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-rw-r--r--testing/web-platform/tests/webnn/META.yml4
-rw-r--r--testing/web-platform/tests/webnn/clamp.https.any.js10
-rw-r--r--testing/web-platform/tests/webnn/concat.https.any.js22
-rw-r--r--testing/web-platform/tests/webnn/idlharness.https.any.js58
-rw-r--r--testing/web-platform/tests/webnn/leaky_relu.https.any.js10
-rw-r--r--testing/web-platform/tests/webnn/relu.https.any.js10
-rw-r--r--testing/web-platform/tests/webnn/reshape.https.any.js20
-rw-r--r--testing/web-platform/tests/webnn/resources/test_data/clamp.json1102
-rw-r--r--testing/web-platform/tests/webnn/resources/test_data/concat.json1799
-rw-r--r--testing/web-platform/tests/webnn/resources/test_data/leaky_relu.json542
-rw-r--r--testing/web-platform/tests/webnn/resources/test_data/relu.json334
-rw-r--r--testing/web-platform/tests/webnn/resources/test_data/reshape.json406
-rw-r--r--testing/web-platform/tests/webnn/resources/test_data/sigmoid.json334
-rw-r--r--testing/web-platform/tests/webnn/resources/test_data/slice.json772
-rw-r--r--testing/web-platform/tests/webnn/resources/test_data/softmax.json70
-rw-r--r--testing/web-platform/tests/webnn/resources/test_data/split.json818
-rw-r--r--testing/web-platform/tests/webnn/resources/test_data/squeeze.json696
-rw-r--r--testing/web-platform/tests/webnn/resources/test_data/tanh.json334
-rw-r--r--testing/web-platform/tests/webnn/resources/test_data/transpose.json679
-rw-r--r--testing/web-platform/tests/webnn/resources/utils.js363
-rw-r--r--testing/web-platform/tests/webnn/sigmoid.https.any.js10
-rw-r--r--testing/web-platform/tests/webnn/slice.https.any.js19
-rw-r--r--testing/web-platform/tests/webnn/softmax.https.any.js10
-rw-r--r--testing/web-platform/tests/webnn/split.https.any.js24
-rw-r--r--testing/web-platform/tests/webnn/squeeze.https.any.js10
-rw-r--r--testing/web-platform/tests/webnn/tanh.https.any.js10
-rw-r--r--testing/web-platform/tests/webnn/transpose.https.any.js10
27 files changed, 8476 insertions, 0 deletions
diff --git a/testing/web-platform/tests/webnn/META.yml b/testing/web-platform/tests/webnn/META.yml
new file mode 100644
index 0000000000..3f87fc8042
--- /dev/null
+++ b/testing/web-platform/tests/webnn/META.yml
@@ -0,0 +1,4 @@
+spec: https://webmachinelearning.github.io/webnn/
+suggested_reviewers:
+ - dontcallmedom
+ - Honry \ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/clamp.https.any.js b/testing/web-platform/tests/webnn/clamp.https.any.js
new file mode 100644
index 0000000000..4cf54d1cea
--- /dev/null
+++ b/testing/web-platform/tests/webnn/clamp.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API clamp operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-clamp
+
+testWebNNOperation('clamp', '/webnn/resources/test_data/clamp.json', buildOperationWithSingleInput); \ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/concat.https.any.js b/testing/web-platform/tests/webnn/concat.https.any.js
new file mode 100644
index 0000000000..2c8950fe99
--- /dev/null
+++ b/testing/web-platform/tests/webnn/concat.https.any.js
@@ -0,0 +1,22 @@
+// META: title=test WebNN API concat operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-concat
+
+const buildConcat = (operationName, builder, resources) => {
+ // MLOperand concat(sequence<MLOperand> inputs, long axis);
+ const namedOutputOperand = {};
+ const inputOperands = [];
+ for (let input of resources.inputs) {
+ inputOperands.push(builder.input(input.name, {type: input.type, dimensions: input.shape}));
+ }
+ // invoke builder.concat()
+ namedOutputOperand[resources.expected.name] = builder[operationName](inputOperands, resources.axis);
+ return namedOutputOperand;
+};
+
+testWebNNOperation('concat', '/webnn/resources/test_data/concat.json', buildConcat); \ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/idlharness.https.any.js b/testing/web-platform/tests/webnn/idlharness.https.any.js
new file mode 100644
index 0000000000..6122134268
--- /dev/null
+++ b/testing/web-platform/tests/webnn/idlharness.https.any.js
@@ -0,0 +1,58 @@
+// META: global=window,dedicatedworker
+// META: script=/resources/WebIDLParser.js
+// META: script=/resources/idlharness.js
+// META: script=./resources/utils.js
+// META: timeout=long
+
+// https://webmachinelearning.github.io/webnn/
+
+'use strict';
+
+idl_test(
+ ['webnn'],
+ ['html', 'webidl', 'webgpu'],
+ async (idl_array) => {
+ if (self.GLOBAL.isWindow()) {
+ idl_array.add_objects({ Navigator: ['navigator'] });
+ } else if (self.GLOBAL.isWorker()) {
+ idl_array.add_objects({ WorkerNavigator: ['navigator'] });
+ }
+
+ idl_array.add_objects({
+ NavigatorML: ['navigator'],
+ ML: ['navigator.ml'],
+ MLContext: ['context'],
+ MLOperand: ['input', 'filter', 'output'],
+ MLOperator: ['relu'],
+ MLGraphBuilder: ['builder'],
+ MLGraph: ['graph']
+ });
+
+ for (const executionType of ExecutionArray) {
+ const isSync = executionType === 'sync';
+ if (self.GLOBAL.isWindow() && isSync) {
+ continue;
+ }
+
+ for (const deviceType of DeviceTypeArray) {
+ if (isSync) {
+ self.context = navigator.ml.createContextSync({deviceType});
+ } else {
+ self.context = await navigator.ml.createContext({deviceType});
+ }
+
+ self.builder = new MLGraphBuilder(self.context);
+ self.input = builder.input('input', {type: 'float32', dimensions: [1, 1, 5, 5]});
+ self.filter = builder.constant({type: 'float32', dimensions: [1, 1, 3, 3]}, new Float32Array(9).fill(1));
+ self.relu = builder.relu();
+ self.output = builder.conv2d(input, filter, {activation: relu, inputLayout: "nchw"});
+
+ if (isSync) {
+ self.graph = builder.buildSync({output});
+ } else {
+ self.graph = await builder.build({output});
+ }
+ }
+ }
+ }
+);
diff --git a/testing/web-platform/tests/webnn/leaky_relu.https.any.js b/testing/web-platform/tests/webnn/leaky_relu.https.any.js
new file mode 100644
index 0000000000..0755f33a90
--- /dev/null
+++ b/testing/web-platform/tests/webnn/leaky_relu.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API leakyRelu operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-leakyrelu
+
+testWebNNOperation('leakyRelu', '/webnn/resources/test_data/leaky_relu.json', buildOperationWithSingleInput); \ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/relu.https.any.js b/testing/web-platform/tests/webnn/relu.https.any.js
new file mode 100644
index 0000000000..19a0d986ca
--- /dev/null
+++ b/testing/web-platform/tests/webnn/relu.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API relu operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-relu
+
+testWebNNOperation('relu', '/webnn/resources/test_data/relu.json', buildOperationWithSingleInput); \ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/reshape.https.any.js b/testing/web-platform/tests/webnn/reshape.https.any.js
new file mode 100644
index 0000000000..40829bd1c3
--- /dev/null
+++ b/testing/web-platform/tests/webnn/reshape.https.any.js
@@ -0,0 +1,20 @@
+// META: title=test WebNN API reshape operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-reshape
+
+const buildReshape = (operationName, builder, resources) => {
+ // MLOperand reshape(MLOperand input, sequence<long> newShape);
+ const namedOutputOperand = {};
+ const inputOperand = createSingleInputOperand(builder, resources);
+ // invoke builder.reshape()
+ namedOutputOperand[resources.expected.name] = builder[operationName](inputOperand, resources.newShape);
+ return namedOutputOperand;
+};
+
+testWebNNOperation('reshape', '/webnn/resources/test_data/reshape.json', buildReshape);
+
diff --git a/testing/web-platform/tests/webnn/resources/test_data/clamp.json b/testing/web-platform/tests/webnn/resources/test_data/clamp.json
new file mode 100644
index 0000000000..93ab5ca0c1
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/clamp.json
@@ -0,0 +1,1102 @@
+{
+ "tests": [
+ // default options
+ {
+ "name": "clamp float32 1D tensor default options",
+ "inputs": {
+ "x": { // use 'x' for input operand name
+ "shape": [24],
+ "data": [
+ -9.817828475355284,
+ -6.024063916325786,
+ -4.072562498632983,
+ -6.575078191902692,
+ -7.7556836912181915,
+ 9.524681107378463,
+ 3.7292487446449307,
+ 6.4816868736447475,
+ -1.5374205904252634,
+ -7.343102426698445,
+ 7.880751290929794,
+ -2.056408790509967,
+ 6.34386375786449,
+ 5.52573787183,
+ 0.8433118207347725,
+ -8.19996033345526,
+ -7.786487326213716,
+ 9.280223823954241,
+ -2.31305948485121,
+ 9.549695091037119,
+ 5.788925460130297,
+ 5.549378312916486,
+ 7.409400528051194,
+ -2.1236145770503745
+ ],
+ "type": "float32"
+ }
+ },
+ "expected": {
+ "name": "output",
+ "shape": [24],
+ "data": [
+ -9.817828178405762,
+ -6.024064064025879,
+ -4.0725626945495605,
+ -6.575078010559082,
+ -7.755683898925781,
+ 9.524681091308594,
+ 3.7292487621307373,
+ 6.481687068939209,
+ -1.537420630455017,
+ -7.34310245513916,
+ 7.880751132965088,
+ -2.0564088821411133,
+ 6.3438639640808105,
+ 5.525737762451172,
+ 0.8433118462562561,
+ -8.199960708618164,
+ -7.786487102508545,
+ 9.280223846435547,
+ -2.3130595684051514,
+ 9.549695014953613,
+ 5.788925647735596,
+ 5.549378395080566,
+ 7.409400463104248,
+ -2.123614549636841
+ ],
+ "type": "float32"
+ }
+ },
+ {
+ "name": "clamp float32 2D tensor default options",
