Source code for streaming.base.shared.array

# Copyright 2022-2024 MosaicML Streaming authors
# SPDX-License-Identifier: Apache-2.0

"""A numpy array of predetermined shape and dtype that lives in shared memory."""

from typing import Any, Union

import numpy as np
from numpy.typing import NDArray

from streaming.base.shared.memory import SharedMemory


[docs]class SharedArray: """A numpy array of predetermined shape and dtype that lives in shared memory. Args: shape (Union[int, Tuple[int]]): Shape of the array. dtype (type): Dtype of the array. name (str): Its name in shared memory. """ def __init__(self, shape: Union[int, tuple[int]], dtype: type, name: str) -> None: self.shape = np.empty(shape).shape self.dtype = dtype self.name = name size = int(np.prod(shape) * dtype(0).nbytes) self.shm = SharedMemory(name=name, size=size)
[docs] def numpy(self) -> NDArray: """Get as a numpy array. We can't just internally store and use this numpy array shared memory wrapper because it's not compatible with spawn. """ return np.ndarray(self.shape, buffer=self.shm.buf, dtype=self.dtype)
def __len__(self) -> int: """Get the length (i.e., size along the first axis). Returns: int: The length. """ return int(self.shape[0]) def __getitem__(self, index: Any) -> Any: """Get the scalar(s) at the given index, slice, or array of indices. Args: index (Any): The index, slice, or array of indices. Returns: The scalar(s) at the given location(s). """ arr = self.numpy() return arr[index] def __setitem__(self, index: Any, value: Any) -> Any: """Set the scalar(s) at the given index, slice, or array of indices. Args: index (Any): The index, slice, or array of indices. value (Any): The scalar(s) at the given location(s). """ arr = self.numpy() arr[index] = value