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)): query_tensors= batch["input_ids"]### Get response from SFTModelresponse_...
epochs = 10 for epoch in tqdm(range(epochs), "epoch: "): for batch in tqdm(ppo_trainer.dataloader): query_tensors = batch["input_ids"] #1、通过SFTModel获得相应response。对应Rollout阶段。 response_tensors = ppo_trainer.generate(query_tensors, **generation_kwargs) batch["response"] = [...
for epoch, batch in enumerate(ppo_trainer.dataloader): ### Get response from gpt2 query_tensors = [] response_tensors = [] query_tensors = [torch.tensor(t).long() for t in batch] for query in batch: input_ids = query.unsqueeze(0) response = [] for _ in range(30): outputs ...
class CustomPPOTrainer(PPOTrainer, Trainer): 定义一个名为CustomPPOTrainer的类,继承自PPOTrainer和Trainer这两个类。 python 复制 r""" Inherits PPOTrainer. """ 文档字符串,说明这个类继承自PPOTrainer。 python 复制 def __init__( self, 定义CustomPPOTrainer类的构造函数。 python 复制 model_args: "Mo...
dataloader)): if epoch >= config.total_ppo_epochs: break question_tensors = batch["input_ids"] response_tensors = ppo_trainer.generate( question_tensors, return_prompt=False, length_sampler=output_length_sampler, **generation_kwargs, ) batch["response"] = tokenizer.batch_decode(response_...
But in thetrainers/base/supervised_trainer.pyandClass SupervisedTrainerBase def get_dataloaders(self, train_data_dtype, eval_data_dtype) -> None: """Get the dataloaders based on data_dtype.""" self.train_template = get_template_class(self.cfgs.data_cfgs.train_template) train_dataset = ...
ppo_update(trajectories, advantages, returns) def collect_trajectories(self, train_data): """采样阶段:收集训练数据""" trajectories = [] for batch in DataLoader(train_data, batch_size=self.batch_size): # 1. Actor生成文本 with torch.no_grad(): actor_outputs = self.actor_model.generate( ...
train() # 切到训练模式 dataloader = DataLoader( preference_data, batch_size=2, shuffle=True, collate_fn=lambda x: { "prompt": [d["prompt"] for d in x], "chosen": [d["chosen"] for d in x], "rejected": [d["rejected"] for d in x] } ) loss_fn = nn.BCEWithLogitsLoss()...
I always get the error that all my tensors are not on the same device at ppo_trainer.step(query_tensors, response_tensors, rewards) despite query_tensors, response_tensors and rewards are all on the same device (i.e., 'cuda:0') when I lo...
Train transformer language models with reinforcement learning. - trl/trl/trainer/ppov2_trainer.py at 3c0a10b1aedbe533005dbfe18f2cc8057093f80b · huggingface/trl