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[ci][onnx] Relax more test tolerances - #11152

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Following on #11042, this changes tolerances to fix some other ONNX test failures that have come up in the past several days

testlink
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3047/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3044/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3068/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11032/2/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-10833/11/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11035/1/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11074/1/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11068/1/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearsigmoid[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11078/1/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-10867/10/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearsigmoid[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/ci-gpu-update/4/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearsigmoid[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3132/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearsigmoid[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3143/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11146/2/pipeline/

cc @areusch

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driazati marked this pull request as ready for review April 27, 2022 22:11
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It has been a while since this PR was updated, @areusch please leave a review or address the outstanding comments. @driazati if this PR is still a work in progress, please convert it to a draft until it is ready for review.

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@driazati
[ci][onnx] Relax more test tolerances
d872145
Following on #11042, this changes tolerances to fix some other ONNX test failures that have come up in the past several days
@areusch

areusch commented May 11, 2022

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cc @altanh can you take a look?

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It has been a while since this PR was updated, @altanh@areusch please leave a review or address the outstanding comments. @driazati if this PR is still a work in progress, please convert it to a draft until it is ready for review.

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I'm suspicious of a single value being ~1e-2 off while the rest are below 1e-5... tricky. We could reduce tolerances to that but that's quite a reduction

This was referenced May 19, 2022
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Closing in favor of #11376, agreed that the tolerances are too high here (and not even enough for all the failures I've seen, some are off by atol=0.02) to merge and would silently indicate nothing is wrong. We don't really have any other choice but to disable these tests until someone can identify and fix the values

@masahi

masahi commented May 19, 2022•
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yeah, for quantized ops, getting accuracy aligned with frameworks is challenging, due to slight difference in how low-level numerics is done (fixed point vs fp32 etc). In PyTorch frontnend, we skip accuracy check entirely for quantized ops / models.

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[ci][onnx] Relax more test tolerances by driazati · Pull Request #11152 · apache/tvm · GitHub
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[ci][onnx] Relax more test tolerances - #11152
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Apr 27, 2022•

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Following on #11042, this changes tolerances to fix some other ONNX test failures that have come up in the past several days
testlink
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3047/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3044/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3068/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11032/2/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-10833/11/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11035/1/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11074/1/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11068/1/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearsigmoid[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11078/1/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-10867/10/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearsigmoid[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/ci-gpu-update/4/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearsigmoid[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3132/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearsigmoid[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3143/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11146/2/pipeline/
cc @areusch
@driazati
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marked this pull request as ready for review
April 27, 2022 22:11
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It has been a while since this PR was updated, @areusch please leave a review or address the outstanding comments. @driazati if this PR is still a work in progress, please convert it to a draft until it is ready for review.
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requested a review
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@driazati
[ci][onnx] Relax more test tolerances
d872145
Following on #11042, this changes tolerances to fix some other ONNX test failures that have come up in the past several days
@areusch
areusch
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May 11, 2022

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cc @altanh can you take a look?
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It has been a while since this PR was updated, @altanh@areusch please leave a review or address the outstanding comments. @driazati if this PR is still a work in progress, please convert it to a draft until it is ready for review.
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I'm suspicious of a single value being ~1e-2 off while the rest are below 1e-5... tricky. We could reduce tolerances to that but that's quite a reduction

This was referenced May 19, 2022
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@driazati
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Closing in favor of #11376, agreed that the tolerances are too high here (and not even enough for all the failures I've seen, some are off by atol=0.02) to merge and would silently indicate nothing is wrong. We don't really have any other choice but to disable these tests until someone can identify and fix the values
@masahi
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May 19, 2022•

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yeah, for quantized ops, getting accuracy aligned with frameworks is challenging, due to slight difference in how low-level numerics is done (fixed point vs fp32 etc). In PyTorch frontnend, we skip accuracy check entirely for quantized ops / models.
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[ci][onnx] Relax more test tolerances by driazati · Pull Request #11152 · apache/tvm · GitHub
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[ci][onnx] Relax more test tolerances - #11152
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Following on #11042, this changes tolerances to fix some other ONNX test failures that have come up in the past several days
testlink
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3047/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3044/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3068/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11032/2/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-10833/11/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11035/1/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11074/1/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11068/1/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearsigmoid[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11078/1/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-10867/10/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearsigmoid[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/ci-gpu-update/4/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearsigmoid[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3132/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearsigmoid[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3143/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11146/2/pipeline/
cc @areusch
@driazati
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marked this pull request as ready for review
April 27, 2022 22:11
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It has been a while since this PR was updated, @areusch please leave a review or address the outstanding comments. @driazati if this PR is still a work in progress, please convert it to a draft until it is ready for review.
@github-actions
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requested a review
from areuschMay 9, 2022 18:54
@driazati
[ci][onnx] Relax more test tolerances
d872145
Following on #11042, this changes tolerances to fix some other ONNX test failures that have come up in the past several days
@areusch
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May 11, 2022

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cc @altanh can you take a look?
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It has been a while since this PR was updated, @altanh@areusch please leave a review or address the outstanding comments. @driazati if this PR is still a work in progress, please convert it to a draft until it is ready for review.
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I'm suspicious of a single value being ~1e-2 off while the rest are below 1e-5... tricky. We could reduce tolerances to that but that's quite a reduction

This was referenced May 19, 2022
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@driazati
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May 19, 2022

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Closing in favor of #11376, agreed that the tolerances are too high here (and not even enough for all the failures I've seen, some are off by atol=0.02) to merge and would silently indicate nothing is wrong. We don't really have any other choice but to disable these tests until someone can identify and fix the values
@masahi
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May 19, 2022•

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yeah, for quantized ops, getting accuracy aligned with frameworks is challenging, due to slight difference in how low-level numerics is done (fixed point vs fp32 etc). In PyTorch frontnend, we skip accuracy check entirely for quantized ops / models.
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[ci][onnx] Relax more test tolerances by driazati · Pull Request #11152 · apache/tvm · GitHub
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[ci][onnx] Relax more test tolerances - #11152
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Following on #11042, this changes tolerances to fix some other ONNX test failures that have come up in the past several days
testlink
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3047/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3044/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3068/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11032/2/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-10833/11/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11035/1/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11074/1/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11068/1/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearsigmoid[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11078/1/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-10867/10/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearsigmoid[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/ci-gpu-update/4/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearsigmoid[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3132/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearsigmoid[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3143/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11146/2/pipeline/
cc @areusch
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[ci][onnx] Relax more test tolerances
d872145
Following on #11042, this changes tolerances to fix some other ONNX test failures that have come up in the past several days
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cc @altanh can you take a look?
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I'm suspicious of a single value being ~1e-2 off while the rest are below 1e-5... tricky. We could reduce tolerances to that but that's quite a reduction

This was referenced May 19, 2022
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Closing in favor of #11376, agreed that the tolerances are too high here (and not even enough for all the failures I've seen, some are off by atol=0.02) to merge and would silently indicate nothing is wrong. We don't really have any other choice but to disable these tests until someone can identify and fix the values
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yeah, for quantized ops, getting accuracy aligned with frameworks is challenging, due to slight difference in how low-level numerics is done (fixed point vs fp32 etc). In PyTorch frontnend, we skip accuracy check entirely for quantized ops / models.
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[ci][onnx] Relax more test tolerances by driazati · Pull Request #11152 · apache/tvm · GitHub
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[ci][onnx] Relax more test tolerances - #11152
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Following on #11042, this changes tolerances to fix some other ONNX test failures that have come up in the past several days
testlink
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3047/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3044/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3068/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11032/2/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-10833/11/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11035/1/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11074/1/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11068/1/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearsigmoid[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11078/1/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-10867/10/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearsigmoid[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/ci-gpu-update/4/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearsigmoid[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3132/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearsigmoid[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3143/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11146/2/pipeline/
cc @areusch
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April 27, 2022 22:11
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It has been a while since this PR was updated, @areusch please leave a review or address the outstanding comments. @driazati if this PR is still a work in progress, please convert it to a draft until it is ready for review.
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[ci][onnx] Relax more test tolerances
d872145
Following on #11042, this changes tolerances to fix some other ONNX test failures that have come up in the past several days
@areusch
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cc @altanh can you take a look?
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It has been a while since this PR was updated, @altanh@areusch please leave a review or address the outstanding comments. @driazati if this PR is still a work in progress, please convert it to a draft until it is ready for review.
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I'm suspicious of a single value being ~1e-2 off while the rest are below 1e-5... tricky. We could reduce tolerances to that but that's quite a reduction

This was referenced May 19, 2022
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Closing in favor of #11376, agreed that the tolerances are too high here (and not even enough for all the failures I've seen, some are off by atol=0.02) to merge and would silently indicate nothing is wrong. We don't really have any other choice but to disable these tests until someone can identify and fix the values
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yeah, for quantized ops, getting accuracy aligned with frameworks is challenging, due to slight difference in how low-level numerics is done (fixed point vs fp32 etc). In PyTorch frontnend, we skip accuracy check entirely for quantized ops / models.
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[ci][onnx] Relax more test tolerances by driazati · Pull Request #11152 · apache/tvm · GitHub
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[ci][onnx] Relax more test tolerances - #11152
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Following on #11042, this changes tolerances to fix some other ONNX test failures that have come up in the past several days
testlink
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3047/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3044/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3068/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11032/2/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-10833/11/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11035/1/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11074/1/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11068/1/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearsigmoid[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11078/1/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-10867/10/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearsigmoid[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/ci-gpu-update/4/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearsigmoid[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3132/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearsigmoid[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3143/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11146/2/pipeline/
cc @areusch
@driazati
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marked this pull request as ready for review
April 27, 2022 22:11
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It has been a while since this PR was updated, @areusch please leave a review or address the outstanding comments. @driazati if this PR is still a work in progress, please convert it to a draft until it is ready for review.
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@driazati
[ci][onnx] Relax more test tolerances
d872145
Following on #11042, this changes tolerances to fix some other ONNX test failures that have come up in the past several days
@areusch
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May 11, 2022

