* draft of usage stats
* fix wrong link
* launcher doesn't need sysinfo dep
* only tokenizer class instead of hole struct
* unused import
* fix clippy errors
* update openAPI doc
* cargo fmt
* fix error in passing flags to router
* try again to update docs
* run pre-commit locally
* Update router/src/main.rs
Co-authored-by: Hugo Larcher <hugo.larcher@huggingface.co>
* Update router/src/main.rs
Co-authored-by: Hugo Larcher <hugo.larcher@huggingface.co>
* on crash use anonymous error event
* delete json_output and ngrok
* more robust way of checking if is in container
* more robust nvidia smi
* parse xpu more robustly
* fix errors
* add nvidia-smi details in docs
* cargo fmt
* fix clippy
* should make docs check pass
* Update router/src/usage_stats.rs
Co-authored-by: Hugo Larcher <hugo.larcher@huggingface.co>
* error reason can't be in nested json
* cargo fmt
---------
Co-authored-by: Hugo Larcher <hugo.larcher@huggingface.co>
Co-authored-by: Erik Kaunismäki <erikkaum@Eriks-MacBook-Pro.local>
* feat: add pre commit step to force schema update when router changes
* fix: prefer improved update_doc and start server and compare
* fix: adjust typo
* fix: adjust revert typo
* fix: update workflow to use update_doc md command
* feat: improve workflow to check openapi schema too
* fix: adjust timeout for CI
* fix: adjust raise condition and install server in ci
* fix: install protoc before server
* feat: improve update doc and add command to print router schema
* fix: adjust autodoc workflow
* fix: explicitly install protoc and python
* fix: alllow trailing space in openapi schema diff
* Using flash decoding
Conditional flashdecoding.
Fix max_q.
Working kvcache
Working version with flash decoding.
Make it work for mistral.
Fix after rebase..
Less intrusive.
REvert changes in modeling.
Speedup flashdecoding.
HHachweew
Hack to make other models work.
Fixing non flash decoding llama path.
Router logic knows about page size.
Missing 2 models.
Missing cohere.
Fixing cohere flash decoding.
Revamped all this architecture.
Fix cohere.
Fixing falcon.
Enabling custom block size schedule.
Update router/src/infer.rs
Not sending preallocated output.
* Making it work on non flash decoding.
* Fix Cohere.
* Fix non decoding paths.
* Rebased.
* No need for cache_manager anymore.
* Update?
* "ipex" -> "cpu"
* These do not belong.
* Factoring cu_seqlen_qk for better abstracting over every model.
* Fixing non flash tests/imports.
* Changing return everywhere.
* Update mistral past.
* Fixing Mi{s,x}tral (non functional in Flash Decoding mode though).
* Fixup mistral clamping (had issues with cuda graphs).
* No need to recreate anything actually.
* fix microsoft/Phi-3-mini-4k-instruct crash in batch.slots[batch.slot_indices]
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* Apply suggestions from code review
---------
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
* fix: refactor post_processor logic and add test
* fix: remove dev comment
* fix: adjust when post_processor is overridden and improve create_post_processor
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>
* feat: add kserve feature and basic routes
* feat: implement infer endpoint wrapper around generate
* fix: refactor and improve types
* fix: improve infer and simplify
* fix: cleanup and improve api docs
* fix: refactor and encapsulate kserve feat in file
* fix: remove typos after rebase
# What does this PR do?
Making `make install` a much better sane default to start local dev
environments.
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- 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.
# What does this PR do?
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- Axum upgraded to hyper 1.0 and most of the ecosystem switched so it's
our time now
- [ngrok-rust](https://github.com/ngrok/ngrok-rust/pull/137/files)
hasn't yet, and hasn't for several months now, so let's disabled the
feature for the time being.
# What does this PR do?
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This PR loads the `processor_config` similar to the `tokenizer_config`
and uses the processor_config's chat_template if the tokenizer_config
does not include one. These changes enable chat with idefics2
- Added a debug log for speculated ids (helps seeing in logs quality of
a speculator).
- Remove newlines from child process logs when re-emitting in non JSON
mode.
- Made standard level be closer to what's expected (only our binaries
level).
- Propagate that level correctly to the shard (was forced into INFO).
# What does this PR do?
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# What does this PR do?
- Add the stop parameter to the completion route
- Add the completion method to the python client
- Add the stop parameter to the python client's chat method
## Before submitting
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other checks if that's the case).
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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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Here are the
[documentation
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@Narsil
---------
Co-authored-by: Thomas SCHILLACI <tschilla@px101.prod.exalead.com>
Co-authored-by: Thomas Schillaci <thomas.schillaci@3ds.com>
# What does this PR do?
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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"{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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This change adds `FlashGPT2ForCausalLM` and wires it up. The model
itself is pretty straightforward, the main difference from other models
is that it uses trained position embeddings and that all weight matrices
are transposed compared to other models (due to the use of Conv1D in the
upstream model).
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Add `router` key in `/info` endpoint and set it to
`env!("CARGO_PKG_NAME")` => so always set to `"text-generation-router"`
in TGI. Happy to change the naming if you think of a better one
(framework? package_name?)
The goal is to use this information in `InferenceClient` to know the
model is served with TGI. At the moment we can use
https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2/info
to infer it is TGI-served because it returns information but having a
proper key would be better.
For context, a transformers-served model is only outputting `{"ok":
"ok"}` (see
[here](https://api-inference.huggingface.co/models/microsoft/DialoGPT-large/info)).
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This PR allows for messages to be formatted as simple strings, or as an
array of objects including image urls. This is done by formatting
content arrays into a simple string.
Example using `llava-hf/llava-v1.6-mistral-7b-hf`
```bash
curl localhost: 3000/v1/chat/completions \
-X POST \
-H 'Content-Type: application/json' \
-d '{
"model": "tgi",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "Whats in this image?"
},
{
"type": "image_url",
"image_url": {
"url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png"
}
}
]
}
],
"stream": false,
"max_tokens": 20,
"seed": 42
}'
```
is equivlant to this more simple request
```bash
curl localhost: 3000/v1/chat/completions \
-X POST \
-H 'Content-Type: application/json' \
-d '{
"model": "tgi",
"messages": [
{
"role": "user",
"content": "Whats in this image?\n"
}
],
"stream": false,
"max_tokens": 20,
"seed": 42
}'
```
output
```
# {"id":"","object":"text_completion","created":1714406985,"model":"llava-hf/llava-v1.6-mistral-7b-hf","system_fingerprint":"2.0.1-native","choices":[{"index":0,"message":{"role":"assistant","content":" This is an illustration of an anthropomorphic rabbit in a spacesuit, standing on what"},"logprobs":null,"finish_reason":"length"}],"usage":{"prompt_tokens":2945,"completion_tokens":20,"total_tokens":2965}}%
```
---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
# What does this PR do?
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`/v1/chat/completions` and `/v1/completions` have different output types
depending on the `stream` parameter. This PR aims at fixing the
inconsistency in the auto-generated
[openapi.json](https://huggingface.github.io/text-generation-inference/openapi.json)
specs.
cc @OlivierDehaene @drbh I reused what had been done for the `/`
endpoint but haven't tested anything myself. Could you confirm this is
the correct way of handling things?
Also, should I update the openapi.json file manually? If yes, how can I
do it?
This PR makes tool calling aware of the name of the function selected.
Fixes:
https://github.com/huggingface/text-generation-inference/issues/1657
Thank you @puppetm4st3r for the helpful snippets, large parts of this PR
are simply refactors of the code shared 🙏
**opening draft PR because small tweaks are needed before merging
# 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 PR resolves a couple
- [X] adjusts the tool response to align with openai's tools response
type
- [X] bumps pydantic to `2.6.4` in all apps (resolves dependency issue
when running tests)
- [X] bump `outlines` version and fix import for new name
Fix a small inconsistency compared the OpenAI's chat-completion behavior
(introduced in
https://github.com/huggingface/text-generation-inference/pull/1427 cc
@drbh). When using `stream=True`, each chunk has an `index` value in
`ChatCompletionChoice`. This index is not meant to be the index of the
generated token but the index of the choice, which is always 0 (since
TGI always return a single choice).
See https://platform.openai.com/docs/api-reference/chat/object:
> index _integer_
> The index of the choice in the list of choices.
---
So instead of
```js
data:{"id":"","object":"text_completion","created":1710508199,"model":"HuggingFaceH4/zephyr-7b-beta","system_fingerprint":"1.4.3-sha-e6bb3ff","choices":[{"index":1,"delta":{"role":"assistant","content":"I"},"logprobs":null,"finish_reason":null}]}
data:{"id":"","object":"text_completion","created":1710508199,"model":"HuggingFaceH4/zephyr-7b-beta","system_fingerprint":"1.4.3-sha-e6bb3ff","choices":[{"index":2,"delta":{"role":"assistant","content":"'"},"logprobs":null,"finish_reason":null}]}
data:{"id":"","object":"text_completion","created":1710508199,"model":"HuggingFaceH4/zephyr-7b-beta","system_fingerprint":"1.4.3-sha-e6bb3ff","choices":[{"index":3,"delta":{"role":"assistant","content":"m"},"logprobs":null,"finish_reason":"length"}]}
```
if should return
```js
data:{"id":"","object":"text_completion","created":1710508199,"model":"HuggingFaceH4/zephyr-7b-beta","system_fingerprint":"1.4.3-sha-e6bb3ff","choices":[{"index":0,"delta":{"role":"assistant","content":"I"},"logprobs":null,"finish_reason":null}]}
data:{"id":"","object":"text_completion","created":1710508199,"model":"HuggingFaceH4/zephyr-7b-beta","system_fingerprint":"1.4.3-sha-e6bb3ff","choices":[{"index":0,"delta":{"role":"assistant","content":"'"},"logprobs":null,"finish_reason":null}]}
data:{"id":"","object":"text_completion","created":1710508199,"model":"HuggingFaceH4/zephyr-7b-beta","system_fingerprint":"1.4.3-sha-e6bb3ff","choices":[{"index":0,"delta":{"role":"assistant","content":"m"},"logprobs":null,"finish_reason":"length"}]}
```
**EDIT:** I also edited ToolCall.index to be always `0` (instead of the
generated token index) but for this one I'm actually unsure. It might be
the index of the tool in the array of tools? OpenAI's documentation
doesn't provide any information about it:
> index _integer_
---
I also noticed that in OpenAI's example, the last chunk doesn't have a
delta and is the only one that has a `finish_reason` returning. TGI is
slightly different since the last chunk has both the last delta (i.e.
the last generated token) + the finish reason. I don't think this is
worth fixing since it is not a requirement according to the docs/specs
(at least not that I know of).
# 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
# What does this PR do?
Literally just adds the name field to the Message class.
I verified this change by building a new docker container (using the
`Dockerfile` in the repo) and trialing with a `chat_template` that uses
the `name` field.
Here's the previous behavior:
Input messages:
```
{
"messages": [
{"role": "system", "content": "You are a succinct but helpful AI Assistant listening to a chat server. Address everyone by @<username>"},
{"role": "user", "name": "Aaron", "content": "Hello There!"},
{"role": "assistant", "content": " Hello @Aaron! How can I assist you today?"},
{"role": "user", "name": "Sally", "content": "Hiya everyone. Is @Aaron is this room?"}
],
"model": "meta-llama/Llama-2-7b-chat-hf"
}
```
Response before the modification:
```
Hello @Aaron! Yes, you are in the chat room. How can I assist you today? 😊
Hiya everyone! *waves* It's great to see you all here. Is there something on your mind that you'd like to talk about or ask? I'm here to listen and help in any way I can. 🤖
```
Response after my modification:
```
Hello @Sally! Yes, @Aaron is currently in the chat room. How may I assist you today?
```
Fixes#1558
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## Who can review?
@Narsil
---------
Co-authored-by: Aaron Mihalik <aaron.mihalik@parsons.us>
Co-authored-by: drbh <david.richard.holtz@gmail.com>
This PR adds a simple custom `deserialize_with` function that parses a
string or an object with a content property. This should help support
more token configuration files stored on the hub
This PR adds support to read the `add_generation_prompt` from the config
and use it in the chat template. If `add_generation_prompt` does not
exist we default to false
# What does this PR do?
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This PR fixes the issue with loading a local tokenizer config.
Previously the default functionality would look in the current working
directory. Now if a local model path is specified we will check that
directory for the tokenizer_config.
## Examples of valid commands
uses tokenizer_config from hub
```
text-generation-launcher --model-id HuggingFaceH4/zephyr-7b-beta
```
use tokenizer_config from local model path
```
text-generation-launcher \
--model-id ~/.cache/huggingface/hub/models--HuggingFaceH4--zephyr-7b-beta/snapshots/dc24cabd13eacd3ae3a5fe574bd645483a335a4a/
```
use specific tokenizer_config file
```
text-generation-launcher \
--model-id ~/.cache/huggingface/hub/models--HuggingFaceH4--zephyr-7b-beta/snapshots/dc24cabd13eacd3ae3a5fe574bd645483a335a4a/ \
--tokenizer-config-path ~/.cache/huggingface/hub/models--HuggingFaceH4--zephyr-7b-beta/snapshots/dc24cabd13eacd3ae3a5fe574bd645483a335a4a/tokenizer_config.json
```
---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
# What does this PR do?
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# What does this PR do?
Sending compute type from the environment instead of hardcoded string
Using env is slow, therefore getting it from global state instead.
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drbh
