Reland "webnn: Remove sync methods"

This is a reland of commit a3e84dde09e099bebbe4793ffaa70679168130d6

Original change's description:
> webnn: Remove sync methods
>
> See webmachinelearning/webnn#548
>
> Deletes all sync methods and simplifies the names of all "Async"
> methods - e.g. BuildAsync() -> Build()
>
> Bug: 40283536, 41481333
> Change-Id: I3b38d987cb4641ea41ab9a974c46dcba16d5c108
> Reviewed-on: https://chromium-review.googlesource.com/c/chromium/src/+/5292884
> Commit-Queue: Austin Sullivan <[email protected]>
> Reviewed-by: Reilly Grant <[email protected]>
> Reviewed-by: ningxin hu <[email protected]>
> Reviewed-by: Alex Gough <[email protected]>
> Cr-Commit-Position: refs/heads/main@{#1261223}

Bug: 40283536, 41481333
Change-Id: Iffcb922b8175a7181deba1f4c2faa953010ae5c0

Author: a-sully

webnn/idlharness.https.any.js

@@ -28,29 +28,18 @@ idl_test(
       MLGraph: ['graph']
     });
 
-    for (const executionType of ExecutionArray) {
-      const isSync = executionType === 'sync';
-      if (self.GLOBAL.isWindow() && isSync) {
-        continue;
-      }
-
-      if (isSync) {
-        self.context = navigator.ml.createContextSync();
-      } else {
-        self.context = await navigator.ml.createContext();
-      }
-
-      self.builder = new MLGraphBuilder(self.context);
-      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"});
-
-      if (isSync) {
-        self.graph = builder.buildSync({output});
-      } else {
-        self.graph = await builder.build({output});
-      }
-    }
+    self.context = await navigator.ml.createContext();
+
+    self.builder = new MLGraphBuilder(self.context);
+    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"});
+
+    self.graph = await builder.build({output});
   }
 );

webnn/resources/utils.js

@@ -1,7 +1,5 @@
 'use strict';
 
-const ExecutionArray = ['sync', 'async'];
-
 // https://webmachinelearning.github.io/webnn/#enumdef-mloperanddatatype
 const TypedArrayDict = {
   // workaround use Uint16 for Float16
@@ -793,25 +791,7 @@ const buildGraph = (operationName, builder, resources, buildFunc) => {
 };
 
 /**
- * Build a graph, synchronously compile graph and execute, then check computed results.
- * @param {String} operationName - An operation name
- * @param {MLContext} context - A ML context
- * @param {MLGraphBuilder} builder - A ML graph builder
- * @param {Object} resources - Resources used for building a graph
- * @param {Function} buildFunc - A build function for an operation
- */
-const runSync = (operationName, context, builder, resources, buildFunc) => {
-  // build a graph
-  const [namedOutputOperands, inputs, outputs] = buildGraph(operationName, builder, resources, buildFunc);
-  // synchronously compile the graph up to the output operand
-  const graph = builder.buildSync(namedOutputOperands);
-  // synchronously execute the compiled graph.
-  context.computeSync(graph, inputs, outputs);
-  checkResults(operationName, namedOutputOperands, outputs, resources);
-};
-
-/**
- * Build a graph, asynchronously compile graph and execute, then check computed results.
+ * Build a graph, compile graph and execute, then check computed results.
  * @param {String} operationName - An operation name
  * @param {MLContext} context - A ML context
  * @param {MLGraphBuilder} builder - A ML graph builder
@@ -821,9 +801,9 @@ const runSync = (operationName, context, builder, resources, buildFunc) => {
 const run = async (operationName, context, builder, resources, buildFunc) => {
   // build a graph
   const [namedOutputOperands, inputs, outputs] = buildGraph(operationName, builder, resources, buildFunc);
-  // asynchronously compile the graph up to the output operand
+  // compile the graph up to the output operand
   const graph = await builder.build(namedOutputOperands);
-  // asynchronously execute the compiled graph
+  // execute the compiled graph
   const result = await context.compute(graph, inputs, outputs);
   checkResults(operationName, namedOutputOperands, result.outputs, resources);
 };
@@ -842,41 +822,18 @@ const testWebNNOperation = (operationName, buildFunc, deviceType = 'cpu') => {
     operationNameArray = operationName;
   }
 
-  ExecutionArray.forEach(executionType => {
-    const isSync = executionType === 'sync';
-    if (self.GLOBAL.isWindow() && isSync) {
-      return;
-    }
-    let context;
-    let builder;
-    if (isSync) {
-      // test sync
-      operationNameArray.forEach((subOperationName) => {
-        const tests = loadTests(subOperationName);
-        setup(() => {
-          context = navigator.ml.createContextSync({deviceType});
-          builder = new MLGraphBuilder(context);
-        });
-        for (const subTest of tests) {
-          test(() => {
-            runSync(subOperationName, context, builder, subTest, buildFunc);
-          }, `${subTest.name} / ${executionType}`);
-        }
-      });
-    } else {
-      // test async
-      operationNameArray.forEach((subOperationName) => {
-        const tests = loadTests(subOperationName);
-        promise_setup(async () => {
-          context = await navigator.ml.createContext({deviceType});
-          builder = new MLGraphBuilder(context);
-        });
-        for (const subTest of tests) {
-          promise_test(async () => {
-            await run(subOperationName, context, builder, subTest, buildFunc);
-          }, `${subTest.name} / ${executionType}`);
-        }
-      });
+  let context;
+  let builder;
+  operationNameArray.forEach((subOperationName) => {
+    const tests = loadTests(subOperationName);
+    promise_setup(async () => {
+      context = await navigator.ml.createContext({deviceType});
+      builder = new MLGraphBuilder(context);
+    });
+    for (const subTest of tests) {
+      promise_test(async () => {
+        await run(subOperationName, context, builder, subTest, buildFunc);
+      }, `${subTest.name}`);
     }
   });
 };