forked from HUSTAI/uie_pytorch
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathuie_predictor.py
681 lines (625 loc) · 27.1 KB
/
uie_predictor.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
# Copyright (c) 2022 Heiheiyoyo. 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.
import re
import numpy as np
import six
import math
import argparse
import os.path
from utils import logger, get_bool_ids_greater_than, get_span, get_id_and_prob, cut_chinese_sent, dbc2sbc
class ONNXInferBackend(object):
def __init__(self,
model_path_prefix,
device='cpu',
use_fp16=False):
from onnxruntime import InferenceSession, SessionOptions
logger.info(">>> [ONNXInferBackend] Creating Engine ...")
onnx_model = float_onnx_file = os.path.join(
model_path_prefix, "inference.onnx")
if not os.path.exists(onnx_model):
raise OSError(f'{onnx_model} not exists!')
infer_model_dir = model_path_prefix
if device == "gpu":
providers = ['CUDAExecutionProvider']
logger.info(">>> [ONNXInferBackend] Use GPU to inference ...")
if use_fp16:
logger.info(">>> [ONNXInferBackend] Use FP16 to inference ...")
from onnxconverter_common import float16
import onnx
fp16_model_file = os.path.join(infer_model_dir,
"fp16_model.onnx")
onnx_model = onnx.load_model(float_onnx_file)
trans_model = float16.convert_float_to_float16(
onnx_model, keep_io_types=True)
onnx.save_model(trans_model, fp16_model_file)
onnx_model = fp16_model_file
else:
providers = ['CPUExecutionProvider']
logger.info(">>> [ONNXInferBackend] Use CPU to inference ...")
sess_options = SessionOptions()
self.predictor = InferenceSession(
onnx_model, sess_options=sess_options, providers=providers)
if device == "gpu":
try:
assert 'CUDAExecutionProvider' in self.predictor.get_providers()
except AssertionError:
raise AssertionError(
f"The environment for GPU inference is not set properly. "
"A possible cause is that you had installed both onnxruntime and onnxruntime-gpu. "
"Please run the following commands to reinstall: \n "
"1) pip uninstall -y onnxruntime onnxruntime-gpu \n 2) pip install onnxruntime-gpu"
)
logger.info(">>> [InferBackend] Engine Created ...")
def infer(self, input_dict: dict):
result = self.predictor.run(None, dict(input_dict))
return result
class PyTorchInferBackend:
def __init__(self,
model_path_prefix,
multilingual=False,
device='cpu',
use_fp16=False):
from model import UIE, UIEM
logger.info(">>> [PyTorchInferBackend] Creating Engine ...")
if multilingual:
self.model = UIEM.from_pretrained(model_path_prefix)
else:
self.model = UIE.from_pretrained(model_path_prefix)
self.model.eval()
self.device = device
if self.device == 'gpu':
logger.info(">>> [PyTorchInferBackend] Use GPU to inference ...")
if use_fp16:
logger.info(
">>> [PyTorchInferBackend] Use FP16 to inference ...")
self.model = self.model.half()
self.model = self.model.cuda()
else:
logger.info(">>> [PyTorchInferBackend] Use CPU to inference ...")
logger.info(">>> [PyTorchInferBackend] Engine Created ...")
def infer(self, input_dict):
import torch
for input_name, input_value in input_dict.items():
input_value = torch.LongTensor(input_value)
if self.device == 'gpu':
input_value = input_value.cuda()
input_dict[input_name] = input_value
outputs = self.model(**input_dict)
start_prob, end_prob = outputs[0], outputs[1]
if self.device == 'gpu':
start_prob, end_prob = start_prob.cpu(), end_prob.cpu()
start_prob = start_prob.detach().numpy()
end_prob = end_prob.detach().numpy()
return start_prob, end_prob
class UIEPredictor(object):
def __init__(self, model, schema, task_path=None, schema_lang="zh", engine='pytorch', device='cpu', position_prob=0.5, max_seq_len=512, batch_size=64, split_sentence=False, use_fp16=False):
assert isinstance(
device, six.string_types
), "The type of device must be string."
