Raise an error on non-hashable static arguments for jax.jit and xla_c…

…omputation.

Up to now, Jax was silently wrapping the object to ensure objects which are not hashable will be hashed using `id` and compared using `is`:

```
class WrapHashably(object):
  __slots__ = ["val"]
  def __init__(self, val):
    self.val = val
  def __hash__(self):
    return id(self.val)
  def __eq__(self, other):
    return self.val is other.val
```

This means that when providing different instances of objects that are non hashable, a recompilation was always occurring. This can be non-intuitive, for example with:

@partial(jax.jit, static_argnums=(1,))
def sum(a, b):
  return a+ b
sum(np.asarray([1,2,3]), np.asarray([4,5,6])
# The next line will recompile, because the 1-indexed argument is non
# hashable and thus compared by identity with different instances
sum(np.asarray([1,2,3]), np.asarray([4,5,6])

or more simply
np.pad(a, [2, 3], 'constant', constant_values=(4, 6))
          ^^^^^^
          non-hashable static argument.

The same problems can occur with any non-hashable types such as lists, dicts, etc. Even JAX itself was having some issues with this (which shows the behaviour was non-trivial to reason about).

If this commit breaks you, you usually have one of the following options:
- If specifying numpy array or jnp arrays arguments as static, you probably simply need to make them non static.
- When using non-hashable values, such as list, dicts or sets, you can simply use non-mutable versions, with tuples, frozendict, and frozenset.
- You can also change the way the function is defined, to capture these non-hashable arguments by closure, returning the jitted function.

PiperOrigin-RevId: 339351798

jax/api_util.py

@@ -88,13 +88,10 @@ def argnums_partial_except(f: lu.WrappedFun, static_argnums: Tuple[int, ...],
     try:
       hash(static_arg)
     except TypeError:
-      logging.warning(
-          "Static argument (index %s) of type %s for function %s is "
-          "non-hashable. As this can lead to unexpected cache-misses, it "
-          "will raise an error in a near future.", i, type(static_arg),
-          f.__name__)
-      # e.g. ndarrays, DeviceArrays
-      fixed_args[i] = WrapHashably(static_arg)  # type: ignore
+      raise ValueError(
+          "Non-hashable static arguments are not supported, as this can lead "
+          f"to unexpected cache-misses. Static argument (index {i}) of type "
+          f"{type(static_arg)} for function {f.__name__} is non-hashable.")
     else:
       fixed_args[i] = Hashable(static_arg)  # type: ignore
 

tests/api_test.py

@@ -415,6 +415,18 @@ def test_jit_reference_dropping(self):
     del g                   # no more references to x
     assert x() is None      # x is gone
 
+  def test_jit_raises_on_first_invocation_on_non_hashable_static_argnum(self):
+    if self.jit != jax.api._python_jit:
+      raise unittest.SkipTest("this test only applies to _python_jit")
+    f = lambda x, y: x + 3
+    jitted_f = self.jit(f, static_argnums=(1,))
+
+    msg = ("Non-hashable static arguments are not supported, as this can lead "
+           "to unexpected cache-misses. Static argument (index 1) of type "
+           "<class 'numpy.ndarray'> for function <lambda> is non-hashable.")
+    with self.assertRaisesRegex(ValueError, re.escape(msg)):
+      jitted_f(1, np.asarray(1))
+
   def test_cpp_jit_raises_on_non_hashable_static_argnum(self):
     if version < (0, 1, 58):
       raise unittest.SkipTest("Disabled because it depends on some future "
@@ -428,9 +440,9 @@ def test_cpp_jit_raises_on_non_hashable_static_argnum(self):
 
     jitted_f(1, 1)
 
-    msg = (
-        """Non-hashable static arguments are not supported. An error occured while trying to hash an object of type <class 'numpy.ndarray'>, 1. The error was:
-TypeError: unhashable type: 'numpy.ndarray'""")
+    msg = ("Non-hashable static arguments are not supported. An error occured "
+           "while trying to hash an object of type <class 'numpy.ndarray'>, 1. "
+           "The error was:\nTypeError: unhashable type: 'numpy.ndarray'")
 
     with self.assertRaisesRegex(ValueError, re.escape(msg)):
       jitted_f(1, np.asarray(1))