What does this PR do?
This PR adds support for Direct Preference Optimization (DPO) training
via the existing HuggingFace inline provider. It introduces a new DPO
training recipe, config schema updates, dataset integration, and
end-to-end testing to support preference-based fine-tuning with TRL.
Test Plan
Added integration test:
tests/integration/post_training/test_post_training.py::TestPostTraining::test_preference_optimize
Ran tests on both CPU and CUDA environments
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Co-authored-by: Ubuntu <ubuntu@ip-172-31-43-83.ec2.internal>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
# What does this PR do?
adds an inline HF SFTTrainer provider. Alongside touchtune -- this is a
super popular option for running training jobs. The config allows a user
to specify some key fields such as a model, chat_template, device, etc
the provider comes with one recipe `finetune_single_device` which works
both with and without LoRA.
any model that is a valid HF identifier can be given and the model will
be pulled.
this has been tested so far with CPU and MPS device types, but should be
compatible with CUDA out of the box
The provider processes the given dataset into the proper format,
establishes the various steps per epoch, steps per save, steps per eval,
sets a sane SFTConfig, and runs n_epochs of training
if checkpoint_dir is none, no model is saved. If there is a checkpoint
dir, a model is saved every `save_steps` and at the end of training.
## Test Plan
re-enabled post_training integration test suite with a singular test
that loads the simpleqa dataset:
https://huggingface.co/datasets/llamastack/simpleqa and a tiny granite
model: https://huggingface.co/ibm-granite/granite-3.3-2b-instruct. The
test now uses the llama stack client and the proper post_training API
runs one step with a batch_size of 1. This test runs on CPU on the
Ubuntu runner so it needs to be a small batch and a single step.
[//]: # (## Documentation)
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Signed-off-by: Charlie Doern <cdoern@redhat.com>
