[Optimization] enable trtllm_all_reduce fusion kernel in glm model

fastdeploy/config.py

@@ -671,6 +671,7 @@ def __init__(
         self.pod_ip: str = None
         # enable the custom all-reduce kernel and fall back to NCCL(dist.all_reduce).
         self.disable_custom_all_reduce: bool = False
+        self.enable_flashinfer_allreduce_fusion: bool = False
         for key, value in args.items():
             if hasattr(self, key):
                 setattr(self, key, value)

fastdeploy/engine/args_utils.py

@@ -274,6 +274,11 @@ class EngineArgs:
     Flag to disable the custom all-reduce kernel.
     """
 
+    enable_flashinfer_allreduce_fusion: bool = False
+    """
+    Flag to enable all reduce fusion kernel in flashinfer.
+    """
+
     use_internode_ll_two_stage: bool = False
     """
     Flag to use the internode_ll_two_stage kernel.
@@ -990,6 +995,12 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
             default=EngineArgs.disable_custom_all_reduce,
             help="Flag to disable custom all-reduce.",
         )
+        parallel_group.add_argument(
+            "--enable-flashinfer-allreduce-fusion",
+            action="store_true",
+            default=EngineArgs.enable_flashinfer_allreduce_fusion,
+            help="Flag to enable all reduce fusion kernel in flashinfer.",
+        )
         parallel_group.add_argument(
             "--use-internode-ll-two-stage",
             action="store_true",

fastdeploy/engine/common_engine.py

@@ -2503,6 +2503,7 @@ def _start_worker_service(self):
             "moe_gate_fp32": self.cfg.model_config.moe_gate_fp32,
             "enable_entropy": self.cfg.model_config.enable_entropy,
             "enable_overlap_schedule": self.cfg.scheduler_config.enable_overlap_schedule,
+            "enable_flashinfer_allreduce_fusion": self.cfg.parallel_config.enable_flashinfer_allreduce_fusion,
         }
         for worker_flag, value in worker_store_true_flag.items():
             if value:

fastdeploy/engine/engine.py

@@ -656,6 +656,7 @@ def _start_worker_service(self):
             "enable_entropy": self.cfg.model_config.enable_entropy,
             "ep_prefill_use_worst_num_tokens": self.cfg.parallel_config.ep_prefill_use_worst_num_tokens,
             "enable_overlap_schedule": self.cfg.scheduler_config.enable_overlap_schedule,
+            "enable_flashinfer_allreduce_fusion": self.cfg.parallel_config.enable_flashinfer_allreduce_fusion,
         }
         for worker_flag, value in worker_store_true_flag.items():
             if value:

