matmulfreellm
Implementation for MatMul-free LM.
Stars: 2838
MatMul-Free LM is a language model architecture that eliminates the need for Matrix Multiplication (MatMul) operations. This repository provides an implementation of MatMul-Free LM that is compatible with the 🤗 Transformers library. It evaluates how the scaling law fits to different parameter models and compares the efficiency of the architecture in leveraging additional compute to improve performance. The repo includes pre-trained models, model implementations compatible with 🤗 Transformers library, and generation examples for text using the 🤗 text generation APIs.
README:
This repo is adapted from flash-linear-attention.
The following requirements should be satisfied
pip install -U git+https://github.com/ridgerchu/matmulfreellm| Model Size | Layer | Hidden dimension | Trained tokens |
|---|---|---|---|
| 370M | 24 | 1024 | 15B |
| 1.3B | 24 | 2048 | 100B |
| 2.7B | 32 | 2560 | 100B |
We provide the implementations of models that are compatible with 🤗 Transformers library.
Here's an example of how to initialize a model from the default configs in matmulfreelm:
This is a huggingface-compatible library that you can use such command to initialize the model with huggingface AutoModel:
>>> from mmfreelm.models import HGRNBitConfig
>>> from transformers import AutoModel
>>> config = HGRNBitConfig()
>>> AutoModel.from_config(config)
HGRNBitModel(
(embeddings): Embedding(32000, 2048)
(layers): ModuleList(
(0): HGRNBitBlock(
(attn_norm): RMSNorm(2048, eps=1e-06)
(attn): HGRNBitAttention(
(i_proj): FusedBitLinear(
in_features=2048, out_features=2048, bias=False
(norm): RMSNorm(2048, eps=1e-08)
)
(f_proj): FusedBitLinear(
in_features=2048, out_features=2048, bias=False
(norm): RMSNorm(2048, eps=1e-08)
)
(g_proj): FusedBitLinear(
in_features=2048, out_features=2048, bias=False
(norm): RMSNorm(2048, eps=1e-08)
)
(g_norm): FusedRMSNormSwishGate()
(o_proj): FusedBitLinear(
in_features=2048, out_features=2048, bias=False
(norm): RMSNorm(2048, eps=1e-08)
)
)
(mlp_norm): RMSNorm(2048, eps=1e-06)
(mlp): HGRNBitMLP(
(gate_proj): FusedBitLinear(
in_features=2048, out_features=11264, bias=False
(norm): RMSNorm(2048, eps=1e-08)
)
(down_proj): FusedBitLinear(
in_features=5632, out_features=2048, bias=False
(norm): RMSNorm(5632, eps=1e-08)
)
(act_fn): SiLU()
)
)
)
>>> Upon successfully pretraining a model, it becomes accessible for generating text using the 🤗 text generation APIs.
In the following, we give a generation example in generate.py:
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
import mmfreelm
from transformers import AutoModelForCausalLM, AutoTokenizer
#Change here to our open-sourced model
name = ''
tokenizer = AutoTokenizer.from_pretrained(name)
model = AutoModelForCausalLM.from_pretrained(name).cuda().half()
input_prompt = "In a shocking finding, scientist discovered a herd of unicorns living in a remote, "
input_ids = tokenizer(input_prompt, return_tensors="pt").input_ids.cuda()
outputs = model.generate(input_ids, max_length=32, do_sample=True, top_p=0.4, temperature=0.6)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])If you use this repo in your work, please cite our preprint:
@article{zhu2024scalable,
title={Scalable MatMul-free Language Modeling},
author={Zhu, Rui-Jie and Zhang, Yu and Sifferman, Ethan and Sheaves, Tyler and Wang, Yiqiao and Richmond, Dustin and Zhou, Peng and Eshraghian, Jason K},
journal={arXiv preprint arXiv:2406.02528},
year={2024}
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