Add integration tests for skill templates

skills/docs/dev/TESTING.md

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+# Testing
+
+The commands assume you are in the `skills/` directory.
+
+## Setup
+
+Set up a local dev environment:
+
+```bash
+python -m venv .venv
+source .venv/bin/activate
+pip install -e ".[dev]"
+```
+
+## Fast Tests
+
+Run the fast tests:
+
+```bash
+pytest -m "not slow"
+```
+
+These are generally lightweight skill validation tests, such as verifying skill frontmatter.
+
+## Integration Tests
+
+Run the integration tests:
+
+```bash
+pytest -m slow -s
+```
+
+These tests deterministically fill in the template project from `skills/udf-gen-test/templates/` with fixture implementations, then actually compile and run Spark tests and benchmark scripts locally.
+
+Thus they require JDK, Maven and Maven repository access, a GPU environment, and (for `cuda` tests) CMake and CUDA toolkit.

skills/pyproject.toml

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+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+[build-system]
+requires = ["setuptools>=61.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "aether-agent"
+version = "0.1.0"
+description = "Convert Spark UDFs into GPU implementations"
+authors = [
+    {name = "Rishi Chandra", email = "rishic@nvidia.com"}
+]
+readme = "README.md"
+requires-python = ">=3.10"
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "Operating System :: OS Independent",
+]
+
+[project.optional-dependencies]
+dev = [
+    "pytest==8.4.1",
+    "PyYAML==6.0.3",
+    "isort==6.0.1",
+    "black==25.1.0",
+    "ruff==0.12.8",
+]
+
+[tool.setuptools]
+packages = []
+
+[tool.pyright]
+typeCheckingMode = "standard"
+
+[tool.pytest.ini_options]
+markers = ["slow: integration tests"]

skills/tests/__init__.py

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+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""
+Tests for the skills templates.
+"""

