251 lines
9.4 KiB
Python
251 lines
9.4 KiB
Python
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# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Test utilities for tf.data benchmarking functionality."""
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import time
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import numpy as np
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from tensorflow.python.client import session
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from tensorflow.python.data.ops import dataset_ops
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from tensorflow.python.data.ops import options as options_lib
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from tensorflow.python.data.util import nest
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from tensorflow.python.eager import context
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from tensorflow.python.platform import test
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class DatasetBenchmarkBase(test.Benchmark):
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"""Base class for dataset benchmarks."""
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def _run_eager_benchmark(self, iterable, iters, warmup):
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"""Benchmark the iterable in eager mode.
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Runs the iterable `iters` times. In each iteration, the benchmark measures
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the time it takes to go execute the iterable.
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Args:
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iterable: The tf op or tf.data Dataset to benchmark.
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iters: Number of times to repeat the timing.
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warmup: If true, warms up the session caches by running an untimed run.
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Returns:
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A float, representing the median time (with respect to `iters`)
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it takes for the iterable to be executed `iters` num of times.
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Raises:
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RuntimeError: When executed in graph mode.
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"""
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deltas = []
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if not context.executing_eagerly():
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raise RuntimeError(
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"Eager mode benchmarking is not supported in graph mode.")
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for _ in range(iters):
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if warmup:
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iterator = iter(iterable)
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next(iterator)
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iterator = iter(iterable)
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start = time.time()
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next(iterator)
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end = time.time()
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deltas.append(end - start)
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return np.median(deltas)
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def _run_graph_benchmark(self,
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iterable,
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iters,
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warmup,
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session_config,
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initializer=None):
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"""Benchmarks the iterable in graph mode.
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Runs the iterable `iters` times. In each iteration, the benchmark measures
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the time it takes to go execute the iterable.
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Args:
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iterable: The tf op or tf.data Dataset to benchmark.
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iters: Number of times to repeat the timing.
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warmup: If true, warms up the session caches by running an untimed run.
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session_config: A ConfigProto protocol buffer with configuration options
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for the session. Applicable only for benchmarking in graph mode.
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initializer: The initializer op required to initialize the iterable.
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Returns:
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A float, representing the median time (with respect to `iters`)
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it takes for the iterable to be executed `iters` num of times.
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Raises:
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RuntimeError: When executed in eager mode.
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"""
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deltas = []
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if context.executing_eagerly():
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raise RuntimeError(
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"Graph mode benchmarking is not supported in eager mode.")
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for _ in range(iters):
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with session.Session(config=session_config) as sess:
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if warmup:
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# Run once to warm up the session caches.
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if initializer:
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sess.run(initializer)
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sess.run(iterable)
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if initializer:
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sess.run(initializer)
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start = time.time()
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sess.run(iterable)
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end = time.time()
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deltas.append(end - start)
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return np.median(deltas)
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def run_op_benchmark(self, op, iters=1, warmup=True, session_config=None):
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"""Benchmarks the op.
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Runs the op `iters` times. In each iteration, the benchmark measures
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the time it takes to go execute the op.
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Args:
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op: The tf op to benchmark.
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iters: Number of times to repeat the timing.
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warmup: If true, warms up the session caches by running an untimed run.
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session_config: A ConfigProto protocol buffer with configuration options
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for the session. Applicable only for benchmarking in graph mode.
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Returns:
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A float, representing the per-execution wall time of the op in seconds.
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This is the median time (with respect to `iters`) it takes for the op
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to be executed `iters` num of times.
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"""
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if context.executing_eagerly():
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return self._run_eager_benchmark(iterable=op, iters=iters, warmup=warmup)
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return self._run_graph_benchmark(
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iterable=op, iters=iters, warmup=warmup, session_config=session_config)
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def run_benchmark(self,
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dataset,
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num_elements,
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iters=1,
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warmup=True,
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apply_default_optimizations=False,
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session_config=None):
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"""Benchmarks the dataset.
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Runs the dataset `iters` times. In each iteration, the benchmark measures
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the time it takes to go through `num_elements` elements of the dataset.
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Args:
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dataset: Dataset to benchmark.
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num_elements: Number of dataset elements to iterate through each benchmark
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iteration.
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iters: Number of times to repeat the timing.
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warmup: If true, warms up the session caches by running an untimed run.
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apply_default_optimizations: Determines whether default optimizations
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should be applied.
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session_config: A ConfigProto protocol buffer with configuration options
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for the session. Applicable only for benchmarking in graph mode.
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Returns:
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A float, representing the per-element wall time of the dataset in seconds.
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This is the median time (with respect to `iters`) it takes for the dataset
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to go through `num_elements` elements, divided by `num_elements.`
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"""
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# The options that have been applied to the dataset are preserved so that
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# they are not overwritten while benchmarking.
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options = options_lib.Options()
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options.experimental_optimization.apply_default_optimizations = (
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apply_default_optimizations)
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dataset = dataset.with_options(options)
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# NOTE: We use `dataset.skip()` to perform the iterations in C++, avoiding
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# the overhead of having to execute a TensorFlow op for each step of the
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# input pipeline. Note that this relies on the underlying implementation of
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# `skip` to execute upstream computation. If it is optimized in the future,
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# we will have to change this code.
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dataset = dataset.skip(num_elements - 1)
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if context.executing_eagerly():
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median_duration = self._run_eager_benchmark(
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iterable=dataset, iters=iters, warmup=warmup)
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return median_duration / float(num_elements)
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iterator = dataset_ops.make_initializable_iterator(dataset)
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next_element = iterator.get_next()
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op = nest.flatten(next_element)[0].op
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median_duration = self._run_graph_benchmark(
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iterable=op,
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iters=iters,
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warmup=warmup,
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session_config=session_config,
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initializer=iterator.initializer)
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return median_duration / float(num_elements)
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def run_and_report_benchmark(self,
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dataset,
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num_elements,
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name,
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iters=5,
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extras=None,
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warmup=True,
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apply_default_optimizations=False,
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session_config=None):
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"""Benchmarks the dataset and reports the stats.
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Runs the dataset `iters` times. In each iteration, the benchmark measures
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the time it takes to go through `num_elements` elements of the dataset.
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This is followed by logging/printing the benchmark stats.
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Args:
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dataset: Dataset to benchmark.
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num_elements: Number of dataset elements to iterate through each benchmark
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iteration.
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name: Name of the benchmark.
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iters: Number of times to repeat the timing.
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extras: A dict which maps string keys to additional benchmark info.
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warmup: If true, warms up the session caches by running an untimed run.
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apply_default_optimizations: Determines whether default optimizations
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should be applied.
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session_config: A ConfigProto protocol buffer with configuration options
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for the session. Applicable only for benchmarking in graph mode.
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Returns:
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A float, representing the per-element wall time of the dataset in seconds.
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This is the median time (with respect to `iters`) it takes for the dataset
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to go through `num_elements` elements, divided by `num_elements.`
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"""
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wall_time = self.run_benchmark(
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dataset=dataset,
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num_elements=num_elements,
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iters=iters,
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warmup=warmup,
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apply_default_optimizations=apply_default_optimizations,
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session_config=session_config)
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if extras is None:
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extras = {}
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if context.executing_eagerly():
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name = "{}.eager".format(name)
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extras["implementation"] = "eager"
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else:
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name = "{}.graph".format(name)
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extras["implementation"] = "graph"
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extras["num_elements"] = num_elements
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self.report_benchmark(
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wall_time=wall_time, iters=iters, name=name, extras=extras)
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return wall_time
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