diff --git a/test/test_pallas.py b/test/test_pallas.py
index 0cba53ac8b9..38ceeaf520e 100644
--- a/test/test_pallas.py
+++ b/test/test_pallas.py
@@ -536,7 +536,7 @@ def test_paged_attention_wrapper(self):
     seq_lens_xla = seq_lens.to("xla")
     page_indices_xla = page_indices.to("xla")
 
-    outputs, _, _ = paged_attention(
+    output = paged_attention(
         q_xla,
         k_pages_xla,
         v_pages_xla,
@@ -544,15 +544,13 @@ def test_paged_attention_wrapper(self):
         page_indices_xla,
         pages_per_compute_block=block_size // page_size,
     )
-    batch_size, num_heads, head_dim = q_xla.shape
-    outputs = outputs.reshape(batch_size, num_heads, head_dim)
 
     q_jax = jnp.array(q.numpy(), dtype=jnp.float32)
     k_pages_jax = jnp.array(k_pages.numpy(), dtype=jnp.float32)
     v_pages_jax = jnp.array(v_pages.numpy(), dtype=jnp.float32)
     seq_lens_jax = jnp.array(seq_lens.numpy(), dtype=jnp.int32)
     page_indices_jax = jnp.array(page_indices.numpy(), dtype=jnp.int32)
-    expected_outputs = torch.from_numpy(
+    expected_output = torch.from_numpy(
         np.array(
             jax_paged_attention(
                 q_jax,
@@ -565,7 +563,87 @@ def test_paged_attention_wrapper(self):
 
     self.assertTrue(
         torch.allclose(
-            outputs.cpu(), expected_outputs.cpu(), atol=1e+1, rtol=1e+1))
+            output.cpu()[seq_lens > 0],
+            expected_output.cpu()[seq_lens > 0],
+            atol=1e-1,
+            rtol=1e-1))
+    jax.config.update('jax_default_matmul_precision', jax.lax.Precision.DEFAULT)
+
+  @unittest.skipIf(xr.device_type() != 'TPU' or tpu.version() < 4,
+                   "This test only works on TPUv4+.")
+  def test_paged_attention_wrapper_with_dynamo(self):
+    jax.config.update('jax_default_matmul_precision', jax.lax.Precision.HIGHEST)
+    from torch_xla.experimental.custom_kernel import paged_attention
+    from jax.experimental.pallas.ops.tpu.paged_attention.paged_attention_kernel import paged_attention as jax_paged_attention
+
+    max_kv_len = 2048
+    block_size = 512
+    page_size = 64
+    num_kv_heads = 8
+    q_kv_head_ratio = 8
+    head_dim = 256
+    dtype = torch.float32
+    seq_lens = torch.tensor([0, 3, 256, 513, 1023, 2048], dtype=torch.int32)
+
+    q, k_pages, v_pages, page_indices = self._pagedattention_generate_qkv(
+        seq_lens,
+        page_size,
+        max_kv_len,
+        num_kv_heads,
+        num_kv_heads * q_kv_head_ratio,
+        head_dim,
+    )
+
+    q_xla = q.to("xla")
+    k_pages_xla = k_pages.to("xla")
+    v_pages_xla = v_pages.to("xla")
+    seq_lens_xla = seq_lens.to("xla")
+    page_indices_xla = page_indices.to("xla")
+
+    def paged_attention_wrapper(q, k, v, seq_lens, page_indices,
+                                pages_per_compute_block):
+      return paged_attention(
+          q_xla,
+          k_pages_xla,
+          v_pages_xla,
+          seq_lens_xla,
+          page_indices_xla,
+          pages_per_compute_block=block_size // page_size,
+      )
+
+    compiled_paged_attention = torch.compile(
+        paged_attention_wrapper, backend="openxla")
+    output = paged_attention_wrapper(
+        q_xla,
+        k_pages_xla,
+        v_pages_xla,
+        seq_lens_xla,
+        page_indices_xla,
+        pages_per_compute_block=block_size // page_size,
+    )
+
+    q_jax = jnp.array(q.numpy(), dtype=jnp.float32)
+    k_pages_jax = jnp.array(k_pages.numpy(), dtype=jnp.float32)
+    v_pages_jax = jnp.array(v_pages.numpy(), dtype=jnp.float32)
+    seq_lens_jax = jnp.array(seq_lens.numpy(), dtype=jnp.int32)
+    page_indices_jax = jnp.array(page_indices.numpy(), dtype=jnp.int32)
+    expected_output = torch.from_numpy(
+        np.array(
+            jax_paged_attention(
+                q_jax,
+                k_pages_jax,
+                v_pages_jax,
+                seq_lens_jax,
+                page_indices_jax,
+                pages_per_compute_block=block_size // page_size,
+            )))
+
+    self.assertTrue(
+        torch.allclose(
+            output.cpu()[seq_lens > 0],
+            expected_output.cpu()[seq_lens > 0],
+            atol=1e-1,
+            rtol=1e-1))
     jax.config.update('jax_default_matmul_precision', jax.lax.Precision.DEFAULT)
 
 
diff --git a/torch_xla/experimental/custom_kernel.py b/torch_xla/experimental/custom_kernel.py
index 3bde331723e..b4bce9b76d2 100644
--- a/torch_xla/experimental/custom_kernel.py
+++ b/torch_xla/experimental/custom_kernel.py
@@ -369,7 +369,7 @@ def paged_attention(q, k_pages, v_pages, lengths, page_indices,
   step = torch.zeros((1,), dtype=torch.int32).to("xla")
   output_shape = torch.Size(list(q.shape[:-1]) + [1])
 
-  output = torch_xla._XLAC._xla_tpu_custom_call(
+  output, _, _ = torch_xla._XLAC._xla_tpu_custom_call(
       [
           lengths,
           page_indices_reshaped,
@@ -381,7 +381,7 @@ def paged_attention(q, k_pages, v_pages, lengths, page_indices,
       ], payload, [q.shape, output_shape, output_shape],
       [q.dtype, torch.float32, torch.float32])
 
-  return output
+  return output.reshape(batch_size, num_heads, head_dim)
 
 
 XLA_LIB.define(