252 lines
8.3 KiB
Plaintext
252 lines
8.3 KiB
Plaintext
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#include "convolution.h"
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#include "util.h"
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#include <mpi.h>
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#include <stdio.h>
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#include <cstdio>
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#include <cuda_runtime.h>
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#define CUDA_CALL(f) \
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{ \
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cudaError_t err = (f); \
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if (err != cudaSuccess) { \
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fprintf(stderr, "CUDA error at [%s:%d] %d %s\n", __FILE__, __LINE__, \
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err, cudaGetErrorString(err)); \
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exit(1); \
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} \
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}
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#define MAX_NUM_CPU 2
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#define MAX_NUM_GPU 4
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#define TS 8
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static float *input, *output, *filter;
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static int N, C, H, W;
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static int K, R, S;
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static int OH, OW;
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static int pad;
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static int dilation;
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static int stride;
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static int mpi_rank, mpi_world_size;
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static int ns[MAX_NUM_CPU], ne[MAX_NUM_CPU];
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static float *input_d[MAX_NUM_GPU];
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static float *filter_d[MAX_NUM_GPU];
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static float *output_d[MAX_NUM_GPU];
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static int Nbegin[MAX_NUM_CPU][MAX_NUM_GPU], Nend[MAX_NUM_CPU][MAX_NUM_GPU];
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int num_devices = 0;
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__global__ void convolution_thread(
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float *input, float *output, float *filter,
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int N, int C, int H, int W,
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int K, int R, int S,
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int pad, int dilation, int stride,
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int OH, int OW) {
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//printf("blockIdx.x:%d\n", blockIdx.x);
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//printf("blockDim.x:%d\n", blockDim.x);
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//printf("threadIdx.x:%d\n", threadIdx.x);
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//printf("blockIdx.y:%d\n", blockIdx.y);
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#if 0
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int oh = (blockIdx.x * blockDim.x + threadIdx.x);
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int ow = (blockIdx.y * blockDim.y + threadIdx.y);
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//int k = (blockIdx.z * blockDim.z + threadIdx.z) % K;
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//int n = (blockIdx.z * blockDim.z + threadIdx.z) / K;
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int k = (blockIdx.z) % K;
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int n = (blockIdx.z) / K;
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#else
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int ow = threadIdx.x;
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int oh = blockIdx.x;
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int k = blockIdx.y;
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int n = blockIdx.z;
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//int ow = threadIdx.x;
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//int oh = blockIdx.z;
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//int k = blockIdx.y;
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//int n = blockIdx.x;
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#endif
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if (oh >= OH || ow >= OW || k >= K || n >= N) return;
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//__shared__ int *filter_d;
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//__syncthreads();
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//
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float o = 0.f;
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for (int c = 0; c < C; ++c) {
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for (int r = 0; r < R; ++r) {
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int h = oh * stride - pad + r * dilation;
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for (int s = 0; s < S; ++s) {
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int w = ow * stride - pad + s * dilation;
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if (h < 0 || h >= H || w < 0 || w >= W) continue;
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float i = input[n * C * H * W + c * H * W + h * W + w];
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float f = filter[k * C * R * S + c * R * S + r * S + s];
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//printf("input[n * C * H * W + c * H * W + h * W + w] = %f\n", i);
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//printf("filter[k * C * R * S + c * R * S + r * S + s] = %f\n", f);
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o += i * f;
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}
