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Merged
facebook-github-bot merged 1 commit into from
Oct 1, 2025
Merged

Update conv replacement pass + tests. #14363

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62 changes: 20 additions & 42 deletions backends/cadence/aot/replace_ops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -699,43 +699,27 @@ def call_operator(self, op, args, kwargs, meta):
# graph operation (in this case a transpose_copy op) to be an explicit
# ProxyValue as well. If not, the view op can be done directly on the
# tensor.
transposed_weight = (
super().call_operator(
exir_ops.edge.aten.transpose_copy.int,
(
weight,
0,
1,
),
kwargs,
meta,
)
if isinstance(weight, ProxyValue)
else weight.transpose(0, 1)
transposed_weight = super().call_operator(
exir_ops.edge.aten.transpose_copy.int,
(
weight,
0,
1,
),
kwargs,
meta,
)

flipped_weight = (
super().call_operator(
exir_ops.edge.aten.flip.default,
(
transposed_weight,
[-1] if transposed_weight.to_tensor().dim() == 3 else [-1, -2],
),
kwargs,
meta,
)
if isinstance(transposed_weight, ProxyValue)
else (
transposed_weight.flip(-1)
if transposed_weight.dim() == 3
else transposed_weight.flip(-1, -2)
)
flipped_weight = super().call_operator(
exir_ops.edge.aten.flip.default,
(
transposed_weight,
[-1] if transposed_weight.to_tensor().dim() == 3 else [-1, -2],
),
kwargs,
meta,
)

# From the previous checks, if flipped_weight is a FakeTensor, it has to be
# a constant (if not, it would be a ProxyValue). Mark it as such.
if isinstance(flipped_weight, FakeTensor):
flipped_weight.constant = flipped_weight
new_args = (
in_tensor,
flipped_weight,
Expand All @@ -751,16 +735,10 @@ def call_operator(self, op, args, kwargs, meta):
# Verify that output_padding is 0.
assert all(
x == 0 for x in output_padding
), "Cannot handle padded output in convolution"
), f"Cannot handle padded output in convolution. Got {output_padding=}"

# If the innermost dim of output tensor is 1, then the stride
# should be 1. Note that the first dimension of output tensor is
# channel
new_stride = stride.copy()
out_shape = meta["val"].shape
assert out_shape is not None
for i, e in enumerate(out_shape[2:]):
new_stride[i] = 1 if e == 1 else stride[i]
# Keep the original stride to maintain correct output dimensions
new_stride = stride

new_args = (
in_tensor,
Expand Down
191 changes: 190 additions & 1 deletion backends/cadence/aot/tests/test_replace_ops_passes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -52,9 +52,10 @@

from executorch.backends.cadence.aot.typing_stubs import expand
from executorch.exir.dialects._ops import ops as exir_ops
from executorch.exir.pass_base import ExportPass
from executorch.exir.pass_base import ExportPass, ProxyValue
from executorch.exir.passes import dead_code_elimination_pass
from torch.fx.passes.infra.pass_base import PassResult
from torch.utils import _pytree as pytree


class TestReplaceOpsPasses(unittest.TestCase):
Expand Down Expand Up @@ -345,6 +346,194 @@ def test_replace_functionally_equivalent_op_targets_unsafe_split(
count_node(graph_after_passes, exir_ops.edge.aten.unsafe_split.Tensor), 0, x
)

def assertTensorMetadataIsSame(
self, a: Sequence[torch.Tensor], b: Sequence[torch.Tensor]
) -> None:
for i, (_a, _b) in enumerate(zip(a, b)):
# TODO: actually compare the tensors.
self.assertTrue(
_a.shape == _b.shape, f"Tensor {i}: {_a.shape} != {_b.shape}"
)
self.assertTrue(
_a.dtype == _b.dtype, f"Tensor {i}: {_a.dtype} != {_b.dtype}"
)

