# MLIR

Multi-Level IR Compiler Framework

# Chapter 2: Emitting Basic MLIR

Now that we’re familiar with our language and the AST, let’s see how MLIR can help to compile Toy.

## Introduction: Multi-Level Intermediate Representation ¶

Other compilers, like LLVM (see the Kaleidoscope tutorial ), offer a fixed set of predefined types and (usually low-level / RISC-like) instructions. It is up to the frontend for a given language to perform any language-specific type-checking, analysis, or transformation before emitting LLVM IR. For example, Clang will use its AST to perform not only static analysis but also transformations, such as C++ template instantiation through AST cloning and rewrite. Finally, languages with construction at a higher-level than C/C++ may require non-trivial lowering from their AST to generate LLVM IR.

As a consequence, multiple frontends end up reimplementing significant pieces of infrastructure to support the need for these analyses and transformation. MLIR addresses this issue by being designed for extensibility. As such, there are few pre-defined instructions (operations in MLIR terminology) or types.

## Interfacing with MLIR ¶

Language reference

MLIR is designed to be a completely extensible infrastructure; there is no closed set of attributes (think: constant metadata), operations, or types. MLIR supports this extensibility with the concept of Dialects . Dialects provide a grouping mechanism for abstraction under a unique namespace.

In MLIR, Operations are the core unit of abstraction and computation, similar in many ways to LLVM instructions. Operations can have application-specific semantics and can be used to represent all of the core IR structures in LLVM: instructions, globals (like functions), modules, etc.

Here is the MLIR assembly for the Toy transpose operations:

%t_tensor = "toy.transpose"(%tensor) {inplace = true} : (tensor<2x3xf64>) -> tensor<3x2xf64> loc("example/file/path":12:1)


Let’s break down the anatomy of this MLIR operation:

• %t_tensor

• The name given to the result defined by this operation (which includes a prefixed sigil to avoid collisions ). An operation may define zero or more results (in the context of Toy, we will limit ourselves to single-result operations), which are SSA values. The name is used during parsing but is not persistent (e.g., it is not tracked in the in-memory representation of the SSA value).
• "toy.transpose"

• The name of the operation. It is expected to be a unique string, with the namespace of the dialect prefixed before the “.”. This can be read as the transpose operation in the toy dialect.
• (%tensor)

• A list of zero or more input operands (or arguments), which are SSA values defined by other operations or referring to block arguments.
• { inplace = true }

• A dictionary of zero or more attributes, which are special operands that are always constant. Here we define a boolean attribute named ‘inplace’ that has a constant value of true.
• (tensor<2x3xf64>) -> tensor<3x2xf64>

• This refers to the type of the operation in a functional form, spelling the types of the arguments in parentheses and the type of the return values afterward.
• loc("example/file/path":12:1)

• This is the location in the source code from which this operation originated.

Shown here is the general form of an operation. As described above, the set of operations in MLIR is extensible. Operations are modeled using a small set of concepts, enabling operations to be reasoned about and manipulated generically. These concepts are:

• A name for the operation.
• A list of SSA operand values.
• A list of attributes .
• A list of types for result values.
• A source location for debugging purposes.
• A list of successors blocks (for branches, mostly).
• A list of regions (for structural operations like functions).

In MLIR, every operation has a mandatory source location associated with it. Contrary to LLVM, where debug info locations are metadata and can be dropped, in MLIR, the location is a core requirement, and APIs depend on and manipulate it. Dropping a location is thus an explicit choice which cannot happen by mistake.

To provide an illustration: If a transformation replaces an operation by another, that new operation must still have a location attached. This makes it possible to track where that operation came from.

It’s worth noting that the mlir-opt tool - a tool for testing compiler passes - does not include locations in the output by default. The -mlir-print-debuginfo flag specifies to include locations. (Run mlir-opt --help for more options.)

### Opaque API ¶

MLIR is designed to allow most IR elements, such as attributes, operations, and types, to be customized. At the same time, IR elements can always be reduced to the above fundamental concepts. This allows MLIR to parse, represent, and round-trip IR for any operation. For example, we could place our Toy operation from above into an .mlir file and round-trip through mlir-opt without registering any dialect:

func @toy_func(%tensor: tensor<2x3xf64>) -> tensor<3x2xf64> {
%t_tensor = "toy.transpose"(%tensor) { inplace = true } : (tensor<2x3xf64>) -> tensor<3x2xf64>
return %t_tensor : tensor<3x2xf64>
}


In the cases of unregistered attributes, operations, and types, MLIR will enforce some structural constraints (SSA, block termination, etc.), but otherwise they are completely opaque. For instance, MLIR has little information about whether an unregistered operation can operate on particular datatypes, how many operands it can take, or how many results it produces. This flexibility can be useful for bootstrapping purposes, but it is generally advised against in mature systems. Unregistered operations must be treated conservatively by transformations and analyses, and they are much harder to construct and manipulate.

