run_squad.py 28.2 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.
thomwolf's avatar
thomwolf committed
16
""" Finetuning the library models for question-answering on SQuAD (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
26
27
28
29
30
31
32
33
34

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

from tensorboardX import SummaryWriter

thomwolf's avatar
thomwolf committed
35
36
37
38
39
40
41
42
from pytorch_transformers import (WEIGHTS_NAME, BertConfig,
                                  BertForQuestionAnswering, BertTokenizer,
                                  XLMConfig, XLMForQuestionAnswering,
                                  XLMTokenizer, XLNetConfig,
                                  XLNetForQuestionAnswering,
                                  XLNetTokenizer)

from pytorch_transformers import AdamW, WarmupLinearSchedule
43

44
45
46
from utils_squad import (read_squad_examples, convert_examples_to_features,
                         RawResult, write_predictions,
                         RawResultExtended, write_predictions_extended)
47

thomwolf's avatar
thomwolf committed
48
49
50
# 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)
51
52
from utils_squad_evaluate import EVAL_OPTS, main as evaluate_on_squad

53
54
logger = logging.getLogger(__name__)

thomwolf's avatar
thomwolf committed
55
56
ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) \
                  for conf in (BertConfig, XLNetConfig, XLMConfig)), ())
thomwolf's avatar
thomwolf committed
57
58

MODEL_CLASSES = {
thomwolf's avatar
thomwolf committed
59
60
61
    'bert': (BertConfig, BertForQuestionAnswering, BertTokenizer),
    'xlnet': (XLNetConfig, XLNetForQuestionAnswering, XLNetTokenizer),
    'xlm': (XLMConfig, XLMForQuestionAnswering, XLMTokenizer),
thomwolf's avatar
thomwolf committed
62
63
}

thomwolf's avatar
thomwolf committed
64
65
66
67
68
69
70
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)

71
72
def to_list(tensor):
    return tensor.detach().cpu().tolist()
thomwolf's avatar
thomwolf committed
73

74
def train(args, train_dataset, model, tokenizer):
thomwolf's avatar
thomwolf committed
75
76
77
78
    """ Train the model """
    if args.local_rank in [-1, 0]:
        tb_writer = SummaryWriter()

79
    args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
thomwolf's avatar
thomwolf committed
80
81
82
83
    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:
84
        t_total = args.max_steps
thomwolf's avatar
thomwolf committed
85
86
        args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1
    else:
87
        t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
thomwolf's avatar
thomwolf committed
88

89
    # Prepare optimizer and schedule (linear warmup and decay)
thomwolf's avatar
thomwolf committed
90
91
    no_decay = ['bias', 'LayerNorm.weight']
    optimizer_grouped_parameters = [
92
        {'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
93
94
        {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
        ]
95
96
    optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
    scheduler = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=t_total)
thomwolf's avatar
thomwolf committed
97
98
99
100
101
102
103
    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)

104
105
106
107
    # multi-gpu training (should be after apex fp16 initialization)
    if args.n_gpu > 1:
        model = torch.nn.DataParallel(model)

thomwolf's avatar
thomwolf committed
108
109
110
111
112
113
    # 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
114
115
116
117
    # Train!
    logger.info("***** Running training *****")
    logger.info("  Num examples = %d", len(train_dataset))
    logger.info("  Num Epochs = %d", args.num_train_epochs)
118
119
120
    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
121
    logger.info("  Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
122
    logger.info("  Total optimization steps = %d", t_total)
thomwolf's avatar
thomwolf committed
123
124
125

    global_step = 0
    tr_loss, logging_loss = 0.0, 0.0
126
127
128
129
130
131
132
    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
133
            batch = tuple(t.to(args.device) for t in batch)
134
            inputs = {'input_ids':       batch[0],
thomwolf's avatar
thomwolf committed
135
136
137
                      'attention_mask':  batch[1], 
                      'token_type_ids':  None if args.model_type == 'xlm' else batch[2],  
                      'start_positions': batch[3], 
138
                      'end_positions':   batch[4]}
139
140
141
            if args.model_type in ['xlnet', 'xlm']:
                inputs.update({'cls_index': batch[5],
                               'p_mask':    batch[6]})
Peiqin Lin's avatar
typos  
Peiqin Lin committed
142
143
            outputs = model(**inputs)
            loss = outputs[0]  # model outputs are always tuple in pytorch-transformers (see doc)
thomwolf's avatar
thomwolf committed
144

