reduce torch.zeros overhead in moe align block size kernel (#6369)

python/sglang/srt/layers/moe/fused_moe_triton/fused_moe.py

@@ -30,6 +30,7 @@ from sglang.srt.utils import (
     is_cuda,
     is_hip,
     log_info_on_rank0,
+    next_power_of_2,
 )
 
 _is_hip = is_hip()
@@ -650,6 +651,61 @@ def moe_align_block_size_triton(
     )
 
 
+@triton.jit
+def init_sorted_ids_and_cumsum_buffer_kernel(
+    sorted_ids_ptr,
+    cumsum_buffer_ptr,
+    max_num_tokens_padded,
+    topk_ids_numel,
+    num_experts: tl.constexpr,
+    BLOCK_SIZE: tl.constexpr,
+    ALIGNED_NUM_EXPERTS_P1: tl.constexpr,
+):
+    pid = tl.program_id(0)
+    offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
+
+    sorted_ids_blocks = tl.cdiv(max_num_tokens_padded, BLOCK_SIZE)
+
+    if pid < sorted_ids_blocks:
+        mask = offsets < max_num_tokens_padded
+        tl.store(
+            sorted_ids_ptr + offsets,
+            tl.full((BLOCK_SIZE,), topk_ids_numel, dtype=tl.int32),
+            mask=mask,
+        )
+    elif pid == sorted_ids_blocks:
+        offset_e = tl.arange(0, ALIGNED_NUM_EXPERTS_P1)
+        mask_e = offset_e < num_experts + 1
+        tl.store(
+            cumsum_buffer_ptr + offset_e,
+            tl.zeros((ALIGNED_NUM_EXPERTS_P1,), dtype=tl.int32),
+            mask=mask_e,
+        )
+
+
+def init_sorted_ids_and_cumsum_buffer(
+    max_num_tokens_padded: int, topk_ids_numel: int, num_experts: int, device="cuda"
+):
+    sorted_ids = torch.empty((max_num_tokens_padded,), dtype=torch.int32, device=device)
+    cumsum_buffer = torch.empty((num_experts + 1,), dtype=torch.int32, device=device)
+
+    BLOCK_SIZE = 1024
+    sorted_ids_blocks = triton.cdiv(max_num_tokens_padded, BLOCK_SIZE)
+    grid = (sorted_ids_blocks + 1,)
+
+    init_sorted_ids_and_cumsum_buffer_kernel[grid](
+        sorted_ids,
+        cumsum_buffer,
+        max_num_tokens_padded,
+        topk_ids_numel,
+        num_experts,
+        BLOCK_SIZE,
+        next_power_of_2(num_experts + 1),
+    )
+
+    return sorted_ids, cumsum_buffer
+
+
 def moe_align_block_size(
     topk_ids: torch.Tensor, block_size: int, num_experts: int
 ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
@@ -691,10 +747,9 @@ def moe_align_block_size(
         by block_size for proper block matrix operations.
     """
     max_num_tokens_padded = topk_ids.numel() + num_experts * (block_size - 1)
-    sorted_ids = torch.empty(
+    sorted_ids, cumsum_buffer = init_sorted_ids_and_cumsum_buffer(
-        (max_num_tokens_padded,), dtype=torch.int32, device=topk_ids.device
+        max_num_tokens_padded, topk_ids.numel(), num_experts, topk_ids.device
     )
-    sorted_ids.fill_(topk_ids.numel())
     max_num_m_blocks = triton.cdiv(max_num_tokens_padded, block_size)
     expert_ids = torch.empty(
         (max_num_m_blocks,), dtype=torch.int32, device=topk_ids.device
@@ -715,9 +770,6 @@ def moe_align_block_size(
             dtype=torch.int32,
             device=topk_ids.device,
         )
-        cumsum_buffer = torch.empty(
-            num_experts + 1, dtype=torch.int32, device=topk_ids.device
-        )
 
         sgl_moe_align_block_size(
             topk_ids,

sgl-kernel/csrc/moe/moe_align_kernel.cu

@@ -197,8 +197,6 @@ void moe_align_block_size(
       size_t num_warps = CEILDIV(padded_num_experts, experts_per_warp);
       size_t shared_mem_size = num_warps * experts_per_warp * sizeof(int32_t);
 
-      cumsum_buffer.zero_();
-
       align_kernel<<<1, threads, shared_mem_size, stream>>>(
           topk_ids.data_ptr<scalar_t>(),
           sorted_token_ids.data_ptr<int32_t>(),