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tensor.https.any.js
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// META: title=test WebNN API tensor operations
// META: global=window,dedicatedworker
// META: variant=?cpu
// META: variant=?gpu
// META: variant=?npu
// META: script=../resources/utils_validation.js
// META: script=../resources/utils.js
// META: timeout=long
'use strict';
// https://www.w3.org/TR/webnn/#api-mltensor
const bytesPerDataType = (dataType) => {
if (dataType === 'int8' || dataType === 'uint8') {
return 1;
} else if (dataType === 'float16') {
return 2;
} else if (
dataType === 'float32' || dataType === 'int32' || dataType === 'uint32') {
return 4;
} else if (dataType === 'int64' || dataType === 'uint64') {
return 8;
} else {
throw new AssertionError(`Data type '${dataType}' is not supported`);
}
};
const sizeOfDescriptor = (descriptor) => {
return descriptor.dimensions.reduce(
(accumulator, currentValue) => accumulator * currentValue,
bytesPerDataType(descriptor.dataType));
};
const getDescriptorFromTensor = (tensor) => {
return {
dataType: tensor.dataType,
dimensions: tensor.shape,
usage: tensor.usage
};
};
/**
* WebNN destroy tensor twice test.
* @param {String} testName - The name of the test operation.
*/
const testDestroyTensor = (testName) => {
let mlContext;
promise_setup(async () => {
try {
mlContext = await navigator.ml.createContext(contextOptions);
} catch (e) {
throw new AssertionError(
`Unable to create context for ${variant} variant. ${e}`);
}
try {
const mlTensor =
await mlContext.createTensor({dataType: 'int32', dimensions: [2, 3]});
} catch (e) {
throw new AssertionError(
`Unable to create tensor for ${variant} variant. ${e}`);
}
});
promise_test(async () => {
let mlTensor =
await mlContext.createTensor({dataType: 'int32', dimensions: [2, 3]});
mlTensor.destroy();
mlTensor.destroy();
}, `${testName}`);
};
/**
* WebNN create tensor test.
* @param {String} testName - The name of the test operation.
* @param {MLTensorDescriptor} tensorDescriptor - The intended tensor specs.
*/
const testCreateTensor = (testName, tensorDescriptor) => {
let mlContext;
promise_setup(async () => {
try {
mlContext = await navigator.ml.createContext(contextOptions);
} catch (e) {
throw new AssertionError(
`Unable to create context for ${variant} variant. ${e}`);
}
});
promise_test(async t => {
if (!mlContext.opSupportLimits().input.dataTypes.includes(
tensorDescriptor.dataType)) {
await promise_rejects_js(
t, TypeError, mlContext.createTensor(tensorDescriptor));
return;
}
const mlTensor = await mlContext.createTensor(tensorDescriptor);
assert_equals(
mlTensor.dataType, tensorDescriptor.dataType,
'tensor data types do not match');
assert_array_equals(
mlTensor.shape, tensorDescriptor.dimensions,
'tensor shapes do not match');
}, `${testName} / ${tensorDescriptor.dataType}`);
};
/**
* Same as above, but expect creating the tensor to fail.
* @param {String} testName - The name of the test operation.
* @param {MLTensorDescriptor} tensorDescriptor - The intended tensor specs.
*/
const testCreateTensorFails = (testName, tensorDescriptor) => {
let mlContext;
promise_setup(async () => {
try {
mlContext = await navigator.ml.createContext(contextOptions);
} catch (e) {
throw new AssertionError(
`Unable to create context for ${variant} variant. ${e}`);
}
});
promise_test(async t => {
await promise_rejects_js(
t, TypeError, mlContext.createTensor(tensorDescriptor));
}, `${testName} / ${tensorDescriptor.dataType}`);
};
/**
* Asserts the tensor data in MLTensor matches expected.
* @param {MLContext} mlContext - The context used to create the tensor.
* @param {MLTensor} mlTensor - The tensor to read and compare data.
* @param {Array} expected - Array of the expected data in the tensor.
*/
const assert_tensor_data_equals = async (mlContext, mlTensor, expected) => {
const actual = await mlContext.readTensor(mlTensor);
assert_array_equals(
new expected.constructor(actual), expected,
'Read tensor data equals expected data.');
};
/**
* WebNN write tensor operation test.
