) return DataLoader( train_dataset, batch_size=self.args.**per_device_train_batch_size**, collate_fn=data_collator, num_workers=self.args.dataloader_num_workers, pin_memory=self.args.dataloader_pin_memory, ) train_sampler = self._get_train_sampler() return DataLoader( train_dataset, batch_...
train_dataloader:一个 torch.utils.data.DataLoader,指定 training dataloader。 eval_dataloader:一个 torch.utils.data.DataLoader,指定 evaluation dataloader。 metrics:一个字典 Dict[str, float],指定由上一次 evaluation 阶段计算得到的指标。 它仅在 on_evaluate 事件中才能访问。 logs:一个字典 Dict[str, floa...
with CodeBlock('训练循环'): with CodeBlock('变量初始化'): # epochs_trained = 0 # 从train_state中恢复的epochs_trained开始迭代 ... ... 跳过已训练epochs:在dataloader中依赖sampler对dataset的顺序进行采样,使用相同的初始随机种子后,为了让恢复训练后的sampler采样结果与中断前相同,需要跳过前epochs_traine...
data_collator = trainer.get_train_dataloader().collate_fn actual_train_set = trainer._remove_unused_columns(trainer.train_dataset) batch = data_collator([actual_train_set[i] for i in range(4)]) print(batch) 输出:{'input_ids': tensor([[ 101, 17158, 2135, 6949, 8301, 25057, 2038, ...
train_args ''' TrainingArguments( _n_gpu=0, adafactor=False, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, auto_find_batch_size=False, bf16=False, bf16_full_eval=False, data_seed=None, dataloader_drop_last=False, dataloader_num_workers=0, dataloader_pin_memory=True, ddp_...
def get_train_dataloader(self): """get train dataloader of mindspore.""" return build_dataset_loader(self.config.train_dataset.data_loader) def get_eval_dataloader(self): """get eval dataloader of mindspore.""" return build_dataset_loader(self.config.eval_dataset.data_loader)...
train_data is like Dataloader. But I get an error"'Trainer' object has no attribute 'train_loader'" No error occurs inYOLONAS Starter Notebook. I get an error in my local environment. Why is that? Tried and tested code trainer.train( model=model, train_params=train_params, train_loader...
self.train_losses = [] self.val_losses = [] def on_train_end(self, trainer, model: "pl.LightningModule") -> None: #记录训练和验证的损失 self.train_losses.append(trainer.train_dataloader.dataset.get_loss() self.val_losses.append(trainer.val_dataloaders[0].dataset.get_loss() ...
然后,我们可以循环数据集中的所有示例并为每个查询生成响应。 然后,我们使用reward_model计算每个生成响应的奖励,并将这些奖励传递给ppo_trainer.step方法。 然后 ppo_trainer.step 方法将使用 PPO 算法优化 SFT 模型。 fromtqdmimporttqdmforepoch, batchintqdm(enumerate(ppo_trainer.dataloader)): ...
_, _, optimizer = train_util.get_optimizer(args, trainable_params=params_to_optimize) # prepare dataloader # strategies are set here because they cannot be referenced in another process. Copy them with the dataset # some strategies can be None train_dataset_group.set_current_strategies(...