auto: pre-test commit

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2026-05-28 16:28:58 +00:00
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/**
* DSV4 FMHA — 6-warp specialized kernel for Blackwell SM100.
*
* ==================================================================
* WARP SPECIALIZATION
* ==================================================================
* Warp 0-3 (tid 0-127): Softmax + correction + epilogue
* - Read S from TMEM, compute softmax, write P to SMEM
* - After PV: read O from TMEM, normalize, write to GMEM
* - For T=1 decode: only warp 0 processes row 0
*
* Warp 4 (tid 128-159): MMA (QK + PV)
* - Call tcgen05.mma for QK and PV
* - TMEM alloc/dealloc
* - Only 1 thread calls MMA, but TMEM ops are warp-collective
*
* Warp 5 (tid 160-191): Data staging (Q/K/V loads)
* - Load Q, K, V from GMEM to SMEM in canonical layout
* - Future: TMA loads with mbarrier
* - Fill sPk from s_p_vals
*
* ==================================================================
* SYNCHRONIZATION
* ==================================================================
* CTA-wide __syncthreads() barriers between phases:
* 1. After Q/K/V loads → QK MMA
* 2. After QK MMA → softmax
* 3. After softmax + P fill + V load → PV MMA
* 4. After PV MMA → epilogue
*
* Future: mbarrier-based producer-consumer sync between warp 5 (producer)
* and warp 4 (consumer) for pipeline overlap.
*
* ==================================================================
* SMEM LAYOUT (shared across all warps)
* ==================================================================
* sQ: (128, 16) canonical = 8 KB (1 K-tile, reused)
* sK: (128, 16) canonical = 8 KB (1 K-tile, reused)
* sPk: (128, 16) canonical = 8 KB (1 sub-tile, reused)
* sV: (16, 16) canonical = 512 bytes (1 N-sub-tile)
* s_p_vals: 128 floats = 512 bytes (softmax output)
* sRowMax: 1 float (row 0 max, for T=1)
* sRowSum: 1 float (row 0 sum, for T=1)
* sTmemBase: 4 bytes (TMEM allocation)
* Total: ~26 KB
*/
#pragma once
#include "fmha_common.cuh"
#include "fmha_umma_desc.cuh"
namespace dsv4::kernels::attention {
template<int HD, int SK_TILE = 128>
__global__ void __launch_bounds__(192)
fmha_6warp_kernel(
const bf16_t* __restrict__ q,
const bf16_t* __restrict__ k,
const bf16_t* __restrict__ v,
bf16_t* __restrict__ o,
int s_k, float scale
) {
static constexpr int NKT_QK = HD / MMA_K_BF16;
static constexpr int NKT_PV = SK_TILE / MMA_K_BF16; // 8
static constexpr int N_NSUB = HD / 16;
static constexpr int TILE_SZ = 128 * MMA_K_BF16; // 2048 BF16
static constexpr int V_SUB_SZ = 256; // (16,16) canonical BF16
static constexpr int TMEM_N = (HD <= 128) ? 128 : 256;
const int tid = threadIdx.x;
const int wid = tid / 32;
const int lane = tid % 32;
// Warp role predicates
const bool is_softmax_warp = (wid < 4); // Warps 0-3
const bool is_mma_warp = (wid == 4); // Warp 4
const bool is_load_warp = (wid == 5); // Warp 5
// ================================================================
// SMEM allocation (shared across all warps)
// ================================================================
extern __shared__ char sbuf[];
uint32_t* sTmemBase = (uint32_t*)sbuf;
float* sRowMax = (float*)(sbuf + 4);
float* sRowSum = sRowMax + 1;
bf16_t* sQ0 = (bf16_t*)(((uintptr_t)(sRowSum + 1) + 15) & ~(uintptr_t)15);
bf16_t* sK0 = sQ0 + TILE_SZ;
bf16_t* sPk = (bf16_t*)(((uintptr_t)(sK0 + TILE_SZ) + 127) & ~(uintptr_t)127);
bf16_t* sV = (bf16_t*)(((uintptr_t)(sPk + TILE_SZ) + 127) & ~(uintptr_t)127);
float* s_p_vals = (float*)(sV + V_SUB_SZ);
// ================================================================
// TMEM allocation (warp 4)
// ================================================================
if (is_mma_warp) {
uint32_t smem_ptr = __cvta_generic_to_shared(sTmemBase);
tmem_alloc(smem_ptr, TMEM_N);
}
__syncthreads();
uint32_t tb = *sTmemBase;
