-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathnpx_array_api.py
175 lines (124 loc) · 5.98 KB
/
npx_array_api.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
from typing import Any, Optional
import numpy as np
from .npx_types import OptParType, ParType, TupleType
class ArrayApiError(RuntimeError):
"""
Raised when a function is not supported by the :epkg:`Array API`.
"""
class BaseArrayApi:
"""
List of supported method by a tensor.
"""
def __array_namespace__(self, api_version: Optional[str] = None):
"""
This method must be overloaded.
"""
raise NotImplementedError("Method '__array_namespace__' must be implemented.")
def generic_method(self, method_name, *args: Any, **kwargs: Any) -> Any:
raise NotImplementedError(
f"Method {method_name!r} must be overwritten "
f"for class {self.__class__.__name__!r}. "
f"Method 'generic_method' can be overwritten "
f"as well to change the behaviour "
f"for all methods supported by class BaseArrayApi."
)
def numpy(self) -> np.ndarray:
return self.generic_method("numpy")
def __neg__(self) -> "BaseArrayApi":
return self.generic_method("__neg__")
def __invert__(self) -> "BaseArrayApi":
return self.generic_method("__invert__")
def __add__(self, ov: "BaseArrayApi") -> "BaseArrayApi":
return self.generic_method("__add__", ov)
def __radd__(self, ov: "BaseArrayApi") -> "BaseArrayApi":
return self.generic_method("__radd__", ov)
def __sub__(self, ov: "BaseArrayApi") -> "BaseArrayApi":
return self.generic_method("__sub__", ov)
def __rsub__(self, ov: "BaseArrayApi") -> "BaseArrayApi":
return self.generic_method("__rsub__", ov)
def __mul__(self, ov: "BaseArrayApi") -> "BaseArrayApi":
return self.generic_method("__mul__", ov)
def __rmul__(self, ov: "BaseArrayApi") -> "BaseArrayApi":
return self.generic_method("__rmul__", ov)
def __matmul__(self, ov: "BaseArrayApi") -> "BaseArrayApi":
res = self.generic_method("__matmul__", ov)
# TODO: It works with float32 but not float64.
return res
def __truediv__(self, ov: "BaseArrayApi") -> "BaseArrayApi":
return self.generic_method("__truediv__", ov)
def __rtruediv__(self, ov: "BaseArrayApi") -> "BaseArrayApi":
return self.generic_method("__rtruediv__", ov)
def __mod__(self, ov: "BaseArrayApi") -> "BaseArrayApi":
return self.generic_method("__mod__", ov)
def __rmod__(self, ov: "BaseArrayApi") -> "BaseArrayApi":
return self.generic_method("__rmod__", ov)
def __pow__(self, ov: "BaseArrayApi") -> "BaseArrayApi":
return self.generic_method("__pow__", ov)
def __rpow__(self, ov: "BaseArrayApi") -> "BaseArrayApi":
return self.generic_method("__rpow__", ov)
def __lt__(self, ov: "BaseArrayApi") -> "BaseArrayApi":
return self.generic_method("__lt__", ov)
def __le__(self, ov: "BaseArrayApi") -> "BaseArrayApi":
return self.generic_method("__le__", ov)
def __gt__(self, ov: "BaseArrayApi") -> "BaseArrayApi":
return self.generic_method("__gt__", ov)
def __ge__(self, ov: "BaseArrayApi") -> "BaseArrayApi":
return self.generic_method("__ge__", ov)
def __eq__(self, ov: "BaseArrayApi") -> "BaseArrayApi":
return self.generic_method("__eq__", ov)
def __ne__(self, ov: "BaseArrayApi") -> "BaseArrayApi":
return self.generic_method("__ne__", ov)
def __lshift__(self, ov: "BaseArrayApi") -> "BaseArrayApi":
return self.generic_method("__lshift__", ov)
def __rshift__(self, ov: "BaseArrayApi") -> "BaseArrayApi":
return self.generic_method("__rshift__", ov)
def __and__(self, ov: "BaseArrayApi") -> "BaseArrayApi":
return self.generic_method("__and__", ov)
def __rand__(self, ov: "BaseArrayApi") -> "BaseArrayApi":
return self.generic_method("__rand__", ov)
def __or__(self, ov: "BaseArrayApi") -> "BaseArrayApi":
return self.generic_method("__or__", ov)
def __ror__(self, ov: "BaseArrayApi") -> "BaseArrayApi":
return self.generic_method("__ror__", ov)
def __xor__(self, ov: "BaseArrayApi") -> "BaseArrayApi":
return self.generic_method("__xor__", ov)
def __rxor__(self, ov: "BaseArrayApi") -> "BaseArrayApi":
return self.generic_method("__rxor__", ov)
@property
def T(self) -> "BaseArrayApi":
return self.generic_method("T")
def astype(self, dtype: Any) -> "BaseArrayApi":
return self.generic_method("astype", dtype=dtype)
@property
def shape(self) -> "BaseArrayApi":
return self.generic_method("shape")
def reshape(self, shape: "BaseArrayApi") -> "BaseArrayApi":
return self.generic_method("reshape", shape)
def sum(
self, axis: OptParType[TupleType[int]] = None, keepdims: ParType[int] = 0
) -> "BaseArrayApi":
return self.generic_method("sum", axis=axis, keepdims=keepdims)
def mean(
self, axis: OptParType[TupleType[int]] = None, keepdims: ParType[int] = 0
) -> "BaseArrayApi":
return self.generic_method("mean", axis=axis, keepdims=keepdims)
def min(
self, axis: OptParType[TupleType[int]] = None, keepdims: ParType[int] = 0
) -> "BaseArrayApi":
return self.generic_method("min", axis=axis, keepdims=keepdims)
def max(
self, axis: OptParType[TupleType[int]] = None, keepdims: ParType[int] = 0
) -> "BaseArrayApi":
return self.generic_method("max", axis=axis, keepdims=keepdims)
def prod(
self, axis: OptParType[TupleType[int]] = None, keepdims: ParType[int] = 0
) -> "BaseArrayApi":
return self.generic_method("prod", axis=axis, keepdims=keepdims)
def copy(self) -> "BaseArrayApi":
return self.generic_method("copy")
def flatten(self) -> "BaseArrayApi":
return self.generic_method("flatten")
def __getitem__(self, index: Any) -> "BaseArrayApi":
return self.generic_method("__getitem__", index)
def __setitem__(self, index: Any, values: Any):
return self.generic_method("__setitem__", index, values)