Use _compile_or_get in JIT groupby apply (#13350)

During #13337 I realized we should have been using this function in groupby, so caching logic and any runtime checks for strings and groupby don't have to be duplicated.

Authors:
  - https://github.com/brandon-b-miller

Approvers:
  - Vyas Ramasubramani (https://github.com/vyasr)

URL: #13350

python/cudf/cudf/core/udf/groupby_utils.py

@@ -20,13 +20,12 @@
 )
 from cudf.core.udf.utils import (
     Row,
-    _generate_cache_key,
+    _compile_or_get,
     _get_extensionty_size,
     _get_kernel,
     _get_udf_return_type,
     _supported_cols_from_frame,
     _supported_dtypes_from_frame,
-    precompiled,
 )
 from cudf.utils.utils import _cudf_nvtx_annotate
 
@@ -146,20 +145,18 @@ def jit_groupby_apply(offsets, grouped_values, function, *args):
     function : callable
         The user-defined function to execute
     """
-    offsets = cp.asarray(offsets)
-    ngroups = len(offsets) - 1
 
-    cache_key = _generate_cache_key(
-        grouped_values, function, args, suffix="__GROUPBY_APPLY_UDF"
+    kernel, return_type = _compile_or_get(
+        grouped_values,
+        function,
+        args,
+        kernel_getter=_get_groupby_apply_kernel,
+        suffix="__GROUPBY_APPLY_UDF",
     )
 
-    if cache_key not in precompiled:
-        precompiled[cache_key] = _get_groupby_apply_kernel(
-            grouped_values, function, args
-        )
-    kernel, return_type = precompiled[cache_key]
+    offsets = cp.asarray(offsets)
+    ngroups = len(offsets) - 1
 
-    return_type = numpy_support.as_dtype(return_type)
     output = cudf.core.column.column_empty(ngroups, dtype=return_type)
 
     launch_args = [

python/cudf/cudf/core/udf/utils.py

@@ -283,7 +283,9 @@ def _generate_cache_key(frame, func: Callable, args, suffix="__APPLY_UDF"):
 
 
 @_cudf_nvtx_annotate
-def _compile_or_get(frame, func, args, kernel_getter=None):
+def _compile_or_get(
+    frame, func, args, kernel_getter=None, suffix="__APPLY_UDF"
+):
     """
     Return a compiled kernel in terms of MaskedTypes that launches a
     kernel equivalent of `f` for the dtypes of `df`. The kernel uses
@@ -308,7 +310,7 @@ def _compile_or_get(frame, func, args, kernel_getter=None):
         raise TypeError("only scalar valued args are supported by apply")
 
     # check to see if we already compiled this function
-    cache_key = _generate_cache_key(frame, func, args)
+    cache_key = _generate_cache_key(frame, func, args, suffix=suffix)
     if precompiled.get(cache_key) is not None:
         kernel, masked_or_scalar = precompiled[cache_key]
         return kernel, masked_or_scalar