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`pytorch-lldb` support pretty printing size and key_set of tensor via #97101 Add same pretty printing for gdb debugging. **Test Result** ```bash $ gdb python (gdb) break at::native::negative (gdb) r >>> import torch >>> t = torch.tensor([1, 2, 3, 4], dtype=torch.float64) >>> t.negative() Thread 1 "python" hit Breakpoint 1, at::native::negative (self=...) at /home/zong/code/pytorch/aten/src/ATen/native/UnaryOps.cpp:854 854 Tensor negative(const Tensor& self) { return self.neg(); } ``` **Before** ```bash (gdb) p self.key_set() $2 = {repr_ = 1271310352385} (gdb) p self.sizes() $3 = {Data = 0x9cb488, Length = 1} ``` **After** ```bash (gdb) torch-int-array-ref-repr self.sizes() [4] (gdb) torch-dispatch-keyset-repr self.key_set() DispatchKeySet(CPU, ADInplaceOrView, AutogradCPU, AutocastCPU) ``` ```bash $ lintrunner ```  Pull Request resolved: https://github.com/pytorch/pytorch/pull/140935 Approved by: https://github.com/drisspg
This folder contains a number of scripts which are used as
part of the PyTorch build process. This directory also doubles
as a Python module hierarchy (thus the __init__.py).
Overview
Modern infrastructure:
- autograd - Code generation for autograd. This includes definitions of all our derivatives.
- jit - Code generation for JIT
- shared - Generic infrastructure that scripts in
tools may find useful.
- module_loader.py - Makes it easier to import arbitrary Python files in a script, without having to add them to the PYTHONPATH first.
Build system pieces:
- setup_helpers - Helper code for searching for third-party dependencies on the user system.
- build_pytorch_libs.py - cross-platform script that builds all of the constituent libraries of PyTorch, but not the PyTorch Python extension itself.
- build_libtorch.py - Script for building libtorch, a standalone C++ library without Python support. This build script is tested in CI.
Developer tools which you might find useful:
- git_add_generated_dirs.sh and git_reset_generated_dirs.sh - Use this to force add generated files to your Git index, so that you can conveniently run diffs on them when working on code-generation. (See also generated_dirs.txt which specifies the list of directories with generated files.)
Important if you want to run on AMD GPU:
- amd_build - HIPify scripts, for transpiling CUDA
into AMD HIP. Right now, PyTorch and Caffe2 share logic for how to
do this transpilation, but have separate entry-points for transpiling
either PyTorch or Caffe2 code.
- build_amd.py - Top-level entry point for HIPifying our codebase.
Tools which are only situationally useful:
- docker - Dockerfile for running (but not developing) PyTorch, using the official conda binary distribution. Context: https://github.com/pytorch/pytorch/issues/1619
- download_mnist.py - Download the MNIST dataset; this is necessary if you want to run the C++ API tests.