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Copy data from Ellpack to GHist. (#8215)

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This commit is contained in:
Jiaming Yuan
2022-09-06 23:05:49 +08:00
committed by GitHub
parent 7ee10e3dbd
commit 441ffc017a
16 changed files with 466 additions and 112 deletions
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29
tests/python-gpu/test_device_quantile_dmatrix.py
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@@ -32,32 +32,41 @@ class TestDeviceQuantileDMatrix:
xgb.DeviceQuantileDMatrix(data, cp.ones(5, dtype=np.float64))
@pytest.mark.skipif(**tm.no_cupy())
def test_from_host(self) -> None:
@pytest.mark.parametrize(
"tree_method,max_bin", [
("hist", 16), ("gpu_hist", 16), ("hist", 64), ("gpu_hist", 64)
]
)
def test_interoperability(self, tree_method: str, max_bin: int) -> None:
import cupy as cp
n_samples = 64
n_features = 3
X, y, w = tm.make_batches(
n_samples, n_features=n_features, n_batches=1, use_cupy=False
)
Xy = xgb.QuantileDMatrix(X[0], y[0], weight=w[0])
booster_0 = xgb.train({"tree_method": "gpu_hist"}, Xy, num_boost_round=4)
# from CPU
Xy = xgb.QuantileDMatrix(X[0], y[0], weight=w[0], max_bin=max_bin)
booster_0 = xgb.train(
{"tree_method": tree_method, "max_bin": max_bin}, Xy, num_boost_round=4
)
X[0] = cp.array(X[0])
y[0] = cp.array(y[0])
w[0] = cp.array(w[0])
Xy = xgb.QuantileDMatrix(X[0], y[0], weight=w[0])
booster_1 = xgb.train({"tree_method": "gpu_hist"}, Xy, num_boost_round=4)
# from GPU
Xy = xgb.QuantileDMatrix(X[0], y[0], weight=w[0], max_bin=max_bin)
booster_1 = xgb.train(
{"tree_method": tree_method, "max_bin": max_bin}, Xy, num_boost_round=4
)
cp.testing.assert_allclose(
booster_0.inplace_predict(X[0]), booster_1.inplace_predict(X[0])
)
with pytest.raises(ValueError, match="not initialized with CPU"):
# Training on CPU with GPU data is not supported.
xgb.train({"tree_method": "hist"}, Xy, num_boost_round=4)
with pytest.raises(ValueError, match=r"Only.*hist.*"):
xgb.train({"tree_method": "approx"}, Xy, num_boost_round=4)
xgb.train(
{"tree_method": "approx", "max_bin": max_bin}, Xy, num_boost_round=4
)
@pytest.mark.skipif(**tm.no_cupy())
def test_metainfo(self) -> None: