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
* 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>
* Fix GPTQ autotune data type to be compatible with Torch 2.4.0
* Update poetry lock file
* Fix small PaliGemma logprob differences after the torch update
* fix crash in multi-modal
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* update according to review comment
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* fix llava_next regression in latest main
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
---------
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* Support passing head_dim through config
* Using `head_dim` as a fallback is necessary since it's a non standard
key in mistralConfig (as defined in transformers).
* Shorter diff.
---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
* Add support for repacking AWQ weights for GPTQ-Marlin
So far we couldn't support AWQ because virtually all AWQ models use
symmetric quantization, which GPTQ-Marlin did not suppors. GPTQ-Marlin
has recently added support AWQ repacking and AWQ asymmetric quantization
(zero_point=True).
This change updates all GPTQ-Marlin kernels from upstream and wires up
AWQ support. For now enabling AWQ using Marlin requires running TGI with
`--quantize gptq`.
* Enable Marlin for supported AWQ configurations by default
This makes the AWQ -> GPTQ repack test redundant, since we are now
testing this with the regular AWQ test.
Deepseek V2 is a MoE model from Deepseek. Relevant variations
compared to other models:
- Grouped top-K in expert selection.
- mscale in yarn is calculated using the `mscale` and `mscale_all_dim`
configuration options.
- `mscale_all_dim` is also used in scaling attention softmax.
- Permuting of the query/key representations before applying rotary
embeddings.
- Some projections cannot be sharded (`q_a_proj`, `kv_a_proj_with_mqa`).
So, we need weight loads that supports quantized weights. To this
end `{Weights,WeightLoader}.get_weight` was added.
- The query/key head dimensionality differs from that of the value,
so we need to pad during attention.
- Heads with size 192, needs an extension to our paged attention
fork and we need to ensure that the KV cache is allocated with the
correct size.
- Shared experts.
* 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>
* Improve the handling of quantized weights
Handling of quantized weights was split between two mechanisms:
- For quantized checkpoints, we used the new weight loader
infrastructure.
- For quantization while loading (EETQ, FP8, bitsandbytes) we
instead relied on conditional in `get_linear`.
Weight loaders support context managers to selectively load
particular layers with different weight loaders, which is useful
for models like Idefics2 AWQ, which uses a quantized text model,
but unquantized vision and connector models. However, the context
manager would be overrided by `get_linear`, which string-checks
`quantizer`. Also, the context manager would not work with
EETQ, FP8, and bitsandbytes.
This change migrates all quantizers to the weight loader infrastructure.
This has several benefits:
- We can use context managers with all quantizers.
- All the implementation details move down to the quantizer layers,
`get_linear` does not need to know how to handle quantizer linear
layers.
- All quantizer weights are strongly typed, we don't pass around
raw tensors.
- We don't have to pass around the `quantizer` string everywhere.
* Exclude non-MLP layers when using FP8 quantization with Llama
Nicolas Patry