# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 """vLLM-specific patches for GPU Memory Service integration. This module contains vLLM-specific patches that are applied when the GMSWorker module is imported: - MemorySnapshot.measure patch (adjusts free memory for read mode) Note: The torch.cuda.empty_cache patch is in integrations/common/patches.py """ from __future__ import annotations import logging from gpu_memory_service import get_gms_client_memory_manager from gpu_memory_service.common.types import GrantedLockType logger = logging.getLogger(__name__) _memory_snapshot_patched = False def patch_memory_snapshot() -> None: """Patch MemorySnapshot.measure to add committed bytes to free_memory.""" global _memory_snapshot_patched if _memory_snapshot_patched: return try: from vllm.utils.mem_utils import MemorySnapshot except ImportError: logger.debug("[GMS Patch] MemorySnapshot not available") return original_measure = MemorySnapshot.measure def patched_measure(self): original_measure(self) manager = get_gms_client_memory_manager() assert manager is not None, "GMS client is not initialized" if manager.mode == GrantedLockType.RO: allocations = manager.list_allocations() committed_bytes = sum(alloc.get("aligned_size", 0) for alloc in allocations) else: # NOTE: by design, we want to assume we have the whole GPU when writing # weights for the first time, so we don't make an adjustment. committed_bytes = 0 logger.info("[GMS] RW mode - skipping committed memory adjustment") original_free = self.free_memory self.free_memory += committed_bytes if committed_bytes > 0: logger.info( "[GMS Patch] Adjusted free_memory: %.2f GiB + %.2f GiB = %.2f GiB", original_free / (1 << 30), committed_bytes / (1 << 30), self.free_memory / (1 << 30), ) MemorySnapshot.measure = patched_measure _memory_snapshot_patched = True logger.info("[GMS Patch] Patched MemorySnapshot.measure")