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"""Summarize Qwen3-Omni full-parameter feasibility gates.
These runs are evidence that full-parameter FSDP can load, prepare, step, and
run short guarded pilots on an 8-GPU remote worker. They are not promoted model results and they do
not publish checkpoints or weights.
"""
from __future__ import annotations
import json
from datetime import datetime, timezone
from pathlib import Path
from typing import Any
ROOT = Path(__file__).resolve().parents[2]
RESULT_ROOT = ROOT / "results" / "omni_finetune"
OUTPUT_JSON = ROOT / "docs/data/qwen3_full_parameter_gates.json"
OUTPUT_MD = RESULT_ROOT / "QWEN3_FULL_PARAMETER_GATES_20260609.md"
RUNS = [
{
"id": "fullparam_smoke_1step",
"title": "Full-Parameter 1-Step Feasibility Smoke",
"summary": RESULT_ROOT
/ "xperience10m_qwen3_omni_128ep_fullparam_smoke_preemptible_8gpu_20260609"
/ "fullparam_feasibility_summary.json",
"scope": "1 optimizer step over 8 train samples",
},
{
"id": "fullparam_shorttrain8",
"title": "Full-Parameter 8-Step Short Train",
"summary": RESULT_ROOT
/ "xperience10m_qwen3_omni_128ep_fullparam_shorttrain8_preemptible_8gpu_20260609"
/ "fullparam_shorttrain8_summary.json",
"scope": "8 optimizer steps over 64 train samples",
},
{
"id": "fullparam_pilot32",
"title": "Full-Parameter 32-Step Pilot",
"summary": RESULT_ROOT
/ "xperience10m_qwen3_omni_128ep_fullparam_pilot32_preemptible_8gpu_20260609"
/ "fullparam_pilot32_summary.json",
"scope": "32 optimizer steps over 256 train samples",
},
{
"id": "fullparam_pilot64",
"title": "Full-Parameter 64-Step Pilot",
"summary": RESULT_ROOT
/ "xperience10m_qwen3_omni_128ep_fullparam_pilot64_preemptible_8gpu_20260609"
/ "fullparam_pilot64_summary.json",
"scope": "64 optimizer steps over 512 train samples",
},
{
"id": "fullparam_pilot128_preempted",
"title": "Full-Parameter 128-Step Opportunistic Pilot",
"summary": RESULT_ROOT
/ "xperience10m_qwen3_omni_128ep_fullparam_pilot128_preemptible_8gpu_20260609"
/ "fullparam_pilot128_summary.json",
"scope": "planned 128 optimizer steps over 1024 train samples; preempted for Qwen v5 handoff",
},
{
"id": "fullparam_pilot128_after_qwen_v5",
"title": "Full-Parameter 128-Step Post-Qwen-v5 Pilot",
"metadata": RESULT_ROOT
/ "xperience10m_qwen3_omni_128ep_fullparam_pilot128_after_qwen_v5_preemptible_8gpu_20260609"
/ "training_metadata.json",
"progress": RESULT_ROOT
/ "xperience10m_qwen3_omni_128ep_fullparam_pilot128_after_qwen_v5_preemptible_8gpu_20260609"
/ "progress.jsonl",
"scope": "128 optimizer steps over 1024 train samples after verified Qwen v5 handoff",
},
{
"id": "fullparam_pilot256_after_qwen_v6",
"title": "Full-Parameter 256-Step Post-Qwen-v6 Pilot",
"metadata": RESULT_ROOT
/ "xperience10m_qwen3_omni_128ep_fullparam_pilot256_after_qwen_v6_preemptible_8gpu_20260611"
/ "training_metadata.json",
"progress": RESULT_ROOT
/ "xperience10m_qwen3_omni_128ep_fullparam_pilot256_after_qwen_v6_preemptible_8gpu_20260611"
/ "progress.jsonl",
"scope": "256 optimizer steps over 2048 train samples after verified Qwen v6 handoff",
},
]
def rel(path: Path) -> str:
return path.relative_to(ROOT).as_posix()
def read_json(path: Path) -> dict[str, Any]:
if not path.exists():
return {}
return json.loads(path.read_text(encoding="utf-8"))
def number(value: Any) -> int | float | None:
return value if isinstance(value, (int, float)) else None
def progress_summary(path: Path, expected_steps: int | None) -> dict[str, Any]:
if not path.exists():
return {}
train_losses: list[float] = []
observed_steps = 0
saw_complete = False
saw_save_skipped = False
saw_max_steps = False
for line in path.read_text(encoding="utf-8").splitlines():
if not line.strip():
continue
try:
event = json.loads(line)
except json.JSONDecodeError:
continue
event_name = event.get("event")
if event_name == "train_step":
observed_steps = max(observed_steps, int(event.get("global_step") or 0))
loss = number(event.get("rank0_batch_loss"))
if loss is not None:
train_losses.append(float(loss))
elif event_name == "train_loop_stopped_max_steps":
saw_max_steps = True
observed_steps = max(observed_steps, int(event.get("global_step") or 0))
elif event_name == "save_skipped":
saw_save_skipped = True
elif event_name == "complete":
saw_complete = True
status = "passed" if saw_complete and saw_save_skipped and observed_steps == expected_steps else "review"
return {
"status": status,
"observed_train_steps": observed_steps or None,
"first_step_loss": train_losses[0] if train_losses else None,
"final_step_loss": train_losses[-1] if train_losses else None,
"min_step_loss": min(train_losses) if train_losses else None,
"max_step_loss": max(train_losses) if train_losses else None,
"saw_max_steps": saw_max_steps,
"saw_save_skipped": saw_save_skipped,
"saw_complete": saw_complete,
}
def row_from_metadata(config: dict[str, Any]) -> dict[str, Any]:
metadata_path = Path(config["metadata"])
progress_path = Path(config["progress"])
payload = read_json(metadata_path)
history = payload.get("history", []) if isinstance(payload.get("history"), list) else []
