"""PyTorch TraVisionLM""" 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 logger = logging.get_logger(__name__) from .configuration_travisionlm import TraVisionLMConfig _CONFIG_FOR_DOC = "TraVisionLMConfig" @dataclass class TraVisionCausalLMOutputWithPast(ModelOutput): """ Base class for TraVision 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 TraVisionMultiModalProjector(nn.Module): """ Multimodal projector that cast the image features into the same dimension space as the language model """ def __init__(self, config: TraVisionLMConfig, 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 ([`TraVisionLMConfig`]): 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 TraVision Model outputting raw hidden-states without any specific head on top.", TRAVISIONLM_START_DOCSTRING, ) class TraVisionPreTrainedModel(PreTrainedModel): config_class = TraVisionLMConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["TraVisionMultiModalProjector"] _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 TraVisionLM model which consists of a vision backbone and a language model.""", TRAVISIONLM_START_DOCSTRING, ) class TraVisionForCausalLM(TraVisionPreTrainedModel): def __init__(self, config: TraVisionLMConfig): super(TraVisionForCausalLM, self).__init__(config) self.vocab_size = 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 = TraVisionMultiModalProjector(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() # Copied from transformers.models.paligemma.modeling_paligemma.PaliGemmaForConditionalGeneration.get_input_embeddings with PaliGemma->TraVisionLM 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->TraVisionLM 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->TraVisionLM 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->TraVisionLM 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->TraVisionLM def set_decoder(self, decoder): self.language_model.set_decoder(decoder) # Copied from transformers.models.paligemma.modeling_paligemma.PaliGemmaForConditionalGeneration.get_decoder with PaliGemma->TraVisionLM def get_decoder(self): return self.language_model.get_decoder() # Copied from transformers.models.paligemma.modeling_paligemma.PaliGemmaForConditionalGeneration.tie_weights with PaliGemma->TraVisionLM 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->TraVisionLM """ !!! Two significant modifications are made to the original code: ------> 1) The pad and eos tokens are set to be the same in TraVisionProcessor. Hence, only the features corresponding to the padding mask are filtered out using the attention mask. ------> 2) The features corresponding to both the prompts (called prefixes in PaliGemma) and labels (called suffixes in PaliGemma) are added the final embedding tensor and the tokens of both the prompts and labels are applied causal attention mask. All the image tokens are attended using full-attention mask. NOTE: In the original PaliGemma implementation, only the suffix tokens are applied causal masking. Check out [PaliGemma arXiv Paper](https://arxiv.org/pdf/2407.07726) for the details. """ def _merge_input_ids_with_image_features( self, image_features, inputs_embeds, input_ids, attention_mask, labels, token_type_ids, cache_position ): _, _, embed_dim = image_features.shape batch_size, sequence_length = input_ids.shape dtype, device = inputs_embeds.dtype, inputs_embeds.device min_dtype = torch.finfo(dtype).min scaled_image_features = image_features / (self.config.hidden_size**0.5) final_embedding = torch.zeros( batch_size, sequence_length, embed_dim, dtype=inputs_embeds.dtype, device=inputs_embeds.device ) text_mask = (input_ids != self.config.image_token_index) & (attention_mask | input_ids != self.config.text_config.pad_token_id) image_mask = input_ids == self.config.image_token_index pad_mask = (attention_mask == 0) & (input_ids == self.config.text_config.pad_token_id) # expand masks to match embedding dimension text_mask_expanded = text_mask.unsqueeze(-1).expand(-1, -1, embed_dim).to(inputs_embeds.device) pad_mask_expanded = pad_mask.unsqueeze(-1).expand(-1, -1, embed_dim).to(inputs_embeds.device) # insert padding and text token embeddings final_embedding = torch.where(text_mask_expanded, inputs_embeds, final_embedding) final_embedding = torch.where(pad_mask_expanded, torch.zeros_like(final_embedding), final_embedding) # insert image embeddings - the image mask is always less or equal to the sentence in length final_embedding = final_embedding.masked_scatter( image_mask.unsqueeze(-1).expand_as(final_embedding).to(device=final_embedding.device), scaled_image_features.to(device=final_embedding.device, dtype=final_embedding.dtype), ) final_embedding = torch.where(pad_mask_expanded, torch.zeros_like(final_embedding), final_embedding) if attention_mask is not None: position_ids = (attention_mask.cumsum(-1)).masked_fill_((attention_mask == 0), 1) else: position_ids = None if token_type_ids is not None: # we are training thus we need to create a full mask on the image, but causal on prompt and suffix target_length = cache_position[-1] + 1 causal_mask = torch.full( (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device ) if sequence_length != 1: causal_mask = torch.triu(causal_mask, diagonal=1) 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 ) # unmask the prefill causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( token_type_ids[:, None, None, :].to(causal_mask.device) == 0, 0 ) padding_mask = padding_mask == 0 causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( padding_mask, min_dtype ) final_labels = None if labels is not None: final_labels = torch.full( (batch_size, sequence_length), self.config.ignore_index, dtype=input_ids.dtype, device=input_ids.device ) final_labels = torch.where((attention_mask | input_ids != self.config.text_config.pad_token_id), labels, final_labels) else: causal_mask = attention_mask.unsqueeze(1).unsqueeze(2) * attention_mask.unsqueeze(1).unsqueeze(-1) # invert causal mask causal_mask = torch.where(causal_mask == 0, min_dtype, 0).to(dtype) final_labels = None return final_embedding, causal_mask, final_labels, position_ids 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, TraVisionCausalLMOutputWithPast]: if labels is not None: use_cache = False if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) input_shape = input_ids.size() input_ids = input_ids.view(-1, input_shape[-1]) batch_size = input_ids.shape[0] elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] batch_size = inputs_embeds.shape[0] else: raise ValueError("You have to specify either 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 if past_key_values is None: past_length = 0 past_key_values = tuple([None] * len(self.language_model.transformer.h)) else: past_length = past_key_values[0][0].size(-2) if position_ids is None: position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=input_ids.device if input_ids is not None else inputs_embeds.device) position_ids = position_ids.unsqueeze(0) # the attention mask is turned 4d after, we keep track of the original one input_attention_mask = attention_mask if inputs_embeds is None: # 1. Extract the input embeddings inputs_embeds = self.get_input_embeddings()(input_ids) # 2. Add the absolute positional embeddings to only text token locations in inputs_embeds if pixel_values is not None and inputs_embeds.shape[1] != 1: # Compute the initial mask for position IDs position_ids_mask = torch.where(input_ids != self.config.image_token_index, position_ids, 1) # Update the mask for positions where input_ids is not zero position_ids_mask[:, :-1] = torch.where(input_ids[:, :-1] != 0, position_ids_mask[:, :-1], 1) # Find the first position embedding locations first_position_embed_locs = torch.sum(position_ids_mask == 1, dim=1) # Adjust the mask by subtracting the first position embedding locations position_ids_mask.sub_(first_position_embed_locs[:, None]) # Ensure all values in the mask are non-negative --> assign values 1 to pad and image token locations position_emb_ids = torch.where(position_ids_mask >= 0, position_ids_mask, 1) # construct position embeddings using position_emb_ids position_embeds = self.language_model.transformer.wpe(position_emb_ids) else: # In this case, we generate from cache with past_key_values pos_emb_ind = position_ids.view(batch_size, -1) position_embeds = self.language_model.transformer.wpe(pos_emb_ind) # Directly add position_embeds to inputs_embeds to get hidden_states hidden_states = inputs_embeds + position_embeds # 3. Merge text and images if pixel_values is not None and input_ids.shape[1] != 1: # make sure that pixel values are of 4D dimensions (batch_size, num_channels, width, height) if pixel_values.dim() == 3: pixel_values = pixel_values.unsqueeze(dim=0) 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) if cache_position is None: cache_position = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) hidden_states, attention_mask, labels, _ = self._merge_input_ids_with_image_features( image_features, hidden_states, input_ids, attention_mask, labels, token_type_ids, cache_position ) else: # In case input_ids.shape[1] == 1 & pixel_values==None & past_key_values != None, we are in the case of # generation with cache, we can use standard causal masking if past_key_values is