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[Quantized DeConv Support] Enable Quantized Transposed Convs with groups==1 #11730

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Jun 18, 2025
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[Quantized DeConv Support] Enable Quantized Transposed Convs with groups==1 #11730

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[Quantized DeConv Support] Enable Quantized Transposed Convs with gro…
mcr229 Jun 16, 2025
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Update on "[Quantized DeConv Support] Enable Quantized Transposed Con…
mcr229 Jun 17, 2025
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28 changes: 26 additions & 2 deletions backends/xnnpack/quantizer/xnnpack_quantizer_utils.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -238,7 +238,19 @@ def _do_annotate_conv(

weight = conv_node.args[1]
assert isinstance(weight, Node)
input_qspec_map[weight] = get_weight_qspec(quantization_config)
weight_qspec = get_weight_qspec(quantization_config)
if is_conv_transpose:
# transposed convs per output channel quantization
weight_qspec = QuantizationSpec(
dtype=weight_qspec.dtype,
quant_min=weight_qspec.quant_min,
quant_max=weight_qspec.quant_max,
qscheme=weight_qspec.qscheme,
ch_axis=1,
is_dynamic=False,
observer_or_fake_quant_ctr=weight_qspec.observer_or_fake_quant_ctr,
)
input_qspec_map[weight] = weight_qspec

# Only annotate dynamically quantized conv if it's 2D and not depthwise
if (
Expand Down Expand Up @@ -311,7 +323,19 @@ def _do_annotate_conv_relu(

weight = conv_node.args[1]
assert isinstance(weight, Node)
input_qspec_map[weight] = get_weight_qspec(quantization_config)
weight_qspec = get_weight_qspec(quantization_config)
if is_conv_transpose:
# transposed convs per output channel quantization
weight_qspec = QuantizationSpec(
dtype=weight_qspec.dtype,
quant_min=weight_qspec.quant_min,
quant_max=weight_qspec.quant_max,
qscheme=weight_qspec.qscheme,
ch_axis=1,
is_dynamic=False,
observer_or_fake_quant_ctr=weight_qspec.observer_or_fake_quant_ctr,
)
input_qspec_map[weight] = weight_qspec

# adding weight node to the partition as well
partition = [relu_node, conv_node, conv_node.args[1]]
Expand Down
130 changes: 32 additions & 98 deletions backends/xnnpack/test/ops/test_conv2d.py
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Original file line number Diff line number Diff line change
Expand Up @@ -221,7 +221,6 @@ def _test(
conv_count=1,
dtype: torch.dtype = torch.float,
check_quantized=True,
delegated=True,
):
# pyre-fixme[29]: `Union[torch._tensor.Tensor,
# torch.nn.modules.module.Module]` is not a function.
Expand All @@ -240,29 +239,20 @@ def _test(

(tester.export().check_count({op: conv_count}).to_edge_transform_and_lower())

if delegated:
(
tester.check_not(
["executorch_exir_dialects_edge__ops_aten_convolution_default"]
)
.check_not(
[
"executorch_exir_dialects_edge__ops__native_batch_norm_legit_no_training_default"
]
)
.check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
.to_executorch()
.serialize()
.run_method_and_compare_outputs(qtol=1)
(
tester.check_not(
["executorch_exir_dialects_edge__ops_aten_convolution_default"]
)
else:
# need quantize ops when ops are not delegated to xnnpack
if has_quantized_ops:
(
tester.to_executorch()
.serialize()
.run_method_and_compare_outputs(qtol=1)
)
.check_not(
[
"executorch_exir_dialects_edge__ops__native_batch_norm_legit_no_training_default"
]
)
.check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
.to_executorch()
.serialize()
.run_method_and_compare_outputs(qtol=1)
)

def _test_dq(
self,
Expand Down Expand Up @@ -325,7 +315,6 @@ def test_qs8_conv2d_per_channel(self) -> None:
self._test(
Conv2d(transpose=transpose),
quant_config=get_symmetric_quantization_config(is_per_channel=True),
delegated=not transpose, # XNNPACK does not support per input channel quantization for transpose convolutions with groups > 1
)

