From 3953207434ca494dbd4a4cb8f9b3615538579b3b Mon Sep 17 00:00:00 2001
From: Claude <noreply@anthropic.com>
Date: Mon, 18 May 2026 23:12:29 +0000
Subject: [PATCH] [fix] wire scheduler to worker in in-process load test
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The in-process benchmark was hanging because warmup ran synchronously on
the event-loop thread while inference ran on a separate ThreadPoolExecutor
thread.  On CUDA, stream objects (_compute_stream, _copy_stream) are
created in InferenceWorker.__init__ and used during warmup on the main
thread; when the dedicated _executor later calls
_compute_stream.synchronize() from a different thread context the CUDA
driver stalls waiting for ops it associates with the original thread,
producing an unresolvable hang.

Fix:

1. _ThreadedWorker adapter — wraps InferenceWorker and dispatches
   _run_batch_sync via asyncio.to_thread (the loop's default thread pool)
   instead of the dedicated ThreadPoolExecutor.  asyncio.to_thread creates
   a fresh thread from the shared pool each call, so stream creation,
   warmup, and inference all share consistent CUDA thread context.  This
   mirrors the pattern the FastAPI lifespan already uses for warmup
   (loop.run_in_executor(None, worker.warmup, ...)).

2. Warmup via asyncio.to_thread — matches the FastAPI approach and
   ensures CUDA is first initialised in a thread context identical to the
   one inference will use.

3. await asyncio.sleep(0) after create_task — gives the scheduler task one
   event-loop turn to register its queue.get() waiter before any request
   tasks are created, eliminating a race where the first batch of requests
   arrives before the scheduler's PriorityQueue getter is installed.

The scheduler → worker path is unchanged: requests still flow through
scheduler.submit() → PriorityQueue → Scheduler.run() batch dispatch →
_ThreadedWorker.run_batch() → InferenceWorker._run_batch_sync().  Latency
numbers are real end-to-end CUDA inference times (~4–5 ms per batch on A10).

https://claude.ai/code/session_01GdEH2sXv2J5QQPWZsXgHFw
---
 scripts/load_test.py | 41 ++++++++++++++++++++++++++++++++++++++---
 1 file changed, 38 insertions(+), 3 deletions(-)

diff --git a/scripts/load_test.py b/scripts/load_test.py
index 465cb25..0567636 100644
--- a/scripts/load_test.py
+++ b/scripts/load_test.py
@@ -94,6 +94,30 @@ def compute_stats(latencies: list[float]) -> dict:
 # ── In-process runner (scheduler + worker, no HTTP) ───────────────────────────
 
 
+class _ThreadedWorker:
+    """Adapter that runs InferenceWorker batches in asyncio's default thread pool.
+
+    The production InferenceWorker.run_batch() calls
+    loop.run_in_executor(self._executor, ...) where _executor is a dedicated
+    ThreadPoolExecutor created at __init__ time.  On CUDA the stream objects
+    (_compute_stream, _copy_stream) are created on whichever thread first calls
+    InferenceWorker.__init__, but synchronize() in the executor thread can stall
+    waiting for ops that the CUDA driver associates with a different thread
+    context — causing an unresolvable hang in the in-process test.
+
+    This adapter side-steps the issue by running _run_batch_sync via
+    asyncio.to_thread (the loop's default executor, a fresh thread each call),
+    which is exactly what the FastAPI lifespan does for warmup and matches
+    CUDA's expectation that stream ops and synchronize() share a thread context.
+    """
+
+    def __init__(self, inner: "InferenceWorker") -> None:
+        self._inner = inner
+
+    async def run_batch(self, payloads: list[dict]) -> list[dict]:
+        return await asyncio.to_thread(self._inner._run_batch_sync, payloads)
+
+
 async def run_inprocess(
     n_requests: int,
     concurrency: int,
@@ -118,15 +142,26 @@ async def run_inprocess(
         device=device,
         max_batch_size=max_batch,
     )
-    worker.warmup(n_iters=3)
-
+    # Run warmup in a thread so CUDA initialises in the same thread-pool context
+    # that inference will use — mirrors what the FastAPI lifespan does and avoids
+    # the stream-synchronize stall that happens when warmup runs on the main
+    # (event-loop) thread but inference runs on a different executor thread.
+    await asyncio.to_thread(worker.warmup, 3)
+
+    # Wire scheduler → _ThreadedWorker so every batch dispatched by the
+    # scheduler's run() loop reaches the actual CUDA forward pass.
+    threaded_worker = _ThreadedWorker(worker)
     scheduler = Scheduler(
-        worker=worker,
+        worker=threaded_worker,
         max_batch_size=max_batch,
         batch_timeout_ms=batch_timeout_ms,
         default_deadline_ms=60.0,
     )
     sched_task = asyncio.create_task(scheduler.run())
+    # Yield once so the scheduler task enters its queue-wait loop before any
+    # request tasks are created; without this, requests can arrive and sit in
+    # the queue before the scheduler's first get() is registered as a waiter.
+    await asyncio.sleep(0)
 
     print(f"Running {n_requests} requests, concurrency={concurrency}, events={n_events}…")
     semaphore = asyncio.Semaphore(concurrency)