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Commit a1bd019e authored by Dong Lin's avatar Dong Lin Committed by Toby Boyd
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Export benchmark stats using tf.test.Benchmark.report_benchmark() (#6103) 

* Export benchmark stats using tf.test.Benchmark.report_benchmark()

* Fix python style using pyformat
parent ed6c805a
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Showing with 207 additions and 125 deletions
official/resnet/estimator_cifar_benchmark.py
View file @ a1bd019e
...@@ -18,6 +18,8 @@ from __future__ import absolute_import ...@@ -18,6 +18,8 @@ from __future__ import absolute_import
from __future__ import division from __future__ import division
from __future__ import print_function from __future__ import print_function
import json
import time
import os import os
from absl import flags from absl import flags
...@@ -29,13 +31,12 @@ from official.resnet import cifar10_main as cifar_main ...@@ -29,13 +31,12 @@ from official.resnet import cifar10_main as cifar_main
DATA_DIR = '/data/cifar10_data/cifar-10-batches-bin' DATA_DIR = '/data/cifar10_data/cifar-10-batches-bin'
class EstimatorCifar10BenchmarkTests(object): class EstimatorCifar10BenchmarkTests(tf.test.Benchmark):
"""Benchmarks and accuracy tests for Estimator ResNet56.""" """Benchmarks and accuracy tests for Estimator ResNet56."""
local_flags = None local_flags = None
def __init__(self, output_dir=None): def __init__(self, output_dir=None):
self.oss_report_object = None
self.output_dir = output_dir self.output_dir = output_dir
def resnet56_1_gpu(self): def resnet56_1_gpu(self):
...@@ -48,8 +49,7 @@ class EstimatorCifar10BenchmarkTests(object): ...@@ -48,8 +49,7 @@ class EstimatorCifar10BenchmarkTests(object):
flags.FLAGS.model_dir = self._get_model_dir('resnet56_1_gpu') flags.FLAGS.model_dir = self._get_model_dir('resnet56_1_gpu')
flags.FLAGS.resnet_size = 56 flags.FLAGS.resnet_size = 56
flags.FLAGS.dtype = 'fp32' flags.FLAGS.dtype = 'fp32'
stats = cifar_main.run_cifar(flags.FLAGS) self._run_and_report_benchmark()
self._fill_report_object(stats)
def resnet56_fp16_1_gpu(self): def resnet56_fp16_1_gpu(self):
"""Test layers FP16 model with Estimator and distribution strategies.""" """Test layers FP16 model with Estimator and distribution strategies."""
...@@ -61,8 +61,7 @@ class EstimatorCifar10BenchmarkTests(object): ...@@ -61,8 +61,7 @@ class EstimatorCifar10BenchmarkTests(object):
flags.FLAGS.model_dir = self._get_model_dir('resnet56_fp16_1_gpu') flags.FLAGS.model_dir = self._get_model_dir('resnet56_fp16_1_gpu')
flags.FLAGS.resnet_size = 56 flags.FLAGS.resnet_size = 56
flags.FLAGS.dtype = 'fp16' flags.FLAGS.dtype = 'fp16'
stats = cifar_main.run_cifar(flags.FLAGS) self._run_and_report_benchmark()
self._fill_report_object(stats)
def resnet56_2_gpu(self): def resnet56_2_gpu(self):
"""Test layers model with Estimator and dist_strat. 2 GPUs.""" """Test layers model with Estimator and dist_strat. 2 GPUs."""
...@@ -74,8 +73,7 @@ class EstimatorCifar10BenchmarkTests(object): ...@@ -74,8 +73,7 @@ class EstimatorCifar10BenchmarkTests(object):
flags.FLAGS.model_dir = self._get_model_dir('resnet56_2_gpu') flags.FLAGS.model_dir = self._get_model_dir('resnet56_2_gpu')
flags.FLAGS.resnet_size = 56 flags.FLAGS.resnet_size = 56
flags.FLAGS.dtype = 'fp32' flags.FLAGS.dtype = 'fp32'
stats = cifar_main.run_cifar(flags.FLAGS) self._run_and_report_benchmark()
self._fill_report_object(stats)
def resnet56_fp16_2_gpu(self): def resnet56_fp16_2_gpu(self):
"""Test layers FP16 model with Estimator and dist_strat. 2 GPUs.""" """Test layers FP16 model with Estimator and dist_strat. 2 GPUs."""
...@@ -87,16 +85,60 @@ class EstimatorCifar10BenchmarkTests(object): ...@@ -87,16 +85,60 @@ class EstimatorCifar10BenchmarkTests(object):
flags.FLAGS.model_dir = self._get_model_dir('resnet56_fp16_2_gpu') flags.FLAGS.model_dir = self._get_model_dir('resnet56_fp16_2_gpu')
flags.FLAGS.resnet_size = 56 flags.FLAGS.resnet_size = 56
flags.FLAGS.dtype = 'fp16' flags.FLAGS.dtype = 'fp16'
self._run_and_report_benchmark()
def unit_test(self):
"""A lightweigth test that can finish quickly"""
self._setup()
flags.FLAGS.num_gpus = 1
flags.FLAGS.data_dir = DATA_DIR
flags.FLAGS.batch_size = 128
flags.FLAGS.train_epochs = 1
flags.FLAGS.model_dir = self._get_model_dir('resnet56_1_gpu')
flags.FLAGS.resnet_size = 8
flags.FLAGS.dtype = 'fp32'
self._run_and_report_benchmark()
def _run_and_report_benchmark(self):
start_time_sec = time.time()
stats = cifar_main.run_cifar(flags.FLAGS) stats = cifar_main.run_cifar(flags.FLAGS)
self._fill_report_object(stats) wall_time_sec = time.time() - start_time_sec
def _fill_report_object(self, stats): self.report_benchmark(
# Also "available global_step" iters=stats['global_step'],
if self.oss_report_object: wall_time=wall_time_sec,
self.oss_report_object.top_1 = stats['accuracy'].item() extras={
self.oss_report_object.top_5 = stats['accuracy_top_5'].item() 'accuracy':
else: self._json_description(stats['accuracy'].item(), priority=0),
raise ValueError('oss_report_object has not been set.') 'accuracy_top_5':
self._json_description(stats['accuracy_top_5'].item()),
})
def _json_description(self,
value,
priority=None,
min_value=None,
max_value=None):
"""Get a json-formatted string describing the attributes for a metric"""
attributes = {}
attributes['value'] = value
if priority:
attributes['priority'] = priority
if min_value:
attributes['min_value'] = min_value
if max_value:
attributes['max_value'] = max_value
if min_value or max_value:
succeeded = True
if min_value and value < min_value:
succeeded = False
if max_value and value > max_value:
succeeded = False
attributes['succeeded'] = succeeded
return json.dumps(attributes)
def _get_model_dir(self, folder_name): def _get_model_dir(self, folder_name):
return os.path.join(self.output_dir, folder_name) return os.path.join(self.output_dir, folder_name)
......
official/resnet/keras/keras_benchmark.py
View file @ a1bd019e
...@@ -19,6 +19,8 @@ from __future__ import division ...@@ -19,6 +19,8 @@ from __future__ import division
from __future__ import print_function from __future__ import print_function
import os import os
import time
import json
from absl import flags from absl import flags
from absl.testing import flagsaver from absl.testing import flagsaver
...@@ -27,12 +29,11 @@ import tensorflow as tf # pylint: disable=g-bad-import-order ...@@ -27,12 +29,11 @@ import tensorflow as tf # pylint: disable=g-bad-import-order
FLAGS = flags.FLAGS FLAGS = flags.FLAGS
class KerasBenchmark(object): class KerasBenchmark(tf.test.Benchmark):
"""Base benchmark class with methods to simplify testing.""" """Base benchmark class with methods to simplify testing."""
local_flags = None local_flags = None
def __init__(self, output_dir=None, default_flags=None, flag_methods=None): def __init__(self, output_dir=None, default_flags=None, flag_methods=None):
self.oss_report_object = None
self.output_dir = output_dir self.output_dir = output_dir
self.default_flags = default_flags or {} self.default_flags = default_flags or {}
self.flag_methods = flag_methods or {} self.flag_methods = flag_methods or {}
...@@ -56,41 +57,76 @@ class KerasBenchmark(object): ...@@ -56,41 +57,76 @@ class KerasBenchmark(object):
else: else:
flagsaver.restore_flag_values(KerasBenchmark.local_flags) flagsaver.restore_flag_values(KerasBenchmark.local_flags)
def fill_report_object(self, stats, top_1_max=None, top_1_min=None, def _report_benchmark(self,
log_steps=None, total_batch_size=None, warmup=1): stats,
"""Fills report object to report results. wall_time_sec,
top_1_max=None,
top_1_min=None,
log_steps=None,
total_batch_size=None,
warmup=1):
"""Report benchmark results by writing to local protobuf file
Args: Args:
stats: dict returned from keras models with known entries. stats: dict returned from keras models with known entries.
wall_time_sec: the during of the benchmark execution in seconds
top_1_max: highest passing level for top_1 accuracy. top_1_max: highest passing level for top_1 accuracy.
top_1_min: lowest passing level for top_1 accuracy. top_1_min: lowest passing level for top_1 accuracy.
log_steps: How often the log was created for stats['step_timestamp_log']. log_steps: How often the log was created for stats['step_timestamp_log'].
total_batch_size: Global batch-size. total_batch_size: Global batch-size.
warmup: number of entries in stats['step_timestamp_log'] to ignore. warmup: number of entries in stats['step_timestamp_log'] to ignore.
