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Add function Eye to the Array API#29

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sdpython merged 4 commits intomainfromasa
Jul 2, 2023
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4 changes: 1 addition & 3 deletions_unittests/onnx-numpy-skips.txt
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@@ -1,10 +1,8 @@
# API failures
# see https://github.com/data-apis/array-api-tests/blob/master/numpy-skips.txt
array_api_tests/test_creation_functions.py::test_asarray_scalars
array_api_tests/test_creation_functions.py::test_arange
# uses __setitem__
array_api_tests/test_creation_functions.py::test_asarray_arrays
array_api_tests/test_creation_functions.py::test_empty
array_api_tests/test_creation_functions.py::test_empty_like
array_api_tests/test_creation_functions.py::test_eye
array_api_tests/test_creation_functions.py::test_linspace
array_api_tests/test_creation_functions.py::test_meshgrid
2 changes: 1 addition & 1 deletion_unittests/test_array_api.sh
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@@ -1,4 +1,4 @@
export ARRAY_API_TESTS_MODULE=onnx_array_api.array_api.onnx_numpy
pytest -v -rxXfE ../array-api-tests/array_api_tests/test_creation_functions.py::test_zeros_like || exit 1
pytest -v -rxXfE ../array-api-tests/array_api_tests/test_creation_functions.py::test_eye || exit 1
# pytest ../array-api-tests/array_api_tests/test_creation_functions.py --help
pytest -v -rxXfE ../array-api-tests/array_api_tests/test_creation_functions.py --hypothesis-explain --skips-file=_unittests/onnx-numpy-skips.txt || exit 1
72 changes: 72 additions & 0 deletions_unittests/ut_array_api/test_hypothesis_array_api.py
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Expand Up@@ -39,6 +39,7 @@ def sh(x):

class TestHypothesisArraysApis(ExtTestCase):
MAX_ARRAY_SIZE = 10000
SQRT_MAX_ARRAY_SIZE = int(10000**0.5)
VERSION = "2021.12"

@classmethod
Expand DownExpand Up@@ -138,9 +139,80 @@ def fctonx(x, kw):
fctonx()
self.assertEqual(len(args_onxp), len(args_np))

def test_square_sizes_strategies(self):
dtypes = dict(
integer_dtypes=self.xps.integer_dtypes(),
uinteger_dtypes=self.xps.unsigned_integer_dtypes(),
floating_dtypes=self.xps.floating_dtypes(),
numeric_dtypes=self.xps.numeric_dtypes(),
boolean_dtypes=self.xps.boolean_dtypes(),
scalar_dtypes=self.xps.scalar_dtypes(),
)

dtypes_onnx = dict(
integer_dtypes=self.onxps.integer_dtypes(),
uinteger_dtypes=self.onxps.unsigned_integer_dtypes(),
floating_dtypes=self.onxps.floating_dtypes(),
numeric_dtypes=self.onxps.numeric_dtypes(),
boolean_dtypes=self.onxps.boolean_dtypes(),
scalar_dtypes=self.onxps.scalar_dtypes(),
)

for k, vnp in dtypes.items():
vonxp = dtypes_onnx[k]
anp = self.xps.arrays(dtype=vnp, shape=shapes(self.xps))
aonxp = self.onxps.arrays(dtype=vonxp, shape=shapes(self.onxps))
self.assertNotEmpty(anp)
self.assertNotEmpty(aonxp)

args_np = []

kws = array_api_kwargs(k=strategies.integers(), dtype=self.xps.numeric_dtypes())
sqrt_sizes = strategies.integers(0, self.SQRT_MAX_ARRAY_SIZE)
ncs = strategies.none() | sqrt_sizes

