from pytorch_pretrained_bert import BertForMaskedLM
    import torch

    def main(args):
        bert_model_origin = 
BertForMaskedLM.from_pretrained("bert-large-uncased")
        example_tensor = torch.randint(0, 100, (1, 256))
        model_int8 = torch.quantization.quantize_dynamic(bert_model_origin, 
quant_layers={torch.nn.Linear}, dtype=torch.qint8)
        model_int8.eval()
        trace_model = torch.jit.trace(model_int8, [example_tensor])
        trace_model.eval()
        shape_list = [(i.debugName().split('.')[0], i.type().sizes()) for i in 
list(trace_model.graph.inputs())[1:]]
        mod_bert, params_bert = 
tvm.relay.frontend.pytorch.from_pytorch(trace_model, shape_list)
        target = tvm.target.Target(target="llvm", host="llvm")
        with tvm.transform.PassContext(opt_level=3):
            lib = relay.build(mod_bert, target=target, params=params_bert)
            lib.export_library(os.path.realpath("net_int18_cpu.tar"))

see code above, when build pre-quantization bert-large masked lm model, it will 
a failure like this:
![image|690x233](upload://lSAlz1g5rkG9VbI3TOkfSZMdFaV.png)





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