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-rw-r--r--testing/web-platform/tests/webnn/resources/test_data/average_pool2d.json335
-rw-r--r--testing/web-platform/tests/webnn/resources/test_data/constant.json754
-rw-r--r--testing/web-platform/tests/webnn/resources/test_data/conv2d.json351
-rw-r--r--testing/web-platform/tests/webnn/resources/test_data/conv_transpose2d.json547
-rw-r--r--testing/web-platform/tests/webnn/resources/test_data/l2_pool2d.json1174
-rw-r--r--testing/web-platform/tests/webnn/resources/test_data/max_pool2d.json335
-rw-r--r--testing/web-platform/tests/webnn/resources/test_data/resample2d.json527
-rw-r--r--testing/web-platform/tests/webnn/resources/test_data/triangular.json1101
-rw-r--r--testing/web-platform/tests/webnn/resources/utils.js172
-rw-r--r--testing/web-platform/tests/webnn/resources/utils_validation.js359
10 files changed, 4028 insertions, 1627 deletions
diff --git a/testing/web-platform/tests/webnn/resources/test_data/average_pool2d.json b/testing/web-platform/tests/webnn/resources/test_data/average_pool2d.json
index 5a9f4e28b1..3d0c432273 100644
--- a/testing/web-platform/tests/webnn/resources/test_data/average_pool2d.json
+++ b/testing/web-platform/tests/webnn/resources/test_data/average_pool2d.json
@@ -534,340 +534,6 @@
}
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@@ -1650,7 +1316,6 @@
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diff --git a/testing/web-platform/tests/webnn/resources/test_data/constant.json b/testing/web-platform/tests/webnn/resources/test_data/constant.json
new file mode 100644
index 0000000000..06fe0a7a95
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/constant.json
@@ -0,0 +1,754 @@
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+ {
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+ 30
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+ "type": "int32"
+ }
+ },
+ {
+ "name": "constant float32 4D tensor of uint32 type",
+ "inputs": {
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+ "step": {
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+ 30
+ ],
+ "type": "uint32"
+ }
+ },
+ {
+ "name": "constant float32 4D tensor of int64 type",
+ "inputs": {
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+ "type": "float32"
+ },
+ "step": {
+ "data": 1,
+ "type": "float32"
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+ "26",
+ "27",
+ "28",
+ "29",
+ "30"
+ ],
+ "type": "int64"
+ }
+ },
+ {
+ "name": "constant float32 4D tensor of int8 type step > 0",
+ "inputs": {
+ "start": {
+ "data": -9,
+ "type": "float32"
+ },
+ "step": {
+ "data": 1,
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+ "outputShape": [2, 2, 2, 3],
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+ 11,
+ 12,
+ 13,
+ 14
+ ],
+ "type": "int8"
+ }
+ },
+ {
+ "name": "constant float32 4D tensor of int8 type step < 0",
+ "inputs": {
+ "start": {
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+ "type": "float32"
+ },
+ "step": {
+ "data": -2,
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+ -31,
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+ -35,
+ -37,
+ -39
+ ],
+ "type": "int8"
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+ },
+ {
+ "name": "constant float32 4D tensor of uint8 type",
+ "inputs": {
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+ "type": "float32"
+ },
+ "step": {
+ "data": 1,
+ "type": "float32"
+ }
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+ "outputShape": [2, 2, 2, 3],
+ "type": "uint8",
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+ ],
+ "type": "uint8"
+ }
+ }
+ ]
+} \ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/conv2d.json b/testing/web-platform/tests/webnn/resources/test_data/conv2d.json
index 5f8cd814a9..13e6b17242 100644
--- a/testing/web-platform/tests/webnn/resources/test_data/conv2d.json
+++ b/testing/web-platform/tests/webnn/resources/test_data/conv2d.json
@@ -421,356 +421,6 @@
}
},
{
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"name": "depthwise conv2d float32 4D input and filter tensors options.groups= input_channels",
"inputs": {
"input": {
@@ -1990,7 +1640,6 @@
"options": {
"padding": [1, 0, 0, 1],
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- "autoPad": "explicit",
"dilations": [1, 1],
"groups": 2,
"inputLayout": "nchw",
diff --git a/testing/web-platform/tests/webnn/resources/test_data/conv_transpose2d.json b/testing/web-platform/tests/webnn/resources/test_data/conv_transpose2d.json
index 42274e6fa3..742752fd41 100644
