#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

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 mat_size[2];
static int NN[MAX_NUM_GPU];
static int OH, OW;

#define TS  8

__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 gr = blockDim.x * blockIdx.x + threadIdx.x;
    const int gc = blockDim.y * blockIdx.y + threadIdx.y;

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

    int n, k, w;

    w = gc;
    n = w / (_K * OW);
    w = w - n * (_K * OW);
    k = w / OW;
    w = w - k * OW;

    int col = w;
    int row = gr;

    if (gr >= OH || gc >= _N * _K * OW) return;
    
    int sr = row * _stride - _pad;
    int sc = col * _stride - _pad;

    float o = 0.0f;

    for (int c = 0; c < _C; ++c) {
      for (int r = 0; r < _R; ++r) {
        for (int s = 0; s < _S; ++s) {

          int h = sr + r * _dilation;
          int w = sc + s * _dilation;

          if (h < 0 || h >= _H || w < 0 || w >= _W) continue;

          float i =  _input[ n*_C*_H*_W + c*_H*_W + h*_W + w];
          float f = _filter[ k*_C*_R*_S + c*_R*_S + r*_S + s];

          o += i * f;
        } 
      } 
    } 
  
    _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 && mat_size[1] !=0) 
    {

      MPI_Isend(&input[mat_size[0]*C*H*W], mat_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 ( mat_size[mpi_rank] < MAX_NUM_GPU) {
          num_devices = mat_size[mpi_rank];
      }

    } else if (mpi_rank == 1 && mat_size[mpi_rank] != 0) {

      alloc_tensor(&input,  mat_size[1], C, H, W);
      alloc_tensor(&output, mat_size[1], K, OH, OW);
      alloc_tensor(&filter, _K, _C, _R, _S);

      MPI_Recv(input,  mat_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 ( mat_size[mpi_rank] < MAX_NUM_GPU) {
          num_devices = mat_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 && mat_size[1] != 0) 
    {
      MPI_Recv(&output[mat_size[0]*K*OH*OW], mat_size[1]*K*OH*OW, MPI_FLOAT, 1, 0, MPI_COMM_WORLD, &status);
    } 
    else if(mpi_rank == 1 && mat_size[1] != 0) 
    {
      MPI_Isend(output, mat_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) mat_size[1] = _N / 2; 
  else                               mat_size[1] = 0;

  mat_size[0] = N - mat_size[1];
	    
  if (mat_size[mpi_rank] < MAX_NUM_GPU) {

    num_devices = mat_size[mpi_rank];

    for (int i = 0; i < mat_size[mpi_rank]; i++) 
	    NN[i] = 1;

  }else{

    num_devices  = MAX_NUM_GPU;
    int quitient = mat_size[mpi_rank] / MAX_NUM_GPU; 
    int remain   = mat_size[mpi_rank] % MAX_NUM_GPU;

    for (int i = 0; i < MAX_NUM_GPU; i++) { 
      NN[i] = quitient;
      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) {
}