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# Summary Add support for pretty printing of tensors when using lldb similiar to what is currently available for gdb <img width="772" alt="Screenshot 2023-03-18 at 6 20 34 PM" src="https://user-images.githubusercontent.com/32754868/226148687-b4e6cfe1-8be1-4657-9ebc-d134f697dd37.png"> <img width="254" alt="Screenshot 2023-03-18 at 6 20 43 PM" src="https://user-images.githubusercontent.com/32754868/226148690-caca6f76-d873-419e-b5e4-6bb403b3d179.png"> I changed it so to override the variable formatting instead of having to call a seperate command you can just do `print <tensor>` I also add one for sizes <img width="309" alt="Screenshot 2023-03-19 at 1 05 49 PM" src="https://user-images.githubusercontent.com/32754868/226206458-e3f0111b-6a97-4d75-8125-48455aa2cf43.png"> Last one: <img width="815" alt="Screenshot 2023-03-19 at 1 39 23 PM" src="https://user-images.githubusercontent.com/32754868/226207687-20bd014f-9e0e-4c01-b2c8-190b7365aa70.png"> If you use the codelldb extension be sure to add: `"lldb.launch.initCommands": ["command source ${env:HOME}/.lldbinit"]` To your setttings .json Pull Request resolved: https://github.com/pytorch/pytorch/pull/97101 Approved by: https://github.com/ngimel
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.
- fast_nvcc - Mostly-transparent wrapper over nvcc that
parallelizes compilation when used to build CUDA files for multiple
architectures at once.
- fast_nvcc.py - Python script, entrypoint to the fast nvcc wrapper.
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.