assert device in [
'cpu', 'gpu'], "The device must be cpu or gpu."
if model in ['uie-m-base', 'uie-m-large']:
self._multilingual = True
else:
self._multilingual = False
self._model = model
self._engine = engine
self._task_path = task_path
self._device = device
self._position_prob = position_prob
self._max_seq_len = max_seq_len
self._batch_size = batch_size
self._split_sentence = split_sentence
self._use_fp16 = use_fp16
self._schema_tree = None
self._is_en = True if model in ['uie-base-en'
] or schema_lang == 'en' else False
self.set_schema(schema)
self._prepare_predictor()
def _prepare_predictor(self):
assert self._engine in ['pytorch',
'onnx'], "engine must be pytorch or onnx!"
if self._task_path is None:
self._task_path = self._model.replace('-', '_')+'_pytorch'
if not os.path.exists(self._task_path):
from convert import check_model, extract_and_convert
check_model(self._model)
extract_and_convert(self._model, self._task_path)
if self._multilingual:
from tokenizer import ErnieMTokenizerFast
self._tokenizer = ErnieMTokenizerFast.from_pretrained(
self._task_path)
else:
from transformers import BertTokenizerFast
self._tokenizer = BertTokenizerFast.from_pretrained(
self._task_path)
if self._engine == 'pytorch':
self.inference_backend = PyTorchInferBackend(
self._task_path, multilingual=self._multilingual, device=self._device, use_fp16=self._use_fp16)
if self._engine == 'onnx':
if os.path.exists(os.path.join(self._task_path, "pytorch_model.bin")) and not os.path.exists(os.path.join(self._task_path, "inference.onnx")):
from export_model import export_onnx
from model import UIE, UIEM
if self._multilingual:
model = UIEM.from_pretrained(self._task_path)
else:
model = UIE.from_pretrained(self._task_path)
input_names = [
'input_ids',
'token_type_ids',
'attention_mask',
]
output_names = [
'start_prob',
'end_prob'
]
logger.info(
"Converting to the inference model cost a little time.")
save_path = export_onnx(
self._task_path, self._tokenizer, model, 'cpu', input_names, output_names)
logger.info(
"The inference model save in the path:{}".format(save_path))
del model
self.inference_backend = ONNXInferBackend(
self._task_path, device=self._device, use_fp16=self._use_fp16)
def set_schema(self, schema):
if isinstance(schema, dict) or isinstance(schema, str):
schema = [schema]
self._schema_tree = self._build_tree(schema)
def __call__(self, inputs):
texts = inputs
if isinstance(texts, str):
texts = [texts]
results = self._multi_stage_predict(texts)
return results
def _multi_stage_predict(self, datas):
"""
Traversal the schema tree and do multi-stage prediction.
