diff --git a/configs/omnidreams_demo_replay_benchmarks.json b/configs/omnidreams_demo_replay_benchmarks.json new file mode 100644 index 00000000..3ac6e401 --- /dev/null +++ b/configs/omnidreams_demo_replay_benchmarks.json @@ -0,0 +1,88 @@ +{ + "schema_version": 1, + "description": "Manual one-minute local benchmark scenarios for comparing the legacy Omnidreams single-view runner against the experimental shared demo replay path. The runner writes the legacy stacked HDMap/RGB canvas while the shared demo writes generated RGB output, so use the report for manual MP4 comparison rather than automatic pixel quality scoring.", + "scenarios": [ + { + "id": "omnidreams-sv-runner-baseline", + "name": "Omnidreams single-view runner baseline", + "description": "Runs the stable legacy Omnidreams single-view runner with the bundled example data for the same one-minute block count used by the shipped Omnidreams baseline.", + "report_group": { + "id": "omnidreams-demo", + "name": "Omnidreams Demo Comparison" + }, + "tags": [ + "manual", + "gpu", + "real-demo", + "omnidreams", + "i2v", + "replay", + "baseline" + ], + "env": { + "CUBLAS_WORKSPACE_CONFIG": ":4096:8", + "PYTORCH_CUDA_ALLOC_CONF": "expandable_segments:True" + }, + "command": [ + "uv", + "run", + "--project", + "integrations/omnidreams", + "flashdreams-run", + "omnidreams-sv-2steps-chunk2-loc6-lightvae-lighttae", + "--example-data", + "True", + "--example-data-uuid", + "239560dc-33d1-11ef-9720-00044bcbccac", + "--total-blocks", + "226" + ], + "warmup_steps": 1, + "quality_baseline_compare": false, + "timeout_s": 7200 + }, + { + "id": "omnidreams-sv-demo-replay", + "name": "Omnidreams shared demo replay", + "description": "Runs the experimental shared demo API replay path with the same stable non-perf preset, bundled example data, and one-minute block count as the legacy runner.", + "report_group": { + "id": "omnidreams-demo", + "name": "Omnidreams Demo Comparison" + }, + "tags": [ + "manual", + "gpu", + "real-demo", + "omnidreams", + "i2v", + "replay", + "shared-demo" + ], + "env": { + "CUBLAS_WORKSPACE_CONFIG": ":4096:8", + "PYTORCH_CUDA_ALLOC_CONF": "expandable_segments:True" + }, + "command": [ + "uv", + "run", + "--project", + "integrations/omnidreams", + "omnidreams-demo", + "replay", + "--preset-id", + "omnidreams-sv-2steps-chunk2-loc6-lightvae-lighttae", + "--example-data", + "--example-data-uuid", + "239560dc-33d1-11ef-9720-00044bcbccac", + "--total-blocks", + "226", + "--output", + "{output_dir}/omnidreams-sv-demo-replay.mp4" + ], + "output_dir_arg": null, + "warmup_steps": 1, + "quality_baseline_compare": false, + "timeout_s": 7200 + } + ] +} diff --git a/docs/inference_runtime_api_design.md b/docs/inference_runtime_api_design.md index d1365d77..6bd1018d 100644 --- a/docs/inference_runtime_api_design.md +++ b/docs/inference_runtime_api_design.md @@ -44,22 +44,24 @@ model. ## Current Implementation Plan Implementation should happen on an experimental integration branch. PRs for this -work should target that branch until the API shape, LingBot migration, and -OmniDreams migration are all working well enough to merge to `main` together. +work should target that branch until the API shape and OmniDreams migration are +working well enough to merge to `main` together. LingBot migration is deferred +to a separate follow-up after the OmniDreams path has clarified the shared demo +API shape. The experimental branch can temporarily break or simplify command-line options -while the demos are being moved to the new API. The required outcome is that the -LingBot and OmniDreams demos still run through the new runtime path, and that -benchmark tooling can confirm they are at least broadly healthy before the -branch is merged back to `main`. +while the demos are being moved to the new API. The required outcome for this +branch is that the OmniDreams demo runs through the new shared demo/runtime path, +and that benchmark and manual WebRTC checks can confirm it is at least broadly +healthy before the branch is merged back to `main`. Initial scope: - define the minimal runtime API envelope; -- migrate LingBot and OmniDreams to use it; +- migrate OmniDreams to use it through a shared demo-level API; - support selectable output modes such as MP4, JPEG/MJPEG stream, WebRTC, and headless/null where appropriate; -- use or update benchmark tooling to verify the migrated demos; +- use or update benchmark tooling to verify the migrated OmniDreams demo; - defer broader model migrations, hosted execution, full autotune, and polished metrics until the first branch proves the API shape. @@ -71,14 +73,30 @@ Initial scope: | T1 | Complete | Minimal API envelope and naming. | Partly. | T0. | `InferenceConfig`, `UserInputs`, `InferenceInput`, runtime/session, output target, and mapping boundaries are defined well enough for demos to use. | | T2 | Complete | Event-based `UserInputs`. | Yes, after T1 direction is agreed. | T1. | User inputs are primarily timestamped events; replay traces and derived snapshots are supported where needed. | | T3 | Complete | `CanonicalInputs`, `InferenceInput`, schemas, and mapping boundary. | Yes, after T1 direction is agreed. | T1. | Models can declare required global/per-step inputs, and mappings can convert canonical inputs into inference inputs. | -| T4 | Planned | `ModelRunner`, `InferenceRuntime`, and `InferenceSession` skeleton. | Partly. | T1. | A minimal standard loop can initialize a runtime, run at least one sequential session, and close cleanly. | +| T4 | Complete | `ModelRunner`, `InferenceRuntime`, and `InferenceSession` skeleton. | Partly. | T1. | A minimal standard loop can initialize a runtime, run at least one sequential session, and close cleanly. | | T5 | Planned | Output mode selection. | Yes, after the result/output shape is agreed. | T1, T4. | A run can choose output behavior such as MP4, JPEG/MJPEG stream, WebRTC, benchmark artifact, or headless/null without changing model code. | -| T6 | Planned | LingBot migration. | Yes, once T2-T4 have a usable skeleton. | T2, T3, T4. | LingBot runs through the new API path with its event inputs mapped into model inputs. | -| T7 | Planned | OmniDreams migration. | Yes, once T2-T4 have a usable skeleton. | T2, T3, T4. | OmniDreams runs through the new API path with its model-specific inputs and mapping preserved. | -| T8 | Planned | Benchmark/smoke verification for LingBot and OmniDreams. | Preparation can run early; final gate is late. | T5, T6, T7. | Existing or updated benchmark tooling can run both migrated demos and produce enough evidence that they still work. | +| T6 | Deferred | LingBot migration. | Yes, but out of scope for this branch. | T2, T3, T4. | LingBot runs through the new API path with its event inputs mapped into model inputs. | +| T7 | Partially complete | OmniDreams migration. | Yes, once T2-T4 have a usable skeleton. | T2, T3, T4. | OmniDreams replay and WebRTC run through the shared demo API path; remaining work is output/stat integration, legacy demo retirement, and cleanup. | +| T8 | Partially complete | Benchmark/smoke verification for OmniDreams. | Preparation can run early; final gate is late. | T5, T7. | Existing or updated benchmark tooling can run the migrated OmniDreams demo and produce enough evidence that it still works. | | T9 | Planned | Metrics and profiling normalization for the branch. | Yes, but final integration is late. | T4, T5, T8. | Basic canonical metrics are emitted for migrated demos; deeper metrics can remain follow-up work. | -| T10 | Planned | CLI compatibility and migration cleanup. | Yes, after demo migrations start. | T6, T7. | Required demo commands are restored or replaced, temporary hacks are removed, and user-facing docs/notes match the branch behavior. | -| T11 | Planned | Stabilize and merge experimental branch to `main`. | No, final integration step. | T6-T10. | LingBot and OmniDreams pass agreed smoke/benchmark checks, review feedback is addressed, and the branch can merge as one API transition. | +| T10 | Planned | CLI compatibility, legacy retirement, and migration cleanup. | Yes, after demo migrations start. | T5, T7, T8. | Required demo commands are restored or replaced, old interactive-drive and old OmniDreams demo/server paths are removed or reduced to compatibility shims, code used only by retired demos is removed, and user-facing docs/notes match the branch behavior. | +| T11 | Planned | Stabilize and merge experimental branch to `main`. | No, final integration step. | T5, T7-T10. | OmniDreams passes agreed smoke/benchmark checks, review feedback is addressed, and the branch can merge as one API transition. | + +Current OmniDreams migration status: + +- The shared `flashdreams.runtime.demo` API and OmniDreams demo adapter exist. +- OmniDreams MP4 replay runs through the shared replay runner and MP4 output + target. +- The one-minute benchmark comparison can run the legacy replay path and the new + shared demo replay path side by side. +- OmniDreams WebRTC runs through `serve_flashdreams_demo(...)` and the shared + WebRTC manager path while still using the existing OmniDreams runtime and + packaged browser app. +- The migration is not complete until the new output target/stat artifact work + lands, the new OmniDreams path is updated to use it, the old interactive-drive + and old OmniDreams demo/server paths are removed or reduced to deliberate + compatibility shims, code used only by retired demos is deleted, and the + experimental demo/runtime/input code is cleaned up. Suggested parallel split: @@ -88,7 +106,8 @@ Suggested parallel split: stay coherent; - one person owns T5/T8/T9, because outputs, benchmarks, and metrics are tightly related; -- LingBot and OmniDreams can be assigned separately once the skeleton is usable; +- LingBot should be tracked as a separate follow-up once OmniDreams has settled + the shared demo API shape; - one person should track branch health, CLI compatibility, and merge readiness. ## Architecture @@ -510,6 +529,11 @@ adapter/runtime still owns deep tensor validation and model semantics. The standard loop should be shared by CLI generation, headless playback, MP4 generation, benchmarks, and simple realtime applications. +The current v0 production loop is `flashdreams.runtime.run_inference_session()`. +It is intentionally narrow: one adapter, one config, one canonicalizer/source, +one selected mapping, one initial input, one output target, one metrics +recorder, and one synchronous sequential session. + A run should: 1. Discover the model or preset without loading checkpoints. @@ -645,9 +669,11 @@ The new API should reuse existing code instead of replacing everything: The task tracker near the start of this document is the source of truth for the first implementation branch. The first milestone is intentionally narrower than -the full design: prove the API with LingBot and OmniDreams, selectable output -modes, and enough benchmark/smoke coverage to merge the experimental branch -back to `main` safely. +the full design: prove the API with OmniDreams, add shared output/stat artifact +selection, retire the old OmniDreams demo paths, clean up the experimental +runtime/demo code, and collect enough benchmark/smoke evidence to merge the +experimental branch back to `main` safely. LingBot should be handled in a +separate follow-up plan. ## Design Risks @@ -709,7 +735,7 @@ registry, standard loop, concrete output modes, or model migrations: registering it? - What package registration mechanism should third-party and internal adapters use for CLI discovery and benchmarks? -- What is the first public model to migrate? +- Which model should migrate after OmniDreams settles the shared demo API shape? - What metrics are required for every benchmark run? - What metadata must be discoverable without loading checkpoints? - What requirements do Dynamo/Reactor-style backends need before we commit to diff --git a/docs/inference_runtime_inputs_implementation.md b/docs/inference_runtime_inputs_implementation.md index 763b9810..75460cce 100644 --- a/docs/inference_runtime_inputs_implementation.md +++ b/docs/inference_runtime_inputs_implementation.md @@ -18,8 +18,9 @@ Implementation lives in `flashdreams.runtime`: and compatibility - `flashdreams/tests/test_runtime_canonical.py` - `flashdreams/tests/test_runtime_input_mapping.py` -- `flashdreams/tests/test_inference_runtime_api.py` — the T1 envelope tests, - including a reference loop that exercises all three layers +- `flashdreams/tests/test_inference_runtime_api.py` — the T1 envelope tests +- `flashdreams/tests/test_runtime_runner.py` — the production standard loop + tests that exercise all three input layers with runtime/session cleanup The supported-model input inventory that informed this work is in `docs/inference_runtime_supported_inputs_inventory.md`. @@ -269,8 +270,9 @@ layer: to `OutputTarget.write()`. Output shape is T5. - **Declared output modalities**, so an output target or quality-eval can state what it requires and be matched the way inputs now are. T5/T8. -- **`Application`**, the class that has-a input system, input map, global - conditioning, session, and output target. T4. +- **Full `Application` ownership**, the class that has-a input system, input + map, global conditioning, session, and output target. T4 now provides the + narrow synchronous runner; richer application ownership remains outside T4. - **Loop ownership** — whether the application or the runtime/session drives the main event loop, and whether inputs are queued and batched. @@ -279,7 +281,8 @@ layer: ```bash .venv/bin/pytest flashdreams/tests/test_runtime_canonical.py \ flashdreams/tests/test_runtime_input_mapping.py \ - flashdreams/tests/test_inference_runtime_api.py -q + flashdreams/tests/test_inference_runtime_api.py \ + flashdreams/tests/test_runtime_runner.py -q .venv/bin/ty check flashdreams/flashdreams/runtime ``` diff --git a/docs/source/developer_guides/local_benchmarks.rst b/docs/source/developer_guides/local_benchmarks.rst index 687d95aa..62842ba3 100644 --- a/docs/source/developer_guides/local_benchmarks.rst +++ b/docs/source/developer_guides/local_benchmarks.rst @@ -132,6 +132,27 @@ input stream is shorter than the requested duration. ``interactive-drive`` is left out of this shipped MP4 suite for now because its public CLI is a live presenter rather than a file-writing runner. +Omnidreams Shared Demo Comparison +--------------------------------- + +``configs/omnidreams_demo_replay_benchmarks.json`` contains a one-minute manual +comparison between the legacy Omnidreams single-view runner and the experimental +shared demo replay path: + +.. code-block:: bash + + uv run flashdreams-benchmark \ + --scenario-file configs/omnidreams_demo_replay_benchmarks.json \ + --scenario omnidreams-sv-runner-baseline \ + --scenario omnidreams-sv-demo-replay \ + --output-dir artifacts/benchmarks/omnidreams-demo-replay-compare + +Use the generated report's MP4 links for side-by-side manual review. The legacy +runner writes the stacked HDMap/RGB canvas while the shared demo writes generated +RGB output, so this comparison intentionally disables automatic baseline quality +scoring until those output layouts are aligned. Both scenarios use ``226`` +blocks, matching the shipped Omnidreams one-minute baseline. + Quality Hooks ------------- diff --git a/flashdreams/flashdreams/runtime/__init__.py b/flashdreams/flashdreams/runtime/__init__.py index 04f84ae5..5f89196b 100644 --- a/flashdreams/flashdreams/runtime/__init__.py +++ b/flashdreams/flashdreams/runtime/__init__.py @@ -56,7 +56,9 @@ RuntimeMetricSample, ) from flashdreams.runtime.output import NullOutputTarget, OutputArtifact, OutputTarget +from flashdreams.runtime.runner import run_inference_session from flashdreams.runtime.types import StepRequest, StepResult +from flashdreams.runtime.video_output import Mp4VideoOutputTarget __all__ = [ "CanonicalInputs", @@ -88,6 +90,7 @@ "MappingCompatibility", "MetricsRecorder", "ModelAdapter", + "Mp4VideoOutputTarget", "NullMetricsRecorder", "NullOutputTarget", "OutputArtifact", @@ -98,6 +101,7 @@ "StepRequest", "StepResult", "TimeWindow", + "run_inference_session", "undeclared_inference_inputs", "UserInputCapability", "UserInputEvent", diff --git a/flashdreams/flashdreams/runtime/demo/__init__.py b/flashdreams/flashdreams/runtime/demo/__init__.py new file mode 100644 index 00000000..3d9d9991 --- /dev/null +++ b/flashdreams/flashdreams/runtime/demo/__init__.py @@ -0,0 +1,31 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""Experimental shared demo API above the inference runtime API.""" + +from flashdreams.runtime.demo.app import run_flashdreams_demo, serve_flashdreams_demo +from flashdreams.runtime.demo.outputs import build_output_target +from flashdreams.runtime.demo.replay import run_replay_demo +from flashdreams.runtime.demo.spec import ( + DemoAdapter, + DemoSpec, + Mp4OutputSpec, + NullOutputSpec, + OutputSpec, + PreparedScenario, + WebRTCOutputSpec, +) + +__all__ = [ + "DemoAdapter", + "DemoSpec", + "Mp4OutputSpec", + "NullOutputSpec", + "OutputSpec", + "PreparedScenario", + "WebRTCOutputSpec", + "build_output_target", + "run_flashdreams_demo", + "run_replay_demo", + "serve_flashdreams_demo", +] diff --git a/flashdreams/flashdreams/runtime/demo/app.py b/flashdreams/flashdreams/runtime/demo/app.py new file mode 100644 index 00000000..7659859b --- /dev/null +++ b/flashdreams/flashdreams/runtime/demo/app.py @@ -0,0 +1,36 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""Experimental shared demo entrypoints.""" + +from __future__ import annotations + +from typing import Any + +from .replay import run_replay_demo +from .spec import DemoAdapter, DemoSpec + + +def run_flashdreams_demo( + *, + spec: DemoSpec, + adapter: DemoAdapter, + **kwargs: Any, +) -> object: + """Run a synchronous replay demo through the shared runtime runner.""" + return run_replay_demo(spec=spec, adapter=adapter, **kwargs) + + +def serve_flashdreams_demo( + *, + spec: DemoSpec, + adapter: DemoAdapter, + **kwargs: Any, +) -> object: + """Serve a WebRTC demo through the shared serving manager.""" + from .webrtc import serve_webrtc_demo + + return serve_webrtc_demo(spec=spec, adapter=adapter, **kwargs) + + +__all__ = ["run_flashdreams_demo", "serve_flashdreams_demo"] diff --git a/flashdreams/flashdreams/runtime/demo/outputs.py b/flashdreams/flashdreams/runtime/demo/outputs.py new file mode 100644 index 00000000..421ec3bb --- /dev/null +++ b/flashdreams/flashdreams/runtime/demo/outputs.py @@ -0,0 +1,45 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""Shared demo output-target construction.""" + +from __future__ import annotations + +from pathlib import Path + +from flashdreams.runtime.output import NullOutputTarget, OutputTarget +from flashdreams.runtime.video_output import Mp4VideoOutputTarget, VideoWriter + +from .spec import Mp4OutputSpec, NullOutputSpec, OutputSpec, WebRTCOutputSpec + + +def build_output_target( + output: OutputSpec, + *, + mp4_writer: VideoWriter | None = None, +) -> OutputTarget: + """Build a replay output target from a demo output spec.""" + if isinstance(output, NullOutputSpec): + return NullOutputTarget(store_results=output.store_results) + if isinstance(output, Mp4OutputSpec): + output_path = Path(output.path) + if mp4_writer is not None: + return Mp4VideoOutputTarget( + output_path=output_path, + fps=output.fps, + output_layout=output.output_layout, + writer=mp4_writer, + move_to_cpu=output.move_to_cpu, + ) + return Mp4VideoOutputTarget( + output_path=output_path, + fps=output.fps, + output_layout=output.output_layout, + move_to_cpu=output.move_to_cpu, + ) + if isinstance(output, WebRTCOutputSpec): + raise ValueError("WebRTC output does not create a replay OutputTarget.") + raise TypeError(f"Unsupported demo output spec: {type(output).