Merge pull request #587 from ajschmidt8/fix-merge-conflicts

Fix merge conflicts [skip gpuci]

.github/ops-bot.yaml

@@ -5,4 +5,4 @@ auto_merger: true
 branch_checker: true
 label_checker: true
 release_drafter: true
-external_contributors: 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

@@ -4,7 +4,7 @@
 
 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#building-raft-c-from-source) 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. 
+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. 
 

README.md

@@ -1,18 +1,17 @@
-# <div align="left"><img src="https://rapids.ai/assets/images/rapids_logo.png" width="90px"/>&nbsp;RAFT: RAPIDS Analytics Framework Toolkit</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 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 in the [RAPIDS](https://rapids.ai) ecosystem. 
+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.
 
-By taking a primitives-based approach to algorithm development, RAFT
+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 the core computations, allowing future optimizations to benefit all algorithms that use them.
+- centralizes core reusable computations, allowing future optimizations to benefit all algorithms that use them.
 
-The algorithms in RAFT span the following general categories:
+While not exhaustive, the following general categories help summarize the accelerated functions in RAFT:
 #####
 | Category | Examples |
 | --- | --- |
 | **Data Formats** | sparse & dense, conversions, data generation |
-| **Data Generation** | sparse, spatial, machine learning datasets |
 | **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 |
@@ -23,18 +22,15 @@ The algorithms in RAFT span the following general categories:
 
 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 a Python library that is currently limited to
-1. a python wrapper around the `raft::handle_t` for managing cuda library resources
-2. definitions for using `raft::handle_t` directly in cython
-3. tools for building multi-node multi-GPU algorithms that leverage [Dask](https://dask.org/)
-
-The Python API is being improved to wrap the algorithms and primitives from the categories above.
+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)
+### RAPIDS Memory Manager (RMM)
 
-RAFT relies heavily on RMM which, like other projects in the RAPIDS ecosystem, eases the burden of configuring different allocation strategies globally across the libraries that use it.
+RAFT relies heavily on RMM which eases the burden of configuring different allocation strategies globally across the libraries that use it.
 
 ### Multi-dimensional Arrays
 
@@ -48,9 +44,9 @@ The `mdarray` forms a convenience layer over RMM and can be constructed in RAFT
 int n_rows = 10;
 int n_cols = 10;
 
-auto scalar = raft::make_device_scalar(handle, 1.0);
-auto vector = raft::make_device_vector(handle, n_cols);
-auto matrix = raft::make_device_matrix(handle, n_rows, n_cols);
+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
@@ -80,38 +76,61 @@ 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 can be installed through conda, cmake-package-manager (cpm), or by building the repository from source. 
+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` contains all the CUDA/C++ headers
-- `libraft-nn` (optional) contains precompiled shared libraries for the nearest neighbors algorithms. If FAISS is not already installed in your environment, this will need to be installed to use the nearest neighbors headers.
-- `libraft-distance` (optional) contains shared libraries for distance algorithms.
-- `pyraft` (optional) contains the Python library
+- `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.
 
-To install RAFT with conda (change to `rapidsai-nightly` for more up-to-date but less stable nightly packages)
+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
-conda install -c rapidsai libraft-headers libraft-nn libraft-distance pyraft
+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. Note that the `COMPONENTS` are optional and will depend on the packages installed.
+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 the [Cmake Package Manager (CPM)](https://github.com/cpm-cmake/CPM.cmake). 
+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 create the `raft::raft` target to add to configure the link libraries for your artifacts.
+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 USE_FAISS_STATIC 
-          COMPILE_LIBRARIES ENABLE_NN_DEPENDENCIES)
+  set(oneValueArgs VERSION FORK PINNED_TAG COMPILE_LIBRARIES)
   cmake_parse_arguments(PKG "${options}" "${oneValueArgs}"
                             "${multiValueArgs}" ${ARGN} )
 
@@ -121,16 +140,15 @@ function(find_and_configure_raft)
 
   rapids_cpm_find(raft ${PKG_VERSION}
           GLOBAL_TARGETS      raft::raft
-          BUILD_EXPORT_SET    proj-exports
-          INSTALL_EXPORT_SET  proj-exports
+          BUILD_EXPORT_SET    projname-exports
+          INSTALL_EXPORT_SET  projname-exports
           CPM_ARGS
           GIT_REPOSITORY https://github.com/${PKG_FORK}/raft.git
           GIT_TAG        ${PKG_PINNED_TAG}
           SOURCE_SUBDIR  cpp
           OPTIONS
           "BUILD_TESTS OFF"
-          "RAFT_ENABLE_NN_DEPENDENCIES ${PKG_ENABLE_NN_DEPENDENCIES}"
-          "RAFT_USE_FAISS_STATIC ${PKG_USE_FAISS_STATIC}"
+          "BUILD_BENCH OFF"
           "RAFT_COMPILE_LIBRARIES ${PKG_COMPILE_LIBRARIES}"
   )
 
@@ -140,35 +158,46 @@ endfunction()
 # To use a different RAFT locally, set the CMake variable
 # CPM_raft_SOURCE=/path/to/local/raft
 find_and_configure_raft(VERSION    ${RAFT_VERSION}.00
-        FORK             rapidsai
-        PINNED_TAG       branch-${RAFT_VERSION}
-
+        FORK             ${RAFT_FORK}
+        PINNED_TAG       ${RAFT_PINNED_TAG}
         COMPILE_LIBRARIES      NO
-        ENABLE_NN_DEPENDENCIES NO
-        USE_FAISS_STATIC       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 RAFT dependencies: `conda env create --name raft_dev -f conda/environments/raft_dev_cuda11.5.yml`
-2. run the build script from the repository root: `./build.sh pyraft libraft --compile-libs`
+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) guide.
+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 other RAPIDS repos (cuDF, cuML, cuGraph...), with the following folders:
+The folder structure mirrors other RAPIDS repos, with the following folders:
 
 - `ci`: Scripts for running CI in PRs
 - `conda`: Conda recipes and development conda environments
-- `cpp`: Source code for all C++ code. 
+- `cpp`: Source code for C++ libraries. 
   - `docs`: Doxygen configuration
-  - `include`: The C++ API is fully-contained here 
+  - `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 all Python source code.
+- `python`: Source code for Python libraries.
 
 ## Contributing
 
@@ -195,4 +224,4 @@ If citing the sparse pairwise distances API, please consider using the following
   journal={arXiv preprint arXiv:2104.06357},
   year={2021}
 }
-```
+```