From 968a30672ab90b6d0f3b6be9a098db567b34e06f Mon Sep 17 00:00:00 2001 From: duzx16 Date: Fri, 31 Mar 2023 10:43:55 +0800 Subject: [PATCH] Add P-Tuning v2 --- README.md | 25 ++- ptuning/README.md | 70 +++++++ ptuning/arguments.py | 217 +++++++++++++++++++++ ptuning/evaluate.sh | 20 ++ ptuning/main.py | 389 +++++++++++++++++++++++++++++++++++++ ptuning/train.sh | 26 +++ ptuning/trainer_seq2seq.py | 245 +++++++++++++++++++++++ 7 files changed, 982 insertions(+), 10 deletions(-) create mode 100644 ptuning/README.md create mode 100644 ptuning/arguments.py create mode 100644 ptuning/evaluate.sh create mode 100644 ptuning/main.py create mode 100644 ptuning/train.sh create mode 100644 ptuning/trainer_seq2seq.py diff --git a/README.md b/README.md index ec90fb8..ff4383b 100644 --- a/README.md +++ b/README.md @@ -10,20 +10,12 @@ ChatGLM-6B 使用了和 ChatGPT 相似的技术，针对中文问答和对话进 *Read this in [English](README_en.md).* ## 更新信息 +**[2023/03/31]** 增加基于 P-Tuning-v2 的微调实现，最低只需 8GB 显存即可进行模型微调。详见[模型微调](ptuning/README.md)。 + **[2023/03/23]** 增加API部署（感谢 [@LemonQu-GIT](https://github.com/LemonQu-GIT)）。增加Embedding量化模型[ChatGLM-6B-INT4-QE](https://huggingface.co/THUDM/chatglm-6b-int4-qe)。增加对基于Apple Silicon的Mac上GPU加速的支持。 **[2023/03/19]** 增加流式输出接口 `stream_chat`，已更新到网页版和命令行 Demo。修复输出中的中文标点。增加量化后的模型 [ChatGLM-6B-INT4](https://huggingface.co/THUDM/chatglm-6b-int4) -## 友情链接 -以下是部分基于本仓库开发的开源项目： -* [ChatGLM-MNN](https://github.com/wangzhaode/ChatGLM-MNN): 一个基于 MNN 的 ChatGLM-6B C++ 推理实现，支持根据显存大小自动分配计算任务给 GPU 和 CPU -* [ChatGLM-Tuning](https://github.com/mymusise/ChatGLM-Tuning): 基于 LoRA 对 ChatGLM-6B 进行微调 - -以下是部分针对本项目的教程/文档： -* [Windows部署文档](https://github.com/ZhangErling/ChatGLM-6B/blob/main/deployment_windows.md) - -如果你有其他好的项目/教程的话，欢迎参照上述格式添加到README中并提出 [PR](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request-from-a-fork). - ## 使用方式 ### 硬件需求 @@ -171,6 +163,9 @@ model = AutoModel.from_pretrained("your local path", trust_remote_code=True).hal ``` 即可使用在 Mac 上使用 GPU 加速模型推理。 +## 模型微调 +详见 [ptuning/README.md](ptuning/README.md)。 + ## ChatGLM-6B 示例 以下是一些使用 `web_demo.py` 得到的示例截图。更多 ChatGLM-6B 的可能，等待你来探索发现！ @@ -259,6 +254,16 @@ model = AutoModel.from_pretrained("your local path", trust_remote_code=True).hal 本仓库的代码依照 [Apache-2.0](LICENSE) 协议开源，ChatGLM-6B 模型的权重的使用则需要遵循 [Model License](MODEL_LICENSE)。 +## 友情链接 +以下是部分基于本仓库开发的开源项目： +* [ChatGLM-MNN](https://github.com/wangzhaode/ChatGLM-MNN): 一个基于 MNN 的 ChatGLM-6B C++ 推理实现，支持根据显存大小自动分配计算任务给 GPU 和 CPU +* [ChatGLM-Tuning](https://github.com/mymusise/ChatGLM-Tuning): 基于 LoRA 对 ChatGLM-6B 进行微调 + +以下是部分针对本项目的教程/文档： +* [Windows部署文档](https://github.com/ZhangErling/ChatGLM-6B/blob/main/deployment_windows.md) + +如果你有其他好的项目/教程的话，欢迎参照上述格式添加到README中并提出 [PR](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request-from-a-fork). + ## 引用 如果你觉得我们的工作有帮助的话，请考虑引用下列论文 diff --git a/ptuning/README.md b/ptuning/README.md new file mode 100644 index 0000000..1fb0ea6 --- /dev/null +++ b/ptuning/README.md @@ -0,0 +1,70 @@ +# ChatGLM-6B-PT +本仓库实现了对于 ChatGLM-6B 模型基于 [P-Tuning v2](https://github.com/THUDM/P-tuning-v2) 的微调。P-Tuning v2将需要微调的参数量减少到原来的0.1%，再通过模型量化、Gradient Checkpoint等方法，最低只需要 8GB 显存即可运行。 + +下面以 [ADGEN](https://aclanthology.org/D19-1321.pdf) (广告生成) 数据集为例介绍代码的使用方法。 + +## 软件依赖 +除 ChatGLM-6B 的依赖之外，还需要按照以下依赖 +``` +pip install rouge_chinese nltk jieba datasets +``` +## 使用方法 + +### 下载数据集 +ADGEN 数据集任务为根据输入（content）生成一段广告词（summary）。 + +```json +{ + "content": "类型#上衣*版型#宽松*版型#显瘦*图案#线条*衣样式#衬衫*衣袖型#泡泡袖*衣款式#抽绳", + "summary": "这件衬衫的款式非常的宽松，利落的线条可以很好的隐藏身材上的小缺点，穿在身上有着很好的显瘦效果。领口装饰了一个可爱的抽绳，漂亮的绳结展现出了十足的个性，配合时尚的泡泡袖型，尽显女性甜美可爱的气息。" +} +``` + +从 [Google Drive](https://drive.google.com/file/d/13_vf0xRTQsyneRKdD1bZIr93vBGOczrk/view?usp=sharing) 或者 [Tsinghua Cloud]() 下载处理好的 ADGEN数据集，将解压后的 `AdvertiseGen` 目录放到本目录下。 + +### 训练 +运行以下指令进行训练： +```shell +bash train.sh +``` +`train.sh` 中的`PRE_SEQ_LEN`和 `LR` 分别是 soft prompt 长度和训练的学习率，可以进行调节以取得最佳的效果。 + +### 推理 + +将`evaluate.sh`中的`CHECKPOINT`更改为训练时保存的checkpoint名称，运行以下指令进行模型推理和评测： +```shell +bash evaluate.sh +``` + +评测指标为中文 Rouge score 和 BLEU-4。生成的结果保存在 +`./output/adgen-chatglm-6b-pt-8-1e-2/generated_predictions.txt`。 + +### 例子 +#### 示例1 +* Input: 类型#上衣\*材质#牛仔布\*颜色#白色\*风格#简约\*图案#刺绣\*衣样式#外套\*衣款式#破洞 +* Label: 简约而不简单的牛仔外套,白色的衣身十分百搭。衣身多处有做旧破洞设计,打破单调乏味,增加一丝造型看点。衣身后背处有趣味刺绣装饰,丰富层次感,彰显别样时尚。 +* 微调前Output: 这件上衣的材质是牛仔布,颜色是白色,风格是简约,图案是刺绣,衣样式是外套,衣款式是破洞。 +* 微调后Output: 这是一款简约的牛仔外套,破洞设计,将牛仔布破洞,带来一种随意与个性。破洞的牛仔外套,展现出时尚气息,带来一种休闲感。同时,刺绣图案,让整件外套更加立体。 + +#### 示例2 + +* Input: 类型#裙\*版型#显瘦\*风格#文艺\*风格#简约\*图案#印花\*图案#撞色\*裙下摆#压褶\*裙长#连衣裙\*裙领型#圆领 +* Label: 文艺个性的印花连衣裙,藏青色底蕴,低调又大气,撞色太阳花分布整个裙身,绚丽而美好,带来时尚减龄的气质。基础款的舒适圆领,简约不失大方,勾勒精致脸庞。