898 lines
36 KiB
Python
898 lines
36 KiB
Python
import json
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import os
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import types
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from collections import Counter
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from dataclasses import fields, is_dataclass
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from pathlib import Path
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from typing import Any, get_args, get_origin
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import yaml
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from vllm.logger import init_logger
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from vllm.sampling_params import RequestOutputKind, SamplingParams
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from vllm.transformers_utils.config import get_config, get_hf_file_to_dict
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from vllm.transformers_utils.repo_utils import file_or_path_exists
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from vllm_omni.config.config_factory import StageConfigFactory, with_trust_remote_code_override
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from vllm_omni.config.pipeline_registry import OMNI_PIPELINES
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from vllm_omni.config.stage_config import _DEPLOY_DIR
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from vllm_omni.config.yaml_util import create_config, load_yaml_config, merge_configs
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from vllm_omni.diffusion.utils.hf_utils import _looks_like_dreamzero
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from vllm_omni.entrypoints.stage_utils import _to_dict
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from vllm_omni.inputs.data import OmniSamplingParams
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from vllm_omni.platforms import current_omni_platform
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# Get the project root directory (2 levels up from this file)
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PROJECT_ROOT = Path(__file__).parent.parent.parent
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logger = init_logger(__name__)
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_DIFFUSERS_CLASS_TO_CONFIG: dict[str, str] = {
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"GlmImagePipeline": "glm_image",
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}
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def inject_omni_kv_config(stage: Any, omni_conn_cfg: dict[str, Any], omni_from: str, omni_to: str) -> None:
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"""Inject connector configuration into stage engine arguments."""
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# Prepare omni_kv_config dict
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omni_conf_dict = {}
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try:
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# Access engine_args safely (might be OmegaConf or dict)
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existing_args = stage.engine_args
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if hasattr(existing_args, "get"):
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_oc = existing_args.get("omni_kv_config", None)
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if _oc:
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if hasattr(_oc, "items"): # dict-like
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omni_conf_dict = dict(_oc)
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else: # object?
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omni_conf_dict = _to_dict(_oc)
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except Exception:
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omni_conf_dict = {}
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# Inject connector info
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omni_conf_dict["connector_config"] = omni_conn_cfg
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omni_conf_dict["omni_from_stage"] = omni_from
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omni_conf_dict["omni_to_stage"] = omni_to
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# Write back to engine_args
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try:
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if hasattr(stage.engine_args, "__setitem__"):
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stage.engine_args["omni_kv_config"] = omni_conf_dict
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else:
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setattr(stage.engine_args, "omni_kv_config", omni_conf_dict)
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except Exception as e:
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# Fallback for OmegaConf or similar if direct set fails?
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logger.error(f"Failed to inject omni connector config into stage: {e}")
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def _try_get_class_name_from_diffusers_config(model: str) -> str | None:
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"""Try to get class name from diffusers model configuration files.
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Args:
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model: Model name or path
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Returns:
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Model type string if found, None otherwise
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"""
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model_index = get_hf_file_to_dict("model_index.json", model, revision=None)
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if model_index and isinstance(model_index, dict) and "_class_name" in model_index:
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logger.debug(f"Found model_type '{model_index['_class_name']}' in model_index.json")
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return model_index["_class_name"]
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return None
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def _filter_dict_like_object(obj: dict | Any) -> dict:
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"""Filter dict-like object by removing callables and recursively converting values.
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Converts dict-like objects to regular dicts while filtering out callable values
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that are incompatible with OmegaConf. Recursively processes values through
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_convert_dataclasses_to_dict for nested object conversion.
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Args:
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obj: Dict or dict-like object to filter
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Returns:
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Regular dict with callables filtered out and values recursively converted
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Raises:
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TypeError: If obj doesn't support .items() method
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ValueError: If dict conversion fails unexpectedly
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"""
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def _is_callable_value(value: Any) -> bool:
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if callable(value):
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return True
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return isinstance(
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value,
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types.FunctionType | types.MethodType | types.BuiltinFunctionType | types.BuiltinMethodType,
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)
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result = {}
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filtered_keys = []
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for k, v in obj.items():
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# Preserve class objects by converting to a fully qualified name string
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# so callers that resolve via import path (e.g. custom_pipeline_args.pipeline_class)
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# still work after OmegaConf round-trip.
