Bug Fixes

• Fix, attribute assignments or deletions where the assigned value or the attribute source was statically raising crashed the compiler.

• Fix, the order of evaluation during optimization was considered in the wrong order for attribute assignments source and value.

• Windows: Fix, when g++ is the path, it was not used automatically, but now it is.

• Windows: Detect the 32 bits variant of MinGW64 too.

• Python3.4: The finalize of compiled generators could corrupt reference counts for shared generator objects. Fixed in 0.5.18.1 already.

• Python3.5: The finalize of compiled coroutines could corrupt reference counts for shared generator objects.

Optimization

• When a variable is known to have dictionary shape (assigned from a constant value, result of dict built-in, or a general dictionary creation), or the branch merge thereof, we lower subscripts from expecting mapping nodes to dictionary specific nodes. These generate more efficient code, and some are then known to not raise an exception.

  def someFunction(a, b):
      value = {a: b}
      value["c"] = 1
      return value
  

  The above function is not yet fully optimized (dictionary key/value tracing is not yet finished), however it at least knows that no exception can raise from assigning value["c"] anymore and creates more efficient code for the typical result = {} functions.

• The use of "logical" sharing during optimization has been replaced with checks for actual sharing. So closure variables that were written to in dead code no longer inhibit optimization of the then no more shared local variable.

• Global variable traces are now faster to decide definite writes without need to check traces for this each time.

Cleanups

• No more using "logical sharing" allowed to remove that function entirely.

• Using "technical sharing" less often for decisions during optimization and instead rely more often on proper variable registry.

• Connected variables with their global variable trace statically avoid the need to check in variable registry for it.

• Removed old and mostly unused "assume unclear locals" indications, we use global variable traces for this now.

Summary

This release aimed at dictionary tracing. As a first step, the value assign is now traced to have a dictionary shape, and this this then used to lower the operations which used to be normal subscript operations to mapping, but now can be more specific.

Making use of the dictionary values knowledge, tracing keys and values is not yet inside the scope, but expected to follow. We got the first signs of type inference here, but to really take advantage, more specific shape tracing will be needed.

SSA (Single State Assignment Form)

The SSA using release has been made last summer. Recent releases have lifted more and more restrictions on where and now it is applied and made sure the internal status is consistent and true. And that trend is going to continue even more.

For shared variables (closure variables and module variables), Nuitka is still too conservative to make optimization. Code does annotate value escapes, but it's not yet trusting it. The next releases will focus on lifting that kind of restriction, and for quality of result, that will mean making a huge jump ahead once that works, so module variables used locally a lot will become even faster to use then and subject to static optimization too.

Function Inlining

When doing my talk to EuroPython 2015, I was demoing it that, and indeed, what a break through. The circumstances under which it is done are still far too limited though. Essentially that ability is there, but will not normally be noticeable yet due to other optimization, e.g. functions are most often module variables and not local to the using function.

More code generation improvements will be needed to be able to inline functions that might raise an exception. Also the "cost" of inlining a function is also very much an unsolved issue. It will become the focus again, once the SSA use as indicated above expands to module variables, as then inlining other things than local functions will be possible too.

So there is a lot of things to do for this to really make a difference to your programs. But it's still great to have that part solved so far.

Scalability

Compatibility

Important things have happened here. Full compatibility mode is planned to not be the default anymore in upcoming releases, but that will only mean to not be stupid compatible, but to e.g. have more complete error messages than CPython, more correct line numbers, or for version differences, the best Python version behaviour.

The stable release has full support for Python 3.5, including the new async and await functions. So recent releases can pronounce it as fully supported which was quite a feat.

I am not sure, if you can fully appreciate the catch up game needed to play here. CPython clearly implements a lot of features, that I have to emulate too. That's going to repeat for every major release.

The good news is that the function type of Nuitka is now specialized to the generators and classes, and that was a massive cleanup of its core that was due anyway. The generators have no more their own function creation stuff and that has been helpful with a lot of other stuff.

Another focus driven from Python3, is to get ahead with type shape tracing and type inference of dictionary, and value tracing. To fully support Python3 classes, we need to work on something that is a dictionary a-like, and that will only ever be efficient if we have that. Good news is that the next release is making progress there too.

Performance

Standalone

The standalone mode of Nuitka was pretty good, and continued to improve further, but I don't care much.

Other Stuff

Future

So, there is multiple things going on:

• Type Inference

  With SSA in place, Nuitka starts to recognize types, and treat things that work something assigned from {} or dict built-in with special nodes and code.

  That's going to be a lot of work. For float and list there are very important use cases, where the code can be much better. But dict is the hardest case, and to get the structure of shape tracing right, we are going there first.

• Shape Analyisis

  The plan for types, is not to use them, but the more general shapes, things that will be more prevalent than actual type information in a program. In fact the precise knowledge will be rare, but more often, we will just have a set of operations performed on a variable, and be able to guess from there.

  Shape analysis will begin though with concrete types like dict. The reason is that some re-formulations like Python3 classes should not use locals, but dictionary accesses throughout for full compatibility. Tracing that correctly to be effectively the same code quality will allow to make that change.

• Plug-ins

  Something I wish I could have shown at EuroPython was plug-ins to Nuitka. It has become more complete, and some demo plug-ins for say Qt plugins or multiprocessing, are starting to work, but it's not progressing recently. The API will need work and of course documentation. Hope is for this to expand Nuitka's reach and appeal to get more contributors.

  It would be sweet, if there were any takers, aiming to complete these things.

• Nested frames

  One result of in-lining will be nested frames still present for exceptions to be properly annotated, or locals giving different sets of locals and so on.

  Some cleanup of these will be needed for code generation and SSA to be able to attach variables to some sort of container, and for a function to be able to reference different sets of these.

Let me know, if you are willing to help. I really need that help to make things happen faster. Nuitka will become more and more important only. And with your help, things will be there sooner.

Release Focus

One thing I have started recently, is to make changes to Nuitka focused to just one goal, and to only deal with the rare bug in other fields, but not much else at all. So instead of across the board improvements in just about everything, I have e.g. in the last release added type inference for dictionaries and special nodes and their code generation for dictionary operations.

This progresses Nuitka in one field. And the next release then e.g. will only focus on making the performance comparison tool, and not continue much in other fields.

That way, more "flow" is possible and more visible progress too. As an example of this, these are the focuses of last releases.

• Full Python 3.5 on a clean base with generators redone so that coroutines fit in nicely.

• Scalability of C compilation with argument parsing redone

• Next release soon: Shape analysis of subscript usages and optimization to exact dictionaries

• Next release thereafter: Comparison benchmarking (vmprof, resolving C level function identifiers easier)

Other focuses will also happen, but that's too far ahead. Mostly like some usability improvements will be the focus of a release some day. Focus is for things that are too complex to attack as a side project, and therefore never happen although surely possible.

Digging into Python3.5 coroutines and their semantics was hard enough, and the structural changes needed to integrate them properly with not too much special casing, but rather removing existing special cases (generator functions) was just too much work to ever happen while also doing other stuff.

Summary

So I am very excited about Nuitka. It feels like the puzzle is coming together finally, with type inference becoming a real thing. And should dictionaries be sorted out, the real important types, say float for scientific use cases, or int, list for others, will be easy to make.

With this, and then harder import association (knowing what other modules are), and module level SSA tracing that can be trusted, we can finally expect Nuitka to be generally fast and deserve to be called a compiler.

That will take a while, but it's likely to happen in 2016. Let's see if I will get the funding to go to EuroPython 2016, that would be great.

Bug Fixes

• Compatibility: The nested arguments functions can now be called using their keyword arguments.

  def someFunction(a, (b, c)):
      return a, b, c
  
  
  someFunction(a=1, **{".1": (2, 3)})
  

• Compatibility: Generators with Python3.4 or higher now also have a __del__ attribute, and therefore properly participate in finalization. This should improve their interactions with garbage collection reference cycles, although no issues had been observed so far.

• Windows: Was outputting command line arguments debug information at program start. Issue#284. Fixed in 0.5.17.1 already.

Optimization

• Code generated for parameter parsing is now a lot less verbose. Python level loops and conditionals to generate code for each variable has been replaced with C level generic code. This will speed up the backend compilation by a lot.

• Function calls with constant arguments were speed up specifically, as their call is now fully prepared, and yet using less code. Variable arguments are also faster, and all defaulted arguments are also much faster. Method calls are not affected by these improvements though.

• Nested argument functions now have a quick call entry point as well, making them faster to call too.

• The slice built-in, and internal creation of slices (e.g. in re-formulations of Python3 slices as subscripts) cannot raise. Issue#262.

• Standalone: Avoid inclusion of bytecode of unittest.test, sqlite3.test, distutils.test, and ensurepip. These are not needed, but simply bloat the amount of bytecode used on e.g. macOS. Issue#272.

• Speed up compilation with Nuitka itself by avoid to copying and constructing variable lists as much as possible using an always accurate variable registry.

Cleanups

• Nested argument functions of Python2 are now re-formulated into a wrapping function that directly calls the actual function body with the unpacking of nested arguments done in nodes explicitly. This allows for better optimization and checks of these steps and potential in-lining of these functions too.

• Unified slice object creation and built-in slice nodes, these were two distinct nodes before.

• The code generation for all statement kinds is now done via dispatching from a dictionary instead of long elif chains.

• Named nodes more often consistently, e.g. all loop related nodes start with Loop now, making them easier to group.

• Parameter specifications got simplified to work without variables where it is possible.

Organizational

• Nuitka is now available on the social code platforms gitlab as well.

Summary

Long standing weaknesses have been addressed in this release, also quite a few structural cleanups have been performed, e.g. strengthening the role of the variable registry to always be accurate, is groundlaying to further improvement of optimization.

