Ray Zhang created MXNET-688:
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Summary: Fix quantization divide by zero errors
Key: MXNET-688
URL: https://issues.apache.org/jira/browse/MXNET-688
Project: Apache MXNet
Issue Type: Bug
Reporter: Ray Zhang
The current quantization strategy for `calib_mode='entropy'` is to calculate
the KL divergence for different thresholds and choose the best threshold. This
assumes that the random variable is nonzero for all reals and is a continuous
random variable. Because we are discretizing the distribution, we smooth the
distribution over the range `[-threshold, threshold]`. What we are not
considering is that the entire sampled distribution may be not in the range
`[-threshold, threshold]` and thus we end up with all zeros in the sampled
candidate `p` distribution inside of `_get_optimal_threshold`.
I have added a check that the distribution(possibly unnormalized) is proper
before attempting to smooth or else we'll run into a divide by 0 error.
In most cases, activation functions and layers for classification type problems
output numbers symmetric around 0. This is not the case for a regressor's last
layer, and there are various other examples where the activation distribution
is not around 0, and this was a major blockage for airbnb's adoption into
mxnet's quantization capabilities.
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