4656b09d5d
* Add a new API function for predicting on `DMatrix`. This function aligns with rest of the `XGBoosterPredictFrom*` functions on semantic of function arguments. * Purge `ntree_limit` from libxgboost, use iteration instead. * [dask] Use `inplace_predict` by default for dask sklearn models. * [dask] Run prediction shape inference on worker instead of client. The breaking change is in the Python sklearn `apply` function, I made it to be consistent with other prediction functions where `best_iteration` is used by default.
447 lines
17 KiB
Python
447 lines
17 KiB
Python
import sys
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import os
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from typing import Type, TypeVar, Any, Dict, List
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import pytest
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import numpy as np
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import asyncio
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import xgboost
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import subprocess
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from collections import OrderedDict
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from inspect import signature
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from hypothesis import given, strategies, settings, note
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from hypothesis._settings import duration
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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_reg # noqa
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from test_with_dask import run_boost_from_prediction # noqa
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from test_with_dask import run_dask_classifier # noqa
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from test_with_dask import run_empty_dmatrix_cls # noqa
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from test_with_dask import _get_client_workers # noqa
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from test_with_dask import generate_array # noqa
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from test_with_dask import kCols as random_cols # noqa
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from test_with_dask import suppress # 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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import xgboost as xgb
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from dask.distributed import Client
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from dask import array as da
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from dask_cuda import LocalCUDACluster
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import cudf
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except ImportError:
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pass
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def run_with_dask_dataframe(DMatrixT: Type, client: Client) -> None:
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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 = DMatrixT(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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T = TypeVar('T')
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def is_df(part: T) -> T:
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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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def run_with_dask_array(DMatrixT: Type, client: Client) -> None:
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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 = DMatrixT(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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def to_cp(x: Any, DMatrixT: Type) -> Any:
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import cupy
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if isinstance(x, np.ndarray) and \
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DMatrixT is dxgb.DaskDeviceQuantileDMatrix:
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X = cupy.array(x)
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else:
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X = x
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return X
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def run_gpu_hist(
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params: Dict,
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num_rounds: int,
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dataset: tm.TestDataset,
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DMatrixT: Type,
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client: Client,
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) -> None:
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params["tree_method"] = "gpu_hist"
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params = dataset.set_params(params)
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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 = to_cp(dataset.X, DMatrixT)
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X = da.from_array(X, chunks=(chunk, dataset.X.shape[1]))
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y = to_cp(dataset.y, DMatrixT)
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y = da.from_array(y, chunks=(chunk,))
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if dataset.w is not None:
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w = to_cp(dataset.w, DMatrixT)
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w = da.from_array(w, chunks=(chunk,))
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else:
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w = None
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if DMatrixT is dxgb.DaskDeviceQuantileDMatrix:
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m = DMatrixT(
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client, data=X, label=y, weight=w, max_bin=params.get("max_bin", 256)
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)
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else:
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m = DMatrixT(client, data=X, label=y, weight=w)
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history = dxgb.train(
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client,
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params=params,
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dtrain=m,
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num_boost_round=num_rounds,
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evals=[(m, "train")],
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)["history"]
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note(history)
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assert tm.non_increasing(history["train"][dataset.metric])
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def test_boost_from_prediction(local_cuda_cluster: LocalCUDACluster) -> None:
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import cudf
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from sklearn.datasets import load_breast_cancer
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with Client(local_cuda_cluster) as client:
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X_, y_ = load_breast_cancer(return_X_y=True)
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X = dd.from_array(X_, chunksize=100).map_partitions(cudf.from_pandas)
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y = dd.from_array(y_, chunksize=100).map_partitions(cudf.from_pandas)
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run_boost_from_prediction(X, y, "gpu_hist", client)
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class TestDistributedGPU:
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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, local_cuda_cluster: LocalCUDACluster) -> None:
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with Client(local_cuda_cluster) as client:
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run_with_dask_dataframe(dxgb.DaskDMatrix, client)
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run_with_dask_dataframe(dxgb.DaskDeviceQuantileDMatrix, client)
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@given(
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params=parameter_strategy,
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num_rounds=strategies.integers(1, 20),
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dataset=tm.dataset_strategy,
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)
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@settings(deadline=duration(seconds=120), suppress_health_check=suppress)
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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.parametrize(
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"local_cuda_cluster", [{"n_workers": 2}], indirect=["local_cuda_cluster"]
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)
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@pytest.mark.mgpu
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def test_gpu_hist(
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self,
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params: Dict,
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num_rounds: int,
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dataset: tm.TestDataset,
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local_cuda_cluster: LocalCUDACluster,
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) -> None:
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with Client(local_cuda_cluster) as client:
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run_gpu_hist(params, num_rounds, dataset, dxgb.DaskDMatrix, client)
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run_gpu_hist(
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params, num_rounds, dataset, dxgb.DaskDeviceQuantileDMatrix, client
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)
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@pytest.mark.skipif(**tm.no_cupy())
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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_dask_array(self, local_cuda_cluster: LocalCUDACluster) -> None:
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with Client(local_cuda_cluster) as client:
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run_with_dask_array(dxgb.DaskDMatrix, client)
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run_with_dask_array(dxgb.DaskDeviceQuantileDMatrix, client)
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@pytest.mark.skipif(**tm.no_cupy())
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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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def test_early_stopping(self, local_cuda_cluster: LocalCUDACluster) -> None:
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from sklearn.datasets import load_breast_cancer
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with Client(local_cuda_cluster) as client:
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X, y = load_breast_cancer(return_X_y=True)
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X, y = da.from_array(X), da.from_array(y)
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m = dxgb.DaskDMatrix(client, X, y)
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valid = dxgb.DaskDMatrix(client, X, y)
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early_stopping_rounds = 5
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booster = dxgb.train(client, {'objective': 'binary:logistic',
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'eval_metric': 'error',
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'tree_method': 'gpu_hist'}, m,
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evals=[(valid, 'Valid')],
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num_boost_round=1000,
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early_stopping_rounds=early_stopping_rounds)[
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'booster']
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assert hasattr(booster, 'best_score')
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dump = booster.get_dump(dump_format='json')
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assert len(dump) - booster.best_iteration == early_stopping_rounds + 1
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valid_X = X
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valid_y = y
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cls = dxgb.DaskXGBClassifier(objective='binary:logistic',
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tree_method='gpu_hist',
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n_estimators=100)
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cls.client = client
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cls.fit(X, y, early_stopping_rounds=early_stopping_rounds,
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eval_set=[(valid_X, valid_y)])
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booster = cls.get_booster()
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dump = booster.get_dump(dump_format='json')
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assert len(dump) - booster.best_iteration == early_stopping_rounds + 1
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@pytest.mark.skipif(**tm.no_cudf())
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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.parametrize("model", ["boosting"])
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def test_dask_classifier(self, model, local_cuda_cluster: LocalCUDACluster) -> None:
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import dask_cudf
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with Client(local_cuda_cluster) as client:
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X_, y_, w_ = generate_array(with_weights=True)
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y_ = (y_ * 10).astype(np.int32)
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X = dask_cudf.from_dask_dataframe(dd.from_dask_array(X_))
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y = dask_cudf.from_dask_dataframe(dd.from_dask_array(y_))
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w = dask_cudf.from_dask_dataframe(dd.from_dask_array(w_))
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run_dask_classifier(X, y, w, model, client)
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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, local_cuda_cluster: LocalCUDACluster) -> None:
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with Client(local_cuda_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_reg(client, parameters)
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run_empty_dmatrix_cls(client, parameters)
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def test_data_initialization(self, local_cuda_cluster: LocalCUDACluster) -> None:
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with Client(local_cuda_cluster) as client:
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X, y, _ = generate_array()
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fw = da.random.random((random_cols, ))
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fw = fw - fw.min()
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m = dxgb.DaskDMatrix(client, X, y, feature_weights=fw)
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workers = list(_get_client_workers(client).keys())
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rabit_args = client.sync(dxgb._get_rabit_args, len(workers), client)
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def worker_fn(worker_addr: str, data_ref: Dict) -> None:
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with dxgb.RabitContext(rabit_args):
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local_dtrain = dxgb._dmatrix_from_list_of_parts(**data_ref)
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fw_rows = local_dtrain.get_float_info("feature_weights").shape[0]
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assert fw_rows == local_dtrain.num_col()
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futures = []
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for i in range(len(workers)):
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futures.append(client.submit(worker_fn, workers[i],
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m.create_fn_args(workers[i]), pure=False,
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workers=[workers[i]]))
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client.gather(futures)
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def test_interface_consistency(self) -> None:
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sig = OrderedDict(signature(dxgb.DaskDMatrix).parameters)
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del sig["client"]
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ddm_names = list(sig.keys())
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sig = OrderedDict(signature(dxgb.DaskDeviceQuantileDMatrix).parameters)
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del sig["client"]
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del sig["max_bin"]
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ddqdm_names = list(sig.keys())
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assert len(ddm_names) == len(ddqdm_names)
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# between dask
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for i in range(len(ddm_names)):
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assert ddm_names[i] == ddqdm_names[i]
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sig = OrderedDict(signature(xgb.DMatrix).parameters)
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del sig["nthread"] # no nthread in dask
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dm_names = list(sig.keys())
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sig = OrderedDict(signature(xgb.DeviceQuantileDMatrix).parameters)
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del sig["nthread"]
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del sig["max_bin"]
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dqdm_names = list(sig.keys())
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# between single node
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assert len(dm_names) == len(dqdm_names)
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for i in range(len(dm_names)):
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assert dm_names[i] == dqdm_names[i]
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# ddm <-> dm
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for i in range(len(ddm_names)):
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assert ddm_names[i] == dm_names[i]
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# dqdm <-> ddqdm
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for i in range(len(ddqdm_names)):
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assert ddqdm_names[i] == dqdm_names[i]
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sig = OrderedDict(signature(xgb.XGBRanker.fit).parameters)
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ranker_names = list(sig.keys())
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sig = OrderedDict(signature(xgb.dask.DaskXGBRanker.fit).parameters)
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dranker_names = list(sig.keys())
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for rn, drn in zip(ranker_names, dranker_names):
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assert rn == drn
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def run_quantile(self, name: str, local_cuda_cluster: LocalCUDACluster) -> None:
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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(
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worker_addr: str, rabit_args: List[bytes]
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) -> subprocess.CompletedProcess:
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port_env = ''
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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_env = arg.decode('utf-8')
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port = port_env.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([str(exe), test], env=env, stdout=subprocess.PIPE)
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with Client(local_cuda_cluster) as client:
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workers = list(_get_client_workers(client).keys())
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rabit_args = client.sync(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.skipif(**tm.no_dask_cuda())
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@pytest.mark.mgpu
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@pytest.mark.gtest
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def test_quantile_basic(self, local_cuda_cluster: LocalCUDACluster) -> None:
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self.run_quantile('AllReduceBasic', local_cuda_cluster)
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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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@pytest.mark.gtest
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def test_quantile_same_on_all_workers(
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self, local_cuda_cluster: LocalCUDACluster
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) -> None:
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self.run_quantile('SameOnAllWorkers', local_cuda_cluster)
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async def run_from_dask_array_asyncio(scheduler_address: str) -> dxgb.TrainReturnT:
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async with Client(scheduler_address, asynchronous=True) as client:
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import cupy as cp
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X, y, _ = generate_array()
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X = X.map_blocks(cp.array)
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y = y.map_blocks(cp.array)
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m = await xgboost.dask.DaskDeviceQuantileDMatrix(client, X, y)
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output = await xgboost.dask.train(client, {'tree_method': 'gpu_hist'},
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dtrain=m)
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with_m = await xgboost.dask.predict(client, output, m)
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with_X = await xgboost.dask.predict(client, output, X)
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inplace = await xgboost.dask.inplace_predict(client, output, X)
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assert isinstance(with_m, da.Array)
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assert isinstance(with_X, da.Array)
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assert isinstance(inplace, da.Array)
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cp.testing.assert_allclose(await client.compute(with_m),
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await client.compute(with_X))
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cp.testing.assert_allclose(await client.compute(with_m),
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await client.compute(inplace))
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client.shutdown()
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return output
|
|
|
|
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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_with_asyncio(local_cuda_cluster: LocalCUDACluster) -> None:
|
|
with Client(local_cuda_cluster) as client:
|
|
address = client.scheduler.address
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|
output = asyncio.run(run_from_dask_array_asyncio(address))
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|
assert isinstance(output['booster'], xgboost.Booster)
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|
assert isinstance(output['history'], dict)
|