from __future__ import annotations
import operator
import types
import uuid
import warnings
from collections.abc import Sequence
from dataclasses import fields, is_dataclass, replace
from functools import partial
from tlz import concat, curry, merge, unique
from dask import config
from dask.base import (
DaskMethodsMixin,
dont_optimize,
is_dask_collection,
named_schedulers,
replace_name_in_key,
)
from dask.base import tokenize as _tokenize
from dask.context import globalmethod
from dask.core import flatten, quote
from dask.highlevelgraph import HighLevelGraph
from dask.typing import Graph, NestedKeys
from dask.utils import (
OperatorMethodMixin,
apply,
funcname,
is_namedtuple_instance,
methodcaller,
)
__all__ = ["Delayed", "delayed"]
DEFAULT_GET = named_schedulers.get("threads", named_schedulers["sync"])
def unzip(ls, nout):
"""Unzip a list of lists into ``nout`` outputs."""
out = list(zip(*ls))
if not out:
out = [()] * nout
return out
def finalize(collection):
assert is_dask_collection(collection)
name = "finalize-" + tokenize(collection)
keys = collection.__dask_keys__()
finalize, args = collection.__dask_postcompute__()
layer = {name: (finalize, keys) + args}
graph = HighLevelGraph.from_collections(name, layer, dependencies=[collection])
return Delayed(name, graph)
def unpack_collections(expr):
"""Normalize a python object and merge all sub-graphs.
- Replace ``Delayed`` with their keys
- Convert literals to things the schedulers can handle
- Extract dask graphs from all enclosed values
Parameters
----------
expr : object
The object to be normalized. This function knows how to handle
dask collections, as well as most builtin python types.
Returns
-------
task : normalized task to be run
collections : a tuple of collections
Examples
--------
>>> import dask
>>> a = delayed(1, 'a')
>>> b = delayed(2, 'b')
>>> task, collections = unpack_collections([a, b, 3])
>>> task
['a', 'b', 3]
>>> collections
(Delayed('a'), Delayed('b'))
>>> task, collections = unpack_collections({a: 1, b: 2})
>>> task
(<class 'dict'>, [['a', 1], ['b', 2]])
>>> collections
(Delayed('a'), Delayed('b'))
"""
if isinstance(expr, Delayed):
return expr._key, (expr,)
if is_dask_collection(expr):
finalized = finalize(expr)
return finalized._key, (finalized,)
if type(expr) is type(iter(list())):
expr = list(expr)
elif type(expr) is type(iter(tuple())):
expr = tuple(expr)
elif type(expr) is type(iter(set())):
expr = set(expr)
typ = type(expr)
if typ in (list, tuple, set):
args, collections = unzip((unpack_collections(e) for e in expr), 2)
args = list(args)
collections = tuple(unique(concat(collections), key=id))
# Ensure output type matches input type
if typ is not list:
args = (typ, args)
return args, collections
if typ is dict:
args, collections = unpack_collections([[k, v] for k, v in expr.items()])
return (dict, args), collections
if typ is slice:
args, collections = unpack_collections([expr.start, expr.stop, expr.step])
return (slice, *args), collections
if is_dataclass(expr):
args, collections = unpack_collections(
[
[f.name, getattr(expr, f.name)]
for f in fields(expr)
if hasattr(expr, f.name) # if init=False, field might not exist
]
)
if not collections:
return expr, ()
try:
_fields = {
f.name: getattr(expr, f.name)
for f in fields(expr)
if hasattr(expr, f.name)
}
replace(expr, **_fields)
except TypeError as e:
raise TypeError(
f"Failed to unpack {typ} instance. "
"Note that using a custom __init__ is not supported."
) from e
except ValueError as e:
raise ValueError(
f"Failed to unpack {typ} instance. "
"Note that using fields with `init=False` are not supported."
) from e
return (apply, typ, (), (dict, args)), collections
if is_namedtuple_instance(expr):
args, collections = unpack_collections([v for v in expr])
return (typ, *args), collections
return expr, ()
def to_task_dask(expr):
"""Normalize a python object and merge all sub-graphs.
- Replace ``Delayed`` with their keys
- Convert literals to things the schedulers can handle
- Extract dask graphs from all enclosed values
Parameters
----------
expr : object
The object to be normalized. This function knows how to handle
``Delayed``s, as well as most builtin python types.
Returns
-------
task : normalized task to be run
dask : a merged dask graph that forms the dag for this task
Examples
--------
>>> import dask
>>> a = delayed(1, 'a')
>>> b = delayed(2, 'b')
>>> task, dask = to_task_dask([a, b, 3]) # doctest: +SKIP
>>> task # doctest: +SKIP
['a', 'b', 3]
>>> dict(dask) # doctest: +SKIP
{'a': 1, 'b': 2}
>>> task, dasks = to_task_dask({a: 1, b: 2}) # doctest: +SKIP
>>> task # doctest: +SKIP
(dict, [['a', 1], ['b', 2]])
>>> dict(dask) # doctest: +SKIP
{'a': 1, 'b': 2}
"""
warnings.warn(
"The dask.delayed.to_dask_dask function has been "
"Deprecated in favor of unpack_collections",
stacklevel=2,
)
if isinstance(expr, Delayed):
return expr.key, expr.dask
if is_dask_collection(expr):
name = "finalize-" + tokenize(expr, pure=True)
keys = expr.__dask_keys__()
opt = getattr(expr, "__dask_optimize__", dont_optimize)
finalize, args = expr.__dask_postcompute__()
dsk = {name: (finalize, keys) + args}
dsk.update(opt(expr.__dask_graph__(), keys))
return name, dsk
if type(expr) is type(iter(list())):
expr = list(expr)
elif type(expr) is type(iter(tuple())):
expr = tuple(expr)
elif type(expr) is type(iter(set())):
expr = set(expr)
typ = type(expr)
if typ in (list, tuple, set):
args, dasks = unzip((to_task_dask(e) for e in expr), 2)
args = list(args)
dsk = merge(dasks)
# Ensure output type matches input type
return (args, dsk) if typ is list else ((typ, args), dsk)
if typ is dict:
args, dsk = to_task_dask([[k, v] for k, v in expr.items()])
return (dict, args), dsk
if is_dataclass(expr):
args, dsk = to_task_dask(
[
[f.name, getattr(expr, f.name)]
for f in fields(expr)
if hasattr(expr, f.name) # if init=False, field might not exist
]
)
return (apply, typ, (), (dict, args)), dsk
if is_namedtuple_instance(expr):
args, dsk = to_task_dask([v for v in expr])
return (typ, *args), dsk
if typ is slice:
args, dsk = to_task_dask([expr.start, expr.stop, expr.step])
return (slice,) + tuple(args), dsk
return expr, {}
def tokenize(*args, pure=None, **kwargs):
"""Mapping function from task -> consistent name.
Parameters
----------
args : object
Python objects that summarize the task.
pure : boolean, optional
If True, a consistent hash function is tried on the input. If this
fails, then a unique identifier is used. If False (default), then a
unique identifier is always used.
"""
if pure is None:
pure = config.get("delayed_pure", False)
if pure:
return _tokenize(*args, **kwargs)
else:
return str(uuid.uuid4())
[docs]@curry
def delayed(obj, name=None, pure=None, nout=None, traverse=True):
"""Wraps a function or object to produce a ``Delayed``.
``Delayed`` objects act as proxies for the object they wrap, but all
operations on them are done lazily by building up a dask graph internally.
Parameters
----------
obj : object
The function or object to wrap
name : Dask key, optional
The key to use in the underlying graph for the wrapped object. Defaults
to hashing content. Note that this only affects the name of the object
wrapped by this call to delayed, and *not* the output of delayed
function calls - for that use ``dask_key_name=`` as described below.
.. note::
Because this ``name`` is used as the key in task graphs, you should
ensure that it uniquely identifies ``obj``. If you'd like to provide
a descriptive name that is still unique, combine the descriptive name
with :func:`dask.base.tokenize` of the ``array_like``. See
:ref:`graphs` for more.
pure : bool, optional
Indicates whether calling the resulting ``Delayed`` object is a pure
operation. If True, arguments to the call are hashed to produce
deterministic keys. If not provided, the default is to check the global
``delayed_pure`` setting, and fallback to ``False`` if unset.
nout : int, optional
The number of outputs returned from calling the resulting ``Delayed``
object. If provided, the ``Delayed`` output of the call can be iterated
into ``nout`` objects, allowing for unpacking of results. By default
iteration over ``Delayed`` objects will error. Note, that ``nout=1``
expects ``obj`` to return a tuple of length 1, and consequently for
``nout=0``, ``obj`` should return an empty tuple.
traverse : bool, optional
By default dask traverses builtin python collections looking for dask
objects passed to ``delayed``. For large collections this can be
expensive. If ``obj`` doesn't contain any dask objects, set
``traverse=False`` to avoid doing this traversal.
Examples
--------
Apply to functions to delay execution:
>>> from dask import delayed
>>> def inc(x):
... return x + 1
>>> inc(10)
11
>>> x = delayed(inc, pure=True)(10)
>>> type(x) == Delayed
True
>>> x.compute()
11
Can be used as a decorator:
>>> @delayed(pure=True)
... def add(a, b):
... return a + b
>>> add(1, 2).compute()
3
``delayed`` also accepts an optional keyword ``pure``. If False, then
subsequent calls will always produce a different ``Delayed``. This is
useful for non-pure functions (such as ``time`` or ``random``).
>>> from random import random
>>> out1 = delayed(random, pure=False)()
>>> out2 = delayed(random, pure=False)()
>>> out1.key == out2.key
False
If you know a function is pure (output only depends on the input, with no
global state), then you can set ``pure=True``. This will attempt to apply a
consistent name to the output, but will fallback on the same behavior of
``pure=False`` if this fails.
>>> @delayed(pure=True)
... def add(a, b):
... return a + b
>>> out1 = add(1, 2)
>>> out2 = add(1, 2)
>>> out1.key == out2.key
True
Instead of setting ``pure`` as a property of the callable, you can also set
it contextually using the ``delayed_pure`` setting. Note that this
influences the *call* and not the *creation* of the callable:
>>> @delayed
... def mul(a, b):
... return a * b
>>> import dask
>>> with dask.config.set(delayed_pure=True):
... print(mul(1, 2).key == mul(1, 2).key)
True
>>> with dask.config.set(delayed_pure=False):
... print(mul(1, 2).key == mul(1, 2).key)
False
The key name of the result of calling a delayed object is determined by
hashing the arguments by default. To explicitly set the name, you can use
the ``dask_key_name`` keyword when calling the function:
>>> add(1, 2) # doctest: +SKIP
Delayed('add-3dce7c56edd1ac2614add714086e950f')
>>> add(1, 2, dask_key_name='three')
Delayed('three')
Note that objects with the same key name are assumed to have the same
result. If you set the names explicitly you should make sure your key names
are different for different results.
>>> add(1, 2, dask_key_name='three')
Delayed('three')
>>> add(2, 1, dask_key_name='three')
Delayed('three')
>>> add(2, 2, dask_key_name='four')
Delayed('four')
``delayed`` can also be applied to objects to make operations on them lazy:
>>> a = delayed([1, 2, 3])
>>> isinstance(a, Delayed)
True
>>> a.compute()
[1, 2, 3]
The key name of a delayed object is hashed by default if ``pure=True`` or
is generated randomly if ``pure=False`` (default). To explicitly set the
name, you can use the ``name`` keyword. To ensure that the key is unique
you should include the tokenized value as well, or otherwise ensure that
it's unique:
>>> from dask.base import tokenize
>>> data = [1, 2, 3]
>>> a = delayed(data, name='mylist-' + tokenize(data))
>>> a # doctest: +SKIP
Delayed('mylist-55af65871cb378a4fa6de1660c3e8fb7')
Delayed results act as a proxy to the underlying object. Many operators
are supported:
>>> (a + [1, 2]).compute()
[1, 2, 3, 1, 2]
>>> a[1].compute()
2
Method and attribute access also works:
>>> a.count(2).compute()
1
Note that if a method doesn't exist, no error will be thrown until runtime:
>>> res = a.not_a_real_method() # doctest: +SKIP
>>> res.compute() # doctest: +SKIP
AttributeError("'list' object has no attribute 'not_a_real_method'")
"Magic" methods (e.g. operators and attribute access) are assumed to be
pure, meaning that subsequent calls must return the same results. This
behavior is not overridable through the ``delayed`` call, but can be
modified using other ways as described below.
To invoke an impure attribute or operator, you'd need to use it in a
delayed function with ``pure=False``:
>>> class Incrementer:
... def __init__(self):
... self._n = 0
... @property
... def n(self):
... self._n += 1
... return self._n
...
>>> x = delayed(Incrementer())
>>> x.n.key == x.n.key
True
>>> get_n = delayed(lambda x: x.n, pure=False)
>>> get_n(x).key == get_n(x).key
False
In contrast, methods are assumed to be impure by default, meaning that
subsequent calls may return different results. To assume purity, set
``pure=True``. This allows sharing of any intermediate values.
