training.py 22.8 KB
Newer Older
1
# coding=utf-8
Mohammad's avatar
Mohammad committed
2
# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
3
4
5
6
7
8
9
10
11
12
13
14
15
#
# 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.

Mohammad's avatar
Mohammad committed
16
"""Pretrain utilities."""
17
18
19

from datetime import datetime
import math
Mohammad's avatar
Mohammad committed
20
import sys
21
import time
22
23
24
25
26

import torch
from torch.nn.parallel.distributed import DistributedDataParallel as torchDDP
from apex.optimizers import FusedAdam as Adam

Mohammad's avatar
Mohammad committed
27
28
29
from megatron import get_args
from megatron import get_timers
from megatron import get_tensorboard_writer
30
from megatron import mpu
Mohammad's avatar
Mohammad committed
31
32
33
from megatron import print_rank_0
from megatron.checkpointing import load_checkpoint
from megatron.checkpointing import save_checkpoint
34
35
from megatron.fp16 import FP16_Module
from megatron.fp16 import FP16_Optimizer
Mohammad's avatar
Mohammad committed
36
from megatron.initialize import initialize_megatron
37
38
39
from megatron.learning_rates import AnnealingLR
from megatron.model import DistributedDataParallel as LocalDDP
from megatron.model import get_params_for_weight_decay_optimization
40
from megatron.mpu.initialize import get_index_ready, get_train_group, get_data_parallel_group, get_gloo_comm_group
Neel Kant's avatar
Neel Kant committed
41
from megatron.model.realm_model import ICTBertModel
42
from megatron.utils import check_adlr_autoresume_termination
43
from megatron.utils import make_data_loader
44
from megatron.utils import report_memory
45
46
47


INDEX_READY = None
48
49


50
def pretrain(train_valid_test_dataset_provider, model_provider,
51
52
             forward_step_func, extra_args_provider=None, args_defaults={},
             initializer_func=None):
53
54
55
    """Main training program.

    This function will run the followings in the order provided:
Mohammad's avatar
Mohammad committed
56
57
        1) initialize Megatron.
        2) setup model, optimizer and lr schedule using the model_provider.
58
        3) call train_val_test_data_provider to get train/val/test datasets.
Mohammad's avatar
Mohammad committed
59
        4) train the modle using the forward_step_func.
60
61

    Arguments:
62
63
64
        train_valid_test_dataset_provider: a function that takes the size of
            train/valid/test dataset and returns `train, valid, test` datasets.
        model_provider: a function that returns a vanilla version of the
Mohammad's avatar
Mohammad committed
65
66
67
68
69
70
71
72
73
74
            model. By vanilla we mean a simple model on cpu with no fp16 or ddp.
        forward_step_func: a function that takes a `data iterator` and `model`,
            and returns a `loss` scalar with a dictionary with key:values being
            the info we would like to monitor during training, for example
            `lm-loss: value`. We also require that this function add
            `batch generator` to the timers class.
        extra_args_provider: a function that takes a parser and adds arguments
            to it. It is used for programs to add their own arguments.
        args_defaults: a dictionary from argument-name to argument-value. It
            to set already parse arguments.
75
76
    """

77
    # Initalize and get arguments, timers, and Tensorboard writer.
78
79
80
81
82
83
84
85
86
    if initializer_func is None:
        initialize_megatron(extra_args_provider=extra_args_provider,
                            args_defaults=args_defaults)
    else:
        initializer_func(extra_args_provider=extra_args_provider,
                         args_defaults=args_defaults)
        global INDEX_READY
        INDEX_READY = get_index_ready()

87
    args = get_args()
Mohammad's avatar
Mohammad committed
88
    timers = get_timers()
89
90

    # Model, optimizer, and learning rate.
Mohammad's avatar
Mohammad committed
91
92
93
    timers('model and optimizer').start()
    model, optimizer, lr_scheduler = setup_model_and_optimizer(model_provider)
    timers('model and optimizer').stop()
94
95

    # Data stuff.
96
97
98
99
100
    timers('train/valid/test data iterators').start()
    train_data_iterator, valid_data_iterator, test_data_iterator \
        = build_train_valid_test_data_iterators(
            train_valid_test_dataset_provider)
    timers('train/valid/test data iterators').stop()
Mohammad's avatar
Mohammad committed
101
102
103

    # Print setup timing.
    print_rank_0('done with setups ...')
104
    timers.log(['model and optimizer', 'train/valid/test data iterators'])
Mohammad's avatar
Mohammad committed
105
    print_rank_0('training ...')
106
107

    iteration = 0
108
    if args.do_train and args.train_iters > 0:
109
110
        iteration, _ = train(forward_step_func,
                             model, optimizer, lr_scheduler,
Neel Kant's avatar
Neel Kant committed
111
                             train_data_iterator, valid_data_iterator)
Mohammad's avatar
Mohammad committed
112

113
114
115
    if args.do_valid:
        prefix = 'the end of training for val data'
        evaluate_and_print_results(prefix, forward_step_func,
116
                                   valid_data_iterator, model,
Mohammad's avatar
Mohammad committed
117
                                   iteration, False)
118
119

    if args.save and iteration != 0:
120
        save_checkpoint(iteration, model, optimizer, lr_scheduler)
121
122
123
124
125
126

    if args.do_test:
        # Run on test data.
        prefix = 'the end of training for test data'
        evaluate_and_print_results(prefix, forward_step_func,
                                   test_data_iterator, model,
Mohammad's avatar
Mohammad committed
127
                                   0, True)
128
129


Mohammad's avatar
Mohammad committed
130
def get_model(model_provider_func):
131
    """Build the model."""
Mohammad's avatar
Mohammad committed
132
    args = get_args()
133
134

    # Build model on cpu.
Mohammad's avatar
Mohammad committed
135
    model = model_provider_func()
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152

    # Print number of parameters.
    if mpu.get_data_parallel_rank() == 0:
        print(' > number of parameters on model parallel rank {}: {}'.format(
            mpu.get_model_parallel_rank(),
            sum([p.nelement() for p in model.parameters()])), flush=True)

    # GPU allocation.
    model.cuda(torch.cuda.current_device())

    # Fp16 conversion.
    if args.fp16:
        model = FP16_Module(model)

    # Wrap model for distributed training."""
    if args.DDP_impl == 'torch':
        i = torch.cuda.current_device()
Mohammad's avatar
Mohammad committed
153
154
        model = torchDDP(model, device_ids=[i], output_device=i,
                         process_group=mpu.get_data_parallel_group())
155
156
        return model
    if args.DDP_impl == 'local':
Mohammad's avatar
Mohammad committed
157
        model = LocalDDP(model)
158
159
        return model

160
    raise NotImplementedError('Unknown DDP implementation specified: {}. '
161
                              'Exiting.'.format(args.DDP_impl))
162
163


Mohammad's avatar
Mohammad committed
164
def get_optimizer(model):
165
    """Set up the optimizer."""
Mohammad's avatar
Mohammad committed
166
    args = get_args()
167
168

    # Build parameter groups (weight decay and non-decay).
Mohammad's avatar
Mohammad committed
169
    while isinstance(model, (torchDDP, LocalDDP, FP16_Module)):
170
171
172
173
174
175
176
177
178
179
        model = model.module
    param_groups = get_params_for_weight_decay_optimization(model)

    # Add model parallel attribute if it is not set.
    for param_group in param_groups:
        for param in param_group['params']:
            if not hasattr(param, 'model_parallel'):
                param.model_parallel = False

    # Use Adam.
Mohammad's avatar
Mohammad committed
180
    optimizer = Adam(param_groups, lr=args.lr, weight_decay=args.weight_decay)
181
182
183
184
185
186
187
188

    # Wrap into fp16 optimizer.
    if args.fp16:
        optimizer = FP16_Optimizer(optimizer,
                                   static_loss_scale=args.loss_scale,
                                   dynamic_loss_scale=args.dynamic_loss_scale,
                                   dynamic_loss_args={
                                       'scale_window': args.loss_scale_window,
Neel Kant's avatar
Neel Kant committed
189
                                       'min_scale': args.min_scale,
190
191
192
193
194
                                       'delayed_shift': args.hysteresis})

    return optimizer


Mohammad's avatar
Mohammad committed
195
def get_learning_rate_scheduler(optimizer):
196
    """Build the learning rate scheduler."""
Mohammad's avatar
Mohammad committed
197
    args = get_args()
198
199
200
201
202
203
204

