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Implement GK sketching on GPU. (#5846)

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* Implement GK sketching on GPU.
* Strong tests on quantile building.
* Handle sparse dataset by binary searching the column index.
* Hypothesis test on dask.
This commit is contained in:
Jiaming Yuan
2020-07-07 12:16:21 +08:00
committed by GitHub
parent ac3f0e78dc
commit 048d969be4
25 changed files with 2045 additions and 405 deletions
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104
tests/python-gpu/test_gpu_with_dask.py
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@@ -1,8 +1,12 @@
import sys
import os
import pytest
import numpy as np
import unittest
import xgboost
import subprocess
from hypothesis import given, strategies, settings, note
from test_gpu_updaters import parameter_strategy
if sys.platform.startswith("win"):
pytest.skip("Skipping dask tests on Windows", allow_module_level=True)
@@ -12,11 +16,13 @@ from test_with_dask import run_empty_dmatrix # noqa
from test_with_dask import generate_array # noqa
import testing as tm # noqa
try:
import dask.dataframe as dd
from xgboost import dask as dxgb
from dask_cuda import LocalCUDACluster
from dask.distributed import Client
from dask import array as da
import cudf
except ImportError:
pass
@@ -42,7 +48,8 @@ class TestDistributedGPU(unittest.TestCase):
y = y.map_partitions(cudf.from_pandas)
dtrain = dxgb.DaskDMatrix(client, X, y)
out = dxgb.train(client, {'tree_method': 'gpu_hist'},
out = dxgb.train(client, {'tree_method': 'gpu_hist',
'debug_synchronize': True},
dtrain=dtrain,
evals=[(dtrain, 'X')],
num_boost_round=4)
@@ -61,7 +68,8 @@ class TestDistributedGPU(unittest.TestCase):
xgboost.DMatrix(X.compute()))
cp.testing.assert_allclose(single_node, predictions)
np.testing.assert_allclose(single_node, series_predictions.to_array())
np.testing.assert_allclose(single_node,
series_predictions.to_array())
predt = dxgb.predict(client, out, X)
assert isinstance(predt, dd.Series)
@@ -77,6 +85,41 @@ class TestDistributedGPU(unittest.TestCase):
cp.testing.assert_allclose(
predt.values.compute(), single_node)
@given(parameter_strategy, strategies.integers(1, 20),
tm.dataset_strategy)
@settings(deadline=None)
@pytest.mark.mgpu
def test_gpu_hist(self, params, num_rounds, dataset):
with LocalCUDACluster(n_workers=2) as cluster:
with Client(cluster) as client:
params['tree_method'] = 'gpu_hist'
params = dataset.set_params(params)
# multi class doesn't handle empty dataset well (empty
# means at least 1 worker has data).
if params['objective'] == "multi:softmax":
return
# It doesn't make sense to distribute a completely
# empty dataset.
if dataset.X.shape[0] == 0:
return
chunk = 128
X = da.from_array(dataset.X,
chunks=(chunk, dataset.X.shape[1]))
y = da.from_array(dataset.y, chunks=(chunk, ))
if dataset.w is not None:
w = da.from_array(dataset.w, chunks=(chunk, ))
else:
w = None
m = dxgb.DaskDMatrix(
client, data=X, label=y, weight=w)
history = dxgb.train(client, params=params, dtrain=m,
num_boost_round=num_rounds,
evals=[(m, 'train')])['history']
note(history)
assert tm.non_increasing(history['train'][dataset.metric])
@pytest.mark.skipif(**tm.no_cupy())
@pytest.mark.mgpu
def test_dask_array(self):
@@ -89,7 +132,8 @@ class TestDistributedGPU(unittest.TestCase):
X = X.map_blocks(cp.asarray)
y = y.map_blocks(cp.asarray)
dtrain = dxgb.DaskDMatrix(client, X, y)
out = dxgb.train(client, {'tree_method': 'gpu_hist'},
out = dxgb.train(client, {'tree_method': 'gpu_hist',
'debug_synchronize': True},
dtrain=dtrain,
evals=[(dtrain, 'X')],
num_boost_round=2)
@@ -107,12 +151,62 @@ class TestDistributedGPU(unittest.TestCase):
single_node,
inplace_predictions)
@pytest.mark.skipif(**tm.no_dask())
@pytest.mark.skipif(**tm.no_dask_cuda())
@pytest.mark.mgpu
def test_empty_dmatrix(self):
with LocalCUDACluster() as cluster:
with Client(cluster) as client:
parameters = {'tree_method': 'gpu_hist'}
parameters = {'tree_method': 'gpu_hist',
'debug_synchronize': True}
run_empty_dmatrix(client, parameters)
def run_quantile(self, name):
if sys.platform.startswith("win"):
pytest.skip("Skipping dask tests on Windows")
exe = None
for possible_path in {'./testxgboost', './build/testxgboost',
'../build/testxgboost', '../gpu-build/testxgboost'}:
if os.path.exists(possible_path):
exe = possible_path
assert exe, 'No testxgboost executable found.'
test = "--gtest_filter=GPUQuantile." + name
def runit(worker_addr, rabit_args):
port = None
# setup environment for running the c++ part.
for arg in rabit_args:
if arg.decode('utf-8').startswith('DMLC_TRACKER_PORT'):
port = arg.decode('utf-8')
port = port.split('=')
env = os.environ.copy()
env[port[0]] = port[1]
return subprocess.run([exe, test], env=env, stdout=subprocess.PIPE)
with LocalCUDACluster() as cluster:
with Client(cluster) as client:
workers = list(dxgb._get_client_workers(client).keys())
rabit_args = dxgb._get_rabit_args(workers, client)
futures = client.map(runit,
workers,
pure=False,
workers=workers,
rabit_args=rabit_args)
results = client.gather(futures)
for ret in results:
msg = ret.stdout.decode('utf-8')
assert msg.find('1 test from GPUQuantile') != -1, msg
assert ret.returncode == 0, msg
@pytest.mark.skipif(**tm.no_dask())
@pytest.mark.mgpu
@pytest.mark.gtest
def test_quantile_basic(self):
self.run_quantile('AllReduceBasic')
@pytest.mark.skipif(**tm.no_dask())
@pytest.mark.mgpu
@pytest.mark.gtest
def test_quantile_same_on_all_workers(self):
self.run_quantile('SameOnAllWorkers')