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.
117 lines
4.5 KiB
117 lines
4.5 KiB
/* Copyright (c) 2016 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 <algorithm>
|
|
#include <vector>
|
|
#include "paddle/fluid/framework/eigen.h"
|
|
#include "paddle/fluid/framework/op_registry.h"
|
|
|
|
namespace paddle {
|
|
namespace operators {
|
|
|
|
template <typename DeviceContext, 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::LoDTensor>("Y");
|
|
bool append = ctx.Attr<bool>("append");
|
|
|
|
framework::TensorCopy(*in, in->place(), out);
|
|
|
|
std::vector<int> level0;
|
|
if (lod_t) {
|
|
if (lod_t->lod().size() > 0) {
|
|
auto y_lod = lod_t->lod();
|
|
auto last_level = y_lod[y_lod.size() - 1];
|
|
PADDLE_ENFORCE_EQ(
|
|
static_cast<int64_t>(last_level.back()), in->dims()[0],
|
|
platform::errors::InvalidArgument(
|
|
"The last value of Input(Y)'s last level LoD should be equal "
|
|
"to the first dimension of Input(X). But received the last "
|
|
"value of Input(Y)'s last level LoD is %d, the first dimension "
|
|
"of Input(X) is %d.",
|
|
static_cast<int64_t>(last_level.back()), in->dims()[0]));
|
|
out->set_lod(y_lod);
|
|
return; // early return, since lod already set
|
|
} else {
|
|
auto* lod = lod_t->data<int>();
|
|
framework::Tensor lod_cpu;
|
|
if (platform::is_gpu_place(lod_t->place())) {
|
|
framework::TensorCopySync(*lod_t, platform::CPUPlace(), &lod_cpu);
|
|
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_GT(
|
|
level0.size(), 1UL,
|
|
platform::errors::InvalidArgument(
|
|
"The size of target LoD should be greater than 1. But received the "
|
|
"size of target LoD is %d.",
|
|
level0.size()));
|
|
PADDLE_ENFORCE_EQ(static_cast<int64_t>(level0[0]), 0,
|
|
platform::errors::InvalidArgument(
|
|
"Target LoD should be a vector starting from 0. But "
|
|
"target LoD starts from %d.",
|
|
static_cast<int64_t>(level0[0])));
|
|
PADDLE_ENFORCE_EQ(
|
|
static_cast<int64_t>(level0.back()), in->dims()[0],
|
|
platform::errors::InvalidArgument(
|
|
"The last value of 'Target LoD''s last level LoD should be equal "
|
|
"to the first dimension of Input(X). But received the 'Target LoD' "
|
|
"is %s, Input(X)'s shape is is %s.",
|
|
framework::make_ddim(level0), in->dims()));
|
|
for (size_t i = 0; i < level0.size() - 1; ++i) {
|
|
PADDLE_ENFORCE_GE(level0[i + 1], level0[i],
|
|
platform::errors::InvalidArgument(
|
|
"'Target LoD' should be an ascending "
|
|
"vector. But received the Target LoD is %s.",
|
|
framework::make_ddim(level0)));
|
|
}
|
|
|
|
// 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); });
|
|
if (append) {
|
|
auto* out_lod = out->mutable_lod();
|
|
out_lod->push_back(ulevel0);
|
|
} else {
|
|
framework::LoD target_lod;
|
|
target_lod.push_back(ulevel0);
|
|
out->set_lod(target_lod);
|
|
}
|
|
}
|
|
};
|
|
|
|
template <typename DeviceContext, 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"));
|
|
|
|
framework::TensorCopy(*d_out, d_out->place(), d_x);
|
|
}
|
|
};
|
|
} // namespace operators
|
|
} // namespace paddle
|