* add 12.8.1 cuda to the base matrix
* use nightly
* bump deepspeed and set no binary
* deepspeed binary fixes hopefully
* install deepspeed by itself
* multiline fix
* make sure ninja is installed
* try with reversion of packaging/setuptools/wheel install
* use license instead of license-file
* try rolling back packaging and setuptools versions
* comment out license for validation for now
* make sure packaging version is consistent
* more parity across tests and docker images for packaging/setuptools
* refactor trainer to prevent circular dependencies later
fix loader default
KD dataset loading and KD with logprobs
filter bad rows
make batch smaller
handle padding/collation for KD datasets
make it work
flipped the slice
cross entropy loss coefficient during KD
make sure to multiply against the correct loss
chore: lint
triton wip
no where support
v2 trial
no torch.exp inside triton kernel
no log etc
no torch.tensor
v3
fix kwarg
don't use triton for now
better rescaling for temperatures
hash for temperature too
use kd_alpha in the correct loss method
fix kd loss so it's causal (fixes repeating tokens)
var naming and add todo
chore: lint
refactor so we can easily add new loss functions
add license block
remove references to triton kd for now
handle token/logprob shifting
support for custom trainer classes from plugins
refactor kd chat template loader
move more things to kd plugin
remove moved class from import
make plugin setup concise
increase logging around loading plugins
add copyrights
remove duplicate code
more info on preprocess for kd and fix import
be a bit pickier about loading dynamic prompt strategies
kd sample packing
make loss torch script compat
support streaming for processing sft datasts?
improve iterable support
ensure that batch vs single is done properly
tweak check for batched prompt data
reward can use same batch check
fix reward trainer calls for tokenization
improve check for batched
reward model doesn't work well with batched
add kd trainer e2e test
linting
rename test files so it gets picked up
make the kd e2e fit in vram for ci and add lora version
set lora_dropout explicitly
lower lr
make sure to set tokenizer from l3 70b and save safetensors
make sure to use the correct tokenizer
fix adapter model check
make sure to use tensorboard to capture loss for checks
chore: lint
chore: lint
improve logprob masking and shift in trainer
more fixes
try tests for kd on l40s
don't shift student logits for kd
no batching for kd chat templates
make sure to truncate logprobs if there are more than top_k
change up logic so we always truncate to top_k
use iter instead of tuple
fix finding the top-k rather than assuming first position has the correct val
apply z-score scaling to kd
kd loss needs to be calculated in full precision
Always re-normalize teacher distribution
various fixes
* support for configurable top-k/softmax ordering
* add attribute check for filter rows and lint
* fix logic
* handle none case for conversion to int
* fix student logit off by one
* set kd_temp to 1.0 for test loss
* address PR feedback
* fix build w pyproject to respect insalled torch version
* include in manifest
* disable duplicate code check for now
* move parser so it can be found
* add checks for correct pytorch version so this doesn't slip by again
* reduce test concurrency to avoid HF rate limiting, test suite parity
* make val_set_size smaller to speed up e2e tests
* more retries for pytest fixture downloads
* val_set_size was too small
* move retry_on_request_exceptions to data utils and add retry strategy
* pre-download ultrafeedback as a test fixture
* refactor download retry into it's own fn
* don't import from data utils
* use retry mechanism now for fixtures
* Fix broken CLI; remove duplicate metadata from setup.py
* Adding tests.yml CLI check
* updating
* remove test with requests to github due to rate limiting
---------
Co-authored-by: Dan Saunders <dan@axolotl.ai>
* feat: add cut_cross_entropy
* fix: add to input
* fix: remove from setup.py
* feat: refactor into an integration
* chore: ignore lint
* feat: add test for cce
* fix: set max_steps for liger test
* chore: Update base model following suggestion
Co-authored-by: Wing Lian <wing.lian@gmail.com>
* chore: update special_tokens following suggestion
Co-authored-by: Wing Lian <wing.lian@gmail.com>
* chore: remove with_temp_dir following comments
* fix: plugins aren't loaded
* chore: update quotes in error message
* chore: lint
* chore: lint
* feat: enable FA on test
* chore: refactor get_pytorch_version
* fix: lock cce commit version
* fix: remove subclassing UT
* fix: downcast even if not using FA and config check
* feat: add test to check different attentions
* feat: add install to CI
* chore: refactor to use parametrize for attention
* fix: pytest not detecting test
* feat: handle torch lower than 2.4
* fix args/kwargs to match docs
* use release version cut-cross-entropy==24.11.4
* fix quotes
* fix: use named params for clarity for modal builder
* fix: handle install from pip
* fix: test check only top level module install
* fix: re-add import check
* uninstall existing version if no transformers submodule in cce
* more dataset fixtures into the cache
---------
Co-authored-by: Wing Lian <wing.lian@gmail.com>
Co-authored-by: Wing Lian <wing@axolotl.ai>
* see if unsloth installs cleanly in ci
* check unsloth install on regular tests, not sdist
* fix ampere check exception for ci
* use cached_property instead
* add an e2e test for unsloth qlora
* reduce seq len and mbsz to prevent oom in ci
* add checks for fp16 and sdp_attention
* pin unsloth to a specific release
* add unsloth to docker image too
* fix flash attn xentropy patch
* fix loss, add check for loss when using fa_xentropy
* fix special tokens for test
* typo
* test fa xentropy with and without gradient accum
* pr feedback changes
* update actions version for node16 deprecation
* update pre-commit/action to use 3.0.1 for actions/cache@v4 dep
* update docker/setup-buildx-action too to v3
* feat: support new arg num_items_in_batch
* use kwargs to manage extra unknown kwargs for now
* upgrade against upstream transformers main
* make sure trl is on latest too
* fix for upgraded trl
* fix: handle trl and transformer signature change
* feat: update trl to handle transformer signature
* RewardDataCollatorWithPadding no longer has max_length
* handle updated signature for tokenizer vs processor class
* invert logic for tokenizer vs processor class
* processing_class, not processor class
* also handle processing class in dpo
* handle model name w model card creation
* upgrade transformers and add a loss check test
* fix install of tbparse requirements
* make sure to add tbparse to req
* feat: revert kwarg to positional kwarg to be explicit
---------
Co-authored-by: Wing Lian <wing.lian@gmail.com>
use a constraint file
use min version of xformers
don't install autoawq with pytorch 2.5.0
debugging for errors
upgrade pip first
fix action yml
add back try/except
retry w/o constraint
use --no-build-isolation
show torch version
install setuptools and wheel
add back try/except
* run tests again on Modal
* make sure to run the full suite of tests on modal
* run cicd steps via shell script
* run tests in different runs
* increase timeout
* split tests into steps on modal
* increase workflow timeout
* retry doing this with only a single script
* fix yml launch for modal ci
* reorder tests to run on modal
* skip dpo tests on modal
* run on L4s, A10G takes too long
* increase CPU and RAM for modal test
* run modal tests on A100s
* skip phi test on modal
* env not arg in modal dockerfile
* upgrade pydantic and fastapi for modal tests
* cleanup stray character
* use A10s instead of A100 for modal
* make mlflow optional
* fix xformers
don't patch swiglu if xformers not working
fix the check for xformers swiglu
* fix install of xformers with extra index url for docker builds
* fix docker build arg quoting
* wip modal for ci
* handle falcon layernorms better
* update
* rebuild the template each time with the pseudo-ARGS
* fix ref
* update tests to use modal
* cleanup ci script
* make sure to install jinja2 also
* kickoff the gh action on gh hosted runners and specify num gpus
