diff --git a/README.md b/README.md index 25dbbd437c6..fb475b097dd 100644 --- a/README.md +++ b/README.md @@ -98,7 +98,7 @@ curl 127.0.0.1:8080/generate_stream \ You can also use [TGI's Messages API](https://huggingface.co/docs/text-generation-inference/en/messages_api) to obtain Open AI Chat Completion API compatible responses. ```bash -curl localhost:3000/v1/chat/completions \ +curl localhost:8080/v1/chat/completions \ -X POST \ -d '{ "model": "tgi", diff --git a/backends/trtllm/src/main.rs b/backends/trtllm/src/main.rs index ec54ccce722..c13c95b9e76 100644 --- a/backends/trtllm/src/main.rs +++ b/backends/trtllm/src/main.rs @@ -54,14 +54,14 @@ struct Args { otlp_service_name: String, #[clap(long, env)] cors_allow_origin: Option>, - #[clap(long, env, default_value_t = false)] - messages_api_enabled: bool, #[clap(default_value = "4", long, env)] max_client_batch_size: usize, #[clap(long, env)] auth_token: Option, #[clap(long, env, help = "Path to the TensorRT-LLM Orchestrator worker")] executor_worker: PathBuf, + #[clap(default_value = "on", long, env)] + usage_stats: usage_stats::UsageStatsLevel, } async fn get_tokenizer( @@ -213,10 +213,10 @@ async fn main() -> Result<(), TensorRtLlmBackendError> { otlp_endpoint, otlp_service_name, cors_allow_origin, - messages_api_enabled, max_client_batch_size, auth_token, executor_worker, + usage_stats, } = args; // Launch Tokio runtime @@ -293,7 +293,6 @@ async fn main() -> Result<(), TensorRtLlmBackendError> { false, None, None, - messages_api_enabled, true, max_client_batch_size, UsageStatsLevel::Off, diff --git a/backends/v2/src/main.rs b/backends/v2/src/main.rs index f53d898e659..ab4b7ce1d97 100644 --- a/backends/v2/src/main.rs +++ b/backends/v2/src/main.rs @@ -44,6 +44,8 @@ struct Args { tokenizer_config_path: Option, #[clap(long, env)] revision: Option, + #[clap(long, env, value_enum)] + trust_remote_code: bool, #[clap(default_value = "2", long, env)] validation_workers: usize, #[clap(long, env)] @@ -63,8 +65,6 @@ struct Args { #[clap(long, env)] ngrok_edge: Option, #[clap(long, env, default_value_t = false)] - messages_api_enabled: bool, - #[clap(long, env, default_value_t = false)] disable_grammar_support: bool, #[clap(default_value = "4", long, env)] max_client_batch_size: usize, @@ -101,6 +101,7 @@ async fn main() -> Result<(), RouterError> { tokenizer_name, tokenizer_config_path, revision, + trust_remote_code, validation_workers, api_key, json_output, @@ -110,7 +111,6 @@ async fn main() -> Result<(), RouterError> { ngrok, ngrok_authtoken, ngrok_edge, - messages_api_enabled, disable_grammar_support, max_client_batch_size, usage_stats, @@ -184,13 +184,13 @@ async fn main() -> Result<(), RouterError> { tokenizer_name, tokenizer_config_path, revision, + trust_remote_code, hostname, port, cors_allow_origin, ngrok, ngrok_authtoken, ngrok_edge, - messages_api_enabled, disable_grammar_support, max_client_batch_size, usage_stats, diff --git a/backends/v3/src/main.rs b/backends/v3/src/main.rs index b4751bd5373..bc4bdb934eb 100644 --- a/backends/v3/src/main.rs +++ b/backends/v3/src/main.rs @@ -44,6 +44,8 @@ struct Args { tokenizer_config_path: Option, #[clap(long, env)] revision: Option, + #[clap(long, env, value_enum)] + trust_remote_code: bool, #[clap(default_value = "2", long, env)] validation_workers: usize, #[clap(long, env)] @@ -63,8 +65,6 @@ struct Args { #[clap(long, env)] ngrok_edge: Option, #[clap(long, env, default_value_t = false)] - messages_api_enabled: bool, - #[clap(long, env, default_value_t = false)] disable_grammar_support: bool, #[clap(default_value = "4", long, env)] max_client_batch_size: usize, @@ -101,6 +101,7 @@ async fn main() -> Result<(), RouterError> { tokenizer_name, tokenizer_config_path, revision, + trust_remote_code, validation_workers, api_key, json_output, @@ -110,7 +111,6 @@ async fn main() -> Result<(), RouterError> { ngrok, ngrok_authtoken, ngrok_edge, - messages_api_enabled, disable_grammar_support, max_client_batch_size, usage_stats, @@ -184,13 +184,13 @@ async fn main() -> Result<(), RouterError> { tokenizer_name, tokenizer_config_path, revision, + trust_remote_code, hostname, port, cors_allow_origin, ngrok, ngrok_authtoken, ngrok_edge, - messages_api_enabled, disable_grammar_support, max_client_batch_size, usage_stats, diff --git a/docs/openapi.json b/docs/openapi.json index d1b60f4d410..e7da2d40c54 100644 --- a/docs/openapi.json +++ b/docs/openapi.json @@ -316,6 +316,98 @@ } } }, + "/invocations": { + "post": { + "tags": [ + "Text Generation Inference" + ], + "summary": "Generate tokens from Sagemaker request", + "operationId": "sagemaker_compatibility", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/SagemakerRequest" + } + } + }, + "required": true + }, + "responses": { + "200": { + "description": "Generated Chat Completion", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/SagemakerResponse" + } + }, + "text/event-stream": { + "schema": { + "$ref": "#/components/schemas/SagemakerStreamResponse" + } + } + } + }, + "422": { + "description": "Input validation error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/ErrorResponse" + }, + "example": { + "error": "Input validation error", + "error_type": "validation" + } + } + } + }, + "424": { + "description": "Generation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/ErrorResponse" + }, + "example": { + "error": "Request failed during generation", + "error_type": "generation" + } + } + } + }, + "429": { + "description": "Model is overloaded", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/ErrorResponse" + }, + "example": { + "error": "Model is overloaded", + "error_type": "overloaded" + } + } + } + }, + "500": { + "description": "Incomplete generation", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/ErrorResponse" + }, + "example": { + "error": "Incomplete generation", + "error_type": "incomplete_generation" + } + } + } + } + } + } + }, "/metrics": { "get": { "tags": [ @@ -1865,6 +1957,45 @@ "type": "string" } }, + "SagemakerRequest": { + "oneOf": [ + { + "$ref": "#/components/schemas/CompatGenerateRequest" + }, + { + "$ref": "#/components/schemas/ChatRequest" + }, + { + "$ref": "#/components/schemas/CompletionRequest" + } + ] + }, + "SagemakerResponse": { + "oneOf": [ + { + "$ref": "#/components/schemas/GenerateResponse" + }, + { + "$ref": "#/components/schemas/ChatCompletion" + }, + { + "$ref": "#/components/schemas/CompletionFinal" + } + ] + }, + "SagemakerStreamResponse": { + "oneOf": [ + { + "$ref": "#/components/schemas/StreamResponse" + }, + { + "$ref": "#/components/schemas/ChatCompletionChunk" + }, + { + "$ref": "#/components/schemas/Chunk" + } + ] + }, "SimpleToken": { "type": "object", "required": [ diff --git a/docs/source/reference/api_reference.md b/docs/source/reference/api_reference.md index 52043c80f8a..45d951bb1e1 100644 --- a/docs/source/reference/api_reference.md +++ b/docs/source/reference/api_reference.md @@ -141,9 +141,7 @@ TGI can be deployed on various cloud providers for scalable and robust text gene ## Amazon SageMaker -To enable the Messages API in Amazon SageMaker you need to set the environment variable `MESSAGES_API_ENABLED=true`. - -This will modify the `/invocations` route to accept Messages dictonaries consisting out of role and content. See the example below on how to deploy Llama with the new Messages API. +Amazon Sagemaker natively supports the message API: ```python import json @@ -161,12 +159,11 @@ except ValueError: hub = { 'HF_MODEL_ID':'HuggingFaceH4/zephyr-7b-beta', 'SM_NUM_GPUS': json.dumps(1), - 'MESSAGES_API_ENABLED': True } # create Hugging Face Model Class huggingface_model = HuggingFaceModel( - image_uri=get_huggingface_llm_image_uri("huggingface",version="1.4.0"), + image_uri=get_huggingface_llm_image_uri("huggingface",version="2.3.2"), env=hub, role=role, ) diff --git a/docs/source/supported_models.md b/docs/source/supported_models.md index 28008bcd1da..ede1fc778f3 100644 --- a/docs/source/supported_models.md +++ b/docs/source/supported_models.md @@ -8,6 +8,7 @@ Text Generation Inference enables serving optimized models. The following sectio - [Llava Next (1.6)](https://huggingface.co/llava-hf/llava-v1.6-vicuna-13b-hf) (Multimodal) - [Llama](https://huggingface.co/collections/meta-llama/llama-31-669fc079a0c406a149a5738f) - [Phi 3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) +- [Granite](https://huggingface.co/ibm-granite/granite-3.0-8b-instruct) - [Gemma](https://huggingface.co/google/gemma-7b) - [PaliGemma](https://huggingface.co/google/paligemma-3b-pt-224) - [Gemma2](https://huggingface.co/collections/google/gemma-2-release-667d6600fd5220e7b967f315) diff --git a/docs/source/usage_statistics.md b/docs/source/usage_statistics.md index a2c406ecc0f..d3878b53ccb 100644 --- a/docs/source/usage_statistics.md +++ b/docs/source/usage_statistics.md @@ -26,7 +26,6 @@ As of release 2.1.2 this is an example of the data collected: "max_top_n_tokens": 5, "max_total_tokens": 2048, "max_waiting_tokens": 20, - "messages_api_enabled": false, "model_config": { "model_type": "Bloom" }, diff --git a/flake.lock b/flake.lock index aacdd30e401..76b4ca2fe38 100644 --- a/flake.lock +++ b/flake.lock @@ -978,15 +978,16 @@ "nixpkgs": "nixpkgs_6" }, "locked": { - "lastModified": 1728381423, - "narHash": "sha256-gpHy1WtlA8ZTd8XmxsdCoDd4Z7DE7co37lH7P+nsADA=", + "lastModified": 1729531056, + "narHash": "sha256-dW9IOA31+j3VS19WAWAmkJW2YCzeVZGqd6HpIJfODtI=", "owner": "huggingface", "repo": "text-generation-inference-nix", - "rev": "93123736c97e9f7bfe825bfaf3d7de0fc9a21a1e", + "rev": "a84a90281a17b15762873845c947e5c78f5a8dd1", "type": "github" }, "original": { "owner": "huggingface", + "ref": "marlin-kernels-0.3.0", "repo": "text-generation-inference-nix", "type": "github" } diff --git a/flake.nix b/flake.nix index edef442f2cb..5c05bfae7fb 100644 --- a/flake.nix +++ b/flake.nix @@ -5,7 +5,7 @@ inputs.nixpkgs.follows = "tgi-nix/nixpkgs"; }; nix-filter.url = "github:numtide/nix-filter"; - tgi-nix.url = "github:huggingface/text-generation-inference-nix"; + tgi-nix.url = "github:huggingface/text-generation-inference-nix/marlin-kernels-0.3.0"; nixpkgs.follows = "tgi-nix/nixpkgs"; flake-utils.url = "github:numtide/flake-utils"; rust-overlay = { @@ -137,6 +137,11 @@ impure = callPackage ./nix/impure-shell.nix { inherit server; }; + impureWithCuda = callPackage ./nix/impure-shell.nix { + inherit server; + withCuda = true; + }; + impure-flash-attn-v1 = callPackage ./nix/impure-shell.nix { server = server.override { flash-attn = python3.pkgs.flash-attn-v1; }; }; diff --git a/integration-tests/models/__snapshots__/test_flash_llama_fp8_kv_cache/test_flash_llama_fp8_kv_cache.json b/integration-tests/models/__snapshots__/test_flash_llama_fp8_kv_cache/test_flash_llama_fp8_kv_cache.json index c55dd593a1d..b82882c00b6 100644 --- a/integration-tests/models/__snapshots__/test_flash_llama_fp8_kv_cache/test_flash_llama_fp8_kv_cache.json +++ b/integration-tests/models/__snapshots__/test_flash_llama_fp8_kv_cache/test_flash_llama_fp8_kv_cache.json @@ -11,27 +11,27 @@ }, { "id": 3923, - "logprob": -5.6328125, + "logprob": -6.1875, "text": "What" }, { "id": 374, - "logprob": -1.2265625, + "logprob": -0.93359375, "text": " is" }, { "id": 5655, - "logprob": -9.1015625, + "logprob": -9.875, "text": " deep" }, { "id": 6975, - "logprob": -1.8085938, + "logprob": -1.1796875, "text": " learning" }, { "id": 30, - "logprob": -1.0439453, + "logprob": -1.75, "text": "?" } ], @@ -39,66 +39,66 @@ "tokens": [ { "id": 18682, - "logprob": -2.1992188, + "logprob": -1.109375, "special": false, "text": " Deep" }, { "id": 6975, - "logprob": -0.079956055, + "logprob": -0.005432129, "special": false, "text": " learning" }, { "id": 374, - "logprob": -0.2763672, + "logprob": -0.028808594, "special": false, "text": " is" }, { "id": 264, - "logprob": -0.37548828, + "logprob": -0.013671875, "special": false, "text": " a" }, { "id": 27084, - "logprob": -1.4628906, + "logprob": -0.69921875, "special": false, "text": " subset" }, { "id": 315, - "logprob": -0.02885437, + "logprob": -0.0005874634, "special": false, "text": " of" }, { "id": 5780, - "logprob": -0.2565918, + "logprob": -0.026855469, "special": false, "text": " machine" }, { "id": 6975, - "logprob": -0.0063438416, + "logprob": -0.00020885468, "special": false, "text": " learning" }, { "id": 430, - "logprob": -1.3056641, + "logprob": -0.17773438, "special": false, "text": " that" }, { - "id": 374, - "logprob": -1.6035156, + "id": 18065, + "logprob": -0.703125, "special": false, - "text": " is" + "text": " involves" } ], "top_tokens": null }, - "generated_text": " Deep learning is a subset of machine learning that is" + "generated_text": " Deep learning is a subset of machine learning that involves" } diff --git a/integration-tests/models/__snapshots__/test_flash_llama_fp8_kv_cache/test_flash_llama_fp8_kv_cache_all_params.json b/integration-tests/models/__snapshots__/test_flash_llama_fp8_kv_cache/test_flash_llama_fp8_kv_cache_all_params.json index d06d6e5662d..8bce3e108d5 100644 --- a/integration-tests/models/__snapshots__/test_flash_llama_fp8_kv_cache/test_flash_llama_fp8_kv_cache_all_params.json +++ b/integration-tests/models/__snapshots__/test_flash_llama_fp8_kv_cache/test_flash_llama_fp8_kv_cache_all_params.json @@ -1,8 +1,8 @@ { "details": { "best_of_sequences": null, - "finish_reason": "eos_token", - "generated_tokens": 3, + "finish_reason": "length", + "generated_tokens": 10, "prefill": [ { "id": 128000, @@ -11,22 +11,22 @@ }, { "id": 374, - "logprob": -22.96875, + "logprob": -18.0, "text": " is" }, { "id": 5655, - "logprob": -10.71875, + "logprob": -11.75, "text": " deep" }, { "id": 6975, - "logprob": -2.6992188, + "logprob": -2.0625, "text": " learning" }, { "id": 30, - "logprob": -4.8398438, + "logprob": -6.0, "text": "?" } ], @@ -34,24 +34,66 @@ "tokens": [ { "id": 720, - "logprob": -0.4411621, + "logprob": 0.0, "special": false, "text": " \n" }, { - "id": 220, - "logprob": -0.35864258, + "id": 34564, + "logprob": -0.11279297, + "special": false, + "text": "Deep" + }, + { + "id": 6975, + "logprob": -0.16015625, "special": false, - "text": " " + "text": " learning" }, { - "id": 128001, + "id": 320, + "logprob": -0.25195312, + "special": false, + "text": " (" + }, + { + "id": 16931, + "logprob": -1.703125, + "special": false, + "text": "DL" + }, + { + "id": 8, "logprob": 0.0, - "special": true, - "text": "<|end_of_text|>" + "special": false, + "text": ")" + }, + { + "id": 374, + "logprob": -1.140625, + "special": false, + "text": " is" + }, + { + "id": 264, + "logprob": 0.0, + "special": false, + "text": " a" + }, + { + "id": 1207, + "logprob": -1.3125, + "special": false, + "text": " sub" + }, + { + "id": 2630, + "logprob": 0.0, + "special": false, + "text": "field" } ], "top_tokens": null }, - "generated_text": "What is deep learning? \n " + "generated_text": "What is deep learning? \nDeep learning (DL) is a subfield" } diff --git a/integration-tests/models/__snapshots__/test_flash_llama_fp8_kv_cache/test_flash_llama_fp8_kv_cache_load.json b/integration-tests/models/__snapshots__/test_flash_llama_fp8_kv_cache/test_flash_llama_fp8_kv_cache_load.json index 46670819f99..c7acee467c6 100644 --- a/integration-tests/models/__snapshots__/test_flash_llama_fp8_kv_cache/test_flash_llama_fp8_kv_cache_load.json +++ b/integration-tests/models/__snapshots__/test_flash_llama_fp8_kv_cache/test_flash_llama_fp8_kv_cache_load.json @@ -12,27 +12,27 @@ }, { "id": 3923, - "logprob": -5.6328125, + "logprob": -6.1875, "text": "What" }, { "id": 374, - "logprob": -1.2265625, + "logprob": -0.93359375, "text": " is" }, { "id": 5655, - "logprob": -9.1015625, + "logprob": -9.875, "text": " deep" }, { "id": 6975, - "logprob": -1.8085938, + "logprob": -1.1796875, "text": " learning" }, { "id": 30, - "logprob": -1.0439453, + "logprob": -1.75, "text": "?" } ], @@ -40,68 +40,68 @@ "tokens": [ { "id": 18682, - "logprob": -2.1992188, + "logprob": -1.109375, "special": false, "text": " Deep" }, { "id": 6975, - "logprob": -0.07897949, + "logprob": -0.0047912598, "special": false, "text": " learning" }, { "id": 374, - "logprob": -0.27734375, + "logprob": -0.025512695, "special": false, "text": " is" }, { "id": 264, - "logprob": -0.37402344, + "logprob": -0.012145996, "special": false, "text": " a" }, { "id": 27084, - "logprob": -1.4511719, + "logprob": -0.72265625, "special": false, "text": " subset" }, { "id": 315, - "logprob": -0.02909851, + "logprob": -0.0005760193, "special": false, "text": " of" }, { "id": 5780, - "logprob": -0.25854492, + "logprob": -0.02722168, "special": false, "text": " machine" }, { "id": 6975, - "logprob": -0.0061798096, + "logprob": -0.00023651123, "special": false, "text": " learning" }, { "id": 430, - "logprob": -1.3046875, + "logprob": -0.17285156, "special": false, "text": " that" }, { - "id": 374, - "logprob": -1.5537109, + "id": 18065, + "logprob": -0.703125, "special": false, - "text": " is" + "text": " involves" } ], "top_tokens": null }, - "generated_text": " Deep learning is a subset of machine learning that is" + "generated_text": " Deep learning is a subset of machine learning that involves" }, { "details": { @@ -116,27 +116,27 @@ }, { "id": 3923, - "logprob": -5.6328125, + "logprob": -6.21875, "text": "What" }, { "id": 