229 lines
11 KiB
ReStructuredText
229 lines
11 KiB
ReStructuredText
.. meta::
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:google-site-verification: S66K6GAclKw1RroxU0Rka_2d1LZFVe27M0gRneEsIVI
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################################################
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The BigDL Project
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################################################
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------
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************************************************
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BigDL-LLM: low-Bit LLM library
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************************************************
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.. raw:: html
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<p>
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<a href="https://github.com/intel-analytics/BigDL/tree/main/python/llm"><code><span>bigdl-llm</span></code></a> is a library for running <strong>LLM</strong> (large language model) on Intel <strong>XPU</strong> (from <em>Laptop</em> to <em>GPU</em> to <em>Cloud</em>) using <strong>INT4</strong> with very low latency <sup><a href="#footnote-perf" id="ref-perf">[1]</a></sup> (for any <strong>PyTorch</strong> model).
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</p>
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.. note::
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It is built on top of the excellent work of `llama.cpp <https://github.com/ggerganov/llama.cpp>`_, `gptq <https://github.com/IST-DASLab/gptq>`_, `bitsandbytes <https://github.com/TimDettmers/bitsandbytes>`_, `qlora <https://github.com/artidoro/qlora>`_, etc.
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============================================
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Latest update
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============================================
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- ``bigdl-llm`` now supports Intel GPU (including Arc, Flex and MAX); see the the latest GPU examples `here <https://github.com/intel-analytics/BigDL/tree/main/python/llm/example/gpu>`_.
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- ``bigdl-llm`` tutorial is released `here <https://github.com/intel-analytics/bigdl-llm-tutorial>`_.
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- Over 20 models have been verified on ``bigdl-llm``, including *LLaMA/LLaMA2, ChatGLM/ChatGLM2, MPT, Falcon, Dolly-v1/Dolly-v2, StarCoder, Whisper, InternLM, QWen, Baichuan, MOSS* and more; see the complete list `here <https://github.com/intel-analytics/BigDL/tree/main/python/llm/README.md#verified-models>`_.
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============================================
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``bigdl-llm`` demos
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============================================
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See the **optimized performance** of ``chatglm2-6b`` and ``llama-2-13b-chat`` models on 12th Gen Intel Core CPU and Intel Arc GPU below.
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.. raw:: html
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<table width="100%">
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<tr>
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<td align="center" colspan="2">12th Gen Intel Core CPU</td>
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<td align="center" colspan="2">Intel Arc GPU</td>
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</tr>
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<tr>
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<td>
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<a href="https://llm-assets.readthedocs.io/en/latest/_images/chatglm2-6b.gif"><img src="https://llm-assets.readthedocs.io/en/latest/_images/chatglm2-6b.gif" ></a>
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</td>
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<td>
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<a href="https://llm-assets.readthedocs.io/en/latest/_images/llama-2-13b-chat.gif"><img src="https://llm-assets.readthedocs.io/en/latest/_images/llama-2-13b-chat.gif"></a>
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</td>
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<td>
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<a href="https://llm-assets.readthedocs.io/en/latest/_images/chatglm2-arc.gif"><img src="https://llm-assets.readthedocs.io/en/latest/_images/chatglm2-arc.gif"></a>
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</td>
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<td>
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<a href="https://llm-assets.readthedocs.io/en/latest/_images/llama2-13b-arc.gif"><img src="https://llm-assets.readthedocs.io/en/latest/_images/llama2-13b-arc.gif"></a>
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</td>
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</tr>
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<tr>
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<td align="center" width="25%"><code>chatglm2-6b</code></td>
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<td align="center" width="25%"><code>llama-2-13b-chat</code></td>
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<td align="center" width="25%"><code>chatglm2-6b</code></td>
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<td align="center" width="25%"><code>llama-2-13b-chat</code></td>
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</tr>
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</table>
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============================================
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``bigdl-llm`` quickstart
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============================================
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- `CPU <#cpu-quickstart>`_
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- `GPU <#gpu-quickstart>`_
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--------------------------------------------
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CPU Quickstart
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--------------------------------------------
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You may install ``bigdl-llm`` on Intel CPU as follows as follows:
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.. code-block:: console
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pip install --pre --upgrade bigdl-llm[all]
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.. note::
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``bigdl-llm`` has been tested on Python 3.9.
