Fit chinese wwm to new datasets (#9887)

* MOD: fit chinese wwm to new datasets

* MOD: move wwm to new folder

* MOD: formate code

* Styling

* MOD add param and recover trainer

Co-authored-by: Sylvain Gugger <sylvain.gugger@gmail.com>

examples/language-modeling/README.md

@@ -100,72 +100,7 @@ sure all your batches have the same length.
 
 ### Whole word masking
 
-The BERT authors released a new version of BERT using Whole Word Masking in May 2019. Instead of masking randomly
-selected tokens (which may be part of words), they mask randomly selected words (masking all the tokens corresponding
-to that word). This technique has been refined for Chinese in [this paper](https://arxiv.org/abs/1906.08101).
-
-To fine-tune a model using whole word masking, use the following script:
-```bash
-python run_mlm_wwm.py \
-    --model_name_or_path roberta-base \
-    --dataset_name wikitext \
-    --dataset_config_name wikitext-2-raw-v1 \
-    --do_train \
-    --do_eval \
-    --output_dir /tmp/test-mlm-wwm
-```
-
-For Chinese models, we need to generate a reference files (which requires the ltp library), because it's tokenized at
-the character level.
-
-**Q :** Why a reference file?
-
-**A :** Suppose we have a Chinese sentence like: `我喜欢你` The original Chinese-BERT will tokenize it as
-`['我','喜','欢','你']` (character level). But `喜欢` is a whole word. For whole word masking proxy, we need a result
-like `['我','喜','##欢','你']`, so we need a reference file to tell the model which position of the BERT original token
-should be added `##`.
-
-**Q :** Why LTP ?
-
-**A :** Cause the best known Chinese WWM BERT is [Chinese-BERT-wwm](https://github.com/ymcui/Chinese-BERT-wwm) by HIT.
-It works well on so many Chines Task like CLUE (Chinese GLUE). They use LTP, so if we want to fine-tune their model,
-we need LTP.
-
-Now LTP only only works well on `transformers==3.2.0`. So we don't add it to requirements.txt.
-You need to create a separate environment with this version of Transformers to run the `run_chinese_ref.py` script that
-will create the reference files. The script is in `examples/contrib`. Once in the proper environment, run the
-following:
-
-
-```bash
-export TRAIN_FILE=/path/to/dataset/wiki.train.raw
-export LTP_RESOURCE=/path/to/ltp/tokenizer
-export BERT_RESOURCE=/path/to/bert/tokenizer
-export SAVE_PATH=/path/to/data/ref.txt
-
-python examples/contrib/run_chinese_ref.py \
-    --file_name=path_to_train_or_eval_file \
-    --ltp=path_to_ltp_tokenizer \
-    --bert=path_to_bert_tokenizer \
-    --save_path=path_to_reference_file
-```
-
-Then you can run the script like this: 
-
-
-```bash
-python run_mlm_wwm.py \
-    --model_name_or_path roberta-base \
-    --train_file path_to_train_file \
-    --validation_file path_to_validation_file \
-    --train_ref_file path_to_train_chinese_ref_file \
-    --validation_ref_file path_to_validation_chinese_ref_file \
-    --do_train \
-    --do_eval \
-    --output_dir /tmp/test-mlm-wwm
-```
-
-**Note:** On TPU, you should the flag `--pad_to_max_length` to make sure all your batches have the same length.
+This part was moved to `examples/research_projects/mlm_wwm`. 
 
