanitize_string(friendlysdmodelname) - loadok = sd_load_model(imgmodel) + loadok = sd_load_model(imgmodel,imgvae,imglora) print("Load Image Model OK: " + str(loadok)) if not loadok: exitcounter = 999 @@ -3623,7 +3735,6 @@ def onready_subprocess(): genout = generate(benchprompt,memory="",images=[],max_length=benchlen,max_context_length=benchmaxctx,temperature=0.1,top_k=1,rep_pen=1,use_default_badwordsids=True) result = genout['text'] result = (result[:5] if len(result)>5 else "") - resultok = (result=="11111") t_pp = float(handle.get_last_process_time())*float(benchmaxctx-benchlen)*0.001 t_gen = float(handle.get_last_eval_time())*float(benchlen)*0.001 s_pp = float(benchmaxctx-benchlen)/t_pp @@ -3641,15 +3752,14 @@ def onready_subprocess(): print(f"GenerationTime: {t_gen:.2f}s") print(f"GenerationSpeed: {s_gen:.2f}T/s") print(f"TotalTime: {(t_pp+t_gen):.2f}s") - print(f"Coherent: {resultok}") print(f"Output: {result}\n-----") if save_to_file: try: with open(args.benchmark, "a") as file: file.seek(0, 2) if file.tell() == 0: #empty file - file.write(f"Timestamp,Backend,Layers,Model,MaxCtx,GenAmount,ProcessingTime,ProcessingSpeed,GenerationTime,GenerationSpeed,TotalTime,Coherent,Output") - file.write(f"\n{datetimestamp},{libname},{args.gpulayers},{benchmodel},{benchmaxctx},{benchlen},{t_pp:.2f},{s_pp:.2f},{t_gen:.2f},{s_gen:.2f},{(t_pp+t_gen):.2f},{resultok},{result}") + file.write(f"Timestamp,Backend,Layers,Model,MaxCtx,GenAmount,ProcessingTime,ProcessingSpeed,GenerationTime,GenerationSpeed,TotalTime,Output") + file.write(f"\n{datetimestamp},{libname},{args.gpulayers},{benchmodel},{benchmaxctx},{benchlen},{t_pp:.2f},{s_pp:.2f},{t_gen:.2f},{s_gen:.2f},{(t_pp+t_gen):.2f},{result}") except Exception as e: print(f"Error writing benchmark to file: {e}") global using_gui_launcher @@ -3723,6 +3833,7 @@ def range_checker(arg: str): if os.cpu_count()!=None and os.cpu_count()>1: physical_core_limit = int(os.cpu_count()/2) default_threads = (physical_core_limit if physical_core_limit<=3 else max(3,physical_core_limit-1)) + default_threads = (8 if default_threads > 8 else default_threads) #there is zero reason to exceed 8 threads by default. this helps avoid e-cores. parser.add_argument("--threads", metavar=('[threads]'), help="Use a custom number of threads if specified. Otherwise, uses an amount based on CPU cores", type=int, default=default_threads) compatgroup = parser.add_mutually_exclusive_group() compatgroup.add_argument("--usecublas", help="Use CuBLAS for GPU Acceleration. Requires CUDA. Select lowvram to not allocate VRAM scratch buffer. Enter a number afterwards to select and use 1 GPU. Leaving no number will use all GPUs. For hipBLAS binaries, please check YellowRoseCx rocm fork.", nargs='*',metavar=('[lowvram|normal] [main GPU ID] [mmq] [rowsplit]'), choices=['normal', 'lowvram', '0', '1', '2', '3', 'mmq', 'rowsplit']) @@ -3753,7 +3864,7 @@ def range_checker(arg: str): advparser.add_argument("--highpriority", help="Experimental flag. If set, increases the process CPU priority, potentially speeding up generation. Use caution.", action='store_true') advparser.add_argument("--foreground", help="Windows only. Sends the terminal to the foreground every time a new prompt is generated. This helps avoid some idle slowdown issues.", action='store_true') advparser.add_argument("--preloadstory", help="Configures a prepared story json save file to be hosted on the server, which frontends (such as Kobold Lite) can access over the API.", default="") - advparser.add_argument("--quiet", help="Enable quiet mode, which hides generation inputs and outputs in the terminal. Quiet mode is automatically enabled when running --hordeconfig.", action='store_true') + advparser.add_argument("--quiet", help="Enable quiet mode, which hides generation inputs and outputs in the terminal. Quiet mode is automatically enabled when running a horde worker.", action='store_true') advparser.add_argument("--ssl", help="Allows all content to be served over SSL instead. A valid UNENCRYPTED SSL cert and key .pem files must be provided", metavar=('[cert_pem]', '[key_pem]'), nargs='+') advparser.add_argument("--nocertify", help="Allows insecure SSL connections. Use this if you have cert errors and need to bypass certificate restrictions.", action='store_true') advparser.add_argument("--mmproj", help="Select a multimodal projector file for LLaVA.", default="") @@ -3762,11 +3873,7 @@ def range_checker(arg: str): advparser.add_argument("--chatcompletionsadapter", help="Select an optional ChatCompletions Adapter JSON file to force custom instruct tags.", default="") advparser.add_argument("--flashattention", help="Enables flash attention (Experimental).", action='store_true') advparser.add_argument("--forceversion", help="If the model file format detection fails (e.g. rogue modified model) you can set this to override the detected format (enter desired version, e.g. 401 for GPTNeoX-Type2).",metavar=('[version]'), type=int, default=0) - - deprecatedgroup = parser.add_argument_group('Deprecated Commands, DO NOT USE!') - deprecatedgroup.add_argument("--smartcontext", help="Command is DEPRECATED and should NOT be used! Instead, use --noshift instead to toggle smartcontext off on old GGML models.", action='store_true') - deprecatedgroup.add_argument("--hordeconfig", help="Command is DEPRECATED and should NOT be used! Instead, use non-positional flags --hordemodelname --hordeworkername --hordekey --hordemaxctx --hordegenlen instead.", nargs='+') - deprecatedgroup.add_argument("--sdconfig", help="Command is DEPRECATED and should NOT be used! Instead, use non-positional flags --sdmodel --sdthreads --sdquant --sdclamped instead.", nargs='+') + advparser.add_argument("--smartcontext", help="Reserving a portion of context to try processing less frequently. Not recommended.", action='store_true') hordeparsergroup = parser.add_argument_group('Horde Worker Commands') hordeparsergroup.add_argument("--hordemodelname", metavar=('[name]'), help="Sets your AI Horde display model name.", default="") @@ -3778,7 +3885,17 @@ def range_checker(arg: str): sdparsergroup = parser.add_argument_group('Image Generation Commands') sdparsergroup.add_argument("--sdmodel", metavar=('[filename]'), help="Specify a stable diffusion safetensors or gguf model to enable image generation.", default="") sdparsergroup.add_argument("--sdthreads", metavar=('[threads]'), help="Use a different number of threads for image generation if specified. Otherwise, has the same value as --threads.", type=int, default=0) - sdparsergroup.add_argument("--sdquant", help="If specified, loads the model quantized to save memory.", action='store_true') sdparsergroup.add_argument("--sdclamped", help="If specified, limit generation steps and resolution settings for shared use.", action='store_true') + sdparsergroupvae = sdparsergroup.add_mutually_exclusive_group() + sdparsergroupvae.add_argument("--sdvae", metavar=('[filename]'), help="Specify a stable diffusion safetensors VAE which replaces the one in the model.", default="") + sdparsergroupvae.add_argument("--sdvaeauto", help="Uses a built-in VAE via TAE SD, which is very fast, and fixed bad VAEs.", action='store_true') + sdparsergrouplora = sdparsergroup.add_mutually_exclusive_group() + sdparsergrouplora.add_argument("--sdquant", help="If specified, loads the model quantized to save memory.", action='store_true') + sdparsergrouplora.add_argument("--sdlora", metavar=('[filename]'), help="Specify a stable diffusion LORA safetensors model to be applied. Cannot be used with quant models.", default="") + sdparsergroup.add_argument("--sdloramult", metavar=('[amount]'), help="Multiplier for the LORA model to be applied.", type=float, default=1.0) + + deprecatedgroup = parser.add_argument_group('Deprecated Commands, DO NOT USE!') + deprecatedgroup.add_argument("--hordeconfig", help=argparse.SUPPRESS, nargs='+') + deprecatedgroup.add_argument("--sdconfig", help=argparse.SUPPRESS, nargs='+') main(parser.parse_args(),start_server=True) diff --git a/koboldcpp.sh b/koboldcpp.sh index a0093494af384..252290ddf65fe 100755 --- a/koboldcpp.sh +++ b/koboldcpp.sh @@ -11,24 +11,24 @@ if [[ ! -f "conda/envs/linux/bin/python" || $1 == "rebuild" ]]; then else KCPP_CUDA=11.5.0 fi - bin/micromamba create --no-shortcuts -r conda -n linux -f environment.tmp.yaml -y - bin/micromamba create --no-shortcuts -r conda -n linux -f environment.tmp.yaml -y - bin/micromamba run -r conda -n linux make clean + bin/micromamba create --no-rc --no-shortcuts -r conda -p conda/envs/linux -f environment.tmp.yaml -y + bin/micromamba create --no-rc --no-shortcuts -r conda -p conda/envs/linux -f environment.tmp.yaml -y + bin/micromamba run -r conda -p conda/envs/linux make clean echo $KCPP_CUDA > conda/envs/linux/cudaver echo rm environment.tmp.yaml fi KCPP_CUDA=$() @@ -223,10 +220,10 @@ enum llm_arch { LLM_ARCH_GPTNEOX, LLM_ARCH_MPT, LLM_ARCH_STARCODER, - LLM_ARCH_PERSIMMON, LLM_ARCH_REFACT, LLM_ARCH_BERT, LLM_ARCH_NOMIC_BERT, + LLM_ARCH_JINA_BERT_V2, LLM_ARCH_BLOOM, LLM_ARCH_STABLELM, LLM_ARCH_QWEN, @@ -250,39 +247,39 @@ enum llm_arch { }; static const std::map LLM_ARCH_NAMES = { - { LLM_ARCH_LLAMA, "llama" }, - { LLM_ARCH_FALCON, "falcon" }, - { LLM_ARCH_GROK, "grok" }, - { LLM_ARCH_GPT2, "gpt2" }, - { LLM_ARCH_GPTJ, "gptj" }, - { LLM_ARCH_GPTNEOX, "gptneox" }, - { LLM_ARCH_MPT, "mpt" }, - { LLM_ARCH_BAICHUAN, "baichuan" }, - { LLM_ARCH_STARCODER, "starcoder" }, - { LLM_ARCH_PERSIMMON, "persimmon" }, - { LLM_ARCH_REFACT, "refact" }, - { LLM_ARCH_BERT, "bert" }, - { LLM_ARCH_NOMIC_BERT, "nomic-bert" }, - { LLM_ARCH_BLOOM, "bloom" }, - { LLM_ARCH_STABLELM, "stablelm" }, - { LLM_ARCH_QWEN, "qwen" }, - { LLM_ARCH_QWEN2, "qwen2" }, - { LLM_ARCH_QWEN2MOE, "qwen2moe" }, - { LLM_ARCH_PHI2, "phi2" }, - { LLM_ARCH_PHI3, "phi3" }, - { LLM_ARCH_PLAMO, "plamo" }, - { LLM_ARCH_CODESHELL, "codeshell" }, - { LLM_ARCH_ORION, "orion" }, - { LLM_ARCH_INTERNLM2, "internlm2" }, - { LLM_ARCH_MINICPM, "minicpm" }, - { LLM_ARCH_GEMMA, "gemma" }, - { LLM_ARCH_STARCODER2, "starcoder2" }, - { LLM_ARCH_MAMBA, "mamba" }, - { LLM_ARCH_XVERSE, "xverse" }, - { LLM_ARCH_COMMAND_R, "command-r" }, - { LLM_ARCH_DBRX, "dbrx" }, - { LLM_ARCH_OLMO, "olmo" }, - { LLM_ARCH_UNKNOWN, "(unknown)" }, + { LLM_ARCH_LLAMA, "llama" }, + { LLM_ARCH_FALCON, "falcon" }, + { LLM_ARCH_GROK, "grok" }, + { LLM_ARCH_GPT2, "gpt2" }, + { LLM_ARCH_GPTJ, "gptj" }, + { LLM_ARCH_GPTNEOX, "gptneox" }, + { LLM_ARCH_MPT, "mpt" }, + { LLM_ARCH_BAICHUAN, "baichuan" }, + { LLM_ARCH_STARCODER, "starcoder" }, + { LLM_ARCH_REFACT, "refact" }, + { LLM_ARCH_BERT, "bert" }, + { LLM_ARCH_NOMIC_BERT, "nomic-bert" }, + { LLM_ARCH_JINA_BERT_V2, "jina-bert-v2" }, + { LLM_ARCH_BLOOM, "bloom" }, + { LLM_ARCH_STABLELM, "stablelm" }, + { LLM_ARCH_QWEN, "qwen" }, + { LLM_ARCH_QWEN2, "qwen2" }, + { LLM_ARCH_QWEN2MOE, "qwen2moe" }, + { LLM_ARCH_PHI2, "phi2" }, + { LLM_ARCH_PHI3, "phi3" }, + { LLM_ARCH_PLAMO, "plamo" }, + { LLM_ARCH_CODESHELL, "codeshell" }, + { LLM_ARCH_ORION, "orion" }, + { LLM_ARCH_INTERNLM2, "internlm2" }, + { LLM_ARCH_MINICPM, "minicpm" }, + { LLM_ARCH_GEMMA, "gemma" }, + { LLM_ARCH_STARCODER2, "starcoder2" }, + { LLM_ARCH_MAMBA, "mamba" }, + { LLM_ARCH_XVERSE, "xverse" }, + { LLM_ARCH_COMMAND_R, "command-r" }, + { LLM_ARCH_DBRX, "dbrx" }, + { LLM_ARCH_OLMO, "olmo" }, + { LLM_ARCH_UNKNOWN, "(unknown)" }, }; enum llm_kv { @@ -325,6 +322,7 @@ enum llm_kv { LLM_KV_ROPE_SCALE_LINEAR, LLM_KV_ROPE_SCALING_TYPE, LLM_KV_ROPE_SCALING_FACTOR, + LLM_KV_ROPE_SCALING_ATTN_FACTOR, LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, LLM_KV_ROPE_SCALING_FINETUNED, @@ -402,6 +400,7 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_ROPE_SCALE_LINEAR, "%s.rope.scale_linear" }, { LLM_KV_ROPE_SCALING_TYPE, "%s.rope.scaling.type" }, { LLM_KV_ROPE_SCALING_FACTOR, "%s.rope.scaling.factor" }, + { LLM_KV_ROPE_SCALING_ATTN_FACTOR, "%s.rope.scaling.attn_factor" }, { LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, "%s.rope.scaling.original_context_length" }, { LLM_KV_ROPE_SCALING_FINETUNED, "%s.rope.scaling.finetuned" }, @@ -457,6 +456,8 @@ enum llm_tensor { LLM_TENSOR_OUTPUT, LLM_TENSOR_OUTPUT_NORM, LLM_TENSOR_ROPE_FREQS, + LLM_TENSOR_ROPE_FACTORS_LONG, + LLM_TENSOR_ROPE_FACTORS_SHORT, LLM_TENSOR_ATTN_Q, LLM_TENSOR_ATTN_K, LLM_TENSOR_ATTN_V, @@ -614,23 +615,6 @@ static const std::map> LLM_TENSOR_NA { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, - { - LLM_ARCH_PERSIMMON, - { - { LLM_TENSOR_TOKEN_EMBD, "token_embd"}, - { LLM_TENSOR_OUTPUT_NORM, "output_norm"}, - { LLM_TENSOR_OUTPUT, "output"}, - { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm"}, - { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv"}, - { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output"}, - { LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm"}, - { LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm"}, - { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm"}, - { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down"}, - { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up"}, - { LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd"}, - }, - }, { LLM_ARCH_MPT, { @@ -713,6 +697,25 @@ static const std::map> LLM_TENSOR_NA { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, + { + LLM_ARCH_JINA_BERT_V2, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" }, + { LLM_TENSOR_TOKEN_TYPES, "token_types" }, + { LLM_TENSOR_ATTN_OUT_NORM, "blk.%d.attn_output_norm" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_LAYER_OUT_NORM, "blk.%d.layer_output_norm" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + }, + }, { LLM_ARCH_BLOOM, { @@ -822,18 +825,20 @@ static const std::map> LLM_TENSOR_NA { LLM_ARCH_PHI3, { - { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, - { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, - { LLM_TENSOR_OUTPUT, "output" }, - { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, - { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" }, - { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, - { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, - { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, - { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, - { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, - { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, - { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ROPE_FACTORS_LONG, "rope_factors_long" }, + { LLM_TENSOR_ROPE_FACTORS_SHORT, "rope_factors_short" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, { @@ -1690,91 +1695,6 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_cpu(bool host_buffer GGML_UNUSED(host_buffer); } -static ggml_backend_buffer_type_t llama_default_buffer_type_offload(int gpu) { - ggml_backend_buffer_type_t buft = nullptr; - -#ifdef GGML_USE_METAL - buft = ggml_backend_metal_buffer_type(); -#elif defined(GGML_USE_CUDA) - buft = ggml_backend_cuda_buffer_type(gpu); -#elif defined(GGML_USE_VULKAN) - buft = ggml_backend_vk_buffer_type(gpu); -#elif defined(GGML_USE_SYCL) - buft = ggml_backend_sycl_buffer_type(gpu); -#elif defined(GGML_USE_CLBLAST) - buft = ggml_backend_opencl_buffer_type(); -#elif defined(GGML_USE_KOMPUTE) - buft = ggml_backend_kompute_buffer_type(gpu); - if (buft == nullptr) { - LLAMA_LOG_WARN("%s: cannot use GPU %d, check `vulkaninfo --summary`\n", __func__, gpu); - } -#endif - - if (buft == nullptr) { - buft = llama_default_buffer_type_cpu(true); - } - return buft; - - GGML_UNUSED(gpu); -} - -static ggml_backend_buffer_type_t llama_default_buffer_type_split(int fallback_gpu, const float * tensor_split) { - ggml_backend_buffer_type_t buft = nullptr; - -#ifdef GGML_USE_CUDA - if (ggml_backend_cuda_get_device_count() > 1) { - buft = ggml_backend_cuda_split_buffer_type(tensor_split); - } -#endif - -#ifdef GGML_USE_SYCL - if (ggml_backend_sycl_get_device_count() > 1) { - buft = ggml_backend_sycl_split_buffer_type(tensor_split); - } -#endif - - if (buft == nullptr) { - buft = llama_default_buffer_type_offload(fallback_gpu); - } - return buft; - - GGML_UNUSED(tensor_split); -} - -static size_t llama_get_device_count() { -#if defined(GGML_USE_CUDA) - return ggml_backend_cuda_get_device_count(); -#elif defined(GGML_USE_SYCL) - return ggml_backend_sycl_get_device_count(); -#elif defined(GGML_USE_VULKAN) - return ggml_backend_vk_get_device_count(); -#else - return 1; -#endif -} - -static size_t llama_get_device_memory(int device) { -#if defined(GGML_USE_CUDA) - size_t total; - size_t free; - ggml_backend_cuda_get_device_memory(device, &free, &total); - return free; -#elif defined(GGML_USE_SYCL) - size_t total; - size_t free; - ggml_backend_sycl_get_device_memory(device, &free, &total); - return free; -#elif defined(GGML_USE_VULKAN) - size_t total; - size_t free; - ggml_backend_vk_get_device_memory(device, &free, &total); - return free; -#else - return 1; - GGML_UNUSED(device); -#endif -} - // // globals // @@ -1783,6 +1703,8 @@ struct llama_state { llama_state() { #ifdef GGML_USE_METAL ggml_backend_metal_log_set_callback(log_callback, log_callback_user_data); +#elif defined(GGML_USE_CUDA) + ggml_backend_cuda_log_set_callback(log_callback, log_callback_user_data); #endif } @@ -1796,17 +1718,24 @@ static llama_state g_state; // available llama models enum e_model { MODEL_UNKNOWN, + MODEL_14M, MODEL_17M, MODEL_22M, MODEL_33M, + MODEL_70M, MODEL_109M, MODEL_137M, + MODEL_160M, MODEL_335M, + MODEL_410M, MODEL_0_5B, MODEL_1B, + MODEL_1_4B, MODEL_2B, + MODEL_2_8B, MODEL_3B, MODEL_4B, + MODEL_6_9B, MODEL_7B, MODEL_8B, MODEL_12B, @@ -1838,6 +1767,7 @@ static const size_t GiB = 1024*MiB; struct llama_hparams { bool vocab_only; bool rope_finetuned; + bool use_par_res; uint32_t n_vocab; uint32_t n_ctx_train; // context size the model was trained on @@ -1856,6 +1786,7 @@ struct llama_hparams { float f_norm_eps; float f_norm_rms_eps; + float rope_attn_factor = 1.0f; float rope_freq_base_train; float rope_freq_scale_train; uint32_t n_yarn_orig_ctx; @@ -1871,7 +1802,7 @@ struct llama_hparams { float f_logit_scale = 0.0f; bool causal_attn = true; - bool use_alibi = false; // currently, we need KQ_pos data for ALiBi-based models + bool use_alibi = false; enum llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_NONE; enum llama_rope_type rope_type = LLAMA_ROPE_TYPE_NONE; @@ -1904,6 +1835,7 @@ struct llama_hparams { if (!is_float_close(this->f_norm_eps, other.f_norm_eps, EPSILON)) return true; if (!is_float_close(this->f_norm_rms_eps, other.f_norm_rms_eps, EPSILON)) return true; + if (!is_float_close(this->rope_attn_factor, other.rope_attn_factor, EPSILON)) return true; if (!is_float_close(this->rope_freq_base_train, other.rope_freq_base_train, EPSILON)) return true; if (!is_float_close(this->rope_freq_scale_train, other.rope_freq_scale_train, EPSILON)) return true; @@ -2038,6 +1970,10 @@ struct llama_layer { // mamba bias struct ggml_tensor * ssm_conv1d_b; struct ggml_tensor * ssm_dt_b; + + // long rope factors + struct ggml_tensor * rope_long = nullptr; + struct ggml_tensor * rope_short = nullptr; }; struct llama_kv_cell { @@ -2219,6 +2155,8 @@ struct llama_model { int main_gpu; int n_gpu_layers; + std::vector rpc_servers; + // gguf metadata std::unordered_map gguf_kv; @@ -2347,7 +2285,6 @@ struct llama_context { struct ggml_tensor * inp_pos; // I32 [n_batch] struct ggml_tensor * inp_out_ids; // I32 [n_outputs] struct ggml_tensor * inp_KQ_mask; // F32 [kv_size, n_batch] - struct ggml_tensor * inp_KQ_pos; // F32 [n_kv] struct ggml_tensor * inp_K_shift; // I32 [kv_size] struct ggml_tensor * inp_mean; // F32 [n_batch, n_batch] struct ggml_tensor * inp_cls; // I32 [n_batch] @@ -2357,11 +2294,105 @@ struct llama_context { // control vectors struct llama_control_vector cvec; +}; + +static ggml_backend_buffer_type_t llama_default_buffer_type_offload(const llama_model & model, int gpu) { + ggml_backend_buffer_type_t buft = nullptr; -#ifdef GGML_USE_MPI - ggml_mpi_context * ctx_mpi = NULL; +#ifdef GGML_USE_RPC + std::string endpoint = model.rpc_servers[gpu]; + buft = ggml_backend_rpc_buffer_type(endpoint.c_str()); +#elif defined(GGML_USE_METAL) + buft = ggml_backend_metal_buffer_type(); +#elif defined(GGML_USE_CUDA) + buft = ggml_backend_cuda_buffer_type(gpu); +#elif defined(GGML_USE_VULKAN) + buft = ggml_backend_vk_buffer_type(gpu); +#elif defined(GGML_USE_SYCL) + buft = ggml_backend_sycl_buffer_type(gpu); +#elif defined(GGML_USE_CLBLAST) + buft = ggml_backend_opencl_buffer_type(); +#elif defined(GGML_USE_KOMPUTE) + buft = ggml_backend_kompute_buffer_type(gpu); + if (buft == nullptr) { + LLAMA_LOG_WARN("%s: cannot use GPU %d, check `vulkaninfo --summary`\n", __func__, gpu); + } #endif -}; + + if (buft == nullptr) { + buft = llama_default_buffer_type_cpu(true); + } + return buft; + GGML_UNUSED(model); + GGML_UNUSED(gpu); +} + +static ggml_backend_buffer_type_t llama_default_buffer_type_split(const llama_model & model, int fallback_gpu, const float * tensor_split) { + ggml_backend_buffer_type_t buft = nullptr; + +#ifdef GGML_USE_CUDA + if (ggml_backend_cuda_get_device_count() > 1) { + buft = ggml_backend_cuda_split_buffer_type(tensor_split); + } +#endif + +#ifdef GGML_USE_SYCL + if (ggml_backend_sycl_get_device_count() > 1) { + buft = ggml_backend_sycl_split_buffer_type(tensor_split); + } +#endif + + if (buft == nullptr) { + buft = llama_default_buffer_type_offload(model, fallback_gpu); + } + return buft; + + GGML_UNUSED(tensor_split); +} + +static size_t llama_get_device_count(const llama_model & model) { +#if defined(GGML_USE_RPC) + return model.rpc_servers.size(); +#elif defined(GGML_USE_CUDA) + return ggml_backend_cuda_get_device_count(); +#elif defined(GGML_USE_SYCL) + return ggml_backend_sycl_get_device_count(); +#elif defined(GGML_USE_VULKAN) + return ggml_backend_vk_get_device_count(); +#else + return 1; +#endif + GGML_UNUSED(model); +} + +static size_t llama_get_device_memory(const llama_model & model, int device) { +#if defined(GGML_USE_RPC) + size_t total; + size_t free; + std::string endpoint = model.rpc_servers[device]; + ggml_backend_rpc_get_device_memory(endpoint.c_str(), &free, &total); + return free; +#elif defined(GGML_USE_CUDA) + size_t total; + size_t free; + ggml_backend_cuda_get_device_memory(device, &free, &total); + return free; +#elif defined(GGML_USE_SYCL) + size_t total; + size_t free; + ggml_backend_sycl_get_device_memory(device, &free, &total); + return free; +#elif defined(GGML_USE_VULKAN) + size_t total; + size_t free; + ggml_backend_vk_get_device_memory(device, &free, &total); + return free; +#else + return 1; +#endif + GGML_UNUSED(model); + GGML_UNUSED(device); +} // // kv cache helpers @@ -2482,7 +2513,6 @@ static bool llama_kv_cache_init( static bool llama_kv_cache_find_slot( struct llama_kv_cache & cache, const struct llama_batch & batch) { - const uint32_t n_ctx = cache.size; const uint32_t n_tokens = batch.n_tokens; if (cache.recurrent) { @@ -2533,16 +2563,16 @@ static bool llama_kv_cache_find_slot( } // otherwise, one cell per token. - if (n_tokens > n_ctx) { - LLAMA_LOG_ERROR("%s: n_tokens=%d > n_ctx=%d\n", __func__, n_tokens, n_ctx); + if (n_tokens > cache.size) { + LLAMA_LOG_ERROR("%s: n_tokens=%d > cache.size=%d\n", __func__, n_tokens, cache.size); return false; } uint32_t n_tested = 0; while (true) { - if (cache.head + n_tokens > n_ctx) { - n_tested += n_ctx - cache.head; + if (cache.head + n_tokens > cache.size) { + n_tested += cache.size - cache.head; cache.head = 0; continue; } @@ -2561,7 +2591,7 @@ static bool llama_kv_cache_find_slot( break; } - if (n_tested >= n_ctx) { + if (n_tested >= cache.size) { //LLAMA_LOG_ERROR("%s: failed to find a slot for %d tokens\n", __func__, n_tokens); return false; } @@ -2815,6 +2845,11 @@ static void llama_kv_cache_defrag(struct llama_kv_cache & cache) { cache.do_defrag = true; } +static uint32_t llama_kv_cache_get_padding(const struct llama_cparams & cparams) { + // the FA kernels require padding to avoid extra runtime boundary checks + return cparams.flash_attn ? 256u : 32u; +} + // // model loading and saving // @@ -3317,6 +3352,39 @@ struct llama_model_loader { return get_arr_n(llm_kv(kid), result, required); } + template + bool get_arr(const std::string & key, std::vector & result, const bool required = true) { + const int kid = gguf_find_key(meta, key.c_str()); + + if (kid < 0) { + if (required) { + throw std::runtime_error(format("key not found in model: %s", key.c_str())); + } + return false; + } + + struct GGUFMeta::ArrayInfo arr_info = + GGUFMeta::GKV::get_kv(meta, kid); + + if (arr_info.gt != GGUF_TYPE_FLOAT32 && arr_info.gt != GGUF_TYPE_INT32) { + throw std::runtime_error(format("%s is not a float32 or int32 array", key.c_str())); + } + + // GGML_ASSERT(gguf_type_size(arr_info.gt) == sizeof(T)); + GGML_ASSERT((arr_info.gt != GGUF_TYPE_FLOAT32 || std::is_same::value)); + GGML_ASSERT((arr_info.gt != GGUF_TYPE_INT32 || std::is_same::value)); + + result.resize(arr_info.length); + result.assign((const T*)arr_info.data, (const T *)arr_info.data + arr_info.length); + + return true; + } + + template + bool get_arr(const enum llm_kv kid, T& result, const bool required = true) { + return get_arr(llm_kv(kid), result, required); + } + template bool get_key(const std::string & key, T & result, const bool required = true) { auto it = kv_overrides.find(key); @@ -3391,11 +3459,15 @@ struct llama_model_loader { return get_tensor_meta(get_tensor_name(i)); } - struct ggml_tensor * create_tensor_for(struct ggml_context * ctx, const struct ggml_tensor * cur) { + struct ggml_tensor * create_tensor_for(struct ggml_context * ctx, const struct ggml_tensor * cur, bool duplicated) { struct ggml_tensor * tensor = ggml_dup_tensor(ctx, cur); ggml_set_name(tensor, ggml_get_name(cur)); - n_created++; + if (duplicated) { + size_data += ggml_nbytes(cur); + } else { + n_created++; + } return tensor; } @@ -3430,14 +3502,17 @@ struct llama_model_loader { return cur; } - struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector & ne, bool required = true) { - const struct ggml_tensor * cur = check_tensor_dims(name, ne, required); + static const int TENSOR_NOT_REQUIRED = 1; + static const int TENSOR_DUPLICATED = 2; + + struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector & ne, int flags = 0) { + const struct ggml_tensor * cur = check_tensor_dims(name, ne, !(flags & TENSOR_NOT_REQUIRED)); if (cur == NULL) { return NULL; } - return create_tensor_for(ctx, cur); + return create_tensor_for(ctx, cur, flags & TENSOR_DUPLICATED); } struct ggml_tensor * create_tensor_as_view(struct ggml_context * ctx, struct ggml_tensor * base, const std::string & name, const std::vector & ne, size_t offset, bool required = true) { @@ -3750,14 +3825,24 @@ static std::string llama_model_ftype_name(llama_ftype ftype) { static const char * llama_model_type_name(e_model type) { switch (type) { + case MODEL_14M: return "14M"; + case MODEL_17M: return "17M"; case MODEL_22M: return "22M"; case MODEL_33M: return "33M"; + case MODEL_70M: return "70M"; case MODEL_109M: return "109M"; case MODEL_137M: return "137M"; + case MODEL_160M: return "160M"; + case MODEL_335M: return "335M"; + case MODEL_410M: return "410M"; case MODEL_0_5B: return "0.5B"; case MODEL_1B: return "1B"; + case MODEL_1_4B: return "1.4B"; case MODEL_2B: return "2B"; + case MODEL_2_8B: return "2.8B"; case MODEL_3B: return "3B"; + case MODEL_4B: return "4B"; + case MODEL_6_9B: return "6.9B"; case MODEL_7B: return "7B"; case MODEL_8B: return "8B"; case MODEL_12B: return "12B"; @@ -3823,6 +3908,12 @@ static void llm_load_hparams( // get hparams kv ml.get_key(LLM_KV_VOCAB_SIZE, hparams.n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, hparams.n_vocab); + + // everything past this point is not vocab-related + if (hparams.vocab_only) { + return; + } + ml.get_key(LLM_KV_CONTEXT_LENGTH, hparams.n_ctx_train); ml.get_key(LLM_KV_EMBEDDING_LENGTH, hparams.n_embd); ml.get_key(LLM_KV_FEED_FORWARD_LENGTH, hparams.n_ff); @@ -3867,6 +3958,8 @@ static void llm_load_hparams( } hparams.rope_freq_scale_train = ropescale == 0.0f ? 1.0f : 1.0f/ropescale; + ml.get_key(LLM_KV_ROPE_SCALING_ATTN_FACTOR, hparams.rope_attn_factor, false); + // sanity check for n_rot (optional) { hparams.n_rot = (hparams.n_head == 0) ? 0 : hparams.n_embd / hparams.n_head; @@ -3904,7 +3997,7 @@ static void llm_load_hparams( switch (hparams.n_layer) { case 22: model.type = e_model::MODEL_1B; break; case 26: model.type = e_model::MODEL_3B; break; - case 32: model.type = hparams.n_head == hparams.n_head_kv ? e_model::MODEL_7B : e_model::MODEL_8B; break; // LLaMa 8B v3 uses GQA + case 32: model.type = hparams.n_vocab < 40000 ? e_model::MODEL_7B : e_model::MODEL_8B; break; case 40: model.type = e_model::MODEL_13B; break; case 48: model.type = e_model::MODEL_34B; break; case 60: model.type = e_model::MODEL_30B; break; @@ -3966,14 +4059,6 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; - case LLM_ARCH_PERSIMMON: - { - ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); - switch (hparams.n_layer) { - case 36: model.type = e_model::MODEL_8B; break; - default: model.type = e_model::MODEL_UNKNOWN; - } - } break; case LLM_ARCH_REFACT: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); @@ -4006,6 +4091,19 @@ static void llm_load_hparams( model.type = e_model::MODEL_335M; break; // bge-large } } break; + case LLM_ARCH_JINA_BERT_V2: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); + ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn); + ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type); + ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type); + hparams.f_max_alibi_bias = 8.0f; + + switch (hparams.n_layer) { + case 4: model.type = e_model::MODEL_33M; break; // jina-embeddings-small + case 12: model.type = e_model::MODEL_137M; break; // jina-embeddings-base + } + } break; case LLM_ARCH_NOMIC_BERT: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); @@ -4102,6 +4200,7 @@ static void llm_load_hparams( switch (hparams.n_layer) { case 24: model.type = e_model::MODEL_1B; break; case 32: model.type = e_model::MODEL_3B; break; + case 40: model.type = e_model::MODEL_14B; break; default: model.type = e_model::MODEL_UNKNOWN; } } break; @@ -4242,6 +4341,52 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_GPTNEOX: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); + ml.get_key(LLM_KV_USE_PARALLEL_RESIDUAL, hparams.use_par_res); + switch (hparams.n_layer) { + case 6: + switch (hparams.n_ff) { + case 512: model.type = e_model::MODEL_14M; break; + case 2048: model.type = e_model::MODEL_70M; break; + default: model.type = e_model::MODEL_UNKNOWN; + } break; + case 12: + switch (hparams.n_ff) { + case 3072: model.type = e_model::MODEL_160M; break; + default: model.type = e_model::MODEL_UNKNOWN; + } break; + case 16: + switch (hparams.n_ff) { + case 8192: model.type = e_model::MODEL_1B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } break; + case 24: + switch (hparams.n_ff) { + case 4096: model.type = e_model::MODEL_410M; break; + case 8192: model.type = e_model::MODEL_1_4B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } break; + case 32: + switch (hparams.n_ff) { + case 10240: model.type = e_model::MODEL_2_8B; break; + case 16384: model.type = e_model::MODEL_6_9B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } break; + case 36: + switch (hparams.n_ff) { + case 20480: model.type = e_model::MODEL_12B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } break; + case 44: + switch (hparams.n_ff) { + case 24576: model.type = e_model::MODEL_20B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; default: (void)0; } @@ -4437,7 +4582,11 @@ static void llm_load_vocab( tokenizer_pre == "starcoder") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_STARCODER; } else if ( - tokenizer_pre == "gpt-2") { + tokenizer_pre == "gpt-2" || + tokenizer_pre == "jina-es" || + tokenizer_pre == "jina-de" || + tokenizer_pre == "jina-v2-es" || + tokenizer_pre == "jina-v2-de") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_GPT2; } else if ( tokenizer_pre == "refact") { @@ -4448,6 +4597,9 @@ static void llm_load_vocab( } else if ( tokenizer_pre == "qwen2") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_QWEN2; + } else if ( + tokenizer_pre == "stablelm2") { + vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_STABLELM2; } else if ( tokenizer_pre == "olmo") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_OLMO; @@ -4578,7 +4730,8 @@ static void llm_load_vocab( (t.first == "<|eot_id|>" || t.first == "<|im_end|>" || t.first == "<|end|>" || - t.first == "" + t.first == "" || + t.first == "<|endoftext|>" ) ) { vocab.special_eot_id = t.second; @@ -4813,13 +4966,13 @@ static bool llm_load_tensors( if (split_mode == LLAMA_SPLIT_MODE_LAYER) { // calculate the split points - int device_count = llama_get_device_count(); + int device_count = llama_get_device_count(model); bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + device_count, [](float x) { return x == 0.0f; }); std::vector splits(device_count); if (all_zero) { // default split, by free memory for (int i = 0; i < device_count; ++i) { - splits[i] = llama_get_device_memory(i); + splits[i] = llama_get_device_memory(model, i); } } else { std::copy(tensor_split, tensor_split + device_count, splits.begin()); @@ -4839,35 +4992,35 @@ static bool llm_load_tensors( int act_gpu_layers = std::min(n_gpu_layers, (int)n_layer + 1); for (int64_t i = i_gpu_start; i < n_layer; ++i) { int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(i - i_gpu_start)/act_gpu_layers) - splits.begin(); - model.buft_layer[i] = llama_default_buffer_type_offload(layer_gpu); + model.buft_layer[i] = llama_default_buffer_type_offload(model, layer_gpu); } // assign the output layer if (n_gpu_layers > n_layer) { int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(act_gpu_layers - 1)/act_gpu_layers) - splits.begin(); - model.buft_output = llama_default_buffer_type_offload(layer_gpu); + model.buft_output = llama_default_buffer_type_offload(model, layer_gpu); } else { model.buft_output = llama_default_buffer_type_cpu(true); } } else { ggml_backend_buffer_type_t split_buft; if (split_mode == LLAMA_SPLIT_MODE_ROW) { - split_buft = llama_default_buffer_type_split(main_gpu, tensor_split); + split_buft = llama_default_buffer_type_split(model, main_gpu, tensor_split); } else { // LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_LAYER in backends where it is not supported - split_buft = llama_default_buffer_type_offload(main_gpu); + split_buft = llama_default_buffer_type_offload(model, main_gpu); } // assign the repeating layers for (int64_t i = i_gpu_start; i < n_layer; ++i) { model.buft_layer[i] = { split_buft, - llama_default_buffer_type_offload(main_gpu) + llama_default_buffer_type_offload(model, main_gpu) }; } // assign the output layer if (n_gpu_layers > n_layer && !clblast_offload_fallback_mode) { model.buft_output = { split_buft, - llama_default_buffer_type_offload(main_gpu) + llama_default_buffer_type_offload(model, main_gpu) }; } else { model.buft_output = llama_default_buffer_type_cpu(true); @@ -4911,6 +5064,7 @@ static bool llm_load_tensors( // create tensors for the weights { const int64_t n_embd = hparams.n_embd; + const int64_t n_embd_head = n_embd / hparams.n_head; const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(); const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(); const int64_t n_embd_gqa = n_embd_v_gqa; @@ -4945,12 +5099,10 @@ static bool llm_load_tensors( { model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); if (model.arch != LLM_ARCH_MINICPM){ - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); // if output is NULL, init from the input tok embed if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - ml.n_created--; // artificial tensor - ml.size_data += ggml_nbytes(model.output); + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } } } @@ -4969,10 +5121,10 @@ static bool llm_load_tensors( layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); // optional bias tensors - layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, false); - layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, false); - layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, false); - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, false); + layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); @@ -4983,7 +5135,7 @@ static bool llm_load_tensors( } else { layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}); - layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, false); + layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED); if (layer.ffn_gate_exps) { layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}); layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}); @@ -5025,12 +5177,10 @@ static bool llm_load_tensors( // output { model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); // if output is NULL, init from the input tok embed if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - ml.n_created--; // artificial tensor - ml.size_data += ggml_nbytes(model.output); + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } } @@ -5053,7 +5203,7 @@ static bool llm_load_tensors( layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}); - layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, false); + layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED); if (layer.ffn_gate_exps) { layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}); layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}); @@ -5155,11 +5305,9 @@ static bool llm_load_tensors( model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); if (!model.output) { - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // needs to be on GPU - ml.n_created--; // artificial tensor - ml.size_data += ggml_nbytes(model.output); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // needs to be on GPU } } @@ -5172,8 +5320,8 @@ static bool llm_load_tensors( layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); - layer.attn_norm_2 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, false); - layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, false); + layer.attn_norm_2 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); @@ -5191,7 +5339,12 @@ static bool llm_load_tensors( { model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + if (!model.output) { + // needs to be on GPU + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); + } + } for (int i = 0; i < n_layer; ++i) { @@ -5219,47 +5372,6 @@ static bool llm_load_tensors( layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); } } break; - case LLM_ARCH_PERSIMMON: - { - model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - - { - model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); - } - - for (int i = 0; i < n_layer; ++i) { - ggml_context * ctx_layer = ctx_for_layer(i); - ggml_context * ctx_split = ctx_for_layer_split(i); - - auto & layer = model.layers[i]; - - layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); - - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); - layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}); - - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); - - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); - layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); - - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); - - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); - - layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {64}); - layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {64}); - - layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {64}); - layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {64}); - } - } break; case LLM_ARCH_BERT: case LLM_ARCH_NOMIC_BERT: { @@ -5312,6 +5424,50 @@ static bool llm_load_tensors( layer.layer_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i), {n_embd}); } } break; + case LLM_ARCH_JINA_BERT_V2: + { + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // word_embeddings + model.type_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type}); //token_type_embeddings + model.tok_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}); // LayerNorm + model.tok_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}); //LayerNorm bias + + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); + + auto & layer = model.layers[i]; // JinaBertLayer + + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); + layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}); + + layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + + layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); + layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}); + + layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + + layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); + layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}); + + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); //output_dens + layer.bo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); //output_dens + + layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}); //output_norm + layer.attn_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i), {n_embd}); + + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); + + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); + layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); + + layer.layer_out_norm = ml.create_tensor(ctx_split, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}); + layer.layer_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i), {n_embd}); + } + } break; case LLM_ARCH_BLOOM: { model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); @@ -5353,18 +5509,16 @@ static bool llm_load_tensors( case LLM_ARCH_MPT: { model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}, false); + model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}, llama_model_loader::TENSOR_NOT_REQUIRED); // output { model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, false); + model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); if (!model.output) { - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // needs to be on GPU - ml.n_created--; // artificial tensor - ml.size_data += ggml_nbytes(model.output); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // needs to be on GPU } } @@ -5375,31 +5529,31 @@ static bool llm_load_tensors( auto & layer = model.layers[i]; layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, false); + layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); - layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, false); + layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, false); + layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, false); + layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); - layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, false); + layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, false); + layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, false); - layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, false); + layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, false); - layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, false); + layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); // AWQ ScaleActivation layer - layer.ffn_act = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, false); + layer.ffn_act = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); } } break; case LLM_ARCH_STABLELM: @@ -5428,17 +5582,17 @@ static bool llm_load_tensors( layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); // optional bias tensors, present in Stable LM 2 1.6B - layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, false); - layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, false); - layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, false); + layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); // optional q and k layernorms, present in StableLM 2 12B - layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head}, false); - layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head_kv}, false); + layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head_kv}, llama_model_loader::TENSOR_NOT_REQUIRED); // optional FFN norm, not present in StableLM 2 12B which uses parallel residual - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, false); - layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, false); + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); @@ -5481,12 +5635,10 @@ static bool llm_load_tensors( // output { model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); // if output is NULL, init from the input tok embed if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - ml.n_created--; // artificial tensor - ml.size_data += ggml_nbytes(model.output); + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } } @@ -5584,8 +5736,8 @@ static bool llm_load_tensors( layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, false); - layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, false); + layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); if (layer.wqkv == nullptr) { layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); @@ -5622,17 +5774,20 @@ static bool llm_load_tensors( ggml_context* ctx_layer = ctx_for_layer(i); ggml_context* ctx_split = ctx_for_layer_split(i); - auto& layer = model.layers[i]; + auto & layer = model.layers[i]; layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }); - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, n_embd + 2 * n_embd_gqa }, false); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd }); + layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, n_embd + 2 * n_embd_gqa }, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd }); layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }); layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }); layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, 2 * n_ff }); + + layer.rope_long = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FACTORS_LONG, "weight"), { n_embd_head/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); + layer.rope_short = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight"), { n_embd_head/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); } } break; case LLM_ARCH_PLAMO: @@ -5801,9 +5956,7 @@ static bool llm_load_tensors( // output model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // same as tok_embd, duplicated to allow offloading - ml.n_created--; // artificial tensor - ml.size_data += ggml_nbytes(model.output); + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading const int64_t n_ff = hparams.n_ff; const int64_t n_embd_head_k = hparams.n_embd_head_k; @@ -5838,12 +5991,10 @@ static bool llm_load_tensors( model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); // if output is NULL, init from the input tok embed if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - ml.n_created--; // artificial tensor - ml.size_data += ggml_nbytes(model.output); + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } } @@ -5894,12 +6045,10 @@ static bool llm_load_tensors( { model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); // if output is NULL, init from the input tok embed, duplicated to allow offloading if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - ml.n_created--; // artificial tensor - ml.size_data += ggml_nbytes(model.output); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } } @@ -5960,9 +6109,7 @@ static bool llm_load_tensors( { model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); // init output from the input tok embed - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - ml.n_created--; // artificial tensor - ml.size_data += ggml_nbytes(model.output); + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } for (int i = 0; i < n_layer; ++i) { @@ -5994,12 +6141,10 @@ static bool llm_load_tensors( // output { - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); // if output is NULL, init from the input tok embed if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - ml.n_created--; // artificial tensor - ml.size_data += ggml_nbytes(model.output); + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } } @@ -6019,6 +6164,41 @@ static bool llm_load_tensors( layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); } } break; + case LLM_ARCH_GPTNEOX: + { + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + // output + { + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); + } + + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); + + auto & layer = model.layers[i]; + + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + + layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); + layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}); + + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); + + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); + + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); + layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); + + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); + } + } break; default: throw std::runtime_error("unknown architecture"); } @@ -6283,10 +6463,7 @@ static struct ggml_tensor * llm_build_inp_embd( inpL = ggml_get_rows(ctx, tok_embd, lctx.inp_tokens); } else { -#ifdef GGML_USE_MPI - GGML_ASSERT(false && "not implemented"); -#endif - lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens); + lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens); inpL = lctx.inp_embd; ggml_set_input(lctx.inp_embd); } @@ -6388,7 +6565,7 @@ static struct ggml_tensor * llm_build_ffn( llm_ffn_gate_type type_gate, const llm_build_cb & cb, int il) { - struct ggml_tensor * tmp = ggml_mul_mat(ctx, up, cur); + struct ggml_tensor * tmp = up ? ggml_mul_mat(ctx, up, cur) : cur; cb(tmp, "ffn_up", il); if (up_b) { @@ -6570,7 +6747,6 @@ static struct ggml_tensor * llm_build_kqv( struct ggml_tensor * wo_b, struct ggml_tensor * q_cur, struct ggml_tensor * kq_mask, - struct ggml_tensor * kq_pos, int32_t n_tokens, int32_t n_kv, float kq_scale, @@ -6582,6 +6758,7 @@ static struct ggml_tensor * llm_build_kqv( const int64_t n_embd_head_k = hparams.n_embd_head_k; const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(); const int64_t n_embd_head_v = hparams.n_embd_head_v; + const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(); struct ggml_tensor * q = ggml_permute(ctx, q_cur, 0, 2, 1, 3); cb(q, "q", il); @@ -6600,31 +6777,27 @@ static struct ggml_tensor * llm_build_kqv( GGML_UNUSED(model); GGML_UNUSED(n_ctx); - // note: if this assert triggers, then some check has failed earlier - // the idea is to detect during context creation that ALiBi would be used and disable Flash Attention - GGML_ASSERT(kq_pos == nullptr && "ALiBi is not yet supported with Flash Attention"); - // split cached v into n_head heads (not transposed) struct ggml_tensor * v = ggml_view_3d(ctx, kv.v_l[il], n_embd_head_v, n_kv, n_head_kv, - ggml_row_size(kv.v_l[il]->type, n_embd_k_gqa), - ggml_row_size(kv.v_l[il]->type, n_embd_head_k), + ggml_row_size(kv.v_l[il]->type, n_embd_v_gqa), + ggml_row_size(kv.v_l[il]->type, n_embd_head_v), 0); cb(v, "v", il); - cur = ggml_flash_attn_ext(ctx, q, k, v, kq_mask, kq_scale); + cur = ggml_flash_attn_ext(ctx, q, k, v, kq_mask, kq_scale, hparams.f_max_alibi_bias); - if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3) { + if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3 || model.arch == LLM_ARCH_GPTNEOX) { ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32); } - cur = ggml_reshape_2d(ctx, cur, n_embd_head_k*n_head, n_tokens); + cur = ggml_reshape_2d(ctx, cur, n_embd_head_v*n_head, n_tokens); } else { struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q); cb(kq, "kq", il); - if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3) { + if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3 || model.arch == LLM_ARCH_GPTNEOX) { // for this arch, we need to perform the KQ multiplication with F32 precision, otherwise we get NaNs // ref: https://github.com/ggerganov/llama.cpp/pull/4490#issuecomment-1859055847 ggml_mul_mat_set_prec(kq, GGML_PREC_F32); @@ -6644,28 +6817,8 @@ static struct ggml_tensor * llm_build_kqv( kq = ggml_scale(ctx, kq, 30); } -#if defined(GGML_USE_KOMPUTE) -#pragma message("TODO: ALiBi support in ggml_soft_max_ext is not implemented for Kompute") -#pragma message(" Falling back to ggml_alibi(). Will become an error in Mar 2024. But koboldcpp will deal with it.") -#pragma message("ref: https://github.com/ggerganov/llama.cpp/pull/5488") - if (hparams.use_alibi) { - kq = ggml_scale(ctx, kq, kq_scale); - cb(kq, "kq_scaled", il); - - kq = ggml_alibi(ctx, kq, /*n_past*/ 0, n_head, hparams.f_max_alibi_bias); - cb(kq, "kq_scaled_alibi", il); - - kq = ggml_add(ctx, kq, kq_mask); - cb(kq, "kq_masked", il); - - kq = ggml_soft_max(ctx, kq); - cb(kq, "kq_soft_max", il); - } else -#endif - { - kq = ggml_soft_max_ext(ctx, kq, kq_mask, kq_pos, kq_scale, hparams.f_max_alibi_bias); - cb(kq, "kq_soft_max_ext", il); - } + kq = ggml_soft_max_ext(ctx, kq, kq_mask, kq_scale, hparams.f_max_alibi_bias); + cb(kq, "kq_soft_max_ext", il); GGML_ASSERT(kv.size == n_ctx); @@ -6684,7 +6837,7 @@ static struct ggml_tensor * llm_build_kqv( struct ggml_tensor * kqv_merged = ggml_permute(ctx, kqv, 0, 2, 1, 3); cb(kqv_merged, "kqv_merged", il); - cur = ggml_cont_2d(ctx, kqv_merged, n_embd_head_k*n_head, n_tokens); + cur = ggml_cont_2d(ctx, kqv_merged, n_embd_head_v*n_head, n_tokens); cb(cur, "kqv_merged_cont", il); } @@ -6715,7 +6868,6 @@ static struct ggml_tensor * llm_build_kv( struct ggml_tensor * v_cur, struct ggml_tensor * q_cur, struct ggml_tensor * kq_mask, - struct ggml_tensor * kq_pos, int32_t n_tokens, int32_t kv_head, int32_t n_kv, @@ -6734,7 +6886,7 @@ static struct ggml_tensor * llm_build_kv( struct ggml_tensor * cur; cur = llm_build_kqv(ctx, model, hparams, cparams, kv, graph, wo, wo_b, - q_cur, kq_mask, kq_pos, n_tokens, n_kv, kq_scale, cb, il); + q_cur, kq_mask, n_tokens, n_kv, kq_scale, cb, il); cb(cur, "kqv_out", il); return cur; @@ -6841,18 +6993,17 @@ struct llm_build_context { ctx0 = ggml_init(params); - lctx.inp_tokens = nullptr; - lctx.inp_embd = nullptr; - lctx.inp_pos = nullptr; + lctx.inp_tokens = nullptr; + lctx.inp_embd = nullptr; + lctx.inp_pos = nullptr; lctx.inp_out_ids = nullptr; lctx.inp_KQ_mask = nullptr; - lctx.inp_KQ_pos = nullptr; lctx.inp_K_shift = nullptr; - lctx.inp_mean = nullptr; - lctx.inp_cls = nullptr; - lctx.inp_s_copy = nullptr; - lctx.inp_s_mask = nullptr; - lctx.inp_s_seq = nullptr; + lctx.inp_mean = nullptr; + lctx.inp_cls = nullptr; + lctx.inp_s_copy = nullptr; + lctx.inp_s_mask = nullptr; + lctx.inp_s_seq = nullptr; } void free() { @@ -6871,17 +7022,20 @@ struct llm_build_context { cb(lctx.inp_K_shift, "K_shift", -1); ggml_set_input(lctx.inp_K_shift); + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * rope_factors = build_rope_factors(il); struct ggml_tensor * tmp = // we rotate only the first n_rot dimensions - ggml_rope_custom_inplace(ctx0, + ggml_rope_ext_inplace(ctx0, ggml_view_3d(ctx0, kv_self.k_l[il], n_embd_head_k, n_head_kv, n_ctx, ggml_row_size(kv_self.k_l[il]->type, n_embd_head_k), ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa), 0), - lctx.inp_K_shift, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, + lctx.inp_K_shift, rope_factors, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); + cb(tmp, "K_shifted", il); ggml_build_forward_expand(gf, tmp); } @@ -6984,6 +7138,17 @@ struct llm_build_context { return lctx.inp_pos; } + struct ggml_tensor * build_rope_factors(int il) { + // choose long/short freq factors based on the context size + const auto n_ctx_pre_seq = cparams.n_ctx / cparams.n_seq_max; + + if (n_ctx_pre_seq > hparams.n_yarn_orig_ctx) { + return model.layers[il].rope_long; + } + + return model.layers[il].rope_short; + } + struct ggml_tensor * build_inp_out_ids() { lctx.inp_out_ids = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_outputs); cb(lctx.inp_out_ids, "inp_out_ids", -1); @@ -7002,19 +7167,6 @@ struct llm_build_context { return flash_attn ? ggml_cast(ctx0, lctx.inp_KQ_mask, GGML_TYPE_F16) : lctx.inp_KQ_mask; } - struct ggml_tensor * build_inp_KQ_pos(bool causal = true) { - if (causal) { - lctx.inp_KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, n_kv); - } else { - // TODO: this will be needed for ALiBi-based BERT models - // https://github.com/ggerganov/llama.cpp/pull/6826 - lctx.inp_KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, n_tokens); - } - cb(lctx.inp_KQ_pos, "KQ_pos", -1); - ggml_set_input(lctx.inp_KQ_pos); - return flash_attn ? ggml_cast(ctx0, lctx.inp_KQ_pos, GGML_TYPE_F16) : lctx.inp_KQ_pos; - } - struct ggml_tensor * build_inp_mean() { lctx.inp_mean = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, n_tokens); cb(lctx.inp_mean, "inp_mean", -1); @@ -7104,15 +7256,15 @@ struct llm_build_context { cb(Vcur, "Vcur", il); } - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -7120,7 +7272,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -7213,9 +7365,6 @@ struct llm_build_context { // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); - // positions of the tokens in the KV cache - struct ggml_tensor * KQ_pos = build_inp_KQ_pos(); - for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * inpSA = inpL; @@ -7237,13 +7386,13 @@ struct llm_build_context { switch (model.type) { case MODEL_7B: - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -7260,7 +7409,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -7330,9 +7479,6 @@ struct llm_build_context { // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); - // positions of the tokens in the KV cache - struct ggml_tensor * KQ_pos = build_inp_KQ_pos(); - for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * inpSA = inpL; @@ -7352,22 +7498,22 @@ struct llm_build_context { struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); cb(Vcur, "Vcur", il); - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Kcur, "Kcur", il); cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -7473,21 +7619,21 @@ struct llm_build_context { Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); // using mode = 2 for neox mode - Qcur = ggml_rope_custom( - ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, + Kcur = ggml_rope_ext( + ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Kcur, "Kcur", il); cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -7596,15 +7742,15 @@ struct llm_build_context { cb(Vcur, "Vcur", il); } - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -7612,7 +7758,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f, cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il); } if (il == n_layer - 1) { @@ -7748,15 +7894,15 @@ struct llm_build_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -7764,7 +7910,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -7876,7 +8022,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -7925,213 +8071,6 @@ struct llm_build_context { return gf; } - struct ggml_cgraph * build_persimmon() { - struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); - - const int64_t n_embd_head = hparams.n_embd_head_v; - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head/2 == hparams.n_rot); - - struct ggml_tensor * cur; - struct ggml_tensor * inpL; - - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); - - // inp_pos - contains the positions - struct ggml_tensor * inp_pos = build_inp_pos(); - - // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); - - for (int il = 0; il < n_layer; ++il) { - struct ggml_tensor * residual = inpL; - - cur = llm_build_norm(ctx0, inpL, hparams, - model.layers[il].attn_norm, - model.layers[il].attn_norm_b, - LLM_NORM, cb, il); - cb(cur, "attn_norm", il); - - // self attention - { - cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur); - cb(cur, "wqkv", il); - - cur = ggml_add(ctx0, cur, model.layers[il].bqkv); - cb(cur, "bqkv", il); - - // split qkv - GGML_ASSERT(n_head_kv == n_head); - - struct ggml_tensor * tmpqkv = ggml_reshape_4d(ctx0, cur, n_embd_head, 3, n_head, n_tokens); - cb(tmpqkv, "tmpqkv", il); - - struct ggml_tensor * tmpqkv_perm = ggml_cont(ctx0, ggml_permute(ctx0, tmpqkv, 0, 3, 1, 2)); - cb(tmpqkv_perm, "tmpqkv", il); - - struct ggml_tensor * tmpq = ggml_view_3d( - ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens, - ggml_element_size(tmpqkv_perm) * n_embd_head, - ggml_element_size(tmpqkv_perm) * n_embd_head * n_head, - 0 - ); - cb(tmpq, "tmpq", il); - - struct ggml_tensor * tmpk = ggml_view_3d( - ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens, - ggml_element_size(tmpqkv_perm) * n_embd_head, - ggml_element_size(tmpqkv_perm) * n_embd_head * n_head, - ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens - ); - cb(tmpk, "tmpk", il); - - // Q/K Layernorm - tmpq = llm_build_norm(ctx0, tmpq, hparams, - model.layers[il].attn_q_norm, - model.layers[il].attn_q_norm_b, - LLM_NORM, cb, il); - cb(tmpq, "tmpq", il); - - tmpk = llm_build_norm(ctx0, tmpk, hparams, - model.layers[il].attn_k_norm, - model.layers[il].attn_k_norm_b, - LLM_NORM, cb, il); - cb(tmpk, "tmpk", il); - - // RoPE the first n_rot of q/k, pass the other half, and concat. - struct ggml_tensor * qrot = ggml_view_3d( - ctx0, tmpq, n_rot, n_head, n_tokens, - ggml_element_size(tmpq) * n_embd_head, - ggml_element_size(tmpq) * n_embd_head * n_head, - 0 - ); - cb(qrot, "qrot", il); - - struct ggml_tensor * krot = ggml_view_3d( - ctx0, tmpk, n_rot, n_head, n_tokens, - ggml_element_size(tmpk) * n_embd_head, - ggml_element_size(tmpk) * n_embd_head * n_head, - 0 - ); - cb(krot, "krot", il); - - // get the second half of tmpq, e.g tmpq[n_rot:, :, :] - struct ggml_tensor * qpass = ggml_view_3d( - ctx0, tmpq, n_rot, n_head, n_tokens, - ggml_element_size(tmpq) * n_embd_head, - ggml_element_size(tmpq) * n_embd_head * n_head, - ggml_element_size(tmpq) * n_rot - ); - cb(qpass, "qpass", il); - - struct ggml_tensor * kpass = ggml_view_3d( - ctx0, tmpk, n_rot, n_head, n_tokens, - ggml_element_size(tmpk) * n_embd_head, - ggml_element_size(tmpk) * n_embd_head * n_head, - ggml_element_size(tmpk) * n_rot - ); - cb(kpass, "kpass", il); - - struct ggml_tensor * qrotated = ggml_rope_custom( - ctx0, qrot, inp_pos, n_rot, rope_type, 0, n_orig_ctx, - freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow - ); - cb(qrotated, "qrotated", il); - - struct ggml_tensor * krotated = ggml_rope_custom( - ctx0, krot, inp_pos, n_rot, rope_type, 0, n_orig_ctx, - freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow - ); - cb(krotated, "krotated", il); - - // ggml currently only supports concatenation on dim=2 - // so we need to permute qrot, qpass, concat, then permute back. - qrotated = ggml_cont(ctx0, ggml_permute(ctx0, qrotated, 2, 1, 0, 3)); - cb(qrotated, "qrotated", il); - - krotated = ggml_cont(ctx0, ggml_permute(ctx0, krotated, 2, 1, 0, 3)); - cb(krotated, "krotated", il); - - qpass = ggml_cont(ctx0, ggml_permute(ctx0, qpass, 2, 1, 0, 3)); - cb(qpass, "qpass", il); - - kpass = ggml_cont(ctx0, ggml_permute(ctx0, kpass, 2, 1, 0, 3)); - cb(kpass, "kpass", il); - - struct ggml_tensor * Qcur = ggml_concat(ctx0, qrotated, qpass); - cb(Qcur, "Qcur", il); - - struct ggml_tensor * Kcur = ggml_concat(ctx0, krotated, kpass); - cb(Kcur, "Kcur", il); - - struct ggml_tensor * Q = ggml_cont(ctx0, ggml_permute(ctx0, Qcur, 2, 1, 0, 3)); - cb(Q, "Q", il); - - Kcur = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 2, 1, 0, 3)); - cb(Kcur, "Kcur", il); - - struct ggml_tensor * Vcur = ggml_view_3d( - ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens, - ggml_element_size(tmpqkv_perm) * n_embd_head, - ggml_element_size(tmpqkv_perm) * n_embd_head * n_head, - ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens * 2 - ); - cb(Vcur, "Vcur", il); - - cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, - model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Q, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); - } - - if (il == n_layer - 1) { - // skip computing output for unused tokens - struct ggml_tensor * inp_out_ids = build_inp_out_ids(); - cur = ggml_get_rows(ctx0, cur, inp_out_ids); - residual = ggml_get_rows(ctx0, residual, inp_out_ids); - } - - struct ggml_tensor * ffn_inp = ggml_add(ctx0, residual, cur); - cb(ffn_inp, "ffn_inp", il); - - // feed-forward network - { - cur = llm_build_norm(ctx0, ffn_inp, hparams, - model.layers[il].ffn_norm, - model.layers[il].ffn_norm_b, - LLM_NORM, cb, il); - cb(cur, "ffn_norm", il); - - cur = llm_build_ffn(ctx0, cur, - model.layers[il].ffn_up, model.layers[il].ffn_up_b, - NULL, NULL, - model.layers[il].ffn_down, model.layers[il].ffn_down_b, - NULL, - LLM_FFN_RELU_SQR, LLM_FFN_SEQ, cb, il); - cb(cur, "ffn_out", il); - } - - cur = ggml_add(ctx0, cur, ffn_inp); - cb(cur, "l_out", il); - - inpL = cur; - } - - cur = inpL; - - cur = llm_build_norm(ctx0, cur, hparams, - model.output_norm, - model.output_norm_b, - LLM_NORM, cb, -1); - cb(cur, "result_norm", -1); - - cur = ggml_mul_mat(ctx0, model.output, cur); - cb(cur, "result_output", -1); - - ggml_build_forward_expand(gf, cur); - - return gf; - } - struct ggml_cgraph * build_refact() { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); @@ -8146,9 +8085,6 @@ struct llm_build_context { // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); - // positions of the tokens in the KV cache - struct ggml_tensor * KQ_pos = build_inp_KQ_pos(); - for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * inpSA = inpL; @@ -8176,7 +8112,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -8238,8 +8174,11 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; + struct ggml_tensor * inp_pos = nullptr; - struct ggml_tensor * inp_pos = build_inp_pos(); + if (model.arch != LLM_ARCH_JINA_BERT_V2) { + inp_pos = build_inp_pos(); + } struct ggml_tensor * inp_mean = build_inp_mean(); struct ggml_tensor * inp_cls = build_inp_cls(); @@ -8270,13 +8209,26 @@ struct llm_build_context { struct ggml_tensor * Vcur; // self-attention - if (model.arch == LLM_ARCH_BERT) { + if (model.arch == LLM_ARCH_BERT || model.arch == LLM_ARCH_JINA_BERT_V2) { Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, cur), model.layers[il].bq); cb(Qcur, "Qcur", il); + if (model.layers[il].attn_q_norm) { + Qcur = llm_build_norm(ctx0, Qcur, hparams, + model.layers[il].attn_q_norm, + model.layers[il].attn_q_norm_b, + LLM_NORM, cb, il); + } + Kcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, cur), model.layers[il].bk); cb(Kcur, "Kcur", il); + if (model.layers[il].attn_k_norm) { + Kcur = llm_build_norm(ctx0, Kcur, hparams, + model.layers[il].attn_k_norm, + model.layers[il].attn_k_norm_b, + LLM_NORM, cb, il); + } Vcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, cur), model.layers[il].bv); cb(Vcur, "Vcur", il); @@ -8295,15 +8247,15 @@ struct llm_build_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -8316,7 +8268,7 @@ struct llm_build_context { struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q); cb(kq, "kq", il); - kq = ggml_soft_max_ext(ctx0, kq, KQ_mask, nullptr, 1.0f/sqrtf(float(n_embd_head)), hparams.f_max_alibi_bias); + kq = ggml_soft_max_ext(ctx0, kq, KQ_mask, 1.0f/sqrtf(float(n_embd_head)), hparams.f_max_alibi_bias); cb(kq, "kq_soft_max_ext", il); struct ggml_tensor * v = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd_gqa, n_tokens))); @@ -8367,6 +8319,13 @@ struct llm_build_context { model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL, LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); + } else if (model.arch == LLM_ARCH_JINA_BERT_V2) { + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, NULL, + model.layers[il].ffn_gate, NULL, + model.layers[il].ffn_down, model.layers[il].ffn_down_b, + NULL, + LLM_FFN_GELU, LLM_FFN_PAR, cb, il); } else { cur = llm_build_ffn(ctx0, cur, model.layers[il].ffn_up, NULL, @@ -8433,9 +8392,6 @@ struct llm_build_context { // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); - // positions of the tokens in the KV cache - struct ggml_tensor * KQ_pos = build_inp_KQ_pos(); - inpL = llm_build_norm(ctx0, inpL, hparams, model.tok_norm, model.tok_norm_b, @@ -8469,7 +8425,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -8534,9 +8490,6 @@ struct llm_build_context { // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); - // positions of the tokens in the KV cache - struct ggml_tensor * KQ_pos = build_inp_KQ_pos(); - if (model.pos_embd) { // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -8600,13 +8553,13 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } else { Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } } @@ -8734,15 +8687,15 @@ struct llm_build_context { } - Qcur = ggml_rope_custom( - ctx0, Qcur, inp_pos, + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, Kcur, inp_pos, + Kcur = ggml_rope_ext( + ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -8750,7 +8703,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -8854,21 +8807,21 @@ struct llm_build_context { Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); // using mode = 2 for neox mode - Qcur = ggml_rope_custom( - ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, + Kcur = ggml_rope_ext( + ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Kcur, "Kcur", il); cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -8965,15 +8918,15 @@ struct llm_build_context { Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv); cb(Vcur, "Vcur", il); - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -8981,7 +8934,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -9079,15 +9032,15 @@ struct llm_build_context { Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv); cb(Vcur, "Vcur", il); - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -9095,7 +9048,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -9231,8 +9184,8 @@ struct llm_build_context { Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); - Qcur = ggml_rope_custom( - ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); @@ -9242,15 +9195,15 @@ struct llm_build_context { Qcur = ggml_scale(ctx0, Qcur, 1.0f/sqrtf(float(n_embd_head))); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, + Kcur = ggml_rope_ext( + ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Kcur, "Kcur", il); cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f, cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il); } if (il == n_layer - 1) { @@ -9319,6 +9272,9 @@ struct llm_build_context { // self-attention { + // rope freq factors for 128k context + struct ggml_tensor * rope_factors = build_rope_factors(il); + struct ggml_tensor* attn_norm_output = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm, NULL, @@ -9350,8 +9306,8 @@ struct llm_build_context { Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); - Qcur = ggml_rope_custom( - ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, rope_factors, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); @@ -9359,15 +9315,15 @@ struct llm_build_context { Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head))); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, + Kcur = ggml_rope_ext( + ctx0, Kcur, inp_pos, rope_factors, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Kcur, "Kcur", il); cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f, cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il); } if (il == n_layer - 1) { @@ -9466,21 +9422,21 @@ struct llm_build_context { struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); cb(Vcur, "Vcur", il); - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_rot, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_rot, n_head, n_tokens), inp_pos, nullptr, n_embd_head, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_rot, n_head_kv, n_tokens), inp_pos, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_rot, n_head_kv, n_tokens), inp_pos, nullptr, n_embd_head, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); cb(Kcur, "Kcur", il); cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } struct ggml_tensor * sa_out = cur; @@ -9583,7 +9539,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -9674,15 +9630,15 @@ struct llm_build_context { cb(tmpk, "tmpk", il); cb(Vcur, "Vcur", il); - struct ggml_tensor * Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, n_tokens), inp_pos, + struct ggml_tensor * Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); - struct ggml_tensor * Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens), inp_pos, + struct ggml_tensor * Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -9690,7 +9646,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -9790,15 +9746,15 @@ struct llm_build_context { // cb(Vcur, "Vcur", il); // } - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -9806,7 +9762,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -9907,15 +9863,15 @@ struct llm_build_context { cb(Vcur, "Vcur", il); } - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -9923,7 +9879,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -10037,15 +9993,15 @@ struct llm_build_context { cb(Vcur, "Vcur", il); } - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -10053,7 +10009,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -10157,8 +10113,8 @@ struct llm_build_context { struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); cb(Vcur, "Vcur", il); - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head, n_tokens), inp_pos, nullptr, n_embd_head_k, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); cb(Qcur, "Qcur", il); @@ -10166,15 +10122,15 @@ struct llm_build_context { Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head_k))); cb(Qcur, "Qcur_scaled", il); - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv, n_tokens), inp_pos, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv, n_tokens), inp_pos, nullptr, n_embd_head_k, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); cb(Kcur, "Kcur", il); cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f, cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il); } if (il == n_layer - 1) { @@ -10277,15 +10233,15 @@ struct llm_build_context { cb(Vcur, "Vcur", il); } - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -10293,7 +10249,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -10567,15 +10523,15 @@ struct llm_build_context { cb(Kcur, "Kcur", il); } - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -10583,7 +10539,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -10698,15 +10654,15 @@ struct llm_build_context { cb(Vcur, "Vcur", il); } - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -10714,7 +10670,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, nullptr, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -10770,6 +10726,140 @@ struct llm_build_context { return gf; } + + struct ggml_cgraph * build_gptneox() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = build_inp_pos(); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); + + for (int il = 0; il < n_layer; ++il) { + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, + model.layers[il].attn_norm_b, + LLM_NORM, cb, il); + cb(cur, "attn_norm", il); + + // self-attention + { + cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur); + cb(cur, "wqkv", il); + + cur = ggml_add(ctx0, cur, model.layers[il].bqkv); + cb(cur, "bqkv", il); + + struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd))); + struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd))); + struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa))); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Qcur, "Qcur", il); + + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Kcur, "Kcur", il); + + cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, + model.layers[il].wo, model.layers[il].bo, + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + } + + if (il == n_layer - 1) { + // skip computing output for unused tokens + struct ggml_tensor * inp_out_ids = build_inp_out_ids(); + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); + } + + // ffn + if (hparams.use_par_res) { + // attention and ffn are computed in parallel + // x = x + attn(ln1(x)) + ffn(ln2(x)) + + struct ggml_tensor * attn_out = cur; + + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].ffn_norm, + model.layers[il].ffn_norm_b, + LLM_NORM, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, model.layers[il].ffn_up_b, + NULL, NULL, + model.layers[il].ffn_down, model.layers[il].ffn_down_b, + NULL, + LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); + cb(cur, "ffn_out", il); + + cur = ggml_add(ctx0, cur, inpL); + cb(cur, "ffn_out", il); + + inpL = ggml_add(ctx0, cur, attn_out); + cb(inpL, "l_out", il); + } else { + // attention and ffn are computed sequentially + // x = x + attn(ln1(x)) + // x = x + ffn(ln2(x)) + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL); + cb(ffn_inp, "ffn_inp", il); + + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, + model.layers[il].ffn_norm_b, + LLM_NORM, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, model.layers[il].ffn_up_b, + NULL, NULL, + model.layers[il].ffn_down, model.layers[il].ffn_down_b, + NULL, + LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); + cb(cur, "ffn_out", il); + + inpL = ggml_add(ctx0, cur, ffn_inp); + cb(inpL, "l_out", il); + } + } + + cur = llm_build_norm(ctx0, inpL, hparams, + model.output_norm, + model.output_norm_b, + LLM_NORM, cb, -1); + cb(cur, "result_norm", -1); + + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } }; static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector & ids) { @@ -10886,15 +10976,12 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_starcoder(); } break; - case LLM_ARCH_PERSIMMON: - { - result = llm.build_persimmon(); - } break; case LLM_ARCH_REFACT: { result = llm.build_refact(); } break; case LLM_ARCH_BERT: + case LLM_ARCH_JINA_BERT_V2: case LLM_ARCH_NOMIC_BERT: { result = llm.build_bert(); @@ -10983,6 +11070,10 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_olmo(); } break; + case LLM_ARCH_GPTNEOX: + { + result = llm.build_gptneox(); + } break; default: GGML_ASSERT(false); } @@ -11102,11 +11193,21 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { if (!lctx.kv_self.cells[i].has_seq_id(seq_id) || lctx.kv_self.cells[i].pos > pos) { f = -INFINITY; } else { - f = 0.0f; + if (hparams.use_alibi) { + f = -fabs(lctx.kv_self.cells[i].pos - pos); + } else { + f = 