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Oct 2, 2025
Merged

Add transposed convolution #14708

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59 changes: 59 additions & 0 deletions backends/cadence/aot/ref_implementations.py
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Original file line number Diff line number Diff line change
Expand Up @@ -960,6 +960,7 @@ def convolution(
_stride: tuple[int, int] | int = stride
_padding: tuple[int, int] | int = padding
_dilation: tuple[int, int] | int = dilation

if conv_is_1d:
conv = torch.nn.functional.conv1d
_stride = stride[0]
Expand All @@ -978,6 +979,64 @@ def convolution(
return conv_out


@impl(m, "transposed_convolution")
def transposed_convolution(
input_tensor: torch.Tensor,
weight: torch.Tensor,
bias: torch.Tensor,
stride: tuple[int, int],
padding: tuple[int, int],
dilation: tuple[int, int],
output_padding: tuple[int, int],
groups: int,
channel_last: bool = False,
) -> torch.Tensor:

conv_is_1d = len(input_tensor.shape) == 3
if channel_last:
if conv_is_1d:
input_tensor = input_tensor.movedim(-1, 1).contiguous()
if len(weight.shape) != 3:
raise ValueError("Weight tensor must be 3D if input is 3D")
weight = weight.movedim(-1, 1).contiguous()
else:
input_tensor = input_tensor.movedim(-1, -3)
if len(weight.shape) != 4:
raise ValueError("Weight tensor must be 4D if input is nd > 3")
weight = torch.permute(weight, (0, -1, 1, 2)).contiguous()

_stride: tuple[int, int] | int = stride
_padding: tuple[int, int] | int = padding
_dilation: tuple[int, int] | int = dilation
_output_padding: tuple[int, int] | int = output_padding
if conv_is_1d:
conv = torch.nn.functional.conv_transpose1d
_stride = stride[0]
_padding = padding[0]
_dilation = dilation[0]
_output_padding = output_padding[0]
else:
conv = torch.nn.functional.conv_transpose2d

conv_out = conv(
input_tensor,
weight,
bias,
_stride,
_padding,
_output_padding,
groups,
_dilation,
)
if channel_last:
if conv_is_1d:
conv_out = conv_out.movedim(1, -1).contiguous()
else:
conv_out = conv_out.movedim(-3, -1).contiguous()

return conv_out


@impl(m, "avg_pool2d")
def avg_pool2d(
input_tensor: torch.Tensor,
Expand Down
137 changes: 137 additions & 0 deletions backends/cadence/aot/tests/test_ref_implementations.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1534,6 +1534,143 @@ def test_convolution(
f"Output values don't match expected in {name}. Got {output}, expected {expected_output}",
)

@expand(
[
# Basic 2D transposed convolution with stride=1 (current test case - corrected name)
(
"basic_2d_stride1",
torch.tensor(
[[[[1.0, 2.0], [3.0, 4.0]]]], dtype=torch.float32
), # input: 1x1x2x2
torch.tensor(
[[[[1.0, 1.0], [1.0, 1.0]]]], dtype=torch.float32
), # weight: 1x1x2x2
torch.tensor([0.0], dtype=torch.float32), # bias
(1, 1), # stride
(0, 0), # padding
(1, 1), # dilation
1, # groups
(0, 0), # output_padding
False, # channel_last
torch.tensor(
[[[[1.0, 3.0, 2.0], [4.0, 10.0, 6.0], [3.0, 7.0, 4.0]]]],
dtype=torch.float32,
),
),
# 2D transposed convolution with channel_last=True (NHWC format)
(
"channel_last_nhwc",
torch.tensor(
[[[[1.0], [2.0]], [[3.0], [4.0]]]], dtype=torch.float32
), # input: 1x2x2x1 (NHWC)
torch.tensor(
[[[[1.0], [1.0]], [[1.0], [1.0]]]], dtype=torch.float32
), # weight: 1x2x2x1 (NHWC)
torch.tensor([0.0], dtype=torch.float32), # bias
(1, 1), # stride
(0, 0), # padding
(1, 1), # dilation
1, # groups
(0, 0), # output_padding
True, # channel_last=True
torch.tensor(
[
[
[[1.0], [3.0], [2.0]],
[[4.0], [10.0], [6.0]],
[[3.0], [7.0], [4.0]],
]
],
dtype=torch.float32,
),
),
# 2D transposed convolution with non-zero bias
(
"with_bias",
torch.tensor(
[[[[1.0, 2.0], [3.0, 4.0]]]], dtype=torch.float32
), # input: 1x1x2x2
torch.tensor(
[[[[1.0, 0.0], [0.0, 1.0]]]], dtype=torch.float32
), # weight: 1x1x2x2
torch.tensor([5.0], dtype=torch.float32), # bias=5.0
(1, 1), # stride
(0, 0), # padding
(1, 1), # dilation
1, # groups
(0, 0), # output_padding
False, # channel_last
torch.tensor(
[[[[6.0, 7.0, 5.0], [8.0, 10.0, 7.0], [5.0, 8.0, 9.0]]]],
dtype=torch.float32,
),
),
# 1D transposed convolution (3D tensor, NLC format)
(
"conv1d_nlc",
torch.tensor(
[[[1.0], [2.0], [3.0]]], dtype=torch.float32
), # input: 1x3x1 (NLC)
torch.tensor(
[[[1.0], [0.5]]], dtype=torch.float32
), # weight: 1x2x1 (NLC)
torch.tensor([0.0], dtype=torch.float32), # bias
(2, 0), # stride
(0, 0), # padding
(1, 1), # dilation
1, # groups
(0, 0), # output_padding
True, # channel_last=True
torch.tensor(
[[[1.0], [0.5], [2.0], [1.0], [3.0], [1.5]]], dtype=torch.float32
),
),
]
)
def test_transposed_convolution(
self,
name: str,
input_tensor: torch.Tensor,
weight: torch.Tensor,
bias: torch.Tensor,
stride: tuple[int, int],
padding: tuple[int, int],
dilation: tuple[int, int],
groups: int,
output_padding: tuple[int, int],
channel_last: bool,
expected_output: torch.Tensor,
) -> None:
output = torch.ops.cadence.transposed_convolution(
input_tensor,
weight,
bias,
stride,
padding,
dilation,
output_padding,
groups,
channel_last,
)

# Verify output properties
self.assertEqual(
output.dtype,
input_tensor.dtype,
f"Output dtype should match input dtype in {name}",
)
self.assertEqual(
output.shape,
expected_output.shape,
f"Output shape should match expected shape in {name}",
)

# Verify output matches expected values
self.assertTrue(
torch.equal(output, expected_output),
f"Output values don't match expected in {name}. Got {output}, expected {expected_output}",
)

@expand(
[
# Basic non-quantized average pooling
Expand Down
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