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89 lines
2.8 KiB
89 lines
2.8 KiB
# Overview
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This is an example showing how to run OPT generation. The OPT model is implemented using ColossalAI.
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It supports tensor parallelism, batching and caching.
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## 🚀Quick Start
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1. Run inference with OPT 125M
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```bash
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docker hpcaitech/tutorial:opt-inference
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docker run -it --rm --gpus all --ipc host -p 7070:7070 hpcaitech/tutorial:opt-inference
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```
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2. Start the http server inside the docker container with tensor parallel size 2
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```bash
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python opt_fastapi.py opt-125m --tp 2 --checkpoint /data/opt-125m
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```
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# How to run
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Run OPT-125M:
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```shell
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python opt_fastapi.py opt-125m
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```
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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.
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## Configure
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### Configure model
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```shell
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python opt_fastapi.py <model>
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```
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Available models: opt-125m, opt-6.7b, opt-30b, opt-175b.
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### Configure tensor parallelism
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```shell
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python opt_fastapi.py <model> --tp <TensorParallelismWorldSize>
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```
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The `<TensorParallelismWorldSize>` can be an integer in `[1, #GPUs]`. Default `1`.
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### Configure checkpoint
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```shell
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python opt_fastapi.py <model> --checkpoint <CheckpointPath>
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```
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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.
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### Configure queue
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```shell
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python opt_fastapi.py <model> --queue_size <QueueSize>
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```
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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).
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### Configure batching
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```shell
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python opt_fastapi.py <model> --max_batch_size <MaxBatchSize>
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```
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The `<MaxBatchSize>` can be an integer in `[1, MAXINT]`. The engine will make batch whose size is less or equal to this value.
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Note that the batch size is not always equal to `<MaxBatchSize>`, as some consecutive requests may not be batched.
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### Configure caching
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```shell
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python opt_fastapi.py <model> --cache_size <CacheSize> --cache_list_size <CacheListSize>
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```
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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.
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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]`.
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### Other configurations
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```shell
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python opt_fastapi.py -h
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```
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# How to benchmark
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```shell
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cd benchmark
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locust
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```
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Then open the web interface link which is on your console.
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# Pre-process pre-trained weights
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## OPT-66B
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See [script/processing_ckpt_66b.py](./script/processing_ckpt_66b.py).
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## OPT-175B
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See [script/process-opt-175b](./script/process-opt-175b/).
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