File size: 11,859 Bytes
079c32c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 |
import os
from functools import lru_cache
import gym
import openai
import numpy as np
from ding.utils import ENV_REGISTRY
from ding.envs import BaseEnv, BaseEnvTimestep
from dizoo.tabmwp.envs.utils import create_example_from_pid, build_prompt, get_gpt3_output, calc_rwkv, calc_internlm,\
extract_prediction, normalize_answer, load_data
@ENV_REGISTRY.register('tabmwp')
class TabMWP(BaseEnv):
model = None
tokenizer = None
def __init__(self, cfg):
self.cfg = cfg
self.enable_replay = cfg.enable_replay
self._init_flag = False
self.problems, self.cand_pids, self.train_pids = None, None, None
self.problem_id = 0
self.cand_examples = []
openai.api_key = cfg.api_key
self.observation_space = gym.spaces.Dict()
self.action_space = gym.spaces.Discrete(self.cfg.cand_number * (self.cfg.cand_number - 1))
self.reward_space = gym.spaces.Box(low=-1, high=1, shape=(1, ), dtype=np.float32)
self.correct_num = 0
# Initialize language model if needed.
assert self.cfg.engine in ['text-davinci-002', 'glm-10B', 'rwkv-7B', 'internlm-7B']
try:
if self.cfg.engine == 'glm-10B' and TabMWP.model is None:
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
TabMWP.tokenizer = AutoTokenizer.from_pretrained("THUDM/glm-10b", trust_remote_code=True)
model = AutoModelForSeq2SeqLM.from_pretrained("THUDM/glm-10b", trust_remote_code=True)
TabMWP.model = model.half()
elif self.cfg.engine == 'rwkv-7B' and TabMWP.model is None:
from transformers import AutoTokenizer, RwkvForCausalLM
TabMWP.tokenizer = AutoTokenizer.from_pretrained("sgugger/rwkv-7b-pile", trust_remote_code=True)
model = RwkvForCausalLM.from_pretrained("sgugger/rwkv-7b-pile")
TabMWP.model = model.half()
elif self.cfg.engine == 'internlm-7B' and TabMWP.model is None:
from transformers import AutoTokenizer, AutoModelForCausalLM
TabMWP.tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-7b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("internlm/internlm-7b", trust_remote_code=True)
TabMWP.model = model.eval()
except ImportError:
import sys
from ditk import logging
logging.warning("not found transformer, please install it using: pip install transformers")
sys.exit(1)
@lru_cache(maxsize=10000)
def get_output(self, inp: str) -> str:
inputs = TabMWP.tokenizer(inp + " [MASK].", return_tensors="pt")
inputs = TabMWP.tokenizer.build_inputs_for_generation(inputs, max_gen_length=512)
inputs = {key: value.cuda() for key, value in inputs.items()}
outputs = TabMWP.model.generate(
**inputs,
max_length=512,
eos_token_id=TabMWP.tokenizer.eop_token_id,
pad_token_id=TabMWP.tokenizer.eos_token_id
)
outputs = TabMWP.tokenizer.decode(outputs[0].tolist())
t0 = outputs.find('<|startofpiece|>') + 16
t1 = outputs.find('<|endofpiece|>')
return outputs[t0:t1]
def seed(self, seed: int, dynamic_seed: bool = False) -> None:
self.cfg.seed = seed
def reset(self) -> dict:
self.problems, self.cand_pids, self.train_pids = load_data(self.cfg)
if TabMWP.model is not None:
TabMWP.model = TabMWP.model.cuda()
if self.enable_replay:
self.cand_pids = [
'32889', '8044', '16892', '5408', '4051', '37355', '17962', '25807', '30602', '5514', '19270', '23713',
'17209', '33379', '34987', '11177'
]
if self.cfg.seed == 0: # train
self.train_pids = [
'14229', '3409', '29980', '799', '5086', '21778', '36441', '34146', '69', '33433', '26979', '18135',
'13347', '17679', '38426', '3454', '10432', '31011', '12162', '13063', '7812', '29661', '24482',
'4970', '4405', '17405', '27781', '26724', '5993', '16442', '30148', '15895', '6855', '29903',
'18107', '29504', '11106', '32964', '29891', '32104', '15712', '24287', '4997', '32581', '21020',
'17247', '31455', '13245', '15850', '10011', '10313', '10158', '1817', '33479', '35842', '14198',
'26039', '3791', '4909', '37056', '7144', '8185', '2131', '4398', '38199', '29520', '37329',
'21388', '28659', '15044', '28510', '12903', '11794', '37095', '32229', '22918', '31680', '15024',
'24607', '26930'
]
model_io_path = 'dizoo/tabmwp/data/model_in_out_train.txt'
if not os.path.exists(model_io_path):
os.system(
f'wget https://opendilab.net/download/DI-zoo/tabmwp/model_in_out_train.txt -O ' +
model_io_path + ' --no-check-certificate'
)
else:
self.train_pids = [
'21037', '22976', '2224', '14145', '27962', '26553', '22110', '16541', '26044', '19492', '31882',
'11991', '27594', '7637', '15394', '7666', '5177', '33761', '13703', '29105'
]
model_io_path = 'dizoo/tabmwp/data/model_in_out_eval.txt'
os.system(
f'wget https://opendilab.net/download/DI-zoo/tabmwp/model_in_out_eval.txt -O ' + model_io_path +
' --no-check-certificate'
)
self.cfg.cand_number = len(self.cand_pids)
self.cfg.train_number = len(self.train_pids)
self.results_memory = []
with open(model_io_path, encoding="ISO-8859-1") as f:
tmp = f.read().split('\n')
for tt in tmp:
if len(tt.strip()) == 0:
continue
self.results_memory.append(eval(tt))
self.cand_examples = []
self.correct_num = 0
for pid in self.cand_pids:
example = create_example_from_pid(pid, self.problems, self.cfg, test=True)
self.cand_examples.append(example)
self._init_flag = True
self.problem_id = 0
train_sample = create_example_from_pid(self.train_pids[self.problem_id], self.problems, self.cfg, test=True)
obs = {'train_sample': train_sample, 'candidate_samples': self.cand_examples}
return obs
def search_answer(self, pid, pids):
for item in self.results_memory:
if item['pid'] != pid:
continue
if item['shot_pids'] == pids:
return item['output']
raise ValueError('item does not exists.')
