Re-add:

- deberta
- debertav2
- distilbert
- electra
- encoder-decoder
- llama
- mbart
- mistral
- mt5
- plbart
- roberta

tests/test_impl/embeddings/test_adapter_embeddings.py

@@ -66,7 +66,7 @@ def test_save_load_embedding(self):
             torch.equal(model.loaded_embeddings["test"].weight, model.loaded_embeddings["test_reloaded"].weight)
         )
         self.assertTrue(torch.equal(output1[0], output2[0]))
-        self.assertEqual(tokenizer.vocab, tokenizer_ref.vocab)
+        self.assertEqual(tokenizer.get_vocab(), tokenizer_ref.get_vocab())
 
     def test_back_to_default(self):
         model = self.get_model()

tests/test_methods/test_deberta.py

@@ -0,0 +1,27 @@
+from transformers import DebertaConfig
+
+from .utils import *
+
+
+class DebertaAdapterTestBase(TextAdapterTestBase):
+    config_class = DebertaConfig
+    config = make_config(
+        DebertaConfig,
+        hidden_size=32,
+        num_hidden_layers=5,
+        num_attention_heads=4,
+        intermediate_size=37,
+        hidden_act="gelu",
+        relative_attention=True,
+        pos_att_type="p2c|c2p",
+    )
+    tokenizer_name = "microsoft/deberta-base"
+
+    def test_parallel_training_lora(self):
+        self.skipTest("Not supported for DeBERTa")
+
+
+method_tests = generate_method_tests(DebertaAdapterTestBase)
+
+for test_class_name, test_class in method_tests.items():
+    globals()[test_class_name] = test_class

tests/test_methods/test_debertaV2.py

@@ -0,0 +1,24 @@
+from transformers import DebertaV2Config
+
+from .utils import *
+
+
+class DebertaV2AdapterTestBase(TextAdapterTestBase):
+    config_class = DebertaV2Config
+    config = make_config(
+        DebertaV2Config,
+        hidden_size=32,
+        num_hidden_layers=5,
+        num_attention_heads=4,
+        intermediate_size=37,
+        hidden_act="gelu",
+        relative_attention=True,
+        pos_att_type="p2c|c2p",
+    )
+    tokenizer_name = "microsoft/deberta-v3-base"
+
+
+method_tests = generate_method_tests(DebertaV2AdapterTestBase)
+
+for test_class_name, test_class in method_tests.items():
+    globals()[test_class_name] = test_class

tests/test_methods/test_distilbert.py

@@ -0,0 +1,21 @@
+from transformers import DistilBertConfig
+
+from .utils import *
+
+
+class DistilBertAdapterTestBase(TextAdapterTestBase):
+    config_class = DistilBertConfig
+    config = make_config(
+        DistilBertConfig,
+        dim=32,
+        n_layers=4,
+        n_heads=4,
+        hidden_dim=37,
+    )
+    tokenizer_name = "distilbert-base-uncased"
+
+
+method_tests = generate_method_tests(DistilBertAdapterTestBase)
+
+for test_class_name, test_class in method_tests.items():
+    globals()[test_class_name] = test_class

tests/test_methods/test_electra.py

@@ -0,0 +1,22 @@
+from transformers import ElectraConfig
+
+from .utils import *
+
+
+class ElectraAdapterTestBase(TextAdapterTestBase):
+    config_class = ElectraConfig
+    config = make_config(
+        ElectraConfig,
+        # vocab_size=99,
+        hidden_size=32,
+        num_hidden_layers=5,
+        num_attention_heads=4,
+        intermediate_size=37,
+    )
+    tokenizer_name = "google/electra-base-generator"
+
+
+method_tests = generate_method_tests(ElectraAdapterTestBase)
+
+for test_class_name, test_class in method_tests.items():
+    globals()[test_class_name] = test_class

