Register allgather/reducescatter buffers with symm memory (#12572)

python/sglang/srt/distributed/device_communicators/pynccl_allocator.py

 import os
 import tempfile
+from contextlib import nullcontext
 
 import torch
 from torch.cuda.memory import CUDAPluggableAllocator
 
 from sglang.srt.distributed.parallel_state import GroupCoordinator
-from sglang.srt.server_args import get_global_server_args
 
 nccl_allocator_source = """
 
@@ -60,6 +60,9 @@ _cur_device = None
 
 
 def is_symmetric_memory_enabled():
+    # Import here to avoid circular import
+    from sglang.srt.server_args import get_global_server_args
+
     return get_global_server_args().enable_symm_mem
 
 
@@ -92,7 +95,7 @@ def get_nccl_mem_pool():
     return _mem_pool
 
 
-class use_symmetric_memory:
+class SymmetricMemoryContext:
     """
     Context manager for using symmetric memory with pynccl.
 
@@ -100,25 +103,17 @@ class use_symmetric_memory:
     by `ncclMemAlloc` and registered by `ncclCommWindowRegister`. Due to this, we introduce
     this context manager. All tensors created under this context will be correctly
     allocated and registered with a custom allocator.
-
-    In addition, developers need to manually tag the tensors that will be used as the input/output
-    of NCCL collectives with `tag(tensor)`.
     """
 
-    def __init__(self, group_coordinator: GroupCoordinator):
-        self.enabled = is_symmetric_memory_enabled()
-
-        if not self.enabled:
-            return
-
+    def __init__(
+        self,
+        group_coordinator: GroupCoordinator,
+    ):
         self.group_coordinator = group_coordinator
         self._mem_pool_ctx = torch.cuda.use_mem_pool(get_nccl_mem_pool())
         self.is_graph_capture = torch.cuda.is_current_stream_capturing()
 
     def __enter__(self):
-        if not self.enabled:
-            return self
-
         assert (
             self.group_coordinator.pynccl_comm is not None
         ), f"Symmetric memory requires pynccl to be enabled in group '{self.group_coordinator.group_name}'"
@@ -139,16 +134,16 @@ class use_symmetric_memory:
         return self
 
     def __exit__(self, exc_type, exc_val, exc_tb):
-        if not self.enabled:
-            return
-
         self._mem_pool_ctx.__exit__(exc_type, exc_val, exc_tb)
 
         if self.is_graph_capture:
             torch._C._cuda_beginAllocateCurrentThreadToPool(_cur_device, _graph_pool_id)
 
-    def tag(self, tensor: torch.Tensor):
-        if not self.enabled:
-            return
 
-        tensor.symmetric_memory = True
+def use_symmetric_memory(group_coordinator: GroupCoordinator, disabled: bool = False):
+    disabled = (
+        not is_symmetric_memory_enabled()
+        or disabled
+        or group_coordinator.world_size == 1
+    )
+    return SymmetricMemoryContext(group_coordinator) if not disabled else nullcontext()

python/sglang/srt/distributed/parallel_state.py

@@ -188,6 +188,27 @@ if _supports_custom_op:
         fake_impl=reg_all_gather_into_tensor_fake,
     )
 
+    def reg_reduce_scatter_tensor(
+        output: torch.Tensor, input: torch.Tensor, group_name: str
+    ) -> None:
+        assert group_name in _groups, f"Group {group_name} is not found."
+        group = _groups[group_name]()
+        if group is None:
+            raise ValueError(f"Group {group_name} is destroyed.")
+        group._reduce_scatter_tensor(output, input)
+
+    def reg_reduce_scatter_tensor_fake(
+        output: torch.Tensor, input: torch.Tensor, group_name: str
+    ) -> None:
+        pass
+
+    direct_register_custom_op(
+        op_name="reg_reduce_scatter_tensor",
+        op_func=reg_reduce_scatter_tensor,
+        mutates_args=["output"],
+        fake_impl=reg_reduce_scatter_tensor_fake,
+    )
+
 
 class GroupCoordinator:
     """
@@ -314,10 +335,16 @@ class GroupCoordinator:
         from sglang.srt.distributed.device_communicators.pynccl import (
             PyNcclCommunicator,
         )
+        from sglang.srt.distributed.device_communicators.pynccl_allocator import (
+            is_symmetric_memory_enabled,
+            use_symmetric_memory,
+        )
         from sglang.srt.distributed.device_communicators.torch_symm_mem import (
             TorchSymmMemCommunicator,
         )
 
