Support numeric, boolean, and string keyword arguments to class metho…

…ds during CPU dispatching (#5236)

This PR:
- Updates the CPU dispatching logic to support keyword arguments that are numeric, boolean, and strings (i.e., things that can't be coerced to cuml arrays). It doesn't support passing sequences, as I believe this use case isn't necessary
- Makes a minor update to the tests to test this dispatching in principle. We may want to expand the testing of keyword arguments in general, but as this is likely worth a broader discussion/test expansion I thought it might be out of scope for this small PR


Closes #5218

This is a replacement for #5223

Authors:
  - Nick Becker (https://github.com/beckernick)
  - Dante Gama Dessavre (https://github.com/dantegd)

Approvers:
  - Dante Gama Dessavre (https://github.com/dantegd)

URL: #5236

python/cuml/internals/base.pyx

@@ -18,6 +18,7 @@
 
 import os
 import inspect
+import numbers
 from importlib import import_module
 from cuml.internals.safe_imports import cpu_only_import
 np = cpu_only_import('numpy')
@@ -36,7 +37,8 @@ from cuml.internals.device_type import DeviceType
 from cuml.internals.input_utils import (
     determine_array_type,
     input_to_cuml_array,
-    input_to_host_array
+    input_to_host_array,
+    is_array_like
 )
 from cuml.internals.memory_utils import determine_array_memtype
 from cuml.internals.mem_type import MemoryType
@@ -626,7 +628,14 @@ class UniversalBase(Base):
         # put all the kwargs on host
         new_kwargs = dict()
         for kw, arg in kwargs.items():
-            new_kwargs[kw] = input_to_host_array(arg)[0]
+            # if array-like, ensure array-like is on the host
+            if is_array_like(arg):
+                new_kwargs[kw] = input_to_host_array(arg)[0]
+            # if Real or string, pass as is
+            elif isinstance(arg, (numbers.Real, str)):
+                new_kwargs[kw] = arg
+            else:
+                raise ValueError(f"Unable to process argument {kw}")
         return new_args, new_kwargs
 
     def dispatch_func(self, func_name, gpu_func, *args, **kwargs):

python/cuml/tests/test_device_selection.py

@@ -511,6 +511,7 @@ def tsvd_test_data(request):
             "input_type": ["numpy", "dataframe", "cupy", "cudf", "numba"],
             "metric": ["euclidean", "cosine"],
             "n_neighbors": [3, 8],
+            "return_distance": [True],
         }
     )
 )
@@ -519,6 +520,7 @@ def nn_test_data(request):
         "metric": request.param["metric"],
         "n_neighbors": request.param["n_neighbors"],
     }
+    infer_func_kwargs = {"return_distance": request.param["return_distance"]}
 
     sk_model = skNearestNeighbors(**kwargs)
     sk_model.fit(X_train_blob)
@@ -529,6 +531,7 @@ def nn_test_data(request):
         "cuEstimator": NearestNeighbors,
         "kwargs": kwargs,
         "infer_func": "kneighbors",
+        "infer_func_kwargs": infer_func_kwargs,
         "assert_func": check_nn,
         "X_train": to_output_type(X_train_blob, input_type),
         "X_test": to_output_type(X_test_blob, input_type),
@@ -563,7 +566,8 @@ def test_train_cpu_infer_cpu(test_data):
         else:
             model.fit(test_data["X_train"])
         infer_func = getattr(model, test_data["infer_func"])
-        cuml_output = infer_func(test_data["X_test"])
+        infer_func_kwargs = test_data.get("infer_func_kwargs", {})
+        cuml_output = infer_func(test_data["X_test"], **infer_func_kwargs)
 
     assert_func = test_data["assert_func"]
     assert_func(cuml_output, test_data)
@@ -582,7 +586,8 @@ def test_train_gpu_infer_cpu(test_data):
             model.fit(test_data["X_train"])
     with using_device_type("cpu"):
         infer_func = getattr(model, test_data["infer_func"])
-        cuml_output = infer_func(test_data["X_test"])
+        infer_func_kwargs = test_data.get("infer_func_kwargs", {})
+        cuml_output = infer_func(test_data["X_test"], **infer_func_kwargs)
 
     assert_func = test_data["assert_func"]
     assert_func(cuml_output, test_data)
@@ -598,7 +603,8 @@ def test_train_cpu_infer_gpu(test_data):
             model.fit(test_data["X_train"])
     with using_device_type("gpu"):
         infer_func = getattr(model, test_data["infer_func"])
-        cuml_output = infer_func(test_data["X_test"])
+        infer_func_kwargs = test_data.get("infer_func_kwargs", {})
+        cuml_output = infer_func(test_data["X_test"], **infer_func_kwargs)
 
     assert_func = test_data["assert_func"]
     assert_func(cuml_output, test_data)
@@ -613,7 +619,8 @@ def test_train_gpu_infer_gpu(test_data):
         else:
             model.fit(test_data["X_train"])
         infer_func = getattr(model, test_data["infer_func"])
-        cuml_output = infer_func(test_data["X_test"])
+        infer_func_kwargs = test_data.get("infer_func_kwargs", {})
+        cuml_output = infer_func(test_data["X_test"], **infer_func_kwargs)
 
     assert_func = test_data["assert_func"]
     assert_func(cuml_output, test_data)