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# What does this PR do?

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

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

This PR access the path on the speculator similar to
`MLPSpeculatorHead.load` and `MedusaHeadV1.load`


these changes resolves this error locally when loading a `MedusaHeadV2`
```
TypeError: expected str, bytes or os.PathLike object, not dict
```

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Not all models were tested in
https://github.com/huggingface/text-generation-inference/pull/1764.

Fixing some more issues (notably starcoder2) here, the full CI will come
shortly once we split `build.yml` in two

cc @Narsil

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>

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Taking the signal handles later, so during loads,
regular signal handling is done, we only need to handle SIGINT and
SIGTERM during real loads to get more graceful shutdowns when queries
are in flight.

Fixes #1842 


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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?

Fixes `Import Error` occurring from mismatch of usage between
torch.nn.Module and nn.Module.

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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).


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---------
Co-authored-by: Joshua Rosenkranz <joshua.rosenkranz@gmail.com>

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Add's support for the Falcon2 11B model architecture.


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---------
Signed-off-by: Raphael Glon <oOraph@users.noreply.github.com>
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Co-authored-by: OlivierDehaene <olivier@huggingface.co>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: oOraph <13552058+oOraph@users.noreply.github.com>
Co-authored-by: Raphael Glon <oOraph@users.noreply.github.com>
Co-authored-by: Julien Chaumond <julien@huggingface.co>
Co-authored-by: OlivierDehaene <23298448+OlivierDehaene@users.noreply.github.com>
Co-authored-by: abhishek thakur <1183441+abhishekkrthakur@users.noreply.github.com>
Co-authored-by: Dong Shin <d0104.shin@gmail.com>
Co-authored-by: Christof Weickhardt <christof@weickhardt.ch>
Co-authored-by: Ikko Eltociear Ashimine <eltociear@gmail.com>
Co-authored-by: drbh <david.richard.holtz@gmail.com>
Co-authored-by: Lucain <lucain@huggingface.co>
Co-authored-by: fxmarty <9808326+fxmarty@users.noreply.github.com>
Co-authored-by: Moritz Laurer <41862082+MoritzLaurer@users.noreply.github.com>
Co-authored-by: dr3s <dr3s@users.noreply.github.com>
Co-authored-by: Wang, Yi <yi.a.wang@intel.com>
Co-authored-by: Morgan Funtowicz <funtowiczmo@gmail.com>
Co-authored-by: Maziyar Panahi <maziyar.panahi@iscpif.fr>
Co-authored-by: Brandon Royal <2762697+brandonroyal@users.noreply.github.com>
Co-authored-by: Mishig <mishig.davaadorj@coloradocollege.edu>
Co-authored-by: Martin Iglesias Goyanes <martinigoyanes@hotmail.com>
Co-authored-by: martini <martin.iglesiasgoyanes@adyen.com>

# What does this PR do?

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Thank you so much for the work you are doing, this is my little
contribution to this great thing you have built. I hope it is useful and
helpful, please don't hesitate to discuss any matters that are not
clear!

I am basing my implementation of frequency penalty on OpenAI's
implementation:
https://platform.openai.com/docs/guides/text-generation/parameter-details

The problem I see with TGI's current implementation is that is not
taking into account the frequency of tokens which have already been
sampled in the current generation stream. Also, the scaling is of the
adjusted token logits is done differently for positive and negative
logits. While in OpenAI's implementation token frequency is taking into
account and the scaling is always done with a subtraction (if penalty is
positive) or add operation (if penalty is negative).

This leads to corrupt generations as I mentioned in issue #1810 .
Moreover, after my tests, other issues are also gone like the one about
some request's with ``penalty_frequency = 1.0`` overruling other
requests (with ``frequency_penalty = 0.0``) in the same batch and
therefore corrupting all generations in the batch. Basically, padding
does not affect this implementation so I believe this ``score *=
input_ids.ne(0)`` is not needed anymore.



Frequency penalty | -1.0 | 0.0 | 1.0
-- | -- | -- | --
Before my change | https://paste.mozilla.org/JxqGJkWY |
https://paste.mozilla.org/hrztJ56h | https://paste.mozilla.org/pBSEH2zw
After my change | https://paste.mozilla.org/7gXCi7zo |
https://paste.mozilla.org/ZR9rJ92g | https://paste.mozilla.org/gHaD2YnC



---------
Co-authored-by: martini <martin.iglesiasgoyanes@adyen.com>

# What does this PR do?

Just unifying some branches and making intentions clearer (no cuda graph
when 0 all the way in the launcher)

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---------
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Co-authored-by: Morgan Funtowicz <funtowiczmo@gmail.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>

# What does this PR do?

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This PR adds support for `do_sample` to chat to enable greedy sampling

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

This PR resolves an issue with the penalty processors during batched
generation where extra padding tokens incorrectly impact the penalty
scores.

generation is impacted in the case where at least one item in the batch
includes a `frequency_penalty`

reproduction script below
```python
import requests
from concurrent import futures
import time

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

json_data = {
    "inputs": "[INST] Whats the capitol of France? [/INST]",
    "parameters": {
        "max_new_tokens": 100,
        "seed": 20,
        "do_sample": False,
    },
}


json_data2 = {
    "inputs": "<s>[INST]Write a mind bending story: I saw a puppy a cat a rat and a raccoon during my bike ride in the park[/INST]",
    "parameters": {
        "max_new_tokens": 100,
        "seed": 2,
        "do_sample": False,
        # OFFENDING LINE
        "frequency_penalty": 1.05,
    },
}

base_url = "http://localhost:3000/generate

"


def req():
    response = requests.post(base_url, headers=headers, json=json_data)
    print("[req ]", response.json())


def req2():
    response = requests.post(base_url, headers=headers, json=json_data2)
    print("[req2]", response.json())


n = 1

for i in range(0, 3):
    print(f"- {n} threads -")
    with futures.ThreadPoolExecutor(max_workers=n) as executor:
        executor.submit(req)
        for i in range(3):
            executor.submit(req2)

    n += 1

# - 1 threads -
# [req ] {'generated_text': ' The capital of France is Paris.'}
# [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"}
# [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"}
# [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"}
# - 2 threads -
# [req ] {'generated_text': ' The capital city'}
# [req2] {'generated_text': ' As""%\n================'}
# [req2] {'generated_text': ' As""%%$\n================'}
# [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"}

# output with this PR's changes:
# - 1 threads -
# [req ] {'generated_text': ' The capital of France is Paris.'}
# [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"}
# [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"}
# [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"}
# - 2 threads -
# [req ] {'generated_text': ' The capital city'}
# [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"}
# [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"}
# [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"}

```

**divergence from expected generation is easier to reproduce with
batched grammar requests as they are more sensitive to unexpected
outputs.

this PR resolves the issue by setting the penalty score to 0 where input
ids are padding tokens (0).

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

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This was ignored up to now, even with `--cuda-graphs 0`.

With this fix, `--cuda-graphs` is obeyed to.

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---------
Co-authored-by: Dong Shin <d0104.shin@gmail.com>