In concurrent code, ensure that operations complete safely before proceeding to prevent race conditions and data corruption. This applies to several scenarios:
In concurrent code, ensure that operations complete safely before proceeding to prevent race conditions and data corruption. This applies to several scenarios:
Lock Management: Use context managers instead of manual lock acquisition to guarantee proper cleanup:
# Good - automatic cleanup
with self.manager_lock:
prefetch_task = self.prefetch_tasks.pop(key)
# Avoid - manual lock management
self.manager_lock.acquire()
prefetch_task = self.prefetch_tasks.pop(key)
self.manager_lock.release()
Async Operation Safety: When data integrity is critical, ensure async operations complete before proceeding:
# Good - synchronous when safety is needed
memory_obj.tensor.copy_(hidden_states)
# Risky - async copy without synchronization
memory_obj.tensor.copy_(hidden_states, non_blocking=True)
Atomic File Operations: Prevent race conditions in file operations using temporary files and atomic renames:
# Good - atomic operation
tmp_path = path + ".tmp"
with open(tmp_path, 'wb') as f:
f.write(data)
os.rename(tmp_path, path) # Atomic on most filesystems
Reference Counting Timing: Ensure reference counting operations happen at the right time relative to object usage:
# Ensure backend consumes object before counting down
backend.submit_put_task(key, obj)
# Backend should call ref_count_down() after consumption
Stream Synchronization: Use proper stream synchronization for CUDA operations:
put_stream = torch.cuda.Stream()
if kv_chunk.device != torch.cpu:
put_stream.wait_stream(torch.cuda.default_stream(kv_chunk.device))
The key principle is to identify critical sections where partial completion could cause issues and ensure these operations are atomic or properly synchronized.
Enter the URL of a public GitHub repository