[Feat] Enable DiT tensor parallel for Diffusion Pipeline(Z-Image) (#735)

Signed-off-by: dongbo910220 <1275604947@qq.com>
Signed-off-by: dongbo910220 <32610838+dongbo910220@users.noreply.github.com>
Signed-off-by: Jiangyun Zhu <riverclouds.zhu@qq.com>
Co-authored-by: Jiangyun Zhu <riverclouds.zhu@qq.com>

tests/e2e/offline_inference/test_zimage_tensor_parallel.py

@@ -0,0 +1,166 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import os
+import sys
+import time
+from pathlib import Path
+
+import numpy as np
+import pytest
+import torch
+from PIL import Image
+from vllm.distributed.parallel_state import cleanup_dist_env_and_memory
+
+# 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.outputs import OmniRequestOutput
+from vllm_omni.utils.platform_utils import is_npu, is_rocm
+
+os.environ["VLLM_TEST_CLEAN_GPU_MEMORY"] = "1"
+
+PROMPT = "a photo of a cat sitting on a laptop keyboard"
+
+
+def _get_zimage_model() -> str:
+    # Allow overriding the model for local/offline environments.
+    # Can be either a HuggingFace repo id or a local path.
+    return os.environ.get("VLLM_TEST_ZIMAGE_MODEL", "Tongyi-MAI/Z-Image-Turbo")
+
+
+def _pil_to_float_rgb_tensor(img: Image.Image) -> torch.Tensor:
+    """Convert PIL image to float32 RGB tensor in [0, 1] with shape [H, W, 3]."""
+    arr = np.asarray(img.convert("RGB"), dtype=np.float32) / 255.0
+    return torch.from_numpy(arr)
+
+
+def _diff_metrics(a: Image.Image, b: Image.Image) -> tuple[float, float]:
+    """Return (mean_abs_diff, max_abs_diff) over RGB pixels in [0, 1]."""
+    ta = _pil_to_float_rgb_tensor(a)
+    tb = _pil_to_float_rgb_tensor(b)
+    assert ta.shape == tb.shape, f"Image shapes differ: {ta.shape} vs {tb.shape}"
+    abs_diff = torch.abs(ta - tb)
+    return abs_diff.mean().item(), abs_diff.max().item()
+
+
+def _extract_single_image(outputs) -> Image.Image:
+    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
+    if images is None or len(images) != 1:
+        raise ValueError(f"Expected 1 image, got {0 if images is None else len(images)}")
+    return images[0]
+
+
+def _run_zimage_generate(
+    *, tp_size: int, height: int, width: int, num_inference_steps: int, seed: int
+) -> tuple[Image.Image, float]:
+    m = Omni(
+        model=_get_zimage_model(),
+        parallel_config=DiffusionParallelConfig(tensor_parallel_size=tp_size),
+    )
+    try:
+        # NOTE: Omni closes itself when a generate() call is exhausted.
+        # To avoid measuring teardown time (process shutdown, memory cleanup),
+        # we measure the latency to produce *subsequent* outputs within a single
+        # generator run.
+        #
+        # This also serves as a warmup: the first output may include extra
+        # compilation/caching overhead, while later outputs are closer to
+        # steady-state inference.
+        num_requests = 4  # 1 warmup + 3 timed
+        gen = m.generate(
+            [PROMPT] * num_requests,
+            height=height,
+            width=width,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=0.0,
+            seed=seed,
+            num_outputs_per_prompt=1,
+            py_generator=True,
+        )
+
+        warmup_output = next(gen)
+        t_prev = time.perf_counter()
+        per_request_times_s: list[float] = []
+        last_output = warmup_output
+        for _ in range(num_requests - 1):
+            last_output = next(gen)
+            t_now = time.perf_counter()
+            per_request_times_s.append(t_now - t_prev)
+            t_prev = t_now
+
+        # Ensure the generator is fully consumed so it can clean up.
+        for _ in gen:
+            pass
+
+        median_time_s = float(np.median(per_request_times_s))
+        return _extract_single_image([last_output]), median_time_s
+    finally:
+        m.close()
+        cleanup_dist_env_and_memory()
+
+
+@pytest.mark.integration
+def test_zimage_tensor_parallel_tp2(tmp_path: Path):
+    if is_npu() or is_rocm():
+        pytest.skip("Z-Image TP e2e test is only supported on CUDA for now.")
+    if not torch.cuda.is_available() or torch.cuda.device_count() < 2:
+        pytest.skip("Z-Image TP=2 requires >= 2 CUDA devices.")
+
+    height = 256
+    width = 256
+    num_inference_steps = 2
+    seed = 42
+
+    tp1_img, tp1_time_s = _run_zimage_generate(
+        tp_size=1,
+        height=height,
+        width=width,
+        num_inference_steps=num_inference_steps,
+        seed=seed,
+    )
+    tp2_img, tp2_time_s = _run_zimage_generate(
+        tp_size=2,
+        height=height,
+        width=width,
+        num_inference_steps=num_inference_steps,
+        seed=seed,
+    )
+
+    tp1_path = tmp_path / "zimage_tp1.png"
+    tp2_path = tmp_path / "zimage_tp2.png"
+    tp1_img.save(tp1_path)
+    tp2_img.save(tp2_path)
+
+    assert tp1_img.width == width and tp1_img.height == height
+    assert tp2_img.width == width and tp2_img.height == height
+
+    mean_abs_diff, max_abs_diff = _diff_metrics(tp1_img, tp2_img)
+    mean_threshold = 3e-2
+    max_threshold = 3.5e-1
+    print(
+        "Z-Image TP image diff stats (TP=1 vs TP=2): "
+        f"mean_abs_diff={mean_abs_diff:.6e}, max_abs_diff={max_abs_diff:.6e}; "
+        f"thresholds: mean<={mean_threshold:.6e}, max<={max_threshold:.6e}; "
+        f"tp1_img={tp1_path}, tp2_img={tp2_path}"
+    )
+    assert mean_abs_diff <= mean_threshold and max_abs_diff <= max_threshold, (
+        f"Image diff exceeded threshold: mean_abs_diff={mean_abs_diff:.6e}, max_abs_diff={max_abs_diff:.6e} "
+        f"(thresholds: mean<={mean_threshold:.6e}, max<={max_threshold:.6e})"
+    )
+
+    print(f"Z-Image TP perf (lower is better): tp1_time_s={tp1_time_s:.6f}, tp2_time_s={tp2_time_s:.6f}")
+    assert tp2_time_s < tp1_time_s, f"Expected TP=2 to be faster than TP=1 (tp1={tp1_time_s}, tp2={tp2_time_s})"