/* 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. */ #pragma once #include "paddle/framework/eigen.h" #include "paddle/framework/op_registry.h" #include "paddle/operators/math/softmax_function.h" namespace paddle { namespace operators { using Tensor = framework::Tensor; using LoDTensor = framework::LoDTensor; template using EigenVector = framework::EigenVector; template using EigenMatrix = framework::EigenMatrix; template class SequenceSoftmaxKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { auto* x = ctx.Input("X"); auto* out = ctx.Output("Out"); auto lod = x->lod(); const size_t level = lod.size() - 1; out->mutable_data(ctx.GetPlace()); for (int i = 0; i < static_cast(lod[level].size()) - 1; ++i) { int start_pos = static_cast(lod[level][i]); int end_pos = static_cast(lod[level][i + 1]); Tensor x_i = x->Slice(start_pos, end_pos); Tensor out_i = out->Slice(start_pos, end_pos); // Reshape from (end_pos - start_pos) x 1UL to 1UL x (end_pos - start_pos) framework::DDim dims = framework::make_ddim({1UL, end_pos - start_pos}); x_i.Resize(dims); out_i.Resize(dims); math::SoftmaxFunctor()(&x_i, &out_i, ctx); } } }; template class SequenceSoftmaxGradKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override {} }; } // namespace operators } // namespace paddle