[Wav2Vec2 - MMS] Correct directly loading adapters weights (#24335)

* Correct direct lang loading

* correct more

* revert black

* Use tie weights instead=

* add tests

* add tests

* make style

src/transformers/models/hubert/modeling_hubert.py

@@ -1134,12 +1134,14 @@ class HubertModel(HubertPreTrainedModel):
 )
 # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForCTC with Wav2Vec2->Hubert, wav2vec2->hubert, WAV_2_VEC_2->HUBERT
 class HubertForCTC(HubertPreTrainedModel):
-    def __init__(self, config, target_lang=None):
+    def __init__(self, config, target_lang: Optional[str] = None):
         super().__init__(config)
 
         self.hubert = HubertModel(config)
         self.dropout = nn.Dropout(config.final_dropout)
 
+        self.target_lang = target_lang
+
         if config.vocab_size is None:
             raise ValueError(
                 f"You are trying to instantiate {self.__class__} with a configuration that "
@@ -1152,15 +1154,29 @@ class HubertForCTC(HubertPreTrainedModel):
         )
         self.lm_head = nn.Linear(output_hidden_size, config.vocab_size)
 
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def tie_weights(self):
+        """
+        This method overwrites [`~PreTrainedModel.tie_weights`] so that adapter weights can be correctly loaded when
+        passing `target_lang=...` to `from_pretrained(...)`.
+
+        This method is **not** supposed to be called by the user and is prone to be changed in the future.
+        """
+
+        # Note that `tie_weights` is usually used to tie input and output embedding weights. The method is re-purposed to
+        # correctly load adapter layers for Hubert so that we do not have to introduce a new API to
+        # [`PreTrainedModel`]. While slightly hacky, Hubert never has to tie input and output embeddings, so that it is
+        # ok to repurpose this function here.
+        target_lang = self.target_lang
+
         if target_lang is not None and getattr(self.config, "adapter_attn_dim", None) is None:
             raise ValueError(f"Cannot pass `target_lang`: {target_lang} if `config.adapter_attn_dim` is not defined.")
         elif target_lang is None and getattr(self.config, "adapter_attn_dim", None) is not None:
             logger.info("By default `target_lang` is set to 'eng'.")
         elif target_lang is not None:
-            self.load_adapter(target_lang)
-
-        # Initialize weights and apply final processing
-        self.post_init()
+            self.load_adapter(target_lang, force_load=True)
 
     def freeze_feature_extractor(self):
         """

src/transformers/models/sew/modeling_sew.py

@@ -969,12 +969,14 @@ class SEWModel(SEWPreTrainedModel):
 )
 # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForCTC with Wav2Vec2->SEW, wav2vec2->sew, WAV_2_VEC_2->SEW
 class SEWForCTC(SEWPreTrainedModel):
-    def __init__(self, config, target_lang=None):
+    def __init__(self, config, target_lang: Optional[str] = None):
         super().__init__(config)
 
         self.sew = SEWModel(config)
         self.dropout = nn.Dropout(config.final_dropout)
 
+        self.target_lang = target_lang
+
         if config.vocab_size is None:
             raise ValueError(
                 f"You are trying to instantiate {self.__class__} with a configuration that "
@@ -987,15 +989,29 @@ class SEWForCTC(SEWPreTrainedModel):
         )
         self.lm_head = nn.Linear(output_hidden_size, config.vocab_size)
 
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def tie_weights(self):
+        """
+        This method overwrites [`~PreTrainedModel.tie_weights`] so that adapter weights can be correctly loaded when
+        passing `target_lang=...` to `from_pretrained(...)`.
+
+        This method is **not** supposed to be called by the user and is prone to be changed in the future.
+        """
+
+        # Note that `tie_weights` is usually used to tie input and output embedding weights. The method is re-purposed to
+        # correctly load adapter layers for SEW so that we do not have to introduce a new API to
+        # [`PreTrainedModel`]. While slightly hacky, SEW never has to tie input and output embeddings, so that it is
+        # ok to repurpose this function here.
+        target_lang = self.target_lang
+
         if target_lang is not None and getattr(self.config, "adapter_attn_dim", None) is None:
             raise ValueError(f"Cannot pass `target_lang`: {target_lang} if `config.adapter_attn_dim` is not defined.")
         elif target_lang is None and getattr(self.config, "adapter_attn_dim", None) is not None:
             logger.info("By default `target_lang` is set to 'eng'.")
         elif target_lang is not None:
-            self.load_adapter(target_lang)
-
-        # Initialize weights and apply final processing
-        self.post_init()
+            self.load_adapter(target_lang, force_load=True)
 
