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[pyspark] Use quantile dmatrix. (#8284)

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Jiaming Yuan 2022-10-12 20:38:53 +08:00 committed by GitHub
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9 changed files with 225 additions and 120 deletions
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2
doc/parameter.rst
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@ -349,7 +349,7 @@ Specify the learning task and the corresponding learning objective. The objectiv
- ``reg:squaredlogerror``: regression with squared log loss :math:`\frac{1}{2}[log(pred + 1) - log(label + 1)]^2`. All input labels are required to be greater than -1. Also, see metric ``rmsle`` for possible issue with this objective.
- ``reg:logistic``: logistic regression.
- ``reg:pseudohubererror``: regression with Pseudo Huber loss, a twice differentiable alternative to absolute loss.
- ``reg:absoluteerror``: Regression with L1 error. When tree model is used, leaf value is refreshed after tree construction.
- ``reg:absoluteerror``: Regression with L1 error. When tree model is used, leaf value is refreshed after tree construction. If used in distributed training, the leaf value is calculated as the mean value from all workers, which is not guaranteed to be optimal.
- ``binary:logistic``: logistic regression for binary classification, output probability
- ``binary:logitraw``: logistic regression for binary classification, output score before logistic transformation
- ``binary:hinge``: hinge loss for binary classification. This makes predictions of 0 or 1, rather than producing probabilities.

43
python-package/xgboost/core.py
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@ -105,6 +105,11 @@ def from_cstr_to_pystr(data: CStrPptr, length: c_bst_ulong) -> List[str]:
return res
def make_jcargs(**kwargs: Any) -> bytes:
"Make JSON-based arguments for C functions."
return from_pystr_to_cstr(json.dumps(kwargs))
IterRange = TypeVar("IterRange", Optional[Tuple[int, int]], Tuple[int, int])
@ -1256,7 +1261,7 @@ class _ProxyDMatrix(DMatrix):
def _set_data_from_cuda_interface(self, data: DataType) -> None:
"""Set data from CUDA array interface."""
interface = data.__cuda_array_interface__
interface_str = bytes(json.dumps(interface, indent=2), "utf-8")
interface_str = bytes(json.dumps(interface), "utf-8")
_check_call(
_LIB.XGProxyDMatrixSetDataCudaArrayInterface(self.handle, interface_str)
)
@ -1357,6 +1362,26 @@ class QuantileDMatrix(DMatrix):
"Only one of the eval_qid or eval_group for each evaluation "
"dataset should be provided."
)
if isinstance(data, DataIter):
if any(
info is not None
for info in (
label,
weight,
base_margin,
feature_names,
feature_types,
group,
qid,
label_lower_bound,
label_upper_bound,
feature_weights,
)
):
raise ValueError(
"If data iterator is used as input, data like label should be "
"specified as batch argument."
)
self._init(
data,
@ -1405,12 +1430,9 @@ class QuantileDMatrix(DMatrix):
"in iterator to fix this error."
)
args = {
"nthread": self.nthread,
"missing": self.missing,
"max_bin": self.max_bin,
}
config = from_pystr_to_cstr(json.dumps(args))
config = make_jcargs(
nthread=self.nthread, missing=self.missing, max_bin=self.max_bin
)
ret = _LIB.XGQuantileDMatrixCreateFromCallback(
None,
it.proxy.handle,
@ -2375,7 +2397,7 @@ class Booster:
"""
length = c_bst_ulong()
cptr = ctypes.POINTER(ctypes.c_char)()
config = from_pystr_to_cstr(json.dumps({"format": raw_format}))
config = make_jcargs(format=raw_format)
_check_call(
_LIB.XGBoosterSaveModelToBuffer(
self.handle, config, ctypes.byref(length), ctypes.byref(cptr)
@ -2570,9 +2592,6 @@ class Booster:
`n_classes`, otherwise they're scalars.
"""
fmap = os.fspath(os.path.expanduser(fmap))
args = from_pystr_to_cstr(
json.dumps({"importance_type": importance_type, "feature_map": fmap})
)
features = ctypes.POINTER(ctypes.c_char_p)()
scores = ctypes.POINTER(ctypes.c_float)()
n_out_features = c_bst_ulong()
@ -2582,7 +2601,7 @@ class Booster:
_check_call(
_LIB.XGBoosterFeatureScore(
self.handle,
args,
make_jcargs(importance_type=importance_type, feature_map=fmap),
ctypes.byref(n_out_features),
ctypes.byref(features),
ctypes.byref(out_dim),