def parse_all_answers(self):
self.cand_pids = [
'32889', '8044', '16892', '5408', '4051', '37355', '17962', '25807', '30602', '5514', '19270', '23713',
'17209', '33379', '34987', '11177', '30218', '26066', '24169', '28492'
]
self.train_pids = [
'14229', '3409', '29980', '799', '5086', '21778', '36441', '34146', '69', '33433', '26979', '18135',
'13347', '17679', '38426', '3454', '10432', '31011', '12162', '13063', '7812', '29661', '24482', '4970',
'4405', '17405', '27781', '26724', '5993', '16442', '30148', '15895', '6855', '29903', '18107', '29504',
'11106', '32964', '29891', '32104', '15712', '24287', '4997', '32581', '21020', '17247', '31455', '13245',
'15850', '10011', '10313', '10158', '1817', '33479', '35842', '14198', '26039', '3791', '4909', '37056',
'7144', '8185', '2131', '4398', '38199', '29520', '37329', '21388', '28659', '15044', '28510', '12903',
'11794', '37095', '32229', '22918', '31680', '15024', '24607', '26930'
]
self.problem_id = 0
self.cfg.train_number = len(self.train_pids)
n = len(self.cand_pids)
with open('sampled_pid.txt', 'w') as f:
f.write(str(self.cand_pids) + '\n')
f.write(str(self.train_pids) + '\n')
with open('model_in_out.txt', 'w') as f:
while self.problem_id < self.cfg.train_number:
for i in range(n):
for j in range(n):
if i == j:
continue
shot_pids = [self.cand_pids[i], self.cand_pids[j]]
pid = self.train_pids[self.problem_id]
# generate the prompt input
prompt = build_prompt(self.problems, shot_pids, pid, self.cfg)
# get the output from LM
# assert self._args.engine == 'text-davinci-002'
output = get_gpt3_output(prompt, self.cfg)
output_txt = {'shot_pids': shot_pids, 'pid': pid, 'prompt': prompt, 'output': output}
f.write(str(output_txt) + '\n')
print(self.problem_id, i, j)
self.problem_id += 1
def close(self) -> None:
self._init_flag = False
def step(self, action: np.array) -> BaseEnvTimestep:
shot_pids = [self.cand_pids[cid] for cid in action]
pid = self.train_pids[self.problem_id]
# generate the prompt input
prompt = build_prompt(self.problems, shot_pids, pid, self.cfg)
# get the output from LM
if self.enable_replay:
output = self.search_answer(pid, shot_pids)
elif self.cfg.engine == 'text-davinci-002':
output = get_gpt3_output(prompt, self.cfg)
elif self.cfg.engine == 'rwkv-7B':
output = calc_rwkv(self.model, self.tokenizer, prompt)
elif self.cfg.engine == 'internlm-7B':
output = calc_internlm(self.model, self.tokenizer, prompt, self.cfg)
else:
output = self.get_output(prompt)
# extract the prediction from the output
prediction = extract_prediction(output, self.problems[pid]['choices'], self.cfg.option_inds)
# normalize the number in the text
prediction_norm = normalize_answer(prediction, self.problems[pid]['unit'])
if prediction_norm.lower() == normalize_answer(self.problems[pid]['answer'],
self.problems[pid]['unit']).lower():
reward = 1
self.correct_num += 1
else:
reward = -1
self.problem_id += 1
if self.problem_id == self.cfg.train_number:
done = True
info = {'eval_episode_return': self.correct_num / self.cfg.train_number}
else:
done = False
info = {}
train_sample = create_example_from_pid(pid, self.problems, self.cfg, test=True)
obs = {'train_sample': train_sample, 'candidate_samples': self.cand_examples}
return BaseEnvTimestep(obs, reward, done, info)
def __repr__(self) -> str:
return "DI-engine tabmwp Env"
if __name__ == '__main__':
from easydict import EasyDict
env_cfg = EasyDict(
dict(
cand_number=16,
train_number=20,
engine='text-davinci-002',
temperature=0.,
max_tokens=512,
top_p=1.,
frequency_penalty=0.,
presence_penalty=0.,
option_inds=["A", "B", "C", "D", "E", "F"],
api_key='xxx',
prompt_format='TQ-A',
enable_replay=True,
seed=0,
)
)
env = TabMWP(env_cfg)
env.seed(0)
env.reset()
env.parse_all_answers()
env.search_answer('22976', ['32889', '8044'])
|