pytorch

6d4b93bd - [quant] adding memoryless observers for embeddingbag QAT work (#65699)

[quant] adding memoryless observers for embeddingbag QAT work (#65699) Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/65699 related to: https://github.com/pytorch/pytorch/pull/65443#discussion_r715132425 The QAT and PAT (pruning aware training) support for embedding bags needs a memoryless observer to work properly. This is necessitated by the changing pruned/non-pruned weights during training which can significantly change the quantization parameters. This PR adds a memoryless flag to the simpler observer classes (not moving average since those explicitly have memory) In addition to the above, I altered the reset_min_max_vals function for MinMaxObserver so that it would preserve the device of the existing self.min_val and self.max_val which was not preserved previously compared to how it is initialized (using factory_kwargs) Test Plan: python test/test_quantization.py TestObserver (added test_memoryless_minmaxobserver, test_memoryless_per_channel_minmaxobserver, test_memoryless_histogramobserver) Imported from OSS Reviewed By: supriyar Differential Revision: D31209773 fbshipit-source-id: 44a63298e44880fbd3576f49ac568e781f3fd79a