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69 lines
2.1 KiB
69 lines
2.1 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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#define EIGEN_USE_GPU
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#include "paddle/operators/cos_sim_op.h"
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namespace paddle {
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namespace operators {
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template <typename T>
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struct CosSimDyFunctor<platform::CUDADeviceContext, T> {
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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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: 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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dy_(dy),
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cols_(static_cast<size_t>(cols)) {}
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inline 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 y_norm_square = y_norm_[0] * y_norm_[0];
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auto reciprocal_y_norm_square = 1 / y_norm_square;
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for (size_t i = 0; i < cols_; ++i) {
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T dy = dz * (x[i] * reciprocal_xy_norm_prod -
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z * y_[i] * reciprocal_y_norm_square);
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paddle::paddleAtomicAdd(dy_ + i, dy)
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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* dy_;
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const size_t cols_;
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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_OP_CUDA_KERNEL(
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cos_sim, ops::CosSimKernel<paddle::platform::CUDADeviceContext, float>);
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REGISTER_OP_CUDA_KERNEL(
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cos_sim_grad,
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ops::CosSimGradKernel<paddle::platform::CUDADeviceContext, float>);
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