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Support distributed CPU env for categorical data. (#7575)

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* Add support for cat data in sketch allreduce.
* Share tests between CPU and GPU.
This commit is contained in:
Jiaming Yuan
2022-01-18 21:56:07 +08:00
committed by GitHub
parent deab0e32ba
commit cc06fab9a7
5 changed files with 299 additions and 150 deletions
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106
tests/python-gpu/test_gpu_with_dask.py
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@@ -1,3 +1,4 @@
"""Copyright 2019-2022 XGBoost contributors"""
import sys
import os
from typing import Type, TypeVar, Any, Dict, List, Tuple
@@ -36,7 +37,8 @@ from test_with_dask import generate_array # noqa
from test_with_dask import kCols as random_cols # noqa
from test_with_dask import suppress # noqa
from test_with_dask import run_tree_stats # noqa
from test_with_dask import run_categorical # noqa
from test_with_dask import make_categorical # noqa
try:
@@ -51,49 +53,6 @@ except ImportError:
pass
def make_categorical(
client: Client,
n_samples: int,
n_features: int,
n_categories: int,
onehot: bool = False,
) -> Tuple[dd.DataFrame, dd.Series]:
workers = _get_client_workers(client)
n_workers = len(workers)
dfs = []
def pack(**kwargs: Any) -> dd.DataFrame:
X, y = tm.make_categorical(**kwargs)
X["label"] = y
return X
meta = pack(
n_samples=1, n_features=n_features, n_categories=n_categories, onehot=False
)
for i, worker in enumerate(workers):
l_n_samples = min(
n_samples // n_workers, n_samples - i * (n_samples // n_workers)
)
future = client.submit(
pack,
n_samples=l_n_samples,
n_features=n_features,
n_categories=n_categories,
onehot=False,
workers=[worker],
)
dfs.append(future)
df = dd.from_delayed(dfs, meta=meta)
y = df["label"]
X = df[df.columns.difference(["label"])]
if onehot:
return dd.get_dummies(X), y
return X, y
def run_with_dask_dataframe(DMatrixT: Type, client: Client) -> None:
import cupy as cp
cp.cuda.runtime.setDevice(0)
@@ -184,69 +143,12 @@ def test_categorical(local_cuda_cluster: LocalCUDACluster) -> None:
with Client(local_cuda_cluster) as client:
import dask_cudf
rounds = 10
X, y = make_categorical(client, 10000, 30, 13)
X = dask_cudf.from_dask_dataframe(X)
X_onehot, _ = make_categorical(client, 10000, 30, 13, True)
X_onehot = dask_cudf.from_dask_dataframe(X_onehot)
parameters = {"tree_method": "gpu_hist"}
m = dxgb.DaskDMatrix(client, X_onehot, y, enable_categorical=True)
by_etl_results = dxgb.train(
client,
parameters,
m,
num_boost_round=rounds,
evals=[(m, "Train")],
)["history"]
m = dxgb.DaskDMatrix(client, X, y, enable_categorical=True)
output = dxgb.train(
client,
parameters,
m,
num_boost_round=rounds,
evals=[(m, "Train")],
)
by_builtin_results = output["history"]
np.testing.assert_allclose(
np.array(by_etl_results["Train"]["rmse"]),
np.array(by_builtin_results["Train"]["rmse"]),
rtol=1e-3,
)
assert tm.non_increasing(by_builtin_results["Train"]["rmse"])
def check_model_output(model: dxgb.Booster) -> None:
with tempfile.TemporaryDirectory() as tempdir:
path = os.path.join(tempdir, "model.json")
model.save_model(path)
with open(path, "r") as fd:
categorical = json.load(fd)
categories_sizes = np.array(
categorical["learner"]["gradient_booster"]["model"]["trees"][-1][
"categories_sizes"
]
)
assert categories_sizes.shape[0] != 0
np.testing.assert_allclose(categories_sizes, 1)
check_model_output(output["booster"])
reg = dxgb.DaskXGBRegressor(
enable_categorical=True, n_estimators=10, tree_method="gpu_hist"
)
reg.fit(X, y)
check_model_output(reg.get_booster())
reg = dxgb.DaskXGBRegressor(
enable_categorical=True, n_estimators=10
)
with pytest.raises(ValueError):
reg.fit(X, y)
run_categorical(client, "gpu_hist", X, X_onehot, y)
def to_cp(x: Any, DMatrixT: Type) -> Any: