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[ONNX] Support quantized::linear_relu #109755

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[ONNX] Support quantized::linear_relu #109755

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0ca3414
torch->onnx export support: quantized::linear_relu
gustavla Sep 21, 2023
e0a6b63
Make bias float32.
gustavla Sep 21, 2023
990711c
Fix bias again, and correct conv tests.
gustavla Sep 21, 2023
641fe7c
One more input shape fix.
gustavla Sep 21, 2023
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38 changes: 35 additions & 3 deletions test/onnx/test_pytorch_onnx_onnxruntime.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13027,7 +13027,7 @@ class M(torch.nn.Module):
def __init__(self):
super().__init__()
self.quant = torch.ao.quantization.QuantStub()
self.conv = torch.nn.Conv2d(2, 4, 3, stride=2)
self.conv = torch.nn.Conv2d(4, 2, 3, stride=2)
self.dequant = torch.ao.quantization.DeQuantStub()

def forward(self, x):
Expand Down Expand Up @@ -13058,7 +13058,7 @@ class M(torch.nn.Module):
def __init__(self):
super().__init__()
self.quant = torch.ao.quantization.QuantStub()
self.conv = torch.nn.Conv2d(2, 4, 3, stride=2)
self.conv = torch.nn.Conv2d(4, 2, 3, stride=2)
self.relu = torch.nn.ReLU()
self.dequant = torch.ao.quantization.DeQuantStub()

Expand Down Expand Up @@ -13091,7 +13091,7 @@ class M(torch.nn.Module):
def __init__(self):
super().__init__()
self.quant = torch.ao.quantization.QuantStub()
self.conv = torch.nn.Conv2d(2, 4, 3, stride=2)
self.conv = torch.nn.Conv2d(4, 2, 3, stride=2)
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@gustavla gustavla Sep 21, 2023 •
edited

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See my comment about these typos. If you expand the context below, the weights are replaced as follows:

        model.conv.weight = torch.nn.Parameter(
            _construct_tensor_for_quantization_test((2, 4, 3, 3), max_val=2)
        )

But this is not consistent with Conv2d(2, 4, 3), it is consistent with Conv2d(4, 2, 3), which is what I'm changing it to.

self.relu = torch.nn.ReLU()
self.dequant = torch.ao.quantization.DeQuantStub()

Expand Down Expand Up @@ -13119,6 +13119,38 @@ def forward(self, x):
)
self.run_test(model, input)

@skipIfUnsupportedMinOpsetVersion(13)
def test_qat_linear_relu_fused(self):
class M(torch.nn.Module):
def __init__(self):
super().__init__()
self.quant = torch.ao.quantization.QuantStub()
self.linear = torch.nn.Linear(4, 2)
self.relu = torch.nn.ReLU()
self.dequant = torch.ao.quantization.DeQuantStub()

def forward(self, x):
x = self.quant(x)
x = self.linear(x)
x = self.relu(x)
x = self.dequant(x)
return x

model = M()
model.qconfig = torch.ao.quantization.get_default_qconfig("fbgemm")
model = torch.ao.quantization.fuse_modules(model.eval(), [["linear", "relu"]])
model = torch.ao.quantization.prepare_qat(model.train())
# Set fixed weight and bias to avoid flaky test.
model.linear.weight = torch.nn.Parameter(
_construct_tensor_for_quantization_test((2, 4), max_val=2)
)
model.linear.bias = torch.nn.Parameter(torch.tensor([0.0, 1.0]))
model = torch.ao.quantization.convert(model)

# Set fixed input to avoid flaky test.
input = _construct_tensor_for_quantization_test((3, 2), offset=-384, max_val=12)
self.run_test(model, input)

@skipIfUnsupportedMinOpsetVersion(10)
def test_qat_maxpool2d(self):
class M(torch.nn.Module):
Expand Down
11 changes: 11 additions & 0 deletions torch/csrc/jit/passes/onnx/unpack_quantized_weights.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -679,6 +679,10 @@ void UnpackQuantizedWeights(
graph(%input, %packed_weight, %w_scale, %w_zero_point):
%r = quantized::linear(%input, %packed_weight, %w_scale, %w_zero_point)
return (%r) )";
std::string qlinear_relu = R"(
graph(%input, %packed_weight, %w_scale, %w_zero_point):
%r = quantized::linear_relu(%input, %packed_weight, %w_scale, %w_zero_point)
return (%r) )";
std::string qconv1d = R"(
graph(%input, %packed_params, %scale, %zero_point):
%r = quantized::conv1d(%input, %packed_params, %scale, %zero_point)
Expand Down Expand Up @@ -722,6 +726,13 @@ void UnpackQuantizedWeights(
"quantized::linear_unpack",
QuantizedParamsType::LINEAR,
caffe2);
unpackQuantizedWeightsHelper(
graph,
paramsDict,
qlinear_relu,
"quantized::linear_unpack",
QuantizedParamsType::LINEAR,
caffe2);
unpackQuantizedWeightsHelper(
graph,
paramsDict,
Expand Down
17 changes: 17 additions & 0 deletions torch/onnx/symbolic_opset10.py
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Original file line number Diff line number Diff line change
Expand Up @@ -58,6 +58,7 @@
"quantized_layer_norm",
"quantized_leaky_relu",
"quantized_linear",
"quantized_linear_relu",
"quantized_mul",
"quantized_sigmoid",
"slice",
Expand Down Expand Up @@ -737,6 +738,22 @@ def quantized_linear(
return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)


@_onnx_symbolic("quantized::linear_relu")
@_beartype.beartype
def quantized_linear_relu(
g: jit_utils.GraphContext, q_input, q_weight, bias, op_scale, op_zero_point
):
input, input_scale, _, _ = symbolic_helper.dequantize_helper(g, q_input)
weight, weight_scale, _, _ = symbolic_helper.dequantize_helper(g, q_weight)
q_bias = symbolic_helper.requantize_bias_helper(g, bias, input_scale, weight_scale)
bias, _, _, _ = symbolic_helper.dequantize_helper(g, q_bias)

output = opset9.linear(g, input, weight, bias)
output = opset9.relu(g, output)

return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)


@_onnx_symbolic("quantized::add")
@_beartype.beartype
def quantized_add(g: jit_utils.GraphContext, x, y, op_scale, op_zero_point):
Expand Down
18 changes: 18 additions & 0 deletions torch/onnx/symbolic_opset13.py
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Original file line number Diff line number Diff line change
Expand Up @@ -888,6 +888,24 @@ def quantized_linear(
return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)


@_onnx_symbolic("quantized::linear_relu")
@_beartype.beartype
def quantized_linear_relu(
g: jit_utils.GraphContext, q_input, q_weight, bias, op_scale, op_zero_point
):
input, input_scale, _, _ = symbolic_helper.dequantize_helper(g, q_input)
weight, weight_scale, _, axis = symbolic_helper.dequantize_helper(g, q_weight)
q_bias = symbolic_helper.requantize_bias_helper(
g, bias, input_scale, weight_scale, axis
)
bias, _, _, _ = symbolic_helper.dequantize_helper(g, q_bias)

output = opset9.linear(g, input, weight, bias)
output = opset9.relu(g, output)

return symbolic_helper.quantize_helper(g, output, op_scale, op_zero_point)


@_onnx_symbolic("quantized::conv1d_relu")
@_beartype.beartype
def quantized_conv1d_relu(
Expand Down