# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project """ Quantization quality gate for diffusion models. Developers adding a new quantization method should: 1. Add their method + model to QUALITY_CONFIGS below 2. Set a max_lpips threshold (use 0.15 for image, 0.20 for video as defaults) 3. Run: pytest tests/diffusion/quantization/test_quantization_quality.py -v -m "" 4. Paste the output table into their PR description The test generates outputs with both BF16 and the quantized method using the same seed, computes similarity metrics, and fails if LPIPS exceeds the threshold. Requirements: pip install lpips Example — run only FP8 tests: pytest tests/diffusion/quantization/test_quantization_quality.py -v -m "" -k "fp8" Example — run a specific model: pytest tests/diffusion/quantization/test_quantization_quality.py -v -m "" -k "z_image" Example — validate a local BF16 baseline against a local pre-quantized checkpoint: export VLLM_OMNI_QUALITY_CONFIGS=/tmp/modelopt_quality_cases.json pytest tests/diffusion/quantization/test_quantization_quality.py -v -m "" -k "qwen_image_2512" Optional artifact dump: export VLLM_OMNI_QUALITY_OUTPUT_DIR=/tmp/modelopt_quality_outputs """ from __future__ import annotations import gc import importlib.util import json import os import sys from dataclasses import dataclass from pathlib import Path from types import SimpleNamespace import numpy as np import pytest import torch from PIL import Image from tests.helpers.mark import hardware_marks _REPO_ROOT = Path(__file__).resolve().parents[3] _BENCH_MODULE_PATH = _REPO_ROOT / "benchmarks" / "diffusion" / "quantization_quality.py" _BENCH_MODULE_NAME = "benchmarks.diffusion.quantization_quality" if _BENCH_MODULE_NAME not in sys.modules: _spec = importlib.util.spec_from_file_location(_BENCH_MODULE_NAME, _BENCH_MODULE_PATH) _mod = importlib.util.module_from_spec(_spec) sys.modules[_BENCH_MODULE_NAME] = _mod _spec.loader.exec_module(_mod) # --------------------------------------------------------------------------- # Configuration — add new quantization methods / models here # --------------------------------------------------------------------------- @dataclass class QualityTestConfig: """Defines a single quantization quality test case.""" id: str # pytest ID, e.g. "fp8_z_image" task: str # "t2i" or "t2v" prompt: str # generation prompt max_lpips: float # fail threshold — higher = more lenient model: str | None = None # HF model name quantization: str | dict[str, object] | None = None # quantization method/config, e.g. "fp8" baseline_model: str | None = None # explicit BF16/local baseline path quantized_model: str | None = None # explicit quantized/local model path height: int = 1024 width: int = 1024 num_inference_steps: int = 20 # keep low for CI speed num_frames: int = 5 # only for t2v seed: int = 42 gpu: str = "H100" # minimum GPU requirement negative_prompt: str = "" guidance_scale: float | None = None def baseline_ref(self) -> str: return self.baseline_model or self.model or "" def quantized_ref(self) -> str: if self.quantized_model is not None: return self.quantized_model return self.model or "" def quantization_ref(self) -> str | None: if self.quantized_model is not None: return None return self.quantization def validate(self) -> None: uses_explicit_models = self.baseline_model is not None or self.quantized_model is not None uses_model_plus_method = self.model is not None or self.quantization is not None if uses_explicit_models and uses_model_plus_method: raise ValueError(f"{self.id}: explicit baseline/quantized paths cannot be mixed with model/quantization") if uses_explicit_models: if self.baseline_model is None or self.quantized_model is None: raise ValueError(f"{self.id}: baseline_model and quantized_model must be provided together") return if self.model is None or self.quantization is None: raise ValueError(f"{self.id}: expected either model+quantization or baseline_model+quantized_model") # Add new quantization methods / models here. # Developers: copy a config, change quantization + max_lpips, run the test. QUALITY_CONFIGS = [ QualityTestConfig( id="fp8_z_image", model="Tongyi-MAI/Z-Image-Turbo", quantization={ "method": "fp8", "ignored_layers": [ "img_mlp", *[ f"layers.{layer_id}.