import os import types from collections import Counter from dataclasses import asdict, fields, is_dataclass from pathlib import Path from typing import Any, get_args, get_origin from omegaconf import OmegaConf from vllm.logger import init_logger from vllm.transformers_utils.config import get_config, get_hf_file_to_dict from vllm.transformers_utils.repo_utils import file_or_path_exists from vllm_omni.entrypoints.stage_utils import _to_dict from vllm_omni.platforms import current_omni_platform # Get the project root directory (2 levels up from this file) PROJECT_ROOT = Path(__file__).parent.parent.parent logger = init_logger(__name__) def inject_omni_kv_config(stage: Any, omni_conn_cfg: dict[str, Any], omni_from: str, omni_to: str) -> None: """Inject connector configuration into stage engine arguments.""" # Prepare omni_kv_config dict omni_conf_dict = {} try: # Access engine_args safely (might be OmegaConf or dict) existing_args = stage.engine_args if hasattr(existing_args, "get"): _oc = existing_args.get("omni_kv_config", None) if _oc: if hasattr(_oc, "items"): # dict-like omni_conf_dict = dict(_oc) else: # object? omni_conf_dict = _to_dict(_oc) except Exception: omni_conf_dict = {} # Inject connector info omni_conf_dict["connector_config"] = omni_conn_cfg omni_conf_dict["omni_from_stage"] = omni_from omni_conf_dict["omni_to_stage"] = omni_to # Write back to engine_args try: if hasattr(stage.engine_args, "__setitem__"): stage.engine_args["omni_kv_config"] = omni_conf_dict else: setattr(stage.engine_args, "omni_kv_config", omni_conf_dict) except Exception as e: # Fallback for OmegaConf or similar if direct set fails? logger.error(f"Failed to inject omni connector config into stage: {e}") def _try_get_class_name_from_diffusers_config(model: str) -> str | None: """Try to get class name from diffusers model configuration files. Args: model: Model name or path Returns: Model type string if found, None otherwise """ model_index = get_hf_file_to_dict("model_index.json", model, revision=None) if model_index and isinstance(model_index, dict) and "_class_name" in model_index: logger.debug(f"Found model_type '{model_index['_class_name']}' in model_index.json") return model_index["_class_name"] return None def _filter_dict_like_object(obj: dict | Any) -> dict: """Filter dict-like object by removing callables and recursively converting values. Converts dict-like objects to regular dicts while filtering out callable values that are incompatible with OmegaConf. Recursively processes values through _convert_dataclasses_to_dict for nested object conversion. Args: obj: Dict or dict-like object to filter Returns: Regular dict with callables filtered out and values recursively converted Raises: TypeError: If obj doesn't support .items() method ValueError: If dict conversion fails unexpectedly """ def _is_callable_value(value: Any) -> bool: if callable(value): return True return isinstance( value, ( types.FunctionType, types.MethodType, types.BuiltinFunctionType, types.BuiltinMethodType, ), ) result = {} filtered_keys = [] for k, v in obj.items(): if _is_callable_value(v): filtered_keys.append(str(k)) else: result[k] = _convert_dataclasses_to_dict(v) if filtered_keys: logger.warning( f"Filtered out {len(filtered_keys)} callable object(s) from base_engine_args " f"that are not compatible with OmegaConf: {filtered_keys}. " ) return result def _convert_dataclasses_to_dict(obj: Any) -> Any: """Recursively convert non-serializable objects to OmegaConf-compatible types. This is needed because OmegaConf cannot handle: - Dataclass objects with Literal type annotations (e.g., StructuredOutputsConfig) - Counter objects (from collections or vllm.utils) - Set objects - Callable objects (functions, methods, etc.) - Other non-primitive types """ # IMPORTANT: Check Counter BEFORE dict, since Counter is a subclass of dict # Handle Counter objects (convert to dict) # Check by class name first to catch both collections.Counter and vllm.utils.Counter if hasattr(obj, "__class__") and obj.__class__.__name__ == "Counter": try: return dict(obj) except (TypeError, ValueError): # If Counter can't be converted to dict, return empty dict return {} # Also check isinstance for collections.Counter (must be before dict check) if isinstance(obj, Counter): return dict(obj) # Handle set objects (convert to list) if isinstance(obj, set): return list(obj) # Handle dataclass objects # Note: asdict() recursively converts nested dataclasses but not Counter objects, # so we need to recursively process the result if is_dataclass(obj): result = asdict(obj) # Recursively process the result to convert any Counter objects return _convert_dataclasses_to_dict(result) # Handle dictionaries (recurse into values) and filter out callables(cause error in OmegaConf.create) # Note: This must come AFTER Counter check since Counter is a dict subclass if isinstance(obj, dict): return _filter_dict_like_object(obj) # Handle callable objects (functions, methods, etc.) - skip them # Note: This comes after dict/list checks to avoid misclassifying dict-like objects if callable(obj): return None # Handle lists and tuples (recurse into items) if isinstance(obj, (list, tuple)): return type(obj)(_convert_dataclasses_to_dict(item) for item in obj if not callable(item)) # Try to convert any dict-like object (has keys/values methods) to dict if hasattr(obj, "keys") and hasattr(obj, "values") and not isinstance(obj, (str, bytes)): try: return _filter_dict_like_object(obj) except (TypeError, ValueError, AttributeError): # If conversion fails, return as-is return obj # Primitive types and other objects that OmegaConf can handle return obj def resolve_model_config_path(model: str) -> str: """Resolve the stage config file path from the model name. Resolves stage configuration path based on the model type and device type. First tries to find a device-specific YAML file from stage_configs/{device_type}/ directory. If not found, falls back to the default config file. Args: model: Model name or path (used to determine model_type) Returns: String path to the stage configuration file Raises: ValueError: If model_type cannot be determined FileNotFoundError: If no stage config file exists for the model type """ # Try to get config from standard transformers format first try: hf_config = get_config(model, trust_remote_code=True) model_type = hf_config.model_type except (ValueError, Exception): # If standard transformers format fails, try diffusers format if file_or_path_exists(model, "model_index.json", revision=None): model_type = _try_get_class_name_from_diffusers_config(model) if model_type is None: raise ValueError( f"Could not determine model_type for diffusers model: {model}. " f"Please ensure the model has 'model_type' in transformer/config.json or model_index.json" ) elif file_or_path_exists(model, "config.json", revision=None): # Try to read config.json manually for custom models like Bagel that fail get_config # but have a valid config.json with model_type try: config_dict = get_hf_file_to_dict("config.json", model, revision=None) if config_dict and "model_type" in config_dict: model_type = config_dict["model_type"] else: raise ValueError(f"config.json found but missing 'model_type' for model: {model}") except Exception as e: raise ValueError(f"Failed to read config.json for model: {model}. Error: {e}") from e else: raise ValueError( f"Could not determine model_type for model: {model}. " f"Model is not in standard transformers format and does not have model_index.json. " f"Please ensure the model has proper configuration files with 'model_type' field" ) default_config_path = current_omni_platform.get_default_stage_config_path() model_type_str = f"{model_type}.yaml" complete_config_path = PROJECT_ROOT / default_config_path / model_type_str if os.path.exists(complete_config_path): return str(complete_config_path) # Fall back to default config stage_config_file = f"vllm_omni/model_executor/stage_configs/{model_type}.yaml" stage_config_path = PROJECT_ROOT / stage_config_file if not os.path.exists(stage_config_path): return None return str(stage_config_path) def load_stage_configs_from_model(model: str, base_engine_args: dict | None = None) -> list: """Load stage configurations from model's default config file. Loads stage configurations based on the model type and device type. First tries to load a device-specific YAML file from stage_configs/{device_type}/ directory. If not found, falls back to the default config file. Args: model: Model name or path (used to determine model_type) Returns: List of stage configuration dictionaries Raises: FileNotFoundError: If no stage config file exists for the model type """ if