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
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
|
"""Testing routines for opt_einsum."""
import random
from typing import Any, Dict, List, Literal, Optional, Tuple, Union, overload
import pytest
from opt_einsum.parser import get_symbol
from opt_einsum.typing import ArrayType, PathType, TensorShapeType
_valid_chars = "abcdefghijklmopqABC"
_sizes = [2, 3, 4, 5, 4, 3, 2, 6, 5, 4, 3, 2, 5, 7, 4, 3, 2, 3, 4]
_default_dim_dict = dict(zip(_valid_chars, _sizes))
def build_shapes(string: str, dimension_dict: Optional[Dict[str, int]] = None) -> Tuple[TensorShapeType, ...]:
"""Builds random tensor shapes for testing.
Parameters:
string: List of tensor strings to build
dimension_dict: Dictionary of index sizes, defaults to indices size of 2-7
Returns:
The resulting shapes.
Examples:
```python
>>> shapes = build_shapes('abbc', {'a': 2, 'b':3, 'c':5})
>>> shapes
[(2, 3), (3, 3, 5), (5,)]
```
"""
if dimension_dict is None:
dimension_dict = _default_dim_dict
shapes = []
terms = string.split("->")[0].split(",")
for term in terms:
dims = [dimension_dict[x] for x in term]
shapes.append(tuple(dims))
return tuple(shapes)
def build_views(
string: str, dimension_dict: Optional[Dict[str, int]] = None, array_function: Optional[Any] = None
) -> Tuple[ArrayType]:
"""Builds random numpy arrays for testing.
Parameters:
string: List of tensor strings to build
dimension_dict: Dictionary of index _sizes
array_function: Function to build the arrays, defaults to np.random.rand
Returns:
The resulting views.
Examples:
```python
>>> view = build_views('abbc', {'a': 2, 'b':3, 'c':5})
>>> view[0].shape
(2, 3, 3, 5)
```
"""
if array_function is None:
np = pytest.importorskip("numpy")
array_function = np.random.rand
views = []
for shape in build_shapes(string, dimension_dict=dimension_dict):
if shape:
views.append(array_function(*shape))
else:
views.append(random.random())
return tuple(views)
@overload
def rand_equation(
n: int,
regularity: int,
n_out: int = ...,
d_min: int = ...,
d_max: int = ...,
seed: Optional[int] = ...,
global_dim: bool = ...,
*,
return_size_dict: Literal[True],
) -> Tuple[str, PathType, Dict[str, int]]: ...
@overload
def rand_equation(
n: int,
regularity: int,
n_out: int = ...,
d_min: int = ...,
d_max: int = ...,
seed: Optional[int] = ...,
global_dim: bool = ...,
return_size_dict: Literal[False] = ...,
) -> Tuple[str, PathType]: ...
def rand_equation(
n: int,
regularity: int,
n_out: int = 0,
d_min: int = 2,
d_max: int = 9,
seed: Optional[int] = None,
global_dim: bool = False,
return_size_dict: bool = False,
) -> Union[Tuple[str, PathType, Dict[str, int]], Tuple[str, PathType]]:
"""Generate a random contraction and shapes.
Parameters:
n: Number of array arguments.
regularity: 'Regularity' of the contraction graph. This essentially determines how
many indices each tensor shares with others on average.
n_out: Number of output indices (i.e. the number of non-contracted indices).
Defaults to 0, i.e., a contraction resulting in a scalar.
d_min: Minimum dimension size.
d_max: Maximum dimension size.
seed: If not None, seed numpy's random generator with this.
global_dim: Add a global, 'broadcast', dimension to every operand.
return_size_dict: Return the mapping of indices to sizes.
Returns:
eq: The equation string.
shapes: The array shapes.
size_dict: The dict of index sizes, only returned if ``return_size_dict=True``.
Examples:
```python
>>> eq, shapes = rand_equation(n=10, regularity=4, n_out=5, seed=42)
>>> eq
'oyeqn,tmaq,skpo,vg,hxui,n,fwxmr,hitplcj,kudlgfv,rywjsb->cebda'
>>> shapes
[(9, 5, 4, 5, 4),
(4, 4, 8, 5),
(9, 4, 6, 9),
(6, 6),
(6, 9, 7, 8),
(4,),
(9, 3, 9, 4, 9),
(6, 8, 4, 6, 8, 6, 3),
(4, 7, 8, 8, 6, 9, 6),
(9, 5, 3, 3, 9, 5)]
```
"""
np = pytest.importorskip("numpy")
if seed is not None:
np.random.seed(seed)
# total number of indices
num_inds = n * regularity // 2 + n_out
inputs = ["" for _ in range(n)]
output = []
size_dict = {get_symbol(i): np.random.randint(d_min, d_max + 1) for i in range(num_inds)}
# generate a list of indices to place either once or twice
def gen():
for i, ix in enumerate(size_dict):
# generate an outer index
if i < n_out:
output.append(ix)
yield ix
# generate a bond
else:
yield ix
yield ix
# add the indices randomly to the inputs
for i, ix in enumerate(np.random.permutation(list(gen()))):
# make sure all inputs have at least one index
if i < n:
inputs[i] += ix
else:
# don't add any traces on same op
where = np.random.randint(0, n)
while ix in inputs[where]:
where = np.random.randint(0, n)
inputs[where] += ix
# possibly add the same global dim to every arg
if global_dim:
gdim = get_symbol(num_inds)
size_dict[gdim] = np.random.randint(d_min, d_max + 1)
for i in range(n):
inputs[i] += gdim
output += gdim
# randomly transpose the output indices and form equation
output = "".join(np.random.permutation(output)) # type: ignore
eq = "{}->{}".format(",".join(inputs), output)
# make the shapes
shapes = [tuple(size_dict[ix] for ix in op) for op in inputs]
ret = (eq, shapes)
if return_size_dict:
return ret + (size_dict,)
else:
return ret
def build_arrays_from_tuples(path: PathType) -> List[Any]:
"""Build random numpy arrays from a path.
Parameters:
path: The path to build arrays from.
Returns:
The resulting arrays.
"""
np = pytest.importorskip("numpy")
return [np.random.rand(*x) for x in path]
|
