7663de956c
This makes GPU Hist robust in distributed environment as some workers might not be associated with any data in either training or evaluation. * Disable rabit mock test for now: See #5012 . * Disable dask-cudf test at prediction for now: See #5003 * Launch dask job for all workers despite they might not have any data. * Check 0 rows in elementwise evaluation metrics. Using AUC and AUC-PR still throws an error. See #4663 for a robust fix. * Add tests for edge cases. * Add `LaunchKernel` wrapper handling zero sized grid. * Move some parts of allreducer into a cu file. * Don't validate feature names when the booster is empty. * Sync number of columns in DMatrix. As num_feature is required to be the same across all workers in data split mode. * Filtering in dask interface now by default syncs all booster that's not empty, instead of using rank 0. * Fix Jenkins' GPU tests. * Install dask-cuda from source in Jenkins' test. Now all tests are actually running. * Restore GPU Hist tree synchronization test. * Check UUID of running devices. The check is only performed on CUDA version >= 10.x, as 9.x doesn't have UUID field. * Fix CMake policy and project variables. Use xgboost_SOURCE_DIR uniformly, add policy for CMake >= 3.13. * Fix copying data to CPU * Fix race condition in cpu predictor. * Fix duplicated DMatrix construction. * Don't download extra nccl in CI script.
652 lines
22 KiB
Python
652 lines
22 KiB
Python
# pylint: disable=too-many-arguments, too-many-locals
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"""Dask extensions for distributed training. See
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https://xgboost.readthedocs.io/en/latest/tutorials/dask.html for simple
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tutorial. Also xgboost/demo/dask for some examples.
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There are two sets of APIs in this module, one is the functional API including
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``train`` and ``predict`` methods. Another is stateful Scikit-Learner wrapper
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inherited from single-node Scikit-Learn interface.
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The implementation is heavily influenced by dask_xgboost:
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https://github.com/dask/dask-xgboost
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"""
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import platform
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import logging
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from collections import defaultdict
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from threading import Thread
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import numpy
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from . import rabit
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from .compat import DASK_INSTALLED
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from .compat import distributed_get_worker, distributed_wait, distributed_comm
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from .compat import da, dd, delayed, get_client
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from .compat import sparse, scipy_sparse
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from .compat import PANDAS_INSTALLED, DataFrame, Series, pandas_concat
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from .compat import CUDF_INSTALLED, CUDF_DataFrame, CUDF_Series, CUDF_concat
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from .core import DMatrix, Booster, _expect
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from .training import train as worker_train
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from .tracker import RabitTracker
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from .sklearn import XGBModel, XGBClassifierBase
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# Current status is considered as initial support, many features are
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# not properly supported yet.
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#
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# TODOs:
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# - Callback.
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# - Label encoding.
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# - CV
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# - Ranking
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def _start_tracker(host, n_workers):
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"""Start Rabit tracker """
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env = {'DMLC_NUM_WORKER': n_workers}
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rabit_context = RabitTracker(hostIP=host, nslave=n_workers)
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env.update(rabit_context.slave_envs())
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rabit_context.start(n_workers)
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thread = Thread(target=rabit_context.join)
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thread.daemon = True
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thread.start()
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return env
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def _assert_dask_support():
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if not DASK_INSTALLED:
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raise ImportError(
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'Dask needs to be installed in order to use this module')
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if platform.system() == 'Windows':
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msg = 'Windows is not officially supported for dask/xgboost,'
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msg += ' contribution are welcomed.'
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logging.warning(msg)
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class RabitContext:
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'''A context controling rabit initialization and finalization.'''
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def __init__(self, args):
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self.args = args
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def __enter__(self):
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rabit.init(self.args)
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logging.debug('-------------- rabit say hello ------------------')
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def __exit__(self, *args):
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rabit.finalize()
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logging.debug('--------------- rabit say bye ------------------')
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def concat(value):
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'''To be replaced with dask builtin.'''
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if isinstance(value[0], numpy.ndarray):
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return numpy.concatenate(value, axis=0)
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if scipy_sparse and isinstance(value[0], scipy_sparse.spmatrix):
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return scipy_sparse.vstack(value, format='csr')
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if sparse and isinstance(value[0], sparse.SparseArray):
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return sparse.concatenate(value, axis=0)
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if PANDAS_INSTALLED and isinstance(value[0], (DataFrame, Series)):
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return pandas_concat(value, axis=0)
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if CUDF_INSTALLED and isinstance(value[0], (CUDF_DataFrame, CUDF_Series)):
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return CUDF_concat(value, axis=0)
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return dd.multi.concat(list(value), axis=0)
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def _xgb_get_client(client):
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'''Simple wrapper around testing None.'''
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ret = get_client() if client is None else client
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return ret
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def _get_client_workers(client):
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workers = client.scheduler_info()['workers']
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return workers
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class DaskDMatrix:
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# pylint: disable=missing-docstring, too-many-instance-attributes
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'''DMatrix holding on references to Dask DataFrame or Dask Array.
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Parameters
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----------
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client: dask.distributed.Client
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Specify the dask client used for training. Use default client
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returned from dask if it's set to None.
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data : dask.array.Array/dask.dataframe.DataFrame
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data source of DMatrix.
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label: dask.array.Array/dask.dataframe.DataFrame
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label used for trainin.
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missing : float, optional
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Value in the input data (e.g. `numpy.ndarray`) which needs
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to be present as a missing value. If None, defaults to np.nan.
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weight : dask.array.Array/dask.dataframe.DataFrame
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Weight for each instance.
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feature_names : list, optional
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Set names for features.
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feature_types : list, optional
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Set types for features
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'''
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_feature_names = None # for previous version's pickle
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_feature_types = None
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def __init__(self,
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client,
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data,
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label=None,
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missing=None,
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weight=None,
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feature_names=None,
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feature_types=None):
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_assert_dask_support()
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self._feature_names = feature_names
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self._feature_types = feature_types
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self._missing = missing
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if len(data.shape) != 2:
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raise ValueError(
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'Expecting 2 dimensional input, got: {shape}'.format(
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shape=data.shape))
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if not isinstance(data, (dd.DataFrame, da.Array)):
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raise TypeError(_expect((dd.DataFrame, da.Array), type(data)))
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if not isinstance(label, (dd.DataFrame, da.Array, dd.Series,
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type(None))):
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raise TypeError(
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_expect((dd.DataFrame, da.Array, dd.Series), type(label)))
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self.worker_map = None
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self.has_label = label is not None
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self.has_weights = weight is not None
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client = _xgb_get_client(client)
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client.sync(self.map_local_data, client, data, label, weight)
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async def map_local_data(self, client, data, label=None, weights=None):
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'''Obtain references to local data.'''
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def inconsistent(left, left_name, right, right_name):
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msg = 'Partitions between {a_name} and {b_name} are not ' \
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'consistent: {a_len} != {b_len}. ' \
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'Please try to repartition/rechunk your data.'.format(
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a_name=left_name, b_name=right_name, a_len=len(left),
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b_len=len(right)
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)
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return msg
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def check_columns(parts):
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# x is required to be 2 dim in __init__
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assert parts.ndim == 1 or parts.shape[1], 'Data should be' \
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' partitioned by row. To avoid this specify the number' \
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' of columns for your dask Array explicitly. e.g.' \
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' chunks=(partition_size, X.shape[1])'
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data = data.persist()
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if label is not None:
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label = label.persist()
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if weights is not None:
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weights = weights.persist()
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# Breaking data into partitions, a trick borrowed from dask_xgboost.
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# `to_delayed` downgrades high-level objects into numpy or pandas
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# equivalents.
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X_parts = data.to_delayed()
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if isinstance(X_parts, numpy.ndarray):
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check_columns(X_parts)
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X_parts = X_parts.flatten().tolist()
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if label is not None:
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y_parts = label.to_delayed()
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if isinstance(y_parts, numpy.ndarray):
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check_columns(y_parts)
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y_parts = y_parts.flatten().tolist()
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if weights is not None:
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w_parts = weights.to_delayed()
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if isinstance(w_parts, numpy.ndarray):
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check_columns(w_parts)
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w_parts = w_parts.flatten().tolist()
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parts = [X_parts]
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if label is not None:
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assert len(X_parts) == len(
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y_parts), inconsistent(X_parts, 'X', y_parts, 'labels')
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parts.append(y_parts)
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if weights is not None:
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assert len(X_parts) == len(
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w_parts), inconsistent(X_parts, 'X', w_parts, 'weights')
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parts.append(w_parts)
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parts = list(map(delayed, zip(*parts)))
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parts = client.compute(parts)
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await distributed_wait(parts) # async wait for parts to be computed
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for part in parts:
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assert part.status == 'finished'
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key_to_partition = {part.key: part for part in parts}
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who_has = await client.scheduler.who_has(
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keys=[part.key for part in parts])
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worker_map = defaultdict(list)
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for key, workers in who_has.items():
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worker_map[next(iter(workers))].append(key_to_partition[key])
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self.worker_map = worker_map
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def get_worker_parts(self, worker):
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'''Get mapped parts of data in each worker.'''
