From a469fba8d28021db1e5d068f3b873784a544f45b Mon Sep 17 00:00:00 2001
From: Austin Sullivan <asully@chromium.org>
Date: Tue, 18 Jun 2024 16:47:27 +0000
Subject: [PATCH] Bug 1902792 [wpt PR 46770] - webnn: Give an MLBuffer an
MLOperandDescriptor, a=testonly
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
Automatic update from web-platform-tests
webnn: Give an MLBuffer an MLOperandDescriptor
This CL gives MLBufferDescriptor an MLOperandDescriptor as per
https://github.com/webmachinelearning/webnn/issues/542
To represent this descriptor, this CL also creates a new typemapped
OperandDescriptor type which ensures that the buffer descriptor is
valid. OperandDescriptor will be used more pervasively within WebNN
in follow-up CLs
1) Move Operand::DataType to DataType (MERGED)
2) Create a typemapped OperandDescriptor class for MLBuffer <-- this CL
3) Use OperandDescriptor in mojom::Operand
4+) Remove duplicate code (especially with //components)
Fuchsia binary size seems to be unavoidable for now, and I suspect
may be temporary once duplicate code is removed in follow-ups.
bloaty shows a binary size increase primarily in
//t/b/r/m/ml/webnn/ml_graph_type_converter.cc, as well as a handful
of other renderer-side files which depend on the mojom component
Bug: 343638938, 325598628
Fuchsia-Binary-Size: See commit description
Cq-Include-Trybots: luci.chromium.try:win11-blink-rel
Change-Id: I775340f5c5e0e80942332cbae750d0d305cdd458
Reviewed-on: https://chromium-review.googlesource.com/c/chromium/src/+/5604163
Reviewed-by: ningxin hu <ningxin.hu@intel.com>
Commit-Queue: Austin Sullivan <asully@chromium.org>
Reviewed-by: Alex Gough <ajgo@chromium.org>
Reviewed-by: Reilly Grant <reillyg@chromium.org>
Cr-Commit-Position: refs/heads/main@{#1315553}
--
wpt-commits: ed9e9309618bdf76de06ff85757edbc8e1d7da82
wpt-pr: 46770
---
.../conformance_tests/buffer.https.any.js | 14 +-
.../tests/webnn/resources/utils.js | 367 +++++++++++++-----
2 files changed, 279 insertions(+), 102 deletions(-)
diff --git a/testing/web-platform/tests/webnn/conformance_tests/buffer.https.any.js b/testing/web-platform/tests/webnn/conformance_tests/buffer.https.any.js
index 9b4c43198b4df..bc838ee7680ce 100644
--- a/testing/web-platform/tests/webnn/conformance_tests/buffer.https.any.js
+++ b/testing/web-platform/tests/webnn/conformance_tests/buffer.https.any.js
@@ -3,6 +3,7 @@
// META: variant=?cpu
// META: variant=?gpu
// META: variant=?npu
+// META: script=../resources/utils_validation.js
// META: script=../resources/utils.js
// META: timeout=long
@@ -11,7 +12,18 @@
// https://webmachinelearning.github.io/webnn/#api-mlbuffer
if (navigator.ml) {
- testCreateWebNNBuffer('create', 4);
+ testCreateWebNNBuffer('create', {dataType: 'float16', dimensions: [2, 3]});
+ testCreateWebNNBuffer('create', {dataType: 'float32', dimensions: [1, 5]});
+ testCreateWebNNBuffer('create', {dataType: 'int32', dimensions: [4]});
+ testCreateWebNNBuffer('create', {dataType: 'uint8', dimensions: [3, 2, 4]});
+
+ testCreateWebNNBufferFails(
+ 'createFailsEmptyDimension', {dataType: 'int32', dimensions: [2, 0, 3]});
+ testCreateWebNNBufferFails('createFailsTooLarge', {
+ dataType: 'int32',
+ dimensions: [kMaxUnsignedLong, kMaxUnsignedLong, kMaxUnsignedLong]
+ });
+
testDestroyWebNNBuffer('destroyTwice');
testReadWebNNBuffer('read');
testWriteWebNNBuffer('write');
diff --git a/testing/web-platform/tests/webnn/resources/utils.js b/testing/web-platform/tests/webnn/resources/utils.js
index df163ca7c2b32..b8a017fb44bfe 100644
--- a/testing/web-platform/tests/webnn/resources/utils.js
+++ b/testing/web-platform/tests/webnn/resources/utils.js
@@ -57,10 +57,33 @@ const getTypedArrayData = (type, size, data) => {
return outData;
};
+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;
+ }
+};
+
const sizeOfShape = (array) => {
return array.reduce((accumulator, currentValue) => accumulator * currentValue, 1);
};
+const sizeOfDescriptor = (descriptor) => {
+ return descriptor.dimensions.reduce(
+ (accumulator, currentValue) => accumulator * currentValue,
+ bytesPerDataType(descriptor.dataType));
+};
+
+const getDescriptorFromBuffer = (buffer) => {
+ return {dataType: buffer.dataType, dimensions: buffer.shape};
+};
+
/**
* Get tests resources from test data JSON file of specified operation name.
