fix: Support to Bool input for Onnx models (#2130)

Signed-off-by: Jason Wang <[email protected]>
microsoft · Nov 14, 2023 · 9195dee · 9195dee
1 parent 4c4fc8a
commit 9195dee
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Showing 2 changed files with 35 additions and 2 deletions.
diff --git a/deep-learning/src/main/scala/com/microsoft/azure/synapse/ml/onnx/ONNXUtils.scala b/deep-learning/src/main/scala/com/microsoft/azure/synapse/ml/onnx/ONNXUtils.scala
@@ -150,6 +150,7 @@ object ONNXUtils {
     inferredShapes.length.toLong +: inferredShapes.head
   }
 
+  // scalastyle:off cyclomatic.complexity
   private[onnx] def loadTensorBuffer(env: OrtEnvironment,
                                      tensorInfo: TensorInfo,
                                      batchedValues: Seq[_],
@@ -168,8 +169,15 @@ object ONNXUtils {
         assertBufferElementsWritten(size, actualCount, shape)
         buffer.rewind()
         OnnxTensor.createTensor(env, buffer, shape)
+      case OnnxJavaType.BOOL =>
+        val buffer = ByteBuffer.allocateDirect(size)
+        val bool2byte: Boolean => Byte = b => if (b) 1.toByte else 0.toByte
+        val actualCount = writeNestedSeqToBuffer[Boolean](batchedValues, (bool2byte andThen buffer.put)(_))
+        assertBufferElementsWritten(size, actualCount, shape)
+        buffer.rewind()
+        OnnxTensor.createTensor(env, buffer, shape, OnnxJavaType.BOOL)
       case OnnxJavaType.INT8 =>
-        val buffer = ByteBuffer.allocate(size)
+        val buffer = ByteBuffer.allocateDirect(size)
         val actualCount = writeNestedSeqToBuffer[Byte](batchedValues, buffer.put(_))
         assertBufferElementsWritten(size, actualCount, shape)
         buffer.rewind()
@@ -197,7 +205,7 @@ object ONNXUtils {
         OnnxTensor.createTensor(env, flattened, shape)
       case other =>
         throw new NotImplementedError(s"Tensor input type $other not supported. " +
-          s"Only FLOAT, DOUBLE, INT8, INT16, INT32, INT64, STRING types are supported.")
+          s"Only FLOAT, DOUBLE, BOOL, INT8, INT16, INT32, INT64, STRING types are supported.")
     }
   }
 

diff --git a/deep-learning/src/test/scala/com/microsoft/azure/synapse/ml/onnx/ONNXModelSuite.scala b/deep-learning/src/test/scala/com/microsoft/azure/synapse/ml/onnx/ONNXModelSuite.scala
@@ -38,6 +38,7 @@ class ONNXModelSuite extends TestBase
     new TestObject(onnxMNIST, testDfMNIST),
     new TestObject(onnxAdultsIncome, testDfAdultsIncome),
     new TestObject(onnxGH1902, testDfGH1902),
+    new TestObject(onnxGH1996, testDfGH1996),
     new TestObject(onnxResNet50, testDfResNet50)
   )
 
@@ -285,6 +286,17 @@ class ONNXModelSuite extends TestBase
       .setMiniBatchSize(5000)
   }
 
+  private lazy val onnxGH1996 = {
+    spark
+    val model = downloadModel("GH1996.onnx", baseUrl)
+    new ONNXModel()
+      .setModelLocation(model.getPath)
+      .setDeviceType("CPU")
+      .setFeedDict(Map("A" -> "i1", "B" -> "i2"))
+      .setFetchDict(Map("Output" -> "Y"))
+      .setMiniBatchSize(5)
+  }
+
   private lazy val testDfGH1902 = {
     val testDf = Seq(
       (39L, " State-gov", 77516L, " Bachelors", 13L, " Never-married", " Adm-clerical",
@@ -296,6 +308,8 @@ class ONNXModelSuite extends TestBase
     testDf
   }
 
+  private lazy val testDfGH1996 = Seq((true, true), (true, false), (false, false)).toDF("i1", "i2")
+
   test("ONNXModel can run transform for issue 1902") {
     val Array(row1, row2) = onnxGH1902.transform(testDfGH1902)
       .select("probability", "prediction")
@@ -310,6 +324,17 @@ class ONNXModelSuite extends TestBase
     assert(row2._2 === 1.0)
   }
 
+  test("ONNXModel can run transform on boolean type (GH1996)") {
+    val Array(row1, row2, row3) = onnxGH1996.transform(testDfGH1996)
+      .orderBy(col("i1"), col("i2"), col("Output"))
+      .as[(Boolean, Boolean, Boolean)]
+      .collect()
+
+    assert(row1 === (false, false, false))
+    assert(row2 === (true, false, false))
+    assert(row3 === (true, true, true))
+  }
+
   test("ONNXModel can translate zipmap output properly") {
     val Array(row1, row2) = onnxAdultsIncome.transform(testDfAdultsIncome)
       .select("probability", "prediction")