You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
112 lines
3.8 KiB
112 lines
3.8 KiB
/* Copyright (c) 2018 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. */
|
|
|
|
#pragma once
|
|
#include "paddle/fluid/framework/eigen.h"
|
|
#include "paddle/fluid/framework/op_registry.h"
|
|
#include "paddle/fluid/framework/operator.h"
|
|
#include "paddle/fluid/operators/detail/safe_ref.h"
|
|
|
|
namespace paddle {
|
|
namespace operators {
|
|
|
|
template <typename DeviceContext, typename Functor>
|
|
class CumKernel : public framework::OpKernel<typename Functor::ELEMENT_TYPE> {
|
|
public:
|
|
using T = typename Functor::ELEMENT_TYPE;
|
|
|
|
void Compute(const framework::ExecutionContext& context) const override {
|
|
auto& X = detail::Ref(context.Input<framework::Tensor>("X"),
|
|
"Cannot get input tensor X, variable name = %s",
|
|
context.op().Input("X"));
|
|
|
|
auto& Out = detail::Ref(context.Output<framework::Tensor>("Out"),
|
|
"Cannot get output tensor Out, variable name = %s",
|
|
context.op().Output("Out"));
|
|
int axis = context.Attr<int>("axis");
|
|
bool exclusive = context.Attr<bool>("exclusive");
|
|
bool reverse = context.Attr<bool>("reverse");
|
|
auto x_dims = X.dims();
|
|
if (axis == -1) {
|
|
axis = x_dims.size() - 1;
|
|
}
|
|
PADDLE_ENFORCE_LT(
|
|
axis, x_dims.size(),
|
|
"axis should be less than the dimensiotn of the input tensor");
|
|
Out.mutable_data<T>(context.GetPlace());
|
|
|
|
int pre = 1;
|
|
int post = 1;
|
|
int mid = x_dims[axis];
|
|
for (int i = 0; i < axis; ++i) {
|
|
pre *= x_dims[i];
|
|
}
|
|
for (int i = axis + 1; i < x_dims.size(); ++i) {
|
|
post *= x_dims[i];
|
|
}
|
|
|
|
auto x = framework::EigenVector<T>::Flatten(X);
|
|
auto out = framework::EigenVector<T>::Flatten(Out);
|
|
auto* place =
|
|
context.template device_context<DeviceContext>().eigen_device();
|
|
|
|
using IndexT = Eigen::DenseIndex;
|
|
if (pre == 1) {
|
|
if (post == 1) {
|
|
ComputeImp(*place, Eigen::DSizes<IndexT, 1>(mid), x, out,
|
|
/* axis= */ 0, reverse, exclusive);
|
|
} else {
|
|
ComputeImp(*place, Eigen::DSizes<IndexT, 2>(mid, post), x, out,
|
|
/* axis= */ 0, reverse, exclusive);
|
|
}
|
|
} else {
|
|
if (post == 1) {
|
|
ComputeImp(*place, Eigen::DSizes<IndexT, 2>(pre, mid), x, out,
|
|
/* axis= */ 1, reverse, exclusive);
|
|
} else {
|
|
ComputeImp(*place, Eigen::DSizes<IndexT, 3>(pre, mid, post), x, out,
|
|
/* axis= */ 1, reverse, exclusive);
|
|
}
|
|
}
|
|
}
|
|
|
|
private:
|
|
template <typename Device, typename Dim, typename X, typename Out>
|
|
void ComputeImp(Device d, const Dim& dims, X x, Out out, int axis,
|
|
bool reverse, bool exclusive) const {
|
|
if (!reverse) {
|
|
out.reshape(dims).device(d) = Functor()(x.reshape(dims), axis, exclusive);
|
|
} else {
|
|
std::array<bool, Dim::count> rev;
|
|
rev.fill(false);
|
|
rev[axis] = reverse;
|
|
out.reshape(dims).device(d) =
|
|
Functor()(x.reshape(dims).reverse(rev), axis, exclusive).reverse(rev);
|
|
}
|
|
}
|
|
};
|
|
|
|
template <typename T>
|
|
struct CumsumFunctor {
|
|
using ELEMENT_TYPE = T;
|
|
template <typename X>
|
|
const typename X::TensorScanSumOp operator()(X x, int axis,
|
|
bool exclusive) const {
|
|
return x.cumsum(axis, exclusive);
|
|
}
|
|
};
|
|
|
|
} // namespace operators
|
|
} // namespace paddle
|