39390cc2ee
- A `DeviceOrd` struct is implemented to indicate the device. It will eventually replace the `gpu_id` parameter. - The `predictor` parameter is removed. - Fallback to `DMatrix` when `inplace_predict` is not available. - The heuristic for choosing a predictor is only used during training.
267 lines
9.1 KiB
Python
267 lines
9.1 KiB
Python
import sys
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from typing import Any, Dict
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import numpy as np
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import pytest
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from hypothesis import assume, given, note, settings, strategies
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import xgboost as xgb
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from xgboost import testing as tm
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from xgboost.testing.params import cat_parameter_strategy, hist_parameter_strategy
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from xgboost.testing.updater import check_init_estimation, check_quantile_loss
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sys.path.append("tests/python")
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import test_updaters as test_up
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pytestmark = tm.timeout(30)
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def train_result(param, dmat: xgb.DMatrix, num_rounds: int) -> dict:
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result: xgb.callback.TrainingCallback.EvalsLog = {}
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booster = xgb.train(
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param,
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dmat,
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num_rounds,
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[(dmat, "train")],
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verbose_eval=False,
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evals_result=result,
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)
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assert booster.num_features() == dmat.num_col()
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assert booster.num_boosted_rounds() == num_rounds
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return result
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class TestGPUUpdatersMulti:
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@given(
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hist_parameter_strategy, strategies.integers(1, 20), tm.multi_dataset_strategy
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)
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@settings(deadline=None, max_examples=50, print_blob=True)
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def test_hist(self, param, num_rounds, dataset):
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param["tree_method"] = "gpu_hist"
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param = dataset.set_params(param)
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result = train_result(param, dataset.get_dmat(), num_rounds)
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note(result)
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assert tm.non_increasing(result["train"][dataset.metric])
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class TestGPUUpdaters:
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cputest = test_up.TestTreeMethod()
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@given(
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hist_parameter_strategy, strategies.integers(1, 20), tm.make_dataset_strategy()
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)
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@settings(deadline=None, max_examples=50, print_blob=True)
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def test_gpu_hist(self, param, num_rounds, dataset):
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param["tree_method"] = "gpu_hist"
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param = dataset.set_params(param)
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result = train_result(param, dataset.get_dmat(), num_rounds)
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note(result)
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assert tm.non_increasing(result["train"][dataset.metric])
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@given(tm.sparse_datasets_strategy)
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@settings(deadline=None, print_blob=True)
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def test_sparse(self, dataset):
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param = {"tree_method": "hist", "max_bin": 64}
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hist_result = train_result(param, dataset.get_dmat(), 16)
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note(hist_result)
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assert tm.non_increasing(hist_result['train'][dataset.metric])
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param = {"tree_method": "gpu_hist", "max_bin": 64}
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gpu_hist_result = train_result(param, dataset.get_dmat(), 16)
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note(gpu_hist_result)
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assert tm.non_increasing(gpu_hist_result['train'][dataset.metric])
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np.testing.assert_allclose(
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hist_result["train"]["rmse"], gpu_hist_result["train"]["rmse"], rtol=1e-2
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)
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@given(strategies.integers(10, 400), strategies.integers(3, 8),
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strategies.integers(1, 2), strategies.integers(4, 7))
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@settings(deadline=None, max_examples=20, print_blob=True)
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@pytest.mark.skipif(**tm.no_pandas())
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def test_categorical_ohe(self, rows, cols, rounds, cats):
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self.cputest.run_categorical_ohe(rows, cols, rounds, cats, "gpu_hist")
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@given(
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tm.categorical_dataset_strategy,
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hist_parameter_strategy,
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cat_parameter_strategy,
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strategies.integers(4, 32),
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)
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@settings(deadline=None, max_examples=20, print_blob=True)
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@pytest.mark.skipif(**tm.no_pandas())
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def test_categorical(
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self,
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dataset: tm.TestDataset,
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hist_parameters: Dict[str, Any],
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cat_parameters: Dict[str, Any],
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n_rounds: int,
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) -> None:
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cat_parameters.update(hist_parameters)
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cat_parameters["tree_method"] = "gpu_hist"
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results = train_result(cat_parameters, dataset.get_dmat(), n_rounds)
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tm.non_increasing(results["train"]["rmse"])
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@given(
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hist_parameter_strategy,
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cat_parameter_strategy,
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)
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@settings(deadline=None, max_examples=10, print_blob=True)
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def test_categorical_ames_housing(
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self,
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hist_parameters: Dict[str, Any],
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cat_parameters: Dict[str, Any],
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) -> None:
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cat_parameters.update(hist_parameters)
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dataset = tm.TestDataset(
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"ames_housing", tm.data.get_ames_housing, "reg:squarederror", "rmse"
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)
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cat_parameters["tree_method"] = "gpu_hist"
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results = train_result(cat_parameters, dataset.get_dmat(), 16)
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tm.non_increasing(results["train"]["rmse"])
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@given(
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strategies.integers(10, 400),
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strategies.integers(3, 8),
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strategies.integers(4, 7)
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)
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@settings(deadline=None, max_examples=20, print_blob=True)
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@pytest.mark.skipif(**tm.no_pandas())
