This directory contains Python packages that are associated with CUTLASS:

• cutlass_cppgen: the CUTLASS Python interface, which enables one to compile and run CUTLASS kernels from within Python. Note that this was previously named cutlass, but was renamed to disambiguate with the CuTe Python DSL.
• cutlass_library: utilities used for enumerating and emitting C++ code for CUTLASS kernels

The CUTLASS Python interface enables one to compile and run CUTLASS operations from within Python.

import cutlass
import numpy as np

plan = cutlass.op.Gemm(element=np.float16, layout=cutlass.LayoutType.RowMajor)
A, B, C, D = [np.ones((1024, 1024), dtype=np.float16) for i in range(4)]
plan.run(A, B, C, D)

The CUTLASS Python interface prioritizes ease of use. It has the following features that support this goal.

• It presents high-level interfaces for operators, that require only few parameters.
• It selects sensible default configurations for an operator given the parameters that have been specified.
• It enumerates configurations for users that are known to work in a given setting.
• It favors emitting descriptive Python run-time exceptions instead of C++ compile-time errors, where possible.
• It simplifies exporting CUTLASS kernels to framework extensions (e.g., PyTorch CUDA extensions).

The CUTLASS Python interface does not intend to:

1. select optimal kernel configurations,
2. act as a fast container for CUTLASS kernels, or
3. act as a Python-to-CUDA-kernel just-in-time (JIT) compilation engine.

Regarding selection of optimal kernel configurations, the interface favors ease-of-use over maximum configurability. Thus, its default selections for operator parameters may not achieve the highest possible performance in all scenarios. Users wishing to achieve the highest performance possible should either

• select parameters by profiling different combinations of them, or
• use a library such as cuBLAS that contains heuristics for selecting kernels.

Regarding acting as a fast container for CUTLASS kernels: the interface does not strive to minimize overhead in its Python functions surrounding the running of a kernel. Those wishing to deploy a CUTLASS kernel should either

• use the C++ emitted by the Python interface directly, or
• use one of the CUTLASS emitters for automatically creating a framework extension for the kernel (e.g., a PyTorch CUDA extension).

Regarding acting as a Python-to-CUDA-kernel JIT compilation engine: the interface enables use of CUTLASS in Python code. It can be used by frameworks for JIT compiling Python to CUDA kernels, but does not set out to be such a framework.

The CUTLASS Python interface builds atop CUTLASS's PyCUTLASS library. PyCUTLASS enables one to declare, compile, and run GEMMs, convolutions, and grouped GEMM operators with nearly the same configuration space as CUTLASS's C++ interface. While this flexibility enables one to achieve the similar levels of functionality as available in CUTLASS's C++ interface, it comes with the burden of needing to specify many configuration parameters to operators -- similar to what one must do in specifying template parameters to operations in CUTLASS's C++ interface.

In contrast, the CUTLASS Python interface aims to provide a higher-level API for declaring, emitting, and compiling kernels that does not require exhaustively defining template parameters.

The CUTLASS Python interface currently supports the following operations:

• GEMMs
• GEMMs with fused elementwise epilogues (e.g., ReLU) (for pre-SM90 kernels)
• Stream K swizzling (for pre-SM90 kernels)
• Grouped GEMM (for pre-SM90 kernels)

We recommend using the CUTLASS Python interface via an NGC PyTorch Docker container:

docker run --gpus all -it --rm nvcr.io/nvidia/pytorch:23.08-py3 -p 8888:8888

The CUTLASS Python interface has been tested with CUDA 11.8, 12.0, and 12.1 on Python 3.8 and 3.9.

Prior to installing the CUTLASS Python interface, one may optionally set the following environment variables:

• CUTLASS_PATH: the path to the cloned CUTLASS repository
• CUDA_INSTALL_PATH: the path to the installation of CUDA

If these environment variables are not set, the installation process will infer them to be the following:

• CUTLASS_PATH: either one directory level above the current directory (i.e., $(pwd)/..) if installed locally or in the source directory of the location in which cutlass_library was installed
• CUDA_INSTALL_PATH: the directory holding /bin/nvcc for the first version of nvcc on $PATH (i.e., which nvcc | awk -F'/bin/nvcc' '{print $1}')

NOTE: The version of cuda-python installed must match the CUDA version in CUDA_INSTALL_PATH.

Stable releases of the CUTLASS Python interface are available via the nvidia-cutlass PyPI package. Any other packages with the name cutlass are not affiliated with NVIDIA CUTLASS.

pip install nvidia-cutlass

The CUTLASS Python interface can also be installed from source by navigating to the root of the CUTLASS directory and performing

pip install .

If you would like to be able to make changes to the CUTLASS Python interface and have them reflected when using the interface, perform:

pip install -e .

To test that your installation was successful, you can run:

import cutlass
import numpy as np

plan = cutlass.op.Gemm(element=np.float16, layout=cutlass.LayoutType.RowMajor)
A, B, C, D = [np.ones((128, 128), dtype=np.float16) for i in range(4)]
plan.run(A, B, C, D)

The CUTLASS Python interface provides utilities for exporting a CUTLASS kernel to a deep learning framework CUDA extensions. Currently, PyTorch CUDA extensions can be exported, but a similar pattern could be applied for other frameworks as well. An example of this is provided here.

Currently, the following operations can be exported to a PyTorch CUDA extension:

• GEMM
• Grouped GEMM
• Conv2d

Jupyter notebook examples of using the CUTLASS Python interface are located in examples/python.

To launch these notebooks from this directory, run:

jupyter-lab ../examples/python

The CUTLASS Python interface uses Sphinx for documentation.

Building the documentation requires additional packages. The following commands will install them.

sudo apt-get install pandoc
pip install --upgrade Sphinx furo pandoc myst-parser sphinx-copybutton nbsphinx nbsphinx-link sphinx-inline-tabs

To build documentation, you must first have installed the CUTLASS Python interface via the installation instructions.

Documentation can then be built via the following commands.

sphinx-apidoc -o docs_src/source/ cutlass/ cutlass/backend*
cd docs_src
make html
mv _build/* ../docs

cutlass_library contains utilities for enumerating and emitting CUTLASS C++ kernels. It is used by the CUTLASS CMake system to construct a library of kernels that can be profiled using the CUTLASS profiler.

To install the cutlass_library package, run

python setup_library.py develop --user

Alternatively, cutlass_library will automatically be installed if you install the CUTLASS Python interface package.

You can also use the generator.py script directly without installing the module.

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