def test_rearrange(shuffle, get):
df = pd.DataFrame({'x': np.random.random(10)})
ddf = dd.from_pandas(df, npartitions=4)
ddf2 = ddf.assign(y=ddf.x % 4)
result = rearrange_by_column(ddf2, 'y', max_branch=32, shuffle=shuffle)
assert result.npartitions == ddf.npartitions
assert set(ddf.dask).issubset(result.dask)
# Every value in exactly one partition
a = result.compute(get=get)
parts = get(result.dask, result._keys())
for i in a.y.drop_duplicates():
assert sum(i in part.y for part in parts) == 1
def test_rearrange(shuffle, scheduler):
df = pd.DataFrame({"x": np.random.random(10)})
ddf = dd.from_pandas(df, npartitions=4)
ddf2 = ddf.assign(_partitions=ddf.x % 4)
result = rearrange_by_column(ddf2, "_partitions", max_branch=32, shuffle=shuffle)
assert result.npartitions == ddf.npartitions
assert set(ddf.dask).issubset(result.dask)
# Every value in exactly one partition
a = result.compute(scheduler=scheduler)
get = dask.base.get_scheduler(scheduler=scheduler)
parts = get(result.dask, result.__dask_keys__())
for i in a._partitions.drop_duplicates():
assert sum(i in set(part._partitions) for part in parts) == 1
def sort_values(
df,
by,
max_branch=None,
divisions=None,
set_divisions=False,
ignore_index=False,
):
""" Sort by the given list/tuple of column names.
"""
npartitions = df.npartitions
if isinstance(by, tuple):
by = list(by)
elif not isinstance(by, list):
by = [by]
# Step 1 - Calculate new divisions (if necessary)
if divisions is None:
divisions = quantile_divisions(df, by, npartitions)
# Step 2 - Perform repartitioning shuffle
meta = df._meta._constructor_sliced([0])
if not isinstance(divisions, (gd.Series, gd.DataFrame)):
dtype = df[by[0]].dtype
divisions = df._meta._constructor_sliced(divisions, dtype=dtype)
partitions = df[by].map_partitions(
_set_partitions_pre, divisions=divisions, meta=meta
)
df2 = df.assign(_partitions=partitions)
df3 = rearrange_by_column(
df2,
"_partitions",
max_branch=max_branch,
npartitions=len(divisions) - 1,
shuffle="tasks",
ignore_index=ignore_index,
).drop(columns=["_partitions"])
df3.divisions = (None,) * (df3.npartitions + 1)
# Step 3 - Return final sorted df
df4 = df3.map_partitions(M.sort_values, by)
if not isinstance(divisions, gd.DataFrame) and set_divisions:
# Can't have multi-column divisions elsewhere in dask (yet)
df4.divisions = methods.tolist(divisions)
return df4
def test_rearrange_disk_cleanup_with_exception():
# ensure temporary files are cleaned up when there's an internal exception.
with mock.patch("dask.dataframe.shuffle.shuffle_group_3", new=mock_shuffle_group_3):
df = pd.DataFrame({"x": np.random.random(10)})
ddf = dd.from_pandas(df, npartitions=4)
ddf2 = ddf.assign(_partitions=ddf.x % 4)
tmpdir = tempfile.mkdtemp()
with dask.config.set(temporay_directory=str(tmpdir)):
with pytest.raises(ValueError, match="Mock exception!"):
result = rearrange_by_column(
ddf2, "_partitions", max_branch=32, shuffle="disk"
)
result.compute(scheduler="processes")
assert len(os.listdir(tmpdir)) == 0
def shuffle(dg, transposed=False, prows=None, pcols=None):
"""
Shuffles the renumbered input distributed graph edgelist into ngpu
partitions. The number of processes/gpus P = prows*pcols. The 2D
partitioning divides the matrix into P*pcols rectangular partitions
as per vertex partitioning performed in renumbering, and then shuffles
these partitions into P gpus.
"""
ddf = dg.edgelist.edgelist_df
ngpus = get_n_workers()
if prows is None and pcols is None:
prows, pcols = get_2D_div(ngpus)
else:
if prows is not None and pcols is not None:
if ngpus != prows*pcols:
raise Exception('prows*pcols should be equal to the\
number of processes')
elif prows is not None:
if ngpus % prows != 0:
raise Exception('prows must be a factor of the number\
of processes')
pcols = int(ngpus/prows)
elif pcols is not None:
if ngpus % pcols != 0:
raise Exception('pcols must be a factor of the number\
of processes')
prows = int(ngpus/pcols)
renumber_vertex_count = dg.renumber_map.implementation.\
ddf.map_partitions(len).compute()
renumber_vertex_cumsum = renumber_vertex_count.cumsum()
src_dtype = ddf['src'].dtype
dst_dtype = ddf['dst'].dtype
vertex_row_partitions = cudf.Series([0], dtype=src_dtype)
vertex_row_partitions = vertex_row_partitions.append(cudf.Series(
renumber_vertex_cumsum, dtype=src_dtype))
num_verts = vertex_row_partitions.iloc[-1]
vertex_col_partitions = []
for i in range(pcols + 1):
vertex_col_partitions.append(vertex_row_partitions.iloc[i*prows])
vertex_col_partitions = cudf.Series(vertex_col_partitions, dtype=dst_dtype)
meta = ddf._meta._constructor_sliced([0])
partitions = ddf.map_partitions(
_set_partitions_pre,
vertex_row_partitions=vertex_row_partitions,
vertex_col_partitions=vertex_col_partitions, prows=prows,
pcols=pcols, transposed=transposed, meta=meta)
ddf2 = ddf.assign(_partitions=partitions)
ddf3 = rearrange_by_column(
ddf2,
"_partitions",
max_branch=None,
npartitions=ngpus,
shuffle="tasks",
ignore_index=True,
).drop(columns=["_partitions"])
return ddf3, num_verts, vertex_row_partitions
def rearrange_by_hash(df,
columns,
npartitions,
max_branch=None,
ignore_index=True):
if npartitions and npartitions != df.npartitions:
# Use main-line dask for new npartitions
meta = df._meta._constructor_sliced([0])
partitions = df[columns].map_partitions(set_partitions_hash,
columns,
npartitions,
meta=meta)
# Note: Dask will use a shallow copy for assign
df2 = df.assign(_partitions=partitions)
return rearrange_by_column(
df2,
"_partitions",
shuffle="tasks",
max_branch=max_branch,
npartitions=npartitions,
ignore_index=ignore_index,
)
n = df.npartitions
if max_branch is False:
stages = 1
else:
max_branch = max_branch or 32
stages = int(math.ceil(math.log(n) / math.log(max_branch)))
if stages > 1:
k = int(math.ceil(n**(1 / stages)))
else:
k = n
if isinstance(columns, str):
columns = [columns]
elif isinstance(columns, tuple):
columns = list(columns)
groups = []
splits = []
combines = []
inputs = [
tuple(digit(i, j, k) for j in range(stages)) for i in range(k**stages)
]
token = tokenize(df, columns, max_branch)
start = {("shuffle-combine-" + token, 0, inp):
(df._name, i) if i < df.npartitions else df._meta
for i, inp in enumerate(inputs)}
for stage in range(1, stages + 1):
group = { # Convert partition into dict of dataframe pieces
("shuffle-group-" + token, stage, inp): (
_shuffle_group,
("shuffle-combine-" + token, stage - 1, inp),
columns,
stage - 1,
k,
n,
ignore_index,
)
for inp in inputs
}
split = { # Get out each individual dataframe piece from the dicts
("shuffle-split-" + token, stage, i, inp): (
getitem,
("shuffle-group-" + token, stage, inp),
i,
)
for i in range(k) for inp in inputs
}
combine = { # concatenate those pieces together, with their friends
("shuffle-combine-" + token, stage, inp): (
_concat,
[(
"shuffle-split-" + token,
stage,
inp[stage - 1],
insert(inp, stage - 1, j),
) for j in range(k)],
ignore_index,
)
for inp in inputs
}
groups.append(group)
splits.append(split)
combines.append(combine)
end = {("shuffle-" + token, i): ("shuffle-combine-" + token, stages, inp)
for i, inp in enumerate(inputs)}
dsk = toolz.merge(start, end, *(groups + splits + combines))
graph = HighLevelGraph.from_collections("shuffle-" + token,
dsk,
dependencies=[df])
df2 = df.__class__(graph, "shuffle-" + token, df, df.divisions)
df2.divisions = (None, ) * (df.npartitions + 1)
return df2
def pagerank(input_graph,
alpha=0.85,
personalization=None,
max_iter=100,
tol=1.0e-5,
nstart=None):
"""
Find the PageRank values for each vertex in a graph using multiple GPUs.
cuGraph computes an approximation of the Pagerank using the power method.
The input graph must contain edge list as dask-cudf dataframe with
one partition per GPU.
Parameters
----------
input_graph : cugraph.DiGraph
cuGraph graph descriptor, should contain the connectivity information
as dask cudf edge list dataframe(edge weights are not used for this
algorithm). Undirected Graph not currently supported.
alpha : float, optional (default=0.85)
The damping factor alpha represents the probability to follow an
outgoing edge, standard value is 0.85.