    # Add linear learning rate scheduler.
    if args.lr_decay_iters is not None:
        num_iters = args.lr_decay_iters
    else:
        num_iters = args.train_iters
    num_iters = max(1, num_iters)
Mohammad's avatar
Mohammad committed
205
    init_step = 0
206
207
208
209
210
    warmup_iter = args.warmup * num_iters
    lr_scheduler = AnnealingLR(
        optimizer,
        start_lr=args.lr,
        warmup_iter=warmup_iter,
Mohammad's avatar
Mohammad committed
211
        total_iters=num_iters,
212
213
214
215
216
217
218
219
220
        decay_style=args.lr_decay_style,
        last_iter=init_step,
        min_lr=args.min_lr,
        use_checkpoint_lr_scheduler=args.use_checkpoint_lr_scheduler,
        override_lr_scheduler=args.override_lr_scheduler)

    return lr_scheduler


Mohammad's avatar
Mohammad committed
221
def setup_model_and_optimizer(model_provider_func):
222
    """Setup model and optimizer."""
Mohammad's avatar
Mohammad committed
223
    args = get_args()
224

Mohammad's avatar
Mohammad committed
225
226
227
    model = get_model(model_provider_func)
    optimizer = get_optimizer(model)
    lr_scheduler = get_learning_rate_scheduler(optimizer)
228
229

    if args.load is not None:
230
        args.iteration = load_checkpoint(model, optimizer, lr_scheduler)
231
232
233
    else:
        args.iteration = 0

Neel Kant's avatar
Neel Kant committed
234
235
236
237
238
239
    if args.iteration == 0 and isinstance(model.module.module, ICTBertModel):
        print("Yes, located ICT model", flush=True)
        model.module.module.init_state_dict_from_bert()
    elif args.iteration == 0:
        print("Ooops", flush=True)

240
241
242
    return model, optimizer, lr_scheduler


Mohammad's avatar
Mohammad committed
243
def backward_step(optimizer, model, loss):
244
    """Backward step."""
Mohammad's avatar
Mohammad committed
245
246
    args = get_args()
    timers = get_timers()
247
    # torch.cuda.synchronize()
248
249

    # Backward pass.
Neel Kant's avatar
Neel Kant committed
250
    # optimizer.zero_grad(set_grads_to_None=True)
251
    if args.fp16:
Neel Kant's avatar
Neel Kant committed
252
        optimizer.zero_grad(set_grads_to_None=True)
253
254
        optimizer.backward(loss, update_master_grads=False)
    else:
Neel Kant's avatar
Neel Kant committed
255
        optimizer.zero_grad()
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
        loss.backward()

    # All-reduce if needed.
    if args.DDP_impl == 'local':
        timers('allreduce').start()
        model.allreduce_params(reduce_after=False,
                               fp32_allreduce=args.fp32_allreduce)
        timers('allreduce').stop()
    # Update master gradients.
    if args.fp16:
        optimizer.update_master_grads()
    # Clipping gradients helps prevent the exploding gradient.
    if args.clip_grad > 0:
        if not args.fp16:
            mpu.clip_grad_norm(model.parameters(), args.clip_grad)
        else:
            optimizer.clip_master_grads(args.clip_grad)


Mohammad's avatar
Mohammad committed
275
276
def train_step(forward_step_func, data_iterator,
               model, optimizer, lr_scheduler):
277
    """Single training step."""
Mohammad's avatar
Mohammad committed
278
279
    args = get_args()
    timers = get_timers()
280
281
282

    # Forward model for one step.
    timers('forward').start()
Mohammad's avatar
Mohammad committed
283
    loss, loss_reduced = forward_step_func(data_iterator, model)
284
285
286
    timers('forward').stop()

    timers('backward').start()
Mohammad's avatar
Mohammad committed
287
    backward_step(optimizer, model, loss)
288
289
    timers('backward').stop()

Neel Kant's avatar
Neel Kant committed
290
291
    # Calculate gradients, reduce across processes, and clip.

292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
    # Update parameters.
    timers('optimizer').start()
    optimizer.step()
    timers('optimizer').stop()

    # Update learning rate.
    skipped_iter = 0
    if not (args.fp16 and optimizer.overflow):
        lr_scheduler.step()
    else:
        skipped_iter = 1

    return loss_reduced, skipped_iter


Mohammad Shoeybi's avatar
Mohammad Shoeybi committed
307
def training_log(loss_dict, total_loss_dict, learning_rate, iteration,
Mohammad's avatar
Mohammad committed
308
309
310
311
312
                 loss_scale, report_memory_flag):
    """Log training information such as losses, timing, ...."""
    args = get_args()
    timers = get_timers()
    writer = get_tensorboard_writer()
Mohammad Shoeybi's avatar
Mohammad Shoeybi committed
313
314
315
316
317
318
319

