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Add array API inspection utilities to dpctl.tensor #1469

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2 changes: 1 addition & 1 deletion .github/workflows/conda-package.yml
Original file line number Diff line number Diff line change
Expand Up @@ -666,7 +666,7 @@ jobs:
python -c "import dpctl; dpctl.lsplatform()"
export ARRAY_API_TESTS_MODULE=dpctl.tensor
cd /home/runner/work/array-api-tests
pytest --ci --json-report --json-report-file=$FILE array_api_tests/ || true
pytest --json-report --json-report-file=$FILE array_api_tests/ || true
- name: Set Github environment variables
shell: bash -l {0}
run: |
Expand Down
3 changes: 3 additions & 0 deletions dpctl/tensor/__init__.py
Original file line number Diff line number Diff line change
Expand Up @@ -93,6 +93,7 @@
from dpctl.tensor._usmarray import usm_ndarray
from dpctl.tensor._utility_functions import all, any

from ._array_api import __array_api_version__, __array_namespace_info__
from ._clip import clip
from ._constants import e, inf, nan, newaxis, pi
from ._elementwise_funcs import (
Expand Down Expand Up @@ -335,4 +336,6 @@
"clip",
"logsumexp",
"reduce_hypot",
"__array_api_version__",
"__array_namespace_info__",
]
207 changes: 207 additions & 0 deletions dpctl/tensor/_array_api.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,207 @@
# Data Parallel Control (dpctl)
#
# Copyright 2020-2023 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import dpctl
import dpctl.tensor as dpt
from dpctl.tensor._tensor_impl import (
default_device_complex_type,
default_device_fp_type,
default_device_index_type,
default_device_int_type,
)


def _isdtype_impl(dtype, kind):
if isinstance(kind, str):
if kind == "bool":
return dtype.kind == "b"
elif kind == "signed integer":
return dtype.kind == "i"
elif kind == "unsigned integer":
return dtype.kind == "u"
elif kind == "integral":
return dtype.kind in "iu"
elif kind == "real floating":
return dtype.kind == "f"
elif kind == "complex floating":
return dtype.kind == "c"
elif kind == "numeric":
return dtype.kind in "iufc"
else:
raise ValueError(f"Unrecognized data type kind: {kind}")

elif isinstance(kind, tuple):
return any(_isdtype_impl(dtype, k) for k in kind)
else:
raise TypeError(f"Unsupported data type kind: {kind}")


__array_api_version__ = "2022.12"


class Info:
"""
namespace returned by `__array_namespace_info__()`
"""

def __init__(self):
self._capabilities = {
"boolean_indexing": True,
"data_dependent_shapes": True,
}
self._all_dtypes = {
"bool": dpt.bool,
"float32": dpt.float32,
"float64": dpt.float64,
"complex64": dpt.complex64,
"complex128": dpt.complex128,
"int8": dpt.int8,
"int16": dpt.int16,
"int32": dpt.int32,
"int64": dpt.int64,
"uint8": dpt.uint8,
"uint16": dpt.uint16,
"uint32": dpt.uint32,
"uint64": dpt.uint64,
}

def capabilities(self):
"""
Returns a dictionary of `dpctl`'s capabilities.

Returns:
dict:
dictionary of `dpctl`'s capabilities
- `boolean_indexing`: bool
- `data_dependent_shapes`: bool
"""
return self._capabilities.copy()

def default_device(self):
"""
Returns the default SYCL device.
"""
return dpctl.select_default_device()

def default_dtypes(self, device=None):
"""
Returns a dictionary of default data types for `device`.

Args:
device (Optional[dpctl.SyclDevice, dpctl.SyclQueue,
dpctl.tensor.Device]):
array API concept of device used in getting default data types.
`device` can be `None` (in which case the default device is
used), an instance of :class:`dpctl.SyclDevice` corresponding
to a non-partitioned SYCL device, an instance of
:class:`dpctl.SyclQueue`, or a `Device` object returned by
:attr:`dpctl.tensor.usm_array.device`. Default: `None`.

Returns:
dict:
a dictionary of default data types for `device`
- `real floating`: dtype
- `complex floating`: dtype
- `integral`: dtype
- `indexing`: dtype
"""
if device is None:
device = dpctl.select_default_device()
elif isinstance(device, dpt.Device):
device = device.sycl_device
return {
"real floating": dpt.dtype(default_device_fp_type(device)),
"complex floating": dpt.dtype(default_device_complex_type(device)),
"integral": dpt.dtype(default_device_int_type(device)),
"indexing": dpt.dtype(default_device_index_type(device)),
}

def dtypes(self, device=None, kind=None):
"""
Returns a dictionary of all Array API data types of a specified `kind`
supported by `device`

This dictionary only includes data types supported by the array API.

See [array API](array_api).

[array_api]: https://data-apis.org/array-api/latest/

Args:
device (Optional[dpctl.SyclDevice, dpctl.SyclQueue,
dpctl.tensor.Device, str]):
array API concept of device used in getting default data types.
`device` can be `None` (in which case the default device is
used), an instance of :class:`dpctl.SyclDevice` corresponding
to a non-partitioned SYCL device, an instance of
:class:`dpctl.SyclQueue`, or a `Device` object returned by
:attr:`dpctl.tensor.usm_array.device`. Default: `None`.

kind (Optional[str, Tuple[str, ...]]):
data type kind.
- if `kind` is `None`, returns a dictionary of all data types
supported by `device`
- if `kind` is a string, returns a dictionary containing the
data types belonging to the data type kind specified.
Supports:
- "bool"
- "signed integer"
- "unsigned integer"
- "integral"
- "real floating"
- "complex floating"
- "numeric"
- if `kind` is a tuple, the tuple represents a union of `kind`
strings, and returns a dictionary containing data types
corresponding to the-specified union.
Default: `None`.

