[GPU-Plugin] Major refactor 2 (#2664)
* Change cmake option * Move source files * Move google tests * Move python tests * Move benchmarks * Move documentation * Remove makefile support * Fix test run * Move GPU tests
This commit is contained in:
Rory Mitchell
GitHub
37
tests/python-gpu/test_gpu_prediction.py
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37
tests/python-gpu/test_gpu_prediction.py
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from __future__ import print_function
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#pylint: skip-file
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import xgboost as xgb
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import testing as tm
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import numpy as np
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import unittest
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from nose.plugins.attrib import attr
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rng = np.random.RandomState(1994)
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@attr('gpu')
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class TestGPUPredict (unittest.TestCase):
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def test_predict(self):
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iterations = 1
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np.random.seed(1)
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test_num_rows = [10,1000,5000]
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test_num_cols = [10,50,500]
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for num_rows in test_num_rows:
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for num_cols in test_num_cols:
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dm = xgb.DMatrix(np.random.randn(num_rows, num_cols), label=[0, 1] * int(num_rows/2))
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watchlist = [(dm, 'train')]
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res = {}
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param = {
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"objective":"binary:logistic",
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"predictor":"gpu_predictor",
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'eval_metric': 'auc',
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}
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bst = xgb.train(param, dm,iterations,evals=watchlist, evals_result=res)
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assert self.non_decreasing(res["train"]["auc"])
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gpu_pred = bst.predict(dm, output_margin=True)
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bst.set_param({"predictor":"cpu_predictor"})
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cpu_pred = bst.predict(dm, output_margin=True)
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np.testing.assert_allclose(cpu_pred, gpu_pred, rtol=1e-5)
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def non_decreasing(self, L):
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return all((x - y) < 0.001 for x, y in zip(L, L[1:]))
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325
tests/python-gpu/test_gpu_updaters.py
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325
tests/python-gpu/test_gpu_updaters.py
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from __future__ import print_function
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#pylint: skip-file
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import sys
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sys.path.append("../../tests/python")
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import xgboost as xgb
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import testing as tm
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import numpy as np
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import unittest
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from nose.plugins.attrib import attr
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rng = np.random.RandomState(1994)
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dpath = 'demo/data/'
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def eprint(*args, **kwargs):
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print(*args, file=sys.stderr, **kwargs)
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print(*args, file=sys.stdout, **kwargs)
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@attr('gpu')
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class TestGPU(unittest.TestCase):
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def test_grow_gpu(self):
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tm._skip_if_no_sklearn()
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from sklearn.datasets import load_digits
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try:
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from sklearn.model_selection import train_test_split
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except:
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from sklearn.cross_validation import train_test_split
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ag_dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
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ag_dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
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ag_param = {'max_depth': 2,
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'tree_method': 'exact',
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'nthread': 0,
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'eta': 1,
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'silent': 1,
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'debug_verbose': 0,
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'objective': 'binary:logistic',
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'eval_metric': 'auc'}
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ag_param2 = {'max_depth': 2,
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'tree_method': 'gpu_exact',
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'nthread': 0,
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'eta': 1,
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'silent': 1,
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'debug_verbose': 0,
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'objective': 'binary:logistic',
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'eval_metric': 'auc'}
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ag_res = {}
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ag_res2 = {}
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num_rounds = 10
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xgb.train(ag_param, ag_dtrain, num_rounds, [(ag_dtrain, 'train'), (ag_dtest, 'test')],
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evals_result=ag_res)
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xgb.train(ag_param2, ag_dtrain, num_rounds, [(ag_dtrain, 'train'), (ag_dtest, 'test')],
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evals_result=ag_res2)
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assert ag_res['train']['auc'] == ag_res2['train']['auc']
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assert ag_res['test']['auc'] == ag_res2['test']['auc']
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digits = load_digits(2)
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X = digits['data']
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y = digits['target']
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X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
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dtrain = xgb.DMatrix(X_train, y_train)
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dtest = xgb.DMatrix(X_test, y_test)
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param = {'objective': 'binary:logistic',
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'nthread': 0,
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'tree_method': 'gpu_exact',
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'max_depth': 3,
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'debug_verbose': 0,
