Refactor embedding input/output getter/setter

examples/modular-transformers/modeling_my_new_model2.py

@@ -333,12 +333,6 @@ def __init__(self, config: MyNewModel2Config):
         # Initialize weights and apply final processing
         self.post_init()
 
-    def get_input_embeddings(self):
-        return self.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.embed_tokens = value
-
     @check_model_inputs
     @auto_docstring
     def forward(
@@ -433,12 +427,6 @@ def __init__(self, config):
         # Initialize weights and apply final processing
         self.post_init()
 
-    def get_input_embeddings(self):
-        return self.model.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.model.embed_tokens = value
-
     @can_return_tuple
     @auto_docstring
     def forward(

examples/modular-transformers/modeling_new_task_model.py

@@ -389,12 +389,6 @@ def get_input_embeddings(self):
     def set_input_embeddings(self, value):
         self.model.set_input_embeddings(value)
 
-    def get_output_embeddings(self):
-        return self.lm_head
-
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head = new_embeddings
-
     def set_decoder(self, decoder):
         self.model.set_decoder(decoder)
 

examples/modular-transformers/modeling_super.py

@@ -332,12 +332,6 @@ def __init__(self, config: SuperConfig):
         # Initialize weights and apply final processing
         self.post_init()
 
-    def get_input_embeddings(self):
-        return self.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.embed_tokens = value
-
     @check_model_inputs
     @auto_docstring
     def forward(

src/transformers/modeling_utils.py

@@ -1902,7 +1902,97 @@ def floating_point_ops(
         return 6 * self.estimate_tokens(input_dict) * self.num_parameters(exclude_embeddings=exclude_embeddings)
 
 
-class PreTrainedModel(nn.Module, ModuleUtilsMixin, PushToHubMixin, PeftAdapterMixin):
+class EmbeddingAccessMixin:
+    """
+    Base utilities to regroup getters and setters for embeddings.
+    Introduces the `input_layer_embed` attribute, which indicates
+    where the input embeddings come from and where they
+    should be set.
+    """
+
+    _input_embed_layer = "embed_tokens"  # default layer that holds input embeddings.
+
+    def get_input_embeddings(self) -> nn.Module:
+        """
+        Returns the model's input embeddings.
+
+        Returns:
+            `nn.Module`: A torch module mapping vocabulary to hidden states.
+        """
+
+        # 1) Check if the model has an attribute named 'embed_tokens' (the standard input embedding layer
+        #  for most NLP models), and if so, return it.
+
+        name = getattr(self, "_input_embed_layer", "embed_tokens")
+
+        if (default_embedding := getattr(self, name, None)) is not None:
+            return default_embedding
+        # 2) encoder/decoder and VLMs like `Gemma3nForConditionalGeneration`
+
+        if hasattr(self, "model") and hasattr(self.model, "embed_tokens"):
+            return self.model.embed_tokens
+
+        # 3) vanilla decoder‑only architectures
+        elif hasattr(self, "embed_tokens"):
+            return self.embed_tokens
+        else:
+            base_model = getattr(self, "base_model_prefix", None)
+            if base_model is not None:
+                base_model = getattr(self, base_model, None)
+                if base_model is not None and base_model is not self:
+                    return base_model.get_input_embeddings()
+            raise NotImplementedError(
+                f"`get_input_embeddings` not auto‑handled for {self.__class__.__name__}; "
+                "please override in the subclass."
+            )
+
+    def set_input_embeddings(self, value: nn.Module):
+        """Fallback setter that handles **~70 %** of models in the code‑base.
+
+        Order of attempts:
+        1. `self.model.embed_tokens`
+        2. `self.embed_tokens`
+        3. delegate to the *base model* if one exists
+        4. otherwise raise `NotImplementedError` so subclasses still can (and
+            should) override for exotic layouts.
+        """
+
+        # 1) encoder/decoder and VLMs like `Gemma3nForConditionalGeneration`
+        name = getattr(self, "_input_embed_layer", "embed_tokens")
+        if hasattr(self, "model") and hasattr(self.model, name):
+            setattr(self.model, name, value)
+        # 2) as well as vanilla decoder‑only architectures
+        elif hasattr(self, name):
+            setattr(self, name, value)
+        # 3) recurse once into the registered *base* model (e.g. for encoder/decoder)
+        elif getattr(self, self.base_model_prefix, self) is not self:
+            base_model = getattr(self, self.base_model_prefix, self)
+            base_model.set_input_embeddings(value)
+        else:
+            raise NotImplementedError(
+                f"`set_input_embeddings` not auto‑handled for {self.__class__.__name__}; please override in the subclass."
+            )
+
+    def get_output_embeddings(self):
+        if not hasattr(self, "lm_head"):
+            return None
+        try:
+            # Speech / vision backbones raise here, so we return None.
+            # Legit use of get_input_embs?
+            self.get_input_embeddings()
+        except NotImplementedError:
+            return None
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        """
+        Sets the model's output embedding, defaulting to setting new_embeddings to lm_head.
+        """
+        if getattr(self, "lm_head"):
+            self.lm_head = new_embeddings
+
+
+class PreTrainedModel(nn.Module, EmbeddingAccessMixin, ModuleUtilsMixin, PushToHubMixin, PeftAdapterMixin):
     r"""
     Base class for all models.
 
