Skip to content

Use device names, not rank ids, for device placement in test helpers #8397

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
delock merged 2 commits into from
Sep 3, 2026
Merged

Use device names, not rank ids, for device placement in test helpers #8397

Show file tree
Hide file tree
Changes from 1 commit
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
8ceca37
Use device names, not rank ids, for device placement in test helpers
delock Sep 3, 2026
91ce198
tests: fix yapf line-length violation in autotp test
delock Sep 3, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
6 changes: 4 additions & 2 deletions tests/unit/common.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -575,8 +575,10 @@ def wrapper(*args: Any, **kwargs: Any):
def reduce_boolean_flags(flag: bool, op=all) -> bool:
if not dist.is_initialized():
return flag
device = get_accelerator().current_device()
tensor_flag = torch.tensor(1 if flag else 0, dtype=torch.int, device=device)
# current_device() is a rank id on CPU, not a valid torch device; use the device name.
device = get_accelerator().current_device_name()

@chatgpt-codex-connector chatgpt-codex-connector Bot Sep 3, 2026

Copy link
Copy Markdown

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

P1 Badge Add the required sign-off trailer

This is a non-merge commit, but its message has no Signed-off-by trailer, so it violates the repository's mandatory commit requirement and may be rejected by DCO/CI checks. Recreate the commit with --signoff using the configured Git identity.

AGENTS.md reference: AGENTS.md:L8-L8

Useful? React with 👍 / 👎.

# gloo rejects 0-dim inputs to all_gather_into_tensor, so carry the flag in a 1-dim tensor.
tensor_flag = torch.tensor([1 if flag else 0], dtype=torch.int, device=device)
world_size = dist.get_world_size()
tensor_flag_buf = torch.zeros(world_size, dtype=torch.int, device=device)
dist.all_gather_into_tensor(tensor_flag_buf, tensor_flag)
Expand Down
30 changes: 15 additions & 15 deletions tests/unit/v1/autotp/test_autotp_training.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -424,7 +424,7 @@ def process_linear_layer(hidden_dim, input, output_dim=None):
torch_linear = nn.Linear(hidden_dim,
output_dim,
dtype=preferred_dtype(),
device=get_accelerator().current_device())
device=get_accelerator().current_device_name())
torch_out = torch_linear(input)
torch_loss = torch_out.sum()
torch_loss.backward()
Expand Down Expand Up @@ -488,7 +488,7 @@ def run_tp_layer_fwd_bwd(tp_size,
hidden_dim,
dtype=preferred_dtype(),
requires_grad=True,
device=get_accelerator().current_device())
device=get_accelerator().current_device_name())
dist.broadcast(input, groups.get_tensor_model_parallel_src_rank(), group=groups.get_tensor_model_parallel_group())

# Note: correctness checks below use standalone TP wrappers and do not
Expand All @@ -498,7 +498,7 @@ def run_tp_layer_fwd_bwd(tp_size,
linear = LinearLayer(deepcopy(torch_linear),
groups.get_tensor_model_parallel_group(),
gather_output=gather_output)
out = linear(input.to(get_accelerator().current_device()))
out = linear(input.to(get_accelerator().current_device_name()))
loss = out.sum()
loss.backward()

Expand All @@ -512,35 +512,35 @@ def run_tp_layer_fwd_bwd(tp_size,
torch_bias_grad = torch_linear.bias.grad.split(output_partition_sizes, dim=0)[tp_rank]