This handling can be observed by crafting what should be an invalid IR for Toy and seeing it round-trip without tripping the verifier:

func @main() {
%0 = "toy.print"() : () -> tensor<2x3xf64>
}


There are multiple problems here: the toy.print operation is not a terminator; it should take an operand; and it shouldn’t return any values. In the next section, we will register our dialect and operations with MLIR, plug into the verifier, and add nicer APIs to manipulate our operations.

## Defining a Toy Dialect ¶

To effectively interface with MLIR, we will define a new Toy dialect. This dialect will model the structure of the Toy language, as well as provide an easy avenue for high-level analysis and transformation.

/// This is the definition of the Toy dialect. A dialect inherits from
/// mlir::Dialect and registers custom attributes, operations, and types (in its
/// constructor). It can also override virtual methods to change some general
/// behavior, which will be demonstrated in later chapters of the tutorial.
class ToyDialect : public mlir::Dialect {
public:
explicit ToyDialect(mlir::MLIRContext *ctx);

/// Provide a utility accessor to the dialect namespace. This is used by
/// several utilities.
static llvm::StringRef getDialectNamespace() { return "toy"; }
};


The dialect can now be registered in the global registry:

  mlir::registerDialect<ToyDialect>();


Any new MLIRContext created from now on will contain an instance of the Toy dialect and invoke specific hooks for things like parsing attributes and types.

## Defining Toy Operations ¶

Now that we have a Toy dialect, we can start registering operations. This will allow for providing semantic information that the rest of the system can hook into. Let’s walk through the creation of the toy.constant operation:

 %4 = "toy.constant"() {value = dense<1.0> : tensor<2x3xf64>} : () -> tensor<2x3xf64>


This operation takes zero operands, a dense elements attribute named value, and returns a single result of TensorType . An operation inherits from the CRTP mlir::Op class which also takes some optional traits to customize its behavior. These traits may provide additional accessors, verification, etc.

class ConstantOp : public mlir::Op<ConstantOp,
/// The ConstantOp takes no inputs.
mlir::OpTrait::ZeroOperands,
/// The ConstantOp returns a single result.
mlir::OpTrait::OneResult,
/// The result of getType is Type.
mlir::OpTraits::OneTypedResult<Type>::Impl> {

public:
/// Inherit the constructors from the base Op class.
using Op::Op;

/// Provide the unique name for this operation. MLIR will use this to register
/// the operation and uniquely identify it throughout the system.
static llvm::StringRef getOperationName() { return "toy.constant"; }

/// Return the value of the constant by fetching it from the attribute.
mlir::DenseElementsAttr getValue();

/// Operations can provide additional verification beyond the traits they
/// define. Here we will ensure that the specific invariants of the constant
/// operation are upheld, for example the result type must be of TensorType.
LogicalResult verify();

/// Provide an interface to build this operation from a set of input values.
/// This interface is used by the builder to allow for easily generating
/// instances of this operation:
///   mlir::OpBuilder::create<ConstantOp>(...)
/// This method populates the given state that MLIR uses to create
/// operations. This state is a collection of all of the discrete elements
/// that an operation may contain.
/// Build a constant with the given return type and value attribute.
static void build(mlir::OpBuilder &builder, mlir::OperationState &state,
mlir::Type result, mlir::DenseElementsAttr value);
/// Build a constant and reuse the type from the given 'value'.
static void build(mlir::OpBuilder &builder, mlir::OperationState &state,
mlir::DenseElementsAttr value);
/// Build a constant by broadcasting the given 'value'.
static void build(mlir::OpBuilder &builder, mlir::OperationState &state,
double value);
};


and we register this operation in the ToyDialect constructor:

ToyDialect::ToyDialect(mlir::MLIRContext *ctx)
: mlir::Dialect(getDialectNamespace(), ctx) {
}


### Op vs Operation: Using MLIR Operations ¶

Now that we have defined an operation, we will want to access and transform it. In MLIR, there are two main classes related to operations: Operation and Op. The Operation class is used to generically model all operations. It is ‘opaque’, in the sense that it does not describe the properties of particular operations or types of operations. Instead, the ‘Operation’ class provides a general API into an operation instance. On the other hand, each specific type of operation is represented by an Op derived class. For instance ConstantOp represents a operation with zero inputs, and one output, which is always set to the same value. Op derived classes act as smart pointer wrapper around a Operation*, provide operation-specific accessor methods, and type-safe properties of operations. This means that when we define our Toy operations, we are simply defining a clean, semantically useful interface for building and interfacing with the Operation class. This is why our ConstantOp defines no class fields; all the data structures are stored in the referenced Operation. A side effect is that we always pass around Op derived classes by value, instead of by reference or pointer (passing by value is a common idiom and applies similarly to attributes, types, etc). Given a generic Operation* instance, we can always get a specific Op instance using LLVM’s casting infrastructure:

void processConstantOp(mlir::Operation *operation) {
ConstantOp op = llvm::dyn_cast<ConstantOp>(operation);

// This operation is not an instance of ConstantOp.
if (!op)
return;

// Get the internal operation instance wrapped by the smart pointer.
mlir::Operation *internalOperation = op.getOperation();
assert(internalOperation == operation &&
"these operation instances are the same");
}


### Using the Operation Definition Specification (ODS) Framework ¶

In addition to specializing the mlir::Op C++ template, MLIR also supports defining operations in a declarative manner. This is achieved via the Operation Definition Specification framework. Facts regarding an operation are specified concisely into a TableGen record, which will be expanded into an equivalent mlir::Op C++ template specialization at compile time. Using the ODS framework is the desired way for defining operations in MLIR given the simplicity, conciseness, and general stability in the face of C++ API changes.

Lets see how to define the ODS equivalent of our ConstantOp:

The first thing to do is to define a link to the Toy dialect that we defined in C++. This is used to link all of the operations that we will define to our dialect:

// Provide a definition of the 'toy' dialect in the ODS framework so that we
// can define our operations.
def Toy_Dialect : Dialect {
// The namespace of our dialect, this corresponds 1-1 with the string we
// provided in ToyDialect::getDialectNamespace.
let name = "toy";

// The C++ namespace that the dialect class definition resides in.
let cppNamespace = "toy";
}


Now that we have defined a link to the Toy dialect, we can start defining operations. Operations in ODS are defined by inheriting from the Op class. To simplify our operation definitions, we will define a base class for operations in the Toy dialect.

// Base class for toy dialect operations. This operation inherits from the base
// Op class in OpBase.td, and provides:
//   * The parent dialect of the operation.
//   * The mnemonic for the operation, or the name without the dialect prefix.
//   * A list of traits for the operation.
class Toy_Op<string mnemonic, list<OpTrait> traits = []> :
Op<Toy_Dialect, mnemonic, traits>;


With all of the preliminary pieces defined, we can begin to define the constant operation.

We define a toy operation by inheriting from our base ‘Toy_Op’ class above. Here we provide the mnemonic and a list of traits for the operation. The mnemonic here matches the one given in ConstantOp::getOperationName without the dialect prefix; toy.. Missing here from our C++ definition are the ZeroOperands and OneResult traits; these will be automatically inferred based upon the arguments and results fields we define later.

def ConstantOp : Toy_Op<"constant"> {
}


At this point you probably might want to know what the C++ code generated by TableGen looks like. Simply run the mlir-tblgen command with the gen-op-decls or the gen-op-defs action like so:

${build_root}/bin/mlir-tblgen -gen-op-defs${mlir_src_root}/examples/toy/Ch2/include/toy/Ops.td -I ${mlir_src_root}/include/  Depending on the selected action, this will print either the ConstantOp class declaration or its implementation. Comparing this output to the hand-crafted implementation is incredibly useful when getting started with TableGen. #### Defining Arguments and Results ¶ With the shell of the operation defined, we can now provide the inputs and outputs to our operation. The inputs, or arguments, to an operation may be attributes or types for SSA operand values. The results correspond to a set of types for the values produced by the operation: def ConstantOp : Toy_Op<"constant"> { // The constant operation takes an attribute as the only input. // F64ElementsAttr corresponds to a 64-bit floating-point ElementsAttr. let arguments = (ins F64ElementsAttr:$value);

// The constant operation returns a single value of TensorType.
// F64Tensor corresponds to a 64-bit floating-point TensorType.
let results = (outs F64Tensor);
}


