Typhon is a virtual machine for Monte. It loads and executes Kernel-Monte from Monte AST files.

You will need RPython from Mercurial:

$ hg clone https://bitbucket.org/pypy/pypy
$ export PYTHONPATH=pypy:.

Typhon operates in both untranslated and translated modes, with an optional JIT. Regardless of mode of operation, you'll need some dependencies (Twisted and RPython), so create a virtualenv:

$ virtualenv local-typhon -p pypy
$ . local-typhon/bin/activate
$ pip install -r requirements.txt

If you don't have a system PyPy, you can leave off -p pypy, but be warned that this will increase run times. Once that's done, Typhon can be run untranslated:

$ python main.py your/awesome/script.ty

Translation is done via the RPython toolchain:

$ python -m rpython -O2 main

The JIT can be enabled with a switch:

$ python -m rpython -Ojit main

You can also build a slow version with libgc, for testing purposes:

$ python -m rpython -O0 main

The resulting executable is immediately usable for any scripts that don't use prelude features:

$ ./mt-typhon your/awesome/script.ty

Note that translation is not cheap. It will require approximately 0.5GiB memory and 2min CPU time on a 64-bit x86 system to translate a non-JIT Typhon executable, or 1GiB memory and 9min CPU time with the JIT enabled.

Without a prelude, Typhon doesn't do much. Most Monte applications have a reasonable expectation of certain non-kernel features, which are implemented in Monte via a prelude and library.

Fortunately, Typhon comes with a self-hosting Monte compiler and a prebuilt prelude, in the boot directory. To use it:

$ ./mt-typhon -l boot script.ty

And the rest of the MAST library can be built immediately:

$ make mast

Then, you can use the newly built prelude and library:

$ ./mt-typhon -l mast another/awesome/script.ty

Contributions are welcome. Please ensure that you're okay with the license!

Unit tests are tested by Travis. You can run them yourself; I recommend the Trial test runner:

$ trial typhon

By default, git won't show diffs of binary files. I don't especially blame it. However, with a bit of a filter, we can give git what it needs:

$ git config diff.typhon.textconv ./dump.py

This configuration option, along with the .gitattributes in the repository, will let git display textual diffs of the binary ASTs.

Here's what you need to know about RPython and things imported from rpython.rlib.

Like Monte, RPython values immutable structures. Whenever a class is immutable, adding the _immutable_ = True annotation will cause RPython to enforce an immutability variant: The fields of an instance of that class can only be assigned to once.

It's possible to make only some fields immutable; just list all the immutable fields in a tuple with _immutable_fields_ = "this", "that". To make a field an immutable list with immutable elements, use a [*], as in _immutable_fields_ = "this", "that", "those[*]".

r.r.jit is mostly about hints to inform the JIT about the behavior of code paths. Some hints are safe and some are not safe.

The JIT colors all values as "red" or "green"; a red value is non-constant and a green value is constant. promote accepts a red value and turns it into a green value. The JIT will reflect this with a guard on the value of the given object. When a value is expected to have a relatively small number of possibilities, a promote can be very effective at improving the performance of the code. promote is safe; it will never cause the JIT to generate wrong code, although it can cause the JIT to perform too much compilation.

jit_debug() can print one string and up to four integers to the JIT log. The computation which prepares the debug message is part of the JIT trace, so it is ideal to have the inputs be green values.

elidable functions must be referentially transparent. In return, the JIT accepts the promise of referential transparency and will try to reorder or remove the call to the elidable function when it can. The function need not actually be pure; it is sufficient for it to appear pure in all cases. If the function is not pure, then elidable is unsafe, since the JIT will not second-guess a promise of elidability.

elidable_promote changes a function so that it is elidable and all of its arguments will be promote'd before entering the function body. It is unsafe, like elidable.

dont_look_inside forces the JIT to not inline calls to a function. It is sometimes necessary to protect events like GIL handling or I/O. It can also be a big improvement for calls to functions which don't inline well due to recursive or other strange behavior. It should be safe.

unroll_safe forces the JIT to consider inlining calls to functions which were not inlinable due to containing loops. This is important because the JIT will otherwise refuse to look inside those functions. Usage of unroll_safe is an informal promise to the JIT that the loops in the function are tightly bounded in the number of iterations which will be performed. While not unsafe, unroll_safe can cause exponential amounts of overcompilation and overtracing, so it should be used sparingly.

How are these used within the codebase? Values that are expected to be green but aren't green-inferred by the JIT are promote'd. Functions that do I/O have dont_look_inside. Functions which are pure and called often are elidable. Lots of factoring has been done to make small chunks of code elidable.

If a function has a loop that is conditionally called, it is useful to factor the loop to a separate function and then consider whether to mark the new function with unroll_safe. Even if the function isn't actually safe to unroll, merely the factorization of code is sufficient to allow the JIT to look into the original function. This happens with every object which is defined in RPython; the dispatch function, callAtom() or similar, is factored to not have loops within it. Since atoms are (usually) green values during execution, this means that callAtom() gets specialized for that atom, and the actual work can usually be inlined.

We use RPython's Unicode database. The magic incantation:

from rpython.rlib.unicodedata import unicodedb_6_2_0 as unicodedb

unicodedb will have plenty of useful functions, like islower() and isalpha(). These functions are not available as methods on unicode objects.

If you create a new object by subclassing Object or calling @runnable, please give it a docstring. The docstrings will be reflected into Monte, so please follow these guidelines:

• The first line should describe the object.
• Subsequent lines should describe specifics of the object's nature which might be helpful to somebody calling help() on the object.
• Docstrings should refer to their object as "this object".
• In-jokes are sometimes allowed. Ask on IRC.
• Dry language is always allowed.
• Unicode is encouraged; do not be afraid to use symbols which are generally available in Unicode fonts. Ask on IRC if unsure.

An example:

▲> help(Any) Result: Object type: AnyGuard A guard which admits the universal set. This object specializes to a guard which admits the union of its subguards: Any[X, Y, Z] =~ X ∪ Y ∪ Z

Also document objects and methods written in Monte. For methods:

• "this method" is the correct self-reference.
• To refer to names defined in the method specification, surround the name in backticks.
• To refer to methods, use atom syntax and backticks: A method with name "meth" and arity 2 would appear as `meth/2`.

Autohelp would like to remind you that subclasses of Object should decorate themselves with @autohelp in order to maintain compliance and safety.

To override pretty-printing for an object, add a toString() method which should return a Unicode string.