[torch.compile] caching of config fields should be opt-out by default (#26468)

Signed-off-by: vnadathur <glvikramn@gmail.com>
Signed-off-by: WorldExplored <srreyansh.sethi@gmail.com>
Signed-off-by: Srreyansh Sethi <srreyansh.sethi@gmail.com>
Signed-off-by: Srreyansh Sethi <107075589+WorldExplored@users.noreply.github.com>
Co-authored-by: WorldExplored <srreyansh.sethi@gmail.com>
Co-authored-by: Srreyansh Sethi <107075589+worldexplored@users.noreply.github.com>
Co-authored-by: vnadathur <236933696+vnadathur@users.noreply.github.com>
Co-authored-by: Luka Govedič <ProExpertProg@users.noreply.github.com>

vllm/config/utils.py

@@ -3,14 +3,19 @@
 """Utility functions for vLLM config dataclasses."""
 
 import ast
+import enum
+import hashlib
 import inspect
+import json
+import pathlib
 import textwrap
-from collections.abc import Iterable
+from collections.abc import Iterable, Mapping, Sequence, Set
 from dataclasses import MISSING, Field, field, fields, is_dataclass, replace
 from itertools import pairwise
 from typing import TYPE_CHECKING, Any, Protocol, TypeVar
 
 import regex as re
+import torch
 from pydantic.fields import FieldInfo
 from typing_extensions import runtime_checkable
 
@@ -176,3 +181,115 @@ def update_config(config: ConfigT, overrides: dict[str, Any]) -> ConfigT:
             )
         processed_overrides[field_name] = value
     return replace(config, **processed_overrides)
+
+
+def normalize_value(x):
+    """Return a stable, JSON-serializable canonical form for hashing.
+    Order: primitives, special types (Enum, callable, torch.dtype, Path), then
+    generic containers (Mapping/Set/Sequence) with recursion.
+    """
+    # Fast path
+    if x is None or isinstance(x, (bool, int, float, str)):
+        return x
+
+    # Enums: tag with FQN to avoid primitive collisions.
+    # Ex: Enum(1) vs int(1) -> ("module.QualName", value).
+    if isinstance(x, enum.Enum):
+        enum_type = f"{x.__class__.__module__}.{x.__class__.__qualname__}"
+        return (enum_type, normalize_value(x.value))
+
+    # Classes (types) are accepted and canonicalized by their fully-qualified
+    # name (module.qualname) for a stable identifier.
+    # Instances are only accepted if they expose uuid(); otherwise they are
+    # rejected to avoid under-hashing object state.
+
+    # Callables: accept classes only; reject funcs/lambdas/methods.
+    # Used by LogitsProcessor types and ModelConfig.hf_overrides.
+    if isinstance(x, type):
+        module = getattr(x, "__module__", "")
+        qual = getattr(x, "__qualname__", getattr(x, "__name__", ""))
+        return ".".join([p for p in (module, qual) if p]) or repr(x)
+
+    # Prefer stable uuid identifiers for objects that provide them, even if
+    # they are callable instances (e.g., InductorPass wrappers).
+    if hasattr(x, "uuid") and callable(getattr(x, "uuid", None)):
+        return x.uuid()
+
+    if callable(x):
+        raise TypeError("normalize_value: function or callable instance unsupported")
+
+    # Torch dtype: stringify (torch.float64 -> "torch.float64").
+    # We rely on the string form here; dtype-bearing fields that need additional
+    # disambiguation should encode that at the config layer.
+    if isinstance(x, torch.dtype):
+        return str(x)
+
+    # Bytes
+    if isinstance(x, (bytes, bytearray)):
+        return x.hex()
+
+    # Paths (canonicalize)
+    if isinstance(x, pathlib.Path):
+        try:
+            return str(x.expanduser().resolve())
+        except Exception:
+            return str(x)
+
+    # Dataclasses: represent as (FQN, sorted(field,value) tuple) for stability.
+    if is_dataclass(x):
+        type_fqn = f"{x.__class__.__module__}.{x.__class__.__qualname__}"
+        items = tuple(
+            (f.name, normalize_value(getattr(x, f.name)))
+            for f in sorted(fields(x), key=lambda f: f.name)
+        )
+        return (type_fqn, items)
+
+    # Containers (generic)
+    if isinstance(x, Mapping):
+        return tuple(sorted((str(k), normalize_value(v)) for k, v in x.items()))
+    if isinstance(x, Set):
+        return tuple(sorted(repr(normalize_value(v)) for v in x))
+    if isinstance(x, Sequence) and not isinstance(x, (str, bytes, bytearray)):
+        return tuple(normalize_value(v) for v in x)
+
+    # PretrainedConfig
+    if hasattr(x, "to_json_string") and callable(x.to_json_string):
+        return x.to_json_string()
+
+    # Unsupported type: e.g., modules, generators, open files, or objects
+    # without a stable JSON/UUID representation. Hard-error to avoid
+    # under-hashing.
+    # If you hit this, either reshape your config to use supported primitives
+    # and containers, or extend normalize_value to provide a stable encoding
+    # (e.g., via uuid() or to_json_string()) for this type.
+    raise TypeError(
+        f"normalize_value: unsupported type '{type(x).__name__}'. "
+        "Ensure config values use supported primitives/containers or add a "
+        "stable representation for this type."
+    )
+
+
+def get_hash_factors(config: ConfigT, ignored_factors: set[str]) -> dict[str, object]:
+    """Gets the factors used for hashing a config class.
+    - Includes all dataclass fields not in `ignored_factors`.
+    - Errors on non-normalizable values.
+    """
+    factors: dict[str, object] = {}
+    for dc_field in fields(config):
+        factor = dc_field.name
+        if factor in ignored_factors:
+            continue
+        value = getattr(config, factor, None)
+        try:
+            factors[factor] = normalize_value(value)
+        except TypeError as e:
+            raise TypeError(
+                f"get_hash_factors: unsupported type for key '{factor}' "
+                f"({type(value).__name__})"
+            ) from e
+    return factors
+
+
+def hash_factors(items: dict[str, object]) -> str:
+    """Return a SHA-256 hex digest of the canonical items structure."""
+    return hashlib.sha256(json.dumps(items, sort_keys=True).encode()).hexdigest()