+        self.is_symmetric_memory_enabled = is_symmetric_memory_enabled
+        self.use_symmetric_memory = use_symmetric_memory
         if is_hip():
             from sglang.srt.distributed.device_communicators.quick_all_reduce import (
                 QuickAllReduce,
@@ -552,7 +579,7 @@ class GroupCoordinator:
         if self.npu_communicator is not None and not self.npu_communicator.disabled:
             return self.npu_communicator.all_reduce(input_)
 
-        if self.pynccl_comm is not None and getattr(input_, "symmetric_memory", False):
+        if self.pynccl_comm is not None and self.is_symmetric_memory_enabled():
             with self.pynccl_comm.change_state(
                 enable=True, stream=get_current_device_stream_fast()
             ):
@@ -627,15 +654,33 @@ class GroupCoordinator:
         else:
             torch.distributed.all_reduce(input_, group=self.device_group)
 
-    def reduce_scatter_tensor(
+    def _reduce_scatter_tensor(
         self,
         output: torch.Tensor,
         input: torch.Tensor,
-    ) -> None:
-        # TODO(ch-wan): support other backends
-        torch.distributed.reduce_scatter_tensor(output, input, group=self.device_group)
+    ) -> torch.Tensor:
+        pynccl_comm = self.pynccl_comm
+        if pynccl_comm is not None and (
+            not pynccl_comm.disabled or self.is_symmetric_memory_enabled()
+        ):
+            with pynccl_comm.change_state(
+                enable=True, stream=get_current_device_stream_fast()
+            ):
+                pynccl_comm.reduce_scatter(output, input)
+        else:
+            torch.distributed.reduce_scatter_tensor(
+                output, input, group=self.device_group
+            )
         return output
 
+    def reduce_scatter_tensor(self, output: torch.Tensor, input: torch.Tensor):
+        if _is_npu or not supports_custom_op():
+            self._reduce_scatter_tensor(output, input)
+        else:
+            torch.ops.sglang.reg_reduce_scatter_tensor(
+                output, input, group_name=self.unique_name
+            )
+
     def reduce_scatter(
         self,
         output: torch.Tensor,
@@ -682,8 +727,13 @@ class GroupCoordinator:
 
     def _all_gather_into_tensor(self, output: torch.Tensor, input: torch.Tensor):
         pynccl_comm = self.pynccl_comm
-        if pynccl_comm is not None and not pynccl_comm.disabled:
-            pynccl_comm.all_gather(output, input)
+        if pynccl_comm is not None and (
+            not pynccl_comm.disabled or self.is_symmetric_memory_enabled()
+        ):
+            with pynccl_comm.change_state(
+                enable=True, stream=get_current_device_stream_fast()
+            ):
+                pynccl_comm.all_gather(output, input)
         else:
             torch.distributed.all_gather_into_tensor(
                 output, input, group=self.device_group
@@ -745,9 +795,10 @@ class GroupCoordinator:
         # torch.compile . see https://github.com/pytorch/pytorch/issues/138795
         output_size = (input_size[0] * world_size,) + input_size[1:]
         # Allocate output tensor.
-        output_tensor = torch.empty(
-            output_size, dtype=input_.dtype, device=input_.device
-        )
+        with self.use_symmetric_memory(self):
+            output_tensor = torch.empty(
+                output_size, dtype=input_.dtype, device=input_.device
+            )
 
         # All-gather.
         if input_.is_cpu:
@@ -787,7 +838,7 @@ class GroupCoordinator:
                 pynccl_comm is not None and not pynccl_comm.disabled
             ), "pynccl is required for all_gatherv"
 
-            def _all_gather_single(
+            def _all_gather_allocate_output(
                 input_: torch.Tensor, sizes: Optional[List[int]] = None
             ):
                 input_size = input_.size()
@@ -801,19 +852,25 @@ class GroupCoordinator:
                 else:
                     output_size = (input_size[0] * world_size,) + input_size[1:]
                 # Allocate output tensor.
-                output_tensor = torch.empty(
-                    output_size, dtype=input_.dtype, device=input_.device
-                )
-                pynccl_comm.all_gather(output_tensor, input_, sizes=sizes)
-                return output_tensor
+                with self.use_symmetric_memory(self, disabled=sizes is not None):
+                    output_tensor = torch.empty(
+                        output_size, dtype=input_.dtype, device=input_.device
+                    )
+                return output_tensor, sizes
 
             if isinstance(input_, torch.Tensor):
-                return _all_gather_single(input_, sizes)
+                input_ = [input_]
 
