Qualify scoped Windows RTX Qwen3.8 FP8 text and image path

2026-09-24 17:01:16 +08:00
1 parent 5bb16af8ee
commit 32a81111bd
@@ -0,0 +1,69 @@
+#!/usr/bin/env python3
+"""Record file-size and bundled CRC32 checks for a native Qwen3.8 FP8 copy."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import time
+import zlib
+from pathlib import Path
+
+
+def crc32(path: Path) -> str:
+    value = 0
+    with path.open("rb") as stream:
+        while chunk := stream.read(8 * 1024 * 1024):
+            value = zlib.crc32(chunk, value)
+    return f"{value:08x}"
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--source", type=Path, required=True)
+    parser.add_argument("--copy", type=Path, required=True)
+    parser.add_argument("--out", type=Path, required=True)
+    args = parser.parse_args()
+    started = time.monotonic()
+    original = {path.name: path.stat().st_size for path in args.source.iterdir() if path.is_file()}
+    copied = {path.name: path.stat().st_size for path in args.copy.iterdir() if path.is_file()}
+    if original != copied:
+        raise ValueError("source and copy differ in file names or sizes")
+    manifest = {}
+    for line in (args.source / "crc32.txt").read_text(encoding="utf-8").splitlines():
+        expected, name = line.split(maxsplit=1)
+        manifest[name] = expected.lower()
+    rows = []
+    for name, expected in manifest.items():
+        actual = crc32(args.copy / name)
+        source_actual = crc32(args.source / name) if actual != expected else None
+        rows.append({"file": name, "bytes": copied[name], "manifest_crc32": expected,
+                     "copy_crc32": actual, "source_crc32_if_manifest_differs": source_actual})
+    weight_rows = [row for row in rows if row["file"].endswith(".safetensors")]
+    mismatches = [row for row in rows if row["manifest_crc32"] != row["copy_crc32"]]
+    if len(weight_rows) != 66 or any(row["manifest_crc32"] != row["copy_crc32"] for row in weight_rows):
+        raise ValueError("at least one safetensors file failed its bundled CRC32")
+    if any(row["source_crc32_if_manifest_differs"] != row["copy_crc32"] for row in mismatches):
+        raise ValueError("copy differs from source on a stale-manifest file")
+    result = {
+        "status": "checked",
+        "source": str(args.source),
+        "copy": str(args.copy),
+        "file_count": len(copied),
+        "total_bytes": sum(copied.values()),
+        "manifest_entries": len(rows),
+        "weight_files_matching_manifest": len(weight_rows),
+        "stale_manifest_nonweight_files": [row["file"] for row in mismatches],
+        "elapsed_s": time.monotonic() - started,
+        "files": rows,
+    }
+    args.out.parent.mkdir(parents=True, exist_ok=True)
+    args.out.write_text(json.dumps(result, indent=2) + "\n", encoding="utf-8")
+    print(json.dumps({key: result[key] for key in (
+        "status", "file_count", "manifest_entries", "weight_files_matching_manifest",
+        "stale_manifest_nonweight_files", "elapsed_s"
+    )}))
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,116 @@
+#!/usr/bin/env python3
+"""Audit the scoped native-Windows Qwen3.8-27B FP8 recovery and serial profile."""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import math
+import re
+from pathlib import Path
+
+
+REVISION = "017b9c7af6b5689d5dd426a76e0bc077eb5ca20a"
+
+
+def digest(path: Path) -> dict[str, int | str]:
+    with path.open("rb") as stream:
+        return {"bytes": path.stat().st_size, "sha256": hashlib.file_digest(stream, "sha256").hexdigest()}
+
+
+def percentile(values: list[float], fraction: float) -> float:
+    return sorted(values)[math.ceil(len(values) * fraction) - 1]
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--evidence-dir", type=Path, required=True)
+    args = parser.parse_args()
+    root = args.evidence_dir
+    names = [
+        "fp8_copy_integrity.json",
+        "fp8_triton_probe.json",
+        "fp8_triton_probe.driver.log",
+        "qwen38_image_fp8_attempt1.json",
+        "qwen38_image_fp8_attempt1.driver.log",
+        "qwen38_image_fp8_triton_attempt1.json",
+        "qwen38_image_fp8_triton_attempt1.driver.log",
+        "qwen38_image_fp8_triton_attempt2.json",
+        "qwen38_image_fp8_triton_attempt2.driver.log",
+        "qwen38_image_fp8_triton_ntfs_attempt1.json",
+        "qwen38_image_fp8_triton_ntfs_attempt1.driver.log",
+        "qwen38_fp8_triton_ntfs_profile20.json",
+        "qwen38_fp8_triton_ntfs_profile20.driver.log",
+    ]
+    paths = {name: root / name for name in names}
+    reports = {name: json.loads(paths[name].read_text(encoding="utf-8")) for name in names if name.endswith(".json")}
+    logs = {name: paths[name].read_text(encoding="utf-8", errors="replace") for name in names if name.endswith(".log")}
+    probe = reports["fp8_triton_probe.json"]
+    copy = reports["fp8_copy_integrity.json"]
+    if (copy["status"] != "checked" or copy["file_count"] != 81
+            or copy["weight_files_matching_manifest"] != 66
+            or set(copy["stale_manifest_nonweight_files"]) != {
+                "chat_template.jinja", "generation_config.json", "tokenizer_config.json"
+            }):
+        raise ValueError("native checkpoint copy integrity record is incomplete")
+    if probe["status"] != "pass" or probe["capability"] != [12, 0] or probe["value"] != 128:
+        raise ValueError("FP8 Triton operation did not pass on SM120")
+    if reports["qwen38_image_fp8_attempt1.json"]["status"] != "failed" or "No compiled cutlass_scaled_mm" not in logs["qwen38_image_fp8_attempt1.driver.log"]:
+        raise ValueError("default CUTLASS failure is missing")
+    if reports["qwen38_image_fp8_triton_attempt1.json"]["status"] != "failed" or "no structured config owner: linear_backend" not in logs["qwen38_image_fp8_triton_attempt1.driver.log"]:
+        raise ValueError("Omni config ownership failure is missing")
+    if reports["qwen38_image_fp8_triton_attempt2.json"]["status"] != "failed" or "0.33 GiB KV cache is needed" not in logs["qwen38_image_fp8_triton_attempt2.driver.log"]:
+        raise ValueError("undersized KV-cache refusal is missing")
+    single = reports["qwen38_image_fp8_triton_ntfs_attempt1.json"]
+    profile = reports["qwen38_fp8_triton_ntfs_profile20.json"]
+    for report in (single, profile):
+        cfg = report["config"]
+        if (report["status"] != "completed" or report["outputs"] != ["Red"]
+                or cfg["linear_backend"] != "triton" or cfg["cpu_offload_gb"] != 12
+                or cfg["gpu_memory_utilization"] != 0.8 or cfg["max_model_len"] != 512
+                or "Qwen3.8-27B-FP8" not in report["model"]):
+            raise ValueError("successful report does not match the bounded FP8 image case")
+    for name in ("qwen38_image_fp8_triton_ntfs_attempt1.driver.log", "qwen38_fp8_triton_ntfs_profile20.driver.log"):
+        if not all(token in logs[name] for token in (
+            "Selected TritonFp8BlockScaledMMKernel",
+            "Loading safetensors checkpoint shards: 100% Completed | 66/66",
+        )):
+            raise ValueError(f"successful log lacks kernel/load/cache evidence: {name}")
+        cache_match = re.search(r"GPU KV cache size: ([\d,]+) tokens", logs[name])
+        if cache_match is None or int(cache_match.group(1).replace(",", "")) < 512:
+            raise ValueError(f"successful log lacks enough KV cache for one request: {name}")
+    if profile["text_output"] != ["ready"]:
+        raise ValueError("text warmup answer changed")
+    timing = {}
+    for kind, expected in (("image", "Red"), ("text", "ready")):
+        rows = [row for row in profile["profile"] if row["kind"] == kind]
+        if len(rows) != 20 or {row["index"] for row in rows} != set(range(20)) or any(row["output"] != expected for row in rows):
+            raise ValueError(f"{kind} serial profile is incomplete or changed output")
+        values = [row["wall_s"] for row in rows]
+        timing[kind] = {"n": len(values), "nearest_rank_p50_s": percentile(values, 0.5),
+                        "nearest_rank_p95_s": percentile(values, 0.95), "min_s": min(values), "max_s": max(values)}
+    result = {
+        "status": "audited_scoped_e2e",
+        "evidence_depth": "P: complete text and synthetic-image requests on native Windows RTX; no long context or broad quality claim",
+        "artifact": "Qwen/Qwen3.8-27B-FP8",
+        "checkpoint_revision": REVISION,
+        "host": "Windows 11 build 26200; Ryzen AI 9 HX 370; RTX 5090 Laptop; driver 610.71",
+        "runtime": "native vLLM 0.29.0+cu134; PyTorch 2.13.0+cu130; Triton FP8 block linear backend",
+        "configuration": profile["config"],
+        "warmups": {"image": 1, "text": 1},
+        "timing": timing,
+        "files": {name: digest(path) for name, path in paths.items()},
+        "limits": [
+            "The FP8 checkpoint and Triton backend are distinct from the NVFP4 Marlin failure.",
+            "The image is a synthetic 96x96 red square and the text prompt is a single-word response; no broad multimodal or long-context quality claim follows.",
+            "The 12 GiB host offload and 0.80 GPU-memory cap are declared settings, not measured loading-peak admission or a sampled power profile.",
+            "Cancellation, public AsyncOmni, concurrency, video, sustained power/thermal behavior and task quality remain unverified for this native-Windows route.",
+        ],
+    }
+    (root / "fp8_audit_report.json").write_text(json.dumps(result, indent=2) + "\n", encoding="utf-8")
+    print(json.dumps({"status": result["status"], "timing": timing}))
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,39 @@
+"""Check the installed Windows vLLM block-FP8 kernel before loading 27B weights."""
+
+import json
+import sys
+import traceback
+from pathlib import Path
+
+import torch
+
+
+def main() -> None:
+    out = Path(sys.argv[1])
+    result = {
+        "torch": torch.__version__,
+        "device": torch.cuda.get_device_name(0),
+        "capability": torch.cuda.get_device_capability(0),
+    }
+    try:
+        import vllm.model_executor.kernels.linear.scaled_mm.triton  # noqa: F401
+
+        a = torch.ones((4, 128), device="cuda", dtype=torch.float8_e4m3fn)
+        b = torch.ones((128, 128), device="cuda", dtype=torch.float8_e4m3fn)
+        sa = torch.ones((4, 1), device="cuda", dtype=torch.float32)
+        sb = torch.ones((1, 1), device="cuda", dtype=torch.float32)
+        y = torch.ops.vllm.w8a8_triton_block_scaled_mm_func(
+            a, b, sa, sb, [128, 128], torch.bfloat16
+        )
+        torch.cuda.synchronize()
+        result.update(status="pass", shape=list(y.shape), value=float(y[0, 0]))
+    except Exception as exc:
+        result.update(status="failed", error=repr(exc), traceback=traceback.format_exc())
+    out.parent.mkdir(parents=True, exist_ok=True)
+    out.write_text(json.dumps(result, indent=2) + "\n", encoding="utf-8")
+    if result["status"] != "pass":
+        raise SystemExit(1)
+
+
+if __name__ == "__main__":
+    main()
@@ -12,6 +12,10 @@ def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--model", required=True)
    parser.add_argument("--out", type=Path, required=True)
+    parser.add_argument("--cpu-offload-gb", type=float, default=2)
+    parser.add_argument("--gpu-memory-utilization", type=float, default=0.85)
+    parser.add_argument("--linear-backend", default="auto")
+    parser.add_argument("--profile-requests", type=int, default=0)
    args = parser.parse_args()
    os.environ.setdefault("VLLM_USE_FLASHINFER_SAMPLER", "0")
    os.environ.setdefault("VLLM_WSL2_ENABLE_PIN_MEMORY", "1")
@@ -42,20 +46,47 @@ def main():
            "max_num_seqs": 1,
            "max_num_batched_tokens": 512,
            "enforce_eager": True,
-            "gpu_memory_utilization": 0.85,
-            "cpu_offload_gb": 2,
+            "gpu_memory_utilization": args.gpu_memory_utilization,
+            "cpu_offload_gb": args.cpu_offload_gb,
            "dtype": "bfloat16",
            "stage_init_timeout": 180,
        }
+        if args.linear_backend != "auto":
+            config["linear_backend"] = args.linear_backend
        record["config"] = config
        engine = Omni(**config)
-        outputs = engine.generate(
-            {"prompt": prompt, "multi_modal_data": {"image": image}},
-            SamplingParams(temperature=0, max_tokens=16),
-        )
+        image_input = {"prompt": prompt, "multi_modal_data": {"image": image}}
+        sampling = SamplingParams(temperature=0, max_tokens=16)
+        request_start = time.perf_counter()
+        outputs = engine.generate(image_input, sampling)
+        record["image_wall_s"] = time.perf_counter() - request_start
        record["outputs"] = [out.outputs[0].text for out in outputs if out.outputs]
        if not record["outputs"]:
            raise RuntimeError("No image-conditioned output")
+        if args.profile_requests:
+            text_prompt = processor.apply_chat_template(
+                [{"role": "user", "content": "Reply with the single word ready."}],
+                tokenize=False,
+                add_generation_prompt=True,
+                enable_thinking=False,
+            )
+            record["text_output"] = [
+                out.outputs[0].text
+                for out in engine.generate({"prompt": text_prompt}, sampling)
+                if out.outputs
+            ]
+            record["profile"] = []
+            for kind, request in (("image", image_input), ("text", {"prompt": text_prompt})):
+                for index in range(args.profile_requests):
+                    started = time.perf_counter()
+                    measured = engine.generate(request, sampling)
+                    wall_s = time.perf_counter() - started
+                    texts = [out.outputs[0].text for out in measured if out.outputs]
+                    if not texts:
+                        raise RuntimeError(f"No {kind} output on measured request {index}")
+                    record["profile"].append(
+                        {"kind": kind, "index": index, "wall_s": wall_s, "output": texts[0]}
+                    )
        record["status"] = "completed"
    except Exception as exc:
        record.update(status="failed", error=repr(exc), traceback=traceback.format_exc())
@@ -1,5 +1,17 @@
 # Qwen3.8-27B NVFP4 native Windows RTX startup attempt

+## FP8/Triton recovery on the same Windows RTX
+
+The separate official Qwen/Qwen3.8-27B-FP8 checkpoint at revision `017b9c7af6b5689d5dd426a76e0bc077eb5ca20a` now passes scoped complete text and image-to-text requests through this checkout's one-stage Omni pipeline on native Windows 11 build 26200 and RTX 5090 Laptop. It uses the installed vLLM 0.29.0+cu134 wheel, PyTorch 2.13.0+cu130, Triton 3.8.0, driver 610.71, explicit `linear_backend=triton`, BF16 compute, 12 GiB CPU parameter offload, 0.80 GPU-memory utilization, one sequence, eager execution and a 512-token context. The image is a synthetic 96×96 red square; the text prompt asks for the single word “ready.” This is a distinct FP8 artifact and backend, not a repair of the NVFP4 Marlin/PTX path below.
+
+The [single image pass](qwen38_image_fp8_triton_ntfs_attempt1.json) answered `Red`; the [serial profile](qwen38_fp8_triton_ntfs_profile20.json) answered `Red` on 20/20 measured image requests and `ready` on 20/20 measured text requests. There was one unmeasured request of each type. Nearest-rank complete-request wall p50/p95 was **4.184/4.380 s for image** and **3.070/3.214 s for text**, concurrency one. The profile's engine initialization was 87.37 s, including 31.35 s reported weight loading; neither startup nor warmups are included in the request percentiles. The worker log selected `TritonFp8BlockScaledMMKernel`, loaded all 66 safetensors files, reported 16.36 GiB GPU model-load allocation and initialized a 1,974-token GPU KV cache. The 16.36 GiB value is the runner's model-load allocation, not a total GPU or host loading peak; sustained power and thermal behavior were not measured.
+
+The checkpoint was copied from its WSL model root to native NTFS for the successful runs. Source and copy have the same 81 file names and sizes. A [full CRC32 record](fp8_copy_integrity.json) of the native copy against the bundled manifest matched all 66 safetensors and 8 other listed files. Three non-weight metadata files (`chat_template.jinja`, `generation_config.json`, `tokenizer_config.json`) differ from that manifest but have identical CRC32 values in source and copy; the manifest is stale for those files. The checkpoint revision comes from the Hugging Face download metadata, whose 81 recorded files all name the same commit. These checks do not substitute for a broad numerical or task-quality evaluation.
+
+The retained failures delimit the fix. The [default FP8/CUTLASS load](qwen38_image_fp8_attempt1.driver.log) reached a model startup operation but the Windows wheel had no compiled `cutlass_scaled_mm` for SM120. A [small Triton FP8 block probe](fp8_triton_probe.json) passed on SM120 with the expected value 128. The [first full Triton attempt](qwen38_image_fp8_triton_attempt1.json) exposed Omni's missing structured owner for `linear_backend`; the current config projection now carries that vLLM option, with a focused test. The [next attempt](qwen38_image_fp8_triton_attempt2.driver.log) loaded all 66 weights but vLLM refused the 0.75 GPU-memory cap: 0.22 GiB available for KV versus 0.33 GiB required for a 512-token request. Raising the cap to 0.80 admitted the successful run. The [FP8 auditor](fp8_audit_report.json) checks all raw reports, failure strings, successful kernel/load/cache evidence, per-request outputs, sample counts and percentile math.
+
+This advances the native Windows RTX × Qwen3.8 cell to a scoped complete text/image path. It does not establish a qualified long context, video path, reference-quality suite, explicit Omni host/VRAM loading-peak admission, cancellation/recovery, public AsyncOmni request, concurrency scaling or sustained power and thermal behavior. The NVFP4 failure remains a separate unresolved artifact/runtime combination.
+
 The pinned Qwen3.8-27B NVFP4 checkpoint, revision `dbb8f445b3145f8a4c18ddc769f032d57d32867c`, was attempted through the current Omni/vLLM 0.29 one-stage pipeline on native Windows 11 build 26200. The bounded image-to-text case used a synthetic 96×96 red image, 512-token context, one sequence, 16 output tokens, eager execution, 0.85 GPU-memory utilization and an explicit 2 GiB CPU offload. This is the same scoped case that [completed on WSL RTX](../../../e2e_recovery_20260923/README.md); Windows **did not reach inference**. No output or latency is claimed for this cell.

 The [first attempt report](qwen38_image_unc_attempt1.json) records an engine-start failure after model load but its console output was not retained. The [second attempt report](qwen38_image_unc_attempt2.json) and [full driver/worker log](qwen38_image_unc_attempt2.driver.log) retain the root cause. All **three safetensors shards completed loading**, then vLLM failed while preparing NVFP4 Marlin weights: `process_weights_after_loading` → `prepare_fp4_layer_for_marlin` → `marlin_permute_scales` raised `torch.AcceleratorError: CUDA error: the provided PTX was compiled with an unsupported toolchain`. The [auditor](audit_report.json) checks the two reports, captured stack, hardware versions and source-file hashes. The probe now exits nonzero after writing a failed report, so automation cannot mistake this startup error for a successful image request.
@@ -0,0 +1,101 @@
+{
+  "status": "audited_scoped_e2e",
+  "evidence_depth": "P: complete text and synthetic-image requests on native Windows RTX; no long context or broad quality claim",
+  "artifact": "Qwen/Qwen3.8-27B-FP8",
+  "checkpoint_revision": "017b9c7af6b5689d5dd426a76e0bc077eb5ca20a",
+  "host": "Windows 11 build 26200; Ryzen AI 9 HX 370; RTX 5090 Laptop; driver 610.71",
+  "runtime": "native vLLM 0.29.0+cu134; PyTorch 2.13.0+cu130; Triton FP8 block linear backend",
+  "configuration": {
+    "model": "C:\\Users\\zhout\\w2\\Qwen3.8-27B-FP8",
+    "language_model_only": false,
+    "max_model_len": 512,
+    "max_num_seqs": 1,
+    "max_num_batched_tokens": 512,
+    "enforce_eager": true,
+    "gpu_memory_utilization": 0.8,
+    "cpu_offload_gb": 12.0,
+    "dtype": "bfloat16",
+    "stage_init_timeout": 180,
+    "linear_backend": "triton"
+  },
+  "warmups": {
+    "image": 1,
+    "text": 1
+  },
+  "timing": {
+    "image": {
+      "n": 20,
+      "nearest_rank_p50_s": 4.183892700006254,
+      "nearest_rank_p95_s": 4.3797133000334725,
+      "min_s": 4.016021100105718,
+      "max_s": 4.443590100039728
+    },
+    "text": {
+      "n": 20,
+      "nearest_rank_p50_s": 3.069557400071062,
+      "nearest_rank_p95_s": 3.213541300036013,
+      "min_s": 3.025600800057873,
+      "max_s": 3.2830592999234796
+    }
+  },
+  "files": {
+    "fp8_copy_integrity.json": {
+      "bytes": 15237,
+      "sha256": "9915945c4b87b2fd360c458ffe1662b0a3ce51d90fa1b4f18f92e62e4566db9d"
+    },
+    "fp8_triton_probe.json": {
+      "bytes": 188,
+      "sha256": "1e3f44d0df5ae15d52a385644c057fda3755654f3a63e035d99b944b664b32b7"
+    },
+    "fp8_triton_probe.driver.log": {
+      "bytes": 405,
+      "sha256": "0db7bcab7a0fe0a04a829e6f086f432b8a620eea96bbdab9bad466c79139ebc2"
+    },
+    "qwen38_image_fp8_attempt1.json": {
+      "bytes": 6212,
+      "sha256": "e3886220a365b2a50831e7b109fb8b82c74121573dc9538c126fe1feb94ba202"
+    },
+    "qwen38_image_fp8_attempt1.driver.log": {
+      "bytes": 68051,
+      "sha256": "0846a9b5e587b0d57dea2319d05d90030894f4175e72b83068b633131924cb63"
+    },
+    "qwen38_image_fp8_triton_attempt1.json": {
+      "bytes": 3096,
+      "sha256": "e0ee3b8ceed607e1af5f2670aed3740729c96abd18e7e8210b8f7d30588e41bf"
+    },
+    "qwen38_image_fp8_triton_attempt1.driver.log": {
+      "bytes": 2622,
+      "sha256": "43ec6bdad30e58a09f68475a9343e55ed8c910876acdab68bd4ee1780a32f28b"
+    },
+    "qwen38_image_fp8_triton_attempt2.json": {
+      "bytes": 6247,
+      "sha256": "db8214d083f2880bd86e88df283cd7a345967da7ee988042ae28e5680da26e69"
+    },
+    "qwen38_image_fp8_triton_attempt2.driver.log": {
+      "bytes": 53487,
+      "sha256": "8986870dec87fb5c0fee1970a861a3a4114be3e8235177700d10f19ca3c06ceb"
+    },
+    "qwen38_image_fp8_triton_ntfs_attempt1.json": {
+      "bytes": 598,
+      "sha256": "29b730bd6701c9d58a4caf173ee32d7da625c0b8e1671a61ef7d0bc52038985b"
+    },
+    "qwen38_image_fp8_triton_ntfs_attempt1.driver.log": {
+      "bytes": 30428,
+      "sha256": "f274a896cb21066b2b5323be74603ed17c26a6f29ef477892d66b2943aa39aee"
+    },
+    "qwen38_fp8_triton_ntfs_profile20.json": {
+      "bytes": 5175,
+      "sha256": "c20c0b9c12b297361487522ac04977a2235c54d16b58ca276f0d09b66376a057"
+    },
+    "qwen38_fp8_triton_ntfs_profile20.driver.log": {
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+    "The 12 GiB host offload and 0.80 GPU-memory cap are declared settings, not measured loading-peak admission or a sampled power profile.",
+    "Cancellation, public AsyncOmni, concurrency, video, sustained power/thermal behavior and task quality remain unverified for this native-Windows route."
