Hi @supriyar @raghuramank100,
Referring to above mentioned issue I want to make it clear about for which layers i have done quantization.

Conv1d (from nightly)
LayerNorm (from nightly)
ReLU
Linear

additional layers:

quantstub / dequantstubs
QFunctional / FloatFunctional

All these layers are quantized and I fused Relu and Conv1d as well ( since beginning Im referring to this documentation Static Quantization with eager mode in pytorch 8
If I use FloatFunctional, I’m not using Quant/Dequantstubs in my model where arithmetic operations are triggered between quantized layers.
After successfully done quantizing, still my model CPU computation is not reduced instead computation has increased after quantization!
Could you tell me in which cases this might happen?
Thanks,
Aravind