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Andreas Bulling 2025-06-24 08:38:09 +02:00
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models/backbones/mini_gpt4v.py Executable file
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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 minigpt4.models.modeling_llama_v2 import LlamaForCausalLM
from minigpt4.conversation.conversation import Conversation, SeparatorStyle, StoppingCriteriaList, StoppingCriteriaSub
from transformers import LlamaTokenizer, CodeLlamaTokenizer, BitsAndBytesConfig
from peft import (
LoraConfig,
get_peft_model,
prepare_model_for_kbit_training
)
import time
import numpy as np
from minigpt4.models import policies
@registry.register_model("mini_gpt4v")
class MiniGPT4v(Blip2Base):
"""
BLIP2 GPT-LLAMA model.
"""
PRETRAINED_MODEL_CONFIG_DICT = {
"pretrain_vicuna": "configs/models/minigpt4.yaml",
}
def __init__(
self,
vit_model="eva_clip_g",
img_size=224,
drop_path_rate=0,
use_grad_checkpoint=False,
vit_precision="fp16",
freeze_vit=True,
llama_model="",
prompt_path="",
prompt_template="",
max_txt_len=32,
low_resource=False, # use 8 bit and put vit in cpu
end_sym='\n',
lora_r = 8,
lora_target_modules = ["q_proj","v_proj"],
lora_alpha=16,
# lora_r = 16,
# lora_target_modules = ["q_proj","v_proj","v_proj"],
lora_dropout= 0.05,
ckpt_path = "",
system_prompt= False,
chat_template=False,
token_pooling=True,
use_grad_checkpoint_llm=False,
max_context_len=3800,
remove_template = False,
):
super().__init__()
self.tokenizer = self.init_tokenizer()
self.low_resource = low_resource
self.token_pooling = token_pooling
self.remove_template = remove_template
print("token pooling", self.token_pooling)
self.use_grad_checkpoint_llm = use_grad_checkpoint_llm
self.max_context_len = max_context_len
self.chat_template = chat_template
# 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
# )
print("vit precision", vit_precision)
self.visual_encoder, self.ln_vision = self.init_vision_encoder(
vit_model, 224, drop_path_rate, use_grad_checkpoint, vit_precision
)
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("freeze the vision encoder")
print('Loading VIT Done')
# print("visual encoder shape", self.visual_encoder.pos_embed.shape)
# assert False
print('Loading LLAMA')
self.B_SYS, self.E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
if 'CodeLlama' in llama_model:
self.llama_tokenizer = CodeLlamaTokenizer.from_pretrained(llama_model, use_fast=False) #
self.llama_tokenizer.pad_token = "$$"
else:
self.llama_tokenizer = LlamaTokenizer.from_pretrained(llama_model, use_fast=False) #
self.llama_tokenizer.pad_token = "$$"
self.system_prompt = system_prompt
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16
)
self.llama_model = LlamaForCausalLM.from_pretrained(
llama_model,
quantization_config=bnb_config,
device_map={"": 0}
)
# self.llama_model.gradient_checkpointing_enable()
self.llama_model = prepare_model_for_kbit_training(self.llama_model)
# self.llama_model.print_trainable_parameters()
print('Loading LLAMA Done')
self.merge_n = 3
self.llama_proj = nn.Linear(
1408 * self.merge_n**2, 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 encode_img(self, image):
device = image.device
if len(image.shape) > 4:
image = image.reshape(-1, *image.shape[-3:])