__name__}.") + + +__all__ = ["build_output_target"] diff --git a/flashdreams/flashdreams/runtime/demo/replay.py b/flashdreams/flashdreams/runtime/demo/replay.py new file mode 100644 index 00000000..18b87325 --- /dev/null +++ b/flashdreams/flashdreams/runtime/demo/replay.py @@ -0,0 +1,93 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""Shared replay demo runner.""" + +from __future__ import annotations + +from collections.abc import Callable, Sequence + +from flashdreams.runtime.metrics import MetricsRecorder, NullMetricsRecorder +from flashdreams.runtime.output import OutputArtifact, OutputTarget +from flashdreams.runtime.runner import run_inference_session + +from .outputs import build_output_target +from .spec import DemoAdapter, DemoSpec, OutputSpec, WebRTCOutputSpec + +OutputTargetFactory = Callable[[OutputSpec], OutputTarget] +InferenceSessionRunner = Callable[..., Sequence[OutputArtifact]] + + +def run_replay_demo( + *, + spec: DemoSpec, + adapter: DemoAdapter, + output_target_factory: OutputTargetFactory = build_output_target, + metrics: MetricsRecorder | None = None, + runner: InferenceSessionRunner = run_inference_session, +) -> tuple[OutputArtifact, ...]: + """Run one prepared demo scenario through the shared runtime runner.""" + _require_supported_mode( + mode=spec.input_mode, + supported=adapter.supported_input_modes(), + label="input_mode", + ) + if spec.input_mode != "replay": + raise ValueError( + "run_replay_demo requires input_mode='replay', " + f"got input_mode={spec.input_mode!r}." + ) + _require_supported_mode( + mode=spec.output.mode, + supported=adapter.supported_output_modes(), + label="output.mode", + ) + if isinstance(spec.output, WebRTCOutputSpec): + raise ValueError("run_replay_demo does not support WebRTC output.") + + prepared = adapter.prepare_scenario(spec) + mapping = prepared.mapping or adapter.default_input_mapping() + if mapping is None: + raise ValueError( + "Demo scenario did not provide an input mapping, and the adapter " + "has no default input mapping." + ) + if spec.config is None: + raise RuntimeError("DemoSpec.config was not initialized.") + + output = output_target_factory(spec.output) + metrics_recorder = metrics or NullMetricsRecorder() + return tuple( + runner( + adapter=adapter, + config=spec.config, + mapping=mapping, + canonicalizer=prepared.canonicalizer, + source_schema=prepared.source_schema, + user_inputs=prepared.user_inputs, + initial_inputs=prepared.initial_inputs, + output=output, + metrics=metrics_recorder, + ) + ) + + +def _require_supported_mode( + *, + mode: str, + supported: tuple[str, ...], + label: str, +) -> None: + if mode in supported: + return + supported_text = ", ".join(repr(each) for each in supported) or "" + raise ValueError( + f"Unsupported demo {label}={mode!r}; supported modes: {supported_text}." + ) + + +__all__ = [ + "InferenceSessionRunner", + "OutputTargetFactory", + "run_replay_demo", +] diff --git a/flashdreams/flashdreams/runtime/demo/spec.py b/flashdreams/flashdreams/runtime/demo/spec.py new file mode 100644 index 00000000..bc2884ab --- /dev/null +++ b/flashdreams/flashdreams/runtime/demo/spec.py @@ -0,0 +1,176 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""Experimental shared demo API data shapes.""" + +from __future__ import annotations + +from collections.abc import Mapping +from dataclasses import dataclass, field, replace +from pathlib import Path +from typing import Any, Literal, Protocol, TypeAlias + +from flashdreams.infra.postprocess import VideoTensorLayout +from flashdreams.runtime._utils import freeze_mapping +from flashdreams.runtime.canonical import InputCanonicalizer +from flashdreams.runtime.config import InferenceConfig +from flashdreams.runtime.inputs import InferenceInput, UserInputs, UserInputSchema +from flashdreams.runtime.interfaces import ModelAdapter +from flashdreams.runtime.mapping import InputMapping + + +@dataclass(frozen=True, kw_only=True, slots=True) +class NullOutputSpec: + """Headless/null replay output.""" + + mode: Literal["null"] = "null" + store_results: bool = False + + +@dataclass(frozen=True, kw_only=True, slots=True) +class Mp4OutputSpec: + """MP4 replay output.""" + + path: str | Path + fps: int | float + mode: Literal["mp4"] = "mp4" + output_layout: VideoTensorLayout = "bvtchw" + move_to_cpu: bool = True + + def __post_init__(self) -> None: + if float(self.fps) <= 0: + raise ValueError("Mp4OutputSpec.fps must be > 0.") + object.__setattr__(self, "path", Path(self.path)) + + +@dataclass(frozen=True, kw_only=True, slots=True) +class WebRTCOutputSpec: + """Shared WebRTC serving output.""" + + mode: Literal["webrtc"] = "webrtc" + host: str = "127.0.0.1" + port: int = 8080 + fps: int = 30 + video_width: int = 1280 + video_height: int = 720 + warmup_chunks: int = 0 + warmup_timeout_s: float = 30.0 + client_liveness_timeout_s: float = 30.0 + web_dir: str | Path | None = None + request_session_path: str = "/request_session" + preload_name: str | None = None + + def __post_init__(self) -> None: + if not self.host.strip(): + raise ValueError("WebRTCOutputSpec.host must be non-empty.") + if not (0 < int(self.port) < 65536): + raise ValueError("WebRTCOutputSpec.port must be between 1 and 65535.") + if self.fps <= 0: + raise ValueError("WebRTCOutputSpec.fps must be > 0.") + if self.video_width <= 0 or self.video_height <= 0: + raise ValueError("WebRTCOutputSpec video dimensions must be > 0.") + if self.warmup_chunks < 0: + raise ValueError("WebRTCOutputSpec.warmup_chunks must be >= 0.") + if self.warmup_timeout_s <= 0: + raise ValueError("WebRTCOutputSpec.warmup_timeout_s must be > 0.") + if self.client_liveness_timeout_s <= 0: + raise ValueError("WebRTCOutputSpec.client_liveness_timeout_s must be > 0.") + if not self.request_session_path.startswith("/"): + raise ValueError( + "WebRTCOutputSpec.request_session_path must start with '/'." + ) + if self.web_dir is not None: + object.__setattr__(self, "web_dir", Path(self.web_dir)) + + +OutputSpec: TypeAlias = NullOutputSpec | Mp4OutputSpec | WebRTCOutputSpec + + +@dataclass(frozen=True, kw_only=True, slots=True) +class DemoSpec: + """User-facing shared demo run description.""" + + __hash__ = None + + model_id: str + input_mode: str + output: OutputSpec + preset_id: str | None = None + scenario: Any | None = None + config: InferenceConfig | None = None + metadata: Mapping[str, Any] = field(default_factory=dict) + + def __post_init__(self) -> None: + if not self.model_id.strip(): + raise ValueError("DemoSpec.model_id must be non-empty.") + if not self.input_mode.strip(): + raise ValueError("DemoSpec.input_mode must be non-empty.") + config = self.config + if config is None: + config = InferenceConfig( + model_id=self.model_id, + preset_id=self.preset_id, + ) + else: + if config.model_id != self.model_id: + raise ValueError( + "DemoSpec.model_id must match InferenceConfig.model_id." + ) + if self.preset_id is None: + object.__setattr__(self, "preset_id", config.preset_id) + elif config.preset_id is None: + config = replace(config, preset_id=self.preset_id) + elif config.preset_id != self.preset_id: + raise ValueError( + "DemoSpec.preset_id must match InferenceConfig.preset_id." + ) + object.__setattr__(self, "config", config) + object.__setattr__(self, "metadata", freeze_mapping(self.metadata)) + + +@dataclass(frozen=True, kw_only=True, slots=True) +class PreparedScenario: + """Runtime-ready scenario prepared by a model demo adapter.""" + + __hash__ = None + + initial_inputs: InferenceInput + user_inputs: UserInputs = field(default_factory=UserInputs) + source_schema: UserInputSchema = field(default_factory=UserInputSchema) + canonicalizer: InputCanonicalizer = field(default_factory=InputCanonicalizer) + mapping: InputMapping | None = None + metadata: Mapping[str, Any] = field(default_factory=dict) + + def __post_init__(self) -> None: + object.__setattr__(self, "metadata", freeze_mapping(self.metadata)) + + +class DemoAdapter(ModelAdapter, Protocol): + """Model-owned adapter surface consumed by shared demo launchers.""" + + def supported_input_modes(self) -> tuple[str, ...]: + """Return demo input modes this adapter can prepare.""" + ... + + def supported_output_modes(self) -> tuple[str, ...]: + """Return demo output modes this adapter can run.""" + ... + + def prepare_scenario(self, spec: DemoSpec) -> PreparedScenario: + """Validate and materialize scenario inputs before runtime creation.""" + ... + + def create_webrtc_runtime(self, spec: DemoSpec) -> Any: + """Create the model-owned runtime consumed by the shared WebRTC manager.""" + ... + + +__all__ = [ + "DemoAdapter", + "DemoSpec", + "Mp4OutputSpec", + "NullOutputSpec", + "OutputSpec", + "PreparedScenario", + "WebRTCOutputSpec", +] diff --git a/flashdreams/flashdreams/runtime/demo/webrtc.py b/flashdreams/flashdreams/runtime/demo/webrtc.py new file mode 100644 index 00000000..f93a855d --- /dev/null +++ b/flashdreams/flashdreams/runtime/demo/webrtc.py @@ -0,0 +1,274 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""Shared WebRTC demo construction.""" + +from __future__ import annotations + +from collections.abc import Callable +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +from aiohttp import web + +from flashdreams.serving.webrtc.bootstrap import run_webrtc_server +from flashdreams.serving.webrtc.manager import BaseWebRTCSessionManager +from flashdreams.serving.webrtc.server import create_webrtc_app + +from .replay import _require_supported_mode +from .spec import DemoAdapter, DemoSpec, WebRTCOutputSpec + + +@dataclass(frozen=True, kw_only=True, slots=True) +class WebRTCDemoRuntimeConfig: + """Runtime config consumed by the shared WebRTC session manager.""" + + video_width: int + video_height: int + warmup_chunks: int + warmup_timeout_s: float + + +class SharedDemoWebRTCSessionManager(BaseWebRTCSessionManager[Any, Any]): + """Generic session manager wrapper for demo adapters.""" + + def __init__( + self, + *, + model_name: str, + runtime: Any, + runtime_config: Any, + fps: int, + client_liveness_timeout_s: float, + ) -> None: + self._demo_model_name = model_name + super().__init__( + runtime=runtime, + runtime_config=runtime_config, + fps=fps, + client_liveness_timeout_s=client_liveness_timeout_s, + ) + + def _model_name(self) -> str: + return self._demo_model_name + + +@dataclass(frozen=True, kw_only=True, slots=True) +class WebRTCDemo: + """Constructed WebRTC demo pieces, before or after serving.""" + + runtime: Any + runtime_config: Any + session_manager: BaseWebRTCSessionManager[Any, Any] + app: web.Application | None + host: str + port: int + + +CreateWebRTCApp = Callable[..., web.Application] +RunWebRTCServer = Callable[..., None] + + +def build_webrtc_demo( + *, + spec: DemoSpec, + adapter: DemoAdapter, + create_app: bool = False, + create_app_fn: CreateWebRTCApp = create_webrtc_app, +) -> WebRTCDemo: + """Build shared WebRTC manager/app pieces for a demo adapter runtime.""" + if not isinstance(spec.output, WebRTCOutputSpec): + raise ValueError("build_webrtc_demo requires WebRTCOutputSpec output.") + _require_supported_mode( + mode=spec.input_mode, + supported=adapter.supported_input_modes(), + label="input_mode", + ) + _require_supported_mode( + mode=spec.output.mode, + supported=adapter.supported_output_modes(), + label="output.mode", + ) + + output = spec.output + runtime = adapter.create_webrtc_runtime(spec) + runtime_config = _create_runtime_config( + spec=spec, + adapter=adapter, + runtime=runtime, + ) + manager = _create_session_manager( + spec=spec, + adapter=adapter, + runtime=runtime, + runtime_config=runtime_config, + fps=output.fps, + client_liveness_timeout_s=output.client_liveness_timeout_s, + ) + app = ( + _create_app( + spec=spec, + adapter=adapter, + session_manager=manager, + create_app_fn=create_app_fn, + ) + if create_app + else None + ) + return WebRTCDemo( + runtime=runtime, + runtime_config=runtime_config, + session_manager=manager, + app=app, + host=output.host, + port=output.port, + ) + + +def serve_webrtc_demo( + *, + spec: DemoSpec, + adapter: DemoAdapter, + world_rank: int = 0, + create_app_fn: CreateWebRTCApp = create_webrtc_app, + server_runner: RunWebRTCServer = run_webrtc_server, +) -> WebRTCDemo: + """Build and serve a shared WebRTC demo.""" + demo = build_webrtc_demo( + spec=spec, + adapter=adapter, + create_app=world_rank == 0, + create_app_fn=create_app_fn, + ) + server_runner( + world_rank=world_rank, + session_manager=demo.session_manager, + app=demo.app, + host=demo.host, + port=demo.port, + ) + return demo + + +def _create_runtime_config( + *, + spec: DemoSpec, + adapter: DemoAdapter, + runtime: Any, +) -> Any: + factory = getattr(adapter, "create_webrtc_runtime_config", None) + if callable(factory): + return factory(spec=spec, runtime=runtime) + + runtime_config = getattr(runtime, "config", None) + if _looks_like_webrtc_runtime_config(runtime_config): + return runtime_config + + output = spec.output + if not isinstance(output, WebRTCOutputSpec): + raise ValueError("WebRTC runtime config creation requires WebRTCOutputSpec.") + return WebRTCDemoRuntimeConfig( + video_width=output.video_width, + video_height=output.video_height, + warmup_chunks=output.warmup_chunks, + warmup_timeout_s=output.warmup_timeout_s, + ) + + +def _looks_like_webrtc_runtime_config(value: Any) -> bool: + return all( + hasattr(value, name) + for name in ( + "video_width", + "video_height", + "warmup_chunks", + "warmup_timeout_s", + ) + ) + + +def _create_session_manager( + *, + spec: DemoSpec, + adapter: DemoAdapter, + runtime: Any, + runtime_config: Any, + fps: int, + client_liveness_timeout_s: float, +) -> BaseWebRTCSessionManager[Any, Any]: + factory = getattr(adapter, "create_webrtc_session_manager", None) + if callable(factory): + return factory( + spec=spec, + runtime=runtime, + runtime_config=runtime_config, + fps=fps, + client_liveness_timeout_s=client_liveness_timeout_s, + ) + + return SharedDemoWebRTCSessionManager( + model_name=spec.model_id, + runtime=runtime, + runtime_config=runtime_config, + fps=fps, + client_liveness_timeout_s=client_liveness_timeout_s, + ) + + +def _create_app( + *, + spec: DemoSpec, + adapter: DemoAdapter, + session_manager: BaseWebRTCSessionManager[Any, Any], + create_app_fn: CreateWebRTCApp, +) -> web.Application: + output = spec.output + if not isinstance(output, WebRTCOutputSpec): + raise ValueError("WebRTC app creation requires WebRTCOutputSpec output.") + factory = getattr(adapter, "create_webrtc_app", None) + if callable(factory): + return factory( + spec=spec, + session_manager=session_manager, + request_session_url=_request_session_url(output), + ) + return _build_webrtc_app( + output=output, + session_manager=session_manager, + create_app_fn=create_app_fn, + preload_name=output.preload_name or spec.model_id, + ) + + +def _build_webrtc_app( + *, + output: WebRTCOutputSpec, + session_manager: BaseWebRTCSessionManager[Any, Any], + create_app_fn: CreateWebRTCApp, + preload_name: str, +) -> web.Application: + if output.web_dir is None: + raise ValueError("WebRTC app creation requires output.web_dir.") + return create_app_fn( + web_dir=Path(output.web_dir), + session_manager=session_manager, + request_session_url=_request_session_url(output), + preload_name=preload_name, + ) + + +def _request_session_url(output: WebRTCOutputSpec) -> str: + host = "127.0.0.1" if output.host in {"0.0.0.0", "::"} else output.host + return f"http://{host}:{output.port}{output.request_session_path}" + + +__all__ = [ + "CreateWebRTCApp", + "RunWebRTCServer", + "SharedDemoWebRTCSessionManager", + "WebRTCDemo", + "WebRTCDemoRuntimeConfig", + "build_webrtc_demo", + "serve_webrtc_demo", +] diff --git a/flashdreams/flashdreams/runtime/mapping.py b/flashdreams/flashdreams/runtime/mapping.py index 710ad804..6dfb5cc4 100644 --- a/flashdreams/flashdreams/runtime/mapping.py +++ b/flashdreams/flashdreams/runtime/mapping.py @@ -369,8 +369,7 @@ def undeclared_inference_inputs( for phase in INPUT_PHASES for key in inputs.for_phase(phase) if not any( - declared.name == key - for declared in mapping_schema.produces_for(phase) + declared.name == key for declared in mapping_schema.produces_for(phase) ) ) diff --git a/flashdreams/flashdreams/runtime/runner.py b/flashdreams/flashdreams/runtime/runner.py new file mode 100644 index 00000000..03d81447 --- /dev/null +++ b/flashdreams/flashdreams/runtime/runner.py @@ -0,0 +1,199 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""Minimal synchronous standard runner for the runtime API.""" + +from __future__ import annotations + +import math + +from flashdreams.runtime.canonical import InputCanonicalizer +from flashdreams.runtime.config import InferenceConfig +from flashdreams.runtime.inputs import ( + CanonicalInputs, + CanonicalInputSchema, + InferenceInput, + TimeWindow, + UserInputs, + UserInputSchema, +) +from flashdreams.runtime.interfaces import ( + InferenceRuntime, + InferenceSession, + ModelAdapter, +) +from flashdreams.runtime.mapping import ( + DeclaresMappingSchema, + InputMapping, + check_mapping_compatibility, +) +from flashdreams.runtime.metrics import MetricsRecorder +from flashdreams.runtime.output import OutputArtifact, OutputTarget +from flashdreams.runtime.types import StepResult + +_DEFAULT_SESSION_HORIZON_S = 3600.0 + + +def run_inference_session( + *, + adapter: ModelAdapter, + config: InferenceConfig, + mapping: InputMapping, + canonicalizer: InputCanonicalizer, + source_schema: UserInputSchema, + user_inputs: UserInputs, + initial_inputs: InferenceInput, + output: OutputTarget, + metrics: MetricsRecorder, +) -> tuple[OutputArtifact, ...]: + """Run one sequential inference session through the standard loop. + + This v0 loop intentionally handles one adapter/runtime/session, one selected + input mapping, one replay/live input batch, one output target, and one + metrics recorder. It is synchronous and owns only orchestration. + """ + + runtime: InferenceRuntime | None = None + session: InferenceSession | None = None + output_opened = False + output_artifacts: tuple[OutputArtifact, ...] = () + primary_error: BaseException | None = None + + try: + adapter.validate_config(config) + canonical_schema = canonicalizer.canonical_schema(source_schema) + _check_declared_mapping_compatibility( + mapping=mapping, + canonical_schema=canonical_schema, + adapter=adapter, + ) + mapping.validate( + canonical_schema=canonical_schema, + inference_input_schema=adapter.inference_input_schema, + ) + canonicalizer.reset() + mapped_initial_inputs = mapping.map_global_conditioning_inputs( + canonical_inputs=CanonicalInputs(), + inference_input=initial_inputs, + ) + runtime = adapter.create_runtime(config) + session = runtime.start_session(mapped_initial_inputs) + output.open() + output_opened = True + step_base_inputs = InferenceInput( + step=initial_inputs.step, + metadata=initial_inputs.metadata, + ) + + while (request := session.next_step_request()) is not None: + step_inputs = mapping.map_step_inputs( + canonical_inputs=canonicalizer.canonicalize( + user_inputs, + window=request.user_input_window + or _all_user_inputs_window(user_inputs), + source_schema=source_schema, + ), + inference_input=step_base_inputs, + request=request, + ) + result = session.step(step_inputs) + output.write(result) + _record_timing_metrics(metrics, result) + except BaseException as exc: + primary_error = exc + raise + finally: + cleanup_error, output_artifacts = _close_run_resources( + output=output if output_opened else None, + session=session, + runtime=runtime, + metrics=metrics, + ) + if cleanup_error is not None and primary_error is None: + raise cleanup_error + + return output_artifacts + + +def _check_declared_mapping_compatibility( + *, + mapping: InputMapping, + canonical_schema: CanonicalInputSchema, + adapter: ModelAdapter, +) -> None: + if not isinstance(mapping, DeclaresMappingSchema): + return + compatibility = check_mapping_compatibility( + canonical_schema=canonical_schema, + inference_input_schema=adapter.inference_input_schema, + mapping_schema=mapping.mapping_schema, + ) + compatibility.raise_if_incompatible() + + +def _all_user_inputs_window(user_inputs: UserInputs) -> TimeWindow: + if not user_inputs.events: + return TimeWindow(start_s=0.0, end_s=_DEFAULT_SESSION_HORIZON_S) + return TimeWindow( + start_s=0.0, + end_s=max( + _DEFAULT_SESSION_HORIZON_S, + math.nextafter(user_inputs.events[-1].timestamp_s, math.inf), + ), + ) + + +def _record_timing_metrics(metrics: MetricsRecorder, result: StepResult) -> None: + for name, value in result.metrics.items(): + if not name.endswith("_s") or isinstance(value, bool): + continue + sample_name = name[:-2] or name + metrics.record_timing( + sample_name, + float(value), + step_index=result.step_index, + ) + + +def _close_run_resources( + *, + output: OutputTarget | None, + session: InferenceSession | None, + runtime: InferenceRuntime | None, + metrics: MetricsRecorder, +) -> tuple[BaseException | None, tuple[OutputArtifact, ...]]: + cleanup_error: BaseException | None = None + artifacts: tuple[OutputArtifact, ...] = () + + def remember_error(exc: BaseException) -> None: + nonlocal cleanup_error + if cleanup_error is None: + cleanup_error = exc + + if output is not None: + try: + artifacts = tuple(output.close()) + except BaseException as exc: + remember_error(exc) + + if session is not None: + try: + session.close() + except BaseException as exc: + remember_error(exc) + + if runtime is not None: + try: + runtime.close() + except BaseException as exc: + remember_error(exc) + + try: + metrics.close() + except BaseException as exc: + remember_error(exc) + + return cleanup_error, artifacts + + +__all__ = ["run_inference_session"] diff --git a/flashdreams/flashdreams/runtime/video_output.py b/flashdreams/flashdreams/runtime/video_output.py new file mode 100644 index 00000000..b5c37212 --- /dev/null +++ b/flashdreams/flashdreams/runtime/video_output.py @@ -0,0 +1,157 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""Video output targets for the runtime API.""" + +from __future__ import annotations + +from collections.abc import Callable, Sequence +from dataclasses import dataclass, field +from pathlib import Path +from typing import cast + +import torch + +from flashdreams.infra.postprocess import VideoTensorLayout +from flashdreams.infra.runner_io import ( + DEFAULT_RUNNER_INSTALL_HINT, + write_video_tensor, +) +from flashdreams.infra.runner_io import ( + VideoTensorLayout as WritableVideoTensorLayout, +) +from flashdreams.infra.video_output import RunnerVideoOutputStream, VideoStepResult +from flashdreams.runtime.output import OutputArtifact +from flashdreams.runtime.types import StepResult + +VideoWriter = Callable[..., Path] + + +@dataclass(slots=True) +class Mp4VideoOutputTarget: + """Write runtime ``VideoStepResult`` chunks to one MP4 artifact.""" + + output_path: Path + fps: int | float + output_layout: VideoTensorLayout = "bvtchw" + writer: VideoWriter = field(default=write_video_tensor, repr=False) + install_hint: str = DEFAULT_RUNNER_INSTALL_HINT + move_to_cpu: bool = True + _opened: bool = field(default=False, init=False, repr=False) + _stream: RunnerVideoOutputStream | None = field( + default=None, + init=False, + repr=False, + ) + + @property + def closed(self) -> bool: + return not self._opened + + def open(self) -> None: + self._stream = RunnerVideoOutputStream( + postprocess_stream=None, + output_layout=self.output_layout, + collect_output=True, + move_to_cpu=self.move_to_cpu, + ) + self._opened = True + + def write(self, result: StepResult) -> None: + if not self._opened or self._stream is None: + raise RuntimeError("Cannot write to a closed output target.") + video_result = result.output + if not isinstance(video_result, VideoStepResult): + raise TypeError( + "Mp4VideoOutputTarget requires StepResult.output to be " + f"VideoStepResult, got {type(video_result).__name__}." + ) + if video_result.layout != self.output_layout: + raise ValueError( + "Mp4VideoOutputTarget received layout " + f"{video_result.layout!r}; expected {self.output_layout!r}." + ) + stats = dict(video_result.stats or result.metrics) + stats_extra: dict[str, object] = { + "step_index": result.step_index, + "frames": video_result.num_frames, + } + if result.output_window is not None: + stats_extra["output_start_s"] = result.output_window.start_s + stats_extra["output_end_s"] = result.output_window.end_s + self._stream.process( + video_result.video_chunk, + autoregressive_index=video_result.chunk_index, + stats=stats if stats else None, + stats_extra=stats_extra, + ) + + def close(self) -> Sequence[OutputArtifact]: + if self._stream is None: + self._opened = False + return () + + stream = self._stream + self._stream = None + self._opened = False + video = stream.finish() + if video is None: + return () + + writable_video, writable_layout = _prepare_video_for_mp4( + video, + layout=self.output_layout, + ) + path = self.writer( + writable_video, + self.output_path, + fps=self.fps, + layout=writable_layout, + install_hint=self.install_hint, + ) + return ( + OutputArtifact( + kind="video/mp4", + uri=str(path), + metadata={ + "fps": self.fps, + "source_layout": self.output_layout, + "write_layout": writable_layout, + "shape": tuple(int(dim) for dim in writable_video.shape), + "stats_history": tuple(stream.stats_history), + }, + ), + ) + + +def _prepare_video_for_mp4( + video: torch.Tensor, + *, + layout: VideoTensorLayout, +) -> tuple[torch.Tensor, WritableVideoTensorLayout]: + """Convert runtime video layouts into layouts accepted by runner I/O.""" + if layout in {"tchw", "btchw", "bcthw"}: + return video, cast(WritableVideoTensorLayout, layout) + if layout == "bvtchw": + if video.ndim != 6: + raise ValueError( + "layout='bvtchw' expects a 6D [B,V,T,C,H,W] tensor, " + f"got {tuple(video.shape)}." + ) + if video.shape[0] != 1: + raise ValueError( + "layout='bvtchw' MP4 writing expects a single batch element, " + f"got {tuple(video.shape)}." + ) + _, views, frames, channels, height, width = video.shape + canvas = ( + video[0] + .permute(1, 3, 0, 4, 2) + .contiguous() + .reshape(frames, height, views * width, channels) + ) + return canvas, "thwc" + raise ValueError(f"unsupported runtime video layout for MP4: {layout!r}") + + +__all__ = ["Mp4VideoOutputTarget"] diff --git a/flashdreams/tests/test_benchmark_harness.py b/flashdreams/tests/test_benchmark_harness.py index d70f9eb5..1de03b5d 100644 --- a/flashdreams/tests/test_benchmark_harness.py +++ b/flashdreams/tests/test_benchmark_harness.py @@ -245,6 +245,50 @@ def test_shipped_one_minute_demo_scenarios_load() -> None: } +def test_shipped_omnidreams_demo_replay_scenarios_load() -> None: + repo_root = Path(__file__).resolve().parents[2] + scenarios = load_scenario_file( + repo_root / "configs" / "omnidreams_demo_replay_benchmarks.json" + ) + + assert set(scenarios) == { + "omnidreams-sv-runner-baseline", + "omnidreams-sv-demo-replay", + } + + baseline = scenarios["omnidreams-sv-runner-baseline"] + assert baseline.report_group is not None + assert baseline.report_group.id == "omnidreams-demo" + assert _command_value(baseline.command, "--total-blocks") == "226" + assert "omnidreams-sv-2steps-chunk2-loc6-lightvae-lighttae" in baseline.command + assert "omnidreams-sv-2steps-chunk2-loc6-lightvae-lighttae-perf" not in ( + baseline.command + ) + assert baseline.quality_baseline_compare is False + + demo = scenarios["omnidreams-sv-demo-replay"] + assert demo.output_dir_arg is None + assert demo.command[:5] == ( + "uv", + "run", + "--project", + "integrations/omnidreams", + "omnidreams-demo", + ) + assert demo.command[5] == "replay" + assert _command_value(demo.command, "--preset-id") == ( + "omnidreams-sv-2steps-chunk2-loc6-lightvae-lighttae" + ) + assert _command_value(demo.command, "--total-blocks") == "226" + assert _command_value(demo.command, "--output") == ( + "{output_dir}/omnidreams-sv-demo-replay.mp4" + ) + assert "omnidreams-sv-2steps-chunk2-loc6-lightvae-lighttae-perf" not in ( + demo.command + ) + assert demo.quality_baseline_compare is False + + def test_shipped_deterministic_quality_scenarios_load() -> None: repo_root = Path(__file__).resolve().parents[2] scenarios = load_scenario_file( diff --git a/flashdreams/tests/test_inference_runtime_api.py b/flashdreams/tests/test_inference_runtime_api.py index 890d7efb..42f75d68 100644 --- a/flashdreams/tests/test_inference_runtime_api.py +++ b/flashdreams/tests/test_inference_runtime_api.py @@ -3,7 +3,6 @@ from __future__ import annotations -from collections.abc import Mapping from dataclasses import fields from typing import Any, cast @@ -11,29 +10,18 @@ from flashdreams.runtime import ( CanonicalInputs, - CanonicalInputSchema, - CanonicalModality, - DeviceConverterSchema, IdentityInputMapping, InferenceConfig, InferenceInput, InferenceInputSchema, - InferenceRuntime, - InferenceSession, InMemoryMetricsRecorder, - InputCanonicalizer, InputField, - InputMapping, - MetricsRecorder, - ModelAdapter, NullOutputTarget, OutputArtifact, - OutputTarget, RuntimeMetricSample, StepRequest, StepResult, TimeWindow, - UserInputCapability, UserInputEvent, UserInputs, UserInputSchema, @@ -42,18 +30,6 @@ pytestmark = pytest.mark.ci_cpu -_SESSION_HORIZON_S = 3600.0 - -_KEYBOARD_SOURCE = UserInputSchema( - capabilities=( - UserInputCapability( - event_type="keyboard.keydown", payload_fields=frozenset({"key"}) - ), - ) -) -_KEYBOARD_CANONICALIZER = InputCanonicalizer() - - def test_inference_config_keeps_runtime_settings_separate() -> None: denied_app_fields = {"prompt", "output_dir", "browser_settings"} config = InferenceConfig( @@ -267,370 +243,3 @@ def test_timing_metric_samples_must_use_seconds() -> None: unit="ms", category="timing", ) - - -def test_runtime_api_components_compose_for_sequential_session() -> None: - adapter = _FakeAdapter() - config = InferenceConfig(model_id="fake-model") - user_inputs = UserInputs( - events=( - UserInputEvent( - timestamp_s=0.25, - event_type="keyboard.keydown", - payload={"key": "w"}, - ), - ) - ) - inference_input = InferenceInput(global_conditioning={"prompt": "drive forward"}) - output = NullOutputTarget(store_results=True) - metrics = InMemoryMetricsRecorder() - - adapter.validate_config(config) - mapping = adapter.default_input_mapping() - assert mapping is not None - _drive_two_step_session( - adapter=adapter, - config=config, - mapping=mapping, - canonicalizer=_KEYBOARD_CANONICALIZER, - source_schema=_KEYBOARD_SOURCE, - user_inputs=user_inputs, - inference_input=inference_input, - output=output, - metrics=metrics, - ) - - assert output.output_count == 2 - assert [result.output for result in output.results] == ["chunk-0", "chunk-1"] - assert [result.frame_count for result in output.results] == [3, 3] - assert output.results[0].output_window == TimeWindow(start_s=0.0, end_s=0.5) - assert [sample.step_index for sample in metrics.samples] == [0, 1] - assert metrics.closed - - -def test_reference_loop_validates_mapping_before_runtime_creation() -> None: - mapping = _OrderCheckingMapping() - adapter = _OrderCheckingAdapter(mapping=mapping) - - _drive_two_step_session( - adapter=adapter, - config=InferenceConfig(model_id="fake-model"), - mapping=mapping, - canonicalizer=_KEYBOARD_CANONICALIZER, - source_schema=_KEYBOARD_SOURCE, - user_inputs=UserInputs(), - inference_input=InferenceInput(global_conditioning={"prompt": "drive forward"}), - output=NullOutputTarget(), - metrics=InMemoryMetricsRecorder(), - ) - - assert mapping.validated - assert adapter.created_runtime_after_validate - - -def test_reference_loop_does_not_canonicalize_global_conditioning() -> None: - mapping = _CanonicalRecordingMapping() - adapter = _FakeAdapter() - canonicalizer = InputCanonicalizer([_CountingDeviceConverter()]) - source_schema = UserInputSchema( - capabilities=( - UserInputCapability( - event_type="stateful_event", - payload_fields=frozenset(), - ), - ) - ) - - _drive_two_step_session( - adapter=adapter, - config=InferenceConfig(model_id="fake-model"), - mapping=mapping, - canonicalizer=canonicalizer, - source_schema=source_schema, - user_inputs=UserInputs( - events=(UserInputEvent(timestamp_s=0.75, event_type="stateful_event"),) - ), - inference_input=InferenceInput(global_conditioning={"prompt": "drive forward"}), - output=NullOutputTarget(), - metrics=InMemoryMetricsRecorder(), - ) - - assert mapping.global_canonical_values == {} - assert mapping.step_canonical_values == ( - {"stateful_counter": {"count": 0}}, - {"stateful_counter": {"count": 1}}, - ) - - -def test_reference_loop_closes_runtime_when_session_start_fails() -> None: - adapter = _FailingStartAdapter() - output = NullOutputTarget() - metrics = InMemoryMetricsRecorder() - - with pytest.raises(RuntimeError, match="start failed"): - _drive_two_step_session( - adapter=adapter, - config=InferenceConfig(model_id="fake-model"), - mapping=IdentityInputMapping(), - canonicalizer=_KEYBOARD_CANONICALIZER, - source_schema=_KEYBOARD_SOURCE, - user_inputs=UserInputs(), - inference_input=InferenceInput( - global_conditioning={"prompt": "drive forward"} - ), - output=output, - metrics=metrics, - ) - - assert adapter.runtime is not None - assert adapter.runtime.closed - assert output.closed - assert metrics.closed - - -def _drive_two_step_session( - *, - adapter: ModelAdapter, - config: InferenceConfig, - mapping: InputMapping, - canonicalizer: InputCanonicalizer, - source_schema: UserInputSchema, - user_inputs: UserInputs, - inference_input: InferenceInput, - output: OutputTarget, - metrics: MetricsRecorder, -) -> None: - mapping.validate( - canonical_schema=adapter.canonical_input_schema, - inference_input_schema=adapter.inference_input_schema, - ) - canonicalizer.reset() - initial_inputs = mapping.map_global_conditioning_inputs( - canonical_inputs=CanonicalInputs(), - inference_input=inference_input, - ) - runtime = adapter.create_runtime(config) - session: InferenceSession | None = None - output_opened = False - try: - session = runtime.start_session(initial_inputs) - output.open() - output_opened = True - while (request := session.next_step_request()) is not None: - step_inputs = mapping.map_step_inputs( - canonical_inputs=canonicalizer.canonicalize( - user_inputs, - window=request.user_input_window - or TimeWindow(start_s=0.0, end_s=_SESSION_HORIZON_S), - source_schema=source_schema, - ), - # Per-step calls carry only the step payload. A changed prompt - # or scene starts or resets a session outside this loop. - inference_input=InferenceInput( - step={"chunk_index": request.step_index}, - ), - request=request, - ) - result = session.step(step_inputs) - output.write(result) - metrics.record_timing( - "model_step", - float(result.metrics["model_step_s"]), - step_index=result.step_index, - ) - finally: - if output_opened: - output.close() - if session is not None: - session.close() - runtime.close() - metrics.close() - - -class _FakeAdapter: - model_id = "fake-model" - inference_input_schema = InferenceInputSchema( - global_conditioning_fields=(InputField(name="prompt"),), - step_fields=(InputField(name="chunk_index"),), - ) - canonical_input_schema = CanonicalInputSchema() - - def default_input_mapping(self) -> InputMapping: - return IdentityInputMapping() - - def validate_config(self, config: InferenceConfig) -> None: - if config.model_id != self.model_id: - raise ValueError(f"Unsupported model_id={config.model_id!r}.") - - def create_runtime(self, config: InferenceConfig) -> InferenceRuntime: - self.validate_config(config) - return _FakeRuntime(inference_input_schema=self.inference_input_schema) - - -class _FakeRuntime: - def __init__(self, *, inference_input_schema: InferenceInputSchema) -> None: - self._inference_input_schema = inference_input_schema - self.closed = False - - def start_session(self, inputs: InferenceInput) -> InferenceSession: - self._inference_input_schema.require_global_conditioning(inputs) - return _FakeSession(inference_input_schema=self._inference_input_schema) - - def close(self) -> None: - self.closed = True - - -class _FailingRuntime(_FakeRuntime): - def start_session(self, inputs: InferenceInput) -> InferenceSession: - del inputs - raise RuntimeError("start failed") - - -class _FakeSession: - def __init__(self, *, inference_input_schema: InferenceInputSchema) -> None: - self._inference_input_schema = inference_input_schema - self.step_index = 0 - self.closed = False - - def next_step_request(self) -> StepRequest | None: - if self.step_index >= 2: - return None - return StepRequest( - step_index=self.step_index, - inference_input_schema=self._inference_input_schema, - user_input_window=TimeWindow( - start_s=0.5 * self.step_index, - end_s=0.5 * (self.step_index + 1), - ), - ) - - def step(self, inputs: InferenceInput) -> StepResult: - self._inference_input_schema.require_step(inputs) - result = StepResult( - step_index=self.step_index, - output=f"chunk-{self.step_index}", - frame_count=3, - output_window=TimeWindow( - start_s=0.5 * self.step_index, - end_s=0.5 * (self.step_index + 1), - ), - metrics={"model_step_s": 0.01}, - ) - self.step_index += 1 - return result - - def reset(self, inputs: InferenceInput | None = None) -> None: - del inputs - self.step_index = 0 - - def close(self) -> None: - self.closed = True - - -class _OrderCheckingMapping(IdentityInputMapping): - def __init__(self) -> None: - self.validated = False - - def validate( - self, - *, - canonical_schema: CanonicalInputSchema | None = None, - inference_input_schema: InferenceInputSchema | None = None, - ) -> None: - super().validate( - canonical_schema=canonical_schema, - inference_input_schema=inference_input_schema, - ) - self.validated = True - - -class _CanonicalRecordingMapping(IdentityInputMapping): - def __init__(self) -> None: - self.global_canonical_values: Mapping[str, Any] | None = None - self._step_canonical_values: list[Mapping[str, Any]] = [] - - @property - def step_canonical_values(self) -> tuple[Mapping[str, Any], ...]: - return tuple(self._step_canonical_values) - - def map_global_conditioning_inputs( - self, - *, - canonical_inputs: CanonicalInputs, - inference_input: InferenceInput, - ) -> InferenceInput: - self.global_canonical_values = canonical_inputs.values - return super().map_global_conditioning_inputs( - canonical_inputs=canonical_inputs, - inference_input=inference_input, - ) - - def map_step_inputs( - self, - *, - canonical_inputs: CanonicalInputs, - inference_input: InferenceInput, - request: StepRequest, - ) -> InferenceInput: - self._step_canonical_values.append(canonical_inputs.values) - return super().map_step_inputs( - canonical_inputs=canonical_inputs, - inference_input=inference_input, - request=request, - ) - - -_STATEFUL_COUNTER = CanonicalModality( - name="stateful_counter", - payload_fields=frozenset({"count"}), -) - - -class _CountingDeviceConverter: - schema = DeviceConverterSchema( - name="stateful-counter", - produces=_STATEFUL_COUNTER, - consumes=(UserInputCapability(event_type="stateful_event"),), - ) - - def __init__(self) -> None: - self.count = 0 - - def reset(self) -> None: - self.count = 0 - - def convert( - self, - user_inputs: UserInputs, - window: TimeWindow, - ) -> Mapping[str, Any] | None: - del window - self.count += len(user_inputs.events) - return _STATEFUL_COUNTER.value({"count": self.count}) - - -class _OrderCheckingAdapter(_FakeAdapter): - canonical_input_schema = CanonicalInputSchema() - - def __init__(self, *, mapping: _OrderCheckingMapping) -> None: - self._mapping = mapping - self.created_runtime_after_validate = False - - def create_runtime(self, config: InferenceConfig) -> InferenceRuntime: - self.validate_config(config) - self.created_runtime_after_validate = self._mapping.validated - return _FakeRuntime(inference_input_schema=self.inference_input_schema) - - -class _FailingStartAdapter(_FakeAdapter): - canonical_input_schema = CanonicalInputSchema() - - def __init__(self) -> None: - self.runtime: _FailingRuntime | None = None - - def create_runtime(self, config: InferenceConfig) -> InferenceRuntime: - self.validate_config(config) - self.runtime = _FailingRuntime( - inference_input_schema=self.inference_input_schema - ) - return self.runtime diff --git a/flashdreams/tests/test_runtime_demo_api.py b/flashdreams/tests/test_runtime_demo_api.py new file mode 100644 index 00000000..7719c7c4 --- /dev/null +++ b/flashdreams/tests/test_runtime_demo_api.py @@ -0,0 +1,458 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +from __future__ import annotations + +from collections.abc import Sequence +from pathlib import Path +from typing import Any + +import pytest +import torch + +from flashdreams.infra.video_output import VideoStepResult +from flashdreams.runtime import ( + CanonicalInputs, + CanonicalInputSchema, + IdentityInputMapping, + InferenceConfig, + InferenceInput, + InferenceInputSchema, + InferenceRuntime, + InferenceSession, + InputCanonicalizer, + InputField, + InputMapping, + InputMappingSchema, + NullMetricsRecorder, + NullOutputTarget, + OutputArtifact, + OutputTarget, + StepRequest, + StepResult, + TimeWindow, + UserInputs, + UserInputSchema, +) +from flashdreams.runtime.demo import ( + DemoSpec, + Mp4OutputSpec, + NullOutputSpec, + PreparedScenario, + WebRTCOutputSpec, + build_output_target, + run_replay_demo, +) +from flashdreams.runtime.demo.webrtc import build_webrtc_demo +from flashdreams.serving.webrtc.manager import BaseWebRTCSessionManager + +pytestmark = pytest.mark.ci_cpu + + +def test_replay_demo_uses_shared_runner() -> None: + adapter = _FakeDemoAdapter() + output = _RecordingOutputTarget() + calls: list[dict[str, Any]] = [] + + def fake_runner(**kwargs: Any) -> Sequence[OutputArtifact]: + calls.append(kwargs) + return (OutputArtifact(kind="test/artifact", uri="memory://artifact"),) + + spec = DemoSpec( + model_id="fake-demo", + scenario="valid-scenario", + input_mode="replay", + output=NullOutputSpec(), + ) + + artifacts = run_replay_demo( + spec=spec, + adapter=adapter, + output_target_factory=lambda output_spec: output, + metrics=NullMetricsRecorder(), + runner=fake_runner, + ) + + assert artifacts == (OutputArtifact(kind="test/artifact", uri="memory://artifact"),) + assert len(calls) == 1 + assert calls[0]["adapter"] is adapter + assert calls[0]["config"] == spec.config + assert calls[0]["mapping"] is adapter.prepared_scenario.mapping + assert calls[0]["canonicalizer"] is adapter.prepared_scenario.canonicalizer + assert calls[0]["source_schema"] is adapter.prepared_scenario.source_schema + assert calls[0]["user_inputs"] is adapter.prepared_scenario.user_inputs + assert calls[0]["initial_inputs"] is adapter.prepared_scenario.initial_inputs + assert calls[0]["output"] is output + assert adapter.prepare_scenario_calls == [spec] + assert not adapter.create_runtime_called + + +def test_replay_demo_builds_output_target_from_spec(tmp_path: Path) -> None: + writer_calls: list[dict[str, Any]] = [] + + def fake_writer( + video: torch.Tensor, + path: Path, + *, + fps: int | float, + layout: str, + install_hint: str, + ) -> Path: + del install_hint + writer_calls.append( + { + "shape": tuple(video.shape), + "path": path, + "fps": fps, + "layout": layout, + } + ) + return path + + spec = DemoSpec( + model_id="fake-demo", + scenario="valid-scenario", + input_mode="replay", + output=Mp4OutputSpec(path=tmp_path / "demo.mp4", fps=12), + ) + + artifacts = run_replay_demo( + spec=spec, + adapter=_FakeDemoAdapter(video_output=True), + output_target_factory=lambda output_spec: build_output_target( + output_spec, + mp4_writer=fake_writer, + ), + ) + + assert len(artifacts) == 1 + assert artifacts[0].kind == "video/mp4" + assert artifacts[0].uri == str(tmp_path / "demo.mp4") + assert writer_calls == [ + { + "shape": (2, 2, 2, 3), + "path": tmp_path / "demo.mp4", + "fps": 12, + "layout": "thwc", + } + ] + + +def test_replay_demo_fails_before_runtime_creation_when_scenario_invalid() -> None: + adapter = _FakeDemoAdapter(scenario_valid=False) + output_factory_calls = 0 + + def output_factory(output_spec: object) -> OutputTarget: + nonlocal output_factory_calls + del output_spec + output_factory_calls += 1 + return NullOutputTarget() + + spec = DemoSpec( + model_id="fake-demo", + scenario="missing-scenario", + input_mode="replay", + output=NullOutputSpec(), + ) + + with pytest.raises(ValueError, match="invalid scenario"): + run_replay_demo( + spec=spec, + adapter=adapter, + output_target_factory=output_factory, + ) + + assert adapter.prepare_scenario_calls == [spec] + assert not adapter.create_runtime_called + assert output_factory_calls == 0 + + +def test_demo_adapter_declares_supported_modes() -> None: + adapter = _FakeDemoAdapter( + input_modes=("replay",), + output_modes=("null", "mp4", "webrtc"), + ) + + assert adapter.supported_input_modes() == ("replay",) + assert adapter.supported_output_modes() == ("null", "mp4", "webrtc") + + with pytest.raises(ValueError, match="input_mode='keyboard-driving'"): + run_replay_demo( + spec=DemoSpec( + model_id="fake-demo", + scenario="valid-scenario", + input_mode="keyboard-driving", + output=NullOutputSpec(), + ), + adapter=adapter, + ) + + assert adapter.prepare_scenario_calls == [] + assert not adapter.create_runtime_called + + +def test_webrtc_demo_uses_existing_session_manager_with_adapter_runtime() -> None: + adapter = _FakeDemoAdapter() + spec = DemoSpec( + model_id="fake-demo", + scenario="valid-scenario", + input_mode="keyboard-driving", + output=WebRTCOutputSpec( + host="0.0.0.0", + port=8082, + fps=24, + video_width=16, + video_height=8, + warmup_chunks=0, + warmup_timeout_s=1.0, + ), + ) + + demo = build_webrtc_demo(spec=spec, adapter=adapter) + + assert isinstance(demo.session_manager, BaseWebRTCSessionManager) + assert demo.runtime is adapter.webrtc_runtime + assert demo.session_manager._runtime is adapter.webrtc_runtime + assert demo.session_manager.runtime_config.video_width == 16 + assert demo.session_manager.runtime_config.video_height == 8 + assert demo.session_manager.fps == 24 + assert demo.session_manager._model_name() == "fake-demo" + assert demo.app is None + assert demo.host == "0.0.0.0" + assert demo.port == 8082 + assert adapter.create_webrtc_runtime_calls == [spec] + assert not adapter.create_runtime_called + + +class _ChunkIndexMapping: + mapping_schema = InputMappingSchema( + name="chunk-index", + produces_global_conditioning=(InputField(name="prompt"),), + produces_step=(InputField(name="chunk_index"),), + ) + + def validate( + self, + *, + canonical_schema: CanonicalInputSchema | None = None, + inference_input_schema: InferenceInputSchema | None = None, + ) -> None: + del canonical_schema, inference_input_schema + + def map_global_conditioning_inputs( + self, + *, + canonical_inputs: CanonicalInputs, + inference_input: InferenceInput, + ) -> InferenceInput: + del canonical_inputs + return inference_input + + def map_step_inputs( + self, + *, + canonical_inputs: CanonicalInputs, + inference_input: InferenceInput, + request: StepRequest, + ) -> InferenceInput: + del canonical_inputs + return InferenceInput( + global_conditioning=inference_input.global_conditioning, + step={"chunk_index": request.step_index}, + metadata=inference_input.metadata, + ) + + +class _FakeDemoAdapter: + model_id = "fake-demo" + inference_input_schema = InferenceInputSchema( + global_conditioning_fields=(InputField(name="prompt"),), + step_fields=(InputField(name="chunk_index"),), + ) + canonical_input_schema = CanonicalInputSchema() + + def __init__( + self, + *, + scenario_valid: bool = True, + video_output: bool = False, + input_modes: tuple[str, ...] = ("replay", "keyboard-driving"), + output_modes: tuple[str, ...] = ("null", "mp4", "webrtc"), + ) -> None: + self._scenario_valid = scenario_valid + self._video_output = video_output + self._input_modes = input_modes + self._output_modes = output_modes + self.mapping = _ChunkIndexMapping() + self.prepared_scenario = PreparedScenario( + initial_inputs=InferenceInput( + global_conditioning={"prompt": "drive forward"}, + ), + user_inputs=UserInputs(), + source_schema=UserInputSchema(), + canonicalizer=InputCanonicalizer(), + mapping=self.mapping, + ) + self.prepare_scenario_calls: list[DemoSpec] = [] + self.create_runtime_called = False + self.runtime: _FakeRuntime | None = None + self.webrtc_runtime: _FakeWebRTCRuntime | None = None + self.create_webrtc_runtime_calls: list[DemoSpec] = [] + + def supported_input_modes(self) -> tuple[str, ...]: + return self._input_modes + + def supported_output_modes(self) -> tuple[str, ...]: + return self._output_modes + + def default_input_mapping(self) -> InputMapping: + return IdentityInputMapping() + + def validate_config(self, config: InferenceConfig) -> None: + if config.model_id != self.model_id: + raise ValueError(f"Unsupported model_id={config.model_id!r}.") + + def create_runtime(self, config: InferenceConfig) -> InferenceRuntime: + self.validate_config(config) + self.create_runtime_called = True + self.runtime = _FakeRuntime( + inference_input_schema=self.inference_input_schema, + video_output=self._video_output, + ) + return self.runtime + + def prepare_scenario(self, spec: DemoSpec) -> PreparedScenario: + self.prepare_scenario_calls.append(spec) + if not self._scenario_valid: + raise ValueError("invalid scenario") + return self.prepared_scenario + + def create_webrtc_runtime(self, spec: DemoSpec) -> "_FakeWebRTCRuntime": + self.create_webrtc_runtime_calls.append(spec) + self.webrtc_runtime = _FakeWebRTCRuntime() + return self.webrtc_runtime + + +class _FakeRuntime: + def __init__( + self, + *, + inference_input_schema: InferenceInputSchema, + video_output: bool, + ) -> None: + self._inference_input_schema = inference_input_schema + self._video_output = video_output + self.session: _FakeSession | None = None + self.closed = False + + def start_session(self, inputs: InferenceInput) -> InferenceSession: + self._inference_input_schema.require_global_conditioning(inputs) + self.session = _FakeSession( + inference_input_schema=self._inference_input_schema, + video_output=self._video_output, + ) + return self.session + + def close(self) -> None: + self.closed = True + + +class _FakeSession: + def __init__( + self, + *, + inference_input_schema: InferenceInputSchema, + video_output: bool, + ) -> None: + self._inference_input_schema = inference_input_schema + self._video_output = video_output + self.step_index = 0 + self.closed = False + + def next_step_request(self) -> StepRequest | None: + if self.step_index >= 2: + return None + return StepRequest( + step_index=self.step_index, + user_input_window=TimeWindow( + start_s=0.5 * self.step_index, + end_s=0.5 * (self.step_index + 1), + ), + ) + + def step(self, inputs: InferenceInput) -> StepResult: + self._inference_input_schema.require_step(inputs) + output: object + if self._video_output: + output = VideoStepResult.from_video_chunk( + chunk_index=self.step_index, + video_chunk=torch.full( + (1, 1, 1, 3, 2, 2), + self.step_index, + dtype=torch.float32, + ), + layout="bvtchw", + ) + else: + output = f"chunk-{self.step_index}" + result = StepResult( + step_index=self.step_index, + output=output, + frame_count=1, + output_window=TimeWindow( + start_s=0.5 * self.step_index, + end_s=0.5 * (self.step_index + 1), + ), + ) + self.step_index += 1 + return result + + def reset(self, inputs: InferenceInput | None = None) -> None: + del inputs + self.step_index = 0 + + def close(self) -> None: + self.closed = True + + +class _RecordingOutputTarget: + def open(self) -> None: + return None + + def write(self, result: StepResult) -> None: + del result + + def close(self) -> Sequence[OutputArtifact]: + return () + + +class _FakeWebRTCRuntime: + async def initialize(self) -> None: + return None + + async def reset_for_new_session(self) -> None: + return None + + def peek_steady_chunk_num_frames(self) -> int: + return 1 + + def peek_next_chunk_num_frames(self) -> int: + return 1 + + async def generate_chunk( + self, + *, + segments: list[Any], + frame_times: list[float], + ) -> Any: + del segments, frame_times + return None + + async def close(self) -> None: + return None + + def send_exit_signal(self) -> None: + return None + + def wait_for_termination(self) -> None: + return None diff --git a/flashdreams/tests/test_runtime_input_mapping.py b/flashdreams/tests/test_runtime_input_mapping.py index b5f0744b..b774371f 100644 --- a/flashdreams/tests/test_runtime_input_mapping.py +++ b/flashdreams/tests/test_runtime_input_mapping.py @@ -201,9 +201,7 @@ def test_model_declares_required_and_optional_fields_per_phase() -> None: ("global_conditioning", "prompt"), ("step", "steering"), } - assert {(phase, f.name) for phase, f in optional} == { - ("step", "camera_delta") - } + assert {(phase, f.name) for phase, f in optional} == {("step", "camera_delta")} def test_required_fields_can_be_filtered_by_phase() -> None: @@ -214,8 +212,7 @@ def test_required_fields_can_be_filtered_by_phase() -> None: def test_field_lookup_is_phase_scoped() -> None: assert ( - DRIVING_MODEL.field_for(name="prompt", phase="global_conditioning") - is not None + DRIVING_MODEL.field_for(name="prompt", phase="global_conditioning") is not None ) assert DRIVING_MODEL.field_for(name="prompt", phase="step") is None @@ -275,12 +272,13 @@ def test_compatible_source_model_and_mapping_can_drive() -> None: ) assert compatibility.can_drive - assert { - (p, f.name) for p, f in compatibility.satisfied_required_model_fields - } == {("global_conditioning", "prompt"), ("step", "steering")} - assert { - (p, f.name) for p, f in compatibility.available_optional_model_fields - } == {("step", "camera_delta")} + assert {(p, f.name) for p, f in compatibility.satisfied_required_model_fields} == { + ("global_conditioning", "prompt"), + ("step", "steering"), + } + assert {(p, f.name) for p, f in compatibility.available_optional_model_fields} == { + ("step", "camera_delta") + } def test_missing_required_model_field_blocks_the_run() -> None: @@ -291,9 +289,9 @@ def test_missing_required_model_field_blocks_the_run() -> None: ) assert not compatibility.can_drive - assert [ - f.name for _, f in compatibility.missing_required_model_fields - ] == ["steering"] + assert [f.name for _, f in compatibility.missing_required_model_fields] == [ + "steering" + ] def test_missing_source_capability_is_reported_when_it_blocks() -> None: @@ -416,9 +414,7 @@ def test_combining_mappings_unions_their_surfaces() -> None: combined = combine_mapping_schemas((PROMPT_MAPPING, STEERING_MAPPING)) assert {m.name for m in combined.consumes} == {"driver_command"} - assert [f.name for f in combined.produces_global_conditioning] == [ - "prompt" - ] + assert [f.name for f in combined.produces_global_conditioning] == ["prompt"] assert [f.name for f in combined.produces_step] == ["steering"] diff --git a/flashdreams/tests/test_runtime_runner.py b/flashdreams/tests/test_runtime_runner.py new file mode 100644 index 00000000..b755ff48 --- /dev/null +++ b/flashdreams/tests/test_runtime_runner.py @@ -0,0 +1,660 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +from __future__ import annotations + +from collections.abc import Mapping, Sequence +from typing import Any + +import pytest + +from flashdreams.runtime import ( + DRIVER_COMMAND, + CanonicalInputs, + CanonicalInputSchema, + CanonicalModality, + DeviceConverterSchema, + IdentityInputMapping, + InferenceConfig, + InferenceInput, + InferenceInputSchema, + InferenceRuntime, + InferenceSession, + InMemoryMetricsRecorder, + InputCanonicalizer, + InputField, + InputMapping, + InputMappingSchema, + NullOutputTarget, + OutputArtifact, + RuntimeMetricSample, + StepRequest, + StepResult, + TimeWindow, + UserInputCapability, + UserInputEvent, + UserInputs, + UserInputSchema, + run_inference_session, +) + +pytestmark = pytest.mark.ci_cpu + + +def test_run_inference_session_completes_two_step_run() -> None: + adapter = _FakeAdapter() + output = NullOutputTarget(store_results=True) + metrics = InMemoryMetricsRecorder() + + artifacts = run_inference_session( + adapter=adapter, + config=InferenceConfig(model_id="fake-model"), + mapping=_ChunkIndexMapping(), + canonicalizer=InputCanonicalizer(), + source_schema=UserInputSchema(), + user_inputs=UserInputs(), + initial_inputs=InferenceInput(global_conditioning={"prompt": "drive forward"}), + output=output, + metrics=metrics, + ) + + assert artifacts == () + assert output.closed + assert output.output_count == 2 + assert [result.output for result in output.results] == ["chunk-0", "chunk-1"] + assert [result.frame_count for result in output.results] == [3, 3] + assert output.results[0].output_window == TimeWindow(start_s=0.0, end_s=0.5) + assert adapter.runtime is not None + assert adapter.runtime.closed + assert adapter.runtime.session is not None + assert adapter.runtime.session.closed + assert [sample.name for sample in metrics.samples] == ["model_step", "model_step"] + assert [sample.step_index for sample in metrics.samples] == [0, 1] + assert metrics.closed + + +def test_runner_preserves_initial_step_inputs_for_identity_mapping() -> None: + adapter = _FakeAdapter() + + run_inference_session( + adapter=adapter, + config=InferenceConfig(model_id="fake-model"), + mapping=IdentityInputMapping(), + canonicalizer=InputCanonicalizer(), + source_schema=UserInputSchema(), + user_inputs=UserInputs(), + initial_inputs=InferenceInput( + global_conditioning={"prompt": "drive forward"}, + step={"chunk_index": 42}, + ), + output=NullOutputTarget(), + metrics=InMemoryMetricsRecorder(), + ) + + assert adapter.runtime is not None + assert adapter.runtime.session is not None + assert [dict(inputs.step) for inputs in adapter.runtime.session.step_inputs] == [ + {"chunk_index": 42}, + {"chunk_index": 42}, + ] + assert [ + dict(inputs.global_conditioning) + for inputs in adapter.runtime.session.step_inputs + ] == [{}, {}] + + +def test_runner_validates_mapping_before_runtime_creation() -> None: + mapping = _OrderCheckingMapping() + adapter = _OrderCheckingAdapter(mapping=mapping) + + run_inference_session( + adapter=adapter, + config=InferenceConfig(model_id="fake-model"), + mapping=mapping, + canonicalizer=InputCanonicalizer(), + source_schema=UserInputSchema(), + user_inputs=UserInputs(), + initial_inputs=InferenceInput(global_conditioning={"prompt": "drive forward"}), + output=NullOutputTarget(), + metrics=InMemoryMetricsRecorder(), + ) + + assert mapping.validated + assert adapter.created_runtime_after_validate + + +def test_runner_closes_runtime_when_session_start_fails() -> None: + adapter = _FailingStartAdapter() + output = _RecordingOutputTarget() + metrics = InMemoryMetricsRecorder() + + with pytest.raises(RuntimeError, match="start failed"): + run_inference_session( + adapter=adapter, + config=InferenceConfig(model_id="fake-model"), + mapping=_ChunkIndexMapping(), + canonicalizer=InputCanonicalizer(), + source_schema=UserInputSchema(), + user_inputs=UserInputs(), + initial_inputs=InferenceInput( + global_conditioning={"prompt": "drive forward"} + ), + output=output, + metrics=metrics, + ) + + assert adapter.runtime is not None + assert adapter.runtime.closed + assert output.events == () + assert metrics.closed + + +def test_runner_does_not_canonicalize_global_conditioning() -> None: + mapping = _CanonicalRecordingMapping() + + run_inference_session( + adapter=_FakeAdapter(), + config=InferenceConfig(model_id="fake-model"), + mapping=mapping, + canonicalizer=InputCanonicalizer([_CountingDeviceConverter()]), + source_schema=UserInputSchema( + capabilities=( + UserInputCapability( + event_type="stateful_event", + payload_fields=frozenset(), + ), + ) + ), + user_inputs=UserInputs( + events=(UserInputEvent(timestamp_s=0.75, event_type="stateful_event"),) + ), + initial_inputs=InferenceInput(global_conditioning={"prompt": "drive forward"}), + output=NullOutputTarget(), + metrics=InMemoryMetricsRecorder(), + ) + + assert mapping.global_canonical_values == {} + assert mapping.step_canonical_values == ( + {"stateful_counter": {"count": 0}}, + {"stateful_counter": {"count": 1}}, + ) + + +def test_runner_closes_opened_resources_after_output_failure() -> None: + events: list[str] = [] + adapter = _RecordingAdapter(events=events) + output = _FailingWriteOutputTarget(events=events) + metrics = _RecordingMetricsRecorder(events=events) + + with pytest.raises(RuntimeError, match="write failed"): + run_inference_session( + adapter=adapter, + config=InferenceConfig(model_id="fake-model"), + mapping=_ChunkIndexMapping(), + canonicalizer=InputCanonicalizer(), + source_schema=UserInputSchema(), + user_inputs=UserInputs(), + initial_inputs=InferenceInput( + global_conditioning={"prompt": "drive forward"} + ), + output=output, + metrics=metrics, + ) + + assert events == [ + "runtime.start_session", + "output.open", + "session.step:0", + "output.write:0", + "output.close", + "session.close", + "runtime.close", + "metrics.close", + ] + + +def test_runner_attempts_later_cleanup_when_output_close_fails() -> None: + events: list[str] = [] + adapter = _RecordingAdapter(events=events) + output = _FailingCloseOutputTarget(events=events) + metrics = _RecordingMetricsRecorder(events=events) + + with pytest.raises(RuntimeError, match="close failed"): + run_inference_session( + adapter=adapter, + config=InferenceConfig(model_id="fake-model"), + mapping=_ChunkIndexMapping(), + canonicalizer=InputCanonicalizer(), + source_schema=UserInputSchema(), + user_inputs=UserInputs(), + initial_inputs=InferenceInput( + global_conditioning={"prompt": "drive forward"} + ), + output=output, + metrics=metrics, + ) + + assert events == [ + "runtime.start_session", + "output.open", + "session.step:0", + "output.write:0", + "session.step:1", + "output.write:1", + "output.close", + "session.close", + "runtime.close", + "metrics.close", + ] + + +def test_runner_checks_declared_mapping_compatibility_before_runtime_creation() -> None: + adapter = _DrivingAdapter() + mapping = _UnfeedableDriverCommandMapping() + metrics = InMemoryMetricsRecorder() + + with pytest.raises(ValueError, match="cannot drive this model"): + run_inference_session( + adapter=adapter, + config=InferenceConfig(model_id="fake-model"), + mapping=mapping, + canonicalizer=InputCanonicalizer(), + source_schema=UserInputSchema(), + user_inputs=UserInputs(), + initial_inputs=InferenceInput( + global_conditioning={"prompt": "drive forward"} + ), + output=NullOutputTarget(), + metrics=metrics, + ) + + assert not adapter.create_runtime_called + assert not mapping.validated + assert metrics.closed + + +class _ChunkIndexMapping: + mapping_schema = InputMappingSchema( + name="chunk-index", + produces_global_conditioning=(InputField(name="prompt"),), + produces_step=(InputField(name="chunk_index"),), + ) + + def __init__(self) -> None: + self.validated = False + + def validate( + self, + *, + canonical_schema: CanonicalInputSchema | None = None, + inference_input_schema: InferenceInputSchema | None = None, + ) -> None: + del canonical_schema, inference_input_schema + self.validated = True + + def map_global_conditioning_inputs( + self, + *, + canonical_inputs: CanonicalInputs, + inference_input: InferenceInput, + ) -> InferenceInput: + del canonical_inputs + return inference_input + + def map_step_inputs( + self, + *, + canonical_inputs: CanonicalInputs, + inference_input: InferenceInput, + request: StepRequest, + ) -> InferenceInput: + del canonical_inputs + return InferenceInput( + global_conditioning=inference_input.global_conditioning, + step={"chunk_index": request.step_index}, + metadata=inference_input.metadata, + ) + + +class _UnfeedableDriverCommandMapping(_ChunkIndexMapping): + mapping_schema = InputMappingSchema( + name="driver-command", + consumes=(DRIVER_COMMAND,), + produces_global_conditioning=(InputField(name="prompt"),), + produces_step=(InputField(name="steering"),), + ) + + def map_step_inputs( + self, + *, + canonical_inputs: CanonicalInputs, + inference_input: InferenceInput, + request: StepRequest, + ) -> InferenceInput: + del request + return InferenceInput( + global_conditioning=inference_input.global_conditioning, + step={ + "steering": canonical_inputs.values[DRIVER_COMMAND.name]["steer"], + }, + metadata=inference_input.metadata, + ) + + +class _CanonicalRecordingMapping(_ChunkIndexMapping): + def __init__(self) -> None: + super().__init__() + self.global_canonical_values: Mapping[str, Any] | None = None + self._step_canonical_values: list[Mapping[str, Any]] = [] + + @property + def step_canonical_values(self) -> tuple[Mapping[str, Any], ...]: + return tuple(self._step_canonical_values) + + def map_global_conditioning_inputs( + self, + *, + canonical_inputs: CanonicalInputs, + inference_input: InferenceInput, + ) -> InferenceInput: + self.global_canonical_values = canonical_inputs.values + return super().map_global_conditioning_inputs( + canonical_inputs=canonical_inputs, + inference_input=inference_input, + ) + + def map_step_inputs( + self, + *, + canonical_inputs: CanonicalInputs, + inference_input: InferenceInput, + request: StepRequest, + ) -> InferenceInput: + self._step_canonical_values.append(canonical_inputs.values) + return super().map_step_inputs( + canonical_inputs=canonical_inputs, + inference_input=inference_input, + request=request, + ) + + +_STATEFUL_COUNTER = CanonicalModality( + name="stateful_counter", + payload_fields=frozenset({"count"}), +) + + +class _CountingDeviceConverter: + schema = DeviceConverterSchema( + name="stateful-counter", + produces=_STATEFUL_COUNTER, + consumes=(UserInputCapability(event_type="stateful_event"),), + ) + + def __init__(self) -> None: + self.count = 0 + + def reset(self) -> None: + self.count = 0 + + def convert( + self, + user_inputs: UserInputs, + window: TimeWindow, + ) -> Mapping[str, Any] | None: + del window + self.count += len(user_inputs.events) + return _STATEFUL_COUNTER.value({"count": self.count}) + + +class _FakeAdapter: + model_id = "fake-model" + inference_input_schema = InferenceInputSchema( + global_conditioning_fields=(InputField(name="prompt"),), + step_fields=(InputField(name="chunk_index"),), + ) + canonical_input_schema = CanonicalInputSchema() + + def __init__(self) -> None: + self.runtime: _FakeRuntime | None = None + self.create_runtime_called = False + + def default_input_mapping(self) -> InputMapping: + return _ChunkIndexMapping() + + def validate_config(self, config: InferenceConfig) -> None: + if config.model_id != self.model_id: + raise ValueError(f"Unsupported model_id={config.model_id!r}.") + + def create_runtime(self, config: InferenceConfig) -> InferenceRuntime: + self.validate_config(config) + self.create_runtime_called = True + self.runtime = _FakeRuntime(inference_input_schema=self.inference_input_schema) + return self.runtime + + +class _DrivingAdapter(_FakeAdapter): + inference_input_schema = InferenceInputSchema( + global_conditioning_fields=(InputField(name="prompt"),), + step_fields=(InputField(name="steering"),), + ) + + +class _OrderCheckingMapping(_ChunkIndexMapping): + pass + + +class _OrderCheckingAdapter(_FakeAdapter): + def __init__(self, *, mapping: _OrderCheckingMapping) -> None: + super().__init__() + self._mapping = mapping + self.created_runtime_after_validate = False + + def create_runtime(self, config: InferenceConfig) -> InferenceRuntime: + self.validate_config(config) + self.created_runtime_after_validate = self._mapping.validated + self.create_runtime_called = True + self.runtime = _FakeRuntime(inference_input_schema=self.inference_input_schema) + return self.runtime + + +class _FailingStartAdapter(_FakeAdapter): + def create_runtime(self, config: InferenceConfig) -> InferenceRuntime: + self.validate_config(config) + self.create_runtime_called = True + self.runtime = _FailingRuntime( + inference_input_schema=self.inference_input_schema + ) + return self.runtime + + +class _RecordingAdapter(_FakeAdapter): + def __init__(self, *, events: list[str]) -> None: + super().__init__() + self._events = events + + def create_runtime(self, config: InferenceConfig) -> InferenceRuntime: + self.validate_config(config) + self.create_runtime_called = True + self.runtime = _RecordingRuntime( + inference_input_schema=self.inference_input_schema, + events=self._events, + ) + return self.runtime + + +class _FakeRuntime: + def __init__(self, *, inference_input_schema: InferenceInputSchema) -> None: + self._inference_input_schema = inference_input_schema + self.session: _FakeSession | None = None + self.closed = False + + def start_session(self, inputs: InferenceInput) -> InferenceSession: + self._inference_input_schema.require_global_conditioning(inputs) + self.session = _FakeSession(inference_input_schema=self._inference_input_schema) + return self.session + + def close(self) -> None: + self.closed = True + + +class _FailingRuntime(_FakeRuntime): + def start_session(self, inputs: InferenceInput) -> InferenceSession: + del inputs + raise RuntimeError("start failed") + + +class _RecordingRuntime(_FakeRuntime): + def __init__( + self, + *, + inference_input_schema: InferenceInputSchema, + events: list[str], + ) -> None: + super().__init__(inference_input_schema=inference_input_schema) + self._events = events + + def start_session(self, inputs: InferenceInput) -> InferenceSession: + self._events.append("runtime.start_session") + self._inference_input_schema.require_global_conditioning(inputs) + self.session = _RecordingSession( + inference_input_schema=self._inference_input_schema, + events=self._events, + ) + return self.session + + def close(self) -> None: + self._events.append("runtime.close") + super().close() + + +class _FakeSession: + def __init__(self, *, inference_input_schema: InferenceInputSchema) -> None: + self._inference_input_schema = inference_input_schema + self.step_index = 0 + self.step_inputs: list[InferenceInput] = [] + self.closed = False + + def next_step_request(self) -> StepRequest | None: + if self.step_index >= 2: + return None + return StepRequest( + step_index=self.step_index, + inference_input_schema=self._inference_input_schema, + user_input_window=TimeWindow( + start_s=0.5 * self.step_index, + end_s=0.5 * (self.step_index + 1), + ), + ) + + def step(self, inputs: InferenceInput) -> StepResult: + self._inference_input_schema.require_step(inputs) + self.step_inputs.append(inputs) + result = StepResult( + step_index=self.step_index, + output=f"chunk-{self.step_index}", + frame_count=3, + output_window=TimeWindow( + start_s=0.5 * self.step_index, + end_s=0.5 * (self.step_index + 1), + ), + metrics={"model_step_s": 0.01, "frames": 3}, + ) + self.step_index += 1 + return result + + def reset(self, inputs: InferenceInput | None = None) -> None: + del inputs + self.step_index = 0 + + def close(self) -> None: + self.closed = True + + +class _RecordingSession(_FakeSession): + def __init__( + self, + *, + inference_input_schema: InferenceInputSchema, + events: list[str], + ) -> None: + super().__init__(inference_input_schema=inference_input_schema) + self._events = events + + def step(self, inputs: InferenceInput) -> StepResult: + self._events.append(f"session.step:{self.step_index}") + return super().step(inputs) + + def close(self) -> None: + self._events.append("session.close") + super().close() + + +class _RecordingOutputTarget: + def __init__(self, *, events: list[str] | None = None) -> None: + self._events = events + self._opened = False + + @property + def events(self) -> tuple[str, ...]: + return () if self._events is None else tuple(self._events) + + def open(self) -> None: + self._opened = True + if self._events is not None: + self._events.append("output.open") + + def write(self, result: StepResult) -> None: + if not self._opened: + raise RuntimeError("Cannot write to a closed output target.") + if self._events is not None: + self._events.append(f"output.write:{result.step_index}") + + def close(self) -> Sequence[OutputArtifact]: + self._opened = False + if self._events is not None: + self._events.append("output.close") + return () + + +class _FailingWriteOutputTarget(_RecordingOutputTarget): + def write(self, result: StepResult) -> None: + super().write(result) + raise RuntimeError("write failed") + + +class _FailingCloseOutputTarget(_RecordingOutputTarget): + def close(self) -> Sequence[OutputArtifact]: + super().close() + raise RuntimeError("close failed") + + +class _RecordingMetricsRecorder: + def __init__(self, *, events: list[str]) -> None: + self._events = events + self.samples: list[RuntimeMetricSample] = [] + + def record(self, sample: RuntimeMetricSample) -> None: + self.samples.append(sample) + + def record_timing( + self, + name: str, + duration_s: float, + *, + step_index: int | None = None, + metadata: Mapping[str, Any] | None = None, + ) -> None: + self.record( + RuntimeMetricSample( + name=name, + value=duration_s, + unit="s", + step_index=step_index, + category="timing", + metadata={} if metadata is None else metadata, + ) + ) + + def close(self) -> None: + self._events.append("metrics.close") diff --git a/flashdreams/tests/test_runtime_video_output.py b/flashdreams/tests/test_runtime_video_output.py new file mode 100644 index 00000000..898acf73 --- /dev/null +++ b/flashdreams/tests/test_runtime_video_output.py @@ -0,0 +1,91 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +from __future__ import annotations + +from pathlib import Path +from typing import Any + +import pytest +import torch + +from flashdreams.infra.video_output import VideoStepResult +from flashdreams.runtime import Mp4VideoOutputTarget, StepResult, TimeWindow + +pytestmark = pytest.mark.ci_cpu + + +def test_mp4_video_output_target_rejects_non_video_payload(tmp_path: Path) -> None: + target = Mp4VideoOutputTarget(output_path=tmp_path / "out.mp4", fps=30) + target.open() + + with pytest.raises(TypeError, match="VideoStepResult"): + target.write(StepResult(step_index=0, output="not-video")) + + +def test_mp4_video_output_target_writes_artifact_on_close(tmp_path: Path) -> None: + calls: list[dict[str, Any]] = [] + + def fake_writer( + video: torch.Tensor, + path: Path, + *, + fps: int | float, + layout: str, + install_hint: str, + ) -> Path: + del install_hint + calls.append( + { + "shape": tuple(video.shape), + "path": path, + "fps": fps, + "layout": layout, + } + ) + return path + + target = Mp4VideoOutputTarget( + output_path=tmp_path / "omnidreams.mp4", + fps=24, + writer=fake_writer, + move_to_cpu=False, + ) + target.open() + target.write( + StepResult( + step_index=3, + output=VideoStepResult.from_video_chunk( + chunk_index=3, + video_chunk=torch.zeros((1, 2, 4, 3, 5, 6)), + layout="bvtchw", + stats={"model_step_s": 0.5}, + ), + frame_count=4, + output_window=TimeWindow(start_s=1.0, end_s=2.0), + ) + ) + + artifacts = target.close() + + assert len(artifacts) == 1 + assert artifacts[0].kind == "video/mp4" + assert artifacts[0].uri == str(tmp_path / "omnidreams.mp4") + assert calls == [ + { + "shape": (4, 5, 12, 3), + "path": tmp_path / "omnidreams.mp4", + "fps": 24, + "layout": "thwc", + } + ] + assert artifacts[0].metadata["stats_history"] == ( + { + "autoregressive_index": 3, + "model_step_s": 0.5, + "step_index": 3, + "frames": 4, + "output_start_s": 1.0, + "output_end_s": 2.0, + }, + ) diff --git a/integrations/omnidreams/omnidreams/demo/README.md b/integrations/omnidreams/omnidreams/demo/README.md new file mode 100644 index 00000000..d69c0170 --- /dev/null +++ b/integrations/omnidreams/omnidreams/demo/README.md @@ -0,0 +1,66 @@ + + +# OmniDreams Shared Demo API + +This folder contains the experimental OmniDreams demo built on +`flashdreams.runtime.demo`. + +Run commands from the FlashDreams workspace root: + +```bash +cd /path/to/flashdreams +export HF_TOKEN= +``` + +## MP4 Replay + +Generate an MP4 from the bundled single-view sample data: + +```bash +mkdir -p outputs +uv run --package flashdreams-omnidreams omnidreams-demo replay \ + --output outputs/omnidreams-demo.mp4 +``` + +This replay path mirrors the benchmark runner path: it uses a prompt, first +frame, and pre-rendered HDMap video. It does not load a Ludus scene or render +HDMaps at runtime. The demo defaults to the stable non-perf OmniDreams preset +used by the benchmark path. + +To provide benchmark-style assets explicitly: + +```bash +uv run --package flashdreams-omnidreams omnidreams-demo replay \ + --prompt "Driving scene from a front-facing car camera." \ + --hdmap-video-paths /path/to/camera_front_wide_120fov_hdmap.mp4 \ + --first-frame-paths /path/to/first_frame.png \ + --camera-names camera_front_wide_120fov \ + --output outputs/omnidreams-demo.mp4 +``` + +Pass `--example-data-uuid ` to select another bundled single-view sample, +or `--no-example-data` to require explicit asset