def test_bert_get_pretrained(model_name, ctx):
assert len(list_pretrained_bert()) > 0
with tempfile.TemporaryDirectory() as root, ctx:
cfg, tokenizer, backbone_params_path, mlm_params_path =\
get_pretrained_bert(model_name, load_backbone=True, load_mlm=True, root=root)
assert cfg.MODEL.vocab_size == len(tokenizer.vocab)
bert_model = BertModel.from_cfg(cfg)
bert_model.load_parameters(backbone_params_path)
bert_mlm_model = BertForMLM(cfg)
if mlm_params_path is not None:
bert_mlm_model.load_parameters(mlm_params_path)
bert_mlm_model = BertForMLM(cfg)
bert_mlm_model.backbone_model.load_parameters(backbone_params_path)
def test_list_pretrained_bert():
assert len(list_pretrained_bert()) > 0
assert_allclose(contextual_embedding.asnumpy(),
mx.np.swapaxes(contextual_embedding_tn, 0, 1).asnumpy(),
1E-3, 1E-3)
assert_allclose(pooled_out.asnumpy(), pooled_out_tn.asnumpy(), 1E-3, 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 BertModel FP16
device_type = ctx.device_type
if device_type == 'gpu':
verify_backbone_fp16(model_cls=BertModel, cfg=cfg, ctx=ctx,
inputs=[inputs, token_types, valid_length])
@pytest.mark.slow
@pytest.mark.remote_required
@pytest.mark.parametrize('model_name', list_pretrained_bert())
def test_bert_get_pretrained(model_name, ctx):
assert len(list_pretrained_bert()) > 0
with tempfile.TemporaryDirectory() as root, ctx:
cfg, tokenizer, backbone_params_path, mlm_params_path =\
get_pretrained_bert(model_name, load_backbone=True, load_mlm=True, root=root)
assert cfg.MODEL.vocab_size == len(tokenizer.vocab)
bert_model = BertModel.from_cfg(cfg)
bert_model.load_parameters(backbone_params_path)
bert_mlm_model = BertForMLM(cfg)
if mlm_params_path is not None:
bert_mlm_model.load_parameters(mlm_params_path)
bert_mlm_model = BertForMLM(cfg)
bert_mlm_model.backbone_model.load_parameters(backbone_params_path)
