def main():
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--data_dir",
default=None,
type=str,
required=True,
help=
"The input data dir. Should contain the .tsv files (or other data files) for the task."
)
#Train File
parser.add_argument("--src_file",
default=None,
type=str,
help="The input data src file name.")
parser.add_argument("--tgt_file",
default=None,
type=str,
help="The input data tgt file name.")
parser.add_argument("--check_file",
default=None,
type=str,
help="The input check knowledge data file name")
#KS File
parser.add_argument("--ks_src_file",
default=None,
type=str,
help="The input ks data src file name.")
parser.add_argument("--ks_tgt_file",
default=None,
type=str,
help="The input ks data tgt file name.")
parser.add_argument("--predict_input_file",
default=None,
type=str,
help="predict_input_file")
parser.add_argument("--predict_output_file",
default=None,
type=str,
help="predict_output_file")
parser.add_argument(
"--bert_model",
default=None,
type=str,
required=True,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese."
)
parser.add_argument("--config_path",
default=None,
type=str,
help="Bert config file path.")
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help=
"The output directory where the model predictions and checkpoints will be written."
)
parser.add_argument(
"--log_dir",
default='',
type=str,
required=True,
help="The output directory where the log will be written.")
parser.add_argument("--model_recover_path",
default=None,
type=str,
required=True,
help="The file of fine-tuned pretraining model.")
parser.add_argument("--optim_recover_path",
default=None,
type=str,
help="The file of pretraining optimizer.")
parser.add_argument("--predict_bleu",
default=0.2,
type=float,
help="The Predicted Bleu for KS Predict ")
# Other parameters
parser.add_argument(
"--max_seq_length",
default=128,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_predict",
action='store_true',
help="Whether to run ks predict.")
parser.add_argument(
"--do_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
default=64,
type=int,
help="Total batch size for eval.")
parser.add_argument("--train_avg_bpe_length",
default=25,
type=int,
help="average bpe length for train.")
parser.add_argument("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--label_smoothing",
default=0,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--weight_decay",
default=0.01,
type=float,
help="The weight decay rate for Adam.")
parser.add_argument("--finetune_decay",
action='store_true',
help="Weight decay to the original weights.")
parser.add_argument("--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument(
"--warmup_proportion_step",
default=300,
type=int,
help=
"Proportion of training to perform linear learning rate warmup for. ")
parser.add_argument("--hidden_dropout_prob",
default=0.1,
type=float,
help="Dropout rate for hidden states.")
parser.add_argument("--attention_probs_dropout_prob",
default=0.1,
type=float,
help="Dropout rate for attention probabilities.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument('--seed',
type=int,
default=67,
help="random seed for initialization")
parser.add_argument(
'--gradient_accumulation_steps',
type=int,
default=1,
help=
"Number of updates steps to accumulate before performing a backward/update pass."
)
parser.add_argument(
'--fp16',
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument(
'--fp32_embedding',
action='store_true',
help=
"Whether to use 32-bit float precision instead of 16-bit for embeddings"
)
parser.add_argument(
'--loss_scale',
type=float,
default=0,
help=
"Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
"0 (default value): dynamic loss scaling.\n"
"Positive power of 2: static loss scaling value.\n")
parser.add_argument('--amp',
action='store_true',
help="Whether to use amp for fp16")
parser.add_argument(
'--from_scratch',
action='store_true',
help=
"Initialize parameters with random values (i.e., training from scratch)."
)
parser.add_argument('--new_segment_ids',
action='store_true',
help="Use new segment ids for bi-uni-directional LM.")
parser.add_argument('--new_pos_ids',
action='store_true',
help="Use new position ids for LMs.")
parser.add_argument('--tokenized_input',
action='store_true',
help="Whether the input is tokenized.")
parser.add_argument('--max_len_a',
type=int,
default=0,
help="Truncate_config: maximum length of segment A.")
parser.add_argument('--max_len_b',
type=int,
default=0,
help="Truncate_config: maximum length of segment B.")
parser.add_argument(
'--trunc_seg',
default='',
help="Truncate_config: first truncate segment A/B (option: a, b).")
parser.add_argument(
'--always_truncate_tail',
action='store_true',
help="Truncate_config: Whether we should always truncate tail.")
parser.add_argument(
"--mask_prob",
default=0.15,
type=float,
help=
"Number of prediction is sometimes less than max_pred when sequence is short."
)
parser.add_argument(
"--mask_prob_eos",
default=0,
type=float,
help=
"Number of prediction is sometimes less than max_pred when sequence is short."
)
parser.add_argument('--max_pred',
type=int,
default=20,
help="Max tokens of prediction.")
parser.add_argument("--num_workers",
default=0,
type=int,
help="Number of workers for the data loader.")
parser.add_argument('--mask_source_words',
action='store_true',
help="Whether to mask source words for training")
parser.add_argument('--skipgram_prb',
type=float,
default=0.0,
help='prob of ngram mask')
parser.add_argument('--skipgram_size',
type=int,
default=1,
help='the max size of ngram mask')
parser.add_argument('--mask_whole_word',
action='store_true',
help="Whether masking a whole word.")
parser.add_argument('--do_l2r_training',
action='store_true',
help="Whether to do left to right training")
parser.add_argument(
'--has_sentence_oracle',
action='store_true',
help="Whether to have sentence level oracle for training. "
"Only useful for summary generation")
parser.add_argument('--max_position_embeddings',
type=int,
default=None,
help="max position embeddings")
parser.add_argument('--relax_projection',
action='store_true',
help="Use different projection layers for tasks.")
parser.add_argument('--ffn_type',
default=0,
type=int,
help="0: default mlp; 1: W((Wx+b) elem_prod x);")
parser.add_argument('--num_qkv',
default=0,
type=int,
help="Number of different <Q,K,V>.")
parser.add_argument('--seg_emb',
action='store_true',
help="Using segment embedding for self-attention.")
parser.add_argument(
'--s2s_special_token',
action='store_true',
help="New special tokens ([S2S_SEP]/[S2S_CLS]) of S2S.")
parser.add_argument('--s2s_add_segment',
action='store_true',
help="Additional segmental for the encoder of S2S.")
parser.add_argument(
'--s2s_share_segment',
action='store_true',
help=
"Sharing segment embeddings for the encoder of S2S (used with --s2s_add_segment)."
)
parser.add_argument('--pos_shift',
action='store_true',
help="Using position shift for fine-tuning.")
args = parser.parse_args()
assert Path(
args.model_recover_path).exists(), "--model_recover_path doesn't exist"
args.output_dir = args.output_dir.replace('[PT_OUTPUT_DIR]',
os.getenv('PT_OUTPUT_DIR', ''))
args.log_dir = args.log_dir.replace('[PT_OUTPUT_DIR]',
os.getenv('PT_OUTPUT_DIR', ''))
os.makedirs(args.output_dir, exist_ok=True)
os.makedirs(args.log_dir, exist_ok=True)
handler = logging.FileHandler(os.path.join(args.log_dir, "train.log"),
encoding='UTF-8')
handler.setLevel(logging.INFO)
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
console = logging.StreamHandler()
console.setLevel(logging.DEBUG)
logger.addHandler(handler)
logger.addHandler(console)
json.dump(args.__dict__,
open(os.path.join(args.output_dir, 'opt.json'), 'w'),
sort_keys=True,
indent=2)
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
dist.init_process_group(backend='nccl')
logger.info(
"device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".
format(device, n_gpu, bool(args.local_rank != -1), args.fp16))
if args.gradient_accumulation_steps < 1:
raise ValueError(
"Invalid gradient_accumulation_steps parameter: {}, should be >= 1"
.format(args.gradient_accumulation_steps))
args.train_batch_size = int(args.train_batch_size /
args.gradient_accumulation_steps)
#Random Seed
#torch.backends.cudnn.enabled = False
#torch.backends.cudnn.benchmark = False
#torch.backends.cudnn.deterministic = True
# if n_gpu > 0:
# torch.cuda.manual_seed_all(args.seed)
if args.local_rank not in (-1, 0):
# Make sure only the first process in distributed training will download model & vocab
dist.barrier()
tokenizer = BertTokenizer.from_pretrained(args.bert_model,
do_lower_case=args.do_lower_case)
if args.max_position_embeddings:
tokenizer.max_len = args.max_position_embeddings
data_tokenizer = WhitespaceTokenizer(
) if args.tokenized_input else tokenizer
if args.local_rank == 0:
dist.barrier()
#Data process pipelines
bi_uni_pipeline = [
seq2seq_loader.Preprocess4Seq2seq(
args.max_pred,
args.mask_prob,
list(tokenizer.vocab.keys()),
tokenizer.convert_tokens_to_ids,
args.max_seq_length,
new_segment_ids=args.new_segment_ids,
truncate_config={
'max_len_a': args.max_len_a,
'max_len_b': args.max_len_b,
'trunc_seg': args.trunc_seg,
'always_truncate_tail': args.always_truncate_tail
},
mask_source_words=args.mask_source_words,
skipgram_prb=args.skipgram_prb,
skipgram_size=args.skipgram_size,
mask_whole_word=args.mask_whole_word,
mode="s2s",
has_oracle=args.has_sentence_oracle,
num_qkv=args.num_qkv,
s2s_special_token=args.s2s_special_token,
s2s_add_segment=args.s2s_add_segment,
s2s_share_segment=args.s2s_share_segment,
pos_shift=args.pos_shift)
]
C_bi_uni_pipeline = [
seq2seq_loader.C_Preprocess4Seq2seq(
args.max_pred,
args.mask_prob,
list(tokenizer.vocab.keys()),
tokenizer.convert_tokens_to_ids,
args.max_seq_length,
new_segment_ids=args.new_segment_ids,
truncate_config={
'max_len_a': args.max_len_a,
'max_len_b': args.max_len_b,
'trunc_seg': args.trunc_seg,
'always_truncate_tail': args.always_truncate_tail
},
mask_source_words=args.mask_source_words,
skipgram_prb=args.skipgram_prb,
skipgram_size=args.skipgram_size,
mask_whole_word=args.mask_whole_word,
mode="s2s",
has_oracle=args.has_sentence_oracle,
num_qkv=args.num_qkv,
s2s_special_token=args.s2s_special_token,
s2s_add_segment=args.s2s_add_segment,
s2s_share_segment=args.s2s_share_segment,
pos_shift=args.pos_shift)
]
ks_predict_bi_uni_pipeline = [
seq2seq_loader.Preprocess4Seq2seq_predict(
args.max_pred,
args.mask_prob,
list(tokenizer.vocab.keys()),
tokenizer.convert_tokens_to_ids,
args.max_seq_length,
new_segment_ids=args.new_segment_ids,
truncate_config={
'max_len_a': args.max_len_a,
'max_len_b': args.max_len_b,
'trunc_seg': args.trunc_seg,
'always_truncate_tail': args.always_truncate_tail
},
mask_source_words=args.mask_source_words,
skipgram_prb=args.skipgram_prb,
skipgram_size=args.skipgram_size,
mask_whole_word=args.mask_whole_word,
mode="s2s",
has_oracle=args.has_sentence_oracle,
num_qkv=args.num_qkv,
s2s_special_token=args.s2s_special_token,
s2s_add_segment=args.s2s_add_segment,
s2s_share_segment=args.s2s_share_segment,
pos_shift=args.pos_shift)
]
if args.do_train:
print("Loading QKR Train Dataset", args.data_dir)
file_oracle = None
if args.has_sentence_oracle:
file_oracle = os.path.join(args.data_dir, 'train.oracle')
fn_src = os.path.join(args.data_dir,
args.src_file if args.src_file else 'train.src')
fn_tgt = os.path.join(args.data_dir,
args.tgt_file if args.tgt_file else 'train.tgt')
fn_check = os.path.join(args.data_dir, args.check_file)
train_dataset = seq2seq_loader.C_Seq2SeqDataset(
fn_src,
fn_tgt,
fn_check,
args.train_batch_size,
data_tokenizer,
args.max_seq_length,
file_oracle=file_oracle,
bi_uni_pipeline=C_bi_uni_pipeline)
if args.local_rank == -1:
train_sampler = RandomSampler(train_dataset, replacement=False)
_batch_size = args.train_batch_size
else:
train_sampler = DistributedSampler(train_dataset)
_batch_size = args.train_batch_size // dist.get_world_size()
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
batch_size=_batch_size,
sampler=train_sampler,
num_workers=args.num_workers,
collate_fn=seq2seq_loader.batch_list_to_batch_tensors,
pin_memory=False)
print("Loading KS Train Dataset", args.data_dir)
ks_fn_src = os.path.join(args.data_dir, args.ks_src_file)
ks_fn_tgt = os.path.join(args.data_dir, args.ks_tgt_file)
ks_train_dataset = seq2seq_loader.Seq2SeqDataset(
ks_fn_src,
ks_fn_tgt,
args.train_batch_size,
data_tokenizer,
args.max_seq_length,
file_oracle=file_oracle,
bi_uni_pipeline=bi_uni_pipeline)
if args.local_rank == -1:
ks_train_sampler = RandomSampler(ks_train_dataset,
replacement=False)
_batch_size = args.train_batch_size
else:
ks_train_sampler = DistributedSampler(ks_train_dataset)
_batch_size = args.train_batch_size // dist.get_world_size()
ks_train_dataloader = torch.utils.data.DataLoader(
ks_train_dataset,
batch_size=_batch_size,
sampler=ks_train_sampler,
num_workers=args.num_workers,
collate_fn=seq2seq_loader.batch_list_to_batch_tensors,
pin_memory=False)
# note: args.train_batch_size has been changed to (/= args.gradient_accumulation_steps)
t_total = int(
len(train_dataloader) * args.num_train_epochs /
args.gradient_accumulation_steps)
amp_handle = None
if args.fp16 and args.amp:
from apex import amp
amp_handle = amp.init(enable_caching=True)
logger.info("enable fp16 with amp")
# Prepare model
cls_num_labels = 2
type_vocab_size = 6 + (
1 if args.s2s_add_segment else 0) if args.new_segment_ids else 2
num_sentlvl_labels = 2 if args.has_sentence_oracle else 0
relax_projection = 4 if args.relax_projection else 0
if args.local_rank not in (-1, 0):
# Make sure only the first process in distributed training will download model & vocab
dist.barrier()
#Recover model
if args.model_recover_path:
logger.info(" ** ** * Recover model: %s ** ** * ",
args.model_recover_path)
model_recover = torch.load(args.model_recover_path, map_location='cpu')
global_step = 0
mask_word_id, eos_word_ids, sos_word_id = tokenizer.convert_tokens_to_ids(
["[MASK]", "[SEP]", "[S2S_SOS]"])
model = BertForPreTrainingLossMask.from_pretrained(
args.bert_model,
state_dict=model_recover,
num_labels=cls_num_labels,
num_rel=0,
type_vocab_size=type_vocab_size,
config_path=args.config_path,
task_idx=3,
num_sentlvl_labels=num_sentlvl_labels,
max_position_embeddings=args.max_position_embeddings,
label_smoothing=args.label_smoothing,
fp32_embedding=args.fp32_embedding,
relax_projection=relax_projection,
new_pos_ids=args.new_pos_ids,
ffn_type=args.ffn_type,
hidden_dropout_prob=args.hidden_dropout_prob,
attention_probs_dropout_prob=args.attention_probs_dropout_prob,
num_qkv=args.num_qkv,
seg_emb=args.seg_emb,
mask_word_id=mask_word_id,
search_beam_size=5,
length_penalty=0,
eos_id=eos_word_ids,
sos_id=sos_word_id,
forbid_duplicate_ngrams=True,
forbid_ignore_set=None,
mode="s2s")
if args.local_rank == 0:
dist.barrier()
if args.fp16:
model.half()
if args.fp32_embedding:
model.bert.embeddings.word_embeddings.float()
model.bert.embeddings.position_embeddings.float()
model.bert.embeddings.token_type_embeddings.float()
model.to(device)
model.tmp_bert_emb.word_embeddings.weight = torch.nn.Parameter(
model.bert.embeddings.word_embeddings.weight.clone())
model.tmp_bert_emb.token_type_embeddings.weight = torch.nn.Parameter(
model.bert.embeddings.token_type_embeddings.weight.clone())
model.tmp_bert_emb.position_embeddings.weight = torch.nn.Parameter(
model.bert.embeddings.position_embeddings.weight.clone())
model.mul_bert_emb.word_embeddings.weight = torch.nn.Parameter(
model.bert.embeddings.word_embeddings.weight.clone())
model.mul_bert_emb.token_type_embeddings.weight = torch.nn.Parameter(
model.bert.embeddings.token_type_embeddings.weight.clone())
model.mul_bert_emb.position_embeddings.weight = torch.nn.Parameter(
model.bert.embeddings.position_embeddings.weight.clone())
if args.local_rank != -1:
try:
from torch.nn.parallel import DistributedDataParallel as DDP
except ImportError:
raise ImportError("DistributedDataParallel")
model = DDP(model,
device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True)
elif n_gpu > 1:
model = DataParallelImbalance(model)
# Prepare optimizer
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params':
[p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
if args.fp16:
try:
from pytorch_bert.optimization_fp16 import FP16_Optimizer_State
from apex.optimizers import FusedAdam
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
)
optimizer = FusedAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
bias_correction=False,
max_grad_norm=1.0)
if args.loss_scale == 0:
optimizer = FP16_Optimizer_State(optimizer,
dynamic_loss_scale=True)
else:
optimizer = FP16_Optimizer_State(optimizer,
static_loss_scale=args.loss_scale)
else:
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=t_total)
if args.optim_recover_path is not None:
logger.info(" ** ** * Recover optimizer from : {} ** ** * ".format(
args.optim_recover_path))
optim_recover = torch.load(args.optim_recover_path, map_location='cpu')
if hasattr(optim_recover, 'state_dict'):
optim_recover = optim_recover.state_dict()
optimizer.load_state_dict(optim_recover)
if args.loss_scale == 0:
logger.info(
" ** ** * Recover optimizer: dynamic_loss_scale ** ** * ")
optimizer.dynamic_loss_scale = True
#logger.info(" ** ** * CUDA.empty_cache() ** ** * ")
torch.cuda.empty_cache()
# ################# TRAIN ############################ #
if args.do_train:
max_F1 = 0
best_step = 0
logger.info(" ** ** * Running training ** ** * ")
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", t_total)
model.train()
start_epoch = 1
for i_epoch in trange(start_epoch,
start_epoch + 1,
desc="Epoch",
disable=args.local_rank not in (-1, 0)):
if args.local_rank != -1:
train_sampler.set_epoch(i_epoch)
step = 0
for batch, ks_batch in zip(train_dataloader, ks_train_dataloader):
# ################# E step + M step + Mutual Information Loss ############################ #
batch = [
t.to(device) if t is not None else None for t in batch
]
input_ids, segment_ids, input_mask, mask_qkv, lm_label_ids, masked_pos, masked_weights, is_next, task_idx, tgt_pos, labels, ks_labels, check_ids = batch
oracle_pos, oracle_weights, oracle_labels = None, None, None
loss_tuple = model(input_ids,
segment_ids,
input_mask,
lm_label_ids,
is_next,
masked_pos=masked_pos,
masked_weights=masked_weights,
task_idx=task_idx,
masked_pos_2=oracle_pos,
masked_weights_2=oracle_weights,
masked_labels_2=oracle_labels,
mask_qkv=mask_qkv,
tgt_pos=tgt_pos,
labels=labels.half(),
ks_labels=ks_labels,
check_ids=check_ids)
masked_lm_loss, next_sentence_loss, KL_loss, Mutual_loss, Golden_loss, predict_kl_loss = loss_tuple
if n_gpu > 1: # mean() to average on multi-gpu.
masked_lm_loss = masked_lm_loss.mean()
next_sentence_loss = next_sentence_loss.mean()
Mutual_loss = Mutual_loss.mean()
Golden_loss = Golden_loss.mean()
KL_loss = KL_loss.mean()
predict_kl_loss = predict_kl_loss.mean()
loss = masked_lm_loss + next_sentence_loss + KL_loss + predict_kl_loss + Mutual_loss + Golden_loss
logger.info("In{}step, masked_lm_loss:{}".format(
step, masked_lm_loss))
logger.info("In{}step, KL_loss:{}".format(step, KL_loss))
logger.info("In{}step, Mutual_loss:{}".format(
step, Mutual_loss))
logger.info("In{}step, Golden_loss:{}".format(
step, Golden_loss))
logger.info("In{}step, predict_kl_loss:{}".format(
step, predict_kl_loss))
logger.info("******************************************* ")
# ensure that accumlated gradients are normalized
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
optimizer.backward(loss)
if amp_handle:
amp_handle._clear_cache()
else:
loss.backward()
if (step + 1) % args.gradient_accumulation_steps == 0:
lr_this_step = args.learning_rate * warmup_linear(
global_step / t_total,
args.warmup_proportion_step / t_total)
if args.fp16:
# modify learning rate with special warm up BERT uses
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
optimizer.step()
optimizer.zero_grad()
global_step += 1
# ################# Knowledge Selection Loss ############################ #
if random.randint(0, 4) == 0:
ks_batch = [
t.to(device) if t is not None else None
for t in ks_batch
]
input_ids, segment_ids, input_mask, mask_qkv, lm_label_ids, masked_pos, masked_weights, is_next, task_idx, _, labels, ks_labels = ks_batch
oracle_pos, oracle_weights, oracle_labels = None, None, None
loss_tuple = model(input_ids,
segment_ids,
input_mask,
lm_label_ids,
is_next,
masked_pos=masked_pos,
masked_weights=masked_weights,
task_idx=task_idx,
masked_pos_2=oracle_pos,
masked_weights_2=oracle_weights,
masked_labels_2=oracle_labels,
mask_qkv=mask_qkv,
labels=labels,
ks_labels=ks_labels,
train_ks=True)
ks_loss, _ = loss_tuple
if n_gpu > 1: # mean() to average on multi-gpu.
ks_loss = ks_loss.mean()
loss = ks_loss
logger.info("In{}step, ks_loss:{}".format(step, ks_loss))
logger.info("******************************************* ")
# ensure that accumlated gradients are normalized
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
optimizer.backward(loss)
if amp_handle:
amp_handle._clear_cache()
else:
loss.backward()
if (step + 1) % args.gradient_accumulation_steps == 0:
lr_this_step = args.learning_rate * warmup_linear(
global_step / t_total,
args.warmup_proportion_step / t_total)
if args.fp16:
# modify learning rate with special warm up BERT uses
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
optimizer.step()
optimizer.zero_grad()
step += 1
###################### Eval Every 5000 Step ############################ #
if (global_step + 1) % 5000 == 0:
next_i = 0
model.eval()
# Know Rank Stage
logger.info(" ** ** * DEV Know Selection Begin ** ** * ")
with open(os.path.join(args.data_dir,
args.predict_input_file),
"r",
encoding="utf-8") as file:
src_file = file.readlines()
with open(os.path.join(args.data_dir,
"train_tgt_pad.empty"),
"r",
encoding="utf-8") as file:
tgt_file = file.readlines()
with open(os.path.join(args.data_dir,
args.predict_output_file),
"w",
encoding="utf-8") as out:
while next_i < len(src_file):
batch_src = src_file[next_i:next_i +
args.eval_batch_size]
batch_tgt = tgt_file[next_i:next_i +
args.eval_batch_size]
next_i += args.eval_batch_size
ex_list = []
for src, tgt in zip(batch_src, batch_tgt):
src_tk = data_tokenizer.tokenize(src.strip())
tgt_tk = data_tokenizer.tokenize(tgt.strip())
ex_list.append((src_tk, tgt_tk))
batch = []
for idx in range(len(ex_list)):
instance = ex_list[idx]
for proc in ks_predict_bi_uni_pipeline:
instance = proc(instance)
batch.append(instance)
batch_tensor = seq2seq_loader.batch_list_to_batch_tensors(
batch)
batch = [
t.to(device) if t is not None else None
for t in batch_tensor
]
input_ids, segment_ids, input_mask, mask_qkv, lm_label_ids, masked_pos, masked_weights, is_next, task_idx = batch
predict_bleu = args.predict_bleu * torch.ones(
[input_ids.shape[0]], device=input_ids.device)
oracle_pos, oracle_weights, oracle_labels = None, None, None
with torch.no_grad():
logits = model(input_ids,
segment_ids,
input_mask,
lm_label_ids,
is_next,
masked_pos=masked_pos,
masked_weights=masked_weights,
task_idx=task_idx,
masked_pos_2=oracle_pos,
masked_weights_2=oracle_weights,
masked_labels_2=oracle_labels,
mask_qkv=mask_qkv,
labels=predict_bleu,
train_ks=True)
logits = torch.nn.functional.softmax(logits,
dim=1)
labels = logits[:, 1].cpu().numpy()
for i in range(len(labels)):
line = batch_src[i].strip()
line += "\t"
line += str(labels[i])
out.write(line)
out.write("\n")
data_path = os.path.join(args.data_dir,
"qkr_dev.ks_score.tk")
src_path = os.path.join(args.data_dir, "qkr_dev.src.tk")
src_out_path = os.path.join(args.data_dir,
"rank_qkr_dev.src.tk")
tgt_path = os.path.join(args.data_dir, "qkr_dev.tgt")
knowledge_selection(data_path, src_path, src_out_path)
logger.info(" ** ** * DEV Know Selection End ** ** * ")
# Decode Stage
logger.info(" ** ** * Dev Decode Begin ** ** * ")
with open(src_out_path, encoding="utf-8") as file:
dev_src_lines = file.readlines()
with open(tgt_path, encoding="utf-8") as file:
golden_response_lines = file.readlines()
decode_result = decode_batch(model, dev_src_lines)
logger.info(" ** ** * Dev Decode End ** ** * ")
# Compute dev F1
assert len(decode_result) == len(golden_response_lines)
C_F1 = f_one(decode_result, golden_response_lines)[0]
logger.info(
"** ** * Current F1 is {} ** ** * ".format(C_F1))
if C_F1 < max_F1:
logger.info(
"** ** * Current F1 is lower than Previous F1. So Stop Training ** ** * "
)
logger.info(
"** ** * The best model is {} ** ** * ".format(
best_step))
break
else:
max_F1 = C_F1
best_step = step
logger.info(
"** ** * Current F1 is larger than Previous F1. So Continue Training ** ** * "
)
# Save trained model
if (args.local_rank == -1
or torch.distributed.get_rank() == 0):
logger.info(
"** ** * Saving fine-tuned model and optimizer ** ** * "
)
model_to_save = model.module if hasattr(
model,
'module') else model # Only save the model it-self
output_model_file = os.path.join(
args.output_dir,
"model.{}_{}.bin".format(i_epoch, global_step))
torch.save(model_to_save.state_dict(),
output_model_file)
output_optim_file = os.path.join(
args.output_dir, "optim.bin")
torch.save(optimizer.state_dict(), output_optim_file)
#logger.info(" ** ** * CUDA.empty_cache() ** ** * ")
torch.cuda.empty_cache()
# ################# Predict ############################ #
if args.do_predict:
bi_uni_pipeline = [
seq2seq_loader.Preprocess4Seq2seq_predict(
args.max_pred,
args.mask_prob,
list(tokenizer.vocab.keys()),
tokenizer.convert_tokens_to_ids,
args.max_seq_length,
new_segment_ids=args.new_segment_ids,
truncate_config={
'max_len_a': args.max_len_a,
'max_len_b': args.max_len_b,
'trunc_seg': args.trunc_seg,
'always_truncate_tail': args.always_truncate_tail
},
mask_source_words=args.mask_source_words,
skipgram_prb=args.skipgram_prb,
skipgram_size=args.skipgram_size,
mask_whole_word=args.mask_whole_word,
mode="s2s",
has_oracle=args.has_sentence_oracle,
num_qkv=args.num_qkv,
s2s_special_token=args.s2s_special_token,
s2s_add_segment=args.s2s_add_segment,
s2s_share_segment=args.s2s_share_segment,
pos_shift=args.pos_shift)
]
next_i = 0
model.eval()
with open(os.path.join(args.data_dir, args.predict_input_file),
"r",
encoding="utf-8") as file:
src_file = file.readlines()
with open("train_tgt_pad.empty", "r", encoding="utf-8") as file:
tgt_file = file.readlines()
with open(os.path.join(args.data_dir, args.predict_output_file),
"w",
encoding="utf-8") as out:
logger.info("** ** * Continue knowledge ranking ** ** * ")
for next_i in tqdm(
range(len(src_file) // args.eval_batch_size + 1)):
#while next_i < len(src_file):
batch_src = src_file[next_i *
args.eval_batch_size:(next_i + 1) *
args.eval_batch_size]
batch_tgt = tgt_file[next_i *
args.eval_batch_size:(next_i + 1) *
args.eval_batch_size]
#next_i += args.eval_batch_size
ex_list = []
for src, tgt in zip(batch_src, batch_tgt):
src_tk = data_tokenizer.tokenize(src.strip())
tgt_tk = data_tokenizer.tokenize(tgt.strip())
ex_list.append((src_tk, tgt_tk))
batch = []
for idx in range(len(ex_list)):
instance = ex_list[idx]
for proc in bi_uni_pipeline:
instance = proc(instance)
batch.append(instance)
batch_tensor = seq2seq_loader.batch_list_to_batch_tensors(
batch)
batch = [
t.to(device) if t is not None else None
for t in batch_tensor
]
input_ids, segment_ids, input_mask, mask_qkv, lm_label_ids, masked_pos, masked_weights, is_next, task_idx = batch
predict_bleu = args.predict_bleu * torch.ones(
[input_ids.shape[0]], device=input_ids.device)
oracle_pos, oracle_weights, oracle_labels = None, None, None
with torch.no_grad():
logits = model(input_ids,
segment_ids,
input_mask,
lm_label_ids,
is_next,
masked_pos=masked_pos,
masked_weights=masked_weights,
task_idx=task_idx,
masked_pos_2=oracle_pos,
masked_weights_2=oracle_weights,
masked_labels_2=oracle_labels,
mask_qkv=mask_qkv,
labels=predict_bleu,
train_ks=True)
logits = torch.nn.functional.softmax(logits, dim=1)
labels = logits[:, 1].cpu().numpy()
for i in range(len(labels)):
line = batch_src[i].strip()
line += "\t"
line += str(labels[i])
out.write(line)
out.write("\n")
def main():
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--data_dir",
default=None,
type=str,
required=True,
help=
"The input data dir. Should contain the .tsv files (or other data files) for the task."
)
parser.add_argument("--src_file",
default=None,
type=str,
help="The input data file name.")
parser.add_argument("--tgt_file",
default=None,
type=str,
help="The output data file name.")
parser.add_argument(
"--bert_model",
default=None,
type=str,
required=True,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese."
)
parser.add_argument("--config_path",
default=None,
type=str,
help="Bert config file path.")
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help=
"The output directory where the model predictions and checkpoints will be written."
)
parser.add_argument(
"--log_dir",
default='',
type=str,
required=True,
help="The output directory where the log will be written.")
parser.add_argument("--model_recover_path",
default=None,
type=str,
required=True,
help="The file of fine-tuned pretraining model.")
parser.add_argument("--optim_recover_path",
default=None,
type=str,
help="The file of pretraining optimizer.")
# Other parameters
parser.add_argument(
"--max_seq_length",
default=128,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--local_debug",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument(
"--do_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
default=64,
type=int,
help="Total batch size for eval.")
parser.add_argument("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--label_smoothing",
default=0,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--weight_decay",
default=0.01,
type=float,
help="The weight decay rate for Adam.")
parser.add_argument("--finetune_decay",
action='store_true',
help="Weight decay to the original weights.")
parser.add_argument("--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument(
"--warmup_proportion",
default=0.1,
type=float,
help=
"Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
parser.add_argument("--hidden_dropout_prob",
default=0.1,
type=float,
help="Dropout rate for hidden states.")
parser.add_argument("--attention_probs_dropout_prob",
default=0.1,
type=float,
help="Dropout rate for attention probabilities.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument(
'--gradient_accumulation_steps',
type=int,
default=1,
help=
"Number of updates steps to accumulate before performing a backward/update pass."
)
parser.add_argument(
'--fp16',
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument(
'--fp32_embedding',
action='store_true',
help=
"Whether to use 32-bit float precision instead of 16-bit for embeddings"
)
parser.add_argument(
'--loss_scale',
type=float,
default=0,
help=
"Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
"0 (default value): dynamic loss scaling.\n"
"Positive power of 2: static loss scaling value.\n")
parser.add_argument('--amp',
action='store_true',
help="Whether to use amp for fp16")
parser.add_argument(
'--from_scratch',
action='store_true',
help=
"Initialize parameters with random values (i.e., training from scratch)."
