Bootstrap examples for PyTorch in quick start (#3655)

Adding bootstrap example

Signed-off-by: Priyanka Dangi <quic_pdangi@quicinc.com>

.pylintrc

@@ -14,6 +14,7 @@ ignore=CVS,
        apply_bn.py,
        create_quantizationsimmodel.py,
        evaluate.py,
+       installation_verification.py,       
        export.py,
        pass_calibration_data.py,
        prepare_model.py

Docs/beta/install/quick-start.rst

@@ -34,12 +34,87 @@ Type the following command to install AIMET for PyTorch framework using pip pack
 
     python3 -m pip install "aimet-torch>=2"
 
-Next steps
+Verification
 ==========
 
-See `Simple example` to test your installation.
+Type the following command to ensure AIMET is installed via pip. 
+
+.. code-block:: bash
+
+    python3 -m pip show aimet-torch
+
+If installed properly, this command will produce no warnings and display information about the package. 
+
+
+Let's run some sample PyTorch code to confirm that we can create QuantSim and perform calibration:
+
+**Step 1**: Let's handle imports and other setup.   
+
+.. literalinclude:: ../snippets/torch/installation_verification.py
+            :language: python
+            :start-after: [step_1]
+            :end-before: [step_2]
+
+**Step 2**: We will create QuantSim and ensure the model contains quantization ops. 
+
+.. literalinclude:: ../snippets/torch/installation_verification.py
+            :language: python
+            :start-after: [step_2]
+            :end-before: [step_3]
+
+The model should be composed of Quantized nn.Modules, similar to the output shown below:
+
+:: 
+
+  MobileNetV2(
+  (features): Sequential(
+    (0): Conv2dNormActivation(
+      (0): QuantizedConv2d(
+        3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False
+        (param_quantizers): ModuleDict(
+          (weight): QuantizeDequantize(shape=(32, 1, 1, 1), qmin=-128, qmax=127, symmetric=True)
+        )
+        (input_quantizers): ModuleList(
+          (0): QuantizeDequantize(shape=(), qmin=0, qmax=65535, symmetric=False)
+        )
+        (output_quantizers): ModuleList(
+          (0): None
+        )
+      )
+    )
+    ...
+  )
+
+
+**Step 3**: We perform calibration. As a proof of concept, random input is being passed in. However, dataloaders are commonly used in real world cases.  
+
+.. literalinclude:: ../snippets/torch/installation_verification.py
+            :language: python
+            :start-after: [step_3]
+            :end-before: [step_4]
+
+**Step 4**: We perform evaluation.  
+
+.. literalinclude:: ../snippets/torch/installation_verification.py
+            :language: python
+            :start-after: [step_4]
+
+The output generated should be of type DequantizedTensor and similar to the one shown below. 
+
+:: 
+
+  DequantizedTensor([[-1.7466,  0.8405,  1.8606,  ..., -0.9714,  0.8366, 2.2363],
+                   [-1.6091,  1.0449,  1.7788,  ..., -0.9904,  1.0861, 2.2431],
+                   [-1.5307,  0.8442,  1.5157,  ..., -0.7793,  0.6327, 2.3861],
+                   ...,
+                   [-1.3610,  1.4499,  2.2068,  ..., -0.8188,  1.1155, 2.5962],
+                   [-1.1619,  1.2217,  2.1050,  ..., -0.5301,  0.9150, 2.1458],
+                   [-1.6340,  0.9826,  2.2459,  ..., -1.0769,  0.9054, 2.2315]], 
+                   device='cuda:0', grad_fn=<AliasBackward0>)
+
 
 See the :ref:`User guide <opt-guide-index>` to read about the model optimization workflow.
 
 See the :ref:`Examples <examples-index>` to try AIMET quantization techniques on your pre-trained models.
 
+

Docs/beta/snippets/torch/installation_verification.py

@@ -0,0 +1,66 @@
+# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2024, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+# pylint: disable=missing-docstring
+[step_1]
+import torch
+from torchvision.models import mobilenet_v2
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+model = mobilenet_v2(weights='DEFAULT').eval().to(device)
+dummy_input = torch.randn((10, 3, 224, 224), device=device)
+[step_2]
+from aimet_torch.v2.quantsim import QuantizationSimModel
+from aimet_common.defs import QuantScheme
+from aimet_common.quantsim_config.utils import get_path_for_per_channel_config
+
+sim = QuantizationSimModel(model, 
+    dummy_input, 
+    quant_scheme=QuantScheme.training_range_learning_with_tf_init, 
+    config_file=get_path_for_per_channel_config(), 
+    default_param_bw=8, 
+    default_output_bw=16)
+
+print(sim)
+[step_3]
+def forward_pass(model):
+    with torch.no_grad():
+        model(torch.randn((10, 3, 224, 224), device=device))
+
+sim.compute_encodings(forward_pass)
+[step_4]
+output = sim.model(dummy_input)
+print(output)