@@ -37,13 +37,13 @@ this:
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| convert_pt2e |
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—--------------------------------------------------------
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|
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- Reference Quantized Model
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+ Quantized Model
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|
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—--------------------------------------------------------
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| Lowering |
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—--------------------------------------------------------
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|
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- Executorch, or Inductor, or <Other Backends>
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+ Executorch, Inductor or <Other Backends>
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The PyTorch 2.0 export quantization API looks like this:
@@ -375,15 +375,15 @@ The following code snippets describes how to quantize the model:
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get_symmetric_quantization_config,
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)
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quantizer = XNNPACKQuantizer()
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- quantizer.set_globa (get_symmetric_quantization_config())
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+ quantizer.set_global (get_symmetric_quantization_config())
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``Quantizer `` is backend specific, and each ``Quantizer `` will provide their
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own way to allow users to configure their model. Just as an example, here is
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the different configuration APIs supported by ``XNNPackQuantizer ``:
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.. code-block :: python
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- quantizer.set_global(qconfig_opt) # qconfig_opt is an optional qconfig, either a valid qconfig or None
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+ quantizer.set_global(qconfig_opt) # qconfig_opt is an optional quantization config
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.set_object_type(torch.nn.Conv2d, qconfig_opt) # can be a module type
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.set_object_type(torch.nn.functional.linear, qconfig_opt) # or torch functional op
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.set_module_name(" foo.bar" , qconfig_opt)
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