wehub-resource-sync
239 lines
8.6 KiB
C++
239 lines
8.6 KiB
C++
/* 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 <memory>
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#include <string>
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#include <vector>
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#include "paddle/fluid/framework/infershape_utils.h"
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/prim/api/composite_backward/composite_backward_api.h"
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#include "paddle/fluid/prim/utils/static/composite_grad_desc_maker.h"
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#include "paddle/fluid/prim/utils/static/desc_tensor.h"
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#include "paddle/phi/core/infermeta_utils.h"
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#include "paddle/phi/infermeta/unary.h"
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namespace paddle {
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namespace operators {
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class TileOp : 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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return phi::KernelKey(OperatorWithKernel::IndicateVarDataType(ctx, "X"),
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ctx.GetPlace());
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}
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phi::KernelKey GetKernelTypeForVar(
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const std::string& var_name,
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const phi::DenseTensor& tensor,
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const phi::KernelKey& expected_kernel_type) const override {
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if (var_name == "repeat_times_tensor" || var_name == "RepeatTimes") {
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return phi::KernelKey(phi::Backend::ALL_BACKEND,
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expected_kernel_type.layout(),
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expected_kernel_type.dtype());
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}
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return phi::KernelKey(
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tensor.place(), tensor.layout(), expected_kernel_type.dtype());
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}
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};
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class TileOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X",
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"(Tensor, default Tensor<float>). X is the input to be titled.");
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AddInput(
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"RepeatTimes",
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"(Tensor<int>, optional). If provided, it is the number of repeat times"
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" along specific axis. It has a higher priority than "
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"repeat_times_tensor and the repeat_times attribute.")
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.AsDispensable();
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AddInput("repeat_times_tensor",
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"(Tensor Tensor<int>), repeat times for X."
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"It has a higher priority than repeat_times, but a lower priority "
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"than RepeatTimes")
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.AsDuplicable()
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.AsDispensable();
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AddOutput("Out",
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"(Tensor, default Tensor<float>). A tensor with rank in [1, 6]."
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"After tiling, size of each dimension of Output(Out) is equal "
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"to size of the corresponding dimension of Input(X) multiplying "
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"the corresponding value given by Attr(repeat_times).");
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AddAttr<std::vector<int>>("repeat_times",
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"The number of repeat times for each dimension.")
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.SetDefault({})
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.SupportTensor();
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AddComment(R"DOC(
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Tile operator repeats the input by given times number. You should set times
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number for each dimension by providing attribute 'repeat_times'. The rank of X
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should be in [1, 6]. Please note that size of 'repeat_times' must be the same
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with X's rank. Following is a using case:
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Input(X) is a 3-D tensor with shape [2, 3, 1]:
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[
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[[1], [2], [3]],
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[[4], [5], [6]]
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]
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Attr(repeat_times): [1, 2, 2]
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Output(Out) is a 3-D tensor with shape [2, 6, 2]:
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[
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[[1, 1], [2, 2], [3, 3], [1, 1], [2, 2], [3, 3]],
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[[4, 4], [5, 5], [6, 6], [4, 4], [5, 5], [6, 6]]
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]
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)DOC");
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}
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};
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class TileGradOp : 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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void InferShape(framework::InferShapeContext* ctx) const override {
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OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "TileGrad");
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OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")),
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"Input",
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framework::GradVarName("Out"),
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"TileGrad");
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auto x_dims = ctx->GetInputDim("X");
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auto x_grad_name = framework::GradVarName("X");
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if (ctx->HasOutput(x_grad_name)) {
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ctx->SetOutputDim(x_grad_name, x_dims);
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}
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}
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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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return phi::KernelKey(OperatorWithKernel::IndicateVarDataType(
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ctx, framework::GradVarName("Out")),
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ctx.GetPlace());
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}
