Decrease DLL Neural Network Compilation Time With C++17
I migrated Deep Learning Library to C++17 reduce compilation time. Here's my test of the results based on the ight official examples from the DLL library.
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Just last week, I've migrated my Expression Templates Library (ETL) library to C++17. It is now also done in my Deep Learning Library (DLL) library. In ETL, this resulted in much nicer code overall, but no real improvement in compilation time.
The objective of the migration of DLL was two-fold. First, I also wanted to simplify some code, especially with
if constexpr. But I also especially wanted to try to reduce the compilation time. In the past, I've already tried a few changes with C++17, with good results on the compilation of the entire test suite. While this is very good, this is not very representative of users of the library. Indeed, normally, you'll have only one network in your source file not several. The new changes will especially help in the case of many networks, but less in the case of a single network per source file.
This time, I decided to test the compilation on the examples. I've tested the eight official examples from the DLL library:
mnist_dbn: A fully connected deep belief network (DBN) on the MNIST dataset with three layers.
char_cnn: A special CNN with embeddings and merge and group layers for text recognition.
imagenet_cnn: A 12-layer convolutional neural network (CNN) for Imagenet.
mnist_ae: A simple two-layer auto-encoder for MNIST.
mnist_cnn: A simple six-layer CNN for MNIST.
mnist_deep_ae: A deep auto-encoder for MNIST, only fully connected.
mnist_lstm: A recurrent neural network (RNN) with long short-term memory (LSTM) cells
mnist_mlp: A simple fully connected network for MNIST, with dropout.
mnist_rnn: A simple RNN with simple cells for MNIST.
This is really representative of what users can do with the library and I think it's much better for compilation time.
For reference, you can find the source code of all the examples online.
Let's start with the results. I've tested this at different stages of the migration with clang 5 and GCC 7.2. I tested the following steps:
- The original C++14 version.
- Simply compiling in c++17 mode (-std=c++17).
- Using the C++17 version of the ETL library.
- Upgrading DLL to C++17 (without ETL).
- ETL and DLL in C++17 versions.
I've compiled each example independently in
release_debug mode. Here are the results for G++ 7.2:
The difference by just enabling C++17 is not significant. On the other hand, some significant gain can be obtained by using the C++17 version of ETL, especially for the DBN version and for the CNN versions. Except for the DBN case, the migration of DLL to C++17 did not bring any significant advantage. When everything is combined, the gains are more important! In the best case, the example is 14.6% faster to compile.
Let's see if it's the same with clang++ 5.0:
First of all, as I have seen time after time, clang is still slower than GCC. It's a not a big difference, but still significant. Overall, the gains are a bit higher on clang than on GCC, but not by much. Interestingly, the migration of DLL to C++17 is less interesting in terms of compilation time for clang. It seems even to slow down compilation on some examples. On the other hand, the migration of ETL is more important than on GCC.
Overall, every example is faster to compile using both libraries in C++17, but we don't have spectacular speed-ups. With clang, we have speedups from 3.3% to 15.3%. With GCC, we have speedup from 1.1% to 14.6%. It's not very high, but I'm already satisfied with these results.
C++17 in DLL
Overall, the migration of DLL to C++17 was quite similar to that of ETL. You can take a look at my previous article if you want more details on C++17 features I've used.
I've replaced a lot of SFINAE functions with
if constexpr. I've also replaced a lot of
if constexpr. There was a large number of these in DLL's code. I also enabled all the
constexpr that were commented for this exact time!
I was also thinking that I could replace a lot of meta-programming stuff with fold expressions. While I was able to replace a few of them, most of them were harder to replace with fold expressions. Indeed, the variadic pack is often hidden behind another class and, therefore, the pack is not directly usable from the network class or the group and merge layers classes. I didn't want to start a big refactoring just to use a C++17 feature; the current state of this code is fine.
I made some use of structured bindings, as well, but again, not as much as I was thinking. In fact, a lot of time, I'm assigning the elements of a pair or tuple to existing variables not declaring new variables and unfortunately, you can only use structured bindings with
Overall, the code is significantly better now, but there was less impact than there was on ETL. It's also a smaller code base, so maybe this is normal and my expectations were too high!
The trunk of DLL is now a C++17 library! I think this improves the quality of the code by a nice margin! Even though there is still some work to be done to improve the code — especially for the DBN pretraining code — the quality is quite good now. Moreover, the switch to C++17 made the compilation of neural networks using the DLL library faster to compile, from 1.1% in the worst case to 15.3% in the best case! I don't know when I will release the next version of DLL, but it will take some time. I'll especially have to polish the RNN support and add a sequence to sequence loss before I will release the 1.1 version of DLL.
I'm quite satisfied with C++17 even if I would have liked a bit more features to play with! I'm already a big fan of
if constexpr; this can make the code much nicer and fold expressions are much more intuitive than their previous recursive template counterpart.
I may also consider migrating some parts of the cpp-utils library, but if I do, it will only be through the use of conditionals in order not to break the other projects that are based on the library.
Published at DZone with permission of Baptiste Wicht, DZone MVB. See the original article here.
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