Merging upstream version 126.0.

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

48 files changed, 3255 insertions, 542 deletions

diff --git a/testing/web-platform/tests/webnn/validation_tests/argMinMax.https.any.js b/testing/web-platform/tests/webnn/validation_tests/argMinMax.https.any.js
new file mode 100644
index 0000000000..6e8d558306
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/argMinMax.https.any.js
@@ -0,0 +1,15 @@
+// META: title=validation tests for WebNN API argMin/Max operations
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+const kArgMinMaxOperators = [
+  'argMin',
+  'argMax',
+];
+
+kArgMinMaxOperators.forEach((operatorName) => {
+  validateOptionsAxes(operatorName);
+  validateInputFromAnotherBuilder(operatorName);
+});
diff --git a/testing/web-platform/tests/webnn/validation_tests/arg_min_max.https.any.js b/testing/web-platform/tests/webnn/validation_tests/arg_min_max.https.any.js
deleted file mode 100644
index 700be83b04..0000000000
--- a/testing/web-platform/tests/webnn/validation_tests/arg_min_max.https.any.js
+++ /dev/null
@@ -1,8 +0,0 @@
-// META: title=validation tests for WebNN API argMin/Max operations
-// META: global=window,dedicatedworker
-// META: script=../resources/utils_validation.js
-// META: timeout=long
-
-'use strict';
-
-validateOptionsAxes(['argMin', 'argMax']);
diff --git a/testing/web-platform/tests/webnn/validation_tests/batch_normalization.https.any.js b/testing/web-platform/tests/webnn/validation_tests/batchNormalization.https.any.js
index 25b542d34e..cafd9f9cd5 100644
--- a/testing/web-platform/tests/webnn/validation_tests/batch_normalization.https.any.js
+++ b/testing/web-platform/tests/webnn/validation_tests/batchNormalization.https.any.js
@@ -1,13 +1,23 @@
 // 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;
 
+const kExampleInputDescriptor = {
+  dataType: 'float32',
+  dimensions: [2, 2]
+};
+// 1D tensor descriptor which may be used for `mean`, `variance`, `scale`, or
+// `bias` inputs.
+const kExample1DTensorDescriptor = {
+  dataType: 'float32',
+  dimensions: [kExampleInputDescriptor.dimensions[/* axis */ 1]]
+};
+
 promise_test(async t => {
   for (let dataType of allWebNNOperandDataTypes) {
     const input = builder.input(`input${++inputIndex}`, {dataType, dimensions: dimensions2D});
@@ -188,3 +198,73 @@ promise_test(async t => {
     }
   }
 }, "[batchNormalization] DataError is expected if bias.dimensions[0] is not equal to input.dimensions[options.axis]");
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const inputFromOtherBuilder =
+      otherBuilder.input('input', kExampleInputDescriptor);
+
+  const mean = builder.input('mean', kExample1DTensorDescriptor);
+  const variance = builder.input('variance', kExample1DTensorDescriptor);
+  assert_throws_js(
+      TypeError, () => builder.batchNormalization(inputFromOtherBuilder, mean, variance));
+}, '[batchNormalization] throw if input is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const meanFromOtherBuilder =
+      otherBuilder.input('mean', kExample1DTensorDescriptor);
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const variance = builder.input('variance', kExample1DTensorDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.batchNormalization(input, meanFromOtherBuilder, variance));
+}, '[batchNormalization] throw if mean is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const varianceFromOtherBuilder =
+      otherBuilder.input('variance', kExample1DTensorDescriptor);
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const mean = builder.input('mean', kExample1DTensorDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.batchNormalization(input, mean, varianceFromOtherBuilder));
+}, '[batchNormalization] throw if variance is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const scaleFromOtherBuilder =
+      otherBuilder.input('scale', kExample1DTensorDescriptor);
+  const options = {scale: scaleFromOtherBuilder};
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const mean = builder.input('mean', kExample1DTensorDescriptor);
+  const variance = builder.input('variance', kExample1DTensorDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.batchNormalization(input, mean, variance, options));
+}, '[batchNormalization] throw if scale option is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const biasFromOtherBuilder =
+      otherBuilder.input('bias', kExample1DTensorDescriptor);
+  const options = {scale: biasFromOtherBuilder};
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const mean = builder.input('mean', kExample1DTensorDescriptor);
+  const variance = builder.input('variance', kExample1DTensorDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.batchNormalization(input, mean, variance, options));
+}, '[batchNormalization] throw if bias option is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const activationFromOtherBuilder = otherBuilder.clamp();
+  const options = {activation: activationFromOtherBuilder};
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const mean = builder.input('mean', kExample1DTensorDescriptor);
+  const variance = builder.input('variance', kExample1DTensorDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.batchNormalization(input, mean, variance, options));
+}, '[batchNormalization] throw if activation option is from another builder');
diff --git a/testing/web-platform/tests/webnn/validation_tests/cast.https.any.js b/testing/web-platform/tests/webnn/validation_tests/cast.https.any.js
new file mode 100644
index 0000000000..f616203a88
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/cast.https.any.js
@@ -0,0 +1,13 @@
+// META: title=validation tests for WebNN API cast operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const inputFromOtherBuilder =
+      otherBuilder.input('input', {dataType: 'int32', dimensions: [2, 2]});
+
+  assert_throws_js(
+      TypeError, () => builder.cast(inputFromOtherBuilder, 'int64'));
+}, '[cast] throw if input is from another builder');
diff --git a/testing/web-platform/tests/webnn/validation_tests/clamp.https.any.js b/testing/web-platform/tests/webnn/validation_tests/clamp.https.any.js
new file mode 100644
index 0000000000..85cd19a566
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/clamp.https.any.js
@@ -0,0 +1,7 @@
+// META: title=validation tests for WebNN API clamp operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+validateInputFromAnotherBuilder('clamp');
diff --git a/testing/web-platform/tests/webnn/validation_tests/compute-multiple-arraybufferviews-sharing-same-arraybuffer.https.any.js b/testing/web-platform/tests/webnn/validation_tests/compute-multiple-arraybufferviews-sharing-same-arraybuffer.https.any.js
new file mode 100644
index 0000000000..42b123a97e
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/compute-multiple-arraybufferviews-sharing-same-arraybuffer.https.any.js
@@ -0,0 +1,50 @@
+// META: title=ensure WebNN MLContext.compute() rejecting detached buffers
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+// These tests are used to reproduce the Chromium issue:
+// https://issues.chromium.org/issues/332002364
+promise_test(async t => {
+    const a = builder.input('a', {dataType: 'float32', dimensions: [2]});
+    const b = builder.input('b', {dataType: 'float32', dimensions: [2]});
+    const c = builder.add(a, b);
+    const graph = await builder.build({c});
+    const arraybuffer = new ArrayBuffer(100);
+    const aBuffer = new Float32Array(arraybuffer, 0, 2);
+    const bBuffer = new Float32Array(arraybuffer, 8, 2);
+    const cBuffer = new Float32Array(2);
+    const promise = context.compute(graph, {'a': aBuffer, 'b': bBuffer}, {'c': cBuffer});
+    promise_rejects_js(t, TypeError, promise);
+  }, 'Throw if two input ArrayBufferViews sharing the same ArrayBuffer');
+
+promise_test(async t => {
+    const a = builder.input('a', {dataType: 'float32', dimensions: [2]});
+    const [b, c] = builder.split(a, 2);
+    const graph = await builder.build({b, c});
+    const aBuffer = new Float32Array(2);
+    const arraybuffer = new ArrayBuffer(100);
+    const bBuffer = new Float32Array(arraybuffer, 0, 1);
+    const cBuffer = new Float32Array(arraybuffer, 4, 1);
+    const promise = context.compute(graph, {'a': aBuffer}, {'b': bBuffer, 'c': cBuffer});
+    promise_rejects_js(t, TypeError, promise);
+}, 'Throw if two output ArrayBufferViews sharing the same ArrayBuffer');
+
+promise_test(async t => {
+    const a = builder.input('a', {dataType: 'float32', dimensions: [2]});
+    const b = builder.relu(a);
+    const graph = await builder.build({b});
+    const arraybuffer = new ArrayBuffer(100);
+    const aBuffer = new Float32Array(arraybuffer, 0, 2);
+    const bBuffer = new Float32Array(arraybuffer, 8, 2);
+    const promise = context.compute(graph, {'a': aBuffer}, {'b': bBuffer});
+    promise_rejects_js(t, TypeError, promise);
+}, 'Throw if input and output ArrayBufferViews sharing the same ArrayBuffer');
+
+promise_test(async t => {
+    const a = builder.input('a', {dataType: 'float32', dimensions: [2]});
+    const b = builder.relu(a);
+    const graph = await builder.build({b});
+    const buffer = new Float32Array(2);
+    const promise = context.compute(graph, {'a': buffer}, {'b': buffer});
+    promise_rejects_js(t, TypeError, promise);
+}, 'Throw if input and output are the same ArrayBufferView');
diff --git a/testing/web-platform/tests/webnn/validation_tests/concat.https.any.js b/testing/web-platform/tests/webnn/validation_tests/concat.https.any.js
new file mode 100644
index 0000000000..b61f2d2bc7
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/concat.https.any.js
@@ -0,0 +1,106 @@
+// META: title=validation tests for WebNN API concat operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+const tests = [
+  {
+    name: '[concat] Test building Concat with one input.',
+    inputs: [{dataType: 'float32', dimensions: [4,4,3]}],
+    axis: 2,
+    output: {dataType: 'float32', dimensions: [4,4,3]}
+  },
+  {
+    name: '[concat] Test building Concat with two inputs',
+    inputs: [{dataType: 'float32', dimensions: [3,1,5]},
+             {dataType: 'float32', dimensions: [3,2,5]}],
+    axis: 1,
+    output: {dataType: 'float32', dimensions: [3,3,5]}
+  },
+  {
+    name: '[concat] Test building Concat with three inputs',
+    inputs: [{dataType: 'float32', dimensions: [3,5,1]},
+             {dataType: 'float32', dimensions: [3,5,2]},
+             {dataType: 'float32', dimensions: [3,5,3]}],
+    axis: 2,
+    output: {dataType: 'float32', dimensions: [3,5,6]}
+  },
+  {
+    name: '[concat] Test building Concat with two 1D inputs.',
+    inputs: [{dataType: 'float32', dimensions: [1]},
+             {dataType: 'float32', dimensions: [1]}],
+    axis: 0,
+    output: {dataType: 'float32', dimensions: [2]}
+  },
+  {
+    name: '[concat] Throw if the inputs are empty.',
+    axis: 0,
+  },
+  {
+    name: '[concat] Throw if the argument types are inconsistent.',
+    inputs: [{dataType: 'float32', dimensions: [1,1]},
+             {dataType: 'int32', dimensions: [1,1]}],
+    axis: 0,
+  },
+  {
+    name: '[concat] Throw if the inputs have different ranks.',
+    inputs: [{dataType: 'float32', dimensions: [1,1]},
+             {dataType: 'float32', dimensions: [1,1,1]}],
+    axis: 0,
+  },
+  {
+    name: '[concat] Throw if the axis is equal to or greater than the size of ranks',
+    inputs: [{dataType: 'float32', dimensions: [1,1]},
+             {dataType: 'float32', dimensions: [1,1]}],
+    axis: 2,
+  },
+  {
+    name: '[concat] Throw if concat with two 0-D scalars.',
+    inputs: [{dataType: 'float32', dimensions: []},
+             {dataType: 'float32', dimensions: []}],
+    axis: 0,
+  },
+  {
+    name: '[concat] Throw if the inputs have other axes with different sizes except on the axis.',
+    inputs: [{dataType: 'float32', dimensions: [1,1,1]},
+             {dataType: 'float32', dimensions: [1,2,3]}],
+    axis: 1,
+  },
+
+];
+
+tests.forEach(test =>
+    promise_test(async t => {
+      let inputs = [];
+      if (test.inputs) {
+        for (let i = 0; i < test.inputs.length; ++i) {
+          inputs[i] = builder.input(
+            `inputs[${i}]`,
+            { dataType: test.inputs[i].dataType, dimensions: test.inputs[i].dimensions }
+          );
+        }
+      }
+      if (test.output) {
+        const output = builder.concat(inputs, test.axis);
+        assert_equals(output.dataType(), test.output.dataType);
+        assert_array_equals(output.shape(), test.output.dimensions);
+      } else {
+        assert_throws_js(TypeError, () => builder.concat(inputs, test.axis));
+      }
+    }, test.name)
+  );
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const operandDescriptor = {dataType: 'float32', dimensions: [2, 2]};
+
+  const inputFromOtherBuilder = otherBuilder.input('input', operandDescriptor);
+
+  const input1 = builder.input('input', operandDescriptor);
+  const input2 = builder.input('input', operandDescriptor);
+  const input3 = builder.input('input', operandDescriptor);
+
+  assert_throws_js(
+      TypeError,
+      () => builder.concat([input1, input2, inputFromOtherBuilder, input3]));
+}, '[concat] throw if any input is from another builder');
diff --git a/testing/web-platform/tests/webnn/validation_tests/constant.https.any.js b/testing/web-platform/tests/webnn/validation_tests/constant.https.any.js
new file mode 100644
index 0000000000..86a60ee209
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/constant.https.any.js
@@ -0,0 +1,141 @@
+// META: title=validation tests for WebNN API constant interface
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+const tests = [
+  // Tests for constant(descriptor, bufferView)
+  {
+    name:
+        '[constant] Test building a 0-D scalar constant without presenting dimensions',
+    descriptor: {dataType: 'float32'},
+    bufferView: {type: Float32Array, byteLength: 1 * 4},
+    output: {dataType: 'float32', dimensions: []}
+  },
+  {
+    name:
+        '[constant] Test building a 0-D scalar constant with empty dimensions',
+    descriptor: {dataType: 'float32', dimensions: []},
+    bufferView: {type: Float32Array, byteLength: 1 * 4},
+    output: {dataType: 'float32', dimensions: []}
+  },
+  {
+    name: '[constant] Test building a constant with float32 data type',
+    descriptor: {dataType: 'float32', dimensions: [2, 3]},
+    bufferView: {type: Float32Array, byteLength: 6 * 4},
+    output: {dataType: 'float32', dimensions: [2, 3]}
+  },
+  {
+    name:
+        '[constant] Throw if byte length of bufferView for float32 doesn\'t match the given dimensions',
+    descriptor: {dataType: 'float32', dimensions: [2, 3]},
+    bufferView: {
+      type: Float32Array,
+      byteLength: 6 * 4 - 4  // The bufferView's byte length is less than the
+                             // one by given dimensions
+    }
+  },
+  // TODO (crbug.com/329702838): Test building a constant with float16 data type
+  {
+    name: '[constant] Test building a constant with int32 data type',
+    descriptor: {dataType: 'int32', dimensions: [2, 3]},
+    bufferView: {type: Int32Array, byteLength: 6 * 4},
+    output: {dataType: 'int32', dimensions: [2, 3]}
+  },
+  {
+    name:
+        '[constant] Throw if byte length of bufferView for int32 doesn\'t match the given dimensions',
+    descriptor: {dataType: 'int32', dimensions: [2, 3]},
+    bufferView: {
+      type: Int32Array,
+      byteLength: 6 * 4 + 4  // The bufferView's byte length is greater than the
+                             // one by given dimensions
+    }
+  },
+  {
+    name: '[constant] Test building a constant with uint32 data type',
+    descriptor: {dataType: 'uint32', dimensions: [2, 3]},
+    bufferView: {type: Uint32Array, byteLength: 6 * 4},
+    output: {dataType: 'uint32', dimensions: [2, 3]}
+  },
+  {
+    name:
+        '[constant] Throw if byte length of bufferView for uint32 doesn\'t match the given dimensions',
+    descriptor: {dataType: 'uint32', dimensions: [2, 3]},
+    bufferView: {type: Uint32Array, byteLength: 6 * 4 + 4}
+  },
+  {
+    name: '[constant] Test building a constant with int64 data type',
+    descriptor: {dataType: 'int64', dimensions: [2, 3]},
+    bufferView: {type: BigInt64Array, byteLength: 6 * 8},
+    output: {dataType: 'int64', dimensions: [2, 3]}
+  },
+  {
+    name:
+        '[constant] Throw if byte length of bufferView for int64 doesn\'t match the given dimensions',
+    descriptor: {dataType: 'int64', dimensions: [2, 3]},
+    bufferView: {type: BigInt64Array, byteLength: 6 * 8 + 8}
+  },
+  {
+    name: '[constant] Test building a constant with uint64 data type',
+    descriptor: {dataType: 'uint64', dimensions: [2, 3]},
+    bufferView: {type: BigUint64Array, byteLength: 6 * 8},
+    output: {dataType: 'uint64', dimensions: [2, 3]}
+  },
+  {
+    name:
+        '[constant] Throw if byte length of bufferView for uint64 doesn\'t match the given dimensions',
+    descriptor: {dataType: 'uint64', dimensions: [2, 3]},
+    bufferView: {type: BigUint64Array, byteLength: 6 * 8 + 8}
+  },
+  {
+    name: '[constant] Test building a constant with int8 data type',
+    descriptor: {dataType: 'int8', dimensions: [2, 3]},
+    bufferView: {type: Int8Array, byteLength: 6 * 1},
+    output: {dataType: 'int8', dimensions: [2, 3]}
+  },
+  {
+    name:
+        '[constant] Throw if byte length of bufferView for int8 doesn\'t match the given dimensions',
+    descriptor: {dataType: 'int8', dimensions: [2, 3]},
+    bufferView: {type: Int8Array, byteLength: 6 * 4 - 4}
+  },
+  {
+    name: '[constant] Test building a constant with uint8 data type',
+    descriptor: {dataType: 'uint8', dimensions: [2, 3]},
+    bufferView: {type: Uint8Array, byteLength: 6 * 1},
+    output: {dataType: 'uint8', dimensions: [2, 3]}
+  },
+  {
+    name:
+        '[constant] Throw if byte length of bufferView for uint8 doesn\'t match the given dimensions',
+    descriptor: {dataType: 'uint8', dimensions: [2, 3]},
+    bufferView: {type: Uint8Array, byteLength: 6 * 4 - 4}
+  },
+  {
+    name: '[constant] Throw if a dimension is 0',
+    descriptor: {dataType: 'float32', dimensions: [2, 0]},
+    bufferView: {type: Float32Array, byteLength: 2 * 4}
+  },
+  {
+    name:
+        '[constant] Throw if bufferView type doesn\'t match the operand data type',
+    descriptor: {dataType: 'float32', dimensions: [2, 3]},
+    bufferView: {type: Int32Array, byteLength: 6 * 4}
+  }
+];
+
+tests.forEach(
+    test => promise_test(async t => {
+      const buffer = new ArrayBuffer(test.bufferView.byteLength);
+      const bufferView = new test.bufferView.type(buffer);
+      if (test.output) {
+        const constantOperand = builder.constant(test.descriptor, bufferView);
+        assert_equals(constantOperand.dataType(), test.output.dataType);
+        assert_array_equals(constantOperand.shape(), test.output.dimensions);
+      } else {
+        assert_throws_js(
+            TypeError, () => builder.constant(test.descriptor, bufferView));
+      }
+    }, test.name));
diff --git a/testing/web-platform/tests/webnn/validation_tests/conv2d.https.any.js b/testing/web-platform/tests/webnn/validation_tests/conv2d.https.any.js
new file mode 100644
index 0000000000..ffc9c2c65d
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/conv2d.https.any.js
@@ -0,0 +1,57 @@
+// META: title=validation tests for WebNN API conv2d operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+// Example input in NCHW layout.
+const kExampleInputDescriptor = {
+  dataType: 'float32',
+  dimensions: [1, 1, 5, 5]
+};
+// Example filter in OIHW layout.
+const kExampleFilterDescriptor = {
+  dataType: 'float32',
+  dimensions: [1, 1, 3, 3]
+};
+const kExampleBiasDescriptor = {
+  dataType: 'float32',
+  dimensions: [/* output channels */ 1]
+};
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const inputFromOtherBuilder =
+      otherBuilder.input('input', kExampleInputDescriptor);
+
+  const filter = builder.input('filter', kExampleFilterDescriptor);
+  assert_throws_js(
+      TypeError, () => builder.conv2d(inputFromOtherBuilder, filter));
+}, '[conv2d] throw if input is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const filterFromOtherBuilder =
+      otherBuilder.input('filter', kExampleFilterDescriptor);
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  assert_throws_js(
+      TypeError, () => builder.conv2d(input, filterFromOtherBuilder));
+}, '[conv2d] throw if filter is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const biasFromOtherBuilder =
+      otherBuilder.input('bias', kExampleBiasDescriptor);
+  const options = {inputLayout: 'nchw', bias: biasFromOtherBuilder};
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const filter = builder.input('filter', kExampleFilterDescriptor);
+  assert_throws_js(TypeError, () => builder.conv2d(input, filter, options));
+}, '[conv2d] throw if bias option is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const activationFromOtherBuilder = otherBuilder.clamp();
+  const options = {activation: activationFromOtherBuilder};
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const filter = builder.input('filter', kExampleFilterDescriptor);
+  assert_throws_js(TypeError, () => builder.conv2d(input, filter, options));
+}, '[conv2d] throw if activation option is from another builder');
diff --git a/testing/web-platform/tests/webnn/validation_tests/convTranspose2d.https.any.js b/testing/web-platform/tests/webnn/validation_tests/convTranspose2d.https.any.js
new file mode 100644
index 0000000000..c14f445bf3
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/convTranspose2d.https.any.js
@@ -0,0 +1,59 @@
+// META: title=validation tests for WebNN API convTranspose2d operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+// Example input in NCHW layout.
+const kExampleInputDescriptor = {
+  dataType: 'float32',
+  dimensions: [1, 1, 5, 5]
+};
+// Example filter in OIHW layout.
+const kExampleFilterDescriptor = {
+  dataType: 'float32',
+  dimensions: [1, 1, 3, 3]
+};
+const kExampleBiasDescriptor = {
+  dataType: 'float32',
+  dimensions: [/* output channels */ 1]
+};
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const inputFromOtherBuilder =
+      otherBuilder.input('input', kExampleInputDescriptor);
+
+  const filter = builder.input('filter', kExampleFilterDescriptor);
+  assert_throws_js(
+      TypeError, () => builder.convTranspose2d(inputFromOtherBuilder, filter));
+}, '[convTranspose2d] throw if input is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const filterFromOtherBuilder =
+      otherBuilder.input('filter', kExampleFilterDescriptor);
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  assert_throws_js(
+      TypeError, () => builder.convTranspose2d(input, filterFromOtherBuilder));
+}, '[convTranspose2d] throw if filter is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const biasFromOtherBuilder =
+      otherBuilder.input('bias', kExampleBiasDescriptor);
+  const options = {inputLayout: 'nchw', bias: biasFromOtherBuilder};
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const filter = builder.input('filter', kExampleFilterDescriptor);
+  assert_throws_js(
+      TypeError, () => builder.convTranspose2d(input, filter, options));
+}, '[convTranspose2d] throw if bias option is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const activationFromOtherBuilder = otherBuilder.clamp();
+  const options = {activation: activationFromOtherBuilder};
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const filter = builder.input('filter', kExampleFilterDescriptor);
+  assert_throws_js(
+      TypeError, () => builder.convTranspose2d(input, filter, options));
+}, '[convTranspose2d] throw if activation option is from another builder');
diff --git a/testing/web-platform/tests/webnn/validation_tests/elementwise-binary.https.any.js b/testing/web-platform/tests/webnn/validation_tests/elementwise-binary.https.any.js
new file mode 100644
index 0000000000..f54626b50d
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/elementwise-binary.https.any.js
@@ -0,0 +1,21 @@
+// META: title=validation tests for WebNN API element-wise binary operations
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+const kElementwiseBinaryOperators = [
+  'add',
+  'sub',
+  'mul',
+  'div',
+  'max',
+  'min',
+  'pow',
+];
+
+kElementwiseBinaryOperators.forEach((operatorName) => {
+  validateTwoInputsOfSameDataType(operatorName);
+  validateTwoInputsBroadcastable(operatorName);
+  validateTwoInputsFromMultipleBuilders(operatorName);
+});
diff --git a/testing/web-platform/tests/webnn/validation_tests/elementwise-logical.https.any copy.js b/testing/web-platform/tests/webnn/validation_tests/elementwise-logical.https.any copy.js
new file mode 100644
index 0000000000..86183b074a
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/elementwise-logical.https.any copy.js
@@ -0,0 +1,20 @@
+// META: title=validation tests for WebNN API element-wise logical operations
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+const kElementwiseLogicalBinaryOperators = [
+  'equal',
+  'greater',
+  'greaterOrEqual',
+  'lesser',
+  'lesserOrEqual',
+];
+
+kElementwiseLogicalBinaryOperators.forEach((operatorName) => {
+  validateTwoInputsFromMultipleBuilders(operatorName);
+});
+
+// The `not()` operator is unary.
+validateInputFromOtherBuilder('not');
diff --git a/testing/web-platform/tests/webnn/validation_tests/elementwise-unary.https.any.js b/testing/web-platform/tests/webnn/validation_tests/elementwise-unary.https.any.js
new file mode 100644
index 0000000000..f87c61b4e4
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/elementwise-unary.https.any.js
@@ -0,0 +1,39 @@
+// META: title=validation tests for WebNN API element-wise unary operations
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+const kElementwiseUnaryOperators = [
+  'abs', 'ceil', 'cos', 'erf', 'exp', 'floor', 'identity', 'log', 'neg',
+  'reciprocal', 'sin', 'sqrt', 'tan'
+];
+
+kElementwiseUnaryOperators.forEach((operatorName) => {
+  validateInputFromAnotherBuilder(operatorName);
+});
+
+const kElementwiseUnaryOperations = [
+  {
+    name: 'abs',
+    supportedDataTypes: [...floatingPointTypes, ...signedIntegerTypes]
+  },
+  {name: 'ceil', supportedDataTypes: floatingPointTypes},
+  {name: 'exp', supportedDataTypes: floatingPointTypes},
+  {name: 'floor', supportedDataTypes: floatingPointTypes},
+  {name: 'log', supportedDataTypes: floatingPointTypes}, {
+    name: 'neg',
+    supportedDataTypes: [...floatingPointTypes, ...signedIntegerTypes]
+  },
+  {name: 'sin', supportedDataTypes: floatingPointTypes},
+  {name: 'tan', supportedDataTypes: floatingPointTypes},
+  {name: 'erf', supportedDataTypes: floatingPointTypes},
+  {name: 'identity', supportedDataTypes: allWebNNOperandDataTypes},
+  {name: 'logicalNot', supportedDataTypes: ['uint8']},
+  {name: 'reciprocal', supportedDataTypes: floatingPointTypes},
+  {name: 'sqrt', supportedDataTypes: floatingPointTypes}
+];
+
+kElementwiseUnaryOperations.forEach((operation) => {
+  validateUnaryOperation(operation.name, operation.supportedDataTypes);
+});
diff --git a/testing/web-platform/tests/webnn/validation_tests/elementwise_binary.https.any.js b/testing/web-platform/tests/webnn/validation_tests/elementwise_binary.https.any.js
deleted file mode 100644
index 97a1a2b93c..0000000000
--- a/testing/web-platform/tests/webnn/validation_tests/elementwise_binary.https.any.js
+++ /dev/null
@@ -1,11 +0,0 @@
-// META: title=validation tests for WebNN API element-wise binary operations
-// META: global=window,dedicatedworker
-// META: script=../resources/utils_validation.js
-// META: timeout=long
-
-'use strict';
-
-['add', 'sub', 'mul', 'div', 'max', 'min', 'pow'].forEach((operationName) => {
-  validateTwoInputsOfSameDataType(operationName);
-  validateTwoInputsBroadcastable(operationName);
-});
diff --git a/testing/web-platform/tests/webnn/validation_tests/elu.https.any.js b/testing/web-platform/tests/webnn/validation_tests/elu.https.any.js
new file mode 100644
index 0000000000..6e842cb691
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/elu.https.any.js
@@ -0,0 +1,7 @@
+// META: title=validation tests for WebNN API elu operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+validateInputFromAnotherBuilder('elu');
diff --git a/testing/web-platform/tests/webnn/validation_tests/expand.https.any.js b/testing/web-platform/tests/webnn/validation_tests/expand.https.any.js
new file mode 100644
index 0000000000..d90ab89468
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/expand.https.any.js
@@ -0,0 +1,14 @@
+// META: title=validation tests for WebNN API expand operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const inputFromOtherBuilder =
+      otherBuilder.input('input', {dataType: 'float32', dimensions: [2, 1, 2]});
+
+  const newShape = [2, 2, 2];
+  assert_throws_js(
+      TypeError, () => builder.expand(inputFromOtherBuilder, newShape));
+}, '[expand] throw if input is from another builder');
diff --git a/testing/web-platform/tests/webnn/validation_tests/gather.https.any.js b/testing/web-platform/tests/webnn/validation_tests/gather.https.any.js
index 67ac7d7be3..184e8033e6 100644
--- a/testing/web-platform/tests/webnn/validation_tests/gather.https.any.js
+++ b/testing/web-platform/tests/webnn/validation_tests/gather.https.any.js
@@ -1,7 +1,6 @@
 // META: title=validation tests for WebNN API gather operation
 // META: global=window,dedicatedworker
 // META: script=../resources/utils_validation.js
-// META: timeout=long
 
