def testFixedLossScaleAppliedToLossWithGetGradients(self):
with ops.Graph().as_default():
var = variables.Variable([2.0])
opt = gradient_descent.SGD(1.0)
loss_scale = 10.
opt = loss_scale_optimizer.LossScaleOptimizer(
opt, dynamic=False, initial_scale=loss_scale)
grad_check_fn = mp_test_util.create_identity_with_grad_check_fn(
loss_scale)
loss = grad_check_fn(var)
run_op = opt.get_gradients(loss, [var])
self.evaluate(variables.global_variables_initializer())
# This will cause an assertion to run, as
# mp_test_util.create_identity_with_grad_check_fn added an assertion op.
self.evaluate(run_op)
def test_fixed_loss_scaling(self, strategy_fn):
# Note: We do not test mixed precision in this method, only loss scaling.
loss_scale = 8.
batch_size = 4
with strategy_fn().scope():
x = layers.Input(shape=(1, ), batch_size=batch_size)
layer = mp_test_util.MultiplyLayer()
y = layer(x)
# The gradient of 'y' at this point is 1. With loss scaling, the gradient
# is 'loss_scale'. We divide by the batch size since the loss is averaged
# across batch elements.
expected_gradient = loss_scale / batch_size
identity_with_grad_check_fn = (
mp_test_util.create_identity_with_grad_check_fn(
[expected_gradient]))
y = core.Lambda(identity_with_grad_check_fn)(y)
model = models.Model(inputs=x, outputs=y)
def loss_fn(y_true, y_pred):
del y_true
return math_ops.reduce_mean(y_pred)
opt = gradient_descent.SGD(1.)
opt = loss_scale_optimizer.LossScaleOptimizer(
opt, dynamic=False, initial_scale=loss_scale)
model.compile(opt,
loss=loss_fn,
run_eagerly=testing_utils.should_run_eagerly())
self.assertEqual(backend.eval(layer.v), 1)
x = np.ones((batch_size, 1))
y = np.ones((batch_size, 1))
dataset = dataset_ops.Dataset.from_tensor_slices(
(x, y)).batch(batch_size)
model.fit(dataset)
# Variable starts at 1, and should have gradient of 1 subtracted from it.
expected = 0
self.assertEqual(backend.eval(layer.v), expected)
def test_dynamic_loss_scaling(self,
strategy_fn,
pass_loss_scale_to_policy=False,
get_config=False,
use_v1_loss_scale_optimizer=False):
strategy = strategy_fn()
initial_loss_scale = 2.
batch_size = 4
expected_gradient = backend.variable([initial_loss_scale / batch_size],
dtype=dtypes.float16)
# If this variable is set to True, the model below will have NaN gradients
have_nan_gradients = backend.variable(False, dtype=dtypes.bool)
with strategy.scope():
opt = gradient_descent.SGD(1.)
if pass_loss_scale_to_policy:
loss_scale = loss_scale_module.DynamicLossScale(
initial_loss_scale=initial_loss_scale, increment_period=2)
p = policy.PolicyV1('mixed_float16', loss_scale=loss_scale)
elif use_v1_loss_scale_optimizer:
loss_scale = loss_scale_module.DynamicLossScale(
initial_loss_scale=initial_loss_scale, increment_period=2)
p = policy.Policy('mixed_float16')
opt = loss_scale_optimizer.LossScaleOptimizerV1(
opt, loss_scale)
else:
p = policy.Policy('mixed_float16')
opt = loss_scale_optimizer.LossScaleOptimizer(
opt,
initial_scale=initial_loss_scale,
dynamic_growth_steps=2)
with policy.policy_scope(p):
x = layers.Input(shape=(1, ),
batch_size=batch_size,
dtype=dtypes.float16)
layer = mp_test_util.MultiplyLayer(assert_type=dtypes.float16)
y = layer(x)
identity_with_nan_grads = (
mp_test_util.create_identity_with_nan_gradients_fn(
have_nan_gradients))
y = core.Lambda(identity_with_nan_grads)(y)
identity_with_grad_check_fn = (
mp_test_util.create_identity_with_grad_check_fn(
expected_dtype=dtypes.float16,
expected_gradient=expected_gradient))
y = core.Lambda(identity_with_grad_check_fn)(y)
model = models.Model(inputs=x, outputs=y)
if get_config:
config = model.get_config()
model = model.__class__.from_config(
config,
custom_objects={
'MultiplyLayer': mp_test_util.MultiplyLayer
})
(layer, ) = (
layer for layer in model.layers
if isinstance(layer, mp_test_util.MultiplyLayer))
def loss_fn(y_true, y_pred):
del y_true
return math_ops.reduce_mean(y_pred)
model.compile(opt,
loss=loss_fn,
run_eagerly=testing_utils.should_run_eagerly())
self.assertEqual(backend.eval(layer.v), 1)
x = np.ones((batch_size, 1))
y = np.ones((batch_size, 1))
dataset = dataset_ops.Dataset.from_tensor_slices(
(x, y)).batch(batch_size)
model.fit(dataset)
# The variables starts with 1 and has a gradient of 1, so will go down by 1
# each step.
self.assertEqual(backend.eval(layer.v), 0)
model.fit(dataset)
self.assertEqual(backend.eval(layer.v), -1)
# There have been two steps without NaNs, so the loss scale will double
backend.set_value(expected_gradient,
backend.get_value(expected_gradient * 2))
model.fit(dataset)
self.assertEqual(backend.eval(layer.v), -2)
# Next test with NaN gradients.
backend.set_value(have_nan_gradients, True)
model.fit(dataset)
# Variable should not be updated
self.assertEqual(backend.eval(layer.v), -2)
# Test with finite gradients again
backend.set_value(have_nan_gradients, False)
# The loss scale will be halved due to the NaNs, so the gradient will also
# be halved
backend.set_value(expected_gradient,
backend.get_value(expected_gradient / 2))
model.fit(dataset)
self.assertEqual(backend.eval(layer.v), -3)
def test_advanced_model(self, strategy_fn, use_loss_scaling=False):
# The advanced model tests mixed-precision-related features that would occur
# in a resnet50 model. It tests a model that has:
# * Multiple layers, some which use auto-cast variables and some which do
# not
# * Regularization on some variables and not others.
# * A fixed loss scale (if use_loss_scaling is True)
strategy = strategy_fn()
if use_loss_scaling:
loss_scale = 8.
learning_rate = 2**-14
with strategy.scope():
with policy.policy_scope(policy.Policy('mixed_float16')):
x = layers.Input(shape=(1, ), batch_size=2)
layer1 = mp_test_util.MultiplyLayer(
assert_type=dtypes.float16,
regularizer=mp_test_util.IdentityRegularizer(),
use_operator=True)
layer2 = mp_test_util.MultiplyLayerWithoutAutoCast(
assert_type=dtypes.float16, use_operator=True)
layer3 = mp_test_util.MultiplyLayer(assert_type=dtypes.float16,
use_operator=False)
layer4 = mp_test_util.MultiplyLayerWithoutAutoCast(
assert_type=dtypes.float16,
regularizer=mp_test_util.IdentityRegularizer(),
use_operator=False)
y = layer1(x)
y = layer2(y)
y = layer3(y)
y = layer4(y)
if use_loss_scaling:
# The gradient of 'y' at this point is 1. With loss scaling, the
# gradient is 'loss_scale'. We divide by the batch size of 2 since the
# loss is averaged across batch elements.
expected_gradient = loss_scale / 2
identity_with_grad_check_fn = (
mp_test_util.create_identity_with_grad_check_fn(
expected_dtype=dtypes.float16,
expected_gradient=[expected_gradient]))
y = core.Lambda(identity_with_grad_check_fn)(y)
model = models.Model(inputs=x, outputs=y)
def loss_fn(y_true, y_pred):
del y_true
return math_ops.reduce_mean(y_pred)
opt = gradient_descent.SGD(learning_rate)
if use_loss_scaling:
opt = loss_scale_optimizer.LossScaleOptimizer(
opt, dynamic=False, initial_scale=loss_scale)
model.compile(opt,
loss=loss_fn,
run_eagerly=testing_utils.should_run_eagerly())
x = np.ones((2, 1))
y = np.ones((2, 1))
dataset = dataset_ops.Dataset.from_tensor_slices((x, y)).batch(2)
model.fit(dataset)
for layer in (layer1, layer2, layer3, layer4):
if layer.losses:
# Layer has weight regularizer
self.assertEqual(backend.eval(layer.v), 1 - 2 * learning_rate)
else:
# Layer does not have weight regularizer
self.assertEqual(backend.eval(layer.v), 1 - learning_rate)
def _run_fn_with_grad_check(self, strategy, var, opt, expected_grad):
grad_check_fn = mp_test_util.create_identity_with_grad_check_fn(
expected_grad)
loss = lambda: grad_check_fn(var) / strategy.num_replicas_in_sync
return lambda: opt.minimize(loss, var_list=[var])
