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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
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