--- a/testing/web-platform/tests/webnn/resources/test_data/conv_transpose2d.json
+++ b/testing/web-platform/tests/webnn/resources/test_data/conv_transpose2d.json
@@ -972,553 +972,6 @@
}
},
{
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diff --git a/testing/web-platform/tests/webnn/resources/test_data/l2_pool2d.json b/testing/web-platform/tests/webnn/resources/test_data/l2_pool2d.json
new file mode 100644
index 0000000000..a65687721a
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/l2_pool2d.json
@@ -0,0 +1,1174 @@
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+ "name": "l2Pool2d float32 4D tensor options.outputSizes ignores options.roundingType=ceil",
+ "inputs": {
+ "input": {
+ "shape": [1, 2, 5, 5],
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+ "type": "float32"
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+ "options": {
+ "windowDimensions": [3, 3],
+ "padding": [1, 0, 0, 1],
+ "strides": [2, 2],
+ "roundingType": "ceil",
+ "outputSizes": [2, 2]
+ },
+ "expected": {
+ "name": "output",
+ "shape": [1, 2, 2, 2],
+ "data": [
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+ ],
+ "type": "float32"
+ }
+ },
+ {
+ "name": "l2Pool2d float32 4D tensor options.dilations with options.strides",
+ "inputs": {
+ "input": {
+ "shape": [1, 7, 7, 2],
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+ "padding": [1, 0, 0, 1],
+ "strides": [2, 2],
+ "dilations": [1, 1],
+ "layout": "nhwc"
+ },
+ "expected": {
+ "name": "output",
+ "shape": [1, 3, 3, 2],
+ "data": [
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+ "type": "float32"
+ }
+ }
+ ]
+} \ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/test_data/max_pool2d.json b/testing/web-platform/tests/webnn/resources/test_data/max_pool2d.json
index 4532843d2b..216b4c55dd 100644
--- a/testing/web-platform/tests/webnn/resources/test_data/max_pool2d.json
+++ b/testing/web-platform/tests/webnn/resources/test_data/max_pool2d.json
@@ -464,340 +464,6 @@
}
},
{
- "name": "maxPool2d float32 4D tensor options.autoPad=explicit",
- "inputs": {
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- "autoPad": "same-lower"
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"name": "maxPool2d float32 4D tensor options.layout=nchw",
"inputs": {
"input": {
@@ -1404,7 +1070,6 @@
"options": {
"windowDimensions": [3, 3],
"padding": [1, 0, 0, 1],
- "autoPad": "explicit",
"strides": [2, 2],
"dilations": [1, 1],
"layout": "nhwc"
diff --git a/testing/web-platform/tests/webnn/resources/test_data/resample2d.json b/testing/web-platform/tests/webnn/resources/test_data/resample2d.json
new file mode 100644
index 0000000000..605d1b55c0
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/resample2d.json
@@ -0,0 +1,527 @@
+{
+ "tests": [
+ {
+ "name": "resample2d float32 4D tensor default options",
+ "inputs": {
+ "input": {
+ "shape": [1, 1, 4, 6], // nchw
+ "data": [
+ 3.8600528355143604,
+ 45.18463077286585,
+ 87.67153742917091,
+ 98.78210347338205,
+ 66.3741434682883,
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+ 25.39634271166711,
+ 67.02175102425608
+ ],
+ "type": "float32"
+ }
+ },
+ "expected": {
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+ "data": [
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+ 25.396343231201172,
+ 67.0217514038086
+ ],
+ "type": "float32"
+ }
+ },
+ {
+ "name": "resample2d(upsample) float32 4D tensor options.scales",
+ "inputs": {
+ "input": {
+ "shape": [1, 1, 2, 3],
+ "data": [
+ 59.92947164849423,
+ 41.989187594696546,
+ 66.39534663077877,
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+ 79.10004839481242
+ ],
+ "type": "float32"
+ }
+ },
+ "options": {
+ "scales": [2.0, 2.0]
+ },
+ "expected": {
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+ "data": [
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+ 59.92947006225586,
+ 41.98918914794922,
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+ 79.10005187988281,
+ 79.10005187988281
+ ],
+ "type": "float32"
+ }
+ },
+ {
+ "name": "resample2d(upsample) float32 4D tensor options.sizes",
+ "inputs": {
+ "input": {
+ "shape": [1, 1, 2, 3],
+ "data": [
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+ 41.989187594696546,
+ 66.39534663077877,
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+ 86.95106056135486,
+ 79.10004839481242
+ ],
+ "type": "float32"
+ }
+ },
+ "options": {
+ "sizes": [4, 6]
+ },
+ "expected": {
+ "shape": [1, 1, 4, 6],
+ "data": [
+ 59.92947006225586,
+ 59.92947006225586,
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+ 66.39534759521484,
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+ 41.98918914794922,
+ 41.98918914794922,
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+ 66.39534759521484,
+ 90.7006607055664,
+ 90.7006607055664,
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+ 86.95105743408203,
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+ 90.7006607055664,
+ 90.7006607055664,
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+ 86.95105743408203,
+ 79.10005187988281,
+ 79.10005187988281
+ ],
+ "type": "float32"
+ }
+ },
+ {
+ "name": "resample2d(upsample) float32 4D tensor options.sizes ignored options.scales",
+ "inputs": {
+ "input": {
+ "shape": [1, 1, 2, 3],
+ "data": [
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+ 90.70066412516924,
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+ 79.10004839481242
+ ],
+ "type": "float32"
+ }
+ },
+ "options": {
+ "scales": [0.5, 0.5],
+ "sizes": [4, 6]
+ },
+ "expected": {
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+ "data": [
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+ ],
+ "type": "float32"
+ }
+ },
+ {
+ "name": "resample2d(upsample) float32 4D tensor options.axes=[1, 2]",
+ "inputs": {
+ "input": {
+ "shape": [1, 2, 3, 1], // nhwc
+ "data": [
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+ 41.989187594696546,
+ 66.39534663077877,
+ 90.70066412516924,
+ 86.95106056135486,
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+ "type": "float32"
+ }
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+ "options": {
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+ "axes": [1, 2]
+ },
+ "expected": {
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+ "data": [
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+ }
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+ {
+ "name": "resample2d(upsample) float32 4D tensor explicit options.axes=[2, 3]",
+ "inputs": {
+ "input": {
+ "shape": [1, 1, 2, 3], // nchw
+ "data": [
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+ 66.39534663077877,
+ 90.70066412516924,
+ 86.95106056135486,
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+ }
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+ {
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+ "type": "float32"
+ }
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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/triangular.json b/testing/web-platform/tests/webnn/resources/test_data/triangular.json
new file mode 100644
index 0000000000..652f780d58
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/test_data/triangular.json
@@ -0,0 +1,1101 @@
+{
+ "tests": [
+ {
+ "name": "triangular float32 2D tensor default options",
+ "inputs": {
+ "input": {
+ "shape": [4, 6],
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+ {
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+ "options": {
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+ "type": "float32"
+ }
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+ "options": {
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+ "expected": {
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+ ],
+ "type": "float32"
+ }
+ },
+ {
+ "name": "triangular float32 4D tensor fully zero options.upper=false options.diagonal=-2",
+ "inputs": {
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+ ],
+ "type": "float32"
+ }
+ },
+ "options": {
+ "upper": false,
+ "diagonal": -2
+ },
+ "expected": {
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+ ],
+ "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
index f91d6622d3..375c71174a 100644
--- a/testing/web-platform/tests/webnn/resources/utils.js
+++ b/testing/web-platform/tests/webnn/resources/utils.js
@@ -1,7 +1,5 @@
'use strict';
-const ExecutionArray = ['sync', 'async'];
-
// https://webmachinelearning.github.io/webnn/#enumdef-mloperanddatatype
const TypedArrayDict = {
// workaround use Uint16 for Float16
@@ -193,7 +191,7 @@ const getMatmulPrecisionTolerance = (resources, operationName) => {
};
/**
- * Get ULP tolerance of averagePool2d operation.
+ * Get ULP tolerance of averagePool2d or l2Pool2d operation.
* @param {Object} resources - Resources used for building a graph
* @param {String} operationName - An operation name
* @returns {Number} A tolerance number
@@ -284,6 +282,32 @@ const getReductionPrecisionTolerance = (resources, operationName) => {
return tolerance;
};
+/**
+ * Get ULP tolerance of resample2d operations.