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if isinstance(v, type):
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module = getattr(v, "__module__", None)
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qualname = getattr(v, "__qualname__", getattr(v, "__name__", None))
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if module and qualname and module != "builtins":
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result[k] = f"{module}.{qualname}"
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else:
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result[k] = qualname
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elif _is_callable_value(v):
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filtered_keys.append(str(k))
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else:
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result[k] = _convert_dataclasses_to_dict(v)
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if filtered_keys:
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logger.warning(
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f"Filtered out {len(filtered_keys)} callable object(s) from base_engine_args "
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f"that are not compatible with OmegaConf: {filtered_keys}. "
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)
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return result
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def _convert_dataclasses_to_dict(obj: Any) -> Any:
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"""Recursively convert non-serializable objects to OmegaConf-compatible types.
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This is needed because OmegaConf cannot handle:
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- Dataclass objects with Literal type annotations (e.g., StructuredOutputsConfig)
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- Counter objects (from collections or vllm.utils)
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- Set objects
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- Callable objects (functions, methods, etc.)
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- Other non-primitive types
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"""
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# IMPORTANT: Check Counter BEFORE dict, since Counter is a subclass of dict
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# Handle Counter objects (convert to dict)
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# Check by class name first to catch both collections.Counter and vllm.utils.Counter
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if hasattr(obj, "__class__") and obj.__class__.__name__ == "Counter":
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try:
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return dict(obj)
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except (TypeError, ValueError):
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# If Counter can't be converted to dict, return empty dict
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return {}
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# Also check isinstance for collections.Counter (must be before dict check)
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if isinstance(obj, Counter):
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return dict(obj)
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# Handle set objects (convert to list)
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if isinstance(obj, set):
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return list(obj)
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# Handle dataclass objects
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# Use field iteration instead of asdict() to:
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# 1. Only include init fields (non-init fields cause "unexpected kwarg" errors)
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# 2. Skip None values matching field defaults (avoids Pydantic validation
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# failures when None is explicitly passed for non-Optional typed fields,
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# e.g. CompilationConfig.cudagraph_capture_sizes: list[int] = None)
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if is_dataclass(obj) and not isinstance(obj, type):
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result = {}
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for f in fields(obj):
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if not f.init:
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continue
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value = getattr(obj, f.name)
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if value is None and f.default is None:
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continue
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result[f.name] = _convert_dataclasses_to_dict(value)
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return result
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# Handle dictionaries (recurse into values) and filter out callables(cause error in OmegaConf.create)
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# Note: This must come AFTER Counter check since Counter is a dict subclass
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if isinstance(obj, dict):
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return _filter_dict_like_object(obj)
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# Preserve class objects by converting to a fully qualified name string.
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if isinstance(obj, type):
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module = getattr(obj, "__module__", None)
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qualname = getattr(obj, "__qualname__", getattr(obj, "__name__", None))
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if module and qualname and module != "builtins":
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return f"{module}.{qualname}"
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return qualname
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# Handle callable objects (functions, methods, etc.) - skip them
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# Note: This comes after dict/list checks to avoid misclassifying dict-like objects
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if callable(obj):
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return None
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# Handle lists and tuples (recurse into items)
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if isinstance(obj, list | tuple):
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return type(obj)(_convert_dataclasses_to_dict(item) for item in obj if not callable(item))
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# Try to convert any dict-like object (has keys/values methods) to dict
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if hasattr(obj, "keys") and hasattr(obj, "values") and not isinstance(obj, str | bytes):
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try:
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return _filter_dict_like_object(obj)
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except (TypeError, ValueError, AttributeError):
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# If conversion fails, return as-is
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return obj
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# Primitive types and other objects that OmegaConf can handle
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return obj
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def _try_resolve_omni_model_type(model: str) -> str | None:
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"""Try to resolve model_type for omni models with empty config.json.
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Searches both the legacy ``stage_configs/*.yaml`` directory and the
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migrated ``deploy/*.yaml`` directory for a stem that substring-matches
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the model path (e.g. ``cosyvoice3`` in
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``FunAudioLLM/Fun-CosyVoice3-0.5B-2512``). The longest match wins so
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``cosyvoice3`` beats ``cosyvoice`` and ``bagel_single_stage`` beats
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``bagel``.
