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Text Generation Inference on Habana Gaudi

Table of contents

Running TGI on Gaudi

To use 🤗 text-generation-inference on Habana Gaudi/Gaudi2/Gaudi3, follow these steps:

  1. Pull the official Docker image with: 
    docker pull ghcr.io/huggingface/tgi-gaudi:2.0.5

Note

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

docker build -t tgi_gaudi .
  1. Use one of the following snippets to launch a local server instance:

Note

For gated models such as meta-llama/Llama-2-7b-hf, you will have to pass -e HF_TOKEN=<token> to the docker run commands below with a valid Hugging Face Hub read token.

i. On 1 Gaudi card

model=meta-llama/Llama-2-7b-hf
hf_token=YOUR_ACCESS_TOKEN
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run

docker run -p 8080:80 -v $volume:/data --runtime=habana -e HABANA_VISIBLE_DEVICES=all \
-e OMPI_MCA_btl_vader_single_copy_mechanism=none -e HF_TOKEN=$hf_token \
-e ENABLE_HPU_GRAPH=true -e LIMIT_HPU_GRAPH=true -e USE_FLASH_ATTENTION=true \
-e FLASH_ATTENTION_RECOMPUTE=true --cap-add=sys_nice --ipc=host \
ghcr.io/huggingface/tgi-gaudi:2.0.5 --model-id $model --max-input-tokens 1024 \
--max-total-tokens 2048

ii. On 1 Gaudi card using PyTorch eager mode with torch compile:

model=meta-llama/Llama-2-7b-hf
hf_token=YOUR_ACCESS_TOKEN
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run

docker run -p 8080:80 -v $volume:/data --runtime=habana -e HABANA_VISIBLE_DEVICES=all \
 -e PT_HPU_LAZY_MODE=0 -e OMPI_MCA_btl_vader_single_copy_mechanism=none \
 -e HF_TOKEN=$hf_token --cap-add=sys_nice --ipc=host \
 ghcr.io/huggingface/tgi-gaudi:2.0.5 --model-id $model --max-input-tokens 1024 --max-total-tokens 2048

iii. On 8 Gaudi cards:

model=meta-llama/Llama-2-70b-hf
hf_token=YOUR_ACCESS_TOKEN
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run

docker run -p 8080:80 -v $volume:/data --runtime=habana -e PT_HPU_ENABLE_LAZY_COLLECTIVES=true \
 -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none \
 -e  HF_TOKEN=$hf_token -e ENABLE_HPU_GRAPH=true -e LIMIT_HPU_GRAPH=true \
 -e USE_FLASH_ATTENTION=true -e FLASH_ATTENTION_RECOMPUTE=true --cap-add=sys_nice \
 --ipc=host ghcr.io/huggingface/tgi-gaudi:2.0.5 --model-id $model --sharded true \
 --num-shard 8 --max-input-tokens 1024 --max-total-tokens 2048
  1. Wait for the TGI-Gaudi server to come online. You will see something like so:

    2024-05-22T19:31:48.302239Z INFO text_generation_router: router/src/main.rs:378: Connected You can then send a simple request to the server from a separate terminal:

    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'
  2. Please note that the model warmup can take several minutes, especially for FP8 inference. To minimize this time in consecutive runs, please refer to Disk Caching Eviction Policy.

TGI-Gaudi Benchmark

Static Batching Benchmark

To run static batching benchmark, 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.

Continuous Batching Benchmark

To run continuous batching benchmark, please refer to README in examples folder.

Tested Models and Configurations

The following table contains models and configurations we have validated on Gaudi2.

Model BF16 FP8 Single Card Multi-Cards
Llama2-7B ✔ ✔ ✔ ✔
Llama2-70B ✔ ✔ ✔
Llama3-8B ✔ ✔ ✔ ✔
Llama3-70B ✔ ✔ ✔
Llama3.1-8B ✔ ✔ ✔ ✔
Llama3.1-70B ✔ ✔ ✔
CodeLlama-13B ✔ ✔ ✔
Mixtral-8x7B ✔ ✔ ✔ ✔
Mistral-7B ✔ ✔ ✔ ✔
Llava-v1.6-Mistral-7B ✔ ✔ ✔ ✔

Running TGI with BF16 Precision

The following are command examples for TGI models inference with BF16 precision.

