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[to #42322933]fix bugs relate to token cls

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1.修复token classification preprocessor finetune结果错误问题
2.修复word segmentation output 无用属性
3. 修复nlp preprocessor传use_fast错误
4. 修复torch model exporter bug
5. 修复文档撰写过程中发现trainer相关bug
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10573269
This commit is contained in:
zhangzhicheng.zzc
2022-10-31 20:52:27 +08:00
committed by yingda.chen
parent e72988c2ba
commit 0d3b7b0df2
11 changed files with 111 additions and 97 deletions
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5
modelscope/exporters/torch_model_exporter.py
View File

@@ -128,7 +128,7 @@ class TorchModelExporter(Exporter):
args_list = list(args)
else:
args_list = [args]
if isinstance(args_list[-1], dict):
if isinstance(args_list[-1], Mapping):
args_dict = args_list[-1]
args_list = args_list[:-1]
n_nonkeyword = len(args_list)
@@ -284,9 +284,8 @@ class TorchModelExporter(Exporter):
'Model property dummy_inputs must be set.')
dummy_inputs = collate_fn(dummy_inputs, device)
if isinstance(dummy_inputs, Mapping):
dummy_inputs = self._decide_input_format(model, dummy_inputs)
dummy_inputs_filter = []
for _input in dummy_inputs:
for _input in self._decide_input_format(model, dummy_inputs):
if _input is not None:
dummy_inputs_filter.append(_input)
else:

11
modelscope/outputs/outputs.py
View File

@@ -491,17 +491,8 @@ TASK_OUTPUTS = {
# word segmentation result for single sample
# {
# "output": "今天 天气 不错 ， 适合 出去 游玩"
# "labels": [
# {'word': '今天', 'label': 'PROPN'},
# {'word': '天气', 'label': 'PROPN'},
# {'word': '不错', 'label': 'VERB'},
# {'word': ',', 'label': 'NUM'},
# {'word': '适合', 'label': 'NOUN'},
# {'word': '出去', 'label': 'PART'},
# {'word': '游玩', 'label': 'ADV'},
# ]
# }
Tasks.word_segmentation: [OutputKeys.OUTPUT, OutputKeys.LABELS],
Tasks.word_segmentation: [OutputKeys.OUTPUT],
# TODO @wenmeng.zwm support list of result check
# named entity recognition result for single sample

4
modelscope/pipelines/nlp/token_classification_pipeline.py
View File

@@ -109,13 +109,13 @@ class TokenClassificationPipeline(Pipeline):
chunk['span'] = text[chunk['start']:chunk['end']]
chunks.append(chunk)
# for cws output
# for cws outputs
if len(chunks) > 0 and chunks[0]['type'] == 'cws':
spans = [
chunk['span'] for chunk in chunks if chunk['span'].strip()
]
seg_result = ' '.join(spans)
outputs = {OutputKeys.OUTPUT: seg_result, OutputKeys.LABELS: []}
outputs = {OutputKeys.OUTPUT: seg_result}
# for ner outputs
else:

6
modelscope/pipelines/nlp/word_segmentation_pipeline.py
View File

@@ -115,15 +115,15 @@ class WordSegmentationPipeline(Pipeline):
chunk['span'] = text[chunk['start']:chunk['end']]
chunks.append(chunk)
# for cws output
# for cws outputs
if len(chunks) > 0 and chunks[0]['type'] == 'cws':
spans = [
chunk['span'] for chunk in chunks if chunk['span'].strip()
]
seg_result = ' '.join(spans)
outputs = {OutputKeys.OUTPUT: seg_result, OutputKeys.LABELS: []}
outputs = {OutputKeys.OUTPUT: seg_result}
# for ner outpus
# for ner output
else:
outputs = {OutputKeys.OUTPUT: chunks}
return outputs