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def test_categorical_missing(self, rows, cols, cats):
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self.cputest.run_categorical_missing(rows, cols, cats, "gpu_hist")
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@pytest.mark.skipif(**tm.no_pandas())
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def test_max_cat(self) -> None:
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self.cputest.run_max_cat("gpu_hist")
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def test_categorical_32_cat(self):
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'''32 hits the bound of integer bitset, so special test'''
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rows = 1000
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cols = 10
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cats = 32
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rounds = 4
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self.cputest.run_categorical_ohe(rows, cols, rounds, cats, "gpu_hist")
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@pytest.mark.skipif(**tm.no_cupy())
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def test_invalid_category(self):
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self.cputest.run_invalid_category("gpu_hist")
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@pytest.mark.skipif(**tm.no_cupy())
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@given(
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hist_parameter_strategy,
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strategies.integers(1, 20),
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tm.make_dataset_strategy(),
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)
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@settings(deadline=None, max_examples=20, print_blob=True)
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def test_gpu_hist_device_dmatrix(
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self, param: dict, num_rounds: int, dataset: tm.TestDataset
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) -> None:
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# We cannot handle empty dataset yet
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assume(len(dataset.y) > 0)
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param['tree_method'] = 'gpu_hist'
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param = dataset.set_params(param)
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result = train_result(
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param,
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dataset.get_device_dmat(max_bin=param.get("max_bin", None)),
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num_rounds
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)
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note(result)
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assert tm.non_increasing(result['train'][dataset.metric], tolerance=1e-3)
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@given(
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hist_parameter_strategy,
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strategies.integers(1, 3),
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tm.make_dataset_strategy(),
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)
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@settings(deadline=None, max_examples=10, print_blob=True)
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def test_external_memory(self, param, num_rounds, dataset):
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if dataset.name.endswith("-l1"):
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return
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# We cannot handle empty dataset yet
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assume(len(dataset.y) > 0)
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param['tree_method'] = 'gpu_hist'
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param = dataset.set_params(param)
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m = dataset.get_external_dmat()
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external_result = train_result(param, m, num_rounds)
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del m
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assert tm.non_increasing(external_result['train'][dataset.metric])
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def test_empty_dmatrix_prediction(self):
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# FIXME(trivialfis): This should be done with all updaters
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kRows = 0
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kCols = 100
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X = np.empty((kRows, kCols))
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y = np.empty((kRows,))
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dtrain = xgb.DMatrix(X, y)
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bst = xgb.train(
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{"verbosity": 2, "tree_method": "gpu_hist", "gpu_id": 0},
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dtrain,
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verbose_eval=True,
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num_boost_round=6,
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evals=[(dtrain, 'Train')]
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)
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kRows = 100
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X = np.random.randn(kRows, kCols)
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dtest = xgb.DMatrix(X)
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predictions = bst.predict(dtest)
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# non-distributed, 0.0 is returned due to base_score estimation with 0 gradient.
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np.testing.assert_allclose(predictions, 0.0, 1e-6)
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@pytest.mark.mgpu
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@given(tm.make_dataset_strategy(), strategies.integers(0, 10))
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@settings(deadline=None, max_examples=10, print_blob=True)
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def test_specified_gpu_id_gpu_update(self, dataset, gpu_id):
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param = {'tree_method': 'gpu_hist', 'gpu_id': gpu_id}
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param = dataset.set_params(param)
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result = train_result(param, dataset.get_dmat(), 10)
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assert tm.non_increasing(result['train'][dataset.metric])
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@pytest.mark.skipif(**tm.no_sklearn())
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@pytest.mark.parametrize("weighted", [True, False])
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def test_adaptive(self, weighted) -> None:
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self.cputest.run_adaptive("gpu_hist", weighted)
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def test_init_estimation(self) -> None:
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check_init_estimation("gpu_hist")
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@pytest.mark.parametrize("weighted", [True, False])
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def test_quantile_loss(self, weighted: bool) -> None:
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check_quantile_loss("gpu_hist", weighted)
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@pytest.mark.skipif(**tm.no_pandas())
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def test_issue8824(self):
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# column sampling by node crashes because shared pointers go out of scope
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import pandas as pd
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data = pd.DataFrame(np.random.rand(1024, 8))
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data.columns = "x" + data.columns.astype(str)
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features = data.columns
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data["y"] = data.sum(axis=1) < 4
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dtrain = xgb.DMatrix(data[features], label=data["y"])
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model = xgb.train(
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dtrain=dtrain,
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params={
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"max_depth": 5,
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"learning_rate": 0.05,
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"objective": "binary:logistic",
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"tree_method": "gpu_hist",
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"colsample_bytree": 0.5,
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"colsample_bylevel": 0.5,
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"colsample_bynode": 0.5, # Causes issues
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"reg_alpha": 0.05,
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"reg_lambda": 0.005,
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"seed": 66,
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"subsample": 0.5,
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"gamma": 0.2,
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"eval_metric": "auc",
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},
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num_boost_round=150,
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)
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