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phi::KernelKey GetKernelTypeForVar(
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const std::string& var_name,
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const phi::DenseTensor& tensor,
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const phi::KernelKey& expected_kernel_type) const override {
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if (var_name == "repeat_times_tensor" || var_name == "RepeatTimes") {
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return phi::KernelKey(phi::Backend::ALL_BACKEND,
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expected_kernel_type.layout(),
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expected_kernel_type.dtype());
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}
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return phi::KernelKey(
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tensor.place(), tensor.layout(), expected_kernel_type.dtype());
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}
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};
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template <typename T>
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class TileGradOpMaker : public framework::SingleGradOpMaker<T> {
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public:
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using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
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protected:
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void Apply(GradOpPtr<T> op) const override {
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op->SetType("tile_grad");
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op->SetInput("X", this->Input("X"));
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op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
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op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
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op->SetInput("repeat_times_tensor", this->Input("repeat_times_tensor"));
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op->SetInput("RepeatTimes", this->Input("RepeatTimes"));
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op->SetAttrMap(this->Attrs());
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}
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};
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class TileCompositeGradOpMaker : 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 x = this->GetSingleForwardInput("X");
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paddle::Tensor out_grad = this->GetSingleOutputGrad("Out");
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paddle::Tensor x_grad = this->GetSingleInputGrad("X");
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paddle::optional<paddle::Tensor> tensor_repeat_times =
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this->GetOptionalSingleForwardInput("RepeatTimes");
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paddle::optional<paddle::Tensor> tensor_repeat_times_attr =
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this->GetOptionalSingleForwardInput("repeat_times_tensor");
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auto dx_ptr = this->GetOutputPtr(&x_grad);
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std::string dx_name = this->GetOutputName(x_grad);
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auto repeat_times = this->Attr<std::vector<int>>("repeat_times");
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if (tensor_repeat_times.is_initialized() ||
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tensor_repeat_times_attr.is_initialized()) {
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PADDLE_THROW(common::errors::Unimplemented(
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"We don't support RepeatTimes from tensor or repeat_times_tensor for "
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"tile composite grad for now. "));
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} else {
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VLOG(6) << "Running tile_grad composite func";
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prim::tile_grad<prim::DescTensor>(
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x, out_grad, paddle::experimental::IntArray(repeat_times), dx_ptr);
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this->RecoverOutputName(x_grad, dx_name);
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}
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}
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};
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template <typename T>
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class TileDoubleGradOpMaker : public framework::SingleGradOpMaker<T> {
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public:
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using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
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protected:
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void Apply(GradOpPtr<T> op) const override {
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op->SetType("tile");
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op->SetInput("X", this->OutputGrad(framework::GradVarName("X")));
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op->SetOutput("Out", this->InputGrad(framework::GradVarName("Out")));
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if (this->HasInput("repeat_times_tensor")) {
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op->SetInput("repeat_times_tensor", this->Input("repeat_times_tensor"));
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}
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if (this->HasInput("RepeatTimes")) {
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op->SetInput("RepeatTimes", this->Input("RepeatTimes"));
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}
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op->SetAttrMap(this->Attrs());
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}
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};
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DECLARE_NO_NEED_BUFFER_VARS_INFERER(TileGradNoNeedBufVarsInferer, "X");
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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DECLARE_INFER_SHAPE_FUNCTOR(tile,
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TileInferMetaFunctor,
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PD_INFER_META(phi::TileInferMeta));
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REGISTER_OPERATOR(tile,
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ops::TileOp,
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ops::TileOpMaker,
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ops::TileGradOpMaker<paddle::framework::OpDesc>,
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ops::TileGradOpMaker<paddle::imperative::OpBase>,
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ops::TileCompositeGradOpMaker,
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TileInferMetaFunctor);
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REGISTER_OPERATOR(tile_grad,
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ops::TileGradOp,
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ops::TileDoubleGradOpMaker<paddle::framework::OpDesc>,
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ops::TileDoubleGradOpMaker<paddle::imperative::OpBase>,
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ops::TileGradNoNeedBufVarsInferer);
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