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from dataclasses import dataclass |
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from typing import List, Optional, Tuple, Union |
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|
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import torch |
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import torch.nn as nn |
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from torch import nn |
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from transformers import PreTrainedModel |
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from transformers.cache_utils import Cache |
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from transformers.modeling_outputs import ModelOutput |
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from transformers.utils import logging |
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|
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from .configuration_aria import AriaConfig |
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from .moe_lm import AriaMoELMForCausalLM |
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from .projector import AriaProjector |
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from .vision_encoder import AriaVisionModel |
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|
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logger = logging.get_logger(__name__) |
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class AriaPretrainedModel(PreTrainedModel): |
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""" |
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An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. |
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""" |
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config_class = AriaConfig |
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base_model_prefix = "model" |
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_no_split_modules = [] |
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supports_gradient_checkpointing = True |
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_skip_keys_device_placement = "past_key_values" |
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_supports_flash_attn_2 = True |
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_supports_cache_class = True |
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|
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@property |
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def _supports_sdpa(self): |
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""" |
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Retrieve language_model's attribute to check whether the model supports |
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SDPA (Scaled Dot Product Attention) or not. |
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""" |
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return self.language_model._supports_sdpa |
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|
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@dataclass |
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|
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class AriaCausalLMOutputWithPast(ModelOutput): |
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""" |
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Base class for Aria causal language model (or autoregressive) outputs. |
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Args: |
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loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): |
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Language modeling loss (for next-token prediction). |
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logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): |
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Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). |
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past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): |
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Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape |
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`(batch_size, num_heads, sequence_length, embed_size_per_head)`) |
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|
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Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see |
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`past_key_values` input) to speed up sequential decoding. |
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hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): |
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Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + |
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one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. |
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|
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Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. |
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attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): |
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Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, |
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sequence_length)`. |
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention |
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heads. |
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image_hidden_states (`tuple(torch.FloatTensor)`, *optional*): |
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Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images, |
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sequence_length, hidden_size)`. |
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|
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image_hidden_states of the model produced by the vision encoder, and optionally by the perceiver |
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""" |
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loss: Optional[torch.FloatTensor] = None |
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logits: torch.FloatTensor = None |
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past_key_values: Optional[List[torch.FloatTensor]] = None |
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hidden_states: Optional[Tuple[torch.FloatTensor]] = None |
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attentions: Optional[Tuple[torch.FloatTensor]] = None |
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image_hidden_states: Optional[Tuple[torch.FloatTensor]] = None |
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def build_mm_projector(config: AriaConfig): |
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""" |
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Builds and returns an AriaProjector instance based on the provided configuration. |
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|
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Args: |
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config (AriaConfig): The configuration object containing necessary parameters. |
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|
