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Support dmatrix construction from cupy array (#5206)

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This commit is contained in:
Rory Mitchell
2020-01-22 13:15:27 +13:00
committed by GitHub
parent 2a071cebc5
commit 9c56480c61
19 changed files with 522 additions and 158 deletions
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1
python-package/xgboost/compat.py
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@@ -115,6 +115,7 @@ except ImportError:
DT_INSTALLED = False
# cudf
try:
from cudf import DataFrame as CUDF_DataFrame
from cudf import Series as CUDF_Series

116
python-package/xgboost/core.py
View File

@@ -24,7 +24,6 @@ from .compat import (
os_fspath, os_PathLike)
from .libpath import find_lib_path
# c_bst_ulong corresponds to bst_ulong defined in xgboost/c_api.h
c_bst_ulong = ctypes.c_uint64
@@ -41,6 +40,7 @@ class EarlyStopException(Exception):
best_iteration : int
The best iteration stopped.
"""
def __init__(self, best_iteration):
super(EarlyStopException, self).__init__()
self.best_iteration = best_iteration
@@ -231,50 +231,16 @@ def c_array(ctype, values):
return (ctype * len(values))(*values)
def _use_columnar_initializer(data):
"""Whether should we use columnar format initializer (pass data in as json
string). Currently cudf is the only valid option. For other dataframe
types, use their sepcific API instead.
"""
if CUDF_INSTALLED and (isinstance(data, (CUDF_DataFrame, CUDF_Series))):
return True
return False
def _extract_interface_from_cudf_series(data):
"""This returns the array interface from the cudf series. This function
should be upstreamed to cudf.
"""
interface = data.__cuda_array_interface__
if data.has_null_mask:
interface['mask'] = interface['mask'].__cuda_array_interface__
return interface
def _extract_interface_from_cudf(df):
"""This function should be upstreamed to cudf."""
if not _use_columnar_initializer(df):
raise ValueError('Only cudf is supported for initializing as json ' +
'columnar format. For other libraries please ' +
'refer to specific API.')
array_interfaces = []
if isinstance(df, CUDF_DataFrame):
for col in df.columns:
array_interfaces.append(
_extract_interface_from_cudf_series(df[col]))
else:
array_interfaces.append(_extract_interface_from_cudf_series(df))
interfaces = bytes(json.dumps(array_interfaces, indent=2), 'utf-8')
return interfaces
PANDAS_DTYPE_MAPPER = {'int8': 'int', 'int16': 'int', 'int32': 'int', 'int64': 'int',
'uint8': 'int', 'uint16': 'int', 'uint32': 'int', 'uint64': 'int',
'float16': 'float', 'float32': 'float', 'float64': 'float',
'bool': 'i'}
# Either object has cuda array interface or contains columns with interfaces
def _has_cuda_array_interface(data):
return hasattr(data, '__cuda_array_interface__') or (
CUDF_INSTALLED and isinstance(data, CUDF_DataFrame))
def _maybe_pandas_data(data, feature_names, feature_types):
"""Extract internal data from pd.DataFrame for DMatrix data"""
@@ -433,7 +399,7 @@ class DMatrix(object):
"""Parameters
----------
data : os.PathLike/string/numpy.array/scipy.sparse/pd.DataFrame/
dt.Frame/cudf.DataFrame
dt.Frame/cudf.DataFrame/cupy.array
Data source of DMatrix.
When data is string or os.PathLike type, it represents the path
libsvm format txt file, csv file (by specifying uri parameter
@@ -500,8 +466,10 @@ class DMatrix(object):
self._init_from_npy2d(data, missing, nthread)
elif isinstance(data, DataTable):
self._init_from_dt(data, nthread)
elif _use_columnar_initializer(data):
self._init_from_columnar(data, missing)
elif hasattr(data, "__cuda_array_interface__"):
self._init_from_array_interface(data, missing, nthread)
elif CUDF_INSTALLED and isinstance(data, CUDF_DataFrame):
self._init_from_array_interface_columns(data, missing, nthread)
else:
try:
csr = scipy.sparse.csr_matrix(data)
@@ -596,7 +564,8 @@ class DMatrix(object):
ptrs[icol] = ctypes.c_void_p(ptr)
else:
# datatable<=0.8.0
from datatable.internal import frame_column_data_r # pylint: disable=no-name-in-module,import-error
from datatable.internal import \
frame_column_data_r # pylint: disable=no-name-in-module,import-error
for icol in range(data.ncols):
ptrs[icol] = frame_column_data_r(data, icol)
@@ -614,16 +583,38 @@ class DMatrix(object):
nthread))
self.handle = handle
def _init_from_columnar(self, df, missing):
def _init_from_array_interface_columns(self, df, missing, nthread):
"""Initialize DMatrix from columnar memory format."""
interfaces = _extract_interface_from_cudf(df)
interfaces = []
for col in df:
interface = df[col].__cuda_array_interface__
if 'mask' in interface:
interface['mask'] = interface['mask'].__cuda_array_interface__
interfaces.append(interface)
handle = ctypes.c_void_p()
has_missing = missing is not None
missing = missing if has_missing else np.nan
missing = missing if missing is not None else np.nan
nthread = nthread if nthread is not None else 1
interfaces_str = bytes(json.dumps(interfaces, indent=2), 'utf-8')
_check_call(
_LIB.XGDMatrixCreateFromArrayInterfaces(
interfaces, ctypes.c_int32(has_missing),
ctypes.c_float(missing), ctypes.byref(handle)))
_LIB.XGDMatrixCreateFromArrayInterfaceColumns(
interfaces_str,
ctypes.c_float(missing), ctypes.c_int(nthread), ctypes.byref(handle)))
self.handle = handle
def _init_from_array_interface(self, data, missing, nthread):
"""Initialize DMatrix from cupy ndarray."""
interface = data.__cuda_array_interface__
if 'mask' in interface:
interface['mask'] = interface['mask'].__cuda_array_interface__
interface_str = bytes(json.dumps(interface, indent=2), 'utf-8')
handle = ctypes.c_void_p()
missing = missing if missing is not None else np.nan
nthread = nthread if nthread is not None else 1
_check_call(
_LIB.XGDMatrixCreateFromArrayInterface(
interface_str,
ctypes.c_float(missing), ctypes.c_int(nthread), ctypes.byref(handle)))
self.handle = handle
def __del__(self):
@@ -694,11 +685,18 @@ class DMatrix(object):
c_bst_ulong(len(data))))
def set_interface_info(self, field, data):
"""Set info type peoperty into DMatrix."""
interfaces = _extract_interface_from_cudf(data)
"""Set info type property into DMatrix."""
# If we are passed a dataframe, extract the series
if isinstance(data, CUDF_DataFrame):
if len(data.columns) != 1:
raise ValueError('Expecting meta-info to contain a single column')
data = data[data.columns[0]]
interface = bytes(json.dumps([data.__cuda_array_interface__], indent=2), 'utf-8')
_check_call(_LIB.XGDMatrixSetInfoFromInterface(self.handle,
c_str(field),
interfaces))
interface))
def set_float_info_npy2d(self, field, data):
"""Set float type property into the DMatrix
@@ -779,7 +777,7 @@ class DMatrix(object):
"""
if isinstance(label, np.ndarray):
self.set_label_npy2d(label)
elif _use_columnar_initializer(label):
elif _has_cuda_array_interface(label):
self.set_interface_info('label', label)
else:
self.set_float_info('label', label)
@@ -812,7 +810,7 @@ class DMatrix(object):
"""
if isinstance(weight, np.ndarray):
self.set_weight_npy2d(weight)
elif _use_columnar_initializer(weight):
elif _has_cuda_array_interface(weight):
self.set_interface_info('weight', weight)
else:
self.set_float_info('weight', weight)
@@ -849,7 +847,7 @@ class DMatrix(object):
margin: array like
Prediction margin of each datapoint
"""
if _use_columnar_initializer(margin):
if _has_cuda_array_interface(margin):
self.set_interface_info('base_margin', margin)
else:
self.set_float_info('base_margin', margin)
@@ -862,7 +860,7 @@ class DMatrix(object):
group : array like
Group size of each group
"""
if _use_columnar_initializer(group):
if _has_cuda_array_interface(group):
self.set_interface_info('group', group)
else:
self.set_uint_info('group', group)
@@ -1073,7 +1071,7 @@ class Booster(object):
ctypes.byref(self.handle)))
if isinstance(params, dict) and \
'validate_parameters' not in params.keys():
'validate_parameters' not in params.keys():
params['validate_parameters'] = 1
self.set_param(params or {})
if (params is not None) and ('booster' in params):