)
parser.add_argument('--new_segment_ids',
action='store_true',
help="Use new segment ids for bi-uni-directional LM.")
parser.add_argument('--new_pos_ids',
action='store_true',
help="Use new position ids for LMs.")
parser.add_argument('--tokenized_input',
action='store_true',
help="Whether the input is tokenized.")
parser.add_argument('--max_len_a',
type=int,
default=0,
help="Truncate_config: maximum length of segment A.")
parser.add_argument('--max_len_b',
type=int,
default=0,
help="Truncate_config: maximum length of segment B.")
parser.add_argument(
'--trunc_seg',
default='',
help="Truncate_config: first truncate segment A/B (option: a, b).")
parser.add_argument(
'--always_truncate_tail',
action='store_true',
help="Truncate_config: Whether we should always truncate tail.")
parser.add_argument(
"--mask_prob",
default=0.15,
type=float,
help=
"Number of prediction is sometimes less than max_pred when sequence is short."
)
parser.add_argument(
"--mask_prob_eos",
default=0,
type=float,
help=
"Number of prediction is sometimes less than max_pred when sequence is short."
)
parser.add_argument('--max_pred',
type=int,
default=20,
help="Max tokens of prediction.")
parser.add_argument("--num_workers",
default=0,
type=int,
help="Number of workers for the data loader.")
parser.add_argument('--mask_source_words',
action='store_true',
help="Whether to mask source words for training")
parser.add_argument('--skipgram_prb',
type=float,
default=0.0,
help='prob of ngram mask')
parser.add_argument('--skipgram_size',
type=int,
default=1,
help='the max size of ngram mask')
parser.add_argument('--mask_whole_word',
action='store_true',
help="Whether masking a whole word.")
parser.add_argument('--do_l2r_training',
action='store_true',
help="Whether to do left to right training")
parser.add_argument(
'--has_sentence_oracle',
action='store_true',
help="Whether to have sentence level oracle for training. "
"Only useful for summary generation")
parser.add_argument('--max_position_embeddings',
type=int,
default=None,
help="max position embeddings")
parser.add_argument('--relax_projection',
action='store_true',
help="Use different projection layers for tasks.")
parser.add_argument('--ffn_type',
default=0,
type=int,
help="0: default mlp; 1: W((Wx+b) elem_prod x);")
parser.add_argument('--num_qkv',
default=0,
type=int,
help="Number of different <Q,K,V>.")
parser.add_argument('--seg_emb',
action='store_true',
help="Using segment embedding for self-attention.")
parser.add_argument(
'--s2s_special_token',
action='store_true',
help="New special tokens ([S2S_SEP]/[S2S_CLS]) of S2S.")
parser.add_argument('--s2s_add_segment',
action='store_true',
help="Additional segmental for the encoder of S2S.")
parser.add_argument(
'--s2s_share_segment',
action='store_true',
help=
"Sharing segment embeddings for the encoder of S2S (used with --s2s_add_segment)."
)
parser.add_argument('--pos_shift',
action='store_true',
help="Using position shift for fine-tuning.")
args = parser.parse_args()
assert Path(
args.model_recover_path).exists(), "--model_recover_path doesn't exist"
args.output_dir = args.output_dir.replace('[PT_OUTPUT_DIR]',
os.getenv('PT_OUTPUT_DIR', ''))
args.log_dir = args.log_dir.replace('[PT_OUTPUT_DIR]',
os.getenv('PT_OUTPUT_DIR', ''))
os.makedirs(args.output_dir, exist_ok=True)
os.makedirs(args.log_dir, exist_ok=True)
json.dump(args.__dict__,
open(os.path.join(args.output_dir, 'opt.json'), 'w'),
sort_keys=True,
indent=2)
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
dist.init_process_group(backend='nccl')
logger.info(
"device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".
format(device, n_gpu, bool(args.local_rank != -1), args.fp16))
if args.gradient_accumulation_steps < 1:
raise ValueError(
"Invalid gradient_accumulation_steps parameter: {}, should be >= 1"
.format(args.gradient_accumulation_steps))
args.train_batch_size = int(args.train_batch_size /
args.gradient_accumulation_steps)
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if not args.do_train and not args.do_eval:
raise ValueError(
"At least one of `do_train` or `do_eval` must be True.")
if args.local_rank not in (-1, 0):
# Make sure only the first process in distributed training will download model & vocab
dist.barrier()
tokenizer = BertTokenizer.from_pretrained(args.bert_model,
do_lower_case=args.do_lower_case)
if args.max_position_embeddings:
tokenizer.max_len = args.max_position_embeddings
data_tokenizer = WhitespaceTokenizer(
) if args.tokenized_input else tokenizer
if args.local_rank == 0:
dist.barrier()
if args.do_train:
print("Loading Train Dataset", args.data_dir)
bi_uni_pipeline = [
seq2seq_loader.Preprocess4Seq2seq(
args.max_pred,
args.mask_prob,
list(tokenizer.vocab.keys()),
tokenizer.convert_tokens_to_ids,
args.max_seq_length,
new_segment_ids=args.new_segment_ids,
truncate_config={
'max_len_a': args.max_len_a,
'max_len_b': args.max_len_b,
'trunc_seg': args.trunc_seg,
'always_truncate_tail': args.always_truncate_tail
},
mask_source_words=args.mask_source_words,
skipgram_prb=args.skipgram_prb,
skipgram_size=args.skipgram_size,
mask_whole_word=args.mask_whole_word,
mode="s2s",
has_oracle=args.has_sentence_oracle,
num_qkv=args.num_qkv,
s2s_special_token=args.s2s_special_token,
s2s_add_segment=args.s2s_add_segment,
s2s_share_segment=args.s2s_share_segment,
pos_shift=args.pos_shift)
]
file_oracle = None
if args.has_sentence_oracle:
file_oracle = os.path.join(args.data_dir, 'train.oracle')
fn_src = os.path.join(args.data_dir,
args.src_file if args.src_file else 'train.src')
fn_tgt = os.path.join(args.data_dir,
args.tgt_file if args.tgt_file else 'train.tgt')
train_dataset = seq2seq_loader.Seq2SeqDataset(
fn_src,
fn_tgt,
args.train_batch_size,
data_tokenizer,
args.max_seq_length,
file_oracle=file_oracle,
bi_uni_pipeline=bi_uni_pipeline,
corpus_preprocessors=corpus_preprocessors)
train_dataset.initial()
print(len(train_dataset.ex_list))
print(train_dataset.batch_size)
# assert 1==0
if args.local_rank == -1:
train_sampler = RandomSampler(train_dataset, replacement=False)
_batch_size = args.train_batch_size
else:
train_sampler = DistributedSampler(train_dataset)
_batch_size = args.train_batch_size // dist.get_world_size()
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
batch_size=_batch_size,
sampler=train_sampler,
num_workers=args.num_workers,
collate_fn=seq2seq_loader.batch_list_to_batch_tensors,
pin_memory=False)
# c = 0
# for i_epoch in trange(0, int(args.num_train_epochs)+1, desc="Epoch", disable=args.local_rank not in (-1, 0)):
# if args.local_rank != -1:
# train_sampler.set_epoch(i_epoch)
# iter_bar = tqdm(train_dataloader, desc='Iter (loss=X.XXX)',
# disable=args.local_rank not in (-1, 0))
# for step, batch in enumerate(iter_bar):
# batch = [
# t.to(device) if t is not None else None for t in batch]
# if args.has_sentence_oracle:
# input_ids, segment_ids, input_mask, mask_qkv, lm_label_ids, masked_pos, masked_weights, is_next, task_idx, sop_label, oracle_pos, oracle_weights, oracle_labels = batch
# else:
# input_ids, segment_ids, input_mask, mask_qkv, lm_label_ids, masked_pos, masked_weights, is_next, task_idx, sop_label = batch
# oracle_pos, oracle_weights, oracle_labels = None, None, None
# c += input_ids.shape[0]
#
# # print(input_ids)
# #
# # print(input_ids.shape)
# # print(segment_ids)
# # print(segment_ids.shape)
# # print(is_next)
# # print(task_idx)
# # print(sop_label)
# # print(task_idx.shape)
# # for i in range(input_mask.shape[0]):
# # print(input_mask[i])
# print(c)
# print(train_dataset.c)
# note: args.train_batch_size has been changed to (/= args.gradient_accumulation_steps)
# t_total = int(math.ceil(len(train_dataset.ex_list) / args.train_batch_size)
t_total = int(
len(train_dataloader) * args.num_train_epochs /
args.gradient_accumulation_steps)
amp_handle = None
if args.fp16 and args.amp:
from apex import amp
amp_handle = amp.init(enable_caching=True)
logger.info("enable fp16 with amp")
# Prepare model
recover_step = _get_max_epoch_model(args.output_dir)
cls_num_labels = 2
type_vocab_size = 6 + \
(1 if args.s2s_add_segment else 0) if args.new_segment_ids else 2
num_sentlvl_labels = 2 if args.has_sentence_oracle else 0
relax_projection = 4 if args.relax_projection else 0
if args.local_rank not in (-1, 0):
# Make sure only the first process in distributed training will download model & vocab
dist.barrier()
if (recover_step is None) and (args.model_recover_path is None):
# if _state_dict == {}, the parameters are randomly initialized
# if _state_dict == None, the parameters are initialized with bert-init
_state_dict = {} if args.from_scratch else None
model = BertForPreTrainingLossMask.from_pretrained(
args.bert_model,
state_dict=_state_dict,
num_labels=cls_num_labels,
num_rel=0,
type_vocab_size=type_vocab_size,
config_path=args.config_path,
task_idx=3,
num_sentlvl_labels=num_sentlvl_labels,
max_position_embeddings=args.max_position_embeddings,
label_smoothing=args.label_smoothing,
fp32_embedding=args.fp32_embedding,
relax_projection=relax_projection,
new_pos_ids=args.new_pos_ids,
ffn_type=args.ffn_type,
hidden_dropout_prob=args.hidden_dropout_prob,
attention_probs_dropout_prob=args.attention_probs_dropout_prob,
num_qkv=args.num_qkv,
seg_emb=args.seg_emb,
local_debug=args.local_debug)
global_step = 0
else:
if recover_step:
logger.info("***** Recover model: %d *****", recover_step)
model_recover = torch.load(os.path.join(
args.output_dir, "model.{0}.bin".format(recover_step)),
map_location='cpu')
# recover_step == number of epochs
global_step = math.floor(recover_step * t_total /
args.num_train_epochs)
elif args.model_recover_path:
logger.info("***** Recover model: %s *****",
args.model_recover_path)
model_recover = torch.load(args.model_recover_path,
map_location='cpu')
global_step = 0
model = BertForPreTrainingLossMask.from_pretrained(
args.bert_model,
state_dict=model_recover,
num_labels=cls_num_labels,
num_rel=0,
type_vocab_size=type_vocab_size,
config_path=args.config_path,
task_idx=3,
num_sentlvl_labels=num_sentlvl_labels,
max_position_embeddings=args.max_position_embeddings,
label_smoothing=args.label_smoothing,
fp32_embedding=args.fp32_embedding,
relax_projection=relax_projection,
new_pos_ids=args.new_pos_ids,
ffn_type=args.ffn_type,
hidden_dropout_prob=args.hidden_dropout_prob,
attention_probs_dropout_prob=args.attention_probs_dropout_prob,
num_qkv=args.num_qkv,
seg_emb=args.seg_emb,
local_debug=args.local_debug)
if args.local_rank == 0:
dist.barrier()
if args.fp16:
model.half()
if args.fp32_embedding:
model.bert.embeddings.word_embeddings.float()
model.bert.embeddings.position_embeddings.float()
model.bert.embeddings.token_type_embeddings.float()
model.to(device)
if args.local_rank != -1:
try:
from torch.nn.parallel import DistributedDataParallel as DDP
except ImportError:
raise ImportError("DistributedDataParallel")
model = DDP(model,
device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True)
elif n_gpu > 1:
# model = torch.nn.DataParallel(model)
model = DataParallelImbalance(model)
# Prepare optimizer
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params':
[p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
if args.fp16:
try:
# from apex.optimizers import FP16_Optimizer
from pytorch_pretrained_bert.optimization_fp16 import FP16_Optimizer_State
from apex.optimizers import FusedAdam
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
)
optimizer = FusedAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
bias_correction=False,
max_grad_norm=1.0)
if args.loss_scale == 0:
optimizer = FP16_Optimizer_State(optimizer,
dynamic_loss_scale=True)
else:
optimizer = FP16_Optimizer_State(optimizer,
static_loss_scale=args.loss_scale)
else:
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=t_total)
if recover_step:
logger.info("***** Recover optimizer: %d *****", recover_step)
optim_recover = torch.load(os.path.join(
args.output_dir, "optim.{0}.bin".format(recover_step)),
map_location='cpu')
if hasattr(optim_recover, 'state_dict'):
optim_recover = optim_recover.state_dict()
optimizer.load_state_dict(optim_recover)
if args.loss_scale == 0:
logger.info("***** Recover optimizer: dynamic_loss_scale *****")
optimizer.dynamic_loss_scale = True
logger.info("***** CUDA.empty_cache() *****")
torch.cuda.empty_cache()
if args.do_train:
logger.info("***** Running training *****")
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", t_total)
model.train()
if recover_step:
start_epoch = recover_step + 1
else:
start_epoch = 1
for i_epoch in trange(start_epoch,
int(args.num_train_epochs) + 1,
desc="Epoch",
disable=args.local_rank not in (-1, 0)):
if args.local_rank != -1:
train_sampler.set_epoch(i_epoch)
iter_bar = tqdm(train_dataloader,
desc='Iter (loss=X.XXX)',
disable=args.local_rank not in (-1, 0))
for step, batch in enumerate(iter_bar):
batch = [
t.to(device) if t is not None else None for t in batch
]
if args.has_sentence_oracle:
input_ids, segment_ids, input_mask, mask_qkv, lm_label_ids, masked_pos, masked_weights, is_next, task_idx, sop_label, oracle_pos, oracle_weights, oracle_labels = batch
else:
input_ids, segment_ids, input_mask, mask_qkv, lm_label_ids, masked_pos, masked_weights, is_next, task_idx, sop_label = batch
print(sop_label)
print(task_idx)
oracle_pos, oracle_weights, oracle_labels = None, None, None
# loss_tuple = model(input_ids, segment_ids, input_mask, lm_label_ids, is_next,
# masked_pos=masked_pos, masked_weights=masked_weights, task_idx=task_idx,
# masked_pos_2=oracle_pos, masked_weights_2=oracle_weights,
# masked_labels_2=oracle_labels, mask_qkv=mask_qkv)
loss_tuple = model(input_ids,
segment_ids,
input_mask,
lm_label_ids,
sop_label,
masked_pos=masked_pos,
masked_weights=masked_weights,
task_idx=task_idx,
masked_pos_2=oracle_pos,
masked_weights_2=oracle_weights,
masked_labels_2=oracle_labels,
mask_qkv=mask_qkv)
masked_lm_loss, next_sentence_loss = loss_tuple
if n_gpu > 1: # mean() to average on multi-gpu.
# loss = loss.mean()
masked_lm_loss = masked_lm_loss.mean()
next_sentence_loss = next_sentence_loss.mean()
print('mask_lm_loss {}'.format(masked_lm_loss))
print('next_sentence_loss {}'.format(next_sentence_loss))
print('----------------------------------------------')
loss = masked_lm_loss + next_sentence_loss
# logging for each step (i.e., before normalization by args.gradient_accumulation_steps)
iter_bar.set_description('Iter (loss=%5.3f)' % loss.item())
# ensure that accumlated gradients are normalized
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
optimizer.backward(loss)
if amp_handle:
amp_handle._clear_cache()
else:
loss.backward()
if (step + 1) % args.gradient_accumulation_steps == 0:
lr_this_step = args.learning_rate * \
warmup_linear(global_step/t_total,
args.warmup_proportion)
if args.fp16:
# modify learning rate with special warm up BERT uses
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
optimizer.step()
optimizer.zero_grad()
global_step += 1
# Save a trained model
if (args.local_rank == -1 or torch.distributed.get_rank() == 0):
logger.info(
"** ** * Saving fine-tuned model and optimizer ** ** * ")
model_to_save = model.module if hasattr(
model, 'module') else model # Only save the model it-self
output_model_file = os.path.join(
args.output_dir, "model.{0}.bin".format(i_epoch))
torch.save(model_to_save.state_dict(), output_model_file)
output_optim_file = os.path.join(
args.output_dir, "optim.{0}.bin".format(i_epoch))
torch.save(optimizer.state_dict(), output_optim_file)
logger.info("***** CUDA.empty_cache() *****")
torch.cuda.empty_cache()
def main():
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--data_dir",
default="../../../dataset/final_data/commongen",
type=str,
help=
"The input data dir. Should contain the .tsv files (or other data files) for the task."
