modeling_taivisionlm.py

"""PyTorch TaiVisionLM"""
import torch
from transformers import PreTrainedModel, AutoModel, AutoModelForCausalLM
from transformers.utils import logging, add_start_docstrings, ModelOutput
from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask_for_sdpa
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
from torch import nn
from transformers.cache_utils import Cache, StaticCache

logger = logging.get_logger(__name__)

from .configuration_taivisionlm import TaiVisionLMConfig

_CONFIG_FOR_DOC = "TaiVisionLMConfig"

@dataclass
class TaiVisionCausalLMOutputWithPast(ModelOutput):
    """
    Base class for TaiVision 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.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 (`tuple(torch.FloatTensor)`, *optional*):
            Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images,
            sequence_length, hidden_size)`.

            image_hidden_states of the model produced by the vision encoder, and optionally by the perceiver
    """
    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[Tuple[torch.FloatTensor]] = None


class TaiVisionMultiModalProjector(nn.Module):
    """
    Multimodal projector that cast the image features into the same dimension space as the language model
    """
    def __init__(self, config: TaiVisionLMConfig, dropout=0.1):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(config.vision_config.projection_dim, 4*config.vision_config.projection_dim, bias=True),
            nn.GELU(),
            nn.Linear(4*config.vision_config.projection_dim, config.hidden_size, bias=True),
            nn.Dropout(dropout)
        )

    def forward(self, image_features):
        hidden_states = self.net(image_features).to(image_features.dtype)
        return hidden_states
    

TRAVISIONLM_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 ([`TaiVisionLMConfig`]):
            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 TaiVision Model outputting raw hidden-states without any specific head on top.",
    TRAVISIONLM_START_DOCSTRING,
)
class TaiVisionPreTrainedModel(PreTrainedModel):
    config_class = TaiVisionLMConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["TaiVisionMultiModalProjector"]
    _skip_keys_device_placement = "past_key_values"
    _supports_flash_attn_2 = True
    _supports_sdpa = True

    def _init_weights(self, module):
        # Do NOT init the weights of the model using this class call, this is a ported version, 
        # hence not intended to be trained from scratch.
        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


@add_start_docstrings(
    """The TaiVisionLM model which consists of a vision backbone and a language model.""",
    TRAVISIONLM_START_DOCSTRING,
)
class TaiVisionForCausalLM(TaiVisionPreTrainedModel):
    def __init__(self, config: TaiVisionLMConfig):
        super(TaiVisionForCausalLM, self).__init__(config)
        self.vocab_size = config.text_config.vocab_size
        self.pad_token_id = -1 if config.pad_token_id == None else config.pad_token_id
        self._attn_implementation = config._attn_implementation
        self.gradient_checkpointing = False

        self.vision_tower = AutoModel.from_config(config=config.vision_config)
        self.vision_projector = TaiVisionMultiModalProjector(config)

        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.post_init()
        
    def load_language_model(self, model_id = "benchang1110/Taiwan-tinyllama-v1.0-chat"):
        language_model = AutoModelForCausalLM.from_pretrained(model_id)
        if language_model.vocab_size != self.vocab_size:
            print("vocab size mismatch, resize the token embeddings for the pretained language model")
            language_model.resize_token_embeddings(self.vocab_size)
        self.language_model.load_state_dict(language_model.state_dict(),strict=True)
        
    def load_vision_model(self,model_id = "google/siglip-base-patch16-224"):
        import transformers
        vision_model = transformers.SiglipVisionModel.from_pretrained(model_id)
        self.vision_tower.load_state_dict(vision_model.state_dict(),strict=True)
        
    # Copied from transformers.models.paligemma.modeling_paligemma.PaliGemmaForConditionalGeneration.get_input_embeddings with PaliGemma->TaiVisionLM
    def get_input_embeddings(self):
        return self.language_model.get_input_embeddings()

    # Copied from transformers.models.paligemma.modeling_paligemma.PaliGemmaForConditionalGeneration.set_input_embeddings with PaliGemma->TaiVisionLM
    def set_input_embeddings(self, value):
        self.language_model.set_input_embeddings(value)

    # Copied from transformers.models.paligemma.modeling_paligemma.PaliGemmaForConditionalGeneration.get_output_embeddings with PaliGemma->TaiVisionLM
    def get_output_embeddings(self):
        return self.language_model.get_output_embeddings()

    # Copied from transformers.models.paligemma.modeling_paligemma.PaliGemmaForConditionalGeneration.set_output_embeddings with PaliGemma->TaiVisionLM
    def set_output_embeddings(self, new_embeddings):
        self.language_model.set_output_embeddings(new_embeddings)

    # Copied from transformers.models.paligemma.modeling_paligemma.PaliGemmaForConditionalGeneration.set_decoder with PaliGemma->TaiVisionLM
    def set_decoder(self, decoder):
        self.language_model.set_decoder(decoder)

    # Copied from transformers.models.paligemma.modeling_paligemma.PaliGemmaForConditionalGeneration.get_decoder with PaliGemma->TaiVisionLM
    def get_decoder(self):
        return self.language_model.get_decoder()

