feat(m3): stage 1 alignment training script + smoke (T3.2)

- src/tsmm/train/stage1.py: streams align.jsonl, freeze_llm, trains
  Encoder+Projector (AdamW lr=1e-4), bs=8/grad_accum=4/ctx=512/BF16,
  gradient checkpointing + enable_input_require_grads, loss = lm + lambda*InfoNCE
  (original vs perturbed TS representation), ckpt + tensorboard every 2000 steps.
- scripts/train_stage1.sh: wrapper with design defaults.
- src/tsmm/data/collator.py: handle JSON null (missing markers) -> NaN -> fill.
- .gitignore: /checkpoints/ and data/*.jsonl.
- Smoke verified: --max_steps 5 on 64 samples, finite loss, peak 5.52 GB (≤6GB).
- 100 tests passing.

scripts/train_stage1.sh

@@ -0,0 +1,23 @@
+#!/usr/bin/env bash
+# Stage ① alignment training (T3.2).
+# Loads align.jsonl (50万 target), freezes LLM, trains Encoder+Projector.
+set -euo pipefail
+
+DATA="${1:-data/align.jsonl}"
+MAX_STEPS="${2:-10000}"
+CKPT_DIR="${3:-checkpoints/stage1}"
+
+cd "$(dirname "$0")/.."
+.venv/bin/python -m tsmm.train.stage1 \
+  --data "$DATA" \
+  --ckpt_dir "$CKPT_DIR" \
+  --max_steps "$MAX_STEPS" \
+  --bs 8 \
+  --grad_accum 4 \
+  --ctx 512 \
+  --lr 1e-4 \
+  --lambda_contrast 0.1 \
+  --perturb_std 0.1 \
+  --log_every 20 \
+  --ckpt_every 2000 \
+  --tensorboard