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from __future__ import annotations
import ast
import pytest
import dask
import dask.array as da
import dask.dataframe as dd
from distributed.diagnostics import SchedulerPlugin
from distributed.utils_test import gen_cluster
np = pytest.importorskip("numpy")
pd = pytest.importorskip("pandas")
from numpy.testing import assert_array_equal
@gen_cluster(client=True)
async def test_combo_of_layer_types(c, s, a, b):
"""Check pack/unpack of a HLG that has everything!"""
def add(x, y, z, extra_arg):
return x + y + z + extra_arg
y = c.submit(lambda x: x, 2)
z = c.submit(lambda x: x, 3)
x = da.blockwise(
add,
"x",
da.zeros((3,), chunks=(1,)),
"x",
da.ones((3,), chunks=(1,)),
"x",
y,
None,
concatenate=False,
dtype=int,
extra_arg=z,
)
df = dd.from_pandas(pd.DataFrame({"a": np.arange(3)}), npartitions=3)
df = df.shuffle("a", shuffle="tasks")
df = df["a"].to_dask_array()
res = x.sum() + df.sum()
res = await c.compute(res, optimize_graph=False)
assert res == 21
@gen_cluster(client=True)
async def test_blockwise(c, s, a, b):
"""Check pack/unpack of blockwise layer"""
def add(x, y, z, extra_arg):
return x + y + z + extra_arg
y = c.submit(lambda x: x, 10)
z = c.submit(lambda x: x, 3)
x = da.blockwise(
add,
"x",
da.zeros((3,), chunks=(1,)),
"x",
da.ones((3,), chunks=(1,)),
"x",
y,
None,
concatenate=False,
dtype=int,
extra_arg=z,
)
res = await c.compute(x.sum(), optimize_graph=False)
assert res == 42
@gen_cluster(client=True)
async def test_shuffle(c, s, a, b):
"""Check pack/unpack of a shuffled dataframe"""
df = dd.from_pandas(
pd.DataFrame(
{"a": np.arange(10, dtype=int), "b": np.arange(10, 0, -1, dtype=float)}
),
npartitions=5,
)
df = df.shuffle("a", shuffle="tasks", max_branch=2)
df = df["a"] + df["b"]
res = await c.compute(df, optimize_graph=False)
assert res.dtypes == np.float64
assert (res == 10.0).all()
class ExampleAnnotationPlugin(SchedulerPlugin):
def __init__(self, priority_fn=None, qux="", resource="", retries=0):
self.priority_fn = priority_fn or (lambda k: 0)
self.qux = qux
self.resource = resource
self.retries = retries
self.priority_matches = 0
self.resource_matches = 0
self.retry_matches = 0
self.qux_matches = 0
def update_graph(self, scheduler, dsk=None, keys=None, restrictions=None, **kwargs):
annots = kwargs["annotations"]
if "priority" in annots:
self.priority_matches = sum(
int(self.priority_fn(ast.literal_eval(k)) == p)
for k, p in annots["priority"].items()
)
if "qux" in annots:
self.qux_matches = sum(int(self.qux == v) for v in annots["qux"].values())
if "custom_resource" in annots:
self.resource_matches = sum(
int(self.resource == v) for v in annots["custom_resource"].values()
)
if "retries" in annots:
self.retry_matches = sum(
int(self.retries == v) for v in annots["retries"].values()
)
@gen_cluster(client=True)
async def test_array_annotations(c, s, a, b):
def fn(k):
return k[1] * 5 + k[2]
qux = "baz"
resource = "widget"
plugin = ExampleAnnotationPlugin(priority_fn=fn, qux=qux, resource=resource)
s.add_plugin(plugin)
assert plugin in s.plugins.values()
with dask.annotate(priority=fn, qux=qux):
A = da.ones((10, 10), chunks=(2, 2))
with dask.annotate(custom_resource=resource):
B = A + 1
with dask.config.set(optimization__fuse__active=False):
result = await c.compute(B)
assert_array_equal(result, 2)
# There are annotation matches per array chunk (i.e. task)
assert plugin.qux_matches == A.npartitions
assert plugin.priority_matches == A.npartitions
assert plugin.resource_matches == B.npartitions
@gen_cluster(client=True)
async def test_dataframe_annotations(c, s, a, b):
retries = 5
plugin = ExampleAnnotationPlugin(retries=retries)
s.add_plugin(plugin)
assert plugin in s.plugins.values()
df = dd.from_pandas(
pd.DataFrame(
{"a": np.arange(10, dtype=int), "b": np.arange(10, 0, -1, dtype=float)}
),
npartitions=5,
)
df = df.shuffle("a", shuffle="tasks", max_branch=2)
acol = df["a"]
bcol = df["b"]
with dask.annotate(retries=retries):
df = acol + bcol
with dask.config.set(optimization__fuse__active=False):
rdf = await c.compute(df)
assert rdf.dtypes == np.float64
assert (rdf == 10.0).all()
# There is an annotation match per partition (i.e. task)
assert plugin.retry_matches == df.npartitions
|