last_history = history[-1] if history else {}
max_train_steps = payload.get("max_train_steps")
progress = progress_summary(progress_path, max_train_steps if isinstance(max_train_steps, int) else None)
status = progress.get("status", "missing") if payload else "missing"
save_mode = payload.get("save_mode")
return {
"id": config["id"],
"title": config["title"],
"status": status,
"scope": config["scope"],
"summary_path": rel(metadata_path),
"progress_path": rel(progress_path),
"run_id": payload.get("run_id"),
"purpose": (
"post_verified_qwen_v6_full_parameter_feasibility_pilot"
if "qwen_v6" in config["id"]
else "post_verified_qwen_v5_full_parameter_feasibility_pilot"
),
"tuning_mode": payload.get("tuning_mode"),
"training_objective": payload.get("backbone", {}).get("training_objective")
if isinstance(payload.get("backbone"), dict)
else None,
"num_processes": payload.get("num_processes"),
"num_train_samples": payload.get("num_train_samples"),
"configured_max_train_steps": max_train_steps,
"observed_train_steps": progress.get("observed_train_steps") or last_history.get("global_step"),
"first_step_loss": number(progress.get("first_step_loss")),
"final_step_loss": number(progress.get("final_step_loss")),
"epoch_train_loss": number(last_history.get("train_loss")),
"min_step_loss": number(progress.get("min_step_loss")),
"max_step_loss": number(progress.get("max_step_loss")),
"model_load_seconds": None,
"accelerator_prepare_seconds": None,
"train_loop_seconds": None,
"save_mode": save_mode,
"checkpoint_saved": False,
"checkpoint_policy": "no full-parameter checkpoint or public weights; save_mode=none",
"preempt_event": None,
"parent_resume_event": None,
"progress_events": {
"max_steps_reached": progress.get("saw_max_steps"),
"save_skipped": progress.get("saw_save_skipped"),
"complete": progress.get("saw_complete"),
},
}
def run_row(config: dict[str, Any]) -> dict[str, Any]:
if "metadata" in config:
return row_from_metadata(config)
path = Path(config["summary"])
payload = read_json(path)
status = payload.get("status", "missing") if payload else "missing"
max_train_steps = (
payload.get("max_train_steps")
or payload.get("configured_max_train_steps")
or payload.get("global_step")
)
final_step_loss = payload.get("final_step_loss", payload.get("rank0_batch_loss"))
epoch_train_loss = payload.get("epoch_train_loss", payload.get("train_loss"))
return {
"id": config["id"],
"title": config["title"],
"status": status,
"scope": config["scope"],
"summary_path": rel(path),
"run_id": payload.get("run_id"),
"purpose": payload.get("purpose"),
"tuning_mode": payload.get("tuning_mode"),
"training_objective": payload.get("training_objective"),
"num_processes": payload.get("num_processes"),
"num_train_samples": payload.get("num_train_samples")
or payload.get("configured_max_train_samples"),
"configured_max_train_steps": max_train_steps,
"observed_train_steps": payload.get("observed_train_steps")
if payload.get("observed_train_steps") is not None
else payload.get("global_step"),
"first_step_loss": number(payload.get("first_step_loss")),
"final_step_loss": number(final_step_loss),
"epoch_train_loss": number(epoch_train_loss),
"min_step_loss": number(payload.get("min_step_loss")),
"max_step_loss": number(payload.get("max_step_loss")),
"model_load_seconds": number(payload.get("model_load_seconds")),
"accelerator_prepare_seconds": number(payload.get("accelerator_prepare_seconds")),
"train_loop_seconds": number(payload.get("train_loop_seconds")),
"save_mode": payload.get("save_mode"),
"checkpoint_saved": bool(payload.get("checkpoint_saved", False)),
"checkpoint_policy": "no full-parameter checkpoint or public weights; save_mode=none",
"preempt_event": payload.get("preempt_event"),
"parent_resume_event": payload.get("parent_resume_event"),
}
def build_payload() -> dict[str, Any]:
runs = [run_row(config) for config in RUNS]
passed = [run for run in runs if run["status"] == "passed"]
preempted = [run for run in runs if str(run["status"]).startswith("preempted")]
missing_or_review = [
run
for run in runs
if run["status"] not in {"passed"} and not str(run["status"]).startswith("preempted")
]
completed_steps = sum(int(run.get("observed_train_steps") or 0) for run in passed)
longest_passed = max(passed, key=lambda run: int(run.get("observed_train_steps") or 0), default=None)
return {
"title": "Qwen3-Omni Full-Parameter Feasibility Gates",
"generated_at_utc": datetime.now(timezone.utc).isoformat(timespec="seconds"),
"status": "pass" if len(passed) >= 5 and len(preempted) <= 1 and not missing_or_review else "review",
"decision": "full_parameter_feasible_for_guarded_short_runs_not_promoted",
"interpretation": (
"The full-parameter gates prove that Qwen3-Omni full-parameter FSDP can load, "
"prepare, run backward/optimizer steps, and complete guarded pilots up to "
"256 optimizer steps on an 8-GPU remote worker. They do not prove a production full-parameter fine-tune, and "
"they intentionally save no full checkpoints or public weights."