* @param {String} testName - The name of the test operation.
*/
const testWriteTensor = (testName) => {
let mlContext;
promise_setup(async () => {
try {
mlContext = await navigator.ml.createContext(contextOptions);
} catch (e) {
throw new AssertionError(
`Unable to create context for ${variant} variant. ${e}`);
}
try {
const mlTensor =
await mlContext.createTensor({dataType: 'int32', dimensions: [2, 3]});
} catch (e) {
throw new AssertionError(
`Unable to create tensor for ${variant} variant. ${e}`);
}
});
promise_test(async () => {
const tensorDescriptor = {
dataType: 'int32',
dimensions: [1],
usage: MLTensorUsage.WRITE,
};
let mlTensor = await mlContext.createTensor(tensorDescriptor);
const tensorByteLength = sizeOfDescriptor(tensorDescriptor);
let arrayBuffer = new ArrayBuffer(tensorByteLength);
// Writing with a size that goes past that source tensor length.
assert_throws_js(
TypeError,
() => mlContext.writeTensor(
mlTensor, new Uint8Array(arrayBuffer), /*srcOffset=*/ 0,
/*srcSize=*/ tensorByteLength + 1));
assert_throws_js(
TypeError,
() => mlContext.writeTensor(
mlTensor, new Uint8Array(arrayBuffer), /*srcOffset=*/ 3,
/*srcSize=*/ tensorByteLength));
// Writing with a source offset that is out of range of the source size.
assert_throws_js(
TypeError,
() => mlContext.writeTensor(
mlTensor, new Uint8Array(arrayBuffer),
/*srcOffset=*/ tensorByteLength + 1));
// Writing with a source offset that is out of range of implicit copy size.
assert_throws_js(
TypeError,
() => mlContext.writeTensor(
mlTensor, new Uint8Array(arrayBuffer),
/*srcOffset=*/ tensorByteLength + 1, /*srcSize=*/ undefined));
assert_throws_js(
TypeError,
() => mlContext.writeTensor(
mlTensor, new Uint8Array(arrayBuffer), /*srcOffset=*/ undefined,
/*srcSize=*/ tensorByteLength + 1));
assert_throws_js(
TypeError,
() => mlContext.writeTensor(
mlTensor, Uint8Array.from([0xEE, 0xEE, 0xEE, 0xEE, 0xEE])));
}, `${testName} / error`);
promise_test(async () => {
const tensorDescriptor = {
dataType: 'int32',
dimensions: [2, 2],
usage: MLTensorUsage.WRITE,
};
let mlTensor = await mlContext.createTensor(tensorDescriptor);
// Writing data to a destroyed MLTensor should throw.
mlTensor.destroy();
assert_throws_dom(
'InvalidStateError',
() => mlContext.writeTensor(
mlTensor, new Uint8Array(sizeOfDescriptor(tensorDescriptor))));
}, `${testName} / destroy`);
promise_test(async () => {
const tensorDescriptor = {
dataType: 'int32',
dimensions: [2, 3],
usage: MLTensorUsage.WRITE,
};
let mlTensor = await mlContext.createTensor(tensorDescriptor);
let anotherMLContext = await navigator.ml.createContext(contextOptions);
let anotherMLTensor = await anotherMLContext.createTensor(tensorDescriptor);
let inputData =
new Uint8Array(sizeOfDescriptor(tensorDescriptor)).fill(0xAA);
assert_throws_js(
TypeError, () => mlContext.writeTensor(anotherMLTensor, inputData));
assert_throws_js(
TypeError, () => anotherMLContext.writeTensor(mlTensor, inputData));
}, `${testName} / context_mismatch`);
promise_test(async () => {
let mlTensor = await mlContext.createTensor({
dataType: 'int32',
dimensions: [1],
usage: MLTensorUsage.WRITE | MLTensorUsage.READ,
});
// Initialize the tensor.
const inputData = Uint8Array.from([0xAA, 0xAA, 0xAA, 0xAA]);
mlContext.writeTensor(mlTensor, inputData);
// Writing zero bytes from a zero write size.
mlContext.writeTensor(mlTensor, Uint8Array.from([0xBB]), 0, 0);
await assert_tensor_data_equals(mlContext, mlTensor, inputData);
// Writing zero bytes at the end of the tensor.
mlContext.writeTensor(
mlTensor, Uint32Array.from([0xBBBBBBBB]), /*srcOffset=*/ 1);
await assert_tensor_data_equals(mlContext, mlTensor, inputData);
}, `${testName} / zero_write`);
promise_test(async () => {
const tensorDescriptor = {
dataType: 'int32',
dimensions: [2, 2],
usage: MLTensorUsage.WRITE | MLTensorUsage.READ,
};
let mlTensor = await mlContext.createTensor(tensorDescriptor);
const tensorByteLength = sizeOfDescriptor(tensorDescriptor);
let inputBuffer = new ArrayBuffer(tensorByteLength);
// Initialize the tensor.
const int32View = new Int32Array(inputBuffer);
int32View.fill(0xBBBBBBBB);
mlContext.writeTensor(mlTensor, int32View);
// Writing to a detached buffer should be ignored.
const detachedBuffer = inputBuffer.transfer();
assert_true(inputBuffer.detached, 'array buffer should be detached.');
mlContext.writeTensor(mlTensor, inputBuffer);
await assert_tensor_data_equals(
mlContext, mlTensor, new Int32Array(detachedBuffer));
}, `${testName} / detached`);
};
/**
* WebNN read tensor operation test.
* @param {String} testName - The name of the test operation.
*/
const testReadTensor = (testName) => {
let mlContext;
promise_setup(async () => {
try {
mlContext = await navigator.ml.createContext(contextOptions);
} catch (e) {
throw new AssertionError(
`Unable to create context for ${variant} variant. ${e}`);
}
try {
const mlTensor =
await mlContext.createTensor({dataType: 'int32', dimensions: [2, 3]});
} catch (e) {
throw new AssertionError(
`Unable to create tensor for ${variant} variant. ${e}`);
}
});
promise_test(async t => {
let mlTensor = await mlContext.createTensor({
dataType: 'int32',
dimensions: [2, 2],
usage: MLTensorUsage.READ,
});
// Reading a destroyed MLTensor should reject.
mlTensor.destroy();
await promise_rejects_dom(
t, 'InvalidStateError', mlContext.readTensor(mlTensor));
}, `${testName} / read_after_destroy`);
promise_test(async t => {
let mlTensor = await mlContext.createTensor({
dataType: 'int32',
dimensions: [2, 3],
usage: MLTensorUsage.READ,
});
let promise = mlContext.readTensor(mlTensor);
let anotherPromise = mlContext.readTensor(mlTensor);
mlTensor.destroy();
await promise_rejects_dom(t, 'InvalidStateError', promise);
await promise_rejects_dom(t, 'InvalidStateError', anotherPromise);
}, `${testName} / read_before_destroy`);
promise_test(async () => {
let mlTensor = await mlContext.createTensor({
dataType: 'int32',
dimensions: [1024],
usage: MLTensorUsage.READ,
});
await assert_tensor_data_equals(mlContext, mlTensor, new Uint32Array(1024));
}, `${testName} / uninitialized`);
promise_test(async () => {
let mlTensor = await mlContext.createTensor({
dataType: 'int32',
dimensions: [1],
usage: MLTensorUsage.READ | MLTensorUsage.WRITE,
});
// Initialize the tensor.
mlContext.writeTensor(mlTensor, Uint8Array.from([0xAA, 0xAA, 0xAA, 0xAA]));
mlContext.writeTensor(mlTensor, Uint32Array.from([0xBBBBBBBB]));
await assert_tensor_data_equals(
mlContext, mlTensor, Uint32Array.from([0xBBBBBBBB]));
;
}, `${testName} / full_size`);
promise_test(async () => {
let mlTensor = await mlContext.createTensor({
dataType: 'int32',
dimensions: [1],
usage: MLTensorUsage.WRITE | MLTensorUsage.READ,
});
// Initialize the tensor.
mlContext.writeTensor(mlTensor, Uint8Array.from([0xAA, 0xAA, 0xAA, 0xAA]));
// Writing to the remainder of the tensor from source offset.
mlContext.writeTensor(
mlTensor, Uint8Array.from([0xCC, 0xCC, 0xBB, 0xBB]),
/*srcOffset=*/ 2);
await assert_tensor_data_equals(
mlContext, mlTensor, Uint8Array.from([0xBB, 0xBB, 0xAA, 0xAA]));
}, `${testName} / src_offset_only`);
promise_test(async () => {
let mlTensor = await mlContext.createTensor({
dataType: 'int32',
dimensions: [1],
usage: MLTensorUsage.WRITE | MLTensorUsage.READ,
});
// Initialize the tensor.
mlContext.writeTensor(mlTensor, Uint8Array.from([0xAA, 0xAA, 0xAA, 0xAA]));
// Writing with both a source offset and size.
mlContext.writeTensor(
mlTensor, Uint8Array.from([0xDD, 0xDD, 0xCC, 0xDD]),
/*srcOffset=*/ 2, /*srcSize=*/ 1);
await assert_tensor_data_equals(
mlContext, mlTensor, Uint8Array.from([0xCC, 0xAA, 0xAA, 0xAA]));
}, `${testName} / src_offset_and_size`);
promise_test(async () => {
let mlTensor = await mlContext.createTensor({
dataType: 'int32',
dimensions: [1],
usage: MLTensorUsage.WRITE | MLTensorUsage.READ,
});
// Initialize the tensor.
mlContext.writeTensor(mlTensor, Uint8Array.from([0xAA, 0xAA, 0xAA, 0xAA]));
// Using an offset allows a larger source tensor to fit.
mlContext.writeTensor(
mlTensor, Uint8Array.from([0xEE, 0xEE, 0xEE, 0xEE, 0xEE]),
/*srcOffset=*/ 1);
await assert_tensor_data_equals(
mlContext, mlTensor, Uint8Array.from([0xEE, 0xEE, 0xEE, 0xEE]));
}, `${testName} / larger_src_data`);
promise_test(async () => {
let mlTensor = await mlContext.createTensor({
dataType: 'int32',
dimensions: [1],
usage: MLTensorUsage.WRITE | MLTensorUsage.READ,
});
const inputData = [0xAA, 0xAA, 0xAA, 0xAA];
// Writing with a source offset of undefined should be treated as 0.
mlContext.writeTensor(
mlTensor, Uint8Array.from(inputData), /*srcOffset=*/ undefined,
/*srcSize=*/ inputData.length);
await assert_tensor_data_equals(
mlContext, mlTensor, Uint8Array.from(inputData));
}, `${testName} / no_src_offset`);
promise_test(async t => {
const tensorDescriptor = {
dataType: 'int32',
dimensions: [2, 3],
usage: MLTensorUsage.READ,
};
let mlTensor = await mlContext.createTensor(tensorDescriptor);
let anotherMLContext = await navigator.ml.createContext(contextOptions);
let anotherMLTensor = await anotherMLContext.createTensor(tensorDescriptor);
await promise_rejects_js(
t, TypeError, mlContext.readTensor(anotherMLTensor));
await promise_rejects_js(
t, TypeError, anotherMLContext.readTensor(mlTensor));
}, `${testName} / context_mismatch`);
};
/**
* WebNN dispatch tensor operation test.
* @param {String} testName - The name of the test operation.
*/
const testDispatchTensor = (testName) => {
let mlContext;
let mlGraph;
const shape = [3, 5];
let inputs = {};
let outputs = {};
promise_setup(async () => {
try {
mlContext = await navigator.ml.createContext(contextOptions);
} catch (e) {
throw new AssertionError(
`Unable to create context for ${variant} variant. ${e}`);
}
// Construct a simple graph: A = B + C, with two outputs.
const builder = new MLGraphBuilder(mlContext);
const tensorDescriptor = {
dataType: 'float32',
dimensions: shape,
usage: MLTensorUsage.WRITE | MLTensorUsage.READ,
};
const lhsOperand = builder.input('lhs', tensorDescriptor);
const rhsOperand = builder.input('rhs', tensorDescriptor);
const output1Operand = builder.add(lhsOperand, rhsOperand);
const output2Operand = builder.add(lhsOperand, rhsOperand);
mlGraph = await builder.build(
{'output1': output1Operand, 'output2': output2Operand});
try {
const mlTensor =
await mlContext.createTensor({dataType: 'int32', dimensions: [2, 3]});
} catch (e) {
throw new AssertionError(
`Unable to create tensor for ${variant} variant. ${e}`);
}
inputs = {
'lhs': await mlContext.createTensor(tensorDescriptor),
'rhs': await mlContext.createTensor(tensorDescriptor),
};
outputs = {
'output1': await mlContext.createTensor(tensorDescriptor),
'output2': await mlContext.createTensor(tensorDescriptor),
};
});
promise_test(async () => {
let anotherMLContext = await navigator.ml.createContext(contextOptions);
// Control case, same context.
mlContext.dispatch(mlGraph, inputs, outputs);
// Test the wrong context being used for inputs.
const lhsTensor = await anotherMLContext.createTensor(
getDescriptorFromTensor(inputs['lhs']));
assert_throws_js(
TypeError,
() => mlContext.dispatch(
mlGraph, {
'lhs': lhsTensor,
'rhs': inputs['rhs'],
},
outputs));
// Test the wrong context being used for outputs.
const outputTensor1 = await anotherMLContext.createTensor(
getDescriptorFromTensor(outputs['output1']));
assert_throws_js(TypeError, () => mlContext.dispatch(mlGraph, inputs, {
'output1': outputTensor1,
'output2': outputs['output2'],
}));
}, `${testName} / context_mismatch`);
promise_test(async () => {
// Control case, valid tensors.
mlContext.dispatch(mlGraph, inputs, outputs);
// Input is a different shape.
const lhsTensor = await mlContext.createTensor({
dataType: inputs['lhs'].dataType,
// Input rank is too high.
dimensions: inputs['lhs'].shape.concat([2])
});
assert_throws_js(
TypeError,
() => mlContext.dispatch(
mlGraph, {
'lhs': lhsTensor,
'rhs': inputs['rhs'],
},
outputs));
const rhsTensor = await mlContext.createTensor({
dataType: inputs['rhs'].dataType,
// Input rank is too low.
dimensions: inputs['rhs'].shape.slice(1)
});
assert_throws_js(
TypeError,
() => mlContext.dispatch(
mlGraph, {
'lhs': inputs['lhs'],
'rhs': rhsTensor,
},
outputs));
// Output is a different shape. Dimension value is too large.
let output1WrongShape = [...outputs['output1'].shape];
output1WrongShape[0] += 2;
const outputTensor1 = await mlContext.createTensor(
{dataType: outputs['output1'].dataType, dimensions: output1WrongShape});
assert_throws_js(TypeError, () => mlContext.dispatch(mlGraph, inputs, {
'output1': outputTensor1,
'output2': outputs['output2'],
}));
// Output is a different shape. Dimension value is too small.
let output2WrongShape = [...outputs['output2'].shape];
output2WrongShape[1] -= 1;
const outputTensor2 = await mlContext.createTensor(
{dataType: outputs['output2'].dataType, dimensions: output2WrongShape});
assert_throws_js(TypeError, () => mlContext.dispatch(mlGraph, inputs, {
'output1': outputs['output1'],
'output2': outputTensor2,
}));
}, `${testName} / invalid shape`);
promise_test(async () => {
// Control case, valid tensors.
mlContext.dispatch(mlGraph, inputs, outputs);
// Inputs are a different data type.
const inputWrongDataType = 'int32';
assert_not_equals(inputs['lhs'].dataType, inputWrongDataType);
assert_not_equals(inputs['rhs'].dataType, inputWrongDataType);
assert_throws_js(
TypeError,
() => mlContext.dispatch(
mlGraph, {
'lhs': mlContext.createTensor({
dataType: inputWrongDataType,
dimensions: inputs['lhs'].shape
}),
'rhs': inputs['rhs'],
},
outputs));
assert_throws_js(
TypeError,
() => mlContext.dispatch(
mlGraph, {
'lhs': inputs['lhs'],
'rhs': mlContext.createTensor({
dataType: inputWrongDataType,
dimensions: inputs['rhs'].shape
}),
},
outputs));
// Outputs are a different data type.
