[Diffusion]: Diffusion Ulysses-Sequence-Parallelism support (#189)

Signed-off-by: Didan Deng <33117903+wtomin@users.noreply.github.com>

tests/e2e/offline_inference/test_sequence_parallel.py

@@ -0,0 +1,72 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+"""
+System test for Ulysses sequence parallel backend.
+
+This test verifies that Ulysses-SP (DeepSpeed Ulysses Sequence Parallel) 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
+
+# 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.diffusion.data import DiffusionParallelConfig
+from vllm_omni.diffusion.distributed.parallel_state import device_count
+from vllm_omni.diffusion.envs import get_device_name
+
+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)
+@pytest.mark.parametrize("ulysses_degree", [1, 2])
+@pytest.mark.parametrize("ring_degree", [1])
+def test_sequence_parallel(model_name: str, ulysses_degree: int, ring_degree: int):
+    """Test SP (Ulysses-SP + Ring-SP) backend with diffusion model."""
+    # Skip if not enough GPUs available
+    if device_count() < ulysses_degree:
+        pytest.skip(f"Test requires {ulysses_degree} GPUs but only {device_count()} available")
+
+    # Configure sequence parallel with DiffusionParallelConfig
+    parallel_config = DiffusionParallelConfig(ulysses_degree=ulysses_degree, ring_degree=ring_degree)
+
+    m = Omni(
+        model=model_name,
+        parallel_config=parallel_config,
+    )
+
+    # Use minimal settings for fast testing
+    height = 256
+    width = 256
+    num_inference_steps = 4  # Minimal steps for fast test
+
+    images = m.generate(
+        "a photo of a cat sitting on a laptop keyboard",
+        height=height,
+        width=width,
+        num_inference_steps=num_inference_steps,
+        guidance_scale=0.0,
+        generator=torch.Generator(get_device_name()).manual_seed(42),
+        num_outputs_per_prompt=1,  # Single output for speed
+    )
+
+    # 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