skills/tests/fixtures.py

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+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""
+Source fixtures for the JVM template integration tests.
+"""
+
+from pathlib import Path
+
+
+def _read_resource(name: str) -> str:
+    return (Path(__file__).parent / "resources" / name).read_text(encoding="utf-8")
+
+
+# ---------------------------------------------------------------------------
+# UDF source code
+# ---------------------------------------------------------------------------
+
+CPU_UDF_NAME = "IntegerMultiplyBy2UDF"
+RAPIDS_UDF_NAME = "IntegerMultiplyBy2RapidsUDF"
+NATIVE_UDF_NAME = "IntegerMultiplyBy2NativeRapidsUDF"
+
+SCALA_UDF_SOURCE = _read_resource(f"{CPU_UDF_NAME}.scala")
+JAVA_UDF_SOURCE = _read_resource(f"{CPU_UDF_NAME}.java")
+SCALA_RAPIDS_UDF_SOURCE = _read_resource(f"{RAPIDS_UDF_NAME}.scala")
+JAVA_RAPIDS_UDF_SOURCE = _read_resource(f"{RAPIDS_UDF_NAME}.java")
+NATIVE_RAPIDS_UDF_SOURCE = _read_resource(f"{NATIVE_UDF_NAME}.java")
+SQL_SOURCE = _read_resource("integer_multiply_by_2.sql")
+JNI_SOURCE = _read_resource("IntegerMultiplyBy2Jni.cpp")
+CUDA_SOURCE = _read_resource("integer_multiply_by_2.cu")
+HEADER_SOURCE = _read_resource("integer_multiply_by_2.hpp")
+
+# ---------------------------------------------------------------------------
+# Unit test methods
+# ---------------------------------------------------------------------------
+
+CREATE_TEST_DATA = """\
+  def createTestData(spark: SparkSession): DataFrame = {
+    val schema = StructType(Seq(
+      StructField("id", IntegerType, nullable = false),
+      StructField("value", IntegerType, nullable = true)
+    ))
+    val testData = Seq(
+      Row(1, 123),
+      Row(2, 0),
+      Row(3, -5),
+      Row(4, null)
+    )
+    spark.createDataFrame(spark.sparkContext.parallelize(testData), schema)
+  }"""
+
+EXECUTE_UDF = """\
+  def executeUDF(spark: SparkSession, udfName: String, testDF: DataFrame): DataFrame = {
+    testDF.createOrReplaceTempView("test_table")
+    spark.sql(s"SELECT *, $udfName(value) AS result FROM test_table")
+  }"""
+
+ASSERT_UDF_RESULTS = """\
+  def assertUDFResults(resultDF: DataFrame, testDF: DataFrame): Unit = {
+    val results = resultDF.collect().sortBy(_.getAs[Int]("id"))
+    assert(results(0).getAs[Int]("result") === 246)
+    assert(results(1).getAs[Int]("result") === 0)
+    assert(results(2).getAs[Int]("result") === -10)
+    assert(results(3).isNullAt(results(3).fieldIndex("result")))
+  }"""
+
+_SCALA_REGISTER_CALL = "spark.udf.register({name}, new com.udf.{cls}())"
+_JAVA_REGISTER_CALL = "spark.udf.register({name}, new com.udf.{cls}(), org.apache.spark.sql.types.IntegerType)"
+
+
+_REGISTER_METHOD = """\
+  def {method}(spark: SparkSession, udfName: String): Unit = {{
+    {register_call}
+  }}"""
+
+
+SCALA_REGISTER_UDF = _REGISTER_METHOD.format(
+    method="registerUDF",
+    register_call=_SCALA_REGISTER_CALL.format(name="udfName", cls=CPU_UDF_NAME),
+)
+JAVA_REGISTER_UDF = _REGISTER_METHOD.format(
+    method="registerUDF",
+    register_call=_JAVA_REGISTER_CALL.format(name="udfName", cls=CPU_UDF_NAME),
+)
+
+SCALA_REGISTER_RAPIDS_UDF = _REGISTER_METHOD.format(
+    method="registerRapidsUDF",
+    register_call=_SCALA_REGISTER_CALL.format(name="udfName", cls=RAPIDS_UDF_NAME),
+)
+JAVA_REGISTER_RAPIDS_UDF = _REGISTER_METHOD.format(
+    method="registerRapidsUDF",
+    register_call=_JAVA_REGISTER_CALL.format(name="udfName", cls=RAPIDS_UDF_NAME),
+)
+NATIVE_REGISTER_RAPIDS_UDF = _REGISTER_METHOD.format(
+    method="registerRapidsUDF",
+    register_call=_JAVA_REGISTER_CALL.format(name="udfName", cls=NATIVE_UDF_NAME),
+)
+
+# ---------------------------------------------------------------------------
+# BenchUtils methods
+# ---------------------------------------------------------------------------
+
+BENCH_GENERATE = """\
+  def generateSyntheticData(
+      spark: SparkSession,
