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language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
model-index:
  - name: LayerSkip Llama3 8B
    results:
      - task:
          type: question-answering
        dataset:
          type: google/boolq
          name: BoolQ
        metrics:
          - name: acc
            type: acc
            value: 0.825
            verified: false
      - task:
          type: question-answering
        dataset:
          type: ybisk/piqa
          name: PIQA
        metrics:
          - name: acc
            type: acc
            value: 0.794
            verified: false
      - task:
          type: question-answering
        dataset:
          type: allenai/social_i_qa
          name: SIQA
        metrics:
          - name: acc
            type: acc
            value: 0.461
            verified: false
      - task:
          type: text-generation
        dataset:
          type: Rowan/hellaswag
          name: HellaSwag
        metrics:
          - name: acc
            type: acc
            value: 0.594
            verified: false
      - task:
          type: question-answering
        dataset:
          type: allenai/winogrande
          name: WinoGrande
        metrics:
          - name: acc
            type: acc
            value: 0.739
            verified: false
      - task:
          type: question-answering
        dataset:
          type: allenai/ai2_arc
          name: ARC (Easy)
        metrics:
          - name: acc
            type: acc
            value: 0.796
            verified: false
      - task:
          type: question-answering
        dataset:
          type: allenai/ai2_arc
          name: ARC (Challenge)
        metrics:
          - name: acc
            type: acc
            value: 0.464
            verified: false
      - task:
          type: question-answering
        dataset:
          type: allenai/openbookqa
          name: OpenBookQA
        metrics:
          - name: acc
            type: acc
            value: 0.344
            verified: false
      - task:
          type: question-answering
        dataset:
          type: ehovy/race
          name: RACE
        metrics:
          - name: acc
            type: acc
            value: 0.393
            verified: false
      - task:
          type: question-answering
        dataset:
          type: cais/mmlu
          name: MMLU
        metrics:
          - name: acc
            type: acc
            value: 0.549
            verified: false
      - task:
          type: text-generation
        dataset:
          type: google-research-datasets/nq_open
          name: Natural Questions
        metrics:
          - name: exact_match
            type: exact_match
            value: 0.173
            verified: false
      - task:
          type: question-answering
        dataset:
          type: sentence-transformers/trivia-qa
          name: TriviaQA
        metrics:
          - name: acc
            type: acc
            value: 0.522
            verified: false
      - task:
          type: text-generation
        dataset:
          type: openai/gsm8k
          name: GSM8K
        metrics:
          - name: exact_match
            type: exact_match
            value: 0.396
            verified: false
      - task:
          type: question-answering
        dataset:
          type: allenai/math_qa
          name: MathQA
        metrics:
          - name: acc
            type: acc
            value: 0.360
            verified: false
      - task:
          type: question-answering
        dataset:
          type: rajpurkar/squad_v2
          name: SQuAD2.0
        metrics:
          - name: exact
            type: exact
            value: 0.225
            verified: false
      - task:
          type: text-classification
        dataset:
          type: toxigen/toxigen-data
          name: ToxiGen
        metrics:
          - name: acc
            type: acc
            value: 0.415
            verified: false
      - task:
          type: text-generation
        dataset:
          type: openai_humaneval
          name: HumanEval
        metrics:
          - name: pass@1
            type: pass@1
            value: 0.079
            verified: false
      - task:
          type: text-generation
        dataset:
          type: mbpp
          name: MBPP
        metrics:
          - name: pass@1
            type: pass@1
            value: 0.298
            verified: false
license: other
license_name: fair
license_link: LICENSE
base_model: meta-llama/Meta-Llama-3-8B
---

# LayerSkip Llama3 8B
Llama3 8B model continually pretrained with LayerSkip as presented in [Layer Skip: Enabling Early Exit Inference and Self-Speculative Decoding
](https://arxiv.org/abs/2404.16710) and is capable of performing self-speculative decoding: decode with earlier layers and verify with remaining layers.

## How to Use

This model is currently run using the following methods:

- [HuggingFace](#huggingface)
- [LayerSkip Codebase](#layerskip-codebase)
- [gpt-fast](#gpt-fast)

### HuggingFace<a name="huggingface"></a>

HuggingFace does not yet have self-speculative decoding support. However, we can re-use it's speculative decoding feature by creating a draft model using a subset of the layers of the main model:

```python
>>> import torch
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> from copy import deepcopy

>>> checkpoint = "facebook/layerskip-llama3-8B"
>>> early_exit = 4
>>> device = "cuda" if torch.cuda.is_available() else "cpu"
>>> prompt = "typing import List\ndef bucket_sort(A: List):"

>>> model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", use_safetensors=True, torch_dtype=torch.float16)
>>> tokenizer = AutoTokenizer.from_pretrained(checkpoint)

>>> generation_config = model.generation_config

>>> weights_memo = {id(w): w for w in model.parameters()}
>>> assistant_model = deepcopy(model, memo=weights_memo) # Clone main model with shared weights
>>> assistant_model.model.layers = assistant_model.model.layers[:early_exit] # Apply early exit
>>> del assistant_model.model.layers[early_exit:]

>>> inputs = tokenizer(prompt, return_tensors="pt").to(device)

>>> outputs = model.generate(**inputs, generation_config=generation_config, assistant_model=assistant_model, max_new_tokens=512)
>>> print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])
```

Please note that this is not an optimal implementation as it requires more memory to save weights and activations of duplicated layers. The optimized implementation that re-uses earlier layers is in 

<details>
  <summary>Benchmark</summary>

If you would like to measure the speedup between self-speculative decoding and autoregressive decoding, we have written this script:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from copy import deepcopy
from time import time
from tqdm import tqdm

prompt = "typing import List\ndef bucket_sort(A: List):"

checkpoint = "facebook/layerskip-llama3-8B"
early_exit = 4
device = "cuda" if torch.cuda.is_available() else "cpu"

max_new_tokens = 512
do_sample = True
top_p = 0.9
temperature = 0.6

warmup = 2
repeat = 10

model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", use_safetensors=True, torch_dtype=torch.float16)
tokenizer = AutoTokenizer.from_pretrained(checkpoint)

# Draft model
# Clone main model with shared weights
weights_memo = {id(w): w for w in model.parameters()}
assistant_model = deepcopy(model, memo=weights_memo)
# Create early exit version
assistant_model.model.layers = assistant_model.model.layers[:early_exit]
del assistant_model.model.layers[early_exit:]

tokenizer = AutoTokenizer.from_pretrained(checkpoint)
inputs = tokenizer(prompt, return_tensors="pt").to(device)

generation_config = {
    "max_new_tokens": max_new_tokens,
    "do_sample": do_sample,
    "top_p": top_p, 
    "temperature": temperature,
    "pad_token_id": tokenizer.eos_token_id,
}

# Warmup
print("Warmup")
for i in tqdm(range(warmup)):
    _ = model.generate(**inputs, **generation_config)
    _ = model.generate(**inputs, **generation_config, assistant_model=assistant_model)

print("Autoregressive Decoding")
total_time = 0
total_tokens = 0
for i in tqdm(range(repeat)):
    start = time()
    outputs = model.generate(**inputs, **generation_config)
    total_time += time() - start
    total_tokens += outputs.numel()
print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])
print("\n\t=========================")
print(f"\tAverage Generation Time: {total_time / repeat:.2f} s")
print(f"\tAverage Tokens per Second: {total_tokens / total_time:.2f} tokens per sec\n\n")

print("Self-Speculative Decoding")
total_time = 0
total_tokens = 0
for i in tqdm(range(repeat)):
    start = time()
    outputs = model.generate(**inputs, **generation_config, assistant_model=assistant_model)
    total_time += time() - start
    total_tokens += outputs.numel()
print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])
print("\n\t=========================")
print(f"\tAverage Generation Time: {total_time / repeat:.2f} s")
print(f"\tAverage Tokens per Second: {total_tokens / total_time:.2f} tokens per sec\n\n")
```

Running this script on a single A100 NVIDIA GPU with `transformers==4.34.1`, `accelerate==1.0.1`, `torch==2.2.1`, `triton==2.2.0`, we obtain:
```
Autoregressive Decoding
        =========================
        Average Generation Time: 8.31 s
        Average Tokens per Second: 31.84 tokens per sec

