Finalize jax.numpy array API compliance

CHANGELOG.md

@@ -8,6 +8,10 @@ Remember to align the itemized text with the first line of an item within a list
 
 ## jax 0.4.29
 
+* New Functionality
+  * The {mod}`jax.numpy` module is now compliant with the Python array API
+    2023 standard.
+
 * Deprecations
   * Removed a number of previously-deprecated APIs:
     * from {mod}`jax.core`: `non_negative_dim`, `DimSize`, `Shape`

jax/_src/basearray.pyi

@@ -213,6 +213,13 @@ class Array(abc.ABC):
   def traceback(self) -> Traceback: ...
   def unsafe_buffer_pointer(self) -> int: ...
 
+  # Array API inclusions
+  @property
+  def device(self) -> Sharding: ...
+  def to_device(self, device: Device | Sharding | None, *,
+               stream: int | Any | None = None) -> Sharding: ...
+  def __array_namespace__(self, /, *,
+                          api_version: None | str = None): ...
 
 ArrayLike = Union[
   Array,  # JAX array type

jax/_src/numpy/_version.py

@@ -0,0 +1,15 @@
+# Copyright 2024 The JAX Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+__array_api_version__ = '2023.12'

jax/_src/numpy/array_methods.py

@@ -45,6 +45,7 @@
 from jax._src.ops import scatter
 from jax._src.typing import Array, ArrayLike, DimSize, DTypeLike, Shape
 from jax._src.util import safe_zip, safe_map
+from jax._src.numpy._version import __array_api_version__
 
 map, unsafe_map = safe_map, map
 zip, unsafe_zip = safe_zip, zip
@@ -56,6 +57,21 @@
 # operator overloads mainly just forward calls to the corresponding lax_numpy
 # functions, which can themselves handle instances from any of these classes.
 
+def _array_namespace(arr: ArrayLike, /, *, api_version: None | str = None):
+  if api_version is not None and api_version != __array_api_version__:
+    raise ValueError(f"{api_version=!r} is not available; "
+                     f"available versions are: {[__array_api_version__]}")
+  return jax.numpy
+
+def _to_device(arr: ArrayLike, device: xc.Device | Sharding | None, *,
+               stream: int | Any | None = None):
+  if stream is not None:
+    raise NotImplementedError("stream argument of array.to_device()")
+  return jax.device_put(arr, device)
+
+def _device(arr: Array) -> Sharding:
+  """Length of one array element in bytes."""
+  return arr.sharding
 
 def _astype(arr: ArrayLike, dtype: DTypeLike, copy: bool = False, device: xc.Device | Sharding | None = None) -> Array:
   """Copy the array and cast to a specified dtype.
@@ -658,6 +674,7 @@ def max(self, values, *, indices_are_sorted=False, unique_indices=False,
 }
 
 _array_methods = {
+  "__array_namespace__": _array_namespace,
   "all": reductions.all,
   "any": reductions.any,
   "argmax": lax_numpy.argmax,
@@ -694,6 +711,7 @@ def max(self, values, *, indices_are_sorted=False, unique_indices=False,
   "sum": reductions.sum,
   "swapaxes": lax_numpy.swapaxes,
   "take": lax_numpy.take,
+  "to_device": _to_device,
   "trace": lax_numpy.trace,
   "transpose": _transpose,
   "var": reductions.var,
@@ -718,6 +736,7 @@ def max(self, values, *, indices_are_sorted=False, unique_indices=False,
   "nbytes": _nbytes,
   "itemsize": _itemsize,
   "at": _IndexUpdateHelper,
+  "device": _device,
 }
 
 def _set_shaped_array_attributes(shaped_array):

jax/_src/numpy/util.py

@@ -17,7 +17,7 @@
 from functools import partial
 import re
 import textwrap
-from typing import Any, Callable, NamedTuple, TypeVar
+from typing import Any, Callable, NamedTuple, TypeVar, Tuple
 