374, - "logprob": -1.2265625, + "logprob": -0.95703125, "text": " is" }, { "id": 5655, - "logprob": -9.1015625, + "logprob": -9.9375, "text": " deep" }, { "id": 6975, - "logprob": -1.8085938, + "logprob": -1.1328125, "text": " learning" }, { "id": 30, - "logprob": -1.0439453, + "logprob": -1.75, "text": "?" } ], @@ -144,68 +144,68 @@ "tokens": [ { "id": 18682, - "logprob": -2.1992188, + "logprob": -1.1796875, "special": false, "text": " Deep" }, { "id": 6975, - "logprob": -0.07897949, + "logprob": -0.005432129, "special": false, "text": " learning" }, { "id": 374, - "logprob": -0.27734375, + "logprob": -0.02758789, "special": false, "text": " is" }, { "id": 264, - "logprob": -0.37402344, + "logprob": -0.013366699, "special": false, "text": " a" }, { "id": 27084, - "logprob": -1.4511719, + "logprob": -0.6953125, "special": false, "text": " subset" }, { "id": 315, - "logprob": -0.02909851, + "logprob": -0.0004863739, "special": false, "text": " of" }, { "id": 5780, - "logprob": -0.25854492, + "logprob": -0.02709961, "special": false, "text": " machine" }, { "id": 6975, - "logprob": -0.0061798096, + "logprob": -0.00022506714, "special": false, "text": " learning" }, { "id": 430, - "logprob": -1.3046875, + "logprob": -0.19726562, "special": false, "text": " that" }, { - "id": 374, - "logprob": -1.5537109, + "id": 18065, + "logprob": -0.77734375, "special": false, - "text": " is" + "text": " involves" } ], "top_tokens": null }, - "generated_text": " Deep learning is a subset of machine learning that is" + "generated_text": " Deep learning is a subset of machine learning that involves" }, { "details": { @@ -220,27 +220,27 @@ }, { "id": 3923, - "logprob": -5.6328125, + "logprob": -6.21875, "text": "What" }, { "id": 374, - "logprob": -1.2265625, + "logprob": -0.95703125, "text": " is" }, { "id": 5655, - "logprob": -9.1015625, + "logprob": -9.9375, "text": " deep" }, { "id": 6975, - "logprob": -1.8085938, + "logprob": -1.1328125, "text": " learning" }, { "id": 30, - "logprob": -1.0439453, + "logprob": -1.75, "text": "?" } ], @@ -248,68 +248,68 @@ "tokens": [ { "id": 18682, - "logprob": -2.1992188, + "logprob": -1.1796875, "special": false, "text": " Deep" }, { "id": 6975, - "logprob": -0.07897949, + "logprob": -0.005432129, "special": false, "text": " learning" }, { "id": 374, - "logprob": -0.27734375, + "logprob": -0.02758789, "special": false, "text": " is" }, { "id": 264, - "logprob": -0.37402344, + "logprob": -0.013366699, "special": false, "text": " a" }, { "id": 27084, - "logprob": -1.4511719, + "logprob": -0.6953125, "special": false, "text": " subset" }, { "id": 315, - "logprob": -0.02909851, + "logprob": -0.0004863739, "special": false, "text": " of" }, { "id": 5780, - "logprob": -0.25854492, + "logprob": -0.02709961, "special": false, "text": " machine" }, { "id": 6975, - "logprob": -0.0061798096, + "logprob": -0.00022506714, "special": false, "text": " learning" }, { "id": 430, - "logprob": -1.3046875, + "logprob": -0.19726562, "special": false, "text": " that" }, { - "id": 374, - "logprob": -1.5537109, + "id": 18065, + "logprob": -0.77734375, "special": false, - "text": " is" + "text": " involves" } ], "top_tokens": null }, - "generated_text": " Deep learning is a subset of machine learning that is" + "generated_text": " Deep learning is a subset of machine learning that involves" }, { "details": { @@ -324,27 +324,27 @@ }, { "id": 3923, - "logprob": -5.6328125, + "logprob": -6.21875, "text": "What" }, { "id": 374, - "logprob": -1.2265625, + "logprob": -0.95703125, "text": " is" }, { "id": 5655, - "logprob": -9.1015625, + "logprob": -9.9375, "text": " deep" }, { "id": 6975, - "logprob": -1.8085938, + "logprob": -1.1328125, "text": " learning" }, { "id": 30, - "logprob": -1.0439453, + "logprob": -1.75, "text": "?" } ], @@ -352,67 +352,67 @@ "tokens": [ { "id": 18682, - "logprob": -2.1992188, + "logprob": -1.1796875, "special": false, "text": " Deep" }, { "id": 6975, - "logprob": -0.07897949, + "logprob": -0.005432129, "special": false, "text": " learning" }, { "id": 374, - "logprob": -0.27734375, + "logprob": -0.02758789, "special": false, "text": " is" }, { "id": 264, - "logprob": -0.37402344, + "logprob": -0.013366699, "special": false, "text": " a" }, { "id": 27084, - "logprob": -1.4511719, + "logprob": -0.6953125, "special": false, "text": " subset" }, { "id": 315, - "logprob": -0.02909851, + "logprob": -0.0004863739, "special": false, "text": " of" }, { "id": 5780, - "logprob": -0.25854492, + "logprob": -0.02709961, "special": false, "text": " machine" }, { "id": 6975, - "logprob": -0.0061798096, + "logprob": -0.00022506714, "special": false, "text": " learning" }, { "id": 430, - "logprob": -1.3046875, + "logprob": -0.19726562, "special": false, "text": " that" }, { - "id": 374, - "logprob": -1.5537109, + "id": 18065, + "logprob": -0.77734375, "special": false, - "text": " is" + "text": " involves" } ], "top_tokens": null }, - "generated_text": " Deep learning is a subset of machine learning that is" + "generated_text": " Deep learning is a subset of machine learning that involves" } ] diff --git a/integration-tests/models/__snapshots__/test_flash_mixtral_gptq/test_flash_mixtral_gptq.json b/integration-tests/models/__snapshots__/test_flash_mixtral_gptq/test_flash_mixtral_gptq.json index 993bdaddc02..b835bf0752d 100644 --- a/integration-tests/models/__snapshots__/test_flash_mixtral_gptq/test_flash_mixtral_gptq.json +++ b/integration-tests/models/__snapshots__/test_flash_mixtral_gptq/test_flash_mixtral_gptq.json @@ -10,80 +10,95 @@ "text": "" }, { - "id": 3735, - "logprob": -11.0078125, - "text": "Test" + "id": 1824, + "logprob": -9.2890625, + "text": "What" }, { - "id": 2159, - "logprob": -13.59375, - "text": "request" + "id": 349, + "logprob": -1.1503906, + "text": "is" + }, + { + "id": 3534, + "logprob": -9.5859375, + "text": "deep" + }, + { + "id": 5168, + "logprob": -1.3945312, + "text": "learning" + }, + { + "id": 28804, + "logprob": -0.4555664, + "text": "?" } ], "seed": null, "tokens": [ { "id": 13, - "logprob": -1.7089844, + "logprob": -0.6953125, "special": false, "text": "\n" }, { "id": 13, - "logprob": -0.68847656, + "logprob": -0.4777832, "special": false, "text": "\n" }, { - "id": 28771, - "logprob": -1.9394531, + "id": 23229, + "logprob": -0.13256836, "special": false, - "text": "#" + "text": "Deep" }, { - "id": 3735, - "logprob": -2.8808594, + "id": 5168, + "logprob": -0.023849487, "special": false, - "text": " Test" + "text": " learning" }, { - "id": 2159, - "logprob": -0.37280273, + "id": 349, + "logprob": -0.13977051, "special": false, - "text": " request" + "text": " is" }, { - "id": 13, - "logprob": -0.26098633, + "id": 264, + "logprob": -0.14489746, "special": false, - "text": "\n" + "text": " a" }, { - "id": 13, - "logprob": -0.0017137527, + "id": 19804, + "logprob": -0.63183594, "special": false, - "text": "\n" + "text": " subset" }, { - "id": 1064, - "logprob": -2.2695312, + "id": 302, + "logprob": -0.010314941, "special": false, - "text": "##" + "text": " of" }, { - "id": 3735, - "logprob": -1.9238281, + "id": 5599, + "logprob": -0.0635376, "special": false, - "text": " Test" + "text": " machine" }, { - "id": 2159, - "logprob": -0.48828125, + "id": 5168, + "logprob": -0.0028572083, "special": false, - "text": " request" + "text": " learning" } ], "top_tokens": null }, - "generated_text": "\n\n# Test request\n\n## Test request" + "generated_text": "\n\nDeep learning is a subset of machine learning" } diff --git a/integration-tests/models/__snapshots__/test_flash_mixtral_gptq/test_flash_mixtral_gptq_all_params.json b/integration-tests/models/__snapshots__/test_flash_mixtral_gptq/test_flash_mixtral_gptq_all_params.json index 94411eefb40..77c8859907e 100644 --- a/integration-tests/models/__snapshots__/test_flash_mixtral_gptq/test_flash_mixtral_gptq_all_params.json +++ b/integration-tests/models/__snapshots__/test_flash_mixtral_gptq/test_flash_mixtral_gptq_all_params.json @@ -10,80 +10,90 @@ "text": "" }, { - "id": 3735, - "logprob": -11.0078125, - "text": "Test" + "id": 349, + "logprob": -12.0546875, + "text": "is" }, { - "id": 2159, - "logprob": -13.59375, - "text": "request" + "id": 3534, + "logprob": -10.53125, + "text": "deep" + }, + { + "id": 5168, + "logprob": -2.71875, + "text": "learning" + }, + { + "id": 28804, + "logprob": -5.0078125, + "text": "?" } ], "seed": 0, "tokens": [ { "id": 13, - "logprob": -0.34838867, + "logprob": 0.0, "special": false, "text": "\n" }, { - "id": 13940, - "logprob": -0.38916016, + "id": 23229, + "logprob": -0.18237305, "special": false, - "text": "``" + "text": "Deep" }, { - "id": 28832, + "id": 17504, "logprob": 0.0, "special": false, - "text": "`" + "text": " Learning" }, { - "id": 3371, - "logprob": -1.2529297, + "id": 349, + "logprob": 0.0, "special": false, - "text": "json" + "text": " is" }, { - "id": 13, + "id": 264, "logprob": 0.0, "special": false, - "text": "\n" + "text": " a" }, { - "id": 28751, + "id": 19804, "logprob": 0.0, "special": false, - "text": "{" + "text": " subset" }, { - "id": 13, + "id": 302, "logprob": 0.0, "special": false, - "text": "\n" + "text": " of" }, { - "id": 2287, - "logprob": 0.0, + "id": 13253, + "logprob": -0.6040039, "special": false, - "text": " " + "text": " Machine" }, { - "id": 345, + "id": 17504, "logprob": 0.0, "special": false, - "text": " \"" + "text": " Learning" }, { - "id": 3134, - "logprob": -0.640625, + "id": 28725, + "logprob": -0.11621094, "special": false, - "text": "request" + "text": "," } ], "top_tokens": null }, - "generated_text": "Test request\n```json\n{\n \"request" + "generated_text": "What is deep learning?\nDeep Learning is a subset of Machine Learning," } diff --git a/integration-tests/models/__snapshots__/test_flash_mixtral_gptq/test_flash_mixtral_gptq_load.json b/integration-tests/models/__snapshots__/test_flash_mixtral_gptq/test_flash_mixtral_gptq_load.json index 19e306a38f1..959e3c5578e 100644 --- a/integration-tests/models/__snapshots__/test_flash_mixtral_gptq/test_flash_mixtral_gptq_load.json +++ b/integration-tests/models/__snapshots__/test_flash_mixtral_gptq/test_flash_mixtral_gptq_load.json @@ -11,82 +11,97 @@ "text": "" }, { - "id": 3735, - "logprob": -11.0078125, - "text": "Test" + "id": 1824, + "logprob": -9.2890625, + "text": "What" }, { - "id": 2159, - "logprob": -13.59375, - "text": "request" + "id": 349, + "logprob": -1.1503906, + "text": "is" + }, + { + "id": 3534, + "logprob": -9.5859375, + "text": "deep" + }, + { + "id": 5168, + "logprob": -1.3945312, + "text": "learning" + }, + { + "id": 28804, + "logprob": -0.4555664, + "text": "?" } ], "seed": null, "tokens": [ { "id": 13, - "logprob": -1.7089844, + "logprob": -0.6953125, "special": false, "text": "\n" }, { "id": 13, - "logprob": -0.68847656, + "logprob": -0.4777832, "special": false, "text": "\n" }, { - "id": 28771, - "logprob": -1.9394531, + "id": 23229, + "logprob": -0.13232422, "special": false, - "text": "#" + "text": "Deep" }, { - "id": 3735, - "logprob": -2.8828125, + "id": 5168, + "logprob": -0.023834229, "special": false, - "text": " Test" + "text": " learning" }, { - "id": 2159, - "logprob": -0.37329102, + "id": 349, + "logprob": -0.13977051, "special": false, - "text": " request" + "text": " is" }, { - "id": 13, - "logprob": -0.2602539, + "id": 264, + "logprob": -0.14416504, "special": false, - "text": "\n" + "text": " a" }, { - "id": 13, - "logprob": -0.0017185211, + "id": 19804, + "logprob": -0.63183594, "special": false, - "text": "\n" + "text": " subset" }, { - "id": 1064, - "logprob": -2.2753906, + "id": 302, + "logprob": -0.010223389, "special": false, - "text": "##" + "text": " of" }, { - "id": 3735, - "logprob": -1.9316406, + "id": 5599, + "logprob": -0.064208984, "special": false, - "text": " Test" + "text": " machine" }, { - "id": 2159, - "logprob": -0.48217773, + "id": 5168, + "logprob": -0.0028266907, "special": false, - "text": " request" + "text": " learning" } ], "top_tokens": null }, - "generated_text": "\n\n# Test request\n\n## Test request" + "generated_text": "\n\nDeep learning is a subset of machine learning" }, { "details": { @@ -100,82 +115,97 @@ "text": "" }, { - "id": 3735, - "logprob": -11.0078125, - "text": "Test" + "id": 1824, + "logprob": -9.2890625, + "text": "What" }, { - "id": 2159, - "logprob": -13.59375, - "text": "request" + "id": 349, + "logprob": -1.1425781, + "text": "is" + }, + { + "id": 3534, + "logprob": -9.59375, + "text": "deep" + }, + { + "id": 5168, + "logprob": -1.390625, + "text": "learning" + }, + { + "id": 28804, + "logprob": -0.45532227, + "text": "?" } ], "seed": null, "tokens": [ { "id": 13, - "logprob": -1.7089844, + "logprob": -0.6953125, "special": false, "text": "\n" }, { "id": 13, - "logprob": -0.68847656, + "logprob": -0.48339844, "special": false, "text": "\n" }, { - "id": 28771, - "logprob": -1.9394531, + "id": 23229, + "logprob": -0.13256836, "special": false, - "text": "#" + "text": "Deep" }, { - "id": 3735, - "logprob": -2.8828125, + "id": 5168, + "logprob": -0.02420044, "special": false, - "text": " Test" + "text": " learning" }, { - "id": 2159, - "logprob": -0.37329102, + "id": 349, + "logprob": -0.13977051, "special": false, - "text": " request" + "text": " is" }, { - "id": 13, - "logprob": -0.2602539, + "id": 264, + "logprob": -0.14501953, "special": false, - "text": "\n" + "text": " a" }, { - "id": 13, - "logprob": -0.0017185211, + "id": 19804, + "logprob": -0.63134766, "special": false, - "text": "\n" + "text": " subset" }, { - "id": 1064, - "logprob": -2.2753906, + "id": 302, + "logprob": -0.010223389, "special": false, - "text": "##" + "text": " of" }, { - "id": 3735, - "logprob": -1.9316406, + "id": 5599, + "logprob": -0.06427002, "special": false, - "text": " Test" + "text": " machine" }, { - "id": 2159, - "logprob": -0.48217773, + "id": 5168, + "logprob": -0.002817154, "special": false, - "text": " request" + "text": " learning" } ], "top_tokens": null }, - "generated_text": "\n\n# Test request\n\n## Test request" + "generated_text": "\n\nDeep learning is a subset of machine learning" }, { "details": { @@ -189,82 +219,97 @@ "text": "" }, { - "id": 3735, - "logprob": -11.0078125, - "text": "Test" + "id": 1824, + "logprob": -9.2890625, + "text": "What" }, { - "id": 2159, - "logprob": -13.59375, - "text": "request" + "id": 349, + "logprob": -1.1425781, + "text": "is" + }, + { + "id": 3534, + "logprob": -9.59375, + "text": "deep" + }, + { + "id": 5168, + "logprob": -1.390625, + "text": "learning" + }, + { + "id": 28804, + "logprob": -0.45532227, + "text": "?" } ], "seed": null, "tokens": [ { "id": 13, - "logprob": -1.7089844, + "logprob": -0.6953125, "special": false, "text": "\n" }, { "id": 13, - "logprob": -0.68847656, + "logprob": -0.48339844, "special": false, "text": "\n" }, { - "id": 28771, - "logprob": -1.9394531, + "id": 23229, + "logprob": -0.13256836, "special": false, - "text": "#" + "text": "Deep" }, { - "id": 3735, - "logprob": -2.8828125, + "id": 5168, + "logprob": -0.02420044, "special": false, - "text": " Test" + "text": " learning" }, { - "id": 2159, - "logprob": -0.37329102, + "id": 349, + "logprob": -0.13977051, "special": false, - "text": " request" + "text": " is" }, { - "id": 13, - "logprob": -0.2602539, + "id": 264, + "logprob": -0.14501953, "special": false, - "text": "\n" + "text": " a" }, { - "id": 13, - "logprob": -0.0017185211, + "id": 19804, + "logprob": -0.63134766, "special": false, - "text": "\n" + "text": " subset" }, { - "id": 1064, - "logprob": -2.2753906, + "id": 302, + "logprob": -0.010223389, "special": false, - "text": "##" + "text": " of" }, { - "id": 3735, - "logprob": -1.9316406, + "id": 5599, + "logprob": -0.06427002, "special": false, - "text": " Test" + "text": " machine" }, { - "id": 2159, - "logprob": -0.48217773, + "id": 5168, + "logprob": -0.002817154, "special": false, - "text": " request" + "text": " learning" } ], "top_tokens": null }, - "generated_text": "\n\n# Test request\n\n## Test request" + "generated_text": "\n\nDeep learning is a subset of machine learning" }, { "details": { @@ -278,81 +323,96 @@ "text": "" }, { - "id": 3735, - "logprob": -11.0078125, - "text": "Test" + "id": 1824, + "logprob": -9.2890625, + "text": "What" }, { - "id": 2159, - "logprob": -13.59375, - "text": "request" + "id": 349, + "logprob": -1.1425781, + "text": "is" + }, + { + "id": 3534, + "logprob": -9.59375, + "text": "deep" + }, + { + "id": 5168, + "logprob": -1.390625, + "text": "learning" + }, + { + "id": 28804, + "logprob": -0.45532227, + "text": "?" } ], "seed": null, "tokens": [ { "id": 13, - "logprob": -1.7089844, + "logprob": -0.6953125, "special": false, "text": "\n" }, { "id": 13, - "logprob": -0.68847656, + "logprob": -0.48339844, "special": false, "text": "\n" }, { - "id": 28771, - "logprob": -1.9394531, + "id": 23229, + "logprob": -0.13256836, "special": false, - "text": "#" + "text": "Deep" }, { - "id": 3735, - "logprob": -2.8828125, + "id": 5168, + "logprob": -0.02420044, "special": false, - "text": " Test" + "text": " learning" }, { - "id": 2159, - "logprob": -0.37329102, + "id": 349, + "logprob": -0.13977051, "special": false, - "text": " request" + "text": " is" }, { - "id": 13, - "logprob": -0.2602539, + "id": 264, + "logprob": -0.14501953, "special": false, - "text": "\n" + "text": " a" }, { - "id": 13, - "logprob": -0.0017185211, + "id": 19804, + "logprob": -0.63134766, "special": false, - "text": "\n" + "text": " subset" }, { - "id": 1064, - "logprob": -2.2753906, + "id": 302, + "logprob": -0.010223389, "special": false, - "text": "##" + "text": " of" }, { - "id": 3735, - "logprob": -1.9316406, + "id": 5599, + "logprob": -0.06427002, "special": false, - "text": " Test" + "text": " machine" }, { - "id": 2159, - "logprob": -0.48217773, + "id": 5168, + "logprob": -0.002817154, "special": false, - "text": " request" + "text": " learning" } ], "top_tokens": null }, - "generated_text": "\n\n# Test request\n\n## Test request" + "generated_text": "\n\nDeep learning is a subset of machine learning" } ] diff --git a/integration-tests/models/__snapshots__/test_flash_phi35_moe/test_flash_phi35_moe.json b/integration-tests/models/__snapshots__/test_flash_phi35_moe/test_flash_phi35_moe.json index 0d6dca31de8..cfabe3c65cd 100644 --- a/integration-tests/models/__snapshots__/test_flash_phi35_moe/test_flash_phi35_moe.json +++ b/integration-tests/models/__snapshots__/test_flash_phi35_moe/test_flash_phi35_moe.json @@ -11,32 +11,32 @@ }, { "id": 338, - "logprob": -0.7133789, + "logprob": -0.6201172, "text": "is" }, { "id": 16030, - "logprob": -13.9296875, + "logprob": -13.6484375, "text": "gradient" }, { "id": 26815, - "logprob": -0.048919678, + "logprob": -0.003894806, "text": "descent" }, { "id": 29973, - "logprob": -3.0078125, + "logprob": -2.6386719, "text": "?" }, { "id": 13, - "logprob": -2.8105469, + "logprob": -6.46875, "text": "\n" }, { "id": 13, - "logprob": -0.84521484, + "logprob": -6.6875, "text": "\n" } ], @@ -44,66 +44,66 @@ "tokens": [ { "id": 25584, - "logprob": -0.017028809, + "logprob": -0.008979797, "special": false, "text": "Grad" }, { "id": 993, - "logprob": -0.0027313232, + "logprob": -8.34465e-07, "special": false, "text": "ient" }, { "id": 26815, - "logprob": -0.023254395, + "logprob": -0.0009407997, "special": false, "text": " descent" }, { "id": 338, - "logprob": -2.0623207e-05, + "logprob": -0.0003838539, "special": false, "text": " is" }, { - "id": 263, - "logprob": -0.5361328, + "id": 385, + "logprob": -0.24499512, "special": false, - "text": " a" + "text": " an" }, { - "id": 937, - "logprob": -0.17578125, + "id": 13883, + "logprob": -0.010406494, "special": false, - "text": " first" + "text": " optimization" }, { - "id": 29899, - "logprob": 0.0, + "id": 5687, + "logprob": -0.00024354458, "special": false, - "text": "-" + "text": " algorithm" }, { - "id": 2098, - "logprob": -0.00011539459, + "id": 15574, + "logprob": -0.6582031, "special": false, - "text": "order" + "text": " commonly" }, { - "id": 13883, - "logprob": -0.47436523, + "id": 1304, + "logprob": -0.00092840195, "special": false, - "text": " optimization" + "text": " used" }, { - "id": 5687, - "logprob": -0.00027680397, + "id": 297, + "logprob": -0.19470215, "special": false, - "text": " algorithm" + "text": " in" } ], "top_tokens": null }, - "generated_text": "Gradient descent is a first-order optimization algorithm" + "generated_text": "Gradient descent is an optimization algorithm commonly used in" } diff --git a/integration-tests/models/__snapshots__/test_flash_phi35_moe/test_flash_phi35_moe_all_params.json b/integration-tests/models/__snapshots__/test_flash_phi35_moe/test_flash_phi35_moe_all_params.json index 38b803353d2..b524859fdc7 100644 --- a/integration-tests/models/__snapshots__/test_flash_phi35_moe/test_flash_phi35_moe_all_params.json +++ b/integration-tests/models/__snapshots__/test_flash_phi35_moe/test_flash_phi35_moe_all_params.json @@ -5,95 +5,95 @@ "generated_tokens": 10, "prefill": [ { - "id": 16030, + "id": 338, "logprob": null, + "text": "is" + }, + { + "id": 16030, + "logprob": -13.328125, "text": "gradient" }, { "id": 26815, - "logprob": -6.4960938, + "logprob": -0.24023438, "text": "descent" }, { "id": 29973, - "logprob": -5.1484375, + "logprob": -3.1386719, "text": "?" }, { "id": 13, - "logprob": -4.0351562, - "text": "\n" - }, - { - "id": 13, - "logprob": -5.2265625, + "logprob": -3.0878906, "text": "\n" } ], "seed": 0, "tokens": [ { - "id": 10994, - "logprob": -1.1542969, + "id": 25584, + "logprob": 0.0, "special": false, - "text": "Hello" + "text": "Grad" }, { - "id": 29991, + "id": 993, "logprob": 0.0, "special": false, - "text": "!" + "text": "ient" }, { - "id": 739, + "id": 2726, "logprob": 0.0, "special": false, - "text": " It" + "text": " Des" }, { - "id": 2444, - "logprob": -0.42260742, + "id": 1760, + "logprob": 0.0, "special": false, - "text": " seems" + "text": "cent" }, { - "id": 366, - "logprob": 0.0, + "id": 313, + "logprob": -0.12322998, "special": false, - "text": " you" + "text": " (" }, { - "id": 29915, + "id": 29954, "logprob": 0.0, "special": false, - "text": "'" + "text": "G" }, { - "id": 276, - "logprob": -0.9838867, + "id": 29928, + "logprob": 0.0, "special": false, - "text": "re" + "text": "D" }, { - "id": 3211, + "id": 29897, "logprob": 0.0, "special": false, - "text": " address" + "text": ")" }, { - "id": 292, - "logprob": 0.0, + "id": 338, + "logprob": -0.6040039, "special": false, - "text": "ing" + "text": " is" }, { - "id": 263, - "logprob": -0.15124512, + "id": 385, + "logprob": -0.1796875, "special": false, - "text": " a" + "text": " an" } ], "top_tokens": null }, - "generated_text": "What is gradient descent?\n\nHello! It seems you're addressing a" + "generated_text": "What is gradient descent?\nGradient Descent (GD) is an" } diff --git a/integration-tests/models/__snapshots__/test_flash_phi35_moe/test_flash_phi35_moe_load.json b/integration-tests/models/__snapshots__/test_flash_phi35_moe/test_flash_phi35_moe_load.json index f1f81152c46..2c977d8b2be 100644 --- a/integration-tests/models/__snapshots__/test_flash_phi35_moe/test_flash_phi35_moe_load.json +++ b/integration-tests/models/__snapshots__/test_flash_phi35_moe/test_flash_phi35_moe_load.json @@ -12,32 +12,32 @@ }, { "id": 338, - "logprob": -0.7133789, + "logprob": -0.6201172, "text": "is" }, { "id": 16030, - "logprob": -13.9296875, + "logprob": -13.6484375, "text": "gradient" }, { "id": 26815, - "logprob": -0.048919678, + "logprob": -0.003894806, "text": "descent" }, { "id": 29973, - "logprob": -3.0078125, + "logprob": -2.6386719, "text": "?" }, { "id": 13, - "logprob": -2.8105469, + "logprob": -6.46875, "text": "\n" }, { "id": 13, - "logprob": -0.84521484, + "logprob": -6.6875, "text": "\n" } ], @@ -45,68 +45,68 @@ "tokens": [ { "id": 25584, - "logprob": -0.017028809, + "logprob": -0.008979797, "special": false, "text": "Grad" }, { "id": 993, - "logprob": -0.0028476715, + "logprob": -8.34465e-07, "special": false, "text": "ient" }, { "id": 26815, - "logprob": -0.023971558, + "logprob": -0.00097084045, "special": false, "text": " descent" }, { "id": 338, - "logprob": -2.0384789e-05, + "logprob": -0.0003838539, "special": false, "text": " is" }, { - "id": 263, - "logprob": -0.5229492, + "id": 385, + "logprob": -0.23840332, "special": false, - "text": " a" + "text": " an" }, { - "id": 937, - "logprob": -0.17602539, + "id": 13883, + "logprob": -0.010406494, "special": false, - "text": " first" + "text": " optimization" }, { - "id": 29899, - "logprob": 0.0, + "id": 5687, + "logprob": -0.0002501011, "special": false, - "text": "-" + "text": " algorithm" }, { - "id": 2098, - "logprob": -0.000116467476, + "id": 15574, + "logprob": -0.6582031, "special": false, - "text": "order" + "text": " commonly" }, { - "id": 13883, - "logprob": -0.47436523, + "id": 1304, + "logprob": -0.00092840195, "special": false, - "text": " optimization" + "text": " used" }, { - "id": 5687, - "logprob": -0.00027871132, + "id": 297, + "logprob": -0.18933105, "special": false, - "text": " algorithm" + "text": " in" } ], "top_tokens": null }, - "generated_text": "Gradient descent is a first-order optimization algorithm" + "generated_text": "Gradient descent is an optimization algorithm commonly used in" }, { "details": { @@ -121,32 +121,32 @@ }, { "id": 338, - "logprob": -0.7128906, + "logprob": -0.6113281, "text": "is" }, { "id": 16030, - "logprob": -13.9375, + "logprob": -13.6640625, "text": "gradient" }, { "id": 26815, - "logprob": -0.05053711, + "logprob": -0.003929138, "text": "descent" }, { "id": 29973, - "logprob": -3.0058594, + "logprob": -2.625, "text": "?" }, { "id": 13, - "logprob": -2.8242188, + "logprob": -6.484375, "text": "\n" }, { "id": 13, - "logprob": -0.84521484, + "logprob": -6.6875, "text": "\n" } ], @@ -154,68 +154,68 @@ "tokens": [ { "id": 25584, - "logprob": -0.018859863, + "logprob": -0.009017944, "special": false, "text": "Grad" }, { "id": 993, - "logprob": -0.002822876, + "logprob": -9.536743e-07, "special": false, "text": "ient" }, { "id": 26815, - "logprob": -0.023254395, + "logprob": -0.00097084045, "special": false, "text": " descent" }, { "id": 338, - "logprob": -2.0384789e-05, + "logprob": -0.0003838539, "special": false, "text": " is" }, { - "id": 263, - "logprob": -0.5229492, + "id": 385, + "logprob": -0.24499512, "special": false, - "text": " a" + "text": " an" }, { - "id": 937, - "logprob": -0.17126465, + "id": 13883, + "logprob": -0.010406494, "special": false, - "text": " first" + "text": " optimization" }, { - "id": 29899, - "logprob": 0.0, + "id": 5687, + "logprob": -0.0002501011, "special": false, - "text": "-" + "text": " algorithm" }, { - "id": 2098, - "logprob": -0.0001155138, + "id": 15574, + "logprob": -0.6435547, "special": false, - "text": "order" + "text": " commonly" }, { - "id": 13883, - "logprob": -0.47436523, + "id": 1304, + "logprob": -0.0009279251, "special": false, - "text": " optimization" + "text": " used" }, { - "id": 5687, - "logprob": -0.00027036667, + "id": 297, + "logprob": -0.18933105, "special": false, - "text": " algorithm" + "text": " in" } ], "top_tokens": null }, - "generated_text": "Gradient descent is a first-order optimization algorithm" + "generated_text": "Gradient descent is an optimization algorithm commonly used in" }, { "details": { @@ -230,32 +230,32 @@ }, { "id": 338, - "logprob": -0.71484375, + "logprob": -0.609375, "text": "is" }, { "id": 16030, - "logprob": -13.9375, + "logprob": -13.671875, "text": "gradient" }, { "id": 26815, - "logprob": -0.049346924, + "logprob": -0.0040016174, "text": "descent" }, { "id": 29973, - "logprob": -3.0078125, + "logprob": -2.6230469, "text": "?" }, { "id": 13, - "logprob": -2.8242188, + "logprob": -6.453125, "text": "\n" }, { "id": 13, - "logprob": -0.86328125, + "logprob": -6.6875, "text": "\n" } ], @@ -263,68 +263,68 @@ "tokens": [ { "id": 25584, - "logprob": -0.017196655, + "logprob": -0.008956909, "special": false, "text": "Grad" }, { "id": 993, - "logprob": -0.0028438568, + "logprob": -8.34465e-07, "special": false, "text": "ient" }, { "id": 26815, - "logprob": -0.023254395, + "logprob": -0.0009407997, "special": false, "text": " descent" }, { "id": 338, - "logprob": -2.026558e-05, + "logprob": -0.0003721714, "special": false, "text": " is" }, { - "id": 263, - "logprob": -0.5229492, + "id": 385, + "logprob": -0.24499512, "special": false, - "text": " a" + "text": " an" }, { - "id": 937, - "logprob": -0.17602539, + "id": 13883, + "logprob": -0.010406494, "special": false, - "text": " first" + "text": " optimization" }, { - "id": 29899, - "logprob": 0.0, + "id": 5687, + "logprob": -0.0002501011, "special": false, - "text": "-" + "text": " algorithm" }, { - "id": 2098, - "logprob": -0.00011622906, + "id": 15574, + "logprob": -0.6435547, "special": false, - "text": "order" + "text": " commonly" }, { - "id": 13883, - "logprob": -0.48608398, + "id": 1304, + "logprob": -0.00092601776, "special": false, - "text": " optimization" + "text": " used" }, { - "id": 5687, - "logprob": -0.00027894974, + "id": 297, + "logprob": -0.19177246, "special": false, - "text": " algorithm" + "text": " in" } ], "top_tokens": null }, - "generated_text": "Gradient descent is a first-order optimization algorithm" + "generated_text": "Gradient descent is an optimization algorithm commonly used in" }, { "details": { @@ -339,32 +339,32 @@ }, { "id": 338, - "logprob": -0.7192383, + "logprob": -0.609375, "text": "is" }, { "id": 16030, - "logprob": -13.9375, + "logprob": -13.6640625, "text": "gradient" }, { "id": 26815, - "logprob": -0.050445557, + "logprob": -0.0038967133, "text": "descent" }, { "id": 29973, - "logprob": -3.0078125, + "logprob": -2.6347656, "text": "?" }, { "id": 13, - "logprob": -2.8242188, + "logprob": -6.453125, "text": "\n" }, { "id": 13, - "logprob": -0.8276367, + "logprob": -6.6875, "text": "\n" } ], @@ -372,67 +372,67 @@ "tokens": [ { "id": 25584, - "logprob": -0.01727295, + "logprob": -0.008979797, "special": false, "text": "Grad" }, { "id": 993, - "logprob": -0.0027542114, + "logprob": -9.536743e-07, "special": false, "text": "ient" }, { "id": 26815, - "logprob": -0.023254395, + "logprob": -0.0009407997, "special": false, "text": " descent" }, { "id": 338, - "logprob": -2.0384789e-05, + "logprob": -0.00038409233, "special": false, "text": " is" }, { - "id": 263, - "logprob": -0.5229492, + "id": 385, + "logprob": -0.24499512, "special": false, - "text": " a" + "text": " an" }, { - "id": 937, - "logprob": -0.17126465, + "id": 13883, + "logprob": -0.010414124, "special": false, - "text": " first" + "text": " optimization" }, { - "id": 29899, - "logprob": 0.0, + "id": 5687, + "logprob": -0.00024354458, "special": false, - "text": "-" + "text": " algorithm" }, { - "id": 2098, - "logprob": -0.00011301041, + "id": 15574, + "logprob": -0.6435547, "special": false, - "text": "order" + "text": " commonly" }, { - "id": 13883, - "logprob": -0.48608398, + "id": 1304, + "logprob": -0.0009279251, "special": false, - "text": " optimization" + "text": " used" }, { - "id": 5687, - "logprob": -0.00027894974, + "id": 297, + "logprob": -0.19470215, "special": false, - "text": " algorithm" + "text": " in" } ], "top_tokens": null }, - "generated_text": "Gradient descent is a first-order optimization algorithm" + "generated_text": "Gradient descent is an optimization algorithm commonly used in" } ] diff --git a/integration-tests/models/test_flash_llama_fp8_kv_cache.py b/integration-tests/models/test_flash_llama_fp8_kv_cache.py index 05e9f0dd9ec..ccd7f78fe6f 100644 --- a/integration-tests/models/test_flash_llama_fp8_kv_cache.py +++ b/integration-tests/models/test_flash_llama_fp8_kv_cache.py @@ -4,7 +4,9 @@ @pytest.fixture(scope="module") def flash_llama_fp8_kv_cache_handle(launcher): with launcher( - "meta-llama/Meta-Llama-3-8B", num_shard=2, kv_cache_dtype="fp8_e5m2" + "neuralmagic/Meta-Llama-3-8B-Instruct-FP8-KV", + num_shard=2, + kv_cache_dtype="fp8_e4m3fn", ) as handle: yield handle @@ -25,7 +27,7 @@ async def test_flash_llama_fp8_kv_cache(flash_llama_fp8_kv_cache, response_snaps assert ( response.generated_text - == " Deep learning is a subset of machine learning that is" + == " Deep learning is a subset of machine learning that involves" ) assert response.details.generated_tokens == 10 assert response == response_snapshot @@ -69,7 +71,7 @@ async def test_flash_llama_fp8_kv_cache_load( assert len(responses) == 4 assert ( responses[0].generated_text - == " Deep learning is a subset of machine learning that is" + == " Deep learning is a subset of machine learning that involves" ) assert all( [r.generated_text == responses[0].generated_text for r in responses] diff --git a/integration-tests/models/test_flash_mixtral_gptq.py b/integration-tests/models/test_flash_mixtral_gptq.py index eb880628481..47bcb0bf3d7 100644 --- a/integration-tests/models/test_flash_mixtral_gptq.py +++ b/integration-tests/models/test_flash_mixtral_gptq.py @@ -3,7 +3,11 @@ @pytest.fixture(scope="module") def flash_mixtral_gptq_handle(launcher): - with launcher("TheBloke/Mixtral-8x7B-Instruct-v0.1-GPTQ", num_shard=2) as handle: + with launcher( + "TheBloke/Mixtral-8x7B-Instruct-v0.1-GPTQ", + revision="gptq-4bit-128g-actorder_True", + num_shard=2, + ) as handle: yield handle @@ -16,7 +20,12 @@ async def flash_mixtral_gptq(flash_mixtral_gptq_handle): @pytest.mark.asyncio async def test_flash_mixtral_gptq(flash_mixtral_gptq, response_snapshot): response = await flash_mixtral_gptq.generate( - "Test request", max_new_tokens=10, decoder_input_details=True + "What is deep learning?", max_new_tokens=10, decoder_input_details=True + ) + + assert response.details.generated_tokens == 10 + assert ( + response.generated_text == "\n\nDeep learning is a subset of machine learning" ) assert response == response_snapshot @@ -25,7 +34,7 @@ async def test_flash_mixtral_gptq(flash_mixtral_gptq, response_snapshot): @pytest.mark.asyncio async def test_flash_mixtral_gptq_all_params(flash_mixtral_gptq, response_snapshot): response = await flash_mixtral_gptq.generate( - "Test request", + "What is deep learning?", max_new_tokens=10, repetition_penalty=1.2, return_full_text=True, @@ -41,6 +50,10 @@ async def test_flash_mixtral_gptq_all_params(flash_mixtral_gptq, response_snapsh ) assert response.details.generated_tokens == 10 + assert ( + response.generated_text + == "What is deep learning?