1 files changed, 190 insertions, 0 deletions
diff --git a/testing/web-platform/tests/webnn/validation_tests/batch_normalization.https.any.js b/testing/web-platform/tests/webnn/validation_tests/batch_normalization.https.any.js
new file mode 100644
index 0000000000..25b542d34e
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/batch_normalization.https.any.js
@@ -0,0 +1,190 @@
+// META: title=validation tests for WebNN API batchNormalization operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+// META: timeout=long
+
+'use strict';
+
+let meanIndex = 0;
+let varianceIndex = 0;
+
+promise_test(async t => {
+  for (let dataType of allWebNNOperandDataTypes) {
+    const input = builder.input(`input${++inputIndex}`, {dataType, dimensions: dimensions2D});
+    const validAxisArray = getAxisArray(dimensions2D);
+    const invalidAxisArray = generateOutOfRangeValuesArray(unsignedLongType);
+    for (let axis of validAxisArray) {
+      let size = dimensions2D[axis];
+      const mean = builder.input(`mean${++meanIndex}`, {dataType, dimensions: [size]});
+      const variance = builder.input(`variance${++varianceIndex}`, {dataType, dimensions: [size]});
+      for (let invalidAxis of invalidAxisArray) {
+        assert_throws_js(TypeError, () => builder.batchNormalization(input, mean, variance, {axis: invalidAxis}));
+      }
+    }
+  }
+}, "[batchNormalization] TypeError is expected if options.axis is outside the 'unsigned long' value range");
+
+promise_test(async t => {
+  for (let dataType of allWebNNOperandDataTypes) {
+    const input = builder.input(`input${++inputIndex}`, {dataType, dimensions: dimensions2D});
+    const validAxisArray = getAxisArray(dimensions2D);
+    for (let axis of validAxisArray) {
+      let size = dimensions2D[axis];
+      const mean = builder.input(`mean${++meanIndex}`, {dataType, dimensions: [size]});
+      const variance = builder.input(`variance${++varianceIndex}`, {dataType, dimensions: [size]});
+      assert_throws_dom('DataError', () => builder.batchNormalization(input, mean, variance, {axis: getRank(dimensions2D)}));
+    }
+  }
+}, "[batchNormalization] DataError is expected if options.axis is 'unsigned long' and it's not in the range 0 to the rank of input, exclusive");
+
+promise_test(async t => {
+  for (let dataType of allWebNNOperandDataTypes) {
+    const input = builder.input(`input${++inputIndex}`, {dataType, dimensions: dimensions2D});
+    const validAxisArray = getAxisArray(dimensions2D);
+    for (let axis of validAxisArray) {
+      let size = dimensions2D[axis];
+      const mean = builder.input(`mean${++meanIndex}`, {dataType, dimensions: [size]});
+      const variance = builder.input(`variance${++varianceIndex}`, {dataType, dimensions: [size]});
+      for (let axis of notUnsignedLongAxisArray) {
+        assert_false(typeof axis === 'number' && Number.isInteger(axis), "[batchNormalization] options.axis should be of 'unsigned long'");
+        assert_throws_js(TypeError, () => builder.batchNormalization(input, mean, variance, {axis}));
+      }
+    }
+  }
+}, '[batchNormalization] TypeError is expected if options.axis is not an unsigned long interger');
+
+promise_test(async t => {
+  for (let dataType of allWebNNOperandDataTypes) {
+    const input = builder.input(`input${++inputIndex}`, {dataType, dimensions: dimensions2D});
+    const validAxisArray = getAxisArray(dimensions2D);
+    for (let axis of validAxisArray) {
+      const variance = builder.input(`variance${++varianceIndex}`, {dataType, dimensions: [dimensions2D[axis]]});
+      for (let dimensions of allWebNNDimensionsArray) {
+        if (dimensions.length !== 1) {
+          // set mean not be 1D tensor
+          const mean = builder.input(`mean${++meanIndex}`, {dataType, dimensions});
+          assert_throws_dom('DataError', () => builder.batchNormalization(input, mean, variance));
+        }
+      }
+    }
+  }
+}, "[batchNormalization] DataError is expected if the size of mean.dimensions is not 1");
+
+promise_test(async t => {
+  for (let dataType of allWebNNOperandDataTypes) {
+    const input = builder.input(`input${++inputIndex}`, {dataType, dimensions: dimensions2D});
+    const validAxisArray = getAxisArray(dimensions2D);
+    for (let axis of validAxisArray) {
+      let size = dimensions2D[axis];
+      const variance = builder.input(`variance${++varianceIndex}`, {dataType, dimensions: [size]});
+      for (let offset of adjustOffsetsArray) {
+        const adjustedSize = size + offset;
+        const mean = builder.input(`mean${++meanIndex}`, {dataType, dimensions: [adjustedSize]});
+        assert_throws_dom('DataError', () => builder.batchNormalization(input, mean, variance, {axis}));
+      }
+    }
+  }
+}, "[batchNormalization] DataError is expected if mean.dimensions[0] is not equal to input.dimensions[options.axis]");
+
+promise_test(async t => {
+  for (let dataType of allWebNNOperandDataTypes) {
+    const input = builder.input(`input${++inputIndex}`, {dataType, dimensions: dimensions2D});
+    const validAxisArray = getAxisArray(dimensions2D);
+    for (let axis of validAxisArray) {
