schedules.py 29.2 KB
Newer Older
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
# coding=utf-8
# Copyright (c) 2020, 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
from contextlib import contextmanager
17
import torch
18
from torch.autograd.variable import Variable
19
from torch.nn.parallel.distributed import DistributedDataParallel as torchDDP
20
21

from megatron import get_args
22
from megatron import get_num_microbatches
23
24
from megatron import get_timers
from megatron import mpu
25
from megatron import p2p_communication
26
27
28
from megatron.utils import unwrap_model
from megatron.model import DistributedDataParallel as LocalDDP
from megatron.model import Float16Module
29
30
from megatron.model import ModelType

31
32
33
34
# >>>
from megatron.mpu.random import assert_viewless_tensor
# <<<

Jared Casper's avatar
Jared Casper committed
35
36
37
38
39
def get_forward_backward_func():
    args = get_args()
    if mpu.get_pipeline_model_parallel_world_size() > 1:
        if args.virtual_pipeline_model_parallel_size is not None:
            forward_backward_func = forward_backward_pipelining_with_interleaving
40
41
42
            assert get_num_microbatches() % args.pipeline_model_parallel_size == 0, \
                'number of microbatches is not divisible by pipeline-parallel ' \
                'size when using interleaved schedule'
Jared Casper's avatar
Jared Casper committed
43
44
45
46
47
48
        else:
            forward_backward_func = forward_backward_pipelining_without_interleaving
    else:
        forward_backward_func = forward_backward_no_pipelining
    return forward_backward_func

49
def free_output_tensor(output_tensors, deallocate_pipeline_outputs):
50
51
52
53
54
55
    '''Pseudo-free (i.e., set to scalar) the output tensor's '.data' field.

    This method should be called right after the output tensor has been
    sent to the next pipeline stage. At this point, the output tensor is
    only useful for its '.grad_fn' field, and not its '.data'.
    '''
56
    if not deallocate_pipeline_outputs or output_tensors is None:
57
58
59
60
        return
    if isinstance(output_tensors, torch.Tensor):
        output_tensors = [output_tensors]
    for output_tensor in output_tensors:
61
62
        output_tensor.data = torch.cuda.FloatTensor([0])
        
63
def custom_backward(output, grad_output):
64
65
66
67
68
69
70
    '''Directly call C++ autograd engine.

    To make the 'free_output_tensor' (above) optimization work, the C++
    autograd engine must be called directly, bypassing Pytorch's
    torch.autograd.backward. Pytorch's 'backward' checks that the output and
    grad have the same shape, while C++'s 'backward' does not.
    '''
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96

    assert output.numel() == 1, \
        "output should be pseudo-'freed' in schedule, to optimize memory"
    assert isinstance(output, torch.Tensor), \
        "output == '%s'." % type(output).__name__
    assert isinstance(grad_output, (torch.Tensor, type(None))), \
        "grad_output == '%s'." % type(grad_output).__name__

    # Handle scalar output
    if grad_output is None:
        assert output.numel() == 1, "implicit grad requires scalar output."
        grad_output = torch.ones_like(
            output,
            memory_format = torch.preserve_format,
        )

    # Call c++ engine [ see torch/csrc/autograd/python_engine.cpp ]
    Variable._execution_engine.run_backward(
        tensors = (output,),
        grad_tensors = (grad_output,),
        keep_graph = False,
        create_graph = False,
        inputs = tuple(),
        allow_unreachable=True,
        accumulate_grad=True,
    )
Jared Casper's avatar
Jared Casper committed
97

98
def forward_step(forward_step_func, data_iterator, model, input_tensor, losses_reduced):
99
100
101
102
103
104
    """Forward step for passed-in model.

    If first stage, input tensor is obtained from data_iterator, otherwise
    passed-in input_tensor is used.

