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DrJessopDrJessop
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Add transposed convolution
Differential Revision: D83602808 Pull Request resolved: #14708
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‎backends/cadence/aot/ref_implementations.py‎

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Original file line numberDiff line numberDiff line change
@@ -960,6 +960,7 @@ def convolution(
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_stride: tuple[int, int] | int = stride
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_padding: tuple[int, int] | int = padding
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_dilation: tuple[int, int] | int = dilation
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963964
if conv_is_1d:
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conv = torch.nn.functional.conv1d
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_stride = stride[0]
@@ -978,6 +979,64 @@ def convolution(
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return conv_out
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982+
@impl(m, "transposed_convolution")
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def transposed_convolution(
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input_tensor: torch.Tensor,
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weight: torch.Tensor,
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bias: torch.Tensor,
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stride: tuple[int, int],
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padding: tuple[int, int],
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dilation: tuple[int, int],
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output_padding: tuple[int, int],
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groups: int,
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channel_last: bool = False,
993+
) -> torch.Tensor:
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conv_is_1d = len(input_tensor.shape) == 3
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if channel_last:
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if conv_is_1d:
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input_tensor = input_tensor.movedim(-1, 1).contiguous()
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if len(weight.shape) != 3:
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raise ValueError("Weight tensor must be 3D if input is 3D")
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weight = weight.movedim(-1, 1).contiguous()
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else:
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input_tensor = input_tensor.movedim(-1, -3)
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if len(weight.shape) != 4:
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raise ValueError("Weight tensor must be 4D if input is nd > 3")
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weight = torch.permute(weight, (0, -1, 1, 2)).contiguous()
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1008+
_stride: tuple[int, int] | int = stride
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_padding: tuple[int, int] | int = padding
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_dilation: tuple[int, int] | int = dilation
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_output_padding: tuple[int, int] | int = output_padding
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if conv_is_1d:
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conv = torch.nn.functional.conv_transpose1d
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_stride = stride[0]
1015+
_padding = padding[0]
1016+
_dilation = dilation[0]
1017+
_output_padding = output_padding[0]
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else:
1019+
conv = torch.nn.functional.conv_transpose2d
1020+
1021+
conv_out = conv(
1022+
input_tensor,
1023+
weight,
1024+
bias,
1025+
_stride,
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_padding,
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_output_padding,
1028+
groups,
1029+
_dilation,
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)
1031+
if channel_last:
1032+
if conv_is_1d:
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conv_out = conv_out.movedim(1, -1).contiguous()
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else:
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conv_out = conv_out.movedim(-3, -1).contiguous()
1036+
1037+
return conv_out
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9811040
@impl(m, "avg_pool2d")
9821041
def avg_pool2d(
9831042
input_tensor: torch.Tensor,

‎backends/cadence/aot/tests/test_ref_implementations.py‎

Lines changed: 137 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -1534,6 +1534,143 @@ def test_convolution(
15341534
f"Output values don't match expected in {name}. Got {output}, expected {expected_output}",
15351535
)
15361536