 ### XLNet and permutation language modeling
 

examples/research_projects/mlm_wwm/README.md

@@ -0,0 +1,92 @@
+<!---
+Copyright 2020 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+## Whole Word Mask Language Model
+
+
+These scripts leverage the 🤗 Datasets library and the Trainer API. You can easily customize them to your needs if you
+need extra processing on your datasets.
+
+The following examples, will run on a datasets hosted on our [hub](https://huggingface.co/datasets) or with your own
+text files for training and validation. We give examples of both below.
+
+
+
+The BERT authors released a new version of BERT using Whole Word Masking in May 2019. Instead of masking randomly
+selected tokens (which may be part of words), they mask randomly selected words (masking all the tokens corresponding
+to that word). This technique has been refined for Chinese in [this paper](https://arxiv.org/abs/1906.08101).
+
+To fine-tune a model using whole word masking, use the following script:
+```bash
+python run_mlm_wwm.py \
+    --model_name_or_path roberta-base \
+    --dataset_name wikitext \
+    --dataset_config_name wikitext-2-raw-v1 \
+    --do_train \
+    --do_eval \
+    --output_dir /tmp/test-mlm-wwm
+```
+
+For Chinese models, we need to generate a reference files (which requires the ltp library), because it's tokenized at
+the character level.
+
+**Q :** Why a reference file?
+
+**A :** Suppose we have a Chinese sentence like: `我喜欢你` The original Chinese-BERT will tokenize it as
+`['我','喜','欢','你']` (character level). But `喜欢` is a whole word. For whole word masking proxy, we need a result
+like `['我','喜','##欢','你']`, so we need a reference file to tell the model which position of the BERT original token
+should be added `##`.
+
+**Q :** Why LTP ?
+
+**A :** Cause the best known Chinese WWM BERT is [Chinese-BERT-wwm](https://github.com/ymcui/Chinese-BERT-wwm) by HIT.
+It works well on so many Chines Task like CLUE (Chinese GLUE). They use LTP, so if we want to fine-tune their model,
+we need LTP.
+
+You could run the following:
+
+
+```bash
+export TRAIN_FILE=/path/to/dataset/wiki.train.raw
+export LTP_RESOURCE=/path/to/ltp/tokenizer
+export BERT_RESOURCE=/path/to/bert/tokenizer
+export SAVE_PATH=/path/to/data/ref.txt
+
+python run_chinese_ref.py \
+    --file_name=path_to_train_or_eval_file \
+    --ltp=path_to_ltp_tokenizer \
+    --bert=path_to_bert_tokenizer \
+    --save_path=path_to_reference_file
+```
+
+Then you can run the script like this: 
+
+
+```bash
+python run_mlm_wwm.py \
+    --model_name_or_path roberta-base \
+    --train_file path_to_train_file \
+    --validation_file path_to_validation_file \
+    --train_ref_file path_to_train_chinese_ref_file \
+    --validation_ref_file path_to_validation_chinese_ref_file \
+    --do_train \
+    --do_eval \
+    --output_dir /tmp/test-mlm-wwm
+```
+
+**Note1:** On TPU, you should the flag `--pad_to_max_length` to make sure all your batches have the same length.
+
+**Note2:** And if you have any questions or something goes wrong when runing this code, don't hesitate to pin @wlhgtc.

