quantization error. For these layers, consider leaving them in
   floating point or adjusting the observer settings to choose a
   better scale and zero_point.
Implementation error
If you are using PyTorch quantization with your own backend you may
see differences between the reference implementation of an operation
(such as "dequant -> op_fp32 -> quant") and the quantized
implementation (such as op_int8) of the op on the target hardware.
This could mean one of two things:


the differences (usually small) are expected due to specific
   behavior of the target kernel on the target hardware compared to
   fp32/cpu. An example of this is accumulating in an integer dtype.
   Unless the kernel guarantees bitwise equivalency with the reference
   implementation, this is expected.


the kernel on the target hardware has an accuracy issue. In this
   case, reach out to the kernel developer.


Numerical Debugging Tooling (prototype)
Warning: