#include "convolution.h"
#include <mpi.h>
#include <stdio.h>
#include <cstdio>
#include <cuda_runtime.h>

#define MAX_NUM_GPU 4
#define TS 8
static float *input, *output, *filter;
static float *in_d[MAX_NUM_GPU], *out_d[MAX_NUM_GPU], *fil_d[MAX_NUM_GPU];
static int N, C, H, W;
static int K, R, S;
static int pad;
static int dilation;
static int stride;
static int mpi_rank, mpi_world_size;
static int num_devices = 1;
static int size[2];
static int NN[MAX_NUM_GPU];
static int OH, OW;

#define CUDA_CALL(f)                                                           \
  {                                                                            \
    cudaError_t err = (f);                                                     \
    if (err != cudaSuccess) {                                                  \
      fprintf(stderr, "CUDA error at [%s:%d] %d %s\n", __FILE__, __LINE__,     \
              err, cudaGetErrorString(err));                                   \
      exit(1);                                                                 \
    }                                                                          \
  }

extern void alloc_tensor(float **t, int D0, int D1, int D2, int D3);

__global__ void conv(
	float *_input, float *_output, float *_filter,
	int _N, int _C, int _H, int _W,
	int _K, int _R, int _S,
	int _pad, int _dilation, int _stride){

  const int globalRow = blockDim.x * blockIdx.x + threadIdx.x;
  const int globalCol = blockDim.y * blockIdx.y + threadIdx.y;
  int OH, OW;

  OH = (_H + 2 * _pad - _dilation * (_R - 1) - 1) / _stride + 1;
  OW = (_W + 2 * _pad - _dilation * (_S - 1) - 1) / _stride + 1;

  int n, k, w;
  w = globalCol;
  n = w / (_K * OW);
  w = w- n *(_K * OW);
  k = w / OW;
  w = w - k * OW;
  int col = w;
  int row = globalRow;

  if (globalRow >= OH || globalCol >= _N*_K*OW) return;

  int start_row = row * _stride - _pad;
  int start_col = col * _stride - _pad;

  float o = 0.0f;
  for (int c = 0; c < _C; c++){
    for (int i = 0; i < _R; i++){
      for (int j = 0; j < _S; j++){
        int h = start_row + i * _dilation;
	int w = start_col + j * _dilation;
	if (h < 0 || w < 0 || h >= _H || w >= _W)  continue;
	float in = _input[n*_C*_W*_H + c*_W*_H + h*_W + w];
	float fil = _filter[k*_C*_R*_S + c*_R*_S + i*_S+j];
	o += in * fil;
      }
    }
  }

  _output[n*_K*OH*OW + k*OH*OW + row * OW + col ] = o;
}

void convolution(
    float *_input, float *_output, float *_filter, 
    int _N, int _C, int _H, int _W,
    int _K, int _R, int _S,
    int _pad, int _dilation, int _stride) {

    int offset = 0;
    MPI_Request request;
    MPI_Status status;

    input = _input;
    output = _output;
    filter = _filter;

    if(mpi_rank == 0 && mpi_world_size == 2 && size[1] != 0){
      MPI_Isend(&input[size[0]*C*H*W], size[1]*C*H*W, MPI_FLOAT, 1, 0, MPI_COMM_WORLD, &request);
      MPI_Isend(filter, _K*_C*_R*_S, MPI_FLOAT, 1, 0, MPI_COMM_WORLD, &request);
        if(size[mpi_rank] < MAX_NUM_GPU){
	      num_devices = size[mpi_rank];
        }
    }
    else if (mpi_rank == 1 && size[mpi_rank] != 0){
      alloc_tensor(&input, size[1], C, H, W);
      alloc_tensor(&output, size[1], K, OH, OW);
      alloc_tensor(&filter, _K, _C, _R, _S);
      MPI_Recv(input, size[1]*C*H*W, MPI_FLOAT, 0, 0, MPI_COMM_WORLD, &status);
      MPI_Recv(filter, _K*_C*_R*_S, MPI_FLOAT, 0, 0, MPI_COMM_WORLD, &status);

      if(size[mpi_rank] < MAX_NUM_GPU){
	      num_devices = size[mpi_rank];
      }
    }

    offset = 0;
    for (int i = 0; i < num_devices; i++){
     CUDA_CALL(cudaMemcpy(in_d[i], input + offset, NN[i]*C*H*W*sizeof(float), cudaMemcpyHostToDevice));
     CUDA_CALL(cudaMemcpy(fil_d[i], filter, K*C*R*S*sizeof(float), cudaMemcpyHostToDevice));
     offset += NN[i] * C * H * W;