However, this release cycle was mostly dedicated to performance of the actual compilation, and more accurate information was needed to e.g. not search for information that should be instant.

Upcoming releases will focus on usability issues and further optimization, it was nice however to see speedups of created code even from these scalability improvements.

Bug Fixes

• Windows: Command line arguments that are unicode strings were not properly working.

• Compatibility: Fix, only the code object attached to exceptions contained all variable names, but not the one of the function object.

• Python3: Support for virtualenv on Windows was using non-portable code and therefore failing. Issue#266.

• The tree displayed with --display-tree duplicated all functions and did not resolve source lines for functions. It also displayed unused functions, which is not helpful.

• Generators with parameters leaked C level memory for each instance of them leading to memory bloat for long running programs that use a lot of generators. Fixed in 0.5.16.1 already.

• Don't drop positional arguments when called with --run, also make it an error if they are present without that option.

New Features

• Added full support for Python3.5, coroutines work now too.

Optimization

• Optimized frame access of generators to not use both a local frame variable and the frame object stored in the generator object itself. This gave about 1% speed up to setting them up.

• Avoid having multiple code objects for functions that can raise and have local variables. Previously one code object would be used to create the function (with parameter variable names only) and when raising an exception, another one would be used (with all local variable names). Creating them both at start-up was wasteful and also needed two tuples to be created, thus more constants setup code.

• The entry point for generators is now shared code instead of being generated for each one over and over. This should make things more cache local and also results in less generated C code.

• When creating frame codes, avoid working with strings, but use proper emission for less memory churn during code generation.

Organizational

• Updated the key for the Debian/Ubuntu repositories to remain valid for 2 more years.

• Added support for Fedora 23.

• MinGW32 is no more supported, use MinGW64 in the 32 bits variant, which has less issues.

Cleanups

• Detecting function type ahead of times, allows to handle generators different from normal functions immediately.

• Massive removal of code duplication between normal functions and generator functions. The later are now normal functions creating generator objects, which makes them much more lightweight.

• The return statement in generators is now immediately set to the proper node as opposed to doing this in variable closure phase only. We can now use the ahead knowledge of the function type.

• The nonlocal statement is now immediately checked for syntax errors as opposed to doing that only in variable closure phase.

• The name of contraction making functions is no longer skewed to empty, but the real thing instead. The code name is solved differently now.

• The local_locals mode for function node was removed, it was always true ever since Python2 list contractions stop using pseudo functions.

• The outline nodes allowed to provide a body when creating them, although creating that body required using the outline node already to create temporary variables. Removed that argument.

• Removed PyLint false positive annotations no more needed for PyLint 1.5 and solved some TODOs.

• Code objects are now mostly created from specs (not yet complete) which are attached and shared between statement frames and function creations nodes, in order to have less guess work to do.

Tests

• Added the CPython3.5 test suite.

• Updated generated doctests to fix typos and use common code in all CPython test suites.

Summary

This release continues to address technical debt. Adding support for Python3.5 was the major driving force, while at the same time removing obstacles to the changes that were needed for coroutine support.

With Python3.5 sorted out, it will be time to focus on general optimization again, but there is more technical debt related to classes, so the cleanup has to continue.

Bug Fixes

• Fix, the len built-in could give false values for dictionary and set creations with the same element.

  # This was falsely optimized to 2 even if "a is b and a == b" was true.
  len({a, b})
  

• Python: Fix, the gi_running attribute of generators is no longer an int, but bool instead.

• Python3: Fix, the int built-in with two arguments, value and base, raised UnicodeDecodeError instead of ValueError for illegal bytes given as value.

• Python3: Using tokenize.open to read source code, instead of reading manually and decoding from tokenize.detect_encoding, this handles corner cases more compatible.

• Fix, the PyLint warnings plug-in could crash in some cases, make sure it's more robust.

• Windows: Fix, the combination of Anaconda Python, MinGW 64 bits and mere acceleration was not working. Issue#254.

• Standalone: Preserve not only namespace packages created by .pth files, but also make the imports done by them. This makes it more compatible with uses of it in Fedora 22.

• Standalone: The extension modules could be duplicated, turned this into an error and cache finding them during compile time and during early import resolution to avoid duplication.

• Standalone: Handle "not found" from ldd output, on some systems not all the libraries wanted are accessible for every library.

• Python3.5: Fixed support for namespace packages, these were not yet working for that version yet.

• Python3.5: Fixes lack of support for unpacking in normal tuple, list, and set creations.

  [*a]  # this has become legal in 3.5 and now works too.
  

  Now also gives compatible SyntaxError for earlier versions. Python2 was good already.

• Python3.5: Fix, need to reduce compiled functions to __qualname__ value, rather than just __name__ or else pickling methods doesn't work.

• Python3.5: Fix, added gi_yieldfrom attribute to generator objects.

• Windows: Fixed harmless warnings for Visual Studio 2015 in --debug mode.

Optimization

• Re-formulate exec and eval to default to globals() as the default for the locals dictionary in modules.