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list_of_parts = self.worker_map[worker.address]
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assert list_of_parts, 'data in ' + worker.address + ' was moved.'
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assert isinstance(list_of_parts, list)
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# `get_worker_parts` is launched inside worker. In dask side
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# this should be equal to `worker._get_client`.
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client = get_client()
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list_of_parts = client.gather(list_of_parts)
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if self.has_label:
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if self.has_weights:
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data, labels, weights = zip(*list_of_parts)
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else:
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data, labels = zip(*list_of_parts)
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weights = None
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else:
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data = [d[0] for d in list_of_parts]
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labels = None
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weights = None
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return data, labels, weights
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def get_worker_data(self, worker):
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'''Get data that local to worker.
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Parameters
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----------
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worker: The worker used as key to data.
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Returns
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-------
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A DMatrix object.
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'''
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if worker.address not in set(self.worker_map.keys()):
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msg = 'worker {address} has an empty DMatrix. ' \
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'All workers associated with this DMatrix: {workers}'.format(
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address=worker.address,
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workers=set(self.worker_map.keys()))
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logging.warning(msg)
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d = DMatrix(numpy.empty((0, 0)),
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feature_names=self._feature_names,
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feature_types=self._feature_types)
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return d
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data, labels, weights = self.get_worker_parts(worker)
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data = concat(data)
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if self.has_label:
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labels = concat(labels)
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else:
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labels = None
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if self.has_weights:
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weights = concat(weights)
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else:
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weights = None
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dmatrix = DMatrix(data,
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labels,
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weight=weights,
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missing=self._missing,
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feature_names=self._feature_names,
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feature_types=self._feature_types)
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return dmatrix
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def get_worker_data_shape(self, worker):
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'''Get the shape of data X in each worker.'''
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data, _, _ = self.get_worker_parts(worker)
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shapes = [d.shape for d in data]
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rows = 0
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cols = 0
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for shape in shapes:
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rows += shape[0]
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c = shape[1]
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assert cols in (0, c), 'Shape between partitions are not the' \
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' same. Got: {left} and {right}'.format(left=c, right=cols)
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cols = c
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return (rows, cols)
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def _get_rabit_args(worker_map, client):
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'''Get rabit context arguments from data distribution in DaskDMatrix.'''
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host = distributed_comm.get_address_host(client.scheduler.address)
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env = client.run_on_scheduler(_start_tracker, host.strip('/:'),
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len(worker_map))
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rabit_args = [('%s=%s' % item).encode() for item in env.items()]
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return rabit_args
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# train and predict methods are supposed to be "functional", which meets the
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# dask paradigm. But as a side effect, the `evals_result` in single-node API
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# is no longer supported since it mutates the input parameter, and it's not
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# intuitive to sync the mutation result. Therefore, a dictionary containing
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# evaluation history is instead returned.
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def train(client, params, dtrain, *args, evals=(), **kwargs):
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'''Train XGBoost model.
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Parameters
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----------
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client: dask.distributed.Client
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Specify the dask client used for training. Use default client
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returned from dask if it's set to None.
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Other parameters are the same as `xgboost.train` except for `evals_result`,
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which is returned as part of function return value instead of argument.
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Returns
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-------
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results: dict
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A dictionary containing trained booster and evaluation history.
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`history` field is the same as `eval_result` from `xgboost.train`.
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.. code-block:: python
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{'booster': xgboost.Booster,
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'history': {'train': {'logloss': ['0.48253', '0.35953']},
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'eval': {'logloss': ['0.480385', '0.357756']}}}
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'''
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_assert_dask_support()
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if 'evals_result' in kwargs.keys():
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raise ValueError(
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'evals_result is not supported in dask interface.',
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'The evaluation history is returned as result of training.')
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client = _xgb_get_client(client)
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workers = list(_get_client_workers(client).keys())
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rabit_args = _get_rabit_args(workers, client)
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def dispatched_train(worker_addr):
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'''Perform training on a single worker.'''
