/** * Test PV GEMM only: feed known P values, load V directly, verify output. * Uses the same 4-warp pattern as test_fmha_gen.cu. * Skips QK and softmax entirely — just PV → O. */ #include #include #include #include #include #include #ifndef HD_VAL #define HD_VAL 64 #endif #include "dsv4/kernels/attention/fmha_common.cuh" #include "dsv4/kernels/attention/fmha_umma_desc.cuh" using namespace dsv4::kernels::attention; static bf16_t f32_to_bf16_host(float f) { uint32_t u; memcpy(&u,&f,4); return (uint16_t)(u>>16); } static float bf16_to_f32_host(bf16_t h) { uint32_t u=(uint32_t)h<<16; float f; memcpy(&f,&u,4); return f; } constexpr int HD = HD_VAL; constexpr int SK = 128; constexpr int BLOCK_MN = 128; constexpr int MMA_K = MMA_K_BF16; constexpr int NKT_PV = SK / MMA_K; constexpr int N_NSUB = HD / 16; constexpr int TILE_SZ = BLOCK_MN * MMA_K; constexpr int TMEM_N = (HD <= 128) ? 128 : 256; constexpr int V_SUB_SZ = 256; // (16,16) canonical constexpr int CORES_MN = 16; constexpr int CORES_MN_V = 2; __global__ void __launch_bounds__(128) test_pv_only_kernel( float* __restrict__ out, // (HD,) — PV output const bf16_t* __restrict__ v, // (HD, SK) const float* __restrict__ p_input // (SK,) — pre-computed P values ) { const int tid = threadIdx.x, wid = tid / 32, lane = tid % 32; extern __shared__ __align__(128) char sbuf[]; size_t off = 0; uint32_t* sTmemBase = (uint32_t*)(sbuf + off); off = 4; off = (off + 15) & ~(size_t)15; float* s_p_vals = (float*)(sbuf + off); off += SK * sizeof(float); off = (off + 15) & ~(size_t)15; bf16_t* sPk = (bf16_t*)(sbuf + off); off += TILE_SZ * sizeof(bf16_t); off = (off + 127) & ~(size_t)127; bf16_t* sV = (bf16_t*)(sbuf + off); off += V_SUB_SZ * sizeof(bf16_t); // Load P values into SMEM for (int j = tid; j < SK; j += 128) s_p_vals[j] = p_input[j]; __syncthreads(); // TMEM alloc if (wid == 1) tmem_alloc(__cvta_generic_to_shared(sTmemBase), TMEM_N); __syncthreads(); uint32_t tb = *sTmemBase; // PV GEMM — EXACT pattern from test_fmha_gen.cu { uint32_t idesc_pv16 = make_idesc(BLOCK_MN, 16); uint64_t dp = make_umma_desc_kmajor_none(__cvta_generic_to_shared(sPk), BLOCK_MN); for (int n = 0; n < N_NSUB; n++) { int d_base = n * 16; for (int kt = 0; kt < NKT_PV; kt++) { for (int i = tid; i < TILE_SZ; i += 128) sPk[i] = 0; if (tid < 16) { int c = tid; int ck = c / 8, lc = c % 8; sPk[ck * CORES_MN * 64 + 0 * 64 + 0 * 8 + lc] = f32_to_bf16(s_p_vals[kt * MMA_K + c]); } for (int i = tid; i < V_SUB_SZ; i += 128) sV[i] = 0; for (int dd = tid; dd < 16; dd += 128) { for (int lr = 0; lr < MMA_K; lr++) { int r = kt * MMA_K + lr; int g_mn = dd / 8, g_k = lr / 8, llr = dd % 8, lc = lr % 8; sV[g_k * CORES_MN_V * 64 + g_mn * 64 + llr * 8 + lc] = v[(d_base + dd) * SK + r]; } } __syncthreads(); uint64_t dv = make_umma_desc_kmajor_none(__cvta_generic_to_shared(sV), 16); if (tid == 0) umma_ss_f16(tb + n * 16, dp, dv, idesc_pv16, kt > 0); asm volatile("tcgen05.fence::after_thread_sync;" ::: "memory"); __syncthreads(); } } } // Read from TMEM if (wid == 0) { float o_vals[HD]; for (int n = 0; n < HD / 8; n++) { float tmp[8]; asm volatile("tcgen05.ld.sync.aligned.32x32b.x8.b32 {%0,%1,%2,%3,%4,%5,%6,%7},[%8];" : "=f"(tmp[0]),"=f"(tmp[1]),"=f"(tmp[2]),"=f"(tmp[3]), "=f"(tmp[4]),"=f"(tmp[5]),"=f"(tmp[6]),"=f"(tmp[7]) : "r"(tb + n * 8)); asm volatile("tcgen05.wait::ld.sync.aligned;"); if (lane == 0) for (int c = 0; c < 8; c++) o_vals[n * 8 + c] = tmp[c]; } if (lane == 0) for (int d = 0; d < HD; d++) out[d] = o_vals[d]; } if (wid == 0) tmem_dealloc(tb, TMEM_N); } int main() { printf("PV-Only Test (HD=%d, SK=%d)\n", (int)HD, (int)SK); bf16_t* h_v = (bf16_t*)malloc(HD * SK * sizeof(bf16_t)); float* h_p = (float*)malloc(SK * sizeof(float)); srand(42); for (int i = 0; i < HD * SK; i++) h_v[i] = f32_to_bf16_host((float)(rand()%100)/100.0f - 0.5f); // Simple P: uniform (all 1/SK) for (int j = 0; j < SK; j++) h_p[j] = 1.0f / SK; bf16_t *d_v; float *d_p, *d_out; cudaMalloc(&d_v, HD * SK * sizeof(bf16_t)); cudaMalloc(&d_p, SK * sizeof(float)); cudaMalloc(&d_out, HD * sizeof(float)); cudaMemcpy(d_v, h_v, HD * SK * sizeof(bf16_t), cudaMemcpyHostToDevice); cudaMemcpy(d_p, h_p, SK * sizeof(float), cudaMemcpyHostToDevice); size_t smem = 4 + 16 + SK*4 + 16 + TILE_SZ*2 + 128 + V_SUB_SZ*2 + 256; cudaFuncSetAttribute(test_pv_only_kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, smem); test_pv_only_kernel<<<1, 128, smem>>>(d_out, d_v, d_p); cudaError_t err = cudaDeviceSynchronize(); if (err != cudaSuccess) { printf("CUDA ERROR: %s\n", cudaGetErrorString(err)); return 1; } float* h_out = (float*)malloc(HD * sizeof(float)); cudaMemcpy(h_out, d_out, HD * sizeof(float), cudaMemcpyDeviceToHost); // Reference: O = P @ V where P = 1/SK, so O[d] = (1/SK) * sum_j V[d,j] int fail = 0; float max_rel = 0; for (int d = 0; d < HD; d++) { float ref = 0.0f; for (int j = 0; j < SK; j++) ref += bf16_to_f32_host(h_v[d * SK + j]) / SK; float got = h_out[d]; float rel = fabsf(ref) > 1e-4f ? fabsf(got - ref) / fabsf(ref) : fabsf(got - ref); if (rel > max_rel) max_rel = rel; if (rel > 0.01f && fail < 5) printf(" d=%d: ref=%.6f got=%.6f\n", d, ref, got); if (rel > 0.01f) fail++; } printf("Max rel err: %.8f, failures: %d\n", max_rel, fail); printf("%s\n", fail == 0 ? "PASSED" : "FAILED"); return fail == 0 ? 0 : 1; }