Returns:
dict:
a dictionary of the supported data types of the specified `kind`
"""
if device is None:
device = dpctl.select_default_device()
elif isinstance(device, dpt.Device):
device = device.sycl_device
_fp64 = device.has_aspect_fp64
if kind is None:
return {
key: val
for key, val in self._all_dtypes.items()
if (key != "float64" or _fp64)
}
else:
return {
key: val
for key, val in self._all_dtypes.items()
if (key != "float64" or _fp64) and _isdtype_impl(val, kind)
}

def devices(self):
"""
Returns a list of supported devices.
"""
return dpctl.get_devices()


def __array_namespace_info__():
"""__array_namespace_info__()

Returns a namespace with Array API namespace inspection utilities.

"""
return Info()
163 changes: 163 additions & 0 deletions dpctl/tests/test_tensor_array_api_inspection.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,163 @@
# Data Parallel Control (dpctl)
#
# Copyright 2020-2023 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import pytest

import dpctl
import dpctl.tensor as dpt
from dpctl.tensor._tensor_impl import (
default_device_complex_type,
default_device_fp_type,
default_device_index_type,
default_device_int_type,
)

_dtypes_no_fp16_fp64 = {
"bool": dpt.bool,
"float32": dpt.float32,
"complex64": dpt.complex64,
"complex128": dpt.complex128,
"int8": dpt.int8,
"int16": dpt.int16,
"int32": dpt.int32,
"int64": dpt.int64,
"uint8": dpt.uint8,
"uint16": dpt.uint16,
"uint32": dpt.uint32,
"uint64": dpt.uint64,
}


class MockDevice:
def __init__(self, fp16: bool, fp64: bool):
self.has_aspect_fp16 = fp16
self.has_aspect_fp64 = fp64


def test_array_api_inspection_methods():
info = dpt.__array_namespace_info__()
assert info.capabilities()
assert info.default_device()
assert info.default_dtypes()
assert info.devices()
assert info.dtypes()


def test_array_api_inspection_default_device():
assert (
dpt.__array_namespace_info__().default_device()
== dpctl.select_default_device()
)


def test_array_api_inspection_devices():
devices1 = dpt.__array_namespace_info__().devices()
devices2 = dpctl.get_devices()
assert len(devices1) == len(devices2)
assert devices1 == devices2


def test_array_api_inspection_capabilities():
capabilities = dpt.__array_namespace_info__().capabilities()
assert capabilities["boolean_indexing"]
assert capabilities["data_dependent_shapes"]


def test_array_api_inspection_default_dtypes():
dev = dpctl.select_default_device()

int_dt = default_device_int_type(dev)
ind_dt = default_device_index_type(dev)
fp_dt = default_device_fp_type(dev)
cm_dt = default_device_complex_type(dev)

info = dpt.__array_namespace_info__()
default_dts_nodev = info.default_dtypes()
default_dts_dev = info.default_dtypes(dev)

assert (
int_dt == default_dts_nodev["integral"] == default_dts_dev["integral"]
)
assert (
ind_dt == default_dts_nodev["indexing"] == default_dts_dev["indexing"]
)
assert (
fp_dt
== default_dts_nodev["real floating"]
== default_dts_dev["real floating"]
)
assert (
cm_dt
== default_dts_nodev["complex floating"]
== default_dts_dev["complex floating"]
)


def test_array_api_inspection_default_device_dtypes():
dev = dpctl.select_default_device()
dtypes = _dtypes_no_fp16_fp64.copy()
if dev.has_aspect_fp64:
dtypes["float64"] = dpt.float64

assert dtypes == dpt.__array_namespace_info__().dtypes()


@pytest.mark.parametrize("fp16", [True, False])
@pytest.mark.parametrize("fp64", [True, False])
def test_array_api_inspection_device_dtypes(fp16, fp64):
dev = MockDevice(fp16, fp64)
dtypes = _dtypes_no_fp16_fp64.copy()
if fp64:
dtypes["float64"] = dpt.float64

assert dtypes == dpt.__array_namespace_info__().dtypes(device=dev)


def test_array_api_inspection_dtype_kind():
info = dpt.__array_namespace_info__()

f_dtypes = info.dtypes(kind="real floating")
assert all([_dt[1].kind == "f" for _dt in f_dtypes.items()])

i_dtypes = info.dtypes(kind="signed integer")
assert all([_dt[1].kind == "i" for _dt in i_dtypes.items()])

u_dtypes = info.dtypes(kind="unsigned integer")
assert all([_dt[1].kind == "u" for _dt in u_dtypes.items()])

ui_dtypes = info.dtypes(kind="unsigned integer")
assert all([_dt[1].kind in "ui" for _dt in ui_dtypes.items()])

c_dtypes = info.dtypes(kind="complex floating")
assert all([_dt[1].kind == "c" for _dt in c_dtypes.items()])

assert info.dtypes(kind="bool") == {"bool": dpt.bool}

_signed_ints = {
"int8": dpt.int8,
"int16": dpt.int16,
"int32": dpt.int32,
"int64": dpt.int64,
}
assert (
info.dtypes(kind=("signed integer", "signed integer")) == _signed_ints
)
assert (
info.dtypes(
kind=("integral", "bool", "real floating", "complex floating")
)
== info.dtypes()
)
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