\nDeep Learning is a subset of Machine Learning," + ) assert response == response_snapshot @@ -49,10 +62,14 @@ async def test_flash_mixtral_gptq_load( flash_mixtral_gptq, generate_load, response_snapshot ): responses = await generate_load( - flash_mixtral_gptq, "Test request", max_new_tokens=10, n=4 + flash_mixtral_gptq, "What is deep learning?", max_new_tokens=10, n=4 ) assert len(responses) == 4 + assert ( + responses[0].generated_text + == "\n\nDeep learning is a subset of machine learning" + ) assert all( [r.generated_text == responses[0].generated_text for r in responses] ), f"{[r.generated_text for r in responses]}" diff --git a/integration-tests/models/test_flash_phi35_moe.py b/integration-tests/models/test_flash_phi35_moe.py index 2173740ad3c..d3043b028a8 100644 --- a/integration-tests/models/test_flash_phi35_moe.py +++ b/integration-tests/models/test_flash_phi35_moe.py @@ -25,7 +25,7 @@ async def test_flash_phi35_moe(flash_phi35_moe, response_snapshot): assert response.details.generated_tokens == 10 assert ( response.generated_text - == "Gradient descent is a first-order optimization algorithm" + == "Gradient descent is an optimization algorithm commonly used in" ) assert response == response_snapshot @@ -33,7 +33,7 @@ async def test_flash_phi35_moe(flash_phi35_moe, response_snapshot): @pytest.mark.asyncio async def test_flash_phi35_moe_all_params(flash_phi35_moe, response_snapshot): response = await flash_phi35_moe.generate( - "What is gradient descent?\n\n", + "What is gradient descent?\n", max_new_tokens=10, repetition_penalty=1.2, return_full_text=True, @@ -51,7 +51,7 @@ async def test_flash_phi35_moe_all_params(flash_phi35_moe, response_snapshot): assert response.details.generated_tokens == 10 assert ( response.generated_text - == "What is gradient descent?\n\nHello! It seems you're addressing a" + == "What is gradient descent?\nGradient Descent (GD) is an" ) assert response == response_snapshot @@ -66,7 +66,7 @@ async def test_flash_phi35_moe_load(flash_phi35_moe, generate_load, response_sna assert responses[0].details.generated_tokens == 10 assert ( responses[0].generated_text - == "Gradient descent is a first-order optimization algorithm" + == "Gradient descent is an optimization algorithm commonly used in" ) assert all( [r.generated_text == responses[0].generated_text for r in responses] diff --git a/launcher/src/main.rs b/launcher/src/main.rs index d9f569fdaeb..71bbcbd8cd9 100644 --- a/launcher/src/main.rs +++ b/launcher/src/main.rs @@ -1104,6 +1104,8 @@ fn log_lines(mut bufread: BufReader) { } } } + } else { + break; } } } @@ -1507,6 +1509,10 @@ fn spawn_webserver( router_args.push(revision.to_string()) } + if args.trust_remote_code { + router_args.push("--trust-remote-code".to_string()); + } + if args.json_output { router_args.push("--json-output".to_string()); } diff --git a/nix/impure-shell.nix b/nix/impure-shell.nix index abed544a155..92e14bc3cb0 100644 --- a/nix/impure-shell.nix +++ b/nix/impure-shell.nix @@ -1,7 +1,12 @@ { + lib, mkShell, black, + cmake, isort, + ninja, + which, + cudaPackages, openssl, pkg-config, protobuf, @@ -11,14 +16,17 @@ ruff, rust-bin, server, + + # Enable dependencies for building CUDA packages. Useful for e.g. + # developing marlin/moe-kernels in-place. + withCuda ? false, }: mkShell { - buildInputs = + nativeBuildInputs = [ black isort - openssl.dev pkg-config (rust-bin.stable.latest.default.override { extensions = [ @@ -31,6 +39,19 @@ mkShell { redocly ruff ] + ++ (lib.optionals withCuda [ + cmake + ninja + which + + # For most Torch-based extensions, setting CUDA_HOME is enough, but + # some custom CMake builds (e.g. vLLM) also need to have nvcc in PATH. + cudaPackages.cuda_nvcc + ]); + buildInputs = + [ + openssl.dev + ] ++ (with python3.pkgs; [ venvShellHook docker @@ -40,10 +61,29 @@ mkShell { pytest pytest-asyncio syrupy - ]); + ]) + ++ (lib.optionals withCuda ( + with cudaPackages; + [ + cuda_cccl + cuda_cudart + cuda_nvrtc + cuda_nvtx + cuda_profiler_api + cudnn + libcublas + libcusolver + libcusparse + ] + )); inputsFrom = [ server ]; + env = lib.optionalAttrs withCuda { + CUDA_HOME = "${lib.getDev cudaPackages.cuda_nvcc}"; + TORCH_CUDA_ARCH_LIST = lib.concatStringsSep ";" python3.pkgs.torch.cudaCapabilities; + }; + venvDir = "./.venv"; postVenvCreation = '' @@ -51,6 +91,7 @@ mkShell { ( cd server ; python -m pip install --no-dependencies -e . ) ( cd clients/python ; python -m pip install --no-dependencies -e . ) ''; + postShellHook = '' unset SOURCE_DATE_EPOCH export PATH=$PATH:~/.cargo/bin diff --git a/router/src/config.rs b/router/src/config.rs index 1a20c40bb67..7139b923768 100644 --- a/router/src/config.rs +++ b/router/src/config.rs @@ -150,6 +150,7 @@ pub enum Config { Idefics2(Idefics2), Ssm, GptBigcode, + Granite, Santacoder, Bloom, Mpt, diff --git a/router/src/lib.rs b/router/src/lib.rs index fdbd931eafb..7c40c7e3dd6 100644 --- a/router/src/lib.rs +++ b/router/src/lib.rs @@ -8,6 +8,7 @@ pub mod validation; mod kserve; pub mod logging; +mod sagemaker; pub mod usage_stats; mod vertex; diff --git a/router/src/main.rs.back b/router/src/main.rs.back deleted file mode 100644 index 36879aa478b..00000000000 --- a/router/src/main.rs.back +++ /dev/null @@ -1,748 +0,0 @@ -use axum::http::HeaderValue; -use clap::Parser; -use clap::Subcommand; -use hf_hub::api::tokio::{Api, ApiBuilder, ApiRepo}; -use hf_hub::{Cache, Repo, RepoType}; -use opentelemetry::sdk::propagation::TraceContextPropagator; -use opentelemetry::sdk::trace; -use opentelemetry::sdk::trace::Sampler; -use opentelemetry::sdk::Resource; -use opentelemetry::{global, KeyValue}; -use opentelemetry_otlp::WithExportConfig; -use std::fs::File; -use std::io::BufReader; -use std::net::{IpAddr, Ipv4Addr, SocketAddr}; -use std::path::{Path, PathBuf}; -use text_generation_router::config::Config; -use text_generation_router::usage_stats; -use text_generation_router::{ - server, HubModelInfo, HubPreprocessorConfig, HubProcessorConfig, HubTokenizerConfig, -}; -use thiserror::Error; -use tokenizers::{processors::template::TemplateProcessing, Tokenizer}; -use tower_http::cors::AllowOrigin; -use tracing_subscriber::layer::SubscriberExt; -use tracing_subscriber::util::SubscriberInitExt; -use tracing_subscriber::{filter::LevelFilter, EnvFilter, Layer}; - -/// App Configuration -#[derive(Parser, Debug)] -#[clap(author, version, about, long_about = None)] -struct Args { - #[command(subcommand)] - command: Option, - - #[clap(default_value = "128", long, env)] - max_concurrent_requests: usize, - #[clap(default_value = "2", long, env)] - max_best_of: usize, - #[clap(default_value = "4", long, env)] - max_stop_sequences: usize, - #[clap(default_value = "5", long, env)] - max_top_n_tokens: u32, - #[clap(default_value = "1024", long, env)] - max_input_tokens: usize, - #[clap(default_value = "2048", long, env)] - max_total_tokens: usize, - #[clap(default_value = "1.2", long, env)] - waiting_served_ratio: f32, - #[clap(default_value = "4096", long, env)] - max_batch_prefill_tokens: u32, - #[clap(long, env)] - max_batch_total_tokens: Option, - #[clap(default_value = "20", long, env)] - max_waiting_tokens: usize, - #[clap(long, env)] - max_batch_size: Option, - #[clap(default_value = "0.0.0.0", long, env)] - hostname: String, - #[clap(default_value = "3000", long, short, env)] - port: u16, - #[clap(default_value = "/tmp/text-generation-server-0", long, env)] - master_shard_uds_path: String, - #[clap(default_value = "bigscience/bloom", long, env)] - tokenizer_name: String, - #[clap(long, env)] - tokenizer_config_path: Option, - #[clap(long, env)] - revision: Option, - #[clap(default_value = "2", long, env)] - validation_workers: usize, - #[clap(long, env)] - json_output: bool, - #[clap(long, env)] - otlp_endpoint: Option, - #[clap(default_value = "text-generation-inference.router", long, env)] - otlp_service_name: String, - #[clap(long, env)] - cors_allow_origin: Option>, - #[clap(long, env)] - api_key: Option, - #[clap(long, env)] - ngrok: bool, - #[clap(long, env)] - ngrok_authtoken: Option, - #[clap(long, env)] - ngrok_edge: Option, - #[clap(long, env, default_value_t = false)] - messages_api_enabled: bool, - #[clap(long, env, default_value_t = false)] - disable_grammar_support: bool, - #[clap(default_value = "4", long, env)] - max_client_batch_size: usize, - #[clap(long, env, default_value_t)] - disable_usage_stats: bool, - #[clap(long, env, default_value_t)] - disable_crash_reports: bool, -} - -#[derive(Debug, Subcommand)] -enum Commands { - PrintSchema, -} - -#[tokio::main] -async fn main() -> Result<(), RouterError> { - let args = Args::parse(); - - // Pattern match configuration - let Args { - max_concurrent_requests, - max_best_of, - max_stop_sequences, - max_top_n_tokens, - max_input_tokens, - max_total_tokens, - waiting_served_ratio, - max_batch_prefill_tokens, - max_batch_total_tokens, - max_waiting_tokens, - max_batch_size, - hostname, - port, - master_shard_uds_path, - tokenizer_name, - tokenizer_config_path, - revision, - validation_workers, - json_output, - otlp_endpoint, - otlp_service_name, - cors_allow_origin, - api_key, - ngrok, - ngrok_authtoken, - ngrok_edge, - messages_api_enabled, - disable_grammar_support, - max_client_batch_size, - disable_usage_stats, - disable_crash_reports, - command, - } = args; - - let print_schema_command = match command { - Some(Commands::PrintSchema) => true, - None => { - // only init logging if we are not running the print schema command - init_logging(otlp_endpoint, otlp_service_name, json_output); - false - } - }; - - // Validate args - if max_input_tokens >= max_total_tokens { - return Err(RouterError::ArgumentValidation( - "`max_input_tokens` must be < `max_total_tokens`".to_string(), - )); - } - if max_input_tokens as u32 > max_batch_prefill_tokens { - return Err(RouterError::ArgumentValidation(format!("`max_batch_prefill_tokens` must be >= `max_input_tokens`. Given: {max_batch_prefill_tokens} and {max_input_tokens}"))); - } - - if validation_workers == 0 { - return Err(RouterError::ArgumentValidation( - "`validation_workers` must be > 0".to_string(), - )); - } - - if let Some(ref max_batch_total_tokens) = max_batch_total_tokens { - if max_batch_prefill_tokens > *max_batch_total_tokens { - return Err(RouterError::ArgumentValidation(format!("`max_batch_prefill_tokens` must be <= `max_batch_total_tokens`. Given: {max_batch_prefill_tokens} and {max_batch_total_tokens}"))); - } - if max_total_tokens as u32 > *max_batch_total_tokens { - return Err(RouterError::ArgumentValidation(format!("`max_total_tokens` must be <= `max_batch_total_tokens`. Given: {max_total_tokens} and {max_batch_total_tokens}"))); - } - } - - // CORS allowed origins - // map to go inside the option and then map to parse from String to HeaderValue - // Finally, convert to AllowOrigin - let cors_allow_origin: Option = cors_allow_origin.map(|cors_allow_origin| { - AllowOrigin::list( - cors_allow_origin - .iter() - .map(|origin| origin.parse::().unwrap()), - ) - }); - - // Parse Huggingface hub token - let authorization_token = std::env::var("HF_TOKEN") - .or_else(|_| std::env::var("HUGGING_FACE_HUB_TOKEN")) - .ok(); - - // Tokenizer instance - // This will only be used to validate payloads - let local_path = Path::new(&tokenizer_name); - - // Shared API builder initialization - let api_builder = || { - let mut builder = ApiBuilder::new() - .with_progress(false) - .with_token(authorization_token); - - if let Ok(cache_dir) = std::env::var("HUGGINGFACE_HUB_CACHE") { - builder = builder.with_cache_dir(cache_dir.into()); - } - - builder - }; - - // Decide if we need to use the API based on the revision and local path - let use_api = revision.is_some() || !local_path.exists() || !local_path.is_dir(); - - // Initialize API if needed - #[derive(Clone)] - enum Type { - Api(Api), - Cache(Cache), - None, - } - let api = if use_api { - if std::env::var("HF_HUB_OFFLINE") == Ok("1".to_string()) { - let cache = std::env::var("HUGGINGFACE_HUB_CACHE") - .map_err(|_| ()) - .map(|cache_dir| Cache::new(cache_dir.into())) - .unwrap_or_else(|_| Cache::default()); - - tracing::warn!("Offline mode active using cache defaults"); - Type::Cache(cache) - } else { - tracing::info!("Using the Hugging Face API"); - match api_builder().build() { - Ok(api) => Type::Api(api), - Err(_) => { - tracing::warn!("Unable to build the Hugging Face API"); - Type::None - } - } - } - } else { - Type::None - }; - - // Load tokenizer and model info - let ( - tokenizer_filename, - config_filename, - tokenizer_config_filename, - preprocessor_config_filename, - processor_config_filename, - model_info, - ) = match api { - Type::None => ( - Some(local_path.join("tokenizer.json")), - Some(local_path.join("config.json")), - Some(local_path.join("tokenizer_config.json")), - Some(local_path.join("preprocessor_config.json")), - Some(local_path.join("processor_config.json")), - None, - ), - Type::Api(api) => { - let api_repo = api.repo(Repo::with_revision( - tokenizer_name.to_string(), - RepoType::Model, - revision.clone().unwrap_or_else(|| "main".to_string()), - )); - - let tokenizer_filename = match api_repo.get("tokenizer.json").await { - Ok(tokenizer_filename) => Some(tokenizer_filename), - Err(_) => get_base_tokenizer(&api, &api_repo).await, - }; - let config_filename = api_repo.get("config.json").await.ok(); - let tokenizer_config_filename = api_repo.get("tokenizer_config.json").await.ok(); - let preprocessor_config_filename = api_repo.get("preprocessor_config.json").await.ok(); - let processor_config_filename = api_repo.get("processor_config.json").await.ok(); - - let model_info = if let Some(model_info) = get_model_info(&api_repo).await { - Some(model_info) - } else { - tracing::warn!("Could not retrieve model info from the Hugging Face hub."); - None - }; - ( - tokenizer_filename, - config_filename, - tokenizer_config_filename, - preprocessor_config_filename, - processor_config_filename, - model_info, - ) - } - Type::Cache(cache) => { - let repo = cache.repo(Repo::with_revision( - tokenizer_name.to_string(), - RepoType::Model, - revision.clone().unwrap_or_else(|| "main".to_string()), - )); - ( - repo.get("tokenizer.json"), - repo.get("config.json"), - repo.get("tokenizer_config.json"), - repo.get("preprocessor_config.json"), - repo.get("processor_config.json"), - None, - ) - } - }; - let config: Option = config_filename.and_then(|filename| { - std::fs::read_to_string(filename) - .ok() - .as_ref() - .and_then(|c| { - let config: Result = serde_json::from_str(c); - if let Err(err) = &config { - tracing::warn!("Could not parse config {err:?}"); - } - config.ok() - }) - }); - let model_info = model_info.unwrap_or_else(|| HubModelInfo { - model_id: tokenizer_name.to_string(), - sha: None, - pipeline_tag: None, - }); - - // Read the JSON contents of the file as an instance of 'HubTokenizerConfig'. - let tokenizer_config: Option = if let Some(filename) = tokenizer_config_path - { - HubTokenizerConfig::from_file(filename) - } else { - tokenizer_config_filename.and_then(HubTokenizerConfig::from_file) - }; - let tokenizer_config = tokenizer_config.unwrap_or_else(|| { - tracing::warn!("Could not find tokenizer config locally and no API specified"); - HubTokenizerConfig::default() - }); - let tokenizer_class = tokenizer_config.tokenizer_class.clone(); - - let tokenizer: Option = tokenizer_filename.and_then(|filename| { - let mut tokenizer = Tokenizer::from_file(filename).ok(); - if let Some(tokenizer) = &mut tokenizer { - if let Some(class) = &tokenizer_config.tokenizer_class { - if class == "LlamaTokenizer" || class == "LlamaTokenizerFast"{ - if let Ok(post_processor) = create_post_processor(tokenizer, &tokenizer_config) { - tracing::info!("Overriding LlamaTokenizer with TemplateProcessing to follow python override defined in https://github.com/huggingface/transformers/blob/4aa17d00690b7f82c95bb2949ea57e22c35b4336/src/transformers/models/llama/tokenization_llama_fast.py#L203-L205"); - tokenizer.with_post_processor(post_processor); - } - } - } - } - tokenizer - }); - - let preprocessor_config = - preprocessor_config_filename.and_then(HubPreprocessorConfig::from_file); - let processor_config = processor_config_filename - .and_then(HubProcessorConfig::from_file) - .unwrap_or_default(); - - tracing::info!("Using config {config:?}"); - if tokenizer.is_none() { - tracing::warn!("Could not find a fast tokenizer implementation for {tokenizer_name}"); - tracing::warn!("Rust input length validation and truncation is disabled"); - } - - // if pipeline-tag == text-generation we default to return_full_text = true - let compat_return_full_text = match &model_info.pipeline_tag { - None => { - tracing::warn!("no pipeline tag found for model {tokenizer_name}"); - true - } - Some(pipeline_tag) => pipeline_tag.as_str() == "text-generation", - }; - - // Determine the server port based on the feature and environment variable. - let port = if cfg!(feature = "google") { - std::env::var("AIP_HTTP_PORT") - .map(|aip_http_port| aip_http_port.parse::().unwrap_or(port)) - .unwrap_or(port) - } else { - port - }; - - let addr = match hostname.parse() { - Ok(ip) => SocketAddr::new(ip, port), - Err(_) => { - tracing::warn!("Invalid hostname, defaulting to 0.0.0.0"); - SocketAddr::new(IpAddr::V4(Ipv4Addr::new(0, 0, 0, 0)), port) - } - }; - - // Only send usage stats when TGI is run in container and the function returns Some - let is_container = matches!