@@ -2004,6 +2094,8 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, PushToHubMixin, PeftAdapterMi
     _supports_attention_backend = False
     _can_record_outputs = None
 
+    # This attribute sets the default parameter to be
+
     @property
     @torch._dynamo.allow_in_graph
     def can_record_outputs(self) -> dict[str, OutputRecorder]:
@@ -2267,6 +2359,101 @@ def _from_config(cls, config, **kwargs):
 
         return model
 
+    @classmethod
+    def _check_attn_implementation(cls, attn_implementation: Union[str, dict]) -> Union[str, dict]:
+        """
+        Checks that the requested attention implementation exists and tries to get the kernel from hub
+        if `attn_implementation` matches hf kernels pattern.
+        """
+        if isinstance(attn_implementation, str) and re.match(r"^[^/:]+/[^/:]+:[^/:]+$", attn_implementation):
+            if not is_kernels_available():
+                raise ValueError("kernels is not installed. Please install it with `pip install kernels`.")
+
+            # Extract repo_id and kernel_name from the string
+            repo_id, kernel_name = attn_implementation.split(":")
+            kernel_name = kernel_name.strip()
+            repo_id = repo_id.strip()
+
+            try:
+                kernel = get_kernel(repo_id)
+                ALL_ATTENTION_FUNCTIONS.register(f"kernel_{repo_id.replace('/', '_')}", getattr(kernel, kernel_name))
+                attn_implementation = f"kernel_{repo_id.replace('/', '_')}"
+            except FileNotFoundError as e:
+                logger.warning(
+                    f"Could not find a kernel repository '{repo_id}' compatible with your devicein the hub: {e}. Using eager attention implementation instead."
+                )
+                attn_implementation = None  # try to dispatch SDPA and fallback eager if not available
+            except AttributeError:
+                raise ValueError(
+                    "the kernel function name or class specified in the attn_implementation argument is not valid. \
+                                 Please check the documentation for the correct format, \
+                                 and check that the kernel exports the class and the function correctly."
+                )
+        if (
+            not isinstance(attn_implementation, dict)
+            and attn_implementation not in ["eager", None] + ALL_ATTENTION_FUNCTIONS.valid_keys()
+        ):
+            message = f'Specified `attn_implementation="{attn_implementation}"` is not supported. The only possible arguments are `attn_implementation="eager"` (manual attention implementation)'
+            # check `supports_flash_attn_2` for BC with custom code. TODO: remove after a few releases
+            if cls._supports_flash_attn or getattr(cls, "_supports_flash_attn_2", False):
+                message += (
+                    ', `"attn_implementation=flash_attention_3"` (implementation using flash attention 3)'
+                    ', `"attn_implementation=flash_attention_2"` (implementation using flash attention 2)'
+                )
+            if cls._supports_sdpa:
+                message += ', `"attn_implementation=sdpa"` (implementation using torch.nn.functional.scaled_dot_product_attention)'
+            if cls._supports_flex_attn:
+                message += ', `"attn_implementation=flex_attention"` (implementation using torch\'s flex_attention)'
+            raise ValueError(message + ".")
+
+        return attn_implementation
+
+    def set_attention_implementation(self, attn_implementation: Union[str, dict]):
+        """
+        Checks and dispatches to the requested attention implementation.
+        """
+        requested_attn_implementation = self._check_attn_implementation(attn_implementation)
+
+        # Composite models consisting of several PretrainedModels can specify attention implementation as a dict where
+        # keys are sub-config names. But most people will specify one `str` which means that should dispatch it for all sub-models.
+        # See https://github.com/huggingface/transformers/pull/32238
+        for key in self.config.sub_configs.keys():
+            sub_config = getattr(self.config, key)
+            curr_attn_implementation = (
+                requested_attn_implementation
+                if not isinstance(requested_attn_implementation, dict)
+                else requested_attn_implementation.get(key, None)
+            )
+            # For models with backbone sub-config might be not initialized. Set the requested att
+            # if the config hasn't got any attn pre-set and the requested attn in not `None` (i.e not the default attn)
+            if (
+                sub_config is not None
+                and sub_config._attn_implementation_internal is None
+                and curr_attn_implementation is not None
+            ):
+                sub_config._attn_implementation_internal = curr_attn_implementation
+
+        if requested_attn_implementation == "flash_attention_3" and self._flash_attn_3_can_dispatch():
+            self.config._attn_implementation = "flash_attention_3"
+        if requested_attn_implementation == "flash_attention_2" and self._flash_attn_2_can_dispatch():
+            self.config._attn_implementation = "flash_attention_2"
+        elif requested_attn_implementation == "flex_attention" and self._flex_attn_can_dispatch():
+            self.config._attn_implementation = "flex_attention"
+        elif (
+            requested_attn_implementation in [None, "sdpa"]
+            and not is_torch_xla_available()
+            and self._sdpa_can_dispatch(hard_check_only=requested_attn_implementation is not None)
+        ):
+            self.config._attn_implementation = "sdpa"
+        elif requested_attn_implementation in ALL_ATTENTION_FUNCTIONS.valid_keys():
+            self.config._attn_implementation = requested_attn_implementation
+        elif isinstance(requested_attn_implementation, dict):
+            self.config._attn_implementation = requested_attn_implementation.get("", None)
+        else:
+            self.config._attn_implementation = "eager"
+
+        self.config._attn_implementation_autoset = True
+
     @classmethod
     def _set_default_torch_dtype(cls, dtype: torch.dtype) -> torch.dtype:
         """
@@ -2769,41 +2956,6 @@ def disable_input_require_grads(self):
         """
         self._require_grads_hook.remove()
 
