// 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.

// Used for compute gpu launch parameter

#pragma once

#ifdef PADDLE_WITH_CUDA

#include <cuda_runtime.h>
#include <stddef.h>
#include <algorithm>
#include <string>
#include <vector>

namespace paddle {
namespace platform {

inline int DivUp(int a, int b) { return (a + b - 1) / b; }

struct GpuLaunchParamConfig {
  dim3 theory_thread_count = dim3(0, 0, 0);
  dim3 thread_per_block = dim3(0, 0, 0);
  dim3 block_per_grid = dim3(0, 0, 0);
};

inline GpuLaunchParamConfig GetGpuLaunchConfig1D(
    const platform::CUDADeviceContext& context, int element_count) {
  PADDLE_ENFORCE_GT(element_count, 0, platform::errors::InvalidArgument(
                                          "element count should greater than 0,"
                                          " but received value is %d.",
                                          element_count));

  const int theory_thread_count = element_count;
  // Get Max threads in all SM
  int max_pyhsical_threads = context.GetMaxPhysicalThreadCount();
  int sm = context.GetSMCount();

  // Compute pyhsical threads we need, should small than max sm threads
  const int physical_thread_count =
      std::min(max_pyhsical_threads, theory_thread_count);

  // Need get from device
  const int thread_per_block = std::min(1024, context.GetMaxThreadsPerBlock());
  // Suppose block count small than factor * sm, factor is a experiments value.
  int factor = 4;
  const int block_count =
      std::min(DivUp(physical_thread_count, thread_per_block), factor * sm);

  GpuLaunchParamConfig config;
  config.theory_thread_count.x = theory_thread_count;
  config.thread_per_block.x = thread_per_block;
  config.block_per_grid.x = block_count;
  return config;
}

inline GpuLaunchParamConfig GetGpuLaunchConfig2D(
    const platform::CUDADeviceContext& context, int xdim, int ydim) {
  PADDLE_ENFORCE_GT(xdim, 0, platform::errors::InvalidArgument(
                                 "x dim number should greater than 0,"
                                 " but received value is:%d",
                                 xdim));
  PADDLE_ENFORCE_GT(ydim, 0, platform::errors::InvalidArgument(
                                 "y dim number should greater than 0,"
                                 " but received value is:%d",
                                 ydim));

  const int kThreadsPerBlock = 256;
  int block_cols = std::min(xdim, kThreadsPerBlock);
  int block_rows = std::max(kThreadsPerBlock / block_cols, 1);

  int max_physical_threads = context.GetMaxPhysicalThreadCount();
  const int max_blocks = std::max(max_physical_threads / kThreadsPerBlock, 1);

  GpuLaunchParamConfig config;
  // Noticed, block size is not align to 32, if needed do it yourself.
  config.theory_thread_count = dim3(xdim, ydim, 1);
  config.thread_per_block = dim3(block_cols, block_rows, 1);

  int grid_x = std::min(DivUp(xdim, block_cols), max_blocks);
  int grid_y = std::min(max_blocks / grid_x, std::max(ydim / block_rows, 1));

  config.block_per_grid = dim3(grid_x, grid_y, 1);
  return config;
}

// 3D will add later

}  // namespace platform
}  // namespace paddle

#endif