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You can then apply INT4 optimizations to any Hugging Face *Transformers* models as follows.
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.. code-block:: python
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#load Hugging Face Transformers model with INT4 optimizations
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from bigdl.llm.transformers import AutoModelForCausalLM
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model = AutoModelForCausalLM.from_pretrained('/path/to/model/', load_in_4bit=True)
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#run the optimized model on Intel CPU
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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input_ids = tokenizer.encode(input_str, ...)
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output_ids = model.generate(input_ids, ...)
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output = tokenizer.batch_decode(output_ids)
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--------------------------------------------
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GPU Quickstart
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--------------------------------------------
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You may install ``bigdl-llm`` on Intel GPU as follows as follows:
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.. code-block:: console
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# below command will install intel_extension_for_pytorch==2.0.110+xpu as default
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# you can install specific ipex/torch version for your need
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pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
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.. note::
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``bigdl-llm`` has been tested on Python 3.9.
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You can then apply INT4 optimizations to any Hugging Face *Transformers* models on Intel GPU as follows.
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.. code-block:: python
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#load Hugging Face Transformers model with INT4 optimizations
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from bigdl.llm.transformers import AutoModelForCausalLM
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model = AutoModelForCausalLM.from_pretrained('/path/to/model/', load_in_4bit=True)
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#run the optimized model on Intel GPU
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model = model.to('xpu')
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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input_ids = tokenizer.encode(input_str, ...).to('xpu')
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output_ids = model.generate(input_ids, ...)
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output = tokenizer.batch_decode(output_ids.cpu())
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**For more details, please refer to the bigdl-llm** `Document <doc/LLM/index.html>`_, `Readme <https://github.com/intel-analytics/BigDL/tree/main/python/llm>`_, `Tutorial <https://github.com/intel-analytics/bigdl-llm-tutorial>`_ and `API Doc <doc/PythonAPI/LLM/index.html>`_.
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------
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************************************************
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Overview of the complete BigDL project
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************************************************
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`BigDL <https://github.com/intel-analytics/bigdl>`_ seamlessly scales your data analytics & AI applications from laptop to cloud, with the following libraries:
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- `LLM <https://github.com/intel-analytics/BigDL/tree/main/python/llm>`_: Low-bit (INT3/INT4/INT5/INT8) large language model library for Intel CPU/GPU
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- `Orca <doc/Orca/index.html>`_: Distributed Big Data & AI (TF & PyTorch) Pipeline on Spark and Ray
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- `Nano <doc/Nano/index.html>`_: Transparent Acceleration of Tensorflow & PyTorch Programs on Intel CPU/GPU
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- `DLlib <doc/DLlib/index.html>`_: "Equivalent of Spark MLlib" for Deep Learning
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- `Chronos <doc/Chronos/index.html>`_: Scalable Time Series Analysis using AutoML
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- `Friesian <doc/Friesian/index.html>`_: End-to-End Recommendation Systems
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- `PPML <doc/PPML/index.html>`_: Secure Big Data and AI (with SGX Hardware Security)
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------
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************************************************
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Choosing the right BigDL library
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************************************************
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.. graphviz::
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digraph BigDLDecisionTree {
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graph [pad=0.1 ranksep=0.3 tooltip=" "]
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node [color="#0171c3" shape=box fontname="Arial" fontsize=14 tooltip=" "]
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edge [tooltip=" "]
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Feature1 [label="Hardware Secured Big Data & AI?"]
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Feature2 [label="Python vs. Scala/Java?"]
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Feature3 [label="What type of application?"]
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Feature4 [label="Domain?"]