+  ]
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@@ -0,0 +1,2 @@
+WARNING 09-24 16:24:29 [fp8_utils.py:852] Using default W8A8 Block FP8 kernel config. Performance might be sub-optimal! Config file not found at C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\model_executor\layers\quantization\utils\configs\N=128,K=128,device_name=NVIDIA_GeForce_RTX_5090_Laptop_GPU,dtype=fp8_w8a8,block_shape=[128,128].json
+remark: fp8_utils.py:737:0: 772 instructions in function
@@ -0,0 +1,14 @@
+{
+  "torch": "2.13.0+cu130",
+  "device": "NVIDIA GeForce RTX 5090 Laptop GPU",
+  "capability": [
+    12,
+    0
+  ],
+  "status": "pass",
+  "shape": [
+    4,
+    128
+  ],
+  "value": 128.0
+}
@@ -0,0 +1,407 @@
+INFO 09-24 16:42:33 [patch.py:259] NVFP4 W4A4 weight_scale NaN-clamp: installed.
+INFO 09-24 16:42:33 [patch.py:510] inductor factorable-divisibility patch: installed.
+INFO 09-24 16:42:37 [omni_base.py:216] [Omni] Initializing with model C:\Users\zhout\w2\Qwen3.8-27B-FP8
+INFO 09-24 16:42:37 [async_omni_engine.py:172] [AsyncOmniEngine] Initializing with model C:\Users\zhout\w2\Qwen3.8-27B-FP8
+INFO 09-24 16:42:37 [async_omni_engine.py:290] [AsyncOmniEngine] Launching Orchestrator thread with 1 stages
+INFO 09-24 16:42:38 [stage_init_utils.py:768] [stage_init] Set VLLM_WORKER_MULTIPROC_METHOD=spawn
+WARNING 09-24 16:42:38 [utils.py:501] Dropping unknown OmniEngineArgs field 'inline_diffusion' (not declared on the dataclass)
+WARNING 09-24 16:42:38 [arg_utils.py:1801] The global random seed is set to 0. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM.
+INFO 09-24 16:42:38 [model.py:684] Resolved architecture: Qwen3_5ForConditionalGeneration
+INFO 09-24 16:42:38 [model.py:2021] Using max model len 512
+WARNING 09-24 16:42:38 [model.py:981] Model does not support mm_device_do_normalize, forcing mm_device_do_normalize = False.
+INFO 09-24 16:42:43 [scheduler.py:277] Chunked prefill is enabled with max_num_batched_tokens=512.
+INFO 09-24 16:42:43 [config.py:625] Mamba cache mode is set to 'align' for Qwen3_5ForConditionalGeneration by default when prefix caching is enabled
+WARNING 09-24 16:42:43 [vllm.py:1211] Auto-disabled DeepGemm for model_type=qwen3_5_text on Blackwell. DeepGemm E8M0 scale format causes accuracy degradation for this architecture. Falling back to CUTLASS. To disable DeepGemm globally, set VLLM_USE_DEEP_GEMM=0.
+WARNING 09-24 16:42:43 [vllm.py:1371] Enforce eager set, disabling torch.compile and CUDAGraphs. This is equivalent to setting -cc.mode=none -cc.cudagraph_mode=none
+WARNING 09-24 16:42:43 [vllm.py:1406] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored.
+INFO 09-24 16:42:43 [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 09-24 16:42:43 [vllm.py:1585] Cudagraph is disabled under eager mode
+INFO 09-24 16:42:43 [compilation.py:329] Enabled custom fusions: norm_quant, act_quant
+WARNING 09-24 16:42:43 [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
+INFO 09-24 16:42:56 [patch.py:259] NVFP4 W4A4 weight_scale NaN-clamp: installed.
+INFO 09-24 16:42:56 [patch.py:510] inductor factorable-divisibility patch: installed.
+(StageEngineCoreProc_stage0_replica0 pid=63532) INFO 09-24 16:43:00 [core.py:123] Initializing a V1 LLM engine (v0.29.0) with config: model='C:\\Users\\zhout\\w2\\Qwen3.8-27B-FP8', speculative_config=None, tokenizer='C:\\Users\\zhout\\w2\\Qwen3.8-27B-FP8', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=512, 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=fp8, 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=C:\Users\zhout\w2\Qwen3.8-27B-FP8, enable_prefix_caching=True, 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': ['+quant_fp8', 'all', '+quant_fp8'], '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': [512], '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='triton')
+(StageEngineCoreProc_stage0_replica0 pid=63532) INFO 09-24 16:43:01 [parallel_state.py:1775] world_size=1 rank=0 local_rank=0 distributed_init_method=file://C:\Users\zhout\AppData\Local\Temp/vllm_dist_d5a7dd0f3a6543fba9ec6e36eadfb2ed backend=nccl
+(StageEngineCoreProc_stage0_replica0 pid=63532) WARNING 09-24 16:43:01 [parallel_state.py:1788] Distributed backend nccl is not available; falling back to gloo.
+(StageEngineCoreProc_stage0_replica0 pid=63532) INFO 09-24 16:43:01 [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=63532) WARNING 09-24 16:43:02 [gpu_ar_worker.py:120] OMNI GPUARWorker forces v1 model runner for omni hooks.
+(StageEngineCoreProc_stage0_replica0 pid=63532) INFO 09-24 16:43:02 [topk_topp_sampler.py:46] FlashInfer top-p/top-k sampling disabled via VLLM_USE_FLASHINFER_SAMPLER=0.
+(StageEngineCoreProc_stage0_replica0 pid=63532) [transformers] The `use_fast` parameter is deprecated and will be removed in a future version. Use `backend="torchvision"` instead of `use_fast=True`, or `backend="pil"` instead of `use_fast=False`.
+(StageEngineCoreProc_stage0_replica0 pid=63532) INFO 09-24 16:43:07 [base.py:138] Offloader set to UVAOffloader
+(StageEngineCoreProc_stage0_replica0 pid=63532) WARNING 09-24 16:43:07 [base.py:173] [LLM Worker 0] Sleep Mode DISABLED.
+(StageEngineCoreProc_stage0_replica0 pid=63532) WARNING 09-24 16:43:07 [base.py:173] [LLM Worker 0] Sleep Mode DISABLED.
+(StageEngineCoreProc_stage0_replica0 pid=63532) INFO 09-24 16:43:07 [gpu_model_runner.py:5379] Starting to load model C:\Users\zhout\w2\Qwen3.8-27B-FP8...
+(StageEngineCoreProc_stage0_replica0 pid=63532) INFO 09-24 16:43:07 [cuda.py:551] Using backend AttentionBackendEnum.FLASH_ATTN for vit attention
+(StageEngineCoreProc_stage0_replica0 pid=63532) INFO 09-24 16:43:07 [mm_encoder_attention.py:372] Using AttentionBackendEnum.FLASH_ATTN for MMEncoderAttention.
+(StageEngineCoreProc_stage0_replica0 pid=63532) INFO 09-24 16:43:08 [uva.py:65] Total CPU offloaded parameters: 0.86
+(StageEngineCoreProc_stage0_replica0 pid=63532) INFO 09-24 16:43:08 [__init__.py:695] Selected TritonFp8BlockScaledMMKernel for Fp8LinearMethod
+(StageEngineCoreProc_stage0_replica0 pid=63532) INFO 09-24 16:43:08 [qwen_gdn_linear_attn.py:167] Using Triton/FLA GDN prefill kernel (requested=auto, head_k_dim=128).
+(StageEngineCoreProc_stage0_replica0 pid=63532) INFO 09-24 16:43:08 [qwen_gdn_linear_attn.py:519] GDN decode kernel: cuda
+(StageEngineCoreProc_stage0_replica0 pid=63532) INFO 09-24 16:43:09 [cuda.py:492] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'TRITON_ATTN', 'FLEX_ATTENTION'].
+(StageEngineCoreProc_stage0_replica0 pid=63532) INFO 09-24 16:43:09 [flash_attn.py:897] Using FlashAttention version 2
+(StageEngineCoreProc_stage0_replica0 pid=63532) INFO 09-24 16:43:13 [uva.py:65] Total CPU offloaded parameters: 12.13
+(StageEngineCoreProc_stage0_replica0 pid=63532) INFO 09-24 16:43:13 [weight_utils.py:863] Filesystem type for checkpoints: unknown. Checkpoint size: 28.75 GiB. Available RAM: 16.12 GiB.
+(StageEngineCoreProc_stage0_replica0 pid=63532) INFO 09-24 16:43:13 [weight_utils.py:893] Auto-prefetch is disabled because the filesystem (unknown) is not a recognized network FS (NFS/Lustre) and the checkpoint size (28.75 GiB) exceeds 90% of available RAM (16.12 GiB).
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+(StageEngineCoreProc_stage0_replica0 pid=63532)
+(StageEngineCoreProc_stage0_replica0 pid=63532) INFO 09-24 16:43:38 [default_loader.py:430] Loading weights took 24.16 seconds
+(StageEngineCoreProc_stage0_replica0 pid=63532) INFO 09-24 16:43:39 [gpu_model_runner.py:5471] Model loading took 16.36 GiB memory and 31.349944 seconds
+(StageEngineCoreProc_stage0_replica0 pid=63532) INFO 09-24 16:43:40 [interface.py:918] Setting attention block size to 784 tokens to ensure that attention page size is >= mamba page size.
+(StageEngineCoreProc_stage0_replica0 pid=63532) INFO 09-24 16:43:40 [interface.py:942] Padding mamba page size by 0.13% to ensure that mamba page size and attention page size are exactly equal.
+(StageEngineCoreProc_stage0_replica0 pid=63532) INFO 09-24 16:43:40 [utils.py:306] Using LBNHC KV cache layout.
+(StageEngineCoreProc_stage0_replica0 pid=63532) INFO 09-24 16:43:40 [gpu_model_runner.py:6519] Skipping memory profiling for multimodal encoder and encoder cache.
+(StageEngineCoreProc_stage0_replica0 pid=63532) WARNING 09-24 16:43:41 [fp8_utils.py:852] Using default W8A8 Block FP8 kernel config. Performance might be sub-optimal! Config file not found at C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\model_executor\layers\quantization\utils\configs\N=16384,K=5120,device_name=NVIDIA_GeForce_RTX_5090_Laptop_GPU,dtype=fp8_w8a8,block_shape=[128,128].json
+(StageEngineCoreProc_stage0_replica0 pid=63532) WARNING 09-24 16:43:41 [fp8_utils.py:852] Using default W8A8 Block FP8 kernel config. Performance might be sub-optimal! Config file not found at C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\model_executor\layers\quantization\utils\configs\N=5120,K=6144,device_name=NVIDIA_GeForce_RTX_5090_Laptop_GPU,dtype=fp8_w8a8,block_shape=[128,128].json
+(StageEngineCoreProc_stage0_replica0 pid=63532) WARNING 09-24 16:43:41 [fp8_utils.py:852] Using default W8A8 Block FP8 kernel config. Performance might be sub-optimal! Config file not found at C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\model_executor\layers\quantization\utils\configs\N=34816,K=5120,device_name=NVIDIA_GeForce_RTX_5090_Laptop_GPU,dtype=fp8_w8a8,block_shape=[128,128].json
+(StageEngineCoreProc_stage0_replica0 pid=63532) WARNING 09-24 16:43:41 [fp8_utils.py:852] Using default W8A8 Block FP8 kernel config. Performance might be sub-optimal! Config file not found at C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\model_executor\layers\quantization\utils\configs\N=5120,K=17408,device_name=NVIDIA_GeForce_RTX_5090_Laptop_GPU,dtype=fp8_w8a8,block_shape=[128,128].json
+(StageEngineCoreProc_stage0_replica0 pid=63532) WARNING 09-24 16:43:42 [fp8_utils.py:852] Using default W8A8 Block FP8 kernel config. Performance might be sub-optimal! Config file not found at C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\model_executor\layers\quantization\utils\configs\N=14336,K=5120,device_name=NVIDIA_GeForce_RTX_5090_Laptop_GPU,dtype=fp8_w8a8,block_shape=[128,128].json
+(StageEngineCoreProc_stage0_replica0 pid=63532) INFO 09-24 16:43:51 [base.py:148] Available KV cache memory: 1.31 GiB (device-level profiling)
+(StageEngineCoreProc_stage0_replica0 pid=63532) INFO 09-24 16:43:51 [kv_cache_utils.py:2032] GPU KV cache size: 1,974 tokens, Maximum concurrency for 512 tokens per request: 3.86x
+(StageEngineCoreProc_stage0_replica0 pid=63532) WARNING 09-24 16:43:51 [base.py:173] [LLM Worker 0] Sleep Mode DISABLED.
+(StageEngineCoreProc_stage0_replica0 pid=63532) INFO 09-24 16:43:52 [kernel_warmup.py:124] JIT kernel warmup starting.
+(StageEngineCoreProc_stage0_replica0 pid=63532)
+JIT kernel warmup (2 compile keys):   0%|          | 0/1 [00:00<?, ?kernel/s]
+JIT kernel warmup (2 compile keys): 100%|██████████| 1/1 [00:00<00:00, 48.74kernel/s, ComputeSlotMappingKernel (2 keys)]
+(StageEngineCoreProc_stage0_replica0 pid=63532) INFO 09-24 16:43:52 [kernel_warmup.py:134] JIT kernel warmup finished in 0.02s.
+(StageEngineCoreProc_stage0_replica0 pid=63532) INFO 09-24 16:43:52 [qwen_triton_warmup.py:270] Warming up Qwen Triton kernels for model_type=qwen3_5_text.
+(StageEngineCoreProc_stage0_replica0 pid=63532) INFO 09-24 16:43:52 [ll_bf16.py:30] cuteDSL (CUTLASS Python) not available, ll_bf16_gemm disabled
+(StageEngineCoreProc_stage0_replica0 pid=63532) INFO 09-24 16:43:52 [gpu_worker.py:860] Free memory on device (22.61/23.89 GiB) on startup. Desired GPU memory utilization is (0.8, 19.11 GiB). Actual usage is 17.74 GiB for consumed memory (weights + non-torch), 0.12 GiB for peak activation, and 0.0 GiB for CUDAGraph memory. Replace gpu_memory_utilization config with `--kv-cache-memory=1186739405` (1.11 GiB) to fit into requested memory, or `--kv-cache-memory=4941637632` (4.6 GiB) to fully utilize gpu memory. Current kv cache memory in use is 1.31 GiB.
+(StageEngineCoreProc_stage0_replica0 pid=63532) INFO 09-24 16:43:53 [jit_monitor.py:85] Kernel JIT monitor activated; monitored JIT compilations during inference will use mode=warn.
+(StageEngineCoreProc_stage0_replica0 pid=63532) INFO 09-24 16:43:54 [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=63532) INFO 09-24 16:43:54 [core.py:368] init engine (profile, create kv cache, warmup model) took 13.61 s
+(StageEngineCoreProc_stage0_replica0 pid=63532) WARNING 09-24 16:43:54 [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=63532) WARNING 09-24 16:43:54 [omni_scheduler_mixin.py:118] OMNI scheduler forces v1 model runner for omni hooks.
+(StageEngineCoreProc_stage0_replica0 pid=63532) WARNING 09-24 16:43:54 [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=63532) WARNING 09-24 16:43:54 [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=63532) INFO 09-24 16:43:54 [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=63532) INFO 09-24 16:43:54 [vllm.py:1585] Cudagraph is disabled under eager mode
+INFO 09-24 16:43:54 [stage_runtime.py:943] [StageRuntime] Stage 0 engine startup completed
+INFO 09-24 16:43:54 [stage_engine_core_client.py:159] [StageEngineCoreClient] stage-0 [rep-0] initializing EngineCore
+(StageEngineCoreProc_stage0_replica0 pid=63532) INFO 09-24 16:43:54 [compilation.py:329] Enabled custom fusions: norm_quant, act_quant
+INFO 09-24 16:43:54 [stage_engine_core_client.py:215] [StageEngineCoreClient] stage-0 [rep-0] EngineCore running
+INFO 09-24 16:43:54 [stage_runtime.py:958] [StageRuntime] Stage 0 initialized
+[transformers] The `use_fast` parameter is deprecated and will be removed in a future version. Use `backend="torchvision"` instead of `use_fast=True`, or `backend="pil"` instead of `use_fast=False`.
+INFO 09-24 16:44:05 [orchestrator.py:604] [Orchestrator] Starting event loop
+INFO 09-24 16:44:05 [async_omni_engine.py:323] [AsyncOmniEngine] Orchestrator ready with 1 stages
+INFO 09-24 16:44:05 [omni_base.py:235] [Omni] AsyncOmniEngine initialized in 87.37 seconds
+INFO 09-24 16:44:05 [omni_base.py:255] [Omni] Initialized with 1 stages for model C:\Users\zhout\w2\Qwen3.8-27B-FP8
+WARNING 09-24 16:44:05 [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=63532) WARNING 09-24 16:44:21 [jit_monitor.py:141] Triton kernel JIT compilation during inference: _bilinear_pos_embed_kernel. This causes a latency spike; consider extending warmup to cover this shape/config.
+(StageEngineCoreProc_stage0_replica0 pid=63532) WARNING 09-24 16:44:21 [jit_monitor.py:141] Triton kernel JIT compilation during inference: rotary_kernel. This causes a latency spike; consider extending warmup to cover this shape/config.
+(StageEngineCoreProc_stage0_replica0 pid=63532) WARNING 09-24 16:44:22 [jit_monitor.py:141] Triton kernel JIT compilation during inference: _w8a8_triton_block_scaled_mm. This causes a latency spike; consider extending warmup to cover this shape/config.
+(StageEngineCoreProc_stage0_replica0 pid=63532) WARNING 09-24 16:44:22 [jit_monitor.py:141] Triton kernel JIT compilation during inference: layer_norm_fwd_kernel. This causes a latency spike; consider extending warmup to cover this shape/config.
+(StageEngineCoreProc_stage0_replica0 pid=63532) INFO 09-24 16:44:24 [prefix_cache.py:160] Omni prefix cache async-write copy stream initialized.
+(StageEngineCoreProc_stage0_replica0 pid=63532) WARNING 09-24 16:44:24 [jit_monitor.py:141] Triton kernel JIT compilation during inference: _causal_conv1d_update_kernel. This causes a latency spike; consider extending warmup to cover this shape/config.
+(StageEngineCoreProc_stage0_replica0 pid=63532) WARNING 09-24 16:44:24 [jit_monitor.py:141] Triton kernel JIT compilation during inference: fused_recurrent_gated_delta_rule_packed_decode_kernel. This causes a latency spike; consider extending warmup to cover this shape/config.