• The try node was making a description of nodes moved to the outside when shrinking its scope, which was using a lot of time, just to not be output, now these can be postponed.

• Refactored how freezing of bytecode works. Uncompiled modules are now explicit nodes too, and in the registry. We only have one or the other of it, avoiding to compile both.

Tests

• When strace or dtruss are not found, given proper error message, so people know what to do.

• The doc tests extracted and then generated for CPython3 test suites were not printing the expressions of the doc test, leading to largely decreased test coverage here.

• The CPython 3.4 test suite is now also using common runner code, and avoids ignoring all Nuitka warnings, instead more white listing was added.

• Started to run CPython 3.5 test suite almost completely, but coroutines are blocking some parts of that, so these tests that use this feature are currently skipped.

• Removed more CPython tests that access the network and are generally useless to testing Nuitka.

• When comparing outputs, normalize typical temporary file names used on posix systems.

• Coverage tests have made some progress, and some changes were made due to its results.

• Added test to cover too complex code module of idna module.

• Added Python3.5 only test for unpacking variants.

Cleanups

• Prepare plug-in interface to allow suppression of import warnings to access the node doing it, making the import node is accessible.

• Have dedicated class function body object, which is a specialization of the function body node base class. This allowed removing class specific code from that class.

• The use of "win_target" as a scons parameter was useless. Make more consistent use of it as a flag indicator in the scons file.

• Compiled types were mixing uses of compiled_ prefixes, something with a space, sometimes with an underscore.

Organizational

• Improved support for Python3.5 missing compatibility with new language features.

• Updated the Developer Manual with changes that SSA is now a fact.

• Added Python3.5 Windows MSI downloads.

• Added repository for Ubuntu Wily (15.10) for download. Removed Ubuntu Utopic package download, no longer supported by Ubuntu.

• Added repository with RPM packages for Fedora 22.

Summary

So this release is mostly to lower the technical debt incurred that holds it back from supporting making more interesting changes. Upcoming releases may have continue that trend for some time.

This release is mostly about catching up with Python3.5, to make sure we did not miss anything important. The new function body variants will make it easier to implement coroutines, and help with optimization and compatibility problems that remain for Python3 classes.

Ultimately it will be nice to require a lot less checks for when function in-line is going to be acceptable. Also code generation will need a continued push to use the new structure in preparation for making type specific code generation a reality.

Bug Fixes

• Standalone: Added implicit import for reportlab package configuration dynamic import. Fixed in 0.5.14.1 already.

• Standalone: Fix, compilation of the ctypes module could happen for some import patterns, and then prevented the distribution to contain all necessary libraries. Now it is made sure to not include compiled and frozen form both. Issue#241. Fixed in 0.5.14.1 already.

• Fix, compilation for conditional statements where the boolean check on the condition cannot raise, could fail compilation. Issue#240. Fixed in 0.5.14.2 already.

• Fix, the __import__ built-in was making static optimization assuming compile time constants to be strings, which in the error case they are not, which was crashing the compiler. Issue#240.

  __import__(("some.module",))  # tuples don't work
  

  This error became only apparent, because now in some cases, Nuitka forward propagates values.

• Windows: Fix, when installing Python2 only for the user, the detection of it via registry failed as it was only searching system key. This was a github pull request. Fixed in 0.5.14.3 already.

• Some modules have extremely complex expressions requiring too deep recursion to work on all platforms. These modules are now included entirely as bytecode fallback. Issue#240.

• The standard library may contain broken code due to installation mistakes. We have to ignore their SyntaxError. Issue#244.

• Fix, pickling compiled methods was failing with the wrong kind of error, because they should not implement __reduce__, but only __deepcopy__. Issue#219.

• Fix, when running under wine, the check for scons binary was fooled by existence of /usr/bin/scons. Issue#251.

New Features

• Added experimental support for Python3.5, coroutines don't work yet, but it works perfectly as a 3.4 replacement.

• Added experimental Nuitka plug-in framework, and use it for the packaging of Qt plugins in standalone mode. The API is not yet stable nor polished.

• New option --debugger that makes --run execute directly in gdb and gives a stack trace on crash.

• New option --profile executes compiled binary and outputs measured performance with vmprof. This is work in progress and not functional yet.

• Started work on --graph to render the SSA state into diagrams. This is work in progress and not functional yet.

• Plug-in framework added. Not yet ready for users. Working PyQt4 and PyQt5 plug-in support. Experimental Windows multiprocessing support. Experimental PyLint warnings disable support. More to come.

• Added support for Anaconda accelerated mode on macOS by modifying the rpath to the Python DLL.

• Added experimental support for multiprocessing on Windows, which needs money patching of the module to support compiled methods.

Optimization

• The SSA analysis is now enabled by default, eliminating variables that are not shared, and can be forward propagated. This is currently limited mostly to compile time constants, but things won't remain that way.

