mindspore2022/mindspore/nn/optim/optimizer.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.
# ============================================================================
"""optimizer"""
from typing import Iterable

import numpy as np

import mindspore
from mindspore.ops import functional as F, composite as C, operations as P
from mindspore.nn.cell import Cell
from mindspore.nn.layer.container import CellList
from mindspore.common.parameter import Parameter, ParameterTuple
from mindspore.common.initializer import initializer
from mindspore.common.tensor import Tensor, RowTensor
import mindspore.common.dtype as mstype
from mindspore._checkparam import Validator as validator
from mindspore import log as logger
from mindspore.parallel._utils import _get_global_rank, _get_device_num, _get_parallel_mode
from mindspore.context import ParallelMode
from mindspore import context
from mindspore.nn.learning_rate_schedule import LearningRateSchedule

__all__ = ['Optimizer']


class Optimizer(Cell):
    """
    Base class for all optimizers.

    Note:
        This class defines the API to add Ops to train a model. Never use
        this class directly, but instead instantiate one of its subclasses.

        Different parameter groups can set different `learning_rate` and `weight_decay`.

        When separating parameter groups, the weight decay in each group will be applied on the parameters if the
        weight_decay is positive. For most optimizer, when not separating parameters, the `weight_decay` in the API will
        be applied on the parameters without 'beta' or 'gamma' in their names if `weight_decay` is positive.

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

    Args:
        learning_rate (Union[float, Tensor, Iterable, LearningRateSchedule]): A value or a graph for the learning
            rate. When the learning_rate is an Iterable or a Tensor in a 1D dimension, use dynamic learning rate, then
            the i-th step will take the i-th value as the learning rate. When the learning_rate is LearningRateSchedule,
            use dynamic learning rate, the i-th learning rate will be calculated during the process of training
            according to the formula of LearningRateSchedule. When the learning_rate is a float or a Tensor in a zero
            dimension, use fixed learning rate. Other cases are not supported. The float learning rate must be
            equal to or greater than 0. If the type of `learning_rate` is int, it will be converted to float.
        parameters (Union[list[Parameter], list[dict]]): When the `parameters` is a list of `Parameter` which will be
            updated, the element in `parameters` must be class `Parameter`. When the `parameters` is a list of `dict`,
            the "params", "lr", "weight_decay" and "order_params" are the keys can be parsed.

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

            - lr: Optional. If "lr" in the keys, the value of corresponding learning rate will be used.
              If not, the `learning_rate` in the API will be used.

            - 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 must 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' must be in one of group parameters.

        weight_decay (float): A floating point value for the weight decay. It must be equal to or greater than 0.
            If the type of `weight_decay` input is int, it will be converted to float. Default: 0.0.
        loss_scale (float): A floating point value for the loss scale. It must be greater than 0. If the
            type of `loss_scale` input is int, it will be converted to float. Default: 1.0.

    Raises:
        ValueError: If the learning_rate is a Tensor, but the dimension of tensor is greater than 1.
        TypeError: If the learning_rate is not any of the three types: float, Tensor, nor Iterable.

    Supported Platforms:
        ``Ascend`` ``GPU``
    """

    def __init__(self, learning_rate, parameters, weight_decay=0.0, loss_scale=1.0):
        super(Optimizer, self).__init__(auto_prefix=False)
        if parameters is not None and not isinstance(parameters, list):
            parameters = list(parameters)

        if not parameters:
            raise ValueError("Optimizer got an empty parameter list.")

        if not isinstance(parameters[0], (dict, Parameter)):
            raise TypeError("Only a list of Parameter or dict can be supported.")

        if isinstance(loss_scale, int):
            loss_scale = float(loss_scale)
        validator.check_value_type("loss_scale", loss_scale, [float], self.cls_name)
        validator.check_positive_float(loss_scale, "loss_scale", self.cls_name)
        self.loss_scale = loss_scale

        weight_decay = self._preprocess_weight_decay(weight_decay)

        self._unique = True
        self._target = context.get_context("device_target")
        self.dynamic_lr = False
        self.assignadd = None
        self.global_step = None
        self.is_group = False
        self.is_group_lr = False
        self.is_group_params_ordered = False
        learning_rate = self._preprocess_single_lr(learning_rate)
        if isinstance(parameters[0], dict):
            self.is_group = True
            self.group_params = []
            self.group_lr = []
            self.group_weight_decay = []
            self._init_group_params(parameters, learning_rate, weight_decay)

        # The final value of dynamic_lr can be determined after the process of parse_single_lr and init_group_params
        if self.dynamic_lr:
            self.assignadd = P.AssignAdd()
            self.global_step = Parameter(initializer(0, [1], mindspore.int32), name='global_step')

        if self.is_group_lr:
            if self.dynamic_lr:
                self.learning_rate = CellList(self.group_lr)
            else:
                self.learning_rate = ParameterTuple(self.group_lr)
        else:
            self.learning_rate = self._build_single_lr(learning_rate, 'learning_rate')
        if self.is_group:
            self.parameters = ParameterTuple(self.group_params)
            self.weight_decay = tuple(self.group_weight_decay)
            self.weight_decay_tensor_tuple = tuple(Tensor(x, mstype.float32) for x in self.group_weight_decay)
            decay_filter = lambda x: x > 0
            self.decay_flags = tuple(decay_filter(x) for x in self.weight_decay)
            self.exec_weight_decay = any(self.decay_flags)
        else:
            self.parameters = ParameterTuple(parameters)
            self.weight_decay = weight_decay * loss_scale
            self.weight_decay_tensor = Tensor(self.weight_decay, mstype.float32)
            decay_filter = lambda x: 'beta' not in x.name and 'gamma' not in x.name
            self.decay_flags = tuple(decay_filter(x) for x in self.parameters)
            self.exec_weight_decay = self.weight_decay > 0
        # when a parameter has been unique, there is no need do another unique in optimizer.
        for param in self.parameters:
            if param.unique:
                self._unique = False
                break
        ps_filter = lambda x: x.is_param_ps
        self.ps_parameters = tuple(ps_filter(x) for x in self.parameters)
        cache_filter = lambda x: x.cache_enable
        self.cache_enable = tuple(cache_filter(x) for x in self.parameters)
        self.reciprocal_scale = Tensor(1.0 / loss_scale, mstype.float32)
        self.need_scale = loss_scale != 1.0
        self.global_step_increase_tensor = Tensor(1, mstype.int32)
        self.param_length = len(self.parameters)
        self.map_ = C.Map()
        if context.get_auto_parallel_context("enable_parallel_optimizer"):
            if _get_parallel_mode() == ParallelMode.DATA_PARALLEL and context.get_context("device_target") == "Ascend":
                self.use_parallel = True
            elif _get_parallel_mode() == ParallelMode.DATA_PARALLEL \
                    and context.get_context("device_target") != "Ascend":
                raise RuntimeError("Parallel optimizer only supports Ascend in data parallel mode.")
            elif _get_parallel_mode() in (ParallelMode.STAND_ALONE, ParallelMode.HYBRID_PARALLEL):
                raise RuntimeError("Parallel optimizer is not supported in {}.".format(_get_parallel_mode()))
            else:
                self.use_parallel = False
        else:
            self.use_parallel = False
        if self.use_parallel:
            if self.cls_name not in ["Lamb", "AdamWeightDecay"]:
                raise RuntimeError("Parallel optimizer does not support optimizer {}".format(self.cls_name))
            self.dev_num = _get_device_num()
            if self.dev_num > self.param_length:
                raise RuntimeError("Parallel optimizer can not be applied when the number of parameters {} is"
                                   " less than the number of devices {}".format(self.param_length, self.dev_num))
            self.param_rank = self._get_parameter_group_id()
            self.optim_filter = tuple(map(lambda x: x == _get_global_rank(), self.param_rank))
            self.param_names = []
            for param in self.parameters:
                self.param_names.append(param.name)

        else:
            self.optim_filter = (True,) * self.param_length

    @property
    def unique(self):
        """The method is to see whether to make unique. The input type is bool. The method is read-only."""
        return self._unique

