base on The Truth Is In There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction # Layer-Selective Rank Reduction This repository contains code for the paper _"The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction,"_ by Pratyusha Sharma, Jordan T. Ash and Dipendra Misra [ICLR 2024](https://arxiv.org/pdf/2312.13558.pdf). **Website:** [https://pratyushasharma.github.io/laser/](https://pratyushasharma.github.io/laser) **Updates:** - **Jan 18th, 2024:** Refactoring is happening in the [refactor](https://github.com/pratyushasharma/laser/tree/refactor) branch. We are working to release it quickly and thank you for your patience. - **Jan 7th, 2024:** Results table has been created on the [website](https://pratyushasharma.github.io/laser/index.html#results). - **Jan 4th, 2024:** Discussions page is open. Feel free to use it to suggest new topics/ideas/results that are not covered by issues. **This is an early development release. We will do a major refactor in Jan 2024 to make the code easier to use and more flexible.** We welcome issues and pull requests. If you report a new result using LASER on a given LLM and NLP task, please issue a pull request and we'll add it to the website's leaderboard. ## What is Layer-Selective Rank Reduction? **LA**yer-**SE**lective **R**ank-Reduction, abbreviated as LASER, is an intervention where we replace a selected weight matrix in the transformer architecture of an LLM with its low-rank approximation. A single LASER transformation consists of 3 hyperparameters: the layer number to modify (&ell;) such as 16th layer, the parameter type (&tau;) such as the first MLP layer, and the fraction of the maximum rank to retain (&rho;) such as 0.01 fraction of the rank. We can write this transformation as (&ell;, &tau;, &rho;) and we can compose these transformations and apply them in parallel. The low-rank approximation is performed using SVD. Figure below from our paper shows an illustration.  LASER can give significant performance improvements on question-answerting tasks without additional model training. Our paper presents various results related to evaluating LASER on 3 different LLMs and several LLM benchmarks. This repository contains the code to reproduce these results. ## How to run a sample code We first discuss installing the code and then discuss how to run an experiment. ### Installation To install the experiment, please install the pip file. We chiefly just need pytorch and the datasets and transformers package from huggingface. It might be a good idea to create a conda environment. ```bash pip3 install -r requirements.txt ``` Optionally, if you want to experiment with the CounterFact dataset then run the following script to download it. All other datasets are available on HuggingFace. ```bash python scripts/get_counterfact.py ``` ### Run a sample code At the moment, each setup is its own file. To run an experiment that performs a single LASER transformer to GPTJ on the Fever dataset, you can run: ```bash python3 intervention_gptj_fever.py --lname fc_in --rate 9.9 --lnum 26 ``` here _lnum_ is &ell;, _lname_ is &tau;, and _rate_ is related to &rho; by &rho; = 1 - 0.1 * rate. The rate is a value between [0, 10.0] and measures how many components to throw away with 10 means all components are thrown away and we get a 0 matrix and 0 means all components are retained and we retain the original matrix. The use of rate is for legacy reasons and we will refactor the code to directly use &rho; in the future. The mapping for _lname_ that we use is: **lname** | **description**| --- | --- | dont | use the base model and dont perform intervention | fc_in | first layer of MLP | fc_out | second layer of MLP | fc_up | a third MLP weight matrix in some LLM, used for Hadamard multiplication | mlp | all MLP weight matrices {fc_in, fc_up, fc_out} | k_proj | key matrix in self attention | v_proj | value matrix in self attention | q_proj | query matrix in self attention | out_proj | output matrix in self attention | attn | all attention weight matrices | **Please do note that if you add a new LLM, then you have to adapt the laser package to implement mappings.** For example, see the mappings for Llama2 [here](https://github.com/pratyushasharma/laser/blob/main/src/laser/llama2_laser.py#L22). You also need to update the Laser wrapper to work with the new LLM [here](https://github.com/pratyushasharma/laser/blob/main/src/laser/LaserWrapper.py#L20). Note that the above experiments will save accuracies and log-losses for each datapoint. In some files, one has to take the validation set (first 20% examples) and do hyperparameter selection separately, and then compute the accuracy on the test set (remaining 80% examples) with the chose hyperparameters. In the future, we will refactor the code to make this very easy to do. ## Code Organization Code is inside the `src` folder. The main experiment files are top-level inside the `src`. The filename convention is `intervention_<llm-name>_<dataset-name>.py` where `<llm-name>` is the name of the LLM and `<dataset-name>` is the name of the dataset. For BigBench, the dataset split is often specified with an additional flag --split. Please see the codebase for details of command line arguments. We will provide a comprehensive tutorial later. The code for performing laser is inside the `laser` package. We use PyTorch to do SVD and compute low-rank approximation. The code for low-rank approximation happens [here](https://github.com/pratyushasharma/laser/blob/main/src/laser/matrix_utils.py#L39). The code for reading and processing dataset is inside `dataset_util`. Finally, metrics and logging are done using the `study_utils`. ## Citation If you find this codebase useful, then please cite the following paper. Additionally, feel free to send a PR or an email and we will cite your result/paper on the leaderboard. ```bash @article{sharma2023truth, title={The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction}, author={Sharma, Pratyusha and Ash, Jordan T and Misra, Dipendra}, journal={arXiv preprint arXiv:2312.13558}, year={2023} } ``` ", Assign "at most 3 tags" to the expected json: {"id":"6658","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"