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				# PDLL - PDL Language

				This document details the PDL Language (PDLL), a custom frontend language for
				writing pattern rewrites targeting MLIR.

				Note: This document assumes a familiarity with MLIR concepts; more specifically
				the concepts detailed within the
				[MLIR Pattern Rewriting](https://mlir.llvm.org/docs/PatternRewriter/) and
				[Operation Definition Specification (ODS)](https://mlir.llvm.org/docs/OpDefinitions/)
				documentation.

				[TOC]

				## Introduction

				Pattern matching is an extremely important component within MLIR, as it
				encompasses many different facets of the compiler. From canonicalization, to
				optimization, to conversion; every MLIR based compiler will heavily rely on the
				pattern matching infrastructure in some capacity.

				The PDL Language (PDLL) provides a declarative pattern language designed from
				the ground up for representing MLIR pattern rewrites. PDLL is designed to
				natively support writing matchers on all of MLIRs constructs via an intuitive
				interface that may be used for both ahead-of-time (AOT) and just-in-time (JIT)
				pattern compilation.

				## Rationale

				This section provides details on various design decisions, their rationale, and
				alternatives considered when designing PDLL. Given the nature of software
				development, this section may include references to areas of the MLIR compiler
				that no longer exist.

				### Why build a new language instead of improving TableGen DRR?

				Note: The section assumes familiarity with
				[TDRR](https://mlir.llvm.org/docs/DeclarativeRewrites/), please refer the
				relevant documentation before continuing.

				Tablegen DRR (TDRR), i.e.
				[Table-driven Declarative Rewrite Rules](https://mlir.llvm.org/docs/DeclarativeRewrites/),
				is a declarative DSL for defining MLIR pattern rewrites within the
				[TableGen](https://llvm.org/docs/TableGen/index.html) language. This
				infrastructure is currently the main way in which patterns may be defined
				declaratively within MLIR. TDRR utilizes TableGen's `dag` support to enable
				defining MLIR patterns that fit nicely within a DAG structure; in a similar way
				in which tablegen has been used to defined patterns for LLVM's backend
				infrastructure (SelectionDAG/Global Isel/etc.). Unfortunately however, the
				TableGen language is not as amenable to the structure of MLIR patterns as it has
				been for LLVM.

				The issues with TDRR largely stem from the use of TableGen as the host language
				for the DSL. These issues have risen from a mismatch in the structure of
				TableGen compared to the structure of MLIR, and from TableGen having different
				motivational goals than MLIR. A majority (or all depending on how stubborn you
				are) of the issues that we've come across with TDRR have been addressable in
				some form; the sticking point here is that the solutions to these problems have
				often been more "creative" than we'd like. This is a problem, and why we decided
				not to invest a larger effort into improving TDRR; users generally don't want
				"creative" APIs, they want something that is intuitive to read/write.

				To highlight some of these issues, below we will take a tour through some of the
				problems that have arisen, and how we "fixed" them.

				#### Multi-result operations

				MLIR natively supports a variable number of operation results. For the DAG based
				structure of TDRR, any form of multiple results (operations in this instance)
				creates a problem. This is because the DAG wants a single root node, and does
				not have nice facilities for indexing or naming the multiple results. Let's take
				a look at a quick example to see how this manifests:

				```tablegen
				// Suppose we have a three result operation, defined as seen below.
				def ThreeResultOp : Op<"three_result_op"> {
				let arguments = (ins ...);

				let results = (outs
				AnyTensor:$output1,
				AnyTensor:$output2,
				AnyTensor:$output3
				);
				}

				// To bind the results of `ThreeResultOp` in a TDRR pattern, we bind all results
				// to a single name and use a special naming convention: `__N`, where `N` is the
				// N-th result.
				def : Pattern<(ThreeResultOp:$results ...),
				[(... $results__0), ..., (... $results__2), ...]>;
				```

				In TDRR, we "solved" the problem of accessing multiple results, but this isn't a
				very intuitive interface for users. Magical naming conventions obfuscate the
				code and can easily introduce bugs and other errors. There are various things
				that we could try to improve this situation, but there is a fundamental limit to
				what we can do given the limits of the TableGen dag structure. In PDLL, however,
				we have the freedom and flexibility to provide a proper interface into
				operations, regardless of their structure:

				```pdll
				// Import our definition of `ThreeResultOp`.
				#include "ops.td"

				Pattern {
				...

				// In PDLL, we can directly reference the results of an operation variable.
				// This provides a closer mental model to what the user expects.
				let threeResultOp = op<my_dialect.three_result_op>;
				let userOp = op<my_dialect.user_op>(threeResultOp.output1, ..., threeResultOp.output3);

				...
				}
				```

				#### Constraints

				In TDRR, the match dag defines the general structure of the input IR to match.
				Any non-structural/non-type constraints on the input are generally relegated to
				a list of constraints specified after the rewrite dag. For very simple patterns
				this may suffice, but with larger patterns it becomes quite problematic as it
				separates the constraint from the entity it constrains and negatively impacts
				the readability of the pattern. As an example, let's look at a simple pattern
				that adds additional constraints to its inputs:

				```tablegen
				// Suppose we have a two result operation, defined as seen below.
				def TwoResultOp : Op<"two_result_op"> {
				let arguments = (ins ...);

				let results = (outs
				AnyTensor:$output1,
				AnyTensor:$output2
				);
				}

				// A simple constraint to check if a value is use_empty.
				def HasNoUseOf: Constraint<CPred<"$_self.use_empty()">, "has no use">;

				// Check if two values have a ShapedType with the same element type.
				def HasSameElementType : Constraint<
				CPred<"$0.getType().cast<ShapedType>().getElementType() == "
				"$1.getType().cast<ShapedType>().getElementType()">,
				"values have same element type">;

				def : Pattern<(TwoResultOp:$results $input),
				[(...), (...)],
				[(HasNoUseOf:$results__1),
				(HasSameElementType $results__0, $input)]>;
				```

				Above, when observing the constraints we need to search through the input dag
				for the inputs (also keeping in mind the magic naming convention for multiple
				results). For this simple pattern it may be just a few lines above, but complex
				patterns often grow to 10s of lines long. In PDLL, these constraints can be
				applied directly on or next to the entities they apply to:

				```pdll
				// The same constraints that we defined above:
				Constraint HasNoUseOf(value: Value) [{
				return success(value.use_empty());
				}];
				Constraint HasSameElementType(value1: Value, value2: Value) [{
				return success(value1.getType().cast<ShapedType>().getElementType() ==
				value2.getType().cast<ShapedType>().getElementType());
				}];

				Pattern {
				// In PDLL, we can apply the constraint as early (or as late) as we want. This
				// enables better structuring of the matcher code, and improves the
				// readability/maintainability of the pattern.
				let op = op<my_dialect.two_result_op>(input: Value);
				HasNoUseOf(op.output2);
				HasSameElementType(input, op.output2);

				// ...
				}
				```

				#### Replacing Multiple Operations

				Often times a pattern will transform N number of input operations into N number
				of result operations. In PDLL, replacing multiple operations is as simple as
				adding two [`replace` statements](#replace-statement). In TDRR, the situation is
				a bit more nuanced. Given the single root structure of the TableGen dag,
				replacing a non-root operation is not nicely supported. It currently isn't
				natively possible, and instead requires using multiple patterns. We could
				potentially add another special rewrite directive, or extend `replaceWithValue`,
				but this simply highlights how even a basic IR transformation is muddled by the
				complexity of the host language.

