Updated documentation for release 2.0 (#4111)

* Added release notes for 1.35.1 and 2.0 releases
* Renamed aimet_torch wheel file name in docs (#3721)
* Updated heading styles. (#3719)
* Updated formatting: no horizontal rule, bold title, other headings non-bold
* Added formatting to make all code block boxes gray. (#3723)
* Edited install and landing pages (#3722)
* Edited landing page, quick start page and installation pages.

Signed-off-by: Hitarth Mehta <quic_hitameht@quicinc.com>
Signed-off-by: Dave Welsch <dwelsch@expertsupport.com>
Co-authored-by: Hitarth Mehta <quic_hitameht@quicinc.com>
Co-authored-by: Dave Welsch <dwelsch@expertsupport.com>

Docs/_static/aimet-furo.css

@@ -1,4 +1,17 @@
-
+/*
+ * AIMET color palette
+ */
+:root {
+    --aimet-blue: #3253dc;
+    --aimet-dark-blue: #0000ff;
+    --aimet-white: #ffffff;
+    --aimet-border-grey: #e0e0e0;
+    --aimet-menu-hover: #e3efff;
+    --aimet-menu-font-active: #0058ff;
+    --aimet-code-grey: #fafafa;
+    --aimet-light-blue: #e7f2fa;
+  }
+
  aside.toc-drawer ul ul ul ul {
    display: none;
 }
@@ -35,9 +48,51 @@
     max-height: 25em;
 }
 
+*.highlight pre, pre.literal-block {
+    background-color: var(--aimet-code-grey);
+}
+
 /*
  * Glossary formatting
 */
 [id^="term-"] {
     font-weight: bold;
 }
+
+/*
+ * Heading formatting
+ */
+
+h1 {
+    font-size:2.5em;
+    margin-top:1.75rem;
+    margin-bottom:1rem;
+}
+
+h2 {
+    font-size:2em;
+    margin-top:1.75rem;
+    margin-bottom:1.25rem;
+    font-weight: normal;
+}
+
+h3 {
+    font-size:1.5em;
+    margin-top:1.25rem;
+    margin-bottom:1.25rem;
+    font-weight: normal;
+}
+
+h4 {
+    font-size:1.25em;
+    margin-top:1.25rem;
+    margin-bottom:1.25rem;
+    font-weight: normal;
+}
+
+h5 {
+    font-size:1.125em;
+    margin-top:1.25rem;
+    margin-bottom:1.25rem;
+    font-weight: normal;
+} 

Docs/index.rst

@@ -19,18 +19,16 @@ AIMET Documentation
    API Reference <apiref/index>
    Release Notes <release_notes>
 
-The AI Model Efficiency Toolkit (AIMET) is a software toolkit designed for quantizing and compressing trained models.
+The AI Model Efficiency Toolkit (AIMET) is a software toolkit for quantizing and compressing trained models. The primary objective of model optimization is to facilitate its deployment on edge devices like mobile phones or laptops.
 
-The primary objective of model optimization is to facilitate its deployment on edge devices like mobile phones or laptops.
+AIMET employs post-training and fine-tuning techniques to minimize accuracy loss during quantization and compression.
 
-AIMET employs post-training and fine-tuning techniques to optimize trained models, aiming to minimize accuracy loss during quantization or compression.
-
-AIMET supports models from PyTorch, TensorFlow/Keras, and ONNX frameworks.
+AIMET supports models from the PyTorch, TensorFlow/Keras, and ONNX frameworks.
 
 Quick Start
 ===========
 
-To quickly install and begin using AIMET with PyTorch, see the :doc:`Quick start guide <../install/quick-start>`.
+To quickly install and begin using AIMET with PyTorch, see the :doc:`Quick Start Guide <../install/quick-start>`.
 
 Installation
 ============
@@ -40,32 +38,37 @@ For other install options, including for TensorFlow and ONNX platforms or to run
 User Guide
 ==========
 
-For a high-level explanation of how to use AIMET to optimize a model, see the :doc:`Quantization user guide <../userguide/index>`.
+For a high-level explanation of how to use AIMET to optimize a model, see the :doc:`Quantization User Guide <../userguide/index>`.
 
 Quantization Simulation Guide
 =============================
 
-For a guide to quantization simulation and its related techniques, see the doc :doc:`Quantization simulation guide <../quantsim/index>`.
+Quantization simulation (QuantSim) emulates the behavior of quantized hardware in a model running on floating point hardware. For a guide to quantization simulation and its related techniques, see the :doc:`Quantization Simulation Guide <../quantsim/index>`.
 
 Feature Guide
 =============
 
-For instructions on applying individual AIMET features, see the :doc:`Feature guide <../featureguide/index>`.
+For instructions on applying individual AIMET features, see the :doc:`Feature Guide <../featureguide/index>`.
 
 Example Notebooks
 =================
 
-To view end-to-end examples of model quantization and compression, and to download the examples in Jupyter notebook format, see :doc:`Example notebooks <../examples/index>`.
+To view end-to-end examples of model quantization and compression, and to download the examples in Jupyter notebook format, see the :doc:`Example Notebooks <../examples/index>` page.
 
