Fixes some typing issues (#3418)

composer/callbacks/eval_output_logging_callback.py

@@ -114,6 +114,10 @@ def eval_batch_end(self, state: State, logger: Logger) -> None:
         self.rows.extend(rows)
 
     def eval_end(self, state: State, logger: Logger) -> None:
+        # eval_batch_end will have set these if there is anything to log
+        if self.name is None or self.columns is None:
+            return
+
         list_of_rows = dist.all_gather_object(self.rows)
         rows = [row for rows in list_of_rows for row in rows]
         for dest_logger in logger.destinations:

composer/core/evaluator.py

@@ -67,7 +67,7 @@ class Evaluator:
 
             When specifying ``eval_interval``, the evaluator(s) are also run at the ``Event.FIT_END`` if it doesn't
             evenly divide the training duration.
-        device_eval_microbatch_size (int, optional): The number of samples to use for each microbatch when evaluating.
+        device_eval_microbatch_size (str | int | float, optional): The number of samples to use for each microbatch when evaluating.
             If set to ``auto``, dynamically decreases device_eval_microbatch_size if microbatch is too large for GPU.
             If None, sets `device_eval_microbatch_size` to per rank batch size. (default: ``None``)
     """
@@ -80,7 +80,7 @@ def __init__(
         metric_names: Optional[list[str]] = None,
         subset_num_batches: Optional[int] = None,
         eval_interval: Optional[Union[int, str, Time, Callable[[State, Event], bool]]] = None,
-        device_eval_microbatch_size: Optional[Union[int, str]] = None,
+        device_eval_microbatch_size: Optional[Union[int, str, float]] = None,
     ):
         self.label = label
         self.dataloader = ensure_data_spec(dataloader)
@@ -142,7 +142,7 @@ def ensure_evaluator(evaluator: Union[Evaluator, DataSpec, Iterable, dict[str, A
         )
 
 
-def _is_auto_microbatching(device_eval_microbatch_size: Optional[Union[int, str]]):
+def _is_auto_microbatching(device_eval_microbatch_size: Optional[Union[int, str, float]]):
     if device_eval_microbatch_size == 'auto':
         warnings.warn((
             "Setting `device_eval_microbatch_size='auto'` is an experimental feature which may cause "
@@ -155,10 +155,10 @@ def _is_auto_microbatching(device_eval_microbatch_size: Optional[Union[int, str]
 
 
 def _get_initial_device_eval_microbatch_size(
-    device_eval_microbatch_size: Optional[Union[int, str]],
+    device_eval_microbatch_size: Optional[Union[int, str, float]],
     auto_microbatching: bool,
     dataloader: Iterable,
-) -> int:
+) -> Union[int, float]:
     """Sets initial value of device_eval_microbatch_size.
 
     If auto_microbatching, sets initial `device_eval_microbatch_size` to per rank batch size.

composer/loggers/mlflow_logger.py

@@ -185,6 +185,9 @@ def __init__(
     def _start_mlflow_run(self, state):
         import mlflow
 
+        # This function is only called if self._enabled is True, and therefore self._experiment_id is not None.
+        assert self._experiment_id is not None
+
         env_run_id = os.getenv(
             mlflow.environment_variables.MLFLOW_RUN_ID.name,  # pyright: ignore[reportGeneralTypeIssues]
             None,
@@ -193,7 +196,6 @@ def _start_mlflow_run(self, state):
             self._run_id = env_run_id
         elif self.resume:
             # Search for an existing run tagged with this Composer run if `self.resume=True`.
-            assert self._experiment_id is not None
             run_name = self.tags['run_name']
             existing_runs = mlflow.search_runs(
                 experiment_ids=[self._experiment_id],