base_engine_args is None: base_engine_args = {} stage_config_path = resolve_model_config_path(model) if stage_config_path is None: return [] stage_configs = load_stage_configs_from_yaml(config_path=stage_config_path, base_engine_args=base_engine_args) return stage_configs def load_stage_configs_from_yaml(config_path: str, base_engine_args: dict | None = None) -> list: """Load stage configurations from a YAML file. Args: config_path: Path to the YAML configuration file Returns: List of stage configuration dictionaries from the file's stage_args """ if base_engine_args is None: base_engine_args = {} config_data = OmegaConf.load(config_path) stage_args = config_data.stage_args global_async_chunk = config_data.get("async_chunk", False) # Convert any nested dataclass objects to dicts before creating OmegaConf base_engine_args = _convert_dataclasses_to_dict(base_engine_args) base_engine_args = OmegaConf.create(base_engine_args) for stage_arg in stage_args: base_engine_args_tmp = base_engine_args.copy() # Update base_engine_args with stage-specific engine_args if they exist if hasattr(stage_arg, "engine_args") and stage_arg.engine_args is not None: base_engine_args_tmp = OmegaConf.merge(base_engine_args_tmp, stage_arg.engine_args) stage_type = getattr(stage_arg, "stage_type", "llm") if hasattr(stage_arg, "runtime") and stage_arg.runtime is not None and stage_type != "diffusion": runtime_cfg = stage_arg.runtime max_batch_size = int(runtime_cfg.get("max_batch_size", 1) or 1) base_engine_args_tmp["max_num_seqs"] = max_batch_size base_engine_args_tmp.async_chunk = global_async_chunk stage_arg.engine_args = base_engine_args_tmp return stage_args def load_and_resolve_stage_configs( model: str, stage_configs_path: str | None, kwargs: dict | None, default_stage_cfg_factory: Any = None, ) -> tuple[str, list]: """Load stage configurations from model or YAML file with fallback to defaults. Args: model: Model name or path stage_configs_path: Optional path to YAML file containing stage configurations kwargs: Engine arguments to merge with stage configs default_stage_cfg_factory: Optional callable that takes no args and returns default stage config list when no configs are found Returns: Tuple of (config_path, stage_configs) """ if stage_configs_path is None: config_path = resolve_model_config_path(model) stage_configs = load_stage_configs_from_model(model, base_engine_args=kwargs) if not stage_configs: if default_stage_cfg_factory is not None: default_stage_cfg = default_stage_cfg_factory() stage_configs = OmegaConf.create(default_stage_cfg) else: stage_configs = [] else: config_path = stage_configs_path stage_configs = load_stage_configs_from_yaml(stage_configs_path, base_engine_args=kwargs) return config_path, stage_configs def get_final_stage_id_for_e2e( output_modalities: list[str] | None, default_modalities: list[str], stage_list: list ) -> int: """Get the final stage id for e2e. Args: stage_list: List of stage configurations Returns: Final stage id for e2e """ last_stage_id = len(stage_list) - 1 if output_modalities is not None: prompt_modalities = [] for modality in output_modalities: if modality not in default_modalities: logger.warning(f"Invalid output modality: {modality}, ignoring it") # TODO: if user specifies unsupported modalities, invalid it and raise an error continue prompt_modalities.append(modality) output_modalities = prompt_modalities else: output_modalities = default_modalities try: for _sid in range(last_stage_id, -1, -1): if ( getattr(stage_list[_sid], "final_output", False) and stage_list[_sid].final_output_type in output_modalities ): final_stage_id_for_e2e = _sid break if final_stage_id_for_e2e < 0: final_stage_id_for_e2e = last_stage_id except Exception as e: logger.debug( "[Orchestrator] Failed to determine final stage for E2E; \ falling back to last: %s", e, exc_info=True, ) final_stage_id_for_e2e = last_stage_id return final_stage_id_for_e2e def filter_dataclass_kwargs(cls: Any, kwargs: dict) -> dict: """Filter kwargs to only include fields defined in the dataclass. Args: cls: Dataclass type kwargs: Keyword arguments to filter Returns: Filtered keyword arguments containing only valid dataclass fields """ if not is_dataclass(cls): raise ValueError(f"{cls} is not a dataclass") if not isinstance(kwargs, dict): raise ValueError("kwargs must be a dictionary") def _filter_value(value: Any, annotation: Any) -> Any: """Recursively filter nested dict/list values based on dataclass annotations.""" if annotation is None: return value origin = get_origin(annotation) if origin is None: if isinstance(annotation, type) and is_dataclass(annotation) and isinstance(value, dict): return filter_dataclass_kwargs(annotation, value) return value if origin in (list, tuple, set): args = get_args(annotation) inner = args[0] if args else None if isinstance(value, (list, tuple, set)): return type(value)(_filter_value(v, inner) for v in value) return value if origin is dict: args = get_args(annotation) val_type = args[1] if len(args) > 1 else None if isinstance(value, dict): return {k: _filter_value(v, val_type) for k, v in value.items()} return value if origin is types.UnionType or origin is getattr(types, "UnionType", None): for arg in get_args(annotation): if isinstance(arg, type) and is_dataclass(arg) and isinstance(value, dict): return filter_dataclass_kwargs(arg, value) # Try container-style filtering for union members filtered = _filter_value(value, arg) if filtered is not value: return filtered return value return value valid_fields = {f.name: f for f in fields(cls) if f.init} filtered_kwargs = {} for k, v in kwargs.items(): if k not in valid_fields: continue field = valid_fields[k] filtered_kwargs[k] = _filter_value(v, field.type) return filtered_kwargs # TODO(wuhang): Remove after PR #1115. def build_base_engine_args(source: Any) -> dict[str, Any] | None: """Build base engine args with tokenizer and parallel configuration. Automatically detects whether source is a dict-like object or namespace object. Args: source: Source object (args namespace or kwargs dict) containing configuration. Returns: Dictionary containing tokenizer and parallel configuration overrides, or None if no configuration is present. """ # Auto-detect source type: dict-like objects have 'get' method is_dict_like = hasattr(source, "get") and callable(getattr(source, "get")) # Extract tokenizer if is_dict_like: tokenizer = source.get("tokenizer", None) else: tokenizer = getattr(source, "tokenizer", None) base_engine_args = {"tokenizer": tokenizer} if tokenizer is not None else None # Extract parallel configuration parallel_keys = [ "tensor_parallel_size", "pipeline_parallel_size", "data_parallel_size", "data_parallel_size_local", "data_parallel_backend", "distributed_executor_backend", ] if is_dict_like: parallel_overrides = {k: source[k] for k in parallel_keys if k in source and source[k] is not None} else: parallel_overrides = { k: getattr(source, k) for k in parallel_keys if hasattr(source, k) and getattr(source, k) is not None } if parallel_overrides: base_engine_args = base_engine_args or {} base_engine_args.update(parallel_overrides) return base_engine_args # The following code detects if the process is running in a container and if # PID host is available. If so, we can use process-scoped memory tracking; # otherwise we need sequential init locks. def _read_text(path: str) -> str | None: try: with open(path, encoding="utf-8", errors="replace") as f: return f.read() except (FileNotFoundError, PermissionError, OSError): return None def in_container() -> bool: # Common Docker signal if os.path.exists("/.dockerenv"): return True # cgroup markers (works for Docker/containerd/K8s/Podman in many setups) cg = _read_text("/proc/1/cgroup") or "" markers = ("docker", "containerd", "kubepods", "libpod", "podman") return any(m in cg for m in markers) def has_pid_host() -> bool | None: """ Returns: True -> very likely running with --pid=host (host PID namespace) False -> very likely isolated PID namespace (default) None -> cannot determine """ # Strong signal: in host pid namespace, PID 2 is usually kthreadd comm2 = _read_text("/proc/2/comm") if comm2 is not None: comm2 = comm2.strip() if comm2 == "kthreadd": return True # If PID 2 exists and is NOT kthreadd, we're almost certainly not in host pid ns return False # Fallback: check for other low-numbered kernel threads (best-effort) for pid, name in [(3, "rcu_gp"), (4, "rcu_par_gp"), (10, "ksoftirqd/0")]: comm = _read_text(f"/proc/{pid}/comm") if comm is not None: if comm.strip() == name: return True else: return False return False def detect_pid_host() -> bool: ic = in_container() if not ic: return True return has_pid_host()