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from typing import Optional
import torch
import torch.nn as nn
def masked_mean(tensor: torch.Tensor, mask: torch.Tensor, dim: int = 1) -> torch.Tensor:
tensor = tensor * mask
tensor = tensor.sum(dim=dim)
mask_sum = mask.sum(dim=dim)
mean = tensor / (mask_sum + 1e-8)
return mean
class GPTLMLoss(nn.Module):
"""
GPT Language Model Loss
"""
def __init__(self):
super().__init__()
self.loss = nn.CrossEntropyLoss()
def forward(self, logits: torch.Tensor, labels: torch.Tensor) -> torch.Tensor:
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
return self.loss(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
class PolicyLoss(nn.Module):
"""
Policy Loss for PPO
"""
def __init__(self, clip_eps: float = 0.2) -> None:
super().__init__()
self.clip_eps = clip_eps
def forward(self,
log_probs: torch.Tensor,
old_log_probs: torch.Tensor,
advantages: torch.Tensor,
action_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
ratio = (log_probs - old_log_probs).exp()
surr1 = ratio * advantages
surr2 = ratio.clamp(1 - self.clip_eps, 1 + self.clip_eps) * advantages
loss = -torch.min(surr1, surr2)
if action_mask is not None:
loss = masked_mean(loss, action_mask)
loss = loss.mean()
return loss
class ValueLoss(nn.Module):
"""
Value Loss for PPO
"""
def __init__(self, clip_eps: float = 0.4) -> None:
super().__init__()
self.clip_eps = clip_eps
def forward(self,
values: torch.Tensor,
old_values: torch.Tensor,
reward: torch.Tensor,
action_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
values_clipped = old_values + (values - old_values).clamp(-self.clip_eps, self.clip_eps)
surr1 = (values_clipped - reward)**2
surr2 = (values - reward)**2
loss = torch.max(surr1, surr2)
loss = loss.mean()
return 0.5 * loss
class PPOPtxActorLoss(nn.Module):
"""
To Do:
PPO-ptx Actor Loss
"""
def __init__(self, policy_clip_eps: float = 0.2, pretrain_coef: float = 0.0, pretrain_loss_fn=GPTLMLoss()) -> None:
super().__init__()
self.pretrain_coef = pretrain_coef
self.policy_loss_fn = PolicyLoss(clip_eps=policy_clip_eps)
self.pretrain_loss_fn = pretrain_loss_fn
def forward(self,
log_probs: torch.Tensor,
old_log_probs: torch.Tensor,
advantages: torch.Tensor,
lm_logits: torch.Tensor,
lm_input_ids: torch.Tensor,
action_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
policy_loss = self.policy_loss_fn(log_probs, old_log_probs, advantages, action_mask=action_mask)
lm_loss = self.pretrain_loss_fn(lm_logits, lm_input_ids)
return policy_loss + self.pretrain_coef * lm_loss
class LogSigLoss(nn.Module):
"""
Pairwise Loss for Reward Model
Details: https://arxiv.org/abs/2203.02155
"""
def forward(self, chosen_reward: torch.Tensor, reject_reward: torch.Tensor) -> torch.Tensor:
probs = torch.sigmoid(chosen_reward - reject_reward)
log_probs = torch.log(probs)
loss = -log_probs.mean()
return loss
class LogExpLoss(nn.Module):
"""
Pairwise Loss for Reward Model
Details: https://arxiv.org/abs/2204.05862
"""
def forward(self, chosen_reward: torch.Tensor, reject_reward: torch.Tensor) -> torch.Tensor:
loss = torch.log(1 + torch.exp(reject_reward - chosen_reward)).mean()
return loss
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