    @unique.setter
    def unique(self, value):
        """Set whether the input value is unique."""
        if not isinstance(value, bool):
            raise TypeError("The value type must be bool, but got value type is {}".format(type(value)))
        self._unique = value

    @property
    def target(self):
        """The method is used to determine whether the parameter is updated on host or device. The input type is str
           and can only be 'CPU', 'Ascend' or 'GPU'."""
        return self._target

    @target.setter
    def target(self, value):
        """If the input value is set to "CPU", the parameters will be updated on the host using the Fused
           optimizer operation."""
        raise NotImplementedError

    def decay_weight(self, gradients):
        """
        Weight decay.

        An approach to reduce the overfitting of a deep learning neural network model.

        Args:
            gradients (tuple[Tensor]): The gradients of `self.parameters`, and have the same shape as
                `self.parameters`.

        Returns:
            tuple[Tensor], The gradients after weight decay.
        """
        if self.exec_weight_decay:
            params = self.parameters
            if self.is_group:
                gradients = self.map_(F.partial(_apply_decay), self.weight_decay_tensor_tuple, self.decay_flags,
                                      params, gradients)
            else:
                gradients = self.map_(F.partial(_apply_decay, self.weight_decay_tensor), self.decay_flags,
                                      params, gradients)

        return gradients

    def scale_grad(self, gradients):
        """
        Loss scale for mixed precision.

        An approach of mixed precision training to improve the speed and energy efficiency of training deep neural
        network.

        Args:
            gradients (tuple[Tensor]): The gradients of `self.parameters`, and have the same shape as
                `self.parameters`.

        Returns:
            tuple[Tensor], The gradients after loss scale.

        """
        if self.need_scale:
            gradients = self.map_(F.partial(_grad_scale, self.reciprocal_scale), gradients)

        return gradients

    def _grad_sparse_indices_deduplicate(self, gradients):
        """ In the case of using big operators, deduplicate the 'indexes' in gradients."""
        if self._target != 'CPU' and self._unique:
            gradients = self.map_(F.partial(_indices_deduplicate), gradients)
        return gradients

    def _preprocess_weight_decay(self, weight_decay):
        """Check weight decay, and convert int to float."""
        if isinstance(weight_decay, (float, int)):
            weight_decay = float(weight_decay)
            validator.check_non_negative_float(weight_decay, "weight_decay", self.cls_name)
            return weight_decay
        raise TypeError("Weight decay should be int or float.")

    def _preprocess_single_lr(self, learning_rate):
        """Check lr value, and convert lr to a float, a Tensor or a LearningRateSchedule."""
        if isinstance(learning_rate, (float, int)):
            learning_rate = float(learning_rate)
            validator.check_non_negative_float(learning_rate, "learning rate", self.cls_name)
            return learning_rate
        if isinstance(learning_rate, Tensor) and learning_rate.ndim == 0:
            return learning_rate

        self.dynamic_lr = True
        if isinstance(learning_rate, Iterable):
            return Tensor(np.array(list(learning_rate)).astype(np.float32))
        if isinstance(learning_rate, Tensor):
            if learning_rate.ndim > 1:
                raise ValueError("The dim of `Tensor` type Learning rate should be a 0 or 1,"
                                 f"but got {learning_rate.ndim}.")
            if learning_rate.ndim == 1 and learning_rate.size < 2:
                logger.warning("If use `Tensor` type dynamic learning rate, please make sure that the number"
                               "of elements in the tensor passed is greater than 1.")
            return learning_rate
        if isinstance(learning_rate, LearningRateSchedule):
            return learning_rate
        raise TypeError("Learning rate should be int, float, Tensor, Iterable or LearningRateSchedule.")

