text-generation-inference/README.md

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# Text Generation Inference on Habana Gaudi

To use [🤗 text-generation-inference](https://github.com/huggingface/text-generation-inference) on Habana Gaudi/Gaudi2, follow these steps:

1. Build the Docker image located in this folder with:
   ```bash
   docker build -t tgi_gaudi .
   ```
2. Launch a local server instance on 1 Gaudi card:
   ```bash
   model=meta-llama/Llama-2-7b-hf
   volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run

   docker run -p 8080:80 -v $volume:/data --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --ipc=host tgi_gaudi --model-id $model
   ```
3. Launch a local server instance on 8 Gaudi cards:
   ```bash
   model=meta-llama/Llama-2-70b-hf
   volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run

   docker run -p 8080:80 -v $volume:/data --runtime=habana -e PT_HPU_ENABLE_LAZY_COLLECTIVES=true -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --ipc=host tgi_gaudi --model-id $model --sharded true --num-shard 8
   ```
4. You can then send a request:
   ```bash
   curl 127.0.0.1:8080/generate \
     -X POST \
     -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \
     -H 'Content-Type: application/json'
   ```
   > The first call will be slower as the model is compiled.
5. To run benchmark test, please refer [TGI's benchmark tool](https://github.com/huggingface/text-generation-inference/tree/main/benchmark).

   To run it on the same machine, you can do the following:
   * `docker exec -it <docker name> bash` , pick the docker started from step 3 or 4 using docker ps
   * `text-generation-benchmark -t <model-id>` , pass the model-id from docker run command
   * after the completion of tests, hit ctrl+c to see the performance data summary.

> For gated models such as [StarCoder](https://huggingface.co/bigcode/starcoder), you will have to pass `-e HUGGING_FACE_HUB_TOKEN=<token>` to the `docker run` command above with a valid Hugging Face Hub read token.

For more information and documentation about Text Generation Inference, checkout [the README](https://github.com/huggingface/text-generation-inference#text-generation-inference) of the original repo.

Not all features of TGI are currently supported as this is still a work in progress.

New changes are added for the current release:
- Sharded feature with support for DeepSpeed-inference auto tensor parallism. Also use HPU graph for performance improvement.
- Torch profile.


Enviroment Variables Added:

<div align="center">

| Name                  | Value(s)       | Default     | Description                       | Usage                                          |
|------------------     |:---------------|:------------|:--------------------              |:---------------------------------
|  MAX_TOTAL_TOKENS     | integer        | 0           | Control the padding of input          | add -e in docker run, such         |
|  ENABLE_HPU_GRAPH     | true/false     | true        | Enable hpu graph or not                                                      |  add -e in docker run command  |
|  PROF_WARMUPSTEP      | integer        | 0           | Enable/disable profile, control profile warmup step, 0 means disable profile |  add -e in docker run command  |
|  PROF_STEP            | interger       | 5           | Control profile step                                                         |  add -e in docker run command  |
|  PROF_PATH            | string         | /root/text-generation-inference                                   | Define profile folder  | add -e in docker run command  |
| LIMIT_HPU_GRAPH       | True/False     | False       | Skip HPU graph usage for prefill to save memory | add -e in docker run command |

</div>


> The license to use TGI on Habana Gaudi is the one of TGI: https://github.com/huggingface/text-generation-inference/blob/main/LICENSE
>
> Please reach out to api-enterprise@huggingface.co if you have any question.