+      numRows: Long,
+      numPartitions: Int
+  ): DataFrame = {
+    val baseDF = spark.range(0, numRows, 1, numPartitions)
+    baseDF.select(
+      col("id"),
+      (rand() * 1000).cast(IntegerType).alias("value")
+    )
+  }"""
+
+
+_BENCH_EXECUTE_METHOD = """\
+  def {method}(spark: SparkSession, df: DataFrame): DataFrame = {{
+    df.createOrReplaceTempView("bench_table")
+    {register}
+    spark.sql("SELECT *, udf(value) AS result FROM bench_table")
+  }}"""
+
+
+BENCH_EXECUTE_SCALA_CPU = _BENCH_EXECUTE_METHOD.format(
+    method="executeCpu",
+    register=_SCALA_REGISTER_CALL.format(name='"udf"', cls=CPU_UDF_NAME),
+)
+BENCH_EXECUTE_JAVA_CPU = _BENCH_EXECUTE_METHOD.format(
+    method="executeCpu",
+    register=_JAVA_REGISTER_CALL.format(name='"udf"', cls=CPU_UDF_NAME),
+)
+BENCH_EXECUTE_SCALA_CUDF = _BENCH_EXECUTE_METHOD.format(
+    method="executeGpu",
+    register=_SCALA_REGISTER_CALL.format(name='"udf"', cls=RAPIDS_UDF_NAME),
+)
+BENCH_EXECUTE_JAVA_CUDF = _BENCH_EXECUTE_METHOD.format(
+    method="executeGpu",
+    register=_JAVA_REGISTER_CALL.format(name='"udf"', cls=RAPIDS_UDF_NAME),
+)
+BENCH_EXECUTE_CUDA = _BENCH_EXECUTE_METHOD.format(
+    method="executeGpu",
+    register=_JAVA_REGISTER_CALL.format(name='"udf"', cls=NATIVE_UDF_NAME),
+)
+
+BENCH_EXECUTE_SQL = """\
+  def executeGpu(spark: SparkSession, df: DataFrame): DataFrame = {
+    df.createOrReplaceTempView("bench_table")
+    val sqlSource = scala.io.Source.fromFile("src/main/resources/integer_multiply_by_2.sql")
+    val sqlContent = try sqlSource.mkString finally sqlSource.close()
+    val benchSql = sqlContent.replace("test_table", "bench_table")
+    spark.sql(benchSql)
+  }"""
+
+# ---------------------------------------------------------------------------
+# MicroBenchRunner methods
+# ---------------------------------------------------------------------------
+
+MICRO_PREPARE_CPU = """\
+  def prepareCpuData(
+      hostColumns: Array[HostColumnVector],
+      numRows: Int
+  ): Array[AnyRef] = {
+    val values = Array.tabulate(numRows) { i =>
+      if (hostColumns(1).isNull(i)) null
+      else Int.box(hostColumns(1).getInt(i))
+    }
+    Array[AnyRef](values)
+  }"""
+
+
+_MICRO_EXECUTE_CPU_METHOD = """\
+  def executeCpu(data: Array[AnyRef], numRows: Int): Unit = {{
+    val values = data(0).asInstanceOf[Array[Integer]]
+    val udf = new com.udf.{cls}()
+    var i = 0
+    while (i < numRows) {{
+      udf.{invoke}(values(i))
+      i += 1
+    }}
+  }}"""
+
+MICRO_EXECUTE_SCALA_CPU = _MICRO_EXECUTE_CPU_METHOD.format(
+    cls=CPU_UDF_NAME,
+    invoke="apply",
+)
+MICRO_EXECUTE_JAVA_CPU = _MICRO_EXECUTE_CPU_METHOD.format(
+    cls=CPU_UDF_NAME,
+    invoke="call",
+)
+
+
+_MICRO_EXECUTE_GPU_METHOD = """\
+  def executeGpu(table: Table, numRows: Int): Unit = {{
+    val udf = new com.udf.{cls}()
+    withResource(udf.evaluateColumnar(numRows, table.getColumn(1))) {{ _ => }}
+  }}"""
+
+MICRO_EXECUTE_CUDF = _MICRO_EXECUTE_GPU_METHOD.format(cls=RAPIDS_UDF_NAME)
+MICRO_EXECUTE_CUDA = _MICRO_EXECUTE_GPU_METHOD.format(cls=NATIVE_UDF_NAME)
+
+# ---------------------------------------------------------------------------
+# Native source paths
+# ---------------------------------------------------------------------------
+
+CMAKE_SOURCE_FILES = """\
+set(SOURCE_FILES
+  "src/IntegerMultiplyBy2Jni.cpp"
+  "src/integer_multiply_by_2.cu"
+)
+"""
+
+NATIVE_PLACEHOLDER_FILES = (
+    "src/main/java/com/udf/PlaceholderUDFNameNativeRapidsUDF.java",
+    "native/src/main/cpp/src/PlaceholderUDFNameJni.cpp",
+    "native/src/main/cpp/src/placeholder_udf_name.cu",
+    "native/src/main/cpp/src/placeholder_udf_name.hpp",
+)
+
+NATIVE_SOURCE_FILES = {
+    "native/src/main/cpp/src/IntegerMultiplyBy2Jni.cpp": JNI_SOURCE,
+    "native/src/main/cpp/src/integer_multiply_by_2.cu": CUDA_SOURCE,
+    "native/src/main/cpp/src/integer_multiply_by_2.hpp": HEADER_SOURCE,
+}

