Hi @jerryzh168,
Now that v1.6 is out, I came back to this issue. As I mentioned,  state_dict() method on traced quantized networks like qresnet from torchvision no longer returns all quantized parameters.
After some digging and thanks to the onnx export implementation below, I found that I can use torch._C._jit_pass_lower_graph(graph, model._c) to get at quantized paremeters I’ve been looking for. Is this the recommend way for third party pkg like TVM to get quantized parameters? Having to pass model._c seems like a very internal API…

  
      github.com
  
  
    pytorch/pytorch/blob/master/torch/onnx/utils.py#L357 1


if isinstance(example_outputs, torch.Tensor):
    example_outputs = [example_outputs]

torch_out = None
if isinstance(model, torch.jit.ScriptModule):
    assert example_outputs is not None, "example_outputs must be provided when exporting a ScriptModule"
    try:
        graph = model.forward.graph
        torch._C._jit_pass_onnx_function_substitution(graph)
        method_graph, params = torch._C._jit_pass_lower_graph(graph, model._c)
        in_vars, in_desc = torch.jit._flatten(tuple(args) + tuple(params))
        graph = _propagate_and_assign_input_shapes(
            method_graph, tuple(in_vars), False, propagate)
    except AttributeError:
        raise RuntimeError('\'forward\' method must be a script method')
elif isinstance(model, torch.jit.ScriptFunction):
    assert example_outputs is not None, "example_outputs must be provided when exporting a TorchScript ScriptFunction"
    method = model
    params = ()
    in_vars, in_desc = torch.jit._flatten(tuple(args))