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Supports function full for the Array API#21

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sdpython merged 5 commits intomainfromfull
Jun 18, 2023
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1 change: 0 additions & 1 deletion_unittests/onnx-numpy-skips.txt
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Expand Up@@ -5,7 +5,6 @@ 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_full
array_api_tests/test_creation_functions.py::test_full_like
array_api_tests/test_creation_functions.py::test_linspace
array_api_tests/test_creation_functions.py::test_meshgrid
Expand Down
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 ../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::test_asarray_scalars || 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
2 changes: 1 addition & 1 deletion_unittests/ut_array_api/test_array_apis.py
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Expand Up@@ -13,7 +13,7 @@ class TestArraysApis(ExtTestCase):
def test_zeros_numpy_1(self):
c = xpn.zeros(1)
d = c.numpy()
self.assertEqualArray(np.array([0], dtype=np.float32), d)
self.assertEqualArray(np.array([0], dtype=np.float64), d)

def test_zeros_ort_1(self):
c = xpo.zeros(1)
Expand Down
34 changes: 34 additions & 0 deletions_unittests/ut_array_api/test_onnx_numpy.py
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Expand Up@@ -19,6 +19,40 @@ def test_zeros(self):
a = xp.absolute(mat)
self.assertEqualArray(np.absolute(mat.numpy()), a.numpy())

def test_zeros_none(self):
c = EagerTensor(np.array([4, 5], dtype=np.int64))
mat = xp.zeros(c)
matnp = mat.numpy()
self.assertEqual(matnp.shape, (4, 5))
self.assertNotEmpty(matnp[0, 0])
self.assertEqualArray(matnp, np.zeros((4, 5)))

def test_ones_none(self):
c = EagerTensor(np.array([4, 5], dtype=np.int64))
mat = xp.ones(c)
matnp = mat.numpy()
self.assertEqual(matnp.shape, (4, 5))
self.assertNotEmpty(matnp[0, 0])
self.assertEqualArray(matnp, np.ones((4, 5)))

def test_full(self):
c = EagerTensor(np.array([4, 5], dtype=np.int64))
mat = xp.full(c, fill_value=5, dtype=xp.int64)
matnp = mat.numpy()
self.assertEqual(matnp.shape, (4, 5))
self.assertNotEmpty(matnp[0, 0])
a = xp.absolute(mat)
self.assertEqualArray(np.absolute(mat.numpy()), a.numpy())

def test_full_bool(self):
c = EagerTensor(np.array([4, 5], dtype=np.int64))
mat = xp.full(c, fill_value=False)
matnp = mat.numpy()
self.assertEqual(matnp.shape, (4, 5))
self.assertNotEmpty(matnp[0, 0])
self.assertEqualArray(matnp, np.full((4, 5), False))


if __name__ == "__main__":
TestOnnxNumpy().test_zeros_none()
unittest.main(verbosity=2)
5 changes: 3 additions & 2 deletions_unittests/ut_npx/test_npx.py
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Expand Up@@ -710,8 +710,8 @@ def impl(
keys = list(sorted(f.onxs))
self.assertIsInstance(f.onxs[keys[0]], ModelProto)
k = keys[-1]
self.assertEqual(len(k),3)
self.assertEqual(k[1:], ("axis", 0))
self.assertEqual(len(k),4)
self.assertEqual(k[1:], ("axis",int,0))

def test_numpy_topk(self):
f = topk(Input("X"), Input("K"))
Expand DownExpand Up@@ -2416,6 +2416,7 @@ def compute_labels(X, centers, use_sqrt=False):
(DType(TensorProto.DOUBLE), 2),
(DType(TensorProto.DOUBLE), 2),
"use_sqrt",
bool,
True,
)
self.assertEqual(f.available_versions, [key])
Expand Down
10 changes: 5 additions & 5 deletionsazure-pipelines.yml
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Expand Up@@ -48,7 +48,7 @@ jobs:
vmImage: 'ubuntu-latest'
strategy:
matrix:
Python310-Linux:
Python311-Linux:
python.version: '3.11'
maxParallel: 3

Expand DownExpand Up@@ -96,7 +96,7 @@ jobs:
strategy:
matrix:
Python310-Linux:
python.version: '3.11'
python.version: '3.10'
maxParallel: 3

steps:
Expand DownExpand Up@@ -149,7 +149,7 @@ jobs:
vmImage: 'ubuntu-latest'
strategy:
matrix:
Python310-Linux:
Python311-Linux:
python.version: '3.11'
maxParallel: 3

Expand DownExpand Up@@ -202,7 +202,7 @@ jobs:
vmImage: 'windows-latest'
strategy:
matrix:
Python310-Windows:
Python311-Windows:
python.version: '3.11'
maxParallel: 3

Expand DownExpand Up@@ -235,7 +235,7 @@ jobs:
vmImage: 'macOS-latest'
strategy:
matrix:
Python310-Mac:
Python311-Mac:
python.version: '3.11'
maxParallel: 3

Expand Down
2 changes: 1 addition & 1 deletiononnx_array_api/_helpers.py
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Expand Up@@ -39,7 +39,7 @@ def np_dtype_to_tensor_dtype(dtype: Any):
elif dtype is int:
dt = TensorProto.INT64
elif dtype is float:
dt = TensorProto.FLOAT64
dt = TensorProto.DOUBLE
else:
raise KeyError(f"Unable to guess type for dtype={dtype}.")
return dt
2 changes: 1 addition & 1 deletiononnx_array_api/array_api/_onnx_common.py
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Expand Up@@ -44,7 +44,7 @@ def template_asarray(
except OverflowError:
v = TEagerTensor(np.asarray(a, dtype=np.uint64))
elif isinstance(a, float):
v = TEagerTensor(np.array(a, dtype=np.float32))
v = TEagerTensor(np.array(a, dtype=np.float64))
elif isinstance(a, bool):
v = TEagerTensor(np.array(a, dtype=np.bool_))
elif isinstance(a, str):
Expand Down
37 changes: 32 additions & 5 deletionsonnx_array_api/array_api/onnx_numpy.py
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Expand Up@@ -3,7 +3,6 @@
"""
from typing import Any, Optional
import numpy as np
from onnx import TensorProto
from ..npx.npx_functions import (
all,
abs,
Expand All@@ -16,10 +15,11 @@
reshape,
take,
)
from ..npx.npx_functions import full as generic_full
from ..npx.npx_functions import ones as generic_ones
from ..npx.npx_functions import zeros as generic_zeros
from ..npx.npx_numpy_tensors import EagerNumpyTensor
from ..npx.npx_types import DType, ElemType, TensorType, OptParType
from ..npx.npx_types import DType, ElemType, TensorType, OptParType, ParType, Scalar
from ._onnx_common import template_asarray
from . import _finalize_array_api

Expand All@@ -31,6 +31,7 @@
"astype",
"empty",
"equal",
"full",
"isdtype",
"isfinite",
"isnan",
Expand DownExpand Up@@ -58,7 +59,7 @@ def asarray(

def ones(
shape: TensorType[ElemType.int64, "I", (None,)],
dtype: OptParType[DType] =DType(TensorProto.FLOAT),
dtype: OptParType[DType] =None,
order: OptParType[str] = "C",
) -> TensorType[ElemType.numerics, "T"]:
if isinstance(shape, tuple):
Expand All@@ -76,7 +77,7 @@ def ones(

def empty(
shape: TensorType[ElemType.int64, "I", (None,)],
dtype: OptParType[DType] =DType(TensorProto.FLOAT),
dtype: OptParType[DType] =None,
order: OptParType[str] = "C",
) -> TensorType[ElemType.numerics, "T"]:
raise RuntimeError(
Expand All@@ -87,7 +88,7 @@ def empty(

def zeros(
shape: TensorType[ElemType.int64, "I", (None,)],
dtype: OptParType[DType] =DType(TensorProto.FLOAT),
dtype: OptParType[DType] =None,
order: OptParType[str] = "C",
) -> TensorType[ElemType.numerics, "T"]:
if isinstance(shape, tuple):
Expand All@@ -103,6 +104,32 @@ def zeros(
return generic_zeros(shape, dtype=dtype, order=order)


def full(
shape: TensorType[ElemType.int64, "I", (None,)],
fill_value: ParType[Scalar] = None,
dtype: OptParType[DType] = None,
order: OptParType[str] = "C",
) -> TensorType[ElemType.numerics, "T"]:
if fill_value is None:
raise TypeError("fill_value cannot be None")
value = fill_value
if isinstance(shape, tuple):
return generic_full(
EagerNumpyTensor(np.array(shape, dtype=np.int64)),
fill_value=value,
dtype=dtype,
order=order,
)
if isinstance(shape, int):
return generic_full(
EagerNumpyTensor(np.array([shape], dtype=np.int64)),
fill_value=value,
dtype=dtype,
order=order,
)
return generic_full(shape, fill_value=value, dtype=dtype, order=order)