// ================================================================
// QK GEMM loop: for each K-tile, load Q+K, then MMA
// ================================================================
for (int kt = 0; kt < NKT_QK; kt++) {
// ---- Warp 5: Load Q and K for this K-tile ----
if (is_load_warp) {
// Load Q K-tile
for (int i = lane; i < TILE_SZ; i += 32) sQ0[i] = 0;
for (int d = lane; d < MMA_K_BF16; d += 32) {
int ck = d / 8, lc = d % 8;
sQ0[ck * 16 * 64 + lc] = q[kt * MMA_K_BF16 + d];
}
// Load K K-tile
for (int i = lane; i < TILE_SZ; i += 32) sK0[i] = 0;
for (int r = 0; r < s_k; r++) {
for (int d = lane; d < MMA_K_BF16; d += 32) {
int ck = d / 8, lc = d % 8;
int tmn = r / 8, lr = r % 8;
sK0[ck * 16 * 64 + tmn * 64 + lr * 8 + lc] = k[r * HD + kt * MMA_K_BF16 + d];
}
}
}
__syncthreads(); // Wait for loads
// ---- Warp 4: QK MMA ----
if (is_mma_warp) {
uint32_t idesc = make_idesc(128, 128);
uint64_t dq = make_umma_desc_kmajor_none(__cvta_generic_to_shared(sQ0), 128);
uint64_t dk = make_umma_desc_kmajor_none(__cvta_generic_to_shared(sK0), 128);
if (tid == 128) umma_ss_f16(tb, dq, dk, idesc, kt > 0);
asm volatile("tcgen05.fence::after_thread_sync;" ::: "memory");
}
__syncthreads(); // Wait for MMA
}
// ================================================================
// Softmax (warp 0, row 0 only for T=1 decode)
// ================================================================
if (wid == 0) {
float s_vals[SK_TILE], row_max = -INFINITY;
for (int n = 0; n < SK_TILE / 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);
if (lane == 0) *sRowMax = row_max;
float row_sum = 0.0f;
if (lane == 0) for (int j=0;j<SK_TILE;j++) {
s_vals[j] = expf(s_vals[j] - row_max);
row_sum += s_vals[j];
}
row_sum = wsum(row_sum);
if (lane == 0) *sRowSum = row_sum;
if (lane == 0) for (int j=0;j<SK_TILE;j++) s_vals[j] /= row_sum;
if (lane == 0) for (int j=0;j<SK_TILE;j++) s_p_vals[j] = s_vals[j];
}
__syncthreads(); // Wait for softmax
// ================================================================
// PV GEMM loop: N=16 sub-tiles × K-tiles
// ================================================================
for (int n = 0; n < N_NSUB; n++) {
int d_base = n * 16;
for (int kt = 0; kt < NKT_PV; kt++) {
// ---- Warp 5: Fill sPk and load V sub-tile ----
if (is_load_warp) {
// Fill sPk from s_p_vals
for (int i = lane; i < TILE_SZ; i += 32) sPk[i] = 0;
if (lane < 16) {
int c = lane;
int ck = c / 8, lc = c % 8;
sPk[ck * 16 * 64 + 0 * 64 + 0 * 8 + lc] = f32_to_bf16(s_p_vals[kt * MMA_K_BF16 + c]);
}
// Load V sub-tile: (16,16) canonical
for (int i = lane; i < V_SUB_SZ; i += 32) sV[i] = 0;
for (int dd = lane; dd < 16; dd += 32) {
for (int lr = 0; lr < MMA_K_BF16; lr++) {
int r = kt * MMA_K_BF16 + lr;
int g_mn = dd / 8, g_k = lr / 8;
int llr = dd % 8, lc = lr % 8;
sV[g_k * 2 * 64 + g_mn * 64 + llr * 8 + lc] = v[(d_base + dd) * SK_TILE + r];
}
}
}
__syncthreads(); // Wait for loads
// ---- Warp 4: PV MMA ----
if (is_mma_warp) {
uint32_t idesc_pv16 = make_idesc(128, 16);
uint64_t dp = make_umma_desc_kmajor_none(__cvta_generic_to_shared(sPk), 128);
uint64_t dv = make_umma_desc_kmajor_none(__cvta_generic_to_shared(sV), 16);
if (tid == 128) umma_ss_f16(tb + n * 16, dp, dv, idesc_pv16, kt > 0);
asm volatile("tcgen05.fence::after_thread_sync;" ::: "memory");
}
__syncthreads(); // Wait for MMA
}
}
// ================================================================
// Epilogue: TMEM → regs → normalize → BF16 → GMEM (warp 0)
// ================================================================
if (wid == 0) {
float inv_sum = 1.0f / *sRowSum;
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] * inv_sum;
}
if (lane == 0) for (int d=0;d<HD;d++) o[d] = f32_to_bf16(o_vals[d]);
}
__syncthreads();
// TMEM dealloc (warp 4)
if (is_mma_warp) {
tmem_dealloc(tb, TMEM_N);
}
}
} // namespace

