#include <cstdio>
#include <cuda_runtime.h>
#include <cuda_runtime_api.h>                  // cudaDeviceSynchronize()
#include "convolution.h"
#include <mpi.h>
//#include <stdio.h>

#include "util.h"
#include <omp.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 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 OH, OW;
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;



//void convolution(
__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;

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

  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;
  if (mpi_rank == 0) {
  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 * _H * _W + c * _H * W + 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(cudaMemcopy(in_d[i], input+offset, NN[i]*C*H*W*sizeof(float), cudaMemcpyHostToDevice));
    CUDA_CALL(cudaMemcopy(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);
  }
  
  // DO NOT REMOVE; NEEDED FOR TIME MEASURE
  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),
//               (Mend[i] - Mbegin[i]) * K * 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) {
}