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| # 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. | |
| """Beam search to find the translated sequence with the highest probability.""" | |
| import tensorflow.compat.v1 as tf | |
| from official.nlp.modeling.ops import beam_search | |
| _StateKeys = beam_search._StateKeys # pylint: disable=protected-access | |
| class SequenceBeamSearch(beam_search.SequenceBeamSearch): | |
| """Implementation of beam search loop.""" | |
| def _process_finished_state(self, finished_state): | |
| alive_seq = finished_state[_StateKeys.ALIVE_SEQ] | |
| alive_log_probs = finished_state[_StateKeys.ALIVE_LOG_PROBS] | |
| finished_seq = finished_state[_StateKeys.FINISHED_SEQ] | |
| finished_scores = finished_state[_StateKeys.FINISHED_SCORES] | |
| finished_flags = finished_state[_StateKeys.FINISHED_FLAGS] | |
| # Account for corner case where there are no finished sequences for a | |
| # particular batch item. In that case, return alive sequences for that batch | |
| # item. | |
| finished_seq = tf.where( | |
| tf.reduce_any(finished_flags, 1), finished_seq, alive_seq) | |
| finished_scores = tf.where( | |
| tf.reduce_any(finished_flags, 1), finished_scores, alive_log_probs) | |
| return finished_seq, finished_scores | |
| def sequence_beam_search(symbols_to_logits_fn, | |
| initial_ids, | |
| initial_cache, | |
| vocab_size, | |
| beam_size, | |
| alpha, | |
| max_decode_length, | |
| eos_id, | |
| padded_decode=False): | |
| """Search for sequence of subtoken ids with the largest probability. | |
| Args: | |
| symbols_to_logits_fn: A function that takes in ids, index, and cache as | |
| arguments. The passed in arguments will have shape: ids -> A tensor with | |
| shape [batch_size * beam_size, index]. index -> A scalar. cache -> A | |
| nested dictionary of tensors [batch_size * beam_size, ...]. | |
| The function must return a tuple of logits and new cache: logits -> A | |
| tensor with shape [batch * beam_size, vocab_size]. new cache -> A nested | |
| dictionary with the same shape/structure as the inputted cache. | |
| initial_ids: An int32 tensor with shape [batch_size]. Starting ids for each | |
| batch item. | |
| initial_cache: A dictionary, containing starting decoder variables | |
| information. | |
| vocab_size: An integer, the size of the vocabulary, used for topk | |
| computation. | |
| beam_size: An integer, the number of beams. | |
| alpha: A float, defining the strength of length normalization. | |
| max_decode_length: An integer, the maximum length to decoded a sequence. | |
| eos_id: An integer, ID of eos token, used to determine when a sequence has | |
| finished. | |
| padded_decode: A bool, indicating if max_sequence_length padding is used for | |
| beam search. | |
| Returns: | |
| Top decoded sequences [batch_size, beam_size, max_decode_length] | |
| sequence scores [batch_size, beam_size] | |
| """ | |
| sbs = SequenceBeamSearch(symbols_to_logits_fn, vocab_size, beam_size, alpha, | |
| max_decode_length, eos_id, padded_decode) | |
| return sbs.search(initial_ids, initial_cache) | |