• Code generation for many constructs now takes into account if a specific operation can raise or not. If e.g. an attribute look-up is known to not raise, then that is now decided by the node the looked is done to, and then more often can determine this, or even directly the value.

• Calls to C-API that we know cannot raise, no longer check, but merely assert the result.

• For attribute look-up and other operations that might be known to not raise, we now only assert that it succeeds.

• Built-in loop-ups cannot fail, merely assert that.

• Creation of built-in exceptions never raises, merely assert that too.

• More Python operation slots now have their own computations and some of these gained overloads for more compile time constant optimization.

• When taking an iterator cannot raise, this is now detected more often.

• The try/finally construct is now represented by duplicating the final block into all kinds of handlers (break, continue, return, or except) and optimized separately. This allows for SSA to trace values more correctly.

• The hash built-in now has dedicated node and code generation too. This is mostly intended to represent the side effects of dictionary look-up, but gives more compact and faster code too.

• Type type built-in cannot raise and has no side effect.

• Speed improvement for in-place float operations for += and *=, as these will be common cases.

Tests

• Made the construct based testing executable with Python3.

• Removed warnings using the new PyLint warnings plug-in for the reflected test. Nuitka now uses the PyLint annotations to not warn. Also do not go into PyQt for reflected test, not needed. Many Python3 improvements for cases where there are differences to report.

• The optimization tests no longer use 2to3 anymore, made the tests portable to all versions.

• Checked more in-place operations for speed.

Organizational

• Many improvements to the coverage taking. We can hope to see public data from this, some improvements were triggered from this already, but full runs of the test suite with coverage data collection are yet to be done.

Summary

The release includes many important new directions. Coverage analysis will be important to remain certain of test coverage of Nuitka itself. This is mostly done, but needs more work to complete.

Then the graphing surely will help us to debug and understand code examples. So instead of tracing, and reading stuff, we should visualize things, to more clearly see, how things evolve under optimization iteration, and where exactly one thing goes wrong. This will be improved as it proves necessary to do just that. So far, this has been rare. Expect this to become end user capable with time. If only to allow you to understand why Nuitka won't optimize code of yours, and what change of Nuitka it will need to improve.

The comparative performance benchmarking is clearly the most important thing to have for users. It deserves to be the top priority. Thanks to the PyPy tool vmprof, we may already be there on the data taking side, but the presenting and correlation part, is still open and a fair bit of work. It will be most important to empower users to make competent performance bug reports, now that Nuitka enters the phase, where these things matter.

As this is a lot of ground to cover. More than ever. We can make this compiler, but only if you help, it will arrive in your life time.

SSA (Single State Assignment Form)

For a long, long time already, each release of Nuitka has worked towards increasing "SSA" usage in Nuitka.

Now it's there. The current pre-release just uses it. There were many things to consider before enabling it, and always a next thing to be found that was needed. Often good changes to Nuitka, it was also annoying the hell out of me at times.

But basically now the forward propagation of variables is in place, with some limitations that are going to fall later.

So the current release, soon to be replaced, still doesn't optimize this code as well as possible:

def f():
    a = 1
    return a

But starting with the next release, the value of a is forward propagated (also in way more complex situations), and that's a serious milestone for the project.

Function Inlining

When submitting my talk to EuroPython 2015, I was putting a lot of pressure on me by promising to demo that. And I did. It was based on the SSA code that only now became completely reliable, but otherwise very few few other changes, and it just worked.

The example I used is this:

def f():
    def g(x, y):
        return x, y

    x = 2
    y = 1

    x, y = g(x, y) # can be inlined

    return x, y

So, the function g is forward propagated to a direct call, as are x and y into the return statement after making the in-line, making this:

def f():
    return 2, 1

Currently function in-lining is not yet activated by default, for this I am waiting for a release cycle to carry the load of SSA in the wild. As you probably know I usually tend to be conservative and to not make too many changes at once.

And as this works for local functions only yet, it's not too important yet either. This will generally become relevant once we have this working across modules and their globally defined functions or methods. This will be a while until Nuitka gets there.

Scalability

Having got Nuitka's memory usage under control, it turned out that there are files that can trigger Python recursion RuntimeError exception when using the ast module to build the Nuitka internal tree. People really have code with many thousands of operations to a + operation.

So, Nuitka here learned to include whole modules as bytecode when it is too complex as there is no easy way to expand the stack on Windows at least. That is kind of a limitation of CPython itself I didn't run into so far, and rather very annoying too.

The scalability of Nuitka also depends much on generated code size. With the optimization become more clever, less code is generated, and that trend will continue as more structural optimization are applied.

Compatibility

Very few things are possible here anymore. For the tests, in full compatibility mode, even more often the less good line number is used.

Also the plug-in work is leading to improved compatibility with Qt plugins of PySide and PyQt. Or another example is the multiprocessing module that on Windows is now supposed to fork compiled code too.

Performance

Standalone

The standalone mode of Nuitka was pretty good, and continued to improve further, now largely with the help of plug-ins.

I now know that PyGTK is an issue and will need a plug-in to work. Once the plug-in interface is public, I hope for more outside contributions here.

Other Stuff

Future

So, there is multiple things going on:

• Function in-lining

  For locally declared functions, it should become possible to avoid their creation, and make direct calls instead of ones that use function objects and expensive parameter handling.

• Nested frames

  One result of in-lining will be nested frames still present for exceptions to be properly annotated, or locals giving different sets of locals and so on.

  Some cleanup of these will be needed for code generation and SSA to be able to attach variables to some sort of container, and for a function to be able to reference different sets of these.

• Type Inference

  With SSA in place, we really can start to recognize types, and treat things that work something assigned from [] different, and with code special to these.

  That's going to be a lot of work. For float and list there are very important use cases, where the code can be much better.

• Shape Analyisis

  My plan for types, is not to use them, but the more general shapes, things that will be more prevalent than actual type information in a program. In fact the precise knowledge will be rare, but more often, we will just have a set of operations performed on a variable, and be able to guess from there.

• Python 3.5 new features

  The coroutines are a new type, and currently it's unclear how deep this is tied into the core of things, i.e. if a compile coroutine can be a premier citizen immediately, or if that needs more work. I hope it just takes for the code object to have the proper flag. But there could be stupid type checks, we shall see.

• Plug-ins

  Something I wish I could have shown at EuroPython was plug-ins to Nuitka. It is recently becoming more complete, and some demo plug-ins for say Qt plugins, or multiprocessing, are starting to work. The API will need work and of course documentation. Hope is for this to expand Nuitka's reach and appeal to get more contributors.

Let me know, if you are willing to help. I really need that help to make things happen faster. Nuitka will become more and more important only.

Bug Fixes

• Python3: Added support assignments trailing star assignment.

  *a, b = 1, 2
  

  This raised ValueError before.

• Python3: Properly detect illegal double star assignments.

  *a, *b = c
  

• Python3: Properly detect the syntax error to star assign from non-tuple/list.

  *a = 1
  

• Python3.4: Fixed a crash of the binary when copying dictionaries with split tables received as star arguments.

• Python3: Fixed reference loss, when using raise a from b where b was an exception instance. Fixed in 0.5.13.8 already.

• Windows: Fix, the flag --disable-windows-console was not properly handled for MinGW32 run time resulting in a crash.

• Python2.7.10: Was not recognizing this as a 2.7.x variant and therefore not applying minor version compatibility levels properly.

• Fix, when choosing to have frozen source references, code objects were not use the same value as __file__ did for its filename.

• Fix, when re-executing itself to drop the site module, make sure we find the same file again, and not according to the PYTHONPATH changes coming from it. Issue#223. Fixed in 0.5.13.4 already.

• Enhanced code generation for del variable statements, where it's clear that the value must be assigned.

• When pressing CTRL-C, the stack traces from both Nuitka and Scons were given, we now avoid the one from Scons.

• Fix, the dump from --xml no longer contains functions that have become unused during analysis.

• Standalone: Creating or running programs from inside unicode paths was not working on Windows. Issue#231 Issue#229 and. Fixed in 0.5.13.7 already.

• Namespace package support was not yet complete, importing the parent of a package was still failing. Issue#230. Fixed in 0.5.13.7 already.

• Python2.6: Compatibility for exception check messages enhanced with newest minor releases.

• Compatibility: The NameError in classes needs to say global name and not just name too.

• Python3: Fixed creation of XML representation, now done without lxml as it doesn't support needed features on that version. Fixed in 0.5.13.5 already.

• Python2: Fix, when creating code for the largest negative constant to still fit into int, that was only working in the main module. Issue#228. Fixed in 0.5.13.5 already.

• Compatibility: The print statement raised an assertion on unicode objects that could not be encoded with ascii codec.

New Features

• Added support for Windows 10.

• Followed changes for Python 3.5 beta 2. Still only usable as a Python 3.4 replacement, no new features.

• Using a self compiled Python running from the source tree is now supported.

• Added support for Anaconda Python distribution. As it doesn't install the Python DLL, we copy it along for acceleration mode.

• Added support for Visual Studio 2015. Issue#222. Fixed in 0.5.13.3 already.

• Added support for self compiled Python versions running from build tree, this is intended to help debug things on Windows.

Optimization

• Function in-lining is now present in the code, but still disabled, because it needs more changes in other areas, before we can generally do it.

• Trivial outlines, result of re-formulations or function in-lining, are now in-lined, in case they just return an expression.

• The re-formulation for or and and has been giving up, eliminating the use of a try/finally expression, at the cost of dedicated boolean nodes and code generation for these.

  This saves around 8% of compile time memory for Nuitka, and allows for faster and more complete optimization, and gets rid of a complicated structure for analysis.

• When a frame is used in an exception, its locals are detached. This was done more often than necessary and even for frames that are not necessary our own ones. This will speed up some exception cases.

