[docs] Update CPU/GPU inference docs (#26881)

* first draft

* remove non-existent paths

* edits

* feedback

* feedback and optimum

* Apply suggestions from code review

Co-authored-by: regisss <[email protected]>
Co-authored-by: Ella Charlaix <[email protected]>

* redirect to correct doc

* _redirects.yml

---------

Co-authored-by: regisss <[email protected]>
Co-authored-by: Ella Charlaix <[email protected]>

docs/source/en/_redirects.yml

@@ -0,0 +1,3 @@
+# Optimizing inference
+
+perf_infer_gpu_many: perf_infer_gpu_one

docs/source/en/_toctree.yml

@@ -155,13 +155,9 @@
     title: Efficient training techniques
   - sections:
     - local: perf_infer_cpu
-      title: Inference on CPU
+      title: CPU inference
     - local: perf_infer_gpu_one
-      title: Inference on one GPU
-    - local: perf_infer_gpu_many
-      title: Inference on many GPUs
-    - local: perf_infer_special
-      title: Inference on Specialized Hardware
+      title: GPU inference
     title: Optimizing inference
   - local: big_models
     title: Instantiating a big model

docs/source/en/perf_infer_cpu.md

@@ -13,63 +13,115 @@ rendered properly in your Markdown viewer.
 
 -->
 
-# Efficient Inference on CPU
+# CPU inference
 
-This guide focuses on inferencing large models efficiently on CPU.
+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.
 
-## `BetterTransformer` for faster inference
+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).
 
-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.
+## BetterTransformer
 
-## PyTorch JIT-mode (TorchScript)
-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.
-Comparing to default eager mode, jit mode in PyTorch normally yields better performance for model inference from optimization methodologies like operator fusion.
+BetterTransformer accelerates inference with its fastpath (native PyTorch specialized implementation of Transformer functions) execution. The two optimizations in the fastpath execution are:
 
-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).
+1. fusion, which combines multiple sequential operations into a single "kernel" to reduce the number of computation steps
+2. skipping the inherent sparsity of padding tokens to avoid unnecessary computation with nested tensors
 
-### IPEX Graph Optimization with JIT-mode
-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.
+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).
 
-Check more detailed information for [IPEX Graph Optimization](https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/features/graph_optimization.html).
+<Tip>
 
-#### IPEX installation:
+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.
 
-IPEX release is following PyTorch, check the approaches for [IPEX installation](https://intel.github.io/intel-extension-for-pytorch/).
+</Tip>
 
-### Usage of JIT-mode
-To enable JIT-mode in Trainer for evaluaion or prediction, users should add `jit_mode_eval` in Trainer command arguments.
+Before you start, make sure you have 🤗 Optimum [installed](https://huggingface.co/docs/optimum/installation).
 
-<Tip warning={true}>
+Enable BetterTransformer with the [`PreTrainedModel.to_bettertransformer`] method:
 
-for PyTorch >= 1.14.0. JIT-mode could benefit any models for prediction and evaluaion since dict input is supported in jit.trace
+```py
+from transformers import AutoModelForCausalLM
 
-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
-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.
+model = AutoModelForCausalLM.from_pretrained("bigcode/starcoder")
+model.to_bettertransformer()
+```
 
-</Tip>
+## TorchScript
+
+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.
 
-Take an example of the use cases on [Transformers question-answering](https://github.com/huggingface/transformers/tree/main/examples/pytorch/question-answering)
+For a gentle introduction to TorchScript, see the [Introduction to PyTorch TorchScript](https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html) tutorial.
 
+With the [`Trainer`] class, you can enable JIT mode for CPU inference by setting the `--jit_mode_eval` flag:
 
-- Inference using jit mode on CPU:
-<pre>python run_qa.py \
+```bash
+python run_qa.py \
 --model_name_or_path csarron/bert-base-uncased-squad-v1 \
 --dataset_name squad \
 --do_eval \
 --max_seq_length 384 \
 --doc_stride 128 \
 --output_dir /tmp/ \
 --no_cuda \
-<b>--jit_mode_eval </b></pre> 
+--jit_mode_eval
+```
+
+<Tip warning={true}>
+
+For PyTorch >= 1.14.0, JIT-mode could benefit any model for prediction and evaluaion since the dict input is supported in `jit.trace`.
+
+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.
+
+</Tip>
+
+## IPEX graph optimization
+
+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.
+
+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):
+
+```bash
+pip install intel_extension_for_pytorch
+```
 
-- Inference with IPEX using jit mode on CPU:
-<pre>python run_qa.py \
+Set the `--use_ipex` and `--jit_mode_eval` flags in the [`Trainer`] class to enable JIT mode with the graph optimizations:
+
+```bash
+python run_qa.py \
 --model_name_or_path csarron/bert-base-uncased-squad-v1 \
 --dataset_name squad \
 --do_eval \
 --max_seq_length 384 \
 --doc_stride 128 \
 --output_dir /tmp/ \
 --no_cuda \
-<b>--use_ipex \</b>
-<b>--jit_mode_eval</b></pre> 
+--use_ipex \
+--jit_mode_eval
+```
+
+## 🤗 Optimum
+
+<Tip>
+
+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.
+
+</Tip>
+
+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.
+
+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:
+
+```py
+from transformers import AutoTokenizer, pipeline
+from optimum.onnxruntime import ORTModelForQuestionAnswering
+
+model = ORTModelForQuestionAnswering.from_pretrained("optimum/roberta-base-squad2")
+tokenizer = AutoTokenizer.from_pretrained("deepset/roberta-base-squad2")
+
+onnx_qa = pipeline("question-answering", model=model, tokenizer=tokenizer)
+
+question = "What's my name?"
+context = "My name is Philipp and I live in Nuremberg."
+pred = onnx_qa(question, context)
+```
+
+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.