Add local E2E profiling evidence and support roadmap

benchmarks/edge_harness/PROFILING.md

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+# Local model qualification and profiling
+
+These runners exercise existing Omni implementations with real weights. They
+do not add device or model support. A completed timing run is distinct from
+reference-quality, streaming, fault-recovery and release qualification.
+
+## Matrix and prerequisites
+
+The [60-cell audit](results/model_device_matrix_20260922/README.md) covers twelve
+PC/mobile/embedded configurations and five model families. Its raw evidence
+distinguishes complete pipelines from components and backend-only probes.
+Unavailable complete pipelines retain a reason and next repair step; a missing
+measurement is never represented as zero latency.
+
+Use the actual installed runtime and exact checkpoint identified in the result,
+including quantization, rather than substituting another family member. The
+current Spark CPU runner uses 1.7B INT8 and the CUDA runner uses 4B BF16. Their
+results do not establish same-model acceleration. Qwen3-TTS uses the pinned
+0.6B CustomVoice checkpoint, English/Vivian, through both Omni stages.
+
+Windows and WSL are distinct configurations on the same physical laptop.
+Run benchmarks serially: they share CPU, physical RAM, GPU and power limits.
+GPU board power is not whole-device energy. Windows used RAM includes WSL;
+do not add Windows and WSL counters as independent memory pools.
+
+## Run the workload
+
+Run from the repository root using the model's validated Python environment:
+
+```bash
+PYTHONPATH=. HF_HUB_OFFLINE=1 VLLM_USE_FLASHINFER_SAMPLER=0 \
+  python benchmarks/edge_harness/profile_local_text.py \
+  --model /absolute/path/to/checkpoint --out /absolute/path/to/new-run
+
+PYTHONPATH=. HF_HUB_OFFLINE=1 VLLM_USE_FLASHINFER_SAMPLER=0 \
+  python benchmarks/edge_harness/profile_local_tts.py \
+  --model /absolute/path/to/customvoice-checkpoint --out /absolute/path/to/new-run
+```
+
+Set the validated CPU thread/KV-cache settings or CUDA compiler environment
+before launching. Native Windows requires UTF-8 Python mode before interpreter
+startup (`PYTHONUTF8=1`, `PYTHONIOENCODING=utf-8`). The common benchmark helper
+uses the active environment's `torch/lib/cudart64_13.dll` when present and a
+short `~/c29` vLLM cache path unless explicitly overridden. These reproduce
+settings required by the tested Windows installation; they are not a general
+claim about other CUDA/runtime versions.
+
+Both runners require a new output directory and default to:
+
+- Three input lengths × concurrency 1/2/4 × 20 measured requests per group.
+- Separate warmup requests, excluded from group percentiles.
+- At least 1800 seconds of continuous medium-input requests.
+- Slow-consumer, cancellation and subsequent-request checks.
+
+Text timing forces 128 output tokens with greedy sampling and records actual
+input lengths, token IDs, sequence/epoch metadata and queue high-water marks.
+It does not recommend those sampling settings for applications. TTS requires
+DELTA output, records every audio chunk including terminal audio, verifies
+finite PCM, and archives warmup float32 chunks with hashes. Playback underruns
+are simulated from chunk arrivals; no sound-device playback or perceptual
+quality is implied.
+
+`profile_host_command.py` wraps an explicit command on Windows to collect
+one-second host-memory, AC/battery and whole-NVIDIA-device telemetry. Each host
+directory retains the exact command, exit status and combined log. Existing
+disk/JIT caches are used. Constructor time is not a cold-disk measurement and
+must remain separate from request latency. Sampled memory peaks are lower
+bounds, not proof that every transient allocation was observed.
+
+On Windows, a live reader can deny replacement of a report snapshot. The
+writer defers such running updates, records the permission error, and requires
+the final snapshot to persist. Request JSONL is the primary sample record.
+Read live reports through Linux when they reside in WSL, or use a Windows
+reader that permits file deletion/replacement. Failed profile/trace reports
+produce a nonzero exit code in the current runners; historical attempts may
+require inspection of the report rather than relying on the process exit code.
+
+## Separate diagnostics
+
+After timing runs finish, use `profile_trace_diagnostic.py --kind spark|tts`
+with the same `--model` and a fresh `--out`. It invokes Omni's profiler RPCs and
+measures three requests before, during and after tracing. Current text traces
+use 32 generated tokens and disable expensive shape/memory instrumentation.
+Compare paired diagnostic requests, not these 32-token times against the
+128-token baseline. Profiler start/stop/export costs are recorded separately
+when supported. The initial archived CPU diagnostic used full instrumentation
+and 128 tokens; its configuration and limitations are retained in its report.
+
+Run `check_text_reliability.py` or `check_tts_reliability.py` separately. They
+test real-model interruption/recovery and deliberately terminate a worker
+belonging to their own engine after verifying process ancestry. Text also
+checks actual consumer-queue saturation, stale handles and oversized-plan
+rejection. TTS's slow consumer does not prove saturation of every internal
+buffer. Inspect individual outcomes: `status=completed` means the probe ran,
+not that all checks passed. Any forced cleanup remains a finding.
+
+## Analyze and qualify
+
+For a separate TTS stage-attribution diagnostic, enable the existing
+`VLLM_OMNI_STEP_STATS_DIR=/absolute/path/to/counters` hook and use
+`VLLM_OMNI_STEP_STATS_SYNC=0`. Run `profile_local_tts.py` with
+`--length-band medium --concurrency 1 --repeats 20 --sustained-seconds 0`
+and a fresh output directory. This is not a full baseline run. The counters
+include warmup, slow-consumer, abort and recovery activity, and nested timers
+overlap. Preserve per-process summaries rather than pooling their percentiles.
+These are host-side timers without accelerator synchronization. Periodic
+flushing can omit the final counter tail after forced process termination.
+Comparisons with an uninstrumented baseline must retain cache/thermal and
+stochastic speech-length caveats.
+Core-step counts include idle/no-work iterations. The current orchestrator
+dispatch hook is only in the optional event-driven loop; the default loop
+does not produce that counter. Absent counters are not zero-time measurements.
+
+```bash
+python benchmarks/edge_harness/summarize_e2e_profiles.py \
+  --matrix benchmarks/edge_harness/results/model_device_matrix_20260922/matrix.json \
+  --runs /absolute/path/to/run-root --out /absolute/path/to/summary
+
+python benchmarks/edge_harness/analyze_profile_resources.py \
+  --runs /absolute/path/to/run-root --out /absolute/path/to/resource-summary.json
+```
+
+Use run directory names `spark-cpu-wsl`, `spark-cuda-wsl`,
+`spark-cuda-windows`, `tts-cuda-wsl` and `tts-cuda-windows`, with `-host`,
+`-trace` and `-reliability` suffixes for their corresponding records. The matrix
+summarizer verifies all sixty pairings and the original evidence hashes. It
+reports nearest-rank p50/p95 and sample counts, checks per-group coverage,
+flags recorded output-contract violations, and retains unavailable metrics.
+Batch-dependent greedy outputs are a numerical/reproducibility finding;
+timings alone cannot establish whether the cause is numerical or state-related.
+Delivery-spacing statistics pool adjacent intervals within each request; their
+sample count is the number of intervals. Text output updates may coalesce
+tokens, so this is delivery timing rather than isolated kernel inter-token
+latency. Effective prefill throughput includes all work before the first output.
+
+Parse large traces only after inference timing has finished. The resource
+analyzer streams trace JSON, separates summed event duration from interval
+union, and excludes telemetry gaps over five seconds from energy integration.
+Actual device placement, fallback, transfer and synchronization conclusions
+must be supported by the trace/runtime records, not merely the intended plan.
+The TTS diagnostic starts each stage with a distinct profiler prefix because
+both workers have rank 0 and the current runtime otherwise overwrites their
+shared trace filename. Both stages are active during traced requests. Sequential
+stop/export RPCs can extend later-stage trace windows with idle/export time;
+trace-window length is not model execution time or GPU utilization.
+Startup summaries retain both constructor time and inclusive host-launch to
+first warmup submission/output. The latter includes artifact hashing, plan
+construction and imports; constructor timing alone excludes that preparation.
+
+Keep compile/load/run failures and their exact configurations. Before declaring
+a pairing qualified, review model-specific reference quality, complete declared
+modalities, state/stream correctness, memory admission, executed placement and
+the performance protocol. Missing artifacts, backends, device access, sensors
+or reference suites require explicit reasons and repair actions.
+
+After every writer exits, archive evidence with reversible compression of large
+uncompressed files and an original/stored byte-hash manifest:
+
+```bash
+python benchmarks/edge_harness/archive_profiles.py create /path/to/run-root /path/to/new-archive
+python benchmarks/edge_harness/archive_profiles.py verify /path/to/new-archive
+python benchmarks/edge_harness/archive_profiles.py restore /path/to/new-archive /path/to/new-run-root
+```
+
+Restore before using original report-relative paths for compressed artifacts.
+The utility does not rewrite raw reports. Integrity verification establishes
+that archived bytes match, not that a model passes its qualification gates.

benchmarks/edge_harness/analyze_profile_resources.py

@@ -0,0 +1,299 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Summarize raw host telemetry and traces without adding overlapping timings."""
+
+import argparse
+import gzip
+import json
+import math
+import re
+from collections import defaultdict
+from pathlib import Path
+
+from summarize_e2e_profiles import read_lines, stats
+
+
+def interval_union(intervals):
+    total = 0.0
+    end = None
+    for start, stop in sorted(intervals):
+        if stop <= start:
+            continue
+        total += stop - start if end is None or start >= end else max(0, stop - end)
+        end = stop if end is None else max(end, stop)
+    return total
+
+
+def trace_events(path):
+    """Read one event at a time, avoiding a multi-GB decoded trace document."""
+    opener = gzip.open if path.suffix == ".gz" else open
+    with opener(path, "rt", encoding="utf-8") as stream:
+        buffer = ""
+        while True:
+            block = stream.read(65536)
+            if not block:
+                raise ValueError("No traceEvents array")
+            buffer += block
+            match = re.search(r'"traceEvents"\s*:\s*\[', buffer)
+            if match:
+                buffer = buffer[match.end() :]
+                break
+            if len(buffer) > 8 * 1024 * 1024:
+                raise ValueError("Trace header exceeds 8 MiB")
+        decoder = json.JSONDecoder()
+        while True:
+            buffer = buffer.lstrip(" \t\r\n,")
+            if buffer.startswith("]"):
+                return
+            try:
+                event, end = decoder.raw_decode(buffer)
+            except json.JSONDecodeError:
+                block = stream.read(65536)
+                if not block:
+                    raise ValueError("Truncated traceEvents array") from None
+                buffer += block
+                if len(buffer) > 16 * 1024 * 1024:
+                    raise ValueError("One trace event exceeds 16 MiB") from None
+                continue
+            buffer = buffer[end:]
+            yield event
+
+
+def trace_summary(path):
+    categories, ops = defaultdict(list), defaultdict(list)
+    for event in trace_events(path):
+        if event.get("ph") != "X" or not isinstance(event.get("dur"), (int, float)):
+            continue
+        categories[event.get("cat", "uncategorized")].append((event["ts"], event["ts"] + event["dur"]))
+        ops[(event.get("cat", ""), event.get("name", ""))].append(float(event["dur"]))
+    return {
+        "source": str(path),
+        "scope": "one process trace; event duration sums overlap and are not E2E time",
+        "categories": {
+            name: {
+                "events": len(group),
+                "duration_sum_us": sum(b - a for a, b in group),
+                "busy_union_us": interval_union(group),
+            }
+            for name, group in categories.items()
+        },
+        "largest_ops_by_duration_sum": sorted(
+            [
+                {"category": cat, "name": name, "duration_sum_us": sum(values), "duration_us": stats(values)}
+                for (cat, name), values in ops.items()
+            ],
+            key=lambda r: r["duration_sum_us"],
+            reverse=True,
+        )[:40],
+    }
+
+
+def telemetry_summary(path, start=None, stop=None):
+    rows = [
+        r for r in read_lines(path) if (start is None or r["unix"] >= start) and (stop is None or r["unix"] <= stop)
+    ]
+    series = defaultdict(list)
+    power = []
+    for row in rows:
+        series["host_available_bytes"].append(row["host_memory"]["available"])
+        series["host_used_bytes"].append(row["host_memory"]["used"])
+        series["host_cpu_percent"].append(row.get("host_cpu_percent"))
+        fields = row.get("gpu_csv", "").split(",")
+        if len(fields) != 10:
+            continue
+        for index, name in [
+            (2, "gpu_memory_mib"),
+            (4, "gpu_temperature_c"),
+            (5, "gpu_power_w"),
+            (6, "gpu_sm_clock_mhz"),
+            (7, "gpu_mem_clock_mhz"),
+            (8, "gpu_utilization_percent"),
+        ]:
+            try:
+                value = float(fields[index])
+            except ValueError:
+                continue
+            if not math.isfinite(value):
+                continue
+            series[name].append(value)
+            if index == 5:
+                power.append((row["unix"], value))
+    gaps = [b["unix"] - a["unix"] for a, b in zip(rows, rows[1:])]
+    intervals = [(a, b) for a, b in zip(power, power[1:]) if 0 < b[0] - a[0] <= 5]
+    energy = sum((b[0] - a[0]) * (a[1] + b[1]) / 2 for a, b in intervals)
+    return {
+        "source": str(path),
+        "samples": len(rows),
+        "span_s": rows[-1]["unix"] - rows[0]["unix"] if len(rows) >= 2 else None,
+        "requested_window": {"start": start, "stop": stop},
+        "sampling_gap_s": stats(gaps),
+        "gaps_over_5s": sum(g > 5 for g in gaps),
+        "metrics": {key: stats(values) for key, values in series.items()},
+        "ac_states": sorted({r["power"]["ACLineStatus"] for r in rows if "power" in r}),
+        "whole_gpu_energy_j_over_observed_intervals": energy if intervals else None,
+        "energy_observed_duration_s": sum(b[0] - a[0] for a, b in intervals),
+        "energy_scope": (
+            "Whole NVIDIA GPU incl. other processes; trapezoids skip gaps >5s; not whole-system, CPU or NPU energy."
+        ),
+    }
+
+
+def main():
+    p = argparse.ArgumentParser(description=__doc__)
+    p.add_argument("--runs", type=Path, required=True)
+    p.add_argument("--out", type=Path, required=True)
+    p.add_argument(
+        "--skip-traces", action="store_true", help="Summarize telemetry only while inference timing is still running."
+    )
+    a = p.parse_args()
+    result = {"telemetry": {}, "traces": [], "trace_analysis": "deferred" if a.skip_traces else "requested"}
+    for host in sorted(a.runs.glob("*-host")):
+        raw = host / "telemetry.jsonl"
+        if not raw.is_file():
+            continue
+        name = host.name.removesuffix("-host")
+        record = {"whole_run": telemetry_summary(raw)}
+        report_path = a.runs / name / "report.json"
+        if report_path.is_file():
+            report = json.loads(report_path.read_text(encoding="utf-8"))
+            host_report_path = host / "status.json"
+            host_report = json.loads(host_report_path.read_text(encoding="utf-8")) if host_report_path.is_file() else {}
+            rows = read_lines(a.runs / name / "requests.jsonl")
+            warmups = [r for r in rows if r.get("phase") == "warmup"]
+            launch, report_start = host_report.get("start_unix"), report.get("start_unix")
+            log_path = host / "command.log"
+            timing_lines = []
+            log_lines = []
+            if log_path.is_file():
+                log_lines = log_path.read_text(encoding="utf-8", errors="replace").splitlines()
+                timing_lines = [
+                    line
+                    for line in log_lines
+                    if any(
+                        marker in line
+                        for marker in (
+                            "Loading model weights took",
+                            "Model loading took",
+                            "init engine (",
+                            "torch.compile took",
+                            "Captured talker_mtp graphs",
+                            "engine startup completed",
+                        )
+                    )
+                ]
+            record["lifecycle_log_observations"] = {
+                "scope": "Counts are log lines, not request counts or a correctness verdict. Inspect source logs.",
+                "markers": {},
+            }
+            for marker in ("Dropping output for unknown req", "force killing remaining process "):
+                matches = [line for line in log_lines if marker in line]
+                record["lifecycle_log_observations"]["markers"][marker] = {
+                    "count": len(matches),
+                    "examples": matches[:3] + matches[-3:] if len(matches) > 6 else matches,
+                }
+            first_submit = warmups[0].get("submitted_unix") if warmups else None
+            first_latency = warmups[0].get("ttft_s") if warmups else None
+            if first_latency is None and warmups and warmups[0].get("ttfa_ms") is not None:
+                first_latency = warmups[0]["ttfa_ms"] / 1000
+            record["startup"] = {
+                "cache_condition": report.get("cache_condition", "See source report"),
+                "constructor_s": report.get("startup_s"),
+                "host_launch_to_benchmark_report_start_s": report_start - launch if launch and report_start else None,
+                "host_launch_to_first_request_s": first_submit - launch if first_submit and launch else None,
+                "host_launch_to_first_output_s": (
+                    first_submit - launch + first_latency
+                    if first_submit and launch and first_latency is not None
+                    else None
+                ),
+                "inclusive_startup_scope": (
+                    "Host launch through first warmup submission/output includes imports, artifact hashing, "
+                    "plan construction and engine startup; uses recorded host/engine wall timestamps. "
+                    "Constructor timing alone excludes preparation before the constructor."
+                ),
+                "pre_constructor_scope": (
+                    "Host launch to report creation includes Python/import/preparation work; "
+                    "not isolated OS process creation."
+                ),
+                "first_warmup": {
+                    key: warmups[0].get(key)
+                    for key in (
+                        "request_id",
+                        "length_band",
+                        "concurrency",
+                        "wall_s",
+                        "ttft_s",
+                        "total_wall_s",
+                        "ttfa_ms",
+                        "rtf_total",
+                    )
+                }
+                if warmups
+                else None,
+                "runtime_loading_compilation_evidence": timing_lines,
+                "scope": (
+                    "Runtime-reported component lines retain original boundaries; "
+                    "do not sum parallel-stage times into startup latency."
+                ),
+            }
+            start, duration = report.get("sustained_start_unix"), report.get("sustained_wall_s")
+            if start and duration:
+                record["sustained"] = telemetry_summary(raw, start, start + duration)
+                record["sustained_first_5min"] = telemetry_summary(raw, start, start + 300)
+                record["sustained_last_5min"] = telemetry_summary(raw, start + duration - 300, start + duration)
+                record["sustained_last_60s"] = telemetry_summary(raw, start + duration - 60, start + duration)
+                requests = [r for r in rows if r.get("phase") == "sustained"]
+                record["sustained_request_summary"] = {
+                    "n": len(requests),
+                    "rtf_above_one_requests": sum((r.get("rtf_total") or 0) > 1 for r in requests),
+                    "simulated_playback_deficit_requests": sum((r.get("stall_at_ttfa_ms") or 0) > 0 for r in requests),
+                    "rtf_total": stats([r.get("rtf_total") for r in requests]),
+                    "scope": "All sustained requests; timing/real-time gates are separate from successful completion.",
+                }
+                record["sustained_request_trends"] = {}
+                for label, begin, end in [
+                    ("first_5min", start, start + 300),
+                    ("last_5min", start + duration - 300, start + duration),
+                    ("last_60s", start + duration - 60, start + duration),
+                ]:
+                    window = [r for r in requests if begin <= r.get("submitted_unix", -1) < end]
+                    record["sustained_request_trends"][label] = {
+                        "n": len(window),
+                        "window_basis": "request submission time",
+                        "metrics": {
+                            metric: stats([r.get(metric) for r in window])
+                            for metric in (
+                                "wall_s",
+                                "ttft_s",
+                                "decode_tok_per_s",
+                                "total_wall_s",
+                                "ttfa_ms",
+                                "rtf_total",
+                                "stall_at_ttfa_ms",
+                            )
+                        },
+                    }
+        result["telemetry"][name] = record
+    for path in [] if a.skip_traces else sorted(a.runs.glob("*trace*/traces/**/*")):
+        if path.is_file() and (path.name.endswith(".json") or path.name.endswith(".json.gz")):
+            report_path = a.runs / path.relative_to(a.runs).parts[0] / "report.json"
+            if (
+                not report_path.is_file()
+                or json.loads(report_path.read_text(encoding="utf-8")).get("status") != "completed"
+            ):
+                result["traces"].append(
+                    {
+                        "source": str(path),
+                        "status": "not_analyzed",
+                        "reason": "Diagnostic has no completed report; trace may be incomplete.",
+                    }
+                )
+                continue
+            try:
+                result["traces"].append(trace_summary(path))
+            except Exception as error:
+                result["traces"].append({"source": str(path), "error": repr(error)})
+    a.out.write_text(json.dumps(result, indent=2) + "\n", encoding="utf-8")
+    print(f"Summarized {len(result['telemetry'])} telemetry runs and {len(result['traces'])} traces")
+
+
+if __name__ == "__main__":
+    main()

benchmarks/edge_harness/archive_profiles.py

@@ -0,0 +1,124 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Archive profiling evidence with byte hashes and reversible large-file compression.
+
+Run only after all writers exit. No benchmark result is changed by this utility.
+"""
+
+import argparse
+import gzip
+import hashlib
+import json
+import shutil
+from pathlib import Path
+
+
+def digest(path, compressed=False):
+    hasher = hashlib.sha256()
+    size = 0
+    opener = gzip.open if compressed else open
+    with opener(path, "rb") as stream:
+        for chunk in iter(lambda: stream.read(1024 * 1024), b""):
+            hasher.update(chunk)
+            size += len(chunk)
+    return hasher.hexdigest(), size
+
+
+def verify(root):
+    manifest = json.loads((root / "manifest.json").read_text(encoding="utf-8"))
+    for record in manifest["files"]:
+        path = root / record["stored_path"]
+        assert path.resolve().is_relative_to(root.resolve()), "Archive path escapes root"
+        assert digest(path) == (record["stored_sha256"], record["stored_bytes"]), path
+        assert digest(path, record["encoding"] == "gzip") == (record["sha256"], record["bytes"]), path
+    return len(manifest["files"])
+
+
+def archive(source, destination):
+    assert source.resolve() != destination.resolve()
+    assert not destination.resolve().is_relative_to(source.resolve())
+    destination.mkdir(parents=True, exist_ok=False)
+    records = []
+    for path in sorted(source.rglob("*")):
+        relative = path.relative_to(source)
+        if not path.is_file() or "__pycache__" in relative.parts or path.suffix in (".pyc", ".tmp"):
+            continue
+        assert not path.is_symlink(), f"Unexpected evidence symlink: {relative}"
+        original_hash, original_size = digest(path)
+        compress = original_size > 50 * 1024 * 1024 and path.suffix != ".gz"
+        stored = Path("evidence") / relative
+        if compress:
+            stored = stored.with_name(stored.name + ".archive.gz")
+        target = destination / stored
+        target.parent.mkdir(parents=True, exist_ok=True)
+        if compress:
+            with path.open("rb") as reader, target.open("wb") as writer:
+                with gzip.GzipFile(filename="", mode="wb", fileobj=writer, mtime=0) as packed:
+                    shutil.copyfileobj(reader, packed, length=1024 * 1024)
+        else:
+            shutil.copyfile(path, target)
+        stored_hash, stored_size = digest(target)
+        assert digest(path) == (original_hash, original_size), f"Source changed during archive: {relative}"
+        records.append(
+            {
+                "source_path": relative.as_posix(),
+                "stored_path": stored.as_posix(),
+                "sha256": original_hash,
+                "bytes": original_size,
+                "stored_sha256": stored_hash,
+                "stored_bytes": stored_size,
+                "encoding": "gzip" if compress else "identity",
+            }
+        )
+    (destination / "manifest.json").write_text(
+        json.dumps(
+            {
+                "schema": 1,
+                "scope": "Raw evidence bytes; hashes establish integrity, not model correctness or qualification.",
+                "files": records,
+            },
+            indent=2,
+        )
+        + "\n",
+        encoding="utf-8",
+    )
+    (destination / ".gitattributes").write_text("evidence/** -text -whitespace\n", encoding="utf-8")
+    return verify(destination)
+
+
+def restore(root, destination):
+    verify(root)
+    destination.mkdir(parents=True, exist_ok=False)
+    manifest = json.loads((root / "manifest.json").read_text(encoding="utf-8"))
+    for record in manifest["files"]:
+        target = destination / record["source_path"]
+        assert target.resolve().is_relative_to(destination.resolve()), "Restore path escapes root"
+        target.parent.mkdir(parents=True, exist_ok=True)
+        opener = gzip.open if record["encoding"] == "gzip" else open
+        with opener(root / record["stored_path"], "rb") as reader, target.open("wb") as writer:
+            shutil.copyfileobj(reader, writer, length=1024 * 1024)
+        assert digest(target) == (record["sha256"], record["bytes"])
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    sub = parser.add_subparsers(dest="operation", required=True)
+    create = sub.add_parser("create")
+    create.add_argument("source", type=Path)
+    create.add_argument("destination", type=Path)
+    check = sub.add_parser("verify")
+    check.add_argument("archive", type=Path)
+    unpack = sub.add_parser("restore")
+    unpack.add_argument("archive", type=Path)
+    unpack.add_argument("destination", type=Path)
+    args = parser.parse_args()
+    if args.operation == "create":
+        print(f"Archived and verified {archive(args.source, args.destination)} files")
+    elif args.operation == "verify":
+        print(f"Verified {verify(args.archive)} files")
+    else:
+        restore(args.archive, args.destination)
+        print(f"Restored evidence to {args.destination}")
+
+
+if __name__ == "__main__":
+    main()

benchmarks/edge_harness/check_text_reliability.py

@@ -0,0 +1,175 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Isolated real-model stream saturation, cancellation and owned-worker failure checks."""
+
+import argparse
+import asyncio
+import copy
+import os
+import sys
+import time
+import traceback
+from pathlib import Path
+
+from profile_local_text import save
+
+
+async def run(args):
+    import psutil
+
+    from vllm_omni.edge.local.engine import LocalTextEngine
+    from vllm_omni.edge.local.plan import plan_text_session
+
+    args.out.mkdir(parents=True, exist_ok=False)
+    report = {
+        "status": "running",
+        "start_unix": time.time(),
+        "argv": sys.argv,
+        "scope": (
+            "LocalTextEngine consumer queue and actual owned engine-process failure; "
+            "not proof of every internal queue being bounded"
+        ),
+    }
+    engine = None
+    owned = []
+    save(args.out / "report.json", report)
+    try:
+        plan = plan_text_session(
+            args.model, max_model_len=4096, max_num_seqs=4, max_num_batched_tokens=512, enforce_eager=True
+        )
+        report["plan"] = plan.to_dict()
+        if not plan.admitted:
+            report["status"] = "rejected"
+            return
+        oversized = plan_text_session(
+            args.model, max_model_len=4096, max_num_seqs=100000, max_num_batched_tokens=512, enforce_eager=True
+        )
+        report["oversized_plan"] = oversized.to_dict()
+        report["oversized_plan_refused"] = not oversized.admitted
+        engine = LocalTextEngine(plan)
+        await engine.start()
+        prompt = "Describe a quiet garden with trees, flowers and a pond."
+
+        async def request(saturate=False):
+            session = engine.open_session()
+            rid, stream = await engine.submit(
+                session, prompt, max_tokens=128, temperature=0, ignore_eos=True, max_chunks=4
+            )
+            if saturate:
+                # Wait for actual capacity, rather than guessing a delay from device speed.
+                deadline = time.monotonic() + 30
+                while stream.stats()["high_water_chunks"] < 4 and time.monotonic() < deadline:
+                    await asyncio.sleep(0.05)
+                report["saturated_queue_before_drain"] = stream.stats()
+            events = [e.to_dict() async for e in stream]
+            record = engine.records[rid].to_dict()
+            record["stream"] = stream.stats()
+            record["events"] = events
+            engine.close_session(session.session_id)
+            assert record["finished"] and not record["error"] and record["output_tokens"] == 128
+            return record
+
+        report["baseline"] = await request()
+        report["saturated"] = await request(saturate=True)
+        report["saturation_reached"] = report["saturated_queue_before_drain"]["high_water_chunks"] == 4
+        report["same_tokens_after_saturation"] = (
+            report["baseline"]["output_token_ids"] == report["saturated"]["output_token_ids"]
+        )
+        session = engine.open_session()
+        stale = copy.deepcopy(session)
+        rid, stream = await engine.submit(session, prompt, max_tokens=512, ignore_eos=True, max_chunks=4)
+        deadline = time.monotonic() + 30
+        while stream.stats()["high_water_chunks"] < 4 and time.monotonic() < deadline:
+            await asyncio.sleep(0.05)
+        report["cancel_queue_before"] = stream.stats()
+        report["cancel"] = await engine.cancel(rid)
+        report["cancel"]["late_events"] = [e.to_dict() async for e in stream]
+        try:
+            await engine.submit(stale, prompt, max_tokens=1)
+        except RuntimeError as error:
+            report["stale_handle_rejected"] = str(error)
+        else:
+            report["stale_handle_rejected"] = False
+        engine.close_session(session.session_id)
+        report["after_cancel"] = await request()
+        save(args.out / "report.json", report)
+
+        # Process handles come from this engine's actual manager, then must also
+        # belong to this probe's process tree. Never select arbitrary system PIDs.
+        client = engine._omni.engine.stage_clients[0]
+        manager = client.resources.engine_manager
+        processes = [p for p in manager.processes if p.is_alive()]
+        descendants = {p.pid: p for p in psutil.Process().children(recursive=True)}
+        assert processes and all(p.pid in descendants for p in processes)
+        manager_tree = {}
+        for handle in processes:
+            core = descendants[handle.pid]
+            manager_tree[core.pid] = core
+            manager_tree.update({child.pid: child for child in core.children(recursive=True)})
+        owned = [(p.pid, p.create_time()) for p in manager_tree.values()]
+        target = processes[0]
+        session = engine.open_session()
+        rid, stream = await engine.submit(session, prompt, max_tokens=512, ignore_eos=True)
+        first = await asyncio.wait_for(stream.get(), 30)
+        assert first is not None and first.kind == "token", "No token before worker-failure injection"
+        report["crash_first"] = first.to_dict()
+        report["crash_injection"] = {
+            "pid": target.pid,
+            "name": target.name,
+            "created": descendants[target.pid].create_time(),
+            "ownership": "engine manager handle and current process descendant",
+        }
+        start = time.perf_counter()
+        target.terminate()
+
+        async def drain():
+            return [e.to_dict() async for e in stream]
+
+        try:
+            events = await asyncio.wait_for(drain(), 30)
+            report["crash_injection"].update(
+                events=events,
+                observed_within_30s=True,
+                explicit_error_reported=bool(engine.records[rid].error) or any(e["kind"] == "error" for e in events),
+                request_record=engine.records[rid].to_dict(),
+            )
+        except asyncio.TimeoutError:
+            report["crash_injection"].update(observed_within_30s=False, error="No terminal stream event within 30s")
+        report["crash_injection"]["observation_s"] = time.perf_counter() - start
+        report["status"] = "completed"
+    except Exception as error:
+        report.update(status="failed", error=repr(error), traceback=traceback.format_exc())
+        traceback.print_exc()
+    finally:
+        if engine is not None:
+            try:
+                await engine.close()
+            except (Exception, asyncio.CancelledError) as error:
+                report.update(status="failed", cleanup_error=repr(error))
+                report.setdefault("error", f"Engine shutdown failed after probe: {error!r}")
+        await asyncio.sleep(2)
+        leftovers = []
+        for pid, created in owned:
+            try:
+                p = psutil.Process(pid)
+                if p.create_time() == created and p.is_running():
+                    leftovers.append({"pid": pid, "created": created, "status": p.status()})
+                    # Contain an isolated diagnostic's leak; report it, do not call cleanup a pass.
+                    p.kill()
+            except psutil.NoSuchProcess:
+                pass
+        report["owned_processes_requiring_harness_cleanup"] = leftovers
+        report["end_unix"] = time.time()
+        save(args.out / "report.json", report)
+    return report["status"] == "completed"
+
+
+if __name__ == "__main__":
+    p = argparse.ArgumentParser(description=__doc__)
+    p.add_argument("--model", required=True)
+    p.add_argument("--out", type=Path, required=True)
+    args = p.parse_args()
+    os.environ.setdefault("VLLM_WORKER_MULTIPROC_METHOD", "spawn")
+    from vllm_omni.windows.aio import install_selector_policy
+
+    install_selector_policy()
+    raise SystemExit(0 if asyncio.run(run(args)) else 1)

benchmarks/edge_harness/check_tts_reliability.py

@@ -0,0 +1,172 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Real-weight TTS slow consumer, abort/recovery and owned vocoder-process failure."""
+
+import argparse
+import asyncio
+import os
+import sys
+import time
+import traceback
+from pathlib import Path
+
+from profile_local_text import save
+
+
+async def run(args):
+    import psutil
+    import torch
+    from transformers import AutoTokenizer
+
+    from vllm_omni import AsyncOmni
+    from vllm_omni.model_executor.models.qwen3_tts.configuration_qwen3_tts import Qwen3TTSConfig
+    from vllm_omni.model_executor.models.qwen3_tts.prompt_embeds_builder import Qwen3TTSPromptEmbedsBuilder
+
+    sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "tts"))
+    from stream_latency_bench import _cat_audio
+
+    args.out.mkdir(parents=True, exist_ok=False)
+    report = {
+        "status": "running",
+        "argv": sys.argv,
+        "start_unix": time.time(),
+        "scope": (
+            "Short real audio delivery, slow consumer, abort fence/recovery, owned vocoder termination. "
+            "Not audio quality or 64 MiB buffer saturation."
+        ),
+    }
+    omni, owned = None, []
+    save(args.out / "report.json", report)
+    try:
+        cfg = Qwen3TTSConfig.from_pretrained(args.model)
+        tok = AutoTokenizer.from_pretrained(args.model)
+
+        def prompt(text):
+            info = {"task_type": ["CustomVoice"], "text": [text], "language": ["English"], "speaker": ["Vivian"]}
+            length = Qwen3TTSPromptEmbedsBuilder.estimate_prompt_len_from_additional_information(
+                additional_information=info,
+                task_type="CustomVoice",
+                tokenize_prompt=lambda x: tok(x, padding=False)["input_ids"],
+                codec_language_id=cfg.talker_config.codec_language_id,
+                spk_is_dialect=cfg.talker_config.spk_is_dialect,
+            )
+            return {"prompt_token_ids": [0] * length, "additional_information": info}
+
+        short = prompt("The garden is quiet. We can hear the birds singing.")
+        long = prompt("This longer sentence keeps the speech model generating while we test interruption. " * 12)
+        omni = AsyncOmni(model=args.model, deploy_profile="edge", stage_init_timeout=180)
+        params = omni.resolve_sampling_params_list(None, allow_delta_coercion=True)
+
+        def event(output, rid):
+            assert output.request_id == rid, "Unexpected request ID in audio stream"
+            mm = output.outputs[0].multimodal_output if output.outputs else None
+            audio = _cat_audio(mm.get("audio") if mm else None)
+            return {
+                "request_id": output.request_id,
+                "finished": output.finished,
+                "samples": int(audio.numel()) if audio is not None else 0,
+                "finite": bool(torch.isfinite(audio).all()) if audio is not None else True,
+            }
+
+        async def first_audio(stream, rid):
+            async for output in stream:
+                row = event(output, rid)
+                if row["samples"]:
+                    assert row["finite"], "Nonfinite audio before fault probe"
+                    return row
+            raise RuntimeError("No audio before fault probe")
+
+        async def collect(stream, rid, pause=False):
+            rows = []
+            async for output in stream:
+                rows.append(event(output, rid))
+                if pause and len(rows) == 1:
+                    await asyncio.sleep(3)
+            return rows
+
+        for rid, slow in [("baseline", False), ("slow-consumer", True)]:
+            rows = await collect(omni.generate(short, request_id=rid, sampling_params_list=params), rid, slow)
+            report[rid] = rows
+            assert any(r["finished"] for r in rows) and any(r["samples"] for r in rows)
+            assert all(r["finite"] for r in rows)
+        rid = "abort-probe"
+        stream = omni.generate(long, request_id=rid, sampling_params_list=params)
+        first = await asyncio.wait_for(first_audio(stream, rid), 60)
+        start = time.perf_counter()
+        await omni.abort(rid)
+        report["abort"] = {"first": first, "ack_s": time.perf_counter() - start}
+        try:
+            late = await asyncio.wait_for(collect(stream, rid), 10)
+            report["abort"]["late_events"] = late
+            report["abort"]["late_audio_samples"] = sum(r["samples"] for r in late)
+        except asyncio.CancelledError:
+            report["abort"]["terminal"] = "CancelledError"
+        except Exception as error:
+            report["abort"]["terminal_error"] = repr(error)
+        rid = "after-abort"
+        rows = await collect(omni.generate(short, request_id=rid, sampling_params_list=params), rid)
+        report[rid] = rows
+        assert any(r["finished"] for r in rows) and any(r["samples"] for r in rows)
+        save(args.out / "report.json", report)
+        descendants = {p.pid: p for p in psutil.Process().children(recursive=True)}
+        core_handles = [
+            p for client in omni.engine.stage_clients for p in client.resources.engine_manager.processes if p.is_alive()
+        ]
+        assert core_handles and all(p.pid in descendants for p in core_handles)
+        manager_tree = {}
+        for handle in core_handles:
+            core = descendants[handle.pid]
+            manager_tree[core.pid] = core
+            manager_tree.update({p.pid: p for p in core.children(recursive=True)})
+        owned = [(p.pid, p.create_time()) for p in manager_tree.values()]
+        target = omni.engine.stage_clients[-1].resources.engine_manager.processes[0]
+        rid = "vocoder-crash"
+        stream = omni.generate(long, request_id=rid, sampling_params_list=params)
+        report["crash_first"] = await asyncio.wait_for(first_audio(stream, rid), 60)
+        report["crash"] = {"pid": target.pid, "name": target.name, "created": descendants[target.pid].create_time()}
+        start = time.perf_counter()
+        target.terminate()
+        try:
+            report["crash"]["events"] = await asyncio.wait_for(collect(stream, rid), 30)
+            report["crash"]["explicit_error_reported"] = False
+        except asyncio.TimeoutError:
+            report["crash"]["timeout"] = True
+        except (Exception, asyncio.CancelledError) as error:
+            report["crash"].update(explicit_error_reported=True, error=repr(error))
+        report["crash"]["observation_s"] = time.perf_counter() - start
+        report["status"] = "completed"
+    except Exception as error:
+        report.update(status="failed", error=repr(error), traceback=traceback.format_exc())
+        traceback.print_exc()
+    finally:
+        if omni is not None:
+            try:
+                omni.shutdown()
+            except (Exception, asyncio.CancelledError) as error:
+                report.update(status="failed", cleanup_error=repr(error))
+                report.setdefault("error", f"Engine shutdown failed after probe: {error!r}")
+        await asyncio.sleep(2)
+        leftovers = []
+        for pid, created in owned:
+            try:
+                p = psutil.Process(pid)
+                if p.create_time() == created and p.is_running():
+                    leftovers.append({"pid": pid, "created": created, "status": p.status()})
+                    p.kill()
+            except psutil.NoSuchProcess:
+                pass
+        report["owned_processes_requiring_harness_cleanup"] = leftovers
+        report["end_unix"] = time.time()
+        save(args.out / "report.json", report)
+    return report["status"] == "completed"
+
+
+if __name__ == "__main__":
+    p = argparse.ArgumentParser(description=__doc__)
+    p.add_argument("--model", required=True)
+    p.add_argument("--out", type=Path, required=True)
+    args = p.parse_args()
+    os.environ.setdefault("VLLM_WORKER_MULTIPROC_METHOD", "spawn")
+    from vllm_omni.windows.aio import install_selector_policy
+
+    install_selector_policy()
+    raise SystemExit(0 if asyncio.run(run(args)) else 1)

benchmarks/edge_harness/profile_host_command.py

@@ -0,0 +1,91 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Run one explicit benchmark command with Windows host and whole-GPU telemetry."""
+
+import argparse
+import ctypes
+import json
+import os
+import platform
+import subprocess
+import sys
+import time
+from pathlib import Path
+
+import psutil
+
+
+class PowerStatus(ctypes.Structure):
+    _fields_ = [
+        ("ACLineStatus", ctypes.c_byte),
+        ("BatteryFlag", ctypes.c_byte),
+        ("BatteryLifePercent", ctypes.c_byte),
+        ("SystemStatusFlag", ctypes.c_byte),
+        ("BatteryLifeTime", ctypes.c_ulong),
+        ("BatteryFullLifeTime", ctypes.c_ulong),
+    ]
+
+
+def main():
+    p = argparse.ArgumentParser(description=__doc__)
+    p.add_argument("--out", type=Path, required=True)
+    p.add_argument("--cwd", type=Path, required=True)
+    p.add_argument("command", nargs=argparse.REMAINDER)
+    a = p.parse_args()
+    command = a.command[1:] if a.command[:1] == ["--"] else a.command
+    a.out.mkdir(parents=True, exist_ok=False)
+    status = {
+        "command": command,
+        "cwd": str(a.cwd),
+        "host": platform.platform(),
+        "scope": "Windows RAM includes WSL; GPU counters are whole-device, not model-exclusive",
+        "sample_interval_s": 1,
+        "start_unix": time.time(),
+        "status": "running",
+    }
+    if os.name == "nt":
+        status["power_scheme"] = subprocess.run(
+            ["powercfg", "/getactivescheme"], capture_output=True, text=True, errors="replace"
+        ).stdout
+    log = (a.out / "command.log").open("w", encoding="utf-8")
+    child = subprocess.Popen(command, cwd=a.cwd, stdout=log, stderr=subprocess.STDOUT)
+    status["pid"] = child.pid
+    (a.out / "status.json").write_text(json.dumps(status, indent=2), encoding="utf-8")
+    with (a.out / "telemetry.jsonl").open("w", encoding="utf-8") as f:
+        while child.poll() is None:
+            sample = {
+                "unix": time.time(),
+                "monotonic": time.monotonic(),
+                "host_memory": psutil.virtual_memory()._asdict(),
+                "host_cpu_percent": psutil.cpu_percent(),
+            }
+            if os.name == "nt":
+                power = PowerStatus()
+                if ctypes.windll.kernel32.GetSystemPowerStatus(ctypes.byref(power)):
+                    sample["power"] = {name: getattr(power, name) for name, _ in power._fields_}
+            try:
+                gpu = subprocess.run(
+                    [
+                        "nvidia-smi",
+                        "--query-gpu=uuid,name,memory.used,memory.total,temperature.gpu,power.draw,clocks.sm,clocks.mem,utilization.gpu,pstate",
+                        "--format=csv,noheader,nounits",
+                    ],
+                    capture_output=True,
+                    text=True,
+                    timeout=5,
+                )
+                sample["gpu_csv"] = gpu.stdout.strip()
+                sample["gpu_returncode"] = gpu.returncode
+            except Exception as error:
+                sample["gpu_error"] = repr(error)
+            f.write(json.dumps(sample) + "\n")
+            f.flush()
+            time.sleep(1)
+    log.close()
+    status.update(status="exited", returncode=child.returncode, end_unix=time.time())
+    (a.out / "status.json").write_text(json.dumps(status, indent=2), encoding="utf-8")
+    print(json.dumps(status), flush=True)
+    return child.returncode
+
+
+if __name__ == "__main__":
+    sys.exit(main())

benchmarks/edge_harness/profile_local_text.py

@@ -0,0 +1,200 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Measure the existing Omni local text path; do not infer model quality from speed."""
+
+import argparse
+import asyncio
+import json
+import os
+import platform
+import sys
+import time
+import traceback
+from pathlib import Path
+
+
+def configure_host_environment():
+    """Reuse the native Windows settings established by the acceptance runs."""
+    if os.name == "nt":
+        cudart = Path(sys.prefix) / "Lib/site-packages/torch/lib/cudart64_13.dll"
+        if cudart.is_file():
+            os.environ.setdefault("VLLM_CUDART_SO_PATH", str(cudart))
+        os.environ.setdefault("VLLM_CACHE_ROOT", str(Path.home() / "c29"))
+    return {
+        key: os.environ[key]
+        for key in ("VLLM_CUDART_SO_PATH", "VLLM_CACHE_ROOT", "CUDA_HOME", "PYTHONUTF8", "PYTHONIOENCODING")
+        if key in os.environ
+    }
+
+
+HOST_ENVIRONMENT = configure_host_environment()
+
+
+def save(path, value):
+    temp = path.with_suffix(".tmp")
+    temp.write_text(json.dumps(value, indent=2, default=str) + "\n", encoding="utf-8")
+    try:
+        temp.replace(path)
+    except PermissionError as error:
+        # Windows readers may deny replacement while holding the previous
+        # snapshot open. Keep request JSONL authoritative and defer a running
+        # snapshot rather than blocking concurrent inference on file I/O.
+        if value.get("status") == "running" and path.is_file():
+            value["report_write_permission_errors"] = value.get("report_write_permission_errors", 0) + 1
+            value["last_report_write_error"] = repr(error)
+            print(f"Deferred report snapshot after PermissionError: {path}", file=sys.stderr, flush=True)
+            return
+        # Initial/final snapshots must persist. This wait is outside measured
+        # request execution; a persistent permission failure remains an error.
+        for _ in range(20):
+            time.sleep(0.05)
+            try:
+                temp.replace(path)
+                return
+            except PermissionError:
+                pass
+        raise
+
+
+async def run(args):
+    from vllm_omni.edge.local.engine import LocalTextEngine, apply_runtime_env
+    from vllm_omni.edge.local.plan import plan_text_session
+
+    args.out.mkdir(parents=True, exist_ok=False)
+    report = {
+        "status": "running",
+        "scope": "complete local text generation; quality gate separate",
+        "argv": sys.argv,
+        "platform": platform.platform(),
+        "python": sys.executable,
+        "start_unix": time.time(),
+        "runtime_env": apply_runtime_env(),
+        "host_environment": HOST_ENVIRONMENT,
+        "settings": vars(args),
+        "cache_condition": "existing disk/JIT cache; not cold disk",
+        "trace": "untraced; API event timings",
+        "requests": 0,
+    }
+    save(args.out / "report.json", report)
+    engine = None
+    try:
+        plan = plan_text_session(
+            args.model,
+            max_model_len=4096,
+            max_num_seqs=4,
+            max_num_batched_tokens=512,
+            enforce_eager=True,
+            digest_weights=True,
+        )
+        report["plan"] = plan.to_dict()
+        if not plan.admitted:
+            report["status"] = "rejected"
+            return
+        engine = LocalTextEngine(plan)
+        start = time.perf_counter()
+        await engine.start()
+        report["startup_s"] = time.perf_counter() - start
+        report["placement"] = engine.report_placement()
+        save(args.out / "report.json", report)
+        # Repeated natural text gives reproducible length bands; record actual token counts.
+        prompts = {
+            name: ("The garden has trees, flowers, and a small pond. " * n) + "\nDescribe the garden in detail."
+            for name, n in [("short", 4), ("medium", 40), ("long", 160)]
+        }
+        samples = (args.out / "requests.jsonl").open("a", encoding="utf-8")
+        counter = 0
+
+        async def request(name, concurrency, phase, slow=False):
+            nonlocal counter
+            counter += 1
+            session = engine.open_session()
+            start = time.perf_counter()
+            rid, stream = await engine.submit(
+                session, prompts[name], max_tokens=128, temperature=0.0, ignore_eos=True, max_chunks=4 if slow else 64
+            )
+            arrivals = []
+            try:
+                async for event in stream:
+                    arrivals.append(
+                        {
+                            "elapsed_s": time.perf_counter() - start,
+                            "kind": event.kind,
+                            "sequence": event.seq,
+                            "epoch": event.epoch,
+                        }
+                    )
+                    if slow:
+                        await asyncio.sleep(0.03)
+                rec = engine.records[rid].to_dict()
+                rec.update(
+                    phase=phase,
+                    length_band=name,
+                    concurrency=concurrency,
+                    wall_s=time.perf_counter() - start,
+                    arrivals=arrivals,
+                    stream=stream.stats(),
+                )
+                samples.write(json.dumps(rec) + "\n")
+                samples.flush()
+                if rec["error"] or rec["output_tokens"] != 128 or not rec["finished"]:
+                    raise RuntimeError(f"Incomplete request: {rid}: {rec['error']}")
+                report["requests"] = counter
+                save(args.out / "report.json", report)
+            finally:
+                engine.close_session(session.session_id)
+
+        for concurrency in (1, 2, 4):
+            for name in prompts:
+                await asyncio.gather(*(request(name, concurrency, "warmup") for _ in range(concurrency)))
+                for i in range(0, args.repeats, concurrency):
+                    await asyncio.gather(
+                        *(request(name, concurrency, "measured") for _ in range(min(concurrency, args.repeats - i)))
+                    )
+                print(f"profiled {name} concurrency={concurrency}", flush=True)
+        thermal_start = time.perf_counter()
+        report["sustained_start_unix"] = time.time()
+        while time.perf_counter() - thermal_start < args.sustained_seconds:
+            await request("medium", 1, "sustained")
+        report["sustained_wall_s"] = time.perf_counter() - thermal_start
+        await request("short", 1, "slow_consumer", slow=True)
+        session = engine.open_session()
+        rid, stream = await engine.submit(session, prompts["short"], max_tokens=512, ignore_eos=True)
+        seen = 0
+        async for event in stream:
+            if event.kind == "token":
+                seen += 1
+            if seen >= 8:
+                break
+        start = time.perf_counter()
+        report["cancel"] = await engine.cancel(rid)
+        report["cancel"]["latency_s"] = time.perf_counter() - start
+        report["cancel"]["remaining_events"] = [e.to_dict() async for e in stream]
+        engine.close_session(session.session_id)
+        await request("short", 1, "after_cancel")
+        samples.close()
+        report["usage"] = engine.report_usage()
+        report["status"] = "completed"
+        report["quality_gate"] = "not rerun against high precision reference; see separate acceptance evidence"
+    except Exception as error:
+        report.update(status="failed", error=repr(error), traceback=traceback.format_exc())
+        traceback.print_exc()
+    finally:
+        if engine is not None:
+            await engine.close()
+            report["usage_after_close"] = engine.report_usage()
+        report["end_unix"] = time.time()
+        save(args.out / "report.json", report)
+    return report["status"] == "completed"
+
+
+if __name__ == "__main__":
+    p = argparse.ArgumentParser(description=__doc__)
+    p.add_argument("--model", required=True)
+    p.add_argument("--out", type=Path, required=True)
+    p.add_argument("--repeats", type=int, default=20)
+    p.add_argument("--sustained-seconds", type=float, default=1800)
+    args = p.parse_args()
+    os.environ.setdefault("VLLM_WORKER_MULTIPROC_METHOD", "spawn")
+    from vllm_omni.windows.aio import install_selector_policy
+
+    install_selector_policy()
+    raise SystemExit(0 if asyncio.run(run(args)) else 1)

