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