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language:
  - en
library_name: transformers
pipeline_tag: text-generation
tags:
  - facebook
  - meta
  - pytorch
  - llama
  - llama-2
license: other
license_name: fair
license_link: LICENSE
base_model: meta-llama/Llama-2-13b-hf

LayerSkip Llama2 13B

Llama2 13B model continually pretrained with LayerSkip as presented in Layer Skip: Enabling Early Exit Inference and Self-Speculative Decoding and is capable of performing self-speculative decoding: decode with earlier layers and verify with remaining layers.

How to Use

We are providing 3 ways to run the model

HuggingFace
LayerSkip Codebase
gpt-fast

HuggingFace

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:

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

>>> checkpoint = "facebook/layerskip-llama2-13B"
>>> early_exit = 8
>>> device = "cuda" if torch.cuda.is_available() else "cpu"
>>> prompt = "typing import List\ndef bucket_sort(A: List):"

>>> model = AutoModelForCausalLM.from_pretrained(checkpoint)
>>> 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:]

>>> model.to(device)
>>> assistant_model.to(device)

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

>>> outputs = model.generate(**inputs, assistant_model=assistant_model, generation_config=generation_config)
>>> 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 KV cache and activations of duplicated layers. The optimized implementation that re-uses earlier layers is in our custom implementation or in our gpt-fast implementation.

Benchmark

If you would like to measure the speedup between self-speculative decoding and autoregressive decoding, we have written this script:

from transformers import LlamaForCausalLM, LlamaConfig, LlamaTokenizer, GenerationConfig
import torch
from copy import deepcopy
from time import time
from tqdm import tqdm

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

checkpoint = "facebook/layerskip-llama2-13B"
early_exit = 8
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

config = LlamaConfig.from_pretrained(checkpoint)
model = LlamaForCausalLM.from_pretrained(checkpoint, config=config, torch_dtype=torch.float16)

# 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:]

model.to(device)
assistant_model.to(device)

tokenizer = LlamaTokenizer.from_pretrained(checkpoint, use_fast=False)
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, torch==2.2.1, triton==2.2.0, we obtain:

Autoregressive Decoding
        =========================
        Average Generation Time: 12.79 s
        Average Tokens per Second: 28.38 tokens per sec

Self-Speculative Decoding
        =========================
        Average Generation Time: 6.35 s
        Average Tokens per Second: 43.64 tokens per sec

LayerSkip Codebase

We have also implemented self-speculative decoding as a separatae branch in PyTorch's gpt-fast 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:

> 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-llama2-13B --generation_strategy self_speculative --exit_layer 8 --num_speculations 4

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

GPT-Fast

To run:

> 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

> mkdir checkpoints

> MODEL_REPO=facebook/layerskip-llama2-13B
> ./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 5 --speculate_k 3

Benchmark

Autoregressive decoding:

> python generate.py --compile --checkpoint_path checkpoints/$MODEL_REPO/model.pth --top_k 100 --temperature 0.6
==========
Average tokens/sec: 61.02
Memory used: 26.67 GB

Self-speculative decoding:

> python generate.py --compile --checkpoint_path checkpoints/$MODEL_REPO/model.pth --top_k 100 --temperature 0.6 --self_speculative --early_exit 5 --speculate_k 3
==========
{'tokens_per_sec': [65.4164788410036, 75.9909526372485, 69.47568346063044, 76.6615810268726, 62.877157626020754], 'accept_counts': [[64, 27, 11, 12], [62, 31, 9, 12], [43, 23, 12, 19], [51, 33, 9, 14], [39, 28, 12, 17], [68, 29, 13, 9]]}
Acceptance probs: [0.5054095826893354, 0.2642967542503864, 0.10200927357032458, 0.12828438948995363]
Mean Accepted: 0.8531684698608965
Average tokens/sec: 70.08
Memory used: 27.57 GB

Training

Our training implementation is work-in-progress. You can check this pull request for details and discussions.

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
Reporting risky content generated by the model: developers.facebook.com/llama_output_feedback
Reporting bugs and security concerns: facebook.com/whitehat/info

License

See the LICENSE file.