@given(n_rows=sqrt_sizes, n_cols=ncs, kw=kws)
def fctnp(n_rows, n_cols, kw):
base = np.asarray(0)
e = np.eye(n_rows, n_cols)
self.assertNotEmpty(e.dtype)
self.assertIsInstance(e, base.__class__)
e = np.eye(n_rows, n_cols, **kw)
self.assertNotEmpty(e.dtype)
self.assertIsInstance(e, base.__class__)
args_np.append((n_rows, n_cols, kw))

fctnp()
self.assertEqual(len(args_np), 100)

args_onxp = []

kws = array_api_kwargs(
k=strategies.integers(), dtype=self.onxps.numeric_dtypes()
)
sqrt_sizes = strategies.integers(0, self.SQRT_MAX_ARRAY_SIZE)
ncs = strategies.none() | sqrt_sizes

@given(n_rows=sqrt_sizes, n_cols=ncs, kw=kws)
def fctonx(n_rows, n_cols, kw):
base = onxp.asarray(0)
e = onxp.eye(n_rows, n_cols)
self.assertIsInstance(e, base.__class__)
self.assertNotEmpty(e.dtype)
e = onxp.eye(n_rows, n_cols, **kw)
self.assertNotEmpty(e.dtype)
self.assertIsInstance(e, base.__class__)
args_onxp.append((n_rows, n_cols, kw))

fctonx()
self.assertEqual(len(args_onxp), len(args_np))


if __name__ == "__main__":
# cl = TestHypothesisArraysApis()
# cl.setUpClass()
# cl.test_scalar_strategies()
# import logging

# logging.basicConfig(level=logging.DEBUG)
unittest.main(verbosity=2)
20 changes: 19 additions & 1 deletion_unittests/ut_array_api/test_onnx_numpy.py
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Expand Up@@ -142,10 +142,28 @@ def test_as_array(self):
self.assertEqual(r.dtype, DType(TensorProto.UINT64))
self.assertEqual(r.numpy(), 9223372036854775809)

def test_eye(self):
nr, nc = xp.asarray(4), xp.asarray(4)
expected = np.eye(nr.numpy(), nc.numpy())
got = xp.eye(nr, nc)
self.assertEqualArray(expected, got.numpy())

def test_eye_nosquare(self):
nr, nc = xp.asarray(4), xp.asarray(5)
expected = np.eye(nr.numpy(), nc.numpy())
got = xp.eye(nr, nc)
self.assertEqualArray(expected, got.numpy())

def test_eye_k(self):
nr = xp.asarray(4)
expected = np.eye(nr.numpy(), k=1)
got = xp.eye(nr, k=1)
self.assertEqualArray(expected, got.numpy())


if __name__ == "__main__":
# import logging

# logging.basicConfig(level=logging.DEBUG)
#TestOnnxNumpy().test_as_array()
TestOnnxNumpy().test_eye()
unittest.main(verbosity=2)
1 change: 1 addition & 0 deletionsonnx_array_api/array_api/__init__.py
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Expand Up@@ -17,6 +17,7 @@
"astype",
"empty",
"equal",
"eye",
"full",
"full_like",
"isdtype",
Expand Down
21 changes: 21 additions & 0 deletionsonnx_array_api/array_api/_onnx_common.py
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@@ -1,6 +1,7 @@
from typing import Any, Optional
import warnings
import numpy as np
from onnx import TensorProto

with warnings.catch_warnings():
warnings.simplefilter("ignore")
Expand All@@ -19,6 +20,8 @@
from ..npx.npx_functions import (
abs as generic_abs,
arange as generic_arange,
copy as copy_inline,
eye as generic_eye,
full as generic_full,
full_like as generic_full_like,
ones as generic_ones,
Expand DownExpand Up@@ -185,6 +188,24 @@ def full(
return generic_full(shape, fill_value=value, dtype=dtype, order=order)


