7aec915dcd
* [Breaking] Rename `data` to `X` in `predict_proba`. (#6555) New Scikit-Learn version uses keyword argument, and `X` is the predefined keyword. * Use pip to install latest Python graphviz on Windows CI. * Suppress health check.
297 lines
10 KiB
Python
297 lines
10 KiB
Python
import sys
|
|
import os
|
|
import pytest
|
|
import numpy as np
|
|
import asyncio
|
|
import xgboost
|
|
import subprocess
|
|
import hypothesis
|
|
from hypothesis import given, strategies, settings, note
|
|
from hypothesis._settings import duration
|
|
from hypothesis import HealthCheck
|
|
from test_gpu_updaters import parameter_strategy
|
|
|
|
if sys.platform.startswith("win"):
|
|
pytest.skip("Skipping dask tests on Windows", allow_module_level=True)
|
|
|
|
sys.path.append("tests/python")
|
|
from test_with_dask import run_empty_dmatrix_reg # noqa
|
|
from test_with_dask import run_empty_dmatrix_cls # noqa
|
|
from test_with_dask import _get_client_workers # noqa
|
|
from test_with_dask import generate_array # noqa
|
|
import testing as tm # noqa
|
|
|
|
if hasattr(HealthCheck, 'function_scoped_fixture'):
|
|
suppress = [HealthCheck.function_scoped_fixture]
|
|
else:
|
|
suppress = hypothesis.utils.conventions.not_set
|
|
|
|
|
|
try:
|
|
import dask.dataframe as dd
|
|
from xgboost import dask as dxgb
|
|
from dask.distributed import Client
|
|
from dask import array as da
|
|
import cudf
|
|
except ImportError:
|
|
pass
|
|
|
|
|
|
def run_with_dask_dataframe(DMatrixT, client):
|
|
import cupy as cp
|
|
cp.cuda.runtime.setDevice(0)
|
|
X, y = generate_array()
|
|
|
|
X = dd.from_dask_array(X)
|
|
y = dd.from_dask_array(y)
|
|
|
|
X = X.map_partitions(cudf.from_pandas)
|
|
y = y.map_partitions(cudf.from_pandas)
|
|
|
|
dtrain = DMatrixT(client, X, y)
|
|
out = dxgb.train(client, {'tree_method': 'gpu_hist',
|
|
'debug_synchronize': True},
|
|
dtrain=dtrain,
|
|
evals=[(dtrain, 'X')],
|
|
num_boost_round=4)
|
|
|
|
assert isinstance(out['booster'], dxgb.Booster)
|
|
assert len(out['history']['X']['rmse']) == 4
|
|
|
|
predictions = dxgb.predict(client, out, dtrain).compute()
|
|
assert isinstance(predictions, np.ndarray)
|
|
|
|
series_predictions = dxgb.inplace_predict(client, out, X)
|
|
assert isinstance(series_predictions, dd.Series)
|
|
series_predictions = series_predictions.compute()
|
|
|
|
single_node = out['booster'].predict(
|
|
xgboost.DMatrix(X.compute()))
|
|
|
|
cp.testing.assert_allclose(single_node, predictions)
|
|
np.testing.assert_allclose(single_node,
|
|
series_predictions.to_array())
|
|
|
|
predt = dxgb.predict(client, out, X)
|
|
assert isinstance(predt, dd.Series)
|
|
|
|
def is_df(part):
|
|
assert isinstance(part, cudf.DataFrame), part
|
|
return part
|
|
|
|
predt.map_partitions(
|
|
is_df,
|
|
meta=dd.utils.make_meta({'prediction': 'f4'}))
|
|
|
|
cp.testing.assert_allclose(
|
|
predt.values.compute(), single_node)
|
|
|
|
|
|
def run_with_dask_array(DMatrixT, client):
|
|
import cupy as cp
|
|
cp.cuda.runtime.setDevice(0)
|
|
X, y = generate_array()
|
|
|
|
X = X.map_blocks(cp.asarray)
|
|
y = y.map_blocks(cp.asarray)
|
|
dtrain = DMatrixT(client, X, y)
|
|
out = dxgb.train(client, {'tree_method': 'gpu_hist',
|
|
'debug_synchronize': True},
|
|
dtrain=dtrain,
|
|
evals=[(dtrain, 'X')],
|
|
num_boost_round=2)
|
|
from_dmatrix = dxgb.predict(client, out, dtrain).compute()
|
|
inplace_predictions = dxgb.inplace_predict(
|
|
client, out, X).compute()
|
|
single_node = out['booster'].predict(
|
|
xgboost.DMatrix(X.compute()))
|
|
np.testing.assert_allclose(single_node, from_dmatrix)
|
|
device = cp.cuda.runtime.getDevice()
|
|
assert device == inplace_predictions.device.id
|
|
single_node = cp.array(single_node)
|
|
assert device == single_node.device.id
|
|
cp.testing.assert_allclose(
|
|
single_node,
|
|
inplace_predictions)
|
|
|
|
|
|
def to_cp(x, DMatrixT):
|
|
import cupy
|
|
if isinstance(x, np.ndarray) and \
|
|
DMatrixT is dxgb.DaskDeviceQuantileDMatrix:
|
|
X = cupy.array(x)
|
|
else:
|
|
X = x
|
|
return X
|
|
|
|
|
|
def run_gpu_hist(params, num_rounds, dataset, DMatrixT, client):
|
|
params['tree_method'] = 'gpu_hist'
|
|
params = dataset.set_params(params)
|
|
# It doesn't make sense to distribute a completely
|
|
# empty dataset.
|
|
if dataset.X.shape[0] == 0:
|
|
return
|
|
|
|
chunk = 128
|
|
X = to_cp(dataset.X, DMatrixT)
|
|
X = da.from_array(X,
|
|
chunks=(chunk, dataset.X.shape[1]))
|
|
y = to_cp(dataset.y, DMatrixT)
|
|
y = da.from_array(y, chunks=(chunk, ))
|
|
if dataset.w is not None:
|
|
w = to_cp(dataset.w, DMatrixT)
|
|
w = da.from_array(w, chunks=(chunk, ))
|
|
else:
|
|
w = None
|
|
|
|
if DMatrixT is dxgb.DaskDeviceQuantileDMatrix:
|
|
m = DMatrixT(client, data=X, label=y, weight=w,
|
|
max_bin=params.get('max_bin', 256))
|
|
else:
|
|
m = DMatrixT(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])
|
|
|
|
|
|
class TestDistributedGPU:
|
|
@pytest.mark.skipif(**tm.no_dask())
|
|
@pytest.mark.skipif(**tm.no_cudf())
|
|
@pytest.mark.skipif(**tm.no_dask_cudf())
|
|
@pytest.mark.skipif(**tm.no_dask_cuda())
|
|
@pytest.mark.mgpu
|
|
def test_dask_dataframe(self, local_cuda_cluster):
|
|
with Client(local_cuda_cluster) as client:
|
|
run_with_dask_dataframe(dxgb.DaskDMatrix, client)
|
|
run_with_dask_dataframe(dxgb.DaskDeviceQuantileDMatrix, client)
|
|
|
|
@given(
|
|
params=parameter_strategy,
|
|
num_rounds=strategies.integers(1, 20),
|
|
dataset=tm.dataset_strategy,
|
|
)
|
|
@settings(deadline=duration(seconds=120), suppress_health_check=suppress)
|
|
@pytest.mark.skipif(**tm.no_dask())
|
|
@pytest.mark.skipif(**tm.no_dask_cuda())
|
|
@pytest.mark.parametrize(
|
|
"local_cuda_cluster", [{"n_workers": 2}], indirect=["local_cuda_cluster"]
|
|
)
|
|
@pytest.mark.mgpu
|
|
def test_gpu_hist(self, params, num_rounds, dataset, local_cuda_cluster):
|
|
with Client(local_cuda_cluster) as client:
|
|
run_gpu_hist(params, num_rounds, dataset, dxgb.DaskDMatrix, client)
|
|
run_gpu_hist(
|
|
params, num_rounds, dataset, dxgb.DaskDeviceQuantileDMatrix, client
|
|
)
|
|
|
|
@pytest.mark.skipif(**tm.no_cupy())
|
|
@pytest.mark.skipif(**tm.no_dask())
|
|
@pytest.mark.skipif(**tm.no_dask_cuda())
|
|
@pytest.mark.mgpu
|
|
def test_dask_array(self, local_cuda_cluster):
|
|
with Client(local_cuda_cluster) as client:
|
|
run_with_dask_array(dxgb.DaskDMatrix, client)
|
|
run_with_dask_array(dxgb.DaskDeviceQuantileDMatrix, client)
|
|
|
|
@pytest.mark.skipif(**tm.no_dask())
|
|
@pytest.mark.skipif(**tm.no_dask_cuda())
|
|
@pytest.mark.mgpu
|
|
def test_empty_dmatrix(self, local_cuda_cluster):
|
|
with Client(local_cuda_cluster) as client:
|
|
parameters = {'tree_method': 'gpu_hist',
|
|
'debug_synchronize': True}
|
|
run_empty_dmatrix_reg(client, parameters)
|
|
run_empty_dmatrix_cls(client, parameters)
|
|
|
|
def run_quantile(self, name, local_cuda_cluster):
|
|
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 Client(local_cuda_cluster) as client:
|
|
workers = list(_get_client_workers(client).keys())
|
|
rabit_args = client.sync(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.skipif(**tm.no_dask_cuda())
|
|
@pytest.mark.mgpu
|
|
@pytest.mark.gtest
|
|
def test_quantile_basic(self, local_cuda_cluster):
|
|
self.run_quantile('AllReduceBasic', local_cuda_cluster)
|
|
|
|
@pytest.mark.skipif(**tm.no_dask())
|
|
@pytest.mark.skipif(**tm.no_dask_cuda())
|
|
@pytest.mark.mgpu
|
|
@pytest.mark.gtest
|
|
def test_quantile_same_on_all_workers(self, local_cuda_cluster):
|
|
self.run_quantile('SameOnAllWorkers', local_cuda_cluster)
|
|
|
|
|
|
async def run_from_dask_array_asyncio(scheduler_address):
|
|
async with Client(scheduler_address, asynchronous=True) as client:
|
|
import cupy as cp
|
|
X, y = generate_array()
|
|
X = X.map_blocks(cp.array)
|
|
y = y.map_blocks(cp.array)
|
|
|
|
m = await xgboost.dask.DaskDeviceQuantileDMatrix(client, X, y)
|
|
output = await xgboost.dask.train(client, {'tree_method': 'gpu_hist'},
|
|
dtrain=m)
|
|
|
|
with_m = await xgboost.dask.predict(client, output, m)
|
|
with_X = await xgboost.dask.predict(client, output, X)
|
|
inplace = await xgboost.dask.inplace_predict(client, output, X)
|
|
assert isinstance(with_m, da.Array)
|
|
assert isinstance(with_X, da.Array)
|
|
assert isinstance(inplace, da.Array)
|
|
|
|
cp.testing.assert_allclose(await client.compute(with_m),
|
|
await client.compute(with_X))
|
|
cp.testing.assert_allclose(await client.compute(with_m),
|
|
await client.compute(inplace))
|
|
|
|
client.shutdown()
|
|
return output
|
|
|
|
|
|
@pytest.mark.skipif(**tm.no_dask())
|
|
@pytest.mark.skipif(**tm.no_dask_cuda())
|
|
@pytest.mark.mgpu
|
|
def test_with_asyncio(local_cuda_cluster):
|
|
with Client(local_cuda_cluster) as client:
|
|
address = client.scheduler.address
|
|
output = asyncio.run(run_from_dask_array_asyncio(address))
|
|
assert isinstance(output['booster'], xgboost.Booster)
|
|
assert isinstance(output['history'], dict)
|