""" """
if self.oss_report_object:
extras = {}
if 'accuracy_top_1' in stats: if 'accuracy_top_1' in stats:
self.oss_report_object.add_top_1(stats['accuracy_top_1'], extras['accuracy'] = self._json_description(
expected_min=top_1_min, stats['accuracy_top_1'],
expected_max=top_1_max) priority=0,
self.oss_report_object.add_other_quality( min_value=top_1_min,
stats['training_accuracy_top_1'], max_value=top_1_max)
'top_1_train_accuracy') extras['top_1_train_accuracy'] = self._json_description(
if (warmup and stats['training_accuracy_top_1'], priority=1)
'step_timestamp_log' in stats and
len(stats['step_timestamp_log']) > warmup): if (warmup and 'step_timestamp_log' in stats and
# first entry in the time_log is start of step 1. The rest of the len(stats['step_timestamp_log']) > warmup):
# entries are the end of each step recorded # first entry in the time_log is start of step 1. The rest of the
time_log = stats['step_timestamp_log'] # entries are the end of each step recorded
elapsed = time_log[-1].timestamp - time_log[warmup].timestamp time_log = stats['step_timestamp_log']
num_examples = (total_batch_size * log_steps * (len(time_log)-warmup-1)) elapsed = time_log[-1].timestamp - time_log[warmup].timestamp
examples_per_sec = num_examples / elapsed num_examples = (
self.oss_report_object.add_examples_per_second(examples_per_sec) total_batch_size * log_steps * (len(time_log) - warmup - 1))
examples_per_sec = num_examples / elapsed
if 'avg_exp_per_second' in stats: extras['exp_per_second'] = self._json_description(
self.oss_report_object.add_result(stats['avg_exp_per_second'], examples_per_sec, priority=2)
'avg_exp_per_second',
'exp_per_second') if 'avg_exp_per_second' in stats:
else: extras['avg_exp_per_second'] = self._json_description(
raise ValueError('oss_report_object has not been set.') stats['avg_exp_per_second'], priority=3)
self.report_benchmark(iters=-1, wall_time=wall_time_sec, extras=extras)
def _json_description(self,
value,
priority=None,
min_value=None,
max_value=None):
"""Get a json-formatted string describing the attributes for a metric"""
attributes = {}
attributes['value'] = value
if priority:
attributes['priority'] = priority
if min_value:
attributes['min_value'] = min_value
if max_value:
attributes['max_value'] = max_value
if min_value or max_value:
succeeded = True
if min_value and value < min_value:
succeeded = False
if max_value and value > max_value:
succeeded = False
attributes['succeeded'] = succeeded
return json.dumps(attributes)
official/resnet/keras/keras_cifar_benchmark.py
View file @ a1bd019e
...@@ -18,6 +18,7 @@ from __future__ import absolute_import ...@@ -18,6 +18,7 @@ from __future__ import absolute_import
from __future__ import division from __future__ import division
from __future__ import print_function from __future__ import print_function
import time
from absl import flags from absl import flags
from official.resnet import cifar10_main as cifar_main from official.resnet import cifar10_main as cifar_main
...@@ -36,11 +37,12 @@ class Resnet56KerasAccuracy(keras_benchmark.KerasBenchmark): ...@@ -36,11 +37,12 @@ class Resnet56KerasAccuracy(keras_benchmark.KerasBenchmark):
"""Accuracy tests for ResNet56 Keras CIFAR-10.""" """Accuracy tests for ResNet56 Keras CIFAR-10."""
def __init__(self, output_dir=None): def __init__(self, output_dir=None):
flag_methods = [keras_common.define_keras_flags, flag_methods = [
cifar_main.define_cifar_flags] keras_common.define_keras_flags, cifar_main.define_cifar_flags
]
super(Resnet56KerasAccuracy, self).__init__(output_dir=output_dir, super(Resnet56KerasAccuracy, self).__init__(
flag_methods=flag_methods) output_dir=output_dir, flag_methods=flag_methods)
def benchmark_graph_1_gpu(self): def benchmark_graph_1_gpu(self):
"""Test keras based model with Keras fit and distribution strategies.""" """Test keras based model with Keras fit and distribution strategies."""
...@@ -51,8 +53,7 @@ class Resnet56KerasAccuracy(keras_benchmark.KerasBenchmark): ...@@ -51,8 +53,7 @@ class Resnet56KerasAccuracy(keras_benchmark.KerasBenchmark):
FLAGS.train_epochs = 182 FLAGS.train_epochs = 182
FLAGS.model_dir = self._get_model_dir('keras_resnet56_1_gpu') FLAGS.model_dir = self._get_model_dir('keras_resnet56_1_gpu')
FLAGS.dtype = 'fp32' FLAGS.dtype = 'fp32'
stats = keras_cifar_main.run(FLAGS) self._run_and_report_benchmark()
self.fill_report_object(stats, FLAGS.batch_size)
def benchmark_1_gpu(self): def benchmark_1_gpu(self):
"""Test keras based model with eager and distribution strategies.""" """Test keras based model with eager and distribution strategies."""
