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| @@ -174,7 +226,22 @@ async def init_weight_transfer_engine(raw_request: Request): | |||
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| @router.post("/start_weight_update") | |||
| async def start_weight_update(raw_request: Request): | |||
| sm: RLStateMachineState = raw_request.app.state.rl_state | |||
| if sm.is_updating: | |||
There was a problem hiding this comment.
⚪ Severity: LOW
TOCTOU race condition: is_updating is checked without the lock, then the engine RPC (await ...start_weight_update()) is an asyncio yield point on line 239 before the locked state transition on line 240. Two concurrent requests can both pass this guard and invoke the engine RPC, bypassing the double-start protection that the state machine was specifically designed to enforce (preventing TMA descriptor errors and engine corruption).
Helpful? Add 👍 / 👎
💡 Fix Suggestion
Suggestion: The TOCTOU race can be fixed by acquiring the state machine's asyncio lock before checking is_updating and holding it through both the engine RPC and the state transition. This ensures two concurrent requests cannot both pass the guard.
Recommended approach (requires changes in two files):
- In
rl_state_machine.py, either make_get_lock()public (rename toget_lock()) or add a new atomic helper method, e.g.:
async def acquire_start_weight_update(self) -> None:
"""Atomically check no update is active, then set the flag.
Caller must invoke the engine RPC *inside* this guard."""
async with self._get_lock():
if self._is_updating:
raise RuntimeError(
"start_weight_update called while a weight update is already "
"in progress. Call finish_weight_update first."
)
self._is_updating = True- In
api_router.py, restructurestart_weight_updateso the guard and engine RPC are under the lock. If using the lock directly:
async def start_weight_update(raw_request: Request):
sm = raw_request.app.state.rl_state
async with sm.get_lock():
if sm.is_updating:
raise HTTPException(status_code=409, detail="...")
await engine_client(raw_request).start_weight_update()
sm._is_updating = True
...Or if using the atomic method, call await sm.acquire_start_weight_update() after the engine RPC check has been moved inside the method.
The key principle: the check (is_updating), the engine RPC, and the flag set (_is_updating = True) must all be within the same critical section so no concurrent coroutine can interleave between them.
| weight_label = body.get("weight_label") | ||
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| state: RLStateMachineState = raw_request.app.state.rl_state | ||
| if not state.is_updating: |
There was a problem hiding this comment.
⚪ Severity: LOW
TOCTOU race condition: is_updating is checked without the asyncio lock, followed by the engine RPC on line 304 (an await yield point), before the locked transition on line 308. Two concurrent finish_weight_update requests can both pass this guard and call the engine's finish_weight_update, corrupting the weight-update protocol state and the engine's internal model state.
Helpful? Add 👍 / 👎
💡 Fix Suggestion
Suggestion: Wrap the is_updating guard, the engine RPC, and the state transition under the same asyncio lock to eliminate the TOCTOU window. The same pattern should also be applied to start_weight_update (line 230).
One approach: add a new method to RLStateMachineState (e.g., finish_weight_update_atomic(callback)) that acquires the lock, checks _is_updating, awaits the engine callback, and then transitions _is_updating = False — all within the same async with self._get_lock(): block. Then replace lines 294–308 with a single call to this new method, passing a callback for the engine RPC.
Alternatively, expose the lock via a public method (e.g., get_lock()) and restructure the endpoint handler to acquire it directly:
state: RLStateMachineState = raw_request.app.state.rl_state
async with state.get_lock():
if not state.is_updating:
raise HTTPException(
status_code=HTTPStatus.CONFLICT.value,
detail=(
"finish_weight_update called without a preceding "
"start_weight_update."
),
)
await engine_client(raw_request).finish_weight_update(weight_version)
state._is_updating = FalseApply the analogous fix to start_weight_update as well.
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Scope correction pushed in The earlier mainline synchronization accidentally included unrelated changes from a local tracking reference that did not match the PR's actual upstream base. Those changes have now been removed from the final PR diff:
I verified the five files against the immutable upstream merge-base This corrects the earlier scope-validation claim. No new GPU execution results are claimed. |
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Updated the shared RL server lifecycle without increasing server startup count.
