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Enables array tests for asarray#20

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sdpython merged 2 commits intomainfromempty
Jun 16, 2023
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2 changes: 1 addition & 1 deletion_unittests/onnx-numpy-skips.txt
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@@ -1,6 +1,6 @@
# API failures
# see https://github.com/data-apis/array-api-tests/blob/master/numpy-skips.txt
array_api_tests/test_creation_functions.py::test_arange
array_api_tests/test_creation_functions.py::test_asarray_scalars
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
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4 changes: 3 additions & 1 deletion_unittests/test_array_api.sh
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@@ -1,2 +1,4 @@
export ARRAY_API_TESTS_MODULE=onnx_array_api.array_api.onnx_numpy
pytest ../array-api-tests/array_api_tests/test_creation_functions.py::test_zeros
# pytest ../array-api-tests/array_api_tests/test_creation_functions.py::test_asarray_arrays || exit 1
# pytest ../array-api-tests/array_api_tests/test_creation_functions.py --help
pytest ../array-api-tests/array_api_tests/test_creation_functions.py --hypothesis-explain --skips-file=_unittests/onnx-numpy-skips.txt || exit 1
6 changes: 5 additions & 1 deletion_unittests/ut_array_api/test_onnx_numpy.py
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Expand Up@@ -6,7 +6,11 @@


class TestOnnxNumpy(ExtTestCase):
def test_abs(self):
def test_empty(self):
c = EagerTensor(np.array([4, 5], dtype=np.int64))
self.assertRaise(lambda: xp.empty(c, dtype=xp.int64), RuntimeError)

def test_zeros(self):
c = EagerTensor(np.array([4, 5], dtype=np.int64))
mat = xp.zeros(c, dtype=xp.int64)
matnp = mat.numpy()
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12 changes: 9 additions & 3 deletions_unittests/ut_npx/test_npx.py
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Expand Up@@ -2501,7 +2501,7 @@ def test_numpy_all(self):
got = ref.run(None, {"A": data})
self.assertEqualArray(y, got[0])

deftest_numpy_all_empty(self):
deftest_numpy_all_zeros(self):
data = np.zeros((0,), dtype=np.bool_)
y = np.all(data)

Expand All@@ -2513,7 +2513,7 @@ def test_numpy_all_empty(self):
self.assertEqualArray(y, got[0])

@unittest.skipIf(True, reason="ReduceMin does not support shape[axis] == 0")
deftest_numpy_all_empty_axis_0(self):
deftest_numpy_all_zeros_axis_0(self):
data = np.zeros((0, 1), dtype=np.bool_)
y = np.all(data, axis=0)

Expand All@@ -2535,7 +2535,13 @@ def test_numpy_all_empty_axis_1(self):
got = ref.run(None, {"A": data})
self.assertEqualArray(y, got[0])

@unittest.skipIf(True, reason="Fails to follow Array API")
def test_get_item(self):
a = EagerNumpyTensor(np.array([True], dtype=np.bool_))
i = a[0]
self.assertEqualArray(i.numpy(), a.numpy()[0])


if __name__ == "__main__":
# TestNpx().test_numpy_all_empty_axis_0()
# TestNpx().test_get_item()
unittest.main(verbosity=2)
4 changes: 2 additions & 2 deletionsazure-pipelines.yml
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Expand Up@@ -131,12 +131,12 @@ jobs:
- script: |
export ARRAY_API_TESTS_MODULE=onnx_array_api.array_api.onnx_numpy
cd array-api-tests
python -m pytest -x array_api_tests/test_creation_functions.py --skips-file=../_unittests/onnx-numpy-skips.txt -v
python -m pytest -x array_api_tests/test_creation_functions.py --skips-file=../_unittests/onnx-numpy-skips.txt --hypothesis-explain
displayName: "numpy test_creation_functions.py"
- script: |
export ARRAY_API_TESTS_MODULE=onnx_array_api.array_api.onnx_ort
cd array-api-tests
python -m pytest -x array_api_tests/test_creation_functions.py --skips-file=../_unittests/onnx-ort-skips.txt -v
python -m pytest -x array_api_tests/test_creation_functions.py --skips-file=../_unittests/onnx-ort-skips.txt --hypothesis-explain
displayName: "ort test_creation_functions.py"
#- script: |
# export ARRAY_API_TESTS_MODULE=onnx_array_api.array_api.onnx_numpy
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10 changes: 9 additions & 1 deletiononnx_array_api/array_api/_onnx_common.py
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Expand Up@@ -34,7 +34,15 @@ def template_asarray(
return a.astype(dtype=dtype)

