keras_common.py 20.5 KB
Newer Older
1
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
2
3
4
5
6
7
8
9
10
11
12
13
14
#
# 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.
# ==============================================================================
15
"""Common util functions and classes used by both keras cifar and imagenet."""
16
17
18
19
20

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

21
22
import multiprocessing
import os
23

24
25
import numpy as np

Toby Boyd's avatar
Toby Boyd committed
26
27
28
# pylint: disable=g-bad-import-order
from absl import flags
import tensorflow as tf
29

30
31
from official.utils.misc import keras_utils
# pylint: disable=ungrouped-imports
32
from tensorflow.core.protobuf import rewriter_config_pb2
33
from tensorflow.python.eager import profiler
34
35
from tensorflow.python.keras.optimizer_v2 import (gradient_descent as
                                                  gradient_descent_v2)
36

Shining Sun's avatar
Shining Sun committed
37
FLAGS = flags.FLAGS
Shining Sun's avatar
Shining Sun committed
38
BASE_LEARNING_RATE = 0.1  # This matches Jing's version.
39
40
TRAIN_TOP_1 = 'training_accuracy_top_1'

Shining Sun's avatar
Shining Sun committed
41

42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
class LearningRateBatchScheduler(tf.keras.callbacks.Callback):
  """Callback to update learning rate on every batch (not epoch boundaries).

  N.B. Only support Keras optimizers, not TF optimizers.

  Args:
      schedule: a function that takes an epoch index and a batch index as input
          (both integer, indexed from 0) and returns a new learning rate as
          output (float).
  """

  def __init__(self, schedule, batch_size, num_images):
    super(LearningRateBatchScheduler, self).__init__()
    self.schedule = schedule
    self.batches_per_epoch = num_images / batch_size
    self.batch_size = batch_size
    self.epochs = -1
    self.prev_lr = -1

  def on_epoch_begin(self, epoch, logs=None):
62
63
    if not hasattr(self.model.optimizer, 'learning_rate'):
      raise ValueError('Optimizer must have a "learning_rate" attribute.')
64
65
66
    self.epochs += 1

  def on_batch_begin(self, batch, logs=None):
67
    """Executes before step begins."""
68
69
70
71
    lr = self.schedule(self.epochs,
                       batch,
                       self.batches_per_epoch,
                       self.batch_size)
72
73
74
    if not isinstance(lr, (float, np.float32, np.float64)):
      raise ValueError('The output of the "schedule" function should be float.')
    if lr != self.prev_lr:
Shining Sun's avatar
Shining Sun committed
75
      self.model.optimizer.learning_rate = lr  # lr should be a float here
76
      self.prev_lr = lr
77
78
79
      tf.compat.v1.logging.debug(
          'Epoch %05d Batch %05d: LearningRateBatchScheduler '
          'change learning rate to %s.', self.epochs, batch, lr)
80

81

82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
class PiecewiseConstantDecayWithWarmup(
    tf.keras.optimizers.schedules.LearningRateSchedule):
  """Piecewise constant decay with warmup schedule."""

  def __init__(self, batch_size, epoch_size, warmup_epochs, boundaries,
               multipliers, compute_lr_on_cpu=True, name=None):
    super(PiecewiseConstantDecayWithWarmup, self).__init__()
    if len(boundaries) != len(multipliers) - 1:
      raise ValueError('The length of boundaries must be 1 less than the '
                       'length of multipliers')

    base_lr_batch_size = 256
    num_batches_per_epoch = epoch_size // batch_size

    self.rescaled_lr = BASE_LEARNING_RATE * batch_size / base_lr_batch_size
    self.step_boundaries = [float(num_batches_per_epoch) * x
                            for x in boundaries]
    self.lr_values = [self.rescaled_lr * m for m in multipliers]
    self.warmup_steps = warmup_epochs * num_batches_per_epoch
    self.compute_lr_on_cpu = compute_lr_on_cpu
    self.name = name

