Add doc for HuggingFace

docs/inference_model_convertor/toolkits/HuggingFace2paddle.md

+# HuggingFace 模型导出为 Paddle 模型教程
+
+X2Paddle 新增对 HuggingFace 模型支持，目前模型支持的列表如下
+
+## TorchScript
+
+<font size=0.5>
+
+| | <font size=2> CausalLM  |<font size=2> MaskedLM  | <font size=2> Seq2SeqLM | <font size=2>SequenceClassification |<font size=2> MultipleChoice |<font size=2>NextSentencePrediction |<font size=2>TokenClassification | <font size=2>QuestionAnswering |<font size=2> AudioClassification |
+|---|---|---|---|---|---|---|---|---|---|
+| <font size=2> [BERT](https://huggingface.co/docs/transformers/main/model_doc/bert#transformers.BertModel) |✅ | ✅ | None |✅|✅|✅|✅|✅|None|
+| <font size=2> [RoBERTa](https://huggingface.co/docs/transformers/main/model_doc/roberta#transformers.RobertaModel) |✅ |✅ | None |✅|✅| None |✅|✅| None |
+|<font size=2>  [T5](https://huggingface.co/docs/transformers/main/model_doc/t5#transformers.T5Model) | None | None |✅|None|None|None|None|None|None|
+|<font size=2>  [GPT2](https://huggingface.co/docs/transformers/main/model_doc/gpt2#transformers.GPT2Model) |✅ | None |None|✅|None|None|✅|None|None|
+|<font size=2>  [MarianMT](https://huggingface.co/docs/transformers/main/model_doc/marian#transformers.MarianModel) |✅ | None |✅|None|None|None|None|None|None|
+|<font size=2>  [ELECTRA](https://huggingface.co/docs/transformers/main/model_doc/electra#transformers.ElectraModel) | None | ✅ |None|✅|✅|None|✅|✅|None|
+|<font size=2>  [DistilBERT](https://huggingface.co/docs/transformers/main/model_doc/distilbert#transformers.DistilBertModel)|None | ✅ |None|✅|✅|None|✅|✅|None|
+|<font size=2>  [BART](https://huggingface.co/docs/transformers/main/model_doc/bart#transformers.BartModel) |✅ |✅ |✅|✅|None|None|None|✅|None|
+|<font size=2>  [XLM-RoBERTa](https://huggingface.co/docs/transformers/main/model_doc/xlm-roberta#transformers.XLMRobertaModel) |✅ |✅ | None |✅|✅|None|✅|✅|None|
+|<font size=2>  [ALBERT](https://huggingface.co/docs/transformers/main/model_doc/albert#transformers.AlbertModel) |None | ✅ |None|✅|✅|None|✅|✅|None|
+|<font size=2>  [LayoutLM](https://huggingface.co/docs/transformers/main/model_doc/layoutlm#transformers.LayoutLMModel) |None | ✅ |None|✅|None|None|✅|None|None|
+|<font size=2>  [BigBird](https://huggingface.co/docs/transformers/main/model_doc/big_bird#transformers.BigBirdModel) |✅ | ✅ |None|✅|✅|None|✅|✅|None|
+|<font size=2>  [Wav2Vec2](https://huggingface.co/docs/transformers/main/model_doc/wav2vec2#transformers.Wav2Vec2Model) | None | None |None|None|None|None|None|None|✅|
+
+Notes:
+
+- 上表的列表示 backbone 类型，行表示任务类型
+- None 表示该 backbone 不支持相关任务（与模型转换无关）
+
+## ONNX
+
+<font size=0.5>
+
+| | <font size=2> CausalLM  |<font size=2> MaskedLM  | <font size=2> Seq2SeqLM | <font size=2>SequenceClassification |<font size=2> MultipleChoice |<font size=2>NextSentencePrediction |<font size=2>TokenClassification | <font size=2>QuestionAnswering |<font size=2> AudioClassification |
+|---|---|---|---|---|---|---|---|---|---|
