saymrwulf/onnxruntime
1
0
Fork 0
mirror of https://github.com/saymrwulf/onnxruntime.git synced 2026-05-18 21:21:17 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions
onnxruntime/js/web/test/data/ops/skip-layer-norm.jsonc

138 lines
3.3 KiB
Text
Raw Normal View History

[JS/Web] Added SkipLayerNormalization operator. (#17102) ### Description Add SkipLayerNormalization operator to JSEP. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. -->
2023-08-18 16:59:03 +00:00
[
{
[js/web] fix test runner with optional input/output (#20399) ### 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.
2024-04-22 19:53:10 +00:00
"name": "SkipLayerNormalization - no output[3]",
[JS/Web] Added SkipLayerNormalization operator. (#17102) ### Description Add SkipLayerNormalization operator to JSEP. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. -->
2023-08-18 16:59:03 +00:00
"operator": "SkipLayerNormalization",
"opset": { "domain": "com.microsoft", "version": 1 },
"attributes": [
{
"name": "epsilon",
"data": 1e-5,
"type": "float"
}
],
"inputShapeDefinitions": [[1, 2, 4], [1, 2, 4], [4], [4], [4]],
"cases": [
{
"name": "default",
"inputs": [
{
"data": [1, 2, 3, 4, 5, 6, 7, 8],
"dims": [1, 2, 4],
"type": "float32"
},
{
"data": [1, 1, 1, 1, 1, 1, 1, 1],
"dims": [1, 2, 4],
"type": "float32"
},
{
"data": [1, 1, 1, 1],
"dims": [4],
"type": "float32"
},
{
"data": [1, 1, 1, 1],
"dims": [4],
"type": "float32"
},
{
"data": [1, 1, 1, 1],
"dims": [4],
"type": "float32"
}
],
"outputs": [
{
"data": [
-0.34163546562194824, 0.5527881383895874, 1.4472118616104126, 2.3416354656219482, -0.34163546562194824,
0.5527881383895874, 1.4472118616104126, 2.3416354656219482
],
"dims": [1, 2, 4],
"type": "float32"
}
[js/web] fix test runner with optional input/output (#20399) ### 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.
2024-04-22 19:53:10 +00:00
// {
// "data": null,
// "type": "float32"
// },
// {
// "data": null,
// "type": "float32"
// },
// {
// "data": null,
// "type": "float32"
// }
[JS/Web] Added SkipLayerNormalization operator. (#17102) ### Description Add SkipLayerNormalization operator to JSEP. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. -->
2023-08-18 16:59:03 +00:00
]
[js/web] fix test runner with optional input/output (#20399) ### 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.
2024-04-22 19:53:10 +00:00
}
]
},
{
"name": "SkipLayerNormalization - has output[3]",
"operator": "SkipLayerNormalization",
"opset": { "domain": "com.microsoft", "version": 1 },
"attributes": [
{
"name": "epsilon",
"data": 1e-5,
"type": "float"
}
],
"inputShapeDefinitions": [[1, 2, 4], [1, 2, 4], [4], [4], [4]],
"cases": [
[JS/Web] Added SkipLayerNormalization operator. (#17102) ### Description Add SkipLayerNormalization operator to JSEP. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. -->
2023-08-18 16:59:03 +00:00
{
"name": "default",
"inputs": [
{
"data": [1, 2, 3, 4, 5, 6, 7, 8],
"dims": [1, 2, 4],
"type": "float32"
},
{
"data": [1, 1, 1, 1, 1, 1, 1, 1],
"dims": [1, 2, 4],
"type": "float32"
},
{
"data": [1, 1, 1, 1],
"dims": [4],
"type": "float32"
},
{
"data": [1, 1, 1, 1],
"dims": [4],
"type": "float32"
},
{
"data": [1, 1, 1, 1],
"dims": [4],
"type": "float32"
}
],
"outputs": [
{
"data": [
-0.34163546562194824, 0.5527881383895874, 1.4472118616104126, 2.3416354656219482, -0.34163546562194824,
0.5527881383895874, 1.4472118616104126, 2.3416354656219482
],
"dims": [1, 2, 4],
"type": "float32"
},
{
"data": null,
"type": "float32"
},
{
"data": null,
"type": "float32"
},
{
"data": [3, 4, 5, 6, 7, 8, 9, 10],
"dims": [1, 2, 4],
"type": "float32"
}
]
}
]
}
]
Reference in a new issue Copy permalink