104
    self.learning_rate_ops_cache = {}
105
106
107
108
109
110
111
112

  def __call__(self, step):
    if tf.executing_eagerly():
      return self._get_learning_rate(step)

    # In an eager function or graph, the current implementation of optimizer
    # repeatedly call and thus create ops for the learning rate schedule. To
    # avoid this, we cache the ops if not executing eagerly.
113
114
    graph = tf.compat.v1.get_default_graph()
    if graph not in self.learning_rate_ops_cache:
115
116
      if self.compute_lr_on_cpu:
        with tf.device('/device:CPU:0'):
117
          self.learning_rate_ops_cache[graph] = self._get_learning_rate(step)
118
      else:
119
120
        self.learning_rate_ops_cache[graph] = self._get_learning_rate(step)
    return self.learning_rate_ops_cache[graph]
121
122
123

  def _get_learning_rate(self, step):
    """Compute learning rate at given step."""
Haoyu Zhang's avatar
Haoyu Zhang committed
124
125
126
127
    with tf.compat.v1.name_scope(self.name, 'PiecewiseConstantDecayWithWarmup',
                                 [self.rescaled_lr, self.step_boundaries,
                                  self.lr_values, self.warmup_steps,
                                  self.compute_lr_on_cpu]):
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
      def warmup_lr(step):
        return self.rescaled_lr * (
            tf.cast(step, tf.float32) / tf.cast(self.warmup_steps, tf.float32))
      def piecewise_lr(step):
        return tf.compat.v1.train.piecewise_constant(
            step, self.step_boundaries, self.lr_values)
      return tf.cond(step < self.warmup_steps,
                     lambda: warmup_lr(step),
                     lambda: piecewise_lr(step))

  def get_config(self):
    return {
        'rescaled_lr': self.rescaled_lr,
        'step_boundaries': self.step_boundaries,
        'lr_values': self.lr_values,
        'warmup_steps': self.warmup_steps,
        'compute_lr_on_cpu': self.compute_lr_on_cpu,
        'name': self.name
    }


149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
class ProfilerCallback(tf.keras.callbacks.Callback):
  """Save profiles in specified step range to log directory."""

  def __init__(self, log_dir, start_step, stop_step):
    super(ProfilerCallback, self).__init__()
    self.log_dir = log_dir
    self.start_step = start_step
    self.stop_step = stop_step

  def on_batch_begin(self, batch, logs=None):
    if batch == self.start_step:
      profiler.start()
      tf.compat.v1.logging.info('Profiler started at Step %s', self.start_step)

  def on_batch_end(self, batch, logs=None):
    if batch == self.stop_step:
      results = profiler.stop()
      profiler.save(self.log_dir, results)
      tf.compat.v1.logging.info(
          'Profiler saved profiles for steps between %s and %s to %s',
          self.start_step, self.stop_step, self.log_dir)


172
def get_config_proto_v1():
173
174
175
  """Return config proto according to flag settings, or None to use default."""
  config = None
  if FLAGS.enable_xla:
Haoyu Zhang's avatar
Haoyu Zhang committed
176
177
178
    # TODO(haoyuzhang): Remove this monkey patch when XLA OOM issue is fixed.
    _monkey_patch_org_assert_broadcastable()

179
    config = tf.compat.v1.ConfigProto()
180
181
182
183
184
185
186
187
188
    config.graph_options.optimizer_options.global_jit_level = (
        tf.OptimizerOptions.ON_2)
    # Disable PinToHostOptimizer in grappler when enabling XLA because it causes
    # OOM and performance regression.
    config.graph_options.rewrite_options.pin_to_host_optimization = (
        rewriter_config_pb2.RewriterConfig.OFF)
  return config


