admin
67eb7fdb48
Evidence-only change: probe kit (serve wrapper, API client, PSNR
comparator, offline fp8_v driver), per-host run summaries, and the
findings report. No code changes to the stack.
Verdicts: not-built on both cards. On the 3090 the served reference
ceiling is two condition images with the conditioning cache on
(2.2x the Diffusers nocache baseline, pair PSNR mean 27.57 dB);
four references OOM on the resident BF16 working set, not the KV
(~0.3 GiB/reference, per-request). On the 5080 the server cannot
initialize under any flag: init_parameters materializes the
unquantized skeleton on-device before quantization/offload engage.
Also documents that the recipe's deploy-YAML extras route for
prefix_kv_cache_dtype does not reach the served transformer on
8484d85d (offline Omni engine kwarg does).
127 lines
4.7 KiB
Python
127 lines
4.7 KiB
Python
#!/usr/bin/env python
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"""Issue 2 probe: offline Qwen-Image-2.1 edit driver with prefix-KV extras.
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The served path cannot reach `extras.prefix_kv_cache_dtype` on commit
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8484d85d (the structured-config bridge drops deploy-YAML extras), so this
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driver exercises the knob through the offline `Omni(...)` engine kwargs —
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the other route the recipe names. It mirrors image_edit.py's request
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construction: same prompt builder, same sampling fields, one request at a
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time.
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Usage: offline_fp8v.py --out RUN_DIR [--kv-dtype fp8_v] [--fixture FID]
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"""
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from __future__ import annotations
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import argparse
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import hashlib
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import json
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import pathlib
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import time
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import torch
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from PIL import Image
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from vllm_omni.entrypoints.omni import Omni
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from vllm_omni.inputs.data import OmniDiffusionSamplingParams
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from vllm_omni.model_extras import (
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build_image_to_image_prompt,
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get_model_class_name,
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)
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from vllm_omni.platforms import current_omni_platform
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W = pathlib.Path.home() / "Projects/vllm-omni-val/issue2"
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SUITE = (
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pathlib.Path.home()
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/ ".local/share/multi-model-helper-server/data/evaluation/media/image/references-suite-2"
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)
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SNAP = (
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pathlib.Path.home()
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/ ".local/state/multi-model-helper-server/state/image-service/weights/hub/models--Qwen--Qwen-Image-2.1/snapshots/790c92633540aa0cb11d9abf19eb46d861714758"
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)
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STEPS = 20
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CFG = 1.0
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def main() -> int:
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parser = argparse.ArgumentParser()
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parser.add_argument("--out", required=True, type=pathlib.Path)
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parser.add_argument("--kv-dtype", default="fp8_v")
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parser.add_argument("--fixture", default="edit-compose-four")
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parser.add_argument("--seeds", type=lambda s: tuple(int(x) for x in s.split(",")), default=(2301,))
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parser.add_argument("--no-extras", action="store_true", help="run without the extras knob (control)")
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args = parser.parse_args()
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fixture = json.loads((W / "fixtures.json").read_text())[args.fixture]
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refs = [Image.open(SUITE / f"{r}.png").convert("RGB") for r in fixture["references"]]
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omni_kwargs = dict(
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model=str(SNAP),
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enforce_eager=True,
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enable_cpu_offload=True,
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init_timeout=1200,
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)
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if not args.no_extras:
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omni_kwargs["extras"] = {"prefix_kv_cache_dtype": args.kv_dtype}
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t0 = time.time()
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omni = Omni(**omni_kwargs)
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print(f"load_s={time.time() - t0:.1f} extras={omni_kwargs.get('extras')}", flush=True)
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model_class_name = get_model_class_name(omni)
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args.out.mkdir(parents=True, exist_ok=True)
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records = []
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for seed in args.seeds:
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generator = torch.Generator(device=current_omni_platform.device_type).manual_seed(seed)
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prompt_dict = build_image_to_image_prompt(
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model_class_name=model_class_name,
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prompt=fixture["prompt"],
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negative_prompt=None,
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input_image=refs[0] if len(refs) == 1 else refs,
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height=fixture["height"],
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width=fixture["width"],
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)
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params = OmniDiffusionSamplingParams(
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generator=generator,
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seed=seed,
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true_cfg_scale=CFG,
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num_inference_steps=STEPS,
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height=fixture["height"],
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width=fixture["width"],
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)
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torch.cuda.reset_peak_memory_stats()
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t1 = time.time()
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rec = {"fixture_id": args.fixture, "seed": seed, "n_references": len(refs), "kv_dtype": args.kv_dtype}
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try:
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outputs = omni.generate(prompt_dict, sampling_params_list=[params])
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wall = time.time() - t1
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images = None
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for output in outputs:
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images = getattr(output, "images", None)
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if images:
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break
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if not images:
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raise RuntimeError("no images in output")
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out_path = args.out / "measured" / f"{args.fixture}.s{seed}.png"
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out_path.parent.mkdir(parents=True, exist_ok=True)
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images[0].save(out_path)
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png = out_path.read_bytes()
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rec.update(
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wall_s=round(wall, 2),
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peak_gpu_mb=round(torch.cuda.max_memory_allocated() / 2**20, 1),
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bytes=len(png),
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sha256=hashlib.sha256(png).hexdigest(),
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)
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except Exception as exc: # noqa: BLE001 - recorded as the probe result
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rec.update(wall_s=round(time.time() - t1, 2), error=f"{type(exc).__name__}: {exc}"[:2000])
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records.append(rec)
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print(json.dumps(rec), flush=True)
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(args.out / f"offline-{args.fixture}.json").write_text(json.dumps(records, indent=1) + "\n")
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ok = all("sha256" in r for r in records)
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print(("PASS" if ok else "FAIL") + f" {len(records)} requests", flush=True)
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return 0 if ok else 1
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if __name__ == "__main__":
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raise SystemExit(main())
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