MLIR

Multi-Level IR Compiler Framework

Generic DAG Rewriter Infrastructure

Introduction and Motivation 

The goal of a compiler IR is to represent code - at various levels of abstraction which pose different sets of tradeoffs in terms of representational capabilities and ease of transformation. However, the ability to represent code is not itself very useful - you also need to be able to implement those transformations.

There are many different sorts of compiler transformations, but this document focuses on a particularly important class of transformation that comes up repeatedly at scale, and is important for the immediate goals of MLIR: that of pattern matching on a set of operations and replacing with another set. This is the key algorithm required to implement the “op fission” algorithm used by the tf2xla bridge, pattern matching rewrites from TF ops to TF/Lite, peephole optimizations like “eliminate identity nodes” or “replace x+0 with x”, as well as a useful abstraction to implement optimization algorithms for MLIR graphs at all levels.

A particular strength of MLIR (and a major difference vs other compiler infrastructures like LLVM, GCC, XLA, TensorFlow, etc) is that it uses a single compiler IR to represent code at multiple levels of abstraction: an MLIR operation can be a “TensorFlow operation”, an “XLA HLO”, a “TF Lite FlatBufferModel op”, a TPU LLO instruction, an LLVM IR instruction (transitively including X86, Lanai, CUDA, and other target specific instructions), or anything else that the MLIR type system can reasonably express. Because MLIR spans such a wide range of different problems, a single infrastructure for performing graph-to-graph rewrites can help solve many diverse domain challenges, including TensorFlow graph level down to the machine code level.

Static single assignment (SSA) representations like MLIR make it easy to access the operands and “users” of an operation. As such, a natural abstraction for these graph-to-graph rewrites is that of DAG pattern matching: clients define DAG tile patterns, and each pattern includes a result DAG to produce and the cost of the result (or, inversely, the benefit of doing the replacement). A common infrastructure efficiently finds and perform the rewrites.

While this concept is simple, the details are more nuanced. This proposal defines and explores a set of abstractions that we feel can solve a wide range of different problems, and can be applied to many different sorts of problems that MLIR is - and is expected to - face over time. We do this by separating the pattern definition and matching algorithm from the “driver” of the computation loop, and make space for the patterns to be defined declaratively in the future.

There is a huge amount of related work to consider, given that pretty much every compiler in existence has to solve this problem many times over. Here are a few graph rewrite systems we have used, along with the pros and cons of this related work. One unifying problem with all of these is that these systems are only trying to solve one particular and usually narrow problem: our proposal would like to solve many of these problems with a single infrastructure. Of these, the most similar design to our proposal is the LLVM DAG-to-DAG instruction selection algorithm at the end.

Constant folding 

A degenerate but pervasive case of DAG-to-DAG pattern matching is constant folding: given an operation whose operands contain constants can often be folded to a result constant value.

MLIR already has constant folding routines which provide a simpler API than a general DAG-to-DAG pattern matcher, and we expect it to remain because the simpler contract makes it applicable in some cases that a generic matcher would not. For example, a DAG-rewrite can remove arbitrary nodes in the current function, which could invalidate iterators. Constant folding as an API does not remove any nodes, it just provides a (list of) constant values and allows the clients to update their data structures as necessary.

AST-Level Pattern Matchers 

The literature is full of source-to-source translators which transform identities in order to improve performance (e.g. transforming X*0 into 0). One large example that I’m aware of is the GCC fold function, which performs many optimizations on ASTs. Clang has similar routines for simple constant folding of expressions (as required by the C++ standard) but doesn’t perform general optimizations on its ASTs.

The primary downside of tree optimizers is that you can’t see across operations that have multiple uses. It is well known in literature that DAG pattern matching is more powerful than tree pattern matching, but OTOH, DAG pattern matching can lead to duplication of computation which needs to be checked for.

“Combiners” and other peephole optimizers 

Compilers end up with a lot of peephole optimizers for various things, e.g. the GCC “combine” routines (which try to merge two machine instructions into a single one), the LLVM Inst Combine pass , LLVM’s DAG Combiner , the Swift compiler’s SIL Combiner , etc. These generally match one or more operations and produce zero or more operations as a result. The LLVM Legalization infrastructure has a different outer loop but otherwise works the same way.