)
parser.add_argument("--src_file",
default=None,
type=str,
help="The input data file name.")
parser.add_argument("--tgt_file",
default=None,
type=str,
help="The output data file name.")
parser.add_argument(
"--bart_model",
default="facebook/bart-large",
type=str,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese."
)
parser.add_argument("--config_path",
default=None,
type=str,
help="Bert config file path.")
parser.add_argument(
"--output_dir",
default="../../../output/train_kgbart",
type=str,
help=
"The output directory where the model predictions and checkpoints will be written."
)
parser.add_argument(
"--log_dir",
default="../../../log/train_kgbart",
type=str,
help="The output directory where the log will be written.")
parser.add_argument("--model_recover_path",
default=None,
type=str,
help="The file of fine-tuned pretraining model.")
parser.add_argument("--optim_recover_path",
default=None,
type=str,
help="The file of pretraining optimizer.")
# Other parameters
parser.add_argument(
"--max_seq_length",
default=64,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--do_train",
default=True,
type=bool,
help="Whether to run training.")
parser.add_argument("--do_eval",
default=True,
type=bool,
help="Whether to run eval on the dev set.")
parser.add_argument(
"--do_lower_case",
default=False,
type=bool,
help="Set this flag if you are using an uncased model.")
parser.add_argument("--train_batch_size",
default=48,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
default=2,
type=int,
help="Total batch size for eval.")
parser.add_argument("--learning_rate",
default=1e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--label_smoothing",
default=0.1,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--weight_decay",
default=0.01,
type=float,
help="The weight decay rate for Adam.")
parser.add_argument("--finetune_decay",
action='store_true',
help="Weight decay to the original weights.")
parser.add_argument("--num_train_epochs",
default=10.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument(
"--warmup_proportion",
default=0.1,
type=float,
help=
"Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
parser.add_argument("--hidden_dropout_prob",
default=0.1,
type=float,
help="Dropout rate for hidden states.")
parser.add_argument("--attention_probs_dropout_prob",
default=0.1,
type=float,
help="Dropout rate for attention probabilities.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument(
'--gradient_accumulation_steps',
type=int,
default=6,
help=
"Number of updates steps to accumulate before performing a backward/update pass."
)
parser.add_argument(
'--fp16',
default=True,
type=bool,
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument(
'--fp32_embedding',
action='store_true',
help=
"Whether to use 32-bit float precision instead of 16-bit for embeddings"
)
parser.add_argument(
'--loss_scale',
type=float,
default=0,
help=
"Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
"0 (default value): dynamic loss scaling.\n"
"Positive power of 2: static loss scaling value.\n")
parser.add_argument('--amp',
default=True,
type=bool,
help="Whether to use amp for fp16")
parser.add_argument(
'--from_scratch',
action='store_true',
help=
"Initialize parameters with random values (i.e., training from scratch)."
)
parser.add_argument('--new_segment_ids',
action='store_true',
help="Use new segment ids for bi-uni-directional LM.")
parser.add_argument('--new_pos_ids',
action='store_true',
help="Use new position ids for LMs.")
parser.add_argument('--tokenized_input',
action='store_true',
help="Whether the input is tokenized.")
parser.add_argument('--max_len_a',
type=int,
default=64,
help="Truncate_config: maximum length of segment A.")
parser.add_argument('--max_len_b',
type=int,
default=64,
help="Truncate_config: maximum length of segment B.")
parser.add_argument(
'--trunc_seg',
default='',
help="Truncate_config: first truncate segment A/B (option: a, b).")
parser.add_argument(
'--always_truncate_tail',
default=True,
type=bool,
help="Truncate_config: Whether we should always truncate tail.")
parser.add_argument(
"--mask_prob",
default=0.70,
type=float,
help=
"Number of prediction is sometimes less than max_pred when sequence is short."
)
parser.add_argument(
"--mask_prob_eos",
default=0,
type=float,
help=
"Number of prediction is sometimes less than max_pred when sequence is short."
)
parser.add_argument('--max_pred',
type=int,
default=30,
help="Max tokens of prediction.")
parser.add_argument("--num_workers",
default=5,
type=int,
help="Number of workers for the data loader.")
parser.add_argument('--mask_source_words',
action='store_true',
help="Whether to mask source words for training")
parser.add_argument('--skipgram_prb',
type=float,
default=0.0,
help='prob of ngram mask')
parser.add_argument('--skipgram_size',
type=int,
default=1,
help='the max size of ngram mask')
parser.add_argument('--mask_whole_word',
action='store_true',
help="Whether masking a whole word.")
parser.add_argument('--do_l2r_training',
action='store_true',
help="Whether to do left to right training")
parser.add_argument('--pretraining_KG',
action='store_true',
help="Whether to pretraining KG. ")
parser.add_argument('--train_pretraining_num',
type=int,
default=20,
help="The number of sample training pretraining KG. ")
parser.add_argument('--val_pretraining_num',
type=int,
default=20,
help="The number of sample validing pretraining KG.")
parser.add_argument('--max_position_embeddings',
type=int,
default=64,
help="max position embeddings")
parser.add_argument('--relax_projection',
action='store_true',
help="Use different projection layers for tasks.")
parser.add_argument('--ffn_type',
default=0,
type=int,
help="0: default mlp; 1: W((Wx+b) elem_prod x);")
parser.add_argument('--num_qkv',
default=0,
type=int,
help="Number of different <Q,K,V>.")
parser.add_argument('--seg_emb',
action='store_true',
help="Using segment embedding for self-attention.")
parser.add_argument(
'--s2s_special_token',
default=False,
type=bool,
help="New special tokens ([S2S_SEP]/[S2S_CLS]) of S2S.")
parser.add_argument('--s2s_add_segment',
default=False,
type=bool,
help="Additional segmental for the encoder of S2S.")
parser.add_argument(
'--s2s_share_segment',
default=False,
type=bool,
help=
"Sharing segment embeddings for the encoder of S2S (used with --s2s_add_segment)."
)
parser.add_argument('--pos_shift',
action='store_true',
help="Using position shift for fine-tuning.")
parser.add_argument("--adam_epsilon",
default=1e-8,
type=float,
help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm",
default=1.0,
type=float,
help="Max gradient norm.")
parser.add_argument(
"--fp16_opt_level",
type=str,
default="O1",
help=
"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html",
)
parser.add_argument("--warmup_steps",
default=0,
type=int,
help="Linear warmup over warmup_steps.")
parser.add_argument("--keep_last_epochs",
default=5,
type=int,
help="Keep the last few epochs.")
args = parser.parse_args()
# assert Path(args.model_recover_path).exists(
# ), "--model_recover_path doesn't exist"
args.output_dir = args.output_dir.replace('[PT_OUTPUT_DIR]',
os.getenv('PT_OUTPUT_DIR', ''))
args.log_dir = args.log_dir.replace('[PT_OUTPUT_DIR]',
os.getenv('PT_OUTPUT_DIR', ''))
os.makedirs(args.output_dir, exist_ok=True)
os.makedirs(args.log_dir, exist_ok=True)
json.dump(args.__dict__,
open(os.path.join(args.output_dir, 'opt.json'), 'w'),
sort_keys=True,
indent=2)
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
dist.init_process_group(backend='nccl')
logger.info(
"device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".
format(device, n_gpu, bool(args.local_rank != -1), args.fp16))
if args.gradient_accumulation_steps < 1:
raise ValueError(
"Invalid gradient_accumulation_steps parameter: {}, should be >= 1"
.format(args.gradient_accumulation_steps))
args.train_batch_size = int(args.train_batch_size /
args.gradient_accumulation_steps)
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if not args.do_train and not args.do_eval:
raise ValueError(
"At least one of `do_train` or `do_eval` must be True.")
if args.local_rank not in (-1, 0):
# Make sure only the first process in distributed training will download model & vocab
dist.barrier()
tokenizer = BartTokenizer.from_pretrained(args.bart_model,
do_lower_case=args.do_lower_case)
# if args.max_position_embeddings:
# tokenizer.max_len = args.max_position_embeddings
data_tokenizer = tokenizer # WhitespaceTokenizer() if args.tokenized_input else
if args.local_rank == 0:
dist.barrier()
bi_uni_pipeline = [
seq2seq_loader.Preprocess4Pretrain(
list(tokenizer.encoder.keys()),
tokenizer.convert_tokens_to_ids,
args.max_seq_length,
new_segment_ids=args.new_segment_ids,
truncate_config={
'max_len_a': args.max_len_a,
'max_len_b': args.max_len_b,
'trunc_seg': args.trunc_seg,
'always_truncate_tail': args.always_truncate_tail
},
mode="s2s",
pretraining_KG=args.pretraining_KG,
num_qkv=args.num_qkv,
s2s_special_token=args.s2s_special_token,
s2s_add_segment=args.s2s_add_segment,
s2s_share_segment=args.s2s_share_segment,
pos_shift=args.pos_shift)
]
file_oracle = None
entity_id = os.path.join(
args.data_dir, args.tgt_file
if args.tgt_file else 'CommonGen_KG/commongen_entity2id.txt')
entity_embedding_path = os.path.join(
args.data_dir, args.tgt_file
if args.tgt_file else 'CommonGen_KG/commongen_ent_embeddings')
relation_id = os.path.join(
args.data_dir, args.tgt_file
if args.tgt_file else 'CommonGen_KG/commongen_relation2id.txt')
relation_embedding_path = os.path.join(
args.data_dir, args.tgt_file
if args.tgt_file else 'CommonGen_KG/commongen_rel_embeddings')
entity_embedding = np.array(pickle.load(open(entity_embedding_path, "rb")))
entity_embedding = np.array(
list(np.zeros((4, 1024))) + list(entity_embedding))
relation_embedding = np.array(
pickle.load(open(relation_embedding_path, "rb")))
if args.do_train:
print("Loading Train Dataset", args.data_dir)
if args.pretraining_KG:
file_oracle = os.path.join(args.data_dir,
'CommonGen_KG/commongen_entity2id.txt')
fn_src = os.path.join(
args.data_dir,
args.src_file if args.src_file else 'commongen.train.src_new.txt')
fn_tgt = os.path.join(
args.data_dir,
args.tgt_file if args.tgt_file else 'commongen.train.tgt.txt')
fn_onehop = os.path.join(
args.data_dir,
args.tgt_file if args.tgt_file else 'commongen.train.onehop_5.txt')
train_dataset = seq2seq_loader.Seq2SeqDataset(
fn_src,
fn_tgt,
entity_id,
relation_id,
fn_onehop,
args.train_batch_size,
data_tokenizer,
args.max_seq_length,
pretraining_KG=file_oracle,
pretraining_num=args.train_pretraining_num,
bi_uni_pipeline=bi_uni_pipeline)
if args.local_rank == -1:
train_sampler = RandomSampler(train_dataset, replacement=False)
_batch_size = args.train_batch_size
else:
train_sampler = DistributedSampler(train_dataset)
_batch_size = args.train_batch_size // dist.get_world_size()
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
batch_size=_batch_size,
sampler=train_sampler,
num_workers=args.num_workers,
collate_fn=seq2seq_loader.batch_list_to_batch_tensors,
pin_memory=False)
if args.do_eval:
print("Loading Dev Dataset", args.data_dir)
if args.pretraining_KG:
file_oracle = os.path.join(args.data_dir,
'CommonGen_KG/commongen_entity2id.txt')
fn_src = os.path.join(
args.data_dir,
args.src_file if args.src_file else 'commongen.dev.src_new.txt')
fn_tgt = os.path.join(
args.data_dir,
args.tgt_file if args.tgt_file else 'commongen.dev.tgt.txt')
fn_onehop = os.path.join(
args.data_dir,
args.tgt_file if args.tgt_file else 'commongen.dev.onehop_5.txt')
dev_dataset = seq2seq_loader.Seq2SeqDataset(
fn_src,
fn_tgt,
entity_id,
relation_id,
fn_onehop,
args.eval_batch_size,
data_tokenizer,
args.max_seq_length,
pretraining_KG=file_oracle,
pretraining_num=args.val_pretraining_num,
bi_uni_pipeline=bi_uni_pipeline)
if args.local_rank == -1:
dev_sampler = RandomSampler(dev_dataset, replacement=False)
_batch_size = args.eval_batch_size
else:
dev_sampler = DistributedSampler(dev_dataset)
_batch_size = args.eval_batch_size // dist.get_world_size()
dev_dataloader = torch.utils.data.DataLoader(
dev_dataset,
batch_size=_batch_size,
sampler=dev_sampler,
num_workers=args.num_workers,
collate_fn=seq2seq_loader.batch_list_to_batch_tensors,
pin_memory=False)
# note: args.train_batch_size has been changed to (/= args.gradient_accumulation_steps)
# t_total = int(math.ceil(len(train_dataset.ex_list) / args.train_batch_size)
t_total = int(
len(train_dataloader) * args.num_train_epochs /
args.gradient_accumulation_steps)
# Prepare model
recover_step = _get_max_epoch_model(args.output_dir)
cls_num_labels = 2
type_vocab_size = 6 + \
(1 if args.s2s_add_segment else 0) if args.new_segment_ids else 2
num_sentlvl_labels = 2 if args.pretraining_KG else 0
relax_projection = 4 if args.relax_projection else 0
if args.local_rank not in (-1, 0):
# Make sure only the first process in distributed training will download model & vocab
dist.barrier()
if (recover_step is None) and (args.model_recover_path is None):
# if _state_dict == {}, the parameters are randomly initialized
# if _state_dict == None, the parameters are initialized with bert-init
_state_dict = {} if args.from_scratch else None
model = KGBartForConditionalGeneration.from_pretrained(
args.bart_model,
entity_weight=entity_embedding,
relation_weight=relation_embedding)
global_step = 0
else:
if recover_step:
logger.info("***** Recover model: %d *****", recover_step)
model_recover = torch.load(os.path.join(
args.output_dir, "model.{0}.bin".format(recover_step)),
map_location='cpu')
# recover_step == number of epochs
global_step = math.floor(recover_step * t_total /
args.num_train_epochs)
elif args.model_recover_path:
logger.info("***** Recover model: %s *****",
args.model_recover_path)
model_recover = torch.load(args.model_recover_path,
map_location='cpu')
global_step = 0
model = KGBartForConditionalGeneration.from_pretrained(
args.bart_model,
state_dict=model_recover,
entity_weight=entity_embedding,
relation_weight=relation_embedding)
if args.local_rank == 0:
dist.barrier()
model.to(device)
if args.local_rank != -1:
try:
from torch.nn.parallel import DistributedDataParallel as DDP
except ImportError:
raise ImportError("DistributedDataParallel")
model = DDP(model,
device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True)
elif n_gpu > 1:
# model = torch.nn.DataParallel(model)
model = DataParallelImbalance(model)
# Prepare optimizer
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params':
[p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
optimizer = AdamW(optimizer_grouped_parameters,
lr=args.learning_rate,
eps=args.adam_epsilon)
scheduler = get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=args.warmup_steps,
num_training_steps=t_total)
if recover_step:
logger.info("***** Recover optimizer: %d *****", recover_step)
optim_recover = torch.load(os.path.join(
args.output_dir, "optim.{0}.bin".format(recover_step)),
map_location='cpu')
if hasattr(optim_recover, 'state_dict'):
optim_recover = optim_recover.state_dict()
optimizer.load_state_dict(optim_recover)
schedule_recover = torch.load(os.path.join(
args.output_dir, "sched.{0}.bin".format(recover_step)),
map_location='cpu')
scheduler.load_state_dict(schedule_recover)
if args.loss_scale == 0:
logger.info("***** Recover optimizer: dynamic_loss_scale *****")
optimizer.dynamic_loss_scale = True
if args.fp16:
try:
from apex import amp
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
)
model, optimizer = amp.initialize(model,
optimizer,
opt_level=args.fp16_opt_level)
logger.info("***** CUDA.empty_cache() *****")
torch.cuda.empty_cache()
best_dev_loss = 1000
output_eval_file = os.path.join(args.log_dir, "eval_results.txt")
writer = open(output_eval_file, "w")
def checkpoint_paths(path, pattern=r"model(\d+)\.pt"):
"""Retrieves all checkpoints found in `path` directory.
