zhaoting
5 years ago
committed by
chang zherui
parent
9a717aa1f7
commit
dcd1f0a504
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# Copyright 2020 Huawei Technologies Co., Ltd
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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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# ============================================================================
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"""rmsprop"""
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from mindspore.ops import functional as F, composite as C, operations as P
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from mindspore.common.initializer import initializer
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from mindspore.common.parameter import Parameter
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from mindspore._checkparam import ParamValidator as validator
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import mindspore.common.dtype as mstype
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from .optimizer import Optimizer, grad_scale
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rmsprop_opt = C.MultitypeFuncGraph("rmsprop_opt")
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centered_rmsprop_opt = C.MultitypeFuncGraph("rmsprop_opt")
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@rmsprop_opt.register("Function", "Number", "Number", "Number", "Number", "Tensor", "Tensor", "Tensor", "Tensor")
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def _rmsprop_opt(opt, learning_rate, decay, epsilon, momentum, weight, ms, mom, grad):
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"""Apply rmsprop optimizer to the weight parameter."""
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success = True
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success = F.depend(success, opt(weight, ms, mom, grad, learning_rate, decay, momentum, epsilon))
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return success
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@rmsprop_opt.register("Function", "Tensor", "Number", "Number", "Number", "Tensor", "Tensor", "Tensor", "Tensor")
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def _rmsprop_opt_dynamic_lr(opt, learning_rate, decay, epsilon, momentum, weight, ms, mom, grad):
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"""Apply rmsprop optimizer to the weight parameter using dynamic learning rate."""
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success = True
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success = F.depend(success, opt(weight, ms, mom, grad, learning_rate, decay, momentum, epsilon))
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return success
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@centered_rmsprop_opt.register("Function", "Number", "Number", "Number", "Number", "Tensor", "Tensor", "Tensor",
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"Tensor", "Tensor")
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def _centered_rmsprop_opt(opt, learning_rate, decay, epsilon, momentum, weight, mg, ms, mom, grad):
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"""Apply centered rmsprop optimizer to the weight parameter."""
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success = True
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success = F.depend(success, opt(weight, mg, ms, mom, grad, learning_rate, decay, momentum, epsilon))
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return success
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@centered_rmsprop_opt.register("Function", "Tensor", "Number", "Number", "Number", "Tensor", "Tensor", "Tensor",
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"Tensor", "Tensor")
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def _centered_rmsprop_opt_dynamic_lr(opt, learning_rate, decay, epsilon, momentum, weight, mg, ms, mom, grad):
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"""Apply centered rmsprop optimizer to the weight parameter using dynamic learning rate."""
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success = True
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success = F.depend(success, opt(weight, mg, ms, mom, grad, learning_rate, decay, momentum, epsilon))
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return success
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class RMSProp(Optimizer):
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"""
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Implements Root Mean Squared Propagation (RMSProp) algorithm.
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Note:
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Update `params` according to the RMSProp algorithm.
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The equation is as follows:
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.. math::
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s_{t} = \\rho s_{t-1} + (1 - \\rho)(\\nabla Q_{i}(w))^2
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.. math::
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m_{t} = \\beta m_{t-1} + \\frac{\\eta} {\\sqrt{s_{t} + \\epsilon}} \\nabla Q_{i}(w)
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.. math::
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w = w - m_{t}
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The first equation calculates moving average of the squared gradient for
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each weight. Then dividing the gradient by :math:`\\sqrt{ms_{t} + \\epsilon}`.
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if centered is True:
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.. math::
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g_{t} = \\rho g_{t-1} + (1 - \\rho)\\nabla Q_{i}(w)
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.. math::
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s_{t} = \\rho s_{t-1} + (1 - \\rho)(\\nabla Q_{i}(w))^2
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.. math::
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m_{t} = \\beta m_{t-1} + \\frac{\\eta} {\\sqrt{s_{t} - g_{t}^2 + \\epsilon}} \\nabla Q_{i}(w)
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.. math::
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w = w - m_{t}
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where, :math:`w` represents `params`, which will be updated.
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:math:`g_{t}` is mean gradients, :math:`g_{t-1}` is the last moment of :math:`g_{t}`.
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:math:`s_{t}` is the mean square gradients, :math:`s_{t-1}` is the last moment of :math:`s_{t}`,
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:math:`m_{t}` is moment, the delta of `w`, :math:`m_{t-1}` is the last moment of :math:`m_{t}`.
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:math:`\\rho` represents `decay`. :math:`\\beta` is the momentum term, represents `momentum`.
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:math:`\\epsilon` is a smoothing term to avoid division by zero, represents `epsilon`.
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:math:`\\eta` is learning rate, represents `learning_rate`. :math:`\\nabla Q_{i}(w)` is gradientse,
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represents `gradients`.
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Args:
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params (list[Parameter]): A list of parameter, which will be updated. The element in `parameters`
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should be class mindspore.Parameter.
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learning_rate (Union[float, Tensor, Iterable]): A value for the learning rate. When the learning_rate is
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Iterable or a Tensor and the dims of the Tensor is 1,
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use dynamic learning rate, then the i-th step will
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take the i-th value as the learning rate.
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When the learning_rate is float or learning_rate is a Tensor
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but the dims of the Tensor is 0, use fixed learning rate.
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Other cases are not supported.
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decay (float): Decay rate.
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momentum (float): Hyperparameter of type float, means momentum for the moving average.
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epsilon (float): Term added to the denominator to improve numerical stability. Should be greater than 0.
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use_locking (bool): Enable a lock to protect the update of variable and accumlation tensors. Default: False.
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centered (bool): If True, gradients are normalized by the estimated variance of the gradient. Default: False
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loss_scale (float): A floating point value for the loss scale. Default: 1.0.
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Inputs:
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- **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`.
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Outputs:
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Tensor[bool], the value is True.
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Examples:
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>>> net = Net()
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>>> loss = nn.SoftmaxCrossEntropyWithLogits()
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>>> opt = RMSProp(params=net.trainable_params(), learning_rate=lr)
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>>> model = Model(net, loss, opt)
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"""
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def __init__(self, params, learning_rate=0.1, decay=0.9, momentum=0.0, epsilon=1e-10,
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use_locking=False, centered=False, loss_scale=1.0):
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super(RMSProp, self).__init__(learning_rate, params)
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if isinstance(momentum, float) and momentum < 0.0:
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raise ValueError("momentum should be at least 0.0, but got momentum {}".format(momentum))
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if decay < 0.0:
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raise ValueError("decay should be at least 0.0, but got dampening {}".format(decay))
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self.decay = decay
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self.epsilon = epsilon
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validator.check_type("use_locking", use_locking, [bool])
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validator.check_type("centered", centered, [bool])
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self.centered = centered
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if centered:
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self.opt = P.ApplyCenteredRMSProp(use_locking)
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self.mg = self.parameters.clone(prefix="mean_grad", init='zeros')
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else:
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self.opt = P.ApplyRMSProp(use_locking)
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self.dynamic_lr = False
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if not isinstance(learning_rate, float):
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self.dynamic_lr = True
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self.gather = P.GatherV2()
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self.assignadd = P.AssignAdd()
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self.global_step = Parameter(initializer(0, [1], mstype.int32), name="global_step")
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self.axis = 0
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self.momentum = momentum
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self.ms = self.parameters.clone(prefix="mean_square", init='zeros')
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self.moment = self.parameters.clone(prefix="moment", init='zeros')
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self.hyper_map = C.HyperMap()
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self.decay = decay
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self.reciprocal_scale = 1.0 / loss_scale
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def construct(self, gradients):
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params = self.parameters
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if self.reciprocal_scale != 1.0:
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gradients = self.hyper_map(F.partial(grad_scale, self.reciprocal_scale), gradients)
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if self.dynamic_lr:
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lr = self.gather(self.learning_rate, self.global_step, self.axis)
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F.control_depend(lr, self.assignadd(self.global_step, self.one))
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else:
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lr = self.learning_rate
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if self.centered:
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success = self.hyper_map(F.partial(centered_rmsprop_opt, self.opt, lr, self.decay, self.epsilon,
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self.momentum), params, self.mg, self.ms, self.moment, gradients)
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else:
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success = self.hyper_map(F.partial(rmsprop_opt, self.opt, lr, self.decay, self.epsilon,
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self.momentum), params, self.ms, self.moment, gradients)
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return success
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