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"""
This script provides an example to wrap TencentPretrain for multi-task classification.
"""
import sys
import os
import random
import argparse
import torch
import torch.nn as nn
tencentpretrain_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
sys.path.append(tencentpretrain_dir)
from tencentpretrain.embeddings import *
from tencentpretrain.encoders import *
from tencentpretrain.utils.constants import *
from tencentpretrain.utils import *
from tencentpretrain.utils.optimizers import *
from tencentpretrain.utils.config import load_hyperparam
from tencentpretrain.utils.seed import set_seed
from tencentpretrain.utils.logging import init_logger
from tencentpretrain.utils.misc import pooling
from tencentpretrain.model_saver import save_model
from tencentpretrain.opts import *
from finetune.run_classifier import count_labels_num, batch_loader, build_optimizer, load_or_initialize_parameters, train_model, read_dataset, evaluate
class MultitaskClassifier(nn.Module):
def __init__(self, args):
super(MultitaskClassifier, self).__init__()
self.embedding = Embedding(args)
for embedding_name in args.embedding:
tmp_emb = str2embedding[embedding_name](args, len(args.tokenizer.vocab))
self.embedding.update(tmp_emb, embedding_name)
self.encoder = str2encoder[args.encoder](args)
self.pooling_type = args.pooling
self.output_layers_1 = nn.ModuleList([nn.Linear(args.hidden_size, args.hidden_size) for _ in args.labels_num_list])
self.output_layers_2 = nn.ModuleList([nn.Linear(args.hidden_size, labels_num) for labels_num in args.labels_num_list])
self.dataset_id = 0
def forward(self, src, tgt, seg, soft_tgt=None):
"""
Args:
src: [batch_size x seq_length]
tgt: [batch_size]
seg: [batch_size x seq_length]
"""
# Embedding.
emb = self.embedding(src, seg)
# Encoder.
output = self.encoder(emb, seg)
# Target.
output = pooling(output, seg, self.pooling_type)
output = torch.tanh(self.output_layers_1[self.dataset_id](output))
logits = self.output_layers_2[self.dataset_id](output)
if tgt is not None:
loss = nn.NLLLoss()(nn.LogSoftmax(dim=-1)(logits), tgt.view(-1))
return loss, logits
else:
return None, logits
def change_dataset(self, dataset_id):
self.dataset_id = dataset_id
def pack_dataset(dataset, dataset_id, batch_size):
packed_dataset = []
src_batch, tgt_batch, seg_batch = [], [], []
for i, sample in enumerate(dataset):
src_batch.append(sample[0])
tgt_batch.append(sample[1])
seg_batch.append(sample[2])
if (i + 1) % batch_size == 0:
packed_dataset.append((dataset_id, torch.LongTensor(src_batch), torch.LongTensor(tgt_batch), torch.LongTensor(seg_batch)))
src_batch, tgt_batch, seg_batch = [], [], []
continue
if len(src_batch) > 0:
packed_dataset.append((dataset_id, torch.LongTensor(src_batch), torch.LongTensor(tgt_batch), torch.LongTensor(seg_batch)))
return packed_dataset
def main():
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
# Path options.
parser.add_argument("--pretrained_model_path", default=None, type=str,
help="Path of the pretrained model.")
parser.add_argument("--dataset_path_list", default=[], nargs='+', type=str, help="Dataset path list.")
parser.add_argument("--output_model_path", default="models/multitask_classifier_model.bin", type=str,
help="Path of the output model.")
parser.add_argument("--config_path", default="models/bert/base_config.json", type=str,
help="Path of the config file.")
# Model options.
model_opts(parser)
# Tokenizer options.
tokenizer_opts(parser)
# Optimizer options.
optimization_opts(parser)
# Training options.
training_opts(parser)
adv_opts(parser)
args = parser.parse_args()
args.soft_targets = False
# Load the hyperparameters from the config file.
args = load_hyperparam(args)
set_seed(args.seed)
# Count the number of labels.
args.labels_num_list = [count_labels_num(os.path.join(path, "train.tsv")) for path in args.dataset_path_list]
args.datasets_num = len(args.dataset_path_list)
# Build tokenizer.
args.tokenizer = str2tokenizer[args.tokenizer](args)
# Build multi-task classification model.
model = MultitaskClassifier(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)
args.model = model
if args.use_adv:
args.adv_method = str2adv[args.adv_type](model)
# Training phase.
dataset_list = [read_dataset(args, os.path.join(path, "train.tsv")) for path in args.dataset_path_list]
packed_dataset_list = [pack_dataset(dataset, i, args.batch_size) for i, dataset in enumerate(dataset_list)]
packed_dataset_all = []
for packed_dataset in packed_dataset_list:
packed_dataset_all += packed_dataset
instances_num = sum([len(dataset) for dataset in dataset_list])
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)
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(packed_dataset_all)
model.train()
for i, (dataset_id, src_batch, tgt_batch, seg_batch) in enumerate(packed_dataset_all):
if hasattr(model, "module"):
model.module.change_dataset(dataset_id)
else:
model.change_dataset(dataset_id)
loss = train_model(args, model, optimizer, scheduler, src_batch, tgt_batch, seg_batch, None)
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
for dataset_id, path in enumerate(args.dataset_path_list):
args.labels_num = args.labels_num_list[dataset_id]
if hasattr(model, "module"):
model.module.change_dataset(dataset_id)
else:
model.change_dataset(dataset_id)
result = evaluate(args, read_dataset(args, os.path.join(path, "dev.tsv")))
save_model(model, args.output_model_path)
if __name__ == "__main__":
main()
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