Checkpoints are identified by matching filename to the specified pattern. If
the pattern contains groups, the result will be sorted by the first group in
descending order.
"""
pt_regexp = re.compile(pattern)
files = os.listdir(path)
entries = []
for i, f in enumerate(files):
m = pt_regexp.fullmatch(f)
if m is not None:
idx = float(m.group(1)) if len(m.groups()) > 0 else int(
f.split(".")[1])
entries.append((idx, m.group(0)))
return [
os.path.join(path, x[1]) for x in sorted(entries, reverse=True)
]
if args.do_train:
logger.info("***** Running training *****")
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", t_total)
if recover_step:
start_epoch = recover_step + 1
else:
start_epoch = 1
for i_epoch in trange(start_epoch,
int(args.num_train_epochs) + 1,
desc="Epoch",
disable=args.local_rank not in (-1, 0)):
model.train()
if args.local_rank != -1:
train_sampler.set_epoch(i_epoch)
# iter_bar = tqdm(BackgroundGenerator(train_dataloader), desc='Iter (loss=X.XXX)',
# disable=args.local_rank not in (-1, 0))
for step, batch in enumerate(
tqdm(train_dataloader,
desc="Training",
position=0,
leave=True)):
batch = [
t.to(device) if t is not None else None for t in batch
]
if args.pretraining_KG:
input_ids, input_entity_ids, subword_mask, word_mask, word_subword, decoder_input_ids, decoder_attention_mask, labels = batch
else:
input_ids, input_entity_ids, subword_mask, word_mask, word_subword, decoder_input_ids, decoder_attention_mask, labels = batch
loss_output = model(
input_ids,
input_entity_ids=input_entity_ids,
attention_mask=subword_mask,
word_mask=word_mask,
word_subword=word_subword,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
labels=labels,
label_smoothing=False)
masked_lm_loss = loss_output.loss
if n_gpu > 1: # mean() to average on multi-gpu.
# loss = loss.mean()
masked_lm_loss = masked_lm_loss.mean()
loss = masked_lm_loss
# logging for each step (i.e., before normalization by args.gradient_accumulation_steps)
# iter_bar.set_description('Iter %d (loss=%5.3f)' % (i_epoch, loss.item()))
if step % 1000 == 0:
print('Iter %d (Gen_loss=%5.3f)' % (i_epoch, loss.item()))
# ensure that accumlated gradients are normalized
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
if (step + 1) % args.gradient_accumulation_steps == 0:
if args.fp16:
torch.nn.utils.clip_grad_norm_(
amp.master_params(optimizer), args.max_grad_norm)
else:
torch.nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
optimizer.step()
scheduler.step() # Update learning rate schedule
model.zero_grad()
global_step += 1
logger.info("***** CUDA.empty_cache() *****")
torch.cuda.empty_cache()
if args.do_eval and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
model.eval()
cur_dev_loss = []
with torch.no_grad():
for step, batch in enumerate(
tqdm(dev_dataloader,
desc="Evaluating",
position=0,
leave=True)):
batch = [
t.to(device) if t is not None else None
for t in batch
]
if args.pretraining_KG:
input_ids, input_entity_ids, subword_mask, word_mask, word_subword, decoder_input_ids, decoder_attention_mask, labels = batch
else:
input_ids, input_entity_ids, subword_mask, word_mask, word_subword, decoder_input_ids, decoder_attention_mask, labels = batch
loss_output = model(
input_ids,
input_entity_ids=input_entity_ids,
attention_mask=subword_mask,
word_mask=word_mask,
word_subword=word_subword,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
labels=labels,
label_smoothing=False)
masked_lm_loss = loss_output.loss
if n_gpu > 1: # mean() to average on multi-gpu.
# loss = loss.mean()
masked_lm_loss = masked_lm_loss.mean()
# next_sentence_loss = next_sentence_loss.mean()
loss = masked_lm_loss
cur_dev_loss.append(float(loss.item()))
# logging for each step (i.e., before normalization by args.gradient_accumulation_steps)
dev_loss = sum(cur_dev_loss) / float(len(cur_dev_loss))
print("the epoch {} DEV loss is {}".format(
i_epoch, dev_loss))
if best_dev_loss > dev_loss:
best_dev_loss = dev_loss
model_to_save = model.module if hasattr(
model,
'module') else model # Only save the model it-self
os.makedirs(args.output_dir + "/best_model",
exist_ok=True)
output_model_file = os.path.join(
args.output_dir, "best_model/model.best.bin")
# output_optim_file = os.path.join(
# args.output_dir, "best_model/optim.best.bin")
# output_schedule_file = os.path.join(
# args.output_dir, "best_model/sched.best.bin")
torch.save(model_to_save.state_dict(),
output_model_file)
# torch.save(optimizer.state_dict(), output_optim_file)
# torch.save(scheduler.state_dict(), output_schedule_file)
logger.info(
"** ** * Saving fine-tuned model and optimizer ** ** * "
)
model_to_save = model.module if hasattr(
model,
'module') else model # Only save the model it-self
output_model_file = os.path.join(
args.output_dir, "model.{0}.bin".format(i_epoch))
output_optim_file = os.path.join(
args.output_dir, "optim.{0}.bin".format(i_epoch))
output_schedule_file = os.path.join(
args.output_dir, "sched.{0}.bin".format(i_epoch))
torch.save(model_to_save.state_dict(), output_model_file)
torch.save(optimizer.state_dict(), output_optim_file)
torch.save(scheduler.state_dict(), output_schedule_file)
writer.write("epoch " + str(i_epoch) + "\n")
writer.write("the current eval accuracy is: " +
str(dev_loss) + "\n")
logger.info("***** CUDA.empty_cache() *****")
torch.cuda.empty_cache()
if args.keep_last_epochs > 0:
# remove old epoch checkpoints; checkpoints are sorted in descending order
checkpoints = checkpoint_paths(
args.output_dir, pattern=r"model.\d+.bin")
for old_chk in checkpoints[args.keep_last_epochs:]:
if os.path.lexists(old_chk):
os.remove(old_chk)
checkpoints = checkpoint_paths(
args.output_dir, pattern=r"optim.\d+.bin")
for old_chk in checkpoints[args.keep_last_epochs:]:
if os.path.lexists(old_chk):
os.remove(old_chk)
checkpoints = checkpoint_paths(
args.output_dir, pattern=r"sched.\d+.bin")
for old_chk in checkpoints[args.keep_last_epochs:]:
if os.path.lexists(old_chk):
os.remove(old_chk)
logger.info("***** CUDA.empty_cache() *****")
torch.cuda.empty_cache()
def main():
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--data_dir",
default=None,
type=str,
required=True,
help=
"The input data dir. Should contain the .tsv files (or other data files) for the task."
)
parser.add_argument("--src_file",
default=None,
type=str,
help="The input data file name.")
parser.add_argument("--topic_model_recover_path",
default=None,
type=str,
help="The file of fine-tuned pretraining topic model.")
parser.add_argument("--topic_model_dict_path",
default=None,
type=str,
help="The file of fine-tuned pretraining topic model.")
parser.add_argument("--tgt_file",
default=None,
type=str,
help="The output data file name.")
parser.add_argument(
"--bert_model",
default=None,
type=str,
required=True,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese."
)
parser.add_argument("--config_path",
default=None,
type=str,
help="Bert config file path.")
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help=
"The output directory where the model predictions and checkpoints will be written."
)
parser.add_argument(
"--log_dir",
default='',
type=str,
required=True,
help="The output directory where the log will be written.")
parser.add_argument("--model_recover_path",
default=None,
type=str,
required=True,
help="The file of fine-tuned pretraining model.")
parser.add_argument("--optim_recover_path",
default=None,
type=str,
help="The file of pretraining optimizer.")
parser.add_argument('--topic_mode',
default=1,
type=float,
help="1:idea1 1.1:idea1_wo_theta 2:idea2 ")
parser.add_argument('--topic_model',
default=False,
type=bool,
help="if only use topic model")
# Other parameters
parser.add_argument(
"--max_seq_length",
default=192,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument(
"--do_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument(
"--train_batch_size",
default=32,
type=int,
help="Total batch size for training.") #batch_size = batch_size/n_gpus
parser.add_argument("--eval_batch_size",
default=16,
type=int,
help="Total batch size for eval.")
parser.add_argument("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--label_smoothing",
default=0,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--weight_decay",
default=0.01,
type=float,
help="The weight decay rate for Adam.")
parser.add_argument("--finetune_decay",
action='store_true',
help="Weight decay to the original weights.")
parser.add_argument("--num_train_epochs",
default=30,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument(
"--warmup_proportion",
default=0.1,
type=float,
help=
"Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
parser.add_argument("--hidden_dropout_prob",
default=0.1,
type=float,
help="Dropout rate for hidden states.")
parser.add_argument("--attention_probs_dropout_prob",
default=0.1,
type=float,
help="Dropout rate for attention probabilities.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument(
'--gradient_accumulation_steps',
type=int,
default=1,
help=
"Number of updates steps to accumulate before performing a backward/update pass."
)
parser.add_argument(
'--fp16',
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument(
'--fp32_embedding',
action='store_true',
help=
"Whether to use 32-bit float precision instead of 16-bit for embeddings"
)
parser.add_argument(
'--loss_scale',
type=float,
default=0,
help=
"Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
"0 (default value): dynamic loss scaling.\n"
"Positive power of 2: static loss scaling value.\n")
parser.add_argument('--amp',
action='store_true',
help="Whether to use amp for fp16")
parser.add_argument(
'--from_scratch',
action='store_true',
help=
"Initialize parameters with random values (i.e., training from scratch)."
)
parser.add_argument('--new_segment_ids',
action='store_true',
help="Use new segment ids for bi-uni-directional LM.")
parser.add_argument('--new_pos_ids',
action='store_true',
help="Use new position ids for LMs.")
parser.add_argument('--tokenized_input',
action='store_true',
help="Whether the input is tokenized.")
parser.add_argument('--max_len_a',
type=int,
default=0,
help="Truncate_config: maximum length of segment A.")
parser.add_argument('--max_len_b',
type=int,
default=0,
help="Truncate_config: maximum length of segment B.")
parser.add_argument(
'--trunc_seg',
default='',
help="Truncate_config: first truncate segment A/B (option: a, b).")
parser.add_argument(
'--always_truncate_tail',
action='store_true',
help="Truncate_config: Whether we should always truncate tail.")
parser.add_argument(
"--mask_prob",
default=0.15,
type=float,
help=
"Number of prediction is sometimes less than max_pred when sequence is short."
)
parser.add_argument(
"--mask_prob_eos",
default=0,
type=float,
help=
"Number of prediction is sometimes less than max_pred when sequence is short."
)
parser.add_argument('--max_pred',
type=int,
default=20,
help="Max tokens of prediction.")
parser.add_argument("--num_workers",
default=0,
type=int,
help="Number of workers for the data loader.")
parser.add_argument('--mask_source_words',
action='store_true',
help="Whether to mask source words for training")
parser.add_argument('--skipgram_prb',
type=float,
default=0.0,
help='prob of ngram mask')
parser.add_argument('--skipgram_size',
type=int,
default=1,
help='the max size of ngram mask')
parser.add_argument('--mask_whole_word',
action='store_true',
help="Whether masking a whole word.")
parser.add_argument('--do_l2r_training',
action='store_true',
help="Whether to do left to right training")
parser.add_argument(
'--has_sentence_oracle',
action='store_true',
help="Whether to have sentence level oracle for training. "
"Only useful for summary generation")
parser.add_argument('--max_position_embeddings',
type=int,
default=None,
help="max position embeddings")
parser.add_argument('--relax_projection',
action='store_true',
help="Use different projection layers for tasks.")
parser.add_argument('--ffn_type',
default=0,
type=int,
help="0: default mlp; 1: W((Wx+b) elem_prod x);")
parser.add_argument('--num_qkv',
default=0,
type=int,
help="Number of different <Q,K,V>.")
parser.add_argument('--seg_emb',
action='store_true',
help="Using segment embedding for self-attention.")
parser.add_argument(
'--s2s_special_token',
action='store_true',
help="New special tokens ([S2S_SEP]/[S2S_CLS]) of S2S.")
parser.add_argument('--s2s_add_segment',
action='store_true',
help="Additional segmental for the encoder of S2S.")
parser.add_argument(
'--s2s_share_segment',
action='store_true',
help=
"Sharing segment embeddings for the encoder of S2S (used with --s2s_add_segment)."
)
parser.add_argument('--pos_shift',
action='store_true',
help="Using position shift for fine-tuning.")
args = parser.parse_args()
assert Path(
args.model_recover_path).exists(), "--model_recover_path doesn't exist"
args.output_dir = args.output_dir.replace('[PT_OUTPUT_DIR]',
os.getenv('PT_OUTPUT_DIR', ''))
args.log_dir = args.log_dir.replace('[PT_OUTPUT_DIR]',
os.getenv('PT_OUTPUT_DIR', ''))
os.makedirs(args.output_dir, exist_ok=True)
os.makedirs(args.log_dir, exist_ok=True)
json.dump(args.__dict__,
open(os.path.join(args.output_dir, 'opt.json'), 'w'),
sort_keys=True,
indent=2)
print("args.local_rank", args.local_rank)
print("args.no_cuda", args.no_cuda)
if args.local_rank == -1 or args.no_cuda: #-1 False
device = torch.device("cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu") #device = cuda
n_gpu = torch.cuda.device_count()
print("n_gpu_1", n_gpu)
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
dist.init_process_group(backend='nccl')
print("n_gpu_1", n_gpu)
logger.info(
"device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".
format(device, n_gpu, bool(args.local_rank != -1), args.fp16))
if args.gradient_accumulation_steps < 1:
raise ValueError(
"Invalid gradient_accumulation_steps parameter: {}, should be >= 1"
.format(args.gradient_accumulation_steps))
args.train_batch_size = int(args.train_batch_size /
args.gradient_accumulation_steps)
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if not args.do_train and not args.do_eval:
raise ValueError(
"At least one of `do_train` or `do_eval` must be True.")
