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345 lines
12 KiB
345 lines
12 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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Indicesou 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 <string>
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#include <unordered_map>
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#include "paddle/fluid/framework/data_layout.h"
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#include "paddle/fluid/framework/eigen.h"
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#include "paddle/fluid/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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class AffineChannelOpMaker : 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) Feature map input can be a 4D tensor with order NCHW "
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"or NHWC. It also can be a 2D tensor and C is the second "
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"dimension.");
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AddInput("Scale",
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"(Tensor) 1D input of shape (C), the c-th element "
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"is the scale factor of the affine transformation "
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"for the c-th channel of the input.");
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AddInput("Bias",
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"(Tensor) 1D input of shape (C), the c-th element "
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"is the bias of the affine transformation for the "
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"c-th channel of the input.");
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AddAttr<std::string>(
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"data_layout",
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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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AddOutput("Out", "(Tensor) A tensor of the same shape and order with X.");
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AddComment(R"DOC(
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Applies a separate affine transformation to each channel of the input. Useful
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for replacing spatial batch norm with its equivalent fixed transformation.
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The input also can be 2D tensor and applies a affine transformation in second
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dimension.
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$$Out = Scale*X + Bias$$
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)DOC");
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}
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};
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class AffineChannelOp : 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"),
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"Input(X) of AffineChannelOp should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("Scale"),
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"Input(Scale) of AffineChannelOp should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("Bias"),
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"Input(Bias) of AffineChannelOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output(Out) of AffineChannelOp should not be null.");
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auto x_dims = ctx->GetInputDim("X");
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auto scale_dims = ctx->GetInputDim("Scale");
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auto b_dims = ctx->GetInputDim("Bias");
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const framework::DataLayout data_layout = framework::StringToDataLayout(
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ctx->Attrs().Get<std::string>("data_layout"));
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const int64_t C = (data_layout == framework::DataLayout::kNCHW
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? x_dims[1]
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: x_dims[x_dims.size() - 1]);
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PADDLE_ENFORCE_EQ(scale_dims.size(), 1UL);
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PADDLE_ENFORCE_EQ(b_dims.size(), 1UL);
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if (ctx->IsRuntime() || scale_dims[0] > 0) {
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PADDLE_ENFORCE_EQ(scale_dims[0], C);
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}
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if (ctx->IsRuntime() || b_dims[0] > 0) {
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PADDLE_ENFORCE_EQ(b_dims[0], C);
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}
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ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
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ctx->ShareLoD("X", "Out");
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}
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};
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class AffineChannelOpGrad : 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(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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PADDLE_ENFORCE(ctx->HasInput("Scale"),
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"Input(Scale) should not be null.");
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ctx->SetOutputDim(framework::GradVarName("X"),
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ctx->GetInputDim(framework::GradVarName("Out")));
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}
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if (ctx->HasOutput(framework::GradVarName("Scale"))) {
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// Scale@GRAD and Bias@GRAD must exist at the same time.
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PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("Bias")),
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"Output(Scale@GRAD) should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null.");
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ctx->SetOutputDim(framework::GradVarName("Scale"),
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ctx->GetInputDim("Scale"));
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ctx->SetOutputDim(framework::GradVarName("Bias"),
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ctx->GetInputDim("Scale"));
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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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return framework::OpKernelType(
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ctx.Input<framework::Tensor>(framework::GradVarName("Out"))->type(),
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ctx.GetPlace());
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}
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};
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class AffineChannelGradMaker : 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* op = new framework::OpDesc();
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op->SetType("affine_channel_grad");
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op->SetInput("X", Input("X"));
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op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
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op->SetInput("Scale", Input("Scale"));
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op->SetAttrMap(Attrs());
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op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
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op->SetOutput(framework::GradVarName("Scale"), InputGrad("Scale"));
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op->SetOutput(framework::GradVarName("Bias"), InputGrad("Bias"));
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return std::unique_ptr<framework::OpDesc>(op);
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}
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};
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template <typename T>
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using EigenArrayMap =
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Eigen::Map<Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
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template <typename T>
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using ConstEigenArrayMap =
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Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
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template <typename T>
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using EigenVectorArrayMap = Eigen::Map<Eigen::Array<T, Eigen::Dynamic, 1>>;
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template <typename T>
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using ConstEigenVectorArrayMap =
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Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, 1>>;
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template <typename DeviceContext, typename T>
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class AffineChannelKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto* x = ctx.Input<framework::Tensor>("X");
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auto* scale = ctx.Input<framework::Tensor>("Scale");
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auto* bias = ctx.Input<framework::Tensor>("Bias");
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auto* y = ctx.Output<framework::Tensor>("Out");
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y->mutable_data<T>(ctx.GetPlace());
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const framework::DataLayout layout =
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framework::StringToDataLayout(ctx.Attr<std::string>("data_layout"));
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auto dims = x->dims();
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int N = dims[0];
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int C = layout == framework::DataLayout::kNCHW ? dims[1]
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: dims[dims.size() - 1];
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int HxW = x->numel() / N / C;
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auto* scale_d = scale->data<T>();
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auto* bias_d = bias->data<T>();
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ConstEigenVectorArrayMap<T> a_e(scale_d, C);
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ConstEigenVectorArrayMap<T> b_e(bias_d, C);
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auto* x_d = x->data<T>();
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auto* y_d = y->data<T>();
