251 lines
12 KiB
Python
251 lines
12 KiB
Python
'''
|
|
This file is AGPL-licensed.
|
|
|
|
Some of the code in this file is copied from PyTorch.
|
|
|
|
The license for PyTorch is shown below:
|
|
|
|
Copyright (c) 2016- Facebook, Inc (Adam Paszke)
|
|
Copyright (c) 2014- Facebook, Inc (Soumith Chintala)
|
|
Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
|
|
Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu)
|
|
Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
|
|
Copyright (c) 2011-2013 NYU (Clement Farabet)
|
|
Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
|
|
Copyright (c) 2006 Idiap Research Institute (Samy Bengio)
|
|
Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
|
|
|
|
Redistribution and use in source and binary forms, with or without
|
|
modification, are permitted provided that the following conditions are met:
|
|
|
|
1. Redistributions of source code must retain the above copyright
|
|
notice, this list of conditions and the following disclaimer.
|
|
|
|
2. Redistributions in binary form must reproduce the above copyright
|
|
notice, this list of conditions and the following disclaimer in the
|
|
documentation and/or other materials provided with the distribution.
|
|
|
|
3. Neither the names of Facebook, Deepmind Technologies, NYU, NEC Laboratories America
|
|
and IDIAP Research Institute nor the names of its contributors may be
|
|
used to endorse or promote products derived from this software without
|
|
specific prior written permission.
|
|
|
|
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
|
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
|
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
|
|
ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
|
|
LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
|
|
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
|
|
SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
|
|
INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
|
|
CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
|
|
ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
|
|
POSSIBILITY OF SUCH DAMAGE.
|
|
'''
|
|
|
|
|
|
import contextlib
|
|
from functools import reduce
|
|
import itertools
|
|
import zipfile
|
|
import pickle
|
|
import torch
|
|
from torch.nn import Module
|
|
from typing import Any, Callable, Dict, Optional, Tuple, Type, Union
|
|
|
|
|
|
_EXTRA_STATE_KEY_SUFFIX = '_extra_state'
|
|
|
|
|
|
class LazyTensor:
|
|
def __init__(self, storage_type: Type[torch._StorageBase], key: str, location: str, seek_offset: Optional[int] = None, shape: Optional[Tuple[int, ...]] = None, stride: Optional[Tuple[int, ...]] = None, requires_grad=False, backward_hooks: Any = None):
|
|
self.storage_type = storage_type
|
|
self.key = key
|
|
self.location = location
|
|
self.seek_offset = seek_offset
|
|
self.shape = shape
|
|
self.stride = stride
|
|
self.requires_grad = requires_grad
|
|
self.backward_hooks = backward_hooks
|
|
|
|
def __view(self, f: Callable):
|
|
return f"{type(self).__name__}(storage_type={f(self.storage_type)}, key={f(self.key)}, location={f(self.location)}, seek_offset={f(self.seek_offset)}, shape={f(self.shape)}, stride={f(self.stride)}, requires_grad={f(self.requires_grad)}, backward_hooks={f(self.backward_hooks)})"
|
|
|
|
def __repr__(self):
|
|
return self.__view(repr)
|
|
|
|
def materialize(self, checkpoint: Union[zipfile.ZipFile, zipfile.ZipExtFile], map_location=None) -> torch.Tensor:
|
|
size = reduce(lambda x, y: x * y, self.shape, 1)
|
|
dtype = self.storage_type(0).dtype
|
|
nbytes = size if dtype is torch.bool else size * ((torch.finfo if dtype.is_floating_point else torch.iinfo)(dtype).bits >> 3)
|
|
if isinstance(checkpoint, zipfile.ZipFile):
|
|
f = checkpoint.open(f"archive/data/{self.key}", "r")
|
|
f.seek(self.seek_offset)
|
|
else:
|
|
f = checkpoint
|
|
try:
|
|
storage = self.storage_type.from_buffer(f.read(nbytes), "little")