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LLM[href="https://github.com/intel-analytics/BigDL/blob/main/python/llm" target="_blank" target="_blank" style="rounded,filled" fontcolor="#ffffff" tooltip="Go to BigDL-LLM document"]
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Orca[href="../doc/Orca/index.html" target="_blank" target="_blank" style="rounded,filled" fontcolor="#ffffff" tooltip="Go to BigDL-Orca document"]
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Nano[href="../doc/Nano/index.html" target="_blank" target="_blank" style="rounded,filled" fontcolor="#ffffff" tooltip="Go to BigDL-Nano document"]
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DLlib1[label="DLlib" href="../doc/DLlib/index.html" target="_blank" style="rounded,filled" fontcolor="#ffffff" tooltip="Go to BigDL-DLlib document"]
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DLlib2[label="DLlib" href="../doc/DLlib/index.html" target="_blank" style="rounded,filled" fontcolor="#ffffff" tooltip="Go to BigDL-DLlib document"]
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Chronos[href="../doc/Chronos/index.html" target="_blank" style="rounded,filled" fontcolor="#ffffff" tooltip="Go to BigDL-Chronos document"]
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Friesian[href="../doc/Friesian/index.html" target="_blank" style="rounded,filled" fontcolor="#ffffff" tooltip="Go to BigDL-Friesian document"]
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PPML[href="../doc/PPML/index.html" target="_blank" style="rounded,filled" fontcolor="#ffffff" tooltip="Go to BigDL-PPML document"]
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ArrowLabel1[label="No" fontsize=12 width=0.1 height=0.1 style=filled color="#c9c9c9"]
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ArrowLabel2[label="Yes" fontsize=12 width=0.1 height=0.1 style=filled color="#c9c9c9"]
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ArrowLabel3[label="Python" fontsize=12 width=0.1 height=0.1 style=filled color="#c9c9c9"]
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ArrowLabel4[label="Scala/Java" fontsize=12 width=0.1 height=0.1 style=filled color="#c9c9c9"]
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ArrowLabel5[label="Large Language Model" fontsize=12 width=0.1 height=0.1 style=filled color="#c9c9c9"]
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ArrowLabel6[label="Big Data + \n AI (TF/PyTorch)" fontsize=12 width=0.1 height=0.1 style=filled color="#c9c9c9"]
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ArrowLabel7[label="Accelerate \n TensorFlow / PyTorch" fontsize=12 width=0.1 height=0.1 style=filled color="#c9c9c9"]
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ArrowLabel8[label="DL for Spark MLlib" fontsize=12 width=0.1 height=0.1 style=filled color="#c9c9c9"]
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ArrowLabel9[label="High Level App Framework" fontsize=12 width=0.1 height=0.1 style=filled color="#c9c9c9"]
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ArrowLabel10[label="Time Series" fontsize=12 width=0.1 height=0.1 style=filled color="#c9c9c9"]
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ArrowLabel11[label="Recommender System" fontsize=12 width=0.1 height=0.1 style=filled color="#c9c9c9"]
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Feature1 -> ArrowLabel1[dir=none]
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ArrowLabel1 -> Feature2
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Feature1 -> ArrowLabel2[dir=none]
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ArrowLabel2 -> PPML
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Feature2 -> ArrowLabel3[dir=none]
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ArrowLabel3 -> Feature3
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Feature2 -> ArrowLabel4[dir=none]
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ArrowLabel4 -> DLlib1
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Feature3 -> ArrowLabel5[dir=none]
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ArrowLabel5 -> LLM
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Feature3 -> ArrowLabel6[dir=none]
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ArrowLabel6 -> Orca
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Feature3 -> ArrowLabel7[dir=none]
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ArrowLabel7 -> Nano
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Feature3 -> ArrowLabel8[dir=none]
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ArrowLabel8 -> DLlib2
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Feature3 -> ArrowLabel9[dir=none]
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ArrowLabel9 -> Feature4
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Feature4 -> ArrowLabel10[dir=none]
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ArrowLabel10 -> Chronos
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Feature4 -> ArrowLabel11[dir=none]
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ArrowLabel11 -> Friesian
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}
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------
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.. raw:: html
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<div>
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<p>
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<sup><a href="#ref-perf" id="footnote-perf">[1]</a>
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Performance varies by use, configuration and other factors. <code><span>bigdl-llm</span></code> may not optimize to the same degree for non-Intel products. Learn more at <a href="https://www.Intel.com/PerformanceIndex">www.Intel.com/PerformanceIndex</a>.
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</sup>
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</p>
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</div>
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