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* add jit mode option and model wrap * Update src/transformers/training_args.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/training_args.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * refine code * Update src/transformers/trainer.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/trainer.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * add ut and refine code * code refine * refine code * add inference doc * Update src/transformers/trainer.py Co-authored-by: Stas Bekman <stas00@users.noreply.github.com> * Update src/transformers/trainer.py Co-authored-by: Stas Bekman <stas00@users.noreply.github.com> * add cpu inference performance doc * Update perf_infer_cpu.mdx * Update perf_infer_cpu.mdx * Update performance.mdx * Update _toctree.yml * refine jit func naming * Update _toctree.yml * Delete perf_infer_gpu_one.mdx * Update perf_infer_cpu.mdx * Update docs/source/en/perf_infer_cpu.mdx Co-authored-by: Stas Bekman <stas00@users.noreply.github.com> * add none check before jit * Update docs/source/en/perf_infer_cpu.mdx Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update docs/source/en/perf_infer_cpu.mdx Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Stas Bekman <stas@stason.org> Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
58 lines
3.1 KiB
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58 lines
3.1 KiB
Plaintext
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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-->
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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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## 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/1.11.200/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, users should add `jit_mode_eval` in Trainer command arguments.
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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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