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dataset_utils.py
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dataset_utils.py
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# coding=utf-8
# Copyright 2024 The Google Research Authors.
#
# 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.
"""Utilities to build TF dataset objects."""
import tensorflow as tf
from alx import als
from alx import batching_utils
def build_datasets(cfg):
local_device_count = len(als.get_local_devices(cfg))
device_count = len(als.get_devices(cfg))
if cfg.is_pre_batched:
return _build_pre_batched_datasets(cfg, local_device_count)
else:
return _batch_and_build_datasets(cfg, local_device_count, device_count)
def _batch_and_build_datasets(cfg, local_device_count,
device_count):
"""Creates TF dataset objects for train, train_t and test files."""
files = tf.data.Dataset.list_files(tf.io.gfile.glob(cfg.train_files))
tf_examples = list(tf.data.TFRecordDataset(files))
examples = batching_utils.tf_examples_to_examples(tf_examples)
train_tf_list = batching_utils.batch_and_create_tf_examples(
examples,
batch_size=cfg.batch_size,
seq_len=cfg.seq_len,
num_rows_per_batch=cfg.num_rows_per_batch,
num_devices=device_count,
to_pad=True)
files = tf.data.Dataset.list_files(
tf.io.gfile.glob(cfg.train_transpose_files))
tf_examples = list(tf.data.TFRecordDataset(files))
examples = batching_utils.tf_examples_to_examples(tf_examples)
train_transpose_tf_list = batching_utils.batch_and_create_tf_examples(
examples,
batch_size=cfg.transpose_batch_size,
seq_len=cfg.seq_len,
num_rows_per_batch=cfg.transpose_num_rows_per_batch,
num_devices=device_count,
to_pad=True)
files = tf.data.Dataset.list_files(tf.io.gfile.glob(cfg.test_files))
tf_examples = list(tf.data.TFRecordDataset(files))
examples = batching_utils.tf_examples_to_examples(tf_examples, is_test=True)
test_tf_list = batching_utils.batch_and_create_tf_examples(
examples,
is_test=True,
batch_size=cfg.eval_batch_size,
seq_len=cfg.seq_len,
num_rows_per_batch=cfg.eval_num_rows_per_batch,
num_devices=device_count,
ground_truth_batch_size=cfg.ground_truth_batch_size,
to_pad=True)
ds = batching_utils.load_dataset_from_generator(
train_tf_list,
batch_size=cfg.batch_size,
seq_len=cfg.seq_len,
num_rows_per_batch=cfg.num_rows_per_batch,
num_devices=local_device_count)
tds = batching_utils.load_dataset_from_generator(
train_transpose_tf_list,
batch_size=cfg.transpose_batch_size,
seq_len=cfg.seq_len,
num_rows_per_batch=cfg.transpose_num_rows_per_batch,
num_devices=local_device_count)
test_ds = batching_utils.load_dataset_from_generator(
test_tf_list,
is_test=True,
batch_size=cfg.eval_batch_size,
seq_len=cfg.seq_len,
num_rows_per_batch=cfg.eval_num_rows_per_batch,
ground_truth_batch_size=cfg.ground_truth_batch_size,
num_devices=local_device_count)
return ds, tds, test_ds
def _build_pre_batched_datasets(cfg, local_device_count):
"""Creates TF datasets for pre-batched train, train_t and test files."""
ds = batching_utils.load_dataset_from_files(
cfg.train_files,
batch_size=cfg.batch_size,
seq_len=cfg.seq_len,
num_rows_per_batch=cfg.num_rows_per_batch,
num_devices=local_device_count)
tds = batching_utils.load_dataset_from_files(
cfg.train_transpose_files,
batch_size=cfg.transpose_batch_size,
seq_len=cfg.seq_len,
num_rows_per_batch=cfg.transpose_num_rows_per_batch,
num_devices=local_device_count)
test_ds = batching_utils.load_dataset_from_files(
cfg.test_files,
is_test=True,
batch_size=cfg.eval_batch_size,
seq_len=cfg.seq_len,
num_rows_per_batch=cfg.eval_num_rows_per_batch,
ground_truth_batch_size=cfg.ground_truth_batch_size,
num_devices=local_device_count)
return ds, tds, test_ds