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Greetings.
I am dealing with a resnet_v2-7.onnx and I expect:
after applying adaround Alg. on all possible Conv/GeMM, this onnx will result in
all non-fused BatchNorm nodes set as fp16,
every Conv/GeMM set as W8A8/W4A8, (int4/8 here, not fp8 of course)
accuracy drop < 3%
Typically, I might utilize a MixPrecisionSimModel class , and reset dtype && bitwidth manually.
Is there an example to achieve above functions?
For aimet-onnx, QuantizationSimModel enables me manually set different int-width for given nodes, only if aimet_onnx/quantsim.py and other necessary files properly revised.
However, any suggestions for fp16 case? aimet-torch verifies a MixedPrecisionConfigurator class, not sure if same concept has already been implemented for aimet-onnx.
The text was updated successfully, but these errors were encountered:
Greetings.
I am dealing with a resnet_v2-7.onnx and I expect:
after applying adaround Alg. on all possible Conv/GeMM, this onnx will result in
Typically, I might utilize a MixPrecisionSimModel class , and reset dtype && bitwidth manually.
Is there an example to achieve above functions?
For aimet-onnx, QuantizationSimModel enables me manually set different int-width for given nodes, only if aimet_onnx/quantsim.py and other necessary files properly revised.
However, any suggestions for fp16 case? aimet-torch verifies a MixedPrecisionConfigurator class, not sure if same concept has already been implemented for aimet-onnx.
The text was updated successfully, but these errors were encountered: