[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