By providing a name to the arguments or results, e.g. $value, ODS will automatically generate a matching accessor: DenseElementsAttr ConstantOp::value(). #### Adding Documentation ¶ The next step after defining the operation is to document it. Operations may provide summary and description fields to describe the semantics of the operation. This information is useful for users of the dialect and can even be used to auto-generate Markdown documents. def ConstantOp : Toy_Op<"constant"> { // Provide a summary and description for this operation. This can be used to // auto-generate documentation of the operations within our dialect. let summary = "constant operation"; let description = [{ Constant operation turns a literal into an SSA value. The data is attached to the operation as an attribute. For example: %0 = "toy.constant"() { value = dense<[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]> : tensor<2x3xf64> } : () -> tensor<2x3xf64> }]; // The constant operation takes an attribute as the only input. // F64ElementsAttr corresponds to a 64-bit floating-point ElementsAttr. let arguments = (ins F64ElementsAttr:$value);

// The generic call operation returns a single value of TensorType.
// F64Tensor corresponds to a 64-bit floating-point TensorType.
let results = (outs F64Tensor);
}


#### Verifying Operation Semantics ¶

At this point we’ve already covered a majority of the original C++ operation definition. The next piece to define is the verifier. Luckily, much like the named accessor, the ODS framework will automatically generate a lot of the necessary verification logic based upon the constraints we have given. This means that we don’t need to verify the structure of the return type, or even the input attribute value. In many cases, additional verification is not even necessary for ODS operations. To add additional verification logic, an operation can override the verifier field. The verifier field allows for defining a C++ code blob that will be run as part of ConstantOp::verify. This blob can assume that all of the other invariants of the operation have already been verified:

def ConstantOp : Toy_Op<"constant"> {
// Provide a summary and description for this operation. This can be used to
// auto-generate documentation of the operations within our dialect.
let summary = "constant operation";
let description = [{
Constant operation turns a literal into an SSA value. The data is attached
to the operation as an attribute. For example:

%0 = "toy.constant"()
{ value = dense<[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]> : tensor<2x3xf64> }
: () -> tensor<2x3xf64>
}];

// The constant operation takes an attribute as the only input.
// F64ElementsAttr corresponds to a 64-bit floating-point ElementsAttr.
let arguments = (ins F64ElementsAttr:$value); // The generic call operation returns a single value of TensorType. // F64Tensor corresponds to a 64-bit floating-point TensorType. let results = (outs F64Tensor); // Add additional verification logic to the constant operation. Here we invoke // a static verify method in a C++ source file. This codeblock is executed // inside of ConstantOp::verify, so we can use this to refer to the current // operation instance. let verifier = [{ return ::verify(*this); }]; }  #### Attaching build Methods ¶ The final missing component here from our original C++ example are the build methods. ODS can generate some simple build methods automatically, and in this case it will generate our first build method for us. For the rest, we define the builders field. This field takes a list of OpBuilder objects that take a string corresponding to a list of C++ parameters, as well as an optional code block that can be used to specify the implementation inline. def ConstantOp : Toy_Op<"constant"> { ... // Add custom build methods for the constant operation. These methods populate // the state that MLIR uses to create operations, i.e. these are used when // using builder.create<ConstantOp>(...). let builders = [ // Build a constant with a given constant tensor value. OpBuilderDAG<(ins "DenseElementsAttr":$value), [{
// Call into an autogenerated build method.
build(builder, result, value.getType(), value);
}]>,