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"""
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model_lower = model.lower().replace("-", "").replace("_", "")
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best_match: str | None = None
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best_len = 0
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for subdir in ("model_executor/stage_configs", "deploy"):
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config_dir = PROJECT_ROOT / "vllm_omni" / subdir
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if not config_dir.exists():
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continue
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for config_file in sorted(config_dir.glob("*.yaml")):
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candidate = config_file.stem.replace("-", "").replace("_", "")
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if candidate and candidate in model_lower and len(candidate) > best_len:
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best_match = config_file.stem
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best_len = len(candidate)
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return best_match
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def _registry_default_deploy_path(model: str) -> str | None:
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"""Return the registered pipeline's default deploy YAML path, if any.
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A checkpoint's HF ``model_type`` is not always the registered Omni pipeline
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key. MiniCPM-o 4.5, for example, reports ``minicpmo`` while its
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predicate-selected pipeline is ``minicpmo_4_5``. Stage construction already
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loads that pipeline's default deploy config, so connector discovery must
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resolve the same path.
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"""
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pipeline_config = StageConfigFactory.get_pipeline_config(
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model=model,
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trust_remote_code=True,
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)
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if pipeline_config is None or pipeline_config.default_deploy_config_name is None:
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return None
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default_deploy_path = _DEPLOY_DIR / pipeline_config.default_deploy_config_name
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if default_deploy_path.is_file():
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return str(default_deploy_path)
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return None
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def resolve_model_config_path(model: str) -> str | None:
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"""Resolve the stage/deploy config file path from the model name.
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Resolves configuration path based on the model type and device type.
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Order:
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1. Device-specific stage config under ``stage_configs/{device_type}/``
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2. If HF ``model_type`` is not an ``OMNI_PIPELINES`` key, the registered
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pipeline's ``default_deploy_config_name`` (keeps connectors aligned with
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stage construction when HF type and pipeline key differ)
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3. ``deploy/{model_type}.yaml``
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4. Legacy ``stage_configs/{model_type}.yaml``
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5. Registered pipeline default deploy config (final fallthrough)
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Args:
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model: Model name or path (used to determine model_type)
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Returns:
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String path to the stage/deploy configuration file, or ``None`` if no
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matching config file exists.
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Raises:
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ValueError: If model_type cannot be determined
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"""
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# Try to get config from standard transformers format first
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try:
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hf_config = get_config(model, trust_remote_code=True)
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model_type = hf_config.model_type
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except (ValueError, Exception):
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# If standard transformers format fails, try diffusers format
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if file_or_path_exists(model, "model_index.json", revision=None):
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model_type = _try_get_class_name_from_diffusers_config(model)
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if model_type is None:
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raise ValueError(
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f"Could not determine model_type for diffusers model: {model}. "
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f"Please ensure the model has 'model_type' in transformer/config.json or model_index.json"
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)
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elif file_or_path_exists(model, "config.json", revision=None):
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# Try to read config.json manually for custom models like Bagel that fail get_config
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# but have a valid config.json with model_type
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try:
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config_dict = get_hf_file_to_dict("config.json", model, revision=None)
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if config_dict and "model_type" in config_dict:
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model_type = config_dict["model_type"]
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else:
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# For models with empty config.json (e.g. CosyVoice3),
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# try matching against registered omni stage configs.
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model_type = _try_resolve_omni_model_type(model)
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if model_type is None:
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raise ValueError(f"config.json found but missing 'model_type' for model: {model}")
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except Exception as e:
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raise ValueError(f"Failed to read config.json for model: {model}. Error: {e}") from e
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else:
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# No config.json at repo root (e.g. GLM-TTS stores configs in
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# subdirectories only). Try matching against registered deploy
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# YAML filenames before giving up.
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model_type = _try_resolve_omni_model_type(model)
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if model_type is None:
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raise ValueError(
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f"Could not determine model_type for model: {model}. "
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f"Model is not in standard transformers format and does not have model_index.json. "
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f"Please ensure the model has proper configuration files with 'model_type' field"
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)
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default_config_path = current_omni_platform.get_default_stage_config_path()
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if model_type == "vla" and _looks_like_dreamzero(model):
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model_type = "dreamzero"
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if model_type in _DIFFUSERS_CLASS_TO_CONFIG:
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normalized_model_type = _DIFFUSERS_CLASS_TO_CONFIG[model_type]
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else:
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normalized_model_type = model_type.replace("-", "_")
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model_type_str = f"{normalized_model_type}.yaml"
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complete_config_path = PROJECT_ROOT / default_config_path / model_type_str
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if os.path.exists(complete_config_path):
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return str(complete_config_path)
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# Prefer the registry default before deploy/<hf_model_type>.yaml when the
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# HF type is not itself a pipeline key. Otherwise a future
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# deploy/minicpmo.yaml would desync connectors from stages for MiniCPM-o 4.5.