Self-Speculative Decoding
        =========================
        Average Generation Time: 4.46 s
        Average Tokens per Second: 47.43 tokens per sec
```
</details>

### LayerSkip Codebase<a name="layerskip-codebase"></a>
Our self-speculative decoding implementation at [github.com/facebookresearch/LayerSkip](https://github.com/facebookresearch/LayerSkip) has an optimized version that does not consume extra memory and re-uses the weights and KV cache of earlier layers in both draft and verification stages.
To run:
```console
> git clone git@github.com:facebookresearch/LayerSkip.git
> cd LayerSkip

> conda create --name layer_skip python=3.10
> conda activate layer_skip

> pip install -r requirements.txt

> torchrun generate.py --model facebook/layerskip-llama3-8B --generation_strategy self_speculative --exit_layer 4 --num_speculations 3
```

You can find more details in the GitHub repo for more options and scripts.

### gpt-fast<a name="gpt-fast"></a>
We have also implemented self-speculative decoding as a [separatae branch in PyTorch's gpt-fast](https://github.com/pytorch-labs/gpt-fast/tree/LayerSkip?tab=readme-ov-file#self-speculative-sampling) if you would to stack our solution on top of other optimizations like `torch.compile()` and quantization. Our gpt-fast implementation is optimized as it does not consume extra memory and re-uses the weights and KV cache of earlier layers in both draft and verification stages.

To run:
```console
> git clone git@github.com:pytorch-labs/gpt-fast.git -b LayerSkip
> cd gpt-fast

> conda create --name gpt_fast python=3.10
> conda activate gpt_fast

> # Install PyTorch (check [here](https://pytorch.org/get-started/locally/) for other hardwares and operating systems)
> pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
> pip install sentencepiece huggingface_hub tiktoken blobfile

> mkdir checkpoints

> MODEL_REPO=facebook/layerskip-llama3-8B
> ./scripts/prepare.sh $MODEL_REPO

> python generate.py --compile --checkpoint_path checkpoints/$MODEL_REPO/model.pth --top_k 100 --temperature 0.6 --self_speculative --early_exit 4 --speculate_k 2
```

<details>
  <summary>Benchmark</summary>

- Autoregressive decoding:

```console
> python generate.py --compile --checkpoint_path checkpoints/$MODEL_REPO/model.pth --top_k 100 --temperature 0.6
==========
Average tokens/sec: 99.35
Memory used: 16.45 GB
```

- Self-speculative decoding:

```console
> python generate.py --compile --checkpoint_path checkpoints/$MODEL_REPO/model.pth --top_k 100 --temperature 0.6 --self_speculative --early_exit 5 --speculate_k 2
==========
{'tokens_per_sec': [120.0120248926913, 112.64537916220596, 102.80705064833688, 114.11851624549094, 110.88261837868764], 'accept_counts': [[33, 17, 44], [32, 13, 47], [38, 24, 38], [56, 22, 33], [36, 20, 41], [39, 29, 34]]}
Acceptance probs: [0.3926174496644295, 0.20973154362416108, 0.3976510067114094]
Mean Accepted: 1.00503355704698
Average tokens/sec: 112.09
Memory used: 16.40 GB
```
</details>

## Training
Our training implementation is work-in-progress. You can check this [pull request](https://github.com/pytorch/torchtune/pull/1076) for details and discussions.

## Evaluation
We have provided evaluation results on various natural language and codinng tasks in the Model Card. You can view them on the top right hand-side bar on the screen.
The numbers reported in this Model Card were evaluated using [Eluether Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) and [BigCode Evaluation Harness](https://github.com/bigcode-project/bigcode-evaluation-harness), while the numbers provided in our paper were evaluated using Meta's internal codebase.

## Issues

Please report any software "bug", or other problems with the models through one of the following means:
- Reporting issues with the model: [https://github.com/facebookresearch/LayerSkip/issues](https://github.com/facebookresearch/LayerSkip/issues)
- Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
- Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)

## License

See the [LICENSE](LICENSE) file.