bs, ch, w, h = image.shape
assert w % 224 == 0
bw = w // 224
assert h % 224 == 0
bh = h // 224
image_patches = image.view(bs, ch, bw, 224, bh, 224).permute(0, 2, 4, 1, 3, 5) # bs, bw, bh, ch, 224, 224
image_patches = image_patches.reshape(bs * bw * bh, ch, 224, 224)
with self.maybe_autocast():
image_patch_embeds = self.ln_vision(self.visual_encoder(image_patches)).to(device)
image_patch_embeds = image_patch_embeds[:,1:,:].reshape(bs, bw, bh, 16, 16, image_patch_embeds.shape[-1])
image_patch_embeds = image_patch_embeds.permute(0, 1, 3, 2, 4, 5) # bs, bw, 16, bh, 16, hs
image_embeds = image_patch_embeds.reshape(bs, bw * 16 * bh * 16, image_patch_embeds.shape[-1])
bs, pn, hs = image_embeds.shape
image_embeds = image_embeds.view(bs, int(pn/self.merge_n**2), int(hs*self.merge_n**2))
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):
img_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(img_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, lengths=None):
if prompts is None or len(prompts) == 0:
# prompts is not provided, just return the original image embedding
return img_embeds, atts_img
elif img_embeds is None:
# prompt is provided but there is no image embedding. return the prompt embedding in right padding
self.llama_tokenizer.padding_side = "right"
prompt_tokens = self.llama_tokenizer(
prompts,
return_tensors="pt",
padding="longest",
add_special_tokens=False
).to(self.device)
prompt_embeds = self.embed_tokens(prompt_tokens.input_ids)
atts_prompt = prompt_tokens.attention_mask
return prompt_embeds, atts_prompt
else:
# return the multi-modal embedding in right padding
emb_lists = []
for idx, (each_img_embed, each_prompt) in enumerate(zip(img_embeds, prompts)):
pn = each_img_embed.shape[-2]
if lengths is not None:
each_img_embed = each_img_embed.reshape(-1, each_img_embed.shape[-1])
each_img_embed = each_img_embed[:lengths[idx] * pn]
p_segs = each_prompt.split('<ImageHere>')
interleave_emb = []
for idx, seg in enumerate(p_segs[:-1]):
p_tokens = self.llama_tokenizer(seg, return_tensors="pt", add_special_tokens=False).to(img_embeds.device)
p_embed = self.embed_tokens(p_tokens.input_ids)
interleave_emb.append(torch.cat([p_embed, each_img_embed[None][:, idx*pn:(idx+1)*pn]], dim=1))
wrapped_emb = torch.cat(interleave_emb, dim=1)
p_tokens = self.llama_tokenizer(p_segs[-1], return_tensors="pt", add_special_tokens=False).to(img_embeds.device)
p_embed = self.embed_tokens(p_tokens.input_ids)
wrapped_emb = torch.cat([wrapped_emb,p_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))
max_length = max(emb_lens) if max(emb_lens) < self.max_context_len else self.max_context_len
wrapped_embs = pad_emb.expand(len(emb_lens), max_length, -1).clone()
wrapped_atts = torch.zeros([len(emb_lens), max_length], dtype=torch.int, device=img_embeds.device)
for i, emb in enumerate(emb_lists):
length = emb_lens[i] if emb_lens[i] < self.max_context_len else self.max_context_len
wrapped_embs[i, :length] = emb[:, :length]
wrapped_atts[i, :length] = 1
return wrapped_embs, wrapped_atts
def concat_emb_input_output(self, input_embs, input_atts, output_embs, output_atts):
"""
Concatenate the batched input embedding and batched output embedding together.
Both the input and the output embedding should be right padded.
"""
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:]
])
)
# print('===================================')
# print('check input emb: ', input_embs[i][this_input_ones-2:this_input_ones])
# print('check pad emb: ', input_embs[i][this_input_ones:this_input_ones+2])
# print('check out emb: ', output_embs[i][:2])
# print('check out pad emb: ', output_embs[i][-2:])
# print('+++++++++++++++++++++++++++++++++++')
#
# print('check attn before: ', input_atts[i][:this_input_ones])
# print('check attn after: ', input_atts[i][this_input_ones:])
# print('check attn gt before: ', output_atts[i][:3])
# print('check attn gt after: ', output_atts[i][-3:])
cat_embs = torch.stack(cat_embs)
cat_atts = torch.stack(cat_atts)
return cat_embs, cat_atts, input_lens
def get_conv_emb(self, conv_q, conv_a, conv_img):
"""concatenate conversation and make sure the model is only trained to regress the answer"""
regress_embs_list = []
targets_list = []
batch_size = len(conv_q)
for batch_idx in range(batch_size):
questions, answers = conv_q[batch_idx], conv_a[batch_idx]
assigned_imgs = conv_img[batch_idx]
questions = [self.prompt_wrap(
img_embeds=img,
atts_img=None,
prompts=[q],
lengths=[img.shape[1]] if img is not None else None) for q, img in zip(questions, assigned_imgs)]
q_embs = [emb for emb, _ in questions]
answers = [self.llama_tokenizer(a, return_tensors="pt", add_special_tokens=False).to(self.device) for a in answers]
cur_emb = []
cur_target = []
for i in range(len(questions)):
cur_emb.append(q_embs[i])
cur_target.append(torch.ones_like(q_embs[i][..., 0], dtype=torch.int) * -100)
cur_emb.append(self.embed_tokens(answers[i].input_ids))
cur_target.append(answers[i].input_ids)
cur_emb = torch.cat(cur_emb, dim=1)
cur_target = torch.cat(cur_target, dim=1)
regress_embs_list.append(cur_emb)
targets_list.append(cur_target)
max_len = min(max([target.shape[1] for target in targets_list]), self.max_txt_len)
regress_embeds = torch.zeros([batch_size, max_len, cur_emb.shape[-1]], device=self.device)
regress_attn = torch.zeros([batch_size, max_len], dtype=torch.int, device=self.device)
targets = torch.ones([batch_size, max_len], dtype=torch.long, device=self.device) * -100
for batch_idx in range(batch_size):
cur_len = regress_embs_list[batch_idx].shape[1]
regress_embeds[batch_idx, :cur_len] = regress_embs_list[batch_idx][0, :max_len]
regress_attn[batch_idx, :cur_len] = 1
targets[batch_idx, :cur_len] = targets_list[batch_idx][0, :max_len]
return regress_embeds, regress_attn, targets
def preparing_embedding(self, samples):
def remove_special_tokens(data):
# if "instruction_input" in data:
data = [instruct.replace(" [caption]","") for instruct in data]
data = [instruct.replace(" [vqa]","") for instruct in data]
data = [instruct.replace(" [grounding]","") for instruct in data]
data = [instruct.replace(" [identify]","") for instruct in data]
data = [instruct.replace(" [refer]","") for instruct in data]
return data
### prepare input tokens
if 'image' in samples:
img_embeds, img_atts = self.encode_img(samples["image"])
else:
img_embeds = img_atts = None
if 'conv_q' in samples:
# handeling conversation datasets
conv_q, conv_a = samples['conv_q'], samples['conv_a']
connect_sym = samples['connect_sym'][0]
conv_q = [q.split(connect_sym)for q in conv_q]
conv_a = [a.split(connect_sym) for a in conv_a]
conv_img = assign_imgs(conv_q, img_embeds)
if self.chat_template:
conv_q = [["[INST] " + item + "[/INST]" for item in items] for items in conv_q]
regress_embeds, regress_atts, part_targets = self.get_conv_emb(conv_q, conv_a, conv_img)
cond_embeds, cond_atts = regress_embeds[:, :0], regress_atts[:, :0]
else:
instruction = samples["instruction_input"] if "instruction_input" in samples else None
# print("instruction before", instruction)
if self.remove_template:
instruction = remove_special_tokens(instruction)
# print("instruction after", instruction)
if self.chat_template:
instruction = ["[INST] " + instruct + "[/INST]" for instruct in instruction]
if 'length' in samples:
# the input is a image train (like videos)
bsz, pn, hs = img_embeds.shape
img_embeds = img_embeds.reshape(len(samples['image']), -1, pn, hs)
cond_embeds, cond_atts = self.prompt_wrap(img_embeds, img_atts, instruction, samples['length'])
else:
cond_embeds, cond_atts = self.prompt_wrap(img_embeds, img_atts, instruction)
### prepare target tokens
self.llama_tokenizer.padding_side = "right"
text = [t + self.end_sym for t in samples["answer"]]
regress_tokens = self.llama_tokenizer(
text,
return_tensors="pt",
padding="longest",
truncation=True,
max_length=self.max_txt_len,
add_special_tokens=False
).to(self.device)
regress_token_ids = regress_tokens.input_ids
regress_atts = regress_tokens.attention_mask
part_targets = regress_token_ids.masked_fill(
regress_token_ids == self.llama_tokenizer.pad_token_id, -100
)
regress_embeds = self.embed_tokens(regress_token_ids)
return cond_embeds, cond_atts, regress_embeds, regress_atts, part_targets
def forward(self, samples, reduction="mean"):
# prepare the embedding to condition and the embedding to regress
cond_embeds, cond_atts, regress_embeds, regress_atts, part_targets = \
self.preparing_embedding(samples)
# concat the embedding to condition and the embedding to regress
inputs_embeds, attention_mask, input_lens = \
self.concat_emb_input_output(cond_embeds, cond_atts, regress_embeds, regress_atts)
# get bos token embedding
bos = torch.ones_like(part_targets[:, :1]) * self.llama_tokenizer.bos_token_id
bos_embeds = self.embed_tokens(bos)
bos_atts = attention_mask[:, :1]
# add bos token at the begining
inputs_embeds = torch.cat([bos_embeds, inputs_embeds], dim=1)
attention_mask = torch.cat([bos_atts, attention_mask], dim=1)
# ensemble the final targets
targets = torch.ones([inputs_embeds.shape[0], inputs_embeds.shape[1]],
dtype=torch.long).to(self.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,
reduction=reduction
)
loss = outputs.loss
return {"loss": loss}
@torch.no_grad()
def generate(
self,
images,
texts,
use_nucleus_sampling=False,
num_beams=1,
max_new_tokens=20,
min_length=1,
top_p=0.9,
repetition_penalty=1,
length_penalty=1,
temperature=1,
do_sample=False,
stop_words_ids=[2],
lengths=None,
):
'''
function for generate test use
'''
stopping_criteria = StoppingCriteriaList([StoppingCriteriaSub(
stops=[torch.tensor([i]).to(self.device) for i in stop_words_ids])])
img_embeds, atts_img = self.encode_img(images.to(self.device))
if lengths is not None:
image_lists = []
img_embeds = img_embeds.reshape(len(lengths), -1, img_embeds.shape[-2], img_embeds.shape[-1])
for idx, img_embed in enumerate(img_embeds):
image_lists.append([img_embed[i][None] for i in range(lengths[idx])])
else:
image_lists = [[image_emb[None]] for image_emb in img_embeds]
assert len(texts) == len(image_lists)
batch_embs = [self.get_context_emb(text, img_list) for text, img_list in zip(texts, image_lists)]
batch_size = len(batch_embs)
max_len = max([emb.shape[1] for emb in batch_embs])
emb_dim = batch_embs[0].shape[2]
dtype = batch_embs[0].dtype
device = batch_embs[0].device
embs = torch.zeros([batch_size, max_len, emb_dim], dtype=dtype, device=device)
attn_mask = torch.zeros([batch_size, max_len], dtype=torch.int, device=device)
for i, emb in enumerate(batch_embs):
emb_len = emb.shape[1]
embs[i, -emb_len:] = emb[0]
attn_mask[i, -emb_len:] = 1
with self.maybe_autocast():
outputs = self.llama_model.generate(
inputs_embeds=embs,
attention_mask=attn_mask,
max_new_tokens=max_new_tokens,
num_beams=num_beams,
do_sample=do_sample,
# stopping_criteria=stopping_criteria,
)
answers = []
for output_token in outputs:
if output_token[0] == 0:
output_token = output_token[1:]
output_texts = self.llama_tokenizer.decode(output_token, skip_special_tokens=True)
output_texts = output_texts.split('</s>')[0] # remove the stop sign </s>
output_texts = output_texts.replace("<s>", "")
output_texts = output_texts.split(r'[/INST]')[-1].strip()
answers.append(output_texts)
return answers
@torch.no_grad()
def multi_select(self, images, texts, answers, num_cand=None):
all_losses = []