0.0f; + } } data[h*(n_kv*n_tokens) + j*n_kv + i] = f; } } + + for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) { + for (int j = 0; j < n_kv; ++j) { + data[h*(n_kv*n_tokens) + i*n_kv + j] = -INFINITY; + } + } } } else { // when using kv cache, the mask needs to match the kv cache size @@ -11125,7 +11226,11 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { float f = -INFINITY; for (int s = 0; s < batch.n_seq_id[i]; ++s) { if (batch.seq_id[i][s] == seq_id) { - f = 0.0f; + if (hparams.use_alibi) { + f = -fabs(batch.pos[i] - batch.pos[j]); + } else { + f = 0.0f; + } break; } } @@ -11141,21 +11246,6 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { } } - // ALiBi requires the KQ_pos tensor to provide the sequence position of each token in the batch - // this allows to process multiple sequences in parallel with ALiBi-based models - if (hparams.use_alibi) { - const int64_t n_kv = kv_self.n; - - GGML_ASSERT(lctx.inp_KQ_pos); - GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_pos->buffer)); - - float * data = (float *) lctx.inp_KQ_pos->data; - - for (int i = 0; i < n_kv; ++i) { - data[i] = float(lctx.kv_self.cells[i].pos); - } - } - if (cparams.pooling_type == LLAMA_POOLING_TYPE_MEAN) { const int64_t n_tokens = batch.n_tokens; @@ -11329,11 +11419,6 @@ static void llama_graph_compute( llama_context & lctx, ggml_cgraph * gf, int n_threads) { -#ifdef GGML_USE_MPI - const int64_t n_layer = lctx.model.hparams.n_layer; - ggml_mpi_graph_compute_pre(lctx.ctx_mpi, gf, n_layer); -#endif - #ifdef GGML_USE_METAL if (ggml_backend_is_metal(lctx.backend_metal)) { ggml_backend_metal_set_n_cb(lctx.backend_metal, n_threads); @@ -11348,10 +11433,6 @@ static void llama_graph_compute( ggml_backend_sched_graph_compute_async(lctx.sched, gf); // fprintf(stderr, "splits: %d\n", ggml_backend_sched_get_n_splits(lctx.sched)); - -#ifdef GGML_USE_MPI - ggml_mpi_graph_compute_post(lctx.ctx_mpi, gf, n_layer); -#endif } // decode a batch of tokens by evaluating the transformer @@ -11389,12 +11470,6 @@ static int llama_decode_internal( } lctx.n_queued_tokens += n_tokens_all; -#ifdef GGML_USE_MPI - // TODO: needs fix after #3228 - GGML_ASSERT(false && "not implemented"); - //ggml_mpi_eval_init(lctx.ctx_mpi, &n_tokens, &n_past, &n_threads); -#endif - auto & kv_self = lctx.kv_self; const int64_t n_embd = hparams.n_embd; @@ -11525,7 +11600,8 @@ static int llama_decode_internal( // a heuristic, to avoid attending the full cache if it is not yet utilized // after enough generations, the benefit from this heuristic disappears // if we start defragmenting the cache, the benefit from this will be more important - kv_self.n = std::min(kv_self.size, std::max(256u, GGML_PAD(llama_kv_cache_cell_max(kv_self), 256))); + const uint32_t pad = llama_kv_cache_get_padding(cparams); + kv_self.n = std::min(kv_self.size, std::max(pad, GGML_PAD(llama_kv_cache_cell_max(kv_self), pad))); //kv_self.n = llama_kv_cache_cell_max(kv_self); } } @@ -12491,13 +12567,14 @@ struct llm_tokenizer_bpe { void tokenize(const std::string & text, std::vector & output) { int final_prev_index = -1; + bool ignore_merges = false; std::vector word_collection; switch (vocab.type) { case LLAMA_VOCAB_TYPE_BPE: switch (vocab.type_pre) { case LLAMA_VOCAB_PRE_TYPE_LLAMA3: - case LLAMA_VOCAB_PRE_TYPE_DBRX: + ignore_merges = true; word_collection = unicode_regex_split(text, { // original regex from tokenizer.json //"(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+", @@ -12506,6 +12583,12 @@ struct llm_tokenizer_bpe { "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+", }); break; + case LLAMA_VOCAB_PRE_TYPE_DBRX: + word_collection = unicode_regex_split(text, { + // same as llama3 + "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+", + }); + break; case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM: word_collection = unicode_regex_split(text, { "[\r\n]", @@ -12557,6 +12640,7 @@ struct llm_tokenizer_bpe { "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)", }); break; + case LLAMA_VOCAB_PRE_TYPE_STABLELM2: case LLAMA_VOCAB_PRE_TYPE_QWEN2: word_collection = unicode_regex_split(text, { // original regex from tokenizer.json @@ -12589,6 +12673,11 @@ struct llm_tokenizer_bpe { int index = 0; size_t offset = 0; + if (ignore_merges && vocab.token_to_id.find(word) != vocab.token_to_id.end()) { + symbols.emplace_back(llm_symbol{-1, -1, word.c_str(), word.size()}); + offset = word.size(); + } + while (offset < word.size()) { llm_symbol sym; size_t char_len = std::min(word.size() - offset, (size_t) ::utf8_len(word[offset])); @@ -12774,16 +12863,16 @@ struct llm_tokenizer_wpm { // to lowercase, pad chinese characters, pad punctuation std::string new_str = ""; for (uint32_t code : cpts_nfd) { - int type = unicode_cpt_type(code); - if (type == CODEPOINT_TYPE_ACCENT_MARK || type == CODEPOINT_TYPE_CONTROL) { + const codepoint_flags flags = unicode_cpt_flags(code); + if (flags.is_accent_mark || flags.is_control) { continue; } code = unicode_tolower(code); - if (type == CODEPOINT_TYPE_SEPARATOR) { + if (flags.is_separator || flags.is_whitespace) { //####FIXME: is_separator ? code = ' '; } std::string s = unicode_cpt_to_utf8(code); - if (type == CODEPOINT_TYPE_PUNCTUATION || is_ascii_punct(code) || is_chinese_char(code)) { + if (flags.is_punctuation || is_ascii_punct(code) || is_chinese_char(code)) { new_str += " "; new_str += s; new_str += " "; @@ -12986,9 +13075,14 @@ static std::vector llama_tokenize_internal(const llama_vocab & // tokenizer.encode('', add_special_tokens=True) returns [1] // tokenizer.encode('', add_special_tokens=False) returns [] + static const bool rtrim = true; //TODO: as param + bool is_prev_special = false; + bool special_token_rtrim = false; + if (add_special && vocab.special_add_bos != 0) { GGML_ASSERT(vocab.special_bos_id != -1); output.push_back(vocab.special_bos_id); + is_prev_special = true; } for (const auto & fragment : fragment_buffer) { @@ -13000,9 +13094,21 @@ static std::vector llama_tokenize_internal(const llama_vocab & // and passing 'add space prefix' as bool argument // auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length); - if (&fragment == &fragment_buffer.front()) { - if (vocab.add_space_prefix) { - raw_text = " " + raw_text; // prefix with space if the first token is not special + + if (special_token_rtrim) { + size_t num_whitespaces = 0; + while (isspace(raw_text[num_whitespaces])) { + num_whitespaces++; + } + if (num_whitespaces == raw_text.size()) { + continue; // skip if all whitespaces + } + raw_text = raw_text.substr(num_whitespaces); + } + + if (vocab.add_space_prefix) { + if (!output.size() || is_prev_special) { // prefix with space if first token + raw_text = " " + raw_text; } } @@ -13014,9 +13120,22 @@ static std::vector llama_tokenize_internal(const llama_vocab & tokenizer.tokenize(raw_text, output); } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN) output.push_back(fragment.token); + is_prev_special = true; + // phi-3 special tokens without rtrim, works fine for llama-spm too + special_token_rtrim = rtrim + && fragment.token != vocab.special_bos_id + && fragment.token != vocab.special_unk_id + && fragment.token != vocab.special_eos_id; } } + if (add_special && vocab.special_add_bos != 0 && output.size() >= 2 && output[1] == vocab.special_bos_id) { + LLAMA_LOG_WARN( + "%s: Added a BOS token to the prompt as specified by the model but the prompt " + "also starts with a BOS token. So now the final prompt starts with 2 BOS tokens. " + "Are you sure this is what you want?\n", __FUNCTION__); + } + if (add_special && vocab.special_add_eos == 1) { GGML_ASSERT(vocab.special_eos_id != -1); output.push_back(vocab.special_eos_id); @@ -13053,7 +13172,17 @@ static std::vector llama_tokenize_internal(const llama_vocab & } } - GGML_ASSERT(vocab.special_add_eos != 1); + if (add_special && vocab.special_add_bos != 0 && output.size() >= 2 && output[1] == vocab.special_bos_id) { + LLAMA_LOG_WARN( + "%s: Added a BOS token to the prompt as specified by the model but the prompt " + "also starts with a BOS token. So now the final prompt starts with 2 BOS tokens. " + "Are you sure this is what you want?\n", __FUNCTION__); + } + + if (add_special && vocab.special_add_eos == 1) { + GGML_ASSERT(vocab.special_add_eos != -1); + output.push_back(vocab.special_eos_id); + } } break; case LLAMA_VOCAB_TYPE_WPM: { @@ -13407,6 +13536,58 @@ static std::vector llama_grammar_reject_candidates( return rejects; } +static bool llama_grammar_detect_left_recursion( + const std::vector> & rules, + size_t rule_index, + std::vector * rules_visited, + std::vector * rules_in_progress, + std::vector * rules_may_be_empty) { + if ((*rules_in_progress)[rule_index]) { + return true; + } + + (*rules_in_progress)[rule_index] = true; + + const std::vector & rule = rules[rule_index]; + + // First check if the rule might produce the empty string. This could be done combined with the second + // step but it's more readable as two steps. + bool at_rule_start = true; + for (size_t i = 0; i < rule.size(); i++) { + if (llama_grammar_is_end_of_sequence(&rule[i])) { + if (at_rule_start) { + (*rules_may_be_empty)[rule_index] = true; + break; + } + at_rule_start = true; + } else { + at_rule_start = false; + } + } + + // Second, recurse into leftmost nonterminals (or next-leftmost as long as the previous nonterminal may + // be empty) + bool recurse_into_nonterminal = true; + for (size_t i = 0; i < rule.size(); i++) { + if (rule[i].type == LLAMA_GRETYPE_RULE_REF && recurse_into_nonterminal) { + if (llama_grammar_detect_left_recursion(rules, (size_t)rule[i].value, rules_visited, rules_in_progress, rules_may_be_empty)) { + return true; + } + if (!((*rules_may_be_empty)[(size_t)rule[i].value])) { + recurse_into_nonterminal = false; + } + } else if (llama_grammar_is_end_of_sequence(&rule[i])) { + recurse_into_nonterminal = true; + } else { + recurse_into_nonterminal = false; + } + } + + (*rules_in_progress)[rule_index] = false; + (*rules_visited)[rule_index] = true; + return false; +} + // // grammar - external // @@ -13426,6 +13607,19 @@ struct llama_grammar * llama_grammar_init( vec_rules[i].push_back({LLAMA_GRETYPE_END, 0}); } + // Check for left recursion + std::vector rules_visited(n_rules); + std::vector rules_in_progress(n_rules); + std::vector rules_may_be_empty(n_rules); + for (size_t i = 0; i < n_rules; i++) { + if (rules_visited[i]) { + continue; + } + if (llama_grammar_detect_left_recursion(vec_rules, i, &rules_visited, &rules_in_progress, &rules_may_be_empty)) { + throw std::runtime_error(format("unsupported grammar, left recursion detected for nonterminal at index %zu", i)); + } + } + // loop over alternates of start rule to build initial stacks std::vector> stacks; pos = vec_rules[start_rule_index].data(); @@ -13448,6 +13642,9 @@ struct llama_grammar * llama_grammar_init( } } while (true); + // Important: vec_rules has to be moved here, not copied, because stacks contains + // pointers to elements of vec_rules. If vec_rules were copied into llama_grammar + // then the pointers would be invalidated when the local vec_rules goes out of scope. return new llama_grammar{ std::move(vec_rules), std::move(stacks), {} }; } @@ -14056,9 +14253,7 @@ llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_ // Sample the next word X using top-k sampling llama_sample_top_k(nullptr, candidates, int(k), 1); - if (ctx) { - ctx->t_sample_us += ggml_time_us() - t_start_sample_us; - } + ctx->t_sample_us += ggml_time_us() - t_start_sample_us; llama_token X = llama_sample_token(ctx, candidates); t_start_sample_us = ggml_time_us(); @@ -14072,9 +14267,7 @@ llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_ // Update mu using the learning rate and error *mu = *mu - eta * e; - if (ctx) { - ctx->t_sample_us += ggml_time_us() - t_start_sample_us; - } + ctx->t_sample_us += ggml_time_us() - t_start_sample_us; return X; } @@ -14659,8 +14852,6 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) && use_more_bits(qs.i_attention_wv, qs.n_attention_wv)) new_type = GGML_TYPE_Q6_K; else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && qs.i_attention_wv < 4) new_type = GGML_TYPE_Q5_K; - else if (QK_K == 64 && (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S) && - (qs.i_attention_wv < qs.n_attention_wv/8 || qs.i_attention_wv >= 7*qs.n_attention_wv/8)) new_type = GGML_TYPE_Q6_K; if (qs.model.type == MODEL_70B) { // In the 70B model we have 8 heads sharing the same attn_v weights. As a result, the attn_v.weight tensor is // 8x smaller compared to attn_q.weight. Hence, we can get a nice boost in quantization accuracy with @@ -15561,6 +15752,7 @@ struct llama_model_params llama_model_default_params() { /*.split_mode =*/ LLAMA_SPLIT_MODE_LAYER, /*.main_gpu =*/ 0, /*.tensor_split =*/ nullptr, + /*.rpc_servers =*/ nullptr, /*.progress_callback =*/ nullptr, /*.progress_callback_user_data =*/ nullptr, /*.kv_overrides =*/ nullptr, @@ -15644,7 +15836,7 @@ bool llama_supports_mlock(void) { bool llama_supports_gpu_offload(void) { #if defined(GGML_USE_CUDA) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL) || defined(GGML_USE_VULKAN) || \ - defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE) + defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE) || defined(GGML_USE_RPC) // Defined when llama.cpp is compiled with support for offloading model layers to GPU. return true; #else @@ -15661,10 +15853,6 @@ void llama_backend_init(void) { struct ggml_context * ctx = ggml_init(params); ggml_free(ctx); } - -#ifdef GGML_USE_MPI - ggml_mpi_backend_init(); -#endif } void llama_numa_init(enum ggml_numa_strategy numa) { @@ -15674,9 +15862,6 @@ void llama_numa_init(enum ggml_numa_strategy numa) { } void llama_backend_free(void) { -#ifdef GGML_USE_MPI - ggml_mpi_backend_free(); -#endif ggml_quantize_free(); } @@ -15707,7 +15892,17 @@ struct llama_model * llama_load_model_from_file( return true; }; } - + if (params.rpc_servers != nullptr) { + // split the servers set them into model->rpc_servers + std::string servers(params.rpc_servers); + size_t pos = 0; + while ((pos = servers.find(",")) != std::string::npos) { + std::string server = servers.substr(0, pos); + model->rpc_servers.push_back(server); + servers.erase(0, pos + 1); + } + model->rpc_servers.push_back(servers); + } int status = llama_model_load(path_model, *model, params); GGML_ASSERT(status <= 0); if (status < 0) { @@ -15746,6 +15941,11 @@ struct llama_context * llama_new_context_with_model( return nullptr; } + if (params.flash_attn && model->arch == LLM_ARCH_GROK) { + LLAMA_LOG_WARN("%s: flash_attn is not compatible with Grok - forcing off\n", __func__); + params.flash_attn = false; + } + llama_context * ctx = new llama_context(*model); const auto & hparams = model->hparams; @@ -15769,7 +15969,7 @@ struct llama_context * llama_new_context_with_model( cparams.rope_freq_scale = params.rope_freq_scale == 0.0f ? hparams.rope_freq_scale_train : params.rope_freq_scale; // this is necessary due to kv_self.n being padded later during inference - cparams.n_ctx = GGML_PAD(cparams.n_ctx, 256); + cparams.n_ctx = GGML_PAD(cparams.n_ctx, llama_kv_cache_get_padding(cparams)); // with causal attention, the batch size is limited by the context size cparams.n_batch = hparams.causal_attn ? std::min(cparams.n_ctx, params.n_batch) : params.n_batch; @@ -15804,6 +16004,7 @@ struct llama_context * llama_new_context_with_model( cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_YARN ? 1.0f : 0.0f; } + cparams.yarn_attn_factor *= hparams.rope_attn_factor; cparams.causal_attn = hparams.causal_attn; if (cparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) { @@ -15814,16 +16015,6 @@ struct llama_context * llama_new_context_with_model( } } - if (cparams.flash_attn && hparams.use_alibi) { - LLAMA_LOG_WARN("%s: flash_attn is not yet compatible with ALiBi - forcing off\n", __func__); - cparams.flash_attn = false; - } - - if (cparams.flash_attn && model->arch == LLM_ARCH_GROK) { - LLAMA_LOG_WARN("%s: flash_attn is not compatible with Grok - forcing off\n", __func__); - cparams.flash_attn = false; - } - if (params.seed == LLAMA_DEFAULT_SEED) { params.seed = time(NULL); } @@ -15859,7 +16050,17 @@ struct llama_context * llama_new_context_with_model( if (!hparams.vocab_only) { // initialize backends -#ifdef GGML_USE_METAL +#if defined(GGML_USE_RPC) + for (auto & server : model->rpc_servers) { + ggml_backend_t backend = ggml_backend_rpc_init(server.c_str()); + if (backend == nullptr) { + LLAMA_LOG_ERROR("%s: failed to connect RPC backend to %s\n", __func__, server.c_str()); + llama_free(ctx); + return nullptr; + } + ctx->backends.push_back(backend); + } +#elif defined(GGML_USE_METAL) if (model->n_gpu_layers > 0) { ctx->backend_metal = ggml_backend_metal_init(); if (ctx->backend_metal == nullptr) { @@ -16015,7 +16216,11 @@ struct llama_context * llama_new_context_with_model( ctx->buf_compute_meta.resize(ggml_tensor_overhead()*LLAMA_MAX_NODES + ggml_graph_overhead_custom(LLAMA_MAX_NODES, false)); // enabling pipeline parallelism in the scheduler increases memory usage, so it is only done when necessary - bool pipeline_parallel = llama_get_device_count() > 1 && model->n_gpu_layers > (int)model->hparams.n_layer && model->split_mode == LLAMA_SPLIT_MODE_LAYER; + bool pipeline_parallel = + llama_get_device_count(*model) > 1 && + model->n_gpu_layers > (int)model->hparams.n_layer && + model->split_mode == LLAMA_SPLIT_MODE_LAYER && + params.offload_kqv; #ifndef GGML_USE_CUDA // pipeline parallelism requires support for async compute and events // currently this is only implemented in the CUDA backend @@ -16058,20 +16263,6 @@ struct llama_context * llama_new_context_with_model( } } -#ifdef GGML_USE_MPI - ctx->ctx_mpi = ggml_mpi_init(); - - if (ggml_mpi_rank(ctx->ctx_mpi) > 0) { - // Enter a blocking eval loop with dummy input, letting rank=0 drive the process - // TODO: needs fix after #3228 - GGML_ASSERT(false && "not implemented"); - //const std::vector tmp(ctx->model.hparams.n_ctx, llama_token_bos(ctx)); - //while (!llama_eval(ctx, tmp.data(), tmp.size(), 0, 0)) {}; - llama_backend_free(); - exit(1); - } -#endif - return ctx; } @@ -16108,11 +16299,11 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { // these models do not use RoPE case LLM_ARCH_GPT2: case LLM_ARCH_GPTJ: - case LLM_ARCH_GPTNEOX: case LLM_ARCH_MPT: case LLM_ARCH_REFACT: case LLM_ARCH_BLOOM: case LLM_ARCH_MAMBA: + case LLM_ARCH_JINA_BERT_V2: return LLAMA_ROPE_TYPE_NONE; // use what we call a normal RoPE, operating on pairs of consecutive head values @@ -16133,7 +16324,6 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { case LLM_ARCH_FALCON: case LLM_ARCH_GROK: case LLM_ARCH_DBRX: - case LLM_ARCH_PERSIMMON: case LLM_ARCH_BERT: case LLM_ARCH_NOMIC_BERT: case LLM_ARCH_STABLELM: @@ -16144,6 +16334,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { case LLM_ARCH_PHI3: case LLM_ARCH_GEMMA: case LLM_ARCH_STARCODER2: + case LLM_ARCH_GPTNEOX: return LLAMA_ROPE_TYPE_NEOX; // all model arches should be listed explicitly here @@ -16303,6 +16494,7 @@ static bool llama_control_vector_init(struct llama_control_vector & cvec, const } // make tensors + cvec.tensors.reserve(model.hparams.n_layer); cvec.tensors.push_back(nullptr); // there's never a tensor for layer 0 for (size_t il = 1; il < model.hparams.n_layer; il++) { struct ggml_context * ctx = ctx_map.at(model.buft_layer[il].buft); @@ -16311,6 +16503,8 @@ static bool llama_control_vector_init(struct llama_control_vector & cvec, const } // allocate tensors / buffers and zero + cvec.ctxs.reserve(ctx_map.size()); + cvec.bufs.reserve(ctx_map.size()); for (auto it : ctx_map) { ggml_backend_buffer_type_t buft = it.first; ggml_context * ctx = it.second; @@ -17134,13 +17328,13 @@ static size_t llama_state_seq_get_data_internal(struct llama_context * ctx, llam } else { if (cell_range_begin != kv_self.size) { - cell_ranges.push_back({ cell_range_begin, i }); + cell_ranges.emplace_back(cell_range_begin, i); cell_range_begin = kv_self.size; } } } if (cell_range_begin != kv_self.size) { - cell_ranges.push_back({ cell_range_begin, kv_self.size }); + cell_ranges.emplace_back(cell_range_begin, kv_self.size); } // DEBUG CHECK: Sum of cell counts in ranges should equal the total cell count @@ -17530,6 +17724,14 @@ void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_ ctx->cparams.n_threads_batch = n_threads_batch; } +uint32_t llama_n_threads(struct llama_context * ctx) { + return ctx->cparams.n_threads; +} + +uint32_t llama_n_threads_batch(struct llama_context * ctx) { + return ctx->cparams.n_threads_batch; +} + void llama_set_abort_callback(struct llama_context * ctx, bool (*abort_callback)(void * data), void * abort_callback_data) { ctx->abort_callback = abort_callback; ctx->abort_callback_data = abort_callback_data; @@ -17968,6 +18170,15 @@ static int32_t llama_chat_apply_template_internal( } } // llama2 templates seem to not care about "add_generation_prompt" + } else if (tmpl == "phi3" || (tmpl.find("<|assistant|>") != std::string::npos && tmpl.find("<|end|>") != std::string::npos)) { + // Phi 3 + for (auto message : chat) { + std::string role(message->role); + ss << "<|" << role << "|>\n" << message->content << "<|end|>\n"; + } + if (add_ass) { + ss << "<|assistant|>\n"; + } } else if (tmpl == "zephyr" || tmpl.find("<|user|>") != std::string::npos) { // zephyr template for (auto message : chat) { @@ -18100,15 +18311,6 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "<|start_header_id|>assistant<|end_header_id|>\n\n"; } - } else if (tmpl == "phi3" || (tmpl.find("<|assistant|>") != std::string::npos && tmpl.find("<|end|>") != std::string::npos )) { - // Phi 3 - for (auto message : chat) { - std::string role(message->role); - ss << "<|" << role << "|>\n" << trim(message->content) << "<|end|>\n"; - } - if (add_ass) { - ss << "<|assistant|>\n"; - } } else { // template not supported return -1; @@ -18230,6 +18432,7 @@ const char * llama_print_system_info(void) { s += "AVX512 = " + std::to_string(ggml_cpu_has_avx512()) + " | "; s += "AVX512_VBMI = " + std::to_string(ggml_cpu_has_avx512_vbmi()) + " | "; s += "AVX512_VNNI = " + std::to_string(ggml_cpu_has_avx512_vnni()) + " | "; + s += "AVX512_BF16 = " + std::to_string(ggml_cpu_has_avx512_bf16()) + " | "; s += "FMA = " + std::to_string(ggml_cpu_has_fma()) + " | "; s += "NEON = " + std::to_string(ggml_cpu_has_neon()) + " | "; s += "ARM_FMA = " + std::to_string(ggml_cpu_has_arm_fma()) + " | "; @@ -18290,6 +18493,8 @@ void llama_log_set(ggml_log_callback log_callback, void * user_data) { g_state.log_callback_user_data = user_data; #ifdef GGML_USE_METAL ggml_backend_metal_log_set_callback(g_state.log_callback, g_state.log_callback_user_data); +#elif defined(GGML_USE_CUDA) + ggml_backend_cuda_log_set_callback(g_state.log_callback, g_state.log_callback_user_data); #endif } diff --git a/llama.h b/llama.h index a9f5cbb264927..f59505d9f00c7 100644 --- a/llama.h +++ b/llama.h @@ -81,9 +81,10 @@ extern "C" { LLAMA_VOCAB_PRE_TYPE_GPT2 = 7, LLAMA_VOCAB_PRE_TYPE_REFACT = 8, LLAMA_VOCAB_PRE_TYPE_COMMAND_R = 9, - LLAMA_VOCAB_PRE_TYPE_QWEN2 = 10, - LLAMA_VOCAB_PRE_TYPE_OLMO = 11, - LLAMA_VOCAB_PRE_TYPE_DBRX = 12, + LLAMA_VOCAB_PRE_TYPE_STABLELM2 = 10, + LLAMA_VOCAB_PRE_TYPE_QWEN2 = 11, + LLAMA_VOCAB_PRE_TYPE_OLMO = 12, + LLAMA_VOCAB_PRE_TYPE_DBRX = 13, }; // note: these values should be synchronized with ggml_rope @@ -242,6 +243,9 @@ extern "C" { // proportion of the model (layers or rows) to offload to each GPU, size: llama_max_devices() const float * tensor_split; + // comma separated list of RPC servers to use for offloading + const char * rpc_servers; + // Called with a progress value between 0.0 and 1.0. Pass NULL to disable. // If the provided progress_callback returns true, model loading continues. // If it returns false, model loading is immediately aborted. @@ -757,6 +761,12 @@ extern "C" { // n_threads_batch is the number of threads used for prompt and batch processing (multiple tokens) LLAMA_API void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_t n_threads_batch); + // Get the number of threads used for generation of a single token. + LLAMA_API uint32_t llama_n_threads(struct llama_context * ctx); + + // Get the number of threads used for prompt and batch processing (multiple token). + LLAMA_API uint32_t llama_n_threads_batch(struct llama_context * ctx); + // Set whether to use causal attention or not // If set to true, the model will only attend to the past tokens LLAMA_API void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn); diff --git a/make_pyinstaller.bat b/make_pyinstaller.bat index 40976d68c685b..e057ada369b73 100644 --- a/make_pyinstaller.bat +++ b/make_pyinstaller.bat @@ -1 +1 @@ -PyInstaller --noconfirm --onefile --clean --console --collect-all customtkinter --icon "./niko.ico" --add-data "./winclinfo.exe;." --add-data "./OpenCL.dll;." --add-data "./klite.embd;." --add-data "./kcpp_docs.embd;." --add-data="./kcpp_sdui.embd;." --add-data "./koboldcpp_default.dll;." --add-data "./koboldcpp_openblas.dll;." --add-data "./koboldcpp_failsafe.dll;." --add-data "./koboldcpp_noavx2.dll;." --add-data "./libopenblas.dll;." --add-data "./koboldcpp_clblast.dll;." --add-data "./koboldcpp_clblast_noavx2.dll;." --add-data "./koboldcpp_vulkan_noavx2.dll;." --add-data "./clblast.dll;." --add-data "./koboldcpp_vulkan.dll;." --add-data "./vulkan-1.dll;." --add-data "./rwkv_vocab.embd;." --add-data "./rwkv_world_vocab.embd;." "./koboldcpp.py" -n "koboldcpp_nocuda.exe" \ No newline at end of file +PyInstaller --noconfirm --onefile --clean --console --collect-all customtkinter --collect-all psutil --icon "./niko.ico" --add-data "./winclinfo.exe;." --add-data "./OpenCL.dll;." --add-data "./klite.embd;." --add-data "./kcpp_docs.embd;." --add-data="./kcpp_sdui.embd;." --add-data="./taesd.embd;." --add-data="./taesd_xl.embd;." --add-data "./koboldcpp_default.dll;." --add-data "./koboldcpp_openblas.dll;." --add-data "./koboldcpp_failsafe.dll;." --add-data "./koboldcpp_noavx2.dll;." --add-data "./libopenblas.dll;." --add-data "./koboldcpp_clblast.dll;." --add-data "./koboldcpp_clblast_noavx2.dll;." --add-data "./koboldcpp_vulkan_noavx2.dll;." --add-data "./clblast.dll;." --add-data "./koboldcpp_vulkan.dll;." --add-data "./vulkan-1.dll;." --add-data "./rwkv_vocab.embd;." --add-data "./rwkv_world_vocab.embd;." "./koboldcpp.py" -n "koboldcpp_nocuda.exe" \ No newline at end of file diff --git a/make_pyinstaller.sh b/make_pyinstaller.sh index 78ab66828a31c..62c303abdbb1c 100755 --- a/make_pyinstaller.sh +++ b/make_pyinstaller.sh @@ -7,10 +7,12 @@ sleep 4 pip install pyinstaller customtkinter && make clean && \ # Ensure all backends are built then build executable file make LLAMA_HIPBLAS=1 LLAMA_CLBLAST=1 LLAMA_OPENBLAS=1 -j$NUMCPUS && \ -pyinstaller --noconfirm --onefile --clean --console --collect-all customtkinter --collect-all libclblast-dev --collect-all libopenblas-dev --collect-all clinfo --icon ".\niko.ico" \ +pyinstaller --noconfirm --onefile --clean --console --collect-all customtkinter --collect-all psutil --collect-all libclblast-dev --collect-all libopenblas-dev --collect-all clinfo --icon ".\niko.ico" \ --add-data "./klite.embd:." \ --add-data "./kcpp_docs.embd:." \ --add-data "./kcpp_sdui.embd:." \ +--add-data "./taesd.embd:." \ +--add-data "./taesd_xl.embd:." \ --add-data "./koboldcpp_default.so:." \ --add-data "./koboldcpp_openblas.so:." \ --add-data "./koboldcpp_failsafe.so:." \ diff --git a/make_pyinstaller_cuda.bat b/make_pyinstaller_cuda.bat index 7323e206b4bcf..b1a2a4d50cdb4 100644 --- a/make_pyinstaller_cuda.bat +++ b/make_pyinstaller_cuda.bat @@ -1 +1 @@ -PyInstaller --noconfirm --onefile --clean --console --collect-all customtkinter --icon "./nikogreen.ico" --add-data "./winclinfo.exe;." --add-data "./OpenCL.dll;." --add-data "./klite.embd;." --add-data "./kcpp_docs.embd;." --add-data="./kcpp_sdui.embd;." --add-data "./koboldcpp_default.dll;." --add-data "./koboldcpp_openblas.dll;." --add-data "./koboldcpp_failsafe.dll;." --add-data "./koboldcpp_noavx2.dll;." --add-data "./libopenblas.dll;." --add-data "./koboldcpp_clblast.dll;." --add-data "./koboldcpp_clblast_noavx2.dll;." --add-data "./koboldcpp_vulkan_noavx2.dll;." --add-data "./clblast.dll;." --add-data "./koboldcpp_vulkan.dll;." --add-data "./vulkan-1.dll;." --add-data "./koboldcpp_cublas.dll;." --add-data "./cublas64_11.dll;." --add-data "./cublasLt64_11.dll;." --add-data "./cudart64_110.dll;." --add-data "./msvcp140.dll;." --add-data "./msvcp140_codecvt_ids.dll;." --add-data "./vcruntime140.dll;." --add-data "./vcruntime140_1.dll;." --add-data "./rwkv_vocab.embd;." --add-data "./rwkv_world_vocab.embd;." "./koboldcpp.py" -n "koboldcpp.exe" \ No newline at end of file +PyInstaller --noconfirm --onefile --clean --console --collect-all customtkinter --collect-all psutil --icon "./nikogreen.ico" --add-data "./winclinfo.exe;." --add-data "./OpenCL.dll;." --add-data "./klite.embd;." --add-data "./kcpp_docs.embd;." --add-data="./kcpp_sdui.embd;." --add-data="./taesd.embd;." --add-data="./taesd_xl.embd;." --add-data "./koboldcpp_default.dll;." --add-data "./koboldcpp_openblas.dll;." --add-data "./koboldcpp_failsafe.dll;." --add-data "./koboldcpp_noavx2.dll;." --add-data "./libopenblas.dll;." --add-data "./koboldcpp_clblast.dll;." --add-data "./koboldcpp_clblast_noavx2.dll;." --add-data "./koboldcpp_vulkan_noavx2.dll;." --add-data "./clblast.dll;." --add-data "./koboldcpp_vulkan.dll;." --add-data "./vulkan-1.dll;." --add-data "./koboldcpp_cublas.dll;." --add-data "./cublas64_11.dll;." --add-data "./cublasLt64_11.dll;." --add-data "./cudart64_110.dll;." --add-data "./msvcp140.dll;." --add-data "./msvcp140_codecvt_ids.dll;." --add-data "./vcruntime140.dll;." --add-data "./vcruntime140_1.dll;." --add-data "./rwkv_vocab.embd;." --add-data "./rwkv_world_vocab.embd;." "./koboldcpp.py" -n "koboldcpp.exe" \ No newline at end of file diff --git a/make_pyinstaller_cuda12.bat b/make_pyinstaller_cuda12.bat index 02af538bb864b..62147e197dbda 100644 --- a/make_pyinstaller_cuda12.bat +++ b/make_pyinstaller_cuda12.bat @@ -1 +1 @@ -PyInstaller --noconfirm --onefile --clean --console --collect-all customtkinter --icon "./nikogreen.ico" --add-data "./winclinfo.exe;." --add-data "./OpenCL.dll;." --add-data "./klite.embd;." --add-data "./kcpp_docs.embd;." --add-data="./kcpp_sdui.embd;." --add-data "./koboldcpp_default.dll;." --add-data "./koboldcpp_openblas.dll;." --add-data "./koboldcpp_failsafe.dll;." --add-data "./koboldcpp_noavx2.dll;." --add-data "./libopenblas.dll;." --add-data "./koboldcpp_clblast.dll;." --add-data "./koboldcpp_clblast_noavx2.dll;." --add-data "./koboldcpp_vulkan_noavx2.dll;." --add-data "./clblast.dll;." --add-data "./koboldcpp_vulkan.dll;." --add-data "./vulkan-1.dll;." --add-data "./koboldcpp_cublas.dll;." --add-data "./cublas64_12.dll;." --add-data "./cublasLt64_12.dll;." --add-data "./cudart64_12.dll;." --add-data "./msvcp140.dll;." --add-data "./msvcp140_codecvt_ids.dll;." --add-data "./vcruntime140.dll;." --add-data "./vcruntime140_1.dll;." --add-data "./rwkv_vocab.embd;." --add-data "./rwkv_world_vocab.embd;." "./koboldcpp.py" -n "koboldcpp_cu12.exe" \ No newline at end of file +PyInstaller --noconfirm --onefile --clean --console --collect-all customtkinter --collect-all psutil --icon "./nikogreen.ico" --add-data "./winclinfo.exe;." --add-data "./OpenCL.dll;." --add-data "./klite.embd;." --add-data "./kcpp_docs.embd;." --add-data="./kcpp_sdui.embd;." --add-data="./taesd.embd;." --add-data="./taesd_xl.embd;." --add-data "./koboldcpp_default.dll;." --add-data "./koboldcpp_openblas.dll;." --add-data "./koboldcpp_failsafe.dll;." --add-data "./koboldcpp_noavx2.dll;." --add-data "./libopenblas.dll;." --add-data "./koboldcpp_clblast.dll;." --add-data "./koboldcpp_clblast_noavx2.dll;." --add-data "./koboldcpp_vulkan_noavx2.dll;." --add-data "./clblast.dll;." --add-data "./koboldcpp_vulkan.dll;." --add-data "./vulkan-1.dll;." --add-data "./koboldcpp_cublas.dll;." --add-data "./cublas64_12.dll;." --add-data "./cublasLt64_12.dll;." --add-data "./cudart64_12.dll;." --add-data "./msvcp140.dll;." --add-data "./msvcp140_codecvt_ids.dll;." --add-data "./vcruntime140.dll;." --add-data "./vcruntime140_1.dll;." --add-data "./rwkv_vocab.embd;." --add-data "./rwkv_world_vocab.embd;." "./koboldcpp.py" -n "koboldcpp_cu12.exe" \ No newline at end of file diff --git a/otherarch/rwkv_vocab.cpp b/otherarch/rwkv_vocab.cpp index 1df437bfe2489..7df753616d610 100644 --- a/otherarch/rwkv_vocab.cpp +++ b/otherarch/rwkv_vocab.cpp @@ -31,7 +31,7 @@ static std::string hexToUnicode(const std::string& hexString) { void read_rwkv_vocab() { std::string line; - auto filepath = executable_path+ "rwkv_vocab.embd"; + auto filepath = executable_path + "rwkv_vocab.embd"; printf("\nReading vocab from %s",filepath.c_str()); std::ifstream myfile(filepath); if (myfile.is_open()) @@ -61,7 +61,7 @@ void read_rwkv_world_vocab() //its in hexadecimal { std::string line; std::string unicodeString; - auto filepath = executable_path+ "rwkv_world_vocab.embd"; + auto filepath = executable_path + "rwkv_world_vocab.embd"; printf("\nReading world vocab from %s",filepath.c_str()); std::ifstream myfile(filepath); if (myfile.is_open()) diff --git a/otherarch/sdcpp/ggml_extend.hpp b/otherarch/sdcpp/ggml_extend.hpp index 71f91fe58f342..3851b0e573640 100644 --- a/otherarch/sdcpp/ggml_extend.hpp +++ b/otherarch/sdcpp/ggml_extend.hpp @@ -701,8 +701,8 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_nn_timestep_embedding( // virtual struct ggml_cgraph* get_ggml_cgraph() = 0; // }; -#define MAX_PARAMS_TENSOR_NUM 10240 -#define MAX_GRAPH_SIZE 10240 +#define MAX_PARAMS_TENSOR_NUM 15360 +#define MAX_GRAPH_SIZE 15360 struct GGMLModule { protected: diff --git a/otherarch/sdcpp/lora.hpp b/otherarch/sdcpp/lora.hpp index 734635b66a961..36b5ee63818a3 100644 --- a/otherarch/sdcpp/lora.hpp +++ b/otherarch/sdcpp/lora.hpp @@ -82,7 +82,16 @@ struct LoraModel : public GGMLModule { } k_tensor = k_tensor.substr(0, k_pos); replace_all_chars(k_tensor, '.', '_'); - std::string lora_up_name = "lora." + k_tensor + ".lora_up.weight"; + // LOG_DEBUG("k_tensor %s", k_tensor.c_str()); + std::string lora_up_name = "lora." + k_tensor + ".lora_up.weight"; + if (lora_tensors.find(lora_up_name) == lora_tensors.end()) { + if (k_tensor == "model_diffusion_model_output_blocks_2_2_conv") { + // fix for some sdxl lora, like lcm-lora-xl + k_tensor = "model_diffusion_model_output_blocks_2_1_conv"; + lora_up_name = "lora." + k_tensor + ".lora_up.weight"; + } + } + std::string lora_down_name = "lora." + k_tensor + ".lora_down.weight"; std::string alpha_name = "lora." + k_tensor + ".alpha"; std::string scale_name = "lora." + k_tensor + ".scale"; diff --git a/otherarch/sdcpp/model.cpp b/otherarch/sdcpp/model.cpp index 07ed53d19a8c4..b1d6b26eed77e 100644 --- a/otherarch/sdcpp/model.cpp +++ b/otherarch/sdcpp/model.cpp @@ -108,14 +108,14 @@ std::unordered_map open_clip_to_hf_clip_model = { {"model.positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"}, {"model.token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"}, {"model.text_projection", "transformer.text_model.text_projection"}, - {"model.visual.class_embedding", "transformer.visual_model.embeddings.class_embedding"}, - {"model.visual.conv1.weight", "transformer.visual_model.embeddings.patch_embedding.weight"}, - {"model.visual.ln_post.bias", "transformer.visual_model.post_layernorm.bias"}, - {"model.visual.ln_post.weight", "transformer.visual_model.post_layernorm.weight"}, - {"model.visual.ln_pre.bias", "transformer.visual_model.pre_layernorm.bias"}, - {"model.visual.ln_pre.weight", "transformer.visual_model.pre_layernorm.weight"}, - {"model.visual.positional_embedding", "transformer.visual_model.embeddings.position_embedding.weight"}, - {"model.visual.proj", "transformer.visual_model.visual_projection"}, + {"model.visual.class_embedding", "transformer.vision_model.embeddings.class_embedding"}, + {"model.visual.conv1.weight", "transformer.vision_model.embeddings.patch_embedding.weight"}, + {"model.visual.ln_post.bias", "transformer.vision_model.post_layernorm.bias"}, + {"model.visual.ln_post.weight", "transformer.vision_model.post_layernorm.weight"}, + {"model.visual.ln_pre.bias", "transformer.vision_model.pre_layernorm.bias"}, + {"model.visual.ln_pre.weight", "transformer.vision_model.pre_layernorm.weight"}, + {"model.visual.positional_embedding", "transformer.vision_model.embeddings.position_embedding.weight"}, + {"model.visual.proj", "transformer.visual_projection.weight"}, }; std::unordered_map open_clip_to_hk_clip_resblock = { @@ -157,6 +157,10 @@ std::string convert_open_clip_to_hf_clip(const std::string& name) { } else if (starts_with(new_name, "cond_stage_model.")) { prefix = "cond_stage_model."; new_name = new_name.substr(strlen("cond_stage_model.")); + } else if (ends_with(new_name, "vision_model.visual_projection.weight")) { + prefix = new_name.substr(0, new_name.size() - strlen("vision_model.visual_projection.weight")); + new_name = prefix + "visual_projection.weight"; + return new_name; } else { return new_name; } @@ -186,7 +190,7 @@ std::string convert_open_clip_to_hf_clip(const std::string& name) { replace_suffix(); open_clip_resblock_prefix = "model.visual.transformer.resblocks."; - hf_clip_resblock_prefix = "transformer.visual_model.encoder.layers."; + hf_clip_resblock_prefix = "transformer.vision_model.encoder.layers."; replace_suffix(); @@ -200,6 +204,25 @@ std::string convert_vae_decoder_name(const std::string& name) { return name; } +/* If not a SDXL LoRA the unet" prefix will have already been replaced by this + * point and "te2" and "te1" don't seem to appear in non-SDXL only "te_" */ +std::string convert_sdxl_lora_name(std::string tensor_name) { + const std::pair sdxl_lora_name_lookup[] = { + {"unet", "model_diffusion_model"}, + {"te2", "cond_stage_model_1_transformer"}, + {"te1", "cond_stage_model_transformer"}, + {"text_encoder_2", "cond_stage_model_1_transformer"}, + {"text_encoder", "cond_stage_model_transformer"}, + }; + for (auto& pair_i : sdxl_lora_name_lookup) { + if (tensor_name.compare(0, pair_i.first.length(), pair_i.first) == 0) { + tensor_name = std::regex_replace(tensor_name, std::regex(pair_i.first), pair_i.second); + break; + } + } + return tensor_name; +} + std::unordered_map> suffix_conversion_underline = { { "attentions", @@ -248,7 +271,7 @@ std::unordered_map> su }, }; -std::string convert_diffusers_name_to_compvis(const std::string& key, char seq) { +std::string convert_diffusers_name_to_compvis(std::string key, char seq) { std::vector m; auto match = [](std::vector& match_list, const std::regex& regex, const std::string& key) { @@ -282,6 +305,11 @@ std::string convert_diffusers_name_to_compvis(const std::string& key, char seq) return inner_key; }; + // convert attn to out + if (ends_with(key, "to_out")) { + key += format("%c0", seq); + } + // unet if (match(m, std::regex(format("unet%cconv_in(.*)", seq)), key)) { return format("model%cdiffusion_model%cinput_blocks%c0%c0", seq, seq, seq, seq) + m[0]; @@ -391,8 +419,8 @@ std::string convert_diffusers_name_to_compvis(const std::string& key, char seq) } std::string convert_tensor_name(const std::string& name) { - std::string new_name; - if (starts_with(name, "cond_stage_model.") || starts_with(name, "conditioner.embedders.")) { + std::string new_name = name; + if (starts_with(name, "cond_stage_model.") || starts_with(name, "conditioner.embedders.") || ends_with(name, ".vision_model.visual_projection.weight")) { new_name = convert_open_clip_to_hf_clip(name); } else if (starts_with(name, "first_stage_model.decoder")) { new_name = convert_vae_decoder_name(name); @@ -406,8 +434,12 @@ std::string convert_tensor_name(const std::string& name) { if (pos != std::string::npos) { std::string name_without_network_parts = name.substr(5, pos - 5); std::string network_part = name.substr(pos + 1); + // LOG_DEBUG("%s %s", name_without_network_parts.c_str(), network_part.c_str()); std::string new_key = convert_diffusers_name_to_compvis(name_without_network_parts, '_'); + /* For dealing with the new SDXL LoRA tensor naming convention */ + new_key = convert_sdxl_lora_name(new_key); + if (new_key.empty()) { new_name = name; } else { @@ -416,6 +448,33 @@ std::string convert_tensor_name(const std::string& name) { } else { new_name = name; } + } else if (contains(name, "lora_up") || contains(name, "lora_down") || + contains(name, "lora.up") || contains(name, "lora.down") || + contains(name, "lora_linear")) { + size_t pos = new_name.find(".processor"); + if (pos != std::string::npos) { + new_name.replace(pos, strlen(".processor"), ""); + } + pos = new_name.rfind("lora"); + if (pos != std::string::npos) { + std::string name_without_network_parts = new_name.substr(0, pos - 1); + std::string network_part = new_name.substr(pos); + // LOG_DEBUG("%s %s", name_without_network_parts.c_str(), network_part.c_str()); + std::string new_key = convert_diffusers_name_to_compvis(name_without_network_parts, '.'); + new_key = convert_sdxl_lora_name(new_key); + replace_all_chars(new_key, '.', '_'); + size_t npos = network_part.rfind("_linear_layer"); + if (npos != std::string::npos) { + network_part.replace(npos, strlen("_linear_layer"), ""); + } + if (starts_with(network_part, "lora.")) { + network_part = "lora_" + network_part.substr(5); + } + if (new_key.size() > 0) { + new_name = "lora." + new_key + "." + network_part; + } + // LOG_DEBUG("new name: %s", new_name.c_str()); + } } else if (starts_with(name, "unet") || starts_with(name, "vae") || starts_with(name, "te")) { // for diffuser size_t pos = name.find_last_of('.'); if (pos != std::string::npos) { @@ -832,8 +891,12 @@ bool ModelLoader::init_from_safetensors_file(const std::string& file_path, const } } - TensorStorage tensor_storage(prefix + name, type, ne, n_dims, file_index, ST_HEADER_SIZE_LEN + header_size_ + begin); + // ggml_n_dims returns 1 for scalars + if (n_dims == 0) { + n_dims = 1; + } + TensorStorage tensor_storage(prefix + name, type, ne, n_dims, file_index, ST_HEADER_SIZE_LEN + header_size_ + begin); tensor_storage.reverse_ne(); size_t tensor_data_size = end - begin; @@ -1172,7 +1235,9 @@ bool ModelLoader::parse_data_pkl(uint8_t* buffer, if (reader.phase == PickleTensorReader::READ_DIMENS) { reader.tensor_storage.reverse_ne(); reader.tensor_storage.file_index = file_index; - reader.tensor_storage.name = prefix + reader.tensor_storage.name; + // if(strcmp(prefix.c_str(), "scarlett") == 0) + // printf(" got tensor %s \n ", reader.tensor_storage.name.c_str()); + reader.tensor_storage.name = prefix + reader.tensor_storage.name; tensor_storages.push_back(reader.tensor_storage); // LOG_DEBUG("%s", reader.tensor_storage.name.c_str()); // reset @@ -1275,7 +1340,8 @@ std::string ModelLoader::load_merges() { return merges_utf8_str; } -void remove_duplicates(std::vector& vec) { +std::vector remove_duplicates(const std::vector& vec) { + std::vector res; std::unordered_map name_to_index_map; for (size_t i = 0; i < vec.size(); ++i) { @@ -1283,13 +1349,16 @@ void remove_duplicates(std::vector& vec) { auto it = name_to_index_map.find(current_name); if (it != name_to_index_map.end()) { - vec[it->second] = vec[i]; + res[it->second] = vec[i]; } else { name_to_index_map[current_name] = i; + res.push_back(vec[i]); } } - vec.resize(name_to_index_map.size()); + // vec.resize(name_to_index_map.size()); + + return res; } bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, ggml_backend_t backend) { @@ -1303,7 +1372,9 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, ggml_backend preprocess_tensor(tensor_storage, processed_tensor_storages); } - remove_duplicates(processed_tensor_storages); + std::vector dedup = remove_duplicates(processed_tensor_storages); + processed_tensor_storages = dedup; + bool success = true; for (size_t file_index = 0; file_index < file_paths_.size(); file_index++) { std::string file_path = file_paths_[file_index]; @@ -1365,7 +1436,6 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, ggml_backend if (tensor_storage.file_index != file_index) { continue; } - ggml_tensor* dst_tensor = NULL; success = on_new_tensor_cb(tensor_storage, &dst_tensor); diff --git a/otherarch/sdcpp/sdtype_adapter.cpp b/otherarch/sdcpp/sdtype_adapter.cpp index daa4ece48a021..16848422f68e4 100644 --- a/otherarch/sdcpp/sdtype_adapter.cpp +++ b/otherarch/sdcpp/sdtype_adapter.cpp @@ -150,7 +150,24 @@ std::string base64_encode(const unsigned char* data, unsigned int data_length) { static std::string sdplatformenv, sddeviceenv, sdvulkandeviceenv; bool sdtype_load_model(const sd_load_model_inputs inputs) { + executable_path = inputs.executable_path; + std::string taesdpath = ""; + std::string lorafilename = inputs.lora_filename; + std::string vaefilename = inputs.vae_filename; printf("\nImageGen Init - Load Model: %s\n",inputs.model_filename); + if(lorafilename!="") + { + printf("With LoRA: %s at %f power\n",lorafilename.c_str(),inputs.lora_multiplier); + } + if(inputs.taesd) + { + taesdpath = executable_path + "taesd.embd"; + printf("With TAE SD VAE: %s\n",taesdpath.c_str()); + } + else if(vaefilename!="") + { + printf("With Custom VAE: %s\n",vaefilename.c_str()); + } //duplicated from expose.cpp int cl_parseinfo = inputs.clblast_info; //first digit is whether configured, second is platform, third is devices @@ -184,6 +201,8 @@ bool sdtype_load_model(const sd_load_model_inputs inputs) { sd_params->n_threads = inputs.threads; //if -1 use physical cores sd_params->input_path = ""; //unused sd_params->batch_count = 1; + sd_params->vae_path = vaefilename; + sd_params->taesd_path = taesdpath; sddebugmode = inputs.debugmode; @@ -230,6 +249,13 @@ bool sdtype_load_model(const sd_load_model_inputs inputs) { return false; } + if(lorafilename!="" && inputs.lora_multiplier>0) + { + printf("\nApply LoRA...\n"); + // sd_ctx->sd->set_pending_lora(lorafilename,inputs.lora_multiplier); + sd_ctx->sd->apply_lora_from_file(lorafilename,inputs.lora_multiplier); + } + return true; } @@ -279,6 +305,7 @@ sd_generation_outputs sdtype_generate(const sd_generation_inputs inputs) sd_params->width = inputs.width; sd_params->height = inputs.height; sd_params->strength = inputs.denoising_strength; + sd_params->clip_skip = inputs.clip_skip; sd_params->mode = (img2img_data==""?SDMode::TXT2IMG:SDMode::IMG2IMG); //for img2img diff --git a/otherarch/sdcpp/stable-diffusion.cpp b/otherarch/sdcpp/stable-diffusion.cpp index c9fb183001076..5a70ed4fa9ef6 100644 --- a/otherarch/sdcpp/stable-diffusion.cpp +++ b/otherarch/sdcpp/stable-diffusion.cpp @@ -57,6 +57,9 @@ void calculate_alphas_cumprod(float* alphas_cumprod, /*=============================================== StableDiffusionGGML ================================================*/ +static std::string pending_apply_lora_fname = ""; +static float pending_apply_lora_power = 1.0f; + class StableDiffusionGGML { public: ggml_backend_t backend = NULL; // general backend @@ -87,6 +90,7 @@ class StableDiffusionGGML { // lora_name => multiplier std::unordered_map curr_lora_state; + std::shared_ptr denoiser = std::make_shared(); StableDiffusionGGML() = default; @@ -121,6 +125,7 @@ class StableDiffusionGGML { schedule_t schedule, bool control_net_cpu) { use_tiny_autoencoder = taesd_path.size() > 0; + std::string taesd_path_fixed = taesd_path; #ifdef SD_USE_CUBLAS LOG_DEBUG("Using CUDA backend"); backend = ggml_backend_cuda_init(0); @@ -165,6 +170,17 @@ class StableDiffusionGGML { return false; } LOG_INFO("Stable Diffusion %s ", model_version_to_str[version]); + + if(use_tiny_autoencoder && version==VERSION_XL) + { + std::string to_search = "taesd.embd"; + std::string to_replace = "taesd_xl.embd"; + size_t pos = taesd_path_fixed.find(to_search); + if (pos != std::string::npos) { + taesd_path_fixed.replace(pos, to_search.length(), to_replace); + } + } + if (wtype == GGML_TYPE_COUNT) { model_data_type = model_loader.get_sd_wtype(); } else { @@ -175,7 +191,7 @@ class StableDiffusionGGML { if (version == VERSION_XL) { scale_factor = 0.13025f; - if (vae_path.size() == 0 && taesd_path.size() == 0) { + if (vae_path.size() == 0 && taesd_path_fixed.size() == 0) { LOG_WARN( "!!!It looks like you are using SDXL model. " "If you find that the generated images are completely black, " @@ -287,7 +303,7 @@ class StableDiffusionGGML { if (!use_tiny_autoencoder) { vae_params_mem_size = first_stage_model->get_params_mem_size(); } else { - if (!tae_first_stage->load_from_file(taesd_path)) { + if (!tae_first_stage->load_from_file(taesd_path_fixed)) { return false; } vae_params_mem_size = tae_first_stage->get_params_mem_size(); @@ -390,6 +406,38 @@ class StableDiffusionGGML { return result < -1; } + void set_pending_lora(const std::string& lora_path, float multiplier) { + pending_apply_lora_fname = lora_path; + pending_apply_lora_power = multiplier; + } + + void apply_lora_from_file(const std::string& lora_path, float multiplier) { + int64_t t0 = ggml_time_ms(); + std::string st_file_path = lora_path; + std::string file_path; + if (file_exists(st_file_path)) { + file_path = st_file_path; + } else { + LOG_WARN("can not find %s for lora %s", st_file_path.c_str(), lora_path.c_str()); + return; + } + LoraModel lora(backend, model_data_type, file_path); + if (!lora.load_from_file()) { + LOG_WARN("load lora tensors from %s failed", file_path.c_str()); + return; + } + + lora.multiplier = multiplier; + lora.apply(tensors, n_threads); + lora.free_params_buffer(); + + int64_t t1 = ggml_time_ms(); + + LOG_INFO("lora '%s' applied, taking %.2fs", + lora_path.c_str(), + (t1 - t0) * 1.0f / 1000); + } + void apply_lora(const std::string& lora_name, float multiplier) { int64_t t0 = ggml_time_ms(); std::string st_file_path = path_join(lora_model_dir, lora_name + ".safetensors"); @@ -1355,6 +1403,12 @@ sd_image_t* txt2img(sd_ctx_t* sd_ctx, int64_t t0 = ggml_time_ms(); sd_ctx->sd->apply_loras(lora_f2m); + if(pending_apply_lora_fname!="" && pending_apply_lora_power>0) + { + printf("\nApplying LoRA now...\n"); + sd_ctx->sd->apply_lora_from_file(pending_apply_lora_fname,pending_apply_lora_power); + pending_apply_lora_fname = ""; + } int64_t t1 = ggml_time_ms(); LOG_INFO("apply_loras completed, taking %.2fs", (t1 - t0) * 1.0f / 1000); struct ggml_init_params params; @@ -1548,6 +1602,12 @@ sd_image_t* img2img(sd_ctx_t* sd_ctx, // load lora from file int64_t t0 = ggml_time_ms(); sd_ctx->sd->apply_loras(lora_f2m); + if(pending_apply_lora_fname!="" && pending_apply_lora_power>0) + { + printf("\nApplying LoRA now...\n"); + sd_ctx->sd->apply_lora_from_file(pending_apply_lora_fname,pending_apply_lora_power); + pending_apply_lora_fname = ""; + } int64_t t1 = ggml_time_ms(); LOG_INFO("apply_loras completed, taking %.2fs", (t1 - t0) * 1.0f / 1000); diff --git a/otherarch/sdcpp/util.cpp b/otherarch/sdcpp/util.cpp index 0811ec0b5c04c..47ae7774c97f5 100644 --- a/otherarch/sdcpp/util.cpp +++ b/otherarch/sdcpp/util.cpp @@ -43,6 +43,13 @@ bool starts_with(const std::string& str, const std::string& start) { return false; } +bool contains(const std::string& str, const std::string& substr) { + if (str.find(substr) != std::string::npos) { + return true; + } + return false; +} + void replace_all_chars(std::string& str, char target, char replacement) { for (size_t i = 0; i < str.length(); ++i) { if (str[i] == target) { diff --git a/otherarch/sdcpp/util.h b/otherarch/sdcpp/util.h index 837f20994ba9c..6a42568f66e08 100644 --- a/otherarch/sdcpp/util.h +++ b/otherarch/sdcpp/util.h @@ -8,6 +8,7 @@ bool ends_with(const std::string& str, const std::string& ending); bool starts_with(const std::string& str, const std::string& start); +bool contains(const std::string& str, const std::string& substr); std::string format(const char* fmt, ...); diff --git a/otherarch/tools/make_old_pyinstaller.bat b/otherarch/tools/make_old_pyinstaller.bat index 9555387221bf8..879f3828e89df 100644 --- a/otherarch/tools/make_old_pyinstaller.bat +++ b/otherarch/tools/make_old_pyinstaller.bat @@ -1,4 +1,4 @@ echo This file is only for my own usage, please do not use it. I am lazy. set PATH=d:\\MainApplications\\KoboldAIGPT\\KoboldAI-Horde-Bridge\\python;d:\\MainApplications\\KoboldAIGPT\\KoboldAI-Horde-Bridge\\python\\Scripts;%PATH% -PyInstaller --noconfirm --onefile --clean --console --collect-all customtkinter --icon "./niko.ico" --add-data "./winclinfo.exe;." --add-data "./OpenCL.dll;." --add-data "./klite.embd;." --add-data "./kcpp_docs.embd;." --add-data="./kcpp_sdui.embd;." --add-data "./koboldcpp_default.dll;." --add-data "./koboldcpp_openblas.dll;." --add-data "./koboldcpp_failsafe.dll;." --add-data "./koboldcpp_noavx2.dll;." --add-data "./libopenblas.dll;." --add-data "./koboldcpp_clblast.dll;." --add-data "./koboldcpp_clblast_noavx2.dll;." --add-data "./koboldcpp_vulkan_noavx2.dll;." --add-data "./clblast.dll;." --add-data "./koboldcpp_vulkan.dll;." --add-data "./vulkan-1.dll;." --add-data "./rwkv_vocab.embd;." --add-data "./rwkv_world_vocab.embd;." "./koboldcpp.py" -n "koboldcpp_nocuda.exe" \ No newline at end of file +PyInstaller --noconfirm --onefile --clean --console --collect-all customtkinter --collect-all psutil --icon "./niko.ico" --add-data "./winclinfo.exe;." --add-data "./OpenCL.dll;." --add-data "./klite.embd;." --add-data "./kcpp_docs.embd;." --add-data="./kcpp_sdui.embd;." --add-data="./taesd.embd;." --add-data="./taesd_xl.embd;." --add-data "./koboldcpp_default.dll;." --add-data "./koboldcpp_openblas.dll;." --add-data "./koboldcpp_failsafe.dll;." --add-data "./koboldcpp_noavx2.dll;." --add-data "./libopenblas.dll;." --add-data "./koboldcpp_clblast.dll;." --add-data "./koboldcpp_clblast_noavx2.dll;." --add-data "./koboldcpp_vulkan_noavx2.dll;." --add-data "./clblast.dll;." --add-data "./koboldcpp_vulkan.dll;." --add-data "./vulkan-1.dll;." --add-data "./rwkv_vocab.embd;." --add-data "./rwkv_world_vocab.embd;." "./koboldcpp.py" -n "koboldcpp_nocuda.exe" \ No newline at end of file diff --git a/otherarch/tools/make_old_pyinstaller_cuda.bat b/otherarch/tools/make_old_pyinstaller_cuda.bat index a451d2eee39dc..e8140cb2e1bca 100644 --- a/otherarch/tools/make_old_pyinstaller_cuda.bat +++ b/otherarch/tools/make_old_pyinstaller_cuda.bat @@ -1,4 +1,4 @@ echo This file is only for my own usage, please do not use it. I am lazy. set PATH=d:\\MainApplications\\KoboldAIGPT\\KoboldAI-Horde-Bridge\\python;d:\\MainApplications\\KoboldAIGPT\\KoboldAI-Horde-Bridge\\python\\Scripts;%PATH% -PyInstaller --noconfirm --onefile --clean --console --collect-all customtkinter --icon "./nikogreen.ico" --add-data "./winclinfo.exe;." --add-data "./OpenCL.dll;." --add-data "./klite.embd;." --add-data "./kcpp_docs.embd;." --add-data="./kcpp_sdui.embd;." --add-data "./koboldcpp_default.dll;." --add-data "./koboldcpp_openblas.dll;." --add-data "./koboldcpp_failsafe.dll;." --add-data "./koboldcpp_noavx2.dll;." --add-data "./libopenblas.dll;." --add-data "./koboldcpp_clblast.dll;." --add-data "./koboldcpp_clblast_noavx2.dll;." --add-data "./koboldcpp_vulkan_noavx2.dll;." --add-data "./clblast.dll;." --add-data "./koboldcpp_vulkan.dll;." --add-data "./vulkan-1.dll;." --add-data "./koboldcpp_cublas.dll;." --add-data "./cublas64_11.dll;." --add-data "./cublasLt64_11.dll;." --add-data "./cudart64_110.dll;." --add-data "./msvcp140.dll;." --add-data "./msvcp140_codecvt_ids.dll;." --add-data "./vcruntime140.dll;." --add-data "./vcruntime140_1.dll;." --add-data "./rwkv_vocab.embd;." --add-data "./rwkv_world_vocab.embd;." "./koboldcpp.py" -n "koboldcpp.exe" \ No newline at end of file +PyInstaller --noconfirm --onefile --clean --console --collect-all customtkinter --collect-all psutil --icon "./nikogreen.ico" --add-data "./winclinfo.exe;." --add-data "./OpenCL.dll;." --add-data "./klite.embd;." --add-data "./kcpp_docs.embd;." --add-data="./kcpp_sdui.embd;." --add-data="./taesd.embd;." --add-data="./taesd_xl.embd;." --add-data "./koboldcpp_default.dll;." --add-data "./koboldcpp_openblas.dll;." --add-data "./koboldcpp_failsafe.dll;." --add-data "./koboldcpp_noavx2.dll;." --add-data "./libopenblas.dll;." --add-data "./koboldcpp_clblast.dll;." --add-data "./koboldcpp_clblast_noavx2.dll;." --add-data "./koboldcpp_vulkan_noavx2.dll;." --add-data "./clblast.dll;." --add-data "./koboldcpp_vulkan.dll;." --add-data "./vulkan-1.dll;." --add-data "./koboldcpp_cublas.dll;." --add-data "./cublas64_11.dll;." --add-data "./cublasLt64_11.dll;." --add-data "./cudart64_110.dll;." --add-data "./msvcp140.dll;." --add-data "./msvcp140_codecvt_ids.dll;." --add-data "./vcruntime140.dll;." --add-data "./vcruntime140_1.dll;." --add-data "./rwkv_vocab.embd;." --add-data "./rwkv_world_vocab.embd;." "./koboldcpp.py" -n "koboldcpp.exe" \ No newline at end of file diff --git a/otherarch/tools/make_pyinstaller_hybrid_henk.bat b/otherarch/tools/make_pyinstaller_hybrid_henk.bat index 7806aa7f8e86c..669cff0eafccd 100644 --- a/otherarch/tools/make_pyinstaller_hybrid_henk.bat +++ b/otherarch/tools/make_pyinstaller_hybrid_henk.bat @@ -2,4 +2,4 @@ cd /d "%~dp0" copy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.4\bin\cudart64_110.dll" .\ /Y copy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.4\bin\cublasLt64_11.dll" .\ /Y copy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.4\bin\cublas64_11.dll" .\ /Y -PyInstaller --noconfirm --onefile --collect-all customtkinter --clean --console --icon ".\niko.ico" --add-data "./winclinfo.exe;." --add-data "./OpenCL.dll;." --add-data "./klite.embd;." --add-data "./kcpp_docs.embd;." --add-data="./kcpp_sdui.embd;." --add-data "./koboldcpp_default.dll;." --add-data "./koboldcpp_openblas.dll;." --add-data "./koboldcpp_failsafe.dll;." --add-data "./koboldcpp_noavx2.dll;." --add-data "./libopenblas.dll;." --add-data "./koboldcpp_clblast.dll;." --add-data "./koboldcpp_clblast_noavx2.dll;." --add-data "./koboldcpp_vulkan_noavx2.dll;." --add-data "./clblast.dll;." --add-data "./koboldcpp_vulkan.dll;." --add-data "./vulkan-1.dll;." --add-data "./koboldcpp_cublas.dll;." --add-data "./cudart64_110.dll;." --add-data "./cublasLt64_11.dll;." --add-data "./cublas64_11.dll;." --add-data "./rwkv_vocab.embd;." --add-data "C:/Windows/System32/msvcp140.dll;." --add-data "C:/Windows/System32/vcruntime140_1.dll;." "./koboldcpp.py" -n "koboldcpp.exe" \ No newline at end of file +PyInstaller --noconfirm --onefile --collect-all customtkinter --collect-all psutil --clean --console --icon ".\niko.ico" --add-data "./winclinfo.exe;." --add-data "./OpenCL.dll;." --add-data "./klite.embd;." --add-data "./kcpp_docs.embd;." --add-data="./kcpp_sdui.embd;." --add-data="./taesd.embd;." --add-data="./taesd_xl.embd;." --add-data "./koboldcpp_default.dll;." --add-data "./koboldcpp_openblas.dll;." --add-data "./koboldcpp_failsafe.dll;." --add-data "./koboldcpp_noavx2.dll;." --add-data "./libopenblas.dll;." --add-data "./koboldcpp_clblast.dll;." --add-data "./koboldcpp_clblast_noavx2.dll;." --add-data "./koboldcpp_vulkan_noavx2.dll;." --add-data "./clblast.dll;." --add-data "./koboldcpp_vulkan.dll;." --add-data "./vulkan-1.dll;." --add-data "./koboldcpp_cublas.dll;." --add-data "./cudart64_110.dll;." --add-data "./cublasLt64_11.dll;." --add-data "./cublas64_11.dll;." --add-data "./rwkv_vocab.embd;." --add-data "C:/Windows/System32/msvcp140.dll;." --add-data "C:/Windows/System32/vcruntime140_1.dll;." "./koboldcpp.py" -n "koboldcpp.exe" \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 37e87234605e4..dc0fd8707c929 100755 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ -numpy==1.24.4 -sentencepiece==0.1.98 +numpy>=1.24.4 +sentencepiece>=0.1.98 transformers>=4.34.0 gguf>=0.1.0 customtkinter>=5.1.0 diff --git a/scripts/debug-test.sh b/scripts/debug-test.sh new file mode 100755 index 0000000000000..7b2b601a96477 --- /dev/null +++ b/scripts/debug-test.sh @@ -0,0 +1,203 @@ +#!/bin/bash + +PROG=${0##*/} +build_dir="build-ci-debug" + +# Print Color Commands +red=$(tput setaf 1) +green=$(tput setaf 2) +yellow=$(tput setaf 3) +blue=$(tput setaf 4) +magenta=$(tput setaf 5) +cyan=$(tput setaf 6) +normal=$(tput sgr0) + + +# Print Help Message +#################### + +print_full_help() { + cat << EOF +Usage: $PROG [OPTION]... (test_number) +Debug specific ctest program. + +Options: + -h, --help display this help and exit + -g run in gdb mode + +Arguments: + (Mandatory) Supply one regex to the script to filter tests + (test_number) (Optional) Test number to run a specific test + +Example: + $PROG test-tokenizer + $PROG test-tokenizer 3 +EOF +} + +abort() { + echo "Error: $1" >&2 + cat << EOF >&2 +Usage: $PROG [OPTION]... (test_number) +Debug specific ctest program. +Refer to --help for full instructions. +EOF + exit 1 +} + + +# Dependency Sanity Check +######################### + +check_dependency() { + command -v "$1" >/dev/null 2>&1 || { + abort "$1 is required but not found. Please install it and try again." + } +} + +check_dependency ctest +check_dependency cmake + + +# Step 0: Check the args +######################## + +if [ x"$1" = x"-h" ] || [ x"$1" = x"--help" ]; then + print_full_help >&2 + exit 0 +fi + +# Parse command-line options +gdb_mode=false +while getopts "g" opt; do + case $opt in + g) + gdb_mode=true + echo "gdb_mode Mode Enabled" + ;; + esac +done + +# Shift the option parameters +shift $((OPTIND - 1)) + +# Positionial Argument Processing : +if [ -z "${1}" ]; then + abort "Test regex is required" +else + test_suite=${1:-} +fi + +# Positionial Argument Processing : (test_number) +test_number=${2:-} + + +# Step 1: Reset and Setup folder context +######################################## + +## Sanity check that we are actually in a git repo +repo_root=$(git rev-parse --show-toplevel) +if [ ! -d "$repo_root" ]; then + abort "Not in a Git repository." +fi + +## Reset folder to root context of git repo and Create and enter build directory +pushd "$repo_root" +rm -rf "$build_dir" && mkdir "$build_dir" || abort "Failed to make $build_dir" + + +# Step 2: Setup Build Environment and Compile Test Binaries +########################################################### + +# Note: test-eval-callback requires -DLLAMA_CURL +cmake -B "./$build_dir" -DCMAKE_BUILD_TYPE=Debug -DLLAMA_CUDA=1 -DLLAMA_CURL=1 || abort "Failed to build enviroment" +pushd "$build_dir" +make -j || abort "Failed to compile" +popd > /dev/null || exit 1 + + +# Step 3: Find all tests available that matches REGEX +#################################################### + +# Ctest Gather Tests +# `-R test-tokenizer` : looks for all the test files named `test-tokenizer*` (R=Regex) +# `-N` : "show-only" disables test execution & shows test commands that you can feed to GDB. +# `-V` : Verbose Mode +printf "\n\nGathering tests that fit REGEX: ${test_suite} ...\n" +pushd "$build_dir" +tests=($(ctest -R ${test_suite} -V -N | grep -E " +Test +#[0-9]+*" | cut -d':' -f2 | awk '{$1=$1};1')) +if [ ${#tests[@]} -eq 0 ]; then + abort "No tests avaliable... check your compliation process..." +fi +popd > /dev/null || exit 1 + + +# Step 4: Identify Test Command for Debugging +############################################# + +# Select test number +if [ -z $test_number ]; then + # List out avaliable tests + printf "Which test would you like to debug?\n" + id=0 + for s in "${tests[@]}" + do + echo "Test# ${id}" + echo " $s" + ((id++)) + done + + # Prompt user which test they wanted to run + printf "\nRun test#? " + read test_number + +else + printf "\nUser Already Requested #${test_number}\n" + +fi + +# Grab all tests commands +pushd "$build_dir" +sIFS=$IFS # Save Initial IFS (Internal Field Separator) +IFS=$'\n' # Change IFS (Internal Field Separator) (So we split ctest output by newline rather than by spaces) +test_args=($(ctest -R ${test_suite} -V -N | grep "Test command" | cut -d':' -f3 | awk '{$1=$1};1' )) # Get test args +IFS=$sIFS # Reset IFS (Internal Field Separator) +popd > /dev/null || exit 1 + +# Grab specific test command +single_test_name="${tests[test_number]}" +single_test_command="${test_args[test_number]}" + + +# Step 5: Execute or GDB Debug +############################## + +printf "${magenta}Running Test #${test_number}: ${single_test_name}${normal}\n" +printf "${cyan}single_test_command: ${single_test_command}${normal}\n" + +if [ "$gdb_mode" = "true" ]; then + # Execute debugger + pushd "$repo_root" || exit 1 + eval "gdb --args ${single_test_command}" + popd > /dev/null || exit 1 + +else + # Execute Test + pushd "$repo_root" || exit 1 + eval "${single_test_command}" + exit_code=$? + popd > /dev/null || exit 1 + + # Print Result + printf "${blue}Ran Test #${test_number}: ${single_test_name}${normal}\n" + printf "${yellow}Command: ${single_test_command}${normal}\n" + if [ $exit_code -eq 0 ]; then + printf "${green}TEST PASS${normal}\n" + else + printf "${red}TEST FAIL${normal}\n" + fi + +fi + +# Return to the directory from which the user ran the command. +popd > /dev/null || exit 1 diff --git a/scripts/gen-unicode-data.py b/scripts/gen-unicode-data.py index 37d1e396c437f..744873c2a4986 100644 --- a/scripts/gen-unicode-data.py +++ b/scripts/gen-unicode-data.py @@ -1,64 +1,134 @@ import regex +import ctypes +import unicodedata -def get_matches(regex_expr): - regex_expr_compiled = regex.compile(regex_expr) - unicode_ranges = [] - current_range = None +class CoodepointFlags (ctypes.Structure): + _fields_ = [ # see definition in unicode.h + ("is_undefined", ctypes.c_uint16, 1), + ("is_number", ctypes.c_uint16, 1), # regex: \p{N} + ("is_letter", ctypes.c_uint16, 1), # regex: \p{L} + ("is_separator", ctypes.c_uint16, 1), # regex: \p{Z} + ("is_accent_mark", ctypes.c_uint16, 1), # regex: \p{M} + ("is_punctuation", ctypes.c_uint16, 1), # regex: \p{P} + ("is_symbol", ctypes.c_uint16, 1), # regex: \p{S} + ("is_control", ctypes.c_uint16, 1), # regex: \p{C} + ] - for codepoint in range(0x110000): - char = chr(codepoint) - if regex_expr_compiled.match(char): - if current_range is None: - current_range = [codepoint, codepoint] - else: - current_range[1] = codepoint - elif current_range is not None: - unicode_ranges.append(tuple(current_range)) - current_range = None - if current_range is not None: - unicode_ranges.append(tuple(current_range)) +assert (ctypes.sizeof(CoodepointFlags) == 2) - return unicode_ranges +MAX_CODEPOINTS = 0x110000 -def print_cat(mode, cat, ranges): - if mode == "range": - print("const std::vector> unicode_ranges_{} = {{".format(cat)) # noqa: NP100 - if mode == "map": - print("const std::map unicode_map_{} = {{".format(cat)) # noqa: NP100 - for i, values in enumerate(ranges): - end = ",\n" if (i % 4 == 3 or i + 1 == len(ranges)) else ", " - values = ["0x%08X" % value for value in values] - print("{" + ", ".join(values) + "}", end=end) # noqa: NP100 - print("};") # noqa: NP100 - print("") # noqa: NP100 +regex_number = regex.compile(r'\p{N}') +regex_letter = regex.compile(r'\p{L}') +regex_separator = regex.compile(r'\p{Z}') +regex_accent_mark = regex.compile(r'\p{M}') +regex_punctuation = regex.compile(r'\p{P}') +regex_symbol = regex.compile(r'\p{S}') +regex_control = regex.compile(r'\p{C}') +regex_whitespace = regex.compile(r'\s') +codepoint_flags = (CoodepointFlags * MAX_CODEPOINTS)() +table_whitespace = [] +table_lowercase = [] +table_uppercase = [] +table_nfd = [] -print_cat("range", "number", get_matches(r'\p{N}')) -print_cat("range", "letter", get_matches(r'\p{L}')) -print_cat("range", "separator", get_matches(r'\p{Z}')) -print_cat("range", "accent_mark", get_matches(r'\p{M}')) -print_cat("range", "punctuation", get_matches(r'\p{P}')) -print_cat("range", "symbol", get_matches(r'\p{S}')) -print_cat("range", "control", get_matches(r'\p{C}')) +for codepoint in range(MAX_CODEPOINTS): + # convert codepoint to unicode character + char = chr(codepoint) -print_cat("range", "whitespace", get_matches(r'\s')) + # regex categories + flags = codepoint_flags[codepoint] + flags.is_number = bool(regex_number.match(char)) + flags.is_letter = bool(regex_letter.match(char)) + flags.is_separator = bool(regex_separator.match(char)) + flags.is_accent_mark = bool(regex_accent_mark.match(char)) + flags.is_punctuation = bool(regex_punctuation.match(char)) + flags.is_symbol = bool(regex_symbol.match(char)) + flags.is_control = bool(regex_control.match(char)) + flags.is_undefined = bytes(flags)[0] == 0 + assert (not flags.is_undefined) + # whitespaces + if bool(regex_whitespace.match(char)): + table_whitespace.append(codepoint) -map_lowercase = [] -map_uppercase = [] -for codepoint in range(0x110000): - char = chr(codepoint) + # lowercase conversion lower = ord(char.lower()[0]) - upper = ord(char.upper()[0]) if codepoint != lower: - map_lowercase.append((codepoint, lower)) + table_lowercase.append((codepoint, lower)) + + # uppercase conversion + upper = ord(char.upper()[0]) if codepoint != upper: - map_uppercase.append((codepoint, upper)) -print_cat("map", "lowercase", map_lowercase) -print_cat("map", "uppercase", map_uppercase) + table_uppercase.append((codepoint, upper)) + + # NFD normalization + norm = ord(unicodedata.normalize('NFD', char)[0]) + if codepoint != norm: + table_nfd.append((codepoint, norm)) + + +# group ranges with same flags +ranges_flags = [(0, codepoint_flags[0])] # start, flags +for codepoint, flags in enumerate(codepoint_flags): + if bytes(flags) != bytes(ranges_flags[-1][1]): + ranges_flags.append((codepoint, flags)) +ranges_flags.append((MAX_CODEPOINTS, CoodepointFlags())) + + +# group ranges with same nfd +ranges_nfd = [(0, 0, 0)] # start, last, nfd +for codepoint, norm in table_nfd: + start = ranges_nfd[-1][0] + if ranges_nfd[-1] != (start, codepoint - 1, norm): + ranges_nfd.append(None) + start = codepoint + ranges_nfd[-1] = (start, codepoint, norm) + + +# Generate 'unicode-data.cpp' + + +def out(line=""): + print(line, end='\n') # noqa + + +out("""\ +// generated with scripts/gen-unicode-data.py + +#include "unicode-data.h" + +#include +#include +#include +#include +""") + +out("const std::vector> unicode_ranges_flags = { // start, flags // last=next_start-1") +for codepoint, flags in ranges_flags: + flags = int.from_bytes(bytes(flags), "little") + out("{0x%06X, 0x%04X}," % (codepoint, flags)) +out("};\n") + +out("const std::unordered_set unicode_set_whitespace = {") +out(", ".join("0x%06X" % cpt for cpt in table_whitespace)) +out("};\n") + +out("const std::unordered_map unicode_map_lowercase = {") +for tuple in table_lowercase: + out("{0x%06X, 0x%06X}," % tuple) +out("};\n") +out("const std::unordered_map unicode_map_uppercase = {") +for tuple in table_uppercase: + out("{0x%06X, 0x%06X}," % tuple) +out("};\n") -# TODO: generate unicode_map_nfd +out("const std::vector unicode_ranges_nfd = { // start, last, nfd") +for triple in ranges_nfd: + out("{0x%06X, 0x%06X, 0x%06X}," % triple) +out("};\n") diff --git a/taesd.embd b/taesd.embd new file mode 100644 index 0000000000000..dbb2169916918 Binary files /dev/null and b/taesd.embd differ diff --git a/taesd_xl.embd b/taesd_xl.embd new file mode 100644 index 0000000000000..24c9d9cf43445 Binary files /dev/null and b/taesd_xl.embd differ diff --git a/tests/test-tokenizer-0.sh b/tests/test-tokenizer-0.sh deleted file mode 100755 index 2fb8632d810c4..0000000000000 --- a/tests/test-tokenizer-0.sh +++ /dev/null @@ -1,34 +0,0 @@ -#!/bin/bash -# -# Usage: -# -# test-tokenizer-0.sh -# - -if [ $# -ne 2 ]; then - printf "Usage: $0 \n" - exit 1 -fi - -name=$1 -input=$2 - -make -j tests/test-tokenizer-0 - -printf "Testing %s on %s ...\n" $name $input - -python3 ./tests/test-tokenizer-0.py ./models/tokenizers/$name --fname-tok $input > /tmp/test-tokenizer-0-$name-py.log 2>&1 -cat /tmp/test-tokenizer-0-$name-py.log | grep "tokenized in" - -./tests/test-tokenizer-0 ./models/ggml-vocab-$name.gguf $input > /tmp/test-tokenizer-0-$name-cpp.log 2>&1 -cat /tmp/test-tokenizer-0-$name-cpp.log | grep "tokenized in" - -diff $input.tok $input.tokcpp > /dev/null 2>&1 - -if [ $? -eq 0 ]; then - printf "Tokenization is correct!\n" -else - diff $input.tok $input.tokcpp | head -n 32 - - printf "Tokenization differs!\n" -fi diff --git a/tests/test-tokenizer-random.py b/tests/test-tokenizer-random.py deleted file mode 100644 index 5b2ab8ef71163..0000000000000 --- a/tests/test-tokenizer-random.py +++ /dev/null @@ -1,295 +0,0 @@ -# Test libllama tokenizer == AutoTokenizer. -# Brute force random tokens/text generation. -# -# Sample usage: -# -# python3 tests/test-tokenizer-random.py ./models/ggml-vocab-llama-bpe.gguf ./models/tokenizers/llama-bpe -# - -import time -import logging -import argparse -import subprocess -import random - -from typing import Iterator - -import cffi -from transformers import AutoTokenizer, PreTrainedTokenizerBase - -logger = logging.getLogger("test-tokenizer-random-bpe") - - -class LibLlama: - - DEFAULT_PATH_LLAMA_H = "./llama.h" - DEFAULT_PATH_LIBLLAMA = "./build/libllama.so" # CMakeLists.txt: BUILD_SHARED_LIBS ON - - def __init__(self, path_llama_h: str = None, path_libllama: str = None): - path_llama_h = path_llama_h or self.DEFAULT_PATH_LLAMA_H - path_libllama = path_libllama or self.DEFAULT_PATH_LIBLLAMA - (self.ffi, self.lib) = self._load_libllama_cffi(path_llama_h, path_libllama) - self.lib.llama_backend_init() - - def _load_libllama_cffi(self, path_llama_h: str, path_libllama: str): - cmd = ["gcc", "-E", "-P", "-D__restrict=", "-D__attribute__(x)=", "-D__asm__(x)=", path_llama_h] - res = subprocess.run(cmd, stdout=subprocess.PIPE) - assert (res.returncode == 0) - source = res.stdout.decode() - ffi = cffi.FFI() - if True: # workarounds for pycparser - source = "typedef struct { } __builtin_va_list;" + "\n" + source - source = source.replace("sizeof (int)", str(ffi.sizeof("int"))) - source = source.replace("sizeof (void *)", str(ffi.sizeof("void*"))) - source = source.replace("sizeof (size_t)", str(ffi.sizeof("size_t"))) - source = source.replace("sizeof(int32_t)", str(ffi.sizeof("int32_t"))) - ffi.cdef(source, override=True) - lib = ffi.dlopen(path_libllama) - return (ffi, lib) - - def model_default_params(self, **kwargs): - mparams = self.lib.llama_model_default_params() - for k, v in kwargs.items(): - setattr(mparams, k, v) - return mparams - - def context_default_params(self, **kwargs): - cparams = self.lib.llama_context_default_params() - for k, v in kwargs.items(): - setattr(cparams, k, v) - return cparams - - -class LibLlamaModel: - - def __init__(self, libllama: LibLlama, path_model: str, mparams={}, cparams={}): - self.lib = libllama.lib - self.ffi = libllama.ffi - if isinstance(mparams, dict): - mparams = libllama.model_default_params(**mparams) - self.model = self.lib.llama_load_model_from_file(path_model.encode(), mparams) - if not self.model: - raise RuntimeError("error: failed to load model '%s'" % path_model) - if isinstance(cparams, dict): - cparams = libllama.context_default_params(**cparams) - self.ctx = self.lib.llama_new_context_with_model(self.model, cparams) - if not self.ctx: - raise RuntimeError("error: failed to create context for model '%s'" % path_model) - n_tokens_max = self.lib.llama_n_ctx(self.ctx) - self.token_ids = self.ffi.new("llama_token[]", n_tokens_max) - - def free(self): - if self.ctx: - self.lib.llama_free(self.ctx) - if self.model: - self.lib.llama_free_model(self.model) - self.ctx = None - self.model = None - self.lib = None - - def tokenize(self, text: str, n_tokens_max: int = 0, add_special: bool = False, parse_special: bool = False) -> list[int]: - n_tokens_max = n_tokens_max if n_tokens_max > 0 else len(self.token_ids) - text = text.encode("utf-8") - num = self.lib.llama_tokenize(self.model, text, len(text), self.token_ids, n_tokens_max, add_special, parse_special) - if num < 0: - return [] - return list(self.token_ids[0:num]) - - -def generator_custom_text() -> Iterator[str]: - """General tests""" - yield from [ - "", - " ", - " ", - " ", - "\t", - "\n", - "\n\n", - "\n\n\n", - "\t\n", - "Hello world", - " Hello world", - "Hello World", - " Hello World", - " Hello World!", - "Hello, world!", - " Hello, world!", - " this is 🦙.cpp", - "w048 7tuijk dsdfhu", - "нещо на Български", - "កាន់តែពិសេសអាចខលចេញ", - "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)", - "Hello", - " Hello", - " Hello", - " Hello", - " Hello", - " Hello\n Hello", - " (", - "\n =", - "' era", - "Hello, y'all! How are you 😁 ?我想在apple工作1314151天~", - "3", - "33", - "333", - "3333", - "33333", - "333333", - "3333333", - "33333333", - "333333333", - ] - - -def generator_custom_text_edge_cases() -> Iterator[str]: - """Edge cases found while debugging""" - yield from [ - '\x1f-a', # unicode_ranges_control, {0x00001C, 0x00001F} - '¼-a', # unicode_ranges_digit, 0x00BC - '½-a', # unicode_ranges_digit, 0x00BD - '¾-a', # unicode_ranges_digit, 0x00BE - 'a 〇b', # unicode_ranges_digit, 0x3007 - 'Ⅵ-a', # unicode_ranges_digit, {0x00002150, 0x0000218F} // Number Forms - '\uFEFF//', # unicode_ranges_control, 0xFEFF (BOM) - 'a' # TODO: Phi-3 fail - ] - - -def generator_random_chars(iterations = 100) -> Iterator[str]: - """Brute force random text with simple characters""" - - WHITESPACES = list(" " * 20 + "\n" * 5 + "\r\n" * 5 + "\t" * 5) - CHARS = list(set(""" - ABCDEFGHIJKLMNOPQRSTUVWXYZ - abcdefghijklmnopqrstuvwxyz - ÁÉÍÓÚÀÈÌÒÙÂÊÎÔÛÄËÏÖÜ - áéíóúàèìòùâêîôûäëïöü - .-,*/-+ª!"·$%&/()=?