These passes have a lot of diversity, but also have a unifying structure: they mostly have a worklist outer loop which visits operations. They then use the C++ visitor pattern (or equivalent) to switch over the class of operation and dispatch to a method. That method contains a long list of hand-written C++ code that pattern-matches various special cases. LLVM introduced a “match” function that allows writing patterns in a somewhat more declarative style using template metaprogramming (MLIR has similar facilities). Here’s a simple example:

  // Y - (X + 1) --> ~X + Y
  if (match(Op1, m_OneUse(m_Add(m_Value(X), m_One()))))
    return BinaryOperator::CreateAdd(Builder.CreateNot(X), Op0);

Here is a somewhat more complicated one (this is not the biggest or most complicated :)

  // C2 is ODD
  // LHS = XOR(Y,C1), Y = AND(Z,C2), C1==(C2+1) => LHS == NEG(OR(Z, ~C2))
  // ADD(LHS, RHS) == SUB(RHS, OR(Z, ~C2))
  if (match(LHS, m_Xor(m_Value(Y), m_APInt(C1))))
    if (C1->countTrailingZeros() == 0)
      if (match(Y, m_And(m_Value(Z), m_APInt(C2))) && *C1 == (*C2 + 1)) {
        Value NewOr = Builder.CreateOr(Z, ~(*C2));
        return Builder.CreateSub(RHS, NewOr, "sub");
      }

These systems are simple to set up, and pattern matching templates have some advantages (they are extensible for new sorts of sub-patterns, look compact at point of use). OTOH, they have lots of well known problems, for example:

  • These patterns are very error prone to write, and contain lots of redundancies.
  • The IR being matched often has identities (e.g. when matching commutative operators) and the C++ code has to handle it manually - take a look at the full code for checkForNegativeOperand that defines the second pattern).
  • The matching code compiles slowly, both because it generates tons of code and because the templates instantiate slowly.
  • Adding new patterns (e.g. for count leading zeros in the example above) is awkward and doesn’t often happen.
  • The cost model for these patterns is not really defined - it is emergent based on the order the patterns are matched in code.
  • They are non-extensible without rebuilding the compiler.
  • It isn’t practical to apply theorem provers and other tools to these patterns - they cannot be reused for other purposes.

In addition to structured “combiners” like these, there are lots of ad-hoc systems like the LLVM Machine code peephole optimizer which are related.

LLVM’s DAG-to-DAG Instruction Selection Infrastructure 

The instruction selection subsystem in LLVM is the result of many years worth of iteration and discovery, driven by the need for LLVM to support code generation for lots of targets, the complexity of code generators for modern instruction sets (e.g. X86), and the fanatical pursuit of reusing code across targets. Eli wrote a nice short overview of how this works, and the LLVM documentation describes it in more depth including its advantages and limitations. It allows writing patterns like this.

def : Pat<(or GR64:$src, (not (add GR64:$src, 1))),
          (BLCI64rr GR64:$src)>;

This example defines a matcher for the “blci” instruction in the X86 target description , there are many others in that file (look for Pat<> patterns, since they aren’t entangled in details of the compiler like assembler/disassembler generation logic).

For our purposes, there is much to like about this system, for example:

  • It is defined in a declarative format.
  • It is extensible to target-defined operations.
  • It automates matching across identities, like commutative patterns.
  • It allows custom abstractions and intense factoring of target-specific commonalities.
  • It generates compact code - it compiles into a state machine, which is interpreted.
  • It allows the instruction patterns to be defined and reused for multiple purposes.
  • The patterns are “type checked” at compile time, detecting lots of bugs early and eliminating redundancy from the pattern specifications.
  • It allows the use of general C++ code for weird/complex cases.

While there is a lot that is good here, there is also a lot of bad things:

  • All of this machinery is only applicable to instruction selection. Even directly adjacent problems like the DAGCombiner and Legalizer can’t use it.
  • This isn’t extensible at compiler runtime, you have to rebuild the compiler to extend it.
  • The error messages when failing to match a pattern are not exactly optimal .
  • It has lots of implementation problems and limitations (e.g. can’t write a pattern for a multi-result operation) as a result of working with the awkward SelectionDAG representation and being designed and implemented lazily.
  • This stuff all grew organically over time and has lots of sharp edges.

