diff --git a/test/unit/test_function.py b/test/unit/test_function.py
index 6a98e6ebef..67e0ca4d65 100644
--- a/test/unit/test_function.py
+++ b/test/unit/test_function.py
@@ -2,6 +2,7 @@ import numpy as np
 import unittest
 from tinygrad.function import function
 from tinygrad import Tensor
+from tinygrad.uop.ops import UOp
 
 class TestFunction(unittest.TestCase):
   def test_simple(self):
@@ -102,7 +103,6 @@ class TestFunction(unittest.TestCase):
     np.testing.assert_allclose(w.grad.numpy(), [4., 5., 6.])
 
   def test_symbolic_index(self):
-    from tinygrad.uop.ops import UOp
     table = Tensor([10,20,30,40]).contiguous().realize()
     @function
     def f(x:Tensor, start_pos:int|UOp) -> Tensor:
@@ -111,6 +111,14 @@ class TestFunction(unittest.TestCase):
     v = UOp.variable("start_pos", 0, 3)
     np.testing.assert_equal(f(Tensor([1,2,3]), v.bind(0)).numpy(), [11,12,13])
 
+  def test_symbolic_shape_input(self):
+    table = Tensor([10,20,30,40]).contiguous().realize()
+    @function
+    def f(x:Tensor) -> Tensor: return x * 2
+    sz = UOp.variable("sz", 1, 3)
+    slic = table[:sz.bind(2)]
+    np.testing.assert_equal(f(slic)[:2].numpy(), [20,40])
+
   def test_nested_calls(self):
     w = Tensor([10., 20., 30.])
     @function
diff --git a/tinygrad/function.py b/tinygrad/function.py
index e67ff2382d..27b5ebaddb 100644
--- a/tinygrad/function.py
+++ b/tinygrad/function.py
@@ -10,7 +10,7 @@ def add_to_ctx(ctx, x:UOp):
   return ret
 
 pm_ctx = PatternMatcher([
-  (UPat(Ops.BUFFER, name="x"), add_to_ctx),
+  (UPat((Ops.BUFFER, Ops.BIND), name="x"), add_to_ctx),
   (UPat((Ops.ASSIGN, Ops.CONTIGUOUS), name="x"),
    lambda ctx,x: add_to_ctx(ctx,x) if not x.op_in_backward_slice_with_self(Ops.PARAM) else None),
 ])
diff --git a/tinygrad/schedule/rangeify.py b/tinygrad/schedule/rangeify.py
index 26942daef8..bc54519eff 100644
--- a/tinygrad/schedule/rangeify.py
+++ b/tinygrad/schedule/rangeify.py
@@ -90,7 +90,7 @@ def resolve_call(c:UOp, allow_param_mismatch=True) -> UOp|None:
 
   dict_map = {x:args[x.arg] for x in params}
   for i, (p, a) in enumerate(dict_map.items()):
-    if p.shape != a.shape: raise TypeError(f"arg {i} shape mismatch: expected {p.shape}, got {a.shape}")
+    if p.max_shape != a.max_shape: raise TypeError(f"arg {i} shape mismatch: expected {p.shape}, got {a.shape}")
     if p.dtype != a.dtype: raise TypeError(f"arg {i} dtype mismatch: expected {p.dtype}, got {a.dtype}")
   return c.src[0].substitute(dict_map, walk=True)