if args.local_rank not in (-1, 0):
# Make sure only the first process in distributed training will download model & vocab
dist.barrier()
tokenizer = BertTokenizer.from_pretrained(args.bert_model,
do_lower_case=args.do_lower_case)
if args.max_position_embeddings:
tokenizer.max_len = args.max_position_embeddings
data_tokenizer = WhitespaceTokenizer(
) if args.tokenized_input else tokenizer
if args.local_rank == 0:
dist.barrier()
if args.do_train:
bi_uni_pipeline = [
seq2seq_loader.Preprocess4Seq2seq(
args.max_pred,
args.mask_prob,
list(tokenizer.vocab.keys()),
tokenizer.convert_tokens_to_ids,
args.max_seq_length,
new_segment_ids=args.new_segment_ids,
truncate_config={
'max_len_a': args.max_len_a,
'max_len_b': args.max_len_b,
'trunc_seg': args.trunc_seg,
'always_truncate_tail': args.always_truncate_tail
},
mask_source_words=args.mask_source_words,
skipgram_prb=args.skipgram_prb,
skipgram_size=args.skipgram_size,
mask_whole_word=args.mask_whole_word,
mode="s2s",
has_oracle=args.has_sentence_oracle,
num_qkv=args.num_qkv,
s2s_special_token=args.s2s_special_token,
s2s_add_segment=args.s2s_add_segment,
s2s_share_segment=args.s2s_share_segment,
pos_shift=args.pos_shift)
]
file_oracle = None
if args.has_sentence_oracle:
file_oracle = os.path.join(args.data_dir, 'train.oracle')
fn_src = os.path.join(args.data_dir,
args.src_file if args.src_file else 'train.src')
fn_tgt = os.path.join(args.data_dir,
args.tgt_file if args.tgt_file else 'train.tgt')
train_dataset = seq2seq_loader.Seq2SeqDataset(
fn_src,
fn_tgt,
args.data_dir,
args.topic_model_dict_path,
args.train_batch_size,
data_tokenizer,
args.max_seq_length,
file_oracle=file_oracle,
bi_uni_pipeline=bi_uni_pipeline)
if args.local_rank == -1:
train_sampler = RandomSampler(train_dataset, replacement=False)
_batch_size = args.train_batch_size
else:
train_sampler = DistributedSampler(train_dataset)
_batch_size = args.train_batch_size // dist.get_world_size()
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
batch_size=_batch_size,
sampler=train_sampler,
num_workers=args.num_workers,
collate_fn=seq2seq_loader.batch_list_to_batch_tensors,
pin_memory=False)
# note: args.train_batch_size has been changed to (/= args.gradient_accumulation_steps)
# t_total = int(math.ceil(len(train_dataset.ex_list) / args.train_batch_size)
t_total = int(
len(train_dataloader) * args.num_train_epochs /
args.gradient_accumulation_steps)
amp_handle = None
if args.fp16 and args.amp:
from apex import amp
amp_handle = amp.init(enable_caching=True)
logger.info("enable fp16 with amp")
# Prepare model
recover_step = _get_max_epoch_model(args.output_dir)
cls_num_labels = 2
type_vocab_size = 6 + \
(1 if args.s2s_add_segment else 0) if args.new_segment_ids else 2 ### type_vocab_size=6
num_sentlvl_labels = 2 if args.has_sentence_oracle else 0
relax_projection = 4 if args.relax_projection else 0
if args.local_rank not in (-1, 0):
# Make sure only the first process in distributed training will download model & vocab
dist.barrier()
if (recover_step is None) and (args.model_recover_path is None):
# if _state_dict == {}, the parameters are randomly initialized
# if _state_dict == None, the parameters are initialized with bert-init
_state_dict = {} if args.from_scratch else None
unilm = BertForPreTrainingLossMask.from_pretrained(
args.bert_model,
state_dict=_state_dict,
num_labels=cls_num_labels,
num_rel=0,
type_vocab_size=type_vocab_size,
config_path=args.config_path,
task_idx=3,
num_sentlvl_labels=num_sentlvl_labels,
max_position_embeddings=args.max_position_embeddings,
label_smoothing=args.label_smoothing,
fp32_embedding=args.fp32_embedding,
relax_projection=relax_projection,
new_pos_ids=args.new_pos_ids,
ffn_type=args.ffn_type,
hidden_dropout_prob=args.hidden_dropout_prob,
attention_probs_dropout_prob=args.attention_probs_dropout_prob,
num_qkv=args.num_qkv,
seg_emb=args.seg_emb)
global_step = 0
else:
if recover_step:
logger.info("***** Recover model: %d *****", recover_step)
model_recover = torch.load(os.path.join(
args.output_dir, "model.{0}.bin".format(recover_step)),
map_location='cpu')
# recover_step == number of epochs
global_step = math.floor(recover_step * t_total /
args.num_train_epochs)
elif args.model_recover_path: # here is the entrance
logger.info("***** Recover model: %s *****",
args.model_recover_path)
model_recover = torch.load(args.model_recover_path,
map_location='cpu')
global_step = 0
unilm = BertForPreTrainingLossMask.from_pretrained(
args.bert_model,
state_dict=model_recover,
num_labels=cls_num_labels,
num_rel=0,
type_vocab_size=type_vocab_size,
config_path=args.config_path,
task_idx=3,
num_sentlvl_labels=num_sentlvl_labels,
max_position_embeddings=args.max_position_embeddings,
label_smoothing=args.label_smoothing,
fp32_embedding=args.fp32_embedding,
relax_projection=relax_projection,
new_pos_ids=args.new_pos_ids,
ffn_type=args.ffn_type,
hidden_dropout_prob=args.hidden_dropout_prob,
attention_probs_dropout_prob=args.attention_probs_dropout_prob,
num_qkv=args.num_qkv,
seg_emb=args.seg_emb)
#1. 模型初始化，入口定义好
gsm = GSM(train_dataset.vocabsize)
gsm_checkpoint = torch.load(args.topic_model_recover_path)
gsm.load_state_dict(gsm_checkpoint["net"])
if args.local_rank == 0:
dist.barrier()
if args.fp16:
unilm.half()
gsm.half()
if args.fp32_embedding:
unilm.bert.embeddings.word_embeddings.float()
unilm.bert.embeddings.position_embeddings.float()
unilm.bert.embeddings.token_type_embeddings.float()
unilm.to(device)
gsm.to(device)
if args.local_rank != -1:
try:
from torch.nn.parallel import DistributedDataParallel as DDP
except ImportError:
raise ImportError("DistributedDataParallel")
unilm = DDP(unilm,
device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True)
elif n_gpu > 1:
# model = torch.nn.DataParallel(model)
unilm = DataParallelImbalance(unilm)
gsm = DataParallelImbalance(gsm)
# Prepare optimizer
total = 0
param_optimizer = list(unilm.named_parameters())
param_optimizer_topic = list(gsm.named_parameters())
for name, parameters in unilm.named_parameters():
if "idea" in name:
if "11" in name and "idea2" in name:
total += np.prod(parameters.size())
# print(name, ':', parameters.size())
else:
total += np.prod(parameters.size())
# print(name, ':', parameters.size())
print("gsm have {} paramerters in total".format(
sum(x.numel() for x in gsm.parameters())))
print("Number of parameter: %.6fM" % (total / 1e6))
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
if not args.topic_model:
optimizer_grouped_parameters = [{
'params': [
p for n, p in param_optimizer
if not any(nd in n for nd in no_decay)
],
'weight_decay':
0.01,
'topic':
False
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0,
'topic':
False
}, {
'params': [p for n, p in param_optimizer_topic],
'weight_decay':
0.0,
'lr':
1e-3,
'topic':
True
}]
else:
optimizer_grouped_parameters = [{
'params': [p for n, p in param_optimizer_topic],
'weight_decay':
0.0,
'lr':
1e-3,
'topic':
True
}]
#一部分是有weight的，一部分是没有weight_dacay的
# print("optimizer_grouped_parameters", optimizer_grouped_parameters)
if args.fp16:
try:
# from apex.optimizers import FP16_Optimizer
from pytorch_pretrained_bert.optimization_fp16 import FP16_Optimizer_State
from apex.optimizers import FusedAdam
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
)
optimizer = FusedAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
bias_correction=False,
max_grad_norm=1.0)
if args.loss_scale == 0:
optimizer = FP16_Optimizer_State(optimizer,
dynamic_loss_scale=True)
else:
optimizer = FP16_Optimizer_State(optimizer,
static_loss_scale=args.loss_scale)
else:
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=t_total)
if recover_step:
logger.info("***** Recover optimizer: %d *****", recover_step)
optim_recover = torch.load(os.path.join(
args.output_dir, "optim.{0}.bin".format(recover_step)),
map_location='cpu')
if hasattr(optim_recover, 'state_dict'):
optim_recover = optim_recover.state_dict()
optimizer.load_state_dict(optim_recover)
if args.loss_scale == 0:
logger.info("***** Recover optimizer: dynamic_loss_scale *****")
optimizer.dynamic_loss_scale = True
logger.info("***** CUDA.empty_cache() *****")
torch.cuda.empty_cache()
if args.do_train:
logger.info("***** Running training *****")
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", t_total)
unilm.train()
gsm.train()
if recover_step:
start_epoch = recover_step + 1
else:
start_epoch = 1
print("000000", args.local_rank, start_epoch,
int(args.num_train_epochs) + 1)
topicloss = []
unilmloss = []
topicloss_lst = []
unilmloss_lst = []
for i_epoch in trange(start_epoch,
int(args.num_train_epochs) + 1,
desc="Epoch",
disable=args.local_rank not in (-1, 0)):
loss_sum = 0.0
ppx_sum = 0.0
word_count = 0.0
doc_count = 0.0
if args.local_rank != -1:
train_sampler.set_epoch(i_epoch)
iter_bar = tqdm(train_dataloader,
desc='Iter (loss=X.XXX)',
disable=args.local_rank not in (-1, 0))
for step, batch in enumerate(iter_bar):
batch = [
t.to(device) if t is not None else None for t in batch
]
if args.has_sentence_oracle: #false
input_ids, segment_ids, input_mask, mask_qkv, lm_label_ids, masked_pos, masked_weights, is_next, task_idx, oracle_pos, oracle_weights, oracle_labels = batch
else: #这里加了bows
input_ids, segment_ids, input_mask, mask_qkv, lm_label_ids, masked_pos, masked_weights, is_next, task_idx, bows = batch
oracle_pos, oracle_weights, oracle_labels = None, None, None
p_x, mus, log_vars, theta, beta, topic_embedding = gsm(bows)
if not args.topic_model:
loss_tuple = unilm(input_ids,
theta,
beta,
topic_embedding,
args.topic_mode,
segment_ids,
input_mask,
lm_label_ids,
is_next,
masked_pos=masked_pos,
masked_weights=masked_weights,
task_idx=task_idx,
masked_pos_2=oracle_pos,
masked_weights_2=oracle_weights,
masked_labels_2=oracle_labels,
mask_qkv=mask_qkv)
masked_lm_loss, next_sentence_loss = loss_tuple
## topic loss
logsoftmax = torch.log(p_x + 1e-10)
rec_loss = -1.0 * torch.sum(
bows * logsoftmax
) #bows*logsoftmax = [batch_size, |V|], 其中torch.sum 把所有的loss全部加起来了，也可以只用加某一维度。
rec_loss_per = -1.0 * torch.sum(bows * logsoftmax, dim=1)
rec_loss_per = rec_loss_per.cpu().detach().numpy()
kl_div = -0.5 * torch.sum(1 + log_vars - mus.pow(2) -
log_vars.exp())
loss_topic = rec_loss + kl_div
if n_gpu > 1: # mean() to average on multi-gpu.
loss_topic = loss_topic.mean()
if not args.topic_model:
masked_lm_loss = masked_lm_loss.mean()
next_sentence_loss = next_sentence_loss.mean()
if not args.topic_model:
loss_unilm = masked_lm_loss + next_sentence_loss
# cal perplexity
word_count_list = []
loss_sum += loss_topic.item()
for bow in bows:
word_num = torch.sum(bow).cpu().numpy()
word_count_list.append(word_num)
word_count += word_num
word_count_np = np.array(word_count_list)
doc_count += len(bows)
ppx_sum += np.sum(np.true_divide(rec_loss_per, word_count_np))
topicloss_lst.append(loss_topic.item() / len(bows))
if not args.topic_model:
unilmloss_lst.append(loss_unilm.item())
#topic_loss end
if not args.topic_model:
loss = loss_unilm + loss_topic
else:
loss = loss_topic
# ensure that accumlated gradients are normalized
if args.gradient_accumulation_steps > 1: # =1
loss = loss / args.gradient_accumulation_steps
if args.fp16:
optimizer.backward(loss)
if amp_handle:
amp_handle._clear_cache()
else:
loss.backward()
if (step + 1) % args.gradient_accumulation_steps == 0:
lr_this_step = args.learning_rate * \
warmup_linear(global_step/t_total,
args.warmup_proportion)
if args.fp16:
# modify learning rate with special warm up BERT uses
for param_group in optimizer.param_groups:
if not param_group['topic']:
param_group['lr'] = lr_this_step
optimizer.step()
optimizer.zero_grad()
global_step += 1
if not args.topic_model:
iter_bar.set_description(
'Iter (loss_unilm=%5.3f),Iter (ppl=%5.3f)' %
(loss_unilm.item(),
np.sum(np.true_divide(rec_loss_per, word_count_np))))
else:
iter_bar.set_description(
'Iter (loss_topic=%5.3f), (ppl=%5.3f)' %
(loss_topic.item(),
np.sum(np.true_divide(rec_loss_per, word_count_np))))
#Save a trained model
ppx_word = np.exp(loss_sum / word_count)
ppx_document = np.exp(ppx_sum / doc_count)
print("********")
print("word_count", word_count)
print("ppx_word", ppx_word)
print("ppx_document", ppx_document)
if (args.local_rank == -1 or torch.distributed.get_rank() == 0):
#save unilm model
logger.info(
"** ** * Saving fine-tuned model and optimizer ** ** * ")
unilm_model_to_save = unilm.module if hasattr(
unilm, 'module') else unilm # Only save the model it-self
output_unilm_model_file = os.path.join(
args.output_dir, "unilm.{0}.bin".format(i_epoch))
torch.save(unilm_model_to_save.state_dict(),
output_unilm_model_file)
#save topic model
logger.info(
"** ** * Saving topic model and optimizer ** ** * ")
topic_model_to_save = gsm.module if hasattr(
gsm, 'module') else gsm # Only save the model it-self
output_topic_model_file = os.path.join(
args.output_dir, "topic.{0}.ckpt".format(i_epoch))
torch.save(topic_model_to_save.state_dict(),
output_topic_model_file)
logger.info("***** CUDA.empty_cache() *****")
torch.cuda.empty_cache()
smth_pts = smooth_curve(topicloss_lst)
# plt.plot(range(len(topicloss_lst)), topicloss_lst)
plt.plot(range(len(smth_pts)), smth_pts)
plt.xlabel('epochs')
plt.title('Topic Model Train Loss')
plt.savefig(args.output_dir + '/topic_loss.png')
plt.cla()
plt.plot(range(len(unilmloss_lst)), unilmloss_lst)
plt.xlabel('epochs')
plt.title('Unilm Train Loss')
plt.savefig(args.output_dir + '/unilm_loss.png')
def main():
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--bert_model", default=None, type=str, required=True,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.")
parser.add_argument("--model_recover_path", default=None, type=str,
help="The file of fine-tuned pretraining model.")
parser.add_argument("--max_seq_length", default=512, type=int,
help="The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
# decoding parameters
parser.add_argument('--fp16', action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument('--amp', action='store_true',
help="Whether to use amp for fp16")
parser.add_argument("--input_file", type=str, help="Input file")
parser.add_argument("--output_file", type=str, help="output file")
parser.add_argument("--split", type=str, default="",
help="Data split (train/val/test).")
parser.add_argument('--tokenized_input', action='store_true',
help="Whether the input is tokenized.")
parser.add_argument('--seed', type=int, default=123,
help="random seed for initialization")
parser.add_argument("--do_lower_case", action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument('--new_segment_ids', action='store_true',
help="Use new segment ids for bi-uni-directional LM.")
parser.add_argument('--batch_size', type=int, default=4,
help="Batch size for decoding.")
parser.add_argument('--beam_size', type=int, default=1,
help="Beam size for searching")
parser.add_argument('--top_k', type=int, default=1,
help="Top k for output")
parser.add_argument('--top_kk', type=int, default=0,
help="Top k sample method for output")
parser.add_argument('--length_penalty', type=float, default=0,
help="Length penalty for beam search")
parser.add_argument('--forbid_duplicate_ngrams', action='store_true')
parser.add_argument('--forbid_ignore_word', type=str, default=None,
help="Forbid the word during forbid_duplicate_ngrams")
parser.add_argument("--min_len", default=None, type=int)
parser.add_argument('--need_score_traces', action='store_true')
parser.add_argument('--ngram_size', type=int, default=3)
parser.add_argument('--mode', default="s2s",
choices=["s2s", "l2r", "both"])
parser.add_argument('--max_tgt_length', type=int, default=128,
help="maximum length of target sequence")
# evaluate parameters
parser.add_argument('--do_predict', action='store_true', help="do_predict")
parser.add_argument("--do_evaluate", action="store_true", help="caculate the scores if have label file")
parser.add_argument("--label_file", type=str, default="")
parser.add_argument("--experiment", type=str, default="full", help="full/title/title-l1/hierachical/title-first/title-first-rouge")
# ranker parameters
parser.add_argument("--ranker_recover_path", type=str, help="ranker model for extract sentence")
parser.add_argument("--ranker_max_len", type=int, default=192, help ="max length of the ranker input")
parser.add_argument("--ranker_batch_size", type=int, default=128)
args = parser.parse_args()
if args.need_score_traces and args.beam_size <= 1:
raise ValueError(
"Score trace is only available for beam search with beam size > 1.")
