diff --git a/llm/llama3_1/cpu/_sources/index.md.txt b/llm/llama3_1/cpu/_sources/index.md.txt new file mode 100644 index 000000000..d10f804fe --- /dev/null +++ b/llm/llama3_1/cpu/_sources/index.md.txt @@ -0,0 +1,177 @@ +# Intel® Extension for PyTorch\* Large Language Model (LLM) Feature Get Started For Llama 3.1 models + +Intel® Extension for PyTorch\* provides dedicated optimization for running Llama 3.1 models faster, including technical points like paged attention, ROPE fusion, etc. And a set of data types are supported for various scenarios, including BF16, Weight Only Quantization INT8/INT4 (prototype), etc. + +# 1. Environment Setup + +There are several environment setup methodologies provided. You can choose either of them according to your usage scenario. The Docker-based ones are recommended. + +## 1.1 [RECOMMENDED] Docker-based environment setup with pre-built wheels + +```bash +# Get the Intel® Extension for PyTorch\* source code +git clone https://github.com/intel/intel-extension-for-pytorch.git +cd intel-extension-for-pytorch +git checkout 2.4-llama-3 +git submodule sync +git submodule update --init --recursive + +# Build an image with the provided Dockerfile by installing from Intel® Extension for PyTorch\* prebuilt wheel files +DOCKER_BUILDKIT=1 docker build -f examples/cpu/inference/python/llm/Dockerfile -t ipex-llm:2.4.0 . + +# Run the container with command below +docker run --rm -it --privileged ipex-llm:2.4.0 bash + +# When the command prompt shows inside the docker container, enter llm examples directory +cd llm + +# Activate environment variables +source ./tools/env_activate.sh +``` + +## 1.2 Conda-based environment setup with pre-built wheels + +```bash +# Get the Intel® Extension for PyTorch\* source code +git clone https://github.com/intel/intel-extension-for-pytorch.git +cd intel-extension-for-pytorch +git checkout 2.4-llama-3 +git submodule sync +git submodule update --init --recursive + +# Create a conda environment (pre-built wheel only available with python=3.10) +conda create -n llm python=3.10 -y +conda activate llm + +# Setup the environment with the provided script +# A sample "prompt.json" file for benchmarking is also downloaded +cd examples/cpu/inference/python/llm +bash ./tools/env_setup.sh 7 + +# Activate environment variables +source ./tools/env_activate.sh +``` +
+ +# 2. How To Run Llama 3.1 with ipex.llm + +**ipex.llm provides a single script to facilitate running generation tasks as below:** + +``` +# if you are using a docker container built from commands above in Sec. 1.1, the placeholder LLM_DIR below is ~/llm +# if you are using a conda env created with commands above in Sec. 1.2, the placeholder LLM_DIR below is intel-extension-for-pytorch/examples/cpu/inference/python/llm +cd +python run.py --help # for more detailed usages +``` + +| Key args of run.py | Notes | +|---|---| +| model id | "--model-name-or-path" or "-m" to specify the , it is model id from Huggingface or downloaded local path | +| generation | default: beam search (beam size = 4), "--greedy" for greedy search | +| input tokens | provide fixed sizes for input prompt size, use "--input-tokens" for in [1024, 2048, 4096, 8192, 32768, 130944]; if "--input-tokens" is not used, use "--prompt" to choose other strings as inputs| +| output tokens | default: 32, use "--max-new-tokens" to choose any other size | +| batch size | default: 1, use "--batch-size" to choose any other size | +| token latency | enable "--token-latency" to print out the first or next token latency | +| generation iterations | use "--num-iter" and "--num-warmup" to control the repeated iterations of generation, default: 100-iter/10-warmup | +| streaming mode output | greedy search only (work with "--greedy"), use "--streaming" to enable the streaming generation output | + +*Note:* You may need to log in your HuggingFace account to access the model files. Please refer to [HuggingFace login](https://huggingface.co/docs/huggingface_hub/quick-start#login). + +## 2.1 Usage of running Llama 3.1 models + +The _\_ in the below commands specifies the Llama 3.1 model you will run, which can be found from [HuggingFace Models](https://huggingface.co/models). + +### 2.1.1 Run generation with multiple instances on multiple CPU numa nodes + +#### 2.1.1.1 Prepare: + +```bash +unset KMP_AFFINITY +``` + +In the DeepSpeed cases below, we recommend "--shard-model" to shard model weight sizes more even for better memory usage when running with DeepSpeed. + +If using "--shard-model", it will save a copy of the shard model weights file in the path of "--output-dir" (default path is "./saved_results" if not provided). +If you have used "--shard-model" and generated such a shard model path (or your model weights files are already well sharded), in further repeated benchmarks, please remove "--shard-model", and replace "-m " with "-m " to skip the repeated shard steps. + +Besides, the standalone shard model function/scripts are also provided in section 2.1.1.4, in case you would like to generate the shard model weights files in advance before running distributed inference. + +#### 2.1.1.2 BF16: + +- Command: +```bash +deepspeed --bind_cores_to_rank run.py --benchmark -m --dtype bfloat16 --ipex --greedy --input-tokens --autotp --shard-model +``` + +#### 2.1.1.3 Weight-only quantization (INT8): + +By default, for weight-only quantization, we use quantization with [Automatic Mixed Precision](https://pytorch.org/tutorials/recipes/recipes/amp_recipe.html) inference ("--quant-with-amp") to get peak performance and fair accuracy. +For weight-only quantization with deepspeed, we quantize the model then run the benchmark. The quantized model won't be saved. + +- Command: +```bash +deepspeed --bind_cores_to_rank run.py --benchmark -m --ipex --ipex-weight-only-quantization --weight-dtype INT8 --quant-with-amp --greedy --input-tokens --autotp --shard-model --output-dir "saved_results" +# Note: you can add "--group-size" to tune good accuracy, suggested range as one of [32, 64, 128, 256, 512]. +``` + +#### 2.1.1.4 How to Shard Model weight files for Distributed Inference with DeepSpeed + +To save memory usage, we could shard the model weights files under the local path before we launch distributed tests with DeepSpeed. + +``` +cd ./utils +# general command: +python create_shard_model.py -m --save-path ./local_llama3_model_shard +# After sharding the model, using "-m ./local_llama3_model_shard" in later tests +``` + +### 2.1.2 Run generation with one socket inference +#### 2.1.2.1 BF16: + +- Command: + +```bash +OMP_NUM_THREADS= numactl -m -C python run.py --benchmark -m --dtype bfloat16 --ipex --greedy --input-tokens +``` + +#### 2.1.2.2 Weight-only quantization (INT8): + +By default, for weight-only quantization, we use quantization with [Automatic Mixed Precision](https://pytorch.org/tutorials/recipes/recipes/amp_recipe.html) inference ("--quant-with-amp") to get peak performance and fair accuracy. + +- Command: + +```bash +OMP_NUM_THREADS= numactl -m -C python run.py --benchmark -m --ipex-weight-only-quantization --weight-dtype INT8 --quant-with-amp --output-dir "saved_results" --greedy --input-tokens +# Note: you can add "--group-size" to tune good accuracy, suggested range as one of [32, 64, 128, 256, 512]. +``` + +#### 2.1.2.3 Weight-only quantization (INT4): +You can use auto-round (part of INC) to generate INT4 WOQ model with following steps. +- Environment installation: +```bash +pip install git+https://github.com/intel/auto-round.git@e24b9074af6cdb099e31c92eb81b7f5e9a4a244e +git clone https://github.com/intel/auto-round.git +git checkout e24b9074af6cdb099e31c92eb81b7f5e9a4a244e +cd auto-round/examples/language-modeling +``` + +- Command (quantize): +```bash +python3 main.py --model_name $model_name --device cpu --sym --nsamples 512 --iters 1000 --group_size 32 --deployment_device cpu --disable_eval --output_dir +``` + +- Command (benchmark): +```bash +cd +IPEX_WOQ_GEMM_LOOP_SCHEME=ACB OMP_NUM_THREADS= numactl -m -C python run.py --benchmark -m --ipex-weight-only-quantization --weight-dtype INT4 --quant-with-amp --output-dir "saved_results" --greedy --input-tokens --cache-weight-for-large-batch --low-precision-checkpoint +``` + +#### 2.1.2.4 Notes: + +(1) [_numactl_](https://linux.die.net/man/8/numactl) is used to specify memory and cores of your hardware to get better performance. _\_ specifies the [numa](https://en.wikipedia.org/wiki/Non-uniform_memory_access) node id (e.g., 0 to use the memory from the first numa node). _\_ specifies phsysical cores which you are using from the _\_ numa node. You can use [_lscpu_](https://man7.org/linux/man-pages/man1/lscpu.1.html) command in Linux to check the numa node information. + +(2) For all quantization benchmarks, both quantization and inference stages will be triggered by default. For quantization stage, it will auto-generate the quantized model named "best_model.pt" in the "--output-dir" path, and for inference stage, it will launch the inference with the quantized model "best_model.pt". For inference-only benchmarks (avoid the repeating quantization stage), you can also reuse these quantized models for by adding "--quantized-model-path " . + + +## Miscellaneous Tips +Intel® Extension for PyTorch\* also provides dedicated optimization for many other Large Language Models (LLM), which cover a set of data types that are supported for various scenarios. For more details, please check this [Intel® Extension for PyTorch\* doc](https://github.com/intel/intel-extension-for-pytorch/blob/release/2.3/README.md). diff --git a/llm/llama3_1/cpu/_static/_sphinx_javascript_frameworks_compat.js b/llm/llama3_1/cpu/_static/_sphinx_javascript_frameworks_compat.js new file mode 100644 index 000000000..81415803e --- /dev/null +++ b/llm/llama3_1/cpu/_static/_sphinx_javascript_frameworks_compat.js @@ -0,0 +1,123 @@ +/* Compatability shim for jQuery and underscores.js. + * + * Copyright Sphinx contributors + * Released under the two clause BSD licence + */ + +/** + * small helper function to urldecode strings + * + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL + */ +jQuery.urldecode = function(x) { + if (!x) { + return x + } + return decodeURIComponent(x.replace(/\+/g, ' ')); +}; + +/** + * small helper function to urlencode strings + */ +jQuery.urlencode = encodeURIComponent; + +/** + * This function returns the parsed url parameters of the + * current request. 