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Fix inplace predict with fallback when base margin is used. (#9536)

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- Copy meta info from proxy DMatrix.
- Use `std::call_once` to emit less warnings.
This commit is contained in:
Jiaming Yuan
2023-09-05 01:04:24 +08:00
committed by GitHub
parent d159ee8547
commit adea842c83
6 changed files with 62 additions and 63 deletions
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24
tests/python-gpu/test_gpu_prediction.py
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@@ -191,14 +191,32 @@ class TestGPUPredict:
np.testing.assert_allclose(predt_0, predt_3)
np.testing.assert_allclose(predt_0, predt_4)
def run_inplace_base_margin(self, booster, dtrain, X, base_margin):
def run_inplace_base_margin(
self, device: int, booster: xgb.Booster, dtrain: xgb.DMatrix, X, base_margin
) -> None:
import cupy as cp
booster.set_param({"device": f"cuda:{device}"})
dtrain.set_info(base_margin=base_margin)
from_inplace = booster.inplace_predict(data=X, base_margin=base_margin)
from_dmatrix = booster.predict(dtrain)
cp.testing.assert_allclose(from_inplace, from_dmatrix)
booster = booster.copy() # clear prediction cache.
booster.set_param({"device": "cpu"})
from_inplace = booster.inplace_predict(data=X, base_margin=base_margin)
from_dmatrix = booster.predict(dtrain)
cp.testing.assert_allclose(from_inplace, from_dmatrix)
booster = booster.copy() # clear prediction cache.
base_margin = cp.asnumpy(base_margin)
if hasattr(X, "values"):
X = cp.asnumpy(X.values)
booster.set_param({"device": f"cuda:{device}"})
from_inplace = booster.inplace_predict(data=X, base_margin=base_margin)
from_dmatrix = booster.predict(dtrain)
cp.testing.assert_allclose(from_inplace, from_dmatrix, rtol=1e-6)
def run_inplace_predict_cupy(self, device: int) -> None:
import cupy as cp
@@ -244,7 +262,7 @@ class TestGPUPredict:
run_threaded_predict(X, rows, predict_dense)
base_margin = cp_rng.randn(rows)
self.run_inplace_base_margin(booster, dtrain, X, base_margin)
self.run_inplace_base_margin(device, booster, dtrain, X, base_margin)
# Create a wide dataset
X = cp_rng.randn(100, 10000)
@@ -318,7 +336,7 @@ class TestGPUPredict:
run_threaded_predict(X, rows, predict_df)
base_margin = cudf.Series(rng.randn(rows))
self.run_inplace_base_margin(booster, dtrain, X, base_margin)
self.run_inplace_base_margin(0, booster, dtrain, X, base_margin)
@given(
strategies.integers(1, 10), tm.make_dataset_strategy(), shap_parameter_strategy