* add system message to template
* readme update
* added code to register new system message
* register chatml template for test
---------
Co-authored-by: Mads Henrichsen <mads@BrbartiendeMads.lan>
Co-authored-by: Wing Lian <wing.lian@gmail.com>
* phi2 multipack
* update validation and examples for phi
* more updates to phi examples
* make sure to use the correct collator for phi multipack
* phi needs attention mask now for multipack
* if the special token already exists in the tokenizer, don't require in lora modules to save
* fix qlora yml for phi, fix phi test validation
* test qlora too
* make sure flash attention is enabled for the test
* don't use remote code for phi anymore
* reduce sequence len for sample packing phi
* cleanup dpo to be a little more extensible, add zephyr/nectar strategy
* fix eos slash
* support for eval split
* fix kwargs
* handle empty evals
* don't load peft model for dpo
* ensure dpo traning args gets bf16 for peft if applicable
* fix duplicate kwargs for bf16
* make sure to respect the configured lr scheduler
* supprt trainer callback to push config to wandb
* set dataloader preload args
* ensure that we are loading the lora when merging
* Update src/axolotl/utils/data.py
Co-authored-by: Agus <agustin.piqueres@gmail.com>
* support local datasets for dpo
Co-authored-by: Agus <agustin.piqueres@gmail.com>
* chore: lint
* dpo/kto/ipo smoke tests w lora, simplify dpo dataset type names
* add split to dpo tests
* fix rebase/merging error
* handle edge case w logging
* use accelerator for dpo datasets so it doesn't break the logger
* missing args
* validate checkpoint is an adapter for now
* log warning when dataset strategy is not loadable
---------
Co-authored-by: Agus <agustin.piqueres@gmail.com>
* also fix multipack for falcon and add smoke tests
* make sure to handle special tokens and added tokens for lora
* fix reference to model_type
* fix tests for falcon
* fix stray typo
* fixes for smoke tests
* set fp16 to false if bf16, update bf16: auto in example YAMLs
* unset fp16 so that it fallsback properly if bf16 isn't available
* Update README.md [skip-ci]
Co-authored-by: NanoCode012 <kevinvong@rocketmail.com>
* test that bf16 disables fp16
---------
Co-authored-by: NanoCode012 <kevinvong@rocketmail.com>
* Add s2_attn to hijack flash code
* Refactor code to account for s2_attn
* Add test for models utils
* Add ``s2_attention`` option to llama configs
* Add ``s2_attention`` option to README config
* Format code to appease linter
* chore: lint
* Remove xpos and llama-landmark [bad merge]
* add e2e smoke tests for shifted sparse attention
* remove stray patch from merge
* update yml with link to paper for s2_attention/longlora
* fix assertion check for full fine tune
* increase sequence len for tests and PR feedback updates
* reduce context len to 16k for tests
* reduce context len to 16k for tests
* reduce batch size for larger context len and udpate test to check message
* fix test for message
---------
Co-authored-by: joecummings <jrcummings@devvm050.nha0.facebook.com>
Co-authored-by: Wing Lian <wing.lian@gmail.com>
* keep gate in fp32 for loras
* add e2e check for lora w/o flash attention for mixtral to check gate
* add checks for gate in fp32 for mixtral, add typehints to train outputs
* mixtral doesn't support basic lora 🤦
add lora tests @ 16bit and fix gate layer check
fix the parameter name, was using the old disco name
don't lora over the gate so we can check that is in fp32
fix dtype check
* ensure we're using fp16/bf16 for 16bit and qlora is always going to be in uint8
* fix: `train_on_inputs: true` ignored for sharegpt
* enable unit test for train_on_inputs for sharegpt
---------
Co-authored-by: Wing Lian <wing.lian@gmail.com>
* attempt to also run e2e tests that needs gpus
* fix stray quote
* checkout specific github ref
* dockerfile for tests with proper checkout
ensure wandb is dissabled for docker pytests
clear wandb env after testing
clear wandb env after testing
make sure to provide a default val for pop
tryin skipping wandb validation tests
explicitly disable wandb in the e2e tests
explicitly report_to None to see if that fixes the docker e2e tests
split gpu from non-gpu unit tests
skip bf16 check in test for now
build docker w/o cache since it uses branch name ref
revert some changes now that caching is fixed
skip bf16 check if on gpu w support
* pytest skip for auto-gptq requirements
* skip mamba tests for now, split multipack and non packed lora llama tests
* split tests that use monkeypatches
* fix relative import for prev commit
* move other tests using monkeypatches to the correct run
* fix double eos token for chatml
* isolate fix to chatml conversation
* fix add special tokens to include rstrip
* add test for train_on_inputs for sharegpt
* don't use rstrip for chatml
* restore to current phi modeling code from phi-2
* enable gradient checkpointing
* don't cast everything to float32 all the time
* gradient checkpointing for phi2 ParallelBlock module too
* fix enabling flash attn for phi2
* add comment about import
* fix phi2 example
* fix model type check for tokenizer
* revert float32 -> bf16 casting changes
* support fused dense flash attn
* fix the repo for flash-attn
* add package name for subdir pkg
* fix the data collator when not using sample packing
* install packaging for pytests in ci
* also fix setup to not install flash attn fused dense subdir if not extras
* split out the fused-dense-lib in extra requires
* don't train w group_by_length for phi
* update integration test to use phi2
* set max steps and save steps for phi e2e tests
* try to workaround ssave issue in ci
* skip phi2 e2e test for now
* [Feat] streaming multipack
* WIP make continued pretraining work w multipack
* fix up hadrcoding, lint
* fix dict check
* update test for updated pretraining multipack code
* fix hardcoded data collator fix for multipack pretraining
* fix the collator to be the max length for multipack pretraining
* don't bother with latest tag for test
* cleanup docker build/test
---------
Co-authored-by: jinwonkim93@github.com <jinwonkim>
Co-authored-by: Wing Lian <wing.lian@gmail.com>
* ipo-dpo trainer
* fix missing abstract method
* chatml template, grad checkpointing kwargs support
* fix steps calc for RL and add dataloader kwargs
* wip to fix dpo and start ppo
* more fixes
* refactor to generalize map fn
* fix dataset loop and handle argilla pref dataset
* set training args
* load reference model on seperate gpu if more than one device
* no auto upload to hub for dpo, don't add lora adapters to ref model for dpo
* fixes for rl training
* support for ipo from yaml
* set dpo training args from the config, add tests
* chore: lint
* set sequence_len for model in test
* add RLHF docs
* bump transformers and update attention class map name
* also run the tests in docker
* add mixtral e2e smoke test
* fix base name for docker image in test
* mixtral lora doesn't seem to work, at least check qlora
* add testcase for mixtral w sample packing
* check monkeypatch for flash attn multipack
* also run the e2e tests in docker
* use all gpus to run tests in docker ci
* use privileged mode too for docker w gpus
* rename the docker e2e actions for gh ci
* set privileged mode for docker and update mixtral model self attn check
* use fp16/bf16 for mixtral w fa2
* skip e2e tests on docker w gpus for now
* tests to validate mistral and mixtral patches
* fix rel import
* Feat: Auto add to modules_to_save when adding tokens
* fix: swap to error instead of warning
* feat: add check when special_tokens differ and add test
* start at index 0
* add test to check for missing turns
* apply black
* Update test_prompt_tokenizers.py
* Update src/axolotl/monkeypatch/fastchat_conversation_turns.py
Co-authored-by: Motoki Wu <tokestermw@gmail.com>
* fix linting
* apply black
* add more tests for llama/sharegpt
* make logic clearer
---------
Co-authored-by: Motoki Wu <tokestermw@gmail.com>
* Respect sequence_len in config for `type: llama2_chat`
It was hardcoded to `4096` I am not sure why? This updates it to pull from the config.
cc: @winglian
* Update llama2_chat.py
* apply black formatting
* fix tokenizer
* update test data
* lint fixtures
* support for mamba
* more mamba fixes
* use fork for mamba kwargs fix
* grad checkpointing doesn't work
* fix extras for mamaba
* mamba loss fix
* use fp32 and remove verbose logging
* mamba fixes
* fix collator for mamba
* set model_type on training_args
* don't save safetensors for mamba
* update mamba config to disable safetensor checkpooints, install for tests
* no evals for mamba tests
* handle save_pretrained
* handle unused safetensors arg
* Feat: Update to handle wandb env better
* chore: rename wandb_run_id to wandb_name
* feat: add new recommendation and update config
* fix: indent and pop disabled env if project passed
* feat: test env set for wandb and recommendation
* feat: update to use wandb_name and allow id
* chore: add info to readme
* 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
