def test_layer_output_range(self, _): # XLA has an obvious numeric issue in this test case. test_layer = transformer.Transformer(num_attention_heads=10, intermediate_size=2048, intermediate_activation='relu') sequence_length = 21 width = 80 batch_size = 6 input_data = 10 * np.random.random_sample( (batch_size, sequence_length, width)) mask_data = np.random.randint(2, size=(batch_size, sequence_length, sequence_length)) output_tensor = test_layer([input_data, mask_data]) # The layer only attends to the first token and outputs the first token # embeeding. new_layer = transformer.Transformer(num_attention_heads=10, intermediate_size=2048, intermediate_activation='relu', output_range=1) _ = new_layer([input_data, mask_data]) new_layer.set_weights(test_layer.get_weights()) new_output_tensor = new_layer([input_data, mask_data]) self.assertAllClose(new_output_tensor, output_tensor[:, 0:1, :], atol=5e-5, rtol=0.003)
def test_layer_invocation_with_mask(self): test_layer = transformer.Transformer(num_attention_heads=10, intermediate_size=2048, intermediate_activation='relu') sequence_length = 21 width = 80 # Create a 3-dimensional input (the first dimension is implicit). data_tensor = tf.keras.Input(shape=(sequence_length, width)) # Create a 2-dimensional input (the first dimension is implicit). mask_tensor = tf.keras.Input(shape=(sequence_length, sequence_length)) output_tensor = test_layer([data_tensor, mask_tensor]) # Create a model from the test layer. model = tf.keras.Model([data_tensor, mask_tensor], output_tensor) # Invoke the model on test data. We can't validate the output data itself # (the NN is too complex) but this will rule out structural runtime errors. batch_size = 6 input_data = 10 * np.random.random_sample( (batch_size, sequence_length, width)) # The attention mask should be of shape (batch, from_seq_len, to_seq_len), # which here is (batch, sequence_length, sequence_length) mask_data = np.random.randint(2, size=(batch_size, sequence_length, sequence_length)) _ = model.predict([input_data, mask_data])
def test_layer_creation(self): test_layer = transformer.Transformer(num_attention_heads=10, intermediate_size=2048, intermediate_activation='relu') sequence_length = 21 width = 80 # Create a 3-dimensional input (the first dimension is implicit). data_tensor = tf.keras.Input(shape=(sequence_length, width)) output_tensor = test_layer(data_tensor) # The default output of a transformer layer should be the same as the input. self.assertEqual(data_tensor.shape.as_list(), output_tensor.shape.as_list())
def test_layer_creation_with_incorrect_mask_fails(self): test_layer = transformer.Transformer(num_attention_heads=10, intermediate_size=2048, intermediate_activation='relu') sequence_length = 21 width = 80 # Create a 3-dimensional input (the first dimension is implicit). data_tensor = tf.keras.Input(shape=(sequence_length, width)) # Create a 2-dimensional input (the first dimension is implicit). mask_tensor = tf.keras.Input(shape=(sequence_length, sequence_length - 3)) with self.assertRaisesRegex(ValueError, 'When passing a mask tensor.*'): _ = test_layer([data_tensor, mask_tensor])
def test_get_config(self): num_attention_heads = 2 encoder_block = transformer.Transformer( num_attention_heads=num_attention_heads, intermediate_size=32, intermediate_activation='relu', dropout_rate=0.1, attention_dropout_rate=0.1, use_bias=False, norm_first=True, norm_epsilon=1e-6) encoder_block_config = encoder_block.get_config() new_encoder_block = transformer.Transformer.from_config( encoder_block_config) self.assertEqual(encoder_block_config, new_encoder_block.get_config())
def test_use_bias_norm_first(self): num_attention_heads = 2 hidden_size = 16 encoder_block = transformer.Transformer( num_attention_heads=num_attention_heads, intermediate_size=32, intermediate_activation='relu', dropout_rate=0.1, attention_dropout_rate=0.1, use_bias=False, norm_first=True, norm_epsilon=1e-6) # Forward path. dummy_tensor = tf.zeros([2, 4, 16], dtype=tf.float32) dummy_mask = tf.zeros([2, 4, 4], dtype=tf.float32) inputs = [dummy_tensor, dummy_mask] output = encoder_block(inputs) self.assertEqual(output.shape, (2, 4, hidden_size))
def test_get_config(self): num_attention_heads = 2 encoder_block = transformer.Transformer( num_attention_heads=num_attention_heads, intermediate_size=32, intermediate_activation='relu', dropout_rate=0.1, attention_dropout_rate=0.1, use_bias=False, norm_first=True, norm_epsilon=1e-6, intermediate_dropout=0.1, attention_initializer=tf.keras.initializers.RandomUniform( minval=0., maxval=1.)) encoder_block_config = encoder_block.get_config() new_encoder_block = transformer.Transformer.from_config( encoder_block_config) self.assertEqual(encoder_block_config, new_encoder_block.get_config())
def test_dynamic_layer_sequence(self): test_layer = transformer.Transformer( num_attention_heads=10, intermediate_size=2048, intermediate_activation='relu', kernel_initializer=tf.keras.initializers.TruncatedNormal( stddev=0.02)) # Create a 3-dimensional input (the first dimension is implicit). width = 30 input_tensor = tf.keras.Input(shape=(None, width)) output_tensor = test_layer(input_tensor) model = tf.keras.Model(input_tensor, output_tensor) input_length = 17 input_data = np.ones((1, input_length, width)) output_data = model.predict(input_data) self.assertAllEqual([1, input_length, width], output_data.shape)
def test_layer_invocation(self): test_layer = transformer.Transformer(num_attention_heads=10, intermediate_size=2048, intermediate_activation='relu') sequence_length = 21 width = 80 # Create a 3-dimensional input (the first dimension is implicit). data_tensor = tf.keras.Input(shape=(sequence_length, width)) output_tensor = test_layer(data_tensor) # Create a model from the test layer. model = tf.keras.Model(data_tensor, output_tensor) # Invoke the model on test data. We can't validate the output data itself # (the NN is too complex) but this will rule out structural runtime errors. batch_size = 6 input_data = 10 * np.random.random_sample( (batch_size, sequence_length, width)) _ = model.predict(input_data)