				### Why not build a DSL in "X"?

				Yes! Well yes and no. To understand why, we have to consider what types of users
				we are trying to serve and what constraints we enforce upon them. The goal of
				PDLL is to provide a default and effective pattern language for MLIR that all
				users of MLIR can interact with immediately, regardless of their host
				environment. This language is available with no extra dependencies and comes
				"free" along with MLIR. If we were to use an existing host language to build our
				new DSL, we would need to make compromises along with it depending on the
				language. For some, there are questions of how to enforce matching environments
				(python2 or python3?, which version?), performance considerations, integration,
				etc. As an LLVM project, this could also mean enforcing a new language
				dependency on the users of MLIR (many of which may not want/need such a
				dependency otherwise). Another issue that comes along with any DSL that is
				embeded in another language: mitigating the user impedance mismatch between what
				the user expects from the host language and what our "backend" supports. For
				example, the PDL IR abstraction only contains limited support for control flow.
				If we were to build a DSL in python, we would need to ensure that complex
				control flow is either handled completely or effectively errors out. Even with
				ideal error handling, not having the expected features available creates user
				frustration. In addition to the environment constraints, there is also the issue
				of language tooling. With PDLL we intend to build a very robust and modern
				toolset that is designed to cater the needs of pattern developers, including
				code completion, signature help, and many more features that are specific to the
				problem we are solving. Integrating custom language tooling into existing
				languages can be difficult, and in some cases impossible (as our DSL would
				merely be a small subset of the existing language).

				These various points have led us to the initial conclusion that the most
				effective tool we can provide for our users is a custom tool designed for the
				problem at hand. With all of that being said, we understand that not all users
				have the same constraints that we have placed upon ourselves. We absolutely
				encourage and support the existence of various PDL frontends defined in
				different languages. This is one of the original motivating factors around
				building the PDL IR abstraction in the first place; to enable innovation and
				flexibility for our users (and in turn their users). For some, such as those in
				research and the Machine Learning space, they may already have a certain
				language (such as Python) heavily integrated into their workflow. For these
				users, a PDL DSL in their language may be ideal and we will remain committed to
				supporting and endorsing that from an infrastructure point-of-view.

				## Language Specification

				Note: PDLL is still under active development, and the designs discussed below
				are not necessarily final and may be subject to change.

				The design of PDLL is heavily influenced and centered around the
				[PDL IR abstraction](https://mlir.llvm.org/docs/Dialects/PDLOps/), which in turn
				is designed as an abstract model of the core MLIR structures. This leads to a
				design and structure that feels very similar to if you were directly writing the
				IR you want to match.

				### Includes

				PDLL supports an `include` directive to import content defined within other
				source files. There are two types of files that may be included: `.pdll` and
				`.td` files.

				#### `.pdll` includes

				When including a `.pdll` file, the contents of that file are copied directly into
				the current file being processed. This means that any patterns, constraints,
				rewrites, etc., defined within that file are processed along with those within
				the current file.

				#### `.td` includes

				When including a `.td` file, PDLL will automatically import any pertinent
				[ODS](https://mlir.llvm.org/docs/OpDefinitions/) information within that file.
				This includes any defined operations, constraints, interfaces, and more, making
				them implicitly accessible within PDLL. This is important, as ODS information
				allows for certain PDLL constructs, such as the
				[`operation` expression](#operation), to become much more powerful.

				### Patterns

				In any pattern descriptor language, pattern definition is at the core. In PDLL,
				patterns start with `Pattern` optionally followed by a name and a set of pattern
				metadata, and finally terminated by a pattern body. A few simple examples are
				shown below:

				```pdll
				// Here we have defined an anonymous pattern:
				Pattern {
				// Pattern bodies are separated into two components:
				// * Match Section
				// - Describes the input IR.
				let root = op<toy.reshape>(op<toy.reshape>(arg: Value));

				// * Rewrite Section
				// - Describes how to transform the IR.
				// - Last statement starts the rewrite.
				replace root with op<toy.reshape>(arg);
				}

				// Here we have defined a pattern named `ReshapeReshapeOptPattern` with a
				// benefit of 10:
				Pattern ReshapeReshapeOptPattern with benefit(10) {
				replace op<toy.reshape>(op<toy.reshape>(arg: Value))
				with op<toy.reshape>(arg);
				}
				```

				After the definition of the pattern metadata, we specify the pattern body. The
				structure of a pattern body is comprised of two main sections, the `match`
				section and the `rewrite` section. The `match` section of a pattern describes
				the expected input IR, whereas the `rewrite` section describes how to transform
				that IR. This distinction is an important one to make, as PDLL handles certain
				variables and expressions differently within the different sections. When
				relevant in each of the sections below, we shall explicitly call out any
				behavioral differences.

				The general layout of the `match` and `rewrite` section is as follows: the
				last statement of the pattern body is required to be a
				[`operation rewrite statement`](#operation-rewrite-statements), and denotes the
				`rewrite` section; every statement before denotes the `match` section.

				#### Pattern metadata

				Rewrite patterns in MLIR have a set of metadata that allow for controlling
				certain behaviors, and providing information to the rewrite driver applying the
				pattern. In PDLL, a pattern can provide a non-default value for this metadata
				after the pattern name. Below, examples are shown for the different types of
				metadata supported:

				##### Benefit

				The benefit of a Pattern is an integer value that represents the "benefit" of
				matching that pattern. It is used by pattern drivers to determine the relative
				priorities of patterns during application; a pattern with a higher benefit is
				generally applied before one with a lower benefit.

