Source code for torch.optim.optimizer
from collections import OrderedDict, defaultdict, abc as container_abcs
import torch
from copy import deepcopy
from itertools import chain
import warnings
import functools
import math
from typing import Callable, Dict, List, Tuple
import torch.utils.hooks as hooks
from torch.utils.hooks import RemovableHandle
from torch._utils import is_compiling
__all__ = ['Optimizer', 'register_optimizer_step_pre_hook', 'register_optimizer_step_post_hook']
_global_optimizer_pre_hooks: Dict[int, Callable] = OrderedDict()
_global_optimizer_post_hooks: Dict[int, Callable] = OrderedDict()
_foreach_supported_types = [torch.Tensor, torch.nn.parameter.Parameter]
class _RequiredParameter:
"""Singleton class representing a required parameter for an Optimizer."""
def __repr__(self):
return "<required parameter>"
required = _RequiredParameter()
def _use_grad_for_differentiable(func):
def _use_grad(self, *args, **kwargs):
prev_grad = torch.is_grad_enabled()
try:
torch.set_grad_enabled(self.defaults['differentiable'])
ret = func(self, *args, **kwargs)
finally:
torch.set_grad_enabled(prev_grad)
return ret
return _use_grad
def _get_value(x):
# item is significantly faster than a cpu tensor in eager mode
if not torch.jit.is_scripting() and is_compiling():
return x
else:
return x.item()
def _stack_if_compiling(x):
if not torch.jit.is_scripting() and is_compiling():
return torch.stack(x)
else:
return x
def _dispatch_sqrt(x: float): # float annotation is needed because of torchscript type inference
if not torch.jit.is_scripting() and isinstance(x, torch.Tensor):
return x.sqrt()
else:
return math.sqrt(x)
# For any optimizer with a faster implementation, we attempt to default to the
# fastest + stablest whenever possible. For foreach, the requirements are to have
# native params all on CUDA. For fused, there's currently the additional requirement
# that the tensors' dtypes must be floating point. Neither alternative supports
# torch.jit.script nor differentiable, so we fall back to the single tensor
# implementation in those cases.
def _default_to_fused_or_foreach(params: List[torch.Tensor],
differentiable: bool,
use_fused: bool = False) -> Tuple[bool, bool]:
if torch.jit.is_scripting() or differentiable:
return False, False
fused = use_fused and all(
p is None or (type(p) in _foreach_supported_types and p.is_cuda and torch.is_floating_point(p)) for p in params
)
foreach = not fused and all(
p is None or (type(p) in _foreach_supported_types and p.is_cuda) for p in params
)
return fused, foreach
# Common doc strings among optimizers
_foreach_doc = r"""foreach (bool, optional): whether foreach implementation of optimizer
is used. If unspecified by the user (so foreach is None), we will try to use
foreach over the for-loop implementation on CUDA, since it is usually
significantly more performant. (default: None)"""
_fused_doc = r"""fused (bool, optional): whether the fused implementation (CUDA only) is used.
Currently, `torch.float64`, `torch.float32`, `torch.float16`, and `torch.bfloat16`
are supported. (default: None)
.. note:: The foreach and fused implementations are typically faster than the for-loop,
single-tensor implementation. Thus, if the user has not specified BOTH flags
(i.e., when foreach = fused = None), we will attempt defaulting to the foreach
implementation when the tensors are all on CUDA. For example, if the user specifies
True for fused but nothing for foreach, we will run the fused implementation. If
the user specifies False for foreach but nothing for fused (or False for fused but
nothing for foreach), we will run the for-loop implementation. If the user specifies
True for both foreach and fused, we will prioritize fused over foreach, as it is
typically faster. We attempt to use the fastest, so the hierarchy goes fused ->
foreach -> for-loop. HOWEVER, since the fused implementation is relatively new,
we want to give it sufficient bake-in time, so we default to foreach and NOT
fused when the user has not specified either flag."""
_capturable_doc = r"""capturable (bool, optional): whether this instance is safe to
capture in a CUDA graph. Passing True can impair ungraphed performance,
so if you don't intend to graph capture this instance, leave it False
(default: False)"""
_differentiable_doc = r"""differentiable (bool, optional): whether autograd should
occur through the optimizer step in training. Otherwise, the step()
function runs in a torch.no_grad() context. Setting to True can impair
performance, so leave it False if you don't intend to run autograd
through this instance (default: False)"""
_maximize_doc = r"""maximize (bool, optional): maximize the params based on the
objective, instead of minimizing (default: False)"""
def register_optimizer_step_pre_hook(hook: Callable[..., None]) -> RemovableHandle:
r"""Register a pre hook common to all optimizers. The hook should have the following
signature::
hook(optimizer, args, kwargs) -> None or modified args and kwargs
Args:
hook (Callable): A user defined hook which is registered on all optimizers.
Returns:
:class:`torch.utils.hooks.RemoveableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
"""
handle = hooks.RemovableHandle(_global_optimizer_pre_hooks)
_global_optimizer_pre_hooks[handle.id] = hook
return handle
def register_optimizer_step_post_hook(hook: Callable[..., None]) -> RemovableHandle:
r"""Register a post hook common to all optimizers. The hook should have the following
signature::
hook(optimizer, args, kwargs) -> None
Args:
hook (Callable): A user defined hook which is registered on all optimizers.
Returns:
:class:`torch.utils.hooks.RemoveableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
"""
handle = hooks.RemovableHandle(_global_optimizer_post_hooks)
_global_optimizer_post_hooks[handle.id] = hook
return handle
[docs]class Optimizer:
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.
Args:
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
self._optimizer_step_pre_hooks: Dict[int, Callable] = OrderedDict()
self._optimizer_step_post_hooks: Dict[int, Callable] = OrderedDict()
self._patch_step_function()
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)
# Allows _cuda_graph_capture_health_check to rig a poor man's TORCH_WARN_ONCE in python,
# which I don't think exists
# https://github.com/pytorch/pytorch/issues/72948
self._warned_capturable_if_run_uncaptured = True
def __getstate__(self):
return {
'defaults': self.defaults,
'state': self.state,
'param_groups': self.param_groups,
}
def __setstate__(self, state):
self.__dict__.update(state)
if '_optimizer_step_pre_hooks' not in self.__dict__:
self._optimizer_step_pre_hooks = OrderedDict()
if '_optimizer_step_post_hooks' not in self.__dict__:
self._optimizer_step_post_hooks = OrderedDict()
self._patch_step_function() # To support multiprocessing pickle/unpickle
self.defaults.setdefault('differentiable', False)
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
# Currently needed by Adam and AdamW
def _cuda_graph_capture_health_check(self):
if torch.has_cuda and torch.cuda.is_available():
capturing = torch.cuda.is_current_stream_capturing()
if capturing and not all(group['capturable'] for group in self.param_groups):
raise RuntimeError("Attempting CUDA graph capture of step() for an instance of " +
self.__class__.__name__ +
" but param_groups' capturable is False.")
if (
(not getattr(self, "_warned_capturable_if_run_uncaptured", False))
and all(group['capturable'] for group in self.param_groups)
and (not capturing)
):
warnings.warn(
"This instance was constructed with capturable=True or some of all the param_groups came with capturable=True, "
"but step() is running without CUDA graph capture. If you never intend to graph-capture this "
"instance, capturable=True can impair performance, and you should set capturable=False."
)
self._warned_capturable_if_run_uncaptured = True
def _optimizer_step_code(self):
"""Entry point for `torch.profile.profiler`.
When python tracing is enabled the profiler will hook into this
function at the CPython level to inspect the optimizer's parameters and
param groups. It is called it after `step()` since many optimizers
lazily initialize state.
This is a workaround due to lack of a proper step hook on the optimizer,
and will be removed if it exists.
"""
pass
@staticmethod
def profile_hook_step(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
self, *_ = args
profile_name = "Optimizer.step#{}.step".format(self.__class__.__name__)
with torch.autograd.profiler.record_function(profile_name):
# call optimizer step pre hooks
for pre_hook in chain(_global_optimizer_pre_hooks.values(), self._optimizer_step_pre_hooks.values()):
result = pre_hook(self, args, kwargs)
if result is not None:
if isinstance(result, tuple) and len(result) == 2:
args, kwargs = result
else:
raise RuntimeError(f"{func} must return None or a tuple of (new_args, new_kwargs),"
f"but got {result}.")
out = func(*args, **kwargs)
self._optimizer_step_code()
# call optimizer step post hooks
for post_hook in chain(self._optimizer_step_post_hooks.values(), _global_optimizer_post_hooks.values()):
post_hook(self, args, kwargs)
return out
return wrapper
def _patch_step_function(self):
self._zero_grad_profile_name = "Optimizer.zero_grad#{}.zero_grad".format(self.__class__.__name__)
hooked = getattr(self.__class__.step, "hooked", None)
if not hooked:
self.__class__.step = self.profile_hook_step(self.__class__.step)
self.__class__.step.hooked = True
def register_step_pre_hook(self, hook: Callable[..., None]) -> RemovableHandle:
r"""Register an optimizer step pre hook which will be called before
optimizer step. It should have the following signature::
hook(optimizer, args, kwargs) -> None or modified args and kwargs
The ``optimizer`` argument is the optimizer instance being used. If
args and kwargs are modified by the pre-hook, then the transformed
values are returned as a tuple containing the new_args and new_kwargs.
Args:
hook (Callable): The user defined hook to be registered.
Returns:
:class:`torch.utils.hooks.RemoveableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
"""
handle = hooks.RemovableHandle(self._optimizer_step_pre_hooks)
self._optimizer_step_pre_hooks[handle.id] = hook
return handle
def register_step_post_hook(self, hook: Callable[..., None]) -> RemovableHandle:
r"""Register an optimizer step post hook which will be called after optimizer step.
It should have the following signature::
hook(optimizer, args, kwargs) -> None
The ``optimizer`` argument is the optimizer instance being used.
Args:
hook (Callable): The user defined hook to be registered.
Returns:
:class:`torch.utils.hooks.RemoveableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
"""
handle = hooks.RemovableHandle(self._optimizer_step_post_hooks)
self._optimizer_step_post_hooks[handle.id] = hook
return handle
[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 list containing all parameter groups where each
parameter group is a dict
"""
# Save order indices instead of Tensors
param_mappings = {}
start_index = 0
def pack_group(group):
nonlocal start_index
packed = {k: v for k, v in group.items() if k != 'params'}
param_mappings.update({id(p): i for i, p in enumerate(group['params'], start_index)
if id(p) not in param_mappings})
packed['params'] = [param_mappings[id(p)] for p in group['params']]
start_index += len(packed['params'])
return packed
param_groups = [pack_group(g) for g in self.param_groups]
# Remap state to use order indices as keys
packed_state = {(param_mappings[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.
Args:
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 = dict(zip(chain.from_iterable((g['params'] for g in saved_groups)),
chain.from_iterable((g['params'] for g in groups))))
def cast(param, value, key=None):
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.
# Make sure state['step'] is not casted https://github.com/pytorch/pytorch/issues/74424
if (key != "step"):
if param.is_floating_point():
value = value.to(param.dtype)
value = value.to(param.device)
return value
elif isinstance(value, dict):
return {k: cast(param, v, key=k) 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, set_to_none: bool = True):
r"""Resets the gradients of all optimized :class:`torch.Tensor` s.
Args:
set_to_none (bool): instead of setting to zero, set the grads to None.
This will in general have lower memory footprint, and can modestly improve performance.
However, it changes certain behaviors. For example:
1. When the user tries to access a gradient and perform manual ops on it,
a None attribute or a Tensor full of 0s will behave differently.
2. If the user requests ``zero_grad(set_to_none=True)`` followed by a backward pass, ``.grad``\ s
are guaranteed to be None for params that did not receive a gradient.
3. ``torch.optim`` optimizers have a different behavior if the gradient is 0 or None
(in one case it does the step with a gradient of 0 and in the other it skips
the step altogether).
"""
foreach = self.defaults.get('foreach', False) or self.defaults.get('fused', False)
if not hasattr(self, "_zero_grad_profile_name"):
self._patch_step_function()
if foreach:
per_device_and_dtype_grads = defaultdict(lambda: defaultdict(list))
with torch.autograd.profiler.record_function(self._zero_grad_profile_name):
for group in self.param_groups:
for p in group['params']:
if p.grad is not None:
if set_to_none:
p.grad = None
else:
if p.grad.grad_fn is not None:
p.grad.detach_()
else:
p.grad.requires_grad_(False)
if (not foreach or p.grad.is_sparse):
p.grad.zero_()
else:
per_device_and_dtype_grads[p.grad.device][p.grad.dtype].append(p.grad)
if foreach:
for per_dtype_grads in per_device_and_dtype_grads.values():
for grads in per_dtype_grads.values():
torch._foreach_zero_(grads)
[docs] def step(self, closure):
r"""Performs a single optimization step (parameter update).
Args:
closure (Callable): A closure that reevaluates the model and
returns the loss. Optional for most optimizers.
.. note::
Unless otherwise specified, this function should not modify the
``.grad`` field of the parameters.
"""
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.
Args:
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 self.defaults.get('differentiable', None) and not (param.is_leaf or param.retains_grad):
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)
params = param_group['params']
if len(params) != len(set(params)):
warnings.warn("optimizer contains a parameter group with duplicate parameters; "
"in future, this will cause an error; "
"see github.com/pytorch/pytorch/issues/40967 for more information", stacklevel=3)
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)
