Record model version during model weight sync

tests/explorer/runner_pool_test.py

@@ -43,10 +43,10 @@ def run(self) -> List[Experience]:
 
 @ray.remote
 class DummyModel(InferenceModel):
-    def sync_model(self, update_weight_args_list):
+    def sync_model(self, model_version, update_weight_args_list):
         return True
 
-    def get_ckp_version(self):
+    def get_model_version(self):
         return 0
 
     def init_process_group(
@@ -65,10 +65,10 @@ def init_process_group(
 
 @ray.remote
 class DummyAuxiliaryModel(InferenceModel):
-    def sync_model(self, update_weight_args_list):
+    def sync_model(self, model_version, update_weight_args_list):
         return True
 
-    def get_ckp_version(self):
+    def get_model_version(self):
         return 0
 
     def init_process_group(

tests/trainer/trainer_test.py

@@ -1,4 +1,5 @@
 """Tests for trainer."""
+
 import multiprocessing
 import os
 import shutil
@@ -83,14 +84,12 @@ def test_trainer(self):
         self.assertEqual(parser.metric_max_step(response_metrics[0]), 8)
         ray.shutdown(_exiting_interpreter=True)
         # check checkpoint
-        from trinity.common.models.utils import get_checkpoint_dir_with_step_num
-
-        checkpoint_step_4 = get_checkpoint_dir_with_step_num(
+        checkpoint_step_4, _ = get_checkpoint_dir_with_step_num(
             checkpoint_root_path=self.config.checkpoint_job_dir,
             trainer_type=self.config.trainer.trainer_type,
             step_num=4,
         )
-        checkpoint_step_8 = get_checkpoint_dir_with_step_num(
+        checkpoint_step_8, _ = get_checkpoint_dir_with_step_num(
             checkpoint_root_path=self.config.checkpoint_job_dir,
             trainer_type=self.config.trainer.trainer_type,
             step_num=8,
@@ -158,13 +157,12 @@ def test_trainer(self):
         self.assertEqual(parser.metric_max_step(response_metrics[0]), 4)
         ray.shutdown(_exiting_interpreter=True)
         # check checkpoint
-        from trinity.common.models.utils import get_checkpoint_dir_with_step_num
 
-        checkpoint_step_4 = get_checkpoint_dir_with_step_num(
+        checkpoint_step_4, step_num = get_checkpoint_dir_with_step_num(
             checkpoint_root_path=self.config.checkpoint_job_dir,
             trainer_type=self.config.trainer.trainer_type,
-            step_num=4,
         )
+        self.assertEqual(step_num, 4)
         self.assertTrue(os.path.exists(checkpoint_step_4))
 
     def tearDown(self):
@@ -374,19 +372,20 @@ def test_fully_async_mode(self):
         explorer2_cache = CacheManager(explorer2_config)
         cache = explorer2_cache.load_explorer()
         self.assertEqual(cache["latest_iteration"], 4)
-        self.assertIsNotNone(
+        # check the lastest checkpoint
+        self.assertEqual(
             get_checkpoint_dir_with_step_num(
                 checkpoint_root_path=explorer1_config.checkpoint_job_dir,
                 trainer_type="verl",
-                step_num=8,
-            )
+            )[1],
+            8,
         )
-        self.assertIsNotNone(
+        self.assertEqual(
             get_checkpoint_dir_with_step_num(
                 checkpoint_root_path=explorer2_config.checkpoint_job_dir,
                 trainer_type="verl",
-                step_num=8,
-            )
+            )[1],
+            8,
         )
         ray.shutdown()
 

trinity/common/models/model.py

@@ -49,7 +49,7 @@ async def convert_messages_to_experience_async(self, messages: List[dict]) -> Ex
         raise NotImplementedError
 
     @abstractmethod
-    def get_ckp_version(self) -> int:
+    def get_model_version(self) -> int:
         """Get the checkpoint version."""
 
     def get_available_address(self) -> Tuple[str, int]:
@@ -99,8 +99,10 @@ def convert_messages_to_experience(self, messages: List[dict]) -> Experience:
         else:
             return ray.get(self.model.convert_messages_to_experience.remote(messages))
 
-    def get_ckp_version(self) -> int:
-        return ray.get(self.model.get_ckp_version.remote())
+    @property
+    def model_version(self) -> int:
+        """Get the version of the model."""
+        return ray.get(self.model.get_model_version.remote())
 
     def get_openai_client(self) -> openai.OpenAI:
         """Get the openai client.

trinity/common/models/utils.py

@@ -105,16 +105,21 @@ def get_checkpoint_dir_with_step_num(
     checkpoint_root_path: str,
     trainer_type: str = "verl",
     step_num: Optional[int] = None,
-) -> str:
+) -> Tuple[str, int]:
     """Get the checkpoint directory from a root checkpoint directory.
 
     Args:
         checkpoint_root_path (str): The root checkpoint directory.
         trainer_type (str): The trainer type. Only support "verl" for now.
-        step_num (Optional[int], optional): The step number. Defaults to None.
+        step_num (Optional[int], optional): The step number. If specified,
+            load the checkpoint with the specified step number. If None,
+            load the latest checkpoint. Defaults to None.
+
+    Returns:
+        Tuple[str, int]: The checkpoint directory and the step number of the checkpoint.
     """
     if trainer_type == "verl":
-        return get_verl_checkpoint_dir(checkpoint_path=checkpoint_root_path, step_num=step_num)
+        return get_verl_checkpoint_info(checkpoint_path=checkpoint_root_path, step_num=step_num)
     else:
         raise NotImplementedError(f"Unsupported trainer type {trainer_type}")
 
@@ -144,8 +149,20 @@ def merge_by_placement(tensors: List[torch.Tensor], placement: Placement):
         raise ValueError(f"Unsupported placement: {placement}")
 
 
-def get_verl_checkpoint_dir(checkpoint_path: str, step_num: Optional[int] = None) -> str:
-    """Get the checkpoint directory from a Verl root checkpoint directory."""
+def get_verl_checkpoint_info(
+    checkpoint_path: str, step_num: Optional[int] = None
+) -> Tuple[str, int]:
+    """Get the checkpoint directory from a Verl root checkpoint directory.
+
+    Args:
+        checkpoint_path (str): The root checkpoint directory.
+        step_num (Optional[int], optional): The step number. If specified,
+            load the checkpoint with the specified step number. If None,
+            load the latest checkpoint. Defaults to None.
+
+    Returns:
+        Tuple[str, int]: The checkpoint directory and the step number of the checkpoint.
+    """
     if step_num is None:
         # load latest checkpoint
         iteration_file = os.path.join(checkpoint_path, "latest_checkpointed_iteration.txt")
@@ -154,12 +171,12 @@ def get_verl_checkpoint_dir(checkpoint_path: str, step_num: Optional[int] = None
                 iteration_file, "r", encoding="utf-8"
             ) as f:  # TODO: this file may be modified simultaneously
                 iteration = f.read().strip()
-                return os.path.join(checkpoint_path, f"global_step_{iteration}")
+                return os.path.join(checkpoint_path, f"global_step_{iteration}"), int(iteration)
         else:
             raise FileNotFoundError(f"No iteration file found in {checkpoint_path}")
     else:
         # load specific iteration checkpoint
-        return os.path.join(checkpoint_path, f"global_step_{step_num}")
+        return os.path.join(checkpoint_path, f"global_step_{step_num}"), step_num
 
 
 # copy from verl/scripts/model_merger.py

trinity/common/models/vllm_async_model.py

@@ -102,7 +102,7 @@ def __init__(
         else:
             self.action_mask_method = tokenize_and_mask_messages_hf
         self.state_dict_meta = None
-        self.ckp_version = 0  # TODO: resume the value from the checkpoint
+        self.model_version = 0  # TODO: resume the value from the checkpoint
         self.api_server_host = None
         self.api_server_port = None
 
@@ -266,13 +266,15 @@ async def _collective_rpc(
                 method, timeout, args, kwargs
             )
 
-    async def sync_model(self, update_weight_args_list: Optional[List[Tuple]] = None) -> bool:
+    async def sync_model(
+        self, model_version: int, update_weight_args_list: Optional[List[Tuple]] = None
+    ) -> bool:
         """Sync model weights to vLLM."""
         if update_weight_args_list is not None:
             await self._collective_rpc("set_state_dict_meta", args=(update_weight_args_list,))
         await self._collective_rpc("update_weight")
         self.logger.info("Sync model weights to vLLM successfully.")
-        self.ckp_version += 1
+        self.model_version = model_version
         return True
 
     async def init_process_group(
@@ -352,8 +354,8 @@ async def api_server_ready(self) -> Tuple[Union[str, None], Union[str, None]]:
     async def reset_prefix_cache(self) -> None:
         await self.async_llm.reset_prefix_cache()
 
