# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import asyncio import queue import threading from types import SimpleNamespace import pytest import torch from pytest_mock import MockerFixture from vllm_omni.diffusion.data import DiffusionOutput, DiffusionRequestAbortedError from vllm_omni.diffusion.diffusion_engine import DiffusionEngine from vllm_omni.diffusion.request import OmniDiffusionRequest from vllm_omni.diffusion.sched import ( DiffusionRequestStatus, RequestScheduler, Scheduler, SchedulerInterface, StepScheduler, ) from vllm_omni.diffusion.sched.interface import CachedRequestData, NewRequestData from vllm_omni.diffusion.worker.utils import RunnerOutput from vllm_omni.inputs.data import OmniDiffusionSamplingParams pytestmark = [pytest.mark.core_model, pytest.mark.cpu, pytest.mark.diffusion] def _make_request(req_id: str) -> OmniDiffusionRequest: return OmniDiffusionRequest( prompts=[f"prompt_{req_id}"], sampling_params=OmniDiffusionSamplingParams(num_inference_steps=1), request_id=req_id, ) def _make_request_output(req_id: str, *, error: str | None = None, finished: bool = True): return RunnerOutput( request_id=req_id, step_index=None, finished=finished, result=DiffusionOutput(output=None, error=error), ) def _make_step_output( req_id: str, step_index: int, *, finished: bool = False, error: str | None = None, ): return RunnerOutput( request_id=req_id, step_index=step_index, finished=finished, result=DiffusionOutput(output=None, error=error) if error is not None else None, ) def _make_step_request( req_id: str, *, num_inference_steps: int = 4, step_index: int | None = None, sampling_params: OmniDiffusionSamplingParams | None = None, ) -> OmniDiffusionRequest: return OmniDiffusionRequest( prompts=[f"prompt_{req_id}"], sampling_params=sampling_params or OmniDiffusionSamplingParams( num_inference_steps=num_inference_steps, step_index=step_index, ), request_id=req_id, ) def _new_ids(sched_output) -> list[str]: return [req.request_id for req in sched_output.scheduled_new_reqs] def _cached_ids(sched_output) -> list[str]: return list(sched_output.scheduled_cached_reqs.request_ids) class _StubScheduler(SchedulerInterface): def __init__(self, request: OmniDiffusionRequest, output) -> None: self._request = request self._output = output self.initialized_with = None self._request_id = request.request_id self._state = None self._scheduled = False self.max_num_running_reqs = 1 def initialize(self, od_config) -> None: self.initialized_with = od_config def add_request(self, request: OmniDiffusionRequest) -> str: assert request is self._request self._state = SimpleNamespace(request_id=self._request_id, req=request) return self._request_id def schedule(self): if self._scheduled or self._state is None: return SimpleNamespace( scheduled_new_reqs=[], scheduled_cached_reqs=CachedRequestData.make_empty(), scheduled_request_ids=[], is_empty=True, ) self._scheduled = True return SimpleNamespace( scheduled_new_reqs=[NewRequestData.from_state(self._state)], scheduled_cached_reqs=CachedRequestData.make_empty(), scheduled_request_ids=[self._state.request_id], is_empty=False, ) def update_from_output(self, sched_output, output) -> set[str]: del sched_output assert output is self._output self._state.status = DiffusionRequestStatus.FINISHED_COMPLETED return {self._request_id} def has_requests(self) -> bool: return not self._scheduled def get_request_state(self, request_id: str): del request_id return self._state def pop_request_state(self, request_id: str): del request_id return self._state def preempt_request(self, request_id: str) -> bool: del request_id return False def finish_requests(self, request_ids, status) -> None: del request_ids, status return None def close(self) -> None: return None class TestGetSamplingParamsKey: """Pure-function tests for the batch-compatibility key builder.""" @staticmethod def _make(lora_int_id: int | None = None, lora_scale: float = 1.0) -> OmniDiffusionRequest: from vllm_omni.lora.request import LoRARequest sp = OmniDiffusionSamplingParams(num_inference_steps=2) if lora_int_id is not None: sp.lora_request = LoRARequest( lora_name=f"adapter-{lora_int_id}", lora_int_id=lora_int_id, lora_path=f"/tmp/lora-{lora_int_id}", ) sp.lora_scale = lora_scale return OmniDiffusionRequest( prompts=["prompt"], sampling_params=sp, request_id=f"req-{lora_int_id}-{lora_scale}", ) def test_distinguishes_lora_id(self) -> None: from vllm_omni.diffusion.sched.base_scheduler import get_sampling_params_key assert get_sampling_params_key(self._make(lora_int_id=1)) != get_sampling_params_key(self._make(lora_int_id=2)) def test_distinguishes_lora_scale(self) -> None: from vllm_omni.diffusion.sched.base_scheduler import get_sampling_params_key assert get_sampling_params_key(self._make(lora_int_id=1, lora_scale=0.5)) != get_sampling_params_key( self._make(lora_int_id=1, lora_scale=1.0) ) def test_treats_no_lora_as_distinct_bucket(self) -> None: from vllm_omni.diffusion.sched.base_scheduler import get_sampling_params_key assert get_sampling_params_key(self._make(lora_int_id=None)) != get_sampling_params_key( self._make(lora_int_id=1) ) def test_equal_for_same_lora_identity(self) -> None: from vllm_omni.diffusion.sched.base_scheduler import get_sampling_params_key a = get_sampling_params_key(self._make(lora_int_id=1, lora_scale=0.5)) b = get_sampling_params_key(self._make(lora_int_id=1, lora_scale=0.5)) assert a == b class TestRequestScheduler: def setup_method(self) -> None: self.scheduler: RequestScheduler = RequestScheduler() self.scheduler.initialize(SimpleNamespace()) def test_single_request_success_lifecycle(self) -> None: req_id = self.scheduler.add_request(_make_request("a")) assert self.scheduler.get_request_state(req_id).status == DiffusionRequestStatus.WAITING sched_output = self.scheduler.schedule() assert _new_ids(sched_output) == [req_id] assert _cached_ids(sched_output) == [] assert sched_output.num_running_reqs == 1 assert sched_output.num_waiting_reqs == 0 finished = self.scheduler.update_from_output(sched_output, _make_request_output(req_id)) assert finished == {req_id} assert self.scheduler.get_request_state(req_id).status == DiffusionRequestStatus.FINISHED_COMPLETED assert self.scheduler.has_requests() is False def test_error_output_marks_finished_error(self) -> None: req_id = self.scheduler.add_request(_make_request("err")) sched_output = self.scheduler.schedule() finished = self.scheduler.update_from_output( sched_output, _make_request_output(req_id, error="worker failed"), ) assert finished == {req_id} state = self.scheduler.get_request_state(req_id) assert state.status == DiffusionRequestStatus.FINISHED_ERROR assert state.error == "worker failed" def test_empty_output_without_error_marks_completed(self) -> None: req_id = self.scheduler.add_request(_make_request("empty")) sched_output = self.scheduler.schedule() finished = self.scheduler.update_from_output(sched_output, _make_request_output(req_id)) assert finished == {req_id} assert self.scheduler.get_request_state(req_id).status == DiffusionRequestStatus.FINISHED_COMPLETED def test_fifo_single_request_scheduling(self) -> None: req_id_a = self.scheduler.add_request(_make_request("a")) req_id_b = self.scheduler.add_request(_make_request("b")) first = self.scheduler.schedule() assert _new_ids(first) == [req_id_a] assert _cached_ids(first) == [] assert first.num_running_reqs == 1 assert first.num_waiting_reqs == 1 # Request A is still running; scheduling again should not pull B. second = self.scheduler.schedule() assert _new_ids(second) == [] assert _cached_ids(second) == [req_id_a] assert second.num_running_reqs == 1 assert second.num_waiting_reqs == 1 self.scheduler.update_from_output(first, _make_request_output(req_id_a)) third = self.scheduler.schedule() assert _new_ids(third) == [req_id_b] assert _cached_ids(third) == [] assert third.num_running_reqs == 1 assert third.num_waiting_reqs == 0 def test_batches_compatible_requests_up_to_max_num_seqs(self) -> None: scheduler = RequestScheduler() scheduler.initialize(SimpleNamespace(max_num_seqs=2)) req_id_a = scheduler.add_request(_make_request("a")) req_id_b = scheduler.add_request(_make_request("b")) sched_output = scheduler.schedule() assert _new_ids(sched_output) == [req_id_a, req_id_b] assert sched_output.num_running_reqs == 2 assert sched_output.num_waiting_reqs == 0 def test_incompatible_waiting_head_blocks_later_compatible_request(self) -> None: scheduler = RequestScheduler() scheduler.initialize(SimpleNamespace(max_num_seqs=3)) req_id_a = scheduler.add_request(_make_request("a")) req_id_b = scheduler.add_request( OmniDiffusionRequest( prompts=["prompt_b"], sampling_params=OmniDiffusionSamplingParams(width=768), request_id="b", ) ) scheduler.add_request(_make_request("c")) first = scheduler.schedule() assert _new_ids(first) == [req_id_a] assert first.num_running_reqs == 1 assert first.num_waiting_reqs == 2 scheduler.update_from_output(first, _make_request_output(req_id_a)) second = scheduler.schedule() assert _new_ids(second) == [req_id_b] assert second.num_running_reqs == 1 assert second.num_waiting_reqs == 1 def test_abort_request_for_waiting_and_running(self) -> None: req_id_a = self.scheduler.add_request(_make_request("a")) req_id_b = self.scheduler.add_request(_make_request("b")) # Abort waiting request. self.scheduler.finish_requests(req_id_b, DiffusionRequestStatus.FINISHED_ABORTED) state_b = self.scheduler.get_request_state(req_id_b) assert state_b.status == DiffusionRequestStatus.FINISHED_ABORTED first = self.scheduler.schedule() assert first.finished_req_ids == {req_id_b} # A should still run normally. assert _new_ids(first) == [req_id_a] # B is already marked finished aborted, scheduling again should not pull it. second = self.scheduler.schedule() assert second.finished_req_ids == set() # Abort running request. self.scheduler.finish_requests(req_id_a, DiffusionRequestStatus.FINISHED_ABORTED) state_a = self.scheduler.get_request_state(req_id_a) assert state_a.status == DiffusionRequestStatus.FINISHED_ABORTED assert self.scheduler.has_requests() is False assert self.scheduler.schedule().scheduled_request_ids == [] def test_has_requests_state_transition(self) -> None: assert self.scheduler.has_requests() is False req_id = self.scheduler.add_request(_make_request("has")) assert self.scheduler.has_requests() is True sched_output = self.scheduler.schedule() assert self.scheduler.has_requests() is True self.scheduler.update_from_output(sched_output, _make_request_output(req_id)) assert self.scheduler.get_request_state(req_id).status == DiffusionRequestStatus.FINISHED_COMPLETED assert self.scheduler.has_requests() is False def test_request_id_is_scheduler_key(self) -> None: request = OmniDiffusionRequest( prompts=["prompt_map_a", "prompt_map_b"], sampling_params=OmniDiffusionSamplingParams(num_inference_steps=1), request_id="map-parent", ) request_id = self.scheduler.add_request(request) assert request_id == "map-parent" state = self.scheduler.get_request_state("map-parent") assert state.request_id == "map-parent" self.scheduler.pop_request_state("map-parent") assert self.scheduler.get_request_state("map-parent") is None def test_duplicate_request_id_is_rejected(self) -> None: self.scheduler.add_request(_make_request("dup")) with pytest.raises(ValueError, match="request_id 'dup' is already active"): self.scheduler.add_request(_make_request("dup")) class TestDiffusionEngine: def test_add_req_and_wait_for_response_single_path(self, mocker: MockerFixture) -> None: engine = DiffusionEngine.