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'eval_metric': 'auc'}
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res = {}
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xgb.train(param, dtrain, num_rounds, [(dtrain, 'train'), (dtest, 'test')],
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evals_result=res)
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assert self.non_decreasing(res['train']['auc'])
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assert self.non_decreasing(res['test']['auc'])
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# fail-safe test for dense data
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from sklearn.datasets import load_svmlight_file
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X2, y2 = load_svmlight_file(dpath + 'agaricus.txt.train')
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X2 = X2.toarray()
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dtrain2 = xgb.DMatrix(X2, label=y2)
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param = {'objective': 'binary:logistic',
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'nthread': 0,
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'tree_method': 'gpu_exact',
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'max_depth': 2,
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'debug_verbose': 0,
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'eval_metric': 'auc'}
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res = {}
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xgb.train(param, dtrain2, num_rounds, [(dtrain2, 'train')], evals_result=res)
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assert self.non_decreasing(res['train']['auc'])
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assert res['train']['auc'][0] >= 0.85
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for j in range(X2.shape[1]):
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for i in rng.choice(X2.shape[0], size=num_rounds, replace=False):
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X2[i, j] = 2
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dtrain3 = xgb.DMatrix(X2, label=y2)
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res = {}
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xgb.train(param, dtrain3, num_rounds, [(dtrain3, 'train')], evals_result=res)
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assert self.non_decreasing(res['train']['auc'])
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assert res['train']['auc'][0] >= 0.85
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for j in range(X2.shape[1]):
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for i in np.random.choice(X2.shape[0], size=num_rounds, replace=False):
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X2[i, j] = 3
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dtrain4 = xgb.DMatrix(X2, label=y2)
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res = {}
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xgb.train(param, dtrain4, num_rounds, [(dtrain4, 'train')], evals_result=res)
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assert self.non_decreasing(res['train']['auc'])
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assert res['train']['auc'][0] >= 0.85
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def test_grow_gpu_hist(self):
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n_gpus=-1
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tm._skip_if_no_sklearn()
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from sklearn.datasets import load_digits
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try:
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from sklearn.model_selection import train_test_split
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except:
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from sklearn.cross_validation import train_test_split
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ag_dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
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ag_dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
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for max_depth in range(3,10): # TODO: Doesn't work with 2 for some tests
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#eprint("max_depth=%d" % (max_depth))
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for max_bin_i in range(3,11):
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max_bin = np.power(2,max_bin_i)
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#eprint("max_bin=%d" % (max_bin))
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# regression test --- hist must be same as exact on all-categorial data
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ag_param = {'max_depth': max_depth,
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'tree_method': 'exact',
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'nthread': 0,
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'eta': 1,
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'silent': 1,
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'debug_verbose': 0,
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'objective': 'binary:logistic',
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'eval_metric': 'auc'}
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ag_param2 = {'max_depth': max_depth,
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'nthread': 0,
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'tree_method': 'gpu_hist',
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'eta': 1,
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'silent': 1,
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'debug_verbose': 0,
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'n_gpus': 1,
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'objective': 'binary:logistic',
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'max_bin': max_bin,
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'eval_metric': 'auc'}
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ag_param3 = {'max_depth': max_depth,
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'nthread': 0,
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'tree_method': 'gpu_hist',
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'eta': 1,
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'silent': 1,
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'debug_verbose': 0,
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'n_gpus': n_gpus,
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'objective': 'binary:logistic',
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'max_bin': max_bin,
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'eval_metric': 'auc'}
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ag_res = {}
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ag_res2 = {}
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ag_res3 = {}
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num_rounds = 10
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#eprint("normal updater");
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xgb.train(ag_param, ag_dtrain, num_rounds, [(ag_dtrain, 'train'), (ag_dtest, 'test')],
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evals_result=ag_res)
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#eprint("grow_gpu_hist updater 1 gpu");
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xgb.train(ag_param2, ag_dtrain, num_rounds, [(ag_dtrain, 'train'), (ag_dtest, 'test')],
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evals_result=ag_res2)
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#eprint("grow_gpu_hist updater %d gpus" % (n_gpus));
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xgb.train(ag_param3, ag_dtrain, num_rounds, [(ag_dtrain, 'train'), (ag_dtest, 'test')],
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evals_result=ag_res3)
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# assert 1==0
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assert ag_res['train']['auc'] == ag_res2['train']['auc']
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assert ag_res['test']['auc'] == ag_res2['test']['auc']
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assert ag_res['test']['auc'] == ag_res3['test']['auc']