def test_fp32_conv2d_seq(self) -> None:
Expand Down Expand Up @@ -485,7 +474,6 @@ def get_inputs(self):
self._test(
ConvReLU(transpose=transpose),
quant_config=get_symmetric_quantization_config(is_per_channel=True),
delegated=not transpose, # XNNPACK does not support per input channel quantization for transpose convolutions with groups > 1
)

def test_qs8_conv2d_dw_relu(self):
Expand Down Expand Up @@ -537,8 +525,6 @@ def get_inputs(self):
quant_config=get_symmetric_quantization_config(
is_per_channel=per_channel_quant
),
# XNNPACK does not support per input channel quantization for transpose convolutions with groups > 1
delegated=not (transpose and per_channel_quant),
)

def test_qs8_conv2d_relu_seq(self):
Expand Down Expand Up @@ -593,7 +579,7 @@ def get_inputs(self):
conv_count=2,
)

def test_qs8_conv_transpose_2d_quantize_per_channel(self):
def test_qs8_conv_transpose_2d_quantize_per_channel_multi_axis(self):
class PerChannelConvTranspose2d(torch.nn.Module):
def __init__(self, input_channels, output_channels, groups, axis):
super().__init__()
Expand Down Expand Up @@ -662,76 +648,24 @@ def get_inputs(self):
)

for groups in (1, 2):
for axis in (0, 1):
self._test(
PerChannelConvTranspose2d(3 * groups, 5 * groups, groups, axis),
quant_config=None,
conv_count=1,
delegated=axis == 1
and groups
== 1, # xnnpack only support output channel axis quantization with groups == 1
)

def test_qs8_conv_transpose_2d_dqd_f32_weights(self):
class TransposeConv2dDQDf32weights(torch.nn.Module):
def __init__(self, input_channels, output_channels, groups, axis):
super().__init__()
self.input_channels = input_channels
self.output_channels = output_channels
self.axis = axis
self.groups = groups
self.transpose = True
self.weights = torch.nn.Parameter(
torch.randn((input_channels, output_channels // groups, 4, 4)),
requires_grad=False,
)

axis_size = self.weights.shape[axis]
self.scale = torch.nn.Parameter(torch.ones(axis_size) * 0.12345)
self.zero_point = torch.nn.Parameter(
torch.zeros((axis_size,), dtype=torch.int64), requires_grad=False
)

def forward(self, x):
dequantize_input = (
exir_ops.edge.quantized_decomposed.dequantize_per_tensor.default(
x, 0.12345, 0, -127, 127, torch.int8
for ch_axis in (1, 2):
if ch_axis == 1 and groups == 1:
self._test(
PerChannelConvTranspose2d(
3 * groups, 5 * groups, groups, ch_axis
), # ch_axis=0
quant_config=None,
conv_count=1,
)
)
x = torch.nn.functional.conv_transpose2d(
dequantize_input, self.weights, groups=self.groups
)

return exir_ops.edge.quantized_decomposed.dequantize_per_tensor.default(
exir_ops.edge.quantized_decomposed.quantize_per_tensor.default(
x,
0.12345,
0,
-127,
127,
torch.int8,
),
0.12345,
0,
-127,
127,
torch.int8,
)

def get_inputs(self):
return (
torch.randint(
low=-127, high=127, size=(3, self.input_channels, 4, 4)
).type(dtype=torch.int8),
)

for groups in (1, 2):
for axis in (0, 1):
self._test(
TransposeConv2dDQDf32weights(3 * groups, 5 * groups, groups, axis),
quant_config=None,
conv_count=1,
)
else:
with self.assertRaises(RuntimeError):
self._test(
PerChannelConvTranspose2d(
3 * groups, 5 * groups, groups, ch_axis
), # ch_axis=0
quant_config=None,
conv_count=1,
)

def test_padded_output_tconv(self):
class TConv2d(torch.nn.Module):
Expand Down Expand Up @@ -761,7 +695,7 @@ def forward(self, x):

(tester.export().check_count({op: conv_count}).to_edge_transform_and_lower())

# tconv should not be offloaded to XNNPack, since output padding is not
# tconv should not be offloaded to XNNPack, since output padding is not supported
(
tester.check(
["executorch_exir_dialects_edge__ops_aten_convolution_default"]
Expand Down
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