if isinstance(a, int):
v = TEagerTensor(np.array(a, dtype=np.int64))
if a is False:
v = TEagerTensor(np.array(False, dtype=np.bool_))
elif a is True:
v = TEagerTensor(np.array(True, dtype=np.bool_))
else:
try:
v = TEagerTensor(np.asarray(a, dtype=np.int64))
except OverflowError:
v = TEagerTensor(np.asarray(a, dtype=np.uint64))
elif isinstance(a, float):
v = TEagerTensor(np.array(a, dtype=np.float32))
elif isinstance(a, bool):
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12 changes: 12 additions & 0 deletionsonnx_array_api/array_api/onnx_numpy.py
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Expand Up@@ -29,6 +29,7 @@
"all",
"asarray",
"astype",
"empty",
"equal",
"isdtype",
"isfinite",
Expand DownExpand Up@@ -73,6 +74,17 @@ def ones(
return generic_ones(shape, dtype=dtype, order=order)


def empty(
shape: TensorType[ElemType.int64, "I", (None,)],
dtype: OptParType[DType] = DType(TensorProto.FLOAT),
order: OptParType[str] = "C",
) -> TensorType[ElemType.numerics, "T"]:
raise RuntimeError(
"ONNX assumes there is no inplace implementation. "
"empty function is only used in that case."
)


def zeros(
shape: TensorType[ElemType.int64, "I", (None,)],
dtype: OptParType[DType] = DType(TensorProto.FLOAT),
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56 changes: 55 additions & 1 deletiononnx_array_api/npx/npx_numpy_tensors.py
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@@ -1,6 +1,6 @@
from typing import Any, Callable, List, Optional, Tuple
import numpy as np
from onnx import ModelProto
from onnx import ModelProto, TensorProto
from onnx.reference import ReferenceEvaluator
from .._helpers import np_dtype_to_tensor_dtype
from .npx_numpy_tensors_ops import ConstantOfShape
Expand DownExpand Up@@ -183,6 +183,60 @@ def __array_namespace__(self, api_version: Optional[str] = None):
f"Unable to return an implementation for api_version={api_version!r}."
)

def __bool__(self):
"Implicit conversion to bool."
if self.dtype != DType(TensorProto.BOOL):
raise TypeError(
f"Conversion to bool only works for bool scalar, not for {self!r}."
)
if self.shape == (0,):
return False
if len(self.shape) != 0:
raise ValueError(
f"Conversion to bool only works for scalar, not for {self!r}."
)
return bool(self._tensor)

def __int__(self):
"Implicit conversion to bool."
if len(self.shape) != 0:
raise ValueError(
f"Conversion to bool only works for scalar, not for {self!r}."
)
if self.dtype not in {
DType(TensorProto.INT64),
DType(TensorProto.INT32),
DType(TensorProto.INT16),
DType(TensorProto.INT8),
DType(TensorProto.UINT64),
DType(TensorProto.UINT32),
DType(TensorProto.UINT16),
DType(TensorProto.UINT8),
}:
raise TypeError(
f"Conversion to int only works for int scalar, "
f"not for dtype={self.dtype}."
)
return int(self._tensor)

def __float__(self):
"Implicit conversion to bool."
if len(self.shape) != 0:
raise ValueError(
f"Conversion to bool only works for scalar, not for {self!r}."
)
if self.dtype not in {
DType(TensorProto.FLOAT),
DType(TensorProto.DOUBLE),
DType(TensorProto.FLOAT16),
DType(TensorProto.BFLOAT16),
}:
raise TypeError(
f"Conversion to int only works for float scalar, "
f"not for dtype={self.dtype}."
)
return float(self._tensor)


class JitNumpyTensor(NumpyTensor, JitTensor):
"""
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4 changes: 4 additions & 0 deletionsonnx_array_api/npx/npx_var.py
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Expand Up@@ -962,6 +962,7 @@ def __getitem__(self, index: Any) -> "Var":

if isinstance(index, Var):
# scenario 2
# TODO: fix this when index is an integer
new_shape = cst(np.array([-1], dtype=np.int64))
new_self = self.reshape(new_shape)
new_index = index.reshape(new_shape)
Expand All@@ -973,6 +974,9 @@ def __getitem__(self, index: Any) -> "Var":

if not isinstance(index, tuple):
index = (index,)
elif len(index) == 0:
# The array contains a scalar and it needs to be returned.
return var(self, op="Identity")

# only one integer?
ni = None
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