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142 lines
4.9 KiB
142 lines
4.9 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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from __future__ import print_function
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import unittest
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import numpy as np
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import paddle.fluid.core as core
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from paddle.fluid.op import Operator
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import paddle.fluid as fluid
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class TestDGCMomentumOp1(unittest.TestCase):
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def get_tensor(self, name, value, place=None):
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tensor = self.scope.var(name).get_tensor()
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tensor.set(value, self.place if place is None else place)
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return name, tensor
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def setup(self, place, step=0.0):
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self.scope = fluid.global_scope()
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self.place = place
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print("place:", place)
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self.op_type = "dgc_momentum"
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self.dtype = np.float32
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nranks_val = 2
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param = np.random.random((123, 321)).astype(self.dtype)
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grad = np.random.random((123, 321)).astype(self.dtype)
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velocity = np.zeros((123, 321)).astype(self.dtype)
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learning_rate = np.array([0.001]).astype(self.dtype)
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current_step = np.full((1), step).astype("float32")
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nranks = np.full((1), nranks_val).astype("float32")
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mu = 0.0001
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use_nesterov = False
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rampup_begin_step = 10.0
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# get tensor
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self.param_name, self.param_tensor = self.get_tensor('Param', param)
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self.grad_name, self.grad_tensor = self.get_tensor('Grad', grad)
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self.velocity_name, self.velocity_tensor = self.get_tensor('Velocity',
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velocity)
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self.learning_rate_name, self.learning_rate_tensor = self.get_tensor(
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'LearningRate', learning_rate)
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self.current_step_name, self.current_step_tensor = self.get_tensor(
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'current_step', current_step, core.CPUPlace())
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self.nranks_name, self.nranks_tensor = self.get_tensor('nranks', nranks,
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core.CPUPlace())
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self.kwargs = {
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# inputs
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'Param': self.param_name,
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'Grad': self.grad_name,
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'Velocity': self.velocity_name,
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'LearningRate': self.learning_rate_name,
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'current_step': self.current_step_name,
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'nranks': self.nranks_name,
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# attrs
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'mu': mu,
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'use_nesterov': use_nesterov,
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'rampup_begin_step': rampup_begin_step,
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# outputs
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'ParamOut': self.param_name,
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'VelocityOut': self.velocity_name,
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'Grad_out': self.grad_name,
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}
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velocity_out = mu * velocity + grad / nranks
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if use_nesterov:
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param_out = param - grad * learning_rate - \
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velocity_out * mu * learning_rate
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else:
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param_out = param - learning_rate * velocity_out
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sgd_out = param - learning_rate * grad / nranks
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self.outputs = {
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'ParamOut': param_out,
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'VelocityOut': velocity_out,
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'SGDOut': sgd_out
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}
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def check(self, actual_t, expect_t, place, out_name, atol=1e-5):
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self.assertTrue(
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np.allclose(
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actual_t, expect_t, atol=atol),
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"Output (" + out_name + ") has diff at " + str(place) + "\nExpect "
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+ str(expect_t) + "\n" + "But Got" + str(actual_t))
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def check_momentum_step(self, place):
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self.setup(place=place)
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dgc_momentum_op = Operator(self.op_type, **self.kwargs)
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dgc_momentum_op.run(self.scope, self.place)
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self.check(
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np.array(self.param_tensor), self.outputs['ParamOut'], self.place,
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self.param_name)
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self.check(
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np.array(self.velocity_tensor), self.outputs['VelocityOut'],
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self.place, self.velocity_name)
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def check_sgd_step(self, place):
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self.setup(place=place, step=15.0)
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dgc_momentum_op = Operator(self.op_type, **self.kwargs)
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dgc_momentum_op.run(self.scope, self.place)
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self.check(
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np.array(self.param_tensor), self.outputs['SGDOut'], self.place,
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self.param_name)
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def test_cuda_place(self):
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if not core.is_compiled_with_cuda():
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return
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place = core.CUDAPlace(0)
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self.check_momentum_step(place)
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self.check_sgd_step(place)
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def test_cpu_place(self):
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place = core.CPUPlace()
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self.check_momentum_step(place)
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self.check_sgd_step(place)
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if __name__ == "__main__":
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unittest.main()
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