* batch api HF adapter for ring-flash-attn; cleanup and improvements
* update
* adding all batch ring-flash-attn methods via single adapter
* removing pad_to_sequence_len=False for now
* fix
* updating docs to include batch SP
* review comments
* fixes for batch API funcs, simplify
* fixes
* fix
* updates
* add batch_zigzag smoke test
* fixes for delinearization, and make qlora work with fsdp2
* Add back mistakenly removed lm_eval
* typo [skip ci]
* patch evals for torch.compile + fsdp2
* also check torch_compile w fsdp2
* lots of fixes for flex attn with llama4
* fix patch check and patch llama4 too
* attempt to make the patches stick
* use transformers 4.51.2
* update configs and README for llama4
* remove torch.compile for CI test
* cleanup any existing singletons
* set singleton cache to None instead of deleting
* use importlib reload with monkeypatch
* don't worry about transformers version, mark inputs with grads, fix regex
* make sure embeds aren't on cpu
* logging and mem improvements
* vllm version and add to docker, make sure to save processor on conversion
* fix ambiguous tensor bool check
* fix vllm to not use v1, upgrade hf transformers
* fix tests
* make flex_attn_compile_kwargs configurable, since this depends on model params
---------
Co-authored-by: Wing Lian <wing@axolotl.ai>
Co-authored-by: Salman Mohammadi <salman.mohammadi@outlook.com>
* [ci] make e2e tests a bit faster by reducing test split size
* use 10% split of alpaca dataset to speed up dataset loading/tokenization
* reduce gas 4->2 for most e2e tests
* increase val set size for packing
* llama4 support
* add xet support [skip ci]
* be flexible on transformers version and skip test on version
* don't use deepspeed for the fix_untrained_tokens test
* reordering to trigger torch 2.6.0 tests first
* slightly smaller train set
* use 4.51.0 for now
* remove stray print, add llama4 chat template to schema, bump peft to 0.15.1
* patches to make llama4 performant
* add preliminary fp8 support
* fsdp2 support
* use accelerate release 1.6.0
* allow 8bit optims with fsdp2
* liger + torch compile fix
* add fsdp2 e2e tests
* use transformers commit with fsdp2 support
* skip zero3 tests for this PR for now
* fix fsdp2 config for ci
* make sure both flex and flash attn work with fsdp2, skip fix untrained tokens
* okay, actually use fdsp2...
* more fixes to flex for fsdp2
* make sure to patch all the loaded models
* additional validation for fsdp2, bump dep versions
* make torch 2.6.0 the default image
* fix tests against upstream main
* fix attribute access
* use fixture dataset
* fix dataset load
* correct the fixtures + tests
* more fixtures
* add accidentally removed shakespeare fixture
* fix conversion from unittest to pytest class
* nightly main ci caches
* build 12.6.3 cuda base image
* override for fix from huggingface/transformers#37162
* address PR feedback
* make gemma3 work with packing
* multi-gpu e2e for ci
* update gemma3 model namespace to use mirror
* add gradient checkpointing to multigpu e2e ci
* update gemma3 examples for use_reentrant and fix ddp find unused params
* fix tests for gemma3
* fix import for test utils
* set correct train loss for gemma3 e2e
* add grpo scale_rewards config for trl#3135
* options to connect to vllm server directly w grpo trl#3094
* temperature support trl#3029
* sampling/generation kwargs for grpo trl#2989
* make vllm_enable_prefix_caching a config param trl#2900
* grpo multi-step optimizeations trl#2899
* remove overrides for grpo trainer
* bump trl to 0.16.0
* add cli to start vllm-serve via trl
* call the python module directly
* update to use vllm with 2.6.0 too now and call trl vllm serve from module
* vllm 0.8.1
* use python3
* use sys.executable
* remove context and wait for start
* fixes to make it actually work
* fixes so the grpo tests pass with new vllm paradigm
* explicit host/port and check in start vllm
* make sure that vllm doesn't hang by setting quiet so outouts go to dev null
* also bump bnb to latest release
* add option for wait from cli and nccl debugging for ci
* grpo + vllm test on separate devices for now
* make sure grpo + vllm tests runs single worker since pynccl comms would conflict
* fix cli
* remove wait and add caching for argilla dataset
* refactoring configs
* chore: lint
* add vllm config
* fixup vllm grpo args
* fix one more incorrect schema/config path
* fix another vlllm reference and increase timeout
* make the tests run a bit faster
* change mbsz back so it is correct for grpo
* another change mbsz back so it is correct for grpo
* fixing cli args
* nits
* adding docs
* docs
* include tensor parallel size for vllm in pydantic schema
* moving start_vllm, more docs
* limit output len for grpo vllm
* vllm enable_prefix_caching isn't a bool cli arg
* fix env ordering in tests and also use pid check when looking for vllm
---------
Co-authored-by: Salman Mohammadi <salman.mohammadi@outlook.com>
* guard return if ring attn alrady registered
* add docs link, bits in multi-gpu docs, remove save model callback (subsumed by HF trainers)
* configurable heads_k_stride from ring-flash-attn hf adapter
* fix: update chat_template
* fix: handle gemma3 showing a lot of no content for turn 0
* fix: remove unknown config from examples
* fix: test
* fix: temporary disable gemma2 test
* fix: stop overwriting config.text_config unnecessarily
* fix: handling of set cache to the text_config section
* feat: add liger gemma support and bump liger to 0.5.5
* fix: add double use_cache setting
* fix: add support for final_logit_softcap in CCE for gemma2/3
* fix: set use_cache before model load
* feat: add missing layernorm override
* fix: handle gemma3 rmsnorm
* fix: use wrapper to pass dim as hidden_size
* fix: change dim to positional
* fix: patch with wrong mlp
* chore: refactor use_cache handling
* fix import issues
* fix tests.e2e.utils import
---------
Co-authored-by: Wing Lian <wing@axolotl.ai>
* hf offline decorator for tests to workaround rate limits
* fail quicker so we can see logs
* try new cache name
* limit files downloaded
* phi mini predownload
* offline decorator for phi tokenizer
* handle meta llama 8b offline too
* make sure to return fixtures if they are wrapped too
* more fixes
* more things offline
* more offline things
* fix the env var
* fix the model name
* handle gemma also
* force reload of modules to recheck offline status
* prefetch mistral too
* use reset_sessions so hub picks up offline mode
* more fixes
* rename so it doesn't seem like a context manager
* fix backoff
* switch out tinyshakespeare dataset since it runs a py script to fetch data and doesn't work offline
* include additional dataset
* more fixes
* more fixes
* replace tiny shakespeaere dataset
* skip some tests for now
* use more robust check using snapshot download to determine if a dataset name is on the hub
* typo for skip reason
* use local_files_only
* more fixtures
* remove local only
* use tiny shakespeare as pretrain dataset and streaming can't be offline even if precached
* make sure fixtures aren't offline
improve the offline reset
try bumping version of datasets
reorder reloading and setting
prime a new cache
run the tests now with fresh cache
try with a static cache
* now run all the ci again with hopefully a correct cache
* skip wonky tests for now
* skip wonky tests for now
* handle offline mode for model card creation
* pass additional info for fix untrained tokens when using distributed + offloading
* use latest version of vendored lib
* use v0.0.5 of contribs lgpl
* fix for no bad tokens and add tests
* use release
* add multigpu test too
* make sure the multigpu zero3 test actually uses zero3
* add muon optimizer
optimizer_cls_and_kwargs is on trainer_kwargs
only add adamw_kwargs if they're non-null
fix mocks
better handling of override and check the optimizer
unwrap optimizer
* fix import
* override special tokens mock code
* fix(doc): remove duplicate config
* feat: replace added_tokens in tokenizer and add test
* make sure to run tokenizer modification on rank 0 only
* use is local main process instead
* feat: rename config
---------
Co-authored-by: NanoCode012 <nano@axolotl.ai>
Co-authored-by: Wing Lian <wing@axolotl.ai>
* Extend MultiPackBatchSampler test to include shorter sequence length and drop long sequences filter
* Fix get_dataset_lengths for datasets that were previously filtered (e.g., with drop_long_seq_in_dataset)
* Update src/axolotl/utils/samplers/utils.py
Fix get_dataset_lengths for datasets that do not have position_ids or length attributes
Co-authored-by: NanoCode012 <kevinvong@rocketmail.com>
---------
Co-authored-by: NanoCode012 <kevinvong@rocketmail.com>
* feat: add config for optional parameters in a chat message
* chore: cleanup
* chore: fix nits and add light docs
* docs: update docs/dataset-formats/conversation.qmd
Co-authored-by: NanoCode012 <kevinvong@rocketmail.com>
* feat: configurable message mappings, jinja template analyzer
* chore: handle bradley terry
* docs: update docs
* refactor: change order of mappings, improve message transform
* refactor: make chat awware of property mappings
* chore: remove .python-version
* chore: revert change
* chore: add dataset validation to tests where appropriate
* chore: add dataset validation to tests where appropriate
* chore: clean up handling of ds_cfg
* chore: recursively serialize config
* make sure to use the return value from validate_config
* DefaultDict pickle/unpickle fix
* fix super call for override
* refactor: message fields
* chore: empty commit
* tests: validate config before using
* chore: add config validation to all e2e tests
* chore: add unneeded logging
* chore: add missed config validation
* chore: pass field_messages to prompter
* test: fix borked test
* chore: remove uninteded file
* chore: add deprecation warning and update chat_datasets script
* chore: lint
* refactor: message fields
* feat: update axolotlinputconfig and test_models
- add configdict import in axolotl/utils/config/models/input/v0_4_1/__init__.py
- remove unnecessary line breaks in sftdataset, dpodataset, ktodataset, stepwisesuperviseddataset classes
- update model_dump method in axolotlinputconfig to exclude none values
- correct typo in test_models.py comment
* feat: simplify dpodataset and ktodataset classes in config models
removed several optional fields from dpodataset and ktodataset classes in axolotl/utils/config/models/input/v0_4_1. this simplifies the configuration subsets for these datasets.
