def main(args):
random_seed(args.seed)
if torch.cuda.is_available():
if not args.cuda:
print(
"WARNING: You have a CUDA device, so you should probably run with --cuda")
device = torch.device("cuda" if args.cuda else "cpu")
corpus = data.Corpus(args.data)
ntokens = len(corpus.dictionary)
print('loaded dictionary')
if args.model == 'Transformer':
model = TransformerModel(
ntokens,
args.emsize,
args.nhead,
args.nhid,
args.nlayers,
args.dropout).to(device)
else:
model = RNNModel(
args.model,
ntokens,
args.emsize,
args.nhid,
args.nlayers,
args.dropout,
args.tied).to(device)
checkpoint = torch.load(args.checkpoint)
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()
print('loaded model')
is_transformer_model = hasattr(
model, 'model_type') and model.model_type == 'Transformer'
if not is_transformer_model:
hidden = model.init_hidden(1)
input = torch.randint(ntokens, (1, 1), dtype=torch.long).to(device)
with open(args.outf, 'w') as outf:
with torch.no_grad(): # no tracking history
for i in range(args.words):
if is_transformer_model:
output = model(input, False)
word_weights = output[-1].squeeze().div(
args.temperature).exp().cpu()
word_idx = torch.multinomial(word_weights, 1)[0]
word_tensor = torch.Tensor([[word_idx]]).long().to(device)
input = torch.cat([input, word_tensor], 0)
else:
output, hidden = model(input, hidden)
word_weights = output.squeeze().div(args.temperature).exp().cpu()
word_idx = torch.multinomial(word_weights, 1)[0]
input.fill_(word_idx)
word = corpus.dictionary.idx2word[word_idx]
outf.write(word + ('\n' if i % 20 == 19 else ' '))
if i % args.log_interval == 0:
print('| Generated {}/{} words'.format(i, args.words))
int_to_vocab = vocab["int_to_vocab"]
ntokens = len(vocab_to_int)
emsize = 512
nhid = 512
nlayers = 4
nhead = 4
dropout = 0.2
model = TransformerModel(ntokens, emsize, nhead, nhid, nlayers,
dropout).to(device)
model_save_path = "./models/transformer/lm-siamzone-v4-space-342.pkl"
model.load_state_dict(
torch.load(model_save_path, map_location=torch.device("cpu")))
model.eval()
print("Model initialized")
def top_k_top_p_filtering(logits,
top_k,
top_p,
temperature,
filter_value=-float("Inf")):
# Hugging Face script to apply top k and nucleus sampling
logits = logits / temperature
top_k = min(top_k, logits.size(-1)) # Safety check
if top_k > 0:
# Remove all tokens with a probability less than the last token of the top-k
def main(args):
random_seed(args.seed)
if torch.cuda.is_available():
if not args.cuda:
print(
"WARNING: You have a CUDA device, so you should probably run with --cuda")
device = torch.device("cuda" if args.cuda else "cpu")
corpus = data.Corpus(args.data)
ntokens = len(corpus.dictionary)
word2idx = corpus.dictionary.word2idx
idx2word = corpus.dictionary.idx2word
args.vocab_size = len(word2idx)
print('loaded dictionary')
if args.model == 'Transformer':
model = TransformerModel(
ntokens,
args.emsize,
args.nhead,
args.nhid,
args.nlayers,
args.dropout).to(device)
else:
model = RNNModel(
args.model,
ntokens,
args.emsize,
args.nhid,
args.nlayers,
args.dropout,
args.tied).to(device)
checkpoint = torch.load(args.checkpoint)
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()
is_transformer_model = hasattr(
model, 'model_type') and model.model_type == 'Transformer'
print('loaded model')
input = torch.randint(ntokens, (1, 1), dtype=torch.long).to(device)
# get as starting words only most common starting word
# from data corpus(heuristics from baseline)
most_common_first_words_ids = [i[0] for i in Counter(corpus.train.tolist()).most_common()
if idx2word[i[0]][0].isupper()][:200]
# most_common_first_words = [corpus.dictionary.idx2word[i]
# for i in most_common_first_words_ids]
# private message(binary code)
bit_stream = open(args.bit_stream_path, 'r').readline()
outfile = open(args.save_path + 'generated' +
str(args.bit_num) + '_bit.txt', 'w')
