* Update rl.py
* Update rl.py
* Update rl.py
* refactor pref dataset loading to reuse load_dataset_w_config
* refactor again after rebase from main
* chore: add docstring and types
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Co-authored-by: Wing Lian <wing@axolotl.ai>
Co-authored-by: NanoCode012 <nano@axolotl.ai>
* fix: drop long seq even if not sample packing
* fix: logging import
* fix: cfg passed being none
* fix: try to fix logging
* fix: refactor call to not use accelerate log
* fix: try to fix circular import issue
* fix: don't drop when skip prepare
* chore: remove duplicate line
* fix: update warning to mention that sequences will be trimmed
* fix: do not drop seq if input_ids don't exist
* fix: increase RM unittest sequence length to reduce trim warnings
* fix: solve conflicts
* fix: default min_seq_len in case of None
* 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
* 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
* add mhenrichsen/alpaca_2k_test with revision dataset download fixture for flaky tests
* log slowest tests
* pin pynvml==11.5.3
* fix load local hub path
* optimize for speed w smaller models and val_set_size
* replace pynvml
* make the resume from checkpoint e2e faster
* make tests smaller
* Add example YAML file for training Mistral using DPO
* added deduplication code
* Add exact deduplication feature and update examples
* Improve deduplication for train/eval overlap
Changed the deduplication function to use a more memory-efficient hashing method. Applied Git suggestions to improve clarity and maintainability.\n\nThe deduplication now handles cases where train and eval datasets have overlapping elements.
* Improve deduplication for train/eval overlap
Changed the deduplication function to use a more memory-efficient hashing method. Applied Git suggestions to improve clarity and maintainability.\n\nThe deduplication now handles cases where train and eval datasets have overlapping elements.
* Apply suggestions from code review
To handle the original case where we do not do deduplication
Co-authored-by: Wing Lian <wing.lian@gmail.com>
* Improve false collision detection to ensure dataset integrity
- Added test cases to simulate and verify handling of forced hash collisions between datasets.
- Ensured that datasets with identical hashes but different content are correctly identified, preventing incorrect deduplication.
- Updated unit tests to include scenarios where collisions occur across both training and evaluation datasets, as well as within a single dataset.
* Moved the constants file to the tests folder
- Relocated `constants.py` to the `tests` folder to improve modularity and maintain a clear separation between source and test files.
- Renamed `cicd/tests.py` to `cicd/cicd_tests.py` to resolve a conflict with `tests/__init__.py`, which caused Mypy to fail due to duplicate module names.
- Updated all references to `cicd.tests` in the codebase to `cicd.cicd_tests` to reflect the renaming and ensure compatibility.
- These changes ensure Mypy passes the pre-commit hook and maintain alignment with the project's structure.
* revert some changes from previous commit and fix relative import
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Co-authored-by: Wing Lian <wing.lian@gmail.com>
Co-authored-by: Wing Lian <wing@axolotl.ai>
* wip add new proposed message structure
* tokenization
* wip
* wip transform builder
* wip make the chat dataset loadable
* wip chatml + llama 3 new chat objects
* chore: lint
* chore: lint
* fix tokenization
* remove dacite dependency since we're using pydantic now
* fix handling when already correctly split in messages
* make sure to remove chat features from tokenized ds
* move chat to be a input transform for messages
* make sure llama3 has the bos token
* remove non-working special token code
* fix messages strat loader
* Add support for `revision` dataset parameter
* only use revision on hf hub backed datasets
* use revision tied to head
* set download to use revision
* feat: add config to model validator class
* feat: add revision config to RL and tests for it
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Co-authored-by: Wing Lian <wing.lian@gmail.com>
Co-authored-by: NanoCode012 <nano@axolotl.ai>
* various batch of fixes
* more tweaks
* fix autoawq requirement for torch flexibility
* simplify conditionals
* multi-node fixes wip
* bump transformers and include 405b qlora+fsdp yaml
* phi-3 support and perplexity metric
* phi-3 chat template
* metrics updates
* chore: lint
* fix assertion on Tensor
* fix tests since tokenization happens in the metric
* fix perplexity value of shorter passage
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Co-authored-by: Wing Lian <wing.lian@gmail.com>
* wrap prepared_ds_path in str() to avoid TypeError in fsspec package
`fsspec` calls `if "::" in path` on `prepared_ds_path`, which will throw an error if it is a `PosixPath` object.
* update test too
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Co-authored-by: Wing Lian <wing.lian@gmail.com>
* Correctly handle splits for datasets.arrow_dataset.Dataset objects
The `load_tokenized_prepared_datasets` function currently has logic for loading a dataset from local path that always checks if a split is in the dataset. The problem is, if the dataset is loaded using `load_from_disk` and it is an Arrow-based dataset, *there is no* split information. Instead what happens is, by calling `split in ds`, it presumably searches through all the rows and columns of the arrow dataset object to find e.g., 'train' assuming `split == 'train'`. This causes the program to hang.
See https://chat.openai.com/share/0d567dbd-d60b-4079-9040-e1de58a4dff3 for context.
* chore: lint
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Co-authored-by: Wing Lian <wing.lian@gmail.com>