13
python-package/xgboost/dask.py
View File

@ -573,6 +573,7 @@ class DaskPartitionIter(DataIter): # pylint: disable=R0902
label_upper_bound: Optional[List[Any]] = None,
feature_names: Optional[FeatureNames] = None,
feature_types: Optional[Union[Any, List[Any]]] = None,
feature_weights: Optional[Any] = None,
) -> None:
self._data = data
self._label = label
@ -583,6 +584,7 @@ class DaskPartitionIter(DataIter): # pylint: disable=R0902
self._label_upper_bound = label_upper_bound
self._feature_names = feature_names
self._feature_types = feature_types
self._feature_weights = feature_weights
assert isinstance(self._data, collections.abc.Sequence)
@ -633,6 +635,7 @@ class DaskPartitionIter(DataIter): # pylint: disable=R0902
label_upper_bound=self._get("_label_upper_bound"),
feature_names=feature_names,
feature_types=self._feature_types,
feature_weights=self._feature_weights,
)
self._iter += 1
return 1
@ -731,19 +734,21 @@ def _create_quantile_dmatrix(
return d
unzipped_dict = _get_worker_parts(parts)
it = DaskPartitionIter(**unzipped_dict)
it = DaskPartitionIter(
**unzipped_dict,
feature_types=feature_types,
feature_names=feature_names,
feature_weights=feature_weights,
)
dmatrix = QuantileDMatrix(
it,
missing=missing,
feature_names=feature_names,
feature_types=feature_types,
nthread=nthread,
max_bin=max_bin,
ref=ref,
enable_categorical=enable_categorical,
)
dmatrix.set_info(feature_weights=feature_weights)
return dmatrix

15
python-package/xgboost/spark/core.py
View File

@ -747,6 +747,7 @@ class _SparkXGBEstimator(Estimator, _SparkXGBParams, MLReadable, MLWritable):
k: v for k, v in train_call_kwargs_params.items() if v is not None
}
dmatrix_kwargs = {k: v for k, v in dmatrix_kwargs.items() if v is not None}
use_qdm = booster_params.get("tree_method") in ("hist", "gpu_hist")
def _train_booster(pandas_df_iter):
"""Takes in an RDD partition and outputs a booster for that partition after
@ -759,20 +760,17 @@ class _SparkXGBEstimator(Estimator, _SparkXGBParams, MLReadable, MLWritable):
context.barrier()
gpu_id = None
if use_qdm and (booster_params.get("max_bin", None) is not None):
dmatrix_kwargs["max_bin"] = booster_params["max_bin"]
if use_gpu:
gpu_id = context.partitionId() if is_local else _get_gpu_id(context)
booster_params["gpu_id"] = gpu_id
# max_bin is needed for qdm
if (
features_cols_names is not None
and booster_params.get("max_bin", None) is not None
):
dmatrix_kwargs["max_bin"] = booster_params["max_bin"]
_rabit_args = {}
if context.partitionId() == 0:
get_logger("XGBoostPySpark").info(
get_logger("XGBoostPySpark").debug(
"booster params: %s\n"
"train_call_kwargs_params: %s\n"
"dmatrix_kwargs: %s",
@ -791,6 +789,7 @@ class _SparkXGBEstimator(Estimator, _SparkXGBParams, MLReadable, MLWritable):
pandas_df_iter,
features_cols_names,
gpu_id,
use_qdm,
dmatrix_kwargs,
enable_sparse_data_optim=enable_sparse_data_optim,
has_validation_col=has_validation_col,