(usage_stats::is_container(), Ok(true)); - - let user_agent = if !disable_usage_stats && is_container { - let reduced_args = usage_stats::Args::new( - config.clone(), - tokenizer_class, - max_concurrent_requests, - max_best_of, - max_stop_sequences, - max_top_n_tokens, - max_input_tokens, - max_total_tokens, - waiting_served_ratio, - max_batch_prefill_tokens, - max_batch_total_tokens, - max_waiting_tokens, - max_batch_size, - revision, - validation_workers, - messages_api_enabled, - disable_grammar_support, - max_client_batch_size, - disable_usage_stats, - disable_crash_reports, - ); - Some(usage_stats::UserAgent::new(reduced_args)) - } else { - None - }; - - if let Some(ref ua) = user_agent { - let start_event = - usage_stats::UsageStatsEvent::new(ua.clone(), usage_stats::EventType::Start, None); - tokio::spawn(async move { - start_event.send().await; - }); - }; - - // Run server - let result = server::run( - master_shard_uds_path, - model_info, - compat_return_full_text, - max_concurrent_requests, - max_best_of, - max_stop_sequences, - max_top_n_tokens, - max_input_tokens, - max_total_tokens, - waiting_served_ratio, - max_batch_prefill_tokens, - max_batch_total_tokens, - max_waiting_tokens, - max_batch_size, - tokenizer, - config, - validation_workers, - addr, - cors_allow_origin, - api_key, - ngrok, - ngrok_authtoken, - ngrok_edge, - tokenizer_config, - preprocessor_config, - processor_config, - messages_api_enabled, - disable_grammar_support, - max_client_batch_size, - print_schema_command, - ) - .await; - - match result { - Ok(_) => { - if let Some(ref ua) = user_agent { - let stop_event = usage_stats::UsageStatsEvent::new( - ua.clone(), - usage_stats::EventType::Stop, - None, - ); - stop_event.send().await; - }; - Ok(()) - } - Err(e) => { - if let Some(ref ua) = user_agent { - if !disable_crash_reports { - let error_event = usage_stats::UsageStatsEvent::new( - ua.clone(), - usage_stats::EventType::Error, - Some(e.to_string()), - ); - error_event.send().await; - } else { - let unknow_error_event = usage_stats::UsageStatsEvent::new( - ua.clone(), - usage_stats::EventType::Error, - Some("unknow_error".to_string()), - ); - unknow_error_event.send().await; - } - }; - Err(RouterError::WebServer(e)) - } - } -} - -/// Init logging using env variables LOG_LEVEL and LOG_FORMAT: -/// - otlp_endpoint is an optional URL to an Open Telemetry collector -/// - otlp_service_name service name to appear in APM -/// - LOG_LEVEL may be TRACE, DEBUG, INFO, WARN or ERROR (default to INFO) -/// - LOG_FORMAT may be TEXT or JSON (default to TEXT) -/// - LOG_COLORIZE may be "false" or "true" (default to "true" or ansi supported platforms) -fn init_logging(otlp_endpoint: Option, otlp_service_name: String, json_output: bool) { - let mut layers = Vec::new(); - - // STDOUT/STDERR layer - let ansi = std::env::var("LOG_COLORIZE") != Ok("1".to_string()); - let fmt_layer = tracing_subscriber::fmt::layer() - .with_file(true) - .with_ansi(ansi) - .with_line_number(true); - - let fmt_layer = match json_output { - true => fmt_layer.json().flatten_event(true).boxed(), - false => fmt_layer.boxed(), - }; - layers.push(fmt_layer); - - // OpenTelemetry tracing layer - if let Some(otlp_endpoint) = otlp_endpoint { - global::set_text_map_propagator(TraceContextPropagator::new()); - - let tracer = opentelemetry_otlp::new_pipeline() - .tracing() - .with_exporter( - opentelemetry_otlp::new_exporter() - .tonic() - .with_endpoint(otlp_endpoint), - ) - .with_trace_config( - trace::config() - .with_resource(Resource::new(vec![KeyValue::new( - "service.name", - otlp_service_name, - )])) - .with_sampler(Sampler::AlwaysOn), - ) - .install_batch(opentelemetry::runtime::Tokio); - - if let Ok(tracer) = tracer { - layers.push(tracing_opentelemetry::layer().with_tracer(tracer).boxed()); - init_tracing_opentelemetry::init_propagator().unwrap(); - }; - } - - // Filter events with LOG_LEVEL - let varname = "LOG_LEVEL"; - let env_filter = if let Ok(log_level) = std::env::var(varname) { - // Override to avoid simple logs to be spammed with tokio level informations - let log_level = match &log_level[..] { - "warn" => "text_generation_launcher=warn,text_generation_router=warn", - "info" => "text_generation_launcher=info,text_generation_router=info", - "debug" => "text_generation_launcher=debug,text_generation_router=debug", - log_level => log_level, - }; - EnvFilter::builder() - .with_default_directive(LevelFilter::INFO.into()) - .parse_lossy(log_level) - } else { - EnvFilter::new("info") - }; - - tracing_subscriber::registry() - .with(env_filter) - .with(layers) - .init(); -} - -/// get model info from the Huggingface Hub -pub async fn get_model_info(api: &ApiRepo) -> Option { - let response = api.info_request().send().await.ok()?; - - if response.status().is_success() { - let hub_model_info: HubModelInfo = - serde_json::from_str(&response.text().await.ok()?).ok()?; - if let Some(sha) = &hub_model_info.sha { - tracing::info!( - "Serving revision {sha} of model {}", - hub_model_info.model_id - ); - } - Some(hub_model_info) - } else { - None - } -} - -/// get base tokenizer -pub async fn get_base_tokenizer(api: &Api, api_repo: &ApiRepo) -> Option { - let config_filename = api_repo.get("config.json").await.ok()?; - - // Open the file in read-only mode with buffer. - let file = File::open(config_filename).ok()?; - let reader = BufReader::new(file); - - // Read the JSON contents of the file as an instance of `User`. - let config: serde_json::Value = serde_json::from_reader(reader).ok()?; - - if let Some(serde_json::Value::String(base_model_id)) = config.get("base_model_name_or_path") { - let api_base_repo = api.repo(Repo::with_revision( - base_model_id.to_string(), - RepoType::Model, - "main".to_string(), - )); - - api_base_repo.get("tokenizer.json").await.ok() - } else { - None - } -} - -/// get tokenizer_config from the Huggingface Hub -pub async fn get_tokenizer_config(api_repo: &ApiRepo) -> Option { - let tokenizer_config_filename = api_repo.get("tokenizer_config.json").await.ok()?; - - // Open the file in read-only mode with buffer. - let file = File::open(tokenizer_config_filename).ok()?; - let reader = BufReader::new(file); - - // Read the JSON contents of the file as an instance of 'HubTokenizerConfig'. - let tokenizer_config: HubTokenizerConfig = serde_json::from_reader(reader) - .map_err(|e| { - tracing::warn!("Unable to parse tokenizer config: {}", e); - e - }) - .ok()?; - - Some(tokenizer_config) -} - -/// Create a post_processor for the LlamaTokenizer -pub fn create_post_processor( - tokenizer: &Tokenizer, - tokenizer_config: &HubTokenizerConfig, -) -> Result { - let add_bos_token = tokenizer_config.add_bos_token.unwrap_or(true); - let add_eos_token = tokenizer_config.add_eos_token.unwrap_or(false); - - let bos_token = tokenizer_config.bos_token.as_ref(); - let eos_token = tokenizer_config.eos_token.as_ref(); - - if add_bos_token && bos_token.is_none() { - panic!("add_bos_token = true but bos_token is None"); - } - - if add_eos_token && eos_token.is_none() { - panic!("add_eos_token = true but eos_token is None"); - } - - let mut single = Vec::new(); - let mut pair = Vec::new(); - let mut special_tokens = Vec::new(); - - if add_bos_token { - if let Some(bos) = bos_token { - let bos_token_id = tokenizer - .token_to_id(bos.as_str()) - .expect("Should have found the bos token id"); - special_tokens.push((bos.as_str(), bos_token_id)); - single.push(format!("{}:0", bos.as_str())); - pair.push(format!("{}:0", bos.as_str())); - } - } - - single.push("$A:0".to_string()); - pair.push("$A:0".to_string()); - - if add_eos_token { - if let Some(eos) = eos_token { - let eos_token_id = tokenizer - .token_to_id(eos.as_str()) - .expect("Should have found the eos token id"); - special_tokens.push((eos.as_str(), eos_token_id)); - single.push(format!("{}:0", eos.as_str())); - pair.push(format!("{}:0", eos.as_str())); - } - } - - if add_bos_token { - if let Some(bos) = bos_token { - pair.push(format!("{}:1", bos.as_str())); - } - } - - pair.push("$B:1".to_string()); - - if add_eos_token { - if let Some(eos) = eos_token { - pair.push(format!("{}:1", eos.as_str())); - } - } - - let post_processor = TemplateProcessing::builder() - .try_single(single)? - .try_pair(pair)? - .special_tokens(special_tokens) - .build()?; - - Ok(post_processor) -} - -#[derive(Debug, Error)] -enum RouterError { - #[error("Argument validation error: {0}")] - ArgumentValidation(String), - #[error("WebServer error: {0}")] - WebServer(#[from] server::WebServerError), - #[error("Tokio runtime failed to start: {0}")] - Tokio(#[from] std::io::Error), -} - -#[cfg(test)] -mod tests { - use super::*; - use text_generation_router::TokenizerConfigToken; - - #[test] - fn test_create_post_processor() { - let tokenizer_config = HubTokenizerConfig { - add_bos_token: None, - add_eos_token: None, - bos_token: Some(TokenizerConfigToken::String("".to_string())), - eos_token: Some(TokenizerConfigToken::String("".to_string())), - chat_template: None, - tokenizer_class: None, - completion_template: None, - }; - - let tokenizer = - Tokenizer::from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0", None).unwrap(); - let post_processor = create_post_processor(&tokenizer, &tokenizer_config).unwrap(); - - let expected = TemplateProcessing::builder() - .try_single(":0 $A:0") - .unwrap() - .try_pair(":0 $A:0 :1 $B:1") - .unwrap() - .special_tokens(vec![("".to_string(), 1)]) - .build() - .unwrap(); - - assert_eq!(post_processor, expected); - } -} diff --git a/router/src/sagemaker.rs b/router/src/sagemaker.rs new file mode 100644 index 00000000000..750ef222bb5 --- /dev/null +++ b/router/src/sagemaker.rs @@ -0,0 +1,82 @@ +use crate::infer::Infer; +use crate::server::{chat_completions, compat_generate, completions, ComputeType}; +use crate::{ + ChatCompletion, ChatCompletionChunk, ChatRequest, Chunk, CompatGenerateRequest, + CompletionFinal, CompletionRequest, ErrorResponse, GenerateResponse, Info, StreamResponse, +}; +use axum::extract::Extension; +use axum::http::StatusCode; +use axum::response::Response; +use axum::Json; +use serde::{Deserialize, Serialize}; +use tracing::instrument; +use utoipa::ToSchema; + +#[derive(Clone, Deserialize, ToSchema)] +#[serde(untagged)] +pub(crate) enum SagemakerRequest { + Generate(CompatGenerateRequest), + Chat(ChatRequest), + Completion(CompletionRequest), +} + +// Used for OpenAPI specs +#[allow(dead_code)] +#[derive(Serialize, ToSchema)] +#[serde(untagged)] +pub(crate) enum SagemakerResponse { + Generate(GenerateResponse), + Chat(ChatCompletion), + Completion(CompletionFinal), +} + +// Used for OpenAPI specs +#[allow(dead_code)] +#[derive(Serialize, ToSchema)] +#[serde(untagged)] +pub(crate) enum SagemakerStreamResponse { + Generate(StreamResponse), + Chat(ChatCompletionChunk), + Completion(Chunk), +} + +/// Generate tokens from Sagemaker request +#[utoipa::path( +post, +tag = "Text Generation Inference", +path = "/invocations", +request_body = SagemakerRequest, +responses( +(status = 200, description = "Generated Chat Completion", +content( +("application/json" = SagemakerResponse), +("text/event-stream" = SagemakerStreamResponse), +)), +(status = 424, description = "Generation Error", body = ErrorResponse, +example = json ! ({"error": "Request failed during generation", "error_type": "generation"})), +(status = 429, description = "Model is overloaded", body = ErrorResponse, +example = json ! ({"error": "Model is overloaded", "error_type": "overloaded"})), +(status = 422, description = "Input validation error", body = ErrorResponse, +example = json ! ({"error": "Input validation error", "error_type": "validation"})), +(status = 500, description = "Incomplete generation", body = ErrorResponse, +example = json ! ({"error": "Incomplete generation", "error_type": "incomplete_generation"})), +) +)] +#[instrument(skip_all)] +pub(crate) async fn sagemaker_compatibility( + default_return_full_text: Extension, + infer: Extension, + compute_type: Extension, + info: Extension, + Json(req): Json, +) -> Result)> { + match req { + SagemakerRequest::Generate(req) => { + compat_generate(default_return_full_text, infer, compute_type, Json(req)).await + } + SagemakerRequest::Chat(req) => chat_completions(infer, compute_type, info, Json(req)).await, + SagemakerRequest::Completion(req) => { + completions(infer, compute_type, info, Json(req)).await + } + } +} diff --git a/router/src/server.rs b/router/src/server.rs index 5e6e696037e..eb1d2544ee3 100644 --- a/router/src/server.rs +++ b/router/src/server.rs @@ -7,6 +7,10 @@ use crate::kserve::{ kerve_server_metadata, kserve_health_live, kserve_health_ready, kserve_model_infer, kserve_model_metadata, kserve_model_metadata_ready, }; +use crate::sagemaker::{ + sagemaker_compatibility, SagemakerRequest, SagemakerResponse, SagemakerStreamResponse, + __path_sagemaker_compatibility, +}; use crate::validation::ValidationError; use crate::vertex::vertex_compatibility; use crate::ChatTokenizeResponse; @@ -83,7 +87,7 @@ example = json ! ({"error": "Incomplete generation"})), ) )] #[instrument(skip(infer, req))] -async fn compat_generate( +pub(crate) async fn compat_generate( Extension(default_return_full_text): Extension, infer: Extension, compute_type: Extension, @@ -678,7 +682,7 @@ time_per_token, seed, ) )] -async fn completions( +pub(crate) async fn completions( Extension(infer): Extension, Extension(compute_type): Extension, Extension(info): Extension, @@ -1202,7 +1206,7 @@ time_per_token, seed, ) )] -async fn chat_completions( +pub(crate) async fn chat_completions( Extension(infer): Extension, Extension(compute_type): Extension, Extension(info): Extension, @@ -1513,11 +1517,13 @@ completions, tokenize, metrics, openai_get_model_info, +sagemaker_compatibility, ), components( schemas( Info, CompatGenerateRequest, +SagemakerRequest, GenerateRequest, GrammarType, ChatRequest, @@ -1540,6 +1546,8 @@ ChatCompletionTopLogprob, ChatCompletion, CompletionRequest, CompletionComplete, +SagemakerResponse, +SagemakerStreamResponse, Chunk, Completion, CompletionFinal, @@ -1601,13 +1609,13 @@ pub async fn run( tokenizer_name: String, tokenizer_config_path: Option, revision: Option, + trust_remote_code: bool, hostname: String, port: u16, cors_allow_origin: Option>, ngrok: bool, _ngrok_authtoken: Option, _ngrok_edge: Option, - messages_api_enabled: bool, disable_grammar_support: bool, max_client_batch_size: usize, usage_stats_level: usage_stats::UsageStatsLevel, @@ -1761,10 +1769,13 @@ pub async fn run( let auto = transformers.getattr("AutoTokenizer")?; let from_pretrained = auto.getattr("from_pretrained")?; let args = (tokenizer_name.to_string(),); - let kwargs = [( - "revision", - revision.clone().unwrap_or_else(|| "main".to_string()), - )] + let kwargs = [ + ( + "revision", + (revision.clone().unwrap_or_else(|| "main".to_string())).into_py(py), + ), + ("trust_remote_code", trust_remote_code.into_py(py)), + ] .into_py_dict_bound(py); let tokenizer = from_pretrained.call(args, Some(&kwargs))?; let save = tokenizer.getattr("save_pretrained")?; @@ -1836,7 +1847,6 @@ pub async fn run( // max_batch_size, revision.clone(), validation_workers, - messages_api_enabled, disable_grammar_support, max_client_batch_size, usage_stats_level, @@ -1878,7 +1888,6 @@ pub async fn run( ngrok, _ngrok_authtoken, _ngrok_edge, - messages_api_enabled, disable_grammar_support, max_client_batch_size, model_info, @@ -1938,7 +1947,6 @@ async fn start( ngrok: bool, _ngrok_authtoken: Option, _ngrok_edge: Option, - messages_api_enabled: bool, disable_grammar_support: bool, max_client_batch_size: usize, model_info: HubModelInfo, @@ -2253,6 +2261,7 @@ async fn start( .route("/v1/chat/completions", post(chat_completions)) .route("/v1/completions", post(completions)) .route("/vertex", post(vertex_compatibility)) + .route("/invocations", post(sagemaker_compatibility)) .route("/tokenize", post(tokenize)); if let Some(api_key) = api_key { @@ -2288,13 +2297,6 @@ async fn start( .route("/metrics", get(metrics)) .route("/v1/models", get(openai_get_model_info)); - // Conditional AWS Sagemaker route - let aws_sagemaker_route = if messages_api_enabled { - Router::new().route("/invocations", post(chat_completions)) // Use 'chat_completions' for OAI_ENABLED - } else { - Router::new().route("/invocations", post(compat_generate)) // Use 'compat_generate' otherwise - }; - let compute_type = ComputeType(std::env::var("COMPUTE_TYPE").unwrap_or("gpu+optimized".to_string())); @@ -2302,8 +2304,7 @@ async fn start( let mut app = Router::new() .merge(swagger_ui) .merge(base_routes) - .merge(info_routes) - .merge(aws_sagemaker_route); + .merge(info_routes); #[cfg(feature = "google")] { diff --git a/router/src/usage_stats.rs b/router/src/usage_stats.rs index 0282ac63492..e9d98327ef6 100644 --- a/router/src/usage_stats.rs +++ b/router/src/usage_stats.rs @@ -93,7 +93,6 @@ pub struct Args { // max_batch_size: Option, revision: Option, validation_workers: usize, - messages_api_enabled: bool, disable_grammar_support: bool, max_client_batch_size: usize, usage_stats_level: UsageStatsLevel, @@ -117,7 +116,6 @@ impl Args { // max_batch_size: Option, revision: Option, validation_workers: usize, - messages_api_enabled: bool, disable_grammar_support: bool, max_client_batch_size: usize, usage_stats_level: UsageStatsLevel, @@ -138,7 +136,6 @@ impl Args { // max_batch_size, revision, validation_workers, - messages_api_enabled, disable_grammar_support, max_client_batch_size, usage_stats_level, diff --git a/server/Makefile b/server/Makefile index 9338b299090..18424dd6d7e 100644 --- a/server/Makefile +++ b/server/Makefile @@ -31,7 +31,7 @@ install: install-cuda echo "Installed server" install-cuda: install-server install-flash-attention-v2-cuda install-vllm-cuda install-flash-attention install-fbgemm - pip install -e ".