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These are the methods that are only valid for actual materialized arrays (i.e. not Tracers) In order to simplify the experience for users, we want to maintain only a single jax.Array type, so we define all methods here and raise explicit errors on Tracer instances.
211 lines
8.6 KiB
Python
211 lines
8.6 KiB
Python
# Copyright 2022 The JAX Authors.
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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, List, Optional, Sequence, Tuple, Union
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import numpy as np
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from jax._src.sharding import Sharding
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from jax._src.array import Device, Shard
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from jax._src.typing import ArrayLike
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# TODO: alias this to xla_client.Traceback
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Traceback = Any
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class Array(abc.ABC):
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dtype: np.dtype
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ndim: int
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size: int
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itemsize: int
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aval: Any
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@property
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def shape(self) -> Tuple[int, ...]: ...
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@property
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def sharding(self) -> Sharding: ...
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@property
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def addressable_shards(self) -> Sequence[Shard]: ...
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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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def __getitem__(self, key, indices_are_sorted=False,
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unique_indices=False) -> Array: ...
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def __setitem__(self, key, value) -> None: ...
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def __len__(self) -> int: ...
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def __iter__(self) -> Any: ...
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def __reversed__(self) -> Any: ...
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def __round__(self, ndigits=None) -> Array: ...
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# Comparisons
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# these return bool for object, so ignore override errors.
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def __lt__(self, other) -> Array: ... # type: ignore[override]
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def __le__(self, other) -> Array: ... # type: ignore[override]
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def __eq__(self, other) -> Array: ... # type: ignore[override]
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def __ne__(self, other) -> Array: ... # type: ignore[override]
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def __gt__(self, other) -> Array: ... # type: ignore[override]
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def __ge__(self, other) -> Array: ... # type: ignore[override]
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# Unary arithmetic
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def __neg__(self) -> Array: ...
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def __pos__(self) -> Array: ...
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def __abs__(self) -> Array: ...
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def __invert__(self) -> Array: ...
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# Binary arithmetic
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def __add__(self, other) -> Array: ...
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def __sub__(self, other) -> Array: ...
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def __mul__(self, other) -> Array: ...
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def __matmul__(self, other) -> Array: ...
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def __truediv__(self, other) -> Array: ...
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def __floordiv__(self, other) -> Array: ...
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def __mod__(self, other) -> Array: ...
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def __divmod__(self, other) -> Array: ...
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def __pow__(self, other) -> Array: ...
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def __lshift__(self, other) -> Array: ...
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def __rshift__(self, other) -> Array: ...
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def __and__(self, other) -> Array: ...
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def __xor__(self, other) -> Array: ...
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def __or__(self, other) -> Array: ...
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def __radd__(self, other) -> Array: ...
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def __rsub__(self, other) -> Array: ...
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def __rmul__(self, other) -> Array: ...
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def __rmatmul__(self, other) -> Array: ...
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def __rtruediv__(self, other) -> Array: ...
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def __rfloordiv__(self, other) -> Array: ...
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def __rmod__(self, other) -> Array: ...
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def __rdivmod__(self, other) -> Array: ...
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def __rpow__(self, other) -> Array: ...
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def __rlshift__(self, other) -> Array: ...
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def __rrshift__(self, other) -> Array: ...
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def __rand__(self, other) -> Array: ...
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def __rxor__(self, other) -> Array: ...
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def __ror__(self, other) -> Array: ...
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def __bool__(self) -> bool: ...
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def __complex__(self) -> complex: ...
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def __int__(self) -> int: ...
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def __float__(self) -> float: ...
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def __index__(self) -> int: ...
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# np.ndarray methods:
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def all(self, axis: Optional[Union[int, Tuple[int, ...]]] = None, out=None,
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keepdims=None) -> Array: ...
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def any(self, axis: Optional[Union[int, Tuple[int, ...]]] = None, out=None,
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keepdims=None) -> Array: ...
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def argmax(self, axis: Optional[int] = None, out=None, keepdims=None) -> Array: ...
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def argmin(self, axis: Optional[int] = None, out=None, keepdims=None) -> Array: ...
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def argpartition(self, kth, axis=-1, kind='introselect', order=None) -> Array: ...
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def argsort(self, axis: Optional[int] = -1, kind='quicksort', order=None) -> Array: ...
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def astype(self, dtype) -> Array: ...
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def broadcast(self, sizes: Sequence[int]) -> Array: ...
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def broadcast_in_dim(self, shape: Sequence[Union[int, Any]],
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broadcast_dimensions: Sequence[int]) -> Array: ...
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def choose(self, choices, out=None, mode='raise') -> Array: ...
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def clip(self, min=None, max=None, out=None) -> Array: ...
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def compress(self, condition, axis: Optional[int] = None, out=None) -> Array: ...
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def conj(self) -> Array: ...
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def conjugate(self) -> Array: ...
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def copy(self) -> Array: ...
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def cumprod(self, axis: Optional[Union[int, Tuple[int, ...]]] = None,
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dtype=None, out=None) -> Array: ...
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def cumsum(self, axis: Optional[Union[int, Tuple[int, ...]]] = None,
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dtype=None, out=None) -> Array: ...
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def diagonal(self, offset=0, axis1: int = 0, axis2: int = 1) -> Array: ...
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def dot(self, b, *, precision=None) -> Array: ...
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def flatten(self) -> Array: ...
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@property
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def imag(self) -> Array: ...
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def item(self, *args) -> Any: ...
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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) -> Array: ...
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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,) -> Array: ...
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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) -> Array: ...
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@property
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def nbytes(self) -> int: ...
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def nonzero(self, *, size=None, fill_value=None) -> Array: ...
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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) -> Array: ...
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def ptp(self, axis: Optional[Union[int, Tuple[int, ...]]] = None, out=None,
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keepdims=False,) -> Array: ...
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def ravel(self, order='C') -> Array: ...
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@property
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def real(self) -> Array: ...
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def repeat(self, repeats, axis: Optional[int] = None, *,
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total_repeat_length=None) -> Array: ...
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def reshape(self, *args, order='C') -> Array: ...
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def round(self, decimals=0, out=None) -> Array: ...
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def searchsorted(self, v, side='left', sorter=None) -> Array: ...
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def sort(self, axis: Optional[int] = -1, kind='quicksort', order=None) -> Array: ...
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def split(self, indices_or_sections: ArrayLike, axis: int = 0) -> List[Array]: ...
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def squeeze(self, axis: Optional[Union[int, Tuple[int, ...]]] = None) -> Array: ...
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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) -> Array: ...
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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) -> Array: ...
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def swapaxes(self, axis1: int, axis2: int) -> Array: ...
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def take(self, indices, axis: Optional[int] = None, out=None,
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mode=None) -> Array: ...
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def tobytes(self, order='C') -> bytes: ...
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def tolist(self) -> List[Any]: ...
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def trace(self, offset=0, axis1: int = 0, axis2: int = 1, dtype=None,
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out=None) -> Array: ...
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def transpose(self, *args) -> Array: ...
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@property
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def T(self) -> Array: ...
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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) -> Array: ...
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def view(self, dtype=None, type=None) -> Array: ...
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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) -> np.ndarray: ...
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def __dlpack__(self) -> Any: ...
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# JAX extensions
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@property
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def at(self) -> Any: ...
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@property
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def weak_type(self) -> bool: ...
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# Methods defined on ArrayImpl, but not on Tracers
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def addressable_data(self, index: int) -> Array: ...
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def block_until_ready(self) -> Array: ...
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def copy_to_host_async(self) -> None: ...
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def delete(self) -> None: ...
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def device(self) -> Device: ...
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def devices(self) -> List[Device]: ...
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@property
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def global_shards(self) -> Sequence[Shard]: ...
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def is_deleted(self) -> bool: ...
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@property
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def is_fully_addressable(self) -> bool: ...
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@property
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def is_fully_replicated(self) -> bool: ...
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def on_device_size_in_bytes(self) -> int: ...
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@property
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def traceback(self) -> Traceback: ...
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def unsafe_buffer_pointer(self) -> int: ...
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