wehub-resource-sync
260 lines
8.4 KiB
Plaintext
260 lines
8.4 KiB
Plaintext
/*
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Kernels for the positional encoder forward pass in GPT-2.
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Compile example:
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nvcc -O3 --use_fast_math -lcublas -lcublasLt encoder_forward.cu -o encoder_forward
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version 1 is naive port from CPU code to kernel: parallelizes over B,T, loops over C
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./encoder_forward 1
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version 2 is more optimized, parallelizes over all of B,T,C
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./encoder_forward 2
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version 3 is like version 2 but uses float4 reads/writes
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./encoder_forward 3
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*/
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#include <stdio.h>
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#include <stdlib.h>
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#include <cuda_runtime.h>
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#include <cassert>
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#define ENABLE_BF16
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#include "common.h"
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// ----------------------------------------------------------------------------
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// CPU code reference
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// GPT-2 positional encoder forward pass
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void encoder_forward_cpu(float* out,
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const int* inp, const float* wte, const float* wpe,
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int B, int T, int C) {
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for (int b = 0; b < B; b++) {
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for (int t = 0; t < T; t++) {
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float* out_bt = out + b * T * C + t * C;
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int ix = inp[b * T + t];
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const float* wte_ix = wte + ix * C;
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const float* wpe_t = wpe + t * C;
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for (int i = 0; i < C; i++) {
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out_bt[i] = wte_ix[i] + wpe_t[i];
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}
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}
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}
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}
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// ----------------------------------------------------------------------------
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// GPU kernels
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// naive implementation into kernel, parallelize over B,T, loop over C
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__global__ void encoder_forward_kernel1(floatX* out,
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const int* inp, const floatX* wte, const floatX* wpe,
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int B, int T, int C) {
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int idx = blockIdx.x * blockDim.x + threadIdx.x;
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int N = B * T;
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if (idx < N) {
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int b = idx / T;
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int t = idx % T;
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floatX* out_bt = out + b * T * C + t * C;
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int ix = inp[b * T + t];
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const floatX* wte_ix = wte + ix * C;
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const floatX* wpe_t = wpe + t * C;
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for (int i = 0; i < C; i++) {
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out_bt[i] = (floatX)((float)wte_ix[i] + (float)wpe_t[i]);
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}
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}
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}
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// optimized implementation: parallelize over all of B,T,C
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__global__ void encoder_forward_kernel2(floatX* out,
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const int* inp, const floatX* wte, const floatX* wpe,
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int B, int T, int C) {
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int idx = blockIdx.x * blockDim.x + threadIdx.x;
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int N = B * T * C;
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if (idx < N) {
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int bt = idx / C;
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int b = bt / T;
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int t = bt % T;
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int c = idx % C;
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int ix = inp[b * T + t];
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floatX* out_btc = out + b * T * C + t * C + c;
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const floatX* wte_ix = wte + ix * C + c;
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const floatX* wpe_tc = wpe + t * C + c;
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*out_btc = (floatX)((float)*wte_ix + (float)*wpe_tc);
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}
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}
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__global__ void encoder_forward_kernel3(floatX* out,
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const int* inp, const floatX* wte, const floatX* wpe,
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int B, int T, int C) {
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int idx = (blockIdx.x * blockDim.x + threadIdx.x) * x128::size;
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int N = B * T * C;
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if (idx < N) {
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int bt = idx / C;
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int b = bt / T;
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int t = bt % T;
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int c = idx % C;
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int ix = inp[b * T + t];
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floatX* out_btc = out + b * T * C + t * C + c;
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const floatX* wte_ix = wte + ix * C + c;
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const floatX* wpe_tc = wpe + t * C + c;
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x128 packed_out;
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x128 wte = load128cs(wte_ix);
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x128 wpe = load128cs(wpe_tc);
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#pragma unroll
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for (int k = 0; k < wte.size; k++) {
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packed_out[k] = (floatX)((float)wte[k] + (float)wpe[k]);
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}
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store128(out_btc, packed_out);
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}
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}
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// ----------------------------------------------------------------------------
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// kernel launcher
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void encoder_forward1(floatX* out,
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const int* inp, const floatX* wte, const floatX* wpe,
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int B, int T, int C,
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const int block_size) {
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const int N = B * T;
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const int grid_size = ceil_div(N, block_size);
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encoder_forward_kernel1<<<grid_size, block_size>>>(out, inp, wte, wpe, B, T, C);
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cudaCheck(cudaGetLastError());
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}
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void encoder_forward2(floatX* out,
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const int* inp, const floatX* wte, const floatX* wpe,
