venv/lib/python3.11/site-packages/trl/trainer/grpo_config.py

# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import warnings
from dataclasses import dataclass, field
from typing import Optional, Union

import transformers
from packaging import version
from transformers import TrainingArguments


@dataclass
class GRPOConfig(TrainingArguments):
    r"""
    Configuration class for the [`GRPOTrainer`].

    Only the parameters specific to GRPO training are listed here. For details on other parameters, refer to the
    [`~transformers.TrainingArguments`] documentation.

    Using [`~transformers.HfArgumentParser`] we can turn this class into
    [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the
    command line.

    Parameters:
        > Parameters that control the model and reference model

        model_init_kwargs (`str`, `dict[str, Any]` or `None`, *optional*, defaults to `None`):
            Keyword arguments for [`~transformers.AutoModelForCausalLM.from_pretrained`], used when the `model`
            argument of the [`GRPOTrainer`] is provided as a string.
        disable_dropout (`bool`, *optional*, defaults to `False`):
            Whether to disable dropout in the model. This is useful for training with a reference model, as it
            prevents the model from generating different logprobs for the same input.

        > Parameters that control the data preprocessing

        remove_unused_columns (`bool`, *optional*, defaults to `False`):
            Whether to only keep the column `"prompt"` in the dataset. If you use a custom reward function that
            requires any column other than `"prompts"` and `"completions"`, you should keep this to `False`.
        max_prompt_length (`int` or `None`, *optional*, defaults to `512`):
            Maximum length of the prompt. If the prompt is longer than this value, it will be truncated left.
        num_generations (`int` or `None`, *optional*, defaults to `8`):
            Number of generations per prompt to sample. The effective batch size (num_processes *
            per_device_batch_size * gradient_accumulation_steps) must be evenly divisible by this value.
        max_completion_length (`int` or `None`, *optional*, defaults to `256`):
            Maximum length of the generated completion.
        ds3_gather_for_generation (`bool`, *optional*, defaults to `True`):
            This setting applies to DeepSpeed ZeRO-3. If enabled, the policy model weights are gathered for generation,
            improving generation speed. However, disabling this option allows training models that exceed the VRAM
            capacity of a single GPU, albeit at the cost of slower generation. Disabling this option is not compatible
            with vLLM generation.
        shuffle_dataset (`bool`, *optional*, defaults to `True`):
            Whether to shuffle the training dataset.

        > Parameters that control generation

        temperature (`float`, defaults to `0.9`):
            Temperature for sampling. The higher the temperature, the more random the completions.
        top_p (`float`, *optional*, defaults to `1.0`):
            Float that controls the cumulative probability of the top tokens to consider. Must be in (0, 1]. Set to
            `1.0` to consider all tokens.
        top_k (`int` or `None`, *optional*, defaults to `50`):
            Number of highest probability vocabulary tokens to keep for top-k-filtering. If `None`, top-k-filtering is
            disabled.
        min_p (`float` or `None`, *optional*, defaults to `None`):
            Minimum token probability, which will be scaled by the probability of the most likely token. It must be a
            value between `0.0` and `1.0`. Typical values are in the `0.01-0.2` range.
        repetition_penalty (`float`, *optional*, defaults to `1.0`):
            Float that penalizes new tokens based on whether they appear in the prompt and the generated text so far.
            Values > `1.0` encourage the model to use new tokens, while values < `1.0` encourage the model to repeat
            tokens.
        cache_implementation (`str` or `None`, *optional*, defaults to `None`):
            Implementation of the cache method for faster generation when use_vllm is set to False.

        > Parameters that control generation acceleration powered by vLLM

        use_vllm (`bool`, *optional*, defaults to `False`):
            Whether to use vLLM for generating completions. If set to `True`, ensure that a GPU is kept unused for
            training, as vLLM will require one for generation. vLLM must be installed (`pip install vllm`).
        vllm_server_host (`str`, *optional*, defaults to `"0.0.0.0"`):
            Host of the vLLM server to connect to.
        vllm_server_port (`int`, *optional*, defaults to `8000`):
            Port of the vLLM server to connect to.
        vllm_server_timeout (`float`, *optional*, defaults to `120.0`):
            Total timeout duration in seconds to wait for the vLLM server to be up. If the server is not up after the
            timeout, a `ConnectionError` is raised.
        vllm_guided_decoding_regex (`str` or `None`, *optional*, defaults to `None`):
            Regex for vLLM guided decoding. If `None` (default), guided decoding is disabled.

        > Parameters that control the training

        learning_rate (`float`, *optional*, defaults to `1e-6`):
            Initial learning rate for [`AdamW`] optimizer. The default value replaces that of
            [`~transformers.TrainingArguments`].
        beta (`float`, *optional*, defaults to `0.04`):
            KL coefficient. If `0.0`, the reference model is not loaded, reducing memory usage and improving training
            speed, but may be numerically unstable for long training runs.
        num_iterations (`int`, *optional*, defaults to `1`):
            Number of iterations per batch (denoted as μ in the algorithm).
        epsilon (`float`, *optional*, defaults to `0.2`):
            Epsilon value for clipping.
        epsilon_high (`float` or `None`, *optional*, defaults to `None`):
            Upper-bound epsilon value for clipping. If not specified, it defaults to the same value as the lower-bound
            specified in argument `epsilon`. Paper [DAPO](https://huggingface.co/papers/2503.14476) recommends `0.28`.
        reward_weights (`list[float]` or `None`, *optional*, defaults to `None`):
            Weights for each reward function. Must match the number of reward functions. If `None`, all rewards are
            weighted equally with weight `1.0`.
        scale_rewards (`bool`, *optional*, defaults to `True`):
            Whether to scale the rewards by dividing them by their standard deviation. If `True` (default), the rewards
            are normalized by the standard deviation, ensuring they have unit variance. If `False`, no scaling is
            applied. The [Dr. GRPO paper](https://huggingface.co/papers/2503.20783) recommends not scaling the rewards,
            as scaling by the standard deviation introduces a question-level difficulty bias.
        loss_type (`str`, *optional*, defaults to `"bnpo"`):
            Specifies the loss formulation to use. Supported values are:

            - `"grpo"`: Aggregates token-level losses by normalizing over sequence length. Not recommended due to
                length bias—this approach tends to prefer shorter completions with positive advantages and longer ones
                with negative advantages.
            - `"bnpo"`: Aggregates token-level losses by normalizing number of active token in the local batch.
                Note that normalization is performed over the local batch only, so results may slightly vary depending
                on the local batch size, despite a constant effective batch size. When using
                `per_device_train_batch_size==1`, the loss is equivalent to the GRPO loss.
            - `"dr_grpo"`: Aggregates token-level losses by normalizing with a global constant. This method was
                introduced in the [Dr. GRPO paper](https://huggingface.co/papers/2503.20783) to eliminate length bias.
                The value of the constant corresponds to `max_completion_length`.
        mask_truncated_completions (`bool`, *optional*, defaults to `False`):
            When enabled, truncated completions are excluded from the loss calculation, preventing them from being
            incorrectly penalized and introducing noise during training. According to the
            [DAPO](https://huggingface.co/papers/2503.14476) paper, this is a good practice for training stability.
        sync_ref_model (`bool`, *optional*, defaults to `False`):
            Whether to synchronize the reference model with the active model every `ref_model_sync_steps` steps, using
            the `ref_model_mixup_alpha` parameter. This synchronization originites from the
            [TR-DPO](https://huggingface.co/papers/2404.09656) paper.
        ref_model_mixup_alpha (`float`, *optional*, defaults to `0.6`):
            α parameter from the [TR-DPO](https://huggingface.co/papers/2404.09656) paper, which controls the mix
            between the current policy and the previous reference policy during updates. The reference policy is
            updated according to the equation: `π_ref = α * π_θ + (1 - α) * π_ref_prev`. To use this parameter, you
            must set `sync_ref_model=True`.
        ref_model_sync_steps (`int`, *optional*, defaults to `512`):
            τ parameter from the [TR-DPO](https://huggingface.co/papers/2404.09656) paper, which determines how
            frequently the current policy is synchronized with the reference policy. To use this parameter, you must
            set `sync_ref_model=True`.
        use_liger_loss (`bool`, *optional*, defaults to `False`):
            Whether to use the Liger GRPO loss.