Summary 

MLIR will face a wide range of pattern matching and graph rewrite problems, and one of the major advantages of having a common representation for code at multiple levels that it allows us to invest in - and highly leverage - a single infra for doing this sort of work.

Goals 

This proposal includes support for defining pattern matching and rewrite algorithms on MLIR. We’d like these algorithms to encompass many problems in the MLIR space, including 1-to-N expansions (e.g. as seen in the TF/XLA bridge when lowering a “tf.AddN” to multiple “add” HLOs), M-to-1 patterns (as seen in Grappler optimization passes, e.g. that convert multiple/add into a single muladd op), as well as general M-to-N patterns (e.g. instruction selection for target instructions). Patterns should have a cost associated with them, and the common infrastructure should be responsible for sorting out the lowest cost match for a given application.

We separate the task of picking a particular locally optimal pattern from a given root node, the algorithm used to rewrite an entire graph given a particular set of goals, and the definition of the patterns themselves. We do this because DAG tile pattern matching is NP complete, which means that there are no known polynomial time algorithms to optimally solve this problem. Additionally, we would like to support iterative rewrite algorithms that progressively transform the input program through multiple steps. Furthermore, we would like to support many different sorts of clients across the MLIR stack, and they may have different tolerances for compile time cost, different demands for optimality, and other algorithmic goals or constraints.

We aim for MLIR transformations to be easy to implement and reduce the likelihood for compiler bugs. We expect there to be a very very large number of patterns that are defined over time, and we believe that these sorts of patterns will have a very large number of legality/validity constraints - many of which are difficult to reason about in a consistent way, may be target specific, and whose implementation may be particularly bug-prone. As such, we aim to design the API around pattern definition to be simple, resilient to programmer errors, and allow separation of concerns between the legality of the nodes generated from the idea of the pattern being defined.

Finally, error handling is a topmost concern: in addition to allowing patterns to be defined in a target-independent way that may not apply for all hardware, we also want failure for any pattern to match to be diagnosable in a reasonable way. To be clear, this is not a solvable problem in general - the space of malfunction is too great to be fully enumerated and handled optimally, but there are better and worse ways to handle the situation. MLIR is already designed to represent the provenance of an operation well. This project aims to propagate that provenance information precisely, as well as diagnose pattern match failures with the rationale for why a set of patterns do not apply.

Non goals 

This proposal doesn’t aim to solve all compiler problems, it is simply a DAG-to-DAG pattern matching system, starting with a greedy driver algorithm. Compiler algorithms that require global dataflow analysis (e.g. common subexpression elimination, conditional constant propagation, and many many others) will not be directly solved by this infrastructure.

This proposal is limited to DAG patterns, which (by definition) prevent the patterns from seeing across cycles in a graph. In an SSA-based IR like MLIR, this means that these patterns don’t see across PHI nodes / basic block arguments. We consider this acceptable given the set of problems we are trying to solve - we don’t know of any other system that attempts to do so, and consider the payoff of worrying about this to be low.

This design includes the ability for DAG patterns to have associated costs (benefits), but those costs are defined in terms of magic numbers (typically equal to the number of nodes being replaced). For any given application, the units of magic numbers will have to be defined.

Overall design 

We decompose the problem into four major pieces:

  1. the code that is used to define patterns to match, cost, and their replacement actions
  2. the driver logic to pick the best match for a given root node
  3. the client that is implementing some transformation (e.g. a combiner)
  4. (future) the subsystem that allows patterns to be described with a declarative syntax, which sugars step #1.