领后是一粒包布扣固定,穿脱十分方便。前片立体的打褶设计,搭配后片压褶的做工,增添层次和空间感,显瘦又有型。 + +* 微调前Output: 类型#裙*版型#显瘦*风格#文艺*风格#简约*图案#印花*图案#撞色*裙下摆#压褶*裙长#连衣裙*裙领型#圆领 1\. 连衣裙:简约风格,裙长为膝盖以上,裙领型为圆领。2\. 裙下摆:压褶设计,使裙摆呈现出流畅的褶皱效果。3\. 裙领型:裙领型为圆领,使穿上连衣裙后更加有型。4\. 版型:采用显瘦设计,让连衣裙看起来更加苗条。5\. 风格:文艺风格,让连衣裙更加有内涵和品味。6\. 图案:印花设计,在连衣裙上印有独特的图案。7\. 撞色:采用撞色设计,让连衣裙在色彩上更加鲜明、富有层次感。 +* 微调后Output: 这是一款文艺范的连衣裙,以印花为元素,采用简约的印花,既能够突出文艺气质,又能够展现简约风。在印花的同时又有领子和裙摆的压褶设计,更加凸显文艺气质。简约而不会过于单调,搭配出街,穿着十分舒适。 + +## 使用自己的数据集 +修改 `train.sh` 和 `evaluate.sh` 中的 `train_file`、`validation_file`和`test_file`为你自己的json格式数据集路径，并将`prompt_column`和`response_column`改为json文件中输入文本和输出文本对应的key。 + +## 引用 + +``` +@inproceedings{liu2022p, + title={P-tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks}, + author={Liu, Xiao and Ji, Kaixuan and Fu, Yicheng and Tam, Weng and Du, Zhengxiao and Yang, Zhilin and Tang, Jie}, + booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)}, + pages={61--68}, + year={2022} +} +``` diff --git a/ptuning/arguments.py b/ptuning/arguments.py new file mode 100644 index 0000000..1c61f97 --- /dev/null +++ b/ptuning/arguments.py @@ -0,0 +1,217 @@ +from dataclasses import dataclass, field +from typing import Optional + + +@dataclass +class ModelArguments: + """ + Arguments pertaining to which model/config/tokenizer we are going to fine-tune from. + """ + + model_name_or_path: str = field( + metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} + ) + config_name: Optional[str] = field( + default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} + ) + tokenizer_name: Optional[str] = field( + default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} + ) + cache_dir: Optional[str] = field( + default=None, + metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"}, + ) + use_fast_tokenizer: bool = field( + default=True, + metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, + ) + model_revision: str = field( + default="main", + metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}, + ) + use_auth_token: bool = field( + default=False, + metadata={ + "help": ( + "Will use the token generated when running `huggingface-cli login` (necessary to use this script " + "with private models)." + ) + }, + ) + resize_position_embeddings: Optional[bool] = field( + default=None, + metadata={ + "help": ( + "Whether to automatically resize the position embeddings if `max_source_length` exceeds " + "the model's position embeddings." + ) + }, + ) + quantization_bit: Optional[int] = field( + default=None + ) + pre_seq_len: Optional[int] = field( + default=None + ) + prefix_projection: bool = field( + default=False + ) + + +@dataclass +class DataTrainingArguments: + """ + Arguments pertaining to what data we are going to input our model for training and eval. + """ + + lang: Optional[str] = field(default=None, metadata={"help": "Language id for summarization."}) + + dataset_name: Optional[str] = field( + default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."} + ) + dataset_config_name: Optional[str] = field( + default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} + ) + prompt_column: Optional[str] = field( + default=None, + metadata={"help": "The name of the column in the datasets containing the full texts (for summarization)."}, + ) + response_column: Optional[str] = field( + default=None, + metadata={"help": "The name of the column in the datasets containing the summaries (for summarization)."}, + ) + train_file: Optional[str] = field( + default=None, metadata={"help": "The input training data file (a jsonlines or csv file)."} + ) + validation_file: Optional[str] = field( + default=None, + metadata={ + "help": ( + "An optional input evaluation data file to evaluate the metrics (rouge) on (a jsonlines or csv file)." + ) + }, + ) + test_file: Optional[str] = field( + default=None, + metadata={ + "help": "An optional input test data file to evaluate the metrics (rouge) on (a jsonlines or csv file)." + }, + ) + overwrite_cache: bool = field( + default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} + ) + preprocessing_num_workers: Optional[int] = field( + default=None, + metadata={"help": "The number of processes to use for the preprocessing."