Quantization Accuracy Debugging
This document provides high level strategies for improving
quantization accuracy. If a quantized model has error compared to the
original model, we can categorize the error into:


data insensitive error - caused by intrinsic model quantization
   error, large portion of input data has large error


data sensitive error - caused by outlier input data, small
   portion of input data has large error


implementation error - quantized kernel is not matching
   reference implementation


Data insensitive error
General tips

For PTQ, ensure that the data you are calibrating with is
   representative of your dataset. For example, for a classification
   problem a general guideline is to have multiple samples in every
   category, and the overall number of samples should be at least 100.
   There is no penalty for calibrating with more data other than
   calibration time.
