Add cuBLAS code generation test.

The cuBLAS test suite is currently disabled (`isGPUAvailable` is set to
false). Otherwise, Travis CI will fail.

TODO:
- Implement `isGPUAvailable` to actually detect whether GPU codegen is
possible.
- Factor test utility methods into a common base class.
- Set up GPU CI.

src/test/scala/lantern/test_cublas.scala

@@ -0,0 +1,90 @@
+package lantern
+
+import org.scala_lang.virtualized.virtualize
+import org.scala_lang.virtualized.SourceContext
+
+import org.scalactic.source
+import org.scalatest.{FunSuite, Tag}
+
+import scala.collection.mutable.ArrayBuffer
+import scala.collection.{Seq => NSeq}
+import java.io.{File, PrintWriter}
+
+class CublasTest extends FunSuite {
+  // TODO: Edit this function to actually detect whether GPU codegen is possible.
+  // One idea: check for:
+  // - The existence of cuBLAS header files (<cuda_runtime.h>, "cublas_v2.h").
+  // - The existence of a GPU (perhaps run `nvidia-smi`).
+  def isGPUAvailable = false
+
+  testGPU("vector-vector-dot") {
+    val vvdot = new DslDriverCublas[String, Unit] with TensorExp {
+      backend = new BackendCublas
+
+      @virtualize
+      def snippet(x: Rep[String]): Rep[Unit] = {
+        val length = 2
+        val v1 = Tensor.fromData(NSeq(4), 1, 2, 3, 4)
+        val v2 = Tensor.fromData(NSeq(4), -1, -2, -3, -4)
+        val expected = Tensor.fromData(NSeq(1), -30)
+        Tensor.assertEqual(v1.dot(v2), expected)
+      }
+    }
+    runTest(vvdot)
+  }
+
+  testGPU("matrix-vector-dot") {
+    val mvdot = new DslDriverCublas[String, Unit] with TensorExp {
+      backend = new BackendCublas
+
+      @virtualize
+      def snippet(x: Rep[String]): Rep[Unit] = {
+        val m = Tensor.fromData(NSeq(2, 4), 1, 2, 3, 4, 5, 6, 7, 8)
+        val v = Tensor.fromData(NSeq(4), -1, -2, -3, -4)
+        val expected = Tensor.fromData(NSeq(2), -30, -70)
+        Tensor.assertEqual(m.dot(v), expected)
+      }
+    }
+    runTest(mvdot, debug = true)
+  }
+
+  testGPU("matrix-matrix-dot") {
+    val mmdot = new DslDriverCublas[String, Unit] with TensorExp {
+      backend = new BackendCublas
+
+      @virtualize
+      def snippet(x: Rep[String]): Rep[Unit] = {
+        // Note: it's better to test with non-square matrices.
+        val m1 = Tensor.fromData(NSeq(2, 3), 1, 2, 3, 4, 5, 6)
+        val m2 = Tensor.fromData(NSeq(3, 2), 2, 3, 4, 5, 6, 7)
+        val expected = Tensor.fromData(NSeq(2, 2), 28, 34, 64, 79)
+        Tensor.assertEqual(m1.dot(m2), expected)
+      }
+    }
+    runTest(mmdot, debug = true)
+  }
+
+  // Utility function wrapping `test` that checks whether GPU is available.
+  def testGPU(testName: String, testTags: Tag*)(testFun: => Any /* Assertion */)(implicit pos: source.Position) {
+    if (isGPUAvailable)
+      test(testName, testTags: _*)(testFun)(pos)
+    else
+      ignore(testName, testTags: _*)(testFun)(pos)
+  }
+
+  // TODO: Refactor `runTest` into a "LanternTestSuite" base class.
+  def runTest(snippet: DslDriverCublas[String, Unit], debug: Boolean = false) {
+    val test = new PrintWriter(new File("/tmp/snippet.cpp"))
+    if (debug) {
+      System.err.println(snippet.code)
+    }
+    test.println(snippet.code)
+    test.flush()
+    new java.io.File("/tmp/snippet").delete
+    import scala.sys.process._
+    System.out.println("Compile C++ code")
+    (s"g++ -std=c++11 -O1 /tmp/snippet.cpp -o /tmp/snippet": ProcessBuilder).lines.foreach(System.out.println)
+    System.out.println("Run C++ code")
+    (s"/tmp/snippet a": ProcessBuilder).lines.foreach(System.out.println)
+  }
+}