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evaluation_PersonaChat.py
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import csv
import random
import logging
from pprint import pformat
from collections import defaultdict
from functools import partial
import torch
from parlai.core.agents import Agent
from parlai.scripts.eval_model import setup_args as base_setup_args
from ParlAI.projects.convai2.eval_hits import eval_hits, setup_args as setup_args_hits
from ParlAI.projects.convai2.eval_f1 import eval_f1, setup_args as setup_args_f1
from transformers import BartTokenizer
from model.modeling_Tmema import TmemaModel
from utils import AttrDict
from eval_PersonaChat_build import create_encoder_input, create_decoder_input, pad_dataset
class TransformerAgent(Agent):
@staticmethod
def add_cmdline_args(argparser):
agent_args = argparser.add_argument_group('Agent parameters')
agent_args.add_argument("--model_checkpoint", type=str, default="persona_original", help="Path, url or short name of the model. Must be OpenAIGPT.")
agent_args.add_argument("--max_history", type=int, default=7, help="Number of previous utterances to keep in history")
agent_args.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu", help="Device (cuda or cpu)")
agent_args.add_argument("--gpu", type=int, default=0)
agent_args.add_argument("--eval_type", type=str, default="hits@1", help="hits@1, ppl or f1")
agent_args.add_argument("--no_sample", action='store_true')
agent_args.add_argument("--max_length", type=int, default=50)
agent_args.add_argument("--min_length", type=int, default=1)
agent_args.add_argument("--seed", type=int, default=0)
agent_args.add_argument("--temperature", type=int, default=0.7)
agent_args.add_argument("--top_k", type=int, default=20)
agent_args.add_argument("--beam", type=int, default=1)
agent_args.add_argument("--top_p", type=float, default=0.9, help="Nucleus filtering (top-p) before sampling (<=0.0: no filtering)")
agent_args.add_argument('--revised', action='store_true', default=False, help='use revised')
return argparser
def __init__(self, opt, shared=None):
super(TransformerAgent, self).__init__(opt, shared)
args = AttrDict(opt)
self.args = args
logging.basicConfig(level=logging.INFO)
self.logger = logging.getLogger(__file__)
self.logger.info(pformat(args))
random.seed(args.seed)
torch.random.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
if shared is None:
self.logger.info("Get pretrained model and tokenizer")
self.tokenizer = BartTokenizer.from_pretrained(args.model_checkpoint)
self.query_id, self.res_id, self.latent_id, self.persona_id = self.tokenizer.convert_tokens_to_ids(['<query>', '<response>', '<latent>', '<persona>'])
self.bos_id = self.tokenizer.bos_token_id
self.eos_id = self.tokenizer.eos_token_id
self.pad_id = self.tokenizer.pad_token_id
self.sep_id = self.tokenizer.sep_token_id
self.model_checkpoint = TmemaModel.from_pretrained(args.model_checkpoint)
if args.gpu != 0:
self.model_checkpoint.to(args.gpu)
else:
self.model_checkpoint.to(args.device)
self.logger.info("Build BPE prefix dictionary")
else:
self.model_checkpoint = shared['model']
self.tokenizer = shared['tokenizer']
self.persona = []
self.history = []
self.labels = []
self.reset()
def observe(self, observation):
if self.episode_done:
self.reset()
if self.labels:
# Add the previous response to the history
self.history.append(self.labels)
if 'labels' in observation or 'eval_labels' in observation:
text = observation.get('labels', observation.get('eval_labels', [[]]))[0]
self.labels = self.tokenizer.convert_tokens_to_ids(self.tokenizer.tokenize(text, add_prefix_space=True))
if 'text' in observation:
text = observation['text']
for subtext in text.split('\n'):
subtext = subtext.strip()
if subtext.startswith('your persona:'):
subtext = subtext.replace('your persona:', '').strip()
self.persona.append(self.tokenizer.convert_tokens_to_ids(self.tokenizer.tokenize(subtext, add_prefix_space=True)))
else:
self.history.append(self.tokenizer.convert_tokens_to_ids(self.tokenizer.tokenize(subtext, add_prefix_space=True)))
self.history = self.history[(-2*self.args.max_history+1):]
candidates = []
if 'label_candidates' in observation:
for candidate in observation['label_candidates']:
candidates.append((self.tokenizer.convert_tokens_to_ids(self.tokenizer.tokenize(candidate, add_prefix_space=True)), candidate))
self.candidates = candidates
self.episode_done = observation['episode_done']
self.observation = observation
return observation
def act(self):
if self.args.eval_type == "hits@1" and len(self.candidates) > 0:
dataset = defaultdict(list)
for candidate, _ in self.candidates:
encoder_input_ids, attention_mask, per_input_ids, per_attention_mask = create_encoder_input(self.persona, self.history, self.query_id,
self.res_id, self.latent_id, self.persona_id,
self.sep_id, self.eos_id)
decoder_lmlabel, decoder_input_ids, decoder_cls_idx, \
decoder_attention_mask = create_decoder_input(candidate, self.res_id, self.eos_id, golden=False)
dataset["input_ids"].append(encoder_input_ids)
dataset["attention_mask"].append(attention_mask)
dataset["per_input_ids"].append(per_input_ids)
dataset["per_attention_mask"].append(per_attention_mask)
dataset["decoder_input_ids"].append(decoder_input_ids)
dataset["decoder_attention_mask"].append(decoder_attention_mask)
dataset["cls_index"].append(decoder_cls_idx)
inputs = pad_dataset(dataset, self.pad_id)
tensor_inputs = {}
for input_name in ["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask",
"cls_index", "per_input_ids", "per_attention_mask"]:
tensor = torch.tensor(inputs[input_name], device=self.args.device if self.args.gpu ==0 else self.args.gpu)
tensor = tensor.view((-1, len(self.candidates)) + tensor.shape[1:])
tensor_inputs[input_name] = tensor
self.model_checkpoint.eval()
with torch.no_grad():
mc_logits = self.model_checkpoint(**tensor_inputs).logits[1]
val, ind = torch.sort(mc_logits.view(1, -1)[0], descending=True)
ypred = self.candidates[ind[0].item()][1] # match
tc = []
for j in range(len(self.candidates)):
tc.append(self.candidates[ind[j].item()][1])
reply = {'text': ypred, 'text_candidates': tc}
else:
input_ids, attention_mask, per_input_ids, per_attention_mask = create_encoder_input(self.persona, self.history, self.query_id,
self.res_id, self.latent_id, self.persona_id, self.sep_id,
self.eos_id)
tensor_input_ids = torch.tensor(input_ids, device=self.args.device if self.args.gpu ==0 else self.args.gpu).unsqueeze(0)
tensor_per_input_ids = torch.tensor(per_input_ids, device=self.args.device if self.args.gpu ==0 else self.args.gpu).unsqueeze(0)
tensor_attention_mask = torch.tensor(attention_mask, device=self.args.device if self.args.gpu ==0 else self.args.gpu).unsqueeze(0)
tensor_per_attention_mask = torch.tensor(per_attention_mask,
device=self.args.device if self.args.gpu == 0 else self.args.gpu).unsqueeze(
0)
self.model_checkpoint.eval()
with torch.no_grad():
out_ids = self.model_checkpoint.generate(input_ids=tensor_input_ids,
attention_mask=tensor_attention_mask,
per_input_ids=tensor_per_input_ids,
per_attention_mask=tensor_per_attention_mask,
max_length=self.args.max_length, num_beams=self.args.beam)
out_text = self.tokenizer.batch_decode(out_ids, skip_special_tokens=True, spaces_between_special_tokens=False,
clean_up_tokenization_spaces=(self.args.eval_type != 'f1'))
ans = out_text[0].strip()
reply = {'text': ans}
#print('\n'.join(self.tokenizer.batch_decode(self.persona)))
#print('\n'.join(self.tokenizer.batch_decode(self.history)))
print(ans)
#print('-'*50)
'''
with open('./wo_EM.tsv', 'a') as f:
tsv_w = csv.writer(f, delimiter='\t')
#tsv_w.writerow(['id', 'name', 'score'])
tsv_w.writerow(['\n'.join(self.tokenizer.batch_decode(self.persona)), '\n'.join(self.tokenizer.batch_decode(self.history)),ans])
'''
return reply
def share(self):
shared = super(TransformerAgent, self).share()
shared['tokenizer'] = self.tokenizer
shared['model'] = self.model_checkpoint
return shared
def reset(self):
self.persona = []
self.history = []
self.labels = []
self.candidates = []
self.episode_done = True
self.observation = None
if __name__ == '__main__':
parser = base_setup_args(None)
parser.add_argument("--beam", type=int, default=1)
parser.add_argument("--model_checkpoint", type=str, default="")
parser.add_argument('--revised', action='store_true', default=False, help='use revised')
parser.set_params(
model='evaluation_PersonaChat:TransformerAgent')
opt = parser.parse_args(print_args=False)
if opt['eval_type'] == "hits@1":
setup_args = setup_args_hits(None, opt["revised"])
eval_fct = partial(eval_hits, print_parser=setup_args, output_file=opt["model_checkpoint"])
elif opt['eval_type'] == "f1":
setup_args = setup_args_f1(None, revised=opt["revised"])
eval_fct = partial(eval_f1, print_parser=setup_args, output_file=opt["model_checkpoint"], beam=opt["beam"])
else:
raise ValueError
setup_args.set_params(
model='evaluation_PersonaChat:TransformerAgent')
opt = setup_args.parse_args(print_args=False)
eval_fct(opt)