+      const mean = builder.input(`mean${++meanIndex}`, {dataType, dimensions: [dimensions2D[axis]]});
+      for (let dimensions of allWebNNDimensionsArray) {
+        if (dimensions.length !== 1) {
+          // set variance not be 1D tensor
+          const variance = builder.input(`variance${++varianceIndex}`, {dataType, dimensions});
+          assert_throws_dom('DataError', () => builder.batchNormalization(input, mean, variance));
+        }
+      }
+    }
+  }
+}, "[batchNormalization] DataError is expected if the size of variance.dimensions is not 1");
+
+promise_test(async t => {
+  for (let dataType of allWebNNOperandDataTypes) {
+    const input = builder.input(`input${++inputIndex}`, {dataType, dimensions: dimensions2D});
+    const validAxisArray = getAxisArray(dimensions2D);
+    for (let axis of validAxisArray) {
+      let size = dimensions2D[axis];
+      const mean = builder.input(`mean${++meanIndex}`, {dataType, dimensions: [size]});
+      for (let offset of adjustOffsetsArray) {
+        const adjustedSize = size + offset;
+        const variance = builder.input(`variance${++varianceIndex}`, {dataType, dimensions: [adjustedSize]});
+        assert_throws_dom('DataError', () => builder.batchNormalization(input, mean, variance, {axis}));
+      }
+    }
+  }
+}, "[batchNormalization] DataError is expected if variance.dimensions[0] is not equal to input.dimensions[options.axis]");
+
+promise_test(async t => {
+  for (let dataType of allWebNNOperandDataTypes) {
+    const input = builder.input(`input${++inputIndex}`, {dataType, dimensions: dimensions2D});
+    const validAxisArray = getAxisArray(dimensions2D);
+    for (let axis of validAxisArray) {
+      const mean = builder.input(`mean${++meanIndex}`, {dataType, dimensions: [dimensions2D[axis]]});
+      const variance = builder.input(`variance${++varianceIndex}`, {dataType, dimensions: [dimensions2D[axis]]});
+      for (let dimensions of allWebNNDimensionsArray) {
+        if (dimensions.length !== 1) {
+          // set scale not be 1D tensor
+          const scale = builder.input('scale', {dataType, dimensions});
+          assert_throws_dom('DataError', () => builder.batchNormalization(input, mean, variance, {axis, scale}));
+        }
+      }
+    }
+  }
+}, "[batchNormalization] DataError is expected if the size of scale.dimensions is not 1");
+
+promise_test(async t => {
+  for (let dataType of allWebNNOperandDataTypes) {
+    const input = builder.input(`input${++inputIndex}`, {dataType, dimensions: dimensions2D});
+    const validAxisArray = getAxisArray(dimensions2D);
+    for (let axis of validAxisArray) {
+      let size = dimensions2D[axis];
+      const mean = builder.input(`mean${++meanIndex}`, {dataType, dimensions: [size]});
+      const variance = builder.input(`variance${++varianceIndex}`, {dataType, dimensions: [size]});
+      for (let offset of adjustOffsetsArray) {
+        const adjustedSize = size + offset;
+        const scale = builder.input('scale', {dataType, dimensions: [adjustedSize]});
+        assert_throws_dom('DataError', () => builder.batchNormalization(input, mean, variance, {axis, scale}));
+      }
+    }
+  }
+}, "[batchNormalization] DataError is expected if scale.dimensions[0] is not equal to input.dimensions[options.axis]");
+
+promise_test(async t => {
+  for (let dataType of allWebNNOperandDataTypes) {
+    const input = builder.input(`input${++inputIndex}`, {dataType, dimensions: dimensions2D});
+    const validAxisArray = getAxisArray(dimensions2D);
+    for (let axis of validAxisArray) {
+      const mean = builder.input(`mean${++meanIndex}`, {dataType, dimensions: [dimensions2D[axis]]});
+      const variance = builder.input(`variance${++varianceIndex}`, {dataType, dimensions: [dimensions2D[axis]]});
+      for (let dimensions of allWebNNDimensionsArray) {
+        if (dimensions.length !== 1) {
+          // set bias not be 1D tensor
+          const bias = builder.input('bias', {dataType, dimensions});
+          assert_throws_dom('DataError', () => builder.batchNormalization(input, mean, variance, {axis, bias}));
+        }
+      }
+    }
+  }
+}, "[batchNormalization] DataError is expected if the size of bias.dimensions is not 1");
+
+promise_test(async t => {
+  for (let dataType of allWebNNOperandDataTypes) {
+    const input = builder.input(`input${++inputIndex}`, {dataType, dimensions: dimensions2D});
+    const validAxisArray = getAxisArray(dimensions2D);
+    for (let axis of validAxisArray) {
+      let size = dimensions2D[axis];
+      const mean = builder.input(`mean${++meanIndex}`, {dataType, dimensions: [size]});
+      const variance = builder.input(`variance${++varianceIndex}`, {dataType, dimensions: [size]});
+      for (let offset of adjustOffsetsArray) {
+        const adjustedSize = size + offset;
+        const bias = builder.input('bias', {dataType, dimensions: [adjustedSize]});
+        assert_throws_dom('DataError', () => builder.batchNormalization(input, mean, variance, {axis, bias}));
+      }
+    }
+  }
+}, "[batchNormalization] DataError is expected if bias.dimensions[0] is not equal to input.dimensions[options.axis]");