* fix: handle legacy conversation data format and check image in data
* feat: add test for llama vision
* feat: add max_steps to test
* fix: incorrect indent and return preprocess
* feat: use smaller model and dataset
* chore: add extra config for sharegpt dataset
* 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
* see if unsloth installs cleanly in ci
* check unsloth install on regular tests, not sdist
* fix ampere check exception for ci
* use cached_property instead
* add an e2e test for unsloth qlora
* reduce seq len and mbsz to prevent oom in ci
* add checks for fp16 and sdp_attention
* pin unsloth to a specific release
* add unsloth to docker image too
* fix flash attn xentropy patch
* fix loss, add check for loss when using fa_xentropy
* fix special tokens for test
* typo
* test fa xentropy with and without gradient accum
* pr feedback changes
* support seperate lr for embeddings, similar to loraplus
* add test case for train w lr embedding scale
* use kwarg for optimizer
* make sure to handle the optimizer creation
* make sure to handle for embedding_lr too
* use smollm for e2e, check for embeddings lr first before wdecay
* Update `get_unpad_data` patching for multipack
* Update src/axolotl/utils/models.py
* Update src/axolotl/utils/models.py
* Add test case
---------
Co-authored-by: Wing Lian <wing.lian@gmail.com>
Co-authored-by: Wing Lian <wing@axolotl.ai>
* remove the bos token from dpo outputs
* don't forget to fix prompt_input_ids too
* use processing_class instead of tokenizer
* fix for processing class
* add more test cases for gradient accumulation and fix zero3
* swap out for smaller model
* fix missing return
* fix missing pad_token in config
* support concurrency for multigpu testing
* cast empty deepspeed to empty string for zero3 check
* fix temp_dir as fixture so parametrize works properly
* fix test file for multigpu evals
* don't use default
* don't use default for fsdp_state_dict_type
* don't use llama tokenizer w smollm
* also automatically cancel multigpu for concurrency
* upgrade liger to 0.3.1
* update docs and example
* skip duplicate code check
* Update src/axolotl/integrations/liger/args.py
Co-authored-by: NanoCode012 <nano@axolotl.ai>
* Update README.md
Co-authored-by: NanoCode012 <nano@axolotl.ai>
* add logging
* chore: lint
* add test case
* upgrade liger and transformers
* also upgrade accelerate
* use kwargs to support patch release
* make sure prepared path is empty for test
* use transfromers 4.46.1 since 4.46.2 breaks fsdp
---------
Co-authored-by: NanoCode012 <nano@axolotl.ai>
* remove skipped test
* use mean_resizing_embeddings with qlora and added tokens
* use </s> as pad_token to prevent resize of embeddings
* make sure local hub test saves to a tmp dir
* use Path so concatenation works
* make sure to use tmp_ds_path for data files
* feat: support new arg num_items_in_batch
* use kwargs to manage extra unknown kwargs for now
* upgrade against upstream transformers main
* make sure trl is on latest too
* fix for upgraded trl
* fix: handle trl and transformer signature change
* feat: update trl to handle transformer signature
* RewardDataCollatorWithPadding no longer has max_length
* handle updated signature for tokenizer vs processor class
* invert logic for tokenizer vs processor class
* processing_class, not processor class
* also handle processing class in dpo
* handle model name w model card creation
* upgrade transformers and add a loss check test
* fix install of tbparse requirements
* make sure to add tbparse to req
* feat: revert kwarg to positional kwarg to be explicit
---------
Co-authored-by: Wing Lian <wing.lian@gmail.com>
* 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
* 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
* 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
* 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
* 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
* 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>
* add example for mistral orpo
* sample_packing: false for orpo
* go to load_dataset (since load_rl_datasets require a transfom_fn, which only dpo uses currently)
* run tests again on Modal
* make sure to run the full suite of tests on modal
* run cicd steps via shell script
* run tests in different runs
* increase timeout
* split tests into steps on modal
* increase workflow timeout
* retry doing this with only a single script
* fix yml launch for modal ci
* reorder tests to run on modal
* skip dpo tests on modal
* run on L4s, A10G takes too long
* increase CPU and RAM for modal test
* run modal tests on A100s
* skip phi test on modal
* env not arg in modal dockerfile
* upgrade pydantic and fastapi for modal tests
* cleanup stray character
* use A10s instead of A100 for modal
* add missing evals_per_epoch setting
* more pydantic fixes
* more fixes
* move test from normalization to validation
* increase eval size for sample packing tests
* make mlflow optional
* fix xformers
don't patch swiglu if xformers not working
fix the check for xformers swiglu
* fix install of xformers with extra index url for docker builds
* fix docker build arg quoting
