Make the TF dummies even smaller (#24071)

* Let's see if we can use the smallest possible dummies

* Make GPT-2's dummies a little longer

* Just use (1,2) as the default shape

* Update other dummies in sync

* Correct imports for Keras 2.13

* Shrink the Wav2Vec2 dummies

src/transformers/modeling_tf_utils.py

@@ -74,7 +74,7 @@ from .utils.hub import convert_file_size_to_int, get_checkpoint_shard_files
 if parse(tf.__version__).minor >= 13:
     from keras import backend as K
     from keras.__internal__ import KerasTensor
-    from keras.engine.base_layer_utils import call_context
+    from keras.src.engine.base_layer_utils import call_context
 elif parse(tf.__version__).minor >= 11:
     from keras import backend as K
     from keras.engine.base_layer_utils import call_context
@@ -1125,7 +1125,11 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin, Pu
         sig = self._prune_signature(self.input_signature)
         for key, spec in sig.items():
             # 2 is the most correct arbitrary size. I will not be taking questions
-            dummies[key] = tf.ones(shape=[dim if dim is not None else 2 for dim in spec.shape], dtype=spec.dtype)
+            dummy_shape = [dim if dim is not None else 2 for dim in spec.shape]
+            if spec.shape[0] is None:
+                # But let's make the batch size 1 to save memory anyway
+                dummy_shape[0] = 1
+            dummies[key] = tf.ones(shape=dummy_shape, dtype=spec.dtype)
             if key == "token_type_ids":
                 # Some models have token_type_ids but with a vocab_size of 1
                 dummies[key] = tf.zeros_like(dummies[key])
@@ -1133,7 +1137,7 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin, Pu
             if "encoder_hidden_states" not in dummies:
                 if self.main_input_name == "input_ids":
                     dummies["encoder_hidden_states"] = tf.ones(
-                        shape=(2, 2, self.config.hidden_size), dtype=tf.float32, name="encoder_hidden_states"
+                        shape=(1, 2, self.config.hidden_size), dtype=tf.float32, name="encoder_hidden_states"
                     )
                 else:
                     raise NotImplementedError(

src/transformers/models/funnel/modeling_tf_funnel.py

@@ -978,7 +978,7 @@ class TFFunnelPreTrainedModel(TFPreTrainedModel):
     @property
     def dummy_inputs(self):
         # Funnel misbehaves with very small inputs, so we override and make them a bit bigger
-        return {"input_ids": tf.ones((3, 3), dtype=tf.int32)}
+        return {"input_ids": tf.ones((1, 3), dtype=tf.int32)}
 
 
 @dataclass

src/transformers/models/sam/modeling_tf_sam.py

@@ -1147,21 +1147,6 @@ class TFSamPreTrainedModel(TFPreTrainedModel):
     base_model_prefix = "sam"
     main_input_name = "pixel_values"
 
-    @property
-    def dummy_inputs(self) -> Dict[str, tf.Tensor]:
-        # We override the default dummy inputs here because SAM has some really explosive memory usage in the
-        # attention layers, so we want to pass the smallest possible batches
-        VISION_DUMMY_INPUTS = tf.random.uniform(
-            shape=(
-                1,
-                self.config.vision_config.num_channels,
-                self.config.vision_config.image_size,
-                self.config.vision_config.image_size,
-            ),
-            dtype=tf.float32,
-        )
-        return {"pixel_values": tf.constant(VISION_DUMMY_INPUTS)}
-
 
 SAM_START_DOCSTRING = r"""
     This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the

src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py

@@ -1194,8 +1194,8 @@ class TFWav2Vec2PreTrainedModel(TFPreTrainedModel):
     @property
     def dummy_inputs(self):
         return {
-            "input_values": tf.random.uniform(shape=(1, 16000), dtype=tf.float32),
-            "attention_mask": tf.ones(shape=(1, 16000), dtype=tf.float32),
+            "input_values": tf.random.uniform(shape=(1, 500), dtype=tf.float32),
+            "attention_mask": tf.ones(shape=(1, 500), dtype=tf.float32),
         }
 
     def __init__(self, config, *inputs, **kwargs):

src/transformers/models/whisper/modeling_tf_whisper.py

@@ -481,9 +481,9 @@ class TFWhisperPreTrainedModel(TFPreTrainedModel):
         """
         return {
             self.main_input_name: tf.random.uniform(
-                [2, self.config.num_mel_bins, self.config.max_source_positions * 2 - 1], dtype=tf.float32
+                [1, self.config.num_mel_bins, self.config.max_source_positions * 2 - 1], dtype=tf.float32
             ),
-            "decoder_input_ids": tf.constant([[2, 3]], dtype=tf.int32),
+            "decoder_input_ids": tf.constant([[1, 3]], dtype=tf.int32),
         }
 
     @property