[Executor] Adjust signal sending order in RL training (#3773) (#4066) (#4178)

* Adjust processing order

* fix bug

* fix update_parameters bug

* refine code

fastdeploy/model_executor/graph_optimization/cudagraph_piecewise_backend.py

@@ -18,6 +18,7 @@ from contextlib import contextmanager
 from dataclasses import dataclass
 from typing import Callable, Dict, Optional
 
+import paddle.jit.dy2static.utils as jit_utils
 import paddle.nn.layer
 from paddle.device.cuda import graphs
 
@@ -51,27 +52,24 @@ class ConcreteSizeEntry:
 
 class Dy2StCudaGraphManager:
     def __init__(self):
-        # NOTE(gongshaotian): Use local import to avoid RLHF version problems
-        from paddle.jit.dy2static.utils import CUDAGraphState
 
-        self.state = CUDAGraphState.DISABLE
+        self.state = jit_utils.CUDAGraphState.DISABLE
         self.captured_batch_size = set()
         self.batch_size = -1
 
     def run_impl(self, original_run_impl, inputs, parameters, attrs):
-        from paddle.jit.dy2static.utils import CUDAGraphState
 
         run_state = self.state
         prog_attrs, cuda_graph_attrs = attrs
-        if run_state == CUDAGraphState.REPLAY:
+        if run_state == jit_utils.CUDAGraphState.REPLAY:
             if self.batch_size not in self.captured_batch_size:
-                run_state = CUDAGraphState.DISABLE
-        elif run_state == CUDAGraphState.CAPTURE:
+                run_state = jit_utils.CUDAGraphState.DISABLE
+        elif run_state == jit_utils.CUDAGraphState.CAPTURE:
             self.captured_batch_size.add(self.batch_size)
 
         cuda_graph_attrs |= {
             "cuda_graph_state": run_state,
-            "cuda_graph_dispatch_key": self.batch_size if run_state != CUDAGraphState.DISABLE else 0,
+            "cuda_graph_dispatch_key": self.batch_size if run_state != jit_utils.CUDAGraphState.DISABLE else 0,
         }
         return original_run_impl(inputs, parameters, (prog_attrs, cuda_graph_attrs))
 
@@ -104,7 +102,6 @@ class CudaGraphPiecewiseBackend:
             self.cuda_graph_manager = Dy2StCudaGraphManager()
 
     def run_static_model(self, entry: ConcreteSizeEntry, **kwargs):
-        from paddle.jit.dy2static.utils import CUDAGraphState
 
         if not entry.captured:
             # Warmup the model
@@ -121,14 +118,14 @@ class CudaGraphPiecewiseBackend:
             entry.input_addresses = input_addresses
 
             # Capture
-            self.cuda_graph_manager.state = CUDAGraphState.CAPTURE
+            self.cuda_graph_manager.state = jit_utils.CUDAGraphState.CAPTURE
             self.cuda_graph_manager.batch_size = entry.real_shape
             entry.captured = True
             with self.cuda_graph_manager.run_impl_guard():
                 entry.runnable(**kwargs)
 
         # Replay
-        self.cuda_graph_manager.state = CUDAGraphState.REPLAY
+        self.cuda_graph_manager.state = jit_utils.CUDAGraphState.REPLAY
         self.cuda_graph_manager.batch_size = entry.real_shape
         with self.cuda_graph_manager.run_impl_guard():
             return entry.runnable(**kwargs)

fastdeploy/rl/dynamic_weight_manager.py

@@ -45,6 +45,7 @@ class DynamicWeightManager:
         self.model: nn.Layer = model
         self._capture_model_state()
         self.update_parameters()
+        self.finalize_update()
 
         logger.info(
             f"✅ DynamicLoad model built successfully by {self.load_config.load_strategy}, "
@@ -81,8 +82,6 @@ class DynamicWeightManager:
 
         logger.info(f"Update parameters in {time.perf_counter()-start_time:.2f}s")
 
-        self._finalize_update(pid)
-
     def _update_ipc_snapshot(self):
         """Update using IPC snapshot strategy for elastic recovery."""
         model_path = os.path.join(
@@ -146,7 +145,7 @@ class DynamicWeightManager:
         if src.shape != dst.shape:
             raise ValueError(f"Shape mismatch for {name}: {src.shape} vs {dst.shape}")
 
-    def _finalize_update(self, pid: int):
+    def finalize_update(self, pid: int = 0):
         """Finalize update process with verification."""
         self._verify_parameters("update")
         if self.parallel_config.tensor_parallel_size > 1:

fastdeploy/worker/gpu_model_runner.py

@@ -1705,25 +1705,27 @@ class GPUModelRunner(ModelRunnerBase):
         paddle.device.cuda.empty_cache()
 
     def clear_parameters(self, pid):
-        """ " Dynamic model loader use to clear parameters use for RL"""
+        """Dynamic model loader use to clear parameters use for RL"""
+        # Clear CUDAGraph
+        if self.use_cudagraph:
+            self.model.clear_grpah_opt_backend()
+        # Clear parameters and Send single
         self.dynamic_weight_manager.clear_parameters(pid)
         self.clear_cache()
         paddle.device.cuda.empty_cache()
 
-        # Clear CudaGraph
-        if self.use_cudagraph:
-            self.model.clear_grpah_opt_backend()
-
         self.dynamic_weight_manager._log_memory("dynamic weight manager clear all memory")
 
     def update_parameters(self, pid):
-        """ " Dynamic model loader use to update parameters use for RL"""
+        """Dynamic model loader use to update parameters use for RL"""
+        # Update parameters
         self.dynamic_weight_manager.update_parameters(pid)
         self.initialize_kv_cache()
-
-        # Recapture CudaGraph
+        # Recapture CUDAGraph
         if self.use_cudagraph:
             self.capture_model()
+        # Send single
+        self.dynamic_weight_manager.finalize_update(pid)
 
         self.dynamic_weight_manager._log_memory("dynamic weight manager update all memory")