0c23c60a1a
This CL adds experimental support for parallel batching to `batch()` and `padded_batch()`. This functionality can be enabled through tf.data.Options(): options = tf.data.Options() options.experimental_optimization.parallel_batch = True dataset = dataset.with_options(options) PiperOrigin-RevId: 244455315
73 lines
2.8 KiB
Python
73 lines
2.8 KiB
Python
# Copyright 2018 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.
|
|
# ==============================================================================
|
|
"""Benchmarks for `tf.data.Dataset.batch()`."""
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
import numpy as np
|
|
|
|
from tensorflow.python.data.benchmarks import benchmark_base
|
|
from tensorflow.python.data.ops import dataset_ops
|
|
from tensorflow.python.framework import sparse_tensor
|
|
|
|
|
|
class BatchBenchmark(benchmark_base.DatasetBenchmarkBase):
|
|
"""Benchmarks for `tf.data.Dataset.batch()`."""
|
|
|
|
def benchmark_batch_sparse(self):
|
|
non_zeros_per_row_values = [0, 1, 5, 10, 100]
|
|
batch_size_values = [1, 32, 64, 128, 1024]
|
|
|
|
for non_zeros_per_row in non_zeros_per_row_values:
|
|
|
|
tensor = sparse_tensor.SparseTensor(
|
|
indices=np.arange(non_zeros_per_row, dtype=np.int64)[:, np.newaxis],
|
|
values=np.arange(non_zeros_per_row, dtype=np.int64),
|
|
dense_shape=[1000])
|
|
|
|
for batch_size in batch_size_values:
|
|
dataset = dataset_ops.Dataset.from_tensors(tensor).repeat().batch(
|
|
batch_size)
|
|
self.run_and_report_benchmark(
|
|
dataset,
|
|
num_elements=100000 // batch_size,
|
|
iters=1,
|
|
name="sparse_num_elements_%d_batch_size_%d" % (non_zeros_per_row,
|
|
batch_size))
|
|
|
|
def benchmark_batch_dense(self):
|
|
for element_exp in [10, 12, 14, 16, 18, 20, 22]:
|
|
for batch_exp in [3, 6, 9]:
|
|
for parallel_copy in [True, False]:
|
|
element_size = 1 << element_exp
|
|
batch_size = 1 << batch_exp
|
|
dataset = dataset_ops.Dataset.from_tensors(
|
|
np.random.rand(element_size)).repeat().batch(batch_size)
|
|
options = dataset_ops.Options()
|
|
options.experimental_optimization.parallel_batch = parallel_copy
|
|
dataset = dataset.with_options(options)
|
|
tag = "_parallel" if parallel_copy else ""
|
|
self.run_and_report_benchmark(
|
|
dataset,
|
|
num_elements=(1 << (22 - batch_exp - element_exp // 2)),
|
|
iters=1,
|
|
name="batch_element_size_%d_batch_size_%d%s" %
|
|
(element_size, batch_size, tag))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
benchmark_base.test.main()
|