    Returns output tensor."""
105
    args = get_args()
106
107
108
    timers = get_timers()

    timers('forward-compute').start()
109
110
    unwrapped_model = unwrap_model(
        model, (torchDDP, LocalDDP, Float16Module))
111
112
113
114
115
116

    unwrap_output_tensor = False
    if not isinstance(input_tensor, list):
        input_tensor = [input_tensor]
        unwrap_output_tensor = True

117
    unwrapped_model.set_input_tensor(input_tensor)
118
    output_tensor, loss_func = forward_step_func(data_iterator, model)
119
    if mpu.is_pipeline_last_stage():
120
        output_tensor = loss_func(output_tensor)
121
122
123
124
125
        loss, loss_reduced = output_tensor
        output_tensor = loss / get_num_microbatches()
        losses_reduced.append(loss_reduced)
    timers('forward-compute').stop()

126
127
128
    # If T5 model (or other model with encoder and decoder)
    # and in decoder stack, then send encoder_hidden_state
    # downstream as well.
129
130
131
132
133
134
    if mpu.is_pipeline_stage_after_split() and \
            args.model_type == ModelType.encoder_and_decoder:
        return [output_tensor, input_tensor[-1]]
    if unwrap_output_tensor:
        return output_tensor
    return [output_tensor]
135
136
137


def backward_step(optimizer, input_tensor, output_tensor, output_tensor_grad):
138
139
140
141
142
143
144
    """Backward step through passed-in output tensor.

    If last stage, output_tensor_grad is None, otherwise gradient of loss
    with respect to stage's output tensor.

    Returns gradient of loss with respect to input tensor (None if first
    stage)."""
145
146
147
148

    # NOTE: This code currently can handle at most one skip connection. It
    # needs to be modified slightly to support arbitrary numbers of skip
    # connections.
149
150
151
152
153
154
    args = get_args()

    timers = get_timers()
    timers('backward-compute').start()

    # Retain the grad on the input_tensor.
155
156
157
158
159
160
161
162
163
164
165
166
    unwrap_input_tensor_grad = False
    if not isinstance(input_tensor, list):
        input_tensor = [input_tensor]
        unwrap_input_tensor_grad = True
    for x in input_tensor:
        if x is not None:
            x.retain_grad()

    if not isinstance(output_tensor, list):
        output_tensor = [output_tensor]
    if not isinstance(output_tensor_grad, list):
        output_tensor_grad = [output_tensor_grad]
167
168

    # Backward pass.
169
170
    if output_tensor_grad[0] is None:
        output_tensor = optimizer.scale_loss(output_tensor[0])
171
172
173
174
175
    if args.deallocate_pipeline_outputs:
        custom_backward(output_tensor[0], output_tensor_grad[0])
    else:
        torch.autograd.backward(output_tensor[0],
                                grad_tensors=output_tensor_grad[0])
176
177

    # Collect the grad of the input_tensor.
178
    input_tensor_grad = [None]
179
    if input_tensor is not None:
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
        input_tensor_grad = []
        for x in input_tensor:
            if x is None:
                input_tensor_grad.append(None)
            else:
                input_tensor_grad.append(x.grad)

    # Handle single skip connection if it exists (encoder_hidden_state in
    # model with encoder and decoder).
    if mpu.get_pipeline_model_parallel_world_size() > 1 and \
            mpu.is_pipeline_stage_after_split() and \
            args.model_type == ModelType.encoder_and_decoder:
        if output_tensor_grad[1] is not None:
            input_tensor_grad[-1].add_(output_tensor_grad[1])
    if unwrap_input_tensor_grad:
        input_tensor_grad = input_tensor_grad[0]
196
197
198
199
200
201

    timers('backward-compute').stop()

    return input_tensor_grad


202
203
204
205
206
207
208
209
@contextmanager
def dummy_handler():
    try:
        yield
    finally:
        pass


210
211
def forward_backward_no_pipelining(forward_step_func, data_iterator, model,
                                   optimizer, timers, forward_only):
212
213
214
215
    """Run forward and backward passes with no pipeline parallelism
    (no inter-stage communication).

    Returns dictionary with losses."""
216
217
218
    assert len(model) == 1
    model = model[0]

219
220
221
222
    context_handler = dummy_handler
    if isinstance(model, torchDDP):
        context_handler = model.no_sync

223
    losses_reduced = []
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
    input_tensor, output_tensor_grad = None, None
    with context_handler():
        for i in range(get_num_microbatches() - 1):
            output_tensor = forward_step(forward_step_func, data_iterator, model,
                                         input_tensor, losses_reduced)
            if not forward_only:
                backward_step(optimizer, input_tensor, output_tensor,
                              output_tensor_grad)

    # Run computation for last microbatch out of context handler (want to
    # synchronize gradients).
    output_tensor = forward_step(forward_step_func, data_iterator, model,
                                 input_tensor, losses_reduced)
    if not forward_only:
        backward_step(optimizer, input_tensor, output_tensor, output_tensor_grad)
239
240
241
242
243
244

    return losses_reduced


def forward_backward_pipelining_with_interleaving(forward_step_func, data_iterator, model,
                                                  optimizer, timers, forward_only):
245
246
247
248
    """Run interleaved 1F1B schedule (model split into model chunks), with
    communication between pipeline stages as needed.

    Returns dictionary with losses if the last stage, empty dict otherwise."""
249
250
251
252
253
254
255
    input_tensors = [[] for _ in range(len(model))]
    output_tensors = [[] for _ in range(len(model))]
    losses_reduced = []
    if not forward_only:
        output_tensor_grads = [[] for _ in range(len(model))]

    pipeline_parallel_size = mpu.get_pipeline_model_parallel_world_size()
256
    pipeline_parallel_rank = mpu.get_pipeline_model_parallel_rank()
257

258
259
260
    args = get_args()
    tensor_shape = (args.seq_length, args.micro_batch_size, args.hidden_size)

261
262
263
264
265
266
267
    # Compute number of warmup and remaining microbatches.
    num_model_chunks = len(model)
    num_microbatches = get_num_microbatches() * num_model_chunks
    all_warmup_microbatches = False
    if forward_only:
        num_warmup_microbatches = num_microbatches
    else:
268
269
270
271
272
273
        # Run all forward passes and then all backward passes if number of
        # microbatches is just the number of pipeline stages.
        # Otherwise, perform (num_model_chunks-1)*pipeline_parallel_size on
        # all workers, followed by more microbatches after depending on
        # stage ID (more forward passes for earlier stages, later stages can
        # immediately start with 1F1B).
274
275
276
277
278
        if get_num_microbatches() == pipeline_parallel_size:
            num_warmup_microbatches = num_microbatches
            all_warmup_microbatches = True
        else:
            num_warmup_microbatches = \
279
                (pipeline_parallel_size - pipeline_parallel_rank - 1) * 2
280
281
282
283
            num_warmup_microbatches += (
                num_model_chunks - 1) * pipeline_parallel_size
            num_warmup_microbatches = min(num_warmup_microbatches,
                                          num_microbatches)
284
285
286
    num_microbatches_remaining = \
        num_microbatches - num_warmup_microbatches

287
    def get_model_chunk_id(microbatch_id, forward):
288
        """Helper method to get the model chunk ID given the iteration number."""
289
290
        microbatch_id_in_group = microbatch_id % (pipeline_parallel_size * num_model_chunks)
        model_chunk_id = microbatch_id_in_group // pipeline_parallel_size
291
        if not forward:
292
293
            model_chunk_id = (num_model_chunks - model_chunk_id - 1)
        return model_chunk_id
294

295
    def forward_step_helper(microbatch_id):
296
297
298
        """Helper method to run forward step with model split into chunks
        (run set_virtual_pipeline_model_parallel_rank() before calling
        forward_step())."""
299
        model_chunk_id = get_model_chunk_id(microbatch_id, forward=True)
300
301
        mpu.set_virtual_pipeline_model_parallel_rank(model_chunk_id)

302
        # forward step
303
        if mpu.is_pipeline_first_stage():
304
305
            if len(input_tensors[model_chunk_id]) == \
                    len(output_tensors[model_chunk_id]):
306
307
                input_tensors[model_chunk_id].append(None)
        input_tensor = input_tensors[model_chunk_id][-1]
308
309
        output_tensor = forward_step(forward_step_func,
                                     data_iterator[model_chunk_id],
310
311
312
                                     model[model_chunk_id],
                                     input_tensor, losses_reduced)
        output_tensors[model_chunk_id].append(output_tensor)
313
        assert_viewless_tensor(output_tensor)
314

315
316
317
318
319
        # if forward-only, no need to save tensors for a backward pass
        if forward_only:
            input_tensors[model_chunk_id].pop()
            output_tensors[model_chunk_id].pop()

320
321
        return output_tensor

322
    def backward_step_helper(microbatch_id):
323
324
325
        """Helper method to run backward step with model split into chunks
        (run set_virtual_pipeline_model_parallel_rank() before calling
        backward_step())."""
326
        model_chunk_id = get_model_chunk_id(microbatch_id, forward=False)
327
328
329
330
331
332
333
334
335
        mpu.set_virtual_pipeline_model_parallel_rank(model_chunk_id)

        if mpu.is_pipeline_last_stage():
            if len(output_tensor_grads[model_chunk_id]) == 0:
                output_tensor_grads[model_chunk_id].append(None)
        input_tensor = input_tensors[model_chunk_id].pop(0)
        output_tensor = output_tensors[model_chunk_id].pop(0)
        output_tensor_grad = output_tensor_grads[model_chunk_id].pop(0)
        input_tensor_grad = \
336
337
338
339
            backward_step(optimizer,
                          input_tensor,
                          output_tensor,
                          output_tensor_grad)
340
341
342
343
344

        return input_tensor_grad

    # Run warmup forward passes.
    mpu.set_virtual_pipeline_model_parallel_rank(0)
345
    input_tensors[0].append(
346
        p2p_communication.recv_forward(tensor_shape, timers=timers))
347
    assert_viewless_tensor(input_tensors[0][-1])
348
349
    for k in range(num_warmup_microbatches):
        output_tensor = forward_step_helper(k)
350
351

        # Determine if tensor should be received from previous stage.
352
353
354
355
356
357
358
        next_forward_model_chunk_id = get_model_chunk_id(k+1, forward=True)
        recv_prev = True
        if mpu.is_pipeline_first_stage(ignore_virtual=True):
            if next_forward_model_chunk_id == 0:
                recv_prev = False
        if k == (num_microbatches - 1):
            recv_prev = False
359
360

        # Don't send tensor downstream if on last stage.
361
362
        if mpu.is_pipeline_last_stage():
            output_tensor = None
363
364
365

        # Send and receive tensors as appropriate (send tensors computed
        # in this iteration; receive tensors for next iteration).
366
367
368
369
370
371
372
        if k == (num_warmup_microbatches - 1) and not forward_only and \
                not all_warmup_microbatches:
            input_tensor_grad = None
            recv_next = True
            if mpu.is_pipeline_last_stage(ignore_virtual=True):
                recv_next = False
            input_tensor, output_tensor_grad = \
373
                p2p_communication.send_forward_backward_recv_forward_backward(
374
375
                        output_tensor, input_tensor_grad,
                        recv_prev=recv_prev, recv_next=recv_next,
376
                        tensor_shape=tensor_shape,
377
378
                        timers=timers)
            output_tensor_grads[num_model_chunks-1].append(output_tensor_grad)
379
            assert_viewless_tensor(output_tensor_grad)
380
        else:
381
            input_tensor = \
382
                p2p_communication.send_forward_recv_forward(
383
384
385
                    output_tensor, recv_prev=recv_prev,
                    tensor_shape=tensor_shape,
                    timers=timers)
386
        free_output_tensor(output_tensor, args.deallocate_pipeline_outputs)
387
        input_tensors[next_forward_model_chunk_id].append(input_tensor)
388
        assert_viewless_tensor(input_tensor)
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425

    # Run 1F1B in steady state.
    for k in range(num_microbatches_remaining):
        # Forward pass.
        forward_k = k + num_warmup_microbatches
        output_tensor = forward_step_helper(forward_k)

        # Backward pass.
        backward_k = k
        input_tensor_grad = backward_step_helper(backward_k)

        # Send output_tensor and input_tensor_grad, receive input_tensor
        # and output_tensor_grad.

        # Determine if current stage has anything to send in either direction,
        # otherwise set tensor to None.
        forward_model_chunk_id = get_model_chunk_id(forward_k, forward=True)
        mpu.set_virtual_pipeline_model_parallel_rank(forward_model_chunk_id)
        if mpu.is_pipeline_last_stage():
            output_tensor = None

        backward_model_chunk_id = get_model_chunk_id(backward_k, forward=False)
        mpu.set_virtual_pipeline_model_parallel_rank(backward_model_chunk_id)
        if mpu.is_pipeline_first_stage():
            input_tensor_grad = None

        # Determine if peers are sending, and where in data structure to put
        # received tensors.
        recv_prev = True
        if mpu.is_pipeline_first_stage(ignore_virtual=True):
            # First stage is ahead of last stage by (pipeline_parallel_size - 1).
            next_forward_model_chunk_id = get_model_chunk_id(
                forward_k - (pipeline_parallel_size - 1), forward=True)
            if next_forward_model_chunk_id == (num_model_chunks - 1):
                recv_prev = False
            next_forward_model_chunk_id += 1
        else:
426
427
            next_forward_model_chunk_id = get_model_chunk_id(forward_k + 1,
                                                             forward=True)
428
429
430
431
432
433
434
435
436
437

        recv_next = True
        if mpu.is_pipeline_last_stage(ignore_virtual=True):
            # Last stage is ahead of first stage by (pipeline_parallel_size - 1).
            next_backward_model_chunk_id = get_model_chunk_id(
                backward_k - (pipeline_parallel_size - 1), forward=False)
            if next_backward_model_chunk_id == 0:
                recv_next = False
            next_backward_model_chunk_id -= 1
        else:
438
439
            next_backward_model_chunk_id = get_model_chunk_id(backward_k + 1,
                                                              forward=False)
440

441
442
        # If last iteration, don't receive; we already received one extra
        # before the start of the for loop.
443
444
445
446
447
        if k == (num_microbatches_remaining - 1):
            recv_prev = False

        # Communicate tensors.
        input_tensor, output_tensor_grad = \
448
            p2p_communication.send_forward_backward_recv_forward_backward(
449
450
                    output_tensor, input_tensor_grad,
                    recv_prev=recv_prev, recv_next=recv_next,
451
                    tensor_shape=tensor_shape, timers=timers)
452
        free_output_tensor(output_tensor, args.deallocate_pipeline_outputs)
453

454
455
        # Put input_tensor and output_tensor_grad in data structures in the
        # right location.
456
457
        if recv_prev:
            input_tensors[next_forward_model_chunk_id].append(input_tensor)
458
            assert_viewless_tensor(input_tensor)
459
        if recv_next:
460
461
            output_tensor_grads[next_backward_model_chunk_id].append(
                output_tensor_grad)
462
            assert_viewless_tensor(output_tensor_grad)
463

464
    # Run cooldown backward passes (flush out pipeline).
465
466
467
    if not forward_only:
        if all_warmup_microbatches:
            output_tensor_grads[num_model_chunks-1].append(
468
                p2p_communication.recv_backward(tensor_shape, timers=timers))
469
            assert_viewless_tensor(output_tensor_grads[num_model_chunks-1][-1])
470
471
472
473
474
475
476
477
478
479
        for k in range(num_microbatches_remaining, num_microbatches):
            input_tensor_grad = backward_step_helper(k)
            next_backward_model_chunk_id = get_model_chunk_id(k+1, forward=False)
            recv_next = True
            if mpu.is_pipeline_last_stage(ignore_virtual=True):
                if next_backward_model_chunk_id == (num_model_chunks - 1):
                    recv_next = False
            if k == (num_microbatches - 1):
                recv_next = False
            output_tensor_grads[next_backward_model_chunk_id].append(
480
                p2p_communication.send_backward_recv_backward(
481
482
483
                    input_tensor_grad, recv_next=recv_next,
                    tensor_shape=tensor_shape,
                    timers=timers))
484
            assert_viewless_tensor(output_tensor_grads[next_backward_model_chunk_id][-1])
485
486
487
488

    return losses_reduced


489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
def get_tensor_shapes(rank, model_type):
    # Determine right tensor sizes (based on position of rank with respect to split
    # rank) and model size.
    # Send two tensors if model is T5 and rank is in decoder stage:
    #     first tensor is decoder (pre-transpose),
    #     second tensor is encoder (post-transpose).
    # If model is T5 and rank is at the boundary:
    #     send one tensor (post-transpose from encoder).
    # Otherwise, send one tensor (pre-transpose).
    args = get_args()
    tensor_shapes = []
    if model_type == ModelType.encoder_and_decoder:
        if mpu.is_pipeline_stage_before_split(rank):
            # If next rank is after split, then need transpose for encoder_hidden_state.
            if mpu.is_pipeline_stage_before_split(rank+1):
                tensor_shapes.append((args.seq_length, args.micro_batch_size, args.hidden_size))
            else:
                tensor_shapes.append((args.micro_batch_size, args.seq_length, args.hidden_size))
        else:
            tensor_shapes.append((args.decoder_seq_length, args.micro_batch_size, args.hidden_size))
            tensor_shapes.append((args.micro_batch_size, args.seq_length, args.hidden_size))
    else:
        tensor_shapes.append((args.seq_length, args.micro_batch_size, args.hidden_size))
    return tensor_shapes


