run_squad.py 31.1 KB
Newer Older
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  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.
16
""" Finetuning the library models for question-answering on SQuAD (DistilBERT, Bert, XLM, XLNet)."""
17
18
19
20
21
22
23

from __future__ import absolute_import, division, print_function

import argparse
import logging
import os
import random
thomwolf's avatar
thomwolf committed
24
import glob
25
import timeit
26
27
28
29
30
31
32

import numpy as np
import torch
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
                              TensorDataset)
from torch.utils.data.distributed import DistributedSampler

33
34
35
36
37
38
try:
    from torch.utils.tensorboard import SummaryWriter
except:
    from tensorboardX import SummaryWriter

from tqdm import tqdm, trange
39

40
from transformers import (WEIGHTS_NAME, BertConfig,
thomwolf's avatar
thomwolf committed
41
42
43
44
                                  BertForQuestionAnswering, BertTokenizer,
                                  XLMConfig, XLMForQuestionAnswering,
                                  XLMTokenizer, XLNetConfig,
                                  XLNetForQuestionAnswering,
45
46
                                  XLNetTokenizer,
                                  DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer)
thomwolf's avatar
thomwolf committed
47

48
from transformers import AdamW, get_linear_schedule_with_warmup
49

50
51
52
from utils_squad import (read_squad_examples, convert_examples_to_features,
                         RawResult, write_predictions,
                         RawResultExtended, write_predictions_extended)
53

thomwolf's avatar
thomwolf committed
54
55
56
# The follwing import is the official SQuAD evaluation script (2.0).
# You can remove it from the dependencies if you are using this script outside of the library
# We've added it here for automated tests (see examples/test_examples.py file)
57
58
from utils_squad_evaluate import EVAL_OPTS, main as evaluate_on_squad

59
60
logger = logging.getLogger(__name__)

thomwolf's avatar
thomwolf committed
61
62
ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) \
                  for conf in (BertConfig, XLNetConfig, XLMConfig)), ())
thomwolf's avatar
thomwolf committed
63
64

MODEL_CLASSES = {
thomwolf's avatar
thomwolf committed
65
66
67
    'bert': (BertConfig, BertForQuestionAnswering, BertTokenizer),
    'xlnet': (XLNetConfig, XLNetForQuestionAnswering, XLNetTokenizer),
    'xlm': (XLMConfig, XLMForQuestionAnswering, XLMTokenizer),
68
    'distilbert': (DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer)
thomwolf's avatar
thomwolf committed
69
70
}

thomwolf's avatar
thomwolf committed
71
72
73
74
75
76
77
def set_seed(args):
    random.seed(args.seed)
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    if args.n_gpu > 0:
        torch.cuda.manual_seed_all(args.seed)

78
79
def to_list(tensor):
    return tensor.detach().cpu().tolist()
thomwolf's avatar
thomwolf committed
80

81
def train(args, train_dataset, model, tokenizer):
thomwolf's avatar
thomwolf committed
82
83
84
85
    """ Train the model """
    if args.local_rank in [-1, 0]:
        tb_writer = SummaryWriter()

86
    args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
thomwolf's avatar
thomwolf committed
87
88
89
90
    train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)
    train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size)

    if args.max_steps > 0:
91
        t_total = args.max_steps
thomwolf's avatar
thomwolf committed
92
93
        args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1
    else:
94
        t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
thomwolf's avatar
thomwolf committed
95

96
    # Prepare optimizer and schedule (linear warmup and decay)
thomwolf's avatar
thomwolf committed
97
98
    no_decay = ['bias', 'LayerNorm.weight']
    optimizer_grouped_parameters = [
99
        {'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': args.weight_decay},
thomwolf's avatar
thomwolf committed
100
101
        {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
        ]
102
    optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
103
    scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total)
thomwolf's avatar
thomwolf committed
104
105
106
107
108
109
110
    if args.fp16:
        try:
            from apex import amp
        except ImportError:
            raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
        model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)