if args.max_tgt_length >= args.max_seq_length - 2:
raise ValueError("Maximum tgt length exceeds max seq length - 2.")
device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
n_gpu = torch.cuda.device_count()
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
tokenizer = BertTokenizer.from_pretrained(
args.bert_model, do_lower_case=args.do_lower_case)
tokenizer.max_len = args.max_seq_length
pair_num_relation = 0
bi_uni_pipeline = []
if args.mode == "s2s" or args.mode == "both":
bi_uni_pipeline.append(seq2seq_loader.Preprocess4Seq2seqDecoder(list(
tokenizer.vocab.keys()), tokenizer.convert_tokens_to_ids, args.max_seq_length, max_tgt_length=args.max_tgt_length, new_segment_ids=args.new_segment_ids, mode="s2s"))
if args.mode == "l2r" or args.mode == "both":
bi_uni_pipeline.append(seq2seq_loader.Preprocess4Seq2seqDecoder(list(
tokenizer.vocab.keys()), tokenizer.convert_tokens_to_ids, args.max_seq_length, max_tgt_length=args.max_tgt_length, new_segment_ids=args.new_segment_ids, mode="l2r"))
if args.experiment == "segsep":
bi_uni_pipeline = []
bi_uni_pipeline.append(Preprocess4SegSepDecoder(list(
tokenizer.vocab.keys()), tokenizer.convert_tokens_to_ids, args.max_seq_length, max_tgt_length=args.max_tgt_length, new_segment_ids=args.new_segment_ids, mode="s2s"))
amp_handle = None
if args.fp16 and args.amp:
from apex import amp
amp_handle = amp.init(enable_caching=True)
logger.info("enable fp16 with amp")
# Prepare model
cls_num_labels = 2
type_vocab_size = 6 if args.new_segment_ids else 2
if args.experiment == "segsep":
type_vocab_size = 11
mask_word_id, eos_word_ids = tokenizer.convert_tokens_to_ids(
["[MASK]", "[SEP]"])
forbid_ignore_set = None
if args.forbid_ignore_word:
w_list = []
for w in args.forbid_ignore_word.split('|'):
if w.startswith('[') and w.endswith(']'):
w_list.append(w.upper())
else:
w_list.append(w)
forbid_ignore_set = set(tokenizer.convert_tokens_to_ids(w_list))
print(args.model_recover_path)
if args.do_predict:
for model_recover_path in glob.glob(args.model_recover_path.strip()):
logger.info("***** Recover model: %s *****", model_recover_path)
model_recover = torch.load(model_recover_path)
model = BertForSeq2SeqDecoder.from_pretrained(args.bert_model, state_dict=model_recover, num_labels=cls_num_labels, num_rel=pair_num_relation, type_vocab_size=type_vocab_size, task_idx=3, mask_word_id=mask_word_id, search_beam_size=args.beam_size,
length_penalty=args.length_penalty, eos_id=eos_word_ids, forbid_duplicate_ngrams=args.forbid_duplicate_ngrams, forbid_ignore_set=forbid_ignore_set, ngram_size=args.ngram_size, min_len=args.min_len, mode=args.mode, max_position_embeddings=args.max_seq_length)
del model_recover
if args.fp16:
model.half()
model.to(device)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
torch.cuda.empty_cache()
model.eval()
next_i = 0
max_src_length = args.max_seq_length - 2 - args.max_tgt_length
if args.experiment in ["full", "title", "title-l1"]:
input_lines = EvalDataset(args.input_file, args.experiment).proc()
elif args.experiment == "single":
input_lines, map_dict = EvalDataset(args.input_file, args.experiment).proc()
elif args.experiment == "title-first":
input_lines = EvalDataset(args.input_file, args.experiment, tokenizer, args.max_seq_length, args.max_seq_length).proc()
elif args.experiment == "segsep":
input_lines = EvalDataset(args.input_file, args.experiment, tokenizer, args.max_seq_length, args.max_seq_length).proc()
elif args.experiment == "heirachical":
logger.info("***** Recover rank model: %s *****", args.ranker_recover_path)
# extract sentences before load data
# load rank model
rank_model_recover = torch.load(args.ranker_recover_path, map_location="cpu")
global_step = 0
rank_model = BertForSentenceRanker.from_pretrained(args.bert_model, state_dict=rank_model_recover, num_labels=2)
# set model for multi GPUs or multi nodes
if args.fp16:
rank_model.half()
rank_model.to(device)
if n_gpu > 1:
rank_model = DataParallelImbalance(rank_model)
DatasetFunc = ScoreEvalDataset
# Load title + sentence pair
print ("Loading Rank Dataset from ", args.input_file)
data_tokenizer = WhitespaceTokenizer() if args.tokenized_input else tokenizer
max_pred = 16
mask_prob = 0.7
rank_bi_uni_pipeline = [Preprocess4Seq2cls(max_pred, mask_prob, list(tokenizer.vocab.keys()), tokenizer.convert_tokens_to_ids, args.ranker_max_len, new_segment_ids=args.new_segment_ids, truncate_config={'max_len_a': 64, 'max_len_b': 16, 'trunc_seg': 'a', 'always_truncate_tail': True}, mask_source_words=False, skipgram_prb=0.0, skipgram_size=1, mask_whole_word=False, mode="s2s", has_oracle=False, num_qkv=0, s2s_special_token=False, s2s_add_segment=False, s2s_share_segment=False, pos_shift=False, eval=True)]
fn_src = args.input_file
fn_tgt = None
eval_dataset = DatasetFunc(
fn_src, fn_tgt, args.ranker_batch_size, data_tokenizer, args.ranker_max_len, bi_uni_pipeline=rank_bi_uni_pipeline
)
eval_sampler = SequentialSampler(eval_dataset)
_batch_size = args.ranker_batch_size
eval_dataloader = torch.utils.data.DataLoader(eval_dataset, batch_size=_batch_size, sampler=eval_sampler, num_workers=24, collate_fn=seq2seq_loader.batch_list_to_batch_tensors, pin_memory=False)
logger.info("***** CUDA.empty_cache() *****")
torch.cuda.empty_cache()
logger.info("***** Runinning ranker *****")
logger.info(" Batch size = %d", _batch_size)
logger.info(" Num steps = %d", int(len(eval_dataset)/ args.ranker_batch_size))
rank_model.to(device)
rank_model.eval()
iter_bar = tqdm(eval_dataloader, desc = "Iter: ")
num_rank_labels = 2
all_labels = []
for step, batch in enumerate(iter_bar):
batch = [t.to(device) if t is not None else None for t in batch]
input_ids, segment_ids, input_mask, mask_qkv, lm_label_ids, masked_pos, masked_weights, is_next, task_idx = batch
logits = rank_model(input_ids, task_idx=task_idx, mask_qkv=mask_qkv)
labels = torch.argmax(logits.view(-1, num_rank_labels), dim=-1)
all_labels.append(labels)
all_labels_results = []
for label in all_labels:
all_labels_results.extend(label.detach().cpu().numpy())
# collect results
logger.info("**** Collect results ******")
clu2doc_dict, doc2sent_dict, all_titles, all_sents = eval_dataset.get_maps()
all_docs = []
for i, doc in enumerate(doc2sent_dict):
text = all_titles[i]
sent_idx = doc2sent_dict[doc]
for idx in sent_idx:
if all_labels_results[idx] == 1:
text += ". " + all_sents[idx]
all_docs.append(text)
input_lines = []
for clu in tqdm(clu2doc_dict):
doc_idx = clu2doc_dict[clu]
input_line = ""
for idx in doc_idx:
input_line += all_docs[idx]
input_lines.append(input_line)
elif args.experiment == "title-first-rank":
logger.info("***** Recover rank model: %s *****", args.ranker_recover_path)
# extract sentences before load data
# load rank model
rank_model_recover = torch.load(args.ranker_recover_path, map_location="cpu")
global_step = 0
rank_model = BertForSentenceRanker.from_pretrained(args.bert_model, state_dict=rank_model_recover, num_labels=2)
# set model for multi GPUs or multi nodes
if args.fp16:
rank_model.half()
rank_model.to(device)
if n_gpu > 1:
rank_model = DataParallelImbalance(rank_model)
DatasetFunc = EvalRankDataset
# Load title + sentence pair
print ("Loading Rank Dataset from ", args.input_file)
data_tokenizer = WhitespaceTokenizer() if args.tokenized_input else tokenizer
max_pred = 16
mask_prob = 0.7
rank_bi_uni_pipeline = [Preprocess4Seq2cls(max_pred, mask_prob, list(tokenizer.vocab.keys()), tokenizer.convert_tokens_to_ids, args.max_seq_length, new_segment_ids=args.new_segment_ids, truncate_config={'max_len_a': 512, 'max_len_b': 16, 'trunc_seg': 'a', 'always_truncate_tail': True}, mask_source_words=False, skipgram_prb=0.0, skipgram_size=1, mask_whole_word=False, mode="s2s", has_oracle=False, num_qkv=0, s2s_special_token=False, s2s_add_segment=False, s2s_share_segment=False, pos_shift=False, eval=True)]
fn_src = args.input_file
fn_tgt = None
eval_dataset = DatasetFunc(
fn_src, fn_tgt, args.ranker_batch_size, data_tokenizer, args.max_seq_length, bi_uni_pipeline=rank_bi_uni_pipeline
)
eval_sampler = SequentialSampler(eval_dataset)
_batch_size = args.ranker_batch_size
eval_dataloader = torch.utils.data.DataLoader(eval_dataset, batch_size=_batch_size, sampler=eval_sampler, num_workers=24, collate_fn=seq2seq_loader.batch_list_to_batch_tensors, pin_memory=False)
logger.info("***** CUDA.empty_cache() *****")
torch.cuda.empty_cache()
logger.info("***** Runinning ranker *****")
logger.info(" Batch size = %d", _batch_size)
logger.info(" Num steps = %d", int(len(eval_dataset)/ args.ranker_batch_size))
rank_model.to(device)
rank_model.eval()
iter_bar = tqdm(eval_dataloader, desc = "Iter: ")
num_rank_labels = 2
all_labels = []
for step, batch in enumerate(iter_bar):
batch = [t.to(device) if t is not None else None for t in batch]
input_ids, segment_ids, input_mask, mask_qkv, lm_label_ids, masked_pos, masked_weights, is_next, task_idx = batch
# print("input_ids", len(input_ids[0]), "segment_ids", len(segment_ids[0]))
with torch.no_grad():
logits = rank_model(input_ids, task_idx=task_idx, mask_qkv=mask_qkv)
labels = logits.view(-1)
all_labels.append(labels)
all_labels_results = []
for label in all_labels:
all_labels_results.extend(label.detach().cpu().numpy())
print("test label results")
print(all_labels_results[0])
# collect results
logger.info("**** Collect results ******")
clu2sent_dict, all_sents, all_titles= eval_dataset.get_maps()
all_clusters = []
input_lines = []
for i, clu_id in enumerate(clu2sent_dict):
text = all_titles[clu_id]
sent_idx = clu2sent_dict[clu_id]
sents_collect = []
for idx in sent_idx:
sents_collect.append([all_sents[idx], all_labels_results[idx]])
sents_collect_sort = sorted(sents_collect, key=lambda x:x[1])
sents_collect = [x[0] for x in sents_collect_sort]
text_tk = tokenizer.tokenize(text)
j = 0
while j < len(sents_collect) and len(text_tk) + len(tokenizer.tokenize(sents_collect[j])) <= args.max_seq_length:
text += " " + sents_collect[j]
j += 1
input_lines.append(text)
else:
input_lines = []
data_tokenizer = WhitespaceTokenizer() if args.tokenized_input else tokenizer
input_lines = [data_tokenizer.tokenize(
x)[:max_src_length] for x in input_lines]
input_lines = sorted(list(enumerate(input_lines)),
key=lambda x: -len(x[1]))
output_lines = [""] * len(input_lines)
score_trace_list = [None] * len(input_lines)
total_batch = math.ceil(len(input_lines) / args.batch_size)
with tqdm(total=total_batch) as pbar:
while next_i < len(input_lines):
_chunk = input_lines[next_i:next_i + args.batch_size]
buf_id = [x[0] for x in _chunk]
buf = [x[1] for x in _chunk]
next_i += args.batch_size
max_a_len = max([len(x) for x in buf])
instances = []
for instance in [(x, max_a_len) for x in buf]:
for proc in bi_uni_pipeline:
instances.append(proc(instance))
with torch.no_grad():
batch = seq2seq_loader.batch_list_to_batch_tensors(
instances)
# print("batch")
# print(batch)
# print(len(batch))
batch = [t.to(device) for t in batch if t is not None]
input_ids, token_type_ids, position_ids, input_mask, task_idx = batch
traces = model(input_ids, token_type_ids,
position_ids, input_mask, task_idx=task_idx)
if args.beam_size > 1:
traces = {k: v.tolist() for k, v in traces.items()}
output_ids = traces['pred_seq']
else:
output_ids = traces.tolist()
for i in range(len(buf)):
scores = traces['scores'][i]
wids_list = traces['wids'][i]
ptrs = traces['ptrs'][i]
eos_id = 102
top_k = args.top_k
# first we need to find the eos frame where all symbols are eos
# any frames after the eos frame are invalid
last_frame_id = len(scores) - 1
for _i, wids in enumerate(wids_list):
if all(wid == eos_id for wid in wids):
last_frame_id = _i
break
frame_id = -1
pos_in_frame = -1
seqs = []
for fid in range(last_frame_id + 1):
for _i, wid in enumerate(wids_list[fid]):
if wid == eos_id or fid == last_frame_id:
s = scores[fid][_i]
frame_id = fid
pos_in_frame = _i
if frame_id != -1 and s < 0:
seq = [wids_list[frame_id][pos_in_frame]]
for _fid in range(frame_id, 0, -1):
pos_in_frame = ptrs[_fid][pos_in_frame]
seq.append(wids_list[_fid - 1][pos_in_frame])
seq.reverse()
seqs.append([seq, s])
seqs = sorted(seqs, key= lambda x:x[1], reverse=True)
w_idss = [seq[0] for seq in seqs[:top_k]]
output_sequences = []
for w_ids in w_idss:
output_buf = tokenizer.convert_ids_to_tokens(w_ids)
output_tokens = []
for t in output_buf:
if t in ("[SEP]", "[PAD]"):
break
output_tokens.append(t)
output_sequence = ' '.join(detokenize(output_tokens))
output_sequences.append(output_sequence)
output_lines[buf_id[i]] = output_sequences
if args.need_score_traces:
score_trace_list[buf_id[i]] = {
'scores': traces['scores'][i], 'wids': traces['wids'][i], 'ptrs': traces['ptrs'][i]}
pbar.update(1)
# collect instances after split
results = []
if args.experiment == "single":
for clu in map_dict:
record = []
clu_ixs = map_dict[clu]
for i in clu_ixs:
record.extend(output_lines[i])
record_top10 = Counter(record).most_common(10)
record_top10 = [x[0] for x in record_top10]
results.append(record_top10)
output_lines = results
if args.output_file:
fn_out = args.output_file
else:
fn_out = model_recover_path+'.'+args.split
with open(fn_out, "w", encoding="utf-8") as fout:
for l in output_lines:
fout.write('\t'.join(l))
fout.write("\n")
if args.need_score_traces:
with open(fn_out + ".trace.pickle", "wb") as fout_trace:
pickle.dump(
{"version": 0.0, "num_samples": len(input_lines)}, fout_trace)
for x in score_trace_list:
pickle.dump(x, fout_trace)
# Evaluate !
if args.do_evaluate:
labels = []
if not os.path.exists(args.label_file):
raise ValueError("Label file not exists")
print("Loading label file from {}".format(args.label_file))
with open(args.label_file) as f:
for line in tqdm(f.readlines()):
line = line.strip().split("\t")
labels.append(line)
results = output_lines
ks = [1, 5, 10]
results_dict = {}
for k in ks:
acc_cul = 0
r_cul = 0
f1_cul = 0
cnt = 0
for predict, true_label in zip(tqdm(results), tqdm(labels)):
predict = predict[:k]
true_label = true_label[:k]
if len(predict) > 0 and len(true_label) > 0:
acc_cul += acc_score(predict, true_label)
r_cul += recall_score(predict, true_label)
f1_cul += f1_score(acc_score(predict, true_label), recall_score(predict, true_label))
cnt += 1
results_dict["P@{}".format(k)] = acc_cul*1.000 / cnt
results_dict["R@{}".format(k)] = r_cul*1.000 / cnt
results_dict["F1@{}".format(k)] = f1_cul*1.000 / cnt
print(results_dict)
def main():
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--data_dir",
default=None,
type=str,
required=True,
help=
"The input data dir. Should contain the .tsv files (or other data files) for the task."
