# Copyright 2019 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Tests for the attention layer.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from absl.testing import parameterized import numpy as np import tensorflow as tf from tensorflow.python.keras import keras_parameterized # pylint: disable=g-direct-tensorflow-import from official.nlp.modeling.layers import attention # This decorator runs the test in V1, V2-Eager, and V2-Functional mode. It # guarantees forward compatibility of this code for the V2 switchover. @keras_parameterized.run_all_keras_modes class MultiHeadAttentionTest(keras_parameterized.TestCase): @parameterized.named_parameters( ("key_value_same_proj", None, None, [40, 80]), ("key_value_different_proj", 32, 60, [40, 60]), ) def test_non_masked_attention(self, value_size, output_shape, output_dims): """Test that the attention layer can be created without a mask tensor.""" test_layer = attention.MultiHeadAttention( num_heads=12, key_size=64, value_size=value_size, output_shape=output_shape) # Create a 3-dimensional input (the first dimension is implicit). query = tf.keras.Input(shape=(40, 80)) value = tf.keras.Input(shape=(20, 80)) output = test_layer([query, value]) self.assertEqual(output.shape.as_list(), [None] + output_dims) def test_non_masked_self_attention(self): """Test with one input (self-attenntion) and no mask tensor.""" test_layer = attention.MultiHeadAttention(num_heads=12, key_size=64) # Create a 3-dimensional input (the first dimension is implicit). query = tf.keras.Input(shape=(40, 80)) output = test_layer([query, query]) self.assertEqual(output.shape.as_list(), [None, 40, 80]) def test_attention_scores(self): """Test attention outputs with coefficients.""" test_layer = attention.MultiHeadAttention( num_heads=12, key_size=64, return_attention_scores=True) # Create a 3-dimensional input (the first dimension is implicit). query = tf.keras.Input(shape=(40, 80)) output, coef = test_layer([query, query]) self.assertEqual(output.shape.as_list(), [None, 40, 80]) self.assertEqual(coef.shape.as_list(), [None, 12, 40, 40]) @parameterized.named_parameters(("with_bias", True), ("no_bias", False)) def test_masked_attention(self, use_bias): """Test with a mask tensor.""" test_layer = attention.MultiHeadAttention( num_heads=2, key_size=2, use_bias=use_bias) # Create a 3-dimensional input (the first dimension is implicit). batch_size = 3 query = tf.keras.Input(shape=(4, 8)) value = tf.keras.Input(shape=(2, 8)) mask_tensor = tf.keras.Input(shape=(4, 2)) output = test_layer([query, value], mask_tensor) # Create a model containing the test layer. model = tf.keras.Model([query, value, mask_tensor], output) # Generate data for the input (non-mask) tensors. from_data = 10 * np.random.random_sample((batch_size, 4, 8)) to_data = 10 * np.random.random_sample((batch_size, 2, 8)) # Invoke the data with a random set of mask data. This should mask at least # one element. mask_data = np.random.randint(2, size=(batch_size, 4, 2)) masked_output_data = model.predict([from_data, to_data, mask_data]) # Invoke the same data, but with a null mask (where no elements are masked). null_mask_data = np.ones((batch_size, 4, 2)) unmasked_output_data = model.predict([from_data, to_data, null_mask_data]) # Because one data is masked and one is not, the outputs should not be the # same. self.assertNotAllClose(masked_output_data, unmasked_output_data) # Tests the layer with three inputs: Q, K, V. key = tf.keras.Input(shape=(2, 8)) output = test_layer([query, value, key], mask_tensor) model = tf.keras.Model([query, value, key, mask_tensor], output) masked_output_data = model.predict([from_data, to_data, to_data, mask_data]) unmasked_output_data = model.predict( [from_data, to_data, to_data, null_mask_data]) # Because one data is masked and one is not, the outputs should not be the # same. self.assertNotAllClose(masked_output_data, unmasked_output_data) if use_bias: self.assertLen(test_layer._query_dense.trainable_variables, 2) self.assertLen(test_layer._output_dense.trainable_variables, 2) else: self.assertLen(test_layer._query_dense.trainable_variables, 1) self.assertLen(test_layer._output_dense.trainable_variables, 1) def test_initializer(self): """Test with a specified initializer.""" test_layer = attention.MultiHeadAttention( num_heads=12, key_size=64, kernel_initializer=tf.keras.initializers.TruncatedNormal(stddev=0.02)) # Create a 3-dimensional input (the first dimension is implicit). query = tf.keras.Input(shape=(40, 80)) output = test_layer([query, query]) self.assertEqual(output.shape.as_list(), [None, 40, 80]) @parameterized.named_parameters( ("4d_inputs_one_free_batch", [3, 4], [3, 2], [4, 2], (2,)), ("4D_inputs_2D_attention", [3, 4], [3, 2], [3, 4, 3, 2], (1, 2)), ("5D_inputs_2D_attention", [5, 3, 4], [5, 3, 2], [3, 4, 3, 2], (2, 3))) def test_high_dim_attention(self, q_dims, v_dims, mask_dims, attention_axes): """Test with a mask tensor.""" test_layer = attention.MultiHeadAttention( num_heads=2, key_size=2, attention_axes=attention_axes) batch_size, hidden_size = 3, 8 # Generate data for the input (non-mask) tensors. query_shape = [batch_size] + q_dims + [hidden_size] value_shape = [batch_size] + v_dims + [hidden_size] mask_shape = [batch_size] + mask_dims query = 10 * np.random.random_sample(query_shape) value = 10 * np.random.random_sample(value_shape) # Invoke the data with a random set of mask data. This should mask at least # one element. mask_data = np.random.randint(2, size=mask_shape).astype("bool") output = test_layer([query, value], mask_data) # Invoke the same data, but with a null mask (where no elements are masked). null_mask_data = np.ones(mask_shape) unmasked_output = test_layer([query, value], null_mask_data) # Because one data is masked and one is not, the outputs should not be the # same. self.assertNotAllClose(output, unmasked_output) class SubclassAttention(attention.MultiHeadAttention): def _build_attention(self, qkv_rank): pass def _compute_attention(self, query_tensor, key_tensor, value_tensor, attention_mask=None): return value_tensor, None @keras_parameterized.run_all_keras_modes class AttentionSubclassTest(keras_parameterized.TestCase): def test_initializer(self): """Test with a specified initializer.""" test_layer = SubclassAttention( num_heads=12, key_size=64) # Create a 3-dimensional input (the first dimension is implicit). query = tf.keras.Input(shape=(40, 80)) output = test_layer([query, query]) self.assertEqual(output.shape.as_list(), [None, 40, 80]) def _create_cache(batch_size, init_decode_length, num_heads, head_size): return { "key": tf.zeros([batch_size, init_decode_length, num_heads, head_size], dtype=tf.float32), "value": tf.zeros([batch_size, init_decode_length, num_heads, head_size], dtype=tf.float32) } @keras_parameterized.run_all_keras_modes class CachedAttentionTest(keras_parameterized.TestCase): def test_masked_attention(self): """Test with a mask tensor.""" num_heads, head_size = 2, 2 # Create a 3-dimensional input (the first dimension is implicit). from_seq_length = 4 batch_size = 3 # GPU/CPU case. init_decode_length = 0 # Directly tests the keras layer. cache = _create_cache(batch_size, init_decode_length, num_heads, head_size) layer = attention.CachedAttention(num_heads=num_heads, key_size=head_size) # Generate data for the input (non-mask) tensors. from_data = tf.zeros((batch_size, from_seq_length, 8), dtype=np.float32) # Invoke the data with a random set of mask data. This should mask at least # one element. mask_data = np.random.randint( 2, size=(batch_size, from_seq_length, from_seq_length)) masked_output_data, cache = layer([from_data, from_data], mask_data, cache) self.assertEqual(masked_output_data.shape, (3, 4, 8)) self.assertEqual(cache["value"].shape, (3, 4, 2, 2)) # Tests inputs without cache. masked_output_data, cache = layer([from_data, from_data, mask_data]) self.assertEqual(masked_output_data.shape, (3, 4, 8)) self.assertIsNone(cache) def test_padded_decode(self): """Test with a mask tensor.""" num_heads, head_size = 2, 2 from_seq_length = 4 # TPU decoding should pre-allocate the entire sequence. batch_size = 3 init_decode_length = from_seq_length # Directly tests the keras layer. cache = _create_cache(batch_size, init_decode_length, num_heads, head_size) layer = attention.CachedAttention(num_heads=num_heads, key_size=head_size) # Generate data for the input (non-mask) tensors. from_data = tf.zeros((batch_size, from_seq_length, 8), dtype=np.float32) decode_loop_step = 2 mask_data = np.random.randint( 2, size=(batch_size, from_seq_length, from_seq_length), dtype=np.int32) # Testing the invocation directly as Keras cannot consume inputs correctly. masked_output_data, cache = layer([from_data, from_data], mask_data, cache, decode_loop_step=decode_loop_step) self.assertEqual(masked_output_data.shape, (3, 4, 8)) self.assertEqual(cache["value"].shape, (3, 4, 2, 2)) if __name__ == "__main__": tf.test.main()