def main():
args = get_args()
prepare(args)
if args.local_rank not in [-1, 0]:
torch.distributed.barrier()
# Make sure only the first process in distributed training will download model & vocab
# Load pretrained model and tokenizer
model, tokenizer = get_model_and_tokenizer(args)
if args.local_rank == 0:
torch.distributed.barrier()
# Make sure only the first process in distributed training will download model & vocab
if args.cached_train_features_file is None:
args.cached_train_features_file = os.path.join(
args.output_dir, "cached_features_for_training.pt")
training_features = utils.load_and_cache_examples(
example_file=args.train_file,
tokenizer=tokenizer,
local_rank=args.local_rank,
cached_features_file=args.cached_train_features_file,
shuffle=True,
)
train(args, training_features, model, tokenizer)
def get_input(args, tokenizer):
max_src_length = args.max_seq_length - 2 - args.max_tgt_length
to_pred = load_and_cache_examples(args.input_file,
tokenizer,
local_rank=-1,
cached_features_file=None,
shuffle=False)
input_lines = []
for line in to_pred:
input_lines.append(
tokenizer.convert_ids_to_tokens(
line["source_ids"])[:max_src_length])
if args.subset > 0:
logger.info("Decoding subset: %d", args.subset)
input_lines = input_lines[:args.subset]
input_lines = sorted(list(enumerate(input_lines)),
key=lambda x: -len(x[1]))
return input_lines
def main():
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--model_type",
default=None,
type=str,
required=True,
help="Model type selected in the list: " +
", ".join(TOKENIZER_CLASSES.keys()))
parser.add_argument("--model_path",
default=None,
type=str,
required=True,
help="Path to the model checkpoint.")
parser.add_argument("--config_path",
default=None,
type=str,
help="Path to config.json for the model.")
# tokenizer_name
parser.add_argument("--tokenizer_name",
default=None,
type=str,
required=True,
help="tokenizer name")
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('--subset',
type=int,
default=0,
help="Decode a subset of the input dataset.")
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('--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('--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=1, 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")
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.")
parser.add_argument(
"--cache_dir",
default=None,
type=str,
help=
"Where do you want to store the pre-trained models downloaded from s3")
parser.add_argument("--workers", default=1, type=int)
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()
logger.info("Device type: %s" % device)
if args.seed > 0:
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)
else:
random_seed = random.randint(0, 10000)
logger.info("Set random seed as: {}".format(random_seed))
random.seed(random_seed)
np.random.seed(random_seed)
torch.manual_seed(random_seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
tokenizer = TOKENIZER_CLASSES[args.model_type].from_pretrained(
args.tokenizer_name,
do_lower_case=args.do_lower_case,
cache_dir=args.cache_dir if args.cache_dir else None)
if args.model_type == "roberta":
vocab = tokenizer.encoder
else:
vocab = tokenizer.vocab
tokenizer.model_max_length = args.max_seq_length
config_file = args.config_path if args.config_path else os.path.join(
args.model_path, "config.json")
logger.info("Read decoding config from: %s" % config_file)
config = BertConfig.from_json_file(config_file)
bi_uni_pipeline = []
bi_uni_pipeline.append(
seq2seq_loader.Preprocess4Seq2seqDecoder(
list(vocab.keys()),
tokenizer.convert_tokens_to_ids,
args.max_seq_length,
max_tgt_length=args.max_tgt_length,
pos_shift=args.pos_shift,
source_type_id=config.source_type_id,
target_type_id=config.target_type_id,
cls_token=tokenizer.cls_token,
sep_token=tokenizer.sep_token,
pad_token=tokenizer.pad_token))
mask_word_id, eos_word_ids, sos_word_id = tokenizer.convert_tokens_to_ids(
[tokenizer.mask_token, tokenizer.sep_token, tokenizer.sep_token])
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_path)
found_checkpoint_flag = False
for model_recover_path in [args.model_path.strip()]:
logger.info("***** Recover model: %s *****", model_recover_path)
found_checkpoint_flag = True
model = BertForSeq2SeqDecoder.from_pretrained(
model_recover_path,
config=config,
mask_word_id=mask_word_id,
search_beam_size=args.beam_size,
length_penalty=args.length_penalty,
eos_id=eos_word_ids,
sos_id=sos_word_id,
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,
pos_shift=args.pos_shift,
)
model.to(device)
if args.fp16:
model.half()
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
to_pred = load_and_cache_examples(args.input_file,
tokenizer,
local_rank=-1,
cached_features_file=None,
shuffle=False,
threads=args.workers)
input_lines = []
for line in to_pred:
input_lines.append(
tokenizer.convert_ids_to_tokens(
line["source_ids"])[:max_src_length])
if args.subset > 0:
logger.info("Decoding subset: %d", args.subset)
input_lines = input_lines[:args.subset]
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:
batch_count = 0
first_batch = True
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
batch_count += 1
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)
batch = [
t.to(device) if t is not None else None for t in batch
]
input_ids, token_type_ids, position_ids, input_mask, mask_qkv, task_idx = batch
traces = model(input_ids,
token_type_ids,
position_ids,
input_mask,
task_idx=task_idx,
mask_qkv=mask_qkv)
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)):
w_ids = output_ids[i]
output_buf = tokenizer.convert_ids_to_tokens(w_ids)
output_tokens = []
for t in output_buf:
if t in (tokenizer.sep_token, tokenizer.pad_token):
break
output_tokens.append(t)
if args.model_type == "roberta":
output_sequence = tokenizer.convert_tokens_to_string(
output_tokens)
else:
output_sequence = ' '.join(
detokenize(output_tokens))
if '\n' in output_sequence:
output_sequence = " [X_SEP] ".join(
output_sequence.split('\n'))
output_lines[buf_id[i]] = output_sequence
# if first_batch or batch_count % 50 == 0:
# logger.info("{} = {}".format(buf_id[i], output_sequence))
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)
first_batch = False
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(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)
if not found_checkpoint_flag:
logger.info("Not found the model checkpoint file!")