     def freeze_feature_extractor(self):
         """

src/transformers/models/sew_d/modeling_sew_d.py

@@ -1509,12 +1509,14 @@ class SEWDModel(SEWDPreTrainedModel):
 )
 # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForCTC with Wav2Vec2->SEWD, wav2vec2->sew_d, WAV_2_VEC_2->SEWD
 class SEWDForCTC(SEWDPreTrainedModel):
-    def __init__(self, config, target_lang=None):
+    def __init__(self, config, target_lang: Optional[str] = None):
         super().__init__(config)
 
         self.sew_d = SEWDModel(config)
         self.dropout = nn.Dropout(config.final_dropout)
 
+        self.target_lang = target_lang
+
         if config.vocab_size is None:
             raise ValueError(
                 f"You are trying to instantiate {self.__class__} with a configuration that "
@@ -1527,15 +1529,29 @@ class SEWDForCTC(SEWDPreTrainedModel):
         )
         self.lm_head = nn.Linear(output_hidden_size, config.vocab_size)
 
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def tie_weights(self):
+        """
+        This method overwrites [`~PreTrainedModel.tie_weights`] so that adapter weights can be correctly loaded when
+        passing `target_lang=...` to `from_pretrained(...)`.
+
+        This method is **not** supposed to be called by the user and is prone to be changed in the future.
+        """
+
+        # Note that `tie_weights` is usually used to tie input and output embedding weights. The method is re-purposed to
+        # correctly load adapter layers for SEWD so that we do not have to introduce a new API to
+        # [`PreTrainedModel`]. While slightly hacky, SEWD never has to tie input and output embeddings, so that it is
+        # ok to repurpose this function here.
+        target_lang = self.target_lang
+
         if target_lang is not None and getattr(self.config, "adapter_attn_dim", None) is None:
             raise ValueError(f"Cannot pass `target_lang`: {target_lang} if `config.adapter_attn_dim` is not defined.")
         elif target_lang is None and getattr(self.config, "adapter_attn_dim", None) is not None:
             logger.info("By default `target_lang` is set to 'eng'.")
         elif target_lang is not None:
-            self.load_adapter(target_lang)
-
-        # Initialize weights and apply final processing
-        self.post_init()
+            self.load_adapter(target_lang, force_load=True)
 
     def freeze_feature_extractor(self):
         """

src/transformers/models/unispeech/modeling_unispeech.py

@@ -1378,12 +1378,14 @@ class UniSpeechForPreTraining(UniSpeechPreTrainedModel):
 )
 # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForCTC with Wav2Vec2->UniSpeech, wav2vec2->unispeech, WAV_2_VEC_2->UNISPEECH
 class UniSpeechForCTC(UniSpeechPreTrainedModel):
-    def __init__(self, config, target_lang=None):
+    def __init__(self, config, target_lang: Optional[str] = None):
         super().__init__(config)
 
         self.unispeech = UniSpeechModel(config)
         self.dropout = nn.Dropout(config.final_dropout)
 
+        self.target_lang = target_lang
+
         if config.vocab_size is None:
             raise ValueError(
                 f"You are trying to instantiate {self.__class__} with a configuration that "
@@ -1396,15 +1398,29 @@ class UniSpeechForCTC(UniSpeechPreTrainedModel):
         )
         self.lm_head = nn.Linear(output_hidden_size, config.vocab_size)
 