    # Update losses.
    for key in loss_dict:
        total_loss_dict[key] = total_loss_dict.get(key, 0.) + loss_dict[key]

    # Logging.
    timers_to_log = []
Neel Kant's avatar
Neel Kant committed
320

Mohammad Shoeybi's avatar
Mohammad Shoeybi committed
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
    def add_to_logging(name):
        if name in timers.timers:
            timers_to_log.append(name)
    add_to_logging('forward')
    add_to_logging('backward')
    add_to_logging('allreduce')
    add_to_logging('optimizer')
    add_to_logging('batch generator')

    # Tensorboard values.
    if writer and torch.distributed.get_rank() == 0:
        writer.add_scalar('learning_rate', learning_rate, iteration)
        for key in loss_dict:
            writer.add_scalar(key, loss_dict[key], iteration)
        if args.fp16:
            writer.add_scalar('loss_scale', loss_scale, iteration)
        normalizer = iteration % args.log_interval
        if normalizer == 0:
            normalizer = args.log_interval
        timers.write(timers_to_log, writer, iteration,
                     normalizer=normalizer)

    if iteration % args.log_interval == 0:
        elapsed_time = timers('interval time').elapsed()
        if writer and torch.distributed.get_rank() == 0:
            writer.add_scalar('iteration_time',
                              elapsed_time / args.log_interval, iteration)
        log_string = ' iteration {:8d}/{:8d} |'.format(iteration,
                                                       args.train_iters)
        log_string += ' elapsed time per iteration (ms): {:.1f} |'.format(
            elapsed_time * 1000.0 / args.log_interval)
        log_string += ' learning rate: {:.3E} |'.format(learning_rate)
        for key in total_loss_dict:
            avg = total_loss_dict[key].item() / args.log_interval
            log_string += ' {}: {:.6E} |'.format(key, avg)
            total_loss_dict[key] = 0.0
        if args.fp16:
            log_string += ' loss scale: {:.1f} |'.format(loss_scale)
        print_rank_0(log_string)
        if report_memory_flag:
            report_memory('after {} iterations'.format(iteration))
            report_memory_flag = False
        timers.log(timers_to_log, normalizer=args.log_interval)

    return report_memory_flag


368
def train(forward_step_func, model, optimizer, lr_scheduler,
369
          train_data_iterator, valid_data_iterator):
370
    """Train the model function."""
Mohammad's avatar
Mohammad committed
371
372
    args = get_args()
    timers = get_timers()
373
374
375
376
377
378
379
380
381
382
383
384
385

    # Turn on training mode which enables dropout.
    model.train()

    # Tracking loss.
    total_loss_dict = {}

    # Iterations.
    iteration = args.iteration
    skipped_iters = 0

    timers('interval time').start()
    report_memory_flag = True
386
    global INDEX_READY
387
    print('>>> Starting train()', flush=True)
388
    # start off by posting a receive call which will be answered.
389
    # synchronize for start
Neel Kant's avatar
Neel Kant committed
390
391
392
393
    if args.max_training_rank is not None:
        torch.distributed.broadcast(INDEX_READY, 0, group=get_gloo_comm_group())
        recv_handle = torch.distributed.broadcast(INDEX_READY, args.max_training_rank, group=get_gloo_comm_group(), async_op=True)
        last_reload_iteration = iteration
394
    while iteration < args.train_iters:
Neel Kant's avatar
Neel Kant committed
395
        if args.max_training_rank is not None and iteration >= last_reload_iteration + 500 and not recv_handle.is_completed():
396
397
398
            time.sleep(5)
            continue

399
        # this only applies for realm right here
400
        if args.max_training_rank is not None and recv_handle.is_completed():
401

402
            # should add check that INDEX_READY == 1 but what else could be happening
403
404
405
406
407
            true_model = model
            if hasattr(true_model, 'module'):
                true_model = true_model.module
                if hasattr(true_model, 'module'):
                    true_model = true_model.module
408
409


410
            print("> Saving model and reloading index", flush=True)
411
412
            if args.rank == 0:
                save_checkpoint(iteration, model, optimizer, lr_scheduler)
413
            true_model.retriever.reload_index()
414
415
416

            if args.rank == 0:
                INDEX_READY = 1 - INDEX_READY
417
            torch.cuda.synchronize()
418
419
420