Llama2-7B on 1 Card

model=meta-llama/Llama-2-7b-chat-hf
hf_token=YOUR_ACCESS_TOKEN
volume=$PWD/data   # share a volume with the Docker container to avoid downloading weights every run

docker run -p 8080:80 \
   --runtime=habana \
   -v $volume:/data \
   -e HABANA_VISIBLE_DEVICES=all \
   -e HF_TOKEN=$hf_token \
   -e OMPI_MCA_btl_vader_single_copy_mechanism=none \
   -e TEXT_GENERATION_SERVER_IGNORE_EOS_TOKEN=true \
   -e MAX_TOTAL_TOKENS=2048 \
   -e PREFILL_BATCH_BUCKET_SIZE=2 \
   -e BATCH_BUCKET_SIZE=32 \
   -e PAD_SEQUENCE_TO_MULTIPLE_OF=256 \
   -e ENABLE_HPU_GRAPH=true \
   -e LIMIT_HPU_GRAPH=true \
   -e USE_FLASH_ATTENTION=true \
   -e FLASH_ATTENTION_RECOMPUTE=true \
   --cap-add=sys_nice \
   --ipc=host \
   ghcr.io/huggingface/tgi-gaudi:2.0.5 \
   --model-id $model \
   --max-input-length 1024 --max-total-tokens 2048 \
   --max-batch-prefill-tokens 2048 --max-batch-total-tokens 65536 \
   --max-waiting-tokens 7 --waiting-served-ratio 1.2 --max-concurrent-requests 64

Llama2-70B on 8 cards

model=meta-llama/Llama-2-70b-chat-hf
hf_token=YOUR_ACCESS_TOKEN
volume=$PWD/data   # share a volume with the Docker container to avoid downloading weights every run

docker run -p 8080:80 \
   --runtime=habana \
   -v $volume:/data \
   -e HABANA_VISIBLE_DEVICES=all \
   -e HF_TOKEN=$hf_token \
   -e OMPI_MCA_btl_vader_single_copy_mechanism=none \
   -e TEXT_GENERATION_SERVER_IGNORE_EOS_TOKEN=true \
   -e PT_HPU_ENABLE_LAZY_COLLECTIVES=true \
   -e MAX_TOTAL_TOKENS=2048 \
   -e BATCH_BUCKET_SIZE=256 \
   -e PREFILL_BATCH_BUCKET_SIZE=4 \
   -e PAD_SEQUENCE_TO_MULTIPLE_OF=64 \
   -e ENABLE_HPU_GRAPH=true \
   -e LIMIT_HPU_GRAPH=true \
   -e USE_FLASH_ATTENTION=true \
   -e FLASH_ATTENTION_RECOMPUTE=true \
   --cap-add=sys_nice \
   --ipc=host \
   ghcr.io/huggingface/tgi-gaudi:2.0.5 \
   --model-id $model \
   --sharded true --num-shard 8 \
   --max-input-length 1024 --max-total-tokens 2048 \
   --max-batch-prefill-tokens 4096 --max-batch-total-tokens 524288 \
   --max-waiting-tokens 7 --waiting-served-ratio 1.2 --max-concurrent-requests 512

Llama3.1-8B on 1 card

model=meta-llama/Meta-Llama-3.1-8B-Instruct
hf_token=YOUR_ACCESS_TOKEN
volume=$PWD/data   # share a volume with the Docker container to avoid downloading weights every run

docker run -p 8080:80 \
   --runtime=habana \
   -v $volume:/data \
   -e HABANA_VISIBLE_DEVICES=all \
   -e HF_TOKEN=$hf_token \
   -e OMPI_MCA_btl_vader_single_copy_mechanism=none \
   -e TEXT_GENERATION_SERVER_IGNORE_EOS_TOKEN=true \
   -e MAX_TOTAL_TOKENS=2048 \
   -e PREFILL_BATCH_BUCKET_SIZE=2 \
   -e BATCH_BUCKET_SIZE=32 \
   -e PAD_SEQUENCE_TO_MULTIPLE_OF=256 \
   -e ENABLE_HPU_GRAPH=true \
   -e LIMIT_HPU_GRAPH=true \
   -e USE_FLASH_ATTENTION=true \
   -e FLASH_ATTENTION_RECOMPUTE=true \
   --cap-add=sys_nice \
   --ipc=host \
   ghcr.io/huggingface/tgi-gaudi:2.0.5 \
   --model-id $model \
   --max-input-length 1024 --max-total-tokens 2048 \
   --max-batch-prefill-tokens 2048 --max-batch-total-tokens 65536 \
   --max-waiting-tokens 7 --waiting-served-ratio 1.2 --max-concurrent-requests 64

