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PPO 训练器

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开始使用

PPO 训练器

TRL 支持使用近端策略优化 (PPO) 训练大语言模型 (LLM)。

参考文献

开始使用

要运行 PPO 脚本以确保训练器可以运行,你可以运行以下命令,使用一个虚拟的奖励模型来训练 PPO 模型。

python examples/scripts/ppo/ppo.py \
    --dataset_name trl-internal-testing/descriptiveness-sentiment-trl-style \
    --dataset_train_split descriptiveness \
    --learning_rate 3e-6 \
    --num_ppo_epochs 1 \
    --num_mini_batches 1 \
    --output_dir models/minimal/ppo \
    --per_device_train_batch_size 64 \
    --gradient_accumulation_steps 1 \
    --total_episodes 10000 \
    --model_name_or_path EleutherAI/pythia-1b-deduped \
    --sft_model_path EleutherAI/pythia-1b-deduped \
    --reward_model_path EleutherAI/pythia-1b-deduped \
    --missing_eos_penalty 1.0

日志指标解释

记录的指标如下。这是一个在 Weights and Biases 上跟踪的运行示例

  • eps: 跟踪每秒的片段(episode)数。
  • objective/kl: 当前策略和参考策略之间的平均库尔贝克-莱布勒(KL)散度。
  • objective/entropy: 策略的平均熵,表示策略选择动作的随机性。
  • objective/non_score_reward: 来自非分数相关来源的平均奖励,基本上是 beta * kl.sum(1),其中 beta 是 KL 惩罚系数,kl 是每个词元的 KL 散度。
  • objective/rlhf_reward: 平均 RLHF 奖励,即 score - non_score_reward
  • objective/scores: 由奖励模型/环境返回的平均分数。
  • policy/approxkl_avg: 连续 PPO 策略之间的平均近似 KL 散度。请注意,这与 objective/kl 不同。
  • policy/clipfrac_avg: 被裁剪的策略更新的平均比例,表示策略更新被限制以防止大幅度变化的频率。
  • loss/policy_avg: 平均策略损失,表示策略的表现如何。
  • loss/value_avg: 平均价值损失,表示预测值与实际奖励之间的差异。
  • val/clipfrac_avg: 被裁剪的价值函数更新的平均比例,与 policy/clipfrac_avg 类似,但针对价值函数。
  • policy/entropy_avg: 训练期间策略的平均熵,表示策略动作的多样性。
  • val/ratio: 当前策略概率与旧策略概率的平均比率,提供了策略变化程度的度量。
  • val/ratio_var: val/ratio 的方差,表示策略变化的可变性。
  • val/num_eos_tokens: 生成的序列结束(EOS)词元的数量,可以表示完整响应的数量。
  • lr: 学习率:优化器当前使用的学习率。
  • episode: 轮次:训练过程中的当前轮次计数。

实用技巧

  • 调试提示:objective/rlhf_reward:这是 RLHF 训练的最终目标。如果训练按预期进行,这个指标应该持续上升。
  • 调试提示:val/ratio:这个数值应该在 1.0 左右浮动,并被 PPO 的代理损失函数中的 --cliprange 0.2 裁剪。所以如果这个 ratio 过高,比如 2.0 或 1000.0,或者过低,比如 0.1,意味着连续策略之间的更新过于剧烈。你应该尝试理解为什么会发生这种情况并尝试修复它。
  • 内存提示:如果内存不足,可以尝试减少 --per_device_train_batch_size 或增加 --gradient_accumulation_steps 来减少内存占用。
  • 内存提示:如果你有多个 GPU,也可以使用 DeepSpeed stage 3 进行训练来减少内存占用 accelerate launch --config_file examples/accelerate_configs/deepspeed_zero3.yaml
  • 使用提示:我们推荐通过 --missing_eos_penalty 使用“EOS 技巧”,它会对未以 EOS 标记结束的补全得分减去一个固定的标量惩罚。这可以帮助模型学习生成更连贯的补全。

我的模型到底在做什么?

