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
|
import torch
from torch.testing._internal.common_utils import TestCase, run_tests
from functorch.experimental.cond import cond
from torch.fx.experimental.proxy_tensor import make_fx
import unittest
class TestControlFlow(TestCase):
def test_cond_no_trace(self):
def true_fn(x):
return x.sin()
def false_fn(x):
return x.cos()
x = torch.randn(4)
result = cond(False, true_fn, false_fn, [x])
self.assertEqual(result, torch.cos(x))
class TestControlFlowTraced(TestCase):
def test_cond_traced_not_nested(self):
def true_fn(x):
return x.sin()
def false_fn(x):
return x.cos()
def f(x, y):
return cond(y, true_fn, false_fn, [x])
x = torch.randn(4)
graph = make_fx(f)(x, torch.tensor(False))
result_true = graph.forward(x, torch.tensor(True))
result_false = graph.forward(x, torch.tensor(False))
self.assertFalse(torch.allclose(result_true, result_false))
self.assertEqual(result_true, torch.sin(x))
self.assertEqual(result_false, torch.cos(x))
@unittest.expectedFailure
def test_cond_nested_traced(self):
def true_nested(y):
return y * y
def false_nested(y):
return y + y
def true_fn(x, pred2):
z = cond(pred2, true_nested, false_nested, [x])
return x + z
def false_fn(x, _):
return x.cos()
def f(x, pred, pred2):
return cond(pred, true_fn, false_fn, [x, pred2])
x = torch.randn(4)
graph = make_fx(f)(x, torch.tensor(False), torch.tensor(False))
result_true_true = graph.forward(x, torch.tensor(True), torch.tensor(True)) # True + True -> x * x
result_true_false = graph.forward(x, torch.tensor(True), torch.tensor(False)) # True + True -> x + x
result_false_true = graph.forward(x, torch.tensor(False), torch.tensor(True)) # False + either -> cos
result_false_false = graph.forward(x, torch.tensor(False), torch.tensor(False)) # False + either -> cos
self.assertNotEqual(result_true_true, result_true_false)
self.assertFalse(torch.allclose(result_false_true, result_true_true))
self.assertEqual(result_false_true, result_false_false)
self.assertEqual(result_true_true, (x * x) + x)
self.assertEqual(result_true_false, x + x + x)
self.assertEqual(result_false_true, torch.cos(x))
@unittest.expectedFailure
def test_cond_nested_traced_other_inputs(self):
def true_nested(y):
return y * y
def false_nested(y):
return y + y
def true_fn(k, pred2):
z = cond(pred2, true_nested, false_nested, [k])
return torch.add(torch.tensor([.25, .25]), z)
def false_fn(k, _):
return k.cos()
def f(k, pred, pred2):
return cond(pred, true_fn, false_fn, [k, pred2])
x = torch.tensor([0.5, 0.5])
graph = make_fx(f)(x, torch.tensor(False), torch.tensor(False))
a = torch.tensor([1.0, 1.0])
result_true_true = graph.forward(a, torch.tensor(True), torch.tensor(True))
self.assertEqual(result_true_true, (a * a) + torch.tensor([0.25, 0.25]))
b = torch.tensor([2.0, 2.0])
result_true_true = graph.forward(b, torch.tensor(True), torch.tensor(True))
self.assertEqual(result_true_true, (b * b) + torch.tensor([0.25, 0.25]))
@unittest.expectedFailure
def test_cond_nested_traced_multi(self):
def true_a(y):
return y * y
def false_a(y):
return y + y
def true_b(y, z):
return y + z
def false_b(y, z):
return y * z
def f(x, pred, pred2):
a_out = cond(pred, true_a, false_a, [x])
b_out = cond(pred2, true_b, false_b, [x, x])
return a_out + b_out
x = torch.randn(4)
graph = make_fx(f)(x, torch.tensor(False), torch.tensor(False))
# Brittle, yet, delicious
out = """
def forward(self, x_1, pred_1, pred2_1):
true_graph_0 = self.true_graph_0
false_graph_0 = self.false_graph_0
conditional = torch.ops.cond(pred_1, true_graph_0, false_graph_0, [[x_1]]);
pred_1 = true_graph_0 = false_graph_0 = None
true_graph_1 = self.true_graph_1
false_graph_1 = self.false_graph_1
conditional_1 = torch.ops.cond(pred2_1, true_graph_1, false_graph_1, [[x_1, x_1]]);
pred2_1 = true_graph_1 = false_graph_1 = x_1 = None
add = torch.ops.aten.add.Tensor(conditional, conditional_1); conditional = conditional_1 = None
return add
"""
code = graph.code
# Normalization hack, cause .code makes some weird whitespace
code = "".join(code.split())
out = "".join(out.split())
self.assertEqual(code, out)
code = graph.true_graph_0.code
out = """
def forward(self, flat_args):
flat_args_1, = fx_pytree.tree_flatten_spec([flat_args], self._in_spec)
mul = torch.ops.aten.mul.Tensor(flat_args_1, flat_args_1); flat_args_1 = None
return pytree.tree_unflatten([mul], self._out_spec)
"""
# Normalization hack, cause .code makes some weird whitespace
code = "".join(code.split())
out = "".join(out.split())
self.assertEqual(code, out)
def test_assert_on_mismatch_type_size(self):
def true_fn(x):
return x.sin()
def false_fn(x):
return (x, x)
def f(x, y):
return cond(y, true_fn, false_fn, [x])
x = torch.randn(4)
with self.assertRaises(AssertionError):
make_fx(f)(x, torch.tensor(False))
@unittest.expectedFailure
def test_assert_on_mismatch_tensor_size(self):
def true_fn(x):
return x.sin()
def false_fn(x):
return torch.zeros([10, 10])
def f(x, y):
return cond(y, true_fn, false_fn, [x])
x = torch.randn(4)
with self.assertRaises(AssertionError):
make_fx(f)(x, torch.tensor(False))
if __name__ == '__main__':
run_tests()
|