bitfile = open(args.save_path + 'bitfile_' +
str(args.bit_num) + '_bit.txt', 'w')
bit_index = random.randint(0, len(word2idx))
soft = torch.nn.Softmax(0)
for uter_id, uter in tqdm.tqdm(
enumerate(range(args.utterances_to_generate))):
# with torch.no_grad(): # no tracking history
input_ = torch.LongTensor([random.choice(
most_common_first_words_ids)]).unsqueeze(0).to(device)
if not is_transformer_model:
hidden = model.init_hidden(1)
output, hidden = model(input_, hidden)
gen = np.random.choice(len(corpus.dictionary), 1,
p=np.array(soft(output.reshape(-1)).tolist()) /
sum(soft(output.reshape(-1)).tolist()))[0]
gen_res = list()
gen_res.append(idx2word[gen])
bit = ""
for word_id, word in enumerate(range(args.len_of_generation - 2)):
if is_transformer_model:
assert NotImplementedError
else:
output, hidden = model(input_, hidden)
p = output.reshape(-1)
sorted_, indices = torch.sort(p, descending=True)
words_prob = [(j, i) for i, j in
zip(sorted_[:2**int(args.bit_num)].tolist(),
indices[:2**int(args.bit_num)].tolist())]
nodes = createNodes([item[1] for item in words_prob])
root = createHuffmanTree(nodes)
codes = huffmanEncoding(nodes, root)
for i in range(2**int(args.bit_num)):
if bit_stream[bit_index:bit_index + i + 1] in codes:
code_index = codes.index(
bit_stream[bit_index:bit_index + i + 1])
gen = words_prob[code_index][0]
test_data = np.int32(gen)
gen_res.append(idx2word[gen])
if idx2word[gen] in ['\n', '', "<eos>"]:
break
bit += bit_stream[bit_index: bit_index + i + 1]
bit_index = bit_index + i + 1
break
gen_sen = ' '.join(
[word for word in gen_res if word not in ["\n", "", "<eos>"]])
outfile.write(gen_sen + "\n")
bitfile.write(bit)
for batch in pb:
record_loss, perplexity = train_one_iter(batch, fp16=True)
update_count += 1
if update_count % num_gradients_accumulation == num_gradients_accumulation - 1:
scheduler.step()
optimizer.step()
optimizer.zero_grad()
# speed measure
end = time.time()
speed = batch_size * num_gradients_accumulation / (end - start)
start = end
pb.set_postfix(loss=record_loss,
perplexity=perplexity,
speed=speed)
"Evaluation"
encoder.eval()
decoder.eval()
ppl = validate(val_dataloader)
checkpointer.save_checkpoint(str(ep), {
"encoder": encoder.state_dict(),
"decoder": decoder.state_dict()
}, {"empty": None},
is_best_so_far=True)
logger.info(f"a={a} b={b} Epoch {ep} Validation perplexity: {ppl}")
logger.info(f"Finish training of alpha={a} beta={b}")
def main():
# from pathlib import Path
# print("File Path:", Path(__file__).absolute())
# print("Directory Path:", Path().absolute())
args = get_args()
args.n_gpu = 1
# noisy_sents_1 = read_strings(os.path.join(args.data_dir, "train_data", "train_data"))
# clean_sents = read_strings(os.path.join(args.data_dir, "train_label"))
# noisy_sents_2 = read_strings(os.path.join(args.data_dir, "train_data", "train_corpus"))
#
# noisy_sents = noisy_sents_1 + noisy_sents_2
# noise_space_ratio = []
#
# for sentence in noisy_sents:
# noise_space_ratio.append(sentence.count(' ') / len(sentence))
#
# clean_space_ratio = []
# for sentence in clean_sents:
# clean_space_ratio.append(sentence.count(' ') / len(sentence))
#
# print("noise_space_ratio: {}, clean_space_ratio: {}".format(sum(noise_space_ratio) / len(noise_space_ratio),
# sum(clean_space_ratio) / len(clean_space_ratio)))
# ##########
# ##for local
# args.num_workers=0
# args.train_batch_size = 4
# args.eval_batch_size = 4
# args.eval_interval = 10
# ##########
set_seed(args)
if args.tokenizer == 'char':
tokenizer = CharTokenizer([])
if args.tokenizer == 'kobert':
print("koBERT tokenizer")
tokenizer = KoBertTokenizer.from_pretrained('monologg/kobert')
args.vocab_size = tokenizer.vocab_size
print(args.vocab_size)
if args.load_vocab != "":
tokenizer.load(args.load_vocab)
args.vocab_size = tokenizer.__len__()
logger.info(f"args: {json.dumps(args.__dict__, indent=2, sort_keys=True)}")
os.environ["CUDA_VISIBLE_DEVICES"] = args.cuda
args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = TransformerModel(
vocab_size=args.vocab_size,
hidden_size=args.hidden_size,
num_attention_heads=args.num_attention_heads,
num_encoder_layers=args.num_encoder_layers,
num_decoder_layers=args.num_decoder_layers,
intermediate_size=args.intermediate_size,
dropout=args.dropout,
).to(args.device)
logger.info(
f"# of model parameters: {sum(p.numel() for p in model.parameters()) * 1e-6:.2f}M"
)
eos_setting = args.eos_setting
bind_nsml(model, tokenizer, args, eos=eos_setting)
if args.pause:
nsml.paused(scope=locals())
if args.mode != 'test' and args.averaging != "":
sess = 't0005/rush1-3/37'
checkpoints = ["4500", "6500", "7500", "8000"]
nsml.load(checkpoint=checkpoints[0], session=sess)
args.vocab_size = tokenizer.__len__()
print(args.vocab_size)
model = TransformerModel(
vocab_size=args.vocab_size,
hidden_size=args.hidden_size,
num_attention_heads=args.num_attention_heads,
num_encoder_layers=args.num_encoder_layers,
num_decoder_layers=args.num_decoder_layers,
intermediate_size=args.intermediate_size,
dropout=args.dropout,
).to(args.device)
params = model.named_parameters()
new_dict_params = dict(params)
for checkpoint in checkpoints:
bind_nsml(model, tokenizer, args, eos=eos_setting)
nsml.load(checkpoint=checkpoint, session=sess)
for name, param in params:
new_dict_params[name] += param / len(checkpoints)
model.load_state_dict(new_dict_params, strict=False)
bind_nsml(model, tokenizer, args, eos=eos_setting)
nsml.save('best')
elif args.mode == 'eval':
print("I'm in EVAL")
checkpoint = 'best'
sess = 't0005/rush1-3/507'
nsml.load(checkpoint=checkpoint, session=sess)
args.vocab_size = tokenizer.__len__()
model = TransformerModel(
vocab_size=args.vocab_size,
hidden_size=args.hidden_size,
num_attention_heads=args.num_attention_heads,
num_encoder_layers=args.num_encoder_layers,
num_decoder_layers=args.num_decoder_layers,
intermediate_size=args.intermediate_size,
dropout=args.dropout,
).to(args.device)
bind_nsml(model, tokenizer, args, eos=eos_setting)
nsml.load(checkpoint=checkpoint, session=sess)
model.eval()
#noisy_sents = open("./naver_data_clean.txt", "r", encoding='utf-8').read().splitlines()
noisy_sents = read_strings(
os.path.join(args.data_dir, "train_data", "train_corpus"))
valid_noisy = noisy_sents[:1000]
prediction = correct_beam(model,
tokenizer,
valid_noisy,
args,
eos=True,
length_limit=0.15)
for i, pred in enumerate(prediction[:1000]):
print("noisy_input: {}, pred: {}".format(valid_noisy[i], pred))
# bind_txt(prediction)
# nsml.save('prediction')
# with open('naver_data_clean_again.txt', 'w',encoding='utf-8') as f:
# for i, pred in enumerate(prediction):
# if i%500==0: print(i)
# f.write("%s\n" % pred)
## only works when char tokenizer
##TODO: kobert tokenizer, different vocabsize if it is needed
elif args.mode != 'test' and args.resubmit != "":
checkpoint = 'best'
sess = 't0005/rush1-3/' + args.resubmit
print(sess)
model = None
tokenizer = CharTokenizer([])
bind_nsml(model, tokenizer, args, eos=eos_setting)
nsml.load(checkpoint=checkpoint, session=sess)
args.vocab_size = len(tokenizer)
print(args.vocab_size)
model = TransformerModel(
vocab_size=args.vocab_size,
hidden_size=args.hidden_size,
num_attention_heads=args.num_attention_heads,
num_encoder_layers=args.num_encoder_layers,
num_decoder_layers=args.num_decoder_layers,
intermediate_size=args.intermediate_size,
dropout=args.dropout,
).to(args.device)
bind_nsml(model, tokenizer, args, eos=eos_setting)
nsml.load(checkpoint=checkpoint, session=sess)
bind_nsml(model, tokenizer, args, eos=eos_setting)
########## testing loaded model & tokenizer ###############
# model.eval()
# noisy_sents = read_strings(os.path.join(args.data_dir, "train_data", "train_data"))
# valid_noisy = noisy_sents[-10:]
#
# prediction = correct(model, tokenizer, valid_noisy, args, eos=True, length_limit=0.1)
#
# for pred in prediction:
# print(pred)
##################
nsml.save("best")
else:
#train_data, valid_data = None, None
if args.mode == "train" or args.mode == "pretrain" or args.mode == "semi-train":
if args.mode == "train":
# noisy_sents = open("./noisy_sejong_500k.txt", "r", encoding='utf-8').read().splitlines()[:20000]
# clean_sents = open("./clean_sejong_500k.txt", "r", encoding='utf-8').read().splitlines()[:20000]
# sents_annotation = ['None'] * len(noisy_sents)
noisy_sents = read_strings(
os.path.join(args.data_dir, "train_data", "train_data"))
sents_annotation = read_strings(
os.path.join(args.data_dir, "train_data",
"train_annotation"))
clean_sents = read_strings(
os.path.join(args.data_dir, "train_label"))
if args.mode == "semi-train":
noisy_sents = read_strings(
os.path.join(args.data_dir, "train_data", "train_data"))
sents_annotation = read_strings(
os.path.join(args.data_dir, "train_data",
"train_annotation"))
clean_sents = read_strings(
os.path.join(args.data_dir, "train_label"))
checkpoint = 'generated_data'
sess = 't0005/rush1-1/' + str(args.semi_dataset)
# five copy
#sess = 't0005/rush1-1/209'
# one copy
#sess = 't0005/rush1-1/224'
semi_noisy_sents, semi_clean_sents = load_generated_data(
checkpoint=checkpoint, session=sess)
semi_sents_annotation = ['None'] * len(semi_noisy_sents)
if args.mode == "pretrain":
print("PRETRAIN MODE ON!!")
noisy_sents = read_strings(
os.path.join('sejong_corpus', args.noisy_file))
clean_sents = read_strings(
os.path.join('sejong_corpus', args.clean_file))
# checkpoint = 'generated_data'
# sess = 't0005/rush1-1/113'
# noisy_sents, clean_sents = load_generated_data(checkpoint=checkpoint, session=sess)
sents_annotation = ['None'] * len(noisy_sents)
error_type_counter = Counter()
for annotation in sents_annotation:
error_type_counter += Counter(annotation.split(','))
print(error_type_counter)
# cleaning noise 버전
# pairs = [{"noisy": preprocess_sentence(noisy), "clean": clean} for noisy, clean in zip(noisy_sents, clean_sents)]
# original 버전
if args.mode == "semi-train":
pairs = [{
"noisy": noisy,
"clean": clean,
"annotation": annot
}
for noisy, clean, annot in zip(
noisy_sents, clean_sents, sents_annotation)]
semi_pairs = [{
"noisy": noisy,
"clean": clean,
"annotation": annot
} for noisy, clean, annot in zip(
semi_noisy_sents, semi_clean_sents, semi_sents_annotation)]
train_data = pairs[:-args.num_val_data] + semi_pairs
valid_data = pairs[-args.num_val_data:]
logger.info(f"# of train data: {len(train_data)}")
logger.info(f"# of valid data: {len(valid_data)}")
train_sents = [x['noisy'] for x in train_data
] + [x['clean'] for x in train_data]
tokenizer = CharTokenizer.from_strings(train_sents,
args.vocab_size)
bind_nsml(model, tokenizer, args, eos=eos_setting)
else:
pairs = [{
"noisy": noisy,
"clean": clean,
"annotation": annot
}
for noisy, clean, annot in zip(
noisy_sents, clean_sents, sents_annotation)]
train_data, valid_data = train_test_split(
pairs, test_size=args.val_ratio,
random_state=args.seed) # test: about 1000
logger.info(f"# of train data: {len(train_data)}")
logger.info(f"# of valid data: {len(valid_data)}")
# print("validation: ", valid_data)
train_sents = [x['noisy'] for x in train_data
] + [x['clean'] for x in train_data]
# train_sents = [x['clean'] for x in train_data]
if args.load_model != "" and args.mode == "train": # Load pretrained model
print("load pretrained model")
model.load_state_dict(
torch.load(args.load_model, map_location=args.device))
if args.freeze:
model.token_embeddings.weight.requires_grad = False
model.decoder_embeddings.weight.requires_grad = False
if args.tokenizer == 'char' and args.load_vocab == "":
tokenizer = CharTokenizer.from_strings(
train_sents, args.vocab_size)
print(
f'tokenizer loaded from strings. len={len(tokenizer)}.'
