Merge pull request #612 from rapidsai/branch-22.04

[RELEASE] v22.04

.github/CODEOWNERS

@@ -1,15 +1,15 @@
 #cpp code owners
-cpp/               @rapidsai/cuml-cpp-codeowners @rapidsai/cugraph-cpp-codeowners
+cpp/               @rapidsai/raft-cpp-codeowners
 
 #python code owners
-python/            @rapidsai/cuml-python-codeowners @rapidsai/cugraph-python-codeowners
+python/            @rapidsai/raft-python-codeowners
 
 #cmake code owners
-**/CMakeLists.txt  @rapidsai/cuml-cmake-codeowners @rapidsai/cugraph-cmake-codeowners
-**/cmake/          @rapidsai/cuml-cmake-codeowners @rapidsai/cugraph-cmake-codeowners
-python/setup.py    @rapidsai/cuml-cmake-codeowners @rapidsai/cugraph-cmake-codeowners
-build.sh           @rapidsai/cuml-cmake-codeowners @rapidsai/cugraph-cmake-codeowners
-**/build.sh        @rapidsai/cuml-cmake-codeowners @rapidsai/cugraph-cmake-codeowners
+**/CMakeLists.txt  @rapidsai/raft-cmake-codeowners
+**/cmake/          @rapidsai/raft-cmake-codeowners
+python/setup.py    @rapidsai/raft-cmake-codeowners
+build.sh           @rapidsai/raft-cmake-codeowners
+**/build.sh        @rapidsai/raft-cmake-codeowners
 
 #build/ops code owners
 .github/           @rapidsai/ops-codeowners

.github/ops-bot.yaml

@@ -0,0 +1,8 @@
+# This file controls which features from the `ops-bot` repository below are enabled.
+# - https://github.com/rapidsai/ops-bot
+
+auto_merger: true
+branch_checker: true
+label_checker: true
+release_drafter: true
+external_contributors: false

.gitignore

@@ -17,8 +17,8 @@ build/
 build_prims/
 dist/
 python/**/**/*.cpp
-python/external_repositories
-python/record.txt
+python/raft/record.txt
+python/pylibraft/record.txt
 log
 .ipynb_checkpoints
 .DS_Store

CONTRIBUTING.md

@@ -23,7 +23,7 @@ into three categories:
 
 ### Your first issue
 
-1. Read the project's [README.md](https://github.com/rapidsai/RAFT/blob/main/README.md)
+1. Read the project's [README.md](https://github.com/rapidsai/raft)
     to learn how to setup the development environment
 2. Find an issue to work on. The best way is to look for the [good first issue](https://github.com/rapidsai/RAFT/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22)
     or [help wanted](https://github.com/rapidsai/RAFT/issues?q=is%3Aissue+is%3Aopen+label%3A%22help+wanted%22) labels

DEVELOPER_GUIDE.md

@@ -0,0 +1,24 @@
+# Developer Guide
+
+## Local Development
+
+Devloping features and fixing bugs for the RAFT library itself is straightforward and only requires building and installing the relevant RAFT artifacts. 
+
+The process for working on a CUDA/C++ feature which spans RAFT and one or more consumers can vary slightly depending on whether the consuming project relies on a source build (as outlined in the [BUILD](BUILD.md#install_header_only_cpp) docs). In such a case, the option `CPM_raft_SOURCE=/path/to/raft/source` can be passed to the cmake of the consuming project in order to build the local RAFT from source. The PR with relevant changes to the consuming project can also pin the RAFT version temporarily by explicitly changing the `FORK` and `PINNED_TAG` arguments to the RAFT branch containing their changes when invoking `find_and_configure_raft`.  The pin should be reverted after the changed is merged to the RAFT project and before it is merged to the dependent project(s) downstream.
+
+If building a feature which spans projects and not using the source build in cmake, the RAFT changes (both C++ and Python) will need to be installed into the environment of the consuming project before they can be used. The ideal integration of RAFT into consuming projects will enable both the source build in the consuming project only for this case but also rely on a more stable packaging (such as conda packaging) otherwise. 
+
+## API stability
+
+Since RAFT is a core library with multiple consumers, it's important that the public APIs maintain stability across versions and any changes to them are done with caution, adding new functions and deprecating the old functions over a couple releases as necessary.
+
+The public APIs should be lightweight wrappers around calls to private APIs inside the `detail` namespace. 
+
+## Testing
+
+It's important for RAFT to maintain a high test coverage in order to minimize the potential for downstream projects to encounter unexpected build or runtime behavior as a result of changes. A well-defined public API can help maintain compile-time stability but means more focus should be placed on testing the functional requirements and verifying execution on the various edge cases within RAFT itself. Ideally, bug fixes and new features should be able to be made to RAFT independently of the consuming projects.
+
+
+## Documentation
+
+Public APIs always require documentation, since those will be exposed directly to users. In addition to summarizing the purpose of each class / function in the public API, the arguments (and relevant templates) should be documented along with brief usage examples.

README.md

@@ -1,15 +1,227 @@
-# <div align="left"><img src="https://rapids.ai/assets/images/rapids_logo.png" width="90px"/>&nbsp;RAFT: RAPIDS Analytics Frameworks Toolset</div>
+# <div align="left"><img src="https://rapids.ai/assets/images/rapids_logo.png" width="90px"/>&nbsp;RAFT: Reusable Accelerated Functions and Tools</div>
 
-RAFT is a repository containining shared utilities, mathematical operations and common functions for the analytics components of RAPIDS. Both the C++ and Python components can be included in consuming libraries.
+RAFT contains fundamental widely-used algorithms and primitives for data science, graph and machine learning. The algorithms are CUDA-accelerated and form building-blocks for rapidly composing analytics.
 
-Refer to the [Build and Development Guide](BUILD.md) for details on RAFT's design, building, testing and development guidelines.
+By taking a primitives-based approach to algorithm development, RAFT 
+- accelerates algorithm construction time
+- reduces the maintenance burden by maximizing reuse across projects, and
+- centralizes core reusable computations, allowing future optimizations to benefit all algorithms that use them.
+
+While not exhaustive, the following general categories help summarize the accelerated functions in RAFT:
+#####
+| Category | Examples |
+| --- | --- |
+| **Data Formats** | sparse & dense, conversions, data generation |
+| **Dense Linear Algebra** | matrix arithmetic, norms, factorization, least squares, svd & eigenvalue problems |
+| **Spatial** | pairwise distances, nearest neighbors, neighborhood graph construction |
+| **Sparse Operations** | linear algebra, eigenvalue problems, slicing, symmetrization, labeling |
+| **Basic Clustering** | spectral clustering, hierarchical clustering, k-means |
+| **Solvers** | combinatorial optimization, iterative solvers |
+| **Statistics** | sampling, moments and summary statistics, metrics |
+| **Distributed Tools** | multi-node multi-gpu infrastructure |
+
+RAFT provides a header-only C++ library and pre-compiled shared libraries that can 1) speed up compile times and 2) enable the APIs to be used without CUDA-enabled compilers.
+
+RAFT also provides 2 Python libraries:
+- `pylibraft` - low-level Python wrappers around RAFT algorithms and primitives.
+- `pyraft` - reusable infrastructure for building analytics, including tools for building both single-GPU and multi-node multi-GPU algorithms.
+
+## Getting started
+
+### RAPIDS Memory Manager (RMM)
+
+RAFT relies heavily on RMM which eases the burden of configuring different allocation strategies globally across the libraries that use it.
+
+### Multi-dimensional Arrays
+
+The APIs in RAFT currently accept raw pointers to device memory and we are in the process of simplifying the APIs with the [mdspan](https://arxiv.org/abs/2010.06474) multi-dimensional array view for representing data in higher dimensions similar to the `ndarray` in the Numpy Python library. RAFT also contains the corresponding owning `mdarray` structure, which simplifies the allocation and management of multi-dimensional data in both host and device (GPU) memory. 
+
+The `mdarray` forms a convenience layer over RMM and can be constructed in RAFT using a number of different helper functions:
+
+```c++
+#include <raft/mdarray.hpp>
+
+int n_rows = 10;
+int n_cols = 10;
+
+auto scalar = raft::make_device_scalar<float>(handle, 1.0);
+auto vector = raft::make_device_vector<float>(handle, n_cols);
+auto matrix = raft::make_device_matrix<float>(handle, n_rows, n_cols);
+```
+
+### C++ Example
+
+Most of the primitives in RAFT accept a `raft::handle_t` object for the management of resources which are expensive to create, such CUDA streams, stream pools, and handles to other CUDA libraries like `cublas` and `cusolver`.
+
+The example below demonstrates creating a RAFT handle and using it with `device_matrix` and `device_vector` to allocate memory, generating random clusters, and computing
+pairwise Euclidean distances:
+```c++
+#include <raft/handle.hpp>
+#include <raft/mdarray.hpp>
+#include <raft/random/make_blobs.cuh>
+#include <raft/distance/distance.cuh>
+
+raft::handle_t handle;
+
+int n_samples = 5000;
+int n_features = 50;
+
+auto input = raft::make_device_matrix<float>(handle, n_samples, n_features);
+auto labels = raft::make_device_vector<int>(handle, n_samples);
+auto output = raft::make_device_matrix<float>(handle, n_samples, n_samples);
+
+raft::random::make_blobs(handle, input.view(), labels.view());
+
+auto metric = raft::distance::DistanceType::L2SqrtExpanded;
+raft::distance::pairwise_distance(handle, input.view(), input.view(), output.view(), metric);
+```
+
+### Python Example
+
+The `pylibraft` package contains a Python API for RAFT algorithms and primitives. The package is currently limited to pairwise distances, and we will continue adding more.
+
+The example below demonstrates computing the pairwise Euclidean distances between cupy arrays. `pylibraft` is a low-level API that prioritizes efficiency and simplicity over being pythonic, which is shown here by pre-allocating the output memory before invoking the `pairwise_distance` function.
+
+```python
+import cupy as cp
+
+from pylibraft.distance import pairwise_distance
+
+n_samples = 5000
+n_features = 50
+
+in1 = cp.random.random_sample((n_samples, n_features), dtype=cp.float32)
+in2 = cp.random.random_sample((n_samples, n_features), dtype=cp.float32)
+output = cp.empty((n_samples, n_samples), dtype=cp.float32)
+
+pairwise_distance(in1, in2, output, metric="euclidean")
+```
+
+## Installing
+
+RAFT itself can be installed through conda, [Cmake Package Manager (CPM)](https://github.com/cpm-cmake/CPM.cmake), or by building the repository from source. Please refer to the [build instructions](BUILD.md) for more a comprehensive guide on building RAFT and using it in downstream projects.
+
+### Conda
+
+The easiest way to install RAFT is through conda and several packages are provided.
+- `libraft-headers` RAFT headers
+- `libraft-nn` (optional) contains shared libraries for the nearest neighbors primitives.
+- `libraft-distance` (optional) contains shared libraries for distance primitives.
+- `pylibraft` (optional) Python wrappers around RAFT algorithms and primitives
+- `pyraft` (optional) contains reusable Python infrastructure and tools to accelerate Python algorithm development.
+
+Use the following command to install RAFT with conda (replace `rapidsai` with `rapidsai-nightly` to install more up-to-date but less stable nightly packages). `mamba` is preferred over the `conda` command.
+```bash
+mamba install -c rapidsai libraft-headers libraft-nn libraft-distance pyraft pylibraft
+```
+
+After installing RAFT, `find_package(raft COMPONENTS nn distance)` can be used in your CUDA/C++ build. `COMPONENTS` are optional and will depend on the packages installed.
+
+### CPM
+
+RAFT uses the [RAPIDS-CMake](https://github.com/rapidsai/rapids-cmake) library, which makes it simple to include in downstream cmake projects. RAPIDS CMake provides a convenience layer around CPM. 
+
+After [installing](https://github.com/rapidsai/rapids-cmake#installation) rapids-cmake in your project, you can begin using RAFT by placing the code snippet below in a file named `get_raft.cmake` and including it in your cmake build with `include(get_raft.cmake)`. This will make available several targets to add to configure the link libraries for your artifacts.
+
+```cmake
+
+set(RAFT_VERSION "22.04")
+set(RAFT_FORK "rapidsai")
+set(RAFT_PINNED_TAG "branch-${RAFT_VERSION}")
+
+function(find_and_configure_raft)
+  set(oneValueArgs VERSION FORK PINNED_TAG COMPILE_LIBRARIES)
+  cmake_parse_arguments(PKG "${options}" "${oneValueArgs}"
+                            "${multiValueArgs}" ${ARGN} )
+
+  #-----------------------------------------------------
+  # Invoke CPM find_package()
+  #-----------------------------------------------------
+
+  rapids_cpm_find(raft ${PKG_VERSION}
+          GLOBAL_TARGETS      raft::raft
+          BUILD_EXPORT_SET    projname-exports
+          INSTALL_EXPORT_SET  projname-exports
+          CPM_ARGS
+          GIT_REPOSITORY https://github.com/${PKG_FORK}/raft.git
+          GIT_TAG        ${PKG_PINNED_TAG}
+          SOURCE_SUBDIR  cpp
+          OPTIONS
+          "BUILD_TESTS OFF"
+          "BUILD_BENCH OFF"
+          "RAFT_COMPILE_LIBRARIES ${PKG_COMPILE_LIBRARIES}"
+  )
+
+endfunction()
+
+# Change pinned tag here to test a commit in CI
+# To use a different RAFT locally, set the CMake variable
+# CPM_raft_SOURCE=/path/to/local/raft
+find_and_configure_raft(VERSION    ${RAFT_VERSION}.00
+        FORK             ${RAFT_FORK}
+        PINNED_TAG       ${RAFT_PINNED_TAG}
+        COMPILE_LIBRARIES      NO
+)
+```
+
+Several CMake targets can be made available by adding components in the table below to the `RAFT_COMPONENTS` list above, separated by spaces. The `raft::raft` target will always be available.
+
+| Component | Target | Description | Base Dependencies |
+| --- | --- | --- | --- |
+| n/a | `raft::raft` | Full RAFT header library | CUDA toolkit library, RMM, std::mdspan, cuCollections, Thrust, NVTools |
+| distance | `raft::distance` | Pre-compiled template specializations for raft::distance | raft::raft |
+| nn | `raft::nn` | Pre-compiled template specializations for raft::spatial::knn | raft::raft, FAISS |
+
+### Source
+
+The easiest way to build RAFT from source is to use the `build.sh` script at the root of the repository:
+1. Create an environment with the needed dependencies: 
+```
+mamba env create --name raft_dev_env -f conda/environments/raft_dev_cuda11.5.yml
+mamba activate raft_dev_env
+```
+```
+./build.sh pyraft pylibraft libraft tests bench --compile-libs
+```
+
+The [build](BUILD.md) instructions contain more details on building RAFT from source and including it in downstream projects. You can also find a more comprehensive version of the above CPM code snippet the [Building RAFT C++ from source](BUILD.md#build_cxx_source) section of the build instructions.
 
