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https://github.com/pytorch/pytorch.git
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b14a14a662
```
import torch
torch._dynamo.config.capture_scalar_outputs = True
torch.manual_seed(42)
def fuzzed_program(arg_0, arg_1, arg_2):
var_node_3 = arg_0 # size=(1,), stride=(1,), dtype=complex128, device=cuda
var_node_4 = torch.full((1,), (-0.29262632146522655-0.7687848816195035j), dtype=torch.complex128) # size=(1,), stride=(1,), dtype=complex128, device=cuda
var_node_2 = torch.ops.aten.add(var_node_3, var_node_4) # size=(1,), stride=(1,), dtype=complex128, device=cuda
var_node_6 = arg_1 # size=(1,), stride=(1,), dtype=complex128, device=cuda
var_node_7 = arg_2 # size=(1,), stride=(1,), dtype=complex128, device=cuda
var_node_5 = torch.ops.aten.add(var_node_6, var_node_7) # size=(1,), stride=(1,), dtype=complex128, device=cuda
var_node_1 = torch.ops.aten.add(var_node_2, var_node_5) # size=(1,), stride=(1,), dtype=complex128, device=cuda
var_node_0 = var_node_1.item() # dtype=complex128
return var_node_0
arg_0 = torch.as_strided(torch.randn(1).to(torch.complex128), (1,), (1,))
arg_1 = torch.as_strided(torch.randn(1).to(torch.complex128), (1,), (1,))
arg_2 = torch.as_strided(torch.randn(1).to(torch.complex128), (1,), (1,))
args = (arg_0, arg_1, arg_2)
result_original = fuzzed_program(*args)
print('✅ eager success')
compiled_program = torch.compile(fuzzed_program, fullgraph=False, dynamic=True)
result_compiled = compiled_program(*args)
print('✅ compile success')
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163812
Approved by: https://github.com/pianpwk
ghstack dependencies: #163743
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.