def recv_forward(tensor_shapes, timers):
    input_tensors = []
    for tensor_shape in tensor_shapes:
        if tensor_shape is None:
            input_tensors.append(None)
        else:
            input_tensors.append(p2p_communication.recv_forward(tensor_shape,
                                                                timers=timers))
523
            assert_viewless_tensor(input_tensors[-1])
524
525
526
527
528
529
530
531
532
533
534
    return input_tensors


def recv_backward(tensor_shapes, timers):
    output_tensor_grads = []
    for tensor_shape in tensor_shapes:
        if tensor_shape is None:
            output_tensor_grads.append(None)
        else:
            output_tensor_grads.append(p2p_communication.recv_backward(tensor_shape,
                                                                       timers=timers))
535
            assert_viewless_tensor(output_tensor_grads[-1])
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
    return output_tensor_grads


def send_forward(output_tensors, tensor_shapes, timers):
    if not isinstance(output_tensors, list):
        output_tensors = [output_tensors]
    for (output_tensor, tensor_shape) in zip(output_tensors, tensor_shapes):
        if tensor_shape is None:
            continue
        p2p_communication.send_forward(output_tensor, tensor_shape, timers=timers)


def send_backward(input_tensor_grads, tensor_shapes, timers):
    if not isinstance(input_tensor_grads, list):
        input_tensor_grads = [input_tensor_grads]
    for (input_tensor_grad, tensor_shape) in zip(input_tensor_grads, tensor_shapes):
        if tensor_shape is None:
            continue
        p2p_communication.send_backward(input_tensor_grad, tensor_shape, timers=timers)


def send_forward_recv_backward(output_tensors, tensor_shapes, timers):
    if not isinstance(output_tensors, list):
        output_tensors = [output_tensors]
    output_tensor_grads = []
    for (output_tensor, tensor_shape) in zip(output_tensors, tensor_shapes):
        if tensor_shape is None:
            output_tensor_grads.append(None)
            continue
        output_tensor_grad = p2p_communication.send_forward_recv_backward(
                output_tensor, tensor_shape, timers=timers)
        output_tensor_grads.append(output_tensor_grad)
568
        assert_viewless_tensor(output_tensor_grad)
569
570
571
572
573
574
575
576
577
578
579
580
581
582
    return output_tensor_grads


def send_backward_recv_forward(input_tensor_grads, tensor_shapes, timers):
    if not isinstance(input_tensor_grads, list):
        input_tensor_grads = [input_tensor_grads]
    input_tensors = []
    for (input_tensor_grad, tensor_shape) in zip(input_tensor_grads, tensor_shapes):
        if tensor_shape is None:
            input_tensors.append(None)
            continue
        input_tensor = p2p_communication.send_backward_recv_forward(
                input_tensor_grad, tensor_shape, timers=timers)
        input_tensors.append(input_tensor)
583
        assert_viewless_tensor(input_tensor)
584
585
586
    return input_tensors


587
588
589
def forward_backward_pipelining_without_interleaving(forward_step_func, data_iterator,
                                                     model, optimizer, timers,
                                                     forward_only):
590
591
592
593
    """Run non-interleaved 1F1B schedule, with communication between pipeline
    stages.

    Returns dictionary with losses if the last stage, empty dict otherwise."""
594
    args = get_args()
595
596
    timers = get_timers()

597
598
599
600
601
602
603
604
605
606
607
608
609
610
    assert len(model) == 1
    model = model[0]

    # Compute number of warmup microbatches.
    num_microbatches = get_num_microbatches()
    num_warmup_microbatches = \
        (mpu.get_pipeline_model_parallel_world_size() -
         mpu.get_pipeline_model_parallel_rank() - 1)
    num_warmup_microbatches = min(
        num_warmup_microbatches,
        num_microbatches)
    num_microbatches_remaining = \
        num_microbatches - num_warmup_microbatches

