### Setup LayerSkip Environment

Source: https://github.com/facebookresearch/layerskip/blob/main/README.md

Set up a Conda environment and install project dependencies. Ensure you have Python 3.10 or a compatible version.

```console
$ conda create --name layer_skip python=3.10
$ conda activate layer_skip

$ pip install -r requirements.txt
```

--------------------------------

### Optimize Dockerfile Dependencies Installation

Source: https://github.com/facebookresearch/layerskip/blob/main/DOCKER.md

Combine RUN commands in your Dockerfile to reduce layers and leverage Docker's build cache. Install dependencies before copying application code.

```dockerfile
RUN conda install pytorch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1 cpuonly -c pytorch -y && \
        pip install --upgrade pip && \
        pip install --no-cache-dir -r /app/requirements.txt
```

--------------------------------

### Run LayerSkip Docker Container (Basic)

Source: https://github.com/facebookresearch/layerskip/blob/main/DOCKER.md

Execute a command inside the LayerSkip Docker container. This example shows how to run a script with the --help flag. Ensure your HuggingFace token is provided as an environment variable.

```bash
docker run -it --rm \
    -e HUGGINGFACE_TOKEN=your_huggingface_token_here \
    layerskip:latest \
    python your_script.py --help
```

--------------------------------

### Optimize Dockerfile Dependencies Installation

Source: https://github.com/facebookresearch/layerskip/blob/main/DOCKER.md

This Dockerfile snippet optimizes build times by copying only the requirements file first and installing dependencies. This allows Docker to cache the dependency layer, speeding up subsequent builds if requirements.txt remains unchanged. It also combines RUN commands for fewer layers.

```dockerfile
RUN conda install pytorch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1 cpuonly -c pytorch -y && \
    pip install --upgrade pip && \
    pip install --no-cache-dir -r /app/requirements.txt
```

--------------------------------

### Verify PyTorch Installation in Docker Container

Source: https://github.com/facebookresearch/layerskip/blob/main/DOCKER.md

Run a command inside the Docker container to check the installed PyTorch version. This confirms that the deep learning environment is set up correctly.

```bash
docker run -it --rm layerskip:latest python -c "import torch; print(torch.__version__)"
```

--------------------------------

### Check Script Help Messages in Docker Container

Source: https://github.com/facebookresearch/layerskip/blob/main/DOCKER.md

Verify that scripts are accessible and their dependencies are correctly installed by checking their help messages within the Docker container.

```bash
docker run -it --rm layerskip:latest python generate.py --help
```

```bash
docker run -it --rm layerskip:latest python benchmark.py --help
```

```bash
docker run -it --rm layerskip:latest python eval.py --help
```

```bash
docker run -it --rm layerskip:latest python sweep.py --help
```

```bash
docker run -it --rm layerskip:latest python correctness.py --help
```

--------------------------------

### Run Generate Script with Docker and Help Flag

Source: https://github.com/facebookresearch/layerskip/blob/main/DOCKER.md

Execute the generate script with the --help flag using Docker. Passes the HuggingFace token as an environment variable.

```bash
docker run -it --rm \
    -e HUGGINGFACE_TOKEN=your_huggingface_token_here \
    layerskip:latest \
    python generate.py --help
```

--------------------------------

### Benchmark LayerSkip Model with Docker

Source: https://github.com/facebookresearch/layerskip/blob/main/DOCKER.md

Execute the benchmark script for the LayerSkip model using Docker. Mounts a log directory and passes the HuggingFace token as an environment variable.

```bash
docker run -it --rm \
    -e HUGGINGFACE_TOKEN=your_huggingface_token_here \
    -v /path/on/host/logs:/app/logs \
    layerskip:latest \
    python benchmark.py --model facebook/layerskip-llama2-7B \
                         --dataset human_eval \
                         --num_samples 100 \
                         --generation_strategy self_speculative \
                         --exit_layer 8 \
                         --num_speculations 6 \
                         --output_dir /app/logs
```

--------------------------------

### Benchmark LayerSkip Model with Docker

Source: https://github.com/facebookresearch/layerskip/blob/main/DOCKER.md

Run the benchmark.py script inside the LayerSkip Docker container to benchmark the model on a specified dataset. Mounts a host directory for logs. Requires HuggingFace token.

```bash
docker run -it --rm \
    -e HUGGINGFACE_TOKEN=your_huggingface_token_here \
    -v /path/on/host/logs:/app/logs \
    layerskip:latest \
    python benchmark.py --model facebook/layerskip-llama2-7B \
                         --dataset cnn_dm_summarization \
                         --num_samples 100 \
                         --generation_strategy self_speculative \
                         --exit_layer 8 \
                         --num_speculations 6 \
                         --output_dir /app/logs
```

--------------------------------

### Evaluate LayerSkip Model with Docker

Source: https://github.com/facebookresearch/layerskip/blob/main/DOCKER.md

Run the evaluation script for a LayerSkip model using Docker. Mounts a log directory and sets the HuggingFace token.

```bash
docker run -it --rm \
    -e HUGGINGFACE_TOKEN=your_huggingface_token_here \
    -v /path/on/host/logs:/app/logs \
    layerskip:latest \
    python eval.py --model facebook/layerskip-llama2-7B \
                    --tasks gsm8k \
                    --limit 10 \
                    --generation_strategy self_speculative \
                    --exit_layer 8 \
                    --num_speculations 6 \
                    --output_dir /app/logs
```

--------------------------------

### Clone LayerSkip Repository

Source: https://github.com/facebookresearch/layerskip/blob/main/README.md

Clone the LayerSkip repository to your local machine. This is the first step to setting up the project.

```console
$ git clone git@github.com:facebookresearch/LayerSkip.git
$ cd LayerSkip
```

--------------------------------

### Test Docker Container with Dummy HuggingFace Token

Source: https://github.com/facebookresearch/layerskip/blob/main/DOCKER.md

Pass a dummy HuggingFace token to ensure environment variables are handled correctly without attempting to access models. This avoids authentication errors during testing.

```bash
docker run -it --rm \
    -e HUGGINGFACE_TOKEN=dummy_token \
    layerskip:latest \
    python generate.py --help
```

--------------------------------

### Check HuggingFace Token Environment Variable

Source: https://github.com/facebookresearch/layerskip/blob/main/DOCKER.md

Verify that environment variables are correctly set within the Docker container by echoing the HuggingFace token.

```bash
docker run -it --rm layerskip:latest bash -c 'echo $HUGGINGFACE_TOKEN'
```

--------------------------------

### Benchmark LayerSkip Model

Source: https://github.com/facebookresearch/layerskip/blob/main/README.md

Use this script to benchmark a LayerSkip model on a specified dataset. Configure dataset, number of samples, generation strategy, exit layer, and number of speculations.

```console
$ torchrun benchmark.py --model facebook/layerskip-llama2-7B \
    --dataset cnn_dm_summarization \
    --num_samples 100 \
    --generation_strategy self_speculative \
    --exit_layer 8 \
    --num_speculations 6 \
    --output_dir ./logs
```

--------------------------------

### Perform Hyperparameter Sweep with Docker

Source: https://github.com/facebookresearch/layerskip/blob/main/DOCKER.md

Execute the sweep script to test different hyperparameters for the LayerSkip model via Docker. Mounts a sweep directory and sets the HuggingFace token.

```bash
docker run -it --rm \
    -e HUGGINGFACE_TOKEN=your_huggingface_token_here \
    -v /path/on/host/sweep:/app/sweep \
    layerskip:latest \
    python sweep.py --model facebook/layerskip-llama2-7B \
                     --dataset human_eval \
                     --generation_strategy self_speculative \
                     --num_samples 150 \
                     --max_steps 256 \
                     --output_dir /app/sweep \
                     --sample False
```

--------------------------------

### Clone LayerSkip Repository

Source: https://github.com/facebookresearch/layerskip/blob/main/DOCKER.md

Clone the LayerSkip repository to your local machine. This is the first step before building the Docker image.

```bash
git clone git@github.com:facebookresearch/LayerSkip.git
cd LayerSkip
```

