-
Notifications
You must be signed in to change notification settings - Fork 835
/
extend_distributed.py
604 lines (534 loc) · 19 KB
/
extend_distributed.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
592
593
594
595
596
597
598
599
600
601
602
603
604
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import builtins
import os
import sys
import torch
import torch.distributed as dist
from torch.autograd import Function
from torch.autograd.profiler import record_function
from torch.nn.parallel import DistributedDataParallel as DDP
try:
import torch_ccl
except ImportError as e:
# print(e)
torch_ccl = False
try:
import torch_ucc
except ImportError as e:
torch_ucc = False
my_rank = -1
my_size = -1
my_local_rank = -1
my_local_size = -1
alltoall_supported = False
a2a_impl = os.environ.get("DLRM_ALLTOALL_IMPL", "")
myreq = None
def env2int(env_list, default=-1):
for e in env_list:
val = int(os.environ.get(e, -1))
if val >= 0:
return val
return default
def get_my_slice(n):
k, m = divmod(n, my_size)
return slice(
my_rank * k + min(my_rank, m), (my_rank + 1) * k + min(my_rank + 1, m), 1
)
def get_split_lengths(n):
k, m = divmod(n, my_size)
if m == 0:
splits = None
my_len = k
else:
splits = [(k + 1) if i < m else k for i in range(my_size)]
my_len = splits[my_rank]
return (my_len, splits)
def init_distributed(rank=-1, local_rank=-1, size=-1, use_gpu=False, backend=""):
global myreq
global my_rank
global my_size
global my_local_rank
global my_local_size
global a2a_impl
global alltoall_supported
# guess MPI ranks from env (works for IMPI, OMPI and MVAPICH2)
num_mpi_ranks = env2int(
["PMI_SIZE", "OMPI_COMM_WORLD_SIZE", "MV2_COMM_WORLD_SIZE", "WORLD_SIZE"]
)
if backend == "" and num_mpi_ranks > 1:
if torch_ccl and env2int(["CCL_WORKER_COUNT"]) > 0:
backend = "ccl"
elif use_gpu and dist.is_nccl_available():
backend = "nccl"
elif dist.is_mpi_available():
backend = "mpi"
else:
print(
"WARNING: MPI multi-process launch detected but PyTorch MPI backend not available."
)
backend = "gloo"
if backend != "":
# guess Rank and size
if rank == -1:
rank = env2int(
["PMI_RANK", "OMPI_COMM_WORLD_RANK", "MV2_COMM_WORLD_RANK", "RANK"], 0
)
if size == -1:
size = env2int(
[
"PMI_SIZE",
"OMPI_COMM_WORLD_SIZE",
"MV2_COMM_WORLD_SIZE",
"WORLD_SIZE",
],
1,
)
if not os.environ.get("RANK", None) and rank != -1:
os.environ["RANK"] = str(rank)
if not os.environ.get("WORLD_SIZE", None) and size != -1:
os.environ["WORLD_SIZE"] = str(size)
if not os.environ.get("MASTER_PORT", None):
os.environ["MASTER_PORT"] = "29500"
if not os.environ.get("MASTER_ADDR", None):
local_size = env2int(
[
"MPI_LOCALNRANKS",
"OMPI_COMM_WORLD_LOCAL_SIZE",
"MV2_COMM_WORLD_LOCAL_SIZE",
],
1,
)
if local_size != size and backend != "mpi":
print(
"Warning: Looks like distributed multinode run but MASTER_ADDR env not set, using '127.0.0.1' as default"
)
print(
"If this run hangs, try exporting rank 0's hostname as MASTER_ADDR"
)
os.environ["MASTER_ADDR"] = "127.0.0.1"
if size > 1:
if local_rank == -1:
my_local_rank = env2int(
[
"MPI_LOCALRANKID",
"OMPI_COMM_WORLD_LOCAL_RANK",
"MV2_COMM_WORLD_LOCAL_RANK",
"LOCAL_RANK",
],
0,
)
else:
my_local_rank = local_rank
my_local_size = env2int(
[
"MPI_LOCALNRANKS",
"OMPI_COMM_WORLD_LOCAL_SIZE",
"MV2_COMM_WORLD_LOCAL_SIZE",
],
1,
)
if use_gpu:
if my_local_size > torch.cuda.device_count():
print(
"Not sufficient GPUs available... local_size = %d, ngpus = %d"
% (my_local_size, torch.cuda.device_count())
)
sys.exit(1)
torch.cuda.set_device(my_local_rank)
dist.init_process_group(backend, rank=rank, world_size=size)
my_rank = dist.get_rank()
my_size = dist.get_world_size()
if my_rank == 0:
print("Running on %d ranks using %s backend" % (my_size, backend))
if hasattr(dist, "all_to_all_single"):
try:
t = torch.zeros([4])
if use_gpu:
t = t.cuda()
dist.all_to_all_single(t, t)
alltoall_supported = True
except RuntimeError as err:
print("fail to enable all_to_all_single primitive: %s" % err)
if a2a_impl == "alltoall" and alltoall_supported == False:
print(
"Requested DLRM_ALLTOALL_IMPL=%s but backend %s does not support it, use scatter/gather based alltoall"
% (a2a_impl, backend)
)
a2a_impl = "scatter"
if a2a_impl != "":
print("Using DLRM_ALLTOALL_IMPL=%s" % a2a_impl)
else:
my_rank = 0
my_size = 1
my_local_rank = 0
my_local_size = 1
print_all(
"world size: %d, current rank: %d, local rank: %d"
% (my_size, my_rank, my_local_rank)
)
myreq = Request()
class Request(object):
def __init__(self):
self.req = None
self.tensor = None
self.WaitFunction = All2All_Scatter_Wait
def wait(self):
ret = self.WaitFunction.apply(*self.tensor)
self.req = None
self.tensor = None
return ret
class All2All_ScatterList_Req(Function):
@staticmethod
def forward(ctx, a2a_info, *inputs):
global myreq
batch_split_lengths = (
a2a_info.global_batch_partition_slices
if a2a_info.global_batch_partition_slices
else a2a_info.local_batch_num
)
table_split_lengths = (
a2a_info.global_table_wise_parition_slices
if a2a_info.global_table_wise_parition_slices
else [a2a_info.local_table_num] * my_size
)
gather_list = []
req_list = []
for i in range(my_size):
for j in range(table_split_lengths[i]):
out_tensor = inputs[0].new_empty(
[a2a_info.local_batch_num, a2a_info.emb_dim]
)
scatter_list = (
list(inputs[j].split(batch_split_lengths, dim=0))
if i == my_rank
else []
)
req = dist.scatter(out_tensor, scatter_list, src=i, async_op=True)
gather_list.append(out_tensor)
req_list.append(req)
myreq.req = req_list
myreq.tensor = tuple(gather_list)
myreq.a2a_info = a2a_info
return myreq.tensor
@staticmethod
def backward(ctx, *grad_output):
global myreq
for r in myreq.req:
r.wait()
myreq.req = None
grad_inputs = myreq.tensor
myreq.tensor = None
return (None, *grad_inputs)
class All2All_ScatterList_Wait(Function):
@staticmethod
def forward(ctx, *output):
global myreq
ctx.a2a_info = myreq.a2a_info
for r in myreq.req:
r.wait()
myreq.req = None
myreq.tensor = None
return output
@staticmethod
def backward(ctx, *grad_output):
global myreq
a2a_info = ctx.a2a_info
grad_output = [t.contiguous() for t in grad_output]
batch_split_lengths = (
a2a_info.global_batch_partition_slices
if a2a_info.global_batch_partition_slices
else [a2a_info.local_batch_num] * my_size
)
per_rank_table_splits = (
a2a_info.global_table_wise_parition_slices
if a2a_info.global_table_wise_parition_slices
else [a2a_info.local_table_num] * my_size
)
grad_inputs = [
grad_output[0].new_empty([ctx.a2a_info.batch_size, ctx.a2a_info.emb_dim])
for _ in range(a2a_info.local_table_num)
]
req_list = []
ind = 0
for i in range(my_size):
for j in range(per_rank_table_splits[i]):
gather_list = (
list(grad_inputs[j].split(batch_split_lengths, dim=0))
if i == my_rank
else None
)
req = dist.gather(grad_output[ind], gather_list, dst=i, async_op=True)
req_list.append(req)
ind += 1
myreq.req = req_list
myreq.tensor = grad_inputs
return tuple(grad_output)
class All2All_Scatter_Req(Function):
@staticmethod
def forward(ctx, a2a_info, *inputs):
global myreq
batch_split_lengths = (
a2a_info.global_batch_partition_slices
if a2a_info.global_batch_partition_slices
else a2a_info.local_batch_num
)
table_split_lengths = (
a2a_info.global_table_wise_parition_slices
if a2a_info.global_table_wise_parition_slices
else [a2a_info.local_table_num] * my_size
)
input = torch.cat(inputs, dim=1)
