test_cache_dit.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

"""
System test for cache-dit backend.

This test verifies that cache-dit acceleration works correctly with diffusion models.
It uses minimal settings to keep test time short for CI.
"""

import os
import sys
from pathlib import Path

import pytest
import torch

from vllm_omni.inputs.data import OmniDiffusionSamplingParams

# ruff: noqa: E402
REPO_ROOT = Path(__file__).resolve().parents[2]
if str(REPO_ROOT) not in sys.path:
    sys.path.insert(0, str(REPO_ROOT))

from vllm_omni import Omni
from vllm_omni.outputs import OmniRequestOutput

os.environ["VLLM_TEST_CLEAN_GPU_MEMORY"] = "1"

# Use random weights model for testing
models = ["riverclouds/qwen_image_random"]


@pytest.mark.parametrize("model_name", models)
def test_cache_dit(model_name: str):
    """Test cache-dit backend with diffusion model."""
    # Configure cache-dit with minimal settings for fast testing
    cache_config = {
        "Fn_compute_blocks": 1,
        "Bn_compute_blocks": 0,
        "max_warmup_steps": 2,  # Minimal warmup for fast test
        "residual_diff_threshold": 0.24,
        "max_continuous_cached_steps": 3,
    }
    m = None
    try:
        m = Omni(
            model=model_name,
            cache_backend="cache_dit",
            cache_config=cache_config,
        )

        # Use minimal settings for fast testing
        height = 256
        width = 256
        num_inference_steps = 4  # Minimal steps for fast test

        outputs = m.generate(
            "a photo of a cat sitting on a laptop keyboard",
            OmniDiffusionSamplingParams(
                height=height,
                width=width,
                num_inference_steps=num_inference_steps,
                guidance_scale=0.0,
                generator=torch.Generator("cuda").manual_seed(42),
                num_outputs_per_prompt=1,  # Single output for speed
            ),
        )
        # Extract images from request_output[0]['images']
        first_output = outputs[0]
        assert first_output.final_output_type == "image"
        if not hasattr(first_output, "request_output") or not first_output.request_output:
            raise ValueError("No request_output found in OmniRequestOutput")

        req_out = first_output.request_output[0]
        if not isinstance(req_out, OmniRequestOutput) or not hasattr(req_out, "images"):
            raise ValueError("Invalid request_output structure or missing 'images' key")

        images = req_out.images

        # Verify generation succeeded
        assert images is not None
        assert len(images) == 1
        # Check image size
        assert images[0].width == width
        assert images[0].height == height
    except Exception as e:
        print(f"Test failed with error: {e}")
        raise
    finally:
        if m is not None and hasattr(m, "close"):
            m.close()