base on For releasing code related to compression methods for transformers, accompanying our publications # Transformer Compression with SliceGPT This repository contains the code for the paper [SliceGPT](https://arxiv.org/abs/2401.15024) (ICLR'24). Also discussed on [Hugging Face](https://huggingface.co/papers/2401.15024). SliceGPT is a new post-training sparsification scheme that makes transformer networks (including LLMs) smaller by first applying orthogonal transformations to each transformer layer that leave the model unchanged, and then slicing off the least-significant rows and columns (chosen by the eigenvalue decay) of the weight matrices. The model structure is left unchanged, but each weight matrix is replaced by a smaller (dense) weight matrix, reducing the embedding dimension of the model. This results in speedups (without any additional code optimization) and a reduced memory footprint. The code is arranged as a package `slicegpt` in `/src`, and scripts to replicate experiments from the paper are in `/experiments`. To install the `slicegpt` package, we recommend ``` pip install -e .[experiment] ``` ## Running SliceGPT To run SliceGPT on `microsoft/phi-2`, from the `experiments` folder, run ``` python run_slicegpt.py \ --model microsoft/phi-2 \ --save-dir dir/to/save/sliced_model/in \ --sparsity 0.25 \ --device cuda:0 \ --eval-baseline \ --no-wandb ``` This will compress the `microsoft/phi-2` model and save the compressed model to the specified directory. Please consult the script for the full set of options. _Note:_ For models that require Hugging Face authentication, set the `--hf-token` argument manually or using a key vault. Alternatively, set the environment variable `HF_TOKEN`. ### Recovery fine-tuning To install additional dependencies required for post-slicing recovery fine-tuning (RFT): ``` pip install -e .[experiment,finetune] ``` The following replicates the experiments in the paper (LoRA hyperparams valid for all Llama-2 and Phi-2 models): ``` python run_finetuning.py \ --model microsoft/phi-2 \ --sliced-model-path path/to/sliced \ --save-dir dir/to/save/finetuned_model/in \ --sparsity 0.25 \ --device cuda:0 \ --ppl-eval-dataset alpaca \ --finetune-dataset alpaca \ --finetune-train-nsamples 8000 \ --finetune-train-seqlen 1024 \ --finetune-train-batch-size 3 \ --lora-alpha 10 \ --lora-r 32 \ --lora-dropout 0.05 \ --lora-target-option attn_head_and_mlp \ --eval-steps 16 \ --save-steps 16 \ --no-wandb ``` Notes: - The script [`bo_finetuning.py`](./experiments/bo_finetuning.py) can be used to run Bayesian optimization over the RFT hyperparameters. - To run finetuning on the original model, specify `--model-path` instead of `--sliced-model-path`. - `sparsity` must be specified when specifying `sliced-model-path` to avoid default sparsity being used ### Evaluation using the [LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness) ``` python run_lm_eval.py \ --model microsoft/phi-2 \ --sliced-model-path path/to/sliced \ --sparsity 0.25 \ --tasks piqa \ --no-wandb ``` Notes: - To run lm-eval on the original model, specify `--model-path` instead of `--sliced-model-path`. - `sparsity` must be specified when specifying `sliced-model-path` to avoid default sparsity being used ## Supported models The following models from Hugging Face hub are currently supported - [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) - [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) - [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b) - [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b) - [meta-llama/Llama-2-70b-hf](https://huggingface.co/meta-llama/Llama-2-70b) - [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) - [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) - [meta-llama/Meta-Llama-3-70B](https://huggingface.co/meta-llama/Meta-Llama-3-70B) - [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) - [facebook/opt-125m](https://huggingface.co/facebook/opt-125m) - [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) - [facebook/opt-2.7b](https://huggingface.co/facebook/opt-2.7b) - [facebook/opt-6.7b](https://huggingface.co/facebook/opt-6.7b) - [facebook/opt-13b](https://huggingface.co/facebook/opt-13b) - [facebook/opt-30b](https://huggingface.co/facebook/opt-30b) - [facebook/opt-66b](https://huggingface.co/facebook/opt-66b) ## Extending support to a new model type The model you wish to support must be in Hugging Face Hub format. The model files can be downloaded from Hugging Face Hub by supplying `--model` argument, or accessed from local storage by using the `--model` and `--model-path` argument. To add SliceGPT support for a new model, one needs to implement a new model adapter and update `hf_utils.get_model_and_tokenizer` before slicing the new model. ### Implementing a new model adapter - Implement the [ModelAdapter](./src/slicegpt/model_adapter.py) interface for the new model. The ModelAdapter class tells SliceGPT how to interact with the model, an instance of which is stored at `self.model`. For example, how to access each of the layers of the model. - Implement the [LayerAdapter](./src/slicegpt/model_adapter.py) interface for the transformer layers. The LayerAdapter class tells SliceGPT how to interact with each transformer layer of the model, an instance of which is stored at `self.layer`. For example, how to access the attention and MLP components of the transformer layer, and how to update the arguments to the transformer layer's forward method. - Implement a compressed transformer layer class that subclasses the transformer layer. This class should also provide an adapted `forward()` method to work with the compressed model. This method should specify how the skip connection orthogonal matrices are used, depending on whether MLP and attention blocks are sequential ([OPT](./src/slicegpt/adapters/opt_adapter.py), [Llama-2/Llama-3](./src/slicegpt/adapters/llama_adapter.py)) or parallel ([Phi-2](./src/slicegpt/adapters/phi2_adapter.py)). The `self.*_shortcut_Q` matrices are attached to the modules during slicing and are available in `forward()`. If the skip connection does not need modification, these matrices will be None, and the `forward()` method can follow the original workflow. For more details on this, please read Section 3 in [the paper](https://arxiv.org/abs/2401.15024). Example: [llama_adapter.py](./src/slicegpt/adapters/llama_adapter.py) ### Using a new model adapter to slice a model Once a model adapter is implemented, compressing the model involves three conceptual steps: - Replace modules with compressed equivalents (via `slicegpt.layernorm_fusion.replace_layers`) - Fuse layer norms and add rotations to skip connections (via `slicegpt.layernorm_fusion.fuse_modules`) - Rotate the inputs and slice the layers (via `slicegpt.rotate.rotate_and_slice`) Example: [run_slicegpt.py](./experiments/run_slicegpt.py) _Note:_ If the model you wish to support is not available in Hugging Face, you will also need to implement custom model loading and initialization functionality. ## Contributing This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com. When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA. This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [[email protected]](mailto:[email protected]) with any additional questions or comments. ## Trademarks This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies. ", Assign "at most 3 tags" to the expected json: {"id":"7430","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"