open-gpt-torch 0.0.7.dev2

An open-source cloud-native of large multi-modal models (LMMs) serving framework.

☄️ OpenGPT

"A playful and whimsical vector art of a Stochastic Tigger, wearing a t-shirt with a "GPT" text printed logo, surrounded by colorful geometric shapes. –ar 1:1 –upbeta"

— Prompts and logo art was produced with PromptPerfect & Stable Diffusion X

OpenGPT is an open-source cloud-native large-scale multimodal models (LMMs) serving framework. It is designed to simplify the deployment and management of large language models, on a distributed cluster of GPUs. We aim to make it a one-stop solution for a centralized and accessible place to gather techniques for optimizing large-scale multimodal models and make them easy to use for everyone.

Table of contents

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

Features

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

• Support for multi-modal models on top of large language 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: open and efficient foundation language models by Meta
  • Pythia: a collection of models developed to facilitate interpretability research by EleutherAI
  • StableLM: series of large language models by Stability AI
  • Vicuna: a chat assistant fine-tuned from LLaMA on user-shared conversations by LMSYS
  • MOSS: conversational language model from Fudan University

• LMM (Large Multi-modal Model)

  • OpenFlamingo: an open source version of DeepMind's Flamingo model
  • MiniGPT-4: aligns a frozen visual encoder with a frozen LLM, Vicuna, using just one projection layer.

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(
    'stabilityai/stablelm-tuned-alpha-3b', 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 use the stabilityai/stablelm-tuned-alpha-3b as the open example model as it is relatively small and fast to download.

Warning In the above example, we use precision='fp16' to reduce the memory usage and speed up the inference with some loss in accuracy on text generation tasks. You can also use precision='fp32' instead as you like for better performance.

Note It usually takes a while (several minutes) for the first time to download and load the model into the memory.

In most cases of large model serving, the model cannot fit into a single GPU. To solve this problem, we also provide a device_map option (supported by accecleate package) to automatically partition the model and distribute it across multiple GPUs:

model = open_gpt.create_model(
    'stabilityai/stablelm-tuned-alpha-3b', precision='fp16', device_map='balanced'
)

In the above example, device_map="balanced" evenly split the model on all available GPUs, making it possible for you to serve large models.

Note The device_map option is supported by the accelerate package.

See examples on how to use opengpt with different models. 🔥

Build a model serving in one line

To do so, you can use the serve command:

opengpt serve stabilityai/stablelm-tuned-alpha-3b --precision fp16 --device_map balanced

💡 Tip: you can inspect the available options with opengpt serve --help.

This will start a gRPC and HTTP server listening on port 51000 and 52000 respectively. Once the server is ready, as shown below:

Click to expand

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:51000/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,
    },
)

What's more, we also provide a Python client (inference-client) for you to easily interact with the server:

from open_gpt import Client

client = Client()

# connect to the model server
model = client.get_model(endpoint='grpc://0.0.0.0:51000')

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,
)

💡 Tip: To display the list of available commands, please use the list command.

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 stabilityai/stablelm-tuned-alpha-3b --device cuda --precision fp16 --provider jina --name opengpt --replicas 2

TBD ...

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.

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Copyright 2020-2022 Jina AI Limited

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