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}
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}
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output[n * K * OH * OW + k * OH * OW + oh * OW + ow] = o;
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//printf("output[n * K * OH * OW + k * OH * OW + oh * OW + ow]= %f\n", o);
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}
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void convolution (
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float *_input, float *_output, float *_filter,
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int _N, int _C, int _H, int _W,
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int _K, int _R, int _S,
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int _pad, int _dilation, int _stride) {
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input = _input;
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output = _output;
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filter = _filter;
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if (mpi_rank != 0) {
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alloc_tensor(&input, N, C, H, W);
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alloc_tensor(&output, N, K, OH, OW);
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alloc_tensor(&filter, K, C, R, S);
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}
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if (mpi_world_size > 1) {
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if (mpi_rank == 0) {
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for (int i = 1; i < mpi_world_size; i++) {
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MPI_Send(input + ns[i] * C * H * W, (ne[i] - ns[i]) * C * H * W, MPI_FLOAT, i, 0,
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MPI_COMM_WORLD);
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MPI_Send(filter, K * C * R * S, MPI_FLOAT, i, 0, MPI_COMM_WORLD);
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}
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} else {
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MPI_Recv(input + ns[mpi_rank] * C * H * W, (ne[mpi_rank] - ns[mpi_rank]) * C * H * W, MPI_FLOAT,
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0, 0, MPI_COMM_WORLD, nullptr);
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MPI_Recv(filter, K * C * R * S, MPI_FLOAT, 0, 0, MPI_COMM_WORLD, nullptr);
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}
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}
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//printf("ns[%d] : %d\n", mpi_rank, ns[mpi_rank]);
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//printf("ne[%d] : %d\n", mpi_rank, ne[mpi_rank]);
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//for(int i = 0; i < num_devices; i++)
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//{
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// printf("Nbegin[%d][%d] : %d\n", mpi_rank,i,Nbegin[mpi_rank][i]);
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// printf("Nend[%d][%d] : %d\n", mpi_rank,i,Nend[mpi_rank][i]);
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//}
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// Cuda start
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for (int i = 0; i < num_devices; i++) {
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if(Nend[mpi_rank][i] - Nbegin[mpi_rank][i] != 0)
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{
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//printf("Nend[mpi_rank][%d]:%d\n", i, Nend[mpi_rank][i]);
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//printf("Nbegin[mpi_rank][%d]:%d\n", i, Nbegin[mpi_rank][i]);
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//printf("%d, %d\n",ns[mpi_rank], Nbegin[mpi_rank][i]);
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CUDA_CALL( cudaMemcpy(input_d[i], input + (ns[mpi_rank] + Nbegin[mpi_rank][i]) * C * H * W,
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(Nend[mpi_rank][i] - Nbegin[mpi_rank][i]) * C * H * W * sizeof(float), cudaMemcpyHostToDevice) );
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CUDA_CALL( cudaMemcpy(filter_d[i], filter, K * C * R * S * sizeof(float), cudaMemcpyHostToDevice) );
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}
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}
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for (int i = 0; i < num_devices; i++) {
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CUDA_CALL( cudaDeviceSynchronize() );
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}
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for (int i = 0; i < num_devices; i++) {
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//dim3 blockDim(OH, OW);
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//dim3 gridDim(Nend[mpi_rank][i] - Nbegin[mpi_rank][i], K);
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//printf("test[%d]\n", mpi_rank);
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if(Nend[mpi_rank][i] - Nbegin[mpi_rank][i] != 0)
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{
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#if 0
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dim3 blockDim(TS, TS);
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dim3 gridDim((OH + TS - 1) / TS, (OW + TS - 1)/ TS, ((Nend[mpi_rank][i] - Nbegin[mpi_rank][i]) * K));
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//printf("Nend[mpi_rank][%d]:%d\n", i, Nend[mpi_rank][i]);
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//printf("Nbegin[mpi_rank][%d]:%d\n", i, Nbegin[mpi_rank][i]);
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#else
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dim3 blockDim(OW);
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dim3 gridDim(OH, K, Nend[mpi_rank][i] - Nbegin[mpi_rank][i]);
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//dim3 gridDim(Nend[mpi_rank][i] - Nbegin[mpi_rank][i], K, OH);
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#endif
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CUDA_CALL( cudaSetDevice(i) );
