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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
   ```
   > Set `LIMIT_HPU_GRAPH=True` for larger sequence/decoding lengths(e.g. 300/212).
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 parallelism. Also, use HPU graphs for performance improvement.
- Torch profile.
- Batch size bucketing for decode and prefill.
- Sequence bucketing for prefill.


Environment 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_WAITSTEP               | integer    | 0                | Control profile wait steps                                                                                                       | add -e in docker run command |
| PROF_WARMUPSTEP             | integer    | 0                | Control profile warmup steps                                                                                                     | add -e in docker run command |
| PROF_STEP                   | integer    | 0                | Enable/disable profile, control profile active steps                                                                             | add -e in docker run command |
| PROF_PATH                   | string     | /tmp/hpu_profile | Define profile folder                                                                                                            | add -e in docker run command |
| PROF_RANKS                  | string     | 0                | Comma-separated list of ranks to profile                                                                                         | add -e in docker run command |
| PROF_RECORD_SHAPES          | true/false | false            | Control record_shapes option in the profiler                                                                                     | add -e in docker run command |
| LIMIT_HPU_GRAPH             | True/False | False            | Skip HPU graph usage for prefill to save memory, set to `True` for large sequence/decoding lengths(e.g. 300/212)                 | add -e in docker run command |
| BATCH_BUCKET_SIZE           | integer    | 8                | Batch size for decode operation will be rounded to the nearest multiple of this number. This limits the number of cached graphs  | add -e in docker run command |
| PREFILL_BATCH_BUCKET_SIZE   | integer    | 4                | Batch size for prefill operation will be rounded to the nearest multiple of this number. This limits the number of cached graphs | add -e in docker run command |
| PAD_SEQUENCE_TO_MULTIPLE_OF | integer    | 128              | For prefill operation, sequences will be padded to a multiple of provided value.                                                 | add -e in docker run command |
| SKIP_TOKENIZER_IN_TGI       | True/False | False            | Skip tokenizer for input/output processing                                                                                       | add -e in docker run command |
| TGI_PROFILER_ENABLED        | True/False | False            | Collect high-level server tracing events                                                                                         | add -e in docker run command |
| WARMUP_ENABLED              | True/False | True             | Enable warmup during server initialization to recompile all graphs. This can increase TGI setup time.                            | add -e in docker run command |
</div>


Maximum batch size is controlled by two arguments:
- For prefill operation, please set `--max-prefill-total-tokens` as `bs * max-input-length`, where `bs` is your expected maximum prefill batch size.
- For decode operation, please set `--max-batch-total-tokens` as `bs * max-total-tokens`, where `bs` is your expected maximum decode batch size.
- Please note that batch size will be always padded to the nearest multiplication of `BATCH_BUCKET_SIZE` and `PREFILL_BATCH_BUCKET_SIZE`.


> 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.