            # send handle
            torch.distributed.broadcast(INDEX_READY, 0, group=get_gloo_comm_group())
421
            torch.distributed.barrier(get_data_parallel_group())
422

423
            recv_handle = torch.distributed.broadcast(INDEX_READY, args.max_training_rank, group=get_gloo_comm_group(), async_op=True)
424
425
            last_reload_iteration = iteration
        elif iteration < 20:
426
            print("moving right along", flush=True)
427
            # report_memory("iteration {}".format(iteration))
428
429
430
431
        loss_dict, skipped_iter = train_step(forward_step_func,
                                             train_data_iterator,
                                             model,
                                             optimizer,
Mohammad's avatar
Mohammad committed
432
                                             lr_scheduler)
433

434
435
436
437
        skipped_iters += skipped_iter
        iteration += 1

        # Logging.
Mohammad's avatar
Mohammad committed
438
439
440
        loss_scale = None
        if args.fp16:
            loss_scale = optimizer.loss_scale
Mohammad Shoeybi's avatar
Mohammad Shoeybi committed
441
442
        report_memory_flag = training_log(loss_dict, total_loss_dict,
                                          optimizer.param_groups[0]['lr'],
Mohammad's avatar
Mohammad committed
443
                                          iteration, loss_scale,
Mohammad's avatar
Mohammad committed
444
                                          report_memory_flag)
445
446

        # Autoresume
447
448
        if args.adlr_autoresume and \
           (iteration % args.adlr_autoresume_interval == 0):
449
            check_adlr_autoresume_termination(iteration, model, optimizer,
450
                                              lr_scheduler)
451
452
453
454

        # Checkpointing
        if args.save and args.save_interval and \
           iteration % args.save_interval == 0:
455
            save_checkpoint(iteration, model, optimizer, lr_scheduler)
456
457
458
459
460
461

        # Evaluation
        if args.eval_interval and iteration % args.eval_interval == 0 and \
           args.do_valid:
            prefix = 'iteration {}'.format(iteration)
            evaluate_and_print_results(prefix, forward_step_func,
462
                                       valid_data_iterator, model,
Mohammad's avatar
Mohammad committed
463
                                       iteration, False)
464
465

        if args.exit_interval and iteration % args.exit_interval == 0:
466
            torch.distributed.barrier(get_data_parallel_group())
467
468
            time_str = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
            rank = torch.distributed.get_rank()
Mohammad's avatar
Mohammad committed
469
470
471
            print_rank_0('rank: {} | time: {} | exiting the program at '
                         'iteration {}'.format(rank, time_str, iteration))
            sys.exit()
472
473
474
475

    return iteration, skipped_iters


Mohammad's avatar
Mohammad committed
476
def evaluate(forward_step_func, data_iterator, model, verbose=False):
477
    """Evaluation."""
Mohammad's avatar
Mohammad committed
478
    args = get_args()
479
480
481
482
483
484
485
486
487
488
489
490
491
492

    # Turn on evaluation mode which disables dropout.
    model.eval()

    total_loss_dict = {}

    with torch.no_grad():
        iteration = 0
        while iteration < args.eval_iters:
            iteration += 1
            if verbose and iteration % args.log_interval == 0:
                print_rank_0('Evaluating iter {}/{}'.format(iteration,
                                                            args.eval_iters))
            # Forward evaluation.
Mohammad's avatar
Mohammad committed
493
            _, loss_dict = forward_step_func(data_iterator, model)
494
495
496
            # Reduce across processes.
            for key in loss_dict:
                total_loss_dict[key] = total_loss_dict.get(key, 0.) + \
Neel Kant's avatar
Neel Kant committed
497
                    loss_dict[key]
498
499
500
501
502
503
504
505
506
507
508
    # Move model back to the train mode.
    model.train()

    for key in total_loss_dict:
        total_loss_dict[key] /= args.eval_iters

    return total_loss_dict


def evaluate_and_print_results(prefix, forward_step_func,
                               data_iterator, model,
Mohammad's avatar
Mohammad committed
509
                               iteration, verbose=False):
510
    """Helper function to evaluate and dump results on screen."""
Mohammad's avatar
Mohammad committed
511
512
513
    writer = get_tensorboard_writer()

    total_loss_dict = evaluate(forward_step_func, data_iterator, model, verbose)
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
    string = ' validation loss at {} | '.format(prefix)
    for key in total_loss_dict:
        string += '{} value: {:.6E} | '.format(key, total_loss_dict[key].item())
        ppl = math.exp(min(20, total_loss_dict[key].item()))
        string += '{} PPL: {:.6E} | '.format(key, ppl)
        if writer and torch.distributed.get_rank() == 0:
            writer.add_scalar('{} value'.format(key),
                              total_loss_dict[key].item(),
                              iteration)
            writer.add_scalar('{} ppl'.format(key), ppl, iteration)

    length = len(string) + 1
    print_rank_0('-' * length)
    print_rank_0(string)
    print_rank_0('-' * length)


