#ifndef __ATTENTION_CUH__ #define __ATTENTION_CUH__ #include #include "../utils/cuda_utils.cuh" #include "softmax.cuh" __global__ void permute_kernel(float* q, float* k, float* v, const float* inp, int B, int N, int NH, int d) { // Implement this int x = blockDim.x * blockIdx.x + threadIdx.x; if( x < B*NH*N*d){ int b = (x / d / N / NH); int nh = (x / d / N) % NH; int t = (x / d) % N; int d_ = x % d; int inp_idx = (b * N * 3 * NH * d) + (t * 3 * NH * d) + (0 * NH * d) + (nh * d) + d_; q[b * NH * N * d + nh * N * d + t * d + d_] = inp[inp_idx]; k[b * NH * N * d + nh * N * d + t * d + d_] = inp[inp_idx + NH * d]; v[b * NH * N * d + nh * N * d + t * d + d_] = inp[inp_idx + 2 * (NH * d)]; } } __global__ void unpermute_kernel(float* inp, float *out, int B, int N, int NH, int d) { // Implement this int x = blockDim.x * blockIdx.x + threadIdx.x; if( x < B*NH*N*d){ int b = (x / d / N / NH); int nh = (x / d / N) % NH; int t = (x / d) % N; int d_ = x % d; out[b*N*NH*d + t*NH*d + nh*d + d_] = inp[b*NH*N*d + nh*N*d + t*d + d_]; } } #define PREATT_T 128 #define PREATT_U 8 #define PREATT_S 16 __global__ void preattention_scores_kernel(float* q, float* k, int B, int T, int NH, int HS, float* preatt){ // each block handles a (b,nh) head and a tile of (t1, t2) int head = blockIdx.z; //whcih pair int b = head / NH; int nh = head % NH; int t1 = blockIdx.x * PREATT_T + threadIdx.x; // row of output query positin int t2 = blockIdx.y * PREATT_U; //start column key position //head pointers const float* q_head = q + (b * NH + nh) * T * HS; //[T,HS] const float* k_head = k + (b * NH + nh) * T * HS; //[T,HS] float* preatt_head = preatt + (b * NH +nh) * T * T; //[T,T] //reg tile for accumulation float accumulation[PREATT_U] = {0.0f}; __shared__ float k_tile[PREATT_S][PREATT_U]; for(int tile_start = 0; tile_start < HS; tile_start+=PREATT_S){ // shared loading // K is [T, HS], K[t2..t2+U, tile_start..tile_start+S] int s_l = threadIdx.x / PREATT_U; int u_l = threadIdx.x % PREATT_U; int hs_idx = tile_start + s_l; int t2_idx = t2 + u_l; if (hs_idx < HS && t2_idx < T) { k_tile[s_l][u_l] = k_head[t2_idx * HS + hs_idx]; } else { k_tile[s_l][u_l] = 0.0f; } __syncthreads(); //each threads now loads its row of queries from global memory into registers and accumulates if (t1 < T){ #pragma unroll for(int s = 0; s < PREATT_S; s++){ int c = tile_start + s; float q_val = (c < HS) ? q_head[t1 * HS +c] : 0.0f; #pragma unroll for(int u = 0; u < PREATT_U; u++){ accumulation[u] += q_val * k_tile[s][u]; } } } __syncthreads(); } if(t1 < T){ #pragma unroll for(int u = 0; u < PREATT_U; u ++){ int t2_u = t2 + u; if(t2_u < T){ preatt_head[t1 * T + t2_u] = accumulation[u]; } } } } #define ATTV_T 128 #define ATTV_U 8 #define ATTV_S 16 __global__ void attention_v_kernel(float* vaccum, float* att, float* v, int B, int T, int NH, int HS){ int head = blockIdx.z; int b = head / NH; int nh = head % NH; int t = blockIdx.x * ATTV_T + threadIdx.x; // row of output query positin int hs_base = blockIdx.y * ATTV_U; //start column key position const float* att_head = att + (b * NH + nh) * T * T; const float* v_head = v + (b * NH + nh) * T * HS; float* out_head = vaccum + (b * NH + nh) * T * HS; float accumulation[ATTV_U] = {0.0f}; __shared__ float v_tile[ATTV_S][ATTV_U]; for(int tile_start = 0; tile_start < T; tile_start += ATTV_S){ int s_l = threadIdx.x / ATTV_U; int u_l = threadIdx.x % ATTV_U; int t2_idx = tile_start + s_l; int hs_idx = hs_base + u_l; if (t2_idx < T && hs_idx < HS) { v_tile[s_l][u_l] = v_head[t2_idx * HS + hs_idx]; } else { v_tile[s_l][u_l] = 0.0f; } __syncthreads(); // Each thread loads att values from registers and accumulates if (t < T) { #pragma unroll for (int s = 0; s < ATTV_S; s++) { int t2 = tile_start + s; float att_val = (t2 < T) ? att_head[t * T + t2] : 0.0f; #pragma unroll for (int u = 0; u < ATTV_U; u++) { accumulation[u] += att_val * v_tile[s][u]; } } } __syncthreads(); } if (t < T) { #pragma unroll for (int u = 0; u < ATTV_U; u++) { int hs = hs_base + u; if (hs < HS) { out_head[t * HS + hs] = accumulation[u]; } } } } // Launch all kernels related to attention here void attention_forward(float* out, float* qkvr, float* att, float* inp, int B, int T, int C, int NH) { // Implement this int HS = C / NH; float *q, *k, *v; q = qkvr + 0 * B * T * C; k = qkvr + 1 * B * T * C; v = qkvr + 2 * B * T * C; dim3 block(32,1,1); dim3 grid((B * NH * T * HS + block.x-1) / block.x,1,1); permute_kernel<<>>(q, k, v, inp, B, T, NH, HS); float* preatt = inp; dim3 block_preatt(PREATT_T); dim3 grid_preatt((T + PREATT_T - 1) / PREATT_T,(T+PREATT_U-1)/PREATT_U,B*NH); preattention_scores_kernel<<>>(q, k, B, T, NH, HS, preatt); float scale = 1.0 / sqrtf(HS); dim3 grid_softmax (( B * NH * T + block.x -1 )/ block.x,1,1); softmax_forward_kernel<<>>(att, scale, preatt, B * NH, T); float* vaccum = inp; dim3 block_attv(PREATT_T); dim3 grid_attv((T + ATTV_T - 1) / ATTV_T,(HS+ATTV_U-1)/ATTV_U,B*NH); attention_v_kernel<<>>(vaccum, att, v, B, T, NH, HS); unpermute_kernel<<>>(vaccum, out, B, T, NH, HS); } #endif // __ATTENTION_CUH__