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505 lines
21 KiB
505 lines
21 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import paddle.fluid.framework as framework
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import paddle.fluid.optimizer as optimizer
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from paddle.fluid.backward import append_backward
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class TestOptimizer(unittest.TestCase):
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def test_sgd_optimizer(self):
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def check_sgd_optimizer(optimizer_attr):
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init_program = framework.Program()
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program = framework.Program()
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block = program.global_block()
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mul_x = block.create_parameter(
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dtype="float32",
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shape=[5, 10],
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lod_level=0,
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name="mul.x",
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optimize_attr=optimizer_attr)
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mul_y = block.create_var(
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dtype="float32", shape=[10, 8], lod_level=0, name="mul.y")
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mul_out = block.create_var(
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dtype="float32", shape=[5, 8], lod_level=0, name="mul.out")
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mean_out = block.create_var(
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dtype="float32", shape=[1], lod_level=0, name="mean.out")
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block.append_op(
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type="mul",
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inputs={"X": mul_x,
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"Y": mul_y},
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outputs={"Out": mul_out},
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attrs={"x_num_col_dims": 1})
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block.append_op(
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type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out})
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sgd_optimizer = optimizer.SGDOptimizer(learning_rate=0.01)
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opts, _ = sgd_optimizer.minimize(mean_out, init_program)
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return opts
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opts = check_sgd_optimizer({'learning_rate': 1.1})
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self.assertEqual(len(opts), 3)
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self.assertEqual([op.type for op in opts],
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["fill_constant", "elementwise_mul", "sgd"])
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opts = check_sgd_optimizer({'learning_rate': 1.0})
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self.assertEqual(len(opts), 1)
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self.assertEqual([op.type for op in opts], ["sgd"])
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class TestMomentumOptimizer(unittest.TestCase):
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class MockMomentum(optimizer.MomentumOptimizer):
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def get_accumulators(self):
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return self._accumulators
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def get_velocity_str(self):
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return self._velocity_acc_str
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def test_vanilla_momentum_optimizer(self):
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init_program = framework.Program()
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program = framework.Program()
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block = program.global_block()
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mul_x = block.create_parameter(
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dtype="float32",
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shape=[5, 10],
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lod_level=0,
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name="mul.x",
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optimize_attr={'learning_rate': 1.1})
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mul_y = block.create_var(
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dtype="float32", shape=[10, 8], lod_level=0, name="mul.y")
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mul_out = block.create_var(
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dtype="float32", shape=[5, 8], lod_level=0, name="mul.out")
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block.append_op(
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type="mul",
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inputs={"X": mul_x,
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"Y": mul_y},
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outputs={"Out": mul_out},
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attrs={"x_num_col_dims": 1})
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learning_rate = 0.01
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momentum_optimizer = self.MockMomentum(
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learning_rate=learning_rate, momentum=0.2)
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mean_out = block.create_var(
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dtype="float32", shape=[1], lod_level=0, name="mean.out")
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block.append_op(
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type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out})
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params_grads = append_backward(mean_out)
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self.assertEqual(len(params_grads), 1)
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self.assertEqual(len(momentum_optimizer.get_accumulators()), 0)
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opts = momentum_optimizer._create_optimization_pass(
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params_grads, mul_out, init_program)
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self.assertEqual(len(opts), 3)
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sgd_op = opts[-1]
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self.assertEqual([op.type for op in opts],
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["fill_constant", "elementwise_mul", "momentum"])
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self.assertFalse(sgd_op.attr('use_nesterov'))
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# Check accumulators
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accumulators = momentum_optimizer.get_accumulators()
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self.assertEqual(len(accumulators), 1)