>>> a.count(2, pure=True).key == a.count(2, pure=True).key
True
As with function calls, method calls also respect the global
``delayed_pure`` setting and support the ``dask_key_name`` keyword:
>>> a.count(2, dask_key_name="count_2")
Delayed('count_2')
>>> import dask
>>> with dask.config.set(delayed_pure=True):
... print(a.count(2).key == a.count(2).key)
True
"""
if isinstance(obj, Delayed):
return obj
if is_dask_collection(obj) or traverse:
task, collections = unpack_collections(obj)
else:
task = quote(obj)
collections = set()
if not (nout is None or (type(nout) is int and nout >= 0)):
raise ValueError("nout must be None or a non-negative integer, got %s" % nout)
if task is obj:
if not name:
try:
prefix = obj.__name__
except AttributeError:
prefix = type(obj).__name__
token = tokenize(obj, nout, pure=pure)
name = f"{prefix}-{token}"
return DelayedLeaf(obj, name, pure=pure, nout=nout)
else:
if not name:
name = f"{type(obj).__name__}-{tokenize(task, pure=pure)}"
layer = {name: task}
graph = HighLevelGraph.from_collections(name, layer, dependencies=collections)
return Delayed(name, graph, nout)
def _swap(method, self, other):
return method(other, self)
def right(method):
"""Wrapper to create 'right' version of operator given left version"""
return partial(_swap, method)
def optimize(dsk, keys, **kwargs):
if not isinstance(keys, (list, set)):
keys = [keys]
if not isinstance(dsk, HighLevelGraph):
dsk = HighLevelGraph.from_collections(id(dsk), dsk, dependencies=())
dsk = dsk.cull(set(flatten(keys)))
return dsk
[docs]class Delayed(DaskMethodsMixin, OperatorMethodMixin):
"""Represents a value to be computed by dask.
Equivalent to the output from a single key in a dask graph.
"""
__slots__ = ("_key", "_dask", "_length", "_layer")
def __init__(self, key, dsk, length=None, layer=None):
self._key = key
self._dask = dsk
self._length = length
# NOTE: Layer is used by `to_delayed` in other collections, but not in normal Delayed use
self._layer = layer or key
if isinstance(dsk, HighLevelGraph) and self._layer not in dsk.layers:
raise ValueError(
f"Layer {self._layer} not in the HighLevelGraph's layers: {list(dsk.layers)}"
)
@property
def key(self):
return self._key
@property
def dask(self):
return self._dask
def __dask_graph__(self) -> Graph:
return self.dask
def __dask_keys__(self) -> NestedKeys:
return [self.key]
def __dask_layers__(self) -> Sequence[str]:
return (self._layer,)
def __dask_tokenize__(self):
return self.key
__dask_scheduler__ = staticmethod(DEFAULT_GET)
__dask_optimize__ = globalmethod(optimize, key="delayed_optimize")
def __dask_postcompute__(self):
return single_key, ()
def __dask_postpersist__(self):
return self._rebuild, ()
def _rebuild(self, dsk, *, rename=None):
key = replace_name_in_key(self.key, rename) if rename else self.key
if isinstance(dsk, HighLevelGraph) and len(dsk.layers) == 1:
# FIXME Delayed is currently the only collection type that supports both high- and low-level graphs.
# The HLG output of `optimize` will have a layer name that doesn't match `key`.
# Remove this when Delayed is HLG-only (because `optimize` will only be passed HLGs, so it won't have
# to generate random layer names).
layer = next(iter(dsk.layers))
else:
layer = None
return Delayed(key, dsk, self._length, layer=layer)
def __repr__(self):
return f"Delayed({repr(self.key)})"
def __hash__(self):
return hash(self.key)
def __dir__(self):
return dir(type(self))
def __getattr__(self, attr):
if attr.startswith("_"):
raise AttributeError(f"Attribute {attr} not found")
if attr == "visualise":
# added to warn users in case of spelling error
# for more details: https://github.com/dask/dask/issues/5721
warnings.warn(
"dask.delayed objects have no `visualise` method. "
"Perhaps you meant `visualize`?"