fastdeploy/model_executor/layers/flashinfer_comm_fusion.py

@@ -0,0 +1,209 @@
+"""
+# Copyright (c) 2026 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+
+from typing import Optional, Tuple
+
+import paddle
+import paddle.distributed as dist
+
+from fastdeploy.config import FDConfig
+from fastdeploy.model_executor.utils import has_flashinfer
+from fastdeploy.utils import get_logger
+
+logger = get_logger("flashinfer", "flashinfer.log")
+
+_flashinfer_comm = None
+_workspace_manager = None
+
+
+def _get_flashinfer_comm():
+    """Lazily import flashinfer.comm to avoid side effects at module load time."""
+    global _flashinfer_comm
+    if _flashinfer_comm is not None:
+        return _flashinfer_comm
+    if has_flashinfer():
+        try:
+            with paddle.use_compat_guard(enable=True, scope={"flashinfer"}):
+                import flashinfer.comm as comm
+
+                _flashinfer_comm = comm
+        except ImportError:
+            logger.warning("flashinfer.comm is not available, falling back to standard " "implementation")
+    return _flashinfer_comm
+
+
+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
+
+        comm = _get_flashinfer_comm()
+        if 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:
+                comm = _get_flashinfer_comm()
+                if comm is not None:
+                    comm.trtllm_destroy_ipc_workspace_for_all_reduce(self.ipc_handles, group=dist.get_group())
+            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(
+    fd_config: FDConfig, max_token_num: int = 2048, hidden_dim: int = 4096, use_fp32_lamport: bool = False
+):
+    """Ensure workspace is initialized"""
+    comm = _get_flashinfer_comm()
+    if not has_flashinfer() or comm is None:
+        return False
+
+    assert fd_config is not None
+    world_size = fd_config.parallel_config.tensor_parallel_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_residual_rmsnorm(
+    fd_config: FDConfig,
+    input_tensor: paddle.Tensor,
+    residual: paddle.Tensor,
+    weight: paddle.Tensor,
+    eps: float = 1e-6,
+    max_token_num: int = 2048,
+    use_oneshot: Optional[bool] = None,
+    trigger_completion_at_end: bool = False,
+    fp32_acc: bool = False,
+) -> Tuple[paddle.Tensor, paddle.Tensor]:
+    """
+    Use FlashInfer's fused allreduce + residual + RMS norm operation
+    """
+    comm = _get_flashinfer_comm()
+    if not has_flashinfer() or comm is None:
+        logger.debug("FlashInfer not available, falling back to standard " "implementation")
+        return None, None
+
+    assert fd_config is not None
+    world_size = fd_config.parallel_config.tensor_parallel_size
+    if world_size <= 1:
+        logger.debug("Single GPU, no need for allreduce fusion")
+        return None, None
+
+    assert input_tensor.shape[0] <= max_token_num
+
+    if not ensure_workspace_initialized(
+        fd_config=fd_config,
+        max_token_num=max_token_num,
+        hidden_dim=input_tensor.shape[-1],
+        use_fp32_lamport=(input_tensor.dtype == paddle.float32),
+    ):
+        logger.debug("FlashInfer workspace not available")
+        return None, None
+
+    token_num, hidden_dim = input_tensor.shape
+
+    residual_out = paddle.empty_like(residual)
+    norm_out = paddle.empty_like(input_tensor)
+    # support empty tensor
+    if input_tensor.shape[0] == 0:
+        return norm_out, residual_out
+    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=(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()

fastdeploy/model_executor/layers/linear.py

@@ -853,6 +853,7 @@ def __init__(
         skip_quant: bool = False,
         weight_dtype: str = "",
         layer_id: int = -1,
+        enable_all_reduce_fusion: bool = None,
     ):
         """
         Initialize a linear layer with additional parameters for inference and quantization.
@@ -864,9 +865,17 @@ def __init__(
             input_size (int): Number of input features. Defaults to None.
             output_size (int): Number of output features. Defaults to None.
             with_bias (bool): Whether to include bias or not. Defaults to False.
-            skip_quant (bool): Whether to skip quantization. Defaults to False.
+            skip_quant (bool): Whether to skip quantization or not. Defaults to False.
+            enable_all_reduce_fusion (bool, optional): Whether to enable all-reduce fusion.
+                If None, it is determined by the config flag and prefix. Defaults to None.
         """
         self.fd_config = fd_config
+        if enable_all_reduce_fusion is None:
+            self.enable_all_reduce_fusion = False
+        else:
+            self.enable_all_reduce_fusion = (
+                fd_config.parallel_config.enable_flashinfer_allreduce_fusion and enable_all_reduce_fusion
+            )
         self.ep_size = fd_config.parallel_config.expert_parallel_size
         self.tp_size = fd_config.parallel_config.tensor_parallel_size
         self.tp_group = fd_config.parallel_config.tp_group
@@ -944,7 +953,10 @@ def forward_cuda(self, x: paddle.Tensor) -> paddle.Tensor:
 
         out = self.quant_method.apply(self, x)
 
-        if self.reduce_results and self.tp_size > 1:
+        need_tp_all_reduce = (
+            self.reduce_results and self.tp_size > 1 and not (self.enable_all_reduce_fusion and out.shape[0] <= 2048)
+        )
+        if need_tp_all_reduce:
             out = tensor_model_parallel_all_reduce(out, self.tp_group)
 
         return out

fastdeploy/model_executor/layers/normalization.py

@@ -35,6 +35,7 @@
     is_batch_invariant_mode_enabled,
     rms_norm_batch_invariant,
 )
+from .flashinfer_comm_fusion import flashinfer_allreduce_residual_rmsnorm
 from .utils import get_tensor, modules_to_convert
 
 
@@ -122,6 +123,10 @@ def __init__(
         self.tp_rank = self.fd_config.parallel_config.tensor_parallel_rank
         self.tp_group = self.fd_config.parallel_config.tp_group
         is_input_norm = prefix.endswith(".input_layernorm")
+        self.enable_all_reduce_fusion = (
+            fd_config.parallel_config.enable_flashinfer_allreduce_fusion and "post_attention_layernorm" in prefix
+        )
+
         self.is_last_norm = prefix.endswith(".norm")
         self.split_x = (
             self.fd_config.parallel_config.use_sequence_parallel_moe
@@ -240,6 +245,12 @@ def forward(
                     norm_out = rms_norm(x, self.weight, self.eps)
                     return norm_out.astype(x_dtype), residual_out
                 norm_out = self.norm_func(x, residual_input, self.weight, self.eps)
+            # enable trtllm all reduce fusion
+            elif self.enable_all_reduce_fusion and x.shape[0] <= 2048:
+                norm_out = flashinfer_allreduce_residual_rmsnorm(
+                    fd_config=self.fd_config, input_tensor=x, residual=residual_input, weight=self.weight, eps=self.eps
+                )
+                assert norm_out[0] is not None, "Trtllm-all-reduce fusion failed!"
             else:
                 if is_batch_invariant_mode_enabled():
                     # M-invariant path: per-row Triton kernel, no cross-row reduction

fastdeploy/model_executor/layers/quantization/mxfp4.py

@@ -14,8 +14,6 @@
 # limitations under the License.
 """
 
-import importlib
-import importlib.util
 import math
 from enum import Enum
 from typing import Callable, Optional
@@ -25,11 +23,12 @@
 
 from fastdeploy import envs
 from fastdeploy.model_executor.layers.moe.fused_moe_backend_base import MoEMethodBase
-from fastdeploy.model_executor.utils import set_weight_attrs
+from fastdeploy.model_executor.utils import has_flashinfer, set_weight_attrs
 from fastdeploy.platforms import current_platform
 
 if current_platform.is_cuda():
     from fastdeploy.model_executor.ops.gpu import moe_expert_dispatch
+
 from fastdeploy.utils import get_logger
 
 from ..moe import FusedMoE
@@ -59,10 +58,6 @@ def check_device_capability(num):
         return False
 
 
-def has_flashinfer():
-    return importlib.util.find_spec("flashinfer") is not None
-
-
 def round_up(a, b):
     return ((a + b - 1) // b) * b
 

fastdeploy/model_executor/models/glm4_moe.py

@@ -130,7 +130,6 @@ def __init__(
         self.tensor_parallel_size = fd_config.parallel_config.tensor_parallel_size
         self.tensor_parallel_rank = fd_config.parallel_config.tensor_parallel_rank
         self.tp_group = fd_config.parallel_config.tp_group
-
         self.use_ep = self.expert_parallel_size > 1
         self.use_tp = self.tensor_parallel_size > 1
 
@@ -229,6 +228,7 @@ def __init__(self, fd_config: FDConfig, layer_id: int, prefix: str = "") -> None
             input_size=fd_config.model_config.num_attention_heads * fd_config.model_config.head_dim,
             output_size=fd_config.model_config.hidden_size,
             layer_id=layer_id,
+            enable_all_reduce_fusion=fd_config.parallel_config.enable_flashinfer_allreduce_fusion,
         )
 
         self.attn = Attention(

fastdeploy/model_executor/utils.py

@@ -14,6 +14,8 @@
 # limitations under the License.
 """
 
+import importlib
+import importlib.util
 import os
 import re
 from collections.abc import Mapping
@@ -553,6 +555,10 @@ def fn(loaded_weight_name, is_moe):
     return fn
 
 
+def has_flashinfer():
+    return importlib.util.find_spec("flashinfer") is not None
+
+
 @cache
 def get_sm_version():
     if paddle.cuda.is_available():