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self.assertTrue(momentum_optimizer.get_velocity_str() in accumulators)
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velocity_acc = accumulators[momentum_optimizer.get_velocity_str()]
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self.assertEqual(len(velocity_acc), 1)
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self.assertTrue(mul_x.name in velocity_acc)
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# Check init_program
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init_ops = init_program.global_block().ops
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self.assertEqual(len(init_ops), 2)
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self.assertEqual(init_ops[0].type, "fill_constant")
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self.assertAlmostEqual(init_ops[0].attr('value'), learning_rate)
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self.assertEqual(init_ops[1].type, "fill_constant")
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self.assertAlmostEqual(init_ops[1].attr('value'), 0.0)
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def test_nesterov_momentum_optimizer(self):
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init_program = framework.Program()
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program = framework.Program()
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block = program.global_block()
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mul_x = block.create_parameter(
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dtype="float32",
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shape=[5, 10],
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lod_level=0,
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name="mul.x",
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optimize_attr={'learning_rate': 1.1})
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mul_y = block.create_var(
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dtype="float32", shape=[10, 8], lod_level=0, name="mul.y")
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mul_out = block.create_var(
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dtype="float32", shape=[5, 8], lod_level=0, name="mul.out")
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block.append_op(
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type="mul",
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inputs={"X": mul_x,
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"Y": mul_y},
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outputs={"Out": mul_out},
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attrs={"x_num_col_dims": 1})
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mean_out = block.create_var(
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dtype="float32", shape=[1], lod_level=0, name="mean.out")
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block.append_op(
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type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out})
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learning_rate = 0.01
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momentum_optimizer = self.MockMomentum(
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learning_rate=learning_rate, momentum=0.2, use_nesterov=True)
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params_grads = append_backward(mean_out)
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self.assertEqual(len(params_grads), 1)
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self.assertEqual(len(momentum_optimizer.get_accumulators()), 0)
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opts = momentum_optimizer._create_optimization_pass(
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params_grads, mul_out, init_program)
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self.assertEqual(len(opts), 3)
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sgd_op = opts[-1]
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self.assertEqual([op.type for op in opts],
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["fill_constant", "elementwise_mul", "momentum"])
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self.assertTrue(sgd_op.attr('use_nesterov'))
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# Check accumulators
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accumulators = momentum_optimizer.get_accumulators()
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self.assertEqual(len(accumulators), 1)
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self.assertTrue(momentum_optimizer.get_velocity_str() in accumulators)
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velocity_acc = accumulators[momentum_optimizer.get_velocity_str()]
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self.assertEqual(len(velocity_acc), 1)
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self.assertTrue(mul_x.name in velocity_acc)
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# Check init_program
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init_ops = init_program.global_block().ops
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self.assertEqual(len(init_ops), 2)
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self.assertEqual(init_ops[0].type, "fill_constant")
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self.assertAlmostEqual(init_ops[0].attr('value'), learning_rate)
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self.assertEqual(init_ops[1].type, "fill_constant")
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self.assertAlmostEqual(init_ops[1].attr('value'), 0.0)
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class TestAdagradOptimizer(unittest.TestCase):
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class MockAdagrad(optimizer.AdagradOptimizer):
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def get_accumulators(self):
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return self._accumulators
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def get_moment_str(self):
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return self._moment_acc_str
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def test_adagrad_optimizer(self):
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init_program = framework.Program()
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program = framework.Program()
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block = program.global_block()
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mul_x = block.create_parameter(
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dtype="float32",
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shape=[5, 10],
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lod_level=0,
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name="mul.x",
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optimize_attr={'learning_rate': 1.1})
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mul_y = block.create_var(
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dtype="float32", shape=[10, 8], lod_level=0, name="mul.y")
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mul_out = block.create_var(
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dtype="float32", shape=[5, 8], lod_level=0, name="mul.out")
