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Update channels last python reference to not use memory_format=channels_last #14035

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Merged
facebook-github-bot merged 1 commit into from
Sep 10, 2025
Merged

Update channels last python reference to not use memory_format=channels_last #14035

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22 changes: 19 additions & 3 deletions backends/cadence/aot/ref_implementations.py
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Original file line number Diff line number Diff line change
Expand Up @@ -458,10 +458,21 @@ def quantized_conv_nhwc_per_tensor(
- out_shift (int): Unused
"""

if not input_tensor.is_contiguous(memory_format=torch.channels_last):
raise ValueError("Input tensor must be in NHWC format")
# Convert to NCHW format to reuse the existing implementation
conv_is_1d = False
if len(input_tensor.shape) == 3:
conv_is_1d = True
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()

return quantized_conv_per_tensor(
nchw_out = quantized_conv_per_tensor(
input_tensor,
weight,
bias,
Expand All @@ -478,6 +489,11 @@ def quantized_conv_nhwc_per_tensor(
out_shift,
)

if conv_is_1d:
return nchw_out.movedim(1, -1).contiguous()
else:
return nchw_out.movedim(-3, -1).contiguous()


def quantized_conv_variant(
layout: str,
Expand Down
25 changes: 17 additions & 8 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 @@ -449,7 +449,7 @@ def test_quantized_layer_norm_per_tensor(
), # expected_output: [1+2, 2+3, 3+4] / 0.5 = [6, 10, 14]
memory_format,
)
for memory_format in [torch.contiguous_format]
for memory_format in [torch.contiguous_format, torch.channels_last]
],
# Test case 5: Multiple output channels
*[
Expand Down Expand Up @@ -686,10 +686,13 @@ def test_quantized_conv_per_tensor(
) -> None:
assert memory_format in [torch.contiguous_format, torch.channels_last]

if len(input_tensor.shape) == 3 and memory_format == torch.channels_last:
self.fail("Channels last format is not supported for 3D input tensors")

input_tensor = input_tensor.to(memory_format=memory_format)
if memory_format == torch.channels_last:
if input_tensor.ndim == 3:
input_tensor = input_tensor.movedim(1, -1)
weight = weight.movedim(1, -1)
else:
input_tensor = input_tensor.movedim(-3, -1)
weight = weight.movedim(-3, -1)

convs = [
(
Expand All @@ -701,7 +704,7 @@ def test_quantized_conv_per_tensor(

optimized_convs = []
if input_tensor.dtype == torch.int8 and weight.dtype == torch.int8:
if input_tensor.is_contiguous(memory_format=torch.contiguous_format):
if memory_format == torch.contiguous_format:
optimized_convs = [
torch.ops.cadence.quantized_conv_nchw_asym8sxsym8s_asym8s.per_tensor,
torch.ops.cadence.quantized_conv_nchw_dilated_asym8sxsym8s_asym8s.per_tensor,
Expand All @@ -715,7 +718,7 @@ def test_quantized_conv_per_tensor(
torch.ops.cadence.quantized_conv_nhwc_depthwise_asym8sxsym8s_asym8s.per_tensor,
]
elif input_tensor.dtype == torch.uint8 and weight.dtype == torch.uint8:
if input_tensor.is_contiguous(memory_format=torch.contiguous_format):
if memory_format == torch.contiguous_format:
optimized_convs = [
torch.ops.cadence.quantized_conv_nchw_asym8uxsym8u_asym8u.per_tensor,
torch.ops.cadence.quantized_conv_nchw_dilated_asym8uxsym8u_asym8u.per_tensor,
Expand Down Expand Up @@ -746,7 +749,13 @@ def test_quantized_conv_per_tensor(
output_zero_point,
out_multiplier,
out_shift,
).to(memory_format=torch.contiguous_format)
)

if memory_format == torch.channels_last:
if input_tensor.ndim == 3:
output = output.movedim(-1, 1)
else:
output = output.movedim(-1, -3)

# Verify output properties
self.assertEqual(output.dtype, dtype, f"Output dtype should be {dtype}")
Expand Down
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