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train.py
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train.py
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import json
import json
import logging
import os
import random
import sys
import torch
import torch.nn as nn
from tensorboardX import SummaryWriter
from transformers.tokenization_gpt2 import GPT2Tokenizer
from transformers.tokenization_openai import OpenAIGPTTokenizer
from config import get_trainer_config
from config import InputConfig
from model.dataset import FacebookDataset
from model.dataset import MixUpDataset
from model.gpt2_model import GPT2DoubleHeadsModel
from model.gpt2_model import GPT2EncoderDecoderModel
from model.gpt2_model import GPT2PrototypeModel
from model.trainer import Trainer
from model.utils import config_logger
from model.utils import f1_score
from model.utils import open
from model.utils import set_seed
from model.seq2seq import TransformerSeq2Seq
from model.seq2seq_vocab import Seq2seqVocab
from model.entailment_score import EntailmentScorer
from metrics import nlp_metrics
from bert_score.score import get_bert_score
class DummyWriter:
""" Used for distributed training (from NVIDIA apex example).
A dummy logger used so that only the main process write and log informations.
"""
def __init__(self, *input, **kwargs):
self.log_dir = "runs/dummy_logs/"
def add_scalar(self, *input, **kwargs):
pass
def modify_tokenizer(tokenizer, data_type):
additional_special_tokens = ['<info_bos>', '<info_eos>', '<talker1_bos>', '<talker1_eos>', '<talker2_bos>',
'<talker2_eos>']
if data_type == 'emoji':
with open('datasets/emoji_talk/emojis.json', 'r') as f:
emojis = json.load(f)['emojis']
additional_special_tokens.extend(emojis)
if data_type == 'daily':
with open('datasets/DailyDialog/daily.json', 'r') as f:
topic_tokens = json.load(f)
additional_special_tokens.extend(topic_tokens)
tokenizer.add_special_tokens({'pad_token': '<pad>', 'bos_token': '<bos>', 'eos_token': '<eos>',
'additional_special_tokens': additional_special_tokens})
tokenizer.eos_id, tokenizer.bos_id, tokenizer.pad_id = tokenizer.eos_token_id, tokenizer.bos_token_id, tokenizer.pad_token_id
tokenizer.sent_dialog_id = tokenizer.bos_token_id
tokenizer.info_dialog_id, tokenizer.info_bos_id = tokenizer.added_tokens_encoder['<info_bos>'], \
tokenizer.added_tokens_encoder[
'<info_bos>']
tokenizer.info_eos_id = tokenizer.added_tokens_encoder['<info_eos>']
tokenizer.talker1_dialog_id, tokenizer.talker1_bos_id = tokenizer.added_tokens_encoder['<talker1_bos>'], \
tokenizer.added_tokens_encoder['<talker1_bos>']
tokenizer.talker1_eos_id = tokenizer.added_tokens_encoder['<talker1_eos>']
tokenizer.talker2_dialog_id, tokenizer.talker2_bos_id = tokenizer.added_tokens_encoder['<talker2_bos>'], \
tokenizer.added_tokens_encoder['<talker2_bos>']
tokenizer.talker2_eos_id = tokenizer.added_tokens_encoder['<talker2_eos>']
return tokenizer, len(additional_special_tokens) + 3
def get_model_and_tokenizer(args, trainer_config, logger):
if args.model_type == 'gpt':
if args.single_input:
model = OpenAIGPTLMHeadModel.from_pretrained('./openai-gpt')
else:
model = OpenAIGPTEncoderDecoderModel.from_pretrained('./openai-gpt')
tokenizer = OpenAIGPTTokenizer.from_pretrained('./openai-gpt')
elif args.model_type == 'dialogpt':
if args.single_input:
model = GPT2DoubleHeadsModel.from_pretrained('./dialogpt')
else:
model = GPT2EncoderDecoderModel.from_pretrained('./dialogpt')
tokenizer = GPT2Tokenizer.from_pretrained('./dialogpt')
elif args.model_type == 'seq2seq' or args.model_type == 'rnn-seq2seq':
