[WebNN] Support MLOperandDataType to align with latest WebNN API Spec (…

…web-platform-tests#42419) * [WebNN] Support MLOperandDataType to align with latest WebNN API Spec * Duplicate "type" key with "dataType“ key
cookiecrook · Nov 14, 2023 · 205a73d · 205a73d
1 parent d0d3b96
commit 205a73d
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Showing 2 changed files with 12 additions and 12 deletions.
diff --git a/webnn/idlharness.https.any.js b/webnn/idlharness.https.any.js
@@ -41,8 +41,8 @@ idl_test(
       }
 
       self.builder = new MLGraphBuilder(self.context);
-      self.input = builder.input('input', {type: 'float32', dimensions: [1, 1, 5, 5]});
-      self.filter = builder.constant({type: 'float32', dimensions: [1, 1, 3, 3]}, new Float32Array(9).fill(1));
+      self.input = builder.input('input', {dataType: 'float32', dimensions: [1, 1, 5, 5]});
+      self.filter = builder.constant({dataType: 'float32', dimensions: [1, 1, 3, 3]}, new Float32Array(9).fill(1));
       self.relu = builder.relu();
       self.output = builder.conv2d(input, filter, {activation: relu, inputLayout: "nchw"});
 

diff --git a/webnn/resources/utils.js b/webnn/resources/utils.js
@@ -2,7 +2,7 @@
 
 const ExecutionArray = ['sync', 'async'];
 
-// https://webmachinelearning.github.io/webnn/#enumdef-mloperandtype
+// https://webmachinelearning.github.io/webnn/#enumdef-mloperanddatatype
 const TypedArrayDict = {
   float32: Float32Array,
   int32: Int32Array,
@@ -349,7 +349,7 @@ const getPrecisonTolerance = (operationName, metricType, resources) => {
  * @param {Number} value
  * @param {String} dataType - A data type string, like "float32", "float16",
  *     more types, please see:
- *     https://webmachinelearning.github.io/webnn/#enumdef-mloperandtype
+ *     https://webmachinelearning.github.io/webnn/#enumdef-mloperanddatatype
  * @return {Number} A 64-bit signed integer.
  */
 const getBitwise = (value, dataType) => {
@@ -375,7 +375,7 @@ const getBitwise = (value, dataType) => {
  * @param {Number} nulp - A BigInt value indicates acceptable ULP distance.
  * @param {String} dataType - A data type string, value: "float32",
  *     more types, please see:
- *     https://webmachinelearning.github.io/webnn/#enumdef-mloperandtype
+ *     https://webmachinelearning.github.io/webnn/#enumdef-mloperanddatatype
  * @param {String} description - Description of the condition being tested.
  */
 const assert_array_approx_equals_ulp = (actual, expected, nulp, dataType, description) => {
@@ -408,7 +408,7 @@ const assert_array_approx_equals_ulp = (actual, expected, nulp, dataType, descri
  * @param {Number} tolerance
  * @param {String} operandType  - An operand type string, value: "float32",
  *     more types, please see:
- *     https://webmachinelearning.github.io/webnn/#enumdef-mloperandtype
+ *     https://webmachinelearning.github.io/webnn/#enumdef-mloperanddatatype
  * @param {String} metricType - Value: 'ULP', 'ATOL'
  */
 const doAssert = (operationName, actual, expected, tolerance, operandType, metricType) => {
@@ -465,7 +465,7 @@ const checkResults = (operationName, namedOutputOperands, outputs, resources) =>
  */
 const createConstantOperand = (builder, resources) => {
   const bufferView = new TypedArrayDict[resources.type](resources.data);
-  return builder.constant({type: resources.type, dimensions: resources.shape}, bufferView);
+  return builder.constant({dataType: resources.type, type: resources.type, dimensions: resources.shape}, bufferView);
 };
 
 /**
@@ -478,7 +478,7 @@ const createConstantOperand = (builder, resources) => {
 const createSingleInputOperand = (builder, resources, inputOperandName) => {
   inputOperandName = inputOperandName ? inputOperandName : Object.keys(resources.inputs)[0];
   const inputResources = resources.inputs[inputOperandName];
-  return builder.input(inputOperandName, {type: inputResources.type, dimensions: inputResources.shape});
+  return builder.input(inputOperandName, {dataType: inputResources.type, type: inputResources.type, dimensions: inputResources.shape});
 };
 
 /**
@@ -525,7 +525,7 @@ const buildOperationWithSingleInput = (operationName, builder, resources) => {
  * @param {Object} resources - Resources used for building a graph
  * @returns {MLNamedOperands}
  */
-const buildOperationWithTwoInputs= (operationName, builder, resources) => {
+const buildOperationWithTwoInputs = (operationName, builder, resources) => {
   // For example: MLOperand matmul(MLOperand a, MLOperand b);
   const namedOutputOperand = {};
   const [inputOperandA, inputOperandB] = createMultiInputOperands(builder, resources);
@@ -561,7 +561,7 @@ const buildConcat = (operationName, builder, resources) => {
   const namedOutputOperand = {};
   const inputOperands = [];
   for (let input of resources.inputs) {
-    inputOperands.push(builder.input(input.name, {type: input.type, dimensions: input.shape}));
+    inputOperands.push(builder.input(input.name, {dataType: input.type, type: input.type, dimensions: input.shape}));
   }
   // invoke builder.concat()
   namedOutputOperand[resources.expected.name] = builder[operationName](inputOperands, resources.axis);
@@ -583,7 +583,7 @@ const buildConvTranspose2d = (operationName, builder, resources) => {
   return namedOutputOperand;
 };
 
-const buildConv2d= (operationName, builder, resources) => {
+const buildConv2d = (operationName, builder, resources) => {
   // MLOperand conv2d(MLOperand input, MLOperand filter, optional MLConv2dOptions options = {});
   const namedOutputOperand = {};
   const [inputOperand, filterOperand] = createMultiInputOperands(builder, resources);
@@ -598,7 +598,7 @@ const buildConv2d= (operationName, builder, resources) => {
   return namedOutputOperand;
 };
 
-const buildGemm= (operationName, builder, resources) => {
+const buildGemm = (operationName, builder, resources) => {
   // MLOperand gemm(MLOperand a, MLOperand b, optional MLGemmOptions options = {});
   const namedOutputOperand = {};
   const [inputOperandA, inputOperandB] = createMultiInputOperands(builder, resources);