Welcome to Cinder!

Cinder is Instagram’s internal performance-oriented production version of
CPython 3.8. It contains a number of performance optimizations, including
bytecode inline caching, eager evaluation of coroutines, a method-at-a-time
JIT, and an experimental bytecode compiler that uses type annotations to emit
type-specialized bytecode that performs better in the JIT.

For more information on CPython, see README.cpython.rst.

Is this supported?

Short answer: no.

We’ve made Cinder publicly available in order to facilitate conversation
about potentially upstreaming some of this work to CPython and to reduce
duplication of effort among people working on CPython performance.

Cinder is not polished or documented for anyone else’s use. We don’t have the
capacity to support Cinder as an independent open-source project, nor any
desire for it to become an alternative to CPython. Our goal in making this
code available is a unified faster CPython. So while we do run Cinder in
production, if you choose to do so you are on your own. We can’t commit to
fixing external bug reports or reviewing pull requests. We make sure Cinder
is sufficiently stable and fast for our production workload, but we make no
assurances about its stability or correctness or performance for any other
workload or use.

That said, if you have experience in dynamic language runtimes and have ideas
to make Cinder faster; or if you work on CPython and want to use Cinder as
inspiration for improvements in CPython (or help upstream parts of Cinder to
CPython), please reach out; we’d love to chat!

How do I build it?

Cinder should build just like CPython; configure and make -j. However
as most development and usage of Cinder occurs in the highly specific context of
Instagram we do not exercise it much in other environments. As such, the most
reliable way to build and run Cinder is to re-use the Docker-based setup from
our GitHub CI workflow. A rough guide is as follows:

  1. Install and setup Docker.
  2. Clone the Cinder repo:
    git clone https://github.com/facebookincubator/cinder
  3. Run a shell in the Docker environment used by the CI:
    docker run -v "$PWD/cinder:/vol" -w /vol -ti ghcr.io/facebookincubator/cinder/python-build-env:latest bash
    The above command does the following:
    • Downloads (if not already cached) a pre-built Docker image used by the
      CI from
    • Makes the Cinder checkout above ($PWD/cinder) available to the
      Docker environment at the mount point /vol.
    • Interactively (-ti) runs bash in the /vol directory.
  4. Build Cinder from the shell started the Docker environment:
    ./configure && make

Please be aware that Cinder is only built or tested on Linux x64; anything else
(including macOS) probably won’t work. The Docker image above is Fedora
Linux-based and built from a Docker spec file in the Cinder repo:

There are some new test targets that might be interesting. make
is pretty much the same as make test except that it skips a
few tests that are problematic in our dev environment. make
runs the test suite with the JIT fully enabled, so all
functions are JIT’ed. make testruntime runs a suite of C++ gtest unit
tests for the JIT. And make test_strict_module runs a test suite for
strict modules (see below).

What’s here?

Immortal Instances

Instagram uses a multi-process webserver architecture; the parent process
starts, performs initialization work (e.g. loading code), and forks tens of
worker processes to handle client requests. Worker processes are restarted
periodically for a number of reasons (e.g. memory leaks, code deployments) and
have a relatively short lifetime. In this model, the OS must copy the entire
page containing an object that was allocated in the parent process when the
object’s reference count is modified. In practice, the objects allocated
in the parent process outlive workers; all the work related to reference
counting them is unnecessary.

Instagram has a very large Python codebase and the overhead due to
copy-on-write from reference counting long-lived objects turned out to be
significant. We developed a solution called “immortal instances” to provide a
way to opt-out objects from reference counting. See Include/object.h for
details. This feature is controlled by defining Py_IMMORTAL_INSTANCES and is
enabled by default in Cinder. This was a large win for us in production (~5%),
but it makes straight-line code slower. Reference counting operations occur
frequently and must check whether or not an object participates in reference
counting when this feature is enabled.


“Shadowcode” or “shadow bytecode” is our inline caching implementation. It
observes particular optimizable cases in the execution of generic Python
opcodes and (for hot functions) dynamically replaces those opcodes with
specialized versions. The core of shadowcode lives in
Python/shadowcode.c, though the implementations for the specialized
bytecodes are in Python/ceval.c with the rest of the eval loop.
Shadowcode-specific tests are in Lib/test/test_shadowcode.py.

Eager coroutine evaluation

If a call to an async function is immediately awaited, we immediately execute
the called function up to its first await. If the called function reaches
a return without needing to await, we will be able to return that value
directly without ever even creating a coroutine object or deferring to the
event loop. This is a significant (~5%) CPU optimization in our async-heavy

This is mostly implemented in Python/ceval.c, via a new vectorcall flag
_Py_AWAITED_CALL_MARKER, indicating the caller is immediately awaiting
this call. Look for uses of the IS_AWAITED() macro and this vectorcall
flag, as well as the _PyEval_EvalEagerCoro function.

The Cinder JIT

The Cinder JIT is a method-at-a-time custom JIT implemented in C++. It is
enabled via the -X jit flag or the PYTHONJIT=1 environment variable.
It supports almost all Python opcodes, and can achieve 1.5-4x speed
improvements on many Python performance benchmarks.

By default when enabled it will JIT-compile every function that is ever
called, which may well make your program slower, not faster, due to overhead
of JIT-compiling rarely-called functions. The option -X
PYTHONJITLISTFILE=/path/to/jitlist.txt can point it to a text file
containing fully qualified function names (in the form
path.to.module:funcname or path.to.module:ClassName.method_name),
one per line, which should be JIT-compiled. We use this option to compile
only a set of hot functions derived from production profiling data. (A more
typical approach for a JIT would be to dynamically compile functions as they
are observed to be called frequently. It hasn’t yet been worth it for us to
implement this, since our production architecture is a pre-fork webserver,
and for memory sharing reasons we wish to do all of our JIT compiling up
front in the initial process before workers are forked, which means we can’t
observe the workload in-process before deciding which functions to

The JIT lives in the Jit/ directory, and its C++ tests live in
RuntimeTests/ (run these with make testruntime). There are also some
Python tests for it in Lib/test/test_cinderjit.py; these aren’t meant to
be exhaustive, since we run the entire CPython test suite under the JIT via
make testcinder_jit; they cover JIT edge cases not otherwise found in the
CPython test suite.