def eye(
TEagerTensor: type,
n_rows: TensorType[ElemType.int64, "I"],
n_cols: OptTensorType[ElemType.int64, "I"] = None,
/,
*,
k: ParType[int] = 0,
dtype: ParType[DType] = DType(TensorProto.DOUBLE),
):
if isinstance(n_rows, int):
n_rows = TEagerTensor(np.array(n_rows, dtype=np.int64))
if n_cols is None:
n_cols = n_rows
elif isinstance(n_cols, int):
n_cols = TEagerTensor(np.array(n_cols, dtype=np.int64))
return generic_eye(n_rows, n_cols, k=k, dtype=dtype)


def full_like(
TEagerTensor: type,
x: TensorType[ElemType.allowed, "T"],
Expand Down
24 changes: 24 additions & 0 deletionsonnx_array_api/npx/npx_functions.py
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Expand Up@@ -473,6 +473,30 @@ def expit(
return var(x, op="Sigmoid")


@npxapi_inline
def eye(
n_rows: TensorType[ElemType.int64, "I"],
n_cols: TensorType[ElemType.int64, "I"],
/,
*,
k: ParType[int] = 0,
dtype: ParType[DType] = DType(TensorProto.DOUBLE),
):
"See :func:`numpy.eye`."
shape = cst(np.array([-1], dtype=np.int64))
shape = var(
var(n_rows, shape, op="Reshape"),
var(n_cols, shape, op="Reshape"),
axis=0,
op="Concat",
)
zero = zeros(shape, dtype=dtype)
res = var(zero, k=k, op="EyeLike")
if dtype is not None:
return var(res, to=dtype.code, op="Cast")
return res


@npxapi_inline
def full(
shape: TensorType[ElemType.int64, "I", (None,)],
Expand Down
10 changes: 10 additions & 0 deletionsonnx_array_api/npx/npx_graph_builder.py
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Expand Up@@ -230,6 +230,11 @@ def make_node(
new_kwargs[k] = v.value
elif isinstance(v, DType):
new_kwargs[k] = v.code
elif isinstance(v, int):
try:
new_kwargs[k] = int(np.array(v, dtype=np.int64))
except OverflowError:
new_kwargs[k] = int(np.iinfo(np.int64).max)
else:
new_kwargs[k] = v

Expand All@@ -246,6 +251,11 @@ def make_node(
f"Unable to create node {op!r}, with inputs={inputs}, "
f"outputs={outputs}, domain={domain!r}, new_kwargs={new_kwargs}."
) from e
except ValueError as e:
raise ValueError(
f"Unable to create node {op!r}, with inputs={inputs}, "
f"outputs={outputs}, domain={domain!r}, new_kwargs={new_kwargs}."
) from e
for p in protos:
node.attribute.append(p)
if attribute_protos is not None:
Expand Down
9 changes: 8 additions & 1 deletiononnx_array_api/npx/npx_jit_eager.py
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Expand Up@@ -510,11 +510,18 @@ def jit_call(self, *values, **kwargs):
from ..plotting.text_plot import onnx_simple_text_plot

text = onnx_simple_text_plot(self.onxs[key])

def catch_len(x):
try:
return len(x)
except TypeError:
return 0

raise RuntimeError(
f"Unable to run function for key={key!r}, "
f"types={[type(x) for x in values]}, "
f"dtypes={[getattr(x, 'dtype', type(x)) for x in values]}, "
f"shapes={[getattr(x, 'shape',len(x)) for x in values]}, "
f"shapes={[getattr(x, 'shape',catch_len(x)) for x in values]}, "
f"kwargs={kwargs}, "
f"self.input_to_kwargs_={self.input_to_kwargs_}, "
f"f={self.f} from module {self.f.__module__!r} "
Expand Down
4 changes: 0 additions & 4 deletionsonnx_array_api/reference/evaluator.py
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Expand Up@@ -7,10 +7,6 @@
from .ops.op_cast_like import CastLike_15, CastLike_19
from .ops.op_constant_of_shape import ConstantOfShape

import onnx

print(onnx.__file__)


logger = getLogger("onnx-array-api-eval")

Expand Down
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