const outputWrongDataType = 'int32';
assert_not_equals(outputs['output1'].dataType, outputWrongDataType);
assert_not_equals(outputs['output2'].dataType, outputWrongDataType);
const outputTensor1 = await mlContext.createTensor(
{dataType: outputWrongDataType, dimensions: outputs['output1'].shape});
assert_throws_js(TypeError, () => mlContext.dispatch(mlGraph, inputs, {
'output1': outputTensor1,
'output2': outputs['output2'],
}));
const outputTensor2 = await mlContext.createTensor(
{dataType: outputWrongDataType, dimensions: outputs['output2'].shape});
assert_throws_js(TypeError, () => mlContext.dispatch(mlGraph, inputs, {
'output1': outputs['output1'],
'output2': outputTensor2,
}));
}, `${testName} / invalid data type`);
promise_test(async () => {
// Control case, valid names.
mlContext.dispatch(mlGraph, inputs, outputs);
// No names is invalid.
assert_throws_js(TypeError, () => mlContext.dispatch(mlGraph, {}, {}));
// Input name is invalid.
assert_throws_js(
TypeError,
() => mlContext.dispatch(
mlGraph, {
'aDifferentInputName': inputs['lhs'],
'rhs': inputs['rhs'],
},
outputs));
assert_throws_js(
TypeError,
() => mlContext.dispatch(
mlGraph, {
'lhs': inputs['lhs'],
'aDifferentInputName': inputs['rhs'],
},
outputs));
// Output name is invalid.
assert_throws_js(TypeError, () => mlContext.dispatch(mlGraph, inputs, {
'aDifferentOutputName': outputs['output1'],
'output2': outputs['output2'],
}));
assert_throws_js(TypeError, () => mlContext.dispatch(mlGraph, inputs, {
'output1': outputs['output1'],
'aDifferentOutputName': outputs['output2'],
}));
// Too few named inputs is invalid.
assert_throws_js(
TypeError,
() => mlContext.dispatch(
mlGraph, {
'lhs': inputs['lhs'],
},
outputs));
// Too many named inputs is invalid.
const anotherRhsTensor =
await mlContext.createTensor(getDescriptorFromTensor(inputs['rhs']));
assert_throws_js(
TypeError,
() => mlContext.dispatch(
mlGraph, {
'lhs': inputs['lhs'],
'rhs': inputs['rhs'],
'aDifferentInputName': anotherRhsTensor,
},
outputs));
// Too few named outputs is invalid.
assert_throws_js(TypeError, () => mlContext.dispatch(mlGraph, inputs, {
'output1': outputs['output1']
}));
// Too many named outputs is invalid.
const anotherOutputTensor2 = await mlContext.createTensor(
getDescriptorFromTensor(outputs['output2']));
assert_throws_js(TypeError, () => mlContext.dispatch(mlGraph, inputs, {
'output1': outputs['output1'],
'output2': outputs['output2'],
'aDifferentOutputName': anotherOutputTensor2,
}));
}, `${testName} / invalid_name`);
promise_test(async () => {
// Control case, valid tensors.
mlContext.dispatch(mlGraph, inputs, outputs);
// Same tensor used as outputs more than once is invalid.
assert_throws_js(TypeError, () => mlContext.dispatch(mlGraph, inputs, {
'output1': outputs['output1'],
'output2': outputs['output1'],
}));
// Same tensor used as input and output is invalid.
assert_throws_js(TypeError, () => mlContext.dispatch(mlGraph, inputs, {
'output1': inputs['lhs'],
'output2': outputs['output2'],
}));
assert_throws_js(
TypeError,
() => mlContext.dispatch(
mlGraph, {
'lhs': outputs['output1'],
'rhs': inputs['rhs'],
},
outputs));
// Tensor that does not exist is invalid.
assert_throws_js(
TypeError,
() => mlContext.dispatch(
mlGraph, {
'lhs': undefined,
'rhs': inputs['rhs'],
},
outputs));
assert_throws_js(TypeError, () => mlContext.dispatch(mlGraph, inputs, {
'output1': undefined,
'output2': outputs['output2'],
}));
}, `${testName} / invalid_tensor`);
promise_test(async () => {
const dispatchInputs = {
'lhs':
await mlContext.createTensor(getDescriptorFromTensor(inputs['lhs'])),
'rhs':
await mlContext.createTensor(getDescriptorFromTensor(inputs['rhs'])),
};
const dispatch1Outputs = {
'output1': await mlContext.createTensor(
getDescriptorFromTensor(outputs['output1'])),
'output2': await mlContext.createTensor(
getDescriptorFromTensor(outputs['output2'])),
};
const dispatch2Outputs = {
'output1': await mlContext.createTensor(
getDescriptorFromTensor(outputs['output1'])),
'output2': await mlContext.createTensor(
getDescriptorFromTensor(outputs['output2'])),
};
// Initialize inputs
const inputData =
new TypedArrayDict['float32'](sizeOfShape(shape)).fill(1.0);
mlContext.writeTensor(dispatchInputs['lhs'], inputData);
mlContext.writeTensor(dispatchInputs['rhs'], inputData);
// Output_1 = LHS + RHS = 1 + 1 = 2
mlContext.dispatch(mlGraph, dispatchInputs, dispatch1Outputs);
// Output_2 = LHS + RHS = 1 + 1 = 2
mlContext.dispatch(mlGraph, dispatchInputs, dispatch2Outputs);
await assert_tensor_data_equals(
mlContext, dispatch1Outputs['output1'],
new Float32Array(sizeOfShape(shape)).fill(2.0));
await assert_tensor_data_equals(
mlContext, dispatch1Outputs['output2'],
new Float32Array(sizeOfShape(shape)).fill(2.0));
await assert_tensor_data_equals(
mlContext, dispatch2Outputs['output1'],
new Float32Array(sizeOfShape(shape)).fill(2.0));
await assert_tensor_data_equals(
mlContext, dispatch2Outputs['output2'],
new Float32Array(sizeOfShape(shape)).fill(2.0));
}, `${testName} / same_inputs`);
promise_test(async () => {
const dispatch1Inputs = {
'lhs':
await mlContext.createTensor(getDescriptorFromTensor(inputs['lhs'])),
'rhs':
await mlContext.createTensor(getDescriptorFromTensor(inputs['rhs'])),
};
const dispatch2Inputs = {
'lhs':
await mlContext.createTensor(getDescriptorFromTensor(inputs['lhs'])),
'rhs':
await mlContext.createTensor(getDescriptorFromTensor(inputs['rhs'])),
};
const dispatchOutputs = {
'output1': await mlContext.createTensor(
getDescriptorFromTensor(outputs['output1'])),
'output2': await mlContext.createTensor(
getDescriptorFromTensor(outputs['output2'])),
};
// Initialize inputs
const input1Data =
new TypedArrayDict['float32'](sizeOfShape(shape)).fill(1.0);
mlContext.writeTensor(dispatch1Inputs['lhs'], input1Data);
mlContext.writeTensor(dispatch1Inputs['rhs'], input1Data);
const input2Data =
new TypedArrayDict['float32'](sizeOfShape(shape)).fill(2.0);
mlContext.writeTensor(dispatch2Inputs['lhs'], input2Data);
mlContext.writeTensor(dispatch2Inputs['rhs'], input2Data);
// Output = LHS_1 + RHS_1 = 1 + 1 = 2
mlContext.dispatch(mlGraph, dispatch1Inputs, dispatchOutputs);
// Output = LHS_2 + RHS_2 = 2 + 2 = 4
mlContext.dispatch(mlGraph, dispatch2Inputs, dispatchOutputs);
await assert_tensor_data_equals(
mlContext, dispatchOutputs['output1'],
new Float32Array(sizeOfShape(shape)).fill(4.0));
await assert_tensor_data_equals(
mlContext, dispatchOutputs['output2'],
new Float32Array(sizeOfShape(shape)).fill(4.0));
}, `${testName} / same_outputs`);
promise_test(async () => {
const dispatchInputs = {
'lhs':
await mlContext.createTensor(getDescriptorFromTensor(inputs['lhs'])),
'rhs':
await mlContext.createTensor(getDescriptorFromTensor(inputs['rhs'])),
};
const dispatchOutputs = {
