Text Generation Inference on Habana Gaudi

To use 🤗 text-generation-inference on Habana Gaudi/Gaudi2, follow these steps:

1. Build the Docker image located in this folder with:

   docker build -t tgi_gaudi .
   

2. Launch a local server instance on 1 Gaudi card:

   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:

   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:

   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.

   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, 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 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 parallelism. Also, use HPU graphs for performance improvement.
• Torch profile.

Environment Variables Added:

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

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.