Bug fix for trainer_state saving

docs/sphinx_doc/source/tutorial/trinity_configs.md

@@ -82,7 +82,7 @@ checkpoint_root_dir: ${oc.env:TRINITY_CHECKPOINT_ROOT_DIR,./checkpoints}   # TRI
   - `explore`: Only launches the explorer.
   - `bench`: Used for benchmarking.
 - `checkpoint_root_dir`: Root directory where all checkpoints and logs will be saved. Checkpoints for this experiment will be stored in `<checkpoint_root_dir>/<project>/<name>/`.
-- `continue_from_checkpoint`: If set to `true`, the experiment will continue from the latest checkpoint in the checkpoint path (if any); otherwise, it will rename the current experiment to `<name>_<timestamp>` and start a new experiment.
+- `continue_from_checkpoint`: If set to `true`, the experiment will continue from the latest checkpoint in the checkpoint path (if any); otherwise, it will rename the current experiment to `<name>_<timestamp>` and start a new experiment. Due to our decoupled design, during recovery from a checkpoint, we can only guarantee that the Trainer's model parameters and its optional auxiliary buffers (`auxiliary_buffers`) are restored to their latest checkpointed states, while the Explorer and Experience Buffer cannot be guaranteed to be restored to the same point in time.
 - `ray_namespace`: Namespace for the modules launched in the current experiment. If not specified, it will be set to `<project>/<name>`.
 
 ---

docs/sphinx_doc/source_zh/tutorial/trinity_configs.md

@@ -82,7 +82,7 @@ checkpoint_root_dir: ${oc.env:TRINITY_CHECKPOINT_ROOT_DIR,./checkpoints}   # TRI
   - `explore`: 仅启动 explorer。
   - `bench`: 用于 benchmark 测试。
 - `checkpoint_root_dir`: 所有检查点和日志的根目录。该实验的检查点将存储在 `<checkpoint_root_dir>/<project>/<name>/` 路径下。
-- `continue_from_checkpoint`: 若设置为 `true`，实验将从检查点路径中的最新检查点继续；否则，会将当前实验重命名为 `<name>_<timestamp>` 并启动新实验。
+- `continue_from_checkpoint`: 若设置为 `true`，实验将从检查点路径中的最新检查点继续；否则，会将当前实验重命名为 `<name>_<timestamp>` 并启动新实验。由于我们的分离式设计，从检查点恢复的时候，我们只能保证Trainer的模型参数以及其使用的可选缓冲区（`auxiliary_buffers`）可以恢复到最新检查点的状态，而Explorer和Experience Buffer不能保证恢复到同一时点。
 - `ray_namespace`: 当前实验中启动模块的命名空间。若未指定，则默认为 `<project>/<name>`。
 
 ---

tests/template/config.yaml

@@ -20,8 +20,8 @@ model:
   max_response_tokens: 2048
   max_model_len: 4096
 cluster:  # 2 for explorer, 2 for trainer
-  node_num: 2
-  gpu_per_node: 2
+  node_num: ${oc.env:NODE_NUM,2}
+  gpu_per_node: ${oc.env:GPU_PER_NODE,2}
 buffer:
   total_epochs: 1
   batch_size: 4

tests/trainer/trainer_test.py

@@ -1,5 +1,6 @@
 """Tests for trainer."""
 
+import json
 import multiprocessing
 import os
 import shutil
@@ -809,7 +810,7 @@ def test_trainer(self):
         self.config.algorithm.policy_loss_fn = "mix"
         self.config.buffer.batch_size = 4
         self.config.buffer.train_batch_size = 32
-        self.config.buffer.total_epochs = 1
+        self.config.buffer.total_steps = 2
         self.config.buffer.explorer_input.taskset = get_unittest_dataset_config("gsm8k")
         self.config.synchronizer.sync_interval = 1
         self.config.trainer.save_interval = 1
@@ -823,6 +824,31 @@ def test_trainer(self):
         self.config.buffer.trainer_input.experience_buffer.max_read_timeout = 20
         self.config.trainer.trainer_config.trainer.max_actor_ckpt_to_keep = 2
         both(self.config)
+        ray.shutdown(_exiting_interpreter=True)
+
+        # check trainer resume metadata
+        trainer_meta_file = os.path.join(self.config.checkpoint_job_dir, "trainer_meta.json")
+        with open(trainer_meta_file) as f:
+            trainer_meta = json.load(f)
+        self.assertEqual(trainer_meta["latest_iteration"], 2)
+        self.assertEqual(
+            trainer_meta["sample_strategy_state"]["expert_buffer"]["current_index"], 32
+        )
+
+        self.config.buffer.total_steps = None
+        self.config.buffer.total_epochs = 1
+        self.config.check_and_update()
+        ray.init(ignore_reinit_error=True, namespace=self.config.ray_namespace)
+        both(self.config)
+
+        # check trainer resume metadata
+        with open(trainer_meta_file) as f:
+            trainer_meta = json.load(f)
+        self.assertEqual(trainer_meta["latest_iteration"], 4)
+        self.assertEqual(
+            trainer_meta["sample_strategy_state"]["expert_buffer"]["current_index"], 64
+        )
+
         parser = TensorBoardParser(os.path.join(self.config.monitor.cache_dir, "tensorboard"))
 
         # test rollout metrics

trinity/algorithm/sample_strategy/mix_sample_strategy.py

@@ -111,3 +111,15 @@ def default_args(cls) -> Dict:
             "expert_data_ratio": 0.5,
             "sft_dataset_name": "sft_dataset",
         }
+
+    def state_dict(self) -> dict:
+        return {
+            "usal_buffer": self.usual_exp_buffer.state_dict(),
+            "expert_buffer": self.expert_exp_buffer.state_dict(),
+        }
+
+    def load_state_dict(self, state_dict: dict) -> None:
+        if state_dict.get("usal_buffer", None):
+            self.usual_exp_buffer.load_state_dict(state_dict["usal_buffer"])
+        if state_dict.get("expert_buffer", None):
+            self.expert_exp_buffer.load_state_dict(state_dict["expert_buffer"])

trinity/algorithm/sample_strategy/sample_strategy.py

@@ -43,6 +43,14 @@ async def sample(self, step: int) -> Tuple[Experiences, Dict, List]:
     def default_args(cls) -> dict:
         """Get the default arguments of the sample strategy."""
 
+    @abstractmethod
+    def state_dict(self) -> dict:
+        """Get the state dict of the sample strategy."""
+
+    @abstractmethod
+    def load_state_dict(self, state_dict: dict) -> None:
+        """Load the state dict of the sample strategy."""
+
 
 @SAMPLE_STRATEGY.register_module("default")
 class DefaultSampleStrategy(SampleStrategy):
@@ -64,6 +72,13 @@ async def sample(self, step: int, **kwargs) -> Tuple[Experiences, Dict, List]:
     def default_args(cls) -> dict:
         return {}
 
+    def state_dict(self) -> dict:
+        return self.exp_buffer.state_dict()
+
+    def load_state_dict(self, state_dict: dict) -> None:
+        if state_dict:
+            self.exp_buffer.load_state_dict(state_dict)
+
 
 @Deprecated
 @SAMPLE_STRATEGY.register_module("warmup")

trinity/buffer/reader/queue_reader.py

@@ -43,9 +43,9 @@ async def read_async(self, batch_size: Optional[int] = None) -> List:
         return exps
 
     def state_dict(self) -> Dict:
-        # SQL Not supporting state dict yet
+        # Queue Not supporting state dict yet
         return {"current_index": 0}
 
     def load_state_dict(self, state_dict):
-        # SQL Not supporting state dict yet
+        # Queue Not supporting state dict yet
         return None

trinity/manager/state_manager.py

@@ -101,14 +101,14 @@ def load_explorer_server_url(self) -> Optional[str]:
 
     def save_trainer(
         self,
-        current_exp_index: int,
         current_step: int,
+        sample_strategy_state: dict,
     ) -> None:
         with open(self.trainer_state_path, "w", encoding="utf-8") as f:
             json.dump(
                 {
-                    "latest_exp_index": current_exp_index,
                     "latest_iteration": current_step,
+                    "sample_strategy_state": sample_strategy_state,
                 },
                 f,
                 indent=2,

trinity/trainer/trainer.py

@@ -14,6 +14,7 @@
 import ray
 
 from trinity.algorithm import SAMPLE_STRATEGY
+from trinity.algorithm.sample_strategy.sample_strategy import SampleStrategy
 from trinity.common.config import Config
 from trinity.common.constants import RunningStatus, SyncMethod, SyncStyle
 from trinity.common.experience import Experiences
@@ -38,9 +39,6 @@ def __init__(self, config: Config) -> None:
             path=config.checkpoint_job_dir, trainer_name=config.trainer.name, config=config
         )
         trainer_state = self.state.load_trainer()
-        config.buffer.trainer_input.experience_buffer.index = trainer_state.get(
-            "latest_exp_index", 0
-        )
         self.last_trainer_sync_step = 0
         self.monitor = MONITOR.get(config.monitor.monitor_type)(
             project=config.project,
@@ -50,10 +48,17 @@ def __init__(self, config: Config) -> None:
             config=config,
         )
         self._sample_exps_to_log = []
-        self.sample_strategy = SAMPLE_STRATEGY.get(config.algorithm.sample_strategy)(
+        self.sample_strategy: SampleStrategy = SAMPLE_STRATEGY.get(
+            config.algorithm.sample_strategy
+        )(
             buffer_config=config.buffer,
             **config.algorithm.sample_strategy_args,
         )
+        if "latest_exp_index" in trainer_state:
+            sample_strategy_state = {"current_index": trainer_state["latest_exp_index"]}
+        else:
+            sample_strategy_state = trainer_state.get("sample_strategy_state", {})
+        self.sample_strategy.load_state_dict(sample_strategy_state)
         self.save_interval = config.trainer.save_interval
         self.last_sync_step = None
         self.last_sync_time = None
@@ -190,8 +195,8 @@ def save_checkpoint(self, block_until_saved: bool = False, save_as_hf: bool = Fa
             self.logger.info(f"Saving checkpoint at step {self.train_step_num}...")
             self.engine.save_checkpoint(block_until_saved=block_until_saved, save_as_hf=save_as_hf)
             self.state.save_trainer(
-                current_exp_index=self.engine.train_step_num * self.config.buffer.train_batch_size,
                 current_step=self.train_step_num,
+                sample_strategy_state=self.sample_strategy.state_dict(),
             )
         return metrics