* @param {String} operationName - An operation name
@@ -919,7 +942,9 @@ const contextOptions = kContextOptionsForVariant[variant];
*/
const isMLBufferSupported =
(ml_context) => {
- return (createBuffer(ml_context, 4) !== undefined);
+ return (
+ createBuffer(ml_context, {dataType: 'int32', dimensions: [2, 2]}) !==
+ undefined);
}
/**
@@ -1069,14 +1094,19 @@ const toHalf = (value) => {
/**
* WebNN buffer creation.
* @param {MLContext} context - the context used to create the buffer.
- * @param {Number} bufferSize - Size of the buffer to create, in bytes.
+ * @param {MLBufferDescriptor} bufferDescriptor - intended specs of the buffer.
* @returns {MLBuffer} the created buffer.
*/
-const createBuffer = (context, bufferSize) => {
+const createBuffer = (context, bufferDescriptor) => {
let buffer;
try {
- buffer = context.createBuffer({size: bufferSize});
- assert_equals(buffer.size, bufferSize);
+ buffer = context.createBuffer(bufferDescriptor);
+ assert_equals(
+ buffer.dataType, bufferDescriptor.dataType,
+ 'buffer data types do not match');
+ assert_array_equals(
+ buffer.shape, bufferDescriptor.dimensions,
+ 'buffer shapes do not match');
} catch (e) {
assert_true(e instanceof DOMException);
assert_equals(e.name, "NotSupportedError");
@@ -1100,8 +1130,8 @@ const testDestroyWebNNBuffer = (testName) => {
}
assert_implements(
supported, `Unable to create context for ${variant} variant`);
- buffer = createBuffer(context, 4);
- });
+ buffer = createBuffer(context, {dataType: 'int32', dimensions: [2, 3]});
+ });
promise_test(async () => {
// MLBuffer is not supported for this deviceType.
if (buffer === undefined) {
@@ -1115,9 +1145,32 @@ const testDestroyWebNNBuffer = (testName) => {
/**
* WebNN create buffer test.
* @param {String} testName - The name of the test operation.
- * @param {Number} bufferSize - Size of the buffer to create, in bytes.
+ * @param {MLBufferDescriptor} bufferDescriptor - The intended buffer specs.
+ */
+const testCreateWebNNBuffer = (testName, bufferDescriptor) => {
+ let context;
+
+ promise_setup(async () => {
+ let supported = false;
+ try {
+ context = await navigator.ml.createContext(contextOptions);
+ supported = true;
+ } catch (e) {
+ }
+ assert_implements(
+ supported, `Unable to create context for ${variant} variant`);
+ });
+ promise_test(async () => {
+ createBuffer(context, bufferDescriptor);
+ }, `${testName} / ${bufferDescriptor.dataType}`);
+};
+
+/**
+ * Same as above, but expect creating the buffer to fail.
+ * @param {String} testName - The name of the test operation.
+ * @param {MLBufferDescriptor} bufferDescriptor - The intended buffer specs.
*/
-const testCreateWebNNBuffer = (testName, bufferSize) => {
+const testCreateWebNNBufferFails = (testName, bufferDescriptor) => {
let context;
promise_setup(async () => {
@@ -1131,10 +1184,11 @@ const testCreateWebNNBuffer = (testName, bufferSize) => {
supported, `Unable to create context for ${variant} variant`);
});
promise_test(async () => {
- createBuffer(context, bufferSize);
- }, `${testName} / ${bufferSize}`);
+ assert_throws_js(TypeError, () => context.createBuffer(bufferDescriptor));
+ }, `${testName} / ${bufferDescriptor.dataType}`);
};
+
/**
* Asserts the buffer data in MLBuffer matches expected.
* @param {MLContext} ml_context - The context used to create the buffer.
@@ -1166,46 +1220,48 @@ const testWriteWebNNBuffer = (testName) => {
});
promise_test(async () => {
- let ml_buffer = createBuffer(ml_context, 4);
+ const descriptor = {dataType: 'int32', dimensions: [1]};
+ let ml_buffer = createBuffer(ml_context, descriptor);
// MLBuffer was unsupported for the deviceType.
if (ml_buffer === undefined) {
return;
}
- let array_buffer = new ArrayBuffer(ml_buffer.size);
+ const bufferByteLength = sizeOfDescriptor(descriptor);
+ let array_buffer = new ArrayBuffer(bufferByteLength);
// Writing with a size that goes past that source buffer length.
assert_throws_js(
TypeError,
() => ml_context.writeBuffer(
ml_buffer, new Uint8Array(array_buffer), /*srcOffset=*/ 0,
- /*srcSize=*/ ml_buffer.size + 1));
+ /*srcSize=*/ bufferByteLength + 1));
assert_throws_js(
TypeError,
() => ml_context.writeBuffer(
ml_buffer, new Uint8Array(array_buffer), /*srcOffset=*/ 3,
- /*srcSize=*/ 4));
+ /*srcSize=*/ bufferByteLength));
// Writing with a source offset that is out of range of the source size.
assert_throws_js(
TypeError,
() => ml_context.writeBuffer(
ml_buffer, new Uint8Array(array_buffer),
- /*srcOffset=*/ ml_buffer.size + 1));
+ /*srcOffset=*/ bufferByteLength + 1));
// Writing with a source offset that is out of range of implicit copy size.
assert_throws_js(
TypeError,
() => ml_context.writeBuffer(
ml_buffer, new Uint8Array(array_buffer),
- /*srcOffset=*/ ml_buffer.size + 1, /*srcSize=*/ undefined));
+ /*srcOffset=*/ bufferByteLength + 1, /*srcSize=*/ undefined));
assert_throws_js(
TypeError,
() => ml_context.writeBuffer(
ml_buffer, new Uint8Array(array_buffer), /*srcOffset=*/ undefined,
- /*srcSize=*/ ml_buffer.size + 1));
+ /*srcSize=*/ bufferByteLength + 1));
assert_throws_js(
TypeError,
@@ -1214,7 +1270,8 @@ const testWriteWebNNBuffer = (testName) => {
}, `${testName} / error`);
promise_test(async () => {
- let ml_buffer = createBuffer(ml_context, 4);
+ const descriptor = {dataType: 'int32', dimensions: [2, 2]};
+ let ml_buffer = createBuffer(ml_context, descriptor);
// MLBuffer was unsupported for the deviceType.
if (ml_buffer === undefined) {
@@ -1226,19 +1283,20 @@ const testWriteWebNNBuffer = (testName) => {
assert_throws_dom(
'InvalidStateError',
- () =>
- ml_context.writeBuffer(ml_buffer, new Uint8Array(ml_buffer.size)));
+ () => ml_context.writeBuffer(
+ ml_buffer, new Uint8Array(sizeOfDescriptor(descriptor))));
}, `${testName} / destroy`);
promise_test(async () => {
- let ml_buffer = createBuffer(ml_context, 4);
+ const descriptor = {dataType: 'int32', dimensions: [2, 2]};
+ let ml_buffer = createBuffer(ml_context, descriptor);
// MLBuffer was unsupported for the deviceType.
if (ml_buffer === undefined) {
return;
}
- const array_buffer = new ArrayBuffer(ml_buffer.size);
+ const array_buffer = new ArrayBuffer(sizeOfDescriptor(descriptor));
const detached_buffer = array_buffer.transfer();
assert_true(array_buffer.detached, 'array buffer should be detached.');
@@ -1246,7 +1304,8 @@ const testWriteWebNNBuffer = (testName) => {
}, `${testName} / detached`);
promise_test(async () => {
- let ml_buffer = createBuffer(ml_context, 4);
+ const bufferDescriptor = {dataType: 'int32', dimensions: [2, 3]};
+ let ml_buffer = createBuffer(ml_context, bufferDescriptor);
// MLBuffer was unsupported for the deviceType.
if (ml_buffer === undefined) {
@@ -1254,9 +1313,10 @@ const testWriteWebNNBuffer = (testName) => {
}
let another_ml_context = await navigator.ml.createContext(contextOptions);
- let another_ml_buffer = createBuffer(another_ml_context, ml_buffer.size);
+ let another_ml_buffer = createBuffer(another_ml_context, bufferDescriptor);
- let input_data = new Uint8Array(ml_buffer.size).fill(0xAA);
+ let input_data =
+ new Uint8Array(sizeOfDescriptor(bufferDescriptor)).fill(0xAA);
assert_throws_js(
TypeError, () => ml_context.writeBuffer(another_ml_buffer, input_data));
assert_throws_js(
@@ -1282,7 +1342,8 @@ const testReadWebNNBuffer = (testName) => {
});
promise_test(async t => {
- let ml_buffer = createBuffer(ml_context, 4);
+ let ml_buffer =
+ createBuffer(ml_context, {dataType: 'int32', dimensions: [2, 2]});
// MLBuffer was unsupported for the deviceType.
