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cb65f9f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 | from transformers import AutoModel, AutoModelForCausalLM
from transformers.activations import ACT2FN
from transformers.modeling_utils import PreTrainedModel
from transformers.generation.utils import GenerationMixin
from transformers.modeling_outputs import CausalLMOutputWithPast
import torch
from torch import nn
from torch.nn import RMSNorm
from typing import List, Optional
from .configuration_qualityv import QualityvConfig, QualityLinearAdapterConfig
class QualityLinearAdapter(nn.Module):
def __init__(self, config: QualityLinearAdapterConfig):
super().__init__()
self.config = config
self.norm = RMSNorm(config.in_hidden_size)
self.act_fn = ACT2FN[config.act_fn]
if config.num_layers == 1:
self.linears = nn.Linear(config.in_hidden_size, config.out_hidden_size)
else:
model_list = []
for _ in range(config.num_layers - 1):
model_list.append(nn.Linear(config.in_hidden_size, config.intermediate_size))
model_list.append(self.act_fn)
model_list.append(nn.Linear(config.intermediate_size, config.out_hidden_size))
self.linears = nn.Sequential(*model_list)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.linears(self.norm(x))
return x
class QualityvForCausalLM(PreTrainedModel, GenerationMixin):
def __init__(self, config: QualityvConfig, *args, **kwargs):
super().__init__(config, *args, **kwargs)
self.config = config
self.llm_model = AutoModelForCausalLM.from_pretrained(config.llm_model_name)
if config.vision_config is not None:
self.vision_model = AutoModel.from_pretrained(config.vision_model_name)
self.vision_adapter = QualityLinearAdapter(config.vision_adapter_config)
if config.audio_config is not None:
self.audio_model = AutoModel.from_pretrained(config.audio_model_name)
self.audio_adapter = QualityLinearAdapter(config.audio_adapter_config)
self.decoder_input_ids = torch.tensor([[1, 1,]]) * self.audio_model.config.decoder_start_token_id
self.post_init()
def get_input_embeddings(self):
return self.llm_model.get_input_embeddings()
def set_input_embeddings(self, value):
self.llm_model.set_input_embeddings(value)
def get_output_embeddings(self):
return self.llm_model.get_output_embeddings()
def set_output_embeddings(self, value):
self.llm_model.set_output_embeddings(value)
def set_decoder(self, decoder):
self.llm_model.set_decoder(decoder)
def get_decoder(self):
return self.llm_model.get_decoder()
def get_vision_model(self):
return self.vision_model
def get_audio_model(self):
return self.audio_model
def get_video_features(self, pixel_values_videos: torch.Tensor) -> torch.Tensor:
video_embeds = self.vision_model(pixel_values_videos).last_hidden_state
video_embeds = self.vision_adapter(video_embeds)
return video_embeds
def get_audio_features(self, audio_values: torch.Tensor) -> torch.Tensor:
audio_embeds = self.audio_model.encoder(audio_values).last_hidden_state
audio_embeds = self.audio_adapter(audio_embeds)
return audio_embeds
def get_image_features(self, pixel_values: torch.Tensor) -> torch.Tensor:
image_embeds = self.vision_model(pixel_values).last_hidden_state
image_embeds = self.vision_adapter(image_embeds)
return image_embeds
def replace_multi_modal_embeddings(self, multi_modal_embeds: torch.Tensor,
input_embeds: torch.Tensor,
input_ids: torch.LongTensor,
multi_modal_token_id: int,
note: str="multi_modal"):
# multi_modal_embeds: batch_size * num_frames, hidden_steps, hidden_size
# input_embeds: batch_size, seq_length, hidden_size
# input_ids: batch_size, seq_length
# multi_modal_token_id: int
# note: str
hidden_size = multi_modal_embeds.shape[-1]
multi_modal_embeds = multi_modal_embeds.view(-1, hidden_size)
n_modal_tokens = (input_ids == multi_modal_token_id).sum()
n_modal_embeds = multi_modal_embeds.shape[0]
if n_modal_tokens != n_modal_embeds:
raise ValueError(f"The number of {note} tokens ({n_modal_tokens}) does not match the number of {note} embeddings ({n_modal_embeds}).")
mask = input_ids == multi_modal_token_id
mask_unsqueezed = mask.unsqueeze(-1)
mask_expanded = mask_unsqueezed.expand_as(input_embeds)
video_mask = mask_expanded.to(input_embeds.device)
multi_modal_embeds = multi_modal_embeds.to(input_embeds.device, dtype=input_embeds.dtype)
input_embeds = input_embeds.masked_scatter(video_mask, multi_modal_embeds)
return input_embeds
def forward(self,
input_ids: 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,
pixel_values: Optional[torch.Tensor] = None,
pixel_values_videos: Optional[torch.FloatTensor] = None,
audio_values: Optional[torch.FloatTensor] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs
):
output_attentions = output_attentions if output_attentions is not None else self.config.llm_config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.llm_config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.llm_config.use_return_dict
if inputs_embeds is None:
inputs_embeds = self.get_input_embeddings()(input_ids)
if pixel_values_videos is not None:
video_features = self.get_video_features(pixel_values_videos)
inputs_embeds = self.replace_multi_modal_embeddings(video_features, inputs_embeds, input_ids, self.config.video_token_id, note="video")
if pixel_values is not None:
image_features = self.get_image_features(pixel_values)
inputs_embeds = self.replace_multi_modal_embeddings(image_features, inputs_embeds, input_ids, self.config.image_token_id, note="image")
if audio_values is not None:
audio_features = self.get_audio_features(audio_values)
inputs_embeds = self.replace_multi_modal_embeddings(audio_features, inputs_embeds, input_ids, self.config.audio_token_id, note="audio")
outputs = self.llm_model(
input_ids=None,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
labels=labels,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
cache_position=cache_position,
**kwargs
)
return outputs
def prepare_inputs_for_generation(self,
input_ids,
past_key_values=None,
attention_mask=None,
use_cache=None,
pixel_values=None,
pixel_values_videos=None,
audio_values=None,
cache_position=None,
**kwargs):
model_inputs = super().prepare_inputs_for_generation(
input_ids=input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
use_cache=use_cache,
pixel_values=pixel_values,
pixel_values_videos=pixel_values_videos,
audio_values=audio_values,
**kwargs
)
if cache_position[0] != 0:
model_inputs["pixel_values"] = None
model_inputs["pixel_values_videos"] = None
return model_inputs
def _expand_inputs_for_generation(self,
expand_size: int = 1,
is_encoder_decoder: bool = False,
input_ids: Optional[torch.LongTensor] = None,
**model_kwargs,
):
"""Expands input tensors for generation when using beam search or sampling.
Args:
expand_size (int, optional): The size to expand the inputs by. Defaults to 1.
is_encoder_decoder (bool, optional): Whether the model is an encoder-decoder model. Defaults to False.
input_ids (Optional[torch.LongTensor], optional): The input token IDs. Defaults to None.
**model_kwargs: Additional model-specific keyword arguments.
Returns:
Tuple[torch.LongTensor, Dict[str, torch.Tensor]]: The expanded input_ids and model_kwargs.
"""
if input_ids is not None:
input_ids = input_ids.repeat_interleave(expand_size, dim=0)
# Expand attention mask if present
if "attention_mask" in model_kwargs:
model_kwargs["attention_mask"] = model_kwargs["attention_mask"].repeat_interleave(expand_size, dim=0)
# Expand position IDs if present
if "position_ids" in model_kwargs:
model_kwargs["position_ids"] = model_kwargs["position_ids"].repeat_interleave(expand_size, dim=0)
# Expand pixel values for images if present
if "pixel_values" in model_kwargs and model_kwargs["pixel_values"] is not None:
model_kwargs["pixel_values"] = model_kwargs["pixel_values"].repeat_interleave(expand_size, dim=0)
# Expand pixel values for videos if present
if "pixel_values_videos" in model_kwargs and model_kwargs["pixel_values_videos"] is not None:
model_kwargs["pixel_values_videos"] = model_kwargs["pixel_values_videos"].repeat_interleave(expand_size, dim=0)
# Expand audio values if present
if "audio_values" in model_kwargs and model_kwargs["audio_values"] is not None:
model_kwargs["audio_values"] = model_kwargs["audio_values"].repeat_interleave(expand_size, dim=0)
# Expand cache position if present
if "cache_position" in model_kwargs and model_kwargs["cache_position"] is not None:
model_kwargs["cache_position"] = model_kwargs["cache_position"].repeat_interleave(expand_size, dim=0)
return input_ids, model_kwargs
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