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Author SHA1 Message Date
Nicolas Patry 15fc64668f
fix(server): Failing quantize config after local read. (#743)
# What does this PR do?

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2023-07-31 17:51:26 +02:00
Nicolas Patry 92bb56b0c1
Local gptq support. (#738)
# What does this PR do?

Redoes #719

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2023-07-31 10:32:52 +02:00
Nicolas Patry a0d55358d2
feat(server): Using `quantize_config.json` instead of GPTQ_BITS env variables. (#671)
- 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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2023-07-25 13:00:27 +02:00
OlivierDehaene 73a4d65d26
feat: add cuda memory fraction (#659)
Close #673
2023-07-24 11:43:58 +02:00
Nicolas Patry d5b5bc750f
feat(server): Add exllama GPTQ CUDA kernel support #553 (#666)
Just trying to get the integration tests to pass.


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---------

Co-authored-by: Felix Marty <9808326+fxmarty@users.noreply.github.com>
2023-07-21 10:59:00 +02:00
ssmi153 3628559516
GPTQ Env vars: catch correct type of error (#596)
# What does this PR do?

When passing in environment variables like gptq_bits, we still get
errors thrown from TGI because the try/catch block is catching the wrong
type of error. This PR aims to fix that.

@Narsil - let me know if this is how you want this formatted. My Python
is a little shaky, so I hope this syntax is correct.
2023-07-12 19:57:46 +02:00
Nicolas Patry 67347950b7
feat(server): Implements sharding for non divisible `vocab_size`. (#583)
- The code is relatively easy (just disable the checks on Embedding and
Head)

This cannot be done in the same easy fashion for hidden_dim/head_dim.
It's relatively easy on some models (classic MHA) but it would make the
other
models (MQA) much more complex, and GPTQ quantization another quite
hairy piece
of code.
2023-07-12 16:43:31 +02:00
ssmi153 2c4bf88268
fix(server): Bug fixes for GPTQ_BITS environment variable passthrough (#590)
# What does this PR do?

This fixes a typo and extends the GPTP_BITS environment variables
through to the second method which requires the same logic. Please let
me know if there's anything I've misunderstood in this change.

Thanks @Narsil for the original fix.
2023-07-12 14:17:35 +02:00
Nicolas Patry 5bd2ab6583
feat(server): Support for env value for GPTQ_BITS and GPTQ_GROUPSIZE. (#580)
# What does this PR do?

Some models are already converted, and do not have those values in the
file, this enables users to use them with less friction.

Went for pure env based because adding flags would end up (imo) very
tedious to maintain. There's a lot of sanitation to do: those flags
would be errors if not used in conjuction with `--quantize gptq`.
Then the flags need to exist in the launcher and the server passing them
all throughout all function calls.

This PR is intended as an easy escape hatch, not the defacto method to
use gptq in TGI.

Fixes #500
2023-07-12 10:00:02 +02:00
OlivierDehaene e74bd41e0f
feat(server): add paged attention to flash models (#516)
Closes #478
2023-06-30 19:09:59 +02:00
Nicolas Patry aefde28b45
feat(server): Add inference support for GPTQ (llama + falcon tested) + Quantization script (#438)
Let's start discussing implementation.

- Need to expose the quantization scripts (either included here or add
doc on how to use https://github.com/qwopqwop200/GPTQ-for-LLaMa)
- Make sure GPTQ works for multiple models (priority to Falcon).

Currently it means that every place we use `get_{tensor|sharded}` to
check for quantization.

My idea is to reintegrate as much as possible into `utils/layer.py` by
expanding `load_multi` to be a bit more generic.
This might require some thinking, but ultimately the
`qweight,qzeros,scales,g_idx` should be in a single place, and
independant of bias presence.

# What does this PR do?

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---------

Co-authored-by: Ubuntu <ubuntu@ip-172-31-41-161.ec2.internal>
Co-authored-by: OlivierDehaene <olivier@huggingface.co>
2023-06-26 12:27:01 +02:00
Nicolas Patry 49b4b33e80
feat(server): Update convert logic. (#483)
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>
2023-06-23 12:40:46 +02:00
Nicolas Patry abd58ff82c
feat(server): Rework model loading (#344)
# 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)

![Screenshot from 2023-05-19
23-19-59](https://github.com/huggingface/text-generation-inference/assets/204321/9a802654-74a3-488c-87a8-073743a6143f)

---------

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>
2023-06-08 14:51:52 +02:00