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
|
import operator_benchmark as op_bench
import torch
"""Microbenchmarks for quantized unary operators (point-wise and reduction)."""
# Configs for pointwise and reduction unary ops
qunary_ops_configs_short = op_bench.config_list(
attr_names=['M', 'N'],
attrs=[
[512, 512],
],
cross_product_configs={
'dtype': [torch.quint8],
},
tags=['short']
)
qunary_ops_configs_long = op_bench.cross_product_configs(
M=[256, 1024],
N=[256, 1024],
dtype=[torch.quint8, torch.qint8, torch.qint32],
tags=['long']
)
class QUnaryOpBenchmark(op_bench.TorchBenchmarkBase):
def init(self, M, N, dtype, op_func):
f_input = torch.rand(M, N)
scale = 1.0
zero_point = 0
self.inputs = {
"q_input": torch.quantize_per_tensor(f_input, scale=scale,
zero_point=zero_point,
dtype=dtype)
}
self.op_func = op_func
def forward(self, q_input):
return self.op_func(q_input)
# TODO: Uncomment the ops whenever they are implemented for quantized tensor.
qunary_ops_list = op_bench.op_list(
attr_names=['op_name', 'op_func'],
attrs=[
# ['q_abs', torch.abs],
# ['q_abs_', torch.abs_],
# ['q_acos', torch.acos],
# ['q_acos_', torch.acos_],
['q_argsort', torch.argsort],
# ['q_asin', torch.asin],
# ['q_asin_', torch.asin_],
# ['q_atan', torch.atan],
# ['q_atan_', torch.atan_],
# ['q_ceil', torch.ceil],
# ['q_ceil_', torch.ceil_],
['q_clone', torch.clone],
# ['q_cos', torch.cos],
# ['q_cos_', torch.cos_],
# ['q_cosh', torch.cosh],
# ['q_digamma', torch.digamma],
# ['q_erf', torch.erf],
# ['q_erf_', torch.erf_],
# ['q_erfc', torch.erfc],
# ['q_erfc_', torch.erfc_],
# ['q_erfinv', torch.erfinv],
# ['q_exp', torch.exp],
# ['q_exp_', torch.exp_],
# ['q_expm1', torch.expm1],
# ['q_expm1_', torch.expm1_],
# ['q_floor', torch.floor],
# ['q_floor_', torch.floor_],
# ['q_frac', torch.frac],
# ['q_frac_', torch.frac_],
# ['q_hardshrink', torch.hardshrink],
# ['q_lgamma', torch.lgamma],
# ['q_log', torch.log],
# ['q_log10', torch.log10],
# ['q_log10_', torch.log10_],
# ['q_log1p', torch.log1p],
# ['q_log1p_', torch.log1p_],
# ['q_log2', torch.log2],
# ['q_log2_', torch.log2_],
# ['q_log_', torch.log_],
['q_mean', torch.mean],
# ['q_neg', torch.neg],
# ['q_neg_', torch.neg_],
# ['q_reciprocal', torch.reciprocal],
# ['q_reciprocal_', torch.reciprocal_],
['q_relu', torch.relu],
['q_relu_', torch.relu_],
# ['q_round', torch.round],
# ['q_round_', torch.round_],
# ['q_rsqrt', torch.rsqrt],
# ['q_rsqrt_', torch.rsqrt_],
# ['q_sigmoid', torch.sigmoid],
# ['q_sigmoid_', torch.sigmoid_],
# ['q_sign', torch.sign],
# ['q_sin', torch.sin],
# ['q_sin_', torch.sin_],
# ['q_sinh', torch.sinh],
['q_sort', torch.sort],
# ['q_sqrt', torch.sqrt],
# ['q_sqrt_', torch.sqrt_],
# ['q_tan', torch.tan],
# ['q_tan_', torch.tan_],
# ['q_tanh', torch.tanh],
# ['q_tanh_', torch.tanh_],
# ['q_trunc', torch.trunc],
# ['q_trunc_', torch.trunc_],
# ['q_unique', torch.unique],
# ['q_zero_', torch.zero_],
# ['q_bernoulli_', lambda t: t.bernoulli_()],
# ['q_cauchy_', lambda t: t.cauchy_()],
# ['q_digamma_', lambda t: t.digamma_()],
# ['q_exponential_', lambda t: t.exponential_()],
# ['q_normal_', lambda t: t.normal_()],
# ['q_random_', lambda t: t.random_()],
# ['q_sign_', lambda t: t.sign_()],
# ['q_uniform_', lambda t: t.uniform_()],
# ['q_half', lambda t: t.half()],
# ['q_long', lambda t: t.long()],
],
)
op_bench.generate_pt_tests_from_op_list(qunary_ops_list,
qunary_ops_configs_short + qunary_ops_configs_long,
QUnaryOpBenchmark)
# === Other unary ops (i.e. the ones that need parameters as args) ===
# Configs for pointwise and reduction unary ops
qunary_ops_topk_configs_short = op_bench.config_list(
attr_names=['M', 'N', 'k'],
attrs=[
[512, 512, 5],
],
cross_product_configs={
'dtype': [torch.quint8],
},
tags=['short']
)
qunary_ops_topk_configs_long = op_bench.cross_product_configs(
M=[256, 1024],
N=[256, 1024],
k=[1, 3, 5],
dtype=[torch.quint8, torch.qint8, torch.qint32],
tags=['long']
)
class QTopkOpBenchmark(op_bench.TorchBenchmarkBase):
def init(self, M, N, dtype, k):
f_input = torch.rand(M, N)
scale = 1.0
zero_point = 0
self.inputs = {
"q_input": torch.quantize_per_tensor(f_input, scale=scale,
zero_point=zero_point,
dtype=dtype),
"k": k
}
self.set_module_name('qtopk')
def forward(self, q_input, k: int):
return torch.topk(q_input, k)
op_bench.generate_pt_test(qunary_ops_topk_configs_short + qunary_ops_topk_configs_long,
QTopkOpBenchmark)
if __name__ == "__main__":
op_bench.benchmark_runner.main()
|