189
190
191
192
193
194
195
196
197
198
199
200
201
202
def set_config_v2():
  """Config eager context according to flag values using TF 2.0 API."""
  if FLAGS.enable_xla:
    # TODO(haoyuzhang): Remove this monkey patch when XLA OOM issue is fixed.
    _monkey_patch_org_assert_broadcastable()

    tf.config.optimizer.set_jit(True)
    # Disable PinToHostOptimizer in grappler when enabling XLA because it
    # causes OOM and performance regression.
    tf.config.optimizer.set_experimental_options(
        {"pin_to_host_optimization": False}
    )


203
204
205
206
207
208
def set_gpu_thread_mode_and_count(flags_obj):
  """Set GPU thread mode and count, and adjust dataset threads count."""
  cpu_count = multiprocessing.cpu_count()
  tf.compat.v1.logging.info('Logical CPU cores: %s', cpu_count)

  # Allocate private thread pool for each GPU to schedule and launch kernels
209
  per_gpu_thread_count = flags_obj.per_gpu_thread_count or 2
210
211
212
213
214
215
216
217
218
219
  os.environ['TF_GPU_THREAD_MODE'] = flags_obj.tf_gpu_thread_mode
  os.environ['TF_GPU_THREAD_COUNT'] = str(per_gpu_thread_count)
  tf.compat.v1.logging.info('TF_GPU_THREAD_COUNT: %s',
                            os.environ['TF_GPU_THREAD_COUNT'])
  tf.compat.v1.logging.info('TF_GPU_THREAD_MODE: %s',
                            os.environ['TF_GPU_THREAD_MODE'])

  # Limit data preprocessing threadpool to CPU cores minus number of total GPU
  # private threads and memory copy threads.
  total_gpu_thread_count = per_gpu_thread_count * flags_obj.num_gpus
220
  num_runtime_threads = flags_obj.num_gpus
221
  if not flags_obj.datasets_num_private_threads:
222
223
224
    flags_obj.datasets_num_private_threads = min(
        cpu_count - total_gpu_thread_count - num_runtime_threads,
        flags_obj.num_gpus * 8)
225
226
227
228
    tf.compat.v1.logging.info('Set datasets_num_private_threads to %s',
                              flags_obj.datasets_num_private_threads)


229
def get_optimizer(learning_rate=0.1):
230
231
  """Returns optimizer to use."""
  # The learning_rate is overwritten at the beginning of each step by callback.
232
  return gradient_descent_v2.SGD(learning_rate=learning_rate, momentum=0.9)
233
234


235
def get_callbacks(learning_rate_schedule_fn, num_images):
236
  """Returns common callbacks."""
237
  time_callback = keras_utils.TimeHistory(FLAGS.batch_size, FLAGS.log_steps)
238
239
240
241
242
243
244
245
  callbacks = [time_callback]

  if not FLAGS.use_tensor_lr:
    lr_callback = LearningRateBatchScheduler(
        learning_rate_schedule_fn,
        batch_size=FLAGS.batch_size,
        num_images=num_images)
    callbacks.append(lr_callback)
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284

  if FLAGS.enable_tensorboard:
    tensorboard_callback = tf.keras.callbacks.TensorBoard(
        log_dir=FLAGS.model_dir)
    callbacks.append(tensorboard_callback)

  if FLAGS.profile_steps:
    profiler_callback = get_profiler_callback()
    callbacks.append(profiler_callback)

  return callbacks


def get_profiler_callback():
  """Validate profile_steps flag value and return profiler callback."""
  profile_steps_error_message = (
      'profile_steps must be a comma separated pair of positive integers, '
      'specifying the first and last steps to be profiled.'
  )
  try:
    profile_steps = [int(i) for i in FLAGS.profile_steps.split(',')]
  except ValueError:
    raise ValueError(profile_steps_error_message)
  if len(profile_steps) != 2:
    raise ValueError(profile_steps_error_message)
  start_step, stop_step = profile_steps
  if start_step < 0 or start_step > stop_step:
    raise ValueError(profile_steps_error_message)
  if FLAGS.enable_tensorboard:
    tf.compat.v1.logging.warn(
        'Both TensorBoard and profiler callbacks are used. Note that the '
        'TensorBoard callback profiles the 2nd step (unless otherwise '
        'specified). Please make sure the steps profiled by the two callbacks '
        'do not overlap.')

  return ProfilerCallback(FLAGS.model_dir, start_step, stop_step)


def build_stats(history, eval_output, callbacks):
285
286
287
288
289
290
291
  """Normalizes and returns dictionary of stats.