not None and pixel_values is not None and input_ids.shape[1] == 1: # Attention mask. _use_sdpa = self._attn_implementation == "sdpa" and output_attentions is False if attention_mask is not None: attention_mask = attention_mask.view(batch_size, -1) if self._attn_implementation == "flash_attention_2": attention_mask = attention_mask if 0 in attention_mask else None elif _use_sdpa: attention_mask = _prepare_4d_causal_attention_mask_for_sdpa( attention_mask=attention_mask, input_shape=(batch_size, input_shape[-1]), inputs_embeds=inputs_embeds, past_key_values_length=past_length, ) else: # We create a 3D attention mask from a 2D tensor mask. # Sizes are [batch_size, 1, 1, to_seq_length] # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] # this attention mask is more simple than the triangular masking of causal attention # used in OpenAI GPT, we just need to prepare the broadcast dimension here. attention_mask = attention_mask[:, None, None, :] # Since attention_mask is 1.0 for positions we want to attend and 0.0 for # masked positions, this operation will create a tensor which is 0.0 for # positions we want to attend and the dtype's smallest value for masked positions. # Since we are adding it to the raw scores before the softmax, this is # effectively the same as removing these entirely. attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min if attention_mask is not None: attention_mask = attention_mask.to(inputs_embeds.dtype) hidden_states = self.language_model.transformer.drop(hidden_states) output_shape = (-1,) + input_shape[1:] + (hidden_states.size(-1),) presents = () if use_cache else None all_self_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None for i, (block, layer_past) in enumerate(zip(self.language_model.transformer.h, past_key_values)): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) outputs = block( hidden_states, layer_past=layer_past, attention_mask=attention_mask, use_cache=use_cache, output_attentions=output_attentions, ) hidden_states = outputs[0] if use_cache is True: presents = presents + (outputs[1],) if output_attentions: all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],) hidden_states = self.language_model.transformer.ln_f(hidden_states) hidden_states = hidden_states.view(output_shape) # Add last hidden state if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) logits = self.language_model.lm_head(hidden_states) logits = logits.float() loss = None if labels is not None: shift_logits = logits[..., :-1, :] shift_labels = labels[..., 1:] # shift to right if input_attention_mask is not None: # we use the input attention mask to shift the logits and labels, because it is 2D. shift_attention_mask = input_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.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, presents, all_hidden_states, all_self_attentions) return (loss,) + output if loss is not None else output return TraVisionCausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_self_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, ): # set position inds here: we are going to use absolute position embeddings, hence carefully track the locs of the past position embedding indices if attention_mask is not None and position_ids is None: if past_key_values: position_ids_mask = (input_ids != self.config.image_token_index) position_ids_mask[:, :-1] &= (input_ids[:, :-1] != self.config.text_config.pad_token_id) last_index = position_ids_mask.sum(dim=1) - 1 position_ids = torch.stack([torch.arange(start, start+cache_position.shape[0], device=input_ids.device) for start in last_index]) # If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens # Exception 1: when passing input_embeds, input_ids may be missing entries # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here if past_key_values is not None: if inputs_embeds is not None: # Exception 1 input_ids = input_ids[:, -cache_position.shape[0] :] elif input_ids.shape[1] != cache_position.shape[0]: # Default case (the "else", a no op, is Exception 2) input_ids = input_ids[:, cache_position] # if `inputs_embeds` are passed, we only want to use them in the 1st generation step if inputs_embeds is not None and cache_position[0] == 0: model_inputs = {"inputs_embeds": inputs_embeds} else: model_inputs = {"input_ids": input_ids.contiguous()} # `contiguous()` needed for compilation use cases model_inputs.update( { "position_ids": position_ids, "past_key_values": past_key_values, "cache_position": cache_position, "use_cache": use_cache, "attention_mask": attention_mask, "pixel_values": pixel_values, "token_type_ids": token_type_ids, } ) return model_inputs