[Test] Initialize tensor-parallel groups in native SANA accuracy fixtures

Signed-off-by: tzhouam <tzhouam@connect.ust.hk>

tests/diffusion/distributed/test_comm.py

@@ -14,7 +14,6 @@ from typing import Literal
 import pytest
 import torch
 
-from tests.helpers.distributed import configure_cpu_collective_worker, start_collective_workers
 from tests.helpers.mark import hardware_marks
 from vllm_omni.diffusion.distributed.comm import RingComm, SeqAllToAll4D, SeqAllToAll5D
 from vllm_omni.diffusion.distributed.parallel_state import (
@@ -57,8 +56,6 @@ def _init_worker(
             "MASTER_PORT": str(master_port),
         }
     )
-    if device_kind == "cpu":
-        configure_cpu_collective_worker()
     backend = "gloo" if device_kind == "cpu" else None
     init_distributed_environment(backend=backend)
 
@@ -230,10 +227,6 @@ def _run_ring_p2p(
             atol=atol,
             msg=f"[Rank {local_rank}] Ring P2P data mismatch",
         )
-        if device_kind == "cpu":
-            # P2P completion only synchronizes neighbors. Keep fast CPU ranks
-            # alive until every rank has finished constructing and using groups.
-            torch.distributed.barrier()
     finally:
         destroy_distributed_env()
 
@@ -250,7 +243,7 @@ def _spawn_4d_identity(
     device_kind: DeviceKind,
     master_port: int,
 ) -> None:
-    start_collective_workers(
+    torch.multiprocessing.spawn(
         _run_4d_identity,
         args=(
             world_size,
@@ -264,7 +257,6 @@ def _spawn_4d_identity(
             master_port,
         ),
         nprocs=world_size,
-        device_kind=device_kind,
     )
 
 
@@ -280,7 +272,7 @@ def _spawn_5d_identity(
     device_kind: DeviceKind,
     master_port: int,
 ) -> None:
-    start_collective_workers(
+    torch.multiprocessing.spawn(
         _run_5d_identity,
         args=(
             world_size,
@@ -294,7 +286,6 @@ def _spawn_5d_identity(
             master_port,
         ),
         nprocs=world_size,
-        device_kind=device_kind,
     )
 
 
@@ -308,11 +299,10 @@ def _spawn_ring_p2p(
     device_kind: DeviceKind,
     master_port: int,
 ) -> None:
-    start_collective_workers(
+    torch.multiprocessing.spawn(
         _run_ring_p2p,
         args=(world_size, dtype, batch_size, num_heads, head_size, device_kind, master_port),
         nprocs=world_size,
-        device_kind=device_kind,
     )
 
 
@@ -509,9 +499,8 @@ def _run_fused_qkv_matches_three_4d(
 @pytest.mark.cpu
 @pytest.mark.parametrize("world_size", [2, 4])
 def test_fused_qkv_all_to_all_matches_three_4d_exchanges_cpu(world_size: int) -> None:
-    start_collective_workers(
+    torch.multiprocessing.spawn(
         _run_fused_qkv_matches_three_4d,
         args=(world_size, "cpu", 29613),
         nprocs=world_size,
-        device_kind="cpu",
     )

tests/diffusion/distributed/test_pipeline_parallel.py

@@ -19,7 +19,6 @@ from vllm.utils.network_utils import get_file_store_init_method
 from vllm.v1.worker.gpu_worker import AsyncIntermediateTensors
 
 import vllm_omni.diffusion.distributed.pipeline_parallel as pp_module
-from tests.helpers.distributed import collective_context, start_collective_workers
 from tests.helpers.mark import hardware_marks
 from vllm_omni.diffusion.distributed.cfg_parallel import CFGParallelMixin
 from vllm_omni.diffusion.distributed.parallel_state import (
@@ -40,25 +39,6 @@ DeviceKind = Literal["cpu", "cuda"]
 _UNIT_MARKS = [pytest.mark.core_model, pytest.mark.diffusion, pytest.mark.cpu]
 
