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Fix on TE to support Mcore Vision Encoder CUDA Graph #2657

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Fix on TE to support Mcore Vision Encoder CUDA Graph #2657

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Fix on TE to support Mcore Vision Encoder CUDA Graph
Feb 6, 2026
503a644
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Feb 6, 2026
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26 changes: 17 additions & 9 deletions transformer_engine/pytorch/graph.py
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Original file line number Diff line number Diff line change
Expand Up @@ -452,9 +452,11 @@ def hook_fn(
inputs = tuple(i for i in static_input_surface if i.requires_grad)
with _none_grad_context_wrapper(inputs):
torch.autograd.backward(
tuple(o for o in outputs if o.requires_grad),
tuple(o for o in outputs if o is not None and o.requires_grad),
grad_tensors=tuple(
torch.empty_like(o) for o in outputs if o.requires_grad
torch.empty_like(o)
for o in outputs
if o is not None and o.requires_grad
),
)
grad_inputs = tuple(input.grad for input in inputs)
Expand Down Expand Up @@ -616,27 +618,32 @@ def hook_fn(
# Note for _reuse_graph_input_output_buffers: grad output is only used
# within backward, so we can reuse the same static buffers every time.
static_grad_outputs_keys = tuple(
(o.shape, o.dtype, o.layout) for o in static_outputs if o.requires_grad
(o.shape, o.dtype, o.layout)
for o in static_outputs
if o is not None and o.requires_grad
)
if static_grad_outputs_keys in static_grad_outputs_dict:
static_grad_outputs = static_grad_outputs_dict[static_grad_outputs_keys]
else:
static_grad_outputs = tuple(
torch.empty_like(o) if o.requires_grad else None
torch.empty_like(o) if o is not None and o.requires_grad else None
for o in static_outputs
)
static_grad_outputs_dict[static_grad_outputs_keys] = static_grad_outputs
else:
static_grad_outputs = tuple(
torch.empty_like(o) if o.requires_grad else None for o in static_outputs
torch.empty_like(o) if o is not None and o.requires_grad else None
for o in static_outputs
)
if is_training:
inputs = tuple(i for i in static_input_surface if i.requires_grad)
with _none_grad_context_wrapper(inputs), _graph_context_wrapper(
bwd_graph, pool=mempool
):
torch.autograd.backward(
tuple(o for o in static_outputs if o.requires_grad),
tuple(
o for o in static_outputs if o is not None and o.requires_grad
),
grad_tensors=tuple(o for o in static_grad_outputs if o is not None),
retain_graph=retain_graph_in_backward,
)
Expand Down Expand Up @@ -719,15 +726,16 @@ def hook_fn(
):
# For now, assumes all static_outputs require grad
static_grad_outputs = tuple(
torch.empty_like(o) if o.requires_grad else None for o in static_outputs
torch.empty_like(o) if o is not None and o.requires_grad else None
for o in static_outputs
)
if is_training:
inputs = tuple(i for i in static_input_surface if i.requires_grad)
with _none_grad_context_wrapper(inputs), _graph_context_wrapper(
bwd_graph, pool=mempool
):
torch.autograd.backward(
tuple(o for o in static_outputs if o.requires_grad),
tuple(o for o in static_outputs if o is not None and o.requires_grad),
grad_tensors=tuple(o for o in static_grad_outputs if o is not None),
retain_graph=retain_graph_in_backward,
)
Expand Down Expand Up @@ -794,7 +802,7 @@ def forward(ctx, skip_fp8_weight_update, *inputs):
# Replay forward graph
fwd_graph.replay()
assert isinstance(static_outputs, tuple)
return tuple(o.detach() for o in static_outputs)
return tuple(o.detach() if o is not None else o for o in static_outputs)

@staticmethod
@torch.autograd.function.once_differentiable
Expand Down