![ALT](/media/images/gemm-hierarchy-with-epilogue-no-labels.png "Complete CUDA GEMM decomposition")

# Python packages associated with CUTLASS
This directory contains Python packages that are associated with CUTLASS:

* `cutlass`: the CUTLASS Python interface, which enables one to compile and run CUTLASS kernels from within Python
* `cutlass_library`: utilities used for enumerating and emitting C++ code for CUTLASS kernels

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

```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)
```

### Overview
The CUTLASS Python interface aims to provide an ease-of-use interface for using CUTLASS via Python. Toward this goal,
the CUTLASS Python interface attempts to:

* Present high-level interfaces for operators that require only few parameters
* Select sensible default configurations for an operator given the parameters that have been specified
* Enumerate configurations for users that are known to work in a given setting
* Reduce the occurrence of C++ compile-time errors in favor of descriptive Python exceptions
* Make it easy to export CUTLASS kernels to framework extensions (e.g., PyTorch CUDA extensions)

#### Non-goals
The CUTLASS Python interface does not intended to:

**Select optimal kernel configurations.**
As an ease-of-use interface, the default selections for operator parameters made by the CUTLASS Python interface may
not achieve the highest possible performance in all scenarios. Users wishing to achieve the highest performance possible
should consider profile different combinations of configuration parameters, or use a library such as [cuBLAS](https://developer.nvidia.com/cublas)
that contains heuristics for selecting kernels.

**Act as a fast container for CUTLASS kernels.**
The CUTLASS Python 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 consider either using the C++ emitted by the Python interface directly, or using
one of the CUTLASS emitters for automatically creating a framework extension for the kernel (e.g., a PyTorch CUDA extension).

**Act as a Python-to-CUDA-kernel JIT compilation engine.**
The CUTLASS Python interface intends to enable one to use CUTLASS via Python. It can be used by frameworks for JIT compiling
Python to CUDA kernels, but does not set out to be such a framework.

#### Comparison to PyCUTLASS
The CUTLASS Python interface builds atop CUTLASS's [PyCUTLASS](https://github.com/NVIDIA/cutlass/tree/v3.0.0/tools/library/scripts/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.

### Current functionality
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)

### Getting started
We recommend using the CUTLASS Python interface via an [NGC PyTorch Docker container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch):

```bash
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.

#### Optional environment variables
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`.

#### Installation
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.
```bash
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
```bash
pip install .
```

If you would like to be able to make changes to CUTLASS Python interface and have them reflected when using the interface, perform:
```bash
pip install -e .
```

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

### Deep learning framework CUDA extensions
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](/examples/python/02_pytorch_extension_grouped_gemm.ipynb).

Currently, the following operations can be exported to a PyTorch CUDA extension:
* GEMM
* Grouped GEMM
* Conv2d

### Examples
Jupyter notebook examples of using the CUTLASS Python interface are located in [examples/python](/examples/python).

To launch these notebooks from this directory, run:
```bash
jupyter-lab ../examples/python
```

### Building documentation
The CUTLASS Python interface uses [Sphinx](https://www.sphinx-doc.org/en/master/) for documentation.

Building the documentation requires additional packages. These can be installed via:
```bash
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](#installation).

Documentation can then be built via the following commands:
```bash
sphinx-apidoc -o docs_src/source/ cutlass/ cutlass/backend*
cd docs_src
make html
mv _build/* ../docs
```

## CUTLASS library package
[cutlass_library](/python/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
```bash
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](/python/cutlass_library/generator.py) script directly without installing the module.

# Copyright

Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
SPDX-License-Identifier: BSD-3-Clause

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