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from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
from transformers import AutoModelForCausalLM, CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from transformers.generation.utils import GenerateOutput
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.models.mistral.modeling_mistral import MistralForCausalLM, MistralModel
from .configuration_lavy import LlavaMistralConfig
IGNORE_INDEX = -100
IMAGE_TOKEN_INDEX = -200
class CLIPVisionTower(nn.Module):
def __init__(self, vision_tower, args, delay_load=False):
super().__init__()
self.is_loaded = False
self.vision_tower_name = vision_tower
self.select_layer = args.mm_vision_select_layer
self.select_feature = getattr(args, "mm_vision_select_feature", "patch")
if not delay_load:
self.load_model()
else:
self.cfg_only = CLIPVisionConfig.from_pretrained(self.vision_tower_name)
def load_model(self, device_map=None):
if self.is_loaded:
return
self.image_processor = CLIPImageProcessor.from_pretrained(self.vision_tower_name)
self.vision_tower = CLIPVisionModel.from_pretrained(self.vision_tower_name, device_map=device_map)
self.vision_tower.requires_grad_(False)
self.is_loaded = True
def feature_select(self, image_forward_outs):
image_features = image_forward_outs.hidden_states[self.select_layer]
if self.select_feature == "patch":
image_features = image_features[:, 1:]
elif self.select_feature != "cls_patch":
raise ValueError(f"Unexpected select feature: {self.select_feature}")
return image_features
@torch.no_grad()
def forward(self, images):
if not self.is_loaded:
self.load_model()
if isinstance(images, list):
image_features = []
for image in images:
image_forward_out = self.vision_tower(
image.to(device=self.device, dtype=self.dtype).unsqueeze(0), output_hidden_states=True
)
image_features.append(self.feature_select(image_forward_out).to(image.dtype))
return image_features
image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype), output_hidden_states=True)
return self.feature_select(image_forward_outs).to(images.dtype)
@property
def dtype(self):
return self.vision_tower.dtype if self.is_loaded else torch.float16
@property
def device(self):
return self.vision_tower.device if self.is_loaded else torch.device("cpu")
@property
def config(self):
return self.vision_tower.config if self.is_loaded else self.cfg_only
@property
def hidden_size(self):
return self.config.hidden_size
def build_vision_projector(config):
projector_type = getattr(config, "mm_projector_type", "linear")
if projector_type == "linear":
return nn.Linear(config.mm_hidden_size, config.hidden_size)
if projector_type == "mlp2x_gelu":
return nn.Sequential(
nn.Linear(config.mm_hidden_size, config.hidden_size),
nn.GELU(),
nn.Linear(config.hidden_size, config.hidden_size),
)
raise ValueError(f"Unknown projector type: {projector_type}")
class LlavaMetaModel:
def __init__(self, config):
super().__init__(config)
if hasattr(config, "mm_vision_tower"):
self.vision_tower = CLIPVisionTower(config.mm_vision_tower, args=config, delay_load=True)
self.mm_projector = build_vision_projector(config)
def get_vision_tower(self):
vision_tower = getattr(self, "vision_tower", None)
if isinstance(vision_tower, list):
vision_tower = vision_tower[0]
return vision_tower
class LlavaMetaForCausalLM(ABC):
@abstractmethod
def get_model(self):
raise NotImplementedError
def get_vision_tower(self):
return self.get_model().get_vision_tower()
def encode_images(self, images):
vision_tower = self.get_vision_tower()
if vision_tower is not None and not vision_tower.is_loaded:
vision_tower.load_model()
image_features = vision_tower(images)
image_features = self.get_model().mm_projector(image_features)
return image_features
def prepare_inputs_labels_for_multimodal(
self, input_ids, position_ids, attention_mask, past_key_values, labels, images, image_sizes=None
):
vision_tower = self.get_vision_tower()
if vision_tower is None or images is None or input_ids.shape[1] == 1:
return input_ids, position_ids, attention_mask, past_key_values, None, labels
if isinstance(images, list) or images.ndim == 5:
if isinstance(images, list):
images = [x.unsqueeze(0) if x.ndim == 3 else x for x in images]
concat_images = torch.cat([image for image in images], dim=0)
image_features = self.encode_images(concat_images)
split_sizes = [image.shape[0] for image in images]
image_features = torch.split(image_features, split_sizes, dim=0)
image_features = [x.flatten(0, 1) for x in image_features]
else:
image_features = self.encode_images(images)
if attention_mask is None:
attention_mask = torch.ones_like(input_ids, dtype=torch.bool)
else:
attention_mask = attention_mask.bool()
if position_ids is None:
position_ids = torch.arange(0, input_ids.shape[1], dtype=torch.long, device=input_ids.device)
if labels is None:
labels = torch.full_like(input_ids, IGNORE_INDEX)
original_labels = labels
original_attention_mask = attention_mask
original_position_ids = position_ids
input_ids = [cur_input_ids[cur_attention_mask] for cur_input_ids, cur_attention_mask in zip(input_ids, attention_mask)]
labels = [cur_labels[cur_attention_mask] for cur_labels, cur_attention_mask in zip(labels, attention_mask)]
new_input_embeds = []
new_labels = []
cur_image_idx = 0
for batch_idx, cur_input_ids in enumerate(input_ids):
num_images = (cur_input_ids == IMAGE_TOKEN_INDEX).sum()
if num_images == 0:
cur_image_features = image_features[cur_image_idx]
cur_input_embeds = self.get_model().embed_tokens(cur_input_ids)
