def predict(self,
input_file,
output_file,
checkpoint_steps=-1,
beam_size=1,
temperature=1.0,
keep_top_k=-1,
vocabulary=None):
"""Predicts targets from the given inputs.
Args:
input_file: str, path to a text file containing newline-separated input
prompts to predict from.
output_file: str, path prefix of output file to write predictions to. Note
the checkpoint step will be appended to the given filename.
checkpoint_steps: int, list of ints, or None. If an int or list of ints,
inference will be run on the checkpoint files in `model_dir` whose
global steps are closest to the global steps provided. If None, run
inference continuously waiting for new checkpoints. If -1, get the
latest checkpoint from the model directory.
beam_size: int, a number >= 1 specifying the number of beams to use for
beam search.
temperature: float, a value between 0 and 1 (must be 0 if beam_size > 1)
0.0 means argmax, 1.0 means sample according to predicted distribution.
keep_top_k: integer, a value between 1 and the vocabulary size. When
sampling, only pick tokens that are in the k most likely.
vocabulary: vocabularies.Vocabulary object to use for tokenization, or
None to use the default SentencePieceVocabulary.
"""
# TODO(sharannarang) : It would be nice to have a function like
# load_checkpoint that loads the model once and then call decode_from_file
# multiple times without having to restore the checkpoint weights again.
# This would be particularly useful in colab demo.
if checkpoint_steps == -1:
checkpoint_steps = utils.get_latest_checkpoint_from_dir(
self._model_dir)
_parse_operative_config(self._model_dir)
with gin.unlock_config():
gin.bind_parameter("Bitransformer.decode.beam_size", beam_size)
gin.bind_parameter("Bitransformer.decode.temperature", temperature)
gin.bind_parameter("Bitransformer.decode.sampling_keep_top_k",
keep_top_k)
gin.bind_parameter("utils.decode_from_file.input_filename",
input_file)
gin.bind_parameter("utils.decode_from_file.output_filename",
output_file)
if vocabulary is None:
vocabulary = utils.get_vocabulary()
mtf_utils.infer_model(self.estimator(vocabulary), vocabulary,
self._sequence_length, self.batch_size,
self._model_type, self._model_dir,
checkpoint_steps)
def predict(self, checkpoint_step, input_file, output_file):
# TODO(sharannarang) : Add the ability to decode from a collection of
# strings instead of always requiring an input file.
# TODO(sharannarang) : It would be nice to have a function like
# load_checkpoint that loads the model once and then call decode_from_file
# multiple times without having to restore the checkpoint weights again.
# This would be particularly useful in colab demo.
utils.infer_model(self._estimator, self._vocabulary,
self._sequence_length, self._batch_size,
self._model_type, self._model_dir, checkpoint_step,
input_file, output_file)
def predict(self,
input_file,
output_file,
checkpoint_steps=-1,
beam_size=1,
temperature=1.0,
sentencepiece_model_path=t5.data.DEFAULT_SPM_PATH,
sampling_keep_top_p=1.0):
"""Predicts targets from the given inputs.
Args:
input_file: str, path to a text file containing newline-separated input
prompts to predict from.
output_file: str, path prefix of output file to write predictions to. Note
the checkpoint step will be appended to the given filename.
checkpoint_steps: int, list of ints, or None. If an int or list of ints,
inference will be run on the checkpoint files in `model_dir` whose
global steps are closest to the global steps provided. If None, run
inference continuously waiting for new checkpoints. If -1, get the
latest checkpoint from the model directory.
beam_size: int, a number >= 1 specifying the number of beams to use for
beam search.
temperature: float, a value between 0 and 1 (must be 0 if beam_size > 1)
0.0 means argmax, 1.0 means sample according to predicted distribution.
sentencepiece_model_path: str, path to the SentencePiece model file to use
for decoding. Must match the one used during training.
"""
# TODO(sharannarang) : It would be nice to have a function like
# load_checkpoint that loads the model once and then call decode_from_file
# multiple times without having to restore the checkpoint weights again.
# This would be particularly useful in colab demo.
if checkpoint_steps == -1:
checkpoint_steps = _get_latest_checkpoint_from_dir(self._model_dir)
with gin.unlock_config():
gin.parse_config_file(_operative_config_path(self._model_dir))
gin.bind_parameter("Bitransformer.decode.beam_size", beam_size)
gin.bind_parameter("Bitransformer.decode.temperature", temperature)
gin.bind_parameter("Bitransformer.decode.sampling_keep_top_p",
sampling_keep_top_p)
vocabulary = t5.data.SentencePieceVocabulary(sentencepiece_model_path)
utils.infer_model(self.estimator(vocabulary), vocabulary,
self._sequence_length, self.batch_size,
self._model_type, self._model_dir, checkpoint_steps,
input_file, output_file)
