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Cleanup Python GPU tests. (#9934)

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* Cleanup Python GPU tests.

- Remove the use of `gpu_hist` and `gpu_id` in cudf/cupy tests.
- Move base margin test into the testing directory.
This commit is contained in:
Jiaming Yuan
2024-01-04 13:15:18 +08:00
committed by GitHub
parent 3c004a4145
commit 9f73127a23
14 changed files with 282 additions and 240 deletions
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96
tests/python-gpu/test_from_cupy.py
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@@ -1,31 +1,25 @@
import json
import sys
import numpy as np
import pytest
import xgboost as xgb
sys.path.append("tests/python")
from test_dmatrix import set_base_margin_info
from xgboost import testing as tm
from xgboost.testing.data import run_base_margin_info
cupy = pytest.importorskip("cupy")
cp = pytest.importorskip("cupy")
def test_array_interface() -> None:
arr = cupy.array([[1, 2, 3, 4], [1, 2, 3, 4]])
arr = cp.array([[1, 2, 3, 4], [1, 2, 3, 4]])
i_arr = arr.__cuda_array_interface__
i_arr = json.loads(json.dumps(i_arr))
ret = xgb.core.from_array_interface(i_arr)
np.testing.assert_equal(cupy.asnumpy(arr), cupy.asnumpy(ret))
np.testing.assert_equal(cp.asnumpy(arr), cp.asnumpy(ret))
def dmatrix_from_cupy(input_type, DMatrixT, missing=np.NAN):
'''Test constructing DMatrix from cupy'''
import cupy as cp
"""Test constructing DMatrix from cupy"""
kRows = 80
kCols = 3
@@ -51,9 +45,7 @@ def dmatrix_from_cupy(input_type, DMatrixT, missing=np.NAN):
def _test_from_cupy(DMatrixT):
'''Test constructing DMatrix from cupy'''
import cupy as cp
"""Test constructing DMatrix from cupy"""
dmatrix_from_cupy(np.float16, DMatrixT, np.NAN)
dmatrix_from_cupy(np.float32, DMatrixT, np.NAN)
dmatrix_from_cupy(np.float64, DMatrixT, np.NAN)
@@ -73,7 +65,6 @@ def _test_from_cupy(DMatrixT):
def _test_cupy_training(DMatrixT):
import cupy as cp
np.random.seed(1)
cp.random.seed(1)
X = cp.random.randn(50, 10, dtype="float32")
@@ -85,19 +76,23 @@ def _test_cupy_training(DMatrixT):
evals_result_cupy = {}
dtrain_cp = DMatrixT(X, y, weight=cupy_weights, base_margin=cupy_base_margin)
params = {'gpu_id': 0, 'nthread': 1, 'tree_method': 'gpu_hist'}
xgb.train(params, dtrain_cp, evals=[(dtrain_cp, "train")],
evals_result=evals_result_cupy)
params = {"tree_method": "hist", "device": "cuda:0"}
xgb.train(
params, dtrain_cp, evals=[(dtrain_cp, "train")], evals_result=evals_result_cupy
)
evals_result_np = {}
dtrain_np = xgb.DMatrix(cp.asnumpy(X), cp.asnumpy(y), weight=weights,
base_margin=base_margin)
xgb.train(params, dtrain_np, evals=[(dtrain_np, "train")],
evals_result=evals_result_np)
assert np.array_equal(evals_result_cupy["train"]["rmse"], evals_result_np["train"]["rmse"])
dtrain_np = xgb.DMatrix(
cp.asnumpy(X), cp.asnumpy(y), weight=weights, base_margin=base_margin
)
xgb.train(
params, dtrain_np, evals=[(dtrain_np, "train")], evals_result=evals_result_np
)
assert np.array_equal(
evals_result_cupy["train"]["rmse"], evals_result_np["train"]["rmse"]
)
def _test_cupy_metainfo(DMatrixT):
import cupy as cp
n = 100
X = np.random.random((n, 2))
dmat_cupy = DMatrixT(cp.array(X))
@@ -106,33 +101,35 @@ def _test_cupy_metainfo(DMatrixT):
uints = np.array([4, 2, 8]).astype("uint32")
cupy_floats = cp.array(floats)
cupy_uints = cp.array(uints)
dmat.set_float_info('weight', floats)
dmat.set_float_info('label', floats)
dmat.set_float_info('base_margin', floats)
dmat.set_uint_info('group', uints)
dmat.set_float_info("weight", floats)
dmat.set_float_info("label", floats)
dmat.set_float_info("base_margin", floats)