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cc @altanh can you take a look?
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It has been a while since this PR was updated, @altanh@areusch please leave a review or address the outstanding comments. @driazati if this PR is still a work in progress, please convert it to a draft until it is ready for review.
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I'm suspicious of a single value being ~1e-2 off while the rest are below 1e-5... tricky. We could reduce tolerances to that but that's quite a reduction

This was referenced May 19, 2022
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Closing in favor of #11376, agreed that the tolerances are too high here (and not even enough for all the failures I've seen, some are off by atol=0.02) to merge and would silently indicate nothing is wrong. We don't really have any other choice but to disable these tests until someone can identify and fix the values
@masahi
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May 19, 2022•

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yeah, for quantized ops, getting accuracy aligned with frameworks is challenging, due to slight difference in how low-level numerics is done (fixed point vs fp32 etc). In PyTorch frontnend, we skip accuracy check entirely for quantized ops / models.
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var __m = "*";
var __re = new RegExp('^' + ".*" + '
[ci][onnx] Relax more test tolerances by driazati · Pull Request #11152 · apache/tvm · GitHub
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[ci][onnx] Relax more test tolerances - #11152
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Following on #11042, this changes tolerances to fix some other ONNX test failures that have come up in the past several days
testlink
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3047/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3044/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3068/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11032/2/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-10833/11/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11035/1/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11074/1/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11068/1/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearsigmoid[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11078/1/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-10867/10/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearsigmoid[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/ci-gpu-update/4/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearsigmoid[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3132/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearsigmoid[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3143/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11146/2/pipeline/
cc @areusch
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@driazati
[ci][onnx] Relax more test tolerances
d872145
Following on #11042, this changes tolerances to fix some other ONNX test failures that have come up in the past several days
@areusch
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cc @altanh can you take a look?
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It has been a while since this PR was updated, @altanh@areusch please leave a review or address the outstanding comments. @driazati if this PR is still a work in progress, please convert it to a draft until it is ready for review.
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I'm suspicious of a single value being ~1e-2 off while the rest are below 1e-5... tricky. We could reduce tolerances to that but that's quite a reduction

This was referenced May 19, 2022
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Closing in favor of #11376, agreed that the tolerances are too high here (and not even enough for all the failures I've seen, some are off by atol=0.02) to merge and would silently indicate nothing is wrong. We don't really have any other choice but to disable these tests until someone can identify and fix the values
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yeah, for quantized ops, getting accuracy aligned with frameworks is challenging, due to slight difference in how low-level numerics is done (fixed point vs fp32 etc). In PyTorch frontnend, we skip accuracy check entirely for quantized ops / models.
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[ci][onnx] Relax more test tolerances by driazati · Pull Request #11152 · apache/tvm · GitHub
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[ci][onnx] Relax more test tolerances - #11152
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Apr 27, 2022•

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Following on #11042, this changes tolerances to fix some other ONNX test failures that have come up in the past several days
testlink
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3047/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3044/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3068/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11032/2/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-10833/11/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11035/1/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11074/1/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11068/1/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearsigmoid[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11078/1/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-10867/10/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearsigmoid[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/ci-gpu-update/4/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearsigmoid[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3132/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearsigmoid[cuda]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/main/3143/pipeline/
tests/python/frontend/onnx/test_forward.py::test_qlinearleakyrelu[llvm]https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-11146/2/pipeline/
cc @areusch
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marked this pull request as ready for review
April 27, 2022 22:11
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May 4, 2022

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It has been a while since this PR was updated, @areusch please leave a review or address the outstanding comments. @driazati if this PR is still a work in progress, please convert it to a draft until it is ready for review.
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@driazati
[ci][onnx] Relax more test tolerances
d872145
Following on #11042, this changes tolerances to fix some other ONNX test failures that have come up in the past several days
@areusch
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May 11, 2022

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cc @altanh can you take a look?
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May 18, 2022

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It has been a while since this PR was updated, @altanh@areusch please leave a review or address the outstanding comments. @driazati if this PR is still a work in progress, please convert it to a draft until it is ready for review.
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May 18, 2022

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I'm suspicious of a single value being ~1e-2 off while the rest are below 1e-5... tricky. We could reduce tolerances to that but that's quite a reduction

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@driazati
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May 19, 2022

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Closing in favor of #11376, agreed that the tolerances are too high here (and not even enough for all the failures I've seen, some are off by atol=0.02) to merge and would silently indicate nothing is wrong. We don't really have any other choice but to disable these tests until someone can identify and fix the values
@masahi
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May 19, 2022•

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yeah, for quantized ops, getting accuracy aligned with frameworks is challenging, due to slight difference in how low-level numerics is done (fixed point vs fp32 etc). In PyTorch frontnend, we skip accuracy check entirely for quantized ops / models.
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