if (ml_buffer === undefined) {
@@ -1297,7 +1358,8 @@ const testReadWebNNBuffer = (testName) => {
}, `${testName} / destroy`);
promise_test(async () => {
- let ml_buffer = createBuffer(ml_context, 4);
+ let ml_buffer =
+ createBuffer(ml_context, {dataType: 'int32', dimensions: [1]});
// MLBuffer was unsupported for the deviceType.
if (ml_buffer === undefined) {
@@ -1315,7 +1377,8 @@ const testReadWebNNBuffer = (testName) => {
}, `${testName} / full_size`);
promise_test(async () => {
- let ml_buffer = createBuffer(ml_context, 4);
+ let ml_buffer =
+ createBuffer(ml_context, {dataType: 'int32', dimensions: [1]});
// MLBuffer was unsupported for the deviceType.
if (ml_buffer === undefined) {
@@ -1335,7 +1398,8 @@ const testReadWebNNBuffer = (testName) => {
}, `${testName} / src_offset_only`);
promise_test(async () => {
- let ml_buffer = createBuffer(ml_context, 4);
+ let ml_buffer =
+ createBuffer(ml_context, {dataType: 'int32', dimensions: [1]});
// MLBuffer was unsupported for the deviceType.
if (ml_buffer === undefined) {
@@ -1354,7 +1418,8 @@ const testReadWebNNBuffer = (testName) => {
}, `${testName} / zero_write`);
promise_test(async () => {
- let ml_buffer = createBuffer(ml_context, 4);
+ let ml_buffer =
+ createBuffer(ml_context, {dataType: 'int32', dimensions: [1]});
// MLBuffer was unsupported for the deviceType.
if (ml_buffer === undefined) {
@@ -1374,7 +1439,8 @@ const testReadWebNNBuffer = (testName) => {
}, `${testName} / src_offset_and_size`);
promise_test(async () => {
- let ml_buffer = createBuffer(ml_context, 4);
+ let ml_buffer =
+ createBuffer(ml_context, {dataType: 'int32', dimensions: [1]});
// MLBuffer was unsupported for the deviceType.
if (ml_buffer === undefined) {
@@ -1394,7 +1460,8 @@ const testReadWebNNBuffer = (testName) => {
}, `${testName} / larger_src_data`);
promise_test(async () => {
- let ml_buffer = createBuffer(ml_context, 4);
+ let ml_buffer =
+ createBuffer(ml_context, {dataType: 'int32', dimensions: [1]});
// MLBuffer was unsupported for the deviceType.
if (ml_buffer === undefined) {
@@ -1412,7 +1479,8 @@ const testReadWebNNBuffer = (testName) => {
}, `${testName} / no_src_offset`);
promise_test(async t => {
- let ml_buffer = createBuffer(ml_context, 4);
+ const bufferDescriptor = {dataType: 'int32', dimensions: [2, 3]};
+ let ml_buffer = createBuffer(ml_context, bufferDescriptor);
// MLBuffer was unsupported for the deviceType.
if (ml_buffer === undefined) {
@@ -1420,7 +1488,7 @@ const testReadWebNNBuffer = (testName) => {
}
let another_ml_context = await navigator.ml.createContext(contextOptions);
- let another_ml_buffer = createBuffer(another_ml_context, ml_buffer.size);
+ let another_ml_buffer = createBuffer(another_ml_context, bufferDescriptor);
await promise_rejects_js(
t, TypeError, ml_context.readBuffer(another_ml_buffer));
@@ -1450,26 +1518,24 @@ const testDispatchWebNNBuffer = (testName) => {
supported, `Unable to create context for ${variant} variant`);
// Construct a simple graph: A = B + C, with two outputs.
const builder = new MLGraphBuilder(ml_context);
- const operandType = {dataType: 'float32', dimensions: shape};
- const lhs_operand = builder.input('lhs', operandType);
- const rhs_operand = builder.input('rhs', operandType);
+ const descriptor = {dataType: 'float32', dimensions: shape};
+ const lhs_operand = builder.input('lhs', descriptor);
+ const rhs_operand = builder.input('rhs', descriptor);
const output_1_operand = builder.add(lhs_operand, rhs_operand);
const output_2_operand = builder.add(lhs_operand, rhs_operand);
ml_graph = await builder.build(
{'output1': output_1_operand, 'output2': output_2_operand});
- const ml_buffer_size =
- TypedArrayDict['float32'].BYTES_PER_ELEMENT * sizeOfShape(shape);
// MLBuffer was unsupported for the deviceType.