...@@ -64,8 +65,7 @@ class Resnet56KerasAccuracy(keras_benchmark.KerasBenchmark): ...@@ -64,8 +65,7 @@ class Resnet56KerasAccuracy(keras_benchmark.KerasBenchmark):
FLAGS.model_dir = self._get_model_dir('keras_resnet56_eager_1_gpu') FLAGS.model_dir = self._get_model_dir('keras_resnet56_eager_1_gpu')
FLAGS.dtype = 'fp32' FLAGS.dtype = 'fp32'
FLAGS.enable_eager = True FLAGS.enable_eager = True
stats = keras_cifar_main.run(flags.FLAGS) self._run_and_report_benchmark()
self.fill_report_object(stats, FLAGS.batch_size)
def benchmark_2_gpu(self): def benchmark_2_gpu(self):
"""Test keras based model with eager and distribution strategies.""" """Test keras based model with eager and distribution strategies."""
...@@ -77,8 +77,7 @@ class Resnet56KerasAccuracy(keras_benchmark.KerasBenchmark): ...@@ -77,8 +77,7 @@ class Resnet56KerasAccuracy(keras_benchmark.KerasBenchmark):
FLAGS.model_dir = self._get_model_dir('keras_resnet56_eager_2_gpu') FLAGS.model_dir = self._get_model_dir('keras_resnet56_eager_2_gpu')
FLAGS.dtype = 'fp32' FLAGS.dtype = 'fp32'
FLAGS.enable_eager = True FLAGS.enable_eager = True
stats = keras_cifar_main.run(FLAGS) self._run_and_report_benchmark()
self.fill_report_object(stats, FLAGS.batch_size)
def benchmark_graph_2_gpu(self): def benchmark_graph_2_gpu(self):
"""Test keras based model with Keras fit and distribution strategies.""" """Test keras based model with Keras fit and distribution strategies."""
...@@ -89,8 +88,7 @@ class Resnet56KerasAccuracy(keras_benchmark.KerasBenchmark): ...@@ -89,8 +88,7 @@ class Resnet56KerasAccuracy(keras_benchmark.KerasBenchmark):
FLAGS.train_epochs = 182 FLAGS.train_epochs = 182
FLAGS.model_dir = self._get_model_dir('keras_resnet56_2_gpu') FLAGS.model_dir = self._get_model_dir('keras_resnet56_2_gpu')
FLAGS.dtype = 'fp32' FLAGS.dtype = 'fp32'
stats = keras_cifar_main.run(FLAGS) self._run_and_report_benchmark()
self.fill_report_object(stats, FLAGS.batch_size)
def benchmark_graph_1_gpu_no_dist_strat(self): def benchmark_graph_1_gpu_no_dist_strat(self):
"""Test keras based model with Keras fit but not distribution strategies.""" """Test keras based model with Keras fit but not distribution strategies."""
...@@ -100,18 +98,21 @@ class Resnet56KerasAccuracy(keras_benchmark.KerasBenchmark): ...@@ -100,18 +98,21 @@ class Resnet56KerasAccuracy(keras_benchmark.KerasBenchmark):
FLAGS.data_dir = DATA_DIR FLAGS.data_dir = DATA_DIR
FLAGS.batch_size = 128 FLAGS.batch_size = 128
FLAGS.train_epochs = 182 FLAGS.train_epochs = 182
FLAGS.model_dir = self._get_model_dir( FLAGS.model_dir = self._get_model_dir('keras_resnet56_no_dist_strat_1_gpu')
'keras_resnet56_no_dist_strat_1_gpu')
FLAGS.dtype = 'fp32' FLAGS.dtype = 'fp32'
self._run_and_report_benchmark()
def _run_and_report_benchmark(self):
start_time_sec = time.time()
stats = keras_cifar_main.run(FLAGS) stats = keras_cifar_main.run(FLAGS)
self.fill_report_object(stats, FLAGS.batch_size) wall_time_sec = time.time() - start_time_sec
def fill_report_object(self, stats, total_batch_size): super(Resnet56KerasAccuracy, self)._report_benchmark(
super(Resnet56KerasAccuracy, self).fill_report_object(
stats, stats,
wall_time_sec,
top_1_min=MIN_TOP_1_ACCURACY, top_1_min=MIN_TOP_1_ACCURACY,
top_1_max=MAX_TOP_1_ACCURACY, top_1_max=MAX_TOP_1_ACCURACY,
total_batch_size=total_batch_size, total_batch_size=FLAGS.batch_size,
log_steps=100) log_steps=100)
...@@ -119,17 +120,25 @@ class Resnet56KerasBenchmarkBase(keras_benchmark.KerasBenchmark): ...@@ -119,17 +120,25 @@ class Resnet56KerasBenchmarkBase(keras_benchmark.KerasBenchmark):
"""Short performance tests for ResNet56 via Keras and CIFAR-10.""" """Short performance tests for ResNet56 via Keras and CIFAR-10."""
