Source code for torch.optim.optimizer
# mypy: allow-untyped-decorators
# mypy: allow-untyped-defs
"""Base optimizer."""
import functools
import warnings
from collections import defaultdict, OrderedDict
from copy import deepcopy
from itertools import chain
from typing import (
Any,
Callable,
cast,
DefaultDict,
Dict,
Hashable,
Iterable,
List,
Optional,
overload,
Set,
Tuple,
TypeVar,
Union,
)
from typing_extensions import ParamSpec, Self, TypeAlias
import torch
import torch.utils.hooks as hooks
from torch._utils import is_compiling
from torch.utils._foreach_utils import (
_get_foreach_kernels_supported_devices,
_get_fused_kernels_supported_devices,
_group_tensors_by_device_and_dtype,
Indices,
TensorListList,
)
from torch.utils.hooks import RemovableHandle
Args: TypeAlias = Tuple[Any, ...]
Kwargs: TypeAlias = Dict[str, Any]
StateDict: TypeAlias = Dict[str, Any]
DeviceDict = Dict[Optional[torch.device], torch.Tensor]
GlobalOptimizerPreHook: TypeAlias = Callable[
["Optimizer", Args, Kwargs], Optional[Tuple[Args, Kwargs]]
]
GlobalOptimizerPostHook: TypeAlias = Callable[["Optimizer", Args, Kwargs], None]
__all__ = [
"Optimizer",
"register_optimizer_step_pre_hook",
"register_optimizer_step_post_hook",
]
_global_optimizer_pre_hooks: Dict[int, GlobalOptimizerPreHook] = OrderedDict()
_global_optimizer_post_hooks: Dict[int, GlobalOptimizerPostHook] = OrderedDict()
_foreach_supported_types = [torch.Tensor, torch.nn.parameter.Parameter]
class _RequiredParameter:
"""Singleton class representing a required parameter for an Optimizer."""
def __repr__(self) -> str:
return "<required parameter>"
required = _RequiredParameter()
def _use_grad_for_differentiable(func):
def _use_grad(self, *args, **kwargs):
import torch._dynamo
prev_grad = torch.is_grad_enabled()
try:
# Note on graph break below:
# we need to graph break to ensure that aot respects the no_grad annotation.
# This is important for perf because without this, functionalization will generate an epilogue
# which updates the mutated parameters of the optimizer which is *not* visible to inductor, as a result,
# inductor will allocate for every parameter in the model, which is horrible.
# With this, aot correctly sees that this is an inference graph, and functionalization will generate
# an epilogue which is appended to the graph, which *is* visible to inductor, as a result, inductor sees that
# step is in place and is able to avoid the extra allocation.
# In the future, we will either 1) continue to graph break on backward, so this graph break does not matter
# or 2) have a fully fused forward and backward graph, which will have no_grad by default, and we can remove this
# graph break to allow the fully fused fwd-bwd-optimizer graph to be compiled.
# see https://github.com/pytorch/pytorch/issues/104053
torch.set_grad_enabled(self.defaults["differentiable"])
torch._dynamo.graph_break()
ret = func(self, *args, **kwargs)
finally:
torch._dynamo.graph_break()
torch.set_grad_enabled(prev_grad)
return ret
functools.update_wrapper(_use_grad, func)
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() if isinstance(x, torch.Tensor) else x
def _stack_if_compiling(x):
if not torch.jit.is_scripting() and is_compiling():
return torch.stack(x)
else:
return x
def _disable_dynamo_if_unsupported(single_tensor_fn=None):
# workaround for torchscript BC
# it requires all called functions to be in the
# global environment at the site at which the
# maybe_fallback closure is created
if single_tensor_fn:
globals()[single_tensor_fn.__name__] = single_tensor_fn
def wrapper(func):
import inspect
disabled_func = torch._disable_dynamo(func)
ps = inspect.signature(func).parameters
has_state_steps = True
try:
state_steps_ind = list(ps.keys()).index("state_steps")
except ValueError:
