Impl simple mamba model #1480
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Impl simple mamba model #1480
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thanks, this will be a great addition as we see more mamba architectures |
drbh
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Jan 30, 2024
drbh
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Jan 30, 2024
server/text_generation_server/models/custom_modeling/mamba_modeling.py
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drbh
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Feb 6, 2024
…-generation-inference into impl-simple-mamba-model
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Apr 29, 2024
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. [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 huggingface/transformers#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` 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>
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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://github.com/johnma2006/mamba-minimal
https://github.com/huggingface/candle/blob/main/candle-examples/examples/mamba-minimal/model.rs
huggingface/transformers#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 768Update / Current State
Integration tests have been added and basic functionality such as model loading is supported.
cd integration-tests pytest -vv models/test_fused_kernel_mamba.pyfetching models tested during dev
The server can be run
cd server MASTER_ADDR=127.0.0.1 MASTER_PORT=5555 python text_generation_server/cli.py serve state-spaces/mamba-2.8brouter
make a request
curl -s localhost:3000/generate \ -X POST \ -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":20}}' \ -H 'Content-Type: application/json' | jqresponse
{ "generated_text": "\n\nDeep learning is a machine learning technique that uses a deep neural network to learn from data." }