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if normalized_model_type not in OMNI_PIPELINES:
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registry_path = _registry_default_deploy_path(model)
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if registry_path is not None:
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return registry_path
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deploy_config_path = _DEPLOY_DIR / model_type_str
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if os.path.exists(deploy_config_path):
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return str(deploy_config_path)
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stage_config_file = f"vllm_omni/model_executor/stage_configs/{normalized_model_type}.yaml"
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stage_config_path = PROJECT_ROOT / stage_config_file
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if os.path.exists(stage_config_path):
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return str(stage_config_path)
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return _registry_default_deploy_path(model)
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def load_stage_configs_from_model(
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model: str,
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*,
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trust_remote_code: bool | None,
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base_engine_args: dict | None = None,
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deploy_config_path: str | None = None,
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stage_overrides: dict[str, dict[str, Any]] | None = None,
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strategy_config_path: str | None = None,
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) -> tuple[list, str | None]:
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"""Load stage configurations from model's default config file.
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For models registered in the pipeline registry (new path), uses
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``StageConfigFactory.create_legacy_stage_configs_from_model()`` which merges
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PipelineConfig + DeployConfig + CLI overrides.
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For other models (legacy path), loads stage configs from YAML.
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Args:
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model: Model name or path (used to determine model_type)
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trust_remote_code: Whether to trust remote code while resolving the model config.
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base_engine_args: Base engine args to merge as CLI overrides.
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deploy_config_path: Optional explicit deploy config path.
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stage_overrides: Per-stage overrides from --stage-overrides.
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strategy_config_path: Optional path to a composable-parallel
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``strategy.yaml`` whose derived sizing is overlaid onto the
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registry-merged stages (opt-in; ignored on the legacy YAML path).
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Returns:
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``(stage_configs, omni_lb_policy)``: the list of stage configuration
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dictionaries plus the strategy-derived pipeline-wide ``omni_lb_policy``
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(``None`` when no strategy set one). The policy is returned rather than
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written into a caller-provided mutable dict.
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"""
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if base_engine_args is None:
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base_engine_args = {}
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cli_overrides = _convert_dataclasses_to_dict(dict(base_engine_args))
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# A False inherited from the engine-args dump is the store_true flag's
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# default, not an explicit choice — drop it so only the tri-state
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# parameter below decides (see with_trust_remote_code_override).
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if not cli_overrides.get("trust_remote_code"):
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cli_overrides.pop("trust_remote_code", None)
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cli_overrides = with_trust_remote_code_override(cli_overrides, trust_remote_code)
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if stage_overrides:
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for stage_id_str, overrides in stage_overrides.items():
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for key, val in overrides.items():
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cli_overrides[f"stage_{stage_id_str}_{key}"] = val
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# Current runtime initialization still consumes legacy OmegaConf stage
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# configs. ``StageConfigFactory.create_from_model`` now produces the
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# structured ``VllmOmniConfig`` object; future RFC #4021 changes will
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# migrate the engine/runtime consumers and replace this legacy resolver.
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strategy_specs = None
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if strategy_config_path is not None:
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from vllm_omni.config.composable_parallel.strategy_loader import load_strategy_specs
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strategy_specs = load_strategy_specs(strategy_config_path)
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stages, omni_lb_policy = StageConfigFactory.create_legacy_stage_configs_from_model(
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model,
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trust_remote_code=trust_remote_code,
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cli_overrides=cli_overrides,
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deploy_config_path=deploy_config_path,
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strategy_specs=strategy_specs,
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)
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if stages is not None:
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# Convert StageConfig objects to OmegaConf for backward compat
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return [stage.to_omegaconf() for stage in stages], omni_lb_policy
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# Legacy fallback: load from YAML. A composable-parallel strategy cannot be
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# applied here (it overlays onto registry-merged stages), so warn rather than
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# silently dropping the operator's --strategy-config.