+ * @param {Object} resources - Resources used for building a graph
+ * @param {String} operationName - An operation name
+ * @returns {Number} A tolerance number
+ */
+const getResample2dPrecisionTolerance = (resources, operationName) => {
+ const options = {...resources.options};
+ let tolerance;
+ if (options.mode && options.mode === 'linear') {
+ // interpolation mode is linear
+ const precisionType = resources.expected.type;
+ if (precisionType === 'float32') {
+ tolerance = 84;
+ } else if (precisionType === 'float16') {
+ tolerance = 10;
+ } else {
+ tolerance = 1;
+ }
+ } else {
+ // interpolation mode is nearest-neighbor
+ tolerance = 0;
+ }
+ return tolerance;
+};
+
// Refer to precision metrics on https://github.com/webmachinelearning/webnn/issues/265#issuecomment-1256242643
const PrecisionMetrics = {
argMax: {ULP: {int64: 0}},
@@ -292,6 +316,7 @@ const PrecisionMetrics = {
cast: {ULP: {float32: 1, float16: 1, int32: 0, uint32: 0, int64: 0, int8: 0, uint8: 0}},
clamp: {ULP: {float32: 0, float16: 0}},
concat: {ULP: {float32: 0, float16: 0}},
+ constant: {ULP: {float32: 2, float16: 2, int32: 0, uint32: 0, int64: 0, int8: 0, uint8: 0}},
conv2d: {ULP: {float32: getConv2dPrecisionTolerance, float16: getConv2dPrecisionTolerance}},
convTranspose2d: {ULP: {float32: getConv2dPrecisionTolerance, float16: getConv2dPrecisionTolerance}},
// Begin Element-wise binary operations
@@ -340,6 +365,7 @@ const PrecisionMetrics = {
pad: {ULP: {float32: 0, float16: 0}},
// Begin Pooling operations
averagePool2d: {ULP: {float32: getAveragePool2dPrecisionTolerance, float16: getAveragePool2dPrecisionTolerance}},
+ l2Pool2d: {ULP: {float32: getAveragePool2dPrecisionTolerance, float16: getAveragePool2dPrecisionTolerance}},
maxPool2d: {ULP: {float32: 0, float16: 0}},
// End Pooling operations
prelu: {ULP: {float32: 1, float16: 1}},
@@ -356,6 +382,7 @@ const PrecisionMetrics = {
reduceSumSquare: {ULP: {float32: getReductionPrecisionTolerance, float16: getReductionPrecisionTolerance}},
// End Reduction operations
relu: {ULP: {float32: 0, float16: 0}},
+ resample2d: {ULP: {float32: getResample2dPrecisionTolerance, float16: getResample2dPrecisionTolerance}},
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}},
@@ -365,6 +392,7 @@ const PrecisionMetrics = {
split: {ULP: {float32: 0, float16: 0}},
tanh: {ATOL: {float32: 1/1024, float16: 1/512}},
transpose: {ULP: {float32: 0, float16: 0}},
+ triangular: {ULP: {float32: 0, float16: 0}},
where: {ULP: {float32: 0, float16: 0}},
};
@@ -635,6 +663,13 @@ const buildConcat = (operationName, builder, resources) => {
return namedOutputOperand;
};
+const buildConstantRange = (operationName, builder, resources) => {
+ const namedOutputOperand = {};
+ // invoke builder.constant(start, step, outputShape, type)
+ namedOutputOperand[resources.expected.name] = builder[operationName](resources.inputs.start, resources.inputs.step, resources.outputShape, resources.type);
+ return namedOutputOperand;
+};
+
const buildConvTranspose2d = (operationName, builder, resources) => {
// MLOperand convTranspose2d(MLOperand input, MLOperand filter, optional MLConvTranspose2dOptions options = {});
const namedOutputOperand = {};
@@ -793,25 +828,7 @@ const buildGraph = (operationName, builder, resources, buildFunc) => {
};
/**
- * 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.