126
python-package/xgboost/spark/data.py
View File

@ -1,13 +1,13 @@
"""Utilities for processing spark partitions."""
from collections import defaultdict, namedtuple
from typing import Any, Callable, Dict, Iterator, List, Optional, Sequence, Tuple
from typing import Any, Callable, Dict, Iterator, List, Optional, Sequence, Tuple, Union
import numpy as np
import pandas as pd
from scipy.sparse import csr_matrix
from xgboost.compat import concat
from xgboost import DataIter, DeviceQuantileDMatrix, DMatrix
from xgboost import DataIter, DMatrix, QuantileDMatrix
from .utils import get_logger # type: ignore
@ -67,10 +67,13 @@ def cache_partitions(
class PartIter(DataIter):
"""Iterator for creating Quantile DMatrix from partitions."""
def __init__(self, data: Dict[str, List], device_id: Optional[int]) -> None:
def __init__(
self, data: Dict[str, List], device_id: Optional[int], **kwargs: Any
) -> None:
self._iter = 0
self._device_id = device_id
self._data = data
self._kwargs = kwargs
super().__init__()
@ -98,6 +101,7 @@ class PartIter(DataIter):
weight=self._fetch(self._data.get(alias.weight, None)),
base_margin=self._fetch(self._data.get(alias.margin, None)),
qid=self._fetch(self._data.get(alias.qid, None)),
**self._kwargs,
)
self._iter += 1
return 1
@ -149,24 +153,52 @@ def _read_csr_matrix_from_unwrapped_spark_vec(part: pd.DataFrame) -> csr_matrix:
)
def make_qdm(
data: Dict[str, List[np.ndarray]],
gpu_id: Optional[int],
meta: Dict[str, Any],
ref: Optional[DMatrix],
params: Dict[str, Any],
) -> DMatrix:
"""Handle empty partition for QuantileDMatrix."""
if not data:
return QuantileDMatrix(np.empty((0, 0)), ref=ref)
it = PartIter(data, gpu_id, **meta)
m = QuantileDMatrix(it, **params, ref=ref)
return m
def create_dmatrix_from_partitions( # pylint: disable=too-many-arguments
iterator: Iterator[pd.DataFrame],
feature_cols: Optional[Sequence[str]],
gpu_id: Optional[int],
use_qdm: bool,
kwargs: Dict[str, Any], # use dict to make sure this parameter is passed.
enable_sparse_data_optim: bool,
has_validation_col: bool,
) -> Tuple[DMatrix, Optional[DMatrix]]:
"""Create DMatrix from spark data partitions. This is not particularly efficient as
we need to convert the pandas series format to numpy then concatenate all the data.
"""Create DMatrix from spark data partitions.
Parameters
----------
iterator :
Pyspark partition iterator.
feature_cols:
A sequence of feature names, used only when rapids plugin is enabled.
gpu_id:
Device ordinal, used when GPU is enabled.
use_qdm :
Whether QuantileDMatrix should be used instead of DMatrix.
kwargs :
Metainfo for DMatrix.
enable_sparse_data_optim :
Whether sparse data should be unwrapped
has_validation:
Whether there's validation data.
Returns
-------
Training DMatrix and an optional validation DMatrix.
"""
# pylint: disable=too-many-locals, too-many-statements
train_data: Dict[str, List[np.ndarray]] = defaultdict(list)
@ -206,16 +238,16 @@ def create_dmatrix_from_partitions( # pylint: disable=too-many-arguments
else:
train_data[name].append(array)
def append_dqm(part: pd.DataFrame, name: str, is_valid: bool) -> None:
"""Preprocessing for DeviceQuantileDMatrix"""
def append_qdm(part: pd.DataFrame, name: str, is_valid: bool) -> None:
"""Preprocessing for QuantileDMatrix."""
nonlocal n_features
if name == alias.data or name in part.columns:
