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path_context_reader.py
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import tensorflow as tf
from typing import Dict, Tuple, NamedTuple, Union, Optional, Iterable
from config import Config
from vocabularies import Code2VecVocabs
import abc
from functools import reduce
from enum import Enum
class EstimatorAction(Enum):
Train = 'train'
Evaluate = 'evaluate'
Predict = 'predict'
@property
def is_train(self):
return self is EstimatorAction.Train
@property
def is_evaluate(self):
return self is EstimatorAction.Evaluate
@property
def is_predict(self):
return self is EstimatorAction.Predict
@property
def is_evaluate_or_predict(self):
return self.is_evaluate or self.is_predict
class ReaderInputTensors(NamedTuple):
"""
Used mostly for convenient-and-clear access to input parts (by their names).
"""
path_source_token_indices: tf.Tensor
path_indices: tf.Tensor
path_target_token_indices: tf.Tensor
context_valid_mask: tf.Tensor
target_index: Optional[tf.Tensor] = None
target_string: Optional[tf.Tensor] = None
path_source_token_strings: Optional[tf.Tensor] = None
path_strings: Optional[tf.Tensor] = None
path_target_token_strings: Optional[tf.Tensor] = None
class ModelInputTensorsFormer(abc.ABC):
"""
Should be inherited by the model implementation.
An instance of the inherited class is passed by the model to the reader in order to help the reader
to construct the input in the form that the model expects to receive it.
This class also enables conveniently & clearly access input parts by their field names.
eg: 'tensors.path_indices' instead if 'tensors[1]'.
This allows the input tensors to be passed as pure tuples along the computation graph, while the
python functions that construct the graph can easily (and clearly) access tensors.
"""
@abc.abstractmethod
def to_model_input_form(self, input_tensors: ReaderInputTensors):
...
@abc.abstractmethod
def from_model_input_form(self, input_row) -> ReaderInputTensors:
...
class PathContextReader:
def __init__(self,
vocabs: Code2VecVocabs,
config: Config,
model_input_tensors_former: ModelInputTensorsFormer,
estimator_action: EstimatorAction,
repeat_endlessly: bool = False):
self.vocabs = vocabs
self.config = config
self.model_input_tensors_former = model_input_tensors_former
self.estimator_action = estimator_action
self.repeat_endlessly = repeat_endlessly
self.CONTEXT_PADDING = ','.join([self.vocabs.token_vocab.special_words.PAD,
self.vocabs.path_vocab.special_words.PAD,
self.vocabs.token_vocab.special_words.PAD])
self.csv_record_defaults = [[self.vocabs.target_vocab.special_words.OOV]] + \
([[self.CONTEXT_PADDING]] * self.config.MAX_CONTEXTS)
# initialize the needed lookup tables (if not already initialized).
self.create_needed_vocabs_lookup_tables(self.vocabs)
self._dataset: Optional[tf.data.Dataset] = None
@classmethod
def create_needed_vocabs_lookup_tables(cls, vocabs: Code2VecVocabs):
vocabs.token_vocab.get_word_to_index_lookup_table()
vocabs.path_vocab.get_word_to_index_lookup_table()
vocabs.target_vocab.get_word_to_index_lookup_table()
@tf.function
def process_input_row(self, row_placeholder):
parts = tf.io.decode_csv(
row_placeholder, record_defaults=self.csv_record_defaults, field_delim=' ', use_quote_delim=False)
# Note: we DON'T apply the filter `_filter_input_rows()` here.
tensors = self._map_raw_dataset_row_to_input_tensors(*parts)
# make it batched (first batch axis is going to have dimension 1)
tensors_expanded = ReaderInputTensors(
**{name: None if tensor is None else tf.expand_dims(tensor, axis=0)
for name, tensor in tensors._asdict().items()})
return self.model_input_tensors_former.to_model_input_form(tensors_expanded)
def process_and_iterate_input_from_data_lines(self, input_data_lines: Iterable) -> Iterable:
for data_row in input_data_lines:
processed_row = self.process_input_row(data_row)
yield processed_row
def get_dataset(self, input_data_rows: Optional = None) -> tf.data.Dataset:
if self._dataset is None:
self._dataset = self._create_dataset_pipeline(input_data_rows)
return self._dataset
def _create_dataset_pipeline(self, input_data_rows: Optional = None) -> tf.data.Dataset:
if input_data_rows is None:
assert not self.estimator_action.is_predict
dataset = tf.data.experimental.CsvDataset(
self.config.data_path(is_evaluating=self.estimator_action.is_evaluate),
record_defaults=self.csv_record_defaults, field_delim=' ', use_quote_delim=False,
buffer_size=self.config.CSV_BUFFER_SIZE)
else:
dataset = tf.data.Dataset.from_tensor_slices(input_data_rows)
dataset = dataset.map(
lambda input_line: tf.io.decode_csv(
tf.reshape(tf.cast(input_line, tf.string), ()),
record_defaults=self.csv_record_defaults,
field_delim=' ', use_quote_delim=False))
if self.repeat_endlessly:
dataset = dataset.repeat()
if self.estimator_action.is_train:
if not self.repeat_endlessly and self.config.NUM_TRAIN_EPOCHS > 1:
dataset = dataset.repeat(self.config.NUM_TRAIN_EPOCHS)
dataset = dataset.shuffle(self.config.SHUFFLE_BUFFER_SIZE, reshuffle_each_iteration=True)
dataset = dataset.map(self._map_raw_dataset_row_to_expected_model_input_form,
num_parallel_calls=self.config.READER_NUM_PARALLEL_BATCHES)
batch_size = self.config.batch_size(is_evaluating=self.estimator_action.is_evaluate)
if self.estimator_action.is_predict:
dataset = dataset.batch(1)
else:
dataset = dataset.filter(self._filter_input_rows)
dataset = dataset.batch(batch_size)
dataset = dataset.prefetch(buffer_size=40) # original: tf.contrib.data.AUTOTUNE) -- got OOM err; 10 seems promising.
