commit 4f25d9d732e74c0793c8e0ee57bdc0b57660d768
parent 7156d0685138b6c9c8869b730a5871352dbcad47
Author: Phillis Tang <phillis@chromium.org>
Date: Mon, 17 Nov 2025 21:50:19 +0000
Bug 2000522 [wpt PR 56046] - webnn: add webgpu interop float16 test, a=testonly
Automatic update from web-platform-tests
webnn: add webgpu interop float16 test
Add test coverage for float16. Also update the tensor shape to not use
the same number for width and height.
Change-Id: Iec6eb063b03c71747eae56229984247d0c282616
Reviewed-on: https://chromium-review.googlesource.com/c/chromium/src/+/7129702
Reviewed-by: Reilly Grant <reillyg@chromium.org>
Commit-Queue: Phillis Tang <phillis@chromium.org>
Cr-Commit-Position: refs/heads/main@{#1545337}
--
wpt-commits: 8aa2da3d7d031b2f651605e8007d173e5c87cabf
wpt-pr: 56046
Diffstat:
1 file changed, 66 insertions(+), 99 deletions(-)
diff --git a/testing/web-platform/tests/webnn/conformance_tests/tensor.https.any.js b/testing/web-platform/tests/webnn/conformance_tests/tensor.https.any.js
@@ -1361,14 +1361,16 @@ const assert_gpu_buffer_data_equals =
* Export to GPU operation test.
* @param {String} testName - The name of the test operation.
*/
-const testExportToGPU = (testName) => {
+const testExportToGPU = (testName, dataType) => {
let gpuAdapter;
let gpuDevice;
let mlContext;
let mlGraph;
- const shape = [2, 2];
+ const shape = [24, 2];
let gpuComputePipeline;
let isExportToGPUSupported = true;
+ const typedArray = dataType == 'float16' ? Float16Array : Float32Array;
+
promise_setup(async () => {
// Initialize GPU
gpuAdapter = navigator.gpu && await navigator.gpu.requestAdapter();
@@ -1377,7 +1379,8 @@ const testExportToGPU = (testName) => {
return;
}
- gpuDevice = await gpuAdapter.requestDevice();
+ gpuDevice =
+ await gpuAdapter.requestDevice({requiredFeatures: ['shader-f16']});
if (!gpuDevice) {
isExportToGPUSupported = false;
return;
@@ -1385,18 +1388,24 @@ const testExportToGPU = (testName) => {
// Construct a GPU custom op which increments each number of the input
// buffer by 1.
+ const bufferType = dataType == 'float16' ? 'f16' : 'f32';
const gpuComputeShaderCode = `
- @group(0) @binding(0) var<storage, read> inputBuffer: array<f32>;
- @group(0) @binding(1) var<storage, read_write> outputBuffer: array<f32>;
+ ${bufferType == 'f16' ? 'enable f16;' : ''}
+ @group(0) @binding(0) var<storage, read> inputBuffer: array<${
+ bufferType}>;
+ @group(0) @binding(1) var<storage, read_write> outputBuffer: array<${
+ bufferType}>;
@compute @workgroup_size(1)
fn main(@builtin(global_invocation_id) global_id: vec3<u32>) {
let index = global_id.x;
- outputBuffer[index] = inputBuffer[index] + 1.0;
+ outputBuffer[index] = inputBuffer[index] + 1.0${
+ bufferType == 'f16' ? 'h' : ''};
}`;
const gpuShaderModule =
gpuDevice.createShaderModule({code: gpuComputeShaderCode});
+
gpuComputePipeline = gpuDevice.createComputePipeline({
layout: 'auto',
compute: {module: gpuShaderModule, entryPoint: 'main'},
@@ -1412,10 +1421,8 @@ const testExportToGPU = (testName) => {
// Check if WebNN interop is supported.
try {
- let mlTensor = await mlContext.createExportableTensor({
- dataType: 'float32',
- shape: shape,
- }, gpuDevice);
+ let mlTensor =
+ await mlContext.createExportableTensor({dataType, shape}, gpuDevice);
await mlContext.exportToGPU(mlTensor);
} catch (e) {
if (e.name === 'NotSupportedError') {
@@ -1427,7 +1434,7 @@ const testExportToGPU = (testName) => {
// Construct a simple graph: OUTPUT = LHS + RHS.
