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Summary: BC NOTE: This change makes it so modules saved with torch.jit.save in PyTorch 1.6 can be loaded by previous versions of PyTorch unless they use torch.div or (soon) torch.full. It also lets tensors saved using torch.save be loaded by previous versions. So this is the opposite of BC-breaking, but I'm using that label to highlight this issue since we don't have a "BC-improving" label. PR NOTE: When an operator's semantics change in PyTorch we want to do two things: 1) Preserve the semantics of older serialized Torchscript programs that use the operator 2) Ensure the new semantics are respected Historically, this meant writing a Versioned Symbol that would remap older versions of the operator into current PyTorch code (1), and bumping the produced file format version (2). Unfortunately, bumping the produced file format version is a nuclear option for ensuring semantics are respected, since it also prevents older versions of PyTorch from loading anything (even tensors!) from newer versions. Dynamic versioning addresses the nuclear consequences of bumping the produced file format version by only bumping it when necessary. That is, when an operator with changed semantics is detected in the serialized Torchscript. This will prevent Torchscript programs that use the changed operator from loading on earlier versions of PyTorch, as desired, but will have no impact on programs that don't use the changed operator. Note that this change is only applicable when using torch.jit.save and torch.jit.load. torch.save pickles the given object using pickle (by default), which saves a function's Python directly. No new tests for this behavior are added since the existing tests for versioned division in test_save_load already validate that models with div are loaded correctly at version 4. Pull Request resolved: https://github.com/pytorch/pytorch/pull/40279 Reviewed By: dzhulgakov Differential Revision: D22168291 Pulled By: mruberry fbshipit-source-id: e71d6380e727e25123c7eedf6d80e5d7f1fe9f95 |
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| .. | ||
| _C | ||
| autograd | ||
| backends | ||
| contrib | ||
| csrc | ||
| cuda | ||
| distributed | ||
| distributions | ||
| for_onnx | ||
| futures | ||
| jit | ||
| legacy | ||
| lib | ||
| multiprocessing | ||
| nn | ||
| onnx | ||
| optim | ||
| quantization | ||
| sparse | ||
| testing | ||
| utils | ||
| __config__.py | ||
| __future__.py | ||
| __init__.py | ||
| _appdirs.py | ||
| _classes.py | ||
| _jit_internal.py | ||
| _linalg_utils.py | ||
| _lobpcg.py | ||
| _lowrank.py | ||
| _namedtensor_internals.py | ||
| _ops.py | ||
| _overrides.py | ||
| _six.py | ||
| _storage_docs.py | ||
| _tensor_docs.py | ||
| _tensor_str.py | ||
| _torch_docs.py | ||
| _utils.py | ||
| _utils_internal.py | ||
| _VF.py | ||
| _vmap_internals.py | ||
| abi-check.cpp | ||
| CMakeLists.txt | ||
| custom_class.h | ||
| custom_class_detail.h | ||
| extension.h | ||
| functional.py | ||
| hub.py | ||
| library.h | ||
| py.typed | ||
| quasirandom.py | ||
| random.py | ||
| README.txt | ||
| script.h | ||
| serialization.py | ||
| storage.py | ||
| tensor.py | ||
| types.py | ||
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.