add dual stream for qwen2_moe (#10252)

python/sglang/srt/models/qwen2_moe.py

@@ -65,10 +65,12 @@ from sglang.srt.managers.schedule_batch import global_server_args_dict
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
 from sglang.srt.model_loader.weight_utils import default_weight_loader
 from sglang.srt.two_batch_overlap import model_forward_maybe_tbo
-from sglang.srt.utils import add_prefix, make_layers
+from sglang.srt.utils import add_prefix, is_cuda, make_layers
 
 logger = logging.getLogger(__name__)
 
+_is_cuda = is_cuda()
+
 
 class Qwen2MoeMLP(nn.Module):
     def __init__(
@@ -122,11 +124,13 @@ class Qwen2MoeSparseMoeBlock(nn.Module):
         layer_id: int,
         config: PretrainedConfig,
         quant_config: Optional[QuantizationConfig] = None,
+        alt_stream: Optional[torch.cuda.Stream] = None,
         prefix: str = "",
     ):
         super().__init__()
         self.tp_size = get_tensor_model_parallel_world_size()
         self.layer_id = layer_id
+        self.alt_stream = alt_stream
         if self.tp_size > config.num_experts:
             raise ValueError(
                 f"Tensor parallel size {self.tp_size} is greater than "
@@ -168,14 +172,7 @@ class Qwen2MoeSparseMoeBlock(nn.Module):
             self.shared_expert = None
         self.shared_expert_gate = torch.nn.Linear(config.hidden_size, 1, bias=False)
 
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        forward_batch: Optional[ForwardBatch] = None,
-        use_reduce_scatter: bool = False,
-    ) -> torch.Tensor:
-        num_tokens, hidden_dim = hidden_states.shape
-        hidden_states = hidden_states.view(-1, hidden_dim)
+    def _forward_shared_experts(self, hidden_states: torch.Tensor):
         shared_output = None
         if self.shared_expert is not None:
             shared_output = self.shared_expert(hidden_states)
@@ -183,11 +180,51 @@ class Qwen2MoeSparseMoeBlock(nn.Module):
                 shared_output = (
                     F.sigmoid(self.shared_expert_gate(hidden_states)) * shared_output
                 )
+        return shared_output
 
+    def _forward_router_experts(self, hidden_states: torch.Tensor):
         # router_logits: (num_tokens, n_experts)
         router_logits, _ = self.gate(hidden_states)
         topk_output = self.topk(hidden_states, router_logits)
-        final_hidden_states = self.experts(hidden_states, topk_output)
+        return self.experts(hidden_states, topk_output)
+
+    def forward_normal_dual_stream(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        current_stream = torch.cuda.current_stream()
+        self.alt_stream.wait_stream(current_stream)
+        shared_output = self._forward_shared_experts(hidden_states)
+
+        with torch.cuda.stream(self.alt_stream):
+            router_output = self._forward_router_experts(hidden_states)
+
+        current_stream.wait_stream(self.alt_stream)
+
+        return router_output, shared_output
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        forward_batch: Optional[ForwardBatch] = None,
+        use_reduce_scatter: bool = False,
+    ) -> torch.Tensor:
+        num_tokens, hidden_dim = hidden_states.shape
+        hidden_states = hidden_states.view(-1, hidden_dim)
+
+        DUAL_STREAM_TOKEN_THRESHOLD = 1024
+        if (
+            self.alt_stream is not None
+            and hidden_states.shape[0] > 0
+            and hidden_states.shape[0] <= DUAL_STREAM_TOKEN_THRESHOLD
+        ):
+            final_hidden_states, shared_output = self.forward_normal_dual_stream(
+                hidden_states
+            )
+        else:
+            shared_output = self._forward_shared_experts(hidden_states)
+            final_hidden_states = self._forward_router_experts(hidden_states)
+
         if shared_output is not None:
             final_hidden_states = final_hidden_states + shared_output
         if self.tp_size > 1 and not use_reduce_scatter:
@@ -346,6 +383,7 @@ class Qwen2MoeDecoderLayer(nn.Module):
                 layer_id=layer_id,
                 config=config,
                 quant_config=quant_config,
+                alt_stream=alt_stream,
                 prefix=add_prefix("mlp", prefix),
             )
         else:
@@ -528,8 +566,12 @@ class Qwen2MoeForCausalLM(nn.Module):
         self.pp_group = get_pp_group()
         self.config = config
         self.quant_config = quant_config
+        alt_stream = torch.cuda.Stream() if _is_cuda else None
         self.model = Qwen2MoeModel(
-            config, quant_config, prefix=add_prefix("model", prefix)
+            config,
+            quant_config,
+            prefix=add_prefix("model", prefix),
+            alt_stream=alt_stream,
         )
         self.lm_head = ParallelLMHead(
             config.vocab_size,