128 lines
7.2 KiB
Markdown
128 lines
7.2 KiB
Markdown
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# CPU inference
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With some optimizations, it is possible to efficiently run large model inference on a CPU. One of these optimization techniques involves compiling the PyTorch code into an intermediate format for high-performance environments like C++. The other technique fuses multiple operations into one kernel to reduce the overhead of running each operation separately.
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You'll learn how to use [BetterTransformer](https://pytorch.org/blog/a-better-transformer-for-fast-transformer-encoder-inference/) for faster inference, and how to convert your PyTorch code to [TorchScript](https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html). If you're using an Intel CPU, you can also use [graph optimizations](https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/features.html#graph-optimization) from [Intel Extension for PyTorch](https://intel.github.io/intel-extension-for-pytorch/cpu/latest/index.html) to boost inference speed even more. Finally, learn how to use 🤗 Optimum to accelerate inference with ONNX Runtime or OpenVINO (if you're using an Intel CPU).
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## BetterTransformer
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BetterTransformer accelerates inference with its fastpath (native PyTorch specialized implementation of Transformer functions) execution. The two optimizations in the fastpath execution are:
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1. fusion, which combines multiple sequential operations into a single "kernel" to reduce the number of computation steps
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2. skipping the inherent sparsity of padding tokens to avoid unnecessary computation with nested tensors
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BetterTransformer also converts all attention operations to use the more memory-efficient [scaled dot product attention](https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention).
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<Tip>
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BetterTransformer is not supported for all models. Check this [list](https://huggingface.co/docs/optimum/bettertransformer/overview#supported-models) to see if a model supports BetterTransformer.
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</Tip>
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Before you start, make sure you have 🤗 Optimum [installed](https://huggingface.co/docs/optimum/installation).
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Enable BetterTransformer with the [`PreTrainedModel.to_bettertransformer`] method:
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```py
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from transformers import AutoModelForCausalLM
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model = AutoModelForCausalLM.from_pretrained("bigcode/starcoder")
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model.to_bettertransformer()
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```
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## TorchScript
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TorchScript is an intermediate PyTorch model representation that can be run in production environments where performance is important. You can train a model in PyTorch and then export it to TorchScript to free the model from Python performance constraints. PyTorch [traces](https://pytorch.org/docs/stable/generated/torch.jit.trace.html) a model to return a [`ScriptFunction`] that is optimized with just-in-time compilation (JIT). Compared to the default eager mode, JIT mode in PyTorch typically yields better performance for inference using optimization techniques like operator fusion.
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For a gentle introduction to TorchScript, see the [Introduction to PyTorch TorchScript](https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html) tutorial.
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With the [`Trainer`] class, you can enable JIT mode for CPU inference by setting the `--jit_mode_eval` flag:
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```bash
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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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--jit_mode_eval
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```
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<Tip warning={true}>
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For PyTorch >= 1.14.0, JIT-mode could benefit any model for prediction and evaluation since the dict input is supported in `jit.trace`.
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For PyTorch < 1.14.0, JIT-mode could benefit a model if its forward parameter order matches the tuple input order in `jit.trace`, such as a question-answering model. If the forward parameter order does not match the tuple input order in `jit.trace`, like a text classification model, `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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## IPEX graph optimization
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Intel® Extension for PyTorch (IPEX) provides further optimizations in JIT mode for Intel CPUs, and we recommend combining it with TorchScript for even faster performance. The IPEX [graph optimization](https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/features/graph_optimization.html) fuses operations like Multi-head attention, Concat Linear, Linear + Add, Linear + Gelu, Add + LayerNorm, and more.
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To take advantage of these graph optimizations, make sure you have IPEX [installed](https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/installation.html):
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```bash
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pip install intel_extension_for_pytorch
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```
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Set the `--use_ipex` and `--jit_mode_eval` flags in the [`Trainer`] class to enable JIT mode with the graph optimizations:
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```bash
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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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--use_ipex \
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--jit_mode_eval
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```
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## 🤗 Optimum
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<Tip>
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Learn more details about using ORT with 🤗 Optimum in the [Optimum Inference with ONNX Runtime](https://huggingface.co/docs/optimum/onnxruntime/usage_guides/models) guide. This section only provides a brief and simple example.
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</Tip>
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ONNX Runtime (ORT) is a model accelerator that runs inference on CPUs by default. ORT is supported by 🤗 Optimum which can be used in 🤗 Transformers, without making too many changes to your code. You only need to replace the 🤗 Transformers `AutoClass` with its equivalent [`~optimum.onnxruntime.ORTModel`] for the task you're solving, and load a checkpoint in the ONNX format.
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For example, if you're running inference on a question answering task, load the [optimum/roberta-base-squad2](https://huggingface.co/optimum/roberta-base-squad2) checkpoint which contains a `model.onnx` file:
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```py
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from transformers import AutoTokenizer, pipeline
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from optimum.onnxruntime import ORTModelForQuestionAnswering
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model = ORTModelForQuestionAnswering.from_pretrained("optimum/roberta-base-squad2")
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tokenizer = AutoTokenizer.from_pretrained("deepset/roberta-base-squad2")
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onnx_qa = pipeline("question-answering", model=model, tokenizer=tokenizer)
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question = "What's my name?"
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context = "My name is Philipp and I live in Nuremberg."
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pred = onnx_qa(question, context)
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```
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If you have an Intel CPU, take a look at 🤗 [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) which supports a variety of compression techniques (quantization, pruning, knowledge distillation) and tools for converting models to the [OpenVINO](https://huggingface.co/docs/optimum/intel/inference) format for higher performance inference.
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