feat(train): crash-resilient resume + EMA loss logging (stage1 & stage2)

Addresses both points from review:

1) Resume (so a killed run converges across multiple restarts, like gen_supervisor):
   - checkpoints now carry optimizer state + EMA value in a 'train_state' field
   - --resume auto-picks the latest step ckpt and continues; --resume_from for explicit
   - stage2 resume rebuilds the LoRA structure via enable_lora (same path as fresh)
     then overlays saved adapter weights via set_peft_model_state_dict. Using
     PeftModel.from_pretrained instead reset requires_grad and shrank the trainable
     set 129->33 tensors, corrupting the optimizer state — verified and fixed.
   - Smoke-verified: stage1 step4->10, stage2 step4->8, both restore optimizer +
     EMA + LoRA with no state mismatch.

2) EMA loss logging (honest answer to 'is the loss swing a real problem?'):
   - the old log printed the raw per-micro-batch loss every N steps; on
     heterogeneous 6-task data that swings 2-3x purely from sampling (anomaly
     JSON is low-loss, forecast numbers high-loss), so it looked 'unstable'
     while the model may well have been converging.
   - EMA(alpha=0.05) now logged alongside raw + as a tensorboard scalar
     (total_loss_ema / sft_loss_ema). Read the EMA, not the raw, to judge
     convergence. lr left unchanged (1e-4) pending EMA evidence.

Adds scripts/train_full_supervisor.sh: stage1->stage2 resume loop, same
crash-convergence guarantee as gen_full_supervisor.

scripts/train_full_supervisor.sh

@@ -0,0 +1,57 @@
+#!/usr/bin/env bash
+# Crash-resilient full-scale training supervisor (stage1 → stage2).
+#
+# Both tsmm.train.stage1 and stage2 now support --resume: each saved checkpoint
+# carries encoder/projector (+LoRA for stage2) + optimizer state + EMA loss, so
+# a kill mid-run keeps all progress and the next --resume continues seamlessly.
+#
+# This supervisor loops "run --resume -> check step" until max_steps is reached.
+# Each surviving iteration advances the step counter by >=1 (ckpt_every), so the
+# run converges across multiple kills.
+#
+# Usage: scripts/train_full_supervisor.sh [stage1_max_steps] [stage2_max_steps]
+set -uo pipefail   # no -e: a killed train sub-call must NOT abort the loop
+cd "$(dirname "$0")/.."
+export PYTHONPATH=src PYTHONUNBUFFERED=1
+
+S1_MAX="${1:-30000}"   # 50万 align, bs=8 ga=4 => 1 epoch ≈ 15625 steps; 2 ep = 31250
+S2_MAX="${2:-10000}"   # 10万 sft,   bs=4 ga=8 => 1 epoch ≈ 3125 steps;  3 ep = 9375
+
+run_until() {  # name  max  ckpt_dir  module  extra_args...
+  local name=$1 max=$2 ckpt_dir=$3 module=$4; shift 4
+  local extra=("$@")
+  while :; do
+    local cur=0
+    # read current step from the latest step ckpt's filename
+    local latest
+    latest=$(ls -1 "$ckpt_dir"/${name}_step*.pt 2>/dev/null | sort -V | tail -1)
+    if [ -n "$latest" ]; then
+      cur=$(echo "$latest" | grep -oE '[0-9]+' | tail -1)
+    elif [ -f "$ckpt_dir/${name}_final.pt" ]; then
+      # final carries step inside the file; assume done if present
+      cur=$max
+    fi
+    if [ "$cur" -ge "$max" ]; then
+      echo "[sup] $name done: step $cur/$max"; return 0
+    fi
+    echo "[sup] $name at step $cur/$max — launching --resume $(date '+%H:%M:%S')"
+    .venv/bin/python -m "$module" \
+      --ckpt_dir "$ckpt_dir" --max_steps "$max" --resume "${extra[@]}"
+    # loop: re-check (train may have been killed or completed)
+  done
+}
+
+echo "[sup] === TRAIN START $(date '+%H:%M:%S') ==="
+echo "[sup] stage1 target=$S1_MAX  stage2 target=$S2_MAX"
+
+run_until stage1 "$S1_MAX" checkpoints/stage1 tsmm.train.stage1 \
+  --data data/align.jsonl --bs 8 --grad_accum 4 --ctx 512 --lr 1e-4 \
+  --lambda_contrast 0.1 --perturb_std 0.1 --log_every 50 --ckpt_every 2000 --tensorboard
+
+run_until stage2 "$S2_MAX" checkpoints/stage2 tsmm.train.stage2 \
+  --data data/sft.jsonl --stage1_ckpt checkpoints/stage1/stage1_final.pt \
+  --bs 4 --grad_accum 8 --ctx 1024 --lr 1e-4 --lora_r 16 \
+  --log_every 50 --ckpt_every 2000 --tensorboard
+
+echo "[sup] === TRAIN ALL DONE $(date '+%H:%M:%S') ==="
+ls -la checkpoints/stage1/stage1_final.pt checkpoints/stage2/stage2_final.pt 2>/dev/null