Thus, 1.0-alpha is the probability to “teleport” to a random vertex.
Alpha should be greater than 0.0 and strictly lower than 1.0.
personalization : cudf.Dataframe, optional (default=None)
GPU Dataframe containing the personalization information.
Currently not supported.
personalization['vertex'] : cudf.Series
Subset of vertices of graph for personalization
personalization['values'] : cudf.Series
Personalization values for vertices
max_iter : int, optional (default=100)
The maximum number of iterations before an answer is returned.
If this value is lower or equal to 0 cuGraph will use the default
value, which is 30.
tol : float, optional (default=1.0e-5)
Set the tolerance the approximation, this parameter should be a small
magnitude value.
The lower the tolerance the better the approximation. If this value is
0.0f, cuGraph will use the default value which is 1.0E-5.
Setting too small a tolerance can lead to non-convergence due to
numerical roundoff. Usually values between 0.01 and 0.00001 are
acceptable.
nstart : not supported
initial guess for pagerank
Returns
-------
PageRank : dask_cudf.DataFrame
GPU data frame containing two dask_cudf.Series of size V: the
vertex identifiers and the corresponding PageRank values.
ddf['vertex'] : dask_cudf.Series
Contains the vertex identifiers
ddf['pagerank'] : dask_cudf.Series
Contains the PageRank score
Examples
--------
>>> # import cugraph.dask as dcg
>>> # ... Init a DASK Cluster
>>> # see https://docs.rapids.ai/api/cugraph/stable/dask-cugraph.html
>>> # Download dataset from https://github.com/rapidsai/cugraph/datasets/..
>>> # chunksize = dcg.get_chunksize(datasets_path / "karate.csv")
>>> # ddf = dask_cudf.read_csv(input_data_path, chunksize=chunksize)
>>> # dg = cugraph.Graph(directed=True)
>>> # dg.from_dask_cudf_edgelist(ddf, source='src', destination='dst',
>>> # edge_attr='value')
>>> # pr = dcg.pagerank(dg)
"""
nstart = None
client = default_client()
input_graph.compute_renumber_edge_list(transposed=True)
ddf = input_graph.edgelist.edgelist_df
vertex_partition_offsets = get_vertex_partition_offsets(input_graph)
num_verts = vertex_partition_offsets.iloc[-1]
num_edges = len(ddf)
data = get_distributed_data(ddf)
src_col_name = input_graph.renumber_map.renumbered_src_col_name
dst_col_name = input_graph.renumber_map.renumbered_dst_col_name
if personalization is not None:
if input_graph.renumbered is True:
personalization = input_graph.add_internal_vertex_id(
personalization, "vertex", "vertex")
# Function to assign partition id to personalization dataframe
def _set_partitions_pre(s, divisions):
partitions = divisions.searchsorted(s, side="right") - 1
partitions[divisions.tail(1).searchsorted(
s, side="right").astype("bool")] = (len(divisions) - 2)
return partitions
# Assign partition id column as per vertex_partition_offsets
df = personalization
by = ['vertex']
meta = df._meta._constructor_sliced([0])
divisions = vertex_partition_offsets
partitions = df[by].map_partitions(_set_partitions_pre,
divisions=divisions,
meta=meta)
df2 = df.assign(_partitions=partitions)
# Shuffle personalization values according to the partition id
df3 = rearrange_by_column(
df2,
"_partitions",
max_branch=None,
npartitions=len(divisions) - 1,
shuffle="tasks",
ignore_index=False,
).drop(columns=["_partitions"])
p_data = get_distributed_data(df3)
result = [
client.submit(call_pagerank,
Comms.get_session_id(),
wf[1],
src_col_name,
dst_col_name,
num_verts,
num_edges,
vertex_partition_offsets,
input_graph.aggregate_segment_offsets,
alpha,
max_iter,
tol,
p_data.worker_to_parts[wf[0]][0],
nstart,
workers=[wf[0]])
for idx, wf in enumerate(data.worker_to_parts.items())
]
else:
result = [
client.submit(call_pagerank,
Comms.get_session_id(),
wf[1],
src_col_name,
dst_col_name,
num_verts,
num_edges,
vertex_partition_offsets,
input_graph.aggregate_segment_offsets,
alpha,
max_iter,
tol,
personalization,
nstart,
workers=[wf[0]])
for idx, wf in enumerate(data.worker_to_parts.items())
]
wait(result)
ddf = dask_cudf.from_delayed(result)
if input_graph.renumbered:
return input_graph.unrenumber(ddf, 'vertex')
return ddf
def shuffle(dg, transposed=False):
"""
Shuffles the renumbered input distributed graph edgelist into ngpu
partitions. The number of processes/gpus P = prows*pcols. The 2D
partitioning divides the matrix into P*pcols rectangular partitions
as per vertex partitioning performed in renumbering, and then shuffles
these partitions into P gpus.
