Fix loading of TwoTowerModel with context query variable

merlin/models/tf/blocks/retrieval/base.py

@@ -195,15 +195,12 @@ def __init__(
     def build(self, input_shapes):
         if isinstance(input_shapes, dict):
             query_shape = input_shapes[self.query_name]
-            self.context.add_variable(
-                tf.Variable(
-                    initial_value=tf.zeros([1, query_shape[-1]], dtype=tf.float32),
-                    name="query",
-                    trainable=False,
-                    validate_shape=False,
-                    dtype=tf.float32,
-                    shape=tf.TensorShape([None, query_shape[-1]]),
-                )
+            self.context.add_weight(
+                name="query",
+                shape=query_shape,
+                dtype=tf.float32,
+                trainable=False,
+                initializer=tf.keras.initializers.Zeros(),
             )
 
         super().build(input_shapes)
@@ -241,7 +238,17 @@ def call(
         """
         if self.cache_query:
             # enabled only during top-k evaluation
-            self.context["query"].assign(tf.cast(inputs[self.query_name], tf.float32))
+
+            query = inputs[self.query_name]
+            context_query_size = tf.shape(self.context["query"])[0]
+            # pad with zeros to match shape of initial query variable
+            padding_size = context_query_size - tf.shape(query)[0]
+            if padding_size > 0:
+                query = tf.pad(query, [[0, padding_size], [0, 0]])
+
+            query = query[:context_query_size]
+
+            self.context["query"].assign(tf.cast(query, tf.float32))
 
         if training or testing:
             return inputs

merlin/models/tf/core/base.py

@@ -94,9 +94,6 @@ class ModelContext(Layer):
     (This is created automatically in the model and doesn't need to be created manually.)
     """
 
-    def add_variable(self, variable):
-        setattr(self, variable.name, variable)
-
     def add_embedding_table(self, name, embedding_table):
         embedding_tables = getattr(self, "embedding_tables", {})
         embedding_tables[name] = embedding_table

merlin/models/tf/core/index.py

@@ -250,7 +250,12 @@ def call_outputs(
             2D Tensors with the one-hot representation of true targets and
             the scores for the top-k implicit negatives.
         """
+
+        n = tf.shape(outputs.positive_item_ids)[0]
+
         queries = self.context["query"]
+        queries = queries[:n]
+
         pred_top_scores, top_ids = self(queries, k=self._k)
 
         targets_sorted = tf.cast(

tests/unit/tf/models/test_retrieval.py

@@ -274,7 +274,7 @@ def test_two_tower_model_save(tmpdir, ecommerce_data: Dataset):
         embedding_options=mm.EmbeddingOptions(infer_embedding_sizes=True),
     )
 
-    testing_utils.model_test(model, dataset, reload_model=False)
+    testing_utils.model_test(model, dataset, reload_model=True)
 
     query_tower = model.retrieval_block.query_block()
     query_tower_path = Path(tmpdir) / "query_tower"