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Co-authored-by: Alexander Suslov <[email protected]>
Co-authored-by: Yamini Nimmagadda <[email protected]>

Author: 3 people

prototype_source/openvino_quantizer.rst

@@ -1,24 +1,23 @@
-PyTorch 2 Export Quantization with OpenVINO backend
+PyTorch 2 Export Quantization for OpenVINO torch.compile backend.
 ===========================================================================
 
 **Author**: dlyakhov, asuslov, aamir, # TODO: add required authors
 
 Introduction
 --------------
 
-This tutorial introduces the steps for utilizing the `Neural Network Compression Framework (nncf) <https://github.com/openvinotoolkit/nncf/tree/develop>`_ to generate a quantized model customized
-for the `OpenVINO torch.compile backend <https://docs.openvino.ai/2024/openvino-workflow/torch-compile.html>`_ and explains how to lower the quantized model into the `OpenVINO <https://docs.openvino.ai/2024/index.html>`_ representation.
+This tutorial demonstrates how to use `OpenVINOQuantizer` from `Neural Network Compression Framework (NNCF) <https://github.com/openvinotoolkit/nncf/tree/develop>`_ in PyTorch 2 Export Quantization flow to generate a quantized model customized for the `OpenVINO torch.compile backend <https://docs.openvino.ai/2024/openvino-workflow/torch-compile.html>`_ and explains how to lower the quantized model into the `OpenVINO <https://docs.openvino.ai/2024/index.html>`_ representation.
 
 The pytorch 2 export quantization flow uses the torch.export to capture the model into a graph and performs quantization transformations on top of the ATen graph.
 This approach is expected to have significantly higher model coverage, better programmability, and a simplified UX.
-OpenVINO is the new backend that compiles the FX Graph generated by TorchDynamo into an optimized OpenVINO model.
+OpenVINO backend compiles the FX Graph generated by TorchDynamo into an optimized OpenVINO model.
 
 The quantization flow mainly includes four steps:
 
 - Step 1: Install OpenVINO and NNCF.
 - Step 2: Capture the FX Graph from the eager Model based on the `torch export mechanism <https://pytorch.org/docs/main/export.html>`_.
-- Step 3: Apply the Quantization flow based on the captured FX Graph.
-- Step 4: Lower the quantized model into OpenVINO representation with the API ``torch.compile``.
+- Step 3: Apply the PyTorch 2 Export Quantization flow with OpenVINOQuantizer based on the captured FX Graph.
+- Step 4: Lower the quantized model into OpenVINO representation with the API `torch.compile <https://docs.openvino.ai/2024/openvino-workflow/torch-compile.html>`_.
 
 The high-level architecture of this flow could look like this:
 
@@ -80,7 +79,6 @@ We will start by performing the necessary imports, capturing the FX Graph from t
     import torchvision.models as models
     from torch.ao.quantization.quantize_pt2e import convert_pt2e
     from torch.ao.quantization.quantize_pt2e import prepare_pt2e
-    from torch.ao.quantization.quantizer.openvino_quantizer import OpenVINOQuantizer
 
     import nncf
     from nncf.torch import disable_patching
@@ -111,6 +109,8 @@ After we capture the FX Module to be quantized, we will import the OpenVINOQuant
 
 .. code-block:: python
 
+    from nncf.experimental.torch.fx.quantization.quantizer.openvino_quantizer import OpenVINOQuantizer
+
     quantizer = OpenVINOQuantizer()
 
 ``OpenVINOQuantizer`` has several optional parameters that allow tuning the quantization process to get a more accurate model.
@@ -208,4 +208,4 @@ Conclusion
 ------------
 
 With this tutorial, we introduce how to use torch.compile with the OpenVINO backend and the OpenVINO quantizer.
-For further information, please visit `OpenVINO deploymet via torch.compile documentation <https://docs.openvino.ai/2024/openvino-workflow/torch-compile.html>`_.
+For further information, please visit `OpenVINO deployment via torch.compile documentation <https://docs.openvino.ai/2024/openvino-workflow/torch-compile.html>`_.