Advanced Configuration

Folder structure

Fab creates files in the Project Workspace

<your $FAB_WORKSPACE>
   <project workspace>
      source/
      build_output/
         *.f90 (preprocessed Fortran files)
         *.mod (compiled module files)
         _prebuild/
            *.an (analysis results)
            *.o (compiled object files)
            *.mod (mod files)
      metrics/
      my_program.exe
      log.txt

The project workspace folder takes its name from the project label passed in to the build configuration.

The source folder is where grab steps place their files.

The build_output folder is where steps put their processed files. For example, a preprocessor reads .F90 from source and writes .f90 to build_output.

The _prebuild folder contains reusable output. Files in this folder include a hash value in their filenames.

The metrics folder contains some useful stats and graphs. See Metrics.

Managed arguments

Fab manages a few command line arguments for some of the tools it uses.

Fortran Preprocessors

Fab knows about some preprocessors which are used with Fortran, currently fpp and cpp. It will ensure the -P flag is present to disable line numbering directives in the output, which is currently required for fparser to parse the output.

Fortran Compilers

Fab knows about some Fortran compilers (currently gfortran or ifort). It will make sure the -c flag is present to compile only (not link).

If the compiler flag which sets the module folder is present, i.e. -J for gfortran or -module for ifort, Fab will remove the flag, with a notification, as it needs to use this flag to control the output location.

Overriding defaults

Command line tools

Fab uses well-known environment variables for tool configuration.

Environment variables

FPP	Fortran preprocessor, e.g fpp or cpp -traditional-cpp -P. Fab ensures the -P is present.
FC	Fortran compiler, e.g gfortran or ifort -c. Fab ensures the -c is present.
FFLAGS	Fortran compiler flags.
CC	C compiler.
CFLAGS	C compiler flags.
LD	Linker, e.g ld.
LFLAGS	Linker flags.

Collection names

You can change the collections which most steps read from and write to. Let’s imagine we need to upgrade a build script, adding a custom step to prepare our Fortran files for preprocessing.

find_source_files(state)  # this was already here

# instead of this
# preprocess_fortran(state)

# we now do this
my_new_step(state, output_collection='my_new_collection')
preprocess_fortran(state, source=CollectionGetter('my_new_collection'))

analyse(state)  # this was already here

Flags

Linker flags

We can add flags to our linker step.

link_exe(state, flags=['-lm', '-lnetcdf'])

Path-specific flags

For preprocessing and compilation, we sometimes need to specify flags per-file. These steps accept both common flags and path specific flags.

...
compile_fortran(
    common_flags=['-O2'],
    path_flags=[
        AddFlags('$output/um/*', ['-I' + '/gcom'])
    ],
)

This will add -O2 to every invocation of the tool, but only add the /gcom include path when processing files in the <project workspace>/build_output/um folder.

Path matching is done using Python’s fnmatch. The $output is a template, see AddFlags.

We can currently only add flags for a path. Future development could add capability to remove or modify flags by path.

Note

This can require some understanding of where and when files are placed in the build_output folder: It will generally match the structure you’ve created in <project workspace>/source, with your grab steps. Early steps like preprocessors generally read files from source and write to build_output. Later steps like compilers generally read files which are already in build_output.

C Code

The C pragma injector creates new C files with “.prag” file extensions, in the source folder. The C preprocessor looks for the output of this step by default. If not found, it will fall back to looking for .c files in the source listing.

...
c_pragma_injector(state)
preprocess_c(state)
...

Custom Steps

If you need a custom build step, you can create a function with the @step decorator.

Fab includes some examples of a custom step. A simple example was created for building JULES. The root_inc_files() step copies all .inc files in the source tree into the root of the source tree, to make subsequent preprocessing flags easier to configure.

That was a simple example that didn’t need to interact with the Artefact Store. Sometimes, inserting a custom step means inserting a new Artefact Collection into the flow of data between steps. We can tell a subsequent step to read our new artefacts, instead of using it’s default Artefacts Getter. We do this using the source argument, which most Fab steps accept.

@step
def custom_step(state):
        state._artefact_store['custom_artefacts'] = do_something(state._artefact_store['step 1 artefacts'])


with BuildConfig(project_label='<project label>') as state:
    fab_step1(state)
    custom_step(state)
    fab_step2(state, source=CollectionGetter('custom_artefacts'))

Steps have access to multiprocessing methods. The Steps module includes a multiprocessing helper function called run_mp() which steps can call to process a collection of artefacts in parallel.

@step
def custom_step(state):
    input_files = artefact_store['custom_artefacts']
    results = run_mp(state, items=input_files, func=do_something)

Parser Workarounds

Unrecognised Dependencies

If a language parser is not able to recognise a dependency within a file, then Fab won’t know the dependency needs to be compiled. For example, some versions of fparser don’t recognise a call on a one-line if statement. In this case we can manually add the dependency using the unreferenced_deps argument to analyse().

Pass in the name of the called function. Fab will find the file containing this symbol and add it, and all its dependencies, to every Build Tree.

...
analyse(state, root_symbol='my_prog', unreferenced_deps=['my_func'])
...

Unparsable Files

If a language parser is not able to process a file at all, then Fab won’t know about any of its symbols and dependencies. This can sometimes happen to valid code which compilers are able to process, for example if the language parser is still maturing and can’t yet handle an uncommon syntax. In this case we can manually give Fab the analysis results using the special_measure_analysis_results argument to analyse().

Pass in a list of FortranParserWorkaround objects, one for every file that can’t be parsed. Each object contains the symbol definitions and dependencies found in one source file.

...
analyse(
    config,
    root_symbol='my_prog',
    special_measure_analysis_results=[
        FortranParserWorkaround(
            fpath=Path(state.build_output / "path/to/file.f90"),
            module_defs={'my_mod'}, symbol_defs={'my_func'},
            module_deps={'other_mod'}, symbol_deps={'other_func'}),
    ])
...

In the above snippet we tell Fab that file.f90 defines a module called my_mod and a function called my_func, and depends on a module called other_mod and a function called other_func.

Custom Step

An alternative approach for some problems is to write a custom step to modify the source so that the language parser can process it. Here’s a simple example, based on a real workaround where the parser gets confused by a variable called NameListFile.

@step
def my_custom_code_fixes(state):
    fpath = state.source_root / 'path/to/file.F90'
    in = open(fpath, "rt").read()
    out = in.replace("NameListFile", "MyRenamedVariable")
    open(fpath, "wt").write(out)

with BuildConfig(project_label='<project_label>') as state:
    # grab steps first
    my_custom_code_fixes(state)
    # find_source_files, preprocess, etc, afterwards

Two-Stage Compilation

The compile_fortran() step compiles files in passes, with each pass identifying all the files which can be compiled next, and compiling them with parallel processing.

Some projects have bottlenecks in their compile order, where lots of files are stuck behind a single file which is slow to compile. Inspired by Busby, Fab can perform two-stage compilation where all the modules are built first in fast passes using the -fsyntax-only flag, and then all the slower object compilation can follow in a single pass.

The potential benefit is that the bottleneck is shortened, but there is a tradeoff with having to run through all the files twice. Some compilers might not have this capability.

Two-stage compilation is configured with the two_stage_flag argument to the Fortran compiler.

compile_fortran(state, two_stage_flag=True)

Configuration Reuse

If you find you have multiple build configurations with duplicated code, it could be helpful to refactor out the commonality into a shared module.

In Fab’s example run configs, we have two build scripts to compile GCOM. Much of the configuration for these two scripts is identical. We extracted the common steps into gcom_build_steps.py and used them in build_gcom_ar.py and build_gcom_so.py.

Separate grab and build scripts

If you are running many builds from the same source, you may wish to grab your repo in a separate script and call it less frequently. In this case your grab script might only contain a single step. You could import your grab configuration to find out where it put the source.

my_grab.py

my_grab_config = BuildConfig(project_label='<project_label>')

if __name__ == '__main__':
    with my_grab_config:
        fcm_export(my_grab_config, src='my_repo')

my_build.py

from my_grab import my_grab_config


if __name__ == '__main__':
    with BuildConfig(project_label='<project_label>') as state:
        grab_folder(state, src=grab_config.source_root),

Housekeeping

Fab will remove old files from the prebuilds folder. By default, it will remove all prebuild files that are not part of the current build. If you add a cleanup_prebuilds() step, you can keep prebuild files for longer. This may be useful, for example, if you often switch between two versions of your code and want to keep the prebuild speed benefits when building both.

Shared prebuilds

You can copy the contents of someone else’s prebuilds folder into your own. Fab uses hashes to keep track of the correct prebuilt files, and will find and use them. There’s also a helper step called grab_pre_build() you can add to your build configurations.

Psykalite (Psyclone overrides)

If you need to override a PSyclone output file with a handcrafted version, you can use the overrides_folder argument to the psyclone() step. This is just a normal folder containing source files. The step will delete any files it creates if there’s a matching filename in the overrides folder.