17
modelscope/preprocessors/nlp/nlp_base.py
View File

@@ -34,6 +34,7 @@ class NLPBasePreprocessor(Preprocessor, ABC):
label=None,
label2id=None,
mode=ModeKeys.INFERENCE,
use_fast=None,
**kwargs):
"""The NLP preprocessor base class.
@@ -45,14 +46,18 @@ class NLPBasePreprocessor(Preprocessor, ABC):
label2id: An optional label2id mapping, the class will try to call utils.parse_label_mapping
if this mapping is not supplied.
mode: Run this preprocessor in either 'train'/'eval'/'inference' mode
use_fast: use the fast version of tokenizer
"""
self.model_dir = model_dir
self.first_sequence = first_sequence
self.second_sequence = second_sequence
self.label = label
self.use_fast = kwargs.pop('use_fast', None)
if self.use_fast is None and os.path.isfile(
self.use_fast = use_fast
if self.use_fast is None and model_dir is None:
self.use_fast = False
elif self.use_fast is None and os.path.isfile(
os.path.join(model_dir, 'tokenizer_config.json')):
with open(os.path.join(model_dir, 'tokenizer_config.json'),
'r') as f:
@@ -61,8 +66,8 @@ class NLPBasePreprocessor(Preprocessor, ABC):
self.use_fast = False if self.use_fast is None else self.use_fast
self.label2id = label2id
if self.label2id is None:
self.label2id = parse_label_mapping(self.model_dir)
if self.label2id is None and model_dir is not None:
self.label2id = parse_label_mapping(model_dir)
super().__init__(mode, **kwargs)
@property
@@ -106,6 +111,7 @@ class NLPTokenizerPreprocessorBase(NLPBasePreprocessor):
label: str = 'label',
label2id: dict = None,
mode: str = ModeKeys.INFERENCE,
use_fast: bool = None,
**kwargs):
"""The NLP tokenizer preprocessor base class.
@@ -122,11 +128,12 @@ class NLPTokenizerPreprocessorBase(NLPBasePreprocessor):
- config.json label2id/id2label
- label_mapping.json
mode: Run this preprocessor in either 'train'/'eval'/'inference' mode, the behavior may be different.
use_fast: use the fast version of tokenizer
kwargs: These kwargs will be directly fed into the tokenizer.
"""
super().__init__(model_dir, first_sequence, second_sequence, label,
label2id, mode)
label2id, mode, use_fast, **kwargs)
self.model_dir = model_dir
self.tokenize_kwargs = kwargs
self.tokenizer = self.build_tokenizer(model_dir)

