chore: import upstream snapshot with attribution

tensorflow/compiler/mlir/g3doc/dialects.md

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+# MLIR dialects
+
+## Overview
+
+
+To separate different hardware and software targets, MLIR has “dialects”,
+including:
+
+* TensorFlow IR, which represents all things possible in TensorFlow graphs.
+* XLA HLO IR, which is designed to take advantage of XLA’s compilation
+  abilities (with output to, among other things, TPUs).
+* An experimental affine dialect, which focuses on
+  [polyhedral representations](https://en.wikipedia.org/wiki/Polytope_model)
+  and optimizations.
+* LLVM IR, which has a 1:1 mapping between it and LLVM’s own representation,
+  allowing MLIR to emit GPU and CPU code through LLVM.
+* TensorFlow Lite, which will translate to running code on mobile platforms.
+
+Each dialect consists of a set of defined operations which have invariants
+placed on them, like: “This is a binary operator, and the inputs and outputs
+have the same types.”
+
+## Adding to MLIR
+
+MLIR has no fixed/built-in list of globally known operations (no “intrinsics”).
+Dialects can define entirely custom types, which is how MLIR can model things
+like the LLVM IR type system (which has first class aggregates), domain
+abstractions important for ML-optimized accelerators like quantized types, and
+even the Swift or Clang type systems (which are built around Swift/Clang
+declaration nodes) in the future.
+
+If you want to connect a new low-level compiler, you would create a new dialect
+and the lowerings between the TensorFlow Graph dialect and your dialect.
+This smooths the path for hardware and compiler makers. You can even target
+dialects at different levels in the same model; the higher-level optimizers
+will respect the unfamiliar parts of the IR and wait for a lower level to handle
+it.