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Fixes Array API with onnxruntime#3

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sdpython merged 10 commits intomainfromortb
Jun 5, 2023
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7 changes: 7 additions & 0 deletionsCHANGELOGS.rst
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@@ -0,0 +1,7 @@
Change Logs
===========

0.2.0
+++++

* :pr:`3`: fixes Array API with onnxruntime
7 changes: 6 additions & 1 deletion_doc/conf.py
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Expand Up@@ -57,6 +57,11 @@
}

epkg_dictionary = {
"Array API": "https://data-apis.org/array-api/",
"ArrayAPI": (
"https://data-apis.org/array-api/",
("2022.12/API_specification/generated/array_api.{0}.html", 1),
),
"DOT": "https://graphviz.org/doc/info/lang.html",
"JIT": "https://en.wikipedia.org/wiki/Just-in-time_compilation",
"onnx": "https://onnx.ai/onnx/",
Expand All@@ -65,7 +70,7 @@
"numpy": "https://numpy.org/",
"numba": "https://numba.pydata.org/",
"onnx-array-api": (
"http://www.xavierdupre.fr/app/" "onnx-array-api/helpsphinx/index.html"
"http://www.xavierdupre.fr/app/onnx-array-api/helpsphinx/index.html"
),
"pyinstrument": "https://github.com/joerick/pyinstrument",
"python": "https://www.python.org/",
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1 change: 1 addition & 0 deletions_doc/index.rst
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Expand Up@@ -34,6 +34,7 @@ well as to execute it.
tutorial/index
api/index
auto_examples/index
../CHANGELOGS

Sources available on
`github/onnx-array-api <https://github.com/sdpython/onnx-array-api>`_,
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19 changes: 6 additions & 13 deletions_unittests/ut_npx/test_sklearn_array_api.py
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@@ -1,7 +1,8 @@
import unittest
import numpy as np
from packaging.version import Version
from onnx.defs import onnx_opset_version
from sklearn import config_context
from sklearn import config_context, __version__ as sklearn_version
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from onnx_array_api.ext_test_case import ExtTestCase
from onnx_array_api.npx.npx_numpy_tensors import EagerNumpyTensor
Expand All@@ -10,23 +11,15 @@
DEFAULT_OPSET = onnx_opset_version()


def take(self, X, indices, *, axis):
# Overwritting method take as it is using iterators.
# When array_api supports `take` we can use this directly
# https://github.com/data-apis/array-api/issues/177
X_np = self._namespace.take(X, indices, axis=axis)
return self._namespace.asarray(X_np)


class TestSklearnArrayAPI(ExtTestCase):
@unittest.skipIf(
Version(sklearn_version) <= Version("1.2.2"),
reason="reshape ArrayAPI not followed",
)
def test_sklearn_array_api_linear_discriminant(self):
from sklearn.utils._array_api import _ArrayAPIWrapper

_ArrayAPIWrapper.take = take
X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
y = np.array([1, 1, 1, 2, 2, 2])
ana = LinearDiscriminantAnalysis()
ana = LinearDiscriminantAnalysis()
ana.fit(X, y)
expected = ana.predict(X)

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39 changes: 39 additions & 0 deletions_unittests/ut_ort/test_sklearn_array_api_ort.py
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@@ -0,0 +1,39 @@
import unittest
import numpy as np
from packaging.version import Version
from onnx.defs import onnx_opset_version
from sklearn import config_context, __version__ as sklearn_version
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from onnx_array_api.ext_test_case import ExtTestCase
from onnx_array_api.ort.ort_tensors import EagerOrtTensor, OrtTensor


DEFAULT_OPSET = onnx_opset_version()


class TestSklearnArrayAPIOrt(ExtTestCase):
@unittest.skipIf(
Version(sklearn_version) <= Version("1.2.2"),
reason="reshape ArrayAPI not followed",
)
def test_sklearn_array_api_linear_discriminant(self):
X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
y = np.array([1, 1, 1, 2, 2, 2])
ana = LinearDiscriminantAnalysis()
ana.fit(X, y)
expected = ana.predict(X)

new_x = EagerOrtTensor(OrtTensor.from_array(X))
self.assertEqual(new_x.device_name, "Cpu")
self.assertStartsWith(
"EagerOrtTensor(OrtTensor.from_array(array([[", repr(new_x)
)
with config_context(array_api_dispatch=True):
got = ana.predict(new_x)
self.assertEqualArray(expected, got.numpy())


if __name__ == "__main__":
# import logging
# logging.basicConfig(level=logging.DEBUG)
unittest.main(verbosity=2)
47 changes: 47 additions & 0 deletionsazure-pipelines.yml
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Expand Up@@ -43,6 +43,53 @@ jobs:
artifactName: 'wheel-linux-wheel-$(python.version)'
targetPath: 'dist'

- job: 'TestLinuxNightly'
pool:
vmImage: 'ubuntu-latest'
strategy:
matrix:
Python310-Linux:
python.version: '3.11'
maxParallel: 3

steps:
- task: UsePythonVersion@0
inputs:
versionSpec: '$(python.version)'
architecture: 'x64'
- script: sudo apt-get update
displayName: 'AptGet Update'
- script: sudo apt-get install -y pandoc
displayName: 'Install Pandoc'
- script: sudo apt-get install -y inkscape
displayName: 'Install Inkscape'
- script: sudo apt-get install -y graphviz
displayName: 'Install Graphviz'
- script: python -m pip install --upgrade pip setuptools wheel
displayName: 'Install tools'
- script: pip install -r requirements.txt
displayName: 'Install Requirements'
- script: pip install -r requirements-dev.txt
displayName: 'Install Requirements dev'
- script: pip uninstall -y scikit-learn
displayName: 'Uninstall scikit-learn'
- script: pip install --pre --extra-index https://pypi.anaconda.org/scipy-wheels-nightly/simple scikit-learn
displayName: 'Install scikit-learn nightly'
- script: pip install onnxmltools --no-deps
displayName: 'Install onnxmltools'
- script: |
ruff .
displayName: 'Ruff'
- script: |
rstcheck -r ./_doc ./onnx_array_api
displayName: 'rstcheck'
- script: |
black --diff .
displayName: 'Black'
- script: |
python -m pytest -v
displayName: 'Runs Unit Tests'

- job: 'TestLinux'
pool:
vmImage: 'ubuntu-latest'
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20 changes: 16 additions & 4 deletionsonnx_array_api/npx/npx_array_api.py
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@@ -1,22 +1,34 @@
from typing import Any
from typing import Any, Optional

import numpy as np

from .npx_types import OptParType, ParType, TupleType


class ArrayApiError(RuntimeError):
"""
Raised when a function is not supported by the :epkg:`Array API`.
"""

pass


class ArrayApi:
"""
List of supported method by a tensor.
"""

def __array_namespace__(self):
def __array_namespace__(self, api_version: Optional[str] = None):
"""
Returns the module holding all the available functions.
"""
from onnx_array_api.npx import npx_functions
if api_version is None or api_version == "2022.12":
from onnx_array_api.npx import npx_functions

return npx_functions
return npx_functions
raise ValueError(
f"Unable to return an implementation for api_version={api_version!r}."
)

def generic_method(self, method_name, *args: Any, **kwargs: Any) -> Any:
raise NotImplementedError(
Expand Down
7 changes: 7 additions & 0 deletionsonnx_array_api/npx/npx_core_api.py
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Expand Up@@ -252,3 +252,10 @@ def npxapi_inline(fn):
to call.
"""
return _xapi(fn, inline=True)


def npxapi_no_inline(fn):
"""
Functions decorated with this decorator are not converted into ONNX.
"""
return fn
33 changes: 30 additions & 3 deletionsonnx_array_api/npx/npx_functions.py
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@@ -1,14 +1,16 @@
from typing import Any, Optional, Tuple, Union

import array_api_compat.numpy as np_array_api
import numpy as np
from onnx import FunctionProto, ModelProto, NodeProto, TensorProto
from onnx.helper import np_dtype_to_tensor_dtype
from onnx.numpy_helper import from_array

from .npx_constants import FUNCTION_DOMAIN
from .npx_core_api import cst, make_tuple, npxapi_inline, var
from .npx_core_api import cst, make_tuple, npxapi_inline,npxapi_no_inline,var
from .npx_tensors import ArrayApi
from .npx_types import (
DType,
ElemType,
OptParType,
ParType,
Expand DownExpand Up@@ -397,6 +399,17 @@ def identity(n: ParType[int], dtype=None) -> TensorType[ElemType.numerics, "T"]:
return v


@npxapi_no_inline
def isdtype(
dtype: DType, kind: Union[DType, str, Tuple[Union[DType, str], ...]]
) -> bool:
"""
See :epkg:`ArrayAPI:isdtype`.
This function is not converted into an onnx graph.
"""
return np_array_api.isdtype(dtype, kind)


@npxapi_inline
def isnan(x: TensorType[ElemType.numerics, "T"]) -> TensorType[ElemType.bool_, "T"]:
"See :func:`numpy.isnan`."
Expand DownExpand Up@@ -460,9 +473,23 @@ def relu(x: TensorType[ElemType.numerics, "T"]) -> TensorType[ElemType.numerics,

@npxapi_inline
def reshape(
x: TensorType[ElemType.numerics, "T"], shape: TensorType[ElemType.int64, "I"]
x: TensorType[ElemType.numerics, "T"],
shape: TensorType[ElemType.int64, "I", (None,)],
) -> TensorType[ElemType.numerics, "T"]:
"See :func:`numpy.reshape`."
"""
See :func:`numpy.reshape`.

.. warning::

Numpy definition is tricky because onnxruntime does not handle well
dimensions with an undefined number of dimensions.
However the array API defines a more stricly signature for
`reshape <https://data-apis.org/array-api/2022.12/
API_specification/generated/array_api.reshape.html>`_.
:epkg:`scikit-learn` updated its code to follow the Array API in
`PR 26030 ENH Forces shape to be tuple when using Array API's reshape
<https://github.com/scikit-learn/scikit-learn/pull/26030>`_.
"""
if isinstance(shape, int):
shape = cst(np.array([shape], dtype=np.int64))
shape_reshaped = var(shape, cst(np.array([-1], dtype=np.int64)), op="Reshape")
Expand Down
17 changes: 17 additions & 0 deletionsonnx_array_api/npx/npx_graph_builder.py
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Expand Up@@ -798,6 +798,23 @@ def to_onnx(
node_inputs.append(input_name)
continue

if isinstance(i, tuple) and all(map(lambda x: isinstance(x, int), i)):
ai = np.array(list(i), dtype=np.int64)
c = Cst(ai)
input_name = self._unique(var._prefix)
self._id_vars[id(i), index] = input_name
self._id_vars[id(c), index] = input_name
self.make_node(
"Constant",
[],
[input_name],
value=from_array(ai),
opset=self.target_opsets[""],
)
self.onnx_names_[input_name] = c
node_inputs.append(input_name)
continue

raise NotImplementedError(
f"Unexpected type {type(i)} for node={domop}."
)
Expand Down
32 changes: 23 additions & 9 deletionsonnx_array_api/npx/npx_jit_eager.py
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Expand Up@@ -367,12 +367,18 @@ def jit_call(self, *values, **kwargs):
try:
res = fct.run(*values)
except Exception as e:
from ..plotting.text_plot import onnx_simple_text_plot

text = onnx_simple_text_plot(self.onxs[key])
raise RuntimeError(
f"Unable to run function for key={key!r}, "
f"types={[type(x) for x in values]}, "
f"dtypes={[x.dtype for x in values]}, "
f"shapes={[x.shape for x in values]}, "
f"kwargs={kwargs}, "
f"self.input_to_kwargs_={self.input_to_kwargs_}, "
f"onnx={self.onxs[key]}."
f"f={self.f} from module {self.f.__module__!r} "
f"onnx=\n---\n{text}\n---\n{self.onxs[key]}"
) from e
self.info("-", "jit_call")
return res
Expand DownExpand Up@@ -492,14 +498,22 @@ def _preprocess_constants(self, *args):
elif isinstance(n, Cst):
new_args.append(self.tensor_class(n.inputs[0]))
modified = True
# elif isinstance(n, tuple):
# if any(map(lambda t: isinstance(t, Var), n)):
# raise TypeError(
# f"Unexpected types in tuple "
# f"({[type(t) for t in n]}) for input {i}, "
# f"function {self.f} from module {self.f.__module__!r}."
# )
# new_args.append(n)
elif isinstance(n, tuple):
if all(map(lambda x: isinstance(x, int), n)):
new_args.append(
self.tensor_class(np.array(list(n), dtype=np.int64))
)
elif any(map(lambda t: isinstance(t, Var), n)):
raise TypeError(
f"Unexpected types in tuple "
f"({[type(t) for t in n]}) for input {i}, "
f"function {self.f} from module {self.f.__module__!r}."
)
else:
raise TypeError(
f"Unsupported tuple {n!r} for input {i}, "
f"function {self.f} from module {self.f.__module__!r}."
)
elif isinstance(n, np.ndarray):
new_args.append(self.tensor_class(n))
modified = True
Expand Down
6 changes: 0 additions & 6 deletionsonnx_array_api/npx/npx_numpy_tensors.py
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Expand Up@@ -162,12 +162,6 @@ def get_ir_version(cls, ir_version):
"""
return ir_version

def const_cast(self, to: Any = None) -> "EagerTensor":
"""
Casts a constant without any ONNX conversion.
"""
return self.__class__(self._tensor.astype(to))

# The class should support whatever Var supports.
# This part is not yet complete.

Expand Down
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