1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
|
import numpy as np
import gc
import pytest
import xgboost as xgb
from hypothesis import given, strategies, assume, settings, note
import sys
import os
# sys.path.append("tests/python")
# import testing as tm
from xgboost import testing as tm
parameter_strategy = strategies.fixed_dictionaries(
{
"max_depth": strategies.integers(0, 11),
"max_leaves": strategies.integers(0, 256),
"max_bin": strategies.integers(2, 1024),
"grow_policy": strategies.sampled_from(["lossguide", "depthwise"]),
"single_precision_histogram": strategies.booleans(),
"min_child_weight": strategies.floats(0.5, 2.0),
"seed": strategies.integers(0, 10),
# We cannot enable subsampling as the training loss can increase
# 'subsample': strategies.floats(0.5, 1.0),
"colsample_bytree": strategies.floats(0.5, 1.0),
"colsample_bylevel": strategies.floats(0.5, 1.0),
}
).filter(
lambda x: (x["max_depth"] > 0 or x["max_leaves"] > 0)
and (x["max_depth"] > 0 or x["grow_policy"] == "lossguide")
)
def train_result(param, dmat, num_rounds):
result = {}
xgb.train(
param,
dmat,
num_rounds,
[(dmat, "train")],
verbose_eval=False,
evals_result=result,
)
return result
class TestSYCLUpdaters:
@given(parameter_strategy, strategies.integers(1, 5), tm.make_dataset_strategy())
@settings(deadline=None)
def test_sycl_hist(self, param, num_rounds, dataset):
param["tree_method"] = "hist"
param["device"] = "sycl"
param["verbosity"] = 0
param = dataset.set_params(param)
result = train_result(param, dataset.get_dmat(), num_rounds)
note(result)
assert tm.non_increasing(result["train"][dataset.metric])
@given(tm.make_dataset_strategy(), strategies.integers(0, 1))
@settings(deadline=None)
def test_specified_device_id_sycl_update(self, dataset, device_id):
# Read the list of sycl-devicese
sycl_ls = os.popen("sycl-ls").read()
devices = sycl_ls.split("\n")
# Test should launch only on gpu
# Find gpus in the list of devices
# and use the id in the list insteard of device_id
target_device_type = "opencl:gpu"
found_devices = 0
for idx in range(len(devices)):
if len(devices[idx]) >= len(target_device_type):
if devices[idx][1 : 1 + len(target_device_type)] == target_device_type:
if found_devices == device_id:
param = {"device": f"sycl:gpu:{idx}"}
param = dataset.set_params(param)
result = train_result(param, dataset.get_dmat(), 10)
assert tm.non_increasing(result["train"][dataset.metric])
else:
found_devices += 1
|
