EET

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Easy and Efficient Transformer

EET(Easy and Efficient Transformer) is a friendly Pytorch inference plugin focus on Transformer-based models to make mega-size model affordable.

Features

• Support Mega-size model with single GPU. EET could make an 11B model available online within 24G Ampere GPU.
• Expertise in inference for multi-modal and NLP tasks (CLIP/GPT-3/Bert/Seq2seq etc.).
• High performance. Make the transformer-based model faster and faster with the effect of CUDA kernel optimization and quantization/sparsity algorithm.
• Out-of-the-box for Transformers and Fairseq. Save your pain of trivial configuration and make your model work within a few lines.

• Model Matirx
• Quick Start
  • Environment
  • Installation
    • From Source
    • From Docker
  • Run
    • operators APIs
    • Model APIs
    • Application APIs
• Performance
• Cite Us
• Contact us

Model Matrix

Quick Start

Environment

• cuda:>=10.1
• python:>=3.7
• gcc:>= 7.4.0
• torch:>=1.5.0
• numpy:>=1.19.1
• fairseq
• transformers

The above environment is the minimum configuration, and it is best to use a newer version.

Installation

Recommend using docker images.

From Source

If you are installing from source, you will need install the necessary environment.Then proceed as follows:

$ git clone https://github.com/NetEase-FuXi/EET.git
$ pip install .

Recommend using nvcr.io/nvidia/pytorch:21.12-py3 and other series of images, you can also use the provided Dockerfile file.

From Docker

$ git clone https://github.com/NetEase-FuXi/EET.git
$ docker build -t eet_docker:0.1 .
$ nvidia-docker run -it --net=host -v /your/project/directory/:/root/workspace  eet_docker:0.1 bash

The EET and its required environment have been installed in docker.

Run

We provide three types of APIs:

• Operators APIs, such as embedding, masked-multi-head-attention, ffn etc. Enable you to define your custom models.
• Model APIs, such as TransformerDecoder, BertEncoder etc. Enable you to integrate EET into your pytorch project.
• Application APIs, such as Transformers Pipeline. Enable you to run your model in a few lines.

Operators APIs

Operators APIs are the intermediate representation of C++/CUDA and Python. We provide almost all the operators required for Transformer models. You can combine different OPs to build other model structures.

• Operators API table

  operators	python API	Remarks
  multi_head_attention	EETSelfAttention	self attention
  masked_multi_head_attention	EETSelfMaskedAttention	causal attention
  cross_multi_head_attention	EETCrossAttention	cross attention
  ffn	EETFeedforward	feed forward network
  embedding	EETBertEmbedding	correspondence to Fairseq and Transfomers
  layernorm	EETLayerNorm	same as nn.LayerNorm

• How to use

  The definition of these OPs is in the file EET/csrc/py11/eet2py.cpp and some using examples were show in the files under python/eet, which tell us how to use those OPs to make up classic models.

Model APIs

As an plugin, EET provides friendly model APIs(python/eet) to integrated into Fairseq and Transformers.

All you need to do is find the corresponding class according to the tables below (usually with a prefix of 'EET') and initialize an object with the from_torch and from_pretrained function.

Note: We now only support pre-padding for GPT-3.

EET and fairseq class comparison table :

EET and Transformers class comparison table :

In addition to the basic model types above, we have extended some task-specific APIs to support different tasks. The table below is part of our task-specific model APIs :

• How to use

This is a code snip to show how to use model APIs :

You can build your application with the model APIs directly with the task-specific APIs. There is an example of a fill-mask:

from eet import EETRobertaForMaskedLM
from transformers import RobertaTokenizer
input = ["My <mask> is Sarah and I live in London"]
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
eet_roberta_model = EETRobertaForMaskedLM.from_pretrained('roberta-base',max_batch = max_batch_size,data_type = data_type)
# first step: tokenize
model_inputs = tokenizer(input,return_tensors = 'pt')
masked_index = torch.nonzero(model_inputs['input_ids'][0] == tokenizer.mask_token_id, as_tuple=False).squeeze(-1)
# second step: predict
prediction_scores = eet_roberta_model(model_inputs['input_ids'].cuda(),attention_mask = model_inputs['attention_mask'])
# third step: argmax
predicted_index = torch.argmax(prediction_scores.logits[0, masked_index]).item()
predicted_token = tokenizer.convert_ids_to_tokens(predicted_index)

For more examples, please refer to example/python/models.

Application APIs

EET provides a ready-made pipelines approach to simplify your application building for different tasks without using the model APIs above.

Here is an example :

import torch
from eet import pipeline
max_batch_size = 1
model_path = 'roberta-base'
data_type = torch.float16
input = ["My <mask> is Sarah and I live in London"]
nlp = pipeline("fill-mask",model = model_path,data_type = data_type,max_batch_size = max_batch_size)
out = nlp(input)

Now we support these tasks：

For more examples, please refer to example/python/pipelines.

Performance

Detailed performance data of GPT-3 and Bert model inference can be viewed at link.

• GPT-3 on A100

• Bert on 2080ti

Cite Us

If you use EET in your research, please cite the following paper.

@misc{https://doi.org/10.48550/arxiv.2104.12470,
  doi = {10.48550/ARXIV.2104.12470},
  url = {https://arxiv.org/abs/2104.12470},
  author = {Li, Gongzheng and Xi, Yadong and Ding, Jingzhen and Wang, Duan and Liu, Bai and Fan, Changjie and Mao, Xiaoxi and Zhao, Zeng},
  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Easy and Efficient Transformer : Scalable Inference Solution For large NLP model},

Video

We have a share on ZhiYuan LIVE, link: https://event.baai.ac.cn/activities/325.

Contact us

You can post your problem with github issues.

You can also contact us by email :

[email protected], [email protected] ,[email protected]