GitLab

* Update Quantization docs and minor doc fix.

* update readme with latest quants info

* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* up

---------
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* add gptj modeling
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>

* fix: update docs for model addition

* fix: adjust syntax typo

* fix: adjust syntax typo again

---------
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Co-authored-by: Wang, Yi A <yi.a.wang@intel.com>

* refactor usage stats

* Update docs/source/usage_statistics.md
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>

* Update router/src/server.rs
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>

* changes based on feedback

* run python3 udpate_doc.py

* fix pre-commit

* Update router/src/server.rs
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>

* delete option around usage stats arg

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

* 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>

* Preparing for release.

* Updating docs.

* Fixing token within the docker image for the launcher.

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.

quick fix

* 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>

* Updating the self check

* Fix.

* Revert the CLI .

* cli.

* Space.

* Revert cargo update.

Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>

* Refactor dead code.

* First working step.

* Remove a lot of duplicated code.

* More dead code.

* More cleanup.

* Fix Santacoder test.

* Fixing the simple tests.

* Fixing sharding.

* Fixes for VLM.

* Fixing santacoder (num_kv_heads hardcoded).

* Removing more dead code.

* Fixing `config.n_head`.

* Stopping earlier because of `<end_of_utterance>` in idefics2.

* Addresses comments.

* Removing the dead code.

* Fuse back mistral into FlashCausalLM.

* Finish removal.

* Fixing docs + causal_lm `batch_class`.

* Fixing docs + causal.lm.

* Add default to Gemma Causality.

* Default value for gemma/gemma2.

* Wrong default.

* Fixing gemma2.

* Adding new model.

* 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>

* Adding Service Name Environment variable for https://github.com/huggingface/text-generation-inference/issues/2069

* Update Docs

* Update README.md

* Update Launcher Docs

* Update Launcher Docs
Removing Option

* Support HF_TOKEN environement variable

* Load test.

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

* doc: adding architecture document

* doc: add architecture to toctree

* fix: avoid cargo lock changes

* fix: avoid cargo lock tweak

---------
Co-authored-by: drbh <david.richard.holtz@gmail.com>

* Update the link for qwen2

* Fix Qwen2 model URL in model table

* Fix too eager staging

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

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

# What does this PR do?

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PR #1986 moved the location of the `flash_attn_triton.py` file. This PR
adjusts sources to changes.


## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
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      Pull Request section?
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Signed-off-by: Emmanuel Ferdman <emmanuelferdman@gmail.com>

# What does this PR do?

I think this will fix the docs from being weirdly formatted. All the
sections after MAX_TOP_N_TOKENS don't show up in the bar on the right
(https://huggingface.co/docs/text-generation-inference/basic_tutorials/launcher#maxtopntokens)


## Before submitting
- [x] 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?
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[forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
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- [ ] Did you write any new necessary tests?


## Who can review?

@merveenoyan

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

As per title

cc @Narsil

Mostly straightforward, changes to existing code:

* Wrap quantizer parameters in a small wrapper to avoid passing
  around untyped tuples and needing to repack them as a dict.
* Move scratch space computation to warmup, because we need the
  maximum input sequence length to avoid allocating huge
  scratch buffers that OOM.

- 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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Fixes # (issue)


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
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      Pull Request section?
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      to it if that's the case.
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# What does this PR do?

Fix a typo; fix a broken link; add one sentence in the guidance docs to
make the word "grammar" less abstract


## Before submitting
- [x] 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
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- [ ] Did you write any new necessary tests?


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@drbh

This PR adds a tutorial to self distill and train medusa heads for a
specific model

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

# What does this PR do?

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Fixes # (issue)


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      Pull Request section?
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      to it if that's the case.
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Fixes an incorrect url in monitoring doc.

## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
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guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      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
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- [ ] Did you write any new necessary tests?


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As per title.

Triton autotune overhead is prohibitive, as it needs to be done for each
different prompt length.

cc @Narsil

As per title. It is very useful.

Adds support for AMD Instinct MI300 in TGI.

Most changes are:
* Support PyTorch TunableOp to pick the GEMM/GEMV kernels for decoding
https://github.com/pytorch/pytorch/tree/main/aten/src/ATen/cuda/tunable.
TunableOp is disabled by default, and can be enabled with
`PYTORCH_TUNABLEOP_ENABLED=1`.
* Update ROCm dockerfile to PyTorch 2.3 (actually patched with changes
from https://github.com/pytorch/pytorch/pull/124362)
* Support SILU & Linear custom kernels contributed by AMD
* Update vLLM paged attention to https://github.com/fxmarty/rocm-vllm/,
branching out of a much more recent commit
https://github.com/ROCm/vllm/commit/3489ce7936c5de588916ae3047c44c23c0b0c308


* Support FA2 Triton kernel as recommended by AMD. Can be used by
specifying `ROCM_USE_FLASH_ATTN_V2_TRITON=1`.
* Update dockerfile to ROCm 6.1

By default, TunableOp tuning results are saved in `/data` (e.g.
`/data/tunableop_meta-llama-Llama-2-70b-chat-hf_tp1_rank0.csv`) in order
to avoid to have to rerun the tuning at each `docker run`.

Example:
```
Validator,PT_VERSION,2.3.0
Validator,ROCM_VERSION,6.1.0.0-82-5fabb4c
Validator,HIPBLASLT_VERSION,0.7.0-1549b021
Validator,GCN_ARCH_NAME,gfx942:sramecc+:xnack-
Validator,ROCBLAS_VERSION,4.1.0-cefa4a9b-dirty
GemmTunableOp_Half_TN,tn_8192_7_28672,Gemm_Rocblas_45475,0.132098
GemmTunableOp_Half_TN,tn_10240_4_8192,Gemm_Rocblas_45546,0.0484431
GemmTunableOp_Half_TN,tn_32000_6_8192,Default,0.149546
GemmTunableOp_Half_TN,tn_32000_3_8192,Gemm_Rocblas_45520,0.147119
GemmTunableOp_Half_TN,tn_8192_3_28672,Gemm_Rocblas_45475,0.132645
GemmTunableOp_Half_TN,tn_10240_3_8192,Gemm_Rocblas_45546,0.0482971
GemmTunableOp_Half_TN,tn_57344_5_8192,Gemm_Rocblas_45520,0.255694
GemmTunableOp_Half_TN,tn_10240_7_8192,Gemm_Rocblas_45517,0.0482522
GemmTunableOp_Half_TN,tn_8192_3_8192,Gemm_Rocblas_45546,0.0444671
GemmTunableOp_Half_TN,tn_8192_5_8192,Gemm_Rocblas_45546,0.0445834
GemmTunableOp_Half_TN,tn_57344_7_8192,Gemm_Rocblas_45520,0.25622
GemmTunableOp_Half_TN,tn_8192_2_28672,Gemm_Rocblas_45475,0.132122
GemmTunableOp_Half_TN,tn_8192_4_8192,Gemm_Rocblas_45517,0.0453191
GemmTunableOp_Half_TN,tn_10240_5_8192,Gemm_Rocblas_45517,0.0482514
GemmTunableOp_Half_TN,tn_8192_5_28672,Gemm_Rocblas_45542,0.133914
GemmTunableOp_Half_TN,tn_8192_2_8192,Gemm_Rocblas_45517,0.0446516
GemmTunableOp_Half_TN,tn_8192_1_28672,Gemm_Hipblaslt_TN_10814,0.131953
GemmTunableOp_Half_TN,tn_10240_2_8192,Gemm_Rocblas_45546,0.0481043
GemmTunableOp_Half_TN,tn_32000_4_8192,Gemm_Rocblas_45520,0.147497
GemmTunableOp_Half_TN,tn_8192_6_28672,Gemm_Rocblas_45529,0.134895
GemmTunableOp_Half_TN,tn_57344_2_8192,Gemm_Rocblas_45520,0.254716
GemmTunableOp_Half_TN,tn_57344_4_8192,Gemm_Rocblas_45520,0.255731
GemmTunableOp_Half_TN,tn_10240_6_8192,Gemm_Rocblas_45517,0.0484816
GemmTunableOp_Half_TN,tn_57344_3_8192,Gemm_Rocblas_45520,0.254701
GemmTunableOp_Half_TN,tn_8192_4_28672,Gemm_Rocblas_45475,0.132159
GemmTunableOp_Half_TN,tn_32000_2_8192,Default,0.147524
GemmTunableOp_Half_TN,tn_32000_5_8192,Default,0.147074
GemmTunableOp_Half_TN,tn_8192_6_8192,Gemm_Rocblas_45546,0.0454045
GemmTunableOp_Half_TN,tn_57344_6_8192,Gemm_Rocblas_45520,0.255582
GemmTunableOp_Half_TN,tn_32000_7_8192,Default,0.146705
GemmTunableOp_Half_TN,tn_8192_7_8192,Gemm_Rocblas_45546,0.0445489
```

---------
Co-authored-by: Mohit Sharma <mohit21sharma.ms@gmail.com>

# What does this PR do?

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This change adds `FlashGPT2ForCausalLM` and wires it up. The model
itself is pretty straightforward, the main difference from other models
is that it uses trained position embeddings and that all weight matrices
are transposed compared to other models (due to the use of Conv1D in the
upstream model).


<!-- Remove if not applicable -->

Fixes # (issue)


## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
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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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@Narsil 

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Fix typo in link to 'using guidance' article

This PR prefers the `huggingface_hub` library, refactors the grammar
docs and adds the new image_url api to the vlm docs.

This PR improves the guidance docs and adds a section that explains how
grammars are applied on a technical level

This PR start to add documentation for visual language models