webnn: Add WPTs asserting input tensors are not modified

The TFLite and CoreML backends explicitly assume that tensors passed as
an input to graph execution are not modified by the underlying
framework. We should specify this behavior

Bug: 366130770
Change-Id: I326975b05ca1163a3cba6a90e81ee22982252a65
Cq-Include-Trybots: luci.chromium.try:win11-blink-rel,mac14-blink-rel,mac14.arm64-blink-rel
Reviewed-on: https://chromium-review.googlesource.com/c/chromium/src/+/5848368
Reviewed-by: Weizhong Xia <[email protected]>
Reviewed-by: ningxin hu <[email protected]>
Commit-Queue: Austin Sullivan <[email protected]>
Cr-Commit-Position: refs/heads/main@{#1354900}

webnn/conformance_tests/inputs-are-not-modified.https.any.js

@@ -0,0 +1,86 @@
+// META: title=test that input tensors are not modified during a call to dispatch()
+// META: global=window,dedicatedworker
+// META: variant=?cpu
+// META: variant=?gpu
+// META: variant=?npu
+// META: script=../resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://www.w3.org/TR/webnn/#api-mlcontext-dispatch
+
+let mlContext;
+
+// Skip tests if WebNN is unimplemented.
+promise_setup(async () => {
+  assert_implements(navigator.ml, 'missing navigator.ml');
+  mlContext = await navigator.ml.createContext(contextOptions);
+});
+
+promise_test(async () => {
+  const builder = new MLGraphBuilder(mlContext);
+  const inputOperand =
+      builder.input('input', {dataType: 'float32', dimensions: [4]});
+  const hardSwishOperand = builder.hardSwish(inputOperand);
+  // Add some other operator for the output tensor to bind to; otherwise there
+  // is no reason to implement hardSwish "in-place".
+  const outputOperand = builder.identity(hardSwishOperand);
+
+  const [inputTensor, outputTensor, mlGraph] = await Promise.all([
+    mlContext.createTensor({
+      dataType: 'float32',
+      dimensions: [4],
+      usage: MLTensorUsage.WRITE_TO | MLTensorUsage.READ_FROM
+    }),
+    mlContext.createTensor(
+        {dataType: 'float32', dimensions: [4], usage: MLTensorUsage.READ_FROM}),
+    builder.build({'output': outputOperand})
+  ]);
+
+  const inputData = Float32Array.from([-4, -1, 1, 4]);
+  mlContext.writeTensor(inputTensor, inputData);
+
+  mlContext.dispatch(mlGraph, {'input': inputTensor}, {'output': outputTensor});
+
+  // Wait for graph execution to complete.
+  await mlContext.readTensor(outputTensor);
+
+  // The input tensor should not be modified.
+  assert_array_equals(
+      new Float32Array(await mlContext.readTensor(inputTensor)), inputData);
+}, 'input tensor is not modified: hardSwish');
+
+promise_test(async () => {
+  const builder = new MLGraphBuilder(mlContext);
+  const inputOperand =
+      builder.input('input', {dataType: 'float32', dimensions: [4]});
+  const constantOperand = builder.constant(
+      {dataType: 'float32', dimensions: [4]}, Float32Array.from([-2, 0, 3, 4]));
+  const mulOperand = builder.mul(inputOperand, constantOperand);
+  // Add some other operator for the output tensor to bind to; otherwise there
+  // is no reason to implement mul "in-place".
+  const outputOperand = builder.add(mulOperand, constantOperand);
+
+  const [inputTensor, outputTensor, mlGraph] = await Promise.all([
+    mlContext.createTensor({
+      dataType: 'float32',
+      dimensions: [4],
+      usage: MLTensorUsage.WRITE_TO | MLTensorUsage.READ_FROM
+    }),
+    mlContext.createTensor(
+        {dataType: 'float32', dimensions: [4], usage: MLTensorUsage.READ_FROM}),
+    builder.build({'output': outputOperand})
+  ]);
+
+  const inputData = Float32Array.from([1, 2, 3, 4]);
+  mlContext.writeTensor(inputTensor, inputData);
+  mlContext.dispatch(mlGraph, {'input': inputTensor}, {'output': outputTensor});
+
+  // Wait for graph execution to complete.
+  await mlContext.readTensor(outputTensor);
+
+  // The input tensor should not be modified.
+  assert_array_equals(
+      new Float32Array(await mlContext.readTensor(inputTensor)), inputData);
+}, 'input tensor is not modified: mul');