diff --git a/tests/distributed/test_pipeline_parallel.py b/tests/distributed/test_pipeline_parallel.py
index 5ff39ddfbf99..f632caba9017 100644
--- a/tests/distributed/test_pipeline_parallel.py
+++ b/tests/distributed/test_pipeline_parallel.py
@@ -9,7 +9,7 @@
 
 import pytest
 
-from ..utils import compare_two_settings
+from ..utils import compare_two_settings, fork_new_process_for_each_test
 
 VLLM_MULTI_NODE = os.getenv("VLLM_MULTI_NODE", "0") == "1"
 
@@ -28,6 +28,7 @@
         (1, 4, 0, 1, "meta-llama/Meta-Llama-3-8B", "mp"),
         (1, 4, 1, 0, "meta-llama/Meta-Llama-3-8B", "mp"),
     ])
+@fork_new_process_for_each_test
 def test_compare_tp(TP_SIZE, PP_SIZE, EAGER_MODE, CHUNKED_PREFILL, MODEL_NAME,
                     DIST_BACKEND):
     if VLLM_MULTI_NODE and DIST_BACKEND == "mp":
@@ -77,6 +78,7 @@ def test_compare_tp(TP_SIZE, PP_SIZE, EAGER_MODE, CHUNKED_PREFILL, MODEL_NAME,
     "FLASH_ATTN",
     "FLASHINFER",
 ])
+@fork_new_process_for_each_test
 def test_pp_cudagraph(PP_SIZE, MODEL_NAME, ATTN_BACKEND):
     cudagraph_args = [
         # use half precision for speed and memory savings in CI environment
diff --git a/tests/utils.py b/tests/utils.py
index 1086591464d4..f3ee801ee774 100644
--- a/tests/utils.py
+++ b/tests/utils.py
@@ -1,4 +1,6 @@
+import functools
 import os
+import signal
 import subprocess
 import sys
 import time
@@ -336,3 +338,40 @@ def wait_for_gpu_memory_to_clear(devices: List[int],
                              f'{dur_s=:.02f} ({threshold_bytes/2**30=})')
 
         time.sleep(5)
+
+
+def fork_new_process_for_each_test(f):
+
+    @functools.wraps(f)
+    def wrapper(*args, **kwargs):
+        # Make the process the leader of its own process group
+        # to avoid sending SIGTERM to the parent process
+        os.setpgrp()
+        from _pytest.outcomes import Skipped
+        pid = os.fork()
+        if pid == 0:
+            try:
+                f(*args, **kwargs)
+            except Skipped as e:
+                # convert Skipped to exit code 0
+                print(str(e))
+                os._exit(0)
+            except Exception:
+                import traceback
+                traceback.print_exc()
+                os._exit(1)
+            else:
+                os._exit(0)
+        else:
+            pgid = os.getpgid(pid)
+            _pid, _exitcode = os.waitpid(pid, 0)
+            # ignore SIGTERM signal itself
+            old_singla_handler = signal.signal(signal.SIGTERM, signal.SIG_IGN)
+            # kill all child processes
+            os.killpg(pgid, signal.SIGTERM)
+            # restore the signal handler
+            signal.signal(signal.SIGTERM, old_singla_handler)
+            assert _exitcode == 0, (f"function {f} failed when called with"
+                                    f" args {args} and kwargs {kwargs}")
+
+    return wrapper
diff --git a/vllm/transformers_utils/tokenizer_group/ray_tokenizer_group.py b/vllm/transformers_utils/tokenizer_group/ray_tokenizer_group.py
index eebdf7bf644d..79081c04ddc1 100644
--- a/vllm/transformers_utils/tokenizer_group/ray_tokenizer_group.py
+++ b/vllm/transformers_utils/tokenizer_group/ray_tokenizer_group.py
@@ -3,7 +3,7 @@
 from typing import List, Optional
 
 try:
-    from ray.exceptions import ActorDiedError
+    from ray.exceptions import ActorDiedError  # type: ignore
 except ImportError:
     # For older versions of Ray
     from ray.exceptions import RayActorError as ActorDiedError  # type: ignore
diff --git a/vllm/utils.py b/vllm/utils.py
index 38e1782a51ab..358788c95f30 100644
--- a/vllm/utils.py
+++ b/vllm/utils.py
@@ -928,7 +928,8 @@ def error_on_invalid_device_count_status():
     with contextlib.suppress(Exception):
         # future pytorch will fix the issue, device_count will not be cached
         # at that time, `.cache_info().currsize` will error out
-        cache_entries = torch.cuda.device_count.cache_info().currsize
+        cache_entries = torch.cuda.device_count.cache_info(  # type: ignore
+        ).currsize
     if cache_entries != 0:
         # the function is already called, and the result is cached
         remembered = torch.cuda.device_count()