Parameters
----------
transposed : bool, optional (default=False)
"""
ddf = dg.edgelist.edgelist_df
ngpus = Comms.get_n_workers()
prows, pcols, partition_type = Comms.get_2D_partition()
renumber_vertex_count = dg.renumber_map.implementation.\
ddf.map_partitions(len).compute()
renumber_vertex_cumsum = renumber_vertex_count.cumsum()
if transposed:
row_dtype = ddf['dst'].dtype
col_dtype = ddf['src'].dtype
else:
row_dtype = ddf['src'].dtype
col_dtype = ddf['dst'].dtype
vertex_partition_offsets = cudf.Series([0], dtype=row_dtype)
vertex_partition_offsets = vertex_partition_offsets.append(
cudf.Series(renumber_vertex_cumsum, dtype=row_dtype))
num_verts = vertex_partition_offsets.iloc[-1]
if partition_type == 1:
vertex_row_partitions = []
for i in range(prows + 1):
vertex_row_partitions.append(vertex_partition_offsets.iloc[i *
pcols])
vertex_row_partitions = cudf.Series(vertex_row_partitions,
dtype=row_dtype)
else:
vertex_row_partitions = vertex_partition_offsets
vertex_col_partitions = []
for i in range(pcols + 1):
vertex_col_partitions.append(vertex_partition_offsets.iloc[i * prows])
vertex_col_partitions = cudf.Series(vertex_col_partitions, dtype=col_dtype)
meta = ddf._meta._constructor_sliced([0])
partitions = ddf.map_partitions(
_set_partitions_pre,
vertex_row_partitions=vertex_row_partitions,
vertex_col_partitions=vertex_col_partitions,
prows=prows,
pcols=pcols,
transposed=transposed,
partition_type=partition_type,
meta=meta)
ddf2 = ddf.assign(_partitions=partitions)
ddf3 = rearrange_by_column(
ddf2,
"_partitions",
max_branch=None,
npartitions=ngpus,
shuffle="tasks",
ignore_index=True,
).drop(columns=["_partitions"])
partition_row_size = pcols
partition_col_size = prows
return (ddf3, num_verts, partition_row_size, partition_col_size,
vertex_partition_offsets)
def sort_values(
df,
by,
max_branch=None,
divisions=None,
set_divisions=False,
ignore_index=False,
ascending=True,
na_position="last",
sort_function=None,
sort_function_kwargs=None,
):
"""Sort by the given list/tuple of column names."""
if not isinstance(ascending, bool):
raise ValueError("ascending must be either True or False")
if na_position not in ("first", "last"):
raise ValueError("na_position must be either 'first' or 'last'")
npartitions = df.npartitions
if isinstance(by, tuple):
by = list(by)
elif not isinstance(by, list):
by = [by]
# parse custom sort function / kwargs if provided
sort_kwargs = {
"by": by,
"ascending": ascending,
"na_position": na_position,
}
if sort_function is None:
sort_function = M.sort_values
if sort_function_kwargs is not None:
sort_kwargs.update(sort_function_kwargs)
# handle single partition case
if npartitions == 1:
return df.map_partitions(sort_function, **sort_kwargs)
# Step 1 - Calculate new divisions (if necessary)
if divisions is None:
divisions = quantile_divisions(df, by, npartitions)
# Step 2 - Perform repartitioning shuffle
meta = df._meta._constructor_sliced([0])
if not isinstance(divisions, (gd.Series, gd.DataFrame)):
dtype = df[by[0]].dtype
divisions = df._meta._constructor_sliced(divisions, dtype=dtype)
partitions = df[by].map_partitions(
_set_partitions_pre,
divisions=divisions,
ascending=ascending,
na_position=na_position,
meta=meta,
)
df2 = df.assign(_partitions=partitions)
df3 = rearrange_by_column(
df2,
"_partitions",
max_branch=max_branch,
npartitions=len(divisions) - 1,
shuffle="tasks",
ignore_index=ignore_index,
).drop(columns=["_partitions"])
df3.divisions = (None, ) * (df3.npartitions + 1)
# Step 3 - Return final sorted df
df4 = df3.map_partitions(sort_function, **sort_kwargs)
if not isinstance(divisions, gd.DataFrame) and set_divisions:
# Can't have multi-column divisions elsewhere in dask (yet)
df4.divisions = methods.tolist(divisions)
return df4
