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e32373c3ea
TODO: * Don't allow `x + y` if `jax.Array` is not fully addressable. * Figure out how to use the already written tests with Array. Might be able to follow the path taken by SDA. PiperOrigin-RevId: 457034779
295 lines
9.9 KiB
Python
295 lines
9.9 KiB
Python
# Copyright 2022 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import abc
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from typing import Any, Tuple, Optional, Union
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from jax import core
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from jax.interpreters import pxla
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from jax._src import device_array
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from jax.experimental import array
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import numpy as np
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class ArrayMeta(abc.ABCMeta):
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"""Metaclass for overriding ndarray isinstance checks."""
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def __instancecheck__(self, instance):
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# Allow tracer instances with avals that are instances of UnshapedArray.
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# We could instead just declare Tracer an instance of the ndarray type, but
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# there can be traced values that are not arrays. The main downside here is
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# that isinstance(x, ndarray) might return true but
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# issubclass(type(x), ndarray) might return false for an array tracer.
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try:
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return (hasattr(instance, "aval") and
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isinstance(instance.aval, core.UnshapedArray))
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except AttributeError:
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super().__instancecheck__(instance)
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class ndarray(metaclass=ArrayMeta):
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dtype: np.dtype
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ndim: int
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shape: Tuple[int, ...]
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size: int
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def __init__(self, shape, dtype=None, buffer=None, offset=0, strides=None,
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order=None):
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raise TypeError("jax.numpy.ndarray() should not be instantiated explicitly."
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" Use jax.numpy.array, or jax.numpy.zeros instead.")
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@abc.abstractmethod
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def __getitem__(self, key, indices_are_sorted=False,
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unique_indices=False) -> Any: ...
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@abc.abstractmethod
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def __setitem__(self, key, value) -> Any: ...
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@abc.abstractmethod
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def __len__(self) -> Any: ...
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@abc.abstractmethod
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def __iter__(self) -> Any: ...
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@abc.abstractmethod
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def __reversed__(self) -> Any: ...
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# Comparisons
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@abc.abstractmethod
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def __lt__(self, other) -> Any: ...
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@abc.abstractmethod
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def __le__(self, other) -> Any: ...
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@abc.abstractmethod
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def __eq__(self, other) -> Any: ...
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@abc.abstractmethod
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def __ne__(self, other) -> Any: ...
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@abc.abstractmethod
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def __gt__(self, other) -> Any: ...
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@abc.abstractmethod
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def __ge__(self, other) -> Any: ...
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# Unary arithmetic
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@abc.abstractmethod
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def __neg__(self) -> Any: ...
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@abc.abstractmethod
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def __pos__(self) -> Any: ...
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@abc.abstractmethod
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def __abs__(self) -> Any: ...
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@abc.abstractmethod
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def __invert__(self) -> Any: ...
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# Binary arithmetic
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@abc.abstractmethod
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def __add__(self, other) -> Any: ...
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@abc.abstractmethod
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def __sub__(self, other) -> Any: ...
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@abc.abstractmethod
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def __mul__(self, other) -> Any: ...
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@abc.abstractmethod
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def __matmul__(self, other) -> Any: ...
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@abc.abstractmethod
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def __truediv__(self, other) -> Any: ...
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@abc.abstractmethod
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def __floordiv__(self, other) -> Any: ...
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@abc.abstractmethod
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def __mod__(self, other) -> Any: ...
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@abc.abstractmethod
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def __divmod__(self, other) -> Any: ...
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@abc.abstractmethod
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def __pow__(self, other) -> Any: ...
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@abc.abstractmethod
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def __lshift__(self, other) -> Any: ...
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@abc.abstractmethod
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def __rshift__(self, other) -> Any: ...
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@abc.abstractmethod
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def __and__(self, other) -> Any: ...
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@abc.abstractmethod
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def __xor__(self, other) -> Any: ...
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@abc.abstractmethod
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def __or__(self, other) -> Any: ...
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@abc.abstractmethod
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def __radd__(self, other) -> Any: ...
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@abc.abstractmethod
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def __rsub__(self, other) -> Any: ...
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@abc.abstractmethod
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def __rmul__(self, other) -> Any: ...
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@abc.abstractmethod
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def __rmatmul__(self, other) -> Any: ...
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@abc.abstractmethod
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def __rtruediv__(self, other) -> Any: ...
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@abc.abstractmethod
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def __rfloordiv__(self, other) -> Any: ...
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@abc.abstractmethod
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def __rmod__(self, other) -> Any: ...
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@abc.abstractmethod
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def __rdivmod__(self, other) -> Any: ...
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@abc.abstractmethod
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def __rpow__(self, other) -> Any: ...
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@abc.abstractmethod
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def __rlshift__(self, other) -> Any: ...
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@abc.abstractmethod
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def __rrshift__(self, other) -> Any: ...
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@abc.abstractmethod
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def __rand__(self, other) -> Any: ...
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@abc.abstractmethod
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def __rxor__(self, other) -> Any: ...
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@abc.abstractmethod
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def __ror__(self, other) -> Any: ...
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@abc.abstractmethod
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def __bool__(self) -> Any: ...
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@abc.abstractmethod
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def __complex__(self) -> Any: ...
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@abc.abstractmethod
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def __int__(self) -> Any: ...
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@abc.abstractmethod
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def __float__(self) -> Any: ...
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@abc.abstractmethod
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def __round__(self, ndigits=None) -> Any: ...
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@abc.abstractmethod
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def __index__(self) -> Any: ...
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# np.ndarray methods:
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@abc.abstractmethod
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def all(self, axis: Optional[Union[int, Tuple[int, ...]]] = None, out=None,
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keepdims=None) -> Any: ...
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@abc.abstractmethod
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def any(self, axis: Optional[Union[int, Tuple[int, ...]]] = None, out=None,
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keepdims=None) -> Any: ...
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@abc.abstractmethod
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def argmax(self, axis: Optional[int] = None, out=None, keepdims=None) -> Any: ...
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@abc.abstractmethod
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def argmin(self, axis: Optional[int] = None, out=None, keepdims=None) -> Any: ...
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@abc.abstractmethod
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def argpartition(self, kth, axis=-1, kind='introselect', order=None) -> Any: ...
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@abc.abstractmethod
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def argsort(self, axis: Optional[int] = -1, kind='quicksort', order=None) -> Any: ...
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@abc.abstractmethod
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def astype(self, dtype) -> Any: ...
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@abc.abstractmethod
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def choose(self, choices, out=None, mode='raise') -> Any: ...
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@abc.abstractmethod
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def clip(self, a_min=None, a_max=None, out=None) -> Any: ...
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@abc.abstractmethod
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def compress(self, condition, axis: Optional[int] = None, out=None) -> Any: ...
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@abc.abstractmethod
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def conj(self) -> Any: ...
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@abc.abstractmethod
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def conjugate(self) -> Any: ...
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@abc.abstractmethod
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def copy(self) -> Any: ...
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@abc.abstractmethod
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def cumprod(self, axis: Optional[Union[int, Tuple[int, ...]]] = None,
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dtype=None, out=None) -> Any: ...
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@abc.abstractmethod
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def cumsum(self, axis: Optional[Union[int, Tuple[int, ...]]] = None,
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dtype=None, out=None) -> Any: ...
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@abc.abstractmethod
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def diagonal(self, offset=0, axis1: int = 0, axis2: int = 1) -> Any: ...
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@abc.abstractmethod
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def dot(self, b, *, precision=None) -> Any: ...
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@abc.abstractmethod
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def flatten(self) -> Any: ...
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@property
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@abc.abstractmethod
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def imag(self) -> Any: ...
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@abc.abstractmethod
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def item(self, *args) -> Any: ...
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@abc.abstractmethod
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def max(self, axis: Optional[Union[int, Tuple[int, ...]]] = None, out=None,
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keepdims=None, initial=None, where=None) -> Any: ...
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@abc.abstractmethod
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def mean(self, axis: Optional[Union[int, Tuple[int, ...]]] = None, dtype=None,
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out=None, keepdims=False, *, where=None,) -> Any: ...
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@abc.abstractmethod
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def min(self, axis: Optional[Union[int, Tuple[int, ...]]] = None, out=None,
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keepdims=None, initial=None, where=None) -> Any: ...
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@property
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@abc.abstractmethod
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def nbytes(self) -> Any: ...
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@abc.abstractmethod
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def nonzero(self, *, size=None, fill_value=None) -> Any: ...
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@abc.abstractmethod
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def prod(self, axis: Optional[Union[int, Tuple[int, ...]]] = None, dtype=None,
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out=None, keepdims=None, initial=None, where=None) -> Any: ...
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@abc.abstractmethod
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def ptp(self, axis: Optional[Union[int, Tuple[int, ...]]] = None, out=None,
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keepdims=False,) -> Any: ...
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@abc.abstractmethod
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def ravel(self, order='C') -> Any: ...
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@property
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@abc.abstractmethod
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def real(self) -> Any: ...
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@abc.abstractmethod
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def repeat(self, repeats, axis: Optional[int] = None, *,
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total_repeat_length=None) -> Any: ...
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@abc.abstractmethod
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def reshape(self, *args, order='C') -> Any: ...
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@abc.abstractmethod
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def round(self, decimals=0, out=None) -> Any: ...
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@abc.abstractmethod
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def searchsorted(self, v, side='left', sorter=None) -> Any: ...
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@abc.abstractmethod
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def sort(self, axis: Optional[int] = -1, kind='quicksort', order=None) -> Any: ...
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@abc.abstractmethod
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def squeeze(self, axis: Optional[Union[int, Tuple[int, ...]]] = None) -> Any: ...
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@abc.abstractmethod
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def std(self, axis: Optional[Union[int, Tuple[int, ...]]] = None,
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dtype=None, out=None, ddof=0, keepdims=False, *, where=None) -> Any: ...
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@abc.abstractmethod
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def sum(self, axis: Optional[Union[int, Tuple[int, ...]]] = None, dtype=None,
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out=None, keepdims=None, initial=None, where=None) -> Any: ...
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@abc.abstractmethod
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def swapaxes(self, axis1: int, axis2: int) -> Any: ...
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@abc.abstractmethod
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def take(self, indices, axis: Optional[int] = None, out=None,
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mode=None) -> Any: ...
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@abc.abstractmethod
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def tobytes(self, order='C') -> Any: ...
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@abc.abstractmethod
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def tolist(self) -> Any: ...
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@abc.abstractmethod
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def trace(self, offset=0, axis1: int = 0, axis2: int = 1, dtype=None,
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out=None) -> Any: ...
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@abc.abstractmethod
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def transpose(self, *args) -> Any: ...
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@abc.abstractmethod
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def var(self, axis: Optional[Union[int, Tuple[int, ...]]] = None,
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dtype=None, out=None, ddof=0, keepdims=False, *, where=None) -> Any: ...
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@abc.abstractmethod
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def view(self, dtype=None, type=None) -> Any: ...
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# Even though we don't always support the NumPy array protocol, e.g., for
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# tracer types, for type checking purposes we must declare support so we
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# implement the NumPy ArrayLike protocol.
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def __array__(self) -> Any: ...
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# JAX extensions
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@property
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@abc.abstractmethod
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def at(self) -> Any: ...
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@property
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@abc.abstractmethod
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def aval(self) -> Any: ...
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@property
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@abc.abstractmethod
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def weak_type(self) -> bool: ...
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ndarray.register(device_array.DeviceArray)
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for t in device_array.device_array_types:
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ndarray.register(t)
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ndarray.register(pxla._SDA_BASE_CLASS)
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ndarray.register(array.Array)
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