				In PDLL, a pattern has a default benefit set to the number of input operations
				in the match section. This rule is driven by an observation that larger matches
				nicolasvasilacheUnsubmitted Done Reply Inline Actions Can you describe a bit more what "the number of input operations in the match section" is? Is it the number of occurrences of `op<>` in the PDLL text or something else ? It can't be too dynamic (e.g. # of Operation* matched or going through input operands and determining whether they come from BBAarg or Op) because the pattern is static. nicolasvasilache: Can you describe a bit more what "the number of input operations in the match section" is? Is…
				rriddleAuthorUnsubmitted Done Reply Inline Actions Yeah, it's effectively the distinct number of `Op` variables/expressions in the input. Effectively the extent of statically guessing the size of the match. rriddle: Yeah, it's effectively the distinct number of `Op` variables/expressions in the input.
				are more beneficial than smaller ones, and if a smaller one is applied first the
				larger one may not apply anymore. Patterns can override this behavior by
				specifying the benefit in the metadata section of the pattern:

				```pdll
				// Here we specify that this pattern has a benefit of `10`, overriding the
				// default behavior.
				Pattern with benefit(10) {
				...
				}
				```

				##### Bounded Rewrite Recursion

				During pattern application, there are situations in which a pattern may be
				applicable to the result of a previous application of that same pattern. If the
				pattern does not properly handle this recusive application, the pattern driver
				could become stuck in an infinite loop of application. To prevent this, patterns
				by-default are assumed to not have proper recursive bounding and will not be
				recursively applied. A pattern can signal that it does have proper handling for
				recursion by specifying the `recusion` flag in the pattern metadata section:

				```pdll
				// Here we signal that this pattern properly bounds recursive application.
				Pattern with recusion {
				...
				nicolasvasilacheUnsubmitted Done Reply Inline Actions This is not yet supported in code (in the PRs I saw saw far) IIRC ? nicolasvasilache: This is not yet supported in code (in the PRs I saw saw far) IIRC ?
				rriddleAuthorUnsubmitted Done Reply Inline Actions Yep, still need to plumb this through PDL (will be in a followup commit). rriddle: Yep, still need to plumb this through PDL (will be in a followup commit).
				}
				```

				#### Single Line "Lambda" Body

				Patterns generally define their body using a compound block of statements, as
				shown below:

				```pdll
				Pattern {
				replace op<my_dialect.foo>(operands: ValueRange) with operands;
				}
				```

				Patterns also support a lambda-like syntax for specifying simple single line
				bodies. The lambda body of a Pattern expects a single
				[operation rewrite statement](#operation-rewrite-statements):

				```pdll
				Pattern => replace op<my_dialect.foo>(operands: ValueRange) with operands;
				```

				### Variables

				Variables in PDLL represent specific instances of IR entities, such as `Value`s,
				`Operation`s, `Type`s, etc. Consider the simple pattern below:

				```pdll
				Pattern {
				let value: Value;
				let root = op<mydialect.foo>(value);

				replace root with value;
				}
				```

				In this pattern we define two variables, `value` and `root`, using the `let`
				statement. The `let` statement allows for defining variables and constraining
				them. Every variable in PDLL is of a certain type, which defines the type of IR
				entity the variable represents. The type of a variable may be determined via
				either a constraint, or an initializer expression.

				#### Variable "Binding"

				In addition to a type, variables must also be "bound", either via an initializer
				expression or a use. "Binding" a variable contextually identifies that variable
				nicolasvasilacheUnsubmitted Done Reply Inline Actions "to having a type" ? nicolasvasilache: "to having a type" ?
				within either the input (i.e. `match` section) or output (i.e. `rewrite`
				section) IR. In the `match` section, this allows for building the match tree
				from the pattern's root operation, which must be "bound" to the
				[operation rewrite statement](#operation-rewrite-statements) that denotes the
				`rewrite` section of the pattern. All non-root variables within the `match`
				section must be bound in some way to the "root" operation. To help illustrate
				the concept, let's take a look at a quick example. Consider the `.mlir` snippet
				below:

				```mlir
				func @baz(%arg: i32) {
				%result = my_dialect.foo %arg, %arg -> i32
				}
				```

				Say that we want to write a pattern that matches `my_dialect.foo` and replaces
				it with its unique input argument. A naive way to write this pattern in PDLL is
				shown below:

				```pdll
				Pattern {
				// match section //
				let arg: Value;
				let root = op<my_dialect.foo>(arg, arg);

				// rewrite section //
				replace root with arg;
				}
				```

				In the above pattern, the `arg` variable is "bound" to its first use; the first
				operand of the `root` operation. Every use of `arg` after the first use is
				constrained to be the same `Value` as the first operand of `root`. The same is
				nicolasvasilacheUnsubmitted Done Reply Inline Actions Can this "first use" ever be different than first occurrence in program string ? In any case, please specify it explicitly. nicolasvasilache: Can this "first use" ever be different than first occurrence in program string ? In any case…
				rriddleAuthorUnsubmitted Done Reply Inline Actions Updated to make it more clear. rriddle: Updated to make it more clear.
				true for the `root` operation, it is bound to the "root" operation of the
				pattern as it is used in input of the top-level
				[`replace` statement](#replace-statement) of the `rewrite` section of the
				pattern. Writing this pattern using the C++ API, the concept of "binding"
				becomes more clear:

				```c++
				struct Pattern : public OpRewritePattern<my_dialect::FooOp> {
				LogicalResult matchAndRewrite(my_dialect::FooOp root, PatternRewriter &rewriter) {
				Value arg = root->getOperand(0);
				if (arg != root->getOperand(1))
				return failure();

				rewriter.replaceOp(root, arg);
				return success();
				}
				};
				```

				If a variable is not "bound" properly, PDLL won't be able to identify what value
				it would correspond to in the IR. As a final example, let's consider a variable
				that hasn't been bound:

				```pdll
				Pattern {
				// match section //
				let arg: Value;
				let root = op<my_dialect.foo>

				// rewrite section //
				replace root with arg;
				}
				```

				If we were to write this exact pattern in C++, we would end up with:

				```c++
				struct Pattern : public OpRewritePattern<my_dialect::FooOp> {
				LogicalResult matchAndRewrite(my_dialect::FooOp root, PatternRewriter &rewriter) {
				// `arg` was never bound, so we don't know what input Value it was meant to
				// correspond to.
				Value arg;

				rewriter.replaceOp(root, arg);
				return success();
				}
				};
				```

				#### Variable Constraints

				```pdll
				// This statement defines a variable `value` that is constrained to be a `Value`.
				let value: Value;

				// This statement defines a variable `value` that is constrained to be a `Value`
				// and constrained to have a single use.
				let value: [Value, HasOneUse];
				```

				Any number of single entity constraints may be attached directly to a variable
				upon declaration. Within the `matcher` section, these constraints may add
				additional checks on the input IR. Within the `rewriter` section, constraints
				are only used to define the type of the variable. There are a number of
				builtin constraints that correlate to the core MLIR constructs: `Attr`, `Op`,
				`Type`, `TypeRange`, `Value`, `ValueRange`. Along with these, users may define
				custom constraints that are implemented within PDLL, or natively (i.e. outside
				of PDLL). See the general [Constraints](#constraints) section for more detailed
				information.

