chore: import upstream snapshot with attribution

tensorflow/compiler/mlir/g3doc/README.md

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+# TensorFlow MLIR
+
+These are the docs for: https://www.tensorflow.org/mlir

tensorflow/compiler/mlir/g3doc/_book.yaml

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+# Copyright 2026 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+upper_tabs:
+# Tabs left of dropdown menu
+- include: /_upper_tabs_left.yaml
+- include: /api_docs/_upper_tabs_api.yaml
+# Dropdown menu
+- name: Resources
+  path: /resources
+  is_default: true
+  menu:
+  - include: /resources/_menu_toc.yaml
+  lower_tabs:
+    # Subsite tabs
+    other:
+    - name: Guide
+      contents:
+      - title: Overview
+        path: /mlir/overview
+      - heading: Dialects
+      - title: Overview
+        path: /mlir/dialects
+      - title: TensorFlow
+        path: /mlir/tf_ops
+      - title: TensorFlow Lite
+        path: /mlir/tfl_ops
+      - heading: Passes
+      - title: TF dialect
+        path: /mlir/tf_passes
+
+- include: /_upper_tabs_right.yaml

tensorflow/compiler/mlir/g3doc/_index.yaml

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+# Copyright 2026 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+book_path: /mlir/_book.yaml
+project_path: /mlir/_project.yaml
+description: An intermediate representation and compiler framework, MLIR unifies the
+ infrastructure for high-performance ML models in TensorFlow.
+landing_page:
+  custom_css_path: /site-assets/css/style.css
+  rows:
+  - heading: MLIR unifies the infrastructure for high-performance ML models in TensorFlow.
+    items:
+    - description: >
+        The <a href="https://mlir.llvm.org/" class="external">MLIR</a> project defines a common
+        intermediate representation (IR) that unifies the infrastructure required to execute high
+        performance machine learning models in TensorFlow and similar ML frameworks. This project
+        will include the application of HPC techniques, along with integration of
+        search algorithms like reinforcement learning. MLIR aims to reduce the
+        cost to bring up new hardware, and improve usability for existing
+        TensorFlow users.
+
+    - code_block: |
+        <pre class = "prettyprint">
+        // Syntactically similar to LLVM:
+        func @testFunction(%arg0: i32) {
+          %x = call @thingToCall(%arg0) : (i32) -> i32
+          br ^bb1
+        ^bb1:
+          %y = arith.addi %x, %x : i32
+          return %y : i32
+        }
+        </pre>
+
+  - classname: devsite-landing-row-cards
+    items:
+    - heading: "Multi-Level Intermediate Representation for Compiler Infrastructure"
+      youtube_id: qzljG6DKgic
+      buttons:
+      - label: Watch the video
+        path: https://www.youtube.com/watch?v=qzljG6DKgic
+    - heading: "A new intermediate representation and compiler framework"
+      image_path: /resources/images/tf-logo-card-16x9.png
+      path: https://blog.tensorflow.org/2019/04/mlir-new-intermediate-representation.html
+      buttons:
+      - label: Read on TensorFlow blog
+        path: https://blog.tensorflow.org/2019/04/mlir-new-intermediate-representation.html
+    - heading: MLIR on GitHub
+      image_path: /resources/images/github-card-16x9.png
+      path: https://github.com/llvm/llvm-project/tree/main/mlir
+      buttons:
+      - label: View on GitHub
+        path: https://github.com/llvm/llvm-project/tree/main/mlir
+    - heading: TensorFlow MLIR on GitHub
+      image_path: /resources/images/github-card-16x9.png
+      path: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/compiler/mlir
+      buttons:
+      - label: View on GitHub
+        path: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/compiler/mlir

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.

tensorflow/compiler/mlir/g3doc/overview.md

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+# MLIR
+
+## Overview
+
+MLIR, or Multi-Level Intermediate Representation, is a representation format
+and library of compiler utilities that sits between the model representation
+and low-level compilers/executors that generate hardware-specific code.
+
+MLIR is, at its heart, a flexible infrastructure for modern optimizing
+compilers. This means it consists of a specification for intermediate
+representations (IR) and a code toolkit to perform transformations on that
+representation. (In compiler parlance, as you move from higher-level
+representations to lower-level representations, these transformations can be
+called “lowerings”)
+
+MLIR is highly influenced by [LLVM](https://llvm.org/) and unabashedly reuses
+many great ideas from it. It has a flexible type system, and allows
+representing, analyzing and transforming graphs combining multiple levels of
+abstraction in the same compilation unit. These abstractions include TensorFlow
+operations, nested polyhedral loop regions, and even LLVM instructions and fixed
+hardware operations and types.
+
+We expect MLIR to be of interest to many groups, including:
+
+*   Compiler researchers and implementers looking to optimize performance and
+    memory consumption of machine learning models
+*   Hardware makers looking for a way to connect their hardware to TensorFlow,
+    such as TPUs, portable neural hardware in phones, and other custom ASICs
+*   People writing language bindings that want to take advantage of optimizing
+    compilers and hardware acceleration.
+
+The TensorFlow ecosystem contains a number of compilers and optimizers that
+operate at multiple levels of the software and hardware stack. We expect the
+gradual adoption of MLIR to simplify every aspect of this stack.
+
+<img alt="MLIR overview diagram" src="./images/mlir-infra.svg"/>

tensorflow/compiler/mlir/g3doc/tf_passes.md

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+# TensorFlow passes
+
+[TOC]
+
+## TF dialect passes
+
+<<_includes/tf_passes.md>>