IBert problems of quant_model=true

[CSuperlei]

Dear Editor,
My first step is to do full-precision finetuning, and I set quant_mode:true. And then I carry out the Integer-only finetuning. When I test the Integer-only finetuning model on the MRPC, the result is very bad. Could you give some guidance？(I test the MRPC sample, the result is tensor([[0.5003, 0.4997]], grad_fn=))

{
"_name_or_path": "/home/rram/storage/cailei/nlp_project/fine_tune/standard_ibert_weights/ibert-roberta-base",
"architectures": [
"IBertForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"eos_token_id": 2,
"finetuning_task": "mrpc",
"force_dequant": "none",
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "not_equivalent",
"1": "equivalent"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"equivalent": 1,
"not_equivalent": 0
},
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "ibert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"quant_mode": true,
"tokenizer_class": "RobertaTokenizer",
"torch_dtype": "int8",
"transformers_version": "4.12.0.dev0",
"type_vocab_size": 1,
"vocab_size": 50265
}

[huu4ontocord]

Same issue #21