 ## Folder Structure and Contents
 
-The folder structure mirrors the main RAPIDS repos (cuDF, cuML, cuGraph...), with the following folders:
+The folder structure mirrors other RAPIDS repos, with the following folders:
 
-- `cpp`: Source code for all C++ code. The code is header only, therefore it is in the `include` folder (with no `src`).
-- `python`: Source code for all Python source code.
 - `ci`: Scripts for running CI in PRs
+- `conda`: Conda recipes and development conda environments
+- `cpp`: Source code for C++ libraries. 
+  - `docs`: Doxygen configuration
+  - `include`: The C++ API is fully-contained here
+  - `src`: Compiled template specializations for the shared libraries
+- `docs`: Source code and scripts for building library documentation (doxygen + pydocs)
+- `python`: Source code for Python libraries.
+
+## Contributing
+
+If you are interested in contributing to the RAFT project, please read our [Contributing guidelines](CONTRIBUTING.md). Refer to the [Developer Guide](DEVELOPER_GUIDE.md) for details on the developer guidelines, workflows, and principals. 
 
+## References
 
+When citing RAFT generally, please consider referencing this Github project.
+```bibtex
+@misc{rapidsai, 
+  title={Rapidsai/raft: RAFT contains fundamental widely-used algorithms and primitives for data science, Graph and machine learning.},
+  url={https://github.com/rapidsai/raft}, 
+  journal={GitHub}, 
+  publisher={Nvidia RAPIDS}, 
+  author={Rapidsai},
+  year={2022}
+}
+```
+If citing the sparse pairwise distances API, please consider using the following bibtex:
+```bibtex
+@article{nolet2021semiring,
+  title={Semiring primitives for sparse neighborhood methods on the gpu},
+  author={Nolet, Corey J and Gala, Divye and Raff, Edward and Eaton, Joe and Rees, Brad and Zedlewski, John and Oates, Tim},
+  journal={arXiv preprint arXiv:2104.06357},
+  year={2021}
+}
+```