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* upgrade to torchao 0.17.0 * chore: lint * refactor attention handling * replace legacy attention boolean flags with capability properties Replace checks with capability-based properties derived from attn_implementation This separates three concerns that were conflated under flash_attention: 1. Backend selection -> attn_implementation enum 2. Packing capability -> attn_supports_packing property 3. Flash-attn library dependency -> attn_uses_flash_lib property * compute attn capability flags in normalizer instead of properties * make attn_implementation the single source of truth * move attention-dependent validators to mode=after * migrate remaining consumers to canonical attn_implementation * expand attention tests + rewrite docs * migrate example configs to canonical attn_implementation * update doc snippets + reject gemma4-hybrid with non-FA2 backend * remove dead gemma4 branch in _set_attention_config * fix duplicate attn_implementation in gpt-oss yamls and flaky caplog tests * drop "Phase 2" naming from attn-implementation tests * regroup attn_implementation tests by feature concern * clean up verbose comments and remove MD Signed-off-by: Wing Lian <wing@axolotl.ai> Co-authored-by: Axolotl Swarm <no-reply@axolotl.ai> * fix(collator): pass return_dict=True at apply_chat_template top level for transformers 5.x In transformers 5.x, ProcessorMixin.apply_chat_template gained its own `return_dict` parameter (defaulting to False). When return_dict=False and tokenize=True the method returns out["input_ids"] directly — a 2-D tensor — rather than the full BatchFeature dict. The old code placed `return_dict=True` inside processor_kwargs. In transformers 5.x those kwargs are forwarded to the underlying processor call self(...) where _merge_kwargs silently ignores any key not present in MllamaProcessorKwargs (emitting a warning). The outer return_dict therefore stayed False, apply_chat_template returned the raw input_ids tensor, and the subsequent `batch["input_ids"]` attempted to index a 2-D tensor with the 9-character string "input_ids", producing: IndexError: too many indices for tensor of dimension 2 The fix is to pass return_dict=True as a top-level keyword argument to apply_chat_template (where it is actually consumed) and remove it from processor_kwargs (where it was silently dropped). No version guard is needed: transformers is pinned to ==5.5.4 in pyproject.toml. Adds a unit-level regression test (tests/test_mm_chat_collator.py) that mocks the processor to return a raw tensor when apply_chat_template is called without top-level return_dict=True, verifying the four invariants: process_rows returns a dict, input_ids is 2-D, labels is 2-D, and apply_chat_template receives return_dict=True as a top-level kwarg. Fixes: tests/e2e/test_llama_vision.py::TestLlamaVision::test_lora_llama_vision_multimodal_dataset Fixes: tests/e2e/test_llama_vision.py::TestLlamaVision::test_lora_llama_vision_text_only_dataset Signed-off-by: Wing Lian <wing@axolotl.ai> Co-authored-by: Axolotl Swarm <no-reply@axolotl.ai> * fix(collator): process_rows returns dict (BatchFeature) shape Two related changes for the multimodal chat collator under transformers 5.x: 1. Wrap apply_chat_template result in dict(...) so process_rows returns a plain dict rather than a BatchFeature instance. BatchFeature is a Mapping but not a dict; downstream code that did batch["labels"] = self.processing_strategy.process_labels(batch["input_ids"]) would index on a tensor when the result wasn't dict-shaped, raising IndexError: too many indices for tensor of dimension 2 2. Soften the regression test's contract from `dict` to `Mapping` so it exercises the actual semantic guarantee (key/value access) rather than the implementation detail (dict vs BatchFeature). Test guards against the original transformers 5.x breakage where apply_chat_template's return_dict default went from True to False. Includes regression test under tests/test_mm_chat_collator.py. Bug surfaced via swarm dispatch task_01KQHPNAYD8XARSNSDJVW1GPF6 against attn-implementation-refactor; squash-merged from agent commits 4de886fd + dc9fcf4f. Signed-off-by: Wing Lian <wing@axolotl.ai> --------- Signed-off-by: Wing Lian <wing@axolotl.ai> Co-authored-by: Axolotl Swarm <no-reply@axolotl.ai>
94 lines
2.2 KiB
YAML
94 lines
2.2 KiB
YAML
# Gemma 4 26B-A4B MoE QLoRA with ScatterMoE kernels
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#
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# Validated: 50 steps on FineTome-100k, loss 8.8 -> 1.8, single RTX 5090 (32GB)
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# torch_compile=true: 21 GiB peak VRAM, ~230 tok/s, 336s total
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#
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# Key notes:
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# - Max sequence length on 32GB GPU: 2048 (micro_batch_size=1, SDP attention).
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# 4096 seq_len OOMs due to head_dim=512 math SDP materializing full score matrix.
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# Use 48GB+ GPUs for longer sequences or multi-GPU with FSDP.
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base_model: google/gemma-4-26B-A4B
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plugins:
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- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
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- axolotl.integrations.kernels.KernelsPlugin
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- axolotl.integrations.liger.LigerPlugin
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use_kernels: true
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use_scattermoe: true
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experts_implementation: scattermoe
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torch_compile: true
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liger_layer_norm: true
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liger_rope: true
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liger_rms_norm: true
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liger_glu_activation: true
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liger_rms_norm_gated: true
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strict: false
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chat_template: gemma4
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datasets:
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- path: mlabonne/FineTome-100k
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type: chat_template
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split: train[:10%]
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field_messages: conversations
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message_property_mappings:
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role: from
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content: value
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val_set_size: 0.05
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output_dir: ./outputs/gemma4-26b-a4b-qlora
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sequence_len: 2048
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sample_packing: true
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load_in_4bit: true
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quantize_moe_experts: true
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adapter: qlora
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lora_r: 16
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lora_alpha: 32
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lora_dropout: 0
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# Restrict LoRA to text backbone only (skip vision/audio encoders)
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# using regex to match only the text decoder attention projections.
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lora_target_modules: 'model.language_model.layers.[\d]+.(_checkpoint_wrapped_module.)?(mlp|self_attn).(up|down|gate|q|k|v|o)_proj'
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# MoE expert LoRA (3D Parameter tensors, not nn.Linear)
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lora_target_parameters:
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- experts.gate_up_proj
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- experts.down_proj
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lora_mlp_kernel: false
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lora_qkv_kernel: false
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lora_o_kernel: false
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bnb_config_kwargs:
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bnb_4bit_use_double_quant: true
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wandb_project:
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wandb_entity:
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wandb_watch:
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wandb_name:
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wandb_log_model:
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gradient_accumulation_steps: 4
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micro_batch_size: 1
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num_epochs: 1
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optimizer: adamw_torch_8bit
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lr_scheduler: cosine
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learning_rate: 0.0002
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bf16: auto
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tf32: true
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gradient_checkpointing: true
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activation_offloading: true
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logging_steps: 1
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# FA2 not supported
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attn_implementation: sdpa
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warmup_ratio: 0.1
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evals_per_epoch: 4
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saves_per_epoch: 1
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weight_decay: 0.0
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special_tokens:
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