Commits on Jan 20, 2021

1. fake_quant: add a more memory efficient version …

   Summary:
   
   Not for review yet, a bunch of TODOs need finalizing.
   
   tl;dr; add an alternative implementation of `fake_quantize` which saves
   a ask during the forward pass and uses it to calculate the backward.
   
   There are two benefits:
   
   1. the backward function no longer needs the input Tensor, and it can be
   gc'ed earlier by autograd.  On MobileNetV2, this reduces QAT overhead
   by ~15% (TODO: link, and absolute numbers).  We add an additional mask Tensor
   to pass around, but its size is 4x smaller than the input tensor. A
   future optimization would be to pack the mask bitwise and unpack in the
   backward.
   
   2. the computation of `qval` can be done only once in the forward and
   reused in the backward. No perf change observed, TODO verify with better
   matrics.
   
   TODO: describe in more detail
   
   Test Plan:
   
   OSS / torchvision / MobileNetV2
   ```
   python references/classification/train_quantization.py
     --print-freq 1
     --data-path /data/local/packages/ai-group.imagenet-256-smallest-side/prod/
     --output-dir ~/nfs/pytorch_vision_tests/
     --backend qnnpack
     --epochs 5
   TODO paste results here
   ```
   
   TODO more
   
   Reviewers:
   
   Subscribers:
   
   Tasks:
   
   Tags:
   
   ghstack-source-id: f932055ee57b6a4e419d3896fb605c58fc063668
   Pull Request resolved: #50561

   

   vkuzo committed Jan 20, 2021
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