Hi @supriyar,

z = self.dequant_tcnet(z)
w = self.dequant_tcnet(w)
v = self.dequant_tcnet(v)
x = self.dequant_tcnet(x)
z = z + w
x = x + v
x = self.quant_tcnet2(x)



Can we perform “addition” on 2 quantized tensor variables? without dequantizing them in any other alternative.

For this i tried QFunctional & FloatFunctional but output is not up to the mark by using this. where as placing quantstubs works good.
I have some concern here. My float model which takes 0.2 - 0.3 second (~300ms) to process single input whereas after i quantizing my model with Int8 Quantization the time taken is increased from 0.2-0.3(float precision) to 0.4 to 0.5(Int8).
Here i show you the exact float model block and quantized model block

  ***** float block ******
   *****Round 1********
    y = self.conv1x12(x)
    y = self.prelu2(y)
    y = self.norm2(y)
    w = self.depthwise_conv12(y)
    #w = w[:,:,:-2*2**2]
    w = w[:,:,:-8]
    y = self.depthwise_conv2(y)
    y = y[:,:,:-8]
    y = self.prelu22(y)
    y = self.norm22(y)
    v = self.pointwise_conv2(y)
    z = z + w
    x = x + v


This is float model block. This block/computation will be repeated 13 more times (total 14 blocks). This is taking 0.2 - 0.3 seconds

Quantized block
round 1**
y = self.conv1x12(x)
y = self.prelu2(y)
y = self.norm2(y)
w = self.depthwise_conv12(y)
w = w[:,:,:-8]
y = self.depthwise_conv2(y)
y = y[:,:,:-8]
y = self.prelu22(y)
y = self.norm22(y)
v = self.pointwise_conv2(y)
w = self.dequant_tcnet(w)
z = self.dequant_tcnet(z)
v = self.dequant_tcnet(v)
x = self.dequant_tcnet(x)
z = z + w
x = x + v
x = self.quant_tcnet3(x)

This is quantized block model where is placed quantstubs for those arthematic operations & remaining all layers are quantized. This quantized model is taking 0.4 - 0.5 seconds
So after quantizing my model, the size of model is optimized but computation time is not optimized. Could you tell me is there any flaw? I crosschecked and the output is also good but computation is not reduced
Thanks @supriyar,
Aravind.