Text Generation Inference on Habana Gaudi

Table of contents

• Running TGI on Gaudi
• Adjusting TGI parameters
• Currently supported configurations
• Environment variables
• Profiler

Running TGI on Gaudi

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

1. Pull the official Docker image with:

   docker pull ghcr.io/huggingface/tgi-gaudi:1.2.1
   

Note

Alternatively, you can build the Docker image using the Dockerfile located in this folder with:

docker build -t tgi_gaudi .

2. Launch a local server instance:

   i. On 1 Gaudi/Gaudi2 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 ghcr.io/huggingface/tgi-gaudi:1.2.1 --model-id $model
   

   For gated models such as LLama or 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.

   ii. On 8 Gaudi/Gaudi2 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 ghcr.io/huggingface/tgi-gaudi:1.2.1 --model-id $model --sharded true --num-shard 8
   

3. You can then send a simple request:

   curl 127.0.0.1:8080/generate \
     -X POST \
     -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":32}}' \
     -H 'Content-Type: application/json'
   

4. To run static benchmark test, please refer to 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 2 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.

5. To run continuous batching test, please refer to examples.

Adjusting TGI parameters

Maximum sequence length is controlled by two arguments:

• --max-input-length is the maximum possible input prompt length. Default value is 1024.
• --max-total-tokens is the maximum possible total length of the sequence (input and output). Default value is 2048.

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.

To ensure greatest performance results, at the begginging of each server run, warmup is performed. It's designed to cover major recompilations while using HPU Graphs. It creates queries with all possible input shapes, based on provided parameters (described in this section) and runs basic TGI operations on them (prefill, decode, concatenate).

Except those already mentioned, there are other parameters that need to be properly adjusted to improve performance or memory usage:

• PAD_SEQUENCE_TO_MULTIPLE_OF determines sizes of input legnth buckets. Since warmup creates several graphs for each bucket, it's important to adjust that value proportionally to input sequence length. Otherwise, some out of memory issues can be observed.
• ENABLE_HPU_GRAPH enables HPU graphs usage, which is crucial for performance results. Recommended value to keep is true .

For more information and documentation about Text Generation Inference, checkout the README of the original repo.

Currently supported configurations

Not all features of TGI are currently supported as this is still a work in progress. Currently supported and validated configurations (other configurations are not guaranted to work or ensure reasonable performance ):

• LLaMA 70b:

  • Num cards: 8
  • Decode batch size: 128
  • Dtype: bfloat16
  • Max input tokens: 1024
  • Max total tokens: 2048

• LLaMA 7b:

  • Num cards: 1
  • Decode batch size: 16
  • Dtype: bfloat16
  • Max input tokens: 1024
  • Max total tokens: 2048

Other sequence lengths can be used with proportionally decreased/increased batch size (the higher sequence length, the lower batch size). Support for other models from Optimum Habana will be added successively.

Environment variables

Name	Value(s)	Default	Description	Usage
ENABLE_HPU_GRAPH	True/False	True	Enable hpu graph or not	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
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
QUEUE_THRESHOLD_MS	integer	120	Controls the threshold beyond which the request are considered overdue and handled with priority. Shorter requests are prioritized otherwise.	add -e in docker run command

Profiler

To collect performance profiling, please set below environment variables:

Name	Value(s)	Default	Description	Usage
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

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.