)
return DelayedAttr(self, attr)
def __setattr__(self, attr, val):
try:
object.__setattr__(self, attr, val)
except AttributeError:
# attr is neither in type(self).__slots__ nor in the __slots__ of any of its
# parent classes, and all the parent classes define __slots__ too.
# This last bit needs to be unit tested: if any of the parent classes omit
# the __slots__ declaration, self will gain a __dict__ and this branch will
# become unreachable.
raise TypeError("Delayed objects are immutable")
def __setitem__(self, index, val):
raise TypeError("Delayed objects are immutable")
def __iter__(self):
if self._length is None:
raise TypeError("Delayed objects of unspecified length are not iterable")
for i in range(self._length):
yield self[i]
def __len__(self):
if self._length is None:
raise TypeError("Delayed objects of unspecified length have no len()")
return self._length
def __call__(self, *args, pure=None, dask_key_name=None, **kwargs):
func = delayed(apply, pure=pure)
if dask_key_name is not None:
return func(self, args, kwargs, dask_key_name=dask_key_name)
return func(self, args, kwargs)
def __bool__(self):
raise TypeError("Truth of Delayed objects is not supported")
__nonzero__ = __bool__
def __get__(self, instance, cls):
if instance is None:
return self
return types.MethodType(self, instance)
@classmethod
def _get_binary_operator(cls, op, inv=False):
method = delayed(right(op) if inv else op, pure=True)
return lambda *args, **kwargs: method(*args, **kwargs)
_get_unary_operator = _get_binary_operator
def call_function(func, func_token, args, kwargs, pure=None, nout=None):
dask_key_name = kwargs.pop("dask_key_name", None)
pure = kwargs.pop("pure", pure)
if dask_key_name is None:
name = "{}-{}".format(
funcname(func),
tokenize(func_token, *args, pure=pure, **kwargs),
)
else:
name = dask_key_name
args2, collections = unzip(map(unpack_collections, args), 2)
collections = list(concat(collections))
if kwargs:
dask_kwargs, collections2 = unpack_collections(kwargs)
collections.extend(collections2)
task = (apply, func, list(args2), dask_kwargs)
else:
task = (func,) + args2
graph = HighLevelGraph.from_collections(
name, {name: task}, dependencies=collections
)
nout = nout if nout is not None else None
return Delayed(name, graph, length=nout)
class DelayedLeaf(Delayed):
__slots__ = ("_obj", "_pure", "_nout")
def __init__(self, obj, key, pure=None, nout=None):
super().__init__(key, None)
self._obj = obj
self._pure = pure
self._nout = nout
@property
def dask(self):
return HighLevelGraph.from_collections(
self._key, {self._key: self._obj}, dependencies=()
)
def __call__(self, *args, **kwargs):
return call_function(
self._obj, self._key, args, kwargs, pure=self._pure, nout=self._nout
)
@property
def __name__(self):
return self._obj.__name__
@property
def __doc__(self):
return self._obj.__doc__
@property
def __wrapped__(self):
return self._obj
class DelayedAttr(Delayed):
__slots__ = ("_obj", "_attr")
def __init__(self, obj, attr):
key = "getattr-%s" % tokenize(obj, attr, pure=True)
super().__init__(key, None)
self._obj = obj
self._attr = attr
def __getattr__(self, attr):
# Calling np.dtype(dask.delayed(...)) used to result in a segfault, as
# numpy recursively tries to get `dtype` from the object. This is
# likely a bug in numpy. For now, we can do a dumb for if
# `x.dtype().dtype()` is called (which shouldn't ever show up in real
# code). See https://github.com/dask/dask/pull/4374#issuecomment-454381465
if attr == "dtype" and self._attr == "dtype":
raise AttributeError("Attribute dtype not found")
return super().__getattr__(attr)
@property
def dask(self):
layer = {self._key: (getattr, self._obj._key, self._attr)}
return HighLevelGraph.from_collections(
self._key, layer, dependencies=[self._obj]
)
def __call__(self, *args, **kwargs):
return call_function(
methodcaller(self._attr), self._attr, (self._obj,) + args, kwargs
)
for op in [
operator.abs,
operator.neg,
operator.pos,
operator.invert,
operator.add,
operator.sub,
operator.mul,
operator.floordiv,
operator.truediv,
operator.mod,
operator.pow,
operator.and_,
operator.or_,
operator.xor,
operator.lshift,
operator.rshift,
operator.eq,
operator.ge,
operator.gt,
operator.ne,
operator.le,
operator.lt,
operator.getitem,
]:
Delayed._bind_operator(op)
try:
Delayed._bind_operator(operator.matmul)
except AttributeError:
pass
def single_key(seq):
"""Pick out the only element of this list, a list of keys"""
return seq[0]