Use reduce scatter for DP

python/sglang/srt/layers/communicator.py

@@ -27,6 +27,7 @@
     attn_tp_all_gather_into_tensor,
     attn_tp_reduce_scatter_tensor,
     dp_gather_partial,
+    dp_reduce_scatter_tensor,
     dp_scatter,
     get_attention_dp_size,
     get_attention_tp_rank,
@@ -149,10 +150,13 @@ def __init__(
         layer_scatter_modes: LayerScatterModes,
         input_layernorm: torch.nn.Module,
         post_attention_layernorm: torch.nn.Module,
+        # Reduce scatter requires skipping all-reduce in model code after MoE/MLP, so only enable for models which have that implemented. Remove flag once done for all models that use LayerCommunicator.
+        allow_reduce_scatter: bool = False,
     ):
         self.layer_scatter_modes = layer_scatter_modes
         self.input_layernorm = input_layernorm
         self.post_attention_layernorm = post_attention_layernorm
+        self.allow_reduce_scatter = allow_reduce_scatter
 
         self._context = CommunicateContext.init_new()
         self._communicate_simple_fn = CommunicateSimpleFn.get_fn(
@@ -239,6 +243,15 @@ def postprocess_layer(
             residual=residual,
             forward_batch=forward_batch,
             context=self._context,
+            allow_reduce_scatter=self.allow_reduce_scatter,
+        )
+
+    def should_use_reduce_scatter(self, forward_batch: ForwardBatch):
+        return (
+            self.allow_reduce_scatter
+            and self._communicate_summable_tensor_pair_fn
+            is CommunicateSummableTensorPairFn._scatter_hidden_states
+            and forward_batch.dp_padding_mode.is_max_len()
         )
 
 
@@ -524,6 +537,7 @@ def _trivial(
         residual: torch.Tensor,
         forward_batch: ForwardBatch,
         context: CommunicateContext,
+        **kwargs,
     ):
         return hidden_states, residual
 
@@ -533,15 +547,17 @@ def _scatter_hidden_states(
         residual: torch.Tensor,
         forward_batch: ForwardBatch,
         context: CommunicateContext,
+        allow_reduce_scatter: bool = False,
     ):
-        # TODO(ch-wan): use reduce-scatter in MLP to avoid this scatter
-        # important: forward batch.gathered_buffer is used both after scatter and after gather.
-        # be careful about this!
         hidden_states, global_hidden_states = (
             forward_batch.gathered_buffer[: forward_batch.input_ids.shape[0]],
             hidden_states,
         )
-        dp_scatter(hidden_states, global_hidden_states, forward_batch)
+        if allow_reduce_scatter and forward_batch.dp_padding_mode.is_max_len():
+            # When using padding, all_reduce is skipped after MLP and MOE and reduce scatter is used here instead.
+            dp_reduce_scatter_tensor(hidden_states, global_hidden_states)
+        else:
+            dp_scatter(hidden_states, global_hidden_states, forward_batch)
         return hidden_states, residual
 
     @staticmethod
@@ -550,6 +566,7 @@ def _gather(
         residual: torch.Tensor,
         forward_batch: ForwardBatch,
         context: CommunicateContext,
+        **kwargs,
     ):
         hidden_states += residual
         residual = None

python/sglang/srt/layers/dp_attention.py

@@ -12,6 +12,7 @@
 
 from sglang.srt.distributed import (
     GroupCoordinator,
+    get_tensor_model_parallel_rank,
     get_tensor_model_parallel_world_size,
     get_tp_group,
     tensor_model_parallel_all_reduce,
@@ -355,6 +356,17 @@ def dp_scatter(
         )
 
 
+def dp_reduce_scatter_tensor(output: torch.Tensor, input: torch.Tensor):
+    if get_tensor_model_parallel_world_size() == get_attention_dp_size():
+        get_tp_group().reduce_scatter_tensor(output, input)
+    else:
+        scattered_local_tokens = input.tensor_split(
+            get_tensor_model_parallel_world_size()
+        )[get_tensor_model_parallel_rank()]
+        get_tp_group().reduce_scatter_tensor(scattered_local_tokens, input)
+        get_attention_tp_group().all_gather_into_tensor(output, scattered_local_tokens)
+
+
 def attn_tp_reduce_scatter_tensor(output: torch.Tensor, input: torch.Tensor):
     return get_attention_tp_group().reduce_scatter_tensor(output, input)
 

python/sglang/srt/layers/linear.py

@@ -1277,7 +1277,7 @@ def weight_loader_v2(self, param: BasevLLMParameter, loaded_weight: torch.Tensor
             # It does not support additional parameters.
             param.load_row_parallel_weight(loaded_weight)
 
-    def forward(self, input_, can_fuse_mlp_allreduce=False):
+    def forward(self, input_, skip_all_reduce=False):
         if self.input_is_parallel:
             input_parallel = input_
         else:
@@ -1294,7 +1294,7 @@ def forward(self, input_, can_fuse_mlp_allreduce=False):
         with use_symmetric_memory(parallel_state.get_tp_group()) as sm:
             output_parallel = self.quant_method.apply(self, input_parallel, bias=bias_)
             sm.tag(output_parallel)
-        if self.reduce_results and self.tp_size > 1 and not can_fuse_mlp_allreduce:
+        if self.reduce_results and self.tp_size > 1 and not skip_all_reduce:
             output = tensor_model_parallel_all_reduce(output_parallel)
         else:
             output = output_parallel

python/sglang/srt/model_executor/forward_batch_info.py

@@ -628,8 +628,10 @@ def prepare_mlp_sync_batch(self, model_runner: ModelRunner):
         self.dp_padding_mode = dp_padding_mode
 
         if dp_padding_mode.is_max_len():
-            # when DP gather mode is all gather, we will use all_gather_into_tensor to gather hidden states,
-            # where transferred tokens should be padded to the same length.
+            # when DP gather mode is all gather, we will use
+            # all_gather_into_tensor to gather hidden states, where transferred
+            # tokens should be padded to the same length. We will also use
+            # reduce-scatter instead of all-reduce after MLP.
             max_num_tokens = max(global_num_tokens)
             global_num_tokens = [max_num_tokens] * sync_group_size
             buffer_len = max_num_tokens * sync_group_size

python/sglang/srt/models/deepseek_v2.py

@@ -208,13 +208,21 @@ def __init__(
             )
         self.act_fn = SiluAndMul()
 