    def _build_single_lr(self, learning_rate, name):
        """Build learning rate value, convert learning rate to a Parameter or a LearningRateSchedule."""
        if isinstance(learning_rate, float):
            learning_rate = Parameter(Tensor(learning_rate, mstype.float32), name)
            if self.is_group_lr and self.dynamic_lr:
                learning_rate = _ConvertToCell(learning_rate)
            return learning_rate
        if isinstance(learning_rate, Tensor) and learning_rate.ndim == 0:
            learning_rate = Parameter(learning_rate, name)
            if self.is_group_lr and self.dynamic_lr:
                learning_rate = _ConvertToCell(learning_rate)
            return learning_rate
        if isinstance(learning_rate, Tensor) and learning_rate.ndim == 1:
            return _IteratorLearningRate(learning_rate, name)
        return learning_rate

    def _check_group_params(self, parameters):
        """Check group params."""
        parse_keys = ['params', 'lr', 'weight_decay', 'order_params']
        for group_param in parameters:
            invalid_key = list(filter(lambda x: x not in parse_keys, group_param.keys()))
            if invalid_key:
                raise KeyError(f'The key "{invalid_key}" cannot be recognized in group params.')

            if 'order_params' in group_param.keys():
                if len(group_param.keys()) > 1:
                    raise ValueError("The order params dict in group parameters should "
                                     "only include the 'order_params' key.")
                if not isinstance(group_param['order_params'], Iterable):
                    raise TypeError("The value of 'order_params' should be an Iterable type.")
                continue

            if not group_param['params']:
                raise ValueError("Optimizer got an empty group parameter list.")

            for param in group_param['params']:
                if not isinstance(param, Parameter):
                    raise TypeError("The group param should be an iterator of Parameter type.")

    def _parse_group_params(self, parameters, learning_rate):
        """Parse group params."""
        self._check_group_params(parameters)
        if isinstance(learning_rate, Tensor) and learning_rate.ndim == 1:
            tensor_lr_length = learning_rate.size
        else:
            tensor_lr_length = 0

        for group_param in parameters:
            if 'order_params' in group_param.keys():
                if len(group_param.keys()) > 1:
                    raise ValueError("The order params dict in group parameters should "
                                     "only include the 'order_params' key.")
                if not isinstance(group_param['order_params'], Iterable):
                    raise TypeError("The value of 'order_params' should be an Iterable type.")
                self.is_group_params_ordered = True
                continue

            if 'lr' in group_param.keys():
                self.is_group_lr = True
                group_lr = self._preprocess_single_lr(group_param['lr'])

                if isinstance(group_lr, Tensor) and group_lr.ndim == 1:
                    group_lr_length = group_lr.size
                    if tensor_lr_length == 0:
                        tensor_lr_length = group_lr_length
                    elif group_lr_length != tensor_lr_length:
                        raise ValueError("The Tensor type dynamic learning rate in group should be the same size.")

    def _init_group_params(self, parameters, learning_rate, weight_decay):
        """Initialize learning rate or weight decay in group params."""
        self._parse_group_params(parameters, learning_rate)
        default_lr = self._build_single_lr(learning_rate, 'learning_rate')

        params_store = []
        for group_num, group_param in enumerate(parameters):
            if 'order_params' in group_param.keys():
                ordered_parameters = group_param['order_params']
                continue

            self.group_params += group_param['params']

            if 'lr' in group_param.keys():
                lr_param_name = 'learning_rate_group_' + str(group_num)
                lr = self._preprocess_single_lr(group_param['lr'])
                lr = self._build_single_lr(lr, lr_param_name)
            else:
                lr = default_lr

            if 'weight_decay' in group_param.keys():
                cur_weight_decay = self._preprocess_weight_decay(group_param['weight_decay'])
                weight_decay_ = cur_weight_decay * self.loss_scale
            else:
                weight_decay_ = weight_decay * self.loss_scale

            for key in group_param.keys():
                if key not in ('params', 'lr', 'weight_decay'):
                    logger.warning(f"The optimizer cannot parse '{key}' when setting parameter groups.")

            for param in group_param['params']:
                validator.check_value_type("parameter", param, [Parameter], self.cls_name)
                if param.name in params_store:
                    raise RuntimeError(f"The {param.name} parameter has appeared in parameter groups.")

                params_store.append(param.name)
                self.group_lr.append(lr)
                self.group_weight_decay.append(weight_decay_)

        if self.is_group_params_ordered:
            self._order_and_adjust_group_params(ordered_parameters)

    def _order_and_adjust_group_params(self, ordered_parameters):
        """
        Order group parameter, learning rate and weight decay in group params.
        """
        params_length = len(self.group_params)
        if len(ordered_parameters) != len(self.group_params):
            raise ValueError(f"The value of 'order_params' should be same with all group parameters.")

        ordered_params = [None] * params_length
        ordered_learning_rate = [None] * params_length
        ordered_weight_decay = [None] * params_length
        params_name = [param.name for param in ordered_parameters]

        for param, lr, wd in zip(self.group_params, self.group_lr, self.group_weight_decay):
            index = params_name.index(param.name)
            ordered_params[index] = param
            ordered_learning_rate[index] = lr
            ordered_weight_decay[index] = wd

        self.group_params = ordered_params
        self.group_lr = ordered_learning_rate
        self.group_weight_decay = ordered_weight_decay

    def get_lr(self):
        """
        Get the learning rate of current step.

        Returns:
            float, the learning rate of current step.
        """
        lr = self.learning_rate
        if self.dynamic_lr:
            if self.is_group_lr:
                lr = ()
                for learning_rate in self.learning_rate:
                    current_dynamic_lr = learning_rate(self.global_step)
                    lr += (current_dynamic_lr,)
            else:
                lr = self.learning_rate(self.global_step)

            self.assignadd(self.global_step, self.global_step_increase_tensor)
        return lr

    def get_lr_parameter(self, param):
        """
        Get the learning rate of parameter.

        Args:
            param (Union[Parameter, list[Parameter]]): The `Parameter` or list of `Parameter`.

        Returns:
            Parameter, single `Parameter` or `list[Parameter]` according to the input type.
        """
        def get_lr_value(learning_rate):
            if isinstance(learning_rate, (_ConvertToCell, _IteratorLearningRate)):
                return learning_rate.learning_rate

            return learning_rate

        if isinstance(param, Parameter):
            param_list = [param]
        elif isinstance(param, list):
            param_list = param
        else:
            raise TypeError(f"The parameter only support 'Parameter' or 'list' type.")

        lr = []
        ids = [id(p) for p in self.parameters]
        for p in param_list:
            validator.check_value_type("parameter", p, [Parameter], self.cls_name)
            if id(p) not in ids:
                raise ValueError(f"The parameter {p.name} is not in optimizer.")
            if self.is_group_lr:
                index = ids.index(id(p))
                lr.append(get_lr_value(self.learning_rate[index]))
            else:
                lr.append(get_lr_value(self.learning_rate))

        return lr if isinstance(param, list) else lr[0]

    def _get_parameter_group_id(self):
        """
        Get the parameter partition group id, which is less than the number of devices.

        Returns:
            tuple, the group id tuple of parameters.
        """
        rank_list = ()
        count = 0
        for _ in range(self.param_length):
            rank_list = rank_list + (count,)
            count = count + 1
            if count == self.dev_num:
                count = 0
        return rank_list

    def broadcast_params(self, optim_result):
        """
        Apply Broadcast operations in the sequential order of parameter groups.

