[upd] update README.md

README.md

@@ -43,14 +43,15 @@ pip install -r requirements.txt
 ```
 4. Compile kernels benchmarks (Optional): Install gcc-11 and CMake (>= 3.24)
 ```
+apt install software-properties-common lsb-release
+apt-get update
+
 curl -s https://apt.kitware.com/keys/kitware-archive-latest.asc 2>/dev/null | gpg --dearmor - | tee /etc/apt/trusted.gpg.d/kitware.gpg >/dev/null
 apt-add-repository "deb https://apt.kitware.com/ubuntu/ $(lsb_release -cs) main"
 apt update
 apt install cmake
 
 cd /PATH_TO_ATOM/kernels
-apt install software-properties-common
-apt-get update
 add-apt-repository -y ppa:ubuntu-toolchain-r/test
 apt-get update
 apt install -y gcc-11 g++-11

kernels/baselines/README.md

@@ -1,27 +1,41 @@
 # Atom: Baseline Kernel Evaluations
 ## Environment Setup
-We use [NVBench](https://github.com/NVIDIA/nvbench.git) to evaluate the kernel performance and we need [libTorch](https://pytorch.org/) with `_GLIBCXX_USE_CXX11_ABI = 1` to make baselines compatible with NVBench. Follow the instructions below to setup the environment.
+We evaluate baselines in CUDA 12.1 to maxmize their performance. Follow the instructions to setup the container.
 ```
+docker pull nvidia/cuda:12.1.0-cudnn8-devel-ubuntu22.04
+docker run -it --gpus all nvidia/cuda:12.1.0-cudnn8-devel-ubuntu22.04 /bin/bash
+```
+Make sure you install wget, git, conda and cmake (>= 3.24). We use [NVBench](https://github.com/NVIDIA/nvbench.git) to evaluate the kernel performance and we need [libTorch](https://pytorch.org/) with `_GLIBCXX_USE_CXX11_ABI = 1` to make baselines compatible with NVBench. Follow the instructions below to setup the environment.
+```
+git clone --recurse-submodules https://github.com/efeslab/Atom
+
 wget https://download.pytorch.org/libtorch/cu121/libtorch-cxx11-abi-shared-with-deps-2.1.2%2Bcu121.zip
 unzip libtorch-cxx11-abi-shared-with-deps-2.1.2+cu121.zip
 mv libtorch /PATH_TO_ATOM/kernels/3rdparty/
 ```
+Install Python dev to include `Python.h` for torch extension.
+```
+apt-get install python3-dev
+```
 Use the following instructions or scripts `build.sh` to build the baseline benchmark.
 ```
+cd /PATH_TO_ATOM/kernels/baselines
 mkdir build
 cd build
 # Fill in your libtorch path
 cmake .. -DCMAKE_PREFIX_PATH=/PATH_TO_ATOM/kernels/3rdparty/libtorch
 make -j
 ```
 ## Result
-8-bit Weight-activation Quantization (SmoothQuant) and 4-bit Weight-only Quantization (AWQ) are evaluated in CUDA 12.1 to maximize their performance. Note that `Elem/s` denotes the computation throughput.
+8-bit Weight-activation Quantization (SmoothQuant) and 4-bit Weight-only Quantization (AWQ) are evaluated in CUDA 12.1 to maximize their performance. Note that `Elem/s` denotes the computation throughput (Flops/s).
 
-W8A8 Evaluation:
+W8A8 Evaluation `./bench_torch_int`:
 ![SmoothQuant](../../figures/bench_torch_int.png)
 
-W4A16 Evaluation:
+W4A16 Evaluation `./bench_awq`:
 ![AWQ](../../figures/bench_awq.png)
 
 We also use PyTorch Extension to evaluate the performance of PyTorch API Kernel. Baselines are installed according to their official codebases. Please refer to this [notebook](./python-api.ipynb) to check the results. Below is a sample figure:
-![PyTorch API](../../figures/python-api.png)
+<div align=center>
+    <img src="../../figures/python-api.png" width="50%" height="50%">
+</div>