1 | /******************************************************************************* |
2 | * Copyright 2022 Intel Corporation |
3 | * |
4 | * Licensed under the Apache License, Version 2.0 (the "License"); |
5 | * you may not use this file except in compliance with the License. |
6 | * You may obtain a copy of the License at |
7 | * |
8 | * http://www.apache.org/licenses/LICENSE-2.0 |
9 | * |
10 | * Unless required by applicable law or agreed to in writing, software |
11 | * distributed under the License is distributed on an "AS IS" BASIS, |
12 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
13 | * See the License for the specific language governing permissions and |
14 | * limitations under the License. |
15 | *******************************************************************************/ |
16 | |
17 | #include "common/utils.hpp" |
18 | |
19 | namespace dnnl { |
20 | namespace impl { |
21 | namespace experimental { |
22 | |
23 | // Bnorm expermental feature: calculate mean & variance in single pass over |
24 | // input tensor. Improves performance by 25-33% but uses numerically unstable |
25 | // formula. |
26 | bool DNNL_API use_bnorm_stats_one_pass() { |
27 | #ifdef DNNL_EXPERIMENTAL |
28 | static const bool stats_onepass_algo |
29 | = getenv_int_user("EXPERIMENTAL_BNORM_STATS_ONE_PASS" , 1); |
30 | #else |
31 | static const bool stats_onepass_algo = false; |
32 | #endif |
33 | return stats_onepass_algo; |
34 | } |
35 | |
36 | } // namespace experimental |
37 | } // namespace impl |
38 | } // namespace dnnl |
39 | |