--------------------------------

### Docker Entrypoint Script to Read Secret

Source: https://github.com/facebookresearch/layerskip/blob/main/DOCKER.md

Bash script for a Docker entrypoint that activates a Conda environment and exports the HuggingFace token from a Docker secret.

```bash
#!/bin/bash
# entrypoint.sh

# Activate the Conda environment
source /opt/conda/etc/profile.d/conda.sh
conda activate layer_skip

# Read HuggingFace token from Docker secret
export HUGGINGFACE_TOKEN=$(cat /run/secrets/huggingface_token)

# Execute the passed command
exec "$@"
```

--------------------------------

### Deploy Docker Service with Secret

Source: https://github.com/facebookresearch/layerskip/blob/main/DOCKER.md

Command to create a Docker Swarm service named 'layerskip_service' using a specific image and mounting a secret.

```bash
docker service create --name layerskip_service \
    --secret huggingface_token \
    layerskip:latest \
    python generate.py --help
```

--------------------------------

### Check Correctness of Self-Speculative Decoding with Docker

Source: https://github.com/facebookresearch/layerskip/blob/main/DOCKER.md

Run the correctness check script for self-speculative decoding using Docker. Mounts a correctness directory and sets the HuggingFace token.

```bash
docker run -it --rm \
    -e HUGGINGFACE_TOKEN=your_huggingface_token_here \
    -v /path/on/host/correctness:/app/correctness \
    layerskip:latest \
    python correctness.py --model facebook/layerskip-llama2-7B \
                            --dataset human_eval \
                            --generation_strategy self_speculative \
                            --num_speculations 6 \
                            --exit_layer 4 \
                            --num_samples 10 \
                            --sample False \
                            --output_dir /app/correctness
```

--------------------------------

### Generate Text with Self-Speculative Decoding (Docker)

Source: https://github.com/facebookresearch/layerskip/blob/main/DOCKER.md

Execute text generation using self-speculative decoding within the LayerSkip Docker container. Specify --exit_layer and --num_speculations for speedup. Requires HuggingFace token.

```bash
docker run -it --rm \
    -e HUGGINGFACE_TOKEN=your_huggingface_token_here \
    layerskip:latest \
    python generate.py --model facebook/layerskip-llama2-7B \
                        --sample True \
                        --max_steps 512 \
                        --generation_strategy self_speculative \
                        --exit_layer 8 \
                        --num_speculations 6
```

--------------------------------

### Generate Text with LayerSkip Docker

Source: https://github.com/facebookresearch/layerskip/blob/main/DOCKER.md

Run the generate.py script within the LayerSkip Docker container for text generation using autoregressive decoding. Requires HuggingFace token.

```bash
docker run -it --rm \
    -e HUGGINGFACE_TOKEN=your_huggingface_token_here \
    layerskip:latest \
    python generate.py --model facebook/layerskip-llama2-7B \
                        --sample True \
                        --max_steps 512
```

--------------------------------

### Evaluate LayerSkip Model with LM Evaluation Harness

Source: https://github.com/facebookresearch/layerskip/blob/main/README.md

Evaluate a LayerSkip model using the Eleuther Language Model Evaluation Harness. Specify tasks, limit, generation strategy, exit layer, and number of speculations.

```console
$ torchrun eval.py --model facebook/layerskip-llama2-7B \
    --tasks gsm8k \
    --limit 10 \
    --generation_strategy self_speculative \
    --exit_layer 8 \
    --num_speculations 6 \
    --output_dir ./logs
```

--------------------------------

### Run Unit/Integration Tests

Source: https://github.com/facebookresearch/layerskip/blob/main/README.md

Execute unit and integration tests for LayerSkip using pytest. Ensure you are in the project's root directory.

```bash
pytest ./tests/
```

--------------------------------

### Build LayerSkip Docker Image

Source: https://github.com/facebookresearch/layerskip/blob/main/DOCKER.md

Build the Docker image for LayerSkip using the Dockerfile in the project root. This command tags the image as 'layerskip:latest'.

```bash
docker build -t layerskip:latest .
```