  Args:
    history: Results of the training step. Supports both categorical_accuracy
      and sparse_categorical_accuracy.
    eval_output: Output of the eval step. Assumes first value is eval_loss and
      second value is accuracy_top_1.
292
293
    callbacks: a list of callbacks which might include a time history callback
      used during keras.fit.
294
295
296
297
298
299
300
301

  Returns:
    Dictionary of normalized results.
  """
  stats = {}
  if eval_output:
    stats['accuracy_top_1'] = eval_output[1].item()
    stats['eval_loss'] = eval_output[0].item()
302

303
304
305
306
307
308
309
310
311
312
  if history and history.history:
    train_hist = history.history
    # Gets final loss from training.
    stats['loss'] = train_hist['loss'][-1].item()
    # Gets top_1 training accuracy.
    if 'categorical_accuracy' in train_hist:
      stats[TRAIN_TOP_1] = train_hist['categorical_accuracy'][-1].item()
    elif 'sparse_categorical_accuracy' in train_hist:
      stats[TRAIN_TOP_1] = train_hist['sparse_categorical_accuracy'][-1].item()

313
314
315
316
317
318
319
320
321
322
323
324
325
326
  if not callbacks:
    return stats

  # Look for the time history callback which was used during keras.fit
  for callback in callbacks:
    if isinstance(callback, keras_utils.TimeHistory):
      timestamp_log = callback.timestamp_log
      stats['step_timestamp_log'] = timestamp_log
      stats['train_finish_time'] = callback.train_finish_time
      if len(timestamp_log) > 1:
        stats['avg_exp_per_second'] = (
            callback.batch_size * callback.log_steps *
            (len(callback.timestamp_log)-1) /
            (timestamp_log[-1].timestamp - timestamp_log[0].timestamp))
327
328
329
  return stats


Shining Sun's avatar
Shining Sun committed
330
def define_keras_flags():
331
  """Define flags for Keras models."""
332

Shining Sun's avatar
Shining Sun committed
333
  flags.DEFINE_boolean(name='enable_eager', default=False, help='Enable eager?')
334
  flags.DEFINE_boolean(name='skip_eval', default=False, help='Skip evaluation?')
Haoyu Zhang's avatar
Haoyu Zhang committed
335
336
  flags.DEFINE_boolean(name='use_trivial_model', default=False,
                       help='Whether to use a trivial Keras model.')
337
338
  flags.DEFINE_boolean(name='report_accuracy_metrics', default=True,
                       help='Report metrics during training and evaluation.')
339
340
  flags.DEFINE_boolean(name='use_tensor_lr', default=False,
                       help='Use learning rate tensor instead of a callback.')
341
342
343
344
  flags.DEFINE_boolean(
      name='enable_xla', default=False,
      help='Whether to enable XLA auto jit compilation. This is still an '
      'experimental feature, and is not yet effective with TF 2.0.')
345
346
347
  flags.DEFINE_boolean(
      name='enable_tensorboard', default=False,
      help='Whether to enable Tensorboard callback.')
Shining Sun's avatar
Shining Sun committed
348
  flags.DEFINE_integer(
349
350
      name='train_steps', default=None,
      help='The number of steps to run for training. If it is larger than '
Shining Sun's avatar
Shining Sun committed
351
      '# batches per epoch, then use # batches per epoch. When this flag is '
352
      'set, only one epoch is going to run for training.')
353
354
355
356
357
358
359
360
  flags.DEFINE_string(
      name='profile_steps', default=None,
      help='Save profiling data to model dir at given range of steps. The '
      'value must be a comma separated pair of positive integers, specifying '
      'the first and last step to profile. For example, "--profile_steps=2,4" '
      'triggers the profiler to process 3 steps, starting from the 2nd step. '
      'Note that profiler has a non-trivial performance overhead, and the '
      'output file can be gigantic if profiling many steps.')
361
  flags.DEFINE_boolean(
362
      name='data_delay_prefetch', default=False,
363
364
365
366
367
      help='Add a small delay in tf.data prefetch to prioritize memory copy of '
      'other tensors over the data minibatch for the (T+1)th step. It should '
      'help improve performance using EagerIterator and function. The codepath '
      'when enabling this feature is experimental and will be removed once the '
      'corresponding performance features are fully supported in TensorFlow.')
368
369
370
371
  flags.DEFINE_boolean(
      name='batchnorm_spatial_persistent', default=True,
      help='Enable the spacial persistent mode for CuDNN batch norm kernel.')
  flags.DEFINE_boolean(
372
      name='clone_model_in_keras_dist_strat', default=None,
373
374
      help='If False, then the experimental code path is used that doesn\'t '
           'clone models for distribution.')
375
376
377
  flags.DEFINE_boolean(
      name='enable_get_next_as_optional', default=False,
      help='Enable get_next_as_optional behavior in DistributedIterator.')
378

Shining Sun's avatar
Shining Sun committed
379
380

def get_synth_input_fn(height, width, num_channels, num_classes,
381
                       dtype=tf.float32, drop_remainder=True):
Shining Sun's avatar
Shining Sun committed
382
383
384
385
386
  """Returns an input function that returns a dataset with random data.

  This input_fn returns a data set that iterates over a set of random data and
  bypasses all preprocessing, e.g. jpeg decode and copy. The host to device
  copy is still included. This used to find the upper throughput bound when
Shining Sun's avatar
Shining Sun committed
387
  tuning the full input pipeline.
Shining Sun's avatar
Shining Sun committed
388
389
390
391
392
393
394
395

  Args:
    height: Integer height that will be used to create a fake image tensor.
    width: Integer width that will be used to create a fake image tensor.
    num_channels: Integer depth that will be used to create a fake image tensor.
    num_classes: Number of classes that should be represented in the fake labels
      tensor
    dtype: Data type for features/images.
396
397
    drop_remainder: A boolean indicates whether to drop the remainder of the
      batches. If True, the batch dimension will be static.
Shining Sun's avatar
Shining Sun committed
398
399
400
401
402
403
404
405
406

  Returns:
    An input_fn that can be used in place of a real one to return a dataset
    that can be used for iteration.
  """
  # pylint: disable=unused-argument
  def input_fn(is_training, data_dir, batch_size, *args, **kwargs):
    """Returns dataset filled with random data."""
    # Synthetic input should be within [0, 255].
407
408
409
410
411
412
413
414
415
416
417
    inputs = tf.random.truncated_normal([height, width, num_channels],
                                        dtype=dtype,
                                        mean=127,
                                        stddev=60,
                                        name='synthetic_inputs')

    labels = tf.random.uniform([1],
                               minval=0,
                               maxval=num_classes - 1,
                               dtype=tf.int32,
                               name='synthetic_labels')
418
419
420
    # Cast to float32 for Keras model.
    labels = tf.cast(labels, dtype=tf.float32)

Shining Sun's avatar
Shining Sun committed
421
    data = tf.data.Dataset.from_tensors((inputs, labels)).repeat()
422
423

    # `drop_remainder` will make dataset produce outputs with known shapes.
424
    data = data.batch(batch_size, drop_remainder=drop_remainder)
425
    data = data.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
Shining Sun's avatar
Shining Sun committed
426
427
428
    return data

  return input_fn
Shining Sun's avatar
Shining Sun committed
429
430


431
432
433
434
435
def is_v2_0():
  """Returns true if using tf 2.0."""
  return tf.__version__.startswith('2')