531
532
533
def build_train_valid_test_data_iterators(
        build_train_valid_test_datasets_provider):
    """XXX"""
Mohammad's avatar
Mohammad committed
534
    args = get_args()
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
    (train_dataloader, valid_dataloader, test_dataloader) = (None, None, None)

    print_rank_0('> building train, validation, and test datasets ...')
    # Data loader only on rank 0 of each model parallel group.
    if mpu.get_model_parallel_rank() == 0:
        # Rank, size, and global batch size.
        data_parallel_size = mpu.get_data_parallel_world_size()
        global_batch_size = args.batch_size * data_parallel_size

        # Number of train/valid/test samples.
        train_iters = args.train_iters
        eval_iters = (train_iters // args.eval_interval + 1) * args.eval_iters
        test_iters = args.eval_iters
        train_val_test_num_samples = [train_iters * global_batch_size,
                                      eval_iters * global_batch_size,
                                      test_iters * global_batch_size]
        print_rank_0(' > datasets target sizes (minimum size):')
        print_rank_0('    train:      {}'.format(train_val_test_num_samples[0]))
        print_rank_0('    validation: {}'.format(train_val_test_num_samples[1]))
        print_rank_0('    test:       {}'.format(train_val_test_num_samples[2]))

        # Build the datasets.
        train_ds, valid_ds, test_ds = build_train_valid_test_datasets_provider(
            train_val_test_num_samples)

        # Build dataloders.
        train_dataloader = make_data_loader(train_ds)
        valid_dataloader = make_data_loader(valid_ds)
        test_dataloader = make_data_loader(test_ds)

        # Flags to know if we need to do training/validation/testing.
        do_train = train_dataloader is not None and args.train_iters > 0
        do_valid = valid_dataloader is not None and args.eval_iters > 0
        do_test = test_dataloader is not None and args.eval_iters > 0
        # Need to broadcast num_tokens and num_type_tokens.
        flags = torch.cuda.LongTensor(
            [int(do_train), int(do_valid), int(do_test)])
    else:
        flags = torch.cuda.LongTensor([0, 0, 0])

    # Broadcast num tokens.
    torch.distributed.broadcast(flags,
                                mpu.get_model_parallel_src_rank(),
                                group=mpu.get_model_parallel_group())
    args.do_train = flags[0].item()
    args.do_valid = flags[1].item()
    args.do_test = flags[2].item()

Mohammad's avatar
Mohammad committed
584
    # Shift the start iterations.
585
586
    if train_dataloader is not None:
        train_dataloader.batch_sampler.start_iter = args.iteration % \
Neel Kant's avatar
Neel Kant committed
587
            len(train_dataloader)
Mohammad's avatar
Mohammad committed
588
        print_rank_0('setting training data start iteration to {}'.
589
590
                     format(train_dataloader.batch_sampler.start_iter))
    if valid_dataloader is not None:
Mohammad's avatar
Mohammad committed
591
        start_iter_val = (args.iteration // args.eval_interval) * \
Neel Kant's avatar
Neel Kant committed
592
            args.eval_iters
593
        valid_dataloader.batch_sampler.start_iter = start_iter_val % \
Neel Kant's avatar
Neel Kant committed
594
            len(valid_dataloader)
Mohammad's avatar
Mohammad committed
595
        print_rank_0('setting validation data start iteration to {}'.
596
                     format(valid_dataloader.batch_sampler.start_iter))
597

598
599
600
    # Build iterators.
    if train_dataloader is not None:
        train_data_iterator = iter(train_dataloader)
601
602
603
    else:
        train_data_iterator = None

604
605
    if valid_dataloader is not None:
        valid_data_iterator = iter(valid_dataloader)
606
    else:
607
        valid_data_iterator = None
608

609
610
    if test_dataloader is not None:
        test_data_iterator = iter(test_dataloader)
611
612
613
    else:
        test_data_iterator = None

614
    return train_data_iterator, valid_data_iterator, test_data_iterator