def main():
#if wargs.ss_type is not None: assert wargs.model == 1, 'Only rnnsearch support schedule sample'
init_dir(wargs.dir_model)
init_dir(wargs.dir_valid)
src = os.path.join(wargs.dir_data, '{}.{}'.format(wargs.train_prefix, wargs.train_src_suffix))
trg = os.path.join(wargs.dir_data, '{}.{}'.format(wargs.train_prefix, wargs.train_trg_suffix))
vocabs = {}
wlog('\nPreparing source vocabulary from {} ... '.format(src))
src_vocab = extract_vocab(src, wargs.src_vcb, wargs.n_src_vcb_plan,
wargs.max_seq_len, char=wargs.src_char)
wlog('\nPreparing target vocabulary from {} ... '.format(trg))
trg_vocab = extract_vocab(trg, wargs.trg_vcb, wargs.n_trg_vcb_plan, wargs.max_seq_len)
n_src_vcb, n_trg_vcb = src_vocab.size(), trg_vocab.size()
wlog('Vocabulary size: |source|={}, |target|={}'.format(n_src_vcb, n_trg_vcb))
vocabs['src'], vocabs['trg'] = src_vocab, trg_vocab
wlog('\nPreparing training set from {} and {} ... '.format(src, trg))
trains = {}
train_src_tlst, train_trg_tlst = wrap_data(wargs.dir_data, wargs.train_prefix,
wargs.train_src_suffix, wargs.train_trg_suffix,
src_vocab, trg_vocab, shuffle=True,
sort_k_batches=wargs.sort_k_batches,
max_seq_len=wargs.max_seq_len,
char=wargs.src_char)
'''
list [torch.LongTensor (sentence), torch.LongTensor, torch.LongTensor, ...]
no padding
'''
batch_train = Input(train_src_tlst, train_trg_tlst, wargs.batch_size,
batch_type=wargs.batch_type, bow=wargs.trg_bow, batch_sort=False)
wlog('Sentence-pairs count in training data: {}'.format(len(train_src_tlst)))
batch_valid = None
if wargs.val_prefix is not None:
val_src_file = os.path.join(wargs.val_tst_dir, '{}.{}'.format(wargs.val_prefix, wargs.val_src_suffix))
val_trg_file = os.path.join(wargs.val_tst_dir, '{}.{}'.format(wargs.val_prefix, wargs.val_ref_suffix))
wlog('\nPreparing validation set from {} and {} ... '.format(val_src_file, val_trg_file))
valid_src_tlst, valid_trg_tlst = wrap_data(wargs.val_tst_dir, wargs.val_prefix,
wargs.val_src_suffix, wargs.val_ref_suffix,
src_vocab, trg_vocab, shuffle=False,
max_seq_len=wargs.dev_max_seq_len,
char=wargs.src_char)
batch_valid = Input(valid_src_tlst, valid_trg_tlst, 1, batch_sort=False)
batch_tests = None
if wargs.tests_prefix is not None:
assert isinstance(wargs.tests_prefix, list), 'Test files should be list.'
init_dir(wargs.dir_tests)
batch_tests = {}
for prefix in wargs.tests_prefix:
init_dir(wargs.dir_tests + '/' + prefix)
test_file = '{}{}.{}'.format(wargs.val_tst_dir, prefix, wargs.val_src_suffix)
wlog('\nPreparing test set from {} ... '.format(test_file))
test_src_tlst, _ = wrap_tst_data(test_file, src_vocab, char=wargs.src_char)
batch_tests[prefix] = Input(test_src_tlst, None, 1, batch_sort=False)
wlog('\n## Finish to Prepare Dataset ! ##\n')
src_emb = WordEmbedding(n_src_vcb, wargs.d_src_emb, wargs.input_dropout,
wargs.position_encoding, prefix='Src')
trg_emb = WordEmbedding(n_trg_vcb, wargs.d_trg_emb, wargs.input_dropout,
wargs.position_encoding, prefix='Trg')
# share the embedding matrix - preprocess with share_vocab required.
if wargs.embs_share_weight:
if n_src_vcb != n_trg_vcb:
raise AssertionError('The `-share_vocab` should be set during '
'preprocess if you use share_embeddings!')