111
112
113
114
    # multi-gpu training (should be after apex fp16 initialization)
    if args.n_gpu > 1:
        model = torch.nn.DataParallel(model)

thomwolf's avatar
thomwolf committed
115
116
117
118
119
120
    # Distributed training (should be after apex fp16 initialization)
    if args.local_rank != -1:
        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank],
                                                          output_device=args.local_rank,
                                                          find_unused_parameters=True)

thomwolf's avatar
thomwolf committed
121
122
123
124
    # Train!
    logger.info("***** Running training *****")
    logger.info("  Num examples = %d", len(train_dataset))
    logger.info("  Num Epochs = %d", args.num_train_epochs)
125
126
127
    logger.info("  Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size)
    logger.info("  Total train batch size (w. parallel, distributed & accumulation) = %d",
                   args.train_batch_size * args.gradient_accumulation_steps * (torch.distributed.get_world_size() if args.local_rank != -1 else 1))
thomwolf's avatar
thomwolf committed
128
    logger.info("  Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
129
    logger.info("  Total optimization steps = %d", t_total)
thomwolf's avatar
thomwolf committed
130
131
132

    global_step = 0
    tr_loss, logging_loss = 0.0, 0.0
133
134
135
136
137
138
139
    model.zero_grad()
    train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
    set_seed(args)  # Added here for reproductibility (even between python 2 and 3)
    for _ in train_iterator:
        epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
        for step, batch in enumerate(epoch_iterator):
            model.train()
thomwolf's avatar
thomwolf committed
140
            batch = tuple(t.to(args.device) for t in batch)
141
            inputs = {'input_ids':       batch[0],
Simon Layton's avatar
Simon Layton committed
142
143
                      'attention_mask':  batch[1],
                      'start_positions': batch[3],
144
                      'end_positions':   batch[4]}
145
146
            if args.model_type != 'distilbert':
                inputs['token_type_ids'] = None if args.model_type == 'xlm' else batch[2]
147
148
            if args.model_type in ['xlnet', 'xlm']:
                inputs.update({'cls_index': batch[5],
thomwolf's avatar
thomwolf committed
149
                               'p_mask':       batch[6]})
150
151
                if args.version_2_with_negative:
                    inputs.update({'is_impossible': batch[7]})
Peiqin Lin's avatar
typos  
Peiqin Lin committed
152
            outputs = model(**inputs)
153
            loss = outputs[0]  # model outputs are always tuple in transformers (see doc)
thomwolf's avatar
thomwolf committed
154

155
            if args.n_gpu > 1:
thomwolf's avatar
thomwolf committed
156
                loss = loss.mean() # mean() to average on multi-gpu parallel (not distributed) training
157
158
            if args.gradient_accumulation_steps > 1:
                loss = loss / args.gradient_accumulation_steps
thomwolf's avatar
thomwolf committed
159

160
161
162
163
164
165
166
167
            if args.fp16:
                with amp.scale_loss(loss, optimizer) as scaled_loss:
                    scaled_loss.backward()
            else:
                loss.backward()

            tr_loss += loss.item()
            if (step + 1) % args.gradient_accumulation_steps == 0:
168
169
170
171
172
                if args.fp16:
                    torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
                else:
                    torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)

173
                optimizer.step()
174
                scheduler.step()  # Update learning rate schedule
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
                model.zero_grad()
                global_step += 1

                if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
                    # Log metrics
                    if args.local_rank == -1 and args.evaluate_during_training:  # Only evaluate when single GPU otherwise metrics may not average well
                        results = evaluate(args, model, tokenizer)
                        for key, value in results.items():
                            tb_writer.add_scalar('eval_{}'.format(key), value, global_step)
                    tb_writer.add_scalar('lr', scheduler.get_lr()[0], global_step)
                    tb_writer.add_scalar('loss', (tr_loss - logging_loss)/args.logging_steps, global_step)
                    logging_loss = tr_loss

