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
|
# Owner(s): ["oncall: distributed"]
import sys
import torch
import torch.distributed as dist
from torch.distributed._shard import (
shard_parameter,
)
from torch.testing._internal.common_distributed import (
requires_nccl,
skip_if_lt_x_gpu,
)
from torch.testing._internal.common_utils import (
TEST_WITH_DEV_DBG_ASAN,
run_tests,
)
from torch.testing._internal.distributed._shard.sharded_tensor import (
TEST_GPU_NUM,
ShardedTensorTestBase,
with_comms,
)
from torch.testing._internal.distributed._shard.sharded_tensor._test_ops_common import (
clone_module_parameter,
generate_chunk_sharding_specs_for_test,
generate_local_weight_sharding_params_for_test,
)
if TEST_WITH_DEV_DBG_ASAN:
print(
"Skip dev-asan as torch + multiprocessing spawn have known issues",
file=sys.stderr,
)
sys.exit(0)
class TestShardedEmbedding(ShardedTensorTestBase):
def _run_sharded_embedding(
self,
spec,
input_size,
num_embeddings,
embedding_dim,
max_norm=None,
norm_type=2.0,
padding_idx=None,
):
# Use same seed.
torch.manual_seed(0)
local_embedding = torch.nn.Embedding(
num_embeddings,
embedding_dim,
max_norm=max_norm,
norm_type=norm_type,
padding_idx=padding_idx,
).cuda(self.rank)
sharded_embedding = torch.nn.Embedding(
num_embeddings,
embedding_dim,
max_norm=max_norm,
norm_type=norm_type,
padding_idx=padding_idx,
)
# Copy the weights from local embedding
sharded_embedding.weight = clone_module_parameter(
local_embedding, "weight"
)
# Shard the parameter.
shard_parameter(sharded_embedding, "weight", spec)
# Run sharded computation
torch.manual_seed(self.rank) # inputs different on each rank
inp = torch.randint(0, num_embeddings, tuple(input_size)).cuda(self.rank)
sharded_output = sharded_embedding(inp)
# If max_norm is set, we need to ensure that the renorm has been applied across
# inputs from all ranks.
if max_norm is not None:
gathered_inputs = [torch.zeros_like(inp) for _ in range(TEST_GPU_NUM)]
dist.all_gather(gathered_inputs, inp)
unique_inp = torch.unique(torch.cat(gathered_inputs))
local_embedding(unique_inp)
# Run local computation
local_output = local_embedding(inp)
# Compare local weight and shared one to ensure the renorm
# as expected.
if max_norm is not None:
sharded_dim = spec.dim
sharded_weight = sharded_embedding.weight.local_shards()[0].tensor
(start_pos, chunk_size) = generate_local_weight_sharding_params_for_test(
local_embedding.weight, sharded_dim, TEST_GPU_NUM, spec, self.rank
)
local_weight_narrowed = local_embedding.weight.narrow(
sharded_dim, start_pos, chunk_size
)
self.assertEqual(local_weight_narrowed, sharded_weight)
# Verify
self.assertEqual(local_output, sharded_output)
# Validate for torch.nn.functional.embedding version.
local_output = torch.nn.functional.embedding(
inp,
local_embedding.weight,
max_norm=max_norm,
norm_type=norm_type,
padding_idx=padding_idx,
)
sharded_output = torch.nn.functional.embedding(
inp,
sharded_embedding.weight,
max_norm=max_norm,
norm_type=norm_type,
padding_idx=padding_idx,
)
self.assertEqual(local_output, sharded_output)
@with_comms(init_rpc=False)
@skip_if_lt_x_gpu(TEST_GPU_NUM)
@requires_nccl()
def test_sharded_embedding_colwise(self):
for spec in generate_chunk_sharding_specs_for_test(1):
self._run_sharded_embedding(spec, [5, 4], 17, 12)
self._run_sharded_embedding(spec, [6, 7, 6], 21, 11)
self._run_sharded_embedding(spec, [8, 6, 5, 4], 23, 13)
self._run_sharded_embedding(spec, [8, 6, 5, 4, 7], 23, 16)
self._run_sharded_embedding(spec, [4], 15, 14)
self._run_sharded_embedding(spec, [34], 15, 14, padding_idx=10)
self._run_sharded_embedding(spec, [8, 6, 5, 4], 23, 13, padding_idx=12)
self._run_sharded_embedding(
spec, [4, 5, 6], 23, 13, max_norm=2.5,
)
self._run_sharded_embedding(
spec, [12, 7, 16], 23, 13, max_norm=2.5,
)
self._run_sharded_embedding(
spec, [8, 16, 20], 12, 12, max_norm=1.25, norm_type=1.0,
)
self._run_sharded_embedding(spec, [30], 15, 14, max_norm=2.0)
@with_comms(init_rpc=False)
@skip_if_lt_x_gpu(TEST_GPU_NUM)
@requires_nccl()
def test_sharded_embedding_rowwise(self):
for spec in generate_chunk_sharding_specs_for_test(0):
# Test even split.
self._run_sharded_embedding(spec, [5, 12], 16, 22)
self._run_sharded_embedding(spec, [5, 4], 32, 12)
self._run_sharded_embedding(spec, [6, 7, 6], 64, 11)
self._run_sharded_embedding(
spec, [5, 12], 16, 22, max_norm=2.5,
)
self._run_sharded_embedding(spec, [6, 7, 6], 64, 11, padding_idx=30)
self._run_sharded_embedding(
spec, [6, 5, 3], 26, 11, max_norm=2.0,
)
# Test uneven split.
self._run_sharded_embedding(spec, [8, 6, 5, 4], 19, 11)
self._run_sharded_embedding(spec, [6, 7, 6], 21, 11)
self._run_sharded_embedding(spec, [4], 21, 11)
self._run_sharded_embedding(spec, [8, 6, 5, 4], 21, 11, padding_idx=10)
self._run_sharded_embedding(
spec, [6, 5, 8], 28, 5, max_norm=2.0,
)
self._run_sharded_embedding(spec, [4], 14, 11, max_norm=2.5)
if __name__ == "__main__":
run_tests()
|