 
-@pytest.fixture(autouse=True)
-def _restore_collective_platforms():
-    """CPU baselines run in the parent process; retain its original platforms."""
-    import vllm.platforms
-
-    from vllm_omni.diffusion.distributed import comm, group_coordinator, parallel_state
-
-    original_platform = vllm.platforms.current_platform
-    original_omni_platforms = {
-        module: module.current_omni_platform for module in (comm, group_coordinator, parallel_state)
-    }
-    try:
-        yield
-    finally:
-        vllm.platforms.current_platform = original_platform
-        for module, platform in original_omni_platforms.items():
-            module.current_omni_platform = platform
-
-
 def _cleanup_distributed() -> None:
     """Destroy omni distributed state left dangling after a test.
 
@@ -429,10 +409,6 @@ def init_dist(
     device = _worker_device(local_rank, device_kind)
     if device_kind == "cuda":
         current_omni_platform.set_device(device)
-    else:
-        from tests.helpers.distributed import configure_cpu_collective_worker
-
-        configure_cpu_collective_worker()
     backend = "gloo" if device_kind == "cpu" else None
     init_distributed_environment(
         world_size=world_size,
@@ -524,14 +500,13 @@ def isend_irecv_worker(
 
 
 def _run_isend_irecv(pp_size: int, device_kind: DeviceKind, init_method: str) -> None:
-    mp_context = collective_context(device_kind)
+    mp_context = torch.multiprocessing.get_context("spawn")
     manager = mp_context.Manager()
     q = manager.Queue()
-    start_collective_workers(
+    torch.multiprocessing.spawn(
         isend_irecv_worker,
         args=(pp_size, init_method, device_kind, q),
         nprocs=pp_size,
-        device_kind=device_kind,
     )
     results = {label: tensor for label, tensor in [q.get(), q.get()]}
     torch.testing.assert_close(
@@ -681,16 +656,15 @@ def _run_predict_noise(
 
     baseline_out = compute_single_gpu_baseline(test_config, dtype, do_true_cfg, device_kind)
 
-    mp_context = collective_context(device_kind)
+    mp_context = torch.multiprocessing.get_context("spawn")
     manager = mp_context.Manager()
     pp_q = manager.Queue()
 
     world_size = pp_size * cfg_size
-    start_collective_workers(
+    torch.multiprocessing.spawn(
         predict_noise_worker,
         args=(world_size, init_method, pp_size, cfg_size, do_true_cfg, dtype, test_config, device_kind, pp_q),
         nprocs=world_size,
-        device_kind=device_kind,
     )
 
     pp_out = pp_q.get()
@@ -926,16 +900,15 @@ def _run_scheduler_step(
 
     baseline = compute_scheduler_step_baseline(test_config, do_true_cfg, device_kind)
 
-    mp_context = collective_context(device_kind)
+    mp_context = torch.multiprocessing.get_context("spawn")
     manager = mp_context.Manager()
     q = manager.Queue()
 
     world_size = pp_size * cfg_size
-    start_collective_workers(
+    torch.multiprocessing.spawn(
         scheduler_step_worker,
         args=(world_size, init_method, pp_size, cfg_size, do_true_cfg, test_config, device_kind, q),
         nprocs=world_size,
-        device_kind=device_kind,
     )
 
     result = q.get()

tests/e2e/accuracy/sana_video/conftest.py

@@ -0,0 +1,26 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
+import pytest
+
+
+@pytest.fixture
+def sana_model_parallel(tmp_path):
+    """Construct native parallel layers with real single-rank GPU groups."""
+    from vllm_omni.diffusion.distributed.parallel_state import (
+        destroy_distributed_env,
+        init_distributed_environment,
+        initialize_model_parallel,
+    )
+
+    init_distributed_environment(
+        world_size=1,
+        rank=0,
+        local_rank=0,
+        distributed_init_method=f"file://{tmp_path / 'sana-distributed'}",
+    )
+    try:
+        initialize_model_parallel()
+        yield
+    finally:
+        destroy_distributed_env()

tests/e2e/accuracy/sana_video/test_sana_video_golden.py

@@ -202,6 +202,7 @@ def test_sana_video_transformer_matches_frozen_golden(
     variant: str,
     _hardware,
     accuracy_artifact_root: Path,
+    sana_model_parallel,
 ) -> None:
     del _hardware
     from vllm.config import LoadConfig

tests/e2e/accuracy/sana_video/test_sana_video_pipeline_alignment.py

@@ -189,6 +189,7 @@ def _cfg_prediction(
 def test_sana_video_quick_pipeline_stages_match_diffusers(
     variant: str,
     _hardware,
+    sana_model_parallel,
 ) -> None:
     """Compare the first denoising step without replacing native attention.
 

tests/helpers/cpu_collective_server.py

@@ -1,19 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
-"""Import preload for the CPU-only collective forkserver.
-
-This module is imported in the forkserver process, before it starts workers.
-Its CUDA visibility does not alter the pytest process or CUDA parity workers.
-"""
-
-import os
-
-os.environ["CUDA_VISIBLE_DEVICES"] = ""
-
-import torch  # noqa: E402
-
-# Retain the expensive worker-module imports across CPU process groups.
-from tests.diffusion.distributed import test_comm, test_pipeline_parallel  # noqa: E402, F401
-
-if torch.cuda.is_initialized():
-    raise RuntimeError("CPU collective forkserver must not initialize CUDA")

tests/helpers/distributed.py

@@ -1,62 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
-"""Process and platform setup for isolated collective test workers."""
-
-from collections.abc import Callable
-from multiprocessing.context import BaseContext
-
-import torch
-
-
-def collective_context(device_kind: str) -> BaseContext:
-    """Reuse imports in a clean CPU forkserver; CUDA workers use spawn."""
-    if device_kind == "cpu" and "forkserver" in torch.multiprocessing.get_all_start_methods():
-        torch.multiprocessing.set_forkserver_preload(["tests.helpers.cpu_collective_server"])
-        return torch.multiprocessing.get_context("forkserver")
-    return torch.multiprocessing.get_context("spawn")
-
-
-def start_collective_workers(
-    worker: Callable[..., None], *, args: tuple[object, ...], nprocs: int, device_kind: str
-) -> None:
-    context = collective_context(device_kind)
-    if context.get_start_method() == "spawn":
-        torch.multiprocessing.spawn(worker, args=args, nprocs=nprocs)
-    else:
-        torch.multiprocessing.start_processes(worker, args=args, nprocs=nprocs, start_method=context.get_start_method())
-
-
-def configure_cpu_collective_worker() -> None:
-    """Select CPU platforms inside a spawned worker that only uses Gloo.
-
-    Upstream coordinators resolve local ranks through the selected
-    platform's visible devices. CPU ranks can outnumber visible GPUs, so they
-    must use the CPU platform even when the test host has CUDA installed.
-    The worker still creates real process groups and runs real collectives.
-    """
-    import vllm.platforms
-    from vllm.platforms.cpu import CpuPlatform
-
-    from vllm_omni.diffusion.distributed import comm, group_coordinator, parallel_state
-    from vllm_omni.platforms.interface import UnspecifiedOmniPlatform
-
-    class CpuCollectivePlatform(UnspecifiedOmniPlatform):
-        dist_backend = "gloo"
-
-        @classmethod
-        def get_device_count(cls) -> int:
-            # CPU tensors share one logical device regardless of process rank.
-            return 1
-
-        @classmethod
-        def set_device(cls, device: torch.device) -> None:
-            assert device.type == "cpu"
-
-        @classmethod
-        def synchronize(cls) -> None:
-            pass
-
-    vllm.platforms.current_platform = CpuPlatform()
-    platform = CpuCollectivePlatform()
-    for module in (comm, group_coordinator, parallel_state):
-        module.current_omni_platform = platform