scripts/train_stage1.sh

@@ -22,3 +22,5 @@ export PYTHONPATH=src PYTHONUNBUFFERED=1
   --log_every 20 \
   --ckpt_every 2000 \
   --tensorboard
+# Resume support: pass --resume to continue from the latest step ckpt.
+# Usage: scripts/train_stage1.sh data/align.jsonl 30000 checkpoints/stage1 --resume

scripts/train_stage2.sh

@@ -22,3 +22,4 @@ export PYTHONPATH=src PYTHONUNBUFFERED=1
   --log_every 20 \
   --ckpt_every 2000 \
   --tensorboard
+# Resume support: pass --resume to continue from the latest step ckpt + lora_adapter.

src/tsmm/train/stage1.py

@@ -14,10 +14,11 @@ Design ref: §4.1/§4.2.
 from __future__ import annotations
 
 import argparse
+import glob
 import json
 import os
 import random
-from typing import Iterator, List
+from typing import Iterator, List, Optional
 
 import torch
 
@@ -32,6 +33,40 @@ LLM_PATH = os.environ.get(
 )
 
 
+class EMA:
+    """Exponential moving average of a scalar — for noise-free loss curves.
+
+    The per-micro-batch loss on heterogeneous 6-task data swings 2-3× between
+    steps purely from sampling (anomaly JSON is low-loss, forecast numbers are
+    high-loss). Logging the raw value every 20 steps is meaningless; the EMA is
+    what tells you whether the model is actually converging.
+    """
+
+    def __init__(self, alpha: float = 0.05) -> None:
+        self.alpha = alpha
+        self.value: Optional[float] = None
+
+    def update(self, x: float) -> float:
+        if self.value is None:
+            self.value = float(x)
+        else:
+            self.value = (1 - self.alpha) * self.value + self.alpha * float(x)
+        return self.value
+
+
+def _latest_step(ckpt_dir: str, prefix: str) -> Optional[str]:
+    """Return the highest-numbered step checkpoint path in ``ckpt_dir``, or None."""
+    steps = sorted(glob.glob(os.path.join(ckpt_dir, f"{prefix}step*.pt")))
+    return steps[-1] if steps else None
+
+
+def _step_number(path: str) -> int:
+    """``stage1_step1500.pt`` -> 1500."""
+    base = os.path.basename(path)
+    num = "".join(ch for ch in base if ch.isdigit())
+    return int(num) if num else 0
+
+
 def stream_jsonl(path: str) -> Iterator[dict]:
     with open(path, "r", encoding="utf-8") as f:
         for line in f:
@@ -89,11 +124,40 @@ def train(args: argparse.Namespace) -> None:
 
         writer = SummaryWriter(os.path.join(args.ckpt_dir, "tb"))
 
+    # --- crash-resilient resume ---
+    # Priority: explicit --resume_from > --resume (auto-pick latest step ckpt).
     step = 0
-    opt.zero_grad()
+    ema = EMA(alpha=0.05)
+    resume_path = args.resume_from
+    if not resume_path and args.resume:
+        resume_path = _latest_step(args.ckpt_dir, "stage1_")
+        # treat stage1_final.pt as a step ckpt too if it's all we have
+        final = os.path.join(args.ckpt_dir, "stage1_final.pt")
+        if os.path.exists(final):
+            if resume_path is None or _step_number(final) > _step_number(resume_path):
+                resume_path = final
+    if resume_path and os.path.exists(resume_path):
+        st = torch.load(resume_path, map_location="cpu")
+        model.encoder.load_state_dict(st["encoder"])
+        model.projector.load_state_dict(st["projector"])
+        step = int(st.get("step", 0))
+        ts = st.get("train_state", {})
+        if "optimizer" in ts:
+            try:
+                opt.load_state_dict(ts["optimizer"])
+                for g in opt.param_groups:
+                    g["lr"] = args.lr  # allow lr override on resume
+                print(f"[stage1] resumed optimizer from {resume_path}")
+            except (ValueError, KeyError) as e:
+                print(f"[stage1] optimizer state mismatch ({e}), fresh AdamW")
+        if "ema" in ts:
+            ema.value = ts["ema"]
+        print(f"[stage1] resumed weights from {resume_path} at step {step}")
+
     accum_loss = 0.0
     print(f"[stage1] training: max_steps={args.max_steps} bs={args.bs} "
-          f"grad_accum={args.grad_accum} ctx={args.ctx} lambda={args.lambda_contrast}")
+          f"grad_accum={args.grad_accum} ctx={args.ctx} lambda={args.lambda_contrast} "
+          f"start_step={step}")
     while step < args.max_steps:
         for batch_samples in batched(stream_jsonl(args.data), args.bs):
             if step >= args.max_steps:
@@ -122,36 +186,45 @@ def train(args: argparse.Namespace) -> None:
                 opt.step()
                 opt.zero_grad()
 
+            ema.update(float(loss))
             if writer is not None:
                 writer.add_scalar("lm_loss", float(lm), step)
                 writer.add_scalar("contrastive_loss", float(contra), step)
                 writer.add_scalar("total_loss", float(loss), step)
+                writer.add_scalar("total_loss_ema", ema.value, step)
 
             if step % args.log_every == 0:
                 peak = (torch.cuda.max_memory_allocated() / 1e9) if torch.cuda.is_available() else 0.0
                 print(f"  step {step:5d}  lm={float(lm):.4f}  contra={float(contra):.4f}  "
-                      f"total={float(loss):.4f}  peak_GB={peak:.2f}")
+                      f"total={float(loss):.4f}  ema={ema.value:.4f}  peak_GB={peak:.2f}")
 
             if (step + 1) % args.ckpt_every == 0:
-                ckpt = os.path.join(args.ckpt_dir, f"stage1_step{step+1}.pt")
-                torch.save({
-                    "encoder": model.encoder.state_dict(),
-                    "projector": model.projector.state_dict(),
-                    "step": step + 1,
-                }, ckpt)
-                print(f"  saved {ckpt}")
+                _save_stage1(model, opt, ema, args.ckpt_dir, step + 1)
             step += 1
 
     # final ckpt
-    ckpt = os.path.join(args.ckpt_dir, "stage1_final.pt")
-    torch.save({
+    _save_stage1(model, opt, ema, args.ckpt_dir, step, final=True)
+    print(f"[stage1] done. saved to {args.ckpt_dir}  avg_loss={accum_loss/max(step,1):.4f}  ema={ema.value:.4f}")
+    if writer is not None:
+        writer.close()
+
+
+def _save_stage1(model, opt, ema, ckpt_dir: str, step: int, final: bool = False) -> None:
+    """Save weights + optimizer + EMA so a kill mid-run can resume seamlessly."""
+    train_state = {
+        "optimizer": opt.state_dict(),
+        "ema": ema.value,
+    }
+    payload = {
         "encoder": model.encoder.state_dict(),
         "projector": model.projector.state_dict(),
         "step": step,
-    }, ckpt)
-    print(f"[stage1] done. saved {ckpt}  avg_loss={accum_loss/max(step,1):.4f}")
-    if writer is not None:
-        writer.close()
+        "train_state": train_state,
+    }
+    name = "stage1_final.pt" if final else f"stage1_step{step}.pt"
+    path = os.path.join(ckpt_dir, name)
+    torch.save(payload, path)
+    print(f"  saved {name}  ema={ema.value:.4f}")
 
 
 def main() -> None:
@@ -169,6 +242,10 @@ def main() -> None:
     p.add_argument("--ckpt_every", type=int, default=2000)
     p.add_argument("--seed", type=int, default=0)
     p.add_argument("--tensorboard", action="store_true")
+    p.add_argument("--resume", action="store_true",
+                   help="auto-pick the latest step ckpt in --ckpt_dir and continue")
+    p.add_argument("--resume_from", type=str, default=None,
+                   help="explicit ckpt path to resume from (overrides --resume)")
     args = p.parse_args()
     train(args)
 

src/tsmm/train/stage2.py

@@ -27,15 +27,85 @@ def load_stage1_ckpt(model, ckpt_path: str) -> None:
     print(f"[stage2] loaded stage1 ckpt: {ckpt_path} (step {state.get('step','?')})")
 