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block.append_op(
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type="mul",
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inputs={"X": mul_x,
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"Y": mul_y},
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outputs={"Out": mul_out},
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attrs={"x_num_col_dims": 1})
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mean_out = block.create_var(
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dtype="float32", shape=[1], lod_level=0, name="mean.out")
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block.append_op(
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type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out})
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learning_rate = 0.01
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adagrad_optimizer = self.MockAdagrad(
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learning_rate=learning_rate, epsilon=1.0e-6)
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params_grads = append_backward(mean_out)
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self.assertEqual(len(params_grads), 1)
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self.assertEqual(len(adagrad_optimizer.get_accumulators()), 0)
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opts = adagrad_optimizer._create_optimization_pass(
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params_grads, mul_out, init_program)
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self.assertEqual(len(opts), 3)
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self.assertEqual([op.type for op in opts],
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["fill_constant", "elementwise_mul", "adagrad"])
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# Check accumulators
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accumulators = adagrad_optimizer.get_accumulators()
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self.assertEqual(len(accumulators), 1)
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self.assertTrue(adagrad_optimizer.get_moment_str() in accumulators)
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moment_acc = accumulators[adagrad_optimizer.get_moment_str()]
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self.assertEqual(len(moment_acc), 1)
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self.assertTrue(mul_x.name in moment_acc)
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# Check init_program
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init_ops = init_program.global_block().ops
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self.assertEqual(len(init_ops), 2)
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self.assertEqual(init_ops[0].type, "fill_constant")
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self.assertAlmostEqual(init_ops[0].attr('value'), learning_rate)
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self.assertEqual(init_ops[1].type, "fill_constant")
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self.assertAlmostEqual(init_ops[1].attr('value'), 0.0)
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class TestAdamOptimizer(unittest.TestCase):
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class MockAdam(optimizer.AdamOptimizer):
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def get_accumulators(self):
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return self._accumulators
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def get_moment1_str(self):
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return self._moment1_acc_str
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def get_moment2_str(self):
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return self._moment2_acc_str
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def test_adam_optimizer(self):
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init_program = framework.Program()
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program = framework.Program()
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block = program.global_block()
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mul_x = block.create_parameter(
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dtype="float32",
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shape=[5, 10],
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lod_level=0,
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name="mul.x",
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optimize_attr={'learning_rate': 1.1})
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mul_y = block.create_var(
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dtype="float32", shape=[10, 8], lod_level=0, name="mul.y")
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mul_out = block.create_var(
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dtype="float32", shape=[5, 8], lod_level=0, name="mul.out")
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block.append_op(
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type="mul",
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inputs={"X": mul_x,
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"Y": mul_y},
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outputs={"Out": mul_out},
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attrs={"x_num_col_dims": 1})
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mean_out = block.create_var(
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dtype="float32", shape=[1], lod_level=0, name="mean.out")
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block.append_op(
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type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out})
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learning_rate = 0.01
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adam_optimizer = self.MockAdam(
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learning_rate=learning_rate, beta1=0.9, beta2=0.999)
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params_grads = append_backward(mean_out)
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self.assertEqual(len(params_grads), 1)
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self.assertEqual(len(adam_optimizer.get_accumulators()), 0)
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opts = adam_optimizer._create_optimization_pass(params_grads, mul_out,
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init_program)
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self.assertEqual(len(opts), 5)
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self.assertEqual(
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[op.type for op in opts],
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["fill_constant", "elementwise_mul", "adam", "scale", "scale"])
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# Check accumulators
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accumulators = adam_optimizer.get_accumulators()
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self.assertEqual(len(accumulators), 4)
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self.assertTrue(adam_optimizer.get_moment1_str() in accumulators)
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self.assertTrue(adam_optimizer.get_moment2_str() in accumulators)
