Multi-Level IR Compiler Framework

MLIR Python Bindings

Current status: Under development and not enabled by default



  • A relatively recent Python3 installation
  • pybind11 must be installed and able to be located by CMake (auto-detected if installed via python -m pip install pybind11). Note: minimum version required: :2.6.0.

CMake variables 


    Enables building the Python bindings. Defaults to OFF.


    Links the native extension against the Python runtime library, which is optional on some platforms. While setting this to OFF can yield some greater deployment flexibility, linking in this way allows the linker to report compile time errors for unresolved symbols on all platforms, which makes for a smoother development workflow. Defaults to ON.


    Specifies the python executable used for the LLVM build, including for determining header/link flags for the Python bindings. On systems with multiple Python implementations, setting this explicitly to the preferred python3 executable is strongly recommended.


Use cases 

There are likely two primary use cases for the MLIR python bindings:

  1. Support users who expect that an installed version of LLVM/MLIR will yield the ability to import mlir and use the API in a pure way out of the box.

  2. Downstream integrations will likely want to include parts of the API in their private namespace or specially built libraries, probably mixing it with other python native bits.

Composable modules 

In order to support use case #2, the Python bindings are organized into composable modules that downstream integrators can include and re-export into their own namespace if desired. This forces several design points:

  • Separate the construction/populating of a py::module from PYBIND11_MODULE global constructor.

  • Introduce headers for C++-only wrapper classes as other related C++ modules will need to interop with it.

  • Separate any initialization routines that depend on optional components into its own module/dependency (currently, things like registerAllDialects fall into this category).

There are a lot of co-related issues of shared library linkage, distribution concerns, etc that affect such things. Organizing the code into composable modules (versus a monolithic cpp file) allows the flexibility to address many of these as needed over time. Also, compilation time for all of the template meta-programming in pybind scales with the number of things you define in a translation unit. Breaking into multiple translation units can significantly aid compile times for APIs with a large surface area.


Generally, the C++ codebase namespaces most things into the mlir namespace. However, in order to modularize and make the Python bindings easier to understand, sub-packages are defined that map roughly to the directory structure of functional units in MLIR.


  • mlir.passes (pass is a reserved word :( )
  • mlir.dialect
  • mlir.execution_engine (aside from namespacing, it is important that “bulky”/optional parts like this are isolated)

In addition, initialization functions that imply optional dependencies should be in underscored (notionally private) modules such as _init and linked separately. This allows downstream integrators to completely customize what is included “in the box” and covers things like dialect registration, pass registration, etc.


LLVM/MLIR is a non-trivial python-native project that is likely to co-exist with other non-trivial native extensions. As such, the native extension (i.e. the .so/.pyd/.dylib) is exported as a notionally private top-level symbol (_mlir), while a small set of Python code is provided in mlir/ and siblings which loads and re-exports it. This split provides a place to stage code that needs to prepare the environment before the shared library is loaded into the Python runtime, and also provides a place that one-time initialization code can be invoked apart from module constructors.

To start with the mlir/ loader shim can be very simple and scale to future need:

from _mlir import *

Use the C-API 

The Python APIs should seek to layer on top of the C-API to the degree possible. Especially for the core, dialect-independent parts, such a binding enables packaging decisions that would be difficult or impossible if spanning a C++ ABI boundary. In addition, factoring in this way side-steps some very difficult issues that arise when combining RTTI-based modules (which pybind derived things are) with non-RTTI polymorphic C++ code (the default compilation mode of LLVM).

Ownership in the Core IR 

There are several top-level types in the core IR that are strongly owned by their python-side reference:

  • PyContext (
  • PyModule (
  • PyOperation ( - but with caveats

All other objects are dependent. All objects maintain a back-reference (keep-alive) to their closest containing top-level object. Further, dependent objects fall into two categories: a) uniqued (which live for the life-time of the context) and b) mutable. Mutable objects need additional machinery for keeping track of when the C++ instance that backs their Python object is no longer valid (typically due to some specific mutation of the IR, deletion, or bulk operation).

Optionality and argument ordering in the Core IR 

The following types support being bound to the current thread as a context manager:

  • PyLocation (loc: = None)
  • PyInsertionPoint (ip: = None)
  • PyMlirContext (context: = None)

In order to support composability of function arguments, when these types appear as arguments, they should always be the last and appear in the above order and with the given names (which is generally the order in which they are expected to need to be expressed explicitly in special cases) as necessary. Each should carry a default value of py::none() and use either a manual or automatic conversion for resolving either with the explicit value or a value from the thread context manager (i.e. DefaultingPyMlirContext or DefaultingPyLocation).

The rationale for this is that in Python, trailing keyword arguments to the right are the most composable, enabling a variety of strategies such as kwarg passthrough, default values, etc. Keeping function signatures composable increases the chances that interesting DSLs and higher level APIs can be constructed without a lot of exotic boilerplate.

Used consistently, this enables a style of IR construction that rarely needs to use explicit contexts, locations, or insertion points but is free to do so when extra control is needed.

