feat(10): structured logging for the trainer

The trainer was nearly silent: outside an example count and a save line it
printed nothing through tokenizer load, the base-model load, example building, or
the training loop, and trajectories dropped for length or empty mask vanished
without a trace.

Add _log.py (a shared stderr logging setup so stdout stays clean for results)
and a module logger. sekft-train gains -v/--verbose and -q/--quiet. Log the run
config and each phase; report dataset accounting (keepers -> usable, with counts
dropped for over-length and empty-mask and a warning when any are dropped);
inside the build loop, a per-trajectory debug line and a progress line every 100;
raise transformers' verbosity during training so the per-step curve shows. Prints
in train() and inspect() are routed through the logger.

src/tiararodney/sekft/_log.py

@@ -0,0 +1,20 @@
+"""Console logging setup shared by the sekft entry points.
+
+Logs go to stderr so stdout stays clean for a command's actual output (metrics
+JSON, a path a caller might capture). Call :func:`setup` once at the top of a
+``main()``; modules then log through ``logging.getLogger("sekft.<area>")``.
+"""
+from __future__ import annotations
+
+import logging
+
+
+def setup(verbose: bool = False, quiet: bool = False) -> None:
+    """Configure root logging to stderr. ``quiet`` shows warnings and worse,
+    ``verbose`` adds debug; the default is info."""
+    level = logging.WARNING if quiet else logging.DEBUG if verbose else logging.INFO
+    logging.basicConfig(
+        level=level,
+        format="%(asctime)s %(levelname)-5s %(name)s  %(message)s",
+        datefmt="%H:%M:%S",
+    )

src/tiararodney/sekft/sft.py

@@ -28,10 +28,16 @@ from __future__ import annotations
 
 import argparse
 import json
+import logging
 from collections.abc import Iterator
 from pathlib import Path
 from typing import Any
 
+from ._log import setup as _setup_logging
+
+log = logging.getLogger("sekft.train")
+
+
 def normalize_for_template(messages: list[dict[str, str]]) -> list[dict[str, str]]:
     """Canonicalise a trajectory for instruct chat templates that have no system
     role and require strict user/assistant alternation (Mistral and friends):
@@ -109,21 +115,44 @@ def train(data_dir: Path, base: str, out: Path, epochs: float, lr: float,
     from peft import LoraConfig, get_peft_model
     from transformers import (AutoModelForCausalLM, AutoTokenizer,
                               DataCollatorForSeq2Seq, Trainer, TrainingArguments)
+    from transformers.utils import logging as hf_logging
 
+    # Surface the Trainer's own per-step curve (loss/lr/grad_norm); it is at
+    # WARNING by default, which is most of why training looks silent.
+    hf_logging.set_verbosity_info()
+
+    log.info("base=%s  data=%s  out=%s", base, data_dir, out)
+    log.info("loading tokenizer: %s", base)
     tok = AutoTokenizer.from_pretrained(base)
     if tok.pad_token is None:
         tok.pad_token = tok.eos_token
 
-    rows = []
+    log.info("building masked examples from %s ...", data_dir)
+    rows: list[dict[str, list[Any]]] = []
+    n_seen = n_long = n_empty = 0
     for turns in iter_keepers(data_dir):
+        n_seen += 1
         ex = build_masked_example(turns, tok)
-        if len(ex["input_ids"]) <= max_len and any(l != -100 for l in ex["labels"]):
+        log.debug("  trajectory %d: %d turns -> %d tokens, %d trained",
+                  n_seen, len(turns), len(ex["input_ids"]), mask_stats(ex)[0])
+        if n_seen % 100 == 0:
+            log.info("  ... %d trajectories processed, %d usable", n_seen, len(rows))
+        if len(ex["input_ids"]) > max_len:
+            n_long += 1
+            continue
+        if not any(l != -100 for l in ex["labels"]):
+            n_empty += 1
+            continue
         rows.append(ex)
     if not rows:
         raise SystemExit(f"no usable keeper trajectories in {data_dir}")
-    print(f"examples: {len(rows)}; "
-          f"trained/total tokens: {sum(mask_stats(r)[0] for r in rows)}"
-          f"/{sum(mask_stats(r)[1] for r in rows)}")
+    trained = sum(mask_stats(r)[0] for r in rows)
+    total = sum(mask_stats(r)[1] for r in rows)
+    log.info("dataset: %d keepers -> %d usable; %d trained / %d tokens (%.1f%% assistant)",
+             n_seen, len(rows), trained, total, 100 * trained / total)
+    if n_long or n_empty:
+        log.warning("dropped %d trajectories: %d over --max-len %d, %d empty-mask",
+                    n_long + n_empty, n_long, max_len, n_empty)
     ds = Dataset.from_list(rows)
 
