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[REVIEW] Optimize pg.get_x_data APIs #3086

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merged 10 commits into from
Jan 3, 2023
Merged

[REVIEW] Optimize pg.get_x_data APIs #3086

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769e281
added benchmarks
VibhuJawa Dec 15, 2022
9ab9d08
Merge branch 'branch-23.02' into optimize_gather_vector_properties
VibhuJawa Dec 16, 2022
0949d07
Remove comments
VibhuJawa Dec 16, 2022
5ea50dc
Merge branch 'optimize_gather_vector_properties' of https://github.co…
VibhuJawa Dec 16, 2022
6040a29
Update cugraph features
VibhuJawa Dec 16, 2022
c6408f5
Update python/cugraph/cugraph/dask/structure/mg_property_graph.py
VibhuJawa Dec 16, 2022
63d5177
optimize_gather_vector_properties
VibhuJawa Dec 16, 2022
dcbb7cf
Update mg_property_graph.py
VibhuJawa Dec 16, 2022
6d001c8
Merge branch 'branch-23.02' into optimize_gather_vector_properties
VibhuJawa Dec 27, 2022
b7814d3
Merge branch 'branch-23.02' into optimize_gather_vector_properties
VibhuJawa Dec 31, 2022
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26 changes: 15 additions & 11 deletions python/cugraph/cugraph/dask/structure/mg_property_graph.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,6 @@

import cudf
import cupy
import numpy as np
import cugraph
import dask_cudf
import cugraph.dask as dcg
Expand Down Expand Up @@ -578,11 +577,13 @@ def get_vertex_data(self, vertex_ids=None, types=None, columns=None):
if vertex_ids is not None:
if isinstance(vertex_ids, int):
vertex_ids = [vertex_ids]
elif not isinstance(
vertex_ids, (list, slice, np.ndarray, self.__series_type)
):
vertex_ids = list(vertex_ids)
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df = df.loc[vertex_ids]
try:
df = df.loc[vertex_ids]
except TypeError:
raise TypeError(
"vertex_ids needs to be a list-like type "
f"compatible with DataFrame.loc[], got {type(vertex_ids)}"
)

if types is not None:
if isinstance(types, str):
Expand Down Expand Up @@ -906,11 +907,14 @@ def get_edge_data(self, edge_ids=None, types=None, columns=None):
if edge_ids is not None:
if isinstance(edge_ids, int):
edge_ids = [edge_ids]
elif not isinstance(
edge_ids, (list, slice, np.ndarray, self.__series_type)
):
edge_ids = list(edge_ids)
df = df.loc[edge_ids]

try:
df = df.loc[edge_ids]
except TypeError:
raise TypeError(
"edge_ids needs to be a list-like type "
f"compatible with DataFrame.loc[], got {type(edge_ids)}"
)

if types is not None:
if isinstance(types, str):
Expand Down
28 changes: 17 additions & 11 deletions python/cugraph/cugraph/structure/property_graph.py
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Original file line number Diff line number Diff line change
Expand Up @@ -512,7 +512,7 @@ def add_vertex_data(
):
"""
Add a dataframe describing vertex properties to the PropertyGraph.
Can contain additional vertices that will not have associatede edges.
Can contain additional vertices that will not have associated edges.

Parameters
----------
Expand Down Expand Up @@ -829,11 +829,14 @@ def get_vertex_data(self, vertex_ids=None, types=None, columns=None):
if vertex_ids is not None:
if isinstance(vertex_ids, int):
vertex_ids = [vertex_ids]
elif not isinstance(
vertex_ids, (list, slice, np.ndarray, self.__series_type)
):
vertex_ids = list(vertex_ids)
df = df.loc[vertex_ids]

try:
df = df.loc[vertex_ids]
except TypeError:
raise TypeError(
"vertex_ids needs to be a list-like type "
f"compatible with DataFrame.loc[], got {type(vertex_ids)}"
)

if types is not None:
if isinstance(types, str):
Expand Down Expand Up @@ -1218,11 +1221,14 @@ def get_edge_data(self, edge_ids=None, types=None, columns=None):
if edge_ids is not None:
if isinstance(edge_ids, int):
edge_ids = [edge_ids]
elif not isinstance(
edge_ids, (list, slice, np.ndarray, self.__series_type)
):
edge_ids = list(edge_ids)
df = df.loc[edge_ids]

try:
df = df.loc[edge_ids]
except TypeError:
raise TypeError(
"edge_ids needs to be a list-like type "
f"compatible with DataFrame.loc[], got {type(edge_ids)}"
)

if types is not None:
if isinstance(types, str):
Expand Down
40 changes: 37 additions & 3 deletions python/cugraph/cugraph/tests/mg/test_mg_property_graph.py
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Original file line number Diff line number Diff line change
Expand Up @@ -866,11 +866,14 @@ def test_renumber_vertices_by_type(dataset1_MGPropertyGraph, prev_id_column):
assert df_id_ranges.loc[key, "stop"] == stop
df = pG.get_vertex_data(types=[key]).compute()
assert len(df) == stop - start + 1
assert (df["_VERTEX_"] == list(range(start, stop + 1))).all()
assert (
df["_VERTEX_"] == df["_VERTEX_"]._constructor(range(start, stop + 1))
).all()
Comment on lines +869 to +871
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These changes are similar to https://github.com/rapidsai/cugraph/pull/3036/files#r1048542911 .

