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--- |
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license: apache-2.0 |
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--- |
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## Installation from source |
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```bash |
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git clone https://github.com/foundation-model-stack/fms-extras |
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cd fms-extras |
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pip install -e . |
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``` |
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## Description |
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This model is intended to be used as an accelerator for [granite-34b-code-instruct](https://huggingface.co./ibm-granite/granite-34b-code-instruct) and takes inspiration |
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from the Medusa speculative decoding architecture. This accelerator modifies the MLP into a multi-stage MLP, where each stage predicts |
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a single token in the draft based on both a state vector and sampled token |
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from the prior stage (the base model can be considered stage 0). |
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The state vector from the base model provides contextual information to the accelerator, |
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while conditioning on prior sampled tokens allows it to produce higher-quality draft n-grams. |
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Note: The underlying MLP speculator is a generic architecture that can be trained with any generative model to accelerate inference. |
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Training is light-weight and can be completed in only a few days depending on base model size and speed. |
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## Repository Links |
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1. [Paged Attention KV-Cache / Speculator](https://github.com/foundation-model-stack/fms-extras) |
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2. [Production Server with speculative decoding](https://github.com/IBM/text-generation-inference.git) |
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3. [Speculator training](https://github.com/foundation-model-stack/fms-fsdp/pull/35) |
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## Samples |
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_Note: For all samples, your environment must have access to cuda_ |
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### Use in IBM Production TGIS |
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*To try this out running in a production-like environment, please use the pre-built docker image:* |
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#### Setup |
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```bash |
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HF_HUB_CACHE=/hf_hub_cache |
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chmod a+w $HF_HUB_CACHE |
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HF_HUB_TOKEN="your huggingface hub token" |
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TGIS_IMAGE=quay.io/wxpe/text-gen-server:main.ddc56ee |
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docker pull $TGIS_IMAGE |
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# optionally download granite-34b-code-instruct if the weights do not already exist |
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docker run --rm \ |
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-v $HF_HUB_CACHE:/models \ |
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-e HF_HUB_CACHE=/models \ |
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-e TRANSFORMERS_CACHE=/models \ |
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$TGIS_IMAGE \ |
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text-generation-server download-weights \ |
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ibm-granite/granite-34b-code-instruct \ |
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--token $HF_HUB_TOKEN |
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# optionally download the speculator model if the weights do not already exist |
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docker run --rm \ |
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-v $HF_HUB_CACHE:/models \ |
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-e HF_HUB_CACHE=/models \ |
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-e TRANSFORMERS_CACHE=/models \ |
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$TGIS_IMAGE \ |
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text-generation-server download-weights \ |
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ibm-granite/granite-34b-code-instruct-accelerator \ |
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--token $HF_HUB_TOKEN |
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# 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> |
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docker run -d --rm --gpus all \ |
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--name my-tgis-server \ |
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-p 8033:8033 \ |
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-v $HF_HUB_CACHE:/models \ |
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-e HF_HUB_CACHE=/models \ |
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-e TRANSFORMERS_CACHE=/models \ |
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-e MODEL_NAME=ibm-granite/granite-34b-code-instruct \ |
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-e SPECULATOR_NAME=ibm-granite/granite-34b-code-instruct-accelerator \ |
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-e FLASH_ATTENTION=true \ |
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-e PAGED_ATTENTION=true \ |
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-e DTYPE=float16 \ |
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$TGIS_IMAGE |
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# check logs and wait for "gRPC server started on port 8033" and "HTTP server started on port 3000" |
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docker logs my-tgis-server -f |
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# get the client sample (Note: The first prompt will take longer as there is a warmup time) |
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conda create -n tgis-client-env python=3.11 |
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conda activate tgis-client-env |
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git clone --branch main --single-branch https://github.com/IBM/text-generation-inference.git |
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cd text-generation-inference/integration_tests |
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make gen-client |
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pip install . --no-cache-dir |
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``` |
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#### Run Sample |
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```bash |
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python sample_client.py |
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``` |
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_Note: first prompt may be slower as there is a slight warmup time_ |
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### Use in Huggingface TGI |
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#### start the server |
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```bash |
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model=ibm-granite/granite-34b-code-instruct-accelerator |
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volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run |
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docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --model-id $model |
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``` |
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_note: for tensor parallel, add --num-shard_ |
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#### make a request |
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```bash |
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curl 127.0.0.1:8080/generate_stream \ |
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-X POST \ |
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-d '{"inputs":"Write a bubble sort in python","parameters":{"max_new_tokens":100}}' \ |
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-H 'Content-Type: application/json' |
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``` |