#include "convolution.h"
#include <mpi.h>
#include "util.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 MAX_NUM_NODE 2
#define MAX_NUM_GPU 4
int num_devices = 0;

#define TS 32
__global__ void cuda_conv(
    float *input, float *output, float *filter, 
    int N, int C, int H, int W,
    int K, int R, int S, int OH, int OW,
    int pad, int dilation, int stride) {
	int x = threadIdx.x;
	int y = threadIdx.y;
  int global_x = blockDim.x * blockIdx.x + x;
  int global_y = blockDim.y * blockIdx.y + y;

  if (global_x >= N * OW || global_y >= K * OH) return; // boundary check

  int n = global_x / OW;
  int ow = global_x % OW;
  int k = global_y / OH;
  int oh = global_y % OH;

  float o = 0.f;
  for (int c = 0; c < C; ++c) {
    for (int r = 0; r < R; ++r) {
      int h = oh * stride - pad + r * dilation;
      if (h < 0 || h >= H) continue;
      for (int s = 0; s < S; ++s) {
        int w = ow * stride - pad + s * dilation;
        if (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) + (oh * OW) + ow] = o;
}

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;

int get_size_per_rank(int rank) {
  const int NN = N / mpi_world_size;

  if (rank == -1) return 0;
  else if (rank != 0) return NN;
  else return (N - (mpi_world_size - 1) * NN);
}

int get_begin_index(int rank) {
  if (rank == 0) return 0;
  else if (rank == 1) return get_size_per_rank(0);
  else return get_begin_index(rank - 1) + get_size_per_rank(rank - 1);
}

// Array of device (GPU) pointers
static float *a_d[MAX_NUM_NODE][MAX_NUM_GPU];
static float *b_d[MAX_NUM_NODE][MAX_NUM_GPU];
static float *c_d[MAX_NUM_NODE][MAX_NUM_GPU];
static int Nbegin[MAX_NUM_NODE][MAX_NUM_GPU], Nend[MAX_NUM_NODE][MAX_NUM_GPU];
static int Nsize[MAX_NUM_NODE][MAX_NUM_GPU];

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

  if (mpi_rank != 0) {
    alloc_tensor(&input, N, C, H, W);
    alloc_tensor(&output, N, K, OH, OW);
    alloc_tensor(&filter, K, C, R, S);
  }
  MPI_Barrier(MPI_COMM_WORLD);

  MPI_Bcast(filter, K * C * R * S, MPI_FLOAT, 0, MPI_COMM_WORLD);
  if (mpi_rank == 0) {
    for (int i = 1; i < mpi_world_size; ++i) {
      MPI_Request request;
      int index = get_begin_index(i) * C * H * W;
      int size = get_size_per_rank(i) * C * H * W;
      MPI_Isend(&input[index], size, MPI_FLOAT, i, 0, MPI_COMM_WORLD, &request);
    }
  } else {
    MPI_Request request;
    int index = get_begin_index(mpi_rank) * C * H * W;
    int size = get_size_per_rank(mpi_rank) * C * H * W;

    MPI_Irecv(&input[index], size, MPI_FLOAT, 0, 0, MPI_COMM_WORLD, &request);
    zero_tensor(output, N, K, OH, OW);
    MPI_Wait(&request, MPI_STATUS_IGNORE);
  }

  // Upload A and B matrix to every GPU
  for (int i = 0; i < num_devices; i++) {
    if (Nsize[mpi_rank][i] == 0) continue;
    CUDA_CALL( cudaSetDevice(i) );
    CUDA_CALL( cudaMemcpy(a_d[mpi_rank][i], &input[Nbegin[mpi_rank][i] * C * H * W],
               Nsize[mpi_rank][i] * C * H * W * sizeof(float),
               cudaMemcpyHostToDevice) );
    CUDA_CALL( cudaMemcpy(b_d[mpi_rank][i], filter, K * C * R * S * sizeof(float), cudaMemcpyHostToDevice) );
  }

  // DO NOT REMOVE; NEEDED FOR TIME MEASURE
  for (int i = 0; i < num_devices; i++) {
    if (Nsize[mpi_rank][i] == 0) continue;
    CUDA_CALL( cudaSetDevice(i) );
    CUDA_CALL( cudaDeviceSynchronize() );
  }
  // Launch kernel on every GPU
  for (int i = 0; i < num_devices; i++) {
    if (Nsize[mpi_rank][i] == 0) continue;
    int gws[3] = {Nsize[mpi_rank][i] * OW, K * OH, 1};
    int lws[3] = {TS, TS, 1};
    for (int j = 0; j < 3; ++j) {
      gws[j] = (gws[j] + lws[j] - 1) / lws[j] * lws[j];
    }
    dim3 blockDim(lws[0], lws[1], lws[2]);
    dim3 gridDim(gws[0] / lws[0], gws[1] / lws[1], gws[2] / lws[2]);