We sketch the first three of these pieces, each in turn. This is not intended to be a concrete API proposal, merely to describe the design

Defining Patterns 

Each pattern will be an instance of a mlir::Pattern class, whose subclasses implement methods like this. Note that this API is meant for exposition, the actual details are different for efficiency and coding standards reasons (e.g. the memory management of PatternState is not specified below, etc):

class Pattern {
  /// Return the benefit (the inverse of "cost") of matching this pattern.  The
  /// benefit of a Pattern is always static - rewrites that may have dynamic
  /// benefit can be instantiated multiple times (different Pattern instances)
  /// for each benefit that they may return, and be guarded by different match
  /// condition predicates.
  PatternBenefit getBenefit() const { return benefit; }

  /// Return the root node that this pattern matches.  Patterns that can
  /// match multiple root types are instantiated once per root.
  OperationName getRootKind() const { return rootKind; }

  /// Attempt to match against code rooted at the specified operation,
  /// which is the same operation code as getRootKind().  On failure, this
  /// returns a None value.  On success it a (possibly null) pattern-specific
  /// state wrapped in a Some.  This state is passed back into its rewrite
  /// function if this match is selected.
  virtual Optional<PatternState*> match(Operation *op) const = 0;

  /// Rewrite the IR rooted at the specified operation with the result of
  /// this pattern, generating any new operations with the specified
  /// rewriter.  If an unexpected error is encountered (an internal
  /// compiler error), it is emitted through the normal MLIR diagnostic
  /// hooks and the IR is left in a valid state.
  virtual void rewrite(Operation *op, PatternState *state,
                       PatternRewriter &rewriter) const;
};

In practice, the first patterns we implement will directly subclass and implement this stuff, but we will define some helpers to reduce boilerplate. When we have a declarative way to describe patterns, this should be automatically generated from the description.

Instances of Pattern have a benefit that is static upon construction of the pattern instance, but may be computed dynamically at pattern initialization time, e.g. allowing the benefit to be derived from domain specific information, like the target architecture). This limitation allows us MLIR to (eventually) perform pattern fusion and compile patterns into an efficient state machine, and Thier, Ertl, and Krall have shown that match predicates eliminate the need for dynamically computed costs in almost all cases: you can simply instantiate the same pattern one time for each possible cost and use the predicate to guard the match.

The two-phase nature of this API (match separate from rewrite) is important for two reasons: 1) some clients may want to explore different ways to tile the graph, and only rewrite after committing to one tiling. 2) We want to support runtime extensibility of the pattern sets, but want to be able to statically compile the bulk of known patterns into a state machine at “compiler compile time”. Both of these reasons lead to us needing to match multiple patterns before committing to an answer.

Picking and performing a replacement 

In the short term, this API can be very simple, something like this can work and will be useful for many clients:

class PatternMatcher {
   // Create a pattern matcher with a bunch of patterns.  This constructor
   // looks across all of the specified patterns, and builds an internal
   // data structure that allows efficient matching.
   PatternMatcher(ArrayRef<Pattern*> patterns);

   // Given a specific operation, see if there is some rewrite that is
   // interesting.  If so, return success and return the list of new
   // operations that were created.  If not, return failure.
   bool matchAndRewrite(Operation *op,
                        SmallVectorImpl<Operation*> &newlyCreatedOps);
};

In practice the interesting part of this class is the acceleration structure it builds internally. It buckets up the patterns by root operation, and sorts them by their static benefit. When performing a match, it tests any dynamic patterns, then tests statically known patterns from highest to lowest benefit.

First Client: A Greedy Worklist Combiner 

We expect that there will be lots of clients for this, but a simple greedy worklist-driven combiner should be powerful enough to serve many important ones, including the TF2XLA op expansion logic , many of the pattern substitution passes of the TOCO compiler for TF-Lite, many Grappler passes, and other general performance optimizations for applying identities.

The structure of this algorithm is straight-forward, here is pseudo code:

  • Walk a function in preorder, adding each operation to a worklist.
  • While the worklist is non-empty, pull something off the back (processing things generally in postorder)
    • Perform matchAndRewrite on the operation. If failed, continue to the next operation.
    • On success, add the newly created ops to the worklist and continue.

Future directions 

It is important to get implementation and usage experience with this, and many patterns can be defined using this sort of framework. Over time, we can look to make it easier to declare patterns in a declarative form (e.g. with the LLVM tblgen tool or something newer/better). Once we have that, we can define an internal abstraction for describing the patterns to match, allowing better high level optimization of patterns (including fusion of the matching logic across patterns, which the LLVM instruction selector does) and allow the patterns to be defined without rebuilding the compiler itself.