forked from huawei/mindspore2022
127 lines
6.2 KiB
Python
127 lines
6.2 KiB
Python
# 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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"""FTRL"""
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from mindspore.ops import functional as F, composite as C, operations as P
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from mindspore.common.parameter import Parameter
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from mindspore.common import Tensor
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import mindspore.common.dtype as mstype
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from mindspore._checkparam import Validator as validator
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from mindspore._checkparam import Rel
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from .optimizer import Optimizer, apply_decay, grad_scale
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ftrl_opt = C.MultitypeFuncGraph("ftrl_opt")
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@ftrl_opt.register("Function", "Tensor", "Number", "Number", "Number", "Tensor", "Tensor", "Tensor", "Tensor")
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def _tensor_run_opt(opt, learning_rate, l1, l2, lr_power, linear, gradient, weight, moment):
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"""Apply ftrl optimizer to the weight parameter."""
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success = True
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success = F.depend(success, opt(weight, moment, linear, gradient, learning_rate, l1, l2, lr_power))
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return success
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def _check_param(initial_accum, learning_rate, lr_power, l1, l2, use_locking, loss_scale=1.0, weight_decay=0.0,
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prim_name=None):
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"""Check param."""
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validator.check_value_type("initial_accum", initial_accum, [float], prim_name)
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validator.check_number("initial_accum", initial_accum, 0.0, Rel.GE, prim_name)
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validator.check_value_type("learning_rate", learning_rate, [float], prim_name)
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validator.check_number("learning_rate", learning_rate, 0.0, Rel.GT, prim_name)
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validator.check_value_type("lr_power", lr_power, [float], prim_name)
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validator.check_number("lr_power", lr_power, 0.0, Rel.LE, prim_name)
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validator.check_value_type("l1", l1, [float], prim_name)
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validator.check_number("l1", l1, 0.0, Rel.GE, prim_name)
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validator.check_value_type("l2", l2, [float], prim_name)
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validator.check_number("l2", l2, 0.0, Rel.GE, prim_name)
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validator.check_value_type("use_locking", use_locking, [bool], prim_name)
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validator.check_value_type("loss_scale", loss_scale, [float], prim_name)
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validator.check_number("loss_scale", loss_scale, 1.0, Rel.GE, prim_name)
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validator.check_value_type("weight_decay", weight_decay, [float], prim_name)
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validator.check_number("weight_decay", weight_decay, 0.0, Rel.GE, prim_name)
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class FTRL(Optimizer):
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"""
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Implement the FTRL algorithm with ApplyFtrl Operator.
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FTRL is an online convex optimization algorithm that adaptively chooses its regularization function
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based on the loss functions. Refer to paper `Adaptive Bound Optimization for Online Convex Optimization
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<https://arxiv.org/abs/1002.4908>`_. Refer to paper `Ad Click Prediction: a View from the Trenches
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<https://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf>`_ for engineering document.
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Args:
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params (list[Parameter]): A list of parameter, which will be updated. The element in `params`
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should be Parameter.
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initial_accum (float): The starting value for accumulators, must be zero or positive values. Default: 0.1.
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learning_rate (float): The learning rate value, should be positive. Default: 0.001.
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lr_power (float): Learning rate power controls how the learning rate decreases during training, must be less
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than or equal to zero. Use fixed learning rate if lr_power is zero. Default: -0.5.
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l1 (float): l1 regularization strength, must be greater than or equal to zero. Default: 0.0.
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l2 (float): l2 regularization strength, must be greater than or equal to zero. Default: 0.0.
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use_locking (bool): If True use locks for update operation. Default: False.
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loss_scale (float): Value for the loss scale. It should be equal to or greater than 1.0. Default: 1.0.
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wegith_decay (float): Weight decay value to multiply weight, must be zero or positive value. Default: 0.0.
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Inputs:
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- **grads** (tuple[Tensor]) - The gradients of `params` in optimizer, the shape is as same as the `params`
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in optimizer.
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Outputs:
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tuple[Parameter], the updated parameters, the shape is the same as `params`.
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Examples:
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>>> net = Net()
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>>> loss = nn.SoftmaxCrossEntropyWithLogits()
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>>> opt = nn.FTRL(net.trainable_params())
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>>> model = Model(net, loss_fn=loss, optimizer=opt, metrics=None)
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"""
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def __init__(self, params, initial_accum=0.1, learning_rate=0.001, lr_power=-0.5, l1=0.0, l2=0.0,
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use_locking=False, loss_scale=1.0, weight_decay=0.0):
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super(FTRL, self).__init__(learning_rate, params)
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_check_param(initial_accum, learning_rate, lr_power, l1, l2, use_locking, loss_scale, weight_decay,
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self.cls_name)
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self.moments = self.parameters.clone(prefix="moments", init=initial_accum)
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self.linear = self.parameters.clone(prefix="linear", init='zeros')
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self.l1 = l1
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self.l2 = l2
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self.lr_power = lr_power
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self.reciprocal_scale = 1.0 / loss_scale
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self.weight_decay = weight_decay
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self.decay_tf = tuple((lambda: True)() for x in self.parameters)
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self.hyper_map = C.HyperMap()
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self.opt = P.ApplyFtrl(use_locking=use_locking)
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self.one = Tensor(1, mstype.int32)
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def construct(self, grads):
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params = self.parameters
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moments = self.moments
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linear = self.linear
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if self.weight_decay > 0.0:
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grads = self.hyper_map(F.partial(apply_decay, self.weight_decay), self.decay_tf, params, grads)
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if self.reciprocal_scale != 1.0:
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grads = self.hyper_map(F.partial(grad_scale, self.reciprocal_scale), grads)
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lr = self.learning_rate
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success = self.hyper_map(F.partial(ftrl_opt, self.opt, lr, self.l1, self.l2, self.lr_power),
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linear, grads, params, moments)
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return success
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