* EBFT wip
* fixes
* more fixeS
* add missing strided module
* ebft fixes for multi-turn
* make ebft work with async
* add example for ebft w qwen3.5
* fix for split thinking and update yaml for lora over linear attention only
* enforce_eager for vllm arg in schema
* fix sync weights
* fix multi-gpu
* handle updated sig for mm
* ddp fixes
* improve multi-gpu handling, don't calculate logits, adaptive completion length
* chore: lint
* chore: lint
* support completion_mean
* Address corereview feedback
* clamp min IS ratio
* Address PR code review
* more fixes identified
* address code review
* Fix property from rebase conflict
* roundup_power2_divisions not needed with newer pytorch versions
* remove typo
* update qwen3.5 moe 35b-a3b yaml for 5090
* more bug fixes
* fix tests to match updated trainer
* don't use fa2 for hooks test
* reset plugins on the instance
* retry download
* fix references to renamed axolotl_cfg property on trainer
* Fix ref to trainer cfg
* feat: LoRA kernel support for bias, dropout, dora, embeddings
* chore: lint
* chore: lint
* address PR feedback, add regression tests, add fsdp2 tests for lora kernels
* update tests for new sigs
* update tests now that bias and dropout are supported
* fix token state json and mistral tokenizer issue
* centralize constants
* forgot to commit constants file
* Fix weakref in pickling relora state dict
* make curl a bit quieter so it doesn't log 2K lines
* fix path traversal for olmoe test
* more test fixes that weren't flagged previously
* chore: lint
* skip tests that fail b/c of OutOfResources
* scattermoe as slow tests
* update fbgemm-genai for torch 2.10
Transformers 5.x routes attention through sdpa_attention.py and no longer
calls the _prepare_4d_causal_attention_mask* or _expand_mask functions that
these patches targeted. This makes the following patches dead code:
- llama_patch_multipack.py (patched _prepare_4d_causal_attention_mask*)
- llama_expand_mask.py (patched _expand_mask, never called)
- Related utility functions in monkeypatch/utils.py
Closesaxolotl-ai-cloud/axolotl#3331
* optimize moe + lora
* more scattermoe optims
* selective dequant
* add correctness unit tests and benchmarks for scattermoe + lora
* handle base+lora split kernel for older moe models
* chore: lint
* fix casting for H200 and B200
* register pressure estimation and pruning for h200/b200
* use soft limit for pruning
* qkv patch for qwen3.5moe
* support text_model for qwen3.5 moe
* nesting of qwen3
* use udpated cce with zero3 support
* Fix decomposed backward for QKV and O projections
eliminates B @ A materialization in LoRA attention backward, replacing full [out, in] matmuls with two small [T, R] matmuls.
* use custom triton kernels for entropy from logits and selective softmax
* PR comments fixes
* fix out of bounds, include tests, include benchmarks
* chore: lint
* async grpo support
* implement data producer
* use fast async
* handle call to create data producer
* fix liger kernel setup
* fix replay buffer
* chore: lint
* make gpus go brrr
* chore: lint
* inplace div_, unwrap model for logits in bf16
* fuse selective softmax and empty cuda cache on each scoring step
* remove waiting for synch time and fix race
* make fp8 work and allow lora kernels w rl
* grpo with lora vllm sync and fixes for sharded distributed
* update docs
* more patches so it works against trl main
* address PR feedback for corerabbit
* consolidate behavioud of routing in scattermoe kernels
* collect telemetry on best chosen autotuned kernel
* properly collect data
* Fix property name and get smem too
* handle issues raised by coderabbit
* add tests for parity before refactoring
* docs: fix codestyle placeholders in CONTRIBUTING.md
Replace unresolved {codestyle} and {URLofCodestyle} template
variables with Ruff, the project's actual linter/formatter
as configured in .pre-commit-config.yaml.
* fix: replace bare except clauses with specific exception types
- quantization.py: use except ImportError for optional torchao imports
(consistent with line 48 which already uses ImportError correctly)
- cli/config.py: use except (RuntimeError, AssertionError) for CUDA
device property query
Prevents masking unrelated errors like KeyboardInterrupt or SystemExit.
* test: add unit tests for convert.py JSON/JSONL utilities
Cover FileReader, FileWriter, StdoutWriter, JsonParser,
JsonlSerializer, and JsonToJsonlConverter with 8 test cases
including roundtrip and edge case (empty list) scenarios.
Previously this module had zero test coverage.