             output_list = []
-            pynccl_comm.group_start()
+            size_list = []
             for inp in input_:
-                output_list.append(_all_gather_single(inp, sizes=sizes))
+                output_tensor, s = _all_gather_allocate_output(inp, sizes=sizes)
+                output_list.append(output_tensor)
+                size_list.append(s)
+
+            pynccl_comm.group_start()
+            for i, inp in enumerate(input_):
+                pynccl_comm.all_gather(output_list[i], inp, sizes=size_list[i])
             pynccl_comm.group_end()
 
             return output_list

python/sglang/srt/layers/communicator.py

@@ -21,8 +21,12 @@ import torch
 
 from sglang.srt.distributed import (
     get_tensor_model_parallel_world_size,
+    get_tp_group,
     tensor_model_parallel_all_reduce,
 )
+from sglang.srt.distributed.device_communicators.pynccl_allocator import (
+    use_symmetric_memory,
+)
 from sglang.srt.layers.dp_attention import (
     attn_tp_all_gather_into_tensor,
     attn_tp_reduce_scatter_tensor,
@@ -34,6 +38,7 @@ from sglang.srt.layers.dp_attention import (
     get_attention_tp_size,
     get_global_dp_buffer,
     get_local_dp_buffer,
+    is_allocation_symmetric,
     is_dp_attention_enabled,
 )
 from sglang.srt.layers.moe import (
@@ -540,7 +545,12 @@ class CommunicateWithAllReduceAndLayerNormFn:
             use_layer_norm_before_gather = context.attn_tp_size == 1
             if use_layer_norm_before_gather and hidden_states.shape[0] != 0:
                 residual = hidden_states
-                hidden_states = layernorm(hidden_states)
+                with use_symmetric_memory(
+                    get_tp_group(),
+                    disabled=not is_allocation_symmetric(),
+                ):
+                    hidden_states = layernorm(hidden_states)
+
             hidden_states, local_hidden_states = (
                 get_global_dp_buffer(),
                 hidden_states,

python/sglang/srt/layers/dp_attention.py

@@ -17,6 +17,9 @@ from sglang.srt.distributed import (
     get_tp_group,
     tensor_model_parallel_all_reduce,
 )
+from sglang.srt.distributed.device_communicators.pynccl_allocator import (
+    use_symmetric_memory,
+)
 from sglang.srt.utils import get_bool_env_var, is_hip
 
 if TYPE_CHECKING:
@@ -86,6 +89,7 @@ class _DpGatheredBufferWrapper:
     _device: torch.device
     _global_dp_buffer_len: int
     _local_dp_buffer_len: int
+    _dp_max_padding: bool
     _global_num_tokens: Optional[List[int]]
     _is_extend_in_batch: bool
 
@@ -100,27 +104,33 @@ class _DpGatheredBufferWrapper:
         cls,
         global_dp_buffer_len: int,
         local_dp_buffer_len: int,
+        dp_max_padding: bool,
         global_num_tokens: Optional[List[int]] = None,
     ):
         cls._global_dp_buffer_len = global_dp_buffer_len
         cls._local_dp_buffer_len = local_dp_buffer_len
+        cls._dp_max_padding = dp_max_padding
         cls._global_num_tokens = global_num_tokens
 
     @classmethod
     def get_global_dp_buffer(cls) -> torch.Tensor:
-        return torch.empty(
-            (cls._global_dp_buffer_len, cls._hidden_size),
-            dtype=cls._dtype,
-            device=cls._device,
-        )
+        with use_symmetric_memory(get_tp_group()):
+            buffer = torch.empty(
+                (cls._global_dp_buffer_len, cls._hidden_size),
+                dtype=cls._dtype,
+                device=cls._device,
+            )
+        return buffer
 
     @classmethod
     def get_local_dp_buffer(cls) -> torch.Tensor:
-        return torch.empty(
-            (cls._local_dp_buffer_len, cls._hidden_size),
-            dtype=cls._dtype,
-            device=cls._device,
-        )
+        with use_symmetric_memory(get_tp_group(), disabled=not cls._dp_max_padding):
+            buffer = torch.empty(
+                (cls._local_dp_buffer_len, cls._hidden_size),
+                dtype=cls._dtype,
+                device=cls._device,
+            )
+        return buffer
 
     @classmethod
     def get_global_dp_buffer_len(cls) -> int:
@@ -154,14 +164,19 @@ class _DpGatheredBufferWrapper:
     def get_is_extend_in_batch(cls) -> bool:
         return cls._is_extend_in_batch
 
+    @classmethod
+    def is_dp_max_padding(cls) -> bool:
+        return cls._dp_max_padding
+
 
 def set_dp_buffer_len(
     global_dp_buffer_len: int,
     local_dp_buffer_len: int,
+    dp_max_padding: bool,
     global_num_tokens: Optional[List[int]] = None,
 ):
     _DpGatheredBufferWrapper.set_dp_buffer_len(
-        global_dp_buffer_len, local_dp_buffer_len, global_num_tokens
+        global_dp_buffer_len, local_dp_buffer_len, dp_max_padding, global_num_tokens
     )
 
 
@@ -205,6 +220,10 @@ def get_is_extend_in_batch() -> bool:
     return _DpGatheredBufferWrapper.get_is_extend_in_batch()
 
 
+def is_dp_max_padding() -> bool:
+    return _DpGatheredBufferWrapper.is_dp_max_padding()
+
+
 def compute_dp_attention_world_info(enable_dp_attention, tp_rank, tp_size, dp_size):
     if not enable_dp_attention:
         return tp_rank, tp_size, 0
@@ -298,6 +317,10 @@ def is_dp_attention_enabled() -> bool:
     return _ENABLE_DP_ATTENTION_FLAG
 
 
+def is_allocation_symmetric() -> bool:
+    return not is_dp_attention_enabled() or is_dp_max_padding()
+
+
 def get_attention_tp_group() -> GroupCoordinator:
     assert _ATTN_TP_GROUP is not None, "dp attention not initialized!"
     return _ATTN_TP_GROUP

python/sglang/srt/layers/linear.py

@@ -21,6 +21,7 @@ from sglang.srt.distributed import (
 from sglang.srt.distributed.device_communicators.pynccl_allocator import (
     use_symmetric_memory,
 )
+from sglang.srt.layers.dp_attention import is_allocation_symmetric
 from sglang.srt.layers.parameter import (
     BasevLLMParameter,
     BlockQuantScaleParameter,
@@ -1372,9 +1373,10 @@ class RowParallelLinear(LinearBase):
         # Only fuse bias add into GEMM for rank 0 (this ensures that
         # bias will not get added more than once in TP>1 case)
         bias_ = None if (self.tp_rank > 0 or self.skip_bias_add) else self.bias
-        with use_symmetric_memory(get_tp_group()) as sm:
+        with use_symmetric_memory(
+            get_tp_group(), disabled=not is_allocation_symmetric()
+        ):
             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 skip_all_reduce:
             output = tensor_model_parallel_all_reduce(output_parallel)

python/sglang/srt/layers/moe/cutlass_moe.py

@@ -97,7 +97,7 @@ def cutlass_fused_experts_fp8(
         b_scales_ptrs (torch.Tensor): Pointers container for calculating offsets of the input scales for each expert.
         use_fp8_blockscale (bool, optional): Flag indicating usage of FP8 with
             block scaling. Currently, only `True` is supported. Defaults to `True`.
-
+        output (torch.Tensor, optional): Output tensor. If not provided, a new tensor will be created.
     Returns:
         torch.Tensor: The computed MoE layer output. Shape: `(m, k)`, dtype matches `a`.
 

python/sglang/srt/layers/moe/fused_moe_triton/layer.py

@@ -18,6 +18,7 @@ from sglang.srt.distributed.device_communicators.pynccl_allocator import (
     use_symmetric_memory,
 )
 from sglang.srt.eplb.expert_location import get_global_expert_location_metadata
+from sglang.srt.layers.dp_attention import is_allocation_symmetric
 from sglang.srt.layers.moe import (
     MoeRunnerConfig,
     get_deepep_mode,
@@ -841,7 +842,9 @@ class FusedMoE(torch.nn.Module):
             **kwargs,
         )
 
-        with use_symmetric_memory(get_tp_group()) as sm:
+        with use_symmetric_memory(
+            get_tp_group(), disabled=not is_allocation_symmetric()
+        ):
             final_hidden_states = self.dispatcher.combine(combine_input=combine_input)
 
             # TODO: should we add some conditions here?
@@ -849,8 +852,6 @@ class FusedMoE(torch.nn.Module):
                 ..., :origin_hidden_states_dim
             ].contiguous()
 
-            sm.tag(final_hidden_states)
-
         if self.reduce_results and (self.moe_tp_size > 1 or self.moe_ep_size > 1):
             final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
 