def _finalize():
"""
Adds common attributes to Array API defined in this modules
Expand Down
2 changes: 1 addition & 1 deletiononnx_array_api/npx/npx_core_api.py
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Expand Up@@ -169,7 +169,7 @@ def wrapper(*inputs, **kwargs):
new_inputs.append(i)
elif isinstance(i, (int, float)):
new_inputs.append(
np.array([i], dtype=np.int64 if isinstance(i, int) else np.float32)
np.array([i], dtype=np.int64 if isinstance(i, int) else np.float64)
)
elif isinstance(i, str):
new_inputs.append(Input(i))
Expand Down
47 changes: 42 additions & 5 deletionsonnx_array_api/npx/npx_functions.py
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Expand Up@@ -15,14 +15,15 @@
SequenceType,
TensorType,
TupleType,
Scalar,
)
from .npx_var import Var


def _cstv(x):
if isinstance(x, Var):
return x
if isinstance(x, (int, float, np.ndarray)):
if isinstance(x, (int, float,bool,np.ndarray)):
return cst(x)
raise TypeError(f"Unexpected constant type {type(x)}.")

Expand DownExpand Up@@ -376,6 +377,42 @@ def expit(x: TensorType[ElemType.numerics, "T"]) -> TensorType[ElemType.numerics
return var(x, op="Sigmoid")


@npxapi_inline
def full(
shape: TensorType[ElemType.int64, "I", (None,)],
dtype: OptParType[DType] = None,
fill_value: ParType[Scalar] = None,
order: OptParType[str] = "C",
) -> TensorType[ElemType.numerics, "T"]:
"""
Implements :func:`numpy.full`.
"""
if order != "C":
raise RuntimeError(f"order={order!r} != 'C' not supported.")
if fill_value is None:
raise TypeError("fill_value cannot be None.")
if dtype is None:
if isinstance(fill_value, bool):
dtype = DType(TensorProto.BOOL)
elif isinstance(fill_value, int):
dtype = DType(TensorProto.INT64)
elif isinstance(fill_value, float):
dtype = DType(TensorProto.DOUBLE)
else:
raise TypeError(
f"Unexpected type {type(fill_value)} for fill_value={fill_value!r}."
)
if isinstance(fill_value, (float, int, bool)):
value = make_tensor(
name="cst", data_type=dtype.code, dims=[1], vals=[fill_value]
)
else:
raise NotImplementedError(
f"Unexpected type ({type(fill_value)} for fill_value={fill_value!r}."
)
return var(shape, value=value, op="ConstantOfShape")


@npxapi_inline
def floor(x: TensorType[ElemType.numerics, "T"]) -> TensorType[ElemType.numerics, "T"]:
"See :func:`numpy.floor`."
Expand DownExpand Up@@ -464,7 +501,7 @@ def matmul(
@npxapi_inline
def ones(
shape: TensorType[ElemType.int64, "I", (None,)],
dtype: OptParType[DType] =DType(TensorProto.FLOAT),
dtype: OptParType[DType] =None,
order: OptParType[str] = "C",
) -> TensorType[ElemType.numerics, "T"]:
"""
Expand All@@ -473,7 +510,7 @@ def ones(
if order != "C":
raise RuntimeError(f"order={order!r} != 'C' not supported.")
if dtype is None:
dtype = DType(TensorProto.FLOAT)
dtype = DType(TensorProto.DOUBLE)
return var(
shape,
value=make_tensor(name="one", data_type=dtype.code, dims=[1], vals=[1]),
Expand DownExpand Up@@ -674,7 +711,7 @@ def where(
@npxapi_inline
def zeros(
shape: TensorType[ElemType.int64, "I", (None,)],
dtype: OptParType[DType] =DType(TensorProto.FLOAT),
dtype: OptParType[DType] =None,
order: OptParType[str] = "C",
) -> TensorType[ElemType.numerics, "T"]:
"""
Expand All@@ -683,7 +720,7 @@ def zeros(
if order != "C":
raise RuntimeError(f"order={order!r} != 'C' not supported.")
if dtype is None:
dtype = DType(TensorProto.FLOAT)
dtype = DType(TensorProto.DOUBLE)
return var(
shape,
value=make_tensor(name="zero", data_type=dtype.code, dims=[1], vals=[0]),
Expand Down
2 changes: 1 addition & 1 deletiononnx_array_api/npx/npx_graph_builder.py
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Expand Up@@ -784,7 +784,7 @@ def to_onnx(
node_inputs.append(input_name)
continue

if isinstance(i, (int, float)):
if isinstance(i, (int, float, bool)):
ni = np.array(i)
c = Cst(ni)
input_name = self._unique(var._prefix)
Expand Down
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