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Support clipping limits for TPU embedding in tpu_estimator.py.

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PiperOrigin-RevId: 243928920
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A. Unique TensorFlower 2019-04-16 20:36:49 -07:00 committed by TensorFlower Gardener
parent 18daf67542
commit 7d2fd55ef6
1 changed files with 42 additions and 16 deletions
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58
tensorflow/python/tpu/tpu_embedding.py
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@ -216,16 +216,23 @@ VariablesAndOps = collections.namedtuple(
class _OptimizationParameters(object): class _OptimizationParameters(object):
"""Parameters common to all optimizations.""" """Parameters common to all optimizations."""
def __init__(self, learning_rate, use_gradient_accumulation): def __init__(self, learning_rate, use_gradient_accumulation,
clip_weight_min, clip_weight_max):
self.learning_rate = learning_rate self.learning_rate = learning_rate
self.use_gradient_accumulation = use_gradient_accumulation self.use_gradient_accumulation = use_gradient_accumulation
self.clip_weight_min = clip_weight_min
self.clip_weight_max = clip_weight_max
class AdagradParameters(_OptimizationParameters): class AdagradParameters(_OptimizationParameters):
"""Optimization parameters for Adagrad.""" """Optimization parameters for Adagrad."""
def __init__(self, learning_rate, initial_accumulator=0.1, def __init__(self,
use_gradient_accumulation=True): learning_rate,
initial_accumulator=0.1,
use_gradient_accumulation=True,
clip_weight_min=None,
clip_weight_max=None):
"""Optimization parameters for Adagrad. """Optimization parameters for Adagrad.
Args: Args:
@ -235,9 +242,12 @@ class AdagradParameters(_OptimizationParameters):
gradients calculation less accurate but faster. Please see gradients calculation less accurate but faster. Please see
`optimization_parameters.proto` for details. `optimization_parameters.proto` for details.
for details. for details.
clip_weight_min: the minimum value to clip by; None means -infinity.
clip_weight_max: the maximum value to clip by; None means +infinity.
""" """
super(AdagradParameters, self).__init__(learning_rate, super(AdagradParameters,
use_gradient_accumulation) self).__init__(learning_rate, use_gradient_accumulation,
clip_weight_min, clip_weight_max)
if initial_accumulator <= 0: if initial_accumulator <= 0:
raise ValueError('Adagrad initial_accumulator must be positive') raise ValueError('Adagrad initial_accumulator must be positive')
self.initial_accumulator = initial_accumulator self.initial_accumulator = initial_accumulator
@ -246,13 +256,16 @@ class AdagradParameters(_OptimizationParameters):
class AdamParameters(_OptimizationParameters): class AdamParameters(_OptimizationParameters):
"""Optimization parameters for Adam.""" """Optimization parameters for Adam."""
def __init__(self, learning_rate, def __init__(self,
learning_rate,
beta1=0.9, beta1=0.9,
beta2=0.999, beta2=0.999,
epsilon=1e-08, epsilon=1e-08,
lazy_adam=True, lazy_adam=True,
sum_inside_sqrt=True, sum_inside_sqrt=True,
use_gradient_accumulation=True): use_gradient_accumulation=True,
clip_weight_min=None,
clip_weight_max=None):
"""Optimization parameters for Adam. """Optimization parameters for Adam.
Args: Args:
@ -270,9 +283,12 @@ class AdamParameters(_OptimizationParameters):
gradients calculation less accurate but faster. Please see gradients calculation less accurate but faster. Please see
`optimization_parameters.proto` for details. `optimization_parameters.proto` for details.
for details. for details.
clip_weight_min: the minimum value to clip by; None means -infinity.
clip_weight_max: the maximum value to clip by; None means +infinity.
""" """
super(AdamParameters, self).__init__(learning_rate, super(AdamParameters,
use_gradient_accumulation) self).__init__(learning_rate, use_gradient_accumulation,
clip_weight_min, clip_weight_max)
if beta1 < 0. or beta1 >= 1.: if beta1 < 0. or beta1 >= 1.:
raise ValueError('beta1 must be between 0. and 1; got {}.'.format(beta1)) raise ValueError('beta1 must be between 0. and 1; got {}.'.format(beta1))
if beta2 < 0. or beta2 >= 1.: if beta2 < 0. or beta2 >= 1.:
@ -291,15 +307,19 @@ class AdamParameters(_OptimizationParameters):
class StochasticGradientDescentParameters(_OptimizationParameters): class StochasticGradientDescentParameters(_OptimizationParameters):
"""Optimization parameters for stochastic gradient descent. """Optimization parameters for stochastic gradient descent."""
Args: def __init__(self, learning_rate, clip_weight_min=None,
learning_rate: a floating point value. The learning rate. clip_weight_max=None):
""" """Optimization parameters for stochastic gradient descent.
def __init__(self, learning_rate): Args:
super(StochasticGradientDescentParameters, self).__init__( learning_rate: a floating point value. The learning rate.
learning_rate, False) clip_weight_min: the minimum value to clip by; None means -infinity.
clip_weight_max: the maximum value to clip by; None means +infinity.
"""
super(StochasticGradientDescentParameters,
self).__init__(learning_rate, False, clip_weight_min, clip_weight_max)
class TPUEmbedding(object): class TPUEmbedding(object):
@ -566,6 +586,12 @@ class TPUEmbedding(object):
optimization_parameters_pb2.GradientAccumulationStatus.ENABLED optimization_parameters_pb2.GradientAccumulationStatus.ENABLED
if self._optimization_parameters.use_gradient_accumulation else if self._optimization_parameters.use_gradient_accumulation else
optimization_parameters_pb2.GradientAccumulationStatus.DISABLED) optimization_parameters_pb2.GradientAccumulationStatus.DISABLED)
if self._optimization_parameters.clip_weight_min is not None:
table_descriptor.optimization_parameters.clipping_limits.lower.value = (
self._optimization_parameters.clip_weight_min)
if self._optimization_parameters.clip_weight_max is not None:
table_descriptor.optimization_parameters.clipping_limits.upper.value = (
self._optimization_parameters.clip_weight_max)
self._optimizer_handler.set_optimization_parameters(table_descriptor) self._optimizer_handler.set_optimization_parameters(table_descriptor)
config_proto.mode = self._mode config_proto.mode = self._mode