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moment1_acc = accumulators[adam_optimizer.get_moment1_str()]
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moment2_acc = accumulators[adam_optimizer.get_moment2_str()]
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self.assertEqual(len(moment1_acc), 1)
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self.assertEqual(len(moment2_acc), 1)
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self.assertTrue(mul_x.name in moment1_acc)
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self.assertTrue(mul_x.name in moment2_acc)
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# Check init_program
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init_ops = init_program.global_block().ops
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self.assertEqual(len(init_ops), 5)
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self.assertEqual(init_ops[0].type, "fill_constant")
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self.assertAlmostEqual(init_ops[0].attr('value'), learning_rate)
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class TestAdamaxOptimizer(unittest.TestCase):
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class MockAdamax(optimizer.AdamaxOptimizer):
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def get_accumulators(self):
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return self._accumulators
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def get_moment_str(self):
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return self._moment_acc_str
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def get_inf_norm_str(self):
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return self._inf_norm_acc_str
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def test_adamax_optimizer(self):
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init_program = framework.Program()
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program = framework.Program()
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block = program.global_block()
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mul_x = block.create_parameter(
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dtype="float32",
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shape=[5, 10],
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lod_level=0,
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name="mul.x",
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optimize_attr={'learning_rate': 1.1})
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mul_y = block.create_var(
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dtype="float32", shape=[10, 8], lod_level=0, name="mul.y")
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mul_out = block.create_var(
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dtype="float32", shape=[5, 8], lod_level=0, name="mul.out")
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block.append_op(
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type="mul",
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inputs={"X": mul_x,
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"Y": mul_y},
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outputs={"Out": mul_out},
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attrs={"x_num_col_dims": 1})
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mean_out = block.create_var(
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dtype="float32", shape=[1], lod_level=0, name="mean.out")
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block.append_op(
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type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out})
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learning_rate = 0.01
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adamax_optimizer = self.MockAdamax(
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learning_rate=learning_rate, beta1=0.9, beta2=0.999)
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params_grads = append_backward(mean_out)
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self.assertEqual(len(params_grads), 1)
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self.assertEqual(len(adamax_optimizer.get_accumulators()), 0)
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opts = adamax_optimizer._create_optimization_pass(params_grads, mul_out,
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init_program)
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self.assertEqual(len(opts), 4)
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self.assertEqual(
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[op.type for op in opts],
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["fill_constant", "elementwise_mul", "adamax", "scale"])
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# Check accumulators
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accumulators = adamax_optimizer.get_accumulators()
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self.assertEqual(len(accumulators), 3)
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self.assertTrue(adamax_optimizer.get_moment_str() in accumulators)
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self.assertTrue(adamax_optimizer.get_inf_norm_str() in accumulators)
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moment_acc = accumulators[adamax_optimizer.get_moment_str()]
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inf_norm_acc = accumulators[adamax_optimizer.get_inf_norm_str()]
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self.assertEqual(len(moment_acc), 1)
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self.assertEqual(len(inf_norm_acc), 1)
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self.assertTrue(mul_x.name in moment_acc)
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self.assertTrue(mul_x.name in inf_norm_acc)
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# Check init_program
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init_ops = init_program.global_block().ops
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self.assertEqual(len(init_ops), 4)
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self.assertEqual(init_ops[0].type, "fill_constant")
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self.assertAlmostEqual(init_ops[0].attr('value'), learning_rate)
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class TestDecayedAdagradOptimizer(unittest.TestCase):
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class MockDecayedAdagrad(optimizer.DecayedAdagradOptimizer):
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def get_accumulators(self):
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return self._accumulators
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def get_moment_str(self):
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return self._moment_acc_str
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def test_decayed_adagrad_optimizer(self):
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init_program = framework.Program()
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program = framework.Program()
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block = program.global_block()
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mul_x = block.create_parameter(
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dtype="float32",