// Build a constant with a given constant floating-point value. This builder
// creates a declaration for ConstantOp::build with the given parameters.
OpBuilderDAG<(ins "double":$value)> ]; }  #### Specifying a Custom Assembly Format ¶ At this point we can generate our “Toy IR”. For example, the following: # User defined generic function that operates on unknown shaped arguments. def multiply_transpose(a, b) { return transpose(a) * transpose(b); } def main() { var a<2, 3> = [[1, 2, 3], [4, 5, 6]]; var b<2, 3> = [1, 2, 3, 4, 5, 6]; var c = multiply_transpose(a, b); var d = multiply_transpose(b, a); print(d); }  Results in the following IR: module { func @multiply_transpose(%arg0: tensor<*xf64>, %arg1: tensor<*xf64>) -> tensor<*xf64> { %0 = "toy.transpose"(%arg0) : (tensor<*xf64>) -> tensor<*xf64> loc("test/Examples/Toy/Ch2/codegen.toy":5:10) %1 = "toy.transpose"(%arg1) : (tensor<*xf64>) -> tensor<*xf64> loc("test/Examples/Toy/Ch2/codegen.toy":5:25) %2 = "toy.mul"(%0, %1) : (tensor<*xf64>, tensor<*xf64>) -> tensor<*xf64> loc("test/Examples/Toy/Ch2/codegen.toy":5:25) "toy.return"(%2) : (tensor<*xf64>) -> () loc("test/Examples/Toy/Ch2/codegen.toy":5:3) } loc("test/Examples/Toy/Ch2/codegen.toy":4:1) func @main() { %0 = "toy.constant"() {value = dense<[[1.000000e+00, 2.000000e+00, 3.000000e+00], [4.000000e+00, 5.000000e+00, 6.000000e+00]]> : tensor<2x3xf64>} : () -> tensor<2x3xf64> loc("test/Examples/Toy/Ch2/codegen.toy":9:17) %1 = "toy.reshape"(%0) : (tensor<2x3xf64>) -> tensor<2x3xf64> loc("test/Examples/Toy/Ch2/codegen.toy":9:3) %2 = "toy.constant"() {value = dense<[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00, 5.000000e+00, 6.000000e+00]> : tensor<6xf64>} : () -> tensor<6xf64> loc("test/Examples/Toy/Ch2/codegen.toy":10:17) %3 = "toy.reshape"(%2) : (tensor<6xf64>) -> tensor<2x3xf64> loc("test/Examples/Toy/Ch2/codegen.toy":10:3) %4 = "toy.generic_call"(%1, %3) {callee = @multiply_transpose} : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64> loc("test/Examples/Toy/Ch2/codegen.toy":11:11) %5 = "toy.generic_call"(%3, %1) {callee = @multiply_transpose} : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64> loc("test/Examples/Toy/Ch2/codegen.toy":12:11) "toy.print"(%5) : (tensor<*xf64>) -> () loc("test/Examples/Toy/Ch2/codegen.toy":13:3) "toy.return"() : () -> () loc("test/Examples/Toy/Ch2/codegen.toy":8:1) } loc("test/Examples/Toy/Ch2/codegen.toy":8:1) } loc(unknown)  One thing to notice here is that all of our Toy operations are printed using the generic assembly format. This format is the one shown when breaking down toy.transpose at the beginning of this chapter. MLIR allows for operations to define their own custom assembly format, either declaratively or imperatively via C++. Defining a custom assembly format allows for tailoring the generated IR into something a bit more readable by removing a lot of the fluff that is required by the generic format. Let’s walk through an example of an operation format that we would like to simplify. ##### toy.print¶ The current form of toy.print is a little verbose. There are a lot of additional characters that we would like to strip away. Let’s begin by thinking of what a good format of toy.print would be, and see how we can implement it. Looking at the basics of toy.print we get: toy.print %5 : tensor<*xf64> loc(...)  Here we have stripped much of the format down to the bare essentials, and it has become much more readable. To provide a custom assembly format, an operation can either override the parser and printer fields for a C++ format, or the assemblyFormat field for the declarative format. Let’s look at the C++ variant first, as this is what the declarative format maps to internally. /// Consider a stripped definition of toy.print here. def PrintOp : Toy_Op<"print"> { let arguments = (ins F64Tensor:$input);

// Divert the printer and parser to static functions in our .cpp
// file that correspond to 'print' and 'printPrintOp'. 'printer' and 'parser'
// here correspond to an instance of a 'OpAsmParser' and 'OpAsmPrinter'. More
// details on these classes is shown below.
let printer = [{ return ::print(printer, *this); }];
let parser = [{ return ::parse$cppClass(parser, result); }]; }  A C++ implementation for the printer and parser is shown below: /// The 'OpAsmPrinter' class is a stream that will allows for formatting /// strings, attributes, operands, types, etc. static void print(mlir::OpAsmPrinter &printer, PrintOp op) { printer << "toy.print " << op.input(); printer.printOptionalAttrDict(op.getAttrs()); printer << " : " << op.input().getType(); } /// The 'OpAsmParser' class provides a collection of methods for parsing /// various punctuation, as well as attributes, operands, types, etc. Each of /// these methods returns a ParseResult. This class is a wrapper around /// LogicalResult that can be converted to a boolean true value on failure, /// or false on success. This allows for easily chaining together a set of /// parser rules. These rules are used to populate an mlir::OperationState /// similarly to the build methods described above. static mlir::ParseResult parsePrintOp(mlir::OpAsmParser &parser, mlir::OperationState &result) { // Parse the input operand, the attribute dictionary, and the type of the // input. mlir::OpAsmParser::OperandType inputOperand; mlir::Type inputType; if (parser.parseOperand(inputOperand) || parser.parseOptionalAttrDict(result.attributes) || parser.parseColon() || parser.parseType(inputType)) return mlir::failure(); // Resolve the input operand to the type we parsed in. if (parser.resolveOperand(inputOperand, inputType, result.operands)) return mlir::failure(); return mlir::success(); }  With the C++ implementation defined, let’s see how this can be mapped to the declarative format . The declarative format is largely composed of three different components: • Directives • A type of builtin function, with an optional set of arguments. • Literals • A keyword or punctuation surrounded by . • Variables • An entity that has been registered on the operation itself, i.e. an argument(attribute or operand), result, successor, etc. In the PrintOp example above, a variable would be $input.

A direct mapping of our C++ format looks something like:

/// Consider a stripped definition of toy.print here.
def PrintOp : Toy_Op<"print"> {
let arguments = (ins F64Tensor:$input); // In the following format we have two directives, attr-dict and type. // These correspond to the attribute dictionary and the type of a given // variable represectively. let assemblyFormat = "$input attr-dict : type(\$input)";
}


The declarative format has many more interesting features, so be sure to check it out before implementing a custom format in C++. After beautifying the format of a few of our operations we now get a much more readable:

module {
func @multiply_transpose(%arg0: tensor<*xf64>, %arg1: tensor<*xf64>) -> tensor<*xf64> {
%0 = toy.transpose(%arg0 : tensor<*xf64>) to tensor<*xf64> loc("test/Examples/Toy/Ch2/codegen.toy":5:10)
%1 = toy.transpose(%arg1 : tensor<*xf64>) to tensor<*xf64> loc("test/Examples/Toy/Ch2/codegen.toy":5:25)
%2 = toy.mul %0, %1 : tensor<*xf64> loc("test/Examples/Toy/Ch2/codegen.toy":5:25)
toy.return %2 : tensor<*xf64> loc("test/Examples/Toy/Ch2/codegen.toy":5:3)
} loc("test/Examples/Toy/Ch2/codegen.toy":4:1)
func @main() {
%0 = toy.constant dense<[[1.000000e+00, 2.000000e+00, 3.000000e+00], [4.000000e+00, 5.000000e+00, 6.000000e+00]]> : tensor<2x3xf64> loc("test/Examples/Toy/Ch2/codegen.toy":9:17)
%1 = toy.reshape(%0 : tensor<2x3xf64>) to tensor<2x3xf64> loc("test/Examples/Toy/Ch2/codegen.toy":9:3)
%2 = toy.constant dense<[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00, 5.000000e+00, 6.000000e+00]> : tensor<6xf64> loc("test/Examples/Toy/Ch2/codegen.toy":10:17)
%3 = toy.reshape(%2 : tensor<6xf64>) to tensor<2x3xf64> loc("test/Examples/Toy/Ch2/codegen.toy":10:3)
%4 = toy.generic_call @multiply_transpose(%1, %3) : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64> loc("test/Examples/Toy/Ch2/codegen.toy":11:11)
%5 = toy.generic_call @multiply_transpose(%3, %1) : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64> loc("test/Examples/Toy/Ch2/codegen.toy":12:11)
toy.print %5 : tensor<*xf64> loc("test/Examples/Toy/Ch2/codegen.toy":13:3)
toy.return loc("test/Examples/Toy/Ch2/codegen.toy":8:1)
} loc("test/Examples/Toy/Ch2/codegen.toy":8:1)
} loc(unknown)


Above we introduce several of the concepts for defining operations in the ODS framework, but there are many more that we haven’t had a chance to: regions, variadic operands, etc. Check out the full specification for more details.

## Complete Toy Example ¶

We can now generate our “Toy IR”. You can build toyc-ch2 and try yourself on the above example: toyc-ch2 test/Examples/Toy/Ch2/codegen.toy -emit=mlir -mlir-print-debuginfo. We can also check our RoundTrip: toyc-ch2 test/Examples/Toy/Ch2/codegen.toy -emit=mlir -mlir-print-debuginfo 2> codegen.mlir followed by toyc-ch2 codegen.mlir -emit=mlir. You should also use mlir-tblgen on the final definition file and study the generated C++ code.

At this point, MLIR knows about our Toy dialect and operations. In the next chapter , we will leverage our new dialect to implement some high-level language-specific analyses and transformations for the Toy language.