|
|
finally:
|
|
if isinstance(checkpoint, zipfile.ZipFile):
|
|
f.close()
|
|
storage = torch.serialization._get_restore_location(map_location)(storage, self.location)
|
|
tensor = torch.tensor([], dtype=storage.dtype, device=storage.device)
|
|
tensor.set_(storage, 0, self.shape, self.stride)
|
|
tensor.requires_grad = self.requires_grad
|
|
tensor._backward_hooks = self.backward_hooks
|
|
return tensor
|
|
|
|
|
|
class _LazyUnpickler(pickle.Unpickler):
|
|
lazy_loaded_storages: Dict[str, LazyTensor]
|
|
|
|
def __init__(self, *args, **kwargs):
|
|
self.lazy_loaded_storages = {}
|
|
return super().__init__(*args, **kwargs)
|
|
|
|
def forced_persistent_load(self, saved_id):
|
|
assert isinstance(saved_id, tuple)
|
|
typename = saved_id[0]
|
|
assert typename == "storage", f"Unknown typename for persistent_load, expected 'storage' but got '{typename}'"
|
|
storage_type, key, location, _ = saved_id[1:]
|
|
return LazyTensor(storage_type, key, location)
|
|
|
|
def load(self, *args, **kwargs):
|
|
self.persistent_load = self.forced_persistent_load
|
|
retval = super().load(*args, **kwargs)
|
|
self.lazy_loaded_storages = {}
|
|
return retval
|
|
|
|
|
|
def _rebuild_tensor(lazy_storage: LazyTensor, storage_offset, shape, stride):
|
|
lazy_storage.shape = shape
|
|
lazy_storage.stride = stride
|
|
dtype = lazy_storage.storage_type(0).dtype
|
|
lazy_storage.seek_offset = storage_offset if dtype is torch.bool else storage_offset * ((torch.finfo if dtype.is_floating_point else torch.iinfo)(dtype).bits >> 3)
|
|
return lazy_storage
|
|
|
|
|
|
# Modified version of https://github.com/pytorch/pytorch/blob/v1.11.0-rc4/torch/nn/modules/module.py#L1346-L1438
|
|
def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
|
|
for hook in self._load_state_dict_pre_hooks.values():
|
|
hook(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
|
|
|
|
persistent_buffers = {k: v for k, v in self._buffers.items() if k not in self._non_persistent_buffers_set}
|
|
local_name_params = itertools.chain(self._parameters.items(), persistent_buffers.items())
|
|
local_state = {k: v for k, v in local_name_params if v is not None}
|
|
|
|
for name, param in local_state.items():
|
|
key = prefix + name
|
|
if key in state_dict:
|
|
input_param = state_dict[key]
|
|
if not torch.overrides.is_tensor_like(input_param):
|
|
error_msgs.append('While copying the parameter named "{}", '
|
|
'expected torch.Tensor or Tensor-like object from checkpoint but '
|
|
'received {}'
|
|
.format(key, type(input_param)))
|
|
continue
|
|
|
|
# This is used to avoid copying uninitialized parameters into
|
|
# non-lazy modules, since they dont have the hook to do the checks
|
|
# in such case, it will error when accessing the .shape attribute.
|
|
is_param_lazy = torch.nn.parameter.is_lazy(param)
|
|
# Backward compatibility: loading 1-dim tensor from 0.3.* to version 0.4+
|
|
if not is_param_lazy and len(param.shape) == 0 and len(input_param.shape) == 1:
|
|
input_param = input_param[0]
|
|
|
|
if not is_param_lazy and input_param.shape != param.shape:
|
|
# local shape should match the one in checkpoint
|
|
error_msgs.append('size mismatch for {}: copying a param with shape {} from checkpoint, '
|
|
'the shape in current model is {}.'
|
|
.format(key, input_param.shape, param.shape))
|
|
continue
|
|
try:
|
|
with torch.no_grad():
|
|
#param.copy_(input_param)
|
|
new_param = torch.nn.Parameter(input_param, requires_grad=param.requires_grad) # This line is new
|
|
if name in self._parameters: # This line is new
|
|
self._parameters[name] = new_param # This line is new
|
|
if name in persistent_buffers: # This line is new
|
|
self._buffers[name] = new_param # This line is new
|
|
except Exception as ex:
|
|
error_msgs.append('While copying the parameter named "{}", '
|
|
'whose dimensions in the model are {} and '
|
|
'whose dimensions in the checkpoint are {}, '
|
|
'an exception occurred : {}.'
|
|
.format(key, param.size(), input_param.size(), ex.args))
|
|
elif strict:
|
|
missing_keys.append(key)
|
|
|
|
extra_state_key = prefix + _EXTRA_STATE_KEY_SUFFIX
|
|
if getattr(self.__class__, "set_extra_state", Module.set_extra_state) is not Module.set_extra_state:
|
|
if extra_state_key in state_dict:
|
|
self.set_extra_state(state_dict[extra_state_key])
|
|
elif strict:
|
|
missing_keys.append(extra_state_key)
|
|
elif strict and (extra_state_key in state_dict):
|
|
unexpected_keys.append(extra_state_key)
|
|
|
|
if strict:
|
|
for key in state_dict.keys():
|
|
if key.startswith(prefix) and key != extra_state_key:
|
|
input_name = key[len(prefix):]
|
|
input_name = input_name.split('.', 1)[0] # get the name of param/buffer/child
|
|
if input_name not in self._modules and input_name not in local_state:
|
|
unexpected_keys.append(key)
|
|
|
|
|
|
@contextlib.contextmanager
|
|
def use_lazy_torch_load(enable=True, callback: Optional[Callable] = None, dematerialized_modules=False):
|
|
if not enable:
|
|
yield False
|
|
return
|
|
|
|
try:
|
|
old_unpickler = pickle.Unpickler
|
|
pickle.Unpickler = _LazyUnpickler
|
|
|
|
old_rebuild_tensor = torch._utils._rebuild_tensor
|
|
torch._utils._rebuild_tensor = _rebuild_tensor
|
|
|
|
old_torch_load = torch.load
|
|
|
|
def torch_load(f, map_location=None, pickle_module=pickle, **pickle_load_args):
|
|
retval = old_torch_load(f=f, map_location=map_location, pickle_module=pickle_module, **pickle_load_args)
|
|
if callback is not None:
|
|
callback(retval, f=f, map_location=map_location, pickle_module=pickle_module, **pickle_load_args)
|
|
return retval
|
|
|
|
torch.load = torch_load
|
|
|
|
if dematerialized_modules:
|
|
old_linear_init = torch.nn.Linear.__init__
|
|
old_embedding_init = torch.nn.Embedding.__init__
|
|
old_layernorm_init = torch.nn.LayerNorm.__init__
|
|
|
|
def linear_init(self, *args, device=None, **kwargs):
|
|
return old_linear_init(self, *args, device="meta", **kwargs)
|
|
|
|
def embedding_init(self, *args, device=None, **kwargs):
|
|
return old_embedding_init(self, *args, device="meta", **kwargs)
|
|
|
|
def layernorm_init(self, *args, device=None, **kwargs):
|
|
return old_layernorm_init(self, *args, device="meta", **kwargs)
|
|
|
|
torch.nn.Linear.__init__ = linear_init
|
|
torch.nn.Embedding.__init__ = embedding_init
|
|
torch.nn.LayerNorm.__init__ = layernorm_init
|
|
old_load_from_state_dict = torch.nn.Module._load_from_state_dict
|
|
torch.nn.Module._load_from_state_dict = _load_from_state_dict
|
|
|
|
yield True
|
|
|
|
finally:
|
|
pickle.Unpickler = old_unpickler
|
|
torch._utils._rebuild_tensor = old_rebuild_tensor
|
|
torch.load = old_torch_load
|
|
if dematerialized_modules:
|
|
torch.nn.Linear.__init__ = old_linear_init
|
|
torch.nn.Embedding.__init__ = old_embedding_init
|
|
torch.nn.LayerNorm.__init__ = old_layernorm_init
|
|
torch.nn.Module._load_from_state_dict = old_load_from_state_dict
|