if name == alias.data:
cname = feature_cols
if name == alias.data and feature_cols is not None:
array = part[feature_cols]
else:
cname = name
array = part[name]
array = stack_series(array)
array = part[cname]
if name == alias.data:
if n_features == 0:
n_features = array.shape[1]
@ -228,6 +260,10 @@ def create_dmatrix_from_partitions( # pylint: disable=too-many-arguments
def make(values: Dict[str, List[np.ndarray]], kwargs: Dict[str, Any]) -> DMatrix:
if len(values) == 0:
get_logger("XGBoostPySpark").warning(
"Detected an empty partition in the training data. Consider to enable"
" repartition_random_shuffle"
)
# We must construct an empty DMatrix to bypass the AllReduce
return DMatrix(data=np.empty((0, 0)), **kwargs)
@ -240,32 +276,62 @@ def create_dmatrix_from_partitions( # pylint: disable=too-many-arguments
data=data, label=label, weight=weight, base_margin=margin, qid=qid, **kwargs
)
is_dmatrix = feature_cols is None
if is_dmatrix:
if enable_sparse_data_optim:
append_fn = append_m_sparse
assert "missing" in kwargs and kwargs["missing"] == 0.0
else:
append_fn = append_m
cache_partitions(iterator, append_fn)
if len(train_data) == 0:
get_logger("XGBoostPySpark").warning(
"Detected an empty partition in the training data. "
"Consider to enable repartition_random_shuffle"
)
dtrain = make(train_data, kwargs)
if enable_sparse_data_optim:
append_fn = append_m_sparse
assert "missing" in kwargs and kwargs["missing"] == 0.0
else:
cache_partitions(iterator, append_dqm)
it = PartIter(train_data, gpu_id)
dtrain = DeviceQuantileDMatrix(it, **kwargs)
append_fn = append_m
def split_params() -> Tuple[Dict[str, Any], Dict[str, Union[int, float, bool]]]:
# FIXME(jiamingy): we really need a better way to bridge distributed frameworks
# to XGBoost native interface and prevent scattering parameters like this.
# parameters that are not related to data.
non_data_keys = (
"max_bin",
"missing",
"silent",
"nthread",
"enable_categorical",
)
non_data_params = {}
meta = {}
for k, v in kwargs.items():
if k in non_data_keys:
non_data_params[k] = v
else:
meta[k] = v
return meta, non_data_params
meta, params = split_params()
if feature_cols is not None: # rapidsai plugin
assert gpu_id is not None
assert use_qdm is True
cache_partitions(iterator, append_qdm)
dtrain: DMatrix = make_qdm(train_data, gpu_id, meta, None, params)
elif use_qdm:
cache_partitions(iterator, append_qdm)
dtrain = make_qdm(train_data, gpu_id, meta, None, params)
else:
cache_partitions(iterator, append_fn)
dtrain = make(train_data, kwargs)
# Using has_validation_col here to indicate if there is validation col
# instead of getting it from iterator, since the iterator may be empty
# in some special case. That is to say, we must ensure every worker
# construct DMatrix even there is no any data since we need to ensure every
# construct DMatrix even there is no data since we need to ensure every
# worker do the AllReduce when constructing DMatrix, or else it may hang
# forever.
dvalid = make(valid_data, kwargs) if has_validation_col else None
if has_validation_col:
if use_qdm:
dvalid: Optional[DMatrix] = make_qdm(
valid_data, gpu_id, meta, dtrain, params
)
else:
dvalid = make(valid_data, kwargs) if has_validation_col else None
else:
dvalid = None
if dvalid is not None:
assert dvalid.num_col() == dtrain.num_col()