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shape=[5, 10],
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lod_level=0,
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name="mul.x",
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optimize_attr={'learning_rate': 1.1})
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mul_y = block.create_var(
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dtype="float32", shape=[10, 8], lod_level=0, name="mul.y")
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mul_out = block.create_var(
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dtype="float32", shape=[5, 8], lod_level=0, name="mul.out")
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block.append_op(
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type="mul",
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inputs={"X": mul_x,
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"Y": mul_y},
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outputs={"Out": mul_out},
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attrs={"x_num_col_dims": 1})
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mean_out = block.create_var(
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dtype="float32", shape=[1], lod_level=0, name="mean.out")
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block.append_op(
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type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out})
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learning_rate = 0.01
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decayed_adagrad_optimizer = self.MockDecayedAdagrad(
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learning_rate=learning_rate, decay=0.95, epsilon=1.0e-6)
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params_grads = append_backward(mean_out)
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self.assertEqual(len(params_grads), 1)
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self.assertEqual(len(decayed_adagrad_optimizer.get_accumulators()), 0)
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opts = decayed_adagrad_optimizer._create_optimization_pass(
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params_grads, mul_out, init_program)
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self.assertEqual(len(opts), 3)
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self.assertEqual(
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[op.type for op in opts],
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["fill_constant", "elementwise_mul", "decayed_adagrad"])
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# Check accumulators
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accumulators = decayed_adagrad_optimizer.get_accumulators()
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self.assertEqual(len(accumulators), 1)
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self.assertTrue(
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decayed_adagrad_optimizer.get_moment_str() in accumulators)
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moment_acc = accumulators[decayed_adagrad_optimizer.get_moment_str()]
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self.assertEqual(len(moment_acc), 1)
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self.assertTrue(mul_x.name in moment_acc)
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# Check init_program
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init_ops = init_program.global_block().ops
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self.assertEqual(len(init_ops), 2)
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self.assertEqual(init_ops[0].type, "fill_constant")
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self.assertAlmostEqual(init_ops[0].attr('value'), learning_rate)
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self.assertEqual(init_ops[1].type, "fill_constant")
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self.assertAlmostEqual(init_ops[1].attr('value'), 0.0)
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|
|
|
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class TestFtrlOptimizer(unittest.TestCase):
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class MockFtrl(optimizer.FtrlOptimizer):
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def get_accumulators(self):
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return self._accumulators
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|
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|
def get_squared_str(self):
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return self._squared_acc_str
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|
|
|
def get_linear_str(self):
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return self._linear_acc_str
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|
|
|
def test_ftrl_optimizer(self):
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init_program = framework.Program()
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|
program = framework.Program()
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|
block = program.global_block()
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|
mul_x = block.create_parameter(
|
|
dtype="float32",
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|
shape=[5, 10],
|
|
lod_level=0,
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|
name="mul.x",
|
|
optimize_attr={'learning_rate': 1.1})
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|
mul_y = block.create_var(
|
|
dtype="float32", shape=[10, 8], lod_level=0, name="mul.y")
|
|
mul_out = block.create_var(
|
|
dtype="float32", shape=[5, 8], lod_level=0, name="mul.out")
|
|
block.append_op(
|
|
type="mul",
|
|
inputs={"X": mul_x,
|
|
"Y": mul_y},
|
|
outputs={"Out": mul_out},
|
|
attrs={"x_num_col_dims": 1})
|
|
mean_out = block.create_var(
|
|
dtype="float32", shape=[1], lod_level=0, name="mean.out")
|
|
block.append_op(
|
|
type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out})
|
|
learning_rate = 0.01
|
|
ftrl_optimizer = self.MockFtrl(
|
|
learning_rate=learning_rate, l1=0.0, l2=0.0, lr_power=-0.5)
|
|
params_grads = append_backward(mean_out)
|
|
self.assertEqual(len(params_grads), 1)
|
|
self.assertEqual(len(ftrl_optimizer.get_accumulators()), 0)
|
|
opts = ftrl_optimizer._create_optimization_pass(params_grads, mul_out,
|
|
init_program)
|
|
self.assertEqual(len(opts), 3)
|
|
self.assertEqual([op.type for op in opts],
|
|
["fill_constant", "elementwise_mul", "ftrl"])
|
|
|
|
# Check accumulators
|
|
accumulators = ftrl_optimizer.get_accumulators()
|
|
self.assertEqual(len(accumulators), 2)
|
|
self.assertTrue(ftrl_optimizer.get_squared_str() in accumulators)
|
|
self.assertTrue(ftrl_optimizer.get_linear_str() in accumulators)
|
|
squared_acc = accumulators[ftrl_optimizer.get_squared_str()]
|
|
linear_acc = accumulators[ftrl_optimizer.get_linear_str()]
|
|
self.assertEqual(len(squared_acc), 1)
|
|
self.assertEqual(len(linear_acc), 1)
|
|
self.assertTrue(mul_x.name in squared_acc)
|
|
self.assertTrue(mul_x.name in linear_acc)
|
|
|
|
# Check init_program
|
|
init_ops = init_program.global_block().ops
|
|
self.assertEqual(len(init_ops), 3)
|
|
self.assertEqual(init_ops[0].type, "fill_constant")
|
|
self.assertAlmostEqual(init_ops[0].attr('value'), learning_rate)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|