204 lines
6.5 KiB
Plaintext
204 lines
6.5 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 <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 TS 8
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#define MAX_NUM_GPU 4
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static float *input, *output, *filter;
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static float *in_d[MAX_NUM_GPU], *out_d[MAX_NUM_GPU], *filter_d[MAX_NUM_GPU];
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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 num_devices = 1;
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static int size_per_node[2];
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static int N_per_gpu[MAX_NUM_GPU];
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__global__ void conv(
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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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const int global_row = blockDim.x * blockIdx.x + threadIdx.x;
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const int global_col = blockDim.y * blockIdx.y + threadIdx.y;
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int OH, OW;
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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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int n,k,w;
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n = global_col / (_K * OW);
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k = (global_col -n *(_K*OW)) / OW;
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w = (global_col -n *(_K*OW))- k * OW;
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int col = w;
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int row = global_row;
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if (global_row >= OH || global_col >= _N*_K*OW) return;
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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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for (int s = 0; s < _S; ++s) {
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int h = row * _stride - _pad + r * _dilation;
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int w = col * _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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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 + row * OW + col] = o;
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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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if (mpi_world_size == 2) {
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size_per_node[1] = _N / 2;
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}
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size_per_node[0] = _N - size_per_node[1];
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if (size_per_node[mpi_rank] < MAX_NUM_GPU) {
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num_devices = size_per_node[mpi_rank];
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for (int i = 0 ; i < size_per_node[mpi_rank] ; i++) {
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N_per_gpu[i] = 1;
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}
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} else {
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num_devices = MAX_NUM_GPU;
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int remain = size_per_node[mpi_rank] % MAX_NUM_GPU;
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int quot = size_per_node[mpi_rank] / MAX_NUM_GPU;
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for (int i = 0 ; i < MAX_NUM_GPU ; i++) {
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N_per_gpu[i] = quot;
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if (i < remain) {
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N_per_gpu[i]++;
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}
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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( cudaSetDevice(i) );
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CUDA_CALL( cudaMalloc(&in_d[i], N_per_gpu[i]*C*H*W*sizeof(float)) );
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CUDA_CALL( cudaMalloc(&out_d[i], N_per_gpu[i]*K*OH*OW*sizeof(float)) );
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CUDA_CALL( cudaMalloc(&filter_d[i], K*C*R*S*sizeof(float)) );
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}
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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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int offset = 0;
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input = _input;
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output = _output;
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filter = _filter;
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MPI_Request request;
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MPI_Status status;
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if (mpi_rank == 0 && mpi_world_size == 2 && size_per_node[1] != 0) {
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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);
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MPI_Isend(filter, _K*_C*_R*_S, MPI_FLOAT, 1, 0, MPI_COMM_WORLD, &request);
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if (size_per_node[mpi_rank] < MAX_NUM_GPU) {
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num_devices = size_per_node[mpi_rank];
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}
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} else if (mpi_rank == 1 && size_per_node[mpi_rank] != 0) {
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alloc_tensor(&input, size_per_node[1], C, H, W);
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alloc_tensor(&output, size_per_node[1], K, OH, OW);
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alloc_tensor(&filter, _K, _C, _R, _S);
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MPI_Recv(input, size_per_node[1]*C*H*W, MPI_FLOAT, 0, 0, MPI_COMM_WORLD, &status);
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MPI_Recv(filter, _K*_C*_R*_S, MPI_FLOAT, 0, 0, MPI_COMM_WORLD, &status);
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if (size_per_node[mpi_rank] < MAX_NUM_GPU) {
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num_devices = size_per_node[mpi_rank];
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}
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}
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offset = 0;
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for (int i = 0 ; i < num_devices ; i++) {
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CUDA_CALL( cudaMemcpy(in_d[i], input + offset, N_per_gpu[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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offset += N_per_gpu[i] * C * H * W;
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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 gridDim((OH+TS-1)/TS, (N_per_gpu[i]*K*OW + TS - 1)/TS, 1);
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dim3 blockDim(TS, TS, 1);
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CUDA_CALL( cudaSetDevice(i) );
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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);
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}
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// DO NOT REMOVE; NEEDED FOR TIME MEASURE
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for (int i = 0; i < num_devices; i++) {
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CUDA_CALL( cudaSetDevice(i) );
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CUDA_CALL( cudaDeviceSynchronize() );
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}
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offset = 0;
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for (int i = 0; i < num_devices; i++) {
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CUDA_CALL( cudaSetDevice(i) );
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CUDA_CALL( cudaMemcpy(output + offset, out_d[i],
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N_per_gpu[i]*K*OH*OW * sizeof(float),
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cudaMemcpyDeviceToHost) );
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offset += N_per_gpu[i]*K*OH*OW;
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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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CUDA_CALL( cudaSetDevice(i) );
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}
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if (mpi_rank == 0 && mpi_world_size == 2 && size_per_node[1] != 0) {
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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);
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} else if(mpi_rank == 1 && size_per_node[1] != 0){
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MPI_Isend(output, size_per_node[1]*K*OH*OW, MPI_FLOAT, 0, 0, MPI_COMM_WORLD, &request);
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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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