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######################################################################
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digits = load_digits(2)
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X = digits['data']
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y = digits['target']
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X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
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dtrain = xgb.DMatrix(X_train, y_train)
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dtest = xgb.DMatrix(X_test, y_test)
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param = {'objective': 'binary:logistic',
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'tree_method': 'gpu_hist',
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'nthread': 0,
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'max_depth': max_depth,
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'n_gpus': 1,
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'max_bin': max_bin,
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'debug_verbose': 0,
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'eval_metric': 'auc'}
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res = {}
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#eprint("digits: grow_gpu_hist updater 1 gpu");
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xgb.train(param, dtrain, num_rounds, [(dtrain, 'train'), (dtest, 'test')],
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evals_result=res)
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assert self.non_decreasing(res['train']['auc'])
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#assert self.non_decreasing(res['test']['auc'])
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param2 = {'objective': 'binary:logistic',
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'nthread': 0,
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'tree_method': 'gpu_hist',
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'max_depth': max_depth,
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'n_gpus': n_gpus,
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'max_bin': max_bin,
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'debug_verbose': 0,
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'eval_metric': 'auc'}
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res2 = {}
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#eprint("digits: grow_gpu_hist updater %d gpus" % (n_gpus));
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xgb.train(param2, dtrain, num_rounds, [(dtrain, 'train'), (dtest, 'test')],
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evals_result=res2)
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assert self.non_decreasing(res2['train']['auc'])
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#assert self.non_decreasing(res2['test']['auc'])
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assert res['train']['auc'] == res2['train']['auc']
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#assert res['test']['auc'] == res2['test']['auc']
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######################################################################
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# fail-safe test for dense data
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from sklearn.datasets import load_svmlight_file
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X2, y2 = load_svmlight_file(dpath + 'agaricus.txt.train')
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X2 = X2.toarray()
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dtrain2 = xgb.DMatrix(X2, label=y2)
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param = {'objective': 'binary:logistic',
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'nthread': 0,
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'tree_method': 'gpu_hist',
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'max_depth': max_depth,
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'n_gpus': n_gpus,
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'max_bin': max_bin,
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'debug_verbose': 0,
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'eval_metric': 'auc'}
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res = {}
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xgb.train(param, dtrain2, num_rounds, [(dtrain2, 'train')], evals_result=res)
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assert self.non_decreasing(res['train']['auc'])
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if max_bin>32:
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assert res['train']['auc'][0] >= 0.85
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for j in range(X2.shape[1]):
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for i in rng.choice(X2.shape[0], size=num_rounds, replace=False):
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X2[i, j] = 2
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dtrain3 = xgb.DMatrix(X2, label=y2)
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res = {}
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xgb.train(param, dtrain3, num_rounds, [(dtrain3, 'train')], evals_result=res)
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assert self.non_decreasing(res['train']['auc'])
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if max_bin>32:
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assert res['train']['auc'][0] >= 0.85
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for j in range(X2.shape[1]):
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for i in np.random.choice(X2.shape[0], size=num_rounds, replace=False):
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X2[i, j] = 3
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dtrain4 = xgb.DMatrix(X2, label=y2)
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res = {}
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xgb.train(param, dtrain4, num_rounds, [(dtrain4, 'train')], evals_result=res)
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assert self.non_decreasing(res['train']['auc'])
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if max_bin>32:
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assert res['train']['auc'][0] >= 0.85
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######################################################################
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# fail-safe test for max_bin
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param = {'objective': 'binary:logistic',
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'nthread': 0,
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'tree_method': 'gpu_hist',
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'max_depth': max_depth,
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'n_gpus': n_gpus,
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'debug_verbose': 0,
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'eval_metric': 'auc',
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'max_bin': max_bin}
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res = {}
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xgb.train(param, dtrain2, num_rounds, [(dtrain2, 'train')], evals_result=res)
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assert self.non_decreasing(res['train']['auc'])
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if max_bin>32:
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assert res['train']['auc'][0] >= 0.85
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######################################################################
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# subsampling
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param = {'objective': 'binary:logistic',
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'nthread': 0,
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'tree_method': 'gpu_hist',
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'max_depth': max_depth,
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'n_gpus': n_gpus,