has_state_steps = False
# Today, there are cases where we stack state steps
# and pass them as the value arg of foreach ops.
# Having state steps on cuda as the value arg is not supported in eager,
# but this only occurs in the rare case that the user explicitly deletes
# the capturable flag. If capturable=True, this is not a problem.
@functools.wraps(func)
def maybe_fallback(*args, **kwargs):
if is_compiling() and (
not kwargs.get("capturable", False)
and has_state_steps
and (args[state_steps_ind] and args[state_steps_ind][0].is_cuda)
or (
"state_steps" in kwargs
and kwargs["state_steps"]
and kwargs["state_steps"][0].is_cuda
)
):
return disabled_func(*args, **kwargs)
else:
return func(*args, **kwargs)
return maybe_fallback
return wrapper
# 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_supported_devices = _get_fused_kernels_supported_devices()
foreach_supported_devices = _get_foreach_kernels_supported_devices()
fused = use_fused and all(
p is None
or (
type(p) in _foreach_supported_types
and p.device.type in fused_supported_devices
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.device.type in foreach_supported_devices
)
for p in params
)
return fused, foreach
def _device_dtype_check_for_fused(
p: torch.Tensor, cuda_unsupported: bool = False
) -> None:
fused_supported_devices = _get_fused_kernels_supported_devices()
if cuda_unsupported:
fused_supported_devices.remove("cuda")
if not (p.device.type in fused_supported_devices and torch.is_floating_point(p)):
raise RuntimeError(
"`fused=True` requires all the params to be floating point Tensors of "
f"supported devices: {fused_supported_devices} but {p.dtype} and {p.device.type}"
)
def _view_as_real(params, *state_and_grads):
for i, p in enumerate(params):
if torch.is_complex(p):
params[i] = torch.view_as_real(params[i])
for s in state_and_grads:
s[i] = torch.view_as_real(s[i])
def _get_scalar_dtype(is_fused=None):
if is_fused:
return torch.float32
return (
torch.float64 if torch.get_default_dtype() == torch.float64 else torch.float32
)
def _get_capturable_supported_devices(supports_xla: bool = True) -> List[str]:
r"""Return the device type list that supports capturable optimizer."""
capturable_supported_devices = ["cuda", "xpu", "hpu"]
if not torch.jit.is_scripting():
capturable_supported_devices.append(torch._C._get_privateuse1_backend_name())
if supports_xla:
capturable_supported_devices.append("xla")
return capturable_supported_devices
# 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. Note that the foreach implementation uses
~ sizeof(params) more peak memory than the for-loop version due to the intermediates
being a tensorlist vs just one tensor. If memory is prohibitive, batch fewer
parameters through the optimizer at a time or switch this flag to False (default: None)"""
_fused_doc = r"""fused (bool, optional): whether the fused implementation 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, with fused being theoretically fastest with both
vertical and horizontal fusion. As such, if the user has not specified either
flag (i.e., when foreach = fused = None), we will attempt defaulting to the foreach
implementation when the tensors are all on CUDA. Why not fused? Since the fused
implementation is relatively new, we want to give it sufficient bake-in time.
To specify fused, pass True for fused. To force running the for-loop
implementation, pass False for either foreach or fused. """
_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 objective with respect to the
params, instead of minimizing (default: False)"""
def register_optimizer_step_pre_hook(hook: GlobalOptimizerPreHook) -> 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.RemovableHandle`:
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: GlobalOptimizerPostHook) -> 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.RemovableHandle`:
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
ParamsT: TypeAlias = Union[Iterable[torch.Tensor], Iterable[Dict[str, Any]]]
_P = ParamSpec("_P")
R = TypeVar("R")
T = TypeVar("T")
[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).
"""
OptimizerPreHook: TypeAlias = Callable[[Self, Args, Kwargs], Optional[Tuple[Args, Kwargs]]] # type: ignore[misc]
OptimizerPostHook: TypeAlias = Callable[[Self, Args, Kwargs], None] # type: ignore[misc]
_optimizer_step_pre_hooks: Dict[int, OptimizerPreHook]
_optimizer_step_post_hooks: Dict[int, OptimizerPostHook]
_optimizer_state_dict_pre_hooks: 'OrderedDict[int, Callable[["Optimizer"], None]]'
_optimizer_state_dict_post_hooks: 'OrderedDict[int, Callable[["Optimizer", StateDict], Optional[StateDict]]]'
_optimizer_load_state_dict_pre_hooks: 'OrderedDict[int, Callable[["Optimizer", StateDict], Optional[StateDict]]]'
_optimizer_load_state_dict_post_hooks: 'OrderedDict[int, Callable[["Optimizer"], None]]'
def __init__(self, params: ParamsT, defaults: Dict[str, Any]) -> None: # noqa: D107
torch._C._log_api_usage_once("python.optimizer")
self.defaults = defaults
self._optimizer_step_pre_hooks = OrderedDict()
self._optimizer_step_post_hooks = OrderedDict()
self._optimizer_state_dict_pre_hooks = OrderedDict()
self._optimizer_state_dict_post_hooks = OrderedDict()
self._optimizer_load_state_dict_pre_hooks = OrderedDict()
self._optimizer_load_state_dict_post_hooks = 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[torch.Tensor, Any] = defaultdict(dict)
self.param_groups: List[Dict[str, Any]] = []
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(cast(dict, 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) -> Dict[str, Any]: # noqa: D105
return {
"defaults": self.defaults,
"state": self.state,
"param_groups": self.param_groups,
}
def __setstate__(self, state: Dict[str, Any]) -> None: # noqa: D105
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()
if "_optimizer_state_dict_pre_hooks" not in self.__dict__:
self._optimizer_state_dict_pre_hooks = OrderedDict()
if "_optimizer_state_dict_post_hooks" not in self.__dict__:
self._optimizer_state_dict_post_hooks = OrderedDict()
if "_optimizer_load_state_dict_pre_hooks" not in self.__dict__:
self._optimizer_load_state_dict_pre_hooks = OrderedDict()
if "_optimizer_load_state_dict_post_hooks" not in self.__dict__:
self._optimizer_load_state_dict_post_hooks = OrderedDict()
self._patch_step_function() # To support multiprocessing pickle/unpickle
self.defaults.setdefault("differentiable", False)
def __repr__(self) -> str: # noqa: D105
format_string = self.__class__.__name__ + " ("
for i, group in enumerate(self.param_groups):
format_string += "\n"
format_string += f"Parameter Group {i}\n"
for key in sorted(group.keys()):
if key != "params":
format_string += f" {key}: {group[key]}\n"
format_string += ")"
return format_string
# Currently needed by Adam and AdamW
def _cuda_graph_capture_health_check(self) -> None:
# Note [torch.compile x capturable]
# If we are compiling, we try to take the capturable path automatically by
# setting the flag to True during tracing. Due to this, we skip all the checks
# normally required for determining whether we can use CUDA graphs and
# shunt the responsibility to torch.inductor. This saves time during tracing
# since the checks are slow without sacrificing UX since inductor will warn
# later if CUDA graphs cannot be enabled, e.g.,
# https://github.com/pytorch/pytorch/blob/d3ba8901d8640eb16f88b2bfef9df7fa383d4b47/torch/_inductor/compile_fx.py#L390.
# Thus, when compiling, inductor will determine if cudagraphs
# can be enabled based on whether there is input mutation or CPU tensors.
if (
not is_compiling()
and torch.backends.cuda.is_built()
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) -> None:
"""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.
