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
|
import numpy as np
import torch
from . import benchmark
class Concat2D2InputBench(benchmark.Benchmark):
def __init__(self, mode, device, dtype, I1_D1, I1_D2, I2_D1, I2_D2, concat_dim):
super().__init__(mode, device, dtype)
self.I1_D1 = I1_D1
self.I1_D2 = I1_D2
self.I2_D1 = I2_D1
self.I2_D2 = I2_D2
self.concat_dim = concat_dim
self.input1 = self.randn(
[I1_D1, I1_D2], device=device, dtype=dtype, requires_grad=self.requires_grad
)
self.input2 = self.randn(
[I2_D1, I2_D2], device=device, dtype=dtype, requires_grad=self.requires_grad
)
self.inputs = [self.input1, self.input2]
def forward(self, input1, input2):
x1 = self.add(input1, 0.00001)
x2 = self.add(input2, 0.00001)
y = self.cat((x1, x2), dim=self.concat_dim)
return y
def reference(self):
return np.concatenate(
(self.numpy(self.input1), self.numpy(self.input2)),
axis=self.concat_dim,
)
def config(self):
return [self.I1_D1, self.I1_D2, self.I2_D1, self.I2_D2, self.concat_dim]
@staticmethod
def module():
return "concat2d2input"
def memory_workload(self):
if self.mode == "fwd":
sol_count = 1 + 1
algorithmic_count = 3 + 1
else:
sol_count = (1 + 1) + (1 + 1)
algorithmic_count = (3 + 1) + (3 + 1)
buffer_size = self.I1_D1 * self.I1_D2 + self.I2_D1 * self.I2_D2
return {
"sol": buffer_size * sol_count,
"algorithmic": buffer_size * algorithmic_count,
}
@staticmethod
def default_configs():
return [
[1, 160, 1, 14, 1],
[1, 580, 1, 174, 1],
[20, 160, 20, 14, 1],
[20, 580, 20, 174, 1],
[8, 512, 8, 512, 1],
[1 << 13, 1060, 1 << 13, 1040, 1],
[1 << 13, 2000, 1 << 13, 1074, 1],
[1 << 15, 1060, 1 << 15, 2670, 1],
[1 << 15, 5120, 1 << 15, 2512, 1],
]
benchmark.register_benchmark_class(Concat2D2InputBench)
class ConcatGraphOptBench(benchmark.Benchmark):
def __init__(self, mode, device, dtype, I1_D1, I1_D2, I2_D1, I2_D2, concat_dim):
super().__init__(mode, device, dtype)
self.I1_D1 = I1_D1
self.I1_D2 = I1_D2
self.I2_D1 = I2_D1
self.I2_D2 = I2_D2
self.concat_dim = concat_dim
self.input1 = self.randn(
[I1_D1, I1_D2], device=device, dtype=dtype, requires_grad=self.requires_grad
)
self.input2 = self.randn(
[I2_D1, I2_D2], device=device, dtype=dtype, requires_grad=self.requires_grad
)
self.inputs = [self.input1, self.input2]
torch._C._jit_override_can_fuse_on_cpu(True)
torch._C._jit_cat_wo_conditionals(True)
def forward(self, input1, input2):
x1 = self.add(input1, 0.00001)
x2 = self.add(input2, 0.00001)
y = self.cat((x1, x2), dim=self.concat_dim)
z = self.relu(y)
return z
def reference(self):
return np.concatenate(
(self.numpy(self.input1), self.numpy(self.input2)),
axis=self.concat_dim,
)
def config(self):
return [self.I1_D1, self.I1_D2, self.I2_D1, self.I2_D2, self.concat_dim]
@staticmethod
def module():
return "concatGraphOpt"
def memory_workload(self):
if self.mode == "fwd":
sol_count = 1 + 1
algorithmic_count = 3 + 1
else:
sol_count = (1 + 1) + (1 + 1)
algorithmic_count = (3 + 1) + (3 + 1)
buffer_size = self.I1_D1 * self.I1_D2 + self.I2_D1 * self.I2_D2
return {
"sol": buffer_size * sol_count,
"algorithmic": buffer_size * algorithmic_count,
}
@staticmethod
def default_configs():
return [
[1 << 13, 1060, 1 << 13, 1040, 1],
[1 << 13, 2000, 1 << 13, 1074, 1],
[1 << 15, 1060, 1 << 15, 2670, 1],
[1 << 15, 5120, 1 << 15, 2512, 1],
]
benchmark.register_benchmark_class(ConcatGraphOptBench)
|