)
bind_nsml(model, tokenizer, args, eos=eos_setting)
if args.tokenizer == 'char' and tokenizer is not None:
tokenizer.save('vocab.txt')
if args.n_gpu > 1:
model = torch.nn.DataParallel(model, dim=1)
if args.mode == "train" or args.mode == "pretrain" or args.mode == 'semi-train':
train(model,
tokenizer,
train_data,
valid_data,
args,
eos=eos_setting)
def predict(dn, rn):
dir_name_format = "../data/{dn}-{rn}-raw"
dir_name = dir_name_format.format(dn=dn, rn=rn)
input_path = os.path.join(dir_name, "src-test.txt")
if not os.path.isfile(input_path):
print(f"File: {input_path} not exist.")
return
output_filename = f"prediction-{dn}-{rn}.txt"
output_path = os.path.join(outputDir, output_filename)
if os.path.isfile(output_path):
print(f"File {output_path} already exists.")
return
# 作用：将src进行index
preprocess = IndexedInputTargetTranslationDataset.preprocess(source_dictionary)
# 作用：将输出逆index为句子
postprocess = lambda x: ''.join(
[token for token in target_dictionary.tokenize_indexes(x) if token != END_TOKEN and token != START_TOKEN and token != PAD_TOKEN])
device = torch.device(f'cuda:{args.device}' if torch.cuda.is_available() and not args.no_cuda else 'cpu')
print('Building model...')
model = TransformerModel(source_dictionary.vocabulary_size, target_dictionary.vocabulary_size,
config['d_model'],
config['nhead'],
config['nhid'],
config['nlayers'])
model.eval()
checkpoint_filepath = checkpoint_path
checkpoint = torch.load(checkpoint_filepath, map_location='cpu')
model.load_state_dict(checkpoint)
translator = Translator(
model=model,
beam_size=args.beam_size,
max_seq_len=args.max_seq_len,
trg_bos_idx=target_dictionary.token_to_index(START_TOKEN),
trg_eos_idx=target_dictionary.token_to_index(END_TOKEN)
).to(device)
from utils.pipe import PAD_INDEX
def pad_src(batch):
sources_lengths = [len(sources) for sources in batch]
sources_max_length = max(sources_lengths)
sources_padded = [sources + [PAD_INDEX] * (sources_max_length - len(sources)) for sources in batch]
sources_tensor = torch.tensor(sources_padded)
return sources_tensor
def process(seq):
seq = seq.strip()
def is_proof(name):
return name.count("balance") > 0 or name.count("one") > 0
if is_proof(data_name) and not is_proof(dn):
seq += ",$,1"
global is_proof_process
if is_proof_process:
print("processing")
is_proof_process = False
return seq
batch_size = args.bs
print(f"Output to {output_path}:")
with open(output_path, 'w', encoding='utf-8') as outFile:
with open(input_path, 'r', encoding='utf-8') as inFile:
seqs = []
for seq in tqdm(inFile):
seq = process(seq)
src_seq = preprocess(seq)
seqs.append(src_seq)
if len(seqs) >= batch_size:
pred_seq = translator.translate_sentence(pad_src(seqs).to(device))
pred_line = [postprocess(pred) for pred in pred_seq]
# print(pred_line)
outFile.writelines([p.strip() + '\n' for p in pred_line])
seqs.clear()
# endif
# endfor
if seqs: # last batch
pred_seq = translator.translate_sentence(pad_src(seqs).to(device))
pred_line = [postprocess(pred).replace(START_TOKEN, '').replace(END_TOKEN, '') for pred in pred_seq]
# print(pred_line)
outFile.writelines([p.strip() + '\n' for p in pred_line])
seqs.clear()
# endwith
# endwith
print(f'[Info] {input_path} Finished.')
