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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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# What does this PR do?


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

- 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 


# What does this PR do?

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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?
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

- 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 #673

This PR directly load GPTBigCode to specified device, avoiding moving
model between devices.

# What does this PR do?
This PR directly load GPTBigCode to specified device, avoiding moving
model between devices.


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@OlivierDehaene OR @Narsil

Just trying to get the integration tests to pass.


# What does this PR do?

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---------
Co-authored-by: Felix Marty <9808326+fxmarty@users.noreply.github.com>

Close
https://github.com/huggingface/text-generation-inference/issues/649
Close
https://github.com/huggingface/text-generation-inference/issues/651
Close
https://github.com/huggingface/text-generation-inference/issues/653
Close #636

As per title & reported
https://github.com/huggingface/text-generation-inference/issues/601#issuecomment-1641435956
https://huggingface.co/TheBloke/Llama-2-70B-chat-GPTQ/discussions/5

Test it:

```
GPTQ_BITS=4 GPTQ_GROUPSIZE=1 text-generation-launcher --model-id TheBloke/Llama-2-70B-chat-GPTQ --port 8080 --num-shard 4 --quantize gptq
```
&
```
curl 127.0.0.1:8080/generate \
    -X POST \
    -d '{"inputs":"hey llama","parameters":{"max_new_tokens":256}}' \
    -H 'Content-Type: application/json'
```

Logger is referenced during the apex importing but is not imported,
causing a NameError

Fixes #541

# What does this PR do?

Some models are already converted, and do not have those values in the
file, this enables users to use them with less friction.

Went for pure env based because adding flags would end up (imo) very
tedious to maintain. There's a lot of sanitation to do: those flags
would be errors if not used in conjuction with `--quantize gptq`.
Then the flags need to exist in the launcher and the server passing them
all throughout all function calls.

This PR is intended as an easy escape hatch, not the defacto method to
use gptq in TGI.

Fixes #500

fix(server): Fixing RW code (it's remote code so the Arch checking doesn't work to see which weights to keep). (#579)

Fixes #555

Close #571

@njhill FYI

This PR allows the MPT model to be loaded from local files. Without this
change, an exception will be thrown by `hf_hub_download` function if
`model_id` is a local path.

# What does this PR do?


This adds a non flash version of MPT.
Flash is harder because we need to create a bias ready cuda kernel of
flash attention.

Fixes
https://github.com/huggingface/text-generation-inference/issues/361
Fixes
https://github.com/huggingface/text-generation-inference/issues/491
Fixes
https://github.com/huggingface/text-generation-inference/issues/290

Closes #478

Let's start discussing implementation.

- Need to expose the quantization scripts (either included here or add
doc on how to use https://github.com/qwopqwop200/GPTQ-for-LLaMa)
- Make sure GPTQ works for multiple models (priority to Falcon).

Currently it means that every place we use `get_{tensor|sharded}` to
check for quantization.

My idea is to reintegrate as much as possible into `utils/layer.py` by
expanding `load_multi` to be a bit more generic.
This might require some thinking, but ultimately the
`qweight,qzeros,scales,g_idx` should be in a single place, and
independant of bias presence.

# What does this PR do?

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---------
Co-authored-by: Ubuntu <ubuntu@ip-172-31-41-161.ec2.internal>
Co-authored-by: OlivierDehaene <olivier@huggingface.co>

Should be more robust to shared tensors (ok when using
      `from_pretrained). But forcing us to add new checks in our loading
      code (since the chosen key to keep might be different from
      `transformers`).

---------
Co-authored-by: Ubuntu <ubuntu@ip-172-31-41-161.ec2.internal>

Closes #471

@lewtun, is this enough?

Closes #458
Closes #456