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def tie_weights(self):
+        """
+        This method overwrites [`~PreTrainedModel.tie_weights`] so that adapter weights can be correctly loaded when
+        passing `target_lang=...` to `from_pretrained(...)`.
+
+        This method is **not** supposed to be called by the user and is prone to be changed in the future.
+        """
+
+        # Note that `tie_weights` is usually used to tie input and output embedding weights. The method is re-purposed to
+        # correctly load adapter layers for UniSpeech so that we do not have to introduce a new API to
+        # [`PreTrainedModel`]. While slightly hacky, UniSpeech never has to tie input and output embeddings, so that it is
+        # ok to repurpose this function here.
+        target_lang = self.target_lang
+
         if target_lang is not None and getattr(self.config, "adapter_attn_dim", None) is None:
             raise ValueError(f"Cannot pass `target_lang`: {target_lang} if `config.adapter_attn_dim` is not defined.")
         elif target_lang is None and getattr(self.config, "adapter_attn_dim", None) is not None:
             logger.info("By default `target_lang` is set to 'eng'.")
         elif target_lang is not None:
-            self.load_adapter(target_lang)
-
-        # Initialize weights and apply final processing
-        self.post_init()
+            self.load_adapter(target_lang, force_load=True)
 
     def freeze_feature_extractor(self):
         """

src/transformers/models/unispeech_sat/modeling_unispeech_sat.py

@@ -1385,12 +1385,14 @@ class UniSpeechSatForPreTraining(UniSpeechSatPreTrainedModel):
 )
 # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForCTC with Wav2Vec2->UniSpeechSat, wav2vec2->unispeech_sat, WAV_2_VEC_2->UNISPEECH_SAT
 class UniSpeechSatForCTC(UniSpeechSatPreTrainedModel):
-    def __init__(self, config, target_lang=None):
+    def __init__(self, config, target_lang: Optional[str] = None):
         super().__init__(config)
 
         self.unispeech_sat = UniSpeechSatModel(config)
         self.dropout = nn.Dropout(config.final_dropout)
 
+        self.target_lang = target_lang
+
         if config.vocab_size is None:
             raise ValueError(
                 f"You are trying to instantiate {self.__class__} with a configuration that "
@@ -1403,15 +1405,29 @@ class UniSpeechSatForCTC(UniSpeechSatPreTrainedModel):
         )
         self.lm_head = nn.Linear(output_hidden_size, config.vocab_size)
 
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def tie_weights(self):
+        """
+        This method overwrites [`~PreTrainedModel.tie_weights`] so that adapter weights can be correctly loaded when
+        passing `target_lang=...` to `from_pretrained(...)`.
+
+        This method is **not** supposed to be called by the user and is prone to be changed in the future.
+        """
+
+        # Note that `tie_weights` is usually used to tie input and output embedding weights. The method is re-purposed to
+        # correctly load adapter layers for UniSpeechSat so that we do not have to introduce a new API to
+        # [`PreTrainedModel`]. While slightly hacky, UniSpeechSat never has to tie input and output embeddings, so that it is
+        # ok to repurpose this function here.
+        target_lang = self.target_lang
+
         if target_lang is not None and getattr(self.config, "adapter_attn_dim", None) is None:
             raise ValueError(f"Cannot pass `target_lang`: {target_lang} if `config.adapter_attn_dim` is not defined.")
         elif target_lang is None and getattr(self.config, "adapter_attn_dim", None) is not None:
             logger.info("By default `target_lang` is set to 'eng'.")
         elif target_lang is not None:
-            self.load_adapter(target_lang)
-
-        # Initialize weights and apply final processing
-        self.post_init()
+            self.load_adapter(target_lang, force_load=True)
 
     def freeze_feature_extractor(self):
         """

src/transformers/models/wav2vec2/modeling_wav2vec2.py

@@ -1207,7 +1207,7 @@ class Wav2Vec2PreTrainedModel(PreTrainedModel):
         if isinstance(self, Wav2Vec2ForCTC):
             self._init_weights(self.lm_head)
 
-    def load_adapter(self, target_lang: str, **kwargs):
+    def load_adapter(self, target_lang: str, force_load=True, **kwargs):
         r"""
         Load a language adapter model from a pre-trained adapter model.
 
@@ -1215,6 +1215,8 @@ class Wav2Vec2PreTrainedModel(PreTrainedModel):
             target_lang (`str`):
                 Has to be a language id of an existing adapter weight. Adapter weights are stored in the format
                 adapter.<lang>.safetensors or adapter.<lang>.bin
+            force_load (`bool`, defaults to `True`):
+                Whether the weights shall be loaded even if `target_lang` matches `self.target_lang`.
             cache_dir (`Union[str, os.PathLike]`, *optional*):
                 Path to a directory in which a downloaded pretrained model configuration should be cached if the
                 standard cache should not be used.
@@ -1271,6 +1273,10 @@ class Wav2Vec2PreTrainedModel(PreTrainedModel):
         if self.config.adapter_attn_dim is None:
             raise ValueError(f"Cannot load_adapter for {target_lang} if `config.adapter_attn_dim` is not defined.")
 
