def test_subs_no_key_data_eq():
# Numpy throws a deprecation warning on bool(array == scalar), which
# pollutes the terminal. This test checks that `subs` never tries to
# compare keys (scalars) with values (which could be arrays)`subs` never
# tries to compare keys (scalars) with values (which could be arrays).
a = MutateOnEq()
subs(a, 'x', 1)
assert a.hit_eq == 0
subs((add, a, 'x'), 'x', 1)
assert a.hit_eq == 0
def test_subs_no_key_data_eq():
# Numpy throws a deprecation warning on bool(array == scalar), which
# pollutes the terminal. This test checks that `subs` never tries to
# compare keys (scalars) with values (which could be arrays)`subs` never
# tries to compare keys (scalars) with values (which could be arrays).
a = MutateOnEq()
subs(a, "x", 1)
assert a.hit_eq == 0
subs((add, a, "x"), "x", 1)
assert a.hit_eq == 0
def test_subs_with_unfriendly_eq():
try:
import numpy as np
except:
return
else:
task = (np.sum, np.array([1, 2]))
assert (subs(task, (4, 5), 1) == task) is True
def test_subs_with_unfriendly_eq():
try:
import numpy as np
except:
return
else:
task = (np.sum, np.array([1, 2]))
assert (subs(task, (4, 5), 1) == task) is True
def test_subs_with_surprisingly_friendly_eq():
try:
import pandas as pd
except:
return
else:
df = pd.DataFrame()
assert subs(df, 'x', 1) is df
def test_subs_with_surprisingly_friendly_eq():
try:
import pandas as pd
except:
return
else:
df = pd.DataFrame()
assert subs(df, 'x', 1) is df
def test_subs_with_unfriendly_eq():
try:
import numpy as np
except:
return
else:
task = (np.sum, np.array([1, 2]))
assert (subs(task, (4, 5), 1) == task) is True
class MyException(Exception):
pass
class F():
def __eq__(self, other):
raise MyException()
task = F()
assert subs(task, 1, 2) is task
def test_subs_with_unfriendly_eq():
try:
import numpy as np
except ImportError:
return
else:
task = (np.sum, np.array([1, 2]))
assert (subs(task, (4, 5), 1) == task) is True
class MyException(Exception):
pass
class F():
def __eq__(self, other):
raise MyException()
task = F()
assert subs(task, 1, 2) is task
def test_subs_unexpected_hashable_key():
class UnexpectedButHashable:
def __init__(self):
self.name = "a"
def __hash__(self):
return hash(self.name)
def __eq__(self, other):
return isinstance(other, UnexpectedButHashable)
assert subs((id, UnexpectedButHashable()), UnexpectedButHashable(), 1) == (id, 1)
def test_subs_unexpected_hashable_key():
class UnexpectedButHashable:
def __init__(self):
self.name = "a"
def __hash__(self):
return hash(self.name)
def __eq__(self, other):
return isinstance(other, UnexpectedButHashable)
assert subs((id, UnexpectedButHashable()), UnexpectedButHashable(), 1) == (id, 1)
def _apply(self, sub_dict):
term = self.rhs
for key, val in sub_dict.items():
term = subs(term, key, val)
return term
def test_subs():
assert subs((sum, [1, 'x']), 'x', 2) == (sum, [1, 2])
assert subs((sum, [1, ['x']]), 'x', 2) == (sum, [1, [2]])
def fuse_linear(dsk, keys=None, dependencies=None, rename_keys=True):
"""Return new dask graph with linear sequence of tasks fused together.
If specified, the keys in ``keys`` keyword argument are *not* fused.
Supply ``dependencies`` from output of ``cull`` if available to avoid
recomputing dependencies.
**This function is mostly superseded by ``fuse``**
Parameters
----------
dsk: dict
keys: list
dependencies: dict, optional
{key: [list-of-keys]}. Must be a list to provide count of each key
This optional input often comes from ``cull``
rename_keys: bool or func, optional
Whether to rename fused keys with ``default_fused_linear_keys_renamer``
or not. Renaming fused keys can keep the graph more understandable
and comprehensive, but it comes at the cost of additional processing.
If False, then the top-most key will be used. For advanced usage, a
func is also accepted, ``new_key = rename_keys(fused_key_list)``.