436
def data_delay_prefetch():
437
438
439
440
441
  """Use unstable code for perf tuning purposes."""
  if not FLAGS.use_synthetic_data:
    _monkey_patch_org_create_device_dataset()


442
443
444
445
446
447
def set_cudnn_batchnorm_mode():
  """Set CuDNN batchnorm mode for better performance. Note that the spatial
     persistent mode may lead to accuracy losses for certain models."""
  if FLAGS.batchnorm_spatial_persistent:
    os.environ['TF_USE_CUDNN_BATCHNORM_SPATIAL_PERSISTENT'] = '1'
  else:
448
    os.environ.pop('TF_USE_CUDNN_BATCHNORM_SPATIAL_PERSISTENT', None)
449
450


Haoyu Zhang's avatar
Haoyu Zhang committed
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
def _monkey_patch_org_assert_broadcastable():
  """Monkey-patch `assert_broadcast` op to avoid OOM when enabling XLA."""
  def no_op_assert_broadcastable(weights, values):
    del weights, values
    tf.compat.v1.logging.info(
        'Using monkey-patched version of assert_broadcastable op, which always '
        'returns an no_op. It should be removed after XLA OOM issue is fixed.')
    return tf.constant([], dtype=tf.float32)

  from tensorflow.python.ops import weights_broadcast_ops  # pylint: disable=g-import-not-at-top
  if not hasattr(weights_broadcast_ops, 'org_assert_broadcastable'):
    weights_broadcast_ops.org_assert_broadcastable = (
        weights_broadcast_ops.assert_broadcastable)
  weights_broadcast_ops.assert_broadcastable = no_op_assert_broadcastable


def _undo_monkey_patch_org_assert_broadcastable():
  from tensorflow.python.ops import weights_broadcast_ops  # pylint: disable=g-import-not-at-top
  if hasattr(weights_broadcast_ops, 'org_assert_broadcastable'):
    weights_broadcast_ops.assert_broadcastable = (
        weights_broadcast_ops.org_assert_broadcastable)
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497


# TODO(haoyuzhang): remove this monkey patch when the "prefetch with slack"
# feature is available in tf.data.
def _monkey_patch_org_create_device_dataset():
  """Monkey-patch `_create_device_dataset` method with delayed prefetch."""

  import ast  # pylint: disable=g-import-not-at-top
  import inspect  # pylint: disable=g-import-not-at-top
  from tensorflow.python.data.ops import multi_device_iterator_ops  # pylint: disable=g-import-not-at-top

  tf.compat.v1.logging.info(
      'Using monkey-patched version of MultiDeviceIterator. It should be '
      'removed when the prefetch with slack feature is implemented in tf.data.')
  cls_multi_device_iterator = ast.parse(
      inspect.getsource(multi_device_iterator_ops.MultiDeviceIterator))
  org_create_device_dataset_code = inspect.getsource(
      multi_device_iterator_ops.MultiDeviceIterator._create_device_dataset)  # pylint: disable=protected-access
  code_lines = org_create_device_dataset_code.split('\n')
  # Insert in reverse order to avoid line number shift by previous insertions
  code_lines.insert(5, '      ds = ds.apply(sleep_ops.sleep(11000))')  # 11ms
  code_lines.insert(2, '    from tensorflow.python.data.experimental.ops import sleep as sleep_ops')  # pylint: disable=line-too-long
  patched_code = '\n'.join(line[2:] for line in code_lines)
  cls_multi_device_iterator.body[0].body[2] = ast.parse(patched_code).body[0]
  exec(compile(cls_multi_device_iterator, '<string>', 'exec'),  # pylint: disable=exec-used
       multi_device_iterator_ops.__dict__)