const mlBuilder = new MLGraphBuilder(mlContext);
- const mlOperandDescriptor = {dataType: 'float32', shape};
+ const mlOperandDescriptor = {dataType, shape};
const lhsOperand = mlBuilder.input('lhs', mlOperandDescriptor);
const rhsOperand = mlBuilder.input('rhs', mlOperandDescriptor);
mlGraph = await mlBuilder.build(
@@ -1461,13 +1468,10 @@ const testExportToGPU = (testName) => {
return;
}
- const mlTensorDescriptor = {
- dataType: 'float32',
- shape: shape,
- };
-
+ const mlTensorDescriptor = {dataType, shape};
const mlTensor = await mlContext.createExportableTensor(mlTensorDescriptor,
gpuDevice);
+
const gpuTensorBuffer = await mlContext.exportToGPU(mlTensor);
assert_equals(
@@ -1482,10 +1486,7 @@ const testExportToGPU = (testName) => {
return;
}
- const mlTensor = await mlContext.createTensor({
- dataType: 'float32',
- shape: shape,
- });
+ const mlTensor = await mlContext.createTensor({dataType, shape});
await promise_rejects_js(t, TypeError, mlContext.exportToGPU(mlTensor));
}, `${testName} / export wrong tensor`);
@@ -1496,13 +1497,11 @@ const testExportToGPU = (testName) => {
}
const maxBufferSizeOOB = gpuDevice.limits.maxBufferSize + 1;
- const elementSize = Float32Array.BYTES_PER_ELEMENT;
+ const elementSize = typedArray.BYTES_PER_ELEMENT;
const shape = [maxBufferSizeOOB / elementSize];
- const mlTensor = await mlContext.createExportableTensor({
- dataType: 'float32',
- shape: shape
- }, gpuDevice);
+ const mlTensor =
+ await mlContext.createExportableTensor({dataType, shape}, gpuDevice);
await mlContext.exportToGPU(mlTensor);
}, `${testName} / export big tensor`)
@@ -1512,15 +1511,11 @@ const testExportToGPU = (testName) => {
return;
}
- const mlTensorDescriptor = {
- dataType: 'float32',
- shape: shape,
- readable: true,
- writable: true
- };
+ const mlTensorDescriptor =
+ {dataType, shape, readable: true, writable: true};
let mlTensor = await mlContext.createExportableTensor(mlTensorDescriptor, gpuDevice);
- const inputData = new Float32Array(sizeOfShape(shape)).fill(1.0);
+ const inputData = new typedArray(sizeOfShape(shape)).fill(1.0);
mlContext.writeTensor(mlTensor, inputData);
const gpuTensorBuffer = await mlContext.exportToGPU(mlTensor);
@@ -1534,15 +1529,11 @@ const testExportToGPU = (testName) => {
return;
}
- const mlTensorDescriptor = {
- dataType: 'float32',
- shape: shape,
- writable: true
- };
+ const mlTensorDescriptor = {dataType, shape, writable: true};
let mlTensor = await mlContext.createExportableTensor(mlTensorDescriptor, gpuDevice);
- const inputData = new Float32Array(sizeOfShape(shape)).fill(1.0);
+ const inputData = new typedArray(sizeOfShape(shape)).fill(1.0);
mlContext.writeTensor(mlTensor, inputData);
const gpuTensorBuffer = await mlContext.exportToGPU(mlTensor);
@@ -1556,15 +1547,13 @@ const testExportToGPU = (testName) => {
return;
}
- const mlTensor = await mlContext.createExportableTensor({
- dataType: 'float32',
- shape: shape,
- }, gpuDevice);
+ const mlTensor =
+ await mlContext.createExportableTensor({dataType, shape}, gpuDevice);
await mlContext.exportToGPU(mlTensor);
assert_throws_js(
TypeError,
() => mlContext.writeTensor(
- mlTensor, new Float32Array([1.0, 2.0, 3.0, 4.0])));
+ mlTensor, new typedArray([1.0, 2.0, 3.0, 4.0])));
}, `${testName} / write tensor after export`);
promise_test(async t => {
@@ -1572,10 +1561,8 @@ const testExportToGPU = (testName) => {
return;
}
- const mlTensor = await mlContext.createExportableTensor({
- dataType: 'float32',
- shape: shape,
- }, gpuDevice);
+ const mlTensor =
+ await mlContext.createExportableTensor({dataType, shape}, gpuDevice);
// Second call rejects because the first export is still pending and multiple
// exports aren’t allowed.
