[bugfix]fix extra npu context in device 0 (#8041)

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### What this PR does / why we need it?
When we launch a PD-disaggregated process and send requests, an
additional processes appear on NPU 0, becasue when a thread has a
primary cuda context, the child thread it creates automatically doesn't
inherit the cuda context. See
https://forums.developer.nvidia.com/t/when-a-thread-has-a-primary-cuda-context-does-the-child-thread-it-creates-automatically-inherit-the-cuda-context/362810.
vLLM has fixed this issue in [pr-37449
](https://github.com/vllm-project/vllm/pull/37449), but version 0.18.0
does not include the fix. Therefore, we need to patch it.
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and bug description.

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### Does this PR introduce _any_ user-facing change?
no
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### How was this patch tested?
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---------

Signed-off-by: zouyida <zouyida@huawei.com>
Co-authored-by: zouyida <zouyida@huawei.com>

vllm_ascend/patch/platform/__init__.py

@@ -14,8 +14,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import os
-
 import vllm_ascend.patch.platform.patch_distributed  # noqa
 import vllm_ascend.patch.platform.patch_fusion_matcher_compat_ops  # noqa
 import vllm_ascend.patch.platform.patch_kv_cache_interface  # noqa
@@ -27,13 +25,11 @@ if not is_310p():
 else:
     import vllm_ascend.patch.platform.patch_mamba_config_310  # noqa
 import vllm_ascend.patch.platform.patch_minimax_m2_config  # noqa
+import vllm_ascend.patch.platform.patch_multiproc_executor  # noqa
 import vllm_ascend.patch.platform.patch_sched_yield  # noqa
 import vllm_ascend.patch.platform.patch_torch_accelerator  # noqa
 import vllm_ascend.patch.platform.patch_minimax_usage_accounting  # noqa
 import vllm_ascend.patch.platform.patch_glm_tool_call_parser  # noqa
 
-if os.getenv("DYNAMIC_EPLB", "false").lower() in ("true", "1") or os.getenv("EXPERT_MAP_RECORD", "false") == "true":
-    import vllm_ascend.patch.platform.patch_multiproc_executor  # noqa
-
 if envs.VLLM_ASCEND_BALANCE_SCHEDULING:
     import vllm_ascend.patch.platform.patch_balance_schedule  # noqa

vllm_ascend/patch/platform/patch_multiproc_executor.py

@@ -1,14 +1,20 @@
 from __future__ import annotations
 
+import os
+import queue
 import weakref
 from collections import deque
 from collections.abc import Callable
 from multiprocessing.synchronize import Lock as LockType
+from threading import Thread
 
 import vllm.v1.executor.multiproc_executor
 from vllm import envs
 from vllm.config import VllmConfig
 from vllm.distributed.device_communicators.shm_broadcast import Handle, MessageQueue
+from vllm.envs import enable_envs_cache
+from vllm.platforms import current_platform
+from vllm.tracing import instrument
 from vllm.utils.network_utils import get_distributed_init_method, get_loopback_ip, get_open_port
 from vllm.utils.system_utils import get_mp_context
 from vllm.v1.executor.abstract import FailureCallback
@@ -19,6 +25,7 @@ from vllm.v1.executor.multiproc_executor import (
     WorkerProc,
     set_multiprocessing_worker_envs,
 )
+from vllm.v1.worker.worker_base import WorkerWrapperBase
 
 
 class AscendMultiprocExecutor(MultiprocExecutor):
@@ -159,6 +166,79 @@ class AscendMultiprocExecutor(MultiprocExecutor):
 
 
 class AscendWorkerProc(WorkerProc):
+    @instrument(span_name="Worker init")
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        local_rank: int,
+        rank: int,
+        distributed_init_method: str,
+        input_shm_handle: Handle,
+        shared_worker_lock: LockType,
+        is_driver_worker: bool,
+    ):
+        self.rank = rank
+        wrapper = WorkerWrapperBase(rpc_rank=local_rank, global_rank=rank)
+        # TODO: move `init_worker` to executor level as a collective rpc call
+        all_kwargs: list[dict] = [{} for _ in range(vllm_config.parallel_config.world_size)]
+        all_kwargs[local_rank] = {
+            "vllm_config": vllm_config,
+            "local_rank": local_rank,
+            "rank": rank,
+            "distributed_init_method": distributed_init_method,
+            "is_driver_worker": is_driver_worker,
+            "shared_worker_lock": shared_worker_lock,
+        }
+        wrapper.init_worker(all_kwargs)
+        self.worker = wrapper
+
+        self.setup_proc_title_and_log_prefix(enable_ep=vllm_config.parallel_config.enable_expert_parallel)
+
+        # Load model
+        is_eep_new_worker = envs.VLLM_ELASTIC_EP_SCALE_UP_LAUNCH
+        if not is_eep_new_worker:
+            self.worker.init_device()
+            # Update process title now that parallel groups are initialized
+            self.setup_proc_title_and_log_prefix(enable_ep=vllm_config.parallel_config.enable_expert_parallel)
+            self.worker.load_model()
+
+        scheduler_config = vllm_config.scheduler_config
+        self.use_async_scheduling = scheduler_config.async_scheduling
+        if self.use_async_scheduling:
+            self.async_output_queue: queue.Queue = queue.Queue()
+            self.async_output_copy_thread = Thread(
+                target=self.async_output_busy_loop,
+                daemon=True,
+                name="WorkerAsyncOutputCopy",
+            )
+            self.async_output_copy_thread.start()
+
+        # Set block size based on the attention backends
+        current_platform.update_block_size_for_backend(vllm_config)
+
+        # Initialize message queues after init_device() since multi-node setups
+        # (nnodes_within_dp > 1) require distributed groups to be initialized
+        self._init_message_queues(input_shm_handle, vllm_config)
+
+        # Enable environment variable cache (e.g. assume no more
+        # environment variable overrides after this point)
+        enable_envs_cache()
+
+    @staticmethod
+    def worker_main(*args, **kwargs):
+        from vllm_ascend.utils import adapt_patch
+
+        adapt_patch(is_global_patch=True)
+        WorkerProc.worker_main(*args, **kwargs)
+
+    def async_output_busy_loop(self):
+        """Entrypoint for the thread which handles outputs asynchronously."""
+        if hasattr(self.worker, "device"):
+            current_platform.set_device(self.worker.device)
+        while True:
+            output = self.async_output_queue.get()
+            self.enqueue_output(output)
+
     @staticmethod
     def make_worker_process(
         vllm_config: VllmConfig,
@@ -192,11 +272,15 @@ class AscendWorkerProc(WorkerProc):
             "inherited_fds": inherited_fds if inherited_fds is not None else [],
         }
         # Run EngineCore busy loop in background process.
+        daemon_mode = not (
+            os.getenv("DYNAMIC_EPLB", "false").lower() in ("true", "1")
+            or os.getenv("EXPERT_MAP_RECORD", "false") == "true"
+        )
         proc = context.Process(
-            target=WorkerProc.worker_main,
+            target=AscendWorkerProc.worker_main,
             kwargs=process_kwargs,
             name=f"VllmWorker-{rank}",
-            daemon=False,
+            daemon=daemon_mode,
         )
 
         proc.start()
@@ -209,3 +293,4 @@ class AscendWorkerProc(WorkerProc):
 
 
 vllm.v1.executor.multiproc_executor.MultiprocExecutor = AscendMultiprocExecutor
+vllm.v1.executor.multiproc_executor.WorkerProc = AscendWorkerProc