+ "inputs": {
+ "x": {
+ "shape": [4, 6],
+ "data": [
+ -9.817828475355284,
+ -6.024063916325786,
+ -4.072562498632983,
+ -6.575078191902692,
+ -7.7556836912181915,
+ 9.524681107378463,
+ 3.7292487446449307,
+ 6.4816868736447475,
+ -1.5374205904252634,
+ -7.343102426698445,
+ 7.880751290929794,
+ -2.056408790509967,
+ 6.34386375786449,
+ 5.52573787183,
+ 0.8433118207347725,
+ -8.19996033345526,
+ -7.786487326213716,
+ 9.280223823954241,
+ -2.31305948485121,
+ 9.549695091037119,
+ 5.788925460130297,
+ 5.549378312916486,
+ 7.409400528051194,
+ -2.1236145770503745
+ ],
+ "type": "float32"
+ }
+ },
+ "expected": {
+ "name": "output",
+ "shape": [4, 6],
+ "data": [
+ -9.817828178405762,
+ -6.024064064025879,
+ -4.0725626945495605,
+ -6.575078010559082,
+ -7.755683898925781,
+ 9.524681091308594,
+ 3.7292487621307373,
+ 6.481687068939209,
+ -1.537420630455017,
+ -7.34310245513916,
+ 7.880751132965088,
+ -2.0564088821411133,
+ 6.3438639640808105,
+ 5.525737762451172,
+ 0.8433118462562561,
+ -8.199960708618164,
+ -7.786487102508545,
+ 9.280223846435547,
+ -2.3130595684051514,
+ 9.549695014953613,
+ 5.788925647735596,
+ 5.549378395080566,
+ 7.409400463104248,
+ -2.123614549636841
+ ],
+ "type": "float32"
+ }
+ },
+ {
+ "name": "clamp float32 3D tensor default options",
+ "inputs": {
+ "x": {
+ "shape": [2, 3, 4],
+ "data": [
+ -9.817828475355284,
+ -6.024063916325786,
+ -4.072562498632983,
+ -6.575078191902692,
+ -7.7556836912181915,
+ 9.524681107378463,
+ 3.7292487446449307,
+ 6.4816868736447475,
+ -1.5374205904252634,
+ -7.343102426698445,
+ 7.880751290929794,
+ -2.056408790509967,
+ 6.34386375786449,
+ 5.52573787183,
+ 0.8433118207347725,
+ -8.19996033345526,
+ -7.786487326213716,
+ 9.280223823954241,
+ -2.31305948485121,
+ 9.549695091037119,
+ 5.788925460130297,
+ 5.549378312916486,
+ 7.409400528051194,
+ -2.1236145770503745
+ ],
+ "type": "float32"
+ }
+ },
+ "expected": {
+ "name": "output",
+ "shape": [2, 3, 4],
+ "data": [
+ -9.817828178405762,
+ -6.024064064025879,
+ -4.0725626945495605,
+ -6.575078010559082,
+ -7.755683898925781,
+ 9.524681091308594,
+ 3.7292487621307373,
+ 6.481687068939209,
+ -1.537420630455017,
+ -7.34310245513916,
+ 7.880751132965088,
+ -2.0564088821411133,
+ 6.3438639640808105,
+ 5.525737762451172,
+ 0.8433118462562561,
+ -8.199960708618164,
+ -7.786487102508545,
+ 9.280223846435547,
+ -2.3130595684051514,
+ 9.549695014953613,
+ 5.788925647735596,
+ 5.549378395080566,
+ 7.409400463104248,
+ -2.123614549636841
+ ],
+ "type": "float32"
+ }
+ },
+ {
+ "name": "clamp float32 4D tensor default options",
+ "inputs": {
+ "x": {
+ "shape": [3, 2, 2, 2],
+ "data": [
+ -9.817828475355284,
+ -6.024063916325786,
+ -4.072562498632983,
+ -6.575078191902692,
+ -7.7556836912181915,
+ 9.524681107378463,
+ 3.7292487446449307,
+ 6.4816868736447475,
+ -1.5374205904252634,
+ -7.343102426698445,
+ 7.880751290929794,
+ -2.056408790509967,
+ 6.34386375786449,
+ 5.52573787183,
+ 0.8433118207347725,
+ -8.19996033345526,
+ -7.786487326213716,
+ 9.280223823954241,
+ -2.31305948485121,
+ 9.549695091037119,
+ 5.788925460130297,
+ 5.549378312916486,
+ 7.409400528051194,
+ -2.1236145770503745
+ ],
+ "type": "float32"
+ }
+ },
+ "expected": {
+ "name": "output",
+ "shape": [3, 2, 2, 2],
+ "data": [
+ -9.817828178405762,
+ -6.024064064025879,
+ -4.0725626945495605,
+ -6.575078010559082,
+ -7.755683898925781,
+ 9.524681091308594,
+ 3.7292487621307373,
+ 6.481687068939209,
+ -1.537420630455017,
+ -7.34310245513916,
+ 7.880751132965088,
+ -2.0564088821411133,
+ 6.3438639640808105,
+ 5.525737762451172,
+ 0.8433118462562561,
+ -8.199960708618164,
+ -7.786487102508545,
+ 9.280223846435547,
+ -2.3130595684051514,
+ 9.549695014953613,
+ 5.788925647735596,
+ 5.549378395080566,
+ 7.409400463104248,
+ -2.123614549636841
+ ],
+ "type": "float32"
+ }
+ },
+ {
+ "name": "clamp float32 5D tensor default options",
+ "inputs": {
+ "x": {
+ "shape": [4, 1, 1, 2, 3],
+ "data": [
+ -9.817828475355284,
+ -6.024063916325786,
+ -4.072562498632983,
+ -6.575078191902692,
+ -7.7556836912181915,
+ 9.524681107378463,
+ 3.7292487446449307,
+ 6.4816868736447475,
+ -1.5374205904252634,
+ -7.343102426698445,
+ 7.880751290929794,
+ -2.056408790509967,
+ 6.34386375786449,
+ 5.52573787183,
+ 0.8433118207347725,
+ -8.19996033345526,
+ -7.786487326213716,
+ 9.280223823954241,
+ -2.31305948485121,
+ 9.549695091037119,
+ 5.788925460130297,
+ 5.549378312916486,
+ 7.409400528051194,
+ -2.1236145770503745
+ ],
+ "type": "float32"
+ }
+ },
+ "expected": {
+ "name": "output",
+ "shape": [4, 1, 1, 2, 3],
+ "data": [
+ -9.817828178405762,
+ -6.024064064025879,
+ -4.0725626945495605,
+ -6.575078010559082,
+ -7.755683898925781,
+ 9.524681091308594,
+ 3.7292487621307373,
+ 6.481687068939209,
+ -1.537420630455017,
+ -7.34310245513916,
+ 7.880751132965088,
+ -2.0564088821411133,
+ 6.3438639640808105,
+ 5.525737762451172,
+ 0.8433118462562561,
+ -8.199960708618164,
+ -7.786487102508545,
+ 9.280223846435547,
+ -2.3130595684051514,
+ 9.549695014953613,
+ 5.788925647735596,
+ 5.549378395080566,
+ 7.409400463104248,
+ -2.123614549636841
+ ],
+ "type": "float32"
+ }
+ },
+ // default options.maxValue and specified options.minValue
+ {
+ "name": "clamp float32 4D tensor default options.maxValue and specified negative options.minValue",
+ "inputs": {
+ "x": {
+ "shape": [2, 1, 4, 3],
+ "data": [
+ -9.817828475355284,
+ -6.024063916325786,
+ -4.072562498632983,
+ -6.575078191902692,
+ -7.7556836912181915,
+ 9.524681107378463,
+ 3.7292487446449307,
+ 6.4816868736447475,
+ -1.5374205904252634,
+ -7.343102426698445,
+ 7.880751290929794,
+ -2.056408790509967,
+ 6.34386375786449,
+ 5.52573787183,
+ 0.8433118207347725,
+ -8.19996033345526,
+ -7.786487326213716,
+ 9.280223823954241,
+ -2.31305948485121,
+ 9.549695091037119,
+ 5.788925460130297,
+ 5.549378312916486,
+ 7.409400528051194,
+ -2.1236145770503745
+ ],
+ "type": "float32"
+ }
+ },
+ "expected": {
+ "name": "output",
+ "shape": [2, 1, 4, 3],
+ "data": [
+ -1,
+ -1,
+ -1,
+ -1,
+ -1,
+ 9.524681091308594,
+ 3.7292487621307373,
+ 6.481687068939209,
+ -1,
+ -1,
+ 7.880751132965088,
+ -1,
+ 6.3438639640808105,
+ 5.525737762451172,
+ 0.8433118462562561,
+ -1,
+ -1,
+ 9.280223846435547,
+ -1,
+ 9.549695014953613,
+ 5.788925647735596,
+ 5.549378395080566,
+ 7.409400463104248,
+ -1
+ ],
+ "type": "float32"
+ },
+ "options": {
+ "minValue": -1.0
+ }
+ },
+ {
+ "name": "clamp float32 3D tensor default options.maxValue and specified options.minValue=0.0",
+ "inputs": {
+ "x": {
+ "shape": [6, 2, 2],
+ "data": [
+ -9.817828475355284,
+ -6.024063916325786,
+ -4.072562498632983,
+ -6.575078191902692,
+ -7.7556836912181915,
+ 9.524681107378463,
+ 3.7292487446449307,
+ 6.4816868736447475,
+ -1.5374205904252634,
+ -7.343102426698445,
+ 7.880751290929794,
+ -2.056408790509967,
+ 6.34386375786449,
+ 5.52573787183,
+ 0.8433118207347725,
+ -8.19996033345526,
+ -7.786487326213716,
+ 9.280223823954241,
+ -2.31305948485121,
+ 9.549695091037119,
+ 5.788925460130297,
+ 5.549378312916486,
+ 7.409400528051194,
+ -2.1236145770503745
+ ],
+ "type": "float32"
+ }
+ },
+ "expected": {
+ "name": "output",
+ "shape": [6, 2, 2],
+ "data": [
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 9.524681091308594,
+ 3.7292487621307373,
+ 6.481687068939209,
+ 0,
+ 0,
+ 7.880751132965088,
+ 0,
+ 6.3438639640808105,
+ 5.525737762451172,
+ 0.8433118462562561,
+ 0,
+ 0,
+ 9.280223846435547,
+ 0,
+ 9.549695014953613,
+ 5.788925647735596,
+ 5.549378395080566,
+ 7.409400463104248,
+ 0
+ ],
+ "type": "float32"
+ },
+ "options": {
+ "minValue": 0.0
+ }
+ },
+ {
+ "name": "clamp float32 2D tensor default options.maxValue and specified positive options.minValue",
+ "inputs": {
+ "x": {
+ "shape": [3, 8],
+ "data": [
+ -9.817828475355284,
+ -6.024063916325786,
+ -4.072562498632983,
+ -6.575078191902692,
+ -7.7556836912181915,
+ 9.524681107378463,
+ 3.7292487446449307,
+ 6.4816868736447475,
+ -1.5374205904252634,
+ -7.343102426698445,
+ 7.880751290929794,
+ -2.056408790509967,
+ 6.34386375786449,
+ 5.52573787183,
+ 0.8433118207347725,
+ -8.19996033345526,
+ -7.786487326213716,
+ 9.280223823954241,
+ -2.31305948485121,
+ 9.549695091037119,
+ 5.788925460130297,
+ 5.549378312916486,
+ 7.409400528051194,
+ -2.1236145770503745
+ ],
+ "type": "float32"
+ }
+ },
+ "expected": {
+ "name": "output",
+ "shape": [3, 8],
+ "data": [
+ 1,
+ 1,
+ 1,
+ 1,
+ 1,
+ 9.524681091308594,
+ 3.7292487621307373,
+ 6.481687068939209,
+ 1,
+ 1,
+ 7.880751132965088,
+ 1,
+ 6.3438639640808105,
+ 5.525737762451172,
+ 1,
+ 1,
+ 1,
+ 9.280223846435547,
+ 1,
+ 9.549695014953613,
+ 5.788925647735596,
+ 5.549378395080566,
+ 7.409400463104248,
+ 1
+ ],
+ "type": "float32"
+ },
+ "options": {
+ "minValue": 1.0
+ }
+ },
+ // default options.minValue and specified options.maxValue
+ {
+ "name": "clamp float32 5D tensor default options.minValue and specified negative options.maxValue",
+ "inputs": {
+ "x": {
+ "shape": [2, 2, 1, 2, 3],
+ "data": [
+ -9.817828475355284,
+ -6.024063916325786,
+ -4.072562498632983,
+ -6.575078191902692,
+ -7.7556836912181915,
+ 9.524681107378463,
+ 3.7292487446449307,
+ 6.4816868736447475,
+ -1.5374205904252634,
+ -7.343102426698445,
