Spaces:
Runtime error
Runtime error
| # Copyright 2023 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.""" | |
| import numpy as np | |
| import tensorflow as tf, tf_keras | |
| from official.nlp.modeling.layers import attention | |
| 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) | |
| } | |
| class CachedAttentionTest(tf.test.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_dim=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( | |
| query=from_data, value=from_data, attention_mask=mask_data, cache=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( | |
| query=from_data, value=from_data, attention_mask=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_dim=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( | |
| query=from_data, | |
| value=from_data, | |
| attention_mask=mask_data, | |
| cache=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() | |