 'use strict';
 
@@ -60,3 +59,23 @@ tests.forEach(
             TypeError, () => builder.gather(input, indices, options));
       }
     }, test.name));
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const inputFromOtherBuilder =
+      otherBuilder.input('input', {dataType: 'float32', dimensions: [2, 2]});
+
+  const indices =
+      builder.input('indices', {dataType: 'int64', dimensions: [2, 2]});
+  assert_throws_js(
+      TypeError, () => builder.gather(inputFromOtherBuilder, indices));
+}, '[gather] throw if input is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const indicesFromOtherBuilder =
+      otherBuilder.input('indices', {dataType: 'int64', dimensions: [2, 2]});
+
+  const input =
+      builder.input('input', {dataType: 'float32', dimensions: [2, 2]});
+  assert_throws_js(
+      TypeError, () => builder.gather(input, indicesFromOtherBuilder));
+}, '[gather] throw if indices is from another builder');
diff --git a/testing/web-platform/tests/webnn/validation_tests/gemm.https.any.js b/testing/web-platform/tests/webnn/validation_tests/gemm.https.any.js
new file mode 100644
index 0000000000..77ce6383cc
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/gemm.https.any.js
@@ -0,0 +1,21 @@
+// META: title=validation tests for WebNN API gemm operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+const kExampleInputDescriptor = {
+  dataType: 'float32',
+  dimensions: [2, 2]
+};
+
+validateTwoInputsFromMultipleBuilders('gemm');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const cFromOtherBuilder = otherBuilder.input('c', kExampleInputDescriptor);
+  const options = {c: cFromOtherBuilder};
+
+  const a = builder.input('a', kExampleInputDescriptor);
+  const b = builder.input('b', kExampleInputDescriptor);
+  assert_throws_js(TypeError, () => builder.gemm(a, b, options));
+}, '[gemm] throw if c option is from another builder');
diff --git a/testing/web-platform/tests/webnn/validation_tests/gru.https.any.js b/testing/web-platform/tests/webnn/validation_tests/gru.https.any.js
index 295baab9c2..50d39d297a 100644
--- a/testing/web-platform/tests/webnn/validation_tests/gru.https.any.js
+++ b/testing/web-platform/tests/webnn/validation_tests/gru.https.any.js
@@ -1,398 +1,445 @@
 // META: title=validation tests for WebNN API gru operation
 // META: global=window,dedicatedworker
 // META: script=../resources/utils_validation.js
-// META: timeout=long
 
 'use strict';
 
-const steps = 2, batchSize = 3, inputSize = 4, hiddenSize = 5,
-    numDirections = 1;
+const steps = 2, batchSize = 3, inputSize = 4, hiddenSize = 5, oneDirection = 1,
+      bothDirections = 2;
+
+// Dimensions required of required inputs.
+const kValidInputDimensions = [steps, batchSize, inputSize];
+const kValidWeightDimensions = [oneDirection, 3 * hiddenSize, inputSize];
+const kValidRecurrentWeightDimensions =
+    [oneDirection, 3 * hiddenSize, hiddenSize];
+// Dimensions required of optional inputs.
+const kValidBiasDimensions = [oneDirection, 3 * hiddenSize];
+const kValidRecurrentBiasDimensions = [oneDirection, 3 * hiddenSize];
+const kValidInitialHiddenStateDimensions =
+    [oneDirection, batchSize, hiddenSize];
+
+// Example descriptors which are valid according to the above dimensions.
+const kExampleInputDescriptor = {
+  dataType: 'float32',
+  dimensions: kValidInputDimensions
+};
+const kExampleWeightDescriptor = {
+  dataType: 'float32',
+  dimensions: kValidWeightDimensions
+};
+const kExampleRecurrentWeightDescriptor = {
+  dataType: 'float32',
+  dimensions: kValidRecurrentWeightDimensions
+};
+const kExampleBiasDescriptor = {
+  dataType: 'float32',
+  dimensions: kValidBiasDimensions
+};
+const kExampleRecurrentBiasDescriptor = {
+  dataType: 'float32',
+  dimensions: kValidRecurrentBiasDimensions
+};
+const kExampleInitialHiddenStateDescriptor = {
+  dataType: 'float32',
+  dimensions: kValidInitialHiddenStateDimensions
+};
 
