mindspore/mindspore/nn/optim/ftrl.py

# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""FTRL"""
from mindspore.ops import functional as F, composite as C, operations as P
from mindspore.common import Tensor
import mindspore.common.dtype as mstype
from mindspore._checkparam import Validator as validator
from mindspore._checkparam import Rel
from .optimizer import Optimizer, _apply_decay, _grad_scale

_ftrl_opt = C.MultitypeFuncGraph("ftrl_opt")


@_ftrl_opt.register("Function", "Function", "Function", "Function", "Number", "Number", "Number", "Tensor", "Tensor",
                    "RowTensor", "Tensor", "Tensor", "Bool")
def _tensor_run_opt_with_sparse(opt, spars_opt, push, pull, l1, l2, lr_power, learning_rate, linear,
                                gradient, weight, moment, ps_parameter):
    """Apply sparse ftrl optimizer to the weight parameter when the gradient is sparse."""
    success = True
    indices = gradient.indices
    values = gradient.values
    if ps_parameter:
        op_shape = P.Shape()
        shapes = (op_shape(weight), op_shape(moment), op_shape(linear), op_shape(values), op_shape(indices))
        success = F.depend(success, pull(push((values, indices), shapes), weight))
    else:
        success = F.depend(success, spars_opt(weight, moment, linear, values, indices))
    return success


@_ftrl_opt.register("Function", "Function", "Function", "Function", "Number", "Number", "Number", "Tensor", "Tensor",
                    "Tensor", "Tensor", "Tensor", "Bool")
def _tensor_run_opt(opt, spars_opt, push, pull, l1, l2, lr_power, learning_rate, linear,
                    gradient, weight, moment, ps_parameter):
    """Apply ftrl optimizer to the weight parameter."""
    success = True
    if ps_parameter:
        op_shape = P.Shape()
        success = F.depend(success, pull(push((gradient, learning_rate, l1, l2, lr_power),
                                              (op_shape(weight), op_shape(moment), op_shape(linear))), weight))
    else:
        success = F.depend(success, opt(weight, moment, linear, gradient, learning_rate, l1, l2, lr_power))
    return success


def _check_param(initial_accum, lr_power, l1, l2, use_locking, prim_name=None):
    """Check param."""
    validator.check_value_type("initial_accum", initial_accum, [float], prim_name)
    validator.check_number("initial_accum", initial_accum, 0.0, Rel.GE, prim_name)

    validator.check_value_type("lr_power", lr_power, [float], prim_name)
    validator.check_number("lr_power", lr_power, 0.0, Rel.LE, prim_name)

    validator.check_value_type("l1", l1, [float], prim_name)
    validator.check_number("l1", l1, 0.0, Rel.GE, prim_name)

    validator.check_value_type("l2", l2, [float], prim_name)
    validator.check_number("l2", l2, 0.0, Rel.GE, prim_name)

    validator.check_value_type("use_locking", use_locking, [bool], prim_name)


class FTRL(Optimizer):
    """
    Implement the FTRL algorithm with ApplyFtrl Operator.

    FTRL is an online convex optimization algorithm that adaptively chooses its regularization function
    based on the loss functions. Refer to paper `Adaptive Bound Optimization for Online Convex Optimization
    <https://arxiv.org/abs/1002.4908>`_. Refer to paper `Ad Click Prediction: a View from the Trenches
    <https://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf>`_ for engineering document.

    Note:
        When separating parameter groups, the weight decay in each group will be applied on the parameters if the
        weight decay is positive. When not separating parameter groups, the `weight_decay` in the API will be applied
        on all of the parameters.

        To improve parameter groups performance, the customized order of parameters can be supported.

        The sparse strategy is applied while the SparseGatherV2 operator being used for forward network.
        The sparse feature is under continuous development. The sparse behavior is currently performed on the CPU.

    Args:
        params (Union[list[Parameter], list[dict]]): When the `params` is a list of `Parameter` which will be updated,
            the element in `params` should be class `Parameter`. When the `params` is a list of `dict`, the "params",
            "lr", "weight_decay" and "order_params" are the keys can be parsed.

            - params: Required. The value should be a list of `Parameter`.

            - lr: Using different learning rate by separating parameters is currently not supported.

            - weight_decay: Optional. If "weight_decay" in the keys, the value of corresponding weight decay
              will be used. If not, the `weight_decay` in the API will be used.

