# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# pyre-ignore-all-errors[2,3,58]
import logging
import os
from collections import defaultdict
from typing import Any, cast, Dict, IO, Iterable, List, NamedTuple, Optional, Tuple
import numpy as np
import torch
import torch.nn as nn
from iopath.common.file_io import HTTPURLHandler, PathManager
from termcolor import colored
from torch.nn.parallel import DataParallel, DistributedDataParallel
TORCH_VERSION: Tuple[int, ...] = tuple(int(x) for x in torch.__version__.split(".")[:2])
if TORCH_VERSION >= (1, 11):
from torch.ao import quantization
from torch.ao.quantization import FakeQuantizeBase, ObserverBase
elif (
TORCH_VERSION >= (1, 8)
and hasattr(torch.quantization, "FakeQuantizeBase")
and hasattr(torch.quantization, "ObserverBase")
):
from torch import quantization
from torch.quantization import FakeQuantizeBase, ObserverBase
__all__ = ["Checkpointer", "PeriodicCheckpointer"]
TORCH_VERSION: Tuple[int, ...] = tuple(int(x) for x in torch.__version__.split(".")[:2])
class _IncompatibleKeys(
NamedTuple(
"IncompatibleKeys",
[
("missing_keys", List[str]),
("unexpected_keys", List[str]),
("incorrect_shapes", List[Tuple[str, Tuple[int], Tuple[int]]]),
],
)
):
pass
[docs]class Checkpointer:
"""
A checkpointer that can save/load model as well as extra checkpointable
objects.
"""
[docs] def __init__(
self,
model: nn.Module,
save_dir: str = "",
*,
save_to_disk: bool = True,
**checkpointables: Any,
) -> None:
"""
Args:
model (nn.Module): model.
save_dir (str): a directory to save and find checkpoints.
save_to_disk (bool): if True, save checkpoint to disk, otherwise
disable saving for this checkpointer.
checkpointables (object): any checkpointable objects, i.e., objects
that have the ``state_dict()`` and ``load_state_dict()`` method. For
example, it can be used like
`Checkpointer(model, "dir", optimizer=optimizer)`.
"""
if isinstance(model, (DistributedDataParallel, DataParallel)):
model = model.module
self.model = model
self.checkpointables: Dict[str, Any] = {}
for k, v in checkpointables.items():
self.add_checkpointable(k, v)
self.logger: logging.Logger = logging.getLogger(__name__)
self.save_dir = save_dir
self.save_to_disk = save_to_disk
# Default PathManager, support HTTP URLs (for backward compatibility in open source).
# A user may want to use a different project-specific PathManager
self.path_manager: PathManager = PathManager()
self.path_manager.register_handler(HTTPURLHandler())
[docs] def add_checkpointable(self, key: str, checkpointable: Any) -> None:
"""
Add checkpointable object for this checkpointer to track.
Args:
key (str): the key used to save the object
checkpointable: any object with ``state_dict()`` and
``load_state_dict()`` method
"""
if key in self.checkpointables:
raise KeyError(f"Key {key} already used in the Checkpointer")
if not hasattr(checkpointable, "state_dict"):
raise TypeError(
"add_checkpointable needs an object with 'state_dict()' method."
)
self.checkpointables[key] = checkpointable
[docs] def save(self, name: str, **kwargs: Any) -> None:
"""
Dump model and checkpointables to a file.
Args:
name (str): name of the file.
kwargs (dict): extra arbitrary data to save.
"""
if not self.save_dir or not self.save_to_disk:
return
data = {}
data["model"] = self.model.state_dict()
for key, obj in self.checkpointables.items():
data[key] = obj.state_dict()
data.update(kwargs)
basename = "{}.pth".format(name)
save_file = os.path.join(self.save_dir, basename)
assert os.path.basename(save_file) == basename, basename
self.logger.info("Saving checkpoint to {}".format(save_file))
with self.path_manager.open(save_file, "wb") as f:
# pyre-fixme[22]: The cast is redundant.
torch.save(data, cast(IO[bytes], f))
self.tag_last_checkpoint(basename)
[docs] def load(
self, path: str, checkpointables: Optional[List[str]] = None
) -> Dict[str, Any]:
"""
Load from the given checkpoint.
Args:
path (str): path or url to the checkpoint. If empty, will not load
anything.
checkpointables (list): List of checkpointable names to load. If not
specified (None), will load all the possible checkpointables.
Returns:
dict:
extra data loaded from the checkpoint that has not been
processed. For example, those saved with
:meth:`.save(**extra_data)`.