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logging.info('Training on %s', str(worker_addr))
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worker = distributed_get_worker()
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with RabitContext(rabit_args):
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local_dtrain = dtrain.get_worker_data(worker)
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local_evals = []
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if evals:
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for mat, name in evals:
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if mat is dtrain:
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local_evals.append((local_dtrain, name))
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continue
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local_mat = mat.get_worker_data(worker)
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local_evals.append((local_mat, name))
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local_history = {}
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local_param = params.copy() # just to be consistent
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bst = worker_train(params=local_param,
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dtrain=local_dtrain,
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*args,
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evals_result=local_history,
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evals=local_evals,
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**kwargs)
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ret = {'booster': bst, 'history': local_history}
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if local_dtrain.num_row() == 0:
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ret = None
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return ret
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futures = client.map(dispatched_train,
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workers,
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pure=False,
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workers=workers)
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results = client.gather(futures)
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return list(filter(lambda ret: ret is not None, results))[0]
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def predict(client, model, data, *args):
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'''Run prediction with a trained booster.
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.. note::
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Only default prediction mode is supported right now.
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Parameters
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----------
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client: dask.distributed.Client
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Specify the dask client used for training. Use default client
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returned from dask if it's set to None.
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model: A Booster or a dictionary returned by `xgboost.dask.train`.
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The trained model.
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data: DaskDMatrix
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Input data used for prediction.
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Returns
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-------
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prediction: dask.array.Array
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'''
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_assert_dask_support()
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if isinstance(model, Booster):
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booster = model
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elif isinstance(model, dict):
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booster = model['booster']
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else:
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raise TypeError(_expect([Booster, dict], type(model)))
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if not isinstance(data, DaskDMatrix):
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raise TypeError(_expect([DaskDMatrix], type(data)))
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worker_map = data.worker_map
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client = _xgb_get_client(client)
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rabit_args = _get_rabit_args(worker_map, client)
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def dispatched_predict(worker_id):
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'''Perform prediction on each worker.'''
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logging.info('Predicting on %d', worker_id)
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worker = distributed_get_worker()
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local_x = data.get_worker_data(worker)
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with RabitContext(rabit_args):
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local_predictions = booster.predict(
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data=local_x, validate_features=local_x.num_row() != 0, *args)
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return local_predictions
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futures = client.map(dispatched_predict,
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range(len(worker_map)),
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pure=False,
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workers=list(worker_map.keys()))
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def dispatched_get_shape(worker_id):
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'''Get shape of data in each worker.'''
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logging.info('Trying to get data shape on %d', worker_id)
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worker = distributed_get_worker()
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rows, _ = data.get_worker_data_shape(worker)
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return rows, 1 # default is 1
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# Constructing a dask array from list of numpy arrays
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# See https://docs.dask.org/en/latest/array-creation.html
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futures_shape = client.map(dispatched_get_shape,
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range(len(worker_map)),
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pure=False,
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workers=list(worker_map.keys()))
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shapes = client.gather(futures_shape)
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arrays = []
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for i in range(len(futures_shape)):
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arrays.append(da.from_delayed(futures[i], shape=(shapes[i][0], ),
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dtype=numpy.float32))
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predictions = da.concatenate(arrays, axis=0)
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return predictions
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def _evaluation_matrices(client, validation_set, sample_weights):
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'''
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Parameters
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----------
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validation_set: list of tuples
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Each tuple contains a validation dataset including input X and label y.
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E.g.:
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.. code-block:: python
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[(X_0, y_0), (X_1, y_1), ... ]
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sample_weights: list of arrays
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The weight vector for validation data.
|
|
|
|
Returns
|
|
-------
|
|
evals: list of validation DMatrix
|
|
'''
|
|
evals = []
|
|
if validation_set is not None:
|
|
assert isinstance(validation_set, list)
|
|
for i, e in enumerate(validation_set):
|
|
w = (sample_weights[i]
|
|
if sample_weights is not None else None)
|
|
dmat = DaskDMatrix(client=client, data=e[0], label=e[1], weight=w)
|
|
evals.append((dmat, 'validation_{}'.format(i)))
|
|
else:
|
|
evals = None
|
|
return evals
|
|
|
|
|
|
class DaskScikitLearnBase(XGBModel):
|
|
'''Base class for implementing scikit-learn interface with Dask'''
|
|
|
|
_client = None
|
|
|
|
# pylint: disable=arguments-differ
|
|
def fit(self,
|
|
X,
|
|
y,
|
|
sample_weights=None,
|
|
eval_set=None,
|
|
sample_weight_eval_set=None):
|
|
'''Fit the regressor.
|
|
|
|
Parameters
|
|
----------
|
|
X : array_like
|
|
Feature matrix
|
|
y : array_like
|
|
Labels
|
|
sample_weight : array_like
|
|
instance weights
|
|
eval_set : list, optional
|
|
A list of (X, y) tuple pairs to use as validation sets, for which
|
|
metrics will be computed.
|
|
Validation metrics will help us track the performance of the model.
|
|
sample_weight_eval_set : list, optional
|
|
A list of the form [L_1, L_2, ..., L_n], where each L_i is a list
|
|
of group weights on the i-th validation set.'''
|
|
raise NotImplementedError
|
|
|
|
def predict(self, data): # pylint: disable=arguments-differ
|
|
'''Predict with `data`.
|
|
Parameters
|
|
----------
|
|
data: data that can be used to construct a DaskDMatrix
|
|
Returns
|
|
-------
|
|
prediction : dask.array.Array'''
|
|
raise NotImplementedError
|
|
|
|
@property
|
|
def client(self):
|
|
'''The dask client used in this model.'''
|
|
client = _xgb_get_client(self._client)
|
|
return client
|
|
|
|
@client.setter
|
|
def client(self, clt):
|
|
self._client = clt
|
|
|
|
|
|
class DaskXGBRegressor(DaskScikitLearnBase):
|
|
# pylint: disable=missing-docstring
|
|
__doc__ = ('Implementation of the scikit-learn API for XGBoost ' +
|
|
'regression. \n\n') + '\n'.join(
|
|
XGBModel.__doc__.split('\n')[2:])
|
|
|
|
def fit(self,
|
|
X,
|
|
y,
|
|
sample_weights=None,
|
|
eval_set=None,
|
|
sample_weight_eval_set=None):
|
|
_assert_dask_support()
|
|
dtrain = DaskDMatrix(client=self.client,
|
|
data=X, label=y, weight=sample_weights)
|
|
params = self.get_xgb_params()
|
|
evals = _evaluation_matrices(self.client,
|
|
eval_set, sample_weight_eval_set)
|
|
|
|
results = train(self.client, params, dtrain,
|
|
num_boost_round=self.get_num_boosting_rounds(),
|
|
evals=evals)
|
|
self._Booster = results['booster']
|
|
# pylint: disable=attribute-defined-outside-init
|
|
self.evals_result_ = results['history']
|
|
return self
|
|
|
|
def predict(self, data): # pylint: disable=arguments-differ
|
|
_assert_dask_support()
|
|
test_dmatrix = DaskDMatrix(client=self.client, data=data)
|
|
pred_probs = predict(client=self.client,
|
|
model=self.get_booster(), data=test_dmatrix)
|
|
return pred_probs
|
|
|
|
|
|
class DaskXGBClassifier(DaskScikitLearnBase, XGBClassifierBase):
|
|
# pylint: disable=missing-docstring
|
|
_client = None
|
|
__doc__ = ('Implementation of the scikit-learn API for XGBoost ' +
|
|
'classification.\n\n') + '\n'.join(
|
|
XGBModel.__doc__.split('\n')[2:])
|
|
|
|
def fit(self,
|
|
X,
|
|
y,
|
|
sample_weights=None,
|
|
eval_set=None,
|
|
sample_weight_eval_set=None):
|
|
_assert_dask_support()
|
|
dtrain = DaskDMatrix(client=self.client,
|
|
data=X, label=y, weight=sample_weights)
|
|
params = self.get_xgb_params()
|
|
|
|
# pylint: disable=attribute-defined-outside-init
|
|
self.classes_ = da.unique(y).compute()
|
|
self.n_classes_ = len(self.classes_)
|
|
|
|
if self.n_classes_ > 2:
|
|
params["objective"] = "multi:softprob"
|
|
params['num_class'] = self.n_classes_
|
|
else:
|
|
params["objective"] = "binary:logistic"
|
|
params.setdefault('num_class', self.n_classes_)
|
|
|
|
evals = _evaluation_matrices(self.client,
|
|
eval_set, sample_weight_eval_set)
|
|
results = train(self.client, params, dtrain,
|
|
num_boost_round=self.get_num_boosting_rounds(),
|
|
evals=evals)
|
|
self._Booster = results['booster']
|
|
# pylint: disable=attribute-defined-outside-init
|
|
self.evals_result_ = results['history']
|
|
return self
|
|
|
|
def predict(self, data): # pylint: disable=arguments-differ
|
|
_assert_dask_support()
|
|
test_dmatrix = DaskDMatrix(client=self.client, data=data)
|
|
pred_probs = predict(client=self.client,
|
|
model=self.get_booster(), data=test_dmatrix)
|
|
return pred_probs
|