# Copyright 2023 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
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# See the License for the specific language governing permissions and
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# ============================================================================
"""optimizer"""
from __future__ import absolute_import
from collections import defaultdict
from typing import Iterable
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.common import Tensor
import mindspore.common.dtype as mstype
from mindspore import _checkparam as validator
from mindspore import log as logger
__all__ = ['Optimizer']
[docs]class Optimizer(Cell):
r"""
Base class for all optimizers.
.. warning::
This is an experimental optimizer API that is subject to change.
This module must be used with lr scheduler module in `LRScheduler Class
<https://www.mindspore.cn/docs/en/r2.4.1/api_python/mindspore.experimental.html#lrscheduler-class>`_ .
Args:
params (Union[list(Parameter), list(dict)]): an iterable of :class:`mindspore.Parameter` or
dict. Specifies what Tensors should be optimized.
defaults (dict): a dict containing default values of optimization
options (used when a parameter group doesn't specify them).
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> import mindspore
>>> from mindspore import nn, Tensor, Parameter
>>> from mindspore import ops
>>> from mindspore.experimental import optim
>>>
>>> class MySGD(optim.Optimizer):
... def __init__(self, params, lr):
... defaults = dict(lr=lr)
... super(MySGD, self).__init__(params, defaults)
...
... def construct(self, gradients):
... for group_id, group in enumerate(self.param_groups):
... id = self.group_start_id[group_id]
... for i, param in enumerate(group["params"]):
... next_param = param + gradients[id+i] * group["lr"]
... ops.assign(param, next_param)
>>>
>>> net = nn.Dense(8, 2)
>>> data = Tensor(np.random.rand(20, 8).astype(np.float32))
>>> label = Tensor(np.random.rand(20, 2).astype(np.float32))
>>>
>>> optimizer = MySGD(net.trainable_params(), 0.01)
>>> optimizer.add_param_group({"params": Parameter([0.01, 0.02])})
>>>
>>> criterion = nn.MAELoss(reduction="mean")
>>>
>>> def forward_fn(data, label):
... logits = net(data)
... loss = criterion(logits, label)
... return loss, logits
>>>
>>> grad_fn = mindspore.value_and_grad(forward_fn, None, optimizer.parameters, has_aux=True)
>>>
>>> def train_step(data, label):
... (loss, _), grads = grad_fn(data, label)
... optimizer(grads)
... print(loss)
>>>
>>> train_step(data, label)
"""
def __init__(self, params, defaults):
super(Optimizer, self).__init__(auto_prefix=False)
param_groups = self._parameters_base_check(params, "params")
self.defaults = defaults
self.state = defaultdict(dict)
self.param_groups = []
self.parameters = []
self.lrs = []
self.map_ = C.Map()
self.group_start_id = [0]
if not isinstance(param_groups[0], dict):
param_groups = [{'params': param_groups}]
for param_group in param_groups:
self.add_param_group(param_group)
self.parameters = ParameterTuple(self.parameters)
self.hyper_map = C.HyperMap()
self.enable_tuple_broaden = True
def __repr__(self):
format_string = self.__class__.__name__ + ' ('
for i, group in enumerate(self.param_groups):
format_string += '\n'
format_string += 'Parameter Group {0}\n'.format(i)
for key in sorted(group.keys()):
if key != 'params':
format_string += ' {0}: {1}\n'.format(key, group[key].value()) \
if key == "lr" and isinstance(group[key], Parameter) \
else ' {0}: {1}\n'.format(key, group[key])
format_string += ')'
return format_string
[docs] def add_param_group(self, param_group):
r"""
Add a param group to the `Optimizer.param_groups`.
Args:
param_group (dict): Specifies what Parameters should be optimized along with group
specific optimization options.