"""
if not path:
# no checkpoint provided
self.logger.info("No checkpoint found. Initializing model from scratch")
return {}
self.logger.info("[Checkpointer] Loading from {} ...".format(path))
# path may not be a local file, but _load_file is responsible to handle it.
checkpoint = self._load_file(path)
incompatible = self._load_model(checkpoint)
if (
incompatible is not None
): # handle some existing subclasses that returns None
self._log_incompatible_keys(incompatible)
for key in self.checkpointables if checkpointables is None else checkpointables:
if key in checkpoint:
self.logger.info("Loading {} from {} ...".format(key, path))
obj = self.checkpointables[key]
obj.load_state_dict(checkpoint.pop(key))
# return any further checkpoint data
return checkpoint
[docs] def has_checkpoint(self) -> bool:
"""
Returns:
bool: whether a checkpoint exists in the target directory.
"""
save_file = os.path.join(self.save_dir, "last_checkpoint")
return self.path_manager.exists(save_file)
[docs] def get_checkpoint_file(self) -> str:
"""
Returns:
str: The latest checkpoint file in target directory.
"""
save_file = os.path.join(self.save_dir, "last_checkpoint")
try:
with self.path_manager.open(save_file, "r") as f:
last_saved = f.read().strip()
except IOError:
# if file doesn't exist, maybe because it has just been
# deleted by a separate process
return ""
return os.path.join(self.save_dir, last_saved)
[docs] def get_all_checkpoint_files(self) -> List[str]:
"""
Returns:
list: All available checkpoint files (.pth files) in target
directory.
"""
all_model_checkpoints = [
os.path.join(self.save_dir, file)
for file in self.path_manager.ls(self.save_dir)
if self.path_manager.isfile(os.path.join(self.save_dir, file))
and file.endswith(".pth")
]
return all_model_checkpoints
[docs] def resume_or_load(self, path: str, *, resume: bool = True) -> Dict[str, Any]:
"""
If `resume` is True, this method attempts to resume from the last
checkpoint, if exists. Otherwise, load checkpoint from the given path.
This is useful when restarting an interrupted training job.
Args:
path (str): path to the checkpoint.
resume (bool): if True, resume from the last checkpoint if it exists
and load the model together with all the checkpointables. Otherwise
only load the model without loading any checkpointables.
Returns:
same as :meth:`load`.
"""
if resume and self.has_checkpoint():
path = self.get_checkpoint_file()
return self.load(path)
else:
return self.load(path, checkpointables=[])
[docs] def tag_last_checkpoint(self, last_filename_basename: str) -> None:
"""
Tag the last checkpoint.
Args:
last_filename_basename (str): the basename of the last filename.
"""
save_file = os.path.join(self.save_dir, "last_checkpoint")
with self.path_manager.open(save_file, "w") as f:
f.write(last_filename_basename)
def _load_file(self, f: str) -> Dict[str, Any]:
"""
Load a checkpoint file. Can be overwritten by subclasses to support
different formats.
Args:
f (str): a locally mounted file path.
Returns:
dict: with keys "model" and optionally others that are saved by
the checkpointer dict["model"] must be a dict which maps strings
to torch.Tensor or numpy arrays.
"""
with self.path_manager.open(f, "rb") as file:
# pyre-fixme[22]: The cast is redundant.
return torch.load(cast(IO[bytes], file), map_location=torch.device("cpu"))
def _load_model(self, checkpoint: Any) -> _IncompatibleKeys:
"""
Load weights from a checkpoint.
Args:
checkpoint (Any): checkpoint contains the weights.
Returns:
``NamedTuple`` with ``missing_keys``, ``unexpected_keys``,
and ``incorrect_shapes`` fields:
* **missing_keys** is a list of str containing the missing keys
* **unexpected_keys** is a list of str containing the unexpected keys
* **incorrect_shapes** is a list of (key, shape in checkpoint, shape in model)
This is just like the return value of
:func:`torch.nn.Module.load_state_dict`, but with extra support
for ``incorrect_shapes``.
"""
checkpoint_state_dict = checkpoint.pop("model")
self._convert_ndarray_to_tensor(checkpoint_state_dict)
# if the state_dict comes from a model that was wrapped in a
# DataParallel or DistributedDataParallel during serialization,
# remove the "module" prefix before performing the matching.
_strip_prefix_if_present(checkpoint_state_dict, "module.")