-    def get_ckp_version(self) -> int:
-        return self.ckp_version
+    def get_model_version(self) -> int:
+        return self.model_version
 
     async def sleep(self, level: int = 1) -> None:
         await self.async_llm.sleep(level=level)

trinity/common/models/vllm_model.py

@@ -87,7 +87,7 @@ def __init__(self, config: InferenceModelConfig):
             self.action_mask_method = tokenize_and_mask_messages_hf
         self.lock = threading.Lock()
         self.state_dict_meta = None
-        self.ckp_version = 0  # TODO: resume the value from the checkpoint
+        self.model_version = 0  # TODO: resume the value from the checkpoint
 
     def init_process_group(
         self,
@@ -278,14 +278,16 @@ def convert_messages_to_experience(self, messages: List[dict]) -> Experience:
     def has_api_server(self) -> bool:
         return False
 
-    def sync_model(self, update_weight_args_list: Optional[List[Tuple]] = None) -> bool:
+    def sync_model(
+        self, model_version: int, update_weight_args_list: Optional[List[Tuple]] = None
+    ) -> bool:
         """Sync model weights to vLLM."""
         if update_weight_args_list is not None:
             self._collective_rpc("set_state_dict_meta", args=(update_weight_args_list,))
         self._collective_rpc("update_weight")
         self.logger.info("Sync model weights to vLLM successfully.")
-        self.ckp_version += 1
+        self.model_version += model_version
         return True
 
-    def get_ckp_version(self) -> int:
-        return self.ckp_version
+    def get_model_version(self) -> int:
+        return self.model_version

trinity/explorer/explorer.py

@@ -124,7 +124,7 @@ def _init_runner_pool(self) -> RunnerPool:
         self.logger.info(f"Setup {self.config.explorer.runner_num} WorkflowRunners")
         return RunnerPool(self.config, self.models, self.auxiliary_models)
 
-    async def _update_model_weight(self, state_dict: dict) -> None:
+    async def _update_model_weight(self, step_num: int, state_dict: dict) -> None:
         # TODO: update model weight
         self.state_dict = state_dict
         if self.state_dict_meta is None:
@@ -135,29 +135,31 @@ async def _update_model_weight(self, state_dict: dict) -> None:
         else:
             update_weight_args_list = None
         await asyncio.gather(
-            *[model.sync_model.remote(update_weight_args_list) for model in self.models]
+            *[model.sync_model.remote(step_num, update_weight_args_list) for model in self.models]
         )
         self.state_dict.clear()
 
     async def _checkpoint_weights_update(self, step_num: Optional[int] = None) -> None:
         # TODO: support more checkpoint types
         try:
-            checkpoint_dir = get_checkpoint_dir_with_step_num(
+            checkpoint_dir, checkpoint_step_num = get_checkpoint_dir_with_step_num(
                 checkpoint_root_path=self.config.checkpoint_job_dir,
                 trainer_type=self.config.trainer.trainer_type,
                 step_num=step_num,
             )
             if checkpoint_dir == self.old_checkpoint:
                 return
             model_weights = load_state_dict(os.path.join(checkpoint_dir, "actor"))
-            await self._update_model_weight(model_weights)
+            await self._update_model_weight(checkpoint_step_num, model_weights)
             self.old_checkpoint = checkpoint_dir
         except Exception as e:
             self.logger.warning(f"Fail to load checkpoint: {e}")
 
     async def _nccl_weights_update(self):
         assert self.state_dict_meta is not None
-        await asyncio.gather(*[model.sync_model.remote() for model in self.models])
+        await asyncio.gather(
+            *[model.sync_model.remote(self.explore_step_num) for model in self.models]
+        )
 
     async def prepare(self) -> None:
         """Preparation before running."""

trinity/explorer/workflow_runner.py

@@ -85,7 +85,7 @@ def run_task(self, task: Task) -> Status:
 
                 if not hasattr(exp, "info") or exp.info is None:
                     exp.info = {}
-                exp.info["model_version"] = self.model_wrapper.get_ckp_version()
+                exp.info["model_version"] = self.model_wrapper.model_version
 
                 if not hasattr(exp, "metrics") or exp.metrics is None:
                     exp.metrics = {}