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.
79 lines
2.8 KiB
79 lines
2.8 KiB
/* 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"
|
|
|
|
namespace paddle {
|
|
namespace operators {
|
|
|
|
template <typename Place, typename T>
|
|
class LoDResetKernel : public framework::OpKernel<T> {
|
|
public:
|
|
void Compute(const framework::ExecutionContext& ctx) const {
|
|
auto* out = ctx.Output<framework::LoDTensor>("Out");
|
|
auto* in = ctx.Input<framework::LoDTensor>("X");
|
|
auto* lod_t = ctx.Input<framework::Tensor>("TargetLoD");
|
|
|
|
std::vector<int> level0;
|
|
if (lod_t) {
|
|
auto* lod = lod_t->data<int>();
|
|
if (platform::is_gpu_place(ctx.GetPlace())) {
|
|
framework::Tensor lod_cpu;
|
|
lod_cpu.CopyFrom(*lod_t, platform::CPUPlace(), ctx.device_context());
|
|
lod = lod_cpu.data<int>();
|
|
}
|
|
level0 = std::vector<int>(lod, lod + lod_t->numel());
|
|
} else {
|
|
level0 = ctx.Attr<std::vector<int>>("target_lod");
|
|
}
|
|
|
|
PADDLE_ENFORCE(level0.size() > 1UL,
|
|
"The size of target LoD should be greater than 1.");
|
|
PADDLE_ENFORCE(level0[0] == 0,
|
|
"Target LoD should be a vector starting from 0.");
|
|
PADDLE_ENFORCE(level0.back() == in->dims()[0],
|
|
"Target LoD should be a vector end with the "
|
|
"first dimension of Input(X).");
|
|
for (size_t i = 0; i < level0.size() - 1; ++i) {
|
|
PADDLE_ENFORCE(level0[i + 1] > level0[i],
|
|
"Target LoD should be an ascending vector.");
|
|
}
|
|
|
|
out->ShareDataWith(*in);
|
|
// cast level0 to size_t
|
|
std::vector<size_t> ulevel0(level0.size(), 0);
|
|
std::transform(level0.begin(), level0.end(), ulevel0.begin(),
|
|
[](int a) { return static_cast<size_t>(a); });
|
|
framework::LoD target_lod;
|
|
target_lod.push_back(ulevel0);
|
|
out->set_lod(target_lod);
|
|
}
|
|
};
|
|
|
|
template <typename Place, typename T>
|
|
class LoDResetGradKernel : public framework::OpKernel<T> {
|
|
public:
|
|
void Compute(const framework::ExecutionContext& ctx) const {
|
|
auto* d_out = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
|
|
auto* d_x = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
|
|
|
|
d_x->ShareDataWith(*d_out);
|
|
}
|
|
};
|
|
} // namespace operators
|
|
} // namespace paddle
|