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Merge pull request #6601 from chengduoZH/profiling/cosine_op
Refine cos-sim-op
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,48 @@ | ||
| /* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. | ||
| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software | ||
| distributed under the License is distributed on an "AS IS" BASIS, | ||
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| See the License for the specific language governing permissions and | ||
| limitations under the License. */ | ||
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| #include "paddle/operators/math/cos_sim_functor.h" | ||
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| namespace paddle { | ||
| namespace operators { | ||
| namespace math { | ||
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| template <typename T> | ||
| struct CosSimDyFunctor<platform::CPUDeviceContext, T> { | ||
| void operator()(const platform::CPUDeviceContext& ctx, const T* x_norm, | ||
| const T* y_norm, const T* x, const T* y, const T* z, | ||
| const T* dz, const size_t rows, const size_t cols, | ||
| T* dy) const { | ||
| for (size_t row_id = 0; row_id < rows; ++row_id) { | ||
| auto xy_norm_prod = x_norm[row_id] * y_norm[0]; | ||
| auto dz_data = dz[row_id]; | ||
| auto z_data = z[row_id]; | ||
| auto* x_data = x + cols * row_id; | ||
| auto reciprocal_xy_norm_prod = 1 / xy_norm_prod; | ||
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| auto y_norm_square = y_norm[0] * y_norm[0]; | ||
| auto reciprocal_y_norm_square = 1 / y_norm_square; | ||
| for (size_t i = 0; i < cols; ++i) { | ||
| dy[i] += dz_data * (x_data[i] * reciprocal_xy_norm_prod - | ||
| z_data * y[i] * reciprocal_y_norm_square); | ||
| } | ||
| } | ||
| } | ||
| }; | ||
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| template class CosSimDyFunctor<platform::CPUDeviceContext, float>; | ||
| template class CosSimDyFunctor<platform::CPUDeviceContext, double>; | ||
| } // namespace math | ||
| } // namespace operators | ||
| } // namespace paddle |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,64 @@ | ||
| /* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. | ||
| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software | ||
| distributed under the License is distributed on an "AS IS" BASIS, | ||
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| See the License for the specific language governing permissions and | ||
| limitations under the License. */ | ||
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| #include "paddle/operators/math/cos_sim_functor.h" | ||
| #include "paddle/platform/cuda_helper.h" | ||
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| namespace paddle { | ||
| namespace operators { | ||
| namespace math { | ||
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| template <typename T> | ||
| __global__ void CosSimDyKernel(const T* x_norm, const T* y_norm, const T* x, | ||
| const T* y, const T* z, const T* dz, | ||
| const size_t rows, const size_t cols, T* dy) { | ||
| int grid_size = blockDim.x * gridDim.x; | ||
| T y_norm_data = y_norm[0]; | ||
| for (int row_id = blockIdx.x * blockDim.x + threadIdx.x; row_id < rows; | ||
| row_id += grid_size) { | ||
| T xy_norm_prod = x_norm[row_id] * y_norm_data; | ||
| T dz_data = dz[row_id]; | ||
| T z_data = z[row_id]; | ||
| const T* x_data = x + cols * row_id; | ||
| T reciprocal_xy_norm_prod = 1 / xy_norm_prod; | ||
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| T y_norm_square = y_norm_data * y_norm_data; | ||
| T reciprocal_y_norm_square = 1 / y_norm_square; | ||
| for (size_t i = 0; i < cols; ++i) { | ||
| T dy_data = dz_data * (x_data[i] * reciprocal_xy_norm_prod - | ||
| z_data * y[i] * reciprocal_y_norm_square); | ||
| platform::CudaAtomicAdd(dy + i, dy_data); | ||
| } | ||
| } | ||
| } | ||
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| template <typename T> | ||
| struct CosSimDyFunctor<platform::CUDADeviceContext, T> { | ||
| void operator()(const platform::CUDADeviceContext& ctx, const T* x_norm, | ||
| const T* y_norm, const T* x, const T* y, const T* z, | ||
| const T* dz, const size_t rows, const size_t cols, | ||
| T* dy) const { | ||
| const int block_size = 512; | ||
| dim3 threads(block_size, 1); | ||
| dim3 grid(1, (rows + block_size - 1) / block_size); | ||
| CosSimDyKernel<T><<<grid, threads, 0, ctx.stream()>>>( | ||
| x_norm, y_norm, x, y, z, dz, rows, cols, dy); | ||
| } | ||
| }; | ||
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| template class CosSimDyFunctor<platform::CUDADeviceContext, float>; | ||
| template class CosSimDyFunctor<platform::CUDADeviceContext, double>; | ||
| } // namespace math | ||
| } // namespace operators | ||
| } // namespace paddle |
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