/* 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 <stdio.h>
#include <thrust/device_vector.h>
#include <thrust/host_vector.h>
#include <vector>
#include "paddle/fluid/operators/ctc_align_op.h"

namespace paddle {
namespace operators {

template <typename T>
__global__ void MergeAndDelCudaKernel(const int64_t num_token, const T* tokens,
                                      const size_t num_seq, size_t* lod0,
                                      const int blank, const int merge_repeated,
                                      size_t* out_lod0, T* output) {
  int ouput_idx = 0;
  out_lod0[0] = 0;

  for (int i = 0; i < num_seq; ++i) {
    T pre_token = -1;
    for (int j = lod0[i]; j < lod0[i + 1]; ++j) {
      if (tokens[j] != blank && !(merge_repeated && tokens[j] == pre_token)) {
        output[ouput_idx] = tokens[j];
        ++ouput_idx;
      }
      pre_token = tokens[j];
    }
    out_lod0[i + 1] = ouput_idx;
  }
}

template <typename T>
__global__ void PaddingMergeAndDelCudaKernel(
    const int64_t num_token, const T* tokens, const T* tokens_length,
    const int blank, const int merge_repeated, const int padding_value,
    const int64_t batch_size, T* output, T* output_length) {
  int ind = blockIdx.x * blockDim.x + threadIdx.x;
  if (ind >= batch_size) return;
  int output_idx = ind * num_token;
  T prev_token = -1;
  for (int i = ind * num_token; i < ind * num_token + tokens_length[ind]; i++) {
    if ((unsigned)tokens[i] != blank &&
        !(merge_repeated && tokens[i] == prev_token)) {
      output[output_idx] = tokens[i];
      ++output_idx;
    }
    prev_token = tokens[i];
  }
  output_length[ind] = output_idx - ind * num_token;
  for (int i = output_idx; i < ind * num_token + num_token; i++) {
    output[i] = padding_value;
  }
}

template <typename T>
class CTCAlignOpCUDAKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& ctx) const override {
    PADDLE_ENFORCE_EQ(platform::is_gpu_place(ctx.GetPlace()), true,
                      platform::errors::InvalidArgument(
                          "CTCAlign operator CUDA kernel must use CUDAPlace "
                          "rather than CPUPlace."));
    auto* input = ctx.Input<LoDTensor>("Input");
    auto* output = ctx.Output<LoDTensor>("Output");
    const int blank = ctx.Attr<int>("blank");
    const int merge_repeated =
        static_cast<int>(ctx.Attr<bool>("merge_repeated"));
    const T* tokens = input->data<T>();
    auto stream = ctx.cuda_device_context().stream();

    // tensor input which has no lod
    if (input->lod().empty()) {
      const int padding_value = ctx.Attr<int>("padding_value");
      auto input_dims = input->dims();
      T* output_data = output->mutable_data<T>({input_dims[0], input_dims[1]},
                                               ctx.GetPlace());
      auto* input_length = ctx.Input<LoDTensor>("InputLength");
      const T* input_length_data = input_length->data<T>();
      auto* output_length = ctx.Output<LoDTensor>("OutputLength");
      T* output_length_data =
          output_length->mutable_data<T>({input_dims[0], 1}, ctx.GetPlace());
      PaddingMergeAndDelCudaKernel<
          T><<<32, (input_dims[0] + 32 - 1) / 32, 0, stream>>>(
          input_dims[1], tokens, input_length_data, blank, merge_repeated,
          padding_value, input_dims[0], output_data, output_length_data);
    } else {
      const size_t level = 0;
      auto input_lod = framework::ToAbsOffset(input->lod());

      const int64_t num_tokens = input->dims()[0];
      const size_t num_seq = input_lod[level].size() - 1;

      // prepare a lod to record lod information while merging elements
      thrust::device_vector<size_t> dev_out_lod0(input_lod[level].size());
      size_t* dev_out_lod0_ptr = thrust::raw_pointer_cast(dev_out_lod0.data());

      // merge elements and delete blank
      T* output_data = output->mutable_data<T>({num_tokens, 1}, ctx.GetPlace());

      paddle::framework::MixVector<size_t> mixv_input_lod(&input_lod[level]);
      MergeAndDelCudaKernel<T><<<1, 1, 0, stream>>>(
          num_tokens, tokens, num_seq,
          mixv_input_lod.CUDAMutableData(ctx.GetPlace()), blank, merge_repeated,
          dev_out_lod0_ptr, output_data);
      mixv_input_lod.CopyToCPU();

      // set output lod
      std::vector<size_t> host_out_lod0(dev_out_lod0.begin(),
                                        dev_out_lod0.end());
      framework::LoD out_lod;
      out_lod.push_back(host_out_lod0);
      output->set_lod(out_lod);

      // resize output dims
      output->Resize({static_cast<int64_t>(host_out_lod0.back()), 1});

      if (host_out_lod0.back() == 0) {
        output->Resize({1, 1});
        output->mutable_data<T>(ctx.GetPlace());
        phi::funcs::SetConstant<platform::CUDADeviceContext, T> set_constant;
        set_constant(ctx.template device_context<platform::CUDADeviceContext>(),
                     output, -1);
      }
    }
  }
};

}  // namespace operators
}  // namespace paddle

REGISTER_OP_CUDA_KERNEL(ctc_align, paddle::operators::CTCAlignOpCUDAKernel<int>,
                        paddle::operators::CTCAlignOpCUDAKernel<int64_t>);
