GitLab

* Move to moe-kernels package and switch to common MoE layer

This change introduces the new `moe-kernels` package:

- Add `moe-kernels` as a dependency.
- Introduce a `SparseMoELayer` module that can be used by MoE
  models.
- Port over Mixtral and Deepseek.

* Make `cargo check` pass

* Update runner

* Fixing odd tokenization self modifications on the Rust side (load and
resave in Python).

* Fixing the builds ?

* Fix the gh action?

* Fixing the location ?

* Validation is odd.

* Try a faster runner

* Upgrade python version.

* Remove sccache

* No sccache.

* Getting libpython maybe ?

* List stuff.

* Monkey it up.

* have no idea at this point

* Tmp.

* Shot in the dark.

* Tmate the hell out of this.

* Desperation.

* WTF.

* -y.

* Apparently 3.10 is not available anymore.

* Updating the dockerfile to make libpython discoverable at runtime too.

* Put back rust tests.

* Why do we want mkl on AMD ?

* Forcing 3.11 ?

* 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>

* All integration tests back everywhere (too many failed CI).

* Upgrade integration tests after 12.4

* Attempt to remove the specifed compute cap.

* Common arch list.

* Punica uses raw ASM which is not valid on 9.0 apparently.

* Further fixes.

* Update the conftest to allow NaN (first logprob).

* Fix the condition.

* Fixing exl2 and other quanize tests again.

* Mark exl2 as non release (so CI tests them, needs to be removed latet).

* Fixing exl2 (by disabling cuda graphs)

* Fix quantization defaults without cuda graphs on exl2 (linked to new
issues with it).

* Removing serde override.

* Go back to released exl2 and remove log.

* Adding warnings for deprecated bitsandbytes + upgrade info to warn.

* Upgrading exl2.

* Fixing the other pathways.

* Fix idefics.

* fix: allocate tmp based on sgmv kernel if available

* fix: re add copy build artifacts step for punica kernels

* 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>

* chore: update to torch 2.4

* remove un-necessary patch

* fix

* feat(fp8): add support for fbgemm

* allow loading fp8 weights directly

* update outlines

* fix makefile

* build fbgemm

* avoid circular import and fix dockerfile

* add default dtype

* refactored weights loader

* fix auto conversion

* fix quantization config parsing

* force new nccl on install

* missing get_weights implementation

* increase timeout

* fix nccl issue

* add note in dockerfile

* use v2.22.3 that also fixes @samsamoa's repro

* poetry actually can't handle the conflict between torch and nccl

* set LD_PRELOAD

* 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>

* Fix cargo-chef prepare

In prepare stage, cargo-chef reads Cargo.lock and transforms it accordingly.
If Cargo.lock is not present, cargo-chef will generate a new one first, which
might vary a lot and invalidate docker build caches.

* Fix Dockerfile_amd and Dockerfile_intel

* Set maximum grpc message receive size to 2GiB

The previous default was 4MiB, which doesn't really work well for
multi-modal models.

* Update to Rust 1.79.0

* Fixup formatting to make PR pass

Add support for GPTQ Marlin kernels

GPTQ Marlin extends the Marlin kernels to support common GPTQ
configurations:

- bits: 4 or 8
- groupsize: -1, 32, 64, or 128
- desc_act: true/false

Using the GPTQ Marlin kernels requires repacking the parameters in the
Marlin quantizer format.

The kernels were contributed by Neural Magic to VLLM. We vendor them
here for convenience.

# What does this PR do?

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      Pull Request section?
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      to it if that's the case.
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This change adds support for Marlin-quantized models. Marlin is an
FP16xINT4 matmul kernel, which provides good speedups decoding batches
of 16-32 tokens. It supports quantized models with symmetric
quantization, groupsize -1 or 128, and 4-bit.

Tested with:

- Llama 2
- Llama 3
- Phi 3

This reverts commit 5aec4154.

Close #2007

# What does this PR do?

Making `make install` a much better sane default to start local dev
environments.

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      Pull Request section?
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      to it if that's the case.
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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"![]({img}){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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      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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Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>

wrap text-generation-launcher in docker image
mask ldconfig failures to user (no need in most cases anyway)

---------
Signed-off-by: Raphael Glon <oOraph@users.noreply.github.com>
Co-authored-by: Raphael Glon <oOraph@users.noreply.github.com>

@Narsil @drbh this will update flash attention v2 and vllm.
You will need to re-install them.

# What does this PR do?

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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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- [ ] Did you write any new necessary tests?


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

This draft PR is a work in progress implementation of the mamba model.
This PR currently loads weights, and produces correct logits after a
single pass.

This PR still needs to correctly integrate this model so it produces
tokens as expected, and apply optimization to avoid all copies during
runtime/unnecessary operations.

#### Helpful resources
[Mamba: Linear-Time Sequence Modeling with Selective State Spaces
(Albert Gu and Tri Dao)](https://arxiv.org/abs/2312.00752)
https://github.com/johnma2006/mamba-minimal

https://github.com/huggingface/candle/blob/main/candle-examples/examples/mamba-minimal/model.rs
https://github.com/huggingface/transformers/pull/28094



Notes: this dev work is currently targeting `state-spaces/mamba-130m`,
so if you want to test please use that model. Additionally when starting
the router the prefill needs to be limited: `cargo run --
--max-batch-prefill-tokens 768 --max-input-length 768`


## Update / Current State

Integration tests have been added and basic functionality such as model
loading is supported.

```bash
cd integration-tests
pytest -vv models/test_fused_kernel_mamba.py
```
- [x] add tests
- [x] load model
- [x] make simple request 
- [ ] resolve warmup issue
- [ ] resolve output issues


fetching models tested during dev
```bash
text-generation-server download-weights state-spaces/mamba-130m
text-generation-server download-weights state-spaces/mamba-1.4b
text-generation-server download-weights state-spaces/mamba-2.8b
```

The server can be run 
```bash
cd server
 MASTER_ADDR=127.0.0.1 MASTER_PORT=5555 python text_generation_server/cli.py serve state-spaces/mamba-2.8b
```

router
```bash
cargo run
```

make a request
```bash
curl -s localhost:3000/generate \
    -X POST \
    -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":20}}' \
    -H 'Content-Type: application/json' | jq
```

response
```json
{
  "generated_text": "\n\nDeep learning is a machine learning technique that uses a deep neural network to learn from data."
}
```

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

This PR adds support for AMD Instinct MI210 & MI250 GPUs, with paged
attention and FAv2 support.

Remaining items to discuss, on top of possible others:
* Should we have a
`ghcr.io/huggingface/text-generation-inference:1.1.0+rocm` hosted image,
or is it too early?
* Should we set up a CI on MI210/MI250? I don't have access to the
runners of TGI though.
* Are we comfortable with those changes being directly in TGI, or do we
need a fork?

---------
Co-authored-by: Felix Marty <felix@hf.co>
Co-authored-by: OlivierDehaene <olivier@huggingface.co>
Co-authored-by: Your Name <you@example.com>

# What does this PR do?

See #1165

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      Pull Request section?
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      to it if that's the case.
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Here are the
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---------
Co-authored-by: Florian Zimmermeister <flozi00.fz@gmail.com>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-24-153.ec2.internal>

Close #1142

# What does this PR do?

Fixes #1079 
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Fixes # (issue)


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [ ] Did you read the [contributor
guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the
[forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes?
Here are the
[documentation
guidelines](https://github.com/huggingface/transformers/tree/main/docs),
and
[here are tips on formatting
docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


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