if (!isMLBufferSupported(ml_context)) {
return;
}
inputs = {
- 'lhs': ml_context.createBuffer({size: ml_buffer_size}),
- 'rhs': ml_context.createBuffer({size: ml_buffer_size}),
+ 'lhs': ml_context.createBuffer(descriptor),
+ 'rhs': ml_context.createBuffer(descriptor),
};
outputs = {
- 'output1': ml_context.createBuffer({size: ml_buffer_size}),
- 'output2': ml_context.createBuffer({size: ml_buffer_size}),
+ 'output1': ml_context.createBuffer(descriptor),
+ 'output2': ml_context.createBuffer(descriptor),
};
});
@@ -1489,16 +1555,16 @@ const testDispatchWebNNBuffer = (testName) => {
TypeError,
() => ml_context.dispatch(
ml_graph, {
- 'lhs':
- another_ml_context.createBuffer({size: inputs['lhs'].size()}),
+ 'lhs': another_ml_context.createBuffer(
+ getDescriptorFromBuffer(inputs['lhs'])),
'rhs': inputs['rhs'],
},
outputs));
// Test the wrong context being used for outputs.
assert_throws_js(TypeError, () => ml_context.dispatch(ml_graph, inputs, {
- 'output1':
- another_ml_context.createBuffer({size: outputs['output1'].size()}),
+ 'output1': another_ml_context.createBuffer(
+ getDescriptorFromBuffer(outputs['output1'])),
'output2': outputs['output2'],
}));
}, `${testName} / context_mismatch`);
@@ -1509,15 +1575,80 @@ const testDispatchWebNNBuffer = (testName) => {
return;
}
- // Control case, valid size.
+ // Control case, valid buffers.
+ ml_context.dispatch(ml_graph, inputs, outputs);
+
+ // Input is a different shape.
+ assert_throws_js(
+ TypeError,
+ () => ml_context.dispatch(
+ ml_graph, {
+ 'lhs': ml_context.createBuffer({
+ dataType: inputs['lhs'].dataType,
+ // Input rank is too high.
+ dimensions: inputs['lhs'].shape.concat([2])
+ }),
+ 'rhs': inputs['rhs'],
+ },
+ outputs));
+
+ assert_throws_js(
+ TypeError,
+ () => ml_context.dispatch(
+ ml_graph, {
+ 'lhs': inputs['lhs'],
+ 'rhs': ml_context.createBuffer({
+ dataType: inputs['rhs'].dataType,
+ // Input rank is too low.
+ dimensions: inputs['rhs'].shape.slice(1)
+ }),
+ },
+ outputs));
+
+ // Output is a different shape. Dimension value is too large.
+ let output1WrongShape = [...outputs['output1'].shape];
+ output1WrongShape[0] += 2;
+ assert_throws_js(TypeError, () => ml_context.dispatch(ml_graph, inputs, {
+ 'output1': ml_context.createBuffer({
+ dataType: outputs['output1'].dataType,
+ dimensions: output1WrongShape
+ }),
+ 'output2': outputs['output2'],
+ }));
+
+ // Output is a different shape. Dimension value is too small.
+ let output2WrongShape = [...outputs['output2'].shape];
+ output2WrongShape[1] -= 1;
+ assert_throws_js(TypeError, () => ml_context.dispatch(ml_graph, inputs, {
+ 'output1': outputs['output1'],
+ 'output2': ml_context.createBuffer({
+ dataType: outputs['output2'].dataType,
+ dimensions: output2WrongShape
+ }),
+ }));
+ }, `${testName} / invalid shape`);
+
+ promise_test(async () => {
+ // MLBuffer was unsupported for the deviceType.
+ if (!isMLBufferSupported(ml_context)) {
+ return;
+ }
+
+ // Control case, valid buffers.
ml_context.dispatch(ml_graph, inputs, outputs);
- // Input is too large.
+ // Inputs are a different data type.
+ const inputWrongDataType = 'int32';
+ assert_not_equals(inputs['lhs'].dataType, inputWrongDataType);
+ assert_not_equals(inputs['rhs'].dataType, inputWrongDataType);
assert_throws_js(
TypeError,
() => ml_context.dispatch(
ml_graph, {
- 'lhs': ml_context.createBuffer({size: inputs['lhs'].size() + 1}),
+ 'lhs': ml_context.createBuffer({
+ dataType: inputWrongDataType,
+ dimensions: inputs['lhs'].shape
+ }),
'rhs': inputs['rhs'],
},
outputs));
@@ -1527,21 +1658,33 @@ const testDispatchWebNNBuffer = (testName) => {
() => ml_context.dispatch(
ml_graph, {
'lhs': inputs['lhs'],
- 'rhs': ml_context.createBuffer({size: inputs['rhs'].size() + 1}),
+ 'rhs': ml_context.createBuffer({
+ dataType: inputWrongDataType,
+ dimensions: inputs['rhs'].shape
+ }),
},
outputs));
- // Output is too large.
+ // Outputs are a different data type.
+ const outputWrongDataType = 'int32';
+ assert_not_equals(outputs['output1'].dataType, outputWrongDataType);
+ assert_not_equals(outputs['output2'].dataType, outputWrongDataType);
assert_throws_js(TypeError, () => ml_context.dispatch(ml_graph, inputs, {
- 'output1': ml_context.createBuffer({size: outputs['output1'].size() + 1}),
+ 'output1': ml_context.createBuffer({
+ dataType: outputWrongDataType,
+ dimensions: outputs['output1'].shape
+ }),
'output2': outputs['output2'],
}));
assert_throws_js(TypeError, () => ml_context.dispatch(ml_graph, inputs, {
'output1': outputs['output1'],
- 'output2': ml_context.createBuffer({size: outputs['output2'].size() + 1}),
+ 'output2': ml_context.createBuffer({
+ dataType: outputWrongDataType,
+ dimensions: outputs['output2'].shape
+ }),
}));
- }, `${testName} / invalid_size`);
+ }, `${testName} / invalid data type`);
promise_test(async () => {
// MLBuffer was unsupported for the deviceType.
@@ -1601,8 +1744,8 @@ const testDispatchWebNNBuffer = (testName) => {
ml_graph, {
'lhs': inputs['lhs'],
'rhs': inputs['rhs'],
- 'a_different_input_name':
- ml_context.createBuffer({size: inputs['rhs'].size()}),
+ 'a_different_input_name': ml_context.createBuffer(
+ getDescriptorFromBuffer(inputs['rhs'])),
},
outputs));
@@ -1616,7 +1759,7 @@ const testDispatchWebNNBuffer = (testName) => {
'output1': outputs['output1'],
'output2': outputs['output2'],
'a_different_output_name':
- ml_context.createBuffer({size: outputs['output2'].size()}),
+ ml_context.createBuffer(getDescriptorFromBuffer(outputs['output2'])),
}));
}, `${testName} / invalid_name`);
@@ -1673,18 +1816,22 @@ const testDispatchWebNNBuffer = (testName) => {
}
const dispatch_inputs = {
- 'lhs': ml_context.createBuffer({size: inputs['lhs'].size}),
- 'rhs': ml_context.createBuffer({size: inputs['rhs'].size}),
+ 'lhs': ml_context.createBuffer(getDescriptorFromBuffer(inputs['lhs'])),
+ 'rhs': ml_context.createBuffer(getDescriptorFromBuffer(inputs['rhs'])),
};
const dispatch_1_outputs = {
- 'output1': ml_context.createBuffer({size: outputs['output1'].size}),
- 'output2': ml_context.createBuffer({size: outputs['output2'].size}),
+ 'output1':
+ ml_context.createBuffer(getDescriptorFromBuffer(outputs['output1'])),
+ 'output2':
+ ml_context.createBuffer(getDescriptorFromBuffer(outputs['output2'])),
};
const dispatch_2_outputs = {
- 'output1': ml_context.createBuffer({size: outputs['output1'].size}),
- 'output2': ml_context.createBuffer({size: outputs['output2'].size}),
+ 'output1':
+ ml_context.createBuffer(getDescriptorFromBuffer(outputs['output1'])),
+ 'output2':
+ ml_context.createBuffer(getDescriptorFromBuffer(outputs['output2'])),
};
// Initialize inputs
@@ -1723,18 +1870,20 @@ const testDispatchWebNNBuffer = (testName) => {
}
const dispatch_1_inputs = {
- 'lhs': ml_context.createBuffer({size: inputs['lhs'].size}),