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000000000..b26690143 --- /dev/null +++ b/llm/llama3_1/cpu/_static/custom.css @@ -0,0 +1,25 @@ +/* make the page 1000px */ +.wy-nav-content { + max-width: 1200px; +} + +/* code block highlight color in rtd changed to lime green, no no no */ + +.rst-content tt.literal, .rst-content code.literal, .highlight { + background: #f0f0f0; +} +.rst-content tt.literal, .rst-content code.literal { + color: #000000; +} + +/* Footer privacy links */ +a#wap_dns { + display: none; +} + +/* replace the copyright to eliminate the copyright symbol enforced by + the ReadTheDocs theme */ + div[role=contentinfo] { + visibility: hidden; + position: absolute; + } diff --git a/llm/llama3_1/cpu/_static/doctools.js b/llm/llama3_1/cpu/_static/doctools.js new file mode 100644 index 000000000..4d67807d1 --- /dev/null +++ b/llm/llama3_1/cpu/_static/doctools.js @@ -0,0 +1,156 @@ +/* + * doctools.js + * ~~~~~~~~~~~ + * + * Base JavaScript utilities for all Sphinx HTML documentation. + * + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ +"use strict"; + +const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([ + "TEXTAREA", + "INPUT", + "SELECT", + "BUTTON", +]); + +const _ready = (callback) => { + if (document.readyState !== "loading") { + callback(); + } else { + document.addEventListener("DOMContentLoaded", callback); + } +}; + +/** + * Small JavaScript module for the documentation. + */ +const Documentation = { + init: () => { + Documentation.initDomainIndexTable(); + Documentation.initOnKeyListeners(); + }, + + /** + * i18n support + */ + TRANSLATIONS: {}, + PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), + LOCALE: "unknown", + + // gettext and ngettext don't access this so that the functions + // can safely bound to a different name (_ = Documentation.gettext) + gettext: (string) => { + const translated = Documentation.TRANSLATIONS[string]; + switch (typeof translated) { + case "undefined": + return string; // no translation + case "string": + return translated; // translation exists + default: + return translated[0]; // (singular, plural) translation tuple exists + } + }, + + ngettext: (singular, plural, n) => { + const translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated !== "undefined") + return translated[Documentation.PLURAL_EXPR(n)]; + return n === 1 ? singular : plural; + }, + + addTranslations: (catalog) => { + Object.assign(Documentation.TRANSLATIONS, catalog.messages); + Documentation.PLURAL_EXPR = new Function( + "n", + `return (${catalog.plural_expr})` + ); + Documentation.LOCALE = catalog.locale; + }, + + /** + * helper function to focus on search bar + */ + focusSearchBar: () => { + document.querySelectorAll("input[name=q]")[0]?.focus(); + }, + + /** + * Initialise the domain index toggle buttons + */ + initDomainIndexTable: () => { + const toggler = (el) => { + const idNumber = el.id.substr(7); + const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); + if (el.src.substr(-9) === "minus.png") { + el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; + toggledRows.forEach((el) => (el.style.display = "none")); + } else { + el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; + toggledRows.forEach((el) => (el.style.display = "")); + } + }; + + const togglerElements = document.querySelectorAll("img.toggler"); + togglerElements.forEach((el) => + el.addEventListener("click", (event) => toggler(event.currentTarget)) + ); + togglerElements.forEach((el) => (el.style.display = "")); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); + }, + + initOnKeyListeners: () => { + // only install a listener if it is really needed + if ( + !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && + !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS + ) + return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.altKey || event.ctrlKey || event.metaKey) return; + + if (!event.shiftKey) { + switch (event.key) { + case "ArrowLeft": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const prevLink = document.querySelector('link[rel="prev"]'); + if (prevLink && prevLink.href) { + window.location.href = prevLink.href; + event.preventDefault(); + } + break; + case "ArrowRight": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const nextLink = document.querySelector('link[rel="next"]'); + if (nextLink && nextLink.href) { + window.location.href = nextLink.href; + event.preventDefault(); + } + break; + } + } + + // some keyboard layouts may need Shift to get / + switch (event.key) { + case "/": + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; + Documentation.focusSearchBar(); + event.preventDefault(); + } + }); + }, +}; + +// quick alias for translations +const _ = Documentation.gettext; + +_ready(Documentation.init); diff --git a/llm/llama3_1/cpu/_static/documentation_options.js b/llm/llama3_1/cpu/_static/documentation_options.js new file mode 100644 index 000000000..900635f15 --- /dev/null +++ b/llm/llama3_1/cpu/_static/documentation_options.js @@ -0,0 +1,13 @@ +const DOCUMENTATION_OPTIONS = { + VERSION: '2.4.0+git910bbf9', + LANGUAGE: 'en', + COLLAPSE_INDEX: false, + BUILDER: 'html', + FILE_SUFFIX: '.html', + LINK_SUFFIX: '.html', + HAS_SOURCE: true, + SOURCELINK_SUFFIX: '.txt', + NAVIGATION_WITH_KEYS: false, + SHOW_SEARCH_SUMMARY: true, + ENABLE_SEARCH_SHORTCUTS: true, +}; \ No newline at end of file diff --git a/llm/llama3_1/cpu/_static/file.png b/llm/llama3_1/cpu/_static/file.png new file mode 100644 index 000000000..a858a410e Binary files /dev/null and b/llm/llama3_1/cpu/_static/file.png differ diff --git a/llm/llama3_1/cpu/_static/htmls/tbl_deepspeed.html b/llm/llama3_1/cpu/_static/htmls/tbl_deepspeed.html new file mode 100644 index 000000000..2b0dd5bc7 --- /dev/null +++ b/llm/llama3_1/cpu/_static/htmls/tbl_deepspeed.html @@ -0,0 +1,124 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
MODEL
FAMILY		MODEL NAME
(Huggingface hub)		BF16Weight-Only
Quantization
INT8

