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https://github.com/saymrwulf/onnxruntime.git
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4385602386
### Description fix test runner with optional input/output. This change fixes the OP test runner (.jsonc format test) with optional input(s) and/or output(s). this fix reveals a problem of dealing with optional outputs: > Take SkipSimplifiedLayerNorm as example: > > if in the ONNX model, the node's outputs are: [ 'output_0', '' ] instead of [ 'output_0' ], the current implementation will fail. The difference is, in the first case, context.outputCount == 2, and then the typescript implementation will try to create a tensor for output[1]. It will eventually call to C++ function (OpKernelContext::Output), and the output.DataRaw() will be nullptr. WebGPU backend will fail because it cannot deal with a TensorView with data == 0. > This problem may need to be fixed or workaround in separated PR. This PR does not fix this problem. Failed test cases are modified to work - please note this PR does not break those test cases as they never work.
137 lines
3.3 KiB
Text
137 lines
3.3 KiB
Text
[
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{
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"name": "SkipLayerNormalization - no output[3]",
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"operator": "SkipLayerNormalization",
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"opset": { "domain": "com.microsoft", "version": 1 },
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"attributes": [
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{
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"name": "epsilon",
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"data": 1e-5,
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"type": "float"
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}
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],
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"inputShapeDefinitions": [[1, 2, 4], [1, 2, 4], [4], [4], [4]],
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"cases": [
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{
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"name": "default",
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"inputs": [
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{
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"data": [1, 2, 3, 4, 5, 6, 7, 8],
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"dims": [1, 2, 4],
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"type": "float32"
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},
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{
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"data": [1, 1, 1, 1, 1, 1, 1, 1],
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"dims": [1, 2, 4],
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"type": "float32"
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},
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{
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"data": [1, 1, 1, 1],
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"dims": [4],
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"type": "float32"
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},
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{
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"data": [1, 1, 1, 1],
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"dims": [4],
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"type": "float32"
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},
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{
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"data": [1, 1, 1, 1],
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"dims": [4],
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"type": "float32"
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}
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],
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"outputs": [
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{
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"data": [
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-0.34163546562194824, 0.5527881383895874, 1.4472118616104126, 2.3416354656219482, -0.34163546562194824,
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0.5527881383895874, 1.4472118616104126, 2.3416354656219482
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],
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"dims": [1, 2, 4],
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"type": "float32"
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}
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// {
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// "data": null,
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// "type": "float32"
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// },
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// {
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// "data": null,
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// "type": "float32"
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// },
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// {
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// "data": null,
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// "type": "float32"
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// }
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]
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}
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]
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},
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{
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"name": "SkipLayerNormalization - has output[3]",
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"operator": "SkipLayerNormalization",
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"opset": { "domain": "com.microsoft", "version": 1 },
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"attributes": [
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{
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"name": "epsilon",
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"data": 1e-5,
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"type": "float"
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}
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],
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"inputShapeDefinitions": [[1, 2, 4], [1, 2, 4], [4], [4], [4]],
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"cases": [
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{
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"name": "default",
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"inputs": [
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{
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"data": [1, 2, 3, 4, 5, 6, 7, 8],
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"dims": [1, 2, 4],
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"type": "float32"
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},
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{
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"data": [1, 1, 1, 1, 1, 1, 1, 1],
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"dims": [1, 2, 4],
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"type": "float32"
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},
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{
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"data": [1, 1, 1, 1],
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"dims": [4],
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"type": "float32"
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},
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{
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"data": [1, 1, 1, 1],
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"dims": [4],
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"type": "float32"
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},
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{
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"data": [1, 1, 1, 1],
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"dims": [4],
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"type": "float32"
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}
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],
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"outputs": [
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{
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"data": [
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-0.34163546562194824, 0.5527881383895874, 1.4472118616104126, 2.3416354656219482, -0.34163546562194824,
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0.5527881383895874, 1.4472118616104126, 2.3416354656219482
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],
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"dims": [1, 2, 4],
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"type": "float32"
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},
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{
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"data": null,
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"type": "float32"
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},
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{
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"data": null,
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"type": "float32"
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},
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{
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"data": [3, 4, 5, 6, 7, 8, 9, 10],
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"dims": [1, 2, 4],
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"type": "float32"
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}
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]
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}
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]
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}
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]
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