Add support for inheritance from class with different suffix in modular (#34077)

* add support for different suffix in modular

* add dummy example, pull new changes for modular

* nide lines order change

examples/modular-transformers/modeling_new_task_model.py

@@ -0,0 +1,546 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from examples/modular-transformers/modular_new_task_model.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_new_task_model.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+from dataclasses import dataclass
+from typing import ClassVar, List, Optional, Tuple, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+
+from ...cache_utils import Cache, StaticCache
+from ...generation import GenerationMixin
+from ...modeling_utils import PreTrainedModel
+from ...utils import (
+    ModelOutput,
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    is_flash_attn_2_available,
+    logging,
+    replace_return_docstrings,
+)
+from .configuration_new_task_model import NewTaskModelConfig
+
+
+if is_flash_attn_2_available():
+    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
+
+from ..auto import AutoModel, AutoModelForCausalLM
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "NewTaskModelConfig"
+
+
+# Adapted from transformers.models.llama.modeling_llama.LlamaModel._prepare_4d_causal_attention_mask_with_cache_position
+# But NewTaskModel has no causal mask on prefix
+def _prepare_4d_causal_attention_mask_with_cache_position(
+    attention_mask: torch.Tensor,
+    sequence_length: int,
+    target_length: int,
+    dtype: torch.dtype,
+    device: torch.device,
+    min_dtype: float,
+    cache_position: torch.Tensor,
+    batch_size: int,
+    is_training: bool = False,
+    token_type_ids: torch.Tensor = None,
+):
+    """
+    Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
+    `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
+
+    Args:
+        attention_mask (`torch.Tensor`):
+            A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`.
+        sequence_length (`int`):
+            The sequence length being processed.
+        target_length (`int`):
+            The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet.
+        dtype (`torch.dtype`):
+            The dtype to use for the 4D attention mask.
+        device (`torch.device`):
+            The device to plcae the 4D attention mask on.
+        min_dtype (`float`):
+            The minimum value representable with the dtype `dtype`.
+        cache_position (`torch.Tensor`):
+            Indices depicting the position of the input sequence tokens in the sequence.
+        batch_size (`torch.Tensor`):
+            Batch size.
+        is_training (`bool`):
+            Whether the model is in training mode or in inference. The condition is checked by presence/absence of `token_type_ids/labels`
+    """
+    if attention_mask is not None and attention_mask.dim() == 4:
+        # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
+        causal_mask = attention_mask
+    else:
+        causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device)
+        # Causal diagonal mask only if training, otherwise attend to the whole prefix. Training-specific attn for prefix is handled below
+        if sequence_length != 1:
+            if is_training:
+                causal_mask = torch.triu(causal_mask, diagonal=1)
+            else:
+                causal_mask[:, :sequence_length] = 0.0
+
+        causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1)
+        causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+        if attention_mask is not None:
+            causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
+            mask_length = attention_mask.shape[-1]
+            padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(causal_mask.device)
+            padding_mask = padding_mask == 0
+            causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+                padding_mask, min_dtype
+            )
+            # we are training thus we need to create a full mask on the image + prefix but causal on suffix
+            if is_training:
+                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+                    token_type_ids[:, None, None, :].to(causal_mask.device) == 0, 0
+                )
+    return causal_mask
+
+
+@dataclass
+class NewTaskModelCausalLMOutputWithPast(ModelOutput):
+    """
+    Base class for NewTaskModelcausal language model (or autoregressive) outputs.
+
+    Args:
+        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+            Language modeling loss (for next-token prediction).
+        logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.text_config.vocab_size)`):
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+            `past_key_values` input) to speed up sequential decoding.
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
+        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+            sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+        image_hidden_states (`torch.FloatTensor`, *optional*):
+            A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
+            image_hidden_states of the model produced by the vision encoder after projecting last hidden state.
+    """
+
+    loss: Optional[torch.FloatTensor] = None
+    logits: torch.FloatTensor = None
+    past_key_values: Optional[Union[List[torch.FloatTensor], Cache]] = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+    image_hidden_states: Optional[torch.FloatTensor] = None
+
+
+class NewTaskModelMultiModalProjector(nn.Module):
+    def __init__(self, config: NewTaskModelConfig):
+        super().__init__()
+        self.linear = nn.Linear(config.vision_config.hidden_size, config.vision_config.projection_dim, bias=True)
+
+    def forward(self, image_features):
+        hidden_states = self.linear(image_features)
+
+        return hidden_states
+
+
+NEW_TASK_MODEL_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`NewTaskModelConfig`] or [`NewTaskModelVisionConfig`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
+    NEW_TASK_MODEL_START_DOCSTRING,
+)
+class NewTaskModelPreTrainedModel(PreTrainedModel):
+    config_class = NewTaskModelConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["NewTaskModelMultiModalProjector"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = False
+    _supports_cache_class = True
+    _supports_quantized_cache = True
+    _supports_static_cache = True
+    _supports_sdpa = True
+    _supports_cache_class = True
+
+    def _init_weights(self, module):
+        # important: this ported version of NewTaskModelisn't meant for training from scratch - only
+        # inference and fine-tuning
+        std = (
+            self.config.initializer_range
+            if hasattr(self.config, "initializer_range")
+            else self.config.text_config.initializer_range
+        )
+
+        if hasattr(module, "class_embedding"):
+            module.class_embedding.data.normal_(mean=0.0, std=std)
+
+        if isinstance(module, (nn.Linear, nn.Conv2d)):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+    @property
+    def _supports_sdpa(self):
+        """
+        Retrieve language_model's attribute to check whether the model supports
+        SDPA or not.
+        """
+        return self.language_model._supports_sdpa
+
+
+NEW_TASK_MODEL_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)):
+            The tensors corresponding to the input images. Pixel values can be obtained using
+            [`AutoImageProcessor`]. See [`SiglipImageProcessor.__call__`] for details ([]`NewTaskModelProcessor`] uses
+            [`SiglipImageProcessor`] for processing images).
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
+        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
+            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
+            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
+            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+        cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+            Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
+            this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
+            the complete sequence length.
+"""
+
+
+@add_start_docstrings(
+    """The NEW_TASK_MODEL model which consists of a vision backbone and a language model.""",
+    NEW_TASK_MODEL_START_DOCSTRING,
+)
+class NewTaskModelForNewTask(NewTaskModelPreTrainedModel, GenerationMixin):
+    main_input_name: ClassVar[str] = "doc_input_ids"  # transformers-related
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.vision_tower = AutoModel.from_config(config=config.vision_config)
+        self.multi_modal_projector = NewTaskModelMultiModalProjector(config)
+        self.vocab_size = config.text_config.vocab_size
+        self._attn_implementation = config._attn_implementation
+
+        language_model = AutoModelForCausalLM.from_config(
+            config=config.text_config, attn_implementation=self._attn_implementation
+        )
+
+        if language_model._tied_weights_keys is not None:
+            self._tied_weights_keys = [f"language_model.{k}" for k in language_model._tied_weights_keys]
+        self.language_model = language_model
+
+        self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1
+
+        self.embedding_dim = self.config.embedding_dim
+        self.custom_text_proj = nn.Linear(self.config.text_config.hidden_size, self.embedding_dim)
+
+        if self.language_model._tied_weights_keys is not None:
+            self._tied_weights_keys = [f"model.language_model.{k}" for k in self.language_model._tied_weights_keys]
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.language_model.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.language_model.set_input_embeddings(value)
+
+    def get_output_embeddings(self):
+        return self.language_model.get_output_embeddings()
+
+    def set_output_embeddings(self, new_embeddings):
+        self.language_model.set_output_embeddings(new_embeddings)
+
+    def set_decoder(self, decoder):
+        self.language_model.set_decoder(decoder)
+
+    def get_decoder(self):
+        return self.language_model.get_decoder()
+
+    def tie_weights(self):
+        return self.language_model.tie_weights()
+
+    def _update_causal_mask(
+        self, attention_mask, token_type_ids, inputs_embeds, past_key_values, cache_position, is_training: bool = False
+    ):
+        using_static_cache = isinstance(past_key_values, StaticCache)
+        dtype = inputs_embeds.dtype
+        min_dtype = torch.finfo(dtype).min
+        sequence_length = inputs_embeds.shape[1]
+        if using_static_cache:
+            target_length = past_key_values.get_max_length()
+        else:
+            target_length = (
+                attention_mask.shape[-1]
+                if isinstance(attention_mask, torch.Tensor)
+                else cache_position[0] + sequence_length + 1
+            )
+
+        if attention_mask is not None and attention_mask.dim() == 4:
+            # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
+            return attention_mask
+
+        causal_mask = torch.full(
+            (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
+        )
+        # Causal diagonal mask only if training, otherwise attend to the whole prefix. Training-specific attn for prefix is handled below
+        if sequence_length != 1:
+            if is_training:
+                causal_mask = torch.triu(causal_mask, diagonal=1)
+            else:
+                causal_mask[:, :sequence_length] = 0.0
+
+        causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1)
+        causal_mask = causal_mask[None, None, :, :].expand(inputs_embeds.shape[0], 1, -1, -1)
+        if attention_mask is not None:
+            causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
+            mask_length = attention_mask.shape[-1]
+            padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(causal_mask.device)
+            padding_mask = padding_mask == 0
+            causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+                padding_mask, min_dtype
+            )
+            # we are training thus we need to create a full mask on the image + prefix but causal on suffix
+            if is_training:
+                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+                    token_type_ids[:, None, None, :].to(causal_mask.device) == 0, 0
+                )
+        return causal_mask
+
+    @add_start_docstrings_to_model_forward(NEW_TASK_MODEL_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=NewTaskModelCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        pixel_values: torch.FloatTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Union[List[torch.FloatTensor], Cache]] = None,
+        token_type_ids: Optional[torch.LongTensor] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        num_logits_to_keep: int = 0,
+    ) -> Union[Tuple, NewTaskModelCausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.text_config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.text_config.vocab_size]`.