skills/tests/resources/IntegerMultiplyBy2Jni.cpp

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+/*
+ * SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#include "integer_multiply_by_2.hpp"
+
+#include <cudf/column/column.hpp>
+#include <cudf/column/column_view.hpp>
+#include <cudf/types.hpp>
+
+#include <jni.h>
+
+#include <memory>
+#include <string>
+
+namespace {
+
+constexpr char const* RUNTIME_ERROR_CLASS = "java/lang/RuntimeException";
+constexpr char const* ILLEGAL_ARG_CLASS = "java/lang/IllegalArgumentException";
+
+void throw_java_exception(JNIEnv* env, char const* class_name, char const* message)
+{
+  jclass ex_class = env->FindClass(class_name);
+  if (ex_class != nullptr) {
+    env->ThrowNew(ex_class, message);
+  }
+}
+
+}  // namespace
+
+extern "C" {
+
+JNIEXPORT jlong JNICALL
+Java_com_udf_IntegerMultiplyBy2NativeRapidsUDF_integerMultiplyBy2(JNIEnv* env,
+                                                                  jclass,
+                                                                  jlong input_view)
+{
+  try {
+    auto input = reinterpret_cast<cudf::column_view const*>(input_view);
+    if (input == nullptr) {
+      throw_java_exception(env, ILLEGAL_ARG_CLASS, "input column view is null");
+      return 0;
+    }
+    if (input->type().id() != cudf::type_id::INT32) {
+      throw_java_exception(env, ILLEGAL_ARG_CLASS, "input must be INT32");
+      return 0;
+    }
+
+    std::unique_ptr<cudf::column> result = integer_multiply_by_2(*input);
+    return reinterpret_cast<jlong>(result.release());
+  } catch (std::bad_alloc const& e) {
+    auto message = std::string("Unable to allocate native memory: ") + e.what();
+    throw_java_exception(env, RUNTIME_ERROR_CLASS, message.c_str());
+  } catch (std::invalid_argument const& e) {
+    throw_java_exception(env, ILLEGAL_ARG_CLASS, e.what());
+  } catch (std::exception const& e) {
+    throw_java_exception(env, RUNTIME_ERROR_CLASS, e.what());
+  }
+  return 0;
+}
+
+}

skills/tests/resources/IntegerMultiplyBy2NativeRapidsUDF.java

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+/*
+ * SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+package com.udf;
+
+import ai.rapids.cudf.ColumnVector;
+import com.nvidia.spark.RapidsUDF;
+import org.apache.hadoop.hive.ql.exec.UDF;
+import org.apache.spark.sql.api.java.UDF1;
+
+public class IntegerMultiplyBy2NativeRapidsUDF extends UDF
+        implements UDF1<Integer, Integer>, RapidsUDF {
+    @Override
+    public Integer call(Integer value) {
+        return value == null ? null : value * 2;
+    }
+
+    @Override
+    public ColumnVector evaluateColumnar(int numRows, ColumnVector... args) {
+        NativeUDFLoader.ensureLoaded();
+        return new ColumnVector(integerMultiplyBy2(args[0].getNativeView()));
+    }
+
+    private static native long integerMultiplyBy2(long inputView);
+}