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'eval_metric': 'auc',
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'colsample_bytree': 0.5,
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'colsample_bylevel': 0.5,
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'subsample': 0.5,
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'debug_verbose': 0,
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'max_bin': max_bin}
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res = {}
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xgb.train(param, dtrain2, num_rounds, [(dtrain2, 'train')], evals_result=res)
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assert self.non_decreasing(res['train']['auc'])
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if max_bin>32:
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assert res['train']['auc'][0] >= 0.85
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######################################################################
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# fail-safe test for max_bin=2
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param = {'objective': 'binary:logistic',
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'nthread': 0,
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'tree_method': 'gpu_hist',
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'max_depth': 2,
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'n_gpus': n_gpus,
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'debug_verbose': 0,
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'eval_metric': 'auc',
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'max_bin': 2}
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res = {}
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xgb.train(param, dtrain2, num_rounds, [(dtrain2, 'train')], evals_result=res)
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assert self.non_decreasing(res['train']['auc'])
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if max_bin>32:
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assert res['train']['auc'][0] >= 0.85
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def non_decreasing(self, L):
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return all((x - y) < 0.001 for x, y in zip(L, L[1:]))
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112
tests/python-gpu/test_large_sizes.py
Normal file
112
tests/python-gpu/test_large_sizes.py
Normal file
@@ -0,0 +1,112 @@
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from __future__ import print_function
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#pylint: skip-file
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import sys
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import time
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sys.path.append("../../tests/python")
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import xgboost as xgb
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import testing as tm
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import numpy as np
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import unittest
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from nose.plugins.attrib import attr
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def eprint(*args, **kwargs):
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print(*args, file=sys.stderr, **kwargs) ; sys.stderr.flush()
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print(*args, file=sys.stdout, **kwargs) ; sys.stdout.flush()
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rng = np.random.RandomState(1994)
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# "realistic" size based upon http://stat-computing.org/dataexpo/2009/ , which has been processed to one-hot encode categoricalsxsy
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cols = 31
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# reduced to fit onto 1 gpu but still be large
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rows3 = 5000 # small
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rows2 = 4360032 # medium
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rows1 = 42360032 # large
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#rows1 = 152360032 # can do this for multi-gpu test (very large)
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rowslist = [rows1, rows2, rows3]
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@attr('slow')
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class TestGPU(unittest.TestCase):
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def test_large(self):
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eprint("Starting test for large data")
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tm._skip_if_no_sklearn()
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for rows in rowslist:
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eprint("Creating train data rows=%d cols=%d" % (rows,cols))
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tmp = time.time()
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np.random.seed(7)
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X = np.random.rand(rows, cols)
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y = np.random.rand(rows)
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print("Time to Create Data: %r" % (time.time() - tmp))
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eprint("Starting DMatrix(X,y)")
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tmp = time.time()
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ag_dtrain = xgb.DMatrix(X,y,nthread=40)
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print("Time to DMatrix: %r" % (time.time() - tmp))
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max_depth=6
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max_bin=1024
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# regression test --- hist must be same as exact on all-categorial data
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ag_param = {'max_depth': max_depth,
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'tree_method': 'exact',
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'nthread': 0,
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'eta': 1,
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'silent': 0,
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'debug_verbose': 5,
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'objective': 'binary:logistic',
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'eval_metric': 'auc'}
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ag_paramb = {'max_depth': max_depth,
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'tree_method': 'hist',
|
||||
'nthread': 0,
|
||||
'eta': 1,
|
||||
'silent': 0,
|
||||
'debug_verbose': 5,
|
||||
'objective': 'binary:logistic',
|
||||
'eval_metric': 'auc'}
|
||||
ag_param2 = {'max_depth': max_depth,
|
||||
'tree_method': 'gpu_hist',
|
||||
'nthread': 0,
|
||||
'eta': 1,
|
||||
'silent': 0,
|
||||
'debug_verbose': 5,
|
||||
'n_gpus': 1,
|
||||
'objective': 'binary:logistic',
|
||||
'max_bin': max_bin,
|
||||
'eval_metric': 'auc'}
|
||||
ag_param3 = {'max_depth': max_depth,
|
||||
'tree_method': 'gpu_hist',
|
||||
'nthread': 0,
|
||||
'eta': 1,
|
||||
'silent': 0,
|
||||
'debug_verbose': 5,
|
||||
'n_gpus': -1,
|
||||
'objective': 'binary:logistic',
|
||||
'max_bin': max_bin,
|
||||
'eval_metric': 'auc'}
|
||||
ag_res = {}
|
||||
ag_resb = {}
|
||||
ag_res2 = {}
|
||||
ag_res3 = {}
|
||||
|
||||
num_rounds = 1
|
||||
tmp = time.time()
|
||||
#eprint("hist updater")
|
||||
#xgb.train(ag_paramb, ag_dtrain, num_rounds, [(ag_dtrain, 'train')],
|
||||
# evals_result=ag_resb)
|
||||
#print("Time to Train: %s seconds" % (str(time.time() - tmp)))
|
||||
|
||||
tmp = time.time()
|
||||
eprint("gpu_hist updater 1 gpu")
|
||||
xgb.train(ag_param2, ag_dtrain, num_rounds, [(ag_dtrain, 'train')],
|
||||
evals_result=ag_res2)
|
||||
print("Time to Train: %s seconds" % (str(time.time() - tmp)))
|
||||
|
||||
tmp = time.time()
|
||||
eprint("gpu_hist updater all gpus")
|
||||
xgb.train(ag_param3, ag_dtrain, num_rounds, [(ag_dtrain, 'train')],
|
||||
evals_result=ag_res3)
|
||||
print("Time to Train: %s seconds" % (str(time.time() - tmp)))
|
||||
|
||||
|
||||
Reference in New Issue
Block a user