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  1. 12 Feb, 2024 1 commit
  2. 09 Feb, 2024 1 commit
    • Ilyas Moutawwakil's avatar
      ROCm AWQ support (#1514) · a4e58016
      Ilyas Moutawwakil authored
      # What does this PR do?
      
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      <!-- Remove if not applicable -->
      
      This PR adds the possibility to run AWQ models with Exllama/GPTQ
      kernels, specifically for ROCm devices that support Exllama kernels but
      not AWQ's GEMM.
      
      This is done by :
      - un-packing, reordering and re-packing AWQ weights when `--quantize
      gptq` but the model's `quant_method=awq`.
      - avoiding overflows when adding 1 to zeros in exllama and triton.
      
      Ref: https://github.com/casper-hansen/AutoAWQ/pull/313
      
      ## Before submitting
      - [ ] This PR fixes a typo or improves the docs (you can dismiss the
      other checks if that's the case).
      - [ ] Did you read the [contributor
      guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
            Pull Request section?
      - [ ] Was this discussed/approved via a Github issue or the
      [forum](https://discuss.huggingface.co/)? Please add a link
            to it if that's the case.
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      Here are the
      [documentation
      guidelines](https://github.com/huggingface/transformers/tree/main/docs),
      and
      [here are tips on formatting
      docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation
      
      ).
      - [ ] Did you write any new necessary tests?
      
      
      ## Who can review?
      
      Anyone in the community is free to review the PR once the tests have
      passed. Feel free to tag
      members/contributors who may be interested in your PR.
      
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      ---------
      Co-authored-by: default avatarNicolas Patry <patry.nicolas@protonmail.com>
      a4e58016
  3. 08 Feb, 2024 2 commits
    • OlivierDehaene's avatar
      09b7c26b
    • drbh's avatar
      Impl simple mamba model (#1480) · bd405e03
      drbh authored
      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: default avatarNicolas Patry <patry.nicolas@protonmail.com>
      bd405e03
  4. 26 Jan, 2024 2 commits
    • OlivierDehaene's avatar
      v1.4.0 (#1494) · c2d4a3b5
      OlivierDehaene authored
      c2d4a3b5
    • Nicolas Patry's avatar
      Reinstate exl2 with tp (#1490) · b9573218
      Nicolas Patry authored
      # What does this PR do?
      
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      Fixes # (issue)
      
      
      ## Before submitting
      - [ ] This PR fixes a typo or improves the docs (you can dismiss the
      other checks if that's the case).
      - [ ] Did you read the [contributor
      guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
            Pull Request section?
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      [forum](https://discuss.huggingface.co/)? Please add a link
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      Here are the
      [documentation
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      b9573218
  5. 25 Jan, 2024 1 commit
    • drbh's avatar
      feat: adds phi model (#1442) · 7e2a7433
      drbh authored
      This PR adds basic modeling for phi-2 
      
      run
      ```bash
      text-generation-server \
          serve \
          microsoft/phi-2 \
          --revision 834565c23f9b28b96ccbeabe614dd906b6db551a
      ```
      
      
      test
      ```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 .
      # {
      #   "generated_text": "\nDeep learning is a subset of machine learning that uses artificial neural networks to learn from data. These"
      # }
      ```
      
      
      
      notes 
      - recently (~1 day ago) the Phi weights and model were updated to
      accommodate adding [GQA/MQA attention to the
      model.](https://github.com/huggingface/transformers/pull/28163) This
      impl expects the original model format so a fixed revision is required
      at the moment.
      - this PR only includes a basic implementation of the model and can
      later be extended for support Flash and Sharded versions as well as make
      use of better optimization
      7e2a7433
  6. 24 Jan, 2024 1 commit
    • Nicolas Patry's avatar
      Fixing non divisible embeddings. (#1476) · 7e542d4d
      Nicolas Patry authored
      # What does this PR do?
      
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      Fixes # (issue)
      
      
      ## Before submitting
      - [ ] This PR fixes a typo or improves the docs (you can dismiss the
      other checks if that's the case).
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      guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
            Pull Request section?
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      7e542d4d
  7. 22 Dec, 2023 1 commit
  8. 15 Dec, 2023 1 commit
  9. 12 Dec, 2023 1 commit
  10. 11 Dec, 2023 4 commits
  11. 30 Nov, 2023 1 commit
  12. 28 Nov, 2023 2 commits
    • Nicolas Patry's avatar
      Make GPTQ test less flaky (#1295) · 624800c4
      Nicolas Patry authored
      # What does this PR do?
      
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      ## Before submitting
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      guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
            Pull Request section?
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      624800c4
    • Nicolas Patry's avatar
      Let each model resolve their own default dtype. (#1287) · ba552e1a
      Nicolas Patry authored
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      ## Before submitting
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      guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
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      ba552e1a
  13. 23 Nov, 2023 1 commit
  14. 05 Oct, 2023 1 commit
    • Nicolas Patry's avatar
      Prepare for v1.1.1 (#1100) · 00b8f36f
      Nicolas Patry authored
      # What does this PR do?
      
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      ## Before submitting
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      guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
            Pull Request section?
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      [documentation
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      00b8f36f
  15. 28 Sep, 2023 1 commit
  16. 27 Sep, 2023 3 commits
    • OlivierDehaene's avatar
      feat: format code (#1070) · 47954b81
      OlivierDehaene authored
      47954b81
    • Nicolas Patry's avatar
      Remove the stripping of the prefix space (and any other mangling that tokenizers might do). (#1065) · b32e9ce9
      Nicolas Patry authored
      Superseed #1024
      
      
      # What does this PR do?
      
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      Fixes # (issue)
      
      
      ## Before submitting
      - [ ] This PR fixes a typo or improves the docs (you can dismiss the
      other checks if that's the case).
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      guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
            Pull Request section?
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      [forum](https://discuss.huggingface.co/)? Please add a link
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      ).
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      ---------
      Co-authored-by: default avatarbangoz <ch_xie@pku.edu.cn>
      b32e9ce9
    • Nicolas Patry's avatar
      Preping 1.1.0 (#1066) · a0498642
      Nicolas Patry authored
      # What does this PR do?
      
      Upgrade all relevant versions and dependencies.
      
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      Fixes # (issue)
      
      
      ## Before submitting
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      guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
            Pull Request section?
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      [forum](https://discuss.huggingface.co/)? Please add a link
            to it if that's the case.
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      a0498642
  17. 25 Sep, 2023 1 commit
    • Nicolas Patry's avatar
      Add AWQ quantization inference support (#1019) (#1054) · c5de7cd8
      Nicolas Patry authored
      # Add AWQ quantization inference support
      
      Fixes
      https://github.com/huggingface/text-generation-inference/issues/781
      
      This PR (partially) adds support for AWQ quantization for inference.
      More information on AWQ [here](https://arxiv.org/abs/2306.00978). In
      general, AWQ is faster and more accurate than GPTQ, which is currently
      supported by TGI.
      
      This PR installs 4-bit GEMM custom CUDA kernels released by AWQ authors
      (in `requirements.txt`, just one line change).
      
      Quick way to test this PR would be bring up TGI as follows:
      
      ```
      text-generation-server download-weights abhinavkulkarni/codellama-CodeLlama-7b-Python-hf-w4-g128-awq
      
      text-generation-launcher \
      --huggingface-hub-cache ~/.cache/huggingface/hub/ \
      --model-id abhinavkulkarni/codellama-CodeLlama-7b-Python-hf-w4-g128-awq \
      --trust-remote-code --port 8080 \
      --max-input-length 2048 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 \
      --quantize awq
      ```
      
      Please note:
      * This PR was tested with FlashAttention v2 and vLLM.
      * This PR adds support for AWQ inference, not quantizing the models.
      That needs to be done outside of TGI, instructions
      
      [here](https://github.com/mit-han-lab/llm-awq/tree/f084f40bd996f3cf3a0633c1ad7d9d476c318aaa).
      * This PR only adds support for `FlashLlama` models for now.
      * Multi-GPU setup has not been tested. 
      * No integration tests have been added so far, will add later if
      maintainers are interested in this change.
      * This PR can be tested on any of the models released
      
      [here](https://huggingface.co/abhinavkulkarni?sort_models=downloads#models).
      
      Please refer to the linked issue for benchmarks for
      
      [abhinavkulkarni/meta-llama-Llama-2-7b-chat-hf-w4-g128-awq](https://huggingface.co/abhinavkulkarni/meta-llama-Llama-2-7b-chat-hf-w4-g128-awq)
      vs
      
      [TheBloke/Llama-2-7b-Chat-GPTQ](https://huggingface.co/TheBloke/Llama-2-7b-Chat-GPTQ).
      
      Please note, AWQ has released faster (and in case of Llama, fused)
      kernels for 4-bit GEMM, currently at the top of the `main` branch at
      https://github.com/mit-han-lab/llm-awq, but this PR uses an older commit
      that has been tested to work. We can switch to latest commit later on.
      
      ## Who can review?
      
      @OlivierDehaene OR @Narsil
      
      ---------
      
      
      
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      Co-authored-by: default avatarAbhinav M Kulkarni <abhinavkulkarni@gmail.com>
      Co-authored-by: default avatarAbhinav Kulkarni <abhinav@concentric.ai>
      c5de7cd8
  18. 29 Aug, 2023 1 commit
    • Nicolas Patry's avatar
      New release. (#941) · 5485c142
      Nicolas Patry authored
      # What does this PR do?
      
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      5485c142
  19. 17 Aug, 2023 1 commit
    • Nicolas Patry's avatar
      Adding Idefics multi modal model. (#842) · bce5e224
      Nicolas Patry authored
      
      Co-Authored-By: default avatarVictor Sanh <victorsanh@gmail.com>
      
      
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      Co-authored-by: default avatarVictor Sanh <victorsanh@gmail.com>
      bce5e224
  20. 24 Jul, 2023 1 commit
  21. 21 Jul, 2023 1 commit
    • Nicolas Patry's avatar
      feat(server): Add exllama GPTQ CUDA kernel support #553 (#666) · d5b5bc75
      Nicolas Patry authored
      Just trying to get the integration tests to pass.
      
      
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      Co-authored-by: default avatarFelix Marty <9808326+fxmarty@users.noreply.github.com>
      d5b5bc75
  22. 03 Jul, 2023 1 commit
  23. 30 Jun, 2023 1 commit
  24. 08 Jun, 2023 1 commit
  25. 02 Jun, 2023 1 commit
  26. 31 May, 2023 2 commits
  27. 30 May, 2023 1 commit
  28. 26 May, 2023 1 commit
  29. 23 May, 2023 2 commits
  30. 22 May, 2023 1 commit