* fix: address CodeRabbit review feedback
- quantization.py: catch (ImportError, RuntimeError) for optional
torchao imports; CUDA wheel/GPU mismatches raise RuntimeError,
not ImportError
- convert.py: remove unused output_file_path parameter from
JsonToJsonlConverter.convert() — FileWriter already holds the
output path from construction
- tests/test_convert.py: update call site to match new signature
* install flash-linear-attention
* handle prequant weights for fsdp2 and ensure loss is not zero
* fix type for cu_seqlen, uninstall causal_conv1d
* chore: lint
* uv pip uninstall doesn't need confirmation
* upgrade transformers==5.3.0 trl==0.29.0 kernels
* use latest deepspeed fixes
* use corect image for cleanup
* fix test outputs for tokenizer fixes upstream
* fix import:
* keep trl at 0.28.0
* handle updated API
* use latest trl since 0.28.0 doesn't work with latest transformers
* use trl experimental for pad to length
* monkeypatch trl with ORPOTrainer so liger doesn't croak
* upgrade accelerate
* more fixes
* move patch for orpotrainer
* load the imports later
* remove use_logits_to_keep
* fix loss_type arg as a list
* fetch hf cache from s3
* just manually download the missing model for now
* lint for pre-commit update
* a few more missing models on disk
* fix: loss_type internally now list
* fix: remove deprecated code and raise deprecate
* fix: remove unneeded blocklist
* fix: remove reliance on transformers api to find package available
* chore: refactor shim for less sideeffect
* fix: silent trl experimental warning
---------
Co-authored-by: NanoCode012 <nano@axolotl.ai>
* mxfp4 axo
* import lint
* test for qat mxfp4
* config for mxfp4
* add qat:
* pass base config
* MXFakeQuantizeConfig
* lint
* tune config so it fits in 32GB VRAM
---------
Co-authored-by: Wing Lian <wing@axolotl.ai>
* fix: run deduplication before saving dataset during preprocessing
Move deduplicate_and_log_datasets call before save_preprocessed_dataset
in both SFT and RL data loading pipelines. This ensures the saved
preprocessed dataset is already de-duplicated, so subsequent loads
from cache don't contain duplicates.
Fixes#2719
* fix: include deduplication flag in dataset hash and warn on skip_prepare_dataset+dedup
- Add dataset_exact_deduplication to the hash string in
generate_dataset_hash_from_config so cached datasets are invalidated
when the dedup setting changes.
- Log a warning when skip_prepare_dataset=True and
dataset_exact_deduplication=True, since dedup will be silently
skipped in that configuration (both SFT and RL paths).
* fix: add ValueError for skip_prepare+dedup, fix test mock target and formatting
- Add config validator (check_deduplication_with_skip_prepare) that raises
ValueError when skip_prepare_dataset=True and dataset_exact_deduplication=True
- Replace runtime warnings in sft.py/rl.py with the validator check
- Fix RL test: patch axolotl.utils.data.rl.load_tokenizer instead of
axolotl.loaders.load_tokenizer to properly mock the imported reference
- Fix ruff lint (remove unused imports) and formatting issues
* refactor: inline deduplicate function per review feedback
* fix test fixture, lint
---------
Co-authored-by: ManasVardhan <manasvardhan@users.noreply.github.com>
Co-authored-by: Wing Lian <wing@axolotl.ai>
* Add test cases to verify that the problem exists in the underlying
* Update the handle_long_sequences function to correctly use Map instead of filter for the truncation strategy. Also remove the minimal length filtering from the truncate_long_samples function, and run it separately and before.
* fix: refactor and add test truncate for non-input id fields
* fix: refactor long seq handling fn
* fix: refactor duplicate fn and simplify route
* add additional tests and make them work on mac
* handle logging exception on empty datasets
---------
Co-authored-by: 2ndset bot <bot@2ndset.ai>
Co-authored-by: NanoCode012 <nano@axolotl.ai>
Co-authored-by: Wing Lian <wing@axolotl.ai>
* scattermoe lora support
* fsdp, bf16, dim fixes
* expert weights aren't needed in save for bwd since they are frozen
* use sonicmoe optim options
* update save model from upstream
* fixes per code review feedback and add tests
* revert removal of CP fix
* misc fixes
* feat: support dot-notation CLI args for nested config options
Add support for overriding nested config fields (like TRL config) via
CLI using dot-notation, e.g.:
axolotl train grpo.yaml --trl.vllm-server-host=10.0.0.1 --trl.beta=0.1
Changes:
- args.py: Detect BaseModel subclass fields and generate dot-notation
CLI options (--parent.child) that map to double-underscore kwargs
(parent__child). Also fix _strip_optional_type for Python 3.10+
union syntax (X | None).
- config.py: Handle double-underscore kwargs in load_cfg by setting
nested dict values on the config.
- Add tests for nested option handling.
Fixes#2702
* Address CodeRabbit review: fix string parent bug, add type hints and docstring
Signed-off-by: Manas Vardhan <manasvardhan@gmail.com>
* Add type coercion for CLI kwargs and fix pre-commit issues
- Add _coerce_value() for YAML-style type inference on string CLI args
- When existing config value has a type (int/float/bool), cast to match
- When no existing value, infer type from string (true/false, ints, floats, null)
- Apply coercion to both flat and nested (dot-notation) kwargs
- Fix unused pytest import (pre-commit/ruff)
- Update tests to pass string values (matching real CLI behavior)
- Add dedicated TestCoerceValue test class
Addresses maintainer feedback on type casting for nested kwargs.
