# coding=utf-8 # Copyright 2024 the HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """PyTorch PaliGemmamodel.""" from dataclasses import dataclass from typing import List, Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from ...cache_utils import Cache, HybridCache, StaticCache from ...generation import GenerationMixin from ...modeling_outputs import CausalLMOutputWithPast from ...modeling_utils import PreTrainedModel from ...utils import ( ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, is_torchdynamo_compiling, logging, replace_return_docstrings, ) from ...utils.deprecation import deprecate_kwarg from ..auto import AutoModel, AutoModelForCausalLM from .configuration_paligemma import PaliGemmaConfig logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "PaliGemmaConfig" # Adapted from transformers.models.llama.modeling_llama.LlamaModel._prepare_4d_causal_attention_mask_with_cache_position # But Paligemma 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: Optional[torch.Tensor] = None, **kwargs, ): """ 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 place 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 PaliGemmaCausalLMOutputWithPast(ModelOutput): """ Base class for PaliGemmacausal 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: Optional[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 PaliGemmaMultiModalProjector(nn.Module): def __init__(self, config: PaliGemmaConfig): 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 PALIGEMMA_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 ([`PaliGemmaConfig`] or [`PaliGemmaVisionConfig`]): 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.", PALIGEMMA_START_DOCSTRING, ) class PaliGemmaPreTrainedModel(PreTrainedModel): config_class = PaliGemmaConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["PaliGemmaMultiModalProjector"] _skip_keys_device_placement = "past_key_values" _supports_cache_class = True _supports_quantized_cache = True _supports_static_cache = True _supports_flash_attn_2 = True _supports_sdpa = True def _init_weights(self, module): # important: this ported version of PaliGemmaisn'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_() PALIGEMMA_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 ([]`PaliGemmaProcessor`] 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 PALIGEMMA model which consists of a vision backbone and a language model.""", PALIGEMMA_START_DOCSTRING, ) class PaliGemmaForConditionalGeneration(PaliGemmaPreTrainedModel, GenerationMixin): def __init__(self, config: PaliGemmaConfig): super().__init__(config) self.vision_tower = AutoModel.from_config(config=config.vision_config) self.multi_modal_projector = PaliGemmaMultiModalProjector(config) self.vocab_size = config.text_config.vocab_size language_model = AutoModelForCausalLM.from_config(config=config.text_config) 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.post_init() # Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.get_input_embeddings with Llava->PaliGemma def get_input_embeddings(self): return self.language_model.get_input_embeddings() # Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.set_input_embeddings with Llava->PaliGemma def set_input_embeddings(self, value): self.language_model.set_input_embeddings(value) # Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.get_output_embeddings with Llava->PaliGemma def get_output_embeddings(self): return self.language_model.get_output_embeddings() # Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.set_output_embeddings with Llava->PaliGemma def set_output_embeddings(self, new_embeddings): self.language_model.set_output_embeddings(new_embeddings) # Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.set_decoder with Llava->PaliGemma def set_decoder(self, decoder): self.language_model.set_decoder(decoder) # Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.get_decoder with Llava->PaliGemma def get_decoder(self): return self.language_model.get_decoder() def _update_causal_mask( self, attention_mask, token_type_ids=None, past_key_values=None, cache_position=None, input_tensor=None, is_training: Optional[bool] = None, ): if self.config.text_config._attn_implementation == "flash_attention_2": if attention_mask is not None and 0.0 in attention_mask: return attention_mask return None is_training = is_training if is_training is not None else self.training using_static_cache = isinstance(past_key_values, StaticCache) min_dtype = torch.finfo(self.dtype).min if input_tensor is None: input_tensor = attention_mask inputs_lead_dim, sequence_length = input_tensor.shape[:2] if using_static_cache: target_length = past_key_values.get_max_cache_shape() elif isinstance(past_key_values, HybridCache): target_length = past_key_values.get_max_cache_shape() 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=self.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_lead_dim, 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] # First unmask prefix tokens during training if is_training: if token_type_ids is None: raise ValueError("Token type ids must be provided during training") causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( token_type_ids[:, None, None, :].to(causal_mask.device) == 0, 0 ) # Then apply padding mask (will mask pad tokens) 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 ) return causal_mask def get_image_features(self, pixel_values: torch.FloatTensor): """ Obtains image last hidden states from the vision tower and apply multimodal projection. Args: pixel_values (`torch.FloatTensor]` of shape `(batch_size, channels, height, width)`) The tensors corresponding to the input images. Returns: image_features (`torch.Tensor`): Image feature tensor of shape `(num_images, image_length, embed_dim)`). """ image_outputs = self.vision_tower(pixel_values) selected_image_feature = image_outputs.last_hidden_state image_features = self.multi_modal_projector(selected_image_feature) image_features = image_features / (self.config.text_config.hidden_size**0.5) return image_features @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep") @add_start_docstrings_to_model_forward(PALIGEMMA_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=PaliGemmaCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, pixel_values: Optional[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, logits_to_keep: Union[int, torch.Tensor] = 0, **lm_kwargs, ) -> Union[Tuple, PaliGemmaCausalLMOutputWithPast]: r""" 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]`. logits_to_keep (`int` or `torch.Tensor`, *optional*): If an `int`, compute logits for the last `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. If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension. This is useful when using packed tensor format (single dimension for batch and sequence length). Returns: Example: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProcessor, PaliGemmaForConditionalGeneration >>> model = PaliGemmaForConditionalGeneration.from_pretrained("google/paligemma2-3b-mix-224") >>> processor = AutoProcessor.from_pretrained("google/paligemma2-3b-mix-224") >>> prompt = "Where is the cat standing?" >>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(images=image, text=prompt, return_tensors="pt") >>> # Generate >>> generate_ids = model.generate(**inputs,) >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "Where is the cat standing?\nsnow" ```""" if (input_ids is None) ^ (inputs_embeds is not None): raise ValueError("You must specify exactly one of input_ids or inputs_embeds") 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 # Replace image id woth PAD if the image token if OOV, to avoid index-errors if input_ids is not None and self.config.image_token_index >= self.vocab_size: special_image_mask = input_ids == self.config.image_token_index llm_input_ids = input_ids.clone() llm_input_ids[special_image_mask] = 0 else: llm_input_ids = input_ids if inputs_embeds is None: inputs_embeds = self.get_input_embeddings()(llm_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_features = self.get_image_features(pixel_values) if input_ids is None: special_image_mask = inputs_embeds == self.get_input_embeddings()( torch.tensor(self.config.image_token_index, dtype=torch.long, device=inputs_embeds.device) ) else: special_image_mask = (input_ids == self.config.image_token_index).unsqueeze(-1) special_image_mask = special_image_mask.expand_as(inputs_embeds).to(inputs_embeds.device) if not is_torchdynamo_compiling() and inputs_embeds[special_image_mask].numel() != image_features.numel(): image_tokens_in_text = (special_image_mask).sum(dim=1).sum(dim=0)[0] 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, past_key_values, cache_position, inputs_embeds, is_training ) outputs: CausalLMOutputWithPast = 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=True, cache_position=cache_position, logits_to_keep=logits_to_keep, **lm_kwargs, ) logits = outputs[0] loss = None if labels is not None: # Upcast to float if we need to compute the loss to avoid potential precision issues logits = logits.float() 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. # we also crop attn mask in case it is longer, which happens in PrefixTuning with peft shift_attention_mask = attention_mask[:, -shift_logits.shape[1] :].to(logits.device) 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) output = PaliGemmaCausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, image_hidden_states=image_features if pixel_values is not None else None, ) return output if return_dict else output.to_tuple() 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, logits_to_keep=None, labels=None, **kwargs, ): # Overwritten -- custom `position_ids` and `pixel_values` handling 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, logits_to_keep=logits_to_keep, token_type_ids=token_type_ids, **kwargs, ) # 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 is_training = token_type_ids is not None and labels is not None if cache_position[0] == 0 and isinstance(past_key_values, HybridCache): input_tensor = inputs_embeds if inputs_embeds is not None else input_ids causal_mask = self._update_causal_mask( attention_mask, token_type_ids, past_key_values, cache_position, input_tensor, is_training ) model_inputs["attention_mask"] = causal_mask return model_inputs __all__ = ["PaliGemmaForConditionalGeneration", "PaliGemmaPreTrainedModel"]