Skip to content

GitLab

"src/lib/vscode:/vscode.git/clone" did not exist on "fa6e21f5e0f9843ea8fbf9022dbf9c8060a7f0bb"
Commit aa548b8a authored by A. Unique TensorFlower's avatar A. Unique TensorFlower
Browse files

Merge pull request #7437 from tfboyd:resnet_benchmark_cull 

PiperOrigin-RevId: 263255352
parents 829f365a 0dfc5730
Show whitespace changes
Inline Side-by-side
Showing with 0 additions and 218 deletions
official/resnet/keras/keras_imagenet_benchmark.py
View file @ aa548b8a
......@@ -112,32 +112,6 @@ class Resnet50KerasAccuracy(keras_benchmark.KerasBenchmark):
FLAGS.use_tensor_lr = True
self._run_and_report_benchmark()
def benchmark_8_gpu_mlperf_like_tweaked(self):
"""Test similar to the rules for MLPerf 0.5.
Listed below are reasons this comparison is not to the MLSpec, but this is
still a decent directional measurement:
- Eval is every 4 epochs and again at the end. ~2 extra times.
- Learning rate is not tuned to hit 75%, but we know the model is correct.
- We measure total time and MLPerf 0.5 excluded some startup time.
- Eval is not on the total set, need to set eval batch_size where
8*batch_size/50K is even. 250 is a good number.
- Not sure if we are doing any extra or too few steps due to epoch bleed.
"""
self._setup()
FLAGS.num_gpus = 8
FLAGS.data_dir = self.data_dir
FLAGS.batch_size = 256 * 8
FLAGS.train_epochs = 61
FLAGS.epochs_between_evals = 4
FLAGS.model_dir = self._get_model_dir('benchmark_8_gpu_mlperf_like_tweaked')
FLAGS.dtype = 'fp16'
FLAGS.enable_eager = True
FLAGS.enable_xla = True
FLAGS.use_tensor_lr = True
FLAGS.tf_gpu_thread_mode = 'gpu_private'
self._run_and_report_benchmark(top_1_min=0.736)
def benchmark_8_gpu_mlperf_like(self):
"""Test similar to the rules for MLPerf 0.5.
......@@ -277,48 +251,6 @@ class Resnet50KerasBenchmarkBase(keras_benchmark.KerasBenchmark):
FLAGS.batch_size = 64
self._run_and_report_benchmark()
def benchmark_1_gpu_no_dist_strat_force_v1_path_run_eagerly(self):
"""Forced v1 execution in tf.compile path and force eager."""
self._setup()
FLAGS.num_gpus = 1
FLAGS.enable_eager = True
FLAGS.run_eagerly = True
FLAGS.distribution_strategy = 'off'
FLAGS.model_dir = self._get_model_dir(
'benchmark_1_gpu_no_dist_strat_force_v1_path_run_eagerly')
FLAGS.batch_size = 64
FLAGS.force_v2_in_keras_compile = False
self._run_and_report_benchmark()
def benchmark_1_gpu_no_dist_strat_force_v1_path_run_eagerly_tweaked(self):
"""Forced v1 execution in tf.compile path and force eager."""
self._setup()
FLAGS.num_gpus = 1
FLAGS.enable_eager = True
FLAGS.run_eagerly = True
FLAGS.explicit_gpu_placement = True
FLAGS.distribution_strategy = 'off'
FLAGS.model_dir = self._get_model_dir(
'benchmark_1_gpu_no_dist_strat_force_v1_path_run_eagerly_tweaked')
FLAGS.batch_size = 64
FLAGS.force_v2_in_keras_compile = False
self._run_and_report_benchmark()
def benchmark_1_gpu_no_dist_strat_force_v1_path(self):
"""No dist strat but forced v1 execution tf.compile path."""
self._setup()
FLAGS.num_gpus = 1
FLAGS.enable_eager = True
FLAGS.distribution_strategy = 'off'
FLAGS.model_dir = self._get_model_dir(
'benchmark_1_gpu_no_dist_strat_force_v1_path')
FLAGS.batch_size = 128
FLAGS.force_v2_in_keras_compile = False
self._run_and_report_benchmark()
def benchmark_1_gpu_no_dist_strat_run_eagerly_fp16(self):
"""Test with 1 GPU, no distribution strategy, fp16, run eagerly."""