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Returns: |
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AriaProjector: An instance of the AriaProjector class. |
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""" |
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return AriaProjector( |
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patch_to_query_dict=config.projector_patch_to_query_dict, |
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embed_dim=config.vision_config.hidden_size, |
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num_heads=config.vision_config.num_attention_heads, |
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kv_dim=config.vision_config.hidden_size, |
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ff_dim=config.text_config.hidden_size, |
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output_dim=config.text_config.hidden_size, |
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) |
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class AriaForConditionalGeneration(AriaPretrainedModel): |
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""" |
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Aria model for conditional generation tasks. |
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|
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This model combines a vision tower, a multi-modal projector, and a language model |
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to perform tasks that involve both image and text inputs. |
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""" |
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|
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def __init__(self, config: AriaConfig): |
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super().__init__(config) |
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self.vision_tower = AriaVisionModel(config.vision_config) |
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self.multi_modal_projector = build_mm_projector(config) |
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self.vocab_size = config.text_config.vocab_size |
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self.language_model = AriaMoELMForCausalLM(config.text_config) |
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self.pad_token_id = ( |
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self.config.pad_token_id if self.config.pad_token_id is not None else -1 |
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) |
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self.post_init() |
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|
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def freeze_vit(self): |
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"""Freeze the parameters of the vision tower.""" |
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for param in self.vision_tower.parameters(): |
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param.requires_grad = False |
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|
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def freeze_projector(self): |
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"""Freeze the parameters of the multi-modal projector.""" |
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for param in self.multi_modal_projector.parameters(): |
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param.requires_grad = False |
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|
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def freeze_llm(self): |
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"""Freeze the parameters of the language model.""" |
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for param in self.language_model.parameters(): |
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param.requires_grad = False |
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def get_input_embeddings(self) -> nn.Module: |
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"""Retrieve the input embeddings from the language model.""" |
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return self.language_model.get_input_embeddings() |
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|
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def set_input_embeddings(self, value): |
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"""Set the input embeddings for the language model.""" |
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self.language_model.set_input_embeddings(value) |
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|
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def set_moe_z_loss_coeff(self, value): |
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""" |
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Set the z-loss coefficient for Mixture of Experts (MoE) models. |
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Args: |
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value: The z-loss coefficient value to set. |
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""" |
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self.language_model.set_z_loss_coeff(value) |
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def set_moe_aux_loss_coeff(self, value): |
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""" |
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Set the auxiliary loss coefficient for Mixture of Experts (MoE) models. |
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Args: |
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value: The auxiliary loss coefficient value to set. |
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""" |
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self.language_model.set_aux_loss_coeff(value) |
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def _merge_input_ids_with_image_features( |
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self, image_features, inputs_embeds, input_ids, attention_mask, labels |
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): |
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""" |
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Merge input IDs with image features to create a combined input representation. |
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|
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This method handles the complex logic of interleaving text and image tokens, |
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adjusting attention masks and labels accordingly. |
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Args: |
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image_features (torch.Tensor): Processed image features. |
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inputs_embeds (torch.Tensor): Text input embeddings. |
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input_ids (torch.Tensor): Input token IDs. |
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attention_mask (torch.Tensor): Attention mask for input tokens. |
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labels (torch.Tensor, optional): Labels for language modeling. |
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|
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Returns: |
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tuple: Contains the merged embeddings, updated attention mask, |