Unsure how CI passed without them

if prev_id_column is not None:
cur = df[prev_id_column].sort_values()
expected = sorted(x for x, *args in data[key][1])
assert (cur == expected).all()
expected = cur._constructor(sorted(x for x, *args in data[key][1]))
assert (cur.values == expected.values).all()
Comment on lines +875 to +876
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These changes are similar to https://github.com/rapidsai/cugraph/pull/3036/files#r1048542911 .

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I made similar changes in my PR. We have to be more careful running MG tests until they are included in CI.

# Make sure we renumber vertex IDs in edge data too
df = pG.get_edge_data().compute()
assert 0 <= df[pG.src_col_name].min() < df[pG.src_col_name].max() < 9
Expand Down Expand Up @@ -905,7 +908,9 @@ def test_renumber_edges_by_type(dataset1_MGPropertyGraph, prev_id_column):
assert df_id_ranges.loc[key, "stop"] == stop
df = pG.get_edge_data(types=[key]).compute()
assert len(df) == stop - start + 1
assert (df[pG.edge_id_col_name] == list(range(start, stop + 1))).all()
actual = df[pG.edge_id_col_name]
expected = actual._constructor(range(start, stop + 1))
assert (actual == expected).all()
Comment on lines +911 to +913
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These changes are similar to https://github.com/rapidsai/cugraph/pull/3036/files#r1048542911 .

if prev_id_column is not None:
assert prev_id_column in df.columns

Expand Down Expand Up @@ -1336,3 +1341,32 @@ def func():
assert len(df) == len(cyber_df)

gpubenchmark(func)


@pytest.mark.slow
@pytest.mark.parametrize("n_rows", [1_000_000])
@pytest.mark.parametrize("n_feats", [128])
def bench_get_vector_features(gpubenchmark, dask_client, n_rows, n_feats):
from cugraph.experimental import MGPropertyGraph

df = cudf.DataFrame(
{
"src": cp.arange(0, n_rows, dtype=cp.int32),
"dst": cp.arange(0, n_rows, dtype=cp.int32) + 1,
}
)
for i in range(n_feats):
df[f"feat_{i}"] = cp.ones(len(df), dtype=cp.int32)
df = dask_cudf.from_cudf(df, npartitions=16)

vector_properties = {"feat": [f"feat_{i}" for i in range(n_feats)]}
pG = MGPropertyGraph()
pG.add_edge_data(
df, vertex_col_names=["src", "dst"], vector_properties=vector_properties
)

def func(pG):
df = pG.get_edge_data(edge_ids=cp.arange(0, 100_000))
df = df.compute()

gpubenchmark(func, pG)
46 changes: 45 additions & 1 deletion python/cugraph/cugraph/tests/test_property_graph.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2384,7 +2384,7 @@ def func():
gpubenchmark(func)


@pytest.mark.slow
# @pytest.mark.slow
@pytest.mark.parametrize("n_rows", [10_000, 100_000, 1_000_000, 10_000_000])
@pytest.mark.parametrize("n_feats", [32, 64, 128])
def bench_add_vector_features(gpubenchmark, n_rows, n_feats):
Expand All @@ -2408,3 +2408,47 @@ def func():
)

gpubenchmark(func)


@pytest.mark.parametrize("n_rows", [1_000_000])
@pytest.mark.parametrize("n_feats", [128])
def bench_get_vector_features_cp_array(benchmark, n_rows, n_feats):
from cugraph.experimental import PropertyGraph

df = cudf.DataFrame(
{
"src": cp.arange(0, n_rows, dtype=cp.int32),
"dst": cp.arange(0, n_rows, dtype=cp.int32) + 1,
}
)
for i in range(n_feats):
df[f"feat_{i}"] = cp.ones(len(df), dtype=cp.int32)

vector_properties = {"feat": [f"feat_{i}" for i in range(n_feats)]}
pG = PropertyGraph()
pG.add_edge_data(
df, vertex_col_names=["src", "dst"], vector_properties=vector_properties
)
benchmark(pG.get_edge_data, edge_ids=cp.arange(0, 100_000))


@pytest.mark.parametrize("n_rows", [1_000_000])
@pytest.mark.parametrize("n_feats", [128])
def bench_get_vector_features_cudf_series(benchmark, n_rows, n_feats):
from cugraph.experimental import PropertyGraph

df = cudf.DataFrame(
{
"src": cp.arange(0, n_rows, dtype=cp.int32),
"dst": cp.arange(0, n_rows, dtype=cp.int32) + 1,
}
)
for i in range(n_feats):
df[f"feat_{i}"] = cp.ones(len(df), dtype=cp.int32)

vector_properties = {"feat": [f"feat_{i}" for i in range(n_feats)]}
pG = PropertyGraph()
pG.add_edge_data(
df, vertex_col_names=["src", "dst"], vector_properties=vector_properties
)
benchmark(pG.get_edge_data, edge_ids=cudf.Series(cp.arange(0, 100_000)))