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if strategy_config_path is not None:
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logger.warning(
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"--strategy-config (%s) was provided but model %r resolves via the "
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"legacy stage_configs YAML path, which does not support "
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"composable-parallel strategies; the strategy is ignored. Use a "
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"registry-based model to apply it.",
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strategy_config_path,
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model,
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)
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stage_config_path = resolve_model_config_path(model)
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if stage_config_path is None:
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return [], None
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stage_configs = load_stage_configs_from_yaml(
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config_path=stage_config_path,
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base_engine_args=base_engine_args,
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prefer_stage_engine_args=True,
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)
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return stage_configs, None
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def load_stage_configs_from_yaml(
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config_path: str,
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base_engine_args: dict | None = None,
|
|
prefer_stage_engine_args: bool = True,
|
|
) -> list:
|
|
"""Load stage configurations from a YAML file (legacy OmegaConf path).
|
|
|
|
TODO(@lishunyang12): remove once all models use PipelineConfig + DeployConfig.
|
|
|
|
Args:
|
|
config_path: Path to the YAML configuration file
|
|
base_engine_args: Engine args supplied by the caller.
|
|
prefer_stage_engine_args: When True, YAML stage args override caller
|
|
engine args. When False, caller engine args override YAML defaults.
|
|
|
|
Returns:
|
|
List of stage configuration dictionaries from the file's stage_args
|
|
"""
|
|
if base_engine_args is None:
|
|
base_engine_args = {}
|
|
config_data = load_yaml_config(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 DictConfig
|
|
base_engine_args = _convert_dataclasses_to_dict(base_engine_args)
|
|
base_engine_args = create_config(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:
|
|
if prefer_stage_engine_args:
|
|
merged_engine_args = merge_configs(base_engine_args_tmp, stage_arg.engine_args)
|
|
else:
|
|
merged_engine_args = merge_configs(stage_arg.engine_args, base_engine_args_tmp)
|
|
base_engine_args_tmp = create_config(merged_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":
|
|
base_engine_args_tmp.async_chunk = global_async_chunk
|
|
stage_arg.engine_args = base_engine_args_tmp
|
|
return stage_args
|
|
|
|
|
|
def filter_stages(
|
|
config_path: str | None,
|
|
stage_configs: list,
|
|
kwargs: dict | None,
|
|
) -> list:
|
|
"""Filter stage configs by mode when YAML defines a `modes` section.
|
|
|
|
The YAML can define, e.g.:
|
|
|
|
modes:
|
|
- mode: text-to-image
|
|
stages: [1]
|
|
- mode: image-to-text
|
|
stages: [0]
|
|
|
|
When users pass `mode="image-to-text"` into Omni(**kwargs), only the stages
|
|
listed for that mode are returned. If no mode is provided, defaults to
|
|
"text-to-image". If no modes are defined or filtering fails, returns the
|
|
original stage_configs unchanged.
|
|
|
|
Args:
|
|
config_path: Path to the YAML config (used to read `modes`).
|
|
stage_configs: Loaded list of stage configs.
|
|
kwargs: Engine/caller kwargs; may contain "mode".
|
|
|
|
Returns:
|
|
Filtered list of stage configs (or original list if filtering not applied).
|
|
"""
|
|
if not stage_configs or config_path is None:
|
|
return stage_configs
|
|
|
|
try:
|
|
cfg = load_yaml_config(config_path)
|
|
yaml_modes = getattr(cfg, "modes", None)
|
|
if yaml_modes is None:
|
|
return stage_configs
|
|
|
|
mode_to_stage_ids: dict[str, list[int]] = {}
|
|
if yaml_modes is not None:
|
|
for entry in yaml_modes:
|
|
mode_name = None
|
|
stages = None
|
|
if hasattr(entry, "mode") or hasattr(entry, "stages"):
|
|
mode_name = getattr(entry, "mode", None)
|
|
stages = getattr(entry, "stages", None)
|
|
elif isinstance(entry, dict):
|
|
mode_name = entry.get("mode")
|
|
stages = entry.get("stages")
|
|
|
|
if mode_name is None or stages is None:
|
|
continue
|
|
|
|
if isinstance(stages, int):
|
|
stage_list = [stages]
|
|
else:
|
|
stage_list = list(stages)
|
|
|
|
mode_to_stage_ids[str(mode_name)] = [int(sid) for sid in stage_list]
