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The resources directory lets ET observer dump any additional data like Triton kernels while capturing the ET. This allows us to use the ET trace to replay PT2 workloads and get visibility into data like generated kernels and their usage in a model, index tensor data etc. We also added a few ways to enable ET and ET Resources through the OS environment variables. Setting `ENABLE_PYTORCH_EXECUTION_TRACE` will enable default Execution Tracing in Pytorch. Additionally setting `ENABLE_PYTORCH_EXECUTION_TRACE_EXTRAS` will enable ET to collect extra resources from the ET run like Triton Kernels. Differential Revision: [D58707846](https://our.internmc.facebook.com/intern/diff/D58707846/) Pull Request resolved: https://github.com/pytorch/pytorch/pull/143430 Approved by: https://github.com/shengfukevin, https://github.com/sraikund16 |
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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_docs.py | ||
| _tensor_str.py | ||
| _tensor.py | ||
| _thread_safe_fork.py | ||
| _torch_docs.py | ||
| _utils_internal.py | ||
| _utils.py | ||
| _VF.py | ||
| _vmap_internals.py | ||
| _weights_only_unpickler.py | ||
| abi-check.cpp | ||
| CMakeLists.txt | ||
| custom_class_detail.h | ||
| custom_class.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.