def train(opt, train_data, eval_data=None):
logger.info("start training task")
dim_input = 6
dim_emb = 64
num_class = train_data.num_class
transformer_nhead = 2
transformer_nlayers = 1
model = TransformerModel(dim_input, dim_emb, transformer_nhead,
num_class,
transformer_nlayers)
if model.cuda:
model = move_to_gpu(model)
summary(model, train_data[0]['x'].shape)
try:
dataloader = DataLoader(
train_data,
batch_size=opt.batch_size,
shuffle=False,
num_workers=4
)
logger.info("create training dataloader")
except Exception as e:
logger.error("fail to create dataloader", e)
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer=model.optimizer,
milestones=[5, 10], gamma=0.1)
model_path = os.path.join(opt.model_dir,opt.model_name+".pth")
global_steps = 0
best = 0
for epoch in tqdm(list(range(opt.epoch)), desc='epoch'):
for step, batch in enumerate(dataloader):
global_steps += 1
metrics = model.train(batch)
if global_steps % opt.log_steps == 0:
logger.debug(f"global steps={global_steps},{metrics}")
if global_steps % opt.save_steps == 0:
val_metrics, eval_result = eval(opt, model, eval_data)
logger.info(f"global steps={global_steps}, current={val_metrics}, best={best}, result={eval_result}")
if val_metrics > best:
best = val_metrics
torch.save(model.state_dict(), model_path)
logger.info(f"global steps={global_steps}, save model:{model_path}")
lr_scheduler.step()
t_total=num_train_optimization_steps)
[encoder, decoder], optimizer = amp.initialize([encoder, decoder],
optimizer,
opt_level='O1')
criterion = SequenceFocalLoss(gamma=a, beta=b)
eval_criterion = SequenceCrossEntropyLoss()
update_count = 0
start = time.time()
for ep in range(5):
"Training"
pb = tqdm.tqdm(train_dataloader)
encoder.train()
decoder.train()
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
if args.restart:
# Resume training from checkpoint
with open(os.path.join(args.restart_dir, 'model.pt'), 'rb') as f:
model = torch.load(f)
if not args.fp16:
model = model.float()
model.apply(update_dropout)
model.apply(update_dropatt)
else:
# Train from the start
model = TransformerModel(ntokens, args.d_model, args.n_head, args.d_inner,
args.n_layer, args.dropout)
for p in model.parameters():
p.requires_grad_(True)
model.train()
model.apply(weights_init)
args.n_all_param = sum([p.nelement() for p in model.parameters()])
if args.fp16:
model = model.half()
if args.multi_gpu:
model = model.to(device)
if args.gpu0_bsz >= 0:
para_model = BalancedDataParallel(args.gpu0_bsz // args.batch_chunk,
model,
dim=1).to(device)
else:
para_model = nn.DataParallel(model, dim=1).to(device)
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()
