Multi-Level IR Compiler Framework

Linalg Dialect Rationale: The Case For Compiler-Friendly Custom Operations



MLIR Codegen Flow

This document describes the key design principles that led to the existing implementation of Linalg and aims at exposing the tradeoffs involved when building higher-level Intermediate Representations (IR) and Dialects to facilitate code generation. Consider the simplified schema describing codegen in MLIR. Linalg is designed to solve the High-level Hierarchical Optimization (HHO box) and to interoperate nicely within a Mixture Of Expert Compilers environment (i.e. the CGSel box). This work is inspired by a wealth of prior art in the field, from which it seeks to learn key lessons. This documentation and introspection effort also comes in the context of the proposal for a working group for discussing the Development of high-level Tensor Compute Primitives dialect(s) and transformations. We hope that the lessons from prior art, the design principles outlined in this doc and the architecture of Linalg can help inform the community on a path to defining these High-Level Tensor Compute Primitives.


Linalg started as a pragmatic dialect to bootstrap code generation in MLIR, by defining away complex code generation problems like precise dependence analysis or polyhedral code generation and by introducing the ability to call into fast library implementations when available. Linalg defines ops and transformations declaratively and was originally restricted to ops with linear-algebra like semantics (pointwise, matmul, conv…). This approach enables building a high-level productivity-first codegen solution that leverages both compiler optimizations and efficient library implementations so as not to miss out on simple performance benefits. For example, if one’s favorite HPC library or ISA has a matmul primitive running at 95% of the achievable peak performance, for operands stored in some memory, one should be able to use the primitive when possible and generate code otherwise.

However, as the design of Linalg co-evolved with the design of MLIR, it became apparent that it could extend to larger application domains than just machine learning on dense tensors.

The design and evolution of Linalg follow a codegen-friendly approach where the IR and the transformations evolve hand-in-hand. The key idea is that op semantics declare and transport information that is traditionally obtained by compiler analyses. This information captures the legality and applicability of transformations and is not lost by lowering prematurely to loop or CFG form. The key transformations are designed so as to preserve this information as long as necessary. For example, linalg.matmul remains linalg.matmul after tiling and fusion.

Furthermore, Linalg decouples transformation validity from profitability considerations and voluntarily leaves the latter aside in the first iteration (see the suitability for search guiding principle).

The first incarnation of these ideas was presented as an example at the EuroLLVM 2019 developer’s meeting as part of the Linalg section of the first MLIR Tutorial.


Since the initial implementation, the design has evolved with, and partially driven the evolution of the core MLIR infrastructure to use Regions, OpInterfaces, ODS and Declarative Rewrite Rules among others. The approach adopted by Linalg was extended to become StructuredOps abstractions, with Linalg becoming its incarnation on tensors and buffers. It is complemented by the Vector dialect, which defines structured operations on vectors, following the same rationale and design principles as Linalg. (Vector dialect includes the higher-level operations on multi-dimensional vectors and abstracts away the lowering to single-dimensional vectors).

The Linalg dialect itself grew beyond linear algebra-like operations to become more expressive, in particular by providing an abstraction of a loop nest supporting parallelism, reductions and sliding windows around arbitrary MLIR regions. It also has the potential of growing beyond dense linear-algebra to support richer data types, such as sparse and ragged tensors and buffers.

Linalg design remains open to evolution and cross-pollination with other dialects and approaches. It has been successfully used as the staging ground for code generation-related abstractions, spinning off the generalization of the following:

  • the !linalg.view type folded into the “Strided MemRef” type while preserving structure to allow calling into external C++ libraries with unsurprising ABI conventions;
  • the linalg.view and linalg.subview ops evolved into the standard dialect;
  • the linalg.for, linalg.load and ops evolved into a prelude to the structured control flow dialect (named LoopOps). More components can be extracted, redesigned and generalized when new uses or requirements arise.

Several design questions remain open in Linalg, which does not claim to be a general solution to all compilation problems. It does aim at driving thinking and implementations of domain-specific abstractions where programmer’s intent can be captured at a very high level, directly in the IR.