src_emb.we.weight = trg_emb.we.weight
nmtModel = build_NMT(src_emb, trg_emb)
if not wargs.copy_attn:
classifier = Classifier(wargs.d_model if wargs.decoder_type == 'att' else 2 * wargs.d_enc_hid,
n_trg_vcb, trg_emb, loss_norm=wargs.loss_norm,
label_smoothing=wargs.label_smoothing,
emb_loss=wargs.emb_loss, bow_loss=wargs.bow_loss)
nmtModel.decoder.classifier = classifier
if wargs.gpu_id is not None:
wlog('push model onto GPU {} ... '.format(wargs.gpu_id), 0)
#nmtModel = nn.DataParallel(nmtModel, device_ids=wargs.gpu_id)
nmtModel.to(tc.device('cuda'))
else:
wlog('push model onto CPU ... ', 0)
nmtModel.to(tc.device('cpu'))
wlog('done.')
if wargs.pre_train is not None:
assert os.path.exists(wargs.pre_train)
from tools.utils import load_model
_dict = load_model(wargs.pre_train)
# initializing parameters of interactive attention model
class_dict = None
if len(_dict) == 5:
model_dict, class_dict, eid, bid, optim = _dict
elif len(_dict) == 4:
model_dict, eid, bid, optim = _dict
for name, param in nmtModel.named_parameters():
if name in model_dict:
param.requires_grad = not wargs.fix_pre_params
param.data.copy_(model_dict[name])
wlog('{:7} -> grad {}\t{}'.format('Model', param.requires_grad, name))
elif name.endswith('map_vocab.weight'):
if class_dict is not None:
param.requires_grad = not wargs.fix_pre_params
param.data.copy_(class_dict['map_vocab.weight'])
wlog('{:7} -> grad {}\t{}'.format('Model', param.requires_grad, name))
elif name.endswith('map_vocab.bias'):
if class_dict is not None:
param.requires_grad = not wargs.fix_pre_params
param.data.copy_(class_dict['map_vocab.bias'])
wlog('{:7} -> grad {}\t{}'.format('Model', param.requires_grad, name))
else: init_params(param, name, init_D=wargs.param_init_D, a=float(wargs.u_gain))
wargs.start_epoch = eid + 1
else:
optim = Optim(wargs.opt_mode, wargs.learning_rate, wargs.max_grad_norm)
#for n, p in nmtModel.named_parameters():
# bias can not be initialized uniformly
#if wargs.encoder_type != 'att' and wargs.decoder_type != 'att':
# init_params(p, n, init_D=wargs.param_init_D, a=float(wargs.u_gain))
wlog(nmtModel)
wlog(optim)
pcnt1 = len([p for p in nmtModel.parameters()])
pcnt2 = sum([p.nelement() for p in nmtModel.parameters()])
wlog('parameters number: {}/{}'.format(pcnt1, pcnt2))
wlog('\n' + '*' * 30 + ' trainable parameters ' + '*' * 30)
for n, p in nmtModel.named_parameters():
if p.requires_grad: wlog('{:60} : {}'.format(n, p.size()))
optim.init_optimizer(nmtModel.parameters())
trainer = Trainer(nmtModel, batch_train, vocabs, optim, batch_valid, batch_tests)
trainer.train()
# voc = list(wv.vocab)
# weight = tc.zeros(n_trg_vcb, 100)
# for i, k in trg_vocab.idx2key.items():
# if k not in wv:
# continue
# weight[i, :] = tc.Tensor(wv[k])
# dic = {
# 'w2v': weight
# }
# tc.save(dic, '../zh_en_w2v_embedding.pt')
model_dict, e_idx, e_bidx, n_steps, optim = load_model(model_file)
from models.embedding import WordEmbedding
src_emb = WordEmbedding(n_src_vcb,
wargs.d_src_emb,
position_encoding=wargs.position_encoding,
prefix='Src')
trg_emb = WordEmbedding(n_trg_vcb,
wargs.d_trg_emb,
position_encoding=wargs.position_encoding,
prefix='Trg')
from models.model_builder import build_NMT
nmtModel = build_NMT(src_emb, trg_emb)
if args.gpu_ids is not None:
wlog('push model onto GPU {} ... '.format(args.gpu_ids[0]), 0)
nmtModel.to(tc.device(type='cuda', index=args.gpu_ids[0]))
else:
wlog('push model onto CPU ... ', 0)
nmtModel.to(tc.device('cpu'))
wlog('done.')