def __init__(self, output_dir=None, default_flags=None): def __init__(self, output_dir=None, default_flags=None):
flag_methods = [keras_common.define_keras_flags, flag_methods = [
cifar_main.define_cifar_flags] keras_common.define_keras_flags, cifar_main.define_cifar_flags
]
super(Resnet56KerasBenchmarkBase, self).__init__( super(Resnet56KerasBenchmarkBase, self).__init__(
output_dir=output_dir, output_dir=output_dir,
flag_methods=flag_methods, flag_methods=flag_methods,
default_flags=default_flags) default_flags=default_flags)
def _run_benchmark(self): def _run_and_report_benchmark(self):
start_time_sec = time.time()
stats = keras_cifar_main.run(FLAGS) stats = keras_cifar_main.run(FLAGS)
self.fill_report_object(stats) wall_time_sec = time.time() - start_time_sec
super(Resnet56KerasBenchmarkBase, self)._report_benchmark(
stats,
wall_time_sec,
total_batch_size=FLAGS.batch_size,
log_steps=FLAGS.log_steps)
def benchmark_1_gpu_no_dist_strat(self): def benchmark_1_gpu_no_dist_strat(self):
self._setup() self._setup()
...@@ -137,8 +146,7 @@ class Resnet56KerasBenchmarkBase(keras_benchmark.KerasBenchmark): ...@@ -137,8 +146,7 @@ class Resnet56KerasBenchmarkBase(keras_benchmark.KerasBenchmark):
FLAGS.enable_eager = True FLAGS.enable_eager = True
FLAGS.turn_off_distribution_strategy = True FLAGS.turn_off_distribution_strategy = True
FLAGS.batch_size = 128 FLAGS.batch_size = 128
self._run_and_report_benchmark()
self._run_benchmark()
def benchmark_graph_1_gpu_no_dist_strat(self): def benchmark_graph_1_gpu_no_dist_strat(self):
self._setup() self._setup()
...@@ -146,8 +154,7 @@ class Resnet56KerasBenchmarkBase(keras_benchmark.KerasBenchmark): ...@@ -146,8 +154,7 @@ class Resnet56KerasBenchmarkBase(keras_benchmark.KerasBenchmark):
FLAGS.enable_eager = False FLAGS.enable_eager = False
FLAGS.turn_off_distribution_strategy = True FLAGS.turn_off_distribution_strategy = True
FLAGS.batch_size = 128 FLAGS.batch_size = 128
self._run_and_report_benchmark()
self._run_benchmark()
def benchmark_1_gpu(self): def benchmark_1_gpu(self):
self._setup() self._setup()
...@@ -155,8 +162,7 @@ class Resnet56KerasBenchmarkBase(keras_benchmark.KerasBenchmark): ...@@ -155,8 +162,7 @@ class Resnet56KerasBenchmarkBase(keras_benchmark.KerasBenchmark):
FLAGS.enable_eager = True FLAGS.enable_eager = True
FLAGS.turn_off_distribution_strategy = False FLAGS.turn_off_distribution_strategy = False
FLAGS.batch_size = 128 FLAGS.batch_size = 128
self._run_and_report_benchmark()
self._run_benchmark()
def benchmark_graph_1_gpu(self): def benchmark_graph_1_gpu(self):
self._setup() self._setup()
...@@ -164,8 +170,7 @@ class Resnet56KerasBenchmarkBase(keras_benchmark.KerasBenchmark): ...@@ -164,8 +170,7 @@ class Resnet56KerasBenchmarkBase(keras_benchmark.KerasBenchmark):
FLAGS.enable_eager = False FLAGS.enable_eager = False
FLAGS.turn_off_distribution_strategy = False FLAGS.turn_off_distribution_strategy = False
FLAGS.batch_size = 128 FLAGS.batch_size = 128
self._run_and_report_benchmark()
self._run_benchmark()
def benchmark_2_gpu(self): def benchmark_2_gpu(self):
self._setup() self._setup()
...@@ -173,8 +178,7 @@ class Resnet56KerasBenchmarkBase(keras_benchmark.KerasBenchmark): ...@@ -173,8 +178,7 @@ class Resnet56KerasBenchmarkBase(keras_benchmark.KerasBenchmark):
FLAGS.enable_eager = True FLAGS.enable_eager = True
