/* 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 "lite/core/op_registry.h"
#include "lite/kernels/cuda/search_fc_compute.h"
namespace paddle {
namespace lite {
namespace kernels {
namespace cuda {
template <typename T>
static void anakin_NV_gemv(cublasHandle_t handle,
                           const bool TransA,
                           const int M,
                           const int N,
                           const T alpha,
                           const T* A,
                           const T* x,
                           const T beta,
                           T* y);
template <>
void anakin_NV_gemv<float>(cublasHandle_t handle,
                           const bool TransA,
                           const int M,
                           const int N,
                           const float alpha,
                           const float* A,
                           const float* x,
                           const float beta,
                           float* y) {
  LOG(INFO) << "1";
  cublasOperation_t cuTransA = (TransA == false) ? CUBLAS_OP_T : CUBLAS_OP_N;
  CUBLAS_CHECK(
      cublasSgemv(handle, cuTransA, N, M, &alpha, A, N, x, 1, &beta, y, 1));
}
template <typename T>
static void anakin_NV_gemm(cublasHandle_t handle,
                           const bool TransA,
                           const bool TransB,
                           const int M,
                           const int N,
                           const int K,
                           const T alpha,
                           const T* A,
                           const T* B,
                           const T beta,
                           T* C);

template <>
void anakin_NV_gemm<float>(cublasHandle_t handle,
                           const bool TransA,
                           const bool TransB,
                           const int M,
                           const int N,
                           const int K,
                           const float alpha,
                           const float* A,
                           const float* B,
                           const float beta,
                           float* C) {
  LOG(INFO) << "1";
  // Note that cublas follows fortran order.
  int lda = (!TransA /* == CblasNoTrans*/) ? K : M;
  int ldb = (!TransB /* == CblasNoTrans*/) ? N : K;
  LOG(INFO) << "1";
  cublasOperation_t cuTransA =
      (!TransA /* == CblasNoTrans*/) ? CUBLAS_OP_N : CUBLAS_OP_T;
  LOG(INFO) << "1";
  cublasOperation_t cuTransB =
      (!TransB /* == CblasNoTrans*/) ? CUBLAS_OP_N : CUBLAS_OP_T;
  LOG(INFO) << "1";
  CUBLAS_CHECK(cublasSgemm(handle,
                           cuTransB,
                           cuTransA,
                           N,
                           M,
                           K,
                           &alpha,
                           B,
                           ldb,
                           A,
                           lda,
                           &beta,
                           C,
                           N));
  LOG(INFO) << "1";
}

template <>
void anakin_NV_gemm<char>(cublasHandle_t handle,
                          const bool TransA,
                          const bool TransB,
                          const int M,
                          const int N,
                          const int K,
                          const char alpha,
                          const char* A,
                          const char* B,
                          const char beta,
                          char* C) {
  LOG(FATAL) << "int8 gemm is not implemented";
}

template <typename T>
static __global__ void add_bias(int n,
                                int output_size,
                                const T* bias,
                                T* dout) {
  int index = blockIdx.x * blockDim.x + threadIdx.x;
  int bias_index = index % output_size;
  if (index < n) {
    dout[index] = dout[index] + bias[bias_index];
  }
}

template <typename T>
void SearchFcCompute<T>::Run() {
  auto& param = this->Param<param_t>();
  auto& ctx = this->ctx_->template As<CUDAContext>();
  auto stream = ctx.exec_stream();
  const Tensor* x_tensor = param.X;
  param.Out->Resize({x_tensor->dims()[0], param.out_size});
  _M = x_tensor->dims().count(0, 1);
  _K = x_tensor->dims().count(1, x_tensor->numel());
  _N = param.out_size;
  const T* din = x_tensor->data<T>();
  Tensor* out_tensor = param.Out;
  T* dout = out_tensor->mutable_data<T>(TARGET(kCUDA));
  const Tensor* w_tensor = param.W;
  const T* weight = w_tensor->data<T>();
  const Tensor* b_tensor = param.b;
  const T* bias = b_tensor->data<T>();
  cublasCreate(&_handle);
  if (_M == 1 && _K > 50000) {
    anakin_NV_gemv<T>(_handle, false, _N, _K, (T)1, weight, din, (T)0, dout);
  } else {
    anakin_NV_gemm<T>(_handle,
                      false,
                      !_flag_trans_weights,
                      _M,
                      _N,
                      _K,
                      (T)1,
                      din,
                      weight,
                      (T)0,
                      dout);
  }
  int total_size = _M * _N;
  add_bias<T><<<CUDA_GET_BLOCKS(total_size), CUDA_NUM_THREADS, 0, stream>>>(
      total_size, _N, bias, dout);
}
}  // namespace cuda
}  // namespace kernels
}  // namespace lite
}  // namespace paddle

REGISTER_LITE_KERNEL(search_fc,
                     kCUDA,
                     kFloat,
                     kNCHW,
                     paddle::lite::kernels::cuda::SearchFcCompute<float>,
                     def)
    .BindInput("X", {LiteType::GetTensorTy(TARGET(kCUDA))})
    .BindInput("W", {LiteType::GetTensorTy(TARGET(kCUDA))})
    .BindInput("b", {LiteType::GetTensorTy(TARGET(kCUDA))})
    .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kCUDA))})
    .Finalize();