* feat: improve readability and structure in dataset configuration models
this commit enhances the readability and structure of the dataset configuration models in the `axolotl/utils/config/models/input/v0_4_1` module. it removes unused `configdict` import and adds line breaks to separate class definitions for better clarity. additionally, a minor documentation fix is included to ensure a newline at the end of the `stepwise_supervised.qmd` file.
* feat: change log level from info to debug in chattemplatestrategy
* feat(prompt_strategies): refactor chattemplateprompter and chattemplatestrategy
- Make `chat_template` a required parameter in `ChatTemplatePrompter` constructor
- Add default value for `message_property_mappings` in `ChatTemplatePrompter` constructor
- Add `messages_array_name` property to `ChatTemplatePrompter`
- Change `processor` type to Optional in `ChatTemplatePrompter`
- Add TypeError check for `processor` in `ChatTemplatePrompter.build_prompt`
- Remove `_messages` property from `ChatTemplateStrategy`
- Make `prompter` a required parameter and add type hint in `ChatTemplateStrategy` constructor
- Remove `messages` getter and setter from `ChatTemplateStrategy`
- Use `prompter.messages_array_name` in `ChatTemplateStrategy.get_conversation_thread`
- Remove condition to set `messages` field in `load` function
* feat(tests/utils): ignore type check in load_model call in test_models.py
* feat: improve type handling and test structure in chat templates
- Add return type hint for `get_chat_template` function in `chat_templates.py`
- Remove unnecessary assignment of `strategy.messages` in several test cases
- Add `messages_array_name` parameter to various test configurations in `test_chat_templates.py` and `test_chat_templates_advanced.py`
- Remove redundant `strategy.messages` assignment in `test_chat_templates_advanced.py`
* feat(axolotl): enhance chat strategy with datasetconfig support
This commit introduces support for DatasetConfig in the ChatTemplateStrategy. It also refines the strategy loader to handle different types of ds_cfg inputs and improves the clarity of the code by formatting and reordering. The key changes include:
- Importing Union from typing and BaseModel from pydantic.
- Adding DatasetConfig as an optional type for ds_cfg in StrategyLoader.
- Adjusting the handling of ds_cfg in StrategyLoader to account for BaseModel instances.
- Refactoring the prompter_params and strategy_params for better readability.
- Changing the reference from prompt[self.messages] to prompt[self.prompter.messages_array_name] in the is_prompt_batched method.
* feat: update message handling in btchattemplatestrategy
* Replace `self.messages` with direct string references to "chosen_messages" and "rejected_messages"
* Append system, user, and assistant content directly to "chosen_messages" and "rejected_messages"
* Add a new attribute "messages_array_name" to the `load` function parameters
* Remove the conditional attribute assignment for "field_messages" in the `load` function
* feat: add config validation in test_kd.py
- Import `validate_config` from `axolotl.utils.config`
- Validate the configuration in `test_llama_kd` and another function in `TestKnowledgeDistillation` class
* feat: enhance config validation and capabilities handling
* Import `EnvCapabilities` and `GPUCapabilities` from `axolotl.utils.config.models.internals`
* Update `validate_config` function to create `KTODataset` and `SFTDataset` instances using `dict(ds_cfg)`
* Replace `capabilities` and `env_capabilities` with instances of `GPUCapabilities` and `EnvCapabilities` respectively in `AxolotlConfigWCapabilities` model dump
* feat: update config validation in axolotl utils
- Remove import of `EnvCapabilities` and `GPUCapabilities` from `axolotl.utils.config.models.internals`
- Update `validate_config` function to use `capabilities` and `env_capabilities` directly instead of creating new instances of `GPUCapabilities` and `EnvCapabilities`
* feat: refactor strategyloader in chat_template.py
- Extracted the creation of strategy parameters into a separate function, `_get_strategy_params(cfg, dataset_config)`
- Created a new function, `_get_strategy_cls()`, to obtain the strategy class
- Replaced `ChatTemplateStrategy` with `strategy_cls` for strategy instantiation
* trigger CI
* chore: revert dataset config changes for kto/dpo
* subject: refactor: rename 'messages_array_name' to 'field_messages'
Body:
- Renamed 'messages_array_name' to 'field_messages' in 'ChatTemplatePrompter' class and its usages in 'chat_template.py'
- Updated 'load' function in 'bradley_terry/chat_template.py' to reflect the change
- Adjusted 'get_chat_template_msg_variables' and 'get_message_vars' methods in 'jinja_template_analyzer.py' to use the new variable name
- Modified 'StrategyLoader' in 'chat_template.py' to use 'field_messages'
- Updated tests in 'test_chat_templates.py' and 'test_chat_templates_advanced.py' to use 'field_messages' instead of 'messages_array_name'
* feat: refactor prompt strategies and update config models
* Remove redundant 'return None' in `axolotl/prompt_strategies/__init__.py`
* Simplify message handling in `axolotl/prompt_strategies/bradley_terry/chat_template.py` by using a single 'messages' list instead of separate 'chosen_messages' and 'rejected_messages' lists
* Update default 'message_property_mappings' in `axolotl/prompt_strategies/bradley_terry/chat_template.py`
* Add 'field_messages' field to `axolotl/utils/config/models/input/v0_4_1/__init__.py` configuration model
* chore: remove unused input
* chore: remove redundant type ignore
* fix: remove old configs and update examples
* fix: type check
* fix: remove loading old config in ChatMessage
* fix: update faq with potential new undefinederror
* fix: add debug if property mapped is not found
* chore: improve explanation for unmapped properties
* fix: update docs with new config
* chore: add note for deprecation config and del old config from dict
---------
Co-authored-by: NanoCode012 <kevinvong@rocketmail.com>
Co-authored-by: Wing Lian <wing@axolotl.ai>
Co-authored-by: NanoCode012 <nano@axolotl.ai>
* fix: drop long seq even if not sample packing
* fix: logging import
* fix: cfg passed being none
* fix: try to fix logging
* fix: refactor call to not use accelerate log
* fix: try to fix circular import issue
* fix: don't drop when skip prepare
* chore: remove duplicate line
* fix: update warning to mention that sequences will be trimmed
* fix: do not drop seq if input_ids don't exist
* fix: increase RM unittest sequence length to reduce trim warnings
* fix: solve conflicts
* fix: default min_seq_len in case of None
* refactor trainer to prevent circular dependencies later
fix loader default
KD dataset loading and KD with logprobs
filter bad rows
make batch smaller
handle padding/collation for KD datasets
make it work
flipped the slice
cross entropy loss coefficient during KD
make sure to multiply against the correct loss
chore: lint
triton wip
no where support
v2 trial
no torch.exp inside triton kernel
no log etc
no torch.tensor
v3
fix kwarg
don't use triton for now
better rescaling for temperatures
hash for temperature too
use kd_alpha in the correct loss method
fix kd loss so it's causal (fixes repeating tokens)
var naming and add todo
chore: lint
refactor so we can easily add new loss functions
add license block
remove references to triton kd for now
handle token/logprob shifting
support for custom trainer classes from plugins
refactor kd chat template loader
move more things to kd plugin
remove moved class from import
make plugin setup concise
increase logging around loading plugins
add copyrights
remove duplicate code
more info on preprocess for kd and fix import
be a bit pickier about loading dynamic prompt strategies
kd sample packing
make loss torch script compat
support streaming for processing sft datasts?
improve iterable support
ensure that batch vs single is done properly
tweak check for batched prompt data
reward can use same batch check
fix reward trainer calls for tokenization
improve check for batched
reward model doesn't work well with batched
add kd trainer e2e test
linting
rename test files so it gets picked up
make the kd e2e fit in vram for ci and add lora version
set lora_dropout explicitly
lower lr
make sure to set tokenizer from l3 70b and save safetensors
make sure to use the correct tokenizer
fix adapter model check
make sure to use tensorboard to capture loss for checks
chore: lint
chore: lint
improve logprob masking and shift in trainer
more fixes
try tests for kd on l40s
don't shift student logits for kd
no batching for kd chat templates
make sure to truncate logprobs if there are more than top_k
change up logic so we always truncate to top_k
use iter instead of tuple
fix finding the top-k rather than assuming first position has the correct val
apply z-score scaling to kd
kd loss needs to be calculated in full precision
Always re-normalize teacher distribution
various fixes
* support for configurable top-k/softmax ordering
* add attribute check for filter rows and lint
* fix logic
* handle none case for conversion to int
* fix student logit off by one
* set kd_temp to 1.0 for test loss
* address PR feedback