Llama3.1-70B 8 cards

model=meta-llama/Meta-Llama-3.1-70B-Instruct
hf_token=YOUR_ACCESS_TOKEN
volume=$PWD/data   # share a volume with the Docker container to avoid downloading weights every run

docker run -p 8080:80 \
   --runtime=habana \
   -v $volume:/data \
   -e HABANA_VISIBLE_DEVICES=all \
   -e HF_TOKEN=$hf_token \
   -e OMPI_MCA_btl_vader_single_copy_mechanism=none \
   -e TEXT_GENERATION_SERVER_IGNORE_EOS_TOKEN=true \
   -e PT_HPU_ENABLE_LAZY_COLLECTIVES=true \
   -e MAX_TOTAL_TOKENS=2048 \
   -e BATCH_BUCKET_SIZE=256 \
   -e PREFILL_BATCH_BUCKET_SIZE=4 \
   -e PAD_SEQUENCE_TO_MULTIPLE_OF=64 \
   -e ENABLE_HPU_GRAPH=true \
   -e LIMIT_HPU_GRAPH=true \
   -e USE_FLASH_ATTENTION=true \
   -e FLASH_ATTENTION_RECOMPUTE=true \
   --cap-add=sys_nice \
   --ipc=host \
   ghcr.io/huggingface/tgi-gaudi:2.0.5 \
   --model-id $model \
   --sharded true --num-shard 8 \
   --max-input-length 1024 --max-total-tokens 2048 \
   --max-batch-prefill-tokens 4096 --max-batch-total-tokens 524288 \
   --max-waiting-tokens 7 --waiting-served-ratio 1.2 --max-concurrent-requests 512

Llava-v1.6-Mistral-7B on 1 card

In Llava-v1.6-Mistral-7B, an image usually accounts for 2000 input tokens. For example, an image of size 512x512 is represented by 2800 tokens. Thus, max-input-tokens must be larger than the number of tokens associated with the image. Otherwise the image may be truncated. We set BASE_IMAGE_TOKENS=2048 as the default image token value. This is the minimum value of max-input-tokens. You can override the environment variable BASE_IMAGE_TOKENS to change this value. The warmup will generate graphs with input length from BASE_IMAGE_TOKENS to max-input-tokens. For Llava-v1.6-Mistral-7B, the value of max-batch-prefill-tokens is 16384, which is calcualted as follows: prefill_batch_size = max-batch-prefill-tokens / max-input-tokens.

model=llava-hf/llava-v1.6-mistral-7b-hf
volume=$PWD/data   # share a volume with the Docker container to avoid downloading weights every run

docker run -p 8080:80 \
   --runtime=habana \
   -v $volume:/data \
   -e HABANA_VISIBLE_DEVICES=all \
   -e OMPI_MCA_btl_vader_single_copy_mechanism=none \
   -e TEXT_GENERATION_SERVER_IGNORE_EOS_TOKEN=true \
   -e PT_HPU_ENABLE_LAZY_COLLECTIVES=true \
   -e HF_HUB_ENABLE_HF_TRANSFER=1 \
   -e ENABLE_HPU_GRAPH=true \
   -e LIMIT_HPU_GRAPH=true \
   -e USE_FLASH_ATTENTION=true \
   -e FLASH_ATTENTION_RECOMPUTE=true \
    -e PREFILL_BATCH_BUCKET_SIZE=1 \
    -e BATCH_BUCKET_SIZE=1 \
   --cap-add=sys_nice \
   --ipc=host \
   ghcr.io/huggingface/tgi-gaudi:2.0.5 \
   --model-id $model \
   --max-input-tokens 4096 --max-batch-prefill-tokens 16384 \
   --max-total-tokens 8192 --max-batch-total-tokens 32768

Send the simple request.

curl -N 127.0.0.1:8080/generate_stream \
    -X POST \
    -d '{"inputs":"![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png)What is this a picture of?\n\n","parameters":{"max_new_tokens":16, "seed": 42}}' \
    -H 'Content-Type: application/json'

Running TGI with FP8 Precision

TGI-Gaudi supports FP8 precision inference with INC (Intel Neural Compressor) and HQT (Habana Quantization Toolkit). FP8 inference can be run by setting QUANT_CONFIG environment variable in the docker command. From TGI-Gaudi 2.0.4 release, INC is used by default for quantization. HQT will be removed in future releases. To use HQT, disable INC by setting -e USE_INC=0 in docker command.

To run FP8 Inference:

  1. Measure statistics by using Optimum Habana measurement script
  2. Run the model in TGI with QUANT_CONFIG setting - e.g. -e QUANT_CONFIG=./quantization_config/maxabs_quant.json.

The following are the commmand examples for FP8 inference based on the assumption that measurement is done in the first step above.

Llama2-7B on 1 Card

model=meta-llama/Llama-2-7b-chat-hf
hf_token=YOUR_ACCESS_TOKEN
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run

docker run -p 8080:80 \
   --runtime=habana \
   -v $volume:/data \
   -v $PWD/quantization_config:/usr/src/quantization_config \
   -v $PWD/hqt_output:/usr/src/hqt_output \
   -e QUANT_CONFIG=./quantization_config/maxabs_quant.json \
   -e HABANA_VISIBLE_DEVICES=all \
   -e HF_TOKEN=$hf_token \
   -e OMPI_MCA_btl_vader_single_copy_mechanism=none \
   -e TEXT_GENERATION_SERVER_IGNORE_EOS_TOKEN=true \
   -e MAX_TOTAL_TOKENS=2048 \
   -e PREFILL_BATCH_BUCKET_SIZE=2 \
   -e BATCH_BUCKET_SIZE=32 \
   -e PAD_SEQUENCE_TO_MULTIPLE_OF=256 \
   -e ENABLE_HPU_GRAPH=true \
   -e LIMIT_HPU_GRAPH=true \
   -e USE_FLASH_ATTENTION=true \
   -e FLASH_ATTENTION_RECOMPUTE=true \
   --cap-add=sys_nice \
   --ipc=host \
   ghcr.io/huggingface/tgi-gaudi:2.0.5 \
   --model-id $model \
   --max-input-length 1024 --max-total-tokens 2048 \
   --max-batch-prefill-tokens 2048 --max-batch-total-tokens 65536 \
   --max-waiting-tokens 7 --waiting-served-ratio 1.2 --max-concurrent-requests 64

Llama2-70B on 8 Cards

model=meta-llama/Llama-2-70b-chat-hf
hf_token=YOUR_ACCESS_TOKEN
volume=$PWD/data   # share a volume with the Docker container to avoid downloading weights every run

docker run -p 8080:80 \
   --runtime=habana \
   -v $volume:/data \
   -v $PWD/quantization_config:/usr/src/quantization_config \
   -v $PWD/hqt_output:/usr/src/hqt_output \
   -e QUANT_CONFIG=./quantization_config/maxabs_quant.json \
   -e HABANA_VISIBLE_DEVICES=all \
   -e HF_TOKEN=$hf_token \
   -e OMPI_MCA_btl_vader_single_copy_mechanism=none \
   -e TEXT_GENERATION_SERVER_IGNORE_EOS_TOKEN=true \
   -e PT_HPU_ENABLE_LAZY_COLLECTIVES=true \
   -e MAX_TOTAL_TOKENS=2048 \
   -e BATCH_BUCKET_SIZE=256 \
   -e PREFILL_BATCH_BUCKET_SIZE=4 \
   -e PAD_SEQUENCE_TO_MULTIPLE_OF=64 \
   -e ENABLE_HPU_GRAPH=true \
   -e LIMIT_HPU_GRAPH=true \
   -e USE_FLASH_ATTENTION=true \
   -e FLASH_ATTENTION_RECOMPUTE=true \
   --cap-add=sys_nice \
   --ipc=host \
   ghcr.io/huggingface/tgi-gaudi:2.0.5 \
   --model-id $model \
   --sharded true --num-shard 8 \
   --max-input-length 1024 --max-total-tokens 2048 \
   --max-batch-prefill-tokens 4096 --max-batch-total-tokens 524288 \
   --max-waiting-tokens 7 --waiting-served-ratio 1.2 --max-concurrent-requests 512

Llama3.1-8B on 1 Card

model=meta-llama/Meta-Llama-3.1-8B-Instruct
hf_token=YOUR_ACCESS_TOKEN
volume=$PWD/data   # share a volume with the Docker container to avoid downloading weights every run

docker run -p 8080:80 \
   --runtime=habana \
   -v $volume:/data \
   -v $PWD/quantization_config:/usr/src/quantization_config \
   -v $PWD/hqt_output:/usr/src/hqt_output \
   -e QUANT_CONFIG=./quantization_config/maxabs_quant.json \
   -e HABANA_VISIBLE_DEVICES=all \
   -e HF_TOKEN=$hf_token \
   -e OMPI_MCA_btl_vader_single_copy_mechanism=none \
   -e TEXT_GENERATION_SERVER_IGNORE_EOS_TOKEN=true \
   -e MAX_TOTAL_TOKENS=2048 \
   -e PREFILL_BATCH_BUCKET_SIZE=2 \
   -e BATCH_BUCKET_SIZE=32 \
   -e PAD_SEQUENCE_TO_MULTIPLE_OF=256 \
   -e ENABLE_HPU_GRAPH=true \
   -e LIMIT_HPU_GRAPH=true \
   -e USE_FLASH_ATTENTION=true \
   -e FLASH_ATTENTION_RECOMPUTE=true \
   --cap-add=sys_nice \
   --ipc=host \
   ghcr.io/huggingface/tgi-gaudi:2.0.5 \
   --model-id $model \
   --max-input-length 1024 --max-total-tokens 2048 \
   --max-batch-prefill-tokens 2048 --max-batch-total-tokens 65536 \
   --max-waiting-tokens 7 --waiting-served-ratio 1.2 --max-concurrent-requests 64

Llama3.1-70B on 8 cards

model=meta-llama/Meta-Llama-3.1-70B-Instruct
hf_token=YOUR_ACCESS_TOKEN
volume=$PWD/data   # share a volume with the Docker container to avoid downloading weights every run

docker run -p 8080:80 \
   --runtime=habana \
   -v $volume:/data \
   -v $PWD/quantization_config:/usr/src/quantization_config \
   -v $PWD/hqt_output:/usr/src/hqt_output \
   -e QUANT_CONFIG=./quantization_config/maxabs_quant.json \
   -e HABANA_VISIBLE_DEVICES=all \
   -e HF_TOKEN=$hf_token \
   -e OMPI_MCA_btl_vader_single_copy_mechanism=none \
   -e TEXT_GENERATION_SERVER_IGNORE_EOS_TOKEN=true \
   -e PT_HPU_ENABLE_LAZY_COLLECTIVES=true \
   -e MAX_TOTAL_TOKENS=2048 \
   -e BATCH_BUCKET_SIZE=256 \
   -e PREFILL_BATCH_BUCKET_SIZE=4 \
   -e PAD_SEQUENCE_TO_MULTIPLE_OF=64 \
   -e ENABLE_HPU_GRAPH=true \
   -e LIMIT_HPU_GRAPH=true \
   -e USE_FLASH_ATTENTION=true \
   -e FLASH_ATTENTION_RECOMPUTE=true \
   --cap-add=sys_nice \
   --ipc=host \
   ghcr.io/huggingface/tgi-gaudi:2.0.5 \
   --model-id $model \
   --sharded true --num-shard 8 \
   --max-input-length 1024 --max-total-tokens 2048 \
   --max-batch-prefill-tokens 4096 --max-batch-total-tokens 524288 \
   --max-waiting-tokens 7 --waiting-served-ratio 1.2 --max-concurrent-requests 512