FLAGS.turn_off_distribution_strategy = False FLAGS.turn_off_distribution_strategy = False
FLAGS.batch_size = 128 * 2 # 2 GPUs FLAGS.batch_size = 128 * 2 # 2 GPUs
self._run_and_report_benchmark()
self._run_benchmark()
def benchmark_graph_2_gpu(self): def benchmark_graph_2_gpu(self):
self._setup() self._setup()
...@@ -182,14 +186,7 @@ class Resnet56KerasBenchmarkBase(keras_benchmark.KerasBenchmark): ...@@ -182,14 +186,7 @@ class Resnet56KerasBenchmarkBase(keras_benchmark.KerasBenchmark):
FLAGS.enable_eager = False FLAGS.enable_eager = False
FLAGS.turn_off_distribution_strategy = False FLAGS.turn_off_distribution_strategy = False
FLAGS.batch_size = 128 * 2 # 2 GPUs FLAGS.batch_size = 128 * 2 # 2 GPUs
self._run_and_report_benchmark()
self._run_benchmark()
def fill_report_object(self, stats):
super(Resnet56KerasBenchmarkBase, self).fill_report_object(
stats,
total_batch_size=FLAGS.batch_size,
log_steps=FLAGS.log_steps)
class Resnet56KerasBenchmarkSynth(Resnet56KerasBenchmarkBase): class Resnet56KerasBenchmarkSynth(Resnet56KerasBenchmarkBase):
...@@ -202,8 +199,8 @@ class Resnet56KerasBenchmarkSynth(Resnet56KerasBenchmarkBase): ...@@ -202,8 +199,8 @@ class Resnet56KerasBenchmarkSynth(Resnet56KerasBenchmarkBase):
def_flags['train_steps'] = 110 def_flags['train_steps'] = 110
def_flags['log_steps'] = 10 def_flags['log_steps'] = 10
super(Resnet56KerasBenchmarkSynth, self).__init__(output_dir=output_dir, super(Resnet56KerasBenchmarkSynth, self).__init__(
default_flags=def_flags) output_dir=output_dir, default_flags=def_flags)
class Resnet56KerasBenchmarkReal(Resnet56KerasBenchmarkBase): class Resnet56KerasBenchmarkReal(Resnet56KerasBenchmarkBase):
...@@ -216,5 +213,5 @@ class Resnet56KerasBenchmarkReal(Resnet56KerasBenchmarkBase): ...@@ -216,5 +213,5 @@ class Resnet56KerasBenchmarkReal(Resnet56KerasBenchmarkBase):
def_flags['train_steps'] = 110 def_flags['train_steps'] = 110
def_flags['log_steps'] = 10 def_flags['log_steps'] = 10
super(Resnet56KerasBenchmarkReal, self).__init__(output_dir=output_dir, super(Resnet56KerasBenchmarkReal, self).__init__(
default_flags=def_flags) output_dir=output_dir, default_flags=def_flags)
official/resnet/keras/keras_imagenet_benchmark.py
View file @ a1bd019e
...@@ -16,6 +16,7 @@ ...@@ -16,6 +16,7 @@
from __future__ import print_function from __future__ import print_function
import os import os
import time
from absl import flags from absl import flags
...@@ -35,43 +36,47 @@ class Resnet50KerasAccuracy(keras_benchmark.KerasBenchmark): ...@@ -35,43 +36,47 @@ class Resnet50KerasAccuracy(keras_benchmark.KerasBenchmark):
"""Benchmark accuracy tests for ResNet50 in Keras.""" """Benchmark accuracy tests for ResNet50 in Keras."""
def __init__(self, output_dir=None): def __init__(self, output_dir=None):
flag_methods = [keras_common.define_keras_flags, flag_methods = [
imagenet_main.define_imagenet_flags] keras_common.define_keras_flags, imagenet_main.define_imagenet_flags
]
super(Resnet50KerasAccuracy, self).__init__(output_dir=output_dir, super(Resnet50KerasAccuracy, self).__init__(
flag_methods=flag_methods) output_dir=output_dir, flag_methods=flag_methods)
def benchmark_graph_8_gpu(self): def benchmark_graph_8_gpu(self):
"""Test Keras model with Keras fit/dist_strat and 8 GPUs.""" """Test Keras model with Keras fit/dist_strat and 8 GPUs."""
self._setup() self._setup()
FLAGS.num_gpus = 8 FLAGS.num_gpus = 8
FLAGS.data_dir = DATA_DIR FLAGS.data_dir = DATA_DIR
FLAGS.batch_size = 128*8 FLAGS.batch_size = 128 * 8
FLAGS.train_epochs = 90 FLAGS.train_epochs = 90
FLAGS.model_dir = self._get_model_dir('keras_resnet50_8_gpu') FLAGS.model_dir = self._get_model_dir('keras_resnet50_8_gpu')
FLAGS.dtype = 'fp32' FLAGS.dtype = 'fp32'
stats = keras_imagenet_main.run(FLAGS) self._run_and_report_benchmark()
self._fill_report_object(stats, FLAGS.batch_size)
def benchmark_8_gpu(self): def benchmark_8_gpu(self):
"""Test Keras model with eager, dist_strat and 8 GPUs.""" """Test Keras model with eager, dist_strat and 8 GPUs."""
self._setup() self._setup()
FLAGS.num_gpus = 8 FLAGS.num_gpus = 8
FLAGS.data_dir = DATA_DIR FLAGS.data_dir = DATA_DIR
FLAGS.batch_size = 128*8 FLAGS.batch_size = 128 * 8
FLAGS.train_epochs = 90 FLAGS.train_epochs = 90
FLAGS.model_dir = self._get_model_dir('keras_resnet50_eager_8_gpu') FLAGS.model_dir = self._get_model_dir('keras_resnet50_eager_8_gpu')
FLAGS.dtype = 'fp32' FLAGS.dtype = 'fp32'
FLAGS.enable_eager = True FLAGS.enable_eager = True
stats = keras_imagenet_main.run(FLAGS) self._run_and_report_benchmark()
self._fill_report_object(stats, FLAGS.batch_size)
def fill_report_object(self, stats, total_batch_size): def _run_and_report_benchmark(self):
super(Resnet50KerasAccuracy, self).fill_report_object( start_time_sec = time.time()
stats = keras_imagenet_main.run(flags.FLAGS)
wall_time_sec = time.time() - start_time_sec
super(Resnet50KerasAccuracy, self)._report_benchmark(
stats, stats,
wall_time_sec,
top_1_min=MIN_TOP_1_ACCURACY, top_1_min=MIN_TOP_1_ACCURACY,
top_1_max=MAX_TOP_1_ACCURACY, top_1_max=MAX_TOP_1_ACCURACY,
total_batch_size=total_batch_size, total_batch_size=FLAGS.batch_size,
log_steps=100) log_steps=100)
def _get_model_dir(self, folder_name): def _get_model_dir(self, folder_name):
...@@ -82,17 +87,25 @@ class Resnet50KerasBenchmarkBase(keras_benchmark.KerasBenchmark): ...@@ -82,17 +87,25 @@ class Resnet50KerasBenchmarkBase(keras_benchmark.KerasBenchmark):
"""Resnet50 benchmarks.""" """Resnet50 benchmarks."""
def __init__(self, output_dir=None, default_flags=None): def __init__(self, output_dir=None, default_flags=None):
flag_methods = [keras_common.define_keras_flags, flag_methods = [
imagenet_main.define_imagenet_flags] keras_common.define_keras_flags, imagenet_main.define_imagenet_flags
]
super(Resnet50KerasBenchmarkBase, self).__init__( super(Resnet50KerasBenchmarkBase, self).__init__(
output_dir=output_dir, output_dir=output_dir,
flag_methods=flag_methods, flag_methods=flag_methods,
default_flags=default_flags) default_flags=default_flags)
def _run_benchmark(self): def _run_and_report_benchmark(self):
start_time_sec = time.time()
stats = keras_imagenet_main.run(FLAGS) stats = keras_imagenet_main.run(FLAGS)
self.fill_report_object(stats) wall_time_sec = time.time() - start_time_sec
super(Resnet50KerasBenchmarkBase, self)._report_benchmark(
stats,
wall_time_sec,
total_batch_size=FLAGS.batch_size,
log_steps=FLAGS.log_steps)
def benchmark_1_gpu_no_dist_strat(self): def benchmark_1_gpu_no_dist_strat(self):
self._setup() self._setup()
...@@ -101,8 +114,7 @@ class Resnet50KerasBenchmarkBase(keras_benchmark.KerasBenchmark): ...@@ -101,8 +114,7 @@ class Resnet50KerasBenchmarkBase(keras_benchmark.KerasBenchmark):
FLAGS.enable_eager = True FLAGS.enable_eager = True
FLAGS.turn_off_distribution_strategy = True FLAGS.turn_off_distribution_strategy = True
FLAGS.batch_size = 128 FLAGS.batch_size = 128
self._run_and_report_benchmark()
self._run_benchmark()
def benchmark_graph_1_gpu_no_dist_strat(self): def benchmark_graph_1_gpu_no_dist_strat(self):
self._setup() self._setup()