为了帮助你理解模型正在做什么,我们会定期记录一些模型的样本补全。这是一个补全的例子。在一个在 Weights and Biases 上跟踪的运行示例中,它看起来如下,让你能看到模型在不同训练阶段的响应。默认情况下,我们在训练期间生成 --num_sample_generations 10 个样本,但你可以自定义生成的数量。

在日志中,采样的生成结果如下:

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┓
┃ query                           ┃ model response                  ┃ score    ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━┩
│  SUBREDDIT: r/AskReddit         │  I'm in love with a friend, and3.921875 │
│                                 │ I don't know how to get rid of  │          │
│ TITLE: How do you get someone   │ those feelings. I'm             │          │
│ out of your head?               │ desperate.<|endoftext|>[PAD][P… │          │
│                                 │                                 │          │
│ POST: Hi,                       │                                 │          │
│ I'm 22, and I have been with my │                                 │          │
│ girlfriend for 5 years now. We  │                                 │          │
│ recently moved together. We've  │                                 │          │
│ always loved each other         │                                 │          │
│ intensely.                      │                                 │          │
│                                 │                                 │          │
│ Problem, I recently started to  │                                 │          │
│ have feelings for an other      │                                 │          │
│ person (a friend). This person  │                                 │          │
│ has had a boyfriend for now 3   │                                 │          │
│ years, and has absolutely no    │                                 │          │
│ ideas. Those feelings were so   │                                 │          │
│ strong, it was hard to hide     │                                 │          │
│ them. After 2 months of me      │                                 │          │
│ being distant and really sad,   │                                 │          │
│ my girlfriend forced me to say  │                                 │          │
│ what was bothering me. I'm not  │                                 │          │
│ a good liar, and now she knows. │                                 │          │
│                                 │                                 │          │
│ We decided to give us a week    │                                 │          │
│ alone, I went to my parents.    │                                 │          │
│                                 │                                 │          │
│ Now, I'm completely lost. I     │                                 │          │
│ keep on thinking about this     │                                 │          │
│ person, and I hate that. I      │                                 │          │
│ would like for those feelings   │                                 │          │
│ to go away, to leave me alone.  │                                 │          │
│ But I can't.                    │                                 │          │
│                                 │                                 │          │
│ What do I do? It's been 3       │                                 │          │
│ months now, and I'm just        │                                 │          │
│ desperate.                      │                                 │          │
│                                 │                                 │          │
│ TL;DR:                          │                                 │          │
├─────────────────────────────────┼─────────────────────────────────┼──────────┤
│  SUBREDDIT: r/pettyrevenge      │  My mom woke me up with a loud  │ 6.84375  │
│                                 │ TV. I blasted Gangnam Style on  │          │
│ TITLE: So, my mom woke me up    │ repeat, with the bass cranked   │          │
│ with a loud TV.                 │ up as high as it could          │          │
│                                 │ go.<|endoftext|>[PAD][PAD][PAD… │          │
│ POST: She was in her living     │                                 │          │
│ room, watching TV. This was at  │                                 │          │
│ about 8:30 in the morning, and  │                                 │          │
│ she was exercising. She turned  │                                 │          │
│ the TV up extra loud to hear it │                                 │          │
│ over her excercycle, and woke   │                                 │          │
│ me up. I went in there asking   │                                 │          │
│ for her to turn it down. She    │                                 │          │
│ said she didn't have to; I      │                                 │          │
│ explained that I always used    │                                 │          │
│ headphones so she didn't have   │                                 │          │
│ to deal with my noise and that  │                                 │          │
│ she should give me a little     │                                 │          │
│ more respect, given that I paid │                                 │          │
│ rent at the time.               │                                 │          │
│                                 │                                 │          │
│ She disagreed. I went back to   │                                 │          │
│ my room, rather pissed off at   │                                 │          │
│ the lack of equality. I had no  │                                 │          │
│ lock on my door; but I had a    │                                 │          │
│ dresser right next to it, so I  │                                 │          │
│ pulled one of the drawers out   │                                 │          │
│ enough so that it caused the    │                                 │          │
│ door to not be openable. Then,  │                                 │          │
│ I turned my speakers up really  │                                 │          │
│ loud and blasted Gangnam Style  │                                 │          │
│ on repeat, with the bass        │                                 │          │
│ cranked up as high as it could  │                                 │          │
│ go.                             │                                 │          │
│                                 │                                 │          │
│ If you hate Gangnam Style for   │                                 │          │
│ being overplayed, you will see  │                                 │          │
│ why I chose that particular     │                                 │          │
│ song. I personally don't mind   │                                 │          │
│ it. But here's the thing about  │                                 │          │
│ my bass; it vibrates the walls, │                                 │          │
│ making one hell of a lot of     │                                 │          │
│ noise. Needless to say, my mom  │                                 │          │
│ was not pleased and shut off    │                                 │          │
│ the internet. But it was oh so  │                                 │          │
│ worth it.                       │                                 │          │
│                                 │                                 │          │
│ TL;DR:                          │                                 │          │
└─────────────────────────────────┴─────────────────────────────────┴──────────┘

