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

tests/config/test_config_utils.py

@@ -0,0 +1,163 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from dataclasses import dataclass
+from enum import Enum
+from typing import Optional
+
+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: Optional[object] = 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
+    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

@@ -4,11 +4,14 @@
 import ast
 import dataclasses
 import hashlib
+import json
+import logging
 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
@@ -22,8 +25,10 @@
     resolve_defined_ops,
 )
 from vllm.config import CompilationConfig, CUDAGraphMode, VllmConfig
+from vllm.config.utils import hash_factors
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
+from vllm.logging_utils import lazy
 from vllm.utils.import_utils import resolve_obj_by_qualname
 from vllm.utils.torch_utils import is_torch_equal_or_newer
 
@@ -574,32 +579,46 @@ def __call__(
         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
 
@@ -627,6 +646,51 @@ def __call__(
             local_cache_dir, disable_cache, self.prefix
         )
 
+        # Reuses existing cache key
+
+        logger.info(
+            "torch.compile cache factors: env=%s cfg=%s comp=%s dir=%s",
+            env_hash,
+            config_hash,
+            compiler_hash,
+            local_cache_dir,
+        )
+        logger.debug("code hash=%s", code_hash)
+
+        # Persist and log only hash-relevant factors together.
+        try:
+            if logger.isEnabledFor(logging.DEBUG):
+                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/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
 
@@ -152,13 +151,29 @@ def compute_hash(self) -> str:
         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",
+            "calculate_kv_scales",
+            "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