423 lines
20 KiB
Python
423 lines
20 KiB
Python
"""Training mode of the task generator (stage 2 data).
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Separate pool `tasks_gen/train` (ids G1000+). Same category and object type mix as the eval plan, plus
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tasks for the common errors (docs/yol-haritasi.md step 3). A new bundle is checked against all eval tasks
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and the earlier training tasks (`harness/overlap.py`); a too close bundle goes back to the model as a
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repair message. K variants are not made here (they need the generic_v0 tool schema).
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python3 -m harness.trainset plan
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python3 -m harness.trainset run --part 0 --parts 3 [--target 200] [--stop-ledger 27]
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"""
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import argparse
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import glob
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import json
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import os
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import time
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from .adt_client import load_env
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from .evalset import SLOTS, RELEASES, accepted_goals
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from .generator import ROOT, generate, make_k_variant
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from .ledger import BudgetExceeded, spent
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from . import overlap
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from . import mix
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POOL = os.path.join(ROOT, "tasks_gen", "train")
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PLAN = os.path.join(POOL, "plan.json")
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FIRST_ID = 1000
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RUN_BASE = 32000 # run numbers stay below 36**3 * 1 (proxy prefix rule); 40 per slot
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N_SLOTS = 224 # about 12 % more slots than the target: some tasks are not accepted
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N_ERROR = 30
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EVAL_HOLD = 3 # eval pool is loaded once per process
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ERROR_HINTS = [
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("named-type", "The contract has methods with short coded character parameters (zone code, currency code, "
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"status flag, region key) of fixed length. A correct solution declares named types for them: "
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"TYPE c LENGTH n is not allowed in a method signature."),
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("reserved-word", "The business domain uses concepts that collide with reserved or SQL words (hours, mode, "
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"order, date, count, group, value). The contract uses safe field and parameter names (for example "
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"work_hours, calc_mode); the solution must also avoid reserved words as field names."),
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("long-names", "The task has at least 10 separate business rules, so a good solution needs at least 10 "
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"own unit tests, each for one rule. Every ABAP name stays at 30 characters or less (the model must "
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"shorten long descriptive test method names)."),
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]
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ERROR_TYPES = ["CLAS", "CLAS", "FUNC", "DDLS", "PROG", "CLAS"] # reserved-word fits DDLS and tables well
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CATEGORY_SHARE = {"A": 10, "B": 15, "C": 15, "D": 10, "E": 15, "F": 10, "G": 10, "I": 5, "H": 10} # eval 8.1
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ERROR_CATEGORY = {"named-type": "A", "reserved-word": "D", "long-names": "C"}
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def build_plan(n_slots=N_SLOTS, n_error=N_ERROR, hard_share=0.0, first_id=FIRST_ID, run_base=RUN_BASE):
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"""Category shares as in the eval plan; object types inside a category as in evalset.SLOTS.
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n_error slots target the common errors (named-type, reserved-word, long-names); hard_share of the other
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slots get difficulty 3 (every 10th slot pattern, deterministic)."""
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share_sum = sum(CATEGORY_SHARE.values())
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items = []
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for cat, share in CATEGORY_SHARE.items():
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k_cat = round(n_slots * share / share_sum)
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per_kind = n_error // 3
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k_err = sum(1 for k in ERROR_CATEGORY if ERROR_CATEGORY[k] == cat) * per_kind
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slots = [x for x in SLOTS if x[0] == cat]
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weight = sum(n for _, _, n, _ in slots)
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k_rest = k_cat - k_err
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quota = [(t, hints, round(k_rest * n / weight)) for _, t, n, hints in slots]
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quota[0] = (quota[0][0], quota[0][1], quota[0][2] + k_rest - sum(q[2] for q in quota))
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pos = 0
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for t, hints, k in quota:
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for i in range(k):
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topic = hints[i % len(hints)] if hints else None
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if cat == "G":
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topic = (topic + "; " if topic else "") + f"release target {RELEASES[pos % 2]}"
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pos += 1
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items.append({"category": cat, "object_type": t, "topic": topic, "frac": pos / (k_cat + 1)})
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for kind, text in ERROR_HINTS:
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if ERROR_CATEGORY[kind] != cat:
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continue
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for i in range(per_kind):
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t = ERROR_TYPES[i % len(ERROR_TYPES)]
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pos += 1
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items.append({"category": cat, "object_type": t, "topic": text, "error_kind": kind,
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"frac": pos / (k_cat + 1)})
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items.sort(key=lambda x: (x["frac"], x["category"], x["object_type"])) # interleave: a cut keeps the mix
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hard_every = round(1 / hard_share) if hard_share else 0
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for n, it in enumerate(items):
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it.pop("frac")
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it["id"] = f"G{first_id + n:04d}"
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it["run_base"] = run_base + 40 * n
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if hard_every and it["category"] != "H" and n % hard_every == hard_every // 2:
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it["difficulty"] = 3
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return items
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def accepted_count():
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n = 0
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for f in glob.glob(os.path.join(POOL, "G*", "generation.json")):
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try:
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n += bool(json.load(open(f)).get("accepted"))
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except (OSError, ValueError):
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pass
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return n
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STOP_FLAG = os.path.join(ROOT, "runs", "pipeline", "STOP") # the pipeline writes it: workers end after the current slot
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BACKLOG_LIMIT = 40 # accepted tasks without a first trajectory run: generation waits above this
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PLAN2 = {"n_slots": 500, "n_error": 100, "hard_share": 0.3, "first_id": 1400, "run_base": 100000}
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# plan 2 (2026-10-05, Opus 5.5 rules): error tasks 20 % (plan 1: 13 %, +50 %), hard (difficulty 3) 30 % of the
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# other slots (plan 1: 20 % assumed from the pilot hard batch), same category shares, ids G1400+, no fixed target
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def plan_path(name):
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return os.path.join(POOL, name + ".json")
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def ensure_plan(name, stop_ledger=27.0, target=200):
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os.makedirs(POOL, exist_ok=True)
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if not os.path.exists(plan_path(name)):
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if name == "plan":
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slots, tgt, stop = build_plan(), target, stop_ledger
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else:
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slots, tgt, stop = build_plan(**PLAN2), None, 1e6 # only the budget guard and the deadline stop it
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json.dump({"slots": slots, "ledger_at_start": spent(), "stop_ledger": stop, "target": tgt,
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"created": time.strftime("%F %T")}, open(plan_path(name), "w"), indent=1)
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return json.load(open(plan_path(name)))
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def backlog():
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"""Accepted training tasks that have no first trajectory run yet."""
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done = set()
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sp = os.path.join(ROOT, "runs", "traj", "summary.jsonl")
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if os.path.exists(sp):
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done = {json.loads(l)["task"] for l in open(sp) if json.loads(l)["attempt"] == 0}
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acc = {os.path.basename(os.path.dirname(f)) for f in glob.glob(os.path.join(POOL, "G*", "generation.json"))
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if json.load(open(f)).get("accepted")}
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return len(acc - done)
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def backlog_by_kind():
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"""{kind: accepted tasks without a first trajectory run}."""
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done = set()
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sp = os.path.join(ROOT, "runs", "traj", "summary.jsonl")
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if os.path.exists(sp):
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done = {json.loads(l)["task"] for l in open(sp) if json.loads(l)["attempt"] == 0}
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out = {}
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for f in glob.glob(os.path.join(POOL, "G*", "generation.json")):
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tid = os.path.basename(os.path.dirname(f))
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try:
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ok = json.load(open(f)).get("accepted")
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except (OSError, ValueError):
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ok = False
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if ok and tid not in done:
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k = mix.kind_of_task_dir(tid)
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out[k] = out.get(k, 0) + 1
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return out
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BAL_KIND_BACKLOG = 8 # a kind with more waiting tasks than this is not generated (its trajectories come first)
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def run(part, parts, target, stop_ledger, plan_name="plan", deadline=None):
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plan = ensure_plan(plan_name, stop_ledger, target)
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stop_at = plan["ledger_at_start"] + plan["stop_ledger"]
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base_url = os.environ.get("LLM_BASE_URL", "http://127.0.0.1:11434/v1")
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evals = overlap.load_pool("eval")
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for idx, s in enumerate(plan["slots"]):
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if idx % parts != part:
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continue
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if os.path.exists(os.path.join(POOL, "_logs", s["id"] + ".json")):
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continue # done or failed: not tried again (delete the log to retry)
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while backlog() > BACKLOG_LIMIT and not os.path.exists(STOP_FLAG) \
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and not (deadline and time.time() > deadline):
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time.sleep(120) # trajectories are the slower side: do not generate tasks that wait for days
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if os.path.exists(STOP_FLAG):
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print("STOP flag", flush=True)
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break
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if deadline and time.time() > deadline:
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print("DEADLINE", flush=True)
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break
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if plan["target"] and accepted_count() >= plan["target"]:
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print("TARGET reached", flush=True)
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break
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if spent() >= stop_at:
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print("PHASE LIMIT", spent(), stop_at, flush=True)
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break
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goals = accepted_goals()
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avoid = [g for g in goals if g] # eval goals and all training goals
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topic = ((s["topic"] + ". ") if s["topic"] else "Choose a new, realistic business topic. ") + \
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"Do not repeat these existing topics: " + "; ".join(avoid[-170:])
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pool_now = evals + overlap.load_pool("train")
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def extra(b, _pool=pool_now):
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hits = overlap.check(overlap.load_bundle(b), _pool)
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return [f"Too close to task {i} (similarity spec {sc['spec']:.2f}, rules {sc['core']:.2f}, "
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f"names {sc['name']:.2f}). Choose a different business topic and different object names."
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for i, sc in hits[:3]]
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try:
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log = generate(s["id"], "train", s["object_type"], s["category"], s.get("difficulty", 2),
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"deepseek-v4.1-flash:cloud",
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base_url, s["run_base"], topic, extra_check=extra)
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except BudgetExceeded as e:
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print("BUDGET", e, flush=True)
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break
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except Exception as e: # noqa: BLE001
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log = {"id": s["id"], "error": str(e)[:500]}
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log.update(error_kind=s.get("error_kind"), difficulty=s.get("difficulty", 2), spent_total=spent())
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os.makedirs(os.path.join(POOL, "_logs"), exist_ok=True)
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json.dump(log, open(os.path.join(POOL, "_logs", s["id"] + ".json"), "w"), indent=1)
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stray = os.path.join(POOL, "generation.json") # generate() writes here when no task dir exists
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if os.path.exists(stray):
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os.remove(stray)
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print(json.dumps(log), flush=True)
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BAL_FIRST_ID = 1910
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BAL_RUN_BASE = 370000 # 40 per slot; below 466560 (a digit must lead the 4-char base36 run)
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BAL_ERROR_KINDS = {"CLAS": ["named-type", "long-names"], "FUNC": ["named-type"], "DDLS": ["reserved-word"],
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"TABL": ["reserved-word"], "STRU": ["reserved-word"]}
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BAL_HINTS = dict(((c, t), h) for c, t, _, h in SLOTS if h)
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def _claims_dir():
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d = os.path.join(POOL, "_claims")
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os.makedirs(d, exist_ok=True)
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return d
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def _claim_slot():
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"""Next free balanced slot number, claimed with O_EXCL (several workers). Returns (n, claim path)."""
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for n in range(BAL_FIRST_ID, BAL_FIRST_ID + 600):
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sid = "G%04d" % n
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if os.path.exists(os.path.join(POOL, "_logs", sid + ".json")):
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continue
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path = os.path.join(_claims_dir(), sid + ".json")
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try:
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fd = os.open(path, os.O_CREAT | os.O_EXCL | os.O_WRONLY)
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except FileExistsError:
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continue
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os.close(fd)
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return n, path
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return None, None
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def _kind_stats():
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"""({kind: accepted}, {kind: attempted}) from the generation logs; claims of running slots count as attempted."""
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acc = mix.accepted_task_counts()
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att = {}
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for f in glob.glob(os.path.join(POOL, "_logs", "G*.json")):
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try:
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l = json.load(open(f))
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except (OSError, ValueError):
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continue
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k = l.get("kind") or l.get("object_type")
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if k and l.get("category") != "K":
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att[k] = att.get(k, 0) + 1
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return acc, att
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def run_balanced(part, parts, deadline):
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"""Generation without a fixed plan: each slot takes the kind with the biggest deficit against mix.TYPE_SHARE.
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A kind with 6 or more tries and an acceptance below 20 % is skipped (a harness or prompt problem: do not burn budget)."""
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base_url = os.environ.get("LLM_BASE_URL", "http://127.0.0.1:11434/v1")
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evals = overlap.load_pool("eval")
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while True:
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if os.path.exists(STOP_FLAG):
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print("STOP flag", flush=True)
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return
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if deadline and time.time() > deadline:
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print("DEADLINE", flush=True)
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return
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acc, att = _kind_stats()
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running = {}
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for f in glob.glob(os.path.join(_claims_dir(), "G*.json")):
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try:
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k = json.load(open(f)).get("kind")
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except (OSError, ValueError):
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k = None
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if k:
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running[k] = running.get(k, 0) + 1
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counts = {k: acc.get(k, 0) + running.get(k, 0) for k in set(acc) | set(running) | set(mix.TYPE_SHARE)}
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blocked = {k for k in mix.TYPE_SHARE if att.get(k, 0) >= 6 and acc.get(k, 0) < 0.2 * att.get(k, 0)}
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if blocked:
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print("kinds skipped (low acceptance):", sorted(blocked), flush=True)
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waiting = {k for k, n in backlog_by_kind().items() if n > BAL_KIND_BACKLOG}
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allowed = (set(mix.TYPE_SHARE) - blocked - waiting) & mix.below_target(counts) # only kinds below their share
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if not allowed:
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time.sleep(120) # every kind has a backlog: the trajectories are the slower side
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continue
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kind = mix.deficit_pick(counts, allowed=allowed)
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n, claim = _claim_slot()
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if n is None:
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print("no free slot", flush=True)
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return
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sid = "G%04d" % n
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json.dump({"kind": kind}, open(claim, "w"))
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otype = "CLAS" if kind == "EXC" else kind
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cats = mix.KIND_CATEGORIES[kind]
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logs = [json.load(open(f)) for f in glob.glob(os.path.join(POOL, "_logs", "G*.json"))]
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ccount = {c: sum(1 for l in logs if l.get("accepted") and l.get("category") == c) for c in cats}
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tot = sum(ccount.values()) + 1
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cat = max(cats, key=lambda c: tot * mix.CATEGORY_SHARE[c] / sum(mix.CATEGORY_SHARE[x] for x in cats) - ccount[c])
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idx = n - BAL_FIRST_ID
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error_kind = None
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if idx % 5 == 2 and kind in BAL_ERROR_KINDS: # 20 % error-targeted slots
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error_kind = BAL_ERROR_KINDS[kind][(idx // 5) % len(BAL_ERROR_KINDS[kind])]
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cat = ERROR_CATEGORY[error_kind] if kind in ("CLAS", "FUNC") else cat
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hard = cat != "H" and idx % 10 in (3, 6, 9) # 30 % hard
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topic = None
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if error_kind:
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topic = dict(ERROR_HINTS)[error_kind]
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elif kind == "EXC":
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topic = "exception class (CX_...): " + ["a domain exception with context attributes and message texts",
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"an exception hierarchy with a common super class",
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"an exception that wraps a previous exception"][idx % 3]
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elif (cat, otype) in BAL_HINTS:
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h = BAL_HINTS[(cat, otype)]
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topic = h[idx % len(h)]
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if cat == "G":
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topic = (topic + "; " if topic else "") + f"release target {RELEASES[idx % 2]}"
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avoid = [g for g in accepted_goals() if g][-170:]
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full = ((topic + ". ") if topic else "Choose a new, realistic business topic. ") + \
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"Do not repeat these existing topics: " + "; ".join(avoid)
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pool_now = evals + overlap.load_pool("train")
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def extra(b, _pool=pool_now):
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hits = overlap.check(overlap.load_bundle(b), _pool)
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return [f"Too close to task {i} (similarity spec {sc['spec']:.2f}, rules {sc['core']:.2f}, "
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f"names {sc['name']:.2f}). Choose a different business topic and different object names."
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for i, sc in hits[:3]]
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try:
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log = generate(sid, "train", otype, cat, 3 if hard else 2, "deepseek-v4.1-flash:cloud", base_url,
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BAL_RUN_BASE + 40 * idx, full, extra_check=extra)
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except BudgetExceeded as e:
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print("BUDGET", e, flush=True)
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os.remove(claim)
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return
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except Exception as e: # noqa: BLE001
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log = {"id": sid, "error": str(e)[:500]}
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log.update(kind=kind, error_kind=error_kind, difficulty=3 if hard else 2, spent_total=spent())
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os.makedirs(os.path.join(POOL, "_logs"), exist_ok=True)
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json.dump(log, open(os.path.join(POOL, "_logs", sid + ".json"), "w"), indent=1)
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stray = os.path.join(POOL, "generation.json")
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if os.path.exists(stray):
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os.remove(stray)
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os.remove(claim)
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print(json.dumps(log), flush=True)
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K_FIRST_ID = 1300
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K_RUN_BASE = 41800 # 20 per variant; above the trajectory run numbers (41000-41700)
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K_COUNT = 18 # K share of the eval plan: 10 of 110 (9 %); counted inside the 200 accepted tasks
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K_STYLES_CYCLE = ["free_text", "incomplete"]
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def run_k(count):
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"""K tasks for training: free-text or incomplete spec of an accepted training task, EPOD tool names
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(no generic_v0). Same reference and hidden tests as the source task."""
