def main(args):
tokenizer = BartTokenizer.from_pretrained(args.tokenizer_path)
bart_config = BartConfig()
bart_config.vocab_size = len(tokenizer)
bart_config.eos_token_id = tokenizer.eos_token_id
bart_config.bos_token_id = tokenizer.bos_token_id
bart_config.pad_token_id = tokenizer.pad_token_id
bart_config.save_pretrained(args.config_path)
def build_graph(self):
"""构建模型"""
if self.config.bart_pre_training:
self.model = CustomBartGeneration.from_pretrained(
self.config.bart_pre_training)
if self.model.config.vocab_size != self.config.vocab_size:
# 使用预训练模型时词汇表发生变化, 重置embedding表的大小
self.model.resize_token_embeddings(self.config.vocab_size)
else:
bart_config = BartConfig()
bart_config.activation_function = self.config.activate_func
bart_config.vocab_size = self.config.vocab_size
bart_config.d_model = self.config.embed_size
bart_config.max_position_embeddings = self.config.embed_size
bart_config.max_length = self.config.max_generate_length
bart_config.num_labels = self.config.num_labels
bart_config.image_para_freeze = self.config.image_para_freeze
bart_config.encoder_layers = self.config.n_layers
bart_config.decoder_layers = self.config.n_layers
bart_config.encoder_attention_heads = self.config.n_head
bart_config.decoder_attention_heads = self.config.n_head
bart_config.encoder_ffn_dim = self.config.ffn_dim
bart_config.decoder_ffn_dim = self.config.ffn_dim
bart_config.pad_token_id = PAD_ID
bart_config.bos_token_id = BOS_ID
bart_config.eos_token_id = EOS_ID
self.model = CustomBartGeneration(config=bart_config)
# multi-task
# bart_config.summary_use_proj = True
# bart_config.summary_activation = None
# bart_config.summary_first_dropout = True
# bart_config.summary_proj_to_labels = 0.1
# bart_config.summary_type = "cls_index"
# self.model = CustomBartGenerationDoubleHeads(config=bart_config)
if torch.cuda.is_available():
self.model.to(self.config.device)
if self.config.checkpoint:
self.checkpoint_dict = self.load_model(self.config.checkpoint)
if self.is_train:
no_decay = ['bias', 'layer_norm.weight']
optimizer_grouped_parameters = [{
'params': [
p for n, p in self.model.named_parameters()
if not any(nd in n for nd in no_decay)
],
'weight_decay':
self.config.weight_decay
}, {
'params': [
p for n, p in self.model.named_parameters()
if any(nd in n for nd in no_decay)
],
'weight_decay':
0.0
}]
self.optimizer = AdamW(optimizer_grouped_parameters,
lr=self.config.learning_rate,
eps=self.config.adam_epsilon)
self.scheduler = get_linear_schedule_with_warmup(
self.optimizer,
num_warmup_steps=self.config.num_warmup_steps,
num_training_steps=self.config.num_training_steps)
if self.config.checkpoint and self.checkpoint_dict:
self.optimizer.load_state_dict(
self.checkpoint_dict["optimizer"]) # 加载优化器参数
self.scheduler.load_state_dict(
self.checkpoint_dict["lr_scheduler"]) # 加载lr_scheduler
