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vxverse.py
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vxverse.py
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import logging
import random
import torch
from torch.cuda.amp import autocast as autocast
import torch.nn as nn
from vxverse.common.registry import registry
from vxverse.models.base_model import disabled_train
from vxverse.models.vxverse_base import VXVERSEBase
from vxverse.models.Qformer import BertConfig, BertLMHeadModel
from vxverse.common.utils import get_abs_path, is_url
@registry.register_model("vxverse")
class VXVERSE(VXVERSEBase):
"""
VXVERSE model
"""
PRETRAINED_MODEL_CONFIG_DICT = {
"pretrain_xverse7b-chat": "configs/models/vxverse_7bchat.yaml",
"pretrain_xverse13b-chat": "configs/models/vxverse_13bchat.yaml",
"pretrain_xverse65b-chat": "configs/models/vxverse_65bchat.yaml",
}
Q_Former_Structure_CONFIG_DICT = {
"bert-base-uncased": "configs/Qformer/bert-base-uncased",
}
def __init__(
self,
vit_model="EVA02-CLIP-bigE-14-224",
vit_path="./eva02/EVA02_CLIP_E_psz14_s4B.pt",
# q_former_model="https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained_flant5xxl.pth",
q_former_model="",
img_size=224,
drop_path_rate=0,
use_grad_checkpoint=False,
vit_precision="fp16",
train_precision="fp16",
freeze_vit=True,
freeze_llm=True,
has_qformer=True,
n_proj_layers=1,
freeze_qformer=True,
num_query_token=32,
llama_model="",
prompt_path="",
prompt_template="",
max_txt_len=128,
max_context_len=800,
end_sym='\n',
low_resource=False, # use 8 bit and put vit in cpu
device_8bit=0, # the device of 8bit model should be set when loading and cannot be changed anymore.
lora_r=0, # lora_r means lora is not used
lora_target_modules=["q_proj", "v_proj"],
lora_alpha=16,
lora_dropout=0.1
):
super().__init__(
vit_model=vit_model,
vit_path=vit_path,
img_size=img_size,
train_precision=train_precision,
drop_path_rate=drop_path_rate,
use_grad_checkpoint=use_grad_checkpoint,
vit_precision=vit_precision,
freeze_vit=freeze_vit,
freeze_llm=freeze_llm,
llama_model=llama_model,
max_txt_len=max_txt_len,
max_context_len=max_context_len,
end_sym=end_sym,
low_resource=low_resource,
device_8bit=device_8bit,
lora_r=lora_r, # lora_r means lora is not used
lora_target_modules=lora_target_modules,
lora_alpha=lora_alpha,
lora_dropout=lora_dropout
)
self.has_qformer = has_qformer
self.n_proj_layers = n_proj_layers
self.train_precision = train_precision
if self.has_qformer:
print('Loading Q-Former')
logging.info('Loading Q-Former')
self.Qformer, self.query_tokens = self.init_Qformer(
num_query_token, self.visual_encoder.num_features, freeze_qformer
)
# TODO
# delete or not?
if q_former_model != "" :
print("Loading QFormer weight from pretrained weight...")
self.load_from_pretrained(url_or_filename=q_former_model) # load q-former weights here
else:
print("Initial QFormer weight randomly and do not load from pretrained when constructing it...")
img_f_dim = self.Qformer.config.hidden_size
print('Loading Q-Former Done')
logging.info('Loading Q-Former Done')
self.llama_proj = nn.Linear(
img_f_dim, self.llama_model.config.hidden_size
)
else:
print('Do not use Q-Former here.')
logging.info('Do not use Q-Former here.')
img_f_dim = self.visual_encoder.num_features
llama_hidden_size = self.llama_model.config.hidden_size
modules = [nn.Linear(img_f_dim, llama_hidden_size)]
print(f">>>>> img_f_dim: {img_f_dim}, llama_hidden_size: {llama_hidden_size}")
print(f">>>>> n_proj_layers: {self.n_proj_layers}")
for _ in range(1, self.n_proj_layers):
modules.append(nn.GELU())
modules.append(nn.Linear(llama_hidden_size, llama_hidden_size))
self.llama_proj = nn.Sequential(*modules)
if prompt_path:
with open(prompt_path, 'r') as f:
raw_prompts = f.read().splitlines()
filted_prompts = [raw_prompt for raw_prompt in raw_prompts if "<ImageHere>" in raw_prompt]
self.prompt_list = [prompt_template.format(p) for p in filted_prompts]
print('Load {} training prompts'.format(len(self.prompt_list)))
print('Prompt Example \n{}'.format(random.choice(self.prompt_list)))
logging.info('Load {} training prompts'.format(len(self.prompt_list)))
logging.info('Prompt Example \n{}'.format(random.choice(self.prompt_list)))
else:
self.prompt_list = []
@classmethod
def init_Qformer(cls, num_query_token, vision_width, freeze):
# TODO
qformer_struct_config = get_abs_path(cls.Q_Former_Structure_CONFIG_DICT['bert-base-uncased'])
encoder_config = BertConfig.from_pretrained(qformer_struct_config)
encoder_config.encoder_width = vision_width
# insert cross-attention layer every other block
encoder_config.add_cross_attention = True
encoder_config.cross_attention_freq = 2
encoder_config.query_length = num_query_token
Qformer = BertLMHeadModel(config=encoder_config)
query_tokens = nn.Parameter(
torch.zeros(1, num_query_token, encoder_config.hidden_size)
)
query_tokens.data.normal_(mean=0.0, std=encoder_config.initializer_range)
Qformer.cls = None
Qformer.bert.embeddings.word_embeddings = None
Qformer.bert.embeddings.position_embeddings = None
for layer in Qformer.bert.encoder.layer:
layer.output = None
layer.intermediate = None
if freeze:
for name, param in Qformer.named_parameters():
param.requires_grad = False
Qformer = Qformer.eval()
Qformer.train = disabled_train
query_tokens.requires_grad = False
print("freeze Qformer")
logging.info("freeze Qformer")
return Qformer, query_tokens
def encode_img(self, image):
if type(image) == torch.Tensor:
if self.train_precision == "bf16":
image = image.to(torch.bfloat16)
device = image.device
with self.maybe_autocast():
image_embeds = self.visual_encoder(image)
image_embeds = self.ln_vision(image_embeds).to(device)
if self.has_qformer:
image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(device)
query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
query_output = self.Qformer.bert(
query_embeds=query_tokens,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
return_dict=True,
)
inputs_llama = self.llama_proj(
query_output.last_hidden_state)
else:
inputs_llama = self.llama_proj(image_embeds)
atts_llama = torch.ones(inputs_llama.size()[:-1], dtype=torch.long).to(device)
return inputs_llama, atts_llama
elif type(image) == list:
inputs_llama_lists, atts_llama_lists = [], []
for per_imgs in image:
if self.train_precision=="bf16":
per_imgs = per_imgs.to(torch.bfloat16)
device = per_imgs.device
with self.maybe_autocast():
image_embeds = self.visual_encoder(per_imgs)
image_embeds = self.ln_vision(image_embeds).to(device)
if self.has_qformer:
image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(device)
query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
query_output = self.Qformer.bert(
query_embeds=query_tokens,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
return_dict=True,
)
inputs_llama = self.llama_proj(query_output.last_hidden_state)
else:
inputs_llama = self.llama_proj(image_embeds)
atts_llama = torch.ones(inputs_llama.size()[:-1], dtype=torch.long).to(device)
inputs_llama_lists.append(inputs_llama)
atts_llama_lists.append(atts_llama)
return inputs_llama_lists, atts_llama_lists
@classmethod
def from_config(cls, cfg):
vit_model = cfg.get("vit_model", "EVA02-CLIP-bigE-14-224")
vit_path = cfg.get("vit_path", "./eva02/EVA02_CLIP_E_psz14_s4B.pt")
q_former_model = cfg.get("q_former_model", "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained_flant5xxl.pth")
img_size = cfg.get("image_size")
num_query_token = cfg.get("num_query_token")
llama_model = cfg.get("llama_model")
drop_path_rate = cfg.get("drop_path_rate", 0)
use_grad_checkpoint = cfg.get("use_grad_checkpoint", False)
vit_precision = cfg.get("vit_precision", "fp16")
train_precision = cfg.get("train_precision", "fp16")
freeze_vit = cfg.get("freeze_vit", True)
freeze_llm = cfg.get("freeze_llm", True)
has_qformer = cfg.get("has_qformer", True)
n_proj_layers = cfg.get("n_proj_layers", 1)
freeze_qformer = cfg.get("freeze_qformer", False)
low_resource = cfg.get("low_resource", False)
device_8bit = cfg.get("device_8bit", 0)
prompt_path = cfg.get("prompt_path", "")
prompt_template = cfg.get("prompt_template", "")
max_txt_len = cfg.get("max_txt_len", 128)
max_context_len = cfg.get("max_context_len", 800)
end_sym = cfg.get("end_sym", '\n')
lora_r = cfg.get("lora_r", 0)
lora_alpha = cfg.get("lora_alpha", 16)
lora_dropout = cfg.get("lora_dropout", 0.01)
lora_target_modules = cfg.get("lora_target_modules", ["q_proj", "v_proj"])
# 检查是否支持bf16
if train_precision == 'bf16' and not torch.cuda.is_bf16_supported():
raise ValueError("bf16 is not supported on your GPU.")
print(f"train_precision is : {train_precision}")
model = cls(
vit_model=vit_model,
vit_path=vit_path,
train_precision=train_precision,
q_former_model=q_former_model,
img_size=img_size,
drop_path_rate=drop_path_rate,
use_grad_checkpoint=use_grad_checkpoint,
vit_precision=vit_precision,
freeze_vit=freeze_vit,
freeze_llm=freeze_llm,
has_qformer=has_qformer,
n_proj_layers=n_proj_layers,
freeze_qformer=freeze_qformer,
num_query_token=num_query_token,
llama_model=llama_model,
prompt_path=prompt_path,
prompt_template=prompt_template,
max_txt_len=max_txt_len,
max_context_len=max_context_len,
end_sym=end_sym,
low_resource=low_resource,
device_8bit=device_8bit,
lora_r=lora_r,
lora_alpha=lora_alpha,
lora_target_modules=lora_target_modules,
lora_dropout=lora_dropout,
)
ckpt_path = cfg.get("ckpt", "") # load weights of Vxverse
if ckpt_path:
print("Loading Visual-Xverse Checkpoint: {}".format(ckpt_path))
ckpt = torch.load(ckpt_path, map_location="cpu")
msg = model.load_state_dict(ckpt['model'], strict=False)
if train_precision == 'bf16':
model.to(torch.bfloat16)
return model