Remove duplicate definitions of fill_defaults (#1674)

torchao/dtypes/uintx/uint4_layout.py

@@ -3,6 +3,8 @@
 import torch.utils._pytree as pytree
 from torch.library import Library, impl
 
+from torchao.utils import fill_defaults
+
 
 def down_size(size):
     assert size[-1] % 2 == 0, f"{size} last dim not divisible by two"
@@ -13,31 +15,6 @@ def up_size(size):
     return (*size[:-1], size[-1] * 2)
 
 
-def fill_defaults(args, n, defaults_tail):
-    """
-    __torch_dispatch__ doesn't guarantee the number of arguments you are
-    passed (e.g., defaulted arguments are not passed); but usually it is
-    convenient to pad out the arguments list with defaults.  This function
-    helps you do that.
-    Args:
-        args: the list of positional arguments passed to __torch_dispatch__
-        n: the number of arguments you are expecting to get
-        defaults_tail: default values for the arguments, starting from the
-            end of the list
-    Example:
-        >>> fill_defaults([1, 2, 3], 5, [3, 4, 5])
-        [1, 2, 3, 4, 5]
-        >>> fill_defaults([1, 2, 3], 5, [None, None, None])
-        [1, 2, 3, None, None]]
-    """
-    if n - len(defaults_tail) > len(args):
-        raise RuntimeError("not enough defaults to fill arguments")
-    r = list(args)
-    for i in range(len(args), n):
-        r.append(defaults_tail[i - n + len(defaults_tail)])
-    return r
-
-
 # from
 # https://github.com/drisspg/transformer_nuggets/blob/9ad3a7fc552a954eb702ade0e276b8d8e09c3db6/transformer_nuggets/quant/qlora.py#L233
 

torchao/prototype/dtypes/uint2.py

@@ -4,16 +4,9 @@
 import torch
 import torch._prims_common as utils
 
-UINT2_OPS_TABLE: Dict[Any, Any] = {}
-
+from torchao.utils import fill_defaults
 
-def fill_defaults(args, n, defaults_tail):
-    if n - len(defaults_tail) > len(args):
-        raise RuntimeError("not enough defaults to fill arguments")
-    r = list(args)
-    for i in range(len(args), n):
-        r.append(defaults_tail[i - n + len(defaults_tail)])
-    return r
+UINT2_OPS_TABLE: Dict[Any, Any] = {}
 
 
 def implements(aten_ops):