Mark model input as dynamically sized (#105)

I tested this out with the 7B and it does not hurt throughput.

CHANGELOG.md

@@ -7,6 +7,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## Unreleased
 
+### Fixed
+
+- (Optimization) Mark model input sizes as dynamic for `torch.compile()` to avoid recompile during evals or variable-sequence / batch size training. This doesn't seem to hurt throughput.
+
 ## [v1.6.3](https://github.com/allenai/OLMo-core/releases/tag/v1.6.3) - 2024-11-15
 
 ### Added

src/olmo_core/train/trainer.py

@@ -48,7 +48,7 @@
     fused_cross_entropy_loss,
 )
 from ..optim import SkipStepOptimizer
-from ..utils import cuda_sync_debug_mode, move_to_device
+from ..utils import cuda_sync_debug_mode, mark_dynamic, move_to_device
 from .callbacks import (
     Callback,
     CheckpointerCallback,
@@ -880,6 +880,15 @@ def model_forward(self, micro_batch: Dict[str, Any]) -> torch.Tensor:
         Run a forward pass on a micro-batch, returning the logits.
         """
         with self._model_forward_context():
+            # NOTE: Input sizes might be dynamic, e.g. when training with variable sequence lengths
+            # or during an eval loop, so we mark them as dynamic for torch.compile up-front to avoid
+            # recompiling later.
+            # In theory this could harm performance a bit when input sizes are actually static
+            # but so far I haven't noticed any dip in throughput with the models I've tested.
+            mark_dynamic(micro_batch["input_ids"], (0, 1))
+            if "doc_lens" in micro_batch:
+                mark_dynamic(micro_batch["doc_lens"], (0, 1))
+
             # shape: (batch_size, seq_len, vocab_size)
             logits = self.model(
                 input_ids=micro_batch["input_ids"],

src/olmo_core/utils.py

@@ -12,7 +12,7 @@
 from itertools import cycle, islice
 from queue import Queue
 from threading import Thread
-from typing import Any, Callable, Dict, List, Optional, TypeVar, Union
+from typing import Any, Callable, Dict, List, Optional, Sequence, TypeVar, Union
 
 import rich
 import torch
@@ -111,6 +111,13 @@ def move_to_device(o: T, device: torch.device, non_blocking: Optional[bool] = No
         return o
 
 
+def mark_dynamic(x: torch.Tensor, dim: Union[int, Sequence[int]]):
+    """
+    Mark a tensor as having dynamic sizes for ``torch.compile()``.
+    """
+    torch._dynamo.mark_dynamic(x, dim)
+
+
 def get_default_device() -> torch.device:
     """
     Get the default device.