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[XNNPACK Quantizer] Select between TConvs and Convs #11732

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Jun 18, 2025
Merged

[XNNPACK Quantizer] Select between TConvs and Convs #11732

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aa92ce6
[XNNPACK Quantizer] Select between TConvs and Convs
mcr229 Jun 16, 2025
0aa7950
Update on "[XNNPACK Quantizer] Select between TConvs and Convs"
mcr229 Jun 17, 2025
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17 changes: 13 additions & 4 deletions backends/xnnpack/quantizer/xnnpack_quantizer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -251,6 +251,15 @@ class QuantPattern:
torch.ops.aten.convolution.default,
}

CONV_TRANSPOSE_TARGETS = {
torch.ops.aten.conv_transpose1d,
torch.ops.aten.conv_transpose1d.default,
torch.ops.aten.conv_transpose2d,
torch.ops.aten.conv_transpose2d.input,
torch.ops.aten.conv_transpose3d,
torch.ops.aten.conv_transpose3d.input,
}

LINEAR_TARGETS = {
torch.ops.aten.linear.default,
}
Expand All @@ -269,14 +278,14 @@ class XNNPACKQuantizer(Quantizer):
SUPPORTED_PATTERNS = [
QuantPattern("conv_bn_relu", False, True, CONV_TARGETS),
QuantPattern("conv_bn", False, True, CONV_TARGETS),
QuantPattern("conv_transpose_bn_relu", False, True, CONV_TARGETS),
QuantPattern("conv_transpose_bn", False, True, CONV_TARGETS),
QuantPattern("conv_transpose_bn_relu", False, True, CONV_TRANSPOSE_TARGETS),
QuantPattern("conv_transpose_bn", False, True, CONV_TRANSPOSE_TARGETS),
QuantPattern("linear_relu", False, False, LINEAR_TARGETS),
QuantPattern("linear", True, False, LINEAR_TARGETS),
QuantPattern("conv", True, False, CONV_TARGETS),
QuantPattern("conv_transpose", True, False, CONV_TARGETS),
QuantPattern("conv_transpose", True, False, CONV_TRANSPOSE_TARGETS),
QuantPattern("conv_relu", False, False, CONV_TARGETS),
QuantPattern("conv_transpose_relu", False, False, CONV_TARGETS),
QuantPattern("conv_transpose_relu", False, False, CONV_TRANSPOSE_TARGETS),
QuantPattern("adaptive_avg_pool2d", False, False, ADAPTIVE_AVG_POOL2D_TARGETS),
QuantPattern("add_relu", False, False, ADD_TARGETS),
QuantPattern("add", False, False, ADD_TARGETS),
Expand Down
110 changes: 110 additions & 0 deletions backends/xnnpack/test/quantizer/test_xnnpack_quantizer.py
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Expand Up @@ -120,6 +120,116 @@ def test_conv1d_with_conv2d(self):
node_list,
)

def test_q_tconv_and_conv2d(self):
class TConv2dConv2d(torch.nn.Module):
def __init__(self):
super().__init__()
self.first = torch.nn.ConvTranspose2d(
in_channels=1,
out_channels=3,
kernel_size=(3, 3),
padding=1,
bias=False,
)
self.second = torch.nn.Conv2d(
in_channels=3,
out_channels=2,
kernel_size=(3, 3),
padding=1,
bias=False,
)

def forward(self, x):
y = self.first(x)
return self.second(y)

def example_inputs(self):
return (torch.randn(1, 1, 3, 3),)

quantizer = XNNPACKQuantizer()
quantization_config = get_symmetric_quantization_config(is_per_channel=True)
quantizer.set_operator_type(
torch.ops.aten.conv_transpose2d.input, quantization_config
)
node_occurrence = {
# input and output are using quantize_per_tensor and weight is using quantize_per_channel
torch.ops.quantized_decomposed.quantize_per_tensor.default: 2,
torch.ops.quantized_decomposed.dequantize_per_tensor.default: 2,
torch.ops.quantized_decomposed.quantize_per_channel.default: 0,
torch.ops.quantized_decomposed.dequantize_per_channel.default: 1,
}
node_list = [
torch.ops.quantized_decomposed.quantize_per_tensor.default,
torch.ops.quantized_decomposed.dequantize_per_tensor.default,
torch.ops.aten.conv_transpose2d.input,
torch.ops.quantized_decomposed.quantize_per_tensor.default,
torch.ops.quantized_decomposed.dequantize_per_tensor.default,
torch.ops.aten.conv2d.default,
]
m = TConv2dConv2d()
self._test_quantizer(
m,
m.example_inputs(),
quantizer,
node_occurrence,
node_list,
is_debug_mode=True,
)

def test_q_conv2_and_tconv2d(self):
class TConv2dConv2d(torch.nn.Module):
def __init__(self):
super().__init__()
self.first = torch.nn.ConvTranspose2d(
in_channels=1,
out_channels=3,
kernel_size=(3, 3),
padding=1,
bias=False,
)
self.second = torch.nn.Conv2d(
in_channels=3,
out_channels=2,
kernel_size=(3, 3),
padding=1,
bias=False,
)

def forward(self, x):
y = self.first(x)
return self.second(y)

def example_inputs(self):
return (torch.randn(1, 1, 3, 3),)

quantizer = XNNPACKQuantizer()
quantization_config = get_symmetric_quantization_config(is_per_channel=True)
quantizer.set_operator_type(torch.ops.aten.conv2d.default, quantization_config)
node_occurrence = {
# input and output are using quantize_per_tensor and weight is using quantize_per_channel
torch.ops.quantized_decomposed.quantize_per_tensor.default: 2,
torch.ops.quantized_decomposed.dequantize_per_tensor.default: 2,
torch.ops.quantized_decomposed.quantize_per_channel.default: 0,
torch.ops.quantized_decomposed.dequantize_per_channel.default: 1,
}
node_list = [
torch.ops.aten.conv_transpose2d.input,
torch.ops.quantized_decomposed.quantize_per_tensor.default,
torch.ops.quantized_decomposed.dequantize_per_tensor.default,
torch.ops.aten.conv2d.default,
torch.ops.quantized_decomposed.quantize_per_tensor.default,
torch.ops.quantized_decomposed.dequantize_per_tensor.default,
]
m = TConv2dConv2d()
self._test_quantizer(
m,
m.example_inputs(),
quantizer,
node_occurrence,
node_list,
is_debug_mode=True,
)

def test_linear(self):
quantizer = XNNPACKQuantizer()
quantization_config = get_symmetric_quantization_config(is_per_channel=True)
Expand Down
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