GitLab

Before this change, the number of reserved image tokens was not the
same as the number of images. Fixes #2029.

While at it, also remove all the image token handling duplication
in `prepare_input`.

* feat: first draft load multiple lora

* feat: load weights within layer and refactor lora pass

* fix: refactor and reduce lora math

* feat: baseline impl single request multi lora support

* feat: prefer lorax implementation and port loading logic

* fix: prefer adapter_data and refactors

* feat: perfer loraxs custom punica kernels and add mlp loras

* fix: adjust batch for bgmv

* fix: adjust adapter_segments logic when in batch

* fix: refactor and move changes to v3 proto

* fix: pass model_id for all flash causal lms

* fix: pass model_id for all causal and seq2seq lms

* fix: add model_id to model test

* feat: add lora support to mistral and refactors

* feat: prefer model id in request

* fix: include rust code for adapter id

* feat: bump launcher and add new lora docs

* feat: support base model generation and refactors

* fix: rename doc to retry ci build

* feat: support if vlm models

* fix: add adapter_data param and avoid missing layers

* fix: add adapter_data param to phi and neox

* fix: update all models forwards to include adapter_data

* fix: add model_id to IdeficsCausalLM

* Update lora.md

Fixed a typo

* Update lora.md

Fixing spam image

* fix: add lora kernel to dockerfile, support running without kernels and refactors

* fix: avoid dockerfile conflict

* fix: refactors and adjust flash llama lora logic

* fix: skip llama test due to CI issue (temp)

* fix: skip llama test CI (temp) 2

* fix: revert skips and prefer updated ci token for tests

* fix: refactors and helpful comments

* fix: add noop in TensorParallelAdapterRowLinear too

* fix: refactor and move shard_lora_weights logic

* fix: exit early if no adapter_data

---------
Co-authored-by: Derek <datavistics@gmail.com>

- Refactor code to allow supporting multiple versions of the
generate.proto at the same time
- Add v3/generate.proto (ISO to generate.proto for now but allow for
future changes without impacting v2 backends)
- Add Schedule trait to abstract queuing and batching mechanisms that
will be different in the future
- Add SchedulerV2/V3 impl

Before this change, the generation input was sent to the backend as a
single string, encoding images as Base64 and packing them in
Markdown-style links.

This change adds a new chunked input representation that separates text
chunks from images chunks. Image chunks contain binary data (for smaller
message sizes) and the image's MIME type.

The stringly-typed inputs are still sent to support backends that do not
support chunked inputs yet.

This PR adds paligemma modeling code

Blog post: https://huggingface.co/blog/paligemma
Transformers PR: https://github.com/huggingface/transformers/pull/30814

install the latest changes and run with
```bash
# get the weights
# text-generation-server download-weights gv-hf/PaliGemma-base-224px-hf

# run TGI
text-generation-launcher --model-id gv-hf/PaliGemma-base-224px-hf
```


basic example sending various requests
```python
from huggingface_hub import InferenceClient

client = InferenceClient("http://127.0.0.1:3000")


images = [
    "https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png",
    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png

",
]

prompts = [
    "What animal is in this image?",
    "Name three colors in this image.",
    "What are 10 colors in this image?",
    "Where is the cow standing?",
    "answer en Where is the cow standing?",
    "Is there a bird in the image?",
    "Is ther a cow in the image?",
    "Is there a rabbit in the image?",
    "how many birds are in the image?",
    "how many rabbits are in the image?",
]

for img in images:
    print(f"\nImage: {img.split('/')[-1]}")
    for prompt in prompts:
        inputs = f"![]({img}){prompt}\n"
        json_data = {
            "inputs": inputs,
            "parameters": {
                "max_new_tokens": 30,
                "do_sample": False,
            },
        }
        generated_output = client.text_generation(prompt, max_new_tokens=30, stream=False)
        print([f"{prompt}\n{generated_output}"])

```

---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>

# What does this PR do?

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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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# 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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This WIP PR starts to add grammar support via outlines, currently this
PR supports very simple regex grammars and does not optimize for
precompiling or caching grammar fsm's.

todo:
- [X] add simple outlines guidance to `NextTokenChooser`
- [X] update protos for grammar
- [X] update generation params API
- [X] constrain simple grammar
- [ ] support parsing more complex grammar into fsm
- [ ] support all outline support grammar types
- [ ] explore optimizations to avoid recompiling grammars

guided request
```bash
curl -s 'http://localhost:3000/generate' \
--header 'Content-Type: application/json' \
--data-raw '{
    "inputs": "make an email for david: \n",
    "parameters": {
        "max_new_tokens": 6,
        "grammar": "[\\w-]+@([\\w-]+\\.)+[\\w-]+"
    }
}' | jq
```
response
```json
{
  "generated_text": "david@example.com"
}
```

unguided request
```bash
curl -s 'http://localhost:3000/generate' \
--header 'Content-Type: application/json' \
--data '{
    "inputs": "make an email for david: \n",
    "parameters": {
        "max_new_tokens": 6
    }
}' | jq
```
response
```json
{
  "generated_text": "    email = 'david"
}
```

# 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 support to make TGI a drop in replacement for OpenAI
clients by exposing the same HTTP interface.

