mirror of https://github.com/llvm/torch-mlir
Adds onnx ConvTranspose support for autopadding. (#3797)
Adds onnx ConvTranspose support for autopadding (https://github.com/nod-ai/SHARK-ModelDev/issues/839). - Adds support for attribute auto_pad="SAME_UPPER" or "SAME_LOWER" which will automatically calculate padding of input based on output shape. - Adds support, during auto-padding, for output_shape=[H,W] which overrides the default output shape of input_shape[i]*stride[i] (for spatial dimensions only). - Adds lit test for auto-padding. - Tests are added by https://github.com/nod-ai/SHARK-TestSuite/pull/370 NOTE: ConvTranspose still doesn't support asymmetric padding, therefore multiple original onnx tests still won't pass.pull/3806/head
David Tanner
GitHub
parent
9c7067649b
commit
02327af998
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@ -1690,20 +1690,6 @@ void mlir::torch::onnx_c::populateDefaultDomainAtoF(
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std::string autoPad;
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std::string autoPad;
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if (binder.customOpNameStringAttr(autoPad, "auto_pad", "NOTSET"))
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if (binder.customOpNameStringAttr(autoPad, "auto_pad", "NOTSET"))
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return failure();
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return failure();
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if (autoPad != "NOTSET") {
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// TODO: Add support for `auto_pad` != "NOTSET"
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return rewriter.notifyMatchFailure(
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binder.op, "unsupported conversion: auto_pad != NOTSET");
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}
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SmallVector<int64_t> outputShape;
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if (binder.s64IntegerArrayAttr(outputShape, "output_shape", {}))
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return failure();
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if (outputShape.size()) {
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// TODO: Add support for non-None output_shape value.
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return rewriter.notifyMatchFailure(
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binder.op,
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"unsupported conversion: output_shape should be absent");
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}
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Torch::ValueTensorType resultType;
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Torch::ValueTensorType resultType;
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Value input, weight;
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Value input, weight;
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int64_t group;
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int64_t group;
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@ -1737,6 +1723,10 @@ void mlir::torch::onnx_c::populateDefaultDomainAtoF(
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}
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}
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}
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}
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}
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}
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} else {
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for (unsigned i = 0; i < weightShape.size() - 2; i++) {
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kernelShape.push_back(weightShape[i + 2]);
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}
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}
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}
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// Determine the rank of input tensor.
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// Determine the rank of input tensor.
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@ -1746,7 +1736,8 @@ void mlir::torch::onnx_c::populateDefaultDomainAtoF(
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"Unimplemented: unranked tensor");
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"Unimplemented: unranked tensor");
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unsigned rank = *maybeRank;
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unsigned rank = *maybeRank;
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SmallVector<int64_t> padding, strides, dilations, outputPadding;
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SmallVector<int64_t> padding, strides, dilations, outputPadding,
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outputShape;
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SmallVector<int64_t> defaultPadding, defaultStrides, defaultDilations,
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SmallVector<int64_t> defaultPadding, defaultStrides, defaultDilations,
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defaultOutputPadding;
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defaultOutputPadding;
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for (unsigned i = 0; i < rank - 2; i++) {
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for (unsigned i = 0; i < rank - 2; i++) {
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@ -1762,13 +1753,6 @@ void mlir::torch::onnx_c::populateDefaultDomainAtoF(
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// x2_begin…x1_end, x2_end,…], where xi_begin the number of pixels added
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// x2_begin…x1_end, x2_end,…], where xi_begin the number of pixels added
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// at the beginning of axis i and xi_end, the number of pixels added at
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// at the beginning of axis i and xi_end, the number of pixels added at
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// the end of axis i.
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// the end of axis i.
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if (binder.s64IntegerArrayAttr(padding, "pads", defaultPadding)) {
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return failure();
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}
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if (padding.size() != rank - 2 && padding.size() != 2 * (rank - 2)) {
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return rewriter.notifyMatchFailure(
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binder.op, "padding list size does not match the number of axes");
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}
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if (binder.s64IntegerArrayAttr(dilations, "dilations",
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if (binder.s64IntegerArrayAttr(dilations, "dilations",
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defaultDilations)) {
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defaultDilations)) {
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return failure();
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return failure();
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@ -1794,7 +1778,60 @@ void mlir::torch::onnx_c::populateDefaultDomainAtoF(
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binder.op,
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binder.op,
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"output_padding list size does not match the number of axes");
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"output_padding list size does not match the number of axes");
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}
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}
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auto inputTensorType = cast<Torch::ValueTensorType>(input.getType());
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if (!inputTensorType || !inputTensorType.hasSizes()) {
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return rewriter.notifyMatchFailure(
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binder.op, "Expected input type having sizes");
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}
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ArrayRef<int64_t> inputShape = inputTensorType.getSizes();
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if (autoPad == "VALID") {
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// Zero padding.