    # Copied from transformers.models.paligemma.modeling_paligemma.PaliGemmaForConditionalGeneration.tie_weights with PaliGemma->TaiVisionLM
    def tie_weights(self):
        return self.language_model.tie_weights()
    
    def resize_token_embeddings(self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None) -> nn.Embedding:
        # TODO: config.vocab_size is deprecated and will be removed in v4.43.
        # `resize_token_embeddings` should work from `modeling_utils.py``
        model_embeds = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
        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

    # Copied from transformers.models.paligemma.modeling_paligemma.PaliGemmaForConditionalGeneration._merge_input_ids_with_image_features with PaliGemma->TaiVisionLM
    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, device = inputs_embeds.dtype, inputs_embeds.device
        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.
            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 = torch.zeros_like(causal_mask)

        causal_mask *= torch.arange(target_length, device=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
    

    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,
    ) -> Union[Tuple, TaiVisionCausalLMOutputWithPast]:
        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.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.vocab_size]`.

        Returns:

        Example:

        ```python
        >>> from PIL import Image
        >>> import requests
        >>> from transformers import AutoProcessor, PaliGemmaForConditionalGeneration

        >>> model = PaliGemmaForConditionalGeneration.from_pretrained("google/PaliGemma-test-224px-hf")
        >>> processor = AutoProcessor.from_pretrained("google/PaliGemma-test-224px-hf")

        >>> prompt = "answer en Where is the cow standing?"
        >>> url = "https://huggingface.co/gv-hf/PaliGemma-test-224px-hf/resolve/main/cow_beach_1.png"
        >>> image = Image.open(requests.get(url, stream=True).raw)

        >>> inputs = processor(text=prompt, images=image, 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"
        ```"""

        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError(
                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
            )

        if pixel_values is not None and inputs_embeds is not None:
            raise ValueError(
                "You cannot specify both pixel_values and inputs_embeds at the same time, and must specify either one"
            )

        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        is_training = token_type_ids is not None and labels is not None

        if inputs_embeds is None:
            inputs_embeds = self.get_input_embeddings()(input_ids)

        if cache_position is None:
            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
            cache_position = torch.arange(
                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
            )

        if position_ids is None:
            position_ids = cache_position.unsqueeze(0) + 1  # Paligemma positions are 1-indexed

        # Merge text and images
        if pixel_values is not None:
            image_outputs = self.vision_tower(pixel_values.to(inputs_embeds.dtype))
            selected_image_feature = image_outputs.last_hidden_state
            image_features = self.vision_projector(selected_image_feature)
            image_features = image_features / (self.config.hidden_size**0.5)

            special_image_mask = (input_ids == self.config.image_token_index).unsqueeze(-1).expand_as(inputs_embeds)
            if inputs_embeds[special_image_mask].numel() != image_features.numel():
                image_tokens_in_text = torch.sum(input_ids == self.config.image_token_index)
                raise ValueError(
                    f"Number of images does not match number of special image tokens in the input text. "
                    f"Got {image_tokens_in_text} image tokens in the text but {image_features.shape[0] * image_features.shape[1]} "
                    "tokens from image embeddings."
                )
            image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
            inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)

        # mask out pad-token-ids in labels for BC
        if labels is not None and self.pad_token_id in labels:
            logger.warning_once(
                "`labels` contains `pad_token_id` which will be masked with `config.ignore_index`. ",
                "You have to mask out `pad_token_id` when preparing `labels`, this behavior will be removed in v.4.46.",
            )
            labels = torch.where(input_ids == self.pad_token_id, self.config.ignore_index, labels)

        causal_mask = self._update_causal_mask(
            attention_mask, token_type_ids, inputs_embeds, past_key_values, cache_position, is_training
        )

        outputs = self.language_model(
            attention_mask=causal_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            cache_position=cache_position,
        )

        logits = outputs.logits
        logits = logits.float()
        loss = None
        if labels is not None:
            shift_logits = logits[..., :-1, :]
            shift_labels = labels[..., 1:]
            if attention_mask is not None:
                # we use the input attention mask to shift the logits and labels, because it is 2D.
                shift_attention_mask = attention_mask[..., 1:]
                shift_logits = shift_logits[shift_attention_mask.to(logits.device) != 0].contiguous()
                shift_labels = shift_labels[shift_attention_mask.to(shift_labels.device) != 0].contiguous()
            else:
                shift_logits = shift_logits.contiguous()
                shift_labels = shift_labels.contiguous()
            # Flatten the tokens
            loss_fct = nn.CrossEntropyLoss()

            flat_logits = shift_logits.view(-1, self.config.text_config.vocab_size)
            flat_labels = shift_labels.view(-1).to(shift_logits.device)
            loss = loss_fct(flat_logits, flat_labels)
        if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss,) + output if loss is not None else output

        return TaiVisionCausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

    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,
        **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,
            cache_position=cache_position,
            **kwargs,
        )

        model_inputs["token_type_ids"] = token_type_ids

        # position_ids in Paligemma 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