[bnb]" + pip install -e ".[bnb,marlin,moe]" pip install nvidia-nccl-cu12==2.22.3 install-rocm: install-server install-flash-attention-v2-rocm install-vllm-rocm diff --git a/server/poetry.lock b/server/poetry.lock index 08f74999f04..1293e883656 100644 --- a/server/poetry.lock +++ b/server/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. 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"https://github.com/danieldk/marlin-kernels/releases/download/v0.2.0/marlin_kernels-0.2.0+cu123torch2.4-cp39-cp39-linux_x86_64.whl", python = "~3.9", optional = true }, - { url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.2.0/marlin_kernels-0.2.0+cu123torch2.4-cp310-cp310-linux_x86_64.whl", python = "~3.10", optional = true }, - { url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.2.0/marlin_kernels-0.2.0+cu123torch2.4-cp311-cp311-linux_x86_64.whl", python = "~3.11", optional = true }, - { url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.2.0/marlin_kernels-0.2.0+cu123torch2.4-cp312-cp312-linux_x86_64.whl", python = "~3.12", optional = true }, + { url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.0/marlin_kernels-0.3.0+cu123torch2.4-cp39-cp39-linux_x86_64.whl", python = "~3.9", optional = true }, + { url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.0/marlin_kernels-0.3.0+cu123torch2.4-cp310-cp310-linux_x86_64.whl", python = "~3.10", optional = true }, + { url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.0/marlin_kernels-0.3.0+cu123torch2.4-cp311-cp311-linux_x86_64.whl", python = "~3.11", optional = true }, + { url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.0/marlin_kernels-0.3.0+cu123torch2.4-cp312-cp312-linux_x86_64.whl", python = "~3.12", optional = true }, ] moe-kernels = [ { url = "https://github.com/danieldk/moe-kernels/releases/download/v0.6.0/moe_kernels-0.6.0+cu123torch2.4-cp39-cp39-linux_x86_64.whl", python = "~3.9", optional = true }, diff --git a/server/requirements_cuda.txt b/server/requirements_cuda.txt index 5de75b6b3e3..e3f6d20f8c7 100644 --- a/server/requirements_cuda.txt +++ b/server/requirements_cuda.txt @@ -1,5 +1,5 @@ certifi==2024.8.30 ; python_version >= "3.9" and python_version < "3.13" -charset-normalizer==3.3.2 ; python_version >= "3.9" and python_version < "3.13" +charset-normalizer==3.4.0 ; python_version >= "3.9" and python_version < "3.13" click==8.1.7 ; python_version >= "3.9" and python_version < "3.13" colorama==0.4.6 ; python_version >= "3.9" and python_version < "3.13" and (sys_platform == "win32" or platform_system == "Windows") deprecated==1.2.14 ; python_version >= "3.9" and python_version < "3.13" @@ -10,7 +10,7 @@ googleapis-common-protos==1.65.0 ; python_version >= "3.9" and python_version < grpc-interceptor==0.15.4 ; python_version >= "3.9" and python_version < "3.13" grpcio-reflection==1.62.3 ; python_version >= "3.9" and python_version < "3.13" grpcio-status==1.62.3 ; python_version >= "3.9" and python_version < "3.13" -grpcio==1.66.1 ; python_version >= "3.9" and python_version < "3.13" +grpcio==1.67.0 ; python_version >= "3.9" and python_version < "3.13" hf-transfer==0.1.8 ; python_version >= "3.9" and python_version < "3.13" huggingface-hub==0.23.5 ; python_version >= "3.9" and python_version < "3.13" idna==3.10 ; python_version >= "3.9" and python_version < "3.13" @@ -38,14 +38,14 @@ pygments==2.18.0 ; python_version >= "3.9" and python_version < "3.13" pyyaml==6.0.2 ; python_version >= "3.9" and python_version < "3.13" regex==2024.9.11 ; python_version >= "3.9" and python_version < "3.13" requests==2.32.3 ; python_version >= "3.9" and python_version < "3.13" -rich==13.8.1 ; python_version >= "3.9" and python_version < "3.13" +rich==13.9.3 ; python_version >= "3.9" and python_version < "3.13" safetensors==0.4.5 ; python_version >= "3.9" and python_version < "3.13" scipy==1.13.1 ; python_version >= "3.9" and python_version < "3.13" sentencepiece==0.2.0 ; python_version >= "3.9" and python_version < "3.13" -setuptools==75.1.0 ; python_version >= "3.9" and python_version < "3.13" -tokenizers==0.20.0 ; python_version >= "3.9" and python_version < "3.13" +setuptools==75.2.0 ; python_version >= "3.9" and python_version < "3.13" +tokenizers==0.20.1 ; python_version >= "3.9" and python_version < "3.13" tqdm==4.66.5 ; python_version >= "3.9" and python_version < "3.13" -transformers==4.45.0 ; python_version >= "3.9" and python_version < "3.13" +transformers==4.45.2 ; python_version >= "3.9" and python_version < "3.13" typer==0.6.1 ; python_version >= "3.9" and python_version < "3.13" typing-extensions==4.12.2 ; python_version >= "3.9" and python_version < "3.13" urllib3==2.2.3 ; python_version >= "3.9" and python_version < "3.13" diff --git a/server/requirements_intel.txt b/server/requirements_intel.txt index 5de75b6b3e3..e3f6d20f8c7 100644 --- a/server/requirements_intel.txt +++ b/server/requirements_intel.txt @@ -1,5 +1,5 @@ certifi==2024.8.30 ; python_version >= "3.9" and python_version < "3.13" -charset-normalizer==3.3.2 ; python_version >= "3.9" and python_version < "3.13" +charset-normalizer==3.4.0 ; python_version >= "3.9" and python_version < "3.13" click==8.1.7 ; python_version >= "3.9" and python_version < "3.13" colorama==0.4.6 ; python_version >= "3.9" and python_version < "3.13" and (sys_platform == "win32" or platform_system == "Windows") deprecated==1.2.14 ; python_version >= "3.9" and python_version < "3.13" @@ -10,7 +10,7 @@ googleapis-common-protos==1.65.0 ; python_version >= "3.9" and python_version < grpc-interceptor==0.15.4 ; python_version >= "3.9" and python_version < "3.13" grpcio-reflection==1.62.3 ; python_version >= "3.9" and python_version < "3.13" grpcio-status==1.62.3 ; python_version >= "3.9" and python_version < "3.13" -grpcio==1.66.1 ; python_version >= "3.9" and python_version < "3.13" +grpcio==1.67.0 ; python_version >= "3.9" and python_version < "3.13" hf-transfer==0.1.8 ; python_version >= "3.9" and python_version < "3.13" huggingface-hub==0.23.5 ; python_version >= "3.9" and python_version < "3.13" idna==3.10 ; python_version >= "3.9" and python_version < "3.13" @@ -38,14 +38,14 @@ pygments==2.18.0 ; python_version >= "3.9" and python_version < "3.13" pyyaml==6.0.2 ; python_version >= "3.9" and python_version < "3.13" regex==2024.9.11 ; python_version >= "3.9" and python_version < "3.13" requests==2.32.3 ; python_version >= "3.9" and python_version < "3.13" -rich==13.8.1 ; python_version >= "3.9" and python_version < "3.13" +rich==13.9.3 ; python_version >= "3.9" and python_version < "3.13" safetensors==0.4.5 ; python_version >= "3.9" and python_version < "3.13" scipy==1.13.1 ; python_version >= "3.9" and python_version < "3.13" sentencepiece==0.2.0 ; python_version >= "3.9" and python_version < "3.13" -setuptools==75.1.0 ; python_version >= "3.9" and python_version < "3.13" -tokenizers==0.20.0 ; python_version >= "3.9" and python_version < "3.13" +setuptools==75.2.0 ; python_version >= "3.9" and python_version < "3.13" +tokenizers==0.20.1 ; python_version >= "3.9" and python_version < "3.13" tqdm==4.66.5 ; python_version >= "3.9" and python_version < "3.13" -transformers==4.45.0 ; python_version >= "3.9" and python_version < "3.13" +transformers==4.45.2 ; python_version >= "3.9" and python_version < "3.13" typer==0.6.1 ; python_version >= "3.9" and python_version < "3.13" typing-extensions==4.12.2 ; python_version >= "3.9" and python_version < "3.13" urllib3==2.2.3 ; python_version >= "3.9" and python_version < "3.13" diff --git a/server/requirements_rocm.txt b/server/requirements_rocm.txt index 5de75b6b3e3..e3f6d20f8c7 100644 --- a/server/requirements_rocm.txt +++ b/server/requirements_rocm.txt @@ -1,5 +1,5 @@ certifi==2024.8.30 ; python_version >= "3.9" and python_version < "3.13" -charset-normalizer==3.3.2 ; python_version >= "3.9" and python_version < "3.13" +charset-normalizer==3.4.0 ; python_version >= "3.9" and python_version < "3.13" click==8.1.7 ; python_version >= "3.9" and python_version < "3.13" colorama==0.4.6 ; python_version >= "3.9" and python_version < "3.13" and (sys_platform == "win32" or platform_system == "Windows") deprecated==1.2.14 ; python_version >= "3.9" and python_version < "3.13" @@ -10,7 +10,7 @@ googleapis-common-protos==1.65.0 ; python_version >= "3.9" and python_version < grpc-interceptor==0.15.4 ; python_version >= "3.9" and python_version < "3.13" grpcio-reflection==1.62.3 ; python_version >= "3.9" and python_version < "3.13" grpcio-status==1.62.3 ; python_version >= "3.9" and python_version < "3.13" -grpcio==1.66.1 ; python_version >= "3.9" and python_version < "3.13" +grpcio==1.67.0 ; python_version >= "3.9" and python_version < "3.13" hf-transfer==0.1.8 ; python_version >= "3.9" and python_version < "3.13" huggingface-hub==0.23.5 ; python_version >= "3.9" and python_version < "3.13" idna==3.10 ; python_version >= "3.9" and python_version < "3.13" @@ -38,14 +38,14 @@ pygments==2.18.0 ; python_version >= "3.9" and python_version < "3.13" pyyaml==6.0.2 ; python_version >= "3.9" and python_version < "3.13" regex==2024.9.11 ; python_version >= "3.9" and python_version < "3.13" requests==2.32.3 ; python_version >= "3.9" and python_version < "3.13" -rich==13.8.1 ; python_version >= "3.9" and python_version < "3.13" +rich==13.9.3 ; python_version >= "3.9" and python_version < "3.13" safetensors==0.4.5 ; python_version >= "3.9" and python_version < "3.13" scipy==1.13.1 ; python_version >= "3.9" and python_version < "3.13" sentencepiece==0.2.0 ; python_version >= "3.9" and python_version < "3.13" -setuptools==75.1.0 ; python_version >= "3.9" and python_version < "3.13" -tokenizers==0.20.0 ; python_version >= "3.9" and python_version < "3.13" +setuptools==75.2.0 ; python_version >= "3.9" and python_version < "3.13" +tokenizers==0.20.1 ; python_version >= "3.9" and python_version < "3.13" tqdm==4.66.5 ; python_version >= "3.9" and python_version < "3.13" -transformers==4.45.0 ; python_version >= "3.9" and python_version < "3.13" +transformers==4.45.2 ; python_version >= "3.9" and python_version < "3.13" typer==0.6.1 ; python_version >= "3.9" and python_version < "3.13" typing-extensions==4.12.2 ; python_version >= "3.9" and python_version < "3.13" urllib3==2.2.3 ; python_version >= "3.9" and python_version < "3.13" diff --git a/server/text_generation_server/layers/attention/__init__.py b/server/text_generation_server/layers/attention/__init__.py index b1d7b864a29..ebe32042c46 100644 --- a/server/text_generation_server/layers/attention/__init__.py +++ b/server/text_generation_server/layers/attention/__init__.py @@ -28,10 +28,11 @@ raise ImportError(f"System {SYSTEM} doesn't support flash/paged attention") # KVCache needs `reshape_and_cache`, so ensure that it is defined already. -from .kv_cache import KVCache +from .kv_cache import KVCache, get_kv_scales __all__ = [ "attention", + "get_kv_scales", "paged_attention", "SUPPORTS_WINDOWING", "KVCache", diff --git a/server/text_generation_server/layers/attention/cuda.py b/server/text_generation_server/layers/attention/cuda.py index 23f3404cc70..d705afb0bd2 100644 --- a/server/text_generation_server/layers/attention/cuda.py +++ b/server/text_generation_server/layers/attention/cuda.py @@ -1,5 +1,5 @@ import torch -from text_generation_server.layers.attention.kv_cache import KVCache +from text_generation_server.layers.attention.kv_cache import KVCache, KVScales from text_generation_server.utils.import_utils import SYSTEM from text_generation_server.models.globals import ( ATTENTION, @@ -8,6 +8,7 @@ from text_generation_server.layers.attention import Seqlen from typing import Optional + major, minor = torch.cuda.get_device_capability() is_sm75 = major == 7 and minor == 5 _PARTITION_SIZE = 512 @@ -21,6 +22,8 @@ def paged_attention( block_tables: torch.Tensor, seqlen: Seqlen, max_s: int, + *, + kv_scales: KVScales, softcap: Optional[float] = None, ): # Adapted from: https://github.com/vllm-project/vllm/blob/f8a1e39fae05ca610be8d5a78be9d40f5274e5fc/vllm/model_executor/layers/attention.py @@ -46,6 +49,8 @@ def paged_attention( num_seqs, num_heads, head_size = query.shape max_num_partitions = (max_s + _PARTITION_SIZE - 1) // _PARTITION_SIZE + can_scale = kv_cache.can_scale(kv_scales) + # NOTE(woosuk): We use a simple heuristic to decide whether to use # PagedAttention V1 or V2. If the number of partitions is 1, we use # V1 to avoid the overhead of reduction. Also, if the number of @@ -55,10 +60,13 @@ def paged_attention( from text_generation_server.layers.attention.flashinfer import decode_state return decode_state.get().forward( + # TODO: remove `contiguous` call once https://github.com/flashinfer-ai/flashinfer/pull/553 is merged. query.contiguous(), paged_kv_cache=(kv_cache.key, kv_cache.value), logits_soft_cap=softcap, sm_scale=softmax_scale, + k_scale=kv_scales.key_scale_cpu if can_scale else 1.0, + v_scale=kv_scales.value_scale_cpu if can_scale else 1.0, ) elif ATTENTION == "flashdecoding": max_q = 1 @@ -204,6 +212,7 @@ def attention( key: torch.Tensor, value: torch.Tensor, kv_cache: KVCache, + kv_scales: KVScales, seqlen: Seqlen, block_tables: torch.Tensor, softmax_scale: float, @@ -211,6 +220,8 @@ def attention( causal: bool = True, softcap: Optional[float] = None, ): + can_scale = kv_cache.can_scale(kv_scales) + if ATTENTION == "flashinfer": from text_generation_server.layers.attention.flashinfer import ( prefill_with_paged_kv_state, @@ -220,12 +231,15 @@ def attention( softcap = 0.0 return prefill_with_paged_kv_state.get().forward( + # TODO: remove `contiguous` call once https://github.com/flashinfer-ai/flashinfer/pull/553 is merged. query.contiguous(), causal=causal, paged_kv_cache=(kv_cache.key, kv_cache.value), logits_soft_cap=softcap, sm_scale=softmax_scale, window_left=window_size_left, + k_scale=kv_scales.key_scale_cpu if can_scale else 1.0, + v_scale=kv_scales.value_scale_cpu if can_scale else 1.0, ) # If we are using flashdecoding or paged, we always use flash-attn for diff --git a/server/text_generation_server/layers/attention/flashinfer.py b/server/text_generation_server/layers/attention/flashinfer.py index d603c6f5f03..26a72d9be71 100644 --- a/server/text_generation_server/layers/attention/flashinfer.py +++ b/server/text_generation_server/layers/attention/flashinfer.py @@ -204,6 +204,7 @@ def use_decode_state( num_kv_heads: int, head_size: int, page_size: int, + kv_cache_dtype: torch.dtype, dtype: torch.dtype, window_left: int, ): @@ -240,7 +241,7 @@ def use_decode_state( num_kv_heads=num_kv_heads, head_dim=head_size, page_size=page_size, - data_type=dtype, + data_type=kv_cache_dtype, q_data_type=dtype, window_left=window_left, ) diff --git a/server/text_generation_server/layers/attention/ipex.py b/server/text_generation_server/layers/attention/ipex.py index e76bb1f42fd..677f3f5647d 100644 --- a/server/text_generation_server/layers/attention/ipex.py +++ b/server/text_generation_server/layers/attention/ipex.py @@ -1,6 +1,6 @@ import intel_extension_for_pytorch as ipex import torch -from text_generation_server.layers.attention.kv_cache import KVCache +from text_generation_server.layers.attention.kv_cache import KVCache, KVScales from text_generation_server.models.flash_causal_lm import BLOCK_SIZE from text_generation_server.layers.attention import Seqlen from typing import Optional @@ -14,6 +14,7 @@ def attention( key: torch.Tensor, value: torch.Tensor, kv_cache: KVCache, + kv_scales: KVScales, seqlen: Seqlen, block_tables: torch.Tensor, softmax_scale: float, @@ -55,6 +56,8 @@ def paged_attention( block_tables: torch.Tensor, seqlen: Seqlen, max_s: int, + *, + kv_scales: KVScales, softcap: Optional[float] = None, ): if softcap is not None: diff --git a/server/text_generation_server/layers/attention/kv_cache.py b/server/text_generation_server/layers/attention/kv_cache.py index d64302c651c..9d739da5ee1 100644 --- a/server/text_generation_server/layers/attention/kv_cache.py +++ b/server/text_generation_server/layers/attention/kv_cache.py @@ -1,8 +1,38 @@ from typing import Tuple +from dataclasses import dataclass, field +from loguru import logger import torch + +from text_generation_server.layers.fp8 import fp8_quantize from text_generation_server.models.globals import ATTENTION, BLOCK_SIZE from text_generation_server.utils.import_utils import SYSTEM +from text_generation_server.utils.log import log_once +from text_generation_server.utils.weights import Weights + + +@dataclass +class KVScales: + """ + Key-value scales for FP8 KV cache. + + This data class stores key and value scales both as a GPU tensor and + as a GPU float. This inconvenience is necessary because some functions + (e.g. scaling kernels) take scales as a GPU tensor, whereas others + (e.g. flashinfer) take scales as a CPU scalar. + """ + + key_scale: torch.Tensor + value_scale: torch.Tensor + key_scale_cpu: float = field(init=False) + value_scale_cpu: float = field(init=False) + + def __post_init__(self): + if self.key_scale.numel() != 1 or