#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 size[2];
static int NN[MAX_NUM_GPU];
static int OH, OW;

#define TS 4


__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;
  
  int kernel_k = 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 (kernel_k >= _K) return;

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

  int col = w;
  int row = g_row;

  if (g_row >= OH || g_col >= _N*OW) return;

  int row_calc = row * _stride - _pad;
  int col_calc = 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 = row_calc + r * _dilation;
        int w = col_calc + s * _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[kernel_k*_C*_R*_S + c*_R*_S + r*_S + s];
        o += in * fil;
      }
    }
  }

  _output[n*_K*OH*OW + kernel_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(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]*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);
	}

	// DO NOT REMOVE; NEDDED 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),
				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]++; 
	  }
	}
	
	// Allocate device memory for each GPU
	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) { 
}