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Make some GPU tests deterministic (#5229)

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Rory Mitchell 2020-01-26 11:53:07 +13:00 committed by GitHub
parent 3eb1279bbf
commit 1b3947d929
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2 changed files with 11 additions and 6 deletions
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8
tests/python-gpu/test_from_columnar.py
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@ -88,9 +88,10 @@ Arrow specification.'''
def test_cudf_training(self):
from cudf import DataFrame as df
import pandas as pd
np.random.seed(1)
X = pd.DataFrame(np.random.randn(50, 10))
y = pd.DataFrame(np.random.randn(50))
weights = np.random.random(50)
weights = np.random.random(50) + 1.0
cudf_weights = df.from_pandas(pd.DataFrame(weights))
base_margin = np.random.random(50)
cudf_base_margin = df.from_pandas(pd.DataFrame(base_margin))
@ -98,11 +99,12 @@ Arrow specification.'''
evals_result_cudf = {}
dtrain_cudf = xgb.DMatrix(df.from_pandas(X), df.from_pandas(y), weight=cudf_weights,
base_margin=cudf_base_margin)
xgb.train({'gpu_id': 0}, dtrain_cudf, evals=[(dtrain_cudf, "train")],
params = {'gpu_id': 0, 'nthread': 1}
xgb.train(params, dtrain_cudf, evals=[(dtrain_cudf, "train")],
evals_result=evals_result_cudf)
evals_result_np = {}
dtrain_np = xgb.DMatrix(X, y, weight=weights, base_margin=base_margin)
xgb.train({}, dtrain_np, evals=[(dtrain_np, "train")],
xgb.train(params, dtrain_np, evals=[(dtrain_np, "train")],
evals_result=evals_result_np)
assert np.array_equal(evals_result_cudf["train"]["rmse"], evals_result_np["train"]["rmse"])

9
tests/python-gpu/test_from_cupy.py
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@ -51,21 +51,24 @@ Arrow specification.'''
@pytest.mark.skipif(**tm.no_cupy())
def test_cupy_training(self):
import cupy as cp
np.random.seed(1)
cp.random.seed(1)
X = cp.random.randn(50, 10, dtype="float32")
y = cp.random.randn(50, dtype="float32")
weights = np.random.random(50)
weights = np.random.random(50) + 1
cupy_weights = cp.array(weights)
base_margin = np.random.random(50)
cupy_base_margin = cp.array(base_margin)
evals_result_cupy = {}
dtrain_cp = xgb.DMatrix(X, y, weight=cupy_weights, base_margin=cupy_base_margin)
xgb.train({'gpu_id': 0}, dtrain_cp, evals=[(dtrain_cp, "train")],
params = {'gpu_id': 0, 'nthread': 1}
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({'gpu_id': 0}, dtrain_np, evals=[(dtrain_np, "train")],
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"])