DeepSpeed Model Implementations for Inference (MII)
The DeepSpeed-MII project is an open-source Python library developed by DeepSpeed, aimed at democratizing model inference by focusing on high-throughput, low latency, and cost-effectiveness. This platform is designed to enhance the efficiency of significant text-generation models, facilitating faster and more effective processing of large language models (LLMs).
Key Technologies
MII for High-Throughput Text Generation
MII employs several cutting-edge technologies to accelerate text-generation inference. These include:
- Blocked KV Caching: Optimizes memory usage during the inference process.
- Continuous Batching: Groups model requests together to increase efficiency.
- Dynamic SplitFuse: Enhances processing speed through more efficient workload handling.
- High Performance CUDA Kernels: Utilizes GPU capabilities to deliver rapid model throughput.
These technologies collectively ensure that MII can deliver results promptly and effectively, with particular benefit to LLMs like Llama-2-70B and Phi-2.
MII Legacy
MII has historically introduced optimizations for low-latency scenarios:
- DeepFusion: For better performance in processing Transformers.
- Multi-GPU Inference with Tensor-Slicing: Spreads workloads across multiple GPUs for efficiency.
- ZeRO-Inference: Reduces resource use in constrained environments.
- Compiler Optimizations: Fine-tunes code execution for better performance.
How Does MII Work?
DeepSpeed-MII operates under the engine of DeepSpeed-Inference. It adjusts its operations based on the model's architecture, size, and available hardware resources. This adaptability ensures reduced latency and increased throughput, automatically optimizing models before deployment.
Supported Models
MII supports over 37,000 models across various architectures such as Llama, Mistral, and Falcon. These models are integrated within Hugging Face's ecosystem, leveraging its infrastructure for model weights and tokenizers.
Getting Started with MII
Beginning to use DeepSpeed-MII is streamlined and efficient. Users can set up both non-persistent and persistent deployments quickly.
Installation
To install DeepSpeed-MII, simply utilize PyPI for a quick setup with:
pip install deepspeed-mii
This method covers most custom kernel requirements, simplifying the setup process.
Non-Persistent Pipeline
A non-persistent pipeline allows users to execute model inference during the runtime of a script, perfect for trial purposes. Here's a quick example:
import mii
pipe = mii.pipeline("mistralai/Mistral-7B-v0.1")
response = pipe(["DeepSpeed is", "Seattle is"], max_new_tokens=128)
print(response)
Persistent Deployment
For long-term and production applications, a persistent deployment is appropriate. It uses a lightweight GRPC server, allowing concurrent client queries:
import mii
client = mii.serve("mistralai/Mistral-7B-v0.1")
response = client.generate(["Deepspeed is", "Seattle is"], max_new_tokens=128)
print(response)
MII also supports multi-GPU utilization through model parallelism and model replicas, ensuring efficient loads and faster processing times.
RESTful API
MII provides RESTful API capabilities, allowing models to be accessed through standard web requests, which fits well in web-based applications.
Contributing to MII
Contributions to the DeepSpeed-MII project are welcome. Contributors must adhere to the Microsoft Open Source Code of Conduct and agree to a Contributor License Agreement. This ensures proper use and integration of all contributions within the project.
Conclusion
DeepSpeed-MII stands out as a robust solution for model inference, offering substantial enhancements in speed and resource management. Its focus on leveraging state-of-the-art technologies ensures that users can efficiently deploy and operate powerful language models with minimal overhead.