Error in inference phase, after loading quantized model

After I used torch.quantization.quantize_dynamic() to quantize the original model, I saved and loaded the quantized model. But, when I ran inference, it returned this error. The original model still ran inference well, I don’t know why

Traceback (most recent call last):
  File "", line 81, in <module>
    output = infer(args.text, model)
  File "", line 30, in infer
    mel_outputs, mel_outputs_postnet, _, alignments = model.inference(sequence)
  File "/media/tma/DATA/Khai-folder/Tacotron2-PyTorch/model/", line 542, in inference
    encoder_outputs = self.encoder.inference(embedded_inputs)
  File "/media/tma/DATA/Khai-folder/Tacotron2-PyTorch/model/", line 219, in inference
  File "/media/tma/DATA/miniconda3/envs/ttsv/lib/python3.6/site-packages/torch/nn/modules/", line 576, in __get
    type(self).__name__, name))
AttributeError: 'LSTM' object has no attribute 'flatten_parameters'

Which pytorch version are you using? I think this is a known problem and should go away with 1.5 release.
Could you wait for that or re-try with the nightly build and see if this issue goes away?

I’m using pytorch version 1.4.0
Hope next release will fix this issue.

@khaidoan25, I am facing the same issue with Pytorch version - 1.5.1 . Have you been able to solve it?

Actually, when you load your quantized model, you need to quantize your initial model first.

quantized_model = torch.quantization.quantize_dynamic(
    model, {nn.LSTM, nn.Linear}, dtype=torch.qint8
)         // Do s.t like this first before loading your quantized model