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Summary: This is intended for use with torchft when we need to do a streaming state dict transfer. This is strictly superior to the prior streaming method in torchft as this supports all tensor subclasses such as DTensor. This supports 100% of the inputs to torch.save/load but is not wire compatible nor intended to have any backwards compatibility. Security wise this fully supports weights_only and defaults to True. It does use pickle for some metadata but uses weights_only for the metadata. Adapted from: https://github.com/pytorch/torchft/pull/101 https://github.com/pytorch/torchft/pull/54 Test Plan: pytest test/distributed/test_serialization.py Pull Request resolved: https://github.com/pytorch/pytorch/pull/146555 Approved by: https://github.com/fegin, https://github.com/mikaylagawarecki Co-authored-by: Krishn Parasar <76171905+Krishn1412@users.noreply.github.com> |
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| .. | ||
| _awaits | ||
| _C | ||
| _C_flatbuffer | ||
| _custom_op | ||
| _decomp | ||
| _dispatch | ||
| _dynamo | ||
| _export | ||
| _functorch | ||
| _higher_order_ops | ||
| _inductor | ||
| _lazy | ||
| _library | ||
| _logging | ||
| _numpy | ||
| _prims | ||
| _prims_common | ||
| _refs | ||
| _strobelight | ||
| _subclasses | ||
| _vendor | ||
| accelerator | ||
| amp | ||
| ao | ||
| autograd | ||
| backends | ||
| compiler | ||
| contrib | ||
| cpu | ||
| csrc | ||
| cuda | ||
| distributed | ||
| distributions | ||
| export | ||
| fft | ||
| func | ||
| futures | ||
| fx | ||
| jit | ||
| legacy | ||
| lib | ||
| linalg | ||
| masked | ||
| monitor | ||
| mps | ||
| mtia | ||
| multiprocessing | ||
| nested | ||
| nn | ||
| onnx | ||
| optim | ||
| package | ||
| profiler | ||
| quantization | ||
| signal | ||
| sparse | ||
| special | ||
| testing | ||
| utils | ||
| xpu | ||
| __config__.py | ||
| __future__.py | ||
| __init__.py | ||
| _appdirs.py | ||
| _classes.py | ||
| _compile.py | ||
| _custom_ops.py | ||
| _deploy.py | ||
| _environment.py | ||
| _guards.py | ||
| _jit_internal.py | ||
| _linalg_utils.py | ||
| _lobpcg.py | ||
| _lowrank.py | ||
| _meta_registrations.py | ||
| _namedtensor_internals.py | ||
| _ops.py | ||
| _python_dispatcher.py | ||
| _size_docs.py | ||
| _sources.py | ||
| _storage_docs.py | ||
| _streambase.py | ||
| _tensor.py | ||
| _tensor_docs.py | ||
| _tensor_str.py | ||
| _thread_safe_fork.py | ||
| _torch_docs.py | ||
| _utils.py | ||
| _utils_internal.py | ||
| _VF.py | ||
| _vmap_internals.py | ||
| _weights_only_unpickler.py | ||
| abi-check.cpp | ||
| CMakeLists.txt | ||
| custom_class.h | ||
| custom_class_detail.h | ||
| extension.h | ||
| functional.py | ||
| hub.py | ||
| library.h | ||
| library.py | ||
| overrides.py | ||
| py.typed | ||
| quasirandom.py | ||
| random.py | ||
| README.txt | ||
| return_types.py | ||
| script.h | ||
| serialization.py | ||
| storage.py | ||
| torch_version.py | ||
| types.py | ||
| version.py.tpl | ||
Note [TH abstraction violation] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ TH/THC provide some hpp headers, which are proper C++ headers rather than C headers. These headers serve double duty as *internal implementation detail* headers, whose contents should largely not be used by external clients. Ideally, we would not install these headers at all; instead, you should use public functions (in headers like `THTensor.h`, NOT `THTensor.hpp`) to manipulate these structs. However, there are a few places in torch/csrc where we violate this abstraction. They are marked with a pointer to this note. Each of those sites will have to be refactored when we refactor the guts of THTensor and related structures.