257 lines
8.0 KiB
257 lines
8.0 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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#pragma once
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#include "paddle/framework/op_registry.h"
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#include "paddle/operators/elementwise_op_function.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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template <typename DeviceContext, typename T>
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struct CosSimDyFunctor {
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CosSimDyFunctor(const T* x_norm, const T* y_norm, const T* x, const T* y,
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const T* z, const T* dz, T* dy, int cols);
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inline void operator()(size_t) const;
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};
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template <typename Callback>
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static void ForEachZip(size_t num, Callback callback) {
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for (size_t i = 0; i < num; ++i) {
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callback(i);
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}
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}
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template <typename T, bool same_row>
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struct CosSimFunctor {
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CosSimFunctor(const T* x, const T* y, T* x_norm, T* y_norm, T* z, int cols)
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: x_norm_(x_norm),
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y_norm_(y_norm),
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x_(x),
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y_(y),
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z_(z),
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cols_(static_cast<size_t>(cols)) {}
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inline HOSTDEVICE void operator()(size_t offset) const {
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auto* x = x_ + cols_ * offset;
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T xx = 0, xy = 0, yy = 0;
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if (same_row) {
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auto* y = y_ + cols_ * offset;
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for (size_t i = 0; i < cols_; ++i) {
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xx += x[i] * x[i];
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yy += y[i] * y[i];
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xy += x[i] * y[i];
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}
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xx = sqrt(xx);
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yy = sqrt(yy);
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y_norm_[offset] = yy;
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x_norm_[offset] = xx;
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z_[offset] = xy / (xx * yy);
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} else { // This can be wrote in a better way.
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for (size_t i = 0; i < cols_; ++i) {
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xx += x[i] * x[i];
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yy += y_[i] * y_[i]; // only need
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xy += x[i] * y_[i];
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}
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xx = sqrt(xx);
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yy = sqrt(yy);
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y_norm_[0] = yy;
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x_norm_[offset] = xx;
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z_[offset] = xy / (xx * yy);
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}
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}
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T* x_norm_;
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T* y_norm_;
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const T* x_;
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const T* y_;
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T* z_;
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const size_t cols_;
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};
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template <typename DeviceContext, typename T>
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class CosSimKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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// get Tensor
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auto* in_x = context.Input<Tensor>("X");
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auto* in_y = context.Input<Tensor>("Y");
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auto* out_z = context.Output<Tensor>("Out");
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auto* out_x_norm = context.Output<Tensor>("XNorm");
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auto* out_y_norm = context.Output<Tensor>("YNorm");
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out_z->mutable_data<T>(context.GetPlace());
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out_x_norm->mutable_data<T>(context.GetPlace());
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out_y_norm->mutable_data<T>(context.GetPlace());
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int rows_x = in_x->dims()[0];
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int rows_y = in_y->dims()[0];
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int cols = framework::product(in_x->dims()) / rows_x;
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if (rows_x == rows_y) {
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CosSimFunctor<T, true> functor(
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in_x->data<T>(), in_y->data<T>(), out_x_norm->data<T>(),
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out_y_norm->data<T>(), out_z->data<T>(), cols);
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ForEachZip(rows_x, functor);
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} else {
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CosSimFunctor<T, false> functor(
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in_x->data<T>(), in_y->data<T>(), out_x_norm->data<T>(),
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out_y_norm->data<T>(), out_z->data<T>(), cols);
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ForEachZip(rows_x, functor);
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}
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}
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};
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template <typename T>
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struct CosSimGradFunctor {
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CosSimGradFunctor(const T* x_norm, const T* y_norm, const T* x, const T* y,
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const T* z, const T* dz, T* dx, int cols)
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: x_norm_(x_norm),
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y_norm_(y_norm),
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x_(x),
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y_(y),
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z_(z),
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dz_(dz),
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dx_(dx),
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cols_(static_cast<size_t>(cols)) {}
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inline HOSTDEVICE void operator()(size_t offset) const {
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auto x_norm_square = x_norm_[offset] * x_norm_[offset];
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auto xy_norm_prod = x_norm_[offset] * y_norm_[offset];
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auto dz = dz_[offset];
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auto z = z_[offset];
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auto* dx = dx_ + cols_ * offset;
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auto* x = x_ + cols_ * offset;