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/**
* Test 6-warp specialized FMHA kernel for HD=16/64/128/256.
* Compile with -DHD_VAL=64 etc.
*/
#include <cuda_runtime.h>
#include <cstdio>
#include <cmath>
#include <cstdlib>
#include <cstring>
#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 NKT_QK = HD / MMA_K_BF16;
constexpr int NKT_PV = SK / MMA_K_BF16;
constexpr int TILE_SZ = 128 * MMA_K_BF16;
constexpr int N_NSUB = HD / 16;
constexpr int V_SUB_SZ = 256;
constexpr int TMEM_N = (HD <= 128) ? 128 : 256;
// Include the kernel
#include "dsv4/kernels/attention/fmha_6warp.cuh"
int main() {
printf("=== 6-warp FMHA HD=%d ===\n", HD);
const float SCALE = 1.0f / sqrtf((float)HD);
bf16_t* h_q = (bf16_t*)malloc(HD*sizeof(bf16_t));
bf16_t* h_k = (bf16_t*)malloc(SK*HD*sizeof(bf16_t));
bf16_t* h_v = (bf16_t*)malloc(HD*SK*sizeof(bf16_t));
bf16_t* h_o = (bf16_t*)calloc(HD, sizeof(bf16_t));
srand(42);
for (int d=0;d<HD;d++) h_q[d] = f32_to_bf16_host((float)(rand()%100)/100.0f-0.5f);
for (int i=0;i<SK*HD;i++) h_k[i] = f32_to_bf16_host((float)(rand()%100)/100.0f-0.5f);
for (int i=0;i<HD*SK;i++) h_v[i] = f32_to_bf16_host((float)(rand()%100)/100.0f-0.5f);
bf16_t *d_q,*d_k,*d_v,*d_o;
cudaMalloc(&d_q, HD*sizeof(bf16_t));
cudaMalloc(&d_k, SK*HD*sizeof(bf16_t));
cudaMalloc(&d_v, HD*SK*sizeof(bf16_t));
cudaMalloc(&d_o, HD*sizeof(bf16_t));
cudaMemcpy(d_q, h_q, HD*sizeof(bf16_t), cudaMemcpyHostToDevice);
cudaMemcpy(d_k, h_k, SK*HD*sizeof(bf16_t), cudaMemcpyHostToDevice);
cudaMemcpy(d_v, h_v, HD*SK*sizeof(bf16_t), cudaMemcpyHostToDevice);
// Compute reference
float o_ref[HD];
{
float s[SK];
for (int j=0;j<SK;j++) {
float dot = 0.0f;
for (int d=0;d<HD;d++) dot += bf16_to_f32_host(h_q[d]) * bf16_to_f32_host(h_k[j*HD+d]);
s[j] = dot * SCALE;
}
float mx = -INFINITY;
for (int j=0;j<SK;j++) mx = fmaxf(mx, s[j]);
float sm = 0.0f;
for (int j=0;j<SK;j++) { s[j] = expf(s[j]-mx); sm += s[j]; }
for (int j=0;j<SK;j++) s[j] /= sm;
for (int d=0;d<HD;d++) {
float ov = 0.0f;
for (int j=0;j<SK;j++) ov += s[j] * bf16_to_f32_host(h_v[d*SK+j]);
o_ref[d] = ov;
}
}
int smem = (4 + 8 + 16 + TILE_SZ*2 + TILE_SZ*2 + TILE_SZ*2 + V_SUB_SZ*2 + SK*4 + 256 + 127) & ~127;
printf("SMEM: %d bytes (%.1f KB), 6 warps (192 threads), TMEM: %d cols\n", smem, smem/1024.0f, TMEM_N);
if (smem > 48 * 1024) {
cudaFuncSetAttribute(fmha_6warp_kernel<HD>, cudaFuncAttributeMaxDynamicSharedMemorySize, smem);
}
fmha_6warp_kernel<HD><<<1, 192, smem>>>(d_q, d_k, d_v, d_o, SK, SCALE);
cudaError_t launch_err = cudaGetLastError();
if (launch_err != cudaSuccess) { printf("LAUNCH ERROR: %s\n", cudaGetErrorString(launch_err)); return 1; }
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);
printf("O[0..7] MMA: "); for(int d=0;d<min(8,HD);d++) printf("%.6f ",bf16_to_f32_host(h_o[d])); printf("\n");
printf("O[0..7] ref: "); for(int d=0;d<min(8,HD);d++) printf("%.6f ",o_ref[d]); printf("\n");
float cs=0,na=0,nb=0;
for (int d=0;d<HD;d++) {
float a=bf16_to_f32_host(h_o[d]),b=o_ref[d];
if(fabsf(b)>1e-4f) { cs+=a*b; na+=a*a; nb+=b*b; }
}
cs /= (sqrtf(na)*sqrtf(nb)+1e-10f);
printf("Filtered cosine: %.8f\n", cs);
printf("Test %s\n", cs > 0.999f ? "PASSED" : "FAILED");
cudaFree(d_q); cudaFree(d_k); cudaFree(d_v); cudaFree(d_o);
free(h_q); free(h_k); free(h_v); free(h_o);
return cs > 0.999f ? 0 : 1;
}

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#define HD_VAL 128
#include "test_fmha_6warp.cu"

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#define HD_VAL 16
#include "test_fmha_6warp.cu"

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#define HD_VAL 256
#include "test_fmha_6warp.cu"

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#define HD_VAL 64
#include "test_fmha_6warp.cu"