                if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
                    # Save model checkpoint
                    output_dir = os.path.join(args.output_dir, 'checkpoint-{}'.format(global_step))
                    if not os.path.exists(output_dir):
                        os.makedirs(output_dir)
                    model_to_save = model.module if hasattr(model, 'module') else model  # Take care of distributed/parallel training
                    model_to_save.save_pretrained(output_dir)
                    torch.save(args, os.path.join(output_dir, 'training_args.bin'))
                    logger.info("Saving model checkpoint to %s", output_dir)

            if args.max_steps > 0 and global_step > args.max_steps:
                epoch_iterator.close()
                break
        if args.max_steps > 0 and global_step > args.max_steps:
            train_iterator.close()
            break

thomwolf's avatar
thomwolf committed
205
206
207
    if args.local_rank in [-1, 0]:
        tb_writer.close()

208
209
210
211
212
213
214
215
216
217
218
219
220
221
    return global_step, tr_loss / global_step


def evaluate(args, model, tokenizer, prefix=""):
    dataset, examples, features = load_and_cache_examples(args, tokenizer, evaluate=True, output_examples=True)

    if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
        os.makedirs(args.output_dir)

    args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
    # Note that DistributedSampler samples randomly
    eval_sampler = SequentialSampler(dataset) if args.local_rank == -1 else DistributedSampler(dataset)
    eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)

ronakice's avatar
ronakice committed
222
223
224
225
    # multi-gpu evaluate
    if args.n_gpu > 1:
        model = torch.nn.DataParallel(model)

226
227
228
229
230
    # Eval!
    logger.info("***** Running evaluation {} *****".format(prefix))
    logger.info("  Num examples = %d", len(dataset))
    logger.info("  Batch size = %d", args.eval_batch_size)
    all_results = []
231
    start_time = timeit.default_timer()
232
233
234
235
236
    for batch in tqdm(eval_dataloader, desc="Evaluating"):
        model.eval()
        batch = tuple(t.to(args.device) for t in batch)
        with torch.no_grad():
            inputs = {'input_ids':      batch[0],
237
                      'attention_mask': batch[1]
thomwolf's avatar
thomwolf committed
238
                      }
239
240
            if args.model_type != 'distilbert':
                inputs['token_type_ids'] = None if args.model_type == 'xlm' else batch[2]  # XLM don't use segment_ids
241
242
243
244
            example_indices = batch[3]
            if args.model_type in ['xlnet', 'xlm']:
                inputs.update({'cls_index': batch[4],
                               'p_mask':    batch[5]})
245
246
247
248
249
            outputs = model(**inputs)

        for i, example_index in enumerate(example_indices):
            eval_feature = features[example_index.item()]
            unique_id = int(eval_feature.unique_id)
250
251
252
253
254
255
256
257
258
259
260
261
262
            if args.model_type in ['xlnet', 'xlm']:
                # XLNet uses a more complex post-processing procedure
                result = RawResultExtended(unique_id            = unique_id,
                                           start_top_log_probs  = to_list(outputs[0][i]),
                                           start_top_index      = to_list(outputs[1][i]),
                                           end_top_log_probs    = to_list(outputs[2][i]),
                                           end_top_index        = to_list(outputs[3][i]),
                                           cls_logits           = to_list(outputs[4][i]))
            else:
                result = RawResult(unique_id    = unique_id,
                                   start_logits = to_list(outputs[0][i]),
                                   end_logits   = to_list(outputs[1][i]))
            all_results.append(result)
263

264
265
266
    evalTime = timeit.default_timer() - start_time
    logger.info("  Evaluation done in total %f secs (%f sec per example)", evalTime, evalTime / len(dataset))

thomwolf's avatar
thomwolf committed
267
    # Compute predictions
268
269
    output_prediction_file = os.path.join(args.output_dir, "predictions_{}.json".format(prefix))
    output_nbest_file = os.path.join(args.output_dir, "nbest_predictions_{}.json".format(prefix))
270
271
272
273
    if args.version_2_with_negative:
        output_null_log_odds_file = os.path.join(args.output_dir, "null_odds_{}.json".format(prefix))
    else:
        output_null_log_odds_file = None
274
275
276
277
278
279