benchmarks/edge_harness/profile_local_tts.py

@@ -0,0 +1,227 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Profile real Qwen3-TTS streams through AsyncOmni, retaining terminal audio separately."""
+
+import argparse
+import asyncio
+import hashlib
+import json
+import os
+import platform
+import sys
+import time
+import traceback
+from pathlib import Path
+
+from profile_local_text import HOST_ENVIRONMENT, save
+
+
+async def run(args):
+    import torch
+    from transformers import AutoTokenizer
+
+    from vllm_omni import AsyncOmni
+    from vllm_omni.edge.local.engine import MemorySampler
+    from vllm_omni.edge.local.manifest import runtime_versions
+    from vllm_omni.model_executor.models.qwen3_tts.configuration_qwen3_tts import Qwen3TTSConfig
+    from vllm_omni.model_executor.models.qwen3_tts.prompt_embeds_builder import Qwen3TTSPromptEmbedsBuilder
+
+    sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "tts"))
+    from stream_latency_bench import _cat_audio, _sample_rate
+    from stream_latency_metrics import request_metrics, underruns_if_start_at
+
+    args.out.mkdir(parents=True, exist_ok=False)
+    report = {
+        "status": "running",
+        "argv": sys.argv,
+        "python": sys.executable,
+        "host_environment": HOST_ENVIRONMENT,
+        "platform": platform.platform(),
+        "runtime": runtime_versions().to_dict(),
+        "settings": vars(args),
+        "start_unix": time.time(),
+        "requests": 0,
+        "cache_condition": "existing disk/JIT caches; not cold disk",
+        "quality_gate": "finite PCM checks only; ASR/speaker/listening reference gates pending",
+        "playback_scope": "arrival-based simulated playback; no sound-device measurement",
+        "terminal_audio": (
+            "DELTA output required; terminal audio is included once in complete playback metrics "
+            "and archived separately"
+        ),
+    }
+    save(args.out / "report.json", report)
+    sampler = MemorySampler()
+    sampler.start()
+    omni = None
+    try:
+        cfg = Qwen3TTSConfig.from_pretrained(args.model)
+        tokenizer = AutoTokenizer.from_pretrained(args.model)
+        texts = {
+            "short": "Hello, this is a speech test.",
+            "medium": (
+                "The garden has trees, flowers, and a small pond. We walk along the path and listen to the birds."
+            ),
+            "long": (
+                "Today we are testing a local speech system. "
+                "It should speak clearly and keep the audio flowing smoothly. " * 4
+            ),
+        }
+        prompts = {}
+        for name, text in texts.items():
+            info = {"task_type": ["CustomVoice"], "text": [text], "language": ["English"], "speaker": ["Vivian"]}
+            length = Qwen3TTSPromptEmbedsBuilder.estimate_prompt_len_from_additional_information(
+                additional_information=info,
+                task_type="CustomVoice",
+                tokenize_prompt=lambda x: tokenizer(x, padding=False)["input_ids"],
+                codec_language_id=cfg.talker_config.codec_language_id,
+                spk_is_dialect=cfg.talker_config.spk_is_dialect,
+            )
+            prompts[name] = {"prompt_token_ids": [0] * length, "additional_information": info}
+        report["prompts"] = prompts
+        start = time.perf_counter()
+        omni = AsyncOmni(model=args.model, deploy_profile="edge", stage_init_timeout=180)
+        report["startup_s"] = time.perf_counter() - start
+        from vllm.sampling_params import RequestOutputKind
+
+        sampling_params = omni.resolve_sampling_params_list(None, allow_delta_coercion=True)
+        report["sampling_params"] = [repr(p) for p in sampling_params]
+        if sampling_params[-1].output_kind != RequestOutputKind.DELTA:
+            raise RuntimeError("Final TTS stage must emit DELTA audio for this playback benchmark")
+        report["memory_after_load"] = sampler.peaks()
+        save(args.out / "report.json", report)
+        counter = 0
+        samples = (args.out / "requests.jsonl").open("a", encoding="utf-8")
+
+        async def request(name, concurrency, phase, slow=False):
+            nonlocal counter
+            counter += 1
+            rid = f"{phase}-{name}-{concurrency}-{counter}"
+            chunks, buffers = [], []
+            submitted_unix = time.time()
+            start = time.perf_counter()
+            finished, sr = False, 24000
+            async for output in omni.generate(prompts[name], request_id=rid, sampling_params_list=sampling_params):
+                if output.request_id != rid:
+                    raise RuntimeError(f"Unexpected output request ID: {output.request_id}; expected {rid}")
+                elapsed = (time.perf_counter() - start) * 1000
+                mm = output.outputs[0].multimodal_output if output.outputs else None
+                sr = _sample_rate(mm, sr)
+                audio = _cat_audio(mm.get("audio") if mm else None)
+                raw = audio.numpy().tobytes() if audio is not None else b""
+                finite = bool(torch.isfinite(audio).all()) if audio is not None else True
+                chunks.append(
+                    {
+                        "idx": len(chunks),
+                        "t_ms": elapsed,
+                        "samples": len(raw) // 4,
+                        "sr": sr,
+                        "request_id": output.request_id,
+                        "terminal": output.finished,
+                        "sha256": hashlib.sha256(raw).hexdigest(),
+                        "finite": finite,
+                    }
+                )
+                if phase == "warmup":
+                    buffers.append(raw)
+                finished = finished or output.finished
+                if not finite:
+                    raise RuntimeError(f"Nonfinite audio: {rid}")
+                if slow:
+                    await asyncio.sleep(0.2)
+            wall = time.perf_counter() - start
+            streamed = [c for c in chunks if not c["terminal"]]
+            terminal = [c for c in chunks if c["terminal"]]
+            rec = {
+                "request_id": rid,
+                "phase": phase,
+                "length_band": name,
+                "submitted_unix": submitted_unix,
+                "finished_unix": time.time(),
+                "concurrency": concurrency,
+                "sr": sr,
+                "chunks_all": chunks,
+                "finished": finished,
+                "terminal_audio_s": sum(c["samples"] for c in terminal) / sr,
+                "prefix_metrics": request_metrics(streamed, sr, wall),
+                **request_metrics(chunks, sr, wall),
+            }
+            rec["underruns"] = underruns_if_start_at(chunks, sr, rec["ttfa_ms"]) if rec["ttfa_ms"] is not None else []
+            # Preserve each raw warmup chunk, including the terminal event, without guessing concatenation semantics.
+            if buffers:
+                audio_dir = args.out / "audio_chunks"
+                audio_dir.mkdir(exist_ok=True)
+                for i, raw in enumerate(buffers):
+                    (audio_dir / f"{rid}-{i}.f32").write_bytes(raw)
+            samples.write(json.dumps(rec) + "\n")
+            samples.flush()
+            if not finished or not any(c["samples"] for c in chunks):
+                raise RuntimeError(f"Missing completion/audio: {rid}")
+            report["requests"] = counter
+            save(args.out / "report.json", report)
+
+        for concurrency in (args.concurrency,) if args.concurrency is not None else (1, 2, 4):
+            for name in (args.length_band,) if args.length_band else prompts:
+                await asyncio.gather(*(request(name, concurrency, "warmup") for _ in range(concurrency)))
+                for i in range(0, args.repeats, concurrency):
+                    await asyncio.gather(
+                        *(request(name, concurrency, "measured") for _ in range(min(concurrency, args.repeats - i)))
+                    )
+                print(f"profiled {name} concurrency={concurrency}", flush=True)
+        report["sustained_wall_s"] = 0.0
+        if args.sustained_seconds > 0:
+            start = time.perf_counter()
+            report["sustained_start_unix"] = time.time()
+            while time.perf_counter() - start < args.sustained_seconds:
+                await request("medium", 1, "sustained")
+            report["sustained_wall_s"] = time.perf_counter() - start
+        await request("medium", 1, "slow_consumer", slow=True)
+        stream = omni.generate(prompts["long"], request_id="cancel-probe", sampling_params_list=sampling_params)
+        async for output in stream:
+            if output.outputs:
+                break
+        start = time.perf_counter()
+        await omni.abort("cancel-probe")
+        await stream.aclose()
+        report["cancel"] = {
+            "abort_ack_s": time.perf_counter() - start,
+            "scope": "abort acknowledgement and subsequent request; late-event gate not verified",
+        }
+        await request("short", 1, "after_cancel")
+        samples.close()
+        report["status"] = "completed"
+    except Exception as error:
+        report.update(status="failed", error=repr(error), traceback=traceback.format_exc())
+        traceback.print_exc()
+    finally:
+        if omni is not None:
+            omni.shutdown()
+        sampler.stop()
+        report["memory"] = sampler.peaks()
+        report["end_unix"] = time.time()
+        save(args.out / "report.json", report)
+    return report["status"] == "completed"
+
+
+if __name__ == "__main__":
+    p = argparse.ArgumentParser(description=__doc__)
+    p.add_argument("--model", required=True)
+    p.add_argument("--out", required=True, type=Path)
+    p.add_argument("--repeats", default=20, type=int)
+    p.add_argument("--sustained-seconds", default=1800, type=float)
+    p.add_argument(
+        "--length-band",
+        choices=("short", "medium", "long"),
+        help="Select one band for a separate instrumentation diagnostic.",
+    )
+    p.add_argument(
+        "--concurrency",
+        type=int,
+        choices=(1, 2, 4),
+        help="Select one concurrency for a separate instrumentation diagnostic.",
+    )
+    args = p.parse_args()
+    os.environ.setdefault("VLLM_WORKER_MULTIPROC_METHOD", "spawn")
+    os.environ.setdefault("VLLM_USE_FLASHINFER_SAMPLER", "0")
+    from vllm_omni.windows.aio import install_selector_policy
+
+    install_selector_policy()
+    raise SystemExit(0 if asyncio.run(run(args)) else 1)

benchmarks/edge_harness/profile_trace_diagnostic.py

@@ -0,0 +1,198 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Separate paired untraced/traced requests through Omni's existing profiler RPC."""
+
+import argparse
+import asyncio
+import hashlib
+import os
+import platform
+import statistics
+import sys
+import time
+import traceback
+from pathlib import Path
+
+from profile_local_text import save
+
+
+async def run(args):
+    from vllm_omni import AsyncOmni
+    from vllm_omni.edge.local.engine import apply_runtime_env
+    from vllm_omni.edge.local.manifest import runtime_versions
+
+    args.out = args.out.resolve()
+    args.out.mkdir(parents=True, exist_ok=False)
+    trace_dir = (args.out / "traces").resolve()
+    report = {
+        "status": "running",
+        "start_unix": time.time(),
+        "argv": sys.argv,
+        "python": sys.executable,
+        "platform": platform.platform(),
+        "runtime": runtime_versions().to_dict(),
+        "environment": apply_runtime_env(),
+        "scope": "diagnostic paired profiling overhead; not steady-state latency percentiles",
+        "text_output_tokens": 32,
+        "requests": [],
+    }
+    config = {
+        "profiler": "torch",
+        "torch_profiler_dir": str(trace_dir),
+        "torch_profiler_with_stack": False,
+        "torch_profiler_record_shapes": False,
+        "torch_profiler_with_memory": False,
+        # Archive compression happens after writers exit, avoiding async gzip/hash races.
+        "torch_profiler_use_gzip": False,
+        "torch_profiler_dump_cuda_time_total": False,
+    }
+    report["profiler_config"] = config
+    save(args.out / "report.json", report)
+    omni = None
+    profiling = False
+    try:
+        params = None
+        if args.kind == "spark":
+            from vllm import SamplingParams
+
+            from vllm_omni.edge.local.plan import plan_text_session
+
+            plan = plan_text_session(
+                args.model, max_model_len=4096, max_num_seqs=4, max_num_batched_tokens=512, enforce_eager=True
+            )
+            report["plan"] = plan.to_dict()
+            if not plan.admitted:
+                report["status"] = "rejected"
+                return
+            kwargs = plan.engine_kwargs
+            prompt = "The garden has trees, flowers, and a small pond. Describe the garden in detail."
+            params = SamplingParams(temperature=0, max_tokens=32, ignore_eos=True)
+        else:
+            from transformers import AutoTokenizer
+
+            from vllm_omni.model_executor.models.qwen3_tts.configuration_qwen3_tts import Qwen3TTSConfig
+            from vllm_omni.model_executor.models.qwen3_tts.prompt_embeds_builder import Qwen3TTSPromptEmbedsBuilder
+
+            cfg = Qwen3TTSConfig.from_pretrained(args.model)
+            tok = AutoTokenizer.from_pretrained(args.model)
+            info = {
+                "task_type": ["CustomVoice"],
+                "text": ["Hello, this is a speech test."],
+                "language": ["English"],
+                "speaker": ["Vivian"],
+            }
+            length = Qwen3TTSPromptEmbedsBuilder.estimate_prompt_len_from_additional_information(
+                additional_information=info,
+                task_type="CustomVoice",
+                tokenize_prompt=lambda x: tok(x, padding=False)["input_ids"],
+                codec_language_id=cfg.talker_config.codec_language_id,
+                spk_is_dialect=cfg.talker_config.spk_is_dialect,
+            )
+            prompt = {"prompt_token_ids": [0] * length, "additional_information": info}
+            kwargs = {"deploy_profile": "edge", "stage_init_timeout": 180}
+        start = time.perf_counter()
+        omni = AsyncOmni(model=args.model, **kwargs, profiler_config=config)
+        report["startup_s"] = time.perf_counter() - start
+
+        async def request(phase, number):
+            start = time.perf_counter()
+            outputs = 0
+            finished = False
+            audio_samples = 0
+            async for output in omni.generate(prompt, sampling_params=params, request_id=f"{phase}-{number}"):
+                outputs += 1
+                finished = finished or output.finished
+                if args.kind == "tts" and output.outputs:
+                    mm = output.outputs[0].multimodal_output
+                    audio = mm.get("audio") if mm else None
+                    parts = audio if isinstance(audio, list) else [audio]
+                    audio_samples += sum(int(part.numel()) for part in parts if part is not None)
+            row = {
+                "phase": phase,
+                "index": number,
+                "wall_s": time.perf_counter() - start,
+                "events": outputs,
+                "finished": finished,
+                "audio_samples": audio_samples,
+            }
+            report["requests"].append(row)
+            save(args.out / "report.json", report)
+            if not finished or not outputs:
+                raise RuntimeError(f"Incomplete diagnostic request: {row}")
+
+        await request("warmup", 0)
+        for i in range(3):
+            await request("untraced_before", i)
+        rpc_started = time.perf_counter()
+        profiling = True
+        if args.kind == "tts":
+            # Both workers are rank 0. A shared prefix overwrites the first stage's trace.
+            report["stage_trace_prefixes"] = {str(stage): f"e2e-stage{stage}" for stage in (0, 1)}
+            report["start_rpc"] = [
+                await omni.start_profile(profile_prefix=prefix, stages=[int(stage)])
+                for stage, prefix in report["stage_trace_prefixes"].items()
+            ]
+        else:
+            report["start_rpc"] = await omni.start_profile(profile_prefix="e2e-diagnostic")
+        report["start_rpc_s"] = time.perf_counter() - rpc_started
+        profiling = True
+        for i in range(3):
+            await request("traced", i)
+        rpc_started = time.perf_counter()
+        report["stop_rpc"] = await omni.stop_profile()
+        report["stop_rpc_s"] = time.perf_counter() - rpc_started
+        profiling = False
+        for i in range(3):
+            await request("untraced_after", i)
+        medians = {
+            phase: statistics.median(r["wall_s"] for r in report["requests"] if r["phase"] == phase)
+            for phase in ("untraced_before", "traced", "untraced_after")
+        }
+        report["median_wall_s"] = medians
+        report["traced_over_untraced_before"] = medians["traced"] / medians["untraced_before"]
+        report["traced_over_untraced_after"] = medians["traced"] / medians["untraced_after"]
+        if args.kind == "tts":
+            report["overhead_caveat"] = (
+                "Same prompt but speech generation can vary; compare audio_samples alongside wall-time ratios."
+            )
+        report["traces"] = [
+            {
+                "path": str(f.relative_to(args.out)),
+                "bytes": f.stat().st_size,
+                "sha256": hashlib.sha256(f.read_bytes()).hexdigest(),
+            }
+            for f in trace_dir.rglob("*")
+            if f.is_file()
+        ]
+        if not report["traces"]:
+            raise RuntimeError("Profiler RPC returned but no trace artifact was written")
+        for prefix in report.get("stage_trace_prefixes", {}).values():
+            if not any(prefix in trace["path"] and ".json" in trace["path"] for trace in report["traces"]):
+                raise RuntimeError(f"Missing trace artifact for {prefix}")
+        report["status"] = "completed"
+    except Exception as error:
+        report.update(status="failed", error=repr(error), traceback=traceback.format_exc())
+        traceback.print_exc()
+    finally:
+        if profiling and omni is not None:
+            try:
+                await omni.stop_profile()
+            except Exception as error:
+                report["stop_error"] = repr(error)
+        if omni is not None:
+            omni.shutdown()
+        report["end_unix"] = time.time()
+        save(args.out / "report.json", report)
+    return report["status"] == "completed"
+
+
+if __name__ == "__main__":
+    p = argparse.ArgumentParser(description=__doc__)
+    p.add_argument("--kind", choices=["spark", "tts"], required=True)
+    p.add_argument("--model", required=True)
+    p.add_argument("--out", type=Path, required=True)
+    args = p.parse_args()
+    os.environ.setdefault("VLLM_WORKER_MULTIPROC_METHOD", "spawn")
+    from vllm_omni.windows.aio import install_selector_policy
+
+    install_selector_policy()
+    raise SystemExit(0 if asyncio.run(run(args)) else 1)

benchmarks/edge_harness/results/e2e_profiling_20260922/.gitattributes

@@ -0,0 +1 @@
+evidence/** -text -whitespace

benchmarks/edge_harness/results/e2e_profiling_20260922/README.md

@@ -0,0 +1,67 @@
+# PC/mobile E2E check and profiling — 2026-09-22
+
+**60 device/model pairs checked; five current desktop paths profiled; 55 retain
+explicit complete-pipeline blockers.** The five paths completed 900 measured
+requests in total and at least 30 minutes of sustained generation each.
+Completed performance protocols do not establish full release qualification.
+
+- [Final findings and measured results](evidence/FINAL_REVIEW.md)
+- [Complete configuration × model matrix and repair steps](evidence/summary/README.md)
+- [Implementation roadmap for all configurations and models](../../../../docs/design/omni_edge_e2e_roadmap.md)
+- [Requirement-by-requirement audit](evidence/requirements-audit.json)
+- [Raw performance summary](evidence/summary/matrix.json)
+- [Memory, startup, power, sustained trends and trace analysis](evidence/resource-summary.json)
+- [Separate TTS stage counters](evidence/attribution-summary.json)
+- [Completion and artifact-integrity checks](evidence/completion-integrity.json)
+- [Chronological experiments, failures and retries](evidence/README.md)
+- [Reproduction harness and measurement boundaries](../../PROFILING.md)
+
+## Main findings
+
+Spark text ran on WSL CPU and on CUDA in WSL/native Windows. CPU uses 1.7B INT8;
+CUDA uses 4B BF16, so these are not same-model speedup comparisons. Batch-dependent
+greedy outputs remain a reference/reproducibility investigation item.
+
+Qwen3-TTS 0.6B CustomVoice ran both stages on CUDA in WSL/native Windows.
+The measured groups had simulated playback deficits in 1/180 WSL requests and
+49/180 native Windows requests. Windows sustained generation slowed late in the
+run: the final-minute RTF p50 was 1.316, with five requests in that window. A later
+driver snapshot reported a 33 W GPU power ceiling; the evidence does not establish
+when or why it changed. Actual sound-device playback and speech reference quality
+remain unqualified. WSL loading headroom and normal vocoder shutdown also need work.
+
+The other cells retain specific runtime, artifact, registration, memory-feasibility
+or device-access gaps. AI Hub component evidence is not promoted to complete
+mobile generation, playback or sustained co-residency. No missing latency is zero,
+and a rejected export is not a permanent rejection of the model/device family.
+
+## Verify and reproduce
+
+From the repository root:
+
+```bash
+python benchmarks/edge_harness/archive_profiles.py verify \
+  benchmarks/edge_harness/results/e2e_profiling_20260922
+
+python benchmarks/edge_harness/archive_profiles.py restore \
+  benchmarks/edge_harness/results/e2e_profiling_20260922 /absolute/path/to/new-run-root
+
+python benchmarks/edge_harness/summarize_e2e_profiles.py \
+  --matrix /absolute/path/to/new-run-root/prior-matrix/matrix.json \
+  --runs /absolute/path/to/new-run-root --out /absolute/path/to/new-summary
+
+python benchmarks/edge_harness/analyze_profile_resources.py \
+  --runs /absolute/path/to/new-run-root --out /absolute/path/to/new-resource-summary.json
+```
+
+[manifest.json](manifest.json) records original and stored hashes/sizes. Large
+uncompressed files are stored as `.archive.gz`; restore before replaying original
+relative trace paths. Raw reports retain their original platform paths and
+measurement metadata. The portable harness is documented separately; historical
+supervisor scripts contain this run's local paths and process identities.
+
+Evidence bytes are protected from Git newline conversion by local attributes.
+Hash verification proves archive integrity, not numerical/model correctness.
+The harness's 17 focused tests and pinned Ruff 0.14.10 checks passed. All trace and
+counter diagnostics finished before archiving; failures and interrupted attempts
+remain available with their original configurations.

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/FINAL_REVIEW.md

@@ -0,0 +1,58 @@
+# End-to-end check and profiling review — 2026-09-22
+
+All 60 device/model pairs have a disposition. Five current desktop execution paths were selected for profiling; the other 55 retain concrete complete-pipeline blockers. This is a check of the current implementation, not a claim that all planned backends or release gates are implemented.
+
+The [full matrix](summary/README.md) contains all cells and next steps. [Requirement audit](requirements-audit.json), [raw performance](summary/matrix.json), [resource/trace analysis](resource-summary.json), and [stage counters](attribution-summary.json) retain the evidence and limits.
+
+## Executed timing protocol
+
+Twenty measured requests per short/medium/long × concurrency 1/2/4 group; separate warmups, a 30-minute medium/concurrency-1 sustained phase, and separate trace/fault diagnostics. Nearest-rank percentiles describe measured requests. Existing disk/JIT caches were retained.
+
+| Path | Protocol complete | Normal requests checked | Constructor startup s | Sustained s | Medium c1 latency p50 / p95 | Medium c1 rate p50 |
+|---|---|---:|---:|---:|---|---|
+| pc_cpu_wsl / Spark-X2.5 | True | 405 | 20.767 | 1805.768 | 0.762 / 0.801 s | 15.663 token/s |
+| pc_cuda_wsl / Spark-X2.5 | True | 839 | 23.693 | 1802.602 | 0.090 / 0.103 s | 46.545 token/s |
+| pc_cuda_wsl / Qwen3-TTS 0.6B CustomVoice | True | 1472 | 345.168 | 1801.183 | 73.956 / 111.604 ms | 0.175 RTF |
+| pc_cuda_windows / Spark-X2.5 | True | 708 | 95.155 | 1803.249 | 0.100 / 0.129 s | 44.656 token/s |
+| pc_cuda_windows / Qwen3-TTS 0.6B CustomVoice | True | 1290 | 79.792 | 1804.298 | 111.521 / 302.522 ms | 0.178 RTF |
+
+Constructor startup excludes earlier imports, artifact hashing and plan construction. Resource analysis also retains inclusive host-launch-to-first-request/output timing. Native Windows Spark reads its checkpoint through the WSL UNC path; native Windows TTS uses a local Windows copy. Startup comparisons therefore include filesystem and preparation differences. Spark CPU uses 1.7B INT8; CUDA Spark uses 4B BF16. These are not same-model speedup comparisons. TTS is the pinned 0.6B CustomVoice checkpoint, English/Vivian. Other voices, languages and TTS modes are unqualified.
+
+## Reliability and output checks
+
+| Path | Recorded normal-output invariants | Targeted probe status | Cancellation / failure observation |
+|---|---|---|---|
+| pc_cpu_wsl / spark | True | completed | queue saturation=True; same tokens after drain=True; oversized refusal=True; post-cancel events=0; worker error=True, 5.907 s; harness cleanup processes=0 |
+| pc_cuda_wsl / spark | True | completed | queue saturation=True; same tokens after drain=True; oversized refusal=True; post-cancel events=0; worker error=True, 4.160 s; harness cleanup processes=0 |
+| pc_cuda_wsl / tts | True | completed | abort ack=0.002 s; late PCM samples=0; worker error=True, 4.107 s; harness cleanup processes=0 |
+| pc_cuda_windows / spark | True | completed | queue saturation=True; same tokens after drain=True; oversized refusal=True; post-cancel events=0; worker error=True, 0.315 s; harness cleanup processes=0 |
+| pc_cuda_windows / tts | True | completed | abort ack=0.003 s; late PCM samples=0; worker error=True, 0.269 s; harness cleanup processes=0 |
+
+A completed probe means it ran. Inspect each outcome. Oversized admission is not an induced OOM; the TTS slow-consumer probe does not saturate every internal buffer. Targeted checks do not establish perceptual/reference quality or all possible state transitions.
+
+## Findings requiring follow-up
+
+- pc_cpu_wsl Spark: 35 measured concurrency-2/4 requests produced a token sequence absent from the corresponding concurrency-1 set. This is a reproducibility/reference gap; timing evidence alone does not identify numerical or state-related causes.
+- pc_cuda_wsl Spark: 32 measured concurrency-2/4 requests produced a token sequence absent from the corresponding concurrency-1 set. This is a reproducibility/reference gap; timing evidence alone does not identify numerical or state-related causes.
+- pc_cuda_wsl TTS: 1/180 measured requests had a simulated post-startup playback deficit. Raw chunk timings/underruns are retained. Actual sound-device playback, speech tail/reference agreement, ASR and perceptual/speaker quality remain unqualified.
+- pc_cuda_wsl TTS sustained: 0/1269 requests had RTF above 1; 0 had simulated playback deficits. Requests submitted in the final 60 seconds: n=42, RTF p50/p95=0.180/0.185. This tail window is separate from the full-run and first/last-five-minute distributions.
+- pc_cuda_windows Spark: 40 measured concurrency-2/4 requests produced a token sequence absent from the corresponding concurrency-1 set. This is a reproducibility/reference gap; timing evidence alone does not identify numerical or state-related causes.
+- pc_cuda_windows TTS: 49/180 measured requests had a simulated post-startup playback deficit. Raw chunk timings/underruns are retained. Actual sound-device playback, speech tail/reference agreement, ASR and perceptual/speaker quality remain unqualified.
+- pc_cuda_windows TTS sustained: 21/1087 requests had RTF above 1; 98 had simulated playback deficits. Requests submitted in the final 60 seconds: n=5, RTF p50/p95=1.316/1.337. This tail window is separate from the full-run and first/last-five-minute distributions.
+- WSL TTS startup briefly coincided with only 24,514,560 bytes of available Windows RAM. The retained minimum sample includes other processes. Native-stage migration into the shared memory ledger and loading/workspace headroom still need work; sampled peaks are lower bounds, not allocation accounting.
+- The normal WSL TTS baseline required forced vocoder shutdown after its grace period. The isolated worker-failure probe later retired owned workers; that does not erase the baseline shutdown finding.
+- The original TTS profiler used the same rank-0 filename for both stages. The diagnostic workaround uses separate public-API stage prefixes. Original collided traces and their logs are preserved under trace-collision-1; production profiler naming remains a repair item.
+- Native Windows TTS slowed substantially at the end of sustained execution while GPU telemetry reported P5 and an 810 MHz memory clock. AC/Performance conditions were still recorded. A subsequent 18:21 driver snapshot reports software power capping active, a 33 W current ceiling versus 95 W default, and inactive GPU thermal slowdown flags ([raw snapshot](gpu-low-clock-diagnostic.txt)). This spot check does not establish when or why the platform changed the limit. The clock transition and latency change are not proof of a thermal cause. Investigate power-state/clock behavior before claiming sustained real-time speech.
+- CPU TTS has a vLLM/Omni compatibility blocker; native Windows CPU lacks the needed attention operators. Pin and qualify matching backend builds before claiming those cells.
+- Qwen3.8-27B lacks the current public Omni pipeline registration; historical bare-vLLM text and vision-component results do not qualify its complete multimedia route.
+- Matching full MiniCPM-o 4.5 and InternVLA-A1 checkpoints/stage artifacts are absent from checked roots. Obtain pinned artifacts, establish desktop reference outputs, then repeat the appropriate modality/action suites.
+- AMD 890M evidence remains a vision component; the tested NPU whole-vision artifacts were rejected. Complete downstream adapters and compatible compiled artifacts are missing. Joint CPU/iGPU/NPU/GPU execution has no complete co-resident serial/overlap comparison.
+- Mobile/embedded and X Elite targets lack device-local application access and integrated stateful model pipelines. AI Hub components do not supply the prefill/decode/KV/sampling loop, playback, co-residency or sustained device thermals. Do not deploy through AI Hub.
+
+## Measurement limits
+
+Windows physical RAM includes WSL; WSL quota, process RSS/PSS and VRAM are distinct scopes and must not be added. Power/temperature/clock samples cover the whole NVIDIA GPU, including other applications. CPU package temperature and CPU/NPU/whole-device energy are unavailable in these environments. CPU effective-frequency trends were not captured; the separate WMI check exposes nominal/aggregate counters, not a validated per-core frequency trace. No model-exclusive energy or causal thermal-throttling claim is made.
+
+Text delivery intervals can combine tokens into output updates. Effective prefill includes queue, host and first-output costs. Torch category duration sums overlap; interval unions are not additive across categories. Sequential profiler stop/export can extend trace windows with idle time. TTS trace overhead comparisons retain speech-length variation. StepStats counters use host timing with synchronization disabled, overlapping scopes and per-process reservoirs; they are not pooled stage GPU latencies. Baseline-versus-counter ratios also include different observed power/clock conditions after the GPU limit change; they are not isolated instrumentation overhead. Engine-core step counters include idle/no-work iterations. The existing orchestrator-dispatch hook is only in the optional event-driven loop, which these runs did not enable; no dispatch latency is inferred from its absent counter.
+
+Failures and corrected retries remain archived. The Windows TTS report-write failure interrupted its first sustained phase; that attempt is not a sustained pass. No universal model/device release qualification follows from a completed performance protocol.

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/README.md

@@ -0,0 +1,467 @@
+# E2E checks and profiling — 2026-09-22
+
+This file is a chronological experiment log, including intermediate states.
+The final review is generated as `FINAL_REVIEW.md` after all serial jobs exit;
+use it together with `summary/README.md` for the final dispositions.
+
+User scope: finish checking and profiling the accepted mobile/PC × model plan;
+where the current implementation does not support a complete pipeline, record
+the reason for later repair. Do not convert component results into E2E claims.
+
+## Current experiment
+
+The prior 60-cell audit is the input backlog. Current read-only preflights in
+`preflight-windows.json` and `preflight-wsl-cpu.json` use real filesystem paths
+(the older Windows audit included a PowerShell provider-prefixed workspace
+path). They retain missing/broken artifact links explicitly. No Android device
+is attached; no complete MiniCPM-o 4.5 or InternVLA-A1 checkpoint is available
+in the checked roots. MiniCPM5-2B is a different model and is not substituted.
+
+Five currently runnable complete request paths are being measured serially:
+
+1. Spark 1.7B INT8, WSL CPU, existing vLLM 0.28 CPU build.
+2. Spark 4B BF16, WSL CUDA, vLLM 0.29.
+3. Spark 4B BF16, native Windows CUDA, vLLM 0.29+cu134.
+4. Qwen3-TTS 0.6B CustomVoice, WSL CUDA, edge deployment profile.
+5. The same pinned Qwen3-TTS checkpoint, native Windows CUDA.
+
+The CPU run started first. `run_remaining.py` waits for its actual live WSL
+process to exit and its host collector to finalize, then runs the four GPU
+benchmarks serially. Inspect actual process/session state before restarting
+anything. `queue.json` is written once the initial wait finishes.
+
+Each run measures three length bands × concurrency 1/2/4 × 20 requests, excluding
+warmups, followed by 1800 seconds of continuous medium-length requests. Spark
+forces 128 output tokens for timing; this is not recommended application
+sampling. Actual prompt token counts are archived. TTS retains terminal audio
+separately from nonterminal chunks and records hashes/finite checks. Warmup PCM
+chunks are archived individually as float32 bytes, with rate/order in JSON.
+Terminal incremental/cumulative semantics and perceptual quality remain separate
+qualification gates. Playback stalls are simulated from arrivals, not measured
+through an audio output device.
+
+Update: inspection of `resolve_sampling_params_list`, `drain_delta_payload` and
+the output processor confirms DELTA payload draining. The TTS recorder now
+resolves and checks the final stage's actual DELTA output setting, includes
+terminal audio once in the primary complete playback metrics, and keeps
+prefix-only metrics and raw terminal bytes separately. Waveform/reference
+quality and correct flush boundaries still require their own evidence.
+
+Each `<run>-host` directory contains the exact command, combined log, process
+exit status and one-second Windows RAM/whole-NVIDIA-device telemetry. AC status
+is sampled. GPU power is not whole-system/CPU/NPU energy. Background desktop
+activity is not disabled. Brief read-only inventory and test-runner development
+occurred during the initial CPU sweep; results are observational and carry
+these conditions, not isolated laboratory SLA claims. The initial CPU collector
+could not decode the localized power-scheme label; AC and battery telemetry
+remain available. The collector's decoding was corrected for subsequent runs.
+
+Model startup uses existing disk/JIT caches. No cold-disk startup or component
+timing sum is reported as E2E latency. Memory peaks sampled by the engine are
+lower bounds; Windows RAM and WSL RAM must not be added.
+
+## Scripts
+
+Source lives in `vllm-omni-edge/benchmarks/edge_harness/`:
+
+- `profile_local_text.py`: existing Omni local text API, raw events, requests,
+  lengths/concurrency, sustained run, slow consumer and cancellation/recovery.
+- `profile_local_tts.py`: public AsyncOmni, pinned real weights, raw chunk
+  arrivals, finite checks, terminal samples, sustained run and abort/recovery.
+- `profile_host_command.py`: Windows host telemetry around one explicit command.
+- `summarize_e2e_profiles.py`: checks all 60 pairings and raw-evidence hashes;
+  joins the five profiling runs; unavailable E2E paths keep their exact reasons.
+- `test_summarize_e2e_profiles.py`: nearest-rank percentile, per-configuration
+  coverage, warmup exclusion and thermal-duration gate tests (3 passed).
+- `profile_trace_diagnostic.py`: three untraced / three traced / three untraced
+  diagnostic requests using existing Omni profiler RPCs. Executed separately
+  after all sweep/thermal runs by `run_diagnostics.py`; its supervisor verifies
+  the live predecessor process identity before waiting. Three samples are
+  diagnostic overhead observations, not latency percentiles.
+- `analyze_profile_resources.py`: whole-GPU telemetry/energy integration and
+  trace category/event summaries. Concurrent event durations are not added
+  and called wall time; long telemetry gaps are excluded from energy
+  integration. Two resource-accounting tests pass (5 analyzer tests total).
+
+CPU sensor probe results: Windows ACPI query returns access denied; WSL exposes
+no hwmon/thermal-zone temperature inputs. CPU package temperature/energy cannot
+be claimed. GPU temperature/power and sustained CPU throughput remain measured.
+The Windows power-scheme query confirms Performance mode; raw probe saved.
+
+The completed CPU sweep exposes a numerical/reproducibility qualification gap:
+all 20 long-prompt concurrency-1 requests share one output sequence, while
+concurrency 2 has two sequences (10 each) and concurrency 4 has four sequences
+(5 each). Inputs all contain 1928 tokens and force 128 output tokens. First
+differences from the c1 sequence appear at output indices 1–4 for differing
+variants. Raw examples are in `spark-cpu-concurrency-output-variants.json`.
+Without captured reference logits, this does not establish state contamination
+or its cause. Do not promote successful timing completion into batch-invariant
+quality acceptance. Snapshot sequence/epoch/queue-bound checks found no stream
+invariant failures; final cancellation checks still run after the sustained phase.
+
+Generate/update the current report from the implementation repository:
+
+```bash
+../.venvs/omni-cpu/bin/python benchmarks/edge_harness/summarize_e2e_profiles.py \
+  --matrix benchmarks/edge_harness/results/model_device_matrix_20260922/matrix.json \
+  --runs ../analysis/experiments/e2e_profiling_20260922 \
+  --out ../analysis/experiments/e2e_profiling_20260922/summary
+```
+
+## Completion requirements still open
+
+Progress at 2026-09-22 15:05 local time:
+
+- CPU sweep completed all nine groups × 20 measured requests, plus warmups,
+  1805.77 seconds sustained generation, slow-consumer and after-cancel requests
+  (405 recorded normal requests). Cancellation acknowledged backend abort,
+  producer stopped, zero in-flight requests, and no delivered stale events.
+  Original CPU workers exited. CPU queue-saturation backpressure still needs
+  a stronger delay probe: the original 30 ms consumer delay can be faster than
+  CPU production, so a bounded queue alone does not prove backpressure engaged.
+- CPU diagnostic completed, with 3 before / 3 traced / 3 after requests and a
+  74,474,545-byte compressed trace. Traced median request time was 1.232× before;
+  this excludes profiler start/stop/export cost. Trace postprocessing reached
+  roughly 27 GiB worker RSS under the 30.9 GiB WSL quota. It completed before
+  any manual intervention; shutdown used a forced process-manager cleanup.
+  The original full instrumentation script is archived as
+  `profile_trace_diagnostic_full.py`. Future diagnostics use 32 text output
+  tokens and disable shape/memory instrumentation; comparisons remain paired
+  within the same diagnostic configuration, separate from 128-token baselines.
+- Initial GPU launches failed before model execution: unquoted inherited Windows
+  PATH expansion in WSL, and native Python's GBK default decoding a Torch template.
+  They are harness failures, not hardware/model failures. Preserved under
+  `launcher-failures-1/`. Retried commands use a minimal Linux PATH and
+  `PYTHONUTF8=1` / `PYTHONIOENCODING=utf-8`. The host wrapper now propagates child
+  exit status rather than always returning zero.
+- `run_retry.py` serializes corrected sweeps then diagnostics. Its live status
+  is `retry-status.json`; `queue.json` records the current sweep supervisor.
+  WSL CUDA Spark has loaded and is generating requests successfully. Do not
+  restart live runs based only on an observation timeout.
+
+Wait for and inspect all five real runs, diagnose runner failures if any, then
+derive p50/p95 by exact configuration from raw samples. Inspect actual device
+placement, state/recovery results, TTS final-tail correctness, memory and
+thermal trends. Run separate diagnostic profiling where the runtime supports
+it, keeping traced latency separate and quantifying overhead. Recheck all
+unavailable pairings against current artifacts/runtime evidence and record
+specific repair actions. Publish a reproducible evidence bundle and update
+the fork only after the final matrix and hashes verify. Do not mark the goal
+complete while benchmark processes or required checks remain outstanding.
+
+### Profiling supervision update — 15:28 local
+
+The completed CPU run has 405 request records with no recorded sequence,
+epoch, completion or queue-bound violations. Batch-dependent token differences
+remain a separate qualification gap. The summarizer now includes recorded
+output invariants and separate trace/reliability reports without treating probe
+completion as a pass. Ten focused analyzer tests pass.
+
+`run_reliability.py` waits for the corrected sweep/trace supervisor, then runs
+three isolated Spark checks. `run_tts_reliability.py` waits for that supervisor
+and runs WSL/native Windows TTS interruption, recovery, slow-consumer and owned
+vocoder-failure probes. Fault injection selects process handles belonging to
+the probe's own engine and confirms process ancestry. Forced cleanup, if needed,
+is retained as a finding. TTS slow-consumer tests do not claim to saturate the
+internal 64 MiB transport buffer.
+
+`run_final_analysis.py` waits for both reliability sequences before parsing
+large traces, keeping that CPU/memory overhead outside timing runs. Its queue
+record is `analysis-queue.json`. Final publication still requires review of the
+actual results, finalized requirement dispositions, reproducible evidence and
+hash checks; none of these supervisors automatically push changes.
+
+### Profiling update — 15:47 local
+
+WSL CUDA Spark completed 839 normal request records, including all 180 measured
+requests and 1802.60 seconds sustained generation. Recorded sequence/epoch/
+completion/queue checks pass. Cancellation acknowledged backend abort, stopped
+the producer, left zero in flight and delivered no remaining stale events.
+Short/medium/long concurrency-1 TTFT p50 values are 0.0352/0.0901/0.3633 s;
+decode p50 values are 50.20/46.55/48.21 tokens/s (4B BF16, forced 128 outputs).
+These are not same-model CPU/GPU comparisons.
+
+Native Windows Spark's second launcher attempt failed before model execution:
+`VLLM_CUDART_SO_PATH` was omitted. The benchmark common helper now reuses the
+acceptance environment's torch DLL and short `~/c29` cache directory; native
+Omni import passed. Before trace diagnostics, `run_diagnostics.py` will archive
+this specific failed attempt under `launcher-failures-2` and retry its full
+baseline after other sweeps finish. This keeps GPU workloads serial. Future
+native TTS/trace/reliability launches inherit the same helper via their shared
+benchmark import. No runtime wheel was changed.
+
+WSL TTS loaded both real-weight stages and initialized in about 345 seconds,
+including graph capture under substantial GPU/host memory pressure. It completed
+the short concurrency-1 group and continues. Preserve this startup behavior;
+do not substitute the earlier 40-second diagnostic initialization measurement.
+
+### Profiling update — 16:23 local
+
+WSL TTS completed all 180 measured requests and 1801.18 seconds sustained
+operation: 1472 normal request records total. Recorded output invariants pass.
+Concurrency-1 short/medium/long TTFA p50 values are 67.58/73.96/68.52 ms; p95
+values are 91.83/111.60/100.57 ms. Corresponding RTF p50 values are
+0.2033/0.1748/0.1758. One of the 180 measured requests (medium, concurrency 4)
+has a 0.66094 ms simulated playback deficit. Actual sound-device playback and
+perceptual/reference speech quality remain unqualified. All 223 archived
+warmup PCM chunks match their hashes and sample counts; see
+`tts-cuda-wsl-warmup-audio-integrity.json`.
+
+Important qualification gaps remain: startup took 345.17 s, whole-GPU memory
+peaked around 23.3 GiB, and whole-Windows-host available RAM briefly reached
+24,514,560 bytes (about 23.4 MiB). The raw minimum sample is retained in
+`tts-cuda-wsl-memory-headroom.json`; it includes other processes and is not
+exclusive model attribution. Normal shutdown force-killed the remaining
+vocoder stage after its grace interval; both stage PIDs and the controller were
+subsequently confirmed absent. Abort acknowledgement took 35.3 ms and the next
+request completed; late-event fencing still needs the queued reliability probe.
+
+Native Windows TTS has passed the corrected import and reached stage startup.
+The Windows Spark baseline retry follows that run before diagnostics. All runs
+remain serialized. The harness now passes the repository-pinned Ruff 0.14.10
+checks and 12 focused analyzer tests. See the new `PROFILING.md` in the harness
+for portable per-run commands; supervisor scripts here are historical local
+orchestration records with specific process IDs, not generic rerun entrypoints.
+
+### Reporting failure and serial retry — 16:40 local
+
+The first native Windows TTS sweep completed all 180 measured requests. It had
+26 simulated underruns (positive durations 8.05–46.87 ms), and all 219 archived
+warmup chunks passed integrity checks. Its sustained phase then stopped at 245
+normal request records because `Path.replace(report.tmp, report.json)` raised
+Windows access denied. This is a reporting failure, not a model-execution
+failure, and the interrupted phase is not a 30-minute sustained pass.
+
+A native Windows reproducer in `report-write-lock-probe/` confirms that a reader
+without FILE_SHARE_DELETE blocks replacement on this workspace UNC path. The
+shared report writer now defers running snapshots under this contention (raw
+request JSONL remains authoritative), records the permission error, and requires
+initial/final snapshots to persist with a bounded retry. Two regression tests
+and the real Windows lock probe pass. Profile and trace CLIs now return a
+nonzero exit code for failed reports. The earlier host exit code 0 must not be
+mistaken for a successful TTS profile.
+
+Read live reports using `inspect_live.py` with WSL Python. Avoid Windows
+Get-Content/native readers on live report files: the currently running native
+Spark process loaded the earlier writer before the fix. It has completed its
+180-request sweep and is in sustained generation.
+
+`run_windows_tts_retry.py` (queue: `windows-tts-retry-queue.json`) waits for the
+existing sweep/trace/reliability/analysis chain to exit, then preserves the
+interrupted native TTS run under `reporting-failures-1/` and performs a complete
+fresh baseline. It reuses a completed, hash-verified paired trace if available,
+otherwise reruns it; then performs native TTS reliability checks and regenerates
+the final analyses. This additional supervisor must finish before publication
+or goal completion. All inference and heavy trace analysis remain serial.
+
+### Additional stage attribution
+
+`run_attribution.py` waits for `run_windows_tts_retry.py` to finish, then runs
+one medium-input/concurrency-1 TTS diagnostic per OS using the existing
+`VLLM_OMNI_STEP_STATS_DIR` hooks, 20 measured requests and no sustained phase.
+These directories are explicitly separate from the five full baselines.
+`VLLM_OMNI_STEP_STATS_SYNC=0` keeps their counters as host-side timing;
+Torch traces provide separate accelerator evidence. Warmup, slow-consumer,
+abort and recovery calls also contribute to the per-process counters. Nested
+counter durations must not be added into E2E latency. The existing counter
+percentiles use rounded sample indices/a capped reservoir and are not pooled
+across processes; forced shutdown may lose the final unflushed counter tail.
+
+The attribution report compares 20 matched input/concurrency requests against
+the uninstrumented baseline, retaining speech-length metrics and the caveat
+that separate-process cache/thermal history and stochastic speech preclude a
+pure causal overhead interpretation. Final task completion also requires
+`attribution-queue.json` to be terminal and its actual process to have exited.
+
+### Final diagnostic and archive preparation
+
+`run_cpu_trace_refresh.py` waits for the attribution supervisor, preserves the
+earlier full CPU diagnostic under `spark-cpu-wsl-trace-full`, and runs the same
+lightweight 32-token diagnostic used for CUDA. The original full CPU trace did
+not record profiler start/stop RPC durations; this separate run fills that
+measurement gap without changing any baseline. Its queue must also be terminal
+before final review/publication.
+
+The summarizer now reports pooled within-request delivery intervals and
+effective prefill throughput. Text timestamps are output-update timestamps and
+can represent coalesced tokens; they are not per-token kernel measurements.
+Intervals never cross request boundaries. Audio intervals exclude empty chunks
+and retain nonempty terminal audio. Fifteen focused analyzer/report-writer tests
+pass after adding this check.
+
+The evidence archive utility preserves original file bytes and hashes. Files
+larger than 50 MiB that are not already gzip are reversibly compressed with an
+explicit original-to-stored mapping in the manifest. In particular, native
+Windows Spark produced a roughly 194 MiB uncompressed trace, which cannot be
+committed directly to GitHub. Restore the archive before replaying original
+report-relative trace paths; unchanged original reports retain original hashes.
+
+### Reliability results — 17:36 local
+
+All three Spark probes reached the four-chunk queue capacity, preserved the
+baseline token sequence after draining, rejected stale post-cancel handles and
+oversized admission plans, and returned no late events after cancellation.
+Terminating an owned model worker produced an explicit request error in
+5.907 s (CPU WSL), 4.160 s (CUDA WSL), and 0.315 s (native Windows CUDA).
+No owned process required harness cleanup. These target specific scenarios;
+they do not prove every internal queue, reference-quality gate or memory-pressure
+case. The oversized-plan check is admission rejection, not destructive OOM.
+
+WSL TTS's slow consumer completed. Abort after the first nonempty finite audio
+chunk acknowledged in 2.438 ms, delivered only an empty terminal event after
+acknowledgement, and the following request completed with finite PCM. Killing
+the owned vocoder produced `OmniEngineDeadError` after 4.107 s. No owned process
+required harness cleanup. This does not qualify perceptual quality or saturation
+of the internal 64 MiB buffer. See the `*-reliability/report.json` records.
+
+### TTS trace filename collision found during evidence review
+
+The first paired TTS diagnostics each produced only one trace. Both stage logs
+explicitly exported to the same `trace_rank0.json`: stage 1 overwrote stage 0.
+These records measure paired RPC/request overhead but preserve only the vocoder
+trace, so they cannot establish full two-stage attribution. The runtime's
+profiler artifact naming needs a stage identifier for general multi-stage use.
+
+The diagnostic harness works around this through the existing public API:
+`start_profile(profile_prefix="e2e-stage0", stages=[0])` and a distinct
+`e2e-stage1` prefix for stage 1, with both active for the traced requests.
+It requires an artifact for each stage. Background gzip is disabled for new
+diagnostics; archive compression occurs after every writer has exited, avoiding
+hashing a file still being compressed.
+
+`run_tts_trace_refresh.py` waits for the final CPU diagnostic, preserves the
+earlier TTS trace/host directories under `trace-collision-1/`, repeats both OS
+diagnostics, then regenerates analyses. Its actual process and
+`tts-trace-refresh-queue.json` must be terminal before publication. Baseline
+timings are unchanged. Windows Spark's trace and both retained vocoder traces
+contain actual CUDA kernel, memcpy and runtime events; file existence alone
+was not used to infer accelerator capture.
+
+### Native TTS retry and final audit preparation — 18:05 local
+
+The fresh native Windows TTS run completed its full 180-request measured sweep
+and is in sustained generation. This sweep has 49 simulated playback deficits,
+positive durations 1.130–136.304 ms; its largest measured RTF is 0.3525. Thus
+RTF below one does not imply the proposed zero-underrun gate passed. The earlier
+interrupted attempt's 26/180 count remains a separate record, not pooled with
+this rerun. Constructor startup is 79.792 s for the fresh run.
+
+The resource analyzer now retains inclusive host-launch-to-first-request/output
+timing, which includes imports, plan construction and artifact hashing before
+the constructor. For example, native Spark's constructor took 95.155 s, while
+host launch to first request was 175.590 s. Its checkpoint was accessed over the
+WSL UNC path; native TTS uses a local Windows checkpoint copy. These are recorded
+conditions, not an isolated OS startup comparison.
+
+Sixteen focused tests now pass, including a check that missing desktop MiniCPM-o
+and InternVLA checkpoints remain the primary machine-readable blocker. A single
+Windows WMI frequency inventory is recorded separately; it is not a sustained
+CPU effective-frequency trace. `render_final_review.py` and
+`finalize_requirements.py` require terminal diagnostic queues before resolving
+the final review and all 23 requirement groups. The evidence secret scan and
+archive verification must run after all writers exit.
+
+### All five baselines complete — 18:17 local
+
+Native Windows TTS completed 1290 normal requests and 1804.298 seconds sustained
+generation. All recorded normal-output invariants pass, and all 218 archived
+warmup PCM chunks match their hashes and lengths. Normal shutdown retired both
+stages without a forced-kill log. Its separate fault probe completed: abort
+acknowledged in 3.488 ms with no late PCM, the following request completed, and
+vocoder termination produced an explicit error in 0.269 s. No owned worker
+required harness cleanup.
+
+Sustained real-time performance failed despite functional completion. Of 1087
+sustained requests, 21 had RTF above one and 98 had simulated playback deficits.
+The first five minutes had RTF p50 0.1775 (n=208); requests submitted in the final
+60 seconds had RTF p50/p95 1.3163/1.3370 (n=5), TTFA p50 346.34 ms and simulated
+playback deficit p50 3674.54 ms. The full-run RTF p95 is only 0.2065, illustrating
+why aggregate request percentiles alone hide this late slowdown. GPU telemetry
+in the last minute reports an 810 MHz memory clock throughout, P5 in raw samples,
+temperature p50 57 C and whole-GPU power p50 33.62 W. These are correlated
+observations, not proof of a thermal cause. AC/Performance conditions remained
+recorded. See `tts-cuda-windows-sustained-tail-check.json` and the raw telemetry.
+
+The resource analyzer now includes the whole sustained request distribution,
+RTF-above-one/deficit counts and a final-60-second window, alongside the existing
+first/last-five-minute windows. Stage attribution and refreshed traces continue
+serially after these completed baselines.
+
+At 18:21, `nvidia-smi -q -d PERFORMANCE,POWER,CLOCK,TEMPERATURE` reported
+software power capping active, a current GPU ceiling of 33 W versus a 95 W
+default, P5 and an 810 MHz memory clock. GPU hardware/software thermal slowdown
+flags were inactive at that instant; cumulative reason counters are not scoped
+to this run. The raw snapshot is `gpu-low-clock-diagnostic.txt`. This later spot
+check does not prove when or why the platform reduced its limit. No power-policy
+setting was changed by this profiling task. Baseline-versus-stage-counter ratios
+therefore include observed power/clock differences and cannot isolate profiler
+overhead. Paired trace-before/during/after requests remain separately recorded.
+
+### Diagnostic deadline correction — 18:36 local
+
+The WSL stage-counter run reached engine readiness after 842.48 s, then its
+900-second launcher deadline expired after one warmup and two measured requests.
+The timeout/controller/owned stage PIDs were confirmed absent. This is an
+insufficient diagnostic time allowance under the observed conditions, not a
+claim that the model cannot execute. The raw last snapshot remains `running`;
+the host exit code 124 is authoritative. The summarizer now resolves such stale
+snapshots as failed/interrupted without editing the original data; 17 tests pass.
+
+Only the still-waiting TTS trace supervisor was replaced, before it launched any
+model, to extend its future WSL deadline to 1800 s. Its prior queue record is
+`tts-trace-refresh-supervisor-superseded.json`. `run_attribution_retry.py` waits
+for those traces, preserves the interrupted WSL counter run and original summary
+under `attribution-timeouts-1/`, and repeats the same 20-request diagnostic with
+an 1800-second allowance. It retains the completed native counter run and
+regenerates summaries. Final review/publication also requires this retry queue
+and actual supervisor process to be terminal. No ongoing inference was restarted
+merely because an observation wait expired.
+
+The native counter run completed all 20 measured requests (RTF p50 1.4450 in
+the reduced-power conditions). Both stage counter files are present. Engine
+step counts include idle/no-work iterations; the default orchestrator loop has
+no dispatch counter hook, so an absent counter is not reported as zero latency.
+The lightweight CPU trace also completed: paired request medians were
+1.8122/1.9962/1.8516 seconds (before/traced/after), with profiler stop/export RPC
+21.5903 seconds. The earlier full CPU trace remains under `spark-cpu-wsl-trace-full`.
+## Corrected two-stage trace results
+
+Both TTS trace refreshes completed with separate `e2e-stage0` and `e2e-stage1`
+artifacts. WSL constructor startup was 937.495 s; before/traced/after request
+medians were 5.0944/4.7426/4.3724 s and stop/export RPC was 15.5334 s. Native
+Windows startup was 56.8888 s; corresponding medians were
+4.2874/4.4784/4.5978 s and stop/export RPC was 18.0903 s. These are the separate
+reduced-power diagnostic conditions, not replacements for baseline timings or
+isolated OS/instrumentation effects. Speech lengths may differ.
+
+The resource analyzer found actual CUDA kernel, memcpy and runtime events in
+both stages on both OS paths. Stage-0/stage-1 kernel event counts were
+196,938/10,977 for WSL and 54,380/10,977 for native Windows. Event counts are
+trace evidence, not throughput or interchangeable work counts across builds.
+Nested event sums and per-stage interval unions must not be added into E2E time.
+The old collided traces remain under `trace-collision-1/`.
+
+The WSL stage-counter retry began at 19:00 after the trace supervisor and its
+analysis exited. It preserves the first timed-out attempt, retains the completed
+native counter run, and repeats the unchanged 20-request medium/c1 workload.
+## Terminal review and publication checks
+
+The WSL counter retry completed all 20 medium/c1 measured requests with exit 0,
+constructor startup 917.27 s and RTF p50 1.4879 under the recorded reduced-power
+conditions. Normal shutdown completed; both known stage processes and the
+supervisor were confirmed absent. The native counter run also has 20 measured
+requests and both stage counter files. Their timings are not an isolated
+instrumentation-overhead estimate.
+
+`FINAL_REVIEW.md` is the canonical human review. All 23 groups in
+`requirements-audit.json` now have terminal dispositions, including explicit
+implementation, reference-quality and sensor gaps. `completion-integrity.json`
+verifies all 60 cells, the 900 measured baseline requests, and original hashes
+for the final trace/counter artifacts. The verifier normalizes Windows path
+separators when reading native reports on Linux; raw reports are unchanged.
+All five baselines completed at least 1800 seconds of sustained generation.
+
+The 245-file text evidence scan had one reviewed false positive: the Qualcomm
+helper selects a client using a token read from stdin/configuration; the flagged
+assignment contains no string literal. The scan reports locations, never values,
+and remains a heuristic rather than proof of absence. See `secret-scan.json`.

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/analysis-queue.json

@@ -0,0 +1,39 @@
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+        "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\benchmarks\\edge_harness\\summarize_e2e_profiles.py",
+        "--matrix",
+        "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\benchmarks\\edge_harness\\results\\model_device_matrix_20260922\\matrix.json",
+        "--runs",
+        "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\analysis\\experiments\\e2e_profiling_20260922",
+        "--out",
+        "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\analysis\\experiments\\e2e_profiling_20260922\\summary"
+      ],
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+        "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\benchmarks\\edge_harness\\analyze_profile_resources.py",
+        "--runs",
+        "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\analysis\\experiments\\e2e_profiling_20260922",
+        "--out",
+        "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\analysis\\experiments\\e2e_profiling_20260922\\resource-summary.json"
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benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/attribution-queue.json

@@ -0,0 +1,55 @@
+{
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+      "command": [
+        "wsl",
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+        "Ubuntu",
+        "--",
+        "bash",
+        "-lc",
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+      ],
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+        "--model",
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+        "--out",
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benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/attribution-retry-queue.json

@@ -0,0 +1,30 @@
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+    "--cwd",
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+    "--",
+    "wsl",
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+  ],
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+}

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/attribution-summary.json

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benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/attribution-timeouts-1/attribution-summary.json

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+              "max": 42.55909999832511
+            }
+          }
+        },
+        "instrumented": {
+          "n": 20,
+          "metrics": {
+            "ttfa_ms": {
+              "n": 20,
+              "p50": 354.54690002370626,
+              "p95": 397.6043000584468,
+              "mean": 359.2399050015956,
+              "min": 321.42510008998215,
+              "max": 399.18359997682273
+            },
+            "rtf_total": {
+              "n": 20,
+              "p50": 1.4450212374940747,
+              "p95": 1.47655767440758,
+              "mean": 1.4417227106009558,
+              "min": 1.3939023665090327,
+              "max": 1.4862167954561298
+            },
+            "total_wall_s": {
+              "n": 20,
+              "p50": 11.558093100087717,
+              "p95": 13.13359290000517,
+              "mean": 11.718756790016778,
+              "min": 10.15871679992415,
+              "max": 13.212468800018542
+            },
+            "streamed_audio_s": {
+              "n": 20,
+              "p50": 8.0,
+              "p95": 8.96,
+              "mean": 8.128,
+              "min": 6.88,
+              "max": 8.96
+            },
+            "stall_at_ttfa_ms": {
+              "n": 20,
+              "p50": 4788.281400012784,
+              "p95": 5484.972200030461,
+              "mean": 4870.361790002789,
+              "min": 4307.558099995367,
+              "max": 5541.990600083955
+            }
+          }
+        }
+      },
+      "p50_ratios": {
+        "ttfa_ms": 3.1792083061007657,
+        "rtf_total": 8.109370534458272,
+        "total_wall_s": 8.033095907491303
+      }
+    }
+  ]
+}

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/attribution-timeouts-1/tts-cuda-wsl-attribution-host/command.log

@@ -0,0 +1,247 @@
+/home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/transformers/modeling_rope_utils.py:1036: FutureWarning: `rope_config_validation` is deprecated and has been removed. Its functionality has been moved to RotaryEmbeddingConfigMixin.validate_rope method. PreTrainedConfig inherits this class, so please call self.validate_rope() instead. Also, make sure to use the new rope_parameters syntax. You can caINFO 09-22 18:16:48 [configuration_qwen3_tts.py:483] talker_config is None. Initializing talker model with default values
+INFO 09-22 18:16:48 [configuration_qwen3_tts.py:486] speaker_encoder_config is None. Initializing talker model with default values
+INFO 09-22 18:16:48 [configuration_qwen3_tts.py:437] code_predictor_config is None.[transformers] Model config: codec_eos_token_id must beINFO 09-22 18:16:48 [configuration_qwen3_tts.py:437] code_predictor_config is None. Initializing code_predictor model with default values
+INFO 09-22 18:16:49 [omni_base.py:216] [AsyncOmni] Initializing with model /home/zhout/.cache/huggingface/hub/models--Qwen--Qwen3-TTS-12Hz-0.6B-CustomVoice/snapshots/85e237c12c027371202489a0ec509ded67b5e4b5
+INFO 09-22 18:16:49 [async_omni_engine.py:172] [AsyncOmniEngine] Initializing with model /home/zhout/.cache/huggingface/hub/models--Qwen--Qwen3-TTS-12Hz-0.6B-CustomVoice/snapshots/85e237c12c027371202489a0ec509ded67b5e4b5
+ERROR 09-22 18:16:49 [repo_utils.py:119] Error parsing config for /ho[transformers] `layer_type_validation` is deprecated and will be removed in v5.20. Use `PreTrainedConfig.validate_layer_type` instead
+[transformers] `layer_type_validation` is deprecated and will be removed in v5.20. Use `PreTrainedConfig.validate_layer_type` instead
+[transformers] `layer_type_validation` is deprecated and will be removed in v5.20. Use `PreTrainedConfig.validate_layer_type` instead
+[transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'mrope_section', 'interleaved'}
+[transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'mrope_section', 'interleaved'}
+[transformers] `layer_type_validation` is deprecated and will be removed in v5.20. Use `PreTrainedConfig.validate_layer_type` instead
+[transformers] `layer_type_validation` is deprecated and will be removed in v5.20. Use `PreTrainedConfig.validate_layer_type` instead
+[transformers] `layer_type_validation` is deprecated and will be removed in v5.20. Use `PreTrainedConfig.validate_layer_type` instead
+(StageEngineCoreProc_stage1_replica0 pid=1528780) ␍Loading safetensors checkpoint shards:   0% Completed | 0/1 [00:00<?, ?it/s]
+(StageEngineCoreProc_stage1_replica0 pid=1528780) ␍Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  1.12it/s]
+(StageEngineCoreProc_stage1_replica0 pid=1528780) ␍Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  1.12it/s]
+(StageEngineCoreProc_stage1_replica0 pid=1528780) 
+(StageEngineCoreProc_stage1_replica0 pid=1528780) ␍Loading safetensors checkpoint shards:   0% Completed | 0/1 [00:00<?, ?it/s]
+(StageEngineCoreProc_stage0_replica0 pid=1528777) ␍Loading safetensors checkpoint shards:   0% Completed | 0/1 [00:00<?, ?it/s]
+(StageEngineCoreProc_stage1_replica0 pid=1528780) ␍Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  1.50it/s]
+(StageEngineCoreProc_stage1_replica0 pid=1528780) ␍Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  1.50it/s]
+(StageEngineCoreProc_stage1_replica0 pid=1528780) 
+(StageEngineCoreProc_stage0_replica0 pid=1528777) ␍Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  1.03it/s]
+(StageEngineCoreProc_stage0_replica0 pid=1528777) ␍Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  1.03it/s]
+(StageEngineCoreProc_stage0_replica0 pid=1528777) 
+(StageEngineCoreProc_stage0_replica0 pid=1528777) ␍Loading safetensors checkpoint shards:   0% Completed | 0/1 [00:00<?, ?it/s]
+(StageEngineCoreProc_stage0_replica0 pid=1528777) ␍Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  7.20it/s]
+(StageEngineCoreProc_stage0_replica0 pid=1528777) ␍Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  7.19it/s]
+(StageEngineCoreProc_stage0_replica0 pid=1528777) 
+(StageEngineCoreProc_stage0_replica0 pid=1528777) ␍Loading safetensors checkpoint shards:   0% Completed | 0/1 [00:00<?, ?it/s]
+(StageEngineCoreProc_stage0_replica0 pid=1528777) ␍Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00, 25.31it/s]
+(StageEngineCoreProc_stage0_replica0 pid=1528777) 
+(StageEngineCoreProc_stage0_replica0 pid=1528777) /home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/transformers/modeling_rope_utils.py:1036: FutureWarning: `rope_config_validation` is deprecated and has been removed. Its functionality has been moved to RotaryEmbeddingConfigMixin.validate_rope method. PreTrainedConfig inherits this class, so please call self.validate_rope() instead. Also, make sure to use the new rope_parameters syntax. You can call self.standardize_rope_params() in the meantime.
+(StageEngineCoreProc_stage0_replica0 pid=1528777)   warnings.warn(
+(StageEngineCoreProc_stage0_replica0 pid=1528777) [transformers] `layer_type_validation` is deprecated and will be removed in v5.20. Use `PreTrainedConfig.validate_layer_type` instead
+(StageEngineCoreProc_stage0_replica0 pid=1528777) [transformers] Model config: codec_eos_token_id must be `None` or an integer within the vocabulary (between 0 and 3071), got 4198. This may result in unexpected behavior.
+(StageEngineCoreProc_stage0_replica0 pid=1528777) [transformers] `layer_type_validation` is deprecated and will be removed in v5.20. Use `PreTrainedConfig.validate_layer_type` instead
+(StageEngineCoreProc_stage0_replica0 pid=1528777) [transformers] `layer_type_validation` is deprecated and will be removed in v5.20. Use `PreTrainedConfig.validate_layer_type` instead
+(StageEngineCoreProc_stage0_replica0 pid=1528777) [transformers] `layer_type_validation` is deprecated and will be removed in v5.20. Use `PreTrainedConfig.validate_layer_type` instead
+(StageEngineCoreProc_stage1_replica0 pid=1528780) ␍JIT kernel warmup (1 compile keys):   0%|          | 0/1 [00:00<?, ?kernel/s](StageEngineCoreProc_stage0_replica0 pid=1528777) ␍JIT kernel warmup (1 compile keys):   0%|          | 0/1 [00:00<?, ?kernel/s]␍JIT kernel warmup (1 compile keys): 100%|██████████| 1/1 [00:00<00:00, 224.27kernel/s, ComputeSlotMappingKernel (1 keys)]
+␍JIT kernel warmup (1 compile keys): 100%|██████████| 1/1 [00:00<00:00, 210.09kernel/s, ComputeSlotMappingKernel (1 keys)]
+(StageEngineCoreProc_stage1_replica0 pid=1528780) /home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/transformers/modeling_rope_utils.py:1036: FutureWarning: `rope_config_validation` is deprecated and has been removed. Its functionality has been moved to RotaryEmbeddingConfigMixin.validate_rope method. PreTrainedConfig inherits this class, so please call self.validate_rope() instead. Also, make sure to use the new rope_parameters syntax. You can call self.standardize_rope_params() in the meantime.
+(StageEngineCoreProc_stage1_replica0 pid=1528780)   warnings.warn(
+(StageEngineCoreProc_stage1_replica0 pid=1528780) [transformers] `layer_type_validation` is deprecated and will be removed in v5.20. Use `PreTrainedConfig.validate_layer_type` instead
+(StageEngineCoreProc_stage1_replica0 pid=1528780) [transformers] Model config: codec_eos_token_id must be `None` or an integer within the vocabulary (between 0 and 3071), got 4198. This may result in unexpected behavior.
+(StageEngineCoreProc_stage1_replica0 pid=1528780) [transformers] `layer_type_validation` is deprecated and will be removed in v5.20. Use `PreTrainedConfig.validate_layer_type` instead
+(StageEngineCoreProc_stage1_replica0 pid=1528780) [transformers] `layer_type_validation` is deprecated and will be removed in v5.20. Use `PreTrainedConfig.validate_layer_type` instead
+(StageEngineCoreProc_stage1_replica0 pid=1528780) [transformers] `layer_type_validation` is deprecated and will be removed in v5.20. Use `PreTrainedConfig.validate_layer_type` instead
+(StageEngineCoreProc_stage0_replica0 pid=1528777) /home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/transformers/modeling_rope_utils.py:1036: FutureWarning: `rope_config_validation` is deprecated and has been removed. Its functionality has been moved to RotaryEmbeddingConfigMixin.validate_rope method. PreTrainedConfig inherits this class, so please call self.validate_rope() instead. Also, make sure to use the new rope_parameters syntax. You can call self.standardize_rope_params() in the meantime.
+(StageEngineCoreProc_stage0_replica0 pid=1528777)   warnings.warn(
+(StageEngineCoreProc_stage0_replica0 pid=1528777) [transformers] `layer_type_validation` is deprecated and will be removed in v5.20. Use `PreTrainedConfig.validate_layer_type` instead
+(StageEngineCoreProc_stage0_replica0 pid=1528777) [transformers] `layer_type_validation` is deprecated and will be removed in v5.20. Use `PreTrainedConfig.validate_layer_type` instead
+(StageEngineCoreProc_stage0_replica0 pid=1528777) [transformers] `layer_type_validation` is deprecated and will be removed in v5.20. Use `PreTrainedConfig.validate_layer_type` instead
+(StageEngineCoreProc_stage0_replica0 pid=1528777) [transformers] `layer_type_validation` is deprecated and will be removed in v5.20. Use `PreTrainedConfig.validate_layer_type` instead
+(StageEngineCoreProc_stage0_replica0 pid=1528777) 2026-09-22 18:17:16,027 - INFO - autotuner.py:2789 - flashinfer.jit: [Autotuner]: Loaded 0 configs from /home/zhout/.cache/vllm/flashinfer_autotune_cache/0.6.18/120f/950d410991c69fa2fad5df4e9799f92ecddcf5c5874e9f613a037097bbc1efcc/autotune_configs.json
+(StageEngineCoreProc_stage0_replica0 pid=1528777) 2026-09-22 18:17:16,028 - INFO - autotuner.py:972 - flashinfer.jit: [Autotuner]: Autotuning process starts ...
+(StageEngineCoreProc_stage0_replica0 pid=1528777) 2026-09-22 18:17:16,067 - INFO - autotuner.py:995 - flashinfer.jit: [Autotuner]: Autotuning process ends
+(StageEngineCoreProc_stage1_replica0 pid=1528780) 2026-09-22 18:17:16,101 - INFO - autotuner.py:2789 - flashinfer.jit: [Autotuner]: Loaded 0 configs from /home/zhout/.cache/vllm/flashinfer_autotune_cache/0.6.18/120f/50f3f22d277815e34018a894a95ec6f31c44909c9954ca6981e93c164b765601/autotune_configs.json
+(StageEngineCoreProc_stage1_replica0 pid=1528780) 2026-09-22 18:17:16,101 - INFO - autotuner.py:972 - flashinfer.jit: [Autotuner]: Autotuning process starts ...
+(StageEngineCoreProc_stage0_replica0 pid=1528777) 2026-09-22 18:17:16,130 - INFO - autotuner.py:2652 - flashinfer.jit: [Autotuner]: Saved 0 configs to /home/zhout/.cache/vllm/flashinfer_autotune_cache/0.6.18/120f/950d410991c69fa2fad5df4e9799f92ecddcf5c5874e9f613a037097bbc1efcc/autotune_configs.json (0 new, 0 from previous config)
+(StageEngineCoreProc_stage0_replica0 pid=1528777) ␍Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):   0%|          | 0/4 [00:00<?, ?it/s]␍Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  25%|██▌       | 1/4 [00:00<00:01,  1.65it/s]␍Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  50%|█████     | 2/4 [00:01<00:01,  1.49it/s]␍Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  75%|███████▌  | 3/4 [00:01<00:00,  2.01it/s]␍Capturing CUDA graphs (mixed prefill-decode, PIECEWISE): 100%|██████████| 4/4 [00:03<00:00,  1.11s/it]␍Capturing CUDA graphs (mixed prefill-decode, PIECEWISE): 100%|██████████| 4/4 [00:03<00:00,  1.09it/s]
+(StageEngineCoreProc_stage0_replica0 pid=1528777) ␍Capturing CUDA graphs (decode, FULL):   0%|          | 0/3 [00:00<?, ?it/s]␍Capturing CUDA graphs (decode, FULL):  33%|███▎      | 1/3 [00:00<00:00,  4.54it/s]␍Capturing CUDA graphs (decode, FULL): 100%|██████████| 3/3 [00:00<00:00,  9.50it/s]
+(StageEngineCoreProc_stage0_replica0 pid=1528777) /home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/transformers/modeling_rope_utils.py:1036: FutureWarning: `rope_config_validation` is deprecated and has been removed. Its functionality has been moved to RotaryEmbeddingConfigMixin.validate_rope method. PreTrainedConfig inherits this class, so please call self.validate_rope() instead. Also, make sure to use the new rope_parameters syntax. You can call self.standardize_rope_params() in the meantime.
+(StageEngineCoreProc_stage0_replica0 pid=1528777)   warnings.warn(
+(StageEngineCoreProc_stage0_replica0 pid=1528777) [transformers] `layer_type_validation` is deprecated and will be removed in v5.20. Use `PreTrainedConfig.validate_layer_type` instead
+(StageEngineCoreProc_stage0_replica0 pid=1528777) [transformers] `layer_type_validation` is deprecated and will be removed in v5.20. Use `PreTrainedConfig.validate_layer_type` instead
+(StageEngineCoreProc_stage0_replica0 pid=1528777) [transformers] `layer_type_validation` is deprecated and will be removed in v5.20. Use `PreTrainedConfig.validate_layer_type` instead
+(StageEngineCoreProc_stage0_replica0 pid=1528777) [transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'interleaved', 'mrope_section'}
+(StageEngineCoreProc_stage0_replica0 pid=1528777) [transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'interleaved', 'mrope_section'}
+(StageEngineCoreProc_stage1_replica0 pid=1528780) 2026-09-22 18:17:45,084 - INFO - autotuner.py:995 - flashinfer.jit: [Autotuner]: Autotuning process ends
+(StageEngineCoreProc_stage1_replica0 pid=1528780) 2026-09-22 18:17:45,164 - INFO - autotuner.py:2652 - flashinfer.jit: [Autotuner]: Saved 0 configs to /home/zhout/.cache/vllm/flashinfer_autotune_cache/0.6.18/120f/50f3f22d277815e34018a894a95ec6f31c44909c9954ca6981e93c164b765601/autotune_configs.json (0 new, 0 from previous config)
+(StageEngineCoreProc_stage1_replica0 pid=1528780) ␍Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):   0%|          | 0/4 [00:00<?, ?it/s]␍Capturing CUDA graphs (mixed prefill-decode, PIECEWISE): 100%|██████████| 4/4 [00:00<00:00, 81.10it/s]
+(StageEngineCoreProc_stage1_replica0 pid=1528780) ␍Capturing CUDA graphs (decode, FULL):   0%|          | 0/3 [00:00<?, ?it/s]␍Capturing CUDA graphs (decode, FULL): 100%|██████████| 3/3 [00:00<00:00, 208.63it/s]
+(StageEngineCoreProc_stage1_replica0 pid=1528780) /home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/transformers/modeling_rope_utils.py:1036: FutureWarning: `rope_config_validation` is deprecated and has been removed. Its functionality has been moved to RotaryEmbeddingConfigMixin.validate_rope method. PreTrainedConfig inherits this class, so please call self.validate_rope() instead. Also, make sure to use the new rope_parameters syntax. You can call self.standardize_rope_params() in the meantime.
+(StageEngineCoreProc_stage1_replica0 pid=1528780)   warnings.warn(
+(StageEngineCoreProc_stage1_replica0 pid=1528780) [transformers] `layer_type_validation` is deprecated and will be removed in v5.20. Use `PreTrainedConfig.validate_layer_type` instead
+(StageEngineCoreProc_stage1_replica0 pid=1528780) [transformers] `layer_type_validation` is deprecated and will be removed in v5.20. Use `PreTrainedConfig.validate_layer_type` instead
+(StageEngineCoreProc_stage1_replica0 pid=1528780) [transformers] `layer_type_validation` is deprecated and will be removed in v5.20. Use `PreTrainedConfig.validate_layer_type` instead
+(StageEngineCoreProc_stage1_replica0 pid=1528780) [transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'interleaved', 'mrope_section'}
+(StageEngineCoreProc_stage1_replica0 pid=1528780) [transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'interleaved', 'mrope_section'}
+[transformers] `layer_type_validation` is deprecated and will be removed in v5.20. Use `PreTrainedConfig.validate_layer_type` instead
+[transformers] `layer_type_validation` is deprecated and will be removed in v5.20. Use `PreTrainedConfig.validate_layer_type` instead
+[transformers] `layer_type_validation` is deprecated and will be removed in v5.20. Use `PreTrainedConfig.validate_layer_type` instead
+[transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'mrope_section', 'interleaved'}
+[transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'mrope_section', 'interleaved'}
+(StageEngineCoreProc_stage0_replica0 pid=1528777) [transformers] `layer_type_validation` is deprecated and will be removed in v5.20. Use `PreTrainedConfig.validate_layer_type` instead
+(StageEngineCoreProc_stage0_replica0 pid=1528777) [transformers] `layer_type_validation` is deprecated and will be removed in v5.20. Use `PreTrainedConfig.validate_layer_type` instead
+(StageEngineCoreProc_stage0_replica0 pid=1528777) [transformers] `layer_type_validation` is deprecated and will be removed in v5.20. Use `PreTrainedConfig.validate_layer_type` instead
+(StageEngineCoreProc_stage0_replica0 pid=1528777) Exception ignored in audit hook:
+(StageEngineCoreProc_stage0_replica0 pid=1528777) Traceback (most recent call last):
+(StageEngineCoreProc_stage0_replica0 pid=1528777)   File "/home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/filelock/_api.py", line 1785, in _audit_fork_safety
+(StageEngineCoreProc_stage0_replica0 pid=1528777)     def _audit_fork_safety(  # pragma: no cover - CPython disables tracing while Python audit hooks run
+(StageEngineCoreProc_stage0_replica0 pid=1528777)     
+(StageEngineCoreProc_stage0_replica0 pid=1528777)   File "/home/zhout/project/edge_infer/vllm-omni-edge/vllm_omni/engine/stage_engine_core_proc.py", line 230, in signal_handler
+(StageEngineCoreProc_stage0_replica0 pid=1528777)     raise SystemExit(_signal_exit_code(signum))
+(StageEngineCoreProc_stage0_replica0 pid=1528777) SystemExit: 143
+[rank0]:[W922 18:31:36.966060849 ProcessGroupNCCL.cpp:1624] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+te.py:1775] world_size=1 rank=0 local_rank=0 distributed_init_method=file:///tmp/vllm_dist_21f6ed8d01a14418bedf5295d27c4cba backend=nccl
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:04 [parallel_state.py:2119] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A, EPLB rank N/A
+(StageEngineCoreProc_stage1_replica0 pid=1528780) WARNING 09-22 18:17:05 [gpu_generation_worker.py:103] OMNI GPUGenerationWorker forces v1 model runner for omni hooks.
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:05 [topk_topp_sampler.py:46] FlashInfer top-p/top-k sampling disabled via VLLM_USE_FLASHINFER_SAMPLER=0.
+(StageEngineCoreProc_stage0_replica0 pid=1528777) WARNING 09-22 18:17:05 [gpu_ar_worker.py:120] OMNI GPUARWorker forces v1 model runner for omni hooks.
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:05 [topk_topp_sampler.py:46] FlashInfer top-p/top-k sampling disabled via VLLM_USE_FLASHINFER_SAMPLER=0.
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:05 [factory.py:46] Created connector: SharedMemoryConnector
+(StageEngineCoreProc_stage0_replica0 pid=1528777) WARNING 09-22 18:17:05 [base.py:173] [LLM Worker 0] Sleep Mode DISABLED.
+(StageEngineCoreProc_stage0_replica0 pid=1528777) WARNING 09-22 18:17:05 [base.py:173] [LLM Worker 0] Sleep Mode DISABLED.
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:05 [gpu_model_runner.py:5379] Starting to load model /home/zhout/.cache/huggingface/hub/models--Qwen--Qwen3-TTS-12Hz-0.6B-CustomVoice/snapshots/85e237c12c027371202489a0ec509ded67b5e4b5...
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:05 [factory.py:46] Created connector: SharedMemoryConnector
+(StageEngineCoreProc_stage1_replica0 pid=1528780) WARNING 09-22 18:17:05 [base.py:173] [LLM Worker 0] Sleep Mode DISABLED.
+(StageEngineCoreProc_stage1_replica0 pid=1528780) WARNING 09-22 18:17:05 [base.py:173] [LLM Worker 0] Sleep Mode DISABLED.
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:05 [gpu_model_runner.py:5379] Starting to load model /home/zhout/.cache/huggingface/hub/models--Qwen--Qwen3-TTS-12Hz-0.6B-CustomVoice/snapshots/85e237c12c027371202489a0ec509ded67b5e4b5...
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:05 [configuration_qwen3_tts_tokenizer_v2.py:156] encoder_config is None. Initializing encoder with default values
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:05 [configuration_qwen3_tts_tokenizer_v2.py:159] decoder_config is None. Initializing decoder with default values
+(StageEngineCoreProc_stage1_replica0 pid=1528780) WARNING 09-22 18:17:06 [vllm.py:2952] `torch.compile` is turned on, but the model /home/zhout/.cache/huggingface/hub/models--Qwen--Qwen3-TTS-12Hz-0.6B-CustomVoice/snapshots/85e237c12c027371202489a0ec509ded67b5e4b5 does not support it. Please open an issue on GitHub if you want it to be supported.
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:06 [weight_utils.py:863] Filesystem type for checkpoints: EXT4. Checkpoint size: 1.69 GiB. Available RAM: 25.92 GiB.
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:06 [weight_utils.py:886] Auto-prefetch is disabled because the filesystem (EXT4) is not a recognized network FS (NFS/Lustre). If you want to force prefetching, start vLLM with --safetensors-load-strategy=prefetch.
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:06 [cuda.py:492] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION'].
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:06 [flash_attn.py:897] Using FlashAttention version 2
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:07 [weight_utils.py:863] Filesystem type for checkpoints: EXT4. Checkpoint size: 0.64 GiB. Available RAM: 25.89 GiB.
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:07 [configuration_qwen3_tts_tokenizer_v2.py:156] encoder_config is None. Initializing encoder with default values
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:07 [configuration_qwen3_tts_tokenizer_v2.py:159] decoder_config is None. Initializing decoder with default values
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:07 [speaker_cache.py:239] Speaker cache ready (max_bytes=536870912)
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:07 [weight_utils.py:863] Filesystem type for checkpoints: EXT4. Checkpoint size: 1.69 GiB. Available RAM: 25.81 GiB.
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:07 [weight_utils.py:886] Auto-prefetch is disabled because the filesystem (EXT4) is not a recognized network FS (NFS/Lustre). If you want to force prefetching, start vLLM with --safetensors-load-strategy=prefetch.
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:07 [modeling_qwen3_tts_tokenizer_v2.py:865] Precomputed exp caches for 29 SnakeBeta activations
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:08 [weight_utils.py:863] Filesystem type for checkpoints: EXT4. Checkpoint size: 0.64 GiB. Available RAM: 25.49 GiB.
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:08 [weight_utils.py:863] Filesystem type for checkpoints: EXT4. Checkpoint size: 0.64 GiB. Available RAM: 25.42 GiB.
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:09 [qwen3_tts_talker.py:1192] Loaded 604 weights for Qwen3TTSTalkerForConditionalGeneration
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:09 [default_loader.py:430] Loading weights took 1.57 seconds
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:09 [segmented_graph_wrapper.py:326] Captured ICL prefix CUDA Graph for batch=1
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:09 [segmented_graph_wrapper.py:331] Captured xvec prefix CUDA Graph for batch=1
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:09 [segmented_graph_wrapper.py:335] Starting CUDA Graph warmup for 6 shapes: batch_sizes=[1] seq_lens=[6, 14, 30, 55, 80, 97]
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:09 [segmented_graph_wrapper.py:346] Captured combined suffix CUDA Graph for batch=1 size=6
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:09 [segmented_graph_wrapper.py:346] Captured combined suffix CUDA Graph for batch=1 size=14
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:10 [segmented_graph_wrapper.py:346] Captured combined suffix CUDA Graph for batch=1 size=30
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:10 [gpu_model_runner.py:5471] Model loading took 1.94 GiB memory and 3.415367 seconds
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:10 [segmented_graph_wrapper.py:346] Captured combined suffix CUDA Graph for batch=1 size=55
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:10 [segmented_graph_wrapper.py:346] Captured combined suffix CUDA Graph for batch=1 size=80
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:10 [segmented_graph_wrapper.py:346] Captured combined suffix CUDA Graph for batch=1 size=97
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:10 [utils.py:306] Using LBNHC KV cache layout.
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:10 [segmented_graph_wrapper.py:361] Captured xvec suffix CUDA Graph for batch=1 size=6
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:10 [segmented_graph_wrapper.py:361] Captured xvec suffix CUDA Graph for batch=1 size=14
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:10 [segmented_graph_wrapper.py:361] Captured xvec suffix CUDA Graph for batch=1 size=30
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:11 [segmented_graph_wrapper.py:361] Captured xvec suffix CUDA Graph for batch=1 size=55
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:11 [segmented_graph_wrapper.py:361] Captured xvec suffix CUDA Graph for batch=1 size=80
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:11 [segmented_graph_wrapper.py:361] Captured xvec suffix CUDA Graph for batch=1 size=97
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:11 [modeling_qwen3_tts_tokenizer_v2.py:909] CUDA Graph enabled for decoder: batch_sizes=[1] seq_lens=[6, 14, 30, 55, 80, 97]
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:11 [qwen3_tts_code2wav.py:206] Code2Wav decoder CUDA Graph enabled
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:11 [default_loader.py:430] Loading weights took 5.29 seconds
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:12 [gpu_model_runner.py:5471] Model loading took 0.54 GiB memory and 5.493188 seconds
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:13 [configuration_qwen3_tts.py:483] talker_config is None. Initializing talker model with default values
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:13 [configuration_qwen3_tts.py:486] speaker_encoder_config is None. Initializing talker model with default values
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:13 [configuration_qwen3_tts.py:437] code_predictor_config is None. Initializing code_predictor model with default values
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:13 [configuration_qwen3_tts.py:437] code_predictor_config is None. Initializing code_predictor model with default values
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:13 [configuration_qwen3_tts.py:437] code_predictor_config is None. Initializing code_predictor model with default values
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:13 [cuda.py:492] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION'].
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:13 [utils.py:306] Using LBNHC KV cache layout.
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:14 [caching.py:343] reconstructed serializable fn from standalone compile artifacts. num_artifacts=4 num_submods=29
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:14 [decorators.py:313] Directly load AOT compilation from path /home/zhout/.cache/vllm/torch_compile_cache/torch_aot_compile/7db261aff95b5f1c2d680f8b909683b7f9c1cb10a8835d2989f18e3dfbe67f7b/rank_0_0/model
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:14 [monitor.py:53] torch.compile took 0.24 s in total
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:14 [monitor.py:81] Initial profiling/warmup run took 0.01 s
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:15 [base.py:148] Available KV cache memory: 3.95 GiB (device-level profiling)
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:15 [kv_cache_utils.py:2032] GPU KV cache size: 36,928 tokens, Maximum concurrency for 4,096 tokens per request: 9.02x
+(StageEngineCoreProc_stage0_replica0 pid=1528777) WARNING 09-22 18:17:15 [base.py:173] [LLM Worker 0] Sleep Mode DISABLED.
+(StageEngineCoreProc_stage1_replica0 pid=1528780) WARNING 09-22 18:17:15 [base.py:173] [LLM Worker 0] Sleep Mode DISABLED.
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:15 [kernel_warmup.py:124] JIT kernel warmup starting.
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:15 [kernel_warmup.py:124] JIT kernel warmup starting.
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:15 [kernel_warmup.py:134] JIT kernel warmup finished in 0.01s.
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:15 [kernel_warmup.py:134] JIT kernel warmup finished in 0.01s.
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:15 [configuration_qwen3_tts.py:483] talker_config is None. Initializing talker model with default values
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:15 [configuration_qwen3_tts.py:486] speaker_encoder_config is None. Initializing talker model with default values
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:15 [configuration_qwen3_tts.py:437] code_predictor_config is None. Initializing code_predictor model with default values
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:15 [configuration_qwen3_tts.py:437] code_predictor_config is None. Initializing code_predictor model with default values
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:15 [configuration_qwen3_tts.py:437] code_predictor_config is None. Initializing code_predictor model with default values
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:15 [configuration_qwen3_tts.py:483] talker_config is None. Initializing talker model with default values
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:15 [configuration_qwen3_tts.py:486] speaker_encoder_config is None. Initializing talker model with default values
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:15 [configuration_qwen3_tts.py:437] code_predictor_config is None. Initializing code_predictor model with default values
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:15 [configuration_qwen3_tts.py:437] code_predictor_config is None. Initializing code_predictor model with default values
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:15 [configuration_qwen3_tts.py:437] code_predictor_config is None. Initializing code_predictor model with default values
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:15 [kernel_warmup.py:309] Using FlashInfer autotune cache file: /home/zhout/.cache/vllm/flashinfer_autotune_cache/0.6.18/120f/950d410991c69fa2fad5df4e9799f92ecddcf5c5874e9f613a037097bbc1efcc/autotune_configs.json
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:16 [kernel_warmup.py:266] Running FlashInfer autotune with 512 tokens.
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:16 [kernel_warmup.py:309] Using FlashInfer autotune cache file: /home/zhout/.cache/vllm/flashinfer_autotune_cache/0.6.18/120f/50f3f22d277815e34018a894a95ec6f31c44909c9954ca6981e93c164b765601/autotune_configs.json
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:16 [kernel_warmup.py:266] Running FlashInfer autotune with 65536 tokens.
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:16 [gpu_model_runner.py:6955] Rank 0: Torch profiler disabled for CUDA graph capture
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:16 [qwen3_tts_code2wav.py:431] Code2Wav codec: frames=4096 q=16 uniq=1 range=[0,0] batch=1
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:21 [gpu_model_runner.py:7001] Graph capturing finished in 5 secs, took 0.05 GiB
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:21 [gpu_ar_model_runner.py:595] Capturing talker_mtp graphs for sizes [8, 4, 2, 1]
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:33 [qwen3_code_predictor.py:1066] code_predictor: warmup done for buckets [1, 2, 4]
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:33 [qwen3_code_predictor.py:929] code_predictor: torch.compile (dynamic=False, no epilogue fusion)
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:40 [gpu_ar_model_runner.py:632] Captured talker_mtp graphs for 4 sizes
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:40 [gpu_worker.py:860] Free memory on device (22.58/23.89 GiB) on startup. Desired GPU memory utilization is (0.25, 5.97 GiB). Actual usage is 2.11 GiB for consumed memory (weights + non-torch), 0.01 GiB for peak activation, and 0.05 GiB for CUDAGraph memory. Replace gpu_memory_utilization config with `--kv-cache-memory=3921297408` (3.65 GiB) to fit into requested memory, or `--kv-cache-memory=21750611968` (20.26 GiB) to fully utilize gpu memory. Current kv cache memory in use is 3.95 GiB.
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:41 [jit_monitor.py:85] Kernel JIT monitor activated; monitored JIT compilations during inference will use mode=warn.
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:42 [torch_utils.py:277] Reducing Torch threads from 12 to 1 for serving. Set OMP_NUM_THREADS in the external environment to override.
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:42 [core.py:361] init engine (profile, create kv cache, warmup model) took 31.71 s (compilation: 0.24 s)
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:42 [configuration_qwen3_tts.py:486] speaker_encoder_config is None. Initializing talker model with default values
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:42 [configuration_qwen3_tts.py:483] talker_config is None. Initializing talker model with default values
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:42 [configuration_qwen3_tts.py:486] speaker_encoder_config is None. Initializing talker model with default values
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:42 [configuration_qwen3_tts.py:437] code_predictor_config is None. Initializing code_predictor model with default values
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:42 [configuration_qwen3_tts.py:437] code_predictor_config is None. Initializing code_predictor model with default values
+(StageEngineCoreProc_stage0_replica0 pid=1528777) WARNING 09-22 18:17:42 [scheduler.py:213] Using custom scheduler class vllm_omni.core.sched.omni_ar_scheduler.OmniARAsyncScheduler. This scheduler interface is not public and compatibility may not be maintained. If you have subclassed Scheduler instead of AsyncScheduler, you will see degraded performance due to async scheduling being disabled.
+(StageEngineCoreProc_stage0_replica0 pid=1528777) WARNING 09-22 18:17:42 [omni_scheduler_mixin.py:118] OMNI scheduler forces v1 model runner for omni hooks.
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:42 [factory.py:46] Created connector: SharedMemoryConnector
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:17:42 [kernel.py:369] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native'])
+INFO 09-22 18:17:42 [stage_runtime.py:943] [StageRuntime] Stage 0 engine startup completed
+INFO 09-22 18:17:42 [stage_engine_core_client.py:159] [StageEngineCoreClient] stage-0 [rep-0] initializing EngineCore
+INFO 09-22 18:17:42 [stage_engine_core_client.py:215] [StageEngineCoreClient] stage-0 [rep-0] EngineCore running
+INFO 09-22 18:17:42 [stage_runtime.py:958] [StageRuntime] Stage 0 initialized
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:17:45 [gpu_model_runner.py:6955] Rank 0: Torch profiler disabled for CUDA graph capture
+(StageEngineCoreProc_stage1_replica0 pid=1528780) WARNING 09-22 18:30:48 [qwen3_tts_code2wav.py:365] Code2Wav input_ids length 8 not divisible by num_quantizers 16; skipping malformed request and suppressing repeats for this length.
+(StageEngineCoreProc_stage1_replica0 pid=1528780) WARNING 09-22 18:30:48 [qwen3_tts_code2wav.py:365] Code2Wav input_ids length 4 not divisible by num_quantizers 16; skipping malformed request and suppressing repeats for this length.
+(StageEngineCoreProc_stage1_replica0 pid=1528780) WARNING 09-22 18:30:48 [qwen3_tts_code2wav.py:365] Code2Wav input_ids length 2 not divisible by num_quantizers 16; skipping malformed request and suppressing repeats for this length.
+(StageEngineCoreProc_stage1_replica0 pid=1528780) WARNING 09-22 18:30:48 [qwen3_tts_code2wav.py:365] Code2Wav input_ids length 1 not divisible by num_quantizers 16; skipping malformed request and suppressing repeats for this length.
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:30:48 [gpu_model_runner.py:7001] Graph capturing finished in 783 secs, took 0.00 GiB
+(StageEngineCoreProc_stage1_replica0 pid=1528780) WARNING 09-22 18:30:48 [gpu_generation_model_runner.py:558] Dummy sampler run is not implemented for generation model
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:30:49 [jit_monitor.py:85] Kernel JIT monitor activated; monitored JIT compilations during inference will use mode=warn.
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:30:49 [torch_utils.py:277] Reducing Torch threads from 12 to 1 for serving. Set OMP_NUM_THREADS in the external environment to override.
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:30:49 [core.py:368] init engine (profile, create kv cache, warmup model) took 816.90 s
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:30:49 [configuration_qwen3_tts.py:486] speaker_encoder_config is None. Initializing talker model with default values
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:30:49 [configuration_qwen3_tts.py:483] talker_config is None. Initializing talker model with default values
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:30:49 [configuration_qwen3_tts.py:486] speaker_encoder_config is None. Initializing talker model with default values
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:30:49 [configuration_qwen3_tts.py:437] code_predictor_config is None. Initializing code_predictor model with default values
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:30:49 [configuration_qwen3_tts.py:437] code_predictor_config is None. Initializing code_predictor model with default values
+(StageEngineCoreProc_stage1_replica0 pid=1528780) WARNING 09-22 18:30:49 [scheduler.py:213] Using custom scheduler class vllm_omni.core.sched.omni_generation_scheduler.OmniGenerationScheduler. This scheduler interface is not public and compatibility may not be maintained. If you have subclassed Scheduler instead of AsyncScheduler, you will see degraded performance due to async scheduling being disabled.
+(StageEngineCoreProc_stage1_replica0 pid=1528780) WARNING 09-22 18:30:49 [core.py:155] Disabling chunked prefill for model without KVCache
+(StageEngineCoreProc_stage1_replica0 pid=1528780) WARNING 09-22 18:30:49 [omni_scheduler_mixin.py:118] OMNI scheduler forces v1 model runner for omni hooks.
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:30:49 [factory.py:46] Created connector: SharedMemoryConnector
+(StageEngineCoreProc_stage1_replica0 pid=1528780) INFO 09-22 18:30:49 [kernel.py:369] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native'])
+INFO 09-22 18:30:49 [stage_runtime.py:943] [StageRuntime] Stage 1 engine startup completed
+INFO 09-22 18:30:49 [stage_engine_core_client.py:159] [StageEngineCoreClient] stage-1 [rep-0] initializing EngineCore
+INFO 09-22 18:30:50 [stage_engine_core_client.py:215] [StageEngineCoreClient] stage-1 [rep-0] EngineCore running
+INFO 09-22 18:30:50 [stage_runtime.py:958] [StageRuntime] Stage 1 initialized
+INFO 09-22 18:30:50 [configuration_qwen3_tts.py:486] speaker_encoder_config is None. Initializing talker model with default values
+INFO 09-22 18:30:50 [configuration_qwen3_tts.py:483] talker_config is None. Initializing talker model with default values
+INFO 09-22 18:30:50 [configuration_qwen3_tts.py:486] speaker_encoder_config is None. Initializing talker model with default values
+INFO 09-22 18:30:50 [configuration_qwen3_tts.py:437] code_predictor_config is None. Initializing code_predictor model with default values
+INFO 09-22 18:30:50 [configuration_qwen3_tts.py:437] code_predictor_config is None. Initializing code_predictor model with default values
+INFO 09-22 18:30:51 [orchestrator.py:604] [Orchestrator] Starting event loop
+INFO 09-22 18:30:51 [async_omni_engine.py:323] [AsyncOmniEngine] Orchestrator ready with 2 stages
+INFO 09-22 18:30:51 [omni_base.py:235] [AsyncOmni] AsyncOmniEngine initialized in 842.48 seconds
+INFO 09-22 18:30:51 [omni_base.py:255] [AsyncOmni] Initialized with 2 stages for model /home/zhout/.cache/huggingface/hub/models--Qwen--Qwen3-TTS-12Hz-0.6B-CustomVoice/snapshots/85e237c12c027371202489a0ec509ded67b5e4b5
+WARNING 09-22 18:30:51 [input_processor.py:315] Passing raw prompts to InputProcessor is deprecated and will be removed in the future. You should instead pass the outputs of Renderer.render_cmpl() or Renderer.render_chat().
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:30:54 [configuration_qwen3_tts.py:486] speaker_encoder_config is None. Initializing talker model with default values
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:30:54 [configuration_qwen3_tts.py:483] talker_config is None. Initializing talker model with default values
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:30:54 [configuration_qwen3_tts.py:486] speaker_encoder_config is None. Initializing talker model with default values
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:30:54 [configuration_qwen3_tts.py:437] code_predictor_config is None. Initializing code_predictor model with default values
+(StageEngineCoreProc_stage0_replica0 pid=1528777) INFO 09-22 18:30:54 [configuration_qwen3_tts.py:437] code_predictor_config is None. Initializing code_predictor model with default values
+(StageEngineCoreProc_stage0_replica0 pid=1528777) WARNING 09-22 18:30:54 [sampling_utils.py:57] min_tokens: dropped stop token ids [151645] that exceed the logits vocabulary (3072); the model head cannot emit them.

