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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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Supports function full for the Array API
  • Loading branch information
@xadupre
xadupre committedJun 18, 2023
commit791eb37fa9fc018abb9a89c4464bec65f5e9516d
2 changes: 1 addition & 1 deletion_unittests/onnx-numpy-skips.txt
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Expand Up@@ -5,7 +5,7 @@ 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
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
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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_full || 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
10 changes: 10 additions & 0 deletions_unittests/ut_array_api/test_onnx_numpy.py
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Expand Up@@ -19,6 +19,16 @@ def test_zeros(self):
a = xp.absolute(mat)
self.assertEqualArray(np.absolute(mat.numpy()), a.numpy())

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())


if __name__ == "__main__":
TestOnnxNumpy().test_full()
unittest.main(verbosity=2)
30 changes: 29 additions & 1 deletiononnx_array_api/array_api/onnx_numpy.py
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Expand Up@@ -16,10 +16,11 @@
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 +32,7 @@
"astype",
"empty",
"equal",
"full",
"isdtype",
"isfinite",
"isnan",
Expand DownExpand Up@@ -103,6 +105,32 @@ 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] = DType(TensorProto.FLOAT),
order: OptParType[str] = "C",
) -> TensorType[ElemType.numerics, "T"]:
if fill_value is None:
raise AttributeError("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
30 changes: 29 additions & 1 deletiononnx_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 +377,33 @@ 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] = DType(TensorProto.FLOAT),
fill_value: ParType[Scalar] = 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 AttributeError("fill_value cannot be None.")
if dtype is None:
dtype = DType(TensorProto.FLOAT)
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 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
14 changes: 8 additions & 6 deletionsonnx_array_api/npx/npx_jit_eager.py
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Expand Up@@ -131,7 +131,7 @@ def make_key(*values, **kwargs):
for iv, v in enumerate(values):
if isinstance(v, (Var, EagerTensor, JitTensor)):
res.append(v.key)
elif isinstance(v, (int, float, DType)):
elif isinstance(v, (int, float,bool,DType)):
res.append(v)
elif isinstance(v, slice):
res.append(("slice", v.start, v.stop, v.step))
Expand All@@ -153,7 +153,7 @@ def make_key(*values, **kwargs):
)
if kwargs:
for k, v in sorted(kwargs.items()):
if isinstance(v, (int, float, str, type, DType)):
if isinstance(v, (int, float, str, type,bool,DType)):
res.append(k)
res.append(v)
elif isinstance(v, tuple):
Expand DownExpand Up@@ -543,12 +543,12 @@ def _preprocess_constants(self, *args):
elif isinstance(n, np.ndarray):
new_args.append(self.tensor_class(n))
modified = True
elif isinstance(n, (int, float)):
elif isinstance(n, (int, float, bool)):
new_args.append(self.tensor_class(np.array(n)))
modified = True
elif isinstance(n, DType):
new_args.append(n)
elif n in (int, float):
elif n in (int, float, bool):
# usually used to cast
new_args.append(n)
elif n is None:
Expand DownExpand Up@@ -586,6 +586,7 @@ def __call__(self, *args, already_eager=False, **kwargs):
EagerTensor,
Cst,
int,
bool,
float,
tuple,
slice,
Expand DownExpand Up@@ -616,12 +617,13 @@ def __call__(self, *args, already_eager=False, **kwargs):
else:
# tries to call the version
try:
res = self.f(*values)
res = self.f(*values, **kwargs)
except (AttributeError, TypeError) as e:
inp1 = ", ".join(map(str, map(type, args)))
inp2 = ", ".join(map(str, map(type, values)))
raise TypeError(
f"Unexpected types, input types are {inp1} " f"and {inp2}."
f"Unexpected types, input types are {inp1} "
f"and {inp2}, kwargs={kwargs}."
) from e

if isinstance(res, EagerTensor) or (
Expand Down
2 changes: 2 additions & 0 deletionsonnx_array_api/npx/npx_numpy_tensors_ops.py
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Expand Up@@ -11,6 +11,8 @@ def _process(value):
cst = np.int64(cst)
elif isinstance(cst, float):
cst = np.float64(cst)
elif isinstance(cst, bool):
cst = np.bool_(cst)
elif cst is None:
cst = np.float32(0)
if not isinstance(
Expand Down
2 changes: 1 addition & 1 deletiononnx_array_api/npx/npx_tensors.py
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Expand Up@@ -133,7 +133,7 @@ def _generic_method_operator(self, method_name, *args: Any, **kwargs: Any) -> An
for a in args:
if isinstance(a, np.ndarray):
new_args.append(self.__class__(a.astype(self.dtype.np_dtype)))
elif isinstance(a, (int, float)):
elif isinstance(a, (int, float, bool)):
new_args.append(
self.__class__(np.array([a]).astype(self.dtype.np_dtype))
)
Expand Down
28 changes: 25 additions & 3 deletionsonnx_array_api/npx/npx_types.py
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Expand Up@@ -292,6 +292,19 @@ def get_set_name(cls, dtypes):
return None


class Scalar:
"""
Defines a scalar.
"""

def __init__(self, value: Union[float, int, bool]):
self.value = value

def __repr__(self):
"usual"
return f"Scalar({self.value!r})"


class ParType(WrapperType):
"""
Defines a parameter type.
Expand All@@ -300,11 +313,18 @@ class ParType(WrapperType):
:param optional: is optional or not
"""

map_names = {int: "int", float: "float", str: "str", DType: "DType"}
map_names = {
int: "int",
float: "float",
str: "str",
DType: "DType",
bool: "bool",
Scalar: "Scalar",
}

@classmethod
def __class_getitem__(cls, dtype):
if isinstance(dtype, (int, float)):
if isinstance(dtype, (int, float, bool)):
msg = str(dtype)
else:
msg = getattr(dtype, "__name__", str(dtype))
Expand All@@ -331,6 +351,8 @@ def onnx_type(cls):
return AttributeProto.INT
if cls.dtype == float:
return AttributeProto.FLOAT
if cls.dtype == bool:
return AttributeProto.BOOL
if cls.dtype == str:
return AttributeProto.STRING
raise RuntimeError(
Expand All@@ -347,7 +369,7 @@ class OptParType(ParType):

@classmethod
def __class_getitem__(cls, dtype):
if isinstance(dtype, (int, float)):
if isinstance(dtype, (int, float, bool)):
msg = str(dtype)
else:
msg = dtype.__name__
Expand Down
16 changes: 10 additions & 6 deletionsonnx_array_api/npx/npx_var.py
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Expand Up@@ -12,7 +12,7 @@ class Par:
Defines a named parameter.

:param name: parameter name
:param dtype: parameter type (int, str, float)
:param dtype: parameter type (bool,int, str, float)
:param value: value of the parameter if known
:param parent_op: node type it belongs to
"""
Expand DownExpand Up@@ -233,7 +233,7 @@ def __call__(self, new_values):

def _setitem1_where(self, index, new_values):
cst, var = Var.get_cst_var()
if isinstance(new_values, (int, float)):
if isinstance(new_values, (int, float, bool)):
new_values = np.array(new_values)
if isinstance(new_values, np.ndarray):
value = var(cst(new_values), self.parent, op="CastLike")
Expand DownExpand Up@@ -446,7 +446,7 @@ def _get_vars(self):
cst = Var.get_cst_var()[0]
replacement_cst[id(i)] = cst(i)
continue
if isinstance(i, (int, float)):
if isinstance(i, (int, float, bool)):
cst = Var.get_cst_var()[0]
replacement_cst[id(i)] = cst(np.array(i))
continue
Expand DownExpand Up@@ -595,13 +595,13 @@ def __iter__(self):

def _binary_op(self, ov: "Var", op_name: str, **kwargs) -> "Var":
var = Var.get_cst_var()[1]
if isinstance(ov, (int, float, np.ndarray, Cst)):
if isinstance(ov, (int, float,bool,np.ndarray, Cst)):
return var(self.self_var, var(ov, self.self_var, op="CastLike"), op=op_name)
return var(self.self_var, ov, op=op_name, **kwargs)

def _binary_op_right(self, ov: "Var", op_name: str, **kwargs) -> "Var":
var = Var.get_cst_var()[1]
if isinstance(ov, (int, float, np.ndarray, Cst)):
if isinstance(ov, (int, float,bool,np.ndarray, Cst)):
return var(var(ov, self.self_var, op="CastLike"), self.self_var, op=op_name)
return var(ov, self.self_var, op=op_name, **kwargs)

Expand DownExpand Up@@ -1112,10 +1112,14 @@ def __init__(self, cst: Any):
Var.__init__(self, np.array(cst, dtype=np.int64), op="Identity")
elif isinstance(cst, float):
Var.__init__(self, np.array(cst, dtype=np.float32), op="Identity")
elif isinstance(cst, bool):
Var.__init__(self, np.array(cst, dtype=np.bool_), op="Identity")
elif isinstance(cst, list):
if all(map(lambda t: isinstance(t, int), cst)):
Var.__init__(self, np.array(cst, dtype=np.int64), op="Identity")
elif all(map(lambda t: isinstance(t, (float, int)), cst)):
elif all(map(lambda t: isinstance(t, bool), cst)):
Var.__init__(self, np.array(cst, dtype=np.bool_), op="Identity")
elif all(map(lambda t: isinstance(t, (float, int, bool)), cst)):
Var.__init__(self, np.array(cst, dtype=np.float64), op="Identity")
else:
raise ValueError(
Expand Down
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