@@ -1048,10 +1049,10 @@ class FlashInferFP4MoE(FusedMoE):
 
         router_logits = router_logits.to(torch.float32)
 
-        with use_symmetric_memory(get_tp_group()) as sm:
+        with use_symmetric_memory(
+            get_tp_group(), disabled=not is_allocation_symmetric()
+        ):
             symm_output = torch.empty_like(hidden_states)
-            sm.tag(symm_output)
-
         result = trtllm_fp4_block_scale_moe(
             routing_logits=router_logits,
             routing_bias=topk_config.correction_bias.to(hidden_states.dtype),

python/sglang/srt/layers/moe/topk.py

@@ -32,12 +32,17 @@ import torch
 import torch.nn.functional as F
 
 from sglang.srt.custom_op import CustomOp
+from sglang.srt.distributed import get_tp_group
+from sglang.srt.distributed.device_communicators.pynccl_allocator import (
+    use_symmetric_memory,
+)
 from sglang.srt.eplb import expert_location_dispatch
 from sglang.srt.eplb.expert_distribution import get_global_expert_distribution_recorder
 from sglang.srt.eplb.expert_location_dispatch import (
     ExpertLocationDispatchInfo,
     topk_ids_logical_to_physical,
 )
+from sglang.srt.layers.dp_attention import is_allocation_symmetric
 from sglang.srt.layers.moe import get_moe_runner_backend
 from sglang.srt.utils import (
     cpu_has_amx_support,
@@ -279,13 +284,17 @@ class TopK(CustomOp):
             )
         else:
             self.topk_config.torch_native = False
-            return select_experts(
-                hidden_states=hidden_states,
-                router_logits=router_logits,
-                topk_config=self.topk_config,
-                num_token_non_padded=num_token_non_padded,
-                expert_location_dispatch_info=expert_location_dispatch_info,
-            )
+            with use_symmetric_memory(
+                get_tp_group(), disabled=not is_allocation_symmetric()
+            ):
+                topk_output = select_experts(
+                    hidden_states=hidden_states,
+                    router_logits=router_logits,
+                    topk_config=self.topk_config,
+                    num_token_non_padded=num_token_non_padded,
+                    expert_location_dispatch_info=expert_location_dispatch_info,
+                )
+            return topk_output
 
     def forward_cpu(
         self,
@@ -386,8 +395,11 @@ class TopK(CustomOp):
 
     def empty_topk_output(self, device: torch.device) -> TopKOutput:
         topk = self.topk_config.top_k - self.topk_config.num_fused_shared_experts
-        topk_weights = torch.empty((0, topk), dtype=torch.float32, device=device)
-        topk_ids = torch.full((0, topk), -1, dtype=torch.int32, device=device)
+        with use_symmetric_memory(
+            get_tp_group(), disabled=not is_allocation_symmetric()
+        ):
+            topk_weights = torch.empty((0, topk), dtype=torch.float32, device=device)
+            topk_ids = torch.full((0, topk), -1, dtype=torch.int32, device=device)
         # FIXME: router_logits should be of size (0, num_experts)
         router_logits = torch.empty((0, topk), dtype=torch.float32, device=device)
         return StandardTopKOutput(topk_weights, topk_ids, router_logits)

python/sglang/srt/layers/quantization/fp8.py

@@ -10,6 +10,12 @@ import torch.nn.functional as F
 from torch.nn import Module
 from torch.nn.parameter import Parameter
 
+from sglang.srt.distributed import get_tp_group
+from sglang.srt.distributed.device_communicators.pynccl_allocator import (
+    use_symmetric_memory,
+)
+from sglang.srt.layers.dp_attention import is_allocation_symmetric
+
 try:
     from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
         apply_fp8_marlin_linear,
@@ -1033,9 +1039,10 @@ class Fp8MoEMethod(FusedMoEMethodBase):
         if get_moe_runner_backend().is_cutlass():
             from sglang.srt.layers.moe.cutlass_moe import cutlass_fused_experts_fp8
 
-            with use_symmetric_memory(get_tp_group()) as sm:
+            with use_symmetric_memory(
+                get_tp_group(), disabled=not is_allocation_symmetric()
+            ):
                 symm_output = torch.empty_like(x)
-                sm.tag(symm_output)
 
             topk_weights, topk_ids, _ = dispatch_output.topk_output
             output = cutlass_fused_experts_fp8(
@@ -1208,12 +1215,14 @@ class Fp8MoEMethod(FusedMoEMethodBase):
             else topk_config.correction_bias.to(x.dtype)
         )
 
-        with use_symmetric_memory(get_tp_group()) as sm:
+        with use_symmetric_memory(
+            get_tp_group(), disabled=not is_allocation_symmetric()
+        ):
             # FIXME: there is a bug in the trtllm_fp8_block_scale_moe.
             # It ignored the `output`` argument. https://github.com/flashinfer-ai/flashinfer/blob/da01b1bd8f9f22aec8c0eea189ad54860b034947/flashinfer/fused_moe/core.py#L1323-L1325
             # so we put the whole function under the ``use_symmetric_memory`` context manager.
             # If the bug is fixed, we can only put the output tensor allocation under the context manager.
-            output = trtllm_fp8_block_scale_moe(
+            return trtllm_fp8_block_scale_moe(
                 routing_logits=router_logits.to(torch.float32),
                 routing_bias=correction_bias,
                 hidden_states=a_q,
@@ -1238,8 +1247,6 @@ class Fp8MoEMethod(FusedMoEMethodBase):
                 routing_method_type=2,  # DeepSeek-styled routing method
                 use_shuffled_weight=False,
             )
-            sm.tag(output)
-        return output
 
     def maybe_apply_hip_fused_experts(
         self,

python/sglang/srt/layers/quantization/modelopt_quant.py

@@ -11,7 +11,11 @@ from sglang.srt.distributed import get_tp_group
 from sglang.srt.distributed.device_communicators.pynccl_allocator import (
     use_symmetric_memory,
 )
-from sglang.srt.layers.dp_attention import get_dp_global_num_tokens, get_local_dp_buffer
+from sglang.srt.layers.dp_attention import (
+    get_dp_global_num_tokens,
+    get_local_dp_buffer,
+    is_allocation_symmetric,
+)
 from sglang.srt.layers.moe import (
     MoeRunner,
     MoeRunnerBackend,
@@ -663,7 +667,9 @@ class ModelOptFp8MoEMethod(FusedMoEMethodBase):
                 None if correction_bias is None else correction_bias.to(torch.bfloat16)
             )
 
-            with use_symmetric_memory(get_tp_group()) as sm:
+            with use_symmetric_memory(
+                get_tp_group(), disabled=not is_allocation_symmetric()
+            ):
                 # FIXME: there is a bug in the trtllm_fp8_block_scale_moe.
                 # It ignored the `output`` argument. https://github.com/flashinfer-ai/flashinfer/blob/da01b1bd8f9f22aec8c0eea189ad54860b034947/flashinfer/fused_moe/core.py#L1323-L1325
                 # so we put the whole function under the ``use_symmetric_memory`` context manager.
@@ -693,7 +699,6 @@ class ModelOptFp8MoEMethod(FusedMoEMethodBase):
                     tile_tokens_dim=None,
                     routing_method_type=routing_method_type,
                 )
-                sm.tag(output)
 
             from sglang.srt.layers.moe.token_dispatcher import StandardCombineInput
 
@@ -1581,38 +1586,42 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase):
             topk_weights, topk_ids = topk_output.topk_weights, topk_output.topk_ids
 
             output_dtype = x.dtype
+            original_col = x.shape[1]
             x_sf = None
+
             if should_use_flashinfer_cutlass_moe_fp4_allgather():
                 from flashinfer import nvfp4_block_scale_interleave
 
                 # Quantize before comm, swizzle after.
-                if x.shape[0] > 0:
-                    x, x_sf = fp4_quantize_flashinfer(
-                        x, layer.w13_input_scale_quant, is_sf_swizzled_layout=False
-                    )
-                else:
-                    x_col = x.shape[1]
-                    x = torch.zeros(0, x_col // 2, dtype=torch.uint8, device=x.device)
-                    x_sf = torch.zeros(
-                        0, x_col // 16, dtype=torch.uint8, device=x.device
-                    )
+                with use_symmetric_memory(
+                    get_tp_group(), disabled=not is_allocation_symmetric()
+                ):
+                    if x.shape[0] > 0:
+                        x, x_sf = fp4_quantize_flashinfer(
+                            x, layer.w13_input_scale_quant, is_sf_swizzled_layout=False
+                        )
+                    else:
+                        x_col = x.shape[1]
+                        x = torch.zeros(
+                            0, x_col // 2, dtype=torch.uint8, device=x.device
+                        )
+                        x_sf = torch.zeros(
+                            0, x_col // 16, dtype=torch.uint8, device=x.device
+                        )
                 topk_weights, topk_ids, x, x_sf = get_tp_group().all_gatherv(
                     [topk_weights, topk_ids, x, x_sf], sizes=get_dp_global_num_tokens()
                 )
                 x_sf = nvfp4_block_scale_interleave(x_sf)
 
-            with use_symmetric_memory(get_tp_group()) as sm:
-                # The x might be packed in the case of fp4. So, use the output dim of the
-                # weight of the second GEMM.
+            with use_symmetric_memory(
+                get_tp_group(), disabled=not is_allocation_symmetric()
+            ):
                 symm_output = torch.empty(
-                    x.shape[0],
-                    layer.w2_weight.shape[1],
-                    dtype=output_dtype,
-                    device=x.device,
+                    x.shape[0], original_col, dtype=output_dtype, device=x.device
                 )
-                sm.tag(symm_output)
 
             output = flashinfer_cutlass_fused_moe(
+                output=symm_output,
                 input=x,
                 token_selected_experts=topk_ids.to(torch.int),
                 token_final_scales=topk_weights,
@@ -1633,7 +1642,6 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase):
                 tp_size=layer.moe_tp_size,
                 tp_rank=layer.moe_tp_rank,
                 tune_max_num_tokens=next_power_of_2(x.shape[0]),
-                output=symm_output,
             )[0]
             if should_use_flashinfer_cutlass_moe_fp4_allgather():
                 output, global_output = get_local_dp_buffer(), output

python/sglang/srt/layers/quantization/mxfp4.py

@@ -26,6 +26,7 @@ from sglang.srt.distributed import get_tp_group
 from sglang.srt.distributed.device_communicators.pynccl_allocator import (
     use_symmetric_memory,
 )
+from sglang.srt.layers.dp_attention import is_allocation_symmetric
 from sglang.srt.layers.moe import MoeRunner, MoeRunnerBackend, MoeRunnerConfig
 from sglang.srt.layers.moe.moe_runner.triton import TritonMoeQuantInfo
 from sglang.srt.layers.moe.utils import get_moe_runner_backend
@@ -640,10 +641,10 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
             top_k = topk_output.topk_config.top_k
             router_logits = topk_output.router_logits
 
-            with use_symmetric_memory(get_tp_group()) as sm:
+            with use_symmetric_memory(
+                get_tp_group(), disabled=not is_allocation_symmetric()
+            ):
                 symm_output = torch.empty_like(x)
-                sm.tag(symm_output)
-
             trtllm_gen_output = trtllm_fp4_block_scale_moe(
                 router_logits.to(torch.bfloat16),
                 None,  # routing_bias

python/sglang/srt/layers/vocab_parallel_embedding.py

@@ -473,9 +473,8 @@ class VocabParallelEmbedding(torch.nn.Module):
         else:
             masked_input = input_
         # Get the embeddings.
-        with use_symmetric_memory(get_tp_group()) as sm:
+        with use_symmetric_memory(get_tp_group(), disabled=not self.enable_tp):
             output_parallel = self.quant_method.embedding(self, masked_input.long())
-            sm.tag(output_parallel)
         # Mask the output embedding.
         if self.tp_size > 1:
             output_parallel.masked_fill_(input_mask.unsqueeze(-1), 0)

python/sglang/srt/model_executor/cuda_graph_runner.py

@@ -660,7 +660,11 @@ class CudaGraphRunner:
         def run_once():
             # Clean intermediate result cache for DP attention
             forward_batch.dp_local_start_pos = forward_batch.dp_local_num_tokens = None
-            set_dp_buffer_len(global_dp_buffer_len, num_tokens)
+            set_dp_buffer_len(
+                global_dp_buffer_len,
+                num_tokens,
+                forward_batch.dp_padding_mode.is_max_len(),
+            )
             set_is_extend_in_batch(False)
 
             kwargs = {}

python/sglang/srt/model_executor/forward_batch_info.py

@@ -719,7 +719,9 @@ class ForwardBatch:
             num_tokens = global_num_tokens[0]
 
         self.global_dp_buffer_len = buffer_len
-        set_dp_buffer_len(buffer_len, num_tokens, global_num_tokens)
+        set_dp_buffer_len(
+            buffer_len, num_tokens, dp_padding_mode.is_max_len(), global_num_tokens
+        )
         set_is_extend_in_batch(self.is_extend_in_batch)
 
         bs = self.batch_size

python/sglang/srt/models/deepseek_v2.py

@@ -480,7 +480,8 @@ class DeepseekV2MLP(nn.Module):
         gate_up, _ = self.gate_up_proj(x)
         x = self.act_fn(gate_up)
         x, _ = self.down_proj(
-            x, skip_all_reduce=should_allreduce_fusion or use_reduce_scatter
+            x,
+            skip_all_reduce=should_allreduce_fusion or use_reduce_scatter,
         )
         return x
 
@@ -814,7 +815,6 @@ class DeepseekV2MoE(nn.Module):
             final_hidden_states *= self.routed_scaling_factor
         if shared_output is not None:
             final_hidden_states += shared_output
-
         if (
             self.tp_size > 1
             and not should_allreduce_fusion
@@ -883,7 +883,9 @@ class DeepseekV2MoE(nn.Module):
         return final_hidden_states
 
     def forward_deepep(
-        self, hidden_states: torch.Tensor, forward_batch: ForwardBatch
+        self,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
     ) -> torch.Tensor:
         shared_output = None
         if hidden_states.shape[0] > 0:

python/sglang/srt/models/glm4_moe.py

@@ -45,6 +45,7 @@ from sglang.srt.layers.communicator import (
 from sglang.srt.layers.dp_attention import (
     get_attention_tp_rank,
     get_attention_tp_size,
+    is_allocation_symmetric,
     is_dp_attention_enabled,
 )
 from sglang.srt.layers.layernorm import RMSNorm
@@ -482,12 +483,13 @@ class Glm4MoeSparseMoeBlock(nn.Module):
                 final_hidden_states *= self.routed_scaling_factor
 
         current_stream.wait_stream(self.alt_stream)
-        with use_symmetric_memory(parallel_state.get_tp_group()) as sm:
+        with use_symmetric_memory(
+            parallel_state.get_tp_group(), disabled=not is_allocation_symmetric()
+        ):
             final_hidden_states_out = torch.empty_like(final_hidden_states)
 
         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 should_allreduce_fusion
@@ -517,11 +519,12 @@ class Glm4MoeSparseMoeBlock(nn.Module):
             # fused in biased_grouped_topk so we can skip here
             final_hidden_states *= self.routed_scaling_factor
         if shared_output is not None:
-            with use_symmetric_memory(parallel_state.get_tp_group()) as sm:
+            with use_symmetric_memory(
+                parallel_state.get_tp_group(), disabled=not is_allocation_symmetric()
+            ):
                 final_hidden_states_out = torch.empty_like(final_hidden_states)
             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 should_allreduce_fusion

python/sglang/srt/operations.py

@@ -85,6 +85,7 @@ class _StageExecutor:
         self._global_dp_buffer_len = forward_batch.global_dp_buffer_len
         self._local_dp_buffer_len = forward_batch.input_ids.shape[0]
         self._global_num_tokens = forward_batch.global_num_tokens_cpu
+        self._is_dp_max_padding = forward_batch.dp_padding_mode.is_max_len()
 
     def next(self):
         assert not self.done
@@ -95,6 +96,7 @@ class _StageExecutor:
             set_dp_buffer_len(
                 self._global_dp_buffer_len,
                 self._local_dp_buffer_len,
+                self._is_dp_max_padding,
                 self._global_num_tokens,
             )
 

python/sglang/srt/speculative/eagle_draft_cuda_graph_runner.py

@@ -263,7 +263,11 @@ class EAGLEDraftCudaGraphRunner:
         def run_once():
             # Clean intermediate result cache for DP attention
             forward_batch.dp_local_start_pos = forward_batch.dp_local_num_tokens = None
-            set_dp_buffer_len(global_dp_buffer_len, num_tokens)
+            set_dp_buffer_len(
+                global_dp_buffer_len,
+                num_tokens,
+                forward_batch.dp_padding_mode.is_max_len(),
+            )
             set_is_extend_in_batch(False)
 
             # Backup two fields, which will be modified in-place in `draft_forward`.

python/sglang/srt/speculative/eagle_draft_extend_cuda_graph_runner.py

@@ -294,7 +294,11 @@ class EAGLEDraftExtendCudaGraphRunner:
         def run_once():
             # Clean intermediate result cache for DP attention
             forward_batch.dp_local_start_pos = forward_batch.dp_local_num_tokens = None
-            set_dp_buffer_len(global_dp_buffer_len, num_tokens)
+            set_dp_buffer_len(
+                global_dp_buffer_len,
+                num_tokens,
+                forward_batch.dp_padding_mode.is_max_len(),
+            )
             set_is_extend_in_batch(False)
 
             # Backup two fields, which will be modified in-place in `draft_forward`.