torch.testing.assert_close(linear.bias.grad,
torch_bias_grad.to(get_accelerator().current_device()),
torch_bias_grad.to(get_accelerator().current_device_name()),
atol=1e-3,
rtol=1e-3)
torch.testing.assert_close(linear.weight.grad,
torch_grad.to(get_accelerator().current_device()),
torch_grad.to(get_accelerator().current_device_name()),
atol=1e-3,
rtol=1e-3)
torch.testing.assert_close(expected_out.to(get_accelerator().current_device()).contiguous(),
torch.testing.assert_close(expected_out.to(get_accelerator().current_device_name()).contiguous(),
out.contiguous(),
atol=1e-2,
rtol=1e-2)
else:
linear = LinearAllreduce(deepcopy(torch_linear), groups.get_tensor_model_parallel_group())
input_ = torch.chunk(input, tp_size, dim=-1)[groups.get_tensor_model_parallel_rank()]
out = linear(input_.to(get_accelerator().current_device()))
out = linear(input_.to(get_accelerator().current_device_name()))
loss = out.sum()
loss.backward()

torch_grad = torch.chunk(torch_linear.weight.grad, tp_size, dim=1)[groups.get_tensor_model_parallel_rank()]
torch_bias_grad = torch_linear.bias.grad
torch.testing.assert_close(linear.bias.grad,
torch_bias_grad.to(get_accelerator().current_device()),
torch_bias_grad.to(get_accelerator().current_device_name()),
atol=1e-3,
rtol=1e-3)
torch.testing.assert_close(linear.weight.grad,
torch_grad.to(get_accelerator().current_device()),
torch_grad.to(get_accelerator().current_device_name()),
atol=1e-3,
rtol=1e-3)
torch.testing.assert_close(out, torch_out.to(get_accelerator().current_device()), atol=1e-2, rtol=1e-2)
torch.testing.assert_close(out, torch_out.to(get_accelerator().current_device_name()), atol=1e-2, rtol=1e-2)


@pytest.mark.sequential
Expand Down Expand Up @@ -577,7 +577,7 @@ def test_standard_cross_entropy_matches_unsharded_reference(self):
set_autotp_mode(training=True)
try:
torch.manual_seed(20260825)
model = UnevenVocabOutputModel(hidden_dim, vocab_size).to(get_accelerator().current_device())
model = UnevenVocabOutputModel(hidden_dim, vocab_size).to(get_accelerator().current_device_name())
reference_model = deepcopy(model)

autotp = AutoTP(module=model,
Expand All @@ -596,13 +596,13 @@ def test_standard_cross_entropy_matches_unsharded_reference(self):
assert model.lm_head.gather_output

torch.manual_seed(17)
reference_input = torch.randn(4, hidden_dim, device=get_accelerator().current_device(), requires_grad=True)
reference_input = torch.randn(4, hidden_dim, device=get_accelerator().current_device_name(), requires_grad=True)
dist.broadcast(reference_input, src=0, group=tp_group)
tp_input = reference_input.detach().clone().requires_grad_(True)

partition_sizes = get_shard_size_list(vocab_size, self.world_size, model.lm_head.tp_meta, "lm_head")
labels = torch.tensor([0, vocab_size - 1, partition_sizes[0], 1],
device=get_accelerator().current_device())
device=get_accelerator().current_device_name())
reference_logits = reference_model(reference_input)
tp_logits = model(tp_input)
reference_loss = nn.functional.cross_entropy(reference_logits, labels)
Expand Down Expand Up @@ -693,7 +693,7 @@ def test(self, layer_type):
if is_model_parallel_parameter(param):
param.gather_params([param])

torch_linear = torch_linear.to(get_accelerator().current_device())
torch_linear = torch_linear.to(get_accelerator().current_device_name())
is_same_weights = all(
torch.equal(param1, param2) for param1, param2 in zip(tp_layer.parameters(), torch_linear.parameters()))

Expand Down Expand Up @@ -759,7 +759,7 @@ def test_uneven_linear_gather_params(self):
if is_model_parallel_parameter(param):
param.gather_params([param])

torch_linear = torch_linear.to(get_accelerator().current_device())
torch_linear = torch_linear.to(get_accelerator().current_device_name())
is_same_weights = all(
torch.equal(param1, param2) for param1, param2 in zip(tp_layer.parameters(), torch_linear.parameters()))

Expand Down
Loading