* feat: implement a templated endpoint for visibility into chat requests
* feat: improve to tokenize too
* fix: adjust return type
* feat: simplify prepare_chat_input logic and adjust start stop chars
* wip
wip
refacto
refacto
Initial setup for CXX binding to TRTLLM
Working FFI call for TGI and TRTLLM backend
Remove unused parameters annd force tokenizer name to be set
Overall build TRTLLM and deps through CMake build system
Enable end to end CMake build
First version loading engines and making it ready for inference
Remembering to check how we can detect support for chunked context
Move to latest TensorRT-LLM version
Specify which default log level to use depending on CMake build type
make leader executor mode working
unconditionally call InitializeBackend on the FFI layer
bind to CUDA::nvml to retrieve compute capabilities at runtime
updated logic and comment to detect cuda compute capabilities
implement the Stream method to send new tokens through a callback
use spdlog release 1.14.1 moving forward
update trtllm to latest version a96cccafcf6365c128f004f779160951f8c0801c
correctly tell cmake to build dependent tensorrt-llm required libraries
create cmake install target to put everything relevant in installation folder
add auth_token CLI argument to provide hf hub authentification token
allow converting huggingface::tokenizers error to TensorRtLlmBackendError
use correct include for spdlog
include guard to build example in cmakelists
working setup of the ffi layer
remove fmt import
use external fmt lib
end to end ffi flow working
make sure to track include/ffi.h to trigger rebuild from cargo
impl the rust backend which currently cannot move the actual computation in background thread
expose shutdown function at ffi layer
impl RwLock scenario for TensorRtLllmBackend
oops missing c++ backend definitions
compute the number of maximum new tokens for each request independently
make sure the context is not dropped in the middle of the async decoding.
remove unnecessary log
add all the necessary plumbery to return the generated content
update invalid doc in cpp file
correctly forward back the log probabilities
remove unneeded scope variable for now
refactor Stream impl for Generation to factorise code
expose the internal missing start/queue timestamp
forward tgi parameters rep/freq penalty
add some more validation about grammar not supported
define a shared struct to hold the result of a decoding step
expose information about potential error happening while decoding
remove logging
add logging in case of decoding error
make sure executor_worker is provided
add initial Dockerfile for TRTLLM backend
add some more information in CMakeLists.txt to correctly install executorWorker
add some more information in CMakeLists.txt to correctly find and install nvrtc wrapper
simplify prebuilt trtllm libraries name definition
do the same name definition stuff for tensorrt_llm_executor_static
leverage pkg-config to probe libraries paths and reuse new install structure from cmake
fix bad copy/past missing nvinfer linkage direction
align all the linker search dependency
add missing pkgconfig folder for MPI in Dockerfile
correctly setup linking search path for runtime layer
fix missing / before tgi lib path
adding missing ld_library_path for cuda stubs in Dockerfile
update tgi entrypoint
commenting out Python part for TensorRT installation
refactored docker image
move to TensorRT-LLM v0.11.0
make docker linter happy with same capitalization rule
fix typo
refactor the compute capabilities detection along with num gpus
update TensorRT-LLM to latest version
update TensorRT install script to latest
update build.rs to link to cuda 12.5
add missing dependant libraries for linking
clean up a bit
install to decoder_attention target
add some custom stuff for nccl linkage
fix envvar CARGO_CFG_TARGET_ARCH set at runtime vs compile time
use std::env::const::ARCH
make sure variable live long enough...
look for cuda 12.5
add some more basic info in README.md
* Rebase.
* Fix autodocs.
* Let's try to enable trtllm backend.
* Ignore backends/v3 by default.
* Fixing client.
* Fix makefile + autodocs.
* Updating the schema thing + redocly.
* Fix trtllm lint.
* Adding pb files ?
* Remove cargo fmt temporarily.
* ?
* Tmp.
* Remove both check + clippy ?
* Backporting telemetry.
* Backporting 457fb0a1
* Remove PB from git.
* Fixing PB with default member backends/client
* update TensorRT-LLM to latest version
* provided None for api_key
* link against libtensorrt_llm and not libtensorrt-llm
---------
Co-authored-by: OlivierDehaene <23298448+OlivierDehaene@users.noreply.github.com>
Co-authored-by: Morgan Funtowicz <morgan@huggingface.co>
* Add API_Key for Auth and conditionally add authorisation for non info/health endpoints.
* change name to info routes
* Fix comment
* convert strings to lowercase for case insensitive comparison
* convert header to string
* fixes and update docs
* update docs again
* revert wrong update
---------
Co-authored-by: Kevin Duffy <kevin.duffy94@gmail.com>
* 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
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
- 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
# 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
# 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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@Narsil
---------
Co-authored-by: Thomas SCHILLACI <tschilla@px101.prod.exalead.com>
Co-authored-by: Thomas Schillaci <thomas.schillaci@3ds.com>
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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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`/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
- 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).
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
