a blog post draft on testing PyPy

Author: cfbolz

posts/2022/03/how-is-pypy-tested.rst

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+.. title: How is PyPy Tested?
+.. slug: how-is-pypy-tested
+.. date: 2022-03-02 12:00:00 UTC
+.. tags:
+.. category: 
+.. link: 
+.. description: 
+.. type: rest
+.. author: Carl Friedrich Bolz-Tereick
+
+===================
+How is PyPy Tested?
+===================
+
+In this post I want to give an overview of how the PyPy project does and thinks
+about testing. PyPy takes testing quite seriously and has done some from the
+start of the project. In the post I want to present the different styles of
+tests that PyPy has, when we use them and how I think about them.
+
+
+Background
+============
+
+To make the blog post self-contained, I am going to start with a small overview
+about PyPy's architecture. If you already know what PyPy is and how it works,
+you can skip this section.
+
+PyPy means "Python in Python". It is an alternative implementation of the Python
+language. Usually, when we speak of "Python", we can mean two different things.
+On the one hand it means "Python as an abstract programming language". On the
+other hand, the main implementation of that language is also often called
+"Python". To more clearly distinguish the two, the implementation is often also
+called "CPython", because it is an interpreter implemented in C code.
+
+Now we can make the statement "PyPy is Python in Python" more precise: PyPy is
+an interpreter for Python 3.9, implemented in RPython. RPython ("Restricted
+Python") is a subset of Python 2, which is statically typed and can be compiled
+to C code. That means we can take our Python 3.9 interpreter, and compile it
+into a C binary that can run Python 3.9 code. The final binary behaves pretty
+similarly to CPython.
+
+The main thing that makes PyPy interesting is that during the translation of our
+interpreter to C, a number of components are automatically inserted into the
+final binary. One component is a reasonably good garbage collector. 
+
+The more exciting component that is inserted into the binary is a just-in-time
+compiler. The insertion of this component is not fully automatic, instead it is
+guided by a small number of annotations in the source code of the interpreter.
+The effect of inserting this JIT compiler into the binary is that the resulting
+binary can run Python code significantly faster than CPython, in many cases.
+How this works is not important for the rest of the post, if you want to see an
+example of concretely doing that to a small interpreter you can look at this
+video_.
+
+.. _video: https://www.youtube.com/watch?v=fZj3uljJl_k
+
+
+PyPy Testing History
+=====================
+
+A few historical notes on the PyPy project and its relationship to testing: The
+PyPy project `was started in 2004`_. At the time when the project was started,
+Extreme Programming and Agile Software Development where up and coming. On the
+methodology side, PyPy was heavily influenced by these, and started using
+Test-Driven Development and pair programming right from the start.
+
+.. _`was started in 2004`: https://www.pypy.org/posts/2018/09/the-first-15-years-of-pypy-3412615975376972020.html
+
+Also technologically, PyPy has been influential on testing in the Python world.
+Originally, PyPy had used the ``unittest`` testing framework, but pretty soon
+the developers got frustrated with it. `Holger Krekel`_, one of the original
+developers who started PyPy, started the pytest_ testing framework soon
+afterwards.
+
+.. _`Holger Krekel`: https://holgerkrekel.net/
+.. _`pytest`: https://pytest.org/
+
+
+Interpreter-Level Tests
+=========================
+
+So, how are tests for PyPy written, concretely? The tests for the interpreter
+are split into two different kinds, which we call "interpreter level tests" and
+"application level tests". The former are tests that can be used to test the
+objects and functions that are used in the implementation of the Python
+interpreter. Since the interpreter is written in Python 2, those tests are also
+written in Python 2, using pytest. They tend to be more on the unit test side of
+things. They are in files with the pattern ``test_*.py``.
+
+Here is an example that tests the implementation of integers (very slightly
+simplified)::
+
+    class TestW_IntObject:
+        ...
+
+        def test_hash(self):
+            w_x = W_IntObject(42)
+            w_result = w_x.descr_hash(self.space)
+            assert isinstance(w_result, W_IntObject)
+            assert w_result.intval == 42
+
+
+This test checks that if you take an object that represents integers in the
+Python language (using the class ``W_IntObject``, a "wrapped integer object")
+with the value 42, computing the hash of that object returns another instance of
+the same class, also with the value 42.