Given the evolution of the scope, it becomes apparent that a better name than “Linalg” could remove some of the confusions related to the dialect (and the underlying approach), its goals and limitations.

Prior Art 

Linalg draws inspiration from decades of prior art to design a modern a pragmatic solution. The following non-exhaustive list refers to some of the projects that influenced Linalg design:

Additionally, experience with the following tools proved very valuable when thinking holistically about how all these components interplay all the way up to the user and down to the hardware:

  • the Torch machine-learning framework,
  • the LLVM compiler, specifically in JIT mode,
  • high-performance libraries (MKL, CUBLAS, FBFFT)
  • the PeachPy assembler
  • current and potentially upcoming hardware ISAs.

The novelty of MLIR’s code base and its unprecedented support for defining and mixing abstractions, enabling one to reflect on and integrate the key elements of the prior art success as well as avoid the common pitfalls in the area of code generation. Thus, instead of diverging into a discussion about the implications of adopting any of the existing solutions, Linalg had the possibility to build on all of them and learn from their experience while leveraging the benefit of hindsight.

The following reflections on prior art have influenced the design of Linalg. The discussion is by no means exhaustive but should capture the key motivations behind Linalg.

Lessons from ONNX 

ONNX is a specification of operations that appear in Machine Learning workloads. As such, it is predominantly driven by the expressiveness requirements of ML, and less by the considerations of IR design for HPC code generation.

Similarly to ONNX, Linalg defines “semantically charged” named ops. But it also considers transformations on these ops as a key component and defines the IR to support the transformations, preferring transformations over expressiveness if necessary.

Linalg hopes to additionally address the following:

  • facilitate frontend-compiler co-design by taking into account compiler transformations and lowerings in op definition;
  • minimize the set of available ops by making them non-overlapping with each other, thus simplifying the intermediate representation.

Lessons from LIFT 

LIFT is a system to write computational kernels based on functional abstractions. Transformations are represented by additional nodes in the IR, whose semantics are at the level of the algorithm (e.g. partialReduce). LIFT applies and composes transformations by using local rewrite rules that embed these additional nodes directly in the functional abstraction.

Similarly to LIFT, Linalg uses local rewrite rules implemented with the MLIR Declarative Rewrite Rules mechanisms.

Linalg builds on, and helps separate concerns in the LIFT approach as follows:

  • transformations are either separated from the representation or expressed as composable attributes that are independent of the actual computation, avoiding intricate effects on performance;
  • abstractions are split into smaller components (e.g., control flow and data structure abstractions) potentially reusable across different dialects in the MLIR’s open ecosystem.

LIFT is expected to further influence the design of Linalg as it evolves. In particular, extending the data structure abstractions to support non-dense tensors can use the experience of LIFT abstractions for sparse and position-dependent arrays.

Lessons from XLA 

XLA is one of the first post-Theano ML compilers that was introduced as a pragmatic compilation solution for TensorFlow. It shines on Google’s xPU hardware and is an important piece of the puzzle. It is particularly good at (1) transforming code back and forth between the scalar and the vector worlds, (2) passing function boundaries for handling both host and device code, and (3) complying to stringent requirements imposed by energy-efficient xPUs. XLA followed a pragmatic design process where the compiler is given perfect knowledge of each op’s semantic, all starting from the mighty conv and matmul ops. XLA transformations consist of writing emitters that compose, as C++ functions. Perfect op semantics knowledge has 2 big benefits: (1) transformations are correct by construction (2) very strong performance on difficult xPU targets.

Similarly, Linalg ops “know their semantics” and “know how to transform and lower themselves”. The means by which this information is made available and how it is used in MLIR are, however, very different.

Linalg hopes to additionally address the following:

  • HLOs are expressive as a whole, but each op has very limited and fixed semantics: ops are not configurable. As a consequence, HLOs have evolved into a too large set of ops whose semantics intersect. This echoes the ops proliferation problem also exhibited by ONNX.
  • Reliance on perfect op knowledge leads to situations where transformations and ops end up needing to know about each other’s semantics (e.g. during fusion). Since the transformations themselves are not simple local rewrite patterns (unlike LIFT), code complexity grows quickly.
  • XLA lacks an independent IR that can be inspected, unit tested and used independently. This monolithic design makes the system not portable: xPU passes and GPU passes do not share much code.

