# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import json import multiprocessing as mp import os import threading import time import uuid import weakref from collections.abc import Callable, Generator, Sequence from concurrent.futures import ThreadPoolExecutor, as_completed from typing import Any, Literal, overload import huggingface_hub import msgspec.msgpack import zmq from omegaconf import OmegaConf from tqdm.auto import tqdm from vllm import SamplingParams from vllm.logger import init_logger from vllm.utils.network_utils import make_zmq_socket from vllm.v1.utils import get_engine_client_zmq_addr from vllm_omni.distributed.omni_connectors import ( get_stage_connector_config, initialize_orchestrator_connectors, ) from vllm_omni.distributed.omni_connectors.adapter import try_send_via_connector from vllm_omni.distributed.omni_connectors.utils.initialization import ( resolve_omni_kv_config_for_stage, ) from vllm_omni.distributed.ray_utils.utils import ( create_placement_group, get_ray_queue_class, try_close_ray, ) from vllm_omni.entrypoints.omni_stage import OmniStage from vllm_omni.entrypoints.stage_utils import SHUTDOWN_TASK, OmniStageTaskType from vllm_omni.entrypoints.stage_utils import maybe_load_from_ipc as _load from vllm_omni.entrypoints.utils import ( get_final_stage_id_for_e2e, inject_omni_kv_config, load_and_resolve_stage_configs, ) from vllm_omni.entrypoints.zmq_utils import ZmqQueue from vllm_omni.inputs.data import OmniDiffusionSamplingParams, OmniPromptType, OmniSamplingParams from vllm_omni.metrics import OrchestratorAggregator, StageRequestStats from vllm_omni.model_executor.model_loader.weight_utils import ( download_weights_from_hf_specific, ) from vllm_omni.outputs import OmniRequestOutput logger = init_logger(__name__) def _weak_close_cleanup( stage_list, stage_in_queues, stage_out_queues, ray_pg, zmq_ctx=None, handshake_stop: threading.Event | None = None, zmq_handshake_socket: zmq.Socket | None = None, handshake_thread: threading.Thread | None = None, ): """Weak reference cleanup function for OmniBase instances.""" if stage_list: for q in stage_in_queues: try: q.put_nowait(SHUTDOWN_TASK) except Exception as e: logger.warning(f"Failed to send shutdown signal to stage input queue: {e}") close_fn = getattr(q, "close", None) if callable(close_fn): close_fn() for q in stage_out_queues: close_fn = getattr(q, "close", None) if callable(close_fn): close_fn() for stage in stage_list: try: stage.stop_stage_worker() except Exception as e: logger.warning(f"Failed to stop stage worker: {e}") try_close_ray(ray_pg) # Gracefully shutdown handshake server thread if handshake_stop is not None: handshake_stop.set() if handshake_thread is not None: handshake_thread.join(timeout=2.0) if handshake_thread.is_alive(): logger.warning("Handshake server thread did not terminate gracefully within timeout") # Close ZMQ resources after thread has exited if zmq_handshake_socket is not None: zmq_handshake_socket.close(0) if zmq_ctx is not None: zmq_ctx.term() def _dummy_snapshot_download(model_id): return model_id def omni_snapshot_download(model_id) -> str: # If it's already a local path, just return it if os.path.exists(model_id): return model_id # TODO: this is just a workaround for quickly use modelscope, we should support # modelscope in weight loading feature instead of using `snapshot_download` if os.environ.get("VLLM_USE_MODELSCOPE", False): from modelscope.hub.snapshot_download import snapshot_download return snapshot_download(model_id) # For other cases (Hugging Face), perform a real download to ensure all # necessary files (including *.pt for audio/diffusion) are available locally # before stage workers are spawned. This prevents initialization timeouts. # Return the original model_id so that model_config.model preserves # HuggingFace semantics (e.g. "Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice") # instead of the resolved cache path. try: download_weights_from_hf_specific( model_name_or_path=model_id, cache_dir=None, allow_patterns=["*"], require_all=True, ) except huggingface_hub.errors.RepositoryNotFoundError: logger.warning(f"Repository not found for '{model_id}'.") return model_id class OmniBase: """Base class for serving Omni models. Args: model: Model name or path to load. **kwargs: Arbitrary keyword arguments. - stage_configs_path: Optional path to YAML file containing stage configurations. If None, configurations are loaded from the model. - log_stats: Whether to enable statistics logging be written to files with stage-specific suffixes. - stage_init_timeout: Per-stage init watchdog (seconds). Measured from when the previous stage finished (possibly a prior Omni run with GPU reuse/overlap) to when the current stage starts to initialize. - shm_threshold_bytes: Threshold in bytes for using shared memory for IPC. Objects larger than this threshold will use shared memory. - worker_backend: Backend for worker processes. Default is "multi_process". - ray_address: Address of Ray cluster for Ray backend, if using Ray backend. - batch_timeout: Timeout in seconds for batching requests within a stage - init_timeout: Timeout in seconds for waiting for all stages to initialize - Additional keyword arguments passed to stage engines. """ def __init__(self, model: str, **kwargs: Any) -> None: model = omni_snapshot_download(model) kwargs["model"] = model # Stage management attributes self.stage_list: list[OmniStage] = [] self._stage_in_queues: list[Any] = [] self._stage_out_queues: list[Any] = [] self._stages_ready: set[int] = set() self._ray_pg = None self._queue_cls = None self._ctx = None self._zmq_ctx: zmq.Context | None = None self._zmq_master_address: str | None = None self._zmq_master_port: int | None = None self._zmq_handshake_socket: zmq.Socket | None = None self._handshake_thread: threading.Thread | None = None self._handshake_stop: threading.Event | None = None self._handshake_endpoints: dict[int, tuple[str, str]] = {} self._handshake_seen: set[int] = set() # Track which stage IDs have completed ZMQ handshake self._single_stage_id: int | None = None # Optional: deploy only a specific stage ID # Initialize stages - each stage will create appropriate instance based on stage_type # Stage workers will automatically create OmniLLM or OmniDiffusion instances # based on stage_type in YAML config (handled in omni_stage.py) logger.info(f"Initializing stages for model: {model}") self._initialize_stages(model, kwargs) def _get_default_cache_config(self, cache_backend: str | None) -> dict[str, Any] | None: if cache_backend == "cache_dit": return { "Fn_compute_blocks": 1, "Bn_compute_blocks": 0, "max_warmup_steps": 4, "residual_diff_threshold": 0.24, "max_continuous_cached_steps": 3, "enable_taylorseer": False, "taylorseer_order": 1, "scm_steps_mask_policy": None, "scm_steps_policy": "dynamic", } if cache_backend == "tea_cache": return { "rel_l1_thresh": 0.2, } return None def _normalize_cache_config(self, cache_backend: str | None, cache_config: Any | None) -> Any | None: if isinstance(cache_config, str): try: cache_config = json.loads(cache_config) except json.JSONDecodeError: logger.warning("Invalid cache_config JSON, using defaults.") cache_config = None if cache_config is None and cache_backend not in (None, "", "none"): cache_config = self._get_default_cache_config(cache_backend) return cache_config def _create_default_diffusion_stage_cfg(self, kwargs: dict[str, Any]) -> dict[str, Any]: """Create default diffusion stage configuration.""" # We temporally create a default config for diffusion stage. # In the future, we should merge the default config with the user-provided config. # TODO: hack, convert dtype to string to avoid non-premitive omegaconf create error. if "dtype" in kwargs: kwargs["dtype"] = str(kwargs["dtype"]) cache_backend = kwargs.get("cache_backend", "none") cache_config = self._normalize_cache_config(cache_backend, kwargs.get("cache_config", None)) # TODO: hack, calculate devices based on parallel config. devices = "0" if "parallel_config" in kwargs: num_devices = kwargs["parallel_config"].world_size for i in range(1, num_devices): devices += f",{i}" default_stage_cfg = [ { "stage_id": 0, "stage_type": "diffusion", "runtime": { "process": True, "devices": devices, "max_batch_size": 1, }, "engine_args": OmegaConf.create( { **kwargs, "cache_backend": cache_backend, "cache_config": cache_config, } ), "final_output": True, "final_output_type": "image", } ] default_stage_cfg[0]["engine_args"]["model_stage"] = "diffusion" return default_stage_cfg def _resolve_stage_configs(self, model: str, kwargs: dict[str, Any]) -> tuple[str, list[Any]]: """Resolve stage configs and inject defaults shared by orchestrator/headless.""" # TODO(wuhang): # Remove kwargs as parameters in the future. # Use dataclass directly for engine args. stage_configs_path = kwargs.get("stage_configs_path", None) # TTS-specific CLI overrides self.tts_max_instructions_length: int | None = kwargs.get("tts_max_instructions_length", None) # Load stage configurations from YAML config_path, stage_configs = load_and_resolve_stage_configs( model, stage_configs_path, kwargs, default_stage_cfg_factory=lambda: self._create_default_diffusion_stage_cfg(kwargs), ) # Inject diffusion LoRA-related knobs from kwargs if not present in the stage config. for cfg in stage_configs: try: if getattr(cfg, "stage_type", None) != "diffusion": continue if not hasattr(cfg, "engine_args") or cfg.engine_args is None: cfg.engine_args = OmegaConf.create({}) if kwargs.get("lora_path") is not None: if not hasattr(cfg.engine_args, "lora_path") or cfg.engine_args.lora_path is None: cfg.engine_args.lora_path = kwargs["lora_path"] lora_scale = kwargs.get("lora_scale") if lora_scale is None: # Backwards compatibility for older callers. lora_scale = kwargs.get("static_lora_scale") if lora_scale is not None: if not hasattr(cfg.engine_args, "lora_scale") or cfg.engine_args.lora_scale is None: cfg.engine_args.lora_scale = lora_scale quantization_config = kwargs.get("quantization_config") if quantization_config is not None: if ( not hasattr(cfg.engine_args, "quantization_config") or cfg.engine_args.quantization_config is None ): cfg.engine_args.quantization_config = quantization_config except Exception as e: logger.warning("Failed to inject LoRA config for stage: %s", e) return config_path, stage_configs def _initialize_stages(self, model: str, kwargs: dict[str, Any]) -> None: """Initialize stage list management.""" stage_init_timeout = kwargs.get("stage_init_timeout", 20) shm_threshold_bytes = kwargs.get("shm_threshold_bytes", 65536) init_timeout = kwargs.get("init_timeout", 300) worker_backend = kwargs.get("worker_backend", "multi_process") ray_address = kwargs.get("ray_address", None) batch_timeout = kwargs.get("batch_timeout", 10) log_stats = kwargs.get("log_stats", False) self._single_stage_id = kwargs.get("stage_id", None) self._zmq_master_address = kwargs.get("omni_master_address", None) if self._zmq_master_address is None: self._zmq_master_address = "127.0.0.1" logger.info("No omni_master_address provided, defaulting to localhost (127.0.0.1)") self._zmq_master_port = kwargs.get("omni_master_port", None) # Resolve stage configs shared by orchestrator/headless paths. self.config_path, self.stage_configs = self._resolve_stage_configs(model, kwargs) # Initialize connectors self.omni_transfer_config, self.connectors = initialize_orchestrator_connectors( self.config_path, worker_backend=worker_backend, shm_threshold_bytes=shm_threshold_bytes ) # Initialize stats paths self.log_stats: bool = bool(log_stats) self.worker_backend = worker_backend self.ray_address = ray_address self.batch_timeout = batch_timeout # async chunk remains the same for each stage self.async_chunk = self._is_async_chunk_enable(self.stage_configs) # Build OmniStage instances in parallel, preserve original order def _build_stage(idx_cfg: tuple[int, Any]) -> tuple[int, OmniStage]: idx, cfg = idx_cfg return idx, OmniStage(cfg, stage_init_timeout=stage_init_timeout) with ThreadPoolExecutor(max_workers=min(len(self.stage_configs), max(1, os.cpu_count() or 1))) as executor: futures = [executor.submit(_build_stage, (idx, cfg)) for idx, cfg in enumerate(self.stage_configs)] results: list[tuple[int, OmniStage]] = [] for fut in as_completed(futures): results.append(fut.result()) results.sort(key=lambda x: x[0]) self.stage_list = [st for _, st in results] self.default_sampling_params_list = [st.default_sampling_params for st in self.stage_list] self.output_modalities = [st.final_output_type for st in self.stage_list] logger.info(f"[{self._name}] Loaded {len(self.stage_list)} stages") if self.worker_backend == "ray": self._queue_cls = get_ray_queue_class() else: self._ctx = mp.get_context("spawn") self._queue_cls = lambda: self._ctx.Queue(maxsize=0) self._stage_init_timeout = max(0, int(stage_init_timeout)) self._shm_threshold_bytes = max(0, int(shm_threshold_bytes)) self._start_stages(model) # Wait for all stages to report readiness before seeding self._wait_for_stages_ready(timeout=init_timeout) def _is_async_chunk_enable(self, stage_args: list) -> bool: """get async chunk flag""" engine_args = getattr(stage_args[0], "engine_args", None) return bool(getattr(engine_args, "async_chunk", False)) def _start_stages(self, model: str) -> None: """Start all stage processes.""" if self.worker_backend == "ray": # Initialize Ray Cluster self._ray_pg = create_placement_group( number_of_stages=len(self.stage_list), address=self.ray_address, strategy="PACK" ) else: # Initialize ZMQ context if self._zmq_ctx is None: self._zmq_ctx = zmq.Context() # Allocate endpoints for each stage total_stages = len(self.stage_configs) self._handshake_endpoints = {} # If --stage-id is not set, use local_only mode local_only = self._single_stage_id is None for sid in range(total_stages): in_endpoint = get_engine_client_zmq_addr(local_only=local_only, host=self._zmq_master_address) out_endpoint = get_engine_client_zmq_addr(local_only=local_only, host=self._zmq_master_address) self._handshake_endpoints[sid] = (in_endpoint, out_endpoint) logger.debug( f"[{self._name}] Allocated endpoints for stage-{sid}: in={in_endpoint}, out={out_endpoint}" ) # Start handshake server self.start_handshake_server() for stage_id, stage in enumerate[OmniStage](self.stage_list): if self.worker_backend == "ray": in_q = self._queue_cls() out_q = self._queue_cls() else: in_endpoint, out_endpoint = self._handshake_endpoints[stage_id] in_q = ZmqQueue(self._zmq_ctx, zmq.PUSH, bind=in_endpoint) out_q = ZmqQueue(self._zmq_ctx, zmq.PULL, bind=out_endpoint) self._stage_in_queues.append(in_q) self._stage_out_queues.append(out_q) stage.attach_queues(in_q, out_q) stage_connectors_config = get_stage_connector_config( self.omni_transfer_config, stage_id, ) # Inject YAML-resolved connector config into omni_kv_config for # in-engine usage (GPU model runner reads model_config.omni_kv_config). try: omni_conn_cfg, omni_from, omni_to = resolve_omni_kv_config_for_stage( self.omni_transfer_config, stage_id ) if omni_conn_cfg: inject_omni_kv_config(stage, omni_conn_cfg, omni_from, omni_to) # type: ignore except Exception as e: logger.debug("[Omni] Failed to inject omni connector config into stage-%s: %s", stage_id, e) if self._single_stage_id is not None and stage_id != int(self._single_stage_id): logger.info( f"[{self._name}] Skipping initialization of stage-{stage_id} worker due to single_stage_id setting" ) continue stage.init_stage_worker( model, is_async=self.is_async, shm_threshold_bytes=self._shm_threshold_bytes, ctx=self._ctx if self.worker_backend != "ray" else None, batch_timeout=self.batch_timeout, connectors_config=stage_connectors_config, worker_backend=self.worker_backend, ray_placement_group=self._ray_pg, ignore_runtime_config=True if self._single_stage_id is not None else False, ) logger.debug(f"[{self._name}] Stage-{stage_id} process started") def _process_stage_ready(self, stage: OmniStage, stage_id: int, result: dict[str, Any]) -> None: self._stages_ready.add(stage_id) logger.info(f"[{self._name}] Stage-{stage_id} reported ready") def _wait_for_stages_ready(self, timeout: int = 120) -> None: """Wait for all stages to report readiness with optimized polling.""" if self._single_stage_id is not None and self.worker_backend != "ray": timeout = self._wait_for_handshakes(timeout) num_stages = len(self.stage_list) deadline = time.time() + max(0, int(timeout)) logger.info(f"[{self._name}] Waiting for {num_stages} stages to initialize (timeout: {timeout}s)") while len(self._stages_ready) < num_stages and time.time() < deadline: progressed = False for stage_id, stage in enumerate(self.stage_list): if stage_id in self._stages_ready: continue # Check if the stage has reported status if result := stage.try_collect(): progressed = True if result.get("type") == "stage_ready": self._process_stage_ready(stage, stage_id, result) if not progressed: time.sleep(0.05) # Handle Final State if len(self._stages_ready) == num_stages: logger.info(f"[{self._name}] All stages initialized successfully") return # Handle Timeout/Failure not_ready = sorted(set(range(num_stages)) - set(self._stages_ready)) logger.warning( f"[{self._name}] Initialization timeout: {len(self._stages_ready)}/{num_stages} " f"stages ready. Missing stages: {not_ready}" ) suggestions = [ f"Ignore this warning if the model weight download / load from disk time is longer than {timeout}s.", "Verify GPU/device assignment in config (runtime.devices) is correct.", "Check GPU/host memory availability; reduce model or batch size if needed.", "Check model weights path and network reachability (if loading remotely).", "Increase initialization wait time (stage_init_timeout or call-site timeout).", ] formatted_suggestions = "\n".join(f" {i + 1}) {msg}" for i, msg in enumerate(suggestions)) logger.warning(f"[{self._name}] Stage initialization timeout. Troubleshooting Steps:\n{formatted_suggestions}") def _is_profiler_enabled(self, stage_id: int) -> bool: """Check if profiler config is set for a given stage.""" stage = self.stage_list[stage_id] # For diffusion stages, profiling is controlled by VLLM_TORCH_PROFILER_DIR env var if stage.stage_type == "diffusion": return True # For LLM stages, check if profiler_config is set in engine_args engine_args = getattr(stage.stage_config, "engine_args", None) if engine_args is None: return False profiler_config = getattr(engine_args, "profiler_config", None) if profiler_config is None: return False profiler = getattr(profiler_config, "profiler", None) return profiler is not None def start_profile(self, stages: list[int] | None = None) -> None: """Start profiling for specified stages. Sends start_profile command to stage workers. Profiling must be enabled via VLLM_TORCH_PROFILER_DIR environment variable. Args: stages: List of stage IDs to start profiling. If None, starts profiling for all stages that have profiling enabled. Example: >>> # Profile all stages >>> omni.start_profile() >>> outputs = omni.generate(prompts, sampling_params) >>> omni.stop_profile() >>> # Profile only stage 0 and 2 >>> omni.start_profile(stages=[0, 2]) """ if stages is None: stages = list(range(len(self.stage_list))) for stage_id in stages: if stage_id < len(self.stage_list): if not self._is_profiler_enabled(stage_id): logger.info( "[%s] Skipping start_profile for stage-%s: profiler config not set", self._name, stage_id, ) continue try: self.stage_list[stage_id].submit({"type": OmniStageTaskType.PROFILER_START}) logger.info("[%s] Sent start_profile to stage-%s", self._name, stage_id) except Exception as e: logger.warning( "[%s] Failed to send start_profile to stage-%s: %s", self._name, stage_id, e, ) def stop_profile(self, stages: list[int] | None = None) -> dict: """ Synchronously stop profiling for specified stages and collect the file paths for traces and tables. """ if stages is None: stages = list(range(len(self.stage_list))) all_results = {"traces": [], "tables": []} for stage_id in stages: if stage_id < len(self.stage_list): if not self._is_profiler_enabled(stage_id): logger.info( "[%s] Skipping stop_profile for stage-%s: profiler config not set", self._name, stage_id, ) continue stage = self.stage_list[stage_id] # Check if the stage object has our new bridge method if hasattr(stage, "stop_profile"): logger.info("[%s] Requesting profile data collection from stage-%s", self._name, stage_id) # This is the blocking call that triggers the RPC chain stage_data = stage.stop_profile() if isinstance(stage_data, dict): # FIX: Handle both single key and list key formats traces = stage_data.get("trace") or stage_data.get("traces") tables = stage_data.get("table") or stage_data.get("tables") # Debug logging logger.debug(f"[{self._name}] Stage-{stage_id} returned: {stage_data.keys()}") if traces: logger.debug(f"[{self._name}] Stage-{stage_id} traces type: {type(traces)}") if tables: logger.debug(f"[{self._name}] Stage-{stage_id} tables type: {type(tables)}") # Handle single strings if traces: if isinstance(traces, str): all_results["traces"].append(traces) elif isinstance(traces, list): all_results["traces"].extend(traces) # Handle single strings if tables: if isinstance(tables, str): all_results["tables"].append(tables) elif isinstance(tables, list): all_results["tables"].extend(tables) else: logger.warning(f"[{self._name}] Stage-{stage_id} returned no table data") else: logger.warning(f"[{self._name}] Stage-{stage_id} returned non-dict data: {type(stage_data)}") else: # Fallback for non-diffusion stages logger.warning( "[%s] Stage-%s does not support synchronous stop_profile. Falling back to async.", self._name, stage_id, ) stage.submit({"type": OmniStageTaskType.PROFILER_STOP}) # Final debug output logger.info( f"[{self._name}] Collected {len(all_results['traces'])} trace(s) and {len(all_results['tables'])} table(s)" ) return all_results def close(self) -> None: """Close all stage processes and clean up resources.""" if hasattr(self, "_weak_finalizer"): self._weak_finalizer() def _process_handshake_message(self, msg: Any) -> dict[str, Any]: """Process incoming handshake message and generate response. Args: msg: Decoded message from client Returns: Response dictionary with ok status and either endpoints or error """ if not isinstance(msg, dict) or msg.get("type") != "handshake": return {"ok": False, "error": "invalid handshake payload"} try: stage_id = int(msg.get("stage_id")) except (TypeError, ValueError) as e: return {"ok": False, "error": f"invalid stage_id: {e}"} endpoints = self._handshake_endpoints.get(stage_id) if endpoints is None: return {"ok": False, "error": f"unknown stage_id: {stage_id}"} # Mark stage as seen and prepare success response self._handshake_seen.add(stage_id) in_endpoint, out_endpoint = endpoints logger.info( "[%s] Handshake received from stage-%s", self._name, stage_id, ) return { "ok": True, "in_endpoint": in_endpoint, "out_endpoint": out_endpoint, } def _run_handshake_server_loop(self) -> None: """Main loop for handshake server - polls for messages and responds.""" poller = zmq.Poller() poller.register(self._zmq_handshake_socket, zmq.POLLIN) try: while not self._handshake_stop.is_set(): events = poller.poll(1000) has_message = any(sock == self._zmq_handshake_socket and event == zmq.POLLIN for sock, event in events) if not has_message: continue msg = msgspec.msgpack.decode(self._zmq_handshake_socket.recv()) response = msgspec.msgpack.encode(self._process_handshake_message(msg)) self._zmq_handshake_socket.send(response) finally: poller.unregister(self._zmq_handshake_socket) def start_handshake_server(self) -> None: """Start the ZMQ handshake server. The handshake server allows distributed stages to discover their queue endpoints by querying the orchestrator with their stage_id. Skips starting if the server is already running or ZMQ is not initialized. """ # Skip if already running or ZMQ not initialized if self._handshake_thread is not None or self._zmq_ctx is None: return # Skip if master address/port not configured if not self._zmq_master_address or self._zmq_master_port is None: return # Create server endpoint and socket endpoint = get_engine_client_zmq_addr( local_only=False, host=self._zmq_master_address, port=int(self._zmq_master_port) ) self._handshake_stop = threading.Event() self._zmq_handshake_socket = make_zmq_socket(self._zmq_ctx, endpoint, zmq.REP, bind=True, linger=5000) # Start server thread self._handshake_thread = threading.Thread( target=self._run_handshake_server_loop, daemon=True, name="zmq-handshake-server" ) self._handshake_thread.start() def _wait_for_handshakes(self, timeout: int = 120) -> int: """Wait for handshakes from all expected stages. Args: timeout: Timeout in seconds for waiting for handshakes. Default is 120s. Returns: Remaining timeout in seconds after waiting for handshakes. """ total_stages = len(self.stage_configs) expected = set(range(total_stages)) - {int(self._single_stage_id)} if not expected: return timeout deadline = time.time() + max(0, int(timeout)) logger.info(f"[{self._name}] Waiting for handshakes from stages: {expected} (timeout: {timeout}s)") # NOTE: _handshake_seen may be updated from the handshake server thread. # It is intentionally used here without additional locking because: # - _handshake_seen only ever grows (stages are added but never removed), and # - we only check membership and set inclusion relative to `expected`. # Under these monotonic semantics and the CPython GIL, concurrent reads/writes # are safe for this usage and cannot violate correctness: we may observe a # slightly stale view, but the loop condition remains valid and eventually # becomes true once all expected stages have handshaked or the timeout elapses. while not expected.issubset(self._handshake_seen) and time.time() < deadline: time.sleep(1.0) remaining_timeout = max(0, int(deadline - time.time())) if not expected.issubset(self._handshake_seen): missing = sorted(expected - self._handshake_seen) logger.warning( f"[{self._name}] Handshake timeout: {len(self._handshake_seen)}/{len(expected)} " f"stages completed handshake. Missing stages: {missing}" ) return remaining_timeout @property def _name(self) -> str: return "OmniBase" @property def is_async(self) -> bool: return False class Omni(OmniBase): """Unified entrypoint for both LLM and Diffusion models for better usability. Args: model: Model name or path to load. **kwargs: Arbitrary keyword arguments. - stage_configs_path: Optional path to YAML file containing stage configurations. If None, configurations are loaded from the model. - log_stats: Whether to enable statistics logging be written to files with stage-specific suffixes. - stage_init_timeout: Per-stage init watchdog (seconds). Measured from when the previous stage finished (possibly a prior Omni run with GPU reuse/overlap) to when the current stage starts to initialize. - shm_threshold_bytes: Threshold in bytes for using shared memory for IPC. Objects larger than this threshold will use shared memory. - worker_backend: Backend for worker processes. Default is "multi_process". - ray_address: Address of Ray cluster for Ray backend, if using Ray backend. - batch_timeout: Timeout in seconds for batching requests within a stage - init_timeout: Timeout in seconds for waiting for all stages to initialize - Additional keyword arguments passed to stage engines. Example: >>> omni = Omni(model="Qwen/Qwen2.5-Omni-7B") >>> outputs = omni.generate(prompts="Hello, world!", sampling_params_list=[SamplingParams()]) >>> print(outputs) """ def __init__(self, model: str, **kwargs: Any) -> None: super().__init__(model, **kwargs) # Register weak reference cleanup (called on garbage collection) self._weak_finalizer = weakref.finalize( self, _weak_close_cleanup, self.stage_list, self._stage_in_queues, self._stage_out_queues, self._ray_pg, self._zmq_ctx, self._handshake_stop, self._zmq_handshake_socket, self._handshake_thread, ) @overload def generate( self, prompts: OmniPromptType | Sequence[OmniPromptType], sampling_params_list: OmniSamplingParams | Sequence[OmniSamplingParams] | None = None, *, py_generator: Literal[True], ) -> Generator[OmniRequestOutput, None, None]: ... @overload def generate( self, prompts: OmniPromptType | Sequence[OmniPromptType], sampling_params_list: OmniSamplingParams | Sequence[OmniSamplingParams] | None = None, *, py_generator: Literal[False] = False, ) -> list[OmniRequestOutput]: ... def generate( self, prompts: OmniPromptType | Sequence[OmniPromptType], sampling_params_list: OmniSamplingParams | Sequence[OmniSamplingParams] | None = None, *, py_generator: bool = False, use_tqdm: bool | Callable[..., tqdm] = True, ) -> Generator[OmniRequestOutput, None, None] | list[OmniRequestOutput]: """Generate outputs for the given prompts. Orchestrates the multi-stage pipeline based on YAML configuration. Each stage will use OmniLLM or OmniDiffusion based on stage_type. Args: prompts: Input prompt(s) for generation. sampling_params_list: Optional list of per-stage parameters. py_generator: Whether the returned result(s) are wrapped in a generator instead of a list. use_tqdm: Whether to use tqdm progress bar Returns: List of OmniRequestOutput objects, one for each input prompt. Each output contains the stage_id, final_output_type, and the request_output from the final stage. Raises: ValueError: If sampling_params_list is None or has incorrect length. """ if sampling_params_list is None: sampling_params_list = self.default_sampling_params_list elif not isinstance(sampling_params_list, Sequence): # TODO: After the recent introduction of BAGEL model (one LLM and one Diffusion), # expect the text_to_image example code to run when only passing one OmniDiffusionSamplingParams # This behavior may be confusing, and future PR can improve it. per_stage_params: list[OmniSamplingParams] = [] for default_stage_sp in self.default_sampling_params_list: default_sp_type = default_stage_sp.