diff --git a/test/test_tpu_paged_attention_kernel.py b/test/test_tpu_paged_attention_kernel.py
index d2d0f4f19a9..44b6bf3a2c9 100644
--- a/test/test_tpu_paged_attention_kernel.py
+++ b/test/test_tpu_paged_attention_kernel.py
@@ -11,7 +11,7 @@
 
 # Set up paged_attention inputs.
 def _generate_qkv(
-    kv_seq_lens,
+    batch_size,
     page_size,
     max_kv_len,
     query_len,
@@ -23,7 +23,6 @@ def _generate_qkv(
 ):
   assert max_kv_len % page_size == 0
   pages_per_sequence = max_kv_len // page_size
-  batch_size = len(kv_seq_lens)
   total_pages = batch_size * pages_per_sequence
   k1, k2, k3, k4 = jax.random.split(prng_key, 4)
   k_pages = jax.random.normal(
@@ -113,7 +112,7 @@ def setUp(self):
       num_queries_per_compute_block=(16, 32),
       block_kv_size=(128, 256),
   )
-  def test_paged_attention_without_query_padding(
+  def _test_paged_attention_without_query_padding(
       self,
       dtype,
       page_size,
@@ -138,7 +137,7 @@ def test_paged_attention_without_query_padding(
     assert max_kv_len <= total_num_pages * page_size
 
     q, k_pages, v_pages, page_indices = _generate_qkv(
-        kv_seq_lens,
+        batch_size,
         page_size,
         max_kv_len,
         query_len,
@@ -224,8 +223,9 @@ def test_paged_attention_with_query_padding(
     # Set query_len>kv_seq_lens
     query_len = max_kv_len
     batch_size = 3
-    kv_seq_lens = jax.random.randint(
-        jax.random.key(0), (batch_size,), 0, max_kv_len)
+    # kv_seq_lens = jax.random.randint(
+    #     jax.random.key(0), (batch_size,), 0, max_kv_len)
+    kv_seq_lens = jnp.array([256, 512, 512])
     effective_q_lens = jax.random.randint(
         jax.random.key(0), (batch_size,), 0, kv_seq_lens)
     for cur_effec_q_len, cur_kv_seq_len in zip(effective_q_lens, kv_seq_lens):
@@ -235,7 +235,7 @@ def test_paged_attention_with_query_padding(
     total_num_pages = batch_size * pages_per_sequence
     assert max_kv_len <= total_num_pages * page_size
     q, k_pages, v_pages, page_indices = _generate_qkv(
-        kv_seq_lens,
+        batch_size,
         page_size,
         max_kv_len,
         query_len,
@@ -292,6 +292,79 @@ def test_paged_attention_with_query_padding(
               atol=atol,
               rtol=rtol))
 
+  def test_paged_attention_store_to_output_correctly(
+      self,
+  ):
+    # Make sure the internal FA store_to_output correctly.
+    dtype = jnp.float32
+    page_size=16
+    num_kv_heads = 8
+    q_kv_head_ratio = 4
+    head_dim = 256
+    num_queries_per_compute_block = 32
+    block_kv_size = 256
+
+    max_kv_len = 512
+    query_len = max_kv_len
+    batch_size = 3
+    # Set various edge case testing the internal flash attention can store_to_output correct
+    kv_seq_lens = jnp.array([block_kv_size-1, block_kv_size+1, 2*block_kv_size])
+    assert len(kv_seq_lens) == batch_size
+    effective_q_lens = jax.random.randint(
+        jax.random.key(0), (batch_size,), 0, kv_seq_lens)
+    for cur_effec_q_len, cur_kv_seq_len in zip(effective_q_lens, kv_seq_lens):
+      assert cur_effec_q_len <= cur_kv_seq_len, f'The effective query len {cur_effec_q_len} should be less than or equal to the kv_len {cur_kv_seq_len} in the current sequence.'
+
+    pages_per_sequence = max_kv_len // page_size
+    total_num_pages = batch_size * pages_per_sequence
+    assert max_kv_len <= total_num_pages * page_size
+    q, k_pages, v_pages, page_indices = _generate_qkv(
+        batch_size,
+        page_size,
+        max_kv_len,
+        query_len,
+        num_kv_heads,
+        num_kv_heads * q_kv_head_ratio,
+        head_dim,
+        jax.random.key(0),
+        dtype,
+    )
+
+    num_kv_pages_per_compute_block = block_kv_size // page_size
+    actual_output = paged_attention(
+        q,
+        k_pages,
+        v_pages,
+        kv_seq_lens,
+        page_indices,
+        effective_q_lens,
+        num_kv_pages_per_compute_block=block_kv_size // page_size,
+        num_queries_per_compute_block=num_queries_per_compute_block,
+    )
+    actual_output = jax.block_until_ready(actual_output)
+
+    # Run the ref impl.
+    expected_output = _ref_jax_extended_paged_attention(
+        q,
+        k_pages,
+        v_pages,
+        kv_seq_lens,
+        page_indices,
+        effective_q_lens,
+    )
+
+    self.assertEqual(actual_output.shape, expected_output.shape)
+
+    atol = 2e-2
+    rtol = 1e-2
+    for b in range(batch_size):
+      effective_q_len = effective_q_lens[b]
+      self.assertTrue(
+          jnp.allclose(
+              expected_output[b, :effective_q_len],
+              actual_output[b, :effective_q_len],
+              atol=atol,
+              rtol=rtol))
 
 if __name__ == "__main__":
   absltest.main(testLoader=jtu.JaxTestLoader())
diff --git a/torch_xla/experimental/pallas_kernels/multi_queries_paged_attention_kernel.py b/torch_xla/experimental/pallas_kernels/multi_queries_paged_attention_kernel.py
index 0bb572c49e1..5e4988a9892 100644
--- a/torch_xla/experimental/pallas_kernels/multi_queries_paged_attention_kernel.py
+++ b/torch_xla/experimental/pallas_kernels/multi_queries_paged_attention_kernel.py
@@ -198,7 +198,8 @@ def start_new_sequence():
   o_curr = jax.lax.dot(p.astype(v.dtype), v, preferred_element_type=jnp.float32)
   acc_scratch_ref[q_head_idx_per_kv] += o_curr * l_broadcast(l_next_inv_safe)
 
-  @pl.when(kv_blk_idx == kv_len // kv_seq_len_per_kv_compute_blk)
+  # @pl.when(kv_blk_idx == kv_len // kv_seq_len_per_kv_compute_blk)
+  @pl.when(kv_blk_idx == pl.cdiv(kv_len, kv_seq_len_per_kv_compute_blk)-1)
   def store_to_output():
     o_ref[0, q_head_idx_per_kv] = acc_scratch_ref[q_head_idx_per_kv].astype(
         o_ref.dtype)
@@ -384,7 +385,7 @@ def prefetch_next_block():  # pylint: disable=unused-variable
 
 MIN_BLOCK_SIZE = 128
 
-
+@jax.profiler.annotate_function
 @functools.partial(
     jax.jit,
     static_argnames=[