¿[]{}<>\\|@#~½¬~;:_ - """)) - - rand = random.Random() - for m in range(iterations): - rand.seed(m) - text = [] - num_words = rand.randint(300, 400) - for i in range(num_words): - k = rand.randint(1, 7) - word = rand.choices(CHARS, k=k) - space = rand.choice(WHITESPACES) - text.append("".join(word) + space) - yield "".join(text) - - -def generator_random_vocab_chars(tokenizer: PreTrainedTokenizerBase, iterations = 100) -> Iterator[str]: - """Brute force random text with vocab characters""" - - vocab_ids = list(tokenizer.vocab.values()) - vocab_text = tokenizer.decode(vocab_ids, skip_special_tokens=True) - vocab_chars = list(set(vocab_text)) - del vocab_ids, vocab_text - - rand = random.Random() - for m in range(iterations): - rand.seed(m) - text = rand.choices(vocab_chars, k=1024) - yield "".join(text) - - -def generator_random_vocab_tokens(tokenizer: PreTrainedTokenizerBase, iterations = 100) -> Iterator[str]: - """Brute force random text from vocab tokens""" - - space_id = tokenizer.encode(" ", add_special_tokens=False)[0] - vocab_ids = list(tokenizer.vocab.values()) - vocab_ids = list(sorted(vocab_ids + vocab_ids)) - for i in range(1, len(vocab_ids), 2): - vocab_ids[i] = space_id - vocab_tokens = tokenizer.decode(vocab_ids, skip_special_tokens=True) - vocab_tokens = vocab_tokens.split(" ") - del vocab_ids - - yield from vocab_tokens - - rand = random.Random() - for m in range(iterations): - rand.seed(m) - text = [] - num_words = rand.randint(300, 400) - for i in range(num_words): - k = rand.randint(1, 3) - tokens = rand.choices(vocab_tokens, k=k) - tokens = [t.strip(" \n\r\t") for t in tokens] - sep = rand.choice(" \n\r\t") - text.append("".join(tokens) + sep) - yield "".join(text) - - -def generator_random_bytes(iterations = 100) -> Iterator[str]: - """Brute force random bytes""" - - WHITESPACES = list(" " * 20 + "\n" * 5 + "\r\n" * 5 + "\t" * 5) - - rand = random.Random() - for m in range(iterations): - rand.seed(m) - text = [] - num_words = rand.randint(300, 400) - for i in range(num_words): - k = rand.randint(1, 8) - word = [chr(r) for r in rand.randbytes(k) if r] - word.append(rand.choice(WHITESPACES)) - text.append("".join(word)) - yield "".join(text) - - -def test_compare_tokenizer(model: LibLlamaModel, tokenizer: PreTrainedTokenizerBase, generator: Iterator[str]): - - def find_first_mismatch(ids1: list[int], ids2: list[int]): - for i, (a,b) in enumerate(zip(ids1, ids2)): - if a != b: - return i - if len(ids1) == len(ids2): - return -1 - return min(len(ids1), len(ids2)) - - t0 = time.perf_counter() - logger.info("%s: %s" % (generator.__name__, "ini")) - for text in generator: - ids1 = model.tokenize(text, add_special=False, parse_special=False) - ids2 = tokenizer.encode(text, add_special_tokens=False) - if ids1 != ids2: - i = find_first_mismatch(ids1, ids2) - ids1 = list(ids1)[max(0, i - 2) : i + 2 + 1] - ids2 = list(ids2)[max(0, i - 2) : i + 2 + 1] - text2 = tokenizer.decode(ids2, skip_special_tokens=True) - assert (text2 in text) - logger.info(" Text: " + repr(text2)) - logger.info(" TokenIDs: " + str(ids1)) - logger.info(" Expected: " + str(ids2)) - raise Exception() - t1 = time.perf_counter() - logger.info("%s: end, time: %.3f secs" % (generator.__name__, t1 - t0)) - - -if __name__ == "__main__": - - parser = argparse.ArgumentParser() - parser.add_argument("vocab_file", help="path to vocab 'gguf' file") - parser.add_argument("dir_tokenizer", help="directory containing 'tokenizer.model' file") - parser.add_argument("--verbose", action="store_true", help="increase output verbosity") - args = parser.parse_args() - - logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO) - - model = LibLlamaModel(LibLlama(), args.vocab_file, mparams=dict(vocab_only=True), cparams=dict(n_ctx=2048)) - - tokenizer = AutoTokenizer.from_pretrained(args.dir_tokenizer) - - test_compare_tokenizer(model, tokenizer, generator_custom_text()) - test_compare_tokenizer(model, tokenizer, generator_custom_text_edge_cases()) - test_compare_tokenizer(model, tokenizer, generator_random_chars(10_000)) - test_compare_tokenizer(model, tokenizer, generator_random_vocab_chars(tokenizer, 10_000)) - test_compare_tokenizer(model, tokenizer, generator_random_vocab_tokens(tokenizer, 10_000)) - # test_compare_tokenizer(model, tokenizer, generator_random_bytes(10_000)) # FAIL - - model.free() diff --git a/unicode-data.cpp b/unicode-data.cpp index c54175fc3b485..d7c1c898d8828 100644 --- a/unicode-data.cpp +++ b/unicode-data.cpp @@ -1,2183 +1,6983 @@ -#include "unicode-data.h" - -#include -#include -#include -#include - // generated with scripts/gen-unicode-data.py -// -// TODO: generate unicode_map_nfd -const std::vector> unicode_ranges_number = { -{0x00000030, 0x00000039}, {0x000000B2, 0x000000B3}, {0x000000B9, 0x000000B9}, {0x000000BC, 0x000000BE}, -{0x00000660, 0x00000669}, {0x000006F0, 0x000006F9}, {0x000007C0, 0x000007C9}, {0x00000966, 0x0000096F}, -{0x000009E6, 0x000009EF}, {0x000009F4, 0x000009F9}, {0x00000A66, 0x00000A6F}, {0x00000AE6, 0x00000AEF}, -{0x00000B66, 0x00000B6F}, {0x00000B72, 0x00000B77}, {0x00000BE6, 0x00000BF2}, {0x00000C66, 0x00000C6F}, -{0x00000C78, 0x00000C7E}, {0x00000CE6, 0x00000CEF}, {0x00000D58, 0x00000D5E}, {0x00000D66, 0x00000D78}, -{0x00000DE6, 0x00000DEF}, {0x00000E50, 0x00000E59}, {0x00000ED0, 0x00000ED9}, {0x00000F20, 0x00000F33}, -{0x00001040, 0x00001049}, {0x00001090, 0x00001099}, {0x00001369, 0x0000137C}, {0x000016EE, 0x000016F0}, -{0x000017E0, 0x000017E9}, {0x000017F0, 0x000017F9}, {0x00001810, 0x00001819}, {0x00001946, 0x0000194F}, -{0x000019D0, 0x000019DA}, {0x00001A80, 0x00001A89}, {0x00001A90, 0x00001A99}, {0x00001B50, 0x00001B59}, -{0x00001BB0, 0x00001BB9}, {0x00001C40, 0x00001C49}, {0x00001C50, 0x00001C59}, {0x00002070, 0x00002070}, -{0x00002074, 0x00002079}, {0x00002080, 0x00002089}, {0x00002150, 0x00002182}, {0x00002185, 0x00002189}, -{0x00002460, 0x0000249B}, {0x000024EA, 0x000024FF}, {0x00002776, 0x00002793}, {0x00002CFD, 0x00002CFD}, -{0x00003007, 0x00003007}, {0x00003021, 0x00003029}, {0x00003038, 0x0000303A}, {0x00003192, 0x00003195}, -{0x00003220, 0x00003229}, {0x00003248, 0x0000324F}, {0x00003251, 0x0000325F}, {0x00003280, 0x00003289}, -{0x000032B1, 0x000032BF}, {0x0000A620, 0x0000A629}, {0x0000A6E6, 0x0000A6EF}, {0x0000A830, 0x0000A835}, -{0x0000A8D0, 0x0000A8D9}, {0x0000A900, 0x0000A909}, {0x0000A9D0, 0x0000A9D9}, {0x0000A9F0, 0x0000A9F9}, -{0x0000AA50, 0x0000AA59}, {0x0000ABF0, 0x0000ABF9}, {0x0000FF10, 0x0000FF19}, {0x00010107, 0x00010133}, -{0x00010140, 0x00010178}, {0x0001018A, 0x0001018B}, {0x000102E1, 0x000102FB}, {0x00010320, 0x00010323}, -{0x00010341, 0x00010341}, {0x0001034A, 0x0001034A}, {0x000103D1, 0x000103D5}, {0x000104A0, 0x000104A9}, -{0x00010858, 0x0001085F}, {0x00010879, 0x0001087F}, {0x000108A7, 0x000108AF}, {0x000108FB, 0x000108FF}, -{0x00010916, 0x0001091B}, {0x000109BC, 0x000109BD}, {0x000109C0, 0x000109CF}, {0x000109D2, 0x000109FF}, -{0x00010A40, 0x00010A48}, {0x00010A7D, 0x00010A7E}, {0x00010A9D, 0x00010A9F}, {0x00010AEB, 0x00010AEF}, -{0x00010B58, 0x00010B5F}, {0x00010B78, 0x00010B7F}, {0x00010BA9, 0x00010BAF}, {0x00010CFA, 0x00010CFF}, -{0x00010D30, 0x00010D39}, {0x00010E60, 0x00010E7E}, {0x00010F1D, 0x00010F26}, {0x00010F51, 0x00010F54}, -{0x00010FC5, 0x00010FCB}, {0x00011052, 0x0001106F}, {0x000110F0, 0x000110F9}, {0x00011136, 0x0001113F}, -{0x000111D0, 0x000111D9}, {0x000111E1, 0x000111F4}, {0x000112F0, 0x000112F9}, {0x00011450, 0x00011459}, -{0x000114D0, 0x000114D9}, {0x00011650, 0x00011659}, {0x000116C0, 0x000116C9}, {0x00011730, 0x0001173B}, -{0x000118E0, 0x000118F2}, {0x00011950, 0x00011959}, {0x00011C50, 0x00011C6C}, {0x00011D50, 0x00011D59}, -{0x00011DA0, 0x00011DA9}, {0x00011F50, 0x00011F59}, {0x00011FC0, 0x00011FD4}, {0x00012400, 0x0001246E}, -{0x00016A60, 0x00016A69}, {0x00016AC0, 0x00016AC9}, {0x00016B50, 0x00016B59}, {0x00016B5B, 0x00016B61}, -{0x00016E80, 0x00016E96}, {0x0001D2C0, 0x0001D2D3}, {0x0001D2E0, 0x0001D2F3}, {0x0001D360, 0x0001D378}, -{0x0001D7CE, 0x0001D7FF}, {0x0001E140, 0x0001E149}, {0x0001E2F0, 0x0001E2F9}, {0x0001E4F0, 0x0001E4F9}, -{0x0001E8C7, 0x0001E8CF}, {0x0001E950, 0x0001E959}, {0x0001EC71, 0x0001ECAB}, {0x0001ECAD, 0x0001ECAF}, -{0x0001ECB1, 0x0001ECB4}, {0x0001ED01, 0x0001ED2D}, {0x0001ED2F, 0x0001ED3D}, {0x0001F100, 0x0001F10C}, -{0x0001FBF0, 0x0001FBF9}, -}; - -const std::vector> unicode_ranges_letter = { -{0x00000041, 0x0000005A}, {0x00000061, 0x0000007A}, {0x000000AA, 0x000000AA}, {0x000000B5, 0x000000B5}, -{0x000000BA, 0x000000BA}, {0x000000C0, 0x000000D6}, {0x000000D8, 0x000000F6}, {0x000000F8, 0x000002C1}, -{0x000002C6, 0x000002D1}, {0x000002E0, 0x000002E4}, {0x000002EC, 0x000002EC}, {0x000002EE, 0x000002EE}, -{0x00000370, 0x00000374}, {0x00000376, 0x00000377}, {0x0000037A, 0x0000037D}, {0x0000037F, 0x0000037F}, -{0x00000386, 0x00000386}, {0x00000388, 0x0000038A}, {0x0000038C, 0x0000038C}, {0x0000038E, 0x000003A1}, -{0x000003A3, 0x000003F5}, {0x000003F7, 0x00000481}, {0x0000048A, 0x0000052F}, {0x00000531, 0x00000556}, -{0x00000559, 0x00000559}, {0x00000560, 0x00000588}, {0x000005D0, 0x000005EA}, {0x000005EF, 0x000005F2}, -{0x00000620, 0x0000064A}, {0x0000066E, 0x0000066F}, {0x00000671, 0x000006D3}, {0x000006D5, 0x000006D5}, -{0x000006E5, 0x000006E6}, {0x000006EE, 0x000006EF}, {0x000006FA, 0x000006FC}, {0x000006FF, 0x000006FF}, -{0x00000710, 0x00000710}, {0x00000712, 0x0000072F}, {0x0000074D, 0x000007A5}, {0x000007B1, 0x000007B1}, -{0x000007CA, 0x000007EA}, {0x000007F4, 0x000007F5}, {0x000007FA, 0x000007FA}, {0x00000800, 0x00000815}, -{0x0000081A, 0x0000081A}, {0x00000824, 0x00000824}, {0x00000828, 0x00000828}, {0x00000840, 0x00000858}, -{0x00000860, 0x0000086A}, {0x00000870, 0x00000887}, {0x00000889, 0x0000088E}, {0x000008A0, 0x000008C9}, -{0x00000904, 0x00000939}, {0x0000093D, 0x0000093D}, {0x00000950, 0x00000950}, {0x00000958, 0x00000961}, -{0x00000971, 0x00000980}, {0x00000985, 0x0000098C}, {0x0000098F, 0x00000990}, {0x00000993, 0x000009A8}, -{0x000009AA, 0x000009B0}, {0x000009B2, 0x000009B2}, {0x000009B6, 0x000009B9}, {0x000009BD, 0x000009BD}, -{0x000009CE, 0x000009CE}, {0x000009DC, 0x000009DD}, {0x000009DF, 0x000009E1}, {0x000009F0, 0x000009F1}, -{0x000009FC, 0x000009FC}, {0x00000A05, 0x00000A0A}, {0x00000A0F, 0x00000A10}, {0x00000A13, 0x00000A28}, -{0x00000A2A, 0x00000A30}, {0x00000A32, 0x00000A33}, {0x00000A35, 0x00000A36}, {0x00000A38, 0x00000A39}, -{0x00000A59, 0x00000A5C}, {0x00000A5E, 0x00000A5E}, {0x00000A72, 0x00000A74}, {0x00000A85, 0x00000A8D}, -{0x00000A8F, 0x00000A91}, {0x00000A93, 0x00000AA8}, {0x00000AAA, 0x00000AB0}, {0x00000AB2, 0x00000AB3}, -{0x00000AB5, 0x00000AB9}, {0x00000ABD, 0x00000ABD}, {0x00000AD0, 0x00000AD0}, {0x00000AE0, 0x00000AE1}, -{0x00000AF9, 0x00000AF9}, {0x00000B05, 0x00000B0C}, {0x00000B0F, 0x00000B10}, {0x00000B13, 0x00000B28}, -{0x00000B2A, 0x00000B30}, {0x00000B32, 0x00000B33}, {0x00000B35, 0x00000B39}, {0x00000B3D, 0x00000B3D}, -{0x00000B5C, 0x00000B5D}, {0x00000B5F, 0x00000B61}, {0x00000B71, 0x00000B71}, {0x00000B83, 0x00000B83}, -{0x00000B85, 0x00000B8A}, {0x00000B8E, 0x00000B90}, {0x00000B92, 0x00000B95}, {0x00000B99, 0x00000B9A}, -{0x00000B9C, 0x00000B9C}, {0x00000B9E, 0x00000B9F}, {0x00000BA3, 0x00000BA4}, {0x00000BA8, 0x00000BAA}, -{0x00000BAE, 0x00000BB9}, {0x00000BD0, 0x00000BD0}, {0x00000C05, 0x00000C0C}, {0x00000C0E, 0x00000C10}, -{0x00000C12, 0x00000C28}, {0x00000C2A, 0x00000C39}, {0x00000C3D, 0x00000C3D}, {0x00000C58, 0x00000C5A}, -{0x00000C5D, 0x00000C5D}, {0x00000C60, 0x00000C61}, {0x00000C80, 0x00000C80}, {0x00000C85, 0x00000C8C}, -{0x00000C8E, 0x00000C90}, {0x00000C92, 0x00000CA8}, {0x00000CAA, 0x00000CB3}, {0x00000CB5, 0x00000CB9}, -{0x00000CBD, 0x00000CBD}, {0x00000CDD, 0x00000CDE}, {0x00000CE0, 0x00000CE1}, {0x00000CF1, 0x00000CF2}, -{0x00000D04, 0x00000D0C}, {0x00000D0E, 0x00000D10}, {0x00000D12, 0x00000D3A}, {0x00000D3D, 0x00000D3D}, -{0x00000D4E, 0x00000D4E}, {0x00000D54, 0x00000D56}, {0x00000D5F, 0x00000D61}, {0x00000D7A, 0x00000D7F}, -{0x00000D85, 0x00000D96}, {0x00000D9A, 0x00000DB1}, {0x00000DB3, 0x00000DBB}, {0x00000DBD, 0x00000DBD}, -{0x00000DC0, 0x00000DC6}, {0x00000E01, 0x00000E30}, {0x00000E32, 0x00000E33}, {0x00000E40, 0x00000E46}, -{0x00000E81, 0x00000E82}, {0x00000E84, 0x00000E84}, {0x00000E86, 0x00000E8A}, {0x00000E8C, 0x00000EA3}, -{0x00000EA5, 0x00000EA5}, {0x00000EA7, 0x00000EB0}, {0x00000EB2, 0x00000EB3}, {0x00000EBD, 0x00000EBD}, -{0x00000EC0, 0x00000EC4}, {0x00000EC6, 0x00000EC6}, {0x00000EDC, 0x00000EDF}, {0x00000F00, 0x00000F00}, -{0x00000F40, 0x00000F47}, {0x00000F49, 0x00000F6C}, {0x00000F88, 0x00000F8C}, {0x00001000, 0x0000102A}, -{0x0000103F, 0x0000103F}, {0x00001050, 0x00001055}, {0x0000105A, 0x0000105D}, {0x00001061, 0x00001061}, -{0x00001065, 0x00001066}, {0x0000106E, 0x00001070}, {0x00001075, 0x00001081}, {0x0000108E, 0x0000108E}, -{0x000010A0, 0x000010C5}, {0x000010C7, 0x000010C7}, {0x000010CD, 0x000010CD}, {0x000010D0, 0x000010FA}, -{0x000010FC, 0x00001248}, {0x0000124A, 0x0000124D}, {0x00001250, 0x00001256}, {0x00001258, 0x00001258}, -{0x0000125A, 0x0000125D}, {0x00001260, 0x00001288}, {0x0000128A, 0x0000128D}, {0x00001290, 0x000012B0}, -{0x000012B2, 0x000012B5}, {0x000012B8, 0x000012BE}, {0x000012C0, 0x000012C0}, {0x000012C2, 0x000012C5}, -{0x000012C8, 0x000012D6}, {0x000012D8, 0x00001310}, {0x00001312, 0x00001315}, {0x00001318, 0x0000135A}, -{0x00001380, 0x0000138F}, {0x000013A0, 0x000013F5}, {0x000013F8, 0x000013FD}, {0x00001401, 0x0000166C}, -{0x0000166F, 0x0000167F}, {0x00001681, 0x0000169A}, {0x000016A0, 0x000016EA}, {0x000016F1, 0x000016F8}, -{0x00001700, 0x00001711}, {0x0000171F, 0x00001731}, {0x00001740, 0x00001751}, {0x00001760, 0x0000176C}, -{0x0000176E, 0x00001770}, {0x00001780, 0x000017B3}, {0x000017D7, 0x000017D7}, {0x000017DC, 0x000017DC}, -{0x00001820, 0x00001878}, {0x00001880, 0x00001884}, {0x00001887, 0x000018A8}, {0x000018AA, 0x000018AA}, -{0x000018B0, 0x000018F5}, {0x00001900, 0x0000191E}, {0x00001950, 0x0000196D}, {0x00001970, 0x00001974}, -{0x00001980, 0x000019AB}, {0x000019B0, 0x000019C9}, {0x00001A00, 0x00001A16}, {0x00001A20, 0x00001A54}, -{0x00001AA7, 0x00001AA7}, {0x00001B05, 0x00001B33}, {0x00001B45, 0x00001B4C}, {0x00001B83, 0x00001BA0}, -{0x00001BAE, 0x00001BAF}, {0x00001BBA, 0x00001BE5}, {0x00001C00, 0x00001C23}, {0x00001C4D, 0x00001C4F}, -{0x00001C5A, 0x00001C7D}, {0x00001C80, 0x00001C88}, {0x00001C90, 0x00001CBA}, {0x00001CBD, 0x00001CBF}, -{0x00001CE9, 0x00001CEC}, {0x00001CEE, 0x00001CF3}, {0x00001CF5, 0x00001CF6}, {0x00001CFA, 0x00001CFA}, -{0x00001D00, 0x00001DBF}, {0x00001E00, 0x00001F15}, {0x00001F18, 0x00001F1D}, {0x00001F20, 0x00001F45}, -{0x00001F48, 0x00001F4D}, {0x00001F50, 0x00001F57}, {0x00001F59, 0x00001F59}, {0x00001F5B, 0x00001F5B}, -{0x00001F5D, 0x00001F5D}, {0x00001F5F, 0x00001F7D}, {0x00001F80, 0x00001FB4}, {0x00001FB6, 0x00001FBC}, -{0x00001FBE, 0x00001FBE}, {0x00001FC2, 0x00001FC4}, {0x00001FC6, 0x00001FCC}, {0x00001FD0, 0x00001FD3}, -{0x00001FD6, 0x00001FDB}, {0x00001FE0, 0x00001FEC}, {0x00001FF2, 0x00001FF4}, {0x00001FF6, 0x00001FFC}, -{0x00002071, 0x00002071}, {0x0000207F, 0x0000207F}, {0x00002090, 0x0000209C}, {0x00002102, 0x00002102}, -{0x00002107, 0x00002107}, {0x0000210A, 0x00002113}, {0x00002115, 0x00002115}, {0x00002119, 0x0000211D}, -{0x00002124, 0x00002124}, {0x00002126, 0x00002126}, {0x00002128, 0x00002128}, {0x0000212A, 0x0000212D}, -{0x0000212F, 0x00002139}, {0x0000213C, 0x0000213F}, {0x00002145, 0x00002149}, {0x0000214E, 0x0000214E}, -{0x00002183, 0x00002184}, {0x00002C00, 0x00002CE4}, {0x00002CEB, 0x00002CEE}, {0x00002CF2, 0x00002CF3}, -{0x00002D00, 0x00002D25}, {0x00002D27, 0x00002D27}, {0x00002D2D, 0x00002D2D}, {0x00002D30, 0x00002D67}, -{0x00002D6F, 0x00002D6F}, {0x00002D80, 0x00002D96}, {0x00002DA0, 0x00002DA6}, {0x00002DA8, 0x00002DAE}, -{0x00002DB0, 0x00002DB6}, {0x00002DB8, 0x00002DBE}, {0x00002DC0, 0x00002DC6}, {0x00002DC8, 0x00002DCE}, -{0x00002DD0, 0x00002DD6}, {0x00002DD8, 0x00002DDE}, {0x00002E2F, 0x00002E2F}, {0x00003005, 0x00003006}, -{0x00003031, 0x00003035}, {0x0000303B, 0x0000303C}, {0x00003041, 0x00003096}, {0x0000309D, 0x0000309F}, -{0x000030A1, 0x000030FA}, {0x000030FC, 0x000030FF}, {0x00003105, 0x0000312F}, {0x00003131, 0x0000318E}, -{0x000031A0, 0x000031BF}, {0x000031F0, 0x000031FF}, {0x00003400, 0x00004DBF}, {0x00004E00, 0x0000A48C}, -{0x0000A4D0, 0x0000A4FD}, {0x0000A500, 0x0000A60C}, {0x0000A610, 0x0000A61F}, {0x0000A62A, 0x0000A62B}, -{0x0000A640, 0x0000A66E}, {0x0000A67F, 0x0000A69D}, {0x0000A6A0, 0x0000A6E5}, {0x0000A717, 0x0000A71F}, -{0x0000A722, 0x0000A788}, {0x0000A78B, 0x0000A7CA}, {0x0000A7D0, 0x0000A7D1}, {0x0000A7D3, 0x0000A7D3}, -{0x0000A7D5, 0x0000A7D9}, {0x0000A7F2, 0x0000A801}, {0x0000A803, 0x0000A805}, {0x0000A807, 0x0000A80A}, -{0x0000A80C, 0x0000A822}, {0x0000A840, 0x0000A873}, {0x0000A882, 0x0000A8B3}, {0x0000A8F2, 0x0000A8F7}, -{0x0000A8FB, 0x0000A8FB}, {0x0000A8FD, 0x0000A8FE}, {0x0000A90A, 0x0000A925}, {0x0000A930, 0x0000A946}, -{0x0000A960, 0x0000A97C}, {0x0000A984, 0x0000A9B2}, {0x0000A9CF, 0x0000A9CF}, {0x0000A9E0, 0x0000A9E4}, -{0x0000A9E6, 0x0000A9EF}, {0x0000A9FA, 0x0000A9FE}, {0x0000AA00, 0x0000AA28}, {0x0000AA40, 0x0000AA42}, -{0x0000AA44, 0x0000AA4B}, {0x0000AA60, 0x0000AA76}, {0x0000AA7A, 0x0000AA7A}, {0x0000AA7E, 0x0000AAAF}, -{0x0000AAB1, 0x0000AAB1}, {0x0000AAB5, 0x0000AAB6}, {0x0000AAB9, 0x0000AABD}, {0x0000AAC0, 0x0000AAC0}, -{0x0000AAC2, 0x0000AAC2}, {0x0000AADB, 0x0000AADD}, {0x0000AAE0, 0x0000AAEA}, {0x0000AAF2, 0x0000AAF4}, -{0x0000AB01, 0x0000AB06}, {0x0000AB09, 0x0000AB0E}, {0x0000AB11, 0x0000AB16}, {0x0000AB20, 0x0000AB26}, -{0x0000AB28, 0x0000AB2E}, {0x0000AB30, 0x0000AB5A}, {0x0000AB5C, 0x0000AB69}, {0x0000AB70, 0x0000ABE2}, -{0x0000AC00, 0x0000D7A3}, {0x0000D7B0, 0x0000D7C6}, {0x0000D7CB, 0x0000D7FB}, {0x0000F900, 0x0000FA6D}, -{0x0000FA70, 0x0000FAD9}, {0x0000FB00, 0x0000FB06}, {0x0000FB13, 0x0000FB17}, {0x0000FB1D, 0x0000FB1D}, -{0x0000FB1F, 0x0000FB28}, {0x0000FB2A, 0x0000FB36}, {0x0000FB38, 0x0000FB3C}, {0x0000FB3E, 0x0000FB3E}, -{0x0000FB40, 0x0000FB41}, {0x0000FB43, 0x0000FB44}, {0x0000FB46, 0x0000FBB1}, {0x0000FBD3, 0x0000FD3D}, -{0x0000FD50, 0x0000FD8F}, {0x0000FD92, 0x0000FDC7}, {0x0000FDF0, 0x0000FDFB}, {0x0000FE70, 0x0000FE74}, -{0x0000FE76, 0x0000FEFC}, {0x0000FF21, 0x0000FF3A}, {0x0000FF41, 0x0000FF5A}, {0x0000FF66, 0x0000FFBE}, -{0x0000FFC2, 0x0000FFC7}, {0x0000FFCA, 0x0000FFCF}, {0x0000FFD2, 0x0000FFD7}, {0x0000FFDA, 0x0000FFDC}, -{0x00010000, 0x0001000B}, {0x0001000D, 0x00010026}, {0x00010028, 0x0001003A}, {0x0001003C, 0x0001003D}, -{0x0001003F, 0x0001004D}, {0x00010050, 0x0001005D}, {0x00010080, 0x000100FA}, {0x00010280, 0x0001029C}, -{0x000102A0, 0x000102D0}, {0x00010300, 0x0001031F}, {0x0001032D, 0x00010340}, {0x00010342, 0x00010349}, -{0x00010350, 0x00010375}, {0x00010380, 0x0001039D}, {0x000103A0, 0x000103C3}, {0x000103C8, 0x000103CF}, -{0x00010400, 0x0001049D}, {0x000104B0, 0x000104D3}, {0x000104D8, 0x000104FB}, {0x00010500, 0x00010527}, -{0x00010530, 0x00010563}, {0x00010570, 0x0001057A}, {0x0001057C, 0x0001058A}, {0x0001058C, 0x00010592}, -{0x00010594, 0x00010595}, {0x00010597, 0x000105A1}, {0x000105A3, 0x000105B1}, {0x000105B3, 0x000105B9}, -{0x000105BB, 0x000105BC}, {0x00010600, 0x00010736}, {0x00010740, 0x00010755}, {0x00010760, 0x00010767}, -{0x00010780, 0x00010785}, {0x00010787, 0x000107B0}, {0x000107B2, 0x000107BA}, {0x00010800, 0x00010805}, -{0x00010808, 0x00010808}, {0x0001080A, 0x00010835}, {0x00010837, 0x00010838}, {0x0001083C, 0x0001083C}, -{0x0001083F, 0x00010855}, {0x00010860, 0x00010876}, {0x00010880, 0x0001089E}, {0x000108E0, 0x000108F2}, -{0x000108F4, 0x000108F5}, {0x00010900, 0x00010915}, {0x00010920, 0x00010939}, {0x00010980, 0x000109B7}, -{0x000109BE, 0x000109BF}, {0x00010A00, 0x00010A00}, {0x00010A10, 0x00010A13}, {0x00010A15, 0x00010A17}, -{0x00010A19, 0x00010A35}, {0x00010A60, 0x00010A7C}, {0x00010A80, 0x00010A9C}, {0x00010AC0, 0x00010AC7}, -{0x00010AC9, 0x00010AE4}, {0x00010B00, 0x00010B35}, {0x00010B40, 0x00010B55}, {0x00010B60, 0x00010B72}, -{0x00010B80, 0x00010B91}, {0x00010C00, 0x00010C48}, {0x00010C80, 0x00010CB2}, {0x00010CC0, 0x00010CF2}, -{0x00010D00, 0x00010D23}, {0x00010E80, 0x00010EA9}, {0x00010EB0, 0x00010EB1}, {0x00010F00, 0x00010F1C}, -{0x00010F27, 0x00010F27}, {0x00010F30, 0x00010F45}, {0x00010F70, 0x00010F81}, {0x00010FB0, 0x00010FC4}, -{0x00010FE0, 0x00010FF6}, {0x00011003, 0x00011037}, {0x00011071, 0x00011072}, {0x00011075, 0x00011075}, -{0x00011083, 0x000110AF}, {0x000110D0, 0x000110E8}, {0x00011103, 0x00011126}, {0x00011144, 0x00011144}, -{0x00011147, 0x00011147}, {0x00011150, 0x00011172}, {0x00011176, 0x00011176}, {0x00011183, 0x000111B2}, -{0x000111C1, 0x000111C4}, {0x000111DA, 0x000111DA}, {0x000111DC, 0x000111DC}, {0x00011200, 0x00011211}, -{0x00011213, 0x0001122B}, {0x0001123F, 0x00011240}, {0x00011280, 0x00011286}, {0x00011288, 0x00011288}, -{0x0001128A, 0x0001128D}, {0x0001128F, 0x0001129D}, {0x0001129F, 0x000112A8}, {0x000112B0, 0x000112DE}, -{0x00011305, 0x0001130C}, {0x0001130F, 0x00011310}, {0x00011313, 0x00011328}, {0x0001132A, 0x00011330}, -{0x00011332, 0x00011333}, {0x00011335, 0x00011339}, {0x0001133D, 0x0001133D}, {0x00011350, 0x00011350}, -{0x0001135D, 0x00011361}, {0x00011400, 0x00011434}, {0x00011447, 0x0001144A}, {0x0001145F, 0x00011461}, -{0x00011480, 0x000114AF}, {0x000114C4, 0x000114C5}, {0x000114C7, 0x000114C7}, {0x00011580, 0x000115AE}, -{0x000115D8, 0x000115DB}, {0x00011600, 0x0001162F}, {0x00011644, 0x00011644}, {0x00011680, 0x000116AA}, -{0x000116B8, 0x000116B8}, {0x00011700, 0x0001171A}, {0x00011740, 0x00011746}, {0x00011800, 0x0001182B}, -{0x000118A0, 0x000118DF}, {0x000118FF, 0x00011906}, {0x00011909, 0x00011909}, {0x0001190C, 0x00011913}, -{0x00011915, 0x00011916}, {0x00011918, 0x0001192F}, {0x0001193F, 0x0001193F}, {0x00011941, 0x00011941}, -{0x000119A0, 0x000119A7}, {0x000119AA, 0x000119D0}, {0x000119E1, 0x000119E1}, {0x000119E3, 0x000119E3}, -{0x00011A00, 0x00011A00}, {0x00011A0B, 0x00011A32}, {0x00011A3A, 0x00011A3A}, {0x00011A50, 0x00011A50}, -{0x00011A5C, 0x00011A89}, {0x00011A9D, 0x00011A9D}, {0x00011AB0, 0x00011AF8}, {0x00011C00, 0x00011C08}, -{0x00011C0A, 0x00011C2E}, {0x00011C40, 0x00011C40}, {0x00011C72, 0x00011C8F}, {0x00011D00, 0x00011D06}, -{0x00011D08, 0x00011D09}, {0x00011D0B, 0x00011D30}, {0x00011D46, 0x00011D46}, {0x00011D60, 0x00011D65}, -{0x00011D67, 0x00011D68}, {0x00011D6A, 0x00011D89}, {0x00011D98, 0x00011D98}, {0x00011EE0, 0x00011EF2}, -{0x00011F02, 0x00011F02}, {0x00011F04, 0x00011F10}, {0x00011F12, 0x00011F33}, {0x00011FB0, 0x00011FB0}, -{0x00012000, 0x00012399}, {0x00012480, 0x00012543}, {0x00012F90, 0x00012FF0}, {0x00013000, 0x0001342F}, -{0x00013441, 0x00013446}, {0x00014400, 0x00014646}, {0x00016800, 0x00016A38}, {0x00016A40, 0x00016A5E}, -{0x00016A70, 0x00016ABE}, {0x00016AD0, 0x00016AED}, {0x00016B00, 0x00016B2F}, {0x00016B40, 0x00016B43}, -{0x00016B63, 0x00016B77}, {0x00016B7D, 0x00016B8F}, {0x00016E40, 0x00016E7F}, {0x00016F00, 0x00016F4A}, -{0x00016F50, 0x00016F50}, {0x00016F93, 0x00016F9F}, {0x00016FE0, 0x00016FE1}, {0x00016FE3, 0x00016FE3}, -{0x00017000, 0x000187F7}, {0x00018800, 0x00018CD5}, {0x00018D00, 0x00018D08}, {0x0001AFF0, 0x0001AFF3}, -{0x0001AFF5, 0x0001AFFB}, {0x0001AFFD, 0x0001AFFE}, {0x0001B000, 0x0001B122}, {0x0001B132, 0x0001B132}, -{0x0001B150, 0x0001B152}, {0x0001B155, 0x0001B155}, {0x0001B164, 0x0001B167}, {0x0001B170, 0x0001B2FB}, -{0x0001BC00, 0x0001BC6A}, {0x0001BC70, 0x0001BC7C}, {0x0001BC80, 0x0001BC88}, {0x0001BC90, 0x0001BC99}, -{0x0001D400, 0x0001D454}, {0x0001D456, 0x0001D49C}, {0x0001D49E, 0x0001D49F}, {0x0001D4A2, 0x0001D4A2}, -{0x0001D4A5, 0x0001D4A6}, {0x0001D4A9, 0x0001D4AC}, {0x0001D4AE, 0x0001D4B9}, {0x0001D4BB, 0x0001D4BB}, -{0x0001D4BD, 0x0001D4C3}, {0x0001D4C5, 0x0001D505}, {0x0001D507, 0x0001D50A}, {0x0001D50D, 0x0001D514}, -{0x0001D516, 0x0001D51C}, {0x0001D51E, 0x0001D539}, {0x0001D53B, 0x0001D53E}, {0x0001D540, 0x0001D544}, -{0x0001D546, 0x0001D546}, {0x0001D54A, 0x0001D550}, {0x0001D552, 0x0001D6A5}, {0x0001D6A8, 0x0001D6C0}, -{0x0001D6C2, 0x0001D6DA}, {0x0001D6DC, 0x0001D6FA}, {0x0001D6FC, 0x0001D714}, {0x0001D716, 0x0001D734}, -{0x0001D736, 0x0001D74E}, {0x0001D750, 0x0001D76E}, {0x0001D770, 0x0001D788}, {0x0001D78A, 0x0001D7A8}, -{0x0001D7AA, 0x0001D7C2}, {0x0001D7C4, 0x0001D7CB}, {0x0001DF00, 0x0001DF1E}, {0x0001DF25, 0x0001DF2A}, -{0x0001E030, 0x0001E06D}, {0x0001E100, 0x0001E12C}, {0x0001E137, 0x0001E13D}, {0x0001E14E, 0x0001E14E}, -{0x0001E290, 0x0001E2AD}, {0x0001E2C0, 0x0001E2EB}, {0x0001E4D0, 0x0001E4EB}, {0x0001E7E0, 0x0001E7E6}, -{0x0001E7E8, 0x0001E7EB}, {0x0001E7ED, 0x0001E7EE}, {0x0001E7F0, 0x0001E7FE}, {0x0001E800, 0x0001E8C4}, -{0x0001E900, 0x0001E943}, {0x0001E94B, 0x0001E94B}, {0x0001EE00, 0x0001EE03}, {0x0001EE05, 0x0001EE1F}, -{0x0001EE21, 0x0001EE22}, {0x0001EE24, 0x0001EE24}, {0x0001EE27, 0x0001EE27}, {0x0001EE29, 0x0001EE32}, -{0x0001EE34, 0x0001EE37}, {0x0001EE39, 0x0001EE39}, {0x0001EE3B, 0x0001EE3B}, {0x0001EE42, 0x0001EE42}, -{0x0001EE47, 0x0001EE47}, {0x0001EE49, 0x0001EE49}, {0x0001EE4B, 0x0001EE4B}, {0x0001EE4D, 0x0001EE4F}, -{0x0001EE51, 0x0001EE52}, {0x0001EE54, 0x0001EE54}, {0x0001EE57, 0x0001EE57}, {0x0001EE59, 0x0001EE59}, -{0x0001EE5B, 0x0001EE5B}, {0x0001EE5D, 0x0001EE5D}, {0x0001EE5F, 0x0001EE5F}, {0x0001EE61, 0x0001EE62}, -{0x0001EE64, 0x0001EE64}, {0x0001EE67, 0x0001EE6A}, {0x0001EE6C, 0x0001EE72}, {0x0001EE74, 0x0001EE77}, -{0x0001EE79, 0x0001EE7C}, {0x0001EE7E, 0x0001EE7E}, {0x0001EE80, 0x0001EE89}, {0x0001EE8B, 0x0001EE9B}, -{0x0001EEA1, 0x0001EEA3}, {0x0001EEA5, 0x0001EEA9}, {0x0001EEAB, 0x0001EEBB}, {0x00020000, 0x0002A6DF}, -{0x0002A700, 0x0002B739}, {0x0002B740, 0x0002B81D}, {0x0002B820, 0x0002CEA1}, {0x0002CEB0, 0x0002EBE0}, -{0x0002EBF0, 0x0002EE5D}, {0x0002F800, 0x0002FA1D}, {0x00030000, 0x0003134A}, {0x00031350, 0x000323AF}, -}; +#include "unicode-data.h" -const std::vector> unicode_ranges_separator = { -{0x00000020, 0x00000020}, {0x000000A0, 0x000000A0}, {0x00001680, 0x00001680}, {0x00002000, 0x0000200A}, -{0x00002028, 0x00002029}, {0x0000202F, 0x0000202F}, {0x0000205F, 0x0000205F}, {0x00003000, 0x00003000}, -}; - -const std::vector> unicode_ranges_whitespace = { -{0x00000009, 0x0000000D}, {0x00000020, 0x00000020}, {0x00000085, 0x00000085}, {0x000000A0, 0x000000A0}, -{0x00001680, 0x00001680}, {0x00002000, 0x0000200A}, {0x00002028, 0x00002029}, {0x0000202F, 0x0000202F}, -{0x0000205F, 0x0000205F}, {0x00003000, 0x00003000}, -}; - -const std::vector> unicode_ranges_accent_mark = { -{0x00000300, 0x0000036F}, {0x00000483, 0x00000489}, {0x00000591, 0x000005BD}, {0x000005BF, 0x000005BF}, -{0x000005C1, 0x000005C2}, {0x000005C4, 0x000005C5}, {0x000005C7, 0x000005C7}, {0x00000610, 0x0000061A}, -{0x0000064B, 0x0000065F}, {0x00000670, 0x00000670}, {0x000006D6, 0x000006DC}, {0x000006DF, 0x000006E4}, -{0x000006E7, 0x000006E8}, {0x000006EA, 0x000006ED}, {0x00000711, 0x00000711}, {0x00000730, 0x0000074A}, -{0x000007A6, 0x000007B0}, {0x000007EB, 0x000007F3}, {0x000007FD, 0x000007FD}, {0x00000816, 0x00000819}, -{0x0000081B, 0x00000823}, {0x00000825, 0x00000827}, {0x00000829, 0x0000082D}, {0x00000859, 0x0000085B}, -{0x00000898, 0x0000089F}, {0x000008CA, 0x000008E1}, {0x000008E3, 0x00000903}, {0x0000093A, 0x0000093C}, -{0x0000093E, 0x0000094F}, {0x00000951, 0x00000957}, {0x00000962, 0x00000963}, {0x00000981, 0x00000983}, -{0x000009BC, 0x000009BC}, {0x000009BE, 0x000009C4}, {0x000009C7, 0x000009C8}, {0x000009CB, 0x000009CD}, -{0x000009D7, 0x000009D7}, {0x000009E2, 0x000009E3}, {0x000009FE, 0x000009FE}, {0x00000A01, 0x00000A03}, -{0x00000A3C, 0x00000A3C}, {0x00000A3E, 0x00000A42}, {0x00000A47, 0x00000A48}, {0x00000A4B, 0x00000A4D}, -{0x00000A51, 0x00000A51}, {0x00000A70, 0x00000A71}, {0x00000A75, 0x00000A75}, {0x00000A81, 0x00000A83}, -{0x00000ABC, 0x00000ABC}, {0x00000ABE, 0x00000AC5}, {0x00000AC7, 0x00000AC9}, {0x00000ACB, 0x00000ACD}, -{0x00000AE2, 0x00000AE3}, {0x00000AFA, 0x00000AFF}, {0x00000B01, 0x00000B03}, {0x00000B3C, 0x00000B3C}, -{0x00000B3E, 0x00000B44}, {0x00000B47, 0x00000B48}, {0x00000B4B, 0x00000B4D}, {0x00000B55, 0x00000B57}, -{0x00000B62, 0x00000B63}, {0x00000B82, 0x00000B82}, {0x00000BBE, 0x00000BC2}, {0x00000BC6, 0x00000BC8}, -{0x00000BCA, 0x00000BCD}, {0x00000BD7, 0x00000BD7}, {0x00000C00, 0x00000C04}, {0x00000C3C, 0x00000C3C}, -{0x00000C3E, 0x00000C44}, {0x00000C46, 0x00000C48}, {0x00000C4A, 0x00000C4D}, {0x00000C55, 0x00000C56}, -{0x00000C62, 0x00000C63}, {0x00000C81, 0x00000C83}, {0x00000CBC, 0x00000CBC}, {0x00000CBE, 0x00000CC4}, -{0x00000CC6, 0x00000CC8}, {0x00000CCA, 0x00000CCD}, {0x00000CD5, 0x00000CD6}, {0x00000CE2, 0x00000CE3}, -{0x00000CF3, 0x00000CF3}, {0x00000D00, 0x00000D03}, {0x00000D3B, 0x00000D3C}, {0x00000D3E, 0x00000D44}, -{0x00000D46, 0x00000D48}, {0x00000D4A, 0x00000D4D}, {0x00000D57, 0x00000D57}, {0x00000D62, 0x00000D63}, -{0x00000D81, 0x00000D83}, {0x00000DCA, 0x00000DCA}, {0x00000DCF, 0x00000DD4}, {0x00000DD6, 0x00000DD6}, -{0x00000DD8, 0x00000DDF}, {0x00000DF2, 0x00000DF3}, {0x00000E31, 0x00000E31}, {0x00000E34, 0x00000E3A}, -{0x00000E47, 0x00000E4E}, {0x00000EB1, 0x00000EB1}, {0x00000EB4, 0x00000EBC}, {0x00000EC8, 0x00000ECE}, -{0x00000F18, 0x00000F19}, {0x00000F35, 0x00000F35}, {0x00000F37, 0x00000F37}, {0x00000F39, 0x00000F39}, -{0x00000F3E, 0x00000F3F}, {0x00000F71, 0x00000F84}, {0x00000F86, 0x00000F87}, {0x00000F8D, 0x00000F97}, -{0x00000F99, 0x00000FBC}, {0x00000FC6, 0x00000FC6}, {0x0000102B, 0x0000103E}, {0x00001056, 0x00001059}, -{0x0000105E, 0x00001060}, {0x00001062, 0x00001064}, {0x00001067, 0x0000106D}, {0x00001071, 0x00001074}, -{0x00001082, 0x0000108D}, {0x0000108F, 0x0000108F}, {0x0000109A, 0x0000109D}, {0x0000135D, 0x0000135F}, -{0x00001712, 0x00001715}, {0x00001732, 0x00001734}, {0x00001752, 0x00001753}, {0x00001772, 0x00001773}, -{0x000017B4, 0x000017D3}, {0x000017DD, 0x000017DD}, {0x0000180B, 0x0000180D}, {0x0000180F, 0x0000180F}, -{0x00001885, 0x00001886}, {0x000018A9, 0x000018A9}, {0x00001920, 0x0000192B}, {0x00001930, 0x0000193B}, -{0x00001A17, 0x00001A1B}, {0x00001A55, 0x00001A5E}, {0x00001A60, 0x00001A7C}, {0x00001A7F, 0x00001A7F}, -{0x00001AB0, 0x00001ACE}, {0x00001B00, 0x00001B04}, {0x00001B34, 0x00001B44}, {0x00001B6B, 0x00001B73}, -{0x00001B80, 0x00001B82}, {0x00001BA1, 0x00001BAD}, {0x00001BE6, 0x00001BF3}, {0x00001C24, 0x00001C37}, -{0x00001CD0, 0x00001CD2}, {0x00001CD4, 0x00001CE8}, {0x00001CED, 0x00001CED}, {0x00001CF4, 0x00001CF4}, -{0x00001CF7, 0x00001CF9}, {0x00001DC0, 0x00001DFF}, {0x000020D0, 0x000020F0}, {0x00002CEF, 0x00002CF1}, -{0x00002D7F, 0x00002D7F}, {0x00002DE0, 0x00002DFF}, {0x0000302A, 0x0000302F}, {0x00003099, 0x0000309A}, -{0x0000A66F, 0x0000A672}, {0x0000A674, 0x0000A67D}, {0x0000A69E, 0x0000A69F}, {0x0000A6F0, 0x0000A6F1}, -{0x0000A802, 0x0000A802}, {0x0000A806, 0x0000A806}, {0x0000A80B, 0x0000A80B}, {0x0000A823, 0x0000A827}, -{0x0000A82C, 0x0000A82C}, {0x0000A880, 0x0000A881}, {0x0000A8B4, 0x0000A8C5}, {0x0000A8E0, 0x0000A8F1}, -{0x0000A8FF, 0x0000A8FF}, {0x0000A926, 0x0000A92D}, {0x0000A947, 0x0000A953}, {0x0000A980, 0x0000A983}, -{0x0000A9B3, 0x0000A9C0}, {0x0000A9E5, 0x0000A9E5}, {0x0000AA29, 0x0000AA36}, {0x0000AA43, 0x0000AA43}, -{0x0000AA4C, 0x0000AA4D}, {0x0000AA7B, 0x0000AA7D}, {0x0000AAB0, 0x0000AAB0}, {0x0000AAB2, 0x0000AAB4}, -{0x0000AAB7, 0x0000AAB8}, {0x0000AABE, 0x0000AABF}, {0x0000AAC1, 0x0000AAC1}, {0x0000AAEB, 0x0000AAEF}, -{0x0000AAF5, 0x0000AAF6}, {0x0000ABE3, 0x0000ABEA}, {0x0000ABEC, 0x0000ABED}, {0x0000FB1E, 0x0000FB1E}, -{0x0000FE00, 0x0000FE0F}, {0x0000FE20, 0x0000FE2F}, {0x000101FD, 0x000101FD}, {0x000102E0, 0x000102E0}, -{0x00010376, 0x0001037A}, {0x00010A01, 0x00010A03}, {0x00010A05, 0x00010A06}, {0x00010A0C, 0x00010A0F}, -{0x00010A38, 0x00010A3A}, {0x00010A3F, 0x00010A3F}, {0x00010AE5, 0x00010AE6}, {0x00010D24, 0x00010D27}, -{0x00010EAB, 0x00010EAC}, {0x00010EFD, 0x00010EFF}, {0x00010F46, 0x00010F50}, {0x00010F82, 0x00010F85}, -{0x00011000, 0x00011002}, {0x00011038, 0x00011046}, {0x00011070, 0x00011070}, {0x00011073, 0x00011074}, -{0x0001107F, 0x00011082}, {0x000110B0, 0x000110BA}, {0x000110C2, 0x000110C2}, {0x00011100, 0x00011102}, -{0x00011127, 0x00011134}, {0x00011145, 0x00011146}, {0x00011173, 0x00011173}, {0x00011180, 0x00011182}, -{0x000111B3, 0x000111C0}, {0x000111C9, 0x000111CC}, {0x000111CE, 0x000111CF}, {0x0001122C, 0x00011237}, -{0x0001123E, 0x0001123E}, {0x00011241, 0x00011241}, {0x000112DF, 0x000112EA}, {0x00011300, 0x00011303}, -{0x0001133B, 0x0001133C}, {0x0001133E, 0x00011344}, {0x00011347, 0x00011348}, {0x0001134B, 0x0001134D}, -{0x00011357, 0x00011357}, {0x00011362, 0x00011363}, {0x00011366, 0x0001136C}, {0x00011370, 0x00011374}, -{0x00011435, 0x00011446}, {0x0001145E, 0x0001145E}, {0x000114B0, 0x000114C3}, {0x000115AF, 0x000115B5}, -{0x000115B8, 0x000115C0}, {0x000115DC, 0x000115DD}, {0x00011630, 0x00011640}, {0x000116AB, 0x000116B7}, -{0x0001171D, 0x0001172B}, {0x0001182C, 0x0001183A}, {0x00011930, 0x00011935}, {0x00011937, 0x00011938}, -{0x0001193B, 0x0001193E}, {0x00011940, 0x00011940}, {0x00011942, 0x00011943}, {0x000119D1, 0x000119D7}, -{0x000119DA, 0x000119E0}, {0x000119E4, 0x000119E4}, {0x00011A01, 0x00011A0A}, {0x00011A33, 0x00011A39}, -{0x00011A3B, 0x00011A3E}, {0x00011A47, 0x00011A47}, {0x00011A51, 0x00011A5B}, {0x00011A8A, 0x00011A99}, -{0x00011C2F, 0x00011C36}, {0x00011C38, 0x00011C3F}, {0x00011C92, 0x00011CA7}, {0x00011CA9, 0x00011CB6}, -{0x00011D31, 0x00011D36}, {0x00011D3A, 0x00011D3A}, {0x00011D3C, 0x00011D3D}, {0x00011D3F, 0x00011D45}, -{0x00011D47, 0x00011D47}, {0x00011D8A, 0x00011D8E}, {0x00011D90, 0x00011D91}, {0x00011D93, 0x00011D97}, -{0x00011EF3, 0x00011EF6}, {0x00011F00, 0x00011F01}, {0x00011F03, 0x00011F03}, {0x00011F34, 0x00011F3A}, -{0x00011F3E, 0x00011F42}, {0x00013440, 0x00013440}, {0x00013447, 0x00013455}, {0x00016AF0, 0x00016AF4}, -{0x00016B30, 0x00016B36}, {0x00016F4F, 0x00016F4F}, {0x00016F51, 0x00016F87}, {0x00016F8F, 0x00016F92}, -{0x00016FE4, 0x00016FE4}, {0x00016FF0, 0x00016FF1}, {0x0001BC9D, 0x0001BC9E}, {0x0001CF00, 0x0001CF2D}, -{0x0001CF30, 0x0001CF46}, {0x0001D165, 0x0001D169}, {0x0001D16D, 0x0001D172}, {0x0001D17B, 0x0001D182}, -{0x0001D185, 0x0001D18B}, {0x0001D1AA, 0x0001D1AD}, {0x0001D242, 0x0001D244}, {0x0001DA00, 0x0001DA36}, -{0x0001DA3B, 0x0001DA6C}, {0x0001DA75, 0x0001DA75}, {0x0001DA84, 0x0001DA84}, {0x0001DA9B, 0x0001DA9F}, -{0x0001DAA1, 0x0001DAAF}, {0x0001E000, 0x0001E006}, {0x0001E008, 0x0001E018}, {0x0001E01B, 0x0001E021}, -{0x0001E023, 0x0001E024}, {0x0001E026, 0x0001E02A}, {0x0001E08F, 0x0001E08F}, {0x0001E130, 0x0001E136}, -{0x0001E2AE, 0x0001E2AE}, {0x0001E2EC, 0x0001E2EF}, {0x0001E4EC, 0x0001E4EF}, {0x0001E8D0, 0x0001E8D6}, -{0x0001E944, 0x0001E94A}, {0x000E0100, 0x000E01EF}, -}; +#include +#include +#include +#include -const std::vector> unicode_ranges_punctuation = { -{0x00000021, 0x00000023}, {0x00000025, 0x0000002A}, {0x0000002C, 0x0000002F}, {0x0000003A, 0x0000003B}, -{0x0000003F, 0x00000040}, {0x0000005B, 0x0000005D}, {0x0000005F, 0x0000005F}, {0x0000007B, 0x0000007B}, -{0x0000007D, 0x0000007D}, {0x000000A1, 0x000000A1}, {0x000000A7, 0x000000A7}, {0x000000AB, 0x000000AB}, -{0x000000B6, 0x000000B7}, {0x000000BB, 0x000000BB}, {0x000000BF, 0x000000BF}, {0x0000037E, 0x0000037E}, -{0x00000387, 0x00000387}, {0x0000055A, 0x0000055F}, {0x00000589, 0x0000058A}, {0x000005BE, 0x000005BE}, -{0x000005C0, 0x000005C0}, {0x000005C3, 0x000005C3}, {0x000005C6, 0x000005C6}, {0x000005F3, 0x000005F4}, -{0x00000609, 0x0000060A}, {0x0000060C, 0x0000060D}, {0x0000061B, 0x0000061B}, {0x0000061D, 0x0000061F}, -{0x0000066A, 0x0000066D}, {0x000006D4, 0x000006D4}, {0x00000700, 0x0000070D}, {0x000007F7, 0x000007F9}, -{0x00000830, 0x0000083E}, {0x0000085E, 0x0000085E}, {0x00000964, 0x00000965}, {0x00000970, 0x00000970}, -{0x000009FD, 0x000009FD}, {0x00000A76, 0x00000A76}, {0x00000AF0, 0x00000AF0}, {0x00000C77, 0x00000C77}, -{0x00000C84, 0x00000C84}, {0x00000DF4, 0x00000DF4}, {0x00000E4F, 0x00000E4F}, {0x00000E5A, 0x00000E5B}, -{0x00000F04, 0x00000F12}, {0x00000F14, 0x00000F14}, {0x00000F3A, 0x00000F3D}, {0x00000F85, 0x00000F85}, -{0x00000FD0, 0x00000FD4}, {0x00000FD9, 0x00000FDA}, {0x0000104A, 0x0000104F}, {0x000010FB, 0x000010FB}, -{0x00001360, 0x00001368}, {0x00001400, 0x00001400}, {0x0000166E, 0x0000166E}, {0x0000169B, 0x0000169C}, -{0x000016EB, 0x000016ED}, {0x00001735, 0x00001736}, {0x000017D4, 0x000017D6}, {0x000017D8, 0x000017DA}, -{0x00001800, 0x0000180A}, {0x00001944, 0x00001945}, {0x00001A1E, 0x00001A1F}, {0x00001AA0, 0x00001AA6}, -{0x00001AA8, 0x00001AAD}, {0x00001B5A, 0x00001B60}, {0x00001B7D, 0x00001B7E}, {0x00001BFC, 0x00001BFF}, -{0x00001C3B, 0x00001C3F}, {0x00001C7E, 0x00001C7F}, {0x00001CC0, 0x00001CC7}, {0x00001CD3, 0x00001CD3}, -{0x00002010, 0x00002027}, {0x00002030, 0x00002043}, {0x00002045, 0x00002051}, {0x00002053, 0x0000205E}, -{0x0000207D, 0x0000207E}, {0x0000208D, 0x0000208E}, {0x00002308, 0x0000230B}, {0x00002329, 0x0000232A}, -{0x00002768, 0x00002775}, {0x000027C5, 0x000027C6}, {0x000027E6, 0x000027EF}, {0x00002983, 0x00002998}, -{0x000029D8, 0x000029DB}, {0x000029FC, 0x000029FD}, {0x00002CF9, 0x00002CFC}, {0x00002CFE, 0x00002CFF}, -{0x00002D70, 0x00002D70}, {0x00002E00, 0x00002E2E}, {0x00002E30, 0x00002E4F}, {0x00002E52, 0x00002E5D}, -{0x00003001, 0x00003003}, {0x00003008, 0x00003011}, {0x00003014, 0x0000301F}, {0x00003030, 0x00003030}, -{0x0000303D, 0x0000303D}, {0x000030A0, 0x000030A0}, {0x000030FB, 0x000030FB}, {0x0000A4FE, 0x0000A4FF}, -{0x0000A60D, 0x0000A60F}, {0x0000A673, 0x0000A673}, {0x0000A67E, 0x0000A67E}, {0x0000A6F2, 0x0000A6F7}, -{0x0000A874, 0x0000A877}, {0x0000A8CE, 0x0000A8CF}, {0x0000A8F8, 0x0000A8FA}, {0x0000A8FC, 0x0000A8FC}, -{0x0000A92E, 0x0000A92F}, {0x0000A95F, 0x0000A95F}, {0x0000A9C1, 0x0000A9CD}, {0x0000A9DE, 0x0000A9DF}, -{0x0000AA5C, 0x0000AA5F}, {0x0000AADE, 0x0000AADF}, {0x0000AAF0, 0x0000AAF1}, {0x0000ABEB, 0x0000ABEB}, -{0x0000FD3E, 0x0000FD3F}, {0x0000FE10, 0x0000FE19}, {0x0000FE30, 0x0000FE52}, {0x0000FE54, 0x0000FE61}, -{0x0000FE63, 0x0000FE63}, {0x0000FE68, 0x0000FE68}, {0x0000FE6A, 0x0000FE6B}, {0x0000FF01, 0x0000FF03}, -{0x0000FF05, 0x0000FF0A}, {0x0000FF0C, 0x0000FF0F}, {0x0000FF1A, 0x0000FF1B}, {0x0000FF1F, 0x0000FF20}, -{0x0000FF3B, 0x0000FF3D}, {0x0000FF3F, 0x0000FF3F}, {0x0000FF5B, 0x0000FF5B}, {0x0000FF5D, 0x0000FF5D}, -{0x0000FF5F, 0x0000FF65}, {0x00010100, 0x00010102}, {0x0001039F, 0x0001039F}, {0x000103D0, 0x000103D0}, -{0x0001056F, 0x0001056F}, {0x00010857, 0x00010857}, {0x0001091F, 0x0001091F}, {0x0001093F, 0x0001093F}, -{0x00010A50, 0x00010A58}, {0x00010A7F, 0x00010A7F}, {0x00010AF0, 0x00010AF6}, {0x00010B39, 0x00010B3F}, -{0x00010B99, 0x00010B9C}, {0x00010EAD, 0x00010EAD}, {0x00010F55, 0x00010F59}, {0x00010F86, 0x00010F89}, -{0x00011047, 0x0001104D}, {0x000110BB, 0x000110BC}, {0x000110BE, 0x000110C1}, {0x00011140, 0x00011143}, -{0x00011174, 0x00011175}, {0x000111C5, 0x000111C8}, {0x000111CD, 0x000111CD}, {0x000111DB, 0x000111DB}, -{0x000111DD, 0x000111DF}, {0x00011238, 0x0001123D}, {0x000112A9, 0x000112A9}, {0x0001144B, 0x0001144F}, -{0x0001145A, 0x0001145B}, {0x0001145D, 0x0001145D}, {0x000114C6, 0x000114C6}, {0x000115C1, 0x000115D7}, -{0x00011641, 0x00011643}, {0x00011660, 0x0001166C}, {0x000116B9, 0x000116B9}, {0x0001173C, 0x0001173E}, -{0x0001183B, 0x0001183B}, {0x00011944, 0x00011946}, {0x000119E2, 0x000119E2}, {0x00011A3F, 0x00011A46}, -{0x00011A9A, 0x00011A9C}, {0x00011A9E, 0x00011AA2}, {0x00011B00, 0x00011B09}, {0x00011C41, 0x00011C45}, -{0x00011C70, 0x00011C71}, {0x00011EF7, 0x00011EF8}, {0x00011F43, 0x00011F4F}, {0x00011FFF, 0x00011FFF}, -{0x00012470, 0x00012474}, {0x00012FF1, 0x00012FF2}, {0x00016A6E, 0x00016A6F}, {0x00016AF5, 0x00016AF5}, -{0x00016B37, 0x00016B3B}, {0x00016B44, 0x00016B44}, {0x00016E97, 0x00016E9A}, {0x00016FE2, 0x00016FE2}, -{0x0001BC9F, 0x0001BC9F}, {0x0001DA87, 0x0001DA8B}, {0x0001E95E, 0x0001E95F}, +const std::vector> unicode_ranges_flags = { // start, flags // last=next_start-1 +{0x000000, 0x0080}, +{0x000020, 0x0008}, +{0x000021, 0x0020}, +{0x000024, 0x0040}, +{0x000025, 0x0020}, +{0x00002B, 0x0040}, +{0x00002C, 0x0020}, +{0x000030, 0x0002}, +{0x00003A, 0x0020}, +{0x00003C, 0x0040}, +{0x00003F, 0x0020}, +{0x000041, 0x0004}, +{0x00005B, 0x0020}, +{0x00005E, 0x0040}, +{0x00005F, 0x0020}, +{0x000060, 0x0040}, +{0x000061, 0x0004}, +{0x00007B, 0x0020}, +{0x00007C, 0x0040}, +{0x00007D, 0x0020}, +{0x00007E, 0x0040}, +{0x00007F, 0x0080}, +{0x0000A0, 0x0008}, +{0x0000A1, 0x0020}, +{0x0000A2, 0x0040}, +{0x0000A7, 0x0020}, +{0x0000A8, 0x0040}, +{0x0000AA, 0x0004}, +{0x0000AB, 0x0020}, +{0x0000AC, 0x0040}, +{0x0000AD, 0x0080}, +{0x0000AE, 0x0040}, +{0x0000B2, 0x0002}, +{0x0000B4, 0x0040}, +{0x0000B5, 0x0004}, +{0x0000B6, 0x0020}, +{0x0000B8, 0x0040}, +{0x0000B9, 