#include "convolution.h"
#include <mpi.h>
#include <stdio.h>
#include "util.h"
#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_X	4	
#define TS_Y	4	
#define TS_Z	64

#define MAX_NUM_GPU 4
int num_devices = 0;

static float *input_d[MAX_NUM_GPU];
static float *filter_d[MAX_NUM_GPU];
static float *output_d[MAX_NUM_GPU];
static int Mbegin[MAX_NUM_GPU], Mend[MAX_NUM_GPU];

void convolution_cuda();
void convolution_cuda_init(int,int);
void convolution_cuda_final(int);

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 p_n, remain, offset;

MPI_Status status;
MPI_Request request;

__global__ void conv_kernel(float *input, float *filter, float *output,  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 k = blockDim.x * blockIdx.x + threadIdx.x;
	int oh = blockDim.y * blockIdx.y + threadIdx.y;
	int ow = blockDim.z * blockIdx.z + threadIdx.z;

	if (k >= K || oh>= OH || ow >= OW) return;

	for(int n = 0; n < N; n++){
		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 = oh * stride - pad + r * dilation;
					int w = ow * 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 + oh * OW + ow] = o;
	}
}

void convolution_cuda() {

	// Launch kernel on every GPU
	for (int i = 0; i < num_devices; i++) {
		dim3 blockDim(TS_X, TS_Y, TS_Z);
		dim3 gridDim((K+TS_X-1)/TS_X, (OH+TS_Y-1)/TS_Y, (OW+TS_Z-1)/TS_Z);

		CUDA_CALL( cudaSetDevice(i) );
		conv_kernel<<<gridDim, blockDim>>>(input_d[i], filter_d[i], output_d[i], Mend[i] - Mbegin[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++) {
		CUDA_CALL( cudaSetDevice(i) );
		CUDA_CALL( cudaDeviceSynchronize() );
	}
}

void convolution_cuda_init() {

	CUDA_CALL( cudaGetDeviceCount(&num_devices) );

	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);
	}

	// Setup problem size for each GPU
	for (int i = 0; i < num_devices; i++) {
		Mbegin[i] = (p_n / num_devices) * i;
		Mend[i] = (p_n / num_devices) * (i + 1); 
	}

	Mend[num_devices - 1] = p_n;

	// Allocate device memory for each GPU
	for (int i = 0; i < num_devices; i++) {
		CUDA_CALL( cudaSetDevice(i) );
		CUDA_CALL( cudaMalloc(&input_d[i], (Mend[i] - Mbegin[i]) * C * H * W * sizeof(float)) );
		CUDA_CALL( cudaMalloc(&filter_d[i], K * C * R * S * sizeof(float)) );
		CUDA_CALL( cudaMalloc(&output_d[i], (Mend[i] - Mbegin[i]) * K* OH * OW * sizeof(float)) );
	}

	// Upload A and B matrix to every GPU
	for (int i = 0; i < num_devices; i++) {
		CUDA_CALL( cudaMemcpy(input_d[i], input + Mbegin[i] * C * H * W,
					(Mend[i] - Mbegin[i]) * C * H * W * sizeof(float),
					cudaMemcpyHostToDevice) );
		CUDA_CALL( cudaMemcpy(filter_d[i], filter, K * C * R * S * sizeof(float), cudaMemcpyHostToDevice) );
	}

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

void convolution_cuda_final() {
	// Do any post-matmul cleanup work here.

	// Download C matrix from GPUs
	for (int i = 0; i < num_devices; i++) {
		CUDA_CALL( cudaMemcpy(output + Mbegin[i] * K * OH * OW, output_d[i],
					(Mend[i] - Mbegin[i]) * K * OH * OW * sizeof(float),
					cudaMemcpyDeviceToHost) );
	}

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

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;

	p_n = N/mpi_world_size;
	remain= N%mpi_world_size;
	offset = remain;

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


	if (mpi_rank == 0) {

		for (int dest=1; dest < mpi_world_size; dest++){
			offset += p_n;
			MPI_Isend(&offset, 1, MPI_INT, dest , 1, MPI_COMM_WORLD,&request);
			MPI_Isend(&p_n, 1, MPI_INT, dest , 1, MPI_COMM_WORLD,&request);
			MPI_Isend(&input[offset*C*H*W], p_n*C*H*W, MPI_FLOAT, dest , 1, MPI_COMM_WORLD,&request);
			MPI_Isend(filter, K*C*R*S, MPI_FLOAT, dest, 1, MPI_COMM_WORLD,&request);
		}

		p_n += remain;
		convolution_cuda_init();
		convolution_cuda();
		convolution_cuda_final();

		for (int source=1; source < mpi_world_size; source++){
			MPI_Recv(&offset, 1, MPI_INT, source, 2, MPI_COMM_WORLD, &status);
			MPI_Recv(&p_n, 1, MPI_INT, source, 2, MPI_COMM_WORLD, &status);
			MPI_Recv(&output[offset*K*OH*OW], p_n*K*OH*OW, MPI_FLOAT, source, 2, MPI_COMM_WORLD, &status);
		} 
	}

	if(mpi_rank > 0){
		alloc_tensor(&input, p_n, C,H,W);
		alloc_tensor(&filter, K,C,R,S);
		alloc_tensor(&output, p_n, K,OH,OW);

		MPI_Recv(&offset, 1, MPI_INT, 0, 1, MPI_COMM_WORLD, &status);
		MPI_Recv(&p_n, 1, MPI_INT, 0, 1, MPI_COMM_WORLD, &status);
		MPI_Recv(input, p_n*C*H*W, MPI_FLOAT, 0, 1, MPI_COMM_WORLD, &status);
		MPI_Recv(filter,  K*C*R*S, MPI_FLOAT, 0, 1, MPI_COMM_WORLD, &status);

		convolution_cuda_init();
		convolution_cuda();
		convolution_cuda_final();

		MPI_Send(&offset, 1, MPI_INT, 0, 2, MPI_COMM_WORLD);
		MPI_Send(&p_n, 1, MPI_INT, 0, 2, MPI_COMM_WORLD);
		MPI_Send(output, p_n*K*OH*OW, MPI_FLOAT, 0, 2, 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);
}

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