mirror of https://github.com/hpcaitech/ColossalAI
91 lines
2.9 KiB
Python
Executable File
91 lines
2.9 KiB
Python
Executable File
from abc import ABC, abstractmethod
|
|
from dataclasses import dataclass
|
|
from typing import Optional
|
|
|
|
import torch
|
|
from coati.models import Critic, RewardModel
|
|
from transformers import PreTrainedModel
|
|
|
|
|
|
@dataclass
|
|
class Experience:
|
|
"""Experience is a batch of data.
|
|
These data should have the sequence length and number of actions.
|
|
Left padding for sequences is applied.
|
|
|
|
Shapes of each tensor:
|
|
sequences: (B, S)
|
|
action_log_probs: (B, A)
|
|
values: (B)
|
|
reward: (B)
|
|
advantages: (B)
|
|
attention_mask: (B, S)
|
|
action_mask: (B, A)
|
|
|
|
"A" is the number of actions.
|
|
"""
|
|
|
|
sequences: torch.Tensor
|
|
action_log_probs: torch.Tensor
|
|
values: torch.Tensor
|
|
reward: torch.Tensor
|
|
kl: torch.Tensor
|
|
advantages: torch.Tensor
|
|
attention_mask: Optional[torch.LongTensor]
|
|
action_mask: Optional[torch.BoolTensor]
|
|
|
|
@torch.no_grad()
|
|
def to_device(self, device: torch.device) -> None:
|
|
self.sequences = self.sequences.to(device)
|
|
self.action_log_probs = self.action_log_probs.to(device)
|
|
self.values = self.values.to(device)
|
|
self.reward = self.reward.to(device)
|
|
self.advantages = self.advantages.to(device)
|
|
self.kl = self.kl.to(device)
|
|
if self.attention_mask is not None:
|
|
self.attention_mask = self.attention_mask.to(device)
|
|
if self.action_mask is not None:
|
|
self.action_mask = self.action_mask.to(device)
|
|
|
|
def pin_memory(self):
|
|
self.sequences = self.sequences.pin_memory()
|
|
self.action_log_probs = self.action_log_probs.pin_memory()
|
|
self.values = self.values.pin_memory()
|
|
self.reward = self.reward.pin_memory()
|
|
self.advantages = self.advantages.pin_memory()
|
|
self.kl = self.kl.pin_memory()
|
|
if self.attention_mask is not None:
|
|
self.attention_mask = self.attention_mask.pin_memory()
|
|
if self.action_mask is not None:
|
|
self.action_mask = self.action_mask.pin_memory()
|
|
return self
|
|
|
|
|
|
class ExperienceMaker(ABC):
|
|
"""
|
|
Base class for experience makers.
|
|
"""
|
|
|
|
def __init__(
|
|
self, actor: PreTrainedModel, critic: Critic, reward_model: RewardModel, initial_model: PreTrainedModel
|
|
) -> None:
|
|
super().__init__()
|
|
self.actor = actor
|
|
self.critic = critic
|
|
self.reward_model = reward_model
|
|
self.initial_model = initial_model
|
|
|
|
@abstractmethod
|
|
def make_experience(self, input_ids: torch.Tensor, attention_mask: torch.Tensor, **generate_kwargs) -> Experience:
|
|
"""
|
|
Abstract method to generate an experience.
|
|
|
|
Args:
|
|
input_ids (torch.Tensor): The input tensor.
|
|
attention_mask (torch.Tensor): The attention mask tensor.
|
|
**generate_kwargs: Additional keyword arguments for generating the experience.
|
|
|
|
Returns:
|
|
Experience: The generated experience.
|
|
"""
|