1 files changed, 50 insertions, 0 deletions
diff --git a/test/test_nn.py b/test/test_nn.py
index 649c3ffb3a..ca113ecf21 100644
--- a/test/test_nn.py
+++ b/test/test_nn.py
@@ -13,6 +13,7 @@ from operator import mul
 from collections import OrderedDict
 import hashlib
 import os
+import threading
 
 import torch
 from torch._six import inf, nan
@@ -3837,6 +3838,55 @@ class TestNN(NNTestCase):
 
     @unittest.skipIf(not TEST_CUDA, 'CUDA not available')
     @unittest.skipIf(not TEST_CUDNN, 'CUDNN not available')
+    @skipIfRocm
+    def test_cudnn_multiple_threads_same_device(self):
+        # This function is intended to test the lazy creation and reuse of per-thread
+        # cudnn handles on each device in aten/src/ATen/cudnn/Handles.cpp.
+        # Failure here likely indicates something wrong with that logic.
+        weight = torch.ones((1, 1, 2, 2), device='cuda')
+
+        results = {}
+
+        num_threads = 2
+        trials = 2
+        test_iters = 100
+
+        with torch.backends.cudnn.flags(enabled=True):
+            def _worker(t, input):
+                my_stream = torch.cuda.Stream()
+                results[t] = input
+                with torch.cuda.stream(my_stream):
+                    for i in range(test_iters):
+                        # If all threads are sharing the same cudnn handle,
+                        # the following sequence may occur:
+                        # thread 0 calls setCuDNNStreamToCurrent()
+                        # thread 1 calls setCuDNNStreamToCurrent()
+                        # thread 0 launches its raw convolution, which it thinks is in
+                        #          its own stream, but is actually in thread 1's stream.
+                        # thread 0 enqueues its div_, which IS is its own stream,
+                        #          but now races with its convolution.
+                        results[t] = torch.nn.functional.conv2d(results[t], weight, padding=0)
+                        results[t].div_(4.0)
+                torch.cuda.current_stream().wait_stream(my_stream)
+
+            for trial in range(trials):
+                for t in range(num_threads):
+                    results[t] = torch.ones((1, 1, 2048, 2048), device='cuda')
+
+                threads = [threading.Thread(target=_worker,
+                                            args=(t, results[t])) for t in range(num_threads)]
+
+                for thread in threads:
+                    thread.start()
+                for thread in threads:
+                    thread.join()
+
+                for t in range(num_threads):
+                    self.assertEqual(results[t].sum().item(),
+                                     (2048 - test_iters) * (2048 - test_iters))
+
+    @unittest.skipIf(not TEST_CUDA, 'CUDA not available')
+    @unittest.skipIf(not TEST_CUDNN, 'CUDNN not available')
     @repeat_test_for_types(ALL_TENSORTYPES)
     @skipIfRocm
     def test_Conv2d_deterministic_cudnn(self, dtype=torch.float):