mirror of
https://github.com/hpcaitech/ColossalAI.git
synced 2025-08-10 12:22:28 +00:00
7b9b86441f
* feat: modify forward fn of critic and reward model * feat: modify calc_action_log_probs * to: add wandb in sft and rm trainer * feat: update train_sft * feat: update train_rm * style: modify type annotation and add warning * feat: pass tokenizer to ppo trainer * to: modify trainer base and maker base * feat: add wandb in ppo trainer * feat: pass tokenizer to generate * test: update generate fn tests * test: update train tests * fix: remove action_mask * feat: remove unused code * fix: fix wrong ignore_index * fix: fix mock tokenizer * chore: update requirements * revert: modify make_experience * fix: fix inference * fix: add padding side * style: modify _on_learn_batch_end * test: use mock tokenizer * fix: use bf16 to avoid overflow * fix: fix workflow * [chat] fix gemini strategy * [chat] fix * sync: update colossalai strategy * fix: fix args and model dtype * fix: fix checkpoint test * fix: fix requirements * fix: fix missing import and wrong arg * fix: temporarily skip gemini test in stage 3 * style: apply pre-commit * fix: temporarily skip gemini test in stage 1&2 --------- Co-authored-by: Mingyan Jiang <1829166702@qq.com>
71 lines
2.3 KiB
Python
71 lines
2.3 KiB
Python
from abc import ABC, abstractmethod
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from dataclasses import dataclass
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from typing import Optional
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import torch
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from coati.models.base import Actor, Critic, RewardModel
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@dataclass
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class Experience:
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"""Experience is a batch of data.
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These data should have the sequence length and number of actions.
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Left padding for sequences is applied.
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Shapes of each tensor:
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sequences: (B, S)
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action_log_probs: (B, A)
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values: (B)
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reward: (B)
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advantages: (B)
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attention_mask: (B, S)
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action_mask: (B, A)
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"A" is the number of actions.
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"""
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sequences: torch.Tensor
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action_log_probs: torch.Tensor
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values: torch.Tensor
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reward: torch.Tensor
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advantages: torch.Tensor
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attention_mask: Optional[torch.LongTensor]
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action_mask: Optional[torch.BoolTensor]
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@torch.no_grad()
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def to_device(self, device: torch.device) -> None:
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self.sequences = self.sequences.to(device)
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self.action_log_probs = self.action_log_probs.to(device)
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self.values = self.values.to(device)
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self.reward = self.reward.to(device)
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self.advantages = self.advantages.to(device)
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if self.attention_mask is not None:
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self.attention_mask = self.attention_mask.to(device)
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if self.action_mask is not None:
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self.action_mask = self.action_mask.to(device)
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def pin_memory(self):
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self.sequences = self.sequences.pin_memory()
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self.action_log_probs = self.action_log_probs.pin_memory()
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self.values = self.values.pin_memory()
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self.reward = self.reward.pin_memory()
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self.advantages = self.advantages.pin_memory()
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if self.attention_mask is not None:
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self.attention_mask = self.attention_mask.pin_memory()
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if self.action_mask is not None:
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self.action_mask = self.action_mask.pin_memory()
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return self
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class ExperienceMaker(ABC):
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def __init__(self, actor: Actor, critic: Critic, reward_model: RewardModel, initial_model: Actor) -> None:
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super().__init__()
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self.actor = actor
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self.critic = critic
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self.reward_model = reward_model
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self.initial_model = initial_model
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@abstractmethod
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def make_experience(self, input_ids: torch.Tensor, attention_mask: torch.Tensor, **generate_kwargs) -> Experience:
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pass
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