diff --git a/test/spmd/test_xla_sharding.py b/test/spmd/test_xla_sharding.py
index 1b128164a22b..5b74f05c35f7 100644
--- a/test/spmd/test_xla_sharding.py
+++ b/test/spmd/test_xla_sharding.py
@@ -653,6 +653,23 @@ def test_send_cpu_data_to_device_with_sharding(self):
         torch_xla._XLAC._get_xla_sharding_spec(xt),
         torch_xla._XLAC._get_xla_sharding_spec(explicit_xt))
 
+  def test_send_cpu_data_to_device_with_multiple_sharding(self):
+    tensors = [torch.randn(16), torch.randn(16, 16), torch.randn(16, 16, 16)]
+    mesh = self._get_mesh((self.n_devices, 1))
+    specs = [
+        xs.ShardingSpec(mesh, spec) for spec in [(0, None), (0, None, None)]
+    ]
+    xtensors = xm.send_cpu_data_to_device(tensors, xm.xla_device(), specs)
+    str_specs = [torch_xla._XLAC._get_xla_sharding_spec(t) for t in xtensors]
+    self.assertEqual(str_specs[0], '{replicated}')
+    if self.n_devices > 1:
+      dev_fmt = (self.n_devices, ','.join(map(str, range(self.n_devices))))
+      self.assertEqual(str_specs[1], "{devices=[%d,1]%s}" % dev_fmt)
+      self.assertEqual(str_specs[2], "{devices=[%d,1,1]%s}" % dev_fmt)
+    else:
+      self.assertEqual(str_specs[1], '{replicated}')
+      self.assertEqual(str_specs[2], '{replicated}')
+
   def test_multiple_operations(self):
     t1 = torch.randn(2, 2)
     t2 = torch.randn(2, 2)
diff --git a/torch_xla/core/xla_model.py b/torch_xla/core/xla_model.py
index e85db1d20a6a..fbeface58b69 100755
--- a/torch_xla/core/xla_model.py
+++ b/torch_xla/core/xla_model.py
@@ -1012,7 +1012,16 @@ def convert_fn(tensors):
     devices = [str(device)] * len(tensors)
     shardings = None
     if input_sharding:
-      shardings = [input_sharding.xla_spec(t) for t in tensors]
+      if isinstance(input_sharding, list):
+        shardings = [None] * len(tensors)
+        # Apply the first matching ShardingSpec to each tensor.
+        for i, tensor in enumerate(tensors):
+          for sharding in input_sharding:
+            if sharding.can_apply(tensor):
+              shardings[i] = sharding.xla_spec(tensor)
+              break
+      else:
+        shardings = [input_sharding.xla_spec(t) for t in tensors]
     xtensors = torch_xla._XLAC._xla_tensors_from_aten(tensors, devices,
                                                       shardings)
     return xtensors
diff --git a/torch_xla/distributed/parallel_loader.py b/torch_xla/distributed/parallel_loader.py
index 8af7196e95ca..64b884a30057 100644
--- a/torch_xla/distributed/parallel_loader.py
+++ b/torch_xla/distributed/parallel_loader.py
@@ -74,8 +74,9 @@ class ParallelLoader(object):
     host_to_device_transfer_threads (int, optional): The number of threads that
       work in parallel to transfer data from loader queue to device queue.
       Default: 1
-    input_sharding (ShardingSpec, optional): Sharding spec to apply to
-      compatible input tensors after loading.
+    input_sharding (Union[ShardingSpec, List[ShardingSpec]], optional): Sharding
+      specs to apply to compatible input tensors when loading. When a list is
+      provided, the first matching sharding spec will be applied.
       Default: None
   """