[mlir][docs] Refactor the layout of the docs folder

[rriddle]

Summary

This revision performs a few refactorings on the main docs folder. Namely it:

• Adds a new Rationale/ folder to contain various rationale documents
• Moves several "getting started" documents to the Tutorials/ folder
• Cleans up the titles of various documents

mlir/docs/Diagnostics.md

-# Introduction and Usage Guide to MLIR's Diagnostics Infrastructure
+# Diagnostic Infrastructure
 
 [TOC]
 
 This document presents an introduction to using and interfacing with MLIR's
 diagnostics infrastructure.
 
 See [MLIR specification](LangRef.md) for more information about MLIR, the
 structure of the IR, operations, etc.

mlir/docs/Interfaces.md

-# Introduction to MLIR Interfaces
+# MLIR Interfaces
 
 MLIR is generic and very extensible; it allows for opaquely representing many
 different dialects that have their own operations, attributes, types, and so on.
 This allows for dialects to be very expressive in their semantics and for MLIR
 to capture many different levels of abstraction. The downside to this is that
 transformations and analyses must be extremely conservative about the operations
 that they encounter, and must special-case the different dialects that they
 support. To combat this, MLIR provides the concept of `interfaces`.

mlir/docs/LangRef.md

-# MLIR Specification
+# MLIR Language Reference
 
 MLIR (Multi-Level IR) is a compiler intermediate representation with
 similarities to traditional three-address SSA representations (like
 [LLVM IR](http://llvm.org/docs/LangRef.html) or
 [SIL](https://github.com/apple/swift/blob/master/docs/SIL.rst)), but which
 introduces notions from polyhedral loop optimization as first-class concepts.
 This hybrid design is optimized to represent, analyze, and transform high level
 dataflow graphs as well as target-specific code generated for high performance

mlir/docs/PassManagement.md

-# Writing a Pass
+# Pass Infrastructure
 
 [TOC]
 
 Passes represent the basic infrastructure for transformation and optimization.
 This document provides a quickstart to the pass infrastructure in MLIR and how
 to use it.
 
 See [MLIR specification](LangRef.md) for more information about MLIR and its

mlir/docs/ShapeInference.md

-# Shape inference
+# Shape Inference
 
 Shape inference as discussed here is considered a specific instance of type
 inference for [ShapedType][ShapedType]. Type constraints are along (at least)
 three axis: 1) elemental type, 2) rank (including static or dynamic), 3)
 dimensions. While some operations have no compile time fixed shape (e.g., output
 shape is dictated by data) we could still have some knowledge of
 constraints/bounds in the system for that operation (e.g., the output of a
 `tf.where` is at most the size of the input data). That is, there are additional

mlir/docs/Traits.md

-# Introduction to MLIR Operation Traits
+# MLIR Operation Traits
 
 [TOC]
 
 MLIR allows for a truly open operation ecosystem, as any dialect may define
 operations that suit a specific level of abstraction. `Traits` are a mechanism
 in which to abstract implementation details and properties that are common
 across many different operations. `Traits` may be used to specify special
 properties and constraints of the operation, including whether the operation has

mlir/docs/Tutorials/DefiningAttributesAndTypes.md

-# Quickstart tutorial to defining custom dialect attributes and types
+# Defining Dialect Attributes and Types
 
 This document is a quickstart to defining dialect specific extensions to the
 [attribute](LangRef.md#attributes) and [type system](LangRef.md#type-system).
 The main part of the tutorial focuses on defining types, but the instructions
 are nearly identical for defining attributes.
 
 See [MLIR specification](LangRef.md) for more information about MLIR, the
 structure of the IR, operations, etc.