/* 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. */

#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/multiplex_op.h"

namespace paddle {
namespace operators {

using Tensor = framework::Tensor;

template <typename Place, typename T>
class MultiplexGPUKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& ctx) const {
    auto ins = ctx.MultiInput<Tensor>("X");
    auto* ids = ctx.Input<Tensor>("Ids");
    auto* out = ctx.Output<Tensor>("Out");
    out->mutable_data<T>(ctx.GetPlace());

    auto rows = ins[0]->dims()[0];
    auto cols = ins[0]->numel() / rows;
    // copy index to cpu
    Tensor index_t_cpu;
    TensorCopy(*ids, platform::CPUPlace(), ctx.device_context(), &index_t_cpu);
    auto* index = index_t_cpu.data<int32_t>();
    auto stream = ctx.cuda_device_context().stream();
    platform::CUDAPlace place = boost::get<platform::CUDAPlace>(ctx.GetPlace());
    for (auto i = 0; i < rows; i++) {
      int32_t k = index[i];
      PADDLE_ENFORCE_GE(k, 0, "index must be nonnegative.");
      PADDLE_ENFORCE_LT((size_t)k, ins.size(),
                        "index exceeds the number of candidate tensors.");
      memory::Copy(place, out->data<T>() + i * cols, place,
                   ins[k]->data<T>() + i * cols, cols * sizeof(T), stream);
    }
  }
};

template <typename Place, typename T>
class MultiplexGradGPUKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& ctx) const {
    auto* d_out = ctx.Input<Tensor>(framework::GradVarName("Out"));
    auto ins = ctx.MultiInput<Tensor>("X");
    auto* ids = ctx.Input<Tensor>("Ids");
    auto d_ins = ctx.MultiOutput<Tensor>(framework::GradVarName("X"));
    for (size_t i = 0; i < d_ins.size(); i++) {
      if (d_ins[i]) {
        d_ins[i]->mutable_data<T>(ctx.GetPlace());
        auto t = framework::EigenVector<T>::Flatten(*d_ins[i]);
        t.device(*ctx.template device_context<Place>().eigen_device()) =
            t.constant(static_cast<T>(0));
      }
    }

    auto rows = ins[0]->dims()[0];
    auto cols = ins[0]->numel() / rows;
    // copy index to cpu
    Tensor index_t_cpu;
    TensorCopy(*ids, platform::CPUPlace(), ctx.device_context(), &index_t_cpu);
    auto* index = index_t_cpu.data<int32_t>();

    auto stream = ctx.cuda_device_context().stream();
    platform::CUDAPlace place = boost::get<platform::CUDAPlace>(ctx.GetPlace());
    for (auto i = 0; i < rows; i++) {
      size_t k = static_cast<size_t>(index[i]);
      if (d_ins[k]) {
        memory::Copy(place, d_ins[k]->data<T>() + i * cols, place,
                     d_out->data<T>() + i * cols, cols * sizeof(T), stream);
      }
    }
  }
};
}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;

REGISTER_OP_CUDA_KERNEL(
    multiplex,
    ops::MultiplexGPUKernel<paddle::platform::CUDADeviceContext, float>,
    ops::MultiplexGPUKernel<paddle::platform::CUDADeviceContext, double>,
    ops::MultiplexGPUKernel<paddle::platform::CUDADeviceContext, int>,
    ops::MultiplexGPUKernel<paddle::platform::CUDADeviceContext, int64_t>);
REGISTER_OP_CUDA_KERNEL(
    multiplex_grad,
    ops::MultiplexGradGPUKernel<paddle::platform::CUDADeviceContext, float>,
    ops::MultiplexGradGPUKernel<paddle::platform::CUDADeviceContext, double>,
    ops::MultiplexGradGPUKernel<paddle::platform::CUDADeviceContext, int>,
    ops::MultiplexGradGPUKernel<paddle::platform::CUDADeviceContext, int64_t>);
