wehub-resource-sync
251 lines
9.5 KiB
C++
251 lines
9.5 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 "paddle/fluid/operators/split_op.h"
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#include <string>
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#include "paddle/fluid/framework/infershape_utils.h"
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#include "paddle/fluid/framework/phi_utils.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/infermeta/unary.h"
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namespace paddle {
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namespace operators {
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using framework::Variable;
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class SplitOp : 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_EQ(ctx->HasInput("X"),
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true,
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common::errors::InvalidArgument(
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"Input(X) of SplitOp should not be null."));
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PADDLE_ENFORCE_GE(ctx->Outputs("Out").size(),
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1UL,
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common::errors::InvalidArgument(
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"Outputs(Out) of SplitOp should not be empty."));
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int axis = static_cast<int>(ctx->Attrs().Get<int>("axis"));
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int num = static_cast<int>(ctx->Attrs().Get<int>("num"));
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std::vector<int> sections = static_cast<std::vector<int>>(
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ctx->Attrs().Get<std::vector<int>>("sections"));
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// Construct MetaTensor for InferMeta Func
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using CompatMetaTensor = framework::CompatMetaTensor;
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CompatMetaTensor x(ctx->GetInputVarPtrs("X")[0], ctx->IsRuntime());
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std::vector<CompatMetaTensor> out;
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size_t out_size = ctx->GetOutputVarPtrs("Out").size();
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out.reserve(out_size);
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for (size_t i = 0; i < out_size; i++) {
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out.emplace_back(
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CompatMetaTensor(ctx->GetOutputVarPtrs("Out")[i], ctx->IsRuntime()));
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}
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std::vector<phi::MetaTensor *> out_ptr(out_size);
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for (size_t i = 0; i < out_size; i++) {
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out_ptr[i] = &out[i];
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}
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phi::Scalar axis_final;
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phi::IntArray sections_final;
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// Construct axis_final
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if (ctx->IsRuntime() && ctx->HasInput("AxisTensor")) {
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Variable *var =
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PADDLE_GET_CONST(Variable *, ctx->GetInputVarPtrs("AxisTensor")[0]);
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axis_final = framework::MakePhiScalarFromVar(*var);
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} else if (!ctx->IsRuntime() && ctx->HasInput("AxisTensor")) {
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axis_final = phi::Scalar(-1);
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axis_final.SetFromTensor(true);
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} else {
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axis_final = phi::Scalar(axis);
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}
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// Construct sections_final
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if (ctx->IsRuntime() && ctx->HasInputs("SectionsTensorList")) {
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int sections_tensor_list_size =
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static_cast<int>(ctx->GetInputVarPtrs("SectionsTensorList").size());
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const paddle::small_vector<framework::InferShapeVarPtr,
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phi::kInputSmallVectorSize>
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§ions_varptr_list = ctx->GetInputVarPtrs("SectionsTensorList");
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std::vector<DenseTensor> sections_from_tensor;
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sections_from_tensor.reserve(sections_tensor_list_size);
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for (const auto §ion_varptr : sections_varptr_list) {
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Variable *var = PADDLE_GET_CONST(Variable *, section_varptr);
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sections_from_tensor.emplace_back(var->Get<DenseTensor>());
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}
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sections_final = phi::IntArray(sections_from_tensor);
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} else if (!ctx->IsRuntime() && ctx->HasInputs("SectionsTensorList")) {
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sections_final = phi::IntArray(std::vector<int>(
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ctx->GetInputVarPtrs("SectionsTensorList").size(), -1));
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sections_final.SetFromTensor(true);
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} else {
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sections_final = phi::IntArray(sections);
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}
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if (!sections.empty()) {
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if (ctx->IsRuntime()) {
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phi::SplitInferMeta(
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x, sections_final, axis_final, out_ptr, {true, false});
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} else {
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phi::SplitInferMeta(
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x, sections_final, axis_final, out_ptr, {false, false});
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}
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} else {
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if (ctx->IsRuntime()) {
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phi::SplitWithNumInferMeta(x, num, axis_final, out_ptr, {true, false});
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} else {
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phi::SplitWithNumInferMeta(x, num, axis_final, out_ptr, {false, false});
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}
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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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auto input_data_type =
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framework::OperatorWithKernel::IndicateVarDataType(ctx, "X");
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#ifdef PADDLE_WITH_DNNL
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if (this->CanONEDNNBeUsed(ctx, input_data_type)) {
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// OneDNN uses blocking format, which cannot be always supported with
