def test_list_pretrained_mobilebert(): assert len(list_pretrained_mobilebert()) > 0
1E-3) assert_allclose(nsp_score.asnumpy(), nsp_score_tn.asnumpy(), 1E-3, 1E-3) assert_allclose(mlm_score.asnumpy(), mlm_score_tn.asnumpy(), 1E-3, 1E-3) # Test for fp16 if ctx.device_type == 'gpu': pytest.skip('MobileBERT will have nan values in FP16 mode.') verify_backbone_fp16(model_cls=MobileBertModel, cfg=cfg, ctx=ctx, inputs=[inputs, token_types, valid_length]) @pytest.mark.remote_required @pytest.mark.parametrize('model_name', list_pretrained_mobilebert()) def test_mobilebert_get_pretrained(model_name): with tempfile.TemporaryDirectory() as root: cfg, tokenizer, backbone_params_path, mlm_params_path =\ get_pretrained_mobilebert(model_name, load_backbone=True, load_mlm=True, root=root) assert cfg.MODEL.vocab_size == len(tokenizer.vocab) mobilebert_model = MobileBertModel.from_cfg(cfg) mobilebert_model.load_parameters(backbone_params_path) mobilebert_pretain_model = MobileBertForPretrain(cfg) if mlm_params_path is not None: mobilebert_pretain_model.load_parameters(mlm_params_path) mobilebert_pretain_model = MobileBertForPretrain(cfg) mobilebert_pretain_model.backbone_model.load_parameters( backbone_params_path)