self._setup()
......@@ -437,20 +369,6 @@ class Resnet50KerasBenchmarkBase(keras_benchmark.KerasBenchmark):
FLAGS.tf_gpu_thread_mode = 'gpu_private'
self._run_and_report_benchmark()
def benchmark_xla_1_gpu_fp16_slack(self):
"""Test Keras model tf.data's experimental_slack functionality."""
self._setup()
FLAGS.num_gpus = 1
FLAGS.enable_eager = True
FLAGS.enable_xla = True
FLAGS.distribution_strategy = 'default'
FLAGS.model_dir = self._get_model_dir('benchmark_xla_1_gpu_fp16_slack')
FLAGS.dtype = 'fp16'
FLAGS.batch_size = 256
FLAGS.tf_data_experimental_slack = True
self._run_and_report_benchmark()
def benchmark_xla_1_gpu_fp16_dynamic(self):
"""Test Keras model with XLA, 1 GPU, fp16, and dynamic loss scaling."""
self._setup()
......@@ -529,21 +447,6 @@ class Resnet50KerasBenchmarkBase(keras_benchmark.KerasBenchmark):
FLAGS.tf_gpu_thread_mode = 'gpu_private'
self._run_and_report_benchmark()
def benchmark_graph_xla_1_gpu_fp16_slack(self):
"""Test model in legacy graph with tf.data's experimental_slack."""
self._setup()
FLAGS.num_gpus = 1
FLAGS.enable_eager = False
FLAGS.enable_xla = True
FLAGS.distribution_strategy = 'default'
FLAGS.model_dir = self._get_model_dir(
'benchmark_graph_xla_1_gpu_fp16_slack')
FLAGS.dtype = 'fp16'
FLAGS.batch_size = 256
FLAGS.tf_data_experimental_slack = True
self._run_and_report_benchmark()
def benchmark_8_gpu(self):
"""Test Keras model with 8 GPUs."""
self._setup()
......@@ -568,18 +471,6 @@ class Resnet50KerasBenchmarkBase(keras_benchmark.KerasBenchmark):
FLAGS.datasets_num_private_threads = 14
self._run_and_report_benchmark()
def benchmark_8_gpu_slack(self):
"""Test Keras model with tf.data's experimental_slack and 8 GPUs."""
self._setup()
FLAGS.num_gpus = 8
FLAGS.enable_eager = True
FLAGS.distribution_strategy = 'default'
FLAGS.model_dir = self._get_model_dir('benchmark_8_gpu_slack')
FLAGS.batch_size = 128 * 8 # 8 GPUs
FLAGS.tf_data_experimental_slack = True
self._run_and_report_benchmark()
def benchmark_xla_8_gpu(self):
"""Test Keras model with XLA and 8 GPUs."""
self._setup()
......@@ -649,24 +540,6 @@ class Resnet50KerasBenchmarkBase(keras_benchmark.KerasBenchmark):
FLAGS.tf_gpu_thread_mode = 'gpu_private'
self._run_and_report_benchmark()
def benchmark_xla_8_gpu_fp16_optional_next(self):
"""Test Keras model with XLA, 8 GPUs and fp16.
This test also enables get_next_as_optional.
"""
self._setup()
FLAGS.num_gpus = 8
FLAGS.dtype = 'fp16'
FLAGS.enable_eager = True
FLAGS.enable_xla = True
FLAGS.distribution_strategy = 'default'
FLAGS.model_dir = self._get_model_dir(
'benchmark_xla_8_gpu_fp16_optional_next')
FLAGS.batch_size = 256 * 8 # 8 GPUs
FLAGS.enable_get_next_as_optional = True
self._run_and_report_benchmark()
def benchmark_xla_8_gpu_fp16(self):
"""Test Keras model with XLA, 8 GPUs and fp16."""
self._setup()
......@@ -716,44 +589,6 @@ class Resnet50KerasBenchmarkBase(keras_benchmark.KerasBenchmark):
FLAGS.train_steps = 310
self._run_and_report_benchmark()
def benchmark_xla_8_gpu_fp16_tweaked_optional_next(self):
"""Test Keras model with manual config tuning, XLA, 8 GPUs, fp16.
This test also enables get_next_as_optional.