0x0002}, +{0x0000BA, 0x0004}, +{0x0000BB, 0x0020}, +{0x0000BC, 0x0002}, +{0x0000BF, 0x0020}, +{0x0000C0, 0x0004}, +{0x0000D7, 0x0040}, +{0x0000D8, 0x0004}, +{0x0000F7, 0x0040}, +{0x0000F8, 0x0004}, +{0x0002C2, 0x0040}, +{0x0002C6, 0x0004}, +{0x0002D2, 0x0040}, +{0x0002E0, 0x0004}, +{0x0002E5, 0x0040}, +{0x0002EC, 0x0004}, +{0x0002ED, 0x0040}, +{0x0002EE, 0x0004}, +{0x0002EF, 0x0040}, +{0x000300, 0x0010}, +{0x000370, 0x0004}, +{0x000375, 0x0040}, +{0x000376, 0x0004}, +{0x000378, 0x0080}, +{0x00037A, 0x0004}, +{0x00037E, 0x0020}, +{0x00037F, 0x0004}, +{0x000380, 0x0080}, +{0x000384, 0x0040}, +{0x000386, 0x0004}, +{0x000387, 0x0020}, +{0x000388, 0x0004}, +{0x00038B, 0x0080}, +{0x00038C, 0x0004}, +{0x00038D, 0x0080}, +{0x00038E, 0x0004}, +{0x0003A2, 0x0080}, +{0x0003A3, 0x0004}, +{0x0003F6, 0x0040}, +{0x0003F7, 0x0004}, +{0x000482, 0x0040}, +{0x000483, 0x0010}, +{0x00048A, 0x0004}, +{0x000530, 0x0080}, +{0x000531, 0x0004}, +{0x000557, 0x0080}, +{0x000559, 0x0004}, +{0x00055A, 0x0020}, +{0x000560, 0x0004}, +{0x000589, 0x0020}, +{0x00058B, 0x0080}, +{0x00058D, 0x0040}, +{0x000590, 0x0080}, +{0x000591, 0x0010}, +{0x0005BE, 0x0020}, +{0x0005BF, 0x0010}, +{0x0005C0, 0x0020}, +{0x0005C1, 0x0010}, +{0x0005C3, 0x0020}, +{0x0005C4, 0x0010}, +{0x0005C6, 0x0020}, +{0x0005C7, 0x0010}, +{0x0005C8, 0x0080}, +{0x0005D0, 0x0004}, +{0x0005EB, 0x0080}, +{0x0005EF, 0x0004}, +{0x0005F3, 0x0020}, +{0x0005F5, 0x0080}, +{0x000606, 0x0040}, +{0x000609, 0x0020}, +{0x00060B, 0x0040}, +{0x00060C, 0x0020}, +{0x00060E, 0x0040}, +{0x000610, 0x0010}, +{0x00061B, 0x0020}, +{0x00061C, 0x0080}, +{0x00061D, 0x0020}, +{0x000620, 0x0004}, +{0x00064B, 0x0010}, +{0x000660, 0x0002}, +{0x00066A, 0x0020}, +{0x00066E, 0x0004}, +{0x000670, 0x0010}, +{0x000671, 0x0004}, +{0x0006D4, 0x0020}, +{0x0006D5, 0x0004}, +{0x0006D6, 0x0010}, +{0x0006DD, 0x0080}, +{0x0006DE, 0x0040}, +{0x0006DF, 0x0010}, +{0x0006E5, 0x0004}, +{0x0006E7, 0x0010}, +{0x0006E9, 0x0040}, +{0x0006EA, 0x0010}, +{0x0006EE, 0x0004}, +{0x0006F0, 0x0002}, +{0x0006FA, 0x0004}, +{0x0006FD, 0x0040}, +{0x0006FF, 0x0004}, +{0x000700, 0x0020}, +{0x00070E, 0x0080}, +{0x000710, 0x0004}, +{0x000711, 0x0010}, +{0x000712, 0x0004}, +{0x000730, 0x0010}, +{0x00074B, 0x0080}, +{0x00074D, 0x0004}, +{0x0007A6, 0x0010}, +{0x0007B1, 0x0004}, +{0x0007B2, 0x0080}, +{0x0007C0, 0x0002}, +{0x0007CA, 0x0004}, +{0x0007EB, 0x0010}, +{0x0007F4, 0x0004}, +{0x0007F6, 0x0040}, +{0x0007F7, 0x0020}, +{0x0007FA, 0x0004}, +{0x0007FB, 0x0080}, +{0x0007FD, 0x0010}, +{0x0007FE, 0x0040}, +{0x000800, 0x0004}, +{0x000816, 0x0010}, +{0x00081A, 0x0004}, +{0x00081B, 0x0010}, +{0x000824, 0x0004}, +{0x000825, 0x0010}, +{0x000828, 0x0004}, +{0x000829, 0x0010}, +{0x00082E, 0x0080}, +{0x000830, 0x0020}, +{0x00083F, 0x0080}, +{0x000840, 0x0004}, +{0x000859, 0x0010}, +{0x00085C, 0x0080}, +{0x00085E, 0x0020}, +{0x00085F, 0x0080}, +{0x000860, 0x0004}, +{0x00086B, 0x0080}, +{0x000870, 0x0004}, +{0x000888, 0x0040}, +{0x000889, 0x0004}, +{0x00088F, 0x0080}, +{0x000898, 0x0010}, +{0x0008A0, 0x0004}, +{0x0008CA, 0x0010}, +{0x0008E2, 0x0080}, +{0x0008E3, 0x0010}, +{0x000904, 0x0004}, +{0x00093A, 0x0010}, +{0x00093D, 0x0004}, +{0x00093E, 0x0010}, +{0x000950, 0x0004}, +{0x000951, 0x0010}, +{0x000958, 0x0004}, +{0x000962, 0x0010}, +{0x000964, 0x0020}, +{0x000966, 0x0002}, +{0x000970, 0x0020}, +{0x000971, 0x0004}, +{0x000981, 0x0010}, +{0x000984, 0x0080}, +{0x000985, 0x0004}, +{0x00098D, 0x0080}, +{0x00098F, 0x0004}, +{0x000991, 0x0080}, +{0x000993, 0x0004}, +{0x0009A9, 0x0080}, +{0x0009AA, 0x0004}, +{0x0009B1, 0x0080}, +{0x0009B2, 0x0004}, +{0x0009B3, 0x0080}, +{0x0009B6, 0x0004}, +{0x0009BA, 0x0080}, +{0x0009BC, 0x0010}, +{0x0009BD, 0x0004}, +{0x0009BE, 0x0010}, +{0x0009C5, 0x0080}, +{0x0009C7, 0x0010}, +{0x0009C9, 0x0080}, +{0x0009CB, 0x0010}, +{0x0009CE, 0x0004}, +{0x0009CF, 0x0080}, +{0x0009D7, 0x0010}, +{0x0009D8, 0x0080}, +{0x0009DC, 0x0004}, +{0x0009DE, 0x0080}, +{0x0009DF, 0x0004}, +{0x0009E2, 0x0010}, +{0x0009E4, 0x0080}, +{0x0009E6, 0x0002}, +{0x0009F0, 0x0004}, +{0x0009F2, 0x0040}, +{0x0009F4, 0x0002}, +{0x0009FA, 0x0040}, +{0x0009FC, 0x0004}, +{0x0009FD, 0x0020}, +{0x0009FE, 0x0010}, +{0x0009FF, 0x0080}, +{0x000A01, 0x0010}, +{0x000A04, 0x0080}, +{0x000A05, 0x0004}, +{0x000A0B, 0x0080}, +{0x000A0F, 0x0004}, +{0x000A11, 0x0080}, +{0x000A13, 0x0004}, +{0x000A29, 0x0080}, +{0x000A2A, 0x0004}, +{0x000A31, 0x0080}, +{0x000A32, 0x0004}, +{0x000A34, 0x0080}, +{0x000A35, 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0x0080}, +{0x016FE0, 0x0004}, +{0x016FE2, 0x0020}, +{0x016FE3, 0x0004}, +{0x016FE4, 0x0010}, +{0x016FE5, 0x0080}, +{0x016FF0, 0x0010}, +{0x016FF2, 0x0080}, +{0x017000, 0x0004}, +{0x0187F8, 0x0080}, +{0x018800, 0x0004}, +{0x018CD6, 0x0080}, +{0x018D00, 0x0004}, +{0x018D09, 0x0080}, +{0x01AFF0, 0x0004}, +{0x01AFF4, 0x0080}, +{0x01AFF5, 0x0004}, +{0x01AFFC, 0x0080}, +{0x01AFFD, 0x0004}, +{0x01AFFF, 0x0080}, +{0x01B000, 0x0004}, +{0x01B123, 0x0080}, +{0x01B132, 0x0004}, +{0x01B133, 0x0080}, +{0x01B150, 0x0004}, +{0x01B153, 0x0080}, +{0x01B155, 0x0004}, +{0x01B156, 0x0080}, +{0x01B164, 0x0004}, +{0x01B168, 0x0080}, +{0x01B170, 0x0004}, +{0x01B2FC, 0x0080}, +{0x01BC00, 0x0004}, +{0x01BC6B, 0x0080}, +{0x01BC70, 0x0004}, +{0x01BC7D, 0x0080}, +{0x01BC80, 0x0004}, +{0x01BC89, 0x0080}, +{0x01BC90, 0x0004}, +{0x01BC9A, 0x0080}, +{0x01BC9C, 0x0040}, +{0x01BC9D, 0x0010}, +{0x01BC9F, 0x0020}, +{0x01BCA0, 0x0080}, +{0x01CF00, 0x0010}, +{0x01CF2E, 0x0080}, +{0x01CF30, 0x0010}, +{0x01CF47, 0x0080}, +{0x01CF50, 0x0040}, +{0x01CFC4, 0x0080}, +{0x01D000, 0x0040}, +{0x01D0F6, 0x0080}, +{0x01D100, 0x0040}, +{0x01D127, 0x0080}, +{0x01D129, 0x0040}, +{0x01D165, 0x0010}, +{0x01D16A, 0x0040}, +{0x01D16D, 0x0010}, +{0x01D173, 0x0080}, +{0x01D17B, 0x0010}, +{0x01D183, 0x0040}, +{0x01D185, 0x0010}, +{0x01D18C, 0x0040}, +{0x01D1AA, 0x0010}, +{0x01D1AE, 0x0040}, +{0x01D1EB, 0x0080}, +{0x01D200, 0x0040}, +{0x01D242, 0x0010}, +{0x01D245, 0x0040}, +{0x01D246, 0x0080}, +{0x01D2C0, 0x0002}, +{0x01D2D4, 0x0080}, +{0x01D2E0, 0x0002}, +{0x01D2F4, 0x0080}, +{0x01D300, 0x0040}, +{0x01D357, 0x0080}, +{0x01D360, 0x0002}, +{0x01D379, 0x0080}, +{0x01D400, 0x0004}, +{0x01D455, 0x0080}, +{0x01D456, 0x0004}, +{0x01D49D, 0x0080}, +{0x01D49E, 0x0004}, +{0x01D4A0, 0x0080}, +{0x01D4A2, 0x0004}, +{0x01D4A3, 0x0080}, +{0x01D4A5, 0x0004}, +{0x01D4A7, 0x0080}, +{0x01D4A9, 0x0004}, +{0x01D4AD, 0x0080}, +{0x01D4AE, 0x0004}, +{0x01D4BA, 0x0080}, +{0x01D4BB, 0x0004}, +{0x01D4BC, 0x0080}, +{0x01D4BD, 0x0004}, +{0x01D4C4, 0x0080}, +{0x01D4C5, 0x0004}, +{0x01D506, 0x0080}, +{0x01D507, 0x0004}, +{0x01D50B, 0x0080}, +{0x01D50D, 0x0004}, +{0x01D515, 0x0080}, +{0x01D516, 0x0004}, +{0x01D51D, 0x0080}, +{0x01D51E, 0x0004}, +{0x01D53A, 0x0080}, +{0x01D53B, 0x0004}, +{0x01D53F, 0x0080}, +{0x01D540, 0x0004}, +{0x01D545, 0x0080}, +{0x01D546, 0x0004}, +{0x01D547, 0x0080}, +{0x01D54A, 0x0004}, +{0x01D551, 0x0080}, +{0x01D552, 0x0004}, +{0x01D6A6, 0x0080}, +{0x01D6A8, 0x0004}, +{0x01D6C1, 0x0040}, +{0x01D6C2, 0x0004}, +{0x01D6DB, 0x0040}, +{0x01D6DC, 0x0004}, +{0x01D6FB, 0x0040}, +{0x01D6FC, 0x0004}, +{0x01D715, 0x0040}, +{0x01D716, 0x0004}, +{0x01D735, 0x0040}, +{0x01D736, 0x0004}, +{0x01D74F, 0x0040}, +{0x01D750, 0x0004}, +{0x01D76F, 0x0040}, +{0x01D770, 0x0004}, +{0x01D789, 0x0040}, +{0x01D78A, 0x0004}, +{0x01D7A9, 0x0040}, +{0x01D7AA, 0x0004}, +{0x01D7C3, 0x0040}, +{0x01D7C4, 0x0004}, +{0x01D7CC, 0x0080}, +{0x01D7CE, 0x0002}, +{0x01D800, 0x0040}, +{0x01DA00, 0x0010}, +{0x01DA37, 0x0040}, +{0x01DA3B, 0x0010}, +{0x01DA6D, 0x0040}, +{0x01DA75, 0x0010}, +{0x01DA76, 0x0040}, +{0x01DA84, 0x0010}, +{0x01DA85, 0x0040}, +{0x01DA87, 0x0020}, +{0x01DA8C, 0x0080}, +{0x01DA9B, 0x0010}, +{0x01DAA0, 0x0080}, +{0x01DAA1, 0x0010}, +{0x01DAB0, 0x0080}, +{0x01DF00, 0x0004}, +{0x01DF1F, 0x0080}, +{0x01DF25, 0x0004}, +{0x01DF2B, 0x0080}, +{0x01E000, 0x0010}, +{0x01E007, 0x0080}, +{0x01E008, 0x0010}, +{0x01E019, 0x0080}, +{0x01E01B, 0x0010}, +{0x01E022, 0x0080}, +{0x01E023, 0x0010}, +{0x01E025, 0x0080}, +{0x01E026, 0x0010}, +{0x01E02B, 0x0080}, +{0x01E030, 0x0004}, +{0x01E06E, 0x0080}, +{0x01E08F, 0x0010}, +{0x01E090, 0x0080}, +{0x01E100, 0x0004}, +{0x01E12D, 0x0080}, +{0x01E130, 0x0010}, +{0x01E137, 0x0004}, +{0x01E13E, 0x0080}, +{0x01E140, 0x0002}, +{0x01E14A, 0x0080}, +{0x01E14E, 0x0004}, +{0x01E14F, 0x0040}, +{0x01E150, 0x0080}, +{0x01E290, 0x0004}, +{0x01E2AE, 0x0010}, +{0x01E2AF, 0x0080}, +{0x01E2C0, 0x0004}, +{0x01E2EC, 0x0010}, +{0x01E2F0, 0x0002}, +{0x01E2FA, 0x0080}, +{0x01E2FF, 0x0040}, +{0x01E300, 0x0080}, +{0x01E4D0, 0x0004}, +{0x01E4EC, 0x0010}, +{0x01E4F0, 0x0002}, +{0x01E4FA, 0x0080}, +{0x01E7E0, 0x0004}, +{0x01E7E7, 0x0080}, +{0x01E7E8, 0x0004}, +{0x01E7EC, 0x0080}, +{0x01E7ED, 0x0004}, +{0x01E7EF, 0x0080}, +{0x01E7F0, 0x0004}, +{0x01E7FF, 0x0080}, +{0x01E800, 0x0004}, +{0x01E8C5, 0x0080}, +{0x01E8C7, 0x0002}, +{0x01E8D0, 0x0010}, +{0x01E8D7, 0x0080}, +{0x01E900, 0x0004}, +{0x01E944, 0x0010}, +{0x01E94B, 0x0004}, +{0x01E94C, 0x0080}, +{0x01E950, 0x0002}, +{0x01E95A, 0x0080}, +{0x01E95E, 0x0020}, +{0x01E960, 0x0080}, +{0x01EC71, 0x0002}, +{0x01ECAC, 0x0040}, +{0x01ECAD, 0x0002}, +{0x01ECB0, 0x0040}, +{0x01ECB1, 0x0002}, +{0x01ECB5, 0x0080}, +{0x01ED01, 0x0002}, +{0x01ED2E, 0x0040}, +{0x01ED2F, 0x0002}, +{0x01ED3E, 0x0080}, +{0x01EE00, 0x0004}, +{0x01EE04, 0x0080}, +{0x01EE05, 0x0004}, +{0x01EE20, 0x0080}, +{0x01EE21, 0x0004}, +{0x01EE23, 0x0080}, +{0x01EE24, 0x0004}, +{0x01EE25, 0x0080}, +{0x01EE27, 0x0004}, +{0x01EE28, 0x0080}, +{0x01EE29, 0x0004}, +{0x01EE33, 0x0080}, +{0x01EE34, 0x0004}, +{0x01EE38, 0x0080}, +{0x01EE39, 0x0004}, +{0x01EE3A, 0x0080}, +{0x01EE3B, 0x0004}, +{0x01EE3C, 0x0080}, +{0x01EE42, 0x0004}, +{0x01EE43, 0x0080}, +{0x01EE47, 0x0004}, +{0x01EE48, 0x0080}, +{0x01EE49, 0x0004}, +{0x01EE4A, 0x0080}, +{0x01EE4B, 0x0004}, +{0x01EE4C, 0x0080}, +{0x01EE4D, 0x0004}, +{0x01EE50, 0x0080}, +{0x01EE51, 0x0004}, +{0x01EE53, 0x0080}, +{0x01EE54, 0x0004}, +{0x01EE55, 0x0080}, +{0x01EE57, 0x0004}, +{0x01EE58, 0x0080}, +{0x01EE59, 0x0004}, +{0x01EE5A, 0x0080}, +{0x01EE5B, 0x0004}, +{0x01EE5C, 0x0080}, +{0x01EE5D, 0x0004}, +{0x01EE5E, 0x0080}, +{0x01EE5F, 0x0004}, +{0x01EE60, 0x0080}, +{0x01EE61, 0x0004}, +{0x01EE63, 0x0080}, +{0x01EE64, 0x0004}, +{0x01EE65, 0x0080}, +{0x01EE67, 0x0004}, +{0x01EE6B, 0x0080}, +{0x01EE6C, 0x0004}, +{0x01EE73, 0x0080}, +{0x01EE74, 0x0004}, +{0x01EE78, 0x0080}, +{0x01EE79, 0x0004}, +{0x01EE7D, 0x0080}, +{0x01EE7E, 0x0004}, +{0x01EE7F, 0x0080}, +{0x01EE80, 0x0004}, +{0x01EE8A, 0x0080}, +{0x01EE8B, 0x0004}, +{0x01EE9C, 0x0080}, +{0x01EEA1, 0x0004}, +{0x01EEA4, 0x0080}, +{0x01EEA5, 0x0004}, +{0x01EEAA, 0x0080}, +{0x01EEAB, 0x0004}, +{0x01EEBC, 0x0080}, +{0x01EEF0, 0x0040}, +{0x01EEF2, 0x0080}, +{0x01F000, 0x0040}, +{0x01F02C, 0x0080}, +{0x01F030, 0x0040}, +{0x01F094, 0x0080}, +{0x01F0A0, 0x0040}, +{0x01F0AF, 0x0080}, +{0x01F0B1, 0x0040}, +{0x01F0C0, 0x0080}, +{0x01F0C1, 0x0040}, +{0x01F0D0, 0x0080}, +{0x01F0D1, 0x0040}, +{0x01F0F6, 0x0080}, +{0x01F100, 0x0002}, +{0x01F10D, 0x0040}, +{0x01F1AE, 0x0080}, +{0x01F1E6, 0x0040}, +{0x01F203, 0x0080}, +{0x01F210, 0x0040}, +{0x01F23C, 0x0080}, +{0x01F240, 0x0040}, +{0x01F249, 0x0080}, +{0x01F250, 0x0040}, +{0x01F252, 0x0080}, +{0x01F260, 0x0040}, +{0x01F266, 0x0080}, +{0x01F300, 0x0040}, +{0x01F6D8, 0x0080}, +{0x01F6DC, 0x0040}, +{0x01F6ED, 0x0080}, +{0x01F6F0, 0x0040}, +{0x01F6FD, 0x0080}, +{0x01F700, 0x0040}, +{0x01F777, 0x0080}, +{0x01F77B, 0x0040}, +{0x01F7DA, 0x0080}, +{0x01F7E0, 0x0040}, +{0x01F7EC, 0x0080}, +{0x01F7F0, 0x0040}, +{0x01F7F1, 0x0080}, +{0x01F800, 0x0040}, +{0x01F80C, 0x0080}, +{0x01F810, 0x0040}, +{0x01F848, 0x0080}, +{0x01F850, 0x0040}, +{0x01F85A, 0x0080}, +{0x01F860, 0x0040}, +{0x01F888, 0x0080}, +{0x01F890, 0x0040}, +{0x01F8AE, 0x0080}, +{0x01F8B0, 0x0040}, +{0x01F8B2, 0x0080}, +{0x01F900, 0x0040}, +{0x01FA54, 0x0080}, +{0x01FA60, 0x0040}, +{0x01FA6E, 0x0080}, +{0x01FA70, 0x0040}, +{0x01FA7D, 0x0080}, +{0x01FA80, 0x0040}, +{0x01FA89, 0x0080}, +{0x01FA90, 0x0040}, +{0x01FABE, 0x0080}, +{0x01FABF, 0x0040}, +{0x01FAC6, 0x0080}, +{0x01FACE, 0x0040}, +{0x01FADC, 0x0080}, +{0x01FAE0, 0x0040}, +{0x01FAE9, 0x0080}, +{0x01FAF0, 0x0040}, +{0x01FAF9, 0x0080}, +{0x01FB00, 0x0040}, +{0x01FB93, 0x0080}, +{0x01FB94, 0x0040}, +{0x01FBCB, 0x0080}, +{0x01FBF0, 0x0002}, +{0x01FBFA, 0x0080}, +{0x020000, 0x0004}, +{0x02A6E0, 0x0080}, +{0x02A700, 0x0004}, +{0x02B73A, 0x0080}, +{0x02B740, 0x0004}, +{0x02B81E, 0x0080}, +{0x02B820, 0x0004}, +{0x02CEA2, 0x0080}, +{0x02CEB0, 0x0004}, +{0x02EBE1, 0x0080}, +{0x02EBF0, 0x0004}, +{0x02EE5E, 0x0080}, +{0x02F800, 0x0004}, +{0x02FA1E, 0x0080}, +{0x030000, 0x0004}, +{0x03134B, 0x0080}, +{0x031350, 0x0004}, +{0x0323B0, 0x0080}, +{0x0E0100, 0x0010}, +{0x0E01F0, 0x0080}, +{0x110000, 0x0000}, }; -const std::vector> unicode_ranges_symbol = { -{0x00000024, 0x00000024}, {0x0000002B, 0x0000002B}, {0x0000003C, 0x0000003E}, {0x0000005E, 0x0000005E}, -{0x00000060, 0x00000060}, {0x0000007C, 0x0000007C}, {0x0000007E, 0x0000007E}, {0x000000A2, 0x000000A6}, -{0x000000A8, 0x000000A9}, {0x000000AC, 0x000000AC}, {0x000000AE, 0x000000B1}, {0x000000B4, 0x000000B4}, -{0x000000B8, 0x000000B8}, {0x000000D7, 0x000000D7}, {0x000000F7, 0x000000F7}, {0x000002C2, 0x000002C5}, -{0x000002D2, 0x000002DF}, {0x000002E5, 0x000002EB}, {0x000002ED, 0x000002ED}, {0x000002EF, 0x000002FF}, -{0x00000375, 0x00000375}, {0x00000384, 0x00000385}, {0x000003F6, 0x000003F6}, {0x00000482, 0x00000482}, -{0x0000058D, 0x0000058F}, {0x00000606, 0x00000608}, {0x0000060B, 0x0000060B}, {0x0000060E, 0x0000060F}, -{0x000006DE, 0x000006DE}, {0x000006E9, 0x000006E9}, {0x000006FD, 0x000006FE}, {0x000007F6, 0x000007F6}, -{0x000007FE, 0x000007FF}, {0x00000888, 0x00000888}, {0x000009F2, 0x000009F3}, {0x000009FA, 0x000009FB}, -{0x00000AF1, 0x00000AF1}, {0x00000B70, 0x00000B70}, {0x00000BF3, 0x00000BFA}, {0x00000C7F, 0x00000C7F}, -{0x00000D4F, 0x00000D4F}, {0x00000D79, 0x00000D79}, {0x00000E3F, 0x00000E3F}, {0x00000F01, 0x00000F03}, -{0x00000F13, 0x00000F13}, {0x00000F15, 0x00000F17}, {0x00000F1A, 0x00000F1F}, {0x00000F34, 0x00000F34}, -{0x00000F36, 0x00000F36}, {0x00000F38, 0x00000F38}, {0x00000FBE, 0x00000FC5}, {0x00000FC7, 0x00000FCC}, -{0x00000FCE, 0x00000FCF}, {0x00000FD5, 0x00000FD8}, {0x0000109E, 0x0000109F}, {0x00001390, 0x00001399}, -{0x0000166D, 0x0000166D}, {0x000017DB, 0x000017DB}, {0x00001940, 0x00001940}, {0x000019DE, 0x000019FF}, -{0x00001B61, 0x00001B6A}, {0x00001B74, 0x00001B7C}, {0x00001FBD, 0x00001FBD}, {0x00001FBF, 0x00001FC1}, -{0x00001FCD, 0x00001FCF}, {0x00001FDD, 0x00001FDF}, {0x00001FED, 0x00001FEF}, {0x00001FFD, 0x00001FFE}, -{0x00002044, 0x00002044}, {0x00002052, 0x00002052}, {0x0000207A, 0x0000207C}, {0x0000208A, 0x0000208C}, -{0x000020A0, 0x000020C0}, {0x00002100, 0x00002101}, {0x00002103, 0x00002106}, {0x00002108, 0x00002109}, -{0x00002114, 0x00002114}, {0x00002116, 0x00002118}, {0x0000211E, 0x00002123}, {0x00002125, 0x00002125}, -{0x00002127, 0x00002127}, {0x00002129, 0x00002129}, {0x0000212E, 0x0000212E}, {0x0000213A, 0x0000213B}, -{0x00002140, 0x00002144}, {0x0000214A, 0x0000214D}, {0x0000214F, 0x0000214F}, {0x0000218A, 0x0000218B}, -{0x00002190, 0x00002307}, {0x0000230C, 0x00002328}, {0x0000232B, 0x00002426}, {0x00002440, 0x0000244A}, -{0x0000249C, 0x000024E9}, {0x00002500, 0x00002767}, {0x00002794, 0x000027C4}, {0x000027C7, 0x000027E5}, -{0x000027F0, 0x00002982}, {0x00002999, 0x000029D7}, {0x000029DC, 0x000029FB}, {0x000029FE, 0x00002B73}, -{0x00002B76, 0x00002B95}, {0x00002B97, 0x00002BFF}, {0x00002CE5, 0x00002CEA}, {0x00002E50, 0x00002E51}, -{0x00002E80, 0x00002E99}, {0x00002E9B, 0x00002EF3}, {0x00002F00, 0x00002FD5}, {0x00002FF0, 0x00002FFF}, -{0x00003004, 0x00003004}, {0x00003012, 0x00003013}, {0x00003020, 0x00003020}, {0x00003036, 0x00003037}, -{0x0000303E, 0x0000303F}, {0x0000309B, 0x0000309C}, {0x00003190, 0x00003191}, {0x00003196, 0x0000319F}, -{0x000031C0, 0x000031E3}, {0x000031EF, 0x000031EF}, {0x00003200, 0x0000321E}, {0x0000322A, 0x00003247}, -{0x00003250, 0x00003250}, {0x00003260, 0x0000327F}, {0x0000328A, 0x000032B0}, {0x000032C0, 0x000033FF}, -{0x00004DC0, 0x00004DFF}, {0x0000A490, 0x0000A4C6}, {0x0000A700, 0x0000A716}, {0x0000A720, 0x0000A721}, -{0x0000A789, 0x0000A78A}, {0x0000A828, 0x0000A82B}, {0x0000A836, 0x0000A839}, {0x0000AA77, 0x0000AA79}, -{0x0000AB5B, 0x0000AB5B}, {0x0000AB6A, 0x0000AB6B}, {0x0000FB29, 0x0000FB29}, {0x0000FBB2, 0x0000FBC2}, -{0x0000FD40, 0x0000FD4F}, {0x0000FDCF, 0x0000FDCF}, {0x0000FDFC, 0x0000FDFF}, {0x0000FE62, 0x0000FE62}, -{0x0000FE64, 0x0000FE66}, {0x0000FE69, 0x0000FE69}, {0x0000FF04, 0x0000FF04}, {0x0000FF0B, 0x0000FF0B}, -{0x0000FF1C, 0x0000FF1E}, {0x0000FF3E, 0x0000FF3E}, {0x0000FF40, 0x0000FF40}, {0x0000FF5C, 0x0000FF5C}, -{0x0000FF5E, 0x0000FF5E}, {0x0000FFE0, 0x0000FFE6}, {0x0000FFE8, 0x0000FFEE}, {0x0000FFFC, 0x0000FFFD}, -{0x00010137, 0x0001013F}, {0x00010179, 0x00010189}, {0x0001018C, 0x0001018E}, {0x00010190, 0x0001019C}, -{0x000101A0, 0x000101A0}, {0x000101D0, 0x000101FC}, {0x00010877, 0x00010878}, {0x00010AC8, 0x00010AC8}, -{0x0001173F, 0x0001173F}, {0x00011FD5, 0x00011FF1}, {0x00016B3C, 0x00016B3F}, {0x00016B45, 0x00016B45}, -{0x0001BC9C, 0x0001BC9C}, {0x0001CF50, 0x0001CFC3}, {0x0001D000, 0x0001D0F5}, {0x0001D100, 0x0001D126}, -{0x0001D129, 0x0001D164}, {0x0001D16A, 0x0001D16C}, {0x0001D183, 0x0001D184}, {0x0001D18C, 0x0001D1A9}, -{0x0001D1AE, 0x0001D1EA}, {0x0001D200, 0x0001D241}, {0x0001D245, 0x0001D245}, {0x0001D300, 0x0001D356}, -{0x0001D6C1, 0x0001D6C1}, {0x0001D6DB, 0x0001D6DB}, {0x0001D6FB, 0x0001D6FB}, {0x0001D715, 0x0001D715}, -{0x0001D735, 0x0001D735}, {0x0001D74F, 0x0001D74F}, {0x0001D76F, 0x0001D76F}, {0x0001D789, 0x0001D789}, -{0x0001D7A9, 0x0001D7A9}, {0x0001D7C3, 0x0001D7C3}, {0x0001D800, 0x0001D9FF}, {0x0001DA37, 0x0001DA3A}, -{0x0001DA6D, 0x0001DA74}, {0x0001DA76, 0x0001DA83}, {0x0001DA85, 0x0001DA86}, {0x0001E14F, 0x0001E14F}, -{0x0001E2FF, 0x0001E2FF}, {0x0001ECAC, 0x0001ECAC}, {0x0001ECB0, 0x0001ECB0}, {0x0001ED2E, 0x0001ED2E}, -{0x0001EEF0, 0x0001EEF1}, {0x0001F000, 0x0001F02B}, {0x0001F030, 0x0001F093}, {0x0001F0A0, 0x0001F0AE}, -{0x0001F0B1, 0x0001F0BF}, {0x0001F0C1, 0x0001F0CF}, {0x0001F0D1, 0x0001F0F5}, {0x0001F10D, 0x0001F1AD}, -{0x0001F1E6, 0x0001F202}, {0x0001F210, 0x0001F23B}, {0x0001F240, 0x0001F248}, {0x0001F250, 0x0001F251}, -{0x0001F260, 0x0001F265}, {0x0001F300, 0x0001F6D7}, {0x0001F6DC, 0x0001F6EC}, {0x0001F6F0, 0x0001F6FC}, -{0x0001F700, 0x0001F776}, {0x0001F77B, 0x0001F7D9}, {0x0001F7E0, 0x0001F7EB}, {0x0001F7F0, 0x0001F7F0}, -{0x0001F800, 0x0001F80B}, {0x0001F810, 0x0001F847}, {0x0001F850, 0x0001F859}, {0x0001F860, 0x0001F887}, -{0x0001F890, 0x0001F8AD}, {0x0001F8B0, 0x0001F8B1}, {0x0001F900, 0x0001FA53}, {0x0001FA60, 0x0001FA6D}, -{0x0001FA70, 0x0001FA7C}, {0x0001FA80, 0x0001FA88}, {0x0001FA90, 0x0001FABD}, {0x0001FABF, 0x0001FAC5}, -{0x0001FACE, 0x0001FADB}, {0x0001FAE0, 0x0001FAE8}, {0x0001FAF0, 0x0001FAF8}, {0x0001FB00, 0x0001FB92}, -{0x0001FB94, 0x0001FBCA}, +const std::unordered_set unicode_set_whitespace = { +0x000009, 0x00000A, 0x00000B, 0x00000C, 0x00000D, 0x000020, 0x000085, 0x0000A0, 0x001680, 0x002000, 0x002001, 0x002002, 0x002003, 0x002004, 0x002005, 0x002006, 0x002007, 0x002008, 0x002009, 0x00200A, 0x002028, 0x002029, 0x00202F, 0x00205F, 0x003000 }; -const std::vector> unicode_ranges_control = { -{0x00000000, 0x0000001F}, {0x0000007F, 0x0000009F}, {0x000000AD, 0x000000AD}, {0x00000378, 0x00000379}, -{0x00000380, 0x00000383}, {0x0000038B, 0x0000038B}, {0x0000038D, 0x0000038D}, {0x000003A2, 0x000003A2}, -{0x00000530, 0x00000530}, {0x00000557, 0x00000558}, {0x0000058B, 0x0000058C}, {0x00000590, 0x00000590}, -{0x000005C8, 0x000005CF}, {0x000005EB, 0x000005EE}, {0x000005F5, 0x00000605}, {0x0000061C, 0x0000061C}, -{0x000006DD, 0x000006DD}, {0x0000070E, 0x0000070F}, {0x0000074B, 0x0000074C}, {0x000007B2, 0x000007BF}, -{0x000007FB, 0x000007FC}, {0x0000082E, 0x0000082F}, {0x0000083F, 0x0000083F}, {0x0000085C, 0x0000085D}, -{0x0000085F, 0x0000085F}, {0x0000086B, 0x0000086F}, {0x0000088F, 0x00000897}, {0x000008E2, 0x000008E2}, -{0x00000984, 0x00000984}, {0x0000098D, 0x0000098E}, {0x00000991, 0x00000992}, {0x000009A9, 0x000009A9}, -{0x000009B1, 0x000009B1}, {0x000009B3, 0x000009B5}, {0x000009BA, 0x000009BB}, {0x000009C5, 0x000009C6}, -{0x000009C9, 0x000009CA}, {0x000009CF, 0x000009D6}, {0x000009D8, 0x000009DB}, {0x000009DE, 0x000009DE}, -{0x000009E4, 0x000009E5}, {0x000009FF, 0x00000A00}, {0x00000A04, 0x00000A04}, {0x00000A0B, 0x00000A0E}, -{0x00000A11, 0x00000A12}, {0x00000A29, 0x00000A29}, {0x00000A31, 0x00000A31}, {0x00000A34, 0x00000A34}, -{0x00000A37, 0x00000A37}, {0x00000A3A, 0x00000A3B}, {0x00000A3D, 0x00000A3D}, {0x00000A43, 0x00000A46}, -{0x00000A49, 0x00000A4A}, {0x00000A4E, 0x00000A50}, {0x00000A52, 0x00000A58}, {0x00000A5D, 0x00000A5D}, -{0x00000A5F, 0x00000A65}, {0x00000A77, 0x00000A80}, {0x00000A84, 0x00000A84}, {0x00000A8E, 0x00000A8E}, -{0x00000A92, 0x00000A92}, {0x00000AA9, 0x00000AA9}, {0x00000AB1, 0x00000AB1}, {0x00000AB4, 0x00000AB4}, -{0x00000ABA, 0x00000ABB}, {0x00000AC6, 0x00000AC6}, {0x00000ACA, 0x00000ACA}, {0x00000ACE, 0x00000ACF}, -{0x00000AD1, 0x00000ADF}, {0x00000AE4, 0x00000AE5}, {0x00000AF2, 0x00000AF8}, {0x00000B00, 0x00000B00}, -{0x00000B04, 0x00000B04}, {0x00000B0D, 0x00000B0E}, {0x00000B11, 0x00000B12}, {0x00000B29, 0x00000B29}, -{0x00000B31, 0x00000B31}, {0x00000B34, 0x00000B34}, {0x00000B3A, 0x00000B3B}, {0x00000B45, 0x00000B46}, -{0x00000B49, 0x00000B4A}, {0x00000B4E, 0x00000B54}, {0x00000B58, 0x00000B5B}, {0x00000B5E, 0x00000B5E}, -{0x00000B64, 0x00000B65}, {0x00000B78, 0x00000B81}, {0x00000B84, 0x00000B84}, {0x00000B8B, 0x00000B8D}, -{0x00000B91, 0x00000B91}, {0x00000B96, 0x00000B98}, {0x00000B9B, 0x00000B9B}, {0x00000B9D, 0x00000B9D}, -{0x00000BA0, 0x00000BA2}, {0x00000BA5, 0x00000BA7}, {0x00000BAB, 0x00000BAD}, {0x00000BBA, 0x00000BBD}, -{0x00000BC3, 0x00000BC5}, {0x00000BC9, 0x00000BC9}, {0x00000BCE, 0x00000BCF}, {0x00000BD1, 0x00000BD6}, -{0x00000BD8, 0x00000BE5}, {0x00000BFB, 0x00000BFF}, {0x00000C0D, 0x00000C0D}, {0x00000C11, 0x00000C11}, -{0x00000C29, 0x00000C29}, {0x00000C3A, 0x00000C3B}, {0x00000C45, 0x00000C45}, {0x00000C49, 0x00000C49}, -{0x00000C4E, 0x00000C54}, {0x00000C57, 0x00000C57}, {0x00000C5B, 0x00000C5C}, {0x00000C5E, 0x00000C5F}, -{0x00000C64, 0x00000C65}, {0x00000C70, 0x00000C76}, {0x00000C8D, 0x00000C8D}, {0x00000C91, 0x00000C91}, -{0x00000CA9, 0x00000CA9}, {0x00000CB4, 0x00000CB4}, {0x00000CBA, 0x00000CBB}, {0x00000CC5, 0x00000CC5}, -{0x00000CC9, 0x00000CC9}, {0x00000CCE, 0x00000CD4}, {0x00000CD7, 0x00000CDC}, {0x00000CDF, 0x00000CDF}, -{0x00000CE4, 0x00000CE5}, {0x00000CF0, 0x00000CF0}, {0x00000CF4, 0x00000CFF}, {0x00000D0D, 0x00000D0D}, -{0x00000D11, 0x00000D11}, {0x00000D45, 0x00000D45}, {0x00000D49, 0x00000D49}, {0x00000D50, 0x00000D53}, -{0x00000D64, 0x00000D65}, {0x00000D80, 0x00000D80}, {0x00000D84, 0x00000D84}, {0x00000D97, 0x00000D99}, -{0x00000DB2, 0x00000DB2}, {0x00000DBC, 0x00000DBC}, {0x00000DBE, 0x00000DBF}, {0x00000DC7, 0x00000DC9}, -{0x00000DCB, 0x00000DCE}, {0x00000DD5, 0x00000DD5}, {0x00000DD7, 0x00000DD7}, {0x00000DE0, 0x00000DE5}, -{0x00000DF0, 0x00000DF1}, {0x00000DF5, 0x00000E00}, {0x00000E3B, 0x00000E3E}, {0x00000E5C, 0x00000E80}, -{0x00000E83, 0x00000E83}, {0x00000E85, 0x00000E85}, {0x00000E8B, 0x00000E8B}, {0x00000EA4, 0x00000EA4}, -{0x00000EA6, 0x00000EA6}, {0x00000EBE, 0x00000EBF}, {0x00000EC5, 0x00000EC5}, {0x00000EC7, 0x00000EC7}, -{0x00000ECF, 0x00000ECF}, {0x00000EDA, 0x00000EDB}, {0x00000EE0, 0x00000EFF}, {0x00000F48, 0x00000F48}, -{0x00000F6D, 0x00000F70}, {0x00000F98, 0x00000F98}, {0x00000FBD, 0x00000FBD}, {0x00000FCD, 0x00000FCD}, -{0x00000FDB, 0x00000FFF}, {0x000010C6, 0x000010C6}, {0x000010C8, 0x000010CC}, {0x000010CE, 0x000010CF}, -{0x00001249, 0x00001249}, {0x0000124E, 0x0000124F}, {0x00001257, 0x00001257}, {0x00001259, 0x00001259}, -{0x0000125E, 0x0000125F}, {0x00001289, 0x00001289}, {0x0000128E, 0x0000128F}, {0x000012B1, 0x000012B1}, -{0x000012B6, 0x000012B7}, {0x000012BF, 0x000012BF}, {0x000012C1, 0x000012C1}, {0x000012C6, 0x000012C7}, -{0x000012D7, 0x000012D7}, {0x00001311, 0x00001311}, {0x00001316, 0x00001317}, {0x0000135B, 0x0000135C}, -{0x0000137D, 0x0000137F}, {0x0000139A, 0x0000139F}, {0x000013F6, 0x000013F7}, {0x000013FE, 0x000013FF}, -{0x0000169D, 0x0000169F}, {0x000016F9, 0x000016FF}, {0x00001716, 0x0000171E}, {0x00001737, 0x0000173F}, -{0x00001754, 0x0000175F}, {0x0000176D, 0x0000176D}, {0x00001771, 0x00001771}, {0x00001774, 0x0000177F}, -{0x000017DE, 0x000017DF}, {0x000017EA, 0x000017EF}, {0x000017FA, 0x000017FF}, {0x0000180E, 0x0000180E}, -{0x0000181A, 0x0000181F}, {0x00001879, 0x0000187F}, {0x000018AB, 0x000018AF}, {0x000018F6, 0x000018FF}, -{0x0000191F, 0x0000191F}, {0x0000192C, 0x0000192F}, {0x0000193C, 0x0000193F}, {0x00001941, 0x00001943}, -{0x0000196E, 0x0000196F}, {0x00001975, 0x0000197F}, {0x000019AC, 0x000019AF}, {0x000019CA, 0x000019CF}, -{0x000019DB, 0x000019DD}, {0x00001A1C, 0x00001A1D}, {0x00001A5F, 0x00001A5F}, {0x00001A7D, 0x00001A7E}, -{0x00001A8A, 0x00001A8F}, {0x00001A9A, 0x00001A9F}, {0x00001AAE, 0x00001AAF}, {0x00001ACF, 0x00001AFF}, -{0x00001B4D, 0x00001B4F}, {0x00001B7F, 0x00001B7F}, {0x00001BF4, 0x00001BFB}, {0x00001C38, 0x00001C3A}, -{0x00001C4A, 0x00001C4C}, {0x00001C89, 0x00001C8F}, {0x00001CBB, 0x00001CBC}, {0x00001CC8, 0x00001CCF}, -{0x00001CFB, 0x00001CFF}, {0x00001F16, 0x00001F17}, {0x00001F1E, 0x00001F1F}, {0x00001F46, 0x00001F47}, -{0x00001F4E, 0x00001F4F}, {0x00001F58, 0x00001F58}, {0x00001F5A, 0x00001F5A}, {0x00001F5C, 0x00001F5C}, -{0x00001F5E, 0x00001F5E}, {0x00001F7E, 0x00001F7F}, {0x00001FB5, 0x00001FB5}, {0x00001FC5, 0x00001FC5}, -{0x00001FD4, 0x00001FD5}, {0x00001FDC, 0x00001FDC}, {0x00001FF0, 0x00001FF1}, {0x00001FF5, 0x00001FF5}, -{0x00001FFF, 0x00001FFF}, {0x0000200B, 0x0000200F}, {0x0000202A, 0x0000202E}, {0x00002060, 0x0000206F}, -{0x00002072, 0x00002073}, {0x0000208F, 0x0000208F}, {0x0000209D, 0x0000209F}, {0x000020C1, 0x000020CF}, -{0x000020F1, 0x000020FF}, {0x0000218C, 0x0000218F}, {0x00002427, 0x0000243F}, {0x0000244B, 0x0000245F}, -{0x00002B74, 0x00002B75}, {0x00002B96, 0x00002B96}, {0x00002CF4, 0x00002CF8}, {0x00002D26, 0x00002D26}, -{0x00002D28, 0x00002D2C}, {0x00002D2E, 0x00002D2F}, {0x00002D68, 0x00002D6E}, {0x00002D71, 0x00002D7E}, -{0x00002D97, 0x00002D9F}, {0x00002DA7, 0x00002DA7}, {0x00002DAF, 0x00002DAF}, {0x00002DB7, 0x00002DB7}, -{0x00002DBF, 0x00002DBF}, {0x00002DC7, 0x00002DC7}, {0x00002DCF, 0x00002DCF}, {0x00002DD7, 0x00002DD7}, -{0x00002DDF, 0x00002DDF}, {0x00002E5E, 0x00002E7F}, {0x00002E9A, 0x00002E9A}, {0x00002EF4, 0x00002EFF}, -{0x00002FD6, 0x00002FEF}, {0x00003040, 0x00003040}, {0x00003097, 0x00003098}, {0x00003100, 0x00003104}, -{0x00003130, 0x00003130}, {0x0000318F, 0x0000318F}, {0x000031E4, 0x000031EE}, {0x0000321F, 0x0000321F}, -{0x0000A48D, 0x0000A48F}, {0x0000A4C7, 0x0000A4CF}, {0x0000A62C, 0x0000A63F}, {0x0000A6F8, 0x0000A6FF}, -{0x0000A7CB, 0x0000A7CF}, {0x0000A7D2, 0x0000A7D2}, {0x0000A7D4, 0x0000A7D4}, {0x0000A7DA, 0x0000A7F1}, -{0x0000A82D, 0x0000A82F}, {0x0000A83A, 0x0000A83F}, {0x0000A878, 0x0000A87F}, {0x0000A8C6, 0x0000A8CD}, -{0x0000A8DA, 0x0000A8DF}, {0x0000A954, 0x0000A95E}, {0x0000A97D, 0x0000A97F}, {0x0000A9CE, 0x0000A9CE}, -{0x0000A9DA, 0x0000A9DD}, {0x0000A9FF, 0x0000A9FF}, {0x0000AA37, 0x0000AA3F}, {0x0000AA4E, 0x0000AA4F}, -{0x0000AA5A, 0x0000AA5B}, {0x0000AAC3, 0x0000AADA}, {0x0000AAF7, 0x0000AB00}, {0x0000AB07, 0x0000AB08}, -{0x0000AB0F, 0x0000AB10}, {0x0000AB17, 0x0000AB1F}, {0x0000AB27, 0x0000AB27}, {0x0000AB2F, 0x0000AB2F}, -{0x0000AB6C, 0x0000AB6F}, {0x0000ABEE, 0x0000ABEF}, {0x0000ABFA, 0x0000ABFF}, {0x0000D7A4, 0x0000D7AF}, -{0x0000D7C7, 0x0000D7CA}, {0x0000D7FC, 0x0000F8FF}, {0x0000FA6E, 0x0000FA6F}, {0x0000FADA, 0x0000FAFF}, -{0x0000FB07, 0x0000FB12}, {0x0000FB18, 0x0000FB1C}, {0x0000FB37, 0x0000FB37}, {0x0000FB3D, 0x0000FB3D}, -{0x0000FB3F, 0x0000FB3F}, {0x0000FB42, 0x0000FB42}, {0x0000FB45, 0x0000FB45}, {0x0000FBC3, 0x0000FBD2}, -{0x0000FD90, 0x0000FD91}, {0x0000FDC8, 0x0000FDCE}, {0x0000FDD0, 0x0000FDEF}, {0x0000FE1A, 0x0000FE1F}, -{0x0000FE53, 0x0000FE53}, {0x0000FE67, 0x0000FE67}, {0x0000FE6C, 0x0000FE6F}, {0x0000FE75, 0x0000FE75}, -{0x0000FEFD, 0x0000FF00}, {0x0000FFBF, 0x0000FFC1}, {0x0000FFC8, 0x0000FFC9}, {0x0000FFD0, 0x0000FFD1}, -{0x0000FFD8, 0x0000FFD9}, {0x0000FFDD, 0x0000FFDF}, {0x0000FFE7, 0x0000FFE7}, {0x0000FFEF, 0x0000FFFB}, -{0x0000FFFE, 0x0000FFFF}, {0x0001000C, 0x0001000C}, {0x00010027, 0x00010027}, {0x0001003B, 0x0001003B}, -{0x0001003E, 0x0001003E}, {0x0001004E, 0x0001004F}, {0x0001005E, 0x0001007F}, {0x000100FB, 0x000100FF}, -{0x00010103, 0x00010106}, {0x00010134, 0x00010136}, {0x0001018F, 0x0001018F}, {0x0001019D, 0x0001019F}, -{0x000101A1, 0x000101CF}, {0x000101FE, 0x0001027F}, {0x0001029D, 0x0001029F}, {0x000102D1, 0x000102DF}, -{0x000102FC, 0x000102FF}, {0x00010324, 0x0001032C}, {0x0001034B, 0x0001034F}, {0x0001037B, 0x0001037F}, -{0x0001039E, 0x0001039E}, {0x000103C4, 0x000103C7}, {0x000103D6, 0x000103FF}, {0x0001049E, 0x0001049F}, -{0x000104AA, 0x000104AF}, {0x000104D4, 0x000104D7}, {0x000104FC, 0x000104FF}, {0x00010528, 0x0001052F}, -{0x00010564, 0x0001056E}, {0x0001057B, 0x0001057B}, {0x0001058B, 0x0001058B}, {0x00010593, 0x00010593}, -{0x00010596, 0x00010596}, {0x000105A2, 0x000105A2}, {0x000105B2, 0x000105B2}, {0x000105BA, 0x000105BA}, -{0x000105BD, 0x000105FF}, {0x00010737, 0x0001073F}, {0x00010756, 0x0001075F}, {0x00010768, 0x0001077F}, -{0x00010786, 0x00010786}, {0x000107B1, 0x000107B1}, {0x000107BB, 0x000107FF}, {0x00010806, 0x00010807}, -{0x00010809, 0x00010809}, {0x00010836, 0x00010836}, {0x00010839, 0x0001083B}, {0x0001083D, 0x0001083E}, -{0x00010856, 0x00010856}, {0x0001089F, 0x000108A6}, {0x000108B0, 0x000108DF}, {0x000108F3, 0x000108F3}, -{0x000108F6, 0x000108FA}, {0x0001091C, 0x0001091E}, {0x0001093A, 0x0001093E}, {0x00010940, 0x0001097F}, -{0x000109B8, 0x000109BB}, {0x000109D0, 0x000109D1}, {0x00010A04, 0x00010A04}, {0x00010A07, 0x00010A0B}, -{0x00010A14, 0x00010A14}, {0x00010A18, 0x00010A18}, {0x00010A36, 0x00010A37}, {0x00010A3B, 0x00010A3E}, -{0x00010A49, 0x00010A4F}, {0x00010A59, 0x00010A5F}, {0x00010AA0, 0x00010ABF}, {0x00010AE7, 0x00010AEA}, -{0x00010AF7, 0x00010AFF}, {0x00010B36, 0x00010B38}, {0x00010B56, 0x00010B57}, {0x00010B73, 0x00010B77}, -{0x00010B92, 0x00010B98}, {0x00010B9D, 0x00010BA8}, {0x00010BB0, 0x00010BFF}, {0x00010C49, 0x00010C7F}, -{0x00010CB3, 0x00010CBF}, {0x00010CF3, 0x00010CF9}, {0x00010D28, 0x00010D2F}, {0x00010D3A, 0x00010E5F}, -{0x00010E7F, 0x00010E7F}, {0x00010EAA, 0x00010EAA}, {0x00010EAE, 0x00010EAF}, {0x00010EB2, 0x00010EFC}, -{0x00010F28, 0x00010F2F}, {0x00010F5A, 0x00010F6F}, {0x00010F8A, 0x00010FAF}, {0x00010FCC, 0x00010FDF}, -{0x00010FF7, 0x00010FFF}, {0x0001104E, 0x00011051}, {0x00011076, 0x0001107E}, {0x000110BD, 0x000110BD}, -{0x000110C3, 0x000110CF}, {0x000110E9, 0x000110EF}, {0x000110FA, 0x000110FF}, {0x00011135, 0x00011135}, -{0x00011148, 0x0001114F}, {0x00011177, 0x0001117F}, {0x000111E0, 0x000111E0}, {0x000111F5, 0x000111FF}, -{0x00011212, 0x00011212}, {0x00011242, 0x0001127F}, {0x00011287, 0x00011287}, {0x00011289, 0x00011289}, -{0x0001128E, 0x0001128E}, {0x0001129E, 0x0001129E}, {0x000112AA, 0x000112AF}, {0x000112EB, 0x000112EF}, -{0x000112FA, 0x000112FF}, {0x00011304, 0x00011304}, {0x0001130D, 0x0001130E}, {0x00011311, 0x00011312}, -{0x00011329, 0x00011329}, {0x00011331, 0x00011331}, {0x00011334, 0x00011334}, {0x0001133A, 0x0001133A}, -{0x00011345, 0x00011346}, {0x00011349, 0x0001134A}, {0x0001134E, 0x0001134F}, {0x00011351, 0x00011356}, -{0x00011358, 0x0001135C}, {0x00011364, 0x00011365}, {0x0001136D, 0x0001136F}, {0x00011375, 0x000113FF}, -{0x0001145C, 0x0001145C}, {0x00011462, 0x0001147F}, {0x000114C8, 0x000114CF}, {0x000114DA, 0x0001157F}, -{0x000115B6, 0x000115B7}, {0x000115DE, 0x000115FF}, {0x00011645, 0x0001164F}, {0x0001165A, 0x0001165F}, -{0x0001166D, 0x0001167F}, {0x000116BA, 0x000116BF}, {0x000116CA, 0x000116FF}, {0x0001171B, 0x0001171C}, -{0x0001172C, 0x0001172F}, {0x00011747, 0x000117FF}, {0x0001183C, 0x0001189F}, {0x000118F3, 0x000118FE}, -{0x00011907, 0x00011908}, {0x0001190A, 0x0001190B}, {0x00011914, 0x00011914}, {0x00011917, 0x00011917}, -{0x00011936, 0x00011936}, {0x00011939, 0x0001193A}, {0x00011947, 0x0001194F}, {0x0001195A, 0x0001199F}, -{0x000119A8, 0x000119A9}, {0x000119D8, 0x000119D9}, {0x000119E5, 0x000119FF}, {0x00011A48, 0x00011A4F}, -{0x00011AA3, 0x00011AAF}, {0x00011AF9, 0x00011AFF}, {0x00011B0A, 0x00011BFF}, {0x00011C09, 0x00011C09}, -{0x00011C37, 0x00011C37}, {0x00011C46, 0x00011C4F}, {0x00011C6D, 0x00011C6F}, {0x00011C90, 0x00011C91}, -{0x00011CA8, 0x00011CA8}, {0x00011CB7, 0x00011CFF}, {0x00011D07, 0x00011D07}, {0x00011D0A, 0x00011D0A}, -{0x00011D37, 0x00011D39}, {0x00011D3B, 0x00011D3B}, {0x00011D3E, 0x00011D3E}, {0x00011D48, 0x00011D4F}, -{0x00011D5A, 0x00011D5F}, {0x00011D66, 0x00011D66}, {0x00011D69, 0x00011D69}, {0x00011D8F, 0x00011D8F}, -{0x00011D92, 0x00011D92}, {0x00011D99, 0x00011D9F}, {0x00011DAA, 0x00011EDF}, {0x00011EF9, 0x00011EFF}, -{0x00011F11, 0x00011F11}, {0x00011F3B, 0x00011F3D}, {0x00011F5A, 0x00011FAF}, {0x00011FB1, 0x00011FBF}, -{0x00011FF2, 0x00011FFE}, {0x0001239A, 0x000123FF}, {0x0001246F, 0x0001246F}, {0x00012475, 0x0001247F}, -{0x00012544, 0x00012F8F}, {0x00012FF3, 0x00012FFF}, {0x00013430, 0x0001343F}, {0x00013456, 0x000143FF}, -{0x00014647, 0x000167FF}, {0x00016A39, 0x00016A3F}, {0x00016A5F, 0x00016A5F}, {0x00016A6A, 0x00016A6D}, -{0x00016ABF, 0x00016ABF}, {0x00016ACA, 0x00016ACF}, {0x00016AEE, 0x00016AEF}, {0x00016AF6, 0x00016AFF}, -{0x00016B46, 0x00016B4F}, {0x00016B5A, 0x00016B5A}, {0x00016B62, 0x00016B62}, {0x00016B78, 0x00016B7C}, -{0x00016B90, 0x00016E3F}, {0x00016E9B, 0x00016EFF}, {0x00016F4B, 0x00016F4E}, {0x00016F88, 0x00016F8E}, -{0x00016FA0, 0x00016FDF}, {0x00016FE5, 0x00016FEF}, {0x00016FF2, 0x00016FFF}, {0x000187F8, 0x000187FF}, -{0x00018CD6, 0x00018CFF}, {0x00018D09, 0x0001AFEF}, {0x0001AFF4, 0x0001AFF4}, {0x0001AFFC, 0x0001AFFC}, -{0x0001AFFF, 0x0001AFFF}, {0x0001B123, 0x0001B131}, {0x0001B133, 0x0001B14F}, {0x0001B153, 0x0001B154}, -{0x0001B156, 0x0001B163}, {0x0001B168, 0x0001B16F}, {0x0001B2FC, 0x0001BBFF}, {0x0001BC6B, 0x0001BC6F}, -{0x0001BC7D, 0x0001BC7F}, {0x0001BC89, 0x0001BC8F}, {0x0001BC9A, 0x0001BC9B}, {0x0001BCA0, 0x0001CEFF}, -{0x0001CF2E, 0x0001CF2F}, {0x0001CF47, 0x0001CF4F}, {0x0001CFC4, 0x0001CFFF}, {0x0001D0F6, 0x0001D0FF}, -{0x0001D127, 0x0001D128}, {0x0001D173, 0x0001D17A}, {0x0001D1EB, 0x0001D1FF}, {0x0001D246, 0x0001D2BF}, -{0x0001D2D4, 0x0001D2DF}, {0x0001D2F4, 0x0001D2FF}, {0x0001D357, 0x0001D35F}, {0x0001D379, 0x0001D3FF}, -{0x0001D455, 0x0001D455}, {0x0001D49D, 0x0001D49D}, {0x0001D4A0, 0x0001D4A1}, {0x0001D4A3, 0x0001D4A4}, -{0x0001D4A7, 0x0001D4A8}, {0x0001D4AD, 0x0001D4AD}, {0x0001D4BA, 0x0001D4BA}, {0x0001D4BC, 0x0001D4BC}, -{0x0001D4C4, 0x0001D4C4}, {0x0001D506, 0x0001D506}, {0x0001D50B, 0x0001D50C}, {0x0001D515, 0x0001D515}, -{0x0001D51D, 0x0001D51D}, {0x0001D53A, 0x0001D53A}, {0x0001D53F, 0x0001D53F}, {0x0001D545, 0x0001D545}, -{0x0001D547, 0x0001D549}, {0x0001D551, 0x0001D551}, {0x0001D6A6, 0x0001D6A7}, {0x0001D7CC, 0x0001D7CD}, -{0x0001DA8C, 0x0001DA9A}, {0x0001DAA0, 0x0001DAA0}, {0x0001DAB0, 0x0001DEFF}, {0x0001DF1F, 0x0001DF24}, -{0x0001DF2B, 0x0001DFFF}, {0x0001E007, 0x0001E007}, {0x0001E019, 0x0001E01A}, {0x0001E022, 0x0001E022}, -{0x0001E025, 0x0001E025}, {0x0001E02B, 0x0001E02F}, {0x0001E06E, 0x0001E08E}, {0x0001E090, 0x0001E0FF}, -{0x0001E12D, 0x0001E12F}, {0x0001E13E, 0x0001E13F}, {0x0001E14A, 0x0001E14D}, {0x0001E150, 0x0001E28F}, -{0x0001E2AF, 0x0001E2BF}, {0x0001E2FA, 0x0001E2FE}, {0x0001E300, 0x0001E4CF}, {0x0001E4FA, 0x0001E7DF}, -{0x0001E7E7, 0x0001E7E7}, {0x0001E7EC, 0x0001E7EC}, {0x0001E7EF, 0x0001E7EF}, {0x0001E7FF, 0x0001E7FF}, -{0x0001E8C5, 0x0001E8C6}, {0x0001E8D7, 0x0001E8FF}, {0x0001E94C, 0x0001E94F}, {0x0001E95A, 0x0001E95D}, -{0x0001E960, 0x0001EC70}, {0x0001ECB5, 0x0001ED00}, {0x0001ED3E, 0x0001EDFF}, {0x0001EE04, 0x0001EE04}, -{0x0001EE20, 0x0001EE20}, {0x0001EE23, 0x0001EE23}, {0x0001EE25, 0x0001EE26}, {0x0001EE28, 0x0001EE28}, -{0x0001EE33, 0x0001EE33}, {0x0001EE38, 0x0001EE38}, {0x0001EE3A, 0x0001EE3A}, {0x0001EE3C, 0x0001EE41}, -{0x0001EE43, 0x0001EE46}, {0x0001EE48, 0x0001EE48}, {0x0001EE4A, 0x0001EE4A}, {0x0001EE4C, 0x0001EE4C}, -{0x0001EE50, 0x0001EE50}, {0x0001EE53, 0x0001EE53}, {0x0001EE55, 0x0001EE56}, {0x0001EE58, 0x0001EE58}, -{0x0001EE5A, 0x0001EE5A}, {0x0001EE5C, 0x0001EE5C}, {0x0001EE5E, 0x0001EE5E}, {0x0001EE60, 0x0001EE60}, -{0x0001EE63, 0x0001EE63}, {0x0001EE65, 0x0001EE66}, {0x0001EE6B, 0x0001EE6B}, {0x0001EE73, 0x0001EE73}, -{0x0001EE78, 0x0001EE78}, {0x0001EE7D, 0x0001EE7D}, {0x0001EE7F, 0x0001EE7F}, {0x0001EE8A, 0x0001EE8A}, -{0x0001EE9C, 0x0001EEA0}, {0x0001EEA4, 0x0001EEA4}, {0x0001EEAA, 0x0001EEAA}, {0x0001EEBC, 0x0001EEEF}, -{0x0001EEF2, 0x0001EFFF}, {0x0001F02C, 0x0001F02F}, {0x0001F094, 0x0001F09F}, {0x0001F0AF, 0x0001F0B0}, -{0x0001F0C0, 0x0001F0C0}, {0x0001F0D0, 0x0001F0D0}, {0x0001F0F6, 0x0001F0FF}, {0x0001F1AE, 0x0001F1E5}, -{0x0001F203, 0x0001F20F}, {0x0001F23C, 0x0001F23F}, {0x0001F249, 0x0001F24F}, {0x0001F252, 0x0001F25F}, -{0x0001F266, 0x0001F2FF}, {0x0001F6D8, 0x0001F6DB}, {0x0001F6ED, 0x0001F6EF}, {0x0001F6FD, 0x0001F6FF}, -{0x0001F777, 0x0001F77A}, {0x0001F7DA, 0x0001F7DF}, {0x0001F7EC, 0x0001F7EF}, {0x0001F7F1, 0x0001F7FF}, -{0x0001F80C, 0x0001F80F}, {0x0001F848, 0x0001F84F}, {0x0001F85A, 0x0001F85F}, {0x0001F888, 0x0001F88F}, -{0x0001F8AE, 0x0001F8AF}, {0x0001F8B2, 0x0001F8FF}, {0x0001FA54, 0x0001FA5F}, {0x0001FA6E, 0x0001FA6F}, -{0x0001FA7D, 0x0001FA7F}, {0x0001FA89, 0x0001FA8F}, {0x0001FABE, 0x0001FABE}, {0x0001FAC6, 0x0001FACD}, -{0x0001FADC, 0x0001FADF}, {0x0001FAE9, 0x0001FAEF}, {0x0001FAF9, 0x0001FAFF}, {0x0001FB93, 0x0001FB93}, -{0x0001FBCB, 0x0001FBEF}, {0x0001FBFA, 0x0001FFFF}, {0x0002A6E0, 0x0002A6FF}, {0x0002B73A, 0x0002B73F}, -{0x0002B81E, 0x0002B81F}, {0x0002CEA2, 0x0002CEAF}, {0x0002EBE1, 0x0002EBEF}, {0x0002EE5E, 0x0002F7FF}, -{0x0002FA1E, 0x0002FFFF}, {0x0003134B, 0x0003134F}, {0x000323B0, 0x000E00FF}, {0x000E01F0, 0x0010FFFF}, +const std::unordered_map unicode_map_lowercase = { +{0x000041, 0x000061}, +{0x000042, 0x000062}, +{0x000043, 0x000063}, +{0x000044, 0x000064}, +{0x000045, 0x000065}, +{0x000046, 0x000066}, +{0x000047, 0x000067}, +{0x000048, 0x000068}, +{0x000049, 0x000069}, +{0x00004A, 0x00006A}, +{0x00004B, 0x00006B}, +{0x00004C, 0x00006C}, +{0x00004D, 0x00006D}, +{0x00004E, 0x00006E}, +{0x00004F, 0x00006F}, +{0x000050, 0x000070}, +{0x000051, 0x000071}, +{0x000052, 0x000072}, +{0x000053, 0x000073}, +{0x000054, 0x000074}, +{0x000055, 0x000075}, +{0x000056, 0x000076}, +{0x000057, 0x000077}, +{0x000058, 0x000078}, +{0x000059, 0x000079}, +{0x00005A, 0x00007A}, +{0x0000C0, 0x0000E0}, +{0x0000C1, 0x0000E1}, +{0x0000C2, 0x0000E2}, +{0x0000C3, 0x0000E3}, +{0x0000C4, 0x0000E4}, +{0x0000C5, 0x0000E5}, +{0x0000C6, 0x0000E6}, +{0x0000C7, 0x0000E7}, +{0x0000C8, 0x0000E8}, +{0x0000C9, 0x0000E9}, +{0x0000CA, 0x0000EA}, +{0x0000CB, 0x0000EB}, +{0x0000CC, 0x0000EC}, +{0x0000CD, 0x0000ED}, +{0x0000CE, 0x0000EE}, +{0x0000CF, 0x0000EF}, +{0x0000D0, 0x0000F0}, +{0x0000D1, 0x0000F1}, +{0x0000D2, 0x0000F2}, +{0x0000D3, 0x0000F3}, +{0x0000D4, 0x0000F4}, +{0x0000D5, 0x0000F5}, +{0x0000D6, 0x0000F6}, +{0x0000D8, 0x0000F8}, +{0x0000D9, 0x0000F9}, +{0x0000DA, 0x0000FA}, +{0x0000DB, 0x0000FB}, +{0x0000DC, 0x0000FC}, +{0x0000DD, 0x0000FD}, +{0x0000DE, 