trl/pr_5607/en/bco_trainer.md

BCO Trainer

TRL supports the Binary Classifier Optimization (BCO). The BCO authors train a binary classifier whose logit serves as a reward so that the classifier maps {prompt, chosen completion} pairs to 1 and {prompt, rejected completion} pairs to 0. For a full example have a look at examples/scripts/bco.py.

Expected dataset type

The experimental.bco.BCOTrainer requires an unpaired preference dataset. The experimental.bco.BCOTrainer supports both conversational and standard dataset formats. When provided with a conversational dataset, the trainer will automatically apply the chat template to the dataset.

Expected model format

The BCO trainer expects a model of AutoModelForCausalLM, compared to PPO that expects AutoModelForCausalLMWithValueHead for the value function.

Using the BCOTrainer

For a detailed example have a look at the examples/scripts/bco.py script. At a high level we need to initialize the BCOTrainer with a model we wish to train and a reference ref_model which we will use to calculate the implicit rewards of the preferred and rejected response.

The beta refers to the hyperparameter of the implicit reward, and the dataset contains the 3 entries listed above. Note that the model and ref_model need to have the same architecture (ie decoder only or encoder-decoder).

from trl.experimental.bco import BCOConfig, BCOTrainer

training_args = BCOConfig(
    beta=0.1,
)

bco_trainer = BCOTrainer(
    model,
    model_ref,
    args=training_args,
    train_dataset=train_dataset,
    processing_class=tokenizer,
)

After this one can then call:

bco_trainer.train()

Underlying Distribution matching (UDM)

In practical scenarios, the thumbs-up and thumbs-down datasets are likely to have divergent underlying distributions of prompts. Consider an LLM deployed for user feedback: if the model excels in writing tasks but underperforms in coding, the thumbs-up dataset will be dominated by writing-related prompts, while the thumbs-down dataset will contain mostly coding-related prompts.
If the prompts in your desired and undesired datasets differ a lot, it is useful to enable UDM.

Choose an embedding model and tokenizer:

embedding_model = AutoModel.from_pretrained(your_model_id)
embedding_tokenizer = AutoTokenizer.from_pretrained(your_model_id)

# customize this function depending on your embedding model
def embed_prompt(input_ids, attention_mask, model):
    outputs = model(input_ids=input_ids, attention_mask=attention_mask)
    return outputs.last_hidden_state.mean(dim=1)

embedding_model = Accelerator().prepare_model(self.embedding_model)
embedding_func = partial(embed_prompt, model=embedding_model)

Set prompt_sample_size to define how many prompts are selected to train the UDM classifier and start the training with the provided embedding function:

training_args = BCOConfig(
    beta=0.1,
    prompt_sample_size=512,
)

bco_trainer = BCOTrainer(
    model,
    model_ref,
    args=training_args,
    train_dataset=train_dataset,
    processing_class=tokenizer,
    embedding_func=embedding_func,
    embedding_tokenizer=self.embedding_tokenizer,
)

bco_trainer.train()

For Mixture of Experts Models: Enabling the auxiliary loss

MOEs are the most efficient if the load is about equally distributed between experts.
To ensure that we train MOEs similarly during preference-tuning, it is beneficial to add the auxiliary loss from the load balancer to the final loss.

This option is enabled by setting output_router_logits=True in the model config (e.g. MixtralConfig).
To scale how much the auxiliary loss contributes to the total loss, use the hyperparameter router_aux_loss_coef=... (default: 0.001).

BCOTrainer[[trl.experimental.bco.BCOTrainer]]

trl.experimental.bco.BCOTrainer[[trl.experimental.bco.BCOTrainer]]

Source

Initialize BCOTrainer from BCO paper.

traintrl.experimental.bco.BCOTrainer.trainhttps://github.com/huggingface/trl/blob/vr_5607/transformers/trainer.py#L1323[{"name": "resume_from_checkpoint", "val": ": str | bool | None = None"}, {"name": "trial", "val": ": optuna.Trial | dict[str, Any] | None = None"}, {"name": "ignore_keys_for_eval", "val": ": list[str] | None = None"}]- resume_from_checkpoint (str or bool, optional) -- If a str, local path to a saved checkpoint as saved by a previous instance of Trainer. If a bool and equals True, load the last checkpoint in args.output_dir as saved by a previous instance of Trainer. If present, training will resume from the model/optimizer/scheduler states loaded here.

• trial (optuna.Trial or dict[str, Any], optional) -- The trial run or the hyperparameter dictionary for hyperparameter search.
• ignore_keys_for_eval (list[str], optional) -- A list of keys in the output of your model (if it is a dictionary) that should be ignored when gathering predictions for evaluation during the training.0~trainer_utils.TrainOutputObject containing the global step count, training loss, and metrics.

Main training entry point.

Parameters:

model (PreTrainedModel) : The model to train, preferably an AutoModelForSequenceClassification.

ref_model (PreTrainedModel) : Hugging Face transformer model with a casual language modelling head. Used for implicit reward computation and loss. If no reference model is provided, the trainer will create a reference model with the same architecture as the model to be optimized.

args (experimental.bco.BCOConfig) : The arguments to use for training.

train_dataset (Dataset) : The dataset to use for training.

eval_dataset (Dataset) : The dataset to use for evaluation.

processing_class (PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin or ProcessorMixin, optional) : Processing class used to process the data. If provided, will be used to automatically process the inputs for the model, and it will be saved along the model to make it easier to rerun an interrupted training or reuse the fine-tuned model.

data_collator (DataCollator, optional) : The data collator to use for training. If None is specified, the default data collator (experimental.utils.DPODataCollatorWithPadding) will be used which will pad the sequences to the maximum length of the sequences in the batch, given a dataset of paired sequences.

model_init (Callable[[], transformers.PreTrainedModel]) : The model initializer to use for training. If None is specified, the default model initializer will be used.

callbacks (list[transformers.TrainerCallback]) : The callbacks to use for training.

optimizers (tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]) : The optimizer and scheduler to use for training.

preprocess_logits_for_metrics (Callable[[torch.Tensor, torch.Tensor], torch.Tensor]) : The function to use to preprocess the logits before computing the metrics.

peft_config (dict, defaults to None) : The PEFT configuration to use for training. If you pass a PEFT configuration, the model will be wrapped in a PEFT model.

compute_metrics (Callable[[EvalPrediction], dict], optional) : The function to use to compute the metrics. Must take a EvalPrediction and return a dictionary string to metric values.

model_adapter_name (str, defaults to None) : Name of the train target PEFT adapter, when using LoRA with multiple adapters.

ref_adapter_name (str, defaults to None) : Name of the reference PEFT adapter, when using LoRA with multiple adapters.

Returns:

~trainer_utils.TrainOutput

Object containing the global step count, training loss, and metrics.

save_model[[trl.experimental.bco.BCOTrainer.save_model]]

Source

Will save the model, so you can reload it using from_pretrained().

Will only save from the main process.

push_to_hub[[trl.experimental.bco.BCOTrainer.push_to_hub]]

Source

Upload self.model and self.processing_class to the 🤗 model hub on the repo self.args.hub_model_id.

Parameters:

commit_message (str, optional, defaults to "End of training") : Message to commit while pushing.

blocking (bool, optional, defaults to True) : Whether the function should return only when the git push has finished.

token (str, optional, defaults to None) : Token with write permission to overwrite Trainer's original args.

revision (str, optional) : The git revision to commit from. Defaults to the head of the "main" branch.

kwargs (dict[str, Any], optional) : Additional keyword arguments passed along to ~Trainer.create_model_card.

Returns:

The URL of the repository where the model was pushed if blocking=False, or a Future object tracking the progress of the commit if blocking=True.

BCOConfig[[trl.experimental.bco.BCOConfig]]

trl.experimental.bco.BCOConfig[[trl.experimental.bco.BCOConfig]]

Source

Configuration class for the experimental.bco.BCOTrainer.

This class includes only the parameters that are specific to BCO training. For a full list of training arguments, please refer to the TrainingArguments documentation. Note that default values in this class may differ from those in TrainingArguments.

Using HfArgumentParser we can turn this class into argparse arguments that can be specified on the command line.

These parameters have default values different from TrainingArguments:

• logging_steps: Defaults to 10 instead of 500.
• gradient_checkpointing: Defaults to True instead of False.
• bf16: Defaults to True if fp16 is not set, instead of False.
• learning_rate: Defaults to 5e-7 instead of 5e-5.

Parameters:

max_length (int or None, optional, defaults to 1024) : Maximum length of the sequences (prompt + completion) in the batch. This argument is required if you want to use the default data collator.

max_completion_length (int, optional) : Maximum length of the completion. This argument is required if you want to use the default data collator and your model is an encoder-decoder.

beta (float, optional, defaults to 0.1) : Parameter controlling the deviation from the reference model. Higher β means less deviation from the reference model.

disable_dropout (bool, optional, defaults to True) : Whether to disable dropout in the model and reference model.

generate_during_eval (bool, optional, defaults to False) : If True, generates and logs completions from both the model and the reference model to W&B or Comet during evaluation.

is_encoder_decoder (bool, optional) : When using the model_init argument (callable) to instantiate the model instead of the model argument, you need to specify if the model returned by the callable is an encoder-decoder model.

precompute_ref_log_probs (bool, optional, defaults to False) : Whether to precompute reference model log probabilities for training and evaluation datasets. This is useful when training without the reference model to reduce the total GPU memory needed.

model_init_kwargs (dict[str, Any], optional) : Keyword arguments to pass to AutoModelForCausalLM.from_pretrained when instantiating the model and reference model from strings.

dataset_num_proc (int, optional) : Number of processes to use for processing the dataset.

prompt_sample_size (int, optional, defaults to 1024) : Number of prompts that are fed to density ratio classifier.

min_density_ratio (float, optional, defaults to 0.5) : Minimum value of the density ratio. The estimated density ratio is clamped to this value.

max_density_ratio (float, optional, defaults to 10.0) : Maximum value of the density ratio. The estimated density ratio is clamped to this value.