* 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>
* Fix XFormers attention for Llama-2 70B (GQA)
Updated XFormers MonkeyPatch to handle GQA as used in Llama-2 70B. All the updated code is taken directly from the Transformers library: 07360b6c9c (diff-06392bad3b9e97be9ade60d4ac46f73b6809388f4d507c2ba1384ab872711c51) from their llama_modeling.py file.
* Catch configs without pretraining_tp
* Whitespace bug fix
Command had accidentally been moved out of if-else block.
* pre-commit formatting fixes
Thanks to @winglian
