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Add full_like for the array API#26

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sdpython merged 4 commits intomainfromfulll
Jul 2, 2023
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1 change: 0 additions & 1 deletion_unittests/onnx-numpy-skips.txt
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Expand Up@@ -6,7 +6,6 @@ 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_like
array_api_tests/test_creation_functions.py::test_linspace
array_api_tests/test_creation_functions.py::test_meshgrid
array_api_tests/test_creation_functions.py::test_zeros_like
6 changes: 3 additions & 3 deletions_unittests/ut_array_api/test_hypothesis_array_api.py
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Expand Up@@ -140,7 +140,7 @@ def fctonx(x, kw):


if __name__ == "__main__":
cl = TestHypothesisArraysApis()
cl.setUpClass()
cl.test_scalar_strategies()
#cl = TestHypothesisArraysApis()
#cl.setUpClass()
#cl.test_scalar_strategies()
unittest.main(verbosity=2)
20 changes: 19 additions & 1 deletion_unittests/ut_array_api/test_onnx_numpy.py
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Expand Up@@ -112,7 +112,25 @@ def test_ones_like_uint16(self):
expected = np.array(1, dtype=np.uint16)
self.assertEqualArray(expected, z.numpy())

def test_full_like(self):
c = EagerTensor(np.array(False))
expected = np.full_like(c.numpy(), fill_value=False)
mat = xp.full_like(c, fill_value=False)
matnp = mat.numpy()
self.assertEqual(matnp.shape, tuple())
self.assertEqualArray(expected, matnp)

def test_full_like_mx(self):
c = EagerTensor(np.array([], dtype=np.uint8))
expected = np.full_like(c.numpy(), fill_value=0)
mat = xp.full_like(c, fill_value=0)
matnp = mat.numpy()
self.assertEqualArray(expected, matnp)


if __name__ == "__main__":
# TestOnnxNumpy().test_ones_like()
# import logging

# logging.basicConfig(level=logging.DEBUG)
# TestOnnxNumpy().test_full_like_mx()
unittest.main(verbosity=2)
7 changes: 4 additions & 3 deletionsazure-pipelines.yml
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Expand Up@@ -246,9 +246,10 @@ jobs:
architecture: 'x64'
- script: gcc --version
displayName: 'gcc version'
- script: |
brew update
displayName: 'brew update'
#- script: brew upgrade
# displayName: 'brew upgrade'
#- script: brew update
# displayName: 'brew update'
- script: export
displayName: 'export'
- script: gcc --version
Expand Down
1 change: 1 addition & 0 deletionsonnx_array_api/array_api/__init__.py
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Expand Up@@ -18,6 +18,7 @@
"empty",
"equal",
"full",
"full_like",
"isdtype",
"isfinite",
"isinf",
Expand Down
18 changes: 18 additions & 0 deletionsonnx_array_api/array_api/_onnx_common.py
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Expand Up@@ -20,6 +20,7 @@
abs as generic_abs,
arange as generic_arange,
full as generic_full,
full_like as generic_full_like,
ones as generic_ones,
zeros as generic_zeros,
)
Expand DownExpand Up@@ -177,6 +178,23 @@ def full(
return generic_full(shape, fill_value=value, dtype=dtype, order=order)


def full_like(
TEagerTensor: type,
x: TensorType[ElemType.allowed, "T"],
/,
fill_value: ParType[Scalar] = None,
*,
dtype: OptParType[DType] = None,
order: OptParType[str] = "C",
) -> EagerTensor[TensorType[ElemType.allowed, "TR"]]:
if dtype is None:
if isinstance(fill_value, TEagerTensor):
dtype = fill_value.dtype
elif isinstance(x, TEagerTensor):
dtype = x.dtype
return generic_full_like(x, fill_value=fill_value, dtype=dtype, order=order)


def ones(
TEagerTensor: type,
shape: EagerTensor[TensorType[ElemType.int64, "I", (None,)]],
Expand Down
47 changes: 45 additions & 2 deletionsonnx_array_api/npx/npx_functions.py
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Expand Up@@ -275,9 +275,9 @@ def astype(
if dtype is int:
to = DType(TensorProto.INT64)
elif dtype is float:
to = DType(TensorProto.FLOAT64)
to = DType(TensorProto.DOUBLE)
elif dtype is bool:
to = DType(TensorProto.FLOAT64)
to = DType(TensorProto.BOOL)
elif dtype is str:
to = DType(TensorProto.STRING)
else:
Expand DownExpand Up@@ -511,6 +511,49 @@ def full(
return var(shape, value=value, op="ConstantOfShape")


@npxapi_inline
def full_like(
x: TensorType[ElemType.allowed, "T"],
/,
*,
fill_value: ParType[Scalar] = None,
dtype: OptParType[DType] = None,
order: OptParType[str] = "C",
) -> TensorType[ElemType.numerics, "T"]:
"""
Implements :func:`numpy.zeros`.
"""
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} "
f"and dtype={dtype!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}."
)

v = var(x.shape, value=value, op="ConstantOfShape")
if dtype is None:
return var(v, x, op="CastLike")
return v


@npxapi_inline
def floor(
x: TensorType[ElemType.numerics, "T"], /
Expand Down
15 changes: 10 additions & 5 deletionsonnx_array_api/npx/npx_jit_eager.py
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Expand Up@@ -58,6 +58,7 @@ def info(
kwargs: Optional[Dict[str, Any]] = None,
key: Optional[Tuple[Any, ...]] = None,
onx: Optional[ModelProto] = None,
output: Optional[Any] = None,
):
"""
Logs a status.
Expand DownExpand Up@@ -93,6 +94,8 @@ def info(
"" if args is None else str(args),
"" if kwargs is None else str(kwargs),
)
if output is not None:
logger.debug("==== [%s]", output)

def status(self, me: str) -> str:
"""
Expand DownExpand Up@@ -517,7 +520,7 @@ def jit_call(self, *values, **kwargs):
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")
self.info("-", "jit_call", output=res)
return res


Expand DownExpand Up@@ -737,11 +740,13 @@ def __call__(self, *args, already_eager=False, **kwargs):
try:
res = self.f(*values, **kwargs)
except (AttributeError, TypeError) as e:
inp1 = ", ".join(map(str, map(type, args)))
inp2 = ", ".join(map(str, map(type, values)))
inp1 = ", ".join(map(str, map(lambda a:type(a).__name__, args)))
inp2 = ", ".join(map(str, map(lambda a:type(a).__name__, values)))
raise TypeError(
f"Unexpected types, input types are {inp1} "
f"and {inp2}, kwargs={kwargs}."
f"Unexpected types, input types are args=[{inp1}], "
f"values=[{inp2}], kwargs={kwargs}. "
f"(values = self._preprocess_constants(args)) "
f"args={args}, values={values}"
) from e

if isinstance(res, EagerTensor) or (
Expand Down
3 changes: 1 addition & 2 deletionsonnx_array_api/npx/npx_numpy_tensors.py
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Expand Up@@ -4,7 +4,6 @@
from onnx import ModelProto, TensorProto
from ..reference import ExtendedReferenceEvaluator
from .._helpers import np_dtype_to_tensor_dtype
from .npx_numpy_tensors_ops import ConstantOfShape
from .npx_tensors import EagerTensor, JitTensor
from .npx_types import DType, TensorType

Expand DownExpand Up@@ -36,7 +35,7 @@ def __init__(
onx: ModelProto,
f: Callable,
):
self.ref = ExtendedReferenceEvaluator(onx, new_ops=[ConstantOfShape])
self.ref = ExtendedReferenceEvaluator(onx)
self.input_names = input_names
self.tensor_class = tensor_class
self._f = f
Expand Down
2 changes: 1 addition & 1 deletiononnx_array_api/npx/npx_types.py
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Expand Up@@ -68,7 +68,7 @@ def __eq__(self, dt: "DType") -> bool:
if dt is bool:
return self.code_ == TensorProto.BOOL
if dt is float:
return self.code_ == TensorProto.FLOAT64
return self.code_ == TensorProto.DOUBLE
if isinstance(dt, list):
return False
if dt in ElemType.numpy_map:
Expand Down
17 changes: 17 additions & 0 deletionsonnx_array_api/reference/evaluator.py
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Original file line numberDiff line numberDiff line change
@@ -1,9 +1,18 @@
from logging import getLogger
from typing import Any, Dict, List, Optional, Union
from onnx import FunctionProto, ModelProto
from onnx.defs import get_schema
from onnx.reference import ReferenceEvaluator
from onnx.reference.op_run import OpRun
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")


class ExtendedReferenceEvaluator(ReferenceEvaluator):
Expand All@@ -24,6 +33,7 @@ class ExtendedReferenceEvaluator(ReferenceEvaluator):
default_ops = [
CastLike_15,
CastLike_19,
ConstantOfShape,
]

@staticmethod
Expand DownExpand Up@@ -88,3 +98,10 @@ def __init__(
new_ops=new_ops,
**kwargs,
)

def _log(self, level: int, pattern: str, *args: List[Any]) -> None:
if level < self.verbose:
new_args = [self._log_arg(a) for a in args]
print(pattern % tuple(new_args))
else:
logger.debug(pattern, *args)
Original file line numberDiff line numberDiff line change
@@ -1,12 +1,18 @@
import numpy as np

from onnx.reference.op_run import OpRun


class ConstantOfShape(OpRun):
@staticmethod
def _process(value):
cst = value[0] if isinstance(value, np.ndarray) else value
cst = value[0] if isinstance(value, np.ndarray) and value.size > 0 else value
if isinstance(value, np.ndarray):
if len(value.shape) == 0:
cst = value
elif value.size > 0:
cst = value.ravel()[0]
else:
raise ValueError(f"Unexpected fill_value={value!r}")
if isinstance(cst, bool):
cst = np.bool_(cst)
elif isinstance(cst, int):
Expand Down
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