0x0000FE}, +{0x000100, 0x000101}, +{0x000102, 0x000103}, +{0x000104, 0x000105}, +{0x000106, 0x000107}, +{0x000108, 0x000109}, +{0x00010A, 0x00010B}, +{0x00010C, 0x00010D}, +{0x00010E, 0x00010F}, +{0x000110, 0x000111}, +{0x000112, 0x000113}, +{0x000114, 0x000115}, +{0x000116, 0x000117}, +{0x000118, 0x000119}, +{0x00011A, 0x00011B}, +{0x00011C, 0x00011D}, +{0x00011E, 0x00011F}, +{0x000120, 0x000121}, +{0x000122, 0x000123}, +{0x000124, 0x000125}, +{0x000126, 0x000127}, +{0x000128, 0x000129}, +{0x00012A, 0x00012B}, +{0x00012C, 0x00012D}, +{0x00012E, 0x00012F}, +{0x000130, 0x000069}, +{0x000132, 0x000133}, +{0x000134, 0x000135}, +{0x000136, 0x000137}, +{0x000139, 0x00013A}, +{0x00013B, 0x00013C}, +{0x00013D, 0x00013E}, +{0x00013F, 0x000140}, +{0x000141, 0x000142}, +{0x000143, 0x000144}, +{0x000145, 0x000146}, +{0x000147, 0x000148}, +{0x00014A, 0x00014B}, +{0x00014C, 0x00014D}, +{0x00014E, 0x00014F}, +{0x000150, 0x000151}, +{0x000152, 0x000153}, +{0x000154, 0x000155}, +{0x000156, 0x000157}, +{0x000158, 0x000159}, +{0x00015A, 0x00015B}, +{0x00015C, 0x00015D}, +{0x00015E, 0x00015F}, +{0x000160, 0x000161}, +{0x000162, 0x000163}, +{0x000164, 0x000165}, +{0x000166, 0x000167}, +{0x000168, 0x000169}, +{0x00016A, 0x00016B}, +{0x00016C, 0x00016D}, +{0x00016E, 0x00016F}, +{0x000170, 0x000171}, +{0x000172, 0x000173}, +{0x000174, 0x000175}, +{0x000176, 0x000177}, +{0x000178, 0x0000FF}, +{0x000179, 0x00017A}, +{0x00017B, 0x00017C}, +{0x00017D, 0x00017E}, +{0x000181, 0x000253}, +{0x000182, 0x000183}, +{0x000184, 0x000185}, +{0x000186, 0x000254}, +{0x000187, 0x000188}, +{0x000189, 0x000256}, +{0x00018A, 0x000257}, +{0x00018B, 0x00018C}, +{0x00018E, 0x0001DD}, +{0x00018F, 0x000259}, +{0x000190, 0x00025B}, +{0x000191, 0x000192}, +{0x000193, 0x000260}, +{0x000194, 0x000263}, +{0x000196, 0x000269}, +{0x000197, 0x000268}, +{0x000198, 0x000199}, +{0x00019C, 0x00026F}, +{0x00019D, 0x000272}, +{0x00019F, 0x000275}, +{0x0001A0, 0x0001A1}, +{0x0001A2, 0x0001A3}, +{0x0001A4, 0x0001A5}, +{0x0001A6, 0x000280}, +{0x0001A7, 0x0001A8}, +{0x0001A9, 0x000283}, +{0x0001AC, 0x0001AD}, +{0x0001AE, 0x000288}, +{0x0001AF, 0x0001B0}, +{0x0001B1, 0x00028A}, +{0x0001B2, 0x00028B}, +{0x0001B3, 0x0001B4}, +{0x0001B5, 0x0001B6}, +{0x0001B7, 0x000292}, +{0x0001B8, 0x0001B9}, +{0x0001BC, 0x0001BD}, +{0x0001C4, 0x0001C6}, +{0x0001C5, 0x0001C6}, +{0x0001C7, 0x0001C9}, +{0x0001C8, 0x0001C9}, +{0x0001CA, 0x0001CC}, +{0x0001CB, 0x0001CC}, +{0x0001CD, 0x0001CE}, +{0x0001CF, 0x0001D0}, +{0x0001D1, 0x0001D2}, +{0x0001D3, 0x0001D4}, +{0x0001D5, 0x0001D6}, +{0x0001D7, 0x0001D8}, +{0x0001D9, 0x0001DA}, +{0x0001DB, 0x0001DC}, +{0x0001DE, 0x0001DF}, +{0x0001E0, 0x0001E1}, +{0x0001E2, 0x0001E3}, +{0x0001E4, 0x0001E5}, +{0x0001E6, 0x0001E7}, +{0x0001E8, 0x0001E9}, +{0x0001EA, 0x0001EB}, +{0x0001EC, 0x0001ED}, +{0x0001EE, 0x0001EF}, +{0x0001F1, 0x0001F3}, +{0x0001F2, 0x0001F3}, +{0x0001F4, 0x0001F5}, +{0x0001F6, 0x000195}, +{0x0001F7, 0x0001BF}, +{0x0001F8, 0x0001F9}, +{0x0001FA, 0x0001FB}, +{0x0001FC, 0x0001FD}, +{0x0001FE, 0x0001FF}, +{0x000200, 0x000201}, +{0x000202, 0x000203}, +{0x000204, 0x000205}, +{0x000206, 0x000207}, +{0x000208, 0x000209}, +{0x00020A, 0x00020B}, +{0x00020C, 0x00020D}, +{0x00020E, 0x00020F}, +{0x000210, 0x000211}, +{0x000212, 0x000213}, +{0x000214, 0x000215}, +{0x000216, 0x000217}, +{0x000218, 0x000219}, +{0x00021A, 0x00021B}, +{0x00021C, 0x00021D}, +{0x00021E, 0x00021F}, +{0x000220, 0x00019E}, +{0x000222, 0x000223}, +{0x000224, 0x000225}, +{0x000226, 0x000227}, +{0x000228, 0x000229}, +{0x00022A, 0x00022B}, +{0x00022C, 0x00022D}, +{0x00022E, 0x00022F}, +{0x000230, 0x000231}, +{0x000232, 0x000233}, +{0x00023A, 0x002C65}, +{0x00023B, 0x00023C}, +{0x00023D, 0x00019A}, +{0x00023E, 0x002C66}, +{0x000241, 0x000242}, +{0x000243, 0x000180}, +{0x000244, 0x000289}, +{0x000245, 0x00028C}, +{0x000246, 0x000247}, +{0x000248, 0x000249}, +{0x00024A, 0x00024B}, +{0x00024C, 0x00024D}, +{0x00024E, 0x00024F}, +{0x000370, 0x000371}, +{0x000372, 0x000373}, +{0x000376, 0x000377}, +{0x00037F, 0x0003F3}, +{0x000386, 0x0003AC}, +{0x000388, 0x0003AD}, +{0x000389, 0x0003AE}, +{0x00038A, 0x0003AF}, +{0x00038C, 0x0003CC}, +{0x00038E, 0x0003CD}, +{0x00038F, 0x0003CE}, +{0x000391, 0x0003B1}, 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0x00FF4A}, +{0x00FF2B, 0x00FF4B}, +{0x00FF2C, 0x00FF4C}, +{0x00FF2D, 0x00FF4D}, +{0x00FF2E, 0x00FF4E}, +{0x00FF2F, 0x00FF4F}, +{0x00FF30, 0x00FF50}, +{0x00FF31, 0x00FF51}, +{0x00FF32, 0x00FF52}, +{0x00FF33, 0x00FF53}, +{0x00FF34, 0x00FF54}, +{0x00FF35, 0x00FF55}, +{0x00FF36, 0x00FF56}, +{0x00FF37, 0x00FF57}, +{0x00FF38, 0x00FF58}, +{0x00FF39, 0x00FF59}, +{0x00FF3A, 0x00FF5A}, +{0x010400, 0x010428}, +{0x010401, 0x010429}, +{0x010402, 0x01042A}, +{0x010403, 0x01042B}, +{0x010404, 0x01042C}, +{0x010405, 0x01042D}, +{0x010406, 0x01042E}, +{0x010407, 0x01042F}, +{0x010408, 0x010430}, +{0x010409, 0x010431}, +{0x01040A, 0x010432}, +{0x01040B, 0x010433}, +{0x01040C, 0x010434}, +{0x01040D, 0x010435}, +{0x01040E, 0x010436}, +{0x01040F, 0x010437}, +{0x010410, 0x010438}, +{0x010411, 0x010439}, +{0x010412, 0x01043A}, +{0x010413, 0x01043B}, +{0x010414, 0x01043C}, +{0x010415, 0x01043D}, +{0x010416, 0x01043E}, +{0x010417, 0x01043F}, +{0x010418, 0x010440}, +{0x010419, 0x010441}, +{0x01041A, 0x010442}, +{0x01041B, 0x010443}, +{0x01041C, 0x010444}, +{0x01041D, 0x010445}, +{0x01041E, 0x010446}, +{0x01041F, 0x010447}, +{0x010420, 0x010448}, +{0x010421, 0x010449}, +{0x010422, 0x01044A}, +{0x010423, 0x01044B}, +{0x010424, 0x01044C}, +{0x010425, 0x01044D}, +{0x010426, 0x01044E}, +{0x010427, 0x01044F}, +{0x0104B0, 0x0104D8}, +{0x0104B1, 0x0104D9}, +{0x0104B2, 0x0104DA}, +{0x0104B3, 0x0104DB}, +{0x0104B4, 0x0104DC}, +{0x0104B5, 0x0104DD}, +{0x0104B6, 0x0104DE}, +{0x0104B7, 0x0104DF}, +{0x0104B8, 0x0104E0}, +{0x0104B9, 0x0104E1}, +{0x0104BA, 0x0104E2}, +{0x0104BB, 0x0104E3}, +{0x0104BC, 0x0104E4}, +{0x0104BD, 0x0104E5}, +{0x0104BE, 0x0104E6}, +{0x0104BF, 0x0104E7}, +{0x0104C0, 0x0104E8}, +{0x0104C1, 0x0104E9}, +{0x0104C2, 0x0104EA}, +{0x0104C3, 0x0104EB}, +{0x0104C4, 0x0104EC}, +{0x0104C5, 0x0104ED}, +{0x0104C6, 0x0104EE}, +{0x0104C7, 0x0104EF}, +{0x0104C8, 0x0104F0}, +{0x0104C9, 0x0104F1}, +{0x0104CA, 0x0104F2}, +{0x0104CB, 0x0104F3}, +{0x0104CC, 0x0104F4}, +{0x0104CD, 0x0104F5}, +{0x0104CE, 0x0104F6}, +{0x0104CF, 0x0104F7}, +{0x0104D0, 0x0104F8}, +{0x0104D1, 0x0104F9}, +{0x0104D2, 0x0104FA}, +{0x0104D3, 0x0104FB}, +{0x010C80, 0x010CC0}, +{0x010C81, 0x010CC1}, +{0x010C82, 0x010CC2}, +{0x010C83, 0x010CC3}, +{0x010C84, 0x010CC4}, +{0x010C85, 0x010CC5}, +{0x010C86, 0x010CC6}, +{0x010C87, 0x010CC7}, +{0x010C88, 0x010CC8}, +{0x010C89, 0x010CC9}, +{0x010C8A, 0x010CCA}, +{0x010C8B, 0x010CCB}, +{0x010C8C, 0x010CCC}, +{0x010C8D, 0x010CCD}, +{0x010C8E, 0x010CCE}, +{0x010C8F, 0x010CCF}, +{0x010C90, 0x010CD0}, +{0x010C91, 0x010CD1}, +{0x010C92, 0x010CD2}, +{0x010C93, 0x010CD3}, +{0x010C94, 0x010CD4}, +{0x010C95, 0x010CD5}, +{0x010C96, 0x010CD6}, +{0x010C97, 0x010CD7}, +{0x010C98, 0x010CD8}, +{0x010C99, 0x010CD9}, +{0x010C9A, 0x010CDA}, +{0x010C9B, 0x010CDB}, +{0x010C9C, 0x010CDC}, +{0x010C9D, 0x010CDD}, +{0x010C9E, 0x010CDE}, +{0x010C9F, 0x010CDF}, +{0x010CA0, 0x010CE0}, +{0x010CA1, 0x010CE1}, +{0x010CA2, 0x010CE2}, +{0x010CA3, 0x010CE3}, +{0x010CA4, 0x010CE4}, +{0x010CA5, 0x010CE5}, +{0x010CA6, 0x010CE6}, +{0x010CA7, 0x010CE7}, +{0x010CA8, 0x010CE8}, +{0x010CA9, 0x010CE9}, +{0x010CAA, 0x010CEA}, +{0x010CAB, 0x010CEB}, +{0x010CAC, 0x010CEC}, +{0x010CAD, 0x010CED}, +{0x010CAE, 0x010CEE}, +{0x010CAF, 0x010CEF}, +{0x010CB0, 0x010CF0}, +{0x010CB1, 0x010CF1}, +{0x010CB2, 0x010CF2}, +{0x0118A0, 0x0118C0}, +{0x0118A1, 0x0118C1}, +{0x0118A2, 0x0118C2}, +{0x0118A3, 0x0118C3}, +{0x0118A4, 0x0118C4}, +{0x0118A5, 0x0118C5}, +{0x0118A6, 0x0118C6}, +{0x0118A7, 0x0118C7}, +{0x0118A8, 0x0118C8}, +{0x0118A9, 0x0118C9}, +{0x0118AA, 0x0118CA}, +{0x0118AB, 0x0118CB}, +{0x0118AC, 0x0118CC}, +{0x0118AD, 0x0118CD}, +{0x0118AE, 0x0118CE}, +{0x0118AF, 0x0118CF}, +{0x0118B0, 0x0118D0}, +{0x0118B1, 0x0118D1}, +{0x0118B2, 0x0118D2}, +{0x0118B3, 0x0118D3}, +{0x0118B4, 0x0118D4}, +{0x0118B5, 0x0118D5}, +{0x0118B6, 0x0118D6}, +{0x0118B7, 0x0118D7}, +{0x0118B8, 0x0118D8}, +{0x0118B9, 0x0118D9}, +{0x0118BA, 0x0118DA}, +{0x0118BB, 0x0118DB}, +{0x0118BC, 0x0118DC}, +{0x0118BD, 0x0118DD}, +{0x0118BE, 0x0118DE}, +{0x0118BF, 0x0118DF}, +{0x016E40, 0x016E60}, +{0x016E41, 0x016E61}, +{0x016E42, 0x016E62}, +{0x016E43, 0x016E63}, +{0x016E44, 0x016E64}, +{0x016E45, 0x016E65}, +{0x016E46, 0x016E66}, +{0x016E47, 0x016E67}, +{0x016E48, 0x016E68}, +{0x016E49, 0x016E69}, +{0x016E4A, 0x016E6A}, +{0x016E4B, 0x016E6B}, +{0x016E4C, 0x016E6C}, +{0x016E4D, 0x016E6D}, +{0x016E4E, 0x016E6E}, +{0x016E4F, 0x016E6F}, +{0x016E50, 0x016E70}, +{0x016E51, 0x016E71}, +{0x016E52, 0x016E72}, +{0x016E53, 0x016E73}, +{0x016E54, 0x016E74}, +{0x016E55, 0x016E75}, +{0x016E56, 0x016E76}, +{0x016E57, 0x016E77}, +{0x016E58, 0x016E78}, +{0x016E59, 0x016E79}, +{0x016E5A, 0x016E7A}, +{0x016E5B, 0x016E7B}, +{0x016E5C, 0x016E7C}, +{0x016E5D, 0x016E7D}, +{0x016E5E, 0x016E7E}, +{0x016E5F, 0x016E7F}, +{0x01E900, 0x01E922}, +{0x01E901, 0x01E923}, +{0x01E902, 0x01E924}, +{0x01E903, 0x01E925}, +{0x01E904, 0x01E926}, +{0x01E905, 0x01E927}, +{0x01E906, 0x01E928}, +{0x01E907, 0x01E929}, +{0x01E908, 0x01E92A}, +{0x01E909, 0x01E92B}, +{0x01E90A, 0x01E92C}, +{0x01E90B, 0x01E92D}, +{0x01E90C, 0x01E92E}, +{0x01E90D, 0x01E92F}, +{0x01E90E, 0x01E930}, +{0x01E90F, 0x01E931}, +{0x01E910, 0x01E932}, +{0x01E911, 0x01E933}, +{0x01E912, 0x01E934}, +{0x01E913, 0x01E935}, +{0x01E914, 0x01E936}, +{0x01E915, 0x01E937}, +{0x01E916, 0x01E938}, +{0x01E917, 0x01E939}, +{0x01E918, 0x01E93A}, +{0x01E919, 0x01E93B}, +{0x01E91A, 0x01E93C}, +{0x01E91B, 0x01E93D}, +{0x01E91C, 0x01E93E}, +{0x01E91D, 0x01E93F}, +{0x01E91E, 0x01E940}, +{0x01E91F, 0x01E941}, +{0x01E920, 0x01E942}, +{0x01E921, 0x01E943}, }; -const std::map unicode_map_lowercase = { -{0x00000041, 0x00000061}, {0x00000042, 0x00000062}, {0x00000043, 0x00000063}, {0x00000044, 0x00000064}, -{0x00000045, 0x00000065}, {0x00000046, 0x00000066}, {0x00000047, 0x00000067}, {0x00000048, 0x00000068}, -{0x00000049, 0x00000069}, {0x0000004A, 0x0000006A}, {0x0000004B, 0x0000006B}, {0x0000004C, 0x0000006C}, -{0x0000004D, 0x0000006D}, {0x0000004E, 0x0000006E}, {0x0000004F, 0x0000006F}, {0x00000050, 0x00000070}, -{0x00000051, 0x00000071}, {0x00000052, 0x00000072}, {0x00000053, 0x00000073}, {0x00000054, 0x00000074}, -{0x00000055, 0x00000075}, {0x00000056, 0x00000076}, {0x00000057, 0x00000077}, {0x00000058, 0x00000078}, -{0x00000059, 0x00000079}, {0x0000005A, 0x0000007A}, {0x000000C0, 0x000000E0}, {0x000000C1, 0x000000E1}, -{0x000000C2, 0x000000E2}, {0x000000C3, 0x000000E3}, {0x000000C4, 0x000000E4}, {0x000000C5, 0x000000E5}, -{0x000000C6, 0x000000E6}, {0x000000C7, 0x000000E7}, {0x000000C8, 0x000000E8}, {0x000000C9, 0x000000E9}, -{0x000000CA, 0x000000EA}, {0x000000CB, 0x000000EB}, {0x000000CC, 0x000000EC}, {0x000000CD, 0x000000ED}, -{0x000000CE, 0x000000EE}, {0x000000CF, 0x000000EF}, {0x000000D0, 0x000000F0}, {0x000000D1, 0x000000F1}, -{0x000000D2, 0x000000F2}, {0x000000D3, 0x000000F3}, {0x000000D4, 0x000000F4}, {0x000000D5, 0x000000F5}, -{0x000000D6, 0x000000F6}, {0x000000D8, 0x000000F8}, {0x000000D9, 0x000000F9}, {0x000000DA, 0x000000FA}, -{0x000000DB, 0x000000FB}, {0x000000DC, 0x000000FC}, {0x000000DD, 0x000000FD}, {0x000000DE, 0x000000FE}, -{0x00000100, 0x00000101}, {0x00000102, 0x00000103}, {0x00000104, 0x00000105}, {0x00000106, 0x00000107}, -{0x00000108, 0x00000109}, {0x0000010A, 0x0000010B}, {0x0000010C, 0x0000010D}, {0x0000010E, 0x0000010F}, -{0x00000110, 0x00000111}, {0x00000112, 0x00000113}, {0x00000114, 0x00000115}, {0x00000116, 0x00000117}, -{0x00000118, 0x00000119}, {0x0000011A, 0x0000011B}, {0x0000011C, 0x0000011D}, {0x0000011E, 0x0000011F}, -{0x00000120, 0x00000121}, {0x00000122, 0x00000123}, {0x00000124, 0x00000125}, {0x00000126, 0x00000127}, -{0x00000128, 0x00000129}, {0x0000012A, 0x0000012B}, {0x0000012C, 0x0000012D}, {0x0000012E, 0x0000012F}, -{0x00000130, 0x00000069}, {0x00000132, 0x00000133}, {0x00000134, 0x00000135}, {0x00000136, 0x00000137}, -{0x00000139, 0x0000013A}, {0x0000013B, 0x0000013C}, {0x0000013D, 0x0000013E}, {0x0000013F, 0x00000140}, -{0x00000141, 0x00000142}, {0x00000143, 0x00000144}, {0x00000145, 0x00000146}, {0x00000147, 0x00000148}, -{0x0000014A, 0x0000014B}, {0x0000014C, 0x0000014D}, {0x0000014E, 0x0000014F}, {0x00000150, 0x00000151}, -{0x00000152, 0x00000153}, {0x00000154, 0x00000155}, {0x00000156, 0x00000157}, {0x00000158, 0x00000159}, -{0x0000015A, 0x0000015B}, {0x0000015C, 0x0000015D}, {0x0000015E, 0x0000015F}, {0x00000160, 0x00000161}, -{0x00000162, 0x00000163}, {0x00000164, 0x00000165}, {0x00000166, 0x00000167}, {0x00000168, 0x00000169}, -{0x0000016A, 0x0000016B}, {0x0000016C, 0x0000016D}, {0x0000016E, 0x0000016F}, {0x00000170, 0x00000171}, -{0x00000172, 0x00000173}, {0x00000174, 0x00000175}, {0x00000176, 0x00000177}, {0x00000178, 0x000000FF}, -{0x00000179, 0x0000017A}, {0x0000017B, 0x0000017C}, {0x0000017D, 0x0000017E}, {0x00000181, 0x00000253}, -{0x00000182, 0x00000183}, {0x00000184, 0x00000185}, {0x00000186, 0x00000254}, {0x00000187, 0x00000188}, -{0x00000189, 0x00000256}, {0x0000018A, 0x00000257}, {0x0000018B, 0x0000018C}, {0x0000018E, 0x000001DD}, -{0x0000018F, 0x00000259}, {0x00000190, 0x0000025B}, {0x00000191, 0x00000192}, {0x00000193, 0x00000260}, -{0x00000194, 0x00000263}, {0x00000196, 0x00000269}, {0x00000197, 0x00000268}, {0x00000198, 0x00000199}, -{0x0000019C, 0x0000026F}, {0x0000019D, 0x00000272}, {0x0000019F, 0x00000275}, {0x000001A0, 0x000001A1}, -{0x000001A2, 0x000001A3}, {0x000001A4, 0x000001A5}, {0x000001A6, 0x00000280}, {0x000001A7, 0x000001A8}, -{0x000001A9, 0x00000283}, {0x000001AC, 0x000001AD}, {0x000001AE, 0x00000288}, {0x000001AF, 0x000001B0}, -{0x000001B1, 0x0000028A}, {0x000001B2, 0x0000028B}, {0x000001B3, 0x000001B4}, {0x000001B5, 0x000001B6}, -{0x000001B7, 0x00000292}, {0x000001B8, 0x000001B9}, {0x000001BC, 0x000001BD}, {0x000001C4, 0x000001C6}, -{0x000001C5, 0x000001C6}, {0x000001C7, 0x000001C9}, {0x000001C8, 0x000001C9}, {0x000001CA, 0x000001CC}, -{0x000001CB, 0x000001CC}, {0x000001CD, 0x000001CE}, {0x000001CF, 0x000001D0}, {0x000001D1, 0x000001D2}, -{0x000001D3, 0x000001D4}, {0x000001D5, 0x000001D6}, {0x000001D7, 0x000001D8}, 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{0x000104C7, 0x000104EF}, -{0x000104C8, 0x000104F0}, {0x000104C9, 0x000104F1}, {0x000104CA, 0x000104F2}, {0x000104CB, 0x000104F3}, -{0x000104CC, 0x000104F4}, {0x000104CD, 0x000104F5}, {0x000104CE, 0x000104F6}, {0x000104CF, 0x000104F7}, -{0x000104D0, 0x000104F8}, {0x000104D1, 0x000104F9}, {0x000104D2, 0x000104FA}, {0x000104D3, 0x000104FB}, -{0x00010C80, 0x00010CC0}, {0x00010C81, 0x00010CC1}, {0x00010C82, 0x00010CC2}, {0x00010C83, 0x00010CC3}, -{0x00010C84, 0x00010CC4}, {0x00010C85, 0x00010CC5}, {0x00010C86, 0x00010CC6}, {0x00010C87, 0x00010CC7}, -{0x00010C88, 0x00010CC8}, {0x00010C89, 0x00010CC9}, {0x00010C8A, 0x00010CCA}, {0x00010C8B, 0x00010CCB}, -{0x00010C8C, 0x00010CCC}, {0x00010C8D, 0x00010CCD}, {0x00010C8E, 0x00010CCE}, {0x00010C8F, 0x00010CCF}, -{0x00010C90, 0x00010CD0}, {0x00010C91, 0x00010CD1}, {0x00010C92, 0x00010CD2}, {0x00010C93, 0x00010CD3}, -{0x00010C94, 0x00010CD4}, {0x00010C95, 0x00010CD5}, {0x00010C96, 0x00010CD6}, {0x00010C97, 0x00010CD7}, -{0x00010C98, 0x00010CD8}, {0x00010C99, 0x00010CD9}, {0x00010C9A, 0x00010CDA}, {0x00010C9B, 0x00010CDB}, -{0x00010C9C, 0x00010CDC}, {0x00010C9D, 0x00010CDD}, {0x00010C9E, 0x00010CDE}, {0x00010C9F, 0x00010CDF}, -{0x00010CA0, 0x00010CE0}, {0x00010CA1, 0x00010CE1}, {0x00010CA2, 0x00010CE2}, {0x00010CA3, 0x00010CE3}, -{0x00010CA4, 0x00010CE4}, {0x00010CA5, 0x00010CE5}, {0x00010CA6, 0x00010CE6}, {0x00010CA7, 0x00010CE7}, -{0x00010CA8, 0x00010CE8}, {0x00010CA9, 0x00010CE9}, {0x00010CAA, 0x00010CEA}, {0x00010CAB, 0x00010CEB}, -{0x00010CAC, 0x00010CEC}, {0x00010CAD, 0x00010CED}, {0x00010CAE, 0x00010CEE}, {0x00010CAF, 0x00010CEF}, -{0x00010CB0, 0x00010CF0}, {0x00010CB1, 0x00010CF1}, {0x00010CB2, 0x00010CF2}, {0x000118A0, 0x000118C0}, -{0x000118A1, 0x000118C1}, {0x000118A2, 0x000118C2}, {0x000118A3, 0x000118C3}, {0x000118A4, 0x000118C4}, -{0x000118A5, 0x000118C5}, {0x000118A6, 0x000118C6}, {0x000118A7, 0x000118C7}, {0x000118A8, 0x000118C8}, -{0x000118A9, 0x000118C9}, {0x000118AA, 0x000118CA}, {0x000118AB, 0x000118CB}, {0x000118AC, 0x000118CC}, -{0x000118AD, 0x000118CD}, {0x000118AE, 0x000118CE}, {0x000118AF, 0x000118CF}, {0x000118B0, 0x000118D0}, -{0x000118B1, 0x000118D1}, {0x000118B2, 0x000118D2}, {0x000118B3, 0x000118D3}, {0x000118B4, 0x000118D4}, -{0x000118B5, 0x000118D5}, {0x000118B6, 0x000118D6}, {0x000118B7, 0x000118D7}, {0x000118B8, 0x000118D8}, -{0x000118B9, 0x000118D9}, {0x000118BA, 0x000118DA}, {0x000118BB, 0x000118DB}, {0x000118BC, 0x000118DC}, -{0x000118BD, 0x000118DD}, {0x000118BE, 0x000118DE}, {0x000118BF, 0x000118DF}, {0x00016E40, 0x00016E60}, -{0x00016E41, 0x00016E61}, {0x00016E42, 0x00016E62}, {0x00016E43, 0x00016E63}, {0x00016E44, 0x00016E64}, -{0x00016E45, 0x00016E65}, {0x00016E46, 0x00016E66}, {0x00016E47, 0x00016E67}, {0x00016E48, 0x00016E68}, -{0x00016E49, 0x00016E69}, {0x00016E4A, 0x00016E6A}, {0x00016E4B, 0x00016E6B}, {0x00016E4C, 0x00016E6C}, -{0x00016E4D, 0x00016E6D}, {0x00016E4E, 0x00016E6E}, {0x00016E4F, 0x00016E6F}, {0x00016E50, 0x00016E70}, -{0x00016E51, 0x00016E71}, {0x00016E52, 0x00016E72}, {0x00016E53, 0x00016E73}, {0x00016E54, 0x00016E74}, -{0x00016E55, 0x00016E75}, {0x00016E56, 0x00016E76}, {0x00016E57, 0x00016E77}, {0x00016E58, 0x00016E78}, -{0x00016E59, 0x00016E79}, {0x00016E5A, 0x00016E7A}, {0x00016E5B, 0x00016E7B}, {0x00016E5C, 0x00016E7C}, -{0x00016E5D, 0x00016E7D}, {0x00016E5E, 0x00016E7E}, {0x00016E5F, 0x00016E7F}, {0x0001E900, 0x0001E922}, -{0x0001E901, 0x0001E923}, {0x0001E902, 0x0001E924}, {0x0001E903, 0x0001E925}, {0x0001E904, 0x0001E926}, -{0x0001E905, 0x0001E927}, {0x0001E906, 0x0001E928}, {0x0001E907, 0x0001E929}, {0x0001E908, 0x0001E92A}, -{0x0001E909, 0x0001E92B}, {0x0001E90A, 0x0001E92C}, {0x0001E90B, 0x0001E92D}, {0x0001E90C, 0x0001E92E}, -{0x0001E90D, 0x0001E92F}, {0x0001E90E, 0x0001E930}, {0x0001E90F, 0x0001E931}, {0x0001E910, 0x0001E932}, -{0x0001E911, 0x0001E933}, {0x0001E912, 0x0001E934}, {0x0001E913, 0x0001E935}, {0x0001E914, 0x0001E936}, -{0x0001E915, 0x0001E937}, {0x0001E916, 0x0001E938}, {0x0001E917, 0x0001E939}, {0x0001E918, 0x0001E93A}, -{0x0001E919, 0x0001E93B}, {0x0001E91A, 0x0001E93C}, {0x0001E91B, 0x0001E93D}, {0x0001E91C, 0x0001E93E}, -{0x0001E91D, 0x0001E93F}, {0x0001E91E, 0x0001E940}, {0x0001E91F, 0x0001E941}, {0x0001E920, 0x0001E942}, -{0x0001E921, 0x0001E943}, +const std::unordered_map unicode_map_uppercase = { +{0x000061, 0x000041}, +{0x000062, 0x000042}, +{0x000063, 0x000043}, +{0x000064, 0x000044}, +{0x000065, 0x000045}, +{0x000066, 0x000046}, +{0x000067, 0x000047}, +{0x000068, 0x000048}, +{0x000069, 0x000049}, +{0x00006A, 0x00004A}, +{0x00006B, 0x00004B}, +{0x00006C, 0x00004C}, +{0x00006D, 0x00004D}, +{0x00006E, 0x00004E}, +{0x00006F, 0x00004F}, +{0x000070, 0x000050}, +{0x000071, 0x000051}, +{0x000072, 0x000052}, +{0x000073, 0x000053}, +{0x000074, 0x000054}, +{0x000075, 0x000055}, +{0x000076, 0x000056}, +{0x000077, 0x000057}, +{0x000078, 0x000058}, +{0x000079, 0x000059}, +{0x00007A, 0x00005A}, +{0x0000B5, 0x00039C}, +{0x0000DF, 0x000053}, +{0x0000E0, 0x0000C0}, +{0x0000E1, 0x0000C1}, +{0x0000E2, 0x0000C2}, +{0x0000E3, 0x0000C3}, +{0x0000E4, 0x0000C4}, +{0x0000E5, 0x0000C5}, +{0x0000E6, 0x0000C6}, +{0x0000E7, 0x0000C7}, +{0x0000E8, 0x0000C8}, +{0x0000E9, 0x0000C9}, +{0x0000EA, 0x0000CA}, +{0x0000EB, 0x0000CB}, +{0x0000EC, 0x0000CC}, +{0x0000ED, 0x0000CD}, +{0x0000EE, 0x0000CE}, +{0x0000EF, 0x0000CF}, +{0x0000F0, 0x0000D0}, +{0x0000F1, 0x0000D1}, +{0x0000F2, 0x0000D2}, +{0x0000F3, 0x0000D3}, +{0x0000F4, 0x0000D4}, +{0x0000F5, 0x0000D5}, +{0x0000F6, 0x0000D6}, +{0x0000F8, 0x0000D8}, +{0x0000F9, 0x0000D9}, +{0x0000FA, 0x0000DA}, +{0x0000FB, 0x0000DB}, +{0x0000FC, 0x0000DC}, +{0x0000FD, 0x0000DD}, +{0x0000FE, 0x0000DE}, +{0x0000FF, 0x000178}, +{0x000101, 0x000100}, +{0x000103, 0x000102}, +{0x000105, 0x000104}, +{0x000107, 0x000106}, +{0x000109, 0x000108}, +{0x00010B, 0x00010A}, +{0x00010D, 0x00010C}, +{0x00010F, 0x00010E}, +{0x000111, 0x000110}, +{0x000113, 0x000112}, +{0x000115, 0x000114}, +{0x000117, 0x000116}, +{0x000119, 0x000118}, +{0x00011B, 0x00011A}, +{0x00011D, 0x00011C}, +{0x00011F, 0x00011E}, +{0x000121, 0x000120}, +{0x000123, 0x000122}, +{0x000125, 0x000124}, +{0x000127, 0x000126}, +{0x000129, 0x000128}, +{0x00012B, 0x00012A}, +{0x00012D, 0x00012C}, +{0x00012F, 0x00012E}, +{0x000131, 0x000049}, +{0x000133, 0x000132}, +{0x000135, 0x000134}, +{0x000137, 0x000136}, +{0x00013A, 0x000139}, +{0x00013C, 0x00013B}, +{0x00013E, 0x00013D}, +{0x000140, 0x00013F}, +{0x000142, 0x000141}, +{0x000144, 0x000143}, +{0x000146, 0x000145}, +{0x000148, 0x000147}, +{0x000149, 0x0002BC}, +{0x00014B, 0x00014A}, +{0x00014D, 0x00014C}, +{0x00014F, 0x00014E}, +{0x000151, 0x000150}, +{0x000153, 0x000152}, +{0x000155, 0x000154}, +{0x000157, 0x000156}, +{0x000159, 0x000158}, +{0x00015B, 0x00015A}, +{0x00015D, 0x00015C}, +{0x00015F, 0x00015E}, +{0x000161, 0x000160}, +{0x000163, 0x000162}, +{0x000165, 0x000164}, +{0x000167, 0x000166}, +{0x000169, 0x000168}, +{0x00016B, 0x00016A}, +{0x00016D, 0x00016C}, +{0x00016F, 0x00016E}, +{0x000171, 0x000170}, +{0x000173, 0x000172}, +{0x000175, 0x000174}, +{0x000177, 0x000176}, +{0x00017A, 0x000179}, +{0x00017C, 0x00017B}, +{0x00017E, 0x00017D}, +{0x00017F, 0x000053}, +{0x000180, 0x000243}, +{0x000183, 0x000182}, +{0x000185, 0x000184}, +{0x000188, 0x000187}, +{0x00018C, 0x00018B}, +{0x000192, 0x000191}, +{0x000195, 0x0001F6}, +{0x000199, 0x000198}, +{0x00019A, 0x00023D}, +{0x00019E, 0x000220}, +{0x0001A1, 0x0001A0}, +{0x0001A3, 0x0001A2}, +{0x0001A5, 0x0001A4}, +{0x0001A8, 0x0001A7}, +{0x0001AD, 0x0001AC}, +{0x0001B0, 0x0001AF}, +{0x0001B4, 0x0001B3}, +{0x0001B6, 0x0001B5}, +{0x0001B9, 0x0001B8}, +{0x0001BD, 0x0001BC}, +{0x0001BF, 0x0001F7}, +{0x0001C5, 0x0001C4}, +{0x0001C6, 0x0001C4}, +{0x0001C8, 0x0001C7}, +{0x0001C9, 0x0001C7}, +{0x0001CB, 0x0001CA}, +{0x0001CC, 0x0001CA}, +{0x0001CE, 0x0001CD}, +{0x0001D0, 0x0001CF}, +{0x0001D2, 0x0001D1}, +{0x0001D4, 0x0001D3}, +{0x0001D6, 0x0001D5}, +{0x0001D8, 0x0001D7}, +{0x0001DA, 0x0001D9}, +{0x0001DC, 0x0001DB}, +{0x0001DD, 0x00018E}, +{0x0001DF, 0x0001DE}, +{0x0001E1, 0x0001E0}, +{0x0001E3, 0x0001E2}, +{0x0001E5, 0x0001E4}, +{0x0001E7, 0x0001E6}, +{0x0001E9, 0x0001E8}, +{0x0001EB, 0x0001EA}, +{0x0001ED, 0x0001EC}, +{0x0001EF, 0x0001EE}, +{0x0001F0, 0x00004A}, +{0x0001F2, 0x0001F1}, +{0x0001F3, 0x0001F1}, +{0x0001F5, 0x0001F4}, +{0x0001F9, 0x0001F8}, +{0x0001FB, 0x0001FA}, +{0x0001FD, 0x0001FC}, +{0x0001FF, 0x0001FE}, +{0x000201, 0x000200}, +{0x000203, 0x000202}, +{0x000205, 0x000204}, +{0x000207, 0x000206}, +{0x000209, 0x000208}, +{0x00020B, 0x00020A}, +{0x00020D, 0x00020C}, +{0x00020F, 0x00020E}, +{0x000211, 0x000210}, +{0x000213, 0x000212}, +{0x000215, 0x000214}, +{0x000217, 0x000216}, +{0x000219, 0x000218}, +{0x00021B, 0x00021A}, +{0x00021D, 0x00021C}, +{0x00021F, 0x00021E}, +{0x000223, 0x000222}, +{0x000225, 0x000224}, +{0x000227, 0x000226}, +{0x000229, 0x000228}, +{0x00022B, 0x00022A}, +{0x00022D, 0x00022C}, +{0x00022F, 0x00022E}, +{0x000231, 0x000230}, +{0x000233, 0x000232}, +{0x00023C, 0x00023B}, +{0x00023F, 0x002C7E}, +{0x000240, 0x002C7F}, +{0x000242, 0x000241}, +{0x000247, 0x000246}, +{0x000249, 0x000248}, +{0x00024B, 0x00024A}, +{0x00024D, 0x00024C}, +{0x00024F, 0x00024E}, +{0x000250, 0x002C6F}, +{0x000251, 0x002C6D}, +{0x000252, 0x002C70}, +{0x000253, 0x000181}, +{0x000254, 0x000186}, +{0x000256, 0x000189}, +{0x000257, 0x00018A}, +{0x000259, 0x00018F}, +{0x00025B, 0x000190}, +{0x00025C, 0x00A7AB}, +{0x000260, 0x000193}, +{0x000261, 0x00A7AC}, +{0x000263, 0x000194}, +{0x000265, 0x00A78D}, +{0x000266, 0x00A7AA}, +{0x000268, 0x000197}, +{0x000269, 0x000196}, +{0x00026A, 0x00A7AE}, +{0x00026B, 0x002C62}, +{0x00026C, 0x00A7AD}, +{0x00026F, 0x00019C}, +{0x000271, 0x002C6E}, +{0x000272, 0x00019D}, +{0x000275, 0x00019F}, +{0x00027D, 0x002C64}, +{0x000280, 0x0001A6}, +{0x000282, 0x00A7C5}, +{0x000283, 0x0001A9}, +{0x000287, 0x00A7B1}, +{0x000288, 0x0001AE}, +{0x000289, 0x000244}, +{0x00028A, 0x0001B1}, +{0x00028B, 0x0001B2}, +{0x00028C, 0x000245}, +{0x000292, 0x0001B7}, +{0x00029D, 0x00A7B2}, +{0x00029E, 0x00A7B0}, 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+{0x010447, 0x01041F}, +{0x010448, 0x010420}, +{0x010449, 0x010421}, +{0x01044A, 0x010422}, +{0x01044B, 0x010423}, +{0x01044C, 0x010424}, +{0x01044D, 0x010425}, +{0x01044E, 0x010426}, +{0x01044F, 0x010427}, +{0x0104D8, 0x0104B0}, +{0x0104D9, 0x0104B1}, +{0x0104DA, 0x0104B2}, +{0x0104DB, 0x0104B3}, +{0x0104DC, 0x0104B4}, +{0x0104DD, 0x0104B5}, +{0x0104DE, 0x0104B6}, +{0x0104DF, 0x0104B7}, +{0x0104E0, 0x0104B8}, +{0x0104E1, 0x0104B9}, +{0x0104E2, 0x0104BA}, +{0x0104E3, 0x0104BB}, +{0x0104E4, 0x0104BC}, +{0x0104E5, 0x0104BD}, +{0x0104E6, 0x0104BE}, +{0x0104E7, 0x0104BF}, +{0x0104E8, 0x0104C0}, +{0x0104E9, 0x0104C1}, +{0x0104EA, 0x0104C2}, +{0x0104EB, 0x0104C3}, +{0x0104EC, 0x0104C4}, +{0x0104ED, 0x0104C5}, +{0x0104EE, 0x0104C6}, +{0x0104EF, 0x0104C7}, +{0x0104F0, 0x0104C8}, +{0x0104F1, 0x0104C9}, +{0x0104F2, 0x0104CA}, +{0x0104F3, 0x0104CB}, +{0x0104F4, 0x0104CC}, +{0x0104F5, 0x0104CD}, +{0x0104F6, 0x0104CE}, +{0x0104F7, 0x0104CF}, +{0x0104F8, 0x0104D0}, +{0x0104F9, 0x0104D1}, +{0x0104FA, 0x0104D2}, +{0x0104FB, 0x0104D3}, +{0x010CC0, 0x010C80}, +{0x010CC1, 0x010C81}, +{0x010CC2, 0x010C82}, +{0x010CC3, 0x010C83}, +{0x010CC4, 0x010C84}, +{0x010CC5, 0x010C85}, +{0x010CC6, 0x010C86}, +{0x010CC7, 0x010C87}, +{0x010CC8, 0x010C88}, +{0x010CC9, 0x010C89}, +{0x010CCA, 0x010C8A}, +{0x010CCB, 0x010C8B}, +{0x010CCC, 0x010C8C}, +{0x010CCD, 0x010C8D}, +{0x010CCE, 0x010C8E}, +{0x010CCF, 0x010C8F}, +{0x010CD0, 0x010C90}, +{0x010CD1, 0x010C91}, +{0x010CD2, 0x010C92}, +{0x010CD3, 0x010C93}, +{0x010CD4, 0x010C94}, +{0x010CD5, 0x010C95}, +{0x010CD6, 0x010C96}, +{0x010CD7, 0x010C97}, +{0x010CD8, 0x010C98}, +{0x010CD9, 0x010C99}, +{0x010CDA, 0x010C9A}, +{0x010CDB, 0x010C9B}, +{0x010CDC, 0x010C9C}, +{0x010CDD, 0x010C9D}, +{0x010CDE, 0x010C9E}, +{0x010CDF, 0x010C9F}, +{0x010CE0, 0x010CA0}, +{0x010CE1, 0x010CA1}, +{0x010CE2, 0x010CA2}, +{0x010CE3, 0x010CA3}, +{0x010CE4, 0x010CA4}, +{0x010CE5, 0x010CA5}, +{0x010CE6, 0x010CA6}, +{0x010CE7, 0x010CA7}, +{0x010CE8, 0x010CA8}, +{0x010CE9, 0x010CA9}, +{0x010CEA, 0x010CAA}, +{0x010CEB, 0x010CAB}, +{0x010CEC, 0x010CAC}, +{0x010CED, 0x010CAD}, +{0x010CEE, 0x010CAE}, +{0x010CEF, 0x010CAF}, +{0x010CF0, 0x010CB0}, +{0x010CF1, 0x010CB1}, +{0x010CF2, 0x010CB2}, +{0x0118C0, 0x0118A0}, +{0x0118C1, 0x0118A1}, +{0x0118C2, 0x0118A2}, +{0x0118C3, 0x0118A3}, +{0x0118C4, 0x0118A4}, +{0x0118C5, 0x0118A5}, +{0x0118C6, 0x0118A6}, +{0x0118C7, 0x0118A7}, +{0x0118C8, 0x0118A8}, +{0x0118C9, 0x0118A9}, +{0x0118CA, 0x0118AA}, +{0x0118CB, 0x0118AB}, +{0x0118CC, 0x0118AC}, +{0x0118CD, 0x0118AD}, +{0x0118CE, 0x0118AE}, +{0x0118CF, 0x0118AF}, +{0x0118D0, 0x0118B0}, +{0x0118D1, 0x0118B1}, +{0x0118D2, 0x0118B2}, +{0x0118D3, 0x0118B3}, +{0x0118D4, 0x0118B4}, +{0x0118D5, 0x0118B5}, +{0x0118D6, 0x0118B6}, +{0x0118D7, 0x0118B7}, +{0x0118D8, 0x0118B8}, +{0x0118D9, 0x0118B9}, +{0x0118DA, 0x0118BA}, +{0x0118DB, 0x0118BB}, +{0x0118DC, 0x0118BC}, +{0x0118DD, 0x0118BD}, +{0x0118DE, 0x0118BE}, +{0x0118DF, 0x0118BF}, +{0x016E60, 0x016E40}, +{0x016E61, 0x016E41}, +{0x016E62, 0x016E42}, +{0x016E63, 0x016E43}, +{0x016E64, 0x016E44}, +{0x016E65, 0x016E45}, +{0x016E66, 0x016E46}, +{0x016E67, 0x016E47}, +{0x016E68, 0x016E48}, +{0x016E69, 0x016E49}, +{0x016E6A, 0x016E4A}, +{0x016E6B, 0x016E4B}, +{0x016E6C, 0x016E4C}, +{0x016E6D, 0x016E4D}, +{0x016E6E, 0x016E4E}, +{0x016E6F, 0x016E4F}, +{0x016E70, 0x016E50}, +{0x016E71, 0x016E51}, +{0x016E72, 0x016E52}, +{0x016E73, 0x016E53}, +{0x016E74, 0x016E54}, +{0x016E75, 0x016E55}, +{0x016E76, 0x016E56}, +{0x016E77, 0x016E57}, +{0x016E78, 0x016E58}, +{0x016E79, 0x016E59}, +{0x016E7A, 0x016E5A}, +{0x016E7B, 0x016E5B}, +{0x016E7C, 0x016E5C}, +{0x016E7D, 0x016E5D}, +{0x016E7E, 0x016E5E}, +{0x016E7F, 0x016E5F}, +{0x01E922, 0x01E900}, +{0x01E923, 0x01E901}, +{0x01E924, 0x01E902}, +{0x01E925, 0x01E903}, +{0x01E926, 0x01E904}, +{0x01E927, 0x01E905}, +{0x01E928, 0x01E906}, +{0x01E929, 0x01E907}, +{0x01E92A, 0x01E908}, +{0x01E92B, 0x01E909}, +{0x01E92C, 0x01E90A}, +{0x01E92D, 0x01E90B}, +{0x01E92E, 0x01E90C}, +{0x01E92F, 0x01E90D}, +{0x01E930, 0x01E90E}, +{0x01E931, 0x01E90F}, +{0x01E932, 0x01E910}, +{0x01E933, 0x01E911}, +{0x01E934, 0x01E912}, +{0x01E935, 0x01E913}, +{0x01E936, 0x01E914}, +{0x01E937, 0x01E915}, +{0x01E938, 0x01E916}, +{0x01E939, 0x01E917}, +{0x01E93A, 0x01E918}, +{0x01E93B, 0x01E919}, +{0x01E93C, 0x01E91A}, +{0x01E93D, 0x01E91B}, +{0x01E93E, 0x01E91C}, +{0x01E93F, 0x01E91D}, +{0x01E940, 0x01E91E}, +{0x01E941, 0x01E91F}, +{0x01E942, 0x01E920}, +{0x01E943, 0x01E921}, }; -const std::map unicode_map_uppercase = { -{0x00000061, 0x00000041}, {0x00000062, 0x00000042}, {0x00000063, 0x00000043}, {0x00000064, 0x00000044}, -{0x00000065, 0x00000045}, {0x00000066, 0x00000046}, {0x00000067, 0x00000047}, {0x00000068, 0x00000048}, -{0x00000069, 0x00000049}, {0x0000006A, 0x0000004A}, {0x0000006B, 0x0000004B}, {0x0000006C, 0x0000004C}, -{0x0000006D, 0x0000004D}, {0x0000006E, 0x0000004E}, {0x0000006F, 0x0000004F}, {0x00000070, 0x00000050}, -{0x00000071, 0x00000051}, {0x00000072, 0x00000052}, {0x00000073, 0x00000053}, {0x00000074, 0x00000054}, -{0x00000075, 0x00000055}, {0x00000076, 0x00000056}, {0x00000077, 0x00000057}, {0x00000078, 0x00000058}, -{0x00000079, 0x00000059}, {0x0000007A, 0x0000005A}, {0x000000B5, 0x0000039C}, {0x000000DF, 0x00000053}, -{0x000000E0, 0x000000C0}, {0x000000E1, 0x000000C1}, {0x000000E2, 0x000000C2}, {0x000000E3, 0x000000C3}, -{0x000000E4, 0x000000C4}, {0x000000E5, 0x000000C5}, {0x000000E6, 0x000000C6}, {0x000000E7, 0x000000C7}, -{0x000000E8, 0x000000C8}, {0x000000E9, 0x000000C9}, {0x000000EA, 0x000000CA}, {0x000000EB, 0x000000CB}, -{0x000000EC, 0x000000CC}, {0x000000ED, 0x000000CD}, {0x000000EE, 0x000000CE}, {0x000000EF, 0x000000CF}, -{0x000000F0, 0x000000D0}, {0x000000F1, 0x000000D1}, {0x000000F2, 0x000000D2}, {0x000000F3, 0x000000D3}, -{0x000000F4, 0x000000D4}, {0x000000F5, 0x000000D5}, {0x000000F6, 0x000000D6}, {0x000000F8, 0x000000D8}, -{0x000000F9, 0x000000D9}, {0x000000FA, 0x000000DA}, {0x000000FB, 0x000000DB}, {0x000000FC, 0x000000DC}, -{0x000000FD, 0x000000DD}, {0x000000FE, 0x000000DE}, {0x000000FF, 0x00000178}, {0x00000101, 0x00000100}, -{0x00000103, 0x00000102}, {0x00000105, 0x00000104}, {0x00000107, 0x00000106}, {0x00000109, 0x00000108}, -{0x0000010B, 0x0000010A}, {0x0000010D, 0x0000010C}, {0x0000010F, 0x0000010E}, {0x00000111, 0x00000110}, -{0x00000113, 0x00000112}, {0x00000115, 0x00000114}, {0x00000117, 0x00000116}, {0x00000119, 0x00000118}, -{0x0000011B, 0x0000011A}, {0x0000011D, 0x0000011C}, {0x0000011F, 0x0000011E}, {0x00000121, 0x00000120}, -{0x00000123, 0x00000122}, {0x00000125, 0x00000124}, {0x00000127, 0x00000126}, {0x00000129, 0x00000128}, -{0x0000012B, 0x0000012A}, {0x0000012D, 0x0000012C}, {0x0000012F, 0x0000012E}, {0x00000131, 0x00000049}, -{0x00000133, 0x00000132}, {0x00000135, 0x00000134}, {0x00000137, 0x00000136}, {0x0000013A, 0x00000139}, -{0x0000013C, 0x0000013B}, {0x0000013E, 0x0000013D}, {0x00000140, 0x0000013F}, {0x00000142, 0x00000141}, -{0x00000144, 0x00000143}, {0x00000146, 0x00000145}, {0x00000148, 0x00000147}, {0x00000149, 0x000002BC}, -{0x0000014B, 0x0000014A}, {0x0000014D, 0x0000014C}, {0x0000014F, 0x0000014E}, {0x00000151, 0x00000150}, -{0x00000153, 0x00000152}, {0x00000155, 0x00000154}, {0x00000157, 0x00000156}, {0x00000159, 0x00000158}, -{0x0000015B, 0x0000015A}, {0x0000015D, 0x0000015C}, {0x0000015F, 0x0000015E}, {0x00000161, 0x00000160}, -{0x00000163, 0x00000162}, {0x00000165, 0x00000164}, {0x00000167, 0x00000166}, {0x00000169, 0x00000168}, -{0x0000016B, 0x0000016A}, {0x0000016D, 0x0000016C}, {0x0000016F, 0x0000016E}, {0x00000171, 0x00000170}, -{0x00000173, 0x00000172}, {0x00000175, 0x00000174}, {0x00000177, 0x00000176}, {0x0000017A, 0x00000179}, -{0x0000017C, 0x0000017B}, {0x0000017E, 0x0000017D}, {0x0000017F, 0x00000053}, {0x00000180, 0x00000243}, -{0x00000183, 0x00000182}, {0x00000185, 0x00000184}, {0x00000188, 0x00000187}, {0x0000018C, 0x0000018B}, -{0x00000192, 0x00000191}, {0x00000195, 0x000001F6}, {0x00000199, 0x00000198}, {0x0000019A, 0x0000023D}, -{0x0000019E, 0x00000220}, {0x000001A1, 0x000001A0}, {0x000001A3, 0x000001A2}, {0x000001A5, 0x000001A4}, -{0x000001A8, 0x000001A7}, {0x000001AD, 0x000001AC}, {0x000001B0, 0x000001AF}, {0x000001B4, 0x000001B3}, -{0x000001B6, 0x000001B5}, {0x000001B9, 0x000001B8}, {0x000001BD, 0x000001BC}, {0x000001BF, 0x000001F7}, -{0x000001C5, 0x000001C4}, {0x000001C6, 0x000001C4}, {0x000001C8, 0x000001C7}, {0x000001C9, 0x000001C7}, -{0x000001CB, 0x000001CA}, {0x000001CC, 0x000001CA}, {0x000001CE, 0x000001CD}, {0x000001D0, 0x000001CF}, -{0x000001D2, 0x000001D1}, {0x000001D4, 0x000001D3}, {0x000001D6, 0x000001D5}, {0x000001D8, 0x000001D7}, -{0x000001DA, 0x000001D9}, {0x000001DC, 0x000001DB}, {0x000001DD, 0x0000018E}, {0x000001DF, 0x000001DE}, -{0x000001E1, 0x000001E0}, {0x000001E3, 0x000001E2}, {0x000001E5, 0x000001E4}, {0x000001E7, 0x000001E6}, -{0x000001E9, 0x000001E8}, {0x000001EB, 0x000001EA}, {0x000001ED, 0x000001EC}, {0x000001EF, 0x000001EE}, -{0x000001F0, 0x0000004A}, {0x000001F2, 0x000001F1}, {0x000001F3, 0x000001F1}, {0x000001F5, 0x000001F4}, -{0x000001F9, 0x000001F8}, {0x000001FB, 0x000001FA}, {0x000001FD, 0x000001FC}, {0x000001FF, 0x000001FE}, -{0x00000201, 0x00000200}, {0x00000203, 0x00000202}, {0x00000205, 0x00000204}, {0x00000207, 0x00000206}, -{0x00000209, 0x00000208}, {0x0000020B, 0x0000020A}, {0x0000020D, 0x0000020C}, {0x0000020F, 0x0000020E}, -{0x00000211, 0x00000210}, {0x00000213, 0x00000212}, {0x00000215, 0x00000214}, {0x00000217, 0x00000216}, -{0x00000219, 0x00000218}, {0x0000021B, 0x0000021A}, {0x0000021D, 0x0000021C}, {0x0000021F, 0x0000021E}, -{0x00000223, 0x00000222}, {0x00000225, 0x00000224}, {0x00000227, 0x00000226}, {0x00000229, 0x00000228}, -{0x0000022B, 0x0000022A}, {0x0000022D, 0x0000022C}, {0x0000022F, 0x0000022E}, {0x00000231, 0x00000230}, -{0x00000233, 0x00000232}, {0x0000023C, 0x0000023B}, {0x0000023F, 0x00002C7E}, {0x00000240, 0x00002C7F}, -{0x00000242, 0x00000241}, {0x00000247, 0x00000246}, {0x00000249, 0x00000248}, {0x0000024B, 0x0000024A}, -{0x0000024D, 0x0000024C}, {0x0000024F, 0x0000024E}, {0x00000250, 0x00002C6F}, {0x00000251, 0x00002C6D}, -{0x00000252, 0x00002C70}, {0x00000253, 0x00000181}, {0x00000254, 0x00000186}, {0x00000256, 0x00000189}, -{0x00000257, 0x0000018A}, {0x00000259, 0x0000018F}, {0x0000025B, 0x00000190}, {0x0000025C, 0x0000A7AB}, -{0x00000260, 0x00000193}, {0x00000261, 0x0000A7AC}, {0x00000263, 0x00000194}, {0x00000265, 0x0000A78D}, -{0x00000266, 0x0000A7AA}, {0x00000268, 0x00000197}, {0x00000269, 0x00000196}, {0x0000026A, 0x0000A7AE}, -{0x0000026B, 0x00002C62}, {0x0000026C, 0x0000A7AD}, {0x0000026F, 0x0000019C}, {0x00000271, 0x00002C6E}, -{0x00000272, 0x0000019D}, {0x00000275, 0x0000019F}, {0x0000027D, 0x00002C64}, {0x00000280, 0x000001A6}, -{0x00000282, 0x0000A7C5}, {0x00000283, 0x000001A9}, {0x00000287, 0x0000A7B1}, {0x00000288, 0x000001AE}, -{0x00000289, 0x00000244}, {0x0000028A, 0x000001B1}, {0x0000028B, 0x000001B2}, {0x0000028C, 0x00000245}, -{0x00000292, 0x000001B7}, {0x0000029D, 0x0000A7B2}, {0x0000029E, 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0x0001040C}, {0x00010435, 0x0001040D}, {0x00010436, 0x0001040E}, {0x00010437, 0x0001040F}, -{0x00010438, 0x00010410}, {0x00010439, 0x00010411}, {0x0001043A, 0x00010412}, {0x0001043B, 0x00010413}, -{0x0001043C, 0x00010414}, {0x0001043D, 0x00010415}, {0x0001043E, 0x00010416}, {0x0001043F, 0x00010417}, -{0x00010440, 0x00010418}, {0x00010441, 0x00010419}, {0x00010442, 0x0001041A}, {0x00010443, 0x0001041B}, -{0x00010444, 0x0001041C}, {0x00010445, 0x0001041D}, {0x00010446, 0x0001041E}, {0x00010447, 0x0001041F}, -{0x00010448, 0x00010420}, {0x00010449, 0x00010421}, {0x0001044A, 0x00010422}, {0x0001044B, 0x00010423}, -{0x0001044C, 0x00010424}, {0x0001044D, 0x00010425}, {0x0001044E, 0x00010426}, {0x0001044F, 0x00010427}, -{0x000104D8, 0x000104B0}, {0x000104D9, 0x000104B1}, {0x000104DA, 0x000104B2}, {0x000104DB, 0x000104B3}, -{0x000104DC, 0x000104B4}, {0x000104DD, 0x000104B5}, {0x000104DE, 0x000104B6}, {0x000104DF, 0x000104B7}, -{0x000104E0, 0x000104B8}, {0x000104E1, 0x000104B9}, {0x000104E2, 0x000104BA}, {0x000104E3, 0x000104BB}, -{0x000104E4, 0x000104BC}, {0x000104E5, 0x000104BD}, {0x000104E6, 0x000104BE}, {0x000104E7, 0x000104BF}, -{0x000104E8, 0x000104C0}, {0x000104E9, 0x000104C1}, {0x000104EA, 0x000104C2}, {0x000104EB, 0x000104C3}, -{0x000104EC, 0x000104C4}, {0x000104ED, 0x000104C5}, {0x000104EE, 0x000104C6}, {0x000104EF, 0x000104C7}, -{0x000104F0, 0x000104C8}, {0x000104F1, 0x000104C9}, {0x000104F2, 0x000104CA}, {0x000104F3, 0x000104CB}, -{0x000104F4, 0x000104CC}, {0x000104F5, 0x000104CD}, {0x000104F6, 0x000104CE}, {0x000104F7, 0x000104CF}, -{0x000104F8, 0x000104D0}, {0x000104F9, 0x000104D1}, {0x000104FA, 0x000104D2}, {0x000104FB, 0x000104D3}, -{0x00010CC0, 0x00010C80}, {0x00010CC1, 0x00010C81}, {0x00010CC2, 0x00010C82}, {0x00010CC3, 0x00010C83}, -{0x00010CC4, 0x00010C84}, {0x00010CC5, 0x00010C85}, {0x00010CC6, 0x00010C86}, {0x00010CC7, 0x00010C87}, -{0x00010CC8, 0x00010C88}, {0x00010CC9, 0x00010C89}, {0x00010CCA, 0x00010C8A}, {0x00010CCB, 0x00010C8B}, -{0x00010CCC, 0x00010C8C}, {0x00010CCD, 0x00010C8D}, {0x00010CCE, 0x00010C8E}, {0x00010CCF, 0x00010C8F}, -{0x00010CD0, 0x00010C90}, {0x00010CD1, 0x00010C91}, {0x00010CD2, 0x00010C92}, {0x00010CD3, 0x00010C93}, -{0x00010CD4, 0x00010C94}, {0x00010CD5, 0x00010C95}, {0x00010CD6, 0x00010C96}, {0x00010CD7, 0x00010C97}, -{0x00010CD8, 0x00010C98}, {0x00010CD9, 0x00010C99}, {0x00010CDA, 0x00010C9A}, {0x00010CDB, 0x00010C9B}, -{0x00010CDC, 0x00010C9C}, {0x00010CDD, 0x00010C9D}, {0x00010CDE, 0x00010C9E}, {0x00010CDF, 0x00010C9F}, -{0x00010CE0, 0x00010CA0}, {0x00010CE1, 0x00010CA1}, {0x00010CE2, 0x00010CA2}, {0x00010CE3, 0x00010CA3}, -{0x00010CE4, 0x00010CA4}, {0x00010CE5, 0x00010CA5}, {0x00010CE6, 0x00010CA6}, {0x00010CE7, 0x00010CA7}, -{0x00010CE8, 0x00010CA8}, {0x00010CE9, 0x00010CA9}, {0x00010CEA, 0x00010CAA}, {0x00010CEB, 0x00010CAB}, -{0x00010CEC, 0x00010CAC}, {0x00010CED, 0x00010CAD}, {0x00010CEE, 0x00010CAE}, {0x00010CEF, 0x00010CAF}, -{0x00010CF0, 0x00010CB0}, {0x00010CF1, 0x00010CB1}, {0x00010CF2, 0x00010CB2}, {0x000118C0, 0x000118A0}, -{0x000118C1, 0x000118A1}, {0x000118C2, 0x000118A2}, {0x000118C3, 0x000118A3}, {0x000118C4, 0x000118A4}, -{0x000118C5, 0x000118A5}, {0x000118C6, 0x000118A6}, {0x000118C7, 0x000118A7}, {0x000118C8, 0x000118A8}, -{0x000118C9, 0x000118A9}, {0x000118CA, 0x000118AA}, {0x000118CB, 0x000118AB}, {0x000118CC, 0x000118AC}, -{0x000118CD, 0x000118AD}, {0x000118CE, 0x000118AE}, {0x000118CF, 0x000118AF}, {0x000118D0, 0x000118B0}, -{0x000118D1, 0x000118B1}, {0x000118D2, 0x000118B2}, {0x000118D3, 0x000118B3}, {0x000118D4, 0x000118B4}, -{0x000118D5, 0x000118B5}, {0x000118D6, 0x000118B6}, {0x000118D7, 0x000118B7}, {0x000118D8, 0x000118B8}, -{0x000118D9, 0x000118B9}, {0x000118DA, 0x000118BA}, {0x000118DB, 0x000118BB}, {0x000118DC, 0x000118BC}, -{0x000118DD, 0x000118BD}, {0x000118DE, 0x000118BE}, {0x000118DF, 0x000118BF}, {0x00016E60, 0x00016E40}, -{0x00016E61, 0x00016E41}, {0x00016E62, 0x00016E42}, {0x00016E63, 0x00016E43}, {0x00016E64, 0x00016E44}, -{0x00016E65, 0x00016E45}, {0x00016E66, 0x00016E46}, {0x00016E67, 0x00016E47}, {0x00016E68, 0x00016E48}, -{0x00016E69, 0x00016E49}, {0x00016E6A, 0x00016E4A}, {0x00016E6B, 0x00016E4B}, {0x00016E6C, 0x00016E4C}, -{0x00016E6D, 0x00016E4D}, {0x00016E6E, 0x00016E4E}, {0x00016E6F, 0x00016E4F}, {0x00016E70, 0x00016E50}, -{0x00016E71, 0x00016E51}, {0x00016E72, 0x00016E52}, {0x00016E73, 0x00016E53}, {0x00016E74, 0x00016E54}, -{0x00016E75, 0x00016E55}, {0x00016E76, 0x00016E56}, {0x00016E77, 0x00016E57}, {0x00016E78, 0x00016E58}, -{0x00016E79, 0x00016E59}, {0x00016E7A, 0x00016E5A}, {0x00016E7B, 0x00016E5B}, {0x00016E7C, 0x00016E5C}, -{0x00016E7D, 0x00016E5D}, {0x00016E7E, 0x00016E5E}, {0x00016E7F, 0x00016E5F}, {0x0001E922, 0x0001E900}, -{0x0001E923, 0x0001E901}, {0x0001E924, 0x0001E902}, {0x0001E925, 0x0001E903}, {0x0001E926, 0x0001E904}, -{0x0001E927, 0x0001E905}, {0x0001E928, 0x0001E906}, {0x0001E929, 0x0001E907}, {0x0001E92A, 0x0001E908}, -{0x0001E92B, 0x0001E909}, {0x0001E92C, 0x0001E90A}, {0x0001E92D, 0x0001E90B}, {0x0001E92E, 0x0001E90C}, -{0x0001E92F, 0x0001E90D}, {0x0001E930, 0x0001E90E}, {0x0001E931, 0x0001E90F}, {0x0001E932, 0x0001E910}, -{0x0001E933, 0x0001E911}, {0x0001E934, 0x0001E912}, {0x0001E935, 0x0001E913}, {0x0001E936, 0x0001E914}, -{0x0001E937, 0x0001E915}, {0x0001E938, 0x0001E916}, {0x0001E939, 0x0001E917}, {0x0001E93A, 0x0001E918}, -{0x0001E93B, 0x0001E919}, {0x0001E93C, 0x0001E91A}, {0x0001E93D, 0x0001E91B}, {0x0001E93E, 0x0001E91C}, -{0x0001E93F, 0x0001E91D}, {0x0001E940, 0x0001E91E}, {0x0001E941, 0x0001E91F}, {0x0001E942, 0x0001E920}, -{0x0001E943, 0x0001E921}, +const std::vector unicode_ranges_nfd = { // start, last, nfd +{0x000000, 0x000000, 0x000000}, +{0x0000C0, 0x0000C5, 0x000041}, +{0x0000C7, 0x0000C7, 0x000043}, +{0x0000C8, 0x0000CB, 0x000045}, +{0x0000CC, 0x0000CF, 0x000049}, +{0x0000D1, 0x0000D1, 0x00004E}, +{0x0000D2, 0x0000D6, 0x00004F}, +{0x0000D9, 0x0000DC, 0x000055}, +{0x0000DD, 0x0000DD, 0x000059}, +{0x0000E0, 0x0000E5, 0x000061}, +{0x0000E7, 0x0000E7, 0x000063}, +{0x0000E8, 0x0000EB, 0x000065}, +{0x0000EC, 0x0000EF, 0x000069}, +{0x0000F1, 0x0000F1, 0x00006E}, +{0x0000F2, 0x0000F6, 0x00006F}, +{0x0000F9, 0x0000FC, 0x000075}, +{0x0000FD, 0x0000FD, 0x000079}, +{0x0000FF, 0x0000FF, 0x000079}, +{0x000100, 0x000100, 0x000041}, +{0x000101, 0x000101, 0x000061}, +{0x000102, 0x000102, 0x000041}, +{0x000103, 0x000103, 0x000061}, +{0x000104, 0x000104, 0x000041}, +{0x000105, 0x000105, 0x000061}, +{0x000106, 0x000106, 0x000043}, +{0x000107, 0x000107, 0x000063}, +{0x000108, 0x000108, 0x000043}, +{0x000109, 0x000109, 0x000063}, +{0x00010A, 0x00010A, 0x000043}, +{0x00010B, 0x00010B, 0x000063}, +{0x00010C, 0x00010C, 0x000043}, +{0x00010D, 0x00010D, 0x000063}, +{0x00010E, 0x00010E, 0x000044}, +{0x00010F, 0x00010F, 0x000064}, +{0x000112, 0x000112, 0x000045}, +{0x000113, 0x000113, 0x000065}, +{0x000114, 0x000114, 0x000045}, +{0x000115, 0x000115, 0x000065}, +{0x000116, 0x000116, 0x000045}, +{0x000117, 0x000117, 0x000065}, +{0x000118, 0x000118, 0x000045}, +{0x000119, 0x000119, 0x000065}, +{0x00011A, 0x00011A, 0x000045}, +{0x00011B, 0x00011B, 0x000065}, +{0x00011C, 0x00011C, 0x000047}, +{0x00011D, 0x00011D, 0x000067}, +{0x00011E, 0x00011E, 0x000047}, +{0x00011F, 0x00011F, 0x000067}, +{0x000120, 0x000120, 0x000047}, +{0x000121, 0x000121, 0x000067}, +{0x000122, 0x000122, 0x000047}, +{0x000123, 0x000123, 0x000067}, +{0x000124, 0x000124, 0x000048}, +{0x000125, 0x000125, 0x000068}, +{0x000128, 0x000128, 0x000049}, +{0x000129, 0x000129, 0x000069}, +{0x00012A, 0x00012A, 0x000049}, +{0x00012B, 0x00012B, 0x000069}, +{0x00012C, 0x00012C, 0x000049}, +{0x00012D, 0x00012D, 0x000069}, +{0x00012E, 0x00012E, 0x000049}, +{0x00012F, 0x00012F, 0x000069}, +{0x000130, 0x000130, 0x000049}, +{0x000134, 0x000134, 0x00004A}, +{0x000135, 0x000135, 0x00006A}, +{0x000136, 0x000136, 0x00004B}, +{0x000137, 0x000137, 0x00006B}, +{0x000139, 0x000139, 0x00004C}, +{0x00013A, 0x00013A, 0x00006C}, +{0x00013B, 0x00013B, 0x00004C}, +{0x00013C, 0x00013C, 0x00006C}, +{0x00013D, 0x00013D, 0x00004C}, +{0x00013E, 0x00013E, 0x00006C}, +{0x000143, 0x000143, 0x00004E}, +{0x000144, 0x000144, 0x00006E}, +{0x000145, 0x000145, 0x00004E}, +{0x000146, 0x000146, 0x00006E}, +{0x000147, 0x000147, 0x00004E}, +{0x000148, 0x000148, 0x00006E}, +{0x00014C, 0x00014C, 0x00004F}, +{0x00014D, 0x00014D, 0x00006F}, +{0x00014E, 0x00014E, 0x00004F}, +{0x00014F, 0x00014F, 0x00006F}, +{0x000150, 0x000150, 0x00004F}, +{0x000151, 0x000151, 0x00006F}, +{0x000154, 0x000154, 0x000052}, +{0x000155, 0x000155, 0x000072}, +{0x000156, 0x000156, 0x000052}, +{0x000157, 0x000157, 0x000072}, +{0x000158, 0x000158, 0x000052}, +{0x000159, 0x000159, 0x000072}, +{0x00015A, 0x00015A, 0x000053}, +{0x00015B, 0x00015B, 0x000073}, +{0x00015C, 0x00015C, 0x000053}, +{0x00015D, 0x00015D, 0x000073}, +{0x00015E, 0x00015E, 0x000053}, +{0x00015F, 0x00015F, 0x000073}, +{0x000160, 0x000160, 0x000053}, +{0x000161, 0x000161, 0x000073}, +{0x000162, 0x000162, 0x000054}, +{0x000163, 0x000163, 0x000074}, +{0x000164, 0x000164, 0x000054}, +{0x000165, 0x000165, 0x000074}, +{0x000168, 0x000168, 0x000055}, +{0x000169, 0x000169, 0x000075}, +{0x00016A, 0x00016A, 0x000055}, +{0x00016B, 0x00016B, 0x000075}, +{0x00016C, 0x00016C, 0x000055}, +{0x00016D, 0x00016D, 0x000075}, +{0x00016E, 0x00016E, 0x000055}, +{0x00016F, 0x00016F, 0x000075}, +{0x000170, 0x000170, 0x000055}, +{0x000171, 0x000171, 0x000075}, +{0x000172, 0x000172, 0x000055}, +{0x000173, 0x000173, 0x000075}, +{0x000174, 0x000174, 0x000057}, +{0x000175, 0x000175, 0x000077}, +{0x000176, 0x000176, 0x000059}, +{0x000177, 0x000177, 0x000079}, +{0x000178, 0x000178, 0x000059}, +{0x000179, 0x000179, 0x00005A}, +{0x00017A, 0x00017A, 0x00007A}, +{0x00017B, 0x00017B, 0x00005A}, +{0x00017C, 0x00017C, 0x00007A}, +{0x00017D, 0x00017D, 0x00005A}, +{0x00017E, 0x00017E, 0x00007A}, +{0x0001A0, 0x0001A0, 0x00004F}, +{0x0001A1, 0x0001A1, 0x00006F}, +{0x0001AF, 0x0001AF, 0x000055}, +{0x0001B0, 0x0001B0, 0x000075}, +{0x0001CD, 0x0001CD, 0x000041}, +{0x0001CE, 0x0001CE, 0x000061}, +{0x0001CF, 0x0001CF, 0x000049}, +{0x0001D0, 0x0001D0, 0x000069}, +{0x0001D1, 0x0001D1, 0x00004F}, +{0x0001D2, 0x0001D2, 0x00006F}, +{0x0001D3, 0x0001D3, 0x000055}, +{0x0001D4, 0x0001D4, 0x000075}, +{0x0001D5, 0x0001D5, 0x000055}, +{0x0001D6, 0x0001D6, 0x000075}, +{0x0001D7, 0x0001D7, 0x000055}, +{0x0001D8, 0x0001D8, 0x000075}, +{0x0001D9, 0x0001D9, 0x000055}, +{0x0001DA, 0x0001DA, 0x000075}, +{0x0001DB, 0x0001DB, 0x000055}, +{0x0001DC, 0x0001DC, 0x000075}, +{0x0001DE, 0x0001DE, 0x000041}, +{0x0001DF, 0x0001DF, 0x000061}, +{0x0001E0, 0x0001E0, 0x000041}, +{0x0001E1, 0x0001E1, 0x000061}, +{0x0001E2, 0x0001E2, 0x0000C6}, +{0x0001E3, 0x0001E3, 0x0000E6}, +{0x0001E6, 0x0001E6, 0x000047}, +{0x0001E7, 0x0001E7, 0x000067}, +{0x0001E8, 0x0001E8, 0x00004B}, +{0x0001E9, 0x0001E9, 0x00006B}, +{0x0001EA, 0x0001EA, 0x00004F}, +{0x0001EB, 0x0001EB, 0x00006F}, +{0x0001EC, 0x0001EC, 0x00004F}, +{0x0001ED, 0x0001ED, 0x00006F}, +{0x0001EE, 0x0001EE, 0x0001B7}, +{0x0001EF, 0x0001EF, 0x000292}, +{0x0001F0, 0x0001F0, 0x00006A}, +{0x0001F4, 0x0001F4, 0x000047}, +{0x0001F5, 0x0001F5, 0x000067}, +{0x0001F8, 0x0001F8, 0x00004E}, +{0x0001F9, 0x0001F9, 0x00006E}, +{0x0001FA, 0x0001FA, 0x000041}, +{0x0001FB, 0x0001FB, 0x000061}, +{0x0001FC, 0x0001FC, 0x0000C6}, +{0x0001FD, 0x0001FD, 0x0000E6}, +{0x0001FE, 0x0001FE, 0x0000D8}, +{0x0001FF, 0x0001FF, 0x0000F8}, +{0x000200, 0x000200, 0x000041}, +{0x000201, 0x000201, 0x000061}, +{0x000202, 0x000202, 0x000041}, +{0x000203, 0x000203, 0x000061}, +{0x000204, 0x000204, 0x000045}, +{0x000205, 0x000205, 0x000065}, +{0x000206, 0x000206, 0x000045}, +{0x000207, 0x000207, 0x000065}, +{0x000208, 0x000208, 0x000049}, +{0x000209, 0x000209, 0x000069}, +{0x00020A, 0x00020A, 0x000049}, +{0x00020B, 0x00020B, 0x000069}, +{0x00020C, 0x00020C, 0x00004F}, +{0x00020D, 0x00020D, 0x00006F}, +{0x00020E, 0x00020E, 0x00004F}, +{0x00020F, 0x00020F, 0x00006F}, +{0x000210, 0x000210, 0x000052}, +{0x000211, 0x000211, 0x000072}, 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+{0x02F9B2, 0x02F9B2, 0x00456B}, +{0x02F9B3, 0x02F9B3, 0x008650}, +{0x02F9B4, 0x02F9B4, 0x00865C}, +{0x02F9B5, 0x02F9B5, 0x008667}, +{0x02F9B6, 0x02F9B6, 0x008669}, +{0x02F9B7, 0x02F9B7, 0x0086A9}, +{0x02F9B8, 0x02F9B8, 0x008688}, +{0x02F9B9, 0x02F9B9, 0x00870E}, +{0x02F9BA, 0x02F9BA, 0x0086E2}, +{0x02F9BB, 0x02F9BB, 0x008779}, +{0x02F9BC, 0x02F9BC, 0x008728}, +{0x02F9BD, 0x02F9BD, 0x00876B}, +{0x02F9BE, 0x02F9BE, 0x008786}, +{0x02F9BF, 0x02F9BF, 0x0045D7}, +{0x02F9C0, 0x02F9C0, 0x0087E1}, +{0x02F9C1, 0x02F9C1, 0x008801}, +{0x02F9C2, 0x02F9C2, 0x0045F9}, +{0x02F9C3, 0x02F9C3, 0x008860}, +{0x02F9C4, 0x02F9C4, 0x008863}, +{0x02F9C5, 0x02F9C5, 0x027667}, +{0x02F9C6, 0x02F9C6, 0x0088D7}, +{0x02F9C7, 0x02F9C7, 0x0088DE}, +{0x02F9C8, 0x02F9C8, 0x004635}, +{0x02F9C9, 0x02F9C9, 0x0088FA}, +{0x02F9CA, 0x02F9CA, 0x0034BB}, +{0x02F9CB, 0x02F9CB, 0x0278AE}, +{0x02F9CC, 0x02F9CC, 0x027966}, +{0x02F9CD, 0x02F9CD, 0x0046BE}, +{0x02F9CE, 0x02F9CE, 0x0046C7}, +{0x02F9CF, 0x02F9CF, 0x008AA0}, +{0x02F9D0, 0x02F9D0, 0x008AED}, +{0x02F9D1, 0x02F9D1, 0x008B8A}, +{0x02F9D2, 0x02F9D2, 0x008C55}, +{0x02F9D3, 0x02F9D3, 0x027CA8}, +{0x02F9D4, 0x02F9D4, 0x008CAB}, +{0x02F9D5, 0x02F9D5, 0x008CC1}, +{0x02F9D6, 0x02F9D6, 0x008D1B}, +{0x02F9D7, 0x02F9D7, 0x008D77}, +{0x02F9D8, 0x02F9D8, 0x027F2F}, +{0x02F9D9, 0x02F9D9, 0x020804}, +{0x02F9DA, 0x02F9DA, 0x008DCB}, +{0x02F9DB, 0x02F9DB, 0x008DBC}, +{0x02F9DC, 0x02F9DC, 0x008DF0}, +{0x02F9DD, 0x02F9DD, 0x0208DE}, +{0x02F9DE, 0x02F9DE, 0x008ED4}, +{0x02F9DF, 0x02F9DF, 0x008F38}, +{0x02F9E0, 0x02F9E0, 0x0285D2}, +{0x02F9E1, 0x02F9E1, 0x0285ED}, +{0x02F9E2, 0x02F9E2, 0x009094}, +{0x02F9E3, 0x02F9E3, 0x0090F1}, +{0x02F9E4, 0x02F9E4, 0x009111}, +{0x02F9E5, 0x02F9E5, 0x02872E}, +{0x02F9E6, 0x02F9E6, 0x00911B}, +{0x02F9E7, 0x02F9E7, 0x009238}, +{0x02F9E8, 0x02F9E8, 0x0092D7}, +{0x02F9E9, 0x02F9E9, 0x0092D8}, +{0x02F9EA, 0x02F9EA, 0x00927C}, +{0x02F9EB, 0x02F9EB, 0x0093F9}, +{0x02F9EC, 0x02F9EC, 0x009415}, +{0x02F9ED, 0x02F9ED, 0x028BFA}, +{0x02F9EE, 0x02F9EE, 0x00958B}, +{0x02F9EF, 0x02F9EF, 0x004995}, +{0x02F9F0, 0x02F9F0, 0x0095B7}, +{0x02F9F1, 0x02F9F1, 0x028D77}, +{0x02F9F2, 0x02F9F2, 0x0049E6}, +{0x02F9F3, 0x02F9F3, 0x0096C3}, +{0x02F9F4, 0x02F9F4, 0x005DB2}, +{0x02F9F5, 0x02F9F5, 0x009723}, +{0x02F9F6, 0x02F9F6, 0x029145}, +{0x02F9F7, 0x02F9F7, 0x02921A}, +{0x02F9F8, 0x02F9F8, 0x004A6E}, +{0x02F9F9, 0x02F9F9, 0x004A76}, +{0x02F9FA, 0x02F9FA, 0x0097E0}, +{0x02F9FB, 0x02F9FB, 0x02940A}, +{0x02F9FC, 0x02F9FC, 0x004AB2}, +{0x02F9FD, 0x02F9FD, 0x029496}, +{0x02F9FE, 0x02F9FF, 0x00980B}, +{0x02FA00, 0x02FA00, 0x009829}, +{0x02FA01, 0x02FA01, 0x0295B6}, +{0x02FA02, 0x02FA02, 0x0098E2}, +{0x02FA03, 0x02FA03, 0x004B33}, +{0x02FA04, 0x02FA04, 0x009929}, +{0x02FA05, 0x02FA05, 0x0099A7}, +{0x02FA06, 0x02FA06, 0x0099C2}, +{0x02FA07, 0x02FA07, 0x0099FE}, +{0x02FA08, 0x02FA08, 0x004BCE}, +{0x02FA09, 0x02FA09, 0x029B30}, +{0x02FA0A, 0x02FA0A, 0x009B12}, +{0x02FA0B, 0x02FA0B, 0x009C40}, +{0x02FA0C, 0x02FA0C, 0x009CFD}, +{0x02FA0D, 0x02FA0D, 0x004CCE}, +{0x02FA0E, 0x02FA0E, 0x004CED}, +{0x02FA0F, 0x02FA0F, 0x009D67}, +{0x02FA10, 0x02FA10, 0x02A0CE}, +{0x02FA11, 0x02FA11, 0x004CF8}, +{0x02FA12, 0x02FA12, 0x02A105}, +{0x02FA13, 0x02FA13, 0x02A20E}, +{0x02FA14, 0x02FA14, 0x02A291}, +{0x02FA15, 0x02FA15, 0x009EBB}, +{0x02FA16, 0x02FA16, 0x004D56}, +{0x02FA17, 0x02FA17, 0x009EF9}, +{0x02FA18, 0x02FA18, 0x009EFE}, +{0x02FA19, 0x02FA19, 0x009F05}, +{0x02FA1A, 0x02FA1A, 0x009F0F}, +{0x02FA1B, 0x02FA1B, 0x009F16}, +{0x02FA1C, 0x02FA1C, 0x009F3B}, +{0x02FA1D, 0x02FA1D, 0x02A600}, }; -const std::multimap unicode_map_nfd = { -{0x000000C0, 0x00000041}, {0x000000C0, 0x00000300}, {0x000000C1, 0x00000041}, {0x000000C1, 0x00000301}, -{0x000000C2, 0x00000041}, {0x000000C2, 0x00000302}, {0x000000C3, 0x00000041}, {0x000000C3, 0x00000303}, -{0x000000C4, 0x00000041}, {0x000000C4, 0x00000308}, {0x000000C5, 0x00000041}, {0x000000C5, 0x0000030A}, -{0x000000C7, 0x00000043}, {0x000000C7, 0x00000327}, {0x000000C8, 0x00000045}, {0x000000C8, 0x00000300}, -{0x000000C9, 0x00000045}, {0x000000C9, 0x00000301}, {0x000000CA, 0x00000045}, {0x000000CA, 0x00000302}, -{0x000000CB, 0x00000045}, {0x000000CB, 0x00000308}, {0x000000CC, 0x00000049}, {0x000000CC, 0x00000300}, -{0x000000CD, 0x00000049}, {0x000000CD, 0x00000301}, {0x000000CE, 0x00000049}, {0x000000CE, 0x00000302}, -{0x000000CF, 0x00000049}, {0x000000CF, 0x00000308}, {0x000000D1, 0x0000004E}, {0x000000D1, 0x00000303}, -{0x000000D2, 0x0000004F}, {0x000000D2, 0x00000300}, {0x000000D3, 0x0000004F}, {0x000000D3, 0x00000301}, -{0x000000D4, 0x0000004F}, {0x000000D4, 0x00000302}, {0x000000D5, 0x0000004F}, {0x000000D5, 0x00000303}, -{0x000000D6, 0x0000004F}, {0x000000D6, 0x00000308}, {0x000000D9, 0x00000055}, {0x000000D9, 0x00000300}, -{0x000000DA, 0x00000055}, {0x000000DA, 0x00000301}, {0x000000DB, 0x00000055}, {0x000000DB, 0x00000302}, -{0x000000DC, 0x00000055}, {0x000000DC, 0x00000308}, {0x000000DD, 0x00000059}, {0x000000DD, 0x00000301}, -{0x000000E0, 0x00000061}, {0x000000E0, 0x00000300}, {0x000000E1, 0x00000061}, {0x000000E1, 0x00000301}, -{0x000000E2, 0x00000061}, {0x000000E2, 0x00000302}, {0x000000E3, 0x00000061}, {0x000000E3, 0x00000303}, -{0x000000E4, 0x00000061}, {0x000000E4, 0x00000308}, {0x000000E5, 0x00000061}, {0x000000E5, 0x0000030A}, -{0x000000E7, 0x00000063}, {0x000000E7, 0x00000327}, {0x000000E8, 0x00000065}, {0x000000E8, 0x00000300}, -{0x000000E9, 0x00000065}, {0x000000E9, 0x00000301}, {0x000000EA, 0x00000065}, {0x000000EA, 0x00000302}, -{0x000000EB, 0x00000065}, {0x000000EB, 0x00000308}, {0x000000EC, 0x00000069}, {0x000000EC, 0x00000300}, -{0x000000ED, 0x00000069}, {0x000000ED, 0x00000301}, {0x000000EE, 0x00000069}, {0x000000EE, 0x00000302}, -{0x000000EF, 0x00000069}, {0x000000EF, 0x00000308}, {0x000000F1, 0x0000006E}, {0x000000F1, 0x00000303}, -{0x000000F2, 0x0000006F}, {0x000000F2, 0x00000300}, {0x000000F3, 0x0000006F}, {0x000000F3, 0x00000301}, -{0x000000F4, 0x0000006F}, {0x000000F4, 0x00000302}, {0x000000F5, 0x0000006F}, {0x000000F5, 0x00000303}, -{0x000000F6, 0x0000006F}, {0x000000F6, 0x00000308}, {0x000000F9, 0x00000075}, {0x000000F9, 0x00000300}, -{0x000000FA, 0x00000075}, {0x000000FA, 0x00000301}, {0x000000FB, 0x00000075}, {0x000000FB, 0x00000302}, -{0x000000FC, 0x00000075}, {0x000000FC, 0x00000308}, {0x000000FD, 0x00000079}, {0x000000FD, 0x00000301}, -{0x000000FF, 0x00000079}, {0x000000FF, 0x00000308}, {0x00000100, 0x00000041}, {0x00000100, 0x00000304}, -{0x00000101, 0x00000061}, {0x00000101, 0x00000304}, {0x00000102, 0x00000041}, {0x00000102, 0x00000306}, -{0x00000103, 0x00000061}, {0x00000103, 0x00000306}, {0x00000104, 0x00000041}, {0x00000104, 0x00000328}, -{0x00000105, 0x00000061}, {0x00000105, 0x00000328}, {0x00000106, 0x00000043}, {0x00000106, 0x00000301}, -{0x00000107, 0x00000063}, {0x00000107, 0x00000301}, {0x00000108, 0x00000043}, {0x00000108, 0x00000302}, -{0x00000109, 0x00000063}, {0x00000109, 0x00000302}, {0x0000010A, 0x00000043}, {0x0000010A, 0x00000307}, -{0x0000010B, 0x00000063}, {0x0000010B, 0x00000307}, {0x0000010C, 0x00000043}, {0x0000010C, 0x0000030C}, -{0x0000010D, 0x00000063}, {0x0000010D, 0x0000030C}, {0x0000010E, 0x00000044}, {0x0000010E, 0x0000030C}, -{0x0000010F, 0x00000064}, {0x0000010F, 0x0000030C}, {0x00000112, 0x00000045}, {0x00000112, 0x00000304}, -{0x00000113, 0x00000065}, {0x00000113, 0x00000304}, {0x00000114, 0x00000045}, {0x00000114, 0x00000306}, -{0x00000115, 0x00000065}, {0x00000115, 0x00000306}, {0x00000116, 0x00000045}, {0x00000116, 0x00000307}, -{0x00000117, 0x00000065}, {0x00000117, 0x00000307}, {0x00000118, 0x00000045}, {0x00000118, 0x00000328}, -{0x00000119, 0x00000065}, {0x00000119, 0x00000328}, {0x0000011A, 0x00000045}, {0x0000011A, 0x0000030C}, -{0x0000011B, 0x00000065}, {0x0000011B, 0x0000030C}, {0x0000011C, 0x00000047}, {0x0000011C, 0x00000302}, -{0x0000011D, 0x00000067}, {0x0000011D, 0x00000302}, {0x0000011E, 0x00000047}, {0x0000011E, 0x00000306}, -{0x0000011F, 0x00000067}, {0x0000011F, 0x00000306}, {0x00000120, 0x00000047}, {0x00000120, 0x00000307}, -{0x00000121, 0x00000067}, {0x00000121, 0x00000307}, {0x00000122, 0x00000047}, {0x00000122, 0x00000327}, -{0x00000123, 0x00000067}, {0x00000123, 0x00000327}, {0x00000124, 0x00000048}, {0x00000124, 0x00000302}, -{0x00000125, 0x00000068}, {0x00000125, 0x00000302}, {0x00000128, 0x00000049}, {0x00000128, 0x00000303}, -{0x00000129, 0x00000069}, {0x00000129, 0x00000303}, {0x0000012A, 0x00000049}, {0x0000012A, 0x00000304}, -{0x0000012B, 0x00000069}, {0x0000012B, 0x00000304}, {0x0000012C, 0x00000049}, {0x0000012C, 0x00000306}, -{0x0000012D, 0x00000069}, {0x0000012D, 0x00000306}, {0x0000012E, 0x00000049}, {0x0000012E, 0x00000328}, -{0x0000012F, 0x00000069}, {0x0000012F, 0x00000328}, {0x00000130, 0x00000049}, {0x00000130, 0x00000307}, -{0x00000134, 0x0000004A}, {0x00000134, 0x00000302}, {0x00000135, 0x0000006A}, {0x00000135, 0x00000302}, -{0x00000136, 0x0000004B}, {0x00000136, 0x00000327}, {0x00000137, 0x0000006B}, {0x00000137, 0x00000327}, -{0x00000139, 0x0000004C}, {0x00000139, 0x00000301}, {0x0000013A, 0x0000006C}, {0x0000013A, 0x00000301}, -{0x0000013B, 0x0000004C}, {0x0000013B, 0x00000327}, {0x0000013C, 0x0000006C}, {0x0000013C, 0x00000327}, -{0x0000013D, 0x0000004C}, {0x0000013D, 0x0000030C}, {0x0000013E, 0x0000006C}, {0x0000013E, 0x0000030C}, -{0x00000143, 0x0000004E}, {0x00000143, 0x00000301}, {0x00000144, 0x0000006E}, {0x00000144, 0x00000301}, -{0x00000145, 0x0000004E}, {0x00000145, 0x00000327}, {0x00000146, 0x0000006E}, {0x00000146, 0x00000327}, -{0x00000147, 0x0000004E}, {0x00000147, 0x0000030C}, {0x00000148, 0x0000006E}, {0x00000148, 0x0000030C}, -{0x0000014C, 0x0000004F}, {0x0000014C, 0x00000304}, {0x0000014D, 0x0000006F}, {0x0000014D, 0x00000304}, -{0x0000014E, 0x0000004F}, {0x0000014E, 0x00000306}, {0x0000014F, 0x0000006F}, {0x0000014F, 0x00000306}, -{0x00000150, 0x0000004F}, {0x00000150, 0x0000030B}, {0x00000151, 0x0000006F}, {0x00000151, 0x0000030B}, -{0x00000154, 0x00000052}, {0x00000154, 0x00000301}, {0x00000155, 0x00000072}, {0x00000155, 0x00000301}, -{0x00000156, 0x00000052}, {0x00000156, 0x00000327}, {0x00000157, 0x00000072}, {0x00000157, 0x00000327}, -{0x00000158, 0x00000052}, {0x00000158, 0x0000030C}, {0x00000159, 0x00000072}, {0x00000159, 0x0000030C}, -{0x0000015A, 0x00000053}, {0x0000015A, 0x00000301}, {0x0000015B, 0x00000073}, {0x0000015B, 0x00000301}, -{0x0000015C, 0x00000053}, {0x0000015C, 0x00000302}, {0x0000015D, 0x00000073}, {0x0000015D, 0x00000302}, -{0x0000015E, 0x00000053}, {0x0000015E, 0x00000327}, {0x0000015F, 0x00000073}, {0x0000015F, 0x00000327}, -{0x00000160, 0x00000053}, {0x00000160, 0x0000030C}, {0x00000161, 0x00000073}, {0x00000161, 0x0000030C}, -{0x00000162, 0x00000054}, {0x00000162, 0x00000327}, {0x00000163, 0x00000074}, {0x00000163, 0x00000327}, -{0x00000164, 0x00000054}, {0x00000164, 0x0000030C}, {0x00000165, 0x00000074}, {0x00000165, 0x0000030C}, -{0x00000168, 0x00000055}, {0x00000168, 0x00000303}, {0x00000169, 0x00000075}, {0x00000169, 0x00000303}, -{0x0000016A, 0x00000055}, {0x0000016A, 0x00000304}, {0x0000016B, 0x00000075}, {0x0000016B, 0x00000304}, -{0x0000016C, 0x00000055}, {0x0000016C, 0x00000306}, {0x0000016D, 0x00000075}, {0x0000016D, 0x00000306}, -{0x0000016E, 0x00000055}, {0x0000016E, 0x0000030A}, {0x0000016F, 0x00000075}, {0x0000016F, 0x0000030A}, -{0x00000170, 0x00000055}, {0x00000170, 0x0000030B}, {0x00000171, 0x00000075}, {0x00000171, 0x0000030B}, -{0x00000172, 0x00000055}, {0x00000172, 0x00000328}, {0x00000173, 0x00000075}, {0x00000173, 0x00000328}, -{0x00000174, 0x00000057}, {0x00000174, 0x00000302}, {0x00000175, 0x00000077}, {0x00000175, 0x00000302}, -{0x00000176, 0x00000059}, {0x00000176, 0x00000302}, {0x00000177, 0x00000079}, {0x00000177, 0x00000302}, -{0x00000178, 0x00000059}, {0x00000178, 0x00000308}, {0x00000179, 0x0000005A}, {0x00000179, 0x00000301}, -{0x0000017A, 0x0000007A}, {0x0000017A, 0x00000301}, {0x0000017B, 0x0000005A}, {0x0000017B, 0x00000307}, -{0x0000017C, 0x0000007A}, {0x0000017C, 0x00000307}, {0x0000017D, 0x0000005A}, {0x0000017D, 0x0000030C}, -{0x0000017E, 0x0000007A}, {0x0000017E, 0x0000030C}, {0x000001A0, 0x0000004F}, {0x000001A0, 0x0000031B}, -{0x000001A1, 0x0000006F}, {0x000001A1, 0x0000031B}, {0x000001AF, 0x00000055}, {0x000001AF, 0x0000031B}, -{0x000001B0, 0x00000075}, {0x000001B0, 0x0000031B}, {0x000001CD, 0x00000041}, {0x000001CD, 0x0000030C}, -{0x000001CE, 0x00000061}, {0x000001CE, 0x0000030C}, {0x000001CF, 0x00000049}, {0x000001CF, 0x0000030C}, -{0x000001D0, 0x00000069}, {0x000001D0, 0x0000030C}, {0x000001D1, 0x0000004F}, {0x000001D1, 0x0000030C}, -{0x000001D2, 0x0000006F}, {0x000001D2, 0x0000030C}, {0x000001D3, 0x00000055}, {0x000001D3, 0x0000030C}, -{0x000001D4, 0x00000075}, {0x000001D4, 0x0000030C}, {0x000001D5, 0x00000055}, {0x000001D5, 0x00000308}, -{0x000001D5, 0x00000304}, {0x000001D6, 0x00000075}, {0x000001D6, 0x00000308}, {0x000001D6, 0x00000304}, -{0x000001D7, 0x00000055}, {0x000001D7, 0x00000308}, {0x000001D7, 0x00000301}, {0x000001D8, 0x00000075}, -{0x000001D8, 0x00000308}, {0x000001D8, 0x00000301}, {0x000001D9, 0x00000055}, {0x000001D9, 0x00000308}, -{0x000001D9, 0x0000030C}, {0x000001DA, 0x00000075}, {0x000001DA, 0x00000308}, {0x000001DA, 0x0000030C}, -{0x000001DB, 0x00000055}, {0x000001DB, 0x00000308}, {0x000001DB, 0x00000300}, {0x000001DC, 0x00000075}, -{0x000001DC, 0x00000308}, {0x000001DC, 0x00000300}, {0x000001DE, 0x00000041}, {0x000001DE, 0x00000308}, -{0x000001DE, 0x00000304}, {0x000001DF, 0x00000061}, {0x000001DF, 0x00000308}, {0x000001DF, 0x00000304}, -{0x000001E0, 0x00000041}, {0x000001E0, 0x00000307}, {0x000001E0, 0x00000304}, {0x000001E1, 0x00000061}, -{0x000001E1, 0x00000307}, {0x000001E1, 0x00000304}, {0x000001E2, 0x000000C6}, {0x000001E2, 0x00000304}, -{0x000001E3, 0x000000E6}, {0x000001E3, 0x00000304}, {0x000001E6, 0x00000047}, {0x000001E6, 0x0000030C}, -{0x000001E7, 0x00000067}, {0x000001E7, 0x0000030C}, {0x000001E8, 0x0000004B}, {0x000001E8, 0x0000030C}, -{0x000001E9, 0x0000006B}, {0x000001E9, 0x0000030C}, {0x000001EA, 0x0000004F}, 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{0x0002FA17, 0x00009EF9}, {0x0002FA18, 0x00009EFE}, {0x0002FA19, 0x00009F05}, -{0x0002FA1A, 0x00009F0F}, {0x0002FA1B, 0x00009F16}, {0x0002FA1D, 0x0002A600}, -}; diff --git a/unicode-data.h b/unicode-data.h index 3cccf20685429..e27fe1770710a 100644 --- a/unicode-data.h +++ b/unicode-data.h @@ -1,17 +1,20 @@ #pragma once #include -#include -#include #include +#include +#include -extern const std::vector> unicode_ranges_number; -extern const std::vector> unicode_ranges_letter; -extern const std::vector> unicode_ranges_separator; -extern const std::vector> unicode_ranges_whitespace; -extern const std::vector> unicode_ranges_accent_mark; -extern const std::vector> unicode_ranges_punctuation; -extern const std::vector> unicode_ranges_symbol; -extern const std::vector> unicode_ranges_control; -extern const std::multimap unicode_map_nfd; -extern const std::map unicode_map_lowercase; +struct range_nfd { + uint32_t first; + uint32_t last; + uint32_t nfd; +}; + +static const uint32_t MAX_CODEPOINTS = 0x110000; + +extern const std::vector> unicode_ranges_flags; +extern const std::unordered_set unicode_set_whitespace; +extern const std::unordered_map unicode_map_lowercase; +extern const std::unordered_map unicode_map_uppercase; +extern const std::vector unicode_ranges_nfd; diff --git a/unicode.cpp b/unicode.cpp index ca03c49d39c7c..056a4c74172c7 100644 --- a/unicode.cpp +++ b/unicode.cpp @@ -1,4 +1,4 @@ -#include "unicode.h" +#include "unicode.h" #include "unicode-data.h" #include @@ -109,57 +109,49 @@ static uint32_t unicode_cpt_from_utf8(const std::string & utf8, size_t & offset) // return result; //} -static std::unordered_map unicode_cpt_type_map() { - std::unordered_map cpt_types; - for (auto p : unicode_ranges_number) { - for (auto i = p.first; i <= p.second; ++i) { - cpt_types[i] = CODEPOINT_TYPE_NUMBER; - } - } - for (auto p : unicode_ranges_letter) { - for (auto i = p.first; i <= p.second; ++i) { - cpt_types[i] = CODEPOINT_TYPE_LETTER; - } - } - for (auto p : unicode_ranges_separator) { - for (auto i = p.first; i <= p.second; ++i) { - cpt_types[i] = CODEPOINT_TYPE_SEPARATOR; +static std::vector unicode_cpt_flags_array() { + std::vector cpt_flags(MAX_CODEPOINTS, codepoint_flags::UNDEFINED); + + assert (unicode_ranges_flags.front().first == 0); + assert (unicode_ranges_flags.back().first == MAX_CODEPOINTS); + for (size_t i = 1; i < unicode_ranges_flags.size(); ++i) { + const auto range_ini = unicode_ranges_flags[i-1]; // codepoint_ini, flags + const auto range_end = unicode_ranges_flags[i]; // codepoint_end, flags + for (uint32_t cpt = range_ini.first; cpt < range_end.first; ++cpt) { + cpt_flags[cpt] = range_ini.second; } } - for (auto p : unicode_ranges_accent_mark) { - for (auto i = p.first; i <= p.second; ++i) { - cpt_types[i] = CODEPOINT_TYPE_ACCENT_MARK; - } + + for (auto cpt : unicode_set_whitespace) { + cpt_flags[cpt].is_whitespace = true; } - for (auto p : unicode_ranges_punctuation) { - for (auto i = p.first; i <= p.second; ++i) { - cpt_types[i] = CODEPOINT_TYPE_PUNCTUATION; - } + + for (auto p : unicode_map_lowercase) { + cpt_flags[p.second].is_lowercase = true; } - for (auto p : unicode_ranges_symbol) { - for (auto i = p.first; i <= p.second; ++i) { - cpt_types[i] = CODEPOINT_TYPE_SYMBOL; - } + + for (auto p : unicode_map_uppercase) { + cpt_flags[p.second].is_uppercase = true; } - for (auto p : unicode_ranges_control) { - for (auto i = p.first; i <= p.second; ++i) { - cpt_types[i] = CODEPOINT_TYPE_CONTROL; - } + + for (auto &range : unicode_ranges_nfd) { // start, last, nfd + cpt_flags[range.nfd].is_nfd = true; } - return cpt_types; + + return cpt_flags; } static std::unordered_map unicode_byte_to_utf8_map() { std::unordered_map map; - for (int ch = u'!'; ch <= u'~'; ++ch) { + for (int ch = 0x21; ch <= 0x7E; ++ch) { // u'!' to u'~' assert(0 <= ch && ch < 256); map[ch] = unicode_cpt_to_utf8(ch); } - for (int ch = u'¡'; ch <= u'¬'; ++ch) { + for (int ch = 0xA1; ch <= 0xAC; ++ch) { // u'¡' to u'¬' assert(0 <= ch && ch < 256); map[ch] = unicode_cpt_to_utf8(ch); } - for (int ch = u'®'; ch <= u'ÿ'; ++ch) { + for (int ch = 0xAE; ch <= 0xFF; ++ch) { // u'®' to u'ÿ' assert(0 <= ch && ch < 256); map[ch] = unicode_cpt_to_utf8(ch); } @@ -175,15 +167,15 @@ static std::unordered_map unicode_byte_to_utf8_map() { static std::unordered_map unicode_utf8_to_byte_map() { std::unordered_map map; - for (int ch = u'!'; ch <= u'~'; ++ch) { + for (int ch = 0x21; ch <= 0x7E; ++ch) { // u'!' to u'~' assert(0 <= ch && ch < 256); map[unicode_cpt_to_utf8(ch)] = ch; } - for (int ch = u'¡'; ch <= u'¬'; ++ch) { + for (int ch = 0xA1; ch <= 0xAC; ++ch) { // u'¡' to u'¬' assert(0 <= ch && ch < 256); map[unicode_cpt_to_utf8(ch)] = ch; } - for (int ch = u'®'; ch <= u'ÿ'; ++ch) { + for (int ch = 0xAE; ch <= 0xFF; ++ch) { // u'®' to u'ÿ' assert(0 <= ch && ch < 256); map[unicode_cpt_to_utf8(ch)] = ch; } @@ -238,8 +230,9 @@ static std::vector unicode_regex_split_custom_gpt2(const std::string & t return (offset_ini <= pos && pos < offset_end) ? cpts[pos] : 0; }; - auto _get_cpt_type = [&] (const size_t pos) -> int { - return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_type(cpts[pos]) : CODEPOINT_TYPE_UNIDENTIFIED; + auto _get_flags = [&] (const size_t pos) -> codepoint_flags { + static const codepoint_flags undef(codepoint_flags::UNDEFINED); + return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_flags(cpts[pos]) : undef; }; size_t _prev_end = offset_ini; @@ -261,7 +254,7 @@ static std::vector unicode_regex_split_custom_gpt2(const std::string & t for (size_t pos = offset_ini; pos < offset_end; /*pos++*/ ) { const char32_t cpt = _get_cpt(pos); - const int cpt_type = _get_cpt_type(pos); + const auto flags = _get_flags(pos); // regex: 's|'t|'re|'ve|'m|'ll|'d if (cpt == '\'' && pos+1 < offset_end) { @@ -281,39 +274,37 @@ static std::vector unicode_regex_split_custom_gpt2(const std::string & t } } - char32_t cpt2 = (cpt == ' ' ? _get_cpt(pos+1) : cpt); - int cpt2_type = (cpt == ' ' ? _get_cpt_type(pos+1) : cpt_type); + auto flags2 = (cpt == ' ' ? _get_flags(pos+1) : flags); // regex: ?\p{L}+ - if (cpt2_type == CODEPOINT_TYPE_LETTER) { + if (flags2.is_letter) { pos += (cpt == ' '); - while (cpt2_type == CODEPOINT_TYPE_LETTER) { - cpt2_type = _get_cpt_type(++pos); + while (flags2.is_letter) { + flags2 = _get_flags(++pos); } _add_token(pos); continue; } // regex: ?\p{N}+ - if (cpt2_type == CODEPOINT_TYPE_NUMBER) { + if (flags2.is_number) { pos += (cpt == ' '); - while (cpt2_type == CODEPOINT_TYPE_NUMBER) { - cpt2_type = _get_cpt_type(++pos); + while (flags2.is_number) { + flags2 = _get_flags(++pos); } _add_token(pos); continue; } // regex: ?[^\s\p{L}\p{N}]+ - if (!unicode_cpt_is_whitespace(cpt2) && cpt2_type != CODEPOINT_TYPE_LETTER && cpt2_type != CODEPOINT_TYPE_NUMBER && cpt2_type != CODEPOINT_TYPE_UNIDENTIFIED) { + if (!(flags2.is_whitespace || flags2.is_letter || flags2.is_number || flags2.is_undefined)) { pos += (cpt == ' '); - while (!unicode_cpt_is_whitespace(cpt2) && cpt2_type != CODEPOINT_TYPE_LETTER && cpt2_type != CODEPOINT_TYPE_NUMBER && cpt2_type != CODEPOINT_TYPE_UNIDENTIFIED) { - cpt2_type = _get_cpt_type(++pos); - cpt2 = _get_cpt(pos); + while (!(flags2.is_whitespace || flags2.is_letter || flags2.is_number || flags2.is_undefined)) { + flags2 = _get_flags(++pos); } _add_token(pos); continue; } size_t num_whitespaces = 0; - while (unicode_cpt_is_whitespace(_get_cpt(pos+num_whitespaces))) { + while (_get_flags(pos+num_whitespaces).is_whitespace) { num_whitespaces++; } @@ -357,8 +348,9 @@ static std::vector unicode_regex_split_custom_llama3(const std::string & return (offset_ini <= pos && pos < offset_end) ? cpts[pos] : 0; }; - auto _get_cpt_type = [&] (const size_t pos) -> int { - return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_type(cpts[pos]) : CODEPOINT_TYPE_UNIDENTIFIED; + auto _get_flags = [&] (const size_t pos) -> codepoint_flags { + static const codepoint_flags undef(codepoint_flags::UNDEFINED); + return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_flags(cpts[pos]) : undef; }; size_t _prev_end = offset_ini; @@ -380,7 +372,7 @@ static std::vector unicode_regex_split_custom_llama3(const std::string & for (size_t pos = offset_ini; pos < offset_end; /*pos++*/ ) { const char32_t cpt = _get_cpt(pos); - const int cpt_type = _get_cpt_type(pos); + const auto flags = _get_flags(pos); // regex: (?i:'s|'t|'re|'ve|'m|'ll|'d) // case insensitive if (cpt == '\'' && pos+1 < offset_end) { @@ -401,10 +393,10 @@ static std::vector unicode_regex_split_custom_llama3(const std::string & } // regex: [^\r\n\p{L}\p{N}]?\p{L}+ //####FIXME: the first \p{L} is correct? - if (cpt != '\r' && cpt != '\n' && /*cpt_type != CODEPOINT_TYPE_LETTER &&*/ cpt_type != CODEPOINT_TYPE_NUMBER) { - if (cpt_type == CODEPOINT_TYPE_LETTER || _get_cpt_type(pos+1) == CODEPOINT_TYPE_LETTER) { // one or more letters + if (!(cpt == '\r' || cpt == '\n' || /*flags.is_letter |*/ flags.is_number)) { + if (flags.is_letter || _get_flags(pos+1).is_letter) { // one or more letters pos++; - while (_get_cpt_type(pos) == CODEPOINT_TYPE_LETTER) { + while (_get_flags(pos).is_letter) { pos++; } _add_token(pos); @@ -413,9 +405,9 @@ static std::vector unicode_regex_split_custom_llama3(const std::string & } // regex: \p{N}{1,3} - if (cpt_type == CODEPOINT_TYPE_NUMBER) { + if (flags.is_number) { size_t ini = pos; - while (_get_cpt_type(pos) == CODEPOINT_TYPE_NUMBER) { + while (_get_flags(pos).is_number) { if (++pos - ini >= 3 ) { _add_token(pos); ini = pos; @@ -426,14 +418,13 @@ static std::vector unicode_regex_split_custom_llama3(const std::string & } // regex: ?[^\s\p{L}\p{N}]+[\r\n]* - char32_t cpt2 = (cpt == ' ' ? _get_cpt(pos+1) : cpt); - int cpt2_type = (cpt == ' ' ? _get_cpt_type(pos+1) : cpt_type); - if (!unicode_cpt_is_whitespace(cpt2) && cpt2_type != CODEPOINT_TYPE_LETTER && cpt2_type != CODEPOINT_TYPE_NUMBER && cpt2_type != CODEPOINT_TYPE_UNIDENTIFIED) { + auto flags2 = (cpt == ' ' ? _get_flags(pos+1) : flags); + if (!(flags2.is_whitespace || flags2.is_letter || flags2.is_number || flags2.is_undefined)) { pos += (cpt == ' '); - while (!unicode_cpt_is_whitespace(cpt2) && cpt2_type != CODEPOINT_TYPE_LETTER && cpt2_type != CODEPOINT_TYPE_NUMBER && cpt2_type != CODEPOINT_TYPE_UNIDENTIFIED) { - cpt2_type = _get_cpt_type(++pos); - cpt2 = _get_cpt(pos); + while (!(flags2.is_whitespace || flags2.is_letter || flags2.is_number || flags2.is_undefined)) { + flags2 = _get_flags(++pos); } + char32_t cpt2 = _get_cpt(pos); while (cpt2 == '\r' || cpt2 == '\n') { cpt2 = _get_cpt(++pos); } @@ -443,7 +434,7 @@ static std::vector unicode_regex_split_custom_llama3(const std::string & size_t num_whitespaces = 0; size_t last_end_r_or_n = 0; - while (unicode_cpt_is_whitespace(_get_cpt(pos+num_whitespaces))) { + while (_get_flags(pos+num_whitespaces).is_whitespace) { char32_t cpt2 = _get_cpt(pos+num_whitespaces); if (cpt2 == '\r' || cpt2 == '\n') { last_end_r_or_n = pos + num_whitespaces + 1; @@ -589,15 +580,14 @@ std::string unicode_cpt_to_utf8(uint32_t cp) { } std::vector unicode_cpts_normalize_nfd(const std::vector & cpts) { - std::vector result; - result.reserve(cpts.size()); + auto comp = [] (const uint32_t cpt, const range_nfd & range) { + return cpt < range.first; + }; + std::vector result(cpts.size()); for (size_t i = 0; i < cpts.size(); ++i) { - auto it = unicode_map_nfd.find(cpts[i]); - if (it == unicode_map_nfd.end()) { - result.push_back(cpts[i]); - } else { - result.push_back(it->second); - } + const uint32_t cpt = cpts[i]; + auto it = std::upper_bound(unicode_ranges_nfd.cbegin(), unicode_ranges_nfd.cend(), cpt, comp) - 1; + result[i] = (it->first <= cpt && cpt <= it->last) ? it->nfd : cpt; } return result; } @@ -611,31 +601,19 @@ std::vector unicode_cpts_from_utf8(const std::string & utf8) { return result; } -int unicode_cpt_type(uint32_t cp) { - static std::unordered_map cpt_types = unicode_cpt_type_map(); - const auto it = cpt_types.find(cp); - return it == cpt_types.end() ? CODEPOINT_TYPE_UNIDENTIFIED : it->second; +codepoint_flags unicode_cpt_flags(const uint32_t cp) { + static const codepoint_flags undef(codepoint_flags::UNDEFINED); + static const auto cpt_flags = unicode_cpt_flags_array(); + return cp < cpt_flags.size() ? cpt_flags[cp] : undef; } -int unicode_cpt_type(const std::string & utf8) { - if (utf8.length() == 0) { - return CODEPOINT_TYPE_UNIDENTIFIED; +codepoint_flags unicode_cpt_flags(const std::string & utf8) { + static const codepoint_flags undef(codepoint_flags::UNDEFINED); + if (utf8.empty()) { + return undef; // undefined } size_t offset = 0; - return unicode_cpt_type(unicode_cpt_from_utf8(utf8, offset)); -} - -bool unicode_cpt_is_whitespace(uint32_t cp) { - static const std::unordered_set is_whitespace = [] { - std::unordered_set is_whitespace; - for (auto p : unicode_ranges_whitespace) { - for (auto i = p.first; i <= p.second; ++i) { - is_whitespace.insert(i); - } - } - return is_whitespace; - }(); - return (bool)is_whitespace.count(cp); + return unicode_cpt_flags(unicode_cpt_from_utf8(utf8, offset)); } std::string unicode_byte_to_utf8(uint8_t byte) { @@ -656,21 +634,21 @@ char32_t unicode_tolower(char32_t cp) { std::vector unicode_regex_split(const std::string & text, const std::vector & regex_exprs) { // unicode categories static const std::map k_ucat_enum = { - { "\\p{N}", CODEPOINT_TYPE_NUMBER }, - { "\\p{L}", CODEPOINT_TYPE_LETTER }, - { "\\p{P}", CODEPOINT_TYPE_PUNCTUATION }, + { "\\p{N}", codepoint_flags::NUMBER }, + { "\\p{L}", codepoint_flags::LETTER }, + { "\\p{P}", codepoint_flags::PUNCTUATION }, }; static const std::map k_ucat_cpt = { - { CODEPOINT_TYPE_NUMBER, 0xD1 }, - { CODEPOINT_TYPE_LETTER, 0xD2 }, - { CODEPOINT_TYPE_PUNCTUATION, 0xD3 }, + { codepoint_flags::NUMBER, 0xD1 }, + { codepoint_flags::LETTER, 0xD2 }, + { codepoint_flags::PUNCTUATION, 0xD3 }, }; static const std::map k_ucat_map = { - { CODEPOINT_TYPE_NUMBER, "\x30-\x39" }, // 0-9 - { CODEPOINT_TYPE_LETTER, "\x41-\x5A\x61-\x7A" }, // A-Za-z - { CODEPOINT_TYPE_PUNCTUATION, "\x21-\x23\x25-\x2A\x2C-\x2F\x3A-\x3B\x3F-\x40\\\x5B-\\\x5D\x5F\\\x7B\\\x7D" }, // !-#%-*,-/:-;?-@\[-\]_\{\} + { codepoint_flags::NUMBER, "\x30-\x39" }, // 0-9 + { codepoint_flags::LETTER, "\x41-\x5A\x61-\x7A" }, // A-Za-z + { codepoint_flags::PUNCTUATION, "\x21-\x23\x25-\x2A\x2C-\x2F\x3A-\x3B\x3F-\x40\\\x5B-\\\x5D\x5F\\\x7B\\\x7D" }, // !-#%-*,-/:-;?-@\[-\]_\{\} }; // compute collapsed codepoints only if needed by at least one regex @@ -701,10 +679,10 @@ std::vector unicode_regex_split(const std::string & text, const std continue; } - const int cpt_type = unicode_cpt_type(cpts[i]); + const int cpt_flag = unicode_cpt_flags(cpts[i]).category_flag(); - if (k_ucat_cpt.find(cpt_type) != k_ucat_cpt.end()) { - text_collapsed[i] = k_ucat_cpt.at(cpt_type); + if (k_ucat_cpt.find(cpt_flag) != k_ucat_cpt.end()) { + text_collapsed[i] = k_ucat_cpt.at(cpt_flag); } else { text_collapsed[i] = (char) 0xD0; // fallback } diff --git a/unicode.h b/unicode.h index d6a14d470bfc3..7513be4ad0d4f 100644 --- a/unicode.h +++ b/unicode.h @@ -4,24 +4,56 @@ #include #include -#define CODEPOINT_TYPE_UNIDENTIFIED 0 -#define CODEPOINT_TYPE_NUMBER 1 -#define CODEPOINT_TYPE_LETTER 2 -#define CODEPOINT_TYPE_SEPARATOR 3 -#define CODEPOINT_TYPE_ACCENT_MARK 4 -#define CODEPOINT_TYPE_PUNCTUATION 5 -#define CODEPOINT_TYPE_SYMBOL 6 -#define CODEPOINT_TYPE_CONTROL 7 +struct codepoint_flags { + enum { + UNDEFINED = 0x0001, + NUMBER = 0x0002, // regex: \p{N} + LETTER = 0x0004, // regex: \p{L} + SEPARATOR = 0x0008, // regex: \p{Z} + ACCENT_MARK = 0x0010, // regex: \p{M} + PUNCTUATION = 0x0020, // regex: \p{P} + SYMBOL = 0x0040, // regex: \p{S} + CONTROL = 0x0080, // regex: \p{C} + MASK_CATEGORIES = 0x00FF, + }; + + // codepoint type + uint16_t is_undefined : 1; + uint16_t is_number : 1; // regex: \p{N} + uint16_t is_letter : 1; // regex: \p{L} + uint16_t is_separator : 1; // regex: \p{Z} + uint16_t is_accent_mark : 1; // regex: \p{M} + uint16_t is_punctuation : 1; // regex: \p{P} + uint16_t is_symbol : 1; // regex: \p{S} + uint16_t is_control : 1; // regex: \p{C} + // helper flags + uint16_t is_whitespace : 1; // regex: \s + uint16_t is_lowercase : 1; + uint16_t is_uppercase : 1; + uint16_t is_nfd : 1; + + // decode from uint16 + inline codepoint_flags(const uint16_t flags=0) { + *reinterpret_cast(this) = flags; + } + + inline uint16_t as_uint() const { + return *reinterpret_cast(this); + } + + inline uint16_t category_flag() const { + return this->as_uint() & MASK_CATEGORIES; + } +}; + std::string unicode_cpt_to_utf8(uint32_t cp); std::vector unicode_cpts_from_utf8(const std::string & utf8); std::vector unicode_cpts_normalize_nfd(const std::vector & cpts); -int unicode_cpt_type(uint32_t cp); -int unicode_cpt_type(const std::string & utf8); - -bool unicode_cpt_is_whitespace(uint32_t cp); +codepoint_flags unicode_cpt_flags(const uint32_t cp); +codepoint_flags unicode_cpt_flags(const std::string & utf8); std::string unicode_byte_to_utf8(uint8_t byte); uint8_t unicode_utf8_to_byte(const std::string & utf8);