Support async in DeepEP (#4610)

Co-authored-by: Cheng Wan <cwan39@gatech.edu>

python/sglang/srt/layers/moe/ep_moe/token_dispatcher.py

@@ -5,7 +5,6 @@ try:
 except ImportError:
     use_deepep = False
 
-import os
 from typing import Optional, Tuple
 
 import torch
@@ -101,6 +100,7 @@ class DeepEPDispatcher:
         num_local_experts: int = None,
         hidden_size: int = None,
         params_dtype: torch.dtype = None,
+        async_finish: bool = False,
     ):
         self.group = group
         self.router_topk = router_topk
@@ -117,6 +117,7 @@ class DeepEPDispatcher:
         self.token_probs = None
         # Handle used for combine operation
         self.handle = None
+        self.async_finish = async_finish
 
         # `num_max_dispatch_tokens_per_rank` (the actual batch size in the decoding engine) should be less than 256
         # https://github.com/deepseek-ai/DeepEP?tab=readme-ov-file#example-use-in-inference-decoding
@@ -182,7 +183,6 @@ class DeepEPDispatcher:
         topk_weights: torch.Tensor,
         num_experts: int,
         forward_mode: ForwardMode,
-        previous_event=None,
         num_max_dispatch_tokens_per_rank: int = 128,
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         topk_idx = topk_idx.to(torch.int64)
@@ -195,9 +195,7 @@ class DeepEPDispatcher:
                 num_recv_tokens_per_expert_list,
                 handle,
                 event,
-            ) = self.dispatch_normal(
-                hidden_states, topk_idx, topk_weights, num_experts, previous_event
-            )
+            ) = self.dispatch_normal(hidden_states, topk_idx, topk_weights, num_experts)
             self.tokens_per_expert = torch.tensor(
                 num_recv_tokens_per_expert_list,
                 device=hidden_states.device,
@@ -213,6 +211,10 @@ class DeepEPDispatcher:
                 )
             )
             self.recv_expert_count = recv_expert_count
+
+        if self.async_finish:
+            event.current_stream_wait()
+
         self.handle = handle
         self.topk_idx = topk_idx
         self.topk_weights = topk_weights
@@ -235,8 +237,9 @@ class DeepEPDispatcher:
         topk_idx: torch.Tensor,
         topk_weights: torch.Tensor,
         num_experts: int,
-        previous_event=None,
     ):
+        previous_event = Buffer.capture() if self.async_finish else None
+
         (
             num_tokens_per_rank,
             num_tokens_per_rdma_rank,
@@ -247,8 +250,8 @@ class DeepEPDispatcher:
             topk_idx,
             num_experts,
             previous_event=previous_event,
-            async_finish=False,
-            allocate_on_comm_stream=False,
+            async_finish=self.async_finish,
+            allocate_on_comm_stream=previous_event is not None,
         )
 
         (
@@ -267,8 +270,8 @@ class DeepEPDispatcher:
             is_token_in_rank=is_token_in_rank,
             num_tokens_per_expert=num_tokens_per_expert,
             previous_event=previous_event,
-            async_finish=False,
-            allocate_on_comm_stream=False,
+            async_finish=self.async_finish,
+            allocate_on_comm_stream=(previous_event is not None) and self.async_finish,
         )
 
         return (
@@ -333,7 +336,7 @@ class DeepEPDispatcher:
                 topk_idx,
                 num_max_dispatch_tokens_per_rank,
                 num_experts,
-                async_finish=False,
+                async_finish=self.async_finish,
                 return_recv_hook=False,  # True for double-batch overlapping, need call hook()
             )
         )
@@ -373,16 +376,22 @@ class DeepEPDispatcher:
             hidden_states, event, hook = self.combine_low_latency(
                 hidden_states, self.topk_idx, self.topk_weights, self.handle
             )
+
+        if self.async_finish:
+            event.current_stream_wait()
+
         self.handle = None
         return hidden_states
 
-    def combine_normal(self, x: torch.Tensor, handle: Tuple, previous_event=None):
+    def combine_normal(self, x: torch.Tensor, handle: Tuple):
+        previous_event = Buffer.capture() if self.async_finish else None
+
         combined_x, _, event = self.buffer_normal.combine(
             x,
             handle,
-            async_finish=False,
+            async_finish=self.async_finish,
             previous_event=previous_event,
-            allocate_on_comm_stream=False,
+            allocate_on_comm_stream=previous_event is not None,
         )
         return combined_x, event
 
@@ -399,7 +408,7 @@ class DeepEPDispatcher:
                 topk_idx,
                 topk_weights,
                 handle,
-                async_finish=False,
+                async_finish=self.async_finish,
                 return_recv_hook=False,  # True for double-batch overlapping, need call hook()
             )
         )

python/sglang/srt/models/deepseek_v2.py

@@ -239,6 +239,7 @@ class DeepseekV2MoE(nn.Module):
                 num_local_experts=config.n_routed_experts // self.tp_size,
                 hidden_size=config.hidden_size,
                 params_dtype=config.torch_dtype,
+                async_finish=True,  # TODO
             )
 
     def forward(