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* Rename index.mdx to index.md * With saved modifs * Address review comment * Treat all files * .mdx -> .md * Remove special char * Update utils/tests_fetcher.py Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr> --------- Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>
76 lines
4.2 KiB
Markdown
76 lines
4.2 KiB
Markdown
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
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# Efficient Inference on CPU
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This guide focuses on inferencing large models efficiently on CPU.
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## `BetterTransformer` for faster inference
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We have recently integrated `BetterTransformer` for faster inference on CPU for text, image and audio models. Check the documentation about this integration [here](https://huggingface.co/docs/optimum/bettertransformer/overview) for more details.
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## PyTorch JIT-mode (TorchScript)
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TorchScript is a way to create serializable and optimizable models from PyTorch code. Any TorchScript program can be saved from a Python process and loaded in a process where there is no Python dependency.
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Comparing to default eager mode, jit mode in PyTorch normally yields better performance for model inference from optimization methodologies like operator fusion.
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For a gentle introduction to TorchScript, see the Introduction to [PyTorch TorchScript tutorial](https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html#tracing-modules).
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### IPEX Graph Optimization with JIT-mode
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Intel® Extension for PyTorch provides further optimizations in jit mode for Transformers series models. It is highly recommended for users to take advantage of Intel® Extension for PyTorch with jit mode. Some frequently used operator patterns from Transformers models are already supported in Intel® Extension for PyTorch with jit mode fusions. Those fusion patterns like Multi-head-attention fusion, Concat Linear, Linear+Add, Linear+Gelu, Add+LayerNorm fusion and etc. are enabled and perform well. The benefit of the fusion is delivered to users in a transparent fashion. According to the analysis, ~70% of most popular NLP tasks in question-answering, text-classification, and token-classification can get performance benefits with these fusion patterns for both Float32 precision and BFloat16 Mixed precision.
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Check more detailed information for [IPEX Graph Optimization](https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/features/graph_optimization.html).
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#### IPEX installation:
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IPEX release is following PyTorch, check the approaches for [IPEX installation](https://intel.github.io/intel-extension-for-pytorch/).
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### Usage of JIT-mode
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To enable JIT-mode in Trainer for evaluaion or prediction, users should add `jit_mode_eval` in Trainer command arguments.
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<Tip warning={true}>
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for PyTorch >= 1.14.0. JIT-mode could benefit any models for prediction and evaluaion since dict input is supported in jit.trace
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for PyTorch < 1.14.0. JIT-mode could benefit models whose forward parameter order matches the tuple input order in jit.trace, like question-answering model
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In the case where the forward parameter order does not match the tuple input order in jit.trace, like text-classification models, jit.trace will fail and we are capturing this with the exception here to make it fallback. Logging is used to notify users.
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</Tip>
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Take an example of the use cases on [Transformers question-answering](https://github.com/huggingface/transformers/tree/main/examples/pytorch/question-answering)
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- Inference using jit mode on CPU:
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<pre>python run_qa.py \
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--model_name_or_path csarron/bert-base-uncased-squad-v1 \
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--dataset_name squad \
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--do_eval \
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--max_seq_length 384 \
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--doc_stride 128 \
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--output_dir /tmp/ \
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--no_cuda \
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<b>--jit_mode_eval </b></pre>
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- Inference with IPEX using jit mode on CPU:
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<pre>python run_qa.py \
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--model_name_or_path csarron/bert-base-uncased-squad-v1 \
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--dataset_name squad \
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--do_eval \
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--max_seq_length 384 \
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--doc_stride 128 \
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--output_dir /tmp/ \
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--no_cuda \
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<b>--use_ipex \</b>
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<b>--jit_mode_eval</b></pre>
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