examples/research_projects/mlm_wwm/requirements.txt

@@ -0,0 +1,4 @@
+datasets >= 1.1.3
+sentencepiece != 0.1.92
+protobuf
+ltp

examples/research_projects/mlm_wwm/run_chinese_ref.py

@@ -0,0 +1,147 @@
+import argparse
+import json
+from typing import List
+
+from ltp import LTP
+from transformers.models.bert.tokenization_bert import BertTokenizer
+
+
+def _is_chinese_char(cp):
+    """Checks whether CP is the codepoint of a CJK character."""
+    # This defines a "chinese character" as anything in the CJK Unicode block:
+    #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
+    #
+    # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
+    # despite its name. The modern Korean Hangul alphabet is a different block,
+    # as is Japanese Hiragana and Katakana. Those alphabets are used to write
+    # space-separated words, so they are not treated specially and handled
+    # like the all of the other languages.
+    if (
+        (cp >= 0x4E00 and cp <= 0x9FFF)
+        or (cp >= 0x3400 and cp <= 0x4DBF)  #
+        or (cp >= 0x20000 and cp <= 0x2A6DF)  #
+        or (cp >= 0x2A700 and cp <= 0x2B73F)  #
+        or (cp >= 0x2B740 and cp <= 0x2B81F)  #
+        or (cp >= 0x2B820 and cp <= 0x2CEAF)  #
+        or (cp >= 0xF900 and cp <= 0xFAFF)
+        or (cp >= 0x2F800 and cp <= 0x2FA1F)  #
+    ):  #
+        return True
+
+    return False
+
+
+def is_chinese(word: str):
+    # word like '180' or '身高' or '神'
+    for char in word:
+        char = ord(char)
+        if not _is_chinese_char(char):
+            return 0
+    return 1
+
+
+def get_chinese_word(tokens: List[str]):
+    word_set = set()
+
+    for token in tokens:
+        chinese_word = len(token) > 1 and is_chinese(token)
+        if chinese_word:
+            word_set.add(token)
+    word_list = list(word_set)
+    return word_list
+
+
+def add_sub_symbol(bert_tokens: List[str], chinese_word_set: set()):
+    if not chinese_word_set:
+        return bert_tokens
+    max_word_len = max([len(w) for w in chinese_word_set])
+
+    bert_word = bert_tokens
+    start, end = 0, len(bert_word)
+    while start < end:
+        single_word = True
+        if is_chinese(bert_word[start]):
+            l = min(end - start, max_word_len)
+            for i in range(l, 1, -1):
+                whole_word = "".join(bert_word[start : start + i])
+                if whole_word in chinese_word_set:
+                    for j in range(start + 1, start + i):
+                        bert_word[j] = "##" + bert_word[j]
+                    start = start + i
+                    single_word = False
+                    break
+        if single_word:
+            start += 1
+    return bert_word
+
+
+def prepare_ref(lines: List[str], ltp_tokenizer: LTP, bert_tokenizer: BertTokenizer):
+    ltp_res = []
+
+    for i in range(0, len(lines), 100):
+        res = ltp_tokenizer.seg(lines[i : i + 100])[0]
+        res = [get_chinese_word(r) for r in res]
+        ltp_res.extend(res)
+    assert len(ltp_res) == len(lines)
+
+    bert_res = []
+    for i in range(0, len(lines), 100):
+        res = bert_tokenizer(lines[i : i + 100], add_special_tokens=True, truncation=True, max_length=512)
+        bert_res.extend(res["input_ids"])
+    assert len(bert_res) == len(lines)
+
+    ref_ids = []
+    for input_ids, chinese_word in zip(bert_res, ltp_res):
+
+        input_tokens = []
+        for id in input_ids:
+            token = bert_tokenizer._convert_id_to_token(id)
+            input_tokens.append(token)
+        input_tokens = add_sub_symbol(input_tokens, chinese_word)
+        ref_id = []
+        # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
+        for i, token in enumerate(input_tokens):
+            if token[:2] == "##":
+                clean_token = token[2:]
+                # save chinese tokens' pos
+                if len(clean_token) == 1 and _is_chinese_char(ord(clean_token)):
+                    ref_id.append(i)
+        ref_ids.append(ref_id)
+
+    assert len(ref_ids) == len(bert_res)
+
+    return ref_ids
+
+
+def main(args):
+    # For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
+    # If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
+    with open(args.file_name, "r", encoding="utf-8") as f:
+        data = f.readlines()
+    data = [line.strip() for line in data if len(line) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
+    ltp_tokenizer = LTP(args.ltp)  # faster in GPU device
+    bert_tokenizer = BertTokenizer.from_pretrained(args.bert)
+
+    ref_ids = prepare_ref(data, ltp_tokenizer, bert_tokenizer)
+
+    with open(args.save_path, "w", encoding="utf-8") as f:
+        data = [json.dumps(ref) + "\n" for ref in ref_ids]
+        f.writelines(data)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="prepare_chinese_ref")
+    parser.add_argument(
+        "--file_name",
+        type=str,
+        default="./resources/chinese-demo.txt",
+        help="file need process, same as training data in lm",
+    )
+    parser.add_argument(
+        "--ltp", type=str, default="./resources/ltp", help="resources for LTP tokenizer, usually a path"
+    )
+    parser.add_argument("--bert", type=str, default="./resources/robert", help="resources for Bert tokenizer")
+    parser.add_argument("--save_path", type=str, default="./resources/ref.txt", help="path to save res")
+
+    args = parser.parse_args()
+    main(args)

examples/research_projects/mlm_wwm/run_mlm_wwm.py

@@ -337,6 +337,10 @@ def main():
         tokenized_datasets["validation"] = add_chinese_references(
             tokenized_datasets["validation"], data_args.validation_ref_file
         )
+    # If we have ref files, need to avoid it removed by trainer
+    has_ref = data_args.train_ref_file or data_args.validation_ref_file
+    if has_ref:
+        training_args.remove_unused_columns = False
 
     # Data collator
     # This one will take care of randomly masking the tokens.

src/transformers/data/data_collator.py

@@ -402,7 +402,7 @@ class DataCollatorForWholeWordMask(DataCollatorForLanguageModeling):
             # For Chinese tokens, we need extra inf to mark sub-word, e.g [喜,欢]-> [喜，##欢]
             if "chinese_ref" in e:
                 ref_pos = tolist(e["chinese_ref"])
-                len_seq = e["input_ids"].size(0)
+                len_seq = len(e["input_ids"])
                 for i in range(len_seq):
                     if i in ref_pos:
                         ref_tokens[i] = "##" + ref_tokens[i]