+| <font size=2> [BERT](https://huggingface.co/docs/transformers/main/model_doc/bert#transformers.BertModel) |✅ | ✅ | None |✅|None|None|✅|✅|None|
+| <font size=2> [RoBERTa](https://huggingface.co/docs/transformers/main/model_doc/roberta#transformers.RobertaModel) |✅ |✅ | None |✅|None| None |✅|✅| None |
+|<font size=2>  [T5](https://huggingface.co/docs/transformers/main/model_doc/t5#transformers.T5Model) | None | None |✅|None|None|None|None|None|None|
+|<font size=2>  [GPT2](https://huggingface.co/docs/transformers/main/model_doc/gpt2#transformers.GPT2Model) |✅ | None |None|✅|None|None|✅|None|None|
+|<font size=2>  [MarianMT](https://huggingface.co/docs/transformers/main/model_doc/marian#transformers.MarianModel) |✅ | None |✅|None|None|None|None|None|None|
+|<font size=2>  [ELECTRA](https://huggingface.co/docs/transformers/main/model_doc/electra#transformers.ElectraModel) | None | ✅ |None|✅|None|None|✅|✅|None|
+|<font size=2>  [DistilBERT](https://huggingface.co/docs/transformers/main/model_doc/distilbert#transformers.DistilBertModel)|None | ✅ |None|✅|None|None|✅|✅|None|
+|<font size=2>  [BART](https://huggingface.co/docs/transformers/main/model_doc/bart#transformers.BartModel) |✅ |None |✅|✅|None|None|None|✅|None|
+|<font size=2>  [XLM-RoBERTa](https://huggingface.co/docs/transformers/main/model_doc/xlm-roberta#transformers.XLMRobertaModel) |✅ |✅ | None |✅|None|None|✅|✅|None|
+|<font size=2>  [ALBERT](https://huggingface.co/docs/transformers/main/model_doc/albert#transformers.AlbertModel) |None | ✅ |None|✅|None|None|✅|✅|None|
+|<font size=2>  [LayoutLM](https://huggingface.co/docs/transformers/main/model_doc/layoutlm#transformers.LayoutLMModel) |None | ✅ |None|✅|None|None|✅|None|None|
+
+Notes:
+
+- 上表的列表示 backbone 类型，行表示任务类型
+- None 表示 HuggingFace 不支持 ONNX 模型导出（与模型转换无关）
+
+## 转换教程
+
+### 环境依赖
+
+- python >= 3.5
+- Huggingface 4.16.0
+- PyTorch 1.7.1
+- PaddlePaddle 2.3.0
+- ONNX 1.9.0
+
+### Torch模型转换到Paddle
+
+以模型为 Bert、任务类型为 SequenceClassification 为例，运行如下代码：
+
+```code
+import torch
+
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from x2paddle.convert import pytorch2paddle
+
+save_dir = "pd_model"
+jit_type = "trace"
+
+# Load tokenizer and PyTorch weights form the Hub
+tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased", return_dict=False)
+inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
+pt_model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased", return_dict=False)
+pt_model.eval()
+result = pt_model(inputs["input_ids"], inputs["attention_mask"], inputs["token_type_ids"])
+
+# convert
+pytorch2paddle(pt_model, save_dir , jit_type, [inputs["input_ids"], inputs["attention_mask"], inputs["token_type_ids"]])
+```
+
+### ONNX 模型转换到 Paddle
+
+#### 步骤一、通过 HuggingFace 导出 ONNX 模型
+
+使用命令行
+
+```shell
+python -m transformers.onnx --model=bert-base-uncased onnx/
+```
+
+更多细节可参考 HuggingFace [ONNX导出教程](https://huggingface.co/docs/transformers/main/serialization#exporting-a-model-to-onnx)
+
+#### 步骤二、通过X2Paddle将ONNX模型转换为Paddle格式
+
+通过 X2Paddle 将 ONNX 模型转换为 Paddle
+
+```shell
+x2paddle --framework=onnx --model=model.onnx --save_dir=pd_model_dynamic
+```