[FEAT] Support fast engine recovery through weight cache - #49879
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| model="/disk3/models/Qwen3-0.6B/", | ||
| load_format="ipc_cache", | ||
| model_loader_extra_config={ | ||
| "socket_dir": "/disk3/lsy/vllm/.wc_test", |
| socket_dir: str | None = None, | ||
| ): | ||
| self.vllm_config = vllm_config | ||
| self.tp_rank = tp_rank |
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Does this only support TP? what about DP, EP and PP?
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Will support more features soon.
| gpu_memory_utilization=0.4, | ||
| enforce_eager=True, | ||
| ) | ||
| print(f"LLM init took {time.perf_counter() - start:.2f}s") |
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I think we need this e2e test to run in CI
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| logger = init_logger(__name__) | ||
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| _CONNECT_TIMEOUT_S = 5.0 |
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I think we need to make this configurable
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| @dataclass(frozen=True) | ||
| class CacheConfig: |
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@claude review |
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Found one blocking correctness issue in the weight export/import path.
| def _export_entries(self) -> None: | ||
| assert self.model is not None | ||
| entries: dict[str, TensorEntry] = {} | ||
| for name, param in self.model.named_parameters(): |
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Reviewed by codex
[P1] Preserve aliases for tied parameters
named_parameters() defaults to remove_duplicate=True, so when two names share one parameter (for example, embed_tokens.weight and a tied lm_head.weight), only one name is exported. In the client, _apply_entries() replaces only that registration; the other alias remains a meta parameter and _materialize_remaining_meta_tensors() converts it into an uninitialized torch.empty_like allocation. This silently corrupts inference for tied-weight models.
Please export explicit alias metadata and restore the shared relationship on import. Using remove_duplicate=False alone is not sufficient to preserve parameter identity. An end-to-end test with tie_word_embeddings=True should verify both aliasing and output parity with normal loading.
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Beyond the two inline findings, this run also checked whether module-level (not just parameter-identity) embedding/lm_head aliasing could hit the same uninitialized-weight issue (ruled out — only identity-tied parameters via named_parameters(remove_duplicate=True) are affected), and whether the daemon's narrow exception handler could be crashed by any arbitrary unhandled exception from a command handler in normal operation, not just malformed messages (ruled out as a distinct, broader claim — the reported gap is specifically ValueError/unpickling errors from recv_msg).
Extended reasoning...
This PR touches security-sensitive, security-adjacent code (a new Unix-socket IPC daemon with pickle deserialization for cross-process weight sharing) and has real, confirmed findings including a local-RCE-capable deserialization issue and a silent-correctness bug for tied-embedding architectures — both already posted as inline comments. That alone rules out approval or a no-bugs-found defer. This note is purely to record two adjacent variants of the reported findings that were independently investigated and ruled out this run, so a future pass doesn't need to re-derive them from scratch.
| def recv_msg(sock: socket.socket) -> Any: | ||
| (length,) = _LEN_STRUCT.unpack(_recv_exact(sock, _LEN_STRUCT.size)) | ||
| if length > MAX_MSG_SIZE: | ||
| raise ValueError(f"Message size {length} exceeds limit {MAX_MSG_SIZE}") | ||
| return pickle.loads(_recv_exact(sock, length)) |
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🔴 Unauthenticated pickle.loads deserialization over a predictable, world-writable Unix socket path allows local RCE. recv_msg (protocol.py:160-164) runs bare pickle.loads on whatever bytes arrive, and get_socket_path defaults socket_dir to tempfile.gettempdir() with a fully predictable name (vllm_weight_cache_gpu{gpu_id}.sock); the client in ipc_loader.py connects and deserializes the response with no SO_PEERCRED/ownership check, so any local user who pre-plants a socket at that path (before the daemon starts, or during a crash/restart window) can serve a malicious pickle payload and get code execution in the engine process. Fix by verifying the peer's UID/socket ownership before connecting, using a restricted unpickler, and/or placing the socket in a private (0700) per-user directory with an unpredictable path (as get_open_zmq_ipc_path() already does via uuid4()).
Extended reasoning...
