Added argument check to all primitives.

jax/api.py

@@ -1590,6 +1590,7 @@ def _check_args(args):
       raise TypeError("Argument '{}' of type {} is not a valid JAX type"
                       .format(arg, type(arg)))
 
+# TODO(necula): this duplicates code in core.valid_jaxtype
 def _valid_jaxtype(arg):
   try:
     xla.abstractify(arg)  # faster than core.get_aval

jax/core.py

@@ -17,7 +17,7 @@
 from operator import attrgetter
 from contextlib import contextmanager
 from collections import namedtuple
-from functools import total_ordering
+from functools import total_ordering, reduce
 import itertools as it
 from weakref import ref
 import threading
@@ -204,8 +204,6 @@ def __repr__(self):
     return '{}'.format(self.name)
 
   def bind(self, *args, **kwargs):
-    assert skip_checks or all(isinstance(arg, Tracer)
-                              or valid_jaxtype(arg) for arg in args), args
     top_trace = find_top_trace(args)
     if top_trace is None:
       return self.impl(*args, **kwargs)
@@ -623,14 +621,22 @@ def full_lower(val):
   else:
     return val
 
-def find_top_trace(xs):
- try:
-   top_trace = max((x._trace for x in xs if isinstance(x, Tracer)),
-                   key=attrgetter('level'))
- except ValueError:
-   return None
- else:
-   return type(top_trace)(top_trace.master, cur_sublevel())
+def find_top_trace(args) -> Optional[Tracer]:
+  """Find the tracer with the highest-level, or None. """
+  def check_arg(top_so_far: Optional[Tracer], arg) -> Optional[Tracer]:
+    if isinstance(arg, Tracer):
+      return (top_so_far
+              if top_so_far and top_so_far.level >= arg._trace.level else arg._trace)
+    # Raises error here for bind on LAX primitives
+    if not valid_jaxtype(arg):
+      raise TypeError(f"Argument '{arg}' of type {type(arg)} is not a valid JAX type")
+    return top_so_far
+
+  top_trace = reduce(check_arg, args, None)
+  if top_trace is not None:
+    return type(top_trace)(top_trace.master, cur_sublevel())  # type: ignore[call-arg]
+  else:
+    return None
 
 @contextmanager
 def initial_style_staging():

jax/lax/lax.py

@@ -1199,7 +1199,7 @@ def top_k(operand: Array, k: int) -> Tuple[Array, Array]:
   return top_k_p.bind(operand, k=k)
 
 def tie_in(x: Array, y: Array) -> Array:
-  """Gives ``y`` a fake data dependence on ``x``.
+  """Returns the value of ``y`` but with a fake data dependence on ``x``.
 
   When staging to XLA (e.g. running under jit or pmap), values that don't depend
   on computation inputs are computed op-by-op, and folded into the XLA
@@ -1209,6 +1209,10 @@ def tie_in(x: Array, y: Array) -> Array:
   When staging to XLA and ``x`` is already staged, then the result of ``tie_in``
   is ``y``, but staged to XLA. Downstream use of the result will also be staged
   to XLA.
+
+  For example, ``lax.sin(const)`` would be constant-folded if ``const`` is
+  a constant array, but ``lax.sin(lax.tie_in(x, const))``, will be staged to
+  XLA as long as ``x`` is staged to XLA.
   """
   return tie_in_p.bind(x, y)
 

tests/lax_test.py

@@ -2633,7 +2633,7 @@ def f2(x, y):
     expected = onp.array(0.0)
     self.assertAllClose(ans, expected, check_dtypes=False)
 
-    with self.assertRaises(TypeError if core.skip_checks else AssertionError):
+    with self.assertRaises(TypeError):
       lax.stop_gradient(lambda x: x)
 
   # TODO(mattjj): make this a more systematic test
@@ -3250,6 +3250,10 @@ def testTopK(self, shape, dtype, k, bdims, rng_factory):
   # TODO Collapse
   # TODO Scatter
 
+  def test_tie_in_error(self):
+    with self.assertRaisesRegex(TypeError,
+                                ".*tuple.* is not a valid JAX type"):
+      api.make_jaxpr(lambda x: lax.tie_in((x, x), 1))(1.)
 
 if __name__ == '__main__':
   absltest.main()