Incorporate feedback for install pages

Docs/install/index.rst

@@ -4,11 +4,35 @@
 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. The following prerequisites apply
 to all frameworks variants.
@@ -29,7 +53,7 @@ to all frameworks variants.
     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.
 
@@ -39,6 +63,9 @@ 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:
@@ -88,26 +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|
 
-.. |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
+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 set up, 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 a locally built Docker image <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 instructions in the
-documentation for your selected release to install the AIMET package for that release.
+        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

Docs/install/quick-start.rst

@@ -1,8 +1,8 @@
 .. _install-quick-start:
 
-###########
-Quick Start
-###########
+#####################
+Quick Start (PyTorch)
+#####################
 
 This page describes how to quickly install the latest version of AIMET for the PyTorch framework.
 
@@ -13,10 +13,10 @@ For all the framework variants and compute platforms, see :ref:`Installation <in
 Tested platform
 ===============
 
-AIMET PyTorch 2.0 has been tested using the following recommended host platform configuration:
+aimet_torch 2 has been tested using the following host platform configuration:
 
 * 64-bit Intel x86-compatible processor
-* Python 3.8 – 3.10
+* Python 3.10
 * Ubuntu 22.04
 * For GPU variants:
     * Nvidia GPU card (Compute capability 5.2 or later)
@@ -25,7 +25,10 @@ AIMET PyTorch 2.0 has been tested using the following recommended host platform
 Installing AIMET
 ================
 
-AIMET PyTorch 2.0 should run on any platform that supports PyTorch using Python 3.8 or later. See :ref:`above <install-quick-start-platform>` for information about tested platforms.
+.. note::
+    aimet_torch 2 should run on any platform that supports PyTorch using Python 3.8 or later.
+
+    See :ref:`tested platform <install-quick-start-platform>` for information about tested host platform configuration.
 
 Type the following command to install AIMET for the PyTorch framework using the pip package manager.
 
@@ -46,7 +49,7 @@ To confirm that AIMET PyTorch is installed correctly, do the following.
     x = torch.randn(100)
 
 
-**Step 2:** Verify AIMET PyTorch by instantiating an 8-bit symmetric affine quantizer.
+**Step 2:** Verify AIMET PyTorch by instantiating an 8-bit symmetric quantizer.
 
 .. code-block:: python
 
@@ -56,7 +59,7 @@ To confirm that AIMET PyTorch is installed correctly, do the following.
     out = Q.affine.quantize(x, scale, offset, qmin=-128, qmax=127)
     print(out)
 
-The quantized output should be something similar to the one shown below.
+The quantized output should be similar to the one shown below.
 
 .. rst-class:: script-output