The bug. protocol.py's recv_msg (lines 160-164) reads a length-prefixed payload off a Unix domain socket and immediately calls bare pickle.loads on it, with no restricted unpickler and no allowlist of permitted classes. Deserializing an untrusted pickle stream is a well-known arbitrary-code-execution primitive — the payload's __reduce__ runs during loads, before any application-level validation of the resulting object happens.
How an attacker reaches it. get_socket_path defaults socket_dir to tempfile.gettempdir() (world-writable /tmp on virtually every deployment) and builds a fully predictable filename, vllm_weight_cache_gpu{gpu_id}.sock. The client side, IpcModelLoader._connect/_resolve_socket_path in ipc_loader.py, computes this same predictable path, opens a plain socket.connect(), sends get_state, and calls recv_msg on whatever comes back — with no peer verification at all: no SO_PEERCRED check, no os.stat ownership check on the socket file before connecting. The daemon's own os.chmod(socket_path, 0o600) in daemon.py only restricts access to a socket the daemon itself has already bound; it does nothing to stop a client from connecting to a different, attacker-owned socket that got there first.
Why nothing else prevents this. Because the filename is deterministic and /tmp is world-writable, any local user can create a Unix socket at that exact path ahead of the real daemon — e.g. before the daemon has started for a given GPU, or during the window while it is restarting/crashed and has not yet rebound. (Note /tmp's sticky bit means the daemon's own os.unlink()+bind() on that path would then fail if the attacker's socket is owned by another user, so the daemon can't even reclaim the path — the rogue listener persists.) The engine, launched with --load-format ipc_cache, has no way to distinguish the attacker's socket from the legitimate daemon's, so it connects, sends its request, and unpickles the crafted response, executing attacker-controlled code in the engine process.
Precedent inside the same codebase makes this a regression, not an accepted tradeoff. vLLM's own get_open_zmq_ipc_path() (vllm/utils/network_utils.py) deliberately embeds a uuid4() specifically to prevent this predictable-path pre-planting attack. Similarly, the existing weight_transfer pickle-over-IPC path (vllm/distributed/weight_transfer/clients.py) gates untrusted pickle deserialization behind VLLM_ALLOW_INSECURE_SERIALIZATION=1, treating it as an explicit, opt-in risk. This new path has neither an unpredictable path nor an opt-in gate nor any restricted unpickler — it is strictly less defended than the precedents already in the tree, despite being reachable by any co-tenant local user.
Proof sketch.
- A GPU box is shared by multiple local users (or a container escape / less-privileged local account exists), and a legitimate operator plans to launch a weight-cache daemon for
gpu_id=0, then start an engine with--load-format ipc_cache. - Attacker computes the exact socket path via
get_socket_path(0)→/tmp/vllm_weight_cache_gpu0.sock(same formula is public in this PR's source) and binds a listening Unix socket there first — either racing the daemon's startup, or waiting for a crash/restart window where the daemon is momentarily unbound. - The victim starts an engine with
--load-format ipc_cache;IpcModelLoader._connectresolves the same path and connects — no check that the socket belongs to the daemon's expected owner/PID. - The engine sends
{"cmd": "get_state", ...}; the attacker's listener replies with bytes that pickle to a__reduce__payload (e.g.os.system(...)). recv_msgcallspickle.loadson that payload inside the engine process, executing arbitrary code as the engine's user — well before anyCacheConfig/entry-shape validation could reject it.
Suggested fix, matching approaches already used elsewhere in vLLM: verify the socket's peer credentials (SO_PEERCRED) and/or os.stat ownership of the socket file before connecting and before trusting a response; use a restricted Unpickler subclass that only allows the specific classes the protocol needs (CacheConfig, TensorEntry, tensor-rebuild helpers); and/or place the socket under a private, per-user 0700 directory (e.g. $XDG_RUNTIME_DIR) with an unpredictable component, mirroring get_open_zmq_ipc_path()'s uuid4() approach.
| def _export_entries(self) -> None: | ||
| assert self.model is not None | ||
| entries: dict[str, TensorEntry] = {} | ||
| for name, param in self.model.named_parameters(): | ||
| entries[name] = TensorEntry.from_tensor(param, "param") | ||
| # named_buffers includes non-persistent buffers (e.g. rotary | ||
| # embedding caches) that state_dict would miss. | ||
| for name, buffer in self.model.named_buffers(): | ||
| if name not in entries: | ||
| entries[name] = TensorEntry.from_tensor(buffer, "buffer") | ||
| self.entries = entries |
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🔴 The daemon's _export_entries uses named_parameters()/named_buffers() with PyTorch's default remove_duplicate=True, so for the ~30 vLLM models that tie lm_head.weight = model.embed_tokens.weight at the parameter level (e.g. granite.py:368, mixtral.py:414, qwen2_moe.py:466), only one of the two names ever gets exported. On the client, _apply_entries only rewires the module it has an entry for, so the other module's weight stays on the meta device and _materialize_remaining_meta_tensors fills it with uninitialized torch.empty_like memory instead of re-establishing the tie — silently producing garbage/NaN logits for these models under load_format=ipc_cache.
Extended reasoning...
The bug: WeightCacheDaemon._export_entries (daemon.py:94-104) builds the exported tensor map from model.named_parameters() and model.named_buffers(). Both methods default to remove_duplicate=True, which dedupes by Python object identity — if two module attributes point at the exact same nn.Parameter object, only the first-encountered name is yielded.
Many vLLM models establish embedding/lm_head tying at the parameter object level rather than the module level, e.g. granite.py:368: self.lm_head.weight = self.model.embed_tokens.weight (also confirmed in mixtral.py:414, qwen2_moe.py:466, and roughly 30 more architectures per the verifiers' grep — arctic, chameleon, ernie45_moe, exaone, granitemoe, hunyuan_v1, nemotron, stablelm, etc). This differs from models like Qwen3 that alias the whole submodule (self.lm_head = self.model.embed_tokens), where both names would resolve to the same registered module and the issue does not apply the same way.
Trigger path:
- In
__init__,self.model(containingembed_tokens) is constructed and registered beforeself.lm_head, somodel.named_parameters()traversal yieldsmodel.embed_tokens.weightfirst and silently skipslm_head.weightsince it's the identical Parameter object. daemon.py's_export_entriestherefore only ever putsmodel.embed_tokens.weightintoself.entries;lm_head.weightnever appears in the cache.- On the client side,
IpcModelLoader._build_modelcallsinitialize_model(...)undertorch.device('meta'), which re-runs__init__and re-establishes the same identity tie on meta tensors (lm_head.weight is model.embed_tokens.weight, both meta). _apply_entries(ipc_loader.py:165ish) iterates only over the entries it received. For themodel.embed_tokens.weightentry, it doesmodule._parameters.pop(leaf)+module.register_parameter(leaf, nn.Parameter(tensor))— but only on theembed_tokensmodule object.lm_headis a distinctnn.Module; its_parameters['weight']still references the old meta Parameter, since there was never an entry namedlm_head.weightto process._materialize_remaining_meta_tensors(ipc_loader.py:281ish) then scans for any parameter still on the meta device, findslm_head.weight, and replaces it withtorch.empty_like(param, device=device)— i.e. uninitialized GPU memory — logging only alogger.warning, not raising.
Why nothing else catches this: process_weights_after_loading is intentionally skipped for this loader (the daemon exports the already-processed state), so there is no downstream step that would re-tie lm_head.weight to the real embedding tensor. Nothing in _apply_entries or _materialize_remaining_meta_tensors is aware of parameter aliasing.
Impact: For any of these tied-embedding architectures loaded with load_format=ipc_cache, the LM head — the projection that produces logits — runs on uninitialized garbage instead of the real embedding weights. This is not a crash; it silently produces garbage or NaN token probabilities, which is worse than a hard failure because it can go undetected until someone notices bad generations.
