[Cherry-pick][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.
@@ -977,6 +982,12 @@ 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

@@ -2509,6 +2509,7 @@ 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

@@ -655,6 +655,7 @@ 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 +1,212 @@
+from typing import Optional, Tuple
+
+import paddle
+import paddle.distributed as dist
+
+# from sglang.srt.distributed import get_tensor_model_parallel_world_size
+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
+
+# fd_config.parallel_config.tensor_parallel_size
+
+if has_flashinfer():
+    try:
+        paddle.compat.enable_torch_proxy(scope={"flashinfer"})
+        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.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"""
+    if not has_flashinfer() or _flashinfer_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
+
+    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[paddle.Tensor, paddle.Tensor]: (norm_output, residual_output)
+    """
+    if not has_flashinfer() or _flashinfer_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
+    _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 fake_flashinfer_allreduce_residual_rmsnorm(
+    input_tensor: paddle.Tensor,
+    residual: paddle.Tensor,
+    weight: paddle.Tensor,
+    eps: float = 1e-6,
+    max_token_num: int = 16384,
+    use_oneshot: Optional[bool] = None,
+    trigger_completion_at_end: bool = False,
+    fp32_acc: bool = False,
+) -> Tuple[paddle.Tensor, paddle.Tensor]:
+    residual_out = paddle.empty_like(residual)
+    norm_out = paddle.empty_like(input_tensor)
+    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

@@ -860,6 +860,9 @@ def __init__(
             skip_quant (bool): Whether to skip quantization. Defaults to False.
         """
         self.fd_config = fd_config
+        self.enable_all_reduce_fusion = (
+            fd_config.parallel_config.enable_flashinfer_allreduce_fusion and "enable_all_reduce" in prefix
+        )
         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
@@ -937,7 +940,10 @@ 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 +245,11 @@ 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
+                )
             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

@@ -129,7 +129,6 @@ 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
 
@@ -224,7 +223,7 @@ def __init__(self, fd_config: FDConfig, layer_id: int, prefix: str = "") -> None
 
         self.o_proj = RowParallelLinear(
             fd_config,
-            prefix=f"{prefix}.o_proj",
+            prefix=f"{prefix}.enable_all_reduce.o_proj",
             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,
@@ -299,15 +298,15 @@ def __init__(
             fd_config,
             hidden_size=fd_config.model_config.hidden_size,
             eps=fd_config.model_config.rms_norm_eps,
-            prefix=f"{prefix}.input_layernorm",
+            prefix=f"{prefix}.enable_all_reduce_fusion.input_layernorm",
             layer_id=layer_id,
         )
 
         self.post_attention_layernorm = RMSNorm(
             fd_config,
             hidden_size=fd_config.model_config.hidden_size,
             eps=fd_config.model_config.rms_norm_eps,
-            prefix=f"{prefix}.post_attention_layernorm",
+            prefix=f"{prefix}.enable_all_reduce_fusion.post_attention_layernorm",
             layer_id=layer_id,
         )