From cdc2fce544a4da5776e77d6a3c821e329c0396f6 Mon Sep 17 00:00:00 2001 From: xiaoma Date: Wed, 10 Apr 2024 05:53:34 +0000 Subject: [PATCH] add edit --- documents/chapter3/README.md | 164 ++++++++++++++++++++++++++ documents/chapter3/assets/1.png | Bin 0 -> 114715 bytes documents/chapter3/assets/2.png | Bin 0 -> 55944 bytes documents/chapter3/assets/3.png | Bin 0 -> 315420 bytes documents/chapter3/assets/4.png | Bin 0 -> 322856 bytes documents/chapter3/assets/5.png | Bin 0 -> 87638 bytes documents/chapter3/dive_edit_0410.pdf | Bin 0 -> 3361016 bytes 7 files changed, 164 insertions(+) create mode 100644 documents/chapter3/README.md create mode 100644 documents/chapter3/assets/1.png create mode 100644 documents/chapter3/assets/2.png create mode 100644 documents/chapter3/assets/3.png create mode 100644 documents/chapter3/assets/4.png create mode 100644 documents/chapter3/assets/5.png create mode 100644 documents/chapter3/dive_edit_0410.pdf diff --git a/documents/chapter3/README.md b/documents/chapter3/README.md new file mode 100644 index 0000000..66d804d --- /dev/null +++ b/documents/chapter3/README.md @@ -0,0 +1,164 @@ +# 动手学大模型:大模型知识编辑 +导读: 语言模型的编辑方法和工具 +> 想操控语言模型在对指定知识的记忆?让我们选择合适的编辑方法,对特定知识进行编辑,并将对编辑后的模型进行验证! + +## 1. 本教程目标: + +- 熟悉使用EasyEdit工具包 +- 掌握语言模型的编辑方法(最简) +- 了解不同类型的编辑方法的选型和应用场景 + +## 2. 工作准备: +### 2.1 了解EasyEdit + +https://github.com/zjunlp/EasyEdit + +EasyEdit 是一个用于编辑语言模型的 Python 包,如 GPT-J、Llama、GPT-NEO、GPT2、T5等,其目标是针对一个特定的知识有效地改变语言模型的行为,而不会对其他输入的性能产生负面影响,同时易于使用且易于扩展。 + +EasyEdit 集成了现有的流行的编辑方法: +![](./assets/1.png) + +### 2.2 主要框架 + +![](./assets/2.png) +EasyEdit包含一个统一的Editor、Method和Evaluate框架,分别代表编辑场景、编辑技术和评估方法。 +- Editor:描述工作场景,包含待编辑的模型,待编辑的知识,以及其他必要的超参数。 +- Method:所使用的具体知识编辑方法(例如ROME、MEND等)。 +- Evaluate:评估知识编辑性能的指标,包含了可靠性、通用性、局部性、可移植性。 +- Trainer:有些编辑方法需要一定的训练过程,由Trainer模块实现。 +## 3. 安装环境: +``` +git clone https://github.com/zjunlp/EasyEdit.git +(可选)conda create -n EasyEdit python=3.9.7 +cd EasyEdit +pip install -r requirements.txt +``` +## 4. 编辑案例 +> 目标:改变GPT-2-XL的知识记忆,将梅西(Lionel Messi)的职业从足球改成篮球(football->basketball). +步骤: +- 选择编辑方法,准备参数 +- 准备知识编辑的数据 +- 实例化Editor +- Run! +下面以ROME方法为例具体介绍: +### 4.1 ROME +Jupiter Notebook: [https://colab.research.google.com/drive/1KkyWqyV3BjXCWfdrrgbR-QS3AAokVZbr?usp=sharing#scrollTo=zWfGkNb9FBJQ] +- 选择编辑方法,准备参数 + - 编辑方法选择为ROME,准备ROME和GPT2-XL所需要的参数。 + - 例如:alg_name: "ROME",model_name: "./hugging_cache/gpt2-xl"或为本地该模型的路径,"device": 使用的GPU序号 + - 其余参数可保持默认 +![](./assets/3.png) +- 准备知识编辑的数据 + ``` + prompts = ['Question:What sport does Lionel Messi play? Answer:'] # x_e + ground_truth = ['football'] # y + target_new = ['basketball'] # y_e + subject = ['Lionel Messi'] + ``` +- 实例化Editor,将准备好的参数传入BaseEditor类进行实例化,得到定制的Editor实例。 + ``` + hparams = ROMEHyperParams.from_hparams('./hparams/ROME/gpt2-xl.yaml') + editor=BaseEditor.from_hparams(hparams) + ``` +- Run! 调用editor的edit方法: + ``` + metrics, edited_model, _ = editor.edit( + prompts=prompts, + ground_truth=ground_truth, + target_new=target_new, + subject=subject, + keep_original_weight=False + ) + ``` +![](./assets/4.png) +> 备注:首次编辑某个模型时会下载Wiki语料,并为该模型计算各层的状态(stats_dir: "./data/stats")并存下,在后续的每次编辑中复用。因此,首次编辑有所耗时,确保网络通畅的情况下可耐心等待。 +### 4.2 验证与评估 +editor.edit会返回metrics(由EasyEdit的Evaluate模块计算)。形式为: +![](./assets/5.png) +要得到通用性、局部性、可移植性的数值,需要在edit方法中传入用于评估的数据。 + +以局部性为例,会导致edit方法计算局部性的指标,即在locality_inputs上模型回答的正确率。 +``` +locality_inputs = { + 'neighborhood':{ + 'prompt': ['Joseph Fischhof, the', 'Larry Bird is a professional', 'In Forssa, they understand'], + 'ground_truth': ['piano', 'basketball', 'Finnish'] + } +} +metrics, edited_model, _ = editor.edit( + prompts=prompts, + ground_truth=ground_truth, + target_new=target_new, + locality_inputs=locality_inputs, + keep_original_weight=False +) +``` +或者直接比较前后模型的generte行为。 +``` +generation_prompts = [ + "Lionel Messi, the", + "The law in Ikaalinen declares the language" +] + +model = GPT2LMHeadModel.from_pretrained('./hugging_cache/gpt2').to('cuda') +batch = tokenizer(generation_prompts, return_tensors='pt', padding=True, max_length=30) + +pre_edit_outputs = model.generate( + input_ids=batch['input_ids'].to('cuda'), + attention_mask=batch['attention_mask'].to('cuda'), + max_new_tokens=3 +) +post_edit_outputs = edited_model.generate( + input_ids=batch['input_ids'].to('cuda'), + attention_mask=batch['attention_mask'].to('cuda'), + max_new_tokens=3 +``` +## 5. 规模化的编辑(可选) +### 5.1 Batch edit +多条数据可以形成并列的列表同时传入edit方法进行batch edit,此时MEMIT为最佳方法。(https://colab.research.google.com/drive/1P1lVklP8bTyh8uxxSuHnHwB91i-1LW6Z) +``` +prompts = ['Question:What sport does Lionel Messi play? Answer:', + 'The law in Ikaalinen declares the language'] +ground_truth = ['football', 'Finnish'] +target_new = ['basketball', 'Swedish'] +subject = ['Lionel Messi', 'Ikaalinen'] +``` +### 5.2 Benchmark上测试 +- Counterfact +- ZsRE +``` +{ + "case_id": 4402, + "pararel_idx": 11185, + "requested_rewrite": { + "prompt": "{} debuted on", + "relation_id": "P449", + "target_new": { + "str": "CBS", + "id": "Q43380" + }, + "target_true": { + "str": "MTV", + "id": "Q43359" + }, + "subject": "Singled Out" + }, + "paraphrase_prompts": [ + "No one on the ground was injured. v", + "The sex ratio was 1063. Singled Out is to debut on" + ], + "neighborhood_prompts": [ + "Daria premieres on", + "Teen Wolf was originally aired on", + "Spider-Man: The New Animated Series was originally aired on", + "Celebrity Deathmatch premiered on", + "\u00c6on Flux premiered on", + "My Super Psycho Sweet 16 premieres on", + "Daria was released on", + "Jersey Shore premiered on", + "Skins was originally aired on", + "All You've Got premiered on" + ] + } + ``` +https://github.com/zjunlp/EasyEdit/blob/main/examples/run_zsre_llama2.py \ No newline at end of file diff --git a/documents/chapter3/assets/1.png b/documents/chapter3/assets/1.png new file mode 100644 index 0000000000000000000000000000000000000000..cc1f73535e8887d2f2af34e9c07e971c14f128ed GIT binary patch literal 114715 zcmeFZcRZJE{5Ndxkey9J5<)_BCE)F z-hTJ}dYoNWJ{J8<1pCftsp}IEkm7%dUQrO? z7lqyMDguH)f-~wW2A)snQ#?Ek40o2KY7%uGo>}amC&~y**r&BW#*CDep5zMC`53{| zb<9k$nZjeiOhLS&ydBCE8QDT44Qw_KpGePgSh(dO5% 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