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

#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);                                                                 \
    }                                                                          \
  }

#define MAX_NUM_GPU	4
#define TS		4

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 size[2];
static int NN[MAX_NUM_GPU];
static int OH, OW;
int num_devices = 1;

__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, int OH, int OW) {

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

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

  if (KK >= _K) return;

  int n, w;
  w = globalCol;
  n = w / OW;
  w = w - n * OW;

  int col = w;
  int row = globalRow;

  if (globalRow >= OH || globalCol >= _N*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[KK*_C*_R*_S + c*_R*_S + i*_S + j];
        o += in * fil;
      }
    }
  }

  _output[n*_K*OH*OW + KK*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);
	dim3 gridDim( (OH+TS-1)/TS, (NN[i]*K*OW + TS - 1)/TS, (K+TS-1)/TS);
	dim3 blockDim( TS, TS, TS);
	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, OH, OW);
  }

  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;

//printf("debug: mpi_world_size=%d\n",mpi_world_size);
//printf("debug: _N=%d\n",_N);

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

//printf("debug: size[0]=%d\n",size[0]);
//printf("debug: size[1]=%d\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 = size[mpi_rank];
     num_devices = 4;
     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]++;
     }
  }

  //printf("debug: num_devices=%d\n",num_devices);
  for(int i = 0; i < num_devices; i++) {
//    if(i<4) {
      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)) );
//    }
 //  else {
 //          printf("error: i num=%d, num_devices=%d\n",i,num_devices);
 //  }
  }

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

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