575 lines
24 KiB
Python
575 lines
24 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Tests for midway prompt update across runner, batch, pipeline, and entrypoint layers."""
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from __future__ import annotations
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import asyncio
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import queue
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import time
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from types import SimpleNamespace
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from typing import Any, cast
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from unittest.mock import MagicMock, patch
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import pytest
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import torch
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from tests.engine.test_orchestrator import (
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OrchestratorFixture,
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_build_harness,
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_wait_for,
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)
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from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig
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from vllm_omni.diffusion.inline_stage_diffusion_client import InlineStageDiffusionClient
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from vllm_omni.diffusion.models.helios.pipeline_helios import HeliosPipeline
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from vllm_omni.diffusion.worker.diffusion_model_runner import DiffusionModelRunner
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from vllm_omni.diffusion.worker.input_batch import InputBatch
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from vllm_omni.diffusion.worker.utils import StepRequestState
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from vllm_omni.engine.async_omni_engine import StageRuntimeInfo
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from vllm_omni.engine.messages import (
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ErrorMessage,
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InteractionMessage,
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ShutdownRequestMessage,
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StageSubmissionMessage,
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)
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from vllm_omni.engine.orchestrator import Orchestrator, OrchestratorRequestState
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from vllm_omni.engine.stage_client import StagePoolClient
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from vllm_omni.engine.stage_init_utils import StageMetadata
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from vllm_omni.engine.stage_pool import StagePool
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from vllm_omni.entrypoints.async_omni import AsyncEventResolver, AsyncOmni
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from vllm_omni.inputs.data import OmniDiffusionSamplingParams, OmniInteractionPrompt
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from vllm_omni.outputs import OmniRequestOutput
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pytestmark = [pytest.mark.core_model, pytest.mark.diffusion, pytest.mark.cpu]
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@pytest.fixture
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def pipeline() -> HeliosPipeline:
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pipeline = object.__new__(HeliosPipeline)
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pipeline.device = torch.device("cpu")
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pipeline.transformer = SimpleNamespace(dtype=torch.float32) # pyright: ignore[reportAttributeAccessIssue]
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pipeline.encode_prompt = MagicMock(
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return_value=(
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torch.full((1, 4, 2), 2.0),
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None,
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)
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)
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pipeline._prepare_next_chunk = MagicMock()
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return pipeline
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def _make_diffusion_request_state(*, request_id: str = "req-1") -> StepRequestState:
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state = StepRequestState(
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request_id=request_id,
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sampling=SimpleNamespace(num_outputs_per_prompt=1, max_sequence_length=226), # pyright: ignore[reportArgumentType]
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prompt="hello",
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)
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state.prompt_embeds = torch.zeros(1, 4, 2)
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state.extra = {}
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return state
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def _make_diffusion_model_runner(*, pipeline, streaming_output: bool = True) -> DiffusionModelRunner:
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runner = object.__new__(DiffusionModelRunner)
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runner.pipeline = pipeline
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runner.state_cache = {}
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runner.od_config = SimpleNamespace( # pyright: ignore[reportAttributeAccessIssue]
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model_class_name="HeliosPipeline",
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streaming_output=streaming_output,
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)
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runner._supports_step_mode = lambda: True
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return runner
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def _prompt_interaction(
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prompt: str = "updated",
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*,
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event_id: str = "ui-update-1",
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transition_chunks: int | None = None,
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) -> OmniInteractionPrompt:
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interaction: dict[str, Any] = {
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"event_id": event_id,
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"event": {"prompt": prompt},
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}
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if transition_chunks is not None:
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interaction["transition_chunks"] = transition_chunks
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return cast(OmniInteractionPrompt, interaction)
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class TestPromptUpdateExecution:
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"""Runner, InputBatch cache, and pipeline chunk-boundary behavior."""
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def test_runner_interaction_delegates_to_helios_pipeline(self, pipeline: HeliosPipeline) -> None:
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"""Runner encodes the new prompt and queues pending embeds on the request state."""