Validation: targeted |
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This pull request has merge conflicts that must be resolved before it can be |
Signed-off-by: Windfeng8 <126474910+Windfeng8@users.noreply.github.com>
Signed-off-by: Windfeng8 <126474910+Windfeng8@users.noreply.github.com>
Signed-off-by: Windfeng8 <126474910+Windfeng8@users.noreply.github.com>
Signed-off-by: Windfeng8 <126474910+Windfeng8@users.noreply.github.com>
Signed-off-by: Windfeng8 <126474910+Windfeng8@users.noreply.github.com>
Signed-off-by: Windfeng8 <126474910+Windfeng8@users.noreply.github.com>
Signed-off-by: Windfeng8 <126474910+Windfeng8@users.noreply.github.com>
Update conftest.py and test_weight_update.py based on PR vllm-project#52144 server lifecycle patterns. - Keep module-scoped server fixtures and MRV1/MRV2 coverage. - Configure backend-specific weight transfer through the shared server harness. - Exercise IPC/NCCL cases when the required CUDA hardware is available. - Preserve protocol/error and reload coverage. Validated on glusterfs-05 with CUDA_VISIBLE_DEVICES=3,4: - g1 changes_output: 4 passed in 156.00s - g2 reload bf16 tiny: 1 passed in 11.51s - g3 reload bf16 3b: 1 passed in 15.63s - g4 reload fp8: 1 passed in 24.48s - g5 reload mxfp8: 1 skipped in 0.42s - g6 rest: 40 passed in 298.28s Signed-off-by: Windfeng8 <126474910+Windfeng8@users.noreply.github.com>
Signed-off-by: Ronald1995 <ronaldautomobile@163.com>
Signed-off-by: Ronald1995 <ronaldautomobile@163.com>
Signed-off-by: Ronald1995 <ronaldautomobile@163.com>
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This pull request has merge conflicts that must be resolved before it can be |
This branch has not been deployed
2 participants
Purpose
This PR adds end-to-end coverage for the RL weight-transfer lifecycle described in RFC #45585 and consolidated in RFC #55421.
The test exercises real weight transfers through the public HTTP client and validates the committed model numerically against the trainer. It covers:
IPC and NCCL weight-transfer backends.
MRV1 and MRV2 model runners.
Repeated RL update rounds, including a level-2 sleep/wake cycle.
Weight-version propagation through the finish handshake.
Explicit prefix-cache invalidation through
pause(clear_cache=True).Per-token log-probability agreement between vLLM and the trainer model.
Shared server lifecycle and cleanup behavior for the existing pause/resume suite.
This PR intentionally does not add product metrics, a frontend-specific state machine, or model/quantization reload matrices. Those concerns are covered separately or remain next to their owning components.
Test Coverage
Weight-transfer matrix
All four combinations use
facebook/opt-125mfor both the trainer and inference server. The trainer mutates a real output-embedding tensor, andModuleSourcesends the resulting model state through the selected backend.Weight-update lifecycle
Each backend/runner case performs the following sequence:
clear_cache=True.B./weight_inforeports versionB;A-restored, wake the KV cache, and resume./weight_inforeportsA-restored;The numerical oracle evaluates the exact tokens generated by vLLM rather than comparing independently generated text. Token log probabilities are compared with an absolute tolerance of
0.05.Server lifecycle and stability
The shared test server now uses the repository’s
RemoteOpenAIServerlifecycle for port allocation, process-group shutdown, and GPU-memory release checks.For the pause/resume suite:
For the weight-transfer suite:
finallyblocks.This preserves server reuse where state can safely be shared without introducing additional server startups.
CI integration
The regular entrypoints job runs tests marked
not distributed, including the single-GPU IPC cases.A dedicated Buildkite step runs the NCCL cases:
The NCCL step requests two H100 GPUs so the trainer and inference server can run on separate devices.
Test results
The weight-transfer and pause/resume groups completed successfully on a dual-H100 system:
Weight-transfer case durations:
The reported results cover all four backend/runner combinations. No metrics, cluster-wide transaction semantics, routing replay, or model/quantization expansion is claimed by this PR.