mirror of https://github.com/hpcaitech/ColossalAI
78 lines
2.4 KiB
Python
78 lines
2.4 KiB
Python
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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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import torch.nn as nn
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from coati.models.base import Actor
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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 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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advatanges: (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,
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actor: Actor,
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critic: nn.Module,
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reward_model: nn.Module,
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initial_model: Actor,
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kl_coef: float = 0.1) -> 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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self.kl_coef = kl_coef
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@abstractmethod
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def make_experience(self, input_ids: torch.Tensor, **generate_kwargs) -> Experience:
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pass
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