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runner = _make_diffusion_model_runner(pipeline=pipeline)
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state = _make_diffusion_request_state()
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runner.state_cache["req-1"] = state
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runner.submit_interaction("req-1", _prompt_interaction(transition_chunks=2))
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pipeline.encode_prompt.assert_called_once() # pyright: ignore[reportAttributeAccessIssue]
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pending = state.extra["pending_prompt_update"]
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assert pending["event_id"] == "ui-update-1"
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assert pending["transition_chunks"] == 2
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assert torch.equal(pending["target_prompt_embeds"], torch.full((1, 4, 2), 2.0))
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def test_runner_prompt_update_rejects_unsupported_pipeline(self) -> None:
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"""Runner rejects prompt updates when the pipeline lacks prompt-update support."""
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class _UnsupportedPipeline:
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supports_step_execution = True
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runner = _make_diffusion_model_runner(pipeline=_UnsupportedPipeline())
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with pytest.raises(ValueError, match="not supported"):
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runner.submit_interaction("req-1", _prompt_interaction())
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def test_runner_prompt_update_rejects_missing_request(self, pipeline) -> None:
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"""Runner rejects prompt updates when no active request state exists."""
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runner = _make_diffusion_model_runner(pipeline=pipeline)
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with pytest.raises(ValueError, match="No active request state"):
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runner.submit_interaction("missing", _prompt_interaction())
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@pytest.mark.parametrize(
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"interaction",
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[
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{},
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{"multi_modal_data": {"camera": {"type": "pose"}}},
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{"event": {"prompt": "updated", "multi_modal_data": {"camera": {"type": "pose"}}}},
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],
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)
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def test_runner_interaction_rejects_structural_payloads_until_implemented(
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self,
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pipeline: HeliosPipeline,
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interaction: dict[str, Any],
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) -> None:
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"""Unsupported interaction dict shapes are preserved to and rejected by the runner."""
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runner = _make_diffusion_model_runner(pipeline=pipeline)
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runner.state_cache["req-1"] = _make_diffusion_request_state()
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with pytest.raises(NotImplementedError, match="Only text-only prompt update interactions"):
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runner.submit_interaction("req-1", cast(Any, interaction))
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def test_input_batch_refreshes_prompt_embeds_on_version_change(self) -> None:
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"""InputBatch rebuilds prompt_embeds when prompt_update_version changes."""
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state = StepRequestState(
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request_id="req-1",
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sampling=SimpleNamespace(), # pyright: ignore[reportArgumentType]
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prompt="hello",
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)
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state.prompt_embeds = torch.zeros(1, 2, 3)
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state.latents = torch.zeros(1, 2)
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state.timesteps = torch.tensor([1.0])
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state.extra["prompt_update_version"] = 0
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batch = InputBatch.make_batch([state])
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assert torch.equal(batch.prompt_embeds, torch.zeros(1, 2, 3)) # pyright: ignore[reportArgumentType]
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state.prompt_embeds = torch.ones(1, 2, 3)
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state.extra["prompt_update_version"] = 1
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refreshed = InputBatch.make_batch([state], cached_batch=batch)
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assert torch.equal(refreshed.prompt_embeds, torch.ones(1, 2, 3)) # pyright: ignore[reportArgumentType]
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def test_helios_prepare_prompt_update_queues_pending_target(self, pipeline: HeliosPipeline) -> None:
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"""HeliosPipeline queues target embeds without mutating current prompt_embeds."""
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state = _make_diffusion_request_state()
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pipeline.prepare_prompt_update(state, "new scene", "ui-update-1", transition_chunks=2)
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pending = state.extra["pending_prompt_update"]
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assert torch.equal(pending["target_prompt_embeds"], torch.full((1, 4, 2), 2.0))
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assert pending["transition_chunks"] == 2
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assert torch.equal(state.prompt_embeds, torch.zeros(1, 4, 2)) # pyright: ignore[reportArgumentType]
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def test_helios_prepare_prompt_update_rejects_before_initial_generation(self, pipeline: HeliosPipeline) -> None:
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"""Reject prompt updates submitted before initial prompt embeds exist."""
