Simplify the _check_loss_fn() logic (#1243)

Summary:

The _check_loss_fn() has exact same logic when sample_wise_grads_per_batch is None and True cases. Thus simplify the logic.

Differential Revision: D54883319

Author: cicichen01

captum/influence/_utils/common.py

@@ -444,7 +444,7 @@ def _check_loss_fn(
     influence_instance: Union["TracInCPBase", "InfluenceFunctionBase"],
     loss_fn: Optional[Union[Module, Callable]],
     loss_fn_name: str,
-    sample_wise_grads_per_batch: Optional[bool] = None,
+    sample_wise_grads_per_batch: bool = True,
 ) -> str:
     """
     This checks whether `loss_fn` satisfies the requirements assumed of all
@@ -469,16 +469,13 @@ def _check_loss_fn(
     # attribute.
     if hasattr(loss_fn, "reduction"):
         reduction = loss_fn.reduction  # type: ignore
-        if sample_wise_grads_per_batch is None:
+        if sample_wise_grads_per_batch:
             assert reduction in [
                 "sum",
                 "mean",
-            ], 'reduction for `loss_fn` must be "sum" or "mean"'
-            reduction_type = str(reduction)
-        elif sample_wise_grads_per_batch:
-            assert reduction in ["sum", "mean"], (
+            ], (
                 'reduction for `loss_fn` must be "sum" or "mean" when '
-                "`sample_wise_grads_per_batch` is True"
+                "`sample_wise_grads_per_batch` is True (i.e. the default value) "
             )
             reduction_type = str(reduction)
         else:
@@ -490,18 +487,7 @@ def _check_loss_fn(
         # if we are unable to access the reduction used by `loss_fn`, we warn
         # the user about the assumptions we are making regarding the reduction
         # used by `loss_fn`
-        if sample_wise_grads_per_batch is None:
-            warnings.warn(
-                f'Since `{loss_fn_name}` has no "reduction" attribute, the '
-                f'implementation  assumes that `{loss_fn_name}` is a "reduction" loss '
-                "function that reduces the per-example losses by taking their *sum*. "
-                f"If `{loss_fn_name}` instead reduces the per-example losses by "
-                f"taking their mean, please set the reduction attribute of "
-                f'`{loss_fn_name}` to "mean", i.e. '
-                f'`{loss_fn_name}.reduction = "mean"`.'
-            )
-            reduction_type = "sum"
-        elif sample_wise_grads_per_batch:
+        if sample_wise_grads_per_batch:
             warnings.warn(
                 f"Since `{loss_fn_name}`` has no 'reduction' attribute, and "
                 "`sample_wise_grads_per_batch` is True, the implementation assumes "