"""
self._setup()
FLAGS.num_gpus = 8
FLAGS.dtype = 'fp16'
FLAGS.enable_eager = True
FLAGS.enable_xla = True
FLAGS.distribution_strategy = 'default'
FLAGS.model_dir = self._get_model_dir(
'benchmark_xla_8_gpu_fp16_tweaked_optional_next')
FLAGS.batch_size = 256 * 8 # 8 GPUs
FLAGS.use_tensor_lr = True
FLAGS.tf_gpu_thread_mode = 'gpu_private'
FLAGS.datasets_num_private_threads = 48
FLAGS.enable_get_next_as_optional = True
self._run_and_report_benchmark()
def benchmark_xla_8_gpu_fp16_slack(self):
"""Test Keras model with XLA, 8 GPUs and fp16.
This test also enable tf.data's experimental_slack functionality.
"""
self._setup()
FLAGS.num_gpus = 8
FLAGS.dtype = 'fp16'
FLAGS.enable_eager = True
FLAGS.enable_xla = True
FLAGS.distribution_strategy = 'default'
FLAGS.model_dir = self._get_model_dir('benchmark_xla_8_gpu_fp16_slack')
FLAGS.batch_size = 256 * 8 # 8 GPUs
FLAGS.tf_data_experimental_slack = True
self._run_and_report_benchmark()
def benchmark_xla_8_gpu_fp16_dynamic_tweaked(self):
"""Test Keras model with config tuning, XLA, 8 GPUs and dynamic fp16."""
self._setup()
......@@ -772,24 +607,6 @@ class Resnet50KerasBenchmarkBase(keras_benchmark.KerasBenchmark):
FLAGS.datasets_num_private_threads = 48
self._run_and_report_benchmark()
def benchmark_xla_8_gpu_fp16_tensorboard_tweaked(self):
"""Test to track Tensorboard performance overhead."""
self._setup()
FLAGS.num_gpus = 8
FLAGS.dtype = 'fp16'
FLAGS.enable_eager = True
FLAGS.enable_xla = True
FLAGS.distribution_strategy = 'default'
FLAGS.model_dir = self._get_model_dir(
'benchmark_xla_8_gpu_fp16_tensorboard_tweaked')
FLAGS.batch_size = 256 * 8 # 8 GPUs
FLAGS.use_tensor_lr = True
FLAGS.tf_gpu_thread_mode = 'gpu_private'
FLAGS.datasets_num_private_threads = 48
FLAGS.enable_tensorboard = True
self._run_and_report_benchmark()
def benchmark_graph_8_gpu(self):
"""Test Keras model in legacy graph mode with 8 GPUs."""
self._setup()
......@@ -888,41 +705,6 @@ class Resnet50KerasBenchmarkBase(keras_benchmark.KerasBenchmark):
FLAGS.train_steps = 310
self._run_and_report_benchmark()
def benchmark_graph_xla_8_gpu_fp16_tweaked_optional_next(self):
"""Test in legacy graph mode with manual config tuning, XLA, 8 GPUs, fp16.
This test also enables get_next_as_optional.
"""
self._setup()
FLAGS.num_gpus = 8
FLAGS.dtype = 'fp16'
FLAGS.enable_eager = False
FLAGS.enable_xla = True
FLAGS.distribution_strategy = 'default'
FLAGS.model_dir = self._get_model_dir(
'benchmark_graph_xla_8_gpu_fp16_tweaked_optional_next')
FLAGS.batch_size = 256 * 8 # 8 GPUs
FLAGS.use_tensor_lr = True
FLAGS.tf_gpu_thread_mode = 'gpu_private'
FLAGS.enable_get_next_as_optional = True
self._run_and_report_benchmark()
def benchmark_graph_xla_8_gpu_fp16_slack(self):
"""Test legacy graph mode with tf.data's experimental_slack."""
self._setup()
FLAGS.num_gpus = 8
FLAGS.dtype = 'fp16'
FLAGS.enable_eager = False
FLAGS.enable_xla = True
FLAGS.distribution_strategy = 'default'
FLAGS.model_dir = self._get_model_dir(
'benchmark_graph_xla_8_gpu_fp16_slack')
FLAGS.batch_size = 256 * 8 # 8 GPUs
FLAGS.tf_data_experimental_slack = True
self._run_and_report_benchmark()
def benchmark_graph_8_gpu_fp16_dynamic_tweaked(self):
"""Test graph Keras with config tuning, 8 GPUs and dynamic fp16."""
self._setup()
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment