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297 lines
11 KiB
297 lines
11 KiB
/* Copyright (c) 2016 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 "paddle/fluid/operators/transpose_op.h"
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#include <string>
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#include <vector>
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#ifdef PADDLE_WITH_MKLDNN
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#include "paddle/fluid/platform/mkldnn_helper.h"
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#endif
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namespace paddle {
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namespace operators {
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using framework::Tensor;
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class TransposeOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext *ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
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PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should not be null");
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auto x_dims = ctx->GetInputDim("X");
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std::vector<int> axis = ctx->Attrs().Get<std::vector<int>>("axis");
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size_t x_rank = x_dims.size();
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size_t axis_size = axis.size();
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PADDLE_ENFORCE_EQ(x_rank, axis_size,
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"The input tensor's rank(%d) "
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"should be equal to the axis's size(%d)",
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x_rank, axis_size);
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std::vector<int> count(axis_size, 0);
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for (size_t i = 0; i < axis_size; i++) {
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PADDLE_ENFORCE(
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axis[i] < static_cast<int>(axis_size) && ++count[axis[i]] == 1,
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"Each element of Attribute axis should be a unique value "
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"range from 0 to (dims - 1), "
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"where the dims is the axis's size");
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}
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framework::DDim out_dims(x_dims);
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for (size_t i = 0; i < axis_size; i++) {
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out_dims[i] = x_dims[axis[i]];
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}
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ctx->SetOutputDim("Out", out_dims);
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}
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protected:
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext &ctx) const override {
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framework::LibraryType library_{framework::LibraryType::kPlain};
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std::string data_format = ctx.Attr<std::string>("data_format");
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framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
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#ifdef PADDLE_WITH_MKLDNN
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if (library_ == framework::LibraryType::kPlain &&
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platform::CanMKLDNNBeUsed(ctx)) {
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library_ = framework::LibraryType::kMKLDNN;
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layout_ = framework::DataLayout::kMKLDNN;
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}
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#endif
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return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
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ctx.GetPlace(), layout_, library_);
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}
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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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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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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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void InferShape(framework::InferShapeContext *ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
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PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
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"Input(Out@GRAD) should not be null");
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auto x_dims = ctx->GetInputDim("X");
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ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
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if (ctx->HasOutput(framework::GradVarName("X"))) {
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ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
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}
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}
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protected:
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext &ctx) const override {
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framework::LibraryType library_{framework::LibraryType::kPlain};
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std::string data_format = ctx.Attr<std::string>("data_format");
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framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
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#ifdef PADDLE_WITH_MKLDNN
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if (library_ == framework::LibraryType::kPlain &&
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platform::CanMKLDNNBeUsed(ctx)) {
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library_ = framework::LibraryType::kMKLDNN;
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layout_ = framework::DataLayout::kMKLDNN;
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}
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#endif
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return framework::OpKernelType(
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ctx.Input<framework::LoDTensor>(framework::GradVarName("Out"))->type(),
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ctx.GetPlace(), layout_, library_);
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}
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};
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// FIXME(zcd): transpose2 adds an intermediate output(XShape) based on
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// transpose, the XShape is used to carry the shape and lod of X which
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// will be used in transpose_grad, in this way, the framework can reuse
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// the memory of X immediately the transpose2_op is finished.
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// Considering compatibility issues, we could not fix transpose2_op
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class Transpose2Op : public TransposeOp {
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public:
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Transpose2Op(const std::string &type,
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const framework::VariableNameMap &inputs,
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const framework::VariableNameMap &outputs,
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const framework::AttributeMap &attrs)
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: TransposeOp(type, inputs, outputs, attrs) {}
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void InferShape(framework::InferShapeContext *ctx) const override {
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TransposeOp::InferShape(ctx);
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PADDLE_ENFORCE(ctx->HasOutput("XShape"),
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"Output(XShape) should not be null");
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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", framework::make_ddim(x_shape_dim));
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ctx->ShareLoD("X", /*->*/ "XShape");
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}
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protected:
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext &ctx) const override {
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framework::LibraryType library_{framework::LibraryType::kPlain};
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std::string data_format = ctx.Attr<std::string>("data_format");
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framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
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#ifdef PADDLE_WITH_MKLDNN
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if (library_ == framework::LibraryType::kPlain &&
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platform::CanMKLDNNBeUsed(ctx)) {
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library_ = framework::LibraryType::kMKLDNN;
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layout_ = framework::DataLayout::kMKLDNN;
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}
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#endif
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return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
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ctx.GetPlace(), layout_, library_);
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}
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};
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class Transpose2OpMaker : public TransposeOpMaker {
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public:
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void Make() override {
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TransposeOpMaker::Make();
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AddOutput("XShape", "(Tensor)The output tensor.").AsIntermediate();
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}
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};
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class Transpose2GradMaker : public framework::SingleGradOpDescMaker {
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public:
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using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
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std::unique_ptr<framework::OpDesc> Apply() const override {
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auto *grad_op = new framework::OpDesc();
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grad_op->SetType("transpose2_grad");
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grad_op->SetInput("XShape", Output("XShape"));
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grad_op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
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grad_op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
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grad_op->SetAttrMap(Attrs());
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return std::unique_ptr<framework::OpDesc>(grad_op);
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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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void InferShape(framework::InferShapeContext *ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("XShape"), "Input(XShape) should not be null");
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PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
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"Input(Out@GRAD) should not be null");
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if (ctx->HasOutput(framework::GradVarName("X"))) {
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auto xshape_dim = ctx->GetInputDim("XShape");
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auto x_shape_dim =
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framework::slice_ddim(xshape_dim, 1, xshape_dim.size());
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ctx->SetOutputDim(framework::GradVarName("X"), x_shape_dim);
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ctx->ShareLoD("XShape", framework::GradVarName("X"));
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}
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}
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protected:
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext &ctx) const override {
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framework::LibraryType library_{framework::LibraryType::kPlain};
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std::string data_format = ctx.Attr<std::string>("data_format");
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framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
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#ifdef PADDLE_WITH_MKLDNN
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if (library_ == framework::LibraryType::kPlain &&
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platform::CanMKLDNNBeUsed(ctx)) {
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library_ = framework::LibraryType::kMKLDNN;
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layout_ = framework::DataLayout::kMKLDNN;
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}
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#endif
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return framework::OpKernelType(
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ctx.Input<framework::LoDTensor>(framework::GradVarName("Out"))->type(),
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ctx.GetPlace(), layout_, library_);
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OPERATOR(transpose, ops::TransposeOp, ops::TransposeOpMaker,
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paddle::framework::DefaultGradOpDescMaker<true>);
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REGISTER_OPERATOR(transpose_grad, ops::TransposeOpGrad);
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REGISTER_OP_CPU_KERNEL(
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transpose, ops::TransposeKernel<paddle::platform::CPUDeviceContext, float>,
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ops::TransposeKernel<paddle::platform::CPUDeviceContext, double>);
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REGISTER_OP_CPU_KERNEL(
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transpose_grad,
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ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, float>,
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ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, double>);
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REGISTER_OPERATOR(transpose2, ops::Transpose2Op, ops::Transpose2OpMaker,
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ops::Transpose2GradMaker);
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REGISTER_OPERATOR(transpose2_grad, ops::Transpose2OpGrad);
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REGISTER_OP_CPU_KERNEL(
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transpose2, ops::TransposeKernel<paddle::platform::CPUDeviceContext, float>,
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ops::TransposeKernel<paddle::platform::CPUDeviceContext, double>);
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REGISTER_OP_CPU_KERNEL(
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transpose2_grad,
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ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, float>,
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ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, double>);
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