diff --git a/tinygrad/mlops.py b/tinygrad/mlops.py
index d73b66b562..8f4cc62800 100644
--- a/tinygrad/mlops.py
+++ b/tinygrad/mlops.py
@@ -1,5 +1,5 @@
 import math
-from typing import Tuple, Optional, cast
+from typing import Tuple, Optional
 from tinygrad.helpers import argsort, DType
 from tinygrad.ops import UnaryOps, BinaryOps, TernaryOps, ReduceOps
 from tinygrad.tensor import Function
@@ -203,9 +203,7 @@ class Shrink(Function):
     return x.shrink(arg)
 
   def backward(self, grad_output:LazyBuffer) -> LazyBuffer:
-    assert all(isinstance(x[0], int) and isinstance(x[1], int) for x in self.narg), "symbolic shrink does not support backward"
-    # need this cast because mypy cannot narrow the type even with assert
-    return grad_output.pad(cast(Tuple[Tuple[int, int], ...], self.narg))
+    return grad_output.pad(self.narg)
 
 class Flip(Function):
   def forward(self, x:LazyBuffer, axis:Tuple[int, ...]) -> LazyBuffer:
diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index c47d5df8e2..9b615d5f28 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -406,9 +406,9 @@ class Tensor:
 
   # NOTE: using slice is discouraged and things should migrate to pad and shrink
   def slice(self, arg:Sequence[Optional[Tuple[int, sint]]], value:float=0) -> Tensor:
-    arg_ = tuple([a if a is not None else (0,s) for s,a in zip(self.shape, arg)])
-    padding = tuple([(max(0, -p[0]), max(0, p[1]-self.shape[i])) for i,p in enumerate(arg_)])
-    return self.pad(padding, value=value).shrink(tuple([(p[0] + padding[i][0], p[1] + padding[i][0]) for i,p in enumerate(arg_)]))
+    arg_ = tuple(a if a is not None else (0, s) for s,a in zip(self.shape, arg))
+    padding = tuple((max(0, -l), max(0, r-s)) for s,(l,r) in zip(self.shape, arg_))
+    return self.pad(padding, value=value).shrink(tuple((l + pl, r + pl) for (l,r),(pl,_) in zip(arg_, padding)))
 
   def gather(self:Tensor, idx:Tensor, dim:int) -> Tensor:
     assert idx.ndim == self.ndim, "self.ndim must equal idx.ndim"
@@ -420,13 +420,13 @@ class Tensor:
     return ((idx == Tensor.arange(self.shape[dim], requires_grad=False, device=self.device)) * self.permute(*permarg).shrink(tuple([*[(0,sh) for sh in idx.shape[1:-1]], (0,self.shape[dim])])).unsqueeze(0)).sum(-1).transpose(ax1=0, ax2=dim)  # noqa: E501
 
   def cat(self:Tensor, *args:Tensor, dim:int=0) -> Tensor:
-    dim = (dim + len(self.shape)) if dim < 0 else dim
+    if dim < 0: dim += self.ndim
     assert all(len(y.shape) == len(self.shape) and all(y.shape[i] == s for i,s in enumerate(self.shape) if i != dim) for y in args)
     catargs = [self, *args]
     assert all(t.shape for t in catargs), "zero-dimensional tensor cannot be concatenated"
     shapes = [s.shape[dim] for s in catargs]
     shape_cumsum = [0, *accumulate(shapes)]
-    slc:List[List[Tuple[sint, sint]]] = [[(0, 0) for _ in self.shape] for _ in catargs]
+    slc:List[List[Optional[Tuple[sint, sint]]]] = [[None for _ in self.shape] for _ in catargs]
     for shp,k,s in zip(shapes, shape_cumsum[:-1], slc): s[dim] = (k, shape_cumsum[-1] - k - shp)
     return reduce(Tensor.__add__, [arg.pad(tuple(s)) for arg,s in zip(catargs, slc)])
 
@@ -457,7 +457,7 @@ class Tensor:
     return self if self.shape[dim] != 1 else self.reshape(self.shape[:dim] + self.shape[dim+1:])
 
   def unsqueeze(self, dim:int) -> Tensor:
-    if dim < 0: dim = len(self.shape) + dim + 1
+    if dim < 0: dim = self.ndim + dim + 1
     return self.reshape(self.shape[:dim] + (1,) + self.shape[dim:])
 
   # (padding_left, padding_right, padding_top, padding_bottom)
@@ -576,10 +576,10 @@ class Tensor:
     x, w = self, weight.reshape(groups, weight.shape[0]//groups, weight.shape[1], *weight.shape[2:]).permute(0,2,1,*trailing).flip(trailing)
     stride = make_pair(stride, len(HW))
     if any(s>1 for s in stride):
-      x = x.reshape(*x.shape[:2], *flatten((k,1) for k in x.shape[2:]))
-      x = x.pad(((0,0), (0,0), *flatten(((0,0),(0,s-1)) for s in stride)))
-      x = x.reshape(*x.shape[:2], *[k*s for k,s in zip(x.shape[2::2], stride)])
-      x = x.shrink(((0,x.shape[0]), (0,x.shape[1]), *[(0,k-(s-1)) for k,s in zip(x.shape[2:], stride)]))
+      x = x.reshape(None, None, *flatten((k,1) for k in x.shape[2:]))
+      x = x.pad((None, None, *flatten((None,(0,s-1)) for s in stride)))
+      x = x.reshape(None, None, *[k*s for k,s in zip(x.shape[2::2], stride)])
+      x = x.shrink((None, None, *[(0,k-(s-1)) for k,s in zip(x.shape[2:], stride)]))
     padding = flatten((((k-1)*d-p,(k-1)*d-p+op) for k,d,p,op in reversed(list(zip(HW, make_pair(dilation, len(HW)), make_pair(padding, len(HW)), make_pair(output_padding, len(HW)))))))  # noqa: E501
     return x.conv2d(w.reshape(w.shape[0]*w.shape[1],*w.shape[2:]), groups=groups, bias=bias, dilation=dilation, padding=padding)