+
+            num_logits_to_keep (`int`, *optional*):
+                Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all
+                `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
+                token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from PIL import Image
+        >>> import requests
+        >>> from transformers import AutoProcessor, NewTaskModelForNewTask
+
+        >>> model = NewTaskModelForNewTask.from_pretrained("google/NewTaskModel-test-224px-hf")
+        >>> processor = AutoProcessor.from_pretrained("google/NewTaskModel-test-224px-hf")
+
+        >>> prompt = "answer en Where is the cow standing?"
+        >>> url = "https://huggingface.co/gv-hf/NewTaskModel-test-224px-hf/resolve/main/cow_beach_1.png"
+        >>> image = Image.open(requests.get(url, stream=True).raw)
+
+        >>> inputs = processor(images=image, text=prompt,  return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(**inputs, max_length=30)
+        >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "answer en Where is the cow standing?\nbeach"
+        ```
+        Returns:
+        """
+        vlm_outputs = super().forward(
+            input_ids=input_ids,
+            pixel_values=pixel_values,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            token_type_ids=token_type_ids,
+            cache_position=cache_position,
+            inputs_embeds=inputs_embeds,
+            labels=labels,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=True,
+            return_dict=True,
+            num_logits_to_keep=num_logits_to_keep,
+        )
+        last_hidden_states = vlm_outputs.hidden_states[-1]  # (batch_size, sequence_length, hidden_size)
+        proj = self.custom_text_proj(last_hidden_states)  # (batch_size, sequence_length, dim)
+
+        # L2 normalization
+        embeddings = proj / proj.norm(dim=-1, keepdim=True)  # (batch_size, sequence_length, dim)
+
+        embeddings = embeddings * attention_mask.unsqueeze(-1)  # (batch_size, sequence_length, dim)
+
+        return (embeddings,) + vlm_outputs
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        inputs_embeds=None,
+        cache_position=None,
+        position_ids=None,
+        pixel_values=None,
+        attention_mask=None,
+        token_type_ids=None,
+        use_cache=True,
+        num_logits_to_keep=None,
+        **kwargs,
+    ):
+        model_inputs = self.language_model.prepare_inputs_for_generation(
+            input_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            cache_position=cache_position,
+            use_cache=use_cache,
+            num_logits_to_keep=num_logits_to_keep,
+            **kwargs,
+        )
+
+        if isinstance(past_key_values, StaticCache) and attention_mask.ndim == 2:
+            if model_inputs["inputs_embeds"] is not None:
+                batch_size, sequence_length, _ = model_inputs["inputs_embeds"].shape
+                device = model_inputs["inputs_embeds"].device
+            else:
+                batch_size, sequence_length = model_inputs["input_ids"].shape
+                device = model_inputs["input_ids"].device
+
+            dtype = self.get_output_embeddings().weight.dtype
+            min_dtype = torch.finfo(dtype).min
+
+            model_inputs["attention_mask"] = _prepare_4d_causal_attention_mask_with_cache_position(
+                attention_mask,
+                sequence_length=sequence_length,
+                target_length=past_key_values.get_max_length(),
+                dtype=dtype,
+                device=device,
+                min_dtype=min_dtype,
+                cache_position=cache_position,
+                batch_size=batch_size,
+            )
+
+        model_inputs["token_type_ids"] = token_type_ids
+
+        # position_ids in NewTaskModel are 1-indexed
+        if model_inputs.get("position_ids") is not None:
+            model_inputs["position_ids"] += 1
+
+        # If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore
+        # Otherwise we need pixel values to be passed to model. NOTE: use_cache=False needs pixel_values always
+        if cache_position[0] == 0:
+            model_inputs["pixel_values"] = pixel_values
+
+        return model_inputs
+
+    def resize_token_embeddings(
+        self,
+        new_num_tokens: Optional[int] = None,
+        pad_to_multiple_of=None,
+    ) -> nn.Embedding:
+        model_embeds = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
+
+        # Update vocab size
+        self.config.text_config.vocab_size = model_embeds.num_embeddings