-    def forward(self, x, forward_batch=None, can_fuse_mlp_allreduce=False):
+    def forward(
+        self,
+        x,
+        forward_batch=None,
+        can_fuse_mlp_allreduce: bool = False,
+        use_reduce_scatter: bool = False,
+    ):
         if (self.tp_size == 1) and x.shape[0] == 0:
             return x
 
         gate_up, _ = self.gate_up_proj(x)
         x = self.act_fn(gate_up)
-        x, _ = self.down_proj(x, can_fuse_mlp_allreduce=can_fuse_mlp_allreduce)
+        x, _ = self.down_proj(
+            x, skip_all_reduce=can_fuse_mlp_allreduce or use_reduce_scatter
+        )
         return x
 
 
@@ -441,6 +449,7 @@ def forward(
         hidden_states: torch.Tensor,
         forward_batch: Optional[ForwardBatch] = None,
         can_fuse_mlp_allreduce: bool = False,
+        use_reduce_scatter: bool = False,
     ) -> torch.Tensor:
         if not self._enable_deepep_moe:
             DUAL_STREAM_TOKEN_THRESHOLD = 1024
@@ -450,15 +459,20 @@ def forward(
                 and hidden_states.shape[0] <= DUAL_STREAM_TOKEN_THRESHOLD
             ):
                 return self.forward_normal_dual_stream(
-                    hidden_states, can_fuse_mlp_allreduce
+                    hidden_states, can_fuse_mlp_allreduce, use_reduce_scatter
                 )
             else:
-                return self.forward_normal(hidden_states, can_fuse_mlp_allreduce)
+                return self.forward_normal(
+                    hidden_states, can_fuse_mlp_allreduce, use_reduce_scatter
+                )
         else:
             return self.forward_deepep(hidden_states, forward_batch)
 
     def forward_normal_dual_stream(
-        self, hidden_states: torch.Tensor, can_fuse_mlp_allreduce: bool = False
+        self,
+        hidden_states: torch.Tensor,
+        can_fuse_mlp_allreduce: bool = False,
+        use_reduce_scatter: bool = False,
     ) -> torch.Tensor:
 
         current_stream = torch.cuda.current_stream()
@@ -486,12 +500,15 @@ def forward_normal_dual_stream(
         torch.add(final_hidden_states, shared_output, out=final_hidden_states_out)
         final_hidden_states = final_hidden_states_out
         sm.tag(final_hidden_states)
-        if self.tp_size > 1 and not can_fuse_mlp_allreduce:
+        if self.tp_size > 1 and not can_fuse_mlp_allreduce and not use_reduce_scatter:
             final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
         return final_hidden_states
 
     def forward_normal(
-        self, hidden_states: torch.Tensor, can_fuse_mlp_allreduce: bool = False
+        self,
+        hidden_states: torch.Tensor,
+        can_fuse_mlp_allreduce: bool = False,
+        use_reduce_scatter: bool = False,
     ) -> torch.Tensor:
         if hasattr(self, "shared_experts") and use_intel_amx_backend(
             self.shared_experts.gate_up_proj
@@ -520,7 +537,7 @@ def forward_normal(
             torch.add(final_hidden_states, shared_output, out=final_hidden_states_out)
             final_hidden_states = final_hidden_states_out
             sm.tag(final_hidden_states)
-        if self.tp_size > 1 and not can_fuse_mlp_allreduce:
+        if self.tp_size > 1 and not can_fuse_mlp_allreduce and not use_reduce_scatter:
             final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
         return final_hidden_states
 
@@ -1822,6 +1839,7 @@ def __init__(
             layer_scatter_modes=self.layer_scatter_modes,
             input_layernorm=self.input_layernorm,
             post_attention_layernorm=self.post_attention_layernorm,
+            allow_reduce_scatter=True,
         )
 
     def _is_layer_sparse(self, layer_id: int, is_nextn: bool) -> bool:
@@ -1884,7 +1902,13 @@ def forward(
             and not self.is_nextn
         )
 
-        hidden_states = self.mlp(hidden_states, forward_batch, can_fuse_mlp_allreduce)
+        # For DP with padding, reduce scatter can be used instead of all-reduce.
+        use_reduce_scatter = self.layer_communicator.should_use_reduce_scatter(
+            forward_batch
+        )
+        hidden_states = self.mlp(
+            hidden_states, forward_batch, can_fuse_mlp_allreduce, use_reduce_scatter
+        )
 
         if can_fuse_mlp_allreduce:
             hidden_states._sglang_needs_allreduce_fusion = True

python/sglang/srt/models/glm4_moe.py

@@ -160,7 +160,7 @@ def forward(self, x, forward_batch=None, can_fuse_mlp_allreduce=False):
 
         gate_up, _ = self.gate_up_proj(x)
         x = self.act_fn(gate_up)
-        x, _ = self.down_proj(x, can_fuse_mlp_allreduce=can_fuse_mlp_allreduce)
+        x, _ = self.down_proj(x, skip_all_reduce=can_fuse_mlp_allreduce)
         return x