+
+These tests can be run on top of any Python 2 implementation, either CPython or
+PyPy. We can then test and debug the internals of the PyPy interpreter using
+familiar tools like indeed pytest and the Python debuggers.
+
+In CPython, these tests don't really have an equivalent. They would correspond
+to tests that are written in C and that can access test the logic of all the C
+functions of CPython that execute certain functionality, accessing the internals
+of C structs in the process.
+
+
+Application-Level Tests
+=========================
+
+There is also a second class of tests for the interpreter. Those are tests that
+don't run on the level of the implementation. Instead, they are executed *by*
+the PyPy Python interpreter, thus running on the level of the applications run
+by PyPy. Since the interpreter is running Python 3, the tests are also written
+in Python 3. They are stored in files with the pattern ``apptest_*.py`` _[#] and
+look like "regular" Python 3 tests.
+
+.. [#] There is also a deprecated different way to write these tests, by putting
+   them in the ``test_*.py`` files that interpreter level tests are using and
+   then having a test class with the pattern ``class AppTest*``. We haven't
+   converted all of them to the new style yet, even though the old style is
+   quite weird: since the ``test_*.py`` files are themselves parsed by
+   Python 2, the tests methods in ``AppTest*`` classes need to be written in the
+   subset of Python 3 that is also valid Python 2 syntax, leading to a lot of
+   confusion.
+
+Here's an example of how you could write a test equivalent to the one above::
+
+    def test_hash():
+        assert hash(42) == 42
+
+This style of test looks more "natural" and is the preferred one in cases where
+the test does not need to access the internals of the logic or the objects of
+the interpreter.
+
+Application level tests can be run in two different ways. On the one hand, we
+can simply run them on CPython 3. This is very useful! Since we want PyPy to
+behave like CPython, running the tests that we write on CPython is useful to
+make sure that the tests themselves aren't wrong.
+
+On the other hand, the main way to run these tests is on top of PyPy, itself
+running on top of a Python 2 implementation. This makes it possible to run the
+test without first bootstrapping PyPy to C. Since bootstrapping to C is a
+relatively slow operation (can take up to an hour) it is crucially important to
+be able to run tests without bootstrapping first. It also again makes it
+possible to debug crashes in the interpreter using the regular Python 2
+debugger. Of course running tests in this way is unfortunately itself not super
+fast, given that they run on a stack of two different interpreters. 
+
+Application-level tests correspond quite closely to CPython's tests suite (which
+is using the unittest framework). Of course in CPython it is not possible to run
+the test suite without building the CPython binary using a C compiler.  _[#]
+
+.. [#] Nit-picky side-note: `C interpreters`_ `are a thing`_! But not that
+   widely used in practice, or only in very specific situations.
+
+.. _`C interpreters`: https://root.cern.ch/root/html534/guides/users-guide/CINT.html
+.. _`are a thing`: https://www.youtube.com/watch?v=yyDD_KRdQQU
+
+So when do we write application-level tests, and when interpreter-level tests?
+Interpreter-level tests are necessary to test internal data structures that
+touch data and logic that is not directly exposed to the Python language. If
+that is not necessary, we try to write application-level tests. App-level tests
+are however by their nature always more on the integration test side of things.
+To be able to run the ``test_hash`` function above, many parts of PyPy need to
+work correctly, the parser, the bytecode compiler, the bytecode interpreter, the
+``hash`` builtin, calling the ``__hash__`` special method, etc, etc.
+
+This observation is also true for CPython! One could argue that CPython has no
+unit tests at all, because in order to be able to even run the tests, most of
+Python needs to be in working order already, so all the tests are really
+implicitly integration tests.