'output1': await mlContext.createTensor(
getDescriptorFromTensor(outputs['output1'])),
'output2': await mlContext.createTensor(
getDescriptorFromTensor(outputs['output2'])),
};
// Initialize inputs
const inputData =
new TypedArrayDict['float32'](sizeOfShape(shape)).fill(1.0);
mlContext.writeTensor(dispatchInputs['lhs'], inputData);
mlContext.writeTensor(dispatchInputs['rhs'], inputData);
// Output = LHS + RHS = 1 + 1 = 2
mlContext.dispatch(mlGraph, dispatchInputs, dispatchOutputs);
mlContext.dispatch(mlGraph, dispatchInputs, dispatchOutputs);
await assert_tensor_data_equals(
mlContext, dispatchOutputs['output1'],
new Float32Array(sizeOfShape(shape)).fill(2.0));
await assert_tensor_data_equals(
mlContext, dispatchOutputs['output2'],
new Float32Array(sizeOfShape(shape)).fill(2.0));
}, `${testName} / same_inputs_and_outputs`);
promise_test(async () => {
const dispatchInputs = {
'lhs':
await mlContext.createTensor(getDescriptorFromTensor(inputs['lhs'])),
'rhs':
await mlContext.createTensor(getDescriptorFromTensor(inputs['rhs'])),
};
const dispatch1Outputs = {
'output1': await mlContext.createTensor(
getDescriptorFromTensor(outputs['output1'])),
'output2': await mlContext.createTensor(
getDescriptorFromTensor(outputs['output2'])),
};
const dispatch2Outputs = {
'output1': await mlContext.createTensor(
getDescriptorFromTensor(outputs['output1'])),
'output2': await mlContext.createTensor(
getDescriptorFromTensor(outputs['output2'])),
};
// Initialize inputs
const inputData =
new TypedArrayDict['float32'](sizeOfShape(shape)).fill(1.0);
mlContext.writeTensor(dispatchInputs['lhs'], inputData);
mlContext.writeTensor(dispatchInputs['rhs'], inputData);
// Output_1 = LHS + RHS = 1 + 1 = 2
mlContext.dispatch(mlGraph, dispatchInputs, dispatch1Outputs);
// Output_2 = Output_1_LHS + Output_1_RHS = 2 + 2 = 4
mlContext.dispatch(
mlGraph, {
'lhs': dispatch1Outputs['output1'],
'rhs': dispatch1Outputs['output2'],
},
dispatch2Outputs);
// Output_1 = Output_2_LHS + Output_2_RHS = 4 + 4 = 8
mlContext.dispatch(
mlGraph, {
'lhs': dispatch2Outputs['output1'],
'rhs': dispatch2Outputs['output2'],
},
dispatch1Outputs);
await assert_tensor_data_equals(
mlContext, dispatch1Outputs['output1'],
new Float32Array(sizeOfShape(shape)).fill(8));
await assert_tensor_data_equals(
mlContext, dispatch1Outputs['output2'],
new Float32Array(sizeOfShape(shape)).fill(8));
}, `${testName} / outputs_as_inputs`);
promise_test(async () => {
// Construct a simple graph: OUTPUT = LHS - RHS.
const builder = new MLGraphBuilder(mlContext);
const operandType = {dataType: 'float32', dimensions: shape};
const lhsOperand = builder.input('lhs', operandType);
const rhsOperand = builder.input('rhs', operandType);
const graph =
await builder.build({'output': builder.sub(lhsOperand, rhsOperand)});
const lhsTensor =
await mlContext.createTensor(getDescriptorFromTensor(inputs['lhs']));
const rhsTensor =
await mlContext.createTensor(getDescriptorFromTensor(inputs['rhs']));
const dispatchOutputs = {
'output': await mlContext.createTensor(
getDescriptorFromTensor(outputs['output1']))
};
// Initialize inputs
mlContext.writeTensor(
lhsTensor, new TypedArrayDict['float32'](sizeOfShape(shape)).fill(5.0));
mlContext.writeTensor(
rhsTensor, new TypedArrayDict['float32'](sizeOfShape(shape)).fill(3.0));
// Output = LHS - RHS = 5 - 3 = 2
mlContext.dispatch(
graph, {
'lhs': lhsTensor,
'rhs': rhsTensor,
},
dispatchOutputs);
await assert_tensor_data_equals(
mlContext, dispatchOutputs['output'],
new Float32Array(sizeOfShape(shape)).fill(2));
// Output = RHS - LHS = 3 - 5 = -2
mlContext.dispatch(
graph, {