LogLM

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From Task-based to Instruction-based Automated Log Analysis

LogLM 최신버젼 다운로드

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# LogLM

This repo contains training codes and the human-calibriated instruction dataset for LogLM, an LLM designed for instruction-based log analysis.

Paper: [LogLM: From Task-based to Instruction-based Automated Log Analysis](https://arxiv.org/abs/2410.09352)

## 📰 News
LogLM has been accepted by International Conference on Software Engineering (ICSE) 2025 (SEIP track)🎉🎉🎉🎉🎉🎉.

## 📣 Introduction

 <img src="asset/illustration.png" width=800 />

Automatic log analysis is essential for the efficient Operation and Maintenance (O\&M) of software systems, providing critical insights into system behaviors. However, existing approaches mostly treat log analysis as training a model to perform an isolated task, using task-specific log-label pairs. These task-based approaches are inflexible in generalizing to complex scenarios, depend on task-specific training data, and cost significantly when deploying multiple models. In this paper, we propose an instruction-based training approach that transforms log-label pairs from multiple tasks and domains into a unified format of instruction-response pairs. Our trained model, LogLM, can follow complex user instructions and generalize better across different tasks, thereby increasing flexibility and reducing the dependence on task-specific training data. By integrating major log analysis tasks into a single model, our approach also relieves model deployment burden. Experimentally, LogLM outperforms existing approaches across five log analysis capabilities, and exhibits strong generalization abilities on complex instructions and unseen tasks.

## 🔰 Installation
```
$ pip install requirements.txt
```
## ✨ Instruction Dataset of LogLM
We created an instruction dataset of 2632 pairs of instruction and response for training LogLM, an instruction-following LLM. The instruction dataset consists of five log analysis tasks and nine distinct log domains.
**This dataset is in the main directory of this repo, named 'Instruction Dataset of LogLM.json'.**

Example:
```
Instruction Dataset of LogLM.json
{
 "Instruction": "Interpret the error message in the given log.",
 "Input": "ValueError: Unable to configure handler 'log_file': [Errno 2] No such file or directory: '/logs/django.log'",
 "Response": "The error message indicates that the Django application's logging system is configured to write logs to a file located at '/logs/django.log'. However, it encountered a ValueError because it could not find the specified file or directory. This could be due to an incorrect path or the directory not existing where it expects to find or create the log file."
}
```
The statistics of the dataset is as follows:

 <img src="asset/instruction dataset.png" width=500 />

## 📝 Training LogLM

LogLM in our paper is implemented by fine-tuning LLaMA-2-7B on our curated instruction dataset. Thanks [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) for implementing an efficient tool to fine-tune LLMs. The training steps of LogLM are as follows:

(1) Prepare the environment

Please make sure to install the packages required. For issues related to environment, please refer to LLaMA-Factory v0.3.2.

(2) Register the training dataset

Register the dataset in data/dataset_info.json.  
```
dataset_info.json
{
  "LogLM_train": {
    "file_name": "Instruction Dataset of LogLM.json",
    "file_sha1": "",
    "columns": {
      "prompt": "instruction",
      "query": "input",
      "response": "output"
    }
  }
}
```

(3) Start training (with multiple GPUs)
```
WANDB_MODE=offline deepspeed --include localhost:0,1 --master_port=9905 src/train_bash.py \
    --deepspeed ds_config_zero2.json \
    --stage sft \
    --model_name_or_path [path to Llama-2-7b-hf] \
    --do_train \
    --dataset LogLM_train \
    --template alpaca \
    --finetuning_type full \
    --output_dir [path to model saving] \
    --overwrite_cache \
    --per_device_train_batch_size 32 \
    --gradient_accumulation_steps 1 \
    --lr_scheduler_type cosine \
    --save_strategy "epoch" \
    --save_total_limit 6 \
    --logging_steps 10 \
    --learning_rate 2e-5 \
    --weight_decay 0. \
    --warmup_ratio 0.03 \
    --num_train_epochs 6 \
    --plot_loss \
    --overwrite_output_dir \
    --bf16 True \
    --tf32 True \
```
The config file ds_config_zero2.json can be found in [DeepSpeed](https://github.com/huggingface/transformers/blob/main/tests/deepspeed/ds_config_zero2.json)

(4) Inference

The inference test datasets in the "test set" folder should also be registerd in data/dataset_info.json, the same way as the training dataset. 
```
python src/train_bash.py \
    --stage sft \
    --model_name_or_path [path to Llama-2-7b-hf] \
    --do_predict \
    --dataset [registered name of your test set] \
    --template alpaca \
    --finetuning_type full \
    --checkpoint_dir [path to finetuned model] \
    --output_dir [result saving path] \
    --per_device_eval_batch_size 32 \
    --predict_with_generate
```
For more information, please refer to LLaMA-Factory v0.3.2.

## **🔬** Additional Experimental Results

The following additional experimental results expand the ablation study in the paper, focusing on two aspect: (1) an ablation study on amount of instruction training data. (2) Comparison with "LLaMA-2-finetuned" on all five tasks, which is the backbone LLM fine-tuned on the same in-domain and in-task data as LogLM.

### (1) Ablation on Amount of Training Data

To disentagle whether the strong performance of LogLM is coming from the diversity of tasks in LogLM's training data or the amount of data itself (For example, it is possible that just seeing more logs is helping), we created an upsampled version of each single-task dataset with the equal size of the full LogLM instruction dataset (with 2632 entries). For example, the training dataset for Log Parsing in HDFS has 200 entries, and is cloned for around 13 times to form a upsampled version. We then train LogLM with the same setting on each of the upsampled dataset and compare the performance. Results are shown in the following tables:

#### Log Parsing

| **Model**                         | **HDFS**  | **Hadoop**    | **Zookeeper**    | **BGL**   | **HPC**   | **Linux** | **Proxifier**    | **Avg.**  |
| --------------------------------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- |
| **LogLM**                   | 0.998     | **0.973** | **0.995** | **0.977** | **0.935** | **0.934** | **0.940**     | **0.965** |
| **LLaMA-2-7B-finetuned** | **1.000** | 0.937     | 0.786     | 0.866     | 0.736     | 0.913     | 0.927     | 0.881     |
| **LLaMA-2-7B-finetuned (Upsampled)**                      | 0.999     | 0.949     | 0.805     | 0.823     | 0.889     | 0.919     | 0.925     | 0.901     |

#### Anomaly Detection

|             **Model**             |    BGL    |  Spirit   |   Avg.    |
| -------------------------------| :-------: | :-------: | :-------: |
|          **LogLM**          | **0.625** | **0.278** | **0.452** |
| **LLaMA-2-7B-finetuned** |   0.203   |   0.073   |   0.138   |
| **LLaMA-2-7B-finetuned (Upsampled)**           |   0.345   |   0.073   |   0.209   |

In the table, **LLaMA-2-7B-finetuned** refers to the model fine-tuned from LLaMA-2 using only in-domain and in-task training data, and **LLaMA-2-7B-finetuned (Upsampled)** refers to the model trained using the upsampled version of in-domain data. Both the results for log parsing and anomaly detection indicate that, although increasing the amount of training data helps with the performance (Upsampled > Non-upsampled), it still falls behind LogLM, which is trained with an instruction dataset containing diverse tasks and domains.

### (2) Comparison with LLaMA-2-7B-finetuned on All Five Log Analysis Tasks

In the paper, we only displayed performance comparison between LogLM and LLaMA-2-7B-finetuned (the pink bar in Fig. 5 in the paper, training using only in-domain and in-task data) for two tasks: Log Parsing and Anomaly Detection. The rest of the five tasks are Log Interpretation, Root Cause Analysis and Solution Recommendation. And results on these tasks are as follows:

#### Log Interpretation
| **Model**       | **BLEU** | **ROUGE-1** | **ROUGE-2** | **ROUGE-L** |
|------------------|----------|-------------|-------------|-------------|
| **LogLM**        | **15.584**   | **46.488**      | **23.087**      | 34.769      |
| **LLaMA-2-7B-finetuned**     | 15.505   | 46.022      | 23.025      | **35.471**  |

#### Root Cause Analysis Task
| **Model**       | **BLEU** | **ROUGE-1** | **ROUGE-2** | **ROUGE-L** |
|------------------|----------|-------------|-------------|-------------|
| **LogLM**        | 12.398   | **40.602**      | **19.042**      | **30.227**      |
| **LLaMA-2-7B-finetuned**     | **13.436** | 40.322      | 18.889      | 29.942      |

#### Solution Recommendation Task
| **Model**       | **BLEU** | **ROUGE-1** | **ROUGE-2** | **ROUGE-L** |
|------------------|----------|-------------|-------------|-------------|
| **LogLM**        | **8.241** | **34.415**  | **13.911**  | **25.431**  |
| **LLaMA-2-7B-finetuned**     | 6.259    | 30.870      | 11.622      | 23.463      |

In general, we found that LogLM demonstrated superior performance against LLaMA-2-7B-finetuned in the vast majority of testing terms.

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