Args:
datas (list): a list of strings
Returns:
list: a list of predictions, where the list's length
equals to the length of `datas`
"""
results = [{} for _ in range(len(datas))]
# input check to early return
if len(datas) < 1 or self._schema_tree is None:
return results
# copy to stay `self._schema_tree` unchanged
schema_list = self._schema_tree.children[:]
while len(schema_list) > 0:
node = schema_list.pop(0)
examples = []
input_map = {}
cnt = 0
idx = 0
if not node.prefix:
for data in datas:
examples.append({
"text": data,
"prompt": dbc2sbc(node.name)
})
input_map[cnt] = [idx]
idx += 1
cnt += 1
else:
for pre, data in zip(node.prefix, datas):
if len(pre) == 0:
input_map[cnt] = []
else:
for p in pre:
if self._is_en:
if re.search(r'\[.*?\]$', node.name):
prompt_prefix = node.name[:node.name.find(
"[", 1)].strip()
cls_options = re.search(
r'\[.*?\]$', node.name).group()
# Sentiment classification of xxx [positive, negative]
prompt = prompt_prefix + p + " " + cls_options
else:
prompt = node.name + p
else:
prompt = p + node.name
examples.append({
"text": data,
"prompt": dbc2sbc(prompt)
})
input_map[cnt] = [i + idx for i in range(len(pre))]
idx += len(pre)
cnt += 1
if len(examples) == 0:
result_list = []
else:
result_list = self._single_stage_predict(examples)
if not node.parent_relations:
relations = [[] for i in range(len(datas))]
for k, v in input_map.items():
for idx in v:
if len(result_list[idx]) == 0:
continue
if node.name not in results[k].keys():
results[k][node.name] = result_list[idx]
else:
results[k][node.name].extend(result_list[idx])
if node.name in results[k].keys():
relations[k].extend(results[k][node.name])
else:
relations = node.parent_relations
for k, v in input_map.items():
for i in range(len(v)):
if len(result_list[v[i]]) == 0:
continue
if "relations" not in relations[k][i].keys():
relations[k][i]["relations"] = {
node.name: result_list[v[i]]
}
elif node.name not in relations[k][i]["relations"].keys(
):
relations[k][i]["relations"][
node.name] = result_list[v[i]]
else:
relations[k][i]["relations"][node.name].extend(
result_list[v[i]])
new_relations = [[] for i in range(len(datas))]
for i in range(len(relations)):
for j in range(len(relations[i])):
if "relations" in relations[i][j].keys(
) and node.name in relations[i][j]["relations"].keys():
for k in range(
len(relations[i][j]["relations"][
node.name])):
new_relations[i].append(relations[i][j][
"relations"][node.name][k])
relations = new_relations
prefix = [[] for _ in range(len(datas))]
for k, v in input_map.items():
for idx in v:
for i in range(len(result_list[idx])):
if self._is_en:
prefix[k].append(" of " +
result_list[idx][i]["text"])
else:
prefix[k].append(result_list[idx][i]["text"] + "的")
for child in node.children:
child.prefix = prefix
child.parent_relations = relations
schema_list.append(child)
return results
def _convert_ids_to_results(self, examples, sentence_ids, probs):
"""
Convert ids to raw text in a single stage.
"""
results = []
for example, sentence_id, prob in zip(examples, sentence_ids, probs):
if len(sentence_id) == 0:
results.append([])
continue
result_list = []
text = example["text"]
prompt = example["prompt"]
for i in range(len(sentence_id)):
start, end = sentence_id[i]
if start < 0 and end >= 0:
continue
if end < 0:
start += (len(prompt) + 1)
end += (len(prompt) + 1)
result = {"text": prompt[start:end],
"probability": prob[i]}
result_list.append(result)
else:
result = {
"text": text[start:end],
"start": start,
"end": end,
"probability": prob[i]
}
result_list.append(result)
results.append(result_list)
return results
def _auto_splitter(self, input_texts, max_text_len, split_sentence=False):
'''
Split the raw texts automatically for model inference.
Args:
input_texts (List[str]): input raw texts.
max_text_len (int): cutting length.
split_sentence (bool): If True, sentence-level split will be performed.
return:
short_input_texts (List[str]): the short input texts for model inference.
input_mapping (dict): mapping between raw text and short input texts.
