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# 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 run_tests, TEST_WITH_DEV_DBG_ASAN
from torch.testing._internal.distributed._shard.sharded_tensor import (
ShardedTensorTestBase,
TEST_GPU_NUM,
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()
|
