import difflib import inspect import os import io import shutil import struct import sys import torch import tarfile import zipfile import tempfile import warnings from contextlib import closing, contextmanager from ._utils import _import_dotted_name from ._six import string_classes as _string_classes if sys.version_info[0] == 2: import cPickle as pickle else: import pickle import pathlib DEFAULT_PROTOCOL = 2 LONG_SIZE = struct.Struct('=l').size INT_SIZE = struct.Struct('=i').size SHORT_SIZE = struct.Struct('=h').size MAGIC_NUMBER = 0x1950a86a20f9469cfc6c PROTOCOL_VERSION = 1001 STORAGE_KEY_SEPARATOR = ',' class SourceChangeWarning(Warning): pass @contextmanager def mkdtemp(): path = tempfile.mkdtemp() yield path shutil.rmtree(path) _package_registry = [] def register_package(priority, tagger, deserializer): queue_elem = (priority, tagger, deserializer) _package_registry.append(queue_elem) _package_registry.sort() def _cpu_tag(obj): if type(obj).__module__ == 'torch': return 'cpu' def _cuda_tag(obj): if type(obj).__module__ == 'torch.cuda': return 'cuda:' + str(obj.get_device()) def _cpu_deserialize(obj, location): if location == 'cpu': return obj def validate_cuda_device(location): if isinstance(location, torch.device): location = str(location) if not isinstance(location, _string_classes): raise ValueError("location should be a string or torch.device") if location[5:] == '': device = 0 else: device = max(int(location[5:]), 0) if not torch.cuda.is_available(): raise RuntimeError('Attempting to deserialize object on a CUDA ' 'device but torch.cuda.is_available() is False. ' 'If you are running on a CPU-only machine, ' 'please use torch.load with map_location=\'cpu\' ' 'to map your storages to the CPU.') if device >= torch.cuda.device_count(): raise RuntimeError('Attempting to deserialize object on CUDA device ' '{} but torch.cuda.device_count() is {}. Please use ' 'torch.load with map_location to map your storages ' 'to an existing device.'.format( device, torch.cuda.device_count())) return device def _cuda_deserialize(obj, location): if location.startswith('cuda'): device = validate_cuda_device(location) if getattr(obj, "_torch_load_uninitialized", False): storage_type = getattr(torch.cuda, type(obj).__name__) with torch.cuda.device(device): return storage_type(obj.size()) else: return obj.cuda(device) register_package(10, _cpu_tag, _cpu_deserialize) register_package(20, _cuda_tag, _cuda_deserialize) def location_tag(storage): for _, tagger, _ in _package_registry: location = tagger(storage) if location: return location raise RuntimeError("don't know how to determine data location of " + torch.typename(storage)) def default_restore_location(storage, location): for _, _, fn in _package_registry: result = fn(storage, location) if result is not None: return result raise RuntimeError("don't know how to restore data location of " + torch.typename(storage) + " (tagged with " + location + ")") def normalize_storage_type(storage_type): return getattr(torch, storage_type.__name__) def storage_to_tensor_type(storage): storage_type = type(storage) module = _import_dotted_name(storage_type.__module__) return getattr(module, storage_type.__name__.replace('Storage', 'Tensor')) def _with_file_like(f, mode, body): """ Executes a body function with a file object for f, opening it in 'mode' if it is a string filename. """ new_fd = False if isinstance(f, str) or \ (sys.version_info[0] == 2 and isinstance(f, unicode)) or \ (sys.version_info[0] == 3 and isinstance(f, pathlib.Path)): new_fd = True f = open(f, mode) try: return body(f) finally: if new_fd: f.close() def _is_compressed_file(f): compress_modules = ['gzip'] try: return f.__module__ in compress_modules except AttributeError: return False def _should_read_directly(f): """ Checks if f is a file that should be read directly. It should be read directly if it is backed by a real file (has a fileno) and is not a a compressed file (e.g. gzip) """ if _is_compressed_file(f): return False try: return f.fileno() >= 0 except io.UnsupportedOperation: return False except AttributeError: return False def _check_seekable(f): def raise_err_msg(patterns, e): for p in patterns: if p in str(e): msg = (str(e) + ". You can only torch.load from a file that is seekable." + " Please pre-load the data into a buffer like io.BytesIO and" + " try to load from it instead.") raise type(e)(msg) raise e try: f.seek(f.tell()) return True except (io.UnsupportedOperation, AttributeError) as e: raise_err_msg(["seek", "tell"], e) def save(obj, f, pickle_module=pickle, pickle_protocol=DEFAULT_PROTOCOL): """Saves an object to a disk file. See also: :ref:`recommend-saving-models` Args: obj: saved object f: a file-like object (has to implement write and flush) or a string containing a file name pickle_module: module used for pickling metadata and objects pickle_protocol: can be specified to override the default protocol .. warning:: If you are using Python 2, torch.save does NOT support StringIO.StringIO as a valid file-like object. This is because the write method should return the number of bytes written; StringIO.write() does not do this. Please use something like io.BytesIO instead. Example: >>> # Save to file >>> x = torch.tensor([0, 1, 2, 3, 4]) >>> torch.save(x, 'tensor.pt') >>> # Save to io.BytesIO buffer >>> buffer = io.BytesIO() >>> torch.save(x, buffer) """ return _with_file_like(f, "wb", lambda f: _save(obj, f, pickle_module, pickle_protocol)) def _save(obj, f, pickle_module, pickle_protocol): if sys.version_info[0] == 2: import StringIO if isinstance(f, StringIO.StringIO): msg = ('torch.save received unsupported StringIO.StringIO file object, whose ' 'write method does not return the number of bytes written. ' 'Please use something like io.BytesIO for torch.save instead.') raise RuntimeError(msg) import torch.nn as nn serialized_container_types = {} serialized_storages = {} def persistent_id(obj): # FIXME: the docs say that persistent_id should only return a string # but torch store returns tuples. This works only in the binary protocol # see # https://docs.python.org/2/library/pickle.html#pickling-and-unpickling-external-objects # https://github.com/python/cpython/blob/master/Lib/pickle.py#L527-L537 if isinstance(obj, type) and issubclass(obj, nn.Module): if obj in serialized_container_types: return None serialized_container_types[obj] = True source_file = source = None try: source_file = inspect.getsourcefile(obj) source = inspect.getsource(obj) except Exception: # saving the source is optional, so we can ignore any errors warnings.warn("Couldn't retrieve source code for container of " "type " + obj.__name__ + ". It won't be checked " "for correctness upon loading.") return ('module', obj, source_file, source) elif torch.is_storage(obj): storage_type = normalize_storage_type(type(obj)) # Offset is always 0, but we keep it for backwards compatibility # with the old serialization format (which supported storage views) offset = 0 obj_key = str(obj._cdata) location = location_tag(obj) serialized_storages[obj_key] = obj is_view = obj._cdata != obj._cdata if is_view: view_metadata = (str(obj._cdata), offset, obj.size()) else: view_metadata = None return ('storage', storage_type, obj_key, location, obj.size(), view_metadata) return None sys_info = dict( protocol_version=PROTOCOL_VERSION, little_endian=sys.byteorder == 'little', type_sizes=dict( short=SHORT_SIZE, int=INT_SIZE, long=LONG_SIZE, ), ) pickle_module.dump(MAGIC_NUMBER, f, protocol=pickle_protocol) pickle_module.dump(PROTOCOL_VERSION, f, protocol=pickle_protocol) pickle_module.dump(sys_info, f, protocol=pickle_protocol) pickler = pickle_module.Pickler(f, protocol=pickle_protocol) pickler.persistent_id = persistent_id pickler.dump(obj) serialized_storage_keys = sorted(serialized_storages.keys()) pickle_module.dump(serialized_storage_keys, f, protocol=pickle_protocol) f.flush() for key in serialized_storage_keys: serialized_storages[key]._write_file(f, _should_read_directly(f)) def load(f, map_location=None, pickle_module=pickle, **pickle_load_args): """Loads an object saved with :func:`torch.save` from a file. :meth:`torch.load` uses Python's unpickling facilities but treats storages, which underlie tensors, specially. They are first deserialized on the CPU and are then moved to the device they were saved from. If this fails (e.g. because the run time system doesn't have certain devices), an exception is raised. However, storages can be dynamically remapped to an alternative set of devices using the `map_location` argument. If `map_location` is a callable, it will be called once for each serialized storage with two arguments: storage and location. The storage argument will be the initial deserialization of the storage, residing on the CPU. Each serialized storage has a location tag associated with it which identifies the device it was saved from, and this tag is the second argument passed to map_location. The builtin location tags are `'cpu'` for CPU tensors and `'cuda:device_id'` (e.g. `'cuda:2'`) for CUDA tensors. `map_location` should return either None or a storage. If `map_location` returns a storage, it will be used as the final deserialized object, already moved to the right device. Otherwise, :math:`torch.load` will fall back to the default behavior, as if `map_location` wasn't specified. If `map_location` is a string, it should be a device tag, where all tensors should be loaded. Otherwise, if `map_location` is a dict, it will be used to remap location tags appearing in the file (keys), to ones that specify where to put the storages (values). User extensions can register their own location tags and tagging and deserialization methods using `register_package`. Args: f: a file-like object (has to implement read, readline, tell, and seek), or a string containing a file name map_location: a function, torch.device, string or a dict specifying how to remap storage locations pickle_module: module used for unpickling metadata and objects (has to match the pickle_module used to serialize file) pickle_load_args: optional keyword arguments passed over to ``pickle_module.load`` and ``pickle_module.Unpickler``, e.g., ``encoding=...``. .. note:: When you call :meth:`torch.load()` on a file which contains GPU tensors, those tensors will be loaded to GPU by default. You can call `torch.load(.., map_location='cpu')` and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint. .. note:: In Python 3, when loading files saved by Python 2, you may encounter ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...``. This is caused by the difference of handling in byte strings in Python2 and Python 3. You may use extra ``encoding`` keyword argument to specify how these objects should be loaded, e.g., ``encoding='latin1'`` decodes them to strings using ``latin1`` encoding, and ``encoding='bytes'`` keeps them as byte arrays which can be decoded later with ``byte_array.decode(...)``. Example: >>> torch.load('tensors.pt') # Load all tensors onto the CPU >>> torch.load('tensors.pt', map_location=torch.device('cpu')) # Load all tensors onto the CPU, using a function >>> torch.load('tensors.pt', map_location=lambda storage, loc: storage) # Load all tensors onto GPU 1 >>> torch.load('tensors.pt', map_location=lambda storage, loc: storage.cuda(1)) # Map tensors from GPU 1 to GPU 0 >>> torch.load('tensors.pt', map_location={'cuda:1':'cuda:0'}) # Load tensor from io.BytesIO object >>> with open('tensor.pt', 'rb') as f: buffer = io.BytesIO(f.read()) >>> torch.load(buffer) """ new_fd = False if isinstance(f, str) or \ (sys.version_info[0] == 2 and isinstance(f, unicode)): new_fd = True f = open(f, 'rb') elif (sys.version_info[0] == 3 and isinstance(f, pathlib.Path)): new_fd = True f = f.open('rb') try: return _load(f, map_location, pickle_module, **pickle_load_args) finally: if new_fd: f.close() def _load(f, map_location, pickle_module, **pickle_load_args): deserialized_objects = {} if map_location is None: restore_location = default_restore_location elif isinstance(map_location, dict): def restore_location(storage, location): location = map_location.get(location, location) return default_restore_location(storage, location) elif isinstance(map_location, _string_classes): def restore_location(storage, location): return default_restore_location(storage, map_location) elif isinstance(map_location, torch.device): def restore_location(storage, location): return default_restore_location(storage, str(map_location)) else: def restore_location(storage, location): result = map_location(storage, location) if result is None: result = default_restore_location(storage, location) return result def _check_container_source(container_type, source_file, original_source): try: current_source = inspect.getsource(container_type) except Exception: # saving the source is optional, so we can ignore any errors warnings.warn("Couldn't retrieve source code for container of " "type " + container_type.__name__ + ". It won't be checked " "for correctness upon loading.") return if original_source != current_source: if container_type.dump_patches: file_name = container_type.__name__ + '.patch' diff = difflib.unified_diff(current_source.split('\n'), original_source.split('\n'), source_file, source_file, lineterm="") lines = '\n'.join(diff) try: with open(file_name, 'a+') as f: file_size = f.seek(0, 2) f.seek(0) if file_size == 0: f.write(lines) elif file_size != len(lines) or f.read() != lines: raise IOError msg = ("Saved a reverse patch to " + file_name + ". " "Run `patch -p0 < " + file_name + "` to revert your " "changes.") except IOError: msg = ("Tried to save a patch, but couldn't create a " "writable file " + file_name + ". Make sure it " "doesn't exist and your working directory is " "writable.") else: msg = ("you can retrieve the original source code by " "accessing the object's source attribute or set " "`torch.nn.Module.dump_patches = True` and use the " "patch tool to revert the changes.") msg = ("source code of class '{}' has changed. {}" .format(torch.typename(container_type), msg)) warnings.warn(msg, SourceChangeWarning) def legacy_load(f): deserialized_objects = {} def persistent_load(saved_id): if isinstance(saved_id, tuple): # Ignore containers that don't have any sources saved if all(saved_id[1:]): _check_container_source(*saved_id) return saved_id[0] return deserialized_objects[int(saved_id)] with closing(tarfile.open(fileobj=f, mode='r:', format=tarfile.PAX_FORMAT)) as tar, \ mkdtemp() as tmpdir: tar.extract('storages', path=tmpdir) with open(os.path.join(tmpdir, 'storages'), 'rb', 0) as f: num_storages = pickle_module.load(f, **pickle_load_args) for i in range(num_storages): args = pickle_module.load(f, **pickle_load_args) key, location, storage_type = args obj = storage_type._new_with_file(f) obj = restore_location(obj, location) deserialized_objects[key] = obj storage_views = pickle_module.load(f, **pickle_load_args) for target_cdata, root_cdata, offset, size in storage_views: root = deserialized_objects[root_cdata] deserialized_objects[target_cdata] = root[offset:offset + size] tar.extract('tensors', path=tmpdir) with open(os.path.join(tmpdir, 'tensors'), 'rb', 0) as f: num_tensors = pickle_module.load(f, **pickle_load_args) for _ in range(num_tensors): args = pickle_module.load(f, **pickle_load_args) key, storage_id, original_tensor_type = args storage = deserialized_objects[storage_id] tensor_type = storage_to_tensor_type(storage) ndim, = struct.unpack('