 
+class EMA:
+    """Exponential moving average of a scalar — see stage1.EMA for rationale."""
+
+    def __init__(self, alpha: float = 0.05) -> None:
+        self.alpha = alpha
+        self.value = None
+
+    def update(self, x: float) -> float:
+        if self.value is None:
+            self.value = float(x)
+        else:
+            self.value = (1 - self.alpha) * self.value + self.alpha * float(x)
+        return self.value
+
+
+def _latest_step(ckpt_dir: str, prefix: str):
+    import glob
+    steps = sorted(glob.glob(os.path.join(ckpt_dir, f"{prefix}step*.pt")))
+    return steps[-1] if steps else None
+
+
+def _step_number(path: str) -> int:
+    base = os.path.basename(path)
+    num = "".join(ch for ch in base if ch.isdigit())
+    return int(num) if num else 0
+
+
 def train(args: argparse.Namespace) -> None:
     device = "cuda" if torch.cuda.is_available() else "cpu"
     torch.manual_seed(args.seed)
 
+    # Decide resume BEFORE enabling LoRA: a stage2 resume ckpt carries its own
+    # encoder/projector (so we skip the stage1 load); a fresh run loads stage1.
+    resume_path = args.resume_from
+    if not resume_path and args.resume:
+        resume_path = _latest_step(args.ckpt_dir, "stage2_")
+        final = os.path.join(args.ckpt_dir, "stage2_final.pt")
+        if os.path.exists(final):
+            if resume_path is None or _step_number(final) > _step_number(resume_path):
+                resume_path = final
+
     model = build_model().to(device)
-    if args.stage1_ckpt and os.path.exists(args.stage1_ckpt):
+    # When resuming, encoder/projector come from the stage2 ckpt (which already
+    # absorbed stage1 + further SFT); only a fresh run bootstraps from stage1.
+    if resume_path and os.path.exists(resume_path):
+        st = torch.load(resume_path, map_location="cpu")
+        model.encoder.load_state_dict(st["encoder"])
+        model.projector.load_state_dict(st["projector"])
+        resume_step = int(st.get("step", 0))
+        print(f"[stage2] resumed weights from {resume_path} at step {resume_step}")
+    elif args.stage1_ckpt and os.path.exists(args.stage1_ckpt):
         load_stage1_ckpt(model, args.stage1_ckpt)
+        resume_step = 0
+    else:
+        resume_step = 0
+
     model.freeze_llm()
+    # Build LoRA structure the SAME way for fresh and resume (so the trainable
+    # param set matches the saved optimizer state). For resume we then overlay
+    # the saved adapter weights via load_adapter (which does NOT reset
+    # requires_grad, unlike PeftModel.from_pretrained).
     model.enable_lora(r=args.lora_r)
+    resume_lora_loaded = False
+    if resume_path and os.path.exists(resume_path):
+        lora_dir = os.path.join(args.ckpt_dir, "lora_adapter")
+        if os.path.exists(lora_dir):
+            try:
+                from peft import set_peft_model_state_dict
+                import json as _json
+                # load adapter weights into the already-built LoRA model
+                sd_path = os.path.join(lora_dir, "adapter_model.safetensors")
+                if os.path.exists(sd_path):
+                    from safetensors.torch import load_file
+                    sd = load_file(sd_path)
+                    set_peft_model_state_dict(model.llm, sd)
+                    print(f"[stage2] resumed LoRA weights from {lora_dir}")
+                    resume_lora_loaded = True
+            except Exception as e:
+                print(f"[stage2] LoRA weight load failed ({e}), using fresh LoRA")
     model.train()
     if hasattr(model.llm, "gradient_checkpointing_enable"):
         model.llm.gradient_checkpointing_enable()
@@ -47,17 +117,34 @@ def train(args: argparse.Namespace) -> None:
     params = [p for p in model.parameters() if p.requires_grad]
     opt = torch.optim.AdamW(params, lr=args.lr)
 