LLAMA

meta-llama/Llama-2-7b-hf

🟩

🟩

LLAMA

meta-llama/Llama-2-13b-hf

🟩

🟩

LLAMA

meta-llama/Llama-2-70b-hf

🟩

🟩

GPT-J

EleutherAI/gpt-j-6b

🟨

🟩

GPT-NEOX

EleutherAI/gpt-neox-20b

🟨

🟩

DOLLY

databricks/dolly-v2-12b

🟨

🟩

FALCON

tiiuae/falcon-40b

🟩

🟩

OPT

facebook/opt-30b

🟨

🟩

OPT

facebook/opt-1.3b

🟩

🟩

Bloom

bigscience/bloom-1b7

🟨

🟩

CodeGen

Salesforce/codegen-2B-multi

🟩

🟩

Baichuan

baichuan-inc/Baichuan2-7B-Chat

🟩

🟩

Baichuan

baichuan-inc/Baichuan2-13B-Chat

🟨

🟩

Baichuan

baichuan-inc/Baichuan-13B-Chat

🟨

🟩

GPTBigCode

bigcode/starcoder

🟩

🟩

T5

google/flan-t5-xl

🟩

🟩

Mistral

mistralai/Mistral-7B-v0.1

🟩

🟩

MPT

mosaicml/mpt-7b

🟩

🟩

+
    +

  • 🟩 signifies that the model can perform well and with good accuracy (<1% difference as compared with FP32).

    • +

  • 🟨 signifies that the model can perform well while accuracy may not been in a perfect state (>1% difference as compared with FP32).

    • +
diff --git a/llm/llama3_1/cpu/_static/htmls/tbl_single.html b/llm/llama3_1/cpu/_static/htmls/tbl_single.html new file mode 100644 index 000000000..8eaefecf8 --- /dev/null +++ b/llm/llama3_1/cpu/_static/htmls/tbl_single.html @@ -0,0 +1,226 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
MODEL
FAMILY		MODEL NAME
(Huggingface hub)		FP32BF16Static
Quantization
INT8		Weight-Only
Quantization
INT8		Weight-Only
Quantization
INT4

LLAMA

meta-llama/Llama-2-7b-hf

🟩

🟩

🟩

🟩

🟨

LLAMA

meta-llama/Llama-2-13b-hf

🟩

🟩

🟩

🟩

🟨

LLAMA

meta-llama/Llama-2-70b-hf

🟩

🟩

🟩

🟩

🟨

GPT-J

EleutherAI/gpt-j-6b

🟩

🟩

🟩

🟩

🟩

GPT-NEOX

EleutherAI/gpt-neox-20b

🟩

🟨

🟨

🟩

🟨

DOLLY

databricks/dolly-v2-12b

🟩

🟨

🟨

🟩

🟨

FALCON

tiiuae/falcon-40b

🟩

🟩

🟩

🟩

🟩

OPT

facebook/opt-30b

🟩

🟩

🟩

🟩

🟨

OPT

facebook/opt-1.3b

🟩

🟩

🟩

🟩

🟨

Bloom

bigscience/bloom-1b7

🟩

🟨

🟩

🟩

🟨

CodeGen

Salesforce/codegen-2B-multi

🟩

🟩

🟨

🟩

🟩

Baichuan

baichuan-inc/Baichuan2-7B-Chat

🟩

🟩

🟩

🟩

Baichuan

baichuan-inc/Baichuan2-13B-Chat

🟩

🟩

🟩

🟩

Baichuan

baichuan-inc/Baichuan-13B-Chat

🟩

🟨

🟩

🟩

ChatGLM

THUDM/chatglm3-6b

🟩

🟩

🟨

🟩

ChatGLM

THUDM/chatglm2-6b

🟩

🟩

🟨

🟩

GPTBigCode

bigcode/starcoder

🟩

🟩

🟨

🟩

🟨

T5

google/flan-t5-xl

🟩

🟩

🟨

🟩

Mistral

mistralai/Mistral-7B-v0.1

🟩

🟩

🟨

🟩

🟨

MPT

mosaicml/mpt-7b

🟩

🟩

🟨

🟩

🟩

Mixtral

mistralai/Mixtral-8x7B-v0.1

🟩

🟩

🟩

Stablelm

stabilityai/stablelm-2-1_6b

🟩

🟩

🟨

Qwen

Qwen/Qwen-7B-Chat

🟩

🟩

🟩

+
    +

  • 🟩 signifies that the model can perform well and with good accuracy (<1% difference as compared with FP32).