+        if target_lang == self.target_lang and not force_load:
+            logger.warn(f"Adapter weights are already set to {target_lang}.")
+            return
+
         cache_dir = kwargs.pop("cache_dir", None)
         force_download = kwargs.pop("force_download", False)
         resume_download = kwargs.pop("resume_download", False)
@@ -1372,6 +1378,9 @@ class Wav2Vec2PreTrainedModel(PreTrainedModel):
         state_dict = {k: v.to(adapter_weights[k]) for k, v in state_dict.items()}
         self.load_state_dict(state_dict, strict=False)
 
+        # set target language corectly
+        self.target_lang = target_lang
+
 
 WAV_2_VEC_2_START_DOCSTRING = r"""
     Wav2Vec2 was proposed in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
@@ -1854,12 +1863,14 @@ class Wav2Vec2ForMaskedLM(Wav2Vec2PreTrainedModel):
     WAV_2_VEC_2_START_DOCSTRING,
 )
 class Wav2Vec2ForCTC(Wav2Vec2PreTrainedModel):
-    def __init__(self, config, target_lang=None):
+    def __init__(self, config, target_lang: Optional[str] = None):
         super().__init__(config)
 
         self.wav2vec2 = Wav2Vec2Model(config)
         self.dropout = nn.Dropout(config.final_dropout)
 
+        self.target_lang = target_lang
+
         if config.vocab_size is None:
             raise ValueError(
                 f"You are trying to instantiate {self.__class__} with a configuration that "
@@ -1872,15 +1883,29 @@ class Wav2Vec2ForCTC(Wav2Vec2PreTrainedModel):
         )
         self.lm_head = nn.Linear(output_hidden_size, config.vocab_size)
 
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def tie_weights(self):
+        """
+        This method overwrites [`~PreTrainedModel.tie_weights`] so that adapter weights can be correctly loaded when
+        passing `target_lang=...` to `from_pretrained(...)`.
+
+        This method is **not** supposed to be called by the user and is prone to be changed in the future.
+        """
+
+        # Note that `tie_weights` is usually used to tie input and output embedding weights. The method is re-purposed to
+        # correctly load adapter layers for Wav2Vec2 so that we do not have to introduce a new API to
+        # [`PreTrainedModel`]. While slightly hacky, Wav2Vec2 never has to tie input and output embeddings, so that it is
+        # ok to repurpose this function here.
+        target_lang = self.target_lang
+
         if target_lang is not None and getattr(self.config, "adapter_attn_dim", None) is None:
             raise ValueError(f"Cannot pass `target_lang`: {target_lang} if `config.adapter_attn_dim` is not defined.")
         elif target_lang is None and getattr(self.config, "adapter_attn_dim", None) is not None:
             logger.info("By default `target_lang` is set to 'eng'.")
         elif target_lang is not None:
-            self.load_adapter(target_lang)
-
-        # Initialize weights and apply final processing
-        self.post_init()
+            self.load_adapter(target_lang, force_load=True)
 
     def freeze_feature_extractor(self):
         """

src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py

@@ -1610,6 +1610,8 @@ class Wav2Vec2ConformerForCTC(Wav2Vec2ConformerPreTrainedModel):
         self.wav2vec2_conformer = Wav2Vec2ConformerModel(config)
         self.dropout = nn.Dropout(config.final_dropout)
 
+        self.target_lang = target_lang
+
         if config.vocab_size is None:
             raise ValueError(
                 f"You are trying to instantiate {self.__class__} with a configuration that "
@@ -1622,13 +1624,6 @@ class Wav2Vec2ConformerForCTC(Wav2Vec2ConformerPreTrainedModel):
         )
         self.lm_head = nn.Linear(output_hidden_size, config.vocab_size)
 
