[Bug]: RuntimeError: aclose(): asynchronous generator is already running

[highheart]

Your current environment

The output of `python collect_env.py`
PyTorch version: 2.3.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.3 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: 14.0.0-1ubuntu1.1
CMake version: version 3.30.0
Libc version: glibc-2.35

Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-5.4.0-48-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.3.107
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA A800-SXM4-80GB
GPU 1: NVIDIA A800-SXM4-80GB
GPU 2: NVIDIA A800-SXM4-80GB
GPU 3: NVIDIA A800-SXM4-80GB
GPU 4: NVIDIA A800-SXM4-80GB
GPU 5: NVIDIA A800-SXM4-80GB
GPU 6: NVIDIA A800-SXM4-80GB
GPU 7: NVIDIA A800-SXM4-80GB

Nvidia driver version: 535.129.03
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.0.0
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                    x86_64
CPU op-mode(s):                  32-bit, 64-bit
Address sizes:                   46 bits physical, 57 bits virtual
Byte Order:                      Little Endian
CPU(s):                          128
On-line CPU(s) list:             0-127
Vendor ID:                       GenuineIntel
Model name:                      Intel(R) Xeon(R) Platinum 8358 CPU @ 2.60GHz
CPU family:                      6
Model:                           106
Thread(s) per core:              2
Core(s) per socket:              32
Socket(s):                       2
Stepping:                        6
Frequency boost:                 enabled
CPU max MHz:                     3400.0000
CPU min MHz:                     800.0000
BogoMIPS:                        5200.00
Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 invpcid_single ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid md_clear pconfig flush_l1d arch_capabilities
Virtualization:                  VT-x
L1d cache:                       3 MiB (64 instances)
L1i cache:                       2 MiB (64 instances)
L2 cache:                        80 MiB (64 instances)
L3 cache:                        96 MiB (2 instances)
NUMA node(s):                    2
NUMA node0 CPU(s):               0-31,64-95
NUMA node1 CPU(s):               32-63,96-127
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:        Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:        Mitigation; Enhanced IBRS, IBPB conditional, RSB filling
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] torch==2.3.0
[pip3] torchvision==0.18.0
[pip3] transformers==4.42.3
[pip3] triton==2.3.0
[pip3] triton-nightly==3.0.0.post20240626041721
[conda] Could not collect
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.5.1
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0    GPU1    GPU2    GPU3    GPU4    GPU5    GPU6    GPU7    NIC0    NIC1    NIC2    NIC3    NIC4    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      NV8     NV8     NV8     NV8     NV8     NV8     NV8     PXB     SYS     SYS     SYS     SYS     0-31,64-95      0               N/A
GPU1    NV8      X      NV8     NV8     NV8     NV8     NV8     NV8     PXB     SYS     SYS     SYS     SYS     0-31,64-95      0               N/A
GPU2    NV8     NV8      X      NV8     NV8     NV8     NV8     NV8     SYS     PXB     SYS     SYS     SYS     0-31,64-95      0               N/A
GPU3    NV8     NV8     NV8      X      NV8     NV8     NV8     NV8     SYS     PXB     SYS     SYS     SYS     0-31,64-95      0               N/A
GPU4    NV8     NV8     NV8     NV8      X      NV8     NV8     NV8     SYS     SYS     PXB     SYS     SYS     32-63,96-127    1               N/A
GPU5    NV8     NV8     NV8     NV8     NV8      X      NV8     NV8     SYS     SYS     PXB     SYS     SYS     32-63,96-127    1               N/A
GPU6    NV8     NV8     NV8     NV8     NV8     NV8      X      NV8     SYS     SYS     SYS     PXB     SYS     32-63,96-127    1               N/A
GPU7    NV8     NV8     NV8     NV8     NV8     NV8     NV8      X      SYS     SYS     SYS     PXB     SYS     32-63,96-127    1               N/A
NIC0    PXB     PXB     SYS     SYS     SYS     SYS     SYS     SYS      X      SYS     SYS     SYS     SYS
NIC1    SYS     SYS     PXB     PXB     SYS     SYS     SYS     SYS     SYS      X      SYS     SYS     SYS
NIC2    SYS     SYS     SYS     SYS     PXB     PXB     SYS     SYS     SYS     SYS      X      SYS     SYS
NIC3    SYS     SYS     SYS     SYS     SYS     SYS     PXB     PXB     SYS     SYS     SYS      X      SYS
NIC4    SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS      X 

