ORPA-pyOpenRPA/Resources/WPy64-3720/python-3.7.2.amd64/Lib/site-packages/dask/local.py

"""
Asynchronous Shared-Memory Scheduler for Dask Graphs.

This scheduler coordinates several workers to execute tasks in a dask graph in
parallel.  It depends on an apply_async function as would be found in thread or
process Pools and a corresponding Queue for worker-to-scheduler communication.

It tries to execute tasks in an order which maintains a small memory footprint
throughout execution.  It does this by running tasks that allow us to release
data resources.


Task Selection Policy
=====================

When we complete a task we add more data in to our set of available data; this
new data makes new tasks available.  We preferentially choose tasks that were
just made available in a last-in-first-out fashion.  We implement this as a
simple stack.  This results in more depth-first rather than breadth first
behavior which encourages us to process batches of data to completion before
starting in on new data when possible.

When the addition of new data readies multiple tasks simultaneously we add
tasks to the stack in sorted order so that tasks with greater keynames are run
first.  This can be handy to break ties in a predictable fashion.


State
=====

Many functions pass around a ``state`` variable that holds the current state of
the computation.  This variable consists of several other dictionaries and
sets, explained below.

Constant state
--------------

1.  dependencies: {x: [a, b ,c]} a,b,c, must be run before x
2.  dependents: {a: [x, y]} a must run before x or y

Changing state
--------------

### Data

1.  cache: available concrete data.  {key: actual-data}
2.  released: data that we've seen, used, and released because it is no longer
    needed

### Jobs

1.  ready: A fifo stack of ready-to-run tasks
2.  running: A set of tasks currently in execution
3.  finished: A set of finished tasks
4.  waiting: which tasks are still waiting on others :: {key: {keys}}
    Real-time equivalent of dependencies
5.  waiting_data: available data to yet-to-be-run-tasks :: {key: {keys}}
    Real-time equivalent of dependents


Examples
--------

>>> import pprint  # doctest: +SKIP
>>> dsk = {'x': 1, 'y': 2, 'z': (inc, 'x'), 'w': (add, 'z', 'y')}  # doctest: +SKIP
>>> pprint.pprint(start_state_from_dask(dsk))  # doctest: +SKIP
{'cache': {'x': 1, 'y': 2},
 'dependencies': {'w': {'z', 'y'}, 'x': set(), 'y': set(), 'z': {'x'}},
 'dependents': {'w': set(), 'x': {'z'}, 'y': {'w'}, 'z': {'w'}},
 'finished': set(),
 'ready': ['z'],
 'released': set(),
 'running': set(),
 'waiting': {'w': {'z'}},
 'waiting_data': {'x': {'z'}, 'y': {'w'}, 'z': {'w'}}}

Optimizations
=============

We build this scheduler with out-of-core array operations in mind.  To this end
we have encoded some particular optimizations.

Compute to release data
-----------------------

When we choose a new task to execute we often have many options.  Policies at
this stage are cheap and can significantly impact performance.  One could
imagine policies that expose parallelism, drive towards a particular output,
etc..

Our current policy is to run tasks that were most recently made available.


Inlining computations
---------------------

We hold on to intermediate computations either in memory or on disk.

For very cheap computations that may emit new copies of the data, like
``np.transpose`` or possibly even ``x + 1`` we choose not to store these as
separate pieces of data / tasks.  Instead we combine them with the computations
that require them.  This may result in repeated computation but saves
significantly on space and computation complexity.

See the function ``inline_functions`` for more information.
"""
from __future__ import absolute_import, division, print_function

import os

from .compatibility import Queue, Empty, reraise, PY2
from .core import (flatten, reverse_dict, get_dependencies,
                   has_tasks, _execute_task)
from . import config
from .order import order
from .callbacks import unpack_callbacks, local_callbacks
from .utils_test import add, inc  # noqa: F401


if PY2:
    # Due to a bug in python 2.7 Queue.get, if a timeout isn't specified then
    # `Queue.get` can't be interrupted. A workaround is to specify an extremely
    # long timeout, which then allows it to be interrupted.
    # For more information see: https://bugs.python.org/issue1360
    def queue_get(q):
        return q.get(block=True, timeout=(365 * 24 * 60 * 60))

elif os.name == 'nt':
    # Python 3 windows Queue.get also doesn't handle interrupts properly. To
    # workaround this we poll at a sufficiently large interval that it
    # shouldn't affect performance, but small enough that users trying to kill
    # an application shouldn't care.
    def queue_get(q):
        while True:
            try:
                return q.get(block=True, timeout=0.1)
            except Empty:
                pass
else:
    def queue_get(q):
        return q.get()


DEBUG = False


def start_state_from_dask(dsk, cache=None, sortkey=None):
    """ Start state from a dask

