Fix TPU tests - for splash attention

keras/src/backend/jax/nn.py

@@ -1340,25 +1340,32 @@ def dot_product_attention(
         if custom_mask is None and is_causal:
             custom_mask = jnp.tril(jnp.ones((q_len, q_len), dtype=jnp.bool_))
 
-        try:
-            output = wrap_flash_attention(
-                query_tpu_layout,
-                key_tpu_layout,
-                value_tpu_layout,
-                decoder_segment_ids=decoder_segment_ids,
-                custom_mask=custom_mask,
-                attn_logits_soft_cap=attn_logits_soft_cap,
-                head_shards=head_shards,
-                q_seq_shards=q_seq_shards,
-            )
-            # Transpose output back to Keras layout
-            return jnp.transpose(output, axes=(0, 2, 1, 3))
-        except Exception:
-            logging.exception(
-                "Failed to apply Splash kernel for flash attention. "
-                "Falling back to JAX native dot_product_attention."
-            )
+        # Splash attention kernel requires concrete mask values for hashing.
+        # If the mask is a tracer (e.g. inside a scan/loop), we must fall back.
+        if isinstance(mask, jax.core.Tracer) or isinstance(
+            custom_mask, jax.core.Tracer
+        ):
             flash_attention = False
+        else:
+            try:
+                output = wrap_flash_attention(
+                    query_tpu_layout,
+                    key_tpu_layout,
+                    value_tpu_layout,
+                    decoder_segment_ids=decoder_segment_ids,
+                    custom_mask=custom_mask,
+                    attn_logits_soft_cap=attn_logits_soft_cap,
+                    head_shards=head_shards,
+                    q_seq_shards=q_seq_shards,
+                )
+                # Transpose output back to Keras layout
+                return jnp.transpose(output, axes=(0, 2, 1, 3))
+            except Exception:
+                logging.exception(
+                    "Failed to apply Splash kernel for flash attention. "
+                    "Falling back to JAX native dot_product_attention."
+                )
+                flash_attention = False
 
     # JAX native dot_product_attention for GPU or fallback for TPU
     if hasattr(jax.nn, "dot_product_attention"):

keras/src/ops/nn_test.py

@@ -1324,6 +1324,40 @@ def test_polar(self):
 
 
 class NNOpsCorrectnessTest(testing.TestCase):
+    @pytest.mark.skipif(backend.backend() != "jax", reason="JAX only")
+    def test_dot_product_attention_inside_scan(self):
+        import jax
+
+        try:
+            if jax.devices()[0].platform != "tpu":
+                self.skipTest("TPU-specific test")
+        except:
+            self.skipTest("TPU-specific test")
+
+        import jax.numpy as jnp
+
+        def attention_scan_body(carry, x):
+            query, key, value = x
+            # dot_product_attention expects 4D inputs (B, H, S, D)
+            query = jnp.expand_dims(query, axis=0)
+            key = jnp.expand_dims(key, axis=0)
+            value = jnp.expand_dims(value, axis=0)
+
+            # Use a mask to trigger the issue
+            mask = jnp.ones((1, 4, 8), dtype="bool")
+            out = knn.dot_product_attention(query, key, value, mask=mask)
+
+            out = jnp.squeeze(out, axis=0)
+            return carry, out
+
+        query = jnp.ones((2, 1, 4, 8))
+        key = jnp.ones((2, 1, 4, 8))
+        value = jnp.ones((2, 1, 4, 8))
+
+        # Scan over the first dimension
+        _, out = jax.lax.scan(attention_scan_body, None, (query, key, value))
+        self.assertEqual(out.shape, (2, 1, 4, 8))
+
     def test_relu(self):
         x = np.array([-1, 0, 1, 2, 3], dtype=np.float32)
         self.assertAllClose(knn.relu(x), [0, 0, 1, 2, 3])