def test_load_adapter_fusion(self):
for adater_fusion_config_name, adapter_fusion_config in ADAPTERFUSION_CONFIG_MAP.items():
model1 = AutoModel.from_config(self.config())
model1.eval()
with self.subTest(model_class=model1.__class__.__name__):
name1 = "name1"
name2 = "name2"
model1.add_adapter(name1)
model1.add_adapter(name2)
model2 = copy.deepcopy(model1)
model2.eval()
model1.add_fusion([name1, name2], adater_fusion_config_name)
with tempfile.TemporaryDirectory() as temp_dir:
model1.save_adapter_fusion(temp_dir, ",".join([name1, name2]))
model2.load_adapter_fusion(temp_dir)
# check if adapter was correctly loaded
self.assertTrue(model1.config.adapter_fusion_models == model2.config.adapter_fusion_models)
# check equal output
in_data = self.get_input_samples((1, 128), config=model1.config)
model1.set_active_adapters([[name1, name2]])
model2.set_active_adapters([[name1, name2]])
output1 = model1(in_data)
output2 = model2(in_data)
self.assertEqual(len(output1), len(output2))
self.assertTrue(torch.equal(output1[0], output2[0]))
def test_add_adapter_fusion(self):
config_name = "pfeiffer"
adapter_config = AdapterConfig.load(config_name)
for adater_fusion_config_name, adapter_fusion_config in ADAPTERFUSION_CONFIG_MAP.items():
model = AutoModel.from_config(self.config())
model.eval()
with self.subTest(model_class=model.__class__.__name__, config=config_name):
name1 = f"{config_name}-1"
name2 = f"{config_name}-2"
model.add_adapter(name1, config=config_name)
model.add_adapter(name2, config=config_name)
# adapter is correctly added to config
self.assertTrue(name1 in model.config.adapters)
self.assertTrue(name2 in model.config.adapters)
self.assertEqual(asdict(adapter_config), asdict(model.config.adapters.get(name1)))
self.assertEqual(asdict(adapter_config), asdict(model.config.adapters.get(name2)))
model.add_fusion([name1, name2], adater_fusion_config_name)
# check forward pass
input_ids = self.get_input_samples((1, 128), config=model.config)
input_data = {"input_ids": input_ids}
model.set_active_adapters([[name1, name2]])
adapter_output = model(**input_data)
model.set_active_adapters(None)
base_output = model(**input_data)
self.assertEqual(len(adapter_output), len(base_output))
self.assertFalse(torch.equal(adapter_output[0], base_output[0]))
def test_model_config_serialization_fusion(self):
"""PretrainedConfigurations should not raise an Exception when serializing the config dict
See, e.g., PretrainedConfig.to_json_string()
"""
for k, v in ADAPTERFUSION_CONFIG_MAP.items():
model = self.get_model()
model.add_adapter("test1")
model.add_adapter("test2")
model.add_adapter_fusion(["test1", "test2"], config=v)
# should not raise an exception
model.config.to_json_string()
def test_model_config_serialization(self):
"""PretrainedConfigurations should not raise an Exception when serializing the config dict
See, e.g., PretrainedConfig.to_json_string()
"""
for model_class in self.model_classes:
for k, v in ADAPTERFUSION_CONFIG_MAP.items():
model_config = model_class.config_class
model = model_class(model_config())
model.add_adapter("test1", AdapterType.text_task)
model.add_adapter("test2", AdapterType.text_task)
model.add_fusion(["test1", "test2"], adapter_fusion_config=v)
# should not raise an exception
model.config.to_json_string()
def test_add_adapter_fusion(self):
for adater_fusion_config_name, adapter_fusion_config in ADAPTERFUSION_CONFIG_MAP.items(
):
for config_name, adapter_config in ADAPTER_CONFIG_MAP.items():
for type_name, adapter_type in AdapterType.__members__.items():
for model_class in self.model_classes:
model_config = model_class.config_class
model = model_class(model_config())
# skip configs without invertible language adapters
if adapter_type == AdapterType.text_lang and not adapter_config.invertible_adapter:
continue
with self.subTest(model_class=model_class,
config=config_name,
adapter_type=type_name):
name1 = f"{type_name}-{config_name}-1"
name2 = f"{type_name}-{config_name}-2"
model.add_adapter(name1,
adapter_type,
config=adapter_config)
model.add_adapter(name2,
adapter_type,
config=adapter_config)
# adapter is correctly added to config
self.assertTrue(name1 in model.config.adapters.
adapter_list(adapter_type))
self.assertTrue(name2 in model.config.adapters.
adapter_list(adapter_type))
self.assertEqual(adapter_config,
model.config.adapters.get(name1))
self.assertEqual(adapter_config,
model.config.adapters.get(name2))
model.add_fusion([name1, name2],
adater_fusion_config_name)
# check forward pass
input_ids = ids_tensor((1, 128), 1000)
input_data = {"input_ids": input_ids}
if adapter_type == AdapterType.text_task or adapter_type == AdapterType.text_lang:
input_data["adapter_names"] = [[name1, name2]]
adapter_output = model(**input_data)
base_output = model(input_ids)
self.assertEqual(len(adapter_output),
len(base_output))
self.assertFalse(
torch.equal(adapter_output[0], base_output[0]))
def test_load_adapter_fusion(self):
for adater_fusion_config_name, adapter_fusion_config in ADAPTERFUSION_CONFIG_MAP.items(
):
for name, adapter_type in AdapterType.__members__.items():
for model_class in self.model_classes:
with self.subTest(model_class=model_class,
adapter_type=name):
model_config = model_class.config_class
model1 = model_class(model_config())
name1 = "name1"
name2 = "name2"
model1.add_adapter(name1, adapter_type)
model1.add_adapter(name2, adapter_type)
model1, model2 = create_twin_models(model1)
model1.add_fusion([name1, name2],
adater_fusion_config_name)
with tempfile.TemporaryDirectory() as temp_dir:
model1.save_adapter_fusion(
temp_dir, ",".join([name1, name2]))
model2.load_adapter_fusion(temp_dir)
model1.eval()
model2.eval()
# check if adapter was correctly loaded
self.assertTrue(model1.config.adapter_fusion_models ==
model2.config.adapter_fusion_models)
# check equal output
in_data = ids_tensor((1, 128), 1000)
output1 = model1(in_data,
adapter_names=[[name1, name2]])
output2 = model2(in_data,
adapter_names=[[name1, name2]])
self.assertEqual(len(output1), len(output2))
self.assertTrue(torch.equal(output1[0], output2[0]))