diff --git a/tinygrad/llops/ops_cpu.py b/tinygrad/llops/ops_cpu.py
index 0ba429e6b8..b9db70ff38 100644
--- a/tinygrad/llops/ops_cpu.py
+++ b/tinygrad/llops/ops_cpu.py
@@ -18,7 +18,7 @@ class CPUBuffer(np.ndarray):
   def flip(x, axis): return np.flip(x, axis)
   def amax(x, *args, **kwargs): return np.amax(x, *args, **kwargs)
   def permute(x, order): return x.transpose(order)
-  def custompad(x, padding): return np.pad(x, padding).view(CPUBuffer) if any(x != 0 or y != 0 for x,y in padding) else x
+  def pad(x, padding): return np.pad(x, padding).view(CPUBuffer) if any(x != 0 or y != 0 for x,y in padding) else x
   def expand(x, new_shape): return np.broadcast_to(x, new_shape).view(CPUBuffer)
   def as_strided(x, size, stride): return np.lib.stride_tricks.as_strided(x, shape=size, strides=[y*x.dtype.itemsize for y in stride]).view(CPUBuffer)
   def contiguous(x): return x.ravel().reshape(x.shape)
@@ -41,20 +41,12 @@ class CPUBuffer(np.ndarray):
       return x.amax(axis, keepdims=True)
 
   def movement_op(x, op, arg=None):
-    if op == MovementOps.RESHAPE:
-      return x.reshape(arg)
-    elif op == MovementOps.PERMUTE:
-      return x.permute(arg)
-    elif op == MovementOps.FLIP:
-      return x.flip(arg)
-    elif op == MovementOps.PAD:
-      return x.custompad(arg)
-    elif op == MovementOps.SHRINK:
+    if op == MovementOps.SHRINK:
       return x[tuple(slice(p[0], p[1], None) for p in arg)]
-    elif op == MovementOps.EXPAND:
-      return x.expand(arg)
     elif op == MovementOps.STRIDED:
       return x.contiguous().as_strided([x[0] for x in arg], [x[1] for x in arg])
+    else:
+      return getattr(x, op.name.lower())(arg)
 
   PREPAD = True
   def processing_op(x,op,w,C):
diff --git a/tinygrad/llops/ops_torch.py b/tinygrad/llops/ops_torch.py
index 3c80ff97ea..c6319a5a55 100644
--- a/tinygrad/llops/ops_torch.py
+++ b/tinygrad/llops/ops_torch.py
@@ -4,7 +4,7 @@ from tinygrad.ops import ProcessingOps
 
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 class TorchBuffer(torch.Tensor):
-  def custompad(x, padding): return torch.nn.functional.pad(x, [item for sublist in padding[::-1] for item in sublist])
+  def pad(x, padding): return torch.nn.functional.pad(x, [item for sublist in padding[::-1] for item in sublist])
 
   @staticmethod
   def fromCPU(data): return TorchBuffer(torch.from_numpy(data).requires_grad_(False)).to(device)