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hariharans29Copilotgithub-actions[bot]edgchen1
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[CPU/CUDA] Add bias input validations for ConvTranspose (#27209)
### Description Takeaway from #26144 Resolve #26144 ### Motivation and Context Improve kernel input validation for ConvTranspose --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
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‎onnxruntime/core/providers/cpu/nn/conv_transpose_attributes.h‎

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@@ -99,6 +99,18 @@ struct ConvTransposeAttributes : public ConvAttributes {
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" group: ", group);
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}
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// Bias shape validation (It should be a 1D tensor with size M)
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// See https://github.com/microsoft/onnxruntime/issues/26144
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if (B != nullptr) {
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if (B->Shape().NumDimensions() != 1 || B->Shape()[0] != num_output_channels) {
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return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
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"Bias shape is not compatible with number of output channels."
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" It should be a 1-D tensor with size num_output_channels(M).",
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" Bias: ", B->Shape(),
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" num_output_channels: ", num_output_channels);
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}
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}
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TensorShapeVector kernel_shape;
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ORT_RETURN_IF_ERROR(ComputeKernelShape(F_Shape, kernel_shape, is_nhwc));
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‎onnxruntime/core/providers/cuda/nn/conv_transpose.cc‎

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@@ -311,6 +311,18 @@ Status ConvTranspose<T, Layout>::UpdateState(OpKernelContext* context, bool dyna
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" group: ", conv_transpose_attrs_.group);
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}
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// Bias shape validation (It should be a 1D tensor with size M)
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// See https://github.com/microsoft/onnxruntime/issues/26144
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if (B != nullptr) {
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if (B->Shape().NumDimensions() != 1 || B->Shape()[0] != num_output_channels) {
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return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
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"Bias shape is not compatible with number of output channels."
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" It should be a 1-D tensor with size num_output_channels(M).",
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" Bias: ", B->Shape(),
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" num_output_channels: ", num_output_channels);
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}
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}
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TensorShapeVector kernel_shape;
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ORT_RETURN_IF_ERROR(conv_transpose_attrs_.ComputeKernelShape(w_shape, kernel_shape, w_in_nhwc));
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‎onnxruntime/test/providers/cpu/nn/conv_transpose_op_test.cc‎

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@@ -529,6 +529,74 @@ TEST(ConvTransposeTest, ConvTranspose_InvalidKernelShape) {
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kDmlExecutionProvider}); // TODO: Unskip when fixed #41968513
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}
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TEST(ConvTransposeTest, ConvTranspose_InvalidBiasShape_1) {
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ConvTransposeOpAttributes attrs = {
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vector<int64_t>{1, 5}, // kernel_shape
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{}, // output_padding
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vector<int64_t>{2, 1, 1, 14}, // output_shape
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vector<int64_t>{0, 0, 0, 0}, // pads
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vector<int64_t>{1, 1}, // strides
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vector<int64_t>{1, 1}, // dilations
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1, // group
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"NOTSET" // auto_pad
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};
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vector<float> X = {0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f,
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10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, 19.0f};
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vector<int64_t> X_shape = {2, 1, 1, 10};
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vector<float> W = {1.0f, 2.0f, 3.0f, 2.0f, 1.0f};
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vector<int64_t> W_shape = {1, 1, 1, 5};
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vector<float> B = {1.0f, 2.0f}; // invalid bias shape, should be {1}
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vector<int64_t> B_shape = {2}; // invalid bias shape, should be {1}
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vector<int64_t> Y_shape = {2, 1, 1, 14};
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vector<float> expected_vals = {1.0f, 2.0f, 5.0f, 11.0f, 19.0f, 28.0f, 37.0f, 46.0f, 55.0f, 64.0f, 63.0f, 51.0f, 27.0f, 10.0f,
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11.0f, 32.0f, 65.0f, 91.0f, 109.0f, 118.0f, 127.0f, 136.0f, 145.0f, 154.0f, 143.0f, 111.0f, 57.0f, 20.0f};
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TestConvTransposeOp(attrs, {X, W, B}, {X_shape, W_shape, B_shape}, expected_vals, Y_shape,
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OpTester::ExpectResult::kExpectFailure,
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// Just ensure that it starts with the expected string.
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"Bias shape is not compatible with number of output channels. "
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"It should be a 1-D tensor with size num_output_channels(M).",
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// The EP exclusions are along the same lines as ConvTranspose_InvalidKernelShape which
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// also tests for invalid shapes. It also includes XnnPack which seems to have its own
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// way of dealing with incorrectly shaped bias.
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{kTensorrtExecutionProvider, kQnnExecutionProvider,
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kDmlExecutionProvider, kXnnpackExecutionProvider,
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kWebGpuExecutionProvider}); // Remove when https://github.com/microsoft/onnxruntime/issues/27210 is fixed
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}
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TEST(ConvTransposeTest, ConvTranspose_InvalidBiasShape_2) {
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ConvTransposeOpAttributes attrs = {
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vector<int64_t>{1, 5}, // kernel_shape
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{}, // output_padding
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vector<int64_t>{2, 1, 1, 14}, // output_shape
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vector<int64_t>{0, 0, 0, 0}, // pads
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vector<int64_t>{1, 1}, // strides
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vector<int64_t>{1, 1}, // dilations
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1, // group
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"NOTSET" // auto_pad
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};
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vector<float> X = {0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f,
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10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, 19.0f};
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vector<int64_t> X_shape = {2, 1, 1, 10};
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vector<float> W = {1.0f, 2.0f, 3.0f, 2.0f, 1.0f};
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vector<int64_t> W_shape = {1, 1, 1, 5};
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vector<float> B = {1.0f, 2.0f};
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vector<int64_t> B_shape = {1, 2}; // invalid bias rank (it should be 1-D)
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vector<int64_t> Y_shape = {2, 1, 1, 14};
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vector<float> expected_vals = {1.0f, 2.0f, 5.0f, 11.0f, 19.0f, 28.0f, 37.0f, 46.0f, 55.0f, 64.0f, 63.0f, 51.0f, 27.0f, 10.0f,
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11.0f, 32.0f, 65.0f, 91.0f, 109.0f, 118.0f, 127.0f, 136.0f, 145.0f, 154.0f, 143.0f, 111.0f, 57.0f, 20.0f};
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TestConvTransposeOp(attrs, {X, W, B}, {X_shape, W_shape, B_shape}, expected_vals, Y_shape,
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OpTester::ExpectResult::kExpectFailure,
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// Just ensure that it starts with the expected string.
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"Bias shape is not compatible with number of output channels. "
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"It should be a 1-D tensor with size num_output_channels(M).",
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// The EP exclusions are along the same lines as ConvTranspose_InvalidKernelShape which
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// also tests for invalid shapes. It also includes XnnPack which seems to have its own
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// way of dealing with incorrectly shaped bias.
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{kTensorrtExecutionProvider, kQnnExecutionProvider,
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kDmlExecutionProvider, kXnnpackExecutionProvider,
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kWebGpuExecutionProvider}); // Remove when https://github.com/microsoft/onnxruntime/issues/27210 is fixed
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}
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TEST(ConvTransposeTest, ConvTranspose_onnx) {
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ConvTransposeOpAttributes attrs = {
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vector<int64_t>{3, 3}, // kernel_shape

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