GitLab

* Choosing input/total tokens automatically based on available VRAM?

* Update doc.

* Remove generated files.

* Trying to fix non chunking targets.

* Attempt #2

* fix.

* QuantLinear is rocm compatible.

* Much simpler logic after the overhead.

* Updating logic + non flash.

* Revert doc text.

* Simple updates.

* Fix integration mt0 (transformers update).

* wip

* rollback

* refactor to use prefix/postfix namming + fix all_input_ids_tensor

* maybe patching vlms?

* fix filter and concat

* wip, no filter, no concat

* current

* add prepare_for_prefill

* working

* load tested

* re-create slots

* re-create slots

* fix slot_filtering_indices

* feedback loop

* remove log

* fix benchmarker

* fix vlm and seq2seq

* rename to cache and input lengths

* fix prefill logprobs

* fix launcher

* fix logprobs?

* idk at this point

* max input length

* omfg

* remove debugging lines

* fix tests

* fix mllama

* fix cargo tests

* remove support chunking for paged

* Fixing non blocked attentions

* Fixing dtype + AMD, Ipex targets.

* lint fix.

* rename

* Fix prefix_caching variable, remove defaults in server (confusing a lot
of the times).

* Add simple resolution when user specifies ATTENTION=paged.

* Put back non default simple tests.

* Fix env name

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

* adding max_token_capacity_metric

* added tgi to name of metric

* Adding max capacity metric.

* Add description for the metrics

---------
Co-authored-by: Edwinhr716 <Edandres249@gmail.com>

Co-authored-by: Daniël de Kok <me@danieldk.eu>

This change adds support for prefix caching to the v3 router. This
is broken up from the backend support to ease reviewing.

For now prefix caching is only enabled with `USE_PREFIX_CACHING=1`
in this case, the router will switch to `RadixAllocator`. This
allocator uses a radix trie to keep track of prefills that were
seen prior. If a new prefill is a prefix of a previously-seen
prefil, the router will send a request with `prefix_len>0`, which
can be used by the backend to decide to reuse KV blocks from the
cache, rather than recomputing them.

Even though backend support is not added in this PR, the backend
will still work with prefix caching enabled. The prefix lengths
are just ignored and not used.

* 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>