wehub-resource-sync
290 lines
10 KiB
C++
290 lines
10 KiB
C++
/* Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include <memory>
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#include <string>
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#include <vector>
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#include "paddle/fluid/operators/transpose_op.h"
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namespace paddle {
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namespace operators {
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phi::KernelKey TransposeOp::GetExpectedKernelType(
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const framework::ExecutionContext &ctx) const {
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auto data_type = OperatorWithKernel::IndicateVarDataType(ctx, "X");
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auto &data_format = ctx.Attr<std::string>("data_format");
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phi::DataLayout layout_ = common::StringToDataLayout(data_format);
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return phi::KernelKey(
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ctx.GetPlace(), layout_, phi::TransToPhiDataType(data_type));
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}
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class TransposeOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput(
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"X",
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"(Tensor) The input tensor, tensors with rank up to 6 are supported.");
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AddOutput("Out", "(Tensor)The output tensor.");
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AddAttr<std::vector<int>>(
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"axis",
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"(vector<int>) A list of values, and the size of the list should be "
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"the same with the input tensor rank. This operator permutes the input "
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"tensor's axes according to the values given.");
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AddAttr<bool>("use_mkldnn",
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"(bool, default false) Only used in mkldnn kernel")
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.SetDefault(false)
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.AsExtra();
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AddAttr<std::string>(
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"data_format",
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"(string, default NCHW) Only used in "
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"An optional string from: \"NHWC\", \"NCHW\". "
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"Defaults to \"NHWC\". Specify the data format of the output data, "
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"the input will be transformed automatically. ")
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.SetDefault("AnyLayout")
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.AsExtra();
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AddAttr<std::string>(
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"mkldnn_data_type",
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"(string, default \"float32\"). Data type of mkldnn kernel")
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.SetDefault("float32")
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.InEnum({"float32", "int8", "bfloat16"})
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.AsExtra();
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AddComment(R"DOC(
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Transpose Operator.
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The input tensor will be permuted according to the axes given.
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The behavior of this operator is similar to how `numpy.transpose` works.
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- suppose the input `X` is a 2-D tensor:
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$$
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X = \begin{pmatrix}
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0 &1 &2 \\
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3 &4 &5
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\end{pmatrix}$$
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the given `axes` is: $[1, 0]$, and $Y$ = transpose($X$, axis)
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then the output $Y$ is:
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$$
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Y = \begin{pmatrix}
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0 &3 \\
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1 &4 \\
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2 &5
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\end{pmatrix}$$
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- Given a input tensor with shape $(N, C, H, W)$ and the `axes` is
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$[0, 2, 3, 1]$, then shape of the output tensor will be: $(N, H, W, C)$.
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)DOC");
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}
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};
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class TransposeOpGrad : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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protected:
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phi::KernelKey GetExpectedKernelType(
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const framework::ExecutionContext &ctx) const override {
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auto data_type = OperatorWithKernel::IndicateVarDataType(
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ctx, framework::GradVarName("Out"));
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std::string data_format = ctx.Attr<std::string>("data_format");
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phi::DataLayout layout_ = common::StringToDataLayout(data_format);
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return phi::KernelKey(
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ctx.GetPlace(), layout_, phi::TransToPhiDataType(data_type));
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}
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};
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void Transpose2Op::InferShape(framework::InferShapeContext *ctx) const {
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using CompatMetaTensor = framework::CompatMetaTensor;
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CompatMetaTensor x(ctx->GetInputVarPtrs("X")[0], ctx->IsRuntime());
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CompatMetaTensor out(ctx->GetOutputVarPtrs("Out")[0], ctx->IsRuntime());
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std::vector<int> axis = ctx->Attrs().Get<std::vector<int>>("axis");
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phi::TransposeInferMeta(x, axis, &out);
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if (!ctx->HasOutput("XShape")) return;
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const auto &in_dims = ctx->GetInputDim("X");
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std::vector<int64_t> x_shape_dim(in_dims.size() + 1);
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x_shape_dim[0] = 0;
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for (int i = 0; i < in_dims.size(); ++i) {
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x_shape_dim[i + 1] = in_dims[i];
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}
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ctx->SetOutputDim("XShape", common::make_ddim(x_shape_dim));
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ctx->ShareLoD("X", /*->*/ "XShape");
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}
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phi::KernelKey Transpose2Op::GetExpectedKernelType(
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const framework::ExecutionContext &ctx) const {
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auto data_type = OperatorWithKernel::IndicateVarDataType(ctx, "X");
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auto &data_format = ctx.Attr<std::string>("data_format");
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phi::DataLayout layout_ = common::StringToDataLayout(data_format);
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return phi::KernelKey(
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ctx.GetPlace(), layout_, phi::TransToPhiDataType(data_type));
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}
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void Transpose2OpMaker::Make() {
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AddInput(
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"X",
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"(Tensor) The input tensor, tensors with rank up to 6 are supported.");
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AddOutput("Out", "(Tensor)The output tensor.");
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AddAttr<std::vector<int>>(
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"axis",
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"(vector<int>) A list of values, and the size of the list should be "
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"the same with the input tensor rank. This operator permutes the input "
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"tensor's axes according to the values given.");
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AddOutput("XShape", "(Tensor)The output tensor.").AsIntermediate().AsExtra();
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AddComment(R"DOC(
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Transpose Operator.
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The input tensor will be permuted according to the axes given.
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The behavior of this operator is similar to how `numpy.transpose` works.
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- suppose the input `X` is a 2-D tensor:
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$$
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X = \begin{pmatrix}
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0 &1 &2 \\
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3 &4 &5
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\end{pmatrix}$$
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the given `axes` is: $[1, 0]$, and $Y$ = transpose($X$, axis)
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then the output $Y$ is:
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$$
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Y = \begin{pmatrix}
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0 &3 \\
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1 &4 \\
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2 &5
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\end{pmatrix}$$
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- Given a input tensor with shape $(N, C, H, W)$ and the `axes` is
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$[0, 2, 3, 1]$, then shape of the output tensor will be: $(N, H, W, C)$.
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)DOC");
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Apply();
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}
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template <typename T>
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class Transpose2GradMaker : public framework::SingleGradOpMaker<T> {
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public:
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using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
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void Apply(GradOpPtr<T> grad_op) const override {
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grad_op->SetType("transpose2_grad");
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grad_op->SetInput("XShape", this->Output("XShape"));
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grad_op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
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grad_op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
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grad_op->SetAttrMap(this->Attrs());
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}
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};
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class Transpose2CompositeGradOpMaker : public prim::CompositeGradOpMakerBase {
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using prim::CompositeGradOpMakerBase::CompositeGradOpMakerBase;
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public:
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void Apply() override {
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paddle::Tensor xshape = this->GetSingleForwardOutput("XShape");
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paddle::Tensor out_grad = this->GetSingleOutputGrad("Out");
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paddle::Tensor dx = this->GetSingleInputGrad("X");
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auto *dx_ptr = this->GetOutputPtr(&dx);
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std::string dx_name = this->GetOutputName(dx);
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std::vector<int> axis =
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static_cast<std::vector<int>>(this->Attr<std::vector<int>>("axis"));
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VLOG(6) << "Running transpose2_grad composite func";
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prim::transpose_grad<prim::DescTensor>(out_grad, axis, dx_ptr);
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this->RecoverOutputName(dx, dx_name);
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}
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};
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template <typename T>
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class Transpose2DoubleGradMaker : public framework::SingleGradOpMaker<T> {
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public:
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using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
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void Apply(GradOpPtr<T> grad_op) const override {
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grad_op->SetType("transpose2");
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grad_op->SetInput("X", this->OutputGrad(framework::GradVarName("X")));
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grad_op->SetOutput("Out", this->InputGrad(framework::GradVarName("Out")));
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grad_op->SetOutput("XShape", this->Input("XShape"));
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grad_op->SetAttrMap(this->Attrs());
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}
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};
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class Transpose2OpGrad : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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protected:
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phi::KernelKey GetExpectedKernelType(
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const framework::ExecutionContext &ctx) const override {
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framework::proto::VarType::Type data_type =
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OperatorWithKernel::IndicateVarDataType(ctx,
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framework::GradVarName("Out"));
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std::string data_format = ctx.Attr<std::string>("data_format");
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phi::DataLayout layout_ = common::StringToDataLayout(data_format);
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return phi::KernelKey(
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ctx.GetPlace(), layout_, phi::TransToPhiDataType(data_type));
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}
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};
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class TransposeGradInferVarType : public framework::VarTypeInference {
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public:
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void operator()(framework::InferVarTypeContext *ctx) const override {
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ctx->SyncTypeAndDataType(framework::GradVarName("Out"),
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framework::GradVarName("X"));
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}
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};
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} // namespace operators
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} // namespace paddle
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DECLARE_INFER_SHAPE_FUNCTOR(transpose,
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TransposeInferShapeFunctor,
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PD_INFER_META(phi::TransposeInferMeta));
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DECLARE_INFER_SHAPE_FUNCTOR(transpose_grad,
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TransposeGradInferShapeFunctor,
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PD_INFER_META(phi::TransposeGradInferMeta));
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DECLARE_INFER_SHAPE_FUNCTOR(transpose2_grad,
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Transpose2GradInferShapeFunctor,
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PD_INFER_META(phi::TransposeGradInferMeta));
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namespace ops = paddle::operators;
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REGISTER_OPERATOR(
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transpose,
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ops::TransposeOp,
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ops::TransposeOpMaker,
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paddle::framework::DefaultGradOpMaker<paddle::framework::OpDesc, true>,
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paddle::framework::DefaultGradOpMaker<paddle::imperative::OpBase, true>,
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TransposeInferShapeFunctor);
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REGISTER_OPERATOR(transpose_grad,
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ops::TransposeOpGrad,
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ops::TransposeGradInferVarType,
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TransposeGradInferShapeFunctor);
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REGISTER_OPERATOR(transpose2,
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ops::Transpose2Op,
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ops::Transpose2OpMaker,
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ops::Transpose2GradMaker<paddle::framework::OpDesc>,
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ops::Transpose2GradMaker<paddle::imperative::OpBase>,
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ops::Transpose2CompositeGradOpMaker);
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REGISTER_OPERATOR(transpose2_grad,
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ops::Transpose2OpGrad,
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ops::TransposeGradInferVarType,
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ops::Transpose2DoubleGradMaker<paddle::framework::OpDesc>,
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ops::Transpose2DoubleGradMaker<paddle::imperative::OpBase>,
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Transpose2GradInferShapeFunctor);
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