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padding = defaultPadding;
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} else if (autoPad == "NOTSET") {
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// Explicit padding; read pads with defaults.
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if (binder.s64IntegerArrayAttr(padding, "pads", defaultPadding))
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return failure();
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} else { // autopad == SAME_UPPER or SAME_LOWER
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// Auto-padding; output_shape defaults to input_shape * strides.
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SmallVector<int64_t> defaultOutputShape;
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for (unsigned i = 0; i < rank - 2; i++) {
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defaultOutputShape.push_back(inputShape[2 + i] * strides[i]);
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}
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if (binder.s64IntegerArrayAttr(outputShape, "output_shape",
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defaultOutputShape))
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return failure();
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SmallVector<int64_t> paddingEnd;
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for (unsigned i = 0; i < rank - 2; i++) {
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int64_t totalPadding =
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strides[i] * (inputShape[2 + i] - 1) + outputPadding[i] +
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((kernelShape[i] - 1) * dilations[i] + 1) - outputShape[i];
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if (totalPadding % 2) {
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// TODO: Add support for different padding values for the
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// beginning and ending along each spatial axis.
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return rewriter.notifyMatchFailure(
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binder.op,
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"unsupported conversion: the combination of stride, "
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"input_shape, kernel_shape, dilation, output_padding and "
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"output_shape caused auto-padding to produce asymmetric "
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"padding which isn't currently supported.");
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}
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int64_t half = totalPadding / 2;
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int64_t remainder = totalPadding - half;
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if (autoPad == "SAME_UPPER") {
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padding.push_back(half);
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paddingEnd.push_back(remainder);
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} else {
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padding.push_back(remainder);
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paddingEnd.push_back(half);
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}
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}
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padding.insert(padding.end(), paddingEnd.begin(), paddingEnd.end());
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}
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if (padding.size() != rank - 2 && padding.size() != 2 * (rank - 2)) {
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return rewriter.notifyMatchFailure(
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binder.op, "padding list size does not match the number of axes");
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}
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SmallVector<Value> cstPadding, cstStrides, cstDilations,
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SmallVector<Value> cstPadding, cstStrides, cstDilations,
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cstOutputPadding;
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cstOutputPadding;
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if (padding.size() != 2 * (rank - 2)) {
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if (padding.size() != 2 * (rank - 2)) {
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@ -1329,6 +1329,78 @@ func.func @test_convtranspose(%arg0: !torch.vtensor<[1,1,3,3],f32>, %arg1: !torc
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// -----
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// -----
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// CHECK-LABEL: @test_convtranspose_autopad_same_upper
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func.func @test_convtranspose_autopad_same_upper(%arg0: !torch.vtensor<[1,1,3,3],f32>, %arg1: !torch.vtensor<[1,2,4,4],f32>) -> !torch.vtensor<[1,2,6,6],f32> attributes {torch.onnx_meta.ir_version = 6 : si64, torch.onnx_meta.opset_version = 11 : si64, torch.onnx_meta.producer_name = "user-test", torch.onnx_meta.producer_version = ""} {
