refactor(llm): 重构 vLLM 推理任务处理方式，支持多任务处理

- 移除任务队列和单任务处理限制
- 使用 asyncio.run_coroutine_threadsafe() 在后台线程中运行推理任务

cosyvoice/llm/llm_vllm.py

@@ -86,46 +86,35 @@ class VllmQwen2LM(Qwen2LM):
         self.task_token_id = self.sos_eos_token_id + 1
         self.zero_token_id = self.task_token_id + 1
 
-        # 不能直接在同步函数正确的使用 异步的生成器函数，即使使用协程也会对vllm造成崩溃
-        # 使用 queue 的方式，后台线程运行推理任务
-        self.task_queue = queue.Queue()
+        # vllm 的推理任务需要在一个固定的事件循环中，因此启动一个后台线程运行转用于推理任务
         self.loop = asyncio.new_event_loop()
         self.loop_thread = threading.Thread(target=self._run_event_loop, daemon=True)
         self.loop_thread.start()
-        # 运行后台协程，用于处理任务队列中的任务
-        # TODO: 目前只能单任务运行，多任务运行需要对 inference_processor 进行修改
-        asyncio.run_coroutine_threadsafe(self.inference_processor(self.task_queue), self.loop)
 
     def _run_event_loop(self):
         asyncio.set_event_loop(self.loop)
         self.loop.run_forever()
 
-    async def inference_processor(self, task_queue):
-        while True:
-            try:
-                logging.debug(f"inference_processor")
-                out_queue, prompt_token_ids, request_id, stop_token_ids, max_tokens = task_queue.get()
-                sampling_params = SamplingParams(**SAMPLING_PARAMS)
-                sampling_params.stop_token_ids = stop_token_ids or [6561]
-                if max_tokens:
-                    sampling_params.max_tokens = max_tokens
-                async for output in self.llm_engine.generate(
-                        {
-                            "prompt_token_ids": prompt_token_ids,
-                        },
-                        sampling_params=sampling_params,
-                        request_id=request_id or f"{time.time()}",
-                ):
-                    out_queue.put((output.outputs[0], output.finished))
-            except Exception as e:
-                logging.error(f"Error in inference_processor: {e}")
+    async def async_llm_inference(self, out_queue, prompt_token_ids, request_id, stop_token_ids, max_tokens):
+        sampling_params = SamplingParams(**SAMPLING_PARAMS)
+        sampling_params.stop_token_ids = stop_token_ids or [6561]
+        if max_tokens:
+            sampling_params.max_tokens = max_tokens
+        async for output in self.llm_engine.generate(
+                {
+                    "prompt_token_ids": prompt_token_ids,
+                },
+                sampling_params=sampling_params,
+                request_id=request_id or f"{time.time()}",
+        ):
+            out_queue.put((output.outputs[0], output.finished))
 
     def llm_inference(self, prompt_token_ids: List[int], request_id: str=None, stop_token_ids=None, max_tokens=None):
-        # 使用 同步转异步 会导致vllm崩溃，目前选择 queue 的方式，后台线程运行推理任务
-        # 提交推理任务到队列中
         out_queue = queue.Queue()
-        self.task_queue.put((out_queue, prompt_token_ids, request_id, stop_token_ids, max_tokens))
-        # 将 out_queue 的结果返回
+        asyncio.run_coroutine_threadsafe(
+            self.async_llm_inference(out_queue, prompt_token_ids, request_id, stop_token_ids, max_tokens), self.loop
+        )
+        # 接收 out_queue 返回的结果
         finished = False
         while not finished:
             (output, finished) = out_queue.get_nowait() if not out_queue.empty() else out_queue.get()