# Pattern Rewriting : Generic DAG-to-DAG Rewriting [TOC] This document details the design and API of the pattern rewriting infrastructure present in MLIR, a general DAG-to-DAG transformation framework. This framework is widely used throughout MLIR for canonicalization, conversion, and general transformation. For an introduction to DAG-to-DAG transformation, and the rationale behind this framework please take a look at the [Generic DAG Rewriter Rationale](Rationale/RationaleGenericDAGRewriter.md). ## Introduction The pattern rewriting framework can largely be decomposed into two parts: Pattern Definition and Pattern Application. ## Defining Patterns Patterns are defined by inheriting from the `RewritePattern` class. This class represents the base class of all rewrite patterns within MLIR, and is comprised of the following components: ### Benefit This is the expected benefit of applying a given pattern. This benefit is static upon construction of the pattern, 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 for performing pattern fusion and compiling patterns into an efficient state machine, and [Thier, Ertl, and Krall](https://dl.acm.org/citation.cfm?id=3179501) 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. ### Root Operation Name (Optional) The name of the root operation that this pattern matches against. If specified, only operations with the given root name will be provided to the `match` and `rewrite` implementation. If not specified, any operation type may be provided. The root operation name should be provided whenever possible, because it simplifies the analysis of patterns when applying a cost model. To match any operation type, a special tag must be provided to make the intent explicit: `MatchAnyOpTypeTag`. ### `match` and `rewrite` implementation This is the chunk of code that matches a given root `Operation` and performs a rewrite of the IR. A `RewritePattern` can specify this implementation either via separate `match` and `rewrite` methods, or via a combined `matchAndRewrite` method. When using the combined `matchAndRewrite` method, no IR mutation should take place before the match is deemed successful. The combined `matchAndRewrite` is useful when non-trivially recomputable information is required by the matching and rewriting phase. See below for examples: ```c++ class MyPattern : public RewritePattern { public: /// This overload constructs a pattern that only matches operations with the /// root name of `MyOp`. MyPattern(PatternBenefit benefit, MLIRContext *context) : RewritePattern(MyOp::getOperationName(), benefit, context) {} /// This overload constructs a pattern that matches any operation type. MyPattern(PatternBenefit benefit) : RewritePattern(benefit, MatchAnyOpTypeTag()) {} /// In this section, the `match` and `rewrite` implementation is specified /// using the separate hooks. LogicalResult match(Operation *op) const override { // The `match` method returns `success()` if the pattern is a match, failure // otherwise. // ... } void rewrite(Operation *op, PatternRewriter &rewriter) { // The `rewrite` method performs mutations on the IR rooted at `op` using // the provided rewriter. All mutations must go through the provided // rewriter. } /// In this section, the `match` and `rewrite` implementation is specified /// using a single hook. LogicalResult matchAndRewrite(Operation *op, PatternRewriter &rewriter) { // The `matchAndRewrite` method performs both the matching and the mutation. // Note that the match must reach a successful point before IR mutation may // take place. } }; ``` #### Restrictions Within the `match` section of a pattern, the following constraints apply: * No mutation of the IR is allowed. Within the `rewrite` section of a pattern, the following constraints apply: * All IR mutations, including creation, *must* be performed by the given `PatternRewriter`. This class provides hooks for performing all of the possible mutations that may take place within a pattern. For example, this means that an operation should not be erased via its `erase` method. To erase an operation, the appropriate `PatternRewriter` hook (in this case `eraseOp`) should be used instead. * The root operation is required to either be: updated in-place, replaced, or erased. ### Application Recursion Recursion is an important topic in the context of pattern rewrites, as a pattern may often be applicable to its own result. For example, imagine a pattern that peels a single iteration from a loop operation. If the loop has multiple peelable iterations, this pattern may apply multiple times during the application process. By looking at the implementation of this pattern, the bound for recursive application may be obvious, e.g. there are no peelable iterations within the loop, but from the perspective of the pattern driver this recursion is potentially dangerous. Often times the recursive application of a pattern indicates a bug in the matching logic. These types of bugs generally do not cause crashes, but create infinite loops within the application process. Given this, the pattern rewriting infrastructure conservatively assumes that no patterns have a proper bounded recursion, and will fail if recursion is detected. A pattern that is known to have proper support for handling recursion can signal this by calling `setHasBoundedRewriteRecursion` when initializing the pattern. This will signal to the pattern driver that recursive application of this pattern may happen, and the pattern is equipped to safely handle it. ### Initialization Several pieces of pattern state require explicit initialization by the pattern, for example setting `setHasBoundedRewriteRecursion` if a pattern safely handles recursive application. This pattern state can be initialized either in the constructor of the pattern or via the utility `initialize` hook. Using the `initialize` hook removes the need to redefine pattern constructors just to inject additional pattern state initialization. An example is shown below: ```c++ class MyPattern : public RewritePattern { public: /// Inherit the constructors from RewritePattern. using RewritePattern::RewritePattern; /// Initialize the pattern. void initialize() { /// Signal that this pattern safely handles recursive application. setHasBoundedRewriteRecursion(); } // ... }; ``` ### Construction Constructing a RewritePattern should be performed by using the static `RewritePattern::create` utility method. This method ensures that the pattern is properly initialized and