#include "convolution.h"
#include "util.h"
#include <mpi.h>
#include <cstdio>
#include <cuda_runtime.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 TS 8
#define MAX_NUM_GPU 4

static float *input, *output, *filter;
static float *in_d[MAX_NUM_GPU], *out_d[MAX_NUM_GPU], *filter_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_per_node[2];
static int N_per_gpu[MAX_NUM_GPU];

__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 global_row = blockDim.x * blockIdx.x + threadIdx.x;
  const int global_col = 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;
	n = global_col / (_K * OW);
	k = (global_col -n *(_K*OW)) / OW;
	w = (global_col -n *(_K*OW))- k * OW;
	int col = w;
	int row = global_row;

	if (global_row >= OH || global_col >= _N*_K*OW) return;

  float o = 0.f;
  for (int c = 0; c < _C; ++c) {
    for (int r = 0; r < _R; ++r) {
      for (int s = 0; s < _S; ++s) {
        int h = row * _stride - _pad + r * _dilation;
        int w = col * _stride - _pad + 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_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) {
	  size_per_node[1] = _N / 2;
	 }
  size_per_node[0] = _N - size_per_node[1];

  if (size_per_node[mpi_rank] < MAX_NUM_GPU) {
    num_devices = size_per_node[mpi_rank];
    for (int i = 0 ; i < size_per_node[mpi_rank] ; i++) {
      N_per_gpu[i] = 1;
    }
  } else {
    num_devices = MAX_NUM_GPU;
    int remain = size_per_node[mpi_rank] % MAX_NUM_GPU;
    int quot = size_per_node[mpi_rank] / MAX_NUM_GPU;
    for (int i = 0 ; i < MAX_NUM_GPU ; i++) {
      N_per_gpu[i] = quot;
      if (i < remain) {
	      N_per_gpu[i]++;
      }
    }
  }

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

}

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;

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

	MPI_Request request;
  MPI_Status status;

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

  offset = 0;
  for (int i = 0 ; i < num_devices ; i++) {
    CUDA_CALL( cudaMemcpy(in_d[i], input + offset, N_per_gpu[i]*C*H*W*sizeof(float), cudaMemcpyHostToDevice) );
    CUDA_CALL( cudaMemcpy(filter_d[i], filter, K*C*R*S*sizeof(float),cudaMemcpyHostToDevice) );
    offset += N_per_gpu[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, (N_per_gpu[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], filter_d[i], N_per_gpu[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],
               N_per_gpu[i]*K*OH*OW * sizeof(float),
               cudaMemcpyDeviceToHost) );
    offset += N_per_gpu[i]*K*OH*OW;
  }

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

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

}


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