AliciaandHongsheng Liu
d3ea94309d
Signed-off-by: Alicia <115451386+congw729@users.noreply.github.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>
99 lines
3.1 KiB
Python
99 lines
3.1 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""
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System test for cache-dit backend.
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This test verifies that cache-dit acceleration works correctly with diffusion models.
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It uses minimal settings to keep test time short for CI.
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"""
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import os
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import sys
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from pathlib import Path
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import pytest
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import torch
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from tests.utils import hardware_test
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from vllm_omni.inputs.data import OmniDiffusionSamplingParams
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# ruff: noqa: E402
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REPO_ROOT = Path(__file__).resolve().parents[2]
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if str(REPO_ROOT) not in sys.path:
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sys.path.insert(0, str(REPO_ROOT))
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from vllm_omni import Omni
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from vllm_omni.outputs import OmniRequestOutput
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from vllm_omni.platforms import current_omni_platform
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os.environ["VLLM_TEST_CLEAN_GPU_MEMORY"] = "1"
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# Use random weights model for testing
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models = ["riverclouds/qwen_image_random"]
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@pytest.mark.core_model
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@pytest.mark.diffusion
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@pytest.mark.cache
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@hardware_test(res={"cuda": "L4", "rocm": "MI325"})
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@pytest.mark.parametrize("model_name", models)
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def test_cache_dit(model_name: str):
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"""Test cache-dit backend with diffusion model."""
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# Configure cache-dit with minimal settings for fast testing
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cache_config = {
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"Fn_compute_blocks": 1,
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"Bn_compute_blocks": 0,
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"max_warmup_steps": 2, # Minimal warmup for fast test
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"residual_diff_threshold": 0.24,
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"max_continuous_cached_steps": 3,
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}
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m = None
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try:
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m = Omni(
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model=model_name,
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cache_backend="cache_dit",
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cache_config=cache_config,
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)
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# Use minimal settings for fast testing
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height = 256
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width = 256
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num_inference_steps = 4 # Minimal steps for fast test
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outputs = m.generate(
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"a photo of a cat sitting on a laptop keyboard",
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OmniDiffusionSamplingParams(
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height=height,
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width=width,
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num_inference_steps=num_inference_steps,
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guidance_scale=0.0,
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generator=torch.Generator(current_omni_platform.device_type).manual_seed(42),
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num_outputs_per_prompt=1, # Single output for speed
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),
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)
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# Extract images from request_output[0]['images']
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first_output = outputs[0]
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assert first_output.final_output_type == "image"
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if not hasattr(first_output, "request_output") or not first_output.request_output:
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raise ValueError("No request_output found in OmniRequestOutput")
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req_out = first_output.request_output[0]
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if not isinstance(req_out, OmniRequestOutput) or not hasattr(req_out, "images"):
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raise ValueError("Invalid request_output structure or missing 'images' key")
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images = req_out.images
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# Verify generation succeeded
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assert images is not None
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assert len(images) == 1
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# Check image size
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assert images[0].width == width
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assert images[0].height == height
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except Exception as e:
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print(f"Test failed with error: {e}")
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raise
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finally:
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if m is not None and hasattr(m, "close"):
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m.close()
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