un1d -> unravel [pr] (tinygrad#8300)

numpy/torch has a similar function called `unravel_index`

tinygrad/shape/view.py

@@ -76,8 +76,9 @@ def _reshape_mask(_mask:Optional[Tuple[Tuple[sint, sint], ...]], old_shape:Tuple
 
   return tuple(reversed(new_mask))
 
-def un1d(shape:Tuple[sint, ...], offset:sint) -> List[sint]:
+def unravel(shape:Tuple[sint, ...], offset:sint) -> List[sint]:
   # find the position of offset on each dimension based on shape
+  # similar to unravel_index in numpy/torch
   ret = []
   for stride in strides_for_shape(shape):
     ret.append(offset // stride if stride != 0 else 0)
@@ -171,12 +172,12 @@ def __add__(self, vm1:View) -> Optional[View]:
     if not all_int(vm1.shape): return None
 
     # Project vm1's offset and strides on to vm2.
-    origin = un1d(vm2.shape, vm1.offset)
+    origin = unravel(vm2.shape, vm1.offset)
     terms: List[List[Tuple[int, sint]]] = [[] for _ in vm2.shape]
     strides: List[sint] = [0] * len(vm1.shape)
     for d1, st in enumerate(vm1.strides):
       if st == 0: continue
-      for d2, (o, s1) in enumerate(zip(origin, un1d(vm2.shape, vm1.offset + st))):
+      for d2, (o, s1) in enumerate(zip(origin, unravel(vm2.shape, vm1.offset + st))):
         if (s1 := s1 - o) == 0: continue
         terms[d2].append((d1, s1))
         strides[d1] += s1 * vm2.strides[d2]