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Supports function full for the Array API#21

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Merged
sdpython merged 5 commits intomainfromfull
Jun 18, 2023
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fix keys by adding types
  • Loading branch information
@xadupre
xadupre committedJun 18, 2023
commit870ba4b8990f6f73730ebfc7d15eb27c717f9b51
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_full || exit 1
pytest ../array-api-tests/array_api_tests/test_creation_functions.py::test_ones || 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
18 changes: 17 additions & 1 deletion_unittests/ut_array_api/test_onnx_numpy.py
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Expand Up@@ -19,6 +19,22 @@ 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)
Expand All@@ -38,5 +54,5 @@ def test_full_bool(self):


if __name__ == "__main__":
TestOnnxNumpy().test_full_bool()
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
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
7 changes: 3 additions & 4 deletionsonnx_array_api/array_api/onnx_numpy.py
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Original file line numberDiff line numberDiff line change
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 DownExpand Up@@ -60,7 +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@@ -78,7 +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@@ -89,7 +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 Down
8 changes: 4 additions & 4 deletionsonnx_array_api/npx/npx_functions.py
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Expand Up@@ -501,7 +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@@ -510,7 +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@@ -711,7 +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@@ -720,7 +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
4 changes: 3 additions & 1 deletiononnx_array_api/npx/npx_jit_eager.py
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Expand Up@@ -132,6 +132,7 @@ def make_key(*values, **kwargs):
if isinstance(v, (Var, EagerTensor, JitTensor)):
res.append(v.key)
elif isinstance(v, (int, float, bool, DType)):
res.append(type(v))
res.append(v)
elif isinstance(v, slice):
res.append(("slice", v.start, v.stop, v.step))
Expand DownExpand Up@@ -170,7 +171,8 @@ def make_key(*values, **kwargs):
newv.append(t)
res.append(tuple(newv))
elif v is None and k in {"dtype"}:
continue
res.append(k)
res.append(v)
else:
raise TypeError(
f"Type {type(v)} is not yet supported, "
Expand Down
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