Comments (3)
例如, 在 LLaMA 的微调中, 我们使用如下代码, 在 deepspeed 和 fsdp 下可以正常工作, 其中 optim_mode 是我们自定义的参数, 根据此参数的值来判断是否使用我们的优化器. 如果有其他更好的做法欢迎提出
def create_optimizer(self):
"""
Setup the optimizer.
We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
Trainer's init through `optimizers`, or subclass and override this method in a subclass.
"""
if self.optimizer is None:
if self.args.optim_mode is None or self.args.optim_mode == "base":
print(f"Use {self.args.optim}")
super().create_optimizer()
else:
assert self.args.optim_mode == "lpmm"
opt_model = self.model
decay_parameters = get_parameter_names(opt_model, [nn.LayerNorm])
decay_parameters = [name for name in decay_parameters if "bias" not in name]
optimizer_grouped_parameters = [
{
"params": [
p for n, p in opt_model.named_parameters() if (n in decay_parameters and p.requires_grad)
],
"weight_decay": self.args.weight_decay,
},
{
"params": [
p for n, p in opt_model.named_parameters() if (n not in decay_parameters and p.requires_grad)
],
"weight_decay": 0.0,
},
]
_, optimizer_kwargs = Trainer.get_optimizer_cls_and_kwargs(self.args)
self.optimizer = lpmm.optim.AdamW(
params=optimizer_grouped_parameters,
factor_second_moment=False,
qconfig=self.args.qconfig,
fused=False,
**optimizer_kwargs,
)
return self.optimizer
from low-bit-optimizers.
你好, transformers Trainer 中没有内置我们的优化器, 目前无法通过直接传递 optim 字段来使用我们的 low-bit optimizers. 如果要搭配 Trainer 使用, 可以通过重载 create_optimizer 方法.
from low-bit-optimizers.
你好, transformers Trainer 中没有内置我们的优化器, 目前无法通过直接传递 optim 字段来使用我们的 low-bit optimizers. 如果要搭配 Trainer 使用, 可以通过重载 create_optimizer 方法.
具体要如何操作呢
from low-bit-optimizers.
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from low-bit-optimizers.