Some fixes to pairwise distances for cupy integration

python/pylibraft/pylibraft/distance/pairwise_distance.pyx

@@ -54,12 +54,14 @@ cdef extern from "raft_distance/pairwise_distance.hpp" \
 
 DISTANCE_TYPES = {
     "l2": DistanceType.L2SqrtUnexpanded,
+    "sqeuclidean": DistanceType.L2Unexpanded,
     "euclidean": DistanceType.L2SqrtUnexpanded,
     "l1": DistanceType.L1,
     "cityblock": DistanceType.L1,
     "inner_product": DistanceType.InnerProduct,
     "chebyshev": DistanceType.Linf,
     "canberra": DistanceType.Canberra,
+    "cosine": DistanceType.CosineExpanded,
     "lp": DistanceType.LpUnexpanded,
     "correlation": DistanceType.CorrelationExpanded,
     "jaccard": DistanceType.JaccardExpanded,
@@ -68,21 +70,26 @@ DISTANCE_TYPES = {
     "jensenshannon": DistanceType.JensenShannon,
     "hamming": DistanceType.HammingUnexpanded,
     "kl_divergence": DistanceType.KLDivergence,
+    "minkowski": DistanceType.LpUnexpanded,
     "russellrao": DistanceType.RusselRaoExpanded,
     "dice": DistanceType.DiceExpanded
 }
 
-SUPPORTED_DISTANCES = list(DISTANCE_TYPES.keys())
+SUPPORTED_DISTANCES = ["euclidean", "l1", "cityblock", "l2", "inner_product",
+                       "chebyshev", "minkowski", "canberra", "kl_divergence",
+                       "correlation", "russellrao", "hellinger", "lp",
+                       "hamming", "jensenshannon", "cosine", "sqeuclidean"]
 
 
-def distance(X, Y, dists, metric="euclidean"):
+def distance(X, Y, dists, metric="euclidean", p=2.0):
     """
     Compute pairwise distances between X and Y
 
     Valid values for metric:
         ["euclidean", "l2", "l1", "cityblock", "inner_product",
          "chebyshev", "canberra", "lp", "hellinger", "jensenshannon",
-         "kl_divergence", "russellrao"]
+         "kl_divergence", "russellrao", "minkowski", "correlation",
+         "cosine"]
 
     Parameters
     ----------
@@ -113,13 +120,16 @@ def distance(X, Y, dists, metric="euclidean"):
         pairwise_distance(in1, in2, output, metric="euclidean")
    """
 
-    # TODO: Validate inputs, shapes, etc...
     x_cai = X.__cuda_array_interface__
     y_cai = Y.__cuda_array_interface__
     dists_cai = dists.__cuda_array_interface__
 
     m = x_cai["shape"][0]
     n = y_cai["shape"][0]
+
+    if x_cai["shape"][1] != y_cai["shape"][1]:
+        raise ValueError("Inputs must have same number of columns.")
+
     k = x_cai["shape"][1]
 
     x_ptr = <uintptr_t>x_cai["data"][0]
@@ -132,6 +142,12 @@ def distance(X, Y, dists, metric="euclidean"):
     y_dt = np.dtype(y_cai["typestr"])
     d_dt = np.dtype(dists_cai["typestr"])
 
+    x_c_contiguous = "strides" not in x_cai or x_cai["strides"] is None
+    y_c_contiguous = "strides" not in y_cai or y_cai["strides"] is None
+
+    if x_c_contiguous != y_c_contiguous:
+        raise ValueError("Inputs must have matching strides")
+
     if metric not in SUPPORTED_DISTANCES:
         raise ValueError("metric %s is not supported" % metric)
 
@@ -149,8 +165,8 @@ def distance(X, Y, dists, metric="euclidean"):
                           <int>n,
                           <int>k,
                           <DistanceType>distance_type,
-                          <bool>True,
-                          <float>0.0)
+                          <bool>x_c_contiguous,
+                          <float>p)
     elif x_dt == np.float64:
         pairwise_distance(deref(h),
                           <double*> x_ptr,
@@ -160,7 +176,7 @@ def distance(X, Y, dists, metric="euclidean"):
                           <int>n,
                           <int>k,
                           <DistanceType>distance_type,
-                          <bool>True,
-                          <float>0.0)
+                          <bool>x_c_contiguous,
+                          <float>p)
     else:
         raise ValueError("dtype %s not supported" % x_dt)

python/pylibraft/pylibraft/test/test_distance.py

@@ -49,7 +49,8 @@ def copy_to_host(self):
 @pytest.mark.parametrize("n_cols", [100])
 @pytest.mark.parametrize("metric", ["euclidean", "cityblock", "chebyshev",
                                     "canberra", "correlation", "hamming",
-                                    "jensenshannon", "russellrao"])
+                                    "jensenshannon", "russellrao", "cosine",
+                                    "sqeuclidean"])
 @pytest.mark.parametrize("dtype", [np.float32, np.float64])
 def test_distance(n_rows, n_cols, metric, dtype):
     input1 = np.random.random_sample((n_rows, n_cols)).astype(dtype)