+        self.config.vocab_size = model_embeds.num_embeddings
+        self.vocab_size = model_embeds.num_embeddings
+
+        return model_embeds

examples/modular-transformers/modular_new_task_model.py

@@ -0,0 +1,84 @@
+from typing import ClassVar, List, Optional, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+
+from transformers.models.paligemma.modeling_paligemma import PaliGemmaForConditionalGeneration
+
+from ...cache_utils import Cache
+
+
+class NewTaskModelForNewTask(PaliGemmaForConditionalGeneration):
+    main_input_name: ClassVar[str] = "doc_input_ids"  # transformers-related
+
+    def __init__(self, config):
+        super().__init__(config=config)
+
+        self.embedding_dim = self.config.embedding_dim
+        self.custom_text_proj = nn.Linear(self.config.text_config.hidden_size, self.embedding_dim)
+
+        if self.language_model._tied_weights_keys is not None:
+            self._tied_weights_keys = [f"model.language_model.{k}" for k in self.language_model._tied_weights_keys]
+
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        pixel_values: torch.FloatTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Union[List[torch.FloatTensor], Cache]] = None,
+        token_type_ids: Optional[torch.LongTensor] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        num_logits_to_keep: int = 0,
+    ):
+        r"""
+        Returns:
+        """
+        vlm_outputs = super().forward(
+            input_ids=input_ids,
+            pixel_values=pixel_values,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            token_type_ids=token_type_ids,
+            cache_position=cache_position,
+            inputs_embeds=inputs_embeds,
+            labels=labels,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=True,
+            return_dict=True,
+            num_logits_to_keep=num_logits_to_keep,
+        )
+        last_hidden_states = vlm_outputs.hidden_states[-1]  # (batch_size, sequence_length, hidden_size)
+        proj = self.custom_text_proj(last_hidden_states)  # (batch_size, sequence_length, dim)
+
+        # L2 normalization
+        embeddings = proj / proj.norm(dim=-1, keepdim=True)  # (batch_size, sequence_length, dim)
+
+        embeddings = embeddings * attention_mask.unsqueeze(-1)  # (batch_size, sequence_length, dim)
+
+        return (embeddings,) + vlm_outputs
+
+    def resize_token_embeddings(
+        self,
+        new_num_tokens: Optional[int] = None,
+        pad_to_multiple_of=None,
+    ) -> nn.Embedding:
+        model_embeds = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
+
+        # Update vocab size
+        self.config.text_config.vocab_size = model_embeds.num_embeddings
+        self.config.vocab_size = model_embeds.num_embeddings
+        self.vocab_size = model_embeds.num_embeddings
+
+        return model_embeds

utils/modular_model_converter.py

@@ -204,7 +204,15 @@ class ReplaceNameTransformer(m.MatcherDecoratableTransformer):
         - LLaMa -> MyNewModel       abd     MyNewModel      -> Llama
     """
 
-    def __init__(self, old_name, new_name, given_old_name=None, given_new_name=None):
+    def __init__(
+        self,
+        old_name,
+        new_name,
+        given_old_name=None,
+        given_new_name=None,
+        old_class_name: str = None,
+        new_class_name: str = None,
+    ):
         super().__init__()
         self.old_name = old_name
         self.new_name = new_name
@@ -220,6 +228,18 @@ class ReplaceNameTransformer(m.MatcherDecoratableTransformer):
         }
         if given_old_name is not None and given_new_name is not None and given_old_name not in self.patterns:
             self.patterns[given_old_name] = given_new_name
+        if self.old_name in CONFIG_MAPPING_NAMES:
+            self.default_old_name = CONFIG_MAPPING_NAMES[self.old_name].replace("Config", "")
+            if self.default_old_name.isupper():
+                self.default_old_name = self.default_old_name.capitalize()
+        if new_class_name is not None and old_class_name is not None and old_class_name not in self.patterns:
+            # In last recourse, when the suffix of the new class is not the same as the old class,
+            # and if the old and new classes start with the default name, we keep the default class name
+            # and replace the old suffix with the new one.
+            # Useful when we have a class like `ColPaliForRetrieval` inheriting from `PaliGemmaForConditionalGeneration`
+            # where a model extends another model, but is used for a different task.