Lessons from Halide and TVM 

Halide is a DSL embedded in C++ that provides a way of metaprogramming the HalideIR and applying transformations declaratively to let the expert user transform and optimize the program in tailored ways. Halide, initially targeted the SIGGRAPH community but is now more generally applicable. TVM is an evolution of Halide into the machine learning and deep-neural network space, based on HalideIR.

The Halide transformation methodology follows similar principles to the URUK and CHiLL compiler transformation frameworks, but without the strengths (and especially complexity) of the polyhedral model.

Halide particularly shines at making the HPC transformation methodology accessible to $\Omega$(10-100) users, at a time when polyhedral tools are still only accessible to $\Omega$(1-10) users. Halide makes heavy usage of canonicalization rules that are also very prevalent in MLIR.

Linalg hopes to additionally address the following:

  • Halide scheduling is powerful and explores a large swath of possible transformations. But it’s still too hard for newcomers to use or extend. The level of performance you get from Halide is very different depending on whether one is a seasoned veteran or a newcomer. This is especially true as the number of transformations grows.
  • Halide raises rather than lowers in two ways, going counter-current to the design goals we set for high-level codegen abstractions in MLIR. First, canonical Halide front-end code uses explicit indexing and math on scalar values, so to target BLAS/DNN libraries one needs to add pattern matching which is similarly brittle as in the affine case. While Halide’s performance is on par with the libraries on programmable targets (CPU/GPU), that approach doesn’t work on mobile accelerators or on xPUs, where the framework ingests whole-tensor operations. Second, reductions and scans are expressed using serial iteration, again requiring pattern matching before they can be transformed (e.g. to do a reduction using atomics, or hierarchically). The lesson to draw is that we should start with higher-level primitives than Halide.

Lessons from Tensor Comprehensions 

Tensor Comprehensions is a high-level language to express tensor computations with a syntax generalizing the Einstein notation, coupled to an end-to-end compilation flow capable of lowering to efficient GPU code. It was integrated with 2 ML frameworks: Caffe2 and PyTorch.

MLIR Codegen Flow

The compilation flow combines Halide and a Polyhedral Compiler derived from ISL and uses both HalideIR and the ISL schedule-tree IR. The compiler provides a collection of polyhedral compilation algorithms to perform fusion and favor multi-level parallelism and promotion to deeper levels of the memory hierarchy. Tensor Comprehensions showed that, fixing a few predefined strategies with parametric transformations and tuning knobs, can already provide great results. In that previous work, simple genetic search combined with an autotuning framework was sufficient to find good implementations in the non-compute bound regime. This requires code versions obtainable by the various transformations to encompass versions that get close to the roofline limit. The ultimate goal of Tensor Comprehensions was to concretely mix Halide high-level transformations with polyhedral mid-level transformations and build a pragmatic system that could take advantage of both styles of compilation.

Linalg hopes to additionally address the following:

  • Halide was never properly used in Tensor Comprehensions beyond shape inference. Most of the investment went into simplifying polyhedral transformations and building a usable end-to-end system. MLIR was deemed a better infrastructure to mix these types of compilation.
  • The early gains provided by reusing established infrastructures (HalideIR and ISL schedule trees) turned into more impedance mismatch problems than could be solved with a small tactical investment.
  • Tensor Comprehensions emitted CUDA code which was then JIT compiled with NVCC from a textual representation. While this was a pragmatic short-term solution it made it hard to perform low-level rewrites that would have helped with register reuse in the compute-bound regime.
  • The same reliance on emitting CUDA code made it difficult to create cost models when time came. This made it artificially harder to prune out bad solutions than necessary. This resulted in excessive runtime evaluation, as reported in the paper Machine Learning Systems are Stuck in a Rut.

Many of those issues are naturally addressed by implementing these ideas in the MLIR infrastructure.

Lessons from Polyhedral compilers 

The polyhedral model has been on the cutting edge of loop-level optimization for decades, with several incarnations in production compilers such as GRAPHITE for GCC and Polly for LLVM. Although it has proved crucial to generate efficient code from domain-specific languages such as PolyMage and Tensor Comprehensions, it has never been fully included into mainstream general-purpose optimization pipelines. Detailed analysis of the role of polyhedral transformations is provided in the simplified polyhedral form document dating back to the inception of MLIR.

In particular, polyhedral abstractions have proved challenging to integrate with a more conventional compiler due to the following.

  • The transformed code (or IR) quickly gets complex and thus hard to analyze and understand.
  • Code generation from the mathematical form used in the polyhedral model relies on non-trivial exponentially complex algorithms.
  • The mathematical form is rarely composable with the SSA representation and related algorithms, on which most mainstream compilers are built today.
  • Expressiveness limitations, although addressed in the scientific literature through, e.g., summary functions, often remain present in actual implementations.

The Affine dialect in MLIR was specifically designed to address the integration problems mention above. In particular, it maintains the IR in the same form (loops with additional constraints on how the bounds are expressed) throughout the transformation, decreasing the need for one-shot conversion between drastically different representations. It also embeds the polyhedral representation into the SSA form by using MLIR regions and thus allows one to combine polyhedral and SSA-based transformations.

Lessons from the Affine dialect 

The Affine dialect in MLIR brings the polyhedral abstraction closer to the conventional SSA representation. It addresses several long-standing integration challenges as described above and is likely to be more suitable when compiling from a C language-level abstraction.

MLIR makes it possible to start from a higher-level abstraction than C, for example in machine learning workloads. In such cases, it may be possible to avoid complex analyses (data-flow analysis across loop iterations is exponentially complex) required for polyhedral transformation by leveraging the information available at higher levels of abstractions, similarly to DSL compilers. Linalg intends to use this information when available and ensure legality of transformations by construction, by integrating legality preconditions in the op semantics (for example, loop tiling can be applied to the loop nest computing a matrix multiplication, no need to additionally rely on affine dependence analysis to check this). This information is not readily available in the Affine dialect, and can only be derived using potentially expensive pattern-matching algorithms.

Informed by the practical experience in polyhedral compilation and with the Affine dialects in particular, Linalg takes the following decisions.

  • Discourage loop skewing: the loop skewing transformation, that is sometimes used to enable parallelization, often has surprising (negative) effects on performance. In particular, polyhedral auto-transformation can be expressed in a simpler way without loop skewing; skewing often leads to complex control flow hampering performance on accelerators such as GPUs. Moreover, the problems loop skewing addresses can be better addressed by other approaches, e.g., diamond tiling. In the more restricted case of ML workloads, multi-for loops with induction variables independent of each other (referred to as hyper-rectangular iteration domains in the literature) such as the proposed affine.parallel are sufficient in the majority of cases.
  • Declarative Tiling: the tiling transformation is ubiquitous in HPC code generation. It can be seen as a decomposition of either the iteration space or the data space into smaller regular parts, referred to as tiles. Polyhedral approaches, including the Affine dialect, mostly opt for iteration space tiling, which introduces additional control flow and complex address expressions. If the tile sizes are not known during the transformation (so called parametric tiling), the address expressions and conditions quickly become non-affine or require exponentially complex algorithms to reason about them. Linalg focuses tiling on the data space instead, creating views into the buffers that leverage MLIR’s strided memref abstraction. These views compose and the complexity of access expressions remains predictable.
  • Preserve high-level information: Linalg maintains the information provided by the op semantics as long as necessary for transformations. For example, the result of tiling a matrix multiplication is loops around a smaller matrix multiplication. Even with pattern-matching on top of the Affine dialect, this would have required another step of pattern-matching after the transformation.

Given these choices, Linalg intends to be a better fit for high-level compilation were significantly more information is readily available in the input representation and should be leveraged before lowering to other abstractions. Affine remains a strong abstraction for mid-level transformation and is used as a lowering target for Linalg, enabling further transformations and combination of semantically-loaded and lower-level inputs. As such, Linalg is intended to complement Affine rather than replace it.

