def test_optimizer_errors(self):
opt = 1
if tf2.enabled():
expected_regex = ('"opt" must be an instance of a '
'tf.keras.optimizers.Optimizer, but got')
else:
expected_regex = ('"opt" must be an instance of a tf.train.Optimizer or '
'a tf.keras.optimizers.Optimizer, but got')
with self.assertRaisesRegexp(ValueError, expected_regex):
enable_mixed_precision_graph_rewrite(opt)
self.assertFalse(config.get_optimizer_experimental_options()
.get('auto_mixed_precision', False))
opt = gradient_descent_v1.GradientDescentOptimizer(1.0)
opt = loss_scale_optimizer_v1.MixedPrecisionLossScaleOptimizer(opt,
'dynamic')
with self.assertRaisesRegexp(ValueError,
'"opt" must not already be an instance of a '
'MixedPrecisionLossScaleOptimizer.'):
enable_mixed_precision_graph_rewrite(opt)
self.assertFalse(config.get_optimizer_experimental_options()
.get('auto_mixed_precision', False))
opt = gradient_descent_v2.SGD(1.0)
opt = loss_scale_optimizer_v2.LossScaleOptimizer(opt, 'dynamic')
with self.assertRaisesRegexp(ValueError,
'"opt" must not already be an instance of a '
'LossScaleOptimizer.'):
enable_mixed_precision_graph_rewrite(opt)
self.assertFalse(config.get_optimizer_experimental_options()
.get('auto_mixed_precision', False))
def testOptimizerBoolOption(self, field):
# TODO(b/128531235): Improve testing of option
options = config.get_optimizer_experimental_options()
self.assertFalse(options.get(field))
config.set_optimizer_experimental_options({field: True})
options[field] = True
self.assertDictEqual(config.get_optimizer_experimental_options(), options)
self.assertDictEqual(
context.context().get_optimizer_experimental_options(), options)
config.set_optimizer_experimental_options({field: False})
options[field] = False
self.assertDictEqual(config.get_optimizer_experimental_options(), options)
self.assertDictEqual(
context.context().get_optimizer_experimental_options(), options)
def testOptimizerToggleOption(self, field):
# TODO(b/128531235): Improve testing of option
options = config.get_optimizer_experimental_options()
self.assertIsNone(options.get(field))
config.set_optimizer_experimental_options({field: True})
options[field] = True
self.assertDictEqual(config.get_optimizer_experimental_options(),
options)
self.assertDictEqual(
context.context().get_optimizer_experimental_options(), options)
config.set_optimizer_experimental_options({field: False})
options[field] = False
self.assertDictEqual(config.get_optimizer_experimental_options(),
options)
self.assertDictEqual(
context.context().get_optimizer_experimental_options(), options)
def test_optimizer_errors(self):
opt = gradient_descent_v2.SGD(1.0)
opt = loss_scale_optimizer_v2.LossScaleOptimizerV1(opt, 'dynamic')
with self.assertRaisesRegex(
ValueError, '"opt" must not already be an instance of a '
'LossScaleOptimizer.'):
enable_mixed_precision_graph_rewrite(opt)
self.assertFalse(config.get_optimizer_experimental_options().get(
'auto_mixed_precision', False))
def test_optimizer_errors(self):
opt = 1
expected_regex = ('"opt" must be an instance of a tf.train.Optimizer or '
'a tf.keras.optimizers.Optimizer, but got')
with self.assertRaisesRegex(ValueError, expected_regex):
mixed_precision.enable_mixed_precision_graph_rewrite_v1(opt)
self.assertFalse(config.get_optimizer_experimental_options()
.get('auto_mixed_precision', False))
opt = gradient_descent_v1.GradientDescentOptimizer(1.0)
opt = loss_scale_optimizer_v1.MixedPrecisionLossScaleOptimizer(opt,
'dynamic')
with self.assertRaisesRegex(
ValueError, '"opt" must not already be an instance of a '
'MixedPrecisionLossScaleOptimizer.'):
mixed_precision.enable_mixed_precision_graph_rewrite_v1(opt)
self.assertFalse(config.get_optimizer_experimental_options()
.get('auto_mixed_precision', False))
def testOptimizerToggleOptionPinToHost(self):
options = config.get_optimizer_experimental_options()
self.assertIsNone(options.get('pin_to_host_optimization'))
@def_function.function
def fun():
op = test_ops.device_placement_op()
return op
# Force optimizer to run for all graphs
config.set_optimizer_experimental_options({'min_graph_nodes': -1})
options['min_graph_nodes'] = -1
# Since pin to host is disabled, the operation should go on GPU
gpu = self.evaluate(fun())
self.assertIn(compat.as_bytes('GPU'), gpu)
config.set_optimizer_experimental_options(
{'pin_to_host_optimization': True})
options['pin_to_host_optimization'] = True
self.assertDictEqual(config.get_optimizer_experimental_options(),
options)
self.assertDictEqual(
context.context().get_optimizer_experimental_options(), options)
# Since pin to host is enabled, the operation should go on CPU
cpu = self.evaluate(fun())
self.assertIn(compat.as_bytes('CPU'), cpu)
config.set_optimizer_experimental_options(
{'pin_to_host_optimization': False})
options['pin_to_host_optimization'] = False
self.assertDictEqual(config.get_optimizer_experimental_options(),
options)
self.assertDictEqual(
context.context().get_optimizer_experimental_options(), options)
# Since pin to host is disabled again, the operation should go on GPU
gpu2 = self.evaluate(fun())
self.assertIn(compat.as_bytes('GPU'), gpu2)
def testOptimizerToggleOptionPinToHost(self):
options = config.get_optimizer_experimental_options()
self.assertIsNone(options.get('pin_to_host_optimization'))
@def_function.function
def fun():
op = test_ops.device_placement_op()
return op
# Force optimizer to run for all graphs
config.set_optimizer_experimental_options({'min_graph_nodes': -1})
options['min_graph_nodes'] = -1
# Since pin to host is disabled, the operation should go on GPU
gpu = self.evaluate(fun())
self.assertIn(compat.as_bytes('GPU'), gpu)
config.set_optimizer_experimental_options(
{'pin_to_host_optimization': True})
options['pin_to_host_optimization'] = True
self.assertDictEqual(config.get_optimizer_experimental_options(), options)
self.assertDictEqual(
context.context().get_optimizer_experimental_options(), options)
# Since pin to host is enabled, the operation should go on CPU
cpu = self.evaluate(fun())
self.assertIn(compat.as_bytes('CPU'), cpu)
config.set_optimizer_experimental_options(
{'pin_to_host_optimization': False})
options['pin_to_host_optimization'] = False
self.assertDictEqual(config.get_optimizer_experimental_options(), options)
self.assertDictEqual(
context.context().get_optimizer_experimental_options(), options)
# Since pin to host is disabled again, the operation should go on GPU
gpu2 = self.evaluate(fun())
self.assertIn(compat.as_bytes('GPU'), gpu2)
