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PiperOrigin-RevId: 558604894

official/nlp/modeling/layers/masked_lm.py

@@ -81,10 +81,16 @@ def call(self, sequence_data, masked_positions):
     lm_data = self.layer_norm(lm_data)
     lm_data = tf.matmul(lm_data, self.embedding_table, transpose_b=True)
     logits = tf.nn.bias_add(lm_data, self.bias)
-    masked_positions_length = masked_positions.shape.as_list()[1] or tf.shape(
-        masked_positions)[1]
-    logits = tf.reshape(logits,
-                        [-1, masked_positions_length, self._vocab_size])
+    masked_positions_length = (
+        masked_positions.shape.as_list()[1] or tf.shape(masked_positions)[1]
+    )
+    batch_size = (
+        masked_positions.shape.as_list()[0] or tf.shape(masked_positions)[0]
+    )
+    logits = tf.reshape(
+        logits,
+        [batch_size, masked_positions_length, self._vocab_size],
+    )
     if self._output_type == 'logits':
       return logits
     return tf.nn.log_softmax(logits)

official/nlp/modeling/layers/masked_lm_test.py

@@ -13,15 +13,15 @@
 # limitations under the License.
 
 """Tests for masked language model network."""
-
+from absl.testing import parameterized
 import numpy as np
 import tensorflow as tf
 
 from official.nlp.modeling.layers import masked_lm
 from official.nlp.modeling.networks import bert_encoder
 
 
-class MaskedLMTest(tf.test.TestCase):
+class MaskedLMTest(tf.test.TestCase, parameterized.TestCase):
 
   def create_layer(self,
                    vocab_size,
@@ -110,11 +110,20 @@ def test_layer_invocation_with_external_logits(self):
     self.assertEqual(expected_output_shape, outputs.shape)
     self.assertAllClose(ref_outputs, outputs)
 
-  def test_layer_invocation(self):
+  @parameterized.named_parameters(
+      dict(
+          testcase_name='default',
+          num_predictions=21,
+      ),
+      dict(
+          testcase_name='zero_predictions',
+          num_predictions=0,
+      ),
+  )
+  def test_layer_invocation(self, num_predictions):
     vocab_size = 100
     sequence_length = 32
     hidden_size = 64
-    num_predictions = 21
     test_layer = self.create_layer(
         vocab_size=vocab_size, hidden_size=hidden_size)
 
@@ -131,7 +140,9 @@ def test_layer_invocation(self):
         (batch_size, sequence_length, hidden_size))
     masked_position_data = np.random.randint(
         2, size=(batch_size, num_predictions))
-    _ = model.predict([lm_input_data, masked_position_data])
+    res = model.predict([lm_input_data, masked_position_data])
+    expected_shape = (batch_size, num_predictions, vocab_size)
+    self.assertEqual(expected_shape, res.shape)
 
   def test_unknown_output_type_fails(self):
     with self.assertRaisesRegex(ValueError, 'Unknown `output` value "bad".*'):