Llava-v1.6-Mistral-7B on 1 Card

model=llava-hf/llava-v1.6-mistral-7b-hf
volume=$PWD/data   # share a volume with the Docker container to avoid downloading weights every run

docker run -p 8080:80 \
   --runtime=habana \
   -v $volume:/data \
   -v $PWD/quantization_config:/usr/src/quantization_config \
   -v $PWD/hqt_output:/usr/src/hqt_output \
   -e QUANT_CONFIG=./quantization_config/maxabs_quant.json \
   -e HABANA_VISIBLE_DEVICES=all \
   -e OMPI_MCA_btl_vader_single_copy_mechanism=none \
   -e TEXT_GENERATION_SERVER_IGNORE_EOS_TOKEN=true \
   -e PT_HPU_ENABLE_LAZY_COLLECTIVES=true \
   -e HF_HUB_ENABLE_HF_TRANSFER=1 \
   -e ENABLE_HPU_GRAPH=true \
   -e LIMIT_HPU_GRAPH=true \
   -e USE_FLASH_ATTENTION=true \
   -e FLASH_ATTENTION_RECOMPUTE=true \
    -e PREFILL_BATCH_BUCKET_SIZE=1 \
    -e BATCH_BUCKET_SIZE=1 \
   --cap-add=sys_nice \
   --ipc=host \
   ghcr.io/huggingface/tgi-gaudi:2.0.5 \
   --model-id $model \
   --max-input-tokens 4096 --max-batch-prefill-tokens 16384 \
   --max-total-tokens 8192 --max-batch-total-tokens 32768

Llava-v1.6-Mistral-7B on 8 Cards

model=llava-hf/llava-v1.6-mistral-7b-hf
volume=$PWD/data   # share a volume with the Docker container to avoid downloading weights every run

docker run -p 8080:80 \
   --runtime=habana \
   -v $volume:/data \
   -v $PWD/quantization_config:/usr/src/quantization_config \
   -v $PWD/hqt_output:/usr/src/hqt_output \
   -e QUANT_CONFIG=./quantization_config/maxabs_quant.json \
   -e HABANA_VISIBLE_DEVICES=all \
   -e OMPI_MCA_btl_vader_single_copy_mechanism=none \
   -e TEXT_GENERATION_SERVER_IGNORE_EOS_TOKEN=true \
   -e PT_HPU_ENABLE_LAZY_COLLECTIVES=true \
   -e HF_HUB_ENABLE_HF_TRANSFER=1 \
   -e ENABLE_HPU_GRAPH=true \
   -e LIMIT_HPU_GRAPH=true \
   -e USE_FLASH_ATTENTION=true \
   -e FLASH_ATTENTION_RECOMPUTE=true \
    -e PREFILL_BATCH_BUCKET_SIZE=1 \
    -e BATCH_BUCKET_SIZE=1 \
   --cap-add=sys_nice \
   --ipc=host \
   ghcr.io/huggingface/tgi-gaudi:2.0.5 \
   --model-id $model \
   --sharded true --num-shard 8 \
   --max-input-tokens 4096 --max-batch-prefill-tokens 16384 \
   --max-total-tokens 8192 --max-batch-total-tokens 32768

Adjusting TGI Parameters

Maximum sequence length is controlled by two arguments:

  • --max-input-tokens is the maximum possible input prompt length. Default value is 4095.
  • --max-total-tokens is the maximum possible total length of the sequence (input and output). Default value is 4096.

Maximum batch size is controlled by two arguments:

  • For prefill operation, please set --max-batch-prefill-tokens as bs * max-input-tokens, 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 beginning 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 length 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.

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
USE_FLASH_ATTENTION True/False False Whether to enable Habana Flash Attention, provided that the model supports it. Currently only llama and mistral supports this feature. Please refer to https://docs.habana.ai/en/latest/PyTorch/Model_Optimization_PyTorch/Optimization_in_PyTorch_Models.html?highlight=fusedsdpa#using-fused-scaled-dot-product-attention-fusedsdpa
FLASH_ATTENTION_RECOMPUTE True/False False Whether to enable Habana Flash Attention in recompute mode on first token generation.

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

License

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 [email protected] if you have any question.