Fix ops.tile shape inference issue on TensorFlow backend

keras/src/backend/tensorflow/numpy.py

@@ -2742,19 +2742,30 @@ def round(x, decimals=0):
 
 def tile(x, repeats):
     x = convert_to_tensor(x)
-    repeats = tf.reshape(convert_to_tensor(repeats, dtype="int32"), [-1])
-    repeats_size = tf.size(repeats)
-    repeats = tf.pad(
-        repeats,
-        [[tf.maximum(x.shape.rank - repeats_size, 0), 0]],
-        constant_values=1,
-    )
-    x_shape = tf.pad(
-        tf.shape(x),
-        [[tf.maximum(repeats_size - x.shape.rank, 0), 0]],
-        constant_values=1,
-    )
-    x = tf.reshape(x, x_shape)
+
+    # Convert repeats to a list (works for both sequences and 1D tensors)
+    repeats = [v for v in repeats]
+
+    # Process list elements: convert concrete scalar tensors to Python ints
+    processed_repeats = []
+    for r in repeats:
+        if hasattr(r, "numpy") and r.shape == ():
+            processed_repeats.append(int(r.numpy()))
+        else:
+            processed_repeats.append(r)
+    repeats = processed_repeats
+
+    # Get x rank
+    x_rank = x.shape.rank
+
+    # Pad repeats if needed
+    if len(repeats) < x_rank:
+        repeats = [1] * (x_rank - len(repeats)) + repeats
+
+    # Add dimensions to x if needed using tf.expand_dims
+    while len(repeats) > x.shape.rank:
+        x = tf.expand_dims(x, 0)
+
     return tf.tile(x, repeats)
 
 

keras/src/ops/numpy.py

@@ -6411,17 +6411,20 @@ def compute_output_spec(self, x):
         repeats = self.repeats
         if isinstance(repeats, int):
             repeats = [repeats]
+        else:
+            repeats = list(repeats)
+
         if len(x_shape) > len(repeats):
             repeats = [1] * (len(x_shape) - len(repeats)) + repeats
         else:
             x_shape = [1] * (len(repeats) - len(x_shape)) + x_shape
 
         output_shape = []
         for x_size, repeat in zip(x_shape, repeats):
-            if x_size is None:
-                output_shape.append(None)
-            else:
+            if isinstance(repeat, int):
                 output_shape.append(x_size * repeat)
+            else:
+                output_shape.append(None)
         return KerasTensor(output_shape, dtype=x.dtype)
 
 

keras/src/ops/numpy_test.py

@@ -1821,6 +1821,10 @@ def test_tile(self):
         self.assertEqual(knp.tile(x, [1, 2]).shape, (None, 6))
         self.assertEqual(knp.tile(x, [2, 1, 2]).shape, (2, None, 6))
 
+        # Test with multi-dimensional input
+        x = KerasTensor((None, 3, 2, 2))
+        self.assertEqual(knp.tile(x, [1, 2, 1, 1]).shape, (None, 6, 2, 2))
+
     def test_trace(self):
         x = KerasTensor((None, 3, None, 5))
         self.assertEqual(knp.trace(x).shape, (None, 5))
@@ -9507,3 +9511,16 @@ def call(self, x):
         model.compile(jit_compile=jit_compile)
 
         model.predict(np.random.randn(1, 8))
+
+
+class TileTest(testing.TestCase):
+    def test_tile_shape_inference_in_layer(self):
+        class TileLayer(keras.layers.Layer):
+            def call(self, x):
+                repeats = [1, 2, 1, 1]
+                return knp.tile(x, repeats)
+
+        inputs = keras.Input(shape=(3, 2, 2))
+        output = TileLayer()(inputs)
+
+        self.assertEqual(output.shape, (None, 6, 2, 2))