[misc][distributed] improve libcudart.so finding (vllm-project#7127)

vllm/distributed/device_communicators/cuda_wrapper.py

@@ -4,9 +4,6 @@
 """
 
 import ctypes
-import glob
-import os
-import sys
 from dataclasses import dataclass
 from typing import Any, Dict, List, Optional
 
@@ -36,24 +33,25 @@ class Function:
     argtypes: List[Any]
 
 
-def get_pytorch_default_cudart_library_path() -> str:
-    # code borrowed from https://github.com/pytorch/pytorch/blob/1cae60a87e5bdda8bcf55724a862eeed98a9747e/torch/__init__.py#L284 # noqa
-    lib_folder = "cuda_runtime"
-    lib_name = "libcudart.so.*[0-9]"
-    lib_path = None
-    for path in sys.path:
-        nvidia_path = os.path.join(path, "nvidia")
-        if not os.path.exists(nvidia_path):
-            continue
-        candidate_lib_paths = glob.glob(
-            os.path.join(nvidia_path, lib_folder, "lib", lib_name))
-        if candidate_lib_paths and not lib_path:
-            lib_path = candidate_lib_paths[0]
-        if lib_path:
-            break
-    if not lib_path:
-        raise ValueError(f"{lib_name} not found in the system path {sys.path}")
-    return lib_path
+def find_loaded_library(lib_name) -> Optional[str]:
+    """
+    According to according to https://man7.org/linux/man-pages/man5/proc_pid_maps.5.html,
+    the file `/proc/self/maps` contains the memory maps of the process, which includes the
+    shared libraries loaded by the process. We can use this file to find the path of the
+    a loaded library.
+    """ # noqa
+    found = False
+    with open("/proc/self/maps") as f:
+        for line in f:
+            if lib_name in line:
+                found = True
+                break
+    if not found:
+        # the library is not loaded in the current process
+        return None
+    start = line.index("/")
+    path = line[start:].strip()
+    return path
 
 
 class CudaRTLibrary:
@@ -100,7 +98,9 @@ class CudaRTLibrary:
 
     def __init__(self, so_file: Optional[str] = None):
         if so_file is None:
-            so_file = get_pytorch_default_cudart_library_path()
+            so_file = find_loaded_library("libcudart.so")
+            assert so_file is not None, \
+                "libcudart.so is not loaded in the current process"
         if so_file not in CudaRTLibrary.path_to_library_cache:
             lib = ctypes.CDLL(so_file)
             CudaRTLibrary.path_to_library_cache[so_file] = lib

vllm/distributed/device_communicators/custom_all_reduce_utils.py

@@ -145,6 +145,7 @@ def can_actually_p2p(
     p_tgt.start()
     p_src.join()
     p_tgt.join()
+    assert p_src.exitcode == 0 and p_tgt.exitcode == 0
     result: List[bool] = []
     for src, tgt in zip(batch_src, batch_tgt):
         a = result_queue.get()
@@ -221,7 +222,8 @@ def gpu_p2p_access_check(src: int, tgt: int) -> bool:
             # wrap raised exception to provide more information
             raise RuntimeError(
                 f"Error happened when batch testing "
-                f"peer-to-peer access from {batch_src} to {batch_tgt}") from e
+                f"peer-to-peer access from {batch_src} to {batch_tgt}:\n"
+                f"{returned.stderr.decode()}") from e
         result = pickle.loads(returned.stdout)
         for _i, _j, r in zip(batch_src, batch_tgt, result):
             cache[f"{_i}->{_j}"] = r