mirror of https://github.com/razor-ai/soup.git
55d1b9312c
Closes #43, #44, #47. #43 Multi-trainer wiring (sft/dpo/pretrain): - New utils/v028_features.apply_v028_speed_memory(model, tcfg, base_model, console) — single shared helper for use_cut_ce, quantization_aware="fp8", kernel_auto_compose. Each feature degrades silently to a yellow advisory if the underlying lib is missing; never crashes training kick-off. - Helpers supports_v028_features(task) and warn_unsupported_features(tcfg, task) drive both the schema validator and runtime advisories. - soup_cli/trainer/dpo.py and trainer/pretrain.py now call the helper after model load (post-LoRA, post-QAT) — same hook point as SFT. - soup_cli/config/schema.py validator _validate_v028_speed_memory_sft_only renamed _validate_v028_speed_memory_supported_tasks; allowlist now {sft, dpo, pretrain}. GRPO/KTO/ORPO/SimPO/IPO/PPO/RewardModel/Embedding still error out at config-load with a precise multi-trainer message. #44 Selective gradient-checkpoint hooks: - New utils/gradient_ckpt.install_selective_hooks(model, granularity) iterates ``model.named_modules()`` looking for transformer-block-shaped names (numeric suffix on layer path), wraps each module's ``forward`` with torch.utils.checkpoint.checkpoint based on tier: - selective: only attention sub-modules - medium: every second transformer block - full: every transformer block - Returns hook count so callers can fall back to HF native checkpointing when zero blocks were found. #47 CrossDocCollator: - New soup_cli/data/collators.CrossDocCollator wraps any base data collator and injects a block-diagonal causal ``cross_doc_attn_mask`` built from per-example ``doc_lengths``. Preferred over TRL's ``packing_strategy="attention_free"`` flag (best-effort across TRL versions). Degrades gracefully when doc_lengths is missing or shapes don't match — base attention_mask preserved, no crash. Tests: +16 in tests/test_part_c.py covering apply_v028_speed_memory (no-features, cut_ce graceful failure), supports/warn helpers extension, schema gate (dpo + pretrain accept, kto still rejects), selective hook installation across full/medium/selective with fake transformer-shaped models, CrossDocCollator passthrough + strip + injection. One existing test in test_training_speed.py updated: dpo+use_cut_ce now accepted. Known limitations: - 7 trainers (GRPO/KTO/ORPO/SimPO/IPO/PPO/RewardModel/Embedding) still reject v0.28.0 flags at config-load. Each is a 5-line addition once schema validation is satisfied; tracked as a v0.33.x follow-up. - install_selective_hooks doesn't undo earlier hooks — caller must be re-init aware. Not an issue for the typical "construct wrapper, train, exit" flow but worth noting. - CrossDocCollator emits ``cross_doc_attn_mask`` (not ``attention_mask``) to avoid clobbering the base collator's contract; downstream consumers must read the new key explicitly. The plan calls for "preferred over TRL's packing_strategy" which we satisfy via opt-in collation, not silent override. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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