* initial changes
* Add support for other vlm
* cleanup comment
* Improve attn_implementation
* Add comments for support of models
* add model
* add model
* fixes and improvements
* update docker
* Add cache position
* Add tests
* remove redundant changes
* remove tr version
* Upgrade doc + fix linting.
* Fixing the CI.
---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
* Fixing the tool calling convention.
* Update tehe doc.
* Fixing some corner cases.
* Fixing the tool call id.
* Fmt.
* Snapshot update with the new updated tool_call_id.
* More qwen2.
* change ChatCompletionChunk to align with "OpenAI Chat Completions streaming API"
Moving after tool_calls2
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
add in Buffering..
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
fix: handle usage outside of stream state and add tests
Simplifying everything quite a bit.
Remove the unused model_dump.
Clippy.
Clippy ?
Ruff.
Uppgrade the flake for latest transformers.
Upgrade after rebase.
Remove potential footgun.
Fix completion test.
* Clippy.
* Tweak for multi prompt.
* Ruff.
* Update the snapshot a bit.
---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
* Making `tool_calls` a vector.
* Arguments output is a string.
* Update all the integration tests.
* Add the requirements.
* Upgrade other tests.
* Clippy.
* Update the old test.
* Making `tool_calls` a vector.
* Update doc.
* Fixing the nix overlay with updated version.
* Add openai dependency.
* Updating the old tests.
* Trying to reduce the logs in the case of errors.
* Less spammy logs too.
* Patch rust release.
* Trying to remove the rust-toolchain hardcoded in action.
* Upgrade rust toolchain.
* Put back the toolchain ?
* Fix neuron dockerfile.
* Move to the proper version of Rust.
* 1.85 since the GH action doesn't respect the override.
* Typo.
* Fixing the github action.
* Fixing docker llamacpp.
* Fixing the github action.
* Update clippy.
* make content field optional in chat request
* add tool_calls field to Message struct
* feat: add test and serialize tool messages
* fix: bump utopia, openapi doc version and improve test
* fix: rerun update docs
* fix: suppoer tool call id in template and remove unnecessary changes
* fix: ruff lint remove unused import
* fix: adjust message types in tests
---------
Co-authored-by: sailesh duddupudi <saileshradar@gmail.com>
* Attempt at automatic max batch prefill.
* Taking into account number of shards.
* Adding more cards.
* Adding A100 + H100
* Adding a few more cards.
* Logprobs cost too much.
* h100 better name, and keep factor of 2
* Damn inflated sparse tflops.
* Typo in h100.
* Updated the flops calculation (checked with fvcore).
* chunking by default.
* Fix prefix caching for chat completion since we removed logprobs.
* More tests.
* Dropping all the prefill logprobs.
* Add a flag that enables users to get logprobs back.
* Repairing prompt token counting.
* Fixing a few tests.
* Remove some scaffolding.
* Attempting to reduces the issues (workarounds for now).
* add OpenAI like tool_choice for named choice
* add tests
* fix: run linter and bump api docs
* fix: consolidate changes and remove old tool type
* feat: improve, simplify and rename tool choice struct add required support and refactor
* fix: simplify tool choice logic, improve tests, openapi and rust docs
* fix: refactor away prepare_chat_input and improve tool grammar apply control flow
* feat: update docs and add tool choice configuration section
* fix: simplify naming, tool choice default and improve test
* fix: adjust tool choice none logic, add test and small refactors
* fix: add missing snapshot file
* fix: adjust tool choice type in test
* fix: adjust default when json tool choice is
* fix: remove trailing space lint after rebase
* fix: remove mostly mocked unit test
---------
Co-authored-by: Linus Bierhoff <linus.bierhoff@icloud.com>
* Remove vLLM dependency for CUDA
This change adds `attention-kernels` as a dependency for paged
attention and cache reshaping. With that, we don't use vLLM
anywhere for CUDA.
Tested run (since we don't have paged attention in CI):
```
❯ ATTENTION=paged python -m pytest integration-tests -k "llama and awq" --release
[...]
5 snapshots passed.
```
* Fix clippy warning
* We can have a tokenizer anywhere.
* Handling potential lack of offsets (python tokenizer)
* Remove redundancy.
* Fixing the tests.
* Flake.lock update ?
* Fixing the GIL locking.
* Fixing mamba by using the transformers version.
* Adding the legacy handle.
* Ellide lifetime.
* Lint.
* Deprecation message.
* Fixing bad rebase.
* Stream options.
* Fetch stuff from nix integration test for easier testing.
* Adding the assert.
* Only send the usage when asked for.
* Update the docs.
* Impure test because we need network.
* develop.
* Optional usage.
* Fixes.
* Workflow
* Making prefix/flashinfer the default and testing the full release tests.
* Include flashinfer in the docker.
* Using prebuilt.
* Allowing window_left_size (dummy version).
* Disabling flashinfer/prefix caching on odd head_dim
* Disable prefix caching for lora.
* More specific codes.
* Update lock
* Updating integration tests with new values with FI/FD.
Remove paged as a default too, and using FD everywhere.
* Update cargo lock ?
* Upgrade to 1.80 because of bitstream...
* Everywhere 1.80
* Forgot last default place.
* Apply suggestions from code review
Co-authored-by: drbh <david.richard.holtz@gmail.com>
* Updated flake lock
* Tmp
* Upgrade resolution system for less errors in resolution.
* Remove lambda for cleaner function.
* Handling debugger.
* OVerride the env in server tests.
* Is this enough to make it work ?
* This seems to be working.
* Downgrade some logs.
* Fixing the default for vlm.
* Don't enable prefix caching on VLM just yet.
* Change `add_special_tokens` in order to have the correct tokens for chat
input and not (since it's super important with the prefixing now)
* Fixing prefix caching for flashdecoding.
* Update all models.
* Fixed flashinfer version.
* add_special_tokens is internal only
* Fixing seqlen with the new vlms.
* Fixing the issue with `add_special_tokens` not being passed around.
* Fixing the test.
* Removing encoder_decoder (seq2seq).
* Update the chat test.
* Fixing the batching tokenization in flash causal lm.
* Truncating left for radix purposes.
* Oops this doesn't belong here.
* Put back default pure shell.
* Update server tests
- Default to throughput test in k6
- Use TGI_WIGGLE_ROOM to adjust wiggle room
* Only n_heads / process_group.size() are necessary.
* Revert the integrationt tests change (seem linked to head_size
modification).
* Adding error message when assert is violated.
* Fixing the free algorithm to handle times where the common prefix is
smaller.
* Apply suggestions from code review
Co-authored-by: OlivierDehaene <olivier@huggingface.co>
* Update server/text_generation_server/layers/attention/common.py
Co-authored-by: OlivierDehaene <olivier@huggingface.co>
* Fix disabling prefix caching - Fix windowing checks.
* Revert the Cohere tokenizer change (for now using a revision instead).
* Fmt.
---------
Co-authored-by: drbh <david.richard.holtz@gmail.com>
Co-authored-by: OlivierDehaene <olivier@huggingface.co>
* fix: improve completions to send a final chunk with usage details
* fix: include finish reason string
* fix: remove dev debug trait and unneeded mut
* fix: update openapi schema
* 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>
* 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>
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>
