Sync from v0.13

vllm/utils/platform_utils.py

@@ -0,0 +1,59 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import multiprocessing
+from collections.abc import Sequence
+from concurrent.futures.process import ProcessPoolExecutor
+from functools import cache
+from typing import Any
+
+import torch
+
+
+def cuda_is_initialized() -> bool:
+    """Check if CUDA is initialized."""
+    if not torch.cuda._is_compiled():
+        return False
+    return torch.cuda.is_initialized()
+
+
+def xpu_is_initialized() -> bool:
+    """Check if XPU is initialized."""
+    if not torch.xpu._is_compiled():
+        return False
+    return torch.xpu.is_initialized()
+
+
+def get_cu_count(device_id: int = 0) -> int:
+    """Returns the total number of compute units (CU) on single GPU."""
+    return torch.cuda.get_device_properties(device_id).multi_processor_count
+
+
+def cuda_get_device_properties(
+    device, names: Sequence[str], init_cuda=False
+) -> tuple[Any, ...]:
+    """Get specified CUDA device property values without initializing CUDA in
+    the current process."""
+    if init_cuda or cuda_is_initialized():
+        props = torch.cuda.get_device_properties(device)
+        return tuple(getattr(props, name) for name in names)
+
+    # Run in subprocess to avoid initializing CUDA as a side effect.
+    mp_ctx = multiprocessing.get_context("fork")
+    with ProcessPoolExecutor(max_workers=1, mp_context=mp_ctx) as executor:
+        return executor.submit(cuda_get_device_properties, device, names, True).result()
+
+
+@cache
+def is_pin_memory_available() -> bool:
+    from vllm.platforms import current_platform
+
+    return current_platform.is_pin_memory_available()
+
+
+@cache
+def is_uva_available() -> bool:
+    """Check if Unified Virtual Addressing (UVA) is available."""
+    # UVA requires pinned memory.
+    # TODO: Add more requirements for UVA if needed.
+    return is_pin_memory_available()