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/attribution-timeouts-1/tts-cuda-wsl-attribution-host/status.json

@@ -0,0 +1,21 @@
+{
+  "command": [
+    "wsl",
+    "-d",
+    "Ubuntu",
+    "--",
+    "bash",
+    "-lc",
+    "cd /home/zhout/project/edge_infer/vllm-omni-edge && PYTHONPATH=. HF_HUB_OFFLINE=1 VLLM_USE_FLASHINFER_SAMPLER=0 VLLM_WSL2_ENABLE_PIN_MEMORY=1 CUDA_HOME=/home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/nvidia/cu13 VLLM_OMNI_STEP_STATS_DIR=/home/zhout/project/edge_infer/analysis/experiments/e2e_profiling_20260922/tts-cuda-wsl-attribution-step-stats VLLM_OMNI_STEP_STATS_SYNC=0 PATH=/home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/nvidia/cu13/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/lib/wsl/lib timeout 900 ../.venvs/omni-cuda-029/bin/python benchmarks/edge_harness/profile_local_tts.py --model /home/zhout/.cache/huggingface/hub/models--Qwen--Qwen3-TTS-12Hz-0.6B-CustomVoice/snapshots/85e237c12c027371202489a0ec509ded67b5e4b5 --out ../analysis/experiments/e2e_profiling_20260922/tts-cuda-wsl-attribution --length-band medium --concurrency 1 --repeats 20 --sustained-seconds 0"
+  ],
+  "cwd": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge",
+  "host": "Windows-11-10.0.26200-SP0",
+  "scope": "Windows RAM includes WSL; GPU counters are whole-device, not model-exclusive",
+  "sample_interval_s": 1,
+  "start_unix": 1790072196.1287558,
+  "status": "exited",
+  "power_scheme": "\u7535\u6e90\u65b9\u6848 GUID: 27fa6203-3987-4dcc-918d-748559d549ec  (Performance)",
+  "pid": 61848,
+  "returncode": 124,
+  "end_unix": 1790073097.1795352
+}

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benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/attribution-timeouts-1/tts-cuda-wsl-attribution-step-stats/stage0_1528777.json

@@ -0,0 +1,160 @@
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benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/completion-integrity.json

@@ -0,0 +1,59 @@
+{
+  "status": "verified",
+  "cells": 60,
+  "profiled_paths": 5,
+  "measured_baseline_requests": 900,
+  "trace_files": [
+    {
+      "path": "spark-cpu-wsl-trace/traces/profiler_out_0.txt",
+      "sha256": "04ca3c8494c773c191a7b89544c5bfd27fad017d7da3a05a3d83af6cb340cbc7"
+    },
+    {
+      "path": "spark-cpu-wsl-trace/traces/1790073491843072609-rank-0.1790073527497371957.pt.trace.json",
+      "sha256": "071c819a66987259e8d6aa420f135c674438d991a5016c1d41615a4280960de0"
+    },
+    {
+      "path": "spark-cuda-wsl-trace/traces/20260922-171224_e2e-diagnostic/trace_rank0.json.gz",
+      "sha256": "096f3cbd5f65e5d614eaabee270495a754a1cd4946e0f75c4703c844db9f741e"
+    },
+    {
+      "path": "spark-cuda-windows-trace/traces/20260922-171601_e2e-diagnostic/trace_rank0.json",
+      "sha256": "cb3db8804f8350585f09010704590d916913e08a511adb1b9b6511618e5bb82b"
+    },
+    {
+      "path": "tts-cuda-wsl-trace/traces/20260922-185614_e2e-stage1/trace_rank0.json",
+      "sha256": "b2ce835bb81df5a2d7e4e3894f7d2a6267cbd3b7a2664de4fceefd0b666d36ca"
+    },
+    {
+      "path": "tts-cuda-wsl-trace/traces/20260922-185614_e2e-stage0/trace_rank0.json",
+      "sha256": "7f7fd56ff97afc0a5d6cc2325ade11032cdd4b50dda10b7da4e081e5bc233df9"
+    },
+    {
+      "path": "tts-cuda-windows-trace/traces/20260922-185848_e2e-stage0/trace_rank0.json",
+      "sha256": "07510fee2ba52a5e2fd823738a829b2100c6125480005a7e3ae379fc629a874a"
+    },
+    {
+      "path": "tts-cuda-windows-trace/traces/20260922-185848_e2e-stage1/trace_rank0.json",
+      "sha256": "82e6e3d6ebd1a77704fb00105d6f99e3647817f292b7c0503b5939ed5141d9cc"
+    }
+  ],
+  "counter_files": [
+    {
+      "path": "tts-cuda-wsl-attribution-step-stats/stage0_1628534.json",
+      "sha256": "cf3c11fd9475d555ec339ae6cdc51f9a3ca508d0ce3343a7a1c0c96e7cc13b78"
+    },
+    {
+      "path": "tts-cuda-wsl-attribution-step-stats/stage1_1628537.json",
+      "sha256": "0109a0871a4db87778f62ffad628e93c29ace126f28bb69784ec95978e8802c4"
+    },
+    {
+      "path": "tts-cuda-windows-attribution-step-stats\\stage0_62016.json",
+      "sha256": "7ff79e2d17f2d1016a7239ccf70c0a6e2df601443fd63284b17d2437cb701a39"
+    },
+    {
+      "path": "tts-cuda-windows-attribution-step-stats\\stage1_13708.json",
+      "sha256": "b098ac3e0e0caca3bb5ab7f3ed0d31c5228c02af82c836af9fcdd99bc5c153f5"
+    }
+  ],
+  "scope": "Terminal sample coverage and source artifact integrity; not model-quality or release qualification."
+}

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/cpu-frequency-availability-windows.json

@@ -0,0 +1,16 @@
+{
+  "checked_at": "2026-09-22T17:51:31+08:00",
+  "scope": "Single read during Windows TTS sustained execution; not a CPU-only sustained-frequency trace",
+  "Win32_Processor": {
+    "Name": "AMD Ryzen AI 9 HX 370 w/ Radeon 890M",
+    "CurrentClockSpeed": 2000,
+    "MaxClockSpeed": 2000
+  },
+  "Win32_PerfFormattedData_Counters_ProcessorInformation": {
+    "Name": "_Total",
+    "ProcessorFrequency": 2000,
+    "PercentProcessorPerformance": 200,
+    "PercentofMaximumFrequency": 100
+  },
+  "qualification": "WMI nominal/aggregate counters are exposed. No calibrated effective per-core frequency mapping or sustained CPU-frequency trace was collected; do not infer CPU clocks or thermal throttling from the GPU counters."
+}

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/cpu-sensor-availability-windows.json

@@ -0,0 +1,6 @@
+{
+  "checked_at": "2026-09-22T14:20:41.7894572+08:00",
+  "scope": "CPU package temperature availability; ACPI zones are not assumed CPU package sensors",
+  "acpi_error": "拒绝访问 ",
+  "power_scheme": "电源方案 GUID: 27fa6203-3987-4dcc-918d-748559d549ec  (Performance)\r\n"
+}

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/cpu-sensor-availability-wsl.json

@@ -0,0 +1,5 @@
+{
+  "timestamp": 1790058042.2009394,
+  "temperature_sensor_paths": [],
+  "note": "Empty list means no exposed hwmon/thermal_zone temperature sensors in this WSL environment"
+}

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/cpu-trace-refresh-queue.json

@@ -0,0 +1,27 @@
+{
+  "status": "exited",
+  "pid": 19252,
+  "predecessor_pid": 27528,
+  "predecessor_created": 1790067499.6468523,
+  "command": [
+    "C:\\Users\\zhout\\w2\\omni029venv\\Scripts\\python.exe",
+    "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\benchmarks\\edge_harness\\profile_host_command.py",
+    "--out",
+    "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\analysis\\experiments\\e2e_profiling_20260922\\spark-cpu-wsl-trace-host",
+    "--cwd",
+    "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge",
+    "--",
+    "wsl",
+    "-d",
+    "Ubuntu",
+    "--",
+    "bash",
+    "-lc",
+    "cd /home/zhout/project/edge_infer/vllm-omni-edge && PYTHONPATH=. HF_HUB_OFFLINE=1 VLLM_CPU_KVCACHE_SPACE=2 OMP_NUM_THREADS=8 timeout 900 ../.venvs/omni-cpu/bin/python benchmarks/edge_harness/profile_trace_diagnostic.py --kind spark --model ../models/Spark-X2.5-1.7B-int8 --out ../analysis/experiments/e2e_profiling_20260922/spark-cpu-wsl-trace"
+  ],
+  "start": 1790073480.1387935,
+  "returncode": 0,
+  "summarize_e2e_profiles.py": 0,
+  "analyze_profile_resources.py": 0,
+  "end": 1790073601.112772
+}

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/diagnostic-queue.json

@@ -0,0 +1,99 @@
+{
+  "status": "exited",
+  "pid": 27376,
+  "predecessor_pid": 52656,
+  "predecessor_created": null,
+  "start": 1790065913.4098895,
+  "jobs": [
+    {
+      "name": "spark-cpu-wsl-trace",
+      "status": "exited",
+      "benchmark_status": "completed",
+      "reused_verified_report": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\analysis\\experiments\\e2e_profiling_20260922\\spark-cpu-wsl-trace\\report.json"
+    },
+    {
+      "name": "spark-cuda-wsl-trace",
+      "command": [
+        "wsl",
+        "-d",
+        "Ubuntu",
+        "--",
+        "bash",
+        "-lc",
+        "cd /home/zhout/project/edge_infer/vllm-omni-edge && PYTHONPATH=. HF_HUB_OFFLINE=1 VLLM_USE_FLASHINFER_SAMPLER=0 VLLM_WSL2_ENABLE_PIN_MEMORY=1 CUDA_HOME=/home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/nvidia/cu13 PATH=/home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/nvidia/cu13/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/lib/wsl/lib timeout 1800 ../.venvs/omni-cuda-029/bin/python benchmarks/edge_harness/profile_trace_diagnostic.py --kind spark --model ../models/Spark-X2.5-4B --out ../analysis/experiments/e2e_profiling_20260922/spark-cuda-wsl-trace"
+      ],
+      "status": "exited",
+      "start": 1790068263.6139073,
+      "returncode": 0,
+      "end": 1790068384.9514291,
+      "benchmark_status": "completed",
+      "error": null
+    },
+    {
+      "name": "spark-cuda-windows-trace",
+      "command": [
+        "C:\\Users\\zhout\\w2\\omni029venv\\Scripts\\python.exe",
+        "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\benchmarks\\edge_harness\\profile_trace_diagnostic.py",
+        "--kind",
+        "spark",
+        "--model",
+        "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\Spark-X2.5-4B",
+        "--out",
+        "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\analysis\\experiments\\e2e_profiling_20260922\\spark-cuda-windows-trace"
+      ],
+      "status": "exited",
+      "start": 1790068384.9575229,
+      "returncode": 0,
+      "end": 1790068648.1229272,
+      "benchmark_status": "completed",
+      "error": null
+    },
+    {
+      "name": "tts-cuda-wsl-trace",
+      "command": [
+        "wsl",
+        "-d",
+        "Ubuntu",
+        "--",
+        "bash",
+        "-lc",
+        "cd /home/zhout/project/edge_infer/vllm-omni-edge && PYTHONPATH=. HF_HUB_OFFLINE=1 VLLM_USE_FLASHINFER_SAMPLER=0 VLLM_WSL2_ENABLE_PIN_MEMORY=1 CUDA_HOME=/home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/nvidia/cu13 PATH=/home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/nvidia/cu13/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/lib/wsl/lib timeout 1800 ../.venvs/omni-cuda-029/bin/python benchmarks/edge_harness/profile_trace_diagnostic.py --kind tts --model /home/zhout/.cache/huggingface/hub/models--Qwen--Qwen3-TTS-12Hz-0.6B-CustomVoice/snapshots/85e237c12c027371202489a0ec509ded67b5e4b5 --out ../analysis/experiments/e2e_profiling_20260922/tts-cuda-wsl-trace"
+      ],
+      "status": "exited",
+      "start": 1790068648.1269243,
+      "returncode": 0,
+      "end": 1790068979.9018085,
+      "benchmark_status": "completed",
+      "error": null
+    },
+    {
+      "name": "tts-cuda-windows-trace",
+      "command": [
+        "C:\\Users\\zhout\\w2\\omni029venv\\Scripts\\python.exe",
+        "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\benchmarks\\edge_harness\\profile_trace_diagnostic.py",
+        "--kind",
+        "tts",
+        "--model",
+        "C:\\Users\\zhout\\w2\\models\\Qwen3-TTS-0.6B-85e237c",
+        "--out",
+        "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\analysis\\experiments\\e2e_profiling_20260922\\tts-cuda-windows-trace"
+      ],
+      "status": "exited",
+      "start": 1790068979.908313,
+      "returncode": 0,
+      "end": 1790069182.5775964,
+      "benchmark_status": "completed",
+      "error": null
+    }
+  ],
+  "baseline_retry": {
+    "name": "spark-cuda-windows",
+    "status": "exited",
+    "reason": "Missing VLLM_CUDART_SO_PATH in native launcher",
+    "start": 1790065913.5339177,
+    "returncode": 0,
+    "benchmark_status": "completed",
+    "end": 1790068263.0334463
+  },
+  "end": 1790069182.5821192
+}

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/finalize_requirements.py

@@ -0,0 +1,45 @@
+"""Resolve each requested requirement against terminal evidence and explicit gaps."""
+import json
+from pathlib import Path
+import time
+
+HERE = Path(__file__).resolve().parent
+for name in ('windows-tts-retry', 'attribution', 'cpu-trace-refresh', 'tts-trace-refresh', 'attribution-retry'):
+    assert json.loads((HERE / (name + '-queue.json')).read_text())['status'] == 'exited', name
+matrix = json.loads((HERE / 'summary/matrix.json').read_text())
+assert len(matrix['cases']) == 60
+runs = [c for c in matrix['cases'] if c['profiling']['status'] != 'not_run']
+assert len(runs) == 5
+assert all(c['profiling'].get('profile_protocol_complete') for c in runs), 'Inspect incomplete timing protocols first'
+assert all(c.get('diagnostics', {}).get('trace', {}).get('status') == 'completed' for c in runs)
+assert all(c.get('diagnostics', {}).get('reliability', {}).get('status') in ('completed', 'failed') for c in runs)
+resources = json.loads((HERE / 'resource-summary.json').read_text())
+assert resources['traces'] and all('error' not in r for r in resources['traces'])
+audit = json.loads((HERE / 'requirements-audit.json').read_text())
+updates = {
+    'S3': ('checked_with_limits', 'Exact local hardware, runtime packages, memory, OS, driver and AC/Performance conditions are recorded. Remote component targets lack full application access and complete device build/RAM/power inventories. CPU package temperature and whole-device/CPU/NPU energy are unavailable.', ['preflight-windows.json', 'preflight-wsl-cpu.json', 'resource-summary.json', 'prior-matrix/matrix.json']),
+    'S5': ('checked_with_blockers', 'Desktop real-model cancellation/recovery and worker-failure probes completed; inspect individual outcomes. Spark reached queue capacity and rejected stale state. Stateful mobile sessions and complete device-local controllers remain absent; TTS internal buffer saturation and universal shared-ledger release are not qualified.', ['summary/matrix.json', 'prior-evidence/README.md']),
+    'S6': ('completed_local_checks', 'Reusable text/TTS/host/trace/reliability runners and matrix/resource analyzers are implemented. All 60 pairs have evidence-backed dispositions; raw requests, warmup PCM, counters, traces and failed attempts are retained. Publication integrity is verified separately by the archive manifest.', ['summary/matrix.json', 'FINAL_REVIEW.md']),
+    'M2': ('checked_with_gaps', 'Both CUDA OS paths completed all nine measured groups and 30-minute sustained generation. Simulated playback deficits, model-specific speech/reference quality, forced normal shutdown and memory headroom remain findings. Mobile TTS and CPU compatibility remain blocked; see exact per-run counts and probe results.', ['FINAL_REVIEW.md', 'summary/matrix.json', 'resource-summary.json']),
+    'COVERAGE': ('completed_check', 'All 12 configurations x five models checked: five runnable current desktop paths profiled, 55 explicitly not E2E-profileable with reasons and next actions. Component and historical results are retained without promotion to complete pipelines.', ['summary/matrix.json', 'prior-matrix/matrix.json']),
+    'P1': ('measured_with_limits', 'Constructor startup, host-launch/import interval, first warmup and runtime-reported load/compile lines are retained separately. Existing disk/JIT caches were not purged; isolated cold compilation, cold-disk startup and exhaustive warm-restart distributions are not qualified.', ['resource-summary.json']),
+    'P2': ('completed_measurement', 'All five paths contain at least 20 measured requests in every short/medium/long x concurrency 1/2/4 group. Raw requests and nearest-rank p50/p95 are retained; warmup/sustained records are excluded from these percentiles.', ['summary/matrix.json']),
+    'P3': ('measured_with_quality_gaps', 'Text TTFT/decode/completion, effective prefill and delivery-spacing proxies; speech TTFA/RTF/chunk gaps/simulated underruns and interruption are measured. Text updates can coalesce tokens. Speech perceptual/tail/reference quality and independent text numerical parity are unqualified. Missing multimodal/VLA pipelines block modality/action/deadline measurements.', ['summary/matrix.json', 'FINAL_REVIEW.md']),
+    'P4': ('measured_with_accounting_gaps', 'Windows host RAM, WSL quota/process measurements and whole-GPU VRAM sampled during startup and runtime. Peaks are lower bounds; per-allocation weights/state/workspace/transfer attribution and complete native multi-pool admission remain unimplemented. WSL TTS briefly coincided with only 24.5 MB Windows RAM available; see raw sample.', ['resource-summary.json', 'tts-cuda-wsl-memory-headroom.json', 'prior-evidence/README.md']),
+    'P5': ('measured_with_limits', 'Actual CPU/CUDA runtime evidence, CUDA kernels/copies/runtime calls, and separate TTS stage counters retained. Stage-specific trace prefixes avoid the observed rank-0 filename collision. Counters are host timers with overlapping scopes, not pooled GPU latency. No unmeasured joint-device execution, fallback-free blanket claim or component-sum E2E speedup.', ['resource-summary.json', 'attribution-summary.json', 'summary/matrix.json']),
+    'P6': ('completed_measurement_with_sensor_gaps', 'All five paths completed at least 1800 seconds of medium-input concurrency-1 generation. Whole-NVIDIA-GPU clocks/power/temperature and host RAM sampled; first/last five-minute trends retained. CPU temperature and CPU/NPU/whole-device energy unavailable. GPU energy excludes telemetry gaps over five seconds and includes other processes.', ['resource-summary.json', 'cpu-sensor-availability-windows.json', 'cpu-sensor-availability-wsl.json']),
+    'P7': ('checked_with_limits', 'Repeated sessions, real slow consumers, cancellation/recovery, stale-state fencing, oversized text admission and owned-worker failures checked. Inspect per-probe flags/errors/cleanup. TTS full buffer saturation, destructive OOM recovery, every internal queue and exhaustive state-boundary cases are unqualified.', ['summary/matrix.json', 'FINAL_REVIEW.md']),
+    'P8': ('completed_diagnostics_with_limits', 'Separate three-before/three-traced/three-after diagnostics with profiler RPC times for each runnable path. Original CPU full trace and overwritten TTS traces preserved; final TTS traces use distinct stage prefixes. Tracing/export overhead is separate from baseline; stochastic speech length and sequential export idle time preclude a pure causal universal overhead ratio.', ['summary/matrix.json', 'resource-summary.json', 'trace-collision-1/tts-cuda-wsl-trace-host/command.log']),
+    'RELEASE': ('not_fully_qualified', 'Checks/profiles are complete for the executable current paths; full universal release gates are not met. Missing artifacts/backends/device access, quality suites, memory accounting and playback guarantees remain an explicit user repair backlog. The fork publication and archive hashes establish delivery/integrity, not model qualification.', ['FINAL_REVIEW.md', 'summary/matrix.json', 'resource-summary.json']),
+}
+for requirement in audit['requirements']:
+    if requirement['id'] in updates:
+        status, finding, evidence = updates[requirement['id']]
+        requirement.update(status=status, finding=finding, evidence=evidence)
+    assert 'pending' not in requirement['status'] and requirement['status'] != 'in_progress'
+    for source in requirement['evidence']:
+        assert (HERE / source).is_file(), source
+audit['generated_unix'] = time.time()
+audit['disposition'] = 'Implementation and unavailable qualification gates are recorded for later user repair; performance completion is separate from release qualification.'
+(HERE / 'requirements-audit.json').write_text(json.dumps(audit, indent=2) + '\n')
+print(f"Resolved {len(audit['requirements'])} requirement groups")

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/gpu-low-clock-diagnostic.txt

@@ -0,0 +1,105 @@
+
+==============NVSMI LOG==============
+
+Timestamp                                              : Tue Sep 22 18:21:17 2026
+Driver Version                                         : 610.71 [Deprecated; will be removed in CUDA 14.0. Use KMD Version instead]
+CUDA Version                                           : 13.3 [Deprecated; will be removed in CUDA 14.0. Use CUDA UMD Version instead]
+KMD Version                                            : 610.71
+CUDA UMD Version                                       : 13.3
+
+Attached GPUs                                          : 1
+GPU 00000000:64:00.0
+    Performance State                                  : P5
+    Clocks Event Reasons
+        Idle                                           : Not Active
+        Applications Clocks Setting                    : Not Active
+        SW Power Cap                                   : Active
+        HW Slowdown                                    : Not Active
+            HW Thermal Slowdown                        : Not Active
+            HW Power Brake Slowdown                    : Not Active
+        Sync Boost                                     : Not Active
+        SW Thermal Slowdown                            : Not Active
+        Display Clock Setting                          : Not Active
+    Clocks Event Reasons Counters
+        SW Power Capping                               : 65277286042 us
+        Sync Boost                                     : 0 us
+        SW Thermal Slowdown                            : 62604198347 us
+        HW Thermal Slowdown                            : 0 us
+        HW Power Braking                               : 17078 us
+    Sparse Operation Mode                              : N/A
+    Temperature
+        GPU Current Temp                               : 59 C
+        GPU Current T.Limit Temp                       : 28 C
+        GPU Shutdown T.Limit Temp Specification        : -5 C
+        GPU Slowdown T.Limit Temp Specification        : -2 C
+        GPU Max Operating T.Limit Temp Specification   : 0 C
+        GPU Target Temperature Specification           : 87 C
+        Memory Current Temp                            : N/A
+        Memory Max Operating T.Limit Temp Specification: N/A
+    GPU Power Readings
+        Average Power Draw                             : 0.00 W
+        Instantaneous Power Draw                       : 32.72 W
+        GPU Ceiling Power Limit
+            Current Power Limit                        : 33.00 W
+            Requested Power Limit                      : N/A
+            Default Power Limit                        : 95.00 W
+        GPU Base Power
+            Current Base Power                         : N/A
+            Requested Base Power                       : N/A
+            Default Base Power                         : N/A
+        Min Power Limit                                : 5.00 W
+        Max Power Limit                                : 130.00 W
+    Power Samples
+        Duration                                       : Not Found
+        Number of Samples                              : Not Found
+        Max                                            : Not Found
+        Min                                            : Not Found
+        Avg                                            : Not Found
+    GPU Memory Power Readings 
+        Average Power Draw                             : N/A
+        Instantaneous Power Draw                       : N/A
+    Module Power Readings
+        Average Power Draw                             : N/A
+        Instantaneous Power Draw                       : N/A
+        Current Power Limit                            : N/A
+        Requested Power Limit                          : N/A
+        Default Power Limit                            : N/A
+        Min Power Limit                                : N/A
+        Max Power Limit                                : N/A
+    EDPp Multiplier                                    : N/A
+    Clocks
+        Graphics                                       : 1665 MHz
+        SM                                             : 1665 MHz
+        Memory                                         : 810 MHz
+        Video                                          : 1462 MHz
+    Applications Clocks
+        Graphics                                       : Requested functionality has been deprecated
+        Memory                                         : Requested functionality has been deprecated
+    Default Applications Clocks
+        Graphics                                       : Requested functionality has been deprecated
+        Memory                                         : Requested functionality has been deprecated
+    Deferred Clocks
+        Memory                                         : N/A
+    Max Clocks
+        Graphics                                       : 3090 MHz
+        SM                                             : 3090 MHz
+        Memory                                         : 14001 MHz
+        Video                                          : 3090 MHz
+    Max Customer Boost Clocks
+        Graphics                                       : N/A
+    SM Clock Samples
+        Duration                                       : N/A
+        Number of Samples                              : N/A
+        Max                                            : N/A
+        Min                                            : N/A
+        Avg                                            : N/A
+    Memory Clock Samples
+        Duration                                       : N/A
+        Number of Samples                              : N/A
+        Max                                            : N/A
+        Min                                            : N/A
+        Avg                                            : N/A
+    Clock Policy
+        Auto Boost                                     : N/A
+        Auto Boost Default                             : N/A
+

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/inspect_diagnostics.py

@@ -0,0 +1,22 @@
+"""Read diagnostic outcomes through Linux without holding Windows report locks."""
+import json,os
+from pathlib import Path
+assert os.name!='nt'
+root=Path(__file__).resolve().parent
+for path in sorted([*root.glob('*-trace/report.json'),*root.glob('*-reliability/report.json')]):
+    try:
+        r=json.loads(path.read_text())
+    except (FileNotFoundError,json.JSONDecodeError):
+        continue
+    d={'name':path.parent.name,'status':r.get('status'),'error':r.get('error')}
+    if path.parent.name.endswith('-trace'):
+        d.update(traces=len(r.get('traces',[])),medians=r.get('median_wall_s'),stop_rpc_s=r.get('stop_rpc_s'))
+    else:
+        for key in ('saturation_reached','same_tokens_after_saturation','stale_handle_rejected','oversized_plan_refused','cleanup_error'):
+            if key in r:
+                d[key]=r[key]
+        crash=r.get('crash_injection',r.get('crash',{}))
+        d['crash_error_reported']=crash.get('explicit_error_reported')
+        if 'owned_processes_requiring_harness_cleanup' in r:
+            d['forced_cleanup_count']=len(r['owned_processes_requiring_harness_cleanup'])
+    print(json.dumps(d))

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/inspect_live.py

@@ -0,0 +1,28 @@
+"""Read live report snapshots from Linux, avoiding Windows deny-delete handles."""
+import json
+import os
+from pathlib import Path
+import time
+assert os.name != 'nt', 'Run with the WSL Python interpreter.'
+root=Path(__file__).resolve().parent
+for name in ('spark-cpu-wsl','spark-cuda-wsl','spark-cuda-windows','tts-cuda-wsl','tts-cuda-windows'):
+    path=root/name/'report.json'
+    if not path.exists():
+        continue
+    try:
+        r=json.loads(path.read_text())
+    except json.JSONDecodeError:
+        print(json.dumps({'name':name,'status':'snapshot_mid_write'}))
+        continue
+    row={'name':name,**{key:r.get(key) for key in ('status','requests','error','sustained_wall_s','report_write_permission_errors')}}
+    if r.get('status')=='running' and r.get('sustained_start_unix'):
+        row['sustained_elapsed_s']=round(time.time()-r['sustained_start_unix'])
+    print(json.dumps(row))
+for name in ('retry-status','diagnostic-queue','reliability-queue','tts-reliability-queue','analysis-queue','windows-tts-retry-queue','attribution-queue','cpu-trace-refresh-queue','tts-trace-refresh-queue','attribution-retry-queue'):
+    path=root/(name+'.json')
+    if path.exists():
+        try:
+            r=json.loads(path.read_text())
+            print(json.dumps({'queue':name,'status':r.get('status'),'pid':r.get('pid'),'baseline_retry':r.get('baseline_retry')}))
+        except json.JSONDecodeError:
+            print(json.dumps({'queue':name,'status':'snapshot_mid_write'}))

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/launcher-failures-1/diagnostic-queue.json

@@ -0,0 +1,102 @@
+{
+  "status": "exited",
+  "pid": 20772,
+  "predecessor_pid": 40704,
+  "predecessor_created": 1790057338.4801023,
+  "start": 1790058105.191317,
+  "jobs": [
+    {
+      "name": "spark-cpu-wsl-trace",
+      "command": [
+        "wsl",
+        "-d",
+        "Ubuntu",
+        "--",
+        "bash",
+        "-lc",
+        "cd /home/zhout/project/edge_infer/vllm-omni-edge && PYTHONPATH=. HF_HUB_OFFLINE=1 VLLM_USE_FLASHINFER_SAMPLER=0 VLLM_WSL2_ENABLE_PIN_MEMORY=1 VLLM_CPU_KVCACHE_SPACE=2 OMP_NUM_THREADS=8 timeout 1800 ../.venvs/omni-cpu/bin/python benchmarks/edge_harness/profile_trace_diagnostic.py --kind spark --model ../models/Spark-X2.5-1.7B-int8 --out ../analysis/experiments/e2e_profiling_20260922/spark-cpu-wsl-trace"
+      ],
+      "status": "exited",
+      "start": 1790060190.5552063,
+      "returncode": 0,
+      "end": 1790060565.9332244,
+      "benchmark_status": "completed",
+      "error": null
+    },
+    {
+      "name": "spark-cuda-wsl-trace",
+      "command": [
+        "wsl",
+        "-d",
+        "Ubuntu",
+        "--",
+        "bash",
+        "-lc",
+        "cd /home/zhout/project/edge_infer/vllm-omni-edge && PYTHONPATH=. HF_HUB_OFFLINE=1 VLLM_USE_FLASHINFER_SAMPLER=0 VLLM_WSL2_ENABLE_PIN_MEMORY=1 CUDA_HOME=/home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/nvidia/cu13 PATH=/home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/nvidia/cu13/bin:$PATH timeout 1800 ../.venvs/omni-cuda-029/bin/python benchmarks/edge_harness/profile_trace_diagnostic.py --kind spark --model ../models/Spark-X2.5-4B --out ../analysis/experiments/e2e_profiling_20260922/spark-cuda-wsl-trace"
+      ],
+      "status": "exited",
+      "start": 1790060565.9372296,
+      "returncode": 2,
+      "end": 1790060567.46022,
+      "benchmark_status": "missing_report",
+      "error": null
+    },
+    {
+      "name": "spark-cuda-windows-trace",
+      "command": [
+        "C:\\Users\\zhout\\w2\\omni029venv\\Scripts\\python.exe",
+        "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\benchmarks\\edge_harness\\profile_trace_diagnostic.py",
+        "--kind",
+        "spark",
+        "--model",
+        "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\Spark-X2.5-4B",
+        "--out",
+        "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\analysis\\experiments\\e2e_profiling_20260922\\spark-cuda-windows-trace"
+      ],
+      "status": "exited",
+      "start": 1790060567.46322,
+      "returncode": 1,
+      "end": 1790060579.0515544,
+      "benchmark_status": "missing_report",
+      "error": null
+    },
+    {
+      "name": "tts-cuda-wsl-trace",
+      "command": [
+        "wsl",
+        "-d",
+        "Ubuntu",
+        "--",
+        "bash",
+        "-lc",
+        "cd /home/zhout/project/edge_infer/vllm-omni-edge && PYTHONPATH=. HF_HUB_OFFLINE=1 VLLM_USE_FLASHINFER_SAMPLER=0 VLLM_WSL2_ENABLE_PIN_MEMORY=1 CUDA_HOME=/home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/nvidia/cu13 PATH=/home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/nvidia/cu13/bin:$PATH timeout 1800 ../.venvs/omni-cuda-029/bin/python benchmarks/edge_harness/profile_trace_diagnostic.py --kind tts --model /home/zhout/.cache/huggingface/hub/models--Qwen--Qwen3-TTS-12Hz-0.6B-CustomVoice/snapshots/85e237c12c027371202489a0ec509ded67b5e4b5 --out ../analysis/experiments/e2e_profiling_20260922/tts-cuda-wsl-trace"
+      ],
+      "status": "exited",
+      "start": 1790060579.05656,
+      "returncode": 2,
+      "end": 1790060580.542679,
+      "benchmark_status": "missing_report",
+      "error": null
+    },
+    {
+      "name": "tts-cuda-windows-trace",
+      "command": [
+        "C:\\Users\\zhout\\w2\\omni029venv\\Scripts\\python.exe",
+        "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\benchmarks\\edge_harness\\profile_trace_diagnostic.py",
+        "--kind",
+        "tts",
+        "--model",
+        "C:\\Users\\zhout\\w2\\models\\Qwen3-TTS-0.6B-85e237c",
+        "--out",
+        "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\analysis\\experiments\\e2e_profiling_20260922\\tts-cuda-windows-trace"
+      ],
+      "status": "exited",
+      "start": 1790060580.5466845,
+      "returncode": 1,
+      "end": 1790060587.851317,
+      "benchmark_status": "missing_report",
+      "error": null
+    }
+  ],
+  "end": 1790060587.8553176
+}

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/launcher-failures-1/queue.json

@@ -0,0 +1,78 @@
+{
+  "status": "exited",
+  "pid": 40704,
+  "start": 1790060171.7258117,
+  "jobs": [
+    {
+      "name": "spark-cuda-wsl",
+      "command": [
+        "wsl",
+        "-d",
+        "Ubuntu",
+        "--",
+        "bash",
+        "-lc",
+        "cd /home/zhout/project/edge_infer/vllm-omni-edge && PYTHONPATH=. HF_HUB_OFFLINE=1 VLLM_USE_FLASHINFER_SAMPLER=0 VLLM_WSL2_ENABLE_PIN_MEMORY=1 CUDA_HOME=/home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/nvidia/cu13 PATH=/home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/nvidia/cu13/bin:$PATH timeout 10800 ../.venvs/omni-cuda-029/bin/python benchmarks/edge_harness/profile_local_text.py --model ../models/Spark-X2.5-4B --out ../analysis/experiments/e2e_profiling_20260922/spark-cuda-wsl"
+      ],
+      "status": "exited",
+      "start": 1790060171.729809,
+      "returncode": 0,
+      "end": 1790060173.2586715,
+      "benchmark_status": "missing_report",
+      "error": null
+    },
+    {
+      "name": "spark-cuda-windows",
+      "command": [
+        "C:\\Users\\zhout\\w2\\omni029venv\\Scripts\\python.exe",
+        "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\benchmarks\\edge_harness\\profile_local_text.py",
+        "--model",
+        "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\Spark-X2.5-4B",
+        "--out",
+        "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\analysis\\experiments\\e2e_profiling_20260922\\spark-cuda-windows"
+      ],
+      "status": "exited",
+      "start": 1790060173.2616706,
+      "returncode": 0,
+      "end": 1790060180.4131043,
+      "benchmark_status": "missing_report",
+      "error": null
+    },
+    {
+      "name": "tts-cuda-wsl",
+      "command": [
+        "wsl",
+        "-d",
+        "Ubuntu",
+        "--",
+        "bash",
+        "-lc",
+        "cd /home/zhout/project/edge_infer/vllm-omni-edge && PYTHONPATH=. HF_HUB_OFFLINE=1 VLLM_USE_FLASHINFER_SAMPLER=0 VLLM_WSL2_ENABLE_PIN_MEMORY=1 CUDA_HOME=/home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/nvidia/cu13 PATH=/home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/nvidia/cu13/bin:$PATH timeout 10800 ../.venvs/omni-cuda-029/bin/python benchmarks/edge_harness/profile_local_tts.py --model /home/zhout/.cache/huggingface/hub/models--Qwen--Qwen3-TTS-12Hz-0.6B-CustomVoice/snapshots/85e237c12c027371202489a0ec509ded67b5e4b5 --out ../analysis/experiments/e2e_profiling_20260922/tts-cuda-wsl"
+      ],
+      "status": "exited",
+      "start": 1790060180.416104,
+      "returncode": 0,
+      "end": 1790060181.8675497,
+      "benchmark_status": "missing_report",
+      "error": null
+    },
+    {
+      "name": "tts-cuda-windows",
+      "command": [
+        "C:\\Users\\zhout\\w2\\omni029venv\\Scripts\\python.exe",
+        "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\benchmarks\\edge_harness\\profile_local_tts.py",
+        "--model",
+        "C:\\Users\\zhout\\w2\\models\\Qwen3-TTS-0.6B-85e237c",
+        "--out",
+        "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\analysis\\experiments\\e2e_profiling_20260922\\tts-cuda-windows"
+      ],
+      "status": "exited",
+      "start": 1790060181.870549,
+      "returncode": 0,
+      "end": 1790060189.1714816,
+      "benchmark_status": "missing_report",
+      "error": null
+    }
+  ],
+  "end": 1790060189.1754813
+}

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/launcher-failures-1/spark-cuda-windows-host/command.log

@@ -0,0 +1,31 @@
+Traceback (most recent call last):
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\benchmarks\edge_harness\profile_local_text.py", line 132, in <module>
+    from vllm_omni.windows.aio import install_selector_policy
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\__init__.py", line 19, in <module>
+    from .version import __version__, __version_tuple__  # isort:skip # noqa: F401
+    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\version.py", line 55, in <module>
+    warn_if_misaligned_vllm_version()
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\version.py", line 30, in warn_if_misaligned_vllm_version
+    from vllm import __version__ as vllm_version
+  File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\__init__.py", line 14, in <module>
+    import vllm.env_override  # noqa: F401
+    ^^^^^^^^^^^^^^^^^^^^^^^^
+  File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\env_override.py", line 858, in <module>
+    _patch_inductor_fallback_allow_list()
+  File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\env_override.py", line 835, in _patch_inductor_fallback_allow_list
+    from torch._inductor import lowering as _lowering
+  File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\_inductor\lowering.py", line 9111, in <module>
+    import_submodule(kernel)
+  File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\_dynamo\utils.py", line 4384, in import_submodule
+    importlib.import_module(f"{mod.__name__}.{filename[:-3]}")
+  File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\importlib\__init__.py", line 90, in import_module
+    return _bootstrap._gcd_import(name[level:], package, level)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\_inductor\kernel\mm_grouped.py", line 142, in <module>
+    source=load_kernel_template("cutedsl_mm_grouped"),
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\_inductor\utils.py", line 4770, in load_template
+    return f.read()
+           ^^^^^^^^
+UnicodeDecodeError: 'gbk' codec can't decode byte 0x94 in position 618: illegal multibyte sequence

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/launcher-failures-1/spark-cuda-windows-host/status.json

@@ -0,0 +1,20 @@
+{
+  "command": [
+    "C:\\Users\\zhout\\w2\\omni029venv\\Scripts\\python.exe",
+    "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\benchmarks\\edge_harness\\profile_local_text.py",
+    "--model",
+    "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\Spark-X2.5-4B",
+    "--out",
+    "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\analysis\\experiments\\e2e_profiling_20260922\\spark-cuda-windows"
+  ],
+  "cwd": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge",
+  "host": "Windows-11-10.0.26200-SP0",
+  "scope": "Windows RAM includes WSL; GPU counters are whole-device, not model-exclusive",
+  "sample_interval_s": 1,
+  "start_unix": 1790060173.6020522,
+  "status": "exited",
+  "power_scheme": "\u9422\u57ab\u7c2e\u93c2\u89c4\ufffd\ufffd GUID: 27fa6203-3987-4dcc-918d-748559d549ec  (Performance)",
+  "pid": 63360,
+  "returncode": 1,
+  "end_unix": 1790060180.3821137
+}

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/launcher-failures-1/spark-cuda-windows-host/telemetry.jsonl

@@ -0,0 +1,5 @@
+{"unix": 1790060173.631572, "monotonic": 514905.812, "host_memory": {"total": 67773509632, "available": 24035213312, "percent": 64.5, "used": 43738296320, "free": 24035213312}, "host_cpu_percent": 25.5, "power": {"ACLineStatus": 1, "BatteryFlag": 1, "BatteryLifePercent": 100, "SystemStatusFlag": 0, "BatteryLifeTime": 4294967295, "BatteryFullLifeTime": 4294967295}, "gpu_csv": "GPU-bf6e910e-657c-15d7-c558-f169df134d8a, NVIDIA GeForce RTX 5090 Laptop GPU, 4046, 24463, 55, 17.09, 1710, 810, 0, P5", "gpu_returncode": 0}
+{"unix": 1790060174.6937401, "monotonic": 514906.875, "host_memory": {"total": 67773509632, "available": 24542744576, "percent": 63.8, "used": 43230765056, "free": 24542744576}, "host_cpu_percent": 22.5, "power": {"ACLineStatus": 1, "BatteryFlag": 1, "BatteryLifePercent": 100, "SystemStatusFlag": 0, "BatteryLifeTime": 4294967295, "BatteryFullLifeTime": 4294967295}, "gpu_csv": "GPU-bf6e910e-657c-15d7-c558-f169df134d8a, NVIDIA GeForce RTX 5090 Laptop GPU, 4046, 24463, 55, 18.20, 1680, 810, 0, P5", "gpu_returncode": 0}
+{"unix": 1790060176.0106814, "monotonic": 514908.187, "host_memory": {"total": 67773509632, "available": 25487683584, "percent": 62.4, "used": 42285826048, "free": 25487683584}, "host_cpu_percent": 17.7, "power": {"ACLineStatus": 1, "BatteryFlag": 1, "BatteryLifePercent": 100, "SystemStatusFlag": 0, "BatteryLifeTime": 4294967295, "BatteryFullLifeTime": 4294967295}, "gpu_csv": "GPU-bf6e910e-657c-15d7-c558-f169df134d8a, NVIDIA GeForce RTX 5090 Laptop GPU, 4046, 24463, 55, 17.79, 1665, 810, 0, P5", "gpu_returncode": 0}
+{"unix": 1790060177.4634295, "monotonic": 514909.64, "host_memory": {"total": 67773509632, "available": 25450708992, "percent": 62.4, "used": 42322800640, "free": 25450708992}, "host_cpu_percent": 11.8, "power": {"ACLineStatus": 1, "BatteryFlag": 1, "BatteryLifePercent": 100, "SystemStatusFlag": 0, "BatteryLifeTime": 4294967295, "BatteryFullLifeTime": 4294967295}, "gpu_csv": "GPU-bf6e910e-657c-15d7-c558-f169df134d8a, NVIDIA GeForce RTX 5090 Laptop GPU, 4046, 24463, 55, 18.16, 1657, 810, 0, P5", "gpu_returncode": 0}
+{"unix": 1790060178.9128952, "monotonic": 514911.093, "host_memory": {"total": 67773509632, "available": 25406222336, "percent": 62.5, "used": 42367287296, "free": 25406222336}, "host_cpu_percent": 7.8, "power": {"ACLineStatus": 1, "BatteryFlag": 1, "BatteryLifePercent": 100, "SystemStatusFlag": 0, "BatteryLifeTime": 4294967295, "BatteryFullLifeTime": 4294967295}, "gpu_csv": "GPU-bf6e910e-657c-15d7-c558-f169df134d8a, NVIDIA GeForce RTX 5090 Laptop GPU, 4046, 24463, 54, 17.26, 1620, 810, 0, P5", "gpu_returncode": 0}

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/launcher-failures-1/spark-cuda-windows-trace-host/command.log

@@ -0,0 +1,31 @@
+Traceback (most recent call last):
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\benchmarks\edge_harness\profile_trace_diagnostic.py", line 140, in <module>
+    from vllm_omni.windows.aio import install_selector_policy
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\__init__.py", line 19, in <module>
+    from .version import __version__, __version_tuple__  # isort:skip # noqa: F401
+    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\version.py", line 55, in <module>
+    warn_if_misaligned_vllm_version()
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\version.py", line 30, in warn_if_misaligned_vllm_version
+    from vllm import __version__ as vllm_version
+  File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\__init__.py", line 14, in <module>
+    import vllm.env_override  # noqa: F401
+    ^^^^^^^^^^^^^^^^^^^^^^^^
+  File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\env_override.py", line 858, in <module>
+    _patch_inductor_fallback_allow_list()
+  File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\env_override.py", line 835, in _patch_inductor_fallback_allow_list
+    from torch._inductor import lowering as _lowering
+  File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\_inductor\lowering.py", line 9111, in <module>
+    import_submodule(kernel)
+  File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\_dynamo\utils.py", line 4384, in import_submodule
+    importlib.import_module(f"{mod.__name__}.{filename[:-3]}")
+  File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\importlib\__init__.py", line 90, in import_module
+    return _bootstrap._gcd_import(name[level:], package, level)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\_inductor\kernel\mm_grouped.py", line 142, in <module>
+    source=load_kernel_template("cutedsl_mm_grouped"),
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\_inductor\utils.py", line 4770, in load_template
+    return f.read()
+           ^^^^^^^^
+UnicodeDecodeError: 'gbk' codec can't decode byte 0x94 in position 618: illegal multibyte sequence

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/launcher-failures-1/spark-cuda-windows-trace-host/status.json

@@ -0,0 +1,22 @@
+{
+  "command": [
+    "C:\\Users\\zhout\\w2\\omni029venv\\Scripts\\python.exe",
+    "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\benchmarks\\edge_harness\\profile_trace_diagnostic.py",
+    "--kind",
+    "spark",
+    "--model",
+    "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\Spark-X2.5-4B",
+    "--out",
+    "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\analysis\\experiments\\e2e_profiling_20260922\\spark-cuda-windows-trace"
+  ],
+  "cwd": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge",
+  "host": "Windows-11-10.0.26200-SP0",
+  "scope": "Windows RAM includes WSL; GPU counters are whole-device, not model-exclusive",
+  "sample_interval_s": 1,
+  "start_unix": 1790060567.8569674,
+  "status": "exited",
+  "power_scheme": "\u9422\u57ab\u7c2e\u93c2\u89c4\ufffd\ufffd GUID: 27fa6203-3987-4dcc-918d-748559d549ec  (Performance)",
+  "pid": 42396,
+  "returncode": 1,
+  "end_unix": 1790060579.0231204
+}

