feat: support moe_align_block_size_triton (#2712)

Co-authored-by: WANDY666 <1060304770@qq.com>

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

@@ -17,15 +17,21 @@ from sglang.srt.layers.moe.topk import select_experts
 from sglang.srt.layers.quantization.fp8_kernel import per_token_group_quant_fp8
 from sglang.srt.utils import direct_register_custom_op, get_device_name, is_hip
 
-not_hip = False
+is_hip_flag = False
 if not is_hip():
     from sgl_kernel import moe_align_block_size as sgl_moe_align_block_size
 
-    not_hip = True
+    is_hip_flag = False
+else:
+    is_hip_flag = True
 
 logger = logging.getLogger(__name__)
 padding_size = 128 if bool(int(os.getenv("MOE_PADDING", "0"))) else 0
 
+enable_moe_align_block_size_triton = bool(
+    int(os.getenv("ENABLE_MOE_ALIGN_BLOCK_SIZE_TRITON", "0"))
+)
+
 
 @triton.jit
 def fused_moe_kernel(
@@ -222,6 +228,139 @@ def fused_moe_kernel(
     tl.store(c_ptrs, accumulator, mask=c_mask)
 
 
+def ceil_div(a, b):
+    return (a + b - 1) // b
+
+
+@triton.jit
+def moe_align_block_size_stage1(
+    topk_ids_ptr,
+    tokens_cnts_ptr,
+    num_experts: tl.constexpr,
+    numel: tl.constexpr,
+    tokens_per_thread: tl.constexpr,
+):
+    pid = tl.program_id(0)
+
+    start_idx = pid * tokens_per_thread
+
+    off_c = (pid + 1) * num_experts
+
+    for i in range(tokens_per_thread):
+        if start_idx + i < numel:
+            idx = tl.load(topk_ids_ptr + start_idx + i)
+            token_cnt = tl.load(tokens_cnts_ptr + off_c + idx)
+            tl.store(tokens_cnts_ptr + off_c + idx, token_cnt + 1)
+
+
+@triton.jit
+def moe_align_block_size_stage2(
+    tokens_cnts_ptr,
+    num_experts: tl.constexpr,
+):
+    pid = tl.program_id(0)
+
+    last_cnt = 0
+    for i in range(1, num_experts + 1):
+        token_cnt = tl.load(tokens_cnts_ptr + i * num_experts + pid)
+        last_cnt = last_cnt + token_cnt
+        tl.store(tokens_cnts_ptr + i * num_experts + pid, last_cnt)
+
+
+@triton.jit
+def moe_align_block_size_stage3(
+    total_tokens_post_pad_ptr,
+    tokens_cnts_ptr,
+    cumsum_ptr,
+    num_experts: tl.constexpr,
+    block_size: tl.constexpr,
+):
+    last_cumsum = 0
+    off_cnt = num_experts * num_experts
+    for i in range(1, num_experts + 1):
+        token_cnt = tl.load(tokens_cnts_ptr + off_cnt + i - 1)
+        last_cumsum = last_cumsum + tl.cdiv(token_cnt, block_size) * block_size
+        tl.store(cumsum_ptr + i, last_cumsum)
+    tl.store(total_tokens_post_pad_ptr, last_cumsum)
+
+
+@triton.jit
+def moe_align_block_size_stage4(
+    topk_ids_ptr,
+    sorted_token_ids_ptr,
+    expert_ids_ptr,
+    tokens_cnts_ptr,
+    cumsum_ptr,
+    num_experts: tl.constexpr,
+    block_size: tl.constexpr,
+    numel: tl.constexpr,
+    tokens_per_thread: tl.constexpr,
+):
+    pid = tl.program_id(0)
+    start_idx = tl.load(cumsum_ptr + pid)
+    end_idx = tl.load(cumsum_ptr + pid + 1)
+
+    for i in range(start_idx, end_idx, block_size):
+        tl.store(expert_ids_ptr + i // block_size, pid)
+
+    start_idx = pid * tokens_per_thread
+    off_t = pid * num_experts
+
+    for i in range(start_idx, tl.minimum(start_idx + tokens_per_thread, numel)):
+        expert_id = tl.load(topk_ids_ptr + i)
+        token_cnt = tl.load(tokens_cnts_ptr + off_t + expert_id)