for answer in answers:
choice_samples = {
'image': images,
'instruction_input': texts,
'answer': answer
}
loss = self.forward(choice_samples, reduction='none')['loss'].reshape(-1, 1)
all_losses.append(loss)
torch.cuda.empty_cache()
all_losses = torch.cat(all_losses, dim=-1)
if num_cand is not None:
for i in range(all_losses.shape[0]):
all_losses[i, num_cand[i]:] = 9999
output_class_ranks = torch.argsort(all_losses, dim=-1)
return output_class_ranks.tolist()
def predict_answers(
self,
samples,
num_beams=5,
inference_method="generate",
max_len=10,
min_len=1,
num_ans_candidates=128,
answer_list=None,
prompt="",
length_penalty=0,
**kwargs
):
'''
function for open-ended VQA
'''
images = samples["image"].cuda()
texts = samples["instruction_input"]
output_text = self.generate(
images=images,
texts=texts,
num_beams=num_beams,
max_new_tokens=max_len,
min_length=min_len,
length_penalty=length_penalty
)
if "apply_lemmatizer" in samples.keys() and samples["apply_lemmatizer"]:
output_text = self._lemmatize(output_text)
return output_text
def predict_class(
self,
samples,
num_beams=5,
inference_method="generate",
max_len=10,
min_len=1,
num_ans_candidates=5,
answer_list=None,
prompt="",
length_penalty=0,
**kwargs
):
'''
function for multi-choice VQA
'''
image = samples["image"].cuda()
instruction = samples['instruction_input']
answers = samples["choices"]
num_cand = samples["num_choices"]
ranks = self.multi_select(image, instruction, answers, num_cand)
pred_ans = []
for i, rank in enumerate(ranks):
pred = answers[rank[0]][i]
pred_ans.append(pred)
return pred_ans
def embed_tokens(self, token_ids):
try:
embeds = self.llama_model.base_model.model.model.embed_tokens(token_ids)
except AttributeError:
embeds = self.llama_model.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)
freeze_qformer = cfg.get("freeze_qformer", True)
low_resource = cfg.get("low_resource", False)
prompt_path = cfg.get("prompt_path", "")
prompt_template = cfg.get("prompt_template", "")
max_txt_len = cfg.get("max_txt_len", 300)
end_sym = cfg.get("end_sym", '\n')
lora_r = cfg.get("lora_r",64)
lora_alpha = cfg.get("lora_alpha",16)
chat_template = cfg.get("chat_template",False)
system_prompt = cfg.get("system_prompt", False)
token_pooling = cfg.get("token_pooling",True)
use_grad_checkpoint_llm = cfg.get("use_grad_checkpoint_llm", False)
max_context_len = cfg.get("max_context_len", 3800)
remove_template = cfg.get("remove_template", False)
model = cls(
vit_model=vit_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,
llama_model=llama_model,
prompt_path=prompt_path,
prompt_template=prompt_template,
max_txt_len=max_txt_len,
low_resource=low_resource,
end_sym=end_sym,
lora_r = lora_r,
lora_alpha = lora_alpha,
chat_template = chat_template,
system_prompt = system_prompt,
token_pooling = token_pooling,
use_grad_checkpoint_llm=use_grad_checkpoint_llm,
max_context_len=max_context_len,
remove_template = remove_template
)
ckpt_path = cfg.get("ckpt", "") # load weights of MiniGPT-4
if ckpt_path:
print("Load Minigpt-4-LLM Checkpoint: {}".format(ckpt_path))
ckpt = torch.load(ckpt_path, map_location="cpu")
msg = model.load_state_dict(ckpt['model'], strict=False)
return model
def assign_imgs(batched_instruct_list, batched_img_embeds):
'''this function is used when the data is interleaved.
the interlevaed data is separated, and this function assign
corresponding image embeddings to each segment'''
if len(batched_img_embeds.shape) == 3:
batched_img_embeds = batched_img_embeds[:, None]
batched_assigned = []
for instruct_list, img_embeds in zip(batched_instruct_list, batched_img_embeds):
img_idx = 0
assigned_img = []
n_assigned = []
for instruct in instruct_list:
n_img = instruct.count('<ImageHere>')
if n_img > 0: # this instruction include images.
assigned_img.append(img_embeds[None, img_idx:img_idx+n_img])
img_idx += n_img
n_assigned.append(n_img)
else: # this instruction doesn't include images
assigned_img.append(None)
n_assigned.append(None)
batched_assigned.append(assigned_img)
return batched_assigned