cur_input_embeds = torch.cat([cur_input_embeds, cur_image_features[0:0]], dim=0)
new_input_embeds.append(cur_input_embeds)
new_labels.append(labels[batch_idx])
cur_image_idx += 1
continue
image_token_indices = [-1] + torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0].tolist() + [cur_input_ids.shape[0]]
cur_input_ids_noim = []
cur_labels = labels[batch_idx]
cur_labels_noim = []
for i in range(len(image_token_indices) - 1):
cur_input_ids_noim.append(cur_input_ids[image_token_indices[i] + 1 : image_token_indices[i + 1]])
cur_labels_noim.append(cur_labels[image_token_indices[i] + 1 : image_token_indices[i + 1]])
split_sizes = [x.shape[0] for x in cur_labels_noim]
cur_input_embeds = self.get_model().embed_tokens(torch.cat(cur_input_ids_noim))
cur_input_embeds_no_im = torch.split(cur_input_embeds, split_sizes, dim=0)
cur_new_input_embeds = []
cur_new_labels = []
for i in range(num_images + 1):
cur_new_input_embeds.append(cur_input_embeds_no_im[i])
cur_new_labels.append(cur_labels_noim[i])
if i < num_images:
cur_image_features = image_features[cur_image_idx]
cur_image_idx += 1
cur_new_input_embeds.append(cur_image_features)
cur_new_labels.append(
torch.full(
(cur_image_features.shape[0],),
IGNORE_INDEX,
device=cur_labels.device,
dtype=cur_labels.dtype,
)
)
new_input_embeds.append(torch.cat([x.to(self.device) for x in cur_new_input_embeds]))
new_labels.append(torch.cat(cur_new_labels))
tokenizer_model_max_length = getattr(self.config, "tokenizer_model_max_length", None)
if tokenizer_model_max_length is not None:
new_input_embeds = [x[:tokenizer_model_max_length] for x in new_input_embeds]
new_labels = [x[:tokenizer_model_max_length] for x in new_labels]
max_len = max(x.shape[0] for x in new_input_embeds)
batch_size = len(new_input_embeds)
new_input_embeds_padded = []
new_labels_padded = torch.full((batch_size, max_len), IGNORE_INDEX, dtype=new_labels[0].dtype, device=new_labels[0].device)
attention_mask = torch.zeros((batch_size, max_len), dtype=attention_mask.dtype, device=attention_mask.device)
position_ids = torch.zeros((batch_size, max_len), dtype=position_ids.dtype, device=position_ids.device)
for i, (cur_new_embed, cur_new_labels) in enumerate(zip(new_input_embeds, new_labels)):
cur_len = cur_new_embed.shape[0]
new_input_embeds_padded.append(
torch.cat(
[
cur_new_embed,
torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device),
],
dim=0,
)
)
if cur_len > 0:
new_labels_padded[i, :cur_len] = cur_new_labels
attention_mask[i, :cur_len] = True
position_ids[i, :cur_len] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device)
new_input_embeds = torch.stack(new_input_embeds_padded, dim=0)
if original_labels is None:
new_labels_padded = None
if original_attention_mask is None:
attention_mask = None
else:
attention_mask = attention_mask.to(dtype=original_attention_mask.dtype)
if original_position_ids is None:
position_ids = None
return None, position_ids, attention_mask, past_key_values, new_input_embeds, new_labels_padded
class LlavaMistralModel(LlavaMetaModel, MistralModel):
config_class = LlavaMistralConfig
def __init__(self, config):
super().__init__(config)
class LlavaMistralForCausalLM(MistralForCausalLM, LlavaMetaForCausalLM):
config_class = LlavaMistralConfig
def __init__(self, config):
super(MistralForCausalLM, self).__init__(config)
self.model = LlavaMistralModel(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.post_init()
def get_model(self):
return self.model
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,
images: Optional[torch.FloatTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
image_sizes: Optional[List[List[int]]] = None,
return_dict: Optional[bool] = None,
**kwargs,
) -> Union[Tuple, CausalLMOutputWithPast]:
if images is None:
images = pixel_values
if inputs_embeds is None:
input_ids, position_ids, attention_mask, past_key_values, inputs_embeds, labels = (
self.prepare_inputs_labels_for_multimodal(
input_ids, position_ids, attention_mask, past_key_values, labels, images, image_sizes
)
)
return super().forward(
input_ids=input_ids,
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,
)
@torch.no_grad()
def generate(
self,
inputs: Optional[torch.Tensor] = None,
images: Optional[torch.Tensor] = None,
pixel_values: Optional[torch.Tensor] = None,
image_sizes: Optional[torch.Tensor] = None,
**kwargs,
) -> Union[GenerateOutput, torch.LongTensor]:
if images is None:
images = pixel_values
position_ids = kwargs.pop("position_ids", None)
attention_mask = kwargs.pop("attention_mask", None)
if "inputs_embeds" in kwargs:
raise NotImplementedError("inputs_embeds is not supported")
if images is not None:
inputs, position_ids, attention_mask, _, inputs_embeds, _ = self.prepare_inputs_labels_for_multimodal(
inputs, position_ids, attention_mask, None, None, images, image_sizes=image_sizes
)
else:
inputs_embeds = self.get_model().embed_tokens(inputs)
return super().generate(position_ids=position_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, **kwargs)
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
images = kwargs.pop("images", kwargs.pop("pixel_values", None))
image_sizes = kwargs.pop("image_sizes", None)
inputs = super().prepare_inputs_for_generation(
input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, **kwargs
)
if images is not None:
inputs["images"] = images
if image_sizes is not None:
inputs["image_sizes"] = image_sizes
return inputs
AutoModelForCausalLM.register(LlavaMistralConfig, LlavaMistralForCausalLM)
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