dmat.set_uint_info("group", uints)
dmat_cupy.set_info(weight=cupy_floats)
dmat_cupy.set_info(label=cupy_floats)
dmat_cupy.set_info(base_margin=cupy_floats)
dmat_cupy.set_info(group=cupy_uints)
# Test setting info with cupy
assert np.array_equal(dmat.get_float_info('weight'),
dmat_cupy.get_float_info('weight'))
assert np.array_equal(dmat.get_float_info('label'),
dmat_cupy.get_float_info('label'))
assert np.array_equal(dmat.get_float_info('base_margin'),
dmat_cupy.get_float_info('base_margin'))
assert np.array_equal(dmat.get_uint_info('group_ptr'),
dmat_cupy.get_uint_info('group_ptr'))
assert np.array_equal(
dmat.get_float_info("weight"), dmat_cupy.get_float_info("weight")
)
assert np.array_equal(
dmat.get_float_info("label"), dmat_cupy.get_float_info("label")
)
assert np.array_equal(
dmat.get_float_info("base_margin"), dmat_cupy.get_float_info("base_margin")
)
assert np.array_equal(
dmat.get_uint_info("group_ptr"), dmat_cupy.get_uint_info("group_ptr")
)
set_base_margin_info(cp.asarray, DMatrixT, "gpu_hist")
run_base_margin_info(cp.asarray, DMatrixT, "cuda")
@pytest.mark.skipif(**tm.no_cupy())
@pytest.mark.skipif(**tm.no_sklearn())
def test_cupy_training_with_sklearn():
import cupy as cp
np.random.seed(1)
cp.random.seed(1)
X = cp.random.randn(50, 10, dtype="float32")
@@ -142,7 +139,7 @@ def test_cupy_training_with_sklearn():
base_margin = np.random.random(50)
cupy_base_margin = cp.array(base_margin)
clf = xgb.XGBClassifier(gpu_id=0, tree_method="gpu_hist")
clf = xgb.XGBClassifier(tree_method="hist", device="cuda:0")
clf.fit(
X,
y,
@@ -155,8 +152,8 @@ def test_cupy_training_with_sklearn():
class TestFromCupy:
'''Tests for constructing DMatrix from data structure conforming Apache
Arrow specification.'''
"""Tests for constructing DMatrix from data structure conforming Apache
Arrow specification."""
@pytest.mark.skipif(**tm.no_cupy())
def test_simple_dmat_from_cupy(self):
@@ -184,19 +181,17 @@ Arrow specification.'''
@pytest.mark.skipif(**tm.no_cupy())
def test_dlpack_simple_dmat(self):
import cupy as cp
n = 100
X = cp.random.random((n, 2))
xgb.DMatrix(X.toDlpack())
@pytest.mark.skipif(**tm.no_cupy())
def test_cupy_categorical(self):
import cupy as cp
n_features = 10
X, y = tm.make_categorical(10, n_features, n_categories=4, onehot=False)
X = cp.asarray(X.values.astype(cp.float32))
y = cp.array(y)
feature_types = ['c'] * n_features
feature_types = ["c"] * n_features
assert isinstance(X, cp.ndarray)
Xy = xgb.DMatrix(X, y, feature_types=feature_types)
@@ -204,7 +199,6 @@ Arrow specification.'''
@pytest.mark.skipif(**tm.no_cupy())
def test_dlpack_device_dmat(self):
import cupy as cp
n = 100
X = cp.random.random((n, 2))
m = xgb.QuantileDMatrix(X.toDlpack())
@@ -213,7 +207,6 @@ Arrow specification.'''
@pytest.mark.skipif(**tm.no_cupy())
def test_qid(self):
import cupy as cp
rng = cp.random.RandomState(1994)
rows = 100
cols = 10
@@ -223,19 +216,16 @@ Arrow specification.'''
Xy = xgb.DMatrix(X, y)
Xy.set_info(qid=qid)
group_ptr = Xy.get_uint_info('group_ptr')
group_ptr = Xy.get_uint_info("group_ptr")
assert group_ptr[0] == 0
assert group_ptr[-1] == rows
@pytest.mark.skipif(**tm.no_cupy())
@pytest.mark.mgpu
def test_specified_device(self):
import cupy as cp
cp.cuda.runtime.setDevice(0)
dtrain = dmatrix_from_cupy(np.float32, xgb.QuantileDMatrix, np.nan)
with pytest.raises(
xgb.core.XGBoostError, match="Invalid device ordinal"
):
with pytest.raises(xgb.core.XGBoostError, match="Invalid device ordinal"):
xgb.train(
{'tree_method': 'gpu_hist', 'gpu_id': 1}, dtrain, num_boost_round=10
{"tree_method": "hist", "device": "cuda:1"}, dtrain, num_boost_round=10
)