From 4470ada1d16f8543816ffc11fc3a113b9b207513 Mon Sep 17 00:00:00 2001 From: heheda Date: Wed, 5 Jun 2024 15:15:27 +0800 Subject: [PATCH] update sysname --- README.md | 14 +++++++------- frontend/utils.py | 2 +- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index f1d5816..f41c8d6 100644 --- a/README.md +++ b/README.md @@ -1,15 +1,15 @@ -# DeepVisor -DeepVisor is a JIT compiler for PyTorch programs. It can extract the operator graph from PyTorch programs and optimize the graph with a wide range of deep learning graph compilers. +# MagPy +MagPy is a JIT compiler for PyTorch programs. It can extract the operator graph from PyTorch programs and optimize the graph with a wide range of deep learning graph compilers. # Installation -DeepVisor now supports Python 3.9. The support of other Python versions is working in progress. +MagPy now supports Python 3.9. The support of other Python versions is working in progress. 1. Install CUDA. CUDA 11.8 is recommended. 2. Install dependencies: ```bash pip install -r requirements.txt -f https://download.pytorch.org/whl/torch_stable.html ``` -3. Install DeepVisor: +3. Install MagPy: ```bash pip install -e . ``` @@ -21,14 +21,14 @@ DeepVisor now supports Python 3.9. The support of other Python versions is worki # Example Usage -The following script compiles and runs a simple PyTorch program with DeepVisor. +The following script compiles and runs a simple PyTorch program with MagPy. ```python LD_PRELOAD=build/ldlong.v3.9.12.so python test/example.py ``` # Citation -If you find DeepVisor useful in your research, please consider citing the following paper: +If you find MagPy useful in your research, please consider citing the following paper: -> DeepVisor: Effective Operator Graph Instantiation for Deep Learning by Execution State Monitoring; Chen Zhang, Rongchao Dong, Haojie Wang, Runxin Zhong, Jike Chen, and Jidong Zhai, Tsinghua University; will be appeared in USENIX ATC'24. +> MagPy: Effective Operator Graph Instantiation for Deep Learning by Execution State Monitoring; Chen Zhang, Rongchao Dong, Haojie Wang, Runxin Zhong, Jike Chen, and Jidong Zhai, Tsinghua University; will be appeared in USENIX ATC'24. diff --git a/frontend/utils.py b/frontend/utils.py index e9d6cec..e1a3aaf 100644 --- a/frontend/utils.py +++ b/frontend/utils.py @@ -229,7 +229,7 @@ def is_user_defined_func(func: Callable[..., Any]) -> bool: return False root_module = get_root_module(func) - print("root module", func, "===is==", root_module, type(root_module)) + # print("root module", func, "===is==", root_module, type(root_module)) if root_module == 'torch' and hasattr( func, '__name__') and func.__name__ == '_call_impl': return True