    if args.model_type in ['xlnet', 'xlm']:
        # XLNet uses a more complex post-processing procedure
        write_predictions_extended(examples, features, all_results, args.n_best_size,
                        args.max_answer_length, output_prediction_file,
                        output_nbest_file, output_null_log_odds_file, args.predict_file,
280
281
                        model.config.start_n_top, model.config.end_n_top,
                        args.version_2_with_negative, tokenizer, args.verbose_logging)
282
283
284
285
286
    else:
        write_predictions(examples, features, all_results, args.n_best_size,
                        args.max_answer_length, args.do_lower_case, output_prediction_file,
                        output_nbest_file, output_null_log_odds_file, args.verbose_logging,
                        args.version_2_with_negative, args.null_score_diff_threshold)
287

thomwolf's avatar
thomwolf committed
288
    # Evaluate with the official SQuAD script
289
290
291
292
293
294
295
296
    evaluate_options = EVAL_OPTS(data_file=args.predict_file,
                                 pred_file=output_prediction_file,
                                 na_prob_file=output_null_log_odds_file)
    results = evaluate_on_squad(evaluate_options)
    return results


def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False):
VictorSanh's avatar
VictorSanh committed
297
    if args.local_rank not in [-1, 0] and not evaluate:
thomwolf's avatar
thomwolf committed
298
299
        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache

300
301
302
    # Load data features from cache or dataset file
    input_file = args.predict_file if evaluate else args.train_file
    cached_features_file = os.path.join(os.path.dirname(input_file), 'cached_{}_{}_{}'.format(
thomwolf's avatar
thomwolf committed
303
        'dev' if evaluate else 'train',
304
        list(filter(None, args.model_name_or_path.split('/'))).pop(),
305
306
        str(args.max_seq_length)))
    if os.path.exists(cached_features_file) and not args.overwrite_cache and not output_examples:
thomwolf's avatar
thomwolf committed
307
308
309
        logger.info("Loading features from cached file %s", cached_features_file)
        features = torch.load(cached_features_file)
    else:
310
311
        logger.info("Creating features from dataset file at %s", input_file)
        examples = read_squad_examples(input_file=input_file,
312
313
                                                is_training=not evaluate,
                                                version_2_with_negative=args.version_2_with_negative)
314
315
316
317
318
        features = convert_examples_to_features(examples=examples,
                                                tokenizer=tokenizer,
                                                max_seq_length=args.max_seq_length,
                                                doc_stride=args.doc_stride,
                                                max_query_length=args.max_query_length,
319
320
321
322
323
                                                is_training=not evaluate,
                                                cls_token_segment_id=2 if args.model_type in ['xlnet'] else 0,
                                                pad_token_segment_id=3 if args.model_type in ['xlnet'] else 0,
                                                cls_token_at_end=True if args.model_type in ['xlnet'] else False,
                                                sequence_a_is_doc=True if args.model_type in ['xlnet'] else False)
thomwolf's avatar
thomwolf committed
324
325
326
327
        if args.local_rank in [-1, 0]:
            logger.info("Saving features into cached file %s", cached_features_file)
            torch.save(features, cached_features_file)

VictorSanh's avatar
VictorSanh committed
328
    if args.local_rank == 0 and not evaluate:
thomwolf's avatar
thomwolf committed
329
330
        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache

thomwolf's avatar
thomwolf committed
331
    # Convert to Tensors and build dataset
332
333
334
    all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
    all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)
    all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)
335
336
    all_cls_index = torch.tensor([f.cls_index for f in features], dtype=torch.long)
    all_p_mask = torch.tensor([f.p_mask for f in features], dtype=torch.float)
337
    if evaluate:
thomwolf's avatar
thomwolf committed
338
        all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
339
340
        dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
                                all_example_index, all_cls_index, all_p_mask)
341
342
343
    else:
        all_start_positions = torch.tensor([f.start_position for f in features], dtype=torch.long)
        all_end_positions = torch.tensor([f.end_position for f in features], dtype=torch.long)
344
        all_is_impossible = torch.tensor([1.0 if f.is_impossible == True else 0.0 for f in features], dtype=torch.float)
345
346
        dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
                                all_start_positions, all_end_positions,
347
                                all_cls_index, all_p_mask, all_is_impossible)
thomwolf's avatar
thomwolf committed
348

