Merge pull request #25544 from jakevdp:array-aval

PiperOrigin-RevId: 707254670

Author: Google-ML-Automation

jax/_src/abstract_arrays.py

@@ -43,13 +43,6 @@
 
 array_types: set[type] = {np.ndarray} | numpy_scalar_types  # pylint: disable=g-bare-generic
 
-def canonical_concrete_aval(val, weak_type=None):
-  weak_type = dtypes.is_weakly_typed(val) if weak_type is None else weak_type
-  dtype = dtypes.canonicalize_dtype(np.result_type(val))
-  dtypes.check_valid_dtype(dtype)
-  sharding = core._get_abstract_sharding(val)
-  return ShapedArray(np.shape(val), dtype, weak_type=weak_type, sharding=sharding)
-
 
 def masked_array_error(*args, **kwargs):
   raise ValueError("numpy masked arrays are not supported as direct inputs to JAX functions. "

jax/_src/array.py

@@ -22,7 +22,6 @@
 import operator as op
 from typing import Any, TYPE_CHECKING, cast
 
-from jax._src import abstract_arrays
 from jax._src import api
 from jax._src import api_util
 from jax._src import basearray
@@ -1027,18 +1026,20 @@ def make_array_from_single_device_arrays(
   return ArrayImpl(aval, sharding, cast(Sequence[ArrayImpl], arrays),
                    committed=True)
 
-
-core.pytype_aval_mappings[ArrayImpl] = abstract_arrays.canonical_concrete_aval
-core.xla_pytype_aval_mappings[ArrayImpl] = op.attrgetter('aval')
 xla.canonicalize_dtype_handlers[ArrayImpl] = pxla.identity
+
 def _get_aval_array(self):
   if config.sharding_in_types.value and isinstance(self.sharding, NamedSharding):
     return self.aval.update(sharding=NamedSharding(
         self.sharding.mesh.abstract_mesh,
         self.sharding.spec._normalized_spec(self.ndim)))
   else:
     return self.aval
+
 api_util._shaped_abstractify_handlers[ArrayImpl] = _get_aval_array
+core.pytype_aval_mappings[ArrayImpl] = _get_aval_array
+core.xla_pytype_aval_mappings[ArrayImpl] = _get_aval_array
+
 # TODO(jakevdp) replace this with true inheritance at the C++ level.
 basearray.Array.register(ArrayImpl)