config, envs: pass use_replayssm to the breakable-graph fallback; register VLLM_FAIL_ON_PROFILING_KV_LEAK

CompilationConfig.resolve_cudagraph_mode_and_sizes read self.cache_config.use_replayssm, but CompilationConfig has
no cache_config, so with VLLM_USE_BREAKABLE_CUDAGRAPH=1 the fallback branch raised AttributeError. Both model
runners now pass cache_config.use_replayssm as an argument. The shipped launchers set VLLM_USE_BREAKABLE_CUDAGRAPH=0
and never reach this branch.

VLLM_FAIL_ON_PROFILING_KV_LEAK was read with a raw os.getenv, so setting it printed the unknown-variable warning. It
is now registered in vllm.envs with the same spellings (unset fails the boot; 0, false or no warn instead), listed
as an ignored compile factor and read through vllm.envs. Tests cover both.

Co-Authored-By: Hinata <hinata@idihore.id>
Signed-off-by: MOVZX <movzx@yahoo.com>

vllm/config/compilation.py

@@ -1390,6 +1390,7 @@ class CompilationConfig:
         max_num_reqs: int | None = None,
         is_profiling: bool = False,
         piecewise_capture_available: bool = True,
+        use_replayssm: bool = False,
     ) -> CUDAGraphMode:
         from vllm.v1.attention.backend import AttentionCGSupport
 
@@ -1443,7 +1444,8 @@ class CompilationConfig:
                 )
             ) or (
                 envs.VLLM_USE_BREAKABLE_CUDAGRAPH
-                and self.cache_config.use_replayssm
+                # CompilationConfig has no cache_config; the runner passes the flag.
+                and use_replayssm
             ):
                 # Breakable CUDA graphs break at the attention kernels at runtime,
                 # with or without torch.compile.

vllm/envs.py

@@ -471,6 +471,11 @@ if TYPE_CHECKING:
     # it changes the ring views' block stride and which block table the PLE
     # state is read through, so ON and OFF must not share a compile cache.
     VLLM_E2_SIDE_CACHE_LAYOUT: bool = True
+    # Profiling-teardown leak policy (vllm/v1/worker/gpu/cudagraph_utils.py): a profiling
+    # KV cache still allocated after teardown fails the boot by default; "0", "false" or
+    # "no" downgrade it to a warning while diagnosing. AOT: no. CAPTURE: no -- it only
+    # decides whether a host-side check raises.
+    VLLM_FAIL_ON_PROFILING_KV_LEAK: bool = True
     # Opt-out for the fork startup guards in vllm/v1/e2_guards.py, read at
     # Qwen4ExpForCausalLM construction. AOT: no. CAPTURE: no -- it changes
     # nothing itself; it only permits a boot whose identity IS different
@@ -2712,6 +2717,11 @@ environment_variables: dict[str, Callable[[], Any]] = {
     "VLLM_E2_SIDE_CACHE_LAYOUT": lambda: _e2_strict_switch(
         "VLLM_E2_SIDE_CACHE_LAYOUT", default=True
     ),
+    # Profiling-teardown leak policy: fail the boot unless "0"/"false"/"no".
+    "VLLM_FAIL_ON_PROFILING_KV_LEAK": lambda: os.getenv(
+        "VLLM_FAIL_ON_PROFILING_KV_LEAK", "1"
+    ).strip().lower()
+    not in ("0", "false", "no"),
     # Seconds between `topk-exact-counters` lines from each GPU worker
     # (0, the default, disables the reader entirely). The line reports the
     # medium-path exact-restart counters; see
@@ -2967,6 +2977,9 @@ def compile_factors() -> dict[str, object]:
         # compiled and the worker's tables are identical in both worlds, so an
         # A/B flip must reuse the same AOT cache.
         "VLLM_E2_MAMBA_RETAIN_CHECKPOINT",
+        # Boot-failure policy of a host-side profiling-teardown check; read by no
+        # compiled code.
+        "VLLM_FAIL_ON_PROFILING_KV_LEAK",
         # Logging cadence for an already-compiled kernel's counters: it gates a
         # host-side timer and a snapshot op, and is read by no compiled code, so
         # hashing it would invalidate every compile cache for a log interval.

vllm/v1/worker/gpu/cudagraph_utils.py

@@ -3,7 +3,6 @@
 import contextlib
 import gc
 import itertools
-import os
 from collections import defaultdict
 from collections.abc import Callable, Iterable, Iterator
 from contextlib import ExitStack
@@ -15,6 +14,7 @@ import torch
 import torch.nn as nn
 from tqdm import tqdm
 
+import vllm.envs as envs
 from vllm.compilation.breakable_cudagraph import (
     BreakableCUDAGraphWrapper,
     is_breakable_cudagraph_enabled,
@@ -1215,8 +1215,6 @@ def _teardown_profiling_state(runner: "GPUModelRunner") -> None:
             # Fail boot by default: this memory is absent from the real KV
             # pool and can invalidate capacity planning. Operators may opt out
             # temporarily while diagnosing with VLLM_FAIL_ON_PROFILING_KV_LEAK=0.
-            if os.getenv("VLLM_FAIL_ON_PROFILING_KV_LEAK", "1").lower() not in (
-                "0", "false", "no"
-            ):
+            if envs.VLLM_FAIL_ON_PROFILING_KV_LEAK:
                 raise RuntimeError(message)
             logger.warning(message)

vllm/v1/worker/gpu/model_runner.py

@@ -804,6 +804,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
             max_num_reqs=self.max_num_reqs,
             is_profiling=is_profiling,
             piecewise_capture_available=piecewise_capture_available,
+            use_replayssm=self.cache_config.use_replayssm,
         )
         self.cudagraph_manager = ModelCudaGraphManager(
             self.vllm_config,

vllm/v1/worker/gpu_model_runner.py

@@ -7132,6 +7132,7 @@ class GPUModelRunner(
             kv_cache_config=self.kv_cache_config,
             max_num_reqs=self.max_num_reqs,
             is_profiling=is_profiling,
+            use_replayssm=self.cache_config.use_replayssm,
         )
         # Trigger cudagraph dispatching keys initialization after
         # resolved cudagraph mode.