* add phi modeling from hf
* update for packing and use new modeling class for phi
* update e2e tests for phi to use new model name
* update example phi to also use new phi model name
* use AutoModelForCausalLM for phi lora since sample packing isn't supported
* use tensorboard to see if resume from checkpoint works
* make sure e2e test is either fp16 or bf16
* set max_steps and save limit so we have the checkpoint when testing resuming
* fix test parameters
* support for sharegpt with assistant talking first, better masking of assistant token, allow remap of roles from dataset
* invalid role is actually not possible
* update tokenized fixture for corrected labels
* Allow usage of native Mistral FA when no sample_packing
* fix: do not apply custom patch when sample_pack off
* chore: lint
* chore: pin transformer to v4.35.0.dev0
* fix: split sample_packing to separate test
* Fix(cfg): Check save_strategy cfg conflict with save_steps
* Fix(cfg): Check evaluation_strategy cfg conflict with eval_steps
* chore: add extra check for steps only
* use fastchat conversations template
* require fastchat (fschat) pip install
* handle roles dynamically from conversation
* tweak fastchat conversation with a monkeypatch to get individual turns
* fix up so it works with multiple conversation styles, and don't strip the turns
* fix sharegpt fixture now that we're using a more correct tokenization
* use a new prompter and support fastchat conversation type
* use sharegpt from prompt strategies now
* update docs, add chatml template
* add a newline after im_end token
* ensure we correctly set system message
* update per PR feedback to handle deprecated sharegpt types
* don't add duplicate wandb req
* make sharegpt fields configurable from yml
* llama2 fixes
* don't fail fatally when turns are improper
* phi sequence packing
* sample packing fixes
* fix linting
* fix inference and phi e2e tests
* update phi example now that sample packing works
* wandb import keeps getting moved around
* return without packing prep/len
* fix remove columns
* fix encode arguments
* add error when max steps not set
* fix test
---------
Co-authored-by: Jan Philipp Harries <jphme@users.noreply.github.com>
* fix attetion mask with packing
* set position ids and use block diagonal attn mask
* fix expand mask for multiple batch items, make sure we pad position_ids
* don't move masks to cpu
* use multi pack dataloader w random sampler
* add position_ids back
* more fixes for dataloader integration
* est total tokens, fix field loop
* more fixes, position_ids seems broken
* more fixes for sample packing
* use distributed sampler, avoid accelerate prepare
* use accelerator prepare for dataloader
* fix for position_ids w packing
* Update src/axolotl/utils/dataloader.py
* validation for sample packing and doc
* more fixes for 4k and optimizations
* optimized expand mask fn
* better handling of variance in multipack dataloader length and trainer hanging when it runs out of data
* fix rounding of len of batches to int
* better handling so that all devices have the same dataloader len
* fix step calc for packing
* pass sample packing efficiency to training args
* add a test for the mask expansion for sequence packing
* only process eval dataset for packing if not None
* don't split batches when packing
* weighted CE losses
* weighted CEL fixes
* limit packing to sequences of max seq len
* seq_len_multiple for packing
* make sure the chunk size is an int
* sample_packing_seq_len_multiplier config
* use cumulative seq len with var len flash attn v2 w packing
* properly calculate max len
* fix flash-attn, xformers, packing, support chatml
* fix chatml system prompt for openorca, legacy tokenizer opts
* add chatml
* add unit tests for cum seq lens, add ability to build cu_seq_lens from positional ids, fix prompt test
* fix test and pylint checks
* more packing and dataset optimizations and fixes
* filter w multiple cpus
* more fixes and optimizations
* fixes and go back to distributed sampler since batch sampler won't work
* fix counts by accounting for num devices
* fix steps calculation
* previous accelerate is still most performant
* add numba to requirements.
* use custom distributed checks
* fix sampler to prevent overfit w new epochs
* let's not cleanup the cached datasets
* calculate cum seq lens with pos_ids instead of mask, simplify packing params, fix distributed barrier
* speed optimizations and set accelerate fsdp env vars
* optimize dataset concatenation?
* more optimizations for dataset handling
* fix import for annotation
* manual pre-commit fixes
* another sum optimization and bug fix for calc steps
* fix packing estimations
* fix formatting
* pylint problems
* add back flash attention branch for handling unpacked sequences seperately
* Address PR feedback
* add optional sample packing config params to readme
* experimental llama 2 chat support
* few small fixes
* llama2_chat
* small fix to follow original implementation
* small fixes and added fixtures/tests
* fix -mixed up inference and finetuning conversations
* args - small fix
* small fix
* small adjustment and warning
* fix with pre-commit
---------
Co-authored-by: Jan Philipp Harries <jpdus@users.noreply.github.com>