|
|
|
|
# No modes section or empty mapping: use all stages and return early.
|
|
active_mode: str | None = None
|
|
if isinstance(kwargs, dict):
|
|
active_mode = kwargs.get("mode")
|
|
|
|
if active_mode is None:
|
|
active_mode = "text-to-image"
|
|
|
|
if active_mode not in mode_to_stage_ids:
|
|
logger.warning(
|
|
"Requested mode '%s' not found in config '%s'; available modes: %s. Using all stages.",
|
|
active_mode,
|
|
config_path,
|
|
sorted(mode_to_stage_ids.keys()),
|
|
)
|
|
return stage_configs
|
|
|
|
allowed_ids = set(mode_to_stage_ids[active_mode])
|
|
filtered_stage_configs = [sc for sc in stage_configs if getattr(sc, "stage_id", None) in allowed_ids]
|
|
if not filtered_stage_configs:
|
|
logger.warning(
|
|
"Mode '%s' in config '%s' resolved to stage ids %s, but none matched loaded stage_args. "
|
|
"Falling back to all stages.",
|
|
active_mode,
|
|
config_path,
|
|
sorted(allowed_ids),
|
|
)
|
|
return stage_configs
|
|
|
|
return filtered_stage_configs
|
|
except Exception as e:
|
|
logger.warning("Failed to apply mode-based stage filtering: %s", e)
|
|
return stage_configs
|
|
|
|
|
|
def parse_stage_overrides(value: Any) -> dict[str, dict[str, Any]] | None:
|
|
"""Parse the ``--stage-overrides`` value into a per-stage override dict.
|
|
|
|
``value`` may be a raw JSON string (as supplied on the CLI) or an
|
|
already-parsed mapping. Returns ``None`` when no overrides are given.
|
|
|
|
Raises:
|
|
ValueError: when ``value`` is a string that is not valid JSON.
|
|
"""
|
|
if not value:
|
|
return None
|
|
if isinstance(value, str):
|
|
try:
|
|
return json.loads(value)
|
|
except json.JSONDecodeError as exc:
|
|
raise ValueError(f"--stage-overrides is not valid JSON: {exc}. Got: {value!r}") from exc
|
|
return value
|
|
|
|
|
|
def load_and_resolve_stage_configs(
|
|
model: str,
|
|
stage_configs_path: str | None,
|
|
kwargs: dict | None,
|
|
*,
|
|
trust_remote_code: bool | None,
|
|
default_stage_cfg_factory: Any = None,
|
|
deploy_config_path: str | None = None,
|
|
stage_overrides: dict[str, dict[str, Any]] | None = None,
|
|
strategy_config_path: str | None = None,
|
|
) -> tuple[str, list, str | None]:
|
|
"""Load stage configurations from model or YAML file with fallback to defaults.
|
|
|
|
Args:
|
|
model: Model name or path
|
|
stage_configs_path: Optional path to legacy YAML (stage_args format)
|
|
kwargs: Engine arguments to merge with stage configs
|
|
trust_remote_code: Whether to trust remote code while resolving the model config.
|
|
default_stage_cfg_factory: Optional callable that takes no args and returns
|
|
default stage config list when no configs are found
|
|
deploy_config_path: Optional path to deploy YAML (new format).
|
|
Mutually exclusive with ``stage_configs_path``.
|
|
stage_overrides: Per-stage overrides from ``--stage-overrides`` JSON.
|
|
Keys are stage_id strings, values are dicts of overrides.
|
|
strategy_config_path: Optional path to a composable-parallel
|
|
``strategy.yaml`` overlaid onto the registry-merged stages.
|
|
|
|
Returns:
|
|
Tuple of ``(config_path, stage_configs, omni_lb_policy)`` — the last is
|
|
the strategy-derived pipeline-wide load-balancer policy (``None`` when no
|
|
strategy set one), returned for the engine to apply.
|
|
"""
|
|
if stage_configs_path is not None and deploy_config_path is not None:
|
|
raise ValueError(
|
|
"--stage-configs-path and --deploy-config are mutually exclusive: "
|
|
"they use different path resolution rules and loading paths. "
|
|
"Use --deploy-config for new-format YAMLs (preferred); "
|
|
"--stage-configs-path is kept only for the legacy `stage_args` format "
|
|
"and will be removed in a future release."
|
|
)
|
|
if stage_configs_path is not None and deploy_config_path is None:
|
|
if not os.path.exists(stage_configs_path):
|
|
raise FileNotFoundError(
|
|
f"--stage-configs-path {stage_configs_path!r} does not exist. "
|
|
"Legacy `stage_configs/` yamls were replaced by `vllm_omni/deploy/<model>.yaml`; "
|
|
"use --deploy-config. See docs/configuration/stage_configs.md."
|
|
)
|
|
with open(stage_configs_path, encoding="utf-8") as f:
|
|
_peek = yaml.safe_load(f) or {}
|
|
if "stages" in _peek and "stage_args" not in _peek:
|
|
deploy_config_path = stage_configs_path
|
|
stage_configs_path = None
|
|
else:
|
|
logger.warning(
|
|
"--stage-configs-path is deprecated; migrate %r and use --deploy-config.",
|
|
stage_configs_path,
|
|
)
|
|
|
|
omni_lb_policy: str | None = None
|
|
if deploy_config_path is not None:
|
|
config_path = deploy_config_path
|
|
stage_configs, omni_lb_policy = load_stage_configs_from_model(
|
|
model,
|
|
trust_remote_code=trust_remote_code,
|
|
base_engine_args=kwargs,
|
|
deploy_config_path=deploy_config_path,
|
|
stage_overrides=stage_overrides,
|
|
strategy_config_path=strategy_config_path,
|
|
)
|
|
if not stage_configs:
|
|
if default_stage_cfg_factory is not None:
|
|
default_stage_cfg = default_stage_cfg_factory()
|
|
stage_configs = create_config(_convert_dataclasses_to_dict(default_stage_cfg))
|
|
else:
|
|
stage_configs = []
|
|
elif stage_configs_path is None:
|
|
config_path = resolve_model_config_path(model)
|
|
stage_configs, omni_lb_policy = load_stage_configs_from_model(
|
|
model,
|
|
trust_remote_code=trust_remote_code,
|
|
base_engine_args=kwargs,
|
|
stage_overrides=stage_overrides,
|
|
strategy_config_path=strategy_config_path,
|
|
)
|
|
if not stage_configs:
|
|
if default_stage_cfg_factory is not None:
|
|
default_stage_cfg = default_stage_cfg_factory()
|
|
stage_configs = create_config(_convert_dataclasses_to_dict(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)
|
|
|
|
stage_configs = filter_stages(config_path, stage_configs, kwargs)
|
|
logger.debug(f"stage_configs: {stage_configs}")
|
|
|
|
return config_path, stage_configs, omni_lb_policy
|
|
|
|
|
|
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:
|
|
final_stage_id_for_e2e = last_stage_id
|
|
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
|
|
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
|
|
|
|
|
|
# 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()
|
|
|
|
|
|
### Helpers for handling delta messages
|
|
def coerce_param_message_types(params: list[OmniSamplingParams], is_streaming: bool):
|
|
"""Iterate over the sampling params and convert to the message types
|
|
to DELTA messages, if streaming is enabled, or FINAL_ONLY if
|
|
it's disabled, while respecting `.skip_clone` on the params.
|
|
|
|
This is needed to avoid emitting redundant multimodal data.
|
|
"""
|
|
# Coerce vLLM's default output kinds as needed to handle streaming
|
|
# (i.e., DELTA output kind). Note that this is only applied to non
|
|
# Diffusion sampling params.
|
|
#
|
|
# NOTE: Hidden states will still be passed between stages.
|
|
for idx, sp in enumerate(params):
|
|
# For OmniDiffusionParams don't set output kind
|
|
if isinstance(sp, SamplingParams):
|
|
params[idx] = maybe_coerce_to_message_type(sp, is_streaming)
|
|
return params
|
|
|
|
|
|
def maybe_coerce_to_message_type(params: SamplingParams, is_streaming: bool):
|
|
"""If this is a CUMULATIVE message, coerce it to DELTA if streaming, otherwise FINAL_ONLY."""
|
|
target_type = RequestOutputKind.DELTA if is_streaming else RequestOutputKind.FINAL_ONLY
|
|
if params.output_kind == target_type:
|
|
return params
|
|
elif is_streaming and params.output_kind == RequestOutputKind.FINAL_ONLY:
|
|
logger.warning("Request appears to be streaming, but got request type final only!")
|
|
elif not is_streaming and params.output_kind == RequestOutputKind.DELTA:
|
|
logger.warning("Request appears to not be streaming, but got request type delta!")
|
|
|
|
if not params.skip_clone:
|
|
params = params.clone()
|
|
params.skip_clone = True
|
|
params.output_kind = target_type
|
|
return params
|