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base_url = os.environ.get("LLM_BASE_URL", "http://127.0.0.1:11434/v1")
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for n in range(count):
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new_id = f"G{K_FIRST_ID + n}"
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if os.path.exists(os.path.join(POOL, "_logs", new_id + ".json")):
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continue
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used = {json.load(open(f)).get("base_task") for f in glob.glob(os.path.join(POOL, "_logs", "G13*.json"))}
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cands = [] # accepted, not K, not H (a stop task has no free-text form), not used yet
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for f in sorted(glob.glob(os.path.join(POOL, "_logs", "G1*.json"))):
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lg = json.load(open(f))
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if lg.get("accepted") and lg.get("category") not in ("H", "K") and lg["id"] not in used:
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cands.append(lg)
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if not cands:
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print("no source task left", flush=True)
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return
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kinds = {}
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for lg in cands: # spread over object types: take the type with the fewest K variants so far
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kinds.setdefault(lg["object_type"], []).append(lg)
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done_types = [json.load(open(f)).get("object_type") for f in glob.glob(os.path.join(POOL, "_logs", "G13*.json"))]
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otype = min(kinds, key=lambda t: done_types.count(t))
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src = kinds[otype][0]
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style = K_STYLES_CYCLE[n % 2]
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if os.path.exists(STOP_FLAG):
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return
|
|
try:
|
|
log = make_k_variant(src["id"], new_id, style, "deepseek-v4.1-flash:cloud", base_url,
|
|
K_RUN_BASE + 20 * n, tool_schema=None, pool="train")
|
|
except BudgetExceeded as e:
|
|
print("BUDGET", e, flush=True)
|
|
return
|
|
log.update(object_type=otype, spent_total=spent())
|
|
stray = os.path.join(POOL, "generation.json")
|
|
if os.path.exists(stray):
|
|
os.remove(stray)
|
|
os.makedirs(os.path.join(POOL, "_logs"), exist_ok=True)
|
|
json.dump(log, open(os.path.join(POOL, "_logs", new_id + ".json"), "w"), indent=1)
|
|
print(json.dumps(log), flush=True)
|
|
|
|
|
|
def main():
|
|
load_env(os.path.join(ROOT, ".env"))
|
|
ap = argparse.ArgumentParser()
|
|
ap.add_argument("cmd", choices=["plan", "run", "k"])
|
|
ap.add_argument("--part", type=int, default=0)
|
|
ap.add_argument("--parts", type=int, default=1)
|
|
ap.add_argument("--target", type=int, default=200)
|
|
ap.add_argument("--plan", default="plan", help="plan (first 223 slots), plan2 (hard and error share raised) or balanced (kind with the biggest deficit)")
|
|
ap.add_argument("--deadline", help="YYYY-MM-DDTHH:MM local time: no new slot after it")
|
|
ap.add_argument("--k-count", type=int, default=K_COUNT)
|
|
ap.add_argument("--stop-ledger", type=float, default=27.0, help="ledger USD for this phase (10 USD usage = 27)")
|
|
a = ap.parse_args()
|
|
if a.cmd == "plan":
|
|
import collections
|
|
p = build_plan()
|
|
print(len(p), "slots", collections.Counter(x["category"] for x in p))
|
|
print(collections.Counter(x["object_type"] for x in p), collections.Counter(x.get("error_kind") for x in p))
|
|
return
|
|
if a.cmd == "k":
|
|
run_k(a.k_count)
|
|
return
|
|
dl = time.mktime(time.strptime(a.deadline, "%Y-%m-%dT%H:%M")) if a.deadline else None
|
|
if a.plan == "balanced":
|
|
run_balanced(a.part, a.parts, dl)
|
|
return
|
|
run(a.part, a.parts, a.target, a.stop_ledger, a.plan, dl)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|