intel/intel-extension-for-pytorch v2.3.110+xpu

Intel® Extension for PyTorch* v2.3.110+xpu Release Notes

on GitHub

2.3.110+xpu

We are excited to announce the release of Intel® Extension for PyTorch* v2.3.110+xpu. This is the new release which supports Intel® GPU platforms (Intel® Data Center GPU Max Series, Intel® Arc™ A-Series Graphics, Intel® Core™ Ultra Processors with Intel® Arc™ Graphics, Intel® Core™ Ultra Series 2 with Intel® Arc™ Graphics and Intel® Data Center GPU Flex Series) based on PyTorch* 2.3.1.

Highlights

• oneDNN v3.5.3 integration

• Intel® oneAPI Base Toolkit 2024.2.1 compatibility

• Intel® Core™ Ultra Series 2 with Intel® Arc™ Graphics support on Windows (Prototype)

• Large Language Model (LLM) optimization

  Intel® Extension for PyTorch* provides a new dedicated module, ipex.llm, to host for Large Language Models (LLMs) specific APIs. With ipex.llm, Intel® Extension for PyTorch* provides comprehensive LLM optimization on FP16 and INT4 datatypes. Specifically for low precision, Weight-Only Quantization is supported for various scenarios. And user can also run Intel® Extension for PyTorch* with Tensor Parallel to fit in the multiple ranks or multiple nodes scenarios to get even better performance.

  A typical API under this new module is ipex.llm.optimize, which is designed to optimize transformer-based models within frontend Python modules, with a particular focus on Large Language Models (LLMs). It provides optimizations for both model-wise and content-generation-wise. ipex.llm.optimize is an upgrade API to replace previous ipex.optimize_transformers, which will bring you more consistent LLM experience and performance. Below shows a simple example of ipex.llm.optimize for fp16 inference:

    import torch
    import intel_extension_for_pytorch as ipex
    import transformers
  
    model= transformers.AutoModelForCausalLM.from_pretrained(model_name_or_path).eval()
  
    dtype = torch.float16
    model = ipex.llm.optimize(model, dtype=dtype, device="xpu")
  
    model.generate(YOUR_GENERATION_PARAMS)

  More examples of this API can be found at LLM optimization API.

  Besides that, we optimized more LLM inference models. A full list of optimized models can be found at LLM Optimizations Overview.

• Serving framework support

  Typical LLM serving frameworks including vLLM and TGI can co-work with Intel® Extension for PyTorch* on Intel® GPU platforms (Intel® Data Center GPU Max Series and Intel® Arc™ A-Series Graphics). Besides the integration of LLM serving frameworks with ipex.llm module level APIs, we enhanced the performance and quality of underneath Intel® Extension for PyTorch* operators such as paged attention and flash attention for better end to end model performance.

• Prototype support of full fine-tuning and LoRA PEFT with mixed precision

  Intel® Extension for PyTorch* also provides new capability for supporting popular recipes with both full fine-tuning and LoRA PEFT for mixed precision with BF16 and FP32. We optimized many typical LLM models including Llama 2 (7B and 70B), Llama 3 8B, Phi-3-Mini 3.8B model families and Chinese model Qwen-7B, on both single GPU and Multi-GPU (distributed fine-tuning based on PyTorch FSDP) use cases.

Breaking Changes

• Block format support: oneDNN Block format integration support is being deprecated and will no longer be available starting from the release after v2.3.110+xpu.

Known Issues

Please refer to Known Issues webpage.