diff --git a/test/test_input_output_aliases.py b/test/test_input_output_aliases.py
index b2c5fc50b21..e90c74201d6 100644
--- a/test/test_input_output_aliases.py
+++ b/test/test_input_output_aliases.py
@@ -1,10 +1,13 @@
 import sys
 
 import torch
+import torch.nn as nn
+import torch.nn.functional as F
 import torch_xla
 import torch_xla.core.xla_model as xm
 import torch_xla.debug.metrics as met
 import unittest
+import copy
 
 
 # TODO(alanwaketan): add test for views.
@@ -82,6 +85,52 @@ def test_aliasing_with_multiple_inplace_update(self):
     self.assertEqual(met.metric_data("InputOutputAliasCount")[1], 1.0)
     torch.allclose(k_cache[slot_mapping[0][0]].cpu(), key[0].cpu())
 
+  def test_grad_accum(self):
+
+    class MLP(nn.Module):
+
+      def __init__(self, input_size=28 * 28, output_size=10):
+        super(MLP, self).__init__()
+        self.fc1 = nn.Linear(input_size, output_size, bias=False)
+
+      def forward(self, x):
+        x = self.fc1(x)
+        return F.log_softmax(x, dim=1)
+
+    def try_grad_accum(model, device, train_x, train_label, accum_steps):
+      loss_fn = nn.NLLLoss()
+      train_x = train_x.to(device)
+      train_label = train_label.to(device)
+      model.zero_grad()
+      for i in range(accum_steps):
+        output = model(train_x)
+        t_loss = loss_fn(output, train_label)
+        t_loss.backward()
+        xm.mark_step()
+      return [p.grad.to('cpu').numpy() for p in model.parameters()]
+
+    dev = xm.xla_device()
+    train_x_sample = torch.rand((1, 28 * 28))
+    train_label_sample = torch.tensor([5])
+    c_model = MLP().to('cpu')
+    t_model = copy.deepcopy(c_model).to(dev)
+    t_model.train()
+    c_model.train()
+    accum_steps = 4
+    c_grads_5 = try_grad_accum(c_model, 'cpu', train_x_sample,
+                               train_label_sample, accum_steps)
+    met.clear_metrics()
+    t_grads_5 = try_grad_accum(t_model, dev, train_x_sample, train_label_sample,
+                               accum_steps)
+    torch.testing.assert_close(t_grads_5, c_grads_5, rtol=3e-2, atol=1e-3)
+    graph_count, alias_count, _ = met.metric_data("InputOutputAliasCount")
+    assert (
+        graph_count == 2
+    ), f"Expect 2 graphs for gradient accumulation test, got {graph_count}"
+    assert (
+        alias_count == 1.0
+    ), f"Expect 1 input-output alias pair for gradient accumulation, got {alias_count}"
+
 
 if __name__ == '__main__':
   test = unittest.main()