				#### Inline Variable Definition

				Along with the `let` statement, variables may also be defined inline by
				specifying the constraint list along with the desired variable name in the first
				place that the variable would be used. After definition, the variable is visible
				from all points forward. See below for an example:

				```pdll
				// `value` is used as an operand to the operation `root`:
				let value: Value;
				let root = op<my_dialect.foo>(value);
				replace root with value;

				// `value` could also be defined "inline":
				let root = op<my_dialect.foo>(value: Value);
				replace root with value;
				```

				##### Wildcard Variable Definition

				Often times when defining a variable inline, the variable isn't intended to be
				used anywhere else in the pattern. For example, this may happen if you want to
				attach constraints to a variable but have no other use for it. In these
				situations, the "wildcard" variable can be used to remove the need to provide a
				name, as "wildcard" variables are not visible outside of the point of
				definition. An example is shown below:

				nicolasvasilacheUnsubmitted Done Reply Inline Actions It is a bit unclear to me whether the point of definition is the PDLL statement or just the internal expression. Can we write: `let root = op<my_dialect.foo>(arg: Value, _: Value, _: Value, arg);` ? Also, side note, can we write: `let root = op<my_dialect.foo>(arg: Value, _: [Value, Int64], _: [Value, Int32], arg);` ? nicolasvasilache: It is a bit unclear to me whether the point of definition is the PDLL statement or just the…
				rriddleAuthorUnsubmitted Done Reply Inline Actions Yes to both. The point of definition is the point of reference expression added some more examples. rriddle: Yes to both. The point of definition is the point of reference expression added some more…
				```pdll
				Pattern {
				let root = op<my_dialect.foo>(arg: Value, _: Value, arg);
				replace root with arg;
				}
				```

				In the above example, the second operand isn't needed for the pattern but we
				need to provide it to signal that a second operand does exist (we just don't
				care what it is in this pattern).

				### Operation Expression

				An operation expression in PDLL represents an MLIR operation. In the `match`
				section of the pattern, this expression models one of the input operations to
				the pattern. In the `rewrite` section of the pattern, this expression models one
				of the operations to create. The general structure of the operation expression
				is very similar to that of the "generic form" of textual MLIR assembly:

				```pdll
				let root = op<my_dialect.foo>(operands: ValueRange) {attr = attr: Attr} -> (resultTypes: TypeRange);
				```

				Let's walk through each of the different components of the expression:

				#### Operation name

				The operation name signifies which type of MLIR Op this operation corresponds
				to. In the `match` section of the pattern, the name may be elided. This would
				cause this pattern to match any operation type that satifies the rest of the
				constraints of the operation. In the `rewrite` section, the name is required.

				```pdll
				// `root` corresponds to an instance of a `my_dialect.foo` operation.
				let root = op<my_dialect.foo>;

				nicolasvasilacheUnsubmitted Done Reply Inline Actions Could we also support `op_interface` ? Seems easily doable by using op<> + native constraint but: maybe there is a more efficient way to target that seems like it would be generally useful to avoid that boilerplate nicolasvasilache: Could we also support `op_interface` ? Seems easily doable by using op<> + native constraint…
				rriddleAuthorUnsubmitted Done Reply Inline Actions Yeah, we will likely end up supporting something like that if it becomes super common. The main hurdle before was the lack of enough information from ODS, which we have now. If this is something you think we'll want sooner rather than later, can you file an issue to track? rriddle: Yeah, we will likely end up supporting something like that if it becomes super common. The main…
				// `root` could be an instance of any operation type.
				let root = op<>;
				```

				#### Operands

				The operands section corresponds to the operands of the operation. This section
				of an operation expression may be elided, in which case the operands are not
				constrained in any way. When present, the operands of an operation expression
				are interpreted in the following ways:

				1) A single instance of type `ValueRange`:

				In this case, the single range is treated as all of the operands of the
				operation:

				```pdll
				// Define an instance with single range of operands.
				let root = op<my_dialect.foo>(allOperands: ValueRange);
				```

				2) A variadic number of either `Value` or `ValueRange`:

				In this case, the inputs are expected to correspond with the operand groups as
				defined on the operation in ODS.

				Given the following operation definition in ODS:

				```tablegen
				def MyIndirectCallOp {
				let arguments = (ins FunctionType:$call, Variadic<AnyType>:$args);
				}
				```

				We can match the operands as so:

				```pdll
				let root = op<my_dialect.indirect_call>(call: Value, args: ValueRange);
				```
				nicolasvasilacheUnsubmitted Done Reply Inline Actions In the future, do you envision this to help allow multiple Variadic packs more naturally than with all the special attribute genuflexions? nicolasvasilache: In the future, do you envision this to help allow multiple Variadic packs more naturally than…
				rriddleAuthorUnsubmitted Done Reply Inline Actions Do you mean in ODS, or in PDLL? rriddle: Do you mean in ODS, or in PDLL?

				#### Results

				The results section corresponds to the result types of the operation. This
				section of an operation expression may be elided, in which case the result types
				are not constrained in any way. When present, the result types of an operation
				expression are interpreted in the following ways:

				1) A single instance of type `TypeRange`:

				In this case, the single range is treated as all of the result types of the
				operation:

				```pdll
				// Define an instance with single range of types.
				let root = op<my_dialect.foo> -> (allResultTypes: TypeRange);
				```

				2) A variadic number of either `Type` or `TypeRange`:

				In this case, the inputs are expected to correspond with the result groups as
				defined on the operation in ODS.

				Given the following operation definition in ODS:

				```tablegen
				def MyOp {
				let results = (outs SomeType:$result, Variadic<SomeType>:$otherResults);
				}
				```

				We can match the result types as so:

				```pdll
				let root = op<my_dialect.op> -> (result: Type, otherResults: TypeRange);
				```
				nicolasvasilacheUnsubmitted Done Reply Inline Actions Same Q re packs and maybe also re. does this mix with type constraints: `otherResults: [TypeRange, I32OrI64]` nicolasvasilache: Same Q re packs and maybe also re. does this mix with type constraints: `otherResults…
				rriddleAuthorUnsubmitted Done Reply Inline Actions Is the second part of this about applying a single element constraint to all of the elements within the range? rriddle: Is the second part of this about applying a single element constraint to all of the elements…

				#### Attributes

				The attributes section of the operation expression corresponds to the attribute
				dictionary of the operation. This section of an operation expression may be
				elided, in which case the attributes are not constrained in any way. The
				composition of this component maps exactly to how attribute dictionaries are
				structured in the MLIR textual assembly format:

				```pdll
				let root = op<my_dialect.foo> {attr1 = attrValue: Attr, attr2 = attrValue2: Attr};
				```

				Within the `{}` attribute entries are specified by an identifier or string name,
				corresponding to the attribute name, followed by an assignment to the attribute
				value. If the attribute value is elided, the value of the attribute is
				implicitly defined as a
				[`UnitAttr`](https://mlir.llvm.org/docs/Dialects/Builtin/#unitattr).

				```pdll
				let unitConstant = op<std.constant> {value};
				```

				##### Accessing Operation Results

				In multi-operation patterns, the result of one operation often feeds as an input
				into another. The result groups of an operation may be accessed by name or by
				index via the `.` operator:

				Note: Remember to import the definition of your operation via
				[include](#`.td`_includes) to ensure it is visible to PDLL.

				Given the following operation definition in ODS:

				```tablegen
				def MyResultOp {
				nicolasvasilacheUnsubmitted Done Reply Inline Actions Do you see a future where tablegen is replace in MLIR (beyond what you are currently doing for TDRRs) ? nicolasvasilache: Do you see a future where tablegen is replace in MLIR (beyond what you are currently doing for…
				rriddleAuthorUnsubmitted Done Reply Inline Actions It depends on how much usability win we get from it. Right now Tablegen seems to be "good enough" for what we want to do with most ODS things, given that ODS is much more focused on record definitions than pattern matching is. There is also a matter of whether we'd want to extend/"fix" tablegen vs make something new. rriddle: It depends on how much usability win we get from it. Right now Tablegen seems to be "good…
				let results = (outs SomeType:$result);
				}
				def MyInputOp {
				let arguments = (ins SomeType:$input, SomeType:$input);
				}
				```

				We can write a pattern where `MyResultOp` feeds into `MyInputOp` as so:

				```pdll
				// Note: If we elide the result types section within the match section, it means
				// they aren't constrained, not that the operation has no results.
				let resultOp = op<my_dialect.result_op>;
				let inputOp = op<my_dialect.input_op>(resultOp.result, resultOp.0);
				zero9178Unsubmitted Done Reply Inline Actions I think it would be nice if either in a comment right about here, or maybe in the sentence above the code block, it'd be mentioned that `resultOp.result` and `resultOp.0` refer to the exact same result. This makes it clear that this is done as a demonstration and there is no actual semantic difference when using one form over the other. zero9178: I think it would be nice if either in a comment right about here, or maybe in the sentence…
				nicolasvasilacheUnsubmitted Done Reply Inline Actions +1 nicolasvasilache: +1
				```

				Along with result name access, variables of `Op` type may implicitly convert to
				`Value` or `ValueRange`. These variables are converted to `Value` when they are
				known (via ODS) to only have one result, in all other cases they convert to
				`ValueRange`:

				```pdll
				// `resultOp` may also convert implicitly to a Value for use in `inputOp`:
				let resultOp = op<my_dialect.result_op>;
				let inputOp = op<my_dialect.input_op>(resultOp);

				// We could also inline `resultOp` directly:
				let inputOp = op<my_dialect.input_op>(op<my_dialect.result_op>);
				```

				### Attribute Expression

				An attribute expression represents a literal MLIR attribute. It allows for
				statically specifying an MLIR attribute to use, by specifying the textual form
				of that attribute.

				```pdll
				let trueConstant = op<std.constant> {value = attr<"true">};
				```

				### Type Expression
				nicolasvasilacheUnsubmitted Done Reply Inline Actions Add an example with an AffineMapAttr to show its full power ? nicolasvasilache: Add an example with an AffineMapAttr to show its full power ?

				A type expression represents a literal MLIR type. It allows for statically
				specifying an MLIR type to use, by specifying the textual form of that type.

				```pdll
				let i32Constant = op<std.constant> -> (type<"i32">);
				```

				### Tuples

				PDLL provides native support for tuples, which are used to group multiple
				elements into a single compound value. The values in a tuple can be of any type,
				and do not need to be of the same type. There is also no limit to the number of
				elements held by a tuple. The elements of a tuple can be accessed by index:

				```pdll
				let tupleValue = (op<my_dialect.foo>, attr<"10 : i32">, type<"i32">);

				let opValue = tupleValue.0;
				let attrValue = tupleValue.1;
				let typeValue = tupleValue.2;
				```

				You can also name the elements of a tuple and use those names to refer to the
				values of the individual elements. An element name consists of an identifier
				followed immediately by an equal (=).

				```pdll
				let tupleValue = (
				opValue = op<my_dialect.foo>,
				attr<"10 : i32">,
				typeValue = type<"i32">
				);

				let opValue = tupleValue.opValue;
				let attrValue = tupleValue.1;
				let typeValue = tupleValue.typeValue;
				```

				Tuples are used to represent multiple results from a
				[constraint](#constraints-with-multiple-results) or
				[rewrite](#rewrites-with-multiple-results).

				### Constraints

				Constraints provide the ability to inject additional checks on the input IR
				within the `match` section of a pattern. Constraints can be applied anywhere
				within the `match` section, and depending on the type can either be applied via
				the constraint list of a [variable](#variables) or via the call operator (e.g.
				`MyConstraint(...)`). There are three main categories of constraints:

				#### Core Constraints

				PDLL defines a number of core constraints that constrain the type of the IR
				entity. These constraints can only be applied via the
				[constraint list](#variable-constraints) of a variable.

				* `Attr` (`<` type `>`)?

				A single entity constraint that corresponds to an `mlir::Attribute`. This
				constraint optionally takes a type component that constrains the result type of
				the attribute.

				```pdll
				// Define a simple variable using the `Attr` constraint.
				let attr: Attr;
				let constant = op<std.constant> {value = attr};

				// Define a simple variable using the `Attr` constraint, that has its type
				// constrained as well.
				nicolasvasilacheUnsubmitted Done Reply Inline Actions Side Q, is there an easy way to get PDL-specific match and apply pattern failure messages without pulling in the whole `-debug` shabang? nicolasvasilache: Side Q, is there an easy way to get PDL-specific match and apply pattern failure messages…
				rriddleAuthorUnsubmitted Done Reply Inline Actions The only thing PDL specific is for the byte code, `pdl-bytecode`, otherwise all of the normal pattern debug logging should also apply. rriddle: The only thing PDL specific is for the byte code, `pdl-bytecode`, otherwise all of the normal…
				let attrType: Type;
				let attr: Attr<attrType>;
				let constant = op<std.constant> {value = attr};
				```

				* `Op` (`<` op-name `>`)?

				A single entity constraint that corresponds to an `mlir::Operation *`.

				```pdll
				// Match only when the input is from another operation.
				let inputOp: Op;
				let root = op<my_dialect.foo>(inputOp);

				// Match only when the input is from another `my_dialect.foo` operation.
				let inputOp: Op<my_dialect.foo>;
				let root = op<my_dialect.foo>(inputOp);
				```

				* `Type`

				A single entity constraint that corresponds to an `mlir::Type`.

				```pdll
				// Define a simple variable using the `Type` constraint.
				let resultType: Type;
				let root = op<my_dialect.foo> -> (resultType);
				```

				* `TypeRange`

				A single entity constraint that corresponds to a `mlir::TypeRange`.

				```pdll
				// Define a simple variable using the `TypeRange` constraint.
				let resultTypes: TypeRange;
				let root = op<my_dialect.foo> -> (resultTypes);
				```

				* `Value` (`<` type-expr `>`)?

				A single entity constraint that corresponds to an `mlir::Value`. This constraint
				optionally takes a type component that constrains the result type of the value.