prepared for insertion into a `RewritePatternSet`. ## Pattern Rewriter A `PatternRewriter` is a special class that allows for a pattern to communicate with the driver of pattern application. As noted above, *all* IR mutations, including creations, are required to be performed via the `PatternRewriter` class. This is required because the underlying pattern driver may have state that would be invalidated when a mutation takes place. Examples of some of the more prevalent `PatternRewriter` API is shown below, please refer to the [class documentation](https://github.com/llvm/llvm-project/blob/main/mlir/include/mlir/IR/PatternMatch.h#L235) for a more up-to-date listing of the available API: * Erase an Operation : `eraseOp` This method erases an operation that either has no results, or whose results are all known to have no uses. * Notify why a `match` failed : `notifyMatchFailure` This method allows for providing a diagnostic message within a `matchAndRewrite` as to why a pattern failed to match. How this message is displayed back to the user is determined by the specific pattern driver. * Replace an Operation : `replaceOp`/`replaceOpWithNewOp` This method replaces an operation's results with a set of provided values, and erases the operation. * Update an Operation in-place : `(start|cancel|finalize)RootUpdate` This is a collection of methods that provide a transaction-like API for updating the attributes, location, operands, or successors of an operation in-place within a pattern. An in-place update transaction is started with `startRootUpdate`, and may either be canceled or finalized with `cancelRootUpdate` and `finalizeRootUpdate` respectively. A convenience wrapper, `updateRootInPlace`, is provided that wraps a `start` and `finalize` around a callback. * OpBuilder API The `PatternRewriter` inherits from the `OpBuilder` class, and thus provides all of the same functionality present within an `OpBuilder`. This includes operation creation, as well as many useful attribute and type construction methods. ## Pattern Application After a set of patterns have been defined, they are collected and provided to a specific driver for application. A driver consists of several high levels parts: * Input `RewritePatternSet` The input patterns to a driver are provided in the form of an `RewritePatternSet`. This class provides a simplified API for building a list of patterns. * Driver-specific `PatternRewriter` To ensure that the driver state does not become invalidated by IR mutations within the pattern rewriters, a driver must provide a `PatternRewriter` instance with the necessary hooks overridden. If a driver does not need to hook into certain mutations, a default implementation is provided that will perform the mutation directly. * Pattern Application and Cost Model Each driver is responsible for defining its own operation visitation order as well as pattern cost model, but the final application is performed via a `PatternApplicator` class. This class takes as input the `RewritePatternSet` and transforms the patterns based upon a provided cost model. This cost model computes a final benefit for a given pattern, using whatever driver specific information necessary. After a cost model has been computed, the driver may begin to match patterns against operations using `PatternApplicator::matchAndRewrite`. An example is shown below: ```c++ class MyPattern : public RewritePattern { public: MyPattern(PatternBenefit benefit, MLIRContext *context) : RewritePattern(MyOp::getOperationName(), benefit, context) {} }; /// Populate the pattern list. void collectMyPatterns(RewritePatternSet &patterns, MLIRContext *ctx) { patterns.add(/*benefit=*/1, ctx); } /// Define a custom PatternRewriter for use by the driver. class MyPatternRewriter : public PatternRewriter { public: MyPatternRewriter(MLIRContext *ctx) : PatternRewriter(ctx) {} /// Override the necessary PatternRewriter hooks here. }; /// Apply the custom driver to `op`. void applyMyPatternDriver(Operation *op, const RewritePatternSet &patterns) { // Initialize the custom PatternRewriter. MyPatternRewriter rewriter(op->getContext()); // Create the applicator and apply our cost model. PatternApplicator applicator(patterns); applicator.applyCostModel([](const Pattern &pattern) { // Apply a default cost model. // Note: This is just for demonstration, if the default cost model is truly // desired `applicator.applyDefaultCostModel()` should be used // instead. return pattern.getBenefit(); }); // Try to match and apply a pattern. LogicalResult result = applicator.matchAndRewrite(op, rewriter); if (failed(result)) { // ... No patterns were applied. } // ... A pattern was successfully applied. } ``` ## Common Pattern Drivers MLIR provides several common pattern drivers that serve a variety of different use cases. ### Dialect Conversion Driver This driver provides a framework in which to perform operation conversions between, and within dialects using a concept of "legality". This framework allows for transforming illegal operations to those supported by a provided conversion target, via a set of pattern-based operation rewriting patterns. This framework also provides support for type conversions. More information on this driver can be found [here](DialectConversion.md). ### Greedy Pattern Rewrite Driver This driver walks the provided operations and greedily applies the patterns that locally have the most benefit. The benefit of a pattern is decided solely by the benefit specified on the pattern, and the relative order of the pattern within the pattern list (when two patterns have the same local benefit). Patterns are iteratively applied to operations until a fixed point is reached, at which point the driver finishes. This driver may be used via the following: `applyPatternsAndFoldGreedily` and `applyOpPatternsAndFold`. The latter of which only applies patterns to the provided operation, and will not traverse the IR. The driver is configurable and supports two modes: 1) you may opt-in to a "top-down" traversal, which seeds the worklist with each operation top down and in a pre-order over the region tree. This is generally more efficient in compile time. 2) the default is a "bottom up" traversal, which builds the initial worklist with a postorder traversal of the region tree. This may match larger patterns with ambiguous pattern sets. Note: This driver is the one used by the [canonicalization](Canonicalization.md) [pass](Passes.md#-canonicalize-canonicalize-operations) in MLIR.