"""
group_id = len(self.param_groups)
param_group = self._preprocess_param_group(param_group)
self.parameters += tuple(param_group.get("params"))
for name, default in self.defaults.items():
if name not in param_group:
param_group.setdefault(name, default)
lr = self._build_single_lr(param_group.get("lr"), 'learning_rate_group_' + str(group_id))
weight_decay = self._preprocess_weight_decay(param_group.get("weight_decay", 0.0))
self.lrs.append(lr)
param_group["lr"] = lr
param_group["weight_decay"] = weight_decay
if "amsgrad" in param_group and param_group.get("amsgrad") and hasattr(self, 'max_v_group'):
param_items = ParameterTuple(tuple(param_group.get("params")))
param_group["max_exp_avg_sq"] = param_items.clone(prefix="max_exp_avg_sq", init='zeros')
self.param_groups.append(param_group)
self.group_start_id.append(self.group_start_id[-1] + len(param_group.get("params")))
@staticmethod
def _parameters_base_check(parameters, param_info):
"""Parameters base check."""
if parameters is None:
raise ValueError(f"For 'Optimizer', the argument {param_info} can not be None.")
if not isinstance(parameters, Iterable):
raise TypeError(f"For 'Optimizer', the argument {param_info} must be Iterable type, "
f"but got {type(parameters)}.")
parameters = list(parameters)
if not parameters:
raise ValueError(f"For 'Optimizer', the argument {param_info} must not be empty.")
return parameters
def _decay_weight(self, weight_decay, params, gradients):
"""Apply weight decay."""
if weight_decay != 0.:
weight_decay = Tensor(weight_decay, mstype.float32)
gradients = self.map_(F.partial(_apply_decay, weight_decay), params, gradients)
return gradients
def _preprocess_param_group(self, param_group):
"""Preprocess param groups."""
if not isinstance(param_group, dict):
raise TypeError('Param group must be a dict.')
params = param_group['params']
if isinstance(params, Parameter):
param_group['params'] = [params]
elif isinstance(params, set):
raise TypeError('Optimizer parameters need to be organized in ordered collections, but '
'the ordering of tensors in sets will change between runs. '
'Please use a list instead.')
else:
param_group['params'] = list(params)
for param in param_group['params']:
if not isinstance(param, Parameter):
raise TypeError("Optimizer can only optimize Parameters, but one of the params is " + type(param))
if len(param_group['params']) != len(set(param_group['params'])):
logger.warning("Optimizer contains a parameter group with duplicate parameters.")
param_set = set()
for group in self.param_groups:
param_set.update(set(group['params']))
if not param_set.isdisjoint(set(param_group['params'])):
raise ValueError("some parameters appear in more than one parameter group.")
return param_group
def _build_single_lr(self, learning_rate, name):
"""Check lr value, and convert lr to a float or a Tensor."""
if isinstance(learning_rate, (float, int)):
learning_rate = float(learning_rate)
validator.check_non_negative_float(learning_rate, "learning rate", self.cls_name)
return Parameter(Tensor(learning_rate, mstype.float32), name)
if isinstance(learning_rate, Tensor):
if learning_rate.ndim == 0:
return Parameter(learning_rate.astype(mstype.float32), name)
raise ValueError(f"For 'Optimizer', if 'learning_rate' is a Tensor, "
f"then it should be scalar Tensor")
raise TypeError("For 'Optimizer', the argument 'learning_rate' must be int, float or Tensor, "
"but got {}.".format(type(learning_rate)))
def _preprocess_weight_decay(self, weight_decay):
"""preprocess weight decay"""
if isinstance(weight_decay, (float, int)):
weight_decay = float(weight_decay)
validator.check_non_negative_float(weight_decay, "weight_decay", self.cls_name)
else:
raise TypeError("For 'Optimizer', the argument 'Weight_decay' must be int or "
"float.but got {}".format(type(weight_decay)))
return weight_decay
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", "Tensor", "RowTensor")
def _tensor_apply_decay_with_sparse(weight_decay, weight, gradient):
"""Get grad with weight_decay."""
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 RowTensorInner(indices, values, shape)
@_apply_decay.register("Tensor", "Tensor", "Tensor")
def _tensor_apply_decay(weight_decay, weight, gradient):
"""Get grad with weight_decay."""
return op_add((op_mul(weight, F.cast(weight_decay, F.dtype(weight))), gradient))
def check_not_less_than(arg_value, arg_name, prim, value=0.0):
if arg_value < value:
raise ValueError("For {}, the {} must be greater than or equal to {}, "
"but got {}.".format(prim, arg_name, value, arg_value))
def check_not_less_than_without_equal(arg_value, arg_name, prim, value=0.0):
if arg_value <= value:
raise ValueError("For {}, the {} must be greater than {}, "
"but got {}.".format(prim, arg_name, value, arg_value))