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Replace if-guards in fusion_pass.py with asserts #17318

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Merged
meta-codesync merged 1 commit into from
Feb 11, 2026
Merged

Replace if-guards in fusion_pass.py with asserts #17318

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94 changes: 39 additions & 55 deletions backends/cadence/aot/quantizer/fusion_pass.py
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Original file line number Diff line number Diff line change
Expand Up @@ -160,20 +160,15 @@ def get_args_and_kwargs_layer_norm(
),
{"dtype": torch.float32},
)
if len(inputs_inputs) > 0:
if "val" in inputs_inputs[0].meta:
fake_mode = inputs_inputs[0].meta["val"].fake_mode
if fake_mode is not None:
with fake_mode:
fake_weight = torch.full(
other_inputs[0], 1, dtype=torch.float32
)
weight.meta["val"] = fake_weight
else:
weight.meta["val"] = torch.full(
other_inputs[0], 1, dtype=torch.float32
)
copy_node_metadata(weight, inputs_inputs[0])
assert (
len(inputs_inputs) == 1
), f"Expected 1 input for layer norm weight, got {len(inputs_inputs)}"
assert "val" in inputs_inputs[0].meta, "Missing val metadata on input node"
fake_mode = inputs_inputs[0].meta["val"].fake_mode
assert fake_mode is not None, "fake_mode is None on input node"
with fake_mode:
weight.meta["val"] = torch.full(other_inputs[0], 1, dtype=torch.float32)
copy_node_metadata(weight, inputs_inputs[0])

bias = other_inputs[2] if len(other_inputs) > 2 else None

Expand All @@ -186,18 +181,15 @@ def get_args_and_kwargs_layer_norm(
),
{"dtype": torch.float32},
)
if len(inputs_inputs) > 0:
if "val" in inputs_inputs[0].meta:
fake_mode = inputs_inputs[0].meta["val"].fake_mode
if fake_mode is not None:
with fake_mode:
fake_bias = torch.full(other_inputs[0], 0, dtype=torch.float32)
bias.meta["val"] = fake_bias
else:
bias.meta["val"] = torch.full(
other_inputs[0], 0, dtype=torch.float32
)
copy_node_metadata(bias, inputs_inputs[0])
assert (
len(inputs_inputs) == 1
), f"Expected 1 input for layer norm bias, got {len(inputs_inputs)}"
assert "val" in inputs_inputs[0].meta, "Missing val metadata on input node"
fake_mode = inputs_inputs[0].meta["val"].fake_mode
assert fake_mode is not None, "fake_mode is None on input node"
with fake_mode:
bias.meta["val"] = torch.full(other_inputs[0], 0, dtype=torch.float32)
copy_node_metadata(bias, inputs_inputs[0])

# Make the args and kwargs for the replacement op
args = tuple(inputs_inputs + [scale, zero_point])
Expand Down Expand Up @@ -373,16 +365,15 @@ def get_args_and_kwargs_softmax(
),
{"dtype": torch.int32},
)
if len(inputs_inputs) > 0:
if "val" in inputs_inputs[0].meta:
fake_mode = inputs_inputs[0].meta["val"].fake_mode
if fake_mode is not None:
with fake_mode:
fake_mask = torch.full(mask_shape, 0.0, dtype=torch.int32)
mask_tensor.meta["val"] = fake_mask
else:
mask_tensor.meta["val"] = torch.full(mask_shape, 0.0, dtype=torch.int32)
copy_node_metadata(mask_tensor, inputs_inputs[0])
assert (
len(inputs_inputs) == 1
), f"Expected 1 input for softmax, got {len(inputs_inputs)}"
assert "val" in inputs_inputs[0].meta, "Missing val metadata on input node"
fake_mode = inputs_inputs[0].meta["val"].fake_mode
assert fake_mode is not None, "fake_mode is None on input node"
with fake_mode:
mask_tensor.meta["val"] = torch.full(mask_shape, 0.0, dtype=torch.int32)
copy_node_metadata(mask_tensor, inputs_inputs[0])
# Make the scale and zero_point tensors
in_scale = dequants_inputs[0].args[1]
in_zero_point = dequants_inputs[0].args[2]
Expand Down Expand Up @@ -636,25 +627,18 @@ def call(self, graph_module: fx.GraphModule) -> PassResult: # noqa: C901
torch.ops.aten.transpose.int,
(weights_inputs[0], 0, 1),
)
if "val" in weights_inputs[0].meta:
original_val = weights_inputs[0].meta["val"]
fake_mode = original_val.fake_mode
if fake_mode is not None:
with fake_mode:
transposed_val = torch.ops.aten.transpose.int(
original_val, 0, 1
)
transposed_weights.meta["val"] = transposed_val
else:
transposed_shape = list(original_val.shape)
transposed_shape[0], transposed_shape[1] = (
transposed_shape[1],
transposed_shape[0],
)
transposed_weights.meta["val"] = torch.zeros(
transposed_shape, dtype=original_val.dtype
)
copy_node_metadata(transposed_weights, weights_inputs[0])
assert (
"val" in weights_inputs[0].meta
), "Missing val metadata on weight node"
original_val = weights_inputs[0].meta["val"]
assert (
original_val.fake_mode is not None
), "fake_mode is None on weight node"
with original_val.fake_mode:
transposed_weights.meta["val"] = (
torch.ops.aten.transpose.int(original_val, 0, 1)
)
copy_node_metadata(transposed_weights, weights_inputs[0])

# Call linear with transposed weight
args, kwargs = get_args_and_kwargs_linear(
Expand Down
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