        Returns:
             bool, the status flag.
        """
        param_group = []
        key_group = []
        for _ in range(self.dev_num):
            param_group.append(F.make_tuple())
            key_group.append(F.make_tuple())
        for i in range(self.param_length):
            param_group[self.param_rank[i]] = param_group[self.param_rank[i]] + (self.parameters[i],)
            key = P.MakeRefKey(self.param_names[i])()
            key_group[self.param_rank[i]] = key_group[self.param_rank[i]] + (key,)
        new_param_group = []
        for root in range(self.dev_num):
            ops = P.Broadcast(root)
            next_params = ops(param_group[root])
            new_param_group.append(next_params)
            for i in range(F.tuple_len(next_params)):
                F.assign(key_group[root][i], next_params[i])
        return new_param_group

    def construct(self, *hyper_params):
        raise NotImplementedError


op_add = P.AddN()
op_gather = P.Gather()
op_mul = P.Mul()

_apply_decay = C.MultitypeFuncGraph("apply_decay")


@_apply_decay.register("Tensor", "Bool", "Tensor", "RowTensor")
def _tensor_apply_decay_with_sparse(weight_decay, if_apply, weight, gradient):
    """Get grad with weight_decay."""
    if if_apply:
        indices = gradient.indices
        values = op_add((op_gather(weight, indices, 0) * F.cast(weight_decay, F.dtype(weight)), gradient.values))
        shape = gradient.dense_shape
        return RowTensor(indices, values, shape)
    return gradient


@_apply_decay.register("Tensor", "Bool", "Tensor", "Tensor")
def _tensor_apply_decay(weight_decay, if_apply, weight, gradient):
    """Get grad with weight_decay."""
    if if_apply:
        return op_add((op_mul(weight, F.cast(weight_decay, F.dtype(weight))), gradient))
    return gradient


_grad_scale = C.MultitypeFuncGraph("grad_scale")
_indices_deduplicate = C.MultitypeFuncGraph("indices_deduplicate")

@_grad_scale.register("Number", "Tensor")
def tensor_grad_scale(scale, grad):
    """Get grad with scale."""
    if scale == 1.0:
        return grad
    return op_mul(grad, F.cast(scale, F.dtype(grad)))

@_grad_scale.register("Tensor", "Tensor")
def tensor_grad_scale_with_tensor(scale, grad):
    """Get grad with scale."""
    return op_mul(grad, F.cast(scale, F.dtype(grad)))

@_grad_scale.register("Tensor", "RowTensor")
def tensor_grad_scale_with_sparse(scale, grad):
    """Get grad with scale."""
    return RowTensor(grad.indices, grad.values * F.cast(scale, F.dtype(grad.values)), grad.dense_shape)


@_indices_deduplicate.register("RowTensor")
def rowtensor_deduplicate_indices_slices(grad):
    """Unique the indices and sums the 'values' corresponding to the duplicate indices."""
    indices = grad.indices
    values = grad.values

    unique_indices, index_position = P.Unique()(indices)
    summed_values = P.UnsortedSegmentSum()(values, index_position, P.DynamicShape()(unique_indices)[0])

    return RowTensor(unique_indices, summed_values, grad.dense_shape)


@_indices_deduplicate.register("Tensor")
def tensor_deduplicate_indice_slices(grad):
    """Return the input gradient directly in the dense sences."""
    return grad


class _ConvertToCell(LearningRateSchedule):
    """Inner api, convert learning rate of scalar to LearningRateSchedule."""
    def __init__(self, learning_rate):
        super(_ConvertToCell, self).__init__()
        if not isinstance(learning_rate, Parameter):
            raise TypeError('Learning rate must be Parameter.')
        self.learning_rate = learning_rate

    def construct(self, global_step):
        return self.learning_rate + 1.0 - 1.0


class _IteratorLearningRate(LearningRateSchedule):
    """Inner api, convert learning rate of Tensor(list) to LearningRateSchedule."""
    def __init__(self, learning_rate, name):
        super(_IteratorLearningRate, self).__init__()
        if isinstance(learning_rate, Tensor):
            if learning_rate.ndim != 1:
                raise ValueError("The dim of `Tensor` type dynamic learning rate should be a 1,"
                                 f"but got {learning_rate.ndim}.")
        else:
            raise TypeError("Learning rate should be Tensor.")

        self.learning_rate = Parameter(learning_rate, name)
        self.gather = P.Gather()

    def construct(self, global_step):
        return self.gather(self.learning_rate, global_step, 0)