Legend:

  X    = Self
  SYS  = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
  NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
  PHB  = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
  PXB  = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
  PIX  = Connection traversing at most a single PCIe bridge
  NV#  = Connection traversing a bonded set of # NVLinks

NIC Legend:

  NIC0: mlx5_0
  NIC1: mlx5_1
  NIC2: mlx5_4
  NIC3: mlx5_5
  NIC4: mlx5_bond_0

🐛 Describe the bug

my code (Some unimportant code was removed):

import asyncio
import json
import os
import threading
from typing import AsyncGenerator

import numpy as np
import triton_python_backend_utils as pb_utils
from vllm import SamplingParams
from vllm.engine.arg_utils import AsyncEngineArgs
from vllm.engine.async_llm_engine import AsyncLLMEngine
from vllm.utils import random_uuid
import sys

sys.path.append("/model")

_VLLM_ENGINE_ARGS_FILENAME = "model.json"


class TritonPythonModel:
    @staticmethod
    def auto_complete_config(auto_complete_model_config):
        '''init params'''

        return auto_complete_model_config

    def initialize(self, args):
        self.logger = pb_utils.Logger
        self.model_config = json.loads(args["model_config"])

        # assert are in decoupled mode. Currently, Triton needs to use
        # decoupled policy for asynchronously forwarding requests to
        # vLLM engine.
        self.using_decoupled = pb_utils.using_decoupled_model_transaction_policy(
            self.model_config
        )
        assert (
            self.using_decoupled
        ), "vLLM Triton backend must be configured to use decoupled model transaction policy"

        engine_args_filepath = os.path.join(
            pb_utils.get_model_dir(), _VLLM_ENGINE_ARGS_FILENAME
        )
        with open(engine_args_filepath) as file:
            vllm_engine_config = json.load(file)

        # Create an AsyncLLMEngine from the config from JSON
        self.logger.log_info(f"engine_args: {vllm_engine_config}")
        self.llm_engine = AsyncLLMEngine.from_engine_args(
            AsyncEngineArgs(**vllm_engine_config)
        )

        output_config = pb_utils.get_output_config_by_name(
            self.model_config, "text_output"
        )
        self.output_dtype = pb_utils.triton_string_to_numpy(output_config["data_type"])

        # Counter to keep track of ongoing request counts
        self.ongoing_request_count = 0

        # Starting asyncio event loop to process the received requests asynchronously.
        self._loop = asyncio.get_event_loop()
        self._loop_thread = threading.Thread(
            target=self.engine_loop, args=(self._loop,)
        )
        self._shutdown_event = asyncio.Event()
        self._loop_thread.start()

    def create_task(self, coro):
        """
        Creates a task on the engine's event loop which is running on a separate thread.
        """
        assert (
                self._shutdown_event.is_set() is False
        ), "Cannot create tasks after shutdown has been requested"

        return asyncio.run_coroutine_threadsafe(coro, self._loop)

    def engine_loop(self, loop):
        """
        Runs the engine's event loop on a separate thread.
        """
        asyncio.set_event_loop(loop)
        self._loop.run_until_complete(self.await_shutdown())

    async def await_shutdown(self):
        """
        Primary coroutine running on the engine event loop. This coroutine is responsible for
        keeping the engine alive until a shutdown is requested.
        """
        # first await the shutdown signal
        while self._shutdown_event.is_set() is False:
            await asyncio.sleep(5)

