6762ca15c4
The session returned by cached_session uses soft placement, something we don't want for XLA_* devices. With soft placement ops lacking XLA kernels silently fall back and run on the CPU, misleading us into thinking we have more test coverage than we actually do. With this test some tests (rightly) start failing because they were testing ops with dtypes the XLA kernels do not support. I've removed these dtypes from the tests. This CL partially addresses b/132430685. It stubs out "cached_session" and "test_session" to raise errors, so we have more confidence that the compiler is being exercised. However, we still use XLA_* devices to exercise XLA, which has a different code path than xla.compile and tpu.rewrite. This needs to be incrementally fixed. PiperOrigin-RevId: 248437673
175 lines
6.6 KiB
Python
175 lines
6.6 KiB
Python
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
|
|
#
|
|
# 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.
|
|
# ==============================================================================
|
|
"""Tests for Proximal Adagrad optimizer."""
|
|
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
import numpy as np
|
|
|
|
from tensorflow.compiler.tests import xla_test
|
|
from tensorflow.python.framework import constant_op
|
|
from tensorflow.python.ops import resource_variable_ops
|
|
from tensorflow.python.ops import variables
|
|
from tensorflow.python.platform import test
|
|
from tensorflow.python.training import adagrad
|
|
from tensorflow.python.training import proximal_adagrad
|
|
|
|
|
|
class ProximalAdagradOptimizerTest(xla_test.XLATestCase):
|
|
|
|
def testResourceProximalAdagradwithoutRegularization(self):
|
|
with self.session(), self.test_scope():
|
|
var0 = resource_variable_ops.ResourceVariable([0.0, 0.0])
|
|
var1 = resource_variable_ops.ResourceVariable([0.0, 0.0])
|
|
grads0 = constant_op.constant([0.1, 0.2])
|
|
grads1 = constant_op.constant([0.01, 0.02])
|
|
opt = proximal_adagrad.ProximalAdagradOptimizer(
|
|
3.0,
|
|
initial_accumulator_value=0.1,
|
|
l1_regularization_strength=0.0,
|
|
l2_regularization_strength=0.0)
|
|
update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
|
|
variables.global_variables_initializer().run()
|
|
|
|
self.assertAllClose([0.0, 0.0], self.evaluate(var0))
|
|
self.assertAllClose([0.0, 0.0], self.evaluate(var1))
|
|
|
|
# Run 3 steps Proximal Adagrad.
|
|
for _ in range(3):
|
|
update.run()
|
|
|
|
self.assertAllClose(
|
|
np.array([-2.60260963, -4.29698515]), self.evaluate(var0))
|
|
self.assertAllClose(
|
|
np.array([-0.28432083, -0.56694895]), self.evaluate(var1))
|
|
opt_vars = opt.variables()
|
|
self.assertStartsWith(opt_vars[0].name, var0._shared_name)
|
|
self.assertStartsWith(opt_vars[1].name, var1._shared_name)
|
|
self.assertEqual(2, len(opt_vars))
|
|
|
|
def testProximalAdagradwithoutRegularization2(self):
|
|
with self.session(), self.test_scope():
|
|
var0 = resource_variable_ops.ResourceVariable([1.0, 2.0])
|
|
var1 = resource_variable_ops.ResourceVariable([4.0, 3.0])
|
|
grads0 = constant_op.constant([0.1, 0.2])
|
|
grads1 = constant_op.constant([0.01, 0.02])
|
|
|
|
opt = proximal_adagrad.ProximalAdagradOptimizer(
|
|
3.0,
|
|
initial_accumulator_value=0.1,
|
|
l1_regularization_strength=0.0,
|
|
l2_regularization_strength=0.0)
|
|
update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
|
|
variables.global_variables_initializer().run()
|
|
|
|
self.assertAllClose([1.0, 2.0], self.evaluate(var0))
|
|
self.assertAllClose([4.0, 3.0], self.evaluate(var1))
|
|
|
|
# Run 3 steps Proximal Adagrad.
|
|
for _ in range(3):
|
|
update.run()
|
|
self.assertAllClose(np.array([-1.60261, -2.296985]), self.evaluate(var0))
|
|
self.assertAllClose(np.array([3.715679, 2.433051]), self.evaluate(var1))
|
|
|
|
def testProximalAdagradWithL1(self):
|
|
with self.session(), self.test_scope():
|
|
var0 = resource_variable_ops.ResourceVariable([1.0, 2.0])
|
|
var1 = resource_variable_ops.ResourceVariable([4.0, 3.0])
|
|
grads0 = constant_op.constant([0.1, 0.2])
|
|
grads1 = constant_op.constant([0.01, 0.02])
|
|
|
|
opt = proximal_adagrad.ProximalAdagradOptimizer(
|
|
3.0,
|
|
initial_accumulator_value=0.1,
|
|
l1_regularization_strength=0.001,
|
|
l2_regularization_strength=0.0)
|
|
update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
|
|
variables.global_variables_initializer().run()
|
|
|
|
self.assertAllClose([1.0, 2.0], self.evaluate(var0))
|
|
self.assertAllClose([4.0, 3.0], self.evaluate(var1))
|
|
|
|
# Run 10 steps Proximal Adagrad
|
|
for _ in range(10):
|
|
update.run()
|
|
self.assertAllClose(np.array([-6.663634, -9.190331]), self.evaluate(var0))
|
|
self.assertAllClose(np.array([2.959304, 1.029232]), self.evaluate(var1))
|
|
|
|
def testProximalAdagradWithL1_L2(self):
|
|
with self.session(), self.test_scope():
|
|
var0 = resource_variable_ops.ResourceVariable([1.0, 2.0])
|
|
var1 = resource_variable_ops.ResourceVariable([4.0, 3.0])
|
|
grads0 = constant_op.constant([0.1, 0.2])
|
|
grads1 = constant_op.constant([0.01, 0.02])
|
|
|
|
opt = proximal_adagrad.ProximalAdagradOptimizer(
|
|
3.0,
|
|
initial_accumulator_value=0.1,
|
|
l1_regularization_strength=0.001,
|
|
l2_regularization_strength=2.0)
|
|
update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
|
|
variables.global_variables_initializer().run()
|
|
|
|
self.assertAllClose([1.0, 2.0], self.evaluate(var0))
|
|
self.assertAllClose([4.0, 3.0], self.evaluate(var1))
|
|
|
|
# Run 10 steps Proximal Adagrad.
|
|
for _ in range(10):
|
|
update.run()
|
|
|
|
self.assertAllClose(np.array([-0.0495, -0.0995]), self.evaluate(var0))
|
|
self.assertAllClose(np.array([-0.0045, -0.0095]), self.evaluate(var1))
|
|
|
|
def applyOptimizer(self, opt, steps=5):
|
|
var0 = resource_variable_ops.ResourceVariable([1.0, 2.0])
|
|
var1 = resource_variable_ops.ResourceVariable([3.0, 4.0])
|
|
grads0 = constant_op.constant([0.1, 0.2])
|
|
grads1 = constant_op.constant([0.01, 0.02])
|
|
|
|
update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
|
|
variables.global_variables_initializer().run()
|
|
|
|
self.assertAllClose([1.0, 2.0], self.evaluate(var0))
|
|
self.assertAllClose([3.0, 4.0], self.evaluate(var1))
|
|
|
|
# Run ProximalAdagrad for a few steps
|
|
for _ in range(steps):
|
|
update.run()
|
|
|
|
return self.evaluate(var0), self.evaluate(var1)
|
|
|
|
def testEquivAdagradwithoutRegularization(self):
|
|
with self.session(), self.test_scope():
|
|
val0, val1 = self.applyOptimizer(
|
|
proximal_adagrad.ProximalAdagradOptimizer(
|
|
3.0,
|
|
initial_accumulator_value=0.1,
|
|
l1_regularization_strength=0.0,
|
|
l2_regularization_strength=0.0))
|
|
|
|
with self.session(), self.test_scope():
|
|
val2, val3 = self.applyOptimizer(
|
|
adagrad.AdagradOptimizer(
|
|
3.0, initial_accumulator_value=0.1))
|
|
|
|
self.assertAllClose(val0, val2)
|
|
self.assertAllClose(val1, val3)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test.main()
|