def main():
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default='bert-base-cased',
type=str,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-large-cased, roberta-base, "
"roberta-large, unilm-base-cased, unilm-large-cased.")
parser.add_argument("--model_recover_path",
default=None,
type=str,
required=True,
help="The file of fine-tuned pretraining model.")
# tokenizer_name
parser.add_argument("--tokenizer_name",
default=None,
type=str,
help="tokenizer name")
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.")
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.")
# 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('--subset',
type=int,
default=0,
help="Decode a subset of the input dataset.")
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_pos_ids',
action='store_true',
help="Use new position ids for LMs.")
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('--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=1, 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")
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()
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()
if args.seed > 0:
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)
else:
random_seed = random.randint(0, 10000)
logger.info("Set random seed as: {}".format(random_seed))
random.seed(random_seed)
np.random.seed(random_seed)
torch.manual_seed(random_seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
setattr(args, "is_roberta", args.model_name.startswith("roberta"))
setattr(args, "no_segment_embedding", args.is_roberta)
if not args.model_name.startswith("unilm1.2"):
setattr(
args, "new_segment_ids",
args.model_name.startswith("unilm-")
or args.model_name.startswith("unilm1-"))
else:
setattr(args, "new_segment_ids", False)
if args.is_roberta:
tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
vocab = tokenizer.encoder
args.model_name = args.model_name.replace("roberta", "bert") + "-cased"
else:
if args.model_name.startswith("bert"):
tokenizer = BertTokenizer.from_pretrained(
args.tokenizer_name
if args.tokenizer_name else args.model_name,
do_lower_case=args.do_lower_case)
else:
tokenizer = UnilmTokenizer.from_pretrained(
args.tokenizer_name
if args.tokenizer_name else args.model_name,
do_lower_case=args.do_lower_case)
args.model_name = 'bert-' + args.model_name.split('-', 1)[-1]
vocab = tokenizer.vocab
tokenizer.max_len = args.max_seq_length
pair_num_relation = 0
bi_uni_pipeline = []
cls_token = '<s>' if args.is_roberta else '[CLS]'
sep_token = '</s>' if args.is_roberta else '[SEP]'
pad_token = '<pad>' if args.is_roberta else '[PAD]'
mask_token = '<mask>' if args.is_roberta else '[MASK]'
bi_uni_pipeline.append(
seq2seq_loader.Preprocess4Seq2seqDecoder(
list(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",
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,
cls_token=cls_token,
sep_token=sep_token,
pad_token=pad_token))
# 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
mask_word_id, eos_word_ids, sos_word_id = tokenizer.convert_tokens_to_ids(
[mask_token, sep_token, sep_token])
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)
found_checkpoint_flag = False
for model_recover_path in glob.glob(args.model_recover_path.strip()):
logger.info("***** Recover model: %s *****", model_recover_path)
found_checkpoint_flag = True
model_recover = torch.load(model_recover_path)
model = BertForSeq2SeqDecoder.from_pretrained(
args.model_name,
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,
sos_id=sos_word_id,
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,
ffn_type=args.ffn_type,
num_qkv=args.num_qkv,
seg_emb=args.seg_emb,
pos_shift=args.pos_shift,
is_roberta=args.is_roberta,
no_segment_embedding=args.no_segment_embedding,
)
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
to_pred = load_and_cache_examples(args.input_file,
tokenizer,
local_rank=-1,
cached_features_file=None,
shuffle=False)
input_lines = []
for line in to_pred:
input_lines.append(
tokenizer.convert_ids_to_tokens(
line["source_ids"])[:max_src_length])
if args.subset > 0:
logger.info("Decoding subset: %d", args.subset)
input_lines = input_lines[:args.subset]
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:
batch_count = 0
first_batch = True
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
batch_count += 1
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)
batch = [
t.to(device) if t is not None else None for t in batch
]
input_ids, token_type_ids, position_ids, input_mask, mask_qkv, task_idx = batch
traces = model(input_ids,
token_type_ids,
position_ids,
input_mask,
task_idx=task_idx,
mask_qkv=mask_qkv)
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)):
w_ids = output_ids[i]
output_buf = tokenizer.convert_ids_to_tokens(w_ids)
output_tokens = []
for t in output_buf:
if t in (sep_token, pad_token):
break
output_tokens.append(t)
if args.is_roberta:
output_sequence = tokenizer.convert_tokens_to_string(
output_tokens)
else:
output_sequence = ' '.join(
detokenize(output_tokens))
if '\n' in output_sequence:
output_sequence = " [X_SEP] ".join(
output_sequence.split('\n'))
output_lines[buf_id[i]] = output_sequence
if first_batch or batch_count % 50 == 0:
logger.info("{} = {}".format(
buf_id[i], output_sequence))
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)
first_batch = False
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(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)
if not found_checkpoint_flag:
logger.info("Not found the model checkpoint file!")