 const tests = [
-    {
-        name: '[gru] Test with default options',
-        input: { dataType: 'float32', dimensions: [steps, batchSize, inputSize] },
-        weight: {
-            dataType: 'float32',
-            dimensions: [numDirections, 3 * hiddenSize, inputSize]
-        },
-        recurrentWeight: {
-            dataType: 'float32',
-            dimensions: [numDirections, 3 * hiddenSize, hiddenSize]
-        },
-        steps: steps,
-        hiddenSize: hiddenSize,
-        outputs: [
-            { dataType: 'float32', dimensions: [numDirections, batchSize, hiddenSize] }
-        ]
-    },
-    {
-        name: '[gru] Test with given options',
-        input: { dataType: 'float32', dimensions: [steps, batchSize, inputSize] },
-        weight: {
-            dataType: 'float32',
-            dimensions: [/*numDirections=*/ 2, 3 * hiddenSize, inputSize]
-        },
-        recurrentWeight: {
-            dataType: 'float32',
-            dimensions: [/*numDirections=*/ 2, 3 * hiddenSize, hiddenSize]
-        },
-        steps: steps,
-        hiddenSize: hiddenSize,
-        options: {
-            bias: {
-                dataType: 'float32',
-                dimensions: [/*numDirections=*/ 2, 3 * hiddenSize]
-            },
-            recurrentBias: {
-                dataType: 'float32',
-                dimensions: [/*numDirections=*/ 2, 3 * hiddenSize]
-            },
-            initialHiddenState: {
-                dataType: 'float32',
-                dimensions: [/*numDirections=*/ 2, batchSize, hiddenSize]
-            },
-            restAfter: true,
-            returnSequence: true,
-            direction: 'both',
-            layout: 'rzn',
-            activations: ['sigmoid', 'relu']
-        },
-        outputs: [
-            {
-                dataType: 'float32',
-                dimensions: [/*numDirections=*/ 2, batchSize, hiddenSize]
-            },
-            {
-                dataType: 'float32',
-                dimensions: [steps, /*numDirections=*/ 2, batchSize, hiddenSize]
-            }
-        ]
-    },
-    {
-        name: '[gru] TypeError is expected if steps equals to zero',
-        input: { dataType: 'float32', dimensions: [steps, batchSize, inputSize] },
-        weight: {
-            dataType: 'float32',
-            dimensions: [numDirections, 4 * hiddenSize, inputSize]
-        },
-        recurrentWeight: {
-            dataType: 'float32',
-            dimensions: [numDirections, 4 * hiddenSize, hiddenSize]
-        },
-        steps: 0,
-        hiddenSize: hiddenSize,
-    },
-    {
-        name: '[gru] TypeError is expected if hiddenSize equals to zero',
-        input: { dataType: 'float32', dimensions: [steps, batchSize, inputSize] },
-        weight: {
-            dataType: 'float32',
-            dimensions: [numDirections, 3 * hiddenSize, inputSize]
-        },
-        recurrentWeight: {
-            dataType: 'float32',
-            dimensions: [numDirections, 3 * hiddenSize, hiddenSize]
-        },
-        steps: steps,
-        hiddenSize: 0
-    },
-    {
-        name: '[gru] TypeError is expected if hiddenSize is too large',
-        input: { dataType: 'float32', dimensions: [steps, batchSize, inputSize] },
-        weight: {
-            dataType: 'float32',
-            dimensions: [numDirections, 3 * hiddenSize, inputSize]
-        },
-        recurrentWeight: {
-            dataType: 'float32',
-            dimensions: [numDirections, 3 * hiddenSize, hiddenSize]
-        },
-        steps: steps,
-        hiddenSize: 4294967295,
+  {
+    name: '[gru] Test with default options',
+    input: kExampleInputDescriptor,
+    weight: kExampleWeightDescriptor,
+    recurrentWeight: kExampleRecurrentWeightDescriptor,
+    steps: steps,
+    hiddenSize: hiddenSize,
+    outputs: [
+      {dataType: 'float32', dimensions: [oneDirection, batchSize, hiddenSize]}
+    ]
+  },
+  {
+    name: '[gru] Test with given options',
+    input: kExampleInputDescriptor,
+    weight: {
+      dataType: 'float32',
+      dimensions: [bothDirections, 3 * hiddenSize, inputSize]
     },
-    {
-        name:
-            '[gru] TypeError is expected if the data type of the inputs is not one of the floating point types',
-        input: { dataType: 'uint32', dimensions: [steps, batchSize, inputSize] },
-        weight: {
-            dataType: 'uint32',
-            dimensions: [numDirections, 3 * hiddenSize, inputSize]
-        },
-        recurrentWeight: {
-            dataType: 'uint32',
-            dimensions: [numDirections, 3 * hiddenSize, hiddenSize]
-        },
-        steps: steps,
-        hiddenSize: hiddenSize
+    recurrentWeight: {
+      dataType: 'float32',
+      dimensions: [bothDirections, 3 * hiddenSize, hiddenSize]
     },
-    {
-        name:
-            '[gru] TypeError is expected if the rank of input is not 3',
-        input: { dataType: 'float32', dimensions: [steps, batchSize] },
-        weight: {
-            dataType: 'float32',
-            dimensions: [numDirections, 3 * hiddenSize, inputSize]
-        },
-        recurrentWeight: {
-            dataType: 'float32',
-            dimensions: [numDirections, 3 * hiddenSize, hiddenSize]
-        },
-        steps: steps,
-        hiddenSize: hiddenSize
+    steps: steps,
+    hiddenSize: hiddenSize,
+    options: {
+      bias: {dataType: 'float32', dimensions: [bothDirections, 3 * hiddenSize]},
+      recurrentBias:
+          {dataType: 'float32', dimensions: [bothDirections, 3 * hiddenSize]},
+      initialHiddenState: {
+        dataType: 'float32',
+        dimensions: [bothDirections, batchSize, hiddenSize]
+      },
+      restAfter: true,
+      returnSequence: true,
+      direction: 'both',
+      layout: 'rzn',
+      activations: ['sigmoid', 'relu']
     },
-    {
-        name:
-            '[gru] TypeError is expected if input.dimensions[0] is not equal to steps',
-        input: { dataType: 'float32', dimensions: [1000, batchSize, inputSize] },
-        weight: {
-            dataType: 'float32',
-            dimensions: [numDirections, 3 * hiddenSize, inputSize]
-        },
-        recurrentWeight: {
-            dataType: 'float32',
-            dimensions: [numDirections, 3 * hiddenSize, hiddenSize]
-        },
-        steps: steps,
-        hiddenSize: hiddenSize
+    outputs: [
+      {
+        dataType: 'float32',
+        dimensions: [bothDirections, batchSize, hiddenSize]
+      },
+      {
+        dataType: 'float32',
+        dimensions: [steps, bothDirections, batchSize, hiddenSize]
+      }
+    ]
+  },
+  {
+    name: '[gru] TypeError is expected if steps equals to zero',
+    input: kExampleInputDescriptor,
+    weight: {
+      dataType: 'float32',
+      dimensions: [oneDirection, 4 * hiddenSize, inputSize]
     },
-    {
-        name: '[gru] TypeError is expected if weight.dimensions[1] is not 3 * hiddenSize',
-        input: { dataType: 'float32', dimensions: [steps, batchSize, inputSize] },
-        weight: {
-            dataType: 'float32',
-            dimensions: [numDirections, 4 * hiddenSize, inputSize]
-        },
-        recurrentWeight: {
-            dataType: 'float32',
-            dimensions: [numDirections, 3 * hiddenSize, hiddenSize]
-        },
-        steps: steps,
-        hiddenSize: hiddenSize
+    recurrentWeight: {
+      dataType: 'float32',
+      dimensions: [oneDirection, 4 * hiddenSize, hiddenSize]
     },
-    {
-        name:
-            '[gru] TypeError is expected if the rank of recurrentWeight is not 3',
-        input: { dataType: 'float32', dimensions: [steps, batchSize, inputSize] },
-        weight: {
-            dataType: 'float32',
-            dimensions: [numDirections, 3 * hiddenSize, inputSize]
-        },
-        recurrentWeight:
-            { dataType: 'float32', dimensions: [numDirections, 3 * hiddenSize] },
-        steps: steps,
-        hiddenSize: hiddenSize
+    steps: 0,
+    hiddenSize: hiddenSize,
+  },
+  {
+    name: '[gru] TypeError is expected if hiddenSize equals to zero',
+    input: kExampleInputDescriptor,
+    weight: kExampleWeightDescriptor,
+    recurrentWeight: kExampleRecurrentWeightDescriptor,
+    steps: steps,
+    hiddenSize: 0
+  },
+  {
+    name: '[gru] TypeError is expected if hiddenSize is too large',
+    input: kExampleInputDescriptor,
+    weight: kExampleWeightDescriptor,
+    recurrentWeight: kExampleRecurrentWeightDescriptor,
+    steps: steps,
+    hiddenSize: 4294967295,
+  },
+  {
+    name:
+        '[gru] TypeError is expected if the data type of the inputs is not one of the floating point types',
+    input: {dataType: 'uint32', dimensions: kValidInputDimensions},
+    weight: {dataType: 'uint32', dimensions: kValidWeightDimensions},
+    recurrentWeight:
+        {dataType: 'uint32', dimensions: kValidRecurrentWeightDimensions},
+    steps: steps,
+    hiddenSize: hiddenSize
+  },
+  {
+    name: '[gru] TypeError is expected if the rank of input is not 3',
+    input: {dataType: 'float32', dimensions: [steps, batchSize]},
+    weight: kExampleWeightDescriptor,
+    recurrentWeight: kExampleRecurrentWeightDescriptor,
+    steps: steps,
+    hiddenSize: hiddenSize
+  },
+  {
+    name:
+        '[gru] TypeError is expected if input.dimensions[0] is not equal to steps',
+    input: {dataType: 'float32', dimensions: [1000, batchSize, inputSize]},
+    weight: kExampleWeightDescriptor,
+    recurrentWeight: kExampleRecurrentWeightDescriptor,
+    steps: steps,
+    hiddenSize: hiddenSize
+  },
+  {
+    name:
+        '[gru] TypeError is expected if weight.dimensions[1] is not 3 * hiddenSize',
+    input: kExampleInputDescriptor,
+    weight: {
+      dataType: 'float32',
+      dimensions: [oneDirection, 4 * hiddenSize, inputSize]
     },
-    {
-        name:
-            '[gru] TypeError is expected if the recurrentWeight.dimensions is invalid',
-        input: { dataType: 'float32', dimensions: [steps, batchSize, inputSize] },
-        weight: {
-            dataType: 'float32',
-            dimensions: [numDirections, 3 * hiddenSize, inputSize]
-        },
-        recurrentWeight:
-            { dataType: 'float32', dimensions: [numDirections, 4 * hiddenSize, inputSize] },
-        steps: steps,
-        hiddenSize: hiddenSize
-    },
-    {
-        name:
-            '[gru] TypeError is expected if the size of options.activations is not 2',
-        input: { dataType: 'float32', dimensions: [steps, batchSize, inputSize] },
-        weight: {
-            dataType: 'float32',
-            dimensions: [numDirections, 3 * hiddenSize, inputSize]
-        },
-        recurrentWeight: {
-            dataType: 'float32',
-            dimensions: [numDirections, 3 * hiddenSize, hiddenSize]
-        },
-        steps: steps,
-        hiddenSize: hiddenSize,
-        options: { activations: ['sigmoid', 'tanh', 'relu'] }
-    },
-    {
-        name:
-            '[gru] TypeError is expected if the rank of options.bias is not 2',
-        input: { dataType: 'float32', dimensions: [steps, batchSize, inputSize] },
-        weight: {
-            dataType: 'float32',
-            dimensions: [numDirections, 3 * hiddenSize, inputSize]
-        },
-        recurrentWeight: {
-            dataType: 'float32',
-            dimensions: [numDirections, 3 * hiddenSize, hiddenSize]
-        },
-        steps: steps,
-        hiddenSize: hiddenSize,
-        options: { bias: { dataType: 'float32', dimensions: [numDirections] } }
-    },
-    {
-        name:
-            '[gru] TypeError is expected if options.bias.dimensions[1] is not 3 * hiddenSize',
-        input: { dataType: 'float32', dimensions: [steps, batchSize, inputSize] },
-        weight: {
-            dataType: 'float32',
-            dimensions: [numDirections, 3 * hiddenSize, inputSize]
-        },
-        recurrentWeight: {
-            dataType: 'float32',
-            dimensions: [numDirections, 3 * hiddenSize, hiddenSize]
-        },
-        steps: steps,
-        hiddenSize: hiddenSize,
-        options: { bias: { dataType: 'float32', dimensions: [numDirections, hiddenSize] } }
-    },
-    {
-        name:
-            '[gru] TypeError is expected if options.recurrentBias.dimensions[1] is not 3 * hiddenSize',
-        input: { dataType: 'float16', dimensions: [steps, batchSize, inputSize] },
-        weight: {
-            dataType: 'float16',
-            dimensions: [numDirections, 3 * hiddenSize, inputSize]
-        },
-        recurrentWeight: {
-            dataType: 'float16',
-            dimensions: [numDirections, 3 * hiddenSize, hiddenSize]
-        },
-        steps: steps,
-        hiddenSize: hiddenSize,
-        options: {
-            recurrentBias: { dataType: 'float16', dimensions: [numDirections, 4 * hiddenSize] }
-        }
+    recurrentWeight: kExampleRecurrentWeightDescriptor,
+    steps: steps,
+    hiddenSize: hiddenSize
+  },
+  {
+    name: '[gru] TypeError is expected if the rank of recurrentWeight is not 3',
+    input: kExampleInputDescriptor,
+    weight: kExampleWeightDescriptor,
+    recurrentWeight:
+        {dataType: 'float32', dimensions: [oneDirection, 3 * hiddenSize]},
+    steps: steps,
+    hiddenSize: hiddenSize
+  },
+  {
+    name:
+        '[gru] TypeError is expected if the recurrentWeight.dimensions is invalid',
+    input: kExampleInputDescriptor,
+    weight: kExampleWeightDescriptor,
+    recurrentWeight: {
+      dataType: 'float32',
+      dimensions: [oneDirection, 4 * hiddenSize, inputSize]
     },
-    {
-        name:
-            '[gru] TypeError is expected if the rank of options.initialHiddenState is not 3',
-        input: { dataType: 'float16', dimensions: [steps, batchSize, inputSize] },
-        weight: {
-            dataType: 'float16',
-            dimensions: [numDirections, 3 * hiddenSize, inputSize]
-        },
-        recurrentWeight: {
-            dataType: 'float16',
-            dimensions: [numDirections, 3 * hiddenSize, hiddenSize]
-        },
-        steps: steps,
-        hiddenSize: hiddenSize,
-        options: {
-            initialHiddenState: {
-                dataType: 'float16',
-                dimensions: [numDirections, batchSize]
-            }
-        }
-    },
-    {
-        name:
-            '[gru] TypeError is expected if options.initialHiddenState.dimensions[2] is not inputSize',
-        input: { dataType: 'float16', dimensions: [steps, batchSize, inputSize] },
-        weight: {
-            dataType: 'float16',
-            dimensions: [numDirections, 3 * hiddenSize, inputSize]
-        },
-        recurrentWeight: {
-            dataType: 'float16',
-            dimensions: [numDirections, 3 * hiddenSize, hiddenSize]
-        },
-        steps: steps,
-        hiddenSize: hiddenSize,
-        options: {
-            initialHiddenState: {
-                dataType: 'float16',
-                dimensions: [numDirections, batchSize, 3 * hiddenSize]
-            }
-        }
-    },
-    {
-        name:
-            '[gru] TypeError is expected if the dataType of options.initialHiddenState is incorrect',
-        input: { dataType: 'float16', dimensions: [steps, batchSize, inputSize] },
-        weight: {
-            dataType: 'float16',
-            dimensions: [numDirections, 3 * hiddenSize, inputSize]
-        },
-        recurrentWeight: {
-            dataType: 'float16',
-            dimensions: [numDirections, 3 * hiddenSize, hiddenSize]
-        },
-        steps: steps,
-        hiddenSize: hiddenSize,
-        options: {
-            initialHiddenState: {
-                dataType: 'uint64',
-                dimensions: [numDirections, batchSize, hiddenSize]
-            }
-        }
+    steps: steps,
+    hiddenSize: hiddenSize
+  },
+  {
+    name:
+        '[gru] TypeError is expected if the size of options.activations is not 2',
+    input: kExampleInputDescriptor,
+    weight: kExampleWeightDescriptor,
+    recurrentWeight: kExampleRecurrentWeightDescriptor,
+    steps: steps,
+    hiddenSize: hiddenSize,
+    options: {activations: ['sigmoid', 'tanh', 'relu']}
+  },
+  {
+    name: '[gru] TypeError is expected if the rank of options.bias is not 2',
+    input: kExampleInputDescriptor,
+    weight: kExampleWeightDescriptor,
+    recurrentWeight: kExampleRecurrentWeightDescriptor,
+    steps: steps,
+    hiddenSize: hiddenSize,
+    options: {bias: {dataType: 'float32', dimensions: [oneDirection]}}
+  },
+  {
+    name:
+        '[gru] TypeError is expected if options.bias.dimensions[1] is not 3 * hiddenSize',
+    input: kExampleInputDescriptor,
+    weight: kExampleWeightDescriptor,
+    recurrentWeight: kExampleRecurrentWeightDescriptor,
+    steps: steps,
+    hiddenSize: hiddenSize,
+    options:
+        {bias: {dataType: 'float32', dimensions: [oneDirection, hiddenSize]}}
+  },
+  {
+    name:
+        '[gru] TypeError is expected if options.recurrentBias.dimensions[1] is not 3 * hiddenSize',
+    input: {dataType: 'float16', dimensions: kValidInputDimensions},
+    weight: {dataType: 'float16', dimensions: kValidWeightDimensions},
+    recurrentWeight:
+        {dataType: 'float16', dimensions: kValidRecurrentWeightDimensions},
+    steps: steps,
+    hiddenSize: hiddenSize,
+    options: {
+      recurrentBias:
+          {dataType: 'float16', dimensions: [oneDirection, 4 * hiddenSize]}
     }
+  },
+  {
+    name:
+        '[gru] TypeError is expected if the rank of options.initialHiddenState is not 3',
+    input: {dataType: 'float16', dimensions: kValidInputDimensions},
+    weight: {dataType: 'float16', dimensions: kValidWeightDimensions},
+    recurrentWeight:
+        {dataType: 'float16', dimensions: kValidRecurrentWeightDimensions},
+    steps: steps,
+    hiddenSize: hiddenSize,
+    options: {
+      initialHiddenState:
+          {dataType: 'float16', dimensions: [oneDirection, batchSize]}
+    }
+  },
+  {
+    name:
+        '[gru] TypeError is expected if options.initialHiddenState.dimensions[2] is not inputSize',
+    input: {dataType: 'float16', dimensions: kValidInputDimensions},
+    weight: {dataType: 'float16', dimensions: kValidWeightDimensions},
+    recurrentWeight:
+        {dataType: 'float16', dimensions: kValidRecurrentWeightDimensions},
+    steps: steps,
+    hiddenSize: hiddenSize,
+    options: {
+      initialHiddenState: {
+        dataType: 'float16',
+        dimensions: [oneDirection, batchSize, 3 * hiddenSize]
+      }
+    }
+  },
+  {
+    name:
+        '[gru] TypeError is expected if the dataType of options.initialHiddenState is incorrect',
+    input: {dataType: 'float16', dimensions: kValidInputDimensions},
+    weight: {dataType: 'float16', dimensions: kValidWeightDimensions},
+    recurrentWeight:
+        {dataType: 'float16', dimensions: kValidRecurrentWeightDimensions},
+    steps: steps,
+    hiddenSize: hiddenSize,
+    options: {
+      initialHiddenState: {
+        dataType: 'uint64',
+        dimensions: [oneDirection, batchSize, hiddenSize]
+      }
+    }
+  }
 ];
 
 tests.forEach(
     test => promise_test(async t => {
-        const input = builder.input(
-            'input',
-            { dataType: test.input.dataType, dimensions: test.input.dimensions });
-        const weight = builder.input(
-            'weight',
-            { dataType: test.weight.dataType, dimensions: test.weight.dimensions });
-        const recurrentWeight = builder.input('recurrentWeight', {
-            dataType: test.recurrentWeight.dataType,
-            dimensions: test.recurrentWeight.dimensions
-        });
+      const input = builder.input(
+          'input',
+          {dataType: test.input.dataType, dimensions: test.input.dimensions});
+      const weight = builder.input(
+          'weight',
+          {dataType: test.weight.dataType, dimensions: test.weight.dimensions});
+      const recurrentWeight = builder.input('recurrentWeight', {
+        dataType: test.recurrentWeight.dataType,
+        dimensions: test.recurrentWeight.dimensions
+      });
 
-        const options = {};
-        if (test.options) {
-            if (test.options.bias) {
-                options.bias = builder.input('bias', {
-                    dataType: test.options.bias.dataType,
-                    dimensions: test.options.bias.dimensions
-                });
-            }
-            if (test.options.recurrentBias) {
-                options.bias = builder.input('recurrentBias', {
-                    dataType: test.options.recurrentBias.dataType,
-                    dimensions: test.options.recurrentBias.dimensions
-                });
-            }
-            if (test.options.initialHiddenState) {
-                options.initialHiddenState = builder.input('initialHiddenState', {
-                    dataType: test.options.initialHiddenState.dataType,
-                    dimensions: test.options.initialHiddenState.dimensions
-                });
-            }
-            if (test.options.resetAfter) {
-                options.resetAfter = test.options.resetAfter;
-            }
-            if (test.options.returnSequence) {
-                options.returnSequence = test.options.returnSequence;
-            }
-            if (test.options.direction) {
-                options.direction = test.options.direction;
-            }
-            if (test.options.layout) {
-                options.layout = test.options.layout;
-            }
-            if (test.options.activations) {
-                options.activations = [];
-                test.options.activations.forEach(
-                    activation => options.activations.push(builder[activation]()));
-            }
+      const options = {};
+      if (test.options) {
+        if (test.options.bias) {
+          options.bias = builder.input('bias', {
+            dataType: test.options.bias.dataType,
+            dimensions: test.options.bias.dimensions
+          });
+        }
+        if (test.options.recurrentBias) {
+          options.bias = builder.input('recurrentBias', {
+            dataType: test.options.recurrentBias.dataType,
+            dimensions: test.options.recurrentBias.dimensions
+          });
+        }
+        if (test.options.initialHiddenState) {
+          options.initialHiddenState = builder.input('initialHiddenState', {
+            dataType: test.options.initialHiddenState.dataType,
+            dimensions: test.options.initialHiddenState.dimensions
+          });
         }
+        if (test.options.resetAfter) {
+          options.resetAfter = test.options.resetAfter;
+        }
+        if (test.options.returnSequence) {
+          options.returnSequence = test.options.returnSequence;
+        }
+        if (test.options.direction) {
+          options.direction = test.options.direction;
+        }
+        if (test.options.layout) {
+          options.layout = test.options.layout;
+        }
+        if (test.options.activations) {
+          options.activations = [];
+          test.options.activations.forEach(
+              activation => options.activations.push(builder[activation]()));
+        }
+      }
 
-        if (test.outputs) {
-            const outputs = builder.gru(
-                input, weight, recurrentWeight, test.steps, test.hiddenSize,
-                options);
-            assert_equals(outputs.length, test.outputs.length);
-            for (let i = 0; i < outputs.length; ++i) {
-                assert_equals(outputs[i].dataType(), test.outputs[i].dataType);
-                assert_array_equals(outputs[i].shape(), test.outputs[i].dimensions);
-            }
-        } else {
-            assert_throws_js(
-                TypeError,
-                () => builder.gru(
-                    input, weight, recurrentWeight, test.steps, test.hiddenSize,
-                    options));
+      if (test.outputs) {
+        const outputs = builder.gru(
+            input, weight, recurrentWeight, test.steps, test.hiddenSize,
+            options);
+        assert_equals(outputs.length, test.outputs.length);
+        for (let i = 0; i < outputs.length; ++i) {
+          assert_equals(outputs[i].dataType(), test.outputs[i].dataType);
+          assert_array_equals(outputs[i].shape(), test.outputs[i].dimensions);
         }
-    }, test.name));
\ No newline at end of file
+      } else {
+        assert_throws_js(
+            TypeError,
+            () => builder.gru(
+                input, weight, recurrentWeight, test.steps, test.hiddenSize,
+                options));
+      }
+    }, test.name));
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const inputFromOtherBuilder =
+      otherBuilder.input('input', kExampleInputDescriptor);
+
+  const weight = builder.input('weight', kExampleWeightDescriptor);
+  const recurrentWeight =
+      builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.gru(
+          inputFromOtherBuilder, weight, recurrentWeight, steps, hiddenSize));
+}, '[gru] throw if input is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const weightFromOtherBuilder =
+      otherBuilder.input('weight', kExampleWeightDescriptor);
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const recurrentWeight =
+      builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.gru(
+          input, weightFromOtherBuilder, recurrentWeight, steps, hiddenSize));
+}, '[gru] throw if weight is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const recurrentWeightFromOtherBuilder =
+      otherBuilder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const weight = builder.input('weight', kExampleWeightDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.gru(
+          input, weight, recurrentWeightFromOtherBuilder, steps, hiddenSize));
+}, '[gru] throw if recurrentWeight is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const biasFromOtherBuilder =
+      otherBuilder.input('bias', kExampleBiasDescriptor);
+  const options = {bias: biasFromOtherBuilder};
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const weight = builder.input('weight', kExampleWeightDescriptor);
+  const recurrentWeight =
+      builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.gru(
+          input, weight, recurrentWeight, steps, hiddenSize, options));
+}, '[gru] throw if bias option is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const recurrentBiasFromOtherBuilder =
+      otherBuilder.input('recurrentBias', kExampleRecurrentBiasDescriptor);
+  const options = {recurrentBias: recurrentBiasFromOtherBuilder};
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const weight = builder.input('weight', kExampleWeightDescriptor);
+  const recurrentWeight =
+      builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.gru(
+          input, weight, recurrentWeight, steps, hiddenSize, options));
+}, '[gru] throw if recurrentBias option is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const initialHiddenStateFromOtherBuilder = otherBuilder.input(
+      'initialHiddenState', kExampleInitialHiddenStateDescriptor);
+  const options = {initialHiddenState: initialHiddenStateFromOtherBuilder};
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const weight = builder.input('weight', kExampleWeightDescriptor);
+  const recurrentWeight =
+      builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.gru(
+          input, weight, recurrentWeight, steps, hiddenSize, options));
+}, '[gru] throw if initialHiddenState option is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const activation = builder.clamp();
+  const activationFromOtherBuilder = otherBuilder.clamp();
+  const options = {activations: [activation, activationFromOtherBuilder]};
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const weight = builder.input('weight', kExampleWeightDescriptor);
+  const recurrentWeight =
+      builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.gru(
+          input, weight, recurrentWeight, steps, hiddenSize, options));
+}, '[gru] throw if any activation option is from another builder');
diff --git a/testing/web-platform/tests/webnn/validation_tests/gruCell.https.any.js b/testing/web-platform/tests/webnn/validation_tests/gruCell.https.any.js
new file mode 100644
index 0000000000..3cd9d32b07
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/gruCell.https.any.js
@@ -0,0 +1,471 @@
+// META: title=validation tests for WebNN API gruCell operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+const batchSize = 3, inputSize = 4, hiddenSize = 5;
+
+// Dimensions required of required inputs.
+const kValidInputDimensions = [batchSize, inputSize];
+const kValidWeightDimensions = [3 * hiddenSize, inputSize];
+const kValidRecurrentWeightDimensions = [3 * hiddenSize, hiddenSize];
+const kValidHiddenStateDimensions = [batchSize, hiddenSize];
+// Dimensions required of optional inputs.
+const kValidBiasDimensions = [3 * hiddenSize];
+const kValidRecurrentBiasDimensions = [3 * hiddenSize];
+// Dimensions required of required output.
+const kValidOutputDimensions = [batchSize, hiddenSize];
+
+// Example descriptors which are valid according to the above dimensions.
+const kExampleInputDescriptor = {
+  dataType: 'float32',
+  dimensions: kValidInputDimensions
+};
+const kExampleWeightDescriptor = {
+  dataType: 'float32',
+  dimensions: kValidWeightDimensions
+};
+const kExampleRecurrentWeightDescriptor = {
+  dataType: 'float32',
+  dimensions: kValidRecurrentWeightDimensions
+};
+const kExampleHiddenStateDescriptor = {
+  dataType: 'float32',
+  dimensions: kValidHiddenStateDimensions
+};
+const kExampleBiasDescriptor = {
+  dataType: 'float32',
+  dimensions: kValidBiasDimensions
+};
+const kExampleRecurrentBiasDescriptor = {
+  dataType: 'float32',
+  dimensions: kValidRecurrentBiasDimensions
+};
+const kExampleOutputDescriptor = {
+    dataType: 'float32',
+    dimensions: kValidOutputDimensions
+  };
+
+const tests = [
+    {
+        name: '[gruCell] Test with default options',
+        input: kExampleInputDescriptor,
+        weight: kExampleWeightDescriptor,
+        recurrentWeight: kExampleRecurrentWeightDescriptor,
+        hiddenState: kExampleHiddenStateDescriptor,
+        hiddenSize: hiddenSize,
+        output: kExampleOutputDescriptor
+    },
+    {
+        name: '[gruCell] Test with given options',
+        input: kExampleInputDescriptor,
+        weight: kExampleWeightDescriptor,
+        recurrentWeight: kExampleRecurrentWeightDescriptor,
+        hiddenState: kExampleHiddenStateDescriptor,
+        hiddenSize: hiddenSize,
+        options: {
+            bias: kExampleBiasDescriptor,
+            recurrentBias: kExampleRecurrentBiasDescriptor,
+            restAfter: true,
+            layout: 'rzn',
+            activations: ['sigmoid', 'relu']
+        },
+        output: kExampleOutputDescriptor
+    },
+    {
+        name: '[gruCell] Throw if hiddenSize equals to zero',
+        input: kExampleInputDescriptor,
+        weight: kExampleWeightDescriptor,
+        recurrentWeight: kExampleRecurrentWeightDescriptor,
+        hiddenState: kExampleHiddenStateDescriptor,
+        hiddenSize: 0
+    },
+    {
+        name: '[gruCell] Throw if hiddenSize is too large',
+        input: kExampleInputDescriptor,
+        weight: kExampleWeightDescriptor,
+        recurrentWeight: kExampleRecurrentWeightDescriptor,
+        hiddenState: kExampleHiddenStateDescriptor,
+        hiddenSize: 4294967295,
+    },
+    {
+        name:
+            '[gruCell] Throw if the data type of the inputs is not one of the floating point types',
+        input: { dataType: 'uint32', dimensions: kValidInputDimensions },
+        weight: { dataType: 'uint32', dimensions: kValidWeightDimensions },
+        recurrentWeight: {
+            dataType: 'uint32',
+            dimensions: kValidRecurrentWeightDimensions
+        },
+        hiddenState: {
+            dataType: 'uint32',
+            dimensions: kValidHiddenStateDimensions
+        },
+        hiddenSize: hiddenSize
+    },
+    {
+        name:
+            '[gruCell] Throw if the rank of input is not 2',
+        input: { dataType: 'float32', dimensions: [batchSize] },
+        weight: kExampleWeightDescriptor,
+        recurrentWeight: kExampleRecurrentWeightDescriptor,
+        hiddenState: kExampleHiddenStateDescriptor,
+        hiddenSize: hiddenSize
+    },
+    {
+        name:
+            '[gruCell] Throw if the input.dimensions[1] is incorrect',
+        input: { dataType: 'float32', dimensions: [inputSize, inputSize] },
+        weight: kExampleWeightDescriptor,
+        recurrentWeight: kExampleRecurrentWeightDescriptor,
+        hiddenState: kExampleHiddenStateDescriptor,
+        hiddenSize: hiddenSize
+    },
+    {
+        name: '[gruCell] Throw if data type of weight is not one of the floating point types',
+        input: kExampleInputDescriptor,
+        weight: {
+            dataType: 'int8',
+            dimensions: [3 * hiddenSize, inputSize]
+        },
+        recurrentWeight: kExampleRecurrentWeightDescriptor,
+        hiddenState: kExampleHiddenStateDescriptor,
+        hiddenSize: hiddenSize
+    },
+    {
+        name: '[gruCell] Throw if rank of weight is not 2',
+        input: kExampleInputDescriptor,
+        weight: {
+            dataType: 'float32',
+            dimensions: [3 * hiddenSize]
+        },
+        recurrentWeight: kExampleRecurrentWeightDescriptor,
+        hiddenState: kExampleHiddenStateDescriptor,
+        hiddenSize: hiddenSize
+    },
+    {
+        name: '[gruCell] Throw if weight.dimensions[0] is not 3 * hiddenSize',
+        input: kExampleInputDescriptor,
+        weight: {
+            dataType: 'float32',
+            dimensions: [4 * hiddenSize, inputSize]
+        },
+        recurrentWeight: kExampleRecurrentWeightDescriptor,
+        hiddenState: kExampleHiddenStateDescriptor,
+        hiddenSize: hiddenSize
+    },
+    {
+        name: '[gruCell] Throw if data type of recurrentWeight is not one of the floating point types',
+        input: kExampleInputDescriptor,
+        weight: kExampleWeightDescriptor,
+        recurrentWeight: {
+            dataType: 'int32',
+            dimensions: [3 * hiddenSize, hiddenSize]
+        },
+        hiddenState: kExampleHiddenStateDescriptor,
+        hiddenSize: hiddenSize
+    },
+    {
+        name:
+            '[gruCell] Throw if the rank of recurrentWeight is not 2',
+        input: kExampleInputDescriptor,
+        weight: kExampleWeightDescriptor,
+        recurrentWeight:
+            { dataType: 'float32', dimensions: [3 * hiddenSize] },
+        hiddenState: kExampleHiddenStateDescriptor,
+        hiddenSize: hiddenSize
+    },
+    {
+        name:
+            '[gruCell] Throw if the recurrentWeight.dimensions is invalid',
+        input: kExampleInputDescriptor,
+        weight: kExampleWeightDescriptor,
+        recurrentWeight:
+            { dataType: 'float32', dimensions: [4 * hiddenSize, inputSize] },
+        hiddenState: kExampleHiddenStateDescriptor,
+        hiddenSize: hiddenSize
+    },
+    {
+        name:
+            '[gruCell] Throw if data type of hiddenState is not one of the floating point types',
+        input: kExampleInputDescriptor,
+        weight: kExampleWeightDescriptor,
+        recurrentWeight:
+            kExampleRecurrentWeightDescriptor,
+        hiddenState: {
+            dataType: 'uint32',
+            dimensions: [batchSize, hiddenSize]
+        },
+        hiddenSize: hiddenSize
+    },
+    {
+        name:
+            '[gruCell] Throw if the rank of hiddenState is not 2',
+        input: kExampleInputDescriptor,
+        weight: kExampleWeightDescriptor,
+        recurrentWeight:
+            kExampleRecurrentWeightDescriptor,
+        hiddenState: {
+            dataType: 'float32',
+            dimensions: [hiddenSize]
+        },
+        hiddenSize: hiddenSize
+    },
+    {
+        name:
+            '[gruCell] Throw if the hiddenState.dimensions is invalid',
+        input: kExampleInputDescriptor,
+        weight: kExampleWeightDescriptor,
+        recurrentWeight: kExampleRecurrentWeightDescriptor,
+        hiddenState: {
+            dataType: 'float32',
+            dimensions: [batchSize, 3 * hiddenSize]
+        },
+        hiddenSize: hiddenSize
+    },
+    {
+        name:
+            '[gruCell] Throw if the size of options.activations is not 2',
+        input: kExampleInputDescriptor,
+        weight: kExampleWeightDescriptor,
+        recurrentWeight: kExampleRecurrentWeightDescriptor,
+        hiddenState: kExampleHiddenStateDescriptor,
+        hiddenSize: hiddenSize,
+        options: { activations: ['sigmoid', 'tanh', 'relu'] }
+    },
+    {
+        name:
+            '[gruCell] Throw if data type of options.bias is not one of the floating point types',
+        input: kExampleInputDescriptor,
+        weight: kExampleWeightDescriptor,
+        recurrentWeight: kExampleRecurrentWeightDescriptor,
+        hiddenState: kExampleHiddenStateDescriptor,
+        hiddenSize: hiddenSize,
+        options: { bias: { dataType: 'uint8', dimensions: [3 * hiddenSize] } }
+    },
+    {
+        name:
+            '[gruCell] Throw if the rank of options.bias is not 1',
+        input: kExampleInputDescriptor,
+        weight: kExampleWeightDescriptor,
+        recurrentWeight: kExampleRecurrentWeightDescriptor,
+        hiddenState: kExampleHiddenStateDescriptor,
+        hiddenSize: hiddenSize,
+        options: { bias: { dataType: 'float32', dimensions: [batchSize, 3 * hiddenSize] } }
+    },
+    {
+        name:
+            '[gruCell] Throw if options.bias.dimensions[0] is not 3 * hiddenSize',
+        input: kExampleInputDescriptor,
+        weight: kExampleWeightDescriptor,
+        recurrentWeight: kExampleRecurrentWeightDescriptor,
+        hiddenState: kExampleHiddenStateDescriptor,
+        hiddenSize: hiddenSize,
+        options: { bias: { dataType: 'float32', dimensions: [2 * hiddenSize] } }
+    },
+    {
+        name:
+            '[gruCell] Throw if data type of options.recurrentBias is not one of the floating point types',
+        input: kExampleInputDescriptor,
+        weight: kExampleWeightDescriptor,
+        recurrentWeight: kExampleRecurrentWeightDescriptor,
+        hiddenState: kExampleHiddenStateDescriptor,
+        hiddenSize: hiddenSize,
+        options: { recurrentBias: { dataType: 'int8', dimensions: [3 * hiddenSize] } }
+    },
+    {
+        name:
+            '[gruCell] Throw if the rank of options.recurrentBias is not 1',
+        input: kExampleInputDescriptor,
+        weight: kExampleWeightDescriptor,
+        recurrentWeight: kExampleRecurrentWeightDescriptor,
+        hiddenState: kExampleHiddenStateDescriptor,
+        hiddenSize: hiddenSize,
+        options: { recurrentBias: { dataType: 'float32', dimensions: [batchSize, 3 * hiddenSize] } }
+    },
+    {
+        name:
+            '[gruCell] Throw if options.recurrentBias.dimensions[0] is not 3 * hiddenSize',
+        input: kExampleInputDescriptor,
+        weight: kExampleWeightDescriptor,
+        recurrentWeight: kExampleRecurrentWeightDescriptor,
+        hiddenState: kExampleHiddenStateDescriptor,
+        hiddenSize: hiddenSize,
+        options: {
+            recurrentBias: { dataType: 'float16', dimensions: [4 * hiddenSize] }
+        }
+    }
+];
+
+tests.forEach(
+    test => promise_test(async t => {
+        const input = builder.input(
+            'input',
+            { dataType: test.input.dataType, dimensions: test.input.dimensions });
+        const weight = builder.input(
+            'weight',
+            { dataType: test.weight.dataType, dimensions: test.weight.dimensions });
+        const recurrentWeight = builder.input('recurrentWeight', {
+            dataType: test.recurrentWeight.dataType,
+            dimensions: test.recurrentWeight.dimensions
+        });
+        const hiddenState = builder.input('hiddenState', {
+            dataType: test.hiddenState.dataType,
+            dimensions: test.hiddenState.dimensions
+        });
+
+        const options = {};
+        if (test.options) {
+            if (test.options.bias) {
+                options.bias = builder.input('bias', {
+                    dataType: test.options.bias.dataType,
+                    dimensions: test.options.bias.dimensions
+                });
+            }
+            if (test.options.recurrentBias) {
+                options.bias = builder.input('recurrentBias', {
+                    dataType: test.options.recurrentBias.dataType,
+                    dimensions: test.options.recurrentBias.dimensions
+                });
+            }
+            if (test.options.resetAfter) {
+                options.resetAfter = test.options.resetAfter;
+            }
+            if (test.options.layout) {
+                options.layout = test.options.layout;
+            }
+            if (test.options.activations) {
+                options.activations = [];
+                test.options.activations.forEach(
+                    activation => options.activations.push(builder[activation]()));
+            }
+        }
+
+        if (test.output) {
+            const output = builder.gruCell(
+                input, weight, recurrentWeight, hiddenState, test.hiddenSize,
+                options);
+            assert_equals(output.dataType(), test.output.dataType);
+            assert_array_equals(output.shape(), test.output.dimensions);
+        } else {
+            assert_throws_js(
+                TypeError,
+                () => builder.gruCell(
+                    input, weight, recurrentWeight, hiddenState, test.hiddenSize,
+                    options));
+        }
+    }, test.name));
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const inputFromOtherBuilder =
+      otherBuilder.input('input', kExampleInputDescriptor);
+
+  const weight = builder.input('weight', kExampleWeightDescriptor);
+  const recurrentWeight =
+      builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
+  const hiddenState =
+      builder.input('hiddenState', kExampleHiddenStateDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.gruCell(
+          inputFromOtherBuilder, weight, recurrentWeight, hiddenState,
+          hiddenSize));
+}, '[gruCell] throw if input is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const weightFromOtherBuilder =
+      otherBuilder.input('weight', kExampleWeightDescriptor);
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const recurrentWeight =
+      builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
+  const hiddenState =
+      builder.input('hiddenState', kExampleHiddenStateDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.gruCell(
+          input, weightFromOtherBuilder, recurrentWeight, hiddenState,
+          hiddenSize));
+}, '[gruCell] throw if weight is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const recurrentWeightFromOtherBuilder =
+      otherBuilder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const weight = builder.input('weight', kExampleWeightDescriptor);
+  const hiddenState =
+      builder.input('hiddenState', kExampleHiddenStateDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.gruCell(
+          input, weight, recurrentWeightFromOtherBuilder, hiddenState,
+          hiddenSize));
+}, '[gruCell] throw if recurrentWeight is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const hiddenStateFromOtherBuilder =
+      otherBuilder.input('hiddenState', kExampleHiddenStateDescriptor);
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const weight = builder.input('weight', kExampleWeightDescriptor);
+  const recurrentWeight =
+      builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.gruCell(
+          input, weight, recurrentWeight, hiddenStateFromOtherBuilder,
+          hiddenSize));
+}, '[gruCell] throw if hiddenState is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const biasFromOtherBuilder =
+      otherBuilder.input('bias', kExampleBiasDescriptor);
+  const options = {bias: biasFromOtherBuilder};
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const weight = builder.input('weight', kExampleWeightDescriptor);
+  const recurrentWeight =
+      builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
+  const hiddenState =
+      builder.input('hiddenState', kExampleHiddenStateDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.gruCell(
+          input, weight, recurrentWeight, hiddenState, hiddenSize, options));
+}, '[gruCell] throw if bias option is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const recurrentBiasFromOtherBuilder =
+      otherBuilder.input('recurrentBias', kExampleRecurrentBiasDescriptor);
+  const options = {recurrentBias: recurrentBiasFromOtherBuilder};
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const weight = builder.input('weight', kExampleWeightDescriptor);
+  const recurrentWeight =
+      builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
+  const hiddenState =
+      builder.input('hiddenState', kExampleHiddenStateDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.gruCell(
+          input, weight, recurrentWeight, hiddenState, hiddenSize, options));
+}, '[gruCell] throw if recurrentBias option is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const activation = builder.clamp();
+  const activationFromOtherBuilder = otherBuilder.clamp();
+  const options = {activations: [activation, activationFromOtherBuilder]};
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const weight = builder.input('weight', kExampleWeightDescriptor);
+  const recurrentWeight =
+      builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
+  const hiddenState =
+      builder.input('hiddenState', kExampleHiddenStateDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.gruCell(
+          input, weight, recurrentWeight, hiddenState, hiddenSize, options));
+}, '[gruCell] throw if any activation option is from another builder');
diff --git a/testing/web-platform/tests/webnn/validation_tests/hardSigmoid.https.any.js b/testing/web-platform/tests/webnn/validation_tests/hardSigmoid.https.any.js
new file mode 100644
index 0000000000..01b24dbc7c
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/hardSigmoid.https.any.js
@@ -0,0 +1,7 @@
+// META: title=validation tests for WebNN API hardSigmoid operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+validateInputFromAnotherBuilder('hardSigmoid');
diff --git a/testing/web-platform/tests/webnn/validation_tests/hardSwish.https.any.js b/testing/web-platform/tests/webnn/validation_tests/hardSwish.https.any.js
new file mode 100644
index 0000000000..97ecfb4142
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/hardSwish.https.any.js
@@ -0,0 +1,10 @@
+// META: title=validation tests for WebNN API hardSwish operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+validateInputFromAnotherBuilder('hardSwish');
+
+validateUnaryOperation(
+    'hardSwish', floatingPointTypes, /*alsoBuildActivation=*/ true);
diff --git a/testing/web-platform/tests/webnn/validation_tests/input.https.any.js b/testing/web-platform/tests/webnn/validation_tests/input.https.any.js
new file mode 100644
index 0000000000..a7561bf628
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/input.https.any.js
@@ -0,0 +1,70 @@
+// META: title=validation tests for WebNN API input interface
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+// Tests for input(name, descriptor)
+const tests = [
+  {
+    testName:
+        '[input] Test building a 0-D scalar input without presenting dimensions',
+    name: 'input',
+    descriptor: {dataType: 'float32'},
+    output: {dataType: 'float32', dimensions: []},
+  },
+  {
+    testName: '[input] Test building a 0-D scalar input with empty dimensions',
+    name: 'input',
+    descriptor: {dataType: 'float32', dimensions: []},
+    output: {dataType: 'float32', dimensions: []},
+  },
+  {
+    testName: '[input] Test building a 1-D input with int64 data type',
+    name: 'input',
+    descriptor: {dataType: 'int64', dimensions: [3]},
+    output: {dataType: 'int64', dimensions: [3]},
+  },
+  {
+    testName: '[input] Test building a 2-D input without errors',
+    name: 'input',
+    descriptor: {dataType: 'float32', dimensions: [3, 4]},
+    output: {dataType: 'float32', dimensions: [3, 4]},
+  },
+  {
+    testName: '[input] Throw if the name is empty',
+    name: '',
+    descriptor: {dataType: 'float32', dimensions: [3, 4]}
+  },
+  {
+    testName: '[input] Throw if a dimension size is 0',
+    name: 'input',
+    descriptor: {dataType: 'float32', dimensions: [3, 0]}
+  },
+  {
+    testName:
+        '[input] Throw if the value of any element in dimensions is outside the \'unsigned long\' value range',
+    name: 'input',
+    descriptor: {dataType: 'float32', dimensions: [kMaxUnsignedLong + 1]}
+  },
+  {
+    testName: '[input] Throw if the number of elements is too large',
+    name: 'input',
+    descriptor: {
+      dataType: 'float32',
+      dimensions: [kMaxUnsignedLong, kMaxUnsignedLong, kMaxUnsignedLong]
+    }
+  }
+];
+
+tests.forEach(
+    test => promise_test(async t => {
+      if (test.output) {
+        const inputOperand = builder.input(test.name, test.descriptor);
+        assert_equals(inputOperand.dataType(), test.output.dataType);
+        assert_array_equals(inputOperand.shape(), test.output.dimensions);
+      } else {
+        assert_throws_js(
+            TypeError, () => builder.input(test.name, test.descriptor));
+      }
+    }, test.testName));
diff --git a/testing/web-platform/tests/webnn/validation_tests/instanceNormalization.https.any.js b/testing/web-platform/tests/webnn/validation_tests/instanceNormalization.https.any.js
new file mode 100644
index 0000000000..bdd338588f
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/instanceNormalization.https.any.js
@@ -0,0 +1,43 @@
+// META: title=validation tests for WebNN API instanceNormalization operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+const kExampleInputDescriptor = {
+  dataType: 'float32',
+  dimensions: [2, 2, 2, 2]
+};
+// 1D tensor descriptor which may be used for `scale`, or `bias` inputs.
+const kExample1DTensorDescriptor = {
+  dataType: 'float32',
+  dimensions: [2]
+};
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const inputFromOtherBuilder =
+      otherBuilder.input('input', kExampleInputDescriptor);
+
+  assert_throws_js(
+      TypeError, () => builder.instanceNormalization(inputFromOtherBuilder));
+}, '[instanceNormalization] throw if input is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const scaleFromOtherBuilder =
+      otherBuilder.input('scale', kExample1DTensorDescriptor);
+  const options = {scale: scaleFromOtherBuilder};
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  assert_throws_js(
+      TypeError, () => builder.instanceNormalization(input, options));
+}, '[instanceNormalization] throw if scale option is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const biasFromOtherBuilder =
+      otherBuilder.input('bias', kExample1DTensorDescriptor);
+  const options = {bias: biasFromOtherBuilder};
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  assert_throws_js(
+      TypeError, () => builder.instanceNormalization(input, options));
+}, '[instanceNormalization] throw if bias option is from another builder');
diff --git a/testing/web-platform/tests/webnn/validation_tests/layerNormalization.https.any.js b/testing/web-platform/tests/webnn/validation_tests/layerNormalization.https.any.js
new file mode 100644
index 0000000000..e9e9141aa6
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/layerNormalization.https.any.js
@@ -0,0 +1,32 @@
+// META: title=validation tests for WebNN API layerNormalization operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+const kExampleInputDescriptor = {
+  dataType: 'float32',
+  dimensions: [2, 2]
+};
+
+validateOptionsAxes('layerNormalization', 4);
+
+validateInputFromAnotherBuilder('layerNormalization');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const scaleFromOtherBuilder =
+      otherBuilder.input('scale', kExampleInputDescriptor);
+  const options = {scale: scaleFromOtherBuilder};
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  assert_throws_js(TypeError, () => builder.layerNormalization(input, options));
+}, '[layerNormalization] throw if scale option is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const biasFromOtherBuilder =
+      otherBuilder.input('bias', kExampleInputDescriptor);
+  const options = {bias: biasFromOtherBuilder};
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  assert_throws_js(TypeError, () => builder.layerNormalization(input, options));
+}, '[layerNormalization] throw if bias option is from another builder');
diff --git a/testing/web-platform/tests/webnn/validation_tests/layer_normalization.https.any.js b/testing/web-platform/tests/webnn/validation_tests/layer_normalization.https.any.js
deleted file mode 100644
index 7dbcf5c74a..0000000000
--- a/testing/web-platform/tests/webnn/validation_tests/layer_normalization.https.any.js
+++ /dev/null
@@ -1,8 +0,0 @@
-// META: title=validation tests for WebNN API
-// META: global=window,dedicatedworker
-// META: script=../resources/utils_validation.js
-// META: timeout=long
-
-'use strict';
-
-validateOptionsAxes('layerNormalization', 4);
diff --git a/testing/web-platform/tests/webnn/validation_tests/leakyRelu.https.any.js b/testing/web-platform/tests/webnn/validation_tests/leakyRelu.https.any.js
new file mode 100644
index 0000000000..6fc19b1f0d
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/leakyRelu.https.any.js
@@ -0,0 +1,7 @@
+// META: title=validation tests for WebNN API leakyRelu operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+validateInputFromAnotherBuilder('leakyRelu');
diff --git a/testing/web-platform/tests/webnn/validation_tests/linear.https.any.js b/testing/web-platform/tests/webnn/validation_tests/linear.https.any.js
new file mode 100644
index 0000000000..99c1daad3f
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/linear.https.any.js
@@ -0,0 +1,7 @@
+// META: title=validation tests for WebNN API linear operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+validateInputFromAnotherBuilder('linear');
diff --git a/testing/web-platform/tests/webnn/validation_tests/lstm.https.any.js b/testing/web-platform/tests/webnn/validation_tests/lstm.https.any.js
index efa05090ca..18d609798c 100644
--- a/testing/web-platform/tests/webnn/validation_tests/lstm.https.any.js
+++ b/testing/web-platform/tests/webnn/validation_tests/lstm.https.any.js
@@ -1,25 +1,56 @@
 // META: title=validation tests for WebNN API lstm operation
 // META: global=window,dedicatedworker
 // META: script=../resources/utils_validation.js
-// META: timeout=long
 