实现细节

这个 PPO 实现基于使用 PPO 进行 RLHF 的 N+ 个实现细节:TL;DR 摘要案例研究

基准实验

为了验证 PPO 实现的有效性,我们在 1B 参数模型上进行了实验。以下是我们用来运行实验的命令。我们直接从使用 PPO 进行 RLHF 的 N+ 个实现细节:TL;DR 摘要案例研究中获取 SFT / RM 模型。

accelerate launch --config_file examples/accelerate_configs/deepspeed_zero2.yaml \
    examples/scripts/ppo/ppo_tldr.py \
    --output_dir models/minimal/ppo_tldr \
    --learning_rate 3e-6 \
    --per_device_train_batch_size 16 \
    --gradient_accumulation_steps 4 \
    --total_episodes 1000000 \
    --model_name_or_path EleutherAI/pythia-1b-deduped \
    --sft_model_path cleanrl/EleutherAI_pythia-1b-deduped__sft__tldr \
    --reward_model_path cleanrl/EleutherAI_pythia-1b-deduped__reward__tldr \
    --local_rollout_forward_batch_size 16 \
    --missing_eos_penalty 1.0 \
    --stop_token eos

检查点和实验跟踪可在以下链接查看:

为了评估,我们使用 vLLM 加载检查点,并使用 GPT-4o mini 作为评判模型,评估生成的 TL;DR 与参考 TL;DR。有关如何使用评判器的更多信息,请参见评判器

$ python examples/scripts/evals/judge_tldr.py --model_name_or_path cleanrl/EleutherAI_pythia-1b-deduped__sft__tldr --judge_model gpt-4o-mini --num_examples 1000
Model win rate: 33.00%
$ python examples/scripts/evals/judge_tldr.py --model_name_or_path vwxyzjn/ppo_tldr --judge_model gpt-4o-mini --num_examples 1000
Model win rate: 64.70%

PPO 检查点获得了 64.7% 的偏好率,而 SFT 检查点的偏好率为 33.0%。这是一个好迹象,表明 PPO 训练按预期工作。

指标

# pip install openrlbenchmark==0.2.1a5
# see https://github.com/openrlbenchmark/openrlbenchmark#get-started for documentation
# to use it, change `?we=huggingface&wpn=trl` to your own project and `?tag=pr-1540` to your own tag
python -m openrlbenchmark.rlops_multi_metrics \
    --filters '?we=huggingface&wpn=trl&xaxis=train/episode&ceik=output_dir&cen=sft_model_path&metrics=train/objective/rlhf_reward&metrics=train/objective/scores&metrics=train/objective/kl&metrics=train/objective/non_score_reward&metrics=train/objective/entropy&metrics=train/policy/approxkl_avg&metrics=train/policy/clipfrac_avg&metrics=train/loss/policy_avg&metrics=train/loss/value_avg&metrics=train/val/clipfrac_avg&metrics=train/policy/entropy_avg&metrics=train/val/ratio&metrics=train/val/ratio_var&metrics=train/val/num_eos_tokens&metrics=train/lr&metrics=train/eps' \
        "cleanrl/EleutherAI_pythia-1b-deduped__sft__tldr?tag=pr-1540" \
    --env-ids models/minimal/ppo_tldr \
    --pc.ncols 4 \
    --pc.ncols-legend 1 \
    --pc.xlabel "Episode" \
    --output-filename benchmark/trl/pr-1540/ppo \
    --scan-history

PPOTrainer

class trl.PPOTrainer

< >

( args: PPOConfig processing_class: typing.Union[transformers.tokenization_utils_base.PreTrainedTokenizerBase, transformers.image_processing_utils.BaseImageProcessor, transformers.feature_extraction_utils.FeatureExtractionMixin, transformers.processing_utils.ProcessorMixin, NoneType] model: Module ref_model: typing.Optional[torch.nn.modules.module.Module] reward_model: Module train_dataset: Dataset value_model: Module data_collator: typing.Optional[transformers.data.data_collator.DataCollatorWithPadding] = None eval_dataset: typing.Union[datasets.arrow_dataset.Dataset, dict[str, datasets.arrow_dataset.Dataset], NoneType] = None optimizers: tuple = (None, None) callbacks: typing.Optional[list[transformers.trainer_callback.TrainerCallback]] = None peft_config: typing.Optional[ForwardRef('PeftConfig')] = None )

train

< >

( )

save_model

< >

( output_dir: typing.Optional[str] = None _internal_call: bool = False )

push_to_hub

< >

( commit_message: typing.Optional[str] = 'End of training' blocking: bool = True token: typing.Optional[str] = None revision: typing.Optional[str] = None **kwargs )

参数

  • commit_message (str, 可选, 默认为 "End of training") — 推送时的提交信息。
  • blocking (bool, 可选, 默认为 True) — 函数是否应在 `git push` 完成后才返回。
  • token (str, 可选, 默认为 None) — 具有写入权限的令牌,用于覆盖 Trainer 的原始参数。
  • revision (str, 可选) — 要提交的 git 修订版本。默认为“main”分支的头部。
  • kwargs (dict[str, Any], 可选) — 传递给 ~Trainer.create_model_card 的附加关键字参数。

将 `self.model` 和 `self.processing_class` 上传到 🤗 模型中心的 `self.args.hub_model_id` 存储库。

PPOConfig

class trl.PPOConfig

< >

( output_dir: typing.Optional[str] = None overwrite_output_dir: bool = False do_train: bool = False do_eval: bool = False do_predict: bool = False eval_strategy: typing.Union[transformers.trainer_utils.IntervalStrategy, str] = 'no' prediction_loss_only: bool = False per_device_train_batch_size: int = 8 per_device_eval_batch_size: int = 8 per_gpu_train_batch_size: typing.Optional[int] = None per_gpu_eval_batch_size: typing.Optional[int] = None gradient_accumulation_steps: int = 1 eval_accumulation_steps: typing.Optional[int] = None eval_delay: typing.Optional[float] = 0 torch_empty_cache_steps: typing.Optional[int] = None learning_rate: float = 5e-05 weight_decay: float = 0.0 adam_beta1: float = 0.9 adam_beta2: float = 0.999 adam_epsilon: float = 1e-08 max_grad_norm: float = 1.0 num_train_epochs: float = 3.0 max_steps: int = -1 lr_scheduler_type: typing.Union[transformers.trainer_utils.SchedulerType, str] = 'linear' lr_scheduler_kwargs: typing.Union[dict[str, typing.Any], str, NoneType] = <factory> warmup_ratio: float = 0.0 warmup_steps: int = 0 log_level: str = 'passive' log_level_replica: str = 'warning' log_on_each_node: bool = True logging_dir: typing.Optional[str] = None logging_strategy: typing.Union[transformers.trainer_utils.IntervalStrategy, str] = 'steps' logging_first_step: bool = False logging_steps: float = 10 logging_nan_inf_filter: bool = True save_strategy: typing.Union[transformers.trainer_utils.SaveStrategy, str] = 'steps' save_steps: float = 500 save_total_limit: typing.Optional[int] = None save_safetensors: typing.Optional[bool] = True save_on_each_node: bool = False save_only_model: bool = False restore_callback_states_from_checkpoint: bool = False no_cuda: bool = False use_cpu: bool = False use_mps_device: bool = False seed: int = 42 data_seed: typing.Optional[int] = None jit_mode_eval: bool = False use_ipex: bool = False bf16: typing.Optional[bool] = None fp16: bool = False fp16_opt_level: str = 'O1' half_precision_backend: str = 'auto' bf16_full_eval: bool = False fp16_full_eval: bool = False tf32: typing.Optional[bool] = None local_rank: int = -1 ddp_backend: typing.Optional[str] = None tpu_num_cores: typing.Optional[int] = None tpu_metrics_debug: bool = False debug: typing.Union[str, list[transformers.debug_utils.DebugOption]] = '' dataloader_drop_last: bool = False eval_steps: typing.Optional[float] = None dataloader_num_workers: int = 0 dataloader_prefetch_factor: typing.Optional[int] = None past_index: int = -1 run_name: typing.Optional[str] = None disable_tqdm: typing.Optional[bool] = None remove_unused_columns: typing.Optional[bool] = True label_names: typing.Optional[list[str]] = None load_best_model_at_end: typing.Optional[bool] = False metric_for_best_model: typing.Optional[str] = None greater_is_better: typing.Optional[bool] = None ignore_data_skip: bool = False fsdp: typing.Union[list[transformers.trainer_utils.FSDPOption], str, NoneType] = '' fsdp_min_num_params: int = 0 fsdp_config: typing.Union[dict[str, typing.Any], str, NoneType] = None fsdp_transformer_layer_cls_to_wrap: typing.Optional[str] = None accelerator_config: typing.Union[dict, str, NoneType] = None deepspeed: typing.Union[dict, str, NoneType] = None label_smoothing_factor: float = 0.0 optim: typing.Union[transformers.training_args.OptimizerNames, str] = 'adamw_torch' optim_args: typing.Optional[str] = None adafactor: bool = False group_by_length: bool = False length_column_name: typing.Optional[str] = 'length' report_to: typing.Union[NoneType, str, list[str]] = None ddp_find_unused_parameters: typing.Optional[bool] = None ddp_bucket_cap_mb: typing.Optional[int] = None ddp_broadcast_buffers: typing.Optional[bool] = None dataloader_pin_memory: bool = True dataloader_persistent_workers: bool = False skip_memory_metrics: bool = True use_legacy_prediction_loop: bool = False push_to_hub: bool = False resume_from_checkpoint: typing.Optional[str] = None hub_model_id: typing.Optional[str] = None hub_strategy: typing.Union[transformers.trainer_utils.HubStrategy, str] = 'every_save' hub_token: typing.Optional[str] = None hub_private_repo: typing.Optional[bool] = None hub_always_push: bool = False hub_revision: typing.Optional[str] = None gradient_checkpointing: bool = False gradient_checkpointing_kwargs: typing.Union[dict[str, typing.Any], str, NoneType] = None include_inputs_for_metrics: bool = False include_for_metrics: list = <factory> eval_do_concat_batches: bool = True fp16_backend: str = 'auto' push_to_hub_model_id: typing.Optional[str] = None push_to_hub_organization: typing.Optional[str] = None push_to_hub_token: typing.Optional[str] = None mp_parameters: str = '' auto_find_batch_size: bool = False full_determinism: bool = False torchdynamo: typing.Optional[str] = None ray_scope: typing.Optional[str] = 'last' ddp_timeout: int = 1800 torch_compile: bool = False torch_compile_backend: typing.Optional[str] = None torch_compile_mode: typing.Optional[str] = None include_tokens_per_second: typing.Optional[bool] = False include_num_input_tokens_seen: typing.Optional[bool] = False neftune_noise_alpha: typing.Optional[float] = None optim_target_modules: typing.Union[NoneType, str, list[str]] = None batch_eval_metrics: bool = False eval_on_start: bool = False use_liger_kernel: typing.Optional[bool] = False liger_kernel_config: typing.Optional[dict[str, bool]] = None eval_use_gather_object: typing.Optional[bool] = False average_tokens_across_devices: typing.Optional[bool] = True dataset_num_proc: typing.Optional[int] = None num_mini_batches: int = 1 total_episodes: typing.Optional[int] = None local_rollout_forward_batch_size: int = 64 num_sample_generations: int = 10 response_length: int = 53 stop_token: typing.Optional[typing.Literal['eos']] = None stop_token_id: typing.Optional[int] = None temperature: float = 0.7 missing_eos_penalty: typing.Optional[float] = None sft_model_path: str = 'EleutherAI/pythia-160m' world_size: typing.Optional[int] = None num_total_batches: typing.Optional[int] = None micro_batch_size: typing.Optional[int] = None local_batch_size: typing.Optional[int] = None batch_size: typing.Optional[int] = None local_mini_batch_size: typing.Optional[int] = None mini_batch_size: typing.Optional[int] = None exp_name: str = 'ppo_config' reward_model_path: str = 'EleutherAI/pythia-160m' model_adapter_name: typing.Optional[str] = None ref_adapter_name: typing.Optional[str] = None num_ppo_epochs: int = 4 whiten_rewards: bool = False kl_coef: float = 0.05 kl_estimator: typing.Literal['k1', 'k3'] = 'k1' cliprange: float = 0.2 vf_coef: float = 0.1 cliprange_value: float = 0.2 gamma: float = 1.0 lam: float = 0.95 ds3_gather_for_generation: bool = True )

