Python based DSL for authoring Linalg op definitions and generating
linalg.generic IR based on them for samples.
linalg_opdsl provides a high level DSL for constructing
structured op definitions in a way that can be exported to built-in, named
structured ops via the above YAML-based definitions or used interactively to
linalg.generic IR for the composition.
Basic usage ¶
The tool is bundled with the MLIR Python bindings. To use from the CMake build
tree, MLIR must be build with Python bindings enabled
-DMLIR_BINDINGS_PYTHON_ENABLED=ON). Then add the
python directory in the
build tree to your
PYTHONPATH environment variable (i.e.
export PYTHONPATH=$PWD/build/python). Optionally, use an installed MLIR
package, if available, to avoid building.
# Dump the `core_named_ops.py` module as YAML. python -m python -m mlir.tools.linalg_opdsl.dump_oplib .ops.core_named_ops
The tool is meant for use during both development and runtime, but not as a build tool of the core compiler: in order to export static named op definitions to be built as part of the compiler, the corresponding Linalg dialect YAML file must be updated and reviewed. TODO: Develop a script to automate op updates to these files.
Language Guide ¶
The language presented here is loosely inspired from the Tensor Comprehensions work, adapted to represent linalg structured ops.
This tool is new and rapidly evolving. For language examples, refer to the
built-in ops in the
lib/Bindings/Python/mlir/tools/linalg_opdsl/ops in the repository).
Using a matmul as an example, we will decompose the language:
T1 = TV.T1 T2 = TV.T2 @linalg_structured_op def matmul(A=TensorDef(T1, S.M, S.K), B=TensorDef(T2, S.K, S.N), C=TensorDef(U, S.M, S.N, output=True)): """Performs a matrix multiplacation of two 2D inputs. Numeric casting is performed on the operands to the inner multiply, promoting them to the same data type as the accumulator/output. """ implements(ContractionOpInterface) C[D.m, D.n] += cast(U, A[D.m, D.k]) * cast(U, B[D.k, D.n])
Here we have a simple type polymorphic contraction that takes arguments
B and outputs
C. Each is bound to a
TensorDef, which specifies:
- The symbolic element type (
- Symbolic shape expressions with symbols that are bound globally for the op ( note that in this simple example, the shape expressions are just symbol references, but they are permitted to be a constrained set of affine expressions).
- Usage (
The docstring will be transferred to the op definition verbatim.
Special identifying op interfaces can be declared for the op via
The bulk of language consists of assignment expressions of the form above. The iteration dimension order is determined lexically based on the order encountered in the expression (following operator precedence if math operators are used). TODO: Introduce a directive to fix the dimension bindings.
Reduction dimensions are inferred to be any dimensions on the RHS that are not on the LHS.
A number of arithmetic primitive functions are supported:
PrimFn.add(a, b)(also via overloading the binary
PrimFn.mul(a, b)(also via overloading the binary
PrimFn.sub(a, b)(also via overloading the binary
Reduction functions can appear as the outer-most function on the RHS:
ReduceFn.add(also overloading the inplace
+=on a LHS)
There are also special forms:
All types in assignment expressions are late bound based on actual input
and output types of constructed ops. Assignment expressions with no
calls will generally require uniform types throughout and will fail to
verify if violated. The presence of a
cast allows for a limited form of
numeric type conversion between element types that can be derived from inputs
and outputs (and in the future, attributes).
cast calls with a
first argument are emitted as
symbolic_cast primitives in the YAML definition.
Casting will perform
int<->float type conversions and will perform any
necessary extension or truncation within type family. Note that presently,
any integer type is assumed to be signed for the purpose of determing how to
extend or truncate. Supporting unsigned integer types is left for future work.
Not all functions are applicable for all numeric types, and on mismatch, op verification will fail.