# Chapter 1: Toy Tutorial Introduction
[TOC]
This tutorial runs through the implementation of a basic toy language on top of
MLIR. The goal of this tutorial is to introduce the concepts of MLIR; in
particular, how [dialects](../../LangRef.md#dialects) can help easily support
language specific constructs and transformations while still offering an easy
path to lower to LLVM or other codegen infrastructure. This tutorial is based on
the model of the
[LLVM Kaleidoscope Tutorial](https://llvm.org/docs/tutorial/MyFirstLanguageFrontend/index.html).
This tutorial assumes you have cloned and built MLIR; if you have not yet done
so, see
[Getting started with MLIR](https://github.com/tensorflow/mlir#getting-started-with-mlir).
## The Chapters
This tutorial is divided in the following chapters:
- [Chapter #1](Ch-1.md): Introduction to the Toy language and the definition
of its AST.
- [Chapter #2](Ch-2.md): Traversing the AST to emit a dialect in MLIR,
introducing base MLIR concepts. Here we show how to start attaching
semantics to our custom operations in MLIR.
- [Chapter #3](Ch-3.md): High-level language-specific optimization using
pattern rewriting system.
- [Chapter #4](Ch-4.md): Writing generic dialect-independent transformations
with Interfaces. Here we will show how to plug dialect specific information
into generic transformations like shape inference and inlining.
- [Chapter #5](Ch-5.md): Partially lowering to lower-level dialects. We'll
convert some our high level language specific semantics towards a generic
affine oriented dialect for optimization.
- [Chapter #6](Ch-6.md): Lowering to LLVM and code generation. Here we'll
target LLVM IR for code generation, and detail more of the lowering
framework.
- [Chapter #7](Ch-7.md): Extending Toy: Adding support for a composite type.
We'll demonstrate how to add a custom type to MLIR, and how it fits in the
existing pipeline.
## The Language
This tutorial will be illustrated with a toy language that we’ll call “Toy”
(naming is hard...). Toy is a tensor-based language that allows you to define
functions, perform some math computation, and print results.
Given that we want to keep things simple, the codegen will be limited to tensors
of rank <= 2, and the only datatype in Toy is a 64-bit floating point type (aka
‘double’ in C parlance). As such, all values are implicitly double precision,
`Values` are immutable (i.e. every operation returns a newly allocated value),
and deallocation is automatically managed. But enough with the long description;
nothing is better than walking through an example to get a better understanding:
```toy
def main() {
# Define a variable `a` with shape <2, 3>, initialized with the literal value.
# The shape is inferred from the supplied literal.
var a = [[1, 2, 3], [4, 5, 6]];
# b is identical to a, the literal tensor is implicitly reshaped: defining new
# variables is the way to reshape tensors (element count must match).
var b<2, 3> = [1, 2, 3, 4, 5, 6];
# transpose() and print() are the only builtin, the following will transpose
# a and b and perform an element-wise multiplication before printing the result.
print(transpose(a) * transpose(b));
}
```
Type checking is statically performed through type inference; the language only
requires type declarations to specify tensor shapes when needed. Functions are
generic: their parameters are unranked (in other words, we know these are
tensors, but we don't know their dimensions). They are specialized for every
newly discovered signature at call sites. Let's revisit the previous example by
adding a user-defined function:
```toy
# User defined generic function that operates on unknown shaped arguments.
def multiply_transpose(a, b) {
return transpose(a) * transpose(b);
}
def main() {
# Define a variable `a` with shape <2, 3>, initialized with the literal value.
var a = [[1, 2, 3], [4, 5, 6]];
var b<2, 3> = [1, 2, 3, 4, 5, 6];
# This call will specialize `multiply_transpose` with <2, 3> for both
# arguments and deduce a return type of <3, 2> in initialization of `c`.
var c = multiply_transpose(a, b);
# A second call to `multiply_transpose` with <2, 3> for both arguments will
# reuse the previously specialized and inferred version and return <3, 2>.
var d = multiply_transpose(b, a);
# A new call with <3, 2> (instead of <2, 3>) for both dimensions will
# trigger another specialization of `multiply_transpose`.
var e = multiply_transpose(c, d);
# Finally, calling into `multiply_transpose` with incompatible shape will
# trigger a shape inference error.
var f = multiply_transpose(transpose(a), c);
}
```
## The AST
The AST from the above code is fairly straightforward; here is a dump of it:
```
Module:
Function
Proto 'multiply_transpose' @test/ast.toy:5:1'
Args: [a, b]
Block {
Return
BinOp: * @test/ast.toy:6:25
Call 'transpose' [ @test/ast.toy:6:10
var: a @test/ast.toy:6:20
]
Call 'transpose' [ @test/ast.toy:6:25
var: b @test/ast.toy:6:35
]
} // Block
Function
Proto 'main' @test/ast.toy:9:1'
Args: []
Block {
VarDecl a<> @test/ast.toy:11:3
Literal: <2, 3>[<3>[1.000000e+00, 2.000000e+00, 3.000000e+00], <3>[4.000000e+00, 5.000000e+00, 6.000000e+00]] @test/ast.toy:11:17
VarDecl b<2, 3> @test/ast.toy:12:3
Literal: <6>[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00, 5.000000e+00, 6.000000e+00] @test/ast.toy:12:17
VarDecl c<> @test/ast.toy:15:3
Call 'multiply_transpose' [ @test/ast.toy:15:11
var: a @test/ast.toy:15:30
var: b @test/ast.toy:15:33
]
VarDecl d<> @test/ast.toy:18:3
Call 'multiply_transpose' [ @test/ast.toy:18:11
var: b @test/ast.toy:18:30
var: a @test/ast.toy:18:33
]
VarDecl e<> @test/ast.toy:21:3
Call 'multiply_transpose' [ @test/ast.toy:21:11
var: b @test/ast.toy:21:30
var: c @test/ast.toy:21:33
]
VarDecl f<> @test/ast.toy:24:3
Call 'multiply_transpose' [ @test/ast.toy:24:11
Call 'transpose' [ @test/ast.toy:24:30
var: a @test/ast.toy:24:40
]
var: c @test/ast.toy:24:44
]
} // Block
```
You can reproduce this result and play with the example in the
`examples/toy/Ch1/` directory; try running `path/to/BUILD/bin/toyc-ch1
test/Examples/Toy/Ch1/ast.toy -emit=ast`.
The code for the lexer is fairly straightforward; it is all in a single header:
`examples/toy/Ch1/include/toy/Lexer.h`. The parser can be found in
`examples/toy/Ch1/include/toy/Parser.h`; it is a recursive descent parser. If
you are not familiar with such a Lexer/Parser, these are very similar to the
LLVM Kaleidoscope equivalent that are detailed in the first two chapters of the
[Kaleidoscope Tutorial](https://llvm.org/docs/tutorial/MyFirstLanguageFrontend/LangImpl02.html).
The [next chapter](Ch-2.md) will demonstrate how to convert this AST into MLIR.