diff --git a/timm/optim/adafactor.py b/timm/optim/adafactor.py
index 37871af1..4cefad18 100644
--- a/timm/optim/adafactor.py
+++ b/timm/optim/adafactor.py
@@ -2,8 +2,9 @@
 
 Lifted from https://github.com/pytorch/fairseq/blob/master/fairseq/optim/adafactor.py
 
-Original header/copyright below.
+Modified by Ross Wightman to fix some issues with factorization dims for non nn.Linear layers
 
+Original header/copyright below.
 """
 # Copyright (c) Facebook, Inc. and its affiliates.
 #
@@ -96,7 +97,7 @@ class Adafactor(torch.optim.Optimizer):
             # nD convs in torch are ND + 2 dim weights with leading in/out chs
             factored = 0, 1
         elif ndim >= 2 and param_shape[-2] > min_size_to_factor and param_shape[-1] > min_size_to_factor:
-            # if the criteria above didn't match, check trailing dims
+            # if the criteria above didn't match, test trailing dims for eligibility
             factored = ndim - 2, ndim - 1
 
         return factored, use_first_moment
diff --git a/timm/optim/adafactor_bv.py b/timm/optim/adafactor_bv.py
index 465f8f0e..3bb6e959 100644
--- a/timm/optim/adafactor_bv.py
+++ b/timm/optim/adafactor_bv.py
@@ -1,3 +1,12 @@
+""" Adafactor (Big Vision variant) for PyTorch
+
+Adapted from the implementation in big vision: https://github.com/google-research/big_vision
+
+Described in 'Scaling Vision Transformers': https://arxiv.org/abs/2106.04560
+
+Adaptation and PyTorch modifications by Ross Wightman
+"""
+
 from typing import List, Optional, Tuple, Union
 
 import torch
@@ -39,6 +48,8 @@ def _factored_dims(
 class AdafactorBigVision(Optimizer):
     """
     PyTorch implementation of BigVision's Adafactor variant with both single and multi tensor implementations.
+
+    Adapted from https://github.com/google-research/big_vision by Ross Wightman
     """
 
     def __init__(
@@ -292,4 +303,5 @@ def _multi_tensor_adafactor(
         clipping_threshold: Optional[float],
         unscaled_wd: bool,
 ):
+    # FIXME TODO
     assert False, 'multi-tensor fn (foreach=True) not implemented yet'