}, + ) + max_source_length: Optional[int] = field( + default=1024, + metadata={ + "help": ( + "The maximum total input sequence length after tokenization. Sequences longer " + "than this will be truncated, sequences shorter will be padded." + ) + }, + ) + max_target_length: Optional[int] = field( + default=128, + metadata={ + "help": ( + "The maximum total sequence length for target text after tokenization. Sequences longer " + "than this will be truncated, sequences shorter will be padded." + ) + }, + ) + val_max_target_length: Optional[int] = field( + default=None, + metadata={ + "help": ( + "The maximum total sequence length for validation target text after tokenization. Sequences longer " + "than this will be truncated, sequences shorter will be padded. Will default to `max_target_length`." + "This argument is also used to override the ``max_length`` param of ``model.generate``, which is used " + "during ``evaluate`` and ``predict``." + ) + }, + ) + pad_to_max_length: bool = field( + default=False, + metadata={ + "help": ( + "Whether to pad all samples to model maximum sentence length. " + "If False, will pad the samples dynamically when batching to the maximum length in the batch. More " + "efficient on GPU but very bad for TPU." + ) + }, + ) + max_train_samples: Optional[int] = field( + default=None, + metadata={ + "help": ( + "For debugging purposes or quicker training, truncate the number of training examples to this " + "value if set." + ) + }, + ) + max_eval_samples: Optional[int] = field( + default=None, + metadata={ + "help": ( + "For debugging purposes or quicker training, truncate the number of evaluation examples to this " + "value if set." + ) + }, + ) + max_predict_samples: Optional[int] = field( + default=None, + metadata={ + "help": ( + "For debugging purposes or quicker training, truncate the number of prediction examples to this " + "value if set." + ) + }, + ) + num_beams: Optional[int] = field( + default=None, + metadata={ + "help": ( + "Number of beams to use for evaluation. This argument will be passed to ``model.generate``, " + "which is used during ``evaluate`` and ``predict``." + ) + }, + ) + ignore_pad_token_for_loss: bool = field( + default=True, + metadata={ + "help": "Whether to ignore the tokens corresponding to padded labels in the loss computation or not." + }, + ) + source_prefix: Optional[str] = field( + default="", metadata={"help": "A prefix to add before every source text (useful for T5 models)."} + ) + + forced_bos_token: Optional[str] = field( + default=None, + metadata={ + "help": ( + "The token to force as the first generated token after the decoder_start_token_id." + "Useful for multilingual models like mBART where the first generated token" + "needs to be the target language token (Usually it is the target language token)" + ) + }, + ) + + + + def __post_init__(self): + if self.dataset_name is None and self.train_file is None and self.validation_file is None: + raise ValueError("Need either a dataset name or a training/validation file.") + else: + if self.train_file is not None: + extension = self.train_file.split(".")[-1] + assert extension in ["csv", "json"], "`train_file` should be a csv or a json file." + if self.validation_file is not None: + extension = self.validation_file.split(".")[-1] + assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file." + if self.val_max_target_length is None: + self.val_max_target_length = self.max_target_length + diff --git a/ptuning/evaluate.sh b/ptuning/evaluate.sh new file mode 100644 index 0000000..db2a8c1 --- /dev/null +++ b/ptuning/evaluate.sh @@ -0,0 +1,20 @@ +PRE_SEQ_LEN=8 +CHECKPOINT=adgen-chatglm-6b-pt-8-1e-2 +STEP=3000 + +CUDA_VISIBLE_DEVICES=0 python3 main.py \ + --do_predict \ + --test_file AdvertiseGen/dev.json \ + --overwrite_cache \ + --prompt_column content \ + --response_column summary \ + --model_name_or_path ./output/$CHECKPOINT/checkpoint-$STEP \ + --output_dir ./output/$CHECKPOINT \ + --overwrite_output_dir \ + --max_source_length 64 \ + --max_target_length 64 \ + --per_device_eval_batch_size 1 \ + --predict_with_generate \ + --max_predict_samples 10 \ + --pre_seq_len $PRE_SEQ_LEN \ + --quantization_bit 4 diff --git a/ptuning/main.py b/ptuning/main.py new file mode 100644 index 0000000..d82fccc --- /dev/null +++ b/ptuning/main.py @@ -0,0 +1,389 @@ +#!/usr/bin/env python +# coding=utf-8 +# Copyright 2021 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Fine-tuning the library models for sequence to sequence. +""" +# You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments. + +import logging +import os +import sys +import json + +import numpy as np +from datasets import load_dataset +import jieba +from rouge_chinese import Rouge +from nltk.translate.bleu_score import sentence_bleu + +import transformers +from transformers import ( + AutoConfig, + AutoModel, + AutoTokenizer, + AutoTokenizer, + DataCollatorForSeq2Seq, + HfArgumentParser, + Seq2SeqTrainingArguments, + set_seed, +) +from trainer_seq2seq import Seq2SeqTrainer + +from arguments import ModelArguments, DataTrainingArguments + +logger = logging.getLogger(__name__) + +def main(): + + parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments)) + if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): + # If we pass only one argument to the script and it's the path to a json file, + # let's parse it to get our arguments. + model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) + else: + model_args, data_args, training_args = parser.parse_args_into_dataclasses() + + # Setup logging + logging.basicConfig( + format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", + datefmt="%m/%d/%Y %H:%M:%S", + handlers=[logging.StreamHandler(sys.stdout)], + ) + + if training_args.should_log: + # The default of training_args.log_level is passive, so we set log level at info here to have that default. + transformers.utils.logging.set_verbosity_info() + + log_level = training_args.get_process_log_level() + logger.setLevel(log_level) + # datasets.utils.logging.set_verbosity(log_level) + transformers.utils.logging.set_verbosity(log_level) + transformers.utils.logging.enable_default_handler() + transformers.utils.logging.enable_explicit_format() + + # Log on each process the small summary: + logger.warning( + f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}" + + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" + ) + logger.info(f"Training/evaluation parameters {training_args}") + + # Set seed before initializing model. + set_seed(training_args.seed) + + # Load dataset + data_files = {} + if data_args.train_file is not None: + data_files["train"] = data_args.train_file + extension = data_args.train_file.split(".")[-1] + if data_args.validation_file is not None: + data_files["validation"] = data_args.validation_file + extension = data_args.validation_file.split(".")[-1] + if data_args.test_file is not None: + data_files["test"] = data_args.test_file + extension = data_args.test_file.split(".")[-1] + + raw_datasets = load_dataset( + extension, + data_files=data_files, + cache_dir=model_args.cache_dir, + use_auth_token=True if model_args.use_auth_token else None, + ) + + # Load pretrained model and tokenizer + config = AutoConfig.from_pretrained(model_args.model_name_or_path, trust_remote_code=True) + config.pre_seq_len = model_args.pre_seq_len + config.prefix_projection = model_args.prefix_projection + + tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, trust_remote_code=True) + + model = AutoModel.from_pretrained(model_args.model_name_or_path, config=config, revision=True, trust_remote_code=True) + + model = model.half() + if model_args.quantization_bit is not None: + print(f"Quantized to {model_args.quantization_bit} bit") + model = model.quantize(model_args.quantization_bit) + model.transformer.prefix_encoder.float() + + prefix = data_args.source_prefix if data_args.source_prefix is not None else "" + + # Preprocessing the datasets. + # We need to tokenize inputs and targets. + if training_args.do_train: + column_names = raw_datasets["train"].column_names + elif training_args.do_eval: + column_names = raw_datasets["validation"].column_names + elif training_args.do_predict: + column_names = raw_datasets["test"].column_names + else: + logger.info("There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.") + return + + # Get the column names for input/target. + prompt_column = data_args.prompt_column + response_column = data_args.response_column + + # Temporarily set max_target_length for training. + max_target_length = data_args.max_target_length + + def preprocess_function_eval(examples): + inputs, targets = [], [] + for i in range(len(examples[prompt_column])): + if examples[prompt_column][i] and examples[response_column][i]: + inputs.append(examples[prompt_column][i]) + targets.append(examples[response_column][i]) + + inputs = [prefix + inp for inp in inputs] + model_inputs = tokenizer(inputs, max_length=data_args.max_source_length, truncation=True) + labels = tokenizer(text_target=targets, max_length=max_target_length, truncation=True) + + if data_args.ignore_pad_token_for_loss: + labels["input_ids"] = [ + [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"] + ] + model_inputs["labels"] = labels["input_ids"] + + return model_inputs + + def preprocess_function_train(examples): + max_seq_length = data_args.max_source_length + data_args.max_target_length + + model_inputs = { + "input_ids": [], + "labels": [], + } + for i in range(len(examples[prompt_column])): + if examples[prompt_column][i] and examples[response_column][i]: + prompt, answer = examples[prompt_column][i], examples[response_column][i] + prompt = prefix + prompt + a_ids = tokenizer.encode(text=prompt, add_special_tokens=False) + b_ids = tokenizer.encode(text=answer, add_special_tokens=False) + + if len(a_ids) > data_args.max_source_length - 1: + a_ids = a_ids[: data_args.max_source_length - 1] + + if len(b_ids) > data_args.max_target_length - 2: + b_ids = b_ids[: data_args.max_target_length - 2] + + input_ids = a_ids + [150001, 150004] + b_ids + [150005] + + context_length = input_ids.index(150004) + mask_position = context_length - 1 + labels = [-100] * context_length + input_ids[mask_position+1:] + + pad_len = max_seq_length - len(input_ids) + input_ids = input_ids + [tokenizer.pad_token_id] * pad_len + labels = labels + [tokenizer.pad_token_id] * pad_len + + model_inputs["input_ids"].append(input_ids) + model_inputs["labels"].append(labels) + + return model_inputs + + def print_dataset_example(example): + print("input_ids",example["input_ids"]) + print("inputs", tokenizer.decode(example["input_ids"])) + print("label_ids", example["labels"]) + print("labels", tokenizer.decode(example["labels"])) + + if training_args.do_train: + if "train" not in raw_datasets: + raise ValueError("--do_train requires a train dataset") + train_dataset = raw_datasets["train"] + if data_args.max_train_samples is not None: + max_train_samples = min(len(train_dataset), data_args.max_train_samples) + train_dataset = train_dataset.select(range(max_train_samples)) + with training_args.main_process_first(desc="train dataset map pre-processing"): + train_dataset = train_dataset.map( + preprocess_function_train, + batched=True, + num_proc=data_args.preprocessing_num_workers, + remove_columns=column_names, + load_from_cache_file=not data_args.overwrite_cache, + desc="Running tokenizer on train dataset", + ) + print_dataset_example(train_dataset[0]) + + if training_args.do_eval: + max_target_length = data_args.val_max_target_length + if "validation" not in raw_datasets: + raise ValueError("--do_eval requires a validation dataset") + eval_dataset = raw_datasets["validation"] + if data_args.max_eval_samples is not None: + max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples) + eval_dataset = eval_dataset.select(range(max_eval_samples)) + with training_args.main_process_first(desc="validation dataset map pre-processing"): + eval_dataset = eval_dataset.map( + preprocess_function_eval, + batched=True, + num_proc=data_args.preprocessing_num_workers, + remove_columns=column_names, + load_from_cache_file=not data_args.overwrite_cache, + desc="Running tokenizer on validation dataset", + ) + print_dataset_example(eval_dataset[0]) + + if training_args.do_predict: + max_target_length = data_args.val_max_target_length + if "test" not in raw_datasets: + raise ValueError("--do_predict requires a test dataset") + predict_dataset = raw_datasets["test"] + if data_args.max_predict_samples is not None: + max_predict_samples = min(len(predict_dataset), data_args.max_predict_samples) + predict_dataset = predict_dataset.select(range(max_predict_samples)) + with training_args.main_process_first(desc="prediction dataset map pre-processing"): + predict_dataset = predict_dataset.map( + preprocess_function_eval, + batched=True, + num_proc=data_args.preprocessing_num_workers, + remove_columns=column_names, + load_from_cache_file=not data_args.overwrite_cache, + desc="Running tokenizer on prediction dataset", + ) + print_dataset_example(predict_dataset[0]) + + # Data collator + label_pad_token_id = -100 if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id + data_collator = DataCollatorForSeq2Seq( + tokenizer, + model=model, + label_pad_token_id=label_pad_token_id, + pad_to_multiple_of=None, + ) + + # Metric + def compute_metrics(eval_preds): + preds, labels = eval_preds + if isinstance(preds, tuple): + preds = preds[0] + decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True) + if data_args.ignore_pad_token_for_loss: + # Replace -100 in the labels as we can't decode them. + labels = np.where(labels != -100, labels, tokenizer.pad_token_id) + decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True) + + score_dict = { + "rouge-1": [], + "rouge-2": [], + "rouge-l": [], + "bleu-4": [] + } + for pred, label in zip(decoded_preds, decoded_labels): + hypothesis = list(jieba.cut(pred)) + reference = list(jieba.cut(label)) + rouge = Rouge() + scores = rouge.get_scores(' '.join(hypothesis) , ' '.join(reference)) + result = scores[0] + + for k, v in result.items(): + score_dict[k].append(round(v["f"] * 100, 4)) + bleu_score = sentence_bleu([list(label)], list(pred)) + score_dict["bleu-4"].append(round(bleu_score * 100, 4)) + + for k, v in score_dict.items(): + score_dict[k] = float(np.mean(v)) + return score_dict + + # Override the decoding parameters of Seq2SeqTrainer + training_args.generation_max_length = ( + training_args.generation_max_length + if training_args.generation_max_length is not None + else data_args.val_max_target_length + ) + training_args.generation_num_beams = ( + data_args.num_beams if data_args.num_beams is not None else training_args.generation_num_beams + ) + # Initialize our Trainer + trainer = Seq2SeqTrainer( + model=model, + args=training_args, + train_dataset=train_dataset if training_args.do_train else None, + eval_dataset=eval_dataset if training_args.do_eval else None, + tokenizer=tokenizer, + data_collator=data_collator, + compute_metrics=compute_metrics if training_args.predict_with_generate else None, + ) + + # Training + if training_args.do_train: + checkpoint = None + if training_args.resume_from_checkpoint is not None: + checkpoint = training_args.resume_from_checkpoint + # elif last_checkpoint is not None: + # checkpoint = last_checkpoint + model.gradient_checkpointing_enable() + model.enable_input_require_grads() + train_result = trainer.train(resume_from_checkpoint=checkpoint) + # trainer.save_model() # Saves the tokenizer too for easy upload + + metrics = train_result.metrics + max_train_samples = ( + data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset) + ) + metrics["train_samples"] = min(max_train_samples, len(train_dataset)) + + trainer.log_metrics("train", metrics) + trainer.save_metrics("train", metrics) + trainer.save_state() + + # Evaluation + results = {} + if training_args.do_eval: + logger.info("*** Evaluate ***") + metrics = trainer.evaluate(metric_key_prefix="eval", do_sample=True, top_p=0.7, max_length=512, temperature=0.95) + max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset) + metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset)) + + trainer.log_metrics("eval", metrics) + trainer.save_metrics("eval", metrics) + + if training_args.do_predict: + logger.info("*** Predict ***") + + predict_results = trainer.predict(predict_dataset, metric_key_prefix="predict", max_length=512, do_sample=True, top_p=0.7, temperature=0.95) + metrics = predict_results.metrics + max_predict_samples = ( + data_args.max_predict_samples if data_args.max_predict_samples is not None else len(predict_dataset) + ) + metrics["predict_samples"] = min(max_predict_samples, len(predict_dataset)) + + trainer.log_metrics("predict", metrics) + trainer.save_metrics("predict", metrics) + + if trainer.is_world_process_zero(): + if training_args.predict_with_generate: + predictions = tokenizer.batch_decode( + predict_results.predictions, skip_special_tokens=True, clean_up_tokenization_spaces=True + ) + predictions = [pred.strip() for pred in predictions] + labels = tokenizer.batch_decode( + predict_results.label_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True + ) + labels = [label.strip() for label in labels] + output_prediction_file = os.path.join(training_args.output_dir, "generated_predictions.txt") + with open(output_prediction_file, "w") as writer: + for p, l in zip(predictions, labels): + writer.write(json.dumps({"labels": l, "predict": p}, ensure_ascii=False)) + return results + + +def _mp_fn(index): + # For xla_spawn (TPUs) + main() + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/ptuning/train.sh b/ptuning/train.sh new file mode 100644 index 0000000..6988596 --- /dev/null +++ b/ptuning/train.sh @@ -0,0 +1,26 @@ +PRE_SEQ_LEN=8 +LR=1e-2 + +CUDA_VISIBLE_DEVICES=0 python3 main.py \ + --do_train \ + --train_file AdvertiseGen/train.json \ + --validation_file AdvertiseGen/dev.json \ + --prompt_column content \ + --response_column summary \ + --overwrite_cache \ + --model_name_or_path /mnt/vepfs/workspace/zxdu/chatglm_6b \ + --output_dir output/adgen-chatglm-6b-pt-$PRE_SEQ_LEN-$LR-dev \ + --overwrite_output_dir \ + --max_source_length 64 \ + --max_target_length 64 \ + --per_device_train_batch_size 8 \ + --per_device_eval_batch_size 1 \ + --gradient_accumulation_steps 2 \ + --predict_with_generate \ + --max_steps 3000 \ + --logging_steps 10 \ + --save_steps 1000 \ + --learning_rate $LR \ + --pre_seq_len $PRE_SEQ_LEN \ + --quantization_bit 4 + diff --git a/ptuning/trainer_seq2seq.py b/ptuning/trainer_seq2seq.py new file mode 100644 index 0000000..0087786 --- /dev/null +++ b/ptuning/trainer_seq2seq.py @@ -0,0 +1,245 @@ +# Copyright 2020 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Any, Dict, List, Optional, Tuple, Union + +import torch +from torch import nn +from torch.utils.data import Dataset + +from transformers.deepspeed import is_deepspeed_zero3_enabled +from transformers.trainer import Trainer +from transformers.trainer_utils import PredictionOutput +from transformers.utils import logging + + +logger = logging.get_logger(__name__) + + +class Seq2SeqTrainer(Trainer): + def evaluate( + self, + eval_dataset: Optional[Dataset] = None, + ignore_keys: Optional[List[str]] = None, + metric_key_prefix: str = "eval", + **gen_kwargs + ) -> Dict[str, float]: + """ + Run evaluation and returns metrics. + + The calling script will be responsible for providing a method to compute metrics, as they are task-dependent + (pass it to the init `compute_metrics` argument). + + You can also subclass and override this method to inject custom behavior. + + Args: + eval_dataset (`Dataset`, *optional*): + Pass a dataset if you wish to override `self.eval_dataset`. If it is an [`~datasets.Dataset`], columns + not accepted by the `model.forward()` method are automatically removed. It must implement the `__len__` + method. + ignore_keys (`List[str]`, *optional*): + A list of keys in the output of your model (if it is a dictionary) that should be ignored when + gathering predictions. + metric_key_prefix (`str`, *optional*, defaults to `"eval"`): + An optional prefix to be used as the metrics key prefix. For example the metrics "bleu" will be named + "eval_bleu" if the prefix is `"eval"` (default) + max_length (`int`, *optional*): + The maximum target length to use when predicting with the generate method. + num_beams (`int`, *optional*): + Number of beams for beam search that will be used when predicting with the generate method. 1 means no + beam search. + gen_kwargs: + Additional `generate` specific kwargs. + + Returns: + A dictionary containing the evaluation loss and the potential metrics computed from the predictions. The + dictionary also contains the epoch number which comes from the training state. + """ + + gen_kwargs = gen_kwargs.copy() + if gen_kwargs.get("max_length") is None and gen_kwargs.get("max_new_tokens") is None: + gen_kwargs["max_length"] = self.args.generation_max_length + gen_kwargs["num_beams"] = ( + gen_kwargs["num_beams"] if gen_kwargs.get("num_beams") is not None else self.args.generation_num_beams + ) + self._gen_kwargs = gen_kwargs + + return super().evaluate(eval_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix) + + def predict( + self, + test_dataset: Dataset, + ignore_keys: Optional[List[str]] = None, + metric_key_prefix: str = "test", + **gen_kwargs + ) -> PredictionOutput: + """ + Run prediction and returns predictions and potential metrics. + + Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method + will also return metrics, like in `evaluate()`. + + Args: + test_dataset (`Dataset`): + Dataset to run the predictions on. If it is a [`~datasets.Dataset`], columns not accepted by the + `model.forward()` method are automatically removed. Has to implement the method `__len__` + ignore_keys (`List[str]`, *optional*): + A list of keys in the output of your model (if it is a dictionary) that should be ignored when + gathering predictions. + metric_key_prefix (`str`, *optional*, defaults to `"eval"`): + An optional prefix to be used as the metrics key prefix. For example the metrics "bleu" will be named + "eval_bleu" if the prefix is `"eval"` (default) + max_length (`int`, *optional*): + The maximum target length to use when predicting with the generate method. + num_beams (`int`, *optional*): + Number of beams for beam search that will be used when predicting with the generate method. 1 means no + beam search. + gen_kwargs: + Additional `generate` specific kwargs. + + + + If your predictions or labels have different sequence lengths (for instance because you're doing dynamic + padding in a token classification task) the predictions will be padded (on the right) to allow for + concatenation into one array. The padding index is -100. + + + + Returns: *NamedTuple* A namedtuple with the following keys: + + - predictions (`np.ndarray`): The predictions on `test_dataset`. + - label_ids (`np.ndarray`, *optional*): The labels (if the dataset contained some). + - metrics (`Dict[str, float]`, *optional*): The potential dictionary of metrics (if the dataset contained + labels). + """ + + gen_kwargs = gen_kwargs.copy() + if gen_kwargs.get("max_length") is None and gen_kwargs.get("max_new_tokens") is None: + gen_kwargs["max_length"] = self.args.generation_max_length + gen_kwargs["num_beams"] = ( + gen_kwargs["num_beams"] if gen_kwargs.get("num_beams") is not None else self.args.generation_num_beams + ) + self._gen_kwargs = gen_kwargs + + + return super().predict(test_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix) + + def prediction_step( + self, + model: nn.Module, + inputs: Dict[str, Union[torch.Tensor, Any]], + prediction_loss_only: bool, + ignore_keys: Optional[List[str]] = None, + ) -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]: + """ + Perform an evaluation step on `model` using `inputs`. + + Subclass and override to inject custom behavior. + + Args: + model (`nn.Module`): + The model to evaluate. + inputs (`Dict[str, Union[torch.Tensor, Any]]`): + The inputs and targets of the model. + + The dictionary will be unpacked before being fed to the model. Most models expect the targets under the + argument `labels`. Check your model's documentation for all accepted arguments. + prediction_loss_only (`bool`): + Whether or not to return the loss only. + + Return: + Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]: A tuple with the loss, logits and + labels (each being optional). + """ + + if not self.args.predict_with_generate or prediction_loss_only: + return super().prediction_step( + model, inputs, prediction_loss_only=prediction_loss_only, ignore_keys=ignore_keys + ) + + has_labels = "labels" in inputs + inputs = self._prepare_inputs(inputs) + + # XXX: adapt synced_gpus for fairscale as well + gen_kwargs = self._gen_kwargs.copy() + if gen_kwargs.get("max_length") is None and gen_kwargs.get("max_new_tokens") is None: + gen_kwargs["max_length"] = self.model.config.max_length + gen_kwargs["num_beams"] = ( + gen_kwargs["num_beams"] if gen_kwargs.get("num_beams") is not None else self.model.config.num_beams + ) + default_synced_gpus = True if is_deepspeed_zero3_enabled() else False + gen_kwargs["synced_gpus"] = ( + gen_kwargs["synced_gpus"] if gen_kwargs.get("synced_gpus") is not None else default_synced_gpus + ) + + if "attention_mask" in inputs: + gen_kwargs["attention_mask"] = inputs.get("attention_mask", None) + if "global_attention_mask" in inputs: + gen_kwargs["global_attention_mask"] = inputs.get("global_attention_mask", None) + + # prepare generation inputs + # some encoder-decoder models can have varying encoder's and thus + # varying model input names + if hasattr(self.model, "encoder") and self.model.encoder.main_input_name != self.model.main_input_name: + generation_inputs = inputs[self.model.encoder.main_input_name] + else: + generation_inputs = inputs[self.model.main_input_name] + + gen_kwargs["input_ids"] = generation_inputs + generated_tokens = self.model.generate(**gen_kwargs) + generated_tokens = generated_tokens[:, generation_inputs.size()[-1]:] + + # in case the batch is shorter than max length, the output should be padded + if gen_kwargs.get("max_length") is not None and generated_tokens.shape[-1] < gen_kwargs["max_length"]: + generated_tokens = self._pad_tensors_to_max_len(generated_tokens, gen_kwargs["max_length"]) + elif gen_kwargs.get("max_new_tokens") is not None and generated_tokens.shape[-1] < ( + gen_kwargs["max_new_tokens"] + 1 + ): + generated_tokens = self._pad_tensors_to_max_len(generated_tokens, gen_kwargs["max_new_tokens"] + 1) + + loss = None + + if self.args.prediction_loss_only: + return (loss, None, None) + + if has_labels: + labels = inputs["labels"] + if gen_kwargs.get("max_length") is not None and labels.shape[-1] < gen_kwargs["max_length"]: + labels = self._pad_tensors_to_max_len(labels, gen_kwargs["max_length"]) + elif gen_kwargs.get("max_new_tokens") is not None and labels.shape[-1] < ( + gen_kwargs["max_new_tokens"] + 1 + ): + labels = self._pad_tensors_to_max_len(labels, (gen_kwargs["max_new_tokens"] + 1)) + else: + labels = None + + return (loss, generated_tokens, labels) + + def _pad_tensors_to_max_len(self, tensor, max_length): + if self.tokenizer is not None and hasattr(self.tokenizer, "pad_token_id"): + # If PAD token is not defined at least EOS token has to be defined + pad_token_id = ( + self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id + ) + else: + if self.model.config.pad_token_id is not None: + pad_token_id = self.model.config.pad_token_id + else: + raise ValueError("Pad_token_id must be set in the configuration of the model, in order to pad tensors") + + padded_tensor = pad_token_id * torch.ones( + (tensor.shape[0], max_length), dtype=tensor.dtype, device=tensor.device + ) + padded_tensor[:, : tensor.shape[-1]] = tensor + return padded_tensor