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/launcher-failures-1/spark-cuda-windows-trace-host/telemetry.jsonl

@@ -0,0 +1,8 @@
+{"unix": 1790060567.8859663, "monotonic": 515300.062, "host_memory": {"total": 67773509632, "available": 2813661184, "percent": 95.8, "used": 64959848448, "free": 2813661184}, "host_cpu_percent": 18.9, "power": {"ACLineStatus": 1, "BatteryFlag": 1, "BatteryLifePercent": 100, "SystemStatusFlag": 0, "BatteryLifeTime": 4294967295, "BatteryFullLifeTime": 4294967295}, "gpu_csv": "GPU-bf6e910e-657c-15d7-c558-f169df134d8a, NVIDIA GeForce RTX 5090 Laptop GPU, 3753, 24463, 53, 17.27, 1702, 810, 0, P5", "gpu_returncode": 0}
+{"unix": 1790060568.9383662, "monotonic": 515301.125, "host_memory": {"total": 67773509632, "available": 3221893120, "percent": 95.2, "used": 64551616512, "free": 3221893120}, "host_cpu_percent": 23.6, "power": {"ACLineStatus": 1, "BatteryFlag": 1, "BatteryLifePercent": 100, "SystemStatusFlag": 0, "BatteryLifeTime": 4294967295, "BatteryFullLifeTime": 4294967295}, "gpu_csv": "GPU-bf6e910e-657c-15d7-c558-f169df134d8a, NVIDIA GeForce RTX 5090 Laptop GPU, 3772, 24463, 53, 12.08, 427, 405, 0, P8", "gpu_returncode": 0}
+{"unix": 1790060570.2571478, "monotonic": 515302.437, "host_memory": {"total": 67773509632, "available": 3608010752, "percent": 94.7, "used": 64165498880, "free": 3608010752}, "host_cpu_percent": 21.5, "power": {"ACLineStatus": 1, "BatteryFlag": 1, "BatteryLifePercent": 100, "SystemStatusFlag": 0, "BatteryLifeTime": 4294967295, "BatteryFullLifeTime": 4294967295}, "gpu_csv": "GPU-bf6e910e-657c-15d7-c558-f169df134d8a, NVIDIA GeForce RTX 5090 Laptop GPU, 3778, 24463, 53, 17.62, 1665, 810, 0, P5", "gpu_returncode": 0}
+{"unix": 1790060571.7240214, "monotonic": 515303.906, "host_memory": {"total": 67773509632, "available": 5151072256, "percent": 92.4, "used": 62622437376, "free": 5151072256}, "host_cpu_percent": 24.0, "power": {"ACLineStatus": 1, "BatteryFlag": 1, "BatteryLifePercent": 100, "SystemStatusFlag": 0, "BatteryLifeTime": 4294967295, "BatteryFullLifeTime": 4294967295}, "gpu_csv": "GPU-bf6e910e-657c-15d7-c558-f169df134d8a, NVIDIA GeForce RTX 5090 Laptop GPU, 3774, 24463, 53, 17.60, 1635, 810, 0, P5", "gpu_returncode": 0}
+{"unix": 1790060573.1758518, "monotonic": 515305.359, "host_memory": {"total": 67773509632, "available": 6478401536, "percent": 90.4, "used": 61295108096, "free": 6478401536}, "host_cpu_percent": 22.4, "power": {"ACLineStatus": 1, "BatteryFlag": 1, "BatteryLifePercent": 100, "SystemStatusFlag": 0, "BatteryLifeTime": 4294967295, "BatteryFullLifeTime": 4294967295}, "gpu_csv": "GPU-bf6e910e-657c-15d7-c558-f169df134d8a, NVIDIA GeForce RTX 5090 Laptop GPU, 3774, 24463, 53, 13.68, 270, 405, 0, P8", "gpu_returncode": 0}
+{"unix": 1790060574.638596, "monotonic": 515306.828, "host_memory": {"total": 67773509632, "available": 6952316928, "percent": 89.7, "used": 60821192704, "free": 6952316928}, "host_cpu_percent": 14.0, "power": {"ACLineStatus": 1, "BatteryFlag": 1, "BatteryLifePercent": 100, "SystemStatusFlag": 0, "BatteryLifeTime": 4294967295, "BatteryFullLifeTime": 4294967295}, "gpu_csv": "GPU-bf6e910e-657c-15d7-c558-f169df134d8a, NVIDIA GeForce RTX 5090 Laptop GPU, 3774, 24463, 53, 11.95, 202, 405, 0, P8", "gpu_returncode": 0}
+{"unix": 1790060576.0972688, "monotonic": 515308.281, "host_memory": {"total": 67773509632, "available": 7677448192, "percent": 88.7, "used": 60096061440, "free": 7677448192}, "host_cpu_percent": 17.3, "power": {"ACLineStatus": 1, "BatteryFlag": 1, "BatteryLifePercent": 100, "SystemStatusFlag": 0, "BatteryLifeTime": 4294967295, "BatteryFullLifeTime": 4294967295}, "gpu_csv": "GPU-bf6e910e-657c-15d7-c558-f169df134d8a, NVIDIA GeForce RTX 5090 Laptop GPU, 3770, 24463, 53, 17.35, 1597, 810, 0, P5", "gpu_returncode": 0}
+{"unix": 1790060577.5458121, "monotonic": 515309.734, "host_memory": {"total": 67773509632, "available": 9116598272, "percent": 86.5, "used": 58656911360, "free": 9116598272}, "host_cpu_percent": 15.6, "power": {"ACLineStatus": 1, "BatteryFlag": 1, "BatteryLifePercent": 100, "SystemStatusFlag": 0, "BatteryLifeTime": 4294967295, "BatteryFullLifeTime": 4294967295}, "gpu_csv": "GPU-bf6e910e-657c-15d7-c558-f169df134d8a, NVIDIA GeForce RTX 5090 Laptop GPU, 3770, 24463, 53, 17.03, 1590, 810, 0, P5", "gpu_returncode": 0}

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/launcher-failures-1/spark-cuda-wsl-host/command.log

@@ -0,0 +1,2 @@
+bash: -c: line 1: syntax error near unexpected token `('
+bash: -c: line 1: `cd /home/zhout/project/edge_infer/vllm-omni-edge && PYTHONPATH=. HF_HUB_OFFLINE=1 VLLM_USE_FLASHINFER_SAMPLER=0 VLLM_WSL2_ENABLE_PIN_MEMORY=1 CUDA_HOME=/home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/nvidia/cu13 PATH=/home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/nvidia/cu13/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/usr/lib/wsl/lib:/mnt/c/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v13.4/bin:/mnt/c/Users/zhout/.cache/codex-runtimes/codex-primary-runtime/dependencies/native/powershell:/mnt/c/Users/zhout/.codex/tmp/arg0/codex-arg07mHRat:/mnt/c/Users/zhout/.cache/codex-runtimes/codex-primary-runtime/dependencies/native/libheif/libheif/bin:/mnt/c/Users/zhout/.cache/codex-runtimes/codex-primary-runtime/dependencies/native/jxrlib/jxrlib/bin:/mnt/c/Users/zhout/.cache/codex-runtimes/codex-primary-runtime/dependencies/native/poppler/Library/bin:/mnt/c/Users/zhout/.cache/codex-runtimes/codex-primary-runtime/dependencies/bin/override:/mnt/c/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v13.4/bin/x64:/mnt/c/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v13.4/bin:/mnt/c/Windows/system32:/mnt/c/Windows:/mnt/c/Windows/System32/Wbem:/mnt/c/Windows/System32/WindowsPowerShell/v1.0/:/mnt/c/Windows/System32/OpenSSH/:/mnt/c/Program Files (x86)/NVIDIA Corporation/PhysX/Common:/mnt/c/Program Files/NVIDIA Corporation/NVIDIA App/NvDLISR:/mnt/c/Windows/System32/WindowsPowerShell/v1.0:/mnt/c/Program Files/Git/cmd:/mnt/c/Program Files/NVIDIA Corporation/Nsight Compute 2026.3.0/:/mnt/c/Users/zhout/.cargo/bin:/mnt/c/Users/zhout/AppData/Local/Programs/Python/Python312/Scripts/:/mnt/c/Users/zhout/AppData/Local/Programs/Python/Python312/:/mnt/c/Users/zhout/AppData/Local/Programs/Python/Launcher/:/mnt/c/Users/zhout/AppData/Local/Programs/cursor/resources/app/codeBin:/mnt/c/Users/zhout/.cache/codex-runtimes/codex-primary-runtime/dependencies/native/powershell:/mnt/c/Users/zhout/AppData/Local/Microsoft/WindowsApps:/mnt/c/Users/zhout/AppData/Local/Programs/cursor/resources/app/bin:/mnt/c/Users/zhout/.cache/codex-runtimes/codex-primary-runtime/dependencies/bin/fallback:/mnt/c/Users/zhout/.cache/codex-runtimes/codex-primary-runtime/dependencies/node/bin:/mnt/c/Users/zhout/.cache/codex-runtimes/codex-primary-runtime/dependencies/native/git/cmd:/mnt/c/Users/zhout/AppData/Local/OpenAI/Codex/bin/73fd6465d9c76545:/mnt/c/Users/zhout/AppData/Local/OpenAI/Codex/bin/247581e40ee272fb timeout 10800 ../.venvs/omni-cuda-029/bin/python benchmarks/edge_harness/profile_local_text.py --model ../models/Spark-X2.5-4B --out ../analysis/experiments/e2e_profiling_20260922/spark-cuda-wsl'

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/launcher-failures-1/spark-cuda-wsl-host/status.json

@@ -0,0 +1,21 @@
+{
+  "command": [
+    "wsl",
+    "-d",
+    "Ubuntu",
+    "--",
+    "bash",
+    "-lc",
+    "cd /home/zhout/project/edge_infer/vllm-omni-edge && PYTHONPATH=. HF_HUB_OFFLINE=1 VLLM_USE_FLASHINFER_SAMPLER=0 VLLM_WSL2_ENABLE_PIN_MEMORY=1 CUDA_HOME=/home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/nvidia/cu13 PATH=/home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/nvidia/cu13/bin:$PATH timeout 10800 ../.venvs/omni-cuda-029/bin/python benchmarks/edge_harness/profile_local_text.py --model ../models/Spark-X2.5-4B --out ../analysis/experiments/e2e_profiling_20260922/spark-cuda-wsl"
+  ],
+  "cwd": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge",
+  "host": "Windows-11-10.0.26200-SP0",
+  "scope": "Windows RAM includes WSL; GPU counters are whole-device, not model-exclusive",
+  "sample_interval_s": 1,
+  "start_unix": 1790060172.150004,
+  "status": "exited",
+  "power_scheme": "\u9422\u57ab\u7c2e\u93c2\u89c4\ufffd\ufffd GUID: 27fa6203-3987-4dcc-918d-748559d549ec  (Performance)",
+  "pid": 64896,
+  "returncode": 2,
+  "end_unix": 1790060173.2406642
+}

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/launcher-failures-1/spark-cuda-wsl-host/telemetry.jsonl

@@ -0,0 +1 @@
+{"unix": 1790060172.1820033, "monotonic": 514904.359, "host_memory": {"total": 67773509632, "available": 22391828480, "percent": 67.0, "used": 45381681152, "free": 22391828480}, "host_cpu_percent": 15.4, "power": {"ACLineStatus": 1, "BatteryFlag": 1, "BatteryLifePercent": 100, "SystemStatusFlag": 0, "BatteryLifeTime": 4294967295, "BatteryFullLifeTime": 4294967295}, "gpu_csv": "GPU-bf6e910e-657c-15d7-c558-f169df134d8a, NVIDIA GeForce RTX 5090 Laptop GPU, 4046, 24463, 55, 14.03, 900, 405, 0, P8", "gpu_returncode": 0}

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/launcher-failures-1/spark-cuda-wsl-trace-host/command.log

@@ -0,0 +1,2 @@
+bash: -c: line 1: syntax error near unexpected token `('
+bash: -c: line 1: `cd /home/zhout/project/edge_infer/vllm-omni-edge && PYTHONPATH=. HF_HUB_OFFLINE=1 VLLM_USE_FLASHINFER_SAMPLER=0 VLLM_WSL2_ENABLE_PIN_MEMORY=1 CUDA_HOME=/home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/nvidia/cu13 PATH=/home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/nvidia/cu13/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/usr/lib/wsl/lib:/mnt/c/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v13.4/bin:/mnt/c/Users/zhout/.cache/codex-runtimes/codex-primary-runtime/dependencies/native/powershell:/mnt/c/Users/zhout/.codex/tmp/arg0/codex-arg07mHRat:/mnt/c/Users/zhout/.cache/codex-runtimes/codex-primary-runtime/dependencies/native/libheif/libheif/bin:/mnt/c/Users/zhout/.cache/codex-runtimes/codex-primary-runtime/dependencies/native/jxrlib/jxrlib/bin:/mnt/c/Users/zhout/.cache/codex-runtimes/codex-primary-runtime/dependencies/native/poppler/Library/bin:/mnt/c/Users/zhout/.cache/codex-runtimes/codex-primary-runtime/dependencies/bin/override:/mnt/c/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v13.4/bin/x64:/mnt/c/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v13.4/bin:/mnt/c/Windows/system32:/mnt/c/Windows:/mnt/c/Windows/System32/Wbem:/mnt/c/Windows/System32/WindowsPowerShell/v1.0/:/mnt/c/Windows/System32/OpenSSH/:/mnt/c/Program Files (x86)/NVIDIA Corporation/PhysX/Common:/mnt/c/Program Files/NVIDIA Corporation/NVIDIA App/NvDLISR:/mnt/c/Windows/System32/WindowsPowerShell/v1.0:/mnt/c/Program Files/Git/cmd:/mnt/c/Program Files/NVIDIA Corporation/Nsight Compute 2026.3.0/:/mnt/c/Users/zhout/.cargo/bin:/mnt/c/Users/zhout/AppData/Local/Programs/Python/Python312/Scripts/:/mnt/c/Users/zhout/AppData/Local/Programs/Python/Python312/:/mnt/c/Users/zhout/AppData/Local/Programs/Python/Launcher/:/mnt/c/Users/zhout/AppData/Local/Programs/cursor/resources/app/codeBin:/mnt/c/Users/zhout/.cache/codex-runtimes/codex-primary-runtime/dependencies/native/powershell:/mnt/c/Users/zhout/AppData/Local/Microsoft/WindowsApps:/mnt/c/Users/zhout/AppData/Local/Programs/cursor/resources/app/bin:/mnt/c/Users/zhout/.cache/codex-runtimes/codex-primary-runtime/dependencies/bin/fallback:/mnt/c/Users/zhout/.cache/codex-runtimes/codex-primary-runtime/dependencies/node/bin:/mnt/c/Users/zhout/.cache/codex-runtimes/codex-primary-runtime/dependencies/native/git/cmd:/mnt/c/Users/zhout/AppData/Local/OpenAI/Codex/bin/73fd6465d9c76545:/mnt/c/Users/zhout/AppData/Local/OpenAI/Codex/bin/247581e40ee272fb timeout 1800 ../.venvs/omni-cuda-029/bin/python benchmarks/edge_harness/profile_trace_diagnostic.py --kind spark --model ../models/Spark-X2.5-4B --out ../analysis/experiments/e2e_profiling_20260922/spark-cuda-wsl-trace'

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/launcher-failures-1/spark-cuda-wsl-trace-host/status.json

@@ -0,0 +1,21 @@
+{
+  "command": [
+    "wsl",
+    "-d",
+    "Ubuntu",
+    "--",
+    "bash",
+    "-lc",
+    "cd /home/zhout/project/edge_infer/vllm-omni-edge && PYTHONPATH=. HF_HUB_OFFLINE=1 VLLM_USE_FLASHINFER_SAMPLER=0 VLLM_WSL2_ENABLE_PIN_MEMORY=1 CUDA_HOME=/home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/nvidia/cu13 PATH=/home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/nvidia/cu13/bin:$PATH timeout 1800 ../.venvs/omni-cuda-029/bin/python benchmarks/edge_harness/profile_trace_diagnostic.py --kind spark --model ../models/Spark-X2.5-4B --out ../analysis/experiments/e2e_profiling_20260922/spark-cuda-wsl-trace"
+  ],
+  "cwd": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge",
+  "host": "Windows-11-10.0.26200-SP0",
+  "scope": "Windows RAM includes WSL; GPU counters are whole-device, not model-exclusive",
+  "sample_interval_s": 1,
+  "start_unix": 1790060566.342099,
+  "status": "exited",
+  "power_scheme": "\u9422\u57ab\u7c2e\u93c2\u89c4\ufffd\ufffd GUID: 27fa6203-3987-4dcc-918d-748559d549ec  (Performance)",
+  "pid": 44684,
+  "returncode": 2,
+  "end_unix": 1790060567.440716
+}

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/launcher-failures-1/spark-cuda-wsl-trace-host/telemetry.jsonl

@@ -0,0 +1 @@
+{"unix": 1790060566.3766065, "monotonic": 515298.562, "host_memory": {"total": 67773509632, "available": 1684791296, "percent": 97.5, "used": 66088718336, "free": 1684791296}, "host_cpu_percent": 14.5, "power": {"ACLineStatus": 1, "BatteryFlag": 1, "BatteryLifePercent": 100, "SystemStatusFlag": 0, "BatteryLifeTime": 4294967295, "BatteryFullLifeTime": 4294967295}, "gpu_csv": "GPU-bf6e910e-657c-15d7-c558-f169df134d8a, NVIDIA GeForce RTX 5090 Laptop GPU, 3763, 24463, 53, 17.71, 1702, 810, 0, P5", "gpu_returncode": 0}

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/launcher-failures-1/tts-cuda-windows-host/command.log

@@ -0,0 +1,31 @@
+Traceback (most recent call last):
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\benchmarks\edge_harness\profile_local_tts.py", line 165, in <module>
+    from vllm_omni.windows.aio import install_selector_policy
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\__init__.py", line 19, in <module>
+    from .version import __version__, __version_tuple__  # isort:skip # noqa: F401
+    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\version.py", line 55, in <module>
+    warn_if_misaligned_vllm_version()
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\version.py", line 30, in warn_if_misaligned_vllm_version
+    from vllm import __version__ as vllm_version
+  File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\__init__.py", line 14, in <module>
+    import vllm.env_override  # noqa: F401
+    ^^^^^^^^^^^^^^^^^^^^^^^^
+  File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\env_override.py", line 858, in <module>
+    _patch_inductor_fallback_allow_list()
+  File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\env_override.py", line 835, in _patch_inductor_fallback_allow_list
+    from torch._inductor import lowering as _lowering
+  File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\_inductor\lowering.py", line 9111, in <module>
+    import_submodule(kernel)
+  File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\_dynamo\utils.py", line 4384, in import_submodule
+    importlib.import_module(f"{mod.__name__}.{filename[:-3]}")
+  File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\importlib\__init__.py", line 90, in import_module
+    return _bootstrap._gcd_import(name[level:], package, level)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\_inductor\kernel\mm_grouped.py", line 142, in <module>
+    source=load_kernel_template("cutedsl_mm_grouped"),
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\_inductor\utils.py", line 4770, in load_template
+    return f.read()
+           ^^^^^^^^
+UnicodeDecodeError: 'gbk' codec can't decode byte 0x94 in position 618: illegal multibyte sequence

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/launcher-failures-1/tts-cuda-windows-host/status.json

@@ -0,0 +1,20 @@
+{
+  "command": [
+    "C:\\Users\\zhout\\w2\\omni029venv\\Scripts\\python.exe",
+    "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\benchmarks\\edge_harness\\profile_local_tts.py",
+    "--model",
+    "C:\\Users\\zhout\\w2\\models\\Qwen3-TTS-0.6B-85e237c",
+    "--out",
+    "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\analysis\\experiments\\e2e_profiling_20260922\\tts-cuda-windows"
+  ],
+  "cwd": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge",
+  "host": "Windows-11-10.0.26200-SP0",
+  "scope": "Windows RAM includes WSL; GPU counters are whole-device, not model-exclusive",
+  "sample_interval_s": 1,
+  "start_unix": 1790060182.2059293,
+  "status": "exited",
+  "power_scheme": "\u9422\u57ab\u7c2e\u93c2\u89c4\ufffd\ufffd GUID: 27fa6203-3987-4dcc-918d-748559d549ec  (Performance)",
+  "pid": 62484,
+  "returncode": 1,
+  "end_unix": 1790060189.144959
+}

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/launcher-failures-1/tts-cuda-windows-host/telemetry.jsonl

@@ -0,0 +1,5 @@
+{"unix": 1790060182.2339296, "monotonic": 514914.421, "host_memory": {"total": 67773509632, "available": 26110607360, "percent": 61.5, "used": 41662902272, "free": 26110607360}, "host_cpu_percent": 6.0, "power": {"ACLineStatus": 1, "BatteryFlag": 1, "BatteryLifePercent": 100, "SystemStatusFlag": 0, "BatteryLifeTime": 4294967295, "BatteryFullLifeTime": 4294967295}, "gpu_csv": "GPU-bf6e910e-657c-15d7-c558-f169df134d8a, NVIDIA GeForce RTX 5090 Laptop GPU, 4046, 24463, 54, 18.72, 1590, 810, 0, P5", "gpu_returncode": 0}
+{"unix": 1790060183.287921, "monotonic": 514915.468, "host_memory": {"total": 67773509632, "available": 25606938624, "percent": 62.2, "used": 42166571008, "free": 25606938624}, "host_cpu_percent": 9.3, "power": {"ACLineStatus": 1, "BatteryFlag": 1, "BatteryLifePercent": 100, "SystemStatusFlag": 0, "BatteryLifeTime": 4294967295, "BatteryFullLifeTime": 4294967295}, "gpu_csv": "GPU-bf6e910e-657c-15d7-c558-f169df134d8a, NVIDIA GeForce RTX 5090 Laptop GPU, 4046, 24463, 54, 14.57, 375, 405, 2, P8", "gpu_returncode": 0}
+{"unix": 1790060184.7676566, "monotonic": 514916.953, "host_memory": {"total": 67773509632, "available": 25494081536, "percent": 62.4, "used": 42279428096, "free": 25494081536}, "host_cpu_percent": 10.4, "power": {"ACLineStatus": 1, "BatteryFlag": 1, "BatteryLifePercent": 100, "SystemStatusFlag": 0, "BatteryLifeTime": 4294967295, "BatteryFullLifeTime": 4294967295}, "gpu_csv": "GPU-bf6e910e-657c-15d7-c558-f169df134d8a, NVIDIA GeForce RTX 5090 Laptop GPU, 4046, 24463, 53, 19.30, 1672, 810, 1, P5", "gpu_returncode": 0}
+{"unix": 1790060186.2106762, "monotonic": 514918.39, "host_memory": {"total": 67773509632, "available": 25424269312, "percent": 62.5, "used": 42349240320, "free": 25424269312}, "host_cpu_percent": 9.3, "power": {"ACLineStatus": 1, "BatteryFlag": 1, "BatteryLifePercent": 100, "SystemStatusFlag": 0, "BatteryLifeTime": 4294967295, "BatteryFullLifeTime": 4294967295}, "gpu_csv": "GPU-bf6e910e-657c-15d7-c558-f169df134d8a, NVIDIA GeForce RTX 5090 Laptop GPU, 4046, 24463, 53, 18.50, 1710, 810, 1, P5", "gpu_returncode": 0}
+{"unix": 1790060187.6714928, "monotonic": 514919.859, "host_memory": {"total": 67773509632, "available": 25400971264, "percent": 62.5, "used": 42372538368, "free": 25400971264}, "host_cpu_percent": 8.6, "power": {"ACLineStatus": 1, "BatteryFlag": 1, "BatteryLifePercent": 100, "SystemStatusFlag": 0, "BatteryLifeTime": 4294967295, "BatteryFullLifeTime": 4294967295}, "gpu_csv": "GPU-bf6e910e-657c-15d7-c558-f169df134d8a, NVIDIA GeForce RTX 5090 Laptop GPU, 4046, 24463, 53, 14.87, 997, 405, 1, P8", "gpu_returncode": 0}

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/launcher-failures-1/tts-cuda-windows-trace-host/command.log

@@ -0,0 +1,31 @@
+Traceback (most recent call last):
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\benchmarks\edge_harness\profile_trace_diagnostic.py", line 140, in <module>
+    from vllm_omni.windows.aio import install_selector_policy
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\__init__.py", line 19, in <module>
+    from .version import __version__, __version_tuple__  # isort:skip # noqa: F401
+    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\version.py", line 55, in <module>
+    warn_if_misaligned_vllm_version()
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\version.py", line 30, in warn_if_misaligned_vllm_version
+    from vllm import __version__ as vllm_version
+  File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\__init__.py", line 14, in <module>
+    import vllm.env_override  # noqa: F401
+    ^^^^^^^^^^^^^^^^^^^^^^^^
+  File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\env_override.py", line 858, in <module>
+    _patch_inductor_fallback_allow_list()
+  File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\env_override.py", line 835, in _patch_inductor_fallback_allow_list
+    from torch._inductor import lowering as _lowering
+  File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\_inductor\lowering.py", line 9111, in <module>
+    import_submodule(kernel)
+  File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\_dynamo\utils.py", line 4384, in import_submodule
+    importlib.import_module(f"{mod.__name__}.{filename[:-3]}")
+  File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\importlib\__init__.py", line 90, in import_module
+    return _bootstrap._gcd_import(name[level:], package, level)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\_inductor\kernel\mm_grouped.py", line 142, in <module>
+    source=load_kernel_template("cutedsl_mm_grouped"),
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\_inductor\utils.py", line 4770, in load_template
+    return f.read()
+           ^^^^^^^^
+UnicodeDecodeError: 'gbk' codec can't decode byte 0x94 in position 618: illegal multibyte sequence

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/launcher-failures-1/tts-cuda-windows-trace-host/status.json

@@ -0,0 +1,22 @@
+{
+  "command": [
+    "C:\\Users\\zhout\\w2\\omni029venv\\Scripts\\python.exe",
+    "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\benchmarks\\edge_harness\\profile_trace_diagnostic.py",
+    "--kind",
+    "tts",
+    "--model",
+    "C:\\Users\\zhout\\w2\\models\\Qwen3-TTS-0.6B-85e237c",
+    "--out",
+    "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\analysis\\experiments\\e2e_profiling_20260922\\tts-cuda-windows-trace"
+  ],
+  "cwd": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge",
+  "host": "Windows-11-10.0.26200-SP0",
+  "scope": "Windows RAM includes WSL; GPU counters are whole-device, not model-exclusive",
+  "sample_interval_s": 1,
+  "start_unix": 1790060580.8961785,
+  "status": "exited",
+  "power_scheme": "\u9422\u57ab\u7c2e\u93c2\u89c4\ufffd\ufffd GUID: 27fa6203-3987-4dcc-918d-748559d549ec  (Performance)",
+  "pid": 13956,
+  "returncode": 1,
+  "end_unix": 1790060587.8268032
+}

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/launcher-failures-1/tts-cuda-windows-trace-host/telemetry.jsonl

@@ -0,0 +1,5 @@
+{"unix": 1790060580.9251828, "monotonic": 515313.109, "host_memory": {"total": 67773509632, "available": 14605930496, "percent": 78.4, "used": 53167579136, "free": 14605930496}, "host_cpu_percent": 13.3, "power": {"ACLineStatus": 1, "BatteryFlag": 1, "BatteryLifePercent": 100, "SystemStatusFlag": 0, "BatteryLifeTime": 4294967295, "BatteryFullLifeTime": 4294967295}, "gpu_csv": "GPU-bf6e910e-657c-15d7-c558-f169df134d8a, NVIDIA GeForce RTX 5090 Laptop GPU, 3786, 24463, 52, 11.48, 180, 405, 1, P8", "gpu_returncode": 0}
+{"unix": 1790060581.9816196, "monotonic": 515314.156, "host_memory": {"total": 67773509632, "available": 14227529728, "percent": 79.0, "used": 53545979904, "free": 14227529728}, "host_cpu_percent": 19.2, "power": {"ACLineStatus": 1, "BatteryFlag": 1, "BatteryLifePercent": 100, "SystemStatusFlag": 0, "BatteryLifeTime": 4294967295, "BatteryFullLifeTime": 4294967295}, "gpu_csv": "GPU-bf6e910e-657c-15d7-c558-f169df134d8a, NVIDIA GeForce RTX 5090 Laptop GPU, 3782, 24463, 53, 19.22, 1327, 810, 1, P5", "gpu_returncode": 0}
+{"unix": 1790060583.4301763, "monotonic": 515315.609, "host_memory": {"total": 67773509632, "available": 14376071168, "percent": 78.8, "used": 53397438464, "free": 14376071168}, "host_cpu_percent": 17.3, "power": {"ACLineStatus": 1, "BatteryFlag": 1, "BatteryLifePercent": 100, "SystemStatusFlag": 0, "BatteryLifeTime": 4294967295, "BatteryFullLifeTime": 4294967295}, "gpu_csv": "GPU-bf6e910e-657c-15d7-c558-f169df134d8a, NVIDIA GeForce RTX 5090 Laptop GPU, 3782, 24463, 53, 17.97, 1717, 810, 1, P5", "gpu_returncode": 0}
+{"unix": 1790060584.9211826, "monotonic": 515317.109, "host_memory": {"total": 67773509632, "available": 15048978432, "percent": 77.8, "used": 52724531200, "free": 15048978432}, "host_cpu_percent": 18.8, "power": {"ACLineStatus": 1, "BatteryFlag": 1, "BatteryLifePercent": 100, "SystemStatusFlag": 0, "BatteryLifeTime": 4294967295, "BatteryFullLifeTime": 4294967295}, "gpu_csv": "GPU-bf6e910e-657c-15d7-c558-f169df134d8a, NVIDIA GeForce RTX 5090 Laptop GPU, 3782, 24463, 53, 17.60, 1725, 810, 1, P5", "gpu_returncode": 0}
+{"unix": 1790060586.369542, "monotonic": 515318.546, "host_memory": {"total": 67773509632, "available": 15909814272, "percent": 76.5, "used": 51863695360, "free": 15909814272}, "host_cpu_percent": 24.0, "power": {"ACLineStatus": 1, "BatteryFlag": 1, "BatteryLifePercent": 100, "SystemStatusFlag": 0, "BatteryLifeTime": 4294967295, "BatteryFullLifeTime": 4294967295}, "gpu_csv": "GPU-bf6e910e-657c-15d7-c558-f169df134d8a, NVIDIA GeForce RTX 5090 Laptop GPU, 3782, 24463, 52, 13.39, 465, 405, 0, P8", "gpu_returncode": 0}

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/launcher-failures-1/tts-cuda-wsl-host/command.log

@@ -0,0 +1,2 @@
+bash: -c: line 1: syntax error near unexpected token `('
+bash: -c: line 1: `cd /home/zhout/project/edge_infer/vllm-omni-edge && PYTHONPATH=. HF_HUB_OFFLINE=1 VLLM_USE_FLASHINFER_SAMPLER=0 VLLM_WSL2_ENABLE_PIN_MEMORY=1 CUDA_HOME=/home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/nvidia/cu13 PATH=/home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/nvidia/cu13/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/usr/lib/wsl/lib:/mnt/c/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v13.4/bin:/mnt/c/Users/zhout/.cache/codex-runtimes/codex-primary-runtime/dependencies/native/powershell:/mnt/c/Users/zhout/.codex/tmp/arg0/codex-arg07mHRat:/mnt/c/Users/zhout/.cache/codex-runtimes/codex-primary-runtime/dependencies/native/libheif/libheif/bin:/mnt/c/Users/zhout/.cache/codex-runtimes/codex-primary-runtime/dependencies/native/jxrlib/jxrlib/bin:/mnt/c/Users/zhout/.cache/codex-runtimes/codex-primary-runtime/dependencies/native/poppler/Library/bin:/mnt/c/Users/zhout/.cache/codex-runtimes/codex-primary-runtime/dependencies/bin/override:/mnt/c/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v13.4/bin/x64:/mnt/c/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v13.4/bin:/mnt/c/Windows/system32:/mnt/c/Windows:/mnt/c/Windows/System32/Wbem:/mnt/c/Windows/System32/WindowsPowerShell/v1.0/:/mnt/c/Windows/System32/OpenSSH/:/mnt/c/Program Files (x86)/NVIDIA Corporation/PhysX/Common:/mnt/c/Program Files/NVIDIA Corporation/NVIDIA App/NvDLISR:/mnt/c/Windows/System32/WindowsPowerShell/v1.0:/mnt/c/Program Files/Git/cmd:/mnt/c/Program Files/NVIDIA Corporation/Nsight Compute 2026.3.0/:/mnt/c/Users/zhout/.cargo/bin:/mnt/c/Users/zhout/AppData/Local/Programs/Python/Python312/Scripts/:/mnt/c/Users/zhout/AppData/Local/Programs/Python/Python312/:/mnt/c/Users/zhout/AppData/Local/Programs/Python/Launcher/:/mnt/c/Users/zhout/AppData/Local/Programs/cursor/resources/app/codeBin:/mnt/c/Users/zhout/.cache/codex-runtimes/codex-primary-runtime/dependencies/native/powershell:/mnt/c/Users/zhout/AppData/Local/Microsoft/WindowsApps:/mnt/c/Users/zhout/AppData/Local/Programs/cursor/resources/app/bin:/mnt/c/Users/zhout/.cache/codex-runtimes/codex-primary-runtime/dependencies/bin/fallback:/mnt/c/Users/zhout/.cache/codex-runtimes/codex-primary-runtime/dependencies/node/bin:/mnt/c/Users/zhout/.cache/codex-runtimes/codex-primary-runtime/dependencies/native/git/cmd:/mnt/c/Users/zhout/AppData/Local/OpenAI/Codex/bin/73fd6465d9c76545:/mnt/c/Users/zhout/AppData/Local/OpenAI/Codex/bin/247581e40ee272fb timeout 10800 ../.venvs/omni-cuda-029/bin/python benchmarks/edge_harness/profile_local_tts.py --model /home/zhout/.cache/huggingface/hub/models--Qwen--Qwen3-TTS-12Hz-0.6B-CustomVoice/snapshots/85e237c12c027371202489a0ec509ded67b5e4b5 --out ../analysis/experiments/e2e_profiling_20260922/tts-cuda-wsl'

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/launcher-failures-1/tts-cuda-wsl-host/status.json

@@ -0,0 +1,21 @@
+{
+  "command": [
+    "wsl",
+    "-d",
+    "Ubuntu",
+    "--",
+    "bash",
+    "-lc",
+    "cd /home/zhout/project/edge_infer/vllm-omni-edge && PYTHONPATH=. HF_HUB_OFFLINE=1 VLLM_USE_FLASHINFER_SAMPLER=0 VLLM_WSL2_ENABLE_PIN_MEMORY=1 CUDA_HOME=/home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/nvidia/cu13 PATH=/home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/nvidia/cu13/bin:$PATH timeout 10800 ../.venvs/omni-cuda-029/bin/python benchmarks/edge_harness/profile_local_tts.py --model /home/zhout/.cache/huggingface/hub/models--Qwen--Qwen3-TTS-12Hz-0.6B-CustomVoice/snapshots/85e237c12c027371202489a0ec509ded67b5e4b5 --out ../analysis/experiments/e2e_profiling_20260922/tts-cuda-wsl"
+  ],
+  "cwd": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge",
+  "host": "Windows-11-10.0.26200-SP0",
+  "scope": "Windows RAM includes WSL; GPU counters are whole-device, not model-exclusive",
+  "sample_interval_s": 1,
+  "start_unix": 1790060180.7591414,
+  "status": "exited",
+  "power_scheme": "\u9422\u57ab\u7c2e\u93c2\u89c4\ufffd\ufffd GUID: 27fa6203-3987-4dcc-918d-748559d549ec  (Performance)",
+  "pid": 57324,
+  "returncode": 2,
+  "end_unix": 1790060181.8515525
+}

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/launcher-failures-1/tts-cuda-wsl-host/telemetry.jsonl

@@ -0,0 +1 @@
+{"unix": 1790060180.7881372, "monotonic": 514912.968, "host_memory": {"total": 67773509632, "available": 26121744384, "percent": 61.5, "used": 41651765248, "free": 26121744384}, "host_cpu_percent": 10.4, "power": {"ACLineStatus": 1, "BatteryFlag": 1, "BatteryLifePercent": 100, "SystemStatusFlag": 0, "BatteryLifeTime": 4294967295, "BatteryFullLifeTime": 4294967295}, "gpu_csv": "GPU-bf6e910e-657c-15d7-c558-f169df134d8a, NVIDIA GeForce RTX 5090 Laptop GPU, 4046, 24463, 54, 18.61, 1605, 810, 0, P5", "gpu_returncode": 0}

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/launcher-failures-1/tts-cuda-wsl-trace-host/command.log

@@ -0,0 +1,2 @@
+bash: -c: line 1: syntax error near unexpected token `('
+bash: -c: line 1: `cd /home/zhout/project/edge_infer/vllm-omni-edge && PYTHONPATH=. HF_HUB_OFFLINE=1 VLLM_USE_FLASHINFER_SAMPLER=0 VLLM_WSL2_ENABLE_PIN_MEMORY=1 CUDA_HOME=/home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/nvidia/cu13 PATH=/home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/nvidia/cu13/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/usr/lib/wsl/lib:/mnt/c/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v13.4/bin:/mnt/c/Users/zhout/.cache/codex-runtimes/codex-primary-runtime/dependencies/native/powershell:/mnt/c/Users/zhout/.codex/tmp/arg0/codex-arg07mHRat:/mnt/c/Users/zhout/.cache/codex-runtimes/codex-primary-runtime/dependencies/native/libheif/libheif/bin:/mnt/c/Users/zhout/.cache/codex-runtimes/codex-primary-runtime/dependencies/native/jxrlib/jxrlib/bin:/mnt/c/Users/zhout/.cache/codex-runtimes/codex-primary-runtime/dependencies/native/poppler/Library/bin:/mnt/c/Users/zhout/.cache/codex-runtimes/codex-primary-runtime/dependencies/bin/override:/mnt/c/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v13.4/bin/x64:/mnt/c/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v13.4/bin:/mnt/c/Windows/system32:/mnt/c/Windows:/mnt/c/Windows/System32/Wbem:/mnt/c/Windows/System32/WindowsPowerShell/v1.0/:/mnt/c/Windows/System32/OpenSSH/:/mnt/c/Program Files (x86)/NVIDIA Corporation/PhysX/Common:/mnt/c/Program Files/NVIDIA Corporation/NVIDIA App/NvDLISR:/mnt/c/Windows/System32/WindowsPowerShell/v1.0:/mnt/c/Program Files/Git/cmd:/mnt/c/Program Files/NVIDIA Corporation/Nsight Compute 2026.3.0/:/mnt/c/Users/zhout/.cargo/bin:/mnt/c/Users/zhout/AppData/Local/Programs/Python/Python312/Scripts/:/mnt/c/Users/zhout/AppData/Local/Programs/Python/Python312/:/mnt/c/Users/zhout/AppData/Local/Programs/Python/Launcher/:/mnt/c/Users/zhout/AppData/Local/Programs/cursor/resources/app/codeBin:/mnt/c/Users/zhout/.cache/codex-runtimes/codex-primary-runtime/dependencies/native/powershell:/mnt/c/Users/zhout/AppData/Local/Microsoft/WindowsApps:/mnt/c/Users/zhout/AppData/Local/Programs/cursor/resources/app/bin:/mnt/c/Users/zhout/.cache/codex-runtimes/codex-primary-runtime/dependencies/bin/fallback:/mnt/c/Users/zhout/.cache/codex-runtimes/codex-primary-runtime/dependencies/node/bin:/mnt/c/Users/zhout/.cache/codex-runtimes/codex-primary-runtime/dependencies/native/git/cmd:/mnt/c/Users/zhout/AppData/Local/OpenAI/Codex/bin/73fd6465d9c76545:/mnt/c/Users/zhout/AppData/Local/OpenAI/Codex/bin/247581e40ee272fb timeout 1800 ../.venvs/omni-cuda-029/bin/python benchmarks/edge_harness/profile_trace_diagnostic.py --kind tts --model /home/zhout/.cache/huggingface/hub/models--Qwen--Qwen3-TTS-12Hz-0.6B-CustomVoice/snapshots/85e237c12c027371202489a0ec509ded67b5e4b5 --out ../analysis/experiments/e2e_profiling_20260922/tts-cuda-wsl-trace'

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/launcher-failures-1/tts-cuda-wsl-trace-host/status.json

@@ -0,0 +1,21 @@
+{
+  "command": [
+    "wsl",
+    "-d",
+    "Ubuntu",
+    "--",
+    "bash",
+    "-lc",
+    "cd /home/zhout/project/edge_infer/vllm-omni-edge && PYTHONPATH=. HF_HUB_OFFLINE=1 VLLM_USE_FLASHINFER_SAMPLER=0 VLLM_WSL2_ENABLE_PIN_MEMORY=1 CUDA_HOME=/home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/nvidia/cu13 PATH=/home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/nvidia/cu13/bin:$PATH timeout 1800 ../.venvs/omni-cuda-029/bin/python benchmarks/edge_harness/profile_trace_diagnostic.py --kind tts --model /home/zhout/.cache/huggingface/hub/models--Qwen--Qwen3-TTS-12Hz-0.6B-CustomVoice/snapshots/85e237c12c027371202489a0ec509ded67b5e4b5 --out ../analysis/experiments/e2e_profiling_20260922/tts-cuda-wsl-trace"
+  ],
+  "cwd": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge",
+  "host": "Windows-11-10.0.26200-SP0",
+  "scope": "Windows RAM includes WSL; GPU counters are whole-device, not model-exclusive",
+  "sample_interval_s": 1,
+  "start_unix": 1790060579.427969,
+  "status": "exited",
+  "power_scheme": "\u9422\u57ab\u7c2e\u93c2\u89c4\ufffd\ufffd GUID: 27fa6203-3987-4dcc-918d-748559d549ec  (Performance)",
+  "pid": 24940,
+  "returncode": 2,
+  "end_unix": 1790060580.5256815
+}

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/launcher-failures-1/tts-cuda-wsl-trace-host/telemetry.jsonl

@@ -0,0 +1 @@
+{"unix": 1790060579.4559724, "monotonic": 515311.64, "host_memory": {"total": 67773509632, "available": 13956501504, "percent": 79.4, "used": 53817008128, "free": 13956501504}, "host_cpu_percent": 18.0, "power": {"ACLineStatus": 1, "BatteryFlag": 1, "BatteryLifePercent": 100, "SystemStatusFlag": 0, "BatteryLifeTime": 4294967295, "BatteryFullLifeTime": 4294967295}, "gpu_csv": "GPU-bf6e910e-657c-15d7-c558-f169df134d8a, NVIDIA GeForce RTX 5090 Laptop GPU, 3770, 24463, 53, 17.26, 1590, 810, 0, P5", "gpu_returncode": 0}

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/launcher-failures-2/queue.json

@@ -0,0 +1,78 @@
+{
+  "status": "exited",
+  "pid": 52656,
+  "start": 1790060589.375201,
+  "jobs": [
+    {
+      "name": "spark-cuda-wsl",
+      "command": [
+        "wsl",
+        "-d",
+        "Ubuntu",
+        "--",
+        "bash",
+        "-lc",
+        "cd /home/zhout/project/edge_infer/vllm-omni-edge && PYTHONPATH=. HF_HUB_OFFLINE=1 VLLM_USE_FLASHINFER_SAMPLER=0 VLLM_WSL2_ENABLE_PIN_MEMORY=1 CUDA_HOME=/home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/nvidia/cu13 PATH=/home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/nvidia/cu13/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/lib/wsl/lib timeout 10800 ../.venvs/omni-cuda-029/bin/python benchmarks/edge_harness/profile_local_text.py --model ../models/Spark-X2.5-4B --out ../analysis/experiments/e2e_profiling_20260922/spark-cuda-wsl"
+      ],
+      "status": "exited",
+      "start": 1790060589.378202,
+      "returncode": 0,
+      "end": 1790062795.1474967,
+      "benchmark_status": "completed",
+      "error": null
+    },
+    {
+      "name": "spark-cuda-windows",
+      "command": [
+        "C:\\Users\\zhout\\w2\\omni029venv\\Scripts\\python.exe",
+        "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\benchmarks\\edge_harness\\profile_local_text.py",
+        "--model",
+        "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\Spark-X2.5-4B",
+        "--out",
+        "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\analysis\\experiments\\e2e_profiling_20260922\\spark-cuda-windows"
+      ],
+      "status": "exited",
+      "start": 1790062795.152503,
+      "returncode": 1,
+      "end": 1790062812.6604095,
+      "benchmark_status": "missing_report",
+      "error": null
+    },
+    {
+      "name": "tts-cuda-wsl",
+      "command": [
+        "wsl",
+        "-d",
+        "Ubuntu",
+        "--",
+        "bash",
+        "-lc",
+        "cd /home/zhout/project/edge_infer/vllm-omni-edge && PYTHONPATH=. HF_HUB_OFFLINE=1 VLLM_USE_FLASHINFER_SAMPLER=0 VLLM_WSL2_ENABLE_PIN_MEMORY=1 CUDA_HOME=/home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/nvidia/cu13 PATH=/home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/nvidia/cu13/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/lib/wsl/lib timeout 10800 ../.venvs/omni-cuda-029/bin/python benchmarks/edge_harness/profile_local_tts.py --model /home/zhout/.cache/huggingface/hub/models--Qwen--Qwen3-TTS-12Hz-0.6B-CustomVoice/snapshots/85e237c12c027371202489a0ec509ded67b5e4b5 --out ../analysis/experiments/e2e_profiling_20260922/tts-cuda-wsl"
+      ],
+      "status": "exited",
+      "start": 1790062812.6633985,
+      "returncode": 0,
+      "end": 1790065331.9386182,
+      "benchmark_status": "completed",
+      "error": null
+    },
+    {
+      "name": "tts-cuda-windows",
+      "command": [
+        "C:\\Users\\zhout\\w2\\omni029venv\\Scripts\\python.exe",
+        "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\benchmarks\\edge_harness\\profile_local_tts.py",
+        "--model",
+        "C:\\Users\\zhout\\w2\\models\\Qwen3-TTS-0.6B-85e237c",
+        "--out",
+        "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\analysis\\experiments\\e2e_profiling_20260922\\tts-cuda-windows"
+      ],
+      "status": "exited",
+      "start": 1790065331.9426181,
+      "returncode": 0,
+      "end": 1790065913.1483374,
+      "benchmark_status": "failed",
+      "error": "PermissionError(13, '\u62d2\u7edd\u8bbf\u95ee\u3002')"
+    }
+  ],
+  "end": 1790065913.152338
+}

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/launcher-failures-2/spark-cuda-windows-host/command.log

@@ -0,0 +1,19 @@
+INFO 09-22 15:40:10 [patch.py:259] NVFP4 W4A4 weight_scale NaN-clamp: installed.
+INFO 09-22 15:40:10 [patch.py:510] inductor factorable-divisibility patch: installed.
+Traceback (most recent call last):
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\benchmarks\edge_harness\profile_local_text.py", line 132, in <module>
+    from vllm_omni.windows.aio import install_selector_policy
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\__init__.py", line 22, in <module>
+    from . import patch  # noqa: F401
+    ^^^^^^^^^^^^^^^^^^^
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\patch.py", line 588, in <module>
+    _patch_cumem_free_callback_cuda()
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\patch.py", line 548, in _patch_cumem_free_callback_cuda
+    from vllm.device_allocator.cumem import CuMemAllocator
+  File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\device_allocator\cumem.py", line 40, in <module>
+    libcudart = CudaRTLibrary()
+                ^^^^^^^^^^^^^^^
+  File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\distributed\device_communicators\cuda_wrapper.py", line 113, in __init__
+    assert so_file is not None, (
+           ^^^^^^^^^^^^^^^^^^^
+AssertionError: libcudart is not loaded in the current process, try setting VLLM_CUDART_SO_PATH

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/launcher-failures-2/spark-cuda-windows-host/status.json

@@ -0,0 +1,20 @@
+{
+  "command": [
+    "C:\\Users\\zhout\\w2\\omni029venv\\Scripts\\python.exe",
+    "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\benchmarks\\edge_harness\\profile_local_text.py",
+    "--model",
+    "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\Spark-X2.5-4B",
+    "--out",
+    "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\analysis\\experiments\\e2e_profiling_20260922\\spark-cuda-windows"
+  ],
+  "cwd": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge",
+  "host": "Windows-11-10.0.26200-SP0",
+  "scope": "Windows RAM includes WSL; GPU counters are whole-device, not model-exclusive",
+  "sample_interval_s": 1,
+  "start_unix": 1790062795.5994885,
+  "status": "exited",
+  "power_scheme": "\u7535\u6e90\u65b9\u6848 GUID: 27fa6203-3987-4dcc-918d-748559d549ec  (Performance)",
+  "pid": 41696,
+  "returncode": 1,
+  "end_unix": 1790062812.6363986
+}

