[b200] support trt-llm allreduce fuse rms_norm_add kernel

python/sglang/srt/layers/communicator.py

@@ -32,8 +32,13 @@
     get_attention_tp_rank,
     get_attention_tp_size,
 )
+from sglang.srt.layers.utils import is_sm100_supported
 from sglang.srt.managers.schedule_batch import global_server_args_dict
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.utils import is_cuda, is_flashinfer_available
+
+_is_flashinfer_available = is_flashinfer_available()
+_is_sm100_supported = is_cuda() and is_sm100_supported()
 
 
 class ScatterMode(Enum):
@@ -397,8 +402,19 @@ def _gather_hidden_states_and_residual(
             if hidden_states.shape[0] != 0:
                 hidden_states = layernorm(hidden_states)
         else:
-            hidden_states = tensor_model_parallel_all_reduce(hidden_states)
-            hidden_states, residual = layernorm(hidden_states, residual)
+            if (
+                _is_sm100_supported
+                and _is_flashinfer_available
+                and hasattr(layernorm, "forward_with_allreduce_fusion")
+                and global_server_args_dict["enable_flashinfer_allreduce_fusion"]
+                and hidden_states.shape[0] <= 1024
+            ):
+                hidden_states, residual = layernorm.forward_with_allreduce_fusion(
+                    hidden_states, residual
+                )
+            else:
+                hidden_states = tensor_model_parallel_all_reduce(hidden_states)
+                hidden_states, residual = layernorm(hidden_states, residual)
         return hidden_states, residual
 
     @staticmethod

python/sglang/srt/layers/flashinfer_comm_fusion.py

@@ -0,0 +1,202 @@
+import logging
+from typing import Tuple
+
+import torch
+import torch.distributed as dist
+
+from sglang.srt.distributed import get_tensor_model_parallel_world_size
+from sglang.srt.utils import is_flashinfer_available
+
+logger = logging.getLogger(__name__)
+
+_flashinfer_comm = None
+_workspace_manager = None
+
+if is_flashinfer_available():
+    try:
+        import flashinfer.comm as comm
+
+        _flashinfer_comm = comm
+    except ImportError:
+        logger.warning(
+            "flashinfer.comm is not available, falling back to standard "
+            "implementation"
+        )
+
+
+class FlashInferWorkspaceManager:
+    def __init__(self):
+        self.workspace_tensor = None
+        self.ipc_handles = None
+        self.world_size = None
+        self.rank = None
+        self.initialized = False
+
+    def initialize(
+        self,
+        world_size: int,
+        rank: int,
+        max_token_num: int,
+        hidden_dim: int,
+        group=None,
+        use_fp32_lamport: bool = False,
+    ):
+        """Initialize workspace"""
+        if self.initialized and self.world_size == world_size:
+            return
+
+        if _flashinfer_comm is None:
+            logger.warning(
+                "FlashInfer comm not available, skipping workspace " "initialization"
+            )
+            return
+
+        self.cleanup()
+
+        self.ipc_handles, self.workspace_tensor = (
+            comm.trtllm_create_ipc_workspace_for_all_reduce_fusion(
+                rank,
+                world_size,
+                max_token_num,
+                hidden_dim,
+                group=group,
+                use_fp32_lamport=use_fp32_lamport,
+            )
+        )
+
+        self.world_size = world_size
+        self.rank = rank
+        self.initialized = True
+
+        logger.info(
+            f"FlashInfer workspace initialized for rank {rank}, "
+            f"world_size {world_size}"
+        )
+
+    def cleanup(self):
+        """Clean up workspace"""
+        if self.initialized and self.ipc_handles is not None:
+            try:
+                _flashinfer_comm.trtllm_destroy_ipc_workspace_for_all_reduce(
+                    self.ipc_handles, group=dist.group.WORLD
+                )
+            except Exception as e:
+                logger.warning(f"Failed to cleanup FlashInfer workspace: {e}")
+            finally:
+                self.workspace_tensor = None
+                self.ipc_handles = None
+                self.initialized = False
+
+
+_workspace_manager = FlashInferWorkspaceManager()
+
+
+def ensure_workspace_initialized(
+    max_token_num: int = 1024, hidden_dim: int = 4096, use_fp32_lamport: bool = False
+):
+    """Ensure workspace is initialized"""
+    if not is_flashinfer_available() or _flashinfer_comm is None:
+        return False
+
+    world_size = get_tensor_model_parallel_world_size()
+    if world_size <= 1:
+        return False
+
+    rank = dist.get_rank()
+
+    if (
+        not _workspace_manager.initialized
+        or _workspace_manager.world_size != world_size
+    ):
+        _workspace_manager.initialize(
+            world_size=world_size,
+            rank=rank,
+            max_token_num=max_token_num,
+            hidden_dim=hidden_dim,
+            use_fp32_lamport=use_fp32_lamport,
+        )
+
+    return _workspace_manager.initialized
+
+
+def flashinfer_allreduce_add_rmsnorm(
+    input_tensor: torch.Tensor,
+    residual: torch.Tensor,
+    weight: torch.Tensor,
+    eps: float = 1e-6,
+    max_token_num: int = 1024,
+    use_oneshot: bool = True,
+    trigger_completion_at_end: bool = False,
+    fp32_acc: bool = False,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """
+    Use FlashInfer's fused allreduce + residual + RMS norm operation
+
+    Args:
+        input_tensor: Input tensor that needs allreduce
+        residual: Residual tensor
+        weight: RMS norm weight
+        eps: RMS norm epsilon
+        max_token_num: Maximum token number
+        use_oneshot: Whether to use oneshot mode
+        trigger_completion_at_end: Whether to trigger completion at end
+        fp32_acc: Whether to use fp32 precision
+
+    Returns:
+        Tuple[torch.Tensor, torch.Tensor]: (norm_output, residual_output)
+    """
+    if not is_flashinfer_available() or _flashinfer_comm is None:
+        logger.debug(
+            "FlashInfer not available, falling back to standard " "implementation"
+        )
+        return None, None
+
+    world_size = get_tensor_model_parallel_world_size()
+    if world_size <= 1:
+        logger.debug("Single GPU, no need for allreduce fusion")
+        return None, None
+
+    if not ensure_workspace_initialized(
+        max_token_num=max_token_num,
+        hidden_dim=input_tensor.shape[-1],
+        use_fp32_lamport=(input_tensor.dtype == torch.float32),
+    ):
+        logger.debug("FlashInfer workspace not available")
+        return None, None
+
+    token_num, hidden_dim = input_tensor.shape
+
+    residual_out = torch.empty_like(residual)
+    norm_out = torch.empty_like(input_tensor)
+
+    _flashinfer_comm.trtllm_allreduce_fusion(
+        allreduce_in=input_tensor,
+        world_size=world_size,
+        world_rank=dist.get_rank(),
+        token_num=token_num,
+        hidden_dim=hidden_dim,
+        workspace_ptrs=_workspace_manager.workspace_tensor,
+        launch_with_pdl=True,
+        use_oneshot=use_oneshot,
+        trigger_completion_at_end=trigger_completion_at_end,
+        fp32_acc=fp32_acc,
+        pattern_code=(_flashinfer_comm.AllReduceFusionPattern.kARResidualRMSNorm),
+        allreduce_out=None,
+        residual_in=residual,
+        residual_out=residual_out,
+        norm_out=norm_out,
+        quant_out=None,
+        scale_out=None,
+        rms_gamma=weight,
+        rms_eps=eps,
+        scale_factor=None,
+        layout_code=None,
+    )
+
+    return norm_out, residual_out
+
+
+def cleanup_flashinfer_workspace():
+    global _workspace_manager
+    if _workspace_manager is not None:
+        _workspace_manager.cleanup()

python/sglang/srt/layers/layernorm.py

@@ -163,6 +163,32 @@ def forward_cpu(
         else:
             return self.forward_native(x, residual)
 
+    def forward_with_allreduce_fusion(
+        self,
+        x: torch.Tensor,
+        residual: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        """
+        Forward method with allreduce fusion, prioritizing flashinfer fused operations
+        """
+        if residual is not None:
+            from sglang.srt.distributed import get_tensor_model_parallel_world_size
+            from sglang.srt.layers.flashinfer_comm_fusion import (
+                flashinfer_allreduce_add_rmsnorm,
+            )
+
+            if get_tensor_model_parallel_world_size() > 1:
+                fused_result = flashinfer_allreduce_add_rmsnorm(
+                    input_tensor=x,
+                    residual=residual,
+                    weight=self.weight,
+                    eps=self.variance_epsilon,
+                )
+                if fused_result[0] is not None:
+                    return fused_result
+
+        return self.forward(x, residual)
+
 
 class GemmaRMSNorm(CustomOp):
     def __init__(

python/sglang/srt/managers/schedule_batch.py

@@ -85,6 +85,7 @@
     "deepep_mode",
     "enable_ep_moe",
     "enable_flashinfer_moe",
+    "enable_flashinfer_allreduce_fusion",
     "moe_dense_tp_size",
     "ep_dispatch_algorithm",
     "deepep_config",

python/sglang/srt/server_args.py

@@ -156,6 +156,7 @@ class ServerArgs:
     enable_ep_moe: bool = False
     enable_deepep_moe: bool = False
     enable_flashinfer_moe: bool = False
+    enable_flashinfer_allreduce_fusion: bool = False
     deepep_mode: Optional[Literal["auto", "normal", "low_latency"]] = "auto"
     ep_num_redundant_experts: int = 0
     ep_dispatch_algorithm: Optional[Literal["static", "dynamic", "fake"]] = None
@@ -1200,6 +1201,11 @@ def add_cli_args(parser: argparse.ArgumentParser):
             action="store_true",
             help="Enable FlashInfer CUTLASS MoE backend for modelopt_fp4 quant on Blackwell. Supports MoE-EP with --enable-ep-moe",
         )
+        parser.add_argument(
+            "--enable-flashinfer-allreduce-fusion",
+            action="store_true",
+            help="Enable FlashInfer allreduce fusion for Add_RMSNorm.",
+        )
         parser.add_argument(
             "--enable-deepep-moe",
             action="store_true",