scatter_list = list(input.split(batch_split_lengths, dim=0))
gather_list = []
req_list = []
for i in range(my_size):
out_tensor = input.new_empty(
[a2a_info.local_batch_num, table_split_lengths[i] * a2a_info.emb_dim]
)
req = dist.scatter(
out_tensor, scatter_list if i == my_rank else [], src=i, async_op=True
)
gather_list.append(out_tensor)
req_list.append(req)
myreq.req = req_list
myreq.tensor = tuple(gather_list)
myreq.a2a_info = a2a_info
ctx.a2a_info = a2a_info
return myreq.tensor
@staticmethod
def backward(ctx, *grad_output):
global myreq
for r in myreq.req:
r.wait()
myreq.req = None
grad_input = myreq.tensor
grad_inputs = grad_input.split(ctx.a2a_info.emb_dim, dim=1)
myreq.tensor = None
return (None, *grad_inputs)
class All2All_Scatter_Wait(Function):
@staticmethod
def forward(ctx, *output):
global myreq
ctx.a2a_info = myreq.a2a_info
for r in myreq.req:
r.wait()
myreq.req = None
myreq.tensor = None
return output
@staticmethod
def backward(ctx, *grad_output):
global myreq
assert len(grad_output) == my_size
scatter_list = [t.contiguous() for t in grad_output]
a2a_info = ctx.a2a_info
batch_split_lengths = (
a2a_info.global_batch_partition_slices
if a2a_info.global_batch_partition_slices
else a2a_info.local_batch_num
)
table_split_lengths = (
a2a_info.global_table_wise_parition_slices
if a2a_info.global_table_wise_parition_slices
else [a2a_info.local_table_num] * my_size
)
grad_input = grad_output[0].new_empty(
[a2a_info.batch_size, a2a_info.emb_dim * a2a_info.local_table_num]
)
gather_list = list(grad_input.split(batch_split_lengths, dim=0))
req_list = []
for i in range(my_size):
req = dist.gather(
scatter_list[i],
gather_list if i == my_rank else [],
dst=i,
async_op=True,
)
req_list.append(req)
myreq.req = req_list
myreq.tensor = grad_input
return grad_output
class All2All_Req(Function):
@staticmethod
def forward(ctx, a2a_info, *inputs):
global myreq
with record_function("DLRM alltoall_req_fwd_single"):
batch_split_lengths = a2a_info.global_batch_partition_slices
if batch_split_lengths:
batch_split_lengths = [
m * a2a_info.emb_dim * a2a_info.local_table_num
for m in batch_split_lengths
]
table_split_lengths = a2a_info.global_table_wise_parition_slices
if table_split_lengths:
table_split_lengths = [
a2a_info.local_batch_num * e * a2a_info.emb_dim
for e in table_split_lengths
]
input = torch.cat(inputs, dim=1).view([-1])
output = input.new_empty(
[
a2a_info.global_table_num
* a2a_info.local_batch_num
* a2a_info.emb_dim
]
)
req = dist.all_to_all_single(
output, input, table_split_lengths, batch_split_lengths, async_op=True
)
myreq.req = req
myreq.tensor = []
myreq.tensor.append(output)
myreq.tensor = tuple(myreq.tensor)
a2a_info.batch_split_lengths = batch_split_lengths
a2a_info.table_split_lengths = table_split_lengths
myreq.a2a_info = a2a_info
ctx.a2a_info = a2a_info
return myreq.tensor
@staticmethod
def backward(ctx, *grad_output):
global myreq
with record_function("DLRM alltoall_req_bwd_single"):
a2a_info = ctx.a2a_info
myreq.req.wait()
myreq.req = None
grad_input = myreq.tensor
grad_inputs = grad_input.view([a2a_info.batch_size, -1]).split(
a2a_info.emb_dim, dim=1
)
grad_inputs = [gin.contiguous() for gin in grad_inputs]
myreq.tensor = None
return (None, *grad_inputs)
class All2All_Wait(Function):
@staticmethod
def forward(ctx, *output):
global myreq
with record_function("DLRM alltoall_wait_fwd_single"):
a2a_info = myreq.a2a_info
ctx.a2a_info = a2a_info
myreq.req.wait()
myreq.req = None
myreq.tensor = None
table_split_lengths = (
a2a_info.table_split_lengths
if a2a_info.table_split_lengths
else a2a_info.local_table_num
* a2a_info.local_batch_num
* a2a_info.emb_dim
)
outputs = output[0].split(table_split_lengths)
outputs = tuple(
[out.view([a2a_info.local_batch_num, -1]) for out in outputs]
)
return outputs
@staticmethod
def backward(ctx, *grad_outputs):
global myreq
with record_function("DLRM alltoall_wait_bwd_single"):
a2a_info = ctx.a2a_info
grad_outputs = [gout.contiguous().view([-1]) for gout in grad_outputs]
grad_output = torch.cat(grad_outputs)
grad_input = grad_output.new_empty(
[a2a_info.batch_size * a2a_info.local_table_num * a2a_info.emb_dim]
)
req = dist.all_to_all_single(
grad_input,
grad_output,
a2a_info.batch_split_lengths,
a2a_info.table_split_lengths,
async_op=True,
)
myreq.req = req
myreq.tensor = grad_input
return (grad_output,)
class AllGather(Function):
@staticmethod
def forward(ctx, input, global_lengths, dim=0):
if not isinstance(global_lengths, (list, tuple)):
global_lengths = [global_lengths] * my_size
assert len(global_lengths) == my_size
assert global_lengths[my_rank] == input.size(dim)
local_start = sum(global_lengths[:my_rank])
output_size = list(input.size())
ctx.dim = dim
ctx.local_start = local_start
ctx.local_length = global_lengths[my_rank]
input = input.contiguous()
if dim == 0:
out_len = sum(global_lengths)
output_size[dim] = out_len
output = input.new_empty(output_size)
gather_list = list(output.split(global_lengths, dim=0))
else:
gather_list = [torch.empty_like(input) for _ in range(my_size)]
gather_list = []
for length in global_lengths:
output_size[dim] = length
gather_list.append(input.new_empty(output_size))
dist.all_gather(gather_list, input)
if dim != 0:
output = torch.cat(gather_list, dim=dim)
return output
@staticmethod
def backward(ctx, grad_output):
# print("Inside All2AllBackward")
dim = ctx.dim
start = ctx.local_start
length = ctx.local_length
grad_input = grad_output.narrow(dim, start, length)
return (grad_input, None, None)
class All2AllInfo(object):
pass
def alltoall(inputs, per_rank_table_splits):
global myreq
batch_size, emb_dim = inputs[0].size()
a2a_info = All2AllInfo()
a2a_info.local_table_num = len(inputs)
a2a_info.global_table_wise_parition_slices = per_rank_table_splits
(
a2a_info.local_batch_num,
a2a_info.global_batch_partition_slices,
) = get_split_lengths(batch_size)
a2a_info.emb_dim = emb_dim
a2a_info.batch_size = batch_size
a2a_info.global_table_num = (
sum(per_rank_table_splits)
if per_rank_table_splits
else a2a_info.local_table_num * my_size
)
if a2a_impl == "" and alltoall_supported or a2a_impl == "alltoall":
# print("Using All2All_Req")
output = All2All_Req.apply(a2a_info, *inputs)
myreq.WaitFunction = All2All_Wait
elif a2a_impl == "" or a2a_impl == "scatter":
# print("Using All2All_Scatter_Req")
output = All2All_Scatter_Req.apply(a2a_info, *inputs)
myreq.WaitFunction = All2All_Scatter_Wait
elif a2a_impl == "scatter_list":
# print("Using All2All_ScatterList_Req")
output = All2All_ScatterList_Req.apply(a2a_info, *inputs)
myreq.WaitFunction = All2All_ScatterList_Wait
else:
print(
"Unknown value set for DLRM_ALLTOALL_IMPL (%s), "
"please use one of [alltoall, scatter, scatter_list]" % a2a_impl
)
return myreq
def all_gather(input, lengths, dim=0):
if not lengths:
lengths = [input.size(0)] * my_size
return AllGather.apply(input, lengths, dim)
def barrier():
if my_size > 1:
dist.barrier()
# Override builtin print function to print only from rank 0
orig_print = builtins.print
def rank0_print(*args, **kwargs):
if my_rank <= 0 or kwargs.get("print_all", False):
orig_print(*args, **kwargs)
builtins.print = rank0_print
# Allow printing from all rank with explicit print_all
def print_all(*args, **kwargs):
orig_print(*args, **kwargs)