tests/test_methods/test_encoder_decoder.py

@@ -0,0 +1,72 @@
+from adapters import init
+from transformers import AutoModelForSeq2SeqLM, BertConfig, EncoderDecoderConfig, EncoderDecoderModel
+
+from .utils import *
+
+
+class EncoderDecoderAdapterTestBase(TextAdapterTestBase):
+    model_class = EncoderDecoderModel
+    config_class = EncoderDecoderConfig
+    config = staticmethod(
+        lambda: EncoderDecoderConfig.from_encoder_decoder_configs(
+            BertConfig(
+                hidden_size=32,
+                num_hidden_layers=4,
+                num_attention_heads=4,
+                intermediate_size=37,
+            ),
+            BertConfig(
+                hidden_size=32,
+                num_hidden_layers=4,
+                num_attention_heads=4,
+                intermediate_size=37,
+                is_decoder=True,
+                add_cross_attention=True,
+            ),
+        )
+    )
+    tokenizer_name = "bert-base-uncased"
+    do_run_train_tests = False
+
+    def test_generation(self):
+        model = AutoModelForSeq2SeqLM.from_config(self.config())
+        init(model)
+        model.add_adapter("test", config="pfeiffer")
+        model.set_active_adapters("test")
+        tokenizer = AutoTokenizer.from_pretrained(self.tokenizer_name, use_fast=False)
+
+        text = "This is a test sentence."
+        input_ids = tokenizer(text, return_tensors="pt").input_ids
+
+        generated_ids = model.generate(input_ids, bos_token_id=100)
+        generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+        self.assertNotEqual("", generated_text)
+
+    def test_invertible_adapter_with_head(self):
+        """This test class is copied and adapted from the identically-named test in test_adapter_heads.py."""
+        raise self.skipTest("AutoModelForSeq2SeqLM does not support using invertible adapters.")
+
+    def test_adapter_fusion_save_with_head(self):
+        # This test is not applicable to the encoder-decoder model since it has no heads.
+        self.skipTest("Not applicable to the encoder-decoder model.")
+
+    def test_forward_with_past(self):
+        # This test is not applicable to the encoder-decoder model since it has no heads.
+        self.skipTest("Not applicable to the encoder-decoder model.")
+
+    def test_output_adapter_gating_scores_unipelt(self):
+        # TODO currently not supported
+        self.skipTest("Not implemented.")
+
+    def test_output_adapter_fusion_attentions(self):
+        # TODO currently not supported
+        self.skipTest("Not implemented.")
+
+
+test_methods = generate_method_tests(
+    EncoderDecoderAdapterTestBase,
+    excluded_tests=["Heads", "ConfigUnion", "Embeddings", "Composition", "PromptTuning", "ClassConversion"],
+)
+
+for test_class_name, test_class in test_methods.items():
+    globals()[test_class_name] = test_class

tests/test_methods/test_llama.py

@@ -0,0 +1,33 @@
+from transformers.models.llama.configuration_llama import LlamaConfig
+
+from .utils import *
+
+
+class LlamaAdapterTestBase(TextAdapterTestBase):
+    config_class = LlamaConfig
+    config = make_config(
+        LlamaConfig,
+        hidden_size=32,
+        num_hidden_layers=5,
+        num_attention_heads=4,
+        intermediate_size=37,
+        hidden_act="gelu",
+        pad_token_id=0,
+    )
+    tokenizer_name = "openlm-research/open_llama_13b"
+
+
+method_tests = generate_method_tests(LlamaAdapterTestBase, excluded_tests=["PromptTuning"])
+
+for test_class_name, test_class in method_tests.items():
+    globals()[test_class_name] = test_class
+
+
+@require_torch
+class ClassConversion(
+    ModelClassConversionTestMixin,
+    LlamaAdapterTestBase,
+    unittest.TestCase,
+):
+    def test_conversion_question_answering_model(self):
+        raise self.skipTest("We don't support the Llama QA model.")

