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ibm-granite/
granite-3.0-8b-instruct-accelerator

Name: granite-3.0-8b-instruct-accelerator
Availability: InStock

pytorch

mlp_speculator

apache-2.0

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Description

This model is intended to be used as an accelerator for Granite-3.0-8b-instruct and takes inspiration from the Medusa speculative decoding architecture. Preliminary evaluations show up to a 2.2x speedup in tokens/step when using the accelerator with the base model. This accelerator modifies the MLP into a multi-stage MLP, where each stage predicts a single token in the draft based on both a state vector and sampled token from the prior stage (the base model can be considered stage 0). The state vector from the base model provides contextual information to the accelerator, while conditioning on prior sampled tokens allows it to produce higher-quality draft n-grams.

Note: The underlying MLP speculator is a generic architecture that can be trained with any generative model to accelerate inference. Training is light-weight and can be completed in only a few days depending on base model size and speed.

Repository Links

Samples

Note: For all samples, your environment must have access to cuda

Use in IBM Production TGIS

To try this out running in a production-like environment, please use the pre-built docker image:

Setup

bash

HF_HUB_CACHE=/hf_hub_cache
chmod a+w $HF_HUB_CACHE
HF_HUB_TOKEN="your huggingface hub token"
TGIS_IMAGE=quay.io/wxpe/text-gen-server:main.ddc56ee

docker pull $TGIS_IMAGE

# optionally download granite-3.0-8b-instruct if the weights do not already exist
docker run --rm \
    -v $HF_HUB_CACHE:/models \
    -e HF_HUB_CACHE=/models \
    -e TRANSFORMERS_CACHE=/models \
    $TGIS_IMAGE \
    text-generation-server download-weights \
    ibm-granite/granite-3.0-8b-instruct \
    --token $HF_HUB_TOKEN

# optionally download the speculator model if the weights do not already exist
docker run --rm \
    -v $HF_HUB_CACHE:/models \
    -e HF_HUB_CACHE=/models \
    -e TRANSFORMERS_CACHE=/models \
    $TGIS_IMAGE \
    text-generation-server download-weights \
    ibm-granite/granite-3.0-8b-instruct-accelerator \
    --token $HF_HUB_TOKEN

# note: if the weights were downloaded separately (not with the above commands), please place them in the HF_HUB_CACHE directory and refer to them with /models/<model_name>
docker run -d --rm --gpus all \
    --name my-tgis-server \
    -p 8033:8033 \
    -v $HF_HUB_CACHE:/models \
    -e HF_HUB_CACHE=/models \
    -e TRANSFORMERS_CACHE=/models \
    -e MODEL_NAME=ibm-granite/granite-3.0-8b-instruct \
    -e SPECULATOR_NAME=ibm-granite/granite-3.0-8b-instruct-accelerator \
    -e FLASH_ATTENTION=true \
    -e PAGED_ATTENTION=true \
    -e DTYPE=float16 \
    $TGIS_IMAGE

# check logs and wait for "gRPC server started on port 8033" and "HTTP server started on port 3000"
docker logs my-tgis-server -f

# get the client sample (Note: The first prompt will take longer as there is a warmup time)
conda create -n tgis-client-env python=3.11
conda activate tgis-client-env
git clone --branch main --single-branch https://github.com/IBM/text-generation-inference.git
cd text-generation-inference/integration_tests
make gen-client
pip install . --no-cache-dir

Run Sample

python sample_client.py

Note: first prompt may be slower as there is a slight warmup time

Use in Huggingface TGI

start the server

bash

model=ibm-granite/granite-3.0-8b-instruct-accelerator
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run

docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --model-id $model

note: for tensor parallel, add --num-shard

make a request

bash

curl 127.0.0.1:8080/generate_stream \
    -X POST \
    -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":20}}' \
    -H 'Content-Type: application/json'

Use in vLLM

from

# Sample prompts.
prompts = [
    "The president of the United States is",
]
# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.0)

# Create an LLM.
llm = LLM(
    model="/path/to/granite-3.0-8b-instruct",
    tensor_parallel_size=4,
    speculative_model="/path/to/granite-3.0-8b-instruct-accelerator",
    speculative_draft_tensor_parallel_size=1,
    use_v2_block_manager=True,
)
# Generate texts from the prompts. The output is a list of RequestOutput objects
# that contain the prompt, generated text, and other information.
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")