+ 7.880751290929794,
+ -2.056408790509967,
+ 6.34386375786449,
+ 5.52573787183,
+ 0.8433118207347725,
+ -8.19996033345526,
+ -7.786487326213716,
+ 9.280223823954241,
+ -2.31305948485121,
+ 9.549695091037119,
+ 5.788925460130297,
+ 5.549378312916486,
+ 7.409400528051194,
+ -2.1236145770503745
+ ],
+ "type": "float32"
+ }
+ },
+ "expected": {
+ "name": "output",
+ "shape": [2, 1, 4, 3],
+ "data": [
+ -9.817828178405762,
+ -6.024064064025879,
+ -4.0725626945495605,
+ -6.575078010559082,
+ -7.755683898925781,
+ -2,
+ -2,
+ -2,
+ -2,
+ -7.34310245513916,
+ -2,
+ -2.0564088821411133,
+ -2,
+ -2,
+ -2,
+ -8.199960708618164,
+ -7.786487102508545,
+ -2,
+ -2.3130595684051514,
+ -2,
+ -2,
+ -2,
+ -2,
+ -2.123614549636841
+ ],
+ "type": "float32"
+ },
+ "options": {
+ "maxValue": -2.0
+ }
+ },
+ {
+ "name": "clamp float32 1D tensor default options.minValue and specified options.maxValue=0.0",
+ "inputs": {
+ "x": {
+ "shape": [24],
+ "data": [
+ -9.817828475355284,
+ -6.024063916325786,
+ -4.072562498632983,
+ -6.575078191902692,
+ -7.7556836912181915,
+ 9.524681107378463,
+ 3.7292487446449307,
+ 6.4816868736447475,
+ -1.5374205904252634,
+ -7.343102426698445,
+ 7.880751290929794,
+ -2.056408790509967,
+ 6.34386375786449,
+ 5.52573787183,
+ 0.8433118207347725,
+ -8.19996033345526,
+ -7.786487326213716,
+ 9.280223823954241,
+ -2.31305948485121,
+ 9.549695091037119,
+ 5.788925460130297,
+ 5.549378312916486,
+ 7.409400528051194,
+ -2.1236145770503745
+ ],
+ "type": "float32"
+ }
+ },
+ "expected": {
+ "name": "output",
+ "shape": [24],
+ "data": [
+ -9.817828178405762,
+ -6.024064064025879,
+ -4.0725626945495605,
+ -6.575078010559082,
+ -7.755683898925781,
+ 0,
+ 0,
+ 0,
+ -1.537420630455017,
+ -7.34310245513916,
+ 0,
+ -2.0564088821411133,
+ 0,
+ 0,
+ 0,
+ -8.199960708618164,
+ -7.786487102508545,
+ 0,
+ -2.3130595684051514,
+ 0,
+ 0,
+ 0,
+ 0,
+ -2.123614549636841
+ ],
+ "type": "float32"
+ },
+ "options": {
+ "maxValue": 0.0
+ }
+ },
+ {
+ "name": "clamp float32 3D tensor default options.minValue and specified positive options.maxValue",
+ "inputs": {
+ "x": {
+ "shape": [3, 4, 2],
+ "data": [
+ -9.817828475355284,
+ -6.024063916325786,
+ -4.072562498632983,
+ -6.575078191902692,
+ -7.7556836912181915,
+ 9.524681107378463,
+ 3.7292487446449307,
+ 6.4816868736447475,
+ -1.5374205904252634,
+ -7.343102426698445,
+ 7.880751290929794,
+ -2.056408790509967,
+ 6.34386375786449,
+ 5.52573787183,
+ 0.8433118207347725,
+ -8.19996033345526,
+ -7.786487326213716,
+ 9.280223823954241,
+ -2.31305948485121,
+ 9.549695091037119,
+ 5.788925460130297,
+ 5.549378312916486,
+ 7.409400528051194,
+ -2.1236145770503745
+ ],
+ "type": "float32"
+ }
+ },
+ "expected": {
+ "name": "output",
+ "shape": [3, 4, 2],
+ "data": [
+ -9.817828178405762,
+ -6.024064064025879,
+ -4.0725626945495605,
+ -6.575078010559082,
+ -7.755683898925781,
+ 3,
+ 3,
+ 3,
+ -1.537420630455017,
+ -7.34310245513916,
+ 3,
+ -2.0564088821411133,
+ 3,
+ 3,
+ 0.8433118462562561,
+ -8.199960708618164,
+ -7.786487102508545,
+ 3,
+ -2.3130595684051514,
+ 3,
+ 3,
+ 3,
+ 3,
+ -2.123614549636841
+ ],
+ "type": "float32"
+ },
+ "options": {
+ "maxValue": 3.0
+ }
+ },
+ // specified both options.minValue and options.maxValue
+ {
+ "name": "clamp float32 5D tensor specified both negative options.minValue and options.maxValue",
+ "inputs": {
+ "x": {
+ "shape": [3, 2, 1, 1, 4],
+ "data": [
+ -9.817828475355284,
+ -6.024063916325786,
+ -4.072562498632983,
+ -6.575078191902692,
+ -7.7556836912181915,
+ 9.524681107378463,
+ 3.7292487446449307,
+ 6.4816868736447475,
+ -1.5374205904252634,
+ -7.343102426698445,
+ 7.880751290929794,
+ -2.056408790509967,
+ 6.34386375786449,
+ 5.52573787183,
+ 0.8433118207347725,
+ -8.19996033345526,
+ -7.786487326213716,
+ 9.280223823954241,
+ -2.31305948485121,
+ 9.549695091037119,
+ 5.788925460130297,
+ 5.549378312916486,
+ 7.409400528051194,
+ -2.1236145770503745
+ ],
+ "type": "float32"
+ }
+ },
+ "expected": {
+ "name": "output",
+ "shape": [3, 2, 1, 1, 4],
+ "data": [
+ -8,
+ -6.024064064025879,
+ -4.0725626945495605,
+ -6.575078010559082,
+ -7.755683898925781,
+ -1,
+ -1,
+ -1,
+ -1.537420630455017,
+ -7.34310245513916,
+ -1,
+ -2.0564088821411133,
+ -1,
+ -1,
+ -1,
+ -8,
+ -7.786487102508545,
+ -1,
+ -2.3130595684051514,
+ -1,
+ -1,
+ -1,
+ -1,
+ -2.123614549636841
+ ],
+ "type": "float32"
+ },
+ "options": {
+ "minValue": -8.0,
+ "maxValue": -1.0
+ }
+ },
+ {
+ "name": "clamp float32 4D tensor specified negative options.minValue and options.maxValue=0.0",
+ "inputs": {
+ "x": {
+ "shape": [1, 4, 3, 2],
+ "data": [
+ -9.817828475355284,
+ -6.024063916325786,
+ -4.072562498632983,
+ -6.575078191902692,
+ -7.7556836912181915,
+ 9.524681107378463,
+ 3.7292487446449307,
+ 6.4816868736447475,
+ -1.5374205904252634,
+ -7.343102426698445,
+ 7.880751290929794,
+ -2.056408790509967,
+ 6.34386375786449,
+ 5.52573787183,
+ 0.8433118207347725,
+ -8.19996033345526,
+ -7.786487326213716,
+ 9.280223823954241,
+ -2.31305948485121,
+ 9.549695091037119,
+ 5.788925460130297,
+ 5.549378312916486,
+ 7.409400528051194,
+ -2.1236145770503745
+ ],
+ "type": "float32"
+ }
+ },
+ "expected": {
+ "name": "output",
+ "shape": [1, 4, 3, 2],
+ "data": [
+ -6,
+ -6,
+ -4.0725626945495605,
+ -6,
+ -6,
+ 0,
+ 0,
+ 0,
+ -1.537420630455017,
+ -6,
+ 0,
+ -2.0564088821411133,
+ 0,
+ 0,
+ 0,
+ -6,
+ -6,
+ 0,
+ -2.3130595684051514,
+ 0,
+ 0,
+ 0,
+ 0,
+ -2.123614549636841
+ ],
+ "type": "float32"
+ },
+ "options": {
+ "minValue": -6.0,
+ "maxValue": 0.0
+ }
+ },
+ {
+ "name": "clamp float32 3D tensor specified negative options.minValue and positive options.maxValue",
+ "inputs": {
+ "x": {
+ "shape": [2, 6, 2],
+ "data": [
+ -9.817828475355284,
+ -6.024063916325786,
+ -4.072562498632983,
+ -6.575078191902692,
+ -7.7556836912181915,
+ 9.524681107378463,
+ 3.7292487446449307,
+ 6.4816868736447475,
+ -1.5374205904252634,
+ -7.343102426698445,
+ 7.880751290929794,
+ -2.056408790509967,
+ 6.34386375786449,
+ 5.52573787183,
+ 0.8433118207347725,
+ -8.19996033345526,
+ -7.786487326213716,
+ 9.280223823954241,
+ -2.31305948485121,
+ 9.549695091037119,
+ 5.788925460130297,
+ 5.549378312916486,
+ 7.409400528051194,
+ -2.1236145770503745
+ ],
+ "type": "float32"
+ }
+ },
+ "expected": {
+ "name": "output",
+ "shape": [2, 6, 2],
+ "data": [
+ -3,
+ -3,
+ -3,
+ -3,
+ -3,
+ 4,
+ 3.7292487621307373,
+ 4,
+ -1.537420630455017,
+ -3,
+ 4,
+ -2.0564088821411133,
+ 4,
+ 4,
+ 0.8433118462562561,
+ -3,
+ -3,
+ 4,
+ -2.3130595684051514,
+ 4,
+ 4,
+ 4,
+ 4,
+ -2.123614549636841
+ ],
+ "type": "float32"
+ },
+ "options": {
+ "minValue": -3.0,
+ "maxValue": 4.0
+ }
+ },
+ {
+ "name": "clamp float32 2D tensor specified options.minValue=0.0 and positive options.maxValue",
+ "inputs": {
+ "x": {
+ "shape": [6, 4],
+ "data": [
+ -9.817828475355284,
+ -6.024063916325786,
+ -4.072562498632983,
+ -6.575078191902692,
+ -7.7556836912181915,
+ 9.524681107378463,
+ 3.7292487446449307,
+ 6.4816868736447475,
+ -1.5374205904252634,
+ -7.343102426698445,
+ 7.880751290929794,
+ -2.056408790509967,
+ 6.34386375786449,
+ 5.52573787183,
+ 0.8433118207347725,
+ -8.19996033345526,
+ -7.786487326213716,
+ 9.280223823954241,
+ -2.31305948485121,
+ 9.549695091037119,
+ 5.788925460130297,
+ 5.549378312916486,
+ 7.409400528051194,
+ -2.1236145770503745
+ ],
+ "type": "float32"
+ }
+ },
+ "expected": {
+ "name": "output",
+ "shape": [6, 4],
+ "data": [
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 6,
+ 3.7292487621307373,
+ 6,
+ 0,
+ 0,
+ 6,
+ 0,
+ 6,
+ 5.525737762451172,
+ 0.8433118462562561,
+ 0,
+ 0,
+ 6,
+ 0,
+ 6,
+ 5.788925647735596,
+ 5.549378395080566,
+ 6,
+ 0
+ ],
+ "type": "float32"
+ },
+ "options": {
+ "minValue": 0.0,
+ "maxValue": 6.0
+ }
+ },
+ {
+ "name": "clamp float32 1D tensor specified both positive options.minValue and options.maxValue",
+ "inputs": {
+ "x": {
+ "shape": [24],
+ "data": [
+ -9.817828475355284,
+ -6.024063916325786,
+ -4.072562498632983,
+ -6.575078191902692,
+ -7.7556836912181915,
+ 9.524681107378463,
+ 3.7292487446449307,
+ 6.4816868736447475,
+ -1.5374205904252634,
+ -7.343102426698445,
+ 7.880751290929794,
+ -2.056408790509967,
+ 6.34386375786449,
+ 5.52573787183,
+ 0.8433118207347725,
+ -8.19996033345526,
+ -7.786487326213716,
+ 9.280223823954241,
+ -2.31305948485121,
+ 9.549695091037119,
+ 5.788925460130297,
+ 5.549378312916486,
+ 7.409400528051194,
+ -2.1236145770503745
+ ],
+ "type": "float32"
+ }
+ },
+ "expected": {
+ "name": "output",
+ "shape": [24],
+ "data": [
+ 2,
+ 2,
+ 2,
+ 2,
+ 2,
+ 7,
+ 3.7292487621307373,
+ 6.481687068939209,
+ 2,
+ 2,
+ 7,
+ 2,
+ 6.3438639640808105,
+ 5.525737762451172,
+ 2,
+ 2,
+ 2,
+ 7,
+ 2,
+ 7,
+ 5.788925647735596,
+ 5.549378395080566,
+ 7,
+ 2
+ ],
+ "type": "float32"
+ },
+ "options": {
+ "minValue": 2.0,
+ "maxValue": 7.0
+ }
+ }
+ ]
+} \ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/concat.json b/testing/web-platform/tests/webnn/resources/test_data/concat.json
new file mode 100644
index 0000000000..9ecc393f89
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/concat.json
@@ -0,0 +1,1799 @@
+{
+ "tests": [
+ // concat 1D tensors
+ {
+ "name": "concat two float32 1D tensors of same shape along axis 0",
+ "inputs": [
+ {
+ "name": "input1",
+ "shape": [12],
+ "data": [
+ -0.39444134019222243,
+ 0.8619825316530809,
+ 0.3379962524218807,
+ -0.9906398615400507,
+ 0.576785657225761,
+ 0.3227640108329237,
+ -0.44735022799701873,
+ 0.11028251232581932,
+ -0.5945112749179908,
+ -0.40284849555754754,
+ -0.9531654171044694,
+ -0.6731740531810844
+ ],
+ "type": "float32"
+ },
+ {
+ "name": "input2",
+ "shape": [12],
+ "data": [
+ 0.4918989118791477,
+ -0.15864110312378976,
+ -0.34188115459083157,
+ -0.9158143500894873,
+ -0.7206121708970712,
+ -0.7993468785008635,
+ 0.6653799854931952,
+ 0.03886038855553897,
+ 0.5182055416768865,
+ -0.8742016938344297,
+ -0.479021891130635,
+ 0.1211843166661235
+ ],
+ "type": "float32"
+ }
+ ],
+ "axis": 0,
+ "expected": {
+ "name": "output",
+ "shape": [24],
+ "data": [
+ -0.3944413363933563,
+ 0.861982524394989,
+ 0.337996244430542,
+ -0.990639865398407,
+ 0.576785683631897,
+ 0.32276400923728943,
+ -0.44735023379325867,
+ 0.11028251051902771,
+ -0.5945112705230713,
+ -0.402848482131958,
+ -0.9531654119491577,
+ -0.6731740236282349,
+ 0.49189892411231995,
+ -0.15864109992980957,
+ -0.3418811559677124,
+ -0.9158143401145935,
+ -0.7206121683120728,
+ -0.7993468642234802,
+ 0.6653800010681152,
+ 0.03886038810014725,
+ 0.5182055234909058,
+ -0.8742017149925232,
+ -0.4790218770503998,
+ 0.1211843192577362
+ ],
+ "type": "float32"
+ }
+ },
+ {
+ "name": "concat three float32 1D tensors of different 1st dimension along axis 0",
+ "inputs": [
+ {
+ "name": "input1",
+ "shape": [4],
+ "data": [
+ -0.39444134019222243,
+ 0.8619825316530809,
+ 0.3379962524218807,
+ -0.9906398615400507
+ ],
+ "type": "float32"
+ },
+ {
+ "name": "input2",
+ "shape": [8],
+ "data": [
+ 0.576785657225761,
+ 0.3227640108329237,
+ -0.44735022799701873,
+ 0.11028251232581932,
+ -0.5945112749179908,
+ -0.40284849555754754,
+ -0.9531654171044694,
+ -0.6731740531810844
+ ],
+ "type": "float32"
+ },
+ {
+ "name": "input3",
+ "shape": [12],
+ "data": [
+ 0.4918989118791477,
+ -0.15864110312378976,
+ -0.34188115459083157,
+ -0.9158143500894873,
+ -0.7206121708970712,
+ -0.7993468785008635,
+ 0.6653799854931952,
+ 0.03886038855553897,
+ 0.5182055416768865,
+ -0.8742016938344297,
+ -0.479021891130635,
+ 0.1211843166661235
+ ],
+ "type": "float32"
+ }
+ ],
+ "axis": 0,
+ "expected": {
+ "name": "output",
+ "shape": [24],
+ "data": [
+ -0.3944413363933563,
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+ "type": "float32"
+ }
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+ {
+ "name": "concat four float32 1D tensors of same 1st dimension along axis 0",
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+ {
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+ ],
+ "type": "float32"
+ }
+ },
+ // concat 2D tensors
+ {
+ "name": "concat two float32 2D tensors of same shape along axis 0",
+ "inputs": [
+ {
+ "name": "input1",
+ "shape": [2, 6],
+ "data": [
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+ {
+ "name": "concat two float32 2D tensors of same others dimensions except different 1st dimension along axis 0",
+ "inputs": [
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+ "type": "float32"
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+ "type": "float32"
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+ {
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+ "inputs": [
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+ "type": "float32"
+ }
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+ {
+ "name": "concat four float32 2D tensors of same others dimensions except different 2nd dimension along axis 1",
+ "inputs": [
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+ "type": "float32"
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+ {
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+ ],
+ "type": "float32"
+ }
+ },
+ // concat 3D tensors
+ {
+ "name": "concat two float32 3D tensors of same others dimensions except different 1st dimension along axis 0",
+ "inputs": [
+ {
+ "name": "input1",
+ "shape": [2, 1, 3],
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+ "axis": 0,
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+ -0.6731740236282349,
+ 0.49189892411231995,
+ -0.15864109992980957,
+ -0.3418811559677124,
+ -0.9158143401145935,
+ -0.7206121683120728,
+ -0.7993468642234802,
+ 0.6653800010681152,
+ 0.03886038810014725,
+ 0.5182055234909058,
+ -0.8742017149925232,
+ -0.4790218770503998,
+ 0.1211843192577362
+ ],
+ "type": "float32"
+ }
+ },
+ {
+ "name": "concat four float32 3D tensors of same others dimensions except different 2nd dimension along axis 1",
+ "inputs": [
+ {
+ "name": "input1",
+ "shape": [3, 1, 1],
+ "data": [
+ -0.39444134019222243,
+ 0.8619825316530809,
+ 0.3379962524218807
+ ],
+ "type": "float32"
+ },
+ {
+ "name": "input2",
+ "shape": [3, 2, 1],
+ "data": [
+ -0.9906398615400507,
+ 0.576785657225761,
+ 0.3227640108329237,
+ -0.44735022799701873,
+ 0.11028251232581932,
+ -0.5945112749179908
+ ],
+ "type": "float32"
+ },
+ {
+ "name": "input3",
+ "shape": [3, 2, 1],
+ "data": [
+ -0.40284849555754754,
+ -0.9531654171044694,
+ -0.6731740531810844,
+ 0.4918989118791477,
+ -0.15864110312378976,
+ -0.34188115459083157
+ ],
+ "type": "float32"
+ },
+ {
+ "name": "input4",
+ "shape": [3, 3, 1],
+ "data": [
+ -0.9158143500894873,
+ -0.7206121708970712,
+ -0.7993468785008635,
+ 0.6653799854931952,
+ 0.03886038855553897,
+ 0.5182055416768865,
+ -0.8742016938344297,
+ -0.479021891130635,
+ 0.1211843166661235
+ ],
+ "type": "float32"
+ }
+ ],
+ "axis": 1,
+ "expected": {
+ "name": "output",
+ "shape": [3, 8, 1],
+ "data": [
+ -0.3944413363933563,
+ -0.990639865398407,
+ 0.576785683631897,
+ -0.402848482131958,
+ -0.9531654119491577,
+ -0.9158143401145935,
+ -0.7206121683120728,
+ -0.7993468642234802,
+ 0.861982524394989,
+ 0.32276400923728943,
+ -0.44735023379325867,
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+ 0.49189892411231995,
+ 0.6653800010681152,
+ 0.03886038810014725,
+ 0.5182055234909058,
+ 0.337996244430542,
+ 0.11028251051902771,
+ -0.5945112705230713,
+ -0.15864109992980957,
+ -0.3418811559677124,
+ -0.8742017149925232,
+ -0.4790218770503998,
+ 0.1211843192577362
+ ],
+ "type": "float32"
+ }
+ },
+ {
+ "name": "concat three float32 3D tensors of same shape along axis 2",
+ "inputs": [
+ {
+ "name": "input1",
+ "shape": [2, 2, 2],
+ "data": [
+ -0.39444134019222243,
+ 0.8619825316530809,
+ 0.3379962524218807,
+ -0.9906398615400507,
+ 0.576785657225761,
+ 0.3227640108329237,
+ -0.44735022799701873,
+ 0.11028251232581932
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+ "type": "float32"
+ },
+ {
+ "name": "input2",
+ "shape": [2, 2, 2],
+ "data": [
+ -0.5945112749179908,
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+ -0.9531654171044694,
+ -0.6731740531810844,
+ 0.4918989118791477,
+ -0.15864110312378976,
+ -0.34188115459083157,
+ -0.9158143500894873
+ ],
+ "type": "float32"
+ },
+ {
+ "name": "input3",
+ "shape": [2, 2, 2],
+ "data": [
+ -0.7206121708970712,
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+ 0.6653799854931952,
+ 0.03886038855553897,
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+ -0.479021891130635,
+ 0.1211843166661235
+ ],
+ "type": "float32"
+ }
+ ],
+ "axis": 2,
+ "expected": {
+ "name": "output",
+ "shape": [2, 2, 6],
+ "data": [
+ -0.3944413363933563,
+ 0.861982524394989,
+ -0.5945112705230713,
+ -0.402848482131958,
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+ -0.7993468642234802,
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+ -0.9158143401145935,
+ -0.4790218770503998,
+ 0.1211843192577362
+ ],
+ "type": "float32"
+ }
+ },
+ // concat 4D tensors
+ {
+ "name": "concat two float32 4D tensors of same others dimensions except different 1st dimension along axis 0",
+ "inputs": [
+ {
+ "name": "input1",
+ "shape": [1, 3, 1, 2],
+ "data": [
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+ "type": "float32"
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+ "shape": [3, 3, 1, 2],
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+ "type": "float32"
+ }
+ ],
+ "axis": 0,
+ "expected": {
+ "name": "output",
+ "shape": [4, 3, 1, 2],
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+ ],
+ "type": "float32"
+ }
+ },
+ {
+ "name": "concat three float32 4D tensors of same shape along axis 1",
+ "inputs": [
+ {
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+ "shape": [2, 2, 1, 2],
+ "data": [
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+ ],
+ "type": "float32"
+ },
+ {
+ "name": "input2",
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+ "type": "float32"
+ },
+ {
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+ "shape": [2, 2, 1, 2],
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+ ],
+ "type": "float32"
+ }
+ ],
+ "axis": 1,
+ "expected": {
+ "name": "output",
+ "shape": [2, 6, 1, 2],
+ "data": [
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+ ],
+ "type": "float32"
+ }
+ },
+ {
+ "name": "concat three float32 4D tensors of same others dimensions except different 3rd dimension along axis 2",
+ "inputs": [
+ {
+ "name": "input1",
+ "shape": [1, 2, 2, 1],
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+ "type": "float32"
+ },
+ {
+ "name": "input2",
+ "shape": [1, 2, 8, 1],
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+ "type": "float32"
+ },
+ {
+ "name": "input3",
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+ "type": "float32"
+ }
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+ "axis": 2,
+ "expected": {
+ "name": "output",
+ "shape": [1, 2, 12, 1],
+ "data": [
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+ -0.4790218770503998,
+ 0.1211843192577362
+ ],
+ "type": "float32"
+ }
+ },
+ {
+ "name": "concat four float32 4D tensors of same others dimensions except different 4th dimension along axis 3",
+ "inputs": [
+ {
+ "name": "input1",
+ "shape": [1, 3, 1, 1],
+ "data": [
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+ "type": "float32"
+ },
+ {
+ "name": "input2",
+ "shape": [1, 3, 1, 1],
+ "data": [
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+ 0.3227640108329237
+ ],
+ "type": "float32"
+ },
+ {
+ "name": "input3",
+ "shape": [1, 3, 1, 2],
+ "data": [
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+ -0.6731740531810844
+ ],
+ "type": "float32"
+ },
+ {
+ "name": "input4",
+ "shape": [1, 3, 1, 4],
+ "data": [
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+ -0.479021891130635,
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+ ],
+ "type": "float32"
+ }
+ ],
+ "axis": 3,
+ "expected": {
+ "name": "output",
+ "shape": [1, 3, 1, 8],
+ "data": [
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+ -0.990639865398407,
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+ -0.15864109992980957,
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+ -0.8742017149925232,
+ -0.4790218770503998,
+ 0.1211843192577362
+ ],
+ "type": "float32"
+ }
+ },
+ // concat 5D tensors
+ {
+ "name": "concat four float32 5D tensors of same shape along axis 0",
+ "inputs": [
+ {
+ "name": "input1",
+ "shape": [1, 2, 1, 1, 3],
+ "data": [
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+ -0.9906398615400507,
+ 0.576785657225761,
+ 0.3227640108329237
+ ],
+ "type": "float32"
+ },
+ {
+ "name": "input2",
+ "shape": [1, 2, 1, 1, 3],
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+ -0.9531654171044694,
+ -0.6731740531810844
+ ],
+ "type": "float32"
+ },
+ {
+ "name": "input3",
+ "shape": [1, 2, 1, 1, 3],
+ "data": [
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+ -0.34188115459083157,
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+ -0.7206121708970712,
+ -0.7993468785008635
+ ],
+ "type": "float32"
+ },
+ {
+ "name": "input4",
+ "shape":[1, 2, 1, 1, 3],
+ "data": [
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+ -0.479021891130635,
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+ ],
+ "type": "float32"
+ }
+ ],
+ "axis": 0,
+ "expected": {
+ "name": "output",
+ "shape": [4, 2, 1, 1, 3],
+ "data": [
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+ ],
+ "type": "float32"
+ }
+ },
+ {
+ "name": "concat two float32 5D tensors of same others dimensions except different 2nd dimension along axis 1",
+ "inputs": [
+ {
+ "name": "input1",
+ "shape": [1, 2, 3, 1, 1],
+ "data": [
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+ "type": "float32"
+ },
+ {
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+ "shape": [1, 6, 3, 1, 1],
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+ "type": "float32"
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+ "axis": 1,
+ "expected": {
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+ "type": "float32"
+ }
+ },
+ {
+ "name": "concat three float32 5D tensors of same others dimensions except different 3rd dimension along axis 2",
+ "inputs": [
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+ "shape": [1, 2, 1, 1, 2],
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+ -0.9906398615400507
+ ],
+ "type": "float32"
+ },
+ {
+ "name": "input2",
+ "shape": [1, 2, 2, 1, 2],
+ "data": [
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+ -0.6731740531810844
+ ],
+ "type": "float32"
+ },
+ {
+ "name": "input3",
+ "shape": [1, 2, 3, 1, 2],
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+ "type": "float32"
+ }
+ ],
+ "axis": 2,
+ "expected": {
+ "name": "output",
+ "shape": [1, 2, 6, 1, 2],
+ "data": [
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diff --git a/testing/web-platform/tests/webnn/resources/test_data/leaky_relu.json b/testing/web-platform/tests/webnn/resources/test_data/leaky_relu.json
new file mode 100644
index 0000000000..a95a9a0cfc
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/leaky_relu.json
@@ -0,0 +1,542 @@
+{
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+ ]
+} \ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/relu.json b/testing/web-platform/tests/webnn/resources/test_data/relu.json
new file mode 100644
index 0000000000..b459789147
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/relu.json
@@ -0,0 +1,334 @@
+{
+ "tests": [ // relu input tensor of 1D to 5D with same data values
+ {
+ "name": "relu float32 1D tensor",
+ "inputs": {
+ "x": {
+ "shape": [24],
+ "data": [
+ 79.04725231657116,
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+ -5.093302411117136,
+ 15.354103550744384,
+ 90.03858807393516
+ ],
+ "type": "float32"
+ }
+ },
+ "expected": {
+ "name": "output",
+ "shape": [24],
+ "data": [
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+ 0,
+ 0,
+ 15.354103088378906,
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+ ],
+ "type": "float32"
+ }
+ },
+ {
+ "name": "relu float32 2D tensor",
+ "inputs": {
+ "x": {
+ "shape": [4, 6],
+ "data": [
+ 79.04725231657116,
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+ "type": "float32"
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+ "expected": {
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+ "type": "float32"
+ }
+ },
+ {
+ "name": "relu float32 3D tensor",
+ "inputs": {
+ "x": {
+ "shape": [2, 3, 4],
+ "data": [
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+ "type": "float32"
+ }
+ },
+ "expected": {
+ "name": "output",
+ "shape": [2, 3, 4],
+ "data": [
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+ "type": "float32"
+ }
+ },
+ {
+ "name": "relu float32 4D tensor",
+ "inputs": {
+ "x": {
+ "shape": [2, 2, 2, 3],
+ "data": [
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+ ],
+ "type": "float32"
+ }
+ },
+ "expected": {
+ "name": "output",
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+ "data": [
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+ "type": "float32"
+ }
+ },
+ {
+ "name": "relu float32 5D tensor",
+ "inputs": {
+ "x": {
+ "shape": [2, 1, 4, 1, 3],
+ "data": [
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+ -5.093302411117136,
+ 15.354103550744384,
+ 90.03858807393516
+ ],
+ "type": "float32"
+ }
+ },
+ "expected": {
+ "name": "output",
+ "shape": [2, 1, 4, 1, 3],
+ "data": [
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+ 2.2503609657287598,
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+ ],
+ "type": "float32"
+ }
+ }
+ ]
+} \ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/reshape.json b/testing/web-platform/tests/webnn/resources/test_data/reshape.json
new file mode 100644
index 0000000000..f04b7b1dbe
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/reshape.json
@@ -0,0 +1,406 @@
+{
+ "tests": [
+ {
+ "name": "reshape float32 tensor to a new shape (reorder all dimensions)",
+ "inputs": {
+ "input": {
+ "shape": [2, 3, 4],
+ "data": [
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+ -2.8698414892852355,
+ 27.903749097190158
+ ],
+ "type": "float32"
+ }
+ },
+ "newShape": [4, 2, 3],
+ "expected": {
+ "name": "output",
+ "shape": [4, 2, 3],
+ "data": [
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+ "type": "float32"
+ }
+ },
+ {
+ "name": "reshape float32 tensor to a new shape (reduce dimensions)",
+ "inputs": {
+ "input": {
+ "shape": [4, 1, 1, 1, 6],
+ "data": [
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+ {
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+ "type": "float32"
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+ {
+ "name": "reshape float32 tensor to a new shape (one dimension being the special value -1)",
+ "inputs": {
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+ "newShape": [4, -1, 3, 1],
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+ "type": "float32"
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+ {
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+ {
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+ ],
+ "type": "float32"
+ }
+ }
+ ]
+} \ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/sigmoid.json b/testing/web-platform/tests/webnn/resources/test_data/sigmoid.json
new file mode 100644
index 0000000000..c233336321
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/sigmoid.json
@@ -0,0 +1,334 @@
+{
+ "tests": [
+ {
+ "name": "sigmoid float32 1D tensor",
+ "inputs": {
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+ }
+ ]
+} \ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/slice.json b/testing/web-platform/tests/webnn/resources/test_data/slice.json
new file mode 100644
index 0000000000..926351cee7
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/slice.json
@@ -0,0 +1,772 @@
+{
+ "tests": [
+ {
+ "name": "slice float32 1D tensor default axes options",
+ "inputs": {
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+ }
+ },
+ "starts": [-3, -2, -1], // [-3, -2, -1] is equal to [1, 1, 1]
+ "sizes": [3, 2, 1],
+ "expected": {
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+ "data": [
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+ "type": "float32"
+ }
+ },
+ {
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+ "inputs": {
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+ "type": "float32"
+ }
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+ "starts": [-1, -2, -1, -1], // [-1, -2, -1, -1] is equal to [1, 0, 2, 1]
+ "sizes": [1, 2, 1, 1],
+ "expected": {
+ "name": "output",
+ "shape": [1, 2, 1, 1],
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+ "type": "float32"
+ }
+ },
+ {
+ "name": "slice float32 4D tensor sizes having special value of -1",
+ "inputs": {
+ "input": {
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+ ],
+ "type": "float32"
+ }
+ },
+ "starts": [1, 0, 2, 1],
+ "sizes": [1, -1, 1, -1], // [1, -1, 1, -1] is equal to [1, 2, 1, 1]
+ "expected": {
+ "name": "output",
+ "shape": [1, 2, 1, 1],
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+ },
+ {
+ "name": "slice float32 5D tensor sizes having special value of -1",
+ "inputs": {
+ "input": {
+ "shape": [2, 2, 3, 2, 1],
+ "data": [
+ 28.846251144212147,
+ 97.9541470229301,
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+ -24.556316258204532,
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+ -4.513182026141791,
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+ -58.46095416849133,
+ 79.80571087632629,
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+ ],
+ "type": "float32"
+ }
+ },
+ "starts": [1, 0, 2, 1, 0],
+ "sizes": [-1, 2, -1, 1, -1], // [-1, 2, -1, 1, -1] is equal to [1, 2, 1, 1, 1]
+ "expected": {
+ "name": "output",
+ "shape": [1, 2, 1, 1, 1],
+ "data": [
+ -27.306041717529297,
+ 42.665199279785156
+ ],
+ "type": "float32"
+ }
+ },
+ {
+ "name": "slice float32 1D tensor options.axes=[0]",
+ "inputs": {
+ "input": {
+ "shape": [24],
+ "data": [
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+ "type": "float32"
+ }
+ },
+ "starts": [12],
+ "sizes": [12],
+ "options": {
+ "axes": [0]
+ },
+ "expected": {
+ "name": "output",
+ "shape": [12],
+ "data": [
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+ "type": "float32"
+ }
+ },
+ {
+ "name": "slice float32 2D tensor positive options.axes=[1]",
+ "inputs": {
+ "input": {
+ "shape": [4, 6],
+ "data": [
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+ "type": "float32"
+ }
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+ "starts": [2],
+ "sizes": [4],
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+ "type": "float32"
+ }
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+ {
+ "name": "slice float32 3D tensor positive options.axes=[1, 2]",
+ "inputs": {
+ "input": {
+ "shape": [4, 3, 2],
+ "data": [
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+ "type": "float32"
+ }
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+ "starts": [1, 1],
+ "sizes": [2, 1],
+ "options": {
+ "axes": [1, 2]
+ },
+ "expected": {
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+ "shape": [4, 2, 1],
+ "data": [
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+ ],
+ "type": "float32"
+ }
+ },
+ {
+ "name": "slice float32 4D tensor positive options.axes=[0, 2, 3]",
+ "inputs": {
+ "input": {
+ "shape": [2, 2, 3, 2],
+ "data": [
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+ "type": "float32"
+ }
+ },
+ "options": {
+ "axes": [0, 2, 3]
+ },
+ "starts": [1, 2, 1],
+ "sizes": [1, 1, 1],
+ "expected": {
+ "name": "output",
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+ "data": [
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+ "type": "float32"
+ }
+ },
+ {
+ "name": "slice float32 3D tensor negative options.axes=[-2, -1]",
+ "inputs": {
+ "input": {
+ "shape": [4, 3, 2],
+ "data": [
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+ ],
+ "type": "float32"
+ }
+ },
+ "starts": [1, 1],
+ "sizes": [2, 1],
+ "options": {
+ "axes": [-2, -1] // [-2, -1] is equal to [1, 2]
+ },
+ "expected": {
+ "name": "output",
+ "shape": [4, 2, 1],
+ "data": [
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+ ],
+ "type": "float32"
+ }
+ },
+ {
+ "name": "slice float32 4D tensor negative options.axes=[-4, -2, -1]",
+ "inputs": {
+ "input": {
+ "shape": [2, 2, 3, 2],
+ "data": [
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+ "type": "float32"
+ }
+ },
+ "options": {
+ "axes": [-4, -2, -1] // [-4, -2, -1] is equal to [0, 2, 3]
+ },
+ "starts": [1, 2, 1],
+ "sizes": [1, 1, 1],
+ "expected": {
+ "name": "output",
+ "shape": [1, 2, 1, 1],
+ "data": [
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+ ],
+ "type": "float32"
+ }
+ }
+ ]
+} \ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/softmax.json b/testing/web-platform/tests/webnn/resources/test_data/softmax.json
new file mode 100644
index 0000000000..b19ce40591
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/softmax.json
@@ -0,0 +1,70 @@
+{
+ "tests": [
+ {
+ "name": "softmax float32 2D tensor",
+ "inputs": {
+ "x": {
+ "shape": [4, 6],
+ "data": [
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+ "type": "float32"
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+ "type": "float32"
+ }
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+} \ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/split.json b/testing/web-platform/tests/webnn/resources/test_data/split.json
new file mode 100644
index 0000000000..33a0704576
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/split.json
@@ -0,0 +1,818 @@
+{
+ "tests": [
+ {
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+ "inputs": {
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+ "splits": 3,
+ "expected": [
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+ ]
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+ "name": "split float32 2D tensor number splits default options",
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+ "splits": 2,
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+ {
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+ "splits": 2,
+ "expected": [
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+ {
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+ "splits": 4,
+ "expected": [
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diff --git a/testing/web-platform/tests/webnn/resources/test_data/squeeze.json b/testing/web-platform/tests/webnn/resources/test_data/squeeze.json
new file mode 100644
index 0000000000..88890fe870
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/squeeze.json
@@ -0,0 +1,696 @@
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diff --git a/testing/web-platform/tests/webnn/resources/test_data/tanh.json b/testing/web-platform/tests/webnn/resources/test_data/tanh.json
new file mode 100644
index 0000000000..9e13b62472
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/tanh.json
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diff --git a/testing/web-platform/tests/webnn/resources/test_data/transpose.json b/testing/web-platform/tests/webnn/resources/test_data/transpose.json
new file mode 100644
index 0000000000..03092031d9
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/transpose.json
@@ -0,0 +1,679 @@
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+ -66.00990219021253,
+ 38.46682821671709,
+ 2.1999381880991393
+ ],
+ "type": "float32"
+ }
+ },
+ "options": {
+ "permutation": [1, 3, 0, 4, 2]
+ },
+ "expected": {
+ "name": "output",
+ "shape": [2, 3, 1, 4, 1],
+ "data": [
+ -45.67443084716797,
+ 53.45924758911133,
+ -60.118492126464844,
+ 38.081748962402344,
+ 78.64247131347656,
+ -69.25324249267578,
+ 1.8434585332870483,
+ 92.8102798461914,
+ 56.100074768066406,
+ 77.05838012695312,
+ 57.46807861328125,
+ -84.74308776855469,
+ 46.38539123535156,
+ -84.89764404296875,
+ 56.70438766479492,
+ -25.695144653320312,
+ 5.62217378616333,
+ -25.66281509399414,
+ 99.46284484863281,
+ -87.58920288085938,
+ -65.3779067993164,
+ -66.00990295410156,
+ 38.466827392578125,
+ 2.1999382972717285
+ ],
+ "type": "float32"
+ }
+ }
+ ]
+} \ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/utils.js b/testing/web-platform/tests/webnn/resources/utils.js
new file mode 100644
index 0000000000..f11ce7c6e0
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/utils.js
@@ -0,0 +1,363 @@
+'use strict';
+
+const ExecutionArray = ['sync', 'async'];
+
+// https://webmachinelearning.github.io/webnn/#enumdef-mldevicetype
+const DeviceTypeArray = ['cpu', 'gpu'];
+
+// https://webmachinelearning.github.io/webnn/#enumdef-mloperandtype
+const TypedArrayDict = {
+ float32: Float32Array,
+ int32: Int32Array,
+ uint32: Uint32Array,
+ int8: Int8Array,
+ uint8: Uint8Array,
+};
+
+const sizeOfShape = (array) => {
+ return array.reduce((accumulator, currentValue) => accumulator * currentValue, 1);
+};
+
+/**
+ * Get JSON resources from specified test resources file.
+ * @param {String} file - A test resources file path
+ * @returns {Object} Test resources
+ */
+const loadResources = (file) => {
+ const loadJSON = () => {
+ let xmlhttp = new XMLHttpRequest();
+ xmlhttp.open("GET", file, false);
+ xmlhttp.overrideMimeType("application/json");
+ xmlhttp.send();
+ if (xmlhttp.status == 200 && xmlhttp.readyState == 4) {
+ return xmlhttp.responseText;
+ } else {
+ throw new Error(`Failed to load ${file}`);
+ }
+ };
+
+ const json = loadJSON();
+ return JSON.parse(json.replace(/\\"|"(?:\\"|[^"])*"|(\/\/.*|\/\*[\s\S]*?\*\/)/g, (m, g) => g ? "" : m));
+};
+
+/**
+ * Get exptected data and data type from given resources with output name.
+ * @param {Array} resources - An array of expected resources
+ * @param {String} outputName - An output name
+ * @returns {Array.<[Number[], String]>} An array of expected data array and data type
+ */
+const getExpectedDataAndType = (resources, outputName) => {
+ let ret;
+ for (let subResources of resources) {
+ if (subResources.name === outputName) {
+ ret = [subResources.data, subResources.type];
+ break;
+ }
+ }
+ if (ret === undefined) {
+ throw new Error(`Failed to get expected data sources and type by ${outputName}`);
+ }
+ return ret;
+};
+
+/**
+ * Get ULP tolerance of softmax operation.
+ * @param {Object} resources - Resources used for building a graph
+ * @returns {Number} A tolerance number
+ */
+const getSoftmaxPrecisionTolerance = (resources) => {
+ // Compute the softmax values of the 2-D input tensor along axis 1.
+ const inputShape = resources.inputs[Object.keys(resources.inputs)[0]].shape;
+ const tolerance = inputShape[1] * 3 + 3;
+ return tolerance;
+};
+
+// Refer to precision metrics on https://github.com/webmachinelearning/webnn/issues/265#issuecomment-1256242643
+const PrecisionMetrics = {
+ clamp: {ULP: {float32: 0, float16: 0}},
+ concat: {ULP: {float32: 0, float16: 0}},
+ leakyRelu: {ULP: {float32: 1, float16: 1}},
+ relu: {ULP: {float32: 0, float16: 0}},
+ reshape: {ULP: {float32: 0, float16: 0}},
+ sigmoid: {ULP: {float32: 32+2, float16: 3}}, // float32 (leaving a few ULP for roundoff)
+ slice: {ULP: {float32: 0, float16: 0}},
+ softmax: {ULP: {float32: getSoftmaxPrecisionTolerance, float16: getSoftmaxPrecisionTolerance}},
+ split: {ULP: {float32: 0, float16: 0}},
+ squeeze: {ULP: {float32: 0, float16: 0}},
+ tanh: {ATOL: {float32: 1/1024, float16: 1/512}},
+ transpose: {ULP: {float32: 0, float16: 0}},
+};
+
+/**
+ * Get precison tolerance value.
+ * @param {String} operationName - An operation name
+ * @param {String} metricType - Value: 'ULP', 'ATOL'
+ * @param {String} precisionType - A precision type string, like "float32", "float16",
+ * more types, please see:
+ * https://webmachinelearning.github.io/webnn/#enumdef-mloperandtype
+ * @returns {Number} A tolerance number
+ */
+const getPrecisonTolerance = (operationName, metricType, precisionType) => {
+ let tolerance = PrecisionMetrics[operationName][metricType][precisionType];
+ // If the tolerance is dynamic, then evaluate the function to get the value.
+ if (tolerance instanceof Function) {
+ tolerance = tolerance(resources, operationName);
+ }
+ return tolerance;
+};
+
+/**
+ * Get bitwise of the given value.
+ * @param {Number} value
+ * @param {String} dataType - A data type string, like "float32", "float16",
+ * more types, please see:
+ * https://webmachinelearning.github.io/webnn/#enumdef-mloperandtype
+ * @return {Number} A 64-bit signed integer.
+ */
+const getBitwise = (value, dataType) => {
+ const buffer = new ArrayBuffer(8);
+ const int64Array = new BigInt64Array(buffer);
+ int64Array[0] = value < 0 ? ~BigInt(0) : BigInt(0);
+ let typedArray;
+ if (dataType === "float32") {
+ typedArray = new Float32Array(buffer);
+ } else {
+ throw new AssertionError(`Data type ${dataType} is not supported`);
+ }
+ typedArray[0] = value;
+ return int64Array[0];
+};
+
+/**
+ * Assert that each array property in ``actual`` is a number being close enough to the corresponding
+ * property in ``expected`` by the acceptable ULP distance ``nulp`` with given ``dataType`` data type.
+ *
+ * @param {Array} actual - Array of test values.
+ * @param {Array} expected - Array of values expected to be close to the values in ``actual``.
+ * @param {Number} nulp - A BigInt value indicates acceptable ULP distance.
+ * @param {String} dataType - A data type string, value: "float32",
+ * more types, please see:
+ * https://webmachinelearning.github.io/webnn/#enumdef-mloperandtype
+ * @param {String} description - Description of the condition being tested.
+ */
+const assert_array_approx_equals_ulp = (actual, expected, nulp, dataType, description) => {
+ /*
+ * Test if two primitive arrays are equal within acceptable ULP distance
+ */
+ assert_true(actual.length === expected.length,
+ `assert_array_approx_equals_ulp: ${description} lengths differ, expected ${expected.length} but got ${actual.length}`);
+ let actualBitwise, expectedBitwise, distance;
+ for (let i = 0; i < actual.length; i++) {
+ actualBitwise = getBitwise(actual[i], dataType);
+ expectedBitwise = getBitwise(expected[i], dataType);
+ distance = actualBitwise - expectedBitwise;
+ distance = distance >= 0 ? distance : -distance;
+ assert_true(distance <= nulp,
+ `assert_array_approx_equals_ulp: ${description} actual ${actual[i]} should be close enough to expected ${expected[i]} by the acceptable ${nulp} ULP distance, but they have ${distance} ULP distance`);
+ }
+};
+
+/**
+ * Assert actual results with expected results.
+ * @param {String} operationName - An operation name
+ * @param {(Number[]|Number)} actual
+ * @param {(Number[]|Number)} expected
+ * @param {Number} tolerance
+ * @param {String} operandType - An operand type string, value: "float32",
+ * more types, please see:
+ * https://webmachinelearning.github.io/webnn/#enumdef-mloperandtype
+ * @param {String} metricType - Value: 'ULP', 'ATOL'
+ */
+const doAssert = (operationName, actual, expected, tolerance, operandType, metricType) => {
+ const description = `test ${operationName} ${operandType}`;
+ if (typeof expected === 'number') {
+ // for checking a scalar output by matmul 1D x 1D
+ expected = [expected];
+ actual = [actual];
+ }
+ if (metricType === 'ULP') {
+ assert_array_approx_equals_ulp(actual, expected, tolerance, operandType, description);
+ } else if (metricType === 'ATOL') {
+ assert_array_approx_equals(actual, expected, tolerance, description);
+ }
+};
+
+/**
+ * Check computed results with expected data.
+ * @param {String} operationName - An operation name
+ * @param {Object.<String, MLOperand>} namedOutputOperands
+ * @param {Object.<MLNamedArrayBufferViews>} outputs - The resources of required outputs
+ * @param {Object} resources - Resources used for building a graph
+ */
+const checkResults = (operationName, namedOutputOperands, outputs, resources) => {
+ const metricType = Object.keys(PrecisionMetrics[operationName])[0];
+ const expected = resources.expected;
+ let tolerance;
+ let operandType;
+ let outputData;
+ let expectedData;
+ if (Array.isArray(expected)) {
+ // the outputs of split() or gru() is a sequence
+ for (let operandName in namedOutputOperands) {
+ outputData = outputs[operandName];
+ // for some operations which may have multi outputs of different types
+ [expectedData, operandType] = getExpectedDataAndType(expected, operandName);
+ tolerance = getPrecisonTolerance(operationName, metricType, operandType);
+ doAssert(operationName, outputData, expectedData, tolerance, operandType, metricType)
+ }
+ } else {
+ outputData = outputs[expected.name];
+ expectedData = expected.data;
+ operandType = expected.type;
+ tolerance = getPrecisonTolerance(operationName, metricType, operandType);
+ doAssert(operationName, outputData, expectedData, tolerance, operandType, metricType)
+ }
+};
+
+/**
+ * Create single input operands for a graph.
+ * @param {MLGraphBuilder} builder - A ML graph builder
+ * @param {Object} resources - Resources used for building a graph
+ * @param {String} [inputOperandName] - An inputOperand name
+ * @returns {MLOperand} An input operand
+ */
+const createSingleInputOperand = (builder, resources, inputOperandName) => {
+ inputOperandName = inputOperandName ? inputOperandName : Object.keys(resources.inputs)[0];
+ const inputResources = resources.inputs[inputOperandName];
+ return builder.input(inputOperandName, {type: inputResources.type, dimensions: inputResources.shape});
+};
+
+/**
+ * Build an operation which has a single input.
+ * @param {String} operationName - An operation name
+ * @param {MLGraphBuilder} builder - A ML graph builder
+ * @param {Object} resources - Resources used for building a graph
+ * @returns {MLNamedOperands}
+ */
+const buildOperationWithSingleInput = (operationName, builder, resources) => {
+ const namedOutputOperand = {};
+ const inputOperand = createSingleInputOperand(builder, resources);
+ const outputOperand = resources.options ?
+ builder[operationName](inputOperand, resources.options) : builder[operationName](inputOperand);
+ namedOutputOperand[resources.expected.name] = outputOperand;
+ return namedOutputOperand;
+};
+
+/**
+ * Build a graph.
+ * @param {String} operationName - An operation name
+ * @param {MLGraphBuilder} builder - A ML graph builder
+ * @param {Object} resources - Resources used for building a graph
+ * @param {Function} buildFunc - A build function for an operation
+ * @returns [namedOperands, inputs, outputs]
+ */
+const buildGraph = (operationName, builder, resources, buildFunc) => {
+ const namedOperands = buildFunc(operationName, builder, resources);
+ let inputs = {};
+ if (Array.isArray(resources.inputs)) {
+ // the inputs of concat() is a sequence
+ for (let subInput of resources.inputs) {
+ inputs[subInput.name] = new TypedArrayDict[subInput.type](subInput.data);
+ }
+ } else {
+ for (let inputName in resources.inputs) {
+ const subTestByName = resources.inputs[inputName];
+ inputs[inputName] = new TypedArrayDict[subTestByName.type](subTestByName.data);
+ }
+ }
+ let outputs = {};
+ if (Array.isArray(resources.expected)) {
+ // the outputs of split() or gru() is a sequence
+ for (let i = 0; i < resources.expected.length; i++) {
+ const subExpected = resources.expected[i];
+ outputs[subExpected.name] = new TypedArrayDict[subExpected.type](sizeOfShape(subExpected.shape));
+ }
+ } else {
+ // matmul 1D with 1D produces a scalar which doesn't have its shape
+ const shape = resources.expected.shape ? resources.expected.shape : [1];
+ outputs[resources.expected.name] = new TypedArrayDict[resources.expected.type](sizeOfShape(shape));
+ }
+ return [namedOperands, inputs, outputs];
+};
+
+/**
+ * Build a graph, synchronously compile graph and execute, then check computed results.
+ * @param {String} operationName - An operation name
+ * @param {MLContext} context - A ML context
+ * @param {MLGraphBuilder} builder - A ML graph builder
+ * @param {Object} resources - Resources used for building a graph
+ * @param {Function} buildFunc - A build function for an operation
+ */
+const runSync = (operationName, context, builder, resources, buildFunc) => {
+ // build a graph
+ const [namedOutputOperands, inputs, outputs] = buildGraph(operationName, builder, resources, buildFunc);
+ // synchronously compile the graph up to the output operand
+ const graph = builder.buildSync(namedOutputOperands);
+ // synchronously execute the compiled graph.
+ context.computeSync(graph, inputs, outputs);
+ checkResults(operationName, namedOutputOperands, outputs, resources);
+};
+
+/**
+ * Build a graph, asynchronously compile graph and execute, then check computed results.
+ * @param {String} operationName - An operation name
+ * @param {MLContext} context - A ML context
+ * @param {MLGraphBuilder} builder - A ML graph builder
+ * @param {Object} resources - Resources used for building a graph
+ * @param {Function} buildFunc - A build function for an operation
+ */
+const run = async (operationName, context, builder, resources, buildFunc) => {
+ // build a graph
+ const [namedOutputOperands, inputs, outputs] = buildGraph(operationName, builder, resources, buildFunc);
+ // asynchronously compile the graph up to the output operand
+ const graph = await builder.build(namedOutputOperands);
+ // asynchronously execute the compiled graph
+ await context.compute(graph, inputs, outputs);
+ checkResults(operationName, namedOutputOperands, outputs, resources);
+};
+
+/**
+ * Run WebNN operation tests.
+ * @param {String} operationName - An operation name
+ * @param {String} file - A test resources file path
+ * @param {Function} buildFunc - A build function for an operation
+ */
+const testWebNNOperation = (operationName, file, buildFunc) => {
+ const resources = loadResources(file);
+ const tests = resources.tests;
+ ExecutionArray.forEach(executionType => {
+ const isSync = executionType === 'sync';
+ if (self.GLOBAL.isWindow() && isSync) {
+ return;
+ }
+ let context;
+ let builder;
+ if (isSync) {
+ // test sync
+ DeviceTypeArray.forEach(deviceType => {
+ setup(() => {
+ context = navigator.ml.createContextSync({deviceType});
+ builder = new MLGraphBuilder(context);
+ });
+ for (const subTest of tests) {
+ test(() => {
+ runSync(operationName, context, builder, subTest, buildFunc);
+ }, `${subTest.name} / ${deviceType} / ${executionType}`);
+ }
+ });
+ } else {
+ // test async
+ DeviceTypeArray.forEach(deviceType => {
+ promise_setup(async () => {
+ context = await navigator.ml.createContext({deviceType});
+ builder = new MLGraphBuilder(context);
+ });
+ for (const subTest of tests) {
+ promise_test(async () => {
+ await run(operationName, context, builder, subTest, buildFunc);
+ }, `${subTest.name} / ${deviceType} / ${executionType}`);
+ }
+ });
+ }
+ });
+}; \ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/sigmoid.https.any.js b/testing/web-platform/tests/webnn/sigmoid.https.any.js
new file mode 100644
index 0000000000..cb22b6eca1
--- /dev/null
+++ b/testing/web-platform/tests/webnn/sigmoid.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API sigmoid operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-sigmoid
+
+testWebNNOperation('sigmoid', '/webnn/resources/test_data/sigmoid.json', buildOperationWithSingleInput); \ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/slice.https.any.js b/testing/web-platform/tests/webnn/slice.https.any.js
new file mode 100644
index 0000000000..8cbcf057c9
--- /dev/null
+++ b/testing/web-platform/tests/webnn/slice.https.any.js
@@ -0,0 +1,19 @@
+// META: title=test WebNN API slice operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-slice
+
+const buildSlice = (operationName, builder, resources) => {
+ // MLOperand slice(MLOperand input, sequence<long> starts, sequence<long> sizes, optional MLSliceOptions options = {});
+ const namedOutputOperand = {};
+ const inputOperand = createSingleInputOperand(builder, resources);
+ // invoke builder.slice()
+ namedOutputOperand[resources.expected.name] = builder[operationName](inputOperand, resources.starts, resources.sizes, resources.options);
+ return namedOutputOperand;
+};
+
+testWebNNOperation('slice', '/webnn/resources/test_data/slice.json', buildSlice); \ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/softmax.https.any.js b/testing/web-platform/tests/webnn/softmax.https.any.js
new file mode 100644
index 0000000000..91afc28385
--- /dev/null
+++ b/testing/web-platform/tests/webnn/softmax.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API softmax operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-softmax
+
+testWebNNOperation('softmax', '/webnn/resources/test_data/softmax.json', buildOperationWithSingleInput); \ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/split.https.any.js b/testing/web-platform/tests/webnn/split.https.any.js
new file mode 100644
index 0000000000..54314d7b7f
--- /dev/null
+++ b/testing/web-platform/tests/webnn/split.https.any.js
@@ -0,0 +1,24 @@
+// META: title=test WebNN API split operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-split
+
+const buildSplit = (operationName, builder, resources) => {
+ // sequence<MLOperand> split(MLOperand input,
+ // (unsigned long or sequence<unsigned long>) splits,
+ // optional MLSplitOptions options = {});
+ const namedOutputOperand = {};
+ const inputOperand = createSingleInputOperand(builder, resources);
+ // invoke builder.split()
+ const outputOperands = builder[operationName](inputOperand, resources.splits, resources.options);
+ resources.expected.forEach((resourceDict, index) => {
+ namedOutputOperand[resourceDict.name] = outputOperands[index];
+ });
+ return namedOutputOperand;
+};
+
+testWebNNOperation('split', '/webnn/resources/test_data/split.json', buildSplit); \ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/squeeze.https.any.js b/testing/web-platform/tests/webnn/squeeze.https.any.js
new file mode 100644
index 0000000000..5e042f34bd
--- /dev/null
+++ b/testing/web-platform/tests/webnn/squeeze.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API squeeze operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-squeeze
+
+testWebNNOperation('squeeze', '/webnn/resources/test_data/squeeze.json', buildOperationWithSingleInput); \ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/tanh.https.any.js b/testing/web-platform/tests/webnn/tanh.https.any.js
new file mode 100644
index 0000000000..603f0930cf
--- /dev/null
+++ b/testing/web-platform/tests/webnn/tanh.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API tanh operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-tanh
+
+testWebNNOperation('tanh', '/webnn/resources/test_data/tanh.json', buildOperationWithSingleInput); \ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/transpose.https.any.js b/testing/web-platform/tests/webnn/transpose.https.any.js
new file mode 100644
index 0000000000..d1303f52ac
--- /dev/null
+++ b/testing/web-platform/tests/webnn/transpose.https.any.js
@@ -0,0 +1,10 @@
+// META: title=test WebNN API transpose operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-transpose
+
+testWebNNOperation('transpose', '/webnn/resources/test_data/transpose.json', buildOperationWithSingleInput); \ No newline at end of file
generated by cgit v1.2.3 (git 2.39.1) at 2024-12-25 09:55:23 +0000