...@@ -111,8 +123,7 @@ class Resnet50KerasBenchmarkBase(keras_benchmark.KerasBenchmark): ...@@ -111,8 +123,7 @@ class Resnet50KerasBenchmarkBase(keras_benchmark.KerasBenchmark):
FLAGS.enable_eager = False FLAGS.enable_eager = False
FLAGS.turn_off_distribution_strategy = True FLAGS.turn_off_distribution_strategy = True
FLAGS.batch_size = 128 FLAGS.batch_size = 128
self._run_and_report_benchmark()
self._run_benchmark()
def benchmark_1_gpu(self): def benchmark_1_gpu(self):
self._setup() self._setup()
...@@ -121,8 +132,7 @@ class Resnet50KerasBenchmarkBase(keras_benchmark.KerasBenchmark): ...@@ -121,8 +132,7 @@ class Resnet50KerasBenchmarkBase(keras_benchmark.KerasBenchmark):
FLAGS.enable_eager = True FLAGS.enable_eager = True
FLAGS.turn_off_distribution_strategy = False FLAGS.turn_off_distribution_strategy = False
FLAGS.batch_size = 128 FLAGS.batch_size = 128
self._run_and_report_benchmark()
self._run_benchmark()
def benchmark_graph_1_gpu(self): def benchmark_graph_1_gpu(self):
self._setup() self._setup()
...@@ -131,8 +141,7 @@ class Resnet50KerasBenchmarkBase(keras_benchmark.KerasBenchmark): ...@@ -131,8 +141,7 @@ class Resnet50KerasBenchmarkBase(keras_benchmark.KerasBenchmark):
FLAGS.enable_eager = False FLAGS.enable_eager = False
FLAGS.turn_off_distribution_strategy = False FLAGS.turn_off_distribution_strategy = False
FLAGS.batch_size = 128 FLAGS.batch_size = 128
self._run_and_report_benchmark()
self._run_benchmark()
def benchmark_8_gpu(self): def benchmark_8_gpu(self):
self._setup() self._setup()
...@@ -141,8 +150,7 @@ class Resnet50KerasBenchmarkBase(keras_benchmark.KerasBenchmark): ...@@ -141,8 +150,7 @@ class Resnet50KerasBenchmarkBase(keras_benchmark.KerasBenchmark):
FLAGS.enable_eager = True FLAGS.enable_eager = True
FLAGS.turn_off_distribution_strategy = False FLAGS.turn_off_distribution_strategy = False
FLAGS.batch_size = 128 * 8 # 8 GPUs FLAGS.batch_size = 128 * 8 # 8 GPUs
self._run_and_report_benchmark()
self._run_benchmark()
def benchmark_graph_8_gpu(self): def benchmark_graph_8_gpu(self):
self._setup() self._setup()
...@@ -151,8 +159,7 @@ class Resnet50KerasBenchmarkBase(keras_benchmark.KerasBenchmark): ...@@ -151,8 +159,7 @@ class Resnet50KerasBenchmarkBase(keras_benchmark.KerasBenchmark):
FLAGS.enable_eager = False FLAGS.enable_eager = False
FLAGS.turn_off_distribution_strategy = False FLAGS.turn_off_distribution_strategy = False
FLAGS.batch_size = 128 * 8 # 8 GPUs FLAGS.batch_size = 128 * 8 # 8 GPUs
self._run_and_report_benchmark()
self._run_benchmark()
def fill_report_object(self, stats): def fill_report_object(self, stats):
super(Resnet50KerasBenchmarkBase, self).fill_report_object( super(Resnet50KerasBenchmarkBase, self).fill_report_object(
...@@ -171,8 +178,8 @@ class Resnet50KerasBenchmarkSynth(Resnet50KerasBenchmarkBase): ...@@ -171,8 +178,8 @@ class Resnet50KerasBenchmarkSynth(Resnet50KerasBenchmarkBase):
def_flags['train_steps'] = 110 def_flags['train_steps'] = 110
def_flags['log_steps'] = 10 def_flags['log_steps'] = 10
super(Resnet50KerasBenchmarkSynth, self).__init__(output_dir=output_dir, super(Resnet50KerasBenchmarkSynth, self).__init__(
default_flags=def_flags) output_dir=output_dir, default_flags=def_flags)
class Resnet50KerasBenchmarkReal(Resnet50KerasBenchmarkBase): class Resnet50KerasBenchmarkReal(Resnet50KerasBenchmarkBase):
...@@ -185,5 +192,5 @@ class Resnet50KerasBenchmarkReal(Resnet50KerasBenchmarkBase): ...@@ -185,5 +192,5 @@ class Resnet50KerasBenchmarkReal(Resnet50KerasBenchmarkBase):
def_flags['train_steps'] = 110 def_flags['train_steps'] = 110
def_flags['log_steps'] = 10 def_flags['log_steps'] = 10
super(Resnet50KerasBenchmarkReal, self).__init__(output_dir=output_dir, super(Resnet50KerasBenchmarkReal, self).__init__(
default_flags=def_flags) output_dir=output_dir, default_flags=def_flags)
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