jit includes tensor inputs in containers (#14043)

* jit includes tensor inputs in containers

* cleanup

test/test_jit.py

@@ -184,16 +184,9 @@ class TestJit(unittest.TestCase):
   def test_array_jit(self):
     @TinyJit
     def add_array(a, arr): return (a+arr[0]).realize()
-    for i in range(5):
-      a = Tensor.randn(10, 10)
-      b = Tensor.randn(10, 10)
-      a.realize(), b.realize()
-      c = add_array(a, [b])
-      if i >= 2:
-        # should fail once jitted since jit can't handle arrays
-        np.testing.assert_allclose(np.any(np.not_equal(c.numpy(),a.numpy()+b.numpy())), True, atol=1e-4, rtol=1e-5)
-      else:
-        np.testing.assert_allclose(c.numpy(), a.numpy()+b.numpy(), atol=1e-4, rtol=1e-5)
+    for _ in range(5):
+      a, b = Tensor.randn(10, 10).realize(), Tensor.randn(10, 10).realize()
+      np.testing.assert_allclose(add_array(a, [b]).numpy(), a.numpy()+b.numpy(), atol=1e-4, rtol=1e-5)
     assert_jit_cache_len(add_array, 1)
 
   def test_jit_copyin(self):

test/test_jit_footguns.py

@@ -6,7 +6,6 @@ Each test shows behavior that works without JIT but changes with JIT.
 Comments marked "should be X!" indicate the intuitively expected value.
 
 SILENT MISMATCHES (highest priority - wrong results, no error):
-  tensors_in_containers_ignored      EASY   only checks t.__class__ is Tensor, could scan lists/dicts
   class_method_shared_across_instances EASY could check if first arg is self and warn
   output_buffer_reuse                MED    performance tradeoff, could add option or better docs
   python_constants_frozen            HARD   inherent to tracing JITs
@@ -65,20 +64,12 @@ class TestJitFootguns(unittest.TestCase):
     with self.assertRaises(JitError):
       f(x, x)
 
-  def test_tensors_in_containers_ignored(self):
-    """Tensors inside lists/dicts are not tracked as inputs."""
+  def test_tensors_in_containers(self):
     @TinyJit
     def f(a, arr): return (a + arr[0]).realize()
-
-    results = []
     for i in range(4):
       a, b = Tensor([1, 1, 1]).realize(), Tensor([i, i, i]).realize()
-      results.append(f(a, [b]).numpy().copy())
-
-    np.testing.assert_array_equal(results[0], [1, 1, 1])  # warmup
-    np.testing.assert_array_equal(results[1], [2, 2, 2])  # capture
-    np.testing.assert_array_equal(results[2], [2, 2, 2])  # should be [3,3,3]!
-    np.testing.assert_array_equal(results[3], [2, 2, 2])  # should be [4,4,4]!
+      np.testing.assert_array_equal(f(a, [b]).numpy(), [1+i, 1+i, 1+i])
 
   def test_nested_jit_fails_on_second_call(self):
     """Nested JIT works on first call but fails on second."""

tinygrad/engine/jit.py

@@ -228,6 +228,10 @@ class CapturedJit(Generic[ReturnType]):
 def _prepare_jit_inputs(args, kwargs):
   input_tensors: list[tuple[int|str, Tensor]] = [(name,t) for name,t in list(enumerate(args))+sorted(kwargs.items()) if t.__class__ is Tensor]
   names, tensors = [name for name,_ in input_tensors], [t for _,t in input_tensors]
+  # extract tensors from containers (shallow, not recursive to avoid grabbing model weights)
+  for x in args + tuple(kwargs.values()):
+    it = x if isinstance(x, (tuple,list)) else x.values() if isinstance(x, dict) else []
+    tensors += [t for t in it if t.__class__ is Tensor and not any(t is y for y in tensors)]
   if len(unrealized_tensors := [x for x in tensors if not x.uop.is_realized]): Tensor.realize(*unrealized_tensors)
   # TODO: this multi unpack stuff is not well tested.
   lbs: list[UOp] = flatten([t.uop.src if t.uop.op is Ops.MULTI else [t.uop] for t in tensors])