344 lines
15 KiB
Python
344 lines
15 KiB
Python
'''
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This file is AGPL-licensed.
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Some of the code in this file is copied from PyTorch.
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The license for PyTorch is shown below:
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Copyright (c) 2016- Facebook, Inc (Adam Paszke)
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Copyright (c) 2014- Facebook, Inc (Soumith Chintala)
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Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
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Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu)
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Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
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Copyright (c) 2011-2013 NYU (Clement Farabet)
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Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
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Copyright (c) 2006 Idiap Research Institute (Samy Bengio)
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Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions are met:
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1. Redistributions of source code must retain the above copyright
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notice, this list of conditions and the following disclaimer.
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2. Redistributions in binary form must reproduce the above copyright
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notice, this list of conditions and the following disclaimer in the
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documentation and/or other materials provided with the distribution.
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3. Neither the names of Facebook, Deepmind Technologies, NYU, NEC Laboratories America
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and IDIAP Research Institute nor the names of its contributors may be
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used to endorse or promote products derived from this software without
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specific prior written permission.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
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LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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POSSIBILITY OF SUCH DAMAGE.
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'''
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import contextlib
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from functools import reduce
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import itertools
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import zipfile
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import pickle
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import torch
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import numpy as np
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import collections
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import _codecs
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import utils
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import os
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from torch.nn import Module
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from typing import Any, Callable, Dict, Optional, Tuple, Type, Union
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_EXTRA_STATE_KEY_SUFFIX = '_extra_state'
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STORAGE_TYPE_MAP = {
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torch.float64: torch.DoubleStorage,
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torch.float32: torch.FloatStorage,
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torch.float16: torch.HalfStorage,
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torch.int64: torch.LongStorage,
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torch.int32: torch.IntStorage,
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torch.int16: torch.ShortStorage,
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torch.int8: torch.CharStorage,
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torch.uint8: torch.ByteStorage,
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torch.bool: torch.BoolStorage,
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torch.bfloat16: torch.BFloat16Storage,
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}
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class LazyTensor:
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def __init__(self, storage_type, key: str, location: str, dtype: Optional[torch.dtype] = None, seek_offset: Optional[int] = None, shape: Optional[Tuple[int, ...]] = None, stride: Optional[Tuple[int, ...]] = None, requires_grad=False, backward_hooks: Any = None):
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self.storage_type = storage_type
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self.key = key
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self.location = location
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self.dtype = dtype
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self.seek_offset = seek_offset
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self.shape = shape
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self.stride = stride
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self.requires_grad = requires_grad
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self.backward_hooks = backward_hooks
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def __view(self, f: Callable):
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return f"{type(self).__name__}(storage_type={f(self.storage_type)}, key={f(self.key)}, location={f(self.location)}, dtype={f(self.dtype)}, 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)})"
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def __repr__(self):
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return self.__view(repr)
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def materialize(self, checkpoint: Union[zipfile.ZipFile, zipfile.ZipExtFile], map_location=None, no_grad=True, filename="pytorch_model.bin") -> torch.Tensor:
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filename = os.path.basename(os.path.normpath(filename)).split('.')[0]
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size = reduce(lambda x, y: x * y, self.shape, 1)
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dtype = self.dtype
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nbytes = size if dtype is torch.bool else size * ((torch.finfo if dtype.is_floating_point else torch.iinfo)(dtype).bits >> 3)
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if isinstance(checkpoint, zipfile.ZipFile):
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try:
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f = checkpoint.open(f"archive/data/{self.key}", "r")
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except:
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f = checkpoint.open(f"{filename}/data/{self.key}", "r")
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f.read(self.seek_offset)
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else:
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f = checkpoint
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try:
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storage = STORAGE_TYPE_MAP[dtype].from_buffer(f.read(nbytes), "little")
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finally:
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if isinstance(checkpoint, zipfile.ZipFile):
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f.close()
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storage = torch.serialization._get_restore_location(map_location)(storage, self.location)
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tensor = torch.tensor([], dtype=storage.dtype, device=storage.device)
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tensor.set_(storage, 0, self.shape, self.stride)
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tensor.requires_grad = not no_grad and self.requires_grad
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tensor._backward_hooks = self.backward_hooks
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return tensor
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class RestrictedUnpickler(pickle.Unpickler):
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def original_persistent_load(self, saved_id):
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return super().persistent_load(saved_id)
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def forced_persistent_load(self, saved_id):
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if saved_id[0] != "storage":
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raise pickle.UnpicklingError("`saved_id[0]` must be 'storage'")
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return self.original_persistent_load(saved_id)
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def find_class(self, module, name):
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if module == "collections" and name == "OrderedDict":
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return collections.OrderedDict
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elif module == "torch._utils" and name == "_rebuild_tensor_v2":
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return torch._utils._rebuild_tensor_v2
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elif module == "torch" and name in (
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"DoubleStorage",
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"FloatStorage",
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"HalfStorage",
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"LongStorage",
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"IntStorage",
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"ShortStorage",
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"CharStorage",
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"ByteStorage",
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"BoolStorage",
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"BFloat16Storage",
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):
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return getattr(torch, name)
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elif module == "numpy.core.multiarray" and name == "scalar":
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return np.core.multiarray.scalar
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elif module == "numpy" and name == "dtype":
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return np.dtype
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elif module == "_codecs" and name == "encode":
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return _codecs.encode
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else:
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# Forbid everything else.
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qualified_name = name if module == "__builtin__" else f"{module}.{name}"
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raise pickle.UnpicklingError(f"`{qualified_name}` is forbidden; the model you are loading probably contains malicious code")
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def load(self, *args, **kwargs):
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self.original_persistent_load = getattr(self, "persistent_load", pickle.Unpickler.persistent_load)
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self.persistent_load = self.forced_persistent_load
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return super().load(*args, **kwargs)
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class _LazyUnpickler(RestrictedUnpickler):
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lazy_loaded_storages: Dict[str, LazyTensor]
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def __init__(self, *args, **kwargs):
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self.lazy_loaded_storages = {}
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return super().__init__(*args, **kwargs)
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def forced_persistent_load(self, saved_id):
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assert isinstance(saved_id, tuple)
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typename = saved_id[0]
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assert typename == "storage", f"Unknown typename for persistent_load, expected 'storage' but got '{typename}'"
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storage_type, key, location, _ = saved_id[1:]
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return LazyTensor(storage_type, key, location)
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def load(self, *args, **kwargs):
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retval = super().load(*args, **kwargs)
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self.lazy_loaded_storages = {}
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return retval
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def _rebuild_tensor(lazy_storage: LazyTensor, storage_offset, shape, stride):
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lazy_storage.shape = shape
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lazy_storage.stride = stride
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dtype = lazy_storage.storage_type.dtype
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if not isinstance(dtype, torch.dtype):
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dtype = lazy_storage.storage_type(0).dtype
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lazy_storage.dtype = dtype
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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)
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return lazy_storage
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# Modified version of https://github.com/pytorch/pytorch/blob/v1.11.0-rc4/torch/nn/modules/module.py#L1346-L1438
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def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
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for hook in self._load_state_dict_pre_hooks.values():
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hook(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
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persistent_buffers = {k: v for k, v in self._buffers.items() if k not in self._non_persistent_buffers_set}
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local_name_params = itertools.chain(self._parameters.items(), persistent_buffers.items())
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local_state = {k: v for k, v in local_name_params if v is not None}
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for name, param in local_state.items():
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key = prefix + name
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if key in state_dict:
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input_param = state_dict[key]
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if not torch.overrides.is_tensor_like(input_param):
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error_msgs.append('While copying the parameter named "{}", '
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'expected torch.Tensor or Tensor-like object from checkpoint but '
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'received {}'
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.format(key, type(input_param)))
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continue
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# This is used to avoid copying uninitialized parameters into
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# non-lazy modules, since they dont have the hook to do the checks
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# in such case, it will error when accessing the .shape attribute.
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is_param_lazy = torch.nn.parameter.is_lazy(param)
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# Backward compatibility: loading 1-dim tensor from 0.3.* to version 0.4+
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if not is_param_lazy and len(param.shape) == 0 and len(input_param.shape) == 1:
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input_param = input_param[0]
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if not is_param_lazy and input_param.shape != param.shape:
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# local shape should match the one in checkpoint
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error_msgs.append('size mismatch for {}: copying a param with shape {} from checkpoint, '
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'the shape in current model is {}.'
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.format(key, input_param.shape, param.shape))
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continue
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try:
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with torch.no_grad():
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#param.copy_(input_param)
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new_param = torch.nn.Parameter(input_param, requires_grad=param.requires_grad) # This line is new
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if name in self._parameters: # This line is new
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self._parameters[name] = new_param # This line is new
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if name in persistent_buffers: # This line is new
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self._buffers[name] = new_param # This line is new
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except Exception as ex:
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error_msgs.append('While copying the parameter named "{}", '
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'whose dimensions in the model are {} and '
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'whose dimensions in the checkpoint are {}, '
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'an exception occurred : {}.'
