Don't reshuffle eval data each "epoch"

olmo/data/__init__.py

@@ -1,5 +1,5 @@
 from pathlib import Path
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List
 
 from torch.utils.data import DataLoader, DistributedSampler
 
@@ -15,14 +15,15 @@
 
 def build_memmap_dataset(train_config: TrainConfig, data_config: DataConfig) -> MemMapDataset:
     paths: List[str]
-    metadata: Optional[List[Dict[str, Any]]] = None
+    metadata: List[Dict[str, Any]] = []
     if data_config.paths:
         if data_config.datasets:
             raise OlmoConfigurationError("DataConfig.paths is mutually exclusive with DataConfig.datasets")
         paths = data_config.paths
+        for path in paths:
+            metadata.append({"path": str(path)})
     elif data_config.datasets:
         paths = []
-        metadata = []
         for label in sorted(data_config.datasets.keys()):
             label_paths = data_config.datasets[label]
             paths.extend(label_paths)

olmo/eval/__init__.py

@@ -7,7 +7,7 @@
 from ..config import EvaluatorConfig, EvaluatorType, TrainConfig
 from ..exceptions import OlmoConfigurationError
 from ..tokenizer import Tokenizer
-from ..util import cycle_through_epochs, get_global_rank, get_world_size
+from ..util import get_global_rank, get_world_size
 from .downstream import ICLMetric, label_to_task_map
 from .evaluator import Evaluator
 
@@ -59,7 +59,6 @@ def build_downstream_evaluator(
         label=eval_cfg.label,
         type=eval_cfg.type,
         eval_loader=ds_eval_dataloader,
-        eval_batches=cycle_through_epochs(ds_eval_dataloader),
         eval_metric=metric.to(device),
         subset_num_batches=eval_cfg.subset_num_batches,
     )
@@ -97,7 +96,6 @@ def make_metric():
             label=eval_config.label,
             type=eval_config.type,
             eval_loader=eval_loader,
-            eval_batches=cycle_through_epochs(eval_loader),
             eval_metric=eval_metric,
             subset_num_batches=eval_config.subset_num_batches,
         )

olmo/eval/evaluator.py

@@ -1,5 +1,5 @@
 from dataclasses import dataclass
-from typing import Any, Dict, Iterator, Optional, Union
+from typing import Any, Dict, Optional, Union
 
 import torch
 from torch.utils.data import DataLoader
@@ -16,7 +16,6 @@ class Evaluator:
     label: str
     type: EvaluatorType
     eval_loader: DataLoader
-    eval_batches: Iterator[Dict[str, Any]]
     eval_metric: Union[Metric, Dict[str, Metric]]
     subset_num_batches: Optional[int] = None
 

olmo/train.py

@@ -677,17 +677,21 @@ def eval(self) -> Dict[str, Any]:
             # Reset metrics.
             evaluator.reset_metrics()
 
-            # Check how many batches to evaluate on.
-            num_eval_batches = evaluator.subset_num_batches
-            if num_eval_batches is None:
-                num_eval_batches = self.cfg.eval_subset_num_batches
-            if num_eval_batches <= 0:
-                num_eval_batches = max(1, len(evaluator.eval_loader))
-            else:
+            # Initialize data loader iterator.
+            eval_batches = iter(evaluator.eval_loader)
+
+            # Adjust how many batches to evaluate on.
+            num_eval_batches = (
+                evaluator.subset_num_batches
+                if evaluator.subset_num_batches is not None
+                else self.cfg.eval_subset_num_batches
+            )
+            if num_eval_batches > 0:
                 num_eval_batches = min(num_eval_batches, len(evaluator.eval_loader))
+                eval_batches = islice(eval_batches, num_eval_batches)
 
             # Run model over batches.
-            for eval_step, eval_batch in enumerate(islice(evaluator.eval_batches, num_eval_batches)):
+            for eval_step, eval_batch in enumerate(eval_batches):
                 self.eval_step(eval_batch, evaluator)
 
                 # Log to console.
@@ -699,6 +703,8 @@ def eval(self) -> Dict[str, Any]:
             eval_metrics.update(metrics)
             self.log_metrics_to_console(f"{evaluator.label}", metrics)
 
+            del eval_batches
+
         return eval_metrics
 
     def fit(self):

olmo/util.py

@@ -4,7 +4,7 @@
 import sys
 import warnings
 from datetime import datetime
-from typing import Any, Dict, Generator, Optional, TypeVar, Union
+from typing import Any, Dict, Optional, TypeVar, Union
 
 import rich
 import torch
@@ -13,7 +13,6 @@
 from rich.highlighter import NullHighlighter
 from rich.text import Text
 from rich.traceback import Traceback
-from torch.utils.data import DataLoader, DistributedSampler
 
 from .config import LogFilterType
 from .exceptions import OlmoCliError, OlmoError
@@ -333,16 +332,6 @@ def peak_gpu_memory(reset: bool = False) -> Optional[float]:
     return peak_mb
 
 
-def cycle_through_epochs(dataloader: DataLoader) -> Generator[Dict[str, Any], None, None]:
-    while True:
-        for batch in dataloader:
-            yield batch
-
-        if isinstance(dataloader.sampler, DistributedSampler):
-            epoch = dataloader.sampler.epoch + 1
-            dataloader.sampler.set_epoch(epoch)
-
-
 def syncronize_flag(flag: bool, device: torch.device) -> bool:
     if dist.is_available() and dist.is_initialized():
         flag_tensor = torch.tensor(flag, device=device)

tests/eval/downstream_test.py

@@ -14,10 +14,6 @@ def test_piqa():
         cfg, cfg.evaluators[1], tokenizer, torch.device("cpu"), is_unit_test=True
     )
     logits = torch.rand(4, 57, 50304)
-    first_batch = next(evaluator.eval_batches)
+    first_batch = next(iter(evaluator.eval_loader))
     evaluator.reset_metrics()
     evaluator.update_metrics(first_batch, logits.sum(), logits)
-
-
-if __name__ == "__main__":
-    test_piqa()