        # Wait for the ongoing_requests
        while self.ongoing_request_count > 0:
            self.logger.log_info(
                "[vllm] Awaiting remaining {} requests".format(
                    self.ongoing_request_count
                )
            )
            await asyncio.sleep(5)

        for task in asyncio.all_tasks(loop=self._loop):
            if task is not asyncio.current_task():
                task.cancel()

        self.logger.log_info("[vllm] Shutdown complete")

    def get_sampling_params_dict(self, params_json):
        """
        This functions parses the dictionary values into their
        expected format.
        """

    def create_response(self, vllm_output, text_len=0):
        """
        Parses the output from the vLLM engine into Triton
        response.
        """
        prompt = vllm_output.prompt
        text_outputs = []
        for output in vllm_output.outputs:
            output_text = prompt + output.text
            text_outputs.append(output_text[text_len:].encode("utf-8"))
            text_len = len(output_text)
        triton_output_tensor = pb_utils.Tensor(
            "text_output", np.asarray(text_outputs, dtype=self.output_dtype)
        )
        model_version_tensor = pb_utils.Tensor(
            "wps_model_version", np.asarray([self.model_version.encode("utf-8")], dtype=self.model_version_dtype)
        )
        return pb_utils.InferenceResponse(output_tensors=[triton_output_tensor, model_version_tensor]), text_len

    async def generate(self, request):
        """
        Forwards single request to LLM engine and returns responses.
        """
        response_sender = request.get_response_sender()
        self.ongoing_request_count += 1
        try:
            prompt = pb_utils.get_input_tensor_by_name(
                request, "PROMPTS"
            ).as_numpy()[0]
            if isinstance(prompt, bytes):
                prompt = prompt.decode("utf-8")

            # Request parameters are not yet supported via
            # BLS. Provide an optional mechanism to receive serialized
            # parameters as an input tensor until support is added

            parameters_input_tensor = pb_utils.get_input_tensor_by_name(
                request, "sampling_parameters"
            )
            if parameters_input_tensor:
                parameters = parameters_input_tensor.as_numpy()[0].decode("utf-8")
            else:
                parameters = request.parameters()

            sampling_params_dict = self.get_sampling_params_dict(parameters)
            sampling_params = SamplingParams(**sampling_params_dict)

            last_output = None
            stream_start_index = 0
            async for output in self.llm_engine.generate(
                    prompt, sampling_params
            ):
                if response_sender.is_cancelled():
                    self.logger.log_info("[vllm] Cancelling the request")
                    await self.llm_engine.abort(request_id)
                    self.logger.log_info("[vllm] Successfully cancelled the request")
                    break
                if stream:
                    response, output_len = self.create_response(output, stream_start_index)
                    response_sender.send(response)
                    stream_start_index = output_len
                else:
                    last_output = output

            if not stream:
                response_sender.send(self.create_response(last_output)[0])

        except Exception as e:
            self.logger.log_info(f"[vllm] Error generating stream: {e}")
            error = pb_utils.TritonError(f"Error generating stream: {e}")
            triton_output_tensor = pb_utils.Tensor(
                "text_output", np.asarray(["N/A"], dtype=self.output_dtype)
            )
            response = pb_utils.InferenceResponse(
                output_tensors=[triton_output_tensor], error=error
            )
            response_sender.send(response)
            raise e
        finally:
            response_sender.send(flags=pb_utils.TRITONSERVER_RESPONSE_COMPLETE_FINAL)
            self.ongoing_request_count -= 1

    def execute(self, requests):
        """
        Triton core issues requests to the backend via this method.