Core Guiding Principles 

Transformations and Simplicity First 

The purpose of the Linalg IR and its operations is primarily to:

  • develop a set of key transformations, and
  • make them correct by construction by carefully curating the set of generic operation properties that drive applicability, and
  • make them very simple to implement, apply, verify and especially maintain.

The problem at hand is fundamentally driven by compilation of domain-specific workloads for high-performance and parallel hardware architectures: this is an HPC compilation problem.

The selection of relevant transformations follows a co-design approach and involves considerations related to:

  • concrete current and future needs of the application domain,
  • concrete current and future hardware properties and ISAs,
  • understanding of strengths and limitations of existing approaches,
  • taking advantage of the coexistence of multiple levels of IR in MLIR,

One needs to be methodical to avoid proliferation and redundancy. A given transformation could exist at multiple levels of abstraction but just because one can write transformation X at level Y absolutely does not mean one should. This is where evaluation of existing systems and acknowledgement of their strengths and weaknesses is crucial: simplicity and maintainability aspects must be first-order concerns. Without this additional effort of introspection, a design will not stand the test of time. At the same time, complexity is very hard to ward off. It seems one needs to suffer complexity to be prompted to take a step back and rethink abstractions.

This is not merely a reimplementation of idea X in system Y: simplicity must be the outcome of this introspection effort.

Preservation of Information 

The last two decades have seen a proliferation of Domain-Specific Languages (DSLs) that have been very successful at limited application domains. The main commonality between these systems is their use of a significantly richer structural information than CFGs or loops. Still, another commonality of existing systems is to lower to LLVM very quickly, and cross a wide abstraction gap in a single step. This process often drops semantic information that later needs to be reconstructed later, when it is not irremediably lost.

These remarks, coupled with MLIR’s suitability for defining IR at multiple levels of abstraction led to the following 2 principles.

Declarative Specification: Avoid Raising 

Compiler transformations need static structural information (e.g. loop-nests, graphs of basic blocks, pure functions, etc). When that structural information is lost, it needs to be reconstructed.

A good illustration of this phenomenon is the notion of raising in polyhedral compilers: multiple polyhedral tools start by raising from a simplified C form or from SSA IR into a higher-level representation that is more amenable to loop transformations.

In advanced polyhedral compilers, a second type of raising may typically exist to detect particular patterns (often variations of BLAS). Such patterns may be broken by transformations making their detection very fragile or even just impossible (incorrect).

MLIR makes it easy to define op semantics declaratively thanks to the use of regions and attributes. This is an ideal opportunity to define new abstractions to convey user-intent directly into the proper abstraction.

Progressive Lowering: Don’t Lose Information too Quickly 

Lowering too quickly to affine, generic loops or CFG form reduces the amount of structure available to derive transformations from. While manipulating loops is a net gain compared to CFG form for a certain class of transformations, important information is still lost (e.g. parallel loops, or mapping of a loop nest to an external implementation).

This creates non-trivial phase ordering issues. For instance, loop fusion may easily destroy the ability to detect a BLAS pattern. One possible alternative is to perform loop fusion, tiling, intra-tile loop distribution and then hope to detect the BLAS pattern. Such a scheme presents difficult phase-ordering constraints that will likely interfere with other decisions and passes. Instead, certain Linalg ops are designed to maintain high-level information across transformations such as tiling and fusion.

MLIR is designed as an infrastructure for progressive lowering. Linalg fully embraces this notion and thinks of codegen in terms of reducing a potential function. That potential function is loosely defined in terms of number of low-level instructions in a particular Linalg ops (i.e. how heavy or lightweight the Linalg op is). Linalg-based codegen and transformations start from higher-level IR ops and dialects. Then each transformation application reduces the potential by introducing lower-level IR ops and smaller Linalg ops. This gradually reduces the potential, all the way to Loops + VectorOps and LLVMIR.

