048d969be4
* Implement GK sketching on GPU. * Strong tests on quantile building. * Handle sparse dataset by binary searching the column index. * Hypothesis test on dask.
213 lines
8.3 KiB
Python
213 lines
8.3 KiB
Python
import sys
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import os
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import pytest
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import numpy as np
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import unittest
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import xgboost
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import subprocess
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from hypothesis import given, strategies, settings, note
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from test_gpu_updaters import parameter_strategy
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if sys.platform.startswith("win"):
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pytest.skip("Skipping dask tests on Windows", allow_module_level=True)
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sys.path.append("tests/python")
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from test_with_dask import run_empty_dmatrix # noqa
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from test_with_dask import generate_array # noqa
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import testing as tm # noqa
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try:
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import dask.dataframe as dd
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from xgboost import dask as dxgb
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from dask_cuda import LocalCUDACluster
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from dask.distributed import Client
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from dask import array as da
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import cudf
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except ImportError:
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pass
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class TestDistributedGPU(unittest.TestCase):
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@pytest.mark.skipif(**tm.no_dask())
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@pytest.mark.skipif(**tm.no_cudf())
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@pytest.mark.skipif(**tm.no_dask_cudf())
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@pytest.mark.skipif(**tm.no_dask_cuda())
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@pytest.mark.mgpu
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def test_dask_dataframe(self):
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with LocalCUDACluster() as cluster:
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with Client(cluster) as client:
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import cupy as cp
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cp.cuda.runtime.setDevice(0)
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X, y = generate_array()
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X = dd.from_dask_array(X)
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y = dd.from_dask_array(y)
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X = X.map_partitions(cudf.from_pandas)
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y = y.map_partitions(cudf.from_pandas)
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dtrain = dxgb.DaskDMatrix(client, X, y)
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out = dxgb.train(client, {'tree_method': 'gpu_hist',
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'debug_synchronize': True},
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dtrain=dtrain,
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evals=[(dtrain, 'X')],
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num_boost_round=4)
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assert isinstance(out['booster'], dxgb.Booster)
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assert len(out['history']['X']['rmse']) == 4
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predictions = dxgb.predict(client, out, dtrain).compute()
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assert isinstance(predictions, np.ndarray)
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series_predictions = dxgb.inplace_predict(client, out, X)
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assert isinstance(series_predictions, dd.Series)
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series_predictions = series_predictions.compute()
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single_node = out['booster'].predict(
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xgboost.DMatrix(X.compute()))
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cp.testing.assert_allclose(single_node, predictions)
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np.testing.assert_allclose(single_node,
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series_predictions.to_array())
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predt = dxgb.predict(client, out, X)
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assert isinstance(predt, dd.Series)
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def is_df(part):
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assert isinstance(part, cudf.DataFrame), part
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return part
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predt.map_partitions(
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is_df,
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meta=dd.utils.make_meta({'prediction': 'f4'}))
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cp.testing.assert_allclose(
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predt.values.compute(), single_node)
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@given(parameter_strategy, strategies.integers(1, 20),
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tm.dataset_strategy)
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@settings(deadline=None)
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@pytest.mark.mgpu
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def test_gpu_hist(self, params, num_rounds, dataset):
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with LocalCUDACluster(n_workers=2) as cluster:
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with Client(cluster) as client:
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params['tree_method'] = 'gpu_hist'
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params = dataset.set_params(params)
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# multi class doesn't handle empty dataset well (empty
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# means at least 1 worker has data).
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if params['objective'] == "multi:softmax":
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return
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# It doesn't make sense to distribute a completely
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# empty dataset.
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if dataset.X.shape[0] == 0:
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return
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chunk = 128
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X = da.from_array(dataset.X,
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chunks=(chunk, dataset.X.shape[1]))
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y = da.from_array(dataset.y, chunks=(chunk, ))
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if dataset.w is not None:
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w = da.from_array(dataset.w, chunks=(chunk, ))
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else:
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w = None
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m = dxgb.DaskDMatrix(
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client, data=X, label=y, weight=w)
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history = dxgb.train(client, params=params, dtrain=m,
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num_boost_round=num_rounds,
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evals=[(m, 'train')])['history']
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note(history)
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assert tm.non_increasing(history['train'][dataset.metric])
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@pytest.mark.skipif(**tm.no_cupy())
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@pytest.mark.mgpu
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def test_dask_array(self):
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with LocalCUDACluster() as cluster:
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with Client(cluster) as client:
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import cupy as cp
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cp.cuda.runtime.setDevice(0)
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X, y = generate_array()
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X = X.map_blocks(cp.asarray)
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y = y.map_blocks(cp.asarray)
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dtrain = dxgb.DaskDMatrix(client, X, y)
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out = dxgb.train(client, {'tree_method': 'gpu_hist',
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'debug_synchronize': True},
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dtrain=dtrain,
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evals=[(dtrain, 'X')],
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num_boost_round=2)
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from_dmatrix = dxgb.predict(client, out, dtrain).compute()
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inplace_predictions = dxgb.inplace_predict(
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client, out, X).compute()
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single_node = out['booster'].predict(
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xgboost.DMatrix(X.compute()))
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np.testing.assert_allclose(single_node, from_dmatrix)
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device = cp.cuda.runtime.getDevice()
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assert device == inplace_predictions.device.id
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single_node = cp.array(single_node)
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assert device == single_node.device.id
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cp.testing.assert_allclose(
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single_node,
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inplace_predictions)
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@pytest.mark.skipif(**tm.no_dask())
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@pytest.mark.skipif(**tm.no_dask_cuda())
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@pytest.mark.mgpu
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def test_empty_dmatrix(self):
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with LocalCUDACluster() as cluster:
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with Client(cluster) as client:
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parameters = {'tree_method': 'gpu_hist',
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'debug_synchronize': True}
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run_empty_dmatrix(client, parameters)
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def run_quantile(self, name):
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if sys.platform.startswith("win"):
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pytest.skip("Skipping dask tests on Windows")
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exe = None
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for possible_path in {'./testxgboost', './build/testxgboost',
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'../build/testxgboost', '../gpu-build/testxgboost'}:
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if os.path.exists(possible_path):
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exe = possible_path
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assert exe, 'No testxgboost executable found.'
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test = "--gtest_filter=GPUQuantile." + name
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def runit(worker_addr, rabit_args):
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port = None
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# setup environment for running the c++ part.
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for arg in rabit_args:
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if arg.decode('utf-8').startswith('DMLC_TRACKER_PORT'):
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port = arg.decode('utf-8')
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port = port.split('=')
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env = os.environ.copy()
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env[port[0]] = port[1]
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return subprocess.run([exe, test], env=env, stdout=subprocess.PIPE)
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with LocalCUDACluster() as cluster:
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with Client(cluster) as client:
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workers = list(dxgb._get_client_workers(client).keys())
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rabit_args = dxgb._get_rabit_args(workers, client)
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futures = client.map(runit,
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workers,
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pure=False,
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workers=workers,
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rabit_args=rabit_args)
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results = client.gather(futures)
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for ret in results:
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msg = ret.stdout.decode('utf-8')
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assert msg.find('1 test from GPUQuantile') != -1, msg
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assert ret.returncode == 0, msg
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@pytest.mark.skipif(**tm.no_dask())
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@pytest.mark.mgpu
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@pytest.mark.gtest
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def test_quantile_basic(self):
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self.run_quantile('AllReduceBasic')
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@pytest.mark.skipif(**tm.no_dask())
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@pytest.mark.mgpu
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@pytest.mark.gtest
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def test_quantile_same_on_all_workers(self):
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self.run_quantile('SameOnAllWorkers')
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