[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>

tests/config/test_config_utils.py

@@ -0,0 +1,166 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from dataclasses import dataclass
+from enum import Enum
+
+import pytest
+
+from vllm.config.utils import get_hash_factors, hash_factors, normalize_value
+
+# Helpers
+
+
+def endswith_fqname(obj, suffix: str) -> bool:
+    # normalize_value(type) returns fully-qualified name
+    # Compare suffix to avoid brittle import paths.
+    out = normalize_value(obj)
+    return isinstance(out, str) and out.endswith(suffix)
+
+
+def expected_path(p_str: str = ".") -> str:
+    import pathlib
+
+    p = pathlib.Path(p_str)
+    return p.expanduser().resolve().as_posix()
+
+
+# Minimal dataclass to test get_hash_factors.
+# Avoid importing heavy vLLM configs.
+@dataclass
+class SimpleConfig:
+    a: object
+    b: object | None = None
+
+
+class DummyLogprobsMode(Enum):
+    RAW_LOGITS = "raw_logits"
+
+
+def test_hash_factors_deterministic():
+    """Test that hash_factors produces consistent SHA-256 hashes"""
+    factors = {"a": 1, "b": "test"}
+    hash1 = hash_factors(factors)
+    hash2 = hash_factors(factors)
+
+    assert hash1 == hash2
+    # Dict key insertion order should not affect the hash.
+    factors_reordered = {"b": "test", "a": 1}
+    assert hash_factors(factors_reordered) == hash1
+    assert len(hash1) == 64
+    assert all(c in "0123456789abcdef" for c in hash1)
+
+
+@pytest.mark.parametrize(
+    "inp, expected",
+    [
+        (None, None),
+        (True, True),
+        (1, 1),
+        (1.0, 1.0),
+        ("x", "x"),
+        (b"ab", "6162"),
+        (bytearray(b"ab"), "6162"),
+        ([1, 2], (1, 2)),
+        ({"b": 2, "a": 1}, (("a", 1), ("b", 2))),
+    ],
+)
+def test_normalize_value_matrix(inp, expected):
+    """Parametric input→expected normalization table."""
+    assert normalize_value(inp) == expected
+
+
+def test_normalize_value_enum():
+    # Enums normalize to (module.QualName, value).
+    # DummyLogprobsMode uses a string payload.
+    out = normalize_value(DummyLogprobsMode.RAW_LOGITS)
+    assert isinstance(out, tuple)
+    assert out[0].endswith("DummyLogprobsMode")
+    # Expect string payload 'raw_logits'.
+    assert out[1] == "raw_logits"
+
+
+def test_normalize_value_set_order_insensitive():
+    # Sets are unordered; normalize_value sorts elements for determinism.
+    assert normalize_value({3, 1, 2}) == normalize_value({1, 2, 3})
+
+
+def test_normalize_value_path_normalization():
+    from pathlib import Path  # local import to avoid global dependency
+
+    # Paths expand/resolve to absolute strings.
+    # Stabilizes hashing across working dirs.
+    assert normalize_value(Path(".")) == expected_path(".")
+
+
+def test_normalize_value_uuid_and_to_json():
+    # Objects may normalize via uuid() or to_json_string().
+    class HasUUID:
+        def uuid(self):
+            return "test-uuid"
+
+    class ToJson:
+        def to_json_string(self):
+            return '{"x":1}'
+
+    assert normalize_value(HasUUID()) == "test-uuid"
+    assert normalize_value(ToJson()) == '{"x":1}'
+
+
+@pytest.mark.parametrize(
+    "bad",
+    [
+        (lambda x: x),
+        (type("CallableInstance", (), {"__call__": lambda self: 0}))(),
+        (lambda: (lambda: 0))(),  # nested function instance
+    ],
+)
+def test_error_cases(bad):
+    """Inputs expected to raise TypeError."""
+    # Reject functions/lambdas/callable instances
+    # to avoid under-hashing.
+    with pytest.raises(TypeError):
+        normalize_value(bad)
+
+
+def test_enum_vs_int_disambiguation():
+    # int stays primitive
+    nf_int = normalize_value(1)
+    assert nf_int == 1
+
+    # enum becomes ("module.QualName", value)
+    nf_enum = normalize_value(DummyLogprobsMode.RAW_LOGITS)
+    assert isinstance(nf_enum, tuple) and len(nf_enum) == 2
+    enum_type, enum_val = nf_enum
+    assert enum_type.endswith(".DummyLogprobsMode")
+    assert enum_val == "raw_logits"
+
+    # Build factor dicts from configs with int vs enum
+    f_int = get_hash_factors(SimpleConfig(1), set())
+    f_enum = get_hash_factors(SimpleConfig(DummyLogprobsMode.RAW_LOGITS), set())
+    # The int case remains a primitive value
+    assert f_int["a"] == 1
+    # The enum case becomes a tagged tuple ("module.QualName", "raw_logits")
+    assert isinstance(f_enum["a"], tuple) and f_enum["a"][1] == "raw_logits"
+    # Factor dicts must differ so we don't collide primitives with Enums.
+    assert f_int != f_enum
+    # Hash digests must differ correspondingly
+    assert hash_factors(f_int) != hash_factors(f_enum)
+
+    # Hash functions produce stable hex strings
+    h_int = hash_factors(f_int)
+    h_enum = hash_factors(f_enum)
+    assert isinstance(h_int, str) and len(h_int) == 64
+    assert isinstance(h_enum, str) and len(h_enum) == 64
+
+
+def test_classes_are_types():
+    """Types normalize to FQNs; include real vLLM types."""
+    # Only classes allowed; functions/lambdas are rejected.
+    # Canonical form is the fully-qualified name.
+    assert isinstance(normalize_value(str), str)
+
+    class LocalDummy:
+        pass
+
+    assert endswith_fqname(LocalDummy, ".LocalDummy")

