forked from huawei/mindspore2022
102 lines
3.8 KiB
Python
Executable File
102 lines
3.8 KiB
Python
Executable File
# 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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"""optimizer"""
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from typing import Iterable
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import logging
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import numpy as np
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from mindspore.ops import functional as F, composite as C, operations as P
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from mindspore.nn.cell import Cell
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from mindspore.common.parameter import Parameter, ParameterTuple
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from mindspore._checkparam import ParamValidator as validator
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from mindspore._checkparam import Rel
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from mindspore.common.tensor import Tensor
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logger = logging.getLogger('Optimizer')
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__all__ = ['Optimizer']
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class Optimizer(Cell):
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"""
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Base class for all optimizers.
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This class defines the API to add Ops to train a model.
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Note:
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This class defines the API to add Ops to train a model. Never use
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this class directly, but instead instantiate one of its subclasses.
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Args:
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learning_rate (float): A floating point value for the learning rate. Should be greater than 0.
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parameters (list): 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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Raises:
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ValueError: If the learning_rate is a Tensor, but the dims of tensor is greater than 1.
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TypeError: If the learning_rate is not any of the three types: float, Tensor, Iterable.
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"""
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def __init__(self, learning_rate, parameters):
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super(Optimizer, self).__init__()
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if isinstance(learning_rate, float):
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validator.check_number_range("learning rate", learning_rate, 0.0, float("inf"), Rel.INC_LEFT)
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elif isinstance(learning_rate, Iterable):
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learning_rate = Tensor(np.array(list(learning_rate)).astype(np.float32))
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elif isinstance(learning_rate, Tensor):
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if learning_rate.dim() > 1:
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raise ValueError("Learning rate should be a 0 or 1 dim `Tensor`,"
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f"but got {learning_rate.dim()}.")
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else:
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raise TypeError("Learning rate should be float, Tensor or Iterable.")
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if isinstance(learning_rate, Tensor) and learning_rate.dim() == 1 and learning_rate.size() < 2:
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logger.warning("If want to use the dynamic learning rate, please make sure that "
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"the number of elements in the list, tuple or tensor passed is greater than 1.")
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self.learning_rate = Parameter(learning_rate, name="learning_rate")
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self.parameters = ParameterTuple(parameters)
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if not self.parameters:
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raise ValueError("optimizer got an empty parameter list.")
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def construct(self, *hyper_params):
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raise NotImplementedError
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op_add = P.AddN()
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apply_decay = C.MultitypeFuncGraph("apply_decay")
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@apply_decay.register("Number", "Bool", "Tensor", "Tensor")
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def _tensor_apply_decay(weight_decay, if_apply, weight, gradient):
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"""Get grad with weight_decay."""
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if if_apply:
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return op_add((gradient, weight * weight_decay))
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return gradient
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grad_scale = C.MultitypeFuncGraph("grad_scale")
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@grad_scale.register("Number", "Tensor")
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def tensor_grad_scale(scale, grad):
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"""Get grad with scale."""
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if scale == 1.0:
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return grad
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cast_op = P.Cast()
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type_op = P.DType()
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return grad * cast_op(F.scalar_to_array(scale), type_op(grad))
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