Text Generation Inference

A Rust and gRPC server for text generation inference. Used in production at HuggingFace to power Bloom, BloomZ and MT0-XXL api-inference widgets.

Features

• Dynamic batching of incoming requests for increased total throughput
• Quantization with bitsandbytes
• Safetensors weight loading
• 45ms per token generation for BLOOM with 8xA100 80GB
• Logits warpers (temperature scaling, topk, repetition penalty ...)
• Stop sequences
• Log probabilities

Officially supported models

• BLOOM
• BLOOMZ
• MT0-XXL
• Galactica (deactivated)
• SantaCoder
• GPT-Neox 20B: use --revision pr/13

Other models are supported on a best effort basis using:

AutoModelForCausalLM.from_pretrained(<model>, device_map="auto")

or

AutoModelForSeq2SeqLM.from_pretrained(<model>, device_map="auto")

Load Tests for BLOOM

See k6/load_test.js

	avg	min	med	max	p(90)	p(95)	RPS
Original code	8.9s	1s	9.12s	16.69s	13.7s	14.26s	5.9
New batching logic	5.44s	959.53ms	5.28s	13.12s	7.78s	8.92s	9.08

Install

make install

Run

BLOOM 560-m

make run-bloom-560m

BLOOM

First you need to download the weights:

make download-bloom

make run-bloom # Requires 8xA100 80GB

You can also quantize the weights with bitsandbytes to reduce the VRAM requirement:

make run-bloom-quantize # Requires 8xA100 40GB

Test

curl 127.0.0.1:3000/generate \
    -v \
    -X POST \
    -d '{"inputs":"Testing API","parameters":{"max_new_tokens":9}}' \
    -H 'Content-Type: application/json'

Develop

make server-dev
make router-dev