mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
3570ef6a0f
Test Plan: revert-hammer Differential Revision: D20876204 Original commit changeset: a719f3583cc4 fbshipit-source-id: 6d00afb3a24754d283a7b832c0b784ed9fce36e1 |
||
|---|---|---|
| .. | ||
| autograd | ||
| backends | ||
| contrib | ||
| csrc | ||
| cuda | ||
| distributed | ||
| distributions | ||
| for_onnx | ||
| jit | ||
| legacy | ||
| lib | ||
| multiprocessing | ||
| nn | ||
| onnx | ||
| optim | ||
| quantization | ||
| sparse | ||
| testing | ||
| utils | ||
| __config__.py | ||
| __future__.py | ||
| __init__.py | ||
| __init__.pyi.in | ||
| _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_internal.py | ||
| _utils.py | ||
| _VF.py | ||
| abi-check.cpp | ||
| CMakeLists.txt | ||
| custom_class_detail.h | ||
| custom_class.h | ||
| extension.h | ||
| functional.py | ||
| hub.py | ||
| py.typed | ||
| quasirandom.py | ||
| random.py | ||
| README.txt | ||
| script.h | ||
| serialization.py | ||
| storage.py | ||
| tensor.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.