-
Notifications
You must be signed in to change notification settings - Fork 5.5k
/
parallel.py
591 lines (469 loc) · 21.5 KB
/
parallel.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except jin compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import six
import numpy as np
import warnings
from collections import OrderedDict
from paddle.fluid import core
from paddle.fluid import framework
from paddle.fluid.dygraph import layers
from paddle.fluid.dygraph import parallel_helper
from paddle.fluid.dygraph import to_variable, no_grad
from paddle.utils import deprecated
import warnings
import paddle
import itertools
__all__ = ["prepare_context", "ParallelEnv", "DataParallel"]
ParallelStrategy = core.ParallelStrategy
@deprecated(since="2.0.0", update_to="paddle.distributed.init_parallel_env")
def prepare_context(strategy=None):
'''
:api_attr: imperative
'''
if strategy is None:
strategy = ParallelStrategy()
strategy.nranks = Env().nranks
strategy.local_rank = Env().local_rank
strategy.trainer_endpoints = Env().trainer_endpoints
strategy.current_endpoint = Env().current_endpoint
if strategy.nranks < 2:
return
assert framework.in_dygraph_mode() is True, \
"dygraph.prepare_context should be used with dygraph mode."
place = framework._current_expected_place()
assert place is not None, \
"dygraph.prepare_context should be used in fluid.dygraph.guard(place) guard."
if not parallel_helper._is_parallel_ctx_initialized():
if isinstance(place, core.CUDAPlace):
parallel_helper._set_parallel_ctx(
core.NCCLParallelContext(strategy, place))
else:
# TODO(Yancey1989): add Gloo Parallel Context to support CPU parallel computation
assert ("Only support CUDAPlace for now.")
parallel_helper._init_parallel_ctx()
return strategy
class ParallelEnv(object):
"""
.. note::
This API is not recommended, if you need to get rank and world_size,
it is recommended to use ``paddle.distributed.get_rank()`` and
``paddle.distributed.get_world_size()`` .
This class is used to obtain the environment variables required for
the parallel execution of ``paddle.nn.Layer`` in dynamic mode.
The parallel execution in dynamic mode needs to be started using ``paddle.distributed.launch``
or ``paddle.distributed.spawn`` .
Examples:
.. code-block:: python
import paddle
import paddle.distributed as dist
def train():
# 1. initialize parallel environment
dist.init_parallel_env()
# 2. get current ParallelEnv
parallel_env = dist.ParallelEnv()
print("rank: ", parallel_env.rank)
print("world_size: ", parallel_env.world_size)
# print result in process 1:
# rank: 1
# world_size: 2
# print result in process 2:
# rank: 2
# world_size: 2
if __name__ == '__main__':
# 1. start by ``paddle.distributed.spawn`` (default)
dist.spawn(train, nprocs=2)
# 2. start by ``paddle.distributed.launch``
# train()
"""
def __init__(self):
self._rank = int(os.getenv("PADDLE_TRAINER_ID", "0"))
self._world_size = int(os.getenv("PADDLE_TRAINERS_NUM", "1"))
# imperative only support one gpu
selected_gpus = os.getenv("FLAGS_selected_gpus", "0").split(",")
self._device_id = int(selected_gpus[0])
self._trainer_endpoints = os.getenv("PADDLE_TRAINER_ENDPOINTS",
"").split(",")
self._current_endpoint = os.getenv("PADDLE_CURRENT_ENDPOINT", "")
self._nrings = int(os.getenv("FLAGS_nccl_nrings", "1"))
assert self._nrings > 0, \
"nccl_nrings must be an integer greater than 0."
assert self._nrings < 9, \
"nccl_nrings should be less than 9, which is enough in most scenarios."
@property
def rank(self):
"""
Rank of current trainer.
Its value is equal to the value of the environment variable ``PADDLE_TRAINER_ID`` . The default value is 0.
Examples:
.. code-block:: python
# execute this command in terminal: export PADDLE_TRAINER_ID=0
import paddle.distributed as dist
env = dist.ParallelEnv()
print("The rank is %d" % env.rank)
# The rank is 0
"""
return self._rank
@property
def world_size(self):
"""
The number of trainers (number of processes participating in current job).
Its value is equal to the value of the environment variable ``PADDLE_TRAINERS_NUM`` . The default value is 1.
Examples:
.. code-block:: python
# execute this command in terminal: export PADDLE_TRAINERS_NUM=4
import paddle.distributed as dist
env = dist.ParallelEnv()
print("The world_size is %d" % env.world_size)
# The world_size is 4
"""
return self._world_size
@property
def device_id(self):
"""
The ID of selected GPU card for parallel training.
Its value is equal to the value of the environment variable ``FLAGS_selected_gpus`` . The default value is 0.
Examples:
.. code-block:: python
# execute this command in terminal: export FLAGS_selected_gpus=1
import paddle.distributed as dist
env = dist.ParallelEnv()
print("The device id are %d" % env.device_id)
# The device id are 1
"""
return self._device_id
@property
def current_endpoint(self):
"""
The endpoint of current trainer, it is in the form of (node IP + port).
Its value is equal to the value of the environment variable ``PADDLE_CURRENT_ENDPOINT`` . The default value is "".
Examples:
.. code-block:: python
# execute this command in terminal: export PADDLE_CURRENT_ENDPOINT=127.0.0.1:6170
import paddle.distributed as dist
env = dist.ParallelEnv()
print("The current endpoint are %s" % env.current_endpoint)
# The current endpoint are 127.0.0.1:6170
"""
return self._current_endpoint
@property
def trainer_endpoints(self):
"""
The endpoints of all trainer nodes in the task,
which are used to broadcast the NCCL ID when NCCL2 is initialized.
Its value is equal to the value of the environment variable ``PADDLE_TRAINER_ENDPOINTS`` . The default value is "".
Examples:
.. code-block:: python
# execute this command in terminal: export PADDLE_TRAINER_ENDPOINTS=127.0.0.1:6170,127.0.0.1:6171
import paddle.distributed as dist
env = dist.ParallelEnv()
print("The trainer endpoints are %s" % env.trainer_endpoints)
# The trainer endpoints are ['127.0.0.1:6170', '127.0.0.1:6171']
"""
return self._trainer_endpoints
@property
def nrings(self):
"""
Nrings of current trainer.
Its value is equal to the value of the environment variable ``FLAGS_nccl_nrings`` . The default value is 1.
Examples:
.. code-block:: python
# execute this command in terminal: export FLAGS_nccl_nrings=1
import paddle.distributed as dist
env = dist.ParallelEnv()
print("The nrings is %d" % env.nrings)
# the number of ring is 1
"""
return self._nrings
# [aliases] Compatible with old method names
local_rank = rank
nranks = world_size
dev_id = device_id
# NOTE: [ Compatible ] Originally this class name is `Env`. The semantics of the old class names
# are inaccurate and may confuse users, so replace it with `ParallelEnv`, but to be compatible
# with the old examples, here still need to keep this name.
Env = ParallelEnv
def _build_default_parallel_strategy():
strategy = ParallelStrategy()
strategy.nranks = ParallelEnv().nranks
strategy.local_rank = ParallelEnv().local_rank
strategy.trainer_endpoints = ParallelEnv().trainer_endpoints
strategy.current_endpoint = ParallelEnv().current_endpoint
return strategy
def _coalesce_tensors(var_groups):
from ..layers import nn
coalesced_grads_and_grad_vars = []
for group_id, grad_vars in var_groups.items():
flattened_vars = []
g_var_shapes = []
for g_var in grad_vars:
g_var_shapes.append(g_var.shape)
flattened_vars.append(
nn.reshape(
x=g_var, shape=[np.prod(g_var.shape)]))
coalesced_grad = nn.concat(flattened_vars)
coalesced_grads_and_grad_vars.append(
[coalesced_grad, grad_vars, g_var_shapes])
return coalesced_grads_and_grad_vars
@framework.dygraph_only
def _reshape_inplace(x, shape):
x_shape = framework._varbase_creator(dtype=x.dtype)
framework._dygraph_tracer().trace_op(
type="reshape2",
inputs={'X': x},
outputs={'Out': x,
'XShape': x_shape},
attrs={'shape': shape})
@framework.dygraph_only
def _split_tensors(coalesced_grads_and_grad_vars):
for coalesced_grad, origin_grad_vars, grad_shapes in coalesced_grads_and_grad_vars:
grad_var_len = [np.prod(g_shape) for g_shape in grad_shapes]
framework._dygraph_tracer().trace_op(
type='split',
inputs={'X': coalesced_grad},
outputs={'Out': origin_grad_vars},
attrs={'sections': grad_var_len,
'axis': 0})
for g_var, g_shape in zip(origin_grad_vars, grad_shapes):
_reshape_inplace(x=g_var, shape=g_shape)
assert g_var.shape == g_shape
def scale_loss(loss):
if not ParallelEnv().world_size > 1:
return loss
loss_scale = to_variable(
np.array([ParallelEnv().world_size]).astype("float32"))
loss_scale.stop_gradient = True
scaled_loss = loss / loss_scale
return scaled_loss
class DataParallel(layers.Layer):
"""
Run the dygraph module with data parallelism.
Currently, DataParallel class only supports to run the dynamic graph
with multi-process.
Now supports two ways to start training:
1. start by ``paddle.distributed.spawn`` method, for example:
``python demo.py`` (spawn need to be called in ``__main__`` method)
2. start by ``paddle.distributed.launch`` module, for example:
``python -m paddle.distributed.launch --gpus=0,1 demo.py`` .
And the content of `demo.py` is the code of examples.
Args:
layers(Layer): The module that should be executed by data parallel.
strategy(ParallelStrategy, optional): (deprecated) The strategy of data parallelism,
contains environment configuration related to parallel execution. Default: None.
comm_buffer_size(int, optional): It limits the memory size(MB) of one buffer
parameters' gradient which is the input of communication
calling(e.g NCCLAllReduce). Default: 25.
last_comm_buffer_size(float, optional): It limits memory size(MB) of last buffer in communication
calling. Making the last communication buffer size small is useful to
improve performance. Default: 1.
Returns:
Layer: The data paralleled module.
Examples:
.. code-block:: python
import paddle
import paddle.nn as nn
import paddle.optimizer as opt
import paddle.distributed as dist
class LinearNet(nn.Layer):
def __init__(self):
super(LinearNet, self).__init__()
self._linear1 = nn.Linear(10, 10)
self._linear2 = nn.Linear(10, 1)
def forward(self, x):
return self._linear2(self._linear1(x))
def train():
# 1. initialize parallel environment
dist.init_parallel_env()
# 2. create data parallel layer & optimizer
layer = LinearNet()
dp_layer = paddle.DataParallel(layer)
loss_fn = nn.MSELoss()
adam = opt.Adam(
learning_rate=0.001, parameters=dp_layer.parameters())
# 3. run layer
inputs = paddle.randn([10, 10], 'float32')
outputs = dp_layer(inputs)
labels = paddle.randn([10, 1], 'float32')
loss = loss_fn(outputs, labels)
loss.backward()
adam.step()
adam.clear_grad()
if __name__ == '__main__':
# 1. start by ``paddle.distributed.spawn`` (default)
dist.spawn(train, nprocs=2)
# 2. start by ``paddle.distributed.launch``
# train()
"""
def __init__(self,
layers,
strategy=None,
comm_buffer_size=25,
last_comm_buffer_size=1):
super(DataParallel,
self).__init__(layers.full_name() + "_data_parallel")
self._layers = layers
# NOTE(chenweihang): The ParallelStrategy here is not strictly a strategy.
# It just stores some environment variables, which can be constructed by
# ParallelEnv. Here it is set as an optional argument.
# This parameter is not removed because of compatibility with 1.x writing.
if strategy is not None:
self._strategy = strategy
else:
self._strategy = _build_default_parallel_strategy()
if self._strategy.nranks > 1:
self.comm_buffer_size = int(comm_buffer_size * 1024 * 1024)
# NOTE(shenliang03): We can set environment variables to control
# the size of the group, Default: 1MB. The role of this small group is:
# when the last group allreduce, the overlap cannot work. Making the
# the last group small is useful to improve performance.
self.last_comm_buffer_size = int(last_comm_buffer_size * 1024 *
1024)
self.init_reducer()
else:
warnings.warn("The program will return to single-card operation. "
"Please check 1, whether you use spawn or fleetrun "
"to start the program. 2, Whether it is a multi-card "
"program. 3, Is the current environment multi-card.")
def init_reducer(self):
layers_param = []
params_set = set()
for sublayer in self.sublayers():
for _, param in sublayer.named_parameters(include_sublayers=False):
if param is None or param in params_set:
continue
params_set.add(param)
if not isinstance(param, core.VarBase):
raise TypeError("The data type of '%s' must be Varbase" %
param.name)
if param.trainable:
layers_param.append((sublayer, param))
trainable_parameters = [param for _, param in layers_param]
# NOTE(shenliang03): Here we can only use the attributes to judge whether
# parameter is sparse(or SelectedRows). The reason is that the sparse message
# can't be obtained when bp hasn't happened yet. So if layer supports sparse parameter,
# we should add the layer here like "paddle.nn.layer.common.Embedding".
def check_layer_sparse(sublayer):
if isinstance(sublayer, paddle.nn.layer.common.Embedding):
return sublayer._sparse
# NOTE(shenliang03):This is for compatibility. If paddle.fluid.dygraph.Embedding
# is removed in the future, the check will also be removed here.
if isinstance(sublayer, paddle.fluid.dygraph.Embedding):
return sublayer._is_sparse
return False
is_sparse_gradient = [
check_layer_sparse(sublayer) for sublayer, _ in layers_param
]
self.group_indices = core.assign_group_by_size(
trainable_parameters, is_sparse_gradient,
[self.last_comm_buffer_size, self.comm_buffer_size])
assert parallel_helper.__parallel_ctx__clz__ is not None, \
"ParallelContext must be initialized before. You should use init_parallel_env() before" \
"constructing the DataParallel."
# TODO(shenliang03) "find_unused_vars" interface will be exposed in the future
# to handle control flow to process unused parameters
find_unused_vars = True
self._reducer = core.Reducer(
trainable_parameters,
list(reversed(self.group_indices)), is_sparse_gradient,
parallel_helper.__parallel_ctx__clz__,
[self.last_comm_buffer_size, self.comm_buffer_size],
find_unused_vars)
def _find_varbase(self, obj):
if isinstance(obj, core.VarBase):
return [obj]
if isinstance(obj, (list, tuple)):
return itertools.chain(*map(self._find_varbase, obj))
if isinstance(obj, dict):
return itertools.chain(*map(self._find_varbase, obj.values()))
return []
def forward(self, *inputs, **kwargs):
outputs = self._layers(*inputs, **kwargs)
if self._strategy.nranks > 1:
self._reducer.prepare_for_backward(
list(self._find_varbase(outputs)))
return outputs
@deprecated(
since="2.0.0", reason="This method does not need to be called anymore.")
def scale_loss(self, loss):
"""
Deprecated method, now ``scale_loss`` is an empty method,
keep this method just for compatibility.
"""
return loss
@deprecated(
since="2.0.0", reason="This method does not need to be called anymore.")
def apply_collective_grads(self):
"""
Deprecated method, now ``apply_collective_grads`` is an empty method,
keep this method just for compatibility.
"""
return
def state_dict(self,
destination=None,
include_sublayers=True,
structured_name_prefix=""):
'''
Get all parameters and persistable buffers of current layer and its sub-layers. And set them into a dict
Parameters:
destination(dict, optional) : If provide, all the parameters and persistable buffers will be set to this dict . Default: None
include_sublayers(bool, optional) : If true, also include the parameters and persistable buffers from sublayers. Default: True
Retruns:
dict: a dict contains all the parameters and persistable buffers.
Examples:
.. code-block:: python
import paddle
import paddle.distributed as dist
dist.init_parallel_env()
emb = fluid.dygraph.Embedding([10, 10])
emb = fluid.dygraph.DataParallel(emb)
state_dict = emb.state_dict()
paddle.save(state_dict, "paddle_dy.pdparams")
'''
return self._layers.state_dict(
destination=destination,
include_sublayers=include_sublayers,
structured_name_prefix=structured_name_prefix)
@framework.deprecate_stat_dict
def set_state_dict(self,
state_dict,
include_sublayers=True,
use_structured_name=True):
'''
Set parameters and persistable buffers from state_dict. All the parameters and buffers will be reset by the tensor in the state_dict
Parameters:
state_dict(dict) : Dict contains all the parameters and persistable buffers.
include_sublayers(bool, optional) : If true, also include the parameters and peresistable buffers from sublayers. Default: True
use_structured_name(bool, optional) : If true, use structured name as key, otherwise, use parameter or buffer name as key.
Default: True
Returns:
None
Examples:
.. code-block:: python
import paddle
import paddle.distributed as dist
dist.init_parallel_env()
emb = paddle.nn.Embedding(10, 10)
emb = fluid.dygraph.DataParallel(emb)
state_dict = emb.state_dict()
paddle.save(state_dict, "paddle_dy.pdparams")
para_state_dict = paddle.load("paddle_dy.pdparams")
emb.set_state_dict(para_state_dict)
'''
self._layers.set_state_dict(
state_dict,
include_sublayers=include_sublayers,
use_structured_name=use_structured_name)
# [aliases] Compatible with old method names
set_dict = set_state_dict
load_dict = set_state_dict