+        rank_post_pad = token_cnt + tl.load(cumsum_ptr + expert_id)
+        tl.store(sorted_token_ids_ptr + rank_post_pad, i)
+        tl.store(tokens_cnts_ptr + off_t + expert_id, token_cnt + 1)
+
+
+def moe_align_block_size_triton(
+    topk_ids: torch.Tensor,
+    num_experts: int,
+    block_size: int,
+    sorted_token_ids: torch.Tensor,
+    expert_ids: torch.Tensor,
+    num_tokens_post_pad: torch.Tensor,
+) -> None:
+    numel = topk_ids.numel()
+    grid = (num_experts,)
+    tokens_cnts = torch.zeros(
+        (num_experts + 1, num_experts), dtype=torch.int32, device=topk_ids.device
+    )
+    cumsum = torch.zeros((num_experts + 1,), dtype=torch.int32, device=topk_ids.device)
+    tokens_per_thread = ceil_div(numel, num_experts)
+
+    moe_align_block_size_stage1[grid](
+        topk_ids,
+        tokens_cnts,
+        num_experts,
+        numel,
+        tokens_per_thread,
+    )
+    moe_align_block_size_stage2[grid](
+        tokens_cnts,
+        num_experts,
+    )
+    moe_align_block_size_stage3[(1,)](
+        num_tokens_post_pad,
+        tokens_cnts,
+        cumsum,
+        num_experts,
+        block_size,
+    )
+    moe_align_block_size_stage4[grid](
+        topk_ids,
+        sorted_token_ids,
+        expert_ids,
+        tokens_cnts,
+        cumsum,
+        num_experts,
+        block_size,
+        numel,
+        tokens_per_thread,
+    )
+
+
 def moe_align_block_size(
     topk_ids: torch.Tensor, block_size: int, num_experts: int
 ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
@@ -272,9 +411,21 @@ def moe_align_block_size(
         (max_num_m_blocks,), dtype=torch.int32, device=topk_ids.device
     )
     num_tokens_post_pad = torch.empty((1), dtype=torch.int32, device=topk_ids.device)
-    if not_hip and num_experts >= 224:
+    if num_experts >= 224:
+        if enable_moe_align_block_size_triton or is_hip_flag:
+            moe_align_block_size_triton(
+                topk_ids,
+                num_experts,
+                block_size,
+                sorted_ids,
+                expert_ids,
+                num_tokens_post_pad,
+            )
+        else:
             token_cnts_buffer = torch.empty(
-            (num_experts + 1) * num_experts, dtype=torch.int32, device=topk_ids.device
+                (num_experts + 1) * num_experts,
+                dtype=torch.int32,
+                device=topk_ids.device,
             )
             cumsum_buffer = torch.empty(
                 num_experts + 1, dtype=torch.int32, device=topk_ids.device
@@ -854,17 +1005,18 @@ def fused_experts_impl(
             block_shape=block_shape,
         )
 
-        if not_hip:
-            torch.sum(
+        if is_hip_flag:
+            ops.moe_sum(
                 intermediate_cache3.view(*intermediate_cache3.shape),
-                dim=1,
-                out=out_hidden_states[begin_chunk_idx:end_chunk_idx],
+                out_hidden_states[begin_chunk_idx:end_chunk_idx],
             )
         else:
-            ops.moe_sum(
+            torch.sum(
                 intermediate_cache3.view(*intermediate_cache3.shape),
-                out_hidden_states[begin_chunk_idx:end_chunk_idx],
+                dim=1,
+                out=out_hidden_states[begin_chunk_idx:end_chunk_idx],
             )
+
     return out_hidden_states