e228c1a121
This PR introduces a new parameter `extmem_concat_pages` to make the page concatenation optional for GPU hist. In addition, the document is updated for the new GPU-based external memory.
116 lines
2.8 KiB
Python
116 lines
2.8 KiB
Python
import sys
|
|
|
|
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.updater import 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
|
|
|
|
|
|
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.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, on_host: bool
|
|
) -> None:
|
|
check_extmem_qdm(n_samples_per_batch, n_features, n_batches, "cuda", on_host)
|
|
|
|
|
|
def test_concat_pages() -> 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"):
|
|
booster = xgb.train(
|
|
{
|
|
"device": "cuda",
|
|
"subsample": 0.5,
|
|
"sampling_method": "gradient_based",
|
|
"extmem_concat_pages": True,
|
|
"objective": "reg:absoluteerror",
|
|
},
|
|
Xy,
|
|
)
|
|
|
|
|
|
@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",
|
|
)
|