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167 lines
4.5 KiB
167 lines
4.5 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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#pragma once
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#include <math.h>
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#include <stdlib.h>
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#include "paddle/fluid/platform/device_context.h"
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#include "paddle/fluid/platform/hostdevice.h"
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namespace paddle {
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namespace operators {
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namespace math {
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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 row_id) const {
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auto* x = x_ + cols_ * row_id;
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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_ * row_id;
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T tep_x, tep_y;
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for (size_t i = 0; i < cols_; ++i) {
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tep_x = x[i];
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tep_y = y[i];
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xx += tep_x * tep_x;
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yy += tep_y * tep_y;
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xy += tep_x * tep_y;
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}
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xx = sqrt(xx);
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yy = sqrt(yy);
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y_norm_[row_id] = yy;
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x_norm_[row_id] = xx;
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z_[row_id] = xy / (xx * yy);
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} else { // This can be wrote in a better way.
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T tep_x, tep_y;
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for (size_t i = 0; i < cols_; ++i) {
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tep_x = x[i];
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tep_y = y_[i];
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xx += tep_x * tep_x;
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yy += tep_y * tep_y;
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xy += tep_x * tep_y;
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}
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xx = sqrt(xx);
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yy = sqrt(yy);
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if (row_id == 0) y_norm_[0] = yy;
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x_norm_[row_id] = xx;
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z_[row_id] = 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 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 row_id) const {
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auto x_norm_square = x_norm_[row_id] * x_norm_[row_id];
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auto xy_norm_prod = x_norm_[row_id] * y_norm_[row_id];
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auto dz = dz_[row_id];
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auto z = z_[row_id];
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auto* dx = dx_ + cols_ * row_id;
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auto* x = x_ + cols_ * row_id;
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auto* y = y_ + cols_ * row_id;
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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 row_id) const {
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auto xy_norm_prod = x_norm_[row_id] * y_norm_[0];
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auto dz = dz_[row_id];
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auto z = z_[row_id];
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auto* x = x_ + cols_ * row_id;
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auto reciprocal_xy_norm_prod = 1 / xy_norm_prod;
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auto x_norm_square = x_norm_[row_id] * x_norm_[row_id];
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auto* dx = dx_ + cols_ * row_id;
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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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struct CosSimDyFunctor {
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void operator()(const DeviceContext& ctx, const T* x_norm, const T* y_norm,
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const T* x, const T* y, const T* z, const T* dz,
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const size_t rows, const size_t cols, T* dy) const;
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};
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} // namespace math
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} // namespace operators
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} // namespace paddle
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