            - order_params: Optional. If "order_params" in the keys, the value should be the order of parameters and
              the order will be followed in optimizer. There are no other keys in the `dict` and the parameters which
              in the value of 'order_params' should be in one of group parameters.

        initial_accum (float): The starting value for accumulators, must be zero or positive values. Default: 0.1.
        learning_rate (float): The learning rate value, should be zero or positive, dynamic learning rate is currently
            not supported. Default: 0.001.
        lr_power (float): Learning rate power controls how the learning rate decreases during training, must be less
            than or equal to zero. Use fixed learning rate if lr_power is zero. Default: -0.5.
        l1 (float): l1 regularization strength, must be greater than or equal to zero. Default: 0.0.
        l2 (float): l2 regularization strength, must be greater than or equal to zero. Default: 0.0.
        use_locking (bool): If True, use locks for updating operation. Default: False.
        loss_scale (float): Value for the loss scale. It should be equal to or greater than 1.0. Default: 1.0.
        weight_decay (float): Weight decay value to multiply weight, must be zero or positive value. Default: 0.0.

    Inputs:
        - **grads** (tuple[Tensor]) - The gradients of `params` in the optimizer, the shape is the same as the `params`
          in optimizer.

    Outputs:
        tuple[Parameter], the updated parameters, the shape is the same as `params`.

    Examples:
        >>> net = Net()
        >>> #1) All parameters use the same learning rate and weight decay
        >>> optim = nn.FTRL(params=net.trainable_params())
        >>>
        >>> #2) Use parameter groups and set different values
        >>> conv_params = list(filter(lambda x: 'conv' in x.name, net.trainable_params()))
        >>> no_conv_params = list(filter(lambda x: 'conv' not in x.name, net.trainable_params()))
        >>> group_params = [{'params': conv_params, 'weight_decay': 0.01},
        >>>                 {'params': no_conv_params},
        >>>                 {'order_params': net.trainable_params()}]
        >>> optim = nn.FTRL(group_params, learning_rate=0.1, weight_decay=0.0)
        >>> # The conv_params's parameters will use weight decay of 0.01.
        >>> # The no_conv_params's parameters will use default weight decay of 0.0.
        >>> # The final parameters order in which the optimizer will be followed is the value of 'order_params'.
        >>>
        >>> loss = nn.SoftmaxCrossEntropyWithLogits()
        >>> model = Model(net, loss_fn=loss, optimizer=optim)
    """
    def __init__(self, params, initial_accum=0.1, learning_rate=0.001, lr_power=-0.5, l1=0.0, l2=0.0,
                 use_locking=False, loss_scale=1.0, weight_decay=0.0):
        super(FTRL, self).__init__(learning_rate, params, weight_decay, loss_scale=loss_scale)
        if self.dynamic_lr or self.is_group_lr:
            raise ValueError('Dynamic learning rate or group learning rate is currently not supported.')
        _check_param(initial_accum, lr_power, l1, l2, use_locking, self.cls_name)
        self.moments = self.parameters.clone(prefix="moments", init=initial_accum)
        self.linear = self.parameters.clone(prefix="linear", init='zeros')
        self.l1 = l1
        self.l2 = l2
        self.lr_power = lr_power
        if not self.is_group:
            self.decay_flags = tuple((lambda: True)() for x in self.parameters)
        self.hyper_map = C.HyperMap()
        self.opt = P.ApplyFtrl(use_locking=use_locking)
        self.sparse_opt = P.FusedSparseFtrl(learning_rate, l1, l2, lr_power, use_locking=use_locking)
        self._ps_pull = P.Pull()
        self._ps_push = P.Push("Ftrl", [0, 1, 2])
        self._ps_push.add_prim_attr("init_accum", initial_accum)
        self._ps_push.add_prim_attr("lr", learning_rate)
        self._ps_push.add_prim_attr("l1", l1)
        self._ps_push.add_prim_attr("l2", l2)
        self._ps_push.add_prim_attr("lr_power", lr_power)

    def construct(self, grads):
        params = self.parameters
        moments = self.moments
        linear = self.linear
        grads = self.decay_weight(grads)
        grads = self.scale_grad(grads)
        lr = self.get_lr()

        success = self.map_(F.partial(_ftrl_opt, self.opt, self.sparse_opt, self._ps_push, self._ps_pull,
                                      self.l1, self.l2, self.lr_power, lr),
                            linear, grads, params, moments, self.ps_parameters)
        return success