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

#define MAX_NODE 2
#define MAX_NUM_GPU 4
#define TS 8

#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);                                                                 \
    }                                                                          \
  }

static float *input, *output, *filter;
static int N, C, H, W;
static int K, R, S;
static int OH, OW;
static int pad;
static int dilation;
static int stride;
static int mpi_rank, mpi_world_size;

static float *in_d[MAX_NUM_GPU], *out_d[MAX_NUM_GPU], *fil_d[MAX_NUM_GPU];
static int num_devices = 1;
static int NN[MAX_NUM_GPU];
static int size[MAX_NODE];	

__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 g_row = blockDim.x * blockIdx.x + threadIdx.x;
	const int g_col = blockDim.y * blockIdx.y + threadIdx.y;

	// Output file size definition
	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;
	n = g_col /(_K * OW);
	w = g_col - (g_col / (_K * OW)) * (_K * OW);
	k = w / OW;
	w = w - k * OW;
	
	int col = w;		// local colume
	int row = g_row;	// local row

	// Boundary check 
	if(g_row >= OH || g_col >= _N*_K*OW) return;
	int s_row = row * _stride - _pad;
	int s_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 = s_row + i * _dilation;
	      int w = s_col + j * _dilation;

              if(h<0 || w<0 || h>=_H || w>=_W) continue;
	      float in  = _input[n*_C*_H*_W + c*_H*_W + h*_W + w];
	      float filter = _filter[k*_C*_R*_S + c*_R*_S + i*_S + j];
	      o += in * filter;
	    }
	  }
	}

	_output[n*_K*OH*OW + k*OH*OW + OW*row + 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, C*K*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) {

}