# coding=utf-8 # Copyright 2023 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 Llava model.""" from dataclasses import dataclass from typing import List, Optional, Tuple, Union import math import torch import torch.utils.checkpoint from torch import nn import os from transformers import PreTrainedModel from transformers.activations import ACT2FN from transformers.cache_utils import Cache from transformers.modeling_outputs import ModelOutput from transformers.utils import ( add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from transformers.models.auto import AutoModel, AutoModelForCausalLM import einops from .configuration_pllava import PllavaConfig import pickle logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "LlavaConfig" PLLAVA_PRETRAINED_MODEL_ARCHIVE_LIST = [ "", "", "", # See all Llava models at https://huggingface.co/models?filter=llava ] @dataclass # Copied from transformers.models.idefics.modeling_idefics.IdeficsCausalLMOutputWithPast with Idefics->Llava class PllavaCausalLMOutputWithPast(ModelOutput): """ Base class for Llava causal 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[List[torch.FloatTensor]] = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None image_hidden_states: Optional[Tuple[torch.FloatTensor]] = None class PllavaMultiModalProjector(nn.Module): supported_highres = ['pad_crop_four', 'slide', ] def __init__(self, config: PllavaConfig): super().__init__() self.use_pooling = config.use_pooling self.frame_shape=config.frame_shape self.num_frames = config.num_frames self.pooling_shape = config.pooling_shape self.pooling = nn.AdaptiveAvgPool3d(config.pooling_shape) self.linear_1 = nn.Linear(config.vision_config.hidden_size, config.text_config.hidden_size, bias=True) self.act = ACT2FN[config.projector_hidden_act] self.linear_2 = nn.Linear(config.text_config.hidden_size, config.text_config.hidden_size, bias=True) def convert_Fembeddings2video(self, input, num_videos, frame_shape): input = einops.rearrange(input, '(num_videos num_frames) (h w) embed_dims -> num_videos embed_dims num_frames h w', num_videos=num_videos, h=frame_shape[0]) return input def convert_video2Fembeddings(self, input): input = einops.rearrange(input, 'num_videos embed_dims num_frames h w -> (num_videos num_frames) (h w) embed_dims ', ) return input def convert_video2MMembeddings(self, input): input = einops.rearrange(input, 'num_videos embed_dims num_frames h w -> num_videos (num_frames h w) embed_dims ', ) return input def forward(self, image_features, media_type, batch_size=None, num_videos=None): frame_shape = self.frame_shape num_frames = self.num_frames assert media_type in ( 'video', 'image'), f'only image or video, but got media_type {media_type}' hidden_states = image_features if media_type == 'image': hidden_states = hidden_states.repeat(num_frames, 1, 1) total_frames, spatial_seqlen, embed_dims = hidden_states.shape #TODO: temporal code, should ensure num_frames == total frames in data loading later if total_frames < num_frames and self.use_pooling: # multiplier = int(num_frames/total_frames)+1 hidden_states= hidden_states.repeat_interleave(multiplier, dim=0)[:num_frames] total_frames, spatial_seqlen, embed_dims = hidden_states.shape assert total_frames % num_frames == 0 assert frame_shape[0] * frame_shape[1] == spatial_seqlen hidden_states = self.linear_1(hidden_states) hidden_states = self.act(hidden_states) hidden_states = self.linear_2(hidden_states) hidden_states_videos = self.convert_Fembeddings2video(hidden_states, num_videos * batch_size, frame_shape) hidden_states_videos = self.pooling(hidden_states_videos) hidden_states = einops.rearrange(hidden_states_videos, 'batch_size_num_videos embed_dims num_frames h w -> batch_size_num_videos num_frames (h w) embed_dims', ) hidden_states = einops.rearrange(hidden_states, 'batch_size_num_videos num_frames hw embed_dims -> batch_size_num_videos (num_frames hw) embed_dims ') return hidden_states PLLAVA_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 ([`LlavaConfig`] or [`LlavaVisionConfig`]): 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.", PLLAVA_START_DOCSTRING, ) class PllavaPreTrainedModel(PreTrainedModel): config_class = PllavaConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["LlavaVisionAttention"] _skip_keys_device_placement = "past_key_values" _supports_flash_attn_2 = True def _init_weights(self, module): # important: this ported version of Llava isn't meant for training from scratch - only # inference and fine-tuning - so the proper init weights code has been removed - the original codebase # https://github.com/haotian-liu/LLaVA/tree/main/llava should serve for that purpose 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_() elif isinstance(module, PllavaMultiModalProjector): # module.register_embed.data.normal_(mean=0.0, std=std) if self.config.register: module.register_embed.data.