Source code for torchtune.rlhf.loss.dpo
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchtune.rlhf._types import ChosenRejectedOutputs
from torchtune.utils._logging import deprecated
[docs]class DPOLoss(nn.Module):
"""
Direct Preference Optimization (DPO) Loss module: https://arxiv.org/abs/2305.18290
Simply stated from the paper:
Intuitively, the DPO update increases the relative log probability of preferred to dispreferred responses,
but it incorporates a dynamic, per-example importance weight that prevents
the model degeneration that we find occurs with a naive probability ratio objective.
Based on the implementation in HF's TRL library:
https://github.com/huggingface/trl/blob/5d1deb1445828cfd0e947cb3a7925b1c03a283fc/trl/trainer/dpo_trainer.py#L844
DPO retains similarities to PPO (https://arxiv.org/abs/2009.01325), where it optimizes a policy
(language) model to align with human preferences, and regularizes the loss function using a baseline
reference (the frozen, initial language model) to prevent over-fitting to the preference dataset.
It differs from PPO by optimizing the policy model directly using labelled preference data, rather
than using an additional reward model to provide feedback.
This significantly simplifies training and reduces compute overhead.
Args:
beta (float): Temperature parameter for the DPO loss, typically in the range of 0.1 to 0.5. Default is 0.1.
label_smoothing (float): Parameter encoding uncertainty about the labels. Default is 0.
"""
def __init__(
self,
beta: float = 0.1,
label_smoothing: float = 0.0,
):
super().__init__()
self.beta = beta
self.label_smoothing = label_smoothing
[docs] def forward(
self,
policy_inputs: ChosenRejectedOutputs,
reference_inputs: ChosenRejectedOutputs,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Compute the DPO loss for a batch of policy and reference model log probabilities.
Args:
policy_inputs (ChosenRejectedOutputs): Policy log-probs and logits required for the calculation.
reference_inputs (ChosenRejectedOutputs): Reference log-probs and logits required for the calculation.
Returns:
tuple[torch.Tensor, torch.Tensor, torch.Tensor]: A tuple of three tensors:
- losses: The DPO loss for each example in the batch.
- chosen_rewards: Rewards for the chosen responses.
- rejected_rewards: Rewards for the rejected responses.
"""
pi_logratios = policy_inputs.chosen_logps - policy_inputs.rejected_logps
ref_logratios = reference_inputs.chosen_logps - reference_inputs.rejected_logps
logits = pi_logratios - ref_logratios
# The beta is a temperature parameter for the DPO loss, typically something in the range of 0.1 to 0.5.
# We ignore the reference model as beta -> 0. The label_smoothing parameter encodes our uncertainty about the labels and
# calculates a conservative DPO loss.
losses = (
-F.logsigmoid(self.beta * logits) * (1 - self.label_smoothing)
- F.logsigmoid(-self.beta * logits) * self.label_smoothing
)
chosen_rewards = (
self.beta
* (policy_inputs.chosen_logps - reference_inputs.chosen_logps).detach()
)
rejected_rewards = (
self.beta
* (policy_inputs.rejected_logps - reference_inputs.rejected_logps).detach()
)
return losses, chosen_rewards, rejected_rewards
[docs]@deprecated(msg="RSOLoss will be deprecated in an upcoming release.")
class RSOLoss(nn.Module):
"""
Statistical Rejection Sampling Optimization (RSO) or "hinge" loss module: https://arxiv.org/abs/2309.06657.
Intuition from the paper:
DPO is a logistic regression on human preference data, and SLiC (https://arxiv.org/abs/2305.10425) is almost
equivalent to a support vector machine (SVM) with hinge loss. [RSO] improve[s] SLiC as the SVM counter part of DPO.
Based on the implementation in HF's TRL library:
https://github.com/huggingface/trl/blob/4dce042a3863db1d375358e8c8092b874b02934b/trl/trainer/dpo_trainer.py#L1141
Args:
gamma (float): Equivalent temperature parameter (from DPO) for the RSO loss.
"""
def __init__(
self,
gamma: float = 0.1,
):
super().__init__()
self.gamma = gamma
def forward(
self,
policy_inputs: ChosenRejectedOutputs,
reference_inputs: ChosenRejectedOutputs,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Compute the RSO loss for a batch of policy and reference model log probabilities.
Args:
policy_inputs (ChosenRejectedOutputs): Policy log-probs and logits required for the calculation.
reference_inputs (ChosenRejectedOutputs): Reference log-probs and logits required for the calculation.
Returns:
tuple[torch.Tensor, torch.Tensor, torch.Tensor]: A tuple of three tensors:
- losses: The RSO loss for each example in the batch.
- chosen_rewards: Rewards for the chosen responses.
- rejected_rewards: Rewards for the rejected responses.
"""
pi_logratios = policy_inputs.chosen_logps - policy_inputs.rejected_logps
ref_logratios = reference_inputs.chosen_logps - reference_inputs.rejected_logps
logits = pi_logratios - ref_logratios
losses = torch.relu(1 - self.gamma * logits)
chosen_rewards = (
self.gamma
* (policy_inputs.chosen_logps - reference_inputs.chosen_logps).detach()
)
rejected_rewards = (
self.gamma
* (policy_inputs.rejected_logps - reference_inputs.rejected_logps).detach()
)
return losses, chosen_rewards, rejected_rewards