vllm/compilation/backends.py

@@ -4,12 +4,14 @@
 import ast
 import dataclasses
 import hashlib
+import json
 import operator
 import os
 import pprint
 import time
 from collections.abc import Callable, Sequence
 from contextlib import contextmanager
+from functools import partial
 from typing import Any
 
 import torch
@@ -23,7 +25,9 @@ from vllm.compilation.partition_rules import (
     should_split,
 )
 from vllm.config import CompilationConfig, CUDAGraphMode, VllmConfig
+from vllm.config.utils import hash_factors
 from vllm.logger import init_logger
+from vllm.logging_utils import lazy
 from vllm.platforms import current_platform
 from vllm.utils.import_utils import resolve_obj_by_qualname
 from vllm.utils.torch_utils import is_torch_equal_or_newer
@@ -580,35 +584,47 @@ class VllmBackend:
     def __call__(
         self, graph: fx.GraphModule, example_inputs
     ) -> VllmSerializableFunction:
-        from .caching import _compute_code_hash, compilation_config_hash_factors
-
         vllm_config = self.vllm_config
+        # Minimal hashing here with existing utilities, reused below.
+
+        env_factors = envs.compile_factors()
+        env_hash = hash_factors(env_factors)
+        # Compute config/compiler/code hashes once and reuse
+        config_hash = vllm_config.compute_hash()
+        compiler_hash = self.compiler_manager.compute_hash(vllm_config)
+        forward_code_files = list(sorted(self.compilation_config.traced_files))
+
+        logger.debug(
+            "Traced files (to be considered for compilation cache):\n%s",
+            lazy(lambda: "\n".join(forward_code_files)),
+        )
+        hash_content = []
+        for filepath in forward_code_files:
+            hash_content.append(filepath)
+            if filepath == "<string>":
+                # This means the function was dynamically generated, with
+                # e.g. exec(). We can't actually check these.
+                continue
+            try:
+                with open(filepath) as f:
+                    hash_content.append(f.read())
+            except Exception:
+                logger.warning("Failed to read file %s", filepath)
+                continue
+        code_hash = hashlib.sha256("\n".join(hash_content).encode()).hexdigest()
+        # Clear after consumption
+        self.compilation_config.traced_files.clear()
         if not self.compilation_config.cache_dir:
             # no provided cache dir, generate one based on the known factors
             # that affects the compilation. if none of the factors change,
             # the cache dir will be the same so that we can reuse the compiled
             # graph.
-
-            factors = compilation_config_hash_factors(vllm_config)
-            # 2. factors come from the code files that are traced by Dynamo (
-            #    it mainly summarizes how the model is used in forward pass)
-            code_hash = _compute_code_hash(self.compilation_config.traced_files)
-            self.compilation_config.traced_files.clear()
-            factors.append(code_hash)
-
-            # 3. compiler hash
-            compiler_hash = self.compiler_manager.compute_hash(vllm_config)
-            factors.append(compiler_hash)
-
-            # combine all factors to generate the cache dir
-            hash_key = hashlib.md5(
-                str(factors).encode(), usedforsecurity=False
-            ).hexdigest()[:10]
-
+            factors = [env_hash, config_hash, code_hash, compiler_hash]
+            # Use SHA-256 for cache key hashing to be consistent across
+            # compute_hash functions. Truncate for a short cache dir name.
+            hash_key = hashlib.sha256(str(factors).encode()).hexdigest()[:10]
             cache_dir = os.path.join(
-                envs.VLLM_CACHE_ROOT,
-                "torch_compile_cache",
-                hash_key,
+                envs.VLLM_CACHE_ROOT, "torch_compile_cache", hash_key
             )
             self.compilation_config.cache_dir = cache_dir
 
