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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Fixes # (issue)
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
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---------
Co-authored-by: Ubuntu <ubuntu@ip-172-31-41-161.ec2.internal>
Co-authored-by: OlivierDehaene <olivier@huggingface.co>
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?
This PR fixes:
- The usage of non posix comparison which may fail depending on the
shell used (`=` will always work, `==` only with bash)
- Typo in the env variable name displayed in the error message
`BUILD_EXTENSION` instead of `BUILD_EXTENSIONS`
<!-- Remove if not applicable -->
Fixes#422
# What does this PR do?
It solves a typo in the comment sections referencing the environment
variable `CUDA_VISIBLE_DEVICES`. No misspelling references to this
variable have been found in code logic leading to undefined behaviour or
bugs. This PR is not expected to perform any code logic modification.
# 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>
# What does this PR do?
Switch security group used for ci
(open outbound rules)
Signed-off-by: Raphael <oOraph@users.noreply.github.com>
Co-authored-by: Raphael <oOraph@users.noreply.github.com>
@njhill,
temporary workaround to be able to run our CI as secrets are not
available to runners run by external contributors. I will ask around to
see if there is a better way.
Co-authored-by: Nick Hill <nickhill@us.ibm.com>
Nicolas Patry