"""
@staticmethod
def profile_hook_step(func: Callable[_P, R]) -> Callable[_P, R]: # noqa: D102
@functools.wraps(func)
def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> R:
self, *_ = args
self = cast(Optimizer, self)
profile_name = f"Optimizer.step#{self.__class__.__name__}.step"
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 # type: ignore[assignment]
else:
raise RuntimeError(
f"{func} must return None or a tuple of (new_args, new_kwargs), 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
@staticmethod
def _group_tensors_by_device_and_dtype(
tensorlistlist: TensorListList,
with_indices: bool = False,
) -> Union[
Dict[Tuple[None, None], Tuple[TensorListList, Indices]],
Dict[Tuple[torch.device, torch.dtype], Tuple[TensorListList, Indices]],
]:
"""Group a list of lists of tensors by device and dtype.
Skips this step if we are compiling since this will occur during inductor lowering.
"""
if is_compiling():
return {(None, None): (tensorlistlist, list(range(len(tensorlistlist[0]))))}
else:
return _group_tensors_by_device_and_dtype(tensorlistlist, with_indices) # type: ignore[return-value, arg-type]
def _patch_step_function(self) -> None:
self._zero_grad_profile_name = (
f"Optimizer.zero_grad#{self.__class__.__name__}.zero_grad"
)
hooked = getattr(self.__class__.step, "hooked", None)
if not hooked:
self.__class__.step = self.profile_hook_step(self.__class__.step) # type: ignore[assignment]
self.__class__.step.hooked = True # type: ignore[attr-defined]
[docs] def register_step_pre_hook(self, hook: OptimizerPreHook) -> 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.RemovableHandle`:
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
[docs] def register_step_post_hook(self, hook: OptimizerPostHook) -> 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.RemovableHandle`:
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 register_state_dict_pre_hook(
self, hook: Callable[["Optimizer"], None], prepend: bool = False
) -> RemovableHandle: # noqa: D101
r"""Register a state dict pre-hook which will be called before :meth:`~torch.optim.Optimizer.state_dict` is called.
It should have the following signature::
hook(optimizer) -> None
The ``optimizer`` argument is the optimizer instance being used.
The hook will be called with argument ``self`` before calling ``state_dict`` on ``self``.
The registered hook can be used to perform pre-processing before the ``state_dict``
call is made.
Args:
hook (Callable): The user defined hook to be registered.
prepend (bool): If True, the provided pre ``hook`` will be fired before
all the already registered pre-hooks on ``state_dict``. Otherwise,
the provided ``hook`` will be fired after all the already registered
pre-hooks. (default: False)
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_state_dict_pre_hooks)
self._optimizer_state_dict_pre_hooks[handle.id] = hook
if prepend:
self._optimizer_state_dict_pre_hooks.move_to_end(handle.id, last=False)
return handle
[docs] def register_state_dict_post_hook(
self,
hook: Callable[["Optimizer", StateDict], Optional[StateDict]],
prepend: bool = False,
) -> RemovableHandle:
r"""Register a state dict post-hook which will be called after :meth:`~torch.optim.Optimizer.state_dict` is called.
It should have the following signature::
hook(optimizer, state_dict) -> state_dict or None
The hook will be called with arguments ``self`` and ``state_dict`` after generating
a ``state_dict`` on ``self``. The hook may modify the state_dict inplace or optionally
return a new one. The registered hook can be used to perform post-processing
on the ``state_dict`` before it is returned.
Args:
hook (Callable): The user defined hook to be registered.
prepend (bool): If True, the provided post ``hook`` will be fired before
all the already registered post-hooks on ``state_dict``. Otherwise,
the provided ``hook`` will be fired after all the already registered
post-hooks. (default: False)
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_state_dict_post_hooks)
self._optimizer_state_dict_post_hooks[handle.id] = hook
if prepend:
self._optimizer_state_dict_post_hooks.move_to_end(handle.id, last=False)
return handle
[docs] @torch._disable_dynamo
def state_dict(self) -> StateDict:
r"""Return 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, but some common characteristics
hold. For example, state is saved per parameter, and the parameter
itself is NOT saved. ``state`` is a Dictionary mapping parameter ids
to a Dict with state corresponding to each parameter.
* ``param_groups``: a List containing all parameter groups where each
parameter group is a Dict. Each parameter group contains metadata
specific to the optimizer, such as learning rate and weight decay,
as well as a List of parameter IDs of the parameters in the group.
NOTE: The parameter IDs may look like indices but they are just IDs
associating state with param_group. When loading from a state_dict,
the optimizer will zip the param_group ``params`` (int IDs) and the
optimizer ``param_groups`` (actual ``nn.Parameter`` s) in order to
match state WITHOUT additional verification.
A returned state dict might look something like:
.. code-block:: text
{
'state': {
0: {'momentum_buffer': tensor(...), ...},
1: {'momentum_buffer': tensor(...), ...},
2: {'momentum_buffer': tensor(...), ...},
3: {'momentum_buffer': tensor(...), ...}
},
'param_groups': [
{
'lr': 0.01,
'weight_decay': 0,
...
'params': [0]
},
{
'lr': 0.001,
'weight_decay': 0.5,
...
'params': [1, 2, 3]
}
]
}
"""
for pre_hook in self._optimizer_state_dict_pre_hooks.values():
pre_hook(self)
# Save order indices instead of Tensors
param_mappings: Dict[int, int] = {}
start_index = 0
def pack_group(group: Dict[str, Any]) -> Dict[str, Any]:
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()
}
state_dict = {
"state": packed_state,
"param_groups": param_groups,
}
for post_hook in self._optimizer_state_dict_post_hooks.values():
hook_result = post_hook(self, state_dict)
if hook_result is not None:
state_dict = hook_result
return state_dict
@staticmethod
def _process_value_according_to_param_policy(
param: torch.Tensor,
value: torch.Tensor,
param_id: int,
param_groups: List[Dict[Any, Any]],
key: Hashable = None,
) -> 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
# UNLESS fused or capturable, see note [special device hosting for step]
fused = False
capturable = False
assert param_groups is not None
for pg in param_groups:
if param_id in pg["params"]:
fused = pg["fused"] if "fused" in pg else False
capturable = pg["capturable"] if "capturable" in pg else False
break
if key == "step":
if capturable or fused:
return value.to(dtype=torch.float32, device=param.device)
else:
return value
else:
if param.is_floating_point():
return value.to(dtype=param.dtype, device=param.device)
else:
return value.to(device=param.device)
[docs] def register_load_state_dict_pre_hook(
self,
hook: Callable[["Optimizer", StateDict], Optional[StateDict]],
prepend: bool = False,
) -> RemovableHandle: # noqa: D205 D400
r"""Register a load_state_dict pre-hook which will be called before
:meth:`~torch.optim.Optimizer.load_state_dict` is called. It should have the
following signature::
hook(optimizer, state_dict) -> state_dict or None
The ``optimizer`` argument is the optimizer instance being used and the
``state_dict`` argument is a shallow copy of the ``state_dict`` the user
passed in to ``load_state_dict``. The hook may modify the state_dict inplace
or optionally return a new one. If a state_dict is returned, it will be used
to be loaded into the optimizer.
The hook will be called with argument ``self`` and ``state_dict`` before
calling ``load_state_dict`` on ``self``. The registered hook can be used to
perform pre-processing before the ``load_state_dict`` call is made.
Args:
hook (Callable): The user defined hook to be registered.
prepend (bool): If True, the provided pre ``hook`` will be fired before
all the already registered pre-hooks on ``load_state_dict``. Otherwise,
the provided ``hook`` will be fired after all the already registered
pre-hooks. (default: False)
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_load_state_dict_pre_hooks)
self._optimizer_load_state_dict_pre_hooks[handle.id] = hook
if prepend:
self._optimizer_load_state_dict_pre_hooks.move_to_end(handle.id, last=False)
return handle
[docs] def register_load_state_dict_post_hook(
self, hook: Callable[["Optimizer"], None], prepend: bool = False
) -> RemovableHandle: # noqa: D205 D400
r"""Register a load_state_dict post-hook which will be called after
:meth:`~torch.optim.Optimizer.load_state_dict` is called. It should have the
following signature::
hook(optimizer) -> None
The ``optimizer`` argument is the optimizer instance being used.
The hook will be called with argument ``self`` after calling
``load_state_dict`` on ``self``. The registered hook can be used to
perform post-processing after ``load_state_dict`` has loaded the
``state_dict``.
Args:
hook (Callable): The user defined hook to be registered.
prepend (bool): If True, the provided post ``hook`` will be fired before
all the already registered post-hooks on ``load_state_dict``. Otherwise,
the provided ``hook`` will be fired after all the already registered
post-hooks. (default: False)
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_load_state_dict_post_hooks)
self._optimizer_load_state_dict_post_hooks[handle.id] = hook
if prepend:
self._optimizer_load_state_dict_post_hooks.move_to_end(handle.id, last=False) # type: ignore[attr-defined]
return handle
[docs] @torch._disable_dynamo
def load_state_dict(self, state_dict: StateDict) -> None:
r"""Load the optimizer state.
Args:
state_dict (dict): optimizer state. Should be an object returned
from a call to :meth:`state_dict`.
"""
# shallow copy, to be consistent with module API
state_dict = state_dict.copy()
for pre_hook in self._optimizer_load_state_dict_pre_hooks.values():
hook_result = pre_hook(self, state_dict)
if hook_result is not None:
state_dict = hook_result
# Validate the state_dict
groups = self.param_groups
# Deepcopy as we write into saved_groups later to update state
saved_groups = deepcopy(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, param_id=None, param_groups=None, key=None):
r"""Make a deep copy of value, casting all tensors to device of param."""
if isinstance(value, torch.Tensor):
return Optimizer._process_value_according_to_param_policy(
param, value, param_id, param_groups, key
)
elif isinstance(value, dict):
return {
k: _cast(
param, v, param_id=param_id, param_groups=param_groups, key=k
)
for k, v in value.items()
}
elif isinstance(value, Iterable):
return type(value)(_cast(param, v, param_id=param_id, param_groups=param_groups) for v in value) # type: ignore[call-arg]
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[torch.Tensor, Dict[Any, Any]] = defaultdict(dict)
for k, v in state_dict["state"].items():
if k in id_map:
param = id_map[k]
state[param] = _cast(
param, v, param_id=k, param_groups=state_dict["param_groups"]
)
else:
state[k] = v
# Update parameter groups, setting their 'params' value
def update_group(
group: Dict[str, Any], new_group: Dict[str, Any]
) -> Dict[str, Any]:
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})
for post_hook in self._optimizer_load_state_dict_post_hooks.values():
post_hook(self)
[docs] @torch._disable_dynamo
def zero_grad(self, set_to_none: bool = True) -> None:
r"""Reset 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()
per_device_and_dtype_grads: Optional[
DefaultDict[torch.device, DefaultDict[torch.dtype, List[torch.Tensor]]]
]
if foreach:
per_device_and_dtype_grads = defaultdict(lambda: defaultdict(list))
else:
per_device_and_dtype_grads = None
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:
assert per_device_and_dtype_grads is not None
per_device_and_dtype_grads[p.grad.device][
p.grad.dtype
].append(p.grad)
if foreach:
assert per_device_and_dtype_grads is not None
for per_dtype_grads in per_device_and_dtype_grads.values():
for grads in per_dtype_grads.values():
torch._foreach_zero_(grads)
@overload
def step(self, closure: None = ...) -> None:
...
@overload
def step(self, closure: Callable[[], float]) -> float:
...
[docs] def step(self, closure: Optional[Callable[[], float]] = None) -> Optional[float]:
r"""Perform a single optimization step to update parameter.
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] @torch._disable_dynamo
def add_param_group(self, param_group: Dict[str, Any]) -> None:
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.
"""
if not isinstance(param_group, dict):
raise TypeError(f"param_group must be a dict, but got {type(param_group)}")
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(
f"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[torch.Tensor] = 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)