 API Reference
 =============
 
-For a detailed look at the AIMET API documentation, see the :doc:`API reference <../apiref/index>`.
+For a detailed look at the AIMET API, see the :doc:`API Reference <../apiref/index>`.
 
 Release Notes
 =============
 
-For information specific to this release, see :doc:`Release notes <release_notes>`.
+For information about new features in this release, see the :doc:`Release Notes <release_notes>`.
+
+Glossary
+========
+
+See the :doc:`glossary <../glossary>` for explanations of terms and acronyms used on this website.
 
 |
 |

Docs/install/index.rst

@@ -4,13 +4,37 @@
 Installation
 ############
 
-Install the latest version of AIMET pacakge for all framework variants and compute platform from
-the **.whl** files hosted at https://github.com/quic/aimet/releases.
+This page describes instructions for installing the latest version of AIMET across all frameworks
+(PyTorch, TensorFlow, and ONNX) and compute platforms. Please choose **ONE** of the available
+installation options based on your needs and preferences.
+
+- :ref:`PyPI <default-package>`
+- :ref:`Alternative packages <alternative-packages>`
+- :ref:`Host install from scratch <host-install-from-scratch>`
+- :ref:`Docker install <docker-install>`
+- :ref:`Building from source <building-from-source>`
+
+.. _default-package:
+
+PyPI
+====
+
+To install the latest version of AIMET for the PyTorch framework, which supports the new **aimet_torch 2**
+interface, use the :ref:`Quick start <install-quick-start>` instructions.
+
+.. _alternative-packages:
+
+Alternative packages
+====================
+
+Install the latest version of AIMET for supported framework variants and compute platforms including
+TensorFlow, ONNX and PyTorch (legacy `aimet_torch.v1` interface) from the .whl files hosted at
+https://github.com/quic/aimet/releases.
 
 Prerequisites
-=============
+-------------
 
-The AIMET package requires the following host platform setup. Following prerequisites apply
+The AIMET package requires the following host platform setup. The following prerequisites apply
 to all frameworks variants.
 
 * 64-bit Intel x86-compatible processor
@@ -21,15 +45,15 @@ to all frameworks variants.
     * Nvidia driver version 455 or later (using the latest driver is recommended; both CUDA and cuDNN are supported)
 
 .. note::
-    Starting with the AIMET 2 release, there is no longer a dependency on ``liblapacke``. Therefore,
-    you should only install the following debian package if you are still using AIMET 1.x.
+    Starting with the AIMET 2 release, there is no longer a dependency on ``liblapacke``. 
+    Install the following Debian package if (and only if) you are still using AIMET 1.x.
 
 .. code-block:: bash
 
     apt-get install liblapacke
 
 Choose and install a package
-============================
+----------------------------
 
 Use one of the following commands to install AIMET based on your choice of framework and compute platform.
 
@@ -39,19 +63,22 @@ Use one of the following commands to install AIMET based on your choice of frame
     .. tab-item:: PyTorch
         :sync: torch
 
+        .. important::
+            Legacy `aimet_torch.v1` necessitates x86_64 architecture, Python 3.10 and PyTorch version 2.1.
+
         **PyTorch 2.1**
 
         With CUDA 12.x:
 
         .. parsed-literal::
 
-           python3 -m pip install |download_url|\ |version|/aimet_torch-|version|\+cu121\ |whl_suffix| -f |torch_pkg_url|
+           python3 -m pip install |download_url|\ |version|/aimet_torch-|version|\+cu121\ |torch_whl_suffix| -f |torch_pkg_url|
 
         With CPU only:
 
         .. parsed-literal::
 
-            python3 -m pip install |download_url|\ |version|/aimet_torch-|version|\+cpu\ |whl_suffix| -f |torch_pkg_url|
+            python3 -m pip install |download_url|\ |version|/aimet_torch-|version|\+cpu\ |torch_whl_suffix| -f |torch_pkg_url|
 
 
     .. tab-item:: TensorFlow
@@ -88,25 +115,113 @@ Use one of the following commands to install AIMET based on your choice of frame
 
             python3 -m pip install |download_url|\ |version|/aimet_onnx-|version|\+cpu\ |whl_suffix| -f |torch_pkg_url|
 
-.. |whl_suffix| replace:: \-cp310-cp310-manylinux_2_34_x86_64.whl
-.. |download_url| replace:: \https://github.com/quic/aimet/releases/download/
-.. |torch_pkg_url| replace:: \https://download.pytorch.org/whl/torch_stable.html
+Verifying the installation
+--------------------------
+
+Verify your installation using the following instructions.
+
+**Step 1:** Handle imports and other setup.
+
+.. code-block:: python
+
+    import numpy as np
+    from aimet_common import libpymo
+
+    x = np.random.randn(100)
+
+    quant_scheme = libpymo.QuantizationMode.QUANTIZATION_TF
+    analyzer = libpymo.EncodingAnalyzerForPython(quant_scheme)
+
+**Step 2:** Compute scale and offset.
+
+.. code-block:: python
+
+    bitwidth = 8
+    is_symmetric, strict_symmetric, unsigned_symmetric = True, False, True
+    use_cuda = False
+    analyzer.updateStats(x, use_cuda)
+    encoding, _ = analyzer.computeEncoding(bitwidth, is_symmetric, strict_symmetric, unsigned_symmetric)
 
-Advanced installation instructions (optional)
-=============================================
+    print(f'Min: {encoding.min}, Max: {encoding.max}, Scale(delta): {encoding.delta}, Offset: {encoding.offset}')
 
-Following are two ways to setup, including prerequisites and dependencies.
+The encodings values should be similar to the one shown below.
 