{suffix}" for layer_id in range(15, 30) for suffix in ( "attention.to_qkv", "attention.to_out.0", "feed_forward.w13", "feed_forward.w2", ) ], *[ f"model.layers.{layer_id}.{suffix}" for layer_id in range(28, 36) for suffix in ( "self_attn.q_proj", "self_attn.k_proj", "self_attn.v_proj", "self_attn.o_proj", "mlp.gate_proj", "mlp.up_proj", "mlp.down_proj", ) ], ], }, task="t2i", prompt=( "A breathtaking twilight scene atop a floating archipelago of crystalline islands suspended in an " "endless nebula-drenched sky, where waterfalls of liquid starlight cascade from the edges of each " "island into the cosmic abyss below. The largest island hosts an ancient, overgrown observatory " "crafted from pearlescent white stone and living silverwood trees whose bioluminescent leaves pulse " "with soft cerulean and violet light. At the observatory's center stands a solitary figure—a young " "astronomer in flowing robes woven from woven moonlight and deep-space fabric, their face illuminated " "by the glow of a holographic star chart hovering above an altar of polished obsidian. The sky swirls " "with the birth of a new galaxy: ribbons of magenta and gold gas clouds twist around nascent stars, " "while distant supernovae bloom like cosmic flowers in the far reaches of the void. Below, the abyss " "reveals faint echoes of forgotten civilizations—ghostly silhouettes of submerged cities shimmering " "in layers of atmospheric haze. Cinematic volumetric lighting cuts through the scene as twin moons—one " "copper-hued, one opalescent—rise on opposite horizons, casting long, intersecting shadows across " "moss-covered ruins and crystalline flora that refract light into prismatic halos. Hyper-detailed, " "photorealistic rendering with the atmospheric depth of Roger Deakins' cinematography, the " "architectural grandeur of Zaha Hadid, and the cosmic wonder of James Jean's illustrations. Shot on " "a mythical 150mm lens with shallow depth of field, 8K resolution, Unreal Engine 5 realism, subsurface " "scattering on organic elements, and ray-traced reflections dancing across every water droplet in the " "starlight waterfalls. Ethereal, melancholic, and transcendent mood—like a moment of quiet revelation " "at the edge of existence." ), max_lpips=0.15, num_inference_steps=20, ), QualityTestConfig( id="fp8_flux", model="black-forest-labs/FLUX.1-dev", quantization="fp8", task="t2i", prompt="a cup of coffee on a wooden table, morning light", max_lpips=0.20, num_inference_steps=10, ), QualityTestConfig( id="fp8_qwen_image", model="Qwen/Qwen-Image", quantization="fp8", task="t2i", prompt="a cup of coffee on a wooden table, morning light", max_lpips=0.35, seed=142, num_inference_steps=20, ), QualityTestConfig( id="fp8_ltx2", model="Lightricks/LTX-2", quantization="fp8", task="t2v", prompt="A serene lakeside sunrise with mist over the water", max_lpips=0.10, height=256, width=256, num_frames=25, num_inference_steps=8, ), ] def _load_extra_quality_configs() -> list[QualityTestConfig]: config_path = os.environ.get("VLLM_OMNI_QUALITY_CONFIGS") if not config_path: return [] payload = json.loads(Path(config_path).read_text(encoding="utf-8")) if not isinstance(payload, list): raise ValueError("VLLM_OMNI_QUALITY_CONFIGS must point to a JSON list") configs: list[QualityTestConfig] = [] for item in payload: if not isinstance(item, dict): raise ValueError("Each quality config entry must be a JSON object") config = QualityTestConfig(**item) config.validate() configs.append(config) return configs def _all_quality_configs() -> list[QualityTestConfig]: configs = [*QUALITY_CONFIGS, *_load_extra_quality_configs()] for config in configs: config.validate() return configs def _output_path(output_dir: Path, config: QualityTestConfig, label: str, suffix: str) -> Path: safe_id = config.id.replace("/", "_") return output_dir / f"{safe_id}_{label}{suffix}" def _maybe_save_output(output_dir: Path | None, config: QualityTestConfig, label: str, output) -> None: if output_dir is None: return output_dir.mkdir(parents=True, exist_ok=True) if config.task == "t2i": output.save(_output_path(output_dir, config, label, ".png")) return if isinstance(output, np.ndarray): np.save(_output_path(output_dir, config, label, ".npy"), output) return raise TypeError(f"Unsupported