Hi Tao, The APIs proposed: "convert_model" and "convert_block" are mainly for inference use cases, where customers bring a FP32 model to convert it to a mixed precision model to get improved performance while not losing out on the accuracy. The PR: https://github.com/apache/incubator-mxnet/pull/14173 is supposed to handle the training use cases and this proposal doesn't cover the AMP feature added in the PR. I think ptrendx@ and canoerst@ are better equipped to answer questions 1 and 2.
> - more generally, what will be saved when users want to serialize their model to disk? Lets say users want to save converted mixed precision model used for inference to disk. It will save both, the symbol with the amp_cast and amp_multicast operators and the params (which are casted if necessary). Anirudh On Mon, Apr 29, 2019 at 6:55 AM Lv, Tao A <[email protected]> wrote: > Thank you for sharing this, Anirudh. > > Curious to know: > - what will be saved in a training checkpoint or snapshot? Can it be > resumed on another platform which might not support the lower precision the > previous one used? > - what will be saved in the final symbol.json and params file when > training is finished? > - more generally, what will be saved when users want to serialize their > model to disk? > > Thank you, > -tao > > -----Original Message----- > From: Anirudh Subramanian [mailto:[email protected]] > Sent: Monday, April 29, 2019 7:00 PM > To: [email protected] > Subject: Proposal for Conversion from FP32 to Mixed Precision Models > > Hi all, > > I have created a doc for conversion from FP32 to Mixed Precision Models: > > https://cwiki.apache.org/confluence/display/MXNET/Conversion+from+FP32+to+Mixed+Precision+Models > > I look forward to your feedback on the same. > > Thanks, > Anirudh >