+    # restore optimizer + EMA when resuming (LoRA already rebuilt above to match)
+    ema = EMA(alpha=0.05)
+    if resume_path and os.path.exists(resume_path):
+        st = torch.load(resume_path, map_location="cpu")
+        ts = st.get("train_state", {})
+        if "optimizer" in ts:
+            try:
+                opt.load_state_dict(ts["optimizer"])
+                for g in opt.param_groups:
+                    g["lr"] = args.lr
+                print(f"[stage2] resumed optimizer from {resume_path}")
+            except (ValueError, KeyError) as e:
+                print(f"[stage2] optimizer state mismatch ({e}), fresh AdamW")
+        if "ema" in ts:
+            ema.value = ts["ema"]
+
     os.makedirs(args.ckpt_dir, exist_ok=True)
     writer = None
     if args.tensorboard:
         from torch.utils.tensorboard import SummaryWriter
         writer = SummaryWriter(os.path.join(args.ckpt_dir, "tb"))
 
-    step = 0
+    step = resume_step
     opt.zero_grad()
     accum = 0.0
     print(f"[stage2] training: max_steps={args.max_steps} bs={args.bs} "
-          f"grad_accum={args.grad_accum} ctx={args.ctx} lora_r={args.lora_r}")
+          f"grad_accum={args.grad_accum} ctx={args.ctx} lora_r={args.lora_r} "
+          f"start_step={step}")
     while step < args.max_steps:
         for batch_samples in batched(stream_jsonl(args.data), args.bs):
             if step >= args.max_steps:
@@ -73,28 +160,36 @@ def train(args: argparse.Namespace) -> None:
                 opt.step()
                 opt.zero_grad()
 
+            ema.update(float(loss))
             if writer is not None:
                 writer.add_scalar("sft_loss", float(loss), step)
+                writer.add_scalar("sft_loss_ema", ema.value, step)
 
             if step % args.log_every == 0:
                 peak = (torch.cuda.max_memory_allocated() / 1e9) if torch.cuda.is_available() else 0.0
-                print(f"  step {step:5d}  sft_loss={float(loss):.4f}  peak_GB={peak:.2f}")
+                print(f"  step {step:5d}  sft_loss={float(loss):.4f}  ema={ema.value:.4f}  peak_GB={peak:.2f}")
 
             if (step + 1) % args.ckpt_every == 0:
-                save_stage2_ckpt(model, args.ckpt_dir, step + 1)
+                save_stage2_ckpt(model, opt, ema, args.ckpt_dir, step + 1)
             step += 1
 
-    save_stage2_ckpt(model, args.ckpt_dir, step, final=True)
-    print(f"[stage2] done. saved to {args.ckpt_dir}  avg_loss={accum/max(step,1):.4f}")
+    save_stage2_ckpt(model, opt, ema, args.ckpt_dir, step, final=True)
+    print(f"[stage2] done. saved to {args.ckpt_dir}  avg_loss={accum/max(step,1):.4f}  ema={ema.value:.4f}")
     if writer is not None:
         writer.close()
 
 
-def save_stage2_ckpt(model, ckpt_dir: str, step: int, final: bool = False) -> None:
+def save_stage2_ckpt(model, opt, ema, ckpt_dir: str, step: int, final: bool = False) -> None:
+    """Save encoder/projector + LoRA adapter + optimizer + EMA for resume."""
+    train_state = {
+        "optimizer": opt.state_dict(),
+        "ema": ema.value,
+    }
     payload = {
         "encoder": model.encoder.state_dict(),
         "projector": model.projector.state_dict(),
         "step": step,
+        "train_state": train_state,
     }
     name = "stage2_final.pt" if final else f"stage2_step{step}.pt"
     torch.save(payload, os.path.join(ckpt_dir, name))
@@ -103,7 +198,7 @@ def save_stage2_ckpt(model, ckpt_dir: str, step: int, final: bool = False) -> No
         model.llm.save_pretrained(os.path.join(ckpt_dir, "lora_adapter"))
     except Exception as e:  # pragma: no cover - best-effort adapter dump
         print(f"  (lora adapter save skipped: {e})")
-    print(f"  saved {name}")
+    print(f"  saved {name}  ema={ema.value:.4f}")
 
 
 def main() -> None:
@@ -121,6 +216,10 @@ def main() -> None:
     p.add_argument("--ckpt_every", type=int, default=2000)
     p.add_argument("--seed", type=int, default=0)
     p.add_argument("--tensorboard", action="store_true")
+    p.add_argument("--resume", action="store_true",
+                   help="auto-pick latest step ckpt + lora_adapter and continue")
+    p.add_argument("--resume_from", type=str, default=None,
+                   help="explicit ckpt path to resume from (overrides --resume)")
     args = p.parse_args()
     train(args)