611
612
613
614
615
616
617
    unwrapped_model = unwrap_model(
        model, (torchDDP, LocalDDP, Float16Module))
    model_type = unwrapped_model.model_type
    rank = mpu.get_pipeline_model_parallel_rank()
    recv_tensor_shapes = get_tensor_shapes(rank-1, model_type)
    send_tensor_shapes = get_tensor_shapes(rank, model_type)

618
619
620
621
622
623
    # Input, output tensors only need to be saved when doing backward passes
    input_tensors = None
    output_tensors = None
    if not forward_only:
        input_tensors = []
        output_tensors = []
624
625
626
627
    losses_reduced = []

    # Run warmup forward passes.
    for i in range(num_warmup_microbatches):
628
        input_tensor = recv_forward(recv_tensor_shapes, timers=timers)
629
630
        output_tensor = forward_step(forward_step_func, data_iterator, model,
                                     input_tensor, losses_reduced)
631
        send_forward(output_tensor, send_tensor_shapes, timers=timers)
632

633
        if not forward_only:
634
635
636
637
638
639
640
641
642
            # >>>
            if input_tensor[0] is not None:
                from lutil import pax
                pax({
                    "input_tensor" : input_tensor,
                })
            # <<<
            assert_viewless_tensor(input_tensor)
            assert_viewless_tensor(output_tensor)
643
644
            input_tensors.append(input_tensor)
            output_tensors.append(output_tensor)
645
            free_output_tensor(output_tensor, args.deallocate_pipeline_outputs)
646
647
648
649
650

    # Before running 1F1B, need to receive first forward tensor.
    # If all microbatches are run in warmup / cooldown phase, then no need to
    # receive this tensor here.
    if num_microbatches_remaining > 0:
651
        input_tensor = recv_forward(recv_tensor_shapes, timers=timers)
652
653
654
655
656
657
658
659

    # Run 1F1B in steady state.
    for i in range(num_microbatches_remaining):
        last_iteration = (i == (num_microbatches_remaining - 1))

        output_tensor = forward_step(forward_step_func, data_iterator, model,
                                     input_tensor, losses_reduced)
        if forward_only:
660
            send_forward(output_tensor, send_tensor_shapes, timers=timers)
661
662

            if not last_iteration:
663
                input_tensor = recv_forward(recv_tensor_shapes, timers=timers)
664

665
        else:
666
            output_tensor_grad = \
667
668
669
                send_forward_recv_backward(output_tensor,
                                           send_tensor_shapes,
                                           timers=timers)
670

671
            # Add input_tensor and output_tensor to end of list.
672
673
674
675
676
677
678
679
680
681
682
            # >>>
            # assert input_tensor[0]._base is None, \
            #     "rank %s; uh oh." % torch.distributed.get_rank()
            # if input_tensor[0] is not None:
            #     from lutil import pax
            #     pax(4, {
            #         "input_tensor[0]" : input_tensor[0],
            #     })
            # <<<
            assert_viewless_tensor(input_tensor)
            assert_viewless_tensor(output_tensor)
683
684
            input_tensors.append(input_tensor)
            output_tensors.append(output_tensor)
685
            free_output_tensor(output_tensor, args.deallocate_pipeline_outputs)
686

687
688
689
690
            # Pop input_tensor and output_tensor from the start of the list for
            # the backward pass.
            input_tensor = input_tensors.pop(0)
            output_tensor = output_tensors.pop(0)
691
692
693
694
695
696
697

            input_tensor_grad = \
                backward_step(optimizer, input_tensor, output_tensor,
                              output_tensor_grad)

            if last_iteration:
                input_tensor = None
698
                send_backward(input_tensor_grad, recv_tensor_shapes, timers=timers)
699
            else:
700
                input_tensor = \
701
702
                    send_backward_recv_forward(
                        input_tensor_grad, recv_tensor_shapes, timers=timers)
703
704
705
706
707
708
709

    # Run cooldown backward passes.
    if not forward_only:
        for i in range(num_warmup_microbatches):
            input_tensor = input_tensors.pop(0)
            output_tensor = output_tensors.pop(0)

710
            output_tensor_grad = recv_backward(send_tensor_shapes, timers=timers)
711
712
713
714
715

            input_tensor_grad = \
                backward_step(optimizer, input_tensor, output_tensor,
                              output_tensor_grad)

716
            send_backward(input_tensor_grad, recv_tensor_shapes, timers=timers)
717
718

    return losses_reduced