import os
from enum import Enum
from dataclasses import dataclass
from typing import List, Optional, Union, Tuple
import torch
import torch.utils.checkpoint
from torch import nn
from transformers import AutoModelForCausalLM
from transformers.models.auto import CONFIG_MAPPING
from transformers.activations import ACT2FN
from transformers.cache_utils import Cache
from transformers.processing_utils import ProcessorMixin
from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_utils import PreTrainedModel
from transformers.modeling_outputs import ModelOutput
from transformers.feature_extraction_utils import BatchFeature
from transformers.tokenization_utils_base import (
TextInput,
TensorType,
PaddingStrategy,
PreTokenizedInput,
TruncationStrategy
)
from transformers.utils import (
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .processor_mm import (
load_and_transform_image_data,
load_and_transform_video_data,
load_and_transform_audio_data
)
from .imagebind_model import *
from .helpers import *
from .multimodal_preprocessors import *
from .transformer import *
class ModalityType(Enum):
TEXT = "text"
IMAGE = "image"
VIDEO = "video"
AUDIO = "audio"
VISION = "vision" # For Imagebind
def __str__(self):
return self.value
def __eq__(self, other):
if isinstance(other, ModalityType):
return self.value == other.value
elif isinstance(other, str):
return self.value == other
return False
def __hash__(self):
return hash(self.value)
_CONFIG_FOR_DOC = "AnyModelConfig"
class AnyModelConfig(PretrainedConfig):
model_type = "any_model"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
modality_config=None,
text_config=None,
ignore_index=-100,
image_token_index=128256,
video_token_index=128257,
audio_token_index=128258,
projector_hidden_act="gelu",
**kwargs,
):
if isinstance(text_config, dict):
text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "llama"
text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
elif text_config is None:
text_config = CONFIG_MAPPING["llama"]()
self.modality_config = modality_config
self.text_config = text_config
self.ignore_index = ignore_index
self.image_token_index = image_token_index
self.video_token_index = video_token_index
self.audio_token_index = audio_token_index
self.projector_hidden_act = projector_hidden_act
super().__init__(
**kwargs,
)
class AnyModelProcessor(ProcessorMixin):
# TODO: Add support for any_model_processor
# attributes = ["any_model_processor", "tokenizer"]
attributes = ["tokenizer"]
valid_kwargs = ["chat_template"]
any_model_processor_class = "AnyModelProcessor"
tokenizer_class = "AutoTokenizer"
def __init__(self, tokenizer=None, **kwargs):
super().__init__(tokenizer, **kwargs)
if self.tokenizer is not None:
self.tokenizer.add_special_tokens({"additional_special_tokens": ["<image>", "<video>", "<audio>"]})
def __call__(
self,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
data_paths: Union[str, List[str]] = None,
modality: Optional[Union[ModalityType, List[ModalityType]]] = None,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = None,
max_length=None,
return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
) -> BatchFeature:
if data_paths is not None:
if modality is None:
raise ValueError("modality must be specified when data_paths is provided")
if isinstance(modality, list):
assert len(set(modality)) == 1, "only one kind modality can be provided in a batch"
modality = modality[0]
proceesor_func = None
if modality == ModalityType.IMAGE:
proceesor_func = load_and_transform_image_data
elif modality == ModalityType.VIDEO:
proceesor_func = load_and_transform_video_data
elif modality == ModalityType.AUDIO:
proceesor_func = load_and_transform_audio_data
else:
raise ValueError("modality must be one of ModalityType.IMAGE, ModalityType.VIDEO, ModalityType.AUDIO")
if isinstance(data_paths, str):
pixel_values = proceesor_func(data_paths)
else:
pixel_values = torch.stack([proceesor_func(data_path) for data_path in data_paths], dim=0)
else:
pixel_values = None
if text is None:
text_inputs = {}
else:
text_inputs = self.tokenizer(
text, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length
)
return BatchFeature(data={**text_inputs, "pixel_values": pixel_values, "modality": modality})
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Llama
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
@property
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names
def model_input_names(self):
tokenizer_input_names = self.tokenizer.model_input_names
feature_extractor_class_input_names = self.feature_extractor_class.model_input_names
return list(dict.fromkeys(tokenizer_input_names + feature_extractor_class_input_names))
@dataclass
# Copied from transformers.models.idefics.modeling_idefics.IdeficsCausalLMOutputWithPast with Idefics->AnyModel
class AnyModelCausalLMOutputWithPast(ModelOutput):
"""
Base class for AnyModel 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.
modality_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)`.
modality_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
modality_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
modality: Optional[ModalityType] = None
class AnyModelMultiModalProjector(nn.Module):
def __init__(self, config: AnyModelConfig):
super().__init__()
self.linear_1 = nn.Linear(config.modality_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 forward(self, modality_features):
hidden_states = self.linear_1(modality_features)
hidden_states = self.act(hidden_states)
hidden_states = self.linear_2(hidden_states)
return hidden_states
class AnyModelPreTrainedModel(PreTrainedModel):
config_class = AnyModelConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["AnyModelAttention"]
_skip_keys_device_placement = "past_key_values"
_supports_flash_attn_2 = True
def __init__(self, config: AnyModelConfig):
self.config = config
super().__init__(config)
def _init_weights(self, module):
# important: this ported version of AnyModel 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_()
@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
ANYMODEL_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 ([]`AnyModelProcessor`] 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.
vision_feature_layer (`int`, *optional*, defaults to -2):
The index of the layer to select the vision feature.
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.
"""
class AnyModelForConditionalGeneration(AnyModelPreTrainedModel):
def __init__(self, config: AnyModelConfig):
super().__init__(config)
self.image_projector = AnyModelMultiModalProjector(config)
self.video_projector = AnyModelMultiModalProjector(config)
self.audio_projector = AnyModelMultiModalProjector(config)
self.language_model = AutoModelForCausalLM.from_config(
config.text_config, attn_implementation=config._attn_implementation
)
self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1
self.modality_tower, _ = \
imagebind_huge(pretrained=True, store_path=os.path.join(config._name_or_path, config.modality_config["imagebind_ckpt_path"]))
self.modality_tower = self.modality_tower.to(self.language_model.device)
self.modality_tower = self.modality_tower.to(self.language_model.dtype)
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.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" "<image>", "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 not `text_positions` needs filling (#29835)
image_to_overwrite = torch.full(
(batch_size, max_embed_dim), True, dtype=torch.bool, device=inputs_embeds.device
)
image_to_overwrite[batch_indices, text_to_overwrite] = False
image_to_overwrite &= image_to_overwrite.cumsum(-1) - 1 >= nb_image_pad[:, None].to(target_device)
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)
# 6. Mask out the embedding at padding positions, as we later use the past_key_value value to determine the non-attended tokens.
batch_indices, pad_indices = torch.where(input_ids == self.pad_token_id)
indices_to_mask = new_token_positions[batch_indices, pad_indices]
final_embedding[batch_indices, indices_to_mask] = 0
if labels is None:
final_labels = None
return final_embedding, final_attention_mask, final_labels, position_ids
def _merge_input_ids_with_video_features(self, video_features, inputs_embeds, input_ids, attention_mask, labels):
num_videos, num_video_patches, embed_dim = video_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 video tokens are
special_video_token_mask = input_ids == self.config.video_token_index
num_special_video_tokens = torch.sum(special_video_token_mask, dim=-1)
# Compute the maximum embed dimension
max_embed_dim = (num_special_video_tokens.max() * (num_video_patches - 1)) + sequence_length
batch_indices, non_video_indices = torch.where(input_ids != self.config.video_token_index)
# 2. Compute the positions where text should be written
# Calculate new positions for text tokens in merged video-text sequence.
# `special_video_token_mask` identifies video tokens. Each video token will be replaced by `nb_text_tokens_per_videos - 1` text tokens.
# `torch.cumsum` computes how each video 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_video_token_mask * (num_video_patches - 1) + 1), -1) - 1
nb_video_pad = max_embed_dim - 1 - new_token_positions[:, -1]
if left_padding:
new_token_positions += nb_video_pad[:, None] # offset for left padding
text_to_overwrite = new_token_positions[batch_indices, non_video_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_video_indices, text_to_overwrite = (
batch_indices.to(target_device),
non_video_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" "<video>", "how", "are"]
# we need to index copy on [0, 577, 578, 579] for the text and [1:576] for the video features
final_embedding[batch_indices, text_to_overwrite] = inputs_embeds[batch_indices, non_video_indices]
final_attention_mask[batch_indices, text_to_overwrite] = attention_mask[batch_indices, non_video_indices]
if labels is not None:
final_labels[batch_indices, text_to_overwrite] = labels[batch_indices, non_video_indices]
# 5. Fill the embeddings corresponding to the videos. Anything that is not `text_positions` needs filling (#29835)
video_to_overwrite = torch.full(
(batch_size, max_embed_dim), True, dtype=torch.bool, device=inputs_embeds.device
)
video_to_overwrite[batch_indices, text_to_overwrite] = False
video_to_overwrite &= video_to_overwrite.cumsum(-1) - 1 >= nb_video_pad[:, None].to(target_device)
if video_to_overwrite.sum() != video_features.shape[:-1].numel():
raise ValueError(
f"The input provided to the model are wrong. The number of video tokens is {torch.sum(special_video_token_mask)} while"
f" the number of video given to the model is {num_videos}. This prevents correct indexing and breaks batch generation."
)
final_embedding[video_to_overwrite] = video_features.contiguous().reshape(-1, embed_dim).to(target_device)
final_attention_mask |= video_to_overwrite
position_ids = (final_attention_mask.cumsum(-1) - 1).masked_fill_((final_attention_mask == 0), 1)
# 6. Mask out the embedding at padding positions, as we later use the past_key_value value to determine the non-attended tokens.
batch_indices, pad_indices = torch.where(input_ids == self.pad_token_id)
indices_to_mask = new_token_positions[batch_indices, pad_indices]
final_embedding[batch_indices, indices_to_mask] = 0
if labels is None:
final_labels = None
return final_embedding, final_attention_mask, final_labels, position_ids
def _merge_input_ids_with_audio_features(self, audio_features, inputs_embeds, input_ids, attention_mask, labels):
num_audios, num_audio_patches, embed_dim = audio_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 audio tokens are
special_audio_token_mask = input_ids == self.config.audio_token_index
num_special_audio_tokens = torch.sum(special_audio_token_mask, dim=-1)
# Compute the maximum embed dimension
max_embed_dim = (num_special_audio_tokens.max() * (num_audio_patches - 1)) + sequence_length
batch_indices, non_audio_indices = torch.where(input_ids != self.config.audio_token_index)
# 2. Compute the positions where text should be written
# Calculate new positions for text tokens in merged audio-text sequence.
# `special_audio_token_mask` identifies audio tokens. Each audio token will be replaced by `nb_text_tokens_per_audios - 1` text tokens.
# `torch.cumsum` computes how each audio 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_audio_token_mask * (num_audio_patches - 1) + 1), -1) - 1
nb_audio_pad = max_embed_dim - 1 - new_token_positions[:, -1]
if left_padding:
new_token_positions += nb_audio_pad[:, None] # offset for left padding
text_to_overwrite = new_token_positions[batch_indices, non_audio_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_audio_indices, text_to_overwrite = (
batch_indices.to(target_device),
non_audio_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" "<audio>", "how", "are"]
# we need to index copy on [0, 577, 578, 579] for the text and [1:576] for the audio features
final_embedding[batch_indices, text_to_overwrite] = inputs_embeds[batch_indices, non_audio_indices]
final_attention_mask[batch_indices, text_to_overwrite] = attention_mask[batch_indices, non_audio_indices]
if labels is not None:
final_labels[batch_indices, text_to_overwrite] = labels[batch_indices, non_audio_indices]
# 5. Fill the embeddings corresponding to the audios. Anything that is not `text_positions` needs filling (#29835)
audio_to_overwrite = torch.full(
(batch_size, max_embed_dim), True, dtype=torch.bool, device=inputs_embeds.device
)
audio_to_overwrite[batch_indices, text_to_overwrite] = False
audio_to_overwrite &= audio_to_overwrite.cumsum(-1) - 1 >= nb_audio_pad[:, None].to(target_device)
if audio_to_overwrite.sum() != audio_features.shape[:-1].numel():
raise ValueError(
f"The input provided to the model are wrong. The number of audio tokens is {torch.sum(special_audio_token_mask)} while"
f" the number of audio given to the model is {num_audios}. This prevents correct indexing and breaks batch generation."
)
final_embedding[audio_to_overwrite] = audio_features.contiguous().reshape(-1, embed_dim).to(target_device)
final_attention_mask |= audio_to_overwrite
position_ids = (final_attention_mask.cumsum(-1) - 1).masked_fill_((final_attention_mask == 0), 1)
# 6. Mask out the embedding at padding positions, as we later use the past_key_value value to determine the non-attended tokens.
batch_indices, pad_indices = torch.where(input_ids == self.pad_token_id)
indices_to_mask = new_token_positions[batch_indices, pad_indices]
final_embedding[batch_indices, indices_to_mask] = 0
if labels is None:
final_labels = None
return final_embedding, final_attention_mask, final_labels, position_ids
@add_start_docstrings_to_model_forward(ANYMODEL_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=AnyModelCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: torch.LongTensor = None,
pixel_values_1: torch.FloatTensor = None,
pixel_values_2: torch.FloatTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
modality: Optional[ModalityType] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
vision_feature_layer: Optional[int] = 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, AnyModelCausalLMOutputWithPast]:
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:
```"""
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 inputs_embeds is None:
# 1. Extra the input embeddings
inputs_embeds = self.get_input_embeddings()(input_ids)
# 2. Merge text and images
if pixel_values_1 is not None and pixel_values_1 is not None and input_ids.shape[1] != 1:
assert modality is not None, "modality must be provided when pixel_values is not None"
'''
if isinstance(modality, list):
assert len(set(modality)) == 1, "only one kind modality can be provided in a batch"
modality = modality[0]
'''
for i in range(2):
pixel_values = pixel_values_1 if i == 0 else pixel_values_2
if modality[0][i] == ModalityType.IMAGE:
modality_outputs = self.modality_tower({
str(ModalityType.VISION): pixel_values
})[str(ModalityType.VISION)] # size = (b, h)
features = self.image_projector(modality_outputs).unsqueeze(1) # size = (b, 1, h)
self.merge_input_ids_with_other_features = self._merge_input_ids_with_image_features
elif modality[0][i] == ModalityType.VIDEO:
modality_outputs = self.modality_tower({
str(ModalityType.VISION): pixel_values
})[str(ModalityType.VISION)] # size = (b, h)
features = self.video_projector(modality_outputs).unsqueeze(1) # size = (b, 1, h)
self.merge_input_ids_with_other_features = self._merge_input_ids_with_video_features
elif modality[0][i] == ModalityType.AUDIO:
modality_outputs = self.modality_tower({
str(ModalityType.AUDIO): pixel_values
})[str(ModalityType.AUDIO)] # size = (b, h)
features = self.audio_projector(modality_outputs).unsqueeze(1) # size = (b, 1, h)
self.merge_input_ids_with_other_features = self._merge_input_ids_with_audio_features
elif modality[0][i] == ModalityType.TEXT:
continue
else:
raise ValueError(f"modality {modality[i]} is not supported")
inputs_embeds = inputs_embeds.to(features.dtype)
'''
print('+++'*10)
print(input_ids)
print(torch.sum(input_ids == self.config.audio_token_index, dim=-1))
print('+++'*10)
'''
inputs_embeds, attention_mask, labels, position_ids = self.merge_input_ids_with_other_features(
features, inputs_embeds, input_ids, attention_mask, labels
)
position_ids = (attention_mask.cumsum(-1) - 1).masked_fill_((attention_mask == 0), 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 AnyModelCausalLMOutputWithPast(
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}
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,
}
)
return model_inputs
def _reorder_cache(self, *args, **kwargs):
return self.language_model._reorder_cache(*args, **kwargs)
@dataclass
class ScoreModelOutput(ModelOutput):
"""Output of the score model."""
scores: torch.FloatTensor | None = None # size = (B, L, D)
clipped_scores: torch.FloatTensor | None = None # size = (B, L-I, D)
end_scores: torch.FloatTensor | None = None # size = (B, D)
last_hidden_state: torch.FloatTensor | None = None # size = (B, L, E)
clipped_states: torch.FloatTensor | None = None # size = (B, L-I, D)
end_last_hidden_state: torch.FloatTensor | None = None # size = (B, E)
end_index: torch.LongTensor | None = None # size = (B,)
class AnyRewardModel(AnyModelForConditionalGeneration):
supports_gradient_checkpointing = True
def __init__(self, config: AnyModelConfig):
super().__init__(config)
self.score_head = nn.Linear(4096, 1, bias=False)
def forward(
self,
input_ids: torch.LongTensor | None = None,
attention_mask: torch.Tensor | None = None,
**kwargs,
) -> torch.Tensor:
outputs = self.model(
input_ids,
attention_mask=attention_mask,
output_hidden_states=True,
**kwargs,
)
last_hidden_state = outputs.hidden_states[-1]
scores = self.score_head(last_hidden_state).float()
B, _, _ = scores.size()
end_index = -torch.ones((B,)) # size = (B,)
end_last_hidden_state = last_hidden_state[:, -1, :].unsqueeze(1)
end_scores = self.score_head(end_last_hidden_state).float()
end_last_hidden_state = end_last_hidden_state.squeeze(dim=1) # size = (B, E)
end_scores = end_scores.squeeze(dim=1) # size = (B, D)
return ScoreModelOutput(
scores=scores, # size = (B, L, D)
end_scores=end_scores, # size = (B, D)
last_hidden_state=last_hidden_state, # size = (B, L, E)
end_last_hidden_state=end_last_hidden_state, # size = (B, E)
end_index=end_index, # size = (B,)
)
from transformers import AutoConfig, AutoModel
AutoConfig.register("any_model", AnyModelConfig)
AutoModel.register(AnyModelConfig, AnyModelForConditionalGeneration)
AutoModel.register(AnyModelConfig, AnyRewardModel)