seq2seq_vocab = Seq2seqVocab(trainer_config.train_datasets, trainer_config.valid_datasets,
trainer_config.test_datasets, args.vocab_path, data_type=trainer_config.data_type,
extend_exist_vocab=args.extend_exist_vocab)
tokenizer = seq2seq_vocab.vocab
# parsed_train_data, parsed_valid_data, parsed_test_data = seq2seq_vocab.all_data[0], seq2seq_vocab.all_data[1], \
# seq2seq_vocab.all_data[2]
args.dialog_embeddings = False
if args.model_type == 'seq2seq':
model = TransformerSeq2Seq(args.emb_dim, args.hidden_dim, args.num_layers, args.heads, args.depth_size,
args.filter_size, tokenizer, args.pretrained_emb_file, args.pointer_gen, logger,
multi_input=not args.single_input, attention_pooling_type=args.attention_pooling_type,
label_smoothing=args.label_smoothing)
else:
model = TransformerSeq2Seq(args.emb_dim, args.hidden_dim, args.num_layers, args.heads, args.depth_size,
args.filter_size, tokenizer, args.pretrained_emb_file, args.pointer_gen, logger,
base_model='gru', label_smoothing=args.label_smoothing)
elif args.model_type == 'gpt2_ptototype':
model = GPT2PrototypeModel.from_pretrained('./gpt2-small')
tokenizer = GPT2Tokenizer.from_pretrained('./gpt2-small')
else:
if args.single_input:
model = GPT2DoubleHeadsModel.from_pretrained('./gpt2-small')
else:
model = GPT2EncoderDecoderModel.from_pretrained('./gpt2-small')
tokenizer = GPT2Tokenizer.from_pretrained('./gpt2-small')
return model, tokenizer
'''Modify the model to make it fit the data'''
def modify_model(args, model, tokenizer):
if args.model_type in ['gpt', 'dialogpt', 'gpt2', 'gpt2_prototype']:
tokenizer, additional_length = modify_tokenizer(tokenizer, args.data_type)
model.embeddings_size = 768
model.n_embeddings = len(tokenizer)
model.shared_attention = (args.shared_attention == 1)
model.shared_module = (args.shared_module == 1)
model.attention_pooling_type = args.attention_pooling_type
model.single_input = args.single_input
if args.model_type == 'gpt':
model_embedding_weight = model.transformer.tokens_embed.weight
model.transformer.tokens_embed = nn.Embedding(model.n_embeddings, 768)
model.lm_head = nn.Linear(768, model.n_embeddings, bias=False)
model.transformer.tokens_embed.weight.data[:-additional_length, :] = model_embedding_weight.data
model.transformer.tokens_embed.weight.data[-additional_length:, :] = 0
model.lm_head.weight = model.transformer.tokens_embed.weight
else:
model_embedding_weight = model.transformer.wte.weight
model.transformer.wte = nn.Embedding(model.n_embeddings, 768)
model.lm_head = nn.Linear(768, model.n_embeddings, bias=False)
model.transformer.wte.weight.data[:-additional_length, :] = model_embedding_weight.data
model.transformer.wte.weight.data[-additional_length:, :] = 0
model.lm_head.weight = model.transformer.wte.weight
# if args.bert_encoder and not model.shared_module:
# model.encoder = BertModel.from_pretrained('./bert-model')
# bert_tokenizer = BertTokenizer.from_pretrained('./bert-model')
# bert_tokenizer = modify_tokenizer(bert_tokenizer)
if not args.single_input:
model.reload_module_dict()
model.sent_dialog_id = tokenizer.sent_dialog_id
model.talker1_id = tokenizer.talker1_bos_id
model.talker2_id = tokenizer.talker2_bos_id
model.padding_idx = tokenizer.pad_id
model.n_pos_embeddings = 512
model.bos_id = tokenizer.bos_id
model.eos_id = tokenizer.eos_id
model.beam_size = args.beam_size
model.diversity_groups = 1
model.max_seq_len = 32
model.dialog_embeddings = args.dialog_embeddings
model.bs_temperature = args.bs_temperature
model.bs_nucleus_p = args.bs_nucleus_p
model.annealing_topk = args.annealing_topk
model.length_penalty_coef = args.length_penalty
model.vocab = None
model.annealing = args.annealing
model.diversity_coef = args.diversity_coef
model.sample = False
model.mixup_soft_loss_weight = args.mixup_soft_loss_weight
model.inference_mode = args.inference_mode
model.response_k = args.response_k
def training_procedure(args, trainer_config, model, tokenizer, device, writer, logger, best_checkpoint_path,
last_checkpoint_path, interrupt_checkpoint_path, log_dir, test_data_type=None):
logger.info("trainer config: {}".format(trainer_config))
logger.info('loading datasets')
train_dataset = FacebookDataset(trainer_config.train_datasets, tokenizer,
max_lengths=model.n_pos_embeddings - 1, # A bit restrictive here
dialog_embeddings=args.dialog_embeddings,
cache=trainer_config.train_datasets_cache,
use_start_end=False,
negative_samples=trainer_config.negative_samples,
augment=trainer_config.persona_augment,
aug_syn_proba=trainer_config.persona_aug_syn_proba,
limit_size=trainer_config.limit_train_size,
max_history_size=trainer_config.max_history_size,
single_input=args.single_input,
data_type=trainer_config.data_type,
task_map_path=args.train_task_map,
extra_train_path=args.extra_train_path,
extra_data_type=args.extra_data_type,
ignore_sample_indices=trainer_config.ignore_train_indices,
extra_cvae_utterances_path=args.extra_cvae_utterances_path)
valid_dataset = FacebookDataset(trainer_config.valid_datasets, tokenizer,
max_lengths=model.n_pos_embeddings - 1, # A bit restrictive here
dialog_embeddings=args.dialog_embeddings,
cache=trainer_config.valid_datasets_cache,
use_start_end=False,
negative_samples=-1, # Keep all negative samples
augment=False,
aug_syn_proba=0.0,
limit_size=trainer_config.limit_eval_size,
max_history_size=trainer_config.max_history_size,
single_input=args.single_input,
data_type=trainer_config.data_type,
task_map_path=args.valid_task_map,
ignore_sample_indices=trainer_config.ignore_train_indices)
if test_data_type is None:
test_data_type = trainer_config.data_type
test_dataset = FacebookDataset(trainer_config.test_datasets, tokenizer,
max_lengths=model.n_pos_embeddings - 1, # A bit restrictive here
dialog_embeddings=args.dialog_embeddings,
cache=trainer_config.test_datasets_cache,
use_start_end=False,
negative_samples=-1, # Keep all negative samples
augment=False,
aug_syn_proba=0.0,
limit_size=trainer_config.limit_eval_size,
max_history_size=trainer_config.max_history_size,
single_input=args.single_input,
data_type=test_data_type,
few_shot=args.few_shot,
task_map_path=args.test_task_map)
mixup_dataset = None
if args.mixup:
logger.info('Load Mixup neighbor dict')
mixup_dataset = MixUpDataset(trainer_config.train_datasets, tokenizer, args.mixup_model_path,
cache=trainer_config.mixup_cache, data_type=args.data_type,
th=args.mixup_candidate_th)
logger.info('train dataset {} valid dataset {} test dataset {}'
.format(len(train_dataset), len(valid_dataset), len(test_dataset)))
'''Normal training will use normal trainer'''
model_trainer = Trainer(model,
train_dataset,
trainer_config,
writer,
logger=logger,
valid_dataset=valid_dataset,
test_dataset=test_dataset,
n_jobs=trainer_config.n_jobs,
device=device,
ignore_idxs=tokenizer.all_special_ids,
local_rank=args.local_rank,
apex_level=None,
apex_loss_scale=trainer_config.apex_loss_scale,
evaluate_full_sequences=trainer_config.evaluate_full_sequences,
full_input=trainer_config.full_input,
uncertainty_loss=args.uncertainty_loss,
best_model_path=best_checkpoint_path,
extra_module_lr_rate=args.extra_module_lr_rate,
no_persona=args.no_persona,
mixup=args.mixup,
mixup_dataset=mixup_dataset,
mixup_ratio=args.mixup_ratio,
bert_mixup=args.bert_mixup,
replace=args.replace,
pointer_gen=args.pointer_gen)