self.value_scale.numel() != 1: + raise ValueError("Key and value scales must be scalar tensors.") + + self.key_scale_cpu = self.key_scale.item() + self.value_scale_cpu = self.value_scale.item() class KVCache: @@ -76,6 +106,33 @@ def __init__( ), ) + def can_scale(self, kv_scales: KVScales) -> bool: + """Check if the cache can be scaled by the given scales.""" + if kv_scales.key_scale_cpu == 1.0 and kv_scales.value_scale_cpu == 1.0: + return False + elif ( + self.dtype == torch.float8_e4m3fn + and ATTENTION == "flashinfer" + and SYSTEM == "cuda" + ): + log_once( + logger.info, + "Using FP8 KV cache scales", + ) + return True + else: + # We have scales, but not the correct FP8 cache type, so warn once. + log_once( + logger.info, + "Ignoring FP8 KV cache scales, only float8_e4m3fn KV cache on flashinfer is supported", + ) + return False + + @property + def dtype(self): + """Get the data type of the cache.""" + return self.kv_cache[0].dtype + @property def key(self): """Get the key cache.""" @@ -94,17 +151,33 @@ def store( key: torch.Tensor, value: torch.Tensor, slots: torch.Tensor, + kv_scales: KVScales, ): """Store the key and value at the given slots.""" key_cache = self.kv_cache[0] value_cache = self.kv_cache[1] + if self.can_scale(kv_scales): + if kv_scales.key_scale_cpu != 1.0: + key = fp8_quantize( + key.float(), + scale=kv_scales.key_scale, + qdtype=self.dtype, + scalar=True, + )[0] + if kv_scales.value_scale_cpu != 1.0: + value = fp8_quantize( + value.float(), + scale=kv_scales.value_scale, + qdtype=self.dtype, + scalar=True, + )[0] + if ATTENTION in {"flashdecoding", "flashinfer"}: - # TODO: add scale key = key.to(key_cache.dtype) value = value.to(value_cache.dtype) - if key_cache.dtype in {torch.float8_e5m2, torch.float8_e4m3fn}: + if key_cache.dtype in {torch.float8_e4m3fn, torch.float8_e5m2}: # Torch index_put does not support float8_{e5m2,e4m3fn} yet, so # put as raw data instead. key_cache = key_cache.view(torch.uint8) @@ -151,5 +224,23 @@ def paged_reshape_and_cache( ) else: raise NotImplementedError( - f"Cannot reshape and cache for paged attention, system '{SYSTEM}' not supportedattention" + f"Cannot reshape and cache for paged attention, system '{SYSTEM}' not supported" ) + + +def get_kv_scales(weights: Weights, prefix: str) -> KVScales: + """Load KV cache scales.""" + + key_scale = torch.tensor(1.0, dtype=torch.float32, device=weights.device) + value_scale = key_scale + if weights.has_tensor(f"{prefix}.k_scale") and weights.has_tensor( + f"{prefix}.v_scale" + ): + key_scale = weights.get_tensor(f"{prefix}.k_scale", to_dtype=False).float() + value_scale = weights.get_tensor(f"{prefix}.v_scale", to_dtype=False).float() + elif weights.has_tensor(f"{prefix}.kv_scale"): + # Fall back to older more coarse-grained scale when available. + key_scale = weights.get_tensor(f"{prefix}.kv_scale").float() + value_scale = key_scale + + return KVScales(key_scale=key_scale, value_scale=value_scale) diff --git a/server/text_generation_server/layers/attention/rocm.py b/server/text_generation_server/layers/attention/rocm.py index 47bf5539c56..ea11c2c2615 100644 --- a/server/text_generation_server/layers/attention/rocm.py +++ b/server/text_generation_server/layers/attention/rocm.py @@ -1,7 +1,7 @@ import os from typing import Optional import torch -from text_generation_server.layers.attention.kv_cache import KVCache +from text_generation_server.layers.attention.kv_cache import KVCache, KVScales from text_generation_server.utils.import_utils import SYSTEM from text_generation_server.layers.attention import Seqlen from text_generation_server.utils.log import log_master @@ -36,6 +36,8 @@ def paged_attention( block_tables: torch.Tensor, seqlen: Seqlen, max_s: int, + *, + kv_scales: KVScales, softcap: Optional[float] = None, ): # Adapted from: https://github.com/vllm-project/vllm/blob/f8a1e39fae05ca610be8d5a78be9d40f5274e5fc/vllm/model_executor/layers/attention.py @@ -210,6 +212,7 @@ def attention( key: torch.Tensor, value: torch.Tensor, kv_cache: KVCache, + kv_scales: KVScales, seqlen: Seqlen, block_tables: torch.Tensor, softmax_scale: float, diff --git a/server/text_generation_server/layers/fp8.py b/server/text_generation_server/layers/fp8.py index 18a40afa3db..a58c7f7b223 100644 --- a/server/text_generation_server/layers/fp8.py +++ b/server/text_generation_server/layers/fp8.py @@ -26,6 +26,12 @@ def is_fbgemm_gpu_available(): return False +try: + import marlin_kernels +except ImportError: + marlin_kernels = None + + if is_fbgemm_gpu_available(): if SYSTEM == "cuda": major, _ = torch.cuda.get_device_capability() @@ -94,6 +100,17 @@ def fp8_quantize( ) return qweight, scale + if marlin_kernels is not None: + shape = weight.shape + qweight, scale = marlin_kernels.scaled_fp8_quant( + weight.reshape(-1, shape[-1]), + dtype=qdtype, + scale=scale, + scale_ub=scale_upper_bound, + ) + + return qweight.reshape(shape), scale + # weight, scale = quant_weights(weight, torch.int8, False) finfo = torch.finfo(qdtype) diff --git a/server/text_generation_server/models/__init__.py b/server/text_generation_server/models/__init__.py index 0860e9ee731..d30154083f5 100644 --- a/server/text_generation_server/models/__init__.py +++ b/server/text_generation_server/models/__init__.py @@ -195,6 +195,11 @@ class ModelType(enum.Enum): "name": "Phi 3", "url": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct", } + GRANITE = { + "type": "granite", + "name": "Granite", + "url": "https://huggingface.co/ibm-granite/granite-3.0-8b-instruct", + } GEMMA = { "type": "gemma", "name": "Gemma", @@ -862,7 +867,12 @@ def get_model( trust_remote_code=trust_remote_code, ) - elif model_type == LLAMA or model_type == BAICHUAN or model_type == PHI3: + elif ( + model_type == LLAMA + or model_type == BAICHUAN + or model_type == PHI3 + or model_type == GRANITE + ): if FLASH_ATTENTION: return FlashCausalLM( model_id=model_id, @@ -876,7 +886,9 @@ def get_model( lora_adapter_ids=lora_adapter_ids, ) elif sharded: - raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Llama")) + raise NotImplementedError( + FLASH_ATT_ERROR_MESSAGE.format(f"Sharded {model_type}") + ) else: return CausalLM.fallback( model_id, diff --git a/server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py b/server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py index 4eee5c20894..68719106fca 100644 --- a/server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py @@ -30,6 +30,7 @@ attention, Seqlen, ) +from text_generation_server.layers.attention.kv_cache import get_kv_scales from text_generation_server.utils.import_utils import SYSTEM from text_generation_server.layers import ( TensorParallelRowLinear, @@ -227,6 +228,7 @@ def __init__( ) self.query_key_value = load_attention(config, prefix, weights) + self.kv_scales = get_kv_scales(weights, f"{prefix}") self.use_qk_norm = config.use_qk_norm if self.use_qk_norm: @@ -289,7 +291,12 @@ def forward( self.rotary_emb(query, key, cos, sin) - kv_cache.store(key=key, value=value, slots=slots) + kv_cache.store( + key=key, + value=value, + slots=slots, + kv_scales=self.kv_scales, + ) # Prefill if cu_seqlen_prefill is not None: @@ -299,6 +306,7 @@ def forward( key=key, value=value, kv_cache=kv_cache, + kv_scales=self.kv_scales, seqlen=seqlen, block_tables=block_tables, softmax_scale=self.softmax_scale, @@ -313,6 +321,7 @@ def forward( block_tables, seqlen, max_s, + kv_scales=self.kv_scales, ) return self.o_proj( diff --git a/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py b/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py index 4ee6774171f..f70bff4f881 100644 --- a/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py @@ -20,6 +20,7 @@ from transformers.activations import ACT2FN from transformers.configuration_utils import PretrainedConfig from typing import Optional, List, Tuple, Any +from text_generation_server.layers.attention.kv_cache import get_kv_scales from text_generation_server.utils.import_utils import SYSTEM if SYSTEM != "ipex": @@ -288,6 +289,7 @@ def __init__( ) self.query_key_value = load_attention(config, prefix, weights) + self.kv_scales = get_kv_scales(weights, f"{prefix}") self.o_proj = TensorParallelRowLinear.load( config, @@ -328,7 +330,12 @@ def forward( self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin) - kv_cache.store(key=kv[:, 0], value=kv[:, 1], slots=slots) + kv_cache.store( + key=kv[:, 0], + value=kv[:, 1], + slots=slots, + kv_scales=self.kv_scales, + ) # Prefill if cu_seqlen_prefill is not None: @@ -338,6 +345,7 @@ def forward( key=kv[:, 0], value=kv[:, 1], kv_cache=kv_cache, + kv_scales=self.kv_scales, seqlen=seqlen, block_tables=block_tables, softmax_scale=self.softmax_scale, @@ -352,6 +360,7 @@ def forward( block_tables, seqlen, max_s, + kv_scales=self.kv_scales, ) return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size)) diff --git a/server/text_generation_server/models/custom_modeling/flash_deepseek_v2_modeling.py b/server/text_generation_server/models/custom_modeling/flash_deepseek_v2_modeling.py index 97b3ea967d5..906a83a4151 100644 --- a/server/text_generation_server/models/custom_modeling/flash_deepseek_v2_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_deepseek_v2_modeling.py @@ -34,6 +34,7 @@ attention, paged_attention, ) +from text_generation_server.layers.attention.kv_cache import KVCache, get_kv_scales from text_generation_server.layers.layernorm import FastRMSNorm from text_generation_server.layers.moe import DenseMoELayer, MoELayer, SparseMoELayer from text_generation_server.layers.rotary import PositionRotaryEmbedding, get_mscale @@ -230,6 +231,8 @@ def __init__( ), ) + self.kv_scales = get_kv_scales(weights, f"{prefix}") + self.kv_a_layernorm = FastRMSNorm.load( prefix=f"{prefix}.kv_a_layernorm", weights=weights, eps=config.rms_norm_eps ) @@ -258,7 +261,7 @@ def forward( cos: torch.Tensor, sin: torch.Tensor, cu_seqlen_prefill: torch.Tensor, - kv_cache: Tuple[torch.Tensor, torch.Tensor], + kv_cache: KVCache, block_tables: torch.Tensor, slots: torch.Tensor, seqlen: Seqlen, @@ -319,7 +322,12 @@ def forward( value, (0, self.head_pad_size - self.value_head_size), value=0 ) - kv_cache.store(key=key, value=value, slots=slots) + kv_cache.store( + key=key, + value=value, + slots=slots, + kv_scales=self.kv_scales, + ) # Prefill if cu_seqlen_prefill is not None: @@ -329,6 +337,7 @@ def forward( key=key, value=value, kv_cache=kv_cache, + kv_scales=self.kv_scales, seqlen=seqlen, block_tables=block_tables, softmax_scale=self.softmax_scale, @@ -343,6 +352,7 @@ def forward( block_tables, seqlen, max_s, + kv_scales=self.kv_scales, ) # Remove padding. diff --git a/server/text_generation_server/models/custom_modeling/flash_gemma2_modeling.py b/server/text_generation_server/models/custom_modeling/flash_gemma2_modeling.py index c962a2aff41..ebf1b80eb03 100644 --- a/server/text_generation_server/models/custom_modeling/flash_gemma2_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_gemma2_modeling.py @@ -39,6 +39,7 @@ TensorParallelMultiAdapterLinear, TensorParallelAdapterRowLinear, ) +from text_generation_server.layers.attention.kv_cache import get_kv_scales from text_generation_server.layers.rotary import PositionRotaryEmbedding from text_generation_server.layers.layernorm import ( FastRMSNorm, @@ -206,6 +207,7 @@ def __init__( ], process_group=weights.process_group, ) + self.kv_scales = get_kv_scales(weights, f"{prefix}") o_proj = TensorParallelRowLinear.load( config, @@ -251,7 +253,12 @@ def forward( self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin) - kv_cache.store(key=kv[:, 0], value=kv[:, 1], slots=slots) + kv_cache.store( + key=kv[:, 0], + value=kv[:, 1], + slots=slots, + kv_scales=self.kv_scales, + ) # Prefill if cu_seqlen_prefill is not None: @@ -261,6 +268,7 @@ def forward( key=kv[:, 0], value=kv[:, 1], kv_cache=kv_cache, + kv_scales=self.kv_scales, seqlen=seqlen, block_tables=block_tables, softmax_scale=self.softmax_scale, @@ -278,6 +286,7 @@ def forward( seqlen, max_s, softcap=self.softcap, + kv_scales=self.kv_scales, ) return self.o_proj( diff --git a/server/text_generation_server/models/custom_modeling/flash_gemma_modeling.py b/server/text_generation_server/models/custom_modeling/flash_gemma_modeling.py index b127f2843c2..ad3be80e51d 100644 --- a/server/text_generation_server/models/custom_modeling/flash_gemma_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_gemma_modeling.py @@ -37,6 +37,7 @@ SpeculativeHead, get_linear, ) +from text_generation_server.layers.attention.kv_cache import get_kv_scales from text_generation_server.layers.rotary import PositionRotaryEmbedding from text_generation_server.layers.layernorm import ( FastRMSNorm, @@ -185,6 +186,7 @@ def __init__(self, prefix: str, config, weights, causal: bool): ) self.query_key_value = load_attention(config, prefix, weights) + self.kv_scales = get_kv_scales(weights, f"{prefix}") self.o_proj = TensorParallelRowLinear.load( config, @@ -222,7 +224,12 @@ def forward( self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin) - kv_cache.store(key=kv[:, 0], value=kv[:, 1], slots=slots) + kv_cache.store( + key=kv[:, 0], + value=kv[:, 1], + slots=slots, + kv_scales=self.kv_scales, + ) # Prefill if cu_seqlen_prefill is not None: @@ -232,6 +239,7 @@ def forward( key=kv[:, 0], value=kv[:, 1], kv_cache=kv_cache, + kv_scales=self.kv_scales, seqlen=seqlen, block_tables=block_tables, softmax_scale=self.softmax_scale, @@ -247,6 +255,7 @@ def forward( block_tables, seqlen, max_s, + kv_scales=self.kv_scales, ) return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size)) diff --git a/server/text_generation_server/models/custom_modeling/flash_gpt2_modeling.py b/server/text_generation_server/models/custom_modeling/flash_gpt2_modeling.py index 2d005734b89..906b34c12b2 100644 --- a/server/text_generation_server/models/custom_modeling/flash_gpt2_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_gpt2_modeling.py @@ -36,6 +36,7 @@ SpeculativeHead, get_linear, ) +from text_generation_server.layers.attention.kv_cache import get_kv_scales def load_qkv(config, prefix: str, weights, head_size, num_heads): @@ -193,6 +194,7 @@ def __init__( head_size=self.head_size, num_heads=self.num_heads, ) + self.kv_scales = get_kv_scales(weights, f"{prefix}") self.o_proj = load_row( config, @@ -222,7 +224,12 @@ def forward( key = key.view(-1, self.num_heads, self.head_size) value = value.view(-1, self.num_heads, self.head_size) - kv_cache.store(key=key, value=value, slots=slots) + kv_cache.store( + key=key, + value=value, + slots=slots, + kv_scales=self.kv_scales, + ) # Prefill if cu_seqlen_prefill is not None: @@ -232,6 +239,7 @@ def forward( key=key, value=value, kv_cache=kv_cache, + kv_scales=self.kv_scales, seqlen=seqlen, block_tables=block_tables, softmax_scale=self.softmax_scale, @@ -246,6 +254,7 @@ def forward( block_tables, seqlen, max_s, + kv_scales=self.kv_scales, ) return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size)) diff --git a/server/text_generation_server/models/custom_modeling/flash_gptj_modeling.py b/server/text_generation_server/models/custom_modeling/flash_gptj_modeling.py index 2eef1dedc57..692f8ca31be 100644 --- a/server/text_generation_server/models/custom_modeling/flash_gptj_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_gptj_modeling.py @@ -24,6 +24,7 @@ from torch import nn from transformers.activations import ACT2FN from typing import Optional, List, Tuple +from text_generation_server.layers.attention.kv_cache import get_kv_scales from text_generation_server.utils.import_utils import SYSTEM from text_generation_server.layers.attention import ( paged_attention, @@ -138,6 +139,7 @@ def __init__( prefix=prefix, weights=weights, ) + self.kv_scales = get_kv_scales(weights, f"{prefix}") self.o_proj = load_row( config, @@ -184,7 +186,12 @@ def forward( else: self.rotary_emb(query, key, cos, sin) - kv_cache.store(key=key, value=value, slots=slots) + kv_cache.store( + key=key, + value=value, + slots=slots, + kv_scales=self.kv_scales, + ) # Prefill if cu_seqlen_prefill is not None: @@ -194,6 +201,7 @@ def forward( key=key, value=value, kv_cache=kv_cache, + kv_scales=self.kv_scales, seqlen=seqlen, block_tables=block_tables, softmax_scale=self.softmax_scale, @@ -208,6 +216,7 @@ def forward( block_tables, seqlen, max_s, + kv_scales=self.kv_scales, ) return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size)) diff --git a/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py b/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py index 5c820bb60d0..b26dd484942 100644 --- a/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py @@ -27,7 +27,10 @@ from torch import nn from transformers.activations import ACT2FN -from text_generation_server.layers.attention import KVCache +from text_generation_server.layers.attention import ( + KVCache, + get_kv_scales, +) from text_generation_server.layers.moe import DenseMoELayer, MoELayer, SparseMoELayer from text_generation_server.utils.import_utils import SYSTEM from text_generation_server.layers.attention import ( @@ -156,7 +159,10 @@ def __init__( device=weights.device, ) - self.softmax_scale = self.head_size**-0.5 + # `config.attention_multiplier` is used in Granite + self.softmax_scale = getattr( + config, "attention_multiplier", self.head_size**-0.5 + ) if self.num_heads % weights.process_group.size() != 0: raise ValueError( @@ -176,11 +182,13 @@ def __init__( self.query_key_value = load_attention(config, prefix, weights, index) self.index = index + self.kv_scales = get_kv_scales(weights, f"{prefix}") + o_proj = TensorParallelRowLinear.load( config, prefix=f"{prefix}.o_proj", weights=weights, - bias=False, + bias=getattr(config, "attention_bias", False), ) self.o_proj = TensorParallelAdapterRowLinear.load( @@ -221,7 +229,12 @@ def forward( self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin) - kv_cache.store(key=kv[:, 0], value=kv[:, 1], slots=slots) + kv_cache.store( + key=kv[:, 0], + value=kv[:, 1], + slots=slots, + kv_scales=self.kv_scales, + ) # Prefill if cu_seqlen_prefill is not None: @@ -230,6 +243,7 @@ def forward( query=query, key=kv[:, 0], value=kv[:, 1], + kv_scales=self.kv_scales, kv_cache=kv_cache, seqlen=seqlen, block_tables=block_tables, @@ -245,6 +259,7 @@ def forward( block_tables, seqlen, max_s, + kv_scales=self.kv_scales, ) return self.o_proj( @@ -436,6 +451,11 @@ def __init__(self, index, prefix, config, weights): eps=config.rms_norm_eps, ) + # Used in Granite + # This could eventually be baked into the weights like we do for the embeddings/lm_head + # but this would mean modifying the lora code + self.residual_multiplier = getattr(config, "residual_multiplier", None) + def forward( self, hidden_states, @@ -466,13 +486,16 @@ def forward( max_s, adapter_data, ) + if self.residual_multiplier is not None: + attn_output *= self.residual_multiplier - # faster post attention rms norm normed_attn_res_output, attn_res = self.post_attention_layernorm( attn_output, res ) mlp_output = self.dense(normed_attn_res_output, adapter_data) + if self.residual_multiplier is not None: + mlp_output *= self.residual_multiplier return mlp_output, attn_res @@ -624,6 +647,11 @@ def __init__(self, prefix: str, config, weights): else: suffix = "lm_head" + # Used in Granite + embedding_multiplier = getattr(config, "embedding_multiplier", None) + if embedding_multiplier is not None: + self.embed_tokens.weight.data *= embedding_multiplier + with no_fp8(weights): self.lm_head = SpeculativeHead.load( config, @@ -631,6 +659,16 @@ def __init__(self, prefix: str, config, weights): weights=weights, ) + # Used in Granite + self.logits_scaling = getattr(config, "logits_scaling", None) + if self.logits_scaling is not None and self.lm_head.head is not None: + try: + # Scale the weights directly + self.lm_head.head.linear.weight.data /= self.logits_scaling + self.logits_scaled = True + except Exception: + self.logits_scaled = False + def forward( self, input_ids: torch.Tensor, @@ -664,4 +702,11 @@ def forward( if lm_head_indices is not None: hidden_states = hidden_states[lm_head_indices] logits, speculative_logits = self.lm_head(hidden_states) + + # Used in Granite + if self.logits_scaling is not None and not self.logits_scaled: + logits /= self.logits_scaling + if speculative_logits is not None: + speculative_logits /= self.logits_scaling + return logits, speculative_logits diff --git a/server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py b/server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py index 7bad429c3f0..c66c732f21d 100644 --- a/server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py @@ -26,6 +26,7 @@ from transformers.configuration_utils import PretrainedConfig from typing import Optional, List, Tuple +from text_generation_server.layers.attention.kv_cache import get_kv_scales from text_generation_server.utils.import_utils import SYSTEM from text_generation_server.layers.attention import ( paged_attention, @@ -158,6 +159,7 @@ def __init__(self, prefix: str, config, weights, layer_id): ], process_group=weights.process_group, ) + self.kv_scales = get_kv_scales(weights, f"{prefix}") o_proj = TensorParallelRowLinear.load( config, @@ -208,7 +210,12 @@ def forward( else: kv_to_cache = kv - kv_cache.store(key=kv_to_cache[:, 0], value=kv_to_cache[:, 1], slots=slots) + kv_cache.store( + key=kv_to_cache[:, 0], + value=kv_to_cache[:, 1], + slots=slots, + kv_scales=self.kv_scales, + ) # Prefill if cu_seqlen_prefill is not None: @@ -218,6 +225,7 @@ def forward( key=kv_to_cache[:, 0], value=kv_to_cache[:, 1], kv_cache=kv_cache, + kv_scales=self.kv_scales, seqlen=seqlen, block_tables=block_tables, softmax_scale=self.softmax_scale, @@ -233,6 +241,7 @@ def forward( block_tables, seqlen, max_s, + kv_scales=self.kv_scales, ) return self.o_proj( diff --git a/server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py b/server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py index 712b7bc4602..a45dd1e615e 100644 --- a/server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py @@ -38,6 +38,7 @@ attention, paged_attention, ) +from text_generation_server.layers.attention.kv_cache import get_kv_scales from text_generation_server.layers.layernorm import FastRMSNorm from text_generation_server.layers.moe import DenseMoELayer, MoELayer, SparseMoELayer from text_generation_server.layers.rotary import PositionRotaryEmbedding @@ -213,6 +214,7 @@ def __init__( ) self.query_key_value = load_attention(config, prefix, weights) + self.kv_scales = get_kv_scales(weights, f"{prefix}") self.o_proj = TensorParallelRowLinear.load( config, @@ -256,7 +258,12 @@ def forward( else: kv_to_cache = kv - kv_cache.store(key=kv_to_cache[:, 0], value=kv_to_cache[:, 1], slots=slots) + kv_cache.store( + key=kv_to_cache[:, 0], + value=kv_to_cache[:, 1], + slots=slots, + kv_scales=self.kv_scales, + ) # Prefill if cu_seqlen_prefill is not None: @@ -266,6 +273,7 @@ def forward( key=kv_to_cache[:, 0], value=kv_to_cache[:, 1], kv_cache=kv_cache, + kv_scales=self.kv_scales, seqlen=seqlen, block_tables=block_tables, softmax_scale=self.softmax_scale, @@ -281,6 +289,7 @@ def forward( block_tables, seqlen, max_s, + kv_scales=self.kv_scales, ) return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size)) diff --git a/server/text_generation_server/models/custom_modeling/flash_neox_modeling.py b/server/text_generation_server/models/custom_modeling/flash_neox_modeling.py index 2ce69d8ea0b..2301b63cff2 100644 --- a/server/text_generation_server/models/custom_modeling/flash_neox_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_neox_modeling.py @@ -38,6 +38,7 @@ SpeculativeHead, get_linear, ) +from text_generation_server.layers.attention.kv_cache import get_kv_scales from text_generation_server.layers.layernorm import ( FastLayerNorm, ) @@ -130,6 +131,7 @@ def __init__(self, config, prefix, weights): head_size=self.head_size, hidden_size=self.hidden_size, ) + self.kv_scales = get_kv_scales(weights, f"{prefix}") self.dense = load_row( config, prefix=f"{prefix}.dense", weights=weights, bias=True ) @@ -163,7 +165,12 @@ def forward( qkv[:, 0] = torch.cat((query_rot, query_pass), dim=-1) qkv[:, 1] = torch.cat((key_rot, key_pass), dim=-1) - kv_cache.store(key=qkv[:, 1], value=qkv[:, 2], slots=slots) + kv_cache.store( + key=qkv[:, 1], + value=qkv[:, 2], + slots=slots, + kv_scales=self.kv_scales, + ) # Prefill if cu_seqlen_prefill is not None: @@ -173,6 +180,7 @@ def forward( key=qkv[:, 1], value=qkv[:, 2], kv_cache=kv_cache, + kv_scales=self.kv_scales, seqlen=seqlen, block_tables=block_tables, softmax_scale=self.softmax_scale, @@ -187,6 +195,7 @@ def forward( block_tables, seqlen, max_s, + kv_scales=self.kv_scales, ) return self.dense(attn_output.view(-1, self.num_heads * self.head_size)) diff --git a/server/text_generation_server/models/custom_modeling/flash_phi_modeling.py b/server/text_generation_server/models/custom_modeling/flash_phi_modeling.py index 62d524c9b5b..7382a7cb9fc 100644 --- a/server/text_generation_server/models/custom_modeling/flash_phi_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_phi_modeling.py @@ -18,6 +18,7 @@ SpeculativeHead, get_linear, ) +from text_generation_server.layers.attention.kv_cache import get_kv_scales from text_generation_server.layers.layernorm import ( FastLayerNorm, ) @@ -137,6 +138,7 @@ def __init__( ) self.query_key_value = load_attention(config, prefix, weights) + self.kv_scales = get_kv_scales(weights, f"{prefix}") # in llama the dense layer is called "o_proj" and has bias=False self.dense = TensorParallelRowLinear.load( @@ -186,7 +188,12 @@ def forward( ) # Reshape key and value and cache - kv_cache.store(key=kv[:, 0], value=kv[:, 1], slots=slots) + kv_cache.store( + key=kv[:, 0], + value=kv[:, 1], + slots=slots, + kv_scales=self.kv_scales, + ) # Prefill if cu_seqlen_prefill is not None: @@ -194,6 +201,7 @@ def forward( query=query, key=kv[:, 0], value=kv[:, 1], + kv_scales=self.kv_scales, kv_cache=kv_cache, seqlen=seqlen, block_tables=block_tables, @@ -209,6 +217,7 @@ def forward( block_tables, seqlen, max_s, + kv_scales=self.kv_scales, ) return self.dense(attn_output.view(-1, self.num_heads * self.head_size)) diff --git a/server/text_generation_server/models/custom_modeling/flash_qwen2_modeling.py b/server/text_generation_server/models/custom_modeling/flash_qwen2_modeling.py index 905dd98fcd6..ab2a177db6a 100644 --- a/server/text_generation_server/models/custom_modeling/flash_qwen2_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_qwen2_modeling.py @@ -16,6 +16,7 @@ TensorParallelEmbedding, SpeculativeHead, ) +from text_generation_server.layers.attention.kv_cache import get_kv_scales from text_generation_server.layers.rotary import PositionRotaryEmbedding from text_generation_server.layers.layernorm import ( FastRMSNorm, @@ -84,6 +85,8 @@ def __init__( self.query_key_value = load_attention(config, prefix, weights) + self.kv_scales = get_kv_scales(weights, f"{prefix}") + self.o_proj = TensorParallelRowLinear.load( config, prefix=f"{prefix}.o_proj", @@ -126,7 +129,12 @@ def forward( else: kv_to_cache = kv - kv_cache.store(key=kv_to_cache[:, 0], value=kv_to_cache[:, 1], slots=slots) + kv_cache.store( + key=kv_to_cache[:, 0], + value=kv_to_cache[:, 1], + slots=slots, + kv_scales=self.kv_scales, + ) # Prefill if cu_seqlen_prefill is not None: @@ -136,6 +144,7 @@ def forward( key=kv_to_cache[:, 0], value=kv_to_cache[:, 1], kv_cache=kv_cache, + kv_scales=self.kv_scales, seqlen=seqlen, block_tables=block_tables, softmax_scale=self.softmax_scale, @@ -151,6 +160,7 @@ def forward( block_tables, seqlen, max_s, + kv_scales=self.kv_scales, ) return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size)) diff --git a/server/text_generation_server/models/custom_modeling/flash_rw_modeling.py b/server/text_generation_server/models/custom_modeling/flash_rw_modeling.py index 8085ff8928b..2dcd1bf3033 100644 --- a/server/text_generation_server/models/custom_modeling/flash_rw_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_rw_modeling.py @@ -12,6 +12,7 @@ TensorParallelRowLinear, get_linear, ) +from text_generation_server.layers.attention.kv_cache import get_kv_scales from text_generation_server.layers.layernorm import FastLayerNorm from text_generation_server.layers.rotary import PositionRotaryEmbedding from text_generation_server.layers.attention import ( @@ -158,6 +159,7 @@ def __init__( weights=weights, bias=config.bias, ) + self.kv_scales = get_kv_scales(weights, f"{prefix}") self.dense = load_row( config, prefix=f"{prefix}.dense", weights=weights, bias=config.bias ) @@ -198,7 +200,12 @@ def forward( # Inplace rotary self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin) - kv_cache.store(key=kv[:, 0], value=kv[:, 1], slots=slots) + kv_cache.store( + key=kv[:, 0], + value=kv[:, 1], + slots=slots, + kv_scales=self.kv_scales, + ) # Prefill if cu_seqlen_prefill is not None: @@ -208,6 +215,7 @@ def forward( key=kv[:, 0], value=kv[:, 1], kv_cache=kv_cache, + kv_scales=self.kv_scales, seqlen=seqlen, block_tables=block_tables, softmax_scale=self.softmax_scale, @@ -222,6 +230,7 @@ def forward( block_tables, seqlen, max_s, + kv_scales=self.kv_scales, ) return self.dense(attn_output.view(-1, self.num_heads * self.head_size)) @@ -276,6 +285,7 @@ def __init__( weights=weights, bias=config.bias, ) + self.kv_scales = get_kv_scales(weights, f"{prefix}") self.dense = load_row( config, prefix=f"{prefix}.dense", weights=weights, bias=config.bias ) @@ -311,7 +321,10 @@ def forward( self.rotary_emb(query, torch.select(kv, dim=2, index=0), cos, sin) kv_cache.store( - key=kv[:, :, 0].contiguous(), value=kv[:, :, 1].contiguous(), slots=slots + key=kv[:, :, 0].contiguous(), + value=kv[:, :, 1].contiguous(), + slots=slots, + kv_scales=self.kv_scales, ) # Prefill @@ -322,6 +335,7 @@ def forward( key=kv[:, :, 0], value=kv[:, :, 1], kv_cache=kv_cache, + kv_scales=self.kv_scales, seqlen=seqlen, block_tables=block_tables, softmax_scale=self.softmax_scale, @@ -336,6 +350,7 @@ def forward( block_tables, seqlen, max_s, + kv_scales=self.kv_scales, ) return self.dense( diff --git a/server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py b/server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py index 52119b64c1d..ed053eb661c 100644 --- a/server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py @@ -17,6 +17,7 @@ TensorParallelEmbedding, get_linear, ) +from text_generation_server.layers.attention.kv_cache import get_kv_scales from text_generation_server.layers.gptq import GPTQWeightsLoader from text_generation_server.layers.layernorm import ( FastLayerNorm, @@ -257,6 +258,7 @@ def __init__(self, prefix, config, weights): self.c_proj = load_row( config, prefix=f"{prefix}.c_proj", weights=weights, bias=True ) + self.kv_scales = get_kv_scales(weights, f"{prefix}") self.kv_head_mapping = torch.zeros( self.num_heads, dtype=torch.int32, device=weights.device ) @@ -282,7 +284,12 @@ def forward( query = query.view(-1, self.num_heads, self.head_size) key_value = key_value.view(-1, 2, 1, self.head_size) - kv_cache.store(key=key_value[:, 0], value=key_value[:, 1], slots=slots) + kv_cache.store( + key=key_value[:, 0], + value=key_value[:, 1], + slots=slots, + kv_scales=self.kv_scales, + ) # Prefill if cu_seqlen_prefill is not None: @@ -292,6 +299,7 @@ def forward( key=key_value[:, 0], value=key_value[:, 1], kv_cache=kv_cache, + kv_scales=self.kv_scales, seqlen=seqlen, block_tables=block_tables, softmax_scale=self.softmax_scale, @@ -306,6 +314,7 @@ def forward( block_tables, seqlen, max_s, + kv_scales=self.kv_scales, ) return self.c_proj(attn_output.view(-1, self.num_heads * self.head_size)) diff --git a/server/text_generation_server/models/custom_modeling/flash_starcoder2_modeling.py b/server/text_generation_server/models/custom_modeling/flash_starcoder2_modeling.py index fe339aee734..c793982d828 100644 --- a/server/text_generation_server/models/custom_modeling/flash_starcoder2_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_starcoder2_modeling.py @@ -38,6 +38,7 @@ SpeculativeHead, get_linear, ) +from text_generation_server.layers.attention.kv_cache import get_kv_scales from text_generation_server.layers.layernorm import ( FastLayerNorm, FastRMSNorm, @@ -188,6 +189,7 @@ def __init__( ) self.query_key_value = load_attention(config, prefix, weights) + self.kv_scales = get_kv_scales(weights, f"{prefix}") self.o_proj = TensorParallelRowLinear.load( config, @@ -231,7 +233,12 @@ def forward( else: kv_to_cache = kv - kv_cache.store(key=kv_to_cache[:, 0], value=kv_to_cache[:, 1], slots=slots) + kv_cache.store( + key=kv_to_cache[:, 0], + value=kv_to_cache[:, 1], + slots=slots, + kv_scales=self.kv_scales, + ) # Prefill if cu_seqlen_prefill is not None: @@ -241,6 +248,7 @@ def forward( key=kv_to_cache[:, 0], value=kv_to_cache[:, 1], kv_cache=kv_cache, + kv_scales=self.kv_scales, seqlen=seqlen, block_tables=block_tables, softmax_scale=self.softmax_scale, @@ -256,6 +264,7 @@ def forward( block_tables, seqlen, max_s, + kv_scales=self.kv_scales, ) return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size)) diff --git a/server/text_generation_server/models/flash_causal_lm.py b/server/text_generation_server/models/flash_causal_lm.py index b1270b4493f..b931671cc0c 100644 --- a/server/text_generation_server/models/flash_causal_lm.py +++ b/server/text_generation_server/models/flash_causal_lm.py @@ -2283,6 +2283,7 @@ def _forward_context( num_kv_heads=self.num_kv_heads, head_size=self.head_size, page_size=BLOCK_SIZE, + kv_cache_dtype=self.kv_cache_dtype, dtype=self.dtype, window_left=self.sliding_window, ) diff --git a/server/text_generation_server/utils/weights.py b/server/text_generation_server/utils/weights.py index 548591e5710..aae64acf3da 100644 --- a/server/text_generation_server/utils/weights.py +++ b/server/text_generation_server/utils/weights.py @@ -207,7 +207,9 @@ def has_tensor(self, tensor_name: str): def get_shape(self, tensor_name: str): return self._get_slice(tensor_name).get_shape() - def get_tensor(self, tensor_name: str, to_device=True, to_dtype=True): + def get_tensor( + self, tensor_name: str, to_device: bool = True, to_dtype: bool = True + ) -> torch.Tensor: filename, tensor_name = self.get_filename(tensor_name) f = self._get_handle(filename) tensor = f.get_tensor(tensor_name) diff --git a/update_doc.py b/update_doc.py index 203aaced025..6357cc0061b 100644 --- a/update_doc.py +++ b/update_doc.py @@ -172,6 +172,8 @@ def check_openapi(check: bool): # allow for trailing whitespace since it's not significant # and the precommit hook will remove it "lint", + "--skip-rule", + "security-defined", filename, ], capture_output=True,