 import warnings
 
@@ -28,6 +28,8 @@
 from jax._src.lax import lax
 from jax._src.util import safe_zip, safe_map
 from jax._src.typing import Array, ArrayLike, DimSize, DType, DTypeLike, Shape
+from jax._src.sharding import Sharding
+from jax._src.lib import xla_client as xc
 
 import numpy as np
 
@@ -453,3 +455,66 @@ def _where(condition: ArrayLike, x: ArrayLike, y: ArrayLike) -> Array:
   except:
     is_always_empty = False  # can fail with dynamic shapes
   return lax.select(condition_arr, x_arr, y_arr) if not is_always_empty else x_arr
+
+class __array_namespace_info__:
+
+  def __init__(self):
+    self._capabilities = {
+      "boolean indexing": True,
+      "data-dependent shapes": False,
+    }
+
+
+  def _build_dtype_dict(self):
+    array_api_types = {
+      "bool", "int8", "int16",
+      "int32", "uint8", "uint16",
+      "uint32", "float32", "complex64"
+    }
+    if config.enable_x64.value:
+      array_api_types |= {"int64", "uint64", "float64", "complex128"}
+    return {category: {t.name: t for t in types if t.name in array_api_types}
+            for category, types in dtypes._dtype_kinds.items()}
+
+  def default_device(self):
+    # By default JAX arrays are uncommitted (device=None), meaning that
+    # JAX is free to choose the most efficient device placement.
+    return None
+
+  def devices(self):
+    return api.devices()
+
+  def capabilities(self):
+    return self._capabilities
+
+  def default_dtypes(self, *, device: xc.Device | Sharding | None = None):
+    # Array API supported dtypes are device-independent in JAX
+    del device
+    default_dtypes = {
+      "real floating": "f",
+      "complex floating": "c",
+      "integral": "i",
+      "indexing": "i",
+    }
+    return {
+      dtype_name: dtypes.canonicalize_dtype(
+        dtypes._default_types.get(kind)
+      ) for dtype_name, kind in default_dtypes.items()
+    }
+
+  def dtypes(
+      self, *,
+      device: xc.Device | Sharding | None = None,
+      kind: str | Tuple[str, ...] | None = None):
+    # Array API supported dtypes are device-independent in JAX
+    del device
+    data_types = self._build_dtype_dict()
+    if kind is None:
+      out_dict = data_types["numeric"] | data_types["bool"]
+    elif isinstance(kind, tuple):
+      out_dict = {}
+      for _kind in kind:
+        out_dict |= data_types[_kind]
+    else:
+      out_dict = data_types[kind]
+    return out_dict

jax/experimental/array_api/__init__.py

@@ -36,12 +36,12 @@
 
 from __future__ import annotations
 
-from jax.experimental.array_api._version import __array_api_version__ as __array_api_version__
-
 from jax.experimental.array_api import fft as fft
 from jax.experimental.array_api import linalg as linalg
 
 from jax.numpy import (
+    __array_api_version__ as __array_api_version__,
+    __array_namespace_info__ as __array_namespace_info__,
     abs as abs,
     acos as acos,
     acosh as acosh,
@@ -203,11 +203,3 @@
     std as std,
     var as var,
 )
-
-from jax.experimental.array_api._utility_functions import (
-    __array_namespace_info__ as __array_namespace_info__,
-)
-
-from jax.experimental.array_api import _array_methods
-_array_methods.add_array_object_methods()
-del _array_methods

jax/numpy/__init__.py

@@ -445,6 +445,11 @@
 
 from jax._src.numpy.vectorize import vectorize as vectorize
 
+from jax._src.numpy._version import __array_api_version__
+from jax._src.numpy.util import (
+    __array_namespace_info__ as __array_namespace_info__,
+)
+
 # Dynamically register numpy-style methods on JAX arrays.
 from jax._src.numpy.array_methods import register_jax_array_methods
 register_jax_array_methods()