* Removing IPEX_AVAIL.
Chose to unify CPU and XPU under `ipex`. Most code is exactly similar
except for a very few spots.
The biggest number of spots is the kv-cache layout and the flash_xxx.py
files.
Since those files should be removed soon and factored away, we should
not need them.
* Forgot a few places.
* Unrelated change.
* Fixing HF_TOKEN.
* HF_TOKEN
* add CPU tgi support
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
* ipex distributed ops support
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
---------
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Co-authored-by: Funtowicz Morgan <mfuntowicz@users.noreply.github.com>
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.
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---------
Co-authored-by: Joshua Rosenkranz <joshua.rosenkranz@gmail.com>
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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>
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- 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.
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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>
# What does this PR do?
Reworked the loading logic. Idea is to use cleaner loading code:
- Remove need for `no_init_weights`
- Remove all weird `bnb_linear` and `load_weights` and
`post_load_weights`.
New code layout:
- New class `Weights` in charge of handling loading the weights from
multiple files into appropiate tensors (potentially sharded)
- TP layers now are "shells", they contain the code to know what kind of
sharding we need + eventual `all_reduce`. They do not inherit from
linear, but they contain some kind of Linear instead
- the contained linear can be either FastLinear, BnbLinear or GPTq
Linear next.
- All modeling code is explictly made for sharding, process group is
just no-ops for non sharded code (removes a lot of test cases)
---------
Co-authored-by: Ubuntu <ubuntu@ip-172-31-41-161.taildb5d.ts.net>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-41-161.ec2.internal>
Co-authored-by: OlivierDehaene <olivier@huggingface.co>
Co-authored-by: OlivierDehaene <23298448+OlivierDehaene@users.noreply.github.com>
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# What does this PR do?
Lifting check_unitialized.
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# What does this PR do?
Hotfixes:
- Uses `model_type`=`gpt_bigcode` for more general usage.
- Hotfixes linked lm_head vs wte_embedding (safetensors file do not
contain the key, correctly when the file is sharded, where as pytorch
copies the tensor)
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Wang, Yi