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int B, int T, int C,
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const int block_size) {
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const int N = B * T * C;
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const int grid_size = ceil_div(N, block_size);
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encoder_forward_kernel2<<<grid_size, block_size>>>(out, inp, wte, wpe, B, T, C);
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cudaCheck(cudaGetLastError());
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}
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void encoder_forward3(floatX* out,
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const int* inp, const floatX* wte, const floatX* wpe,
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int B, int T, int C,
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const int block_size) {
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const int N = B * T * C;
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const int grid_size = ceil_div(N, (int)(block_size * x128::size));
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encoder_forward_kernel3<<<grid_size, block_size>>>(out, inp, wte, wpe, B, T, C);
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cudaCheck(cudaGetLastError());
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}
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// kernel version dispatch
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void encoder_forward(int kernel_num,
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floatX* out,
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const int* inp, const floatX* wte, const floatX* wpe,
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int B, int T, int C,
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const int block_size) {
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switch (kernel_num) {
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case 1:
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encoder_forward1(out, inp, wte, wpe, B, T, C, block_size);
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break;
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case 2:
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encoder_forward2(out, inp, wte, wpe, B, T, C, block_size);
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break;
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case 3:
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encoder_forward3(out, inp, wte, wpe, B, T, C, block_size);
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break;
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default:
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printf("Invalid kernel number\n");
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exit(1);
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}
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}
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// ----------------------------------------------------------------------------
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int main(int argc, char **argv) {
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setup_main();
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int B = 8;
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int T = 1024;
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int C = 768;
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int V = 50257;
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int deviceIdx = 0;
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cudaCheck(cudaSetDevice(deviceIdx));
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// create host memory of random numbers
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float* out = (float*)malloc(B * T * C * sizeof(float));
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int* inp = make_random_int(B * T, V);
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float* wte = make_random_float(V * C);
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float* wpe = make_random_float(T * C);
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// move to GPU
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floatX* d_out;
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int* d_inp;
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floatX* d_wte;
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floatX* d_wpe;
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cudaCheck(cudaMalloc(&d_out, B * T * C * sizeof(floatX)));
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cudaCheck(cudaMalloc(&d_inp, B * T * sizeof(int)));
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cudaCheck(cudaMalloc(&d_wte, V * C * sizeof(floatX)));
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cudaCheck(cudaMalloc(&d_wpe, T * C * sizeof(floatX)));
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cudaCheck(cudaMemcpy(d_inp, inp, B * T * sizeof(int), cudaMemcpyHostToDevice));
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cudaCheck(memcpy_convert(d_wte, wte, V * C));
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cudaCheck(memcpy_convert(d_wpe, wpe, T * C));
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// read kernel_num from command line
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int kernel_num = 2;
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if (argc > 1) {
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kernel_num = atoi(argv[1]);
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}
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printf("Using kernel %d\n", kernel_num);
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// first check the correctness of the kernel
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encoder_forward_cpu(out, inp, wte, wpe, B, T, C);
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// time the kernel at different block sizes
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int block_sizes[] = {32, 64, 128, 256, 512, 1024};
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for (int j = 0; j < sizeof(block_sizes) / sizeof(int); j++) {
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int block_size = block_sizes[j];
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printf("Checking block size %d.\n", block_size);
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encoder_forward(kernel_num, d_out, d_inp, d_wte, d_wpe, B, T, C, block_size);
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#if !defined(ENABLE_BF16) && !defined(ENABLE_FP16)
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float tol = 1e-5;
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#else
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float tol = 1e-2f;
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#endif
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validate_result(d_out, out, "out", B * T * C, tol);
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}
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printf("All results match. Starting benchmarks.\n\n");
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for (int j = 0; j < sizeof(block_sizes) / sizeof(int); j++) {
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int block_size = block_sizes[j];
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int repeat_times = 1000;
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float elapsed_time = benchmark_kernel(repeat_times, encoder_forward,
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kernel_num, d_out, d_inp, d_wte, d_wpe, B, T, C, block_size
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);
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// napkin math: estimate the memory bandwidth achieved
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// for each (B,T,C) output element, we do 3 reads and 1 write, 4 bytes each
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// and e.g. A100 40GB PCIe is advertised at 1,555GB/s
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long memory_ops = B * T * C * 4 * 4;
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float memory_bandwidth = memory_ops / elapsed_time / 1e6;
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printf("block_size %4d | time %.4f ms | bandwidth %.2f GB/s\n", block_size, elapsed_time, memory_bandwidth);
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}
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// free memory
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free(out);
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free(inp);
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free(wte);
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free(wpe);
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cudaCheck(cudaFree(d_out));
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cudaCheck(cudaFree(d_inp));
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cudaCheck(cudaFree(d_wte));
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cudaCheck(cudaFree(d_wpe));
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return 0;
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} |