),
"aggregate": {
"run_count": len(runs),
"passed_run_count": len(passed),
"preempted_run_count": len(preempted),
"review_or_missing_run_count": len(missing_or_review),
"completed_full_parameter_train_steps": completed_steps,
"longest_passed_run_id": longest_passed.get("run_id") if longest_passed else None,
"longest_passed_steps": longest_passed.get("observed_train_steps") if longest_passed else None,
"num_processes": sorted({run.get("num_processes") for run in runs if run.get("num_processes")}),
"checkpoint_saved": any(run.get("checkpoint_saved") for run in runs),
},
"runs": runs,
"publication_policy": {
"public_summary_allowed": True,
"publish_full_parameter_weights": False,
"publish_full_checkpoints": False,
"reason": "All completed full-parameter gate runs used save_mode=none; the preempted pilot saved nothing. These are feasibility evidence only.",
},
"next_steps": [
"Keep the verified Qwen3-Omni LoRA adapter as the published production result for the 128-episode suite.",
"For a production full-parameter run, add a sharded checkpoint/resume plan before any long training launch.",
"Run a separate checkpointed full-parameter pilot only when GPUs are not needed by verified LoRA evaluation/publication work.",
],
}
def fmt(value: Any) -> str:
if value is None:
return ""
if isinstance(value, float):
return f"{value:.4f}"
return str(value)
def markdown(payload: dict[str, Any]) -> str:
lines = [
"# Qwen3-Omni Full-Parameter Feasibility Gates",
"",
f"Generated: `{payload['generated_at_utc']}`",
"",
payload["interpretation"],
"",
"## Summary",
"",
f"- Status: `{payload['status']}`",
f"- Decision: `{payload['decision']}`",
f"- Passed runs: `{payload['aggregate']['passed_run_count']}`",
f"- Preempted runs: `{payload['aggregate']['preempted_run_count']}`",
f"- Review/missing runs: `{payload['aggregate']['review_or_missing_run_count']}`",
f"- Completed full-parameter optimizer steps: `{payload['aggregate']['completed_full_parameter_train_steps']}`",
f"- Longest passed run: `{payload['aggregate']['longest_passed_run_id']}` ({payload['aggregate']['longest_passed_steps']} steps)",
f"- Checkpoint saved: `{payload['aggregate']['checkpoint_saved']}`",
"",
"## Runs",
"",
"| run | status | steps | samples | final loss | epoch/train loss | policy | source |",
"| --- | --- | ---: | ---: | ---: | ---: | --- | --- |",
]
for run in payload["runs"]:
lines.append(
"| {title} | {status} | {steps} | {samples} | {final_loss} | {epoch_loss} | {policy} | `{source}` |".format(
title=run["title"],
status=run["status"],
steps=fmt(run.get("observed_train_steps")),
samples=fmt(run.get("num_train_samples")),
final_loss=fmt(run.get("final_step_loss")),
epoch_loss=fmt(run.get("epoch_train_loss")),
policy="no weights/checkpoints",
source=run["summary_path"],
)
)
lines.extend(
[
"",
"## Publication Policy",
"",
"- Public summary allowed: `true`",
"- Publish full-parameter weights: `false`",
"- Publish full checkpoints: `false`",
f"- Reason: {payload['publication_policy']['reason']}",
"",
"## Next Steps",
"",
]
)
lines.extend(f"- {item}" for item in payload["next_steps"])
lines.append("")
return "\n".join(lines)
def main() -> int:
payload = build_payload()
OUTPUT_JSON.parent.mkdir(parents=True, exist_ok=True)
OUTPUT_MD.parent.mkdir(parents=True, exist_ok=True)
OUTPUT_JSON.write_text(json.dumps(payload, indent=2) + "\n", encoding="utf-8")
OUTPUT_MD.write_text(markdown(payload), encoding="utf-8")
print(f"PASS: wrote {rel(OUTPUT_JSON)}")
print(f"PASS: wrote {rel(OUTPUT_MD)}")
return 0
if __name__ == "__main__":
raise SystemExit(main())
|