diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index 87f18d9417..2a6a5c6147 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -1947,21 +1947,15 @@ class Tensor(SimpleMathTrait):  # pylint: disable=abstract-method
     noop_, i_, ksz_ = [None] * len(self.shape[:-len(k_)]), self.shape[-len(k_):], [d*(k-1)+1 for k,d in  zip(k_,d_)]
     assert all(ksz <= i for ksz,i in zip(ksz_, i_)), "kernel size cannot be greater than actual input size"
     o_ = [ceildiv(i - d * (k-1), s) for i,d,k,s in zip(i_, d_, k_, s_)]
-    if any(resolve(k > s) for k,s in zip(k_, s_)) or any(d != 1 for d in d_):
-      # repeats such that we don't need padding
-      xup = self.repeat([1]*len(noop_) + [o+ceildiv((o*s),i) for o,i,s in zip(o_,i_,s_)])
-      # handle stride
-      xup = xup.shrink(tuple(noop_ + [(0, o*(i+s)) for o,i,s in zip(o_,i_,s_)])).reshape(noop_ + flatten((o,i+s) for o,i,s in zip(o_,i_,s_)))
-      # handle dilation
-      xup = xup.shrink(
-        tuple(noop_ + flatten((None, (0,ksz+d-1)) for ksz,d in zip(ksz_, d_)))).reshape(noop_ + flatten((o,k,d) for o,k,d in zip(o_,k_,d_)))
-      xup = xup.shrink(tuple(noop_ + flatten(((0,o), (0,k), (0,1)) for o,k in zip(o_,k_)))).reshape(noop_ + flatten((o,k) for o,k in zip(o_,k_)))
-      # permute to move reduce to the end
-      return xup.permute(*range(len(noop_)), *[len(noop_)+i*2 for i in range(len(i_))], *[len(noop_)+i*2+1 for i in range(len(i_))])
-    # TODO: once the shapetracker can optimize well, remove this alternative implementation
-    xup = self.pad(tuple(noop_ + [(0, max(0,o*s-i)) for i,o,s in zip(i_, o_, s_)])).shrink(tuple(noop_ + [(0,o*s) for o,s in zip(o_, s_)]))
-    xup = xup.reshape(noop_ + flatten(((o,s) for o,s in zip(o_, s_))))
-    xup = xup.shrink(tuple(noop_ + flatten(((0,o), (0,k)) for o,k in zip(o_, k_))))
+    # repeats such that we don't need padding
+    xup = self.repeat([1]*len(noop_) + [o+ceildiv((o*s),i) for o,i,s in zip(o_,i_,s_)])
+    # handle stride
+    xup = xup.shrink(tuple(noop_ + [(0, o*(i+s)) for o,i,s in zip(o_,i_,s_)])).reshape(noop_ + flatten((o,i+s) for o,i,s in zip(o_,i_,s_)))
+    # handle dilation
+    xup = xup.shrink(
+      tuple(noop_ + flatten((None, (0,ksz+d-1)) for ksz,d in zip(ksz_, d_)))).reshape(noop_ + flatten((o,k,d) for o,k,d in zip(o_,k_,d_)))
+    xup = xup.shrink(tuple(noop_ + flatten(((0,o), (0,k), (0,1)) for o,k in zip(o_,k_)))).reshape(noop_ + flatten((o,k) for o,k in zip(o_,k_)))
+    # permute to move reduce to the end
     return xup.permute(*range(len(noop_)), *[len(noop_)+i*2 for i in range(len(i_))], *[len(noop_)+i*2+1 for i in range(len(i_))])
 
   def _padding2d(self, padding:Union[int, Sequence[int]], dims:int) -> Sequence[int]: