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Colocate slot variables with their base variable in keras OptimizerV2

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Not doing so results in errors because ops such as ResourceApplyRMSProp
take base as well as slot variables. Such ops cannot be placed when the
variable devices are different.

PiperOrigin-RevId: 226924297
This commit is contained in:
Igor Ganichev 2018-12-26 09:05:31 -08:00 committed by TensorFlower Gardener
parent 2fc6decf7b
commit 2696015d85
4 changed files with 108 additions and 5 deletions
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53
tensorflow/contrib/optimizer_v2/rmsprop_test.py
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@ -25,10 +25,12 @@ from absl.testing import parameterized
import numpy as np
from tensorflow.contrib.optimizer_v2 import rmsprop
from tensorflow.python.eager import context
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import embedding_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import resource_variable_ops
@ -448,5 +450,56 @@ class RMSPropOptimizerTest(test.TestCase, parameterized.TestCase):
]), var1.eval())
class SlotColocationTest(test.TestCase, parameterized.TestCase):
@parameterized.parameters([True, False])
@test_util.run_in_graph_and_eager_modes
def testRunMinimizeOnGPUForCPUVariables(self, use_resource):
if not context.context().num_gpus():
self.skipTest("No GPUs found")
with ops.device("/device:CPU:0"):
if use_resource:
var0 = resource_variable_ops.ResourceVariable([1.0, 2.0],
dtype=dtypes.float32)
var1 = resource_variable_ops.ResourceVariable([3.0, 4.0],
dtype=dtypes.float32)
global_step = resource_variable_ops.ResourceVariable(
array_ops.zeros([], dtypes.int64), name="global_step")
else:
var0 = variables.Variable([1.0, 2.0], dtype=dtypes.float32)
var1 = variables.Variable([3.0, 4.0], dtype=dtypes.float32)
global_step = variables.Variable(
array_ops.zeros([], dtypes.int64), name="global_step")
def loss():
return 5 * var0 + 3 * var1
opt = rmsprop.RMSPropOptimizer(
learning_rate=1.0, decay=0.9, momentum=0.5, epsilon=1.0)
# Fetch params to validate initial values
self.evaluate(variables.global_variables_initializer())
self.assertAllClose([1.0, 2.0], self.evaluate(var0))
self.assertAllClose([3.0, 4.0], self.evaluate(var1))
# Run 1 step through optimizer on GPU.
# Slot variables are created the first time optimizer is used on some
# variable. This tests that slot variables will be colocated with the base
# variable.
with ops.device("/device:GPU:0"):
# Note that for eager execution, minimize expects a function instead of a
# Tensor.
opt_op = opt.minimize(loss, global_step, [var0, var1])
self.evaluate(variables.global_variables_initializer())
self.evaluate(opt_op)
# Validate updated params, All variables should have decreased.
self.assertTrue(all(v < 0.0 for v in self.evaluate(var0)),
msg="updated variables: %s" % self.evaluate(var0))
self.assertTrue(all(v < 2.0 for v in self.evaluate(var1)),
msg="updated variables: %s" % self.evaluate(var1))
if __name__ == "__main__":
test.main()

1
tensorflow/python/keras/optimizer_v2/BUILD
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@ -201,6 +201,7 @@ cuda_py_test(
srcs = ["rmsprop_test.py"],
additional_deps = [
":optimizer_v2",
"@absl_py//absl/testing:parameterized",
"//tensorflow/python/eager:def_function",
"//tensorflow/python:client_testlib",
"//tensorflow/python:framework",

1
tensorflow/python/keras/optimizer_v2/optimizer_v2.py
View File

@ -443,6 +443,7 @@ class OptimizerV2(checkpointable.CheckpointableBase):
initializer, shape=var.shape, dtype=var.dtype)
else:
initial_value = initializer
with ops._colocate_with_for_gradient(var, None): # pylint: disable=protected-access
weight = tf_variables.Variable(
name="%s/%s" % (var._shared_name, slot_name), # pylint: disable=protected-access
dtype=var.dtype,

48
tensorflow/python/keras/optimizer_v2/rmsprop_test.py
View File

@ -22,6 +22,7 @@ import copy
import itertools
import math
from absl.testing import parameterized
import numpy as np
from tensorflow.python.eager import context
@ -421,5 +422,52 @@ class RMSpropOptimizerTest(test.TestCase):
self.assertEqual(opt_3.lr, 0.1)
class SlotColocationTest(test.TestCase, parameterized.TestCase):
@parameterized.parameters([True, False])
@test_util.run_in_graph_and_eager_modes
def testRunMinimizeOnGPUForCPUVariables(self, use_resource):
if not context.context().num_gpus():
self.skipTest("No GPUs found")
with ops.device("/device:CPU:0"):
if use_resource:
var0 = resource_variable_ops.ResourceVariable([1.0, 2.0],
dtype=dtypes.float32)
var1 = resource_variable_ops.ResourceVariable([3.0, 4.0],
dtype=dtypes.float32)
else:
var0 = variables.Variable([1.0, 2.0], dtype=dtypes.float32)
var1 = variables.Variable([3.0, 4.0], dtype=dtypes.float32)
def loss():
return 5 * var0 + 3 * var1
opt = rmsprop.RMSprop(
learning_rate=1.0, decay=0.9, momentum=0.5, epsilon=1.0)
# Fetch params to validate initial values
self.evaluate(variables.global_variables_initializer())
self.assertAllClose([1.0, 2.0], self.evaluate(var0))
self.assertAllClose([3.0, 4.0], self.evaluate(var1))
# Run 1 step through optimizer on GPU.
# Slot variables are created the first time optimizer is used on some
# variable. This tests that slot variables will be colocated with the base
# variable.
with ops.device("/device:GPU:0"):
# Note that for eager execution, minimize expects a function instead of a
# Tensor.
opt_op = opt.minimize(loss, [var0, var1])
self.evaluate(variables.global_variables_initializer())
self.evaluate(opt_op)
# Validate updated params, All variables should have decreased.
self.assertTrue(all(v < 0.0 for v in self.evaluate(var0)),
msg="updated variables: %s" % self.evaluate(var0))
self.assertTrue(all(v < 2.0 for v in self.evaluate(var1)),
msg="updated variables: %s" % self.evaluate(var1))
if __name__ == "__main__":
test.main()