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// CHECK: %[[C1:.*]] = torch.constant.int 1
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// CHECK: %[[C1_0:.*]] = torch.constant.int 1
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// CHECK: %[[C1_1:.*]] = torch.constant.int 1
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// CHECK: %[[C1_2:.*]] = torch.constant.int 1
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// CHECK: %[[C2:.*]] = torch.constant.int 2
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// CHECK: %[[C2_3:.*]] = torch.constant.int 2
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// CHECK: %[[C0:.*]] = torch.constant.int 0
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// CHECK: %[[C0_4:.*]] = torch.constant.int 0
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// CHECK: %[[PADDING:.*]] = torch.prim.ListConstruct %[[C1]], %[[C1_0]] : (!torch.int, !torch.int) -> !torch.list<int>
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// CHECK: %[[DILATIONS:.*]] = torch.prim.ListConstruct %[[C1_1]], %[[C1_2]] : (!torch.int, !torch.int) -> !torch.list<int>
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// CHECK: %[[STRIDE:.*]] = torch.prim.ListConstruct %[[C2]], %[[C2_3]] : (!torch.int, !torch.int) -> !torch.list<int>
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// CHECK: %[[OUTPUT_PADDING:.*]] = torch.prim.ListConstruct %[[C0]], %[[C0_4]] : (!torch.int, !torch.int) -> !torch.list<int>
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// CHECK: %[[TRANSPOSED:.*]] = torch.constant.bool true
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// CHECK: %[[BIAS:.*]] = torch.constant.none
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// CHECK: %[[GROUPS:.*]] = torch.constant.int 1
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// CHECK: torch.aten.convolution %arg0, %arg1, %[[BIAS]], %[[STRIDE]], %[[PADDING]], %[[DILATIONS]], %[[TRANSPOSED]], %[[OUTPUT_PADDING]], %[[GROUPS]] : !torch.vtensor<[1,1,3,3],f32>, !torch.vtensor<[1,2,4,4],f32>, !torch.none, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[1,2,6,6],f32>
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%4 = torch.operator "onnx.ConvTranspose"(%arg0, %arg1) {torch.onnx.auto_pad="SAME_UPPER", torch.onnx.strides = [2 : si64, 2 : si64]} : (!torch.vtensor<[1,1,3,3],f32>, !torch.vtensor<[1,2,4,4],f32>) -> !torch.vtensor<[1,2,6,6],f32>
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return %4 : !torch.vtensor<[1,2,6,6],f32>
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}
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// -----
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// CHECK-LABEL: @test_convtranspose_autopad_same_lower
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func.func @test_convtranspose_autopad_same_lower(%arg0: !torch.vtensor<[1,1,3,3],f32>, %arg1: !torch.vtensor<[1,2,4,4],f32>) -> !torch.vtensor<[1,2,6,6],f32> attributes {torch.onnx_meta.ir_version = 6 : si64, torch.onnx_meta.opset_version = 11 : si64, torch.onnx_meta.producer_name = "user-test", torch.onnx_meta.producer_version = ""} {
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// CHECK: %[[C1:.*]] = torch.constant.int 1
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// CHECK: %[[C1_0:.*]] = torch.constant.int 1
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// CHECK: %[[C1_1:.*]] = torch.constant.int 1
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// CHECK: %[[C1_2:.*]] = torch.constant.int 1
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// CHECK: %[[C2:.*]] = torch.constant.int 2
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// CHECK: %[[C2_3:.*]] = torch.constant.int 2
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// CHECK: %[[C0:.*]] = torch.constant.int 0
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// CHECK: %[[C0_4:.*]] = torch.constant.int 0
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// CHECK: %[[PADDING:.*]] = torch.prim.ListConstruct %[[C1]], %[[C1_0]] : (!torch.int, !torch.int) -> !torch.list<int>
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// CHECK: %[[DILATIONS:.*]] = torch.prim.ListConstruct %[[C1_1]], %[[C1_2]] : (!torch.int, !torch.int) -> !torch.list<int>
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// CHECK: %[[STRIDE:.*]] = torch.prim.ListConstruct %[[C2]], %[[C2_3]] : (!torch.int, !torch.int) -> !torch.list<int>
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// CHECK: %[[OUTPUT_PADDING:.*]] = torch.prim.ListConstruct %[[C0]], %[[C0_4]] : (!torch.int, !torch.int) -> !torch.list<int>
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// CHECK: %[[TRANSPOSED:.*]] = torch.constant.bool true
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// CHECK: %[[BIAS:.*]] = torch.constant.none
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// CHECK: %[[GROUPS:.*]] = torch.constant.int 1
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// CHECK: torch.aten.convolution %arg0, %arg1, %[[BIAS]], %[[STRIDE]], %[[PADDING]], %[[DILATIONS]], %[[TRANSPOSED]], %[[OUTPUT_PADDING]], %[[GROUPS]] : !torch.vtensor<[1,1,3,3],f32>, !torch.vtensor<[1,2,4,4],f32>, !torch.none, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[1,2,6,6],f32>
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%4 = torch.operator "onnx.ConvTranspose"(%arg0, %arg1) {torch.onnx.auto_pad="SAME_LOWER", torch.onnx.strides = [2 : si64, 2 : si64]} : (!torch.vtensor<[1,1,3,3],f32>, !torch.vtensor<[1,2,4,4],f32>) -> !torch.vtensor<[1,2,6,6],f32>
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return %4 : !torch.vtensor<[1,2,6,6],f32>
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}
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// -----
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// CHECK-LABEL: @test_convtranspose_autopad_valid
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func.func @test_convtranspose_autopad_valid(%arg0: !torch.vtensor<[1,1,3,3],f32>, %arg1: !torch.vtensor<[1,2,4,4],f32>) -> !torch.vtensor<[1,2,8,8],f32> attributes {torch.onnx_meta.ir_version = 6 : si64, torch.onnx_meta.opset_version = 11 : si64, torch.onnx_meta.producer_name = "user-test", torch.onnx_meta.producer_version = ""} {
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// CHECK: %[[C0:.*]] = torch.constant.int 0
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// CHECK: %[[C0_0:.*]] = torch.constant.int 0
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// CHECK: %[[C1:.*]] = torch.constant.int 1
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// CHECK: %[[C1_1:.*]] = torch.constant.int 1
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// CHECK: %[[C2:.*]] = torch.constant.int 2
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// CHECK: %[[C2_2:.*]] = torch.constant.int 2
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// CHECK: %[[C0_3:.*]] = torch.constant.int 0
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// CHECK: %[[C0_4:.*]] = torch.constant.int 0
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// CHECK: %[[PADDING:.*]] = torch.prim.ListConstruct %[[C0]], %[[C0_0]] : (!torch.int, !torch.int) -> !torch.list<int>
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// CHECK: %[[DILATIONS:.*]] = torch.prim.ListConstruct %[[C1]], %[[C1_1]] : (!torch.int, !torch.int) -> !torch.list<int>
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// CHECK: %[[STRIDE:.*]] = torch.prim.ListConstruct %[[C2]], %[[C2_2]] : (!torch.int, !torch.int) -> !torch.list<int>
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// CHECK: %[[OUTPUT_PADDING:.*]] = torch.prim.ListConstruct %[[C0_3]], %[[C0_4]] : (!torch.int, !torch.int) -> !torch.list<int>
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// CHECK: %[[TRANSPOSED:.*]] = torch.constant.bool true
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// CHECK: %[[BIAS:.*]] = torch.constant.none
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// CHECK: %[[GROUPS:.*]] = torch.constant.int 1
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// CHECK: torch.aten.convolution %arg0, %arg1, %[[BIAS]], %[[STRIDE]], %[[PADDING]], %[[DILATIONS]], %[[TRANSPOSED]], %[[OUTPUT_PADDING]], %[[GROUPS]] : !torch.vtensor<[1,1,3,3],f32>, !torch.vtensor<[1,2,4,4],f32>, !torch.none, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[1,2,8,8],f32>
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%4 = torch.operator "onnx.ConvTranspose"(%arg0, %arg1) {torch.onnx.auto_pad="VALID", torch.onnx.strides = [2 : si64, 2 : si64]} : (!torch.vtensor<[1,1,3,3],f32>, !torch.vtensor<[1,2,4,4],f32>) -> !torch.vtensor<[1,2,8,8],f32>
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return %4 : !torch.vtensor<[1,2,8,8],f32>
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}
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// -----
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// CHECK-LABEL: @test_batchnorm_epsilon
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// CHECK-LABEL: @test_batchnorm_epsilon
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func.func @test_batchnorm_epsilon(%arg0: !torch.vtensor<[2,3,4,5],f32>, %arg1: !torch.vtensor<[3],f32>, %arg2: !torch.vtensor<[3],f32>, %arg3: !torch.vtensor<[3],f32>, %arg4: !torch.vtensor<[3],f32>) -> !torch.vtensor<[2,3,4,5],f32> attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 15 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
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func.func @test_batchnorm_epsilon(%arg0: !torch.vtensor<[2,3,4,5],f32>, %arg1: !torch.vtensor<[3],f32>, %arg2: !torch.vtensor<[3],f32>, %arg3: !torch.vtensor<[3],f32>, %arg4: !torch.vtensor<[3],f32>) -> !torch.vtensor<[2,3,4,5],f32> attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 15 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
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// CHECK: %[[FALSE:.*]] = torch.constant.bool false
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// CHECK: %[[FALSE:.*]] = torch.constant.bool false
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