 'use strict';
 
 const steps = 10, batchSize = 5, inputSize = 3, hiddenSize = 8,
       numDirections = 1;
 
+// Dimensions required of required inputs.
+const kValidInputDimensions = [steps, batchSize, inputSize];
+const kValidWeightDimensions = [numDirections, 4 * hiddenSize, inputSize];
+const kValidRecurrentWeightDimensions =
+    [numDirections, 4 * hiddenSize, hiddenSize];
+// Dimensions required of optional inputs.
+const kValidBiasDimensions = [numDirections, 4 * hiddenSize];
+const kValidPeepholeWeightDimensions = [numDirections, 3 * hiddenSize];
+const kValidInitialHiddenStateDimensions =
+    [numDirections, batchSize, hiddenSize];
+
+// Example descriptors which are valid according to the above dimensions.
+const kExampleInputDescriptor = {
+  dataType: 'float32',
+  dimensions: kValidInputDimensions
+};
+const kExampleWeightDescriptor = {
+  dataType: 'float32',
+  dimensions: kValidWeightDimensions
+};
+const kExampleRecurrentWeightDescriptor = {
+  dataType: 'float32',
+  dimensions: kValidRecurrentWeightDimensions
+};
+const kExampleBiasDescriptor = {
+  dataType: 'float32',
+  dimensions: kValidBiasDimensions
+};
+const kExamplePeepholeWeightDescriptor = {
+  dataType: 'float32',
+  dimensions: kValidPeepholeWeightDimensions
+};
+const kExampleInitialHiddenStateDescriptor = {
+  dataType: 'float32',
+  dimensions: kValidInitialHiddenStateDimensions
+};
+
 const tests = [
   {
     name: '[lstm] Test with default options',
-    input: {dataType: 'float16', dimensions: [steps, batchSize, inputSize]},
-    weight: {
-      dataType: 'float16',
-      dimensions: [numDirections, 4 * hiddenSize, inputSize]
-    },
-    recurrentWeight: {
-      dataType: 'float16',
-      dimensions: [numDirections, 4 * hiddenSize, hiddenSize]
-    },
+    input: {dataType: 'float16', dimensions: kValidInputDimensions},
+    weight: {dataType: 'float16', dimensions: kValidWeightDimensions},
+    recurrentWeight:
+        {dataType: 'float16', dimensions: kValidRecurrentWeightDimensions},
     steps: steps,
     hiddenSize: hiddenSize,
     outputs: [
@@ -29,7 +60,7 @@ const tests = [
   },
   {
     name: '[lstm] Test with given options',
-    input: {dataType: 'float32', dimensions: [steps, batchSize, inputSize]},
+    input: kExampleInputDescriptor,
     weight: {
       dataType: 'float32',
       dimensions: [/*numDirections=*/ 2, 4 * hiddenSize, inputSize]
@@ -83,73 +114,43 @@ const tests = [
   },
   {
     name: '[lstm] DataError is expected if hiddenSize equals to zero',
-    input: {dataType: 'float32', dimensions: [steps, batchSize, inputSize]},
-    weight: {
-      dataType: 'float32',
-      dimensions: [numDirections, 4 * hiddenSize, inputSize]
-    },
-    recurrentWeight: {
-      dataType: 'float32',
-      dimensions: [numDirections, 4 * hiddenSize, hiddenSize]
-    },
+    input: kExampleInputDescriptor,
+    weight: kExampleWeightDescriptor,
+    recurrentWeight: kExampleRecurrentWeightDescriptor,
     steps: steps,
     hiddenSize: 0
   },
   {
     name: '[lstm] DataError is expected if hiddenSize is too large',
-    input: {dataType: 'float32', dimensions: [steps, batchSize, inputSize]},
-    weight: {
-      dataType: 'float32',
-      dimensions: [numDirections, 4 * hiddenSize, inputSize]
-    },
-    recurrentWeight: {
-      dataType: 'float32',
-      dimensions: [numDirections, 4 * hiddenSize, hiddenSize]
-    },
+    input: kExampleInputDescriptor,
+    weight: kExampleWeightDescriptor,
+    recurrentWeight: kExampleRecurrentWeightDescriptor,
     steps: steps,
     hiddenSize: 4294967295,
   },
   {
     name: '[lstm] DataError is expected if steps equals to zero',
-    input: {dataType: 'float32', dimensions: [steps, batchSize, inputSize]},
-    weight: {
-      dataType: 'float32',
-      dimensions: [numDirections, 4 * hiddenSize, inputSize]
-    },
-    recurrentWeight: {
-      dataType: 'float32',
-      dimensions: [numDirections, 4 * hiddenSize, hiddenSize]
-    },
+    input: kExampleInputDescriptor,
+    weight: kExampleWeightDescriptor,
+    recurrentWeight: kExampleRecurrentWeightDescriptor,
     steps: 0,
     hiddenSize: hiddenSize,
   },
   {
     name:
         '[lstm] DataError is expected if the data type is not one of the floating point types',
-    input: {dataType: 'uint32', dimensions: [steps, batchSize, inputSize]},
-    weight: {
-      dataType: 'uint32',
-      dimensions: [numDirections, 4 * hiddenSize, inputSize]
-    },
-    recurrentWeight: {
-      dataType: 'uint32',
-      dimensions: [numDirections, 4 * hiddenSize, hiddenSize]
-    },
+    input: {dataType: 'uint32', dimensions: kValidInputDimensions},
+    weight: {dataType: 'uint32', dimensions: kValidWeightDimensions},
+    recurrentWeight:
+        {dataType: 'uint32', dimensions: kValidRecurrentWeightDimensions},
     steps: steps,
     hiddenSize: hiddenSize
   },
   {
-    name:
-        '[lstm] DataError is expected if the rank of input is not 3',
+    name: '[lstm] DataError is expected if the rank of input is not 3',
     input: {dataType: 'float32', dimensions: [steps, batchSize]},
-    weight: {
-      dataType: 'float32',
-      dimensions: [numDirections, 4 * hiddenSize, inputSize]
-    },
-    recurrentWeight: {
-      dataType: 'float32',
-      dimensions: [numDirections, 4 * hiddenSize, hiddenSize]
-    },
+    weight: kExampleWeightDescriptor,
+    recurrentWeight: kExampleRecurrentWeightDescriptor,
     steps: steps,
     hiddenSize: hiddenSize
   },
@@ -157,39 +158,27 @@ const tests = [
     name:
         '[lstm] DataError is expected if input.dimensions[0] is not equal to steps',
     input: {dataType: 'float32', dimensions: [1000, batchSize, inputSize]},
-    weight: {
-      dataType: 'float32',
-      dimensions: [numDirections, 4 * hiddenSize, inputSize]
-    },
-    recurrentWeight: {
-      dataType: 'float32',
-      dimensions: [numDirections, 4 * hiddenSize, hiddenSize]
-    },
+    weight: kExampleWeightDescriptor,
+    recurrentWeight: kExampleRecurrentWeightDescriptor,
     steps: steps,
     hiddenSize: hiddenSize
   },
   {
     name: '[lstm] DataError is expected if the shape of weight is incorrect',
-    input: {dataType: 'float32', dimensions: [steps, batchSize, inputSize]},
+    input: kExampleInputDescriptor,
     weight: {
       dataType: 'float32',
       dimensions: [numDirections, 4 * hiddenSize, 1000]
     },
-    recurrentWeight: {
-      dataType: 'float32',
-      dimensions: [numDirections, 4 * hiddenSize, hiddenSize]
-    },
+    recurrentWeight: kExampleRecurrentWeightDescriptor,
     steps: steps,
     hiddenSize: hiddenSize
   },
   {
     name:
         '[lstm] DataError is expected if the rank of recurrentWeight is not 3',
-    input: {dataType: 'float32', dimensions: [steps, batchSize, inputSize]},
-    weight: {
-      dataType: 'float32',
-      dimensions: [numDirections, 4 * hiddenSize, inputSize]
-    },
+    input: kExampleInputDescriptor,
+    weight: kExampleWeightDescriptor,
     recurrentWeight:
         {dataType: 'float32', dimensions: [numDirections, 4 * hiddenSize]},
     steps: steps,
@@ -198,31 +187,19 @@ const tests = [
   {
     name:
         '[lstm] DataError is expected if the size of options.activations is not 3',
-    input: {dataType: 'float32', dimensions: [steps, batchSize, inputSize]},
-    weight: {
-      dataType: 'float32',
-      dimensions: [numDirections, 4 * hiddenSize, inputSize]
-    },
-    recurrentWeight: {
-      dataType: 'float32',
-      dimensions: [numDirections, 4 * hiddenSize, hiddenSize]
-    },
+    input: kExampleInputDescriptor,
+    weight: kExampleWeightDescriptor,
+    recurrentWeight: kExampleRecurrentWeightDescriptor,
     steps: steps,
     hiddenSize: hiddenSize,
     options: {activations: ['sigmoid', 'tanh']}
   },
   {
-    name:
-        '[lstm] DataError is expected if the rank of options.bias is not 2',
-    input: {dataType: 'float16', dimensions: [steps, batchSize, inputSize]},
-    weight: {
-      dataType: 'float16',
-      dimensions: [numDirections, 4 * hiddenSize, inputSize]
-    },
-    recurrentWeight: {
-      dataType: 'float16',
-      dimensions: [numDirections, 4 * hiddenSize, hiddenSize]
-    },
+    name: '[lstm] DataError is expected if the rank of options.bias is not 2',
+    input: {dataType: 'float16', dimensions: kValidInputDimensions},
+    weight: {dataType: 'float16', dimensions: kValidWeightDimensions},
+    recurrentWeight:
+        {dataType: 'float16', dimensions: kValidRecurrentWeightDimensions},
     steps: steps,
     hiddenSize: hiddenSize,
     options: {bias: {dataType: 'float16', dimensions: [numDirections]}}
@@ -230,15 +207,10 @@ const tests = [
   {
     name:
         '[lstm] DataError is expected if the shape of options.recurrentBias.dimensions is incorrect',
-    input: {dataType: 'float16', dimensions: [steps, batchSize, inputSize]},
-    weight: {
-      dataType: 'float16',
-      dimensions: [numDirections, 4 * hiddenSize, inputSize]
-    },
-    recurrentWeight: {
-      dataType: 'float16',
-      dimensions: [numDirections, 4 * hiddenSize, hiddenSize]
-    },
+    input: {dataType: 'float16', dimensions: kValidInputDimensions},
+    weight: {dataType: 'float16', dimensions: kValidWeightDimensions},
+    recurrentWeight:
+        {dataType: 'float16', dimensions: kValidRecurrentWeightDimensions},
     steps: steps,
     hiddenSize: hiddenSize,
     options: {
@@ -248,15 +220,10 @@ const tests = [
   {
     name:
         '[lstm] DataError is expected if the dataType of options.peepholeWeight is incorrect',
-    input: {dataType: 'float16', dimensions: [steps, batchSize, inputSize]},
-    weight: {
-      dataType: 'float16',
-      dimensions: [numDirections, 4 * hiddenSize, inputSize]
-    },
-    recurrentWeight: {
-      dataType: 'float16',
-      dimensions: [numDirections, 4 * hiddenSize, hiddenSize]
-    },
+    input: {dataType: 'float16', dimensions: kValidInputDimensions},
+    weight: {dataType: 'float16', dimensions: kValidWeightDimensions},
+    recurrentWeight:
+        {dataType: 'float16', dimensions: kValidRecurrentWeightDimensions},
     steps: steps,
     hiddenSize: hiddenSize,
     options: {
@@ -267,15 +234,10 @@ const tests = [
   {
     name:
         '[lstm] DataError is expected if the dataType of options.initialHiddenState is incorrect',
-    input: {dataType: 'float16', dimensions: [steps, batchSize, inputSize]},
-    weight: {
-      dataType: 'float16',
-      dimensions: [numDirections, 4 * hiddenSize, inputSize]
-    },
-    recurrentWeight: {
-      dataType: 'float16',
-      dimensions: [numDirections, 4 * hiddenSize, hiddenSize]
-    },
+    input: {dataType: 'float16', dimensions: kValidInputDimensions},
+    weight: {dataType: 'float16', dimensions: kValidWeightDimensions},
+    recurrentWeight:
+        {dataType: 'float16', dimensions: kValidRecurrentWeightDimensions},
     steps: steps,
     hiddenSize: hiddenSize,
     options: {
@@ -288,15 +250,9 @@ const tests = [
   {
     name:
         '[lstm] DataError is expected if the shape of options.initialCellState is incorrect',
-    input: {dataType: 'float32', dimensions: [steps, batchSize, inputSize]},
-    weight: {
-      dataType: 'float32',
-      dimensions: [numDirections, 4 * hiddenSize, inputSize]
-    },
-    recurrentWeight: {
-      dataType: 'float32',
-      dimensions: [numDirections, 4 * hiddenSize, hiddenSize]
-    },
+    input: kExampleInputDescriptor,
+    weight: kExampleWeightDescriptor,
+    recurrentWeight: kExampleRecurrentWeightDescriptor,
     steps: steps,
     hiddenSize: hiddenSize,
     options: {
@@ -384,3 +340,132 @@ tests.forEach(
                 options));
       }
     }, test.name));
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const inputFromOtherBuilder =
+      otherBuilder.input('input', kExampleInputDescriptor);
+  const weight = builder.input('weight', kExampleWeightDescriptor);
+  const recurrentWeight =
+      builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
+
+  assert_throws_js(
+      TypeError,
+      () => builder.lstm(
+          inputFromOtherBuilder, weight, recurrentWeight, steps, hiddenSize));
+}, '[lstm] throw if input is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const input = builder.input('input', kExampleInputDescriptor);
+  const weightFromOtherBuilder =
+      otherBuilder.input('weight', kExampleWeightDescriptor);
+  const recurrentWeight =
+      builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
+
+  assert_throws_js(
+      TypeError,
+      () => builder.lstm(
+          input, weightFromOtherBuilder, recurrentWeight, steps, hiddenSize));
+}, '[lstm] throw if weight is from another builder');
+
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const input = builder.input('input', kExampleInputDescriptor);
+  const weight = builder.input('weight', kExampleWeightDescriptor);
+  const recurrentWeightFromOtherBuilder =
+      otherBuilder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
+
+  assert_throws_js(
+      TypeError,
+      () => builder.lstm(
+          input, weight, recurrentWeightFromOtherBuilder, steps, hiddenSize));
+}, '[lstm] throw if recurrentWeight is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const biasFromOtherBuilder =
+      otherBuilder.input('bias', kExampleBiasDescriptor);
+  const options = {bias: biasFromOtherBuilder};
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const weight = builder.input('weight', kExampleWeightDescriptor);
+  const recurrentWeight =
+      builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.lstm(
+          input, weight, recurrentWeight, steps, hiddenSize, options));
+}, '[lstm] throw if bias option is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const recurrentBiasFromOtherBuilder =
+      otherBuilder.input('bias', kExampleBiasDescriptor);
+  const options = {recurrentBias: recurrentBiasFromOtherBuilder};
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const weight = builder.input('weight', kExampleWeightDescriptor);
+  const recurrentWeight =
+      builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.lstm(
+          input, weight, recurrentWeight, steps, hiddenSize, options));
+}, '[lstm] throw if recurrentBias option is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const peepholeWeightFromOtherBuilder =
+      otherBuilder.input('peepholeWeight', kExamplePeepholeWeightDescriptor);
+  const options = {peepholeWeight: peepholeWeightFromOtherBuilder};
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const weight = builder.input('weight', kExampleWeightDescriptor);
+  const recurrentWeight =
+      builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.lstm(
+          input, weight, recurrentWeight, steps, hiddenSize, options));
+}, '[lstm] throw if peepholeWeight option is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const initialHiddenStateFromOtherBuilder = otherBuilder.input(
+      'initialHiddenState', kExampleInitialHiddenStateDescriptor);
+  const options = {initialHiddenState: initialHiddenStateFromOtherBuilder};
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const weight = builder.input('weight', kExampleWeightDescriptor);
+  const recurrentWeight =
+      builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.lstm(
+          input, weight, recurrentWeight, steps, hiddenSize, options));
+}, '[lstm] throw if initialHiddenState option is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const initialCellStateFromOtherBuilder = otherBuilder.input(
+      'initialCellState', kExampleInitialHiddenStateDescriptor);
+  const options = {initialCellState: initialCellStateFromOtherBuilder};
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const weight = builder.input('weight', kExampleWeightDescriptor);
+  const recurrentWeight =
+      builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.lstm(
+          input, weight, recurrentWeight, steps, hiddenSize, options));
+}, '[lstm] throw if initialCellState option is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const activation = builder.clamp();
+  const activationFromOtherBuilder = otherBuilder.clamp();
+  const options = {activations: [activation, activationFromOtherBuilder]};
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const weight = builder.input('weight', kExampleWeightDescriptor);
+  const recurrentWeight =
+      builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.lstm(
+          input, weight, recurrentWeight, steps, hiddenSize, options));
+}, '[lstm] throw if any activation option is from another builder');
diff --git a/testing/web-platform/tests/webnn/validation_tests/lstmCell.https.any.js b/testing/web-platform/tests/webnn/validation_tests/lstmCell.https.any.js
new file mode 100644
index 0000000000..c3769c828d
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/lstmCell.https.any.js
@@ -0,0 +1,600 @@
+// META: title=validation tests for WebNN API lstmCell operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+const batchSize = 3, inputSize = 4, hiddenSize = 5;
+
+// Dimensions required of required inputs.
+const kValidInputDimensions = [batchSize, inputSize];
+const kValidWeightDimensions = [4 * hiddenSize, inputSize];
+const kValidRecurrentWeightDimensions = [4 * hiddenSize, hiddenSize];
+const kValidHiddenStateDimensions = [batchSize, hiddenSize];
+const kValidCellStateDimensions = [batchSize, hiddenSize];
+// Dimensions required of optional inputs.
+const kValidBiasDimensions = [4 * hiddenSize];
+const kValidPeepholeWeightDimensions = [3 * hiddenSize];
+
+// Example descriptors which are valid according to the above dimensions.
+const kExampleInputDescriptor = {
+  dataType: 'float32',
+  dimensions: kValidInputDimensions
+};
+const kExampleWeightDescriptor = {
+  dataType: 'float32',
+  dimensions: kValidWeightDimensions
+};
+const kExampleRecurrentWeightDescriptor = {
+  dataType: 'float32',
+  dimensions: kValidRecurrentWeightDimensions
+};
+const kExampleHiddenStateDescriptor = {