150
modelscope/preprocessors/nlp/token_classification_preprocessor.py
View File

@@ -2,6 +2,7 @@
from typing import Any, Dict, Tuple, Union
import numpy as np
import torch
from modelscope.metainfo import Preprocessors
@@ -20,9 +21,7 @@ class WordSegmentationBlankSetToLabelPreprocessor(NLPBasePreprocessor):
"""
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.first_sequence: str = kwargs.pop('first_sequence',
'first_sequence')
self.first_sequence: str = kwargs.pop('first_sequence', 'tokens')
self.label = kwargs.pop('label', OutputKeys.LABELS)
def __call__(self, data: str) -> Union[Dict[str, Any], Tuple]:
@@ -80,10 +79,9 @@ class TokenClassificationPreprocessor(NLPTokenizerPreprocessorBase):
'is_split_into_words', False)
if 'label2id' in kwargs:
kwargs.pop('label2id')
self.tokenize_kwargs = kwargs
@type_assert(object, str)
def __call__(self, data: str) -> Dict[str, Any]:
@type_assert(object, (str, dict))
def __call__(self, data: Union[dict, str]) -> Dict[str, Any]:
"""process the raw input data
Args:
@@ -99,18 +97,24 @@ class TokenClassificationPreprocessor(NLPTokenizerPreprocessorBase):
text = None
labels_list = None
if isinstance(data, str):
# for inference inputs without label
text = data
self.tokenize_kwargs['add_special_tokens'] = False
elif isinstance(data, dict):
# for finetune inputs with label
text = data.get(self.first_sequence)
labels_list = data.get(self.label)
if isinstance(text, list):
self.tokenize_kwargs['is_split_into_words'] = True
input_ids = []
label_mask = []
offset_mapping = []
if self.is_split_into_words:
for offset, token in enumerate(list(data)):
subtoken_ids = self.tokenizer.encode(
token, add_special_tokens=False)
token_type_ids = []
if self.is_split_into_words and self._mode == ModeKeys.INFERENCE:
for offset, token in enumerate(list(text)):
subtoken_ids = self.tokenizer.encode(token,
**self.tokenize_kwargs)
if len(subtoken_ids) == 0:
subtoken_ids = [self.tokenizer.unk_token_id]
input_ids.extend(subtoken_ids)
@@ -119,10 +123,9 @@ class TokenClassificationPreprocessor(NLPTokenizerPreprocessorBase):
else:
if self.tokenizer.is_fast:
encodings = self.tokenizer(
text,
add_special_tokens=False,
return_offsets_mapping=True,
**self.tokenize_kwargs)
text, return_offsets_mapping=True, **self.tokenize_kwargs)
attention_mask = encodings['attention_mask']
token_type_ids = encodings['token_type_ids']
input_ids = encodings['input_ids']
word_ids = encodings.word_ids()
for i in range(len(word_ids)):
@@ -143,69 +146,80 @@ class TokenClassificationPreprocessor(NLPTokenizerPreprocessorBase):
label_mask, offset_mapping = self.get_label_mask_and_offset_mapping(
text)
if len(input_ids) >= self.sequence_length - 2:
input_ids = input_ids[:self.sequence_length - 2]
label_mask = label_mask[:self.sequence_length - 2]
input_ids = [self.tokenizer.cls_token_id
] + input_ids + [self.tokenizer.sep_token_id]
label_mask = [0] + label_mask + [0]
attention_mask = [1] * len(input_ids)
offset_mapping = offset_mapping[:sum(label_mask)]
if not self.is_transformer_based_model:
input_ids = input_ids[1:-1]
attention_mask = attention_mask[1:-1]
label_mask = label_mask[1:-1]
if self._mode == ModeKeys.INFERENCE:
if len(input_ids) >= self.sequence_length - 2:
input_ids = input_ids[:self.sequence_length - 2]
label_mask = label_mask[:self.sequence_length - 2]
input_ids = [self.tokenizer.cls_token_id
] + input_ids + [self.tokenizer.sep_token_id]
label_mask = [0] + label_mask + [0]
attention_mask = [1] * len(input_ids)
offset_mapping = offset_mapping[:sum(label_mask)]
if not self.is_transformer_based_model:
input_ids = input_ids[1:-1]
attention_mask = attention_mask[1:-1]
label_mask = label_mask[1:-1]
input_ids = torch.tensor(input_ids).unsqueeze(0)
attention_mask = torch.tensor(attention_mask).unsqueeze(0)
label_mask = torch.tensor(
label_mask, dtype=torch.bool).unsqueeze(0)
# the token classification
output = {
'text': text,
'input_ids': input_ids,
'attention_mask': attention_mask,
'label_mask': label_mask,
'offset_mapping': offset_mapping
}
# the token classification
output = {
'text': text,
'input_ids': input_ids,
'attention_mask': attention_mask,
'label_mask': label_mask,
'offset_mapping': offset_mapping
}
else:
output = {
'input_ids': input_ids,
'token_type_ids': token_type_ids,
'attention_mask': attention_mask,
'label_mask': label_mask,
}
# align the labels with tokenized text
if labels_list is not None:
assert self.label2id is not None
# Map that sends B-Xxx label to its I-Xxx counterpart
b_to_i_label = []
label_enumerate_values = [
k for k, v in sorted(
self.label2id.items(), key=lambda item: item[1])
]
for idx, label in enumerate(label_enumerate_values):
if label.startswith('B-') and label.replace(
'B-', 'I-') in label_enumerate_values:
b_to_i_label.append(
label_enumerate_values.index(
label.replace('B-', 'I-')))
else:
b_to_i_label.append(idx)
label_row = [self.label2id[lb] for lb in labels_list]
previous_word_idx = None
label_ids = []
for word_idx in word_ids:
if word_idx is None:
label_ids.append(-100)
elif word_idx != previous_word_idx:
label_ids.append(label_row[word_idx])
else:
if self.label_all_tokens:
label_ids.append(b_to_i_label[label_row[word_idx]])
# align the labels with tokenized text
if labels_list is not None:
assert self.label2id is not None
# Map that sends B-Xxx label to its I-Xxx counterpart
b_to_i_label = []
label_enumerate_values = [
k for k, v in sorted(
self.label2id.items(), key=lambda item: item[1])
]
for idx, label in enumerate(label_enumerate_values):
if label.startswith('B-') and label.replace(
'B-', 'I-') in label_enumerate_values:
b_to_i_label.append(
label_enumerate_values.index(
label.replace('B-', 'I-')))
else:
b_to_i_label.append(idx)
label_row = [self.label2id[lb] for lb in labels_list]
previous_word_idx = None
label_ids = []
for word_idx in word_ids:
if word_idx is None:
label_ids.append(-100)
previous_word_idx = word_idx
labels = label_ids
output['labels'] = labels
elif word_idx != previous_word_idx:
label_ids.append(label_row[word_idx])
else:
if self.label_all_tokens:
label_ids.append(b_to_i_label[label_row[word_idx]])
else:
label_ids.append(-100)
previous_word_idx = word_idx
labels = label_ids
output['labels'] = labels
output = {
k: np.array(v) if isinstance(v, list) else v
for k, v in output.items()
}
return output
def get_tokenizer_class(self):

2
modelscope/trainers/nlp/text_generation_trainer.py
View File

@@ -18,7 +18,7 @@ class TextGenerationTrainer(NlpEpochBasedTrainer):
return tokenizer.decode(tokens.tolist(), skip_special_tokens=True)
def evaluation_step(self, data):
model = self.model
model = self.model.module if self._dist else self.model
model.eval()
with torch.no_grad():

6
modelscope/trainers/nlp_trainer.py
View File

@@ -586,14 +586,16 @@ class NlpEpochBasedTrainer(EpochBasedTrainer):
preprocessor_mode=ModeKeys.TRAIN,
**model_args,
**self.train_keys,
mode=ModeKeys.TRAIN)
mode=ModeKeys.TRAIN,
use_fast=True)
eval_preprocessor = Preprocessor.from_pretrained(
self.model_dir,
cfg_dict=self.cfg,
preprocessor_mode=ModeKeys.EVAL,
**model_args,
**self.eval_keys,
mode=ModeKeys.EVAL)
mode=ModeKeys.EVAL,
use_fast=True)
return train_preprocessor, eval_preprocessor

2
modelscope/trainers/trainer.py
View File

@@ -876,7 +876,7 @@ class EpochBasedTrainer(BaseTrainer):
Subclass and override to inject custom behavior.
"""
model = self.model
model = self.model.module if self._dist else self.model
model.eval()
if is_parallel(model):

3
tests/outputs/test_model_outputs.py
View File

@@ -21,9 +21,10 @@ class TestModelOutput(unittest.TestCase):
self.assertEqual(outputs['logits'], torch.Tensor([1]))
self.assertEqual(outputs[0], torch.Tensor([1]))
self.assertEqual(outputs.logits, torch.Tensor([1]))
outputs.loss = torch.Tensor([2])
logits, loss = outputs
self.assertEqual(logits, torch.Tensor([1]))
self.assertTrue(loss is None)
self.assertTrue(loss is not None)
if __name__ == '__main__':

2
tests/trainers/test_finetune_token_classificatin.py
View File

@@ -87,7 +87,7 @@ class TestFinetuneTokenClassification(unittest.TestCase):
cfg['dataset'] = {
'train': {
'labels': label_enumerate_values,
'first_sequence': 'first_sequence',
'first_sequence': 'tokens',
'label': 'labels',
}
}