class TrainLoop_Transformer():
def __init__(self, opt):
self.opt = opt
self.dict = json.load(open(args.bpe2index, encoding='utf-8'))
self.index2word = {self.dict[key]: key for key in self.dict}
self.batch_size = self.opt['batch_size']
self.epoch = self.opt['epoch']
self.use_cuda = opt['use_cuda']
print('self.use_cuda:', self.use_cuda)
self.device = 'cuda:{}'.format(
self.opt['gpu']) if self.use_cuda else 'cpu'
self.opt['device'] = self.device
self.movie_ids = pkl.load(open("data/movie_ids.pkl", "rb"))
# self.metrics_gen = {
# "ppl": 0,
# "dist1": 0,
# "dist2": 0,
# "dist3": 0,
# "dist4": 0,
# "bleu1": 0,
# "bleu2": 0,
# "bleu3": 0,
# "bleu4": 0,
# "count": 0
# }
self.build_data()
self.build_model()
# self.init_optim(
# [p for p in self.model.parameters() if p.requires_grad],
# optim_states=states.get('optimizer'),
# saved_optim_type=states.get('optimizer_type')
# )
self.init_optim(
[p for p in self.model.parameters() if p.requires_grad])
def build_data(self):
if self.opt['process_data']:
self.train_dataset = dataset(
"../../data/data1030/output/train_cut.pkl", self.opt, 'train')
self.valid_dataset = dataset(
"../../data/data1030/output/valid_cut.pkl", self.opt, 'valid')
self.test_dataset = dataset(
"../../data/data1030/output/test_cut.pkl", self.opt, 'test')
self.train_processed_set = self.train_dataset.data_process(True)
self.valid_processed_set = self.valid_dataset.data_process(True)
self.test_processed_set = self.test_dataset.data_process(True)
pickle.dump(self.train_processed_set,
open('data/train_processed_set.pkl', 'wb'))
pickle.dump(self.valid_processed_set,
open('data/valid_processed_set.pkl', 'wb'))
pickle.dump(self.test_processed_set,
open('data/test_processed_set.pkl', 'wb'))
logger.info("[Save processed data]")
else:
try:
self.train_processed_set = pickle.load(
open('data/train_processed_set.pkl', 'rb'))
self.valid_processed_set = pickle.load(
open('data/valid_processed_set.pkl', 'rb'))
self.test_processed_set = pickle.load(
open('data/test_processed_set.pkl', 'rb'))
except:
assert 1 == 0, "No processed data"
logger.info("[Load processed data]")
def build_model(self):
self.model = TransformerModel(self.opt, self.dict)
# todo
if self.opt['embedding_type'] != 'random':
pass
if self.opt['load_dict'] is not None:
logger.info('[ Loading existing model params from {} ]'
''.format(self.opt['load_dict']))
self.model.load_model(self.opt['load_dict'])
if self.use_cuda:
self.model.to(self.device)
def train(self):
losses = []
best_val_gen = 1000
gen_stop = False
patience = 0
max_patience = 5
num = 0
# file_temp = open('temp.txt', 'w')
# train_output_file = open(f"output_train_tf.txt", 'w', encoding='utf-8')
for i in range(self.epoch):
train_set = CRSdataset(self.train_processed_set,
self.opt['n_entity'], self.opt['n_concept'])
train_dataset_loader = torch.utils.data.DataLoader(
dataset=train_set, batch_size=self.batch_size,
shuffle=True) # shuffle
for context,c_lengths,response,r_length,mask_response, \
mask_r_length,entity,entity_vector,movie,\
concept_mask,dbpedia_mask,concept_vec, \
db_vec,rec in tqdm(train_dataset_loader):
####################################### 检验输入输出ok
# file_temp.writelines("[Context] ", self.vector2sentence(context))
# file_temp.writelines("[Response] ", self.vector2sentence(response))
# file_temp.writelines("\n")
seed_sets = []
batch_size = context.shape[0]
for b in range(batch_size):
seed_set = entity[b].nonzero().view(-1).tolist()
seed_sets.append(seed_set)
self.model.train()
self.zero_grad()
scores, preds, rec_scores, rec_loss, gen_loss, mask_loss, info_db_loss, info_con_loss= \