• When the default arguments, or the keyword default arguments (Python3) or the annotations (Python3) were raising an exception, the function definition is now replaced with the exception, saving a code generation. This happens frequently with Python2/Python3 compatible code guarded by version checks.

• The SSA analysis for loops now properly traces "break" statement situations and merges the post-loop situation from all of them. This significantly allows for and improves optimization of code following the loop.

• The SSA analysis of try/finally statements has been greatly enhanced. The handler for finally is now optimized for exception raise and no exception raise individually, as well as for break, continue and return in the tried code. The SSA analysis for after the statement is now the result of merging these different cases, should they not abort.

• The code generation for del statements is now taking advantage should there be definite knowledge of previous value. This speed them up slightly.

• The SSA analysis of del statements now properly decided if the statement can raise or not, allowing for more optimization.

• For list contractions, the re-formulation was enhanced using the new outline construct instead of a pseudo function, leading to better analysis and code generation.

• Comparison chains are now re-formulated into outlines too, allowing for better analysis of them.

• Exceptions raised in function creations, e.g. in default values, are now propagated, eliminating the function's code. This happens most often with Python2/Python3 in branches. On the other hand, function creations that cannot are also annotated now.

• Closure variables that become unreferenced outside of the function become normal variables leading to better tracing and code generation for them.

• Function creations cannot raise except their defaults, keyword defaults or annotations do.

• Built-in references can now be converted to strings at compile time, e.g. when printed.

Organizational

• Removed gitorious mirror of the git repository, they shut down.

• Make it more clear in the documentation that Python2 is needed at compile time to create Python3 executables.

Cleanups

• Moved more parts of code generation to their own modules, and used registry for code generation for more expression kinds.

• Unified try/except and try/finally into a single construct that handles both through try/except/break/continue/return semantics. Finally is now solved via duplicating the handler into cases necessary.

  No longer are nodes annotated with information if they need to publish the exception or not, this is now all done with the dedicated nodes.

• The try/finally expressions have been replaced with outline function bodies, that instead of side effect statements, are more like functions with return values, allowing for easier analysis and dedicated code generation of much lower complexity.

• No more "tolerant" flag for release nodes, we now decide this fully based on SSA information.

• Added helper for assertions that code flow does not reach certain positions, e.g. a function must return or raise, aborting statements do not continue and so on.

• To keep cloning of code parts as simple as possible, the limited use of makeCloneAt has been changed to a new makeClone which produces identical copies, which is what we always do. And a generic cloning based on "details" has been added, requiring to make constructor arguments and details complete and consistent.

• The re-formulation code helpers have been improved to be more convenient at creating nodes.

• The old nuitka.codegen module Generator was still used for many things. These now all got moved to appropriate code generation modules, and their users got updated, also moving some code generator functions in the process.

• The module nuitka.codegen.CodeTemplates got replaces with direct uses of the proper topic module from nuitka.codegen.templates, with some more added, and their names harmonized to be more easily recognizable.

• Added more assertions to the generated code, to aid bug finding.

• The autoformat now sorts pylint markups for increased consistency.

• Releases no longer have a tolerant flag, this was not needed anymore as we use SSA.

• Handle CTRL-C in scons code preventing per job messages that are not helpful and avoid tracebacks from scons, also remove more unused tools like rpm from out in-line copy.

Tests

• Added the CPython3.4 test suite.

• The CPython3.2, CPython3.3, and CPython3.4 test suite now run with Python2 giving the same errors. Previously there were a few specific errors, some with line numbers, some with different SyntaxError be raised, due to different order of checks.

  This increases the coverage of the exception raising tests somewhat.

• Also the CPython3.x test suites now all pass with debug Python, as does the CPython 2.6 test suite with 2.6 now.

• Added tests to cover all forms of unpacking assignments supported in Python3, to be sure there are no other errors unknown to us.

• Started to document the reference count tests, and to make it more robust against SSA optimization. This will take some time and is work in progress.

• Made the compile library test robust against modules that raise a syntax error, checking that Nuitka does the same.

• Refined more tests to be directly executable with Python3, this is an ongoing effort.

Summary

This release is clearly major. It represents a huge step forward for Nuitka as it improves nearly every aspect of code generation and analysis. Removing the try/finally expression nodes proved to be necessary in order to even have the correct SSA in their cases. Very important optimization was blocked by it.

Going forward, the try/finally statements will be removed and dead variable elimination will happen, which then will give function inlining. This is expected to happen in one of the next releases.

This release is a consolidation of 8 hotfix releases, and many refactorings needed towards the next big step, which might also break things, and for that reason is going to get its own release cycle.

Bug Fixes

• Fix, relative imports in packages were not working with absolute imports enabled via future flags. Fixed in 0.5.12.1 already.

• Loops were not properly degrading knowledge from inside the loop at loop exit, and therefore this could have lead missing checks and releases in code generation for cases, for del statements in the loop body. Fixed in 0.5.12.1 already.

