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"""
Copyright (c) 2023, salesforce.com, inc.
All rights reserved.
SPDX-License-Identifier: BSD-3-Clause
For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
"""
import logging
from packaging import version
import torch
from torch.cuda.amp import autocast as autocast
import torch.nn as nn
import torch.nn.functional as F
from lavis.common.registry import registry
from lavis.models.blip2_models.blip2 import Blip2Base, Blip2ProteinBase, disabled_train
from transformers import AutoTokenizer, LlamaTokenizer, MistralForCausalLM, MistralConfig
import transformers
import esm
import random
from lavis.models.base_model import FAPMConfig
def comb(s):
s_list = [i.strip() for i in s.split(';')]
random.shuffle(s_list)
return '; '.join(s_list)
def process_text(txts, probs):
res = dict()
for txt, prob in zip(txts, probs):
txt_sep = [x.strip() for x in txt.split(';')]
for txt_sub in txt_sep:
txt_sub = txt_sub.replace('|', '')
if txt_sub not in res and txt_sub != '':
res[txt_sub] = round(prob.item(),3)
return '; '.join([str((k, v)) for k, v in res.items()])
@registry.register_model("blip2_protein_mistral")
class Blip2ProteinMistral(Blip2ProteinBase):
PRETRAINED_MODEL_CONFIG_DICT = {
"pretrain_protein_mistral7b": "configs/models/blip2/pretrain_protein_mistral7b.yaml",
}
config_class = FAPMConfig
def __init__(
self,
config,
num_query_token=32,
prompt="",
max_txt_len=128,
max_protein_len=128,
apply_lemmatizer=False,
get_eval=False,
esm_size='650m'
):
"""
apply_lemmatizer: when set to True, postprocess predict_answers() result with lemmas.
"""
super().__init__(config)
transformers_version = version.parse(transformers.__version__)
assert transformers_version >= version.parse("4.27"), "BLIP-2 mistral requires transformers>=4.27"
self.tokenizer = self.init_tokenizer()
'''
self.ln_vision, alphabet = esm.pretrained.esm2_t33_650M_UR50D()
if freeze_vit:
self.ln_vision = self.ln_vision.half()
self.visual_encoder = alphabet.get_batch_converter(truncation_seq_length=max_protein_len)
self.padding_idx = alphabet.padding_idx
self.vis_layers = self.ln_vision.num_layers
if freeze_vit:
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")
else:
for name, param in self.ln_vision.named_parameters():
if 'contact_head' in name or 'emb_layer_norm_after' in name or 'lm_head' in name:
param.requires_grad = False
'''
if esm_size == '650m':
self.Qformer, self.query_tokens = self.init_Qformer(num_query_token, 1280)
elif esm_size == '3b':
self.Qformer, self.query_tokens = self.init_Qformer(num_query_token, 2560)
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.mistral_tokenizer = LlamaTokenizer.from_pretrained("teknium/OpenHermes-2.5-Mistral-7B")
# self.mistral_tokenizer = LlamaTokenizer.from_pretrained("/cluster/home/wenkai/.cache/huggingface/hub/models--teknium--OpenHermes-2.5-Mistral-7B", use_fast=False)
# configuration = MistralConfig()
self.mistral_tokenizer.pad_token = '<pad>'
self.mistral_model = MistralForCausalLM.from_pretrained("teknium/OpenHermes-2.5-Mistral-7B")
# self.mistral_model = MistralForCausalLM.from_pretrained("/cluster/home/wenkai/.cache/huggingface/hub/models--teknium--OpenHermes-2.5-Mistral-7B", torch_dtype=torch.float16)
# self.mistral_model = MistralForCausalLM(configuration)
for name, param in self.mistral_model.named_parameters():
param.requires_grad = False
#self.mistral_model.lm_head = self.mistral_model.lm_head.float()
#for param in self.mistral_model.lm_head.parameters():
# param.requires_grad = True
#self.eos_token_id = self.mistral_tokenizer(
# "\n", add_special_tokens=False
#).input_ids[0]
self.eos_token_id = self.mistral_tokenizer(
"\n", add_special_tokens=False
).input_ids[1]
print(f"LLM hidden size: {self.mistral_model.config.hidden_size}")