self.model(context.to(self.device), response.to(self.device), mask_response.to(self.device), concept_mask, dbpedia_mask, seed_sets, movie, \
concept_vec, db_vec, entity_vector.to(self.device), rec, test=False)
##########################################
# train_output_file.writelines(
# ["Loss per batch = %f\n" % gen_loss.item()])
# train_output_file.writelines(['[GroundTruth] ' + ' '.join(sen_gt)+'\n' \
# + '[Generated] ' + ' '.join(sen_gen)+'\n\n' \
# for sen_gt, sen_gen in zip(self.vector2sentence(response.cpu()), self.vector2sentence(preds.cpu()))])
losses.append([gen_loss])
self.backward(gen_loss)
self.update_params()
if num % 50 == 0:
loss = sum([l[0] for l in losses]) / len(losses)
ppl = exp(loss)
logger.info('gen loss is %f, ppl is %f' % (loss, ppl))
losses = []
num += 1
output_metrics_gen = self.val(epoch=i)
_ = self.val(True, epoch=i)
if best_val_gen < output_metrics_gen["ppl"]:
patience += 1
logger.info('Patience = ', patience)
if patience >= 5:
gen_stop = True
else:
patience = 0
best_val_gen = output_metrics_gen["ppl"]
self.model.save_model(self.opt['model_save_path'])
logger.info(
f"[generator model saved in {self.opt['model_save_path']}"
"------------------------------------------------]")
if gen_stop:
break
# train_output_file.close()
# _ = self.val(is_test=True)
def val(self, is_test=False, epoch=-1):
# count是response数量
self.model.eval()
if is_test:
valid_processed_set = self.test_processed_set
else:
valid_processed_set = self.valid_processed_set
val_set = CRSdataset(valid_processed_set, self.opt['n_entity'],
self.opt['n_concept'])
val_dataset_loader = torch.utils.data.DataLoader(
dataset=val_set, batch_size=self.batch_size, shuffle=False)
inference_sum = []
tf_inference_sum = []
golden_sum = []
# context_sum = []
losses = []
recs = []
for context, c_lengths, response, r_length, mask_response, mask_r_length, \
entity, entity_vector, movie, concept_mask, dbpedia_mask, concept_vec, db_vec, rec \
in tqdm(val_dataset_loader):
with torch.no_grad():
seed_sets = []
batch_size = context.shape[0]
for b in range(batch_size):
seed_set = entity[b].nonzero().view(-1).tolist()
seed_sets.append(seed_set)
# 使用teacher force下的回复生成，
_, tf_preds, _, _, gen_loss, mask_loss, info_db_loss, info_con_loss = \
self.model(context.to(self.device), response.to(self.device), mask_response.to(self.device), concept_mask, dbpedia_mask, \
seed_sets, movie, concept_vec, db_vec, entity_vector.to(self.device), rec, test=False)
# 使用greedy模式下的回复生成，限定maxlen=20？
# todo
scores, preds, rec_scores, rec_loss, _, mask_loss, info_db_loss, info_con_loss = \
self.model(context.to(self.device), response.to(self.device), mask_response.to(self.device), concept_mask, dbpedia_mask, \
seed_sets, movie, concept_vec, db_vec, entity_vector.to(self.device), rec, test=True, maxlen=20, bsz=batch_size)
golden_sum.extend(self.vector2sentence(response.cpu()))
inference_sum.extend(self.vector2sentence(preds.cpu()))
# tf_inference_sum.extend(self.vector2sentence(tf_preds.cpu()))
# context_sum.extend(self.vector2sentence(context.cpu()))
recs.extend(rec.cpu())
losses.append(torch.mean(gen_loss))
#logger.info(losses)
#exit()
subset = 'valid' if not is_test else 'test'
# 原版： gen-loss来自teacher force，inference_sum来自greedy
ppl = exp(sum(loss for loss in losses) / len(losses))
output_dict_gen = {'ppl': ppl}
logger.info(f"{subset} set metrics = {output_dict_gen}")
# logger.info(f"{subset} set gt metrics = {self.metrics_gt}")
# f=open('context_test.txt','w',encoding='utf-8')
# f.writelines([' '.join(sen)+'\n' for sen in context_sum])
# f.close()
# 将生成的回复输出
with open(f"output/output_{subset}_gen_epoch_{epoch}.txt",
