Draft: Dev ptq

TODO

• Normalize layers
  • Get weight max value for each layer (Fc/Conv)-> alpha
  • Divide weights by alpha
  • Divide bias by alpha
  • Add PerOutputChannel normalization
    • TODO : add steps to do this ...
• Normalize activation
  • Normalize stimuli between [0;1]
    • or [-1,1]
  • Forward on validation dataset
  • Get max value of activation for each layer -> beta
    • Develop hook sytsem
    • Develop get max activation hook
  • Add scaling factor beta to the activation
  • Add scaling factor beta to the bias
  • Rescale activation by parent bias scaling
• Quantization
  • Input: multiply by (2^n-1)-1 (singed) or (2^n-1) (unsigned)
  • Weights: Multiply by (2^n-1)-1 + round + store as a signed integer of nbbits size
  • Biases: Multiply by (2^n-1)-1 + Multiply by (2^n-1)-1 (singed) or (2^n-1) (unsigned) + store as a signed integer of nbbits size
  • Activation scaling:
    • Input scaling: (2^n-1)-1 + Multiply by (2^n-1)-1 (singed) or (2^n-1) (unsigned)
    • Output scaling: (2^n-1)-1 (singed) or (2^n-1) (unsigned)
    • Activation sclaing : divide by (input scaling divided bu output_scaling)
• Activaiton clipping
  • generate histogram of outputs for each layer (using validation dataset)
  • MSE
    • TODO : add steps to do this
  • KL-Divergence
    • TODO : add steps to do this
• Weights clipping
  • TODO : add steps to do this
• Better scaling operation
  • Fixed-point scaling
  • Single shift scaling
  • Double shift scaling
• Bind method in Python
• LeNet integration test
• Refactor OutputRange hook to use Tensor Getter/Setter
• Add docstring to OutputRange hook
• Refactor hook system to not use registrar ?

python_binding/pybind_QuantPTQ.cpp

@@ -15,7 +15,7 @@
 #include <string>
 
 #include "aidge/QuantPTQ.hpp"
-#include "aidge/hook/hook.hpp"
+#include "aidge/hook/Hook.hpp"
 
 namespace py = pybind11;