def main():
# if wargs.ss_type is not None: assert wargs.model == 1, 'Only rnnsearch support schedule sample'
init_dir(wargs.dir_model)
init_dir(wargs.dir_valid)
src = os.path.join(
wargs.dir_data, '{}.{}'.format(wargs.train_prefix,
wargs.train_src_suffix))
trg = os.path.join(
wargs.dir_data, '{}.{}'.format(wargs.train_prefix,
wargs.train_trg_suffix))
src, trg = os.path.abspath(src), os.path.abspath(trg)
vocabs = {}
if wargs.share_vocab is False:
wlog('\nPreparing source vocabulary from {} ... '.format(src))
src_vocab = extract_vocab(src,
wargs.src_vcb,
wargs.n_src_vcb_plan,
wargs.max_seq_len,
char=wargs.src_char)
wlog('\nPreparing target vocabulary from {} ... '.format(trg))
trg_vocab = extract_vocab(trg, wargs.trg_vcb, wargs.n_trg_vcb_plan,
wargs.max_seq_len)
n_src_vcb, n_trg_vcb = src_vocab.size(), trg_vocab.size()
wlog('Vocabulary size: |source|={}, |target|={}'.format(
n_src_vcb, n_trg_vcb))
else:
wlog('\nPreparing the shared vocabulary from \n\t{}\n\t{}'.format(
src, trg))
trg_vocab = src_vocab = extract_vocab(src,
wargs.src_vcb,
wargs.n_src_vcb_plan,
wargs.max_seq_len,
share_vocab=True,
trg_file=trg)
n_src_vcb, n_trg_vcb = src_vocab.size(), trg_vocab.size()
wlog('Shared vocabulary size: |vocab|={}'.format(src_vocab.size()))
vocabs['src'], vocabs['trg'] = src_vocab, trg_vocab
wlog('\nPreparing training set from {} and {} ... '.format(src, trg))
trains = {}
train_src_tlst, train_trg_tlst = wrap_data(
wargs.dir_data,
wargs.train_prefix,
wargs.train_src_suffix,
wargs.train_trg_suffix,
src_vocab,
trg_vocab,
shuffle=True,
sort_k_batches=wargs.sort_k_batches,
max_seq_len=wargs.max_seq_len,
char=wargs.src_char)
'''
list [torch.LongTensor (sentence), torch.LongTensor, torch.LongTensor, ...]
no padding
'''
batch_train = Input(train_src_tlst,
train_trg_tlst,
wargs.batch_size,
batch_type=wargs.batch_type,
bow=wargs.trg_bow,
batch_sort=False,
gpu_ids=device_ids)
wlog('Sentence-pairs count in training data: {}'.format(
len(train_src_tlst)))
batch_valid = None
if wargs.val_prefix is not None:
val_src_file = os.path.join(
wargs.val_tst_dir, '{}.{}'.format(wargs.val_prefix,
wargs.val_src_suffix))
val_trg_file = os.path.join(
wargs.val_tst_dir, '{}.{}'.format(wargs.val_prefix,
wargs.val_ref_suffix))
val_src_file, val_trg_file = os.path.abspath(
val_src_file), os.path.abspath(val_trg_file)
wlog('\nPreparing validation set from {} and {} ... '.format(
val_src_file, val_trg_file))
valid_src_tlst, valid_trg_tlst = wrap_data(
wargs.val_tst_dir,
wargs.val_prefix,
wargs.val_src_suffix,
wargs.val_ref_suffix,
src_vocab,
trg_vocab,
shuffle=False,
max_seq_len=wargs.dev_max_seq_len,
char=wargs.src_char)
batch_valid = Input(valid_src_tlst,
valid_trg_tlst,
batch_size=wargs.valid_batch_size,
batch_sort=False,
gpu_ids=device_ids)
batch_tests = None
if wargs.tests_prefix is not None:
assert isinstance(wargs.tests_prefix,
list), 'Test files should be list.'
init_dir(wargs.dir_tests)
batch_tests = {}
for prefix in wargs.tests_prefix:
init_dir(wargs.dir_tests + '/' + prefix)
test_file = '{}{}.{}'.format(wargs.val_tst_dir, prefix,
wargs.val_src_suffix)
test_file = os.path.abspath(test_file)
wlog('\nPreparing test set from {} ... '.format(test_file))
test_src_tlst, _ = wrap_tst_data(test_file,
src_vocab,
char=wargs.src_char)
batch_tests[prefix] = Input(test_src_tlst,
None,
batch_size=wargs.test_batch_size,
batch_sort=False,
gpu_ids=device_ids)
wlog('\n## Finish to Prepare Dataset ! ##\n')
src_emb = WordEmbedding(n_src_vcb,
wargs.d_src_emb,
wargs.input_dropout,
wargs.position_encoding,
prefix='Src')
trg_emb = WordEmbedding(n_trg_vcb,
wargs.d_trg_emb,
wargs.input_dropout,
wargs.position_encoding,
prefix='Trg')
# share the embedding matrix between the source and target
if wargs.share_vocab is True: src_emb.we.weight = trg_emb.we.weight
nmtModel = build_NMT(src_emb, trg_emb)
if device_ids is not None:
wlog('push model onto GPU {} ... '.format(device_ids[0]), 0)
nmtModel_par = nn.DataParallel(nmtModel, device_ids=device_ids)
nmtModel_par.to(device)
else:
wlog('push model onto CPU ... ', 0)
nmtModel.to(tc.device('cpu'))
wlog('done.')