+  dataType: 'float32',
+  dimensions: kValidHiddenStateDimensions
+};
+const kExampleCellStateDescriptor = {
+  dataType: 'float32',
+  dimensions: kValidCellStateDimensions
+};
+const kExampleBiasDescriptor = {
+  dataType: 'float32',
+  dimensions: kValidBiasDimensions
+};
+const kExamplePeepholeWeightDescriptor = {
+  dataType: 'float32',
+  dimensions: kValidPeepholeWeightDimensions
+};
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const inputFromOtherBuilder =
+      otherBuilder.input('input', kExampleInputDescriptor);
+
+  const weight = builder.input('weight', kExampleWeightDescriptor);
+  const recurrentWeight =
+      builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
+  const hiddenState =
+      builder.input('hiddenState', kExampleHiddenStateDescriptor);
+  const cellState = builder.input('cellState', kExampleCellStateDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.lstmCell(
+          inputFromOtherBuilder, weight, recurrentWeight, hiddenState,
+          cellState, hiddenSize));
+}, '[lstmCell] throw if input is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const weightFromOtherBuilder =
+      otherBuilder.input('weight', kExampleWeightDescriptor);
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const recurrentWeight =
+      builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
+  const hiddenState =
+      builder.input('hiddenState', kExampleHiddenStateDescriptor);
+  const cellState = builder.input('cellState', kExampleCellStateDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.lstmCell(
+          input, weightFromOtherBuilder, recurrentWeight, hiddenState,
+          cellState, hiddenSize));
+}, '[lstmCell] throw if weight is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const recurrentWeightFromOtherBuilder =
+      otherBuilder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const weight = builder.input('weight', kExampleWeightDescriptor);
+  const hiddenState =
+      builder.input('hiddenState', kExampleHiddenStateDescriptor);
+  const cellState = builder.input('cellState', kExampleCellStateDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.lstmCell(
+          input, weight, recurrentWeightFromOtherBuilder, hiddenState,
+          cellState, hiddenSize));
+}, '[lstmCell] throw if recurrentWeight is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const hiddenStateFromOtherBuilder =
+      otherBuilder.input('hiddenState', kExampleHiddenStateDescriptor);
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const weight = builder.input('weight', kExampleWeightDescriptor);
+  const recurrentWeight =
+      builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
+  const cellState = builder.input('cellState', kExampleCellStateDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.lstmCell(
+          input, weight, recurrentWeight, hiddenStateFromOtherBuilder,
+          cellState, hiddenSize));
+}, '[lstmCell] throw if hiddenState is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const cellStateFromOtherBuilder =
+      otherBuilder.input('cellState', kExampleCellStateDescriptor);
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const weight = builder.input('weight', kExampleWeightDescriptor);
+  const recurrentWeight =
+      builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
+  const hiddenState =
+      builder.input('hiddenState', kExampleHiddenStateDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.lstmCell(
+          input, weight, recurrentWeight, hiddenState,
+          cellStateFromOtherBuilder, hiddenSize));
+}, '[lstmCell] throw if cellState is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const biasFromOtherBuilder =
+      otherBuilder.input('bias', kExampleBiasDescriptor);
+  const options = {bias: biasFromOtherBuilder};
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const weight = builder.input('weight', kExampleWeightDescriptor);
+  const recurrentWeight =
+      builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
+  const hiddenState =
+      builder.input('hiddenState', kExampleHiddenStateDescriptor);
+  const cellState = builder.input('cellState', kExampleCellStateDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.lstmCell(
+          input, weight, recurrentWeight, hiddenState, cellState, hiddenSize,
+          options));
+}, '[lstmCell] throw if bias option is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const recurrentBiasFromOtherBuilder =
+      otherBuilder.input('bias', kExampleBiasDescriptor);
+  const options = {recurrentBias: recurrentBiasFromOtherBuilder};
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const weight = builder.input('weight', kExampleWeightDescriptor);
+  const recurrentWeight =
+      builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
+  const hiddenState =
+      builder.input('hiddenState', kExampleHiddenStateDescriptor);
+  const cellState = builder.input('cellState', kExampleCellStateDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.lstmCell(
+          input, weight, recurrentWeight, hiddenState, cellState, hiddenSize,
+          options));
+}, '[lstmCell] throw if recurrentBias option is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const peepholeWeightFromOtherBuilder =
+      otherBuilder.input('peepholeWeight', kExamplePeepholeWeightDescriptor);
+  const options = {peepholeWeight: peepholeWeightFromOtherBuilder};
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const weight = builder.input('weight', kExampleWeightDescriptor);
+  const recurrentWeight =
+      builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
+  const hiddenState =
+      builder.input('hiddenState', kExampleHiddenStateDescriptor);
+  const cellState = builder.input('cellState', kExampleCellStateDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.lstmCell(
+          input, weight, recurrentWeight, hiddenState, cellState, hiddenSize,
+          options));
+}, '[lstmCell] throw if peepholeWeight option is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const activation = builder.clamp();
+  const activationFromOtherBuilder = otherBuilder.clamp();
+  const options = {activations: [activation, activationFromOtherBuilder]};
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const weight = builder.input('weight', kExampleWeightDescriptor);
+  const recurrentWeight =
+      builder.input('recurrentWeight', kExampleRecurrentWeightDescriptor);
+  const hiddenState =
+      builder.input('hiddenState', kExampleHiddenStateDescriptor);
+  const cellState = builder.input('cellState', kExampleCellStateDescriptor);
+  assert_throws_js(
+      TypeError,
+      () => builder.lstmCell(
+          input, weight, recurrentWeight, hiddenState, cellState, hiddenSize,
+          options));
+}, '[lstmCell] throw if activation option is from another builder');
+
+const tests = [
+  {
+    name: '[lstmCell] Test with default options',
+    input: {dataType: 'float16', dimensions: [batchSize, inputSize]},
+    weight: {dataType: 'float16', dimensions: [4 * hiddenSize, inputSize]},
+    recurrentWeight:
+        {dataType: 'float16', dimensions: [4 * hiddenSize, hiddenSize]},
+    hiddenState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},
+    cellState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},
+    hiddenSize: hiddenSize,
+    outputs: [
+      {dataType: 'float16', dimensions: [batchSize, hiddenSize]},
+      {dataType: 'float16', dimensions: [batchSize, hiddenSize]}
+    ]
+  },
+  {
+    name: '[lstmCell] Test with given options',
+    input: {dataType: 'float32', dimensions: [batchSize, inputSize]},
+    weight: {dataType: 'float32', dimensions: [4 * hiddenSize, inputSize]},
+    recurrentWeight:
+        {dataType: 'float32', dimensions: [4 * hiddenSize, hiddenSize]},
+    hiddenState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},
+    cellState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},
+    hiddenSize: hiddenSize,
+    options: {
+      bias: {dataType: 'float32', dimensions: [4 * hiddenSize]},
+      recurrentBias: {dataType: 'float32', dimensions: [4 * hiddenSize]},
+      peepholeWeight: {dataType: 'float32', dimensions: [3 * hiddenSize]},
+      layout: 'ifgo',
+      activations: ['sigmoid', 'relu', 'tanh']
+    },
+    outputs: [
+      {dataType: 'float32', dimensions: [batchSize, hiddenSize]},
+      {dataType: 'float32', dimensions: [batchSize, hiddenSize]}
+    ]
+  },
+  {
+    name: '[lstmCell] Throw if hiddenSize is equal to zero',
+    input: {dataType: 'float32', dimensions: [batchSize, inputSize]},
+    weight: {dataType: 'float32', dimensions: [4 * hiddenSize, inputSize]},
+    recurrentWeight:
+        {dataType: 'float32', dimensions: [4 * hiddenSize, hiddenSize]},
+    hiddenState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},
+    cellState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},
+    hiddenSize: 0
+  },
+  {
+    name: '[lstmCell] Throw if hiddenSize is too large',
+    input: {dataType: 'float32', dimensions: [batchSize, inputSize]},
+    weight: {dataType: 'float32', dimensions: [4 * hiddenSize, inputSize]},
+    recurrentWeight:
+        {dataType: 'float32', dimensions: [4 * hiddenSize, hiddenSize]},
+    hiddenState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},
+    cellState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},
+    hiddenSize: 4294967295
+  },
+  {
+    name:
+        '[lstmCell] Throw if the input data type is not one of the floating point types',
+    input: {dataType: 'uint32', dimensions: [batchSize, inputSize]},
+    weight: {dataType: 'float32', dimensions: [4 * hiddenSize, inputSize]},
+    recurrentWeight:
+        {dataType: 'float32', dimensions: [4 * hiddenSize, hiddenSize]},
+    hiddenState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},
+    cellState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},
+    hiddenSize: hiddenSize
+  },
+  {
+    name: '[lstmCell] Throw if the rank of input is not 2',
+    input: {dataType: 'float32', dimensions: [batchSize]},
+    weight: {dataType: 'float32', dimensions: [4 * hiddenSize, inputSize]},
+    recurrentWeight:
+        {dataType: 'float32', dimensions: [4 * hiddenSize, hiddenSize]},
+    hiddenState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},
+    cellState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},
+    hiddenSize: hiddenSize
+  },
+  {
+    name: '[lstmCell] Throw if the shape of input is incorrect',
+    input: {dataType: 'float32', dimensions: [batchSize, 1000]},
+    weight: {dataType: 'float32', dimensions: [4 * hiddenSize, inputSize]},
+    recurrentWeight:
+        {dataType: 'float32', dimensions: [4 * hiddenSize, hiddenSize]},
+    hiddenState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},
+    cellState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},
+    hiddenSize: hiddenSize
+  },
+  {
+    name: '[lstmCell] Throw if the data type of weight is incorrect',
+    input: {dataType: 'float32', dimensions: [batchSize, inputSize]},
+    weight: {dataType: 'float16', dimensions: [4 * hiddenSize, inputSize]},
+    recurrentWeight:
+        {dataType: 'float32', dimensions: [4 * hiddenSize, hiddenSize]},
+    hiddenState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},
+    cellState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},
+    hiddenSize: hiddenSize
+  },
+  {
+    name: '[lstmCell] Throw if the rank of weight is not 2',
+    input: {dataType: 'float32', dimensions: [batchSize, inputSize]},
+    weight:
+        {dataType: 'float32', dimensions: [4 * hiddenSize, inputSize, 1000]},
+    recurrentWeight:
+        {dataType: 'float32', dimensions: [4 * hiddenSize, hiddenSize]},
+    hiddenState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},
+    cellState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},
+    hiddenSize: hiddenSize
+  },
+  {
+    name: '[lstmCell] Throw if the shape of weight is incorrect',
+    input: {dataType: 'float32', dimensions: [batchSize, inputSize]},
+    weight: {dataType: 'float32', dimensions: [1000, inputSize]},
+    recurrentWeight:
+        {dataType: 'float32', dimensions: [4 * hiddenSize, hiddenSize]},
+    hiddenState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},
+    cellState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},
+    hiddenSize: hiddenSize
+  },
+  {
+    name: '[lstmCell] Throw if the data type of recurrentWeight is incorrect',
+    input: {dataType: 'float32', dimensions: [batchSize, inputSize]},
+    weight: {dataType: 'float32', dimensions: [4 * hiddenSize, inputSize]},
+    recurrentWeight:
+        {dataType: 'float16', dimensions: [4 * hiddenSize, hiddenSize]},
+    hiddenState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},
+    cellState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},
+    hiddenSize: hiddenSize
+  },
+  {
+    name: '[lstmCell] Throw if the rank of recurrentWeight is not 2',
+    input: {dataType: 'float32', dimensions: [batchSize, inputSize]},
+    weight: {dataType: 'float32', dimensions: [4 * hiddenSize, inputSize]},
+    recurrentWeight:
+        {dataType: 'float32', dimensions: [1000, 4 * hiddenSize, hiddenSize]},
+    hiddenState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},
+    cellState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},
+    hiddenSize: hiddenSize
+  },
+  {
+    name: '[lstmCell] Throw if the shape of recurrentWeight is incorrect',
+    input: {dataType: 'float32', dimensions: [batchSize, inputSize]},
+    weight: {dataType: 'float32', dimensions: [4 * hiddenSize, inputSize]},
+    recurrentWeight: {dataType: 'float32', dimensions: [1000, hiddenSize]},
+    hiddenState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},
+    cellState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},
+    hiddenSize: hiddenSize
+  },
+  {
+    name: '[lstmCell] Throw if the data type of hiddenState is incorrect',
+    input: {dataType: 'float16', dimensions: [batchSize, inputSize]},
+    weight: {dataType: 'float16', dimensions: [4 * hiddenSize, inputSize]},
+    recurrentWeight:
+        {dataType: 'float16', dimensions: [4 * hiddenSize, hiddenSize]},
+    hiddenState: {dataType: 'int64', dimensions: [batchSize, hiddenSize]},
+    cellState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},
+    hiddenSize: hiddenSize
+  },
+  {
+    name: '[lstmCell] Throw if the rank of hiddenState is not 2',
+    input: {dataType: 'float32', dimensions: [batchSize, inputSize]},
+    weight: {dataType: 'float32', dimensions: [4 * hiddenSize, inputSize]},
+    recurrentWeight:
+        {dataType: 'float32', dimensions: [4 * hiddenSize, hiddenSize]},
+    hiddenState: {dataType: 'float32', dimensions: [batchSize]},
+    cellState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},
+    hiddenSize: hiddenSize
+  },
+  {
+    name: '[lstmCell] Throw if the shape of hiddenState is incorrect',
+    input: {dataType: 'float32', dimensions: [batchSize, inputSize]},
+    weight: {dataType: 'float32', dimensions: [4 * hiddenSize, inputSize]},
+    recurrentWeight:
+        {dataType: 'float32', dimensions: [4 * hiddenSize, hiddenSize]},
+    hiddenState: {dataType: 'float32', dimensions: [batchSize, 1000]},
+    cellState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},
+    hiddenSize: hiddenSize
+  },
+  {
+    name: '[lstmCell] Throw if the data type of cellState is incorrect',
+    input: {dataType: 'float16', dimensions: [batchSize, inputSize]},
+    weight: {dataType: 'float16', dimensions: [4 * hiddenSize, inputSize]},
+    recurrentWeight:
+        {dataType: 'float16', dimensions: [4 * hiddenSize, hiddenSize]},
+    hiddenState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},
+    cellState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},
+    hiddenSize: hiddenSize
+  },
+  {
+    name: '[lstmCell] Throw if the rank of cellState is not 2',
+    input: {dataType: 'float32', dimensions: [batchSize, inputSize]},
+    weight: {dataType: 'float32', dimensions: [4 * hiddenSize, inputSize]},
+    recurrentWeight:
+        {dataType: 'float32', dimensions: [4 * hiddenSize, hiddenSize]},
+    hiddenState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},
+    cellState: {dataType: 'float32', dimensions: [batchSize]},
+    hiddenSize: hiddenSize
+  },
+  {
+    name: '[lstmCell] Throw if the shape of cellState is incorrect',
+    input: {dataType: 'float16', dimensions: [batchSize, inputSize]},
+    weight: {dataType: 'float16', dimensions: [4 * hiddenSize, inputSize]},
+    recurrentWeight:
+        {dataType: 'float16', dimensions: [4 * hiddenSize, hiddenSize]},
+    hiddenState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},
+    cellState: {dataType: 'float16', dimensions: [batchSize, 1000]},
+    hiddenSize: hiddenSize
+  },
+  {
+    name: '[lstmCell] Throw if the data type of options.bias is incorrect',
+    input: {dataType: 'float16', dimensions: [batchSize, inputSize]},
+    weight: {dataType: 'float16', dimensions: [4 * hiddenSize, inputSize]},
+    recurrentWeight:
+        {dataType: 'float16', dimensions: [4 * hiddenSize, hiddenSize]},
+    hiddenState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},
+    cellState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},
+    hiddenSize: hiddenSize,
+    options: {bias: {dataType: 'int8', dimensions: [4 * hiddenSize]}}
+  },
+  {
+    name: '[lstmCell] Throw if the rank of options.bias is not 1',
+    input: {dataType: 'float16', dimensions: [batchSize, inputSize]},
+    weight: {dataType: 'float16', dimensions: [4 * hiddenSize, inputSize]},
+    recurrentWeight:
+        {dataType: 'float16', dimensions: [4 * hiddenSize, hiddenSize]},
+    hiddenState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},
+    cellState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},
+    hiddenSize: hiddenSize,
+    options: {bias: {dataType: 'float16', dimensions: [4 * hiddenSize, 1000]}}
+  },