        > Parameters that control the logging

        log_completions (`bool`, *optional*, defaults to `False`):
            Whether to log a sample of (prompt, completion) pairs every `logging_steps` steps. If `rich` is
            installed, it prints the sample. If `wandb` logging is enabled, it logs it to `wandb`.
        num_completions_to_print (`int` or `None`, *optional*, defaults to `None`):
            Number of completions to print with `rich`. If `None`, all completions are logged.
        wandb_log_unique_prompts (`bool`, *optional*, defaults to `False`):
            Whether to log unique prompts in wandb. If `True`, only unique prompts are logged. If `False`, all
            prompts are logged.
    """

    if version.parse(transformers.__version__) <= version.parse("4.50.3"):
        from transformers.training_args import _VALID_DICT_FIELDS

        _VALID_DICT_FIELDS.append("model_init_kwargs")
    else:
        _VALID_DICT_FIELDS = TrainingArguments._VALID_DICT_FIELDS + ["model_init_kwargs"]

    # Parameters that control the model and reference model
    model_init_kwargs: Optional[Union[dict, str]] = field(
        default=None,
        metadata={
            "help": "Keyword arguments for `transformers.AutoModelForCausalLM.from_pretrained`, used when the `model` "
            "argument of the `GRPOTrainer` is provided as a string."
        },
    )
    disable_dropout: bool = field(
        default=False,
        metadata={
            "help": "Whether to disable dropout in the model. This is useful for training with a reference model, as "
            "it prevents the model from generating different logprobs for the same input."
        },
    )

    # Parameters that control the data preprocessing
    # The default value remove_unused_columns is overwritten from the parent class, because in GRPO we usually rely on
    # additional columns to compute the reward
    remove_unused_columns: Optional[bool] = field(
        default=False,
        metadata={
            "help": "Whether to only keep the column 'prompt' in the dataset. If you use a custom reward function "
            "that requires any column other than 'prompts' and 'completions', you should keep this to `False`."
        },
    )
    max_prompt_length: Optional[int] = field(
        default=512,
        metadata={
            "help": "Maximum length of the prompt. If the prompt is longer than this value, it will be truncated left."
        },
    )
    num_generations: Optional[int] = field(
        default=8,
        metadata={
            "help": "Number of generations to sample. The effective batch size (num_processes * per_device_batch_size "
            "* gradient_accumulation_steps) must be evenly divisible by this value."
        },
    )
    max_completion_length: Optional[int] = field(
        default=256,
        metadata={"help": "Maximum length of the generated completion."},
    )
    ds3_gather_for_generation: bool = field(
        default=True,
        metadata={
            "help": "This setting applies to DeepSpeed ZeRO-3. If enabled, the policy model weights are gathered for "
            "generation, improving generation speed. However, disabling this option allows training models that "
            "exceed the VRAM capacity of a single GPU, albeit at the cost of slower generation. Disabling this option "
            "is not compatible with vLLM generation."
        },
    )
    shuffle_dataset: Optional[bool] = field(
        default=True,
        metadata={"help": "Whether to shuffle the training dataset."},
    )

    # Parameters that control generation
    temperature: float = field(
        default=0.9,
        metadata={"help": "Temperature for sampling. The higher the temperature, the more random the completions."},
    )
    top_p: float = field(
        default=1.0,
        metadata={
            "help": "Float that controls the cumulative probability of the top tokens to consider. Must be in (0, 1]. "
            "Set to 1.0 to consider all tokens."
        },
    )
    top_k: Optional[int] = field(
        default=50,
        metadata={
            "help": "Number of highest probability vocabulary tokens to keep for top-k-filtering. If `None`, "
            "top-k-filtering is disabled."
        },
    )
    min_p: Optional[float] = field(
        default=None,
        metadata={
            "help": "Minimum token probability, which will be scaled by the probability of the most likely token. It "
            "must be a value between 0.0 and 1.0. Typical values are in the 0.01-0.2 range."
        },
    )
    repetition_penalty: float = field(
        default=1.0,
        metadata={
            "help": "Float that penalizes new tokens based on whether they appear in the prompt and the generated "
            "text so far. Values > 1.0 encourage the model to use new tokens, while values < 1.0 encourage the model "
            "to repeat tokens."
        },
    )
    cache_implementation: Optional[str] = field(
        default=None,
        metadata={"help": "Implementation of the cache method for faster generation when use_vllm is set to False."},
    )

    # Parameters that control generation acceleration powered by vLLM
    use_vllm: bool = field(
        default=False,
        metadata={
            "help": "Whether to use vLLM for generating completions. If set to `True`, ensure that a vLLM server is "
            "running. To run the server, install vLLM (`pip install vllm`) and run `trl vllm-serve`."
        },
    )
    vllm_server_host: str = field(
        default="0.0.0.0",
        metadata={"help": "Host of the vLLM server to connect to."},
    )
    vllm_server_port: int = field(
        default=8000,
        metadata={"help": "Port of the vLLM server to connect to."},
    )
    vllm_server_timeout: float = field(
        default=240.0,
        metadata={
            "help": "Total timeout duration in seconds to wait for the vLLM server to be up. If the server is not up "
            "after the timeout, a `ConnectionError` is raised."
        },
    )
    vllm_guided_decoding_regex: Optional[str] = field(
        default=None,
        metadata={"help": "Regex for vLLM guided decoding. If `None` (default), guided decoding is disabled."},
    )

    # Parameters that control the training
    learning_rate: float = field(
        default=1e-6,
        metadata={
            "help": "Initial learning rate for `AdamW` optimizer. The default value replaces that of "
            "`transformers.TrainingArguments`."
        },
    )
    beta: float = field(
        default=0.04,
        metadata={
            "help": "KL coefficient. If `0.0`, the reference model is not loaded, reducing memory usage and improving "
            "training speed, but may be numerically unstable for long training runs."
        },
    )
    num_iterations: int = field(
        default=1,
        metadata={"help": "Number of iterations per batch (denoted as μ in the algorithm)."},
    )
    epsilon: float = field(
        default=0.2,
        metadata={"help": "Epsilon value for clipping."},
    )
    epsilon_high: Optional[float] = field(
        default=None,
        metadata={
            "help": "Upper-bound epsilon value for clipping. If not specified, it defaults to the same value as the "
            "lower-bound specified in argument `epsilon`. Paper DAPO recommends `0.28`."
        },
    )
    reward_weights: Optional[list[float]] = field(
        default=None,
        metadata={
            "help": "Weights for each reward function. Must match the number of reward functions. If `None`, all "
            "rewards are weighted equally with weight `1.0`."
        },
    )
    scale_rewards: bool = field(
        default=True,
        metadata={
            "help": "Whether to scale the rewards by dividing them by their standard deviation. If `True` (default), "
            "the rewards are normalized by the standard deviation, ensuring they have unit variance. If `False`, no "
            "scaling is applied. The Dr. GRPO paper recommends not scaling the rewards, as scaling by the standard "
            "deviation introduces a question-level difficulty bias."
        },
    )
    loss_type: str = field(
        default="bnpo",
        metadata={
            "help": "Specifies the loss formulation to use. Supported values are `grpo`, `bnpo`, and `dr_grpo`. "
            "`'grpo'`: Aggregates token-level losses by normalizing over sequence length. Not recommended due to "
            "length bias—this approach tends to prefer shorter completions with positive advantages and longer ones "
            "with negative advantages. "
            "`'bnpo'`: Aggregates token-level losses by normalizing number of active token in the local batch. "
            "Note that normalization is performed over the local batch only, so results may slightly vary depending "
            "on the local batch size, despite a constant effective batch size. When using "
            "`per_device_train_batch_size==1`, the loss is equivalent to the GRPO loss. "
            "`'dr_grpo'`: Aggregates token-level losses by normalizing with a global constant. This method was "
            "introduced in the Dr. GRPO paper to eliminate length bias. The value of the constant corresponds to "
            "`max_completion_length`."
        },
    )
    mask_truncated_completions: bool = field(
        default=False,
        metadata={
            "help": "When enabled, truncated completions are excluded from the loss calculation, preventing them from "
            "being incorrectly penalized and introducing noise during training. According to the DAPO paper, this is "
            "a good practice for training stability."
        },
    )
    sync_ref_model: bool = field(
        default=False,
        metadata={
            "help": "Whether to synchronize the reference model with the active model every `ref_model_sync_steps` "
            "steps, using the `ref_model_mixup_alpha` parameter."
        },
    )
    ref_model_mixup_alpha: float = field(
        default=0.6,
        metadata={
            "help": "α parameter from the TR-DPO paper, which controls the mix between the current policy and the "
            "previous reference policy during updates. The reference policy is updated according to the equation: "
            "`π_ref = α * π_θ + (1 - α) * π_ref_prev`. To use this parameter, you must set `sync_ref_model=True`."
        },
    )
    ref_model_sync_steps: int = field(
        default=512,
        metadata={
            "help": "τ parameter from the TR-DPO paper, which determines how frequently the current policy is "
            "synchronized with the reference policy. To use this parameter, you must set `sync_ref_model=True`."
        },
    )
    use_liger_loss: bool = field(
        default=False,
        metadata={"help": "Whether to use the Liger GRPO loss."},
    )

    # Parameters that control the logging
    log_completions: bool = field(
        default=False,
        metadata={
            "help": "Whether to log a sample of (prompt, completion) pairs every `logging_steps` steps. If `rich` is "
            "installed, it prints the sample. If `wandb` logging is enabled, it logs it to `wandb`."
        },
    )
    num_completions_to_print: Optional[int] = field(
        default=None,
        metadata={"help": "Number of completions to print with `rich`. If `None`, all completions are logged."},
    )
    wandb_log_unique_prompts: Optional[bool] = field(
        default=False,
        metadata={
            "help": "Whether to log unique prompts in wandb. If `True`, only unique prompts are logged. If `False`, "
            "all prompts are logged."
        },
    )

    # Deprecated parameters
    vllm_device: Optional[str] = field(
        default=None,
        metadata={
            "help": "This parameter is deprecated and will be removed in version 0.18.0. To use vLLM, start a vLLM "
            "server with the `trl vllm-serve` command."
        },
    )
    vllm_gpu_memory_utilization: Optional[float] = field(
        default=None,
        metadata={
            "help": "This parameter is deprecated and will be removed in version 0.18.0. To control the GPU memory "
            "utilization for vLLM, you should now use the `gpu_memory_utilization` parameter in the vLLM server "
            "configuration."
        },
    )
    vllm_dtype: Optional[str] = field(
        default=None,
        metadata={
            "help": "This parameter is deprecated and will be removed in version 0.18.0. To control the data type for "
            "vLLM generation, you should now use the `dtype` parameter in the vLLM server configuration."
        },
    )
    vllm_max_model_len: Optional[int] = field(
        default=None,
        metadata={
            "help": "This parameter is deprecated and will be removed in version 0.18.0. To control the "
            "`max_model_len` for vLLM, you should now use the `max_model_len` parameter in the vLLM server "
            "configuration."
        },
    )
    vllm_enable_prefix_caching: Optional[bool] = field(
        default=None,
        metadata={
            "help": "This parameter is deprecated and will be removed in version 0.18.0. To control prefix caching in "
            "vLLM, you should now use the `enable_prefix_caching` parameter in the vLLM server configuration."
        },
    )

    def __post_init__(self):
        super().__post_init__()

        if self.vllm_device is not None:
            warnings.warn(
                "`vllm_device` is deprecated and will be removed in version 0.18.0. To use vLLM, start a vLLM server "
                "with the `trl vllm-serve` command.",
                DeprecationWarning,
            )

        if self.vllm_gpu_memory_utilization is not None:
            warnings.warn(
                "`vllm_gpu_memory_utilization` is deprecated and will be removed in v0.18. To control the GPU memory "
                "utilization for vLLM, you should now use the `gpu_memory_utilization` parameter in the vLLM server "
                "configuration.",
                DeprecationWarning,
            )

        if self.vllm_dtype is not None:
            warnings.warn(
                "`vllm_dtype` is deprecated and will be removed in version 0.18.0. To control the data type for vLLM "
                "generation, you should now use the `dtype` parameter in the vLLM server configuration.",
                DeprecationWarning,
            )

        if self.vllm_max_model_len is not None:
            warnings.warn(
                "`vllm_max_model_len` is deprecated and will be removed in version 0.18.0. To control the "
                "`max_model_len` for vLLM, you should now use the `max_model_len` parameter in the vLLM server "
                "configuration.",
                DeprecationWarning,
            )

        if self.vllm_enable_prefix_caching is not None:
            warnings.warn(
                "`vllm_enable_prefix_caching` is deprecated and will be removed in version 0.18.0. To control prefix "
                "caching in vLLM, you should now use the `enable_prefix_caching` parameter in the vLLM server "
                "configuration.",
                DeprecationWarning,
            )