@@ -1594,17 +1581,14 @@ const testExportToGPU = (testName) => {
}
// Initialize the tensor buffers from WebNN.
- let mlTensorInput = await mlContext.createExportableTensor({
- dataType: 'float32',
- shape: shape,
- writable: true
- }, gpuDevice);
+ let mlTensorInput = await mlContext.createExportableTensor(
+ {dataType, shape, writable: true}, gpuDevice);
- const inputData1 = new Float32Array(sizeOfShape(shape)).fill(1.0);
+ const inputData1 = new typedArray(sizeOfShape(shape)).fill(1.0);
mlContext.writeTensor(mlTensorInput, inputData1);
- let mlTensorOutput = await mlContext.createExportableTensor(
- {dataType: 'float32', shape: shape}, gpuDevice);
+ let mlTensorOutput =
+ await mlContext.createExportableTensor({dataType, shape}, gpuDevice);
let gpuTensorBufferInput = await mlContext.exportToGPU(mlTensorInput);
let gpuTensorBufferOutput = await mlContext.exportToGPU(mlTensorOutput);
@@ -1617,7 +1601,7 @@ const testExportToGPU = (testName) => {
gpuTensorBufferOutput.destroy();
// Write different data to the input tensor.
- const inputData2 = new Float32Array(sizeOfShape(shape)).fill(2.0);
+ const inputData2 = new typedArray(sizeOfShape(shape)).fill(2.0);
mlContext.writeTensor(mlTensorInput, inputData2);
gpuTensorBufferInput = await mlContext.exportToGPU(mlTensorInput);
@@ -1637,20 +1621,14 @@ const testExportToGPU = (testName) => {
}
// Initialize the tensor buffers from WebNN.
- let mlTensorInput = await mlContext.createExportableTensor({
- dataType: 'float32',
- shape: shape,
- writable: true
- }, gpuDevice);
+ let mlTensorInput = await mlContext.createExportableTensor(
+ {dataType, shape, writable: true}, gpuDevice);
- const inputData = new Float32Array(sizeOfShape(shape)).fill(1.0);
+ const inputData = new typedArray(sizeOfShape(shape)).fill(1.0);
mlContext.writeTensor(mlTensorInput, inputData);
- let mlTensorOutput = await mlContext.createExportableTensor({
- dataType: 'float32',
- shape: shape,
- readable: true
- }, gpuDevice);
+ let mlTensorOutput = await mlContext.createExportableTensor(
+ {dataType, shape, readable: true}, gpuDevice);
let gpuTensorBufferInput = await mlContext.exportToGPU(mlTensorInput);
let gpuTensorBufferOutput = await mlContext.exportToGPU(mlTensorOutput);
@@ -1677,17 +1655,14 @@ const testExportToGPU = (testName) => {
}
// Initialize the tensor buffers from WebNN.
