Branch 23.10 merge 23.08

ci/test_wheel.sh

@@ -13,7 +13,7 @@ if [[ "$(arch)" == "aarch64" ]]; then
 fi
 
 # Always install latest dask for testing
-python -m pip install git+https://github.com/dask/dask.git@main git+https://github.com/dask/distributed.git@main git+https://github.com/rapidsai/[email protected]
+python -m pip install git+https://github.com/dask/dask.git@2023.7.1 git+https://github.com/dask/distributed.git@2023.7.1 git+https://github.com/rapidsai/[email protected]
 
 # echo to expand wildcard before adding `[extra]` requires for pip
 python -m pip install $(echo ./dist/cuml*.whl)[test]

conda/environments/all_cuda-118_arch-x86_64.yaml

@@ -16,12 +16,12 @@ dependencies:
 - cupy>=12.0.0
 - cxx-compiler
 - cython>=0.29,<0.30
-- dask-core>=2023.5.1
+- dask-core==2023.7.1
 - dask-cuda==23.10.*
 - dask-cudf==23.10.*
 - dask-ml
-- dask>=2023.5.1
-- distributed>=2023.5.1
+- dask==2023.7.1
+- distributed==2023.7.1
 - doxygen=1.8.20
 - gcc_linux-64=11.*
 - gmock>=1.13.0

conda/environments/all_cuda-120_arch-x86_64.yaml

@@ -18,12 +18,12 @@ dependencies:
 - cupy>=12.0.0
 - cxx-compiler
 - cython>=0.29,<0.30
-- dask-core>=2023.5.1
+- dask-core==2023.7.1
 - dask-cuda==23.10.*
 - dask-cudf==23.10.*
 - dask-ml
-- dask>=2023.5.1
-- distributed>=2023.5.1
+- dask==2023.7.1
+- distributed==2023.7.1
 - doxygen=1.8.20
 - gcc_linux-64=11.*
 - gmock>=1.13.0

conda/recipes/cuml/meta.yaml

@@ -76,9 +76,9 @@ requirements:
     - cudf ={{ minor_version }}
     - cupy >=12.0.0
     - dask-cudf ={{ minor_version }}
-    - dask >=2023.5.1
-    - dask-core>=2023.5.1
-    - distributed >=2023.5.1
+    - dask ==2023.7.1
+    - dask-core==2023.7.1
+    - distributed ==2023.7.1
     - joblib >=0.11
     - libcuml ={{ version }}
     - libcumlprims ={{ minor_version }}

cpp/src/umap/knn_graph/algo.cuh

@@ -71,6 +71,9 @@ inline void launcher(const raft::handle_t& handle,
                                       out.knn_indices,
                                       out.knn_dists,
                                       n_neighbors,
+                                      true,
+                                      true,
+                                      static_cast<std::vector<int64_t>*>(nullptr),
                                       params->metric,
                                       params->p);
 }

dependencies.yaml

@@ -175,10 +175,10 @@ dependencies:
       - output_types: [conda, requirements, pyproject]
         packages:
           - cudf==23.10.*
-          - dask>=2023.5.1
+          - dask==2023.7.1
           - dask-cuda==23.10.*
           - dask-cudf==23.10.*
-          - distributed>=2023.5.1
+          - distributed==2023.7.1
           - joblib>=0.11
           - numba>=0.57
             # TODO: Is scipy really a hard dependency, or should
@@ -192,7 +192,7 @@ dependencies:
           - cupy>=12.0.0
       - output_types: conda
         packages:
-          - dask-core>=2023.5.1
+          - dask-core==2023.7.1
       - output_types: pyproject
         packages:
           - *treelite_runtime
@@ -360,9 +360,11 @@ dependencies:
     common:
       - output_types: [conda, requirements]
         packages:
+          - dask-ml==2023.3.24
           - jupyter
           - matplotlib
           - numpy
           - pandas
           - *scikit_learn
           - seaborn
+

python/README.md

@@ -70,8 +70,8 @@ Packages required for multigpu algorithms*:
 - ucx-py version matching the cuML version
 - dask-cudf version matching the cuML version
 - nccl>=2.5
-- dask>=2023.5.1
-- distributed>=2023.5.1
+- dask==2023.7.1
+- distributed==2023.7.1
 
 * this can be avoided with `--singlegpu` argument flag.
 

python/cuml/manifold/simpl_set.pyx

@@ -22,7 +22,8 @@ from cuml.internals.safe_imports import gpu_only_import
 cp = gpu_only_import('cupy')
 
 from cuml.manifold.umap_utils cimport *
-from cuml.manifold.umap_utils import GraphHolder, find_ab_params
+from cuml.manifold.umap_utils import GraphHolder, find_ab_params, \
+    metric_parsing
 
 from cuml.internals.input_utils import input_to_cuml_array
 from cuml.internals.array import CumlArray
@@ -82,10 +83,17 @@ def fuzzy_simplicial_set(X,
         structure to the detriment of the larger picture.
     random_state: numpy RandomState or equivalent
         A state capable being used as a numpy random state.
-    metric: string or function (optional, default 'euclidean')
-        unused
-    metric_kwds: dict (optional, default {})
-        unused
+    metric: string (default='euclidean').
+        Distance metric to use. Supported distances are ['l1, 'cityblock',
+        'taxicab', 'manhattan', 'euclidean', 'l2', 'sqeuclidean', 'canberra',
+        'minkowski', 'chebyshev', 'linf', 'cosine', 'correlation', 'hellinger',
+        'hamming', 'jaccard']
+        Metrics that take arguments (such as minkowski) can have arguments
+        passed via the metric_kwds dictionary.
+        Note: The 'jaccard' distance metric is only supported for sparse
+        inputs.
+    metric_kwds: dict (optional, default=None)
+        Metric argument
     knn_indices: array of shape (n_samples, n_neighbors) (optional)
         If the k-nearest neighbors of each point has already been calculated
         you can pass them in here to save computation time. This should be
@@ -138,6 +146,14 @@ def fuzzy_simplicial_set(X,
     umap_params.deterministic = <bool> deterministic
     umap_params.set_op_mix_ratio = <float> set_op_mix_ratio
     umap_params.local_connectivity = <float> local_connectivity
+    try:
+        umap_params.metric = metric_parsing[metric.lower()]
+    except KeyError:
+        raise ValueError(f"Invalid value for metric: {metric}")
+    if metric_kwds is None:
+        umap_params.p = <float> 2.0
+    else:
+        umap_params.p = <float> metric_kwds.get("p", 2.0)
     umap_params.verbosity = <int> verbose
 
     X_m, _, _, _ = \
@@ -245,10 +261,17 @@ def simplicial_set_embedding(
             * A numpy array of initial embedding positions.
     random_state: numpy RandomState or equivalent
         A state capable being used as a numpy random state.
-    metric: string or callable
-        unused
-    metric_kwds: dict
-        unused
+    metric: string (default='euclidean').
+        Distance metric to use. Supported distances are ['l1, 'cityblock',
+        'taxicab', 'manhattan', 'euclidean', 'l2', 'sqeuclidean', 'canberra',
+        'minkowski', 'chebyshev', 'linf', 'cosine', 'correlation', 'hellinger',
+        'hamming', 'jaccard']
+        Metrics that take arguments (such as minkowski) can have arguments
+        passed via the metric_kwds dictionary.
+        Note: The 'jaccard' distance metric is only supported for sparse
+        inputs.
+    metric_kwds: dict (optional, default=None)
+        Metric argument
     output_metric: function
         Function returning the distance between two points in embedding space
         and the gradient of the distance wrt the first argument.
@@ -306,6 +329,14 @@ def simplicial_set_embedding(
         umap_params.init = <int> 0
     umap_params.random_state = <int> random_state
     umap_params.deterministic = <bool> deterministic
+    try:
+        umap_params.metric = metric_parsing[metric.lower()]
+    except KeyError:
+        raise ValueError(f"Invalid value for metric: {metric}")
+    if metric_kwds is None:
+        umap_params.p = <float> 2.0
+    else:
+        umap_params.p = <float> metric_kwds.get("p", 2.0)
     if output_metric == 'euclidean':
         umap_params.target_metric = MetricType.EUCLIDEAN
     else:  # output_metric == 'categorical'