				```pdll
				// Define a simple variable using the `Value` constraint.
				let value: Value;
				let root = op<my_dialect.foo>(value);

				// Define a variable using the `Value` constraint, that has its type constrained
				nicolasvasilacheUnsubmitted Done Reply Inline Actions Should there be some examples with the `let value: [Value, I64]` form ? The variable constraint section says: See the general [Constraints](#constraints) section for more detailed information. nicolasvasilache: Should there be some examples with the `let value: [Value, I64]` form ? The variable…
				nicolasvasilacheUnsubmitted Done Reply Inline Actions Ah scratch this, this is touched on in "Defining Constraints Inline" below. So given that: PDLL Constraints and Native Constraints defined in PDLL may be specified inline at any level of nesting we should be able to write `let root = op<my_dialect.foo>(arg: Value, _: [Value, Int64], _: [Value, Int32], arg);` correct ? nicolasvasilache: Ah scratch this, this is touched on in "Defining Constraints Inline" below. So given that: ```…
				rriddleAuthorUnsubmitted Done Reply Inline Actions Yep, I added some examples in the inline variable section about this. rriddle: Yep, I added some examples in the inline variable section about this.
				// to be same as the result type of the `root` op.
				let valueType: Type;
				let input: Value<valueType>;
				let root = op<my_dialect.foo>(input) -> (valueType);
				```

				* `ValueRange` (`<` type-expr `>`)?

				A single entity constraint that corresponds to a `mlir::ValueRange`. This
				constraint optionally takes a type component that constrains the result types of
				the value range.

				```pdll
				// Define a simple variable using the `ValueRange` constraint.
				let inputs: ValueRange;
				let root = op<my_dialect.foo>(inputs);

				// Define a variable using the `ValueRange` constraint, that has its types
				// constrained to be same as the result types of the `root` op.
				let valueTypes: TypeRange;
				let inputs: ValueRange<valueTypes>;
				let root = op<my_dialect.foo>(inputs) -> (valueTypes);
				```

				#### Defining Constraints in PDLL

				Aside from the core constraints, additional constraints can also be defined
				within PDLL. This allows for building matcher fragments that can be composed
				across many different patterns. A constraint in PDLL is defined similarly to a
				function in traditional programming languages; it contains a name, a set of
				input arguments, a set of result types, and a body. Results of a constraint are
				returned via a `return` statement. A few examples are shown below:

				```pdll
				/// A constraint that takes an input and constrains the use to an operation of
				/// a given type.
				Constraint UsedByFooOp(value: Value) {
				op<my_dialect.foo>(value);
				}

				/// A constraint that returns a result of an existing operation.
				Constraint ExtractResult(op: Op<my_dialect.foo>) -> Value {
				return op.result;
				}

				Pattern {
				let value = ExtractResult(op<my_dialect.foo>);
				UsedByFooOp(value);
				}
				```

				##### Constraints with multiple results

				Constraints can return multiple results by returning a tuple of values. When
				returning multiple results, each result can also be assigned a name to use when
				indexing that tuple element. Tuple elements can be referenced by their index
				number, or by name if they were assigned one.

				```pdll
				// A constraint that returns multiple results, with some of the results assigned
				// a more readable name.
				Constraint ExtractMultipleResults(op: Op<my_dialect.foo>) -> (Value, result1: Value) {
				return (op.result1, op.result2);
				}

				Pattern {
				// Return a tuple of values.
				let result = ExtractMultipleResults(op: op<my_dialect.foo>);

				// Index the tuple elements by index, or by name.
				replace op<my_dialect.foo> with (result.0, result.1, result.result1);
				}
				```

				##### Constraint result type inference

				In addition to explicitly specifying the results of the constraint via the
				constraint signature, PDLL defined constraints also support inferring the result
				type from the return statement. Result type inferrence is active whenever the
				constraint is defined with no result constraints:

				```pdll
				// This constraint returns a derived operation.
				Constraint ReturnSelf(op: Op<my_dialect.foo>) {
				return op;
				}
				// This constraint returns a tuple of two Values.
				Constraint ExtractMultipleResults(op: Op<my_dialect.foo>) {
				return (result1 = op.result1, result2 = op.result2);
				}

				Pattern {
				let values = ExtractMultipleResults(op<my_dialect.foo>);
				replace op<my_dialect.foo> with (values.result1, values.result2);
				}
				```

				##### Single Line "Lambda" Body

				Constraints generally define their body using a compound block of statements, as
				shown below:

				```pdll
				Constraint ReturnSelf(op: Op<my_dialect.foo>) {
				return op;
				}
				Constraint ExtractMultipleResults(op: Op<my_dialect.foo>) {
				return (result1 = op.result1, result2 = op.result2);
				}
				```

				Constraints also support a lambda-like syntax for specifying simple single line
				bodies. The lambda body of a Constraint expects a single expression, which is
				implicitly returned:

				```pdll
				Constraint ReturnSelf(op: Op<my_dialect.foo>) => op;

				Constraint ExtractMultipleResults(op: Op<my_dialect.foo>)
				=> (result1 = op.result1, result2 = op.result2);
				```

				#### Native Constraints

				Constraints may also be defined outside of PDLL, and registered natively within
				the C++ API.

				##### Importing existing Native Constraints

				Constraints defined externally can be imported into PDLL by specifying a
				constraint "declaration". This is similar to the PDLL form of defining a
				constraint but omits the body. Importing the declaration in this form allows for
				PDLL to statically know the expected input and output types.

				```pdll
				// Import a single entity value native constraint that checks if the value has a
				// single use. This constraint must be registered by the consumer of the
				// compiled PDL.
				Constraint HasOneUse(value: Value);

				// Import a multi-entity type constraint that checks if two values have the same
				// element type.
				Constraint HasSameElementType(value1: Value, value2: Value);

				Pattern {
				// A single entity constraint can be applied via the variable argument list.
				let value: HasOneUse;

				// Otherwise, constraints can be applied via the call operator:
				let value: Value = ...;
				let value2: Value = ...;
				HasOneUse(value);
				HasSameElementType(value, value2);
				}
				```

				##### Defining Native Constraints in PDLL

				In addition to importing native constraints, PDLL also supports defining native
				constraints directly when compiling ahead-of-time (AOT) for C++. These
				constraints can be defined by specifying a string code block after the
				constraint declaration:

				```pdll
				Constraint HasOneUse(value: Value) [{
				return success(value.hasOneUse());
				}];
				Constraint HasSameElementType(value1: Value, value2: Value) [{
				return success(value1.getType().cast<ShapedType>().getElementType() ==
				value2.getType().cast<ShapedType>().getElementType());
				}];
				nicolasvasilacheUnsubmitted Done Reply Inline Actions How would you do with passing PoD (e.g. an int argument to the constraint)? Would we have to explicitly use an attribute and unpack it in C++ ? Also, could you elaborate a bit on the debugging story? Would we have to go look at the generated C++ in case of mismatches or do you envision good error messages at this level? nicolasvasilache: How would you do with passing PoD (e.g. an int argument to the constraint)? Would we have to…
				rriddleAuthorUnsubmitted Done Reply Inline Actions All constants passed to constraints will need to be passed through attributes. We can definitely develop some better APIs on the C++ side to, for example, unwrap IntegerAttr to something like int64_t/APInt/etc. The main thing is that because we go through PDL, we have to be able to encode things via attributes. For debugging, there are various different factors involved. In the constraint, though this example doesn't show it (maybe it should?) you have access to the rewriter and could return a `rewriter.notifyMatchFailure(...)` in the case of failure. I think if the user doesn't, then we could likely generate some default messages based on the name of the constraint/how it was applied/etc. This is definitely something that we will want to have a dedicated effort into verifying/validating. It's also one of the things that we need a base end-to-end toolchain to start really experiencing. We'll have to ensure that we don't lose the debuggability aspect of patterns just because we go declarative. This is also something that we really need to figure out at the PDL level, given that PDL performs merging on the input sections (which drops a lot of the current match context, i.e. you can't really say "which pattern am I in?") rriddle: All constants passed to constraints will need to be passed through attributes. We can…