output type for saving: {type(output)!r}") # --------------------------------------------------------------------------- # Helpers # --------------------------------------------------------------------------- def _generate_image(omni, config: QualityTestConfig): """Generate a single image, return (PIL.Image, peak_mem_gib).""" from vllm_omni.inputs.data import OmniDiffusionSamplingParams from vllm_omni.platforms import current_omni_platform generator = torch.Generator( device=current_omni_platform.device_type, ).manual_seed(config.seed) torch.accelerator.reset_peak_memory_stats() outputs = omni.generate( {"prompt": config.prompt, "negative_prompt": config.negative_prompt}, OmniDiffusionSamplingParams( height=config.height, width=config.width, generator=generator, num_inference_steps=config.num_inference_steps, guidance_scale=config.guidance_scale, ), ) peak_mem = torch.accelerator.max_memory_allocated() / (1024**3) first = outputs[0] if hasattr(first, "images") and first.images: return first.images[0], peak_mem inner = first.request_output if inner is not None and hasattr(inner, "images") and inner.images: return inner.images[0], peak_mem raise ValueError("Could not extract image from output.") def _generate_video(omni, config: QualityTestConfig): """Generate a video, return (np.ndarray [F,H,W,C], peak_mem_gib).""" from vllm_omni.inputs.data import OmniDiffusionSamplingParams from vllm_omni.outputs import OmniRequestOutput from vllm_omni.platforms import current_omni_platform generator = torch.Generator( device=current_omni_platform.device_type, ).manual_seed(config.seed) torch.accelerator.reset_peak_memory_stats() outputs = omni.generate( {"prompt": config.prompt, "negative_prompt": config.negative_prompt}, OmniDiffusionSamplingParams( height=config.height, width=config.width, generator=generator, num_inference_steps=config.num_inference_steps, num_frames=config.num_frames, guidance_scale=config.guidance_scale, ), ) peak_mem = torch.accelerator.max_memory_allocated() / (1024**3) first = outputs[0] if hasattr(first, "request_output") and isinstance(first.request_output, list): inner = first.request_output[0] if isinstance(inner, OmniRequestOutput) and hasattr(inner, "images"): frames = inner.images[0] if inner.images else None else: frames = inner elif hasattr(first, "images") and first.images: frames = first.images[0] else: raise ValueError("Could not extract video frames from output.") # LTX-2 (audio+video) may surface (video, audio) tuples or {"video": ...} dicts if isinstance(frames, dict): frames = frames.get("video") or frames.get("frames") elif isinstance(frames, tuple) and len(frames) == 2: frames = frames[0] if frames is None: raise ValueError("Could not extract video frames from output.") if isinstance(frames, torch.Tensor): video = frames.detach().cpu() if video.dim() == 5: video = video[0] if video.dim() == 4 and video.shape[0] in (3, 4): video = video.permute(1, 2, 3, 0) if video.is_floating_point(): video = video.clamp(-1, 1) * 0.5 + 0.5 return video.float().numpy(), peak_mem frames_array = np.asarray(frames) if frames_array.ndim == 5: # strip the leading batch dim frames_array = frames_array[0] return frames_array, peak_mem def _compute_lpips(baseline, quantized, task: str) -> float: """Compute LPIPS between baseline and quantized outputs.""" from benchmarks.diffusion.quantization_quality import ( compute_lpips_images, compute_lpips_video, ) if task == "t2i": return compute_lpips_images([baseline], [quantized])[0] return compute_lpips_video(baseline, quantized) def _to_float_array(output, task: str) -> np.ndarray: if task == "t2i": array = np.asarray(output.convert("RGB"), dtype=np.float32) / 255.0 else: array = np.asarray(output, dtype=np.float32) if array.max() > 1.0 or array.min() < 0.0: array = np.clip(array, 0.0, 255.0) / 255.0 else: array = np.clip(array, 0.0, 1.0) return array def _compute_psnr_and_mae(baseline, quantized, task: str) -> tuple[float, float]: baseline_array = _to_float_array(baseline, task) quantized_array = _to_float_array(quantized, task) if baseline_array.shape != quantized_array.shape: raise ValueError( "Output shapes do not match for metric computation: " f"baseline={baseline_array.shape}, quantized={quantized_array.shape}" ) diff = baseline_array - quantized_array mae = float(np.mean(np.abs(diff))) mse = float(np.mean(np.square(diff))) psnr = float("inf") if mse == 0.0 else float(20.0 * np.log10(1.0 / np.sqrt(mse))) return psnr, mae def _free_gpu_memory(): gc.collect() if torch.cuda.is_available(): torch.accelerator.empty_cache() torch.accelerator.synchronize() # --------------------------------------------------------------------------- # Tests # --------------------------------------------------------------------------- def test_benchmark_generate_image_unwraps_nested_omni_request_output(monkeypatch): from benchmarks.diffusion.quantization_quality import _generate_image as benchmark_generate_image from vllm_omni.outputs import OmniRequestOutput from vllm_omni.platforms import current_omni_platform monkeypatch.setattr(current_omni_platform, "device_type", "cpu", raising=False) monkeypatch.setattr(torch.accelerator, "reset_peak_memory_stats", lambda: None, raising=False) monkeypatch.setattr(torch.accelerator, "max_memory_allocated", lambda: 0, raising=False) image = Image.new("RGB", (2, 2)) inner = OmniRequestOutput.from_diffusion(request_id="req", images=[image]) outer = OmniRequestOutput( request_id="req", stage_id=0, final_output_type="image", request_output=inner, finished=True, ) class DummyOmni: def generate(self, *_args, **_kwargs): return [outer] args = SimpleNamespace(height=2, width=2, num_inference_steps=1) output, _elapsed, peak_mem = benchmark_generate_image(DummyOmni(), args, "prompt", 42) assert output is image assert peak_mem == 0.0 _marks = hardware_marks(res={"cuda": "H100"}) _OUTPUT_DIR = Path(os.environ["VLLM_OMNI_QUALITY_OUTPUT_DIR"]) if "VLLM_OMNI_QUALITY_OUTPUT_DIR" in os.environ else None def _quality_param(c: QualityTestConfig): marks = list(_marks) if c.id == "fp8_qwen_image": marks.append( pytest.mark.skip(reason="Qwen-Image FP8 quality gate temporarily disabled (see CI / issue tracker).") ) return pytest.param(c, id=c.id, marks=marks) @pytest.mark.full_model @pytest.mark.diffusion @pytest.mark.parametrize( "config", [_quality_param(c) for c in _all_quality_configs()], ) def test_quantization_quality(config: QualityTestConfig): """Validate that quantized output stays within LPIPS threshold of BF16.""" from vllm_omni.entrypoints.omni import Omni generate_fn = _generate_video if config.task == "t2v" else _generate_image # --- BF16 baseline --- omni_bl = Omni(model=config.baseline_ref()) baseline_out, bl_mem = generate_fn(omni_bl, config) omni_bl.shutdown() del omni_bl _free_gpu_memory() _maybe_save_output(_OUTPUT_DIR, config, "baseline", baseline_out) # --- Quantized --- quantization = config.quantization_ref() if quantization is None: omni_qt = Omni(model=config.quantized_ref()) else: omni_qt = Omni(model=config.quantized_ref(), quantization_config=quantization) quant_out, qt_mem = generate_fn(omni_qt, config) omni_qt.shutdown() del omni_qt _free_gpu_memory() _maybe_save_output(_OUTPUT_DIR, config, "quantized", quant_out) # --- Similarity metrics --- lpips_score = _compute_lpips(baseline_out, quant_out, config.task) psnr_score, mae_score = _compute_psnr_and_mae(baseline_out, quant_out, config.task) assert lpips_score <= config.max_lpips, ( f"LPIPS {lpips_score:.4f} exceeds threshold {config.max_lpips} " f"for {config.quantization_ref() or 'pre-quantized checkpoint'} on {config.quantized_ref()}" ) # --- Report --- mem_reduction = (bl_mem - qt_mem) / bl_mem * 100 if bl_mem > 0 else 0 print(f"\n{'=' * 60}") print(f"Quantization Quality: {config.id}") print(f"{'=' * 60}") print(f" Baseline: {config.baseline_ref()}") print(f" Quantized: {config.quantized_ref()}") print(f" Method: {config.quantization_ref() or 'pre-quantized checkpoint'}") print(f" LPIPS: {lpips_score:.4f} (threshold: {config.max_lpips})") print(f" PSNR: {psnr_score:.4f} dB (higher is better)") print(f" MAE: {mae_score:.6f} (lower is better)") print(f" BF16 memory: {bl_mem:.2f} GiB") print(f" Quant memory: {qt_mem:.2f} GiB ({mem_reduction:.0f}% reduction)") print(f" Result: {'PASS' if lpips_score <= config.max_lpips else 'FAIL'}") print(f"{'=' * 60}\n") assert np.isfinite(psnr_score) or np.isinf(psnr_score), f"PSNR is invalid for {config.id}: {psnr_score}" assert np.isfinite(mae_score), f"MAE is not finite for {config.id}: {mae_score}"