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import sys
from types import LambdaType
from typing import Callable, TYPE_CHECKING, Generator, Optional, overload, TypeVar
if TYPE_CHECKING:
from ...types import T
from ... import Document, DocumentArray
T_DA = TypeVar('T_DA')
class ParallelMixin:
"""Helper functions that provide parallel map to :class:`DocumentArray`"""
@overload
def apply(
self: 'T',
func: Callable[['Document'], 'Document'],
backend: str = 'thread',
num_worker: Optional[int] = None,
) -> 'T':
"""Apply each element in itself with ``func``, return itself after modified.
:param func: a function that takes :class:`Document` as input and outputs :class:`Document`.
:param backend: if to use multi-`process` or multi-`thread` as the parallelization backend. In general, if your
``func`` is IO-bound then perhaps `thread` is good enough. If your ``func`` is CPU-bound then you may use `process`.
In practice, you should try yourselves to figure out the best value. However, if you wish to modify the elements
in-place, regardless of IO/CPU-bound, you should always use `thread` backend.
.. warning::
When using `process` backend, you should not expect ``func`` modify elements in-place. This is because
the multiprocessing backing pass the variable via pickle and work in another process. The passed object
and the original object do **not** share the same memory.
:param num_worker: the number of parallel workers. If not given, then the number of CPUs in the system will be used.
"""
...
def apply(self: 'T', *args, **kwargs) -> 'T':
"""
# noqa: DAR102
# noqa: DAR101
# noqa: DAR201
:return: a new :class:`DocumentArray`
"""
from ... import DocumentArray
new_da = DocumentArray(self.map(*args, **kwargs))
self.clear()
self.extend(new_da)
return self
def map(
self,
func: Callable[['Document'], 'T'],
backend: str = 'thread',
num_worker: Optional[int] = None,
) -> Generator['T', None, None]:
"""Return an iterator that applies function to every **element** of iterable in parallel, yielding the results.
.. seealso::
- To process on a batch of elements, please use :meth:`.map_batch`;
- To return a :class:`DocumentArray`, please use :meth:`.apply`.
:param func: a function that takes :class:`Document` as input and outputs anything. You can either modify elements
in-place (only with `thread` backend) or work later on return elements.
:param backend: if to use multi-`process` or multi-`thread` as the parallelization backend. In general, if your
``func`` is IO-bound then perhaps `thread` is good enough. If your ``func`` is CPU-bound then you may use `process`.
In practice, you should try yourselves to figure out the best value. However, if you wish to modify the elements
in-place, regardless of IO/CPU-bound, you should always use `thread` backend.
.. warning::
When using `process` backend, you should not expect ``func`` modify elements in-place. This is because
the multiprocessing backing pass the variable via pickle and work in another process. The passed object
and the original object do **not** share the same memory.
:param num_worker: the number of parallel workers. If not given, then the number of CPUs in the system will be used.
:yield: anything return from ``func``
"""
if _is_lambda_or_local_function(func) and backend == 'process':
func = _globalize_lambda_function(func)
with _get_pool(backend, num_worker) as p:
for x in p.imap(func, self):
yield x
@overload
def apply_batch(
self: 'T',
func: Callable[['DocumentArray'], 'DocumentArray'],
batch_size: int,
backend: str = 'thread',
num_worker: Optional[int] = None,
shuffle: bool = False,
) -> 'T':
"""Apply each element in itself with ``func``, return itself after modified.
:param func: a function that takes :class:`Document` as input and outputs :class:`Document`.
:param backend: if to use multi-`process` or multi-`thread` as the parallelization backend. In general, if your
``func`` is IO-bound then perhaps `thread` is good enough. If your ``func`` is CPU-bound then you may use `process`.
In practice, you should try yourselves to figure out the best value. However, if you wish to modify the elements
in-place, regardless of IO/CPU-bound, you should always use `thread` backend.
.. warning::
When using `process` backend, you should not expect ``func`` modify elements in-place. This is because
the multiprocessing backing pass the variable via pickle and work in another process. The passed object
and the original object do **not** share the same memory.
:param num_worker: the number of parallel workers. If not given, then the number of CPUs in the system will be used.
:param batch_size: Size of each generated batch (except the last one, which might be smaller, default: 32)
:param shuffle: If set, shuffle the Documents before dividing into minibatches.
"""
...
def apply_batch(self: 'T', *args, **kwargs) -> 'T':
"""
# noqa: DAR102
# noqa: DAR101
# noqa: DAR201
:return: a new :class:`DocumentArray`
"""
from ... import DocumentArray
new_da = DocumentArray()
for _b in self.map_batch(*args, **kwargs):
new_da.extend(_b)
self.clear()
self.extend(new_da)
return self
def map_batch(
self: 'T_DA',
func: Callable[['DocumentArray'], 'T'],
batch_size: int,
backend: str = 'thread',
num_worker: Optional[int] = None,
shuffle: bool = False,
) -> Generator['T', None, None]:
"""Return an iterator that applies function to every **minibatch** of iterable in parallel, yielding the results.
Each element in the returned iterator is :class:`DocumentArray`.
.. seealso::
- To process single element, please use :meth:`.map`;
- To return :class:`DocumentArray`, please use :meth:`.apply_batch`.
:param batch_size: Size of each generated batch (except the last one, which might be smaller, default: 32)
:param shuffle: If set, shuffle the Documents before dividing into minibatches.
:param func: a function that takes :class:`DocumentArray` as input and outputs anything. You can either modify elements
in-place (only with `thread` backend) or work later on return elements.
:param backend: if to use multi-`process` or multi-`thread` as the parallelization backend. In general, if your
``func`` is IO-bound then perhaps `thread` is good enough. If your ``func`` is CPU-bound then you may use `process`.
In practice, you should try yourselves to figure out the best value. However, if you wish to modify the elements
in-place, regardless of IO/CPU-bound, you should always use `thread` backend.
.. warning::
When using `process` backend, you should not expect ``func`` modify elements in-place. This is because
the multiprocessing backing pass the variable via pickle and work in another process. The passed object
and the original object do **not** share the same memory.
:param num_worker: the number of parallel workers. If not given, then the number of CPUs in the system will be used.
:yield: anything return from ``func``
"""
if _is_lambda_or_local_function(func) and backend == 'process':
func = _globalize_lambda_function(func)
with _get_pool(backend, num_worker) as p:
for x in p.imap(func, self.batch(batch_size=batch_size, shuffle=shuffle)):
yield x
def _get_pool(backend, num_worker):
if backend == 'thread':
from multiprocessing.pool import ThreadPool as Pool
return Pool(processes=num_worker)
elif backend == 'process':
from multiprocessing.pool import Pool
return Pool(processes=num_worker)
else:
raise ValueError(
f'`backend` must be either `process` or `thread`, receiving {backend}'
)
def _is_lambda_or_local_function(func):
return (isinstance(func, LambdaType) and func.__name__ == '<lambda>') or (
'<locals>' in func.__qualname__
)
def _globalize_lambda_function(func):
def result(*args, **kwargs):
return func(*args, **kwargs)
from ...helper import random_identity
result.__name__ = result.__qualname__ = random_identity()
setattr(sys.modules[result.__module__], result.__name__, result)
return result