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auto* y = y_ + cols_ * offset;
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auto reciprocal_xy_norm_prod = 1 / xy_norm_prod;
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auto reciprocal_x_norm_square = 1 / x_norm_square;
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for (size_t i = 0; i < cols_; ++i) {
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dx[i] = dz * (y[i] * reciprocal_xy_norm_prod -
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z * x[i] * reciprocal_x_norm_square);
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}
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}
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const T* x_norm_;
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const T* y_norm_;
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const T* x_;
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const T* y_;
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const T* z_;
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const T* dz_;
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T* dx_;
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const size_t cols_;
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};
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template <typename T>
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struct CosSimDxFunctor {
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CosSimDxFunctor(const T* x_norm, const T* y_norm, const T* x, const T* y,
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const T* z, const T* dz, T* dx, int cols)
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: x_norm_(x_norm),
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y_norm_(y_norm),
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x_(x),
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y_(y),
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z_(z),
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dz_(dz),
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dx_(dx),
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cols_(static_cast<size_t>(cols)) {}
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inline HOSTDEVICE void operator()(size_t offset) const {
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auto xy_norm_prod = x_norm_[offset] * y_norm_[0];
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auto dz = dz_[offset];
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auto z = z_[offset];
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auto* x = x_ + cols_ * offset;
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auto reciprocal_xy_norm_prod = 1 / xy_norm_prod;
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auto x_norm_square = x_norm_[offset] * x_norm_[offset];
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auto* dx = dx_ + cols_ * offset;
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auto reciprocal_x_norm_square = 1 / x_norm_square;
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for (size_t i = 0; i < cols_; ++i) {
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dx[i] = dz * (y_[i] * reciprocal_xy_norm_prod -
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z * x[i] * reciprocal_x_norm_square);
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}
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}
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const T* x_norm_;
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const T* y_norm_;
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const T* x_;
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const T* y_;
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const T* z_;
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const T* dz_;
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T* dx_;
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const size_t cols_;
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};
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template <typename DeviceContext, typename T>
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class CosSimGradKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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// get Tensor
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auto* in_x = context.Input<Tensor>("X");
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auto* in_y = context.Input<Tensor>("Y");
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auto* in_z = context.Input<Tensor>("Out");
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auto* in_x_norm = context.Input<Tensor>("XNorm");
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auto* in_y_norm = context.Input<Tensor>("YNorm");
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auto* out_grad_x = context.Output<Tensor>(framework::GradVarName("X"));
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auto* out_grad_y = context.Output<Tensor>(framework::GradVarName("Y"));
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auto* in_grad_z = context.Input<Tensor>(framework::GradVarName("Out"));
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// compute gradident
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int rows_x = in_x->dims()[0];
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int rows_y = in_y->dims()[0];
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int cols = framework::product(in_x->dims()) / rows_x;
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if (rows_x == rows_y) {
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if (out_grad_x) {
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CosSimGradFunctor<T> functor(
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in_x_norm->data<T>(), in_y_norm->data<T>(), in_x->data<T>(),
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in_y->data<T>(), in_z->data<T>(), in_grad_z->data<T>(),
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out_grad_x->mutable_data<T>(context.GetPlace()), cols);
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ForEachZip(rows_x, functor);
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}
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if (out_grad_y) {
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CosSimGradFunctor<T> functor(
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in_y_norm->data<T>(), in_x_norm->data<T>(), in_y->data<T>(),
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in_x->data<T>(), in_z->data<T>(), in_grad_z->data<T>(),
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out_grad_y->mutable_data<T>(context.GetPlace()), cols);
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ForEachZip(rows_x, functor);
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}
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} else {
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if (out_grad_x) {
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CosSimDxFunctor<T> functor(
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in_x_norm->data<T>(), in_y_norm->data<T>(), in_x->data<T>(),
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in_y->data<T>(), in_z->data<T>(), in_grad_z->data<T>(),
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out_grad_x->mutable_data<T>(context.GetPlace()), cols);
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ForEachZip(rows_x, functor);
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}
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if (out_grad_y) {
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out_grad_y->mutable_data<T>(context.GetPlace());
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math::SetConstant<DeviceContext, T> set_zero;
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auto& dev_ctx = context.template device_context<DeviceContext>();
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set_zero(dev_ctx, out_grad_y, static_cast<T>(0));
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CosSimDyFunctor<DeviceContext, T> functor(
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in_x_norm->data<T>(), in_y_norm->data<T>(), in_x->data<T>(),
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in_y->data<T>(), in_z->data<T>(), in_grad_z->data<T>(),
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out_grad_y->data<T>(), cols);
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ForEachZip(rows_x, functor);
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}
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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