masahi opened a new pull request #5839: URL: https://github.com/apache/incubator-tvm/pull/5839
This is a workaround for the issue reported in https://github.com/pytorch/pytorch/issues/39690 In short, if a quantized PyTorch model is serialized and loaded back, dtypes of output tensors are dropped and the loaded model doesn't have `QUInt8` types at all. This becomes a problem when converting some Torch ops. For example, below the output dtype of `quantize_per_tensor` becomes float (wrong), so `aten::adaptive_avg_pool2d` thinks this is a float operation. But obviously the output of `aten::quantize_per_tensor` should be a quantized tensor, so `aten::adaptive_avg_pool2d` has to be converted to the quantized version. The quantized resnet in torchvision uses `aten::adaptive_avg_pool2d`. So right now if we save and load back the qresnet, we get garbage result. ``` %input.1 : Tensor = aten::quantize_per_tensor(%X.1, %7, %8, %9) # /home/masa/anaconda3 ... %Xq.1 : Tensor = aten::adaptive_avg_pool2d(%input.1, %12) ``` please review @siju-samuel @anijain2305 ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