• The or and and re-formulation could trigger false assertions, due to early releases for compatibility. Fixed in 0.5.12.1 already.

• Fix, optimizion of calls of constant objects (always an exception), crashed the compiler. This corrects Issue#202. Fixed in 0.5.12.2 already.

• Standalone: Added support for site.py installations with a leading def or class statement, which is defeating our attempt to patch __file__ for it. This corrects Issue#189.

• Compatibility: In full compatibility mode, the tracebacks of or and and expressions are now as wrong as they are in CPython. Does not apply to --improved mode.

• Standalone: Added missing dependency on QtGui by QtWidgets for PyQt5.

• macOS: Improved parsing of otool output to avoid duplicate entries, which can also be entirely wrong in the case of Qt plugins at least.

• Avoid relative paths for main program with file reference mode original, as it otherwise changes as the file moves.

• MinGW: The created modules depended on MinGW to be in PATH for their usage. This is no longer necessary, as we now link these libraries statically for modules too.

• Windows: For modules, the option --run to immediately load the modules had been broken for a while.

• Standalone: Ignore Windows DLLs that were attempted to be loaded, but then failed to load. This happens e.g. when both PySide and PyQt are installed, and could cause the dreaded conflicting DLLs message. The DLL loaded in error is now ignored, which avoids this.

• MinGW: The resource file used might be empty, in which case it doesn't get created, avoiding an error due to that.

• MinGW: Modules can now be created again. The run time relative code uses an API that is WinXP only, and MinGW failed to find it without guidance.

Optimization

• Make direct calls out of called function creations. Initially this applies to lambda functions only, but it's expected to become common place in coming releases. This is now 20x faster than CPython.

  # Nuitka avoids creating a function object, parsing function arguments:
  (lambda x: x)(something)
  

• Propagate assignments from non-mutable constants forward based on SSA information. This is the first step of using SSA for real compile time optimization.

• Specialized the creation of call nodes at creation, avoiding to have all kinds be the most flexible form (keyword and plain arguments), but instead only what kind of call they really are. This saves lots of memory, and makes the tree faster to visit.

• Added support for optimizing the slice built-in with compile time constant arguments to constants. The re-formulation for slices in Python3 uses these a lot. And the lack of this optimization prevented a bunch of optimization in this area. For Python2 the built-in is optimized too, but not as important probably.

• Added support for optimizing isinstance calls with compile time constant arguments. This avoids static exception raises in the exec re-formulation which tests for file type, and then optimization couldn't tell that a str is not a file instance. Now it can.

• Lower in-place operations on immutable types to normal operations. This will allow to compile time compute these more accurately.

• The re-formulation of loops puts the loop condition as a conditional statement with break. The not that needs to apply was only added in later optimization, leading to unnecessary compile time efforts.

• Removed per variable trace visit from optimization, removing useless code and compile time overhead. We are going to optimize things by making decision in assignment and reference nodes based on forward looking statements using the last trace collection.

New Features

• Added experimental support for Python 3.5, which seems to be passing the test suites just fine. The new @ matrix multiplicator operators are not yet supported though.

• Added support for patching source on the fly. This is used to work around a (now fixed) issue with numexpr.cpuinfo making type checks with the is operation, about the only thing we cannot detect.

Organizational

• Added repository for Ubuntu Vivid (15.04) for download. Removed Ubuntu Saucy and Ubuntu Raring package downloads, these are no longer supported by Ubuntu.

• Added repository for Debian Stretch, after Jessie release.

• Make it more clear in the documentation that in order to compile Python3, a Python2 is needed to execute Scons, but that the end result is a Python3 binary.

• The PyLint checker tool now can operate on directories given on the command line, and whitelists an error that is Windows only.

Cleanups

• Split up standalone code further, moving depends.exe handling to a separate module.

• Reduced code complexity of scons interface.

• Cleaned up where trace collection is being done. It was partially still done inside the collection itself instead in the owner.

• In case of conflicting DLLs for standalone mode, these are now output with nicer formatting, that makes it easy to recognize what is going on.

• Moved code to fetch depends.exe to dedicated module, so it's not as much in the way of standalone code.

Tests

• Made BuiltinsTest directly executable with Python3.

• Added construct test to demonstrate the speed up of direct lambda calls.

• The deletion of @test for the CPython test suite is more robust now, esp. on Windows, the symbolic links are now handled.

• Added test to cover or usage with in-place assignment.

• Cover local relative import from . with absolute_import future flag enabled.

• Again, more basic tests are now directly executable with Python3.

Summary

This release is major due to amount of ground covered. The reduction in memory usage of Nuitka itself (the C++ compiler will still use much memory) is very massive and an important aspect of scalability too.

Then the SSA changes are truly the first sign of major improvements to come. In their current form, without eliminating dead assignments, the full advantage is not taken yet, but the next releases will do this, and that's a major milestone to Nuitka.

The other optimization mostly stem from looking at things closer, and trying to work towards function in-lining, for which we are making a lot of progress now.