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+INFO 09-24 16:46:55 [omni_base.py:781] [Omni] Shutting down
+INFO 09-24 16:46:55 [async_omni_engine.py:1807] [AsyncOmniEngine] Shutting down Orchestrator
+INFO 09-24 16:46:55 [orchestrator.py:705] [Orchestrator] Received shutdown signal
+INFO 09-24 16:46:55 [orchestrator.py:3092] [Orchestrator] Shutting down all 1 client(s)
+INFO 09-24 16:46:55 [core_client.py:689] [shutdown] MPClient: start timeout=default
+INFO 09-24 16:46:55 [core_client.py:691] [shutdown] MPClient: stopping engine manager
+INFO 09-24 16:46:55 [utils.py:626] [shutdown] Process manager: send sigterm to process StageEngineCoreProc_stage0_replica0
+INFO 09-24 16:46:57 [core_client.py:693] [shutdown] MPClient: engine manager stopped
+INFO 09-24 16:46:57 [core_client.py:694] [shutdown] MPClient: cleaning up background resources
+INFO 09-24 16:46:57 [core_client.py:696] [shutdown] MPClient: complete
+INFO 09-24 16:46:57 [stage_pool.py:1354] [StagePool] Stage 0 replica 0 shut down
@@ -0,0 +1,269 @@
+{
+  "model": "C:\\Users\\zhout\\w2\\Qwen3.8-27B-FP8",
+  "scope": "one synthetic-image Omni image-to-text request",
+  "start": 1790239341.673995,
+  "config": {
+    "model": "C:\\Users\\zhout\\w2\\Qwen3.8-27B-FP8",
+    "language_model_only": false,
+    "max_model_len": 512,
+    "max_num_seqs": 1,
+    "max_num_batched_tokens": 512,
+    "enforce_eager": true,
+    "gpu_memory_utilization": 0.8,
+    "cpu_offload_gb": 12.0,
+    "dtype": "bfloat16",
+    "stage_init_timeout": 180,
+    "linear_backend": "triton"
+  },
+  "image_wall_s": 21.38783759996295,
+  "outputs": [
+    "Red"
+  ],
+  "text_output": [
+    "ready"
+  ],
+  "profile": [
+    {
+      "kind": "image",
+      "index": 0,
+      "wall_s": 4.133468899992295,
+      "output": "Red"
+    },
+    {
+      "kind": "image",
+      "index": 1,
+      "wall_s": 4.016021100105718,
+      "output": "Red"
+    },
+    {
+      "kind": "image",
+      "index": 2,
+      "wall_s": 4.2073019000235945,
+      "output": "Red"
+    },
+    {
+      "kind": "image",
+      "index": 3,
+      "wall_s": 4.282730099977925,
+      "output": "Red"
+    },
+    {
+      "kind": "image",
+      "index": 4,
+      "wall_s": 4.183892700006254,
+      "output": "Red"
+    },
+    {
+      "kind": "image",
+      "index": 5,
+      "wall_s": 4.16138189996127,
+      "output": "Red"
+    },
+    {
+      "kind": "image",
+      "index": 6,
+      "wall_s": 4.3797133000334725,
+      "output": "Red"
+    },
+    {
+      "kind": "image",
+      "index": 7,
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@@ -0,0 +1,746 @@
+INFO 09-24 16:17:42 [patch.py:259] NVFP4 W4A4 weight_scale NaN-clamp: installed.
+INFO 09-24 16:17:43 [patch.py:510] inductor factorable-divisibility patch: installed.
+INFO 09-24 16:17:47 [omni_base.py:216] [Omni] Initializing with model \\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\models\Qwen3.8-27B-FP8
+INFO 09-24 16:17:47 [async_omni_engine.py:172] [AsyncOmniEngine] Initializing with model \\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\models\Qwen3.8-27B-FP8
+INFO 09-24 16:17:47 [async_omni_engine.py:290] [AsyncOmniEngine] Launching Orchestrator thread with 1 stages
+INFO 09-24 16:17:48 [stage_init_utils.py:768] [stage_init] Set VLLM_WORKER_MULTIPROC_METHOD=spawn
+WARNING 09-24 16:17:48 [utils.py:501] Dropping unknown OmniEngineArgs field 'inline_diffusion' (not declared on the dataclass)
+WARNING 09-24 16:17:48 [arg_utils.py:1801] The global random seed is set to 0. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM.
+INFO 09-24 16:17:48 [model.py:684] Resolved architecture: Qwen3_5ForConditionalGeneration
+INFO 09-24 16:17:48 [model.py:2021] Using max model len 512
+WARNING 09-24 16:17:48 [model.py:981] Model does not support mm_device_do_normalize, forcing mm_device_do_normalize = False.
+INFO 09-24 16:17:54 [scheduler.py:277] Chunked prefill is enabled with max_num_batched_tokens=512.
+INFO 09-24 16:17:54 [config.py:625] Mamba cache mode is set to 'align' for Qwen3_5ForConditionalGeneration by default when prefix caching is enabled
+WARNING 09-24 16:17:54 [vllm.py:1211] Auto-disabled DeepGemm for model_type=qwen3_5_text on Blackwell. DeepGemm E8M0 scale format causes accuracy degradation for this architecture. Falling back to CUTLASS. To disable DeepGemm globally, set VLLM_USE_DEEP_GEMM=0.
+WARNING 09-24 16:17:54 [vllm.py:1371] Enforce eager set, disabling torch.compile and CUDAGraphs. This is equivalent to setting -cc.mode=none -cc.cudagraph_mode=none
+WARNING 09-24 16:17:54 [vllm.py:1406] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored.
+INFO 09-24 16:17:54 [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 09-24 16:17:54 [vllm.py:1585] Cudagraph is disabled under eager mode
+INFO 09-24 16:17:54 [compilation.py:329] Enabled custom fusions: norm_quant, act_quant
+WARNING 09-24 16:17:54 [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
+INFO 09-24 16:18:07 [patch.py:259] NVFP4 W4A4 weight_scale NaN-clamp: installed.
+INFO 09-24 16:18:07 [patch.py:510] inductor factorable-divisibility patch: installed.
+(StageEngineCoreProc_stage0_replica0 pid=64968) INFO 09-24 16:18:10 [core.py:123] Initializing a V1 LLM engine (v0.29.0) with config: model='\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\Qwen3.8-27B-FP8', speculative_config=None, tokenizer='\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\Qwen3.8-27B-FP8', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=512, 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=fp8, 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\Qwen3.8-27B-FP8, enable_prefix_caching=True, 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': ['+quant_fp8', 'all', '+quant_fp8'], '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': [512], '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=64968) INFO 09-24 16:18:13 [parallel_state.py:1775] world_size=1 rank=0 local_rank=0 distributed_init_method=file://C:\Users\zhout\AppData\Local\Temp/vllm_dist_88968d4ecf3e4723a96e88303bc28481 backend=nccl
+(StageEngineCoreProc_stage0_replica0 pid=64968) WARNING 09-24 16:18:13 [parallel_state.py:1788] Distributed backend nccl is not available; falling back to gloo.
+(StageEngineCoreProc_stage0_replica0 pid=64968) INFO 09-24 16:18:13 [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=64968) WARNING 09-24 16:18:13 [gpu_ar_worker.py:120] OMNI GPUARWorker forces v1 model runner for omni hooks.
+(StageEngineCoreProc_stage0_replica0 pid=64968) INFO 09-24 16:18:13 [topk_topp_sampler.py:46] FlashInfer top-p/top-k sampling disabled via VLLM_USE_FLASHINFER_SAMPLER=0.
+(StageEngineCoreProc_stage0_replica0 pid=64968) [transformers] The `use_fast` parameter is deprecated and will be removed in a future version. Use `backend="torchvision"` instead of `use_fast=True`, or `backend="pil"` instead of `use_fast=False`.
+(StageEngineCoreProc_stage0_replica0 pid=64968) INFO 09-24 16:18:19 [base.py:138] Offloader set to UVAOffloader
+(StageEngineCoreProc_stage0_replica0 pid=64968) WARNING 09-24 16:18:19 [base.py:173] [LLM Worker 0] Sleep Mode DISABLED.
+(StageEngineCoreProc_stage0_replica0 pid=64968) WARNING 09-24 16:18:19 [base.py:173] [LLM Worker 0] Sleep Mode DISABLED.
+(StageEngineCoreProc_stage0_replica0 pid=64968) INFO 09-24 16:18:19 [gpu_model_runner.py:5379] Starting to load model \\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\models\Qwen3.8-27B-FP8...
+(StageEngineCoreProc_stage0_replica0 pid=64968) INFO 09-24 16:18:19 [cuda.py:551] Using backend AttentionBackendEnum.FLASH_ATTN for vit attention
+(StageEngineCoreProc_stage0_replica0 pid=64968) INFO 09-24 16:18:19 [mm_encoder_attention.py:372] Using AttentionBackendEnum.FLASH_ATTN for MMEncoderAttention.
+(StageEngineCoreProc_stage0_replica0 pid=64968) INFO 09-24 16:18:20 [uva.py:65] Total CPU offloaded parameters: 0.86
+(StageEngineCoreProc_stage0_replica0 pid=64968) INFO 09-24 16:18:20 [__init__.py:695] Selected CutlassFp8BlockScaledMMKernel for Fp8LinearMethod
+(StageEngineCoreProc_stage0_replica0 pid=64968) INFO 09-24 16:18:20 [qwen_gdn_linear_attn.py:167] Using Triton/FLA GDN prefill kernel (requested=auto, head_k_dim=128).
+(StageEngineCoreProc_stage0_replica0 pid=64968) INFO 09-24 16:18:20 [qwen_gdn_linear_attn.py:519] GDN decode kernel: cuda
+(StageEngineCoreProc_stage0_replica0 pid=64968) INFO 09-24 16:18:20 [cuda.py:492] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'TRITON_ATTN', 'FLEX_ATTENTION'].
+(StageEngineCoreProc_stage0_replica0 pid=64968) INFO 09-24 16:18:20 [flash_attn.py:897] Using FlashAttention version 2
+(StageEngineCoreProc_stage0_replica0 pid=64968) INFO 09-24 16:18:26 [uva.py:65] Total CPU offloaded parameters: 12.13
+(StageEngineCoreProc_stage0_replica0 pid=64968) INFO 09-24 16:18:27 [weight_utils.py:863] Filesystem type for checkpoints: unknown. Checkpoint size: 28.75 GiB. Available RAM: 8.70 GiB.
+(StageEngineCoreProc_stage0_replica0 pid=64968) INFO 09-24 16:18:27 [weight_utils.py:893] Auto-prefetch is disabled because the filesystem (unknown) is not a recognized network FS (NFS/Lustre) and the checkpoint size (28.75 GiB) exceeds 90% of available RAM (8.70 GiB).
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+(StageEngineCoreProc_stage0_replica0 pid=64968)
+(StageEngineCoreProc_stage0_replica0 pid=64968) INFO 09-24 16:22:08 [default_loader.py:430] Loading weights took 221.78 seconds
+(StageEngineCoreProc_stage0_replica0 pid=64968) INFO 09-24 16:22:14 [gpu_model_runner.py:5471] Model loading took 16.36 GiB memory and 233.655999 seconds
+(StageEngineCoreProc_stage0_replica0 pid=64968) INFO 09-24 16:22:15 [interface.py:918] Setting attention block size to 784 tokens to ensure that attention page size is >= mamba page size.
+(StageEngineCoreProc_stage0_replica0 pid=64968) INFO 09-24 16:22:15 [interface.py:942] Padding mamba page size by 0.13% to ensure that mamba page size and attention page size are exactly equal.
+(StageEngineCoreProc_stage0_replica0 pid=64968) INFO 09-24 16:22:15 [utils.py:306] Using LBNHC KV cache layout.
+(StageEngineCoreProc_stage0_replica0 pid=64968) INFO 09-24 16:22:15 [gpu_model_runner.py:6519] Skipping memory profiling for multimodal encoder and encoder cache.
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242] StageEngineCoreProc failed to start.
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242] Traceback (most recent call last):
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_engine_core_proc.py", line 189, in run_stage_core
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]     engine_core = StageEngineCoreProc(
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]                   ^^^^^^^^^^^^^^^^^^^^
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\tracing\otel.py", line 178, in sync_wrapper
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]     return func(*args, **kwargs)
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]            ^^^^^^^^^^^^^^^^^^^^^
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\v1\engine\core.py", line 1093, in __init__
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]     super().__init__(
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\v1\engine\core.py", line 145, in __init__
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]     kv_cache_config = self._initialize_kv_caches(vllm_config)
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\tracing\otel.py", line 178, in sync_wrapper
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]     return func(*args, **kwargs)
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]            ^^^^^^^^^^^^^^^^^^^^^
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\v1\engine\core.py", line 308, in _initialize_kv_caches
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]     available_gpu_memory = self.model_executor.determine_available_memory()
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]                            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\v1\executor\abstract.py", line 149, in determine_available_memory
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]     return self.collective_rpc("determine_available_memory")
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\v1\executor\uniproc_executor.py", line 103, in collective_rpc
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]     result = run_method(self.driver_worker, method, args, kwargs)
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\v1\serial_utils.py", line 510, in run_method
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]     return func(*args, **kwargs)
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]            ^^^^^^^^^^^^^^^^^^^^^
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\utils\_contextlib.py", line 124, in decorate_context
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]     return func(*args, **kwargs)
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]            ^^^^^^^^^^^^^^^^^^^^^
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\worker\base.py", line 124, in determine_available_memory
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]     self.model_runner.profile_run()
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\v1\worker\gpu_model_runner.py", line 6574, in profile_run
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]     hidden_states, last_hidden_states = self._dummy_run(
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]                                         ^^^^^^^^^^^^^^^^
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\utils\_contextlib.py", line 124, in decorate_context
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]     return func(*args, **kwargs)
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]            ^^^^^^^^^^^^^^^^^^^^^
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\worker\gpu_model_runner.py", line 1174, in _dummy_run
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]     outputs = self.model(
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]               ^^^^^^^^^^^
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\nn\modules\module.py", line 1778, in _wrapped_call_impl
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]     return self._call_impl(*args, **kwargs)
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\nn\modules\module.py", line 1789, in _call_impl
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]     return forward_call(*args, **kwargs)
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\model_executor\models\qwen3_5.py", line 591, in forward
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]     hidden_states = self.language_model.model(
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]                     ^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\compilation\decorators.py", line 509, in __call__
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]     return self.forward(*args, **kwargs)
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\model_executor\models\qwen3_next.py", line 714, in forward
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]     hidden_states, residual = layer(
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]                               ^^^^^^
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\nn\modules\module.py", line 1778, in _wrapped_call_impl
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]     return self._call_impl(*args, **kwargs)
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\nn\modules\module.py", line 1789, in _call_impl
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]     return forward_call(*args, **kwargs)
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\model_executor\models\qwen3_next.py", line 566, in forward
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]     hidden_states = self.linear_attn(hidden_states=hidden_states)
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\nn\modules\module.py", line 1778, in _wrapped_call_impl
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]     return self._call_impl(*args, **kwargs)
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\nn\modules\module.py", line 1789, in _call_impl
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]     return forward_call(*args, **kwargs)
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\model_executor\layers\mamba\gdn\qwen_gdn_linear_attn.py", line 832, in forward
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]     return self._forward_method(hidden_states)
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\model_executor\layers\mamba\gdn\qwen_gdn_linear_attn.py", line 903, in forward_cuda
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]     mixed_qkvz, _ = self.in_proj_qkvz(hidden_states)
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\nn\modules\module.py", line 1778, in _wrapped_call_impl
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]     return self._call_impl(*args, **kwargs)
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\nn\modules\module.py", line 1789, in _call_impl
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]     return forward_call(*args, **kwargs)
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\model_executor\layers\linear.py", line 600, in forward
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]     output_parallel = self.quant_method.apply(self, input_, bias)
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\model_executor\layers\quantization\fp8.py", line 459, in apply
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]     return self.fp8_linear.apply_weights(layer, x, bias)
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\model_executor\kernels\linear\scaled_mm\BlockScaledMMLinearKernel.py", line 132, in apply_weights
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]     output = self.apply_block_scaled_mm(
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]              ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\model_executor\kernels\linear\scaled_mm\cutlass.py", line 320, in apply_block_scaled_mm
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]     return ops.cutlass_scaled_mm(
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]            ^^^^^^^^^^^^^^^^^^^^^^
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\_custom_ops.py", line 870, in cutlass_scaled_mm
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]     torch.ops._C.cutlass_scaled_mm(out, a, b, scale_a, scale_b, bias)
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\_ops.py", line 1279, in __call__
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]     return self._op(*args, **kwargs)
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242]            ^^^^^^^^^^^^^^^^^^^^^^^^^
+
+(StageEngineCoreProc_stage0_replica0 pid=64968) ERROR 09-24 16:22:15 [stage_engine_core_proc.py:242] RuntimeError: cutlass_scaled_mm, C:\Users\zhout\w2\vllm-src-0.29.0\csrc\libtorch_stable\quantization\w8a8\cutlass\scaled_mm_entry.cu:269, NotImplementedError: No compiled cutlass_scaled_mm for a compute capability less than CUDA device capability: 120
+(StageEngineCoreProc_stage0_replica0 pid=64968) Process StageEngineCoreProc_stage0_replica0:
+(StageEngineCoreProc_stage0_replica0 pid=64968) Traceback (most recent call last):
+(StageEngineCoreProc_stage0_replica0 pid=64968)   File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\multiprocessing\process.py", line 314, in _bootstrap
+(StageEngineCoreProc_stage0_replica0 pid=64968)     self.run()
+(StageEngineCoreProc_stage0_replica0 pid=64968)   File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\multiprocessing\process.py", line 108, in run
+(StageEngineCoreProc_stage0_replica0 pid=64968)     self._target(*self._args, **self._kwargs)
+(StageEngineCoreProc_stage0_replica0 pid=64968)   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_engine_core_proc.py", line 189, in run_stage_core
+(StageEngineCoreProc_stage0_replica0 pid=64968)     engine_core = StageEngineCoreProc(
+(StageEngineCoreProc_stage0_replica0 pid=64968)                   ^^^^^^^^^^^^^^^^^^^^
+(StageEngineCoreProc_stage0_replica0 pid=64968)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\tracing\otel.py", line 178, in sync_wrapper
+(StageEngineCoreProc_stage0_replica0 pid=64968)     return func(*args, **kwargs)
+(StageEngineCoreProc_stage0_replica0 pid=64968)            ^^^^^^^^^^^^^^^^^^^^^
+(StageEngineCoreProc_stage0_replica0 pid=64968)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\v1\engine\core.py", line 1093, in __init__
+(StageEngineCoreProc_stage0_replica0 pid=64968)     super().__init__(
+(StageEngineCoreProc_stage0_replica0 pid=64968)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\v1\engine\core.py", line 145, in __init__
+(StageEngineCoreProc_stage0_replica0 pid=64968)     kv_cache_config = self._initialize_kv_caches(vllm_config)
+(StageEngineCoreProc_stage0_replica0 pid=64968)                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+(StageEngineCoreProc_stage0_replica0 pid=64968)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\tracing\otel.py", line 178, in sync_wrapper
+(StageEngineCoreProc_stage0_replica0 pid=64968)     return func(*args, **kwargs)
+(StageEngineCoreProc_stage0_replica0 pid=64968)            ^^^^^^^^^^^^^^^^^^^^^
+(StageEngineCoreProc_stage0_replica0 pid=64968)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\v1\engine\core.py", line 308, in _initialize_kv_caches
+(StageEngineCoreProc_stage0_replica0 pid=64968)     available_gpu_memory = self.model_executor.determine_available_memory()
+(StageEngineCoreProc_stage0_replica0 pid=64968)                            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+(StageEngineCoreProc_stage0_replica0 pid=64968)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\v1\executor\abstract.py", line 149, in determine_available_memory
+(StageEngineCoreProc_stage0_replica0 pid=64968)     return self.collective_rpc("determine_available_memory")
+(StageEngineCoreProc_stage0_replica0 pid=64968)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+(StageEngineCoreProc_stage0_replica0 pid=64968)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\v1\executor\uniproc_executor.py", line 103, in collective_rpc
+(StageEngineCoreProc_stage0_replica0 pid=64968)     result = run_method(self.driver_worker, method, args, kwargs)
+(StageEngineCoreProc_stage0_replica0 pid=64968)              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+(StageEngineCoreProc_stage0_replica0 pid=64968)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\v1\serial_utils.py", line 510, in run_method
+(StageEngineCoreProc_stage0_replica0 pid=64968)     return func(*args, **kwargs)
+(StageEngineCoreProc_stage0_replica0 pid=64968)            ^^^^^^^^^^^^^^^^^^^^^
+(StageEngineCoreProc_stage0_replica0 pid=64968)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\utils\_contextlib.py", line 124, in decorate_context
+(StageEngineCoreProc_stage0_replica0 pid=64968)     return func(*args, **kwargs)
+(StageEngineCoreProc_stage0_replica0 pid=64968)            ^^^^^^^^^^^^^^^^^^^^^
+(StageEngineCoreProc_stage0_replica0 pid=64968)   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\worker\base.py", line 124, in determine_available_memory
+(StageEngineCoreProc_stage0_replica0 pid=64968)     self.model_runner.profile_run()
+(StageEngineCoreProc_stage0_replica0 pid=64968)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\v1\worker\gpu_model_runner.py", line 6574, in profile_run
+(StageEngineCoreProc_stage0_replica0 pid=64968)     hidden_states, last_hidden_states = self._dummy_run(
+(StageEngineCoreProc_stage0_replica0 pid=64968)                                         ^^^^^^^^^^^^^^^^
+(StageEngineCoreProc_stage0_replica0 pid=64968)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\utils\_contextlib.py", line 124, in decorate_context
+(StageEngineCoreProc_stage0_replica0 pid=64968)     return func(*args, **kwargs)
+(StageEngineCoreProc_stage0_replica0 pid=64968)            ^^^^^^^^^^^^^^^^^^^^^
+(StageEngineCoreProc_stage0_replica0 pid=64968)   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\worker\gpu_model_runner.py", line 1174, in _dummy_run
+(StageEngineCoreProc_stage0_replica0 pid=64968)     outputs = self.model(
+(StageEngineCoreProc_stage0_replica0 pid=64968)               ^^^^^^^^^^^
+(StageEngineCoreProc_stage0_replica0 pid=64968)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\nn\modules\module.py", line 1778, in _wrapped_call_impl
+(StageEngineCoreProc_stage0_replica0 pid=64968)     return self._call_impl(*args, **kwargs)
+(StageEngineCoreProc_stage0_replica0 pid=64968)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+(StageEngineCoreProc_stage0_replica0 pid=64968)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\nn\modules\module.py", line 1789, in _call_impl
+(StageEngineCoreProc_stage0_replica0 pid=64968)     return forward_call(*args, **kwargs)
+(StageEngineCoreProc_stage0_replica0 pid=64968)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+(StageEngineCoreProc_stage0_replica0 pid=64968)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\model_executor\models\qwen3_5.py", line 591, in forward
+(StageEngineCoreProc_stage0_replica0 pid=64968)     hidden_states = self.language_model.model(
+(StageEngineCoreProc_stage0_replica0 pid=64968)                     ^^^^^^^^^^^^^^^^^^^^^^^^^^
+(StageEngineCoreProc_stage0_replica0 pid=64968)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\compilation\decorators.py", line 509, in __call__
+(StageEngineCoreProc_stage0_replica0 pid=64968)     return self.forward(*args, **kwargs)
+(StageEngineCoreProc_stage0_replica0 pid=64968)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+(StageEngineCoreProc_stage0_replica0 pid=64968)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\model_executor\models\qwen3_next.py", line 714, in forward
+(StageEngineCoreProc_stage0_replica0 pid=64968)     hidden_states, residual = layer(
+(StageEngineCoreProc_stage0_replica0 pid=64968)                               ^^^^^^
+(StageEngineCoreProc_stage0_replica0 pid=64968)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\nn\modules\module.py", line 1778, in _wrapped_call_impl
+(StageEngineCoreProc_stage0_replica0 pid=64968)     return self._call_impl(*args, **kwargs)
+(StageEngineCoreProc_stage0_replica0 pid=64968)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+(StageEngineCoreProc_stage0_replica0 pid=64968)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\nn\modules\module.py", line 1789, in _call_impl
+(StageEngineCoreProc_stage0_replica0 pid=64968)     return forward_call(*args, **kwargs)
+(StageEngineCoreProc_stage0_replica0 pid=64968)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+(StageEngineCoreProc_stage0_replica0 pid=64968)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\model_executor\models\qwen3_next.py", line 566, in forward
+(StageEngineCoreProc_stage0_replica0 pid=64968)     hidden_states = self.linear_attn(hidden_states=hidden_states)
+(StageEngineCoreProc_stage0_replica0 pid=64968)                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+(StageEngineCoreProc_stage0_replica0 pid=64968)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\nn\modules\module.py", line 1778, in _wrapped_call_impl
+(StageEngineCoreProc_stage0_replica0 pid=64968)     return self._call_impl(*args, **kwargs)
+(StageEngineCoreProc_stage0_replica0 pid=64968)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+(StageEngineCoreProc_stage0_replica0 pid=64968)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\nn\modules\module.py", line 1789, in _call_impl
+(StageEngineCoreProc_stage0_replica0 pid=64968)     return forward_call(*args, **kwargs)
+(StageEngineCoreProc_stage0_replica0 pid=64968)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+(StageEngineCoreProc_stage0_replica0 pid=64968)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\model_executor\layers\mamba\gdn\qwen_gdn_linear_attn.py", line 832, in forward
+(StageEngineCoreProc_stage0_replica0 pid=64968)     return self._forward_method(hidden_states)
+(StageEngineCoreProc_stage0_replica0 pid=64968)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+(StageEngineCoreProc_stage0_replica0 pid=64968)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\model_executor\layers\mamba\gdn\qwen_gdn_linear_attn.py", line 903, in forward_cuda
+(StageEngineCoreProc_stage0_replica0 pid=64968)     mixed_qkvz, _ = self.in_proj_qkvz(hidden_states)
+(StageEngineCoreProc_stage0_replica0 pid=64968)                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+(StageEngineCoreProc_stage0_replica0 pid=64968)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\nn\modules\module.py", line 1778, in _wrapped_call_impl
+(StageEngineCoreProc_stage0_replica0 pid=64968)     return self._call_impl(*args, **kwargs)
+(StageEngineCoreProc_stage0_replica0 pid=64968)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+(StageEngineCoreProc_stage0_replica0 pid=64968)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\nn\modules\module.py", line 1789, in _call_impl
+(StageEngineCoreProc_stage0_replica0 pid=64968)     return forward_call(*args, **kwargs)
+(StageEngineCoreProc_stage0_replica0 pid=64968)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+(StageEngineCoreProc_stage0_replica0 pid=64968)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\model_executor\layers\linear.py", line 600, in forward
+(StageEngineCoreProc_stage0_replica0 pid=64968)     output_parallel = self.quant_method.apply(self, input_, bias)
+(StageEngineCoreProc_stage0_replica0 pid=64968)                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+(StageEngineCoreProc_stage0_replica0 pid=64968)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\model_executor\layers\quantization\fp8.py", line 459, in apply
+(StageEngineCoreProc_stage0_replica0 pid=64968)     return self.fp8_linear.apply_weights(layer, x, bias)
+(StageEngineCoreProc_stage0_replica0 pid=64968)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+(StageEngineCoreProc_stage0_replica0 pid=64968)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\model_executor\kernels\linear\scaled_mm\BlockScaledMMLinearKernel.py", line 132, in apply_weights
+(StageEngineCoreProc_stage0_replica0 pid=64968)     output = self.apply_block_scaled_mm(
+(StageEngineCoreProc_stage0_replica0 pid=64968)              ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+(StageEngineCoreProc_stage0_replica0 pid=64968)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\model_executor\kernels\linear\scaled_mm\cutlass.py", line 320, in apply_block_scaled_mm
+(StageEngineCoreProc_stage0_replica0 pid=64968)     return ops.cutlass_scaled_mm(
+(StageEngineCoreProc_stage0_replica0 pid=64968)            ^^^^^^^^^^^^^^^^^^^^^^
+(StageEngineCoreProc_stage0_replica0 pid=64968)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\_custom_ops.py", line 870, in cutlass_scaled_mm
+(StageEngineCoreProc_stage0_replica0 pid=64968)     torch.ops._C.cutlass_scaled_mm(out, a, b, scale_a, scale_b, bias)
+(StageEngineCoreProc_stage0_replica0 pid=64968)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\torch\_ops.py", line 1279, in __call__
+(StageEngineCoreProc_stage0_replica0 pid=64968)     return self._op(*args, **kwargs)
+(StageEngineCoreProc_stage0_replica0 pid=64968)            ^^^^^^^^^^^^^^^^^^^^^^^^^
+(StageEngineCoreProc_stage0_replica0 pid=64968) RuntimeError: cutlass_scaled_mm, C:\Users\zhout\w2\vllm-src-0.29.0\csrc\libtorch_stable\quantization\w8a8\cutlass\scaled_mm_entry.cu:269, NotImplementedError: No compiled cutlass_scaled_mm for a compute capability less than CUDA device capability: 120
+ERROR 09-24 16:22:21 [stage_runtime.py:265] [StageRuntime] Stage initialization failed; shutting down 0 initialized client(s)
+
+ERROR 09-24 16:22:21 [stage_runtime.py:265] Traceback (most recent call last):
+
+ERROR 09-24 16:22:21 [stage_runtime.py:265]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 252, in initialize
+
+ERROR 09-24 16:22:21 [stage_runtime.py:265]     initialized_clients = self._initialize_stage_replicas(stage_plans, self._stage_init_timeout)
+
+ERROR 09-24 16:22:21 [stage_runtime.py:265]                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+ERROR 09-24 16:22:21 [stage_runtime.py:265]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 589, in _initialize_stage_replicas
+
+ERROR 09-24 16:22:21 [stage_runtime.py:265]     raise primary_exc
+
+ERROR 09-24 16:22:21 [stage_runtime.py:265]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 550, in _init_group
+
+ERROR 09-24 16:22:21 [stage_runtime.py:265]     client = self._initialize_replica(
+
+ERROR 09-24 16:22:21 [stage_runtime.py:265]              ^^^^^^^^^^^^^^^^^^^^^^^^^
+
+ERROR 09-24 16:22:21 [stage_runtime.py:265]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 828, in _initialize_replica
+
+ERROR 09-24 16:22:21 [stage_runtime.py:265]     return self._initialize_local_llm_replica(plan, stage_init_timeout)
+
+ERROR 09-24 16:22:21 [stage_runtime.py:265]            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+ERROR 09-24 16:22:21 [stage_runtime.py:265]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 934, in _initialize_local_llm_replica
+
+ERROR 09-24 16:22:21 [stage_runtime.py:265]     launch_stack.close()
+
+ERROR 09-24 16:22:21 [stage_runtime.py:265]   File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\contextlib.py", line 618, in close
+
+ERROR 09-24 16:22:21 [stage_runtime.py:265]     self.__exit__(None, None, None)
+
+ERROR 09-24 16:22:21 [stage_runtime.py:265]   File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\contextlib.py", line 610, in __exit__
+
+ERROR 09-24 16:22:21 [stage_runtime.py:265]     raise exc_details[1]
+
+ERROR 09-24 16:22:21 [stage_runtime.py:265]   File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\contextlib.py", line 595, in __exit__
+
+ERROR 09-24 16:22:21 [stage_runtime.py:265]     if cb(*exc_details):
+
+ERROR 09-24 16:22:21 [stage_runtime.py:265]        ^^^^^^^^^^^^^^^^
+
+ERROR 09-24 16:22:21 [stage_runtime.py:265]   File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\contextlib.py", line 144, in __exit__
+
+ERROR 09-24 16:22:21 [stage_runtime.py:265]     next(self.gen)
+
+ERROR 09-24 16:22:21 [stage_runtime.py:265]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_engine_startup.py", line 1211, in launch_stage_replica
+
+ERROR 09-24 16:22:21 [stage_runtime.py:265]     wait_for_engine_startup(
+
+ERROR 09-24 16:22:21 [stage_runtime.py:265]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\v1\engine\utils.py", line 1307, in wait_for_engine_startup
+
+ERROR 09-24 16:22:21 [stage_runtime.py:265]     raise RuntimeError(
+
+ERROR 09-24 16:22:21 [stage_runtime.py:265] RuntimeError: Engine core initialization failed. See root cause above. Failed proc(s): {'StageEngineCoreProc_stage0_replica0': 1}
+ERROR 09-24 16:22:21 [async_omni_engine.py:474] [AsyncOmniEngine] Orchestrator thread crashed
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474] Traceback (most recent call last):
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\async_omni_engine.py", line 468, in _bootstrap_orchestrator
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]     loop.run_until_complete(_run_orchestrator())
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]   File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\asyncio\base_events.py", line 691, in run_until_complete
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]     return future.result()
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]            ^^^^^^^^^^^^^^^
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\async_omni_engine.py", line 424, in _run_orchestrator
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]     self._initialize_stages(stage_init_timeout)
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\async_omni_engine.py", line 350, in _initialize_stages
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]     self._initialize_stages_impl(stage_init_timeout)
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\async_omni_engine.py", line 371, in _initialize_stages_impl
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]     self._runtime.initialize()
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 278, in initialize
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]     raise exc
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 252, in initialize
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]     initialized_clients = self._initialize_stage_replicas(stage_plans, self._stage_init_timeout)
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 589, in _initialize_stage_replicas
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]     raise primary_exc
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 550, in _init_group
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]     client = self._initialize_replica(
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]              ^^^^^^^^^^^^^^^^^^^^^^^^^
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 828, in _initialize_replica
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]     return self._initialize_local_llm_replica(plan, stage_init_timeout)
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 934, in _initialize_local_llm_replica
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]     launch_stack.close()
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]   File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\contextlib.py", line 618, in close
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]     self.__exit__(None, None, None)
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]   File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\contextlib.py", line 610, in __exit__
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]     raise exc_details[1]
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]   File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\contextlib.py", line 595, in __exit__
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]     if cb(*exc_details):
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]        ^^^^^^^^^^^^^^^^
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]   File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\contextlib.py", line 144, in __exit__
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]     next(self.gen)
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_engine_startup.py", line 1211, in launch_stage_replica
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]     wait_for_engine_startup(
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\v1\engine\utils.py", line 1307, in wait_for_engine_startup
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474]     raise RuntimeError(
+
+ERROR 09-24 16:22:21 [async_omni_engine.py:474] RuntimeError: Engine core initialization failed. See root cause above. Failed proc(s): {'StageEngineCoreProc_stage0_replica0': 1}
+INFO 09-24 16:22:21 [async_omni_engine.py:1807] [AsyncOmniEngine] Shutting down Orchestrator
+Exception in thread orchestrator:
+Traceback (most recent call last):
+  File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\threading.py", line 1075, in _bootstrap_inner
+    self.run()
+  File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\threading.py", line 1012, in run
+    self._target(*self._args, **self._kwargs)
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\async_omni_engine.py", line 468, in _bootstrap_orchestrator
+    loop.run_until_complete(_run_orchestrator())
+  File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\asyncio\base_events.py", line 691, in run_until_complete
+    return future.result()
+           ^^^^^^^^^^^^^^^
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\async_omni_engine.py", line 424, in _run_orchestrator
+    self._initialize_stages(stage_init_timeout)
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\async_omni_engine.py", line 350, in _initialize_stages
+    self._initialize_stages_impl(stage_init_timeout)
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\async_omni_engine.py", line 371, in _initialize_stages_impl
+    self._runtime.initialize()
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 278, in initialize
+    raise exc
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 252, in initialize
+    initialized_clients = self._initialize_stage_replicas(stage_plans, self._stage_init_timeout)
+                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 589, in _initialize_stage_replicas
+    raise primary_exc
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 550, in _init_group
+    client = self._initialize_replica(
+             ^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 828, in _initialize_replica
+    return self._initialize_local_llm_replica(plan, stage_init_timeout)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 934, in _initialize_local_llm_replica
+    launch_stack.close()
+  File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\contextlib.py", line 618, in close
+    self.__exit__(None, None, None)
+  File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\contextlib.py", line 610, in __exit__
+    raise exc_details[1]
+  File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\contextlib.py", line 595, in __exit__
+    if cb(*exc_details):
+       ^^^^^^^^^^^^^^^^
+  File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\contextlib.py", line 144, in __exit__
+    next(self.gen)
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_engine_startup.py", line 1211, in launch_stage_replica
+    wait_for_engine_startup(
+  File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\v1\engine\utils.py", line 1307, in wait_for_engine_startup
+    raise RuntimeError(
+RuntimeError: Engine core initialization failed. See root cause above. Failed proc(s): {'StageEngineCoreProc_stage0_replica0': 1}
+Traceback (most recent call last):
+  File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\threading.py", line 1075, in _bootstrap_inner
+    self.run()
+  File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\threading.py", line 1012, in run
+    self._target(*self._args, **self._kwargs)
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\async_omni_engine.py", line 468, in _bootstrap_orchestrator
+    loop.run_until_complete(_run_orchestrator())
+  File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\asyncio\base_events.py", line 691, in run_until_complete
+    return future.result()
+           ^^^^^^^^^^^^^^^
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\async_omni_engine.py", line 424, in _run_orchestrator
+    self._initialize_stages(stage_init_timeout)
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\async_omni_engine.py", line 350, in _initialize_stages
+    self._initialize_stages_impl(stage_init_timeout)
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\async_omni_engine.py", line 371, in _initialize_stages_impl
+    self._runtime.initialize()
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 278, in initialize
+    raise exc
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 252, in initialize
+    initialized_clients = self._initialize_stage_replicas(stage_plans, self._stage_init_timeout)
+                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 589, in _initialize_stage_replicas
+    raise primary_exc
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 550, in _init_group
+    client = self._initialize_replica(
+             ^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 828, in _initialize_replica
+    return self._initialize_local_llm_replica(plan, stage_init_timeout)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 934, in _initialize_local_llm_replica
+    launch_stack.close()
+  File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\contextlib.py", line 618, in close
+    self.__exit__(None, None, None)
+  File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\contextlib.py", line 610, in __exit__
+    raise exc_details[1]
+  File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\contextlib.py", line 595, in __exit__
+    if cb(*exc_details):
+       ^^^^^^^^^^^^^^^^
+  File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\contextlib.py", line 144, in __exit__
+    next(self.gen)
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_engine_startup.py", line 1211, in launch_stage_replica
+    wait_for_engine_startup(
+  File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\v1\engine\utils.py", line 1307, in wait_for_engine_startup
+    raise RuntimeError(
+RuntimeError: Engine core initialization failed. See root cause above. Failed proc(s): {'StageEngineCoreProc_stage0_replica0': 1}
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\benchmarks\edge_harness\probe_qwen35_image.py", line 53, in main
+    engine = Omni(**config)
+             ^^^^^^^^^^^^^^
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\entrypoints\omni_base.py", line 223, in __init__
+    self.engine = AsyncOmniEngine(
+                  ^^^^^^^^^^^^^^^^
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\async_omni_engine.py", line 305, in __init__
+    self._wait_for_orchestrator_init(startup_future, startup_timeout)
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\async_omni_engine.py", line 539, in _wait_for_orchestrator_init
+    startup_future.result(
+  File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\concurrent\futures\_base.py", line 456, in result
+    return self.__get_result()
+           ^^^^^^^^^^^^^^^^^^^
+  File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\concurrent\futures\_base.py", line 401, in __get_result
+    raise self._exception
+RuntimeError: Orchestrator initialization failed: Engine core initialization failed. See root cause above. Failed proc(s): {'StageEngineCoreProc_stage0_replica0': 1}
@@ -0,0 +1,21 @@
+{
+  "model": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\Qwen3.8-27B-FP8",
+  "scope": "one synthetic-image Omni image-to-text request",
+  "start": 1790237850.5871127,
+  "config": {
+    "model": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\Qwen3.8-27B-FP8",
+    "language_model_only": false,
+    "max_model_len": 512,
+    "max_num_seqs": 1,
+    "max_num_batched_tokens": 512,
+    "enforce_eager": true,
+    "gpu_memory_utilization": 0.75,
+    "cpu_offload_gb": 12.0,
+    "dtype": "bfloat16",
+    "stage_init_timeout": 180
+  },
+  "status": "failed",
+  "error": "RuntimeError(\"Orchestrator initialization failed: Engine core initialization failed. See root cause above. Failed proc(s): {'StageEngineCoreProc_stage0_replica0': 1}\")",
+  "traceback": "Traceback (most recent call last):\n  File \"C:\\Users\\zhout\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\threading.py\", line 1075, in _bootstrap_inner\n    self.run()\n  File \"C:\\Users\\zhout\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\threading.py\", line 1012, in run\n    self._target(*self._args, **self._kwargs)\n  File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\vllm_omni\\engine\\async_omni_engine.py\", line 468, in _bootstrap_orchestrator\n    loop.run_until_complete(_run_orchestrator())\n  File \"C:\\Users\\zhout\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 691, in run_until_complete\n    return future.result()\n           ^^^^^^^^^^^^^^^\n  File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\vllm_omni\\engine\\async_omni_engine.py\", line 424, in _run_orchestrator\n    self._initialize_stages(stage_init_timeout)\n  File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\vllm_omni\\engine\\async_omni_engine.py\", line 350, in _initialize_stages\n    self._initialize_stages_impl(stage_init_timeout)\n  File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\vllm_omni\\engine\\async_omni_engine.py\", line 371, in _initialize_stages_impl\n    self._runtime.initialize()\n  File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\vllm_omni\\engine\\stage_runtime.py\", line 278, in initialize\n    raise exc\n  File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\vllm_omni\\engine\\stage_runtime.py\", line 252, in initialize\n    initialized_clients = self._initialize_stage_replicas(stage_plans, self._stage_init_timeout)\n                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\vllm_omni\\engine\\stage_runtime.py\", line 589, in _initialize_stage_replicas\n    raise primary_exc\n  File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\vllm_omni\\engine\\stage_runtime.py\", line 550, in _init_group\n    client = self._initialize_replica(\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\vllm_omni\\engine\\stage_runtime.py\", line 828, in _initialize_replica\n    return self._initialize_local_llm_replica(plan, stage_init_timeout)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\vllm_omni\\engine\\stage_runtime.py\", line 934, in _initialize_local_llm_replica\n    launch_stack.close()\n  File \"C:\\Users\\zhout\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\contextlib.py\", line 618, in close\n    self.__exit__(None, None, None)\n  File \"C:\\Users\\zhout\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\contextlib.py\", line 610, in __exit__\n    raise exc_details[1]\n  File \"C:\\Users\\zhout\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\contextlib.py\", line 595, in __exit__\n    if cb(*exc_details):\n       ^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\zhout\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\contextlib.py\", line 144, in __exit__\n    next(self.gen)\n  File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\vllm_omni\\engine\\stage_engine_startup.py\", line 1211, in launch_stage_replica\n    wait_for_engine_startup(\n  File \"C:\\Users\\zhout\\w2\\omni029venv\\Lib\\site-packages\\vllm\\v1\\engine\\utils.py\", line 1307, in wait_for_engine_startup\n    raise RuntimeError(\nRuntimeError: Engine core initialization failed. See root cause above. Failed proc(s): {'StageEngineCoreProc_stage0_replica0': 1}\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\benchmarks\\edge_harness\\probe_qwen35_image.py\", line 53, 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 305, in __init__\n    self._wait_for_orchestrator_init(startup_future, startup_timeout)\n  File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\vllm_omni\\engine\\async_omni_engine.py\", line 539, in _wait_for_orchestrator_init\n    startup_future.result(\n  File \"C:\\Users\\zhout\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\concurrent\\futures\\_base.py\", line 456, in resultLine truncated
+  "end": 1790238141.9250593
+}
@@ -0,0 +1,30 @@
+INFO 09-24 16:25:08 [patch.py:259] NVFP4 W4A4 weight_scale NaN-clamp: installed.
+INFO 09-24 16:25:08 [patch.py:510] inductor factorable-divisibility patch: installed.
+INFO 09-24 16:25:13 [omni_base.py:216] [Omni] Initializing with model \\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\models\Qwen3.8-27B-FP8
+INFO 09-24 16:25:13 [async_omni_engine.py:172] [AsyncOmniEngine] Initializing with model \\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\models\Qwen3.8-27B-FP8
+Traceback (most recent call last):
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\benchmarks\edge_harness\probe_qwen35_image.py", line 55, in main
+    engine = Omni(**config)
+             ^^^^^^^^^^^^^^
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\entrypoints\omni_base.py", line 223, in __init__
+    self.engine = AsyncOmniEngine(
+                  ^^^^^^^^^^^^^^^^
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\async_omni_engine.py", line 228, in __init__
+    self.config_path, self.stage_configs = self._resolve_stage_configs(
+                                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  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
+    load_and_resolve_stage_configs(
+  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
+    return resolve_omni_config(
+           ^^^^^^^^^^^^^^^^^^^^
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\config\resolver.py", line 238, in resolve_omni_config
+    structured_config = StageConfigFactory.create_from_model(
+                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\config\config_factory.py", line 401, in create_from_model
+    return VllmOmniConfig.from_pipeline_config(
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\config\omni_config.py", line 1952, in from_pipeline_config
+    _validate_global_stage_cli_ownership(pipeline_cfg, cli_overrides)
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\config\omni_config.py", line 368, in _validate_global_stage_cli_ownership
+    raise ValueError(
+ValueError: Pipeline 'qwen3_5' has explicit engine argument(s) with no structured config owner: linear_backend
@@ -0,0 +1,22 @@
+{
+  "model": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\Qwen3.8-27B-FP8",
+  "scope": "one synthetic-image Omni image-to-text request",
+  "start": 1790238290.0872524,
+  "config": {
+    "model": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\Qwen3.8-27B-FP8",
+    "language_model_only": false,
+    "max_model_len": 512,
+    "max_num_seqs": 1,
+    "max_num_batched_tokens": 512,
+    "enforce_eager": true,
+    "gpu_memory_utilization": 0.75,
+    "cpu_offload_gb": 12.0,
+    "linear_backend": "triton",
+    "dtype": "bfloat16",
+    "stage_init_timeout": 180
+  },
+  "status": "failed",
+  "error": "ValueError(\"Pipeline 'qwen3_5' has explicit engine argument(s) with no structured config owner: linear_backend\")",
+  "traceback": "Traceback (most recent call last):\n  File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\benchmarks\\edge_harness\\probe_qwen35_image.py\", line 55, 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 238, in resolve_omni_config\n    structured_config = StageConfigFactory.create_from_model(\n                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\vllm_omni\\config\\config_factory.py\", line 401, in create_from_model\n    return VllmOmniConfig.from_pipeline_config(\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\vllm_omni\\config\\omni_config.py\", line 1952, in from_pipeline_config\n    _validate_global_stage_cli_ownership(pipeline_cfg, cli_overrides)\n  File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\vllm_omni\\config\\omni_config.py\", line 368, in _validate_global_stage_cli_ownership\n    raise ValueError(\nValueError: Pipeline 'qwen3_5' has explicit engine argument(s) with no structured config owner: linear_backend\n",
+  "end": 1790238314.2134175
+}
@@ -0,0 +1,612 @@
+INFO 09-24 16:26:33 [patch.py:259] NVFP4 W4A4 weight_scale NaN-clamp: installed.
+INFO 09-24 16:26:33 [patch.py:510] inductor factorable-divisibility patch: installed.
+INFO 09-24 16:26:37 [omni_base.py:216] [Omni] Initializing with model \\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\models\Qwen3.8-27B-FP8
+INFO 09-24 16:26:37 [async_omni_engine.py:172] [AsyncOmniEngine] Initializing with model \\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\models\Qwen3.8-27B-FP8
+INFO 09-24 16:26:37 [async_omni_engine.py:290] [AsyncOmniEngine] Launching Orchestrator thread with 1 stages
+INFO 09-24 16:26:38 [stage_init_utils.py:768] [stage_init] Set VLLM_WORKER_MULTIPROC_METHOD=spawn
+WARNING 09-24 16:26:38 [utils.py:501] Dropping unknown OmniEngineArgs field 'inline_diffusion' (not declared on the dataclass)
+WARNING 09-24 16:26:38 [arg_utils.py:1801] The global random seed is set to 0. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM.
+INFO 09-24 16:26:38 [model.py:684] Resolved architecture: Qwen3_5ForConditionalGeneration
+INFO 09-24 16:26:38 [model.py:2021] Using max model len 512
+WARNING 09-24 16:26:38 [model.py:981] Model does not support mm_device_do_normalize, forcing mm_device_do_normalize = False.
+INFO 09-24 16:26:44 [scheduler.py:277] Chunked prefill is enabled with max_num_batched_tokens=512.
+INFO 09-24 16:26:44 [config.py:625] Mamba cache mode is set to 'align' for Qwen3_5ForConditionalGeneration by default when prefix caching is enabled
+WARNING 09-24 16:26:44 [vllm.py:1211] Auto-disabled DeepGemm for model_type=qwen3_5_text on Blackwell. DeepGemm E8M0 scale format causes accuracy degradation for this architecture. Falling back to CUTLASS. To disable DeepGemm globally, set VLLM_USE_DEEP_GEMM=0.
+WARNING 09-24 16:26:44 [vllm.py:1371] Enforce eager set, disabling torch.compile and CUDAGraphs. This is equivalent to setting -cc.mode=none -cc.cudagraph_mode=none
+WARNING 09-24 16:26:44 [vllm.py:1406] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored.
+INFO 09-24 16:26:44 [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 09-24 16:26:44 [vllm.py:1585] Cudagraph is disabled under eager mode
+INFO 09-24 16:26:44 [compilation.py:329] Enabled custom fusions: norm_quant, act_quant
+WARNING 09-24 16:26:44 [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
+INFO 09-24 16:26:57 [patch.py:259] NVFP4 W4A4 weight_scale NaN-clamp: installed.
+INFO 09-24 16:26:57 [patch.py:510] inductor factorable-divisibility patch: installed.
+(StageEngineCoreProc_stage0_replica0 pid=52424) INFO 09-24 16:27:00 [core.py:123] Initializing a V1 LLM engine (v0.29.0) with config: model='\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\Qwen3.8-27B-FP8', speculative_config=None, tokenizer='\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\Qwen3.8-27B-FP8', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=512, 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=fp8, 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\Qwen3.8-27B-FP8, enable_prefix_caching=True, 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': ['+quant_fp8', 'all', '+quant_fp8'], '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': [512], '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='triton')
+(StageEngineCoreProc_stage0_replica0 pid=52424) INFO 09-24 16:27:03 [parallel_state.py:1775] world_size=1 rank=0 local_rank=0 distributed_init_method=file://C:\Users\zhout\AppData\Local\Temp/vllm_dist_074ca444a0cc45079a7b9f12455c8f66 backend=nccl
+(StageEngineCoreProc_stage0_replica0 pid=52424) WARNING 09-24 16:27:03 [parallel_state.py:1788] Distributed backend nccl is not available; falling back to gloo.
+(StageEngineCoreProc_stage0_replica0 pid=52424) INFO 09-24 16:27:03 [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=52424) WARNING 09-24 16:27:03 [gpu_ar_worker.py:120] OMNI GPUARWorker forces v1 model runner for omni hooks.
+(StageEngineCoreProc_stage0_replica0 pid=52424) INFO 09-24 16:27:03 [topk_topp_sampler.py:46] FlashInfer top-p/top-k sampling disabled via VLLM_USE_FLASHINFER_SAMPLER=0.
+(StageEngineCoreProc_stage0_replica0 pid=52424) [transformers] The `use_fast` parameter is deprecated and will be removed in a future version. Use `backend="torchvision"` instead of `use_fast=True`, or `backend="pil"` instead of `use_fast=False`.
+(StageEngineCoreProc_stage0_replica0 pid=52424) INFO 09-24 16:27:09 [base.py:138] Offloader set to UVAOffloader
+(StageEngineCoreProc_stage0_replica0 pid=52424) WARNING 09-24 16:27:09 [base.py:173] [LLM Worker 0] Sleep Mode DISABLED.
+(StageEngineCoreProc_stage0_replica0 pid=52424) WARNING 09-24 16:27:09 [base.py:173] [LLM Worker 0] Sleep Mode DISABLED.
+(StageEngineCoreProc_stage0_replica0 pid=52424) INFO 09-24 16:27:09 [gpu_model_runner.py:5379] Starting to load model \\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\models\Qwen3.8-27B-FP8...
+(StageEngineCoreProc_stage0_replica0 pid=52424) INFO 09-24 16:27:09 [cuda.py:551] Using backend AttentionBackendEnum.FLASH_ATTN for vit attention
+(StageEngineCoreProc_stage0_replica0 pid=52424) INFO 09-24 16:27:09 [mm_encoder_attention.py:372] Using AttentionBackendEnum.FLASH_ATTN for MMEncoderAttention.
+(StageEngineCoreProc_stage0_replica0 pid=52424) INFO 09-24 16:27:10 [uva.py:65] Total CPU offloaded parameters: 0.86
+(StageEngineCoreProc_stage0_replica0 pid=52424) INFO 09-24 16:27:10 [__init__.py:695] Selected TritonFp8BlockScaledMMKernel for Fp8LinearMethod
+(StageEngineCoreProc_stage0_replica0 pid=52424) INFO 09-24 16:27:10 [qwen_gdn_linear_attn.py:167] Using Triton/FLA GDN prefill kernel (requested=auto, head_k_dim=128).
+(StageEngineCoreProc_stage0_replica0 pid=52424) INFO 09-24 16:27:10 [qwen_gdn_linear_attn.py:519] GDN decode kernel: cuda
+(StageEngineCoreProc_stage0_replica0 pid=52424) INFO 09-24 16:27:11 [cuda.py:492] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'TRITON_ATTN', 'FLEX_ATTENTION'].
+(StageEngineCoreProc_stage0_replica0 pid=52424) INFO 09-24 16:27:11 [flash_attn.py:897] Using FlashAttention version 2
+(StageEngineCoreProc_stage0_replica0 pid=52424) INFO 09-24 16:27:19 [uva.py:65] Total CPU offloaded parameters: 12.13
+(StageEngineCoreProc_stage0_replica0 pid=52424) INFO 09-24 16:27:20 [weight_utils.py:863] Filesystem type for checkpoints: unknown. Checkpoint size: 28.75 GiB. Available RAM: 5.39 GiB.
+(StageEngineCoreProc_stage0_replica0 pid=52424) INFO 09-24 16:27:20 [weight_utils.py:893] Auto-prefetch is disabled because the filesystem (unknown) is not a recognized network FS (NFS/Lustre) and the checkpoint size (28.75 GiB) exceeds 90% of available RAM (5.39 GiB).
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+Loading safetensors checkpoint shards: 100% Completed | 66/66 [07:12<00:00, 23.51s/it]
+(StageEngineCoreProc_stage0_replica0 pid=52424)
+Loading safetensors checkpoint shards: 100% Completed | 66/66 [07:12<00:00,  6.56s/it]
+(StageEngineCoreProc_stage0_replica0 pid=52424)
+(StageEngineCoreProc_stage0_replica0 pid=52424) INFO 09-24 16:34:34 [default_loader.py:430] Loading weights took 434.06 seconds
+(StageEngineCoreProc_stage0_replica0 pid=52424) INFO 09-24 16:34:39 [gpu_model_runner.py:5471] Model loading took 16.36 GiB memory and 448.537252 seconds
+(StageEngineCoreProc_stage0_replica0 pid=52424) INFO 09-24 16:34:40 [interface.py:918] Setting attention block size to 784 tokens to ensure that attention page size is >= mamba page size.
+(StageEngineCoreProc_stage0_replica0 pid=52424) INFO 09-24 16:34:40 [interface.py:942] Padding mamba page size by 0.13% to ensure that mamba page size and attention page size are exactly equal.
+(StageEngineCoreProc_stage0_replica0 pid=52424) INFO 09-24 16:34:40 [utils.py:306] Using LBNHC KV cache layout.
+(StageEngineCoreProc_stage0_replica0 pid=52424) INFO 09-24 16:34:40 [gpu_model_runner.py:6519] Skipping memory profiling for multimodal encoder and encoder cache.
+(StageEngineCoreProc_stage0_replica0 pid=52424) WARNING 09-24 16:34:40 [fp8_utils.py:852] Using default W8A8 Block FP8 kernel config. Performance might be sub-optimal! Config file not found at C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\model_executor\layers\quantization\utils\configs\N=16384,K=5120,device_name=NVIDIA_GeForce_RTX_5090_Laptop_GPU,dtype=fp8_w8a8,block_shape=[128,128].json
+remark: fp8_utils.py:737:0: 775 instructions in function
+remark: fused_gdn_prefill_post_conv.py:20:0: 380 instructions in function
+(StageEngineCoreProc_stage0_replica0 pid=52424) C:\Users\zhout\w2\omni029venv\Lib\site-packages\triton\language\core.py:2625: UserWarning: tl.make_block_ptr is deprecated. Use TensorDescriptor or tl.make_tensor_descriptor instead.
+(StageEngineCoreProc_stage0_replica0 pid=52424)   warn("tl.make_block_ptr is deprecated. Use TensorDescriptor or tl.make_tensor_descriptor instead.")
+remark: cumsum.py:27:0: 87 instructions in function
+remark: cumsum.py:27:0: 71 instructions in function
+remark: cumsum.py:27:0: 75 instructions in function
+remark: cumsum.py:27:0: 75 instructions in function
+remark: chunk_scaled_dot_kkt.py:47:0: 975 instructions in function
+remark: chunk_scaled_dot_kkt.py:47:0: 993 instructions in function
+remark: chunk_scaled_dot_kkt.py:47:0: 1001 instructions in function
+remark: chunk_scaled_dot_kkt.py:47:0: 677 instructions in function
+remark: chunk_scaled_dot_kkt.py:47:0: 691 instructions in function
+remark: chunk_scaled_dot_kkt.py:47:0: 697 instructions in function
+remark: chunk_scaled_dot_kkt.py:47:0: 469 instructions in function
+remark: chunk_scaled_dot_kkt.py:47:0: 482 instructions in function
+remark: chunk_scaled_dot_kkt.py:47:0: 486 instructions in function
+remark: chunk_scaled_dot_kkt.py:47:0: 1256 instructions in function
+remark: chunk_scaled_dot_kkt.py:47:0: 1274 instructions in function
+remark: chunk_scaled_dot_kkt.py:47:0: 1293 instructions in function
+remark: chunk_scaled_dot_kkt.py:47:0: 845 instructions in function
+remark: chunk_scaled_dot_kkt.py:47:0: 859 instructions in function
+remark: chunk_scaled_dot_kkt.py:47:0: 870 instructions in function
+remark: chunk_scaled_dot_kkt.py:47:0: 598 instructions in function
+remark: chunk_scaled_dot_kkt.py:47:0: 610 instructions in function
+remark: chunk_scaled_dot_kkt.py:47:0: 617 instructions in function
+remark: chunk_scaled_dot_kkt.py:47:0: 1623 instructions in function
+remark: chunk_scaled_dot_kkt.py:47:0: 1623 instructions in function
+remark: chunk_scaled_dot_kkt.py:47:0: 1623 instructions in function
+remark: chunk_scaled_dot_kkt.py:47:0: 1097 instructions in function
+remark: chunk_scaled_dot_kkt.py:47:0: 1097 instructions in function
+remark: chunk_scaled_dot_kkt.py:47:0: 1097 instructions in function
+remark: chunk_scaled_dot_kkt.py:47:0: 803 instructions in function
+remark: chunk_scaled_dot_kkt.py:47:0: 803 instructions in function
+remark: chunk_scaled_dot_kkt.py:47:0: 803 instructions in function
+remark: solve_tril.py:238:0: 1942 instructions in function
+remark: solve_tril.py:238:0: 1942 instructions in function
+remark: solve_tril.py:238:0: 1942 instructions in function
+remark: solve_tril.py:238:0: 1942 instructions in function
+remark: solve_tril.py:238:0: 1104 instructions in function
+remark: solve_tril.py:238:0: 1104 instructions in function
+remark: solve_tril.py:238:0: 1104 instructions in function
+remark: solve_tril.py:238:0: 1104 instructions in function
+remark: solve_tril.py:238:0: 698 instructions in function
+remark: solve_tril.py:238:0: 698 instructions in function
+remark: solve_tril.py:238:0: 698 instructions in function
+remark: solve_tril.py:238:0: 698 instructions in function
+remark: wy_fast.py:29:0: 1227 instructions in function
+remark: wy_fast.py:29:0: 1373 instructions in function
+remark: wy_fast.py:29:0: 1419 instructions in function
+remark: wy_fast.py:29:0: 1093 instructions in function
+remark: wy_fast.py:29:0: 1187 instructions in function
+remark: wy_fast.py:29:0: 1213 instructions in function
+remark: wy_fast.py:29:0: 611 instructions in function
+remark: wy_fast.py:29:0: 675 instructions in function
+remark: wy_fast.py:29:0: 691 instructions in function
+remark: chunk_delta_h.py:45:0: 1529 instructions in function
+remark: chunk_delta_h.py:45:0: 2447 instructions in function
+remark: chunk_delta_h.py:45:0: 1768 instructions in function
+remark: chunk_delta_h.py:45:0: 2739 instructions in function
+remark: chunk_delta_h.py:45:0: 1952 instructions in function
+remark: chunk_delta_h.py:45:0: 2951 instructions in function
+remark: chunk_delta_h.py:45:0: 980 instructions in function
+remark: chunk_delta_h.py:45:0: 1441 instructions in function
+remark: chunk_delta_h.py:45:0: 1109 instructions in function
+remark: chunk_delta_h.py:45:0: 1597 instructions in function
+remark: chunk_delta_h.py:45:0: 1211 instructions in function
+remark: chunk_delta_h.py:45:0: 1713 instructions in function
+remark: chunk_o.py:42:0: 1020 instructions in function
+remark: chunk_o.py:42:0: 1058 instructions in function
+remark: chunk_o.py:42:0: 1080 instructions in function
+remark: chunk_o.py:42:0: 847 instructions in function
+remark: chunk_o.py:42:0: 871 instructions in function
+remark: chunk_o.py:42:0: 883 instructions in function
+remark: chunk_o.py:42:0: 1386 instructions in function
+remark: chunk_o.py:42:0: 1422 instructions in function
+remark: chunk_o.py:42:0: 1448 instructions in function
+remark: chunk_o.py:42:0: 1021 instructions in function
+remark: chunk_o.py:42:0: 1041 instructions in function
+remark: chunk_o.py:42:0: 1055 instructions in function
+remark: chunk_o.py:42:0: 1170 instructions in function
+remark: chunk_o.py:42:0: 1234 instructions in function
+remark: chunk_o.py:42:0: 1279 instructions in function
+remark: chunk_o.py:42:0: 935 instructions in function
+remark: chunk_o.py:42:0: 967 instructions in function
+remark: chunk_o.py:42:0: 992 instructions in function
+remark: chunk_o.py:42:0: 1482 instructions in function
+remark: chunk_o.py:42:0: 1548 instructions in function
+remark: chunk_o.py:42:0: 1601 instructions in function
+remark: chunk_o.py:42:0: 1075 instructions in function
+remark: chunk_o.py:42:0: 1101 instructions in function
+remark: chunk_o.py:42:0: 1130 instructions in function
+(StageEngineCoreProc_stage0_replica0 pid=52424) WARNING 09-24 16:35:40 [fp8_utils.py:852] Using default W8A8 Block FP8 kernel config. Performance might be sub-optimal! Config file not found at C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\model_executor\layers\quantization\utils\configs\N=5120,K=6144,device_name=NVIDIA_GeForce_RTX_5090_Laptop_GPU,dtype=fp8_w8a8,block_shape=[128,128].json
+remark: fp8_utils.py:737:0: 775 instructions in function
+(StageEngineCoreProc_stage0_replica0 pid=52424) WARNING 09-24 16:35:40 [fp8_utils.py:852] Using default W8A8 Block FP8 kernel config. Performance might be sub-optimal! Config file not found at C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\model_executor\layers\quantization\utils\configs\N=34816,K=5120,device_name=NVIDIA_GeForce_RTX_5090_Laptop_GPU,dtype=fp8_w8a8,block_shape=[128,128].json
+(StageEngineCoreProc_stage0_replica0 pid=52424) WARNING 09-24 16:35:40 [fp8_utils.py:852] Using default W8A8 Block FP8 kernel config. Performance might be sub-optimal! Config file not found at C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\model_executor\layers\quantization\utils\configs\N=5120,K=17408,device_name=NVIDIA_GeForce_RTX_5090_Laptop_GPU,dtype=fp8_w8a8,block_shape=[128,128].json
+(StageEngineCoreProc_stage0_replica0 pid=52424) WARNING 09-24 16:35:41 [fp8_utils.py:852] Using default W8A8 Block FP8 kernel config. Performance might be sub-optimal! Config file not found at C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\model_executor\layers\quantization\utils\configs\N=14336,K=5120,device_name=NVIDIA_GeForce_RTX_5090_Laptop_GPU,dtype=fp8_w8a8,block_shape=[128,128].json
+remark: fused_qk_norm_rope.py:17:0: 160 instructions in function
+(StageEngineCoreProc_stage0_replica0 pid=52424) INFO 09-24 16:35:49 [base.py:148] Available KV cache memory: 0.26 GiB (device-level profiling)
+(StageEngineCoreProc_stage0_replica0 pid=52424) Process StageEngineCoreProc_stage0_replica0:
+(StageEngineCoreProc_stage0_replica0 pid=52424) ERROR 09-24 16:35:49 [stage_engine_core_proc.py:242] StageEngineCoreProc failed to start.
