Generating Flex and Bison rules with X-Macros

Lately I found myself working on a pet project for which I needed a parser for an assembly-like programming language for a real architecture. Since I wanted to use this opportunity to learn something new, I decided to use a combination of tools, i.e. Flex (for lexing) and Bison (for parsing)... obviously in C++ :)

However I found myself facing a pretty interesting problem for which I could not find a workaround, therefore I set myself with the goal of filling that void with this post. 

The problem is easily explained, in the language I am parsing (let's call it X for brevity), there  is a large amount of opcodes, e.g. add, sub, bra. These operations have different properties (e.g. accepting different number and type of parameter) and I wanted to have a centralized specification for those properties which I can pull-out as needed throughout my project. This is a typical case where X-macros should be used. However since flex and bison specification files are not a C-like language, I cannot use the preprocessor capabilities. Before we jump to the problem I wanted to recap what X-Macros are and why they are awesome. 

Let us consider the following example:

By properly defining the two macros in the opcodes.def file, and thus take advantage of the C preprocessor, we can reshape the information in different ways. As an example consider a function which determines if an argument is valid for a given operation:

As you expect the output of the code above is:
Is 'sub(_,_,_,reg)' valid? false
Is 'add(_,reg,_)'   valid? true
Is 'add(_,imm,_)'   valid? false
Is 'sub(_,_,imm)'   valid? true
Is 'sub(_,_,lab)'   valid? false

Cool eh? and think how easy is going to be adding a new opcode or change their semantics. This is quite standard practice in compiler-related project, beware that working with macros can get messy... but what not to love here? If you start to get weird errors by your compiler, sometimes it is instructive to run only through the preprocessor and have a look at the generated code. This can be done in GCC using the -E option (checkout an example for the code above here).
In my experience many programmers consider these meta-programming techniques (i.e., macros and templates) harmful and therefore to avoid like the plague. Of course everyone is entitled to his opinion but in a problem like this one, X-macros can save you an huge amount of time. The generated code is also a lot faster and more memory efficient then any implementation based on lookup tables.

Anyhow I am digressing... I hope all those macro haters have had enough by now and they left this post so that we can start to get things to the next level. Without all those 'C is better than C++' holding us back, can move on solving the actual problem which inspired this post. The idea is to use the the properties of X-macros inside the lexer and parser specification for a language parser. Since we are working in C++, the natural choice of tools is Flex (for lexing) and Bison (for parsing) [another choice could be Boost.Spirit, but I never felt the tool to be mature enough to be practical useful, feel free to comment on this]. For those who are not aware of parser generator tools, these are (meta-)programs which take a language specification (usually in a EBNF form) and produce code to determine whether an input stream matches that specification.

This is not going to be a tutorial on how to use Flex and Bison, there are other tutorials/blogs you can check out for that (Google is your friend). I start by setting up a basic flex and bison specification for parsing our language X. X allows 1 instruction per line, where each instruction is composed by an operator and a list of comma-separated arguments. Labels are allowed as well. For example the following is valid X program:

add r1, r2, r3 
sub r1, r1, #10
bra r1, exit
...
exit:
...

Follows the Flex and Bison specification files for the X language:
and
In order to compile this code we need a Makefile which triggers the generation of the lexer and parser. We are going to base our setup on cmake, checkout the CMakeLists.txt file for details. You can test out the code so far by checking out the following github repository (refer to the no_autogen branch).

You may start to notice the problem now. Our opcodes, which we defined in the opcodes.def file, need to be defined (again and again) both in flex and bison specifications. Since these are not C/C++ files, we cannot rely on the preprocessor for generating these rules automatically. This makes adding new opcodes to the X language very cumbersome since many files need to be modified. However there is my solution to the problem.

The idea is to generate temporary C/C++ files used to reshape the information contained in the opcodes.def file in a form that it is suitable for flex and bison. After that we replace the content generated by X-Macros in the language specification. The workflow is not particularly difficult, however we the main effort would be on automatizing this entire process so that specification files are refreshed when needed (i.e. when we lunch make).

Let's start with the Flex specification file (lexer.ll). My solution of the problem is the following. We define a placeholder, i.e. @@{{OPCODES}}@@ (you can choose any name for the placeholder, however make sure that it is unique within your flex specification),  which we are going to replace with some content automatically generated by the preprocessor. We also rename the file into lexer.in.ll which we are now going to call the template file. Next thing we need to do is to add to CMake the necessary action for generating and replacing the placeholder with content.
Similar thing can be done for the bison specification file. Note that here things are a little bit more complex since we need to declare opcodes both in the token section (line 28 of the parser.y file) and later in the grammar itself within the parsing rules (line 53 of the parser.y file). The full CMakeLists.txt file is available here.

In order to make replacement of placeholders with the preprocessor output I wrote a simple python script (i.e. xmacro_patcher.py). The script does remove empty lines and comments produced by the preprocessor and collapse the lines if needed. I actually spent lot of time finding out a way of doing this without the need of an additional script. I found a command line which will do the same work but unfortunately the way CMake treats the command line arguments in the COMMAND section made it impossible to use (damn you CMake). Anyway, in the case anyone is curious

awk -vf2="$(while read x; do echo $x; done)" '/$1/{print;print f2;next}1' $2

This like will read content from stdin into variable 'f2', then match the input file $2 with the regex provided in $1 and append the content of f2 at that location. The patched file will be retuned on the standard output.

And that's really it. We can now add new operators to our opcodes.def, press make... and our parser will now accept the broader language.

C++ <3

My take on serialization (Part III: deserialize)

After seeing serialization, one fundamental thing is missing, i.e. deserialization!

There is not much to say here, this is the inverse operation we performed during serialization... therefore similar patters apply.

