import logging
import random
import torch
from torch.cuda.amp import autocast as autocast
import torch.nn as nn
from minigpt4.common.registry import registry
from minigpt4.models.blip2 import Blip2Base, disabled_train
from transformers.models.llama.modeling_llama import LlamaForCausalLM
from transformers import LlamaTokenizer
from peft import (
LoraConfig,
get_peft_model,
get_peft_model_state_dict,
prepare_model_for_int8_training,
set_peft_model_state_dict,
)
@registry.register_model("mini_gpt4")
class MiniGPT4(Blip2Base):
"""
BLIP2 GPT-LLAMA model.
"""
PRETRAINED_MODEL_CONFIG_DICT = {
"pretrain_vicuna0": "configs/models/minigpt4_vicuna0.yaml",
"pretrain_llama2": "configs/models/minigpt4_llama2.yaml",
}
def __init__(
self,
vit_model="eva_clip_g",
q_former_model="https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained_flant5xxl.pth",
img_size=224,
drop_path_rate=0,
use_grad_checkpoint=False,
vit_precision="fp16",
freeze_vit=True,
has_qformer=True,
freeze_qformer=True,
num_query_token=32,
llama_model="",
prompt_path="",
prompt_template="",
max_txt_len=32,
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_target_modules=["q_proj", "v_proj"],
lora_alpha=16,
lora_dropout=0.05,
):
super().__init__()
self.tokenizer = self.init_tokenizer()
self.low_resource = low_resource
print('Loading VIT')
self.visual_encoder, self.ln_vision = self.init_vision_encoder(
vit_model, img_size, drop_path_rate, use_grad_checkpoint, vit_precision
)
if freeze_vit:
for name, param in self.visual_encoder.named_parameters():
param.requires_grad = False
self.visual_encoder = self.visual_encoder.eval()
self.visual_encoder.train = disabled_train
for name, param in self.ln_vision.named_parameters():
param.requires_grad = False
self.ln_vision = self.ln_vision.eval()
self.ln_vision.train = disabled_train
logging.info("freeze vision encoder")
print('Loading VIT Done')
self.has_qformer = has_qformer
if self.has_qformer:
print('Loading Q-Former')
self.Qformer, self.query_tokens = self.init_Qformer(
num_query_token, self.visual_encoder.num_features
)
self.Qformer.cls = None
self.Qformer.bert.embeddings.word_embeddings = None
self.Qformer.bert.embeddings.position_embeddings = None
for layer in self.Qformer.bert.encoder.layer:
layer.output = None
layer.intermediate = None
self.load_from_pretrained(url_or_filename=q_former_model)
if freeze_qformer:
for name, param in self.Qformer.named_parameters():
param.requires_grad = False
self.Qformer = self.Qformer.eval()
self.Qformer.train = disabled_train
self.query_tokens.requires_grad = False
logging.info("freeze Qformer")
img_f_dim = self.Qformer.config.hidden_size
print('Loading Q-Former Done')
else:
img_f_dim = self.visual_encoder.num_features * 4
print('Do not use Q-Former here.')
print('Loading LLAMA')
self.llama_tokenizer = LlamaTokenizer.from_pretrained(llama_model, use_fast=False)
self.llama_tokenizer.pad_token = "$$"
if self.low_resource:
self.llama_model = LlamaForCausalLM.from_pretrained(
llama_model,
torch_dtype=torch.float16,
load_in_8bit=True,
device_map={'': device_8bit}
)
else:
self.llama_model = LlamaForCausalLM.from_pretrained(
llama_model,
torch_dtype=torch.float16,
)
if lora_r > 0:
self.llama_model = prepare_model_for_int8_training(self.llama_model)
loraconfig = LoraConfig(
r=lora_r,
lora_alpha=lora_alpha,
target_modules=lora_target_modules,
lora_dropout=lora_dropout,
bias="none",
task_type="CAUSAL_LM"
)
self.llama_model = get_peft_model(self.llama_model, loraconfig)
# if ckpt_path:
# print('load the llm under lora')
# ckpt = torch.load(ckpt_path)
# set_peft_model_state_dict(self.llama_model,ckpt)
self.llama_model.print_trainable_parameters()
else:
for name, param in self.llama_model.named_parameters():
param.requires_grad = False
print('Loading LLAMA Done')
self.llama_proj = nn.Linear(
img_f_dim, self.llama_model.config.hidden_size
)
self.max_txt_len = max_txt_len
self.end_sym = end_sym
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)))
else:
self.prompt_list = []
def vit_to_cpu(self):
self.ln_vision.to("cpu")
self.ln_vision.float()
self.visual_encoder.to("cpu")
self.visual_encoder.float()
def encode_img(self, image):
device = image.device
if self.low_resource:
self.vit_to_cpu()
image = image.to("cpu")
with self.maybe_autocast():
image_embeds = self.ln_vision(self.visual_encoder(image)).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:
image_embeds = image_embeds[:, 1:, :]
bs, pn, hs = image_embeds.shape
image_embeds = image_embeds.view(bs, int(pn / 4), int(hs * 4))
inputs_llama = self.llama_proj(image_embeds)
atts_llama = torch.ones(inputs_llama.size()[:-1], dtype=torch.long).to(image.device)
return inputs_llama, atts_llama
def get_context_emb(self, prompt, img_list):
device = img_list[0].device
prompt_segs = prompt.split('<ImageHere>')
assert len(prompt_segs) == len(img_list) + 1, "Unmatched numbers of image placeholders and images."
seg_tokens = [
self.llama_tokenizer(
seg, return_tensors="pt", add_special_tokens=i == 0).to(device).input_ids
# only add bos to the first seg
for i, seg in enumerate(prompt_segs)
]
seg_embs = [self.embed_tokens(seg_t) for seg_t in seg_tokens]
mixed_embs = [emb for pair in zip(seg_embs[:-1], img_list) for emb in pair] + [seg_embs[-1]]
mixed_embs = torch.cat(mixed_embs, dim=1)
return mixed_embs
def prompt_wrap(self, img_embeds, atts_img, prompts):
if prompts:
emb_lists = []
if isinstance(prompts, str):
prompts = [prompts] * len(img_embeds)
for each_img_embed, each_prompt in zip(img_embeds, prompts):
p_before, p_after = each_prompt.split('<ImageHere>')
p_before_tokens = self.llama_tokenizer(
p_before, return_tensors="pt", add_special_tokens=False).to(img_embeds.device)
p_after_tokens = self.llama_tokenizer(
p_after, return_tensors="pt", add_special_tokens=False).to(img_embeds.device)
p_before_embed = self.embed_tokens(p_before_tokens.input_ids)
p_after_embed = self.embed_tokens(p_after_tokens.input_ids)
wrapped_emb = torch.cat([p_before_embed, each_img_embed[None], p_after_embed], dim=1)
emb_lists.append(wrapped_emb)
emb_lens = [emb.shape[1] for emb in emb_lists]
pad_emb = self.embed_tokens(torch.tensor(self.llama_tokenizer.pad_token_id, device=img_embeds.device))
wrapped_embs = pad_emb.expand(len(emb_lens), max(emb_lens), -1).clone()
wrapped_atts = torch.zeros([len(emb_lens), max(emb_lens)], dtype=torch.int, device=img_embeds.device)
for i, emb in enumerate(emb_lists):
wrapped_embs[i, :emb_lens[i]] = emb
wrapped_atts[i, :emb_lens[i]] = 1
return wrapped_embs, wrapped_atts
else:
return img_embeds, atts_img
def concat_emb_input_output(self, input_embs, input_atts, output_embs, output_atts):
input_lens = []
cat_embs = []
cat_atts = []
for i in range(input_embs.size(0)):
input_len = input_atts[i].sum()
input_lens.append(input_len)
cat_embs.append(
torch.cat([
input_embs[i][:input_len],
output_embs[i],
input_embs[i][input_len:]
])
)
cat_atts.append(
torch.cat([
input_atts[i][:input_len],
output_atts[i],
input_atts[i][input_len:]
])
)
cat_embs = torch.stack(cat_embs)
cat_atts = torch.stack(cat_atts)
return cat_embs, cat_atts, input_lens
def forward(self, samples):
image = samples["image"]
img_embeds, atts_img = self.encode_img(image)
if self.prompt_list:
instruction = random.choice(self.prompt_list)
else:
instruction = samples["instruction_input"] if "instruction_input" in samples else None
img_embeds, atts_img = self.prompt_wrap(img_embeds, atts_img, instruction)
self.llama_tokenizer.padding_side = "right"
text = [t + self.end_sym for t in samples["answer"]]
to_regress_tokens = self.llama_tokenizer(
text,
return_tensors="pt",
padding="longest",
truncation=True,
max_length=self.max_txt_len,
add_special_tokens=False
).to(image.device)
batch_size = img_embeds.shape[0]
bos = torch.ones([batch_size, 1],
dtype=to_regress_tokens.input_ids.dtype,
device=to_regress_tokens.input_ids.device) * self.llama_tokenizer.bos_token_id
bos_embeds = self.embed_tokens(bos)
atts_bos = atts_img[:, :1]
to_regress_embeds = self.embed_tokens(to_regress_tokens.input_ids)
inputs_embeds, attention_mask, input_lens = \
self.concat_emb_input_output(img_embeds, atts_img, to_regress_embeds, to_regress_tokens.attention_mask)
inputs_embeds = torch.cat([bos_embeds, inputs_embeds], dim=1)
attention_mask = torch.cat([atts_bos, attention_mask], dim=1)
part_targets = to_regress_tokens.input_ids.masked_fill(
to_regress_tokens.input_ids == self.llama_tokenizer.pad_token_id, -100
)
targets = (
torch.ones([inputs_embeds.shape[0], inputs_embeds.shape[1]],
dtype=torch.long).to(image.device).fill_(-100)
)
for i, target in enumerate(part_targets):
targets[i, input_lens[i] + 1:input_lens[i] + len(target) + 1] = target # plus 1 for bos
with self.maybe_autocast():
outputs = self.llama_model(
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
return_dict=True,
labels=targets,
)
loss = outputs.loss
return {"loss": loss}
def embed_tokens(self, token_ids):
if hasattr(self.llama_model.base_model, 'model'): ## lora wrapped model
embeds = self.llama_model.base_model.model.model.embed_tokens(token_ids)
else:
embeds = self.llama_model.base_model.embed_tokens(token_ids)
return embeds
@classmethod
def from_config(cls, cfg):
vit_model = cfg.get("vit_model", "eva_clip_g")
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")
freeze_vit = cfg.get("freeze_vit", True)
has_qformer = cfg.get("has_qformer", True)
freeze_qformer = cfg.get("freeze_qformer", True)
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", 32)
end_sym = cfg.get("end_sym", '\n')
lora_r = cfg.get("lora_r", 0)
lora_alpha = cfg.get("lora_alpha", 32)
model = cls(
vit_model=vit_model,
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,
has_qformer=has_qformer,
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,
end_sym=end_sym,
low_resource=low_resource,
device_8bit=device_8bit,
lora_r=lora_r,
lora_alpha=lora_alpha,
)
ckpt_path = cfg.get("ckpt", "") # load weights of MiniGPT-4
if ckpt_path:
print("Load BLIP2-LLM Checkpoint: {}".format(ckpt_path))
ckpt = torch.load(ckpt_path, map_location="cpu")
msg = model.load_state_dict(ckpt['model'], strict=False)
return model