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import sys
import numpy as np
import pytest
from hypothesis import given, settings, strategies
import xgboost as xgb
from xgboost import testing as tm
from xgboost.testing import no_cupy
from xgboost.testing.data_iter import check_invalid_cat_batches, check_uneven_sizes
from xgboost.testing.updater import (
check_categorical_missing,
check_categorical_ohe,
check_extmem_qdm,
check_quantile_loss_extmem,
)
sys.path.append("tests/python")
from test_data_iterator import run_data_iterator
from test_data_iterator import test_single_batch as cpu_single_batch
# There are lots of warnings if XGBoost is not running on ATS-enabled systems.
pytestmark = pytest.mark.filterwarnings("ignore")
def test_gpu_single_batch() -> None:
cpu_single_batch("hist", "cuda")
@pytest.mark.skipif(**no_cupy())
@given(
strategies.integers(0, 1024),
strategies.integers(1, 7),
strategies.integers(0, 8),
strategies.booleans(),
strategies.booleans(),
strategies.booleans(),
)
@settings(deadline=None, max_examples=16, print_blob=True)
def test_gpu_data_iterator(
n_samples_per_batch: int,
n_features: int,
n_batches: int,
subsample: bool,
use_cupy: bool,
on_host: bool,
) -> None:
run_data_iterator(
n_samples_per_batch,
n_features,
n_batches,
"hist",
subsample=subsample,
device="cuda",
use_cupy=use_cupy,
on_host=on_host,
)
def test_cpu_data_iterator() -> None:
"""Make sure CPU algorithm can handle GPU inputs"""
run_data_iterator(
1024,
2,
3,
"approx",
device="cuda",
subsample=False,
use_cupy=True,
on_host=False,
)
@given(
strategies.integers(1, 2048),
strategies.integers(1, 8),
strategies.integers(1, 4),
strategies.integers(2, 16),
strategies.booleans(),
)
@settings(deadline=None, max_examples=10, print_blob=True)
def test_extmem_qdm(
n_samples_per_batch: int,
n_features: int,
n_batches: int,
n_bins: int,
on_host: bool,
) -> None:
check_extmem_qdm(
n_samples_per_batch,
n_features,
n_batches=n_batches,
n_bins=n_bins,
device="cuda",
on_host=on_host,
is_cat=False,
)
@given(
strategies.integers(1, 2048),
strategies.integers(1, 4),
strategies.integers(2, 16),
strategies.booleans(),
)
@settings(deadline=None, max_examples=10, print_blob=True)
@pytest.mark.skipif(**tm.no_cudf())
@pytest.mark.skipif(**tm.no_cupy())
def test_categorical_extmem_qdm(
n_samples_per_batch: int,
n_batches: int,
n_bins: int,
on_host: bool,
) -> None:
check_extmem_qdm(
n_samples_per_batch,
4,
n_batches=n_batches,
n_bins=n_bins,
device="cuda",
on_host=on_host,
is_cat=True,
)
def test_invalid_device_extmem_qdm() -> None:
it = tm.IteratorForTest(
*tm.make_batches(16, 4, 2, use_cupy=False), cache="cache", on_host=True
)
Xy = xgb.ExtMemQuantileDMatrix(it)
with pytest.raises(ValueError, match="cannot be used for GPU"):
xgb.train({"device": "cuda"}, Xy)
it = tm.IteratorForTest(
*tm.make_batches(16, 4, 2, use_cupy=True), cache="cache", on_host=True
)
Xy = xgb.ExtMemQuantileDMatrix(it)
with pytest.raises(ValueError, match="cannot be used for CPU"):
xgb.train({"device": "cpu"}, Xy)
def test_concat_pages_invalid() -> None:
it = tm.IteratorForTest(*tm.make_batches(64, 16, 4, use_cupy=True), cache=None)
Xy = xgb.ExtMemQuantileDMatrix(it)
with pytest.raises(ValueError, match="can not be used with concatenated pages"):
xgb.train(
{
"device": "cuda",
"subsample": 0.5,
"sampling_method": "gradient_based",
"extmem_single_page": True,
"objective": "reg:absoluteerror",
},
Xy,
)
def test_concat_pages() -> None:
boosters = []
for min_cache_page_bytes in [0, 256, 386, np.iinfo(np.int64).max]:
it = tm.IteratorForTest(
*tm.make_batches(64, 16, 4, use_cupy=True),
cache=None,
min_cache_page_bytes=min_cache_page_bytes,
on_host=True,
)
Xy = xgb.ExtMemQuantileDMatrix(it)
booster = xgb.train(
{
"device": "cuda",
"objective": "reg:absoluteerror",
},
Xy,
)
boosters.append(booster.save_raw(raw_format="json"))
for model in boosters[1:]:
assert str(model) == str(boosters[0])
@given(
strategies.integers(1, 64),
strategies.integers(1, 8),
strategies.integers(1, 4),
)
@settings(deadline=None, max_examples=10, print_blob=True)
def test_quantile_objective(
n_samples_per_batch: int, n_features: int, n_batches: int
) -> None:
check_quantile_loss_extmem(
n_samples_per_batch,
n_features,
n_batches,
"hist",
"cuda",
)
check_quantile_loss_extmem(
n_samples_per_batch,
n_features,
n_batches,
"approx",
"cuda",
)
@pytest.mark.parametrize("tree_method", ["hist", "approx"])
@pytest.mark.skipif(**tm.no_cudf())
@pytest.mark.skipif(**tm.no_cupy())
def test_categorical_missing(tree_method: str) -> None:
check_categorical_missing(
1024, 4, 5, device="cuda", tree_method=tree_method, extmem=True
)
@pytest.mark.parametrize("tree_method", ["hist", "approx"])
@pytest.mark.skipif(**tm.no_cudf())
@pytest.mark.skipif(**tm.no_cupy())
def test_categorical_ohe(tree_method: str) -> None:
check_categorical_ohe(
rows=1024,
cols=16,
rounds=4,
cats=5,
device="cuda",
tree_method=tree_method,
extmem=True,
)
@pytest.mark.skipif(**tm.no_cudf())
@pytest.mark.skipif(**tm.no_cupy())
def test_invalid_cat_batches() -> None:
check_invalid_cat_batches("cuda")
def test_uneven_sizes() -> None:
check_uneven_sizes("cuda")
|