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/launcher-failures-2/spark-cuda-windows-host/telemetry.jsonl

@@ -0,0 +1,16 @@
+{"unix": 1790062795.6344872, "monotonic": 517527.812, "host_memory": {"total": 67773509632, "available": 16295677952, "percent": 76.0, "used": 51477831680, "free": 16295677952}, "host_cpu_percent": 19.8, "power": {"ACLineStatus": 1, "BatteryFlag": 1, "BatteryLifePercent": 100, "SystemStatusFlag": 0, "BatteryLifeTime": 4294967295, "BatteryFullLifeTime": 4294967295}, "gpu_csv": "GPU-bf6e910e-657c-15d7-c558-f169df134d8a, NVIDIA GeForce RTX 5090 Laptop GPU, 3748, 24463, 59, 26.99, 690, 405, 16, P8", "gpu_returncode": 0}
+{"unix": 1790062796.697404, "monotonic": 517528.875, "host_memory": {"total": 67773509632, "available": 15751299072, "percent": 76.8, "used": 52022210560, "free": 15751299072}, "host_cpu_percent": 24.8, "power": {"ACLineStatus": 1, "BatteryFlag": 1, "BatteryLifePercent": 100, "SystemStatusFlag": 0, "BatteryLifeTime": 4294967295, "BatteryFullLifeTime": 4294967295}, "gpu_csv": "GPU-bf6e910e-657c-15d7-c558-f169df134d8a, NVIDIA GeForce RTX 5090 Laptop GPU, 4024, 24463, 60, 29.87, 1995, 9001, 10, P4", "gpu_returncode": 0}
+{"unix": 1790062797.7641768, "monotonic": 517529.953, "host_memory": {"total": 67773509632, "available": 15692275712, "percent": 76.8, "used": 52081233920, "free": 15692275712}, "host_cpu_percent": 15.9, "power": {"ACLineStatus": 1, "BatteryFlag": 1, "BatteryLifePercent": 100, "SystemStatusFlag": 0, "BatteryLifeTime": 4294967295, "BatteryFullLifeTime": 4294967295}, "gpu_csv": "GPU-bf6e910e-657c-15d7-c558-f169df134d8a, NVIDIA GeForce RTX 5090 Laptop GPU, 4018, 24463, 59, 25.65, 1852, 9001, 3, P4", "gpu_returncode": 0}
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benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/preflight-windows.json

@@ -0,0 +1,194 @@
+{
+  "timestamp": 1790057418.269803,
+  "python": "C:\\Users\\zhout\\w2\\omni029venv\\Scripts\\python.exe",
+  "platform": "Windows-11-10.0.26200-SP0",
+  "packages": {
+    "vllm": "0.29.0+cu134",
+    "torch": "2.13.0+cu130",
+    "vllm-omni": "0.29.0",
+    "transformers": "5.14.1",
+    "qai-hub": null,
+    "onnxruntime": null
+  },
+  "roots": [
+    {
+      "path": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models",
+      "exists": true
+    },
+    {
+      "path": "C:\\Users\\zhout\\.cache\\huggingface\\hub",
+      "exists": true
+    },
+    {
+      "path": "C:\\Users\\zhout\\w2\\models",
+      "exists": true
+    }
+  ],
+  "artifacts": [
+    {
+      "path": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\Qwen3.8-27B-FP8",
+      "model_type": "qwen3_5",
+      "weight_shards": 66,
+      "missing_shards": [],
+      "weight_bytes": 30866866928
+    },
+    {
+      "path": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\spark2_5-gptq-g128",
+      "model_type": "spark2_5",
+      "weight_shards": 1,
+      "missing_shards": [],
+      "weight_bytes": 1287662032
+    },
+    {
+      "path": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\spark2_5-int4-g32",
+      "model_type": "spark2_5",
+      "weight_shards": 1,
+      "missing_shards": [],
+      "weight_bytes": 1068136688
+    },
+    {
+      "path": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\Spark-X2.5-1.7B-int8",
+      "model_type": "spark2_5",
+      "weight_shards": 1,
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+    },
+    {
+      "path": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\spark2_5-int4-g64-rtn",
+      "model_type": "spark2_5",
+      "weight_shards": 1,
+      "missing_shards": [],
+      "weight_bytes": 961443656
+    },
+    {
+      "path": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\Qwen3.8-27B-NVFP4",
+      "model_type": "qwen3_5",
+      "weight_shards": 3,
+      "missing_shards": [],
+      "weight_bytes": 21921697280
+    },
+    {
+      "path": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\spark2_5-int4-g64",
+      "model_type": "spark2_5",
+      "weight_shards": 1,
+      "missing_shards": [],
+      "weight_bytes": 961443656
+    },
+    {
+      "path": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\spark2_5-int4-g32-expanded",
+      "model_type": "spark2_5",
+      "weight_shards": 1,
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+    },
+    {
+      "path": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\spark2_5-int4-g128-nodequant",
+      "model_type": "spark2_5",
+      "weight_shards": 1,
+      "missing_shards": [
+        "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\spark2_5-int4-g128-nodequant\\model.safetensors"
+      ],
+      "weight_bytes": 0
+    },
+    {
+      "path": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\spark2_5-int4-g32-rtn",
+      "model_type": "spark2_5",
+      "weight_shards": 1,
+      "missing_shards": [],
+      "weight_bytes": 1068136688
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+      "weight_shards": 1,
+      "missing_shards": [],
+      "weight_bytes": 1347320208
+    },
+    {
+      "path": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\spark2_5-int4-g64-nocustomop",
+      "model_type": "spark2_5",
+      "weight_shards": 1,
+      "missing_shards": [
+        "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\spark2_5-int4-g64-nocustomop\\model.safetensors"
+      ],
+      "weight_bytes": 0
+    },
+    {
+      "path": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\spark2_5-int4-g64-expanded",
+      "model_type": "spark2_5",
+      "weight_shards": 1,
+      "missing_shards": [],
+      "weight_bytes": 3415340632
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+    {
+      "path": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\spark2_5-gptq-asym-g128",
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+      "weight_shards": 1,
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+    {
+      "path": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\spark2_5-gptq-asym-g64",
+      "model_type": "spark2_5",
+      "weight_shards": 1,
+      "missing_shards": [],
+      "weight_bytes": 1460472840
+    },
+    {
+      "path": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\spark2_5-gptq-g128-head",
+      "model_type": "spark2_5",
+      "weight_shards": 1,
+      "missing_shards": [],
+      "weight_bytes": 1427131536
+    },
+    {
+      "path": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\MiniCPM5-2B",
+      "model_type": "llama",
+      "weight_shards": 1,
+      "missing_shards": [],
+      "weight_bytes": 5033557096
+    },
+    {
+      "path": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\spark2_5-int4-g128",
+      "model_type": "spark2_5",
+      "weight_shards": 1,
+      "missing_shards": [],
+      "weight_bytes": 908097200
+    },
+    {
+      "path": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\spark2_5-gptq-g64",
+      "model_type": "spark2_5",
+      "weight_shards": 1,
+      "missing_shards": [],
+      "weight_bytes": 1460473032
+    },
+    {
+      "path": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\Spark-X2.5-4B",
+      "model_type": "spark2_5",
+      "weight_shards": 5,
+      "missing_shards": [],
+      "weight_bytes": 8224192408
+    },
+    {
+      "path": "C:\\Users\\zhout\\w2\\models\\Qwen3-TTS-0.6B-85e237c",
+      "model_type": "qwen3_tts",
+      "weight_shards": 1,
+      "missing_shards": [],
+      "weight_bytes": 1811626576
+    }
+  ],
+  "source_sha256": {
+    "profile_local_text.py": "33df55fea28437c5cc79e1d3a8017374b3bbbeb91afc1cb572568b1facb62e95",
+    "profile_local_tts.py": "89b30bb6f7e123383a7e2bec1e423cee67dec00019eeec04479819adc841e70e",
+    "profile_host_command.py": "6e0636470c29402aaccc9b0600cdbbbf3340ae97b30a0bc2795b89d9ebf602c7",
+    "profile_cpu_step.py": "818cb9409a0fc308cf5a08b6794da4b1dfee16cb45c48d6b84de5c130a572b59"
+  },
+  "git": {
+    "returncode": 128,
+    "stdout": "",
+    "stderr": "fatal: detected dubious ownership in repository at '//wsl.localhost/Ubuntu/home/zhout/project/edge_infer/vllm-omni-edge'\n'//wsl.localhost/Ubuntu/home/zhout/project/edge_infer/vllm-omni-edge/.git' may refer to a non-local directory\nTo add an exception for this directory, call:\n\n\tgit config --global --add safe.directory '%(prefix)///wsl.localhost/Ubuntu/home/zhout/project/edge_infer/vllm-omni-edge'\n"
+  },
+  "adb": {
+    "error": "FileNotFoundError(2, '\u7cfb\u7edf\u627e\u4e0d\u5230\u6307\u5b9a\u7684\u6587\u4ef6\u3002', None, 2, None)"
+  }
+}

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/preflight-wsl-cpu.json

@@ -0,0 +1,210 @@
+{
+  "timestamp": 1790057420.958391,
+  "python": "/home/zhout/project/edge_infer/.venvs/omni-cpu/bin/python",
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+    "onnxruntime": "1.29.0"
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+  "roots": [
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+      "path": "/home/zhout/project/edge_infer/models",
+      "exists": true
+    },
+    {
+      "path": "/home/zhout/.cache/huggingface/hub",
+      "exists": true
+    },
+    {
+      "path": "/mnt/c/Users/zhout/w2/models",
+      "exists": true
+    }
+  ],
+  "artifacts": [
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+    },
+    {
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+    {
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+    },
+    {
+      "path": "/home/zhout/project/edge_infer/models/spark2_5-gptq-g64",
+      "model_type": "spark2_5",
+      "weight_shards": 1,
+      "missing_shards": [],
+      "weight_bytes": 1460473032
+    },
+    {
+      "path": "/home/zhout/project/edge_infer/models/Spark-X2.5-4B",
+      "model_type": "spark2_5",
+      "weight_shards": 5,
+      "missing_shards": [],
+      "weight_bytes": 8224192408
+    },
+    {
+      "path": "/home/zhout/.cache/huggingface/hub/models--Qwen--Qwen3.8-27B-FP8/snapshots/017b9c7af6b5689d5dd426a76e0bc077eb5ca20a",
+      "model_type": "qwen3_5",
+      "weight_shards": 0,
+      "missing_shards": [],
+      "weight_bytes": 0
+    },
+    {
+      "path": "/home/zhout/.cache/huggingface/hub/models--Qwen--Qwen3-TTS-12Hz-0.6B-CustomVoice/snapshots/85e237c12c027371202489a0ec509ded67b5e4b5",
+      "model_type": "qwen3_tts",
+      "weight_shards": 1,
+      "missing_shards": [],
+      "weight_bytes": 1811626576
+    },
+    {
+      "path": "/mnt/c/Users/zhout/w2/models/Qwen3-TTS-0.6B-85e237c",
+      "model_type": "qwen3_tts",
+      "weight_shards": 1,
+      "missing_shards": [],
+      "weight_bytes": 1811626576
+    }
+  ],
+  "source_sha256": {
+    "profile_local_text.py": "33df55fea28437c5cc79e1d3a8017374b3bbbeb91afc1cb572568b1facb62e95",
+    "profile_local_tts.py": "89b30bb6f7e123383a7e2bec1e423cee67dec00019eeec04479819adc841e70e",
+    "profile_host_command.py": "6e0636470c29402aaccc9b0600cdbbbf3340ae97b30a0bc2795b89d9ebf602c7",
+    "profile_cpu_step.py": "818cb9409a0fc308cf5a08b6794da4b1dfee16cb45c48d6b84de5c130a572b59"
+  },
+  "git": {
+    "returncode": 0,
+    "stdout": "ab2f2a3d658db5be5ec96a950da55cb74b48e7aa\n",
+    "stderr": ""
+  },
+  "adb": {
+    "returncode": 0,
+    "stdout": "List of devices attached\n\n",
+    "stderr": ""
+  }
+}

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/preflight.py

@@ -0,0 +1,48 @@
+"""Read-only current inventory without loading model runtimes during a benchmark."""
+import argparse
+import hashlib
+import importlib.metadata
+import json
+import platform
+import subprocess
+import sys
+import time
+from pathlib import Path
+
+p = argparse.ArgumentParser()
+p.add_argument('--root', required=True, type=Path)
+p.add_argument('--out', required=True, type=Path)
+a = p.parse_args()
+r = {'timestamp': time.time(), 'python': sys.executable, 'platform': platform.platform(),
+     'packages': {}, 'roots': [], 'artifacts': [], 'source_sha256': {}}
+for name in ('vllm', 'torch', 'vllm-omni', 'transformers', 'qai-hub', 'onnxruntime'):
+    try:
+        r['packages'][name] = importlib.metadata.version(name)
+    except importlib.metadata.PackageNotFoundError:
+        r['packages'][name] = None
+roots = [a.root/'models', Path.home()/'.cache/huggingface/hub',
+         Path('C:/Users/zhout/w2/models') if sys.platform == 'win32' else Path('/mnt/c/Users/zhout/w2/models')]
+for root in roots:
+    r['roots'].append({'path': str(root), 'exists': root.is_dir()})
+    if not root.is_dir():
+        continue
+    for item in root.iterdir():
+        if not any(s in item.name.lower() for s in ('spark', 'qwen3', 'minicpm', 'internvla')):
+            continue
+        for cfg in list(item.glob('config.json')) + list(item.glob('snapshots/*/config.json')):
+            data = json.loads(cfg.read_text(encoding='utf-8'))
+            weights = list(cfg.parent.glob('*.safetensors'))
+            r['artifacts'].append({'path': str(cfg.parent), 'model_type': data.get('model_type'),
+                                   'weight_shards': len(weights),
+                                   'missing_shards': [str(w) for w in weights if not w.is_file()],
+                                   'weight_bytes': sum(w.stat().st_size for w in weights if w.is_file())})
+for path in (a.root/'vllm-omni-edge/benchmarks/edge_harness').glob('profile_*.py'):
+    r['source_sha256'][path.name] = hashlib.sha256(path.read_bytes()).hexdigest()
+for cmd in (['git', '-C', str(a.root/'vllm-omni-edge'), 'rev-parse', 'HEAD'], ['adb', 'devices', '-l']):
+    try:
+        result = subprocess.run(cmd, capture_output=True, text=True, errors='replace', timeout=10)
+        r[cmd[0]] = {'returncode': result.returncode, 'stdout': result.stdout, 'stderr': result.stderr}
+    except Exception as error:
+        r[cmd[0]] = {'error': repr(error)}
+a.out.write_text(json.dumps(r, indent=2))
+print(f'{len(r["artifacts"])} artifact configurations inventoried: {a.out}')

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/prior-evidence/README.md

@@ -0,0 +1,241 @@
+# Omni abstraction implementation and qualification — 2026-09-22
+
+Implementation: `vllm-omni-edge`, branch `codex/omni-edge-abstraction`, based on
+`00510ce38bbb96ef2e4ce462618d06ab142fae7c`. Implementation committed as
+`723c5502` and pushed to the user's
+[vLLM-Omni fork branch](https://github.com/tzhouam/vllm-omni/tree/codex/omni-edge-abstraction).
+The working tree is clean. The pre-commit wheel/source identities below remain
+the original validation snapshot; the committed runtime matches it, with the
+hardware-support and validation table added to the guide before publication.
+The original dirty `vllm-omni` and clean `vllm-omni-029` trees were preserved.
+See the [backend guide](../../../vllm-omni-edge/docs/design/omni_edge_backends.md)
+and [completion plan](../../plan_omni_edge_completion_20260922.md).
+
+## Implemented boundary
+
+- Standalone `omni_stage_contracts`: version/features, tensors, requests/events,
+  opaque state, physical topology and hashed artifacts; compatible legacy imports.
+- Explicit host routes, worker bootstrap, Windows process identity and verified
+  cross-domain retirement. Host-local initialization locks work from UNC checkouts.
+- `external.graph.v1` driven by real `PipelineConfig`, `StageRuntime`, `StagePool`,
+  orchestrator and public `Omni`/`AsyncOmni`; native execution/KV/batching stay vLLM.
+- Graph-containing plans reserve all declared memory pools before loading.
+  CPU/iGPU/NPU shared RAM, discrete VRAM and WSL quota are separate constraints.
+  Unverified release is quarantined.
+- One admitted graph request, bounded copied tensors, consumer ACK,
+  epoch/generation fencing and worker retirement for non-preemptible cancellation.
+- Native Windows SHM uses host-local locks, exact payload framing and retained
+  producer handles until a generation-specific consumer ACK. Retention is capped
+  at 64 MiB / 256 buffers; cancellation/close releases unconsumed mappings.
+
+This implements stateless, fixed-bucket graphs. Full native admission migration,
+stateful mobile generation, general fan-out and all model/device qualification
+are unfinished. G1/G2/G3 remain open at their full accepted scope.
+
+## Evidence matrix
+
+D = detection, B = backend execution, C = model component, P = complete specified
+pipeline, E = estimate. Synthetic topology and test doubles are contract tests.
+
+| Route | Raw record | Proven scope |
+|---|---|---|
+| WSL Spark 4B BF16 CUDA | [wsl-m0-cuda-1.log](wsl-m0-cuda-1.log), 11 passed | P for the M0 test workload: 12 prompts × 128 tokens, context/state/cancel and memory checks. Not a new reference-token parity benchmark. |
+| Native Windows Spark 4B BF16 CUDA | [windows-m0-cuda-6.log](windows-m0-cuda-6.log), 11 passed | P for the same M0 acceptance workload on this native Windows environment; includes UNC checkpoint/worktree paths. |
+| WSL Qwen3-TTS 0.6B, real weights | [wsl-tts-6-real.log](wsl-tts-6-real.log), 1 passed | Two-stage edge-profile smoke test only; helper caps generation at two codec frames, so this does not close streaming/M2. |
+| Native Windows Qwen3-TTS 0.6B | [tts-stream-windows-5.log](tts-stream-windows-5.log) | Public AsyncOmni two-stage real-weight streaming completes with audio and clean shutdown. Playback stalls remain; not full M2 acceptance. |
+| WSL ORT CPU Add | [graph-cpu-wsl-2.json](graph-cpu-wsl-2.json) | B, public Omni, 20 requests, zero retained reservation after shutdown. |
+| Native Windows ORT CPU Add | [graph-cpu-windows-2.json](graph-cpu-windows-2.json) | B, native controller/worker, 20 requests. Not CPU text support. |
+| WSL → Windows DirectML Add | [graph-dml-crossdomain.json](graph-dml-crossdomain.json) | B, actual DML nodes, 20 requests. Not the historical 890M vision component. |
+| WSL → Windows AMD NPU A16W8 | [graph-npu-crossdomain-2.json](graph-npu-crossdomain-2.json) | B, VitisAI=1 / CPU=2 nodes explicitly allowed; verified retirement. |
+| Same A16W8 artifact on CPU | [graph-npu-cpu-reference.json](graph-npu-cpu-reference.json) | Backend reference, not original FP checkpoint quality. |
+| Installed Omni wheel outside checkout | [installed-wheel-graph.json](installed-wheel-graph.json) | Public API + real ORT CPU, 20 requests. |
+| Rebuilt Omni wheel after transport fixes | [installed-wheel-bounded.json](installed-wheel-bounded.json) | Same public API test from `/tmp`, actual worker under `/tmp/omni-platform-bounded-check`, exact outputs and zero retained reservation. Earlier builds remain separately recorded. |
+| Qualcomm S25 component | [inference](aihub-s25-infer.json), [profile](aihub-s25-profile.json) | C, existing Spark attention component; not full Spark or an Android Omni controller. |
+
+[Numerical checks](numerical-checks.json): CPU/Windows/DML Add outputs are exact.
+NPU versus the same quantized CPU graph: maximum absolute error
+`4.458427429199219e-05`, normalized maximum `3.111236521857185e-05`, below the
+preselected `0.002` bound. These probes do not validate model quality.
+
+Raw timings are functional-check samples, not controlled performance comparisons.
+Some initialization overlapped independent CPU checks. Power policy and thermal
+equilibrium were not controlled. No speedup or sustained power claim is made.
+
+## Desktop TTS streaming acceptance remains open
+
+The real-weight edge-profile smoke test passes, and a longer Chinese utterance
+runs through `AsyncOmni` with the existing 2→4→8→16→25-frame ramp and 72-frame
+left context. [Raw streaming record](tts-stream/Qwen3-TTS-12Hz-0.6B-CustomVoice/profile_edge/20260922-120641_abstraction.json)
+and [log](tts-stream.log) preserve one warmup and **one measured request**, concurrency
+one, same pinned checkpoint, BF16, RTX 5090 Laptop, native vLLM sampler.
+
+The measured request emitted six nonterminal chunks (6.4 seconds of audio), then
+a terminal tail. First audio was about 411 ms, but playback starting then would
+stall about 4.42 seconds in total. Thus functional streaming works while the
+continuous-playback acceptance criterion is **not met**. This is a diagnostic
+single-request result, not a qualified latency percentile or performance claim.
+Saved WAVs contain the streamed prefix only; the existing benchmark does not
+append the terminal tail. No listening/ASR quality or interruption/recovery gate
+has been passed. Peak GPU usage in this tool is explicitly whole-GPU, not exclusive
+model attribution; power/thermal conditions were not fixed.
+
+Native Windows now also completes the same two-stage stream after the SHM fixes.
+[Raw Windows record](tts-stream-windows/Qwen3-TTS-0.6B-85e237c/profile_edge/20260922-123626_abstraction.json)
+has one warmup and one measured request using the byte-identical native checkpoint
+bundle. The measured request produced six nonterminal chunks / 6.4 seconds of
+audio, first audio about 387 ms, and about 4.08 seconds of playback stall when
+starting at first audio. Wall time was 11.51 seconds. These are diagnostic samples,
+not a Windows-versus-WSL performance comparison. Both worker processes retired
+normally. [Audio checks](tts-audio-checks.json) verify finite, nonzero 24 kHz saved
+prefixes; they do not establish listening quality or terminal-tail correctness.
+
+Reproduce from the worktree, using the CUDA_HOME/PATH of the inventoried venv:
+
+```bash
+VLLM_USE_FLASHINFER_SAMPLER=0 HF_HUB_OFFLINE=1 PYTHONPATH=. \
+  ../.venvs/omni-cuda-029/bin/python benchmarks/tts/stream_latency_bench.py \
+  --model Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice --query-type CustomVoice \
+  --txt-prompts ../analysis/experiments/omni_abstraction_20260922/tts-prompts.txt \
+  --deploy-profile edge --warmup 1 --repeat 1 --save-wav \
+  --output-dir ../analysis/experiments/omni_abstraction_20260922/tts-stream
+```
+
+Next desktop experiment: attribute the native-sampler/per-frame synchronization
+and transport cost under the same input before changing stage placement. Do not
+split onto another accelerator just because it is available.
+
+## Qualcomm AI Hub Workbench
+
+The user authorized this test facility. The supplied token was passed to the SDK
+in memory via stdin and is not stored in the repository or runtime configuration.
+AI Hub remains outside the local deployment path.
+
+- [Inventory](aihub-inventory.json): SDK 0.55.0, 78 device/OS entries, including
+  S25 Android 15, Snapdragon X Elite/X2 Elite Windows 11, SA8775P and RB3. Listing
+  is D, not qualification of every listed device.
+- Existing compile [jgdd1ljrg](https://workbench.aihub.qualcomm.com/jobs/jgdd1ljrg/),
+  target `mnowl6xpq`. The historical label `full1024` means **one full-attention
+  component with a 1024 cache bucket**, not the complete model.
+- Fresh inference [jp0mvkj6g](https://workbench.aihub.qualcomm.com/jobs/jp0mvkj6g/)
+  and NPU profile [jpyo7nx75](https://workbench.aihub.qualcomm.com/jobs/jpyo7nx75/)
+  succeeded on Samsung Galaxy S25, Android 15, Snapdragon 8 Elite for Galaxy.
+- Inference reused dataset `d7m83gjy2` from `j57er3q9p`, with the same compiled
+  W8A16 artifact. [Repeatability](aihub-repeatability.json): all three outputs
+  finite and exactly equal. This does not establish original-checkpoint quality.
+- The profile preserves raw `all_inference_times`, load/memory fields, input
+  shapes/types, options, graph detail and Hub version. `estimated_inference_time`
+  is not relabeled a median or full-model latency.
+
+`qualcomm_workbench.py` inventories, submits jobs for explicit existing model IDs,
+and collects results. It uses a configured SDK profile or `--token-stdin`.
+Example with an already configured profile, from the workspace root:
+
+```bash
+.venvs/aihub/bin/python analysis/experiments/omni_abstraction_20260922/qualcomm_workbench.py \
+  collect --job jpyo7nx75 --out /tmp/s25-profile.json
+```
+
+No ADB device was connected. M1 still requires local prefill, 128-token generation,
+KV/ring/sampling, 512/1024 transitions, cancel/reset and full resident memory.
+M2 still requires local NPU/GPU co-residency, streaming recovery and 30-minute
+thermal behavior. Separate Hub jobs do not establish these properties.
+
+## Reproduction and environment
+
+Final focused regression: [WSL](wsl-regression-shm.log) **415 passed, 1 skipped, 20
+deselected**; [native Windows](windows-regression-shm.log) **78 passed, 2
+skipped** (the controller lacks ONNX for two tests; actual ORT runs use separate
+worker environments). The final Windows buffer/cancellation/retained-key follow-up
+also passes [three tests](windows-shm-bounded.log). Ruff and diff whitespace checks pass. Earlier snapshots
+and failed test-fixture setup attempts remain alongside the final logs.
+
+[WSL inventory](wsl-inventory.json), [Windows inventory](windows-inventory.json),
+[CPU](windows-cpu.json), [display](windows-display.json) and
+[source baseline](source-baseline.txt) record actual imports/packages and hardware.
+GPU: RTX 5090 **Laptop**, 24463 MiB, Windows driver 610.71. Desktop/server numbers
+are not interchangeable. Commands below run from `vllm-omni-edge`:
+
+```bash
+../.venvs/omni-cuda-029/bin/python -m pytest -q -m 'not omni and not local_model' \
+  tests/engine/test_graph_backend.py tests/edge/local \
+  tests/config/test_vllm_native_pipelines.py tests/engine/test_async_omni_engine_stage_init.py \
+  tests/engine/test_parallel_stage_init.py tests/engine/test_stage_runtime_launch_concurrency.py \
+  tests/engine/test_stage_pool_collective_rpc.py tests/engine/test_stage_metadata.py \
+  tests/entrypoints/test_omni_entrypoints.py tests/entrypoints/test_async_omni.py \
+  tests/worker/test_forward_kwargs_filtering.py
+
+../.venvs/omni-cuda-029/bin/python -m pytest -q tests/edge/local/test_e2e_local_text.py
+```
+
+Generate hashed probe bundles using `prepare_probes.py` from the workspace root.
+Use the [public graph runner](../../../vllm-omni-edge/examples/edge/run_graph_stage.py)
+and [route examples](../../../vllm-omni-edge/examples/edge/README.md). Reports retain
+route, artifact identity, placement and reservation. Workers:
+`.venvs/omni-cuda-029/bin/python` (WSL),
+`C:\Users\zhout\npu-ep\Scripts\python.exe` (Windows CPU/VitisAI), and
+`C:\Users\zhout\dml-ep\Scripts\python.exe` (DirectML).
+
+Windows controller: `C:\Users\zhout\w2\omni029venv\Scripts\python.exe`;
+`PYTHONPATH` is the ordinary UNC worktree path, `PYTHONUTF8=1`, and
+`VLLM_CUDART_SO_PATH` points to that environment's `torch\lib\cudart64_13.dll`.
+Usage statistics were disabled. No vendor wheel was modified here.
+
+Package source/wheel identities are in [release-identities.json](release-identities.json).
+The tested main wheel and standalone contracts wheel/sdist are preserved in
+[`packages/`](packages/) in this evidence directory.
+Build commands: `uv build --wheel --out-dir /tmp/omni-platform-shm-wheels` and
+`uv build packages/omni-stage-contracts --out-dir /tmp/omni-contracts-final-wheels`.
+The contracts sdist also installs in a fresh, dependency-free environment and
+imports under `python -I` without NumPy, torch, vLLM or Omni. For the final main
+wheel, install with `uv pip install --no-deps --target /tmp/omni-platform-bounded-check
+/tmp/omni-platform-shm-wheels/*.whl`, then run the graph example from `/tmp`
+with `PYTHONPATH=/tmp/omni-platform-bounded-check` using the existing vLLM environment.
+
+## Retained failures
+
+- Initial NPU execution passed but shutdown quarantined memory: socket closure
+  preceded native identity verification. `graph-npu-crossdomain.json` retains it;
+  the corrected `-2` run verifies release.
+- Windows M0 `-2` failed in default worker-path discovery; planning passes after
+  the fix. `-3`/`-4` were stopped while stalled. `-5` traced a blocking `/tmp`
+  open through UNC, then exited with native access violation. The host-local
+  path/creation fix is covered by lock and model reruns.
+- TTS `-1` lacked complete artifacts. Pinned CustomVoice 0.6B revision
+  `85e237c12c027371202489a0ec509ded67b5e4b5` was then downloaded. `-2`/`-3` lacked
+  CUDA compiler/ninja paths; `-4` lacked the C++ frontend needed by FlashInfer
+  sampling; `-5` exposed CUDA wrapper introspection. These tests defaulted to
+  **dummy weights** and are not model-quality evidence. The introspection fix
+  is tested with the actual vLLM wrapper. Real-weight tests require
+  `--run-level advanced_model`; this environment explicitly selects the native
+  vLLM sampler with `VLLM_USE_FLASHINFER_SAMPLER=0`.
+- Native Windows `windows-tts-real-1.log` stopped in test-fixture setup because
+  an unrelated diffusion test helper imports missing `diffusers`. Direct public
+  benchmark `tts-stream-windows-1.log` then reported an incomplete snapshot:
+  Windows could not follow the Linux cache symlinks. A native copy of the same
+  pinned checkpoint was made without overwriting an existing bundle; all 13 files
+  were SHA256-compared with the source. [Bundle provenance](tts-native-bundle.json).
+- `tts-stream-windows-2.log` loaded the real talker weights, then failed publishing
+  the compiler cache at a 261-character Windows path (`WinError 3`). A separate
+  rerun explicitly sets `VLLM_CACHE_ROOT=C:\Users\zhout\c29`, shortening that
+  target to 253 characters. This uses the runtime's cache setting, with no global
+  Windows policy change and no precision/model substitution.
+- `tts-stream-windows-3.log` passed stage compilation and initialized both
+  stages with the short cache path, then stalled on the first request. The
+  benchmark created `asyncio.run` before importing Omni and installing Windows
+  selector policy. Its entry point now explicitly installs the existing policy
+  before creating the loop, as the local-text CLI already does. The original
+  attempt was retired with its two worker processes before retrying.
+- `tts-stream-windows-4.log` still stalled with a verified selector loop.
+  [Talker stack](windows-tts-talker-stack.txt) located the blocked file lock:
+  `/dev/shm` resolved through the UNC checkout. Paths now select the actual host
+  directory. Windows named mappings also needed retained producer handles and
+  payload-length framing; closing the only handle immediately can destroy them.
+  The bounded generation/ACK implementation and permanent-lock-error handling
+  pass native cross-process and cancellation tests. The `-5` stream then completed.
+- `forward-regression.log` used a nonexistent path; `-2` passed. Metadata fixture
+  setup failures are retained; the corrected metadata/lock suite passed 95 tests
+  in `metadata-lock-regression.log`.
+
+Three early graph reports have ephemeral localhost handshake tokens redacted;
+measurements remain unchanged. Historical vision/CPU/mobile/VLA results are not
+promoted to proof of this implementation. Remaining required rows stay open.

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/prior-evidence/aihub-s25-infer.json

@@ -0,0 +1,34 @@
+{
+  "timestamp_utc": "2026-09-22T03:52:48.169689+00:00",
+  "qai_hub_version": "0.55.0",
+  "facility": "Qualcomm AI Hub Workbench",
+  "command": "collect",
+  "job_id": "jp0mvkj6g",
+  "url": "https://workbench.aihub.qualcomm.com/jobs/jp0mvkj6g/",
+  "status": "SUCCESS",
+  "status_message": "",
+  "options": "--compute_unit npu",
+  "hub_version": "aihub-2026.09.11.0",
+  "device": {
+    "name": "Samsung Galaxy S25",
+    "os": "15",
+    "attributes": [
+      "os:android",
+      "framework:tflite",
+      "framework:onnx",
+      "abi:aarch64-android",
+      "vendor:samsung",
+      "format:phone",
+      "chipset:qualcomm-snapdragon-8-elite-for-galaxy",
+      "chipset:sm8750-ac",
+      "hexagon:v79",
+      "soc-model:69",
+      "htp-supports-fp16:true",
+      "htp-supports-weight-sharing:true",
+      "framework:qnn"
+    ]
+  },
+  "model_id": "mnowl6xpq",
+  "input_dataset_id": "d7m83gjy2",
+  "outputs": "aihub-s25-infer.npz"
+}

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/prior-matrix/.gitattributes

@@ -0,0 +1,2 @@
+# Preserve raw experiment bytes for SHA256 verification, including original whitespace.
+evidence/** -text -whitespace

