mirror of https://github.com/hpcaitech/ColossalAI
36 lines
1.1 KiB
Python
36 lines
1.1 KiB
Python
from typing import Optional
|
|
|
|
import torch
|
|
from transformers import BloomConfig, BloomForCausalLM, BloomModel
|
|
|
|
from ..base import Actor
|
|
|
|
|
|
class BLOOMActor(Actor):
|
|
"""
|
|
BLOOM Actor model.
|
|
|
|
Args:
|
|
pretrained (str): Pretrained model name or path.
|
|
config (BloomConfig): Model config.
|
|
checkpoint (bool): Enable gradient checkpointing.
|
|
lora_rank (int): LoRA rank.
|
|
lora_train_bias (str): LoRA bias training mode.
|
|
"""
|
|
|
|
def __init__(self,
|
|
pretrained: str = None,
|
|
config: Optional[BloomConfig] = None,
|
|
checkpoint: bool = False,
|
|
lora_rank: int = 0,
|
|
lora_train_bias: str = 'none') -> None:
|
|
if pretrained is not None:
|
|
model = BloomForCausalLM.from_pretrained(pretrained)
|
|
elif config is not None:
|
|
model = BloomForCausalLM(config)
|
|
else:
|
|
model = BloomForCausalLM(BloomConfig())
|
|
if checkpoint:
|
|
model.gradient_checkpointing_enable()
|
|
super().__init__(model, lora_rank, lora_train_bias)
|