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state = _make_diffusion_request_state()
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state.prompt_embeds = None
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with pytest.raises(
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ValueError,
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match="prompt_update is not allowed before initial generation has started",
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):
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pipeline.prepare_prompt_update(state, "new scene", "ui-update-1", transition_chunks=2)
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assert "pending_prompt_update" not in state.extra
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def test_helios_apply_prompt_update_at_chunk_boundary_starts_transition(self, pipeline: HeliosPipeline) -> None:
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"""At chunk boundary, starts transition state and bumps prompt_update_version."""
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state = _make_diffusion_request_state()
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pipeline.prepare_prompt_update(state, "new scene", "ui-update-1", transition_chunks=2)
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pipeline._apply_prompt_update_at_chunk_boundary(state)
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assert state.extra.get("prompt_update_state") is not None
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assert torch.equal(state.prompt_embeds, torch.ones(1, 4, 2)) # pyright: ignore[reportArgumentType]
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assert state.extra["prompt_update_version"] == 1
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assert state.extra["prompt_update_chunk_metadata"] == {
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"started_event_ids": ["ui-update-1"],
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"active_event_ids": ["ui-update-1"],
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"completed_event_ids": [],
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}
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def test_helios_apply_prompt_update_advances_transition_over_chunks(self, pipeline: HeliosPipeline) -> None:
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"""At chunk boundary, interpolates embeds until the target prompt is reached."""
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state = _make_diffusion_request_state()
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pipeline.prepare_prompt_update(state, "new scene", "ui-update-1", transition_chunks=3)
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pipeline._apply_prompt_update_at_chunk_boundary(state)
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assert torch.allclose(state.prompt_embeds, torch.full((1, 4, 2), 2.0 / 3.0)) # pyright: ignore[reportArgumentType]
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for _ in range(2):
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pipeline._apply_prompt_update_at_chunk_boundary(state)
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assert torch.allclose(state.prompt_embeds, torch.full((1, 4, 2), 2.0)) # pyright: ignore[reportArgumentType]
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assert state.extra["prompt_update_version"] == 3
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# Further chunk boundaries after completion must not bump the version.
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pipeline._apply_prompt_update_at_chunk_boundary(state)
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assert state.extra["prompt_update_version"] == 3
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class TestPromptUpdateIntegration:
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"""AsyncOmni prompt update through orchestrator, inline client, and runner."""
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@pytest.mark.asyncio
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async def test_orchestrator_prompt_update_failure_surfaces_non_fatal_error(
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self,
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) -> None:
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"""Worker-side prompt_update rejection is reported without crashing the orchestrator."""
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class _RejectingStagePool:
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async def submit_interaction(self, *args: Any, **kwargs: Any) -> None:
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del args, kwargs
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raise ValueError("prompt embeds are not ready")
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output_queue: asyncio.Queue[ErrorMessage] = asyncio.Queue()
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orchestrator = object.__new__(Orchestrator)
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orchestrator.stage_pools = [_RejectingStagePool()] # pyright: ignore[reportAttributeAccessIssue]
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orchestrator.output_async_queue = output_queue # pyright: ignore[reportAttributeAccessIssue]
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orchestrator.request_states = {"req-1": OrchestratorRequestState(request_id="req-1")}
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await orchestrator._handle_interaction(
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InteractionMessage(
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request_id="req-1",
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interaction=_prompt_interaction("new scene", event_id="ui-update-1", transition_chunks=2),
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)
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)
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msg = output_queue.get_nowait()
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assert msg == ErrorMessage(
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error="Failed interaction for request req-1: prompt embeds are not ready",
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fatal=False,
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request_id="req-1",
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event_id="ui-update-1",
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stage_id=0,
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)
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@pytest.mark.asyncio
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async def test_runner_prompt_update_failure_surfaces_non_fatal_error(self, pipeline: HeliosPipeline) -> None:
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"""Runner-side prepare_prompt_update rejection is reported through the orchestrator."""
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pipeline.prepare_prompt_update = MagicMock( # pyright: ignore[reportAttributeAccessIssue, reportMethodAssignment]
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side_effect=ValueError("prompt embeds are not ready")
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)
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runner = _make_diffusion_model_runner(pipeline=pipeline)
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runner.state_cache["req-1"] = _make_diffusion_request_state(request_id="req-1")
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prompt_update_engine = self._PromptUpdateEngine(self._IncrementalStreamingPipeline(), runner)
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inline_client = self._make_inline_pipeline_client(prompt_update_engine)
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output_queue: asyncio.Queue[ErrorMessage] = asyncio.Queue()
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orchestrator = object.__new__(Orchestrator)
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orchestrator.stage_pools = [ # pyright: ignore[reportAttributeAccessIssue]
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StagePool(0, cast(StagePoolClient, inline_client))
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]
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orchestrator.output_async_queue = output_queue # pyright: ignore[reportAttributeAccessIssue]
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orchestrator.request_states = {"req-1": OrchestratorRequestState(request_id="req-1")}
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try:
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await orchestrator._handle_interaction(
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InteractionMessage(
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request_id="req-1",
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interaction=_prompt_interaction("new scene", event_id="ui-update-1", transition_chunks=2),
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)
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)
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finally:
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inline_client.shutdown()
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pipeline.prepare_prompt_update.assert_called_once_with( # pyright: ignore[reportAttributeAccessIssue]
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runner.state_cache["req-1"],
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"new scene",
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"ui-update-1",
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2,
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)
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msg = output_queue.get_nowait()
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assert msg == ErrorMessage(
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error="Failed interaction for request req-1: prompt embeds are not ready",
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fatal=False,
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request_id="req-1",
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event_id="ui-update-1",
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stage_id=0,
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)
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@pytest.mark.asyncio
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async def test_prompt_update_reaches_runner_from_async_omni(self, pipeline: HeliosPipeline) -> None:
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"""Midway prompt update submitted via AsyncOmni reaches the diffusion runner."""
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streaming_pipeline = self._IncrementalStreamingPipeline()
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runner = _make_diffusion_model_runner(pipeline=pipeline)
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prompt_update_engine = self._PromptUpdateEngine(streaming_pipeline, runner)
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inline_client = self._make_inline_pipeline_client(prompt_update_engine)
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fixture = _build_harness([inline_client])
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omni = self._make_async_omni(self._OrchestratorBridgeEngine(fixture))
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generate_task: asyncio.Task[list[OmniRequestOutput]] | None = None
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try:
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generate_task = asyncio.create_task(self._collect_generate_outputs(omni))
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await _wait_for(
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lambda: (
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len(runner.state_cache) > 0
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and any(state.external_request_id == "req-omni" for state in omni.request_states.values())
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)
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)
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internal_request_id = next(iter(runner.state_cache))
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await omni.submit_interaction_async(
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"req-omni",
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interaction=_prompt_interaction("new scene", event_id="ui-update-1", transition_chunks=2),
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)
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outputs = await generate_task
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pending = runner.state_cache[internal_request_id].extra["pending_prompt_update"]
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assert pending["transition_chunks"] == 2
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assert torch.equal(pending["target_prompt_embeds"], torch.full((1, 4, 2), 2.0))
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pipeline.encode_prompt.assert_called_once() # pyright: ignore[reportAttributeAccessIssue]
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assert [output.custom_output["chunk"] for output in outputs] == [0, 1]
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finally:
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if generate_task is not None and not generate_task.done():
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generate_task.cancel()
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await asyncio.gather(generate_task, return_exceptions=True)
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await self._shutdown_pipeline_omni_harness(omni, fixture, inline_client)
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@staticmethod
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async def _collect_generate_outputs(omni: AsyncOmni) -> list[OmniRequestOutput]:
