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"""
This script provides an example to wrap TencentPretrain for ChID (a multiple choice dataset).
"""
import sys
import os
import argparse
import json
import random
import torch
tencentpretrain_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
sys.path.append(tencentpretrain_dir)
from tencentpretrain.utils.constants import *
from tencentpretrain.utils.tokenizers import *
from tencentpretrain.utils.optimizers import *
from tencentpretrain.utils import *
from tencentpretrain.utils.config import load_hyperparam
from tencentpretrain.utils.seed import set_seed
from tencentpretrain.utils.logging import init_logger
from tencentpretrain.model_saver import save_model
from tencentpretrain.opts import finetune_opts, adv_opts
from finetune.run_c3 import MultipleChoice
from finetune.run_classifier import build_optimizer, load_or_initialize_parameters, train_model, batch_loader, evaluate
def tokenize_chid(text):
output = []
first_idiom = True
while True:
if first_idiom:
idiom_index = text.find("#idiom")
output.extend(text[:idiom_index])
output.append(text[idiom_index : idiom_index + 13])
pre_idiom_index = idiom_index
first_idiom = False
else:
if text[idiom_index + 1 :].find("#idiom") == -1:
output.extend(text[pre_idiom_index + 13 :])
break
else:
idiom_index = idiom_index + 1 + text[idiom_index + 1 :].find("#idiom")
output.extend(text[pre_idiom_index + 13 : idiom_index])
output.append(text[idiom_index : idiom_index + 13])
pre_idiom_index = idiom_index
return output
def add_tokens_around(tokens, idiom_index, tokens_num):
left_tokens_num = tokens_num // 2
right_tokens_num = tokens_num - left_tokens_num
if idiom_index >= left_tokens_num and (len(tokens) - 1 - idiom_index) >= right_tokens_num:
left_tokens = tokens[idiom_index - left_tokens_num : idiom_index]
right_tokens = tokens[idiom_index + 1 : idiom_index + 1 + right_tokens_num]
elif idiom_index < left_tokens_num:
left_tokens = tokens[:idiom_index]
right_tokens = tokens[idiom_index + 1 : idiom_index + 1 + tokens_num - len(left_tokens)]
elif (len(tokens) - 1 - idiom_index) < right_tokens_num:
right_tokens = tokens[idiom_index + 1 :]
left_tokens = tokens[idiom_index - (tokens_num - len(right_tokens)) : idiom_index]
return left_tokens, right_tokens
def read_dataset(args, data_path, answer_path):
if answer_path is not None:
answers = json.load(open(answer_path))
dataset = []
max_tokens_for_doc = args.seq_length - 3
group_index = 0
for line in open(data_path, mode="r", encoding="utf-8"):
example = json.loads(line)
options = example["candidates"]
for context in example["content"]:
chid_tokens = tokenize_chid(context)
tags = [token for token in chid_tokens if "#idiom" in token]
for tag in tags:
if answer_path is not None:
tgt = answers[tag]
else:
tgt = -1
tokens = []
for i, token in enumerate(chid_tokens):
if "#idiom" in token:
sub_tokens = [str(token)]
else:
sub_tokens = args.tokenizer.tokenize(token)
for sub_token in sub_tokens:
tokens.append(sub_token)
idiom_index = tokens.index(tag)
left_tokens, right_tokens = add_tokens_around(tokens, idiom_index, max_tokens_for_doc - 1)
for i in range(len(left_tokens)):
if "#idiom" in left_tokens[i] and left_tokens[i] != tag:
left_tokens[i] = MASK_TOKEN
for i in range(len(right_tokens)):
if "#idiom" in right_tokens[i] and right_tokens[i] != tag:
right_tokens[i] = MASK_TOKEN
dataset.append(([], tgt, [], tag, group_index))
for option in options:
option_tokens = args.tokenizer.tokenize(option)
tokens = [CLS_TOKEN] + option_tokens + [SEP_TOKEN] + left_tokens + [SEP_TOKEN] + right_tokens + [SEP_TOKEN]
src = args.tokenizer.convert_tokens_to_ids(tokens)[: args.seq_length]
seg = [0] * len(src)
while len(src) < args.seq_length:
src.append(0)
seg.append(0)
dataset[-1][0].append(src)
dataset[-1][2].append(seg)
while len(dataset[-1][0]) < args.max_choices_num:
dataset[-1][0].append([0] * args.seq_length)
dataset[-1][2].append([0] * args.seq_length)
group_index += 1
return dataset
def main():
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
finetune_opts(parser)
parser.add_argument("--vocab_path", default=None, type=str,
help="Path of the vocabulary file.")
parser.add_argument("--spm_model_path", default=None, type=str,
help="Path of the sentence piece model.")
parser.add_argument("--train_answer_path", type=str, required=True,
help="Path of the answers for trainset.")
parser.add_argument("--dev_answer_path", type=str, required=True,
help="Path of the answers for devset.")
parser.add_argument("--max_choices_num", default=10, type=int,
help="The maximum number of cadicate answer, shorter than this will be padded.")
adv_opts(parser)
args = parser.parse_args()
args.labels_num = args.max_choices_num
# Load the hyperparameters from the config file.
args = load_hyperparam(args)
set_seed(args.seed)
# Build tokenizer.
args.tokenizer = CharTokenizer(args)
# Build multiple choice model.
model = MultipleChoice(args)
# Load or initialize parameters.
load_or_initialize_parameters(args, model)
# Get logger.
args.logger = init_logger(args)
args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(args.device)
# Training phase.
trainset = read_dataset(args, args.train_path, args.train_answer_path)
instances_num = len(trainset)
batch_size = args.batch_size
args.train_steps = int(instances_num * args.epochs_num / batch_size) + 1
args.logger.info("Batch size: {}".format(batch_size))
args.logger.info("The number of training instances: {}".format(instances_num))
optimizer, scheduler = build_optimizer(args, model)
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)
args.amp = amp
if torch.cuda.device_count() > 1:
args.logger.info("{} GPUs are available. Let's use them.".format(torch.cuda.device_count()))
model = torch.nn.DataParallel(model)
args.model = model
if args.use_adv:
args.adv_method = str2adv[args.adv_type](model)
total_loss, result, best_result = 0.0, 0.0, 0.0
args.logger.info("Start training.")
for epoch in range(1, args.epochs_num + 1):
random.shuffle(trainset)
src = torch.LongTensor([example[0] for example in trainset])
tgt = torch.LongTensor([example[1] for example in trainset])
seg = torch.LongTensor([example[2] for example in trainset])
model.train()
for i, (src_batch, tgt_batch, seg_batch, _) in enumerate(batch_loader(batch_size, src, tgt, seg)):
loss = train_model(args, model, optimizer, scheduler, src_batch, tgt_batch, seg_batch)
total_loss += loss.item()
if (i + 1) % args.report_steps == 0:
args.logger.info("Epoch id: {}, Training steps: {}, Avg loss: {:.3f}".format(epoch, i + 1, total_loss / args.report_steps))
total_loss = 0.0
result = evaluate(args, read_dataset(args, args.dev_path, args.dev_answer_path))
if result[0] > best_result:
best_result = result[0]
save_model(model, args.output_model_path)
if __name__ == "__main__":
main()
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