a HSic &@sdZddlZddlZddlZddlmZddlmZddlm Z dddZ ddd Z dd d Z d d Z ddZdddZddZdS)zSerialization This module contains functionality for serializing TorchScript modules, notably: * torch.jit.save * torch.jit.load This is not intended to be imported directly; please use the exposed functionalities in `torch.jit`. N)string_classes)wrap_cpp_module)validate_cuda_devicecCsL|dur i}t|ts"t|tjr2|j||dn|j|d}||dS)aw Save an offline version of this module for use in a separate process. The saved module serializes all of the methods, submodules, parameters, and attributes of this module. It can be loaded into the C++ API using ``torch::jit::load(filename)`` or into the Python API with :func:`torch.jit.load `. To be able to save a module, it must not make any calls to native Python functions. This means that all submodules must be subclasses of :class:`ScriptModule` as well. .. DANGER:: All modules, no matter their device, are always loaded onto the CPU during loading. This is different from :func:`torch.load`'s semantics and may change in the future. Args: m: A :class:`ScriptModule` to save. f: A file-like object (has to implement write and flush) or a string containing a file name. _extra_files: Map from filename to contents which will be stored as part of `f`. .. note:: torch.jit.save attempts to preserve the behavior of some operators across versions. For example, dividing two integer tensors in PyTorch 1.5 performed floor division, and if the module containing that code is saved in PyTorch 1.5 and loaded in PyTorch 1.6 its division behavior will be preserved. The same module saved in PyTorch 1.6 will fail to load in PyTorch 1.5, however, since the behavior of division changed in 1.6, and 1.5 does not know how to replicate the 1.6 behavior. Example: .. testcode:: import torch import io class MyModule(torch.nn.Module): def forward(self, x): return x + 10 m = torch.jit.script(MyModule()) # Save to file torch.jit.save(m, 'scriptmodule.pt') # This line is equivalent to the previous m.save("scriptmodule.pt") # Save to io.BytesIO buffer buffer = io.BytesIO() torch.jit.save(m, buffer) # Save with extra files extra_files = {'foo.txt': b'bar'} torch.jit.save(m, 'scriptmodule.pt', _extra_files=extra_files) N) _extra_files) isinstancestrpathlibPathsavesave_to_bufferwrite)mfrretrT/var/www/html/django/DPS/env/lib/python3.9/site-packages/torch/jit/_serialization.pyr s ; r cCst|tr>tj|s$td|tj|r>td|t|}|durRi}t j }t|t srt|t jrt j |t |||}nt j ||||}t|S)a Load a :class:`ScriptModule` or :class:`ScriptFunction` previously saved with :func:`torch.jit.save ` All previously saved modules, no matter their device, are first loaded onto CPU, and then are moved to the devices they were saved from. If this fails (e.g. because the run time system doesn't have certain devices), an exception is raised. Args: f: a file-like object (has to implement read, readline, tell, and seek), or a string containing a file name map_location (string or torch.device): A simplified version of ``map_location`` in `torch.jit.save` used to dynamically remap storages to an alternative set of devices. _extra_files (dictionary of filename to content): The extra filenames given in the map would be loaded and their content would be stored in the provided map. Returns: A :class:`ScriptModule` object. Example: .. testcode:: import torch import io torch.jit.load('scriptmodule.pt') # Load ScriptModule from io.BytesIO object with open('scriptmodule.pt', 'rb') as f: buffer = io.BytesIO(f.read()) # Load all tensors to the original device torch.jit.load(buffer) # Load all tensors onto CPU, using a device buffer.seek(0) torch.jit.load(buffer, map_location=torch.device('cpu')) # Load all tensors onto CPU, using a string buffer.seek(0) torch.jit.load(buffer, map_location='cpu') # Load with extra files. extra_files = {'foo.txt': ''} # values will be replaced with data torch.jit.load('scriptmodule.pt', _extra_files=extra_files) print(extra_files['foo.txt']) .. testoutput:: :hide: ... .. testcleanup:: import os os.remove("scriptmodule.pt") 'The provided filename {} does not exist'The provided filename {} is a directoryN)rrospathexists ValueErrorformatisdirvalidate_map_locationtorch_CCompilationUnitrrr import_ir_moduleimport_ir_module_from_bufferreadr)r map_locationrcu cpp_modulerrrloadWs?     r$cCsXt|trt|}n(|dus>t|tjs>tdtt|t|drTt||S)NzJmap_location should be either None, string or torch.device, but got type: cuda)rrrdevicertype startswithr)r!rrrrs   rcCs4zddlm}|WSty.tdYn0dS)Nrz4Please include //caffe2:_C_flatbuffer as dependency.)Ztorch._C_flatbufferZ _C_flatbuffer ImportErrorprint)ffrrr get_ff_modules   r,cCst}t|trDtj|s*td|tj|rDtd|t|t sZt|t j rpt |}t | |St ||SdS)Nrr)r,rrrrrrrrrrr rZ_load_jit_module_from_fileZ_load_jit_module_from_bytesr )rr+rrrjit_module_from_flatbuffers   r-cCsb|}|duri}t}t|ts,t|tjrFt|}||j||n||j|}||dS)a Save an offline version of this module for use in a separate process. The saved module serializes all of the methods, submodules, parameters, and attributes of this module. It can be loaded into the C++ API using ``torch::jit::load_jit_module_from_file(filename)`` or into the Python API with :func:`torch.jit.jit_module_from_flatbuffer`. To be able to save a module, it must not make any calls to native Python functions. This means that all submodules must be subclasses of :class:`ScriptModule` as well. .. DANGER:: All modules, no matter their device, are always loaded onto the CPU during loading. This is different from :func:`torch.load`'s semantics and may change in the future. Args: m: A :class:`ScriptModule` to save. f: A string for file path Example: .. testcode:: import torch import io class MyModule(torch.nn.Module): def forward(self, x): return x + 10 m = torch.jit.script(MyModule()) # Save to file torch.jit.save_jit_module_to_flatbuffer(m, 'scriptmodule.ff') N) r,rrrr Z_save_jit_module_cZ_save_jit_module_to_bytesr )r rr extra_filesr+srrrsave_jit_module_to_flatbuffers'r1cCsbt}t|tst|tjrPt|d}|}WdqX1sD0Yn|}||S)aGet some information regarding a model file in flatbuffer format. Args: path_or_file: Either str, Path or file like object (BytesIO OK). If it's str or Path, we will read the file referenced by that path as Bytes. Returns: A dict with metadata on what that file contains, currently looks like this: { 'bytecode_version': 4, # int 'operator_version': 4, # int 'function_names': { '__torch__.___torch_mangle_0.Foo.forward'}, # set 'type_names': set(), # set 'opname_to_num_args': {'aten::linear': 3} # Dict[str, int] } rbN)r,rrrr openr Z _get_module_info_from_flatbuffer)Z path_or_filer+rZ all_bytesrrrget_flatbuffer_module_infos  (r4)N)NN)N)N)__doc__rrr torch._sixrtorch.jit._recursiverZtorch.serializationrr r$rr,r-r1r4rrrrs     D U   4