__class__ if default_sp_type == sampling_params_list.__class__: per_stage_params.append(sampling_params_list) else: per_stage_params.append(default_stage_sp) sampling_params_list = per_stage_params try: if py_generator: return self._run_generation_with_generator(prompts, sampling_params_list) else: outputs = list(self._run_generation(prompts, sampling_params_list, use_tqdm)) return outputs except Exception as e: logger.exception("[Orchestrator] Failed to run generation: %s", e) # Always close on exception to ensure cleanup self.close() raise e def _run_generation_with_generator( self, prompts: OmniPromptType | Sequence[OmniPromptType], sampling_params_list: Sequence[OmniSamplingParams], ) -> Generator[OmniRequestOutput, None, None]: """Run generation through all stages in the pipeline and return a generator.""" gen = self._run_generation(prompts, sampling_params_list) try: yield from gen except Exception as e: logger.exception("[Orchestrator] Failed to run generation: %s", e) raise e finally: # Cleanup when generator is exhausted or closed self.close() def _run_generation( self, prompts: OmniPromptType | Sequence[OmniPromptType], sampling_params_list: Sequence[OmniSamplingParams], use_tqdm: bool | Callable[..., tqdm] = True, ) -> Generator[OmniRequestOutput, None, None]: """Run generation through all stages in the pipeline.""" logger.debug(f"[{self._name}] generate() called") if sampling_params_list is None: raise ValueError("sampling_params_list is required for pipelined generation") if len(sampling_params_list) != len(self.stage_list): raise ValueError(f"Expected {len(self.stage_list)} sampling params, got {len(sampling_params_list)}") for i, (stage, sp) in enumerate(zip(self.stage_list, sampling_params_list)): ExpectedSPType = OmniDiffusionSamplingParams if stage.stage_type == "diffusion" else SamplingParams if not isinstance(sp, ExpectedSPType): raise ValueError( f"Expected sampling parameters with type {ExpectedSPType} in stage {i}, got {sp.__class__}" ) # Normalize prompts to a list for per-request iteration # str is also Sequence but only test list-like containers here if isinstance(prompts, str) or not isinstance(prompts, Sequence): request_prompts: list[OmniPromptType] = [prompts] else: request_prompts = list(prompts) # Orchestrator keeps stage objects for input derivation num_stages = len(self.stage_list) # Generate globally unique request IDs and map them to original prompts request_ids = [f"{i}_{uuid.uuid4()}" for i in range(len(request_prompts))] request_id_to_prompt = {rid: p for rid, p in zip(request_ids, request_prompts)} # Track per-request start time for end-to-end timing _req_start_ts: dict[str, float] = {} _wall_start_ts: float = time.time() # Determine the final stage for E2E stats (highest stage_id with final_output=True; fallback to last stage) final_stage_id_to_prompt: dict[str, int] = {} for rid, prompt in request_id_to_prompt.items(): if isinstance(prompt, dict): prompt_modalities = prompt.get("modalities", None) else: prompt_modalities = None final_stage_id_for_e2e = get_final_stage_id_for_e2e( prompt_modalities, self.output_modalities, self.stage_list ) final_stage_id_to_prompt[rid] = final_stage_id_for_e2e # Metrics/aggregation helper metrics = OrchestratorAggregator( num_stages, self.log_stats, _wall_start_ts, final_stage_id_to_prompt, ) it = request_id_to_prompt.items() if use_tqdm: tqdm_func = use_tqdm if callable(use_tqdm) else tqdm it = tqdm_func(it, desc="Adding requests") # Seed stage-0 queue with all requests logger.debug(f"[{self._name}] Seeding {len(request_prompts)} requests into stage-0") # Mark first input time for stage-0 metrics.stage_first_ts[0] = metrics.stage_first_ts[0] or time.time() for req_id, prompt in request_id_to_prompt.items(): sp0 = sampling_params_list[0] # type: ignore[index] task = { "request_id": req_id, "engine_inputs": prompt, "sampling_params": sp0, } self.stage_list[0].submit(task) _req_start_ts[req_id] = time.time() logger.debug(f"[{self._name}] Enqueued request {req_id} to stage-0") pbar = None if use_tqdm: tqdm_func = use_tqdm if callable(use_tqdm) else tqdm pbar = tqdm_func( total=len(request_prompts), desc="Processed prompts", dynamic_ncols=True, postfix=(f"est. speed input: {0:.2f} unit/s, output: {0:.2f} unit/s"), ) # For each stage, forward results to next stage; collect finals at the end # We pipeline by continually polling output queues in stage order remaining_by_stage: list[int] = [len(request_prompts)] + [0] * (num_stages - 1) completed_requests = 0 total_requests = len(request_prompts) logger.debug( f"[{self._name}] Entering scheduling loop: total_requests={total_requests}, stages={num_stages}", ) while completed_requests < total_requests: made_progress = False for stage_id, stage in enumerate(self.stage_list): result = stage.try_collect() if result is None: continue made_progress = True req_id = result.get("request_id") if "error" in result: logger.error( f"[{self._name}] Stage {stage_id} error on request {req_id}: {result['error']}", ) continue if result.get("type") == "stage_ready": # Only happens when stage is initialized slower than expected, # so we wait for a short time and try again time.sleep(0.05) continue engine_outputs = _load(result, obj_key="engine_outputs", shm_key="engine_outputs_shm") # Mark last output time for this stage whenever we receive outputs metrics.stage_last_ts[stage_id] = max(metrics.stage_last_ts[stage_id] or 0.0, time.time()) try: _m: StageRequestStats = result.get("metrics") if _m is not None: # Accumulate generation time metrics.accumulated_gen_time_ms[req_id][stage_id] += _m.stage_gen_time_ms # For diffusion stages, we also accumulate diffusion time metrics.accumulate_diffusion_metrics(stage.stage_type, req_id, engine_outputs) metrics.on_stage_metrics(stage_id, req_id, _m, stage.final_output_type) if pbar: elapsed = pbar.format_dict["elapsed"] or 1e-6 # Aggregate total tokens/images across all stages total_out = sum(metrics.stage_total_tokens) out_spd = total_out / elapsed modality = self.output_modalities[stage_id] unit = "img" if modality == "image" else "tok" # Pre-calculate for cleaner string formatting if metrics.e2e_count > 0: avg_lat = metrics.e2e_total_ms / metrics.e2e_count else: avg_lat = 0 # Align with vLLM's wording "est. speed" using multi-line parentheses pbar.postfix = ( f"est. speed stage-{stage_id} {unit}/s: {out_spd:.2f}, avg e2e_lat: {avg_lat:.1f}ms" ) except Exception as e: logger.exception( f"[{self._name}] Failed to process metrics for stage {stage_id}, req {req_id}: {e}", ) logger.debug( f"[{self._name}] Stage-{stage_id} completed request {req_id}; forwarding or finalizing", ) stage.set_engine_outputs(engine_outputs) if getattr(stage, "final_output", False): logger.debug( f"[{self._name}] Request {req_id} finalized at stage-{stage_id}", ) # End-to-end timing and time-per-token for final output # (only once per request at the designated final stage) try: if stage_id == final_stage_id_to_prompt[req_id]: metrics.on_finalize_request( stage_id, req_id, _req_start_ts.get(req_id, _wall_start_ts), ) except Exception as e: logger.exception( f"[{self._name}] Finalize request handling error for req {req_id} at stage {stage_id}: {e}", ) output_to_yield = OmniRequestOutput( stage_id=stage_id, final_output_type=stage.final_output_type, # type: ignore[attr-defined] request_output=engine_outputs, ) # Record audio generated frames (only when finished) try: finished = ( engine_outputs.finished if hasattr(engine_outputs, "finished") else ( engine_outputs[0].finished if isinstance(engine_outputs, list) and engine_outputs and hasattr(engine_outputs[0], "finished") else False ) ) if finished: metrics.record_audio_generated_frames(output_to_yield, stage_id, req_id) except Exception as e: logger.exception( f"[{self._name}] Failed to record audio metrics for req {req_id} at stage {stage_id}: {e}", ) yield output_to_yield next_stage_id = stage_id + 1 if next_stage_id <= final_stage_id_to_prompt[req_id]: next_stage: OmniStage = self.stage_list[next_stage_id] try: # Derive inputs for the next stage, record preprocess time with metrics.stage_postprocess_timer(stage_id, req_id): next_inputs = next_stage.process_engine_inputs( self.stage_list, [request_id_to_prompt[req_id]] ) except Exception as e: logger.exception( f"[{self._name}] Process engine inputs error for req {req_id}" f" at stage {next_stage_id}: {e}", ) continue sp_next = sampling_params_list[next_stage_id] # type: ignore[index] # Check if we have a connector for this edge connector_key = (str(stage_id), str(next_stage_id)) connector = self.connectors.get(connector_key) sent_via_connector = False if connector: sent_via_connector = try_send_via_connector( connector=connector, stage_id=stage_id, next_stage_id=next_stage_id, req_id=req_id, next_inputs=next_inputs, sampling_params=sp_next, original_prompt=request_id_to_prompt[req_id], next_stage_queue_submit_fn=self.stage_list[next_stage_id].submit, metrics=metrics, ) if not sent_via_connector: raise RuntimeError( f"[{self._name}] Failed to send request {req_id} to stage-{next_stage_id} via connector. " "Configure a connector for this edge or inspect connector logs for details." ) logger.debug( f"[{self._name}] Forwarded request {req_id} to stage-{next_stage_id}", ) remaining_by_stage[next_stage_id] += 1 else: completed_requests += 1 if pbar: final_mod = self.output_modalities[final_stage_id_to_prompt[req_id]] pbar.unit = "img" if final_mod == "image" else "req" pbar.update(1) logger.debug( f"[{self._name}] Request {req_id} fully completed ({completed_requests}/{total_requests})", ) if not made_progress: time.sleep(0.005) logger.debug(f"[{self._name}] All requests completed") if pbar: pbar.close() # Summarize and print stats try: metrics.build_and_log_summary() except Exception as e: logger.exception(f"[{self._name}] Failed to build/log summary: {e}") @property def _name(self) -> str: return "Orchestrator"