4
tests/python-gpu/test_gpu_spark/test_data.py
View File

@ -20,4 +20,6 @@ from test_spark.test_data import run_dmatrix_ctor
@pytest.mark.skipif(**tm.no_cudf())
def test_qdm_ctor() -> None:
run_dmatrix_ctor(True)
run_dmatrix_ctor(is_dqm=True, on_gpu=True)
with pytest.raises(AssertionError):
run_dmatrix_ctor(is_dqm=False, on_gpu=True)

5
tests/python-gpu/test_gpu_with_dask/test_gpu_with_dask.py
View File

@ -188,12 +188,9 @@ def run_gpu_hist(
# See note on `ObjFunction::UpdateTreeLeaf`.
update_leaf = dataset.name.endswith("-l1")
if update_leaf and len(history) == 2:
if update_leaf:
assert history[0] + 1e-2 >= history[-1]
return
if update_leaf and len(history) > 2:
assert history[0] >= history[-1]
return
else:
assert tm.non_increasing(history)

17
tests/python/test_spark/test_data.py
View File

@ -37,7 +37,7 @@ def test_stack() -> None:
assert b.shape == (2, 1)
def run_dmatrix_ctor(is_dqm: bool) -> None:
def run_dmatrix_ctor(is_dqm: bool, on_gpu: bool) -> None:
rng = np.random.default_rng(0)
dfs: List[pd.DataFrame] = []
n_features = 16
@ -57,7 +57,7 @@ def run_dmatrix_ctor(is_dqm: bool) -> None:
df = pd.DataFrame(
{alias.label: y, alias.margin: m, alias.weight: w, alias.valid: valid}
)
if is_dqm:
if on_gpu:
for j in range(X.shape[1]):
df[f"feat-{j}"] = pd.Series(X[:, j])
else:
@ -65,14 +65,18 @@ def run_dmatrix_ctor(is_dqm: bool) -> None:
dfs.append(df)
kwargs = {"feature_types": feature_types}
if is_dqm:
if on_gpu:
cols = [f"feat-{i}" for i in range(n_features)]
train_Xy, valid_Xy = create_dmatrix_from_partitions(
iter(dfs), cols, 0, kwargs, False, True
iter(dfs), cols, 0, is_dqm, kwargs, False, True
)
elif is_dqm:
train_Xy, valid_Xy = create_dmatrix_from_partitions(
iter(dfs), None, None, True, kwargs, False, True
)
else:
train_Xy, valid_Xy = create_dmatrix_from_partitions(
iter(dfs), None, None, kwargs, False, True
iter(dfs), None, None, False, kwargs, False, True
)
assert valid_Xy is not None
@ -106,7 +110,8 @@ def run_dmatrix_ctor(is_dqm: bool) -> None:
def test_dmatrix_ctor() -> None:
run_dmatrix_ctor(False)
run_dmatrix_ctor(is_dqm=False, on_gpu=False)
run_dmatrix_ctor(is_dqm=True, on_gpu=False)
def test_read_csr_matrix_from_unwrapped_spark_vec() -> None:

