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This draft PR is a work in progress implementation of the mamba model. This PR currently loads weights, and produces correct logits after a single pass. This PR still needs to correctly integrate this model so it produces tokens as expected, and apply optimization to avoid all copies during runtime/unnecessary operations. #### Helpful resources [Mamba: Linear-Time Sequence Modeling with Selective State Spaces (Albert Gu and Tri Dao)](https://arxiv.org/abs/2312.00752) https://github.com/johnma2006/mamba-minimal https://github.com/huggingface/candle/blob/main/candle-examples/examples/mamba-minimal/model.rs https://github.com/huggingface/transformers/pull/28094 Notes: this dev work is currently targeting `state-spaces/mamba-130m`, so if you want to test please use that model. Additionally when starting the router the prefill needs to be limited: `cargo run -- --max-batch-prefill-tokens 768 --max-input-length 768` ## Update / Current State Integration tests have been added and basic functionality such as model loading is supported. ```bash cd integration-tests pytest -vv models/test_fused_kernel_mamba.py ``` - [x] add tests - [x] load model - [x] make simple request - [ ] resolve warmup issue - [ ] resolve output issues fetching models tested during dev ```bash text-generation-server download-weights state-spaces/mamba-130m text-generation-server download-weights state-spaces/mamba-1.4b text-generation-server download-weights state-spaces/mamba-2.8b ``` The server can be run ```bash cd server MASTER_ADDR=127.0.0.1 MASTER_PORT=5555 python text_generation_server/cli.py serve state-spaces/mamba-2.8b ``` router ```bash cargo run ``` make a request ```bash curl -s localhost:3000/generate \ -X POST \ -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":20}}' \ -H 'Content-Type: application/json' | jq ``` response ```json { "generated_text": "\n\nDeep learning is a machine learning technique that uses a deep neural network to learn from data." } ``` --------- Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
34 lines
1.3 KiB
Makefile
34 lines
1.3 KiB
Makefile
include Makefile-flash-att
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include Makefile-flash-att-v2
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include Makefile-vllm
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include Makefile-awq
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include Makefile-eetq
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include Makefile-selective-scan
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unit-tests:
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pytest -s -vv -m "not private" tests
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gen-server:
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# Compile protos
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pip install grpcio-tools==1.51.1 mypy-protobuf==3.4.0 'types-protobuf>=3.20.4' --no-cache-dir
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mkdir text_generation_server/pb || true
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python -m grpc_tools.protoc -I../proto --python_out=text_generation_server/pb \
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--grpc_python_out=text_generation_server/pb --mypy_out=text_generation_server/pb ../proto/generate.proto
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find text_generation_server/pb/ -type f -name "*.py" -print0 -exec sed -i -e 's/^\(import.*pb2\)/from . \1/g' {} \;
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touch text_generation_server/pb/__init__.py
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install-megablocks:
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pip install git+https://github.com/OlivierDehaene/megablocks@181709df192de9a941fdf3a641cdc65a0462996e
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install: gen-server
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pip install pip --upgrade
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pip install -r requirements_cuda.txt
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pip install -e ".[bnb, accelerate, quantize, peft]"
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run-dev:
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SAFETENSORS_FAST_GPU=1 python -m torch.distributed.run --nproc_per_node=2 text_generation_server/cli.py serve bigscience/bloom-560m --sharded
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export-requirements:
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poetry export -o requirements_cuda.txt --extras bnb --without-hashes
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poetry export -o requirements_rocm.txt --without-hashes
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