/** * Full UMMA FMHA — HD=64, SK=128, T=1 (decode) * * Pipeline: Q×K^T (UMMA SS) → softmax (TMEM) → PV (register math) → output * PV computed in registers (decode T=1): O[d] = Σ P[0,j] × V[d,j] */ #include #include #include #include #include #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 = 64, SK = 128, NKT = HD / MMA_K_BF16; constexpr int BLOCK_MN = 128, TILE_SZ = BLOCK_MN * MMA_K_BF16, CORES_MN = BLOCK_MN / 8; __global__ void __launch_bounds__(128) test_fmha_hd64(const bf16_t* q, const bf16_t* k, const bf16_t* v, bf16_t* o_out, float* o_scalar, float scale) { const int tid = threadIdx.x, wid = tid / 32, lane = tid % 32; extern __shared__ char sbuf[]; uint32_t* sTmemBase = (uint32_t*)sbuf; bf16_t* sQ0 = (bf16_t*)(((uintptr_t)(sbuf + 4) + 15) & ~(uintptr_t)15); bf16_t* sK0 = sQ0 + NKT * TILE_SZ; // Load Q and K (same as working QK test) for (int i = tid; i < NKT * TILE_SZ; i += 128) { sQ0[i] = 0; sK0[i] = 0; } for (int kt = 0; kt < NKT; kt++) { bf16_t* sq = sQ0 + kt * TILE_SZ; for (int d = tid; d < MMA_K_BF16; d += 128) { int ck = d / 8, lc = d % 8; sq[ck * CORES_MN * 64 + lc] = q[kt * MMA_K_BF16 + d]; } bf16_t* sk = sK0 + kt * TILE_SZ; for (int r = 0; r < SK; r++) { for (int d = tid; d < MMA_K_BF16; d += 128) { int ck = d / 8, lc = d % 8; int tmn = r / 8, lr = r % 8; sk[ck * CORES_MN * 64 + tmn * 64 + lr * 8 + lc] = k[r * HD + kt * MMA_K_BF16 + d]; } } } __syncthreads(); if (wid == 1) tmem_alloc(__cvta_generic_to_shared(sTmemBase), 128); __syncthreads(); uint32_t tb = *sTmemBase; // QK GEMM bf16_t* sQ_arr[4] = {sQ0, sQ0+TILE_SZ, sQ0+2*TILE_SZ, sQ0+3*TILE_SZ}; bf16_t* sK_arr[4] = {sK0, sK0+TILE_SZ, sK0+2*TILE_SZ, sK0+3*TILE_SZ}; uint32_t idesc = make_idesc(BLOCK_MN, BLOCK_MN); for (int kt = 0; kt < NKT; kt++) { uint64_t dq = make_umma_desc_kmajor_none(__cvta_generic_to_shared(sQ_arr[kt]), BLOCK_MN); uint64_t dk = make_umma_desc_kmajor_none(__cvta_generic_to_shared(sK_arr[kt]), BLOCK_MN); if (tid == 0) umma_ss_f16(tb, dq, dk, idesc, kt > 0); asm volatile("tcgen05.fence::after_thread_sync;" ::: "memory"); __syncthreads(); } asm volatile("tcgen05.fence::after_thread_sync;" ::: "memory"); __syncthreads(); // Softmax + PV: warp 0 computes softmax and then PV in registers if (wid == 0) { float s_vals[SK], row_max = -INFINITY; for (int n = 0; n < SK / 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++) { s_vals[n*8+c] = tmp[c]*scale; row_max = fmaxf(row_max, tmp[c]*scale); } } row_max = wmax(row_max); float row_sum = 0.0f; if (lane == 0) for (int j=0;j>>(d_q, d_k, d_v, d_o, d_o_scalar, SCALE); cudaError_t err = cudaDeviceSynchronize(); if (err != cudaSuccess) { printf("CUDA ERROR: %s\n", cudaGetErrorString(err)); return 1; } cudaMemcpy(h_o, d_o, HD*sizeof(bf16_t), cudaMemcpyDeviceToHost); cudaMemcpy(h_o_scalar, d_o_scalar, HD*sizeof(float), cudaMemcpyDeviceToHost); printf("O[0..7] MMA: "); for(int d=0;d<8;d++) printf("%.6f ",bf16_to_f32_host(h_o[d])); printf("\n"); printf("O[0..7] ref: "); for(int d=0;d<8;d++) printf("%.6f ",h_o_scalar[d]); printf("\n"); float max_diff=0, max_val=0; for (int d=0;d0 ? max_diff/max_val : max_diff; float cos_sim=0, na=0, nb=0; for (int d=0;d 0.999f ? "PASSED" : "FAILED"); cudaFree(d_q); cudaFree(d_k); cudaFree(d_v); cudaFree(d_o); cudaFree(d_o_scalar); free(h_q); free(h_k); free(h_v); free(h_o); free(h_o_scalar); return cos_sim > 0.999f ? 0 : 1; }