- let mlTensorInput = await mlContext.createExportableTensor({
- dataType: 'float32',
- shape: shape,
- writable: true,
- }, gpuDevice);
+ let mlTensorInput = await mlContext.createExportableTensor(
+ {dataType, shape, writable: true}, gpuDevice);
- const inputData = new Float32Array(sizeOfShape(shape)).fill(1.0);
+ const inputData = new typedArray(sizeOfShape(shape)).fill(1.0);
mlContext.writeTensor(mlTensorInput, inputData);
- let mlTensorOutput = await mlContext.createExportableTensor(
- {dataType: 'float32', shape: shape}, gpuDevice);
+ let mlTensorOutput =
+ await mlContext.createExportableTensor({dataType, shape}, gpuDevice);
let gpuTensorBufferInput = await mlContext.exportToGPU(mlTensorInput);
let gpuTensorBufferOutput = await mlContext.exportToGPU(mlTensorOutput);
@@ -1717,7 +1692,7 @@ const testExportToGPU = (testName) => {
await assert_gpu_buffer_data_equals(
gpuDevice, gpuTensorBufferOutput,
- new Float32Array(sizeOfShape(shape)).fill(5.0));
+ new typedArray(sizeOfShape(shape)).fill(5.0));
}, `${testName} / dispatch from webgpu then webnn`);
promise_test(async () => {
@@ -1727,13 +1702,10 @@ const testExportToGPU = (testName) => {
let anotherMLContext = await navigator.ml.createContext(contextOptions);
- let mlTensor = await anotherMLContext.createExportableTensor({
- dataType: 'float32',
- shape: shape,
- writable: true,
- }, gpuDevice);
+ let mlTensor = await anotherMLContext.createExportableTensor(
+ {dataType, shape, writable: true}, gpuDevice);
- const inputData = new Float32Array(sizeOfShape(shape)).fill(1.0);
+ const inputData = new typedArray(sizeOfShape(shape)).fill(1.0);
anotherMLContext.writeTensor(mlTensor, inputData);
const gpuTensorBuffer = await anotherMLContext.exportToGPU(mlTensor);
@@ -1749,17 +1721,14 @@ const testExportToGPU = (testName) => {
}
let anotherGPUAdapter = await navigator.gpu.requestAdapter();
- let anotherGPUDevice = await anotherGPUAdapter.requestDevice();
+ let anotherGPUDevice = await anotherGPUAdapter.requestDevice(
+ {requiredFeatures: ['shader-f16']});
let anotherMLContext = await navigator.ml.createContext(contextOptions);
- let mlTensor = await anotherMLContext.createExportableTensor({
- dataType: 'float32',
- shape: shape,
- readable: true,
- writable: true,
- }, anotherGPUDevice);
+ let mlTensor = await anotherMLContext.createExportableTensor(
+ {dataType, shape, readable: true, writable: true}, anotherGPUDevice);
- const inputData = new Float32Array(sizeOfShape(shape)).fill(1.0);
+ const inputData = new typedArray(sizeOfShape(shape)).fill(1.0);
anotherMLContext.writeTensor(mlTensor, inputData);
const gpuTensorBuffer = await anotherMLContext.exportToGPU(mlTensor);
@@ -1777,16 +1746,13 @@ const testExportToGPU = (testName) => {
}
let anotherGPUAdapter = await navigator.gpu.requestAdapter();
- let anotherGPUDevice = await anotherGPUAdapter.requestDevice();
+ let anotherGPUDevice = await anotherGPUAdapter.requestDevice(
+ {requiredFeatures: ['shader-f16']});
let anotherMLContext = await navigator.ml.createContext(contextOptions);
- let mlTensor = await anotherMLContext.createExportableTensor({
- dataType: 'float32',
- shape: shape,
- readable: true,
- writable: true
- }, anotherGPUDevice);
- const inputData = new Float32Array(sizeOfShape(shape)).fill(1.0);
+ let mlTensor = await anotherMLContext.createExportableTensor(
+ {dataType, shape, readable: true, writable: true}, anotherGPUDevice);
+ const inputData = new typedArray(sizeOfShape(shape)).fill(1.0);
anotherMLContext.writeTensor(mlTensor, inputData);
anotherGPUDevice.destroy();
@@ -1821,7 +1787,8 @@ if (navigator.ml) {
testReadTensor('read');
testWriteTensor('write');
testDispatchTensor('dispatch');
- testExportToGPU('interop');
+ testExportToGPU('interop float16', 'float16');
+ testExportToGPU('interop float32', 'float32');
} else {
test(() => assert_implements(navigator.ml, 'missing navigator.ml'));
}
