* 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
* support for llama multipack using updated code/patches
* also support unsloth patches
* incorrect arg
* add config validation for unsloth
* add missing return to validation
* add another missing return to validation
* 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
* sanity check ranges in freeze.py
this will catch problems earlier and more clearly.
in my case, it appears that deepspeed zero3 sets layer tensor shapes
to [0], which doesn't play well with automatically inferred ranges.
through a bit of luck, inverting ranges still appears to work correctly.
* simplify chained comparison
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
The strategy now supports configuring several fields: * The data field holding message arrays * the role and
content fields for each message * role mapping from source to target types
additionally this adds a sample llama3-8b instruct template using the chat template
* Switch to parallel FFD bin packing algorithm.
Add support for packing in a distributed context.
Add packing efficiency estimate back.
* revert changes to distributed code
* chore: lint
* fix config w new params for packing test
* add sample_packing_group_size and sample_packing_bin_size to cfg schema
* fix lamdbda function
* fix sampler/dataloader calculations for packing
---------
Co-authored-by: dsesclei <dave@sescleifer.com>
* Fix llama3 chat_template (the {{eos_token}} leads to an extra <|eot_id|> being added in the last turn). Output now matches official Llama 3 Instruct model
* add tests
* chore: lint
---------
Co-authored-by: Wing Lian <wing.lian@gmail.com>
* add kto support
* test cleanup
* fix outdated comment
* fix llama3 ultra
* chore: lint
* update to use rl_beta instead of dpo_beta
---------
Co-authored-by: Wing Lian <wing.lian@gmail.com>
* WIP for unsloth integrations
* import the unsloth code in the right context
* add unsloth mlp, qkv, o lora optimizations
* apply unsloth mlp and qkv kernels
* FIX: TRL trainer preprocessing step was running in one process
* FIX: max_length and max_prompt_length was not being sent to ORPOTrainer
* FIX: Change ORPO max prompt length to 1/4 of max length, otherwise we get strange behaviour
* FIX: Removed change from a different PR
* FIX: Black fix
* explicitly set max prompt len for orpo config
---------
Co-authored-by: Ali Mosavian <ali.mosavian@kry.se>
Co-authored-by: Wing Lian <wing.lian@gmail.com>