    • +

  • 🟨 signifies that the model can perform well while accuracy may not been in a perfect state (>1% difference as compared with FP32).

    • +
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// [C]VC[V] is m=1 + var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 + var s_v = "^(" + C + ")?" + v; // vowel in stem + + this.stemWord = function (w) { + var stem; + var suffix; + var firstch; + var origword = w; + + if (w.length < 3) + return w; + + var re; + var re2; + var re3; + var re4; + + firstch = w.substr(0,1); + if (firstch == "y") + w = firstch.toUpperCase() + w.substr(1); + + // Step 1a + re = /^(.+?)(ss|i)es$/; + re2 = /^(.+?)([^s])s$/; + + if (re.test(w)) + w = w.replace(re,"$1$2"); + else if (re2.test(w)) + w = w.replace(re2,"$1$2"); + + // Step 1b + re = /^(.+?)eed$/; + re2 = /^(.+?)(ed|ing)$/; + if (re.test(w)) { + var fp = re.exec(w); + re = new RegExp(mgr0); + if (re.test(fp[1])) { + re = /.$/; + w = w.replace(re,""); + } + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1]; + re2 = new RegExp(s_v); + if (re2.test(stem)) { + w = stem; + re2 = /(at|bl|iz)$/; + re3 = new RegExp("([^aeiouylsz])\\1$"); + re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re2.test(w)) + w = w + "e"; + else if (re3.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + else if (re4.test(w)) + w = w + "e"; + } + } + + // Step 1c + re = /^(.+?)y$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(s_v); + if (re.test(stem)) + w = stem + "i"; + } + + // Step 2 + re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step2list[suffix]; + } + + // Step 3 + re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step3list[suffix]; + } + + // Step 4 + re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; + re2 = /^(.+?)(s|t)(ion)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + if (re.test(stem)) + w = stem; + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1] + fp[2]; + re2 = new RegExp(mgr1); + if (re2.test(stem)) + w = stem; + } + + // Step 5 + re = /^(.+?)e$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + re2 = new RegExp(meq1); + re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) + w = stem; + } + re = /ll$/; + re2 = new RegExp(mgr1); + if (re.test(w) && re2.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + + // and turn initial Y back to y + if (firstch == "y") + w = firstch.toLowerCase() + w.substr(1); + return w; + } +} + diff --git a/llm/llama3_1/cpu/_static/minus.png b/llm/llama3_1/cpu/_static/minus.png new file mode 100644 index 000000000..d96755fda Binary files /dev/null and b/llm/llama3_1/cpu/_static/minus.png differ diff --git a/llm/llama3_1/cpu/_static/plus.png b/llm/llama3_1/cpu/_static/plus.png new file mode 100644 index 000000000..7107cec93 Binary files /dev/null and b/llm/llama3_1/cpu/_static/plus.png differ diff --git a/llm/llama3_1/cpu/_static/pygments.css b/llm/llama3_1/cpu/_static/pygments.css new file mode 100644 index 000000000..84ab3030a --- /dev/null +++ b/llm/llama3_1/cpu/_static/pygments.css @@ -0,0 +1,75 @@ +pre { line-height: 125%; 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+ +/** + * Simple result scoring code. + */ +if (typeof Scorer === "undefined") { + var Scorer = { + // Implement the following function to further tweak the score for each result + // The function takes a result array [docname, title, anchor, descr, score, filename] + // and returns the new score. + /* + score: result => { + const [docname, title, anchor, descr, score, filename] = result + return score + }, + */ + + // query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms, highlightTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + const contentRoot = document.documentElement.dataset.content_root; + + const [docName, title, anchor, descr, score, _filename] = item; + + let listItem = document.createElement("li"); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = contentRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = contentRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) { + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + // highlight search terms in the description + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + } + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms, anchor) + ); + // highlight search terms in the summary + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = _( + "Search finished, found ${resultCount} page(s) matching the search query." + ).replace('${resultCount}', resultCount); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms, + highlightTerms, +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms, highlightTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; +// Helper function used by query() to order search results. +// Each input is an array of [docname, title, anchor, descr, score, filename]. +// Order the results by score (in opposite order of appearance, since the +// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically. +const _orderResultsByScoreThenName = (a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString, anchor) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + for (const removalQuery of [".headerlinks", "script", "style"]) { + htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() }); + } + if (anchor) { + const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`); + if (anchorContent) return anchorContent.textContent; + + console.warn( + `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.` + ); + } + + // if anchor not specified or not found, fall back to main content + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent) return docContent.textContent; + + console.warn( + "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + _parseQuery: (query) => { + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + return [query, searchTerms, excludedTerms, highlightTerms, objectTerms]; + }, + + /** + * execute search (requires search index to be loaded) + */ + _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // Collect multiple result groups to be sorted separately and then ordered. + // Each is an array of [docname, title, anchor, descr, score, filename]. + const normalResults = []; + const nonMainIndexResults = []; + + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase().trim(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + let score = Math.round(100 * queryLower.length / title.length) + normalResults.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id, isMain] of foundEntries) { + const score = Math.round(100 * queryLower.length / entry.length); + const result = [ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + ]; + if (isMain) { + normalResults.push(result); + } else { + nonMainIndexResults.push(result); + } + } + } + } + + // lookup as object + objectTerms.forEach((term) => + normalResults.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) { + normalResults.forEach((item) => (item[4] = Scorer.score(item))); + nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item))); + } + + // Sort each group of results by score and then alphabetically by name. + normalResults.sort(_orderResultsByScoreThenName); + nonMainIndexResults.sort(_orderResultsByScoreThenName); + + // Combine the result groups in (reverse) order. + // Non-main index entries are typically arbitrary cross-references, + // so display them after other results. + let results = [...nonMainIndexResults, ...normalResults]; + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + return results.reverse(); + }, + + query: (query) => { + const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query); + const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms, highlightTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + if (!terms.hasOwnProperty(word)) { + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + } + if (!titleTerms.hasOwnProperty(word)) { + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: titleTerms[term], score: Scorer.partialTitle }); + }); + } + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); + }); + + // create the mapping + files.forEach((file) => { + if (!fileMap.has(file)) fileMap.set(file, [word]); + else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + ]); + } + return results; + }, + + /** + * helper function to return a node containing the + * search summary for a given text. keywords is a list + * of stemmed words. + */ + makeSearchSummary: (htmlText, keywords, anchor) => { + const text = Search.htmlToText(htmlText, anchor); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? "..." : ""; + + let summary = document.createElement("p"); + summary.classList.add("context"); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; + + return summary; + }, +}; + +_ready(Search.init); diff --git a/llm/llama3_1/cpu/_static/sphinx_highlight.js b/llm/llama3_1/cpu/_static/sphinx_highlight.js new file mode 100644 index 000000000..8a96c69a1 --- /dev/null +++ b/llm/llama3_1/cpu/_static/sphinx_highlight.js @@ -0,0 +1,154 @@ +/* Highlighting utilities for Sphinx HTML documentation. */ +"use strict"; + +const SPHINX_HIGHLIGHT_ENABLED = true + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + const rest = document.createTextNode(val.substr(pos + text.length)); + parent.insertBefore( + span, + parent.insertBefore( + rest, + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + /* There may be more occurrences of search term in this node. So call this + * function recursively on the remaining fragment. + */ + _highlight(rest, addItems, text, className); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + +/** + * Small JavaScript module for the documentation. + */ +const SphinxHighlight = { + + /** + * highlight the search words provided in localstorage in the text + */ + highlightSearchWords: () => { + if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight + + // get and clear terms from localstorage + const url = new URL(window.location); + const highlight = + localStorage.getItem("sphinx_highlight_terms") + || url.searchParams.get("highlight") + || ""; + localStorage.removeItem("sphinx_highlight_terms") + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + + // get individual terms from highlight string + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '

' + + '' + + _("Hide Search Matches") + + "

" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(() => { + /* Do not call highlightSearchWords() when we are on the search page. + * It will highlight words from the *previous* search query. + */ + if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); + SphinxHighlight.initEscapeListener(); +}); diff --git a/llm/llama3_1/cpu/_static/twemoji.css b/llm/llama3_1/cpu/_static/twemoji.css new file mode 100644 index 000000000..878d070d1 --- /dev/null +++ b/llm/llama3_1/cpu/_static/twemoji.css @@ -0,0 +1,6 @@ +img.emoji { + height: 1em; + width: 1em; + margin: 0 .05em 0 .1em; + vertical-align: -0.1em; +} diff --git a/llm/llama3_1/cpu/_static/twemoji.js b/llm/llama3_1/cpu/_static/twemoji.js new file mode 100644 index 000000000..91bc868f4 --- /dev/null +++ b/llm/llama3_1/cpu/_static/twemoji.js @@ -0,0 +1,10 @@ +function addEvent(element, eventName, fn) { + if (element.addEventListener) + element.addEventListener(eventName, fn, false); + else if (element.attachEvent) + element.attachEvent('on' + eventName, fn); +} + +addEvent(window, 'load', function() { + twemoji.parse(document.body, {'folder': 'svg', 'ext': '.svg'}); +}); diff --git a/llm/llama3_1/cpu/genindex.html b/llm/llama3_1/cpu/genindex.html new file mode 100644 index 000000000..aef196891 --- /dev/null +++ b/llm/llama3_1/cpu/genindex.html @@ -0,0 +1,114 @@ + + + + + + Index — Intel&#174 Extension for PyTorch* 2.4.0+git910bbf9 documentation + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Index
    • +
  • +
    • +
+
+
+
+
+ + +

Index

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

© Copyright .

