2 Commits

Author	SHA1	Message	Date
Alpamys	892fd33f9e	feat(trainers): v0.35.0 — Trainer Coverage (closes #60, #61, #45) ... Wires v0.28.0 speed/memory features into every transformer-backend trainer (grpo / kto / orpo / simpo / ipo / ppo / reward_model / embedding) plus closes the v0.33.0 #43 oversight where dpo / pretrain accepted activation_offloading without installing offload hooks. Auto-quant --auto-quant now forwards the picked candidate's quantization to vLLM via an explicit named parameter (kwarg-splat hazard removed). Kernel auto-compose runs a forward-only benchmark loop on the trainer's actual model under torch.no_grad() so live training gradients aren't polluted (this was a critical-class bug caught by code-review pre-tag and fixed before merge). Schema gate lifted with distinct MLX-backend vs unknown-task error messages so users get the right fix. fp8 / int8 QAT guard fixed in 6 trainers (the legacy unguarded `if tcfg.quantization_aware:` would have crashed the int8 path with the string "fp8"). Net +187 tests (3928 -> 4115). New file tests/test_trainer_coverage_v035.py provides a parametrised matrix proof that every trainer x every feature is exercised on every CI matrix job. All four review-agent waves (python / code / security / tdd) clean with every CRITICAL -> LOW finding fixed. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-04-28 15:01:42 +05:00
Alpamys	e09a742167	feat(training): Training Speed & Memory — CCE, FP8, grad-ckpt tiers, kernel picker, cross-doc attn, activation offload (v0.28.0) ... Six new training speed/memory features, SFT-only in v0.28.0: - use_cut_ce: Cut Cross-Entropy for 128k-vocab models (8-24GB save) - quantization_aware: "fp8" — Hopper+ float8 training via torchao.float8 - gradient_checkpointing: bool | selective|medium|full|auto (VRAM-based auto) - kernel_auto_compose: benchmark + pick fastest kernel combo - packing_cross_doc_attn_mask: block-diagonal mask for sample packing - activation_offloading: cpu|disk saved-tensor offload Config-load validator rejects non-SFT tasks when speed/memory flags are set — prevents int8-QAT-wrapper crash on the string "fp8" and silent no-ops on DPO/GRPO/KTO/ORPO/SimPO/IPO/PPO/Pretrain/Reward/Embedding. Multi-trainer wiring tracked for v0.28.1. Security: - FP8 path: CUDA + Hopper+ SM capability + transformers backend - Activation-offload disk: is_under_cwd containment, TOCTOU-safe mkstemp (fd held through torch.save), weights_only=True reload, crash-safe cleanup - Kernel picker raises when all candidates lack finite time_ms - Cut CE detector matches last path component only (deepseek-ai/...-phi-... org-prefix does not trigger Phi patch on DeepSeek) - Cross-doc mask numpy-vectorised (np.tril) at max_length=1M - @model_validator gates: packing_cross_doc_attn_mask requires packing=true; v0.28.0 features require task=sft New optional extra: pip install 'soup-cli[cce]' Tests: 2585 -> 2685 (+100 in tests/test_training_speed.py, +1 file). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-04-22 22:51:15 +05:00