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# What does this PR do?

Fix stray import.

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Close #2007

# What does this PR do?

Do not attempt to allocate ExLlamaV2 scratch buffers when there are no
ExLlama2 layers. Avoids a crash in warmup for models that cannot use
exllama when ExLlamaV2 is installed.

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# What does this PR do?

Fixes initial and subsequent installs (protection for folder creation
should only be for git commit, checking out correct commit should be on
both.

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# What does this PR do?

Making `make install` a much better sane default to start local dev
environments.

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# What does this PR do?

The GPTQ code path for column-packed packed tensors assumed that this is
always a QKV matrix. However, models (e.g. Phi-3) can also have
column-packed MLP up/gate matrices.

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- Refactor code to allow supporting multiple versions of the
generate.proto at the same time
- Add v3/generate.proto (ISO to generate.proto for now but allow for
future changes without impacting v2 backends)
- Add Schedule trait to abstract queuing and batching mechanisms that
will be different in the future
- Add SchedulerV2/V3 impl

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


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Signed-off-by: Emmanuel Ferdman <emmanuelferdman@gmail.com>

# What does this PR do?

We were using the wrong parallelism in the up-projection.

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Before this change, the generation input was sent to the backend as a
single string, encoding images as Base64 and packing them in
Markdown-style links.

This change adds a new chunked input representation that separates text
chunks from images chunks. Image chunks contain binary data (for smaller
message sizes) and the image's MIME type.

The stringly-typed inputs are still sent to support backends that do not
support chunked inputs yet.

# What does this PR do?

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

# What does this PR do?

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# 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)


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## Who can review?

@merveenoyan

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

# What does this PR do?

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Purely refactors paged/attention into `layers/attention` and make hardware differences more obvious with 1 file per hardware. (#1986)

# What does this PR do?

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As per title

cc @Narsil

This test fails somewhat regularly due to non-determinism and this
test is primarily to verify that we are loading a model which doesn't
have `float16` as the default dtype correctly.

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.

This PR updates `load_attention` to prefer loading specific attention
based on the model type. Additionally there were two cases where
`TensorParallelColumnLinear.load_multi` was called and this reduces it
to a single path

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Two issues:

1. When one of the stdout/stderr pipe buffers of a process started
   with `subprocess.Popen` is full, the process can get blocked until
   the buffer is drained.
2. Calling `Popen.wait` can deadlock when called before draining
   the pipe buffers (if they are full).

This avoids the issue altogether by giving the child process a
temporary file to write to.

- 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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# 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


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@drbh

This PR loads the `processor_config` similar to the `tokenizer_config`
and uses the processor_config's chat_template if the tokenizer_config
does not include one. These changes enable chat with idefics2

# What does this PR do?

Fix GPTQ for models which do not have float16 at the default dtype

Before this change GPTQ models would not work if the model's default
data type is not `float16`. For example, Gemma GPTQ models would fail
because the default dtype of Gemma is `bfloat16`. There are two issues:

If the default `dtype` is not `float16`, the quantizer's `float16`
parameters get converted to that dtype. The kernels cannot deal
with non-`float16` types. The same applies to inputs of quantized ops.

This is resolved by setting the dtype of gptq/awq-quantized models to
`float16`.

Simpler version of #1951.

**Draft:** just testing...

## Before submitting
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- The need for the slow tokenizer default stems from back
  when llama 1 was introduced and all the flags where not
  supported in `tokenizers`.

- Fixes #1891


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- Added a debug log for speculated ids (helps seeing in logs quality of
  a speculator).
- Remove newlines from child process logs when re-emitting in non JSON
  mode.
- Made standard level be closer to what's expected (only our binaries
  level).
- Propagate that level correctly to the shard (was forced into INFO).


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# What does this PR do?

- Add the stop parameter to the completion route
- Add the completion method to the python client
- Add the stop parameter to the python client's chat method


## Before submitting
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other checks if that's the case).
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      Pull Request section?
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@Narsil

---------
Co-authored-by: Thomas SCHILLACI <tschilla@px101.prod.exalead.com>
Co-authored-by: Thomas Schillaci <thomas.schillaci@3ds.com>

# What does this PR do?

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

# What does this PR do?

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

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>

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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This PR simply includes the HF token in the client tests similar to how
it's included in the server tests. This helps avoid CI failure due to
rate limiting

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Fixes an incorrect url in monitoring doc.

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