tests/test_methods/test_mbart.py

@@ -0,0 +1,26 @@
+from transformers import MBartConfig
+
+from .utils import *
+
+
+class MBartAdapterTestBase(TextAdapterTestBase):
+    config_class = MBartConfig
+    config = make_config(
+        MBartConfig,
+        d_model=16,
+        encoder_layers=2,
+        decoder_layers=2,
+        encoder_attention_heads=4,
+        decoder_attention_heads=4,
+        encoder_ffn_dim=4,
+        decoder_ffn_dim=4,
+        vocab_size=250027,
+    )
+    tokenizer_name = "facebook/mbart-large-cc25"
+
+
+method_tests = generate_method_tests(
+    MBartAdapterTestBase, excluded_tests=["ConfigUnion", "Embeddings", "PromptTuning"]
+)
+for test_class_name, test_class in method_tests.items():
+    globals()[test_class_name] = test_class

tests/test_methods/test_mistral.py

@@ -0,0 +1,24 @@
+from transformers.models.mistral.configuration_mistral import MistralConfig
+
+from .utils import *
+
+
+class MistralAdapterTestBase(TextAdapterTestBase):
+    config_class = MistralConfig
+    config = make_config(
+        MistralConfig,
+        hidden_size=32,
+        num_hidden_layers=5,
+        num_attention_heads=8,
+        intermediate_size=37,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        pad_token_id=0,
+    )
+    tokenizer_name = "HuggingFaceH4/zephyr-7b-beta"
+
+
+test_methods = generate_method_tests(MistralAdapterTestBase, excluded_tests=["PromptTuning", "ConfigUnion"])
+
+for test_class_name, test_class in test_methods.items():
+    globals()[test_class_name] = test_class

tests/test_methods/test_mt5.py

@@ -0,0 +1,26 @@
+from transformers import MT5Config
+
+from .utils import *
+
+
+@require_torch
+class MT5AdapterTestBase(TextAdapterTestBase):
+    config_class = MT5Config
+    config = make_config(
+        MT5Config,
+        d_model=16,
+        num_layers=2,
+        num_decoder_layers=2,
+        num_heads=4,
+        d_ff=4,
+        d_kv=16 // 4,
+        tie_word_embeddings=False,
+        decoder_start_token_id=0,
+    )
+    tokenizer_name = "google/mt5-base"
+
+
+method_tests = generate_method_tests(MT5AdapterTestBase, excluded_tests=["PromptTuning", "ConfigUnion"])
+
+for test_name, test_class in method_tests.items():
+    globals()[test_name] = test_class

tests/test_methods/test_plbart.py

@@ -0,0 +1,25 @@
+from transformers import PLBartConfig
+
+from .utils import *
+
+
+class PLBartAdapterTestBase(TextAdapterTestBase):
+    config_class = PLBartConfig
+    config = make_config(
+        PLBartConfig,
+        d_model=16,
+        encoder_layers=2,
+        decoder_layers=2,
+        encoder_attention_heads=4,
+        decoder_attention_heads=4,
+        encoder_ffn_dim=4,
+        decoder_ffn_dim=4,
+        scale_embedding=False,  # Required for embedding tests
+    )
+    tokenizer_name = "uclanlp/plbart-base"
+
+
+method_tests = generate_method_tests(PLBartAdapterTestBase, excluded_tests=["PromptTuning"])
+
+for test_name, test_class in method_tests.items():
+    globals()[test_name] = test_class

tests/test_methods/test_roberta.py

@@ -0,0 +1,22 @@
+from transformers import RobertaConfig
+
+from .utils import *
+
+
+class RobertaAdapterTestBase(TextAdapterTestBase):
+    config_class = RobertaConfig
+    config = make_config(
+        RobertaConfig,
+        hidden_size=32,
+        num_hidden_layers=4,
+        num_attention_heads=4,
+        intermediate_size=37,
+        vocab_size=50265,
+    )
+    tokenizer_name = "roberta-base"
+
+
+method_tests = generate_method_tests(RobertaAdapterTestBase)
+
+for test_name, test_class in method_tests.items():
+    globals()[test_name] = test_class