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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>
82 lines
2.3 KiB
YAML
82 lines
2.3 KiB
YAML
# EBFT: Energy-Based Fine-Tuning with Llama-3.2-1B on OpenCodeInstruct
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#
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# Paper: "Matching Features, Not Tokens" (Jelassi et al., 2026)
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# https://arxiv.org/abs/2603.12248
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#
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# Prerequisites:
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# 1. Start vLLM server on a separate GPU:
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# CUDA_VISIBLE_DEVICES=1 python -m trl.scripts.vllm_serve \
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# --model meta-llama/Llama-3.2-1B \
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# --host 0.0.0.0 --port 8000 \
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# --gpu-memory-utilization 0.4 --dtype bfloat16
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#
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# 2. Run training:
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# CUDA_VISIBLE_DEVICES=0 axolotl train examples/ebft/llama-1b-ebft-opencode.yaml
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base_model: meta-llama/Llama-3.2-1B
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chat_template: llama3
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# --- Training method ---
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rl: ebft
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# --- EBFT configuration ---
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ebft:
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feature_layers: [0.25, 0.5, 0.75] # extract hidden states at 25%, 50%, 75% depth
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embed_method: last_token # pool to sequence embedding via last token
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use_whitening: false # SVD whitening (disable for speed in small runs)
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alignment_coef: 1.0 # cosine similarity with ground-truth features
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diversity_coef: 1.0 # pairwise similarity penalty
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ce_coef: 0.0 # cross-entropy on ground-truth (0 = pure feature matching)
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# --- Generation settings (via TRL/GRPO infrastructure) ---
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trl:
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num_generations: 4 # samples per prompt for RLOO
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max_completion_length: 256 # max generated tokens
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temperature: 1.0
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use_vllm: true
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scale_rewards: true
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loss_type: grpo
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epsilon: 0.2
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# --- Dataset ---
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datasets:
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- path: nvidia/OpenCodeInstruct
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type: ebft_opencode.transform
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split: train[:1%] # first 1% for validation runs
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# --- Training hyperparameters ---
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sequence_len: 1024
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micro_batch_size: 2
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gradient_accumulation_steps: 4
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num_epochs: 1
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max_steps: 50
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learning_rate: 1.0e-5
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optimizer: adamw_torch_fused
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lr_scheduler: cosine
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warmup_steps: 5
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weight_decay: 0.01
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# --- LoRA (recommended to reduce memory with frozen feature network) ---
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adapter: lora
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lora_r: 16
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lora_alpha: 32
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lora_dropout: 0.05
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lora_target_linear: true
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# --- Hardware ---
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bf16: auto
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attn_implementation: flash_attention_2
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gradient_checkpointing: true
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special_tokens:
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pad_token: "<|end_of_text|>"
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val_set_size: 0.0
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output_dir: ./outputs/ebft-llama-1b-opencode
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# --- Logging ---
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use_tensorboard: true
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logging_steps: 1
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save_steps: 25
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