[callback] Fix io_callback for callbacks that return Python literals.

jax/_src/interpreters/mlir.py

@@ -2461,15 +2461,22 @@ def _wrapped_callback(*args):
       raise RuntimeError(
           "Mismatched number of outputs from callback. "
           "Expected: {}, Actual: {}".format(len(result_avals), len(out_vals)))
+    # Handle Python literals, and custom arrays, e.g., tf.Tensor.
+    out_vals = tuple(np.asarray(a) for a in out_vals)
     for i, (out_val, out_aval) in enumerate(zip(out_vals, result_avals)):
       if out_val.shape != out_aval.shape:
         raise RuntimeError(
-            f"Incorrect output shape for return value {i}: "
-            "Expected: {}, Actual: {}".format(out_aval.shape, out_val.shape))
+            f"Incorrect output shape for return value #{i}: "
+            f"Expected: {out_aval.shape}, Actual: {out_val.shape}")
+      if out_val.dtype != dtypes.canonicalize_dtype(out_val.dtype):
+        raise RuntimeError(
+            "Cannot return 64-bit values when `jax_enable_x64` is disabled. "
+            f"Actual: {out_val.dtype}")
       if out_val.dtype != out_aval.dtype:
         raise RuntimeError(
-            f"Incorrect output dtype for return value {i}: "
-            "Expected: {}, Actual: {}".format(out_aval.dtype, out_val.dtype))
+            f"Incorrect output dtype for return value #{i}: "
+            f"Expected: {out_aval.dtype}, Actual: {out_val.dtype}")
+
     if platform == "tpu":
       # On TPU we cannot receive empty arrays. So, we return from the wrapped
       # callback only the non-empty results, and we will create empty constants

tests/python_callback_test.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import collections
+import contextlib
 import functools
 import logging
 import textwrap
@@ -27,11 +28,11 @@
 from jax._src import core
 from jax._src import dispatch
 from jax._src import maps
-from jax._src.maps import xmap
 from jax._src import test_util as jtu
 from jax._src import util
 from jax._src.lib import xla_client
 from jax._src.lib import xla_extension_version
+from jax._src.maps import xmap
 from jax.experimental import io_callback
 from jax.experimental import pjit
 from jax.experimental.shard_map import shard_map
@@ -72,6 +73,7 @@ def tearDownModule():
   for flavor in ("io_unordered", "io_ordered", "pure")
 )
 
+
 class PythonCallbackTest(jtu.JaxTestCase):
 
   def setUp(self):
@@ -93,6 +95,73 @@ def f(x):
     out = f(0.)
     self.assertEqual(out, 1.)
 
+  @parameterized.named_parameters(
+      dict(
+          testcase_name=f"{flavor}_expect_dtype_{expect_dtype}",
+          callback=dict(
+              io_unordered=io_calback_unordered,
+              io_ordered=io_callback_ordered,
+              pure=jax.pure_callback,
+          )[flavor],
+          expect_dtype=expect_dtype,
+      )
+      for flavor in ("io_unordered", "io_ordered", "pure")
+      for expect_dtype in (np.int32, np.int64, np.float32, np.float64)
+  )
+  def test_callback_returning_python_literal(self, *, callback, expect_dtype):
+    returned_literal = 42 if expect_dtype in (np.int32, np.int64) else 42.0
+
+    @jax.jit
+    def f(x):
+      return callback(
+          lambda x: returned_literal, core.ShapedArray((), expect_dtype), x
+      )
+
+    if not config.enable_x64.value:
+      ctx = self.assertRaisesRegex(Exception, "Cannot return 64-bit values")
+    elif expect_dtype in (np.int32, np.float32):
+      ctx = self.assertRaisesRegex(Exception, "Incorrect output dtype")
+    else:
+      ctx = contextlib.nullcontext()
+
+    with ctx:
+      out = f(0.0)
+      jax.effects_barrier()
+      self.assertEqual(out, returned_literal)
+
+  @with_pure_and_io_callbacks
+  def test_callback_returning_custom_array(self, *, callback):
+    # Some users write the callback in TF, returning a tf.Tensor. We don't
+    # want to add TF as a dependency, but simulate that use case with a
+    # custom array class.
+    class CustomArray:
+
+      def __init__(self, a: np.ndarray):
+        self.a = a
+
+      @property
+      def shape(self):
+        return self.a.shape
+
+      @property
+      def dtype(self):
+        return self.a.dtype
+
+      def __array__(self):
+        return self.a
+
+    @jax.jit
+    def f(x):
+      return callback(
+          lambda x: CustomArray(np.array(42.0, dtype=np.float32)),
+          core.ShapedArray((), np.float32),
+          x,
+      )
+
+    out = f(0.0)
+    jax.effects_barrier()
+    self.assertEqual(out, 42.0)
+
   @parameterized.named_parameters(
     dict(testcase_name=f"{flavor}_{dtype}",
          dtype=dtype,