Transformer Compression with SliceGPT

This repository contains the code for the paper SliceGPT (ICLR'24). Also discussed on Hugging Face.

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 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

    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
• microsoft/Phi-3-mini-4k-instruct
• meta-llama/Llama-2-7b-hf
• meta-llama/Llama-2-13b-hf
• meta-llama/Llama-2-70b-hf
• meta-llama/Meta-Llama-3-8B
• meta-llama/Meta-Llama-3-8B-Instruct
• meta-llama/Meta-Llama-3-70B
• meta-llama/Meta-Llama-3-70B-Instruct
• facebook/opt-125m
• facebook/opt-1.3b
• facebook/opt-2.7b
• facebook/opt-6.7b
• facebook/opt-13b
• facebook/opt-30b
• 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 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 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, Llama-2/Llama-3) or parallel (Phi-2). 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.

Example: 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

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

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