Composable and Declarative Transformations 

Complex and impactful transformations need not be hard to manipulate, write or maintain. Mixing XLA-style high-level op semantics knowledge with generic properties to describe these semantics, directly in MLIR, is a promising way to:

  • Design transformations that are correct by construction, easy to write, easy to verify and easy to maintain.
  • Provide a way to specify transformations and the units of IR they manipulate declaratively. In turn this allows using local pattern rewrite rules in MLIR (i.e. DRR).
  • Allow creating customizable passes declaratively by simply selecting rewrite rules. This allows mixing transformations, canonicalizations, constant folding and other enabling rewrites in a single pass. The result is a system where pass fusion is very simple to obtain and gives hope for solving certain phase ordering issues.

Suitability for Search and Machine Learning 

Compiler heuristics are hand-crafted human-engineered features: it is ripe for disruption by machine-learning techniques. To enable search, compiler transformations should be fine-grained, composable and expose tuning parameters that can modify their behavior, guided by lessons from previous experience with Tensor Comprehensions.

Of course, we are not advocating for using ML everywhere in the stack immediately: low-level compilation and machine models are still quite performant in LLVM. However, for the high-level and mid-level optimization problems, models need to be conditioned (probabilistically) on the low-level compiler which acts as a blackbox. For these reasons we prioritize the design of IR and transformations with search-friendly properties over building cost models. Still, this does not mean Linalg refuses cost models: instead we prefer to invest in infrastructure that will enable ML-based techniques to automatically build cost models.

Extensibility and Future-Proofness 

MLIR allows defining IR for structured control flow and structured data types. We choose to take advantage of these properties for the reasons described above. In particular, the MemRefType represents dense non-contiguous memory regions. This structure should extend beyond simple dense data types and generalize to ragged, sparse and mixed dense/sparse tensors as well as to trees, hash tables, tables of records and maybe even graphs.

For such more advanced data types, the control-flow required to traverse the data structures, termination conditions, etc are much less simple to analyze and characterize statically. As a consequence we need to also design solutions that stand a chance of evolving into runtime-adaptive computations (e.g. inspector-executor in which an inspector runs a cheap runtime analysis on the data to configure how the executor should run). While there is no concrete solution today to solve these problems in MLIR, it is pretty clear that perfect static knowledge and analyses will not be serious contenders for these problems.

Key Observations 

The following key observations have influenced the design of Linalg and helped reconcile core guiding principles with real-world requirements when producing an implementation based on MLIR.

Algorithms + Data Structures = Programs 

This is a twist on Niklaus Wirth’s formulation but captures the essence of the design of Linalg: control-flow does not exist in a vacuum, independently of data. On the contrary, there is a very strong relationship between control-flow and data structures: one cannot exist without the other. This has multiple implications on the semantics of Linalg Ops and their transformations. In particular, this observation influences whether certain transformations are better done: - as control flow or data structure manipulation, - on Linalg ops attributes or on loops after some partial lowering occurred, - as extensions to the Linalg dialect in terms of new ops or attributes.

The Dialect Need not be Closed Under Transformations 

This is probably the most surprising and counter-intuitive observation. When one designs IR for transformations, closed-ness is often a non-negotiable property. This is a key design principle of polyhedral IRs such as URUK and ISL-based IRs: they are closed under affine transformations. In MLIR, multiple dialects coexist and form a coherent whole. After experimenting with different alternatives, it became clear that strict dialect closed-ness wasn’t necessary and could be relaxed. Previous systems did not have simple and principled means of building new IR and probably suffered from this limitation. We conjecture this is a key reason they required the IR to be closed under transformations.

Despite the fact that Linalg ops only allow perfectly nested semantics, once tiling and fusion kick in, imperfectly nested loops are gradually introduced. In other words, imperfectly nested control flow appears as the result of applying key transformations.

Considering the potential described during the discussion on Progressive Lowering, closed-ness under transformation would dictate that the potential remains constant. In contrast, Linalg advocates for monotonicity under transformations.

Summary of Existing Alternatives a Picture 

Lastly, we summarize our observations of lessons from Prior Art—when viewed under the lense of our Core Guiding Principles—with the following picture.

MLIR Codegen Flow

This figure is not meant to be perfectly accurate but a rough map of how we view the distribution of structural information in existing systems, from a codegen-friendly angle. Unsurprisingly, the Linalg Dialect and its future evolutions aspire to a position in the top-right of this map.