__new__(DiffusionEngine) engine.scheduler = RequestScheduler() engine.scheduler.initialize(SimpleNamespace()) engine._rpc_lock = threading.RLock() engine._cv = threading.Condition(engine._rpc_lock) engine._closed = False engine.abort_queue = queue.Queue() request = _make_request("engine") runner_output = _make_request_output("engine") engine.execute_fn = mocker.Mock(return_value=runner_output) output = engine.add_req_and_wait_for_response(request) assert output is runner_output.result engine.execute_fn.assert_called_once() def test_supports_scheduler_interface_injection(self, mocker: MockerFixture) -> None: request = _make_request("engine_iface") runner_output = _make_request_output("engine_iface") scheduler = _StubScheduler(request, runner_output) engine = DiffusionEngine.__new__(DiffusionEngine) engine.scheduler = scheduler engine._rpc_lock = threading.RLock() engine._cv = threading.Condition(engine._rpc_lock) engine._closed = False engine.abort_queue = queue.Queue() engine.execute_fn = mocker.Mock(return_value=runner_output) output = engine.add_req_and_wait_for_response(request) assert output is runner_output.result engine.execute_fn.assert_called_once() def test_initializes_injected_scheduler( self, monkeypatch: pytest.MonkeyPatch, mocker: MockerFixture, ) -> None: request = _make_request("init") scheduler = _StubScheduler(request, DiffusionOutput(output=None)) od_config = SimpleNamespace(model_class_name="mock_model") fake_executor_cls = mocker.Mock(return_value=mocker.Mock()) monkeypatch.setattr( "vllm_omni.diffusion.diffusion_engine.get_diffusion_post_process_func", lambda *args, **kwargs: None, ) monkeypatch.setattr( "vllm_omni.diffusion.diffusion_engine.get_diffusion_pre_process_func", lambda *args, **kwargs: None, ) monkeypatch.setattr( "vllm_omni.diffusion.diffusion_engine.DiffusionExecutor.get_class", lambda *args, **kwargs: fake_executor_cls, ) monkeypatch.setattr(DiffusionEngine, "_dummy_run", lambda self: None) DiffusionEngine(od_config, scheduler=scheduler) assert scheduler.initialized_with is od_config fake_executor_cls.assert_called_once_with(od_config) def test_scheduler_alias_keeps_default_request_scheduler(self) -> None: scheduler = Scheduler() scheduler.initialize(SimpleNamespace()) req_id = scheduler.add_request(_make_request("alias")) sched_output = scheduler.schedule() finished = scheduler.update_from_output(sched_output, _make_request_output(req_id)) assert req_id in finished assert scheduler.get_request_state(req_id).status == DiffusionRequestStatus.FINISHED_COMPLETED @pytest.mark.asyncio async def test_step_raises_aborted_error(self, mocker: MockerFixture) -> None: engine = DiffusionEngine.__new__(DiffusionEngine) engine._closed = False engine._loop_started = True engine._init_lock = asyncio.Lock() engine.main_loop = asyncio.get_running_loop() engine.stop_event = threading.Event() engine.pre_process_func = None engine.async_add_req_and_wait_for_response = mocker.AsyncMock( return_value=DiffusionOutput(aborted=True, abort_message="Request req-abort aborted.") ) with pytest.raises(DiffusionRequestAbortedError, match="Request req-abort aborted"): await engine.step(_make_request("req-abort")) def test_abort_queue_marks_request_finished_aborted(self) -> None: engine = DiffusionEngine.__new__(DiffusionEngine) engine._rpc_lock = threading.RLock() engine._cv = threading.Condition(engine._rpc_lock) engine._closed = False engine.scheduler = RequestScheduler() engine.scheduler.initialize(SimpleNamespace()) engine.abort_queue = queue.Queue() req_id = engine.scheduler.add_request(_make_request("req-abort")) engine.abort("req-abort") engine._process_aborts_queue() assert engine.scheduler.get_request_state(req_id).status == DiffusionRequestStatus.FINISHED_ABORTED def test_finalize_finished_request_returns_aborted_output(self) -> None: engine = DiffusionEngine.__new__(DiffusionEngine) engine.scheduler = RequestScheduler() engine.scheduler.initialize(SimpleNamespace()) req_id = engine.scheduler.add_request(_make_request("req-finalize")) engine.scheduler.finish_requests(req_id, DiffusionRequestStatus.FINISHED_ABORTED) output = engine._finalize_finished_request(req_id) assert output.aborted is True assert output.abort_message == "Request req-finalize aborted." def test_initializes_step_scheduler_when_step_execution_enabled( self, monkeypatch: pytest.MonkeyPatch, mocker: MockerFixture, ) -> None: od_config = SimpleNamespace(model_class_name="mock_model") od_config.step_execution = True fake_executor = mocker.Mock() fake_executor_cls = mocker.Mock(return_value=fake_executor) monkeypatch.setattr( "vllm_omni.diffusion.diffusion_engine.get_diffusion_post_process_func", lambda *args, **kwargs: None, ) monkeypatch.setattr( "vllm_omni.diffusion.diffusion_engine.get_diffusion_pre_process_func", lambda *args, **kwargs: None, ) monkeypatch.setattr( "vllm_omni.diffusion.diffusion_engine.DiffusionExecutor.get_class", lambda *args, **kwargs: fake_executor_cls, ) monkeypatch.setattr(DiffusionEngine, "_dummy_run", lambda self: None) engine = DiffusionEngine(od_config) assert isinstance(engine.scheduler, StepScheduler) assert engine.execute_fn is fake_executor.execute_step fake_executor_cls.assert_called_once_with(od_config) def test_dummy_run_raises_on_output_error(self, mocker: MockerFixture) -> None: engine = DiffusionEngine.__new__(DiffusionEngine) engine.od_config = SimpleNamespace(model_class_name="mock_model", diffusion_load_format="default") engine.pre_process_func = None engine.add_req_and_wait_for_response = mocker.Mock(return_value=DiffusionOutput(error="boom")) with pytest.raises(RuntimeError, match="Dummy run failed: boom"): engine._dummy_run() class TestStepScheduler: def setup_method(self) -> None: self.scheduler: StepScheduler = StepScheduler() self.scheduler.initialize(SimpleNamespace()) def test_single_request_step_lifecycle(self) -> None: request = _make_step_request("step", num_inference_steps=3) req_id = self.scheduler.add_request(request) first = self.scheduler.schedule() assert _new_ids(first) == [req_id] assert _cached_ids(first) == [] assert first.num_running_reqs == 1 assert first.num_waiting_reqs == 0 finished = self.scheduler.update_from_output(first, _make_step_output(req_id, step_index=1)) assert finished == set() assert self.scheduler.get_request_state(req_id).status == DiffusionRequestStatus.RUNNING assert request.sampling_params.step_index == 1 assert self.scheduler.has_requests() is True second = self.scheduler.schedule() assert _new_ids(second) == [] assert _cached_ids(second) == [req_id] assert second.num_running_reqs == 1 assert second.num_waiting_reqs == 0 finished = self.scheduler.update_from_output(second, _make_step_output(req_id, step_index=2)) assert finished == set() assert request.sampling_params.step_index == 2 third = self.scheduler.schedule() assert _new_ids(third) == [] assert _cached_ids(third) == [req_id] finished = self.scheduler.update_from_output( third, _make_step_output(req_id, step_index=3, finished=True), ) assert finished == {req_id} assert self.scheduler.get_request_state(req_id).status == DiffusionRequestStatus.FINISHED_COMPLETED assert request.sampling_params.step_index == 3 assert self.scheduler.has_requests() is False def test_fifo_single_request_scheduling(self) -> None: req_id_a = self.scheduler.add_request(_make_step_request("a", num_inference_steps=2)) req_id_b = self.scheduler.add_request(_make_step_request("b", num_inference_steps=2)) first = self.scheduler.schedule() assert _new_ids(first) == [req_id_a] assert _cached_ids(first) == [] assert first.num_running_reqs == 1 assert first.num_waiting_reqs == 1 finished = self.scheduler.update_from_output(first, _make_step_output(req_id_a, step_index=1)) assert finished == set() second = self.scheduler.schedule() assert _new_ids(second) == [] assert _cached_ids(second) == [req_id_a] assert second.num_running_reqs == 1 assert second.num_waiting_reqs == 1 finished = self.scheduler.update_from_output( second, _make_step_output(req_id_a, step_index=2, finished=True), ) assert finished == {req_id_a} third = self.scheduler.schedule() assert _new_ids(third) == [req_id_b] assert _cached_ids(third) == [] assert third.num_running_reqs == 1 assert third.num_waiting_reqs == 0 def test_error_output_marks_finished_error(self) -> None: req_id = self.scheduler.add_request(_make_step_request("err", num_inference_steps=3)) sched_output = self.scheduler.schedule() assert _new_ids(sched_output) == [req_id] finished = self.scheduler.update_from_output( sched_output, _make_step_output(req_id, step_index=1, finished=True, error="worker failed"), ) assert finished == {req_id} state = self.scheduler.get_request_state(req_id) assert state.status == DiffusionRequestStatus.FINISHED_ERROR assert state.error == "worker failed" assert self.scheduler.has_requests() is False def test_missing_step_index_marks_finished_error(self) -> None: req_id = self.scheduler.add_request(_make_step_request("missing", num_inference_steps=3)) sched_output = self.scheduler.schedule() finished = self.scheduler.update_from_output( sched_output, RunnerOutput( request_id=req_id, step_index=None, finished=True, result=None, ), ) assert finished == {req_id} state = self.scheduler.get_request_state(req_id) assert state.status == DiffusionRequestStatus.FINISHED_ERROR assert state.error == "Missing step_index in RunnerOutput" def test_abort_request_for_waiting_and_running(self) -> None: req_id_a = self.scheduler.add_request(_make_step_request("a", num_inference_steps=2)) req_id_b = self.scheduler.add_request(_make_step_request("b", num_inference_steps=2)) self.scheduler.finish_requests(req_id_b, DiffusionRequestStatus.FINISHED_ABORTED) assert self.scheduler.get_request_state(req_id_b).status == DiffusionRequestStatus.FINISHED_ABORTED running = self.scheduler.schedule() assert _new_ids(running) == [req_id_a] self.scheduler.finish_requests(req_id_a, DiffusionRequestStatus.FINISHED_ABORTED) assert self.scheduler.get_request_state(req_id_a).status == DiffusionRequestStatus.FINISHED_ABORTED assert self.scheduler.has_requests() is False def test_has_requests_state_transition(self) -> None: assert self.scheduler.has_requests() is False req_id = self.scheduler.add_request(_make_step_request("has", num_inference_steps=2)) assert self.scheduler.has_requests() is True sched_output = self.scheduler.schedule() assert self.scheduler.has_requests() is True finished = self.scheduler.update_from_output( sched_output, _make_step_output(req_id, step_index=2, finished=True), ) assert finished == {req_id} assert self.scheduler.get_request_state(req_id).status == DiffusionRequestStatus.FINISHED_COMPLETED assert self.scheduler.has_requests() is False def test_scheduled_request_aborted_before_update_is_returned_finished(self) -> None: req_id = self.scheduler.add_request(_make_step_request("abort-late", num_inference_steps=2)) sched_output = self.scheduler.schedule() self.scheduler.finish_requests(req_id, DiffusionRequestStatus.FINISHED_ABORTED) finished = self.scheduler.update_from_output( sched_output, _make_step_output(req_id, step_index=1), ) assert finished == {req_id} assert self.scheduler.get_request_state(req_id).status == DiffusionRequestStatus.FINISHED_ABORTED def test_batches_compatible_step_requests(self) -> None: scheduler = StepScheduler() scheduler.initialize(SimpleNamespace(max_num_seqs=2)) req_a = scheduler.add_request(_make_step_request("a")) req_b = scheduler.add_request(_make_step_request("b")) sched_output = scheduler.schedule() assert _new_ids(sched_output) == [req_a, req_b] assert sched_output.num_running_reqs == 2 assert sched_output.num_waiting_reqs == 0 def test_step_batch_allows_different_num_inference_steps(self) -> None: scheduler = StepScheduler() scheduler.initialize(SimpleNamespace(max_num_seqs=2)) req_a = scheduler.add_request(_make_step_request("a", num_inference_steps=2)) req_b = scheduler.add_request(_make_step_request("b", num_inference_steps=4)) sched_output = scheduler.schedule() assert _new_ids(sched_output) == [req_a, req_b] assert sched_output.num_running_reqs == 2 assert sched_output.num_waiting_reqs == 0 def test_step_batch_rejects_different_sampling_key(self) -> None: scheduler = StepScheduler() scheduler.initialize(SimpleNamespace(max_num_seqs=3)) req_a = scheduler.add_request(_make_step_request("a")) req_b = scheduler.add_request( _make_step_request( "b", sampling_params=OmniDiffusionSamplingParams( height=768, num_inference_steps=4, ), ) ) scheduler.add_request(_make_step_request("c")) sched_output = scheduler.schedule() assert _new_ids(sched_output) == [req_a] assert sched_output.num_running_reqs == 1 assert sched_output.num_waiting_reqs == 2 scheduler.update_from_output( sched_output, _make_step_output(req_a, step_index=4, finished=True), ) second = scheduler.schedule() assert _new_ids(second) == [req_b] assert second.num_running_reqs == 1 assert second.num_waiting_reqs == 1 def test_step_batch_co_schedules_requests_sharing_lora(self) -> None: """Multiple requests with the same LoRA (id + scale) co-batch.""" from vllm_omni.lora.request import LoRARequest scheduler = StepScheduler() scheduler.initialize(SimpleNamespace(max_num_seqs=3)) lora = LoRARequest(lora_name="adapter", lora_int_id=42, lora_path="/tmp/lora") def _with_lora(req_id: str) -> OmniDiffusionRequest: sp = OmniDiffusionSamplingParams(num_inference_steps=4) sp.lora_request = lora sp.lora_scale = 0.5 return _make_step_request(req_id, sampling_params=sp) req_a = scheduler.add_request(_with_lora("a")) req_b = scheduler.add_request(_with_lora("b")) req_c = scheduler.add_request(_with_lora("c")) sched_output = scheduler.schedule() assert _new_ids(sched_output) == [req_a, req_b, req_c] assert sched_output.num_running_reqs == 3 assert sched_output.num_waiting_reqs == 0 def test_step_batch_separates_requests_with_different_lora_ids(self) -> None: """Different LoRA adapters → distinct batches admitted in FIFO order.""" from vllm_omni.lora.request import LoRARequest scheduler = StepScheduler() scheduler.initialize(SimpleNamespace(max_num_seqs=4)) lora_a = LoRARequest(lora_name="adapter-A", lora_int_id=1, lora_path="/tmp/lora-a") lora_b = LoRARequest(lora_name="adapter-B", lora_int_id=2, lora_path="/tmp/lora-b") def _build(req_id: str, lora: LoRARequest) -> OmniDiffusionRequest: sp = OmniDiffusionSamplingParams(num_inference_steps=2) sp.lora_request = lora return _make_step_request(req_id, sampling_params=sp) req_a1 = scheduler.add_request(_build("a1", lora_a)) req_b1 = scheduler.add_request(_build("b1", lora_b)) req_a2 = scheduler.add_request(_build("a2", lora_a)) # Strict FIFO admission: a1 starts; b1 (different LoRA) blocks the # queue head, so a2 (compatible with a1) is *not* skipped ahead. first = scheduler.schedule() assert _new_ids(first) == [req_a1] assert first.num_waiting_reqs == 2 # Drain a1 → b1 becomes head-of-line and is admitted with its LoRA. scheduler.update_from_output(first, _make_step_output(req_a1, step_index=2, finished=True)) second = scheduler.schedule() assert _new_ids(second) == [req_b1] assert second.num_waiting_reqs == 1 # Drain b1 → a2 is admitted next; LoRA-A is re-activated for it. scheduler.update_from_output(second, _make_step_output(req_b1, step_index=2, finished=True)) third = scheduler.schedule() assert _new_ids(third) == [req_a2] assert third.num_waiting_reqs == 0 def test_step_batch_separates_requests_with_different_lora_scale(self) -> None: """Same adapter id but different scales → still separate batches.""" from vllm_omni.lora.request import LoRARequest scheduler = StepScheduler() scheduler.initialize(SimpleNamespace(max_num_seqs=4)) lora = LoRARequest(lora_name="adapter", lora_int_id=7, lora_path="/tmp/lora") def _build(req_id: str, scale: float) -> OmniDiffusionRequest: sp = OmniDiffusionSamplingParams(num_inference_steps=2) sp.lora_request = lora sp.lora_scale = scale return _make_step_request(req_id, sampling_params=sp) req_full = scheduler.add_request(_build("full", 1.0)) req_half = scheduler.add_request(_build("half", 0.5)) sched_output = scheduler.schedule() admitted = _new_ids(sched_output) assert admitted == [req_full] assert req_half not in admitted assert sched_output.num_waiting_reqs == 1 def test_step_batch_separates_lora_from_no_lora(self) -> None: """A LoRA request and a no-LoRA request do not share a batch.""" from vllm_omni.lora.request import LoRARequest scheduler = StepScheduler() scheduler.initialize(SimpleNamespace(max_num_seqs=4)) lora = LoRARequest(lora_name="adapter", lora_int_id=3, lora_path="/tmp/lora") sp_with = OmniDiffusionSamplingParams(num_inference_steps=2) sp_with.lora_request = lora req_with = scheduler.add_request(_make_step_request("with", sampling_params=sp_with)) req_without = scheduler.add_request(_make_step_request("without", num_inference_steps=2)) sched_output = scheduler.schedule() admitted = _new_ids(sched_output) assert admitted == [req_with] assert req_without not in admitted assert sched_output.num_waiting_reqs == 1 def test_preempt_request_preserves_step_index(self) -> None: request = _make_step_request("preempt", num_inference_steps=3) req_id = self.scheduler.add_request(request) first = self.scheduler.schedule() assert self.scheduler.update_from_output(first, _make_step_output(req_id, step_index=1)) == set() assert request.sampling_params.step_index == 1 second = self.scheduler.schedule() assert _cached_ids(second) == [req_id] assert self.scheduler.preempt_request(req_id) is True assert self.scheduler.get_request_state(req_id).status == DiffusionRequestStatus.PREEMPTED assert request.sampling_params.step_index == 1 third = self.scheduler.schedule() assert _cached_ids(third) == [req_id] assert request.sampling_params.step_index == 1 @pytest.mark.parametrize( ("sampling_params", "expected_steps"), [ ( OmniDiffusionSamplingParams( timesteps=torch.tensor([1.0, 0.5, 0.0]), sigmas=[1.0, 0.5, 0.25, 0.0], num_inference_steps=5, ), 3, ), ( OmniDiffusionSamplingParams( sigmas=[1.0, 0.5], num_inference_steps=5, ), 2, ), ( OmniDiffusionSamplingParams( num_inference_steps=4, ), 4, ), ], ) def test_total_steps_priority(self, sampling_params: OmniDiffusionSamplingParams, expected_steps: int) -> None: request = _make_step_request("priority", sampling_params=sampling_params) req_id = self.scheduler.add_request(request) for _ in range(expected_steps - 1): sched_output = self.scheduler.schedule() assert sched_output.scheduled_request_ids == [req_id] next_step = request.sampling_params.step_index + 1 assert ( self.scheduler.update_from_output( sched_output, _make_step_output(req_id, step_index=next_step), ) == set() ) final_output = self.scheduler.schedule() assert final_output.scheduled_request_ids == [req_id] assert self.scheduler.update_from_output( final_output, _make_step_output(req_id, step_index=expected_steps, finished=True), ) == {req_id} assert self.scheduler.get_request_state(req_id).status == DiffusionRequestStatus.FINISHED_COMPLETED @pytest.mark.parametrize( "sampling_params", [ OmniDiffusionSamplingParams(num_inference_steps=0), OmniDiffusionSamplingParams(num_inference_steps=3, step_index=3), OmniDiffusionSamplingParams(num_inference_steps=3, step_index=-1), ], ) def test_rejects_invalid_initial_step_state(self, sampling_params: OmniDiffusionSamplingParams) -> None: request = _make_step_request("invalid", sampling_params=sampling_params) with pytest.raises(ValueError): self.scheduler.add_request(request)