# workaround https://github.com/pytorch/pytorch/issues/24139
model_state_dict = self.model.state_dict()
incorrect_shapes = []
for k in list(checkpoint_state_dict.keys()):
if k in model_state_dict:
model_param = model_state_dict[k]
# Allow mismatch for uninitialized parameters
if TORCH_VERSION >= (1, 8) and isinstance(
model_param, nn.parameter.UninitializedParameter
):
continue
shape_model = tuple(model_param.shape)
shape_checkpoint = tuple(checkpoint_state_dict[k].shape)
if shape_model != shape_checkpoint:
has_observer_base_classes = (
TORCH_VERSION >= (1, 8)
and hasattr(quantization, "ObserverBase")
and hasattr(quantization, "FakeQuantizeBase")
)
if has_observer_base_classes:
# Handle the special case of quantization per channel observers,
# where buffer shape mismatches are expected.
def _get_module_for_key(
model: torch.nn.Module, key: str
) -> torch.nn.Module:
# foo.bar.param_or_buffer_name -> [foo, bar]
key_parts = key.split(".")[:-1]
cur_module = model
for key_part in key_parts:
cur_module = getattr(cur_module, key_part)
return cur_module
cls_to_skip = (
ObserverBase,
FakeQuantizeBase,
)
target_module = _get_module_for_key(self.model, k)
if isinstance(target_module, cls_to_skip):
# Do not remove modules with expected shape mismatches
# them from the state_dict loading. They have special logic
# in _load_from_state_dict to handle the mismatches.
continue
incorrect_shapes.append((k, shape_checkpoint, shape_model))
checkpoint_state_dict.pop(k)
incompatible = self.model.load_state_dict(checkpoint_state_dict, strict=False)
return _IncompatibleKeys(
missing_keys=incompatible.missing_keys,
unexpected_keys=incompatible.unexpected_keys,
incorrect_shapes=incorrect_shapes,
)
def _log_incompatible_keys(self, incompatible: _IncompatibleKeys) -> None:
"""
Log information about the incompatible keys returned by ``_load_model``.
"""
for k, shape_checkpoint, shape_model in incompatible.incorrect_shapes:
self.logger.warning(
"Skip loading parameter '{}' to the model due to incompatible "
"shapes: {} in the checkpoint but {} in the "
"model! You might want to double check if this is expected.".format(
k, shape_checkpoint, shape_model
)
)
if incompatible.missing_keys:
missing_keys = _filter_reused_missing_keys(
self.model, incompatible.missing_keys
)
if missing_keys:
self.logger.warning(get_missing_parameters_message(missing_keys))
if incompatible.unexpected_keys:
self.logger.warning(
get_unexpected_parameters_message(incompatible.unexpected_keys)
)
def _convert_ndarray_to_tensor(self, state_dict: Dict[str, Any]) -> None:
"""
In-place convert all numpy arrays in the state_dict to torch tensor.
Args:
state_dict (dict): a state-dict to be loaded to the model.
Will be modified.
"""
# model could be an OrderedDict with _metadata attribute
# (as returned by Pytorch's state_dict()). We should preserve these
# properties.
for k in list(state_dict.keys()):
v = state_dict[k]
if not isinstance(v, np.ndarray) and not isinstance(v, torch.Tensor):
raise ValueError(
"Unsupported type found in checkpoint! {}: {}".format(k, type(v))
)
if not isinstance(v, torch.Tensor):
state_dict[k] = torch.from_numpy(v)
[docs]class PeriodicCheckpointer:
"""
Save checkpoints periodically. When `.step(iteration)` is called, it will
execute `checkpointer.save` on the given checkpointer, if iteration is a
multiple of period or if `max_iter` is reached.
Attributes:
checkpointer (Checkpointer): the underlying checkpointer object
"""
[docs] def __init__(
self,
checkpointer: Checkpointer,
period: int,
max_iter: Optional[int] = None,
max_to_keep: Optional[int] = None,
file_prefix: str = "model",
) -> None:
"""
Args:
checkpointer: the checkpointer object used to save checkpoints.
period (int): the period to save checkpoint.
max_iter (int): maximum number of iterations. When it is reached,
a checkpoint named "{file_prefix}_final" will be saved.
max_to_keep (int): maximum number of most current checkpoints to keep,
previous checkpoints will be deleted
file_prefix (str): the prefix of checkpoint's filename
"""
self.checkpointer = checkpointer
self.period = int(period)
self.max_iter = max_iter
if max_to_keep is not None:
assert max_to_keep > 0
self.max_to_keep = max_to_keep
self.recent_checkpoints: List[str] = []
self.path_manager: PathManager = checkpointer.path_manager
self.file_prefix = file_prefix
[docs] def step(self, iteration: int, **kwargs: Any) -> None:
"""
Perform the appropriate action at the given iteration.
Args:
iteration (int): the current iteration, ranged in [0, max_iter-1].
kwargs (Any): extra data to save, same as in
:meth:`Checkpointer.save`.
"""
iteration = int(iteration)
additional_state = {"iteration": iteration}
additional_state.update(kwargs)
if (iteration + 1) % self.period == 0:
self.checkpointer.save(
"{}_{:07d}".format(self.file_prefix, iteration), **additional_state
)
if self.max_to_keep is not None:
self.recent_checkpoints.append(self.checkpointer.get_checkpoint_file())
if len(self.recent_checkpoints) > self.max_to_keep:
file_to_delete = self.recent_checkpoints.pop(0)
if self.path_manager.exists(
file_to_delete
) and not file_to_delete.endswith(f"{self.file_prefix}_final.pth"):
self.path_manager.rm(file_to_delete)
if self.max_iter is not None:
if iteration >= self.max_iter - 1:
self.checkpointer.save(f"{self.file_prefix}_final", **additional_state)
[docs] def save(self, name: str, **kwargs: Any) -> None:
"""
Same argument as :meth:`Checkpointer.save`.
Use this method to manually save checkpoints outside the schedule.
Args:
name (str): file name.
kwargs (Any): extra data to save, same as in
:meth:`Checkpointer.save`.
"""
self.checkpointer.save(name, **kwargs)
def _filter_reused_missing_keys(model: nn.Module, keys: List[str]) -> List[str]:
"""
Filter "missing keys" to not include keys that have been loaded with another name.
"""
keyset = set(keys)
param_to_names = defaultdict(set) # param -> names that points to it
for module_prefix, module in _named_modules_with_dup(model):
for name, param in list(module.named_parameters(recurse=False)) + list(
module.named_buffers(recurse=False)
):
full_name = (module_prefix + "." if module_prefix else "") + name
param_to_names[param].add(full_name)
for names in param_to_names.values():
# if one name appears missing but its alias exists, then this
# name is not considered missing
if any(n in keyset for n in names) and not all(n in keyset for n in names):
[keyset.remove(n) for n in names if n in keyset]
return list(keyset)
def get_missing_parameters_message(keys: List[str]) -> str:
"""
Get a logging-friendly message to report parameter names (keys) that are in
the model but not found in a checkpoint.
Args:
keys (list[str]): List of keys that were not found in the checkpoint.
Returns:
str: message.
"""
groups = _group_checkpoint_keys(keys)
msg_per_group = sorted(k + _group_to_str(v) for k, v in groups.items())
msg = "Some model parameters or buffers are not found in the checkpoint:\n"
msg += "\n".join([colored(x, "blue") for x in msg_per_group])
return msg
def get_unexpected_parameters_message(keys: List[str]) -> str:
"""
Get a logging-friendly message to report parameter names (keys) that are in
the checkpoint but not found in the model.
Args:
keys (list[str]): List of keys that were not found in the model.
Returns:
str: message.
"""
groups = _group_checkpoint_keys(keys)
msg = "The checkpoint state_dict contains keys that are not used by the model:\n"
msg += "\n".join(
" " + colored(k + _group_to_str(v), "magenta") for k, v in groups.items()
)
return msg
def _strip_prefix_if_present(state_dict: Dict[str, Any], prefix: str) -> None:
"""
Strip the prefix in metadata, if any.
Args:
state_dict (OrderedDict): a state-dict to be loaded to the model.
prefix (str): prefix.
"""
keys = sorted(state_dict.keys())
if not all(len(key) == 0 or key.startswith(prefix) for key in keys):
return
for key in keys:
newkey = key[len(prefix) :]
state_dict[newkey] = state_dict.pop(key)
# also strip the prefix in metadata, if any..
try:
metadata = state_dict._metadata # pyre-ignore
except AttributeError:
pass
else:
for key in list(metadata.keys()):
# for the metadata dict, the key can be:
# '': for the DDP module, which we want to remove.
# 'module': for the actual model.
# 'module.xx.xx': for the rest.
if len(key) == 0:
continue
newkey = key[len(prefix) :]
metadata[newkey] = metadata.pop(key)
def _group_checkpoint_keys(keys: List[str]) -> Dict[str, List[str]]:
"""
Group keys based on common prefixes. A prefix is the string up to the final
"." in each key.
Args:
keys (list[str]): list of parameter names, i.e. keys in the model
checkpoint dict.
Returns:
dict[list]: keys with common prefixes are grouped into lists.
"""
groups = defaultdict(list)
for key in keys:
pos = key.rfind(".")
if pos >= 0:
head, tail = key[:pos], [key[pos + 1 :]]
else:
head, tail = key, []
groups[head].extend(tail)
return groups
def _group_to_str(group: List[str]) -> str:
"""
Format a group of parameter name suffixes into a loggable string.
Args:
group (list[str]): list of parameter name suffixes.
Returns:
str: formated string.
"""
if len(group) == 0:
return ""
if len(group) == 1:
return "." + group[0]
return ".{" + ", ".join(sorted(group)) + "}"
def _named_modules_with_dup(
model: nn.Module, prefix: str = ""
) -> Iterable[Tuple[str, nn.Module]]:
"""
The same as `model.named_modules()`, except that it includes
duplicated modules that have more than one name.
"""
yield prefix, model
for name, module in model._modules.items():
if module is None:
continue
submodule_prefix = prefix + ("." if prefix else "") + name
yield from _named_modules_with_dup(module, submodule_prefix)