class AdapterCompositionTest(unittest.TestCase):
def setUp(self):
self.model = BertForSequenceClassification(BertConfig())
self.model.add_adapter("a")
self.model.add_adapter("b")
self.model.add_adapter("c")
self.model.add_adapter("d")
self.model.to(torch_device)
self.model.train()
def training_pass(self):
inputs = {}
inputs["input_ids"] = ids_tensor((1, 128), 1000)
inputs["labels"] = torch.ones(1, dtype=torch.long)
loss = self.model(**inputs).loss
loss.backward()
def test_simple_split(self):
# pass over split setup
self.model.set_active_adapters(Split("a", "b", 64))
self.training_pass()
def test_stacked_split(self):
# split into two stacks
self.model.set_active_adapters(
Split(Stack("a", "b"), Stack("c", "d"), split_index=64))
self.training_pass()
def test_stacked_fusion(self):
self.model.add_fusion(Fuse("b", "d"))
# fuse two stacks
self.model.set_active_adapters(Fuse(Stack("a", "b"), Stack("c", "d")))
self.training_pass()
def test_mixed_stack(self):
self.model.add_fusion(Fuse("a", "b"))
self.model.set_active_adapters(
Stack("a", Split("c", "d", split_index=64), Fuse("a", "b")))
self.training_pass()
def test_nested_split(self):
# split into two stacks
self.model.set_active_adapters(
Split(Split("a", "b", split_index=32), "c", split_index=64))
self.training_pass()
def test_parallel(self):
self.model.set_active_adapters(Parallel("a", "b", "c", "d"))
inputs = {}
inputs["input_ids"] = ids_tensor((1, 128), 1000)
logits = self.model(**inputs).logits
self.assertEqual(logits.shape, (4, 2))
def test_nested_parallel(self):
self.model.set_active_adapters(
Stack("a", Parallel(Stack("b", "c"), "d")))
inputs = {}
inputs["input_ids"] = ids_tensor((1, 128), 1000)
logits = self.model(**inputs).logits
self.assertEqual(logits.shape, (2, 2))