Examples
--------
>>> def inc(x):
... return x + 1
>>> def add(x, y):
... return x + y
>>> d = {'a': 1, 'b': (inc, 'a'), 'c': (inc, 'b')}
>>> dsk, dependencies = fuse(d)
>>> dsk # doctest: +SKIP
{'a-b-c': (inc, (inc, 1)), 'c': 'a-b-c'}
>>> dsk, dependencies = fuse(d, rename_keys=False)
>>> dsk # doctest: +ELLIPSIS
{'c': (<function inc at ...>, (<function inc at ...>, 1))}
>>> dsk, dependencies = fuse(d, keys=['b'], rename_keys=False)
>>> dsk # doctest: +ELLIPSIS
{'b': (<function inc at ...>, 1), 'c': (<function inc at ...>, 'b')}
Returns
-------
dsk: output graph with keys fused
dependencies: dict mapping dependencies after fusion. Useful side effect
to accelerate other downstream optimizations.
"""
if keys is not None and not isinstance(keys, set):
if not isinstance(keys, list):
keys = [keys]
keys = set(flatten(keys))
if dependencies is None:
dependencies = {k: get_dependencies(dsk, k, as_list=True) for k in dsk}
# locate all members of linear chains
child2parent = {}
unfusible = set()
for parent in dsk:
deps = dependencies[parent]
has_many_children = len(deps) > 1
for child in deps:
if keys is not None and child in keys:
unfusible.add(child)
elif child in child2parent:
del child2parent[child]
unfusible.add(child)
elif has_many_children:
unfusible.add(child)
elif child not in unfusible:
child2parent[child] = parent
# construct the chains from ancestor to descendant
chains = []
parent2child = dict(map(reversed, child2parent.items()))
while child2parent:
child, parent = child2parent.popitem()
chain = [child, parent]
while parent in child2parent:
parent = child2parent.pop(parent)
del parent2child[parent]
chain.append(parent)
chain.reverse()
while child in parent2child:
child = parent2child.pop(child)
del child2parent[child]
chain.append(child)
chains.append(chain)
dependencies = {k: set(v) for k, v in dependencies.items()}
if rename_keys is True:
key_renamer = default_fused_linear_keys_renamer
elif rename_keys is False:
key_renamer = None
else:
key_renamer = rename_keys
# create a new dask with fused chains
rv = {}
fused = set()
aliases = set()
is_renamed = False
for chain in chains:
if key_renamer is not None:
new_key = key_renamer(chain)
is_renamed = (
new_key is not None and new_key not in dsk and new_key not in rv
)
child = chain.pop()
val = dsk[child]
while chain:
parent = chain.pop()
dependencies[parent].update(dependencies.pop(child))
dependencies[parent].remove(child)
val = subs(dsk[parent], child, val)
fused.add(child)
child = parent
fused.add(child)
if is_renamed:
rv[new_key] = val
rv[child] = new_key
dependencies[new_key] = dependencies[child]
dependencies[child] = {new_key}
aliases.add(child)
else:
rv[child] = val
for key, val in dsk.items():
if key not in fused:
rv[key] = val
if aliases:
for key, deps in dependencies.items():
for old_key in deps & aliases:
new_key = rv[old_key]
deps.remove(old_key)
deps.add(new_key)
rv[key] = subs(rv[key], old_key, new_key)
if keys is not None:
for key in aliases - keys:
del rv[key]
del dependencies[key]
return rv, dependencies
def fuse(
dsk,
keys=None,
dependencies=None,
ave_width=_default,
max_width=_default,
max_height=_default,
max_depth_new_edges=_default,
rename_keys=_default,
fuse_subgraphs=_default,
):
"""Fuse tasks that form reductions; more advanced than ``fuse_linear``
This trades parallelism opportunities for faster scheduling by making tasks
less granular. It can replace ``fuse_linear`` in optimization passes.
This optimization applies to all reductions--tasks that have at most one
dependent--so it may be viewed as fusing "multiple input, single output"
groups of tasks into a single task. There are many parameters to fine
tune the behavior, which are described below. ``ave_width`` is the
natural parameter with which to compare parallelism to granularity, so
it should always be specified. Reasonable values for other parameters
will be determined using ``ave_width`` if necessary.
Parameters
----------
dsk: dict
dask graph
keys: list or set, optional
Keys that must remain in the returned dask graph
dependencies: dict, optional
{key: [list-of-keys]}. Must be a list to provide count of each key
This optional input often comes from ``cull``
ave_width: float (default 1)
Upper limit for ``width = num_nodes / height``, a good measure of
parallelizability.
dask.config key: ``optimization.fuse.ave-width``
max_width: int (default infinite)
Don't fuse if total width is greater than this.
dask.config key: ``optimization.fuse.max-width``
max_height: int or None (default None)
Don't fuse more than this many levels. Set to None to dynamically
adjust to ``1.5 + ave_width * log(ave_width + 1)``.
dask.config key: ``optimization.fuse.max-height``
max_depth_new_edges: int or None (default None)
Don't fuse if new dependencies are added after this many levels.
Set to None to dynamically adjust to ave_width * 1.5.
dask.config key: ``optimization.fuse.max-depth-new-edges``
rename_keys: bool or func, optional (default True)
Whether to rename the fused keys with ``default_fused_keys_renamer``
or not. Renaming fused keys can keep the graph more understandable
and comprehensive, but it comes at the cost of additional processing.
If False, then the top-most key will be used. For advanced usage, a
function to create the new name is also accepted.
dask.config key: ``optimization.fuse.rename-keys``
fuse_subgraphs : bool or None, optional (default None)
Whether to fuse multiple tasks into ``SubgraphCallable`` objects.
Set to None to let the default optimizer of individual dask collections decide.
If no collection-specific default exists, None defaults to False.
dask.config key: ``optimization.fuse.subgraphs``
Returns
-------
dsk
output graph with keys fused
dependencies
dict mapping dependencies after fusion. Useful side effect to accelerate other
downstream optimizations.
"""
# Perform low-level fusion unless the user has
# specified False explicitly.
if config.get("optimization.fuse.active") is False:
return dsk, dependencies
if keys is not None and not isinstance(keys, set):
if not isinstance(keys, list):
keys = [keys]
keys = set(flatten(keys))
# Read defaults from dask.yaml and/or user-defined config file
if ave_width is _default:
ave_width = config.get("optimization.fuse.ave-width")
assert ave_width is not _default
if max_height is _default:
max_height = config.get("optimization.fuse.max-height")
assert max_height is not _default
if max_depth_new_edges is _default:
max_depth_new_edges = config.get("optimization.fuse.max-depth-new-edges")
assert max_depth_new_edges is not _default
if max_depth_new_edges is None:
max_depth_new_edges = ave_width * 1.5
if max_width is _default:
max_width = config.get("optimization.fuse.max-width")
assert max_width is not _default
if max_width is None:
max_width = 1.5 + ave_width * math.log(ave_width + 1)
if fuse_subgraphs is _default:
fuse_subgraphs = config.get("optimization.fuse.subgraphs")
assert fuse_subgraphs is not _default
if fuse_subgraphs is None:
fuse_subgraphs = False
if not ave_width or not max_height:
return dsk, dependencies
if rename_keys is _default:
rename_keys = config.get("optimization.fuse.rename-keys")
assert rename_keys is not _default
if rename_keys is True:
key_renamer = default_fused_keys_renamer
elif rename_keys is False:
key_renamer = None
elif not callable(rename_keys):
raise TypeError("rename_keys must be a boolean or callable")
else:
key_renamer = rename_keys
rename_keys = key_renamer is not None
if dependencies is None:
deps = {k: get_dependencies(dsk, k, as_list=True) for k in dsk}
else:
deps = dict(dependencies)
rdeps = {}
for k, vals in deps.items():
for v in vals:
if v not in rdeps:
rdeps[v] = [k]
else:
rdeps[v].append(k)
deps[k] = set(vals)
reducible = {k for k, vals in rdeps.items() if len(vals) == 1}
if keys:
reducible -= keys
for k, v in dsk.items():
if type(v) is not tuple and not isinstance(v, (numbers.Number, str)):
reducible.discard(k)
if not reducible and (
not fuse_subgraphs or all(len(set(v)) != 1 for v in rdeps.values())
):
# Quick return if there's nothing to do. Only progress if there's tasks
# fusible by the main `fuse`, or by `fuse_subgraphs` if enabled.
return dsk, deps
rv = dsk.copy()
fused_trees = {}
# These are the stacks we use to store data as we traverse the graph
info_stack = []
children_stack = []
# For speed
deps_pop = deps.pop
reducible_add = reducible.add
reducible_pop = reducible.pop
reducible_remove = reducible.remove
fused_trees_pop = fused_trees.pop
info_stack_append = info_stack.append
info_stack_pop = info_stack.pop
children_stack_append = children_stack.append
children_stack_extend = children_stack.extend
children_stack_pop = children_stack.pop
while reducible:
parent = reducible_pop()
reducible_add(parent)
while parent in reducible:
# Go to the top
parent = rdeps[parent][0]
children_stack_append(parent)
children_stack_extend(reducible & deps[parent])
while True:
child = children_stack[-1]
if child != parent:
children = reducible & deps[child]
while children:
# Depth-first search
children_stack_extend(children)
parent = child
child = children_stack[-1]
children = reducible & deps[child]
children_stack_pop()
# This is a leaf node in the reduction region
# key, task, fused_keys, height, width, number of nodes, fudge, set of edges
info_stack_append(
(
child,
rv[child],
[child] if rename_keys else None,
1,
1,
1,
0,
deps[child] - reducible,
)
)
else:
children_stack_pop()
# Calculate metrics and fuse as appropriate
deps_parent = deps[parent]
edges = deps_parent - reducible
children = deps_parent - edges
num_children = len(children)
if num_children == 1:
(
child_key,
child_task,
child_keys,
height,
width,
num_nodes,
fudge,
children_edges,
) = info_stack_pop()
num_children_edges = len(children_edges)
if fudge > num_children_edges - 1 >= 0:
fudge = num_children_edges - 1
edges |= children_edges
no_new_edges = len(edges) == num_children_edges
if not no_new_edges:
fudge += 1
if (
(num_nodes + fudge) / height <= ave_width
and
# Sanity check; don't go too deep if new levels introduce new edge dependencies
(no_new_edges or height < max_depth_new_edges)
):
# Perform substitutions as we go
val = subs(dsk[parent], child_key, child_task)
deps_parent.remove(child_key)
deps_parent |= deps_pop(child_key)
del rv[child_key]
reducible_remove(child_key)
if rename_keys:
child_keys.append(parent)
fused_trees[parent] = child_keys
fused_trees_pop(child_key, None)
if children_stack:
if no_new_edges:
# Linear fuse
info_stack_append(
(
parent,
val,
child_keys,
height,
width,
num_nodes,
fudge,
edges,
)
)
else:
info_stack_append(
(
parent,
val,
child_keys,
height + 1,
width,
num_nodes + 1,
fudge,
edges,
)
)
else:
rv[parent] = val
break
else:
rv[child_key] = child_task
reducible_remove(child_key)
if children_stack:
# Allow the parent to be fused, but only under strict circumstances.
# Ensure that linear chains may still be fused.
if fudge > int(ave_width - 1):
fudge = int(ave_width - 1)
# This task *implicitly* depends on `edges`
info_stack_append(
(
parent,
rv[parent],
[parent] if rename_keys else None,
1,
width,
1,
fudge,
edges,
)
)
else:
break
else:
child_keys = []
height = 1
width = 0
num_single_nodes = 0
num_nodes = 0
fudge = 0
children_edges = set()
max_num_edges = 0
children_info = info_stack[-num_children:]
del info_stack[-num_children:]
for (
cur_key,
cur_task,
cur_keys,
cur_height,
cur_width,
cur_num_nodes,
cur_fudge,
cur_edges,
) in children_info:
if cur_height == 1:
num_single_nodes += 1
elif cur_height > height:
height = cur_height
width += cur_width
num_nodes += cur_num_nodes
fudge += cur_fudge
if len(cur_edges) > max_num_edges:
max_num_edges = len(cur_edges)
children_edges |= cur_edges
# Fudge factor to account for possible parallelism with the boundaries
num_children_edges = len(children_edges)
fudge += min(
num_children - 1, max(0, num_children_edges - max_num_edges)
)
if fudge > num_children_edges - 1 >= 0:
fudge = num_children_edges - 1
edges |= children_edges
no_new_edges = len(edges) == num_children_edges
if not no_new_edges:
fudge += 1
if (
(num_nodes + fudge) / height <= ave_width
and num_single_nodes <= ave_width
and width <= max_width
and height <= max_height
and
# Sanity check; don't go too deep if new levels introduce new edge dependencies
(no_new_edges or height < max_depth_new_edges)
):
# Perform substitutions as we go
val = dsk[parent]
children_deps = set()
for child_info in children_info:
cur_child = child_info[0]
val = subs(val, cur_child, child_info[1])
del rv[cur_child]
children_deps |= deps_pop(cur_child)
reducible_remove(cur_child)
if rename_keys:
fused_trees_pop(cur_child, None)
child_keys.extend(child_info[2])
deps_parent -= children
deps_parent |= children_deps
if rename_keys:
child_keys.append(parent)
fused_trees[parent] = child_keys
if children_stack:
info_stack_append(
(
parent,
val,
child_keys,
height + 1,
width,
num_nodes + 1,
fudge,
edges,
)
)
else:
rv[parent] = val
break
else:
for child_info in children_info:
rv[child_info[0]] = child_info[1]
reducible_remove(child_info[0])
if children_stack:
# Allow the parent to be fused, but only under strict circumstances.
# Ensure that linear chains may still be fused.
if width > max_width:
width = max_width
if fudge > int(ave_width - 1):
fudge = int(ave_width - 1)
# key, task, height, width, number of nodes, fudge, set of edges
# This task *implicitly* depends on `edges`
info_stack_append(
(
parent,
rv[parent],
[parent] if rename_keys else None,
1,
width,
1,
fudge,
edges,
)
)
else:
break
# Traverse upwards
parent = rdeps[parent][0]
if fuse_subgraphs:
_inplace_fuse_subgraphs(rv, keys, deps, fused_trees, rename_keys)
if key_renamer:
for root_key, fused_keys in fused_trees.items():
alias = key_renamer(fused_keys)
if alias is not None and alias not in rv:
rv[alias] = rv[root_key]
rv[root_key] = alias
deps[alias] = deps[root_key]
deps[root_key] = {alias}
return rv, deps
def inline(dsk, keys=None, inline_constants=True, dependencies=None):
"""Return new dask with the given keys inlined with their values.
Inlines all constants if ``inline_constants`` keyword is True. Note that
the constant keys will remain in the graph, to remove them follow
``inline`` with ``cull``.
Examples
--------
>>> def inc(x):
... return x + 1
>>> def add(x, y):
... return x + y
>>> d = {'x': 1, 'y': (inc, 'x'), 'z': (add, 'x', 'y')}
>>> inline(d) # doctest: +ELLIPSIS
{'x': 1, 'y': (<function inc at ...>, 1), 'z': (<function add at ...>, 1, 'y')}
>>> inline(d, keys='y') # doctest: +ELLIPSIS
{'x': 1, 'y': (<function inc at ...>, 1), 'z': (<function add at ...>, 1, (<function inc at ...>, 1))}
>>> inline(d, keys='y', inline_constants=False) # doctest: +ELLIPSIS
{'x': 1, 'y': (<function inc at ...>, 'x'), 'z': (<function add at ...>, 'x', (<function inc at ...>, 'x'))}
"""
if dependencies and isinstance(next(iter(dependencies.values())), list):
dependencies = {k: set(v) for k, v in dependencies.items()}
keys = _flat_set(keys)
if dependencies is None:
dependencies = {k: get_dependencies(dsk, k) for k in dsk}
if inline_constants:
keys.update(
k
for k, v in dsk.items()
if (ishashable(v) and v in dsk) or (not dependencies[k] and not istask(v))
)
# Keys may depend on other keys, so determine replace order with toposort.
# The values stored in `keysubs` do not include other keys.
replaceorder = toposort(
{k: dsk[k] for k in keys if k in dsk}, dependencies=dependencies
)
keysubs = {}
for key in replaceorder:
val = dsk[key]
for dep in keys & dependencies[key]:
if dep in keysubs:
replace = keysubs[dep]
else:
replace = dsk[dep]
val = subs(val, dep, replace)
keysubs[key] = val
# Make new dask with substitutions
dsk2 = keysubs.copy()
for key, val in dsk.items():
if key not in dsk2:
for item in keys & dependencies[key]:
val = subs(val, item, keysubs[item])
dsk2[key] = val
return dsk2
def _apply(self, sub_dict):
term = self.rhs
for key, val in sub_dict.items():
term = subs(term, key, val)
return term
def test_subs():
assert subs((sum, [1, "x"]), "x", 2) == (sum, [1, 2])
assert subs((sum, [1, ["x"]]), "x", 2) == (sum, [1, [2]])
def test_subs():
assert subs((sum, [1, 'x']), 'x', 2) == (sum, [1, 2])
assert subs((sum, [1, ['x']]), 'x', 2) == (sum, [1, [2]])