				Pattern {
				// A single entity constraint can be applied via the variable argument list.
				let value: HasOneUse;

				// Otherwise, constraints can be applied via the call operator:
				let value: Value = ...;
				let value2: Value = ...;
				HasOneUse(value);
				HasSameElementType(value, value2);
				}
				```

				The arguments of the constraint are accessible within the code block via the
				same name. The type of these native variables are mapped directly to the
				corresponding MLIR type of the [core constraint](#core-constraints) used. For
				example, an `Op` corresponds to a variable of type `Operation *`.

				The results of the constraint can be populated using the provided `results`
				variable. This variable is a `PDLResultList`, and expects results to be
				populated in the order that they are defined within the result list of the
				constraint declaration.

				In addition to the above, the code block may also access the current
				`PatternRewriter` using `rewriter`.

				#### Defining Constraints Inline

				In addition to global scope, PDLL Constraints and Native Constraints defined in
				PDLL may be specified inline at any level of nesting. This means that they may
				be defined in Patterns, other Constraints, Rewrites, etc:

				```pdll
				Constraint GlobalConstraint() {
				Constraint LocalConstraint(value: Value) {
				...
				};
				Constraint LocalNativeConstraint(value: Value) [{
				...
				}];
				let someValue: [LocalConstraint, LocalNativeConstraint] = ...;
				}
				```

				Constraints that are defined inline may also elide the name when used directly:

				```pdll
				Constraint GlobalConstraint(inputValue: Value) {
				Constraint(value: Value) { ... }(inputValue);
				Constraint(value: Value) [{ ... }](inputValue);
				}
				```

				When defined inline, PDLL constraints may reference any previously defined
				variable:

				```pdll
				Constraint GlobalConstraint(op: Op<my_dialect.foo>) {
				Constraint LocalConstraint() {
				let results = op.results;
				};
				}
				```

				### Rewriters

				Rewriters define the set of transformations to be performed within the `rewrite`
				section of a pattern, and, more specifically, how to transform the input IR
				after a successful pattern match. All PDLL rewrites must be defined within the
				`rewrite` section of the pattern. The `rewrite` section is denoted by the last
				statement within the body of the `Pattern`, which is required to be an
				[operation rewrite statement](#operation-rewrite-statements). There are two main
				categories of rewrites in PDLL: operation rewrite statements, and user defined
				rewrites.

				#### Operation Rewrite statements

				Operation rewrite statements are builtin PDLL statements that perform an IR
				transformation given a root operation. These statements are the only ones able
				to start the `rewrite` section of a pattern, as they allow for properly
				["binding"](#variable-binding) the root operation of the pattern.

				##### `erase` statement

				```pdll
				// A pattern that erases all `my_dialect.foo` operations.
				Pattern => erase op<my_dialect.foo>;
				```

				The `erase` statement erases a given operation.

				##### `replace` statement

				```pdll
				// A pattern that replaces the root operation with its input value.
				Pattern {
				let root = op<my_dialect.foo>(input: Value);
				replace root with input;
				}

				// A pattern that replaces the root operation with multiple input values.
				Pattern {
				let root = op<my_dialect.foo>(input: Value, _: Value, input2: Value);
				replace root with (input, input2);
				}

				// A pattern that replaces the root operation with another operation.
				Pattern {
				// Note: In this pattern we also inlined the `root` expression.
				replace op<my_dialect.foo> with op<my_dialect.bar>;
				}
				```

				nicolasvasilacheUnsubmitted Done Reply Inline Actions This is quite implicit, what is the behavior on operands and attributes ? Are they all forwarded from `my_dialect.foo` to `my_dialect.bar` ? nicolasvasilache: This is quite implicit, what is the behavior on operands and attributes ? Are they all…
				The `replace` statement allows for replacing a given root operation with either
				another operation, or a set of input `Value` and `ValueRange` values.

				##### `rewrite` statement

				```pdll
				// A simple pattern that replaces the root operation with its input value.
				Pattern {
				let root = op<my_dialect.foo>(input: Value);
				rewrite root with {
				...

				replace root with input;
				};
				}
				```

				The `rewrite` statement allows for rewriting a given root operation with a block
				of nested rewriters. The root operation is not implicitly erased or replaced,
				and any transformations to it must be expressed within the nested rewrite block.
				The inner body may contain any number of other rewrite statements, variables, or
				expressions.

				#### Defining Rewriters in PDLL

				Additional rewrites can also be defined within PDLL, which allows for building
				rewrite fragments that can be composed across many different patterns. A
				rewriter in PDLL is defined similarly to a function in traditional programming
				languages; it contains a name, a set of input arguments, a set of result types,
				and a body. Results of a rewrite are returned via a `return` statement. A few
				examples are shown below:

				```pdll
				// A rewrite that constructs and returns a new operation, given an input value.
				Rewrite BuildFooOp(value: Value) -> Op {
				return op<my_dialect.foo>(value);
				}

				nicolasvasilacheUnsubmitted Done Reply Inline Actions We touched on this a while back during your presentation: for rewriters defined in PDLL, do you envision a primitive templating addition that would allow at least some unrolling and simple indexing arithmetic to occur? This would be needed to move a bunch of non-trivial patterns to PDLL. nicolasvasilache: We touched on this a while back during your presentation: for rewriters defined in PDLL, do you…
				rriddleAuthorUnsubmitted Done Reply Inline Actions Yeah, I can definitely foresee various different primitives that we'll want to develop in that area. The main thing is that we'll need use cases to figure out how exactly we want to structure it in the language, which contexts we support it, etc. rriddle: Yeah, I can definitely foresee various different primitives that we'll want to develop in that…
				Pattern {
				// We invoke the rewrite in the same way as functions in traditional
				// languages.
				replace op<my_dialect.old_op>(input: Value) with BuildFooOp(input);
				}
				```

				##### Rewrites with multiple results

				Rewrites can return multiple results by returning a tuple of values. When
				returning multiple results, each result can also be assigned a name to use when
				indexing that tuple element. Tuple elements can be referenced by their index
				number, or by name if they were assigned one.