'''
input_mapping = {}
short_input_texts = []
cnt_org = 0
cnt_short = 0
for text in input_texts:
if not split_sentence:
sens = [text]
else:
sens = cut_chinese_sent(text)
for sen in sens:
lens = len(sen)
if lens <= max_text_len:
short_input_texts.append(sen)
if cnt_org not in input_mapping.keys():
input_mapping[cnt_org] = [cnt_short]
else:
input_mapping[cnt_org].append(cnt_short)
cnt_short += 1
else:
temp_text_list = [
sen[i:i + max_text_len]
for i in range(0, lens, max_text_len)
]
short_input_texts.extend(temp_text_list)
short_idx = cnt_short
cnt_short += math.ceil(lens / max_text_len)
temp_text_id = [
short_idx + i for i in range(cnt_short - short_idx)
]
if cnt_org not in input_mapping.keys():
input_mapping[cnt_org] = temp_text_id
else:
input_mapping[cnt_org].extend(temp_text_id)
cnt_org += 1
return short_input_texts, input_mapping
def _single_stage_predict(self, inputs):
input_texts = []
prompts = []
for i in range(len(inputs)):
input_texts.append(inputs[i]["text"])
prompts.append(inputs[i]["prompt"])
# max predict length should exclude the length of prompt and summary tokens
max_predict_len = self._max_seq_len - len(max(prompts)) - 3
short_input_texts, self.input_mapping = self._auto_splitter(
input_texts, max_predict_len, split_sentence=self._split_sentence)
short_texts_prompts = []
for k, v in self.input_mapping.items():
short_texts_prompts.extend([prompts[k] for i in range(len(v))])
short_inputs = [{
"text": short_input_texts[i],
"prompt": short_texts_prompts[i]
} for i in range(len(short_input_texts))]
sentence_ids = []
probs = []
input_ids = []
token_type_ids = []
attention_mask = []
offset_maps = []
if self._multilingual:
padding_type = "max_length"
else:
padding_type = "longest"
encoded_inputs = self._tokenizer(
text=short_texts_prompts,
text_pair=short_input_texts,
stride=2,
truncation=True,
max_length=self._max_seq_len,
padding=padding_type,
add_special_tokens=True,
return_offsets_mapping=True,
return_tensors="np")
start_prob_concat, end_prob_concat = [], []
for batch_start in range(0, len(short_input_texts), self._batch_size):
input_ids = encoded_inputs["input_ids"][batch_start:batch_start+self._batch_size]
token_type_ids = encoded_inputs["token_type_ids"][batch_start:batch_start+self._batch_size]
attention_mask = encoded_inputs["attention_mask"][batch_start:batch_start+self._batch_size]
offset_maps = encoded_inputs["offset_mapping"][batch_start:batch_start+self._batch_size]
if self._multilingual:
input_ids = np.array(
input_ids, dtype="int64")
attention_mask = np.array(
attention_mask, dtype="int64")
position_ids = (np.cumsum(np.ones_like(input_ids), axis=1)
- np.ones_like(input_ids))*attention_mask
input_dict = {
"input_ids": input_ids,
"attention_mask": attention_mask,
"position_ids": position_ids
}
else:
input_dict = {
"input_ids": np.array(
input_ids, dtype="int64"),
"token_type_ids": np.array(
token_type_ids, dtype="int64"),
"attention_mask": np.array(
attention_mask, dtype="int64")
}
outputs = self.inference_backend.infer(input_dict)
start_prob, end_prob = outputs[0], outputs[1]
start_prob_concat.append(start_prob)
end_prob_concat.append(end_prob)
start_prob_concat = np.concatenate(start_prob_concat)
end_prob_concat = np.concatenate(end_prob_concat)
start_ids_list = get_bool_ids_greater_than(
start_prob_concat, limit=self._position_prob, return_prob=True)
end_ids_list = get_bool_ids_greater_than(
end_prob_concat, limit=self._position_prob, return_prob=True)
input_ids = input_dict['input_ids']
sentence_ids = []
probs = []
for start_ids, end_ids, ids, offset_map in zip(start_ids_list,
end_ids_list,
input_ids.tolist(),
offset_maps):
for i in reversed(range(len(ids))):
if ids[i] != 0:
ids = ids[:i]
break
span_list = get_span(start_ids, end_ids, with_prob=True)
sentence_id, prob = get_id_and_prob(span_list, offset_map.tolist())
sentence_ids.append(sentence_id)
probs.append(prob)
results = self._convert_ids_to_results(short_inputs, sentence_ids,
probs)
results = self._auto_joiner(results, short_input_texts,
self.input_mapping)
