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419 lines
16 KiB
419 lines
16 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/data_norm_op.h"
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#include <memory>
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#include <string>
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#include "paddle/fluid/framework/data_layout.h"
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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 Tensor = framework::Tensor;
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using LoDTensor = framework::LoDTensor;
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using DataLayout = framework::DataLayout;
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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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class DataNormOp : 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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PADDLE_ENFORCE(ctx->HasInput("BatchSize"), "");
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PADDLE_ENFORCE(ctx->HasInput("BatchSum"), "");
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PADDLE_ENFORCE(ctx->HasInput("BatchSquareSum"), "");
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PADDLE_ENFORCE(ctx->HasOutput("Means"), "");
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PADDLE_ENFORCE(ctx->HasOutput("Scales"), "");
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PADDLE_ENFORCE(ctx->HasOutput("Y"), "");
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const auto x_dims = ctx->GetInputDim("X");
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const DataLayout data_layout = framework::StringToDataLayout(
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ctx->Attrs().Get<std::string>("data_layout"));
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PADDLE_ENFORCE(x_dims.size() >= 2 && x_dims.size() <= 5,
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"Input X must have 2 to 5 dimensions.");
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const int64_t C =
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(data_layout == DataLayout::kNCHW ? x_dims[1]
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: x_dims[x_dims.size() - 1]);
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PADDLE_ENFORCE_EQ(ctx->GetInputDim("BatchSize").size(), 1UL);
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PADDLE_ENFORCE_EQ(ctx->GetInputDim("BatchSum").size(), 1UL);
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PADDLE_ENFORCE_EQ(ctx->GetInputDim("BatchSquareSum").size(), 1UL);
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if (ctx->IsRuntime()) {
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PADDLE_ENFORCE_EQ(ctx->GetInputDim("BatchSize")[0], C);
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PADDLE_ENFORCE_EQ(ctx->GetInputDim("BatchSum")[0], C);
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PADDLE_ENFORCE_EQ(ctx->GetInputDim("BatchSquareSum")[0], C);
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}
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ctx->SetOutputDim("Y", x_dims);
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ctx->SetOutputDim("Means", {C});
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ctx->SetOutputDim("Scales", {C});
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ctx->ShareLoD("X", "Y");
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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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auto input_data_type = ctx.Input<Tensor>("X")->type();
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// By default, the type of the scale, bias, mean,
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// and var tensors should both be float. (For float or float16 input tensor)
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// or double (For double input tensor).
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auto dn_param_type = framework::proto::VarType::FP32;
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if (input_data_type == framework::proto::VarType::FP64) {
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dn_param_type = framework::proto::VarType::FP64;
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}
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PADDLE_ENFORCE_EQ(dn_param_type, ctx.Input<Tensor>("BatchSize")->type(),
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"BatchSize input should be of float type");
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PADDLE_ENFORCE_EQ(dn_param_type, ctx.Input<Tensor>("BatchSum")->type(),
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"BatchSum input should be of float type");
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PADDLE_ENFORCE_EQ(dn_param_type,
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ctx.Input<Tensor>("BatchSquareSum")->type(),
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"BatchSquareSum input should be of float type");
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// TODO(pzelazko-intel): enable MKLDNN layout when it's ready
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framework::LibraryType library = framework::LibraryType::kPlain;
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framework::DataLayout layout = framework::DataLayout::kAnyLayout;
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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(input_data_type, ctx.GetPlace(), layout,
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library);
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}
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};
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class DataNormOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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// AddAttr<bool>("is_test", "").SetDefault(false);
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AddAttr<float>("epsilon", "")
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.SetDefault(1e-4)
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.AddCustomChecker([](const float &epsilon) {
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PADDLE_ENFORCE(epsilon >= 0.0f && epsilon <= 0.001f,
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"'epsilon' should be between 0.0 and 0.001.");
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});
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AddAttr<std::string>("data_layout", "").SetDefault("NCHW");
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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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AddInput("X", "The input tensor");
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AddInput("BatchSize",
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"BatchSize is a 1-dimensional tensor of size C "
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"that is applied to the output");
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AddInput("BatchSum",
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"BatchSum is a 1-dimensional tensor of size C "
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"that is applied to the output");
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AddInput("BatchSquareSum",
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"The global BatchSquareSum (for training) or "
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"estimated BatchSquareSum (for testing)");
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AddOutput("Y", "result after normalization");
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AddOutput("Means",
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"Mean of the history data batch, "
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"will apply to output when training")
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.AsIntermediate();
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AddOutput("Scales",
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"Scales of the history data batch, "
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"will apply to output when training")
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.AsIntermediate();
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AddComment(R"DOC(
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Data Normalization.