349
350
    if output_examples:
        return dataset, examples, features
thomwolf's avatar
thomwolf committed
351
352
    return dataset

353
354
355
356
357

def main():
    parser = argparse.ArgumentParser()

    ## Required parameters
thomwolf's avatar
thomwolf committed
358
359
360
361
    parser.add_argument("--train_file", default=None, type=str, required=True,
                        help="SQuAD json for training. E.g., train-v1.1.json")
    parser.add_argument("--predict_file", default=None, type=str, required=True,
                        help="SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json")
362
363
364
365
    parser.add_argument("--model_type", default=None, type=str, required=True,
                        help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()))
    parser.add_argument("--model_name_or_path", default=None, type=str, required=True,
                        help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS))
366
367
368
369
    parser.add_argument("--output_dir", default=None, type=str, required=True,
                        help="The output directory where the model checkpoints and predictions will be written.")

    ## Other parameters
370
371
372
373
374
375
376
    parser.add_argument("--config_name", default="", type=str,
                        help="Pretrained config name or path if not the same as model_name")
    parser.add_argument("--tokenizer_name", default="", type=str,
                        help="Pretrained tokenizer name or path if not the same as model_name")
    parser.add_argument("--cache_dir", default="", type=str,
                        help="Where do you want to store the pre-trained models downloaded from s3")

thomwolf's avatar
thomwolf committed
377
378
379
380
381
    parser.add_argument('--version_2_with_negative', action='store_true',
                        help='If true, the SQuAD examples contain some that do not have an answer.')
    parser.add_argument('--null_score_diff_threshold', type=float, default=0.0,
                        help="If null_score - best_non_null is greater than the threshold predict null.")

382
383
384
385
386
387
388
389
    parser.add_argument("--max_seq_length", default=384, type=int,
                        help="The maximum total input sequence length after WordPiece tokenization. Sequences "
                             "longer than this will be truncated, and sequences shorter than this will be padded.")
    parser.add_argument("--doc_stride", default=128, type=int,
                        help="When splitting up a long document into chunks, how much stride to take between chunks.")
    parser.add_argument("--max_query_length", default=64, type=int,
                        help="The maximum number of tokens for the question. Questions longer than this will "
                             "be truncated to this length.")
thomwolf's avatar
thomwolf committed
390
391
    parser.add_argument("--do_train", action='store_true',
                        help="Whether to run training.")
392
    parser.add_argument("--do_eval", action='store_true',
thomwolf's avatar
thomwolf committed
393
                        help="Whether to run eval on the dev set.")
394
395
    parser.add_argument("--evaluate_during_training", action='store_true',
                        help="Rul evaluation during training at each logging step.")
thomwolf's avatar
thomwolf committed
396
    parser.add_argument("--do_lower_case", action='store_true',
397
                        help="Set this flag if you are using an uncased model.")
thomwolf's avatar
thomwolf committed
398

399
400
401
402
    parser.add_argument("--per_gpu_train_batch_size", default=8, type=int,
                        help="Batch size per GPU/CPU for training.")
    parser.add_argument("--per_gpu_eval_batch_size", default=8, type=int,
                        help="Batch size per GPU/CPU for evaluation.")
thomwolf's avatar
thomwolf committed
403
404
405
406
    parser.add_argument("--learning_rate", default=5e-5, type=float,
                        help="The initial learning rate for Adam.")
    parser.add_argument('--gradient_accumulation_steps', type=int, default=1,
                        help="Number of updates steps to accumulate before performing a backward/update pass.")
407
408
409
410
411
412
    parser.add_argument("--weight_decay", default=0.0, type=float,
                        help="Weight deay if we apply some.")
    parser.add_argument("--adam_epsilon", default=1e-8, type=float,
                        help="Epsilon for Adam optimizer.")
    parser.add_argument("--max_grad_norm", default=1.0, type=float,
                        help="Max gradient norm.")
413
414
    parser.add_argument("--num_train_epochs", default=3.0, type=float,
                        help="Total number of training epochs to perform.")
415
416
417
418
    parser.add_argument("--max_steps", default=-1, type=int,
                        help="If > 0: set total number of training steps to perform. Override num_train_epochs.")
    parser.add_argument("--warmup_steps", default=0, type=int,
                        help="Linear warmup over warmup_steps.")
419
    parser.add_argument("--n_best_size", default=20, type=int,
thomwolf's avatar
thomwolf committed
420
                        help="The total number of n-best predictions to generate in the nbest_predictions.json output file.")
421
422
423
424
425
426
    parser.add_argument("--max_answer_length", default=30, type=int,
                        help="The maximum length of an answer that can be generated. This is needed because the start "
                             "and end predictions are not conditioned on one another.")
    parser.add_argument("--verbose_logging", action='store_true',
                        help="If true, all of the warnings related to data processing will be printed. "
                             "A number of warnings are expected for a normal SQuAD evaluation.")
thomwolf's avatar
thomwolf committed
427

428
429
430
431
432
433
    parser.add_argument('--logging_steps', type=int, default=50,
                        help="Log every X updates steps.")
    parser.add_argument('--save_steps', type=int, default=50,
                        help="Save checkpoint every X updates steps.")
    parser.add_argument("--eval_all_checkpoints", action='store_true',
                        help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number")
thomwolf's avatar
thomwolf committed
434
    parser.add_argument("--no_cuda", action='store_true',
435
                        help="Whether not to use CUDA when available")
436
437
438
439
    parser.add_argument('--overwrite_output_dir', action='store_true',
                        help="Overwrite the content of the output directory")
    parser.add_argument('--overwrite_cache', action='store_true',
                        help="Overwrite the cached training and evaluation sets")
thomwolf's avatar
thomwolf committed
440
    parser.add_argument('--seed', type=int, default=42,
441
                        help="random seed for initialization")
442

thomwolf's avatar
thomwolf committed
443
    parser.add_argument("--local_rank", type=int, default=-1,
444
                        help="local_rank for distributed training on gpus")
thomwolf's avatar
thomwolf committed
445
446
447
448
449
    parser.add_argument('--fp16', action='store_true',
                        help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit")
    parser.add_argument('--fp16_opt_level', type=str, default='O1',
                        help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
                             "See details at https://nvidia.github.io/apex/amp.html")
450
451
452
453
    parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.")
    parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.")
    args = parser.parse_args()

thomwolf's avatar
thomwolf committed
454
455
456
    if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir:
        raise ValueError("Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(args.output_dir))

457
    # Setup distant debugging if needed
458
459
460
461
462
463
464
    if args.server_ip and args.server_port:
        # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
        import ptvsd
        print("Waiting for debugger attach")
        ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
        ptvsd.wait_for_attach()

thomwolf's avatar
thomwolf committed
465
    # Setup CUDA, GPU & distributed training
466
467
    if args.local_rank == -1 or args.no_cuda:
        device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
thomwolf's avatar
thomwolf committed
468
469
        args.n_gpu = torch.cuda.device_count()
    else:  # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
470
471
472
        torch.cuda.set_device(args.local_rank)
        device = torch.device("cuda", args.local_rank)
        torch.distributed.init_process_group(backend='nccl')
thomwolf's avatar
thomwolf committed
473
474
        args.n_gpu = 1
    args.device = device
475

thomwolf's avatar
thomwolf committed
476
    # Setup logging
477
478
479
    logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
                        datefmt = '%m/%d/%Y %H:%M:%S',
                        level = logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
thomwolf's avatar
thomwolf committed
480
    logger.warning("Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
481
                    args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16)
482

483
484
    # Set seed
    set_seed(args)
485

thomwolf's avatar
thomwolf committed
486
    # Load pretrained model and tokenizer
487
    if args.local_rank not in [-1, 0]:
488
489
        torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab

490
    args.model_type = args.model_type.lower()
491
    config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
thomwolf's avatar
thomwolf committed
492
493
494
495
496
497
498
499
500
    config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path,
                                          cache_dir=args.cache_dir if args.cache_dir else None)
    tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
                                                do_lower_case=args.do_lower_case,
                                                cache_dir=args.cache_dir if args.cache_dir else None)
    model = model_class.from_pretrained(args.model_name_or_path,
                                        from_tf=bool('.ckpt' in args.model_name_or_path),
                                        config=config,
                                        cache_dir=args.cache_dir if args.cache_dir else None)
501
502

    if args.local_rank == 0:
503
        torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
504

thomwolf's avatar
thomwolf committed
505
    model.to(args.device)
506

507
508
    logger.info("Training/evaluation parameters %s", args)

Simon Layton's avatar
Simon Layton committed
509
510
511
512
513
514
515
516
517
518
    # Before we do anything with models, we want to ensure that we get fp16 execution of torch.einsum if args.fp16 is set.
    # Otherwise it'll default to "promote" mode, and we'll get fp32 operations. Note that running `--fp16_opt_level="O2"` will
    # remove the need for this code, but it is still valid.
    if args.fp16:
        try:
            import apex
            apex.amp.register_half_function(torch, 'einsum')
        except ImportError:
            raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")

thomwolf's avatar
thomwolf committed
519
    # Training
520
    if args.do_train:
521
        train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False)
522
        global_step, tr_loss = train(args, train_dataset, model, tokenizer)
523
        logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
524

525

thomwolf's avatar
thomwolf committed
526
    # Save the trained model and the tokenizer
Peng Qi's avatar
Peng Qi committed
527
    if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
528
529
530
531
532
533
534
535
536
537
        # Create output directory if needed
        if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
            os.makedirs(args.output_dir)

        logger.info("Saving model checkpoint to %s", args.output_dir)
        # Save a trained model, configuration and tokenizer using `save_pretrained()`.
        # They can then be reloaded using `from_pretrained()`
        model_to_save = model.module if hasattr(model, 'module') else model  # Take care of distributed/parallel training
        model_to_save.save_pretrained(args.output_dir)
        tokenizer.save_pretrained(args.output_dir)
538
539

        # Good practice: save your training arguments together with the trained model
540
        torch.save(args, os.path.join(args.output_dir, 'training_args.bin'))
541

542
543
        # Load a trained model and vocabulary that you have fine-tuned
        model = model_class.from_pretrained(args.output_dir)
Peng Qi's avatar
Peng Qi committed
544
        tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
545
546
547
        model.to(args.device)


thomwolf's avatar
thomwolf committed
548
    # Evaluation - we can ask to evaluate all the checkpoints (sub-directories) in a directory
549
550
551
552
553
    results = {}
    if args.do_eval and args.local_rank in [-1, 0]:
        checkpoints = [args.output_dir]
        if args.eval_all_checkpoints:
            checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True)))
554
            logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN)  # Reduce model loading logs
thomwolf's avatar
thomwolf committed
555

556
        logger.info("Evaluate the following checkpoints: %s", checkpoints)
thomwolf's avatar
thomwolf committed
557

558
        for checkpoint in checkpoints:
thomwolf's avatar
thomwolf committed
559
            # Reload the model
560
561
562
            global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else ""
            model = model_class.from_pretrained(checkpoint)
            model.to(args.device)
thomwolf's avatar
thomwolf committed
563
564

            # Evaluate
565
            result = evaluate(args, model, tokenizer, prefix=global_step)
thomwolf's avatar
thomwolf committed
566

567
568
            result = dict((k + ('_{}'.format(global_step) if global_step else ''), v) for k, v in result.items())
            results.update(result)
thomwolf's avatar
thomwolf committed
569

570
    logger.info("Results: {}".format(results))
thomwolf's avatar
thomwolf committed
571

572
    return results
573
574
575
576


if __name__ == "__main__":
    main()