* feat: support excess_length_strategy for RL trainers
Previously, RL data loading always dropped sequences exceeding
sequence_len. This adds support for the existing `excess_length_strategy`
config option (`drop`, `truncate`, `raise`) in RL training pipelines,
matching the behavior already available for SFT.
- `drop` (default): unchanged behavior, filters out long samples
- `truncate`: tokenizes text components, truncates responses to fit
within sequence_len while preserving the full prompt, then decodes
back to text. Handles DPO/IPO/ORPO/SIMPO and KTO datasets.
- `raise`: raises ValueError if any sample exceeds sequence_len
Closes#3547
* improve RL truncation strategy robustness and performance
---------
Co-authored-by: yurekami <yurekami@users.noreply.github.com>
Co-authored-by: Wing Lian <wing@axolotl.ai>
* Skip redundant evaluation when resuming from checkpoint
* add condition check for adding callback
---------
Co-authored-by: Wing Lian <wing@axolotl.ai>
* better handling of dora merge on Conv layers in Qwen 3.5
* address issues from code review
* stricter efficient merges for dora since we now have meta model to reference
* upgrade to torchao 0.17.0
* upgrade mistral-common too
* chore: lint
* patch fix for torchao low bit optimizers
* fix up
* propagate dtype
* fix test for ao change
* address PR comments
* feat: add sonicmoe fused lora support
* fix: forgot to add file
* feat: add test
* feat: add lora support for other routes
* fix: add int8 lora support
* fix: add qwen35_moe interleave support
* fix: qwen3_5_moe loss
* chore: lint
* address some pr comments
* fix test imports
* add support matrix for moe kernels [skip ci]
---------
Co-authored-by: Wing Lian <wing@axolotl.ai>
* fix: DPO tool role KeyError, dataset hash output_dir, config validators [skip-e2e]
- Add 'tool' to default role_map_inv in dpo/chat_template.py default() and
argilla_chat() so datasets with tool-call messages no longer raise
KeyError: 'tool' (closes#3217)
- Fix generate_dataset_hash_from_config to use canonical tokenizer config +
overrides content instead of tokenizer.name_or_path when added_tokens_overrides
is set, preventing cache busting when only output_dir changes (closes#3303)
- Add three Pydantic config validators to AxolotlConfigWCapabilities:
* save_strategy: 'best' requires metric_for_best_model
* streaming=True is incompatible with val_set_size > 0
* lora_target_modules list entries must be valid Python regex patterns
- Tests for all three changes
* review: condense comment in shared.py, swap Mistral model for SmolLM2-135M in test_hash
* chore: lint
* move the validators out of the w/ capabilities schema
---------
Co-authored-by: Wing Lian <wing@axolotl.ai>
* qat patch
* tests fixes
* fixup per PR code review
* use state dict hooks to handle dequant for saving safetensors from transformers
* use transformers torch ao quantizer hooks to save mx quantized model
---------
Co-authored-by: Wing Lian <wing@axolotl.ai>
Co-authored-by: Wing Lian <wing.lian@gmail.com>
* Add precompute_ref_log_probs to config schema
* chore: add description for config
* Add test for precompute_ref_log_probs and move to training args
* useing precompute logprobs as the default slows down CI as it has to precompute
---------
Co-authored-by: NanoCode012 <nano@axolotl.ai>
Co-authored-by: Wing Lian <wing@axolotl.ai>
* allow bf16 flag but warn
Reason: when doing e.g. LoRA merges with CUDA_VISIBLE_DEVICES=, this will unnecessarily crash, even though the LoRA merge operation would have finished successfully. This seems to warrant changing it to a warning instead, as the code will most likely crash later if bf16 is unavailable and training begins anyway.
* don't use deprecated LOG.warn
* update tests to reflect validation change
* Deperecate dpo_norm_loss
* Rename chosen/rejected_input_ids to chosen/rejected_ids to match TRL https://github.com/huggingface/trl/pull/5179
* Remove deprecated rpo_alpha
* Remove dead_code tokenize_row
* Add _tokenize override to prevent double bos token on Llama DPO
* Fix DPO loss type now list not string
* Linting fix
* PR fixes
* update _tokenize override for DPO for multimodal
* support flattening/packing for GRPO
* more flattening
* fix tests
* improve dead vllm handling
* refactor out process handling for vllm serve and move bench flattening tests to gpu tests
* add validation for flattening with liger
* isolate batch flattening test
* flaky test
* nemo gym integration with grpo wip
* mostly working
* cleanup
* simplify
* update docs
* nemo gym support wip
* cleanup
* chore: lint
* address PR review and add more tests
* chore: lint
* post merge lora fixes for CI (#3536) [skip ci]
* post merge lora fixes for CI
* handle lora kernel auto-enable for moe without grouped_mm
* prefer not to import torch in schema validation
* address pr comments, add timeout, add tests
* roundup_power2_divisions not needed with newer pytorch versions (#3540)
* 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
* fix: robust handling of race condition on patching check (#3543) [skip ci]
* EBFT: Matching Features, Not Tokens: Energy-Based Fine-Tuning of Language Models (#3527) [skip ci]
* 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
* fix for ebft sync and update docs
* make trainer loss patch check a solo test
---------
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* 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>
