Add test to ensure models can take int64 inputs (#17210)

* Add test to ensure models can take int64 inputs

* is_integer is an attribute, not a method

* Fix test when some inputs aren't tensors

* Add casts to blenderbot and blenderbot-small

* Add casts to the other failing models

src/transformers/models/blenderbot/modeling_tf_blenderbot.py

@@ -1287,7 +1287,7 @@ class TFBlenderbotForConditionalGeneration(TFBlenderbotPreTrainedModel, TFCausal
         if labels is not None:
             labels = tf.where(
                 labels == self.config.pad_token_id,
-                tf.fill(shape_list(labels), -100),
+                tf.cast(tf.fill(shape_list(labels), -100), labels.dtype),
                 labels,
             )
             use_cache = False

src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py

@@ -1265,7 +1265,7 @@ class TFBlenderbotSmallForConditionalGeneration(TFBlenderbotSmallPreTrainedModel
         if labels is not None:
             labels = tf.where(
                 labels == self.config.pad_token_id,
-                tf.fill(shape_list(labels), -100),
+                tf.cast(tf.fill(shape_list(labels), -100), labels.dtype),
                 labels,
             )
             use_cache = False

src/transformers/models/flaubert/modeling_tf_flaubert.py

@@ -182,8 +182,8 @@ def get_masks(slen, lengths, causal, padding_mask=None):
         mask = padding_mask
     else:
         # assert lengths.max().item() <= slen
-        alen = tf.range(slen)
-        mask = tf.math.less(alen, tf.expand_dims(lengths, axis=1))
+        alen = tf.range(slen, dtype=lengths.dtype)
+        mask = alen < tf.expand_dims(lengths, axis=1)
 
     # attention mask is the same as mask, or triangular inferior attention (causal)
     if causal:

src/transformers/models/mbart/modeling_tf_mbart.py

@@ -1300,7 +1300,7 @@ class TFMBartForConditionalGeneration(TFMBartPreTrainedModel, TFCausalLanguageMo
         if labels is not None:
             labels = tf.where(
                 labels == self.config.pad_token_id,
-                tf.fill(shape_list(labels), -100),
+                tf.cast(tf.fill(shape_list(labels), -100), labels.dtype),
                 labels,
             )
             use_cache = False

src/transformers/models/pegasus/modeling_tf_pegasus.py

@@ -1317,7 +1317,7 @@ class TFPegasusForConditionalGeneration(TFPegasusPreTrainedModel, TFCausalLangua
         if labels is not None:
             labels = tf.where(
                 labels == self.config.pad_token_id,
-                tf.fill(shape_list(labels), -100),
+                tf.cast(tf.fill(shape_list(labels), -100), labels.dtype),
                 labels,
             )
             use_cache = False

src/transformers/models/tapas/modeling_tf_tapas.py

@@ -1726,7 +1726,10 @@ class ProductIndexMap(IndexMap):
             raise ValueError("outer_index.batch_dims and inner_index.batch_dims " "must be the same.")
 
         super(ProductIndexMap, self).__init__(
-            indices=(inner_index.indices + outer_index.indices * inner_index.num_segments),
+            indices=(
+                inner_index.indices
+                + outer_index.indices * tf.cast(inner_index.num_segments, inner_index.indices.dtype)
+            ),
             num_segments=inner_index.num_segments * outer_index.num_segments,
             batch_dims=inner_index.batch_dims,
         )
@@ -1785,7 +1788,7 @@ def flatten(index, name="segmented_flatten"):
     for _ in range(index.batch_dims, index.indices.shape.rank):
         offset = tf.expand_dims(offset, -1)
 
-    indices = offset + index.indices
+    indices = tf.cast(offset, index.indices.dtype) + index.indices
     return IndexMap(indices=tf.reshape(indices, [-1]), num_segments=index.num_segments * batch_size, batch_dims=0)
 
 

src/transformers/models/transfo_xl/modeling_tf_transfo_xl_utilities.py

@@ -111,7 +111,7 @@ class TFAdaptiveSoftmaxMask(tf.keras.layers.Layer):
     @staticmethod
     def _gather_logprob(logprob, target):
         lp_size = shape_list(logprob)
-        r = tf.range(lp_size[0])
+        r = tf.range(lp_size[0], dtype=target.dtype)
         idx = tf.stack([r, target], 1)
         return tf.gather_nd(logprob, idx)
 

src/transformers/models/xlm/modeling_tf_xlm.py

@@ -92,8 +92,8 @@ def get_masks(slen, lengths, causal, padding_mask=None):
         mask = padding_mask
     else:
         # assert lengths.max().item() <= slen
-        alen = tf.range(slen)
-        mask = tf.math.less(alen, tf.expand_dims(lengths, axis=1))
+        alen = tf.range(slen, dtype=lengths.dtype)
+        mask = alen < tf.expand_dims(lengths, axis=1)
 
     # attention mask is the same as mask, or triangular inferior attention (causal)
     if causal:

tests/test_modeling_tf_common.py

@@ -1372,6 +1372,26 @@ class TFModelTesterMixin:
                 val_loss2 = history2.history["val_loss"][0]
                 self.assertTrue(np.allclose(val_loss1, val_loss2, atol=1e-2, rtol=1e-3))
 
+    def test_int64_inputs(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        for model_class in self.all_model_classes:
+            prepared_for_class = self._prepare_for_class(
+                inputs_dict.copy(),
+                model_class,
+                return_labels=True if "labels" in inspect.signature(model_class.call).parameters.keys() else False,
+            )
+            if not any(
+                [tensor.dtype.is_integer for tensor in prepared_for_class.values() if isinstance(tensor, tf.Tensor)]
+            ):
+                return  # No integer inputs means no need for this test
+
+            prepared_for_class = {
+                key: tf.cast(tensor, tf.int64) if isinstance(tensor, tf.Tensor) and tensor.dtype.is_integer else tensor
+                for key, tensor in prepared_for_class.items()
+            }
+            model = model_class(config)
+            model(**prepared_for_class)  # No assertion, we're just checking this doesn't throw an error
+
     def test_generate_with_headmasking(self):
         attention_names = ["encoder_attentions", "decoder_attentions", "cross_attentions"]
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()