use sgl-kernel moe_align_block_size (#2581)

Co-authored-by: ispobock <ispobaoke@163.com>
Co-authored-by: HandH1998 <1335248067@qq.com>

python/pyproject.toml

@@ -21,9 +21,9 @@ runtime_common = ["aiohttp", "decord", "fastapi",
     "orjson", "outlines>=0.0.44,<0.1.0",
     "packaging", "pillow", "prometheus-client>=0.20.0",
     "psutil", "pydantic", "python-multipart",
-    "pyzmq>=25.1.2", "torchao>=0.7.0", "gemlite", "uvicorn", "uvloop",
+    "pyzmq>=25.1.2", "torchao>=0.7.0", "uvicorn", "uvloop",
     "xgrammar>=0.1.6"]
-srt = ["sglang[runtime_common]", "torch", "vllm>=0.6.3.post1,<=0.6.4.post1", "cuda-python", "flashinfer==0.1.6"]
+srt = ["sglang[runtime_common]", "torch", "vllm>=0.6.3.post1,<=0.6.4.post1", "cuda-python", "flashinfer==0.1.6", "sgl-kernel"]
 
 # HIP (Heterogeneous-computing Interface for Portability) for AMD
 # => base docker rocm/vllm-dev:20241022, not from public vllm whl

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

@@ -11,6 +11,7 @@ from typing import Any, Callable, Dict, List, Optional, Tuple
 import torch
 import triton
 import triton.language as tl
+from sgl_kernel import moe_align_block_size as sgl_moe_align_block_size
 from vllm import _custom_ops as ops
 
 from sglang.srt.layers.moe.topk import select_experts
@@ -266,8 +267,24 @@ 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)
+    # FIXME(zhyncs)
+    if num_experts >= 256:
+        sgl_moe_align_block_size(
+            topk_ids,
+            num_experts,
+            block_size,
+            sorted_ids,
+            expert_ids,
+            num_tokens_post_pad,
+        )
+    else:
         ops.moe_align_block_size(
-        topk_ids, num_experts, block_size, sorted_ids, expert_ids, num_tokens_post_pad
+            topk_ids,
+            num_experts,
+            block_size,
+            sorted_ids,
+            expert_ids,
+            num_tokens_post_pad,
         )
     return sorted_ids, expert_ids, num_tokens_post_pad
 

python/sglang/srt/model_executor/model_runner.py

@@ -95,6 +95,12 @@ class ModelRunner:
         ):
             logger.info("MLA optimization is turned on. Use triton backend.")
             self.server_args.attention_backend = "triton"
+            # FIXME(HandH1998)
+            if (
+                "DeepseekV3ForCausalLM" in self.model_config.hf_config.architectures
+                and not self.server_args.disable_cuda_graph
+            ):
+                self.server_args.disable_cuda_graph = True
 
         if self.server_args.enable_double_sparsity:
             logger.info(