[LLM] update function name (#2985)

* [LLM] update function name

fastdeploy/worker/gcu_model_runner.py

@@ -105,9 +105,9 @@ class GCUModelRunner(ModelRunnerBase):
             self.local_rank + int(self.parallel_config.engine_worker_queue_port)
         )
 
-    def prefill_finished(self):
+    def exist_prefill(self):
         """
-        check whether prefill stage finished
+        check whether prefill stage exist
         """
         if int(paddle.max(self.share_inputs["seq_lens_encoder"])) != 0:
             return 1

fastdeploy/worker/gcu_worker.py

@@ -69,11 +69,11 @@ class GcuWorker(WorkerBase):
             local_rank=self.local_rank,
         )
 
-    def prefill_finished(self):
+    def exist_prefill(self):
         """
-        check whether prefill stage finished
+        check whether prefill stage exist
         """
-        return self.model_runner.prefill_finished()
+        return self.model_runner.exist_prefill()
 
     def determine_available_memory(self) -> int:
         """

fastdeploy/worker/gpu_model_runner.py

@@ -148,9 +148,9 @@ class GPUModelRunner(ModelRunnerBase):
             self.local_rank + int(self.parallel_config.engine_worker_queue_port)
         )
 
-    def prefill_finished(self):
+    def exist_prefill(self):
         """
-        Check whether prefill stage finished
+        check whether prefill stage exist
         """
         if int(paddle.max(self.share_inputs["seq_lens_encoder"])) != 0:
             return 1

fastdeploy/worker/gpu_worker.py

@@ -78,11 +78,11 @@ class GpuWorker(WorkerBase):
             local_rank=self.local_rank,
         )
 
-    def prefill_finished(self):
+    def exist_prefill(self):
         """
-        Check whether prefill stage finished
+        check whether prefill stage exist
         """
-        return self.model_runner.prefill_finished()
+        return self.model_runner.exist_prefill()
 
     def determine_available_memory(self) -> int:
         """

fastdeploy/worker/iluvatar_model_runner.py

@@ -105,9 +105,9 @@ class IluvatarModelRunner(ModelRunnerBase):
             self.local_rank + int(self.parallel_config.engine_worker_queue_port)
         )
 
-    def prefill_finished(self):
+    def exist_prefill(self):
         """
-        check whether prefill stage finished
+        check whether prefill stage exist
         """
         if int(paddle.max(self.share_inputs["seq_lens_encoder"])) != 0:
             return 1

fastdeploy/worker/iluvatar_worker.py

@@ -69,11 +69,11 @@ class IluvatarWorker(WorkerBase):
             local_rank=self.local_rank,
         )
 
-    def prefill_finished(self):
+    def exist_prefill(self):
         """
-        check whether prefill stage finished
+        check whether prefill stage exist
         """
-        return self.model_runner.prefill_finished()
+        return self.model_runner.exist_prefill()
 
     def determine_available_memory(self) -> int:
         """

fastdeploy/worker/worker_base.py

@@ -96,6 +96,6 @@ class WorkerBase(ABC):
         """Basic health check (override for device-specific checks)."""
         return NotImplementedError
 
-    def prefill_finished(self):
+    def exist_prefill(self):
-        """check whether prefill stage finished."""
+        """check whether prefill stage exist."""
         return True

fastdeploy/worker/worker_process.py

@@ -286,7 +286,7 @@ class PaddleDisWorkerProc:
             if self.local_rank % mp_num_per_node == 0:
                 if self.task_queue.num_tasks() > 0:
                     # VL only support 1 batch to prefill
-                    if not self.fd_config.model_config.enable_mm or not self.worker.prefill_finished():
+                    if not self.fd_config.model_config.enable_mm or not self.worker.exist_prefill():
                         if self.nnode > 1:
                             self.task_queue.read_finish_flag.set(1)
                         else:
@@ -346,7 +346,7 @@ class PaddleDisWorkerProc:
             # Execute model to generate token. The generated token will be written to the buffer.
             # These generated tokens can be obtained through get_output op.
             self.worker.execute_model(req_dicts)
-            self.exist_prefill_task_signal.value[0] = self.worker.prefill_finished()
+            self.exist_prefill_task_signal.value[0] = self.worker.exist_prefill()
 
     def initialize_kv_cache(self) -> None:
         """Profiles the peak memory usage of the model to determine how many

fastdeploy/worker/xpu_model_runner.py

@@ -584,9 +584,9 @@ class XPUModelRunner(ModelRunnerBase):
         logger.warn("XPU not support cuda graph currently")
         pass
 
-    def prefill_finished(self):
+    def exist_prefill(self):
         """
-        check whether prefill stage finished
+        check whether prefill stage exist
         """
         if int(paddle.max(self.share_inputs["seq_lens_encoder"])) != 0:
             return 1

fastdeploy/worker/xpu_worker.py

@@ -143,11 +143,11 @@ class XpuWorker(WorkerBase):
         output = self.model_runner.execute_model(model_forward_batch)
         return output
 
-    def prefill_finished(self):
+    def exist_prefill(self):
         """
-        check whether prefill stage finished
+        check whether prefill stage exist
         """
-        return self.model_runner.prefill_finished()
+        return self.model_runner.exist_prefill()
 
     def preprocess_new_task(self, req_dicts: List[Request]) -> None:
         """Process new requests and then start the decode loop