Add local rollout logging (#124)

trlx/data/configs.py

@@ -98,6 +98,9 @@ class TrainConfig:
 
     :param entity_name: Entity name for wandb
     :type entity_name: str
+
+    :param rollout_logging_dir: Directory to store generated rollouts for use in Algorithm Distillation. Only used by AcceleratePPOModel.
+    :type rollout_logging_dir: Optional[str]
     """
 
     total_steps: int
@@ -122,6 +125,8 @@ class TrainConfig:
     entity_name: Optional[str] = None
     seed: int = 1000
 
+    rollout_logging_dir: Optional[str] = None
+
     @classmethod
     def from_dict(cls, config: Dict[str, Any]):
         return cls(**config)

trlx/model/accelerate_ppo_model.py

@@ -1,4 +1,5 @@
 from typing import Tuple
+import uuid, os, json
 
 import torch
 from torchtyping import TensorType
@@ -21,6 +22,12 @@ class AcceleratePPOModel(AccelerateRLModel):
     def __init__(self, config):
         super().__init__(config)
 
+        if config.train.rollout_logging_dir is not None:
+            self.log_rollouts = True
+            self.setup_rollout_logging(config)
+        else:
+            self.log_rollouts = False
+
         self.store = PPORolloutStorage(self.tokenizer.pad_token_id)
 
         rollout_loader = self.store.create_loader(
@@ -103,7 +110,24 @@ def loss(self, batch: PPORLBatch):
         self.approx_kl = stats["policy/approx_kl"]  # Update kl controller stats
         return loss, stats
 
+    def setup_rollout_logging(self, config):
+        # Make rollout logging dir for this run and store config
+        exists = os.path.exists(config.train.rollout_logging_dir)
+        isdir = os.path.isdir(config.train.rollout_logging_dir)
+        assert exists and isdir
+
+        self.run_id = f"run-{uuid.uuid4()}"
+        self.rollout_logging_dir = os.path.join(
+            config.train.rollout_logging_dir, self.run_id
+        )
+        os.mkdir(self.rollout_logging_dir)
+
+        with open(os.path.join(self.rollout_logging_dir, "config.json"), "w") as f:
+            f.write(json.dumps(config.to_dict(), indent=2))
+
     def post_epoch_callback(self):
+        if self.log_rollouts:
+            self.store.export_history(location=self.rollout_logging_dir)
         self.store.clear_history()
         self.orch.make_experience(
             self.config.method.num_rollouts, self.iter_count

trlx/pipeline/ppo_pipeline.py

@@ -1,4 +1,6 @@
-from typing import Iterable
+import os, json, time
+
+from typing import Iterable, Optional
 
 from torch.nn.utils.rnn import pad_sequence
 from torch.utils.data import DataLoader
@@ -25,6 +27,15 @@ def push(self, exps: Iterable[PPORLElement]):
     def clear_history(self):
         self.history = []
 
+    def export_history(self, location: str):
+        assert os.path.exists(location)
+
+        fpath = os.path.join(location, f"epoch-{str(time.time())}.json")
+        exp_to_dict = lambda exp: {k: v.cpu().tolist() for k, v in exp.__dict__.items()}
+        data = [exp_to_dict(exp) for exp in self.history]
+        with open(fpath, "w") as f:
+            f.write(json.dumps(data, indent=2))
+
     def __getitem__(self, index: int) -> PPORLElement:
         return self.history[index]