+  {
+    name: '[lstmCell] Throw if the shape of options.bias is incorrect',
+    input: {dataType: 'float16', dimensions: [batchSize, inputSize]},
+    weight: {dataType: 'float16', dimensions: [4 * hiddenSize, inputSize]},
+    recurrentWeight:
+        {dataType: 'float16', dimensions: [4 * hiddenSize, hiddenSize]},
+    hiddenState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},
+    cellState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},
+    hiddenSize: hiddenSize,
+    options: {bias: {dataType: 'float16', dimensions: [1000]}}
+  },
+  {
+    name:
+        '[lstmCell] Throw if the data type of options.recurrentBias is incorrect',
+    input: {dataType: 'float16', dimensions: [batchSize, inputSize]},
+    weight: {dataType: 'float16', dimensions: [4 * hiddenSize, inputSize]},
+    recurrentWeight:
+        {dataType: 'float16', dimensions: [4 * hiddenSize, hiddenSize]},
+    hiddenState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},
+    cellState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},
+    hiddenSize: hiddenSize,
+    options: {recurrentBias: {dataType: 'uint8', dimensions: [4 * hiddenSize]}}
+  },
+  {
+    name: '[lstmCell] Throw if the rank of options.recurrentBias is not 1',
+    input: {dataType: 'float16', dimensions: [batchSize, inputSize]},
+    weight: {dataType: 'float16', dimensions: [4 * hiddenSize, inputSize]},
+    recurrentWeight:
+        {dataType: 'float16', dimensions: [4 * hiddenSize, hiddenSize]},
+    hiddenState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},
+    cellState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},
+    hiddenSize: hiddenSize,
+    options: {
+      recurrentBias: {dataType: 'float16', dimensions: [4 * hiddenSize, 1000]}
+    }
+  },
+  {
+    name: '[lstmCell] Throw if the shape of options.recurrentBias is incorrect',
+    input: {dataType: 'float16', dimensions: [batchSize, inputSize]},
+    weight: {dataType: 'float16', dimensions: [4 * hiddenSize, inputSize]},
+    recurrentWeight:
+        {dataType: 'float16', dimensions: [4 * hiddenSize, hiddenSize]},
+    hiddenState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},
+    cellState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},
+    hiddenSize: hiddenSize,
+    options: {recurrentBias: {dataType: 'float16', dimensions: [1000]}}
+  },
+  {
+    name:
+        '[lstmCell] Throw if the data type of options.peepholeWeight is incorrect',
+    input: {dataType: 'float16', dimensions: [batchSize, inputSize]},
+    weight: {dataType: 'float16', dimensions: [4 * hiddenSize, inputSize]},
+    recurrentWeight:
+        {dataType: 'float16', dimensions: [4 * hiddenSize, hiddenSize]},
+    hiddenState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},
+    cellState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},
+    hiddenSize: hiddenSize,
+    options:
+        {peepholeWeight: {dataType: 'float32', dimensions: [3 * hiddenSize]}}
+  },
+  {
+    name: '[lstmCell] Throw if the rank of options.peepholeWeight is not 1',
+    input: {dataType: 'float16', dimensions: [batchSize, inputSize]},
+    weight: {dataType: 'float16', dimensions: [4 * hiddenSize, inputSize]},
+    recurrentWeight:
+        {dataType: 'float16', dimensions: [4 * hiddenSize, hiddenSize]},
+    hiddenState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},
+    cellState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},
+    hiddenSize: hiddenSize,
+    options: {peepholeWeight: {dataType: 'float16', dimensions: []}}
+  },
+  {
+    name:
+        '[lstmCell] Throw if the shape of options.peepholeWeight is incorrect',
+    input: {dataType: 'float16', dimensions: [batchSize, inputSize]},
+    weight: {dataType: 'float16', dimensions: [4 * hiddenSize, inputSize]},
+    recurrentWeight:
+        {dataType: 'float16', dimensions: [4 * hiddenSize, hiddenSize]},
+    hiddenState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},
+    cellState: {dataType: 'float16', dimensions: [batchSize, hiddenSize]},
+    hiddenSize: hiddenSize,
+    options: {peepholeWeight: {dataType: 'float16', dimensions: [1000]}}
+  },
+  {
+    name: '[lstmCell] Throw if the size of options.activations is not 3',
+    input: {dataType: 'float32', dimensions: [batchSize, inputSize]},
+    weight: {dataType: 'float32', dimensions: [4 * hiddenSize, inputSize]},
+    recurrentWeight:
+        {dataType: 'float32', dimensions: [4 * hiddenSize, hiddenSize]},
+    hiddenState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},
+    cellState: {dataType: 'float32', dimensions: [batchSize, hiddenSize]},
+    hiddenSize: hiddenSize,
+    options: {activations: ['sigmoid', 'tanh', 'sigmoid', 'tanh']}
+  }
+];
+
+tests.forEach(
+    test => promise_test(async t => {
+      const input = builder.input(
+          'input',
+          {dataType: test.input.dataType, dimensions: test.input.dimensions});
+      const weight = builder.input(
+          'weight',
+          {dataType: test.weight.dataType, dimensions: test.weight.dimensions});
+      const recurrentWeight = builder.input('recurrentWeight', {
+        dataType: test.recurrentWeight.dataType,
+        dimensions: test.recurrentWeight.dimensions
+      });
+      const hiddenState = builder.input('hiddenState', {
+        dataType: test.hiddenState.dataType,
+        dimensions: test.hiddenState.dimensions
+      });
+      const cellState = builder.input('cellState', {
+        dataType: test.cellState.dataType,
+        dimensions: test.cellState.dimensions
+      });
+
+      const options = {};
+      if (test.options) {
+        if (test.options.bias) {
+          options.bias = builder.input('bias', {
+            dataType: test.options.bias.dataType,
+            dimensions: test.options.bias.dimensions
+          });
+        }
+        if (test.options.recurrentBias) {
+          options.bias = builder.input('recurrentBias', {
+            dataType: test.options.recurrentBias.dataType,
+            dimensions: test.options.recurrentBias.dimensions
+          });
+        }
+        if (test.options.peepholeWeight) {
+          options.peepholeWeight = builder.input('peepholeWeight', {
+            dataType: test.options.peepholeWeight.dataType,
+            dimensions: test.options.peepholeWeight.dimensions
+          });
+        }
+        if (test.options.layout) {
+          options.layout = test.options.layout;
+        }
+        if (test.options.activations) {
+          options.activations = [];
+          test.options.activations.forEach(
+              activation => options.activations.push(builder[activation]()));
+        }
+      }
+
+      if (test.outputs) {
+        const outputs = builder.lstmCell(
+            input, weight, recurrentWeight, hiddenState, cellState,
+            test.hiddenSize, options);
+        assert_equals(outputs.length, test.outputs.length);
+        for (let i = 0; i < outputs.length; ++i) {
+          assert_equals(outputs[i].dataType(), test.outputs[i].dataType);
+          assert_array_equals(outputs[i].shape(), test.outputs[i].dimensions);
+        }
+      } else {
+        assert_throws_js(
+            TypeError,
+            () => builder.lstmCell(
+                input, weight, recurrentWeight, hiddenState, cellState,
+                test.hiddenSize, options));
+      }
+    }, test.name));
diff --git a/testing/web-platform/tests/webnn/validation_tests/matmul.https.any.js b/testing/web-platform/tests/webnn/validation_tests/matmul.https.any.js
new file mode 100644
index 0000000000..03616ddb01
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/matmul.https.any.js
@@ -0,0 +1,7 @@
+// META: title=validation tests for WebNN API matmul operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+validateTwoInputsFromMultipleBuilders('matmul');
diff --git a/testing/web-platform/tests/webnn/validation_tests/pad.https.any.js b/testing/web-platform/tests/webnn/validation_tests/pad.https.any.js
new file mode 100644
index 0000000000..11c6a8f7ef
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/pad.https.any.js
@@ -0,0 +1,17 @@
+// META: title=validation tests for WebNN API pad operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const inputFromOtherBuilder =
+      otherBuilder.input('input', {dataType: 'float32', dimensions: [2, 2]});
+
+  const beginningPadding = [1, 1];
+  const endingPadding = [1, 1];
+  assert_throws_js(
+      TypeError,
+      () =>
+          builder.pad(inputFromOtherBuilder, beginningPadding, endingPadding));
+}, '[pad] throw if input is from another builder');
diff --git a/testing/web-platform/tests/webnn/validation_tests/pooling.https.any.js b/testing/web-platform/tests/webnn/validation_tests/pooling.https.any.js
new file mode 100644
index 0000000000..e8add0511f
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/pooling.https.any.js
@@ -0,0 +1,275 @@
+// META: title=validation tests for WebNN API pooling operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+const kPoolingOperators = ['averagePool2d', 'l2Pool2d', 'maxPool2d'];
+
+kPoolingOperators.forEach((operatorName) => {
+  validateInputFromAnotherBuilder(
+      operatorName, {dataType: 'float32', dimensions: [2, 2, 2, 2]});
+});
+
+
+const tests = [
+  {
+    name: 'Test pool2d with default options.',
+    input: {dataType: 'float32', dimensions: [1, 3, 4, 4]},
+    output: {dataType: 'float32', dimensions: [1, 3, 1, 1]}
+  },
+  {
+    name: 'Test pool2d with windowDimensions',
+    input: {dataType: 'float32', dimensions: [1, 3, 4, 4]},
+    options: {
+      windowDimensions: [3, 3],
+    },
+    output: {dataType: 'float32', dimensions: [1, 3, 2, 2]}
+  },
+  {
+    name: 'Test pool2d with padding.',
+    input: {dataType: 'float32', dimensions: [1, 3, 5, 5]},
+    options: {
+      windowDimensions: [5, 5],
+      padding: [2, 2, 2, 2],
+    },
+    output: {dataType: 'float32', dimensions: [1, 3, 5, 5]}
+  },
+  {
+    name: 'Test pool2d with strides.',
+    input: {dataType: 'float32', dimensions: [1, 3, 5, 5]},
+    options: {
+      windowDimensions: [2, 2],
+      strides: [2, 2],
+    },
+    output: {dataType: 'float32', dimensions: [1, 3, 2, 2]}
+  },
+  {
+    name: 'Test pool2d with strides and padding.',
+    input: {dataType: 'float32', dimensions: [1, 3, 5, 5]},
+    options: {
+      windowDimensions: [3, 3],
+      padding: [1, 1, 1, 1],
+      strides: [2, 2],
+    },
+    output: {dataType: 'float32', dimensions: [1, 3, 3, 3]}
+  },
+  {
+    name: 'Test pool2d with strides and asymmetric padding.',
+    input: {dataType: 'float32', dimensions: [1, 3, 7, 7]},
+    options: {
+      windowDimensions: [4, 4],
+      padding: [2, 1, 2, 1],
+      strides: [2, 2],
+    },
+    output: {dataType: 'float32', dimensions: [1, 3, 4, 4]}
+  },
+  {
+    name: 'Test pool2d with strides, padding and roundingType="floor".',
+    input: {dataType: 'float32', dimensions: [1, 3, 7, 7]},
+    options: {
+      windowDimensions: [4, 4],
+      padding: [1, 1, 1, 1],
+      strides: [2, 2],
+      roundingType: 'floor',
+    },
+    output: {dataType: 'float32', dimensions: [1, 3, 3, 3]}
+  },
+  {
+    name: 'Test pool2d with strides, padding and roundingType="ceil".',
+    input: {dataType: 'float32', dimensions: [1, 3, 7, 7]},
+    options: {
+      windowDimensions: [4, 4],
+      padding: [1, 1, 1, 1],
+      strides: [2, 2],
+      roundingType: 'ceil',
+    },
+    output: {dataType: 'float32', dimensions: [1, 3, 4, 4]}
+  },
+  {
+    name: 'Test pool2d with explicit outputSizes ignored roundingType',
+    input: {dataType: 'float32', dimensions: [1, 3, 7, 7]},
+    options: {
+      windowDimensions: [4, 4],
+      padding: [1, 1, 1, 1],
+      strides: [2, 2],
+      roundingType: 'ceil',
+      outputSizes: [3, 3],
+    },
+    output: {dataType: 'float32', dimensions: [1, 3, 3, 3]}
+  },
+  {
+    name: 'Test pool2d with strides, padding and outputSizes=[3, 3].',
+    input: {dataType: 'float32', dimensions: [1, 3, 7, 7]},
+    options: {
+      windowDimensions: [4, 4],
+      padding: [1, 1, 1, 1],
+      strides: [2, 2],
+      outputSizes: [3, 3],
+    },
+    output: {dataType: 'float32', dimensions: [1, 3, 3, 3]}
+  },
+  {
+    name: 'Test pool2d with strides, padding and outputSizes=[4, 4].',
+    input: {dataType: 'float32', dimensions: [1, 3, 7, 7]},
+    options: {
+      windowDimensions: [4, 4],
+      padding: [1, 1, 1, 1],
+      strides: [2, 2],
+      outputSizes: [4, 4],
+    },
+    output: {dataType: 'float32', dimensions: [1, 3, 4, 4]}
+  },
+  {
+    name: 'Test pool2d with layout="nchw".',
+    input: {dataType: 'float32', dimensions: [1, 2, 5, 5]},
+    options: {
+      windowDimensions: [3, 3],
+      layout: 'nchw',
+    },
+    output: {dataType: 'float32', dimensions: [1, 2, 3, 3]}
+  },
+  {
+    name: 'Test pool2d with layout="nhwc".',
+    input: {dataType: 'float32', dimensions: [1, 5, 5, 2]},
+    options: {
+      windowDimensions: [3, 3],
+      layout: 'nhwc',
+    },
+    output: {dataType: 'float32', dimensions: [1, 3, 3, 2]}
+  },
+  {
+    name: 'Throw if the input is not a 4-D tensor.',
+    input: {dataType: 'float32', dimensions: [1, 5, 5]},
+  },
+  {
+    name: 'Throw if the output sizes is incorrect.',
+    input: {dataType: 'float32', dimensions: [1, 2, 5, 5]},
+    options: {
+      windowDimensions: [2, 2],
+      padding: [2, 2, 2, 2],
+      strides: [2, 2],
+      outputSizes: [3, 3],
+    },
+  },
+  {
+    name: 'Throw if the length of output sizes is not 2.',
+    input: {dataType: 'float32', dimensions: [1, 2, 5, 5]},
+    options: {
+      windowDimensions: [2, 2],
+      padding: [2, 2, 2, 2],
+      strides: [2, 2],
+      outputSizes: [1, 2, 4, 4],
+    },
+  },
+  {
+    name: 'Throw if the length of window dimensions is not 2.',
+    input: {dataType: 'float32', dimensions: [1, 2, 5, 5]},
+    options: {
+      windowDimensions: [1, 1, 1, 1],
+    },
+  },
+  {
+    name: 'Throw if any window dimension is lesser than 1.',
+    input: {dataType: 'float32', dimensions: [1, 2, 5, 5]},
+    options: {
+      windowDimensions: [0, 2],
+    },
+  },
+  {
+    name:
+        'Throw if the input height is too small to fill the pool window height.',
+    input: {dataType: 'float32', dimensions: [1, 2, 5, 5]},
+    options: {
+      windowDimensions: [8, 2],
+    },
+  },
+  {
+    name:
+        'Throw if the input width is too small to fill the pool window width.',
+    input: {dataType: 'float32', dimensions: [1, 2, 5, 5]},
+    options: {
+      windowDimensions: [2, 8],
+    },
+  },
+  {
+    name: 'Throw if the calculated output height is equal to 0.',
+    input: {dataType: 'float32', dimensions: [1, 2, 5, 5]},
+    options: {
+      windowDimensions: [6, 3],
+    },
+  },
+  {
+    name: 'Throw if the calculated output width is equal to 0.',
+    input: {dataType: 'float32', dimensions: [1, 2, 5, 5]},
+    options: {
+      windowDimensions: [3, 6],
+    },
+  },
+  {
+    name: 'Throw if the length of padding is not 4.',
+    input: {dataType: 'float32', dimensions: [1, 2, 5, 5]},
+    options: {
+      padding: [2, 2],
+    },
+  },
+  {
+    name: 'Throw if the length of strides is not 2.',
+    input: {dataType: 'float32', dimensions: [1, 2, 5, 5]},
+    options: {
+      strides: [2],
+    },
+  },
+  {
+    name: 'Throw if one stride value is smaller than 1.',
+    input: {dataType: 'float32', dimensions: [1, 2, 5, 5]},
+    options: {
+      strides: [0, 2],
+    },
+  },
+  {
+    name: 'Throw if the length of dilations is not 2.',
+    input: {dataType: 'float32', dimensions: [1, 2, 5, 5]},
+    options: {
+      dilations: [1, 1, 2],
+    },
+  },
+  {
+    name: 'Throw if one dilation value is smaller than 1.',
+    input: {dataType: 'float32', dimensions: [1, 2, 5, 5]},
+    options: {
+      dilations: [1, 0],
+    },
+  },
+  {
+    name: 'Throw if the padding height value is too large',
+    input: {dataType: 'float32', dimensions: [1, 3, 5, 5]},
+    options: {
+      padding: [kMaxUnsignedLong, kMaxUnsignedLong, 0, 0],
+    },
+  },
+  {
+    name: 'Throw if the padding width value is too large',
+    input: {dataType: 'float32', dimensions: [1, 3, 5, 5]},
+    options: {
+      padding: [0, 0, kMaxUnsignedLong, kMaxUnsignedLong],
+    },
+  },
+];
+
+tests.forEach(
+    test => promise_test(async t => {
+      const input = builder.input(
+          'input',
+          {dataType: test.input.dataType, dimensions: test.input.dimensions});
+      kPoolingOperators.forEach((operatorName) => {
+        if (test.output) {
+          const output = builder[operatorName](input, test.options);
+          assert_equals(output.dataType(), test.output.dataType);
+          assert_array_equals(output.shape(), test.output.dimensions);
+        } else {
+          assert_throws_js(
+              TypeError, () => builder[operatorName](input, test.options));
+        }
+      });
+    }, test.name));
diff --git a/testing/web-platform/tests/webnn/validation_tests/prelu.https.any.js b/testing/web-platform/tests/webnn/validation_tests/prelu.https.any.js
new file mode 100644
index 0000000000..865f9f684c
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/prelu.https.any.js
@@ -0,0 +1,7 @@
+// META: title=validation tests for WebNN API prelu operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+validateTwoInputsFromMultipleBuilders('prelu');
diff --git a/testing/web-platform/tests/webnn/validation_tests/reduction.https.any.js b/testing/web-platform/tests/webnn/validation_tests/reduction.https.any.js
index 65b71239b9..60f0978678 100644
--- a/testing/web-platform/tests/webnn/validation_tests/reduction.https.any.js
+++ b/testing/web-platform/tests/webnn/validation_tests/reduction.https.any.js
@@ -1,11 +1,10 @@
-// META: title=validation tests for WebNN API reduction  operation
+// META: title=validation tests for WebNN API reduction operation
 // META: global=window,dedicatedworker
 // META: script=../resources/utils_validation.js
-// META: timeout=long
 