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// reorders, because if blocked dimension is not divisible by 8 or
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// 16(depending on which blocking format is used) submemory cannot be
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// created, so in that scenario a fallback is needed
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const auto x_md = ctx.Input<DenseTensor>("X")->mem_desc();
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if (x_md.get_inner_nblks() == 0) {
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return phi::KernelKey(phi::Backend::ONEDNN,
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phi::DataLayout::ONEDNN,
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phi::TransToPhiDataType(input_data_type));
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}
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}
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#endif
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return phi::KernelKey(input_data_type, 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 DenseTensor &tensor,
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const phi::KernelKey &expected_kernel_type) const override {
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if (var_name == "AxisTensor" || var_name == "SectionsTensorList") {
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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 SplitOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X", "(Tensor) Input tensor of the split operator.");
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AddInput("AxisTensor",
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"(Tensor) The axis which the input will be split on. "
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"It has higher priority than Attr(axis). "
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"The shape of AxisTensor must be [1]")
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.AsDispensable();
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AddInput("SectionsTensorList",
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"(vector<Tensor<int>>, optional). "
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"The length of each output along the specified axis. "
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"It has a higher priority than Attr(sections)."
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"The shape of the element in vector must be [1].")
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.AsDuplicable()
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.AsDispensable();
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AddOutput("Out", "(Tensor) Output tensors of the split operator.")
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.AsDuplicable();
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AddComment(R"DOC(
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Split operator
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This operator splits the input tensor into multiple sub-tensors.
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Example:
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Input = [[1,2],
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[3,4],
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[5,6]]
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sections = [2,1]
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axis = 0
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Output[0] = [[1,2],
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[3,4]]
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Output[1] = [[5,6]]
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)DOC");
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AddAttr<std::vector<int>>("sections",
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"(vector<int>) "
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"the length of each output along the "
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"specified axis.")
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.SetDefault(std::vector<int>{});
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AddAttr<int>("num",
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"(int, default 0)"
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"Number of sub-tensors. This must evenly divide "
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"Input.dims()[axis]")
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.SetDefault(0);
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AddAttr<int>("axis",
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"(int, default 0) "
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"The axis which the input will be split on.")
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.SetDefault(0);
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}
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};
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class SplitCompositeGradOpMaker : 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::optional<std::vector<paddle::Tensor>> tensor_sections =
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this->GetOptionalMultiForwardInput("SectionsTensorList");
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paddle::optional<paddle::Tensor> tensor_axis =
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this->GetOptionalSingleForwardInput("AxisTensor");
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int axis = static_cast<int>(this->Attr<int>("axis"));
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std::vector<int> sections =
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static_cast<std::vector<int>>(this->Attr<std::vector<int>>("sections"));
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paddle::Tensor input_grad = this->GetSingleInputGrad("X");
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auto dx_ptr = this->GetOutputPtr(&input_grad);
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std::string dx_name = this->GetOutputName(input_grad);
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std::vector<paddle::Tensor> out_grad = this->GetMultiOutputGrad("Out");
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if (tensor_axis.is_initialized() || tensor_sections.is_initialized()) {
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PADDLE_THROW(common::errors::Unimplemented(
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"We don't support dynamic index or sections from tensor for split "
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"composite grad for now. "));
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} else {
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VLOG(6) << "Running split_grad composite func";
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prim::split_grad<prim::DescTensor>(out_grad, axis, dx_ptr);
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this->RecoverOutputName(input_grad, dx_name);
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}
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}
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};
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class SplitInferVarType : 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("X", "Out");
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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(split,
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ops::SplitOp,
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ops::SplitOpMaker,
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ops::SplitCompositeGradOpMaker,
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ops::SplitInferVarType,
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ops::SplitGradMaker<paddle::framework::OpDesc>,
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ops::SplitGradMaker<paddle::imperative::OpBase>);
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