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/prior-matrix/README.md

@@ -0,0 +1,572 @@
+# Mobile / PC × model qualification audit
+
+2026-09-22: **60 named pairings checked**, not 60 successful executions.
+
+PASS = specified full workload passed; PARTIAL = functional output with acceptance gaps; COMPONENT = one model component only; PROFILE_ONLY = component timing without quality; FAILED = attempted execution/startup failed; REJECTED = specific artifact refused; BLOCKED = a named prerequisite is missing. Historical evidence is marked in each finding.
+
+| Device / execution configuration | Spark-X2.5 | Qwen3-TTS 0.6B CustomVoice | Qwen3.8-27B | MiniCPM-o 4.5 | InternVLA-A1 |
+|---|---|---|---|---|---|
+| PC HX370: CPU / WSL | [PASS](#pc_cpu_wsl-spark) | [FAILED](#pc_cpu_wsl-tts) | [BLOCKED](#pc_cpu_wsl-qwen27) | [BLOCKED](#pc_cpu_wsl-minicpm) | [BLOCKED](#pc_cpu_wsl-vla) |
+| PC HX370: CPU / native Windows | [BLOCKED](#pc_cpu_windows-spark) | [BLOCKED](#pc_cpu_windows-tts) | [BLOCKED](#pc_cpu_windows-qwen27) | [BLOCKED](#pc_cpu_windows-minicpm) | [BLOCKED](#pc_cpu_windows-vla) |
+| PC HX370 + RTX 5090 Laptop / WSL | [PASS](#pc_cuda_wsl-spark) | [PARTIAL](#pc_cuda_wsl-tts) | [FAILED](#pc_cuda_wsl-qwen27) | [BLOCKED](#pc_cuda_wsl-minicpm) | [BLOCKED](#pc_cuda_wsl-vla) |
+| PC HX370 + RTX 5090 Laptop / Windows | [PASS](#pc_cuda_windows-spark) | [PARTIAL](#pc_cuda_windows-tts) | [FAILED](#pc_cuda_windows-qwen27) | [BLOCKED](#pc_cuda_windows-minicpm) | [BLOCKED](#pc_cuda_windows-vla) |
+| PC HX370 + Radeon 890M / Windows worker | [BLOCKED](#pc_igpu-spark) | [BLOCKED](#pc_igpu-tts) | [COMPONENT](#pc_igpu-qwen27) | [BLOCKED](#pc_igpu-minicpm) | [BLOCKED](#pc_igpu-vla) |
+| PC HX370 + AMD NPU / Windows worker | [BLOCKED](#pc_npu-spark) | [BLOCKED](#pc_npu-tts) | [REJECTED](#pc_npu-qwen27) | [BLOCKED](#pc_npu-minicpm) | [BLOCKED](#pc_npu-vla) |
+| PC CPU+iGPU+NPU +/- discrete GPU, joint model execution | [BLOCKED](#pc_joint-spark) | [BLOCKED](#pc_joint-tts) | [BLOCKED](#pc_joint-qwen27) | [BLOCKED](#pc_joint-minicpm) | [BLOCKED](#pc_joint-vla) |
+| PC Snapdragon X Elite CRD / AI Hub | [BLOCKED](#pc_xelite-spark) | [PROFILE_ONLY](#pc_xelite-tts) | [BLOCKED](#pc_xelite-qwen27) | [BLOCKED](#pc_xelite-minicpm) | [BLOCKED](#pc_xelite-vla) |
+| Mobile Galaxy S25 / Snapdragon 8 Elite for Galaxy | [COMPONENT](#mobile_s25-spark) | [COMPONENT](#mobile_s25-tts) | [BLOCKED](#mobile_s25-qwen27) | [COMPONENT](#mobile_s25-minicpm) | [BLOCKED](#mobile_s25-vla) |
+| Mobile Galaxy S24 / Snapdragon 8 Gen 3 | [BLOCKED](#mobile_s24-spark) | [COMPONENT](#mobile_s24-tts) | [BLOCKED](#mobile_s24-qwen27) | [COMPONENT](#mobile_s24-minicpm) | [BLOCKED](#mobile_s24-vla) |
+| Embedded SA8775P ADP / AI Hub | [BLOCKED](#embedded_sa8775p-spark) | [PROFILE_ONLY](#embedded_sa8775p-tts) | [BLOCKED](#embedded_sa8775p-qwen27) | [BLOCKED](#embedded_sa8775p-minicpm) | [BLOCKED](#embedded_sa8775p-vla) |
+| Embedded RB3 Gen 2 / QCS6490 / AI Hub | [BLOCKED](#embedded_rb3-spark) | [PROFILE_ONLY](#embedded_rb3-tts) | [BLOCKED](#embedded_rb3-qwen27) | [BLOCKED](#embedded_rb3-minicpm) | [BLOCKED](#embedded_rb3-vla) |
+
+## Scope and reproduction
+
+PC rows are execution configurations on the HX370/890M/AMD NPU/RTX 5090 Laptop machine. X Elite is a separate Windows ARM device accessed through AI Hub. The two embedded kits are not phones. Other SKUs are outside this finite audit, not implied supported.
+
+The checked roots contain MiniCPM5-2B, which must not substitute for MiniCPM-o 4.5. Missing complete MiniCPM-o and InternVLA artifacts block those local runs. ADB reports no attached phone; Hub component jobs cannot prove a device-local controller or sustained co-residency.
+
+The CPU venv is vLLM 0.28; the CUDA/native-Windows venvs are 0.29. The CPU TTS tuple failure is specific to the mixed-version environment. Preserve failures instead of describing these as universal model limitations.
+
+Raw selected reports are in `evidence/`. `matrix.json` retains every source path/hash, cell finding and next step. Fresh probe scripts are in `repro/`; see their `--help` and the workspace experiment README for exact environments. No model weights or API credentials are included.
+
+## Per-pair results
+
+<a id="pc_cpu_wsl-spark"></a>
+### PC HX370: CPU / WSL × Spark-X2.5
+
+**PASS — P**. Fresh Spark 1.7B INT8 acceptance: 12 prompts x 128 tokens; clean cancellation. Current Omni branch with the existing vLLM 0.28 CPU wheel; version mismatch warning retained, not a claim about a 0.29 CPU wheel.
+
+Evidence: [spark-cpu](evidence/spark-cpu.json), [spark-cpu-log](evidence/spark-cpu-log.log).
+
+Next: Qualify a matching vLLM 0.29 CPU build and token parity separately.
+
+<a id="pc_cpu_wsl-tts"></a>
+### PC HX370: CPU / WSL × Qwen3-TTS 0.6B CustomVoice
+
+**FAILED — execution**. Fresh real-weight two-frame probe loaded both stages but generation failed in sanitize_min_tokens_stop_ids: expected four tuple fields, got three. Existing CPU wheel is vLLM 0.28 versus Omni 0.29. No audio pass.
+
+Evidence: [tts-cpu](evidence/tts-cpu.json), [tts-cpu-log](evidence/tts-cpu-log.log).
+
+Next: Use a matching CPU runtime or explicitly validated compatibility fix; repeat real audio, then full streaming gates.
+
+<a id="pc_cpu_wsl-qwen27"></a>
+### PC HX370: CPU / WSL × Qwen3.8-27B
+
+**BLOCKED — none**. No compatible CPU artifact/deployment for this model was found in the checked model roots. Available Qwen 27B FP8/NVFP4 CUDA artifacts do not establish a CPU route.
+
+Evidence: [host-wsl](evidence/host-wsl.json).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="pc_cpu_wsl-minicpm"></a>
+### PC HX370: CPU / WSL × MiniCPM-o 4.5
+
+**BLOCKED — none**. No complete matching checkpoint found in the inventoried workspace, HF cache and native model roots. MiniCPM5-2B is not MiniCPM-o 4.5. Runtime registration alone is not a model run.
+
+Evidence: [host-wsl](evidence/host-wsl.json), [host-windows](evidence/host-windows.json).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="pc_cpu_wsl-vla"></a>
+### PC HX370: CPU / WSL × InternVLA-A1
+
+**BLOCKED — none**. No complete matching checkpoint found in the inventoried workspace, HF cache and native model roots. MiniCPM5-2B is not MiniCPM-o 4.5. Runtime registration alone is not a model run.
+
+Evidence: [host-wsl](evidence/host-wsl.json), [host-windows](evidence/host-windows.json).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="pc_cpu_windows-spark"></a>
+### PC HX370: CPU / native Windows × Spark-X2.5
+
+**BLOCKED — none**. The installed native Windows vLLM extension lacks cpu_attn_reshape_and_cache and cpu_attn_get_scheduler_metadata. The separate ORT CPU graph worker is not a whole-model text/TTS adapter; a qualified CPU build/binding is missing.
+
+Evidence: [host-windows](evidence/host-windows.json), [windows-cpu-kernels](evidence/windows-cpu-kernels.json), [backend-factory](evidence/backend-factory.py).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="pc_cpu_windows-tts"></a>
+### PC HX370: CPU / native Windows × Qwen3-TTS 0.6B CustomVoice
+
+**BLOCKED — none**. The installed native Windows vLLM extension lacks cpu_attn_reshape_and_cache and cpu_attn_get_scheduler_metadata. The separate ORT CPU graph worker is not a whole-model text/TTS adapter; a qualified CPU build/binding is missing.
+
+Evidence: [host-windows](evidence/host-windows.json), [windows-cpu-kernels](evidence/windows-cpu-kernels.json), [backend-factory](evidence/backend-factory.py).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="pc_cpu_windows-qwen27"></a>
+### PC HX370: CPU / native Windows × Qwen3.8-27B
+
+**BLOCKED — none**. The installed native Windows vLLM extension lacks cpu_attn_reshape_and_cache and cpu_attn_get_scheduler_metadata. The separate ORT CPU graph worker is not a whole-model text/TTS adapter; a qualified CPU build/binding is missing.
+
+Evidence: [host-windows](evidence/host-windows.json), [windows-cpu-kernels](evidence/windows-cpu-kernels.json), [backend-factory](evidence/backend-factory.py).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="pc_cpu_windows-minicpm"></a>
+### PC HX370: CPU / native Windows × MiniCPM-o 4.5
+
+**BLOCKED — none**. No complete matching checkpoint found in the inventoried workspace, HF cache and native model roots. MiniCPM5-2B is not MiniCPM-o 4.5. Runtime registration alone is not a model run.
+
+Evidence: [host-wsl](evidence/host-wsl.json), [host-windows](evidence/host-windows.json).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="pc_cpu_windows-vla"></a>
+### PC HX370: CPU / native Windows × InternVLA-A1
+
+**BLOCKED — none**. No complete matching checkpoint found in the inventoried workspace, HF cache and native model roots. MiniCPM5-2B is not MiniCPM-o 4.5. Runtime registration alone is not a model run.
+
+Evidence: [host-wsl](evidence/host-wsl.json), [host-windows](evidence/host-windows.json).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="pc_cuda_wsl-spark"></a>
+### PC HX370 + RTX 5090 Laptop / WSL × Spark-X2.5
+
+**PASS — P**. Spark 4B BF16: 11 M0 tests passed on this branch for text, state, cancellation and memory; scoped to the recorded workload.
+
+Evidence: [spark-cuda-wsl](evidence/spark-cuda-wsl.log).
+
+Next: Broaden only with a named workload/device/precision qualification.
+
+<a id="pc_cuda_wsl-tts"></a>
+### PC HX370 + RTX 5090 Laptop / WSL × Qwen3-TTS 0.6B CustomVoice
+
+**PARTIAL — P-limited**. Real-weight two-stage audio completes. One measured utterance has playback stalls; speech quality, long streaming, interruption/recovery and thermal gates remain open.
+
+Evidence: [tts-cuda-wsl](evidence/tts-cuda-wsl.json).
+
+Next: Resolve and measure playback stalls; run quality, interruption and thermal tests.
+
+<a id="pc_cuda_wsl-qwen27"></a>
+### PC HX370 + RTX 5090 Laptop / WSL × Qwen3.8-27B
+
+**FAILED — startup**. Fresh public Omni startup rejects the real NVFP4 checkpoint: no registered Omni pipeline. Historical bare-vLLM short-text success does not qualify this integration or multimodal inference.
+
+Evidence: [qwen-wsl](evidence/qwen-wsl.json), [qwen-text-historical](evidence/qwen-text-historical.json).
+
+Next: Add/validate an Omni pipeline binding; rerun text, image, context and artifact-quality tests.
+
+<a id="pc_cuda_wsl-minicpm"></a>
+### PC HX370 + RTX 5090 Laptop / WSL × MiniCPM-o 4.5
+
+**BLOCKED — none**. No complete matching checkpoint found in the inventoried workspace, HF cache and native model roots. MiniCPM5-2B is not MiniCPM-o 4.5. Runtime registration alone is not a model run.
+
+Evidence: [host-wsl](evidence/host-wsl.json), [host-windows](evidence/host-windows.json).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="pc_cuda_wsl-vla"></a>
+### PC HX370 + RTX 5090 Laptop / WSL × InternVLA-A1
+
+**BLOCKED — none**. No complete matching checkpoint found in the inventoried workspace, HF cache and native model roots. MiniCPM5-2B is not MiniCPM-o 4.5. Runtime registration alone is not a model run.
+
+Evidence: [host-wsl](evidence/host-wsl.json), [host-windows](evidence/host-windows.json).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="pc_cuda_windows-spark"></a>
+### PC HX370 + RTX 5090 Laptop / Windows × Spark-X2.5
+
+**PASS — P**. Spark 4B BF16: 11 M0 tests passed on this branch for text, state, cancellation and memory; scoped to the recorded workload.
+
+Evidence: [spark-cuda-windows](evidence/spark-cuda-windows.log).
+
+Next: Broaden only with a named workload/device/precision qualification.
+
+<a id="pc_cuda_windows-tts"></a>
+### PC HX370 + RTX 5090 Laptop / Windows × Qwen3-TTS 0.6B CustomVoice
+
+**PARTIAL — P-limited**. Real-weight two-stage audio completes. One measured utterance has playback stalls; speech quality, long streaming, interruption/recovery and thermal gates remain open.
+
+Evidence: [tts-cuda-windows](evidence/tts-cuda-windows.json).
+
+Next: Resolve and measure playback stalls; run quality, interruption and thermal tests.
+
+<a id="pc_cuda_windows-qwen27"></a>
+### PC HX370 + RTX 5090 Laptop / Windows × Qwen3.8-27B
+
+**FAILED — startup**. Fresh public Omni startup rejects the real NVFP4 checkpoint: no registered Omni pipeline. Historical bare-vLLM short-text success does not qualify this integration or multimodal inference.
+
+Evidence: [qwen-windows](evidence/qwen-windows.json), [qwen-text-historical](evidence/qwen-text-historical.json).
+
+Next: Add/validate an Omni pipeline binding; rerun text, image, context and artifact-quality tests.
+
+<a id="pc_cuda_windows-minicpm"></a>
+### PC HX370 + RTX 5090 Laptop / Windows × MiniCPM-o 4.5
+
+**BLOCKED — none**. No complete matching checkpoint found in the inventoried workspace, HF cache and native model roots. MiniCPM5-2B is not MiniCPM-o 4.5. Runtime registration alone is not a model run.
+
+Evidence: [host-wsl](evidence/host-wsl.json), [host-windows](evidence/host-windows.json).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="pc_cuda_windows-vla"></a>
+### PC HX370 + RTX 5090 Laptop / Windows × InternVLA-A1
+
+**BLOCKED — none**. No complete matching checkpoint found in the inventoried workspace, HF cache and native model roots. MiniCPM5-2B is not MiniCPM-o 4.5. Runtime registration alone is not a model run.
+
+Evidence: [host-wsl](evidence/host-wsl.json), [host-windows](evidence/host-windows.json).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="pc_igpu-spark"></a>
+### PC HX370 + Radeon 890M / Windows worker × Spark-X2.5
+
+**BLOCKED — none**. No placement- and quality-verified whole-model graph bundle/stateful adapter for this model on this AMD route. Small backend graphs do not establish model support.
+
+Evidence: [backend-dml](evidence/backend-dml.json), [graph-contract](evidence/graph-contract.py).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="pc_igpu-tts"></a>
+### PC HX370 + Radeon 890M / Windows worker × Qwen3-TTS 0.6B CustomVoice
+
+**BLOCKED — none**. No placement- and quality-verified whole-model graph bundle/stateful adapter for this model on this AMD route. Small backend graphs do not establish model support.
+
+Evidence: [backend-dml](evidence/backend-dml.json), [graph-contract](evidence/graph-contract.py).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="pc_igpu-qwen27"></a>
+### PC HX370 + Radeon 890M / Windows worker × Qwen3.8-27B
+
+**COMPONENT — C-historical**. FP32 448x448 vision tower: DirectML 85/113 nodes; normalized max error 9.095e-06 versus torch CPU. Historical component path, not full 27B or current graph-stage image-to-text integration.
+
+Evidence: [qwen-vision-dml](evidence/qwen-vision-dml.json).
+
+Next: Recover the hashed tower bundle and qualify its current Omni graph handoff with the language stage.
+
+<a id="pc_igpu-minicpm"></a>
+### PC HX370 + Radeon 890M / Windows worker × MiniCPM-o 4.5
+
+**BLOCKED — none**. No placement- and quality-verified whole-model graph bundle/stateful adapter for this model on this AMD route. Small backend graphs do not establish model support.
+
+Evidence: [backend-dml](evidence/backend-dml.json), [graph-contract](evidence/graph-contract.py).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="pc_igpu-vla"></a>
+### PC HX370 + Radeon 890M / Windows worker × InternVLA-A1
+
+**BLOCKED — none**. No placement- and quality-verified whole-model graph bundle/stateful adapter for this model on this AMD route. Small backend graphs do not establish model support.
+
+Evidence: [backend-dml](evidence/backend-dml.json), [graph-contract](evidence/graph-contract.py).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="pc_npu-spark"></a>
+### PC HX370 + AMD NPU / Windows worker × Spark-X2.5
+
+**BLOCKED — none**. No placement- and quality-verified whole-model graph bundle/stateful adapter for this model on this AMD route. Small backend graphs do not establish model support.
+
+Evidence: [backend-npu](evidence/backend-npu.json), [graph-contract](evidence/graph-contract.py).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="pc_npu-tts"></a>
+### PC HX370 + AMD NPU / Windows worker × Qwen3-TTS 0.6B CustomVoice
+
+**BLOCKED — none**. No placement- and quality-verified whole-model graph bundle/stateful adapter for this model on this AMD route. Small backend graphs do not establish model support.
+
+Evidence: [backend-npu](evidence/backend-npu.json), [graph-contract](evidence/graph-contract.py).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="pc_npu-qwen27"></a>
+### PC HX370 + AMD NPU / Windows worker × Qwen3.8-27B
+
+**REJECTED — artifact**. The tested A16W8 whole-vision-tower export failed NPU placement. This conclusion is specific to that export, not all future Qwen/NPU combinations.
+
+Evidence: [qwen-vision-npu](evidence/qwen-vision-npu.json).
+
+Next: Validate a supported export or smaller useful component, including same-checkpoint numerical quality.
+
+<a id="pc_npu-minicpm"></a>
+### PC HX370 + AMD NPU / Windows worker × MiniCPM-o 4.5
+
+**BLOCKED — none**. No placement- and quality-verified whole-model graph bundle/stateful adapter for this model on this AMD route. Small backend graphs do not establish model support.
+
+Evidence: [backend-npu](evidence/backend-npu.json), [graph-contract](evidence/graph-contract.py).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="pc_npu-vla"></a>
+### PC HX370 + AMD NPU / Windows worker × InternVLA-A1
+
+**BLOCKED — none**. No placement- and quality-verified whole-model graph bundle/stateful adapter for this model on this AMD route. Small backend graphs do not establish model support.
+
+Evidence: [backend-npu](evidence/backend-npu.json), [graph-contract](evidence/graph-contract.py).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="pc_joint-spark"></a>
+### PC CPU+iGPU+NPU +/- discrete GPU, joint model execution × Spark-X2.5
+
+**BLOCKED — none**. No qualifying same-model multi-accelerator deployment/artifact plan or overlap/recovery evidence. Individual routes are not a joint pipeline.
+
+Evidence: [backend-dml](evidence/backend-dml.json), [backend-npu](evidence/backend-npu.json), [graph-contract](evidence/graph-contract.py).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="pc_joint-tts"></a>
+### PC CPU+iGPU+NPU +/- discrete GPU, joint model execution × Qwen3-TTS 0.6B CustomVoice
+
+**BLOCKED — none**. No qualifying same-model multi-accelerator deployment/artifact plan or overlap/recovery evidence. Individual routes are not a joint pipeline.
+
+Evidence: [backend-dml](evidence/backend-dml.json), [backend-npu](evidence/backend-npu.json), [graph-contract](evidence/graph-contract.py).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="pc_joint-qwen27"></a>
+### PC CPU+iGPU+NPU +/- discrete GPU, joint model execution × Qwen3.8-27B
+
+**BLOCKED — none**. No qualifying same-model multi-accelerator deployment/artifact plan or overlap/recovery evidence. Individual routes are not a joint pipeline.
+
+Evidence: [backend-dml](evidence/backend-dml.json), [backend-npu](evidence/backend-npu.json), [graph-contract](evidence/graph-contract.py).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="pc_joint-minicpm"></a>
+### PC CPU+iGPU+NPU +/- discrete GPU, joint model execution × MiniCPM-o 4.5
+
+**BLOCKED — none**. No qualifying same-model multi-accelerator deployment/artifact plan or overlap/recovery evidence. Individual routes are not a joint pipeline.
+
+Evidence: [backend-dml](evidence/backend-dml.json), [backend-npu](evidence/backend-npu.json), [graph-contract](evidence/graph-contract.py).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="pc_joint-vla"></a>
+### PC CPU+iGPU+NPU +/- discrete GPU, joint model execution × InternVLA-A1
+
+**BLOCKED — none**. No qualifying same-model multi-accelerator deployment/artifact plan or overlap/recovery evidence. Individual routes are not a joint pipeline.
+
+Evidence: [backend-dml](evidence/backend-dml.json), [backend-npu](evidence/backend-npu.json), [graph-contract](evidence/graph-contract.py).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="pc_xelite-spark"></a>
+### PC Snapdragon X Elite CRD / AI Hub × Spark-X2.5
+
+**BLOCKED — none**. No complete qualified target artifact and device-local stateful Omni backend. Hub jobs provide component execution, not an on-device generation/streaming controller; ADB has no attached device.
+
+Evidence: [hub-inventory](evidence/hub-inventory.json), [host-wsl](evidence/host-wsl.json), [backend-factory](evidence/backend-factory.py), [graph-contract](evidence/graph-contract.py).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="pc_xelite-tts"></a>
+### PC Snapdragon X Elite CRD / AI Hub × Qwen3-TTS 0.6B CustomVoice
+
+**PROFILE_ONLY — C-timing-only**. Historical QNN talker profile re-fetched successfully; no numerical/full-pipeline gate. Historical TFLite vocoder target was refused on this Windows ARM device.
+
+Evidence: [tts-xelite](evidence/tts-xelite.json), [tts-xelite-vocoder-rejected](evidence/tts-xelite-vocoder-rejected.json), [tts-other-devices](evidence/tts-other-devices.json), [tts-other-device-code](evidence/tts-other-device-code.py).
+
+Next: Validate target-specific numerical quality, complete local execution, memory and sustained behavior.
+
+<a id="pc_xelite-qwen27"></a>
+### PC Snapdragon X Elite CRD / AI Hub × Qwen3.8-27B
+
+**BLOCKED — none**. No complete qualified target artifact and device-local stateful Omni backend. Hub jobs provide component execution, not an on-device generation/streaming controller; ADB has no attached device.
+
+Evidence: [hub-inventory](evidence/hub-inventory.json), [host-wsl](evidence/host-wsl.json), [backend-factory](evidence/backend-factory.py), [graph-contract](evidence/graph-contract.py).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="pc_xelite-minicpm"></a>
+### PC Snapdragon X Elite CRD / AI Hub × MiniCPM-o 4.5
+
+**BLOCKED — none**. No complete qualified target artifact and device-local stateful Omni backend. Hub jobs provide component execution, not an on-device generation/streaming controller; ADB has no attached device.
+
+Evidence: [hub-inventory](evidence/hub-inventory.json), [host-wsl](evidence/host-wsl.json), [backend-factory](evidence/backend-factory.py), [graph-contract](evidence/graph-contract.py).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="pc_xelite-vla"></a>
+### PC Snapdragon X Elite CRD / AI Hub × InternVLA-A1
+
+**BLOCKED — none**. No complete qualified target artifact and device-local stateful Omni backend. Hub jobs provide component execution, not an on-device generation/streaming controller; ADB has no attached device.
+
+Evidence: [hub-inventory](evidence/hub-inventory.json), [host-wsl](evidence/host-wsl.json), [backend-factory](evidence/backend-factory.py), [graph-contract](evidence/graph-contract.py).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="mobile_s25-spark"></a>
+### Mobile Galaxy S25 / Snapdragon 8 Elite for Galaxy × Spark-X2.5
+
+**COMPONENT — C**. Fresh 2026-09-22 W8A16 full-attention component inference/profile passed; full1024 denotes one cache bucket. No full prefill/decode/KV/ring/sampling loop.
+
+Evidence: [spark-s25](evidence/spark-s25.json), [spark-s25-parity](evidence/spark-s25-parity.json).
+
+Next: Implement and validate local 128-token generation, 512/1024 transitions, cancellation and resident memory.
+
+<a id="mobile_s25-tts"></a>
+### Mobile Galaxy S25 / Snapdragon 8 Elite for Galaxy × Qwen3-TTS 0.6B CustomVoice
+
+**COMPONENT — C**. Fresh FP16 predictor inference passed; its one output exactly matches the same compiled artifact/dataset reference. Historical decoder parity exists. No complete co-resident NPU/GPU stream.
+
+Evidence: [tts-s25](evidence/tts-s25.json), [hub-repeatability](evidence/hub-repeatability.json), [tts-mobile-parity](evidence/tts-mobile-parity.json).
+
+Next: Validate the device-local talker/predictor/vocoder pipeline, output quality, recovery and 30-minute thermal behavior.
+
+<a id="mobile_s25-qwen27"></a>
+### Mobile Galaxy S25 / Snapdragon 8 Elite for Galaxy × Qwen3.8-27B
+
+**BLOCKED — none**. No complete qualified target artifact and device-local stateful Omni backend. Hub jobs provide component execution, not an on-device generation/streaming controller; ADB has no attached device.
+
+Evidence: [hub-inventory](evidence/hub-inventory.json), [host-wsl](evidence/host-wsl.json), [backend-factory](evidence/backend-factory.py), [graph-contract](evidence/graph-contract.py).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="mobile_s25-minicpm"></a>
+### Mobile Galaxy S25 / Snapdragon 8 Elite for Galaxy × MiniCPM-o 4.5
+
+**COMPONENT — C**. Fresh FP16 speech-head inference passed; all 41 outputs exactly match the same artifact/dataset reference. Historical FP16 numerical parity passed; tested W8A16 top-1 parity failed. Thinker/encoders/vocoder are not qualified together.
+
+Evidence: [minicpm-s25](evidence/minicpm-s25.json), [hub-repeatability](evidence/hub-repeatability.json), [minicpm-mobile-parity](evidence/minicpm-mobile-parity.json).
+
+Next: Supply a qualified complete mobile checkpoint/export and validate the full multimodal/stateful pipeline.
+
+<a id="mobile_s25-vla"></a>
+### Mobile Galaxy S25 / Snapdragon 8 Elite for Galaxy × InternVLA-A1
+
+**BLOCKED — none**. No complete qualified target artifact and device-local stateful Omni backend. Hub jobs provide component execution, not an on-device generation/streaming controller; ADB has no attached device.
+
+Evidence: [hub-inventory](evidence/hub-inventory.json), [host-wsl](evidence/host-wsl.json), [backend-factory](evidence/backend-factory.py), [graph-contract](evidence/graph-contract.py).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="mobile_s24-spark"></a>
+### Mobile Galaxy S24 / Snapdragon 8 Gen 3 × Spark-X2.5
+
+**BLOCKED — none**. No complete qualified target artifact and device-local stateful Omni backend. Hub jobs provide component execution, not an on-device generation/streaming controller; ADB has no attached device.
+
+Evidence: [hub-inventory](evidence/hub-inventory.json), [host-wsl](evidence/host-wsl.json), [backend-factory](evidence/backend-factory.py), [graph-contract](evidence/graph-contract.py).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="mobile_s24-tts"></a>
+### Mobile Galaxy S24 / Snapdragon 8 Gen 3 × Qwen3-TTS 0.6B CustomVoice
+
+**COMPONENT — C-historical**. Historical FP16 decoder parity has SNR 39.13 dB. Tested calibrated INT8 decoder parity failed with negative SNR. Neither result is a full local TTS stream.
+
+Evidence: [tts-mobile-parity](evidence/tts-mobile-parity.json).
+
+Next: Validate the complete local pipeline and a quality-passing artifact set.
+
+<a id="mobile_s24-qwen27"></a>
+### Mobile Galaxy S24 / Snapdragon 8 Gen 3 × Qwen3.8-27B
+
+**BLOCKED — none**. No complete qualified target artifact and device-local stateful Omni backend. Hub jobs provide component execution, not an on-device generation/streaming controller; ADB has no attached device.
+
+Evidence: [hub-inventory](evidence/hub-inventory.json), [host-wsl](evidence/host-wsl.json), [backend-factory](evidence/backend-factory.py), [graph-contract](evidence/graph-contract.py).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="mobile_s24-minicpm"></a>
+### Mobile Galaxy S24 / Snapdragon 8 Gen 3 × MiniCPM-o 4.5
+
+**COMPONENT — C-historical**. Historical FP16 speech-head job re-fetched successfully; SNR 49.22 dB/top-1 match in the recorded parity test. W8A16 top-1 failed. Not complete MiniCPM-o.
+
+Evidence: [minicpm-s24](evidence/minicpm-s24.json), [minicpm-mobile-parity](evidence/minicpm-mobile-parity.json).
+
+Next: Qualify thinker/encoders/vocoder and stateful local integration.
+
+<a id="mobile_s24-vla"></a>
+### Mobile Galaxy S24 / Snapdragon 8 Gen 3 × InternVLA-A1
+
+**BLOCKED — none**. No complete qualified target artifact and device-local stateful Omni backend. Hub jobs provide component execution, not an on-device generation/streaming controller; ADB has no attached device.
+
+Evidence: [hub-inventory](evidence/hub-inventory.json), [host-wsl](evidence/host-wsl.json), [backend-factory](evidence/backend-factory.py), [graph-contract](evidence/graph-contract.py).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="embedded_sa8775p-spark"></a>
+### Embedded SA8775P ADP / AI Hub × Spark-X2.5
+
+**BLOCKED — none**. No complete qualified target artifact and device-local stateful Omni backend. Hub jobs provide component execution, not an on-device generation/streaming controller; ADB has no attached device.
+
+Evidence: [hub-inventory](evidence/hub-inventory.json), [host-wsl](evidence/host-wsl.json), [backend-factory](evidence/backend-factory.py), [graph-contract](evidence/graph-contract.py).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="embedded_sa8775p-tts"></a>
+### Embedded SA8775P ADP / AI Hub × Qwen3-TTS 0.6B CustomVoice
+
+**PROFILE_ONLY — C-timing-only**. Historical NPU talker and GPU vocoder profiles re-fetched successfully; no complete pipeline, parity or co-residency qualification.
+
+Evidence: [tts-sa8775p-npu](evidence/tts-sa8775p-npu.json), [tts-sa8775p-gpu](evidence/tts-sa8775p-gpu.json), [tts-other-devices](evidence/tts-other-devices.json), [tts-other-device-code](evidence/tts-other-device-code.py).
+
+Next: Validate target-specific numerical quality, complete local execution, memory and sustained behavior.
+
+<a id="embedded_sa8775p-qwen27"></a>
+### Embedded SA8775P ADP / AI Hub × Qwen3.8-27B
+
+**BLOCKED — none**. No complete qualified target artifact and device-local stateful Omni backend. Hub jobs provide component execution, not an on-device generation/streaming controller; ADB has no attached device.
+
+Evidence: [hub-inventory](evidence/hub-inventory.json), [host-wsl](evidence/host-wsl.json), [backend-factory](evidence/backend-factory.py), [graph-contract](evidence/graph-contract.py).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="embedded_sa8775p-minicpm"></a>
+### Embedded SA8775P ADP / AI Hub × MiniCPM-o 4.5
+
+**BLOCKED — none**. No complete qualified target artifact and device-local stateful Omni backend. Hub jobs provide component execution, not an on-device generation/streaming controller; ADB has no attached device.
+
+Evidence: [hub-inventory](evidence/hub-inventory.json), [host-wsl](evidence/host-wsl.json), [backend-factory](evidence/backend-factory.py), [graph-contract](evidence/graph-contract.py).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="embedded_sa8775p-vla"></a>
+### Embedded SA8775P ADP / AI Hub × InternVLA-A1
+
+**BLOCKED — none**. No complete qualified target artifact and device-local stateful Omni backend. Hub jobs provide component execution, not an on-device generation/streaming controller; ADB has no attached device.
+
+Evidence: [hub-inventory](evidence/hub-inventory.json), [host-wsl](evidence/host-wsl.json), [backend-factory](evidence/backend-factory.py), [graph-contract](evidence/graph-contract.py).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="embedded_rb3-spark"></a>
+### Embedded RB3 Gen 2 / QCS6490 / AI Hub × Spark-X2.5
+
+**BLOCKED — none**. No complete qualified target artifact and device-local stateful Omni backend. Hub jobs provide component execution, not an on-device generation/streaming controller; ADB has no attached device.
+
+Evidence: [hub-inventory](evidence/hub-inventory.json), [host-wsl](evidence/host-wsl.json), [backend-factory](evidence/backend-factory.py), [graph-contract](evidence/graph-contract.py).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="embedded_rb3-tts"></a>
+### Embedded RB3 Gen 2 / QCS6490 / AI Hub × Qwen3-TTS 0.6B CustomVoice
+
+**PROFILE_ONLY — C-timing-only**. Historical GPU vocoder profile re-fetched successfully. The attempted NPU talker build was rejected for floating-point inputs. No full model pipeline.
+
+Evidence: [tts-rb3-gpu](evidence/tts-rb3-gpu.json), [tts-rb3-talker-rejected](evidence/tts-rb3-talker-rejected.json), [tts-other-devices](evidence/tts-other-devices.json), [tts-other-device-code](evidence/tts-other-device-code.py).
+
+Next: Validate target-specific numerical quality, complete local execution, memory and sustained behavior.
+
+<a id="embedded_rb3-qwen27"></a>
+### Embedded RB3 Gen 2 / QCS6490 / AI Hub × Qwen3.8-27B
+
+**BLOCKED — none**. No complete qualified target artifact and device-local stateful Omni backend. Hub jobs provide component execution, not an on-device generation/streaming controller; ADB has no attached device.
+
+Evidence: [hub-inventory](evidence/hub-inventory.json), [host-wsl](evidence/host-wsl.json), [backend-factory](evidence/backend-factory.py), [graph-contract](evidence/graph-contract.py).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="embedded_rb3-minicpm"></a>
+### Embedded RB3 Gen 2 / QCS6490 / AI Hub × MiniCPM-o 4.5
+
+**BLOCKED — none**. No complete qualified target artifact and device-local stateful Omni backend. Hub jobs provide component execution, not an on-device generation/streaming controller; ADB has no attached device.
+
+Evidence: [hub-inventory](evidence/hub-inventory.json), [host-wsl](evidence/host-wsl.json), [backend-factory](evidence/backend-factory.py), [graph-contract](evidence/graph-contract.py).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.
+
+<a id="embedded_rb3-vla"></a>
+### Embedded RB3 Gen 2 / QCS6490 / AI Hub × InternVLA-A1
+
+**BLOCKED — none**. No complete qualified target artifact and device-local stateful Omni backend. Hub jobs provide component execution, not an on-device generation/streaming controller; ADB has no attached device.
+
+Evidence: [hub-inventory](evidence/hub-inventory.json), [host-wsl](evidence/host-wsl.json), [backend-factory](evidence/backend-factory.py), [graph-contract](evidence/graph-contract.py).
+
+Next: Supply and validate the missing matching artifact/backend, then run the complete specified workload.

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/prior-matrix/evidence/backend-dml.json

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benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/prior-matrix/evidence/backend-factory.py

@@ -0,0 +1,10 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Factories for non-vLLM stages owned by the existing StageRuntime."""
+
+
+def create_graph_client(metadata, config, ledger, reservation):
+    from .graph import GraphStageClient
+
+    if config.get("backend", {}).get("name") != "external.graph.v1":
+        raise ValueError("graph stage requires backend.name=external.graph.v1")
+    return GraphStageClient(metadata, config["backend"], ledger, reservation)

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/prior-matrix/evidence/backend-npu.json

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+          "type": "tensor(float)",
+          "shape": [
+            256,
+            1024
+          ]
+        }
+      ],
+      "warmup_s": 0.002900300023611635,
+      "profile_path": "onnxruntime_profile__2026-09-22_11-18-52_927.json",
+      "node_counts": {
+        "CPUExecutionProvider": 2,
+        "vitisai": 1
+      },
+      "total_nodes": 3,
+      "target_nodes": 1,
+      "fraction_on_target": 0.3333333333333333,
+      "rss_bytes": 283721728
+    }
+  },
+  "finite": true,
+  "status": "passed",
+  "ledger_after_shutdown": {
+    "capacities": {
+      "machine:ram": 2147483648
+    },
+    "reserved": {
+      "machine:ram": 0
+    },
+    "owners": [],
+    "quarantined": []
+  },
+  "ended_unix": 1790047136.6063926
+}

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/prior-matrix/evidence/graph-contract.py

@@ -0,0 +1,219 @@
+# SPDX-License-Identifier: Apache-2.0
+"""One bounded, non-preemptible graph stage using the existing worker.
+
+The runtime owns startup and capacity. This adapter owns one in-flight call,
+including its output until acknowledged by the consumer. No token scheduler,
+provider fallback, or automatic worker replay is introduced here.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import dataclasses
+import math
+import uuid
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+
+from omni_stage_contracts import ArtifactManifest, BufferRef, StageEvent, StageRequest
+from vllm_omni.edge.local.external.client import ExternalWorker
+from vllm_omni.engine.resource_ledger import ResourceUnavailable
+from vllm_omni.engine.stage_client import StageClientBase
+from vllm_omni.host.routes import resolve_route
+from vllm_omni.outputs import OmniRequestOutput
+
+
+class GraphStageClient(StageClientBase):
+    def __init__(self, metadata, config, ledger, reservation) -> None:
+        for name, value in vars(metadata).items():
+            setattr(self, name, value)
+        self.stage_type = "graph"
+        self._ledger, self._reservation = ledger, reservation
+        self._generation = uuid.uuid4().hex
+        self._config = dict(config)
+        self._worker = None
+        self._closed = False
+        self._active: str | None = None
+        self._task: asyncio.Task | None = None
+        self._output: OmniRequestOutput | None = None
+        self._epoch = 0
+        self._max_io_bytes = int(config.get("max_io_bytes", 8 << 20))
+        if self._max_io_bytes <= 0 or self._max_io_bytes > min(reservation.demands.values()):
+            raise ValueError("graph I/O limit must be positive and fit the stage reservation")
+        try:
+            manifest_path = Path(config["manifest"]).resolve()
+            manifest = ArtifactManifest.read(manifest_path)
+            graph_name = manifest.metadata["graph_file"]
+            examples_name = manifest.metadata["example_inputs_file"]
+            if graph_name not in manifest.files or examples_name not in manifest.files:
+                raise ValueError("graph and placement examples must be hashed artifact payloads")
+            if sum((manifest_path.parent / n).stat().st_size for n in manifest.files) > min(
+                reservation.demands.values()
+            ):
+                raise ResourceUnavailable("artifact files exceed stage memory reservation")
+            route = resolve_route(config["route"])
+            self._worker = ExternalWorker(
+                route, start_timeout_s=float(config.get("start_timeout_s", 120)), max_payload_bytes=self._max_io_bytes
+            )
+            self._worker.start()
+            with np.load(manifest_path.parent / examples_name, allow_pickle=False) as example:
+                inputs = {name: example[name] for name in example.files}
+            self._describe(inputs)
+            self.load_report = self._worker.load(
+                manifest_path.parent / graph_name,
+                example_inputs=inputs,
+                ep_dir=config.get("ep_dir"),
+                intra_op_num_threads=config.get("intra_op_num_threads"),
+                profile_prefix=config.get("profile_prefix"),
+                artifact_files=[manifest_path.parent / name for name in manifest.files],
+            )
+            report = self.load_report
+            minimum = float(config.get("min_fraction_on_target", 1.0))
+            if not 0 < minimum <= 1:
+                raise ValueError("placement threshold must be in (0, 1]")
+            if (
+                report.requested_provider_missing
+                or report.fraction_on_target is None
+                or not math.isfinite(report.fraction_on_target)
+                or not 0 < report.fraction_on_target <= 1
+                or report.target_nodes <= 0
+                or report.fraction_on_target < minimum
+            ):
+                raise RuntimeError(f"REFUSE_EP_PLACEMENT: {report.summary()}")
+            allowed = set(config.get("allowed_providers", report.session_providers if route.ep == "cpu" else ()))
+            if not allowed or set(report.node_counts) - allowed:
+                raise RuntimeError("REFUSE_EP_PLACEMENT: declare all permitted execution providers")
+            if report.rss_bytes > min(reservation.demands.values()):
+                raise ResourceUnavailable("loaded worker RSS exceeds stage memory reservation")
+            self._input_schema = {name: (value.dtype.str, value.shape) for name, value in inputs.items()}
+            from omni_stage_contracts.types import file_digest
+
+            self.execution_plan = {
+                "backend": "external.graph.v1",
+                "stage_id": self.stage_id,
+                "worker_generation": self._generation,
+                "artifact_sha256": file_digest(manifest_path),
+                "component": manifest.component,
+                "route": route.to_dict(),
+                "reserved_bytes": dict(reservation.demands),
+                "max_io_bytes": self._max_io_bytes,
+                "placement": report.to_dict(),
+                "worker": {k: v for k, v in self._worker.hello.items() if k != "token"},
+                "evidence": "B",
+                "stateful": False,
+            }
+        except BaseException:
+            self.shutdown()
+            raise
+
+    def _describe(self, tensors: dict[str, np.ndarray]) -> tuple[BufferRef, ...]:
+        refs = tuple(
+            BufferRef(
+                name, str(self.stage_id), self._generation, array.dtype.name, tuple(array.shape), int(array.nbytes)
+            )
+            for name, array in tensors.items()
+        )
+        if sum(r.nbytes for r in refs) > self._max_io_bytes:
+            raise ResourceUnavailable("graph payload exceeds admitted I/O bound")
+        return refs
+
+    async def add_request_async(self, request_id: str, prompt: Any, params: Any = None) -> None:
+        self.check_health()
+        if self._active is not None:
+            raise ResourceUnavailable("graph stage has an unacknowledged request; capacity is one")
+        if not isinstance(prompt, dict) or not isinstance(prompt.get("tensors"), dict):
+            raise ValueError("graph prompt must contain a tensors mapping supplied by its model adapter")
+        inputs = {name: np.asarray(value) for name, value in prompt["tensors"].items()}
+        refs = self._describe(inputs)
+        if {name: (value.dtype.str, value.shape) for name, value in inputs.items()} != self._input_schema:
+            raise ValueError("input names/dtypes/shapes differ from the placement-verified artifact bucket")
+        # Copy at submission: application mutation cannot change an admitted call.
+        inputs = {name: value.copy() for name, value in inputs.items()}
+        self._epoch += 1
+        epoch = self._epoch
+        request = StageRequest(request_id, self.stage_id, epoch, self._generation, inputs=refs)
+        self._active = request_id
+        self._task = asyncio.create_task(self._run(request, inputs), name=f"graph-{self.stage_id}-{request_id}")
+
+    async def _run(self, request: StageRequest, inputs: dict[str, np.ndarray]) -> None:
+        try:
+            outputs, timing = await asyncio.to_thread(self._worker.run, inputs)
+            refs = self._describe(outputs)
+            if sum(x.nbytes for x in outputs.values()) + sum(x.nbytes for x in inputs.values()) > self._max_io_bytes:
+                raise ResourceUnavailable("combined graph input/output exceeds admitted I/O bound")
+            event = StageEvent(
+                request.request_id, self.stage_id, request.epoch, 1, "tensor", self._generation, refs, terminal=True
+            )
+            output = OmniRequestOutput(
+                request_id=request.request_id,
+                stage_id=self.stage_id,
+                final_output_type=self.final_output_type or "latent",
+                _custom_output={"tensors": outputs, "stage_event": dataclasses.asdict(event)},
+                metrics={"graph_timing": timing.to_dict()},
+            )
+        except asyncio.CancelledError:
+            raise
+        except Exception as exc:
+            output = OmniRequestOutput.from_error(request.request_id, str(exc))
+            output.stage_id = self.stage_id
+        if not self._closed and self._epoch == request.epoch:
+            loop = asyncio.get_running_loop()
+            output._stage_release = lambda: loop.call_soon_threadsafe(
+                self.acknowledge, request.request_id, request.epoch, request.worker_generation
+            )
+            self._output = output
+
+    def get_graph_output_nowait(self):
+        # Delivery is separate from release: the slot stays charged until ACK.
+        output, self._output = self._output, None
+        return output
+
+    def acknowledge(self, request_id: str, epoch: int, generation: str) -> None:
+        if (
+            self._active == request_id
+            and epoch == self._epoch
+            and generation == self._generation
+            and (self._task is None or self._task.done())
+        ):
+            self._active = None
+            self._task = None
+
+    async def abort_requests_async(self, request_ids: list[str]) -> None:
+        if self._active not in request_ids:
+            return
+        self._epoch += 1
+        self._output = None
+        # A completed call needs no worker kill. Otherwise cancellation retires
+        # this single-session worker; the runtime will report it unavailable.
+        if self._task is not None and not self._task.done():
+            drained = await asyncio.to_thread(self._worker.terminate)
+            self._closed = True
+            done, _ = await asyncio.wait({self._task}, timeout=5)
+            self._ledger.release(self._reservation, drained=drained and bool(done) and not self._task.cancelled())
+        self._active = None
+
+    def check_health(self) -> None:
+        if self._closed:
+            from vllm.v1.engine.exceptions import EngineDeadError
+
+            raise EngineDeadError()
+
+    async def collective_rpc_async(self, method, timeout=None, args=(), kwargs=None):
+        raise NotImplementedError(f"graph backend does not implement collective RPC {method}")
+
+    def shutdown(self) -> None:
+        self._closed = True
+        self._output = None
+        drained = self._worker is None or self._worker.terminate()
+        task = self._task
+        if task is not None and not task.done():
+            # Process exit does not release Python input/transport references.
+            # Keep them charged until the call coroutine actually unwinds.
+            self._ledger.release(self._reservation, drained=False)
+            task.add_done_callback(
+                lambda done: self._ledger.release(self._reservation, drained=drained and not done.cancelled())
+            )
+        else:
+            self._ledger.release(self._reservation, drained=drained)

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/prior-matrix/evidence/host-windows.json

@@ -0,0 +1,54 @@
+{
+  "timestamp": "2026-09-22T05:37:31.241692+00:00",
+  "platform": "Windows-11-10.0.26200-SP0",
+  "python": "C:\\Users\\zhout\\w2\\omni029venv\\Scripts\\python.exe",
+  "evidence": "preflight",
+  "packages": {
+    "vllm": "0.29.0+cu134",
+    "torch": "2.13.0+cu130",
+    "transformers": "5.14.1",
+    "onnxruntime": null,
+    "onnxruntime-directml": null
+  },
+  "search_roots": [
+    {
+      "path": "Microsoft.PowerShell.Core\\FileSystem::\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models",
+      "exists": false
+    },
+    {
+      "path": "C:\\Users\\zhout\\.cache\\huggingface\\hub",
+      "exists": true
+    },
+    {
+      "path": "C:\\Users\\zhout\\w2\\models",
+      "exists": true
+    }
+  ],
+  "artifacts": [
+    {
+      "directory": "C:\\Users\\zhout\\w2\\models\\Qwen3-TTS-0.6B-85e237c",
+      "name": "Qwen3-TTS-0.6B-85e237c",
+      "model_type": "qwen3_tts",
+      "architectures": [
+        "Qwen3TTSForConditionalGeneration"
+      ],
+      "weight_shards": 1,
+      "weight_bytes": 1811626576
+    }
+  ],
+  "vllm_platform": "cuda",
+  "registered_pipelines": {
+    "spark2_5": true,
+    "qwen3_tts": true,
+    "qwen3_5": false,
+    "minicpmo_4_5": true
+  },
+  "nvidia-smi": {
+    "returncode": 0,
+    "stdout": "NVIDIA GeForce RTX 5090 Laptop GPU, 24463 MiB, 20263 MiB\n",
+    "stderr": ""
+  },
+  "adb": {
+    "error": "[WinError 2] \u7cfb\u7edf\u627e\u4e0d\u5230\u6307\u5b9a\u7684\u6587\u4ef6\u3002"
+  }
+}

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/prior-matrix/evidence/host-wsl.json

@@ -0,0 +1,264 @@
+{
+  "timestamp": "2026-09-22T05:37:30.425887+00:00",
+  "platform": "Linux-6.18.33.2-microsoft-standard-WSL2-x86_64-with-glibc2.43",
+  "python": "/home/zhout/project/edge_infer/.venvs/omni-cuda-029/bin/python",
+  "evidence": "preflight",
+  "packages": {
+    "vllm": "0.29.0",
+    "torch": "2.13.0",
+    "transformers": "5.14.1",
+    "onnxruntime": "1.30.0",
+    "onnxruntime-directml": null
+  },
+  "search_roots": [
+    {
+      "path": "/home/zhout/project/edge_infer/models",
+      "exists": true
+    },
+    {
+      "path": "/home/zhout/.cache/huggingface/hub",
+      "exists": true
+    },
+    {
+      "path": "/mnt/c/Users/zhout/w2/models",
+      "exists": true
+    }
+  ],
+  "artifacts": [
+    {
+      "directory": "/home/zhout/project/edge_infer/models/Qwen3.8-27B-FP8",
+      "name": "Qwen3.8-27B-FP8",
+      "model_type": "qwen3_5",
+      "architectures": [
+        "Qwen3_5ForConditionalGeneration"
+      ],
+      "weight_shards": 66,
+      "weight_bytes": 30866866928
+    },
+    {
+      "directory": "/home/zhout/project/edge_infer/models/spark2_5-gptq-g128",
+      "name": "spark2_5-gptq-g128",
+      "model_type": "spark2_5",
+      "architectures": [
+        "Spark2_5ForCausalLM"
+      ],
+      "weight_shards": 1,
+      "weight_bytes": 1287662032
+    },
+    {
+      "directory": "/home/zhout/project/edge_infer/models/spark2_5-int4-g32",
+      "name": "spark2_5-int4-g32",
+      "model_type": "spark2_5",
+      "architectures": [
+        "Spark2_5ForCausalLM"
+      ],
+      "weight_shards": 1,
+      "weight_bytes": 1068136688
+    },
+    {
+      "directory": "/home/zhout/project/edge_infer/models/Spark-X2.5-1.7B-int8",
+      "name": "Spark-X2.5-1.7B-int8",
+      "model_type": "spark2_5",
+      "architectures": [
+        "Spark2_5ForCausalLM"
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+      "weight_shards": 1,
+      "weight_bytes": 1979003288
+    },
+    {
+      "directory": "/home/zhout/project/edge_infer/models/spark2_5-int4-g64-rtn",
+      "name": "spark2_5-int4-g64-rtn",
+      "model_type": "spark2_5",
+      "architectures": [
+        "Spark2_5ForCausalLM"
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+      "weight_shards": 1,
+      "weight_bytes": 961443656
+    },
+    {
+      "directory": "/home/zhout/project/edge_infer/models/Qwen3.8-27B-NVFP4",
+      "name": "Qwen3.8-27B-NVFP4",
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+      ],
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+      "weight_bytes": 21921697280
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+      "name": "spark2_5-int4-g64",
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+      "architectures": [
+        "Spark2_5ForCausalLM"
+      ],
+      "weight_shards": 1,
+      "weight_bytes": 961443656
+    },
+    {
+      "directory": "/home/zhout/project/edge_infer/models/spark2_5-int4-g32-expanded",
+      "name": "spark2_5-int4-g32-expanded",
+      "model_type": "spark2_5",
+      "architectures": [
+        "Spark2_5ForCausalLM"
+      ],
+      "weight_shards": 1,
+      "weight_bytes": 3415340632
+    },
+    {
+      "directory": "/home/zhout/project/edge_infer/models/spark2_5-int4-g128-nodequant",
+      "error": "[Errno 2] No such file or directory: '/home/zhout/project/edge_infer/models/spark2_5-int4-g128-nodequant/model.safetensors'"
+    },
+    {
+      "directory": "/home/zhout/project/edge_infer/models/spark2_5-int4-g32-rtn",
+      "name": "spark2_5-int4-g32-rtn",
+      "model_type": "spark2_5",
+      "architectures": [
+        "Spark2_5ForCausalLM"
+      ],
+      "weight_shards": 1,
+      "weight_bytes": 1068136688
+    },
+    {
+      "directory": "/home/zhout/project/edge_infer/models/spark2_5-int4-g64-fpembed",
+      "name": "spark2_5-int4-g64-fpembed",
+      "model_type": "spark2_5",
+      "architectures": [
+        "Spark2_5ForCausalLM"
+      ],
+      "weight_shards": 1,
+      "weight_bytes": 1347320208
+    },
+    {
+      "directory": "/home/zhout/project/edge_infer/models/spark2_5-int4-g64-nocustomop",
+      "error": "[Errno 2] No such file or directory: '/home/zhout/project/edge_infer/models/spark2_5-int4-g64-nocustomop/model.safetensors'"
+    },
+    {
+      "directory": "/home/zhout/project/edge_infer/models/spark2_5-int4-g64-expanded",
+      "name": "spark2_5-int4-g64-expanded",
+      "model_type": "spark2_5",
+      "architectures": [
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+      "weight_bytes": 3415340632
+    },
+    {
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+      "name": "spark2_5-gptq-asym-g64",
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+      "architectures": [
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+      ],
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+    {
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+      "name": "spark2_5-gptq-g128-head",
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+      "architectures": [
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+    {
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+      "name": "MiniCPM5-2B",
+      "model_type": "llama",
+      "architectures": [
+        "LlamaForCausalLM"
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+      "name": "spark2_5-int4-g128",
+      "model_type": "spark2_5",
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+        "Spark2_5ForCausalLM"
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+      "weight_bytes": 908097200
+    },
+    {
+      "directory": "/home/zhout/project/edge_infer/models/spark2_5-gptq-g64",
+      "name": "spark2_5-gptq-g64",
+      "model_type": "spark2_5",
+      "architectures": [
+        "Spark2_5ForCausalLM"
+      ],
+      "weight_shards": 1,
+      "weight_bytes": 1460473032
+    },
+    {
+      "directory": "/home/zhout/project/edge_infer/models/Spark-X2.5-4B",
+      "name": "Spark-X2.5-4B",
+      "model_type": "spark2_5",
+      "architectures": [
+        "Spark2_5ForCausalLM"
+      ],
+      "weight_shards": 5,
+      "weight_bytes": 8224192408
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+    {
+      "directory": "/home/zhout/.cache/huggingface/hub/models--Qwen--Qwen3.8-27B-FP8/snapshots/017b9c7af6b5689d5dd426a76e0bc077eb5ca20a",
+      "name": "models--Qwen--Qwen3.8-27B-FP8",
+      "model_type": "qwen3_5",
+      "architectures": [
+        "Qwen3_5ForConditionalGeneration"
+      ],
+      "weight_shards": 0,
+      "weight_bytes": 0
+    },
+    {
+      "directory": "/home/zhout/.cache/huggingface/hub/models--Qwen--Qwen3-TTS-12Hz-0.6B-CustomVoice/snapshots/85e237c12c027371202489a0ec509ded67b5e4b5",
+      "name": "models--Qwen--Qwen3-TTS-12Hz-0.6B-CustomVoice",
+      "model_type": "qwen3_tts",
+      "architectures": [
+        "Qwen3TTSForConditionalGeneration"
+      ],
+      "weight_shards": 1,
+      "weight_bytes": 1811626576
+    },
+    {
+      "directory": "/mnt/c/Users/zhout/w2/models/Qwen3-TTS-0.6B-85e237c",
+      "name": "Qwen3-TTS-0.6B-85e237c",
+      "model_type": "qwen3_tts",
+      "architectures": [
+        "Qwen3TTSForConditionalGeneration"
+      ],
+      "weight_shards": 1,
+      "weight_bytes": 1811626576
+    }
+  ],
+  "vllm_platform": "cuda",
+  "registered_pipelines": {
+    "spark2_5": true,
+    "qwen3_tts": true,
+    "qwen3_5": false,
+    "minicpmo_4_5": true
+  },
+  "nvidia-smi": {
+    "returncode": 0,
+    "stdout": "NVIDIA GeForce RTX 5090 Laptop GPU, 24463 MiB, 20263 MiB\n",
+    "stderr": ""
+  },
+  "adb": {
+    "returncode": 0,
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benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/prior-matrix/evidence/hub-repeatability.json

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benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/prior-matrix/evidence/minicpm-mobile-parity.json

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benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/prior-matrix/evidence/minicpm-s24.json

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benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/prior-matrix/evidence/minicpm-s25.json

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benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/prior-matrix/evidence/qwen-text-historical.json

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benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/prior-matrix/evidence/qwen-vision-dml.json

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benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/prior-matrix/evidence/qwen-vision-npu.json

@@ -0,0 +1,355 @@
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+   "fraction_on_target": 0.0,
+   "session_create_s": 75.55920579998929,
+   "warmup_s": 2.251566899998579,
+   "rss_bytes": 931209216,
+   "inputs": [
+    {
+     "name": "patches",
+     "type": "tensor(float)",
+     "shape": [
+      784,
+      1536
+     ]
+    }
+   ],
+   "outputs": [
+    {
+     "name": "embeds",
+     "type": "tensor(float)",
+     "shape": [
+      196,
+      5120
+     ]
+    }
+   ],
+   "profile_path": "\\\\wsl.localhost\\Ubuntu\\tmp\\claude-1000\\-home-zhout-project-edge-infer\\e1528a86-4037-4e80-b83f-3a2f6cd1e24f\\scratchpad\\qwen3_5_vision_tower_448_a16w8__2026-09-16_09-44-15_178.json",
+   "ep_directory": "C:\\Program Files\\WindowsApps\\WindowsWorkload.EP.AMD.VitisAI.Framework.1.8_1.8.63.0_x64__8wekyb3d8bbwe\\ExecutionProvider",
+   "requested_provider_missing": "",
+   "fallback_note": "",
+   "onnxruntime": "1.30.0",
+   "note": ""
+  }
+ }
+}

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/prior-matrix/evidence/qwen-windows.json

@@ -0,0 +1,21 @@
+{
+  "platform": "Windows-11-10.0.26200-SP0",
+  "config": {
+    "model": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\Qwen3.8-27B-NVFP4",
+    "language_model_only": true,
+    "max_model_len": 512,
+    "max_num_seqs": 1,
+    "max_num_batched_tokens": 128,
+    "enforce_eager": true,
+    "gpu_memory_utilization": 0.8,
+    "cpu_offload_gb": 2,
+    "dtype": "bfloat16",
+    "stage_init_timeout": 120
+  },
+  "status": "failed",
+  "scope": "public Omni, short text only; explicit 2 GiB native offload",
+  "start": 1790055532.5138774,
+  "error": "ValueError(\"Model '\\\\\\\\\\\\\\\\wsl.localhost\\\\\\\\Ubuntu\\\\\\\\home\\\\\\\\zhout\\\\\\\\project\\\\\\\\edge_infer\\\\\\\\models\\\\\\\\Qwen3.8-27B-NVFP4' did not resolve to a registered Omni pipeline or a supported diffusion model.\")",
+  "traceback": "Traceback (most recent call last):\n  File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\analysis\\experiments\\model_device_matrix_20260922\\qwen_omni_probe.py\", line 30, in main\n    engine = Omni(**config)\n             ^^^^^^^^^^^^^^\n  File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\vllm_omni\\entrypoints\\omni_base.py\", line 223, in __init__\n    self.engine = AsyncOmniEngine(\n                  ^^^^^^^^^^^^^^^^\n  File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\vllm_omni\\engine\\async_omni_engine.py\", line 228, in __init__\n    self.config_path, self.stage_configs = self._resolve_stage_configs(\n                                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\vllm_omni\\engine\\async_omni_engine.py\", line 1101, in _resolve_stage_configs\n    load_and_resolve_stage_configs(\n  File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\vllm_omni\\engine\\async_omni_engine.py\", line 106, in load_and_resolve_stage_configs\n    return resolve_omni_config(\n           ^^^^^^^^^^^^^^^^^^^^\n  File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\vllm_omni\\config\\resolver.py\", line 254, in resolve_omni_config\n    raise ValueError(\nValueError: Model '\\\\\\\\wsl.localhost\\\\Ubuntu\\\\home\\\\zhout\\\\project\\\\edge_infer\\\\models\\\\Qwen3.8-27B-NVFP4' did not resolve to a registered Omni pipeline or a supported diffusion model.\n",
+  "end": 1790055533.0640798
+}

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/prior-matrix/evidence/qwen-wsl.json

@@ -0,0 +1,21 @@
+{
+  "platform": "Linux-6.18.33.2-microsoft-standard-WSL2-x86_64-with-glibc2.43",
+  "config": {
+    "model": "/home/zhout/project/edge_infer/models/Qwen3.8-27B-NVFP4",
+    "language_model_only": true,
+    "max_model_len": 512,
+    "max_num_seqs": 1,
+    "max_num_batched_tokens": 128,
+    "enforce_eager": true,
+    "gpu_memory_utilization": 0.8,
+    "cpu_offload_gb": 2,
+    "dtype": "bfloat16",
+    "stage_init_timeout": 120
+  },
+  "status": "failed",
+  "scope": "public Omni, short text only; explicit 2 GiB native offload",
+  "start": 1790055472.1485431,
+  "error": "ValueError(\"Model '/home/zhout/project/edge_infer/models/Qwen3.8-27B-NVFP4' did not resolve to a registered Omni pipeline or a supported diffusion model.\")",
+  "traceback": "Traceback (most recent call last):\n  File \"/home/zhout/project/edge_infer/analysis/experiments/model_device_matrix_20260922/qwen_omni_probe.py\", line 30, in main\n    engine = Omni(**config)\n             ^^^^^^^^^^^^^^\n  File \"/home/zhout/project/edge_infer/vllm-omni-edge/vllm_omni/entrypoints/omni_base.py\", line 223, in __init__\n    self.engine = AsyncOmniEngine(\n                  ^^^^^^^^^^^^^^^^\n  File \"/home/zhout/project/edge_infer/vllm-omni-edge/vllm_omni/engine/async_omni_engine.py\", line 228, in __init__\n    self.config_path, self.stage_configs = self._resolve_stage_configs(\n                                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/zhout/project/edge_infer/vllm-omni-edge/vllm_omni/engine/async_omni_engine.py\", line 1101, in _resolve_stage_configs\n    load_and_resolve_stage_configs(\n  File \"/home/zhout/project/edge_infer/vllm-omni-edge/vllm_omni/engine/async_omni_engine.py\", line 106, in load_and_resolve_stage_configs\n    return resolve_omni_config(\n           ^^^^^^^^^^^^^^^^^^^^\n  File \"/home/zhout/project/edge_infer/vllm-omni-edge/vllm_omni/config/resolver.py\", line 254, in resolve_omni_config\n    raise ValueError(\nValueError: Model '/home/zhout/project/edge_infer/models/Qwen3.8-27B-NVFP4' did not resolve to a registered Omni pipeline or a supported diffusion model.\n",
+  "end": 1790055473.065098
+}

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/prior-matrix/evidence/spark-cpu-log.log

