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import logging
import random
import torch
from torch.cuda.amp import autocast as autocast
import torch.nn as nn
import sys
from minigpt4.common.registry import registry
from minigpt4.models.blip2 import Blip2Base, disabled_train
from minigpt4.models.modeling_llama import LlamaForCausalLM
from transformers import LlamaTokenizer
from transformers import AutoModelForCausalLM, AutoTokenizer
@registry.register_model("mini_gpt4")
class MiniGPT4(Blip2Base):
"""
BLIP2 GPT-LLAMA model.
"""
PRETRAINED_MODEL_CONFIG_DICT = {
"pretrain_vicuna": "../configs/minigpt4.yaml", # "configs/models/minigpt4.yaml",
}
def __init__(
self,
llama_model="",
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.
):
super().__init__()
self.tokenizer = self.init_tokenizer()
self.low_resource = low_resource
print('Loading LLAMA')
self.llama_tokenizer = AutoTokenizer.from_pretrained(llama_model, use_fast=False)
self.llama_tokenizer.pad_token = self.llama_tokenizer.eos_token
if self.low_resource:
self.llama_model = AutoModelForCausalLM.from_pretrained(
llama_model,
torch_dtype=torch.float16,
load_in_8bit=True,
device_map={'': device_8bit}
)
else:
self.llama_model = AutoModelForCausalLM.from_pretrained(
llama_model,
torch_dtype=torch.float16,
)
for name, param in self.llama_model.named_parameters():
param.requires_grad = False
print('Loading LLAMA Done')
self.esm_struct_llama_proj = nn.Linear(
512, self.llama_model.config.hidden_size
)
self.esm_seq_llama_proj = nn.Linear(
# 1280, self.llama_model.config.hidden_size
2560, self.llama_model.config.hidden_size
)
self.max_txt_len = max_txt_len
self.end_sym = end_sym
self.prompt_template = prompt_template
def encode_protein_struct(self, protein_struct_encode):
device = protein_struct_encode.device
protein_embeds = protein_struct_encode.to(device)
# input llama shape: [B, 32, 5120]
inputs_llama = self.esm_struct_llama_proj(protein_embeds.squeeze(dim=2))
# atts_llama shape: [B, 32]
atts_llama = torch.ones(inputs_llama.size()[:-1], dtype=torch.long).to(device)
return inputs_llama, atts_llama
def encode_protein_seq(self, protein_seq_encode):
device = protein_seq_encode.device
protein_embeds = protein_seq_encode.to(device)
# input llama is of shape [B, 32, 5120]
inputs_llama = self.esm_seq_llama_proj(protein_embeds.squeeze(dim=2))
# atts_llama is of shape [B, 32]
atts_llama = torch.ones(inputs_llama.size()[:-1], dtype=torch.long).to(device)
return inputs_llama, atts_llama
def prompt_wrap(self, img_embeds, atts_img, prompt):
if prompt:
batch_size = img_embeds.shape[0]
p_before, p_after = prompt.split('<proteinHere>')
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_embeds = self.llama_model.model.embed_tokens(p_before_tokens.input_ids).expand(batch_size, -1, -1)
p_after_embeds = self.llama_model.model.embed_tokens(p_after_tokens.input_ids).expand(batch_size, -1, -1)
# print(p_before_embeds.shape, img_embeds.shape, p_after_embeds.shape)
wrapped_img_embeds = torch.cat([p_before_embeds, img_embeds, p_after_embeds], dim=1)
wrapped_atts_img = atts_img[:, :1].expand(-1, wrapped_img_embeds.shape[1])
return wrapped_img_embeds, wrapped_atts_img
else:
return img_embeds, atts_img
def forward(self, samples):
# structure
pdb_encode = samples["pdb_encoder_out"]
pdb_device = pdb_encode.device
pdb_encode = pdb_encode[0]
pdb_encode = pdb_encode.permute(1, 0, 2) # Reshape [X, 1, Y] -> [1, X, Y]
pdb_embeds, atts_pdb = self.encode_protein_struct(pdb_encode)
# sequence
seq_encode = samples["seq_encoder_out"]
seq_device = seq_encode.device
seq_encode = seq_encode[0]
seq_embeds, atts_seq = self.encode_protein_seq(seq_encode)
img_embeds = torch.cat([pdb_embeds, seq_embeds], dim=1)
atts_img = torch.cat([atts_pdb, atts_seq], dim=1)
# skips over this branch for stage 1 and 2
if hasattr(samples, 'question_split'): # VQA dataset
print('VQA Batch')
vqa_prompt = '###Human: <protein><proteinHere></protein> '
img_embeds, atts_img = self.prompt_wrap(img_embeds, atts_img, vqa_prompt)
# TO check: print out when needed (run stage 2 and print out some stuff to see which branch it goes to)
elif "q_input" in samples: # prompt path (alignment.txt provided) then takes this path to random choose form the list
prompt = self.prompt_template.format("<protein><proteinHere></protein> " + samples["q_input"][0])
img_embeds, atts_img = self.prompt_wrap(img_embeds, atts_img, prompt)
# stage 1 directly skip the branches above
self.llama_tokenizer.padding_side = "right"
text = []
if "q_input" in samples:
text = [t + self.end_sym for t in samples["a_input"]]
else:
text = [t + self.end_sym for t in samples["text_input"]]
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(pdb_device)
targets = to_regress_tokens.input_ids.masked_fill(
to_regress_tokens.input_ids == self.llama_tokenizer.pad_token_id, -100
)
empty_targets = (
torch.ones([atts_img.shape[0], atts_img.shape[1]+1],
dtype=torch.long).to(pdb_device).fill_(-100) # plus one for bos
)
targets = torch.cat([empty_targets, targets], dim=1)
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.llama_model.model.embed_tokens(bos)
atts_bos = atts_img[:, :1]
to_regress_embeds = self.llama_model.model.embed_tokens(to_regress_tokens.input_ids)
inputs_embeds = torch.cat([bos_embeds, img_embeds, to_regress_embeds], dim=1)
attention_mask = torch.cat([atts_bos, atts_img, to_regress_tokens.attention_mask], dim=1)
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}
@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_protein_encoder = cfg.get("freeze_protein_encoder", True)
freeze_qformer = cfg.get("freeze_qformer", True)
low_resource = cfg.get("low_resource", False)
device_8bit = cfg.get("device_8bit", 0)
prompt_template = cfg.get("prompt_template", "")
max_txt_len = cfg.get("max_txt_len", 32)
end_sym = cfg.get("end_sym", '\n')
model = cls(
llama_model=llama_model,
prompt_template=prompt_template,
max_txt_len=max_txt_len,
end_sym=end_sym,
low_resource=low_resource,
device_8bit=device_8bit,
)
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
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