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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,170 @@ | ||
| // Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. | ||
| // | ||
| // 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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| #pragma once | ||
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| #include <algorithm> | ||
| #include <vector> | ||
| #include "paddle/fluid/operators/reduce_ops/reduce_op_function.h" | ||
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| namespace paddle { | ||
| namespace operators { | ||
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| #define HANDLE_DIM(NDIM, RDIM) \ | ||
| if (ndim == NDIM && rdim == RDIM) { \ | ||
| paddle::operators::ReduceFunctor<DeviceContext, OutT, NDIM, RDIM, \ | ||
| LogsumexpFunctor>( \ | ||
| context.template device_context<DeviceContext>(), *input, output, \ | ||
| axis, keepdim); \ | ||
| } | ||
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| struct LogsumexpFunctor { | ||
| template <typename DeviceContext, typename X, typename Y, typename Dim> | ||
| void operator()(const DeviceContext& place, X* x, Y* y, const Dim& dim) { | ||
| auto x_dim = x->dimensions(); | ||
| auto t_dim = x_dim; | ||
| for (int i = 0; i < static_cast<int>(dim.size()); i++) { | ||
| t_dim[dim[i]] = 1; | ||
| } | ||
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| auto r_dim = x_dim; | ||
| for (int i = 0; i < static_cast<int>(r_dim.size()); i++) { | ||
| r_dim[i] = 1; | ||
| } | ||
| for (int i = 0; i < static_cast<int>(dim.size()); i++) { | ||
| r_dim[dim[i]] = x_dim[dim[i]]; | ||
| } | ||
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| auto y_dim = y->dimensions(); | ||
| auto x_max = x->maximum(dim); | ||
| y->device(place) = | ||
| (x_max + | ||
| (*x - x_max.reshape(t_dim).broadcast(r_dim)).exp().sum(dim).log()) | ||
| .reshape(y_dim); | ||
| } | ||
| }; | ||
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| struct LogsumexpGradFunctor { | ||
| template <typename DeviceContext, typename X, typename Y, typename DX, | ||
| typename DY, typename Dim> | ||
| void operator()(const DeviceContext& place, X* x, Y* y, DX* dx, DY* dy, | ||
| const Dim& dim, int size) { | ||
| dx->device(place) = dy->broadcast(dim) * (*x - y->broadcast(dim)).exp(); | ||
| } | ||
| }; | ||
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| template <typename DeviceContext, typename OutT> | ||
| class LogsumexpKernel : public framework::OpKernel<OutT> { | ||
| public: | ||
| void Compute(const framework::ExecutionContext& context) const override { | ||
| auto* input = context.Input<Tensor>("X"); | ||
| auto* output = context.Output<Tensor>("Out"); | ||
| output->mutable_data<OutT>(context.GetPlace()); | ||
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| auto axis = context.Attr<std::vector<int>>("axis"); | ||
| auto keepdim = context.Attr<bool>("keepdim"); | ||
| auto reduce_all = context.Attr<bool>("reduce_all"); | ||
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| const auto& input_dim_size = input->dims().size(); | ||
| // The dims has full dim, set the reduce_all is True | ||
| reduce_all |= (static_cast<const int>(axis.size()) == input_dim_size); | ||
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| if (reduce_all) { | ||
| // Flatten and reduce 1-D tensor | ||
| auto x = EigenVector<OutT>::Flatten(*input); | ||
| auto out = EigenScalar<OutT>::From(*output); | ||
| auto& place = | ||
| *context.template device_context<DeviceContext>().eigen_device(); | ||
| auto reduce_dim = Eigen::array<int, 1>({{0}}); | ||
| LogsumexpFunctor()(place, &x, &out, reduce_dim); | ||
| } else { | ||
| int ndim = input_dim_size; | ||
| int rdim = axis.size(); | ||
| // comments for accelerating compiling temporarily. | ||
| // HANDLE_DIM(6, 5); | ||
| // HANDLE_DIM(6, 4); | ||
| // HANDLE_DIM(6, 3); | ||
| // HANDLE_DIM(6, 2); | ||
| // HANDLE_DIM(6, 1); | ||
| // HANDLE_DIM(5, 4); | ||
| // HANDLE_DIM(5, 3); | ||
| // HANDLE_DIM(5, 2); | ||
| // HANDLE_DIM(5, 1); | ||
| HANDLE_DIM(4, 3); | ||
| HANDLE_DIM(4, 2); | ||
| HANDLE_DIM(4, 1); | ||
| HANDLE_DIM(3, 2); | ||
| HANDLE_DIM(3, 1); | ||
| HANDLE_DIM(2, 1); | ||
| } | ||
| } | ||
| }; | ||
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| template <typename DeviceContext, typename T> | ||
| class LogsumexpGradKernel : public framework::OpKernel<T> { | ||
| public: | ||
| void Compute(const framework::ExecutionContext& context) const override { | ||
| auto* input = context.Input<Tensor>("X"); | ||
| auto* output = context.Input<Tensor>("Out"); | ||
| auto* output_grad = context.Input<Tensor>(framework::GradVarName("Out")); | ||
| auto* input_grad = context.Output<Tensor>(framework::GradVarName("X")); | ||
| input_grad->mutable_data<T>(context.GetPlace()); | ||
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| auto axis = context.Attr<std::vector<int>>("axis"); | ||
| auto reduce_all = context.Attr<bool>("reduce_all"); | ||
| const auto input_dim_size = context.Input<Tensor>("X")->dims().size(); | ||
| reduce_all |= (static_cast<const int>(axis.size()) == input_dim_size); | ||
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| if (reduce_all) { | ||
| auto x = EigenVector<T>::Flatten(*input); | ||
| auto y = EigenVector<T>::Flatten(*output); | ||
| auto dy = EigenVector<T>::Flatten(*output_grad); | ||
| auto dx = EigenVector<T>::Flatten(*input_grad); | ||
| auto& place = | ||
| *context.template device_context<DeviceContext>().eigen_device(); | ||
| auto broadcast_dim = | ||
| Eigen::array<int, 1>({{static_cast<int>(input->numel())}}); | ||
| LogsumexpGradFunctor()(place, &x, &y, &dx, &dy, broadcast_dim, | ||
| broadcast_dim[0]); | ||
| } else { | ||
| int rank = input->dims().size(); | ||
| LogsumexpGradFunctor functor; | ||
| switch (rank) { | ||
| case 1: | ||
| ReduceGradFunctor<DeviceContext, T, 1, LogsumexpGradFunctor>( | ||
| context.template device_context<DeviceContext>(), *input, *output, | ||
| *output_grad, input_grad, functor, axis); | ||
| break; | ||
| case 2: | ||
| ReduceGradFunctor<DeviceContext, T, 2, LogsumexpGradFunctor>( | ||
| context.template device_context<DeviceContext>(), *input, *output, | ||
| *output_grad, input_grad, functor, axis); | ||
| break; | ||
| case 3: | ||
| ReduceGradFunctor<DeviceContext, T, 3, LogsumexpGradFunctor>( | ||
| context.template device_context<DeviceContext>(), *input, *output, | ||
| *output_grad, input_grad, functor, axis); | ||
| break; | ||
| case 4: | ||
| ReduceGradFunctor<DeviceContext, T, 4, LogsumexpGradFunctor>( | ||
| context.template device_context<DeviceContext>(), *input, *output, | ||
| *output_grad, input_grad, functor, axis); | ||
| break; | ||
| } | ||
| } | ||
| } | ||
| }; | ||
|
|
||
| } // namespace operators | ||
| } // namespace paddle |
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