+
+(StageEngineCoreProc_stage0_replica0 pid=52424) ERROR 09-24 16:35:49 [stage_engine_core_proc.py:242] Traceback (most recent call last):
+
+(StageEngineCoreProc_stage0_replica0 pid=52424) ERROR 09-24 16:35:49 [stage_engine_core_proc.py:242]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_engine_core_proc.py", line 189, in run_stage_core
+
+(StageEngineCoreProc_stage0_replica0 pid=52424) ERROR 09-24 16:35:49 [stage_engine_core_proc.py:242]     engine_core = StageEngineCoreProc(
+
+(StageEngineCoreProc_stage0_replica0 pid=52424) ERROR 09-24 16:35:49 [stage_engine_core_proc.py:242]                   ^^^^^^^^^^^^^^^^^^^^
+
+(StageEngineCoreProc_stage0_replica0 pid=52424) ERROR 09-24 16:35:49 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\tracing\otel.py", line 178, in sync_wrapper
+
+(StageEngineCoreProc_stage0_replica0 pid=52424) ERROR 09-24 16:35:49 [stage_engine_core_proc.py:242]     return func(*args, **kwargs)
+
+(StageEngineCoreProc_stage0_replica0 pid=52424) ERROR 09-24 16:35:49 [stage_engine_core_proc.py:242]            ^^^^^^^^^^^^^^^^^^^^^
+
+(StageEngineCoreProc_stage0_replica0 pid=52424) ERROR 09-24 16:35:49 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\v1\engine\core.py", line 1093, in __init__
+
+(StageEngineCoreProc_stage0_replica0 pid=52424) ERROR 09-24 16:35:49 [stage_engine_core_proc.py:242]     super().__init__(
+
+(StageEngineCoreProc_stage0_replica0 pid=52424) ERROR 09-24 16:35:49 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\v1\engine\core.py", line 145, in __init__
+
+(StageEngineCoreProc_stage0_replica0 pid=52424) ERROR 09-24 16:35:49 [stage_engine_core_proc.py:242]     kv_cache_config = self._initialize_kv_caches(vllm_config)
+
+(StageEngineCoreProc_stage0_replica0 pid=52424) ERROR 09-24 16:35:49 [stage_engine_core_proc.py:242]                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+(StageEngineCoreProc_stage0_replica0 pid=52424) ERROR 09-24 16:35:49 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\tracing\otel.py", line 178, in sync_wrapper
+
+(StageEngineCoreProc_stage0_replica0 pid=52424) ERROR 09-24 16:35:49 [stage_engine_core_proc.py:242]     return func(*args, **kwargs)
+
+(StageEngineCoreProc_stage0_replica0 pid=52424) ERROR 09-24 16:35:49 [stage_engine_core_proc.py:242]            ^^^^^^^^^^^^^^^^^^^^^
+
+(StageEngineCoreProc_stage0_replica0 pid=52424) ERROR 09-24 16:35:49 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\v1\engine\core.py", line 319, in _initialize_kv_caches
+
+(StageEngineCoreProc_stage0_replica0 pid=52424) ERROR 09-24 16:35:49 [stage_engine_core_proc.py:242]     kv_cache_configs = get_kv_cache_configs(
+
+(StageEngineCoreProc_stage0_replica0 pid=52424) ERROR 09-24 16:35:49 [stage_engine_core_proc.py:242]                        ^^^^^^^^^^^^^^^^^^^^^
+
+(StageEngineCoreProc_stage0_replica0 pid=52424) ERROR 09-24 16:35:49 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\v1\core\kv_cache_utils.py", line 2331, in get_kv_cache_configs
+
+(StageEngineCoreProc_stage0_replica0 pid=52424) ERROR 09-24 16:35:49 [stage_engine_core_proc.py:242]     _check_enough_kv_cache_memory(
+
+(StageEngineCoreProc_stage0_replica0 pid=52424) ERROR 09-24 16:35:49 [stage_engine_core_proc.py:242]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\v1\core\kv_cache_utils.py", line 879, in _check_enough_kv_cache_memory
+
+(StageEngineCoreProc_stage0_replica0 pid=52424) ERROR 09-24 16:35:49 [stage_engine_core_proc.py:242]     raise ValueError(
+
+(StageEngineCoreProc_stage0_replica0 pid=52424) ERROR 09-24 16:35:49 [stage_engine_core_proc.py:242] ValueError: To serve at least one request with the model's max seq len (512), (0.33 GiB KV cache is needed, which is larger than the available KV cache memory (0.22 GiB). Try increasing `gpu_memory_utilization` (which also controls CPU memory on the CPU backend) or decreasing `max_model_len` when initializing the engine. See https://docs.vllm.ai/en/latest/configuration/conserving_memory/ for more details.
+(StageEngineCoreProc_stage0_replica0 pid=52424) Traceback (most recent call last):
+(StageEngineCoreProc_stage0_replica0 pid=52424)   File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\multiprocessing\process.py", line 314, in _bootstrap
+(StageEngineCoreProc_stage0_replica0 pid=52424)     self.run()
+(StageEngineCoreProc_stage0_replica0 pid=52424)   File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\multiprocessing\process.py", line 108, in run
+(StageEngineCoreProc_stage0_replica0 pid=52424)     self._target(*self._args, **self._kwargs)
+(StageEngineCoreProc_stage0_replica0 pid=52424)   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_engine_core_proc.py", line 189, in run_stage_core
+(StageEngineCoreProc_stage0_replica0 pid=52424)     engine_core = StageEngineCoreProc(
+(StageEngineCoreProc_stage0_replica0 pid=52424)                   ^^^^^^^^^^^^^^^^^^^^
+(StageEngineCoreProc_stage0_replica0 pid=52424)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\tracing\otel.py", line 178, in sync_wrapper
+(StageEngineCoreProc_stage0_replica0 pid=52424)     return func(*args, **kwargs)
+(StageEngineCoreProc_stage0_replica0 pid=52424)            ^^^^^^^^^^^^^^^^^^^^^
+(StageEngineCoreProc_stage0_replica0 pid=52424)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\v1\engine\core.py", line 1093, in __init__
+(StageEngineCoreProc_stage0_replica0 pid=52424)     super().__init__(
+(StageEngineCoreProc_stage0_replica0 pid=52424)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\v1\engine\core.py", line 145, in __init__
+(StageEngineCoreProc_stage0_replica0 pid=52424)     kv_cache_config = self._initialize_kv_caches(vllm_config)
+(StageEngineCoreProc_stage0_replica0 pid=52424)                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+(StageEngineCoreProc_stage0_replica0 pid=52424)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\tracing\otel.py", line 178, in sync_wrapper
+(StageEngineCoreProc_stage0_replica0 pid=52424)     return func(*args, **kwargs)
+(StageEngineCoreProc_stage0_replica0 pid=52424)            ^^^^^^^^^^^^^^^^^^^^^
+(StageEngineCoreProc_stage0_replica0 pid=52424)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\v1\engine\core.py", line 319, in _initialize_kv_caches
+(StageEngineCoreProc_stage0_replica0 pid=52424)     kv_cache_configs = get_kv_cache_configs(
+(StageEngineCoreProc_stage0_replica0 pid=52424)                        ^^^^^^^^^^^^^^^^^^^^^
+(StageEngineCoreProc_stage0_replica0 pid=52424)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\v1\core\kv_cache_utils.py", line 2331, in get_kv_cache_configs
+(StageEngineCoreProc_stage0_replica0 pid=52424)     _check_enough_kv_cache_memory(
+(StageEngineCoreProc_stage0_replica0 pid=52424)   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\v1\core\kv_cache_utils.py", line 879, in _check_enough_kv_cache_memory
+(StageEngineCoreProc_stage0_replica0 pid=52424)     raise ValueError(
+(StageEngineCoreProc_stage0_replica0 pid=52424) ValueError: To serve at least one request with the model's max seq len (512), (0.33 GiB KV cache is needed, which is larger than the available KV cache memory (0.22 GiB). Try increasing `gpu_memory_utilization` (which also controls CPU memory on the CPU backend) or decreasing `max_model_len` when initializing the engine. See https://docs.vllm.ai/en/latest/configuration/conserving_memory/ for more details.
+ERROR 09-24 16:35:55 [stage_runtime.py:265] [StageRuntime] Stage initialization failed; shutting down 0 initialized client(s)
+
+ERROR 09-24 16:35:55 [stage_runtime.py:265] Traceback (most recent call last):
+
+ERROR 09-24 16:35:55 [stage_runtime.py:265]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 252, in initialize
+
+ERROR 09-24 16:35:55 [stage_runtime.py:265]     initialized_clients = self._initialize_stage_replicas(stage_plans, self._stage_init_timeout)
+
+ERROR 09-24 16:35:55 [stage_runtime.py:265]                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+ERROR 09-24 16:35:55 [stage_runtime.py:265]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 589, in _initialize_stage_replicas
+
+ERROR 09-24 16:35:55 [stage_runtime.py:265]     raise primary_exc
+
+ERROR 09-24 16:35:55 [stage_runtime.py:265]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 550, in _init_group
+
+ERROR 09-24 16:35:55 [stage_runtime.py:265]     client = self._initialize_replica(
+
+ERROR 09-24 16:35:55 [stage_runtime.py:265]              ^^^^^^^^^^^^^^^^^^^^^^^^^
+
+ERROR 09-24 16:35:55 [stage_runtime.py:265]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 828, in _initialize_replica
+
+ERROR 09-24 16:35:55 [stage_runtime.py:265]     return self._initialize_local_llm_replica(plan, stage_init_timeout)
+
+ERROR 09-24 16:35:55 [stage_runtime.py:265]            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+ERROR 09-24 16:35:55 [stage_runtime.py:265]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 934, in _initialize_local_llm_replica
+
+ERROR 09-24 16:35:55 [stage_runtime.py:265]     launch_stack.close()
+
+ERROR 09-24 16:35:55 [stage_runtime.py:265]   File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\contextlib.py", line 618, in close
+
+ERROR 09-24 16:35:55 [stage_runtime.py:265]     self.__exit__(None, None, None)
+
+ERROR 09-24 16:35:55 [stage_runtime.py:265]   File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\contextlib.py", line 610, in __exit__
+
+ERROR 09-24 16:35:55 [stage_runtime.py:265]     raise exc_details[1]
+
+ERROR 09-24 16:35:55 [stage_runtime.py:265]   File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\contextlib.py", line 595, in __exit__
+
+ERROR 09-24 16:35:55 [stage_runtime.py:265]     if cb(*exc_details):
+
+ERROR 09-24 16:35:55 [stage_runtime.py:265]        ^^^^^^^^^^^^^^^^
+
+ERROR 09-24 16:35:55 [stage_runtime.py:265]   File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\contextlib.py", line 144, in __exit__
+
+ERROR 09-24 16:35:55 [stage_runtime.py:265]     next(self.gen)
+
+ERROR 09-24 16:35:55 [stage_runtime.py:265]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_engine_startup.py", line 1211, in launch_stage_replica
+
+ERROR 09-24 16:35:55 [stage_runtime.py:265]     wait_for_engine_startup(
+
+ERROR 09-24 16:35:55 [stage_runtime.py:265]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\v1\engine\utils.py", line 1307, in wait_for_engine_startup
+
+ERROR 09-24 16:35:55 [stage_runtime.py:265]     raise RuntimeError(
+
+ERROR 09-24 16:35:55 [stage_runtime.py:265] RuntimeError: Engine core initialization failed. See root cause above. Failed proc(s): {'StageEngineCoreProc_stage0_replica0': 1}
+ERROR 09-24 16:35:55 [async_omni_engine.py:474] [AsyncOmniEngine] Orchestrator thread crashed
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474] Traceback (most recent call last):
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\async_omni_engine.py", line 468, in _bootstrap_orchestrator
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]     loop.run_until_complete(_run_orchestrator())
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]   File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\asyncio\base_events.py", line 691, in run_until_complete
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]     return future.result()
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]            ^^^^^^^^^^^^^^^
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\async_omni_engine.py", line 424, in _run_orchestrator
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]     self._initialize_stages(stage_init_timeout)
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\async_omni_engine.py", line 350, in _initialize_stages
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]     self._initialize_stages_impl(stage_init_timeout)
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\async_omni_engine.py", line 371, in _initialize_stages_impl
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]     self._runtime.initialize()
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 278, in initialize
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]     raise exc
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 252, in initialize
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]     initialized_clients = self._initialize_stage_replicas(stage_plans, self._stage_init_timeout)
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 589, in _initialize_stage_replicas
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]     raise primary_exc
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 550, in _init_group
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]     client = self._initialize_replica(
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]              ^^^^^^^^^^^^^^^^^^^^^^^^^
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 828, in _initialize_replica
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]     return self._initialize_local_llm_replica(plan, stage_init_timeout)
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 934, in _initialize_local_llm_replica
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]     launch_stack.close()
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]   File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\contextlib.py", line 618, in close
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]     self.__exit__(None, None, None)
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]   File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\contextlib.py", line 610, in __exit__
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]     raise exc_details[1]
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]   File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\contextlib.py", line 595, in __exit__
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]     if cb(*exc_details):
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]        ^^^^^^^^^^^^^^^^
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]   File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\contextlib.py", line 144, in __exit__
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]     next(self.gen)
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]   File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_engine_startup.py", line 1211, in launch_stage_replica
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]     wait_for_engine_startup(
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]   File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\v1\engine\utils.py", line 1307, in wait_for_engine_startup
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474]     raise RuntimeError(
+
+ERROR 09-24 16:35:55 [async_omni_engine.py:474] RuntimeError: Engine core initialization failed. See root cause above. Failed proc(s): {'StageEngineCoreProc_stage0_replica0': 1}
+INFO 09-24 16:35:55 [async_omni_engine.py:1807] [AsyncOmniEngine] Shutting down Orchestrator
+Exception in thread orchestrator:
+Traceback (most recent call last):
+  File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\threading.py", line 1075, in _bootstrap_inner
+    self.run()
+  File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\threading.py", line 1012, in run
+    self._target(*self._args, **self._kwargs)
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\async_omni_engine.py", line 468, in _bootstrap_orchestrator
+    loop.run_until_complete(_run_orchestrator())
+  File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\asyncio\base_events.py", line 691, in run_until_complete
+    return future.result()
+           ^^^^^^^^^^^^^^^
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\async_omni_engine.py", line 424, in _run_orchestrator
+    self._initialize_stages(stage_init_timeout)
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\async_omni_engine.py", line 350, in _initialize_stages
+    self._initialize_stages_impl(stage_init_timeout)
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\async_omni_engine.py", line 371, in _initialize_stages_impl
+    self._runtime.initialize()
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 278, in initialize
+    raise exc
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 252, in initialize
+    initialized_clients = self._initialize_stage_replicas(stage_plans, self._stage_init_timeout)
+                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 589, in _initialize_stage_replicas
+    raise primary_exc
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 550, in _init_group
+    client = self._initialize_replica(
+             ^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 828, in _initialize_replica
+    return self._initialize_local_llm_replica(plan, stage_init_timeout)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 934, in _initialize_local_llm_replica
+    launch_stack.close()
+  File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\contextlib.py", line 618, in close
+    self.__exit__(None, None, None)
+  File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\contextlib.py", line 610, in __exit__
+    raise exc_details[1]
+  File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\contextlib.py", line 595, in __exit__
+    if cb(*exc_details):
+       ^^^^^^^^^^^^^^^^
+  File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\contextlib.py", line 144, in __exit__
+    next(self.gen)
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_engine_startup.py", line 1211, in launch_stage_replica
+    wait_for_engine_startup(
+  File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\v1\engine\utils.py", line 1307, in wait_for_engine_startup
+    raise RuntimeError(
+RuntimeError: Engine core initialization failed. See root cause above. Failed proc(s): {'StageEngineCoreProc_stage0_replica0': 1}
+Traceback (most recent call last):
+  File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\threading.py", line 1075, in _bootstrap_inner
+    self.run()
+  File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\threading.py", line 1012, in run
+    self._target(*self._args, **self._kwargs)
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\async_omni_engine.py", line 468, in _bootstrap_orchestrator
+    loop.run_until_complete(_run_orchestrator())
+  File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\asyncio\base_events.py", line 691, in run_until_complete
+    return future.result()
+           ^^^^^^^^^^^^^^^
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\async_omni_engine.py", line 424, in _run_orchestrator
+    self._initialize_stages(stage_init_timeout)
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\async_omni_engine.py", line 350, in _initialize_stages
+    self._initialize_stages_impl(stage_init_timeout)
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\async_omni_engine.py", line 371, in _initialize_stages_impl
+    self._runtime.initialize()
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 278, in initialize
+    raise exc
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 252, in initialize
+    initialized_clients = self._initialize_stage_replicas(stage_plans, self._stage_init_timeout)
+                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 589, in _initialize_stage_replicas
+    raise primary_exc
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 550, in _init_group
+    client = self._initialize_replica(
+             ^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 828, in _initialize_replica
+    return self._initialize_local_llm_replica(plan, stage_init_timeout)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_runtime.py", line 934, in _initialize_local_llm_replica
+    launch_stack.close()
+  File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\contextlib.py", line 618, in close
+    self.__exit__(None, None, None)
+  File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\contextlib.py", line 610, in __exit__
+    raise exc_details[1]
+  File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\contextlib.py", line 595, in __exit__
+    if cb(*exc_details):
+       ^^^^^^^^^^^^^^^^
+  File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\contextlib.py", line 144, in __exit__
+    next(self.gen)
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\stage_engine_startup.py", line 1211, in launch_stage_replica
+    wait_for_engine_startup(
+  File "C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\v1\engine\utils.py", line 1307, in wait_for_engine_startup
+    raise RuntimeError(
+RuntimeError: Engine core initialization failed. See root cause above. Failed proc(s): {'StageEngineCoreProc_stage0_replica0': 1}
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\benchmarks\edge_harness\probe_qwen35_image.py", line 55, in main
+    record["config"] = config
+             ^^^^^^^^^^^^^^
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\entrypoints\omni_base.py", line 223, in __init__
+    self.engine = AsyncOmniEngine(
+                  ^^^^^^^^^^^^^^^^
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\async_omni_engine.py", line 305, in __init__
+    self._wait_for_orchestrator_init(startup_future, startup_timeout)
+  File "\\wsl.localhost\Ubuntu\home\zhout\project\edge_infer\vllm-omni-edge\vllm_omni\engine\async_omni_engine.py", line 539, in _wait_for_orchestrator_init
+    startup_future.result(
+  File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\concurrent\futures\_base.py", line 456, in result
+    return self.__get_result()
+           ^^^^^^^^^^^^^^^^^^^
+  File "C:\Users\zhout\AppData\Local\Programs\Python\Python312\Lib\concurrent\futures\_base.py", line 401, in __get_result
+    raise self._exception
+RuntimeError: Orchestrator initialization failed: Engine core initialization failed. See root cause above. Failed proc(s): {'StageEngineCoreProc_stage0_replica0': 1}
@@ -0,0 +1,22 @@
+{
+  "model": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\Qwen3.8-27B-FP8",
+  "scope": "one synthetic-image Omni image-to-text request",
+  "start": 1790238380.8128512,
+  "config": {
+    "model": "\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\models\\Qwen3.8-27B-FP8",
+    "language_model_only": false,
+    "max_model_len": 512,
+    "max_num_seqs": 1,
+    "max_num_batched_tokens": 512,
+    "enforce_eager": true,
+    "gpu_memory_utilization": 0.75,
+    "cpu_offload_gb": 12.0,
+    "linear_backend": "triton",
+    "dtype": "bfloat16",
+    "stage_init_timeout": 180
+  },
+  "status": "failed",
+  "error": "RuntimeError(\"Orchestrator initialization failed: Engine core initialization failed. See root cause above. Failed proc(s): {'StageEngineCoreProc_stage0_replica0': 1}\")",
+  "traceback": "Traceback (most recent call last):\n  File \"C:\\Users\\zhout\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\threading.py\", line 1075, in _bootstrap_inner\n    self.run()\n  File \"C:\\Users\\zhout\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\threading.py\", line 1012, in run\n    self._target(*self._args, **self._kwargs)\n  File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\vllm_omni\\engine\\async_omni_engine.py\", line 468, in _bootstrap_orchestrator\n    loop.run_until_complete(_run_orchestrator())\n  File \"C:\\Users\\zhout\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 691, in run_until_complete\n    return future.result()\n           ^^^^^^^^^^^^^^^\n  File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\vllm_omni\\engine\\async_omni_engine.py\", line 424, in _run_orchestrator\n    self._initialize_stages(stage_init_timeout)\n  File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\vllm_omni\\engine\\async_omni_engine.py\", line 350, in _initialize_stages\n    self._initialize_stages_impl(stage_init_timeout)\n  File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\vllm_omni\\engine\\async_omni_engine.py\", line 371, in _initialize_stages_impl\n    self._runtime.initialize()\n  File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\vllm_omni\\engine\\stage_runtime.py\", line 278, in initialize\n    raise exc\n  File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\vllm_omni\\engine\\stage_runtime.py\", line 252, in initialize\n    initialized_clients = self._initialize_stage_replicas(stage_plans, self._stage_init_timeout)\n                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\vllm_omni\\engine\\stage_runtime.py\", line 589, in _initialize_stage_replicas\n    raise primary_exc\n  File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\vllm_omni\\engine\\stage_runtime.py\", line 550, in _init_group\n    client = self._initialize_replica(\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\vllm_omni\\engine\\stage_runtime.py\", line 828, in _initialize_replica\n    return self._initialize_local_llm_replica(plan, stage_init_timeout)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\vllm_omni\\engine\\stage_runtime.py\", line 934, in _initialize_local_llm_replica\n    launch_stack.close()\n  File \"C:\\Users\\zhout\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\contextlib.py\", line 618, in close\n    self.__exit__(None, None, None)\n  File \"C:\\Users\\zhout\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\contextlib.py\", line 610, in __exit__\n    raise exc_details[1]\n  File \"C:\\Users\\zhout\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\contextlib.py\", line 595, in __exit__\n    if cb(*exc_details):\n       ^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\zhout\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\contextlib.py\", line 144, in __exit__\n    next(self.gen)\n  File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\vllm_omni\\engine\\stage_engine_startup.py\", line 1211, in launch_stage_replica\n    wait_for_engine_startup(\n  File \"C:\\Users\\zhout\\w2\\omni029venv\\Lib\\site-packages\\vllm\\v1\\engine\\utils.py\", line 1307, in wait_for_engine_startup\n    raise RuntimeError(\nRuntimeError: Engine core initialization failed. See root cause above. Failed proc(s): {'StageEngineCoreProc_stage0_replica0': 1}\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\benchmarks\\edge_harness\\probe_qwen35_image.py\", line 55, in main\n    record[\"config\"] = 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 305, in __init__\n    self._wait_for_orchestrator_init(startup_future, startup_timeout)\n  File \"\\\\wsl.localhost\\Ubuntu\\home\\zhout\\project\\edge_infer\\vllm-omni-edge\\vllm_omni\\engine\\async_omni_engine.py\", line 539, in _wait_for_orchestrator_init\n    startup_future.result(\n  File \"C:\\Users\\zhout\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\concurrent\\futures\\_base.py\", line 456, in reLine truncated
+  "end": 1790238956.060437
+}
@@ -0,0 +1,260 @@
+INFO 09-24 16:39:07 [patch.py:259] NVFP4 W4A4 weight_scale NaN-clamp: installed.
+INFO 09-24 16:39:07 [patch.py:510] inductor factorable-divisibility patch: installed.
+INFO 09-24 16:39:12 [omni_base.py:216] [Omni] Initializing with model C:\Users\zhout\w2\Qwen3.8-27B-FP8
+INFO 09-24 16:39:12 [async_omni_engine.py:172] [AsyncOmniEngine] Initializing with model C:\Users\zhout\w2\Qwen3.8-27B-FP8
+INFO 09-24 16:39:12 [async_omni_engine.py:290] [AsyncOmniEngine] Launching Orchestrator thread with 1 stages
+INFO 09-24 16:39:12 [stage_init_utils.py:768] [stage_init] Set VLLM_WORKER_MULTIPROC_METHOD=spawn
+WARNING 09-24 16:39:12 [utils.py:501] Dropping unknown OmniEngineArgs field 'inline_diffusion' (not declared on the dataclass)
+WARNING 09-24 16:39:12 [arg_utils.py:1801] The global random seed is set to 0. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM.
+INFO 09-24 16:39:12 [model.py:684] Resolved architecture: Qwen3_5ForConditionalGeneration
+INFO 09-24 16:39:12 [model.py:2021] Using max model len 512
+WARNING 09-24 16:39:12 [model.py:981] Model does not support mm_device_do_normalize, forcing mm_device_do_normalize = False.
+INFO 09-24 16:39:18 [scheduler.py:277] Chunked prefill is enabled with max_num_batched_tokens=512.
+INFO 09-24 16:39:18 [config.py:625] Mamba cache mode is set to 'align' for Qwen3_5ForConditionalGeneration by default when prefix caching is enabled
+WARNING 09-24 16:39:18 [vllm.py:1211] Auto-disabled DeepGemm for model_type=qwen3_5_text on Blackwell. DeepGemm E8M0 scale format causes accuracy degradation for this architecture. Falling back to CUTLASS. To disable DeepGemm globally, set VLLM_USE_DEEP_GEMM=0.
+WARNING 09-24 16:39:18 [vllm.py:1371] Enforce eager set, disabling torch.compile and CUDAGraphs. This is equivalent to setting -cc.mode=none -cc.cudagraph_mode=none
+WARNING 09-24 16:39:18 [vllm.py:1406] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored.
+INFO 09-24 16:39:18 [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 09-24 16:39:18 [vllm.py:1585] Cudagraph is disabled under eager mode
+INFO 09-24 16:39:18 [compilation.py:329] Enabled custom fusions: norm_quant, act_quant
+WARNING 09-24 16:39:18 [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
+INFO 09-24 16:39:32 [patch.py:259] NVFP4 W4A4 weight_scale NaN-clamp: installed.
+INFO 09-24 16:39:32 [patch.py:510] inductor factorable-divisibility patch: installed.
+(StageEngineCoreProc_stage0_replica0 pid=66956) INFO 09-24 16:39:35 [core.py:123] Initializing a V1 LLM engine (v0.29.0) with config: model='C:\\Users\\zhout\\w2\\Qwen3.8-27B-FP8', speculative_config=None, tokenizer='C:\\Users\\zhout\\w2\\Qwen3.8-27B-FP8', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=512, 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=fp8, 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=C:\Users\zhout\w2\Qwen3.8-27B-FP8, enable_prefix_caching=True, 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': ['+quant_fp8', 'all', '+quant_fp8'], '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': [512], '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='triton')
+(StageEngineCoreProc_stage0_replica0 pid=66956) INFO 09-24 16:39:37 [parallel_state.py:1775] world_size=1 rank=0 local_rank=0 distributed_init_method=file://C:\Users\zhout\AppData\Local\Temp/vllm_dist_184a36a1caf0425a8339674b858f95e7 backend=nccl
+(StageEngineCoreProc_stage0_replica0 pid=66956) WARNING 09-24 16:39:37 [parallel_state.py:1788] Distributed backend nccl is not available; falling back to gloo.
+(StageEngineCoreProc_stage0_replica0 pid=66956) INFO 09-24 16:39:37 [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=66956) WARNING 09-24 16:39:37 [gpu_ar_worker.py:120] OMNI GPUARWorker forces v1 model runner for omni hooks.
+(StageEngineCoreProc_stage0_replica0 pid=66956) INFO 09-24 16:39:37 [topk_topp_sampler.py:46] FlashInfer top-p/top-k sampling disabled via VLLM_USE_FLASHINFER_SAMPLER=0.
+(StageEngineCoreProc_stage0_replica0 pid=66956) [transformers] The `use_fast` parameter is deprecated and will be removed in a future version. Use `backend="torchvision"` instead of `use_fast=True`, or `backend="pil"` instead of `use_fast=False`.
+(StageEngineCoreProc_stage0_replica0 pid=66956) INFO 09-24 16:39:42 [base.py:138] Offloader set to UVAOffloader
+(StageEngineCoreProc_stage0_replica0 pid=66956) WARNING 09-24 16:39:42 [base.py:173] [LLM Worker 0] Sleep Mode DISABLED.
+(StageEngineCoreProc_stage0_replica0 pid=66956) WARNING 09-24 16:39:42 [base.py:173] [LLM Worker 0] Sleep Mode DISABLED.
+(StageEngineCoreProc_stage0_replica0 pid=66956) INFO 09-24 16:39:42 [gpu_model_runner.py:5379] Starting to load model C:\Users\zhout\w2\Qwen3.8-27B-FP8...
+(StageEngineCoreProc_stage0_replica0 pid=66956) INFO 09-24 16:39:43 [cuda.py:551] Using backend AttentionBackendEnum.FLASH_ATTN for vit attention
+(StageEngineCoreProc_stage0_replica0 pid=66956) INFO 09-24 16:39:43 [mm_encoder_attention.py:372] Using AttentionBackendEnum.FLASH_ATTN for MMEncoderAttention.
+(StageEngineCoreProc_stage0_replica0 pid=66956) INFO 09-24 16:39:43 [uva.py:65] Total CPU offloaded parameters: 0.86
+(StageEngineCoreProc_stage0_replica0 pid=66956) INFO 09-24 16:39:43 [__init__.py:695] Selected TritonFp8BlockScaledMMKernel for Fp8LinearMethod
+(StageEngineCoreProc_stage0_replica0 pid=66956) INFO 09-24 16:39:43 [qwen_gdn_linear_attn.py:167] Using Triton/FLA GDN prefill kernel (requested=auto, head_k_dim=128).
+(StageEngineCoreProc_stage0_replica0 pid=66956) INFO 09-24 16:39:43 [qwen_gdn_linear_attn.py:519] GDN decode kernel: cuda
+(StageEngineCoreProc_stage0_replica0 pid=66956) INFO 09-24 16:39:44 [cuda.py:492] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'TRITON_ATTN', 'FLEX_ATTENTION'].
+(StageEngineCoreProc_stage0_replica0 pid=66956) INFO 09-24 16:39:44 [flash_attn.py:897] Using FlashAttention version 2
+(StageEngineCoreProc_stage0_replica0 pid=66956) INFO 09-24 16:39:49 [uva.py:65] Total CPU offloaded parameters: 12.13
+(StageEngineCoreProc_stage0_replica0 pid=66956) INFO 09-24 16:39:49 [weight_utils.py:863] Filesystem type for checkpoints: unknown. Checkpoint size: 28.75 GiB. Available RAM: 16.81 GiB.
+(StageEngineCoreProc_stage0_replica0 pid=66956) INFO 09-24 16:39:49 [weight_utils.py:893] Auto-prefetch is disabled because the filesystem (unknown) is not a recognized network FS (NFS/Lustre) and the checkpoint size (28.75 GiB) exceeds 90% of available RAM (16.81 GiB).
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+(StageEngineCoreProc_stage0_replica0 pid=66956)
+(StageEngineCoreProc_stage0_replica0 pid=66956) INFO 09-24 16:40:10 [default_loader.py:430] Loading weights took 21.14 seconds
+(StageEngineCoreProc_stage0_replica0 pid=66956) INFO 09-24 16:40:12 [gpu_model_runner.py:5471] Model loading took 16.36 GiB memory and 28.736326 seconds
+(StageEngineCoreProc_stage0_replica0 pid=66956) INFO 09-24 16:40:12 [interface.py:918] Setting attention block size to 784 tokens to ensure that attention page size is >= mamba page size.
+(StageEngineCoreProc_stage0_replica0 pid=66956) INFO 09-24 16:40:12 [interface.py:942] Padding mamba page size by 0.13% to ensure that mamba page size and attention page size are exactly equal.
+(StageEngineCoreProc_stage0_replica0 pid=66956) INFO 09-24 16:40:12 [utils.py:306] Using LBNHC KV cache layout.
+(StageEngineCoreProc_stage0_replica0 pid=66956) INFO 09-24 16:40:13 [gpu_model_runner.py:6519] Skipping memory profiling for multimodal encoder and encoder cache.
+(StageEngineCoreProc_stage0_replica0 pid=66956) WARNING 09-24 16:40:13 [fp8_utils.py:852] Using default W8A8 Block FP8 kernel config. Performance might be sub-optimal! Config file not found at C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\model_executor\layers\quantization\utils\configs\N=16384,K=5120,device_name=NVIDIA_GeForce_RTX_5090_Laptop_GPU,dtype=fp8_w8a8,block_shape=[128,128].json
+(StageEngineCoreProc_stage0_replica0 pid=66956) WARNING 09-24 16:40:14 [fp8_utils.py:852] Using default W8A8 Block FP8 kernel config. Performance might be sub-optimal! Config file not found at C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\model_executor\layers\quantization\utils\configs\N=5120,K=6144,device_name=NVIDIA_GeForce_RTX_5090_Laptop_GPU,dtype=fp8_w8a8,block_shape=[128,128].json
+(StageEngineCoreProc_stage0_replica0 pid=66956) WARNING 09-24 16:40:14 [fp8_utils.py:852] Using default W8A8 Block FP8 kernel config. Performance might be sub-optimal! Config file not found at C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\model_executor\layers\quantization\utils\configs\N=34816,K=5120,device_name=NVIDIA_GeForce_RTX_5090_Laptop_GPU,dtype=fp8_w8a8,block_shape=[128,128].json
+(StageEngineCoreProc_stage0_replica0 pid=66956) WARNING 09-24 16:40:14 [fp8_utils.py:852] Using default W8A8 Block FP8 kernel config. Performance might be sub-optimal! Config file not found at C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\model_executor\layers\quantization\utils\configs\N=5120,K=17408,device_name=NVIDIA_GeForce_RTX_5090_Laptop_GPU,dtype=fp8_w8a8,block_shape=[128,128].json
+(StageEngineCoreProc_stage0_replica0 pid=66956) WARNING 09-24 16:40:14 [fp8_utils.py:852] Using default W8A8 Block FP8 kernel config. Performance might be sub-optimal! Config file not found at C:\Users\zhout\w2\omni029venv\Lib\site-packages\vllm\model_executor\layers\quantization\utils\configs\N=14336,K=5120,device_name=NVIDIA_GeForce_RTX_5090_Laptop_GPU,dtype=fp8_w8a8,block_shape=[128,128].json
+(StageEngineCoreProc_stage0_replica0 pid=66956) INFO 09-24 16:40:23 [base.py:148] Available KV cache memory: 1.57 GiB (device-level profiling)
+(StageEngineCoreProc_stage0_replica0 pid=66956) INFO 09-24 16:40:23 [kv_cache_utils.py:2032] GPU KV cache size: 2,340 tokens, Maximum concurrency for 512 tokens per request: 4.57x
+(StageEngineCoreProc_stage0_replica0 pid=66956) WARNING 09-24 16:40:23 [base.py:173] [LLM Worker 0] Sleep Mode DISABLED.
+remark: utils.py:58:0: 58 instructions in function
+(StageEngineCoreProc_stage0_replica0 pid=66956) INFO 09-24 16:40:23 [kernel_warmup.py:124] JIT kernel warmup starting.
+(StageEngineCoreProc_stage0_replica0 pid=66956)
+JIT kernel warmup (2 compile keys):   0%|          | 0/1 [00:00<?, ?kernel/s]remark: block_table.py:412:0: 285 instructions in function
+
+JIT kernel warmup (2 compile keys): 100%|██████████| 1/1 [00:00<00:00,  3.68kernel/s, ComputeSlotMappingKernel (2 keys)]
+JIT kernel warmup (2 compile keys): 100%|██████████| 1/1 [00:00<00:00,  3.67kernel/s, ComputeSlotMappingKernel (2 keys)]
+(StageEngineCoreProc_stage0_replica0 pid=66956) INFO 09-24 16:40:23 [kernel_warmup.py:134] JIT kernel warmup finished in 0.27s.
+(StageEngineCoreProc_stage0_replica0 pid=66956) INFO 09-24 16:40:23 [qwen_triton_warmup.py:270] Warming up Qwen Triton kernels for model_type=qwen3_5_text.
+remark: causal_conv1d.py:17:0: 273 instructions in function
+remark: fused_gdn_prefill_post_conv.py:20:0: 381 instructions in function
+remark: fused_gdn_prefill_post_conv.py:20:0: 382 instructions in function
+remark: fused_sigmoid_gating.py:24:0: 772 instructions in function
+(StageEngineCoreProc_stage0_replica0 pid=66956) INFO 09-24 16:40:25 [ll_bf16.py:30] cuteDSL (CUTLASS Python) not available, ll_bf16_gemm disabled
+(StageEngineCoreProc_stage0_replica0 pid=66956) INFO 09-24 16:40:25 [gpu_worker.py:860] Free memory on device (22.61/23.89 GiB) on startup. Desired GPU memory utilization is (0.8, 19.11 GiB). Actual usage is 17.48 GiB for consumed memory (weights + non-torch), 0.12 GiB for peak activation, and 0.0 GiB for CUDAGraph memory. Replace gpu_memory_utilization config with `--kv-cache-memory=1474032845` (1.37 GiB) to fit into requested memory, or `--kv-cache-memory=5228931072` (4.87 GiB) to fully utilize gpu memory. Current kv cache memory in use is 1.57 GiB.
+remark: fp8_utils.py:737:0: 694 instructions in function
+remark: fp8_utils.py:737:0: 694 instructions in function
+(StageEngineCoreProc_stage0_replica0 pid=66956) INFO 09-24 16:40:27 [jit_monitor.py:85] Kernel JIT monitor activated; monitored JIT compilations during inference will use mode=warn.
+(StageEngineCoreProc_stage0_replica0 pid=66956) INFO 09-24 16:40:28 [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=66956) INFO 09-24 16:40:28 [core.py:368] init engine (profile, create kv cache, warmup model) took 15.15 s
+(StageEngineCoreProc_stage0_replica0 pid=66956) WARNING 09-24 16:40:28 [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=66956) WARNING 09-24 16:40:28 [omni_scheduler_mixin.py:118] OMNI scheduler forces v1 model runner for omni hooks.
+(StageEngineCoreProc_stage0_replica0 pid=66956) WARNING 09-24 16:40:28 [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=66956) WARNING 09-24 16:40:28 [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=66956) INFO 09-24 16:40:28 [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=66956) INFO 09-24 16:40:28 [vllm.py:1585] Cudagraph is disabled under eager mode
+INFO 09-24 16:40:28 [stage_runtime.py:943] [StageRuntime] Stage 0 engine startup completed
+INFO 09-24 16:40:28 [stage_engine_core_client.py:159] [StageEngineCoreClient] stage-0 [rep-0] initializing EngineCore
+(StageEngineCoreProc_stage0_replica0 pid=66956) INFO 09-24 16:40:28 [compilation.py:329] Enabled custom fusions: norm_quant, act_quant
+INFO 09-24 16:40:28 [stage_engine_core_client.py:215] [StageEngineCoreClient] stage-0 [rep-0] EngineCore running
+INFO 09-24 16:40:28 [stage_runtime.py:958] [StageRuntime] Stage 0 initialized
+[transformers] The `use_fast` parameter is deprecated and will be removed in a future version. Use `backend="torchvision"` instead of `use_fast=True`, or `backend="pil"` instead of `use_fast=False`.
+INFO 09-24 16:40:36 [orchestrator.py:604] [Orchestrator] Starting event loop
+INFO 09-24 16:40:36 [async_omni_engine.py:323] [AsyncOmniEngine] Orchestrator ready with 1 stages
+INFO 09-24 16:40:36 [omni_base.py:235] [Omni] AsyncOmniEngine initialized in 84.24 seconds
+INFO 09-24 16:40:36 [omni_base.py:255] [Omni] Initialized with 1 stages for model C:\Users\zhout\w2\Qwen3.8-27B-FP8
+WARNING 09-24 16:40:36 [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().
+
+Processed prompts:   0%|          | 0/1 [00:00<?, ?it/s]remark: qwen3_vl.py:180:0: 267 instructions in function
+(StageEngineCoreProc_stage0_replica0 pid=66956) WARNING 09-24 16:40:48 [jit_monitor.py:141] Triton kernel JIT compilation during inference: _bilinear_pos_embed_kernel. This causes a latency spike; consider extending warmup to cover this shape/config.
+remark: rotary.py:13:0: 131 instructions in function
+(StageEngineCoreProc_stage0_replica0 pid=66956) WARNING 09-24 16:40:49 [jit_monitor.py:141] Triton kernel JIT compilation during inference: rotary_kernel. This causes a latency spike; consider extending warmup to cover this shape/config.
+remark: fp8_utils.py:737:0: 783 instructions in function
+(StageEngineCoreProc_stage0_replica0 pid=66956) WARNING 09-24 16:40:49 [jit_monitor.py:141] Triton kernel JIT compilation during inference: _w8a8_triton_block_scaled_mm. This causes a latency spike; consider extending warmup to cover this shape/config.
+remark: layernorm_guard.py:67:0: 362 instructions in function
+(StageEngineCoreProc_stage0_replica0 pid=66956) WARNING 09-24 16:40:50 [jit_monitor.py:141] Triton kernel JIT compilation during inference: layer_norm_fwd_kernel. This causes a latency spike; consider extending warmup to cover this shape/config.
+remark: fp8_utils.py:737:0: 783 instructions in function
+(StageEngineCoreProc_stage0_replica0 pid=66956) INFO 09-24 16:40:52 [prefix_cache.py:160] Omni prefix cache async-write copy stream initialized.
+remark: causal_conv1d.py:763:0: 136 instructions in function
+(StageEngineCoreProc_stage0_replica0 pid=66956) WARNING 09-24 16:40:52 [jit_monitor.py:141] Triton kernel JIT compilation during inference: _causal_conv1d_update_kernel. This causes a latency spike; consider extending warmup to cover this shape/config.
+remark: fused_recurrent.py:256:0: 1781 instructions in function
+(StageEngineCoreProc_stage0_replica0 pid=66956) WARNING 09-24 16:40:53 [jit_monitor.py:141] Triton kernel JIT compilation during inference: fused_recurrent_gated_delta_rule_packed_decode_kernel. This causes a latency spike; consider extending warmup to cover this shape/config.
+remark: layernorm_guard.py:67:0: 119 instructions in function
+
+Processed prompts: 100%|██████████| 1/1 [00:06<00:00,  6.96s/it]
+Processed prompts: 100%|██████████| 1/1 [00:06<00:00,  6.96s/it]
+INFO 09-24 16:40:55 [omni_base.py:781] [Omni] Shutting down
+INFO 09-24 16:40:55 [async_omni_engine.py:1807] [AsyncOmniEngine] Shutting down Orchestrator
+INFO 09-24 16:40:55 [orchestrator.py:705] [Orchestrator] Received shutdown signal
+INFO 09-24 16:40:55 [orchestrator.py:3092] [Orchestrator] Shutting down all 1 client(s)
+INFO 09-24 16:40:55 [core_client.py:689] [shutdown] MPClient: start timeout=default
+INFO 09-24 16:40:55 [core_client.py:691] [shutdown] MPClient: stopping engine manager
+INFO 09-24 16:40:55 [utils.py:626] [shutdown] Process manager: send sigterm to process StageEngineCoreProc_stage0_replica0
+INFO 09-24 16:40:57 [core_client.py:693] [shutdown] MPClient: engine manager stopped
+INFO 09-24 16:40:57 [core_client.py:694] [shutdown] MPClient: cleaning up background resources
+INFO 09-24 16:40:57 [core_client.py:696] [shutdown] MPClient: complete
+INFO 09-24 16:40:57 [stage_pool.py:1354] [StagePool] Stage 0 replica 0 shut down
@@ -0,0 +1,23 @@
+{
+  "model": "C:\\Users\\zhout\\w2\\Qwen3.8-27B-FP8",
+  "scope": "one synthetic-image Omni image-to-text request",
+  "start": 1790239130.280365,
+  "config": {
+    "model": "C:\\Users\\zhout\\w2\\Qwen3.8-27B-FP8",
+    "language_model_only": false,
+    "max_model_len": 512,
+    "max_num_seqs": 1,
+    "max_num_batched_tokens": 512,
+    "enforce_eager": true,
+    "gpu_memory_utilization": 0.8,
+    "cpu_offload_gb": 12.0,
+    "dtype": "bfloat16",
+    "stage_init_timeout": 180,
+    "linear_backend": "triton"
+  },
+  "outputs": [
+    "Red"
+  ],
+  "status": "completed",
+  "end": 1790239257.3718076
+}
@@ -14,6 +14,8 @@ A separate [native Windows RTX Spark GGUF Omni profile](../../../e2e_expansion_2

 The [native Windows RTX Qwen3.8-27B NVFP4 attempt](../../../e2e_expansion_20260924/evidence/qwen38_omni_windows_rtx/README.md) loaded all three pinned weight shards but failed before inference during vLLM Marlin post-load scale permutation with an unsupported PTX toolchain error. The current Windows driver reports CUDA 13.3 while the installed vLLM wheel was built with CUDA 13.4. This narrows the Windows blocker to the tested runtime combination; it does not reverse the WSL scoped text/image pass or qualify a Windows output.

