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fix: handle non-tensor sample_kwargs in static_input_surface #1

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fix: handle non-tensor sample_kwargs in static_input_surface #1

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ad90b78
fix: handle non-tensor sample_kwargs in static_input_surface
shjwudp Jun 26, 2026
bc69022
fix: handle positional args in functionalized during graph replay
shjwudp Jun 26, 2026
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38 changes: 22 additions & 16 deletions src/te_graph_runtime/graph.py
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Original file line number Diff line number Diff line change
Expand Up @@ -867,7 +867,7 @@ def hook_fn(module, inputs, outputs, func_idx=func_idx): # pylint: disable=unus

def _run_warmup_backward(func_idx, func, outputs, warmup_iter, callable_idx) -> None:
static_input_surface = per_callable_static_input_surfaces[func_idx]
inputs = tuple(i for i in static_input_surface if i.requires_grad)
inputs = tuple(i for i in static_input_surface if i is not None and i.requires_grad)

@buptzyb buptzyb Jun 29, 2026

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Blocker: this still crashes for non-None, non-tensor kwargs such as the img_shapes=[[1,64,64]] example in the PR body. i is not None and i.requires_grad will call .requires_grad on a Python list/int/etc. The guard needs to be isinstance(i, torch.Tensor) and i.requires_grad here and in the corresponding backward-capture paths below.

outputs_requiring_grad = tuple(o for o in outputs if o is not None and o.requires_grad)
grad_outputs = _make_grad_outputs(outputs)

Expand All @@ -883,10 +883,13 @@ def _run_warmup_backward(func_idx, func, outputs, warmup_iter, callable_idx) ->
# Filter module params that get None grad from grad_inputs and remove them
# from static_input_surface. This is to ensure that the backward hooks
# registered to these params are not wrongly triggered.
num_required_grad_sample_args = sum(arg.requires_grad for arg in flatten_sample_args[func_idx])
num_required_grad_sample_args = sum(
isinstance(arg, torch.Tensor) and arg.requires_grad
for arg in flatten_sample_args[func_idx]
)
required_grad_input_idx = []
for i, arg in enumerate(static_input_surface):
if arg.requires_grad:
if isinstance(arg, torch.Tensor) and arg.requires_grad:
required_grad_input_idx.append(i)
module_params_with_grad = []
for grad_inputs_idx, inputs_idx in enumerate(required_grad_input_idx):
Expand Down Expand Up @@ -1108,7 +1111,7 @@ def _run_warmup_backward(func_idx, func, outputs, warmup_iter, callable_idx) ->
func,
static_grad_outputs,
)
inputs = tuple(i for i in static_input_surface if i.requires_grad)
inputs = tuple(i for i in static_input_surface if i is not None and i.requires_grad)
with _none_grad_context_wrapper(inputs), _graph_context_wrapper(
bwd_graph, pool=mempool
):
Expand Down Expand Up @@ -1232,7 +1235,7 @@ def _run_warmup_backward(func_idx, func, outputs, warmup_iter, callable_idx) ->
if is_training:
func = graph_callables[bwd_idx]
_call_capture_time_backward_pre_hooks(bwd_idx, func, static_grad_outputs)
inputs = tuple(i for i in static_input_surface if i.requires_grad)
inputs = tuple(i for i in static_input_surface if i is not None and i.requires_grad)
with _none_grad_context_wrapper(inputs), _graph_context_wrapper(
bwd_graph, pool=mempool
):
Expand Down Expand Up @@ -1311,6 +1314,7 @@ def forward(ctx, skip_fp8_weight_update, cuda_graph_stream, cuda_graph_event, *i
for i in range(len_user_args):
if (
isinstance(static_input_surface[i], torch.Tensor)
and inputs[i] is not None
and static_input_surface[i].data_ptr() != inputs[i].data_ptr()
):
static_input_surface[i].copy_(inputs[i])
Expand Down Expand Up @@ -1416,21 +1420,23 @@ def functionalized(*user_args, **user_kwargs):
user_kwargs.pop("cuda_graph_event")
else:
cuda_graph_event = None
# Check that required kwargs are provided
for key in kwargs_keys:
if key not in user_kwargs:
raise TypeError(
f"Graphed callable was initialized with kwarg {key} ,"
"but it was not provided in graph replay"
)

# Runs the autograd function with inputs == all inputs to
# the graph that might require grad (explicit user args +
# module parameters)
# Assumes module params didn't change since capture.
flatten_user_args, _ = _tree_flatten(user_args)
flatten_user_kwargs, _ = _tree_flatten([user_kwargs[key] for key in kwargs_keys])
func_args = tuple(flatten_user_args) + tuple(flatten_user_kwargs) + module_params
# Reconstruct the same flattened arg order as capture time.
# User may pass some recorded kwargs as positional args, so
# check user_args first (by position), then user_kwargs.
user_pos_args = list(user_args)
kwarg_values = []
for key in kwargs_keys:
if key in user_kwargs:
kwarg_values.append(user_kwargs[key])
elif user_pos_args:
kwarg_values.append(user_pos_args.pop(0))
# else: key was a default not passed — skip (not a tensor)
flatten_user_kwargs, _ = _tree_flatten(kwarg_values)
func_args = tuple(flatten_user_kwargs) + module_params

@buptzyb buptzyb Jun 29, 2026

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Blocker: this drops normal replay positional inputs. user_args is only consumed as a fallback source for captured kwargs, and func_args is built from flatten_user_kwargs + module_params, so graphed(x) with no captured kwargs passes only module params, and graphed(x, scale=scale) drops x. This needs to preserve the flattened explicit user args and then append the captured kwarg values in the same order used during capture.

out = Graphed.apply(
skip_fp8_weight_update, cuda_graph_stream, cuda_graph_event, *func_args
)
Expand Down