return results
def _auto_joiner(self, short_results, short_inputs, input_mapping):
concat_results = []
is_cls_task = False
for short_result in short_results:
if short_result == []:
continue
elif 'start' not in short_result[0].keys(
) and 'end' not in short_result[0].keys():
is_cls_task = True
break
else:
break
for k, vs in input_mapping.items():
if is_cls_task:
cls_options = {}
single_results = []
for v in vs:
if len(short_results[v]) == 0:
continue
if short_results[v][0]['text'] not in cls_options.keys():
cls_options[short_results[v][0][
'text']] = [1, short_results[v][0]['probability']]
else:
cls_options[short_results[v][0]['text']][0] += 1
cls_options[short_results[v][0]['text']][
1] += short_results[v][0]['probability']
if len(cls_options) != 0:
cls_res, cls_info = max(cls_options.items(),
key=lambda x: x[1])
concat_results.append([{
'text': cls_res,
'probability': cls_info[1] / cls_info[0]
}])
else:
concat_results.append([])
else:
offset = 0
single_results = []
for v in vs:
if v == 0:
single_results = short_results[v]
offset += len(short_inputs[v])
else:
for i in range(len(short_results[v])):
if 'start' not in short_results[v][
i] or 'end' not in short_results[v][i]:
continue
short_results[v][i]['start'] += offset
short_results[v][i]['end'] += offset
offset += len(short_inputs[v])
single_results.extend(short_results[v])
concat_results.append(single_results)
return concat_results
def predict(self, input_data):
results = self._multi_stage_predict(input_data)
return results
@classmethod
def _build_tree(cls, schema, name='root'):
"""
Build the schema tree.
"""
schema_tree = SchemaTree(name)
for s in schema:
if isinstance(s, str):
schema_tree.add_child(SchemaTree(s))
elif isinstance(s, dict):
for k, v in s.items():
if isinstance(v, str):
child = [v]
elif isinstance(v, list):
child = v
else:
raise TypeError(
"Invalid schema, value for each key:value pairs should be list or string"
"but {} received".format(type(v)))
schema_tree.add_child(cls._build_tree(child, name=k))
else:
raise TypeError(
"Invalid schema, element should be string or dict, "
"but {} received".format(type(s)))
return schema_tree
class SchemaTree(object):
"""
Implementataion of SchemaTree
"""
def __init__(self, name='root', children=None):
self.name = name
self.children = []
self.prefix = None
self.parent_relations = None
if children is not None:
for child in children:
self.add_child(child)
def __repr__(self):
return self.name
def add_child(self, node):
assert isinstance(
node, SchemaTree
), "The children of a node should be an instacne of SchemaTree."
self.children.append(node)
def parse_args():
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"-m",
"--model",
type=str,
default='uie-base',
help="The model to be used.", )
parser.add_argument(
"-t",
"--task_path",
type=str,
default=None,
help="The path prefix of custom inference model to be used.", )
parser.add_argument(
"-p",
"--position_prob",
default=0.5,
type=float,
help="Probability threshold for start/end index probabiliry.", )
parser.add_argument(
"--use_fp16",
action='store_true',
help="Whether to use fp16 inference, only takes effect when deploying on gpu.",
)
parser.add_argument(
"--max_seq_len",
default=512,
type=int,
help="The maximum input sequence length. Sequences longer than this will be split automatically.",
)
parser.add_argument(
"-D",
"--device",
choices=['cpu', 'gpu'],
default="gpu",
help="Select which device to run model, defaults to gpu."
)
parser.add_argument(
"-e",
"--engine",
choices=['pytorch', 'onnx'],
default="pytorch",
help="Select which engine to run model, defaults to pytorch."
)
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
args.schema = ['航母']
args.schema_lang = "en"
uie = UIEPredictor(model=args.model, task_path=args.task_path, schema_lang=args.schema_lang, schema=args.schema, engine=args.engine, device=args.device,
position_prob=args.position_prob, max_seq_len=args.max_seq_len, batch_size=64, split_sentence=False, use_fp16=args.use_fp16)
print(uie("印媒所称的“印度第一艘国产航母”—“维克兰特”号"))