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updated labels, and position IDs. |
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""" |
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num_images, num_image_patches, embed_dim = image_features.shape |
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batch_size, sequence_length = input_ids.shape |
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left_padding = not torch.sum( |
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input_ids[:, -1] == torch.tensor(self.pad_token_id) |
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) |
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special_image_token_mask = input_ids == self.config.image_token_index |
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num_special_image_tokens = torch.sum(special_image_token_mask, dim=-1) |
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|
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max_embed_dim = ( |
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num_special_image_tokens.max() * (num_image_patches - 1) |
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) + sequence_length |
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batch_indices, non_image_indices = torch.where( |
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input_ids != self.config.image_token_index |
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) |
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new_token_positions = ( |
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torch.cumsum((special_image_token_mask * (num_image_patches - 1) + 1), -1) |
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- 1 |
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) |
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nb_image_pad = max_embed_dim - 1 - new_token_positions[:, -1] |
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if left_padding: |
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new_token_positions += nb_image_pad[:, None] |
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text_to_overwrite = new_token_positions[batch_indices, non_image_indices] |
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|
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final_embedding = torch.zeros( |
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batch_size, |
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max_embed_dim, |
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embed_dim, |
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dtype=inputs_embeds.dtype, |
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device=inputs_embeds.device, |
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) |
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final_attention_mask = torch.zeros( |
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batch_size, |
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max_embed_dim, |
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dtype=attention_mask.dtype, |
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device=inputs_embeds.device, |
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) |
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if labels is not None: |
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final_labels = torch.full( |
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(batch_size, max_embed_dim), |
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self.config.ignore_index, |
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dtype=input_ids.dtype, |
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device=input_ids.device, |
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) |
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target_device = inputs_embeds.device |
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batch_indices, non_image_indices, text_to_overwrite = ( |
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batch_indices.to(target_device), |
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non_image_indices.to(target_device), |
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text_to_overwrite.to(target_device), |
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) |
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attention_mask = attention_mask.to(target_device) |
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final_embedding[batch_indices, text_to_overwrite] = inputs_embeds[ |
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batch_indices, non_image_indices |
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] |
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final_attention_mask[batch_indices, text_to_overwrite] = attention_mask[ |
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batch_indices, non_image_indices |
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] |
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if labels is not None: |
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final_labels[batch_indices, text_to_overwrite] = labels[ |
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batch_indices, non_image_indices |
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] |
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|
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image_to_overwrite = torch.full( |
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(batch_size, max_embed_dim), |
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True, |
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dtype=torch.bool, |
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device=inputs_embeds.device, |
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) |
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image_to_overwrite[batch_indices, text_to_overwrite] = False |
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image_to_overwrite &= image_to_overwrite.cumsum(-1) - 1 >= nb_image_pad[ |
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:, None |
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].to(target_device) |
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|
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if image_to_overwrite.sum() != image_features.shape[:-1].numel(): |
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raise ValueError( |
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f"The input provided to the model are wrong. The number of image tokens is {torch.sum(special_image_token_mask)} while" |
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f" the number of image given to the model is {num_images}. This prevents correct indexing and breaks batch generation." |
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) |
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|
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final_embedding[image_to_overwrite] = ( |
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image_features.contiguous().reshape(-1, embed_dim).to(target_device) |
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) |
|
final_attention_mask |= image_to_overwrite |
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position_ids = (final_attention_mask.cumsum(-1) - 1).masked_fill_( |
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(final_attention_mask == 0), 1 |
