[OpenVINO Quantizer] Update OpenVINO Quantizer Tutorial#3889
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/tutorials/3889
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anzr299
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May 11, 2026
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| * ``target_device`` - defines the target device, the specificity of which will be taken into account during optimization. The following values are supported: ``ANY`` (default), ``CPU``, ``CPU_SPR``, ``GPU``, and ``NPU``. | ||
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| .. code-block:: python | ||
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| OpenVINOQuantizer(target_device=nncf.TargetDevice.CPU) | ||
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@daniil-lyakhov I couldn't find the target_device being used inside of openvino quantizer.
| * ``mode`` - defines quantization scheme for the model. Multiple modes are supported: | ||
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| * ``PERFORMANCE`` (default) - defines symmetric quantization of weights and activations | ||
| * ``INT8_SYM`` (default) - defines symmetric quantization of weights and activations. This is the best for performance |
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Why leave weight compression behind? Can we extend the example with WC?
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In the optional part? I agree I will add it there.
Also, maybe we can change the link which points to some example for PTQ in executorch like yolo instead of nncf resnet example. What do you think?
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Description
Recently the Openvino quantizer was moved from nncf -> executorch. This would break the imports mentioned in this tutorial.
This PR fixes the imports to use executorch.
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