pytorch

a89851a0 - [quant][fx][graphmode] Adding a new convert function that produces reference pattern by default (#66925)

[quant][fx][graphmode] Adding a new convert function that produces reference pattern by default (#66925) Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/66925 Current convert_fx implementation is using "The Interpreter Pattern" in https://pytorch.org/docs/stable/fx.html There are two things that's changed which make the approach in this PR possible and needed: 1). original convert implementation is developed at the initial prototype where fx does not allow mutations, now fx supports mutations 2). original convert needs to work for a lot of fbgemm/qnnpack specific logic, which is not needed for reference patterns Therefore it makes sense for us to write a new convert function just for reference patterns, the implementation is significantly easier to understand than the original convert implementation Current support: * we should be able to support all non-weighted ops like relu, add etc. Missing: * linear and conv * some advanced features like standalone modules, input_quantized_idxs etc. will add linear and conv support and start defining the backend_config_dict based on this version of convert Test Plan: python test/test_quantization.py TestQuantizeFxOpsNew Imported from OSS Reviewed By: vkuzo Differential Revision: D31786241 fbshipit-source-id: 2a32156eb6d3c5271cb44906cd863055785fb5d4