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + +

© Intel Corporation. Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others. No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by this document, with the sole exception that code included in this document is licensed subject to the Zero-Clause BSD open source license (OBSD), http://opensource.org/licenses/0BSD.
+ + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/llm/llama3_1/cpu/index.html b/llm/llama3_1/cpu/index.html new file mode 100644 index 000000000..b90b262d4 --- /dev/null +++ b/llm/llama3_1/cpu/index.html @@ -0,0 +1,351 @@ + + + + + + + Intel® Extension for PyTorch* Large Language Model (LLM) Feature Get Started For Llama 3.1 models — Intel&#174 Extension for PyTorch* 2.4.0+git910bbf9 documentation + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Intel® Extension for PyTorch* Large Language Model (LLM) Feature Get Started For Llama 3.1 models
    • +
  • + View page source +
    • +
+
+
+
+
+ +
+

Intel® Extension for PyTorch* Large Language Model (LLM) Feature Get Started For Llama 3.1 models

+

Intel® Extension for PyTorch* provides dedicated optimization for running Llama 3.1 models faster, including technical points like paged attention, ROPE fusion, etc. And a set of data types are supported for various scenarios, including BF16, Weight Only Quantization INT8/INT4 (prototype), etc.

+
+
+

1. Environment Setup

+

There are several environment setup methodologies provided. You can choose either of them according to your usage scenario. The Docker-based ones are recommended.

+ +
+

1.2 Conda-based environment setup with pre-built wheels

+
# Get the Intel® Extension for PyTorch\* source code
+git clone https://github.com/intel/intel-extension-for-pytorch.git
+cd intel-extension-for-pytorch
+git checkout 2.4-llama-3
+git submodule sync
+git submodule update --init --recursive
+
+# Create a conda environment (pre-built wheel only available with python=3.10)
+conda create -n llm python=3.10 -y
+conda activate llm
+
+# Setup the environment with the provided script
+# A sample "prompt.json" file for benchmarking is also downloaded
+cd examples/cpu/inference/python/llm
+bash ./tools/env_setup.sh 7
+
+# Activate environment variables
+source ./tools/env_activate.sh
+
+
+
+
+
+

2. How To Run Llama 3.1 with ipex.llm

+

ipex.llm provides a single script to facilitate running generation tasks as below:

+
# if you are using a docker container built from commands above in Sec. 1.1, the placeholder LLM_DIR below is ~/llm
+# if you are using a conda env created with commands above in Sec. 1.2, the placeholder LLM_DIR below is intel-extension-for-pytorch/examples/cpu/inference/python/llm
+cd <LLM_DIR>
+python run.py --help # for more detailed usages
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Key args of run.pyNotes
model id"--model-name-or-path" or "-m" to specify the , it is model id from Huggingface or downloaded local path
generationdefault: beam search (beam size = 4), "--greedy" for greedy search
input tokensprovide fixed sizes for input prompt size, use "--input-tokens" for in [1024, 2048, 4096, 8192, 32768, 130944]; if "--input-tokens" is not used, use "--prompt" to choose other strings as inputs
output tokensdefault: 32, use "--max-new-tokens" to choose any other size
batch sizedefault: 1, use "--batch-size" to choose any other size
token latencyenable "--token-latency" to print out the first or next token latency
generation iterationsuse "--num-iter" and "--num-warmup" to control the repeated iterations of generation, default: 100-iter/10-warmup
streaming mode outputgreedy search only (work with "--greedy"), use "--streaming" to enable the streaming generation output

Note: You may need to log in your HuggingFace account to access the model files. Please refer to HuggingFace login.

+
+

2.1 Usage of running Llama 3.1 models

+

The <LLAMA3_MODEL_ID_OR_LOCAL_PATH> in the below commands specifies the Llama 3.1 model you will run, which can be found from HuggingFace Models.

+
+

2.1.1 Run generation with multiple instances on multiple CPU numa nodes

+
+

2.1.1.1 Prepare:

+
unset KMP_AFFINITY
+
+
+

In the DeepSpeed cases below, we recommend “–shard-model” to shard model weight sizes more even for better memory usage when running with DeepSpeed.

+

If using “–shard-model”, it will save a copy of the shard model weights file in the path of “–output-dir” (default path is “./saved_results” if not provided). +If you have used “–shard-model” and generated such a shard model path (or your model weights files are already well sharded), in further repeated benchmarks, please remove “–shard-model”, and replace “-m <LLAMA3_MODEL_ID_OR_LOCAL_PATH>” with “-m ” to skip the repeated shard steps.

+

Besides, the standalone shard model function/scripts are also provided in section 2.1.1.4, in case you would like to generate the shard model weights files in advance before running distributed inference.

+
+
+

2.1.1.2 BF16:

+
    +

  • Command:

    • +
+
deepspeed --bind_cores_to_rank  run.py --benchmark -m <LLAMA3_MODEL_ID_OR_LOCAL_PATH> --dtype bfloat16 --ipex  --greedy --input-tokens <INPUT_LENGTH> --autotp --shard-model
+
+
+
+
+

2.1.1.3 Weight-only quantization (INT8):

+

By default, for weight-only quantization, we use quantization with Automatic Mixed Precision inference (”–quant-with-amp”) to get peak performance and fair accuracy. +For weight-only quantization with deepspeed, we quantize the model then run the benchmark. The quantized model won’t be saved.

