OpenGPT

OpenGPT is an open-source cloud-native large multi-modal models (LMMs) serving solution. It is designed to simplify the deployment and management of large language models, on a distributed cluster of GPUs.

Note The content of README.md is just a placeholder to remind me of what I want to do.

Table of contents

• Features
• Supported models
• Get started
• Build a model serving in one line
• Cloud-native deployment
  • JCloud
  • Kubernetes
• Roadmap

Features

OpenGPT provides the following features to make it easy to deploy and serve large multi-modal models (LMMs) in production:

• Support for multi-modal models
• Scalable architecture for handling high traffic loads
• Optimized for low-latency inference
• Automatic model partitioning and distribution across multiple GPUs
• Centralized model management and monitoring
• REST API for easy integration with existing applications

Updates

• 2023-05-12: 🎉 We have released the first version v0.0.1 of OpenGPT. You can install it with pip install open_gpt_torch.

Supported Models

OpenGPT supports the following models out of the box:

• LLM (Large Language Model)

  • Llama
  • Pythia
  • StableLM
  • Vicuna
  • MOSS

• LMM (Large Multi-modal Model)

  • OpenFlamingo
  • MiniGPT4 (WIP)

For more details about the supported models, please see the Model Zoo.

Roadmap

You can view our roadmap with features that are planned, started, and completed on the Roadmap discussion category.

Get Started

Installation

Install the package with pip:

pip install open_gpt_torch

Quickstart

import open_gpt

model = open_gpt.create_model('facebook/llama-7b', device='cuda', precision='fp16')

prompt = "The quick brown fox jumps over the lazy dog."

output = model.generate(
    prompt,
    max_length=100,
    temperature=0.9,
    top_k=50,
    top_p=0.95,
    repetition_penalty=1.2,
    do_sample=True,
    num_return_sequences=1,
)

We also provide some advanced features to allow you to host your models cost-effectively:

• Offloading: you can offload parts of the model to CPU to reduce the cost of inference.

• Quantization: you can quantize the model to reduce the cost of inference.

For more details, please see the documentation.

Build a model serving in one line

You can serve your models with OpenGPT. To do so, you can use the serve command:

opengpt serve facebook/llama-9b --device cuda --precision fp16 --port 5000

This will start a server on port 5000. You can then send requests to the server:

import requests

prompt = "The quick brown fox jumps over the lazy dog."

response = requests.post(
    "http://localhost:5000/generate",
    json={
        "prompt": prompt,
        "max_length": 100,
        "temperature": 0.9,
        "top_k": 50,
        "top_p": 0.95,
        "repetition_penalty": 1.2,
        "do_sample": True,
        "num_return_sequences": 1,
    },
)


# SSE support
from aiohttp_sse_client import client as sse_client

async with sse_client.EventSource(
    'http://localhost:5000/stream/generate?prompt=The+quick+brown+fox+jumps+over+the+lazy+dog.&max_length=100&temperature=0.9&top_k=50&top_p=0.95&repetition_penalty=1.2&do_sample=True&num_return_sequences=1'
) as event_source:
    try:
        async for event in event_source:
            print(event)
    except ConnectionError:
        pass

Note that the server will only accept requests from the same machine. If you want to accept requests from other machines, you can use the --host flag to specify the host to bind to.

Cloud-native deployment

You can also deploy the server to a cloud provider like Jina Cloud or AWS. To do so, you can use deploy command:

• Jina Cloud

opengpt deploy facebook/llama-9b --device cuda --precision fp16 --provider jina --name opengpt --replicas 2

• AWS

To deploy to AWS, you need to install extra dependencies:

pip install opegpt[aws]

And you need to specify the region:

opengpt deploy facebook/llama-9b --device cuda --precision fp16 --provider aws --region us-east-1 --name opengpt --replicas 2

This will deploy the model to the cloud provider. You can then send requests to the server:

import requests

prompt = "The quick brown fox jumps over the lazy dog."

response = requests.post(
    "https://opengpt.jina.ai/generate",
    json={
        "prompt": prompt,
        "max_length": 100,
        "temperature": 0.9,
        "top_k": 50,
        "top_p": 0.95,
        "repetition_penalty": 1.2,
        "do_sample": True,
        "num_return_sequences": 1,
    },
)

Kubernetes

To deploy OpenGPT on your Kubernetes cluster, follow these steps:

1. Install the OpenGPT operator on your Kubernetes cluster using Helm:

   helm install opengpt ./helm/opengpt --namespace opengpt
   

2. Create a custom resource for your GPT model:

   apiVersion: opengpt.io/v1alpha1
   kind: GptModel
   metadata:
     name: my-gpt-model
     namespace: opengpt
   spec:
     modelPath: s3://my-bucket/my-model
     modelName: my-model
     maxBatchSize: 16
     inputShape:
       - 1024
       - 1024
       - 3
     outputShape:
       - 1024
       - 1024
       - 3
   

3. Apply the custom resource to your cluster:

   kubectl apply -f my-gpt-model.yaml
   

4. Monitor the status of your GPT model using the OpenGPT dashboard:

   kubectl port-forward -n opengpt svc/opengpt-dashboard 8080:80
   

Accessing models via API

You can also access the online models via API. To do so, you can use the inference_client package:

from inference_client import Client

client = Client(token='<your access token>')

model = client.get_model('facebook/llama-9b')

prompt = "The quick brown fox jumps over the lazy dog."

output = model.generate(
    prompt,
    max_length=100,
    temperature=0.9,
    top_k=50,
    top_p=0.95,
    repetition_penalty=1.2,
    do_sample=True,
    num_return_sequences=1,
)

By this way, you can access the models without deploying them to your own machine.

Advanced Usage

Model Offloading

You can also apply the model offloading techniques (based on FlexTensor) to OpenGPT. To do so, you can use the --offload-percents flag:

opengpt serve facebook/llama-9b --device cuda --precision fp16 --port 5000 --offload-percents 10,90,50,50,0,100

This will offload parts of the model to the CPU. You can also use the --offload-strategy flag to specify the offloading strategy:

opengpt serve facebook/llama-9b --device cuda --precision fp16 --port 5000 --offload-strategy "cpu,cpu,cpu,cpu,cpu,cpu"

Model Quantization

You can also apply the model quantization techniques.

• 8-bit quantization

opengpt serve facebook/llama-9b --device cuda --precision fp16 --port 5000 --quantize 8bit

Fine-tuning Models

We currently support fine-tuning models by using the finetune command:

opengpt finetune facebook/llama-9b --dataset wikitext-2 --device cuda --precision fp16 --batch-size 32 --learning-rate 1e-4 --epochs 10

Specifically, we implement the following fine-tuning methods:

• LLaMA-Adapter: Efficient Fine-tuning of LLaMA: Fine-tuning model to follow instructions within 1 Hour and 1.2M Parameters
• low-rank adaptation (LoRA): Low-Rank Adaptation for Efficient Language Model Fine-Tuning

Documentation

For more information, check out the documentation.

Contributing

We welcome contributions from the community! To contribute, please submit a pull request following our contributing guidelines.

License

OpenGPT is licensed under the Apache License, Version 2.0. See LICENSE for the full license text. Copyright 2020-2022 Jina AI Limited. All rights reserved.

                             Apache License
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Copyright 2020-2022 Jina AI Limited

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