And we are done! 

In the std::tuple<T...> deserializer I would have liked to use the commented line. With that line I could remove 20+ lines of code which are used by the deserialize_tuple method. However, in that way the object is deserialized in the inverse order. The type is correct, but since it seems that the arguments of the make_tuple function are evaluated right-to-left, the resulting elements of the tuple are inverted. Therefore a serialized tuple (1,2,3) is deserialized back as (3,2,1) :(. This is caused by the fact that the apply function has side-effects and in C++ we cannot rely on the evaluation order of the arguments of a function, therefore this code is not safe and better take the safe solution (however it might work in some C++ compiler). 

Just to see if everything is working fine we write our usual test cases using google-test: 

Last thing to do is running a complete benchmark where we serialize and deserialize an object and compare it with boost::serialization. This time I compiled everything with optimizations enabled (-O3) and I am using gcc 4.8.0 20130502 (pre-release).

The code is similar to the one we saw in the previous post this time I add a call to deserialize and a stupid if to be sure the compiler is not doing any dead-code elimination. The code for boost::serialization is similar, just trust me (I know I am Italian... it might be difficult... but come on, as my supervisor says... "give me a break").

The result is well... quite impressive. I didn't do this exercise with performance in mind, rather than my goal was to eliminate a dependency on the boost libraries. Now I realize that boost is definitely doing something really wrong in the serialization library. The added storing of typing info does not justify the huge performance penalty. My solution is 20x faster! Since the messages I produce are half of the size (thanks to the missing typing info) I would expect boost to be twice as slow.

I am frankly quite pleased by the performance improvements I saw within the libWater project after replacing boost::serialization with this solution. We had a 10% performance improvement which in HPC is quite welcome.

The full code, plus the test cases are available on github (under the BSD license): https://github.com/motonacciu/meta-serialization
(contributions are welcome)

Read: PART I: get_size(...)

Read: PART II: serialize(...)

C++ <3

My take on serialization (Part II: serialize)

After discussed the get_size(...) functor, which given an object returns its serialized size in bytes, we can go on and write the serialize function.

We can follow the same pattern of the get_size, but this time we have to store the content to a stream.

As before, we have a specialization of the serialize_helper template for tuples, vectors, strings and POD datatypes. In order to be performance efficient we presize the vector in lines 66-67 (because we know how many bytes we need to store the object) thanks to the get_size predicate.

Let's write some unit tests (because they are very important):

So it's time for running some benchmarks. YAY! Let's compare this serializer with boost and check whether spending time re implementing this was worth some. Since I don't have much time, we only run 1 experiment... if you are interested you can run more :) 

So, as long as size of the serialized stream is concerned, boost uses 63 bytes while ours only 26 bytes (more than half). Performance-wise boost::serialization needs 4.410 secs to run the test, our serialization solution 0.975 seconds, more than 4 times faster. Of course we are not saying that boost::serialization sucks... as a matter of fact it solves a different problem since it also store the typing information which allows everyone (even a Java program) to reload the stream. Our serialization strategy (as I explained in the first post) is based on the fact that the type is known at receiver side (therefore we can avoid to store typing).

Let's show some graphs, comparison of our serialization strategy relative to boost::serialization:

Read: PART I: get_size(...)
Read: PART III: deserialize(...)

C++ <3

My take on C++ serialization (Part I: get_size)

Long time, no see!

Lately I have been busy writing... and writing... and we know that: "All writing and no coding makes Simone a dull boy" :) so I needed to go back to C++ writing... which means new blog entries!

Lately, in a project (libwater, soon released) I have been using boost::serialization in order to transfer C++ objects from one node to another via network. Boost serialization is pretty neat, especially when you are under a deadline, but releasing the code with a dependency on boost is in most of the case an overkill.  Especially when the boost library you are using is not an "header-only" library, which means the user needs it installed in his system. Since the project focuses on large clusters, this is always a big problem because these advanced libraries are in the majority of the cases missing (or a very old version is installed) meaning that the user has to do lot of installation work just to try out our library.

Beside that, boost::serialization was doing more than we actually needed, in fact I am in the particular case where I exactly know the type of the object I am receiving on the remote node, therefore I don't need to store in the serialization stream the type information like boost does. This is typical when for example you are implementing a network protocol where the structure of messages is known. In such case we can assign a tag to each message type and easily assign a C++ type to it. Since the list of messages is known statically, we can use some meta-programming just to have some fun.

Actually I could literally half the size of each transferred message by only storing the object content. Therefore I started to work on a "header-only"  serialization interface... of course it had to involve template meta-programming! :) I am going to post the various pieces of my take on object serialization on multiple posts (otherwise it may get to heavy to read).

The first thing (which I will introduce today) was defining a functor which given an object returns the size (in bytes) of its serialized representation, I called it size_t get_size(const T& obj). This is some easy code which needs to be written, so let's get to it.

Stated that the types I deal with are (for now) restricted to std::string, integral types (uint8_t, uint16_t, uint32_t, uint64_t), std::vector<T> and std::tuple<T...>, this code should make the trick. While for tuples and integral types we directly store the value, for vectors and string we prepend the number of elements which will be stored (since this information is not explicit in the type). 

For tuples I I hope I had a better solution using template expansion. This could be possible if for example the get_size was more like sizeof... which means it can be computed based on the object type . This would allow me to write something like this: std::sum(sizeof(T)...).

However, since we may have strings and vectors as elements of the tuple, this is not possible because we would need to also unpack the elements of the tuple (not only the type). Actually I would appreciated anyone that could give me a better solution for that. 

A simple test:

Read: PART II: serialize(...)

Read: PART III: deserialize(...) 

C++ <3