        When this method returns, new requests can be issued to the backend. Blocking
        this function would prevent the backend from pulling additional requests from
        Triton into the vLLM engine. This can be done if the kv cache within vLLM engine
        is too loaded.
        We are pushing all the requests on vllm and let it handle the full traffic.
        """
        for request in requests:
            self.create_task(self.generate(request))
        return None

    def finalize(self):
        """
        Triton virtual method; called when the model is unloaded.
        """
        self.logger.log_info("[vllm] Issuing finalize to vllm backend")
        self._shutdown_event.set()
        if self._loop_thread is not None:
            self._loop_thread.join()
            self._loop_thread = None

The following error occurs occasionally during the call：

ERROR:asyncio:Task exception was never retrieved
future: <Task finished name='Task-424643' coro=<<async_generator_athrow without __name__>()> exception=RuntimeError('aclose(): asynchronous generator is already running')>
RuntimeError: aclose(): asynchronous generator is already running

ERROR:asyncio:Task was destroyed but it is pending!
task: <Task pending name='Task-424498' coro=<TritonPythonModel.generate() done, defined at /triton_deploy/model_common.py:278> wait_for=<Future pending cb=[Task.task_wakeup()]> cb=[_chain_future.<locals>._call_set_state() at /usr/lib/python3.10/asyncio/futures.py:392]>

ERROR:asyncio:Task exception was never retrieved
future: <Task finished name='Task-424644' coro=<<async_generator_athrow without __name__>()> exception=RuntimeError('aclose(): asynchronous generator is already running')>
RuntimeError: aclose(): asynchronous generator is already running

Then the error is as follows：

ERROR 07-10 13:59:59 async_llm_engine.py:53] Engine background task failed
ERROR 07-10 13:59:59 async_llm_engine.py:53] Traceback (most recent call last):
ERROR 07-10 13:59:59 async_llm_engine.py:53]   File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 43, in _log_task_completion
ERROR 07-10 13:59:59 async_llm_engine.py:53]     return_value = task.result()
ERROR 07-10 13:59:59 async_llm_engine.py:53]   File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 595, in run_engine_loop
ERROR 07-10 13:59:59 async_llm_engine.py:53]     result = task.result()
ERROR 07-10 13:59:59 async_llm_engine.py:53]   File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 540, in engine_step
ERROR 07-10 13:59:59 async_llm_engine.py:53]     request_outputs = await self.engine.step_async(virtual_engine)
ERROR 07-10 13:59:59 async_llm_engine.py:53]   File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 251, in step_async
ERROR 07-10 13:59:59 async_llm_engine.py:53]     self.do_log_stats(scheduler_outputs, output)
ERROR 07-10 13:59:59 async_llm_engine.py:53]   File "/usr/local/lib/python3.10/dist-packages/vllm/engine/llm_engine.py", line 903, in do_log_stats
ERROR 07-10 13:59:59 async_llm_engine.py:53]     logger.log(self._get_stats(scheduler_outputs, model_output))
ERROR 07-10 13:59:59 async_llm_engine.py:53]   File "/usr/local/lib/python3.10/dist-packages/vllm/engine/metrics.py", line 429, in log
ERROR 07-10 13:59:59 async_llm_engine.py:53]     self._log_prometheus(stats)
ERROR 07-10 13:59:59 async_llm_engine.py:53]   File "/usr/local/lib/python3.10/dist-packages/vllm/engine/metrics.py", line 386, in _log_prometheus
ERROR 07-10 13:59:59 async_llm_engine.py:53]     self._log_counter(self.metrics.counter_generation_tokens,
ERROR 07-10 13:59:59 async_llm_engine.py:53]   File "/usr/local/lib/python3.10/dist-packages/vllm/engine/metrics.py", line 354, in _log_counter
ERROR 07-10 13:59:59 async_llm_engine.py:53]     counter.labels(**self.labels).inc(data)
ERROR 07-10 13:59:59 async_llm_engine.py:53]   File "/usr/local/lib/python3.10/dist-packages/prometheus_client/metrics.py", line 313, in inc
ERROR 07-10 13:59:59 async_llm_engine.py:53]     raise ValueError('Counters can only be incremented by non-negative amounts.')
ERROR 07-10 13:59:59 async_llm_engine.py:53] ValueError: Counters can only be incremented by non-negative amounts.
ERROR:asyncio:Exception in callback _log_task_completion(error_callback=<bound method...7f351ecc6170>>)(<Task finishe...ve amounts.')>) at /usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py:33

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