/* 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 <vector>
#include "lite/core/op_registry.h"
#include "lite/kernels/cuda/match_matrix_tensor_compute.h"

namespace paddle {
namespace lite {
namespace kernels {
namespace cuda {
using Tensor = lite::Tensor;

void MatchMatrixTensorCompute::PrepareForRun() {
  gemm_impl_.reset(new lite::cuda::math::Gemm<float, float>);
}

void MatchMatrixTensorCompute::Run() {
  CHECK(ctx_) << "running context should be set first";
  auto& param = this->Param<param_t>();
  auto& context = this->ctx_->template As<CUDAContext>();

  auto* x = param.x;
  auto* w = param.w;
  auto* y = param.y;
  auto* out = param.out;
  auto* tmp = param.tmp;
  int dim_t = param.dim_t;
  int dim_in = x->dims()[1];

  const auto& offset_l = x->lod()[0];
  const auto& offset_r = y->lod()[0];

  std::vector<size_t> top_offset;
  int top_size = 0;
  top_offset.push_back(top_size);
  for (size_t b = 0; b < x->lod()[0].size() - 1; b++) {
    int len_l = offset_l[b + 1] - offset_l[b];
    int len_r = offset_r[b + 1] - offset_r[b];
    top_size += dim_t * len_l * len_r;
    top_offset.push_back(top_size);
  }

  auto* bottom_l_data = x->data<float>();
  auto* bottom_r_data = y->data<float>();
  auto* t_data = w->data<float>();
  auto* out_data = out->mutable_data<float>(TARGET(kCUDA));
  auto* bottom_l_trans_data = tmp->mutable_data<float>(TARGET(kCUDA));

  gemm_impl_->init(
      false, false, x->dims()[0], dim_t * dim_in, dim_in, &context);
  gemm_impl_->run(
      1.0f, 0.0f, bottom_l_data, t_data, bottom_l_trans_data, &context);

  for (size_t b = 0; b < x->lod()[0].size() - 1; b++) {
    for (int t = 0; t < dim_t; t++) {
      int len_l = offset_l[b + 1] - offset_l[b];
      int len_r = offset_r[b + 1] - offset_r[b];
      auto* top_data = out_data + top_offset[b] + t * len_l * len_r;
      const auto* l_t_data =
          bottom_l_trans_data + offset_l[b] * dim_t * dim_in + t * dim_in;
      const auto* r_data = bottom_r_data + offset_r[b] * dim_in;

      gemm_impl_->init(false,
                       true,
                       len_l,
                       len_r,
                       dim_in,
                       dim_t * dim_in,
                       dim_in,
                       len_r,
                       &context);
      gemm_impl_->run(1.0f, 0.0f, l_t_data, r_data, top_data, &context);
    }
  }

  int batch_size = x->lod()[0].size() - 1;
  int lod_lv1_size = batch_size * dim_t;
  int lod_lv2_size = x->lod()[0].back() * dim_t;
  std::vector<size_t> out_lod0(batch_size + 1, 0);
  std::vector<size_t> out_lod1(lod_lv1_size + 1, 0);
  std::vector<size_t> out_lod2(lod_lv2_size + 1, 0);
  for (int i = 0; i < batch_size; i++) {
    out_lod0[i + 1] = out_lod0[i] + dim_t;
    int len_l = offset_l[i + 1] - offset_l[i];

    for (int j = 0; j < dim_t; j++) {
      out_lod1[i * dim_t + j + 1] = out_lod1[i * dim_t + j] + len_l;
      int len_r = offset_r[i + 1] - offset_r[i];

      for (int k = 0; k < len_l; k++) {
        out_lod2[offset_l[i] * dim_t + j * len_l + k + 1] =
            out_lod2[offset_l[i] * dim_t + j * len_l + k] + len_r;
      }
    }
  }

  LoD out_lod;
  out_lod.push_back(top_offset);
  out_lod.push_back(offset_l);
  out_lod.push_back(offset_r);
  out->set_lod(out_lod);
}

}  // namespace cuda
}  // namespace kernels
}  // namespace lite
}  // namespace paddle

REGISTER_LITE_KERNEL(match_matrix_tensor,
                     kCUDA,
                     kFloat,
                     kNCHW,
                     paddle::lite::kernels::cuda::MatchMatrixTensorCompute,
                     def)
    .BindInput("X",
               {LiteType::GetTensorTy(TARGET(kCUDA),
                                      PRECISION(kFloat),
                                      DATALAYOUT(kNCHW))})
    .BindInput("W",
               {LiteType::GetTensorTy(TARGET(kCUDA),
                                      PRECISION(kFloat),
                                      DATALAYOUT(kNCHW))})
    .BindInput("Y",
               {LiteType::GetTensorTy(TARGET(kCUDA),
                                      PRECISION(kFloat),
                                      DATALAYOUT(kNCHW))})
    .BindOutput("Out",
                {LiteType::GetTensorTy(TARGET(kCUDA),
                                       PRECISION(kFloat),
                                       DATALAYOUT(kNCHW))})
    .BindOutput("Tmp",
                {LiteType::GetTensorTy(TARGET(kCUDA),
                                       PRECISION(kFloat),
                                       DATALAYOUT(kNCHW))})
    .Finalize();