I am trying to quantize the model of image network. and I faced the error.
For the call the function below
dummy_input = torch.randn(1, 3, 320, 320).cpu()
script_model = torch.jit.trace(net, dummy_input)
the tensor operation of sum shows the Exception
norm = x.sum(dim=1, keepdim=True).sqrt()
RuntimeError: Could not run ‘aten::empty.memory_format’ with arguments from the ‘QuantizedCPU’ backend. ‘aten::empty.memory_format’ is only available for these backends: [CPU, CUDA, MkldnnCPU, SparseCPU, SparseCUDA, BackendSelect, Autograd, Profiler, Tracer].
Is there any way bypassing this error?
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jerryzh168
October 8, 2020, 10:10pm
2
it’s because quantized::sum is not supported, can you put dequant/quant around the sum op?
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hi, how to put dequant/quant around the sum op?? sorry,i am new
There is an example here: https://pytorch.org/docs/stable/quantization.html - if you search that page for torch.quantization.QuantStub() and torch.quantization.DeQuantStub(), that should help.
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escorciav
February 20, 2023, 2:09pm
6
I did the dequant/quant biz, and it works (as it stopped complaining).
However, I’m not sure that’s the proper way. I believe that the PTQ tutorial (InvertedResiual, sum) has the “proper” way of doing it. Let me know
@ptrblck do you know about quantization?
jcaip
February 22, 2023, 4:33pm
7
Hi Victor,
Can you link the tutorial that you are referring to?
escorciav
February 22, 2023, 5:31pm
8
Here you go
https://pytorch.org/tutorials/advanced/static_quantization_tutorial.html#post-training-static-quantization
Out of curiosity what kind of model are you trying to quantize?
jcaip
February 22, 2023, 6:28pm
9
I am the current pytorch quantization oncall
torch.ao.quantization.convert will add in Quant/Dequant pairs for you, but not all ops are supported for quantized inputs, hence the need to manually add in dequant to run your op in fp32.
Also, if you have additional questions it would be better if you created a new question.
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