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outputs: list[OmniRequestOutput] = []
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async for output in omni.generate(
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prompt={"prompt": "a cat"},
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request_id="req-omni",
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sampling_params_list=[OmniDiffusionSamplingParams()],
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output_modalities=["image"],
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):
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outputs.append(output)
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return outputs
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@classmethod
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def _make_inline_pipeline_client(cls, engine: _PromptUpdateEngine) -> InlineStageDiffusionClient:
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metadata = StageMetadata(
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stage_id=0,
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stage_type="diffusion",
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engine_output_type="image",
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is_comprehension=False,
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requires_multimodal_data=False,
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engine_input_source=[],
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final_output=True,
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final_output_type="image",
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default_sampling_params=OmniDiffusionSamplingParams(),
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custom_process_input_func=None,
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model_stage=None,
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runtime_cfg=None,
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)
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with patch.object(InlineStageDiffusionClient, "_enrich_config"):
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with patch(
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"vllm_omni.diffusion.inline_stage_diffusion_client.DiffusionEngine.make_engine",
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return_value=engine,
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):
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od_config = MagicMock(spec=OmniDiffusionConfig)
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od_config.streaming_output = True
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return InlineStageDiffusionClient(
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model="test_model",
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od_config=od_config,
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metadata=metadata,
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batch_size=1,
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)
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@staticmethod
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def _make_async_omni(engine: _OrchestratorBridgeEngine) -> AsyncOmni:
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omni = object.__new__(AsyncOmni)
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omni.engine = engine # pyright: ignore[reportAttributeAccessIssue]
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omni.log_stats = False
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omni._pause_cond = asyncio.Condition()
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omni._paused = False
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omni.request_states = {}
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omni.final_output_task = None
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omni.event_resolver = AsyncEventResolver()
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omni._enable_ar_profiler = False
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omni.prom_metrics = MagicMock()
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omni.mod_metrics = MagicMock()
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omni.resolve_sampling_params_list = lambda params, allow_delta_coercion: params # pyright: ignore[reportAttributeAccessIssue]
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omni._compute_final_stage_id = lambda output_modalities: 0
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omni._compute_final_output_stage_ids = lambda output_modalities: [0]
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omni.default_sampling_params_list = engine.default_sampling_params_list # pyright: ignore[reportAttributeAccessIssue]
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omni._log_summary_and_cleanup = lambda request_id: omni.request_states.pop(request_id, None) # pyright: ignore[reportAttributeAccessIssue]
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return omni
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@staticmethod
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async def _shutdown_pipeline_omni_harness(
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omni: AsyncOmni,
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fixture: OrchestratorFixture,
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inline_client: InlineStageDiffusionClient,
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) -> None:
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if omni.final_output_task is not None:
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omni.final_output_task.cancel()
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await asyncio.gather(omni.final_output_task, return_exceptions=True)
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inline_client.shutdown()
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fixture.request_sync_q.put_nowait(ShutdownRequestMessage())
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await asyncio.to_thread(fixture.thread.join, 5)
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class _IncrementalStreamingPipeline:
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"""Returns one streaming chunk per ``step_outputs`` call."""
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supports_step_execution = True
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def __init__(self) -> None:
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self.requests: list[Any] = []
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self._step_index = 0
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def step_outputs(self, request):
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self.requests.append(request)
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if self._step_index == 0:
|
|
self._step_index += 1
|
|
return [DiffusionOutput(output={"chunk": 0}, finished=False)]
|
|
return [DiffusionOutput(output={"chunk": 1}, finished=True)]
|
|
|
|
class _OrchestratorBridgeEngine:
|
|
"""Minimal AsyncOmni engine facade backed by a live Orchestrator harness."""