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Can be used as a normalizer function for data
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The required data format for this layer is one of the following:
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1. NHWC `[batch, in_height, in_width, in_channels]`
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2. NCHW `[batch, in_channels, in_height, in_width]`
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)DOC");
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}
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};
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template <typename T>
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class DataNormKernel<platform::CPUDeviceContext, T>
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: 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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// const bool is_test = ctx.Attr<bool>("is_test");
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const std::string data_layout_str = ctx.Attr<std::string>("data_layout");
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const DataLayout data_layout =
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framework::StringToDataLayout(data_layout_str);
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const auto *x = ctx.Input<Tensor>("X");
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const auto &x_dims = x->dims();
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PADDLE_ENFORCE(x_dims.size() == 2, "The Input dim size should be 2");
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const int N = x_dims[0];
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const int C =
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(data_layout == DataLayout::kNCHW ? x_dims[1]
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: x_dims[x_dims.size() - 1]);
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auto *y = ctx.Output<Tensor>("Y");
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auto *mean_out = ctx.Output<Tensor>("Means");
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auto *scales = ctx.Output<Tensor>("Scales");
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// alloc memory
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y->mutable_data<T>(ctx.GetPlace());
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Eigen::Array<T, Eigen::Dynamic, 1> inv_std(C);
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ConstEigenVectorArrayMap<T> b_size_arr(
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ctx.Input<Tensor>("BatchSize")->data<T>(), C);
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ConstEigenVectorArrayMap<T> b_sum_arr(
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ctx.Input<Tensor>("BatchSum")->data<T>(), C);
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ConstEigenVectorArrayMap<T> b_square_sum_arr(
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ctx.Input<Tensor>("BatchSquareSum")->data<T>(), C);
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EigenVectorArrayMap<T> means_arr(mean_out->mutable_data<T>(ctx.GetPlace()),
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C);
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EigenVectorArrayMap<T> scales_arr(scales->mutable_data<T>(ctx.GetPlace()),
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C);
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means_arr = b_sum_arr / b_size_arr;
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scales_arr = (b_size_arr / b_square_sum_arr).sqrt();
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switch (data_layout) {
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case DataLayout::kNCHW: // because it's two dimensions, so make no
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// difference
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case DataLayout::kNHWC: {
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EigenArrayMap<T>(y->mutable_data<T>(ctx.GetPlace()), C, N) =
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(ConstEigenArrayMap<T>(x->data<T>(), C, N).colwise() - means_arr)
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.colwise() *
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scales_arr;
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break;
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}
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default:
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PADDLE_THROW("Unknown storage order: %d", data_layout);
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}
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}
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};
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class DataNormGradOp : 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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// check input
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PADDLE_ENFORCE(ctx->HasInput("X"));
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PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Y")), "");
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PADDLE_ENFORCE(ctx->HasInput("BatchSize"), "");
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PADDLE_ENFORCE(ctx->HasInput("BatchSum"), "");
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PADDLE_ENFORCE(ctx->HasInput("BatchSquareSum"), "");
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PADDLE_ENFORCE(ctx->HasInput("Means"), "");
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PADDLE_ENFORCE(ctx->HasInput("Scales"), "");
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// check output
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PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("BatchSize")), "");
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PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("BatchSum")), "");
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PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("BatchSquareSum")),
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"");
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const auto x_dims = ctx->GetInputDim("X");
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const DataLayout data_layout = framework::StringToDataLayout(
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ctx->Attrs().Get<std::string>("data_layout"));
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const int C =
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(data_layout == DataLayout::kNCHW ? x_dims[1]
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: x_dims[x_dims.size() - 1]);
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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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ctx->SetOutputDim(framework::GradVarName("BatchSize"), {C});
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ctx->SetOutputDim(framework::GradVarName("BatchSum"), {C});
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ctx->SetOutputDim(framework::GradVarName("BatchSquareSum"), {C});
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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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const auto *var = ctx.InputVar(framework::GradVarName("Y"));
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if (var == nullptr) {
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PADDLE_THROW("can't find Y@GRAD");
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}
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const Tensor *t = nullptr;
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if (var->IsType<Tensor>()) {
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t = &var->Get<Tensor>();
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} else if (var->IsType<LoDTensor>()) {
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t = &var->Get<LoDTensor>();
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}
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if (t == nullptr) {
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PADDLE_THROW("can't find Y@GRAD");
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}
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// TODO(pzelazko-intel): enable MKLDNN layout when it's ready
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framework::LibraryType library = framework::LibraryType::kPlain;
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framework::DataLayout layout = framework::DataLayout::kAnyLayout;
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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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template <typename T>
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class DataNormGradKernel<platform::CPUDeviceContext, T>
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: 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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const auto *x = ctx.Input<Tensor>("X");
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const auto *d_y = ctx.Input<Tensor>(framework::GradVarName("Y"));
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const auto *batch_size = ctx.Input<Tensor>("BatchSize");
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const auto *batch_sum = ctx.Input<Tensor>("BatchSum");
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const auto *batch_square_sum = ctx.Input<Tensor>("BatchSquareSum");
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const auto *scales = ctx.Input<Tensor>("Scales");
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const auto *means = ctx.Input<Tensor>("Means");
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const std::string data_layout_str = ctx.Attr<std::string>("data_layout");
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const DataLayout data_layout =
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framework::StringToDataLayout(data_layout_str);
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// Get the size for each dimension.
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// NCHW [batch_size, in_channels, in_height, in_width]
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const auto &x_dims = x->dims();
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PADDLE_ENFORCE(x_dims.size() == 2, "The Input dim size should be 2");
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const int N = x_dims[0];
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const int C =
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(data_layout == DataLayout::kNCHW ? x_dims[1]
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: x_dims[x_dims.size() - 1]);
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// init output
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Tensor *d_x = nullptr;
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if (ctx.HasOutput(framework::GradVarName("X"))) {
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d_x = ctx.Output<Tensor>(framework::GradVarName("X"));
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}
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auto *d_batch_size =
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ctx.Output<Tensor>(framework::GradVarName("BatchSize"));
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auto *d_batch_sum = ctx.Output<Tensor>(framework::GradVarName("BatchSum"));
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auto *d_batch_square_sum =
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ctx.Output<Tensor>(framework::GradVarName("BatchSquareSum"));
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EigenVectorArrayMap<T> d_batch_size_arr(
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d_batch_size->mutable_data<T>(ctx.GetPlace()), C);
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EigenVectorArrayMap<T> d_batch_sum_arr(
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d_batch_sum->mutable_data<T>(ctx.GetPlace()), C);
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EigenVectorArrayMap<T> d_batch_square_sum_arr(
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d_batch_square_sum->mutable_data<T>(ctx.GetPlace()), C);
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d_batch_size_arr.setZero();
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d_batch_sum_arr.setZero();
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d_batch_square_sum_arr.setZero();
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const float epsilon = ctx.Attr<float>("epsilon");
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switch (
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data_layout) { // because it's two dimensions, so make no difference
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case DataLayout::kNCHW:
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case DataLayout::kNHWC: {
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ConstEigenVectorArrayMap<T> scales_arr(scales->data<T>(), C);
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ConstEigenVectorArrayMap<T> means_arr(means->data<T>(), C);
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ConstEigenArrayMap<T> x_arr(x->data<T>(), C, N);
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ConstEigenArrayMap<T> d_y_arr(d_y->data<T>(), C, N);
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if (d_x != nullptr) {
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EigenArrayMap<T> d_x_arr(d_x->mutable_data<T>(ctx.GetPlace()), C, N);
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d_x_arr.setZero();
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for (int nc = 0; nc < N; ++nc) {
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d_x_arr.col(nc) = d_y_arr.col(nc) * scales_arr;
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}
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}
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// calculate data sum and squre sum
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ConstEigenVectorArrayMap<T> batch_size_arr(batch_size->data<T>(), C);
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ConstEigenVectorArrayMap<T> batch_sum_arr(batch_sum->data<T>(), C);
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ConstEigenVectorArrayMap<T> batch_square_sum_arr(
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batch_square_sum->data<T>(), C);
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Eigen::Array<T, Eigen::Dynamic, 1> sample_sum(C);
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Eigen::Array<T, Eigen::Dynamic, 1> sample_square_sum(C);
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// calculate data sample sum and square sum
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sample_sum.setZero();
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sample_square_sum.setZero();
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for (int nc = 0; nc < N; ++nc) {
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sample_sum += x_arr.col(nc);
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sample_square_sum += (x_arr.col(nc) - means_arr).square();
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}
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// calculate gradient
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d_batch_size_arr.setConstant(N);
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d_batch_sum_arr = sample_sum;
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d_batch_square_sum_arr = sample_square_sum + d_batch_size_arr * epsilon;
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break;
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}
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default:
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PADDLE_THROW("Unknown storage order: %s", data_layout_str);
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}
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}
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};
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class DataNormGradMaker : public framework::SingleGradOpDescMaker {
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public:
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using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
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protected:
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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("data_norm_grad");
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op->SetInput("X", Input("X"));
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op->SetInput(framework::GradVarName("Y"), OutputGrad("Y"));
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op->SetInput("BatchSize", Input("BatchSize"));
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op->SetInput("BatchSum", Input("BatchSum"));
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op->SetInput("BatchSquareSum", Input("BatchSquareSum"));
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op->SetInput("Scales", Output("Scales"));
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op->SetInput("Means", Output("Means"));
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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("BatchSize"), InputGrad("BatchSize"));
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op->SetOutput(framework::GradVarName("BatchSum"), InputGrad("BatchSum"));
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op->SetOutput(framework::GradVarName("BatchSquareSum"),
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InputGrad("BatchSquareSum"));
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return std::unique_ptr<framework::OpDesc>(op);
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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(data_norm, ops::DataNormOp, ops::DataNormOpMaker,
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ops::DataNormGradMaker);
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REGISTER_OPERATOR(data_norm_grad, ops::DataNormGradOp);
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REGISTER_OP_CPU_KERNEL(
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data_norm, ops::DataNormKernel<paddle::platform::CPUDeviceContext, float>,
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ops::DataNormKernel<paddle::platform::CPUDeviceContext, double>);
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REGISTER_OP_CPU_KERNEL(
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data_norm_grad,
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ops::DataNormGradKernel<paddle::platform::CPUDeviceContext, float>,
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ops::DataNormGradKernel<paddle::platform::CPUDeviceContext, double>);
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