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if (layout == framework::DataLayout::kNCHW) {
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int stride = C * HxW;
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for (int i = 0; i < N; i++) {
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ConstEigenArrayMap<T> x_e(x_d, HxW, C);
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EigenArrayMap<T> y_e(y_d, HxW, C);
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y_e = (x_e.rowwise() * a_e.transpose()).rowwise() + b_e.transpose();
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x_d += stride;
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y_d += stride;
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}
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} else {
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int num = N * HxW;
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ConstEigenArrayMap<T> x_e(x_d, C, num);
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EigenArrayMap<T> y_e(y_d, C, num);
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y_e = (x_e.colwise() * a_e).colwise() + b_e;
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}
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}
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};
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template <typename DeviceContext, typename T>
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class AffineChannelGradKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto* x = ctx.Input<framework::Tensor>("X");
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auto* scale = ctx.Input<framework::Tensor>("Scale");
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auto* dy = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
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auto* dx = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
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auto* dscale =
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ctx.Output<framework::Tensor>(framework::GradVarName("Scale"));
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auto* dbias = ctx.Output<framework::Tensor>(framework::GradVarName("Bias"));
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const framework::DataLayout layout =
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framework::StringToDataLayout(ctx.Attr<std::string>("data_layout"));
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auto dims = x->dims();
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int N = dims[0];
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int C = layout == framework::DataLayout::kNCHW ? dims[1]
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: dims[dims.size() - 1];
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int HxW = x->numel() / N / C;
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auto* dy_d = dy->data<T>();
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auto* scale_d = scale->data<T>();
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ConstEigenVectorArrayMap<T> scale_e(scale_d, C);
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T* dx_d = dx ? dx->mutable_data<T>(ctx.GetPlace()) : nullptr;
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T* dscale_d = dscale ? dscale->mutable_data<T>(ctx.GetPlace()) : nullptr;
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T* dbias_d = dbias ? dbias->mutable_data<T>(ctx.GetPlace()) : nullptr;
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EigenVectorArrayMap<T> dscale_e(dscale_d, C);
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EigenVectorArrayMap<T> dbias_e(dbias_d, C);
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if (layout == framework::DataLayout::kNCHW) {
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// compute dscale and dbias
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int stride = C * HxW;
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auto* original_dy_d = dy_d;
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if (dscale && dbias) {
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auto* x_d = x->data<T>();
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for (int i = 0; i < N; i++) {
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ConstEigenArrayMap<T> x_e(x_d, HxW, C);
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ConstEigenArrayMap<T> dy_e(dy_d, HxW, C);
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if (i == 0) {
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dscale_e = (x_e * dy_e).colwise().sum();
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} else {
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dscale_e += (x_e * dy_e).colwise().sum();
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}
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if (i == 0) {
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dbias_e = dy_e.colwise().sum();
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} else {
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dbias_e += dy_e.colwise().sum();
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}
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x_d += stride;
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dy_d += stride;
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}
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}
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// compute dx
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if (dx) {
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dy_d = original_dy_d;
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for (int i = 0; i < N; i++) {
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ConstEigenArrayMap<T> dy_e(dy_d, HxW, C);
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EigenArrayMap<T> dx_e(dx_d, HxW, C);
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dx_e = dy_e.rowwise() * scale_e.transpose();
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dy_d += stride;
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dx_d += stride;
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}
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}
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} else {
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int num = N * HxW;
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ConstEigenArrayMap<T> dy_e(dy_d, C, num);
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// compute dscale and dbias
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if (dscale && dbias) {
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auto* x_d = x->data<T>();
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ConstEigenArrayMap<T> x_e(x_d, C, num);
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dscale_e = (x_e * dy_e).rowwise().sum();
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dbias_e = dy_e.rowwise().sum();
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}
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// compute dx
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if (dx) {
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EigenArrayMap<T> dx_e(dx_d, C, num);
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dx_e = dy_e.colwise() * scale_e;
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}
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}
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}
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};
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class AffineChannelNoNeedBufferVarsInference
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: public framework::NoNeedBufferVarsInference {
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public:
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using framework::NoNeedBufferVarsInference::NoNeedBufferVarsInference;
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private:
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inline bool HasOutput(const std::string& name) const {
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auto& outputs = Outputs();
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auto iter = outputs.find(name);
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if (iter == outputs.end() || iter->second.empty()) {
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return false;
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} else {
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return iter->second[0] != framework::kEmptyVarName;
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}
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}
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public:
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std::unordered_set<std::string> operator()() const override {
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if (!HasOutput(framework::GradVarName("Scale")) &&
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!HasOutput(framework::GradVarName("Bias"))) {
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return {"X"};
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} else {
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return {};
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}
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}
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};
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DECLARE_INPLACE_OP_INFERER(AffineChannelInplaceInferer, {"X", "Out"});
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DECLARE_INPLACE_OP_INFERER(AffineChannelGradInplaceInferer,
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{framework::GradVarName("Out"),
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framework::GradVarName("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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using CPU = paddle::platform::CPUDeviceContext;
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REGISTER_OPERATOR(affine_channel, ops::AffineChannelOp,
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ops::AffineChannelOpMaker, ops::AffineChannelGradMaker,
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ops::AffineChannelInplaceInferer);
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REGISTER_OPERATOR(affine_channel_grad, ops::AffineChannelOpGrad,
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ops::AffineChannelNoNeedBufferVarsInference,
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ops::AffineChannelGradInplaceInferer);
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REGISTER_OP_CPU_KERNEL(affine_channel, ops::AffineChannelKernel<CPU, float>,
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ops::AffineChannelKernel<CPU, double>);
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REGISTER_OP_CPU_KERNEL(affine_channel_grad,
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ops::AffineChannelGradKernel<CPU, float>,
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ops::AffineChannelGradKernel<CPU, double>);
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