    Examples
    --------

    >>> dsk = {'x': 1, 'y': 2, 'z': (inc, 'x'), 'w': (add, 'z', 'y')}  # doctest: +SKIP
    >>> from pprint import pprint  # doctest: +SKIP
    >>> pprint(start_state_from_dask(dsk))  # doctest: +SKIP
    {'cache': {'x': 1, 'y': 2},
     'dependencies': {'w': {'z', 'y'}, 'x': set(), 'y': set(), 'z': {'x'}},
     'dependents': {'w': set(), 'x': {'z'}, 'y': {'w'}, 'z': {'w'}},
     'finished': set(),
     'ready': ['z'],
     'released': set(),
     'running': set(),
     'waiting': {'w': {'z'}},
     'waiting_data': {'x': {'z'}, 'y': {'w'}, 'z': {'w'}}}
    """
    if sortkey is None:
        sortkey = order(dsk).get
    if cache is None:
        cache = config.get('cache', None)
    if cache is None:
        cache = dict()
    data_keys = set()
    for k, v in dsk.items():
        if not has_tasks(dsk, v):
            cache[k] = v
            data_keys.add(k)

    dsk2 = dsk.copy()
    dsk2.update(cache)

    dependencies = {k: get_dependencies(dsk2, k) for k in dsk}
    waiting = {k: v.copy()
               for k, v in dependencies.items()
               if k not in data_keys}

    dependents = reverse_dict(dependencies)
    for a in cache:
        for b in dependents.get(a, ()):
            waiting[b].remove(a)
    waiting_data = dict((k, v.copy()) for k, v in dependents.items() if v)

    ready_set = set([k for k, v in waiting.items() if not v])
    ready = sorted(ready_set, key=sortkey, reverse=True)
    waiting = dict((k, v) for k, v in waiting.items() if v)

    state = {'dependencies': dependencies,
             'dependents': dependents,
             'waiting': waiting,
             'waiting_data': waiting_data,
             'cache': cache,
             'ready': ready,
             'running': set(),
             'finished': set(),
             'released': set()}

    return state


'''
Running tasks
-------------

When we execute tasks we both

1.  Perform the actual work of collecting the appropriate data and calling the function
2.  Manage administrative state to coordinate with the scheduler
'''


def execute_task(key, task_info, dumps, loads, get_id, pack_exception):
    """
    Compute task and handle all administration

    See Also
    --------
    _execute_task - actually execute task
    """
    try:
        task, data = loads(task_info)
        result = _execute_task(task, data)
        id = get_id()
        result = dumps((result, id))
        failed = False
    except BaseException as e:
        result = pack_exception(e, dumps)
        failed = True
    return key, result, failed


def release_data(key, state, delete=True):
    """ Remove data from temporary storage

    See Also
        finish_task
    """
    if key in state['waiting_data']:
        assert not state['waiting_data'][key]
        del state['waiting_data'][key]

    state['released'].add(key)

    if delete:
        del state['cache'][key]


def finish_task(dsk, key, state, results, sortkey, delete=True,
                release_data=release_data):
    """
    Update execution state after a task finishes

    Mutates.  This should run atomically (with a lock).
    """
    for dep in sorted(state['dependents'][key], key=sortkey, reverse=True):
        s = state['waiting'][dep]
        s.remove(key)
        if not s:
            del state['waiting'][dep]
            state['ready'].append(dep)

    for dep in state['dependencies'][key]:
        if dep in state['waiting_data']:
            s = state['waiting_data'][dep]
            s.remove(key)
            if not s and dep not in results:
                if DEBUG:
                    from chest.core import nbytes
                    print("Key: %s\tDep: %s\t NBytes: %.2f\t Release" % (key, dep,
                          sum(map(nbytes, state['cache'].values()) / 1e6)))
                release_data(dep, state, delete=delete)
        elif delete and dep not in results:
            release_data(dep, state, delete=delete)

    state['finished'].add(key)
    state['running'].remove(key)

    return state


def nested_get(ind, coll):
    """ Get nested index from collection

    Examples
    --------

    >>> nested_get(1, 'abc')
    'b'
    >>> nested_get([1, 0], 'abc')
    ('b', 'a')
    >>> nested_get([[1, 0], [0, 1]], 'abc')
    (('b', 'a'), ('a', 'b'))
    """
    if isinstance(ind, list):
        return tuple([nested_get(i, coll) for i in ind])
    else:
        return coll[ind]


def default_get_id():
    """Default get_id"""
    return None


def default_pack_exception(e, dumps):
    raise


def identity(x):
    """ Identity function. Returns x.

    >>> identity(3)
    3
    """
    return x


'''
Task Selection
--------------

We often have a choice among many tasks to run next.  This choice is both
cheap and can significantly impact performance.

We currently select tasks that have recently been made ready.  We hope that
this first-in-first-out policy reduces memory footprint
'''

'''
`get`
-----

The main function of the scheduler.  Get is the main entry point.
'''


def get_async(apply_async, num_workers, dsk, result, cache=None,
              get_id=default_get_id, rerun_exceptions_locally=None,
              pack_exception=default_pack_exception, raise_exception=reraise,
              callbacks=None, dumps=identity, loads=identity, **kwargs):
    """ Asynchronous get function

    This is a general version of various asynchronous schedulers for dask.  It
    takes a an apply_async function as found on Pool objects to form a more
    specific ``get`` method that walks through the dask array with parallel
    workers, avoiding repeat computation and minimizing memory use.

    Parameters
    ----------
    apply_async : function
        Asynchronous apply function as found on Pool or ThreadPool
    num_workers : int
        The number of active tasks we should have at any one time
    dsk : dict
        A dask dictionary specifying a workflow
    result : key or list of keys
        Keys corresponding to desired data
    cache : dict-like, optional
        Temporary storage of results
    get_id : callable, optional
        Function to return the worker id, takes no arguments. Examples are
        `threading.current_thread` and `multiprocessing.current_process`.
    rerun_exceptions_locally : bool, optional
        Whether to rerun failing tasks in local process to enable debugging
        (False by default)
    pack_exception : callable, optional
        Function to take an exception and ``dumps`` method, and return a
        serialized tuple of ``(exception, traceback)`` to send back to the
        scheduler. Default is to just raise the exception.
    raise_exception : callable, optional
        Function that takes an exception and a traceback, and raises an error.
    dumps: callable, optional
        Function to serialize task data and results to communicate between
        worker and parent.  Defaults to identity.
    loads: callable, optional
        Inverse function of `dumps`.  Defaults to identity.
    callbacks : tuple or list of tuples, optional
        Callbacks are passed in as tuples of length 5. Multiple sets of
        callbacks may be passed in as a list of tuples. For more information,
        see the dask.diagnostics documentation.

    See Also
    --------
    threaded.get
    """
    queue = Queue()

    if isinstance(result, list):
        result_flat = set(flatten(result))
    else:
        result_flat = set([result])
    results = set(result_flat)

    dsk = dict(dsk)
    with local_callbacks(callbacks) as callbacks:
        _, _, pretask_cbs, posttask_cbs, _ = unpack_callbacks(callbacks)
        started_cbs = []
        succeeded = False
        try:
            keyorder = order(dsk)

            state = start_state_from_dask(dsk, cache=cache, sortkey=keyorder.get)

            # extend started_cbs AFTER state is initialized
            for cb in callbacks:
                if cb[0]:
                    cb[0](dsk)
                started_cbs.append(cb)

            for _, start_state, _, _, _ in callbacks:
                if start_state:
                    start_state(dsk, state)

            if rerun_exceptions_locally is None:
                rerun_exceptions_locally = config.get('rerun_exceptions_locally', False)

            if state['waiting'] and not state['ready']:
                raise ValueError("Found no accessible jobs in dask")

            def fire_task():
                """ Fire off a task to the thread pool """
                # Choose a good task to compute
                key = state['ready'].pop()
                state['running'].add(key)
                for f in pretask_cbs:
                    f(key, dsk, state)

                # Prep data to send
                data = dict((dep, state['cache'][dep])
                            for dep in get_dependencies(dsk, key))
                # Submit
                apply_async(execute_task,
                            args=(key, dumps((dsk[key], data)),
                                  dumps, loads, get_id, pack_exception),
                            callback=queue.put)

            # Seed initial tasks into the thread pool
            while state['ready'] and len(state['running']) < num_workers:
                fire_task()

            # Main loop, wait on tasks to finish, insert new ones
            while state['waiting'] or state['ready'] or state['running']:
                key, res_info, failed = queue_get(queue)
                if failed:
                    exc, tb = loads(res_info)
                    if rerun_exceptions_locally:
                        data = dict((dep, state['cache'][dep])
                                    for dep in get_dependencies(dsk, key))
                        task = dsk[key]
                        _execute_task(task, data)  # Re-execute locally
                    else:
                        raise_exception(exc, tb)
                res, worker_id = loads(res_info)
                state['cache'][key] = res
                finish_task(dsk, key, state, results, keyorder.get)
                for f in posttask_cbs:
                    f(key, res, dsk, state, worker_id)

                while state['ready'] and len(state['running']) < num_workers:
                    fire_task()

            succeeded = True

        finally:
            for _, _, _, _, finish in started_cbs:
                if finish:
                    finish(dsk, state, not succeeded)

    return nested_get(result, state['cache'])


""" Synchronous concrete version of get_async

Usually we supply a multi-core apply_async function.  Here we provide a
sequential one.  This is useful for debugging and for code dominated by the
GIL
"""


def apply_sync(func, args=(), kwds={}, callback=None):
    """ A naive synchronous version of apply_async """
    res = func(*args, **kwds)
    if callback is not None:
        callback(res)


def get_sync(dsk, keys, **kwargs):
    """A naive synchronous version of get_async

    Can be useful for debugging.
    """
    kwargs.pop('num_workers', None)    # if num_workers present, remove it
    return get_async(apply_sync, 1, dsk, keys, **kwargs)


def sortkey(item):
    """ Sorting key function that is robust to different types

    Both strings and tuples are common key types in dask graphs.
    However In Python 3 one can not compare strings with tuples directly.
    This function maps many types to a form where they can be compared

    Examples
    --------
    >>> sortkey('Hello')
    ('str', 'Hello')

    >>> sortkey(('x', 1))
    ('tuple', ('x', 1))
    """
    return (type(item).__name__, item)