216 lines
7.1 KiB
Markdown
216 lines
7.1 KiB
Markdown
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
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# 使用 🤗 PEFT 加载adapters
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[[open-in-colab]]
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[参数高效微调（PEFT）方法](https://huggingface.co/blog/peft)在微调过程中冻结预训练模型的参数，并在其顶部添加少量可训练参数（adapters）。adapters被训练以学习特定任务的信息。这种方法已被证明非常节省内存，同时具有较低的计算使用量，同时产生与完全微调模型相当的结果。
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使用PEFT训练的adapters通常比完整模型小一个数量级，使其方便共享、存储和加载。
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<div class="flex flex-col justify-center">
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/peft/PEFT-hub-screenshot.png"/>
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<figcaption class="text-center">与完整尺寸的模型权重（约为700MB）相比，存储在Hub上的OPTForCausalLM模型的adapter权重仅为~6MB。</figcaption>
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</div>
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如果您对学习更多关于🤗 PEFT库感兴趣，请查看[文档](https://huggingface.co/docs/peft/index)。
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## 设置
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首先安装 🤗 PEFT：
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```bash
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pip install peft
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```
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如果你想尝试全新的特性，你可能会有兴趣从源代码安装这个库：
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```bash
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pip install git+https://github.com/huggingface/peft.git
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```
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## 支持的 PEFT 模型
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Transformers原生支持一些PEFT方法，这意味着你可以加载本地存储或在Hub上的adapter权重，并使用几行代码轻松运行或训练它们。以下是受支持的方法：
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- [Low Rank Adapters](https://huggingface.co/docs/peft/conceptual_guides/lora)
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- [IA3](https://huggingface.co/docs/peft/conceptual_guides/ia3)
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- [AdaLoRA](https://arxiv.org/abs/2303.10512)
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如果你想使用其他PEFT方法，例如提示学习或提示微调，或者关于通用的 🤗 PEFT库，请参阅[文档](https://huggingface.co/docs/peft/index)。
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## 加载 PEFT adapter
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要从huggingface的Transformers库中加载并使用PEFTadapter模型，请确保Hub仓库或本地目录包含一个`adapter_config.json`文件和adapter权重，如上例所示。然后，您可以使用`AutoModelFor`类加载PEFT adapter模型。例如，要为因果语言建模加载一个PEFT adapter模型：
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1. 指定PEFT模型id
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2. 将其传递给[`AutoModelForCausalLM`]类
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```py
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from transformers import AutoModelForCausalLM, AutoTokenizer
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peft_model_id = "ybelkada/opt-350m-lora"
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model = AutoModelForCausalLM.from_pretrained(peft_model_id)
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```
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<Tip>
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你可以使用`AutoModelFor`类或基础模型类（如`OPTForCausalLM`或`LlamaForCausalLM`）来加载一个PEFT adapter。
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</Tip>
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您也可以通过`load_adapter`方法来加载 PEFT adapter。
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```py
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "facebook/opt-350m"
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peft_model_id = "ybelkada/opt-350m-lora"
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model = AutoModelForCausalLM.from_pretrained(model_id)
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model.load_adapter(peft_model_id)
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```
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## 基于8bit或4bit进行加载
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`bitsandbytes`集成支持8bit和4bit精度数据类型，这对于加载大模型非常有用，因为它可以节省内存（请参阅`bitsandbytes`[指南](./quantization#bitsandbytes-integration)以了解更多信息）。要有效地将模型分配到您的硬件，请在[`~PreTrainedModel.from_pretrained`]中添加`load_in_8bit`或`load_in_4bit`参数，并将`device_map="auto"`设置为：
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```py
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from transformers import AutoModelForCausalLM, AutoTokenizer
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peft_model_id = "ybelkada/opt-350m-lora"
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model = AutoModelForCausalLM.from_pretrained(peft_model_id, device_map="auto", load_in_8bit=True)
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```
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## 添加新的adapter
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你可以使用[`~peft.PeftModel.add_adapter`]方法为一个已有adapter的模型添加一个新的adapter，只要新adapter的类型与当前adapter相同即可。例如，如果你有一个附加到模型上的LoRA adapter：
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```py
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from transformers import AutoModelForCausalLM, OPTForCausalLM, AutoTokenizer
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from peft import PeftConfig
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model_id = "facebook/opt-350m"
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model = AutoModelForCausalLM.from_pretrained(model_id)
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lora_config = LoraConfig(
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target_modules=["q_proj", "k_proj"],
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init_lora_weights=False
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)
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model.add_adapter(lora_config, adapter_name="adapter_1")
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```
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添加一个新的adapter：
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```py
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# attach new adapter with same config
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model.add_adapter(lora_config, adapter_name="adapter_2")
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```
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现在您可以使用[`~peft.PeftModel.set_adapter`]来设置要使用的adapter。
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```py
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# use adapter_1
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model.set_adapter("adapter_1")
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output = model.generate(**inputs)
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print(tokenizer.decode(output_disabled[0], skip_special_tokens=True))
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# use adapter_2
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model.set_adapter("adapter_2")
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output_enabled = model.generate(**inputs)
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print(tokenizer.decode(output_enabled[0], skip_special_tokens=True))
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```
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## 启用和禁用adapters
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一旦您将adapter添加到模型中，您可以启用或禁用adapter模块。要启用adapter模块：
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```py
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from transformers import AutoModelForCausalLM, OPTForCausalLM, AutoTokenizer
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from peft import PeftConfig
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model_id = "facebook/opt-350m"
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adapter_model_id = "ybelkada/opt-350m-lora"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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text = "Hello"
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inputs = tokenizer(text, return_tensors="pt")
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model = AutoModelForCausalLM.from_pretrained(model_id)
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peft_config = PeftConfig.from_pretrained(adapter_model_id)
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# to initiate with random weights
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peft_config.init_lora_weights = False
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model.add_adapter(peft_config)
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model.enable_adapters()
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output = model.generate(**inputs)
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```
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要禁用adapter模块：
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```py
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model.disable_adapters()
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output = model.generate(**inputs)
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```
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## 训练一个 PEFT adapter
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PEFT适配器受[`Trainer`]类支持，因此您可以为您的特定用例训练适配器。它只需要添加几行代码即可。例如，要训练一个LoRA adapter：
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<Tip>
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如果你不熟悉如何使用[`Trainer`]微调模型，请查看[微调预训练模型](training)教程。
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</Tip>
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1. 使用任务类型和超参数定义adapter配置（参见[`~peft.LoraConfig`]以了解超参数的详细信息）。
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```py
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from peft import LoraConfig
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peft_config = LoraConfig(
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lora_alpha=16,
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lora_dropout=0.1,
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r=64,
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bias="none",
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task_type="CAUSAL_LM",
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)
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```
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2. 将adapter添加到模型中。
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```py
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model.add_adapter(peft_config)
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```
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3. 现在可以将模型传递给[`Trainer`]了！
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```py
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trainer = Trainer(model=model, ...)
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trainer.train()
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```
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要保存训练好的adapter并重新加载它：
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```py
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model.save_pretrained(save_dir)
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model = AutoModelForCausalLM.from_pretrained(save_dir)
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```
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<!--
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TODO: (@younesbelkada @stevhliu)
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- Link to PEFT docs for further details
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- Trainer
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- 8-bit / 4-bit examples ?
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-->
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