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README.md	[doc] fix typo in opt inference tutorial (#2849 )	2023-02-21 17:16:13 +08:00
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requirements.txt	[tutorial] added energonai to opt inference requirements (#2625 )	2023-02-07 16:58:06 +08:00

README.md

Overview

This is an example showing how to run OPT generation. The OPT model is implemented using ColossalAI.

It supports tensor parallelism, batching and caching.

🚀Quick Start

Run inference with OPT 125M

docker hpcaitech/tutorial:opt-inference
docker run -it --rm --gpus all --ipc host -p 7070:7070 hpcaitech/tutorial:opt-inference

Start the http server inside the docker container with tensor parallel size 2

python opt_fastapi.py opt-125m --tp 2 --checkpoint /data/opt-125m

How to run

Run OPT-125M:

python opt_fastapi.py opt-125m

It will launch a HTTP server on 0.0.0.0:7070 by default and you can customize host and port. You can open localhost:7070/docs in your browser to see the openapi docs.

Configure

Configure model

python opt_fastapi.py <model>

Available models: opt-125m, opt-6.7b, opt-30b, opt-175b.

Configure tensor parallelism

python opt_fastapi.py <model> --tp <TensorParallelismWorldSize>

The <TensorParallelismWorldSize> can be an integer in [1, #GPUs]. Default 1.

Configure checkpoint

python opt_fastapi.py <model> --checkpoint <CheckpointPath>

The <CheckpointPath> can be a file path or a directory path. If it's a directory path, all files under the directory will be loaded.

Configure queue

python opt_fastapi.py <model> --queue_size <QueueSize>

The <QueueSize> can be an integer in [0, MAXINT]. If it's 0, the request queue size is infinite. If it's a positive integer, when the request queue is full, incoming requests will be dropped (the HTTP status code of response will be 406).

Configure batching

python opt_fastapi.py <model> --max_batch_size <MaxBatchSize>

The <MaxBatchSize> can be an integer in [1, MAXINT]. The engine will make batch whose size is less or equal to this value.

Note that the batch size is not always equal to <MaxBatchSize>, as some consecutive requests may not be batched.

Configure caching

python opt_fastapi.py <model> --cache_size <CacheSize> --cache_list_size <CacheListSize>

This will cache <CacheSize> unique requests. And for each unique request, it cache <CacheListSize> different results. A random result will be returned if the cache is hit.

The <CacheSize> can be an integer in [0, MAXINT]. If it's 0, cache won't be applied. The <CacheListSize> can be an integer in [1, MAXINT].

Other configurations

python opt_fastapi.py -h

How to benchmark

cd benchmark
locust

Then open the web interface link which is on your console.

Pre-process pre-trained weights

OPT-66B

See script/processing_ckpt_66b.py.

OPT-175B

See script/process-opt-175b.