def test_transformer_cfg_registry():
assert len(transformer_cfg_reg.list_keys()) > 0
layout=layout)
inference_model = TransformerInference(model=model)
model.initialize()
if train_hybridize:
model.hybridize()
verify_nmt_model(model)
if inference_hybridize:
inference_model.hybridize()
verify_nmt_inference(train_model=model, inference_model=inference_model)
def test_transformer_cfg_registry():
assert len(transformer_cfg_reg.list_keys()) > 0
@pytest.mark.parametrize('cfg_key', transformer_cfg_reg.list_keys())
def test_transformer_cfg(cfg_key):
cfg = TransformerModel.get_cfg(cfg_key)
cfg.defrost()
cfg.MODEL.src_vocab_size = 32
cfg.MODEL.tgt_vocab_size = 32
cfg.freeze()
model = TransformerModel.from_cfg(cfg)
model.initialize()
model.hybridize()
cfg.defrost()
cfg.MODEL.layout = 'TN'
cfg.freeze()
model_tn = TransformerModel.from_cfg(cfg)
model_tn.share_parameters(model.collect_params())
model_tn.hybridize()
