Change Seq2SeqTransformer inputs to dictionary.

PiperOrigin-RevId: 338309524

official/nlp/modeling/models/seq2seq_transformer.py

@@ -130,9 +130,9 @@ class Seq2SeqTransformer(tf.keras.Model):
     """Calculate target logits or inferred target sequences.
 
     Args:
-      inputs: input tensor list of size 1 or 2.
-        First item, inputs: int tensor with shape [batch_size, input_length].
-        Second item (optional), targets: None or int tensor with shape
+      inputs: a dictionary of tensors.
+        Feature `inputs`: int tensor with shape [batch_size, input_length].
+        Feature `targets` (optional): None or int tensor with shape
           [batch_size, target_length].
 
     Returns:
@@ -147,12 +147,8 @@ class Seq2SeqTransformer(tf.keras.Model):
     Raises:
       NotImplementedError: If try to use padded decode method on CPU/GPUs.
     """
-    inputs = inputs if isinstance(inputs, list) else [inputs]
-    if len(inputs) == 2:
-      sources, targets = inputs[0], inputs[1]
-    else:
-      # Decoding path.
-      sources, targets = inputs[0], None
+    sources = inputs["inputs"]
+    targets = inputs.get("targets", None)
     attention_bias = model_utils.get_padding_bias(sources)
     attention_bias = tf.cast(attention_bias, self._dtype)
     # Prepare inputs to the layer stack by adding positional encodings and

official/nlp/modeling/models/seq2seq_transformer_test.py

@@ -82,15 +82,15 @@ class Seq2SeqTransformerTest(tf.test.TestCase, parameterized.TestCase):
         return tf.nest.map_structure(distribution.experimental_local_results,
                                      outputs)
 
-      fake_inputs = [np.zeros((batch_size, decode_max_length), dtype=np.int32)]
+      fake_inputs = dict(
+          inputs=np.zeros((batch_size, decode_max_length), dtype=np.int32))
       local_outputs = step(fake_inputs)
       logging.info("local_outputs=%s", local_outputs)
       self.assertEqual(local_outputs["outputs"][0].shape, (4, 10))
 
-      fake_inputs = [
-          np.zeros((batch_size, decode_max_length), dtype=np.int32),
-          np.zeros((batch_size, 8), dtype=np.int32)
-      ]
+      fake_inputs = dict(
+          inputs=np.zeros((batch_size, decode_max_length), dtype=np.int32),
+          targets=np.zeros((batch_size, 8), dtype=np.int32))
       local_outputs = step(fake_inputs)
       logging.info("local_outputs=%s", local_outputs)
       self.assertEqual(local_outputs[0].shape, (4, 8, 100))
@@ -108,7 +108,7 @@ class Seq2SeqTransformerTest(tf.test.TestCase, parameterized.TestCase):
 
       @tf.function
       def serve(self, inputs):
-        return self.model.call([inputs])
+        return self.model.call(dict(inputs=inputs))
 
     save_module = SaveModule(model)
     if padded_decode:

official/nlp/transformer/transformer_forward_test.py

@@ -70,7 +70,8 @@ def _create_model(params, is_train):
     inputs = tf.keras.layers.Input((None,), dtype="int64", name="inputs")
     targets = tf.keras.layers.Input((None,), dtype="int64", name="targets")
     internal_model = models.Seq2SeqTransformer(**model_kwargs)
-    logits = internal_model([inputs, targets], training=is_train)
+    logits = internal_model(
+        dict(inputs=inputs, targets=targets), training=is_train)
     vocab_size = params["vocab_size"]
     label_smoothing = params["label_smoothing"]
     if params["enable_metrics_in_training"]:
@@ -90,7 +91,7 @@ def _create_model(params, is_train):
                                  dtype="int64",
                                  name="inputs")
   internal_model = models.Seq2SeqTransformer(**model_kwargs)
-  ret = internal_model([inputs], training=is_train)
+  ret = internal_model(dict(inputs=inputs), training=is_train)
   outputs, scores = ret["outputs"], ret["scores"]
   return tf.keras.Model(inputs, [outputs, scores])