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[mlir] Add tutorial index.md pages
Sets the content for the section entry pages Hugo side. Differential Revision: https://reviews.llvm.org/D87969
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# Chapter 1: Toy Tutorial Introduction
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# Chapter 1: Toy Language and AST
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[TOC]
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This tutorial runs through the implementation of a basic toy language on top of
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MLIR. The goal of this tutorial is to introduce the concepts of MLIR; in
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particular, how [dialects](../../LangRef.md#dialects) can help easily support
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language specific constructs and transformations while still offering an easy
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path to lower to LLVM or other codegen infrastructure. This tutorial is based on
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the model of the
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[LLVM Kaleidoscope Tutorial](https://llvm.org/docs/tutorial/MyFirstLanguageFrontend/index.html).
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This tutorial assumes you have cloned and built MLIR; if you have not yet done
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so, see
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[Getting started with MLIR](https://mlir.llvm.org/getting_started/).
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## The Chapters
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This tutorial is divided in the following chapters:
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- [Chapter #1](Ch-1.md): Introduction to the Toy language and the definition
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of its AST.
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- [Chapter #2](Ch-2.md): Traversing the AST to emit a dialect in MLIR,
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introducing base MLIR concepts. Here we show how to start attaching
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semantics to our custom operations in MLIR.
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- [Chapter #3](Ch-3.md): High-level language-specific optimization using
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pattern rewriting system.
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- [Chapter #4](Ch-4.md): Writing generic dialect-independent transformations
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with Interfaces. Here we will show how to plug dialect specific information
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into generic transformations like shape inference and inlining.
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- [Chapter #5](Ch-5.md): Partially lowering to lower-level dialects. We'll
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convert some of our high level language specific semantics towards a generic
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affine oriented dialect for optimization.
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- [Chapter #6](Ch-6.md): Lowering to LLVM and code generation. Here we'll
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target LLVM IR for code generation, and detail more of the lowering
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framework.
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- [Chapter #7](Ch-7.md): Extending Toy: Adding support for a composite type.
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We'll demonstrate how to add a custom type to MLIR, and how it fits in the
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existing pipeline.
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## The Language
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This tutorial will be illustrated with a toy language that we’ll call “Toy”
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# Toy Tutorial
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This tutorial runs through the implementation of a basic toy language on top of
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MLIR. The goal of this tutorial is to introduce the concepts of MLIR; in
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particular, how [dialects](../../LangRef.md#dialects) can help easily support
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language specific constructs and transformations while still offering an easy
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path to lower to LLVM or other codegen infrastructure. This tutorial is based on
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the model of the
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[LLVM Kaleidoscope Tutorial](https://llvm.org/docs/tutorial/MyFirstLanguageFrontend/index.html).
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This tutorial assumes you have cloned and built MLIR; if you have not yet done
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so, see
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[Getting started with MLIR](https://mlir.llvm.org/getting_started/).
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This tutorial is divided in the following chapters:
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- [Chapter #1](Ch-1.md): Introduction to the Toy language and the definition
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of its AST.
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- [Chapter #2](Ch-2.md): Traversing the AST to emit a dialect in MLIR,
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introducing base MLIR concepts. Here we show how to start attaching
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semantics to our custom operations in MLIR.
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- [Chapter #3](Ch-3.md): High-level language-specific optimization using
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pattern rewriting system.
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- [Chapter #4](Ch-4.md): Writing generic dialect-independent transformations
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with Interfaces. Here we will show how to plug dialect specific information
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into generic transformations like shape inference and inlining.
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- [Chapter #5](Ch-5.md): Partially lowering to lower-level dialects. We'll
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convert some of our high level language specific semantics towards a generic
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affine oriented dialect for optimization.
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- [Chapter #6](Ch-6.md): Lowering to LLVM and code generation. Here we'll
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target LLVM IR for code generation, and detail more of the lowering
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framework.
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- [Chapter #7](Ch-7.md): Extending Toy: Adding support for a composite type.
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We'll demonstrate how to add a custom type to MLIR, and how it fits in the
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existing pipeline.
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The [first chapter](Ch-1.md) will introduce the Toy language and AST.
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# Tutorials
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This section contains multiple MLIR tutorials.
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See [Toy tutorial](toy) for an introduction to using MLIR infrastructure.
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