---------
Signed-off-by: Manas Vardhan <manasvardhan@gmail.com>
* upgrade transformers to 5.1.0 and torchao to 0.16.0
* upgrade trl for parity
* handle trl api changes
* orpo doesn't have max_prompt_len to check anymore
* cpoconfig doesn't take max_prompt_length and fix cpu offload
* slow fsdp1 test
* triton min 3.4.0 and liger to 0.7.0
* use transformers main for now for zero3 fix
* handle group_by_length change
* fix changes upstream
* mark skip flaky test
* use transformers latest release 5.2.0
* Prepare for transformers v5 upgrade
* fix hf cli
* update for hf hub changes
* fix tokenizer apply_chat_template args
* remap include_tokens_per_second
* fix tps
* handle migration for warmup
* use latest hf hub
* Fix scan -> ls
* fix import
* fix for renaming of mistral common tokenizer -> backend
* update for fixed tokenziation for llama
* Skip phi35 tests for now
* remove mistral patch fixed upstream in huggingface/transformers#41439
* use namespacing for patch
* don't rely on sdist for e2e tests for now
* run modal ci without waiting too
* Fix dep for ci
* fix imports
* Fix fp8 check
* fsdp2 fixes
* fix version handling
* update fsdp version tests for new v5 behavior
* Fail multigpu tests after 3 failures
* skip known v5 broken tests for now and cleanup
* bump deps
* unmark skipped test
* re-enable test_fsdp_qlora_prequant_packed test
* increase multigpu ci timeout
* skip broken gemma3 test
* reduce timout back to original 120min now that the hanging test is skipped
* fix for un-necessary collator for pretraining with bsz=1
* fix: safe_serialization deprecated in transformers v5 rc01 (#3318)
* torch_dtype deprecated
* load model in float32 for consistency with tests
* revert some test fixtures back
* use hf cache ls instead of scan
* don't strip fsdp_version
more fdsp_Version fixes for v5
fix version in fsdp_config
fix aliasing
fix fsdp_version check
check fsdp_version is 2 in both places
* Transformers v5 rc2 (#3347)
* bump dep
* use latest fbgemm, grab model config as part of fixture, un-skip test
* import AutoConfig
* don't need more problematic autoconfig when specifying config.json manually
* add fixtures for argilla ultrafeedback datasets
* download phi4-reasoning
* fix arg
* update tests for phi fast tokenizer changes
* use explicit model types for gemma3
---------
Co-authored-by: Wing Lian <wing@axolotl.ai>
* fix: AutoModelForVision2Seq -> AutoModelForImageTextToText
* chore: remove duplicate
* fix: attempt fix gemma3 text mode
* chore: lint
* ga release of v5
* need property setter for name_or_path for mistral tokenizer
* vllm not compatible with transformers v5
* setter for chat_template w mistral too
---------
Co-authored-by: NanoCode012 <nano@axolotl.ai>
Co-authored-by: salman <salman.mohammadi@outlook.com>
* upgrade dependencies
* don't use reset sessions
* downgrade transformers, upgrade other deps
* upgrade bnb to 0.49.0
* restore s3 cache
* explicit use local files w hub
* decompress and strip top level dir
* use 2 levels for strip components
* try to preserve permissions for symlinks
* use updated tar
* fix#3293 for distributed
* downgrade bnb
* fast fail after 4
* fix total tokens device
* patch accelerate CP/SP (#3309)
---------
Co-authored-by: salman <salman.mohammadi@outlook.com>
* compute loss only if training
* save total_tokens for checkpiont
* check if string
* refactor total_tokens/ num_tokens
* refactor 2
* rplc trainable_step/trian_per_sec_per_gpu
* lint + log trainable/tokens
* consolidate it in the callback.
* test for total_tokes aftr remuse
* check if tokenstate exist after ckpt
---------
Co-authored-by: Ved <ved.work2024@gmail.com>
* feature: raise on long sequence drop
It is sometimes not desired that sequences are silently dropped from the dataset, especially when the dataset has been carefully crafted and pre-fitted for the training context. This would then suggest that an error occurred somewhere in the process. This feature adds a third value for excess_length_strategy called 'raise', which will raise a ValueError if a sequence is encountered that is too long and would have normally been dropped/truncated.
* tests: add excess_length_strategy tests
* doc: updated return value description for drop_long_seq_in_dataset
* add @enable_hf_offline
* fixed cfg modified after validate_config called
* hf offline fix
* fix tqdm desc when raise is used
* test: added test for non-batched case
* accidental code change revert
* test: use pytest.raises
* test: simplified drop_seq_len tests
* test: moved excess_length_strat test to test_data.py
---------
Co-authored-by: salman <salman.mohammadi@outlook.com>