return dataset
def _filter_input_rows(self, *row_parts) -> tf.bool:
row_parts = self.model_input_tensors_former.from_model_input_form(row_parts)
#assert all(tensor.shape == (self.config.MAX_CONTEXTS,) for tensor in
# {row_parts.path_source_token_indices, row_parts.path_indices,
# row_parts.path_target_token_indices, row_parts.context_valid_mask})
# FIXME: Does "valid" here mean just "no padding" or "neither padding nor OOV"? I assumed just "no padding".
any_word_valid_mask_per_context_part = [
tf.not_equal(tf.reduce_max(row_parts.path_source_token_indices, axis=0),
self.vocabs.token_vocab.word_to_index[self.vocabs.token_vocab.special_words.PAD]),
tf.not_equal(tf.reduce_max(row_parts.path_target_token_indices, axis=0),
self.vocabs.token_vocab.word_to_index[self.vocabs.token_vocab.special_words.PAD]),
tf.not_equal(tf.reduce_max(row_parts.path_indices, axis=0),
self.vocabs.path_vocab.word_to_index[self.vocabs.path_vocab.special_words.PAD])]
any_contexts_is_valid = reduce(tf.logical_or, any_word_valid_mask_per_context_part) # scalar
if self.estimator_action.is_evaluate:
cond = any_contexts_is_valid # scalar
else: # training
word_is_valid = tf.greater(
row_parts.target_index, self.vocabs.target_vocab.word_to_index[self.vocabs.target_vocab.special_words.OOV]) # scalar
cond = tf.logical_and(word_is_valid, any_contexts_is_valid) # scalar
return cond # scalar
def _map_raw_dataset_row_to_expected_model_input_form(self, *row_parts) -> \
Tuple[Union[tf.Tensor, Tuple[tf.Tensor, ...], Dict[str, tf.Tensor]], ...]:
tensors = self._map_raw_dataset_row_to_input_tensors(*row_parts)
return self.model_input_tensors_former.to_model_input_form(tensors)
def _map_raw_dataset_row_to_input_tensors(self, *row_parts) -> ReaderInputTensors:
row_parts = list(row_parts)
target_str = row_parts[0]
target_index = self.vocabs.target_vocab.lookup_index(target_str)
contexts_str = tf.stack(row_parts[1:(self.config.MAX_CONTEXTS + 1)], axis=0)
split_contexts = tf.compat.v1.string_split(contexts_str, sep=',', skip_empty=False)
# dense_split_contexts = tf.sparse_tensor_to_dense(split_contexts, default_value=self.vocabs.token_vocab.special_words.PAD)
sparse_split_contexts = tf.sparse.SparseTensor(
indices=split_contexts.indices, values=split_contexts.values, dense_shape=[self.config.MAX_CONTEXTS, 3])
dense_split_contexts = tf.reshape(
tf.sparse.to_dense(sp_input=sparse_split_contexts, default_value=self.vocabs.token_vocab.special_words.PAD),
shape=[self.config.MAX_CONTEXTS, 3]) # (max_contexts, 3)
path_source_token_strings = tf.squeeze(
tf.slice(dense_split_contexts, begin=[0, 0], size=[self.config.MAX_CONTEXTS, 1]), axis=1) # (max_contexts,)
path_strings = tf.squeeze(
tf.slice(dense_split_contexts, begin=[0, 1], size=[self.config.MAX_CONTEXTS, 1]), axis=1) # (max_contexts,)
path_target_token_strings = tf.squeeze(
tf.slice(dense_split_contexts, begin=[0, 2], size=[self.config.MAX_CONTEXTS, 1]), axis=1) # (max_contexts,)
path_source_token_indices = self.vocabs.token_vocab.lookup_index(path_source_token_strings) # (max_contexts, )
path_indices = self.vocabs.path_vocab.lookup_index(path_strings) # (max_contexts, )
path_target_token_indices = self.vocabs.token_vocab.lookup_index(path_target_token_strings) # (max_contexts, )
# FIXME: Does "valid" here mean just "no padding" or "neither padding nor OOV"? I assumed just "no padding".
valid_word_mask_per_context_part = [
tf.not_equal(path_source_token_indices, self.vocabs.token_vocab.word_to_index[self.vocabs.token_vocab.special_words.PAD]),
tf.not_equal(path_target_token_indices, self.vocabs.token_vocab.word_to_index[self.vocabs.token_vocab.special_words.PAD]),
tf.not_equal(path_indices, self.vocabs.path_vocab.word_to_index[self.vocabs.path_vocab.special_words.PAD])] # [(max_contexts, )]
context_valid_mask = tf.cast(reduce(tf.logical_or, valid_word_mask_per_context_part), dtype=tf.float32) # (max_contexts, )
#assert all(tensor.shape == (self.config.MAX_CONTEXTS,) for tensor in {path_source_token_indices, path_indices, path_target_token_indices, context_valid_mask})
return ReaderInputTensors(
path_source_token_indices=path_source_token_indices,
path_indices=path_indices,
path_target_token_indices=path_target_token_indices,
context_valid_mask=context_valid_mask,
target_index=target_index,
target_string=target_str,
path_source_token_strings=path_source_token_strings,
path_strings=path_strings,
path_target_token_strings=path_target_token_strings
)