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fix test #3361

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fix test #3361

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57 changes: 40 additions & 17 deletions torchrec/distributed/tests/test_init_parameters.py
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Original file line number Diff line number Diff line change
Expand Up @@ -60,6 +60,25 @@ def initialize_and_test_parameters(
local_size: Optional[int] = None,
) -> None:
with MultiProcessContext(rank, world_size, backend, local_size) as ctx:
# Set seed again in each process to ensure consistency
torch.manual_seed(42)
if torch.cuda.is_available():
torch.cuda.manual_seed(42)

key = (
f"embeddings.{table_name}.weight"
if isinstance(embedding_tables, EmbeddingCollection)
else f"embedding_bags.{table_name}.weight"
)

# Create the same fixed tensor in each process
fixed_tensor = torch.randn(10, 64, generator=torch.Generator().manual_seed(42))

# Load the fixed tensor into the embedding_tables to ensure consistency
embedding_tables.load_state_dict({key: fixed_tensor})

# Store the original tensor on CPU for comparison BEFORE creating the model
original_tensor = embedding_tables.state_dict()[key].clone().cpu()

module_sharding_plan = construct_module_sharding_plan(
embedding_tables,
Expand All @@ -79,12 +98,8 @@ def initialize_and_test_parameters(
env=ShardingEnv.from_process_group(ctx.pg),
sharders=sharders,
device=ctx.device,
)

key = (
f"embeddings.{table_name}.weight"
if isinstance(embedding_tables, EmbeddingCollection)
else f"embedding_bags.{table_name}.weight"
init_data_parallel=False,
init_parameters=False,
)

if isinstance(model.state_dict()[key], DTensor):
Expand All @@ -96,28 +111,26 @@ def initialize_and_test_parameters(
gathered_tensor = model.state_dict()[key].full_tensor()
if ctx.rank == 0:
torch.testing.assert_close(
gathered_tensor,
embedding_tables.state_dict()[key],
gathered_tensor.cpu(), original_tensor, rtol=1e-5, atol=1e-6
)
elif isinstance(model.state_dict()[key], ShardedTensor):
if ctx.rank == 0:
gathered_tensor = torch.empty_like(
embedding_tables.state_dict()[key], device=ctx.device
)
gathered_tensor = torch.empty_like(original_tensor, device=ctx.device)
else:
gathered_tensor = None

model.state_dict()[key].gather(dst=0, out=gathered_tensor)

if ctx.rank == 0:
torch.testing.assert_close(
none_throws(gathered_tensor).to("cpu"),
embedding_tables.state_dict()[key],
none_throws(gathered_tensor).cpu(),
original_tensor,
rtol=1e-5,
atol=1e-6,
)
elif isinstance(model.state_dict()[key], torch.Tensor):
torch.testing.assert_close(
embedding_tables.state_dict()[key].cpu(),
model.state_dict()[key].cpu(),
model.state_dict()[key].cpu(), original_tensor, rtol=1e-5, atol=1e-6
)
else:
raise AssertionError(
Expand Down Expand Up @@ -161,6 +174,9 @@ def test_initialize_parameters_ec(self, sharding_type: str) -> None:
backend = "nccl"
table_name = "free_parameters"

# Set seed for deterministic tensor generation
torch.manual_seed(42)

# Initialize embedding table on non-meta device, in this case cuda:0
embedding_tables = EmbeddingCollection(
tables=[
Expand All @@ -173,8 +189,10 @@ def test_initialize_parameters_ec(self, sharding_type: str) -> None:
],
)

# Use a fixed tensor with explicit seeding for consistent testing
fixed_tensor = torch.randn(10, 64, generator=torch.Generator().manual_seed(42))
embedding_tables.load_state_dict(
{f"embeddings.{table_name}.weight": torch.randn(10, 64)}
{f"embeddings.{table_name}.weight": fixed_tensor}
)

self._run_multi_process_test(
Expand Down Expand Up @@ -210,6 +228,9 @@ def test_initialize_parameters_ebc(self, sharding_type: str) -> None:
backend = "nccl"
table_name = "free_parameters"

# Set seed for deterministic tensor generation
torch.manual_seed(42)

# Initialize embedding bag on non-meta device, in this case cuda:0
embedding_tables = EmbeddingBagCollection(
tables=[
Expand All @@ -222,8 +243,10 @@ def test_initialize_parameters_ebc(self, sharding_type: str) -> None:
],
)

# Use a fixed tensor with explicit seeding for consistent testing
fixed_tensor = torch.randn(10, 64, generator=torch.Generator().manual_seed(42))
embedding_tables.load_state_dict(
{f"embedding_bags.{table_name}.weight": torch.randn(10, 64)}
{f"embedding_bags.{table_name}.weight": fixed_tensor}
)

self._run_multi_process_test(
Expand Down
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