94 lines
2.2 KiB
Markdown
94 lines
2.2 KiB
Markdown
# Text Generation Inference
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<div align="center">
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</div>
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A Rust and gRPC server for text generation inference.
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## Features
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- [Dynamic bathing of incoming requests](https://github.com/huggingface/text-generation-inference/blob/main/router/src/batcher.rs#L88) for increased total throughput
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- Quantization with [bitsandbytes](https://github.com/TimDettmers/bitsandbytes)
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- [Safetensors](https://github.com/huggingface/safetensors) weight loading
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- 45ms per token generation for BLOOM with 8xA100 80GB
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## Officialy supported models
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- BLOOM
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- BLOOMZ
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- BLOOM-560m
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Other models are supported on a best effort basis using:
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`AutoModelForCausalLM.from_pretrained(<model>, device_map="auto")`
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or
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`AutoModelForSeq2SeqLM.from_pretrained(<model>, device_map="auto")`
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## Load Tests for BLOOM
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See `k6/load_test.js`
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| | avg | min | med | max | p(90) | p(95) | RPS |
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|--------------------------------------------------------------|-----------|--------------|-----------|------------|-----------|-----------|----------|
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| [Original code](https://github.com/huggingface/transformers_bloom_parallel) | 8.9s | 1s | 9.12s | 16.69s | 13.7s | 14.26s | 5.9 |
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| New batching logic | **5.44s** | **959.53ms** | **5.28s** | **13.12s** | **7.78s** | **8.92s** | **9.08** |
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## Install
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```shell
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make install
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```
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## Run
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### BLOOM 560-m
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```shell
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make run-bloom-560m
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```
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### BLOOM
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First you need to download the weights:
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```shell
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make download-bloom
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```
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```shell
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make run-bloom # Requires 8xA100 80GB
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```
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You can also quantize the weights with bitsandbytes to reduce the VRAM requirement:
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```shell
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make run-bloom-quantize # Requires 8xA100 40GB
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```
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## Test
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```shell
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curl 127.0.0.1:3000/generate \
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-v \
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-X POST \
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-d '{"inputs":"Testing API","parameters":{"max_new_tokens":9}}' \
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-H 'Content-Type: application/json'
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```
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## Develop
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```shell
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make server-dev
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make router-dev
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```
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## TODO:
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- [ ] Add tests for the `server/model` logic
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- [ ] Backport custom CUDA kernels to Transformers
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- [ ] Install safetensors with pip |