--------------------------------

### Mount HuggingFace Cache Directory in Docker

Source: https://github.com/facebookresearch/layerskip/blob/main/DOCKER.md

Mount the host's HuggingFace cache directory to the container to avoid re-downloading models. This speeds up model loading times on subsequent runs.

```bash
docker run -it --rm \
    -e HUGGINGFACE_TOKEN=your_huggingface_token_here \
    -v /path/on/host/huggingface_cache:/root/.cache/huggingface \
    layerskip:latest \
    python generate.py --help
```

--------------------------------

### Run Model with Autoregressive Decoding

Source: https://github.com/facebookresearch/layerskip/blob/main/README.md

Generate text using a LayerSkip model with standard autoregressive decoding. This is useful for baseline generation.

```console
$ torchrun generate.py --model facebook/layerskip-llama2-7B \
    --sample True \
    --max_steps 512
```

--------------------------------

### Sweep LayerSkip Hyperparameters

Source: https://github.com/facebookresearch/layerskip/blob/main/README.md

Sweep over a grid of exit_layer and num_speculations to find optimal inference hyperparameters. Configure model, dataset, generation strategy, and sampling parameters.

```console
$ torchrun sweep.py --model facebook/layerskip-llama2-7B \
    --dataset human_eval \
    --generation_strategy self_speculative \
    --num_samples 150 \
    --max_steps 256 \
    --output_dir ./logs/ \
    --sample False
```

--------------------------------

### Create Docker Secret for HuggingFace Token

Source: https://github.com/facebookresearch/layerskip/blob/main/DOCKER.md

Command to create a Docker secret named 'huggingface_token' with the provided token value.

```bash
echo "your_huggingface_token_here" | docker secret create huggingface_token -
```

--------------------------------

### Run Model with Self-Speculative Decoding

Source: https://github.com/facebookresearch/layerskip/blob/main/README.md

Generate text using a LayerSkip model with self-speculative decoding for faster inference. Specify `--exit_layer` and `--num_speculations` to enable this feature.

```console
$ torchrun generate.py --model facebook/layerskip-llama2-7B \
    --sample True \
    --max_steps 512 \
    --generation_strategy self_speculative \
    --exit_layer 8 \
    --num_speculations 6
```

--------------------------------

### Expose and Map Port 8000 in Docker

Source: https://github.com/facebookresearch/layerskip/blob/main/DOCKER.md

Expose a port in the Dockerfile and map it when running the container to allow access to web servers or services running inside.

```dockerfile
EXPOSE 8000
```

```bash
docker run -it --rm \
    -e HUGGINGFACE_TOKEN=your_huggingface_token_here \
    -p 8000:8000 \
    layerskip:latest \
    python your_web_server_script.py
```

--------------------------------

### Verify Correctness of Self-Speculative Decoding

Source: https://github.com/facebookresearch/layerskip/blob/main/README.md

Verify that generated tokens from self-speculative decoding match autoregressive decoding. Ensure no sampling is enabled for guaranteed equivalence.

```console
$ torchrun correctness.py --model facebook/layerskip-llama2-7B \
    --dataset human_eval \
    --generation_strategy self_speculative \
    --num_speculations 6 \
    --exit_layer 4 \
    --num_samples 10 \
    --sample False \
    --output_dir ./logs
```

--------------------------------

### LayerSkip Citation

Source: https://github.com/facebookresearch/layerskip/blob/main/README.md

BibTeX entry for citing LayerSkip in academic research. This entry provides all necessary details for proper attribution.

```bibtex
@misc{layerskip,
    title={LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding},
    author={Mostafa Elhoushi and Akshat Shrivastava and Diana Liskovich and Basil Hosmer and Bram Wasti and Liangzhen Lai and Anas Mahmoud and Bilge Acun and Saurabh Agarwal and Ahmed Roman and Ahmed A Aly and Beidi Chen and Carole-Jean Wu},
    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.acl-long.681",
    doi = "10.18653/v1/2024.acl-long.681",
    pages = "12622--12642",
}
```

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