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) |
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|
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batch_indices, pad_indices = torch.where(input_ids == self.pad_token_id) |
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indices_to_mask = new_token_positions[batch_indices, pad_indices] |
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|
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final_embedding[batch_indices, indices_to_mask] = 0 |
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|
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if labels is None: |
|
final_labels = None |
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|
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return final_embedding, final_attention_mask, final_labels, position_ids |
|
|
|
def forward( |
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self, |
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input_ids: torch.LongTensor = None, |
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pixel_values: torch.FloatTensor = None, |
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pixel_mask: torch.LongTensor = None, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
position_ids: Optional[torch.LongTensor] = None, |
|
past_key_values: Optional[List[torch.FloatTensor]] = 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, AriaCausalLMOutputWithPast]: |
|
""" |
|
Forward pass of the AriaForConditionalGeneration model. |
|
|
|
This method processes both text and image inputs, merges them if necessary, |
|
and generates output using the language model. |
|
|
|
Args: |
|
input_ids (torch.LongTensor, optional): Input token ids. |
|
pixel_values (torch.FloatTensor, optional): Pixel values of the images. |
|
pixel_mask (torch.LongTensor, optional): Mask for the pixel values. |
|
attention_mask (torch.Tensor, optional): Attention mask. |
|
position_ids (torch.LongTensor, optional): Position ids. |
|
past_key_values (List[torch.FloatTensor], optional): Past key values for efficient processing. |
|
inputs_embeds (torch.FloatTensor, optional): Input embeddings. |
|
labels (torch.LongTensor, optional): Labels for computing the language modeling loss. |
|
use_cache (bool, optional): Whether to use the model's cache mechanism. |
|
output_attentions (bool, optional): Whether to output attention weights. |
|
output_hidden_states (bool, optional): Whether to output hidden states. |
|
return_dict (bool, optional): Whether to return a ModelOutput object. |
|
|
|
Returns: |
|
Union[Tuple, AriaCausalLMOutputWithPast]: Model outputs. |
|
""" |
|
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 = ( |
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return_dict if return_dict is not None else self.config.use_return_dict |
|
) |
|
|
|
if inputs_embeds is None: |
|
|
|
inputs_embeds = self.get_input_embeddings()(input_ids) |
|
|
|
|
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if pixel_values is not None and input_ids.shape[1] != 1: |
|
image_outputs, image_attn_mask = self.vision_tower( |
|
pixel_values, |
|
pixel_mask=pixel_mask, |
|
) |
|
selected_image_feature = image_outputs.last_hidden_state |
|
|
|
image_features = self.multi_modal_projector( |
|
selected_image_feature, attn_mask=image_attn_mask |
|
) |
|
|
|
inputs_embeds = inputs_embeds.to(image_features.dtype) |
|
( |
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inputs_embeds, |
|
attention_mask, |
|
labels, |
|
position_ids, |
|
) = self._merge_input_ids_with_image_features( |
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image_features, inputs_embeds, input_ids, attention_mask, labels |
|
) |
|
|
|
|
|
|
|
elif ( |
|
past_key_values is not None |
|
and pixel_values is not None |
|
and input_ids.shape[1] == 1 |
|
): |
|
|
|
|
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first_layer_past_key_value = past_key_values[0][0][:, :, :, 0] |
|
|
|
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|
|
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batch_index, non_attended_tokens = torch.where( |
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first_layer_past_key_value.float().sum(-2) == 0 |
|
) |
|
|
|
|
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target_length = input_ids.shape[1] |
|
past_length = first_layer_past_key_value.shape[-1] |
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|
|
extended_attention_mask = torch.ones( |
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(attention_mask.shape[0], past_length), |
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dtype=attention_mask.dtype, |
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device=attention_mask.device, |
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) |
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|
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|
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valid_indices = non_attended_tokens < extended_attention_mask.size(-1) |
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new_batch_index = batch_index[valid_indices] |
|
new_non_attended_tokens = non_attended_tokens[valid_indices] |
|
|
|
|
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extended_attention_mask[new_batch_index, new_non_attended_tokens] = 0 |
|
|
|
attention_mask = torch.cat( |
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(extended_attention_mask, attention_mask[:, -target_length:]), 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: |
|
|
|
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() |
|
|
|
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 AriaCausalLMOutputWithPast( |
|
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, |
|
pixel_mask=None, |
|
attention_mask=None, |
|
**kwargs, |
|
): |
|
""" |
|
Prepare inputs for generation step. |
|
|
|
This method prepares the inputs for the generation step, handling both |
|
text and image inputs, and managing the model's cache mechanism. |
|
|
|
Args: |
|
input_ids (torch.LongTensor): Input token ids. |
|
past_key_values (Cache or List[torch.FloatTensor], optional): Past key values for efficient processing. |
|
inputs_embeds (torch.FloatTensor, optional): Input embeddings. |
|
pixel_values (torch.FloatTensor, optional): Pixel values of the images. |
|
pixel_mask (torch.LongTensor, optional): Mask for the pixel values. |
|
attention_mask (torch.Tensor, optional): Attention mask. |
|
**kwargs: Additional keyword arguments. |
|
|
|
Returns: |
|
dict: A dictionary containing the prepared inputs for the generation step. |
|
""" |
|
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] |
|
|
|
|
|
|
|
|
|
|
|
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) :] |
|
|
|
|
|
elif past_length < input_ids.shape[1]: |
|
input_ids = input_ids[:, past_length:] |
|
|
|
elif self.config.image_token_index in input_ids: |
|
input_ids = input_ids[:, input_ids.shape[1] - 1 :] |
|
|
|
|
|
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: |
|
|
|
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 is not None and past_key_values is None: |
|
model_inputs = {"inputs_embeds": inputs_embeds} |
|
else: |
|
model_inputs = {"input_ids": input_ids} |
|
|
|
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, |
|
"pixel_mask": pixel_mask, |
|
} |
|
) |
|
return model_inputs |
|
|