Source code for cerebras.modelzoo.losses.DPOLoss

# Copyright 2022 Cerebras Systems.
#
# 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
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# See the License for the specific language governing permissions and
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# Based on https://github.com/huggingface/trl/blob/main/trl/trainer/dpo_trainer.py

import torch
import torch.nn as nn
import torch.nn.functional as F

from cerebras.modelzoo.common.registry import registry


[docs]@registry.register_loss("DPOLoss") class DPOLoss(nn.Module): """ DPO Loss :param beta: 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. :param reference_free: If True, we ignore the _provided_ reference model and implicitly use a reference model that assigns equal probability to all responses. """ def __init__( self, beta=0.1, loss_type="sigmoid", reference_free=False, ): super(DPOLoss, self).__init__() self.beta = beta self.loss_type = loss_type self.reference_free = reference_free def forward( self, policy_chosen_logps, policy_rejected_logps, reference_chosen_logps, reference_rejected_logps, ): pi_logratios = policy_chosen_logps - policy_rejected_logps if self.reference_free: ref_logratios = 0 else: ref_logratios = reference_chosen_logps - reference_rejected_logps logits = pi_logratios - ref_logratios if self.loss_type == "sigmoid": losses = -F.logsigmoid(self.beta * logits) elif self.loss_type == "hinge": losses = torch.relu(1 - self.beta * logits) elif self.loss_type == "ipo": losses = torch.pow(logits - 1 / (2 * self.beta), 2.0) else: raise ValueError( f"Unknown loss type: {self.loss_type}. Should be one of ['sigmoid', 'hinge', 'ipo']" ) return losses.mean()