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.format(key, param.size(), input_param.size(), ex.args))
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elif strict:
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missing_keys.append(key)
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extra_state_key = prefix + _EXTRA_STATE_KEY_SUFFIX
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if hasattr(Module, "set_extra_state") and getattr(self.__class__, "set_extra_state", Module.set_extra_state) is not Module.set_extra_state: # if getattr(self.__class__, "set_extra_state", Module.set_extra_state) is not Module.set_extra_state:
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if extra_state_key in state_dict:
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self.set_extra_state(state_dict[extra_state_key])
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elif strict:
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missing_keys.append(extra_state_key)
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elif strict and (extra_state_key in state_dict):
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unexpected_keys.append(extra_state_key)
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if strict:
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for key in state_dict.keys():
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if key.startswith(prefix) and key != extra_state_key:
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input_name = key[len(prefix):]
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input_name = input_name.split('.', 1)[0] # get the name of param/buffer/child
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if input_name not in self._modules and input_name not in local_state:
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unexpected_keys.append(key)
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@contextlib.contextmanager
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def use_custom_unpickler(unpickler: Type[pickle.Unpickler] = RestrictedUnpickler):
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try:
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old_unpickler = pickle.Unpickler
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pickle.Unpickler = unpickler
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old_pickle_load = pickle.load
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def new_pickle_load(*args, **kwargs):
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return pickle.Unpickler(*args, **kwargs).load()
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pickle.load = new_pickle_load
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yield
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finally:
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pickle.Unpickler = old_unpickler
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pickle.load = old_pickle_load
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@contextlib.contextmanager
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def use_lazy_torch_load(enable=True, callback: Optional[Callable] = None, dematerialized_modules=False, use_accelerate_init_empty_weights=False):
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if not enable:
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with use_custom_unpickler(RestrictedUnpickler):
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yield False
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return
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try:
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old_rebuild_tensor = torch._utils._rebuild_tensor
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torch._utils._rebuild_tensor = _rebuild_tensor
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old_torch_load = torch.load
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def torch_load(f, map_location=None, pickle_module=pickle, **pickle_load_args):
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retval = old_torch_load(f=f, map_location=map_location, pickle_module=pickle_module, **pickle_load_args)
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if callback is not None:
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callback(retval, f=f, map_location=map_location, pickle_module=pickle_module, **pickle_load_args)
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return retval
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torch.load = torch_load
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if dematerialized_modules:
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if use_accelerate_init_empty_weights and utils.HAS_ACCELERATE:
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import accelerate
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init_empty_weights = accelerate.init_empty_weights()
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init_empty_weights.__enter__()
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else:
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old_linear_init = torch.nn.Linear.__init__
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old_embedding_init = torch.nn.Embedding.__init__
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old_layernorm_init = torch.nn.LayerNorm.__init__
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def linear_init(self, *args, device=None, **kwargs):
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return old_linear_init(self, *args, device="meta", **kwargs)
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def embedding_init(self, *args, device=None, **kwargs):
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return old_embedding_init(self, *args, device="meta", **kwargs)
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def layernorm_init(self, *args, device=None, **kwargs):
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return old_layernorm_init(self, *args, device="meta", **kwargs)
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torch.nn.Linear.__init__ = linear_init
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torch.nn.Embedding.__init__ = embedding_init
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torch.nn.LayerNorm.__init__ = layernorm_init
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old_load_from_state_dict = torch.nn.Module._load_from_state_dict
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torch.nn.Module._load_from_state_dict = _load_from_state_dict
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with use_custom_unpickler(_LazyUnpickler):
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yield True
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finally:
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torch._utils._rebuild_tensor = old_rebuild_tensor
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torch.load = old_torch_load
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if dematerialized_modules:
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if use_accelerate_init_empty_weights and utils.HAS_ACCELERATE:
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init_empty_weights.__exit__(None, None, None)
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else:
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torch.nn.Linear.__init__ = old_linear_init
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torch.nn.Embedding.__init__ = old_embedding_init
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torch.nn.LayerNorm.__init__ = old_layernorm_init
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torch.nn.Module._load_from_state_dict = old_load_from_state_dict
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