1537+
@expand(
1538+
[
1539+
# Basic 2D transposed convolution with stride=1 (current test case - corrected name)
1540+
(
1541+
"basic_2d_stride1",
1542+
torch.tensor(
1543+
[[[[1.0, 2.0], [3.0, 4.0]]]], dtype=torch.float32
1544+
), # input: 1x1x2x2
1545+
torch.tensor(
1546+
[[[[1.0, 1.0], [1.0, 1.0]]]], dtype=torch.float32
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), # weight: 1x1x2x2
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torch.tensor([0.0], dtype=torch.float32), # bias
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(1, 1), # stride
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(0, 0), # padding
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(1, 1), # dilation
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1, # groups
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(0, 0), # output_padding
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False, # channel_last
1555+
torch.tensor(
1556+
[[[[1.0, 3.0, 2.0], [4.0, 10.0, 6.0], [3.0, 7.0, 4.0]]]],
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dtype=torch.float32,
1558+
),
1559+
),
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# 2D transposed convolution with channel_last=True (NHWC format)
1561+
(
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"channel_last_nhwc",
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torch.tensor(
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[[[[1.0], [2.0]], [[3.0], [4.0]]]], dtype=torch.float32
1565+
), # input: 1x2x2x1 (NHWC)
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torch.tensor(
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[[[[1.0], [1.0]], [[1.0], [1.0]]]], dtype=torch.float32
1568+
), # weight: 1x2x2x1 (NHWC)
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torch.tensor([0.0], dtype=torch.float32), # bias
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(1, 1), # stride
1571+
(0, 0), # padding
1572+
(1, 1), # dilation
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1, # groups
1574+
(0, 0), # output_padding
1575+
True, # channel_last=True
1576+
torch.tensor(
1577+
[
1578+
[
1579+
[[1.0], [3.0], [2.0]],
1580+
[[4.0], [10.0], [6.0]],
1581+
[[3.0], [7.0], [4.0]],
1582+
]
1583+
],
1584+
dtype=torch.float32,
1585+
),
1586+
),
1587+
# 2D transposed convolution with non-zero bias
1588+
(
1589+
"with_bias",
1590+
torch.tensor(
1591+
[[[[1.0, 2.0], [3.0, 4.0]]]], dtype=torch.float32
1592+
), # input: 1x1x2x2
1593+
torch.tensor(
1594+
[[[[1.0, 0.0], [0.0, 1.0]]]], dtype=torch.float32
1595+
), # weight: 1x1x2x2
1596+
torch.tensor([5.0], dtype=torch.float32), # bias=5.0
1597+
(1, 1), # stride
1598+
(0, 0), # padding
1599+
(1, 1), # dilation
1600+
1, # groups
1601+
(0, 0), # output_padding
1602+
False, # channel_last
1603+
torch.tensor(
1604+
[[[[6.0, 7.0, 5.0], [8.0, 10.0, 7.0], [5.0, 8.0, 9.0]]]],
1605+
dtype=torch.float32,
1606+
),
1607+
),
1608+
# 1D transposed convolution (3D tensor, NLC format)
1609+
(
1610+
"conv1d_nlc",
1611+
torch.tensor(
1612+
[[[1.0], [2.0], [3.0]]], dtype=torch.float32
1613+
), # input: 1x3x1 (NLC)
1614+
torch.tensor(
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[[[1.0], [0.5]]], dtype=torch.float32
1616+
), # weight: 1x2x1 (NLC)
1617+
torch.tensor([0.0], dtype=torch.float32), # bias
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(2, 0), # stride
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(0, 0), # padding
1620+
(1, 1), # dilation
1621+
1, # groups
1622+
(0, 0), # output_padding
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True, # channel_last=True
1624+
torch.tensor(
1625+
[[[1.0], [0.5], [2.0], [1.0], [3.0], [1.5]]], dtype=torch.float32
1626+
),
1627+
),
1628+
]
1629+
)
1630+
def test_transposed_convolution(
1631+
self,
1632+
name: str,
1633+
input_tensor: torch.Tensor,
1634+
weight: torch.Tensor,
1635+
bias: torch.Tensor,
1636+
stride: tuple[int, int],
1637+
padding: tuple[int, int],
1638+
dilation: tuple[int, int],
1639+
groups: int,
1640+
output_padding: tuple[int, int],
1641+
channel_last: bool,
1642+
expected_output: torch.Tensor,
1643+
) -> None:
1644+
output = torch.ops.cadence.transposed_convolution(
1645+
input_tensor,
1646+
weight,
1647+
bias,
1648+
stride,
1649+
padding,
1650+
dilation,
1651+
output_padding,
1652+
groups,
1653+
channel_last,
1654+
)
1655+
1656+
# Verify output properties
1657+
self.assertEqual(
1658+
output.dtype,
1659+
input_tensor.dtype,
1660+
f"Output dtype should match input dtype in {name}",
1661+
)
1662+
self.assertEqual(
1663+
output.shape,
1664+
expected_output.shape,
1665+
f"Output shape should match expected shape in {name}",
1666+
)
1667+
1668+
# Verify output matches expected values
1669+
self.assertTrue(
1670+
torch.equal(output, expected_output),
1671+
f"Output values don't match expected in {name}. Got {output}, expected {expected_output}",
1672+
)
1673+
15371674
@expand(
15381675
[
15391676
# Basic non-quantized average pooling

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