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# What does this PR do?

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`make install-flash-attn-v2-cuda` should work like `make install-flash-attn-v2` used to work. (#1294)

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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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---------
Co-authored-by: Florian Zimmermeister <flozi00.fz@gmail.com>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-24-153.ec2.internal>

# What does this PR do?

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This forces the use of `bfloat16` for IDEFICS. The issue is that with
`float16` the 80b model gives garbage output. Let me know if this
solution is not appropriate and I'll adjust accordingly. For the details
see below.

The current behaviour:
```sh
$ curl 127.0.0.1:8080/generate -X POST -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":20}}' -H 'Content-Type: application/json'
{"generated_text":""}
```

On closer inspection with:
```python
import requests

headers = { "Content-Type": "application/json"}

query = "What is Deep Learning?"
data = {
    "inputs": query,
    "parameters": {
        "max_new_tokens": 10,
        "return_full_text": True,
        "decoder_input_details": True,
        "do_sample": False,
    },
}

api_url = "http://127.0.0.1:8080"
response = requests.post(api_url + "/generate", headers=headers, json=data).json()

for i in ['prefill', 'tokens']:
    print(f'### {i}')
    print(repr(''.join([t['text'] for t in response['details'][i]])))
```

Prints:
```
### prefill
'<s>WhatisDeepLearning?'
### tokens
'<unk><unk><unk><unk><unk><unk><unk><unk><unk><unk>'
########
```

With the change in this PR it prints:
```
### prefill
'<s>WhatisDeepLearning?'
### tokens
'\n\nDeep Learning is a subset of machine'
```

Note, using the Transformers implementation (with
`IdeficsForVisionText2Text.from_pretrained`) produces the latter
(correct) output as well.
This only happens with the 80b model, the 9b model is not as sensitive
to the dtype (as also mentioned in the code).

The reason for "forcing" this in the IDEFICS init method, is because if
quantization is used, then the dtype cannot be set explicitly. And since
it's left as `None`, it's set to `float16` by default
[here](https://github.com/huggingface/text-generation-inference/blob/96a982ad8fc232479384476b1596a880697cc1d0/server/text_generation_server/models/__init__.py#L90).
I.e. there's no other way to manually change the dtype if someone is
using quantization:
```sh
$ docker run .... ghcr.io/huggingface/text-generation-inference:latest --model-id HuggingFaceM4/idefics-80b-instruct --dtype bfloat16 --quantize bitsandbytes-nf4
.....
2023-10-31T12:42:26.710401Z  INFO shard-manager: text_generation_launcher: Starting shard rank=0
2023-10-31T12:42:30.315734Z ERROR shard-manager: text_generation_launcher: Shard complete standard error output:

Traceback (most recent call last):

  File "/opt/conda/bin/text-generation-server", line 8, in <module>
    sys.exit(app())

  File "/opt/conda/lib/python3.9/site-packages/text_generation_server/cli.py", line 80, in serve
    raise RuntimeError(

RuntimeError: Only 1 can be set between `dtype` and `quantize`, as they both decide how goes the final model.
 rank=0
Error: ShardCannotStart
2023-10-31T12:42:30.414010Z ERROR text_generation_launcher: Shard 0 failed to start
2023-10-31T12:42:30.414044Z  INFO text_generation_launcher: Shutting down shards
```

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

Close #1142

# What does this PR do?

Enables PEFT weights to be loaded from a local directory, as opposed to
a hf hub repository. It is a continuation of the work in PR
https://github.com/huggingface/text-generation-inference/pull/762

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Fixes #1259 


## Before submitting
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Pull Request section? **Yes but I don't know how to run the tests for
this repo, and it doesn't look like this code is covered anyway**
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comment](https://github.com/huggingface/text-generation-inference/pull/762#issuecomment-1728089505)**
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**I didn't see any documentation added to the [original
PR](https://github.com/huggingface/text-generation-inference/pull/762

),
and am not sure where this belongs. Let me know and I can add some**
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test coverage for this python module**


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

Peft loading function was missing the
`trust_remote_code=trust_remote_code` argument causing the custom
tokenizer code to be not found.


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@Narsil

Close #1225

# What does this PR do?

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Fixes # (issue)


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See #1049

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

This token is not used by your action.
Secret is removed from the repository.

This PR addresses an issue where calling `model = model.cuda()` would
throw a ValueError when `quantize` is set to "bitsandbytes".

```
> File "/opt/conda/lib/python3.9/site-packages/text_generation_server/server.py", line 147, in serve_inner
    model = get_model(
  File "/opt/conda/lib/python3.9/site-packages/text_generation_server/models/__init__.py", line 295, in get_model
    return CausalLM(
  File "/opt/conda/lib/python3.9/site-packages/text_generation_server/models/causal_lm.py", line 515, in __init__
    model = model.cuda()
  File "/opt/conda/lib/python3.9/site-packages/transformers/modeling_utils.py", line 1998, in cuda
    raise ValueError(
ValueError: Calling `cuda()` is not supported for `4-bit` or `8-bit` quantized models. Please use the model as it is, since the model has already been set to the correct devices and casted to the correct `dtype`.
```
Co-authored-by: mmnga <mmnga1mmnga@gmail.com>

Fixes a link in doc

# What does this PR do?

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# What does this PR do?


Fixes #1017

Not sure if there's a mistake here but 

- NousResearch/Yarn-Llama-2-7b-128k seems to be working fine
- TheBloke/Yarn-Llama-2-13B-128K-GPTQ outputs garbage



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# What does this PR do?

Fixes #1098 
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# What does this PR do?
Fixing a list type hint definition (I believe this was a typo).

Allows backward compatibility with Python 3.8 (relevant for
JetPack-enabled systems).

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# What does this PR do?

Now clients which do not specify a max_length will be implying
`max_new_tokens = max_total_tokens - input_length`.
This is a serious change, but which seems more in line with what users
expect from standing server.

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---------
Co-authored-by: OlivierDehaene <olivier@huggingface.co>

# What does this PR do?

Fix #1095

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