|
|
|
|
def __init__(self, fixture: OrchestratorFixture) -> None:
|
|
self._fixture = fixture
|
|
self.stage_metadata = [
|
|
StageRuntimeInfo(
|
|
stage_type="diffusion",
|
|
final_output=True,
|
|
final_output_type="image",
|
|
)
|
|
]
|
|
self.stage_configs: list[Any] = [SimpleNamespace(stage_type="diffusion")]
|
|
self.default_sampling_params_list = [OmniDiffusionSamplingParams()]
|
|
self.num_stages = 1
|
|
self.supported_tasks = ("generate",)
|
|
self._alive = True
|
|
|
|
async def add_request_async(
|
|
self,
|
|
*,
|
|
request_id: str,
|
|
prompt: Any,
|
|
sampling_params_list: list[Any],
|
|
final_stage_id: int,
|
|
**kwargs: Any,
|
|
) -> None:
|
|
self._fixture.request_sync_q.put_nowait(
|
|
StageSubmissionMessage(
|
|
type="add_request",
|
|
request_id=request_id,
|
|
prompt=prompt,
|
|
original_prompt=prompt,
|
|
output_prompt_text=None,
|
|
sampling_params_list=sampling_params_list,
|
|
final_stage_id=final_stage_id,
|
|
final_output_stage_ids=kwargs.get("final_output_stage_ids"),
|
|
preprocess_ms=0.0,
|
|
request_timestamp=kwargs.get("arrival_time", time.time()),
|
|
enqueue_ts=time.perf_counter(),
|
|
)
|
|
)
|
|
|
|
async def submit_interaction_async(
|
|
self,
|
|
request_id: str,
|
|
*,
|
|
interaction: dict[str, Any],
|
|
) -> None:
|
|
self._fixture.request_sync_q.put_nowait(
|
|
InteractionMessage(
|
|
request_id=request_id,
|
|
interaction=cast(Any, interaction),
|
|
)
|
|
)
|
|
|
|
async def try_get_output_async(self) -> Any | None:
|
|
try:
|
|
return self._fixture.output_sync_q.get_nowait()
|
|
except queue.Empty:
|
|
return None
|
|
|
|
def get_stage_metadata(self, stage_id: int) -> StageRuntimeInfo:
|
|
return self.stage_metadata[stage_id]
|
|
|
|
def is_alive(self) -> bool:
|
|
return self._fixture.thread.is_alive()
|
|
|
|
async def abort_async(self, request_ids: list[str]) -> None:
|
|
del request_ids
|
|
|
|
class _PromptUpdateEngine:
|
|
"""DiffusionEngine stand-in that streams chunks and routes prompt_update to a runner."""
|
|
|
|
def __init__(
|
|
self,
|
|
streaming_pipeline: TestPromptUpdateIntegration._IncrementalStreamingPipeline,
|
|
runner: DiffusionModelRunner,
|
|
) -> None:
|
|
self.streaming_pipeline = streaming_pipeline
|
|
self._runner = runner
|
|
self.executor = SimpleNamespace(
|
|
register_failure_callback=MagicMock(),
|
|
check_health=MagicMock(),
|
|
)
|
|
|
|
async def step_streaming(self, request):
|
|
if request.request_id not in self._runner.state_cache:
|
|
state = _make_diffusion_request_state(request_id=request.request_id)
|
|
self._runner.state_cache[request.request_id] = state
|
|
|
|
state = self._runner.state_cache[request.request_id]
|
|
|
|
outputs = self.streaming_pipeline.step_outputs(request)
|
|
output = outputs[0]
|
|
custom_output = cast(dict[str, Any], output.output) or {}
|
|
yield [
|
|
OmniRequestOutput.from_diffusion(
|
|
request_id=request.request_id,
|
|
images=list(custom_output.get("images") or []),
|
|
custom_output=custom_output,
|
|
finished=output.finished,
|
|
)
|
|
]
|
|
|
|
deadline = time.monotonic() + 5.0
|
|
while "pending_prompt_update" not in state.extra:
|
|
if time.monotonic() >= deadline:
|
|
raise TimeoutError("timed out waiting for prompt update during streaming")
|
|
await asyncio.sleep(0.01)
|
|
|
|
outputs = self.streaming_pipeline.step_outputs(request)
|
|
output = outputs[0]
|
|
custom_output = cast(dict[str, Any], output.output) or {}
|
|
yield [
|
|
OmniRequestOutput.from_diffusion(
|
|
request_id=request.request_id,
|
|
images=list(custom_output.get("images") or []),
|
|
custom_output=custom_output,
|
|
finished=output.finished,
|
|
)
|
|
]
|
|
|
|
def collective_rpc(self, method, timeout, args, kwargs, unique_reply_rank):
|
|
del timeout, kwargs, unique_reply_rank
|
|
if method == "submit_interaction":
|
|
request_id, interaction = args
|
|
self._runner.submit_interaction(request_id, interaction)
|
|
return None
|
|
raise NotImplementedError(f"collective_rpc not mocked for {method!r}")
|
|
|
|
def abort(self, request_id: str) -> None:
|
|
del request_id
|