+ * Build a graph, 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
@@ -821,9 +838,9 @@ const runSync = (operationName, context, builder, resources, buildFunc) => {
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
+ // compile the graph up to the output operand
const graph = await builder.build(namedOutputOperands);
- // asynchronously execute the compiled graph
+ // execute the compiled graph
const result = await context.compute(graph, inputs, outputs);
checkResults(operationName, namedOutputOperands, result.outputs, resources);
};
@@ -835,6 +852,10 @@ const run = async (operationName, context, builder, resources, buildFunc) => {
* @param {String} deviceType - The execution device type for this test
*/
const testWebNNOperation = (operationName, buildFunc, deviceType = 'cpu') => {
+ test(() => assert_not_equals(navigator.ml, undefined, "ml property is defined on navigator"));
+ if (navigator.ml === undefined) {
+ return;
+ }
let operationNameArray;
if (typeof operationName === 'string') {
operationNameArray = [operationName];
@@ -842,41 +863,18 @@ const testWebNNOperation = (operationName, buildFunc, deviceType = 'cpu') => {
operationNameArray = operationName;
}
- ExecutionArray.forEach(executionType => {
- const isSync = executionType === 'sync';
- if (self.GLOBAL.isWindow() && isSync) {
- return;
- }
- let context;
- let builder;
- if (isSync) {
- // test sync
- operationNameArray.forEach((subOperationName) => {
- const tests = loadTests(subOperationName);
- setup(() => {
- context = navigator.ml.createContextSync({deviceType});
- builder = new MLGraphBuilder(context);
- });
- for (const subTest of tests) {
- test(() => {
- runSync(subOperationName, context, builder, subTest, buildFunc);
- }, `${subTest.name} / ${executionType}`);
- }
- });
- } else {
- // test async
- operationNameArray.forEach((subOperationName) => {
- const tests = loadTests(subOperationName);
- promise_setup(async () => {
- context = await navigator.ml.createContext({deviceType});
- builder = new MLGraphBuilder(context);
- });
- for (const subTest of tests) {
- promise_test(async () => {
- await run(subOperationName, context, builder, subTest, buildFunc);
- }, `${subTest.name} / ${executionType}`);
- }
- });
+ let context;
+ let builder;
+ operationNameArray.forEach((subOperationName) => {
+ const tests = loadTests(subOperationName);
+ promise_setup(async () => {
+ context = await navigator.ml.createContext({deviceType});
+ builder = new MLGraphBuilder(context);
+ });
+ for (const subTest of tests) {
+ promise_test(async () => {
+ await run(subOperationName, context, builder, subTest, buildFunc);
+ }, `${subTest.name}`);
}
});
};
@@ -926,4 +924,60 @@ const toHalf = (value) => {
* the exponent, which is OK. */
bits += m & 1;
return bits;
+};
+
+
+/**
+ * WebNN buffer creation.
+ * @param {MLContext} context - the context used to create the buffer.
+ * @param {Number} bufferSize - Size of the buffer to create, in bytes.
+ */
+const createBuffer = (context, bufferSize) => {
+ let buffer;
+ try {
+ buffer = context.createBuffer({size: bufferSize});
+ assert_equals(buffer.size, bufferSize);
+ } catch (e) {
+ assert_true(e instanceof DOMException);
+ assert_equals(e.name, "NotSupportedError");
+ }
+ return buffer;
+};
+
+/**
+ * WebNN destroy buffer twice test.
+ * @param {String} testName - The name of the test operation.
+ * @param {String} deviceType - The execution device type for this test.
+ */
+const testDestroyWebNNBuffer = (testName, deviceType = 'cpu') => {
+ let context;
+ let buffer;
+ promise_setup(async () => {
+ context = await navigator.ml.createContext({deviceType});
+ buffer = createBuffer(context, 4);
+ });
+ promise_test(async () => {
+ // MLBuffer is not supported for this deviceType.
+ if (buffer === undefined) {
+ return;
+ }
+ buffer.destroy();
+ buffer.destroy();
+ }, `${testName}`);
+};
+
+/**
+ * WebNN create buffer test.
+ * @param {String} testName - The name of the test operation.
+ * @param {Number} bufferSize - Size of the buffer to create, in bytes.
+ * @param {String} deviceType - The execution device type for this test.
+ */
+const testCreateWebNNBuffer = (testName, bufferSize, deviceType = 'cpu') => {
+ let context;
+ promise_setup(async () => {
+ context = await navigator.ml.createContext({deviceType});
+ });
+ promise_test(async () => {
+ createBuffer(context, bufferSize);
+ }, `${testName} / ${bufferSize}`);
}; \ No newline at end of file
diff --git a/testing/web-platform/tests/webnn/resources/utils_validation.js b/testing/web-platform/tests/webnn/resources/utils_validation.js
new file mode 100644
index 0000000000..7f1d4a4a94
--- /dev/null
+++ b/testing/web-platform/tests/webnn/resources/utils_validation.js
@@ -0,0 +1,359 @@
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#enumdef-mloperanddatatype
+const allWebNNOperandDataTypes = [
+ 'float32',
+ 'float16',
+ 'int32',
+ 'uint32',
+ 'int64',
+ 'uint64',
+ 'int8',
+ 'uint8'
+];
+
+const unsignedLongType = 'unsigned long';
+
+const dimensions0D = [];
+const dimensions1D = [2];
+const dimensions2D = [2, 3];
+const dimensions3D = [2, 3, 4];
+const dimensions4D = [2, 3, 4, 5];
+const dimensions5D = [2, 3, 4, 5, 6];
+
+const adjustOffsetsArray = [
+ // Decrease 1
+ -1,
+ // Increase 1
+ 1
+];
+
+// TODO
+// Add more 5+ dimensions
+const allWebNNDimensionsArray = [
+ dimensions0D,
+ dimensions1D,
+ dimensions2D,
+ dimensions3D,
+ dimensions4D,
+ dimensions5D
+];
+
+const notUnsignedLongAxisArray = [
+ // String
+ 'abc',
+ // BigInt
+ BigInt(100),
+ // Object
+ {
+ value: 1
+ },
+ // Array Object
+ [0, 1],
+ // Date Object
+ new Date("2024-01-01"),
+];
+
+function getRank(inputDimensions) {
+ return inputDimensions.length;
+}
+
+function getAxisArray(inputDimensions) {
+ return Array.from({length: inputDimensions.length}, (_, i) => i);
+}
+
+function getAxesArrayContainSameValues(inputDimensions) {
+ // TODO
+ // Currently this function returns an array containing each element which all have the same value.
+ // For example axes: [0, 1, 2] for 3D input tensor
+ // this function returns
+ // [
+ // // two values are same
+ // [0, 0],
+ // [1, 1],
+ // [2, 2],
+ // // three values are same
+ // [0, 0, 0],
+ // [1, 1, 1]
+ // [2, 2, 2]
+ // ]
+ // while it should return
+ // [
+ // // two values are same
+ // [0, 0],
+ // [1, 1],
+ // [2, 2],
+ // [0, 0, 1],
+ // [0, 0, 2],
+ // [0, 1, 0],
+ // [0, 2, 0],
+ // [1, 0, 0],
+ // [2, 0, 0],
+ // [1, 1, 0],
+ // [1, 1, 2],
+ // [1, 0, 1],
+ // [1, 2, 1],
+ // [0, 1, 1],
+ // [2, 1, 1],
+ // [2, 2, 0],
+ // [2, 2, 1],
+ // [2, 0, 2],
+ // [2, 1, 2],
+ // [0, 2, 2],
+ // [1, 2, 2],
+ // // three (all) values are same
+ // [0, 0, 0],
+ // [1, 1, 1]
+ // [2, 2, 2]
+ // ]
+ const axesArrayContainSameValues = [];
+ const length = inputDimensions.length;
+ if (length >= 2) {
+ const validAxesArrayFull = getAxisArray(inputDimensions);
+ for (let index = 0; index < length; index++) {
+ axesArrayContainSameValues.push(new Array(2).fill(validAxesArrayFull[index]));
+ if (length > 2) {
+ axesArrayContainSameValues.push(new Array(3).fill(validAxesArrayFull[index]));
+ }
+ }
+ }
+ return axesArrayContainSameValues;
+}
+
+function generateUnbroadcastableDimensionsArray(dimensions) {
+ // Currently this function returns an array of some unbroadcastable dimensions.
+ // for example given dimensions [2, 3, 4]
+ // this function returns
+ // [
+ // [3, 3, 4],
+ // [2, 2, 4],
+ // [2, 4, 4],
+ // [2, 3, 3],
+ // [2, 3, 5],
+ // [3],
+ // [5],
+ // [1, 3],
+ // [1, 5],
+ // [1, 1, 3],
+ // [1, 1, 5],
+ // [1, 1, 1, 3],
+ // [1, 1, 1, 5],
+ // ]
+ if (dimensions.every(v => v === 1)) {
+ throw new Error(`[${dimensions}] always can be broadcasted`);
+ }
+ const resultDimensions = [];
+ const length = dimensions.length;
+ if (!dimensions.slice(0, length - 1).every(v => v === 1)) {
+ for (let i = 0; i < length; i++) {
+ if (dimensions[i] !== 1) {
+ for (let offset of [-1, 1]) {
+ const dimensionsB = dimensions.slice();
+ dimensionsB[i] += offset;
+ if (dimensionsB[i] !== 1) {
+ resultDimensions.push(dimensionsB);
+ }
+ }
+ }
+ }
+ }
+ const lastDimensionSize = dimensions[length - 1];
+ if (lastDimensionSize !== 1) {
+ for (let j = 0; j <= length; j++) {
+ if (lastDimensionSize > 2) {
+ resultDimensions.push(Array(j).fill(1).concat([lastDimensionSize - 1]));
+ }
+ resultDimensions.push(Array(j).fill(1).concat([lastDimensionSize + 1]));
+ }
+ }
+ return resultDimensions;
+}
+
+function generateOutOfRangeValuesArray(type) {
+ let range, outsideValueArray;
+ switch (type) {
+ case 'unsigned long':
+ // https://webidl.spec.whatwg.org/#idl-unsigned-long
+ // The unsigned long type is an unsigned integer type that has values in the range [0, 4294967295].
+ range = [0, 4294967295];
+ break;
+ default:
+ throw new Error(`Unsupport ${type}`);
+ }
+ outsideValueArray = [range[0] - 1, range[1] + 1];
+ return outsideValueArray;
+}
+
+let inputIndex = 0;
+let inputAIndex = 0;
+let inputBIndex = 0;
+let context, builder;
+
+test(() => assert_not_equals(navigator.ml, undefined, "ml property is defined on navigator"));
+
+promise_setup(async () => {
+ if (navigator.ml === undefined) {
+ return;
+ }
+ context = await navigator.ml.createContext();
+ builder = new MLGraphBuilder(context);
+}, {explicit_timeout: true});
+
+function validateTwoInputsBroadcastable(operationName) {
+ if (navigator.ml === undefined) {
+ return;
+ }
+ promise_test(async t => {
+ for (let dataType of allWebNNOperandDataTypes) {
+ for (let dimensions of allWebNNDimensionsArray) {
+ if (dimensions.length > 0) {
+ const inputA = builder.input(`inputA${++inputAIndex}`, {dataType, dimensions});
+ const unbroadcastableDimensionsArray = generateUnbroadcastableDimensionsArray(dimensions);
+ for (let unbroadcastableDimensions of unbroadcastableDimensionsArray) {
+ const inputB = builder.input(`inputB${++inputBIndex}`, {dataType, dimensions: unbroadcastableDimensions});
+ assert_throws_dom('DataError', () => builder[operationName](inputA, inputB));
+ assert_throws_dom('DataError', () => builder[operationName](inputB, inputA));
+ }
+ }
+ }
+ }
+ }, `[${operationName}] DataError is expected if two inputs aren't broadcastable`);
+}
+
+function validateTwoInputsOfSameDataType(operationName) {
+ if (navigator.ml === undefined) {
+ return;
+ }
+ let operationNameArray;
+ if (typeof operationName === 'string') {
+ operationNameArray = [operationName];
+ } else if (Array.isArray(operationName)) {
+ operationNameArray = operationName;
+ } else {
+ throw new Error(`${operationName} should be an operation name string or an operation name string array`);
+ }
+ for (let subOperationName of operationNameArray) {
+ promise_test(async t => {
+ for (let dataType of allWebNNOperandDataTypes) {
+ for (let dimensions of allWebNNDimensionsArray) {
+ const inputA = builder.input(`inputA${++inputAIndex}`, {dataType, dimensions});
+ for (let dataTypeB of allWebNNOperandDataTypes) {
+ if (dataType !== dataTypeB) {
+ const inputB = builder.input(`inputB${++inputBIndex}`, {dataType: dataTypeB, dimensions});
+ assert_throws_dom('DataError', () => builder[subOperationName](inputA, inputB));
+ }
+ }
+ }
+ }
+ }, `[${subOperationName}] DataError is expected if two inputs aren't of same data type`);
+ }
+}
+
+/**
+ * Validate options.axes by given operation and input rank for
+ * argMin/Max / layerNormalization / Reduction operations / resample2d operations
+ * @param {(String[]|String)} operationName - An operation name array or an operation name
+ * @param {Number} [inputRank]
+ */
+function validateOptionsAxes(operationName, inputRank) {
+ if (navigator.ml === undefined) {
+ return;
+ }
+ let operationNameArray;
+ if (typeof operationName === 'string') {
+ operationNameArray = [operationName];
+ } else if (Array.isArray(operationName)) {
+ operationNameArray = operationName;
+ } else {
+ throw new Error(`${operationName} should be an operation name string or an operation name string array`);
+ }
+ const invalidAxisArray = generateOutOfRangeValuesArray(unsignedLongType);
+ for (let subOperationName of operationNameArray) {
+ // TypeError is expected if any of options.axes elements is not an unsigned long interger
+ promise_test(async t => {
+ if (inputRank === undefined) {
+ // argMin/Max / layerNormalization / Reduction operations
+ for (let dataType of allWebNNOperandDataTypes) {
+ for (let dimensions of allWebNNDimensionsArray) {
+ const rank = getRank(dimensions);
+ if (rank >= 1) {
+ const input = builder.input(`input${++inputIndex}`, {dataType, dimensions});
+ for (let invalidAxis of invalidAxisArray) {
+ assert_throws_js(TypeError, () => builder[subOperationName](input, {axes: invalidAxis}));
+ }
+ for (let axis of notUnsignedLongAxisArray) {
+ assert_false(typeof axis === 'number' && Number.isInteger(axis), `[${subOperationName}] any of options.axes elements should be of 'unsigned long'`);
+ assert_throws_js(TypeError, () => builder[subOperationName](input, {axes: [axis]}));
+ }
+ }
+ }
+ }
+ } else {
+ // resample2d
+ for (let dataType of allWebNNOperandDataTypes) {
+ const input = builder.input(`input${++inputIndex}`, {dataType, dimensions: allWebNNDimensionsArray[inputRank]});
+ for (let invalidAxis of invalidAxisArray) {
+ assert_throws_js(TypeError, () => builder[subOperationName](input, {axes: invalidAxis}));
+ }
+ for (let axis of notUnsignedLongAxisArray) {
+ assert_false(typeof axis === 'number' && Number.isInteger(axis), `[${subOperationName}] any of options.axes elements should be of 'unsigned long'`);
+ assert_throws_js(TypeError, () => builder[subOperationName](input, {axes: [axis]}));
+ }
+ }
+ }
+ }, `[${subOperationName}] TypeError is expected if any of options.axes elements is not an unsigned long interger`);
+
+ // DataError is expected if any of options.axes elements is greater or equal to the size of input
+ promise_test(async t => {
+ if (inputRank === undefined) {
+ // argMin/Max / layerNormalization / Reduction operations
+ for (let dataType of allWebNNOperandDataTypes) {
+ for (let dimensions of allWebNNDimensionsArray) {
+ const rank = getRank(dimensions);
+ if (rank >= 1) {
+ const input = builder.input(`input${++inputIndex}`, {dataType, dimensions});
+ assert_throws_dom('DataError', () => builder[subOperationName](input, {axes: [rank]}));
+ assert_throws_dom('DataError', () => builder[subOperationName](input, {axes: [rank + 1]}));
+ }
+ }
+ }
+ } else {
+ // resample2d
+ for (let dataType of allWebNNOperandDataTypes) {
+ const input = builder.input(`input${++inputIndex}`, {dataType, dimensions: allWebNNDimensionsArray[inputRank]});
+ assert_throws_dom('DataError', () => builder[subOperationName](input, {axes: [inputRank]}));
+ assert_throws_dom('DataError', () => builder[subOperationName](input, {axes: [inputRank + 1]}));
+ }
+ }
+ }, `[${subOperationName}] DataError is expected if any of options.axes elements is greater or equal to the size of input`);
+
+ // DataError is expected if two or more values are same in the axes sequence
+ promise_test(async t => {
+ if (inputRank === undefined) {
+ // argMin/Max / layerNormalization / Reduction operations
+ for (let dataType of allWebNNOperandDataTypes) {
+ for (let dimensions of allWebNNDimensionsArray) {
+ const rank = getRank(dimensions);
+ if (rank >= 2) {
+ const input = builder.input(`input${++inputIndex}`, {dataType, dimensions});
+ const axesArrayContainSameValues = getAxesArrayContainSameValues(dimensions);
+ for (let axes of axesArrayContainSameValues) {
+ assert_throws_dom('DataError', () => builder[subOperationName](input, {axes}));
+ }
+ }
+ }
+ }
+ } else {
+ // resample2d
+ for (let dataType of allWebNNOperandDataTypes) {
+ const dimensions = allWebNNDimensionsArray[inputRank];
+ const input = builder.input(`input${++inputIndex}`, {dataType, dimensions});
+ const axesArrayContainSameValues = getAxesArrayContainSameValues(dimensions);
+ for (let axes of axesArrayContainSameValues) {
+ assert_throws_dom('DataError', () => builder[subOperationName](input, {axes}));
+ }
+ }
+ }
+ }, `[${subOperationName}] DataError is expected if two or more values are same in the axes sequence`);
+ }
+}