ECE 408 sem project

2026-06-07 13:43:20 -05:00
1 parent 1d79ccd2e3
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+#ifndef __ATTENTION_CUH__
+#define __ATTENTION_CUH__
+
+#include <cuda_runtime.h>
+#include "../utils/cuda_utils.cuh"
+#include "softmax.cuh"
+
+__global__ void permute_kernel(float* __restrict__ q, float* __restrict__ k, float*  __restrict__ v, const float* __restrict__ 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(const float* __restrict__ inp, float* __restrict__ 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_];
+    }
+
+}
+
+__global__ void preattention_scores_kernel(const float* __restrict__ q, const float* __restrict__ k, int B, int T, int NH, int HS, float* __restrict__ preatt){
+    int x = blockDim.x * blockIdx.x + threadIdx.x;
+
+    if(x < B* T* NH* T){
+        int b = (x / T / T / NH);
+        int nh = (x / T / T) % NH;
+        int t1 = (x / T) % T;
+        int t2 = x % T;
+
+        float sum = 0.0f;
+        const float* __restrict__ q_ptr = q + b * NH * T * HS + nh * T * HS + t1 * HS;
+        const float* __restrict__ k_ptr = k + b * NH * T * HS + nh * T * HS + t2 * HS;
+        for (int i = 0; i < HS; i++) {
+            sum += q_ptr[i] * k_ptr[i];
+        }
+        preatt[b * NH * T * T + nh * T * T + t1 * T + t2] = sum;
+    }   
+}
+
+__global__ void attention_v_kernel(float* __restrict__ vaccum, const float* __restrict__ att, const float* __restrict__ v, int B, int T, int NH, int HS){
+    int x = blockDim.x * blockIdx.x + threadIdx.x;
+
+    if(x < B*NH*T*HS){
+        int b   = (x / HS / T / NH);
+        int nh  = (x / HS / T) % NH;
+        int t   = (x / HS) % T;
+        int hs  = x % HS;
+        float sum = 0.0f;
+        const float* __restrict__ att_ptr = att + b * NH * T * T + nh * T * T + t * T;
+        for (int t2 = 0; t2 < T; t2++) {
+            sum += att_ptr[t2] * v[b*NH*T*HS + nh*T*HS + t2*HS + hs];
+        }
+
+        vaccum[b * NH * T * HS + nh * T * HS + t * HS + hs] = sum;
+    }
+}
+
+// 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<<<grid, block>>>(q, k, v, inp, B, T, NH, HS);
+
+    float* preatt = inp;
+    dim3 grid_preatt((B * NH * T * T + block.x-1) / block.x,1,1);
+    preattention_scores_kernel<<<grid_preatt, block>>>(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<<<grid_softmax, block>>>(att, scale, preatt, B * NH, T);
+
+    float* vaccum = inp;
+    attention_v_kernel<<<grid_preatt,block>>>(vaccum, att, v, B, T, NH, HS);
+
+    unpermute_kernel<<<grid, block>>>(vaccum, out, B, T, NH, HS);
+}
+
+#endif // __ATTENTION_CUH__