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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: 0.36
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 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 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:
>>> 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
Benchmark
If you would like to measure the speedup between self-speculative decoding and autoregressive decoding, we have written this script:
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
LayerSkip Codebase
Our self-speculative decoding implementation at 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:
> git clone [email protected]: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
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 [email protected]: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
Benchmark
- Autoregressive decoding:
> python generate.py --compile --checkpoint_path checkpoints/$MODEL_REPO/model.pth --top_k 100 --temperature 0.6
==========
Average tokens/sec: 99.35
Memory used: 16.45 GB
- Self-speculative decoding:
> python generate.py --compile --checkpoint_path checkpoints/$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
Training
Our training implementation is work-in-progress. You can check this pull request 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 and 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
- 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.