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 PLLAVA_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 [`CLIPImageProcessor.__call__`] for details ([]`LlavaProcessor`] uses [`CLIPImageProcessor`] 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. """ @add_start_docstrings( """The LLAVA model which consists of a vision backbone and a language model.""", PLLAVA_START_DOCSTRING, ) class PllavaForConditionalGeneration(PllavaPreTrainedModel): def __init__(self, config: PllavaConfig): super().__init__(config) self.config = config self.vision_tower = AutoModel.from_config(config.vision_config) self.multi_modal_projector = PllavaMultiModalProjector(config) self.vocab_size = config.vocab_size # self.language_model = AutoModelForCausalLM.from_config(config.text_config, torch_dtype=config.torch_dtype, attn_implementation="flash_attention_2") self.language_model = AutoModelForCausalLM.from_config(config.text_config, torch_dtype=config.torch_dtype, attn_implementation="eager") self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else self.config.text_config.pad_token_id assert self.pad_token_id is not None, 'provide the model with pad_token_id, this would be used to arranging new embedings' 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 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 def _merge_input_ids_with_image_features(self, image_features, inputs_embeds, input_ids, attention_mask, labels): num_images, num_image_patches, embed_dim = image_features.shape batch_size, sequence_length = input_ids.shape left_padding = not torch.sum(input_ids[:, -1] == torch.tensor(self.pad_token_id)) # 1. Create a mask to know where special image tokens are special_image_token_mask = input_ids == self.config.image_token_index num_special_image_tokens = torch.sum(special_image_token_mask, dim=-1) # Compute the maximum embed dimension max_embed_dim = (num_special_image_tokens.max() * (num_image_patches - 1)) + sequence_length batch_indices, non_image_indices = torch.where(input_ids != self.config.image_token_index) # 2. Compute the positions where text should be written # Calculate new positions for text tokens in merged image-text sequence. # `special_image_token_mask` identifies image tokens. Each image token will be replaced by `nb_text_tokens_per_images - 1` text tokens. # `torch.cumsum` computes how each image token shifts subsequent text token positions. # - 1 to adjust for zero-based indexing, as `cumsum` inherently increases indices by one. new_token_positions = torch.cumsum((special_image_token_mask * (num_image_patches - 1) + 1), -1) - 1 nb_image_pad = max_embed_dim - 1 - new_token_positions[:, -1] if left_padding: new_token_positions += nb_image_pad[:, None] # offset for left padding text_to_overwrite = new_token_positions[batch_indices, non_image_indices] # 3. Create the full embedding, already padded to the maximum position final_embedding = torch.zeros( batch_size, max_embed_dim, embed_dim, dtype=inputs_embeds.dtype, device=inputs_embeds.device ) final_attention_mask = torch.zeros( batch_size, max_embed_dim, dtype=attention_mask.dtype, device=inputs_embeds.device ) if labels is not None: final_labels = torch.full( (batch_size, max_embed_dim), self.config.ignore_index, dtype=input_ids.dtype, device=input_ids.device ) # In case the Vision model or the Language model has been offloaded to CPU, we need to manually # set the corresponding tensors into their correct target device. target_device = inputs_embeds.device batch_indices, non_image_indices, text_to_overwrite = ( batch_indices.to(target_device), non_image_indices.to(target_device), text_to_overwrite.to(target_device), ) attention_mask = attention_mask.to(target_device) # 4. Fill the embeddings based on the mask. If we have ["hey" "", "how", "are"] # we need to index copy on [0, 577, 578, 579] for the text and [1:576] for the image features