+
+
+The CPython Test Suite
+========================
+
+We also use the CPython Test suite as a final check to see whether our
+interpreter correctly implements all the features of the Python language. In
+that sense it acts as some kind of compliance test suite that checks whether we
+implement the language correctly. The test suite is not perfect for this
+purpose. Since it is written for CPython's purposes during its development, a
+lot of the tests check really specific CPython implementation details. Examples
+for these are tests that check that ``__del__`` is called immediately after
+objects go out of scope (which only happens if you use reference counting as a
+garbage collection strategy, which PyPy doesn't do). Other examples are checking
+for exception error messages very explicitly. However, the CPython test suite
+has gotten a lot better in these regards over time, by adding
+``support.gc_collect()`` calls to fix the former problem, and by marking some
+very specific tests with the ``@impl_detail`` decorator. Thanks to all the
+CPython developers who have worked on this!
+
+In the process of re-implementing CPython's functionality and running CPython's
+tests suite, PyPy can often also be a good way to find bugs in CPython. While we
+think about the corner cases of some Python feature we occasionally find
+situations where CPython didn't get everything completely correct either, which
+we then report back.
+
+
+Testing Performance
+=====================
+
+All the tests we described so far are checking *behaviour*. But one of PyPy's
+important goals is to be a *fast* implementation not "just" a correct one. Some
+aspects of performance can be tested by regular unit tests, either application-
+or interpreter-level. In order to check whether some performance shortcut is
+taken in the interpreter, we sometimes can write tests that monkeypatch the slow
+default implementation to always error. Then, if the fast path is taken
+properly, that slow default implementation is never reached.
+
+But we also have additional tests that test the correct interaction with the JIT
+explicitly. For that, we have a special style of test that checks that the JIT
+will produce the correct machine code for a small snippet of Python code. To
+make this kind of test somewhat more robust, we don't check the machine code
+directly, but instead the architecture independent `intermediate
+representation`_ that the JIT uses to produce machine code from.
+
+.. _`intermediate representation`: https://www.pypy.org/posts/2018/09/the-first-15-years-of-pypy-3412615975376972020.html
+
+As an example, here is a small test that loading the attribute of a constant
+global instance can be completely constant folded away::
+
+    def test_load_attr(self):
+        src = '''
+            class A(object):
+                pass
+            a = A()
+            a.x = 1
+            def main(n):
+                i = 0
+                while i < n:
+                    i = i + a.x
+                return i
+        '''
+        log = self.run(src, [1000])
+        assert log.result == 1000
+        loop, = log.loops_by_filename(self.filepath)
+        assert loop.match("""
+            i9 = int_lt(i5, i6)
+            guard_true(i9, descr=...)
+            guard_not_invalidated(descr=...)
+            i10 = int_add(i5, 1)
+            --TICK--
+            jump(..., descr=...)
+        """)
+
+The string passed to the ``loop.match`` function is a string representation of
+the intermediate representation code that is generated for the ``while`` loop in
+the ``main`` function given in the source. The important part of that
+intermediate representation is that the ``i = i + a.x`` addition is optimized
+into an ``int_add(x, 1)`` operation. The second argument for the addition is the
+constant ``1``, because the JIT noted that the global ``a`` is a constant, and
+the attribute ``x`` of that instance is always ``1``. The test thus checks that
+this optimization still works.
+
+Those tests are again more on the unit test side of things (and can thus
+unfortunately be a bit brittle sometimes and break). The integration test
+equivalent for performance is the `PyPy Speed Center`_ which tracks the
+performance of micro- and macro-benchmarks over time and lets us see when big
+performance regressions are happening. The speed center is not really an
+automatic test and does not produce pass/fail outcomes. Instead, it requires
+human judgement and intervention in order to interpret the performance changes.
+Having a real pass/fail mechanism is something that would be `great to have`_
+but is probably `quite tricky in practice`_.
+
+.. _`great to have`: https://twitter.com/glyph/status/1495122754286198790
+.. _`quite tricky in practice`: https://arxiv.org/abs/1602.00602
+
+.. _`PyPy Speed Center`: https://speed.pypy.org/
+
+
+Conclusion
+===========
+
+This concludes my overview of some of the different styles of tests that we use
+to develop the PyPy Python interpreter.
+
+There is a whole other set of tests for the development of the RPython language,
+the garbage collectors it provides as well as the code that does the automatic
+JIT insertion, maybe I'll cover these in a future post.