120
tests/python/test_spark/test_spark_local.py
View File

@ -1047,67 +1047,79 @@ class XgboostLocalTest(SparkTestCase):
for row in pred_result:
assert np.isclose(row.prediction, row.expected_prediction, rtol=1e-3)
def test_empty_validation_data(self):
df_train = self.session.createDataFrame(
[
(Vectors.dense(10.1, 11.2, 11.3), 0, False),
(Vectors.dense(1, 1.2, 1.3), 1, False),
(Vectors.dense(14.0, 15.0, 16.0), 0, False),
(Vectors.dense(1.1, 1.2, 1.3), 1, True),
],
["features", "label", "val_col"],
)
classifier = SparkXGBClassifier(
num_workers=2,
min_child_weight=0.0,
reg_alpha=0,
reg_lambda=0,
validation_indicator_col="val_col",
)
model = classifier.fit(df_train)
pred_result = model.transform(df_train).collect()
for row in pred_result:
self.assertEqual(row.prediction, row.label)
def test_empty_validation_data(self) -> None:
for tree_method in [
"hist",
"approx",
]: # pytest.mark conflict with python unittest
df_train = self.session.createDataFrame(
[
(Vectors.dense(10.1, 11.2, 11.3), 0, False),
(Vectors.dense(1, 1.2, 1.3), 1, False),
(Vectors.dense(14.0, 15.0, 16.0), 0, False),
(Vectors.dense(1.1, 1.2, 1.3), 1, True),
],
["features", "label", "val_col"],
)
classifier = SparkXGBClassifier(
num_workers=2,
tree_method=tree_method,
min_child_weight=0.0,
reg_alpha=0,
reg_lambda=0,
validation_indicator_col="val_col",
)
model = classifier.fit(df_train)
pred_result = model.transform(df_train).collect()
for row in pred_result:
self.assertEqual(row.prediction, row.label)
def test_empty_train_data(self):
df_train = self.session.createDataFrame(
[
(Vectors.dense(10.1, 11.2, 11.3), 0, True),
(Vectors.dense(1, 1.2, 1.3), 1, True),
(Vectors.dense(14.0, 15.0, 16.0), 0, True),
(Vectors.dense(1.1, 1.2, 1.3), 1, False),
],
["features", "label", "val_col"],
)
classifier = SparkXGBClassifier(
num_workers=2,
min_child_weight=0.0,
reg_alpha=0,
reg_lambda=0,
validation_indicator_col="val_col",
)
model = classifier.fit(df_train)
pred_result = model.transform(df_train).collect()
for row in pred_result:
self.assertEqual(row.prediction, 1.0)
def test_empty_train_data(self) -> None:
for tree_method in [
"hist",
"approx",
]: # pytest.mark conflict with python unittest
df_train = self.session.createDataFrame(
[
(Vectors.dense(10.1, 11.2, 11.3), 0, True),
(Vectors.dense(1, 1.2, 1.3), 1, True),
(Vectors.dense(14.0, 15.0, 16.0), 0, True),
(Vectors.dense(1.1, 1.2, 1.3), 1, False),
],
["features", "label", "val_col"],
)
classifier = SparkXGBClassifier(
num_workers=2,
min_child_weight=0.0,
reg_alpha=0,
reg_lambda=0,
tree_method=tree_method,
validation_indicator_col="val_col",
)
model = classifier.fit(df_train)
pred_result = model.transform(df_train).collect()
for row in pred_result:
assert row.prediction == 1.0
def test_empty_partition(self):
# raw_df.repartition(4) will result int severe data skew, actually,
# there is no any data in reducer partition 1, reducer partition 2
# see https://github.com/dmlc/xgboost/issues/8221
raw_df = self.session.range(0, 100, 1, 50).withColumn(
"label", spark_sql_func.when(spark_sql_func.rand(1) > 0.5, 1).otherwise(0)
)
vector_assembler = (
VectorAssembler().setInputCols(["id"]).setOutputCol("features")
)
data_trans = vector_assembler.setHandleInvalid("keep").transform(raw_df)
data_trans.show(100)
for tree_method in [
"hist",
"approx",
]: # pytest.mark conflict with python unittest
raw_df = self.session.range(0, 100, 1, 50).withColumn(
"label",
spark_sql_func.when(spark_sql_func.rand(1) > 0.5, 1).otherwise(0),
)
vector_assembler = (
VectorAssembler().setInputCols(["id"]).setOutputCol("features")
)
data_trans = vector_assembler.setHandleInvalid("keep").transform(raw_df)
classifier = SparkXGBClassifier(
num_workers=4,
)
classifier.fit(data_trans)
classifier = SparkXGBClassifier(num_workers=4, tree_method=tree_method)
classifier.fit(data_trans)
def test_early_stop_param_validation(self):
classifier = SparkXGBClassifier(early_stopping_rounds=1)