@@ -0,0 +1,185 @@
+/home/zhout/project/edge_infer/vllm-omni-edge/vllm_omni/version.py:55: RuntimeWarning: vLLM and vLLM-Omni appear to have mismatched major/minor versions:
+ --> vLLM-Omni version 0.29.0rc2.dev71+g00510ce38.d20260922
+ --> vLLM version 0.28.0
+This will likely cause compatibility issues.
+  warn_if_misaligned_vllm_version()
+W0922 13:34:23.252000 629687 .venvs/omni-cpu/lib/python3.12/site-packages/torch/utils/_pytree.py:630] <enum 'KernelPreference'> is an Enum subclass and is now natively supported by torch.compile as an opaque value type. Calling register_constant() on Enum subclasses is deprecated and will be an error in a future release.
+W0922 13:34:23.278000 629687 .venvs/omni-cpu/lib/python3.12/site-packages/torch/utils/_pytree.py:630] <enum 'ScaleCalculationMode'> is an Enum subclass and is now natively supported by torch.compile as an opaque value type. Calling register_constant() on Enum subclasses is deprecated and will be an error in a future release.
+INFO 09-22 13:34:24 [importing.py:53] Triton is installed but 0 active driver(s) found (expected 1). Disabling Triton to prevent runtime errors.
+WARNING 09-22 13:34:24 [importing.py:65] Triton is installed, but doesn't include CPU backend. Disabling Triton.
+INFO 09-22 13:34:24 [importing.py:88] Triton not installed or not compatible; certain GPU-related functions will not be available.
+INFO 09-22 13:34:25 [patch.py:259] NVFP4 W4A4 weight_scale NaN-clamp: installed.
+INFO 09-22 13:34:25 [patch.py:510] inductor factorable-divisibility patch: installed.
+INFO 09-22 13:34:25 [patch.py:640] CPU AOT compile-cache load shim installed: compiled models are reused across engine starts instead of being recompiled.
+INFO 09-22 13:34:25 [patch.py:750] CPU weight-load memory release installed (set VLLM_OMNI_CPU_RELEASE_MEMORY=0 to disable).
+[transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'sliding_attention', 'full_attention'}
+[transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'sliding_attention', 'full_attention'}
+========================================================================
+capability-masked startup (--mask gpu_discrete): the no-NVIDIA class
+========================================================================
+ADMITTED on cpu (AMD Ryzen AI 9 HX 370 w/ Radeon 890M) via vllm:cpu
+  artifact  e25985ca3f2a44a2 compressed-tensors:int8 1.84GiB
+  budget    6.55GiB of 27.96GiB host_ram (available)
+    weights                 1887 MiB  host_ram  session_weights D  1 safetensors shard(s), on-disk size
+    kv_cache                 280 MiB  host_ram  session         E  flat budget at 1x context; 96 MiB if the backend accounts 21 sliding layers separately
+    activations               96 MiB  host_ram  request         E  2048-token prefill chunk x 12 live tensors
+    backend_workspace        512 MiB  host_ram  session         E  attention scratch, driver/allocator context
+    load_transient          1887 MiB  host_ram  load            E  freed once the weights are resident; peaks with the largest shard
+    external_reserve        1024 MiB  host_ram  session         E  memory this engine does not own on a shared pool
+    safety_margin           1024 MiB  host_ram  session         E  fragmentation and allocator slack
+  kv        280 MiB flat; 96 MiB if the backend accounts 21 sliding@512 layers separately (2.91x)
+  not used: [no_exported_graph] npu:amd: npu:amd executes exported graphs (['onnx:a16w8']), not checkpoints, so a whole autoregressive text session cannot be placed on it
+  not used: [no_exported_graph] gpu_integrated:amd: gpu_integrated:amd executes exported graphs (['onnx:fp16', 'onnx:fp32']), not checkpoints, so a whole autoregressive text session cannot be placed on it
+
+========================================================================
+device-visible startup
+========================================================================
+ADMITTED on cpu (AMD Ryzen AI 9 HX 370 w/ Radeon 890M) via vllm:cpu
+  artifact  e25985ca3f2a44a2 compressed-tensors:int8 1.84GiB
+  budget    6.55GiB of 27.99GiB host_ram (available)
+    weights                 1887 MiB  host_ram  session_weights D  1 safetensors shard(s), on-disk size
+    kv_cache                 280 MiB  host_ram  session         E  flat budget at 1x context; 96 MiB if the backend accounts 21 sliding layers separately
+    activations               96 MiB  host_ram  request         E  2048-token prefill chunk x 12 live tensors
+    backend_workspace        512 MiB  host_ram  session         E  attention scratch, driver/allocator context
+    load_transient          1887 MiB  host_ram  load            E  freed once the weights are resident; peaks with the largest shard
+    external_reserve        1024 MiB  host_ram  session         E  memory this engine does not own on a shared pool
+    safety_margin           1024 MiB  host_ram  session         E  fragmentation and allocator slack
+  kv        280 MiB flat; 96 MiB if the backend accounts 21 sliding@512 layers separately (2.91x)
+  not used: [no_exported_graph] npu:amd: npu:amd executes exported graphs (['onnx:a16w8']), not checkpoints, so a whole autoregressive text session cannot be placed on it
+  not used: [no_exported_graph] gpu_integrated:amd: gpu_integrated:amd executes exported graphs (['onnx:fp16', 'onnx:fp32']), not checkpoints, so a whole autoregressive text session cannot be placed on it
+
+INFO 09-22 13:34:27 [engine.py:392] [local] loading /home/zhout/project/edge_infer/models/Spark-X2.5-1.7B-int8 on cpu with {'max_model_len': 4096, 'max_num_seqs': 1, 'max_num_batched_tokens': 2048, 'enforce_eager': True, 'enable_prefix_caching': False, 'gpu_memory_utilization': 0.24, 'kv_cache_memory_bytes': 293601280, 'dtype': 'bfloat16'}
+INFO 09-22 13:34:27 [omni_base.py:216] [AsyncOmni] Initializing with model /home/zhout/project/edge_infer/models/Spark-X2.5-1.7B-int8
+INFO 09-22 13:34:27 [async_omni_engine.py:172] [AsyncOmniEngine] Initializing with model /home/zhout/project/edge_infer/models/Spark-X2.5-1.7B-int8
+[transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'sliding_attention', 'full_attention'}
+[transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'sliding_attention', 'full_attention'}
+[transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'sliding_attention', 'full_attention'}
+INFO 09-22 13:34:27 [async_omni_engine.py:290] [AsyncOmniEngine] Launching Orchestrator thread with 1 stages
+INFO 09-22 13:34:27 [stage_init_utils.py:768] [stage_init] Set VLLM_WORKER_MULTIPROC_METHOD=spawn
+WARNING 09-22 13:34:27 [utils.py:501] Dropping unknown OmniEngineArgs field 'inline_diffusion' (not declared on the dataclass)
+[transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'sliding_attention', 'full_attention'}
+[transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'sliding_attention', 'full_attention'}
+[transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'sliding_attention', 'full_attention'}
+INFO 09-22 13:34:27 [model.py:672] Resolved architecture: Spark2_5ForCausalLM
+INFO 09-22 13:34:27 [model.py:1965] Using max model len 4096
+INFO 09-22 13:34:28 [scheduler.py:242] Chunked prefill is enabled with max_num_batched_tokens=2048.
+WARNING 09-22 13:34:28 [vllm.py:1285] Enforce eager set, disabling torch.compile and CUDAGraphs. This is equivalent to setting -cc.mode=none -cc.cudagraph_mode=none
+WARNING 09-22 13:34:28 [vllm.py:1354] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored.
+INFO 09-22 13:34:28 [kernel.py:308] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native'])
+INFO 09-22 13:34:28 [patch.py:692] Restored explicit kv_cache_memory_bytes=293601280 (0.273 GiB); VLLM_CPU_KVCACHE_SPACE would have overridden it.
+WARNING 09-22 13:34:28 [vllm.py:652] Model Runner V2 requires Triton; using the V1 model runner instead.
+INFO 09-22 13:34:28 [compilation.py:329] Enabled custom fusions: norm_quant, act_quant
+WARNING 09-22 13:34:28 [stage_runtime.py:778] [stage_init] Stage-0 replica 0 physical devices None are not integer GPU ids; all serial LLM replicas will share one init group
+/home/zhout/project/edge_infer/vllm-omni-edge/vllm_omni/version.py:55: RuntimeWarning: vLLM and vLLM-Omni appear to have mismatched major/minor versions:
+ --> vLLM-Omni version 0.29.0rc2.dev71+g00510ce38.d20260922
+ --> vLLM version 0.28.0
+This will likely cause compatibility issues.
+  warn_if_misaligned_vllm_version()
+W0922 13:34:33.190000 629819 .venvs/omni-cpu/lib/python3.12/site-packages/torch/utils/_pytree.py:630] <enum 'KernelPreference'> is an Enum subclass and is now natively supported by torch.compile as an opaque value type. Calling register_constant() on Enum subclasses is deprecated and will be an error in a future release.
+W0922 13:34:33.225000 629819 .venvs/omni-cpu/lib/python3.12/site-packages/torch/utils/_pytree.py:630] <enum 'ScaleCalculationMode'> is an Enum subclass and is now natively supported by torch.compile as an opaque value type. Calling register_constant() on Enum subclasses is deprecated and will be an error in a future release.
+INFO 09-22 13:34:34 [importing.py:53] Triton is installed but 0 active driver(s) found (expected 1). Disabling Triton to prevent runtime errors.
+WARNING 09-22 13:34:34 [importing.py:65] Triton is installed, but doesn't include CPU backend. Disabling Triton.
+INFO 09-22 13:34:34 [importing.py:88] Triton not installed or not compatible; certain GPU-related functions will not be available.
+INFO 09-22 13:34:35 [patch.py:259] NVFP4 W4A4 weight_scale NaN-clamp: installed.
+INFO 09-22 13:34:35 [patch.py:510] inductor factorable-divisibility patch: installed.
+INFO 09-22 13:34:35 [patch.py:640] CPU AOT compile-cache load shim installed: compiled models are reused across engine starts instead of being recompiled.
+INFO 09-22 13:34:35 [patch.py:750] CPU weight-load memory release installed (set VLLM_OMNI_CPU_RELEASE_MEMORY=0 to disable).
+(StageEngineCoreProc_stage0_replica0 pid=629819) INFO 09-22 13:34:36 [core.py:122] Initializing a V1 LLM engine (v0.28.0) with config: model='/home/zhout/project/edge_infer/models/Spark-X2.5-1.7B-int8', speculative_config=None, tokenizer='/home/zhout/project/edge_infer/models/Spark-X2.5-1.7B-int8', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=4096, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, decode_context_parallel_size=1, dcp_comm_backend=ag_rs, disable_custom_all_reduce=True, quantization=compressed-tensors, quantization_config=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cpu, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False, jit_monitor_mode='warn', jit_monitor_verbose=False), seed=0, served_model_name=/home/zhout/project/edge_infer/models/Spark-X2.5-1.7B-int8, enable_prefix_caching=False, enable_chunked_prefill=True, pooler_config=None, compilation_config={'mode': <CompilationMode.NONE: 0>, 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'ir_enable_torch_wrap': False, 'splitting_ops': [], 'compile_mm_encoder': False, 'cudagraph_mm_encoder': False, 'encoder_cudagraph_token_budgets': [], 'encoder_cudagraph_max_vision_items_per_batch': 0, 'encoder_cudagraph_max_frames_per_batch': None, 'compile_sizes': None, 'compile_ranges_endpoints': [2048], 'inductor_compile_config': {'enable_auto_functionalized_v2': False}, 'inductor_passes': {}, 'cudagraph_mode': <CUDAGraphMode.NONE: 0>, 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': True, 'fuse_act_quant': True, 'fuse_attn_quant': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'enable_qk_norm_rope_fusion': False, 'fuse_rope_kvcache_cat_mla': False, 'fuse_act_padding': False, 'fuse_qk_norm_rope_kvcache': False}, 'max_cudagraph_capture_size': None, 'dynamic_shapes_config': {'type': <DynamicShapesType.BACKED: 'backed'>, 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': False, 'static_all_moe_layers': []}, kernel_config=KernelConfig(ir_op_priority=IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native']), enable_flashinfer_autotune=True, enable_cutedsl_warmup=True, enable_jit_warmup=True, enable_bf16x3_router_gemm=False, moe_backend='auto', linear_backend='auto')
+(StageEngineCoreProc_stage0_replica0 pid=629819) INFO 09-22 13:34:36 [multiproc_executor.py:150] DP group leader: node_rank=0, node_rank_within_dp=0, master_addr=127.0.0.1, mq_connect_ip=172.21.141.90 (local), world_size=1, local_world_size=1
+(StageEngineCoreProc_stage0_replica0 pid=629819) INFO 09-22 13:34:36 [ompmultiprocessing.py:185] OpenMP thread binding info: 
+(StageEngineCoreProc_stage0_replica0 pid=629819) INFO 09-22 13:34:36 [ompmultiprocessing.py:185] 	VLLM_CPU_OMP_THREADS_BIND='auto', auto_setup=True, skip_setup=False
+(StageEngineCoreProc_stage0_replica0 pid=629819) INFO 09-22 13:34:36 [ompmultiprocessing.py:185] 	local_world_size=1, reserve_cpu_num=1
+(StageEngineCoreProc_stage0_replica0 pid=629819) INFO 09-22 13:34:36 [ompmultiprocessing.py:185] 	local_rank=0, core ids=[1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21]
+(StageEngineCoreProc_stage0_replica0 pid=629819) INFO 09-22 13:34:36 [ompmultiprocessing.py:185] 	reserved_cpus=[23]
+W0922 13:34:40.073000 629853 .venvs/omni-cpu/lib/python3.12/site-packages/torch/utils/_pytree.py:630] <enum 'KernelPreference'> is an Enum subclass and is now natively supported by torch.compile as an opaque value type. Calling register_constant() on Enum subclasses is deprecated and will be an error in a future release.
+W0922 13:34:40.099000 629853 .venvs/omni-cpu/lib/python3.12/site-packages/torch/utils/_pytree.py:630] <enum 'ScaleCalculationMode'> is an Enum subclass and is now natively supported by torch.compile as an opaque value type. Calling register_constant() on Enum subclasses is deprecated and will be an error in a future release.
+INFO 09-22 13:34:41 [importing.py:53] Triton is installed but 0 active driver(s) found (expected 1). Disabling Triton to prevent runtime errors.
+WARNING 09-22 13:34:41 [importing.py:65] Triton is installed, but doesn't include CPU backend. Disabling Triton.
+INFO 09-22 13:34:41 [importing.py:88] Triton not installed or not compatible; certain GPU-related functions will not be available.
+/home/zhout/project/edge_infer/vllm-omni-edge/vllm_omni/version.py:55: RuntimeWarning: vLLM and vLLM-Omni appear to have mismatched major/minor versions:
+ --> vLLM-Omni version 0.29.0rc2.dev71+g00510ce38.d20260922
+ --> vLLM version 0.28.0
+This will likely cause compatibility issues.
+  warn_if_misaligned_vllm_version()
+INFO 09-22 13:34:42 [patch.py:259] NVFP4 W4A4 weight_scale NaN-clamp: installed.
+INFO 09-22 13:34:42 [patch.py:510] inductor factorable-divisibility patch: installed.
+INFO 09-22 13:34:42 [patch.py:640] CPU AOT compile-cache load shim installed: compiled models are reused across engine starts instead of being recompiled.
+INFO 09-22 13:34:42 [patch.py:750] CPU weight-load memory release installed (set VLLM_OMNI_CPU_RELEASE_MEMORY=0 to disable).
+[W922 13:34:42.576317158 utils.cpp:68] Warning: NUMA binding: Using MEMBIND policy for memory allocation on the NUMA nodes (0). Memory allocations will be strictly bound to these NUMA nodes. (function init_cpu_memory_env)
+WARNING 09-22 13:34:42 [vllm.py:652] Model Runner V2 requires Triton; using the V1 model runner instead.
+(Worker pid=629853) WARNING 09-22 13:34:42 [cpu_worker.py:127] libiomp is not found in LD_PRELOAD. For best performance, please follow the section `set LD_PRELOAD` in https://docs.vllm.ai/en/latest/getting_started/installation/cpu/ to setup required pre-loaded libraries.
+(Worker pid=629853) INFO 09-22 13:34:42 [parallel_state.py:1638] world_size=1 rank=0 local_rank=0 distributed_init_method=file:///tmp/vllm_dist_18e922052e6e4cc9a96df0c8f74a41a3 backend=gloo
+(Worker pid=629853) INFO 09-22 13:34:43 [parallel_state.py:1982] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A, EPLB rank N/A
+(Worker pid=629853) INFO 09-22 13:34:43 [cpu_model_runner.py:131] Starting to load model /home/zhout/project/edge_infer/models/Spark-X2.5-1.7B-int8...
+(Worker pid=629853) INFO 09-22 13:34:43 [__init__.py:755] Selected CPUInt8ScaledMMLinearKernel for CompressedTensorsW8A8Int8
+(Worker pid=629853) INFO 09-22 13:34:43 [selector.py:217] Using HND KV cache layout for CPU_ATTN backend.
+(Worker pid=629853) INFO 09-22 13:34:43 [weight_utils.py:858] Filesystem type for checkpoints: EXT4. Checkpoint size: 1.84 GiB. Available RAM: 26.90 GiB.
+(Worker pid=629853) INFO 09-22 13:34:43 [weight_utils.py:881] Auto-prefetch is disabled because the filesystem (EXT4) is not a recognized network FS (NFS/Lustre). If you want to force prefetching, start vLLM with --safetensors-load-strategy=prefetch.
+(Worker pid=629853) ␍Loading safetensors checkpoint shards:   0% Completed | 0/1 [00:00<?, ?it/s]
+(Worker pid=629853) ␍Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:01<00:00,  1.20s/it]
+(Worker pid=629853) ␍Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:01<00:00,  1.20s/it]
+(Worker pid=629853) 
+(Worker pid=629853) INFO 09-22 13:34:44 [default_loader.py:430] Loading weights took 1.22 seconds
+(Worker pid=629853) INFO 09-22 13:34:45 [memory.py:91] Released 104 MiB after weight load (anonymous RSS 3570 -> 3466 MiB) via ['libc.so.6:malloc_trim', 'libtcmalloc_minimal.so.4:MallocExtension_ReleaseFreeMemory']
+(StageEngineCoreProc_stage0_replica0 pid=629819) WARNING 09-22 13:34:45 [torch_utils.py:250] OMP_NUM_THREADS=8 is set; leaving Torch threads at 8 for serving. Multi-threaded torch CPU ops during serving can degrade performance through spin-wait contention and cgroup CPU-quota throttling.
+(Worker pid=629853) INFO 09-22 13:34:45 [cpu_worker.py:255] Explicitly set (0.27/30.91) GiB for KV cache on node 0.
+(StageEngineCoreProc_stage0_replica0 pid=629819) INFO 09-22 13:34:45 [kv_cache_utils.py:1869] GPU KV cache size: 6,898 tokens, Maximum concurrency for 4,096 tokens per request: 1.68x
+(Worker pid=629853) INFO 09-22 13:34:45 [memory.py:91] Released -0 MiB after warm-up (anonymous RSS 3746 -> 3746 MiB) via ['libc.so.6:malloc_trim', 'libtcmalloc_minimal.so.4:MallocExtension_ReleaseFreeMemory']
+(StageEngineCoreProc_stage0_replica0 pid=629819) INFO 09-22 13:34:45 [core.py:354] init engine (profile, create kv cache, warmup model) took 0.06 s
+(StageEngineCoreProc_stage0_replica0 pid=629819) [transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'full_attention', 'sliding_attention'}
+(StageEngineCoreProc_stage0_replica0 pid=629819) [transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'full_attention', 'sliding_attention'}
+(StageEngineCoreProc_stage0_replica0 pid=629819) [transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'full_attention', 'sliding_attention'}
+(StageEngineCoreProc_stage0_replica0 pid=629819) WARNING 09-22 13:34:46 [scheduler.py:182] Using custom scheduler class vllm_omni.core.sched.omni_ar_scheduler.OmniARAsyncScheduler. This scheduler interface is not public and compatibility may not be maintained. If you have subclassed Scheduler instead of AsyncScheduler, you will see degraded performance due to async scheduling being disabled.
+(StageEngineCoreProc_stage0_replica0 pid=629819) WARNING 09-22 13:34:46 [vllm.py:652] Model Runner V2 requires Triton; using the V1 model runner instead.
+(StageEngineCoreProc_stage0_replica0 pid=629819) WARNING 09-22 13:34:46 [vllm.py:1285] Enforce eager set, disabling torch.compile and CUDAGraphs. This is equivalent to setting -cc.mode=none -cc.cudagraph_mode=none
+(StageEngineCoreProc_stage0_replica0 pid=629819) WARNING 09-22 13:34:46 [vllm.py:1354] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored.
+(StageEngineCoreProc_stage0_replica0 pid=629819) INFO 09-22 13:34:46 [kernel.py:308] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native'])
+(StageEngineCoreProc_stage0_replica0 pid=629819) INFO 09-22 13:34:46 [patch.py:692] Restored explicit kv_cache_memory_bytes=293601280 (0.273 GiB); VLLM_CPU_KVCACHE_SPACE would have overridden it.
+INFO 09-22 13:34:46 [stage_runtime.py:943] [StageRuntime] Stage 0 engine startup completed
+(StageEngineCoreProc_stage0_replica0 pid=629819) INFO 09-22 13:34:46 [compilation.py:329] Enabled custom fusions: norm_quant, act_quant
+INFO 09-22 13:34:46 [stage_engine_core_client.py:159] [StageEngineCoreClient] stage-0 [rep-0] initializing EngineCore
+INFO 09-22 13:34:46 [stage_engine_core_client.py:215] [StageEngineCoreClient] stage-0 [rep-0] EngineCore running
+INFO 09-22 13:34:46 [stage_runtime.py:958] [StageRuntime] Stage 0 initialized
+[transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'sliding_attention', 'full_attention'}
+[transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'sliding_attention', 'full_attention'}
+[transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'sliding_attention', 'full_attention'}
+INFO 09-22 13:34:49 [orchestrator.py:604] [Orchestrator] Starting event loop
+INFO 09-22 13:34:49 [async_omni_engine.py:323] [AsyncOmniEngine] Orchestrator ready with 1 stages
+INFO 09-22 13:34:49 [omni_base.py:235] [AsyncOmni] AsyncOmniEngine initialized in 21.30 seconds
+INFO 09-22 13:34:49 [omni_base.py:255] [AsyncOmni] Initialized with 1 stages for model /home/zhout/project/edge_infer/models/Spark-X2.5-1.7B-int8
+loaded in 21.30 s
+WARNING 09-22 13:34:49 [input_processor.py:296] Passing raw prompts to InputProcessor is deprecated and will be removed in v0.18. You should instead pass the outputs of Renderer.render_cmpl() or Renderer.render_chat().
+  chat_short   in=28 out=128 ttft=0.127s decode=16.41 tok/s
+  chat_tools   in=137 out=128 ttft=0.268s decode=16.29 tok/s
+  chat_long    in=2465 out=128 ttft=4.257s decode=15.51 tok/s
+  plain_0      in=5 out=128 ttft=0.067s decode=16.66 tok/s
+  plain_1      in=13 out=128 ttft=0.073s decode=16.43 tok/s
+  plain_2      in=18 out=128 ttft=0.079s decode=16.38 tok/s
+  plain_3      in=8 out=128 ttft=0.064s decode=15.84 tok/s
+  plain_4      in=7 out=128 ttft=0.068s decode=16.78 tok/s
+  plain_5      in=7 out=128 ttft=0.059s decode=16.51 tok/s
+  plain_6      in=3 out=128 ttft=0.070s decode=16.03 tok/s
+  plain_7      in=9 out=128 ttft=0.075s decode=16.22 tok/s
+  plain_8      in=9 out=128 ttft=0.065s decode=16.48 tok/s
+INFO 09-22 13:36:28 [orchestrator.py:917] [Orchestrator] Aborted request(s) ['09e230fd09a74804-12-bce4064d']
+INFO 09-22 13:36:28 [orchestrator.py:917] [Orchestrator] Aborted request(s) ['09e230fd09a74804-12-bce4064d']
+INFO 09-22 13:36:28 [async_omni.py:665] [AsyncOmni] Request 09e230fd09a74804-12-bce4064d aborted.
+  cancel: aborted=True after 8 events, leaked=0
+INFO 09-22 13:36:29 [omni_base.py:781] [AsyncOmni] Shutting down
+INFO 09-22 13:36:29 [async_omni_engine.py:1807] [AsyncOmniEngine] Shutting down Orchestrator
+INFO 09-22 13:36:29 [orchestrator.py:705] [Orchestrator] Received shutdown signal
+INFO 09-22 13:36:29 [orchestrator.py:3092] [Orchestrator] Shutting down all 1 client(s)
+INFO 09-22 13:36:29 [core_client.py:689] [shutdown] MPClient: start timeout=default
+INFO 09-22 13:36:29 [core_client.py:691] [shutdown] MPClient: stopping engine manager
+INFO 09-22 13:36:29 [utils.py:615] [shutdown] Process manager: send sigterm to process StageEngineCoreProc_stage0_replica0
+(Worker pid=629853) INFO 09-22 13:36:29 [multiproc_executor.py:833] Parent process exited, terminating worker queues
+(StageEngineCoreProc_stage0_replica0 pid=629819) INFO 09-22 13:36:29 [multiproc_executor.py:469] [shutdown] Executor: waiting for worker exit count=1
+(StageEngineCoreProc_stage0_replica0 pid=629819) INFO 09-22 13:36:31 [multiproc_executor.py:476] [shutdown] Executor: all workers exited gracefully
+INFO 09-22 13:36:34 [core_client.py:693] [shutdown] MPClient: engine manager stopped
+INFO 09-22 13:36:34 [core_client.py:694] [shutdown] MPClient: cleaning up background resources
+INFO 09-22 13:36:34 [core_client.py:696] [shutdown] MPClient: complete
+INFO 09-22 13:36:34 [stage_pool.py:1354] [StagePool] Stage 0 replica 0 shut down
+
+acceptance: PASS ({'prompts': 12, 'min_tokens_required': 128, 'all_reached_min': True, 'cancel_clean': True})
+
+wrote /home/zhout/project/edge_infer/analysis/experiments/model_device_matrix_20260922/spark-cpu-current.json

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/prior-matrix/evidence/spark-cuda-windows.log

@@ -0,0 +1,149 @@
+INFO 09-22 11:56:02 [patch.py:259] NVFP4 W4A4 weight_scale NaN-clamp: installed.
+INFO 09-22 11:56:02 [patch.py:510] inductor factorable-divisibility patch: installed.
+INFO 09-22 11:56:02 [patch.py:583] [cumem-cuda] CuMemAllocator._python_free_callback patched: asleep guard extended to all platforms.
+INFO 09-22 11:56:08 [scheduler.py:277] Chunked prefill is enabled with max_num_batched_tokens=2048.
+INFO 09-22 11:56:08 [kernel.py:369] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['native'], fused_add_rms_norm=['native'])
+--- Running test: test_the_masked_startup_reaches_a_decision
+[transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'full_attention', 'sliding_attention'}
+.--- Running test: test_a_refused_plan_cannot_be_started
+[transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'full_attention', 'sliding_attention'}
+.--- Running test: test_int8_is_refused_on_cuda_and_admitted_on_cpu
+[transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'full_attention', 'sliding_attention'}
+[transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'full_attention', 'sliding_attention'}
+.INFO 09-22 11:56:26 [engine.py:392] [local] loading \\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\models\Spark-X2.5-4B on cuda:0 with {'max_model_len': 4096, 'max_num_seqs': 1, 'max_num_batched_tokens': 2048, 'enforce_eager': True, 'enable_prefix_caching': False, 'gpu_memory_utilization': 0.53, 'kv_cache_memory_bytes': 754974720, 'dtype': 'bfloat16'}
+INFO 09-22 11:56:26 [omni_base.py:216] [AsyncOmni] Initializing with model \\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\models\Spark-X2.5-4B
+INFO 09-22 11:56:26 [async_omni_engine.py:172] [AsyncOmniEngine] Initializing with model \\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\models\Spark-X2.5-4B
+[transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'full_attention', 'sliding_attention'}
+[transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'full_attention', 'sliding_attention'}
+[transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'full_attention', 'sliding_attention'}
+INFO 09-22 11:56:26 [async_omni_engine.py:290] [AsyncOmniEngine] Launching Orchestrator thread with 1 stages
+WARNING 09-22 11:56:27 [utils.py:501] Dropping unknown OmniEngineArgs field 'inline_diffusion' (not declared on the dataclass)
+[transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'full_attention', 'sliding_attention'}
+[transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'full_attention', 'sliding_attention'}
+[transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'full_attention', 'sliding_attention'}
+INFO 09-22 11:56:27 [model.py:684] Resolved architecture: Spark2_5ForCausalLM
+INFO 09-22 11:56:27 [model.py:2021] Using max model len 4096
+WARNING 09-22 11:56:27 [vllm.py:1371] Enforce eager set, disabling torch.compile and CUDAGraphs. This is equivalent to setting -cc.mode=none -cc.cudagraph_mode=none
+WARNING 09-22 11:56:27 [vllm.py:1406] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored.
+INFO 09-22 11:56:27 [kernel.py:369] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['vllm_c', 'native'], fused_add_rms_norm=['vllm_c', 'native'])
+INFO 09-22 11:56:27 [vllm.py:1585] Cudagraph is disabled under eager mode
+INFO 09-22 11:56:27 [compilation.py:329] Enabled custom fusions: norm_quant, act_quant
+WARNING 09-22 11:56:27 [stage_runtime.py:765] [stage_init] Stage-0 replica 0 physical devices None are not integer GPU ids; all serial LLM replicas will share one init group
+INFO 09-22 11:56:45 [patch.py:259] NVFP4 W4A4 weight_scale NaN-clamp: installed.
+INFO 09-22 11:56:45 [patch.py:510] inductor factorable-divisibility patch: installed.
+INFO 09-22 11:56:45 [patch.py:583] [cumem-cuda] CuMemAllocator._python_free_callback patched: asleep guard extended to all platforms.
+(StageEngineCoreProc_stage0_replica0 pid=13348) INFO 09-22 11:56:49 [core.py:123] Initializing a V1 LLM engine (v0.29.0) with config: model='\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\Spark-X2.5-4B', speculative_config=None, tokenizer='\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\Spark-X2.5-4B', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=4096, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, decode_context_parallel_size=1, dcp_comm_backend=ag_rs, disable_custom_all_reduce=False, quantization=None, quantization_config=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, per_request_spec_decode_metrics='none', kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False, jit_monitor_mode='warn', jit_monitor_verbose=False), seed=0, served_model_name=\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\models\Spark-X2.5-4B, enable_prefix_caching=False, enable_chunked_prefill=True, pooler_config=None, compilation_config={'mode': <CompilationMode.NONE: 0>, 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['all'], 'ir_enable_torch_wrap': False, 'splitting_ops': [], 'compile_mm_encoder': False, 'cudagraph_mm_encoder': False, 'encoder_cudagraph_token_budgets': [], 'encoder_cudagraph_max_vision_items_per_batch': 0, 'encoder_cudagraph_max_frames_per_batch': None, 'compile_sizes': [], 'compile_ranges_endpoints': [2048], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': <CUDAGraphMode.NONE: 0>, 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': True, 'fuse_act_quant': True, 'fuse_attn_quant': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'enable_qk_norm_rope_fusion': False, 'fuse_rope_kvcache_cat_mla': False, 'fuse_act_padding': False, 'fuse_qk_norm_rope_kvcache': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': <DynamicShapesType.BACKED: 'backed'>, 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': False, 'static_all_moe_layers': []}, kernel_config=KernelConfig(ir_op_priority=IrOpPriorityConfig(rms_norm=['vllm_c', 'native'], fused_add_rms_norm=['vllm_c', 'native']), enable_flashinfer_autotune=True, enable_cutedsl_warmup=True, enable_jit_warmup=True, enable_bf16x3_router_gemm=False, moe_backend='auto', linear_backend='auto')
+(StageEngineCoreProc_stage0_replica0 pid=13348) INFO 09-22 11:56:50 [parallel_state.py:1775] world_size=1 rank=0 local_rank=0 distributed_init_method=file://C:\Users\zhout\AppData\Local\Temp/vllm_dist_cc490ec74fd54cbea88ba183b7556c4c backend=nccl
+(StageEngineCoreProc_stage0_replica0 pid=13348) WARNING 09-22 11:56:50 [parallel_state.py:1788] Distributed backend nccl is not available; falling back to gloo.
+(StageEngineCoreProc_stage0_replica0 pid=13348) INFO 09-22 11:56:51 [parallel_state.py:2119] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A, EPLB rank N/A
+(StageEngineCoreProc_stage0_replica0 pid=13348) WARNING 09-22 11:56:51 [gpu_ar_worker.py:120] OMNI GPUARWorker forces v1 model runner for omni hooks.
+(StageEngineCoreProc_stage0_replica0 pid=13348) INFO 09-22 11:56:51 [topk_topp_sampler.py:46] FlashInfer top-p/top-k sampling disabled via VLLM_USE_FLASHINFER_SAMPLER=0.
+(StageEngineCoreProc_stage0_replica0 pid=13348) WARNING 09-22 11:56:51 [base.py:173] [LLM Worker 0] Sleep Mode DISABLED.
+(StageEngineCoreProc_stage0_replica0 pid=13348) WARNING 09-22 11:56:51 [base.py:173] [LLM Worker 0] Sleep Mode DISABLED.
+(StageEngineCoreProc_stage0_replica0 pid=13348) INFO 09-22 11:56:51 [gpu_model_runner.py:5379] Starting to load model \\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\models\Spark-X2.5-4B...
+(StageEngineCoreProc_stage0_replica0 pid=13348) INFO 09-22 11:56:52 [cuda.py:492] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'TRITON_ATTN', 'FLEX_ATTENTION'].
+(StageEngineCoreProc_stage0_replica0 pid=13348) INFO 09-22 11:56:52 [flash_attn.py:897] Using FlashAttention version 2
+(StageEngineCoreProc_stage0_replica0 pid=13348) INFO 09-22 11:56:53 [weight_utils.py:863] Filesystem type for checkpoints: unknown. Checkpoint size: 7.66 GiB. Available RAM: 9.69 GiB.
+(StageEngineCoreProc_stage0_replica0 pid=13348) INFO 09-22 11:56:53 [weight_utils.py:886] Auto-prefetch is disabled because the filesystem (unknown) is not a recognized network FS (NFS/Lustre). If you want to force prefetching, start vLLM with --safetensors-load-strategy=prefetch.
+(StageEngineCoreProc_stage0_replica0 pid=13348) 
+Loading safetensors checkpoint shards:   0% Completed | 0/5 [00:00<?, ?it/s]
+(StageEngineCoreProc_stage0_replica0 pid=13348) 
+Loading safetensors checkpoint shards:  20% Completed | 1/5 [00:22<01:31, 23.00s/it]
+(StageEngineCoreProc_stage0_replica0 pid=13348) 
+Loading safetensors checkpoint shards:  40% Completed | 2/5 [00:47<01:11, 23.75s/it]
+(StageEngineCoreProc_stage0_replica0 pid=13348) 
+Loading safetensors checkpoint shards:  60% Completed | 3/5 [01:10<00:47, 23.59s/it]
+(StageEngineCoreProc_stage0_replica0 pid=13348) 
+Loading safetensors checkpoint shards:  80% Completed | 4/5 [01:34<00:23, 23.57s/it]
+(StageEngineCoreProc_stage0_replica0 pid=13348) 
+Loading safetensors checkpoint shards: 100% Completed | 5/5 [01:37<00:00, 16.25s/it]
+(StageEngineCoreProc_stage0_replica0 pid=13348) 
+Loading safetensors checkpoint shards: 100% Completed | 5/5 [01:37<00:00, 19.49s/it]
+(StageEngineCoreProc_stage0_replica0 pid=13348) 
+(StageEngineCoreProc_stage0_replica0 pid=13348) INFO 09-22 11:58:30 [default_loader.py:430] Loading weights took 97.54 seconds
+(StageEngineCoreProc_stage0_replica0 pid=13348) INFO 09-22 11:58:31 [gpu_model_runner.py:5471] Model loading took 7.66 GiB memory and 98.388669 seconds
+(StageEngineCoreProc_stage0_replica0 pid=13348) INFO 09-22 11:58:31 [utils.py:306] Using LBNHC KV cache layout.
+(StageEngineCoreProc_stage0_replica0 pid=13348) INFO 09-22 11:58:32 [kv_cache_utils.py:2032] GPU KV cache size: 5,105 tokens, Maximum concurrency for 4,096 tokens per request: 1.25x
+(StageEngineCoreProc_stage0_replica0 pid=13348) WARNING 09-22 11:58:32 [base.py:173] [LLM Worker 0] Sleep Mode DISABLED.
+(StageEngineCoreProc_stage0_replica0 pid=13348) INFO 09-22 11:58:32 [kernel_warmup.py:124] JIT kernel warmup starting.
+(StageEngineCoreProc_stage0_replica0 pid=13348) 
+JIT kernel warmup (1 compile keys):   0%|          | 0/1 [00:00<?, ?kernel/s]
+JIT kernel warmup (1 compile keys): 100%|██████████| 1/1 [00:00<00:00,  1.31kernel/s, ComputeSlotMappingKernel (1 keys)]
+JIT kernel warmup (1 compile keys): 100%|██████████| 1/1 [00:00<00:00,  1.31kernel/s, ComputeSlotMappingKernel (1 keys)]
+(StageEngineCoreProc_stage0_replica0 pid=13348) INFO 09-22 11:58:33 [kernel_warmup.py:134] JIT kernel warmup finished in 0.76s.
+(StageEngineCoreProc_stage0_replica0 pid=13348) INFO 09-22 11:58:34 [ll_bf16.py:30] cuteDSL (CUTLASS Python) not available, ll_bf16_gemm disabled
+(StageEngineCoreProc_stage0_replica0 pid=13348) INFO 09-22 11:58:34 [jit_monitor.py:85] Kernel JIT monitor activated; monitored JIT compilations during inference will use mode=warn.
+(StageEngineCoreProc_stage0_replica0 pid=13348) INFO 09-22 11:58:35 [torch_utils.py:277] Reducing Torch threads from 12 to 1 for serving. Set OMP_NUM_THREADS in the external environment to override.
+(StageEngineCoreProc_stage0_replica0 pid=13348) INFO 09-22 11:58:35 [core.py:368] init engine (profile, create kv cache, warmup model) took 3.41 s
+(StageEngineCoreProc_stage0_replica0 pid=13348) [transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'sliding_attention', 'full_attention'}
+(StageEngineCoreProc_stage0_replica0 pid=13348) [transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'sliding_attention', 'full_attention'}
+(StageEngineCoreProc_stage0_replica0 pid=13348) [transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'sliding_attention', 'full_attention'}
+(StageEngineCoreProc_stage0_replica0 pid=13348) WARNING 09-22 11:58:36 [scheduler.py:213] Using custom scheduler class vllm_omni.core.sched.omni_ar_scheduler.OmniARAsyncScheduler. This scheduler interface is not public and compatibility may not be maintained. If you have subclassed Scheduler instead of AsyncScheduler, you will see degraded performance due to async scheduling being disabled.
+(StageEngineCoreProc_stage0_replica0 pid=13348) WARNING 09-22 11:58:36 [omni_scheduler_mixin.py:118] OMNI scheduler forces v1 model runner for omni hooks.
+INFO 09-22 11:58:36 [stage_runtime.py:930] [StageRuntime] Stage 0 engine startup completed
+INFO 09-22 11:58:36 [stage_engine_core_client.py:159] [StageEngineCoreClient] stage-0 [rep-0] initializing EngineCore
+(StageEngineCoreProc_stage0_replica0 pid=13348) WARNING 09-22 11:58:36 [vllm.py:1371] Enforce eager set, disabling torch.compile and CUDAGraphs. This is equivalent to setting -cc.mode=none -cc.cudagraph_mode=none
+(StageEngineCoreProc_stage0_replica0 pid=13348) WARNING 09-22 11:58:36 [vllm.py:1406] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored.
+(StageEngineCoreProc_stage0_replica0 pid=13348) INFO 09-22 11:58:36 [kernel.py:369] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['vllm_c', 'native'], fused_add_rms_norm=['vllm_c', 'native'])
+(StageEngineCoreProc_stage0_replica0 pid=13348) INFO 09-22 11:58:36 [vllm.py:1585] Cudagraph is disabled under eager mode
+(StageEngineCoreProc_stage0_replica0 pid=13348) INFO 09-22 11:58:36 [compilation.py:329] Enabled custom fusions: norm_quant, act_quant
+INFO 09-22 11:58:36 [stage_engine_core_client.py:215] [StageEngineCoreClient] stage-0 [rep-0] EngineCore running
+INFO 09-22 11:58:36 [stage_runtime.py:945] [StageRuntime] Stage 0 initialized
+[transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'full_attention', 'sliding_attention'}
+[transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'full_attention', 'sliding_attention'}
+[transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='default': {'full_attention', 'sliding_attention'}
+INFO 09-22 11:58:39 [orchestrator.py:604] [Orchestrator] Starting event loop
+INFO 09-22 11:58:39 [async_omni_engine.py:323] [AsyncOmniEngine] Orchestrator ready with 1 stages
+INFO 09-22 11:58:39 [omni_base.py:235] [AsyncOmni] AsyncOmniEngine initialized in 132.84 seconds
+INFO 09-22 11:58:39 [omni_base.py:255] [AsyncOmni] Initialized with 1 stages for model \\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\models\Spark-X2.5-4B
+--- Running test: test_the_engine_loads_and_reports_where_it_landed
+.--- Running test: test_the_kv_pool_is_the_size_the_plan_asked_for
+.--- Running test: test_twelve_prompts_each_reach_128_tokens
+WARNING 09-22 11:58:39 [input_processor.py:315] Passing raw prompts to InputProcessor is deprecated and will be removed in the future. You should instead pass the outputs of Renderer.render_cmpl() or Renderer.render_chat().
+.--- Running test: test_the_budget_bounded_the_measured_peak
+.--- Running test: test_usage_separates_what_was_measured_from_what_was_estimated
+.--- Running test: test_cancel_releases_and_delivers_nothing_from_the_retired_epoch
+INFO 09-22 11:59:34 [orchestrator.py:917] [Orchestrator] Aborted request(s) ['56e2a41427434ee8-12-b33cf904']
+INFO 09-22 11:59:34 [orchestrator.py:917] [Orchestrator] Aborted request(s) ['56e2a41427434ee8-12-b33cf904']
+INFO 09-22 11:59:34 [async_omni.py:665] [AsyncOmni] Request 56e2a41427434ee8-12-b33cf904 aborted.
+.--- Running test: test_a_retired_handle_cannot_be_resubmitted
+INFO 09-22 11:59:35 [orchestrator.py:917] [Orchestrator] Aborted request(s) ['d9c8163be55a49af-13-becca8b2']
+INFO 09-22 11:59:35 [orchestrator.py:917] [Orchestrator] Aborted request(s) ['d9c8163be55a49af-13-becca8b2']
+INFO 09-22 11:59:35 [async_omni.py:665] [AsyncOmni] Request d9c8163be55a49af-13-becca8b2 aborted.
+.--- Running test: test_the_engine_still_generates_after_a_cancel
+.INFO 09-22 11:59:37 [omni_base.py:781] [AsyncOmni] Shutting down
+INFO 09-22 11:59:37 [async_omni_engine.py:1807] [AsyncOmniEngine] Shutting down Orchestrator
+INFO 09-22 11:59:37 [orchestrator.py:705] [Orchestrator] Received shutdown signal
+INFO 09-22 11:59:37 [orchestrator.py:3092] [Orchestrator] Shutting down all 1 client(s)
+INFO 09-22 11:59:37 [core_client.py:689] [shutdown] MPClient: start timeout=default
+INFO 09-22 11:59:37 [core_client.py:691] [shutdown] MPClient: stopping engine manager
+INFO 09-22 11:59:37 [utils.py:626] [shutdown] Process manager: send sigterm to process StageEngineCoreProc_stage0_replica0
+INFO 09-22 11:59:37 [core_client.py:693] [shutdown] MPClient: engine manager stopped
+INFO 09-22 11:59:37 [core_client.py:694] [shutdown] MPClient: cleaning up background resources
+INFO 09-22 11:59:37 [core_client.py:696] [shutdown] MPClient: complete
+INFO 09-22 11:59:37 [stage_pool.py:1354] [StagePool] Stage 0 replica 0 shut down
+
+============================== warnings summary ===============================
+C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\jit\_script.py:365: 14 warnings
+  C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\jit\_script.py:365: DeprecationWarning: `torch.jit.script_method` is deprecated. Please switch to `torch.compile` or `torch.export`.
+    warnings.warn(
+
+tests/edge/local/test_e2e_local_text.py::test_the_masked_startup_reaches_a_decision
+  C:\Users\zhout\w2\omni029venv\Lib\site-packages\pytest_asyncio\plugin.py:916: PytestDeprecationWarning: Overriding the "event_loop_policy" fixture is deprecated and will be removed in a future version of pytest-asyncio. Use the "pytest_asyncio_loop_factories" hook to customize event loop creation.
+    warnings.warn(
+
+tests/edge/local/test_e2e_local_text.py::test_the_masked_startup_reaches_a_decision
+  tests\edge\local\test_e2e_local_text.py:109: PytestWarning: The test <Function test_the_masked_startup_reaches_a_decision> is marked with '@pytest.mark.asyncio' but it is not an async function. Please remove the asyncio mark. If the test is not marked explicitly, check for global marks applied via 'pytestmark'.
+    def test_the_masked_startup_reaches_a_decision(model_dir):
+
+tests/edge/local/test_e2e_local_text.py::test_a_refused_plan_cannot_be_started
+  tests\edge\local\test_e2e_local_text.py:124: PytestWarning: The test <Function test_a_refused_plan_cannot_be_started> is marked with '@pytest.mark.asyncio' but it is not an async function. Please remove the asyncio mark. If the test is not marked explicitly, check for global marks applied via 'pytestmark'.
+    def test_a_refused_plan_cannot_be_started(model_dir):
+
+tests/edge/local/test_e2e_local_text.py::test_int8_is_refused_on_cuda_and_admitted_on_cpu
+  tests\edge\local\test_e2e_local_text.py:133: PytestWarning: The test <Function test_int8_is_refused_on_cuda_and_admitted_on_cpu> is marked with '@pytest.mark.asyncio' but it is not an async function. Please remove the asyncio mark. If the test is not marked explicitly, check for global marks applied via 'pytestmark'.
+    @pytest.mark.skipif(
+
+-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
+--- Running Summary
+11 passed, 18 warnings in 210.11s (0:03:30)

benchmarks/edge_harness/results/e2e_profiling_20260922/evidence/prior-matrix/evidence/spark-cuda-wsl.log

@@ -0,0 +1,25 @@
+...........                                                              [100%]
+=============================== warnings summary ===============================
+../.venvs/omni-cuda-029/lib/python3.12/site-packages/torch/jit/_script.py:365: 14 warnings
+  /home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/torch/jit/_script.py:365: DeprecationWarning: `torch.jit.script_method` is deprecated. Please switch to `torch.compile` or `torch.export`.
+    warnings.warn(
+
+tests/edge/local/test_e2e_local_text.py::test_the_masked_startup_reaches_a_decision
+  /home/zhout/project/edge_infer/.venvs/omni-cuda-029/lib/python3.12/site-packages/pytest_asyncio/plugin.py:916: PytestDeprecationWarning: Overriding the "event_loop_policy" fixture is deprecated and will be removed in a future version of pytest-asyncio. Use the "pytest_asyncio_loop_factories" hook to customize event loop creation.
+    warnings.warn(
+
+tests/edge/local/test_e2e_local_text.py::test_the_masked_startup_reaches_a_decision
+  tests/edge/local/test_e2e_local_text.py:109: PytestWarning: The test <Function test_the_masked_startup_reaches_a_decision> is marked with '@pytest.mark.asyncio' but it is not an async function. Please remove the asyncio mark. If the test is not marked explicitly, check for global marks applied via 'pytestmark'.
+    def test_the_masked_startup_reaches_a_decision(model_dir):
+
+tests/edge/local/test_e2e_local_text.py::test_a_refused_plan_cannot_be_started
+  tests/edge/local/test_e2e_local_text.py:124: PytestWarning: The test <Function test_a_refused_plan_cannot_be_started> is marked with '@pytest.mark.asyncio' but it is not an async function. Please remove the asyncio mark. If the test is not marked explicitly, check for global marks applied via 'pytestmark'.
+    def test_a_refused_plan_cannot_be_started(model_dir):
+
+tests/edge/local/test_e2e_local_text.py::test_int8_is_refused_on_cuda_and_admitted_on_cpu
+  tests/edge/local/test_e2e_local_text.py:133: PytestWarning: The test <Function test_int8_is_refused_on_cuda_and_admitted_on_cpu> is marked with '@pytest.mark.asyncio' but it is not an async function. Please remove the asyncio mark. If the test is not marked explicitly, check for global marks applied via 'pytestmark'.
+    @pytest.mark.skipif(
+
+-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
+--- Running Summary
+11 passed, 18 warnings in 261.25s (0:04:21)