)
parser.add_argument("--dev_src_file",
default=None,
type=str,
help="The input data file name.")
parser.add_argument("--dev_tgt_file",
default=None,
type=str,
help="The output data file name.")
parser.add_argument("--dev_check_file",
default=None,
type=str,
help="The output style response/know data file name.")
parser.add_argument("--dev_style_file",
default=None,
type=str,
help="The output style response/know data file name.")
parser.add_argument(
"--bert_model",
default=None,
type=str,
required=True,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese."
)
parser.add_argument("--config_path",
default=None,
type=str,
help="Bert config file path.")
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help=
"The output directory where the model predictions and checkpoints will be written."
)
parser.add_argument(
"--log_dir",
default='',
type=str,
required=True,
help="The output directory where the log will be written.")
parser.add_argument("--model_recover_path",
default=None,
type=str,
required=True,
help="The file of fine-tuned pretraining model.")
parser.add_argument("--optim_recover_path",
default=None,
type=str,
help="The file of pretraining optimizer.")
parser.add_argument("--predict_bleu",
default=0.5,
type=float,
help="The Predicted Bleu for KS Predict ")
parser.add_argument("--train_vae",
action='store_true',
help="Whether to train vae.")
# Other parameters
parser.add_argument(
"--max_seq_length",
default=128,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_predict",
action='store_true',
help="Whether to run ks predict.")
parser.add_argument("--do_eval",
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument(
"--do_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
default=64,
type=int,
help="Total batch size for eval.")
parser.add_argument("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--label_smoothing",
default=0,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--weight_decay",
default=0.01,
type=float,
help="The weight decay rate for Adam.")
parser.add_argument("--finetune_decay",
action='store_true',
help="Weight decay to the original weights.")
parser.add_argument("--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument(
"--warmup_proportion_step",
default=300,
type=int,
help=
"Proportion of training to perform linear learning rate warmup for. ")
parser.add_argument("--hidden_dropout_prob",
default=0.1,
type=float,
help="Dropout rate for hidden states.")
parser.add_argument("--attention_probs_dropout_prob",
default=0.1,
type=float,
help="Dropout rate for attention probabilities.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument(
'--gradient_accumulation_steps',
type=int,
default=1,
help=
"Number of updates steps to accumulate before performing a backward/update pass."
)
parser.add_argument(
'--fp16',
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument(
'--fp32_embedding',
action='store_true',
help=
"Whether to use 32-bit float precision instead of 16-bit for embeddings"
)
parser.add_argument(
'--loss_scale',
type=float,
default=0,
help=
"Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
"0 (default value): dynamic loss scaling.\n"
"Positive power of 2: static loss scaling value.\n")
parser.add_argument('--amp',
action='store_true',
help="Whether to use amp for fp16")
parser.add_argument(
'--from_scratch',
action='store_true',
help=
"Initialize parameters with random values (i.e., training from scratch)."
)
parser.add_argument('--new_segment_ids',
action='store_true',
help="Use new segment ids for bi-uni-directional LM.")
parser.add_argument('--new_pos_ids',
action='store_true',
help="Use new position ids for LMs.")
parser.add_argument('--tokenized_input',
action='store_true',
help="Whether the input is tokenized.")
parser.add_argument('--max_len_a',
type=int,
default=0,
help="Truncate_config: maximum length of segment A.")
parser.add_argument('--max_len_b',
type=int,
default=0,
help="Truncate_config: maximum length of segment B.")
parser.add_argument(
'--trunc_seg',
default='',
help="Truncate_config: first truncate segment A/B (option: a, b).")
parser.add_argument(
'--always_truncate_tail',
action='store_true',
help="Truncate_config: Whether we should always truncate tail.")
parser.add_argument(
"--mask_prob",
default=0.15,
type=float,
help=
"Number of prediction is sometimes less than max_pred when sequence is short."
)
parser.add_argument(
"--mask_prob_eos",
default=0,
type=float,
help=
"Number of prediction is sometimes less than max_pred when sequence is short."
)
parser.add_argument('--max_pred',
type=int,
default=20,
help="Max tokens of prediction.")
parser.add_argument("--num_workers",
default=0,
type=int,
help="Number of workers for the data loader.")
parser.add_argument('--mask_source_words',
action='store_true',
help="Whether to mask source words for training")
parser.add_argument('--skipgram_prb',
type=float,
default=0.0,
help='prob of ngram mask')
parser.add_argument('--skipgram_size',
type=int,
default=1,
help='the max size of ngram mask')
parser.add_argument('--mask_whole_word',
action='store_true',
help="Whether masking a whole word.")
parser.add_argument('--do_l2r_training',
action='store_true',
help="Whether to do left to right training")
parser.add_argument(
'--has_sentence_oracle',
action='store_true',
help="Whether to have sentence level oracle for training. "
"Only useful for summary generation")
parser.add_argument('--max_position_embeddings',
type=int,
default=None,
help="max position embeddings")
parser.add_argument('--relax_projection',
action='store_true',
help="Use different projection layers for tasks.")
parser.add_argument('--ffn_type',
default=0,
type=int,
help="0: default mlp; 1: W((Wx+b) elem_prod x);")
parser.add_argument('--num_qkv',
default=0,
type=int,
help="Number of different <Q,K,V>.")
parser.add_argument('--seg_emb',
action='store_true',
help="Using segment embedding for self-attention.")
parser.add_argument(
'--s2s_special_token',
action='store_true',
help="New special tokens ([S2S_SEP]/[S2S_CLS]) of S2S.")
parser.add_argument('--s2s_add_segment',
action='store_true',
help="Additional segmental for the encoder of S2S.")
parser.add_argument(
'--s2s_share_segment',
action='store_true',
help=
"Sharing segment embeddings for the encoder of S2S (used with --s2s_add_segment)."
)
parser.add_argument('--pos_shift',
action='store_true',
help="Using position shift for fine-tuning.")
args = parser.parse_args()
assert Path(
args.model_recover_path).exists(), "--model_recover_path doesn't exist"
args.output_dir = args.output_dir.replace('[PT_OUTPUT_DIR]',
os.getenv('PT_OUTPUT_DIR', ''))
args.log_dir = args.log_dir.replace('[PT_OUTPUT_DIR]',
os.getenv('PT_OUTPUT_DIR', ''))
os.makedirs(args.output_dir, exist_ok=True)
os.makedirs(args.log_dir, exist_ok=True)
handler = logging.FileHandler(os.path.join(args.log_dir, "train.log"),
encoding='UTF-8')
handler.setLevel(logging.INFO)
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
console = logging.StreamHandler()
console.setLevel(logging.DEBUG)
logger.addHandler(handler)
logger.addHandler(console)
json.dump(args.__dict__,
open(os.path.join(args.output_dir, 'opt.json'), 'w'),
sort_keys=True,
indent=2)
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
dist.init_process_group(backend='nccl')
logger.info(
"device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".
format(device, n_gpu, bool(args.local_rank != -1), args.fp16))
if args.gradient_accumulation_steps < 1:
raise ValueError(
"Invalid gradient_accumulation_steps parameter: {}, should be >= 1"
.format(args.gradient_accumulation_steps))
args.train_batch_size = int(args.train_batch_size /
args.gradient_accumulation_steps)
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if args.local_rank not in (-1, 0):
# Make sure only the first process in distributed training will download model & vocab
dist.barrier()
tokenizer = BertTokenizer.from_pretrained(args.bert_model,
do_lower_case=args.do_lower_case)
if args.max_position_embeddings:
tokenizer.max_len = args.max_position_embeddings
data_tokenizer = WhitespaceTokenizer(
) if args.tokenized_input else tokenizer
if args.local_rank == 0:
dist.barrier()
C_bi_uni_pipeline = [
seq2seq_loader.C_Preprocess4Seq2seq(
args.max_pred,
args.mask_prob,
list(tokenizer.vocab.keys()),
tokenizer.convert_tokens_to_ids,
args.max_seq_length,
new_segment_ids=args.new_segment_ids,
truncate_config={
'max_len_a': args.max_len_a,
'max_len_b': args.max_len_b,
'trunc_seg': args.trunc_seg,
'always_truncate_tail': args.always_truncate_tail
},
mask_source_words=args.mask_source_words,
skipgram_prb=args.skipgram_prb,
skipgram_size=args.skipgram_size,
mask_whole_word=args.mask_whole_word,
mode="s2s",
has_oracle=args.has_sentence_oracle,
num_qkv=args.num_qkv,
s2s_special_token=args.s2s_special_token,
s2s_add_segment=args.s2s_add_segment,
s2s_share_segment=args.s2s_share_segment,
pos_shift=args.pos_shift)
]
logger.info("Loading Dataset from {}".format(args.data_dir))
fn_src = os.path.join(args.data_dir, args.dev_src_file)
fn_tgt = os.path.join(args.data_dir, args.dev_tgt_file)
dev_reddit_dataset = seq2seq_loader.C_Seq2SeqDataset(
fn_src,
fn_tgt,
args.eval_batch_size,
data_tokenizer,
args.max_seq_length,
file_oracle=None,
bi_uni_pipeline=C_bi_uni_pipeline)
if args.local_rank == -1:
dev_reddit_sampler = RandomSampler(dev_reddit_dataset,
replacement=False)
_batch_size = args.eval_batch_size
else:
dev_reddit_sampler = DistributedSampler(dev_reddit_dataset)
_batch_size = args.eval_batch_size // dist.get_world_size()
dev_reddit_dataloader = torch.utils.data.DataLoader(
dev_reddit_dataset,
batch_size=_batch_size,
sampler=dev_reddit_sampler,
num_workers=args.num_workers,
collate_fn=seq2seq_loader.batch_list_to_batch_tensors,
pin_memory=False)
# note: args.train_batch_size has been changed to (/= args.gradient_accumulation_steps)
amp_handle = None
if args.fp16 and args.amp:
from apex import amp
amp_handle = amp.init(enable_caching=True)
logger.info("enable fp16 with amp")
# Prepare model
recover_step = _get_max_epoch_model(args.output_dir)
cls_num_labels = 2
type_vocab_size = 6 + \
(1 if args.s2s_add_segment else 0) if args.new_segment_ids else 2
num_sentlvl_labels = 2 if args.has_sentence_oracle else 0
relax_projection = 4 if args.relax_projection else 0
if args.local_rank not in (-1, 0):
# Make sure only the first process in distributed training will download model & vocab
dist.barrier()
if args.model_recover_path:
logger.info("***** Recover model: %s *****", args.model_recover_path)
model_recover = torch.load(args.model_recover_path, map_location='cpu')
model = BertForPreTrainingLossMask.from_pretrained(
args.bert_model,
state_dict=model_recover,
num_labels=cls_num_labels,
num_rel=0,
type_vocab_size=type_vocab_size,
config_path=args.config_path,
task_idx=3,
num_sentlvl_labels=num_sentlvl_labels,
max_position_embeddings=args.max_position_embeddings,
label_smoothing=args.label_smoothing,
fp32_embedding=args.fp32_embedding,
relax_projection=relax_projection,
new_pos_ids=args.new_pos_ids,
ffn_type=args.ffn_type,
hidden_dropout_prob=args.hidden_dropout_prob,
attention_probs_dropout_prob=args.attention_probs_dropout_prob,
num_qkv=args.num_qkv,
seg_emb=args.seg_emb)
if args.local_rank == 0:
dist.barrier()
if args.fp16:
model.half()
if args.fp32_embedding:
model.bert.embeddings.word_embeddings.float()
model.bert.embeddings.position_embeddings.float()
model.bert.embeddings.token_type_embeddings.float()
model.to(device)
if args.local_rank != -1:
try:
from torch.nn.parallel import DistributedDataParallel as DDP
except ImportError:
raise ImportError("DistributedDataParallel")
model = DDP(model,
device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True)
elif n_gpu > 1:
# model = torch.nn.DataParallel(model)
model = DataParallelImbalance(model)
# Prepare optimizer
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params':
[p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
if args.fp16:
try:
# from apex.optimizers import FP16_Optimizer
from optimization_fp16 import FP16_Optimizer_State
from apex.optimizers import FusedAdam
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
)
optimizer = FusedAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
bias_correction=False,
max_grad_norm=1.0)
if args.loss_scale == 0:
optimizer = FP16_Optimizer_State(optimizer,
dynamic_loss_scale=True)
else:
optimizer = FP16_Optimizer_State(optimizer,
static_loss_scale=args.loss_scale)
else:
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=t_total)
if args.optim_recover_path is not None:
logger.info("***** Recover optimizer from : {} *****".format(
args.optim_recover_path))
optim_recover = torch.load(args.optim_recover_path, map_location='cpu')
if hasattr(optim_recover, 'state_dict'):
optim_recover = optim_recover.state_dict()
optimizer.load_state_dict(optim_recover)
if args.loss_scale == 0:
logger.info("***** Recover optimizer: dynamic_loss_scale *****")
optimizer.dynamic_loss_scale = True
logger.info("***** CUDA.empty_cache() *****")
torch.cuda.empty_cache()
if args.do_train:
pretrain_step = -1
logger.info("***** Running training *****")
logger.info(" Batch size = %d", args.train_batch_size)
model.train()
if recover_step:
start_epoch = recover_step + 1
else:
start_epoch = 1
for i_epoch in trange(start_epoch,
int(args.num_train_epochs) + 1,
desc="Epoch",
disable=args.local_rank not in (-1, 0)):
if args.local_rank != -1:
train_sampler.set_epoch(i_epoch)
logger.info("***** Running QKR evaling *****")
logger.info(" Batch size = %d", args.eval_batch_size)
if args.local_rank != -1:
train_sampler.set_epoch(i_epoch)
dev_iter_bar = tqdm(dev_reddit_dataloader,
desc='Iter (loss=X.XXX)',
disable=args.local_rank not in (-1, 0))
total_lm_loss = 0
for qkr_dev_step, batch in enumerate(dev_iter_bar):
batch = [
t.to(device) if t is not None else None for t in batch
]
if args.has_sentence_oracle:
input_ids, segment_ids, input_mask, mask_qkv, lm_label_ids, masked_pos, masked_weights, is_next, task_idx, oracle_pos, oracle_weights, oracle_labels = batch
else:
input_ids, segment_ids, input_mask, mask_qkv, lm_label_ids, masked_pos, masked_weights, is_next, task_idx, tgt_pos, labels, ks_labels, style_ids, style_labels, check_ids = batch
oracle_pos, oracle_weights, oracle_labels = None, None, None
with torch.no_grad():
loss_tuple = model(input_ids,
segment_ids,
input_mask,
lm_label_ids,
is_next,
masked_pos=masked_pos,
masked_weights=masked_weights,
task_idx=task_idx,
masked_pos_2=oracle_pos,
masked_weights_2=oracle_weights,
masked_labels_2=oracle_labels,
mask_qkv=mask_qkv,
tgt_pos=tgt_pos,
labels=labels,
ks_labels=ks_labels,
train_vae=args.train_vae,
style_ids=style_ids,
style_labels=style_labels,
check_ids=check_ids,
pretrain=None)
masked_lm_loss, next_sentence_loss, KL_loss, Mutual_loss, Golden_loss, cosine_similarity_loss, predict_kl_loss = loss_tuple
if n_gpu > 1: # mean() to average on multi-gpu.
masked_lm_loss = masked_lm_loss.mean()
# logging for each step (i.e., before normalization by args.gradient_accumulation_steps)
total_lm_loss += masked_lm_loss.item()
# ensure that accumlated gradients are normalized
total_mean_lm_loss = total_lm_loss / (qkr_dev_step + 1)
print(total_mean_lm_loss)
logger.info("** ** * Evaling mean loss ** ** * ")
logger.info("In{}epoch,dev_lm_loss:{}".format(
i_epoch, total_mean_lm_loss))
logger.info("ppl:{}".format(np.exp(total_mean_lm_loss)))
logger.info("******************************************* ")
break