'w',
encoding='utf-8') as f:
f.writelines([
'[Generated] ' + re.sub('@\d+', '__UNK__', ' '.join(sen)) +
'\n' for sen in inference_sum
])
# gt shuchu
with open(f"output/output_{subset}_gt_epoch_{epoch}.txt",
'w',
encoding='utf-8') as f:
for sen in golden_sum:
mask_sen = re.sub('@\d+', '__UNK__', ' '.join(sen))
mask_sen = re.sub(' ([!,.?])', '\\1', mask_sen)
f.writelines(['[GT] ' + mask_sen + '\n'])
# 将生成的回复与gt一起输出
with open(f"output/output_{subset}_both_epoch_{epoch}.txt",
'w',
encoding='utf-8') as f:
f.writelines(['[GroundTruth] ' + re.sub('@\d+', '__UNK__',' '.join(sen_gt))+'\n' \
+ '[Generated] ' + re.sub('@\d+', '__UNK__',' '.join(sen_gen))+'\n\n' \
for sen_gt, sen_gen in zip(golden_sum, inference_sum)])
self.save_embedding()
return output_dict_gen
def save_embedding(self):
json.dump(loop.dict, open('output/tf_bpe2index.json', 'w'))
def vector2sentence(self, batch_sen):
# 一个batch的sentence 从id换成token
sentences = []
for sen in batch_sen.numpy().tolist():
sentence = []
for word in sen:
if word > 3:
sentence.append(self.index2word[word])
elif word == 3:
sentence.append('_UNK_')
sentences.append(sentence)
return sentences
@classmethod
def optim_opts(self):
"""
Fetch optimizer selection.
By default, collects everything in torch.optim, as well as importing:
- qhm / qhmadam if installed from github.com/facebookresearch/qhoptim
Override this (and probably call super()) to add your own optimizers.
"""
# first pull torch.optim in
optims = {
k.lower(): v
for k, v in optim.__dict__.items()
if not k.startswith('__') and k[0].isupper()
}
try:
import apex.optimizers.fused_adam as fused_adam
optims['fused_adam'] = fused_adam.FusedAdam
except ImportError:
pass
try:
# https://openreview.net/pdf?id=S1fUpoR5FQ
from qhoptim.pyt import QHM, QHAdam
optims['qhm'] = QHM
optims['qhadam'] = QHAdam
except ImportError:
# no QHM installed
pass
logger.info(optims)
return optims
def init_optim(self, params, optim_states=None, saved_optim_type=None):
"""
Initialize optimizer with model parameters.
:param params:
parameters from the model
:param optim_states:
optional argument providing states of optimizer to load
:param saved_optim_type:
type of optimizer being loaded, if changed will skip loading
optimizer states
"""
opt = self.opt
# set up optimizer args
lr = opt['learningrate']
kwargs = {'lr': lr}
# kwargs['amsgrad'] = True
# kwargs['betas'] = (0.9, 0.999)
optim_class = self.optim_opts()[opt['optimizer']]
logger.info(f'optim_class = {optim_class}')
self.optimizer = optim_class(params, **kwargs)
def backward(self, loss):
"""
Perform a backward pass. It is recommended you use this instead of
loss.backward(), for integration with distributed training and FP16
training.
"""
loss.backward()
def update_params(self):
"""
Perform step of optimization, clipping gradients and adjusting LR
schedule if needed. Gradient accumulation is also performed if agent
is called with --update-freq.
It is recommended (but not forced) that you call this in train_step.
"""
update_freq = 1
if update_freq > 1:
# we're doing gradient accumulation, so we don't only want to step
# every N updates instead
self._number_grad_accum = (self._number_grad_accum +
1) % update_freq
if self._number_grad_accum != 0:
return
#0.1是不是太小了，原版就是这样
if self.opt['gradient_clip'] > 0:
torch.nn.utils.clip_grad_norm_(self.model.parameters(),
self.opt['gradient_clip'])
self.optimizer.step()
def zero_grad(self):
"""
Zero out optimizer.
It is recommended you call this in train_step. It automatically handles
gradient accumulation if agent is called with --update-freq.
"""
self.optimizer.zero_grad()