See Jit/pyjit.cpp for some other -X options and environment variables
that influence the behavior of the JIT. There is also a cinderjit module
defined in that file which exposes some JIT utilities to Python code (e.g.
forcing a specific function to compile, checking if a function is compiled,
disabling the JIT). Note that cinderjit.disable() only disables future
compilation; it immediately compiles all known functions and keeps existing
JIT-compiled functions.

The JIT first lowers Python bytecode to a high-level intermediate
representation (HIR); this is implemented in Jit/hir/. HIR maps
reasonably closely to Python bytecode, though it is a register machine
instead of a stack machine, it is a bit lower level, it is typed, and some
details that are obscured by Python bytecode but important for performance
(notably reference counting) are exposed explicitly in HIR. HIR is
transformed into SSA form, some optimization passes are performed on it, and
then reference counting operations are automatically inserted into it
according to metadata about the refcount and memory effects of HIR opcodes.

HIR is then lowered to a low-level intermediate representation (LIR), which
is an abstraction over assembly, implemented in Jit/lir/. In LIR we do
register allocation, some additional optimization passes, and then finally
LIR is lowered to assembly (in Jit/codegen/) using the excellent
asmjit library.

The JIT is in its early stages. While it can already eliminate interpreter
loop overhead and offers significant performance improvements for many
functions, we’ve only begun to scratch the surface of possible optimizations.
Many common compiler optimizations are not yet implemented. Our
prioritization of optimizations is largely driven by the characteristics of
the Instagram production workload.

Strict Modules

Strict modules is a few things rolled into one:

1. A static analyzer capable of validating that executing a module’s
top-level code will not have side effects visible outside that module.

2. An immutable StrictModule type usable in place of Python’s default
module type.

3. A Python module loader capable of recognizing modules opted in to strict
mode (via an import __strict__ at the top of the module), analyzing them
to validate no import side effects, and populating them in sys.modules as
a StrictModule object.

The version of strict modules that we currently use in production is written
in Python and is not part of Cinder. The StrictModules/ directory in
Cinder is an in-progress C++ rewrite of the import side effects analyzer.

Static Python

Static Python is an experimental bytecode compiler that makes use of type
annotations to emit type-specialized and type-checked Python bytecode. Used
along with the Cinder JIT, it can deliver performance similar to MyPyC or
Cython in many cases, while offering a pure-Python developer experience
(normal Python syntax, no extra compilation step). Static Python plus Cinder
JIT achieves 7x the performance of stock CPython on a typed version of the
Richards benchmark. At Instagram we have successfully used Static Python in
production to replace the majority of the Cython modules in our primary
webserver codebase, with no performance regression.

The Static Python compiler is built on top of the Python compiler module
that was removed from the standard library in Python 3 and has since been
maintained and updated externally; this compiler is incorporated into Cinder
in Lib/compiler. The Static Python compiler is implemented in
Lib/compiler/static/, and its tests are in

Classes defined in Static Python modules are automatically given typed slots
(based on inspection of their typed class attributes and annotated
assignments in __init__), and attribute loads and stores against
instances of these types use new STORE_FIELD and LOAD_FIELD opcodes,
which in the JIT become direct loads/stores from/to a fixed memory offset in
the object, with none of the indirection of a LOAD_ATTR or
STORE_ATTR. Classes also gain vtables of their methods, for use by the
INVOKE_* opcodes mentioned below. The runtime support for these features
is located in Include/classloader.h and Python/classloader.c.

A static Python function begins with a new CHECK_ARGS opcode which checks
that the supplied arguments’ types match the type annotations, and raises
TypeError if not. Calls from a static Python function to another static
Python function will skip this opcode (since the types are already validated
by the compiler). Static to static calls can also avoid much of the overhead
of a typical Python function call. We emit an INVOKE_FUNCTION or
INVOKE_METHOD opcode which carries with it metadata about the called
function or method; this plus optionally immutable modules (via
StrictModule) and types (via cinder.freeze_type(), which we currently
apply to all types in strict and static modules in our import loader, but in
future may become an inherent part of Static Python) and compile-time
knowledge of the callee signature allow us to (in the JIT) turn many Python
function calls into direct calls to a fixed memory address using the x64
calling convention, with little more overhead than a C function call.

Static Python is still gradually typed, and supports code that is only
partially annotated or uses unknown types by falling back to normal Python
dynamic behavior. In some cases (e.g. when a value of statically-unknown type
is returned from a function with a return annotation), a runtime CAST
opcode is inserted which will raise TypeError if the runtime type does
not match the expected type.

Static Python also supports new types for machine integers, bools, doubles,
and vectors/arrays. In the JIT these are handled as unboxed values, and e.g.
primitive integer arithmetic avoids all Python overhead. Some operations on
builtin types (e.g. list or dictionary subscript or len()) are also

Cinder doesn’t currently come bundled with a module loader that is able to
automatically detect static modules and load them as static with cross-module
compilation; we currently do this via our strict/static import loader which
is not part of Cinder. Currently the best way to experiment with static
python in Cinder is to use ./python -m compiler --static some_module.py,
which will compile the module as static Python and execute it.

See CinderDoc/static_python.rst for more detailed documentation.


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