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Showing content from https://github.com/intel/intel-extension-for-pytorch/tree/xpu-main below:

GitHub - intel/intel-extension-for-pytorch at xpu-main

Intel® Extension for PyTorch*

Intel® Extension for PyTorch* extends PyTorch* with up-to-date features optimizations for an extra performance boost on Intel hardware. Optimizations take advantage of AVX-512 Vector Neural Network Instructions (AVX512 VNNI) and Intel® Advanced Matrix Extensions (Intel® AMX) on Intel CPUs as well as Intel X^e Matrix Extensions (XMX) AI engines on Intel discrete GPUs. Moreover, through PyTorch* xpu device, Intel® Extension for PyTorch* provides easy GPU acceleration for Intel discrete GPUs with PyTorch*.

Intel® Extension for PyTorch* provides optimizations for both eager mode and graph mode, however, compared to eager mode, graph mode in PyTorch* normally yields better performance from optimization techniques, such as operation fusion. Intel® Extension for PyTorch* amplifies them with more comprehensive graph optimizations. Therefore we recommend you to take advantage of Intel® Extension for PyTorch* with TorchScript whenever your workload supports it. You could choose to run with torch.jit.trace() function or torch.jit.script() function, but based on our evaluation, torch.jit.trace() supports more workloads so we recommend you to use torch.jit.trace() as your first choice.

The extension can be loaded as a Python module for Python programs or linked as a C++ library for C++ programs. In Python scripts users can enable it dynamically by importing intel_extension_for_pytorch.

Check CPU tutorial for detailed information of Intel® Extension for PyTorch* for Intel® CPUs. Source code is available at the main branch.
Check GPU tutorial for detailed information of Intel® Extension for PyTorch* for Intel® GPUs. Source code is available at the xpu-main branch.

Large Language Models (LLMs) Optimization

In the current technological landscape, Generative AI (GenAI) workloads and models have gained widespread attention and popularity. Large Language Models (LLMs) have emerged as the dominant models driving these GenAI applications. Starting from 2.1.0, specific optimizations for certain LLM models are introduced in the Intel® Extension for PyTorch*. Check LLM optimizations CPU and LLM optimizations GPU for details.

MODEL FAMILY Verified < MODEL ID > (Huggingface hub) FP16 Weight only quantization INT4 Optimized on Intel® Data Center GPU Max Series (1550/1100) Optimized on Intel® Arc™ A-Series Graphics (A770) Optimized on Intel® Arc™ B-Series Graphics (B580) Llama 2 "meta-llama/Llama-2-7b-hf", "meta-llama/Llama-2-13b-hf", "meta-llama/Llama-2-70b-hf" ✅ ✅ ✅ ✅ $✅^1$ Llama 3 "meta-llama/Meta-Llama-3-8B", "meta-llama/Meta-Llama-3-70B" ✅ ✅ ✅ ✅ $✅^2$ Phi-3 mini "microsoft/Phi-3-mini-128k-instruct", "microsoft/Phi-3-mini-4k-instruct" ✅ ✅ ✅ ✅ $✅^3$ GPT-J "EleutherAI/gpt-j-6b" ✅ ✅ ✅ ✅ Qwen "Qwen/Qwen2-7B" ✅ ✅ ✅ ✅ Qwen "Qwen/Qwen2-7B-Instruct" ✅ OPT "facebook/opt-6.7b", "facebook/opt-30b" ✅ ✅ Bloom "bigscience/bloom-7b1", "bigscience/bloom" ✅ ✅ ChatGLM3-6B "THUDM/chatglm3-6b" ✅ ✅ Baichuan2-13B "baichuan-inc/Baichuan2-13B-Chat" ✅ ✅ Benchmark mode FP16 Weight only quantization INT4 Single instance ✅ ✅ Distributed (autotp) ✅

Note: Intel® Data Center Max 1550 GPU: support all the models in the model list above. Intel® Core™ Ultra Processors with Intel® Arc™ Graphics: support Llama 2 7B, Llama 3 8B and Phi-3-Mini 3.8B.

MODEL FAMILY Verified < MODEL ID > (Hugging Face hub) Mixed Precision (BF16+FP32) Full fine-tuning LoRA Intel® Data Center Max 1550 GPU Intel® Core™ Ultra Processors with Intel® Arc™ Graphics Llama 2 7B "meta-llama/Llama-2-7b-hf" ✅ ✅ ✅ ✅ ✅ Llama 2 70B "meta-llama/Llama-2-70b-hf" ✅ ✅ ✅ Llama 3 8B "meta-llama/Meta-Llama-3-8B" ✅ ✅ ✅ ✅ ✅ Qwen 7B "Qwen/Qwen-7B" ✅ ✅ ✅ ✅ Phi-3-mini 3.8B "Phi-3-mini-4k-instruct" ✅ ✅ ✅ ✅ Benchmark mode Full fine-tuning LoRA Single-GPU ✅ Multi-GPU (FSDP) ✅ ✅

✅ signifies that it is supported.
A blank signifies that it is not supported yet.
1: signifies that Llama-2-7b-hf is verified.
2: signifies that Meta-Llama-3-8B is verified.
3: signifies that Phi-3-mini-4k-instruct is verified.

The team tracks bugs and enhancement requests using GitHub issues. Before submitting a suggestion or bug report, search the existing GitHub issues to see if your issue has already been reported.

Apache License, Version 2.0. As found in LICENSE file.

See Intel's Security Center for information on how to report a potential security issue or vulnerability.