Triton Ascend Case Reduction Weighted Swiglu

Weighted SwiGLU Backward 融合算子优化

任务特征

• 数据尺寸：(16, 1024, 2048) × 3，3D融合算子
• 特点：先逐元素操作，再reduce最后一根轴

优化 1：Reshape 降维

# 将前两个维度(B, M)合并为单个维度BM
x_reshaped = x.reshape(BM, N)
weight_reshaped = weight.reshape(BM, N)
grad_reshaped = grad.reshape(BM, N)

weighted_x_reshaped = weighted_x.reshape(BM, N)
grad_weight_reshaped = grad_weight.reshape(BM, N)
grad_x_reshaped = grad_x.reshape(BM)

优势：简化并行策略，优化内存访问模式，提高内核执行效率。

优化 2：行二次切分

for bm_start in range(0, BLOCK_SIZE_BM, SUB_BLOCK_SIZE_BM):
    bm_offsets = pid * BLOCK_SIZE_BM + bm_start + tl.arange(0, SUB_BLOCK_SIZE_BM)
    bm_mask = bm_offsets < BM

Autotune 配置

# （AI core=40）
# 1. grid=512>40，reduce轴切分较小，UB占满 -> 1105.84 us
triton.Config({'BLOCK_SIZE_BM': 32, 'SUB_BLOCK_SIZE_BM': 32, 'BLOCK_SIZE_N': 128})

# 2. grid=1024>40，reduce轴切分增至256 -> 1110.47 us
triton.Config({'BLOCK_SIZE_BM': 16, 'SUB_BLOCK_SIZE_BM': 16, 'BLOCK_SIZE_N': 256})

# 3. grid=2048>40，reduce轴切分增至512 -> 1091.26 us 最优
triton.Config({'BLOCK_SIZE_BM': 8, 'SUB_BLOCK_SIZE_BM': 8, 'BLOCK_SIZE_N': 512})

# 4. grid=32<40，reduce轴切分较大，UB占满 -> 1098.53 us
triton.Config({'BLOCK_SIZE_BM': 512, 'SUB_BLOCK_SIZE_BM': 8, 'BLOCK_SIZE_N': 512})

# 5. grid=40，有尾块，reduce轴切分较大，UB占满 -> 1094.60 us
triton.Config({'BLOCK_SIZE_BM': 416, 'SUB_BLOCK_SIZE_BM': 8, 'BLOCK_SIZE_N': 512})

总结

1. Reshape降维可简化并行策略，优化内存访问
2. 在优先占满UB前提下为reduce轴分配较大切分尺寸
3. Grid数较大时，可能性能更优（配置3）