Notes
- TGI inits a single model at startup so the `model` field is unused in
HTTP requests.
- `max_tokens` and `stream` should work as expected but other params may
be (unimplemented or not supported)

General approach
- fetch the `tokenizer_config` at startup from the hub
- pass `tokenizer_config` into `Infer` so we have it at request time
- use the `chat_template` on the config to format chat request
- parse jinja template and render chat string
- pass inputs into existing generate function
- wrap generation output in expected structure before returning

# How to test

### Streaming curl
```bash
curl localhost:3000/v1/chat/completions \
    -X POST \
    -d '{
  "model": "tgi",
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful assistant."
    },
    {
      "role": "user",
      "content": "What is deep learning?"
    }
  ],
  "stream": true,
  "max_tokens": 20
}' \
    -H 'Content-Type: application/json'
```


It is also possible to use the `openai` python library and change the
base url

###  🌊 STREAMING REQUEST
```python
from openai import OpenAI

# init the client but point it to TGI
client = OpenAI(
    base_url="http://localhost:3000/v1",
    api_key="not needed for a local LLM"
)

chat_completion = client.chat.completions.create(
    model="tgi",
    messages=[
        {"role": "system", "content": "You are a helpful assistant." },
        {"role": "user", "content": "What is deep learning?"}
    ],
    stream=True
)

# iterate and print stream
for message in chat_completion:
    print(message)

# ChatCompletionChunk(id='', choices=[Choice(delta=ChoiceDelta(content=' that', function_call=None, role='assistant', tool_calls=None), finish_reason=None, index=2, logprobs=None)], created=1704486761, model='', object='text_completion', system_fingerprint='')
```

### 🚗 SYNCHRONOUS REQUEST
```python
from openai import OpenAI

# init the client but point it to TGI
client = OpenAI(
    base_url="http://localhost:3000/v1",
    api_key="not needed for a local LLM"
)

chat_completion = client.chat.completions.create(
    model="tgi",
    messages=[
        {"role": "system", "content": "You are a helpful assistant." },
        {"role": "user", "content": "What is deep learning?"}
    ],
    stream=False
)

print(chat_completion)
# ChatCompletion(id='', choices=[Choice(finish_reason=None, index=0, logprobs=None, message=ChatCompletionMessage(content='\nDeep learning is a new field of research that has been gaining traction in the last ...', role='assistant', function_call=None, tool_calls=None))], created=1704486762, model='', object='text_completion', system_fingerprint='', usage=CompletionUsage(completion_tokens=100, prompt_tokens=76, total_tokens=176))
```


## How to run dev

```bash
cd text-generation-inference/server
MASTER_ADDR=127.0.0.1 MASTER_PORT=5555 text-generation-server serve --trust-remote-code gpt2
```

***note many of the existing `chat_templates` use non standard `jinja`
(ie. adding a `raise` to the template) which will throw an error when
parsing; hence using `upstage/SOLAR-10.7B-Instruct-v1.0` since it has a
valid template
```bash
cd text-generation-inference/router
cargo run -- --tokenizer-name upstage/SOLAR-10.7B-Instruct-v1.0
```

trigger
```bash
curl localhost:3000/v1/chat/completions \
    -X POST \
    -d '{ "model": "gpt-3.5-turbo", "messages": [ { "role": "system", "content": "You are a helpful assistant." }, { "role": "user", "content": "What is the IP address of the Google DNS servers?" } ], "stream": true, "max_tokens": 20, "logprobs": true }' \
    -H 'Content-Type: application/json'
```

^ supports `stream: true` and `stream: false` requests

@oOraph

---------
Signed-off-by: Raphael Glon <oOraph@users.noreply.github.com>
Co-authored-by: Raphael Glon <oOraph@users.noreply.github.com>

Close #1225

See #1049

---------
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Co-authored-by: Wang, Yi <yi.a.wang@intel.com>

# 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?

Upgrade all relevant versions and dependencies.

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---------
Co-authored-by: Vincent Brouwers <vincent.brouwers@ing.com>

# 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?
- [ ] 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.
- [ ] Did you make sure to update the documentation with your changes?
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
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Close #288

Co-authored-by: OlivierDehaene <23298448+OlivierDehaene@users.noreply.github.com>
Co-authored-by: OlivierDehaene <olivier@huggingface.co>

Co-authored-by: Nick Hill <nickhill@us.ibm.com>

closes #189

closes #111

closes #112