# What does this PR do?
compliation -> compilation
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# What does this PR do?
- Renamed `max_input_length` into `max_input_tokens` for consistency
(backward compatible change, will yell if both are set.)
- Will now use the config for `max_input_tokens` `max_total_token` and
`max_batch_total_tokens`.
- Capping the values to 16k in order to save VRAM on behalf of users
(overriddable by simply setting the values).
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---------
Co-authored-by: Dong Shin <d0104.shin@gmail.com>
# What does this PR do?
- Changed all models to extract `embed_tokens` in order to enable llava
to separately call the embeddings and the core model layers.
- Added VlmCausalLM to inherit from FlashMistral in order to be
maximally supported. The only added logics sits on top and parses images
into pixel values, preallocates input_ids space for the image
embeddings, and passes them for the model.
- Added Clip for the vision tower.
- Didn't add flash for the vision tower since there's no padding anyway.
- Added heuristic (potentially incomplete) to calculate number of
features *before* calculating the clip patches (allows for easier logic
reuse of the LLM under the hood).
Still needs to be done:
- [x] Implement the image parsing in the controller side, to avoid
downloading n times per TP shard and also refusing requests too large
early and avoid issues where the truncation actually truncates the
image.
- [ ] Make sure it works with quantization properly.
- [x] Make sure it works with TP>1
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# What does this PR do?
```
text-generation-launcher --model-id XXX # Uses cuda graphs by default
text-generation-launcher --model-id XXX --cuda-graphs "1,2" #Restrict the number of cuda graphs which saves VRAM
text-generation-launcher --model-id XXX --cuda-graphs "0" # Disabling it entirely
```
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## Before submitting
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Signed-off-by: Sachin Varghese <sachin.mathew31@gmail.com>
Falcon models are long superseded by better models like Zephyr and
OpenHermes. This PR updates the docs accordingly
# What does this PR do?
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# What does this PR do?
It was meant to be in seconds float
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This work in progress PR begins to add support for tools. Tools relies
on grammar support and still has some unsolved challenges. Opening the
PR for visibility and feedback
update messages api docs and add Hugging Face Inference Endpoints
integrations section/instructions
---------
Co-authored-by: Philipp Schmid <32632186+philschmid@users.noreply.github.com>
# What does this PR do?
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---------
Co-authored-by: Andres Restrepo <andres@thelinuxkid.com>
# What does this PR do?
Ideally this is done client side, but this is a recurring request,
therefore we implemented it.
- Runs only if rust tokenizer is present (not encumbering the main
inference pipeline is important).
- Returns simple results, ID, text (gotten with offsets from the
original string) and offsets (so users can do things like highlighting
text).
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This PR adds a new page to the docs that describes the Messages API and
how to use it.
Additionally this page will contain cloud provider specific information
for enabling and using this feature. This PR includes a SageMaker
example/information.
This PR adds support for AMD Instinct MI210 & MI250 GPUs, with paged
attention and FAv2 support.
Remaining items to discuss, on top of possible others:
* Should we have a
`ghcr.io/huggingface/text-generation-inference:1.1.0+rocm` hosted image,
or is it too early?
* Should we set up a CI on MI210/MI250? I don't have access to the
runners of TGI though.
* Are we comfortable with those changes being directly in TGI, or do we
need a fork?
---------
Co-authored-by: Felix Marty <felix@hf.co>
Co-authored-by: OlivierDehaene <olivier@huggingface.co>
Co-authored-by: Your Name <you@example.com>
# What does this PR do?
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# What does this PR do?
Now clients which do not specify a max_length will be implying
`max_new_tokens = max_total_tokens - input_length`.
This is a serious change, but which seems more in line with what users
expect from standing server.
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---------
Co-authored-by: OlivierDehaene <olivier@huggingface.co>
# What does this PR do?
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<!-- Remove if not applicable -->
Just removed `--` from the arguments.
With `--` bitsandbytes and bitsandbytes-nf4 are considered an option
which they are not
## Before submitting
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other checks if that's the case).
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Adding a new line to escape between heading and codeblock. However, it
is a hotfix and I will work on a permanent solution on
https://github.com/huggingface/doc-builder
I had to open this PR since I initially worked from my fork, and it
requires a handful of work to trigger a new github action on my fork's
specific branch (couldn't find a way, at least, despite trying all of
them).
---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Added note on serving supported models from a different folder without
re-downloading them.
---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
# What does this PR do?
Upgrade all relevant versions and dependencies.
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# 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](f084f40bd9).
* 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
---------
# What does this PR do?
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---------
Co-authored-by: Abhinav M Kulkarni <abhinavkulkarni@gmail.com>
Co-authored-by: Abhinav Kulkarni <abhinav@concentric.ai>
IDK what else to add in this guide, I looked for relevant code in TGI
codebase and saw that it's used in quantization as well (maybe I could
add that?)
PR for conceptual guide on flash attention. I will add more info unless
I'm told otherwise.
---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: Omar Sanseviero <osanseviero@gmail.com>
# What does this PR do?
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List of changes
- No need to specify `model` in `text_generation` if it's already
specified in `InferenceClient`
- I separated the explanation of `stream=True` and `details=True`
- I found the details explanation a bit repetitive (it says two times
what it returns), so removed a sentence
- Add mention of async client
# What does this PR do?
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I added ToC for docs v1 & started setting up for doc-builder. cc @Narsil
@osanseviero
---------
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Co-authored-by: osanseviero <osanseviero@gmail.com>
Co-authored-by: Mishig <mishig.davaadorj@coloradocollege.edu>
OlivierDehaene