    CUDA_CALL( cudaSetDevice(i) );
    cuda_conv<<<gridDim, blockDim>>>(a_d[mpi_rank][i], c_d[mpi_rank][i], b_d[mpi_rank][i], Nsize[mpi_rank][i], C, H, W, K, R, S, OH, OW, pad, dilation, stride);
}

  // DO NOT REMOVE; NEEDED FOR TIME MEASURE
  for (int i = 0; i < num_devices; i++) {
    if (Nsize[mpi_rank][i] == 0) continue;
    CUDA_CALL( cudaSetDevice(i) );
    CUDA_CALL( cudaDeviceSynchronize() );
  }

  // Download C matrix from GPUs
  for (int i = 0; i < num_devices; i++) {
    if (Nsize[mpi_rank][i] == 0) continue;
    CUDA_CALL( cudaMemcpy(output + Nbegin[mpi_rank][i] * K * OH * OW, c_d[mpi_rank][i],
               Nsize[mpi_rank][i] * K * OH * OW * sizeof(float),
               cudaMemcpyDeviceToHost) );
  }

  // DO NOT REMOVE; NEEDED FOR TIME MEASURE
  for (int i = 0; i < num_devices; i++) {
    if (Nsize[mpi_rank][i] == 0) continue;
    CUDA_CALL( cudaSetDevice(i) );
    CUDA_CALL( cudaDeviceSynchronize() );
  }

  if (mpi_rank == 0) {
    MPI_Request request[mpi_world_size];
    for (int i = 1; i < mpi_world_size; ++i) {
      int index = get_begin_index(i) * K * OH * OW;
      int size = get_size_per_rank(i) * K * OH * OW;
      MPI_Irecv(&output[index], size, MPI_FLOAT, i, 0, MPI_COMM_WORLD, &request[i]);
    }
    for (int i = 1; i < mpi_world_size; ++i) {
      MPI_Wait(&request[i], MPI_STATUS_IGNORE);
    }
  } else {
    int index = get_begin_index(mpi_rank) * K * OH * OW;
    int size = get_size_per_rank(mpi_rank) * K * OH * OW;
    MPI_Send(&output[index], size, MPI_FLOAT, 0, 0, MPI_COMM_WORLD);
  }
}

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);

  CUDA_CALL( cudaGetDeviceCount(&num_devices) );
  if (num_devices > MAX_NUM_GPU)
    num_devices = MAX_NUM_GPU;

  printf("Using %d devices\n", num_devices);
  for (int i = 0; i < num_devices; i++) {
    cudaDeviceProp prop;
    CUDA_CALL( cudaGetDeviceProperties(&prop, i) );

    // Try printing more detailed information here
    printf("[GPU %d] %s\n", i, prop.name);
  }

  if (num_devices <= 0) {
    printf("No CUDA device found. Aborting\n");
    exit(1);
  }

  OH = (H + 2 * pad - dilation * (R - 1) - 1) / stride + 1;
  OW = (W + 2 * pad - dilation * (S - 1) - 1) / stride + 1;

  // Setup problem size for each GPU
  int NN = get_size_per_rank(mpi_rank);
  int NNbegin = get_begin_index(mpi_rank);
  for (int i = 0; i < num_devices; i++) {
    Nbegin[mpi_rank][i] = (NN / num_devices) * i + NNbegin;
    Nend[mpi_rank][i] = (NN / num_devices) * (i + 1) + NNbegin;
    Nsize[mpi_rank][i] = Nend[mpi_rank][i] - Nbegin[mpi_rank][i];
  }
  Nend[mpi_rank][num_devices - 1] = NN + NNbegin;
  Nsize[mpi_rank][num_devices - 1] = Nend[mpi_rank][num_devices - 1] - Nbegin[mpi_rank][num_devices - 1];

  // Allocate device memory for each GPU
  for (int i = 0; i < num_devices; i++) {
    if (Nsize[mpi_rank][i] == 0) continue;
    CUDA_CALL( cudaSetDevice(i) );
    CUDA_CALL( cudaMalloc(&a_d[mpi_rank][i], Nsize[mpi_rank][i] * C * H * W * sizeof(float)) );
    CUDA_CALL( cudaMalloc(&b_d[mpi_rank][i], K * C * R * S * sizeof(float)) );
    CUDA_CALL( cudaMalloc(&c_d[mpi_rank][i], Nsize[mpi_rank][i] * K * OH * OW * 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) {

}