feat(m2): MultimodalTSModel wrapper — end-to-end fwd/generate + LoRA (T2.4)
- src/tsmm/model/wrapper.py: MultimodalTSModel combining TS Encoder + Projector
+ Qwen2.5-0.5B LLM + LoRA. Two modes: freeze_llm() (stage ①) and
enable_lora(r=16) (stage ②, peft on q/v_proj). forward() takes a raw batch
{series, attributes, timestamps, events, question, answer} end-to-end
(encoder→projector→splice→LLM) OR pre-spliced inputs_embeds.
- src/tsmm/model/multimodal.py: device-aware splice (move ids/ts_embeds to
the bound embedding layer's device).
- tests/test_wrapper.py: 6 tests — finite loss, grad flows into
encoder/projector under freeze_llm, generate returns text, freeze_llm and
enable_lora mode invariants, GPU memory budget. 80 tests passing.
- Measured: stage ① fwd+bwd peak 1.87 GB (design budget ~5 GB).
Spec clarifications (small tier, comet-build Step 4):
- Wrapper batch contract = {series, attributes, timestamps, events, question,
answer} (lists of str + series tensor). Collator (T2.5) will produce this.
- Encoder/Projector kept fp32; their output cast to LLM dtype (bf16) before
splice, so inputs_embeds matches LLM weights.
张宗平
aec8fd5ef1