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Remove colocate_with function from FTRL optimizer for use with DistributionStrategy.

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PiperOrigin-RevId: 286981819
Change-Id: I76e644013c8f84e0a3e90b5e2039acb2904023e6
This commit is contained in:
A. Unique TensorFlower 2019-12-23 21:58:16 -08:00 committed by TensorFlower Gardener
parent 49f453f1f9
commit 6a4342982f
3 changed files with 16 additions and 7 deletions
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6
tensorflow/python/distribute/minimize_loss_test.py
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@ -44,7 +44,11 @@ from tensorflow.python.ops.losses import losses_impl
VAR_MAP_V1 = { VAR_MAP_V1 = {
"GradientDescent": ("dense/kernel", "dense/bias"), "GradientDescent": ("dense/kernel", "dense/bias"),
"Adagrad": ("dense/kernel/Adagrad", "dense/kernel", "dense/bias/Adagrad", "Adagrad": ("dense/kernel/Adagrad", "dense/kernel", "dense/bias/Adagrad",
"dense/bias") "dense/bias"),
"Ftrl": ("dense/kernel/Ftrl", "dense/kernel", "dense/bias/Ftrl",
"dense/bias", "dense/kernel/Ftrl_1", "dense/bias/Ftrl_1"),
"RMSProp": ("dense/kernel", "dense/bias/RMSProp", "dense/bias/RMSProp_1",
"dense/bias", "dense/kernel/RMSProp_1", "dense/kernel/RMSProp")
} }
VAR_MAP_V2 = { VAR_MAP_V2 = {

8
tensorflow/python/distribute/strategy_combinations.py
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@ -40,6 +40,7 @@ from tensorflow.python.tpu import device_assignment as device_assignment_lib
from tensorflow.python.tpu import tpu_strategy_util from tensorflow.python.tpu import tpu_strategy_util
from tensorflow.python.training import adagrad from tensorflow.python.training import adagrad
from tensorflow.python.training import adam from tensorflow.python.training import adam
from tensorflow.python.training import ftrl
from tensorflow.python.training import gradient_descent from tensorflow.python.training import gradient_descent
from tensorflow.python.training import rmsprop from tensorflow.python.training import rmsprop
@ -130,11 +131,16 @@ adagrad_optimizer_v1_fn = combinations.NamedObject(
"AdagradV1", lambda: adagrad.AdagradOptimizer(0.001)) "AdagradV1", lambda: adagrad.AdagradOptimizer(0.001))
adam_optimizer_v1_fn = combinations.NamedObject( adam_optimizer_v1_fn = combinations.NamedObject(
"AdamV1", lambda: adam.AdamOptimizer(0.001, epsilon=1)) "AdamV1", lambda: adam.AdamOptimizer(0.001, epsilon=1))
ftrl_optimizer_v1_fn = combinations.NamedObject(
"FtrlV1", lambda: ftrl.FtrlOptimizer(0.001))
rmsprop_optimizer_v1_fn = combinations.NamedObject( rmsprop_optimizer_v1_fn = combinations.NamedObject(
"RmsPropV1", lambda: rmsprop.RMSPropOptimizer(0.001)) "RmsPropV1", lambda: rmsprop.RMSPropOptimizer(0.001))
# TODO(shiningsun): consider adding the other v1 optimizers # TODO(shiningsun): consider adding the other v1 optimizers
optimizers_v1 = [gradient_descent_optimizer_v1_fn, adagrad_optimizer_v1_fn] optimizers_v1 = [
gradient_descent_optimizer_v1_fn, adagrad_optimizer_v1_fn,
ftrl_optimizer_v1_fn, rmsprop_optimizer_v1_fn
]
adadelta_optimizer_keras_v2_fn = combinations.NamedObject( adadelta_optimizer_keras_v2_fn = combinations.NamedObject(
"AdadeltaKerasV2", lambda: adadelta_keras_v2.Adadelta(0.001)) "AdadeltaKerasV2", lambda: adadelta_keras_v2.Adadelta(0.001))

1
tensorflow/python/training/ftrl.py
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@ -132,7 +132,6 @@ class FtrlOptimizer(optimizer.Optimizer):
def _create_slots(self, var_list): def _create_slots(self, var_list):
# Create the "accum" and "linear" slots. # Create the "accum" and "linear" slots.
for v in var_list: for v in var_list:
with ops.colocate_with(v):
val = constant_op.constant( val = constant_op.constant(
self._initial_accumulator_value, dtype=v.dtype, shape=v.get_shape()) self._initial_accumulator_value, dtype=v.dtype, shape=v.get_shape())
self._get_or_make_slot(v, val, "accum", self._accum_name or self._name) self._get_or_make_slot(v, val, "accum", self._accum_name or self._name)