paths. + +The `omnidreams-sv-2steps-chunk2-loc6-lightvae-lighttae-perf` preset remains an +explicit `--preset-id` opt-in. It should become the default only after the +compile/cache behavior is reliable enough for the demo path. + +## WebRTC + +WebRTC uses the shared demo launcher around the existing Omnidreams live WebRTC +runtime. It is still scene-driven and uses Ludus to render HDMap conditioning +from a scene: + +```bash +uv run --package flashdreams-omnidreams omnidreams-demo webrtc \ + --host 0.0.0.0 \ + --port 8082 +``` + +The scene UUID is optional; when omitted, the runtime uses the default +Hugging Face WebRTC scene. Override the scene with `--scene-uuid`, select a +weather variant with `--scene-variant default|rain|snow`, or use +`--scene-dir /path/to/local/scene` for a local staged scene. diff --git a/integrations/omnidreams/omnidreams/demo/__init__.py b/integrations/omnidreams/omnidreams/demo/__init__.py new file mode 100644 index 00000000..6fa3a9b2 --- /dev/null +++ b/integrations/omnidreams/omnidreams/demo/__init__.py @@ -0,0 +1,20 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""Experimental OmniDreams demo adapter built on ``flashdreams.runtime.demo``.""" + +from omnidreams.demo.adapter import OmnidreamsDemoAdapter +from omnidreams.demo.spec import ( + DEFAULT_OMNIDREAMS_PRESET, + OMNIDREAMS_MODEL_ID, + OmnidreamsReplayScenario, + OmnidreamsWebRTCScenario, +) + +__all__ = [ + "DEFAULT_OMNIDREAMS_PRESET", + "OMNIDREAMS_MODEL_ID", + "OmnidreamsDemoAdapter", + "OmnidreamsReplayScenario", + "OmnidreamsWebRTCScenario", +] diff --git a/integrations/omnidreams/omnidreams/demo/adapter.py b/integrations/omnidreams/omnidreams/demo/adapter.py new file mode 100644 index 00000000..e16c5c0a --- /dev/null +++ b/integrations/omnidreams/omnidreams/demo/adapter.py @@ -0,0 +1,279 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""OmniDreams adapter for the shared demo API.""" + +from __future__ import annotations + +from collections.abc import Callable +from dataclasses import replace +from typing import Any + +from omnidreams.config import OMNIDREAMS_CONFIGS, OMNIDREAMS_RUNNERS +from omnidreams.webrtc.session import ( + OmnidreamsInferenceRuntime, + OmnidreamsRuntimeConfig, +) + +from flashdreams.infra.postprocess import VideoPostprocessChainConfig +from flashdreams.runtime import ( + CanonicalInputSchema, + IdentityInputMapping, + InferenceConfig, + InferenceInput, + InferenceInputSchema, + InputCanonicalizer, + InputField, + UserInputSchema, +) +from flashdreams.runtime.demo import ( + DemoSpec, + Mp4OutputSpec, + PreparedScenario, + WebRTCOutputSpec, +) +from flashdreams.runtime.interfaces import InferenceRuntime + +from .replay import ( + OmnidreamsReplayRuntime, + OmnidreamsReplayRuntimeOptions, + PipelineFactory, +) +from .spec import ( + DEFAULT_OMNIDREAMS_PRESET, + OMNIDREAMS_MODEL_ID, + resolve_replay_scenario, + resolve_webrtc_scenario, +) +from .webrtc import ( + OmnidreamsDemoWebRTCSessionManager, + create_omnidreams_webrtc_app, + validate_postprocess_preset, +) + +ReplayRuntimeFactory = Callable[..., InferenceRuntime] +WebRTCRuntimeFactory = Callable[..., Any] + + +class OmnidreamsDemoAdapter: + """Model-owned OmniDreams adapter consumed by shared demo launchers.""" + + def __init__( + self, + *, + replay_runtime_factory: ReplayRuntimeFactory = OmnidreamsReplayRuntime, + webrtc_runtime_factory: WebRTCRuntimeFactory = OmnidreamsInferenceRuntime, + pipeline_factory: PipelineFactory | None = None, + ) -> None: + self._replay_runtime_factory = replay_runtime_factory + self._webrtc_runtime_factory = webrtc_runtime_factory + self._pipeline_factory = pipeline_factory + self._mapping = IdentityInputMapping() + + @property + def model_id(self) -> str: + return OMNIDREAMS_MODEL_ID + + @property + def inference_input_schema(self) -> InferenceInputSchema: + return InferenceInputSchema( + global_conditioning_fields=( + InputField( + name="scenario", + input_modality="omnidreams/replay-scenario", + description="Resolved OmniDreams replay scenario.", + ), + ) + ) + + @property + def canonical_input_schema(self) -> CanonicalInputSchema | None: + return None + + def default_input_mapping(self) -> IdentityInputMapping: + return self._mapping + + def supported_input_modes(self) -> tuple[str, ...]: + return ("replay", "keyboard-driving") + + def supported_output_modes(self) -> tuple[str, ...]: + return ("mp4", "webrtc") + + def prepare_scenario(self, spec: DemoSpec) -> PreparedScenario: + if spec.input_mode != "replay": + raise ValueError( + "OmniDreams prepare_scenario currently supports only " + f"input_mode='replay', got {spec.input_mode!r}." + ) + if not isinstance(spec.output, Mp4OutputSpec): + raise ValueError("OmniDreams replay demo currently requires MP4 output.") + scenario = resolve_replay_scenario( + spec.scenario, + default_prompt=self._default_replay_prompt(spec.config), + ) + return PreparedScenario( + initial_inputs=InferenceInput( + global_conditioning={"scenario": scenario}, + ), + source_schema=UserInputSchema(description="fixed OmniDreams replay input"), + canonicalizer=InputCanonicalizer(), + mapping=self._mapping, + metadata={ + "model_id": self.model_id, + "preset_id": self._preset_id(spec.config), + "num_views": len(scenario.camera_names), + }, + ) + + def validate_config(self, config: InferenceConfig) -> None: + if config.model_id != self.model_id: + raise ValueError( + f"OmniDreams adapter requires model_id={self.model_id!r}, " + f"got {config.model_id!r}." + ) + self._pipeline_config(config) + + def create_runtime(self, config: InferenceConfig) -> InferenceRuntime: + self.validate_config(config) + return self._replay_runtime_factory( + config=config, + options=OmnidreamsReplayRuntimeOptions( + pipeline_config=self._pipeline_config(config), + pipeline_factory=self._pipeline_factory, + ), + ) + + def create_webrtc_runtime(self, spec: DemoSpec) -> Any: + runtime_config = self.create_webrtc_runtime_config(spec=spec, runtime=None) + return self._webrtc_runtime_factory(config=runtime_config) + + def create_webrtc_runtime_config( + self, + *, + spec: DemoSpec, + runtime: Any, + ) -> OmnidreamsRuntimeConfig: + runtime_config = getattr(runtime, "config", None) + if isinstance(runtime_config, OmnidreamsRuntimeConfig): + return runtime_config + if spec.input_mode != "keyboard-driving": + raise ValueError( + "OmniDreams WebRTC requires input_mode='keyboard-driving', " + f"got {spec.input_mode!r}." + ) + if not isinstance(spec.output, WebRTCOutputSpec): + raise ValueError("OmniDreams WebRTC requires WebRTC output.") + config = spec.config + if config is None: + raise RuntimeError("DemoSpec.config was not initialized.") + self.validate_config(config) + scenario = resolve_webrtc_scenario(spec.scenario) + validate_postprocess_preset(scenario.postprocess_preset) + + preset_id = self._preset_id(config) + pipeline_config = self._pipeline_config(config) + seed = _option(config, "seed", 42) + device = config.device or str(_option(config, "device", "cuda:0")) + runtime_config = OmnidreamsRuntimeConfig( + pipeline_config_name=preset_id, + pipeline_config=pipeline_config, + scene_dir=scenario.scene_dir, + scene_uuid=scenario.scene_uuid, + scene_variant=scenario.scene_variant, + seed=None if seed is None else int(seed), + device=device, + video_height=spec.output.video_height, + video_width=spec.output.video_width, + fps=spec.output.fps, + camera_name=scenario.camera_name, + warmup_chunks=spec.output.warmup_chunks, + warmup_timeout_s=spec.output.warmup_timeout_s, + debug_serve_hdmaps=scenario.debug_serve_hdmaps, + postprocess=VideoPostprocessChainConfig(preset=scenario.postprocess_preset), + encoder_backend="default" if scenario.prefer_sw_encoder else "auto", + ) + return _apply_webrtc_runtime_options(runtime_config, config.runtime_options) + + def create_webrtc_session_manager( + self, + *, + spec: DemoSpec, + runtime: Any, + runtime_config: OmnidreamsRuntimeConfig, + fps: int, + client_liveness_timeout_s: float, + ) -> OmnidreamsDemoWebRTCSessionManager: + del spec + return OmnidreamsDemoWebRTCSessionManager( + runtime=runtime, + runtime_config=runtime_config, + fps=fps, + client_liveness_timeout_s=client_liveness_timeout_s, + ) + + def create_webrtc_app( + self, + *, + spec: DemoSpec, + session_manager: Any, + request_session_url: str, + ) -> Any: + return create_omnidreams_webrtc_app( + spec=spec, + session_manager=session_manager, + request_session_url=request_session_url, + ) + + def _preset_id(self, config: InferenceConfig | None) -> str: + return ( + DEFAULT_OMNIDREAMS_PRESET + if config is None or config.preset_id is None + else config.preset_id + ) + + def _pipeline_config(self, config: InferenceConfig) -> Any: + custom = config.runtime_options.get("pipeline_config") + if custom is not None: + return custom + preset_id = self._preset_id(config) + try: + return OMNIDREAMS_CONFIGS[preset_id] + except KeyError as exc: + supported = ", ".join(sorted(OMNIDREAMS_CONFIGS)) + raise ValueError( + f"Unsupported OmniDreams preset_id={preset_id!r}. " + f"Supported presets: {supported}." + ) from exc + + def _default_replay_prompt(self, config: InferenceConfig | None) -> str: + runner = OMNIDREAMS_RUNNERS.get(self._preset_id(config)) + return "" if runner is None else str(getattr(runner, "prompt", "")) + + +def _option(config: InferenceConfig, name: str, default: Any) -> Any: + return config.runtime_options.get(name, default) + + +def _apply_webrtc_runtime_options( + runtime_config: OmnidreamsRuntimeConfig, + options: Any, +) -> OmnidreamsRuntimeConfig: + if not isinstance(options, dict): + options = dict(options) + overrides: dict[str, Any] = {} + for name in ( + "move_speed_per_s", + "rotate_speed_rad_per_s", + "encoder_bitrate_bps", + "encoder_gop", + ): + if name in options: + overrides[name] = options[name] + return replace(runtime_config, **overrides) if overrides else runtime_config + + +__all__ = [ + "OmnidreamsDemoAdapter", + "ReplayRuntimeFactory", + "WebRTCRuntimeFactory", +] diff --git a/integrations/omnidreams/omnidreams/demo/cli.py b/integrations/omnidreams/omnidreams/demo/cli.py new file mode 100644 index 00000000..d35a62b7 --- /dev/null +++ b/integrations/omnidreams/omnidreams/demo/cli.py @@ -0,0 +1,187 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""CLI for the experimental shared OmniDreams demo path.""" + +from __future__ import annotations + +import argparse +from pathlib import Path + +import torch +import torch.distributed as dist +from omnidreams.runner import DEFAULT_EXAMPLE_DATA_UUID_1V + +from flashdreams.core.distributed import init as distributed_init +from flashdreams.runtime import InferenceConfig +from flashdreams.runtime.demo import ( + DemoSpec, + Mp4OutputSpec, + WebRTCOutputSpec, + run_flashdreams_demo, + serve_flashdreams_demo, +) +from flashdreams.serving.webrtc.bootstrap import ( + configure_logging, + initialize_cuda_distributed, +) + +from .adapter import OmnidreamsDemoAdapter +from .spec import ( + DEFAULT_OMNIDREAMS_PRESET, + OMNIDREAMS_MODEL_ID, + OmnidreamsWebRTCScenario, +) + + +def parse_args(argv: list[str] | None = None) -> argparse.Namespace: + parser = argparse.ArgumentParser( + description="Experimental OmniDreams demo using flashdreams.runtime.demo." + ) + subparsers = parser.add_subparsers(dest="command", required=True) + + replay = subparsers.add_parser("replay", help="Run an MP4 replay demo.") + replay.add_argument("--preset-id", default=DEFAULT_OMNIDREAMS_PRESET) + replay.add_argument("--device", default="cuda") + replay.add_argument("--prompt", default=None) + replay.add_argument("--hdmap-video-paths", type=_split_paths, default=()) + replay.add_argument("--first-frame-paths", type=_split_paths, default=()) + replay.add_argument("--camera-names", type=_split_strings, default=()) + replay.add_argument( + "--example-data", + action=argparse.BooleanOptionalAction, + default=None, + help=( + "Use the bundled single-view HF sample when asset paths are omitted " + "(default: auto)." + ), + ) + replay.add_argument("--example-data-uuid", default=DEFAULT_EXAMPLE_DATA_UUID_1V) + replay.add_argument("--total-blocks", type=int, default=60) + replay.add_argument("--pixel-height", type=int, default=704) + replay.add_argument("--pixel-width", type=int, default=1280) + replay.add_argument("--fps", type=int, default=30) + replay.add_argument("--output", type=Path, required=True) + + webrtc = subparsers.add_parser("webrtc", help="Serve a WebRTC driving demo.") + webrtc.add_argument("--preset-id", default=DEFAULT_OMNIDREAMS_PRESET) + webrtc.add_argument("--host", default="0.0.0.0") + webrtc.add_argument("--port", type=int, default=8082) + webrtc.add_argument("--device", default="cuda:0") + webrtc.add_argument("--seed", type=int, default=42) + webrtc.add_argument("--scene-dir", type=Path, default=None) + webrtc.add_argument("--scene-uuid", default=None) + webrtc.add_argument("--scene-variant", default="default") + webrtc.add_argument("--camera-name", default="camera_front_wide_120fov") + webrtc.add_argument("--fps", type=int, default=30) + webrtc.add_argument("--video-height", type=int, default=704) + webrtc.add_argument("--video-width", type=int, default=1280) + webrtc.add_argument("--warmup-chunks", type=int, default=10) + webrtc.add_argument("--warmup-timeout-s", type=float, default=600.0) + webrtc.add_argument("--client-liveness-timeout-s", type=float, default=10.0) + webrtc.add_argument("--debug-serve-hdmaps", action="store_true") + webrtc.add_argument("--postprocess-preset", default="") + webrtc.add_argument("--prefer-sw-encoder", action="store_true") + return parser.parse_args(argv) + + +def main(argv: list[str] | None = None) -> None: + configure_logging() + args = parse_args(argv) + adapter = OmnidreamsDemoAdapter() + if args.command == "replay": + run_flashdreams_demo(spec=_replay_spec(args), adapter=adapter) + return + if args.command == "webrtc": + context = initialize_cuda_distributed( + default_device=args.device, + distributed_init_fn=distributed_init, + configure_logging_fn=configure_logging, + torch_module=torch, + dist_module=dist, + ) + serve_flashdreams_demo( + spec=_webrtc_spec(args, device=str(context.device)), + adapter=adapter, + world_rank=context.world_rank, + ) + return + raise AssertionError(f"Unhandled command: {args.command}") + + +def _replay_spec(args: argparse.Namespace) -> DemoSpec: + scenario: dict[str, object] = { + "example_data": args.example_data, + "example_data_uuid": args.example_data_uuid, + "total_blocks": args.total_blocks, + "pixel_height": args.pixel_height, + "pixel_width": args.pixel_width, + "fps": args.fps, + } + if args.prompt: + scenario["prompt"] = args.prompt + if args.hdmap_video_paths: + scenario["hdmap_video_paths"] = args.hdmap_video_paths + if args.first_frame_paths: + scenario["first_frame_paths"] = args.first_frame_paths + if args.camera_names: + scenario["camera_names"] = args.camera_names + + return DemoSpec( + model_id=OMNIDREAMS_MODEL_ID, + preset_id=args.preset_id, + input_mode="replay", + scenario=scenario, + output=Mp4OutputSpec(path=args.output, fps=args.fps), + config=InferenceConfig( + model_id=OMNIDREAMS_MODEL_ID, + preset_id=args.preset_id, + device=args.device, + ), + ) + + +def _webrtc_spec(args: argparse.Namespace, *, device: str) -> DemoSpec: + return DemoSpec( + model_id=OMNIDREAMS_MODEL_ID, + preset_id=args.preset_id, + input_mode="keyboard-driving", + scenario=OmnidreamsWebRTCScenario( + scene_dir=args.scene_dir, + scene_uuid=args.scene_uuid, + scene_variant=args.scene_variant, + camera_name=args.camera_name, + debug_serve_hdmaps=args.debug_serve_hdmaps, + postprocess_preset=args.postprocess_preset, + prefer_sw_encoder=args.prefer_sw_encoder, + ), + output=WebRTCOutputSpec( + host=args.host, + port=args.port, + fps=args.fps, + video_width=args.video_width, + video_height=args.video_height, + warmup_chunks=args.warmup_chunks, + warmup_timeout_s=args.warmup_timeout_s, + client_liveness_timeout_s=args.client_liveness_timeout_s, + preload_name="Omnidreams", + ), + config=InferenceConfig( + model_id=OMNIDREAMS_MODEL_ID, + preset_id=args.preset_id, + device=device, + runtime_options={"seed": args.seed}, + ), + ) + + +def _split_paths(value: str) -> tuple[Path, ...]: + return tuple(Path(part) for part in value.split(",") if part) + + +def _split_strings(value: str) -> tuple[str, ...]: + return tuple(part for part in value.split(",") if part) + + +if __name__ == "__main__": + main() diff --git a/integrations/omnidreams/omnidreams/demo/replay.py b/integrations/omnidreams/omnidreams/demo/replay.py new file mode 100644 index 00000000..908d8456 --- /dev/null +++ b/integrations/omnidreams/omnidreams/demo/replay.py @@ -0,0 +1,277 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""OmniDreams replay runtime for the shared demo runner.""" + +from __future__ import annotations + +import os +import time +from collections.abc import Callable, Mapping +from dataclasses import dataclass +from typing import Any + +import torch +import torch.distributed as dist +from loguru import logger +from omnidreams.runner import _load_video + +from flashdreams.core.distributed import init as init_distributed +from flashdreams.infra.postprocess import VideoTensorLayout +from flashdreams.infra.runner_io import ( + DEFAULT_RUNNER_INSTALL_HINT, + load_first_frame_tensor, +) +from flashdreams.infra.video_output import VideoStepResult +from flashdreams.runtime.config import InferenceConfig +from flashdreams.runtime.inputs import InferenceInput +from flashdreams.runtime.interfaces import InferenceSession +from flashdreams.runtime.types import StepRequest, StepResult + +from .spec import OmnidreamsReplayScenario + +PipelineFactory = Callable[[Any, str], Any] + + +@dataclass(frozen=True, kw_only=True, slots=True) +class OmnidreamsReplayRuntimeOptions: + """Construction knobs for the replay runtime.""" + + pipeline_config: Any + pipeline_factory: PipelineFactory | None = None + output_layout: VideoTensorLayout = "bvtchw" + + +class OmnidreamsReplayRuntime: + """Heavyweight OmniDreams runtime consumed by ``run_inference_session``.""" + + def __init__( + self, + *, + config: InferenceConfig, + options: OmnidreamsReplayRuntimeOptions, + ) -> None: + self.config = config + self.options = options + if _is_torchrun_env() and not dist.is_initialized(): + init_distributed() + + if dist.is_initialized(): + self.local_rank = int(os.environ.get("LOCAL_RANK", "0")) + self.world_size = dist.get_world_size() + self.global_rank = dist.get_rank() + device = f"cuda:{self.local_rank}" + else: + self.local_rank = 0 + self.world_size = 1 + self.global_rank = 0 + device = config.device or "cuda" + + self.is_rank_zero = self.global_rank == 0 + factory = options.pipeline_factory or _default_pipeline_factory + self.pipeline = factory(options.pipeline_config, device) + + def start_session(self, inputs: InferenceInput) -> InferenceSession: + scenario = _scenario_from_inputs(inputs) + return OmnidreamsReplaySession( + pipeline=self.pipeline, + scenario=scenario, + device=torch.device(f"cuda:{self.local_rank}") + if dist.is_initialized() + else torch.device(self.config.device or "cuda"), + is_rank_zero=self.is_rank_zero, + output_layout=self.options.output_layout, + ) + + def close(self) -> None: + pipeline = getattr(self, "pipeline", None) + if pipeline is not None: + close = getattr(pipeline, "close", None) + if callable(close): + close() + del self.pipeline + device = torch.device(self.config.device or "cuda") + if device.type == "cuda" and torch.cuda.is_available(): + torch.cuda.empty_cache() + + +class OmnidreamsReplaySession: + """One MP4 replay rollout over a prepared scenario.""" + + def __init__( + self, + *, + pipeline: Any, + scenario: OmnidreamsReplayScenario, + device: torch.device, + is_rank_zero: bool, + output_layout: VideoTensorLayout, + ) -> None: + self.pipeline = pipeline + self.scenario = scenario + self.device = device + self.is_rank_zero = is_rank_zero + self.output_layout = output_layout + self.dtype = torch.bfloat16 + self._closed = False + self._step_index = 0 + self._frame_start = 0 + self._cache = self._initialize_cache() + self._hdmap_videos = self._load_hdmaps() + if self.device.type == "cuda" and torch.cuda.is_available(): + torch.cuda.synchronize(device=self.device) + if dist.is_initialized(): + dist.barrier() + + def next_step_request(self) -> StepRequest | None: + if self._closed: + return None + if self._step_index >= self.scenario.total_blocks: + return None + num_frames = int(self.pipeline.get_num_frames(self._step_index)) + if self._frame_start + num_frames > self._hdmap_videos.shape[2]: + return None + return StepRequest(step_index=self._step_index) + + def step(self, inputs: InferenceInput) -> StepResult: + del inputs + if self._closed: + raise RuntimeError("OmniDreams replay session is closed.") + + step_index = self._step_index + num_frames = int(self.pipeline.get_num_frames(step_index)) + frame_end = self._frame_start + num_frames + logger.info( + "OmniDreams demo replay step {} frames=[{}, {})", + step_index, + self._frame_start, + frame_end, + ) + start_t = time.perf_counter() + video_chunk = self.pipeline.generate( + autoregressive_index=step_index, + cache=self._cache, + hdmap=self._hdmap_videos[:, :, self._frame_start : frame_end], + ) + stats = self.pipeline.finalize( + autoregressive_index=step_index, + cache=self._cache, + ) + elapsed_s = time.perf_counter() - start_t + self._step_index += 1 + self._frame_start = frame_end + + metrics = _numeric_stats(stats) + metrics.setdefault("model_step_s", elapsed_s) + return StepResult( + step_index=step_index, + output=VideoStepResult.from_video_chunk( + chunk_index=step_index, + video_chunk=video_chunk, + layout=self.output_layout, + stats=metrics, + ), + frame_count=num_frames, + metrics=metrics, + ) + + def reset(self, inputs: InferenceInput | None = None) -> None: + if inputs is not None: + scenario = _scenario_from_inputs(inputs) + if scenario != self.scenario: + raise ValueError("OmniDreams replay reset cannot swap scenarios.") + cache = getattr(self, "_cache", None) + if cache is not None: + del self._cache + self._cache = self._initialize_cache() + self._step_index = 0 + self._frame_start = 0 + + def close(self) -> None: + self._closed = True + cache = getattr(self, "_cache", None) + if cache is not None: + del self._cache + + def _initialize_cache(self) -> Any: + scenario = self.scenario + first_frames = [ + load_first_frame_tensor( + path, + pixel_height=scenario.pixel_height, + pixel_width=scenario.pixel_width, + device=self.device, + dtype=self.dtype, + allow_video=True, + install_hint=DEFAULT_RUNNER_INSTALL_HINT, + ) + for path in scenario.first_frame_paths + ] + first_frames_t = torch.stack(first_frames, dim=0).unsqueeze(0) + cache = self.pipeline.initialize_cache( + text=[list(scenario.prompts)], + image=first_frames_t, + view_names=list(scenario.camera_names), + ) + release = getattr(self.pipeline, "release_oneshot_encoders", None) + if callable(release): + release() + return cache + + def _load_hdmaps(self) -> torch.Tensor: + scenario = self.scenario + videos = [ + _load_video( + path, + pixel_height=scenario.pixel_height, + pixel_width=scenario.pixel_width, + device=self.device, + dtype=self.dtype, + ) + for path in scenario.hdmap_video_paths + ] + # [B=1, V, T, C, H, W] + hdmap_videos = torch.stack(videos, dim=0).unsqueeze(0) + if self.is_rank_zero: + logger.info( + "Loaded OmniDreams demo HDMaps shape={} views={}", + tuple(hdmap_videos.shape), + len(scenario.camera_names), + ) + return hdmap_videos + + +def _default_pipeline_factory(pipeline_config: Any, device: str) -> Any: + return pipeline_config.setup().to(device=device).eval() + + +def _scenario_from_inputs(inputs: InferenceInput) -> OmnidreamsReplayScenario: + scenario = inputs.global_conditioning.get("scenario") + if not isinstance(scenario, OmnidreamsReplayScenario): + raise TypeError( + "OmniDreams replay runtime requires global_conditioning['scenario'] " + "to be an OmnidreamsReplayScenario." + ) + return scenario + + +def _numeric_stats(stats: Any) -> dict[str, float | int]: + if not isinstance(stats, Mapping): + return {} + return { + str(key): value + for key, value in stats.items() + if isinstance(value, (float, int)) and not isinstance(value, bool) + } + + +def _is_torchrun_env() -> bool: + return "RANK" in os.environ and "WORLD_SIZE" in os.environ + + +__all__ = [ + "OmnidreamsReplayRuntime", + "OmnidreamsReplayRuntimeOptions", + "OmnidreamsReplaySession", + "PipelineFactory", +] diff --git a/integrations/omnidreams/omnidreams/demo/spec.py b/integrations/omnidreams/omnidreams/demo/spec.py new file mode 100644 index 00000000..0a5dcc06 --- /dev/null +++ b/integrations/omnidreams/omnidreams/demo/spec.py @@ -0,0 +1,271 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""OmniDreams demo-specific scenario shapes.""" + +from __future__ import annotations + +from collections.abc import Mapping, Sequence +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +from omnidreams.runner import ( + DEFAULT_EXAMPLE_DATA_UUID_1V, + DEFAULT_VIDEO_HEIGHT, + DEFAULT_VIDEO_WIDTH, + _ensure_hf_single_view_example_data_synced, + _example_camera_names, +) +from omnidreams.scenes import SCENE_VARIANT_DEFAULT +from omnidreams.webrtc.session import DEFAULT_WEBRTC_SCENE_UUID + +DEFAULT_OMNIDREAMS_PRESET = "omnidreams-sv-2steps-chunk2-loc6-lightvae-lighttae" +OMNIDREAMS_MODEL_ID = "omnidreams" + + +@dataclass(frozen=True, kw_only=True, slots=True) +class OmnidreamsReplayScenario: + """Resolved replay assets for the shared MP4 demo path.""" + + prompts: tuple[str, ...] + hdmap_video_paths: tuple[Path, ...] + first_frame_paths: tuple[Path, ...] + camera_names: tuple[str, ...] + total_blocks: int = 60 + pixel_height: int = DEFAULT_VIDEO_HEIGHT + pixel_width: int = DEFAULT_VIDEO_WIDTH + fps: int = 30 + + def __post_init__(self) -> None: + if not self.prompts: + raise ValueError("OmnidreamsReplayScenario.prompts must be non-empty.") + num_views = len(self.prompts) + for name, values in ( + ("hdmap_video_paths", self.hdmap_video_paths), + ("first_frame_paths", self.first_frame_paths), + ("camera_names", self.camera_names), + ): + if len(values) != num_views: + raise ValueError( + f"OmnidreamsReplayScenario.{name} has {len(values)} " + f"entries but prompts has {num_views}." + ) + if self.total_blocks <= 0: + raise ValueError("OmnidreamsReplayScenario.total_blocks must be > 0.") + if self.pixel_height <= 0 or self.pixel_width <= 0: + raise ValueError("OmnidreamsReplayScenario pixel dimensions must be > 0.") + if self.fps <= 0: + raise ValueError("OmnidreamsReplayScenario.fps must be > 0.") + object.__setattr__( + self, + "hdmap_video_paths", + tuple(Path(path) for path in self.hdmap_video_paths), + ) + object.__setattr__( + self, + "first_frame_paths", + tuple(Path(path) for path in self.first_frame_paths), + ) + + +@dataclass(frozen=True, kw_only=True, slots=True) +class OmnidreamsWebRTCScenario: + """Scene/options for the shared WebRTC demo path.""" + + scene_dir: Path | None = None + scene_uuid: str | None = DEFAULT_WEBRTC_SCENE_UUID + scene_variant: str = SCENE_VARIANT_DEFAULT + camera_name: str = "camera_front_wide_120fov" + debug_serve_hdmaps: bool = False + postprocess_preset: str = "" + prefer_sw_encoder: bool = False + + def __post_init__(self) -> None: + if self.scene_dir is not None: + object.__setattr__(self, "scene_dir", Path(self.scene_dir)) + if not self.scene_variant.strip(): + raise ValueError("OmnidreamsWebRTCScenario.scene_variant is required.") + if not self.camera_name.strip(): + raise ValueError("OmnidreamsWebRTCScenario.camera_name is required.") + + +def resolve_replay_scenario( + value: Any, + *, + default_prompt: str = "", +) -> OmnidreamsReplayScenario: + """Normalize a user/demo scenario into a validated replay scenario.""" + if isinstance(value, OmnidreamsReplayScenario): + _require_existing_paths(value.hdmap_video_paths, label="hdmap_video_paths") + _require_existing_paths(value.first_frame_paths, label="first_frame_paths") + return value + if value is None: + value = {} + if not isinstance(value, Mapping): + raise TypeError( + "OmniDreams replay scenario must be an OmnidreamsReplayScenario " + "a mapping, or None." + ) + + hdmap_paths = _path_tuple(value.get("hdmap_video_paths", ())) + first_paths = _path_tuple(value.get("first_frame_paths", ())) + example_data = _resolve_example_data_default(value) + if example_data and (not hdmap_paths or not first_paths): + example_hdmaps, example_first_frames = ( + _ensure_hf_single_view_example_data_synced( + str(value.get("example_data_uuid", DEFAULT_EXAMPLE_DATA_UUID_1V)) + ) + ) + if not hdmap_paths: + hdmap_paths = example_hdmaps + if not first_paths: + first_paths = example_first_frames + + _require_existing_paths(hdmap_paths, label="hdmap_video_paths") + _require_existing_paths(first_paths, label="first_frame_paths") + if len(hdmap_paths) != len(first_paths): + raise ValueError( + "OmniDreams replay scenario requires one HDMap video and first " + "frame per view." + ) + + num_views = len(hdmap_paths) + prompts = _resolve_prompts(value, num_views, default_prompt=default_prompt) + camera_names = _string_tuple(value.get("camera_names", ())) + if not camera_names: + camera_names = ( + _example_camera_names(num_views) + if example_data + else tuple(f"view_{i}" for i in range(num_views)) + ) + + return OmnidreamsReplayScenario( + prompts=prompts, + hdmap_video_paths=hdmap_paths, + first_frame_paths=first_paths, + camera_names=camera_names, + total_blocks=int(value.get("total_blocks", 60)), + pixel_height=int(value.get("pixel_height", DEFAULT_VIDEO_HEIGHT)), + pixel_width=int(value.get("pixel_width", DEFAULT_VIDEO_WIDTH)), + fps=int(value.get("fps", 30)), + ) + + +def resolve_webrtc_scenario(value: Any) -> OmnidreamsWebRTCScenario: + """Normalize a user/demo scenario into a WebRTC scenario.""" + if value is None: + return OmnidreamsWebRTCScenario() + if isinstance(value, OmnidreamsWebRTCScenario): + return value + if not isinstance(value, Mapping): + raise TypeError( + "OmniDreams WebRTC scenario must be an OmnidreamsWebRTCScenario, " + "a mapping, or None." + ) + scene_dir = value.get("scene_dir") + return OmnidreamsWebRTCScenario( + scene_dir=Path(scene_dir) if scene_dir is not None else None, + scene_uuid=value.get("scene_uuid", DEFAULT_WEBRTC_SCENE_UUID), + scene_variant=str(value.get("scene_variant", SCENE_VARIANT_DEFAULT)), + camera_name=str(value.get("camera_name", "camera_front_wide_120fov")), + debug_serve_hdmaps=bool(value.get("debug_serve_hdmaps", False)), + postprocess_preset=str(value.get("postprocess_preset", "")), + prefer_sw_encoder=bool(value.get("prefer_sw_encoder", False)), + ) + + +def _resolve_prompts( + value: Mapping[str, Any], + num_views: int, + *, + default_prompt: str, +) -> tuple[str, ...]: + prompts = _string_tuple(value.get("prompts", ())) + if prompts: + if len(prompts) != num_views: + raise ValueError( + f"OmniDreams replay prompts has {len(prompts)} entries but " + f"there are {num_views} views." + ) + return prompts + prompt = str(value.get("prompt", "")).strip() + if not prompt: + prompt = default_prompt.strip() + if not prompt: + raise ValueError("OmniDreams replay scenario requires prompt or prompts.") + return (prompt,) * num_views + + +def _resolve_example_data_default(value: Mapping[str, Any]) -> bool: + explicit = value.get("example_data") + if explicit is not None: + return _bool_value(explicit) + return not ( + _has_nonempty_value(value, "hdmap_video_paths") + or _has_nonempty_value(value, "first_frame_paths") + ) + + +def _bool_value(value: Any) -> bool: + if isinstance(value, bool): + return value + if isinstance(value, str): + lowered = value.strip().lower() + if lowered in {"1", "true", "yes", "on"}: + return True + if lowered in {"0", "false", "no", "off"}: + return False + return bool(value) + + +def _has_nonempty_value(value: Mapping[str, Any], key: str) -> bool: + if key not in value: + return False + raw = value[key] + if raw is None or raw == "": + return False + if isinstance(raw, Sequence) and not isinstance(raw, str): + return len(raw) > 0 + return True + + +def _path_tuple(value: Any) -> tuple[Path, ...]: + if value is None or value == "": + return () + if isinstance(value, (str, Path)): + return (Path(value),) + if isinstance(value, Sequence): + return tuple(Path(path) for path in value) + raise TypeError(f"Expected path or path sequence, got {type(value).__name__}.") + + +def _string_tuple(value: Any) -> tuple[str, ...]: + if value is None or value == "": + return () + if isinstance(value, str): + return (value,) + if isinstance(value, Sequence): + return tuple(str(item) for item in value) + raise TypeError(f"Expected string or string sequence, got {type(value).__name__}.") + + +def _require_existing_paths(paths: tuple[Path, ...], *, label: str) -> None: + if not paths: + raise ValueError(f"OmniDreams replay scenario requires {label}.") + missing = tuple(path for path in paths if not path.exists()) + if missing: + raise FileNotFoundError( + f"OmniDreams replay scenario missing {label}: " + + ", ".join(str(path) for path in missing) + ) + + +__all__ = [ + "DEFAULT_OMNIDREAMS_PRESET", + "OMNIDREAMS_MODEL_ID", + "OmnidreamsReplayScenario", + "OmnidreamsWebRTCScenario", + "resolve_replay_scenario", + "resolve_webrtc_scenario", +] diff --git a/integrations/omnidreams/omnidreams/demo/webrtc.py b/integrations/omnidreams/omnidreams/demo/webrtc.py new file mode 100644 index 00000000..699d4709 --- /dev/null +++ b/integrations/omnidreams/omnidreams/demo/webrtc.py @@ -0,0 +1,178 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +"""OmniDreams WebRTC hooks for the shared demo API.""" + +from __future__ import annotations + +from typing import Any, cast + +from aiohttp import web +from omnidreams.webrtc.session import ( + OmnidreamsRuntimeConfig, + OmnidreamsRuntimeError, + OmnidreamsSessionInput, + _validate_requested_postprocess_preset, +) + +from flashdreams.plugins.registry import resolve_postprocess_preset +from flashdreams.runtime.demo import DemoSpec +from flashdreams.runtime.demo.webrtc import SharedDemoWebRTCSessionManager +from flashdreams.serving.webrtc.controls import WSAD_SUPPORTED_KEYS +from flashdreams.serving.webrtc.manager import DEFAULT_CLIENT_LIVENESS_TIMEOUT_S +from flashdreams.serving.webrtc.server import ( + SESSION_MANAGER_KEY, + SessionBusyError, + create_packaged_webrtc_app, +) +from flashdreams.serving.webrtc.server import ( + close_package_resources as _close_package_resources, +) + + +class OmnidreamsDemoWebRTCSessionManager(SharedDemoWebRTCSessionManager): + """Shared WebRTC manager customized for OmniDreams session semantics.""" + + _busy_message = "An Omnidreams session is already active." + _warmup_label = "Omnidreams WebRTC" + _runtime_error_types = (OmnidreamsRuntimeError,) + _close_session_on_generation_error = True + _resampler_supported_keys = WSAD_SUPPORTED_KEYS + + runtime_config: OmnidreamsRuntimeConfig + _runtime: Any + + def __init__( + self, + *, + runtime: Any, + runtime_config: OmnidreamsRuntimeConfig, + fps: int, + client_liveness_timeout_s: float = DEFAULT_CLIENT_LIVENESS_TIMEOUT_S, + ) -> None: + super().__init__( + model_name=runtime_config.pipeline_config_name, + runtime=runtime, + runtime_config=runtime_config, + fps=fps, + client_liveness_timeout_s=client_liveness_timeout_s, + ) + self._pending_session_input: OmnidreamsSessionInput | None = None + + def _model_name(self) -> str: + return self.runtime_config.pipeline_config_name + + def _chunk_done_extra(self) -> dict[str, Any]: + return { + "stream": "hdmap" if self.runtime_config.debug_serve_hdmaps else "rgb", + "postprocess_preset": self._runtime.postprocess_preset, + } + + def _peek_pending_session_input(self) -> OmnidreamsSessionInput | None: + return self._pending_session_input + + def _clear_pending_session_input(self) -> None: + self._pending_session_input = None + + async def _reset_runtime_for_session( + self, session_input: OmnidreamsSessionInput | None + ) -> None: + await self._runtime.reset_for_new_session(session_input=session_input) + + def set_pending_session_input(self, session_input: OmnidreamsSessionInput) -> None: + if self.has_active_session(): + raise SessionBusyError(self._busy_message) + preset = session_input.postprocess_preset + if preset: + _validate_requested_postprocess_preset( + requested_preset=preset, + configured_preset=self.runtime_config.postprocess.preset, + ) + self._pending_session_input = session_input + + +async def postprocess_options(request: web.Request) -> web.StreamResponse: + """Return the postprocess preset selected at server launch.""" + manager = _get_omnidreams_manager(request.app) + configured_preset = manager.runtime_config.postprocess.preset + presets = [configured_preset] if configured_preset else [] + return web.json_response( + { + "default_preset": configured_preset, + "presets": presets, + } + ) + + +async def session_input(request: web.Request) -> web.StreamResponse: + """Apply browser-selected settings to the next WebRTC rollout.""" + try: + payload = await request.json() + except Exception as exc: + raise web.HTTPBadRequest(reason="Expected JSON session input.") from exc + if not isinstance(payload, dict): + raise web.HTTPBadRequest(reason="Session input must be a JSON object.") + preset = payload.get("postprocess_preset") + if not isinstance(preset, str): + raise web.HTTPBadRequest( + reason="Session input must include string 'postprocess_preset'." + ) + + manager = _get_omnidreams_manager(request.app) + try: + manager.set_pending_session_input( + OmnidreamsSessionInput(postprocess_preset=preset) + ) + except SessionBusyError as exc: + raise web.HTTPConflict(reason=str(exc)) from exc + except ValueError as exc: + raise web.HTTPBadRequest(reason=str(exc)) from exc + return web.json_response({"postprocess_preset": preset}) + + +def configure_omnidreams_webrtc_app(app: web.Application) -> None: + """Register OmniDreams browser support routes on a shared WebRTC app.""" + app.router.add_get("/api/postprocess/options", postprocess_options) + app.router.add_post("/api/session/input", session_input) + + +def create_omnidreams_webrtc_app( + *, + spec: DemoSpec, + session_manager: Any, + request_session_url: str, +) -> web.Application: + """Create the packaged OmniDreams browser app through shared serving glue.""" + from importlib.resources import as_file, files + + output_preload_name = getattr(spec.output, "preload_name", None) + preload_name = output_preload_name if isinstance(output_preload_name, str) else "" + return create_packaged_webrtc_app( + web_resource=files("omnidreams.webrtc").joinpath("web"), + session_manager=session_manager, + preload_name=preload_name or "Omnidreams", + request_session_url=request_session_url, + configure_app=configure_omnidreams_webrtc_app, + as_file_fn=as_file, + cleanup_callback=_close_package_resources, + ) + + +def validate_postprocess_preset(preset: str) -> None: + """Validate a configured preset without enabling the output system broadly.""" + if preset: + resolve_postprocess_preset(preset) + + +def _get_omnidreams_manager(app: web.Application) -> OmnidreamsDemoWebRTCSessionManager: + return cast(OmnidreamsDemoWebRTCSessionManager, app[SESSION_MANAGER_KEY]) + + +__all__ = [ + "OmnidreamsDemoWebRTCSessionManager", + "configure_omnidreams_webrtc_app", + "create_omnidreams_webrtc_app", + "postprocess_options", + "session_input", + "validate_postprocess_preset", +] diff --git a/integrations/omnidreams/pyproject.toml b/integrations/omnidreams/pyproject.toml index 99d35d73..eafd80a1 100644 --- a/integrations/omnidreams/pyproject.toml +++ b/integrations/omnidreams/pyproject.toml @@ -22,7 +22,8 @@ name = "flashdreams-omnidreams" version = "0.1.0" description = "Omnidreams inference with flashdreams (webrtc / gRPC servers + the interactive-drive desktop demo)" readme = "README.md" -requires-python = ">=3.10,<3.14" +# PyNvVideoCodec 2.1 currently publishes wheels through CPython 3.12. +requires-python = ">=3.10,<3.13" dependencies = [ # Core inference / serving deps (consumed by ``omnidreams.webrtc``, # ``omnidreams.grpc``, and the ``omnidreams.interactive_drive`` desktop @@ -104,6 +105,10 @@ omnidreams-prepare = "omnidreams.prepare:main" # FlashDreams generation, and DrivingGen adapter setup. omnidreams-eval = "omnidreams.eval.cli:main" +# Experimental shared demo API path. This coexists with the legacy +# WebRTC/gRPC/interactive-drive demos until the new adapter is proven. +omnidreams-demo = "omnidreams.demo.cli:main" + # Desktop interactive-drive demo entry point. Requires the # ``interactive-drive`` extra (it adds slangpy); without it the # presenter import fails fast with a clear message. diff --git a/integrations/omnidreams/tests/test_demo_api.py b/integrations/omnidreams/tests/test_demo_api.py new file mode 100644 index 00000000..d9411475 --- /dev/null +++ b/integrations/omnidreams/tests/test_demo_api.py @@ -0,0 +1,577 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +from __future__ import annotations + +from collections.abc import Sequence +from pathlib import Path +from typing import Any, cast + +import omnidreams.demo.spec as spec_module +import omnidreams.demo.webrtc as demo_webrtc_module +import pytest +import torch +from aiohttp import web +from omnidreams.config import OMNIDREAMS_RUNNERS +from omnidreams.demo import ( + DEFAULT_OMNIDREAMS_PRESET, + OMNIDREAMS_MODEL_ID, + OmnidreamsDemoAdapter, + OmnidreamsReplayScenario, + OmnidreamsWebRTCScenario, +) +from omnidreams.demo.cli import _replay_spec, _webrtc_spec, parse_args +from omnidreams.demo.replay import ( + OmnidreamsReplayRuntime, + OmnidreamsReplayRuntimeOptions, +) +from omnidreams.demo.webrtc import OmnidreamsDemoWebRTCSessionManager + +from flashdreams.infra.video_output import VideoStepResult +from flashdreams.runtime import ( + InferenceConfig, + InferenceInput, + OutputArtifact, + OutputTarget, + StepResult, +) +from flashdreams.runtime.demo import ( + DemoSpec, + Mp4OutputSpec, + WebRTCOutputSpec, + serve_flashdreams_demo, +) +from flashdreams.runtime.demo.replay import run_replay_demo +from flashdreams.runtime.demo.webrtc import WebRTCDemo, build_webrtc_demo +from flashdreams.serving.webrtc.server import SESSION_MANAGER_KEY + +pytestmark = pytest.mark.ci_cpu + + +def test_omnidreams_demo_defaults_to_stable_non_perf_preset() -> None: + args = parse_args(["replay", "--output", "demo.mp4"]) + + assert args.preset_id == "omnidreams-sv-2steps-chunk2-loc6-lightvae-lighttae" + assert not args.preset_id.endswith("-perf") + + +def test_omnidreams_demo_adapter_declares_mp4_and_webrtc_modes() -> None: + adapter = OmnidreamsDemoAdapter() + + assert adapter.model_id == OMNIDREAMS_MODEL_ID + assert adapter.supported_input_modes() == ("replay", "keyboard-driving") + assert adapter.supported_output_modes() == ("mp4", "webrtc") + + +def test_omnidreams_replay_demo_uses_shared_runner(tmp_path: Path) -> None: + hdmap = tmp_path / "hdmap.mp4" + first_frame = tmp_path / "first.png" + hdmap.write_bytes(b"fake") + first_frame.write_bytes(b"fake") + pipeline_config = object() + adapter = OmnidreamsDemoAdapter() + output = _RecordingOutputTarget() + calls: list[dict[str, Any]] = [] + + def fake_runner(**kwargs: Any) -> Sequence[OutputArtifact]: + calls.append(kwargs) + return (OutputArtifact(kind="video/mp4", uri="memory://omnidreams"),) + + spec = DemoSpec( + model_id=OMNIDREAMS_MODEL_ID, + preset_id=DEFAULT_OMNIDREAMS_PRESET, + input_mode="replay", + scenario={ + "prompt": "drive through a city", + "hdmap_video_paths": (hdmap,), + "first_frame_paths": (first_frame,), + "camera_names": ("camera_front_wide_120fov",), + "total_blocks": 1, + }, + output=Mp4OutputSpec(path=tmp_path / "demo.mp4", fps=30), + config=InferenceConfig( + model_id=OMNIDREAMS_MODEL_ID, + preset_id=DEFAULT_OMNIDREAMS_PRESET, + runtime_options={"pipeline_config": pipeline_config}, + ), + ) + + artifacts = run_replay_demo( + spec=spec, + adapter=adapter, + output_target_factory=lambda output_spec: output, + runner=fake_runner, + ) + + assert artifacts == (OutputArtifact(kind="video/mp4", uri="memory://omnidreams"),) + assert len(calls) == 1 + assert calls[0]["adapter"] is adapter + assert calls[0]["config"] == spec.config + scenario = calls[0]["initial_inputs"].global_conditioning["scenario"] + assert isinstance(scenario, OmnidreamsReplayScenario) + assert scenario.prompts == ("drive through a city",) + assert scenario.hdmap_video_paths == (hdmap,) + assert scenario.first_frame_paths == (first_frame,) + assert scenario.camera_names == ("camera_front_wide_120fov",) + + +def test_omnidreams_replay_invalid_scenario_fails_before_runtime_creation( + tmp_path: Path, +) -> None: + adapter = OmnidreamsDemoAdapter( + replay_runtime_factory=lambda **kwargs: pytest.fail( + f"runtime should not be created: {kwargs}" + ) + ) + output_factory_calls = 0 + + def output_factory(output_spec: object) -> OutputTarget: + nonlocal output_factory_calls + del output_spec + output_factory_calls += 1 + return _RecordingOutputTarget() + + spec = DemoSpec( + model_id=OMNIDREAMS_MODEL_ID, + input_mode="replay", + scenario={ + "prompt": "drive", + "hdmap_video_paths": (tmp_path / "missing-hdmap.mp4",), + "first_frame_paths": (tmp_path / "missing-first.png",), + }, + output=Mp4OutputSpec(path=tmp_path / "demo.mp4", fps=30), + config=InferenceConfig( + model_id=OMNIDREAMS_MODEL_ID, + runtime_options={"pipeline_config": object()}, + ), + ) + + with pytest.raises(FileNotFoundError, match="missing hdmap_video_paths"): + run_replay_demo( + spec=spec, + adapter=adapter, + output_target_factory=output_factory, + ) + + assert output_factory_calls == 0 + + +def test_omnidreams_replay_cli_defaults_to_hf_example_data( + tmp_path: Path, + monkeypatch: pytest.MonkeyPatch, +) -> None: + hdmap = tmp_path / "hf-hdmap.mp4" + first_frame = tmp_path / "hf-first.png" + hdmap.write_bytes(b"fake") + first_frame.write_bytes(b"fake") + synced_uuids: list[str] = [] + + def fake_sync(uuid: str) -> tuple[tuple[Path, ...], tuple[Path, ...]]: + synced_uuids.append(uuid) + return (hdmap,), (first_frame,) + + monkeypatch.setattr( + spec_module, + "_ensure_hf_single_view_example_data_synced", + fake_sync, + ) + args = parse_args(["replay", "--output", str(tmp_path / "demo.mp4")]) + spec = _replay_spec(args) + + prepared = OmnidreamsDemoAdapter().prepare_scenario(spec) + + scenario = prepared.initial_inputs.global_conditioning["scenario"] + assert isinstance(scenario, OmnidreamsReplayScenario) + assert synced_uuids == ["239560dc-33d1-11ef-9720-00044bcbccac"] + assert scenario.hdmap_video_paths == (hdmap,) + assert scenario.first_frame_paths == (first_frame,) + assert scenario.camera_names == ("camera_front_wide_120fov",) + assert scenario.prompts == ( + str(getattr(OMNIDREAMS_RUNNERS[DEFAULT_OMNIDREAMS_PRESET], "prompt")), + ) + + +def test_omnidreams_replay_cli_can_disable_example_data(tmp_path: Path) -> None: + args = parse_args( + ["replay", "--no-example-data", "--output", str(tmp_path / "demo.mp4")] + ) + spec = _replay_spec(args) + + with pytest.raises(ValueError, match="requires hdmap_video_paths"): + OmnidreamsDemoAdapter().prepare_scenario(spec) + + +def test_omnidreams_replay_runtime_generates_video_step_result( + tmp_path: Path, + monkeypatch: pytest.MonkeyPatch, +) -> None: + import omnidreams.demo.replay as replay_module + + hdmap = tmp_path / "hdmap.mp4" + first_frame = tmp_path / "first.png" + hdmap.write_bytes(b"fake") + first_frame.write_bytes(b"fake") + pipeline = _FakeOmnidreamsPipeline() + monkeypatch.setattr( + replay_module, + "load_first_frame_tensor", + lambda *args, **kwargs: torch.zeros(1, 3, 2, 2), + ) + monkeypatch.setattr( + replay_module, + "_load_video", + lambda *args, **kwargs: torch.zeros(2, 3, 2, 2), + ) + + runtime = OmnidreamsReplayRuntime( + config=InferenceConfig(model_id=OMNIDREAMS_MODEL_ID, device="cpu"), + options=OmnidreamsReplayRuntimeOptions( + pipeline_config=object(), + pipeline_factory=lambda pipeline_config, device: pipeline, + ), + ) + scenario = OmnidreamsReplayScenario( + prompts=("drive",), + hdmap_video_paths=(hdmap,), + first_frame_paths=(first_frame,), + camera_names=("camera_front_wide_120fov",), + total_blocks=1, + pixel_height=2, + pixel_width=2, + fps=30, + ) + session = runtime.start_session( + InferenceInput(global_conditioning={"scenario": scenario}) + ) + + request = session.next_step_request() + assert request is not None + assert request.step_index == 0 + result = session.step(InferenceInput()) + + assert result.step_index == 0 + assert result.frame_count == 1 + assert isinstance(result.output, VideoStepResult) + assert result.output.layout == "bvtchw" + assert result.output.video_chunk.shape == (1, 1, 1, 3, 2, 2) + assert result.metrics["denoise_s"] == 0.25 + assert session.next_step_request() is None + assert pipeline.initialize_cache_calls == [ + { + "text": [["drive"]], + "image_shape": (1, 1, 1, 3, 2, 2), + "view_names": ["camera_front_wide_120fov"], + } + ] + runtime.close() + + +def test_omnidreams_webrtc_cli_builds_keyboard_driving_spec(tmp_path: Path) -> None: + args = parse_args( + [ + "webrtc", + "--host", + "127.0.0.1", + "--port", + "9090", + "--device", + "cuda:2", + "--seed", + "123", + "--scene-dir", + str(tmp_path / "scene"), + "--scene-uuid", + "scene-1", + "--scene-variant", + "rain", + "--camera-name", + "camera_front_wide_120fov", + "--fps", + "24", + "--video-height", + "32", + "--video-width", + "64", + "--warmup-chunks", + "0", + "--warmup-timeout-s", + "1.5", + "--client-liveness-timeout-s", + "2.5", + "--debug-serve-hdmaps", + "--prefer-sw-encoder", + ] + ) + + spec = _webrtc_spec(args, device="cuda:3") + + assert spec.model_id == OMNIDREAMS_MODEL_ID + assert spec.preset_id == DEFAULT_OMNIDREAMS_PRESET + assert spec.input_mode == "keyboard-driving" + assert isinstance(spec.scenario, OmnidreamsWebRTCScenario) + assert spec.scenario.scene_dir == tmp_path / "scene" + assert spec.scenario.scene_uuid == "scene-1" + assert spec.scenario.scene_variant == "rain" + assert spec.scenario.camera_name == "camera_front_wide_120fov" + assert spec.scenario.debug_serve_hdmaps is True + assert spec.scenario.prefer_sw_encoder is True + assert isinstance(spec.output, WebRTCOutputSpec) + assert spec.output.host == "127.0.0.1" + assert spec.output.port == 9090 + assert spec.output.fps == 24 + assert spec.output.video_width == 64 + assert spec.output.video_height == 32 + assert spec.output.warmup_chunks == 0 + assert spec.output.warmup_timeout_s == 1.5 + assert spec.output.client_liveness_timeout_s == 2.5 + assert spec.config is not None + assert spec.config.device == "cuda:3" + assert spec.config.runtime_options["seed"] == 123 + + +def test_omnidreams_webrtc_demo_uses_shared_manager_with_model_config() -> None: + pipeline_config = object() + adapter = OmnidreamsDemoAdapter(webrtc_runtime_factory=_FakeWebRTCRuntime) + spec = DemoSpec( + model_id=OMNIDREAMS_MODEL_ID, + preset_id=DEFAULT_OMNIDREAMS_PRESET, + input_mode="keyboard-driving", + scenario=OmnidreamsWebRTCScenario( + scene_uuid="scene-1", + scene_variant="rain", + camera_name="camera_front_wide_120fov", + debug_serve_hdmaps=True, + prefer_sw_encoder=True, + ), + output=WebRTCOutputSpec( + host="0.0.0.0", + port=8082, + fps=24, + video_width=64, + video_height=32, + warmup_chunks=0, + warmup_timeout_s=1.0, + ), + config=InferenceConfig( + model_id=OMNIDREAMS_MODEL_ID, + preset_id=DEFAULT_OMNIDREAMS_PRESET, + device="cuda:7", + runtime_options={"pipeline_config": pipeline_config, "seed": 123}, + ), + ) + + demo = build_webrtc_demo(spec=spec, adapter=adapter) + + assert isinstance(demo.runtime, _FakeWebRTCRuntime) + assert isinstance(demo.session_manager, OmnidreamsDemoWebRTCSessionManager) + assert demo.session_manager._runtime is demo.runtime + assert demo.session_manager.runtime_config is demo.runtime.config + assert demo.runtime_config is demo.runtime.config + assert demo.runtime_config.pipeline_config is pipeline_config + assert demo.runtime_config.pipeline_config_name == DEFAULT_OMNIDREAMS_PRESET + assert demo.runtime_config.scene_uuid == "scene-1" + assert demo.runtime_config.scene_variant == "rain" + assert demo.runtime_config.seed == 123 + assert demo.runtime_config.device == "cuda:7" + assert demo.runtime_config.video_width == 64 + assert demo.runtime_config.video_height == 32 + assert demo.runtime_config.fps == 24 + assert demo.runtime_config.debug_serve_hdmaps is True + assert demo.runtime_config.encoder_backend == "default" + assert demo.session_manager._model_name() == DEFAULT_OMNIDREAMS_PRESET + assert demo.host == "0.0.0.0" + assert demo.port == 8082 + + +def test_omnidreams_webrtc_demo_installs_model_routes( + monkeypatch: pytest.MonkeyPatch, +) -> None: + app_calls: list[dict[str, Any]] = [] + + def fake_create_packaged_webrtc_app(**kwargs: Any) -> web.Application: + app_calls.append(kwargs) + app = web.Application() + app[SESSION_MANAGER_KEY] = kwargs["session_manager"] + kwargs["configure_app"](app) + return app + + monkeypatch.setattr( + demo_webrtc_module, + "create_packaged_webrtc_app", + fake_create_packaged_webrtc_app, + ) + adapter = OmnidreamsDemoAdapter(webrtc_runtime_factory=_FakeWebRTCRuntime) + spec = DemoSpec( + model_id=OMNIDREAMS_MODEL_ID, + preset_id=DEFAULT_OMNIDREAMS_PRESET, + input_mode="keyboard-driving", + scenario=OmnidreamsWebRTCScenario(), + output=WebRTCOutputSpec( + host="0.0.0.0", + port=8082, + warmup_timeout_s=1.0, + preload_name="Test Omnidreams", + ), + config=InferenceConfig( + model_id=OMNIDREAMS_MODEL_ID, + preset_id=DEFAULT_OMNIDREAMS_PRESET, + runtime_options={"pipeline_config": object()}, + ), + ) + + demo = build_webrtc_demo(spec=spec, adapter=adapter, create_app=True) + + assert demo.app is not None + assert app_calls[0]["session_manager"] is demo.session_manager + assert app_calls[0]["request_session_url"] == ( + "http://127.0.0.1:8082/request_session" + ) + assert app_calls[0]["preload_name"] == "Test Omnidreams" + route_paths = {resource.canonical for resource in demo.app.router.resources()} + assert "/api/postprocess/options" in route_paths + assert "/api/session/input" in route_paths + + +def test_omnidreams_webrtc_demo_serves_through_shared_runner( + monkeypatch: pytest.MonkeyPatch, +) -> None: + server_calls: list[dict[str, Any]] = [] + + def fake_create_packaged_webrtc_app(**kwargs: Any) -> web.Application: + app = web.Application() + app[SESSION_MANAGER_KEY] = kwargs["session_manager"] + kwargs["configure_app"](app) + return app + + def fake_server_runner(**kwargs: Any) -> None: + server_calls.append(kwargs) + + monkeypatch.setattr( + demo_webrtc_module, + "create_packaged_webrtc_app", + fake_create_packaged_webrtc_app, + ) + adapter = OmnidreamsDemoAdapter(webrtc_runtime_factory=_FakeWebRTCRuntime) + spec = DemoSpec( + model_id=OMNIDREAMS_MODEL_ID, + preset_id=DEFAULT_OMNIDREAMS_PRESET, + input_mode="keyboard-driving", + scenario={"scene_uuid": "scene-1"}, + output=WebRTCOutputSpec( + host="0.0.0.0", + port=8082, + warmup_timeout_s=1.0, + ), + config=InferenceConfig( + model_id=OMNIDREAMS_MODEL_ID, + preset_id=DEFAULT_OMNIDREAMS_PRESET, + runtime_options={"pipeline_config": object()}, + ), + ) + + demo = cast( + WebRTCDemo, + serve_flashdreams_demo( + spec=spec, + adapter=adapter, + world_rank=0, + server_runner=fake_server_runner, + ), + ) + + assert len(server_calls) == 1 + assert server_calls[0]["world_rank"] == 0 + assert server_calls[0]["session_manager"] is demo.session_manager + assert server_calls[0]["app"] is demo.app + assert server_calls[0]["host"] == "0.0.0.0" + assert server_calls[0]["port"] == 8082 + assert isinstance(demo.session_manager, OmnidreamsDemoWebRTCSessionManager) + + +class _RecordingOutputTarget: + def open(self) -> None: + return None + + def write(self, result: StepResult) -> None: + del result + + def close(self) -> Sequence[OutputArtifact]: + return () + + +class _FakeOmnidreamsPipeline: + def __init__(self) -> None: + self.initialize_cache_calls: list[dict[str, Any]] = [] + self.released_encoders = False + + def initialize_cache( + self, + *, + text: list[list[str]], + image: torch.Tensor, + view_names: list[str], + ) -> object: + self.initialize_cache_calls.append( + { + "text": text, + "image_shape": tuple(image.shape), + "view_names": view_names, + } + ) + return object() + + def release_oneshot_encoders(self) -> None: + self.released_encoders = True + + def get_num_frames(self, autoregressive_index: int) -> int: + del autoregressive_index + return 1 + + def generate( + self, + *, + autoregressive_index: int, + cache: object, + hdmap: torch.Tensor, + ) -> torch.Tensor: + del cache, hdmap + return torch.full((1, 1, 1, 3, 2, 2), float(autoregressive_index)) + + def finalize(self, *, autoregressive_index: int, cache: object) -> dict[str, float]: + del autoregressive_index, cache + return {"denoise_s": 0.25} + + +class _FakeWebRTCRuntime: + def __init__(self, config: Any) -> None: + self.config = config + + async def initialize(self) -> None: + return None + + async def reset_for_new_session(self, *args: Any, **kwargs: Any) -> None: + return None + + def peek_steady_chunk_num_frames(self) -> int: + return 1 + + def peek_next_chunk_num_frames(self) -> int: + return 1 + + async def generate_chunk( + self, + *, + segments: list[Any], + frame_times: list[float], + ) -> Any: + del segments, frame_times + return None + + async def close(self) -> None: + return None + + def send_exit_signal(self) -> None: + return None + + def wait_for_termination(self) -> None: + return None diff --git a/uv.lock b/uv.lock index a077bb38..4d0a32ba 100644 --- a/uv.lock +++ b/uv.lock @@ -1,6 +1,6 @@ version = 1 revision = 3 -requires-python = ">=3.10, <3.14" +requires-python = ">=3.10, <3.13" resolution-markers = [ "python_full_version >= '3.12' and sys_platform == 'win32'", "python_full_version == '3.11.*' and sys_platform == 'win32'", @@ -131,7 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