diff --git a/test/test_dataloader.py b/test/test_dataloader.py
index ef92b4f1b82d..a0745deae987 100644
--- a/test/test_dataloader.py
+++ b/test/test_dataloader.py
@@ -133,11 +133,28 @@ supported_multiprocessing_contexts = [None] + list(
 )
 
 
-# collate_fn that returns the batch cloned; defined globally here for pickle purposes.
+# The following collate functions are defined globally here for pickle purposes.
+
+
+# collate_fn that returns the batch cloned
 def _clone_collate(b):
     return [x.clone() for x in b]
 
 
+# collate_fn that returns the batch of sparse coo tensors cloned
+def _sparse_coo_collate(b):
+    lst = []
+    for x in b:
+        t = x.clone()
+        lst.append(t)
+        # Force sparse tensor invariants checks. check_pinning=True
+        # reproduces gh-153143.
+        torch._validate_sparse_coo_tensor_args(
+            t._indices(), t._values(), t.size(), t.is_coalesced(), check_pinning=False
+        )
+    return lst
+
+
 @unittest.skipIf(
     TEST_WITH_TSAN,
     "Fails with TSAN with the following error: starting new threads after multi-threaded "
@@ -2893,8 +2910,9 @@ class TestDataLoaderDeviceType(TestCase):
     def test_nested_tensor_multiprocessing(self, device, context):
         # The 'fork' multiprocessing context doesn't work for CUDA so skip it
         if "cuda" in device and context == "fork":
-            # TODO: Skip this better in a better way when the test framework allows
-            return
+            self.skipTest(
+                f"{context} multiprocessing context not supported for {device}"
+            )
 
         dataset = [
             torch.nested.nested_tensor([torch.randn(5)], device=device)
@@ -2932,6 +2950,37 @@ class TestDataLoaderDeviceType(TestCase):
 
             next(iter(loader))
 
+    @parametrize(
+        "context",
+        [ctx for ctx in supported_multiprocessing_contexts if ctx is not None],
+    )
+    @unittest.skipIf(not TEST_CUDA_IPC, "CUDA IPC not available")
+    def test_sparse_tensor_multiprocessing(self, device, context):
+        # The 'fork' multiprocessing context doesn't work for CUDA so skip it
+        if "cuda" in device and context == "fork":
+            self.skipTest(
+                f"{context} multiprocessing context not supported for {device}"
+            )
+
+        dataset = [torch.randn(5, 5).to_sparse().to(device) for _ in range(10)]
+
+        pin_memory_settings = [False]
+        if device == "cpu" and torch.cuda.is_available():
+            pin_memory_settings.append(True)
+
+        for pin_memory in pin_memory_settings:
+            loader = torch.utils.data.DataLoader(
+                dataset,
+                batch_size=1,
+                num_workers=4,
+                collate_fn=_sparse_coo_collate,
+                pin_memory=pin_memory,
+                multiprocessing_context=context,
+            )
+
+            for i, batch in enumerate(loader):
+                self.assertEqual(batch[0], dataset[i])
+
 
 class IntegrationTestDataLoaderDataPipe(TestCase):
     r"""
diff --git a/torch/_utils.py b/torch/_utils.py
index 7b15ca28ae20..8e818f58d988 100644
--- a/torch/_utils.py
+++ b/torch/_utils.py
@@ -282,14 +282,17 @@ def _validate_loaded_sparse_tensors():
         _sparse_tensors_to_validate.clear()
         return
     try:
+        # We disable pinning check (see check_pinning=False below) to
+        # avoid gh-153143. In fact, pinning check is unnecessary
+        # anywhy when loading sparse data from external sources.
         for t in _sparse_tensors_to_validate:
-            if True:
-                # Temporarily disable sparse tensor validation due to
-                # gh-153143.
-                pass
-            elif t.layout is torch.sparse_coo:
+            if t.layout is torch.sparse_coo:
                 torch._validate_sparse_coo_tensor_args(
-                    t._indices(), t._values(), t.size(), t.is_coalesced()
+                    t._indices(),
+                    t._values(),
+                    t.size(),
+                    t.is_coalesced(),
+                    check_pinning=False,
                 )
             elif t.layout in {
                 torch.sparse_csr,
@@ -310,7 +313,12 @@ def _validate_loaded_sparse_tensors():
                         t.row_indices(),
                     )
                 torch._validate_sparse_compressed_tensor_args(
-                    compressed_indices, plain_indices, t.values(), t.size(), t.layout
+                    compressed_indices,
+                    plain_indices,
+                    t.values(),
+                    t.size(),
+                    t.layout,
+                    check_pinning=False,
                 )
             else:
                 raise NotImplementedError(