Operation hierarchy 

As mentioned above, PyOperation is special because it can exist in either a top-level or dependent state. The life-cycle is unidirectional: operations can be created detached (top-level) and once added to another operation, they are then dependent for the remainder of their lifetime. The situation is more complicated when considering construction scenarios where an operation is added to a transitive parent that is still detached, necessitating further accounting at such transition points (i.e. all such added children are initially added to the IR with a parent of their outer-most detached operation, but then once it is added to an attached operation, they need to be re-parented to the containing module).

Due to the validity and parenting accounting needs, PyOperation is the owner for regions and blocks and needs to be a top-level type that we can count on not aliasing. This let’s us do things like selectively invalidating instances when mutations occur without worrying that there is some alias to the same operation in the hierarchy. Operations are also the only entity that are allowed to be in a detached state, and they are interned at the context level so that there is never more than one Python object for a unique MlirOperation, regardless of how it is obtained.

The C/C++ API allows for Region/Block to also be detached, but it simplifies the ownership model a lot to eliminate that possibility in this API, allowing the Region/Block to be completely dependent on its owning operation for accounting. The aliasing of Python Region/Block instances to underlying MlirRegion/MlirBlock is considered benign and these objects are not interned in the context (unlike operations).

If we ever want to re-introduce detached regions/blocks, we could do so with new “DetachedRegion” class or similar and also avoid the complexity of accounting. With the way it is now, we can avoid having a global live list for regions and blocks. We may end up needing an op-local one at some point TBD, depending on how hard it is to guarantee how mutations interact with their Python peer objects. We can cross that bridge easily when we get there.

Module, when used purely from the Python API, can’t alias anyway, so we can use it as a top-level ref type without a live-list for interning. If the API ever changes such that this cannot be guaranteed (i.e. by letting you marshal a native-defined Module in), then there would need to be a live table for it too.


In general, for the core parts of MLIR, the Python bindings should be largely isomorphic with the underlying C++ structures. However, concessions are made either for practicality or to give the resulting library an appropriately “Pythonic” flavor.

Properties vs get*() methods 

Generally favor converting trivial methods like getContext(), getName(), isEntryBlock(), etc to read-only Python properties (i.e. context). It is primarily a matter of calling def_property_readonly vs def in binding code, and makes things feel much nicer to the Python side.

For example, prefer:

m.def_property_readonly("context", ...)


m.def("getContext", ...)

repr methods 

Things that have nice printed representations are really great :) If there is a reasonable printed form, it can be a significant productivity boost to wire that to the __repr__ method (and verify it with a doctest ).

CamelCase vs snake_case 

Name functions/methods/properties in snake_case and classes in CamelCase. As a mechanical concession to Python style, this can go a long way to making the API feel like it fits in with its peers in the Python landscape.

If in doubt, choose names that will flow properly with other PEP 8 style names .

Prefer pseudo-containers 

Many core IR constructs provide methods directly on the instance to query count and begin/end iterators. Prefer hoisting these to dedicated pseudo containers.

For example, a direct mapping of blocks within regions could be done this way:

region = ...

for block in region:


However, this way is preferred:

region = ...

for block in region.blocks:



Instead of leaking STL-derived identifiers (front, back, etc), translate them to appropriate __dunder__ methods and iterator wrappers in the bindings.

Note that this can be taken too far, so use good judgment. For example, block arguments may appear container-like but have defined methods for lookup and mutation that would be hard to model properly without making semantics complicated. If running into these, just mirror the C/C++ API.

Provide one stop helpers for common things 

One stop helpers that aggregate over multiple low level entities can be incredibly helpful and are encouraged within reason. For example, making Context have a parse_asm or equivalent that avoids needing to explicitly construct a SourceMgr can be quite nice. One stop helpers do not have to be mutually exclusive with a more complete mapping of the backing constructs.


Tests should be added in the test/Bindings/Python directory and should typically be .py files that have a lit run line.

While lit can run any python module, prefer to lay tests out according to these rules:

  • For tests of the API surface area, prefer doctest .
  • For generative tests (those that produce IR), define a Python module that constructs/prints the IR and pipe it through FileCheck.
  • Parsing should be kept self-contained within the module under test by use of raw constants and an appropriate parse_asm call.
  • Any file I/O code should be staged through a tempfile vs relying on file artifacts/paths outside of the test module.

Sample Doctest 


  >>> m = load_test_module()
Test basics:
  >>> m.operation.is_registered
  >>> ... etc ...

Verify that repr prints:
  >>> m.operation
  <operation 'module'>

import mlir

func @test_operation_correct_regions() {
  // ...

# TODO: Move to a test utility class once any of this actually exists.
def load_test_module():
  ctx =
  ctx.allow_unregistered_dialects = True
  module = ctx.parse_asm(TEST_MLIR_ASM)
  return module

if __name__ == "__main__":
  import doctest

Sample FileCheck test 

# RUN: %PYTHON %s | mlir-opt -split-input-file | FileCheck

# TODO: Move to a test utility class once any of this actually exists.
def print_module(f):
  m = f()
  print("// -----")
  print("// TEST_FUNCTION:", f.__name__)
  return f

def create_my_op():
  m =
  builder = m.new_op_builder()
  # CHECK: mydialect.my_operation ...
  return m