@@ -621,6 +637,7 @@ class VllmBackend:
         os.makedirs(local_cache_dir, exist_ok=True)
         self.compilation_config.local_cache_dir = local_cache_dir
 
+        # Honors opt-outs such as CompilationMode.NONE or VLLM_DISABLE_COMPILE_CACHE.
         disable_cache = not is_compile_cache_enabled(
             self.compilation_config.inductor_compile_config
         )
@@ -638,6 +655,50 @@ class VllmBackend:
             local_cache_dir, disable_cache, self.prefix
         )
 
+        # Reuses existing cache key
+
+        logger.debug(
+            "torch.compile cache factors: env=%s cfg=%s comp=%s code=%s dir=%s",
+            env_hash,
+            config_hash,
+            compiler_hash,
+            code_hash,
+            local_cache_dir,
+        )
+
+        # Persist and log only hash-relevant factors together.
+        try:
+            logger.debug(
+                "Compile env factors (raw):\n%s\nVllm config hash: %s",
+                lazy(partial(pprint.pformat, env_factors, width=120)),
+                config_hash,
+            )
+            meta_path = os.path.join(local_cache_dir, "cache_key_factors.json")
+            if not os.path.exists(meta_path):
+                with open(meta_path, "w") as f:
+                    json.dump(
+                        {
+                            "env": env_factors,  # raw factors used for env_hash
+                            "config_hash": config_hash,
+                            "code_hash": code_hash,
+                            "compiler_hash": compiler_hash,
+                        },
+                        f,
+                        indent=2,
+                        sort_keys=True,
+                    )
+        except Exception:
+            # Best-effort only; metadata write failures are non-fatal.
+            logger.warning(
+                (
+                    "Could not write compile cache metadata at %s; continuing without "
+                    "metadata. Compiled cache remains valid; diagnostics may be "
+                    "limited."
+                ),
+                local_cache_dir,
+                exc_info=True,
+            )
+
         # when dynamo calls the backend, it means the bytecode
         # transform and analysis are done
         compilation_counter.num_graphs_seen += 1

vllm/compilation/pass_manager.py

@@ -127,7 +127,7 @@ class PostGradPassManager(CustomGraphPass):
         affects compilation caching. Its uuid depends on the UUIDs of all
         dependent passes and the pass config. See InductorPass for more info.
         """
-        state = {"pass_config": self.pass_config.uuid(), "passes": []}
+        state = {"pass_config": self.pass_config.compute_hash(), "passes": []}
         for pass_ in self.passes:
             state["passes"].append(pass_.uuid())
         state["passes"].append(self.fix_functionalization.uuid())

vllm/config/cache.py

@@ -1,7 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-import hashlib
 from dataclasses import field
 from typing import TYPE_CHECKING, Any, Literal
 
@@ -160,13 +159,29 @@ class CacheConfig:
         excluding anything before input ids/embeddings and after
         the final hidden states.
         """
-        factors: list[Any] = []
-        factors.append(self.cache_dtype)
-        factors.append(self.mamba_cache_dtype)
-        factors.append(self.mamba_ssm_cache_dtype)
-        # `cpu_offload_gb` does not use `torch.compile` yet.
-        hash_str = hashlib.md5(str(factors).encode(), usedforsecurity=False).hexdigest()
-        return hash_str
+        ignored_factors = {
+            # Runtime/derived knobs that don't affect compiled graph shape
+            "gpu_memory_utilization",
+            "swap_space",
+            "is_attention_free",
+            "num_gpu_blocks_override",
+            "enable_prefix_caching",
+            "prefix_caching_hash_algo",
+            # `cpu_offload_gb` does not use `torch.compile` yet.
+            "cpu_offload_gb",
+            "cpu_kvcache_space_bytes",
+            "mamba_page_size_padded",
+            # Post-init/derived counters
+            "num_gpu_blocks",
+            "num_cpu_blocks",
+            # WIP feature toggle not impacting compiled graph shape
+            "kv_sharing_fast_prefill",
+        }
+
+        from vllm.config.utils import get_hash_factors, hash_factors
+
+        factors = get_hash_factors(self, ignored_factors)
+        return hash_factors(factors)
 
     def metrics_info(self):
         # convert cache_config to dict(key: str, value: str) for prometheus

vllm/config/compilation.py

@@ -2,7 +2,6 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import enum
-import hashlib
 from collections import Counter
 from collections.abc import Callable
 from dataclasses import asdict, field
@@ -160,7 +159,7 @@ class PassConfig:
             current_platform.get_device_capability().to_int(), {}
         )
 
-    def uuid(self):
+    def compute_hash(self) -> str:
         """
         Produces a hash unique to the pass configuration.
         Any new fields that affect compilation should be added to the hash.
@@ -506,28 +505,33 @@ class CompilationConfig:
 
     def compute_hash(self) -> str:
         """
-        WARNING: Whenever a new field is added to this config,
-        ensure that it is included in the factors list if
-        it affects the computation graph.
-
         Provide a hash that uniquely identifies all the configs
         that affect the structure of the computation
         graph from input ids/embeddings to the final hidden states,
         excluding anything before input ids/embeddings and after
         the final hidden states.
         """
-        factors: list[Any] = []
-        factors.append(self.mode)
-        factors.append(self.backend)
-        factors.append(self.custom_ops)
-        factors.append(self.splitting_ops)
-        factors.append(self.use_inductor)
-        factors.append(self.use_inductor_graph_partition)
-        factors.append(self.inductor_compile_config)
-        factors.append(self.inductor_passes)
-        factors.append(self.pass_config.uuid())
-        factors.append(self.compile_cache_save_format)
-        return hashlib.sha256(str(factors).encode()).hexdigest()
+        # Opt-out: default-include declared fields; keep a tiny exclude set;
+        # normalize types; keep SHA-256. For nested opaque configs, include a
+        # stable identifier (e.g., pass_config.compute_hash()) instead of object id.
+
+        ignored_factors = {
+            # Paths/dirs and runtime/metrics that don’t affect compiled graph
+            "debug_dump_path",
+            "cache_dir",
+            "local_cache_dir",
+            "bs_to_padded_graph_size",
+            "traced_files",
+            "compilation_time",
+            "static_forward_context",
+            "pass_config",  # handled separately below
+        }
+
+        from vllm.config.utils import get_hash_factors, hash_factors
+
+        factors = get_hash_factors(self, ignored_factors)
+        factors["pass_config"] = self.pass_config.compute_hash()
+        return hash_factors(factors)
 
     def __repr__(self) -> str:
         exclude = {

vllm/config/model.py

@@ -1,8 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-import hashlib
-import json
 import warnings
 from collections.abc import Callable
 from dataclasses import InitVar, field
@@ -18,7 +16,7 @@ import vllm.envs as envs
 from vllm.config.multimodal import MMCacheType, MMEncoderTPMode, MultiModalConfig
 from vllm.config.pooler import PoolerConfig
 from vllm.config.scheduler import RunnerType
-from vllm.config.utils import assert_hashable, config, getattr_iter
+from vllm.config.utils import config, getattr_iter
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
 from vllm.transformers_utils.config import (
@@ -324,50 +322,50 @@ class ModelConfig:
         excluding anything before input ids/embeddings and after
         the final hidden states.
         """
-        factors: list[Any] = []
-        factors.append(self.model)
-        factors.append(self.dtype)
-        factors.append(self.quantization)
-        factors.append(self.revision)
-        factors.append(self.code_revision)
-        factors.append(self.max_model_len)
-        factors.append(self.max_logprobs)
-        factors.append(self.disable_sliding_window)
-        factors.append(self.trust_remote_code)
-        factors.append(self.generation_config)
-        factors.append(self.model_impl)
-        factors.append(self.override_generation_config)
-        factors.append(self.video_pruning_rate)
-        factors.append(self.enable_prompt_embeds)
+        ignored_factors = {
+            "runner",
+            "convert",
+            "task",
+            "tokenizer",
+            "tokenizer_mode",
+            "seed",
+            "hf_config_path",
+            "allowed_local_media_path",
+            "allowed_media_domains",
+            "tokenizer_revision",
+            "spec_target_max_model_len",
+            "enforce_eager",
+            "logprobs_mode",
+            "disable_cascade_attn",
+            "skip_tokenizer_init",
+            "enable_prompt_embeds",
+            "served_model_name",
+            "config_format",
+            "hf_token",
+            "hf_overrides",
+            "logits_processor_pattern",
+            "enable_sleep_mode",
+            "override_attention_dtype",
+            "logits_processors",
+            "io_processor_plugin",
+            "pooler_config",
+            "override_pooler_config",
+            "multimodal_config",
+            "limit_mm_per_prompt",
+            "media_io_kwargs",
+            "mm_processor_kwargs",
+            "mm_processor_cache_gb",
+            "mm_processor_cache_type",
+            "mm_shm_cache_max_object_size_mb",
+            "mm_encoder_tp_mode",
+            "interleave_mm_strings",
+            "skip_mm_profiling",
+        }
 
-        # hf_config can control how the model looks!
-        try:
-            hf_config_json = self.hf_config.to_json_string(use_diff=False)
-        except TypeError:
-            from transformers import PretrainedConfig
+        from vllm.config.utils import get_hash_factors, hash_factors
 
-            from vllm.utils.jsontree import json_map_leaves
-
-            # Handle nested HF configs with unserializable values gracefully
-            hf_config_json = (
-                json.dumps(
-                    json_map_leaves(
-                        lambda v: v.to_dict()
-                        if isinstance(v, PretrainedConfig)
-                        else str(v),
-                        self.hf_config.to_dict(),
-                    ),
-                    indent=2,
-                    sort_keys=True,
-                )
-                + "\n"
-            )
-
-        factors.append(hf_config_json)
-
-        str_factors = str(factors)
-        assert_hashable(str_factors)
-        return hashlib.sha256(str(factors).encode()).hexdigest()
+        factors = get_hash_factors(self, ignored_factors)
+        return hash_factors(factors)
 
     def _update_nested(
         self,

vllm/config/parallel.py

@@ -1,7 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-import hashlib
 import os
 from typing import TYPE_CHECKING, Any, Literal
 
@@ -448,19 +447,41 @@ class ParallelConfig:
         This hash is also used for DP worker configuration validation
         to prevent hangs from mismatched collective communication patterns.
         """
-        factors: list[Any] = []
-        factors.append(self.pipeline_parallel_size)
-        factors.append(self.tensor_parallel_size)
-        factors.append(self.enable_expert_parallel)
-        factors.append(self.data_parallel_size)
-        factors.append(self.all2all_backend)
-        factors.append(self.enable_eplb)
-        if self.enable_eplb:
-            factors.append(self.eplb_config.log_balancedness)
-            factors.append(self.eplb_config.window_size)
-            factors.append(self.eplb_config.step_interval)
-            factors.append(self.eplb_config.num_redundant_experts)
-        return hashlib.sha256(str(factors).encode()).hexdigest()
+        ignored_factors = {
+            # Derived/runtime topology, networking, or launch details
+            "data_parallel_rank",
+            "data_parallel_rank_local",
+            "data_parallel_backend",
+            "data_parallel_external_lb",
+            "data_parallel_hybrid_lb",
+            "data_parallel_master_ip",
+            "data_parallel_master_port",
+            "_data_parallel_master_port_list",
+            "data_parallel_rpc_port",
+            "rank",
+            "master_addr",
+            "master_port",
+            "node_rank",
+            "nnodes",
+            "max_parallel_loading_workers",
+            "disable_custom_all_reduce",
+            "ray_workers_use_nsight",
+            "ray_runtime_env",
+            "placement_group",
+            "distributed_executor_backend",
+            "worker_cls",
+            "sd_worker_cls",
+            "worker_extension_cls",
+            "_api_process_count",
+            "_api_process_rank",
+        }
+
+        from vllm.config.utils import get_hash_factors, hash_factors
+
+        factors = get_hash_factors(self, ignored_factors)
+        # Explicitly include backend affecting env factor as before
+        factors["VLLM_ALL2ALL_BACKEND"] = str(envs.VLLM_ALL2ALL_BACKEND)
+        return hash_factors(factors)
 
     def __post_init__(self) -> None:
         # Set all2all_backend from env var if not specified, with deprecation warning

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()

vllm/envs.py

@@ -2,8 +2,8 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import functools
-import hashlib
 import json
+import logging
 import os
 import sys
 import tempfile
@@ -426,6 +426,8 @@ def get_vllm_port() -> int | None:
 
 # --8<-- [start:env-vars-definition]
 
+logger = logging.getLogger(__name__)
+
 environment_variables: dict[str, Callable[[], Any]] = {
     # ================== Installation Time Env Vars ==================
     # Target device of vLLM, supporting [cuda (by default),
@@ -1540,85 +1542,88 @@ def is_set(name: str):
     raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
 
 
-def compute_hash() -> str:
-    """
-    WARNING: Whenever a new key is added to this environment
-    variables, ensure that it is included in the factors list if
-    it affects the computation graph. For example, different values
-    of VLLM_PP_LAYER_PARTITION will generate different computation
-    graphs, so it is included in the factors list. The env vars that
-    affect the choice of different kernels or attention backends should
-    also be included in the factors list.
-    """
+def compile_factors() -> dict[str, object]:
+    """Return env vars used for torch.compile cache keys.
 
-    # The values of envs may affects the computation graph.
-    # TODO(DefTruth): hash all environment variables?
-    # for key in environment_variables:
-    #     factorize(key)
-    environment_variables_to_hash = [
-        "VLLM_PP_LAYER_PARTITION",
-        "VLLM_MLA_DISABLE",
-        "VLLM_FLASH_ATTN_MAX_NUM_SPLITS_FOR_CUDA_GRAPH",
-        "VLLM_USE_TRITON_AWQ",
-        "VLLM_DP_RANK",
-        "VLLM_DP_SIZE",
-        "VLLM_USE_STANDALONE_COMPILE",
-        "VLLM_FUSED_MOE_CHUNK_SIZE",
-        "VLLM_FLASHINFER_MOE_BACKEND",
-        "VLLM_V1_USE_PREFILL_DECODE_ATTENTION",
-        "VLLM_ATTENTION_BACKEND",
-        "VLLM_USE_FLASHINFER_SAMPLER",
-        "VLLM_DISABLED_KERNELS",
-        "VLLM_USE_DEEP_GEMM",
-        "VLLM_MOE_USE_DEEP_GEMM",
-        "VLLM_USE_DEEP_GEMM_E8M0",
-        "VLLM_USE_FUSED_MOE_GROUPED_TOPK",
-        "VLLM_USE_FLASHINFER_MOE_FP16",
-        "VLLM_USE_FLASHINFER_MOE_FP8",
-        "VLLM_USE_FLASHINFER_MOE_FP4",
-        "VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8",
-        "VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8_CUTLASS",
-        "VLLM_USE_FLASHINFER_MOE_MXFP4_BF16",
-        "VLLM_FLASHINFER_WORKSPACE_BUFFER_SIZE",
-        "VLLM_USE_CUDNN_PREFILL",
-        "VLLM_USE_TRTLLM_RAGGED_DEEPSEEK_PREFILL",
-        "VLLM_USE_TRTLLM_ATTENTION",
-        "VLLM_FLASHINFER_DISABLE_Q_QUANTIZATION",
-        "VLLM_ROCM_USE_AITER",
-        "VLLM_ROCM_USE_AITER_PAGED_ATTN",
-        "VLLM_ROCM_USE_AITER_LINEAR",
-        "VLLM_ROCM_USE_AITER_MOE",
-        "VLLM_ROCM_USE_AITER_RMSNORM",
-        "VLLM_ROCM_USE_AITER_MLA",
-        "VLLM_ROCM_USE_AITER_MHA",
-        "VLLM_ROCM_USE_AITER_FP4_ASM_GEMM",
-        "VLLM_ROCM_USE_AITER_TRITON_ROPE",
-        "VLLM_ROCM_USE_AITER_FP8BMM",
-        "VLLM_ROCM_USE_AITER_UNIFIED_ATTENTION",
-        "VLLM_ROCM_USE_AITER_TRITON_GEMM",
-        "VLLM_ROCM_USE_SKINNY_GEMM",
-        "VLLM_ROCM_FP8_PADDING",
-        "VLLM_ROCM_MOE_PADDING",
-        "VLLM_ROCM_CUSTOM_PAGED_ATTN",
-        "VLLM_ROCM_QUICK_REDUCE_QUANTIZATION",
-        "VLLM_ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16",
-        "VLLM_ROCM_QUICK_REDUCE_MAX_SIZE_BYTES_MB",
-        "VLLM_ROCM_FP8_MFMA_PAGE_ATTN",
-        "VLLM_ENABLE_INDUCTOR_MAX_AUTOTUNE",
-        "VLLM_ENABLE_INDUCTOR_COORDINATE_DESCENT_TUNING",
-        "VLLM_NVFP4_GEMM_BACKEND",
-        "VLLM_USE_FBGEMM",
-        "VLLM_DEEPEP_HIGH_THROUGHPUT_FORCE_INTRA_NODE",
-        "VLLM_DEEPEP_LOW_LATENCY_USE_MNNVL",
-    ]
-    for key in environment_variables_to_hash:
-        # if this goes out of sync with environment_variables,
-        # it's not a user error, it's a bug
-        assert key in environment_variables, (
-            "Please update environment_variables_to_hash in envs.py"
-        )
+    Start with every known vLLM env var; drop entries in `ignored_factors`;
+    hash everything else. This keeps the cache key aligned across workers."""
 
-    factors = [environment_variables[key]() for key in environment_variables_to_hash]
+    ignored_factors: set[str] = {
+        "MAX_JOBS",
+        "VLLM_RPC_BASE_PATH",
+        "VLLM_USE_MODELSCOPE",
+        "VLLM_RINGBUFFER_WARNING_INTERVAL",
+        "VLLM_DEBUG_DUMP_PATH",
+        "VLLM_PORT",
+        "VLLM_CACHE_ROOT",
+        "LD_LIBRARY_PATH",
+        "VLLM_SERVER_DEV_MODE",
+        "VLLM_DP_MASTER_IP",
+        "VLLM_DP_MASTER_PORT",
+        "VLLM_RANDOMIZE_DP_DUMMY_INPUTS",
+        "VLLM_CI_USE_S3",
+        "VLLM_MODEL_REDIRECT_PATH",
+        "VLLM_HOST_IP",
+        "S3_ACCESS_KEY_ID",
+        "S3_SECRET_ACCESS_KEY",
+        "S3_ENDPOINT_URL",
+        "VLLM_USAGE_STATS_SERVER",
+        "VLLM_NO_USAGE_STATS",
+        "VLLM_DO_NOT_TRACK",
+        "VLLM_LOGGING_LEVEL",
+        "VLLM_LOGGING_PREFIX",
+        "VLLM_LOGGING_STREAM",
+        "VLLM_LOGGING_CONFIG_PATH",
+        "VLLM_LOG_STATS_INTERVAL",
+        "VLLM_DEBUG_LOG_API_SERVER_RESPONSE",
+        "VLLM_TUNED_CONFIG_FOLDER",
+        "VLLM_ENGINE_ITERATION_TIMEOUT_S",
+        "VLLM_HTTP_TIMEOUT_KEEP_ALIVE",
+        "VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS",
+        "VLLM_KEEP_ALIVE_ON_ENGINE_DEATH",
+        "VLLM_SLEEP_WHEN_IDLE",
+        "VLLM_IMAGE_FETCH_TIMEOUT",
+        "VLLM_VIDEO_FETCH_TIMEOUT",
+        "VLLM_AUDIO_FETCH_TIMEOUT",
+        "VLLM_MEDIA_URL_ALLOW_REDIRECTS",
+        "VLLM_MEDIA_LOADING_THREAD_COUNT",
+        "VLLM_MAX_AUDIO_CLIP_FILESIZE_MB",
+        "VLLM_VIDEO_LOADER_BACKEND",
+        "VLLM_MEDIA_CONNECTOR",
+        "VLLM_ASSETS_CACHE",
+        "VLLM_ASSETS_CACHE_MODEL_CLEAN",
+        "VLLM_MM_INPUT_CACHE_GIB",
+        "VLLM_WORKER_MULTIPROC_METHOD",
+        "VLLM_ENABLE_V1_MULTIPROCESSING",
+        "VLLM_V1_OUTPUT_PROC_CHUNK_SIZE",
+        "VLLM_CPU_KVCACHE_SPACE",
+        "VLLM_CPU_OMP_THREADS_BIND",
+        "VLLM_CPU_NUM_OF_RESERVED_CPU",
+        "VLLM_CPU_MOE_PREPACK",
+        "VLLM_CPU_SGL_KERNEL",
+        "VLLM_TEST_FORCE_LOAD_FORMAT",
+        "LOCAL_RANK",
+        "CUDA_VISIBLE_DEVICES",
+    }
+
+    from vllm.config.utils import normalize_value
+
+    factors: dict[str, object] = {}
+    for factor, getter in environment_variables.items():
+        if factor in ignored_factors:
+            continue
+
+        try:
+            raw = getter()
+        except Exception as exc:  # pragma: no cover - defensive logging
+            logger.warning(
+                "Skipping environment variable %s while hashing compile factors: %s",
+                factor,
+                exc,
+            )
+            continue
+
+        factors[factor] = normalize_value(raw)
 
     ray_noset_env_vars = [
         # Refer to
@@ -1641,8 +1646,8 @@ def compute_hash() -> str:
         "RAY_EXPERIMENTAL_NOSET_ONEAPI_DEVICE_SELECTOR",
         "RAY_EXPERIMENTAL_NOSET_RBLN_RT_VISIBLE_DEVICES",
     ]
-    factors.extend([os.getenv(var) for var in ray_noset_env_vars])
 
-    hash_str = hashlib.md5(str(factors).encode(), usedforsecurity=False).hexdigest()
+    for var in ray_noset_env_vars:
+        factors[var] = normalize_value(os.getenv(var))
 
-    return hash_str
+    return factors

vllm/logging_utils/__init__.py

@@ -2,9 +2,11 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 from vllm.logging_utils.formatter import NewLineFormatter
+from vllm.logging_utils.lazy import lazy
 from vllm.logging_utils.log_time import logtime
 
 __all__ = [
     "NewLineFormatter",
+    "lazy",
     "logtime",
 ]

vllm/logging_utils/lazy.py

@@ -0,0 +1,20 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from collections.abc import Callable
+from typing import Any
+
+
+class lazy:
+    """Wrap a zero-argument callable evaluated only during log formatting."""
+
+    __slots__ = ("_factory",)
+
+    def __init__(self, factory: Callable[[], Any]) -> None:
+        self._factory = factory
+
+    def __str__(self) -> str:
+        return str(self._factory())
+
+    def __repr__(self) -> str:
+        return str(self)