    }

    for(int i = 0; i<num_devices; i++){
      CUDA_CALL(cudaDeviceSynchronize());
    }

    for(int i = 0; i<num_devices; i++){
      dim3 gridDim((OH+TS-1)/TS, (NN[i]*K*OW+TS-1)/TS,1);
      dim3 blockDim(TS, TS, 1);

      CUDA_CALL(cudaSetDevice(i));
      conv<<<gridDim, blockDim>>>(in_d[i], out_d[i], fil_d[i], NN[i], _C, _H, _W, _K, _R, _S, _pad, _dilation, _stride);
    }

    for(int i = 0; i<num_devices; i++){
      CUDA_CALL(cudaSetDevice(i));
      CUDA_CALL(cudaDeviceSynchronize());
    }

    offset = 0;
    for (int i =0; i < num_devices; i++){
      CUDA_CALL(cudaSetDevice(i));
      CUDA_CALL(cudaMemcpy(output + offset, out_d[i], NN[i]*K*OH*OW*sizeof(float),cudaMemcpyDeviceToHost));
      offset += NN[i]*K*OH*OW;
    }

    for(int i = 0; i<num_devices; i++){
      CUDA_CALL(cudaSetDevice(i));
      CUDA_CALL(cudaDeviceSynchronize());
    }

    if(mpi_rank == 0 && mpi_world_size ==2 && size[1] !=0){
      MPI_Recv(&output[size[0]*K*OH*OW], size[1]*K*OH*OW, MPI_FLOAT, 1, 0, MPI_COMM_WORLD, &status);
    }
    else if(mpi_rank == 1 && size[1] != 0){
      MPI_Isend(output, size[1]*K*OH*OW, MPI_FLOAT, 0, 0, MPI_COMM_WORLD, &request);
    }
}

void convolution_init(
    int _N, int _C, int _H, int _W,
    int _K, int _R, int _S,
    int _pad, int _dilation, int _stride) {
  N = _N; C = _C; H = _H; W = _W;
  K = _K; R = _R; S = _S;
  pad = _pad;
  dilation = _dilation;
  stride = _stride;

  MPI_Comm_rank(MPI_COMM_WORLD, &mpi_rank);
  MPI_Comm_size(MPI_COMM_WORLD, &mpi_world_size);

  OH = (H+2*pad - dilation * (R-1)-1)/stride + 1;
  OW = (W+2*pad - dilation * (S-1)-1)/stride + 1;

  if (mpi_world_size ==2 && _N > 4) size[1] = _N / 2;
  else size[1] = 0;
  size[0] = N - size[1];

  if(size[mpi_rank] < MAX_NUM_GPU){
    num_devices = size[mpi_rank];
    for(int i = 0; i < size[mpi_rank]; i++){
       NN[i] = 1;
    }
  }
  else
  {
    num_devices = MAX_NUM_GPU;
    int quotient = size[mpi_rank] / MAX_NUM_GPU;
    int remain = size[mpi_rank] % MAX_NUM_GPU;
    for (int i = 0; i < MAX_NUM_GPU; i++){
      NN[i] = quotient;
      if (i < remain) NN[i]++;
    }

  }

  for (int i = 0; i < num_devices; i++){
    CUDA_CALL(cudaSetDevice(i));
    CUDA_CALL(cudaMalloc(&in_d[i], NN[i]*C*H*W*sizeof(float)));
    CUDA_CALL(cudaMalloc(&out_d[i], NN[i]*K*OH*OW*sizeof(float)));
    CUDA_CALL(cudaMalloc(&fil_d[i], K*C*R*S*sizeof(float)));
  }
}

void convolution_final(
    int _N, int _C, int _H, int _W,
    int _K, int _R, int _S,
    int _pad, int _dilation, int _stride) {
}