     # 4-bit (QLoRA) shrinks the base from ~14 GB to ~4 GB to move across the
@@ -136,6 +165,8 @@ def train(data_dir: Path, base: str, out: Path, epochs: float, lr: float,
             load_in_4bit=True, bnb_4bit_quant_type="nf4",
             bnb_4bit_compute_dtype=torch.float16, bnb_4bit_use_double_quant=True,
         )
+    log.info("loading base model: %s (%s)", base,
+             "4-bit QLoRA" if load_4bit else "fp16")
     model = AutoModelForCausalLM.from_pretrained(
         base, dtype=torch.float16, quantization_config=quant)
     if load_4bit:
@@ -148,7 +179,9 @@ def train(data_dir: Path, base: str, out: Path, epochs: float, lr: float,
         r=lora_r, lora_alpha=lora_r * 2, lora_dropout=0.05, task_type="CAUSAL_LM",
         target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
     ))
-    model.print_trainable_parameters()
+    n_train, n_all = model.get_nb_trainable_parameters()
+    log.info("LoRA r=%d: %d trainable / %d params (%.3f%%)",
+             lora_r, n_train, n_all, 100 * n_train / n_all)
 
     args = TrainingArguments(
         output_dir=str(out), per_device_train_batch_size=batch,
@@ -161,18 +194,21 @@ def train(data_dir: Path, base: str, out: Path, epochs: float, lr: float,
         model=model, args=args, train_dataset=ds,
         data_collator=DataCollatorForSeq2Seq(tok, padding=True, label_pad_token_id=-100),
     )
+    log.info("training: %g epochs, lr=%g, batch=%d x accum=%d (effective %d), max_len=%d",
+             epochs, lr, batch, accum, batch * accum, max_len)
     trainer.train()
     model.save_pretrained(str(out))
     tok.save_pretrained(str(out))
     # durable, greppable record of the curve (loss/lr/grad_norm per step).
     (out / "log_history.jsonl").write_text(
         "\n".join(json.dumps(r) for r in trainer.state.log_history))
-    print(f"saved LoRA adapter + log_history.jsonl -> {out}  "
-          f"(tensorboard: --logdir {out / 'runs'})")
+    log.info("saved LoRA adapter + log_history.jsonl -> %s  (tensorboard: --logdir %s)",
+             out, out / "runs")
 
 
 def inspect(data_dir: Path, base: str) -> None:
     from transformers import AutoTokenizer
+    log.info("loading tokenizer: %s", base)
     tok = AutoTokenizer.from_pretrained(base)
     n = tt = tr = 0
     for turns in iter_keepers(data_dir):
@@ -181,8 +217,8 @@ def inspect(data_dir: Path, base: str) -> None:
         tr += t; tt += total; n += 1
     if not n:
         raise SystemExit(f"no keeper trajectories in {data_dir}")
-    print(f"{n} keeper trajectories; {tr}/{tt} tokens trained "
-          f"({100*tr/tt:.1f}% assistant, rest masked)")
+    log.info("%d keeper trajectories; %d/%d tokens trained (%.1f%% assistant, rest masked)",
+             n, tr, tt, 100 * tr / tt)
 
 
 def main() -> None:
@@ -199,7 +235,10 @@ def main() -> None:
     ap.add_argument("--lora-r", type=int, default=16)
     ap.add_argument("--load-4bit", action="store_true",
                     help="QLoRA: load base in 4-bit (less to move over the link, less VRAM)")
+    ap.add_argument("-v", "--verbose", action="store_true", help="debug-level logging")
+    ap.add_argument("-q", "--quiet", action="store_true", help="warnings and errors only")
     ns = ap.parse_args()
+    _setup_logging(verbose=ns.verbose, quiet=ns.quiet)
     if ns.inspect:
         inspect(ns.data, ns.base)
     else: