93 lines
3.3 KiB
Python
93 lines
3.3 KiB
Python
"""
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Checkpoint scanning utilities for discovering training runs and their checkpoints.
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"""
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import json
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import logging
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from pathlib import Path
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from typing import List, Optional, Tuple
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logger = logging.getLogger(__name__)
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def _read_checkpoint_loss(checkpoint_path: Path) -> Optional[float]:
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"""
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Read the training loss from a checkpoint's trainer_state.json.
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Returns the loss from the last log_history entry, or None if unavailable.
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"""
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trainer_state = checkpoint_path / "trainer_state.json"
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if not trainer_state.exists():
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return None
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try:
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with open(trainer_state) as f:
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state = json.load(f)
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log_history = state.get("log_history", [])
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if log_history:
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return log_history[-1].get("loss")
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except Exception as e:
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logger.debug(f"Could not read loss from {trainer_state}: {e}")
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return None
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def scan_checkpoints(
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outputs_dir: str = "./outputs",
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) -> List[Tuple[str, List[Tuple[str, str, Optional[float]]]]]:
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"""
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Scan outputs folder for training runs and their checkpoints.
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Returns:
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List of tuples: [(model_name, [(display_name, checkpoint_path, loss), ...]), ...]
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The first entry in each checkpoint list is the main adapter; its loss is
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set to the loss of the last (highest-step) intermediate checkpoint.
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"""
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models = []
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outputs_path = Path(outputs_dir)
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if not outputs_path.exists():
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logger.warning(f"Outputs directory not found: {outputs_dir}")
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return models
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try:
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for item in outputs_path.iterdir():
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if not item.is_dir():
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continue
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config_file = item / "config.json"
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adapter_config = item / "adapter_config.json"
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if not (config_file.exists() or adapter_config.exists()):
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continue
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# This is a valid training run
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checkpoints = []
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# Placeholder for the main adapter — loss filled from last checkpoint below
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checkpoints.append((item.name, str(item), None))
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# Scan for intermediate checkpoints (checkpoint-N subdirs)
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for sub in sorted(item.iterdir()):
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if not sub.is_dir() or not sub.name.startswith("checkpoint-"):
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continue
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sub_config = sub / "config.json"
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sub_adapter = sub / "adapter_config.json"
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if sub_config.exists() or sub_adapter.exists():
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loss = _read_checkpoint_loss(sub)
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checkpoints.append((sub.name, str(sub), loss))
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# Assign the last checkpoint's loss to the main adapter entry
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if len(checkpoints) > 1:
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last_checkpoint_loss = checkpoints[-1][2]
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checkpoints[0] = (checkpoints[0][0], checkpoints[0][1], last_checkpoint_loss)
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models.append((item.name, checkpoints))
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logger.debug(f"Found model: {item.name} with {len(checkpoints)} checkpoint(s)")
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# Sort by modification time (newest first)
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models.sort(key=lambda x: Path(x[1][0][1]).stat().st_mtime, reverse=True)
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logger.info(f"Found {len(models)} training runs in {outputs_dir}")
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return models
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except Exception as e:
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logger.error(f"Error scanning checkpoints: {e}")
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return []
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