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convolution_thread<<<gridDim, blockDim>>>(input_d[i], output_d[i], filter_d[i], (Nend[mpi_rank][i] - Nbegin[mpi_rank][i]),
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_C, _H, _W, _K, _R, _S, _pad, _dilation, _stride, OH, OW);
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}
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}
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for (int i = 0; i < num_devices; i++) {
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CUDA_CALL( cudaDeviceSynchronize() );
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}
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for (int i = 0; i < num_devices; i++) {
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if(Nend[mpi_rank][i] - Nbegin[mpi_rank][i] != 0)
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{
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CUDA_CALL( cudaMemcpy(output + (ns[mpi_rank] + Nbegin[mpi_rank][i]) * K * OH * OW, output_d[i],
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(Nend[mpi_rank][i] - Nbegin[mpi_rank][i]) * K * OH * OW * sizeof(float),
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cudaMemcpyDeviceToHost) );
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}
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}
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// Cuda end
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if (mpi_world_size > 1) {
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if (mpi_rank == 0) {
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for (int i = 1; i <mpi_world_size; i++) {
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MPI_Recv(output + ns[i] * K * OH * OW, (ne[i] - ns[i]) * K * OH * OW, MPI_FLOAT, i, 0,
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MPI_COMM_WORLD, nullptr);
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}
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} else {
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MPI_Send(output + ns[mpi_rank] * K * OH * OW, (ne[mpi_rank] - ns[mpi_rank]) * K * OH * OW, MPI_FLOAT, 0, 0, MPI_COMM_WORLD);
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}
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}
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}
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void convolution_init(
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int _N, int _C, int _H, int _W,
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int _K, int _R, int _S,
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int _pad, int _dilation, int _stride) {
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N = _N; C = _C; H = _H; W = _W;
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K = _K; R = _R; S = _S;
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pad = _pad;
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dilation = _dilation;
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stride = _stride;
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MPI_Comm_rank(MPI_COMM_WORLD, &mpi_rank);
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MPI_Comm_size(MPI_COMM_WORLD, &mpi_world_size);
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OH = (H + 2 * pad - dilation * (R - 1) - 1) / stride + 1;
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OW = (W + 2 * pad - dilation * (S - 1) - 1) / stride + 1;
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CUDA_CALL( cudaGetDeviceCount(&num_devices) );
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for (int i = 0; i < mpi_world_size; i++) {
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ns[i] = N / mpi_world_size * i;
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ne[i] = N / mpi_world_size * (i + 1);
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for (int j = 0; j < num_devices; j++) {
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Nbegin[i][j] = ((ne[i] - ns[i]) / num_devices) * j;
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Nend[i][j] = ((ne[i] - ns[i]) / num_devices) * (j + 1);
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}
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}
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ne[mpi_world_size - 1] = N;
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for(int i = 0; i < mpi_world_size; i++) {
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Nend[i][num_devices - 1] = ne[i] - ns[i];
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}
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//for (int i = 0; i< num_devices; i++) {
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// printf("%d: Nbegin[%d]: %d\n",mpi_rank, i, Nbegin[mpi_rank][i]);
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// printf("%d: Nend[%d]: %d\n",mpi_rank, i, Nend[mpi_rank][i]);
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//}
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for (int i = 0; i < num_devices; i++) {
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if(Nend[mpi_rank][i] - Nbegin[mpi_rank][i] != 0)
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{
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CUDA_CALL( cudaSetDevice(i) );
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CUDA_CALL( cudaMalloc(&input_d[i], (Nend[mpi_rank][i] - Nbegin[mpi_rank][i]) * C * H * W * sizeof(float)) );
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CUDA_CALL( cudaMalloc(&filter_d[i], K * C * R * S* sizeof(float)) );
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CUDA_CALL( cudaMalloc(&output_d[i], (Nend[mpi_rank][i] - Nbegin[mpi_rank][i]) * K * OH * OW * sizeof(float)) );
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}
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}
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}
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void convolution_final(
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int _N, int _C, int _H, int _W,
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int _K, int _R, int _S,
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int _pad, int _dilation, int _stride) {
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}
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