mirror of
https://github.com/ROCm/jax.git
synced 2025-04-26 00:46:06 +00:00
137 lines
4.5 KiB
Python
137 lines
4.5 KiB
Python
# Copyright 2022 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.
|
|
|
|
# Note that type annotations for this file are defined in basearray.pyi
|
|
|
|
from __future__ import annotations
|
|
|
|
import abc
|
|
import numpy as np
|
|
from typing import Any, Union
|
|
from collections.abc import Sequence
|
|
|
|
# TODO(jakevdp): fix import cycles and define these.
|
|
Shard = Any
|
|
Sharding = Any
|
|
|
|
# Array is a type annotation for standard JAX arrays and tracers produced by
|
|
# core functions in jax.lax and jax.numpy; it is not meant to include
|
|
# future non-standard array types like KeyArray and BInt.
|
|
class Array(abc.ABC):
|
|
"""Array base class for JAX
|
|
|
|
``jax.Array`` is the public interface for instance checks and type annotation
|
|
of JAX arrays and tracers. Its main applications are in instance checks and
|
|
type annotations; for example::
|
|
|
|
x = jnp.arange(5)
|
|
isinstance(x, jax.Array) # returns True both inside and outside traced functions.
|
|
|
|
def f(x: Array) -> Array: # type annotations are valid for traced and non-traced types.
|
|
return x
|
|
|
|
``jax.Array`` should not be used directly for creation of arrays; instead you
|
|
should use array creation routines offered in :mod:`jax.numpy`, such as
|
|
:func:`jax.numpy.array`, :func:`jax.numpy.zeros`, :func:`jax.numpy.ones`,
|
|
:func:`jax.numpy.full`, :func:`jax.numpy.arange`, etc.
|
|
"""
|
|
# Note: abstract methods for this class are defined dynamically in
|
|
# lax_numpy.py
|
|
# For the sake of static type analysis, these definitions are mirrored in the
|
|
# associated basearray.pyi file.
|
|
|
|
__slots__ = ['__weakref__']
|
|
|
|
@property
|
|
@abc.abstractmethod
|
|
def dtype(self) -> np.dtype:
|
|
"""The data type (:class:`numpy.dtype`) of the array."""
|
|
|
|
@property
|
|
@abc.abstractmethod
|
|
def ndim(self) -> int:
|
|
"""The number of dimensions in the array."""
|
|
|
|
@property
|
|
@abc.abstractmethod
|
|
def size(self) -> int:
|
|
"""The total number of elements in the array."""
|
|
|
|
@property
|
|
@abc.abstractmethod
|
|
def shape(self) -> tuple[int, ...]:
|
|
"""The shape of the array."""
|
|
|
|
# Documentation for sharding-related methods and properties defined on ArrayImpl:
|
|
@abc.abstractmethod
|
|
def addressable_data(self, index: int) -> Array:
|
|
"""Return an array of the addressable data at a particular index."""
|
|
|
|
@property
|
|
@abc.abstractmethod
|
|
def addressable_shards(self) -> Sequence[Shard]:
|
|
"""List of addressable shards."""
|
|
|
|
@property
|
|
@abc.abstractmethod
|
|
def global_shards(self) -> Sequence[Shard]:
|
|
"""List of global shards."""
|
|
|
|
@property
|
|
@abc.abstractmethod
|
|
def is_fully_addressable(self) -> bool:
|
|
"""Is this Array fully addressable?
|
|
|
|
A jax.Array is fully addressable if the current process can address all of
|
|
the devices named in the :class:`Sharding`. ``is_fully_addressable`` is
|
|
equivalent to "is_local" in multi-process JAX.
|
|
|
|
Note that fully replicated is not equal to fully addressable i.e.
|
|
a jax.Array which is fully replicated can span across multiple hosts and is
|
|
not fully addressable.
|
|
"""
|
|
|
|
@property
|
|
@abc.abstractmethod
|
|
def is_fully_replicated(self) -> bool:
|
|
"""Is this Array fully replicated?"""
|
|
|
|
@property
|
|
@abc.abstractmethod
|
|
def sharding(self) -> Sharding:
|
|
"""The sharding for the array."""
|
|
|
|
|
|
Array.__module__ = "jax"
|
|
|
|
# StaticScalar is the Union of all scalar types that can be converted to
|
|
# JAX arrays, and are possible to mark as static arguments.
|
|
StaticScalar = Union[
|
|
np.bool_, np.number, # NumPy scalar types
|
|
bool, int, float, complex, # Python scalar types
|
|
]
|
|
StaticScalar.__doc__ = "Type annotation for JAX-compatible static scalars."
|
|
|
|
|
|
# ArrayLike is a Union of all objects that can be implicitly converted to a
|
|
# standard JAX array (i.e. not including future non-standard array types like
|
|
# KeyArray and BInt). It's different than np.typing.ArrayLike in that it doesn't
|
|
# accept arbitrary sequences, nor does it accept string data.
|
|
ArrayLike = Union[
|
|
Array, # JAX array type
|
|
np.ndarray, # NumPy array type
|
|
StaticScalar, # valid scalars
|
|
]
|
|
ArrayLike.__doc__ = "Type annotation for JAX array-like objects."
|