- 'rhs': ml_context.createBuffer({size: inputs['rhs'].size}),
+ 'lhs': ml_context.createBuffer(getDescriptorFromBuffer(inputs['lhs'])),
+ 'rhs': ml_context.createBuffer(getDescriptorFromBuffer(inputs['rhs'])),
};
const dispatch_2_inputs = {
- 'lhs': ml_context.createBuffer({size: inputs['lhs'].size}),
- 'rhs': ml_context.createBuffer({size: inputs['rhs'].size}),
+ 'lhs': ml_context.createBuffer(getDescriptorFromBuffer(inputs['lhs'])),
+ 'rhs': ml_context.createBuffer(getDescriptorFromBuffer(inputs['rhs'])),
};
const dispatch_outputs = {
- 'output1': ml_context.createBuffer({size: outputs['output1'].size}),
- 'output2': ml_context.createBuffer({size: outputs['output2'].size}),
+ 'output1':
+ ml_context.createBuffer(getDescriptorFromBuffer(outputs['output1'])),
+ 'output2':
+ ml_context.createBuffer(getDescriptorFromBuffer(outputs['output2'])),
};
// Initialize inputs
@@ -1770,13 +1919,15 @@ const testDispatchWebNNBuffer = (testName) => {
}
const dispatch_inputs = {
- 'lhs': ml_context.createBuffer({size: inputs['lhs'].size}),
- 'rhs': ml_context.createBuffer({size: inputs['rhs'].size}),
+ 'lhs': ml_context.createBuffer(getDescriptorFromBuffer(inputs['lhs'])),
+ 'rhs': ml_context.createBuffer(getDescriptorFromBuffer(inputs['rhs'])),
};
const dispatch_outputs = {
- 'output1': ml_context.createBuffer({size: outputs['output1'].size}),
- 'output2': ml_context.createBuffer({size: outputs['output2'].size}),
+ 'output1':
+ ml_context.createBuffer(getDescriptorFromBuffer(outputs['output1'])),
+ 'output2':
+ ml_context.createBuffer(getDescriptorFromBuffer(outputs['output2'])),
};
// Initialize inputs
@@ -1805,18 +1956,22 @@ const testDispatchWebNNBuffer = (testName) => {
}
const dispatch_inputs = {
- 'lhs': ml_context.createBuffer({size: inputs['lhs'].size}),
- 'rhs': ml_context.createBuffer({size: inputs['rhs'].size}),
+ 'lhs': ml_context.createBuffer(getDescriptorFromBuffer(inputs['lhs'])),
+ 'rhs': ml_context.createBuffer(getDescriptorFromBuffer(inputs['rhs'])),
};
const dispatch_1_outputs = {
- 'output1': ml_context.createBuffer({size: outputs['output1'].size}),
- 'output2': ml_context.createBuffer({size: outputs['output2'].size}),
+ 'output1':
+ ml_context.createBuffer(getDescriptorFromBuffer(outputs['output1'])),
+ 'output2':
+ ml_context.createBuffer(getDescriptorFromBuffer(outputs['output2'])),
};
const dispatch_2_outputs = {
- 'output1': ml_context.createBuffer({size: outputs['output1'].size}),
- 'output2': ml_context.createBuffer({size: outputs['output2'].size}),
+ 'output1':
+ ml_context.createBuffer(getDescriptorFromBuffer(outputs['output1'])),
+ 'output2':
+ ml_context.createBuffer(getDescriptorFromBuffer(outputs['output2'])),
};
// Initialize inputs
@@ -1867,11 +2022,14 @@ const testDispatchWebNNBuffer = (testName) => {
const graph =
await builder.build({'output': builder.sub(lhsOperand, rhsOperand)});
- const lhsBuffer = ml_context.createBuffer({size: inputs['lhs'].size});
- const rhsBuffer = ml_context.createBuffer({size: inputs['rhs'].size});
+ const lhsBuffer =
+ ml_context.createBuffer(getDescriptorFromBuffer(inputs['lhs']));
+ const rhsBuffer =
+ ml_context.createBuffer(getDescriptorFromBuffer(inputs['rhs']));
const dispatchOutputs = {
- 'output': ml_context.createBuffer({size: outputs['output1'].size})
+ 'output':
+ ml_context.createBuffer(getDescriptorFromBuffer(outputs['output1']))
};
// Initialize inputs
@@ -1912,14 +2070,14 @@ const testDispatchWebNNBuffer = (testName) => {
}
const dispatchInputs = {
- 'lhs': ml_context.createBuffer({size: inputs['lhs'].size}),
- 'rhs': ml_context.createBuffer({size: inputs['rhs'].size}),
