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[OpenVINOQuantizer] Minor improvements #2581

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[OpenVINOQuantizer] Minor improvements #2581

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11 changes: 7 additions & 4 deletions docs/source/tutorials_source/pt2e_quant_openvino_inductor.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -74,7 +74,7 @@ OpenVINO and NNCF could be easily installed via `pip distribution <https://docs.
.. code-block:: bash

pip install -U pip
pip install openvino, nncf
pip install openvino nncf


1. Capture FX Graph
Expand All @@ -84,7 +84,6 @@ We will start by performing the necessary imports, capturing the FX Graph from t

.. code-block:: python

import copy
import openvino.torch
import torch
import torchvision.models as models
Expand All @@ -106,7 +105,7 @@ We will start by performing the necessary imports, capturing the FX Graph from t
example_inputs = (x,)

# Capture the FX Graph to be quantized
with torch.no_grad(), nncf.torch.disable_patching():
with torch.no_grad():
exported_model = torch.export.export(model, example_inputs).module()


Expand Down Expand Up @@ -204,7 +203,7 @@ After that the FX Graph can utilize OpenVINO optimizations using `torch.compile(

.. code-block:: python

with torch.no_grad(), nncf.torch.disable_patching():
with torch.no_grad():
optimized_model = torch.compile(quantized_model, backend="openvino")

# Running some benchmark
Expand Down Expand Up @@ -235,6 +234,10 @@ These advanced NNCF algorithms can be accessed via the NNCF `quantize_pt2e` API:


calibration_dataset = nncf.Dataset(calibration_loader, transform_fn)

with torch.no_grad():
exported_model = torch.export.export(model, example_inputs).module()

quantized_model = quantize_pt2e(
exported_model, quantizer, calibration_dataset, smooth_quant=True, fast_bias_correction=False
)
Expand Down