* bug-fix: only apply patches when CUDA is available
This will otherwise crash when performing operations with CUDA_VISIBLE_DEVICES=, such as LoRA merging on CPU.
This patch only patches the Qwen 3.5 model, since that's the only one I've tested. This patch should most likely check torch.cuda for all other models as well. One limitation here is that I'm assuming the user runs CUDA, but that assumption is not restricted to this patch so it is probably fine.
* include patch_qwen3_next_modeling_packing, patch_qwen3_5_moe_modeling_packing, and patch_qwen3_5_vlm_flash_attention in cuda guard
* 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
* fix: handle get_open_port import across TRL versions
TRL 0.29+ removed get_open_port from exports; fall back to importing
directly from vllm.utils or vllm.utils.network_utils.
* support DP with vllm and make generation_batch_size confifurable
* 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
* fix: replace shell=True subprocess with argument list in modal CLI
Using shell=True with a formatted string containing docker_image
(a user-controlled value) is a command injection risk (Bandit B602).
Replace with an argument list, which passes args directly to the
process without shell interpretation, removing the nosec annotation.
* fix: add nosec annotation to suppress bandit B603/B607 warnings
Removing shell=True (B602) surfaces B603 (subprocess without shell)
and B607 (partial executable path for 'docker'). Use bare # nosec
to suppress both, consistent with other nosec usages in the codebase.
* 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
* fix: explicit set workflow permission and move secrets to necessary
steps only
* fix: comment
* fix: more permission restrict
* chore: add read for pypi
* 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
