from contextlib import contextmanager import torch from vllm.logger import logger from vllm_ascend.compilation.acl_graph import get_draft_graph_params, get_graph_params, weak_ref_workspaces @contextmanager def torch_cuda_wrapper(): try: torch.cuda.Event = torch.npu.Event torch.cuda.Stream = torch.npu.Stream torch.cuda.stream = torch.npu.stream torch.cuda.default_stream = torch.npu.default_stream torch.cuda.current_stream = torch.npu.current_stream torch.cuda.graph_pool_handle = torch.npu.graph_pool_handle torch.cuda.CUDAGraph = torch.npu.NPUGraph torch.cuda.graph = torch_npu_graph_wrapper torch.cuda.synchronize = torch.npu.synchronize torch.cuda.set_stream = torch.npu.set_stream torch.cuda.current_device = torch.npu.current_device torch.cuda.mem_get_info = torch.npu.mem_get_info logger.info_once("Wrapping torch.cuda with torch.npu.") yield finally: pass @contextmanager def communicator_switch(): import vllm.distributed.device_communicators.cuda_communicator from vllm_ascend.distributed.device_communicators.npu_communicator import NPUCommunicator CudaCommunicator = vllm.distributed.device_communicators.cuda_communicator.CudaCommunicator vllm.distributed.device_communicators.cuda_communicator.CudaCommunicator = NPUCommunicator logger.debug("Switched CudaCommunicator -> NPUCommunicator for graph capture.") try: yield finally: vllm.distributed.device_communicators.cuda_communicator.CudaCommunicator = CudaCommunicator logger.debug("Restored CudaCommunicator after graph capture.") @contextmanager def torch_npu_graph_wrapper(*args, **kwargs): # MRV2-specific cleanup hook: intentionally reuse the graph context # manager's exit to weak-ref graph workspaces after each capture, # without adding another upstream monkey patch. try: with torch.npu.graph(*args, **kwargs): yield finally: weak_ref_workspaces(get_graph_params()) weak_ref_workspaces(get_draft_graph_params())