Add support for GPTQ Marlin kernels
GPTQ Marlin extends the Marlin kernels to support common GPTQ
configurations:
- bits: 4 or 8
- groupsize: -1, 32, 64, or 128
- desc_act: true/false
Using the GPTQ Marlin kernels requires repacking the parameters in the
Marlin quantizer format.
The kernels were contributed by Neural Magic to VLLM. We vendor them
here for convenience.
* feat: add kserve feature and basic routes
* feat: implement infer endpoint wrapper around generate
* fix: refactor and improve types
* fix: improve infer and simplify
* fix: cleanup and improve api docs
* fix: refactor and encapsulate kserve feat in file
* fix: remove typos after rebase
Add support for Phi-3-medium
The main difference between the medium and mini models is that medium
uses grouped query attention with a packed QKV matrix. This change adds
support for GQA with packed matrixes to `Weights.get_weights_col_packed`
and uses it for Phi-3. This also allows us to remove the custom
implementation of GQA from dbrx attention loading.
* update vllm commit & fix models using sliding window
* update
* update commit
* fix bug where tunableop is bound to cuda graph even when cuda graph are disabled
* enable tunableop by default
* fix sliding window
* address review
* dead code
* precise comment
* is it flaky?
The router will now send the input as chunks besides as a single
string. This change modifies the server to process chunked input
rather than strings. This also allows us to remove the image
extraction code from the server.
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Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
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This change adds support for Marlin-quantized models. Marlin is an
FP16xINT4 matmul kernel, which provides good speedups decoding batches
of 16-32 tokens. It supports quantized models with symmetric
quantization, groupsize -1 or 128, and 4-bit.
Tested with:
- Llama 2
- Llama 3
- Phi 3
There was a new release of the python client with version upped to 0.7.0
on pip and on the pyproject.toml, but it wasn't changed on the
__init__.py so when one does:
```python
import text_generation
print(text_generation.__version__)
```
It still outputs "0.6.0"
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# What does this PR do?
Fix stray import.
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# 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.
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---------
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
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Fixes # (issue)
## Before submitting
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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
# What does this PR do?
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Fixes # (issue)
## Before submitting
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Pull Request section?
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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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Fixes # (issue)
## Before submitting
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guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
Pull Request section?
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[forum](https://discuss.huggingface.co/)? Please add a link
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[documentation
guidelines](https://github.com/huggingface/transformers/tree/main/docs),
and
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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
Tiezhen WANG