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114 lines
3.8 KiB
114 lines
3.8 KiB
/* Copyright (c) 2018 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 <array>
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#include "paddle/fluid/framework/eigen.h"
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/framework/operator.h"
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#include "paddle/fluid/operators/detail/safe_ref.h"
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namespace paddle {
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namespace operators {
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template <typename DeviceContext, typename Functor>
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class CumKernel : public framework::OpKernel<typename Functor::ELEMENT_TYPE> {
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public:
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using T = typename Functor::ELEMENT_TYPE;
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void Compute(const framework::ExecutionContext& context) const override {
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auto& X = detail::Ref(context.Input<framework::Tensor>("X"),
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"Cannot get input tensor X, variable name = %s",
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context.InputName("X"));
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auto& Out = detail::Ref(context.Output<framework::Tensor>("Out"),
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"Cannot get output tensor Out, variable name = %s",
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context.OutputName("Out"));
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int axis = context.Attr<int>("axis");
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bool exclusive = context.Attr<bool>("exclusive");
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bool reverse = context.Attr<bool>("reverse");
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auto x_dims = X.dims();
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if (axis == -1) {
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axis = x_dims.size() - 1;
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}
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PADDLE_ENFORCE_LT(
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axis, x_dims.size(),
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"axis should be less than the dimensiotn of the input tensor");
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Out.mutable_data<T>(context.GetPlace());
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int pre = 1;
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int post = 1;
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int mid = x_dims[axis];
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for (int i = 0; i < axis; ++i) {
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pre *= x_dims[i];
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}
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for (int i = axis + 1; i < x_dims.size(); ++i) {
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post *= x_dims[i];
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}
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auto x = framework::EigenVector<T>::Flatten(X);
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auto out = framework::EigenVector<T>::Flatten(Out);
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auto* place =
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context.template device_context<DeviceContext>().eigen_device();
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using IndexT = Eigen::DenseIndex;
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if (pre == 1) {
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if (post == 1) {
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ComputeImp(*place, Eigen::DSizes<IndexT, 1>(mid), x, out,
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/* axis= */ 0, reverse, exclusive);
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} else {
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ComputeImp(*place, Eigen::DSizes<IndexT, 2>(mid, post), x, out,
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/* axis= */ 0, reverse, exclusive);
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}
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} else {
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if (post == 1) {
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ComputeImp(*place, Eigen::DSizes<IndexT, 2>(pre, mid), x, out,
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/* axis= */ 1, reverse, exclusive);
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} else {
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ComputeImp(*place, Eigen::DSizes<IndexT, 3>(pre, mid, post), x, out,
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/* axis= */ 1, reverse, exclusive);
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}
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}
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}
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private:
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template <typename Device, typename Dim, typename X, typename Out>
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void ComputeImp(Device d, const Dim& dims, X x, Out out, int axis,
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bool reverse, bool exclusive) const {
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if (!reverse) {
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out.reshape(dims).device(d) = Functor()(x.reshape(dims), axis, exclusive);
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} else {
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std::array<bool, Dim::count> rev;
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rev.fill(false);
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rev[axis] = reverse;
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out.reshape(dims).device(d) =
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Functor()(x.reshape(dims).reverse(rev), axis, exclusive).reverse(rev);
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}
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}
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};
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template <typename T>
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struct CumsumFunctor {
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using ELEMENT_TYPE = T;
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template <typename X>
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const typename X::TensorScanSumOp operator()(X x, int axis,
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bool exclusive) const {
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return x.cumsum(axis, exclusive);
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}
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};
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} // namespace operators
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} // namespace paddle
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