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model.py
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model.py
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import os
import sys
import torch
from transformers import pipeline
from utils import timeit
import time
import openai
from models import *
import json
def load_featurizer(args):
model_file = args.config['model_file']
if args.model == 'MolR':
model = MolEFeaturizer(args, args.device)
else:
raise NotImplementedError('Model path not supported!')
return model
def load_model(args, load=True):
model_file = args.config['model_file']
if args.model == 'MolR':
model = MolRGNN(args.config, device = args.device)
model_file = os.path.join(model_file, 'model.pt')
elif args.model =='LocalRetro':
exp_config = args.config
model = LocalRetro(
node_in_feats=exp_config['in_node_feats'],
edge_in_feats=exp_config['in_edge_feats'],
node_out_feats=exp_config['node_out_feats'],
edge_hidden_feats=exp_config['edge_hidden_feats'],
num_step_message_passing=exp_config['num_step_message_passing'],
attention_heads = exp_config['attention_heads'],
attention_layers = exp_config['attention_layers'],
AtomTemplate_n = exp_config['AtomTemplate_n'],
BondTemplate_n = exp_config['BondTemplate_n'],
device = args.device
)
elif args.model =='Megan':
exp_config = args.config
model = load_megan(
save_path=args.config['save_path'],
device=args.device
)
else:
raise NotImplementedError('Model path not supported!')
# loading model from file
if load:
assert os.path.isfile(model_file), f'<{model_file}> not found'
model_param = torch.load(model_file, map_location=torch.device(args.device))
if 'model_state_dict' in model_param:
model_param = model_param['model_state_dict']
model.load_state_dict(model_param)
print(f'load model from {model_file}')
model = model.to(args.device)
return model
class LLM():
def __init__(self, args):
self.llm = args.llm.lower()
self.device = args.device
if self.llm == 'gpt':
assert args.openai_key != None
openai.api_key = args.openai_key
self.gpt_temp = args.gpt_temp
elif self.llm == 'galactica':
self.task = "text-generation"
model_name = "facebook/galactica-30b"
self.model = pipeline(self.task, model=model_name, device=self.device)
elif self.llm == 'opt':
self.task = "text-generation"
model_name = "facebook/opt-6.7b"
self.model = pipeline(self.task, model=model_name, device=self.device)
elif self.llm == 'vicuna':
self.task = "text2text-generation"
model_name = "lmsys/fastchat-t5-3b-v1.0"
self.model = pipeline(self.task, model=model_name, device=self.device)
else:
raise NotImplementedError('LLM not supported!')
self.max_tokens = args.max_tokens
self.useJson = args.useJson
# @timeit
def __call__(self, query):
if self.llm == 'gpt':
# open Ai has rate limit, no parallelization here
answer = []
for q in query:
response = self.gpt3_infer(q)
answer.append(self.parse_gpt(response))
elif self.task == "text-generation":
answer = self.model(query, max_new_tokens=self.max_tokens)
answer = [ans['generated_text'] for ans in answer]
answer = [self.parse_answer_with_score(a[len(q):]) for q,a in zip(query, answer)]
elif self.task == "text2text-generation":
answer = self.model(query, max_new_tokens=self.max_tokens)
answer = [ans['generated_text'] for ans in answer]
answer = [self.parse_answer_with_score(a) for a in answer]
return answer
def gpt3_infer(self, query, retry_=0):
if retry_ > 0:
print('retrying...', file=sys.stderr)
st = 2 ** retry_
time.sleep(st)
try:
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo-16k",
messages=query,
temperature=self.gpt_temp,
max_tokens=self.max_tokens,
stop=['<|endoftext|>']
)
except Exception as e:
print(type(e), file=sys.stderr)
if str(e) == 'You exceeded your current quota, please check your plan and billing details.':
exit(1)
return self.gpt3_infer(query, retry_ + 1)
return response
def parse_json(self, ans):
try:
ans = ans.replace('\t', '')
ans = json.loads(ans)
assert 'answer' in ans
ans = ans['answer']
assert 'choice' in ans
assert 'confidence' in ans
choice, score = ans['choice'], ans['confidence']
return ord(choice)-ord('A'), int(score)
except:
print('error occured when parsing json')
print(ans)
return 0, 0
def parse_answer_with_score(self, ans):
try:
ans = ans.strip()
for c in '\n\r\t':
ans = ans.replace(c, ' ')
ans = ans.split(' ')
choice, score = 0, 1
for c in ans:
c=''.join([i for i in c if i.isalnum()])
if len(c)==0 or len(c) >= 2:
continue
if 'A' <= c <= 'Z':
choice = c
elif '0' <= c <= '9':
score = c
return ord(choice)-ord('A'), int(score)
except:
print(f'{ans} is not a valid input!')
return 0, 2
def parse_gpt(self, response):
ans = response['choices'][0]['message']['content']
if self.useJson:
ans, score = self.parse_json(ans)
else:
ans, score = self.parse_answer_with_score(ans)
return ans, score