+[A native Windows RTX Qwen3.8-27B FP8 recovery](../../../e2e_expansion_20260924/evidence/qwen38_omni_windows_rtx/README.md) completed scoped Omni text and synthetic image-to-text requests via an explicit Triton FP8 linear backend. One warmup plus 20/20 serial measured requests of each kind produced stable outputs at 3.070/3.214 s text and 4.184/4.380 s image p50/p95. The separate NVFP4 Marlin/PTX failure remains unresolved.
+
 A [native Windows RTX Qwen3-TTS Omni hybrid route](../../../e2e_expansion_20260924/evidence/qwen_tts_windows_rtx/README.md) now returns complete 24 kHz WAVs with talker/codec on RTX Vulkan0 and predictor on CPU FP32. One warmup plus 20/20 serial outputs had identical PCM at p50/p95 complete-request wall 3.153/3.231 s for 2.72 s audio. Public audio, explicit host/GPU admission, refusal of an undersized GPU reservation and fresh-stage recovery after server-started cancellation passed. The PCM differs from the Radeon hybrid, and the complete-WAV RTF remains above 1; the existing CUDA sustained playback findings are separate.

 A complete pinned [Spark 1.7B BF16 WSL CPU checkpoint and acceptance run](../../../e2e_expansion_20260924/evidence/spark_bf16_wsl_cpu/README.md) adds a second current-Omni text artifact to the GGUF route. Twelve prompts produced 128 tokens each with clean cancellation; all 12 token-ID sequences exactly matched standalone vLLM on the same checkpoint. One warmup plus 20 serial 28-prompt-token/64-output-token requests measured p50/p95 complete-request wall time 5.411/5.751 s and TTFT 0.129/0.146 s on the fork branch. A fresh-session restart after an eight-token cancellation reproduced the same 128 greedy token IDs, while the retired handle was explicitly rejected. This is WSL CPU full-text scope, not mobile generation or broad quality qualification.
@@ -84,7 +86,7 @@ NOT E2E means no complete device/model workload was verified. “Scoped E2E” i
 | PC HX370 + RTX 5090 Laptop / WSL | InternVLA-A1 | SYNTHETIC_POLICY_PASS_OMNI_CUDA; NOT_TASK_E2E | The real Place_Markpen checkpoint ran through an admitted Omni whole-policy graph stage with BF16 policy and Cosmos encoder on the RTX 5090 Laptop GPU. One warmup plus 20/20 serial patterned synthetic observation/state/noise requests returned identical finite float32 [1,50,32] actions with terminal events; nearest-rank complete-request wall p50/p95 was 0.382/0.399 s, excluding 43.82 s startup. The 16 GiB host-RAM and 16 GiB CUDA demands were within explicit 28/21 GiB ceilings checked against available memory. Actual CUDA placement was verified, PyTorch peak reserved VRAM reached 7.86 GB, and an 8 GiB VRAM plan was refused before worker launch. An in-flight abort and shutdown released both ledgers with no stale output; a separate public AsyncOmni.generate action request passed. Relative to a separate patterned WSL CPU output, action relative L2 difference was 2.006% with no task tolerance. The earlier one-forward zero-input direct CUDA run was a different experiment. This is synthetic action-policy integration, not robot-task E2E; units/order/step time remain unverified and control_ready=false. | Validate real observations/reference actions and physical action semantics, post-cancel restart, concurrent admission, loading peak, deadlines and sustained latency/power/thermal behavior; [Omni CUDA evidence](../../../e2e_expansion_20260923/evidence/internvla_omni_cuda/README.md), [earlier direct result](../../../e2e_recovery_20260923/evidence/internvla/internvla_finetune_synthetic.json). |
 | PC HX370 + RTX 5090 Laptop / Windows | Spark-X2.5 | SCOPED_E2E_OMNI_COMPLETE_REQUEST_GGUF_VULKAN; RESTART_AFTER_INFLIGHT_CANCEL_PASS; SEPARATE_4B_BF16_CUDA_PROFILE | The pinned Spark-X2.5-1.7B Q4_K_M GGUF ran through an Omni whole-session StageRuntime/StagePool backend on RTX Vulkan0. The server log identifies NVIDIA GeForce RTX 5090 Laptop GPU, 29/29 layers offloaded and a 1051.02 MiB Vulkan0 model buffer. The short and 120-item inventory prompts answered `Paris` and `1511`; one warmup plus 20/20 serial inventory requests answered `1511` with nearest-rank complete-request wall p50/p95 0.294/0.302 s (2,463 prompt tokens, five completion tokens), excluding 4.319 s startup. A public AsyncOmni request also answered `Paris` on the RTX. An over-context request was rejected without truncation, followed by a correct recovery request. In a separate cancellation check, the owned server task started before abort; the worker and host-RAM reservation drained, and a fresh stage with the same artifact and new generation answered `Paris` without stale output. This GGUF/Vulkan profile is distinct from the earlier Spark 4B BF16 vLLM CUDA profile; the precisions, model sizes and workloads preclude a speedup comparison. There is no incremental token stream, automatic same-stage restart, broad token/quality parity, explicit GPU-VRAM peak admission, concurrency or sustained power/thermal qualification. | Validate streaming, larger quality and parity workloads, VRAM/load peaks, concurrent requests and sustained power; [Omni RTX raw evidence](../../../e2e_expansion_20260924/evidence/spark_omni_windows_rtx/README.md). |
 | PC HX370 + RTX 5090 Laptop / Windows | Qwen3-TTS 0.6B CustomVoice | SCOPED_E2E_OMNI_COMPLETE_WAV_RTX_VULKAN_CPU; RESTART_AFTER_INFLIGHT_CANCEL_PASS; SEPARATE_CUDA_STREAM_PLAYBACK_JITTER | The pinned Q8_0 CustomVoice talker and F16 codec ran on RTX Vulkan0 with the code predictor pinned to CPU FP32 through an admitted Omni whole-session stage. The server logs identify the RTX and actual codec placement. Two named text-to-WAV requests returned finite 24 kHz PCM; one warmup plus 20/20 serial 2.72 s outputs had identical PCM and nearest-rank complete-request wall p50/p95 3.153/3.231 s (RTF 1.16/1.19), excluding startup. Pinned Whisper tiny.en transcribed the first RTX WAV exactly (WER 0 as an intelligibility proxy); RTX PCM differs from the Radeon route, so no same-output speedup is claimed. Public AsyncOmni audio returned the same PCM. The owned server entered prefill before cancellation; the worker and separate declared 8 GiB host-RAM/4 GiB GPU-VRAM reservations drained, then a fresh stage with a new generation returned identical PCM without late output. An undersized 3 GiB GPU-VRAM demand was refused before server launch; the Radeon route still passed a one-request regression. This is complete-WAV Vulkan+CPU execution, not incremental playable audio, an NPU route or a measured GPU-memory peak. Separately, the historical complete-model vLLM CUDA stream slowed sharply around minute 26–27: all 17 requests in minutes 27–30 had simulated playback underruns and median RTF 1.331. A current-source instrumented 30-minute CUDA repeat completed 1,232 serial medium requests; its final 119 had p50/p95 RTF 0.185/0.203 and three brief simulated underruns needing at most 26.6 ms extra prebuffer. The late collapse did not recur, but 69 requests across the full windows had simulated underruns; the original cause and underrun-free sustained playback remain open. These CUDA and Vulkan routes use different artifacts, workloads and output behavior. | Validate playable chunk streaming, broader voice/speaker quality, measured VRAM/load peaks, concurrency and an underrun-free sustained run; [RTX Omni raw evidence](../../../e2e_expansion_20260924/evidence/qwen_tts_windows_rtx/README.md), [CUDA sustained runs](../../../e2e_expansion_20260924/evidence/qwen_tts_sustained_drift/README.md). |
-| PC HX370 + RTX 5090 Laptop / Windows | Qwen3.8-27B | NVFP4_POST_LOAD_PTX_STARTUP_FAILED; NOT_E2E_PROFILEABLE | The pinned NVFP4 checkpoint passed scoped text/image cases in WSL, but two native Windows attempts did not reach inference. In the captured retry, all three safetensors shards loaded; vLLM 0.29.0+cu134 then failed in NVFP4 Marlin scale permutation during post-load processing with `CUDA error: the provided PTX was compiled with an unsupported toolchain`. The installed RTX driver 610.71 reports CUDA UMD 13.3, while the local CUDA toolkit/wheel use 13.4. NVIDIA documents PTX JIT limitations across that driver gap; this is a likely runtime compatibility cause, not an A/B-proven driver fix or a model-wide unsupported conclusion. No native Windows text/image output, admission or latency is qualified. | Validate an R615-or-later compatible driver or a wheel/toolchain supported by R610, then rerun the same 512-token text/image workload and broaden quality, memory and profiling; [Windows failure evidence](../../../e2e_expansion_20260924/evidence/qwen38_omni_windows_rtx/README.md), [WSL pass](../../../e2e_recovery_20260923/README.md). |
+| PC HX370 + RTX 5090 Laptop / Windows | Qwen3.8-27B | SCOPED_E2E_OMNI_FP8_TEXT_IMAGE_RTX; NVFP4_POST_LOAD_PTX_STARTUP_FAILED | The separate pinned Qwen/Qwen3.8-27B-FP8 checkpoint (revision 017b9c7) now ran complete native-Windows Omni text and synthetic red-image requests on RTX 5090 Laptop using vLLM 0.29.0+cu134 with the explicit Triton block-FP8 linear backend, BF16 compute, 12 GiB host offload, 0.80 GPU-memory cap, one sequence and 512-token context. One warmup of each kind plus 20/20 serial measured image outputs Red and 20/20 text outputs ready passed; nearest-rank complete-request wall p50/p95 was 4.184/4.380 s image and 3.070/3.214 s text. The profile initialized in 87.37 s, with 31.35 s reported weight load and a 1,974-token GPU KV cache; startup is excluded from request timing. The default FP8 CUTLASS operator lacked an SM120 kernel, and a 0.75 cap refused the 0.33 GiB required KV allocation; both failures are retained. The different NVFP4 artifact still fails in Marlin post-load PTX under driver 610.71. These two toy prompts do not qualify long context, video, broad quality, cancellation, explicit loading-peak admission, concurrency or sustained behavior. | Validate public AsyncOmni and cancellation/restart, explicit host/VRAM admission and load peaks, longer multimodal/quality cases and sustained power; keep NVFP4 driver/toolchain repair separate; [FP8 and NVFP4 raw evidence](../../../e2e_expansion_20260924/evidence/qwen38_omni_windows_rtx/README.md). |
 | PC HX370 + RTX 5090 Laptop / Windows | MiniCPM-o 4.5 | SCOPED_E2E | The verified checkpoint completed native Windows three-stage BF16 text, one synthetic image, and one synthetic audio input to text+speech in separate constrained plans with 5 GiB thinker host offload and explicit 2,048/512-token limits. Text and all 32 thinker token IDs matched WSL CUDA; a 5.2 s WAV was nonzero. The red-square image produced the same description and 27 thinker token IDs as WSL plus a nonzero 5.2 s WAV in 17.15 s. The 2 s/440 Hz tone produced the same whistling description and nine IDs as WSL plus a nonzero 2.16 s WAV in 8.26 s. The initial text run needed 900/600 s startup limits after a 300 s timeout; a separate 1-warmup/20-measured serial text run had request wall p50 18.38 s/p95 18.73 s, with sampled host available RAM as low as 0.78 GB and pagefile use up to 11.17 GB. These synthetic inputs do not establish general image/audio or speech quality. | Check real image/audio suites, video input, speech intelligibility/alignment, concurrency, interruption, loading-peak admission and sustained thermal/power behavior before broader qualification; [native evidence](../../../e2e_expansion_20260923/README.md). |
 | PC HX370 + RTX 5090 Laptop / Windows | InternVLA-A1 | SYNTHETIC_POLICY_PASS_OMNI_CUDA; NOT_TASK_E2E | The real Place_Markpen checkpoint ran through an admitted native Windows Omni whole-policy graph stage with BF16 policy and Cosmos encoder on the RTX 5090 Laptop GPU. After one warmup, 20/20 serial patterned synthetic requests returned identical finite float32 [1,50,32] actions with terminal events; nearest-rank complete-request wall p50/p95 was 0.746/0.806 s, excluding 147.77 s startup. The 16 GiB host-RAM and 16 GiB CUDA demands were within explicit 18/21 GiB ceilings checked against available memory. Actual CUDA placement was verified, PyTorch peak reserved VRAM reached 7.86 GB, and an 8 GiB VRAM plan was refused before worker launch. In-flight cancellation and shutdown released both ledgers with no stale output; a separate public AsyncOmni.generate request returned the same action hash. Relative to the separate native CPU patterned output, action relative L2 difference was 2.340% with no task tolerance; native-vs-WSL CUDA relative L2 was 1.555%. The earlier one-forward zero-input direct CUDA run was a different experiment. This is synthetic action-policy integration, not robot-task E2E; units/order/step time remain unverified and control_ready=false. | Validate real observations/reference actions and physical action semantics, post-cancel restart, concurrent admission, loading peak, deadlines and sustained latency/power/thermal behavior; [Omni Windows CUDA evidence](../../../e2e_expansion_20260923/evidence/internvla_omni_windows_cuda/README.md), [earlier direct result](../../../e2e_expansion_20260923/README.md). |
 | PC HX370 + Radeon 890M / Windows worker | Spark-X2.5 | SCOPED_E2E_OMNI_COMPLETE_REQUEST; RESTART_AFTER_INFLIGHT_CANCEL_PASS | The same pinned Spark-X2.5-1.7B Q4_K_M GGUF completed text prefill/decode through a bounded Omni whole-session stage with llama.cpp Vulkan1. The log identifies Radeon 890M and 29/29 layers offloaded there, rather than RTX 5090; an intentionally wrong device name was refused. `Paris` and `1511` passed; one warmup plus 20 serial complete requests were 20/20 correct, nearest-rank wall p50/p95 2.10/2.14 s. A public AsyncOmni request passed; an oversized context was rejected with a later correct request; in-flight cancellation produced no stale output and released the ledger. The prior standalone run was 2.18/2.20 s. A separate confirmed server-start cancellation of the 120-item request terminated the Vulkan1 worker, left no stale output or reservation, and a fresh StageRuntime returned Paris under a new worker generation with Radeon 890M placement again verified. This is fresh-stage recovery, not same-session continuation. No streaming, GPU-memory peak or sustained power/thermal check is claimed. | Qualify public configuration, broader text/token quality, streaming, concurrent recovery, shared-RAM/GPU peak and sustained use; [restart evidence](../../../e2e_expansion_20260924/evidence/spark_omni_windows_restart/README.md), [Omni raw evidence](../../../e2e_expansion_20260923/evidence/spark_omni_llamacpp/README.md), [standalone evidence](../../../e2e_expansion_20260923/evidence/spark_radeon890m_llamacpp/README.md). |
@@ -331,6 +331,8 @@ The [native Windows RTX Spark GGUF follow-up](../../benchmarks/edge_harness/resu

 A [native Windows Qwen3.8-27B NVFP4 attempt](../../benchmarks/edge_harness/results/e2e_expansion_20260924/evidence/qwen38_omni_windows_rtx/README.md) did not reach inference despite loading all three weight shards. The installed Windows vLLM 0.29+cu134 wheel failed during Marlin post-load scale permutation with an unsupported PTX toolchain error under RTX driver 610.71/CUDA UMD 13.3. NVIDIA's PTX compatibility guidance makes a driver/toolchain mismatch likely; an upgraded-driver or compatible-wheel control is still needed before rerunning the 512-token text/image cases. This failure constrains the tested Windows route, not the previously passed WSL checkpoint path.

+The separate official Qwen3.8-27B FP8 checkpoint now has a [scoped native-Windows Omni RTX text/image pass and 20-request serial profile per modality](../../benchmarks/edge_harness/results/e2e_expansion_20260924/evidence/qwen38_omni_windows_rtx/README.md). The installed Windows wheel lacks an SM120 CUTLASS block-FP8 operator, so an explicit Omni-owned Triton linear backend was added and verified. The 0.75 GPU-memory cap correctly refused the 512-token KV requirement; 0.80 with 12 GiB declared host offload admitted the run. This is a different artifact/backend from NVFP4 and covers only short text plus a synthetic red image. The next gates remain public request, cancellation/recovery, loading-peak admission, longer multimodal and quality cases, plus sustained power/thermal behavior.
+
 The [native Windows RTX Qwen3-TTS Omni hybrid follow-up](../../benchmarks/edge_harness/results/e2e_expansion_20260924/evidence/qwen_tts_windows_rtx/README.md) extends the existing whole-session CrispASR stage to the optional discrete-GPU route. The RTX Vulkan0 talker/codec and CPU FP32 predictor produced two named complete WAVs, 20 identical serial measured outputs at 3.153/3.231 s p50/p95 for 2.72 s audio, and public AsyncOmni audio. Pinned Whisper tiny.en transcribed the first sentence exactly; the RTX PCM differs from the Radeon PCM, so the timings are not a same-output acceleration comparison. Separate 8 GiB host-RAM and 4 GiB GPU-VRAM reservations, a prelaunch GPU-budget refusal, in-flight cancellation and fresh-stage restart passed; the original Radeon route passed a regression probe. M2 still needs playable chunks, RTF below 1 for the declared workload, broader speaker quality, measured VRAM/load peaks, concurrency and an underrun-free sustained run. This Vulkan+CPU route does not qualify mobile NPU+GPU co-residency.

 The [Qwen3-TTS Omni hybrid follow-up](../../benchmarks/edge_harness/results/e2e_expansion_20260923/evidence/qwen_tts_omni_hybrid/README.md) adds one complete-request stage for the native Windows HX370 CPU+Radeon 890M route. A pinned Q8_0 talker and F16 codec produced two named PCM outputs exactly matching the prior standalone backend; one warmup plus 20 serial requests measured 3.298/3.336 s p50/p95 for 2.56 s of audio. Public `AsyncOmni` audio, cancellation drain and explicit 8 GiB host admission passed. A separate [in-flight cancellation audit](../../benchmarks/edge_harness/results/e2e_expansion_20260924/evidence/qwen_tts_omni_windows_restart/README.md) confirmed that the request reached server prefill before cancellation; a fresh stage with the same artifacts and hybrid placement emitted the identical PCM under a new worker generation, without stale output or a retained reservation. This is progress toward M2 integration, not M2 acceptance: RTF remains above 1, and playable chunk streaming, same-session continuation, loading/iGPU peaks, broader voice quality and 30-minute thermal behavior remain open. No NPU participated.
@@ -715,6 +715,7 @@ def test_sub_config_fields_match_structured_scopes():
        "enforce_eager",
        "language_model_only",
        "cpu_offload_gb",
+        "linear_backend",
        "max_cudagraph_capture_size",
        "enable_flashinfer_autotune",
        "enable_multithread_weight_load",
@@ -1282,12 +1283,13 @@ def test_qwen3_5_large_model_execution_overrides_have_a_stage_owner():

    stage = _from_pipeline_key(
        "qwen3_5",
-        cli_overrides={"language_model_only": True, "cpu_offload_gb": 2.0},
+        cli_overrides={"language_model_only": True, "cpu_offload_gb": 2.0, "linear_backend": "triton"},
    ).stage_by_id(0)

    args = _project_omni_stage_engine_args(stage)
    assert args["language_model_only"] is True
    assert args["cpu_offload_gb"] == 2.0
+    assert args["linear_backend"] == "triton"


 def test_diffusion_config_preserves_existing_coercion_hooks():
@@ -171,6 +171,7 @@ class _ModelEngineOverrides(TypedDict, total=False):
    # vLLM model execution switches needed by large conditional generators.
    language_model_only: bool
    cpu_offload_gb: float
+    linear_backend: str


 class _LoadEngineOverrides(TypedDict, total=False):
@@ -445,6 +446,7 @@ class OmniStageModelConfig(_TrackExplicitConfigFields):
    disable_autocast: bool = False
    language_model_only: bool | None = None
    cpu_offload_gb: float | None = Field(default=None, ge=0.0)
+    linear_backend: str | None = None
    # Per-stage checkpoint/tokenizer subdirectories under the model root
    # (e.g. Audex stage 0 → checkpoint_folder_audiogen). Mirrors
    # StagePipelineConfig.model_subdir/tokenizer_subdir on the legacy path.