[Feature] support clear data (#4185)

* fix

* fix

* fix

* [Feature] support clear data

* update

* fix

* fix

* fix

* fix

fastdeploy/engine/common_engine.py

@@ -751,6 +751,18 @@ class EngineSevice:
     def check_and_free_block_tables(self):
         self.resource_manager.check_and_free_block_tables()
 
+    def clear_data(self):
+        try:
+            llm_logger.info("Clear Data: Start")
+            self.token_processor.clear_data()
+            self.engine_worker_queue.clear_data()
+            self.zmq_server.req_dict.clear()
+            llm_logger.info("Clear Data: Successfully")
+            return True
+        except Exception as e:
+            llm_logger.error(f"Clear data error: {e}")
+            return False
+
     def _exit_sub_services(self):
         """
         exit sub services

fastdeploy/engine/sched/resource_manager_v1.py

@@ -548,3 +548,7 @@ class ResourceManagerV1(ResourceManager):
                     del self.requests[req_id]
         except Exception as e:
             llm_logger.error(f"finish_request err: {e}, {str(traceback.format_exc())}")
+
+    def clear_data(self):
+        self.waiting: deque[Request] = deque()
+        self.to_be_rescheduled_request_id_set = set()

fastdeploy/entrypoints/engine_client.py

@@ -359,3 +359,6 @@ class EngineClient:
             return False, "clear model weight timeout"
         time.sleep(1)
         return True, ""
+
+    def check_model_weight_status(self):
+        return self.model_weights_status_signal.value[0] < 0

fastdeploy/entrypoints/openai/api_server.py

@@ -478,6 +478,7 @@ def reset_scheduler():
 
     if llm_engine is None:
         return Response("Engine not loaded", status_code=500)
+    llm_engine.engine.clear_data()
     llm_engine.engine.scheduler.reset()
     return Response("Scheduler Reset Successfully", status_code=200)
 

fastdeploy/entrypoints/openai/serving_chat.py

@@ -210,6 +210,8 @@ class OpenAIServingChat:
                 decoder_base_url=self.tokenizer_base_url,
             )
             while num_choices > 0:
+                if self.engine_client.check_model_weight_status():
+                    raise ValueError("Engine is clearing model weight")
                 try:
                     response = await asyncio.wait_for(response_queue.get(), timeout=10)
                     current_waiting_time = 0
@@ -425,6 +427,8 @@ class OpenAIServingChat:
                 decoder_base_url=self.tokenizer_base_url,
             )
             while True:
+                if self.engine_client.check_model_weight_status():
+                    return ErrorResponse(code=400, message="Model weight cleared")
                 try:
                     response = await asyncio.wait_for(response_queue.get(), timeout=10)
                     current_waiting_time = 0
@@ -513,6 +517,7 @@ class OpenAIServingChat:
 
         if final_res.get("error_msg") is not None and "Recover" in final_res["error_msg"]:
             choice.finish_reason = "recover_stop"
+
         choices.append(choice)
 
         num_prompt_tokens = len(prompt_token_ids)

fastdeploy/entrypoints/openai/serving_completion.py

@@ -216,6 +216,8 @@ class OpenAIServingCompletion:
             completion_batched_token_ids = [[] for _ in range(num_choices)]
             current_waiting_time = 0
             while num_choices > 0:
+                if self.engine_client.check_model_weight_status():
+                    return ErrorResponse(message="Model weight cleared", code=400)
                 try:
                     response = await asyncio.wait_for(response_queue.get(), timeout=10)
                     current_waiting_time = 0
@@ -270,7 +272,6 @@ class OpenAIServingCompletion:
             return res
         except Exception as e:
             api_server_logger.error(f"Error in completion_full_generator: {e}", exc_info=True)
-            raise
         finally:
             self.engine_client.semaphore.release()
             if dealer is not None:
@@ -333,6 +334,8 @@ class OpenAIServingCompletion:
             )
             current_waiting_time = 0
             while num_choices > 0:
+                if self.engine_client.check_model_weight_status():
+                    raise ValueError("Engine is clearing model weight")
                 try:
                     response = await asyncio.wait_for(response_queue.get(), timeout=10)
                     current_waiting_time = 0

fastdeploy/inter_communicator/engine_worker_queue.py

@@ -392,6 +392,13 @@ class EngineWorkerQueue:
         llm_logger.debug("get tasks from queue success")
         return item
 
+    def clear_data(self):
+        self.lock.acquire()
+        self.tasks[:] = list()
+        self.client_read_flag[:] = [1] * self.num_client
+        self.lock.release()
+        llm_logger.info("clear data for engine worker queue")
+
     def cleanup(self):
         """
         Exit the worker queue gracefully.

fastdeploy/output/token_processor.py

@@ -516,6 +516,31 @@ class TokenProcessor:
                     single_head_acceptance_rate
                 )
 
+    def clear_data(self):
+        if envs.ENABLE_V1_KVCACHE_SCHEDULER:
+            self.resource_manager.clear_data()
+        for i in range(self.cfg.max_num_seqs):
+            if self.resource_manager.stop_flags[i]:
+                continue
+            task = self.resource_manager.tasks_list[i]
+            result = RequestOutput(
+                request_id=task.request_id,
+                outputs=CompletionOutput(
+                    index=i,
+                    send_idx=self.tokens_counter[task.request_id],
+                    token_ids=task.eos_token_ids,
+                    draft_token_ids=[],
+                ),
+                finished=True,
+                metrics=RequestMetrics(
+                    arrival_time=time.time(),
+                    request_start_time=task.arrival_time,
+                ),
+            )
+            is_prefill = task.disaggregate_info is not None and task.disaggregate_info["role"] == "prefill"
+            self._recycle_resources(task.request_id, i, task, result, is_prefill)
+            llm_logger.warning(f"clear data for task {task.request_id}")
+
 
 class WarmUpTokenProcessor(TokenProcessor):
     """

fastdeploy/rl/dynamic_weight_manager.py

@@ -256,6 +256,7 @@ class DynamicWeightManager:
                 model_runner.update_parameters(pid)
             elif model_weights_status.value[0] == -1:
                 logger.info("infer engine stopped! start to clear checkpoint...")
+                model_runner.clear_requests()
                 model_runner.clear_parameters(pid)
 
             while True:

fastdeploy/worker/gcu_model_runner.py

@@ -1199,6 +1199,10 @@ class GCUModelRunner(ModelRunnerBase):
         paddle.device.cuda.empty_cache()
         self.dynamic_weight_manager._log_memory("dynamic weight manager clear all memory")
 
+    def clear_requests(self):
+        """Dynamic model loader use to clear requests use for RL"""
+        self.share_inputs["stop_flags"][:] = True
+
     def update_parameters(self, pid):
         """ " Dynamic model loader use to update parameters use for RL"""
         self.dynamic_weight_manager.update_parameters(pid)

fastdeploy/worker/gpu_model_runner.py

@@ -1729,6 +1729,10 @@ class GPUModelRunner(ModelRunnerBase):
 
         self.dynamic_weight_manager._log_memory("dynamic weight manager clear all memory")
 
+    def clear_requests(self):
+        """Dynamic model loader use to clear requests use for RL"""
+        self.share_inputs["stop_flags"][:] = True
+
     def update_parameters(self, pid):
         """Dynamic model loader use to update parameters use for RL"""
         # Update parameters