 'use strict';
 
-[
+const kReductionOperators = [
   'reduceL1',
   'reduceL2',
   'reduceLogSum',
@@ -16,6 +15,9 @@
   'reduceProduct',
   'reduceSum',
   'reduceSumSquare',
-].forEach((operationName) => {
-  validateOptionsAxes(operationName);
+];
+
+kReductionOperators.forEach((operatorName) => {
+  validateOptionsAxes(operatorName);
+  validateInputFromAnotherBuilder(operatorName);
 });
diff --git a/testing/web-platform/tests/webnn/validation_tests/relu.https.any.js b/testing/web-platform/tests/webnn/validation_tests/relu.https.any.js
new file mode 100644
index 0000000000..237c1c3eda
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/relu.https.any.js
@@ -0,0 +1,10 @@
+// META: title=validation tests for WebNN API relu operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+validateInputFromAnotherBuilder('relu');
+
+validateUnaryOperation(
+    'relu', allWebNNOperandDataTypes, /*alsoBuildActivation=*/ true);
diff --git a/testing/web-platform/tests/webnn/validation_tests/resample2d.https.any.js b/testing/web-platform/tests/webnn/validation_tests/resample2d.https.any.js
index 2e00cf297c..de44c6a333 100644
--- a/testing/web-platform/tests/webnn/validation_tests/resample2d.https.any.js
+++ b/testing/web-platform/tests/webnn/validation_tests/resample2d.https.any.js
@@ -1,8 +1,158 @@
 // META: title=validation tests for WebNN API resample2d operation
 // META: global=window,dedicatedworker
 // META: script=../resources/utils_validation.js
-// META: timeout=long
 
 'use strict';
 
-validateOptionsAxes('resample2d', 4);
+// Tests for resample2d(input, options)
+const tests = [
+  {
+    name: '[resample2d] Test building resample2d with default options',
+    input: {dataType: 'float32', dimensions: [1, 1, 2, 4]},
+    output: {dataType: 'float32', dimensions: [1, 1, 2, 4]},
+  },
+  {
+    name: '[resample2d] Test building resample2d with scales=[2.0, 2.0]',
+    input: {dataType: 'float32', dimensions: [1, 1, 2, 4]},
+    options: {scales: [2.0, 2.0]},
+    output: {dataType: 'float32', dimensions: [1, 1, 4, 8]},
+  },
+  {
+    name: '[resample2d] Test building resample2d with scales=[0.5, 0.5]',
+    input: {dataType: 'float32', dimensions: [1, 1, 5, 5]},
+    options: {scales: [0.5, 0.5]},
+    output: {dataType: 'float32', dimensions: [1, 1, 2, 2]},
+  },
+  {
+    name:
+        '[resample2d] Test building resample2d with scales=[0.5, 0.5] and explicit axes=[2, 3]',
+    input: {dataType: 'float32', dimensions: [1, 1, 5, 5]},
+    options: {scales: [0.5, 0.5], axes: [2, 3]},
+    output: {dataType: 'float32', dimensions: [1, 1, 2, 2]},
+  },
+  {
+    name:
+        '[resample2d] Test building resample2d with scales=[1.0, 2.0] and axes=[0, 1]',
+    input: {dataType: 'float32', dimensions: [1, 1, 2, 4]},
+    options: {scales: [1.0, 2.0], axes: [0, 1]},
+    output: {dataType: 'float32', dimensions: [1, 2, 2, 4]},
+  },
+  {
+    name:
+        '[resample2d] Test building resample2d with scales=[2.0, 2.0] and axes=[1, 2]',
+    input: {dataType: 'float32', dimensions: [1, 1, 2, 4]},
+    options: {scales: [2.0, 2.0], axes: [1, 2]},
+    output: {dataType: 'float32', dimensions: [1, 2, 4, 4]},
+  },
+  {
+    name:
+        '[resample2d] Test building resample2d with sizes=[3, 6] ignored scales',
+    input: {dataType: 'float32', dimensions: [1, 1, 2, 4]},
+    options: {scales: [2.0, 2.0], sizes: [3, 6]},
+    output: {dataType: 'float32', dimensions: [1, 1, 3, 6]},
+  },
+  {
+    name: '[resample2d] Throw if the rank of input is not 4',
+    input: {dataType: 'float32', dimensions: [2, 4]},
+  },
+  {
+    name: '[resample2d] Throw if the length of scales is not 2',
+    input: {dataType: 'float32', dimensions: [1, 1, 2, 4]},
+    options: {scales: [1.0, 1.0, 2.0, 2.0]},
+  },
+  {
+    name: '[resample2d] Throw if any scale value is negative',
+    input: {dataType: 'float32', dimensions: [1, 1, 2, 4]},
+    options: {scales: [1.0, -2.0]},
+  },
+  {
+    name: '[resample2d] Throw if any scale value is 0',
+    input: {dataType: 'float32', dimensions: [1, 1, 2, 4]},
+    options: {scales: [0, 2.0]},
+  },
+  {
+    name: '[resample2d] Throw if the length of sizes is not 2',
+    input: {dataType: 'float32', dimensions: [1, 1, 2, 4]},
+    options: {sizes: [1, 1, 4, 6]},
+  },
+  {
+    name:
+        '[resample2d] Throw if any size value is out of \'unsigned long\' value range',
+    input: {dataType: 'float32', dimensions: [1, 1, 2, 4]},
+    options: {sizes: [kMaxUnsignedLong + 1, kMaxUnsignedLong + 1]},
+  },
+  {
+    name:
+        '[resample2d] Throw if outputHeight being floor(scaleHeight*inputHeight) is too large',
+    input: {dataType: 'float32', dimensions: [1, 1, 2, 4]},
+    // The maximum dimension size is kMaxUnsignedLong (2 ** 32 - 1).
+    // Here scaleHeight=kMaxUnsignedLong and inputHeight=2,
+    // so outputHeight being kMaxUnsignedLong*2 > kMaxUnsignedLong .
+    options: {scales: /*[scaleHeight, scaleWidth]*/[kMaxUnsignedLong, 1]},
+  },
+  {
+    name: '[resample2d] Throw if scaleHeight is too small',
+    input: {dataType: 'float32', dimensions: [1, 1, 2, 4]},
+    // Here scaleHeight=0.02 and inputHeight=2,
+    // so outputHeight would be 0.
+    // Link to https://github.com/webmachinelearning/webnn/issues/391.
+    options: {scales: /*[scaleHeight, scaleWidth]*/[0.02, 0.8]},
+  },
+  {
+    name:
+        '[resample2d] Throw if outputWidth being floor(scaleWidth*inputWidth) is too large',
+    input: {dataType: 'float32', dimensions: [1, 1, 4, 2]},
+    // The maximum dimension size is kMaxUnsignedLong (2 ** 32 - 1).
+    // Here scaleWidth=kMaxUnsignedLong and inputWidth=2,
+    // so outputWidth being kMaxUnsignedLong*2 > kMaxUnsignedLong .
+    options: {scales: /*[scaleHeight, scaleWidth]*/[1, kMaxUnsignedLong]},
+  },
+  {
+    name: '[resample2d] Throw if scaleWidth is too small',
+    input: {dataType: 'float32', dimensions: [1, 1, 2, 4]},
+    // Here scaleWidth=0.1 and inputWidth=4,
+    // so outputWidth would be 0.
+    // Link to https://github.com/webmachinelearning/webnn/issues/391.
+    options: {scales: /*[scaleHeight, scaleWidth]*/[0.7, 0.1]},
+  },
+  {
+    name: '[resample2d] Throw if the length of axes is not 2',
+    input: {dataType: 'float32', dimensions: [1, 1, 2, 4]},
+    options: {axes: [0, 1, 2]},
+  },
+  {
+    name:
+        '[resample2d] Throw if any axis value is greater than or equal to the input rank',
+    input: {dataType: 'float32', dimensions: [1, 1, 2, 4]},
+    options: {axes: [3, 4]},
+  },
+  {
+    // The valid values in the axes sequence are [0, 1], [1, 2] or [2, 3]
+    name: '[resample2d] Throw if the values of axes are inconsecutive',
+    input: {dataType: 'float32', dimensions: [1, 1, 2, 4]},
+    options: {axes: [0, 2]},
+  },
+  {
+    name: '[resample2d] Throw if the values of axes are same',
+    input: {dataType: 'float32', dimensions: [1, 1, 2, 4]},
+    options: {axes: [0, 0]},
+  },
+];
+
+tests.forEach(
+    test => promise_test(async t => {
+      const input = builder.input(
+          'input',
+          {dataType: test.input.dataType, dimensions: test.input.dimensions});
+      const options = test.options ?? {};
+      if (test.output) {
+        const output = builder.resample2d(input, options);
+        assert_equals(output.dataType(), test.output.dataType);
+        assert_array_equals(output.shape(), test.output.dimensions);
+      } else {
+        assert_throws_js(TypeError, () => builder.resample2d(input, options));
+      }
+    }, test.name));
+
+validateInputFromAnotherBuilder(
+    'resample2d', {dataType: 'float32', dimensions: [2, 2, 2, 2]});
diff --git a/testing/web-platform/tests/webnn/validation_tests/reshape.https.any.js b/testing/web-platform/tests/webnn/validation_tests/reshape.https.any.js
new file mode 100644
index 0000000000..435551b716
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/reshape.https.any.js
@@ -0,0 +1,14 @@
+// META: title=validation tests for WebNN API reshape operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const inputFromOtherBuilder =
+      otherBuilder.input('input', {dataType: 'float32', dimensions: [1, 2, 3]});
+
+  const newShape = [3, 2, 1];
+  assert_throws_js(
+      TypeError, () => builder.reshape(inputFromOtherBuilder, newShape));
+}, '[reshape] throw if input is from another builder');
diff --git a/testing/web-platform/tests/webnn/validation_tests/sigmoid.https.any.js b/testing/web-platform/tests/webnn/validation_tests/sigmoid.https.any.js
new file mode 100644
index 0000000000..b40ddc3fd4
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/sigmoid.https.any.js
@@ -0,0 +1,10 @@
+// META: title=validation tests for WebNN API sigmoid operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+validateInputFromAnotherBuilder('sigmoid');
+
+validateUnaryOperation(
+    'sigmoid', floatingPointTypes, /*alsoBuildActivation=*/ true);
diff --git a/testing/web-platform/tests/webnn/validation_tests/slice.https.any.js b/testing/web-platform/tests/webnn/validation_tests/slice.https.any.js
new file mode 100644
index 0000000000..a45ecd3fcb
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/slice.https.any.js
@@ -0,0 +1,15 @@
+// META: title=validation tests for WebNN API slice operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const inputFromOtherBuilder =
+      otherBuilder.input('input', {dataType: 'float32', dimensions: [2, 2]});
+
+  const starts = [1, 1];
+  const sizes = [1, 1];
+  assert_throws_js(
+      TypeError, () => builder.slice(inputFromOtherBuilder, starts, sizes));
+}, '[slice] throw if input is from another builder');
diff --git a/testing/web-platform/tests/webnn/validation_tests/softmax.https.any.js b/testing/web-platform/tests/webnn/validation_tests/softmax.https.any.js
new file mode 100644
index 0000000000..68891b27d8
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/softmax.https.any.js
@@ -0,0 +1,7 @@
+// META: title=validation tests for WebNN API softmax operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+validateInputFromAnotherBuilder('softmax');
diff --git a/testing/web-platform/tests/webnn/validation_tests/softplus.https.any.js b/testing/web-platform/tests/webnn/validation_tests/softplus.https.any.js
new file mode 100644
index 0000000000..347dfcd938
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/softplus.https.any.js
@@ -0,0 +1,7 @@
+// META: title=validation tests for WebNN API softplus operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+validateInputFromAnotherBuilder('softplus');
diff --git a/testing/web-platform/tests/webnn/validation_tests/softsign.https.any.js b/testing/web-platform/tests/webnn/validation_tests/softsign.https.any.js
new file mode 100644
index 0000000000..58ec487159
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/softsign.https.any.js
@@ -0,0 +1,10 @@
+// META: title=validation tests for WebNN API softsign operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+validateInputFromAnotherBuilder('softsign');
+
+validateUnaryOperation(
+    'softsign', floatingPointTypes, /*alsoBuildActivation=*/ true);
diff --git a/testing/web-platform/tests/webnn/validation_tests/split.https.any.js b/testing/web-platform/tests/webnn/validation_tests/split.https.any.js
new file mode 100644
index 0000000000..38f3126603
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/split.https.any.js
@@ -0,0 +1,14 @@
+// META: title=validation tests for WebNN API split operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const inputFromOtherBuilder =
+      otherBuilder.input('input', {dataType: 'float32', dimensions: [4, 4]});
+
+  const splits = 2;
+  assert_throws_js(
+      TypeError, () => builder.split(inputFromOtherBuilder, splits));
+}, '[split] throw if input is from another builder');
diff --git a/testing/web-platform/tests/webnn/validation_tests/tanh.https.any.js b/testing/web-platform/tests/webnn/validation_tests/tanh.https.any.js
new file mode 100644
index 0000000000..4f9de919f6
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/tanh.https.any.js
@@ -0,0 +1,10 @@
+// META: title=validation tests for WebNN API tanh operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+validateInputFromAnotherBuilder('tanh');
+
+validateUnaryOperation(
+    'tanh', floatingPointTypes, /*alsoBuildActivation=*/ true);
diff --git a/testing/web-platform/tests/webnn/validation_tests/transpose.https.any.js b/testing/web-platform/tests/webnn/validation_tests/transpose.https.any.js
new file mode 100644
index 0000000000..9ea5a5dcf8
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/transpose.https.any.js
@@ -0,0 +1,7 @@
+// META: title=validation tests for WebNN API transpose operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+validateInputFromAnotherBuilder('transpose');
diff --git a/testing/web-platform/tests/webnn/validation_tests/triangular.https.any.js b/testing/web-platform/tests/webnn/validation_tests/triangular.https.any.js
index 4e4c368f82..ee8958659c 100644
--- a/testing/web-platform/tests/webnn/validation_tests/triangular.https.any.js
+++ b/testing/web-platform/tests/webnn/validation_tests/triangular.https.any.js
@@ -1,7 +1,6 @@
 // META: title=validation tests for WebNN API triangular operation
 // META: global=window,dedicatedworker
 // META: script=../resources/utils_validation.js
-// META: timeout=long
 
 'use strict';
 
@@ -14,3 +13,5 @@ promise_test(async t => {
     }
   }
 }, "[triangular] DataError is expected if input's rank is less than 2");
+
+validateInputFromAnotherBuilder('triangular');
diff --git a/testing/web-platform/tests/webnn/validation_tests/where.https.any.js b/testing/web-platform/tests/webnn/validation_tests/where.https.any.js
new file mode 100644
index 0000000000..a26fa24931
--- /dev/null
+++ b/testing/web-platform/tests/webnn/validation_tests/where.https.any.js
@@ -0,0 +1,129 @@
+// META: title=validation tests for WebNN API where operation
+// META: global=window,dedicatedworker
+// META: script=../resources/utils_validation.js
+
+'use strict';
+
+const kExampleConditionDescriptor = {
+  dataType: 'uint8',
+  dimensions: [2, 4]
+};
+const kExampleInputDescriptor = {
+  dataType: 'float32',
+  dimensions: [2, 4]
+};
+
+const tests = [
+  {
+    name:
+        '[where] Throw if the condition data type is not uint8.',
+    condition: {dataType: 'float32', dimensions: [2, 4]},
+    input: {dataType: 'float32', dimensions: [2, 4]},
+    other: {dataType: 'float32', dimensions: [2, 4]},
+  },
+  {
+    name:
+        '[where] Throw if the data types of input and other do not match',
+    condition: {dataType: 'uint8', dimensions: [2, 4]},
+    input: {dataType: 'float16', dimensions: [2, 4]},
+    other: {dataType: 'float32', dimensions: [2, 4]},
+  },
+  {
+    name:
+        '[where] Throw if the shapes of input and other are not broadcastable',
+    condition: {dataType: 'uint8', dimensions: [2, 4]},
+    input: {dataType: 'float32', dimensions: [2, 4]},
+    other: {dataType: 'float32', dimensions: [2, 3]},
+  },
+  {
+    name:
+        '[where] Throw if the condition shape is not broadcastable',
+    condition: {dataType: 'uint8', dimensions: [2, 4]},
+    input: {dataType: 'float32', dimensions: [2, 3]},
+    other: {dataType: 'float32', dimensions: [2, 1]},
+  },
+  {
+    name:
+        '[where] Test building where with 2-D condition, 2-D input and 2-D other using broadcast',
+    condition: {dataType: 'uint8', dimensions: [2, 1]},
+    input: {dataType: 'float32', dimensions: [2, 4]},
+    other: {dataType: 'float32', dimensions: [2, 4]},
+    output: {dataType: 'float32', dimensions: [2, 4]},
+  },
+  {
+    name:
+        '[where] Test building where with 2-D condition, 2-D input and 3-D other using broadcast',
+    condition: {dataType: 'uint8', dimensions: [1, 4]},
+    input: {dataType: 'float32', dimensions: [3, 4]},
+    other: {dataType: 'float32', dimensions: [2, 3, 4]},
+    output: {dataType: 'float32', dimensions: [2, 3, 4]},
+  },
+  {
+    name:
+        '[where] Test building where with 3-D condition, 3-D input and 2-D other using broadcast',
+    condition: {dataType: 'uint8', dimensions: [2, 1, 4]},
+    input: {dataType: 'float32', dimensions: [2, 3, 4]},
+    other: {dataType: 'float32', dimensions: [1, 4]},
+    output: {dataType: 'float32', dimensions: [2, 3, 4]},
+  },
+  {
+    name:
+        '[where] Test building where with 4-D condition, 3-D input and 2-D other using broadcast',
+    condition: {dataType: 'uint8', dimensions: [2, 3, 4, 5]},
+    input: {dataType: 'float32', dimensions: [3, 4, 5]},
+    other: {dataType: 'float32', dimensions: [4, 5]},
+    output: {dataType: 'float32', dimensions: [2, 3, 4, 5]},
+  }
+];
+
+tests.forEach(
+    test => promise_test(async t => {
+      const condition = builder.input('condition', {
+        dataType: test.condition.dataType,
+        dimensions: test.condition.dimensions
+      });
+      const input = builder.input(
+          'input',
+          {dataType: test.input.dataType, dimensions: test.input.dimensions});
+      const other = builder.input(
+          'other',
+          {dataType: test.other.dataType, dimensions: test.other.dimensions});
+      if (test.output) {
+        const output = builder.where(condition, input, other);
+        assert_equals(output.dataType(), test.output.dataType);
+        assert_array_equals(output.shape(), test.output.dimensions);
+      } else {
+        assert_throws_js(
+            TypeError, () => builder.where(condition, input, other));
+      }
+    }, test.name));
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const conditionFromOtherBuilder =
+      otherBuilder.input('condition', kExampleConditionDescriptor);
+
+  const input = builder.input('input', kExampleInputDescriptor);
+  const other = builder.input('other', kExampleInputDescriptor);
+  assert_throws_js(
+      TypeError, () => builder.where(conditionFromOtherBuilder, input, other));
+}, '[where] throw if condition is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const inputFromOtherBuilder =
+      otherBuilder.input('input', kExampleInputDescriptor);
+
+  const condition = builder.input('condition', kExampleConditionDescriptor);
+  const other = builder.input('other', kExampleInputDescriptor);
+  assert_throws_js(
+      TypeError, () => builder.where(condition, inputFromOtherBuilder, other));
+}, '[where] throw if input is from another builder');
+
+multi_builder_test(async (t, builder, otherBuilder) => {
+  const otherFromOtherBuilder =
+      otherBuilder.input('other', kExampleInputDescriptor);
+
+  const condition = builder.input('condition', kExampleConditionDescriptor);
+  const input = builder.input('input', kExampleInputDescriptor);
+  assert_throws_js(
+      TypeError, () => builder.where(condition, input, otherFromOtherBuilder));
+}, '[where] throw if other is from another builder');