final_embedding[batch_indices, text_to_overwrite] = inputs_embeds[batch_indices, non_image_indices] final_attention_mask[batch_indices, text_to_overwrite] = attention_mask[batch_indices, non_image_indices] if labels is not None: final_labels[batch_indices, text_to_overwrite] = labels[batch_indices, non_image_indices] # 5. Fill the embeddings corresponding to the images. Anything that is still zeros needs filling image_to_overwrite = torch.all(final_embedding == 0, dim=-1) image_to_overwrite &= image_to_overwrite.cumsum(-1) > nb_image_pad[:, None].to(target_device) # # somthing really weird here. # temp1 = (image_to_overwrite.cumsum(-1) > nb_image_pad[:, None].to(target_device)) & image_to_overwrite # # this is for right padding # temp2 = (image_to_overwrite.cumsum(-1) <= num_special_image_tokens.max() * num_image_patches - nb_image_pad[:, None]) & image_to_overwrite if image_to_overwrite.sum() != image_features.shape[:-1].numel(): raise ValueError( f"The input provided to the model are wrong. The number of image tokens is {torch.sum(special_image_token_mask)} while" f" the number of image given to the model is {num_images}. This prevents correct indexing and breaks batch generation." ) final_embedding[image_to_overwrite] = image_features.contiguous().reshape(-1, embed_dim).to(target_device) final_attention_mask |= image_to_overwrite position_ids = (final_attention_mask.cumsum(-1) - 1).masked_fill_((final_attention_mask == 0), 1) if labels is None: final_labels = None return final_embedding, final_attention_mask, final_labels, position_ids @add_start_docstrings_to_model_forward(PLLAVA_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=PllavaCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: torch.LongTensor = None, pixel_values: torch.FloatTensor = None, attention_mask: Optional[torch.Tensor] = None, media_type: str = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, vision_feature_layer: Optional[int] = None, vision_feature_select_strategy: Optional[str] = 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, PllavaCausalLMOutputWithPast]: 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, LlavaForConditionalGeneration >>> model = LlavaForConditionalGeneration.from_pretrained("llava-hf/llava-1.5-7b-hf") >>> processor = AutoProcessor.from_pretrained("llava-hf/llava-1.5-7b-hf") >>> prompt = "\nUSER: What's the content of the image?\nASSISTANT:" >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg" >>> 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] "\nUSER: What's the content of the image?\nASSISTANT: The image features a stop sign on a street corner" ```""" 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 vision_feature_layer = ( vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer ) vision_feature_select_strategy = ( vision_feature_select_strategy if vision_feature_select_strategy is not None else self.config.vision_feature_select_strategy ) if inputs_embeds is None: # 1. Extra the input embeddings no_img_input_ids = torch.where(input_ids!=self.config.image_token_index, input_ids, self.pad_token_id) # some model used up all the embeddings inputs_embeds = self.get_input_embeddings()(no_img_input_ids) batch_size = inputs_embeds.shape[0] # 2. Merge text and images if pixel_values is not None and input_ids.shape[1] != 1: image_outputs = self.vision_tower(pixel_values, output_hidden_states=True) # this is not memory efficient at all (output_hidden_states=True) will save all the hidden stated. selected_image_feature = image_outputs.hidden_states[vision_feature_layer] # ( b, img_seqlen, embed_dim) if vision_feature_select_strategy == "default": selected_image_feature = selected_image_feature[:, 1:] elif vision_feature_select_strategy == "full": raise ValueError("not implemented") selected_image_feature = selected_image_feature else: raise ValueError( f"Unexpected select feature strategy: {self.config.vision_feature_select_strategy}" ) image_features = self.multi_modal_projector(selected_image_feature, media_type, batch_size=batch_size, num_videos=pixel_values.shape[0]//self.config.num_frames//batch_size,) inputs_embeds, attention_mask, labels, position_ids = self._merge_input_ids_with_image_features( image_features, inputs_embeds, input_ids, attention_mask, labels ) if labels is None: labels = torch.full_like(attention_mask, self.config.ignore_index).to(torch.long) else: # In case input_ids.shape[1] == 1 & pixel_values==None & past_key_values != None, we are in the case of # generation with cache if past_key_values is not None and pixel_values is not None and input_ids.shape[1] == 1: # Retrieve the first layer to inspect the logits and mask out the hidden states # that are set to 0 first_layer_past_key_value = past_key_values[0][0][:, :, :, 0] # Sum all dimensions of head_dim (-2) to avoid random errors such as: https://github.com/huggingface/transformers/pull/28032#issuecomment-1863691941 batch_index, non_attended_tokens = torch.where(first_layer_past_key_value.float().sum(-2) == 0) # Get the target length target_seqlen = first_layer_past_key_value.shape[-1] + 1 extended_attention_mask = torch.ones( (attention_mask.shape[0], target_seqlen - attention_mask.shape[1]), dtype=attention_mask.dtype, device=attention_mask.device, ) # Filter out only the tokens that can be un-attended, this can happen # if one uses Llava + Fused modules where the cache on the # first iteration is already big enough, or if one passes custom cache valid_indices = non_attended_tokens < extended_attention_mask.size(-1) new_batch_index = batch_index[valid_indices] new_non_attended_tokens = non_attended_tokens[valid_indices] # Zero-out the places where we don't need to attend extended_attention_mask[new_batch_index, new_non_attended_tokens] = 0 attention_mask = torch.cat((attention_mask, extended_attention_mask), dim=1) position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1 outputs = self.language_model( attention_mask=attention_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, ) logits = outputs[0] loss = None if labels is not None: # Shift so that tokens < n predict n if attention_mask is not None: shift_attention_mask = attention_mask[..., 1:] shift_logits = logits[..., :-1, :][shift_attention_mask.to(logits.device) != 0].contiguous() shift_labels = labels[..., 1:][shift_attention_mask.to(labels.device) != 0].contiguous() else: shift_logits = logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() # Flatten the tokens loss_fct = nn.CrossEntropyLoss() loss = loss_fct( shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1).to(shift_logits.device) ) if not return_dict: output = (logits,) + outputs[1:] return (loss,) + output if loss is not None else output return PllavaCausalLMOutputWithPast( 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, pixel_values=None, attention_mask=None, **kwargs ): if past_key_values is not None: if isinstance(past_key_values, Cache): cache_length = past_key_values.get_seq_length() past_length = past_key_values.seen_tokens else: cache_length = past_length = past_key_values[0][0].shape[2] # Keep only the unprocessed tokens: # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as # input) if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]: input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :] # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard # input_ids based on the past_length. elif past_length < input_ids.shape[1]: input_ids = input_ids[:, past_length:] # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens. elif self.config.image_token_index in input_ids: input_ids = input_ids[:, input_ids.shape[1] - 1 :] # If the cache has seen more tokens than it can hold, then the cache has a size limit. Let's discard the # older attention values, as their corresponding values are not part of the input. if cache_length < past_length and attention_mask is not None: attention_mask = attention_mask[:, -(cache_length + input_ids.shape[1]) :] position_ids = kwargs.get("position_ids", None) if attention_mask is not None and position_ids is None: # create position_ids on the fly for batch generation position_ids = attention_mask.long().cumsum(-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) if past_key_values: position_ids = position_ids[:, -input_ids.shape[1] :] # if `inputs_embeds` are passed, we only want to use them in the 1st generation step if inputs_embeds is not None and past_key_values is None: model_inputs = {"inputs_embeds": inputs_embeds} else: model_inputs = {"input_ids": input_ids} media_type = kwargs.get('media_type', None) model_inputs.update( { "position_ids": position_ids, "past_key_values": past_key_values, "use_cache": kwargs.get("use_cache"), "attention_mask": attention_mask, "pixel_values": pixel_values, "media_type": media_type, } ) return model_inputs def _reorder_cache(self, *args, **kwargs): return self.language_model._reorder_cache(*args, **kwargs)