-        if target_lang is not None and getattr(self.config, "adapter_attn_dim", None) is None:
-            raise ValueError(f"Cannot pass `target_lang`: {target_lang} if `config.adapter_attn_dim` is not defined.")
-        elif target_lang is None and getattr(self.config, "adapter_attn_dim", None) is not None:
-            logger.info("By default `target_lang` is set to 'eng'.")
-        elif target_lang is not None:
-            self.load_adapter(target_lang)
-
         # Initialize weights and apply final processing
         self.post_init()
 

src/transformers/models/wavlm/modeling_wavlm.py

@@ -1272,12 +1272,14 @@ class WavLMModel(WavLMPreTrainedModel):
 )
 # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForCTC with Wav2Vec2->WavLM, wav2vec2->wavlm, WAV_2_VEC_2->WAVLM
 class WavLMForCTC(WavLMPreTrainedModel):
-    def __init__(self, config, target_lang=None):
+    def __init__(self, config, target_lang: Optional[str] = None):
         super().__init__(config)
 
         self.wavlm = WavLMModel(config)
         self.dropout = nn.Dropout(config.final_dropout)
 
+        self.target_lang = target_lang
+
         if config.vocab_size is None:
             raise ValueError(
                 f"You are trying to instantiate {self.__class__} with a configuration that "
@@ -1290,15 +1292,29 @@ class WavLMForCTC(WavLMPreTrainedModel):
         )
         self.lm_head = nn.Linear(output_hidden_size, config.vocab_size)
 
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def tie_weights(self):
+        """
+        This method overwrites [`~PreTrainedModel.tie_weights`] so that adapter weights can be correctly loaded when
+        passing `target_lang=...` to `from_pretrained(...)`.
+
+        This method is **not** supposed to be called by the user and is prone to be changed in the future.
+        """
+
+        # Note that `tie_weights` is usually used to tie input and output embedding weights. The method is re-purposed to
+        # correctly load adapter layers for WavLM so that we do not have to introduce a new API to
+        # [`PreTrainedModel`]. While slightly hacky, WavLM never has to tie input and output embeddings, so that it is
+        # ok to repurpose this function here.
+        target_lang = self.target_lang
+
         if target_lang is not None and getattr(self.config, "adapter_attn_dim", None) is None:
             raise ValueError(f"Cannot pass `target_lang`: {target_lang} if `config.adapter_attn_dim` is not defined.")
         elif target_lang is None and getattr(self.config, "adapter_attn_dim", None) is not None:
             logger.info("By default `target_lang` is set to 'eng'.")
         elif target_lang is not None:
-            self.load_adapter(target_lang)
-
-        # Initialize weights and apply final processing
-        self.post_init()
+            self.load_adapter(target_lang, force_load=True)
 
     def freeze_feature_extractor(self):
         """

tests/models/wav2vec2/test_modeling_wav2vec2.py

@@ -1117,6 +1117,40 @@ class Wav2Vec2RobustModelTest(ModelTesterMixin, unittest.TestCase):
     def test_feed_forward_chunking(self):
         pass
 
+    def test_load_and_set_attn_adapter(self):
+        processor = Wav2Vec2Processor.from_pretrained(
+            "hf-internal-testing/tiny-random-wav2vec2", return_attention_mask=True
+        )
+
+        def get_logits(model, input_features):
+            model = model.to(torch_device)
+            batch = processor(
+                input_features,
+                padding=True,
+                sampling_rate=processor.feature_extractor.sampling_rate,
+                return_tensors="pt",
+            )
+
+            with torch.no_grad():
+                logits = model(
+                    input_values=batch["input_values"].to(torch_device),
+                    attention_mask=batch["attention_mask"].to(torch_device),
+                ).logits
+            return logits
+
+        input_features = [np.random.random(16_000 * s) for s in [1, 3, 2, 6]]
+
+        model = Wav2Vec2ForCTC.from_pretrained("hf-internal-testing/tiny-random-wav2vec2-adapter", target_lang="it")
+
+        logits = get_logits(model, input_features)
+
+        model_2 = Wav2Vec2ForCTC.from_pretrained("hf-internal-testing/tiny-random-wav2vec2-adapter")
+        model_2.load_adapter("it")
+
+        logits_2 = get_logits(model_2, input_features)
+
+        self.assertTrue(torch.allclose(logits, logits_2, atol=1e-3))
+
     def test_load_attn_adapter(self):
         processor = Wav2Vec2Processor.from_pretrained(
             "hf-internal-testing/tiny-random-wav2vec2", return_attention_mask=True