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.
122 lines
4.2 KiB
122 lines
4.2 KiB
// Copyright (c) 2019 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 <atomic>
|
|
#include <cstring>
|
|
#include <ctime>
|
|
#include <random>
|
|
#include <string>
|
|
#include <vector>
|
|
#include "glog/logging.h"
|
|
#include "paddle/fluid/framework/eigen.h"
|
|
#include "paddle/fluid/framework/lod_tensor.h"
|
|
#include "paddle/fluid/framework/mixed_vector.h"
|
|
#include "paddle/fluid/framework/op_registry.h"
|
|
#include "paddle/fluid/memory/memcpy.h"
|
|
#include "paddle/fluid/platform/timer.h"
|
|
|
|
namespace paddle {
|
|
namespace operators {
|
|
using Tensor = framework::Tensor;
|
|
using LoDTensor = framework::LoDTensor;
|
|
#if defined(PADDLE_WITH_CUDA)
|
|
template <typename T>
|
|
using Vector = framework::Vector<T>;
|
|
#else
|
|
template <typename T>
|
|
using Vector = framework::CPUVector<T>;
|
|
#endif
|
|
|
|
template <typename T>
|
|
class ShuffleBatchKernel : public framework::OpKernel<T> {
|
|
public:
|
|
void Compute(const framework::ExecutionContext &context) const override {
|
|
auto *x = context.Input<LoDTensor>("X");
|
|
auto *seed = context.Input<LoDTensor>("Seed");
|
|
auto *out = context.Output<LoDTensor>("Out");
|
|
auto *shuffleidx = context.Output<LoDTensor>("ShuffleIdx");
|
|
auto *seed_out = context.Output<LoDTensor>("SeedOut");
|
|
|
|
auto x_embed_size = x->dims()[x->dims().size() - 1];
|
|
auto elem_size = 1;
|
|
for (auto i = 0; i < x->dims().size() - 1; i++) elem_size *= x->dims()[i];
|
|
|
|
std::vector<int64_t> idx_vec; // record shuffled order
|
|
idx_vec.reserve(elem_size);
|
|
for (auto i = 0; i < elem_size; i++) {
|
|
idx_vec.push_back(i);
|
|
}
|
|
int64_t seed_int = 0;
|
|
if (seed->IsInitialized()) {
|
|
seed_int = *seed->data<int64_t>();
|
|
} else {
|
|
seed_int = context.Attr<int>("startup_seed");
|
|
}
|
|
std::default_random_engine engine;
|
|
engine.seed(seed_int);
|
|
std::shuffle(idx_vec.begin(), idx_vec.end(), engine);
|
|
|
|
// ShuffleIdx record shuffle order
|
|
shuffleidx->Resize(framework::make_ddim({(int64_t)idx_vec.size()}));
|
|
auto *shuffleidx_data =
|
|
shuffleidx->mutable_data<int64_t>(context.GetPlace());
|
|
for (size_t i = 0; i < idx_vec.size(); i++) {
|
|
shuffleidx_data[i] = idx_vec[i];
|
|
}
|
|
// copy data according to idx_vec
|
|
auto *x_data = x->data<T>();
|
|
auto *out_data = out->mutable_data<T>(context.GetPlace());
|
|
for (auto i = 0; i < elem_size; i++) {
|
|
memcpy(out_data + idx_vec[i] * x_embed_size, x_data + i * x_embed_size,
|
|
x_embed_size * sizeof(T));
|
|
}
|
|
// set new seed
|
|
*seed_out->mutable_data<int64_t>(framework::make_ddim({1}),
|
|
context.GetPlace()) = engine();
|
|
}
|
|
};
|
|
|
|
template <typename T>
|
|
class ShuffleBatchGradKernel : public framework::OpKernel<T> {
|
|
public:
|
|
void Compute(const framework::ExecutionContext &context) const override {
|
|
auto *out_grad = context.Input<LoDTensor>(framework::GradVarName("Out"));
|
|
auto *shuffleidx = context.Input<LoDTensor>("ShuffleIdx");
|
|
auto *x_grad = context.Output<LoDTensor>(framework::GradVarName("X"));
|
|
|
|
auto embed_size = out_grad->dims()[out_grad->dims().size() - 1];
|
|
auto elem_size = 1;
|
|
for (auto i = 0; i < out_grad->dims().size() - 1; i++)
|
|
elem_size *= out_grad->dims()[i];
|
|
|
|
std::vector<int> idx_vec_grad(elem_size);
|
|
auto *shuffleidx_data = shuffleidx->data<int64_t>();
|
|
for (size_t i = 0; i < idx_vec_grad.size(); i++) {
|
|
idx_vec_grad[shuffleidx_data[i]] = i;
|
|
}
|
|
|
|
// copy data according to idx_vec_grad
|
|
auto *out_grad_data = out_grad->data<T>();
|
|
auto *x_grad_data = x_grad->mutable_data<T>(context.GetPlace());
|
|
for (auto i = 0; i < elem_size; i++) {
|
|
memcpy(x_grad_data + idx_vec_grad[i] * embed_size,
|
|
out_grad_data + i * embed_size, embed_size * sizeof(T));
|
|
}
|
|
}
|
|
};
|
|
} // namespace operators
|
|
} // namespace paddle
|