Source code for mindspore.nn.optim.optimizer

# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
from typing import Iterable
import logging

import numpy as np

from mindspore.ops import functional as F, composite as C, operations as P
from mindspore.nn.cell import Cell
from mindspore.common.parameter import Parameter, ParameterTuple
from mindspore._checkparam import ParamValidator as validator
from mindspore._checkparam import Rel
from mindspore.common.tensor import Tensor

logger = logging.getLogger('Optimizer')

__all__ = ['Optimizer']

[docs]class Optimizer(Cell): """ Base class for all optimizers. This class defines the API to add Ops to train a model. 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. Args: learning_rate (float): A floating point value for the learning rate. Should be greater than 0. parameters (list): A list of parameter, which will be updated. The element in `parameters` should be class mindspore.Parameter. Raises: ValueError: If the learning_rate is a Tensor, but the dims of tensor is greater than 1. TypeError: If the learning_rate is not any of the three types: float, Tensor, Iterable. """ def __init__(self, learning_rate, parameters): super(Optimizer, self).__init__() if isinstance(learning_rate, float): validator.check_number_range("learning rate", learning_rate, 0.0, float("inf"), Rel.INC_LEFT) elif isinstance(learning_rate, Iterable): learning_rate = Tensor(np.array(list(learning_rate)).astype(np.float32)) elif isinstance(learning_rate, Tensor): if learning_rate.dim() > 1: raise ValueError("Learning rate should be a 0 or 1 dim `Tensor`," f"but got {learning_rate.dim()}.") else: raise TypeError("Learning rate should be float, Tensor or Iterable.") if isinstance(learning_rate, Tensor) and learning_rate.dim() == 1 and learning_rate.size() < 2: logger.warning("If want to use the dynamic learning rate, please make sure that " "the number of elements in the list, tuple or tensor passed is greater than 1.") self.learning_rate = Parameter(learning_rate, name="learning_rate") self.parameters = ParameterTuple(parameters) if not self.parameters: raise ValueError("optimizer got an empty parameter list.") def construct(self, *hyper_params): raise NotImplementedError
op_add = P.AddN() apply_decay = C.MultitypeFuncGraph("apply_decay") @apply_decay.register("Number", "Bool", "Tensor", "Tensor") def _tensor_apply_decay(weight_decay, if_apply, weight, gradient): """Get grad with weight_decay.""" if if_apply: return op_add((gradient, weight * F.scalar_to_array(weight_decay))) return gradient grad_scale = C.MultitypeFuncGraph("grad_scale") @grad_scale.register("Number", "Tensor") def tensor_grad_scale(scale, grad): """Get grad with scale.""" if scale == 1.0: return grad cast_op = P.Cast() type_op = P.DType() return grad * cast_op(F.scalar_to_array(scale), type_op(grad))