+ 'lhs': ml_context.createBuffer(getDescriptorFromBuffer(inputs['lhs'])),
+ 'rhs': ml_context.createBuffer(getDescriptorFromBuffer(inputs['rhs'])),
};
const outputBuffer1 =
- ml_context.createBuffer({size: outputs['output1'].size});
+ ml_context.createBuffer(getDescriptorFromBuffer(outputs['output1']));
const outputBuffer2 =
- ml_context.createBuffer({size: outputs['output2'].size});
+ ml_context.createBuffer(getDescriptorFromBuffer(outputs['output2']));
// Initialize inputs
const inputData1 =
@@ -1941,7 +2099,8 @@ const testDispatchWebNNBuffer = (testName) => {
ml_context.dispatch(ml_graph, dispatchInputs, {
'output1': outputBuffer1,
- 'output2': ml_context.createBuffer({size: outputs['output2'].size}),
+ 'output2':
+ ml_context.createBuffer(getDescriptorFromBuffer(outputs['output2'])),
});
// Ensure the last dispatch() did not modify the original second output
@@ -1958,13 +2117,15 @@ const testDispatchWebNNBuffer = (testName) => {
}
const dispatchInputs = {
- 'lhs': ml_context.createBuffer({size: inputs['lhs'].size}),
- 'rhs': ml_context.createBuffer({size: inputs['rhs'].size}),
+ 'lhs': ml_context.createBuffer(getDescriptorFromBuffer(inputs['lhs'])),
+ 'rhs': ml_context.createBuffer(getDescriptorFromBuffer(inputs['rhs'])),
};
const dispatchOutputs = {
- 'output1': ml_context.createBuffer({size: outputs['output1'].size}),
- 'output2': ml_context.createBuffer({size: outputs['output2'].size}),
+ 'output1':
+ ml_context.createBuffer(getDescriptorFromBuffer(outputs['output1'])),
+ 'output2':
+ ml_context.createBuffer(getDescriptorFromBuffer(outputs['output2'])),
};
// Initialize inputs
@@ -1978,7 +2139,8 @@ const testDispatchWebNNBuffer = (testName) => {
// Check destroyed input buffers cannot be re-used in subsequent dispatches.
dispatchInputs['lhs'].destroy();
- dispatchInputs['lhs'] = ml_context.createBuffer({size: inputs['lhs'].size});
+ dispatchInputs['lhs'] =
+ ml_context.createBuffer(getDescriptorFromBuffer(inputs['lhs']));
const newInputData =
new TypedArrayDict['float32'](sizeOfShape(shape)).fill(2.0);
@@ -1992,7 +2154,8 @@ const testDispatchWebNNBuffer = (testName) => {
new Float32Array(sizeOfShape(shape)).fill(3));
dispatchInputs['rhs'].destroy();
- dispatchInputs['rhs'] = ml_context.createBuffer({size: inputs['rhs'].size});
+ dispatchInputs['rhs'] =
+ ml_context.createBuffer(getDescriptorFromBuffer(inputs['rhs']));
ml_context.writeBuffer(dispatchInputs['rhs'], newInputData);
// Output = LHS + RHS = 2 + 2 = 4
@@ -2010,13 +2173,15 @@ const testDispatchWebNNBuffer = (testName) => {
}
const dispatchInputs = {
- 'lhs': ml_context.createBuffer({size: inputs['lhs'].size}),
- 'rhs': ml_context.createBuffer({size: inputs['rhs'].size}),
+ 'lhs': ml_context.createBuffer(getDescriptorFromBuffer(inputs['lhs'])),
+ 'rhs': ml_context.createBuffer(getDescriptorFromBuffer(inputs['rhs'])),
};
const dispatchOutputs = {
- 'output1': ml_context.createBuffer({size: outputs['output1'].size}),
- 'output2': ml_context.createBuffer({size: outputs['output2'].size}),
+ 'output1':
+ ml_context.createBuffer(getDescriptorFromBuffer(outputs['output1'])),
+ 'output2':
+ ml_context.createBuffer(getDescriptorFromBuffer(outputs['output2'])),
};
// Initialize inputs
@@ -2031,8 +2196,8 @@ const testDispatchWebNNBuffer = (testName) => {
// Check destroyed output buffers cannot be re-used in subsequent
// dispatches.
dispatchOutputs['output1'].destroy();
- dispatchOutputs['output1'] =
- ml_context.createBuffer({size: outputs['output1'].size});
+ dispatchOutputs['output1'] = ml_context.createBuffer(
+ getDescriptorFromBuffer(outputs['output1']));
const newInputData =
new TypedArrayDict['float32'](sizeOfShape(shape)).fill(2.0);