+
    +

  • Command:

    • +
+
deepspeed --bind_cores_to_rank run.py  --benchmark -m <LLAMA3_MODEL_ID_OR_LOCAL_PATH> --ipex --ipex-weight-only-quantization --weight-dtype INT8 --quant-with-amp --greedy --input-tokens <INPUT_LENGTH>  --autotp --shard-model --output-dir "saved_results"
+# Note: you can add "--group-size" to tune good accuracy, suggested range as one of [32, 64, 128, 256, 512].
+
+
+
+
+

2.1.1.4 How to Shard Model weight files for Distributed Inference with DeepSpeed

+

To save memory usage, we could shard the model weights files under the local path before we launch distributed tests with DeepSpeed.

+
cd ./utils
+# general command:
+python create_shard_model.py -m <LLAMA3_MODEL_ID_OR_LOCAL_PATH>  --save-path ./local_llama3_model_shard
+# After sharding the model, using "-m ./local_llama3_model_shard" in later tests
+
+
+
+
+
+

2.1.2 Run generation with one socket inference

+
+

2.1.2.1 BF16:

+
    +

  • Command:

    • +
+
OMP_NUM_THREADS=<physical cores num> numactl -m <node N> -C <physical cores list> python run.py --benchmark -m <LLAMA3_MODEL_ID_OR_LOCAL_PATH> --dtype bfloat16 --ipex --greedy --input-tokens <INPUT_LENGTH>
+
+
+
+
+

2.1.2.2 Weight-only quantization (INT8):

+

By default, for weight-only quantization, we use quantization with Automatic Mixed Precision inference (”–quant-with-amp”) to get peak performance and fair accuracy.

+
    +

  • Command:

    • +
+
OMP_NUM_THREADS=<physical cores num> numactl -m <node N> -C <physical cores list>  python run.py  --benchmark -m <LLAMA3_MODEL_ID_OR_LOCAL_PATH> --ipex-weight-only-quantization --weight-dtype INT8 --quant-with-amp --output-dir "saved_results"  --greedy --input-tokens <INPUT_LENGTH>
+# Note: you can add "--group-size" to tune good accuracy, suggested range as one of [32, 64, 128, 256, 512].
+
+
+
+
+

2.1.2.3 Weight-only quantization (INT4):

+

You can use auto-round (part of INC) to generate INT4 WOQ model with following steps.

+
    +

  • Environment installation:

    • +
+
pip install git+https://github.com/intel/auto-round.git@e24b9074af6cdb099e31c92eb81b7f5e9a4a244e
+git clone https://github.com/intel/auto-round.git
+git checkout e24b9074af6cdb099e31c92eb81b7f5e9a4a244e
+cd auto-round/examples/language-modeling
+
+
+
    +

  • Command (quantize):

    • +
+
python3 main.py --model_name  $model_name --device cpu --sym --nsamples 512 --iters 1000 --group_size 32 --deployment_device cpu --disable_eval --output_dir <INT4_MODEL_SAVE_PATH>
+
+
+
    +

  • Command (benchmark):

    • +
+
cd <LLM_DIR>
+IPEX_WOQ_GEMM_LOOP_SCHEME=ACB OMP_NUM_THREADS=<physical cores num> numactl -m <node N> -C <physical cores list>  python run.py  --benchmark -m <LLAMA3_MODEL_ID_OR_LOCAL_PATH> --ipex-weight-only-quantization --weight-dtype INT4 --quant-with-amp --output-dir "saved_results"  --greedy --input-tokens <INPUT_LENGTH> --cache-weight-for-large-batch --low-precision-checkpoint <INT4_MODEL_SAVE_PATH>
+
+
+
+
+

2.1.2.4 Notes:

+

(1) numactl is used to specify memory and cores of your hardware to get better performance. <node N> specifies the numa node id (e.g., 0 to use the memory from the first numa node). <physical cores list> specifies phsysical cores which you are using from the <node N> numa node. You can use lscpu command in Linux to check the numa node information.

+

(2) For all quantization benchmarks, both quantization and inference stages will be triggered by default. For quantization stage, it will auto-generate the quantized model named “best_model.pt” in the “–output-dir” path, and for inference stage, it will launch the inference with the quantized model “best_model.pt”. For inference-only benchmarks (avoid the repeating quantization stage), you can also reuse these quantized models for by adding “–quantized-model-path <output_dir + “best_model.pt”>” .

+
+
+
+
+

Miscellaneous Tips

+

Intel® Extension for PyTorch* also provides dedicated optimization for many other Large Language Models (LLM), which cover a set of data types that are supported for various scenarios. For more details, please check this Intel® Extension for PyTorch* doc.

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

© Copyright .

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + +

© Intel Corporation. Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others. No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by this document, with the sole exception that code included in this document is licensed subject to the Zero-Clause BSD open source license (OBSD), http://opensource.org/licenses/0BSD.
+ + +
+
+
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© Intel Corporation. Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others. No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by this document, with the sole exception that code included in this document is licensed subject to the Zero-Clause BSD open source license (OBSD), http://opensource.org/licenses/0BSD.
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