-    def get_input_embeddings(self) -> nn.Module:
-        """
-        Returns the model's input embeddings.
-
-        Returns:
-            `nn.Module`: A torch module mapping vocabulary to hidden states.
-        """
-        base_model = getattr(self, self.base_model_prefix, self)
-        if base_model is not self:
-            return base_model.get_input_embeddings()
-        else:
-            raise NotImplementedError
-
-    def set_input_embeddings(self, value: nn.Module):
-        """
-        Set model's input embeddings.
-
-        Args:
-            value (`nn.Module`): A module mapping vocabulary to hidden states.
-        """
-        base_model = getattr(self, self.base_model_prefix, self)
-        if base_model is not self:
-            base_model.set_input_embeddings(value)
-        else:
-            raise NotImplementedError
-
-    def get_output_embeddings(self) -> nn.Module:
-        """
-        Returns the model's output embeddings.
-
-        Returns:
-            `nn.Module`: A torch module mapping hidden states to vocabulary.
-        """
-        return None  # Overwrite for models with output embeddings
-
     def _init_weights(self, module):
         """
         Initialize the weights. This method should be overridden by derived class and is

src/transformers/models/arcee/modeling_arcee.py

@@ -356,12 +356,6 @@ def __init__(self, config: ArceeConfig):
         # Initialize weights and apply final processing
         self.post_init()
 
-    def get_input_embeddings(self):
-        return self.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.embed_tokens = value
-
     @check_model_inputs
     @auto_docstring
     def forward(
@@ -438,18 +432,6 @@ def __init__(self, config):
         # Initialize weights and apply final processing
         self.post_init()
 
-    def get_input_embeddings(self):
-        return self.model.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.model.embed_tokens = value
-
-    def get_output_embeddings(self):
-        return self.lm_head
-
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head = new_embeddings
-
     def set_decoder(self, decoder):
         self.model = decoder
 
@@ -533,12 +515,6 @@ def __init__(self, config):
         # Initialize weights and apply final processing
         self.post_init()
 
-    def get_input_embeddings(self):
-        return self.model.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.model.embed_tokens = value
-
     @can_return_tuple
     @auto_docstring
     def forward(
@@ -685,12 +661,6 @@ def __init__(self, config):
         # Initialize weights and apply final processing
         self.post_init()
 
-    def get_input_embeddings(self):
-        return self.model.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.model.embed_tokens = value
-
     @can_return_tuple
     @auto_docstring
     def forward(