# MLIR

Multi-Level IR Compiler Framework

# linalg_opdsl tool

Python based DSL for authoring Linalg op definitions and generating linalg.generic IR based on them for samples.

The tool 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 emit corresponding 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_ENALBE_BINDINGS_PYTHON=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 mlir.tools.linalg_opdsl.ops package (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 multiplication 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 A and B and outputs C. Each is bound to a TensorDef, which specifies:

• The symbolic element type (T1, T2, U above).
• 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 (output=True).

The docstring will be transferred to the op definition verbatim.

Special identifying op interfaces can be declared for the op via implements(interface1[, interface2...]).

## Parameters ¶

Structured operations can take two types of parameters namely input/output tensors and captures. Assignment expressions index the tensor parameters to access the individual elements, while captures are scalars that can be accessed directly.

The following example demonstrates the use of the two parameter types:

@linalg_structured_op
def copy_and_scale(I=TensorDef(T, S.M, S.K),
O=TensorDef(T, S.M, S.K, output=True),
val=CaptureDef(T)):
"""Scale the input by the captured value and store the result"""
O[D.m, D.n] = I[D.m, D.n] * val


The operation scales the input tensor I scales its elements by the value val and writes the result to the output tensor out. The capture val is bound to a CaptureDef, which specifies the type of the captured value. The tensors are bound to a TensorDef as demonstrated by the matmul example. All parameters appear in the parameter list of the operation:

fill(in_tensor, outs=[out_tensor], captures=[captured_val])


## Assignments ¶

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 + operator)
• PrimFn.exp(a)
• PrimFn.log(a)
• PrimFn.mul(a, b) (also via overloading the binary * operator)
• PrimFn.max(a, b)
• PrimFn.sub(a, b) (also via overloading the binary - operator)

Reduction functions can appear as the outer-most function on the RHS:

• ReduceFn.add (also overloading the inplace += on a LHS)
• ReduceFn.mul
• ReduceFn.max

There are also special forms:

• cast(TypeVar, operand) casts the operand to the target type TypeVar.
• const(TypeVar, value) returns a constant value of type TypeVar.
• index(dim) returns the iteration index in the given dimension dim.

## Types ¶

All types in assignment expressions are late bound based on actual input and output types of constructed ops. An exception are predefined types such as I32, I64, F32, and F64. These hardwired types enable intermediate computations with a type that is independent of the input and output types. For example, parts of floating point computation may require double precision arithmetic despite all inputs and outputs being single precision values. Assignment expressions with no cast 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 TypeVar first argument are emitted as symbolic_cast primitives in the YAML definition.

Casting will perform int<->float and index->int 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 determining 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.