python/cuml/manifold/umap.pyx

@@ -29,7 +29,8 @@ cupyx = gpu_only_import('cupyx')
 cuda = gpu_only_import('numba.cuda')
 
 from cuml.manifold.umap_utils cimport *
-from cuml.manifold.umap_utils import GraphHolder, find_ab_params
+from cuml.manifold.umap_utils import GraphHolder, find_ab_params, \
+    metric_parsing, DENSE_SUPPORTED_METRICS, SPARSE_SUPPORTED_METRICS
 
 from cuml.common.sparsefuncs import extract_knn_infos
 from cuml.internals.safe_imports import gpu_only_import_from
@@ -47,7 +48,6 @@ from cuml.internals.array import CumlArray
 from cuml.internals.array_sparse import SparseCumlArray
 from cuml.internals.mixins import CMajorInputTagMixin
 from cuml.common.sparse_utils import is_sparse
-from cuml.metrics.distance_type cimport DistanceType
 
 from cuml.manifold.simpl_set import fuzzy_simplicial_set  # no-cython-lint
 from cuml.manifold.simpl_set import simplicial_set_embedding  # no-cython-lint
@@ -152,13 +152,17 @@ class UMAP(UniversalBase,
     n_components: int (optional, default 2)
         The dimension of the space to embed into. This defaults to 2 to
         provide easy visualization, but can reasonably be set to any
-    metric : string (default='euclidean').
+    metric: string (default='euclidean').
         Distance metric to use. Supported distances are ['l1, 'cityblock',
         'taxicab', 'manhattan', 'euclidean', 'l2', 'sqeuclidean', 'canberra',
         'minkowski', 'chebyshev', 'linf', 'cosine', 'correlation', 'hellinger',
         'hamming', 'jaccard']
         Metrics that take arguments (such as minkowski) can have arguments
         passed via the metric_kwds dictionary.
+        Note: The 'jaccard' distance metric is only supported for sparse
+        inputs.
+    metric_kwds: dict (optional, default=None)
+        Metric argument
     n_epochs: int (optional, default None)
         The number of training epochs to be used in optimizing the
         low dimensional embedding. Larger values result in more accurate
@@ -419,7 +423,7 @@ class UMAP(UniversalBase,
             raise ValueError("min_dist should be <= spread")
 
     @staticmethod
-    def _build_umap_params(cls):
+    def _build_umap_params(cls, sparse):
         cdef UMAPParams* umap_params = new UMAPParams()
         umap_params.n_neighbors = <int> cls.n_neighbors
         umap_params.n_components = <int> cls.n_components
@@ -448,37 +452,20 @@ class UMAP(UniversalBase,
         umap_params.random_state = <uint64_t> cls.random_state
         umap_params.deterministic = <bool> cls.deterministic
 
-        # metric
-        metric_parsing = {
-            "l2": DistanceType.L2SqrtUnexpanded,
-            "euclidean": DistanceType.L2SqrtUnexpanded,
-            "sqeuclidean": DistanceType.L2Unexpanded,
-            "cityblock": DistanceType.L1,
-            "l1": DistanceType.L1,
-            "manhattan": DistanceType.L1,
-            "taxicab": DistanceType.L1,
-            "minkowski": DistanceType.LpUnexpanded,
-            "chebyshev": DistanceType.Linf,
-            "linf": DistanceType.Linf,
-            "cosine": DistanceType.CosineExpanded,
-            "correlation": DistanceType.CorrelationExpanded,
-            "hellinger": DistanceType.HellingerExpanded,
-            "hamming": DistanceType.HammingUnexpanded,
-            "jaccard": DistanceType.JaccardExpanded,
-            "canberra": DistanceType.Canberra
-        }
-
-        if cls.metric.lower() in metric_parsing:
+        try:
             umap_params.metric = metric_parsing[cls.metric.lower()]
-        else:
-            raise ValueError("Invalid value for metric: {}"
-                             .format(cls.metric))
-
+            if sparse:
+                if umap_params.metric not in SPARSE_SUPPORTED_METRICS:
+                    raise NotImplementedError(f"Metric '{cls.metric}' not supported for sparse inputs.")
+            elif umap_params.metric not in DENSE_SUPPORTED_METRICS:
+                raise NotImplementedError(f"Metric '{cls.metric}' not supported for dense inputs.")
+
+        except KeyError:
+            raise ValueError(f"Invalid value for metric: {cls.metric}")
         if cls.metric_kwds is None:
             umap_params.p = <float> 2.0
         else:
-            umap_params.p = <float>cls.metric_kwds.get('p')
-
+            umap_params.p = <float> cls.metric_kwds.get("p", 2.0)
         cdef uintptr_t callback_ptr = 0
         if cls.callback:
             callback_ptr = cls.callback.get_native_callback()
@@ -576,7 +563,7 @@ class UMAP(UniversalBase,
         cdef uintptr_t embed_raw = self.embedding_.ptr
 