145
            if args.n_gpu > 1:
thomwolf's avatar
thomwolf committed
146
                loss = loss.mean() # mean() to average on multi-gpu parallel (not distributed) training
147
148
            if args.gradient_accumulation_steps > 1:
                loss = loss / args.gradient_accumulation_steps
thomwolf's avatar
thomwolf committed
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
            if args.fp16:
                with amp.scale_loss(loss, optimizer) as scaled_loss:
                    scaled_loss.backward()
                torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
            else:
                loss.backward()
                torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)

            tr_loss += loss.item()
            if (step + 1) % args.gradient_accumulation_steps == 0:
                scheduler.step()  # Update learning rate schedule
                optimizer.step()
                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
192
193
194
    if args.local_rank in [-1, 0]:
        tb_writer.close()

195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
    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)

    # 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 = []
    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],
thomwolf's avatar
thomwolf committed
219
220
221
                      'attention_mask': batch[1],
                      'token_type_ids': None if args.model_type == 'xlm' else batch[2]  # XLM don't use segment_ids
                      }
222
223
224
225
            example_indices = batch[3]
            if args.model_type in ['xlnet', 'xlm']:
                inputs.update({'cls_index': batch[4],
                               'p_mask':    batch[5]})
226
227
228
229
230
            outputs = model(**inputs)

        for i, example_index in enumerate(example_indices):
            eval_feature = features[example_index.item()]
            unique_id = int(eval_feature.unique_id)
231
232
233
234
235
236
237
238
239
240
241
242
243
            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)
244

thomwolf's avatar
thomwolf committed
245
    # Compute predictions
246
247
    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))
248
249
250
251
    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
252
253
254
255
256
257

    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,
258
259
                        model.config.start_n_top, model.config.end_n_top,
                        args.version_2_with_negative, tokenizer, args.verbose_logging)
260
261
262
263
264
    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)
265

thomwolf's avatar
thomwolf committed
266
    # Evaluate with the official SQuAD script
267
268
269
270
271
272
273
274
275
276
277
    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):
    # 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
278
        'dev' if evaluate else 'train',
279
        list(filter(None, args.model_name_or_path.split('/'))).pop(),
280
281
        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
282
283
284
        logger.info("Loading features from cached file %s", cached_features_file)
        features = torch.load(cached_features_file)
    else:
285
286
        logger.info("Creating features from dataset file at %s", input_file)
        examples = read_squad_examples(input_file=input_file,
287
288
                                                is_training=not evaluate,
                                                version_2_with_negative=args.version_2_with_negative)
289
290
291
292
293
294
        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,
                                                is_training=not evaluate)
thomwolf's avatar
thomwolf committed
295
296
297
298
299
        if args.local_rank in [-1, 0]:
            logger.info("Saving features into cached file %s", cached_features_file)
            torch.save(features, cached_features_file)

    # Convert to Tensors and build dataset
300
301
302
    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)
303
304
    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)
305
    if evaluate:
thomwolf's avatar
thomwolf committed
306
        all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
307
308
        dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
                                all_example_index, all_cls_index, all_p_mask)
309
310
311
    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)
312
313
314
        dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
                                all_start_positions, all_end_positions,
                                all_cls_index, all_p_mask)
thomwolf's avatar
thomwolf committed
315

316
317
    if output_examples:
        return dataset, examples, features
thomwolf's avatar
thomwolf committed
318
319
    return dataset

320
321
322
323
324

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

    ## Required parameters
thomwolf's avatar
thomwolf committed
325
326
327
328
    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")
329
330
331
332
    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))
333
334
335
336
    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
337
338
339
340
341
342
343
    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
344
345
346
347
348
    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.")

349
350
351
352
353
354
355
356
    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
357
358
    parser.add_argument("--do_train", action='store_true',
                        help="Whether to run training.")
359
    parser.add_argument("--do_eval", action='store_true',
thomwolf's avatar
thomwolf committed
360
                        help="Whether to run eval on the dev set.")
361
362
    parser.add_argument("--evaluate_during_training", action='store_true',
                        help="Rul evaluation during training at each logging step.")
thomwolf's avatar
thomwolf committed
363
    parser.add_argument("--do_lower_case", action='store_true',
364
                        help="Set this flag if you are using an uncased model.")
thomwolf's avatar
thomwolf committed
365

366
367
368
369
    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
370
371
372
373
    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.")
374
375
376
377
378
379
    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.")
380
381
    parser.add_argument("--num_train_epochs", default=3.0, type=float,
                        help="Total number of training epochs to perform.")
382
383
384
385
    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.")
386
    parser.add_argument("--n_best_size", default=20, type=int,
thomwolf's avatar
thomwolf committed
387
                        help="The total number of n-best predictions to generate in the nbest_predictions.json output file.")
388
389
390
391
392
393
    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
394

395
396
397
398
399
400
    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
401
    parser.add_argument("--no_cuda", action='store_true',
402
                        help="Whether not to use CUDA when available")
403
404
405
406
    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
407
    parser.add_argument('--seed', type=int, default=42,
408
                        help="random seed for initialization")
409

thomwolf's avatar
thomwolf committed
410
    parser.add_argument("--local_rank", type=int, default=-1,
411
                        help="local_rank for distributed training on gpus")
thomwolf's avatar
thomwolf committed
412
413
414
415
416
    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")
417
418
419
420
    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
421
422
423
    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))

424
    # Setup distant debugging if needed
425
426
427
428
429
430
431
    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
432
    # Setup CUDA, GPU & distributed training
433
434
    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
435
436
        args.n_gpu = torch.cuda.device_count()
    else:  # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
437
438
439
        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
440
441
        args.n_gpu = 1
    args.device = device
442

thomwolf's avatar
thomwolf committed
443
    # Setup logging
444
445
446
    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
447
    logger.warning("Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
448
                    args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16)
449

450
451
    # Set seed
    set_seed(args)
452

thomwolf's avatar
thomwolf committed
453
    # Load pretrained model and tokenizer
454
    if args.local_rank not in [-1, 0]:
455
456
        torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab

457
    args.model_type = args.model_type.lower()
458
    config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
459
460
461
    config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path)
    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)
    model = model_class.from_pretrained(args.model_name_or_path, from_tf=bool('.ckpt' in args.model_name_or_path), config=config)
462
463

    if args.local_rank == 0:
464
        torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
465

thomwolf's avatar
thomwolf committed
466
    model.to(args.device)
467

468
469
    logger.info("Training/evaluation parameters %s", args)

thomwolf's avatar
thomwolf committed
470
    # Training
471
    if args.do_train:
472
473
474
        train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False)
        global_step, tr_loss = train(args, train_dataset, model, tokenizer)
        logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
475

476

thomwolf's avatar
thomwolf committed
477
    # Save the trained model and the tokenizer
478
479
480
481
482
483
484
485
486
487
488
    if args.local_rank == -1 or torch.distributed.get_rank() == 0:
        # 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)
489
490

        # Good practice: save your training arguments together with the trained model
491
        torch.save(args, os.path.join(args.output_dir, 'training_args.bin'))
492

493
494
495
496
497
498
        # Load a trained model and vocabulary that you have fine-tuned
        model = model_class.from_pretrained(args.output_dir)
        tokenizer = tokenizer_class.from_pretrained(args.output_dir)
        model.to(args.device)


thomwolf's avatar
thomwolf committed
499
    # Evaluation - we can ask to evaluate all the checkpoints (sub-directories) in a directory
500
501
502
503
504
    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)))
thomwolf's avatar
thomwolf committed
505
506
            logging.getLogger("pytorch_transformers.modeling_utils").setLevel(logging.WARN)  # Reduce model loading logs

507
        logger.info("Evaluate the following checkpoints: %s", checkpoints)
thomwolf's avatar
thomwolf committed
508

509
        for checkpoint in checkpoints:
thomwolf's avatar
thomwolf committed
510
            # Reload the model
511
512
513
            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
514
515

            # Evaluate
516
            result = evaluate(args, model, tokenizer, prefix=global_step)
thomwolf's avatar
thomwolf committed
517

518
519
            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
520

521
    logger.info("Results: {}".format(results))
thomwolf's avatar
thomwolf committed
522

523
    return results
524
525
526
527


if __name__ == "__main__":
    main()