pytorch

c3f3cb5b - [quant][pt2e] Support conv bn fusion in convert step for QAT flow (#100442)

[quant][pt2e] Support conv bn fusion in convert step for QAT flow (#100442) Summary: This PR adds support for folding bn weights into conv for QAT flow, this is equivalent to the QAT branch of `from_float` in eager mode quantized conv module: https://github.com/pytorch/pytorch/blob/main/torch/ao/nn/quantized/modules/conv.py#L223 Items that needs followup: * there are some workaround I did because quantize_per_tensor is using float/int args and dynamo does not support these args, need to fix after we change the quantized model representation and also change these args to Tensor Test Plan: buck2 test @//mode/opt //caffe2/test:quantization_pt2e -- --exact 'caffe2/test:quantization_pt2e - test_convert_qat_conv_bn_fusion (quantization.pt2e.test_quantize_pt2e.TestQuantizePT2E)' Reviewed By: andrewor14 Differential Revision: D45344281 Pull Request resolved: https://github.com/pytorch/pytorch/pull/100442 Approved by: https://github.com/kimishpatel