self.opt_proj = nn.Linear(
self.Qformer.config.hidden_size, self.mistral_model.config.hidden_size
)
self.max_txt_len = max_txt_len
self.prompt = prompt
prompt_tokens = self.mistral_tokenizer(self.prompt, return_tensors="pt")
self.prompt_length = prompt_tokens.attention_mask.sum(1)
self._apply_lemmatizer = apply_lemmatizer
self._lemmatizer = None
def forward(self, samples):
'''
image = samples["image"]
image = [('protein{}'.format(i), x) for i, x in enumerate(image)]
with self.maybe_autocast():
_, _, batch_tokens = self.visual_encoder(image)
image_embeds = self.ln_vision(batch_tokens.to(self.device), repr_layers=[self.vis_layers], return_contacts=True)["representations"][self.vis_layers].contiguous()
'''
image_embeds = samples["image"]
image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(
self.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_mistral = self.opt_proj(query_output.last_hidden_state)
#torch.save(query_output.last_hidden_state, '/cluster/home/wenkai/LAVIS/output/mf_bp_cc/query_output_mf/{}.pt'.format(samples['name'][0]))
#torch.save(inputs_mistral, '/cluster/home/wenkai/LAVIS/output/mf_bp_cc/inputs_mistral_mf/{}.pt'.format(samples['name'][0]))
atts_mistral = torch.ones(inputs_mistral.size()[:-1], dtype=torch.long).to(self.device)
# prompt
prompt = samples["prompt"]
prompt_tokens = self.mistral_tokenizer(prompt, padding="longest", return_tensors="pt")
prompt_length = prompt_tokens.attention_mask.sum(1)
self.mistral_tokenizer.padding_side = "right"
text = [p+' '+comb(t) + "\n" for p, t in zip(prompt, samples["text_input"])]
text = [p+' '+ t + "\n" for p, t in zip(prompt, samples["text_input"])]
mistral_tokens = self.mistral_tokenizer(
text,
return_tensors="pt",
padding="longest",
truncation=True,
max_length=self.max_txt_len,
).to(self.device)
targets = mistral_tokens.input_ids.masked_fill(
mistral_tokens.input_ids == self.mistral_tokenizer.pad_token_id, -100
)
for i, pl in enumerate(prompt_length):
targets[i, :pl] = -100 # do not apply loss to the prompt
#print(prompt_tokens, '\n', mistral_tokens, '\n', prompt_length)
#if self.prompt:
# targets[:, : self.prompt_length] = -100 # do not apply loss to the prompt
empty_targets = (
torch.ones(atts_mistral.size(), dtype=torch.long).to(self.device).fill_(-100)
)
targets = torch.cat([empty_targets, targets], dim=1)
#inputs_embeds = self.mistral_model.model.decoder.embed_tokens(mistral_tokens.input_ids)
inputs_embeds = self.mistral_model.model.embed_tokens(mistral_tokens.input_ids)
inputs_embeds = torch.cat([inputs_mistral, inputs_embeds], dim=1)
attention_mask = torch.cat([atts_mistral, mistral_tokens.attention_mask], dim=1)
with self.maybe_autocast():
outputs = self.mistral_model(
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
return_dict=True,
labels=targets,
)
loss = outputs.loss
return {"loss": loss}
@torch.no_grad()
def generate(
self,
samples,
# use_nucleus_sampling=False,
num_beams=15,
max_length=32,
min_length=1,
# top_p=0.9,
repetition_penalty=1.0,
length_penalty=0.,
num_captions=10,
temperature=1,
):
"""
Args:
samples (dict): A dictionary containing the following keys:
- image (torch.Tensor): A tensor of shape (batch_size, 3, H, W)
use_nucleus_sampling (bool): Whether to use nucleus sampling. If False, use top-k sampling.
num_beams (int): Number of beams for beam search. 1 means no beam search.
max_length (int): The maximum length of the sequence to be generated.
min_length (int): The minimum length of the sequence to be generated.
top_p (float): The cumulative probability for nucleus sampling.
repetition_penalty (float): The parameter for repetition penalty. 1.0 means no penalty.
num_captions (int): Number of captions to be generated for each image.
Returns:
captions (list): A list of strings of length batch_size * num_captions.
"""
with self.maybe_autocast():
image_embeds = samples["image"]
image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(
self.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_mistral = self.opt_proj(query_output.last_hidden_state)
atts_mistral = torch.ones(inputs_mistral.size()[:-1], dtype=torch.long).to(self.device)
label = samples["text_input"]
name = samples['name']
text = samples['prompt']
# text = ['' for i in range(len(label))]
mistral_tokens = self.mistral_tokenizer(
text,
return_tensors="pt",
padding="longest",
truncation=True,
max_length=self.max_txt_len,
).to(self.device)
# inputs_embeds = self.mistral_model.model.decoder.embed_tokens(mistral_tokens.input_ids)
inputs_embeds = self.mistral_model.model.embed_tokens(mistral_tokens.input_ids)
inputs_embeds = torch.cat([inputs_mistral, inputs_embeds], dim=1)
attention_mask = torch.cat([atts_mistral, mistral_tokens.attention_mask], dim=1)
# if name[0] == 'Pin':
# torch.save(inputs_embeds, '/cluster/home/wenkai/LAVIS/output/inputs_embeds.pt')
# torch.save(attention_mask, '/cluster/home/wenkai/LAVIS/output/attention_mask.pt')
# self.get_eval = False
#'''
#num_txt = 15
#return_num_txt = 10
with torch.no_grad():
outputs = self.mistral_model.generate(inputs_embeds=inputs_embeds, attention_mask=attention_mask, min_length=min_length,
max_new_tokens=max_length, temperature=temperature, return_dict_in_generate=True,
output_scores=True,
repetition_penalty=repetition_penalty, num_beams=num_beams,
length_penalty=length_penalty, num_return_sequences=num_captions,
eos_token_id=self.eos_token_id)
output_text = self.mistral_tokenizer.batch_decode(outputs['sequences'], skip_special_tokens=True)
'''
num_txt = 5
return_num_txt = 1
with torch.no_grad():
outputs = self.mistral_model.generate(inputs_embeds=inputs_embeds, attention_mask=attention_mask, min_length=1,
max_length=96,temperature=1.,return_dict_in_generate=True, output_scores=True,
repetition_penalty=1., num_beams=num_txt,
length_penalty=1, num_return_sequences=return_num_txt,eos_token_id=self.eos_token_id)
output_text = self.mistral_tokenizer.batch_decode(outputs['sequences'], skip_special_tokens=True)
'''
probs = F.softmax(outputs['sequences_scores'])
# print(output_text)
output_text = [x.replace('\n', '').strip() for x in output_text]
output_text_ = []
for i in range(len(label)):
# output_text_.append(';'.join(output_text[i*return_num_txt:(i+1)*return_num_txt]))
output_text_.append(process_text(output_text[i * num_captions:(i + 1) * num_captions],
probs[i * num_captions:(i + 1) * num_captions]))
#output_text_ = ['; '.join(list(set([i.strip() for i in x.split(';')]))) for x in output_text_]
# with open('/cluster/home/wenkai/LAVIS/output/mf_bp_cc/output_test_mf_exp_493552.txt', 'a+', encoding="utf-8") as f:
# for i in range(len(label)):
# f.write(name[i] + "|" +output_text_[i]+"|"+label[i]+'\n')
return output_text_
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
):
image_embeds = samples["image"]
image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(
self.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_mistral = self.opt_proj(query_output.last_hidden_state)
atts_mistral = torch.ones(inputs_mistral.size()[:-1], dtype=torch.long).to(self.device)
label = samples["text_input"]
name = samples['name']
text = samples['prompt']
# text = ['' for i in range(len(label))]
mistral_tokens = self.mistral_tokenizer(
text,
return_tensors="pt",
padding="longest",
truncation=True,
max_length=self.max_txt_len,
).to(self.device)
# inputs_embeds = self.mistral_model.model.decoder.embed_tokens(mistral_tokens.input_ids)
inputs_embeds = self.mistral_model.model.embed_tokens(mistral_tokens.input_ids)
inputs_embeds = torch.cat([inputs_mistral, inputs_embeds], dim=1)
attention_mask = torch.cat([atts_mistral, mistral_tokens.attention_mask], dim=1)
# if name[0] == 'Pin':
# torch.save(inputs_embeds, '/cluster/home/wenkai/LAVIS/output/inputs_embeds.pt')
# torch.save(attention_mask, '/cluster/home/wenkai/LAVIS/output/attention_mask.pt')
# self.get_eval = False
# '''
# num_txt = 15
# return_num_txt = 10
num_txt = 15
return_num_txt = 10
with torch.no_grad():
outputs = self.mistral_model.generate(inputs_embeds=inputs_embeds, attention_mask=attention_mask,
min_length=1,
max_length=32, temperature=1., return_dict_in_generate=True,
output_scores=True,
repetition_penalty=1., num_beams=num_txt,
length_penalty=0., num_return_sequences=return_num_txt,
eos_token_id=self.eos_token_id)
output_text = self.mistral_tokenizer.batch_decode(outputs['sequences'], skip_special_tokens=True)
'''
num_txt = 5
return_num_txt = 1
with torch.no_grad():
outputs = self.mistral_model.generate(inputs_embeds=inputs_embeds, attention_mask=attention_mask, min_length=1,
max_length=96,temperature=1.,return_dict_in_generate=True, output_scores=True,
repetition_penalty=1., num_beams=num_txt,
length_penalty=1, num_return_sequences=return_num_txt,eos_token_id=self.eos_token_id)
output_text = self.mistral_tokenizer.batch_decode(outputs['sequences'], skip_special_tokens=True)
'''
probs = F.softmax(outputs['sequences_scores'])
# print(output_text)
output_text = [x.replace('\n', '').strip() for x in output_text]
output_text_ = []
for i in range(len(label)):
# output_text_.append(';'.join(output_text[i*return_num_txt:(i+1)*return_num_txt]))
output_text_.append(process_text(output_text[i * return_num_txt:(i + 1) * return_num_txt],
probs[i * return_num_txt:(i + 1) * return_num_txt]))
return output_text_
def _lemmatize(self, answers):
def apply(answer):
doc = self.lemmatizer(answer)
words = []
for token in doc:
if token.pos_ in ["NOUN", "VERB"]:
words.append(token.lemma_)
else:
words.append(token.text)
answer = " ".join(words)
return answer
return [apply(answer) for answer in answers]
@property
def lemmatizer(self):
if self._lemmatizer is None:
try:
import spacy
self._lemmatizer = spacy.load("en_core_web_sm")
except ImportError:
logging.error(
"""
Please install spacy and en_core_web_sm model to apply lemmatization.
python -m spacy download en_core_web_sm
OR
import spacy.cli
spacy.cli.download("en_core_web_sm")
"""
)
exit(1)
return self._lemmatizer
@classmethod
def from_config(cls, cfg):
num_query_token = cfg.get("num_query_token")
get_eval = cfg.get("get_eval", False)
esm_size = cfg.get("esm_size", '650m')
prompt = cfg.get("prompt", "")
max_txt_len = cfg.get("max_txt_len", 128)
max_protein_len = cfg.get("max_protein_len", 128)
apply_lemmatizer = cfg.get("apply_lemmatizer", False)
model = cls(
num_query_token=num_query_token,
prompt=prompt,
max_txt_len=max_txt_len,
max_protein_len=max_protein_len,
apply_lemmatizer=apply_lemmatizer,
get_eval=get_eval,
esm_size=esm_size,
)
model.load_checkpoint_from_config(cfg)
return model
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