calibration time.


If your model has Conv-BN or Linear-BN patterns, consider fusing
   them. If you are using FX graph mode quantization, this is done
   automatically by the workflow. If you are using Eager mode
   quantization, you can do this manually with the
   "torch.ao.quantization.fuse_modules" API.


Increase the precision of dtype of the problematic ops. Usually,
   fp32 will have the highest accuracy, followed by fp16, followed by
   dynamically quantized int8, followed by statically quantized int8.


Note: this is trading off performance for accuracy.


Note: availability of kernels per dtype per op can vary by
      backend.


Note: dtype conversions add an additional performance cost. For
      example, "fp32_op -> quant -> int8_op -> dequant -> fp32_op ->
      quant -> int8_op -> dequant" will have a performance penalty
      compared to "fp32_op -> fp32_op -> quant -> int8_op -> int8_op
      -> dequant" because of a higher number of required dtype
      conversions.