if wargs.pre_train is not None:
wlog(wargs.pre_train)
assert os.path.exists(wargs.pre_train)
from tools.utils import load_model
_dict = load_model(wargs.pre_train)
# initializing parameters of interactive attention model
class_dict = None
if len(_dict) == 5:
# model_dict, e_idx, e_bidx, n_steps, optim = _dict['model'], _dict['epoch'], _dict['batch'], _dict['steps'], _dict['optim']
model_dict, e_idx, e_bidx, n_steps, optim = _dict
elif len(_dict) == 4:
model_dict, e_idx, e_bidx, optim = _dict
for name, param in nmtModel.named_parameters():
if name in model_dict:
param.requires_grad = not wargs.fix_pre_params
param.data.copy_(model_dict[name])
# wlog('{:7} -> grad {}\t{}'.format('Model', param.requires_grad, name))
elif name.endswith('map_vocab.weight'):
if class_dict is not None:
param.requires_grad = not wargs.fix_pre_params
param.data.copy_(class_dict['map_vocab.weight'])
# wlog('{:7} -> grad {}\t{}'.format('Model', param.requires_grad, name))
elif name.endswith('map_vocab.bias'):
if class_dict is not None:
param.requires_grad = not wargs.fix_pre_params
param.data.copy_(class_dict['map_vocab.bias'])
# wlog('{:7} -> grad {}\t{}'.format('Model', param.requires_grad, name))
else:
init_params(param,
name,
init_D=wargs.param_init_D,
a=float(wargs.u_gain))
# wargs.start_epoch = e_idx + 1
# # 不重新开始
# optim.n_current_steps = 0
else:
optim = Optim(wargs.opt_mode, wargs.learning_rate, wargs.max_grad_norm)
for n, p in nmtModel.named_parameters():
# bias can not be initialized uniformly
if 'norm' in n:
wlog('ignore layer norm init ...')
continue
if 'emb' in n:
wlog('ignore word embedding weight init ...')
continue
if 'vcb_proj' in n:
wlog('ignore vcb_proj weight init ...')
continue
init_params(p, n, init_D=wargs.param_init_D, a=float(wargs.u_gain))
# if wargs.encoder_type != 'att' and wargs.decoder_type != 'att':
# init_params(p, n, init_D=wargs.param_init_D, a=float(wargs.u_gain))
# wlog(nmtModel)
wlog(optim)
pcnt1 = len([p for p in nmtModel.parameters()])
pcnt2 = sum([p.nelement() for p in nmtModel.parameters()])
wlog('parameters number: {}/{}'.format(pcnt1, pcnt2))
# wlog('\n' + '*' * 30 + ' trainable parameters ' + '*' * 30)
# for n, p in nmtModel.named_parameters():
# if p.requires_grad: wlog('{:60} : {}'.format(n, p.size()))
opt_state = None
if wargs.pre_train:
opt_state = optim.optimizer.state_dict()
if wargs.use_reinfore_ce is False:
criterion = LabelSmoothingCriterion(
trg_emb.n_vocab, label_smoothing=wargs.label_smoothing)
else:
word2vec = tc.load(wargs.word2vec_weight)['w2v']
# criterion = Word2VecDistanceCriterion(word2vec)
criterion = CosineDistance(word2vec)
if device_ids is not None:
wlog('push criterion onto GPU {} ... '.format(device_ids[0]), 0)
criterion = criterion.to(device)
wlog('done.')
# if wargs.reinfore_type == 0 or wargs.reinfore_type == 1:
# param = list(nmtModel.parameters())
# else:
# param = list(nmtModel.parameters()) + list(criterion.parameters())
param = list(nmtModel.parameters())
optim.init_optimizer(param)
lossCompute = MultiGPULossCompute(
nmtModel.generator,
criterion,
wargs.d_model if wargs.decoder_type == 'att' else 2 * wargs.d_enc_hid,
n_trg_vcb,
trg_emb,
nmtModel.bowMapper,
loss_norm=wargs.loss_norm,
chunk_size=wargs.chunk_size,
device_ids=device_ids)
trainer = Trainer(nmtModel_par, batch_train, vocabs, optim, lossCompute,
nmtModel, batch_valid, batch_tests, writer)
trainer.train()
writer.close()