* add ds zero3 to multigpu biweekly tests
* fix for upstream api change
* use updated accelerate and fix deepspeed tests
* stringify the Path, and run multigpu tests if the multigpu tests change for a PR
* use correct json rather than yaml
* revert accelerate for deepspeed
* 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
---------
Co-authored-by: Wing Lian <wing.lian@gmail.com>
Co-authored-by: NanoCode012 <nano@axolotl.ai>
* Add first version of a Comet integration
* Remove debug prints
* Add test for Comet Configuration transformation to env variables
* Fix last lint warning
* Update Readme for Comet logging documentation
* Update Comet integration to be optional, update code and tests
* Add documentation for Comet configuration
* Add missing check
* Attempt to run multigpu in PR CI for now to ensure it works
* fix yaml file
* forgot to include multigpu tests
* fix call to cicd.multigpu
* dump dictdefault to dict for yaml conversion
* use to_dict instead of casting
* 16bit-lora w flash attention, 8bit lora seems problematic
* add llama fsdp test
* more tests
* Add test for qlora + fsdp with prequant
* limit accelerate to 2 processes and disable broken qlora+fsdp+bnb test
* move multigpu tests to biweekly
* Add flexible configuration options for chat dataset training
- Introduce roles_to_train parameter to set training labels by role
- Add train_on_eos option to configure training on end-of-sequence tokens
- Implement per-message training configuration in dataset
- Allow fine-grained control over training specific portions of messages
- Add message_field_training and message_field_training_detail settings
- Implement mapping between dataset character offsets and tokenized prompt
- Enhance test suite to cover new functionality
* Fix missing field inits, things weren't working from yaml.
* Add flexible configuration options for chat dataset training
- Introduce roles_to_train parameter to set training labels by role
- Add train_on_eos option to configure training on end-of-sequence tokens
- Implement per-message training configuration in dataset
- Allow fine-grained control over training specific portions of messages
- Add message_field_training and message_field_training_detail settings
- Implement mapping between dataset character offsets and tokenized prompt
- Enhance test suite to cover new functionality
* Fix missing field inits, things weren't working from yaml.
* chore: lint
* Revert test repo back to NousResearch after opening PR to fix the tokenizer_config.json.
---------
Co-authored-by: Wing Lian <wing.lian@gmail.com>
* swaps to use newer sample packing for mistral
* fix multipack patch test
* patch the common fa utils
* update for refactor of flash attn unpad
* remove un-needed drop attn mask for mistral
* bump transformers to main to pick up latest mistral fix for 12b and refactor of fa2
* update test
* Implementing a basic chat_template strategy for DPO datasets
This mimics the sft chat_template strategy such that users can:
* Specify the messages field
* Specify the per message role and content fields
* speicfy the chosen and rejected fields
* Let the tokenizer construct the raw prompt
* Ensure the chosen and rejected fields don't have any prefix tokens
* Adding additional dpo chat template unittests
* Rename test class
* fixes to accelerator so that iterable pretraining datasets work
* fix the pretraining test params
* split batches, not dispatch batches needs to be set
* update c4 datasets
* set epochs in pretrain config test
* need to set both split_batches and dispatch_batches to false for pretraining
* fix bool val in comment
* add support for optimi_adamw optimizer w kahan summation
* pydantic validator for optimi_adamw
* workaround for setting optimizer for fsdp
* make sure to install optimizer packages
* make sure to have parity for model parameters passed to optimizer
* add smoke test for optimi_adamw optimizer
* don't use foreach optimi by default
* bump flash attention 2.5.8 -> 2.6.1
* use triton implementation of cross entropy from flash attn
* add smoke test for flash attn cross entropy patch
* fix args to xentropy.apply
* handle tuple from triton loss fn
* ensure the patch tests run independently
* use the wrapper already built into flash attn for cross entropy
* mark pytest as forked for patches
* use pytest xdist instead of forked, since cuda doesn't like forking
* limit to 1 process and use dist loadfile for pytest
* change up pytest for fixture to reload transformers w monkeypathc
Allow in message objects the additional key `weight`, which can be set
to 0 (or 1) to cause that message to be masked out (or left unmasked)
for training (similar to [1]). This is helpful for training the model to be robust and
capable of error recovery upon a bad assistant message.
A missing `weight` key defaults to weight 1, to guarantee downward compatibility.
[1]: https://github.com/mistralai/mistral-finetune
* 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
---------
Co-authored-by: Wing Lian <wing.lian@gmail.com>
* re-enable DPO for tests in modal ci
* workaround for training args
* don't mixin AxolotlTrainingArguments
* fix mixin order so MRO doesn't result in
TypeError: non-default argument follows default argument error
* use smaller datasets for dpo tests