@expand(
[
[(1, 8, 18), 8, 16, 3],
[(1, 8, 18), 8, 16, 5, 2],
# depthwise + bias
[(1, 8, 18), 8, 16, 5, 2, 0, 1, True],
# no bias
[(1, 8, 18), 8, 16, 3, 2, 4, 3, False, False],
# bias + transposed
[(1, 8, 18), 8, 16, 5, 2, 0, 1, False, True],
# Stride of 2 needed.
[(1, 8, 3), 8, 8, 48, 2, 23],
]
)
@torch.no_grad()
def test_replace_aten_conv_with_cadence_conv(
self,
shape: Tuple[int, ...],
in_channels: int,
out_channels: int,
kernel: int,
stride: int = 1,
padding: int = 0,
dilation: int = 1,
depthwise: bool = False,
bias_enabled: bool = True,
output_padding: Optional[int] = None,
) -> None:
groups = in_channels if depthwise else 1
builder = GraphBuilder()
x_tensor = torch.randn(*shape, dtype=torch.float32)
x = builder.placeholder("x", x_tensor)
weights_tensor = torch.randn(
[out_channels, in_channels // groups, kernel], dtype=torch.float32
)
weights = builder.placeholder("weights", weights_tensor)
bias: Optional[ProxyValue] = None
bias_tensor: Optional[torch.Tensor] = None
if bias_enabled:
bias_tensor = torch.randn([out_channels], dtype=torch.float32)
bias = builder.placeholder("bias", bias_tensor)
convolution = builder.call_operator(
op=exir_ops.edge.aten.convolution.default,
args=(
x,
weights,
bias,
[stride],
[padding],
[dilation],
False,
[output_padding] if output_padding else [0],
groups,
),
)
builder.output([convolution])
original_gm = builder.get_graph_module()

replacement_pass_result = (
ReplaceAtenConvolutionWithCadenceConvolutionPass().call(original_gm)
)
self.assertIsNotNone(replacement_pass_result)
graph_after_passes = replacement_pass_result.graph_module

self.assertEqual(
count_node(graph_after_passes, exir_ops.edge.aten.convolution.default),
0,
)
self.assertEqual(
count_node(graph_after_passes, exir_ops.edge.cadence.convolution.default),
1,
)
self.assertEqual(
count_node(
graph_after_passes, exir_ops.edge.cadence.transposed_convolution.default
),
0,
)

inputs = (x.to_tensor(), weights.to_tensor())
if bias is not None:
inputs += (bias.to_tensor(),)
self.assertTensorMetadataIsSame(
pytree.tree_flatten(original_gm.forward(*inputs))[0],
pytree.tree_flatten(graph_after_passes.forward(*inputs))[0],
)

@expand(
[
[(1, 8, 18), 8, 16, 3],
[(1, 8, 18), 8, 16, 5, 2],
# depthwise + bias
[(1, 8, 18), 8, 16, 5, 2, 0, 1, True, True],
# no bias
[(1, 8, 18), 8, 16, 3, 2, 4, 3, False, False],
# depthwise + no bias
[(1, 8, 18), 8, 16, 3, 1, 0, 1, True, False],
# bias
[(1, 8, 18), 8, 16, 5, 2, 0, 1, False, True],
]
)
@torch.no_grad()
def test_replace_aten_transposed_conv_with_cadence_transposed_conv(
self,
shape: Tuple[int, ...],
in_channels: int,
out_channels: int,
kernel: int,
stride: int = 1,
padding: int = 0,
dilation: int = 1,
depthwise: bool = False,
bias_enabled: bool = True,
output_padding: Optional[int] = None,
) -> None:
groups = in_channels if depthwise else 1
builder = GraphBuilder()
x = builder.placeholder("x", torch.randn(*shape, dtype=torch.float32))
weights_shape = [in_channels, out_channels // groups, kernel]
weights = builder.placeholder(
"weights",
torch.randn(weights_shape, dtype=torch.float32),
)
bias = (
builder.placeholder(
"bias", torch.randn([out_channels], dtype=torch.float32)
)
if bias_enabled
else None
)
convolution = builder.call_operator(
op=exir_ops.edge.aten.convolution.default,
args=(
x,
weights,
bias,
[stride],
[padding],
[dilation],
True,
[output_padding] if output_padding else [0],
groups,
),
)
builder.output([convolution])
original_gm = builder.get_graph_module()

replacement_pass_result = (
ReplaceAtenConvolutionWithCadenceConvolutionPass().call(original_gm)
)
self.assertIsNotNone(replacement_pass_result)
graph_after_passes = replacement_pass_result.graph_module

self.assertEqual(
count_node(graph_after_passes, exir_ops.edge.aten.convolution.default),
0,
)
self.assertEqual(
count_node(graph_after_passes, exir_ops.edge.cadence.convolution.default),
0,
)
self.assertEqual(
count_node(
graph_after_passes, exir_ops.edge.cadence.transposed_convolution.default
),
1,
)

inputs = (x.to_tensor(), weights.to_tensor())
if bias is not None:
inputs += (bias.to_tensor(),)
self.assertTensorMetadataIsSame(
pytree.tree_flatten(original_gm.forward(*inputs))[0],
pytree.tree_flatten(graph_after_passes.forward(*inputs))[0],
)

@expand(
[
[(1, 8, 33), 8, 16, 3],
Expand Down
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