datasets:
- wikipedia
- allenai/c4
language:
- en
tags:
- MoE
LLaMA-8x265M-MoE
👋 Very nice to meet you here~
❤️ This repo contains the model LLaMA-8x265M-MoE(970M totally), which activates 2 out of 8 experts (332M parameters). This model is trained from scratch with FP32 precision. We firstly train the model through wikipedia dataset with 1 epoch and then through 10% of C4 dataset (10 data shards among 1024 data shards) with 1 epoch. This is NOT fine-tuned by instruction pairs, so it may not be good enough to act like a chatbot.
📢 This series also includes a dense version (without MoE structure), see 🤗this repo.
1. 🚀QuickStart
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_dir = "JuncaiL/llama-8x265m-moe"
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_dir, trust_remote_code=True)
model.eval()
model.to("cuda:0")
input_text = "Beijing is a famous city"
inputs = tokenizer(input_text, return_tensors="pt",return_token_type_ids=False)
inputs = inputs.to("cuda:0")
pred = model.generate(**inputs, max_length=50, temperature=0.0)
print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True))
# Beijing is a famous city in China. It is the capital of the Beijing Province and the largest city in China. It is also the home of the world’s largest city, Beijing.
#The city is the
2. 📑Checkpoint Details and Evaluation
Model Parameter
| Model | #Experts | #Activated Experts | #Params | # Activated Params | Flops(T) per sample （se q=2048） | Model Weights |
|---|---|---|---|---|---|---|
| 265M | - | - | 265M | 265M | 0.48 | 🤗 llama-265m |
| 8 $\times$ 265M MoE | 8 | 2 | 970M | 332M | 0.76 | 🤗 llama-8x265m-moe |
| llama-7b | - | - | 7B | 7B | 25.29 |
Model Evaluation
We use the "Average number of tokens verified" $N$ ( see reference link ） as the metric to evaluate these models. This metric demonstrates that giving the same input to the small speculative model and llama-7b, counting from the first predicted tokens, how many successive tokens in the output sentence of the small speculative model are the same as the output sentence of the llama-7b.
- Average number of tokens verified
| Dataset | 8 $\times$ 265M MoE | GPT without MoE |
|---|---|---|
| tatsu-lab/alpaca | 3.2362 | 3.0334 |
| alespalla/chatbot_instruction_prompts | 3.2031 | 3.0823 |
| web_questions | 2.7201 | 2.5541 |
| MohamedRashad/ChatGPT-prompts | 3.0954 | 2.9768 |
Supposed that the small speculative model can have a hit rate $p$ for the next token when giving the same input. Then we have
We can get the hit rate as follow.
- Hit Rate
| Dataset | 8 $\times$ 265M MoE | GPT without MoE |
|---|---|---|
| tatsu-lab/alpaca | 0.578 | 0.567 |
| alespalla/chatbot_instruction_prompts | 0.576 | 0.570 |
| web_questions | 0.550 | 0.540 |
| MohamedRashad/ChatGPT-prompts | 0.571 | 0.565 |
3. 🚧Limitation and Future Plans
For the MoE model, we only show the accuracy of how this small speculative model approximates the performance of llama-7b. In practice, to achieve physically low latency, the implementation of our MoE needs to be improved. In this version, we calculate the result of MoE expert by expert (sequentially) , and we need to fuse the calculation of these experts.
Acknowledgment
- My implementation of MoE structure is based on the repo
https://huggingface.co/llama-moe/LLaMA-MoE-v1-3_5B-2_8 - My inspiration for Speculative Inference comes from the paper "SpecInfer: Accelerating Generative Large Language Model Serving with Tree-based Speculative Inference and Verification" (link) . I am very appreciative of the help and suggestions from the SpecInfer group. ❤️
Citation
@misc{specmoe-2024,
title={SpecMoE: Building A Speculative MoE Model To Accelerate Inference},
author={Juncai Liu},
year={2024},
month={March},
url={https://github.com/JuncaiL/SpecMoE/}
}
Contact
If you have any interest or question about this project, please feel free to contact me.
[email protected] （before June 30, 2024） or [email protected] （After June 30, 2024）