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from transformers import AutoModelForCausalLM, AutoTokenizer
import warnings
from .base import BaseModel
from ..dataset import DATASET_TYPE
class Monkey(BaseModel):
INSTALL_REQ = False
INTERLEAVE = False
def __init__(self, model_path='echo840/Monkey', **kwargs):
assert model_path is not None
self.model_path = model_path
self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_path, device_map='cpu', trust_remote_code=True).eval()
self.model = model.cuda()
self.kwargs = kwargs
warnings.warn(f'Following kwargs received: {self.kwargs}, will use as generation config. ')
torch.cuda.empty_cache()
def generate_vanilla(self, image_path, prompt):
cur_prompt = f'<img>{image_path}</img> {prompt} Answer: '
input_ids = self.tokenizer(cur_prompt, return_tensors='pt', padding='longest')
attention_mask = input_ids.attention_mask
input_ids = input_ids.input_ids
output_ids = self.model.generate(
input_ids=input_ids.cuda(),
attention_mask=attention_mask.cuda(),
do_sample=False,
num_beams=1,
max_new_tokens=512,
min_new_tokens=1,
length_penalty=1,
num_return_sequences=1,
output_hidden_states=True,
use_cache=True,
pad_token_id=self.tokenizer.eod_id,
eos_token_id=self.tokenizer.eod_id,
)
response = self.tokenizer.decode(
output_ids[0][input_ids.size(1):].cpu(),
skip_special_tokens=True
).strip()
return response
def generate_multichoice(self, image_path, prompt):
cur_prompt = f'<img>{image_path}</img> \n {prompt} Answer: '
input_ids = self.tokenizer(cur_prompt, return_tensors='pt', padding='longest')
attention_mask = input_ids.attention_mask
input_ids = input_ids.input_ids
output_ids = self.model.generate(
input_ids=input_ids.cuda(),
attention_mask=attention_mask.cuda(),
do_sample=False,
num_beams=1,
max_new_tokens=10,
min_new_tokens=1,
length_penalty=1,
num_return_sequences=1,
output_hidden_states=True,
use_cache=True,
pad_token_id=self.tokenizer.eod_id,
eos_token_id=self.tokenizer.eod_id,
)
response = self.tokenizer.decode(
output_ids[0][input_ids.size(1):].cpu(),
skip_special_tokens=True
).strip()
return response
def generate_inner(self, message, dataset=None):
prompt, image_path = self.message_to_promptimg(message, dataset=dataset)
if dataset is None:
return self.generate_vanilla(image_path, prompt)
assert isinstance(dataset, str)
if DATASET_TYPE(dataset) == 'MCQ' or DATASET_TYPE(dataset) == 'Y/N' or dataset == 'HallusionBench':
return self.generate_multichoice(image_path, prompt)
else:
return self.generate_vanilla(image_path, prompt)
class MonkeyChat(BaseModel):
INSTALL_REQ = False
INTERLEAVE = False
def __init__(self, model_path='echo840/Monkey-Chat', **kwargs):
assert model_path is not None
self.model_path = model_path
self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_path, device_map='cpu', trust_remote_code=True).eval()
self.model = model.cuda()
self.kwargs = kwargs
self.tokenizer.padding_side = 'left'
self.tokenizer.pad_token_id = self.tokenizer.eod_id
warnings.warn(f'Following kwargs received: {self.kwargs}, will use as generation config. ')
torch.cuda.empty_cache()
def generate_vanilla(self, image_path, prompt):
cur_prompt = f'<img>{image_path}</img> {prompt} Answer: '
input_ids = self.tokenizer(cur_prompt, return_tensors='pt', padding='longest')
attention_mask = input_ids.attention_mask
input_ids = input_ids.input_ids
output_ids = self.model.generate(
input_ids=input_ids.cuda(),
attention_mask=attention_mask.cuda(),
do_sample=False,
num_beams=1,
max_new_tokens=512,
min_new_tokens=1,
length_penalty=1,
num_return_sequences=1,
output_hidden_states=True,
use_cache=True,
pad_token_id=self.tokenizer.eod_id,
eos_token_id=self.tokenizer.eod_id,
)
response = self.tokenizer.decode(
output_ids[0][input_ids.size(1):].cpu(),
skip_special_tokens=True
).strip()
return response
def generate_multichoice(self, image_path, prompt):
cur_prompt = f'<img>{image_path}</img> \n {prompt} Answer: '
input_ids = self.tokenizer(cur_prompt, return_tensors='pt', padding='longest')
attention_mask = input_ids.attention_mask
input_ids = input_ids.input_ids
output_ids = self.model.generate(
input_ids=input_ids.cuda(),
attention_mask=attention_mask.cuda(),
do_sample=False,
num_beams=1,
max_new_tokens=10,
min_new_tokens=1,
length_penalty=1,
num_return_sequences=1,
output_hidden_states=True,
use_cache=True,
pad_token_id=self.tokenizer.eod_id,
eos_token_id=self.tokenizer.eod_id,
)
response = self.tokenizer.decode(
output_ids[0][input_ids.size(1):].cpu(),
skip_special_tokens=True
).strip()
return response
def generate_inner(self, message, dataset=None):
prompt, image_path = self.message_to_promptimg(message, dataset=dataset)
if dataset is None:
return self.generate_vanilla(image_path, prompt)
assert isinstance(dataset, str)
if DATASET_TYPE(dataset) == 'MCQ' or DATASET_TYPE(dataset) == 'Y/N' or dataset == 'HallusionBench':
return self.generate_multichoice(image_path, prompt)
else:
return self.generate_vanilla(image_path, prompt)
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