         cdef UMAPParams* umap_params = \
-            <UMAPParams*> <size_t> UMAP._build_umap_params(self)
+            <UMAPParams*> <size_t> UMAP._build_umap_params(self, self.sparse_fit)
 
         cdef uintptr_t y_raw = 0
 
@@ -742,7 +729,7 @@ class UMAP(UniversalBase,
         cdef uintptr_t embed_ptr = self.embedding_.ptr
 
         cdef UMAPParams* umap_params = \
-            <UMAPParams*> <size_t> UMAP._build_umap_params(self)
+            <UMAPParams*> <size_t> UMAP._build_umap_params(self, self.sparse_fit)
 
         if self.sparse_fit:
             transform_sparse(handle_[0],

python/cuml/manifold/umap_utils.pyx

@@ -19,6 +19,7 @@
 from rmm._lib.memory_resource cimport get_current_device_resource
 from pylibraft.common.handle cimport handle_t
 from cuml.manifold.umap_utils cimport *
+from cuml.metrics.distance_type cimport DistanceType
 from libcpp.utility cimport move
 from cuml.internals.safe_imports import cpu_only_import
 np = cpu_only_import('numpy')
@@ -130,3 +131,53 @@ def find_ab_params(spread, min_dist):
     yv[xv >= min_dist] = np.exp(-(xv[xv >= min_dist] - min_dist) / spread)
     params, _ = curve_fit(curve, xv, yv)
     return params[0], params[1]
+
+
+metric_parsing = {
+    "l2": DistanceType.L2SqrtUnexpanded,
+    "euclidean": DistanceType.L2SqrtUnexpanded,
+    "sqeuclidean": DistanceType.L2Unexpanded,
+    "cityblock": DistanceType.L1,
+    "l1": DistanceType.L1,
+    "manhattan": DistanceType.L1,
+    "taxicab": DistanceType.L1,
+    "minkowski": DistanceType.LpUnexpanded,
+    "chebyshev": DistanceType.Linf,
+    "linf": DistanceType.Linf,
+    "cosine": DistanceType.CosineExpanded,
+    "correlation": DistanceType.CorrelationExpanded,
+    "hellinger": DistanceType.HellingerExpanded,
+    "hamming": DistanceType.HammingUnexpanded,
+    "jaccard": DistanceType.JaccardExpanded,
+    "canberra": DistanceType.Canberra
+}
+
+
+DENSE_SUPPORTED_METRICS = [
+    DistanceType.Canberra,
+    DistanceType.CorrelationExpanded,
+    DistanceType.CosineExpanded,
+    DistanceType.HammingUnexpanded,
+    DistanceType.HellingerExpanded,
+    # DistanceType.JaccardExpanded,  # not supported
+    DistanceType.L1,
+    DistanceType.L2SqrtUnexpanded,
+    DistanceType.L2Unexpanded,
+    DistanceType.Linf,
+    DistanceType.LpUnexpanded,
+]
+
+
+SPARSE_SUPPORTED_METRICS = [
+    DistanceType.Canberra,
+    DistanceType.CorrelationExpanded,
+    DistanceType.CosineExpanded,
+    DistanceType.HammingUnexpanded,
+    DistanceType.HellingerExpanded,
+    DistanceType.JaccardExpanded,
+    DistanceType.L1,
+    DistanceType.L2SqrtUnexpanded,
+    DistanceType.L2Unexpanded,
+    DistanceType.Linf,
+    DistanceType.LpUnexpanded,
+]