diff --git a/_unittests/onnx-numpy-skips.txt b/_unittests/onnx-numpy-skips.txt
index a3eaa47..1eac9e2 100644
--- a/_unittests/onnx-numpy-skips.txt
+++ b/_unittests/onnx-numpy-skips.txt
@@ -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
diff --git a/_unittests/ut_array_api/test_hypothesis_array_api.py b/_unittests/ut_array_api/test_hypothesis_array_api.py
index e29af65..fdf48f9 100644
--- a/_unittests/ut_array_api/test_hypothesis_array_api.py
+++ b/_unittests/ut_array_api/test_hypothesis_array_api.py
@@ -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)
diff --git a/_unittests/ut_array_api/test_onnx_numpy.py b/_unittests/ut_array_api/test_onnx_numpy.py
index 859c802..78f8872 100644
--- a/_unittests/ut_array_api/test_onnx_numpy.py
+++ b/_unittests/ut_array_api/test_onnx_numpy.py
@@ -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)
diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index c449f2e..709ced3 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -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
diff --git a/onnx_array_api/array_api/__init__.py b/onnx_array_api/array_api/__init__.py
index e1e09b8..bd762be 100644
--- a/onnx_array_api/array_api/__init__.py
+++ b/onnx_array_api/array_api/__init__.py
@@ -18,6 +18,7 @@
"empty",
"equal",
"full",
+ "full_like",
"isdtype",
"isfinite",
"isinf",
diff --git a/onnx_array_api/array_api/_onnx_common.py b/onnx_array_api/array_api/_onnx_common.py
index 2a67f22..98a89f2 100644
--- a/onnx_array_api/array_api/_onnx_common.py
+++ b/onnx_array_api/array_api/_onnx_common.py
@@ -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,
)
@@ -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,)]],
diff --git a/onnx_array_api/npx/npx_functions.py b/onnx_array_api/npx/npx_functions.py
index c0f0a7b..8a886b2 100644
--- a/onnx_array_api/npx/npx_functions.py
+++ b/onnx_array_api/npx/npx_functions.py
@@ -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:
@@ -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"], /
diff --git a/onnx_array_api/npx/npx_jit_eager.py b/onnx_array_api/npx/npx_jit_eager.py
index b49d7ce..e06c944 100644
--- a/onnx_array_api/npx/npx_jit_eager.py
+++ b/onnx_array_api/npx/npx_jit_eager.py
@@ -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.
@@ -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:
"""
@@ -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
@@ -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 (
diff --git a/onnx_array_api/npx/npx_numpy_tensors.py b/onnx_array_api/npx/npx_numpy_tensors.py
index cfc90f3..a106b95 100644
--- a/onnx_array_api/npx/npx_numpy_tensors.py
+++ b/onnx_array_api/npx/npx_numpy_tensors.py
@@ -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
@@ -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
diff --git a/onnx_array_api/npx/npx_types.py b/onnx_array_api/npx/npx_types.py
index fe7b287..54cc618 100644
--- a/onnx_array_api/npx/npx_types.py
+++ b/onnx_array_api/npx/npx_types.py
@@ -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:
diff --git a/onnx_array_api/reference/evaluator.py b/onnx_array_api/reference/evaluator.py
index 737b15d..aa26127 100644
--- a/onnx_array_api/reference/evaluator.py
+++ b/onnx_array_api/reference/evaluator.py
@@ -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):
@@ -24,6 +33,7 @@ class ExtendedReferenceEvaluator(ReferenceEvaluator):
default_ops = [
CastLike_15,
CastLike_19,
+ ConstantOfShape,
]
@staticmethod
@@ -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)
diff --git a/onnx_array_api/npx/npx_numpy_tensors_ops.py b/onnx_array_api/reference/ops/op_constant_of_shape.py
similarity index 78%
rename from onnx_array_api/npx/npx_numpy_tensors_ops.py
rename to onnx_array_api/reference/ops/op_constant_of_shape.py
index b4639ae..33308af 100644
--- a/onnx_array_api/npx/npx_numpy_tensors_ops.py
+++ b/onnx_array_api/reference/ops/op_constant_of_shape.py
@@ -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):
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