				```pdll
				// A rewrite that returns multiple results, with some of the results assigned
				// a more readable name.
				Rewrite CreateRewriteOps() -> (Op, result1: ValueRange) {
				return (op<my_dialect.bar>, op<my_dialect.foo>);
				}

				Pattern {
				rewrite root: Op<my_dialect.foo> with {
				// Return a tuple of values.
				let result = Rewrite();
				zero9178Unsubmitted Done Reply Inline Actions Should this be `CreateRewriteOps`? zero9178: Should this be `CreateRewriteOps`?

				// Index the tuple elements by index, or by name.
				replace root with (result.0, result.1, result.result1);
				nicolasvasilacheUnsubmitted Done Reply Inline Actions Hmm the match part has a piece called `Create...`. Is this a misnomer or will it be able to produce IR If the latter, does this all get properly reverted if some later condition fail to match? Maybe document the expectations more explicitly in `Language Specification > Patterns` ? Atm I see: // Pattern bodies are separated into two components: // * Match Section // - Describes the input IR. // - [Proposed] Is allowed / not allowed to create IR ... // * Rewrite Section // - Describes how to transform the IR. // - Last statement starts the rewrite. This seems nicolasvasilache: Hmm the match part has a piece called `Create...`. Is this a misnomer or will it be able to…
				rriddleAuthorUnsubmitted Done Reply Inline Actions This `Create` is actually within the `rewrite` block: Pattern { rewrite ... with { // This is in a rewrite scope now. } } rriddle: This `Create` is actually within the `rewrite` block: ``` Pattern { rewrite ... with {…
				}
				}
				```

				##### Rewrite result type inference

				In addition to explicitly specifying the results of the rewrite via the rewrite
				signature, PDLL defined rewrites also support inferring the result type from the
				return statement. Result type inferrence is active whenever the rewrite is
				defined with no result constraints:

				```pdll
				nicolasvasilacheUnsubmitted Done Reply Inline Actions typo: inference nicolasvasilache: typo: inference
				// This rewrite returns a derived operation.
				Rewrite ReturnSelf(op: Op<my_dialect.foo>) => op;
				// This rewrite returns a tuple of two Values.
				Rewrite ExtractMultipleResults(op: Op<my_dialect.foo>) {
				return (result1 = op.result1, result2 = op.result2);
				}

				Pattern {
				rewrite root: Op<my_dialect.foo> with {
				let values = ExtractMultipleResults(op<my_dialect.foo>);
				replace root with (values.result1, values.result2);
				}
				}
				```

				##### Single Line "Lambda" Body

				Rewrites generally define their body using a compound block of statements, as
				shown below:

				```pdll
				Rewrite ReturnSelf(op: Op<my_dialect.foo>) {
				return op;
				}
				Rewrite EraseOp(op: Op) {
				erase op;
				}
				```

				Rewrites also support a lambda-like syntax for specifying simple single line
				bodies. The lambda body of a Rewrite expects a single expression, which is
				implicitly returned, or a single
				[operation rewrite statement](#operation-rewrite-statements):

				```pdll
				Rewrite ReturnSelf(op: Op<my_dialect.foo>) => op;
				Rewrite EraseOp(op: Op) => erase op;
				```

				#### Native Rewriters

				Rewriters may also be defined outside of PDLL, and registered natively within
				the C++ API.

				##### Importing existing Native Rewrites

				Rewrites defined externally can be imported into PDLL by specifying a
				rewrite "declaration". This is similar to the PDLL form of defining a
				rewrite but omits the body. Importing the declaration in this form allows for
				PDLL to statically know the expected input and output types.

				```pdll
				// Import a single input native rewrite that returns a new operation. This
				// rewrite must be registered by the consumer of the compiled PDL.
				Rewrite BuildOp(value: Value) -> Op;

				Pattern {
				replace op<my_dialect.old_op>(input: Value) with BuildOp(input);
				nicolasvasilacheUnsubmitted Done Reply Inline Actions Maybe sketch the C++ API this would hook to (e.g. is there a rewriter somewhere ?) nicolasvasilache: Maybe sketch the C++ API this would hook to (e.g. is there a rewriter somewhere ?)
				rriddleAuthorUnsubmitted Done Reply Inline Actions Done. The intention here eventually is to link to the PDL documentation, which is the part that should describe this in further detail. rriddle: Done. The intention here eventually is to link to the PDL documentation, which is the part that…
				}
				```

				##### Defining Native Rewrites in PDLL

				In addition to importing native rewrites, PDLL also supports defining native
				rewrites directly when compiling ahead-of-time (AOT) for C++. These rewrites can
				be defined by specifying a string code block after the rewrite declaration:

				```pdll
				Rewrite BuildOp(value: Value) -> (foo: Op<my_dialect.foo>, bar: Op<my_dialect.bar>) [{
				// We push back the results into the `results` variable in the order defined
				// by the result list of the constraint declaration.
				results.push_back(rewriter.create<my_dialect::FooOp>(value));
				results.push_back(rewriter.create<my_dialect::BarOp>());
				}];

				Pattern {
				let root = op<my_dialect.foo>(input: Value);
				rewrite root with {
				// Invoke the native constraint and use the results when replacing the root.
				let results = BuildOp(input);
				replace root with (results.foo, results.bar);
				}
				}
				```

				The arguments of the rewrite are accessible within the code block via the
				same name. The type of these native variables are mapped directly to the
				corresponding MLIR type of the [core constraint](#core-constraints) used. For
				example, an `Op` corresponds to a variable of type `Operation *`.

				The results of the rewrite can be populated using the provided `results`
				variable. This variable is a `PDLResultList`, and expects results to be
				nicolasvasilacheUnsubmitted Done Reply Inline Actions Same Q as above re passing a PoD (e.g. `int`) around. nicolasvasilache: Same Q as above re passing a PoD (e.g. `int`) around.
				populated in the order that they are defined within the result list of the
				rewrite declaration.

				In addition to the above, the code block may also access the current
				`PatternRewriter` using `rewriter`.

				#### Defining Rewrites Inline

				In addition to global scope, PDLL Rewrites and Native Rewrites defined in PDLL
				may be specified inline at any level of nesting. This means that they may be
				defined in Patterns, other Rewrites, etc:

				```pdll
				Rewrite GlobalRewrite(inputValue: Value) {
				Rewrite localRewrite(value: Value) {
				...
				};
				Rewrite localNativeRewrite(value: Value) [{
				...
				}];
				localRewrite(inputValue);
				localNativeRewrite(inputValue);
				}
				```

				Rewrites that are defined inline may also elide the name when used directly:

				```pdll
				Rewrite GlobalRewrite(inputValue: Value) {
				Rewrite(value: Value) { ... }(inputValue);
				Rewrite(value: Value) [{ ... }](inputValue);
				}
				```

				When defined inline, PDLL rewrites may reference any previously defined
				variable:

				```pdll
				Rewrite GlobalRewrite(op: Op<my_dialect.foo>) {
				Rewrite localRewrite() {
				let results = op.results;
				};
				}
				```

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