Source code for torch.optim.optimizer

from collections import defaultdict
from torch._six import container_abcs

import torch
from copy import deepcopy
from itertools import chain

class _RequiredParameter(object):
    """Singleton class representing a required parameter for an Optimizer."""
    def __repr__(self):
        return "<required parameter>"

required = _RequiredParameter()

[docs]class Optimizer(object): r"""Base class for all optimizers. .. warning:: Parameters need to be specified as collections that have a deterministic ordering that is consistent between runs. Examples of objects that don't satisfy those properties are sets and iterators over values of dictionaries. Arguments: params (iterable): an iterable of :class:`torch.Tensor` s or :class:`dict` s. 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). """ def __init__(self, params, defaults): torch._C._log_api_usage_once("python.optimizer") self.defaults = defaults if isinstance(params, torch.Tensor): raise TypeError("params argument given to the optimizer should be " "an iterable of Tensors or dicts, but got " + torch.typename(params)) self.state = defaultdict(dict) self.param_groups = [] param_groups = list(params) if len(param_groups) == 0: raise ValueError("optimizer got an empty parameter list") if not isinstance(param_groups[0], dict): param_groups = [{'params': param_groups}] for param_group in param_groups: self.add_param_group(param_group) def __getstate__(self): return { 'defaults': self.defaults, 'state': self.state, 'param_groups': self.param_groups, } def __setstate__(self, state): self.__dict__.update(state) 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]) format_string += ')' return format_string
[docs] def state_dict(self): r"""Returns the state of the optimizer as a :class:`dict`. It contains two entries: * state - a dict holding current optimization state. Its content differs between optimizer classes. * param_groups - a dict containing all parameter groups """ # Save ids instead of Tensors def pack_group(group): packed = {k: v for k, v in group.items() if k != 'params'} packed['params'] = [id(p) for p in group['params']] return packed param_groups = [pack_group(g) for g in self.param_groups] # Remap state to use ids as keys packed_state = {(id(k) if isinstance(k, torch.Tensor) else k): v for k, v in self.state.items()} return { 'state': packed_state, 'param_groups': param_groups, }
[docs] def load_state_dict(self, state_dict): r"""Loads the optimizer state. Arguments: state_dict (dict): optimizer state. Should be an object returned from a call to :meth:`state_dict`. """ # deepcopy, to be consistent with module API state_dict = deepcopy(state_dict) # Validate the state_dict groups = self.param_groups saved_groups = state_dict['param_groups'] if len(groups) != len(saved_groups): raise ValueError("loaded state dict has a different number of " "parameter groups") param_lens = (len(g['params']) for g in groups) saved_lens = (len(g['params']) for g in saved_groups) if any(p_len != s_len for p_len, s_len in zip(param_lens, saved_lens)): raise ValueError("loaded state dict contains a parameter group " "that doesn't match the size of optimizer's group") # Update the state id_map = {old_id: p for old_id, p in zip(chain(*(g['params'] for g in saved_groups)), chain(*(g['params'] for g in groups)))} def cast(param, value): r"""Make a deep copy of value, casting all tensors to device of param.""" if isinstance(value, torch.Tensor): # Floating-point types are a bit special here. They are the only ones # that are assumed to always match the type of params. if param.is_floating_point(): value = value = return value elif isinstance(value, dict): return {k: cast(param, v) for k, v in value.items()} elif isinstance(value, container_abcs.Iterable): return type(value)(cast(param, v) for v in value) else: return value # Copy state assigned to params (and cast tensors to appropriate types). # State that is not assigned to params is copied as is (needed for # backward compatibility). state = defaultdict(dict) for k, v in state_dict['state'].items(): if k in id_map: param = id_map[k] state[param] = cast(param, v) else: state[k] = v # Update parameter groups, setting their 'params' value def update_group(group, new_group): new_group['params'] = group['params'] return new_group param_groups = [ update_group(g, ng) for g, ng in zip(groups, saved_groups)] self.__setstate__({'state': state, 'param_groups': param_groups})
[docs] def zero_grad(self): r"""Clears the gradients of all optimized :class:`torch.Tensor` s.""" for group in self.param_groups: for p in group['params']: if p.grad is not None: p.grad.detach_() p.grad.zero_()
[docs] def step(self, closure): r"""Performs a single optimization step (parameter update). Arguments: closure (callable): A closure that reevaluates the model and returns the loss. Optional for most optimizers. """ raise NotImplementedError
[docs] def add_param_group(self, param_group): r"""Add a param group to the :class:`Optimizer` s `param_groups`. This can be useful when fine tuning a pre-trained network as frozen layers can be made trainable and added to the :class:`Optimizer` as training progresses. Arguments: param_group (dict): Specifies what Tensors should be optimized along with group specific optimization options. """ assert isinstance(param_group, dict), "param group must be a dict" params = param_group['params'] if isinstance(params, torch.Tensor): 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, torch.Tensor): raise TypeError("optimizer can only optimize Tensors, " "but one of the params is " + torch.typename(param)) if not param.is_leaf: raise ValueError("can't optimize a non-leaf Tensor") for name, default in self.defaults.items(): if default is required and name not in param_group: raise ValueError("parameter group didn't specify a value of required optimization parameter " + name) else: param_group.setdefault(name, default) 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") self.param_groups.append(param_group)