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109 lines
4.1 KiB
109 lines
4.1 KiB
# Copyright (c) 2019 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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from __future__ import print_function
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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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import paddle.compat as cpt
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from paddle.fluid.backward import append_backward
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from paddle.fluid.transpiler.details import program_to_code
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class TestDGCMomentumOptimizer(unittest.TestCase):
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class MockDGCMomentum(optimizer.DGCMomentumOptimizer):
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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._u_velocity_acc_str
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def check_dgc_momentum_optimizer(self, dims=[5, 10, 8], name="momentum"):
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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=[dims[0], dims[1]],
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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",
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shape=[dims[1], dims[2]],
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lod_level=0,
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name="mul.y")
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mul_out = block.create_var(
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dtype="float32",
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shape=[dims[0], dims[2]],
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lod_level=0,
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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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dgc_momentum_optimizer = self.MockDGCMomentum(
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learning_rate=learning_rate, momentum=0.2, rampup_begin_step=0)
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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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params_grads = dgc_momentum_optimizer.backward(mean_out)
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accumulator_count = 1 if name == "momentum" else 2
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self.assertEqual(len(params_grads), 1)
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self.assertEqual(
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len(dgc_momentum_optimizer.get_accumulators()), accumulator_count)
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with framework.program_guard(program, init_program):
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opts = dgc_momentum_optimizer.apply_gradients(params_grads)
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self.assertEqual(len(opts), 2)
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sgd_op = opts[-1]
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self.assertEqual([op.type for op in opts], ["scale", name])
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self.assertFalse(sgd_op.attr('use_nesterov'))
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# Check accumulators
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accumulators = dgc_momentum_optimizer.get_accumulators()
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self.assertEqual(len(accumulators), accumulator_count)
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self.assertTrue(
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dgc_momentum_optimizer.get_velocity_str() in accumulators)
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velocity_acc = accumulators[dgc_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), 1)
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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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with open("test_dgc_optimizer_" + name + ".log", "w") as f:
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program_to_code(program, fout=f)
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def test_momentum_without_dgc(self):
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self.check_dgc_momentum_optimizer()
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def test_momentum_with_dgc(self):
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# 16 * 1024 = 16384, use dgc momentum
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self.check_dgc_momentum_optimizer(
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dims=[16, 1024, 8], name="dgc_momentum")
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if __name__ == '__main__':
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unittest.main()
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