大改

vllm_npu/patch/worker/patch_distributed.py

@@ -0,0 +1,115 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# This file is a part of the vllm-ascend project.
+#
+# 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 List, Optional, Union
+
+import torch
+import vllm
+from torch.distributed import Backend
+from vllm.distributed.parallel_state import (GroupCoordinator,
+                                             _get_unique_name, _register_group)
+
+from vllm_npu.distributed.communicator import NPUCommunicator
+from vllm_npu.utils import create_hccl_pg_options
+
+
+class GroupCoordinatorPatch(GroupCoordinator):
+
+    def __init__(
+        self,
+        group_ranks: list[list[int]],
+        local_rank: int,
+        torch_distributed_backend: Union[str, Backend],
+        use_device_communicator: bool,  # whether to use device communicator
+        use_message_queue_broadcaster: bool = False,
+        group_name: Optional[str] = None,
+    ):
+        group_name = group_name or "anonymous"
+        self.unique_name = _get_unique_name(group_name)
+        _register_group(self)
+
+        self.rank = torch.distributed.get_rank()
+        self.local_rank = local_rank
+
+        self_device_group = None
+        self_cpu_group = None
+        hccl_pg_options = create_hccl_pg_options(group_name)
+
+        for ranks in group_ranks:
+            device_group = torch.distributed.new_group(
+                ranks,
+                backend=torch_distributed_backend,
+                pg_options=hccl_pg_options)
+
+            # a group with `gloo` backend, to allow direct coordination between
+            # processes through the CPU.
+            cpu_group = torch.distributed.new_group(ranks, backend="gloo")
+            if self.rank in ranks:
+                self.ranks = ranks
+                self.world_size = len(ranks)
+                self.rank_in_group = ranks.index(self.rank)
+                self_device_group = device_group
+                self_cpu_group = cpu_group
+
+        assert self_cpu_group is not None
+        assert self_device_group is not None
+
+        self.cpu_group = self_cpu_group
+        self.device_group = self_device_group
+        self.device = torch.npu.current_device()
+
+        self.use_device_communicator = use_device_communicator
+        self.device_communicator = None
+        if use_device_communicator and self.world_size > 1:
+            self.device_communicator = NPUCommunicator(
+                cpu_group=self.cpu_group,
+                device=self.device,
+                device_group=self.device_group,
+                unique_name=self.unique_name,
+            )
+
+        from vllm.distributed.device_communicators.shm_broadcast import \
+            MessageQueue
+        self.mq_broadcaster: Optional[MessageQueue] = None
+        if use_message_queue_broadcaster and self.world_size > 1:
+            self.mq_broadcaster = MessageQueue.create_from_process_group(
+                self.cpu_group, 1 << 22, 6)
+
+        self.use_custom_op_call = False
+        self.use_cpu_custom_send_recv = False
+
+    def all_to_all(self,
+                   input_: torch.Tensor,
+                   scatter_dim: int = 0,
+                   gather_dim: int = -1,
+                   scatter_sizes: Optional[List[int]] = None,
+                   gather_sizes: Optional[List[int]] = None) -> torch.Tensor:
+        if self.world_size == 1:
+            return input_
+        assert -input_.dim() <= scatter_dim < input_.dim(), (
+            f"Invalid scatter dim ({scatter_dim}) for input tensor with shape {input_.size()}"
+        )
+        assert -input_.dim() <= gather_dim < input_.dim(), (
+            f"Invalid gather dim ({gather_dim}) for input tensor with shape {input_.size()}"
+        )
+        assert self.device_communicator is not None, "device_communicator should be initialized when world_size > 1"
+        return self.device_communicator.all_to_all(input_, scatter_dim,
+                                                   gather_dim, scatter_sizes,
+                                                   gather_sizes)
+
+
+vllm.distributed.parallel_state.GroupCoordinator = GroupCoordinatorPatch