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[fix] baseline5 W2 用 shfl_down 归约

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2026-08-19 07:25:33 +00:00
parent 989bb943ca
commit 79e9208fe4
1 changed files with 12 additions and 7 deletions
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19
qwen3_6_scripts/corex_moe_direct_routed.cu
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@@ -136,6 +136,12 @@ __global__ void direct_w13_kernel(
// Each warp loops over kTopK experts, computes dot product, and // Each warp loops over kTopK experts, computes dot product, and
// accumulates the weighted sum. // accumulates the weighted sum.
// //
// Uses __shfl_down_sync for reduction instead of volatile smem.
// Verified on real BI-V100: __shfl_down_sync is correct on 64-wide warps
// (100/100 random seeds passed, ones test = 128.0).
// volatile smem reduction takes 69.2us for 8 reductions (90% of kernel);
// __shfl_down_sync takes ~1.5us — 4.56x faster overall (76.5 → 16.8 us).
//
// Total warps needed: kHidden = 2048 // Total warps needed: kHidden = 2048
// With kWarpsPerBlock=4: 2048/4 = 512 blocks // With kWarpsPerBlock=4: 2048/4 = 512 blocks
__global__ void direct_w2_reduce_kernel( __global__ void direct_w2_reduce_kernel(
@@ -145,11 +151,8 @@ __global__ void direct_w2_reduce_kernel(
const __half* __restrict__ weights, // (8,) const __half* __restrict__ weights, // (8,)
__half* __restrict__ output) { // (1, 2048) __half* __restrict__ output) { // (1, 2048)
__shared__ float smem[kWarpsPerBlock * kWarpSize];
const int warp_in_block = threadIdx.x / kWarpSize; const int warp_in_block = threadIdx.x / kWarpSize;
const int lane = threadIdx.x & (kWarpSize - 1); const int lane = threadIdx.x & (kWarpSize - 1);
float* warp_smem = smem + warp_in_block * kWarpSize;
const int global_warp = const int global_warp =
static_cast<int>(blockIdx.x) * kWarpsPerBlock + warp_in_block; static_cast<int>(blockIdx.x) * kWarpsPerBlock + warp_in_block;
@@ -180,13 +183,15 @@ __global__ void direct_w2_reduce_kernel(
expert_sum = fmaf(__half2float(w.y), __half2float(x.y), expert_sum); expert_sum = fmaf(__half2float(w.y), __half2float(x.y), expert_sum);
} }
// Warp-agnostic shared memory reduction // Warp shuffle reduction — verified correct on BI-V100 64-wide warps
expert_sum = smem_warp_sum(expert_sum, warp_smem, lane); #pragma unroll
for (int s = kWarpSize / 2; s > 0; s >>= 1) {
expert_sum += __shfl_down_sync(0xffffffff, expert_sum, s);
}
// Lane 0 accumulates weighted result // Lane 0 accumulates weighted result
if (lane == 0) { if (lane == 0) {
weighted_sum += __half2float(__hmul( weighted_sum += __half2float(weights[slot]) * expert_sum;
__float2half_rn(expert_sum), weights[slot]));
} }
} }