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Add update method for ema (#17812)

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dependabot/pip/python/requests-2.20.0
Yibing Liu 6 years ago committed by GitHub
parent c10157a5df
commit d6d33fd748
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GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 28 additions and 15 deletions
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1
paddle/fluid/API.spec
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@ -528,6 +528,7 @@ paddle.fluid.optimizer.LambOptimizer.minimize (ArgSpec(args=['self', 'loss', 'st
paddle.fluid.optimizer.ExponentialMovingAverage.__init__ (ArgSpec(args=['self', 'decay', 'thres_steps', 'name'], varargs=None, keywords=None, defaults=(0.999, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.ExponentialMovingAverage.apply (ArgSpec(args=['self', 'executor', 'need_restore'], varargs=None, keywords=None, defaults=(True,)), ('document', '30f494752ac8921dc5835a63637f453a'))
paddle.fluid.optimizer.ExponentialMovingAverage.restore (ArgSpec(args=['self', 'executor'], varargs=None, keywords=None, defaults=None), ('document', '8c8a1791608b02a1ede53d6dd3a4fcec'))
paddle.fluid.optimizer.ExponentialMovingAverage.update (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'ea10f08af6d7aac3b7974aa976e4085f'))
paddle.fluid.backward.append_backward (ArgSpec(args=['loss', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '08a5dd9f6f376ff3d55e0b1d92115cbd'))
paddle.fluid.regularizer.L1DecayRegularizer.__init__ (ArgSpec(args=['self', 'regularization_coeff'], varargs=None, keywords=None, defaults=(0.0,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.regularizer.L2DecayRegularizer.__init__ (ArgSpec(args=['self', 'regularization_coeff'], varargs=None, keywords=None, defaults=(0.0,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))

42
python/paddle/fluid/optimizer.py
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@ -2333,10 +2333,10 @@ class ExponentialMovingAverage(object):
\\text{EMA}_t & = \\text{decay} * \\text{EMA}_{t-1} + (1 - \\text{decay}) * \\theta_t
The average results will be saved in temporary variables which are created
and maintained by the object, and can be applied to parameters of current
model by calling **apply()** method. And the **restore()** method is used to
restore the parameters.
The average results calculated by **update()** method will be saved in
temporary variables which are created and maintained by the object, and can
be applied to parameters of current model by calling **apply()** method. And
the **restore()** method is used to restore the parameters.
**Bias correction**. All EMAs are initialized to :math:`0` and hence they will be
zero biased, which can be corrected by divided by a factor
@ -2382,6 +2382,7 @@ class ExponentialMovingAverage(object):
global_steps = fluid.layers.learning_rate_scheduler._decay_step_counter()
ema = fluid.optimizer.ExponentialMovingAverage(0.999, thres_steps=global_steps)
ema.update()
# pseudo code
for pass_id in range(args.pass_num):
@ -2407,7 +2408,7 @@ class ExponentialMovingAverage(object):
self._name = name if name is not None else ''
self._decay_var = self._get_ema_decay()
self.params_tmps = []
self._params_tmps = []
for param in default_main_program().global_block().all_parameters():
if param.do_model_average != False:
tmp = param.block.create_var(
@ -2416,22 +2417,22 @@ class ExponentialMovingAverage(object):
dtype=param.dtype,
persistable=False,
stop_gradient=True)
self.params_tmps.append((param, tmp))
self._params_tmps.append((param, tmp))
ema_vars = {}
for param, tmp in self.params_tmps:
self._ema_vars = {}
for param, tmp in self._params_tmps:
with param.block.program._optimized_guard(
[param, tmp]), name_scope('moving_average'):
ema_vars[param.name] = self._append_ema_ops(param)
self._ema_vars[param.name] = self._create_ema_vars(param)
self.apply_program = Program()
block = self.apply_program.global_block()
with program_guard(main_program=self.apply_program):
decay_pow = self._get_decay_pow(block)
for param, tmp in self.params_tmps:
for param, tmp in self._params_tmps:
param = block._clone_variable(param)
tmp = block._clone_variable(tmp)
ema = block._clone_variable(ema_vars[param.name])
ema = block._clone_variable(self._ema_vars[param.name])
layers.assign(input=param, output=tmp)
# bias correction
ema = ema / (1.0 - decay_pow)
@ -2440,7 +2441,7 @@ class ExponentialMovingAverage(object):
self.restore_program = Program()
block = self.restore_program.global_block()
with program_guard(main_program=self.restore_program):
for param, tmp in self.params_tmps:
for param, tmp in self._params_tmps:
tmp = block._clone_variable(tmp)
param = block._clone_variable(param)
layers.assign(input=tmp, output=param)
@ -2472,7 +2473,7 @@ class ExponentialMovingAverage(object):
decay_pow_acc = layers.elementwise_pow(decay_var, global_steps + 1)
return decay_pow_acc
def _append_ema_ops(self, param):
def _create_ema_vars(self, param):
param_ema = layers.create_global_var(
name=unique_name.generate(self._name + param.name + '_ema'),
shape=param.shape,
@ -2480,10 +2481,21 @@ class ExponentialMovingAverage(object):
dtype=param.dtype,
persistable=True)
ema_t = param_ema * self._decay_var + param * (1 - self._decay_var)
layers.assign(input=ema_t, output=param_ema)
return param_ema
def update(self):
"""
Update Exponential Moving Average. Should only call this method in
train program.
"""
for param, tmp in self._params_tmps:
with param.block.program._optimized_guard(
[param, tmp]), name_scope('moving_average'):
param_ema = self._ema_vars[param.name]
ema_t = param_ema * self._decay_var + param * (1 -
self._decay_var)
layers.assign(input=ema_t, output=param_ema)
@signature_safe_contextmanager
def apply(self, executor, need_restore=True):
"""

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