Concrete proof (granite.py):
model.embed_tokens.weightandlm_head.weightare the same Parameter object after__init__.- Daemon exports
{'model.embed_tokens.weight': TensorEntry(...)}; nolm_head.weightkey exists inself.entries. - Client builds the meta model;
lm_head.weight is model.embed_tokens.weight(both meta) at this point. _apply_entriesprocesses themodel.embed_tokens.weightentry:embed_tokens.register_parameter('weight', nn.Parameter(real_tensor)). This does NOT touchlm_head._parameters['weight'], which is still the old meta Parameter object (module-level dict entries aren't linked once one side is popped/replaced)._materialize_remaining_meta_tensorswalks all modules, findslm_head.weightstilldevice.type == 'meta', and executesmodule._parameters[leaf] = nn.Parameter(torch.empty_like(param, device=device))— uninitialized memory.- Every forward pass now computes logits via
lm_headusing garbage weights, disconnected from the real, correctly-loaded embedding table.
Suggested fix: After _apply_entries (or as part of it), detect parameter identity ties from the meta-initialized model before replacement (e.g. group meta parameters by id() or scan model.named_parameters(remove_duplicate=False) to find aliased names), and when one alias is filled from the cache, rewire all other modules that shared that meta Parameter object to point at the same real tensor, rather than relying solely on the (deduplicated) cache entries.
| try: | ||
| while True: | ||
| conn, _ = server.accept() | ||
| with conn: | ||
| try: | ||
| self._handle_connection(conn) | ||
| except (ConnectionError, EOFError): | ||
| logger.warning("Client disconnected mid-request") | ||
| finally: | ||
| server.close() | ||
| if os.path.exists(socket_path): | ||
| os.unlink(socket_path) |
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🟡 In WeightCacheDaemon.serve_forever (daemon.py:127-130), the per-connection try/except only catches (ConnectionError, EOFError), but recv_msg (protocol.py) can also raise ValueError when a message exceeds MAX_MSG_SIZE, or pickle.UnpicklingError/other exceptions on a garbled or protocol-incompatible payload. Any of these escapes the accept loop, hits the finally block that unlinks the socket, and kills the whole daemon process — permanently taking down the persistent weight cache for that GPU until it's manually relaunched. Widening the except clause (e.g. to bare Exception) around each connection would let the daemon keep serving other clients.
Extended reasoning...
The bug: serve_forever wraps each accepted connection in a narrow exception handler:
try:
while True:
conn, _ = server.accept()
with conn:
try:
self._handle_connection(conn)
except (ConnectionError, EOFError):
logger.warning("Client disconnected mid-request")
finally:
server.close()
if os.path.exists(socket_path):
os.unlink(socket_path)_handle_connection calls recv_msg, which is not limited to raising ConnectionError/EOFError. Looking at protocol.py:
def recv_msg(sock: socket.socket) -> Any:
(length,) = _LEN_STRUCT.unpack(_recv_exact(sock, _LEN_STRUCT.size))
if length > MAX_MSG_SIZE:
raise ValueError(f"Message size {length} exceeds limit {MAX_MSG_SIZE}")
return pickle.loads(_recv_exact(sock, length))Two exception paths are not caught by the inner except:
ValueError— raised directly whenever the declared length prefix exceedsMAX_MSG_SIZE.pickle.UnpicklingError(orAttributeError/TypeError/etc.) — raised bypickle.loadson a fully-received but garbled or version-incompatible payload, e.g. a client built against a different vLLM version whoseCacheConfig/TensorEntrydataclass shape has changed.
Code path: any client that connects and sends a bad length prefix or a payload that fails to unpickle causes the exception to propagate out of the while True: loop, since it isn't one of (ConnectionError, EOFError). It then executes finally: server.close(); os.unlink(socket_path) and serve_forever returns via the unhandled exception, terminating _run_daemon and the daemon process.
Why nothing currently prevents this: the handler already anticipates some failure modes (it logs "Client disconnected mid-request" for connection resets), showing the intent was to keep the daemon alive across bad connections — but the catch is scoped too narrowly to also cover the size-guard and unpickling failure modes that the protocol module itself defines.
Impact: WeightCacheDaemon is designed to be a long-lived, one-per-GPU process that every future engine restart on that GPU depends on. Killing it via a single bad connection is disproportionate: every subsequent engine restart loses the fast-path and must fall back to loading from disk (or fail outright if fallback=False), until an operator notices and manually relaunches the daemon. This is most likely to bite exactly when it matters most — during a rolling upgrade, where an old client and a new daemon (or vice versa) disagree on the pickled message shape.
Step-by-step proof:
- Daemon calls
server.accept()and gets a connection from some process. - That process sends 8 bytes encoding a length field of, say,
2**40(exceedsMAX_MSG_SIZE = 1 << 34), or sends a valid length followed by bytes that are not a valid pickle stream (e.g. truncated after a version upgrade changed theTensorEntrydataclass fields). recv_msgraisesValueError(...)orpickle.UnpicklingErrorinside_handle_connection.- This exception is not
ConnectionErrororEOFError, so it is not caught byexcept (ConnectionError, EOFError):at daemon.py:129. - The exception propagates past the
with conn:block and out ofwhile True:, straight intofinally:at daemon.py:131-134, which closes the server socket and unlinks the socket file. serve_forever()exits via the unhandled exception;_run_daemon(which calleddaemon.serve_forever()with no surrounding try/except) exits, terminating the spawned daemon process for that GPU.- Every other engine that later tries to connect to that GPU's socket path gets a connection error and falls back to disk loading (or raises, if
fallback=False), even though nothing is wrong with their own request — the cache is simply gone.
Fix: broaden the per-connection except clause to catch Exception generally (or at minimum add ValueError and pickle.PickleError) and log+continue, matching the existing intent of staying alive across client-side send/format problems.
Severity note: this requires a malformed, oversized, or version-incompatible message rather than a well-formed same-version request, so ordinary same-version traffic is unaffected — it's a hardening gap in a persistent service rather than a break in the common path.
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Will fix soon. |
Signed-off-by: liusy58 <liusy58@smail.nju.edu.cn>
| # trust decision, so only ownership/symlink safety is enforced. | ||
| strict_perms = self.socket_path is None and self.socket_dir is None | ||
| try: | ||
| verify_socket_owner(socket_path, strict_perms=strict_perms) |
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⚪ Severity: LOW
TOCTOU race between verify_socket_owner() (which uses os.lstat) and sock.connect() at line 277. A local attacker with write access to the socket directory (possible when strict_perms=False with operator-configured socket_dir) can swap the socket file after the ownership check passes, causing the engine to connect to a rogue socket that serves malicious pickle payloads for deserialization.
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💡 Fix Suggestion
Suggestion: Add peer credential verification after sock.connect() to eliminate the TOCTOU race. The verify_peer_is_owner() function already exists in protocol.py (used by the daemon at daemon.py:170) and checks SO_PEERCRED to verify the connected peer's UID matches the current user. Call it on the client side as well, right after the sock.connect(socket_path) call at line 277.
Specifically, inside the try block in _connect(), add verify_peer_is_owner(sock) after sock.connect(socket_path) (line 277) and before return sock. You'll also need to import verify_peer_is_owner if it isn't already imported. The PermissionError raised by verify_peer_is_owner should be caught and converted to a WeightCacheUnavailableError (and sock.close() called).
Example modification for lines 276-283:
try:
sock.connect(socket_path)
verify_peer_is_owner(sock)
except PermissionError as e:
sock.close()
raise WeightCacheUnavailableError(
f"Weight cache peer credential check failed: {e}"
) from e
except OSError as e:
sock.close()
raise WeightCacheUnavailableError(
f"Cannot connect to weight cache daemon at {socket_path}: {e}"
) from e
return sockThis uses kernel-level SO_PEERCRED which checks the actual connected peer's credentials, making the filesystem-based TOCTOU race irrelevant.
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3 participants
Purpose
Author: Siyu Liu @liusy58 Michael Qiu qiudayu.qdy@antgroup.com;
Engine restarts are dominated by weight loading from disk. This PR proposes a persistent per-GPU daemon holds post-quantized, TP-sharded weights in GPU memory; restarting engines map them via CUDA IPC (zero-copy) instead of reloading from disk.
Test Plan
Test Result
Essential Elements of an Effective PR Description Checklist
supported_models.mdandexamplesfor a new model.