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if there's no cuda. disable custom kernels

# What does this PR do?

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

As more and more people begin to use Baichuan's open-source models, the
influence of Baichuan models is growing, especially in China. Many
community members are interested in adding support for Baichuan models
to TGI. Meanwhile, Baichuan is a very open company, and in the future,
it plans to open-source more and more models, taking all this into
consideration, we would like to add support for the Baichuan model to
TGI. To do this, we need to make some changes, which we hope can be
merged into the main branch of TGI. In the future, we would be happy to
help maintain support for Baichuan models in TGI. We sincerely hope that
our pull request can be accepted. Thank you.

By the way, the changes of this time mainly for supporting Baichuan-7B.

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

# What does this PR do?

Fix __call__ vs forward.

To reproduce error just launch:
TheBloke/WizardLM-Uncensored-Falcon-7B-GPTQ with gptq (it fails because
falcon code uses `__call__` instead for `forward` calls)

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Proposal to fix
https://github.com/huggingface/text-generation-inference/issues/962

# What does this PR do?
The
[changes](https://github.com/huggingface/text-generation-inference/pull/986/files#diff-b72e45030214e50c8ff6e3be837057b3f3368b9779fd942ca680f949fe069eafR176)
disabling exllama on old compute had unintended consequences of not
setting `use_exllama` to `False` if `HAS_EXLLAMA` equals `False` **and**
`CAN_EXLLAMA` equals `False`. This fixes this.

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# What does this PR do?

Disabling exllama on old compute.

Exllama + T4 don't play nice together, this will disable it right away
to avoid issues at runtime.

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transposing the fixes from
https://github.com/huggingface/transformers/pull/25787

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# What does this PR do?
During the safetensor conversion, duplicate weights are removed.
However, which of the duplicates gets removed, differs per checkpoint.
In some, like `h2oai/h2ogpt-oig-oasst1-falcon-40b`, the weight
`transformer.word_embeddings.weightSafetensor` gets removed. In others,
`lm_head.weight` gets removed. Long story long, we need to support both.

Originally, f0181436 mapped `lm_head` to `word_embeddings`. Then ac736fd8
switched this around. This commit merges them and allows for both.

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## Who can review?

@Narsil, you wrote both commits I referenced in this PR. I think you'll
understand this change :)

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---------
Co-authored-by: Vincent Brouwers <vincent.brouwers@ing.com>

# What does this PR do?

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Co-Authored-By: Victor Sanh <victorsanh@gmail.com>


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---------
Co-authored-by: Victor Sanh <victorsanh@gmail.com>

# What does this PR do?

- New "falcon" layout on this repo
- No alibi
- `transformers` already modifying cache layout in our stead (same
  modifications).
- Output is garbage. Not sure why.

Does not fix #826 but it's a step.

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# What does this PR do?

Fixes #843 
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# What does this PR do?

A few tokenizer_config in huggingface use LlamaTokenizer, so I think I
would have selected `LlamaTokenizer` before.

For a few cases where you're using a llama structure but not a llama
tokenizer, why not make it to call the AutoTokenizer in exception
handling.

In the case of `decapoda-research/llama-7b-hf`, LLamaTokenizer is still
being used in config.json, so it should be called through`
LlamaTokenizer`.
Also, if an exception is thrown by LlamaTokenizer, it will cause
`LlamaTokenzierFast` to be called from AutoTokenizer.


Fixes # 560


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Fixes #816

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# What does this PR do?

Fixes #531

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# What does this PR do?

Reflecting
https://github.com/huggingface/transformers/pull/24998

Current status wants to make sure integration tests *are* broken with
this.


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# What does this PR do?

Fixes #787
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Fixes #784 

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# What does this PR do?

Typo

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See #626 
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---------
Co-authored-by: krzim <zimmerk4@live.com>

# What does this PR do?

Fixes #732 
And remove duplicate AutoTokenizer import

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- Will detect `peft` model by finding `adapter_config.json`.
- This triggers a totally dedicated `download-weights` path
- This path, loads the adapter config, finds the base model_id
- It loads the base_model
- Then peft_model
- Then `merge_and_unload()`
- Then `save_pretrained(.., safe_serialization=True)
- Add back the config + tokenizer.merge_and_unload()`
- Then `save_pretrained(.., safe_serialization=True)
- Add back the config + tokenizer.
- The chosen location is a **local folder with the name of the user
  chosen model id**

PROs:

- Easier than to expect user to merge manually
- Barely any change outside of `download-weights` command.
- This means everything will work in a single load.
- Should enable out of the box SM + HFE

CONs:

- Creates a local merged model in unusual location, potentially
  not saved across docker reloads, or ovewriting some files if the PEFT
  itself was local and containing other files in addition to the lora

Alternatives considered:
- Add `local_files_only=True` every where (discard because of massive
  code change for not a good enough reason)
- Return something to `launcher` about the new model-id (a cleaner
  location for this new model), but it would
  introduce new communication somewhere where we didn't need it before.
- Using the HF cache folder and *stopping* the flow after
  `download-weights` and asking user to restart with the actual local
  model location


Fix #482 


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# What does this PR do?

Fixes #749 

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Co-authored-by: p_spozzhang <p_spozzhang@tencent.com>

# What does this PR do?

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# What does this PR do?
Fixed typo.
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implemetation -> implementation


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# What does this PR do?


- Adds Rope NTK scaling.

Done because
https://github.com/huggingface/text-generation-inference/pull/529 was
closed
Took some code from
https://github.com/huggingface/transformers/pull/24653

- `--rope-scaling` and `--rope-factor` are added separately. I
considered having a single one and parsing something line ("linear:4.0"
, or "dynamic") but decided against
it because it would push more parsing+validation a bit everywhere (both
in the launcher and the server).


Fixes #512




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## What does this PR do?

This PR fixes a minor type annotation issue in the signature of
`Model.generate_token`.

All existing overrides of `Model.generate_token` return
`Tuple[List[Generation], Optional[B]]`:

https://github.com/huggingface/text-generation-inference/blob/3ef5ffbc6400370ff2e1546550a6bad3ac61b079/server/text_generation_server/models/causal_lm.py#L535-L537

https://github.com/huggingface/text-generation-inference/blob/3ef5ffbc6400370ff2e1546550a6bad3ac61b079/server/text_generation_server/models/flash_causal_lm.py#L802-L804

https://github.com/huggingface/text-generation-inference/blob/3ef5ffbc6400370ff2e1546550a6bad3ac61b079/server/text_generation_server/models/seq2seq_lm.py#L589-L591

I suspect that back in 017a2a8c when `GeneratedText` and `Generation`
were separated, the function signature was not updated.

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CC @OlivierDehaene

# What does this PR do?

Redoes #719

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- Current PR is not great because we're side stepping the
  `Weights.__init__` but Weights shouldn't requires anything related
  to the config or the model_id as it aims to be a simple Wrapper
  over multi file loading.
- Ideal solution would be to use something like Rust enum
  ```
  enum Quantize{
    Bitandbytes(Bitsandbytes),
    GPTQ(bits: usize, groupsize: usize)
  ```
  And passing that around during load. Unfortunately we don't
  have access to this, so for now, side-stepping seems easier.

- Re-enabling groupsize<0 with exllama (confirmed it works.)

Helps #601 

In next steps we should make sure our quantization script uses that
format and make it standard.


# What does this PR do?

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Close #683

Close #673