if args.load_last:
state_dict = torch.load(trainer_config.load_last, map_location=device)
model_trainer.load_state_dict(state_dict)
# helpers -----------------------------------------------------
def external_metrics_func(full_references, full_predictions, epoch, is_best=False):
if epoch == -1:
if is_best:
references_file_path = os.path.join(writer.logdir, trainer_config.test_references_file)
predictions_file_path = os.path.join(writer.logdir, trainer_config.test_predictions_file_best)
else:
references_file_path = os.path.join(writer.logdir, trainer_config.test_references_file)
predictions_file_path = os.path.join(writer.logdir, trainer_config.test_predictions_file_last)
else:
references_file_path = os.path.join(writer.logdir, trainer_config.eval_references_file)
predictions_file_path = os.path.join(writer.logdir,
trainer_config.eval_predictions_file + "_{}".format(epoch))
if not os.path.exists(references_file_path):
with open(references_file_path, 'w', encoding='utf-8') as f:
f.write('\n'.join(full_references))
# print(len(full_predictions))
with open(os.path.join(writer.logdir, 'tt.json'), 'w') as f:
json.dump(full_predictions, f)
with open(predictions_file_path, 'w', encoding='utf-8') as f:
if len(full_predictions[-1]) == 0:
full_predictions[-1] = 'a '
f.write('\n'.join(full_predictions))
bleu, bleu_list, nist, nist_list, nist_bleu, nist_bleu_list, s_dist, c_dist, entropy, meteor, \
rouge_l, f1_score, avg_length = nlp_metrics(references_file_path, predictions_file_path, root_path=log_dir)
metrics = {'meteor': meteor * 100, 'avg_len': avg_length, 'rouge-l': rouge_l * 100, 'bleu': bleu, 'nist': nist,
'nist-bleu': nist_bleu, 'f1': f1_score * 100}
for name, metric in (
('bleu', bleu_list), ('nist', nist_list), ('nist_bleu', nist_bleu_list), ('entropy', entropy),
('sentence_div', s_dist), ('corpus_div', c_dist)):
for i, m in enumerate(metric, 1):
if name == 'sentence_div' or name == 'corpus_div':
metrics['{}_{}'.format(name, i)] = m * 100
else:
metrics['{}_{}'.format(name, i)] = m
if args.entail_score_refs_file and epoch == -1:
entailment_scorer = EntailmentScorer(predictions_file_path, args.entail_score_refs_file,
args.entail_model_path, device)
metrics['entail_score'] = entailment_scorer.calculate_entailment_score()
if args.bert_score_model_path is not None and epoch == -1:
all_preds = get_bert_score(
full_predictions,
full_references,
model_type=args.bert_score_model_path,
rescale_with_baseline=args.rescale_with_baseline,
baseline_path=args.baseline_path,
num_layers=16,
batch_size=16,
)
metrics['bert_score_p'] = torch.mean(all_preds[0]).item()
metrics['bert_score_r'] = torch.mean(all_preds[1]).item()
metrics['bert_score_f'] = torch.mean(all_preds[2]).item()
for k, v in metrics.items():
metrics[k] = round(v, 6)
return metrics
def save_func(epoch):
if epoch != -1:
torch.save(model_trainer.model.state_dict(), last_checkpoint_path)
logger.info('Model on Epoch %d has been saved', epoch)
def sample_text_func(epoch):
n_samples = 0
model_trainer.model.eval()
samples_idxs = random.sample(range(len(valid_dataset)), n_samples)
samples = [valid_dataset[idx] for idx in samples_idxs]
for persona_info, dialog, target, _ in samples:
contexts = [torch.tensor([c], dtype=torch.long, device=model_trainer.device) for c in [persona_info, dialog]
if len(c) > 0]
prediction = model_trainer.model.predict(contexts)[0]
persona_info_str = tokenizer.ids2string(persona_info[1:-1])
dialog_str = tokenizer.ids2string(dialog)
dialog_str = dialog_str.replace(tokenizer.talker1_bos, '\n\t- ').replace(tokenizer.talker2_bos, '\n\t- ')
dialog_str = dialog_str.replace(tokenizer.talker1_eos, '').replace(tokenizer.talker2_eos, '')
target_str = tokenizer.ids2string(target[1:-1])
prediction_str = tokenizer.ids2string(prediction)
logger.info('\n')
logger.info('Persona info:\n\t{}'.format(persona_info_str))
logger.info('Dialog:{}'.format(dialog_str))
logger.info('Target:\n\t{}'.format(target_str))
logger.info('Prediction:\n\t{}'.format(prediction_str))
def test_func(epoch):
if (epoch + 1) % trainer_config.test_period == 0:
metric_funcs = {'f1_score': f1_score}
model_trainer.test(metric_funcs, external_metrics_func, epoch)
def f1_risk(predictions, targets):
scores = f1_score(predictions, targets, average=False)
assert all([0 <= s <= 1.0 for s in scores])
return [1 - s for s in scores]
def get_risk_metric_func(risk_metric):
""" risk_metric selected in:
f1, meteor, avg_len, nist_{1, 2, 3, 4}, entropy_{1, 2, 3, 4}, div_{1, 2}, bleu_{1, 2, 3, 4}
"""
def external_metric_risk(predictions, targets):
string_targets = list(tokenizer.ids2string(t) for t in targets)
string_predictions = list(tokenizer.ids2string(t) for t in predictions)
metrics = [external_metrics_func([t], [p], epoch=-1, metric=risk_metric) for p, t in
zip(string_predictions, string_targets)]
if any([s in risk_metric for s in ['entropy', 'nist', 'avg_len']]):
return [-m for m in metrics]
assert all([0 <= s <= 1.0 for s in metrics]), metrics
return [1 - m for m in metrics]
if risk_metric == 'f1':
return f1_risk
return external_metric_risk
# helpers -----------------------------------------------------
try:
model_trainer.train(after_epoch_funcs=[save_func, sample_text_func, test_func],
risk_func=get_risk_metric_func(trainer_config.risk_metric))
except (KeyboardInterrupt, Exception, RuntimeError) as e:
if args.local_rank in [-1, 0]:
torch.save(model_trainer.state_dict(), interrupt_checkpoint_path)
raise e
def main():
args = InputConfig().args
logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt = '%m/%d/%Y %H:%M:%S',
level = logging.INFO if args.local_rank in [-1, 0] else logging.ERROR)
if args.server_ip and args.server_port and args.local_rank in [-1, 0]:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
ptvsd.wait_for_attach()
trainer_config = get_trainer_config(args)
# Log only on main process
if args.local_rank not in [-1, 0]:
sys.stdout = open("./runs/log_distributed_{}".format(args.local_rank), "w") # dump sdtout
writer = DummyWriter()
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S', level=logging.ERROR)
logger = logging.getLogger(__file__)
else:
from datetime import datetime
current_time = datetime.now().strftime('%b%d_%H-%M-%S')
if args.single_input:
comment = '_{}_{}_single'.format(args.model_type, args.data_type)
else:
if args.model_type == 'seq2seq':
comment = '_seq2seq_multi_{}_{}'.format(args.data_type, args.attention_pooling_type)
else:
comment = '_{}_{}_{}_{}_{}'.format(args.model_type, args.data_type, args.attention_pooling_type,
('sm' if args.shared_module == 1 else 'nm'), ('sa' if args.shared_attention == 1 else 'na'))
if args.curriculum_learning:
comment = comment + '_curriculum'
if args.mixup:
comment += '_mixup{}'.format(args.mixup_ratio)
if args.bert_mixup:
comment += '_bert'
if args.replace:
comment += '_replace'
logdir = os.path.join('runs', current_time + comment)
writer = SummaryWriter(logdir=logdir)
logger = config_logger(os.path.join(logdir, 'train.log'))
log_dir = writer.logdir
logger.info("Training args: {}".format(args))
interrupt_checkpoint_path = os.path.join(log_dir, trainer_config.interrupt_checkpoint_path)
last_checkpoint_path = os.path.join(log_dir, trainer_config.last_checkpoint_path)
best_checkpoint_path = os.path.join(log_dir, 'best_model')
logger.info("Logging to {}".format(log_dir)) # Let's save everything on an experiment in the ./runs/XXX/directory
if args.local_rank in [-1, 0]:
with open(os.path.join(log_dir, "trainer_config.json"), "w") as f:
json.dump(trainer_config, f)
set_seed(trainer_config.seed)
device = torch.device(trainer_config.device)
if args.curriculum_learning:
curriculum_trainer_config = get_trainer_config(args, True)
if args.curriculum_reverse:
model, tokenizer = get_model_and_tokenizer(args, trainer_config, logger)
else:
model, tokenizer = get_model_and_tokenizer(args, curriculum_trainer_config, logger)
logger.info('Load tokenizer, vocab size is %d', tokenizer.vocab_size if hasattr(tokenizer, 'vocab_size') else
tokenizer.n_words)
modify_model(args, model, tokenizer)
entail_score_refs_file = args.entail_score_refs_file
args.entail_score_refs_file = None
logger.info('==================================================================================')
logger.info('==================================================================================')
logger.info('Start curriculum Learning Stage 1')
logger.info('\n')
interrupt_checkpoint_path_stage1 = interrupt_checkpoint_path + '_stage1'
last_checkpoint_path_stage1 = last_checkpoint_path + '_stage1'
best_checkpoint_path_stage1 = best_checkpoint_path + '_stage1'
curriculum_trainer_config.ignore_train_indices = './datasets/ConvAI2/filter_indices/ignore_idx_train.json'
curriculum_trainer_config.ignore_dev_indices = './datasets/ConvAI2/filter_indices/ignore_idx_dev.json'
training_procedure(args, curriculum_trainer_config, model, tokenizer, device, writer, logger,
best_checkpoint_path_stage1, last_checkpoint_path_stage1, interrupt_checkpoint_path_stage1,
log_dir)
trainer_config.load_last = best_checkpoint_path_stage1
logger.info('\n')
logger.info('End curriculum Learning Stage 1')
logger.info('==================================================================================')
logger.info('==================================================================================')
logger.info('\n')
logger.info('==================================================================================')
logger.info('==================================================================================')
logger.info('Start curriculum Learning Stage 2')
logger.info('\n')
args.entail_score_refs_file = entail_score_refs_file
trainer_config.ignore_train_indices = None
trainer_config.ignore_dev_indices = None
if args.curriculum_reverse:
training_procedure(args, trainer_config, model, tokenizer, device, writer, logger, best_checkpoint_path,
last_checkpoint_path, interrupt_checkpoint_path, log_dir, curriculum_trainer_config.data_type)
else:
training_procedure(args, trainer_config, model, tokenizer, device, writer, logger, best_checkpoint_path,
last_checkpoint_path, interrupt_checkpoint_path, log_dir)
logger.info('\n')
logger.info('End curriculum Learning Stage 2')
logger.info('==================================================================================')
logger.info('==================================================================================')
else:
model, tokenizer = get_model_and_tokenizer(args, trainer_config, logger)
logger.info('Load tokenizer, vocab size is %d', tokenizer.vocab_size if hasattr(tokenizer, 'vocab_size') else
tokenizer.n_words)
modify_model(args, model, tokenizer)
training_procedure(args, trainer_config, model, tokenizer, device, writer, logger, best_checkpoint_path,
last_checkpoint_path, interrupt_checkpoint_path, log_dir, test_data_type=args.test_data_type)
if __name__ == '__main__':
main()