+            if old_class_name.startswith(self.default_old_name) and new_class_name.startswith(self.default_name):
+                self.patterns[old_class_name[len(self.default_old_name) :]] = new_class_name[len(self.default_name) :]
 
     def preserve_case_replace(self, text):
         # Create a regex pattern to match all variations
@@ -235,7 +255,9 @@ class ReplaceNameTransformer(m.MatcherDecoratableTransformer):
 
     def convert_to_camelcase(self, text):
         # Regex pattern to match consecutive uppercase letters and lowercase the first set
-        result = re.sub(r"^[A-Z]+(?=[A-Z][a-z])", lambda m: m.group(0).capitalize(), text, count=1)
+        result = re.sub(
+            rf"^({self.old_name})(?=[a-z]+)", lambda m: self.default_old_name, text, flags=re.IGNORECASE, count=1
+        )
         return result
 
     @m.leave(m.Name() | m.SimpleString() | m.Comment())
@@ -249,9 +271,24 @@ class ReplaceNameTransformer(m.MatcherDecoratableTransformer):
         return updated_node.with_changes(name=cst.Name(self.convert_to_camelcase(updated_node.name.value)))
 
 
-def find_classes_in_file(module: cst.Module, old_id="llama", new_id="gemma", given_old_name=None, given_new_name=None):
+def find_classes_in_file(
+    module: cst.Module,
+    old_id="llama",
+    new_id="gemma",
+    given_old_name=None,
+    given_new_name=None,
+    old_class_name=None,
+    new_class_name=None,
+):
     """Helper function to rename and then parse a source file using the ClassFinder"""
-    transformer = ReplaceNameTransformer(old_id, new_id, given_old_name, given_new_name)
+    transformer = ReplaceNameTransformer(
+        old_id,
+        new_id,
+        given_old_name=given_old_name,
+        given_new_name=given_new_name,
+        old_class_name=old_class_name,
+        new_class_name=new_class_name,
+    )
     new_module = module.visit(transformer)
 
     wrapper = MetadataWrapper(new_module)
@@ -868,7 +905,7 @@ class ModularConverterTransformer(CSTTransformer):
                     dep: class_finder.class_start_line.get(dep, 1000)
                     for dep in class_finder.class_dependency_mapping.get(class_name, [])
                 }
-            if list_dependencies == []:
+            if len(list_dependencies) == 0:
                 # so, maybe standard renaming did not work (the class name is different)
                 # we try with another renaming pattern
                 potential_given_name = get_new_part(class_name, super_class)
@@ -884,6 +921,30 @@ class ModularConverterTransformer(CSTTransformer):
                     dep: class_finder.class_start_line.get(dep, 1000)
                     for dep in class_finder.class_dependency_mapping.get(class_name, [])
                 }
+            if len(list_dependencies) == 0:
+                # last recourse, if the suffix of the new class is different from the one of the super class
+                # e.g. MyNewClassForSegmentation extends MyOldClassForObjectDetection
+                # we try with another renaming pattern
+                class_finder = find_classes_in_file(
+                    self.transformers_imports[super_file_name],
+                    model_name,
+                    self.model_name,
+                    self.given_old_name,
+                    self.given_new_name,
+                    super_class,
+                    class_name,
+                )
+                visited_module[super_file_name] = class_finder
+                list_dependencies = {
+                    dep: class_finder.class_start_line.get(dep, 1000)
+                    for dep in class_finder.class_dependency_mapping.get(class_name, [])
+                }
+            if len(list_dependencies) == 0:
+                raise ValueError(
+                    f"We were unable to find dependencies for {class_name} (based on inheriting from {super_class})"
+                    f"   Here are all the global dependencies that we found in you modular file: {list(class_finder.class_dependency_mapping.keys())}."
+                    f"   This usually means that the name of `{class_name}` does not match the pattern of `{super_class}`"
+                )
 
             list_dependencies = sorted(list_dependencies.items(), key=lambda x: x[1], reverse=True)
             start_insert_idx = self.global_scope_index
@@ -917,12 +978,6 @@ class ModularConverterTransformer(CSTTransformer):
 
             if len(list_dependencies) > 0:
                 updated_node = replace_call_to_super(class_finder, updated_node, class_name, all_bases)
-            else:
-                raise ValueError(
-                    f"We were unable to find dependencies for {class_name} (based on inheriting from {super_class})"
-                    f"   Here are all the global dependencies that we found in you modular file: {list(class_finder.class_dependency_mapping.keys())}."
-                    f"   This usually means that the name of `{class_name}` does not match the pattern of `{super_class}`"
-                )
 
         # Now, if a class was defined without parents, we look for the name
         match_pattern = "|".join(TYPE_TO_FILE_TYPE.keys())