参数

  • exp_name (str, 可选, 默认为 os.path.basename(__file__)[ ---3]):此实验的名称。
  • reward_model_path (str, 可选, 默认为 "EleutherAI/pythia-160m") — 奖励模型的路径。
  • model_adapter_name (strNone, 可选, 默认为 None) — 当使用带有多个适配器的LoRA时,训练目标PEFT适配器的名称。
  • ref_adapter_name (strNone, 可选, 默认为 None) — 当使用带有多个适配器的LoRA时,参考PEFT适配器的名称。
  • num_ppo_epochs (int, 可选, 默认为 4) — 训练的轮数。
  • whiten_rewards (bool, 可选, 默认为 False) — 是否白化奖励。
  • kl_coef (float, 可选, 默认为 0.05) — KL系数。
  • kl_estimator (Literal["k1", "k3"], 可选, 默认为 "k1") — 从近似KL散度中选择哪个KL散度估计器。默认为“k1”,这是一个直接的、无偏的估计器。可以设置为“k3”,这是一个方差更小的无偏估计器,似乎是“一个严格更好的估计器”。不能设置为“k2”,因为它用于日志记录。
  • cliprange (float, 可选, 默认为 0.2) — 裁剪范围。
  • vf_coef (float, 可选, 默认为 0.1) — 值函数系数。
  • cliprange_value (float, 可选, 默认为 0.2) — 值函数的裁剪范围。
  • gamma (float, 可选, 默认为 1.0) — 折扣因子。
  • lam (float, 可选, 默认为 0.95) — GAE 的 Lambda 值。
  • ds3_gather_for_generation (bool, 可选, 默认为 True) — 此设置适用于 DeepSpeed ZeRO-3。如果启用,策略模型权重将被收集用于生成,从而提高生成速度。但是,禁用此选项可以训练超过单个 GPU VRAM 容量的模型,尽管代价是生成速度较慢。

PPOTrainer 的配置类。

此类仅包含特定于 PPO 训练的参数。有关训练参数的完整列表,请参阅 TrainingArgumentsOnPolicyConfig 文档。请注意,此类中的默认值可能与 TrainingArguments 中的默认值不同。

使用 HfArgumentParser,我们可以将此类别转换为可在命令行上指定的 argparse 参数。

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