-* :ref:`On your host machine <install-host>`
-* :ref:`Using our pre-built or locally built Docker images <install-docker>`
+.. rst-class:: script-output
 
-Installing an older version
-===========================
+    .. code-block:: none
 
-View the release notes for older versions at https://github.com/quic/aimet/releases. Follow the
-documentation corresponding to that release to select and install the appropriate AIMET package.
+        Min: -3.3734087606114667, Max: 3.3470540046691895, Scale(delta): 0.026354755942277083, Offset: -128.0
+
+**Step 3:** Perform quantize-dequantize.
+
+.. code-block:: python
+
+    quantizer = libpymo.TensorQuantizationSimForPython()
+    out = quantizer.quantizeDequantize(x,
+                                       encoding,
+                                       libpymo.RoundingMode.ROUND_NEAREST,
+                                       bitwidth,
+                                       use_cuda)
+    print(out)
+
+The quantized-dequantized output should be similar to the one shown below.
+
+.. rst-class:: script-output
+
+    .. code-block:: none
+
+        [-1.291383    0.36896658  1.0541903  -1.2123188  -2.2137995   1.2650282
+         -0.23719281  0.10541902  0.50074035 -0.05270951 -0.94877124  0.
+          0.10541902  0.52709514 -0.7115784   2.2401543  -0.34261182  2.0293162
+          0.34261182 -0.6061594  -0.36896658 -0.6588689  -1.5022211  -0.10541902
+         -1.4758663  -0.8433522   0.7115784  -0.23719281  0.44803086 -0.94877124
+          0.18448329 -1.0014807   0.55344987 -0.13177378  0.15812853 -0.7115784
+         -0.4216761   1.1068997  -0.07906426  1.6603496   0.55344987 -0.47438562
+         -0.6325141   0.4216761  -1.4495116   1.5549306  -0.6325141  -1.2123188
+          0.50074035  1.291383    0.07906426 -1.2123188  -2.0820258   1.0014807
+         -0.18448329 -0.4216761   1.0278355  -0.21083805  0.52709514  1.6867044
+         -0.68522364  1.0278355  -0.55344987 -0.26354757  0.10541902 -0.02635476
+          0.6588689  -0.34261182 -0.05270951  3.347054    0.07906426 -1.080545
+         -0.57980466  1.4231569  -0.6588689   1.291383   -0.13177378  0.31625706
+         -0.36896658  0.05270951 -0.81699747 -1.4231569  -1.1068997  -0.68522364
+          0.7115784  -1.2650282  -0.7115784   0.50074035  0.28990233 -0.73793316
+          0.21083805  2.4246376  -0.15812853  0.52709514 -0.02635476 -0.13177378
+         -1.8711877   0.4216761  -0.55344987 -0.76428795]
+
+Old versions
+------------
+
+You can also view the release notes for older AIMET versions at https://github.com/quic/aimet/releases.
+Follow the documentation corresponding to that release to select and install the appropriate AIMET package.
+
+.. _host-install-from-scratch:
+
+Host install from scratch
+=========================
+
+The :ref:`Host install from scratch <install-host>` page contains the procedure to prepare the environment
+and manually install and setup AIMET (including prerequisites and dependencies for all framework and
+variants) on a fresh Linux host machine. Use it if you experienced problems installing and/or
+using AIMET via any of the previous installation procedures.
+
+.. _docker-install:
+
+Docker install
+==============
+
+The :ref:`Docker install <install-docker>` page describes how to install AIMET in a Docker
+container using pre-built or locally built Docker images.
+
+.. _building-from-source:
 
 Building from source
 ====================
 
-To build the latest AIMET code from the source, see `build AIMET from source <https://github.com/quic/aimet/blob/develop/packaging/docker_install.md>`_.
+For most users, installing the pre-built AIMET package via the pip package manager offers the best
+experience. However, if you want to use the latest code or contribute to AIMET, you need to build it
+from source. To build the latest AIMET code from the source, see `build AIMET from source <https://github.com/quic/aimet/blob/develop/packaging/docker_install.md>`_.
+
+.. |torch_whl_suffix| replace:: \-cp310-none-any.whl
+.. |whl_suffix| replace:: \-cp310-cp310-manylinux_2_34_x86_64.whl
+.. |download_url| replace:: \https://github.com/quic/aimet/releases/download/
+.. |torch_pkg_url| replace:: \https://download.pytorch.org/whl/torch_stable.html