[ONNX] Fix an assertion failure involving Slice (#71965) #72989
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Before this change, exporting a model to ONNX involving Slice crashes at `axes[i]` in line 153 if C++ assertions are enabled: ``` /usr/include/c++/11.1.0/bits/stl_vector.h:1045: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = long int; _Alloc = std::allocator<long int>; std::vector<_Tp, _Alloc>::reference = long int&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]: Assertion '__n < this->size()' failed. ``` The relevant check is https://github.com/gcc-mirror/gcc/blob/releases/gcc-11.1.0/libstdc++-v3/include/bits/stl_vector.h#L1045, which checks the vector index. The issue can be reproduced by exporting Mask R-CNN or similar ones. For example, ```Python import io import torch import torchvision as tv model = tv.models.detection.maskrcnn_resnet50_fpn(pretrained=False) x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)] with io.BytesIO() as f: torch.onnx.export(model, x, f, opset_version=11) ``` (extracted from [onnxoptimizer tests](https://github.com/onnx/optimizer/blob/master/onnxoptimizer/test/optimizer_test.py)) Tested environment: Arch Linux x86_64 with pytorch and torchvisoin installed from [the official repo](https://github.com/archlinux/svntogit-community/blob/packages/python-pytorch/trunk/PKGBUILD) and [AUR](https://aur.archlinux.org/cgit/aur.git/tree/PKGBUILD?h=python-torchvision), respectively. [ghstack-poisoned]
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Before this change, exporting a model to ONNX involving Slice crashes at `axes[i]` in line 153 if C++ assertions are enabled: ``` /usr/include/c++/11.1.0/bits/stl_vector.h:1045: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = long int; _Alloc = std::allocator<long int>; std::vector<_Tp, _Alloc>::reference = long int&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]: Assertion '__n < this->size()' failed. ``` The relevant check is https://github.com/gcc-mirror/gcc/blob/releases/gcc-11.1.0/libstdc++-v3/include/bits/stl_vector.h#L1045, which checks the vector index. The issue can be reproduced by exporting Mask R-CNN or similar ones. For example, ```Python import io import torch import torchvision as tv model = tv.models.detection.maskrcnn_resnet50_fpn(pretrained=False) x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)] with io.BytesIO() as f: torch.onnx.export(model, x, f, opset_version=11) ``` (extracted from [onnxoptimizer tests](https://github.com/onnx/optimizer/blob/master/onnxoptimizer/test/optimizer_test.py)) Tested environment: Arch Linux x86_64 with pytorch and torchvisoin installed from [the official repo](https://github.com/archlinux/svntogit-community/blob/packages/python-pytorch/trunk/PKGBUILD) and [AUR](https://aur.archlinux.org/cgit/aur.git/tree/PKGBUILD?h=python-torchvision), respectively. [ghstack-poisoned]
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cyyever
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Feb 21, 2022
Before this change, exporting a model to ONNX involving Slice crashes at `axes[i]` in line 153 if C++ assertions are enabled: ``` /usr/include/c++/11.1.0/bits/stl_vector.h:1045: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = long int; _Alloc = std::allocator<long int>; std::vector<_Tp, _Alloc>::reference = long int&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]: Assertion '__n < this->size()' failed. ``` The relevant check is https://github.com/gcc-mirror/gcc/blob/releases/gcc-11.1.0/libstdc++-v3/include/bits/stl_vector.h#L1045, which checks the vector index. The issue can be reproduced by exporting Mask R-CNN or similar ones. For example, ```Python import io import torch import torchvision as tv model = tv.models.detection.maskrcnn_resnet50_fpn(pretrained=False) x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)] with io.BytesIO() as f: torch.onnx.export(model, x, f, opset_version=11) ``` (extracted from [onnxoptimizer tests](https://github.com/onnx/optimizer/blob/master/onnxoptimizer/test/optimizer_test.py)) Tested environment: Arch Linux x86_64 with pytorch and torchvisoin installed from [the official repo](https://github.com/archlinux/svntogit-community/blob/packages/python-pytorch/trunk/PKGBUILD) and [AUR](https://aur.archlinux.org/cgit/aur.git/tree/PKGBUILD?h=python-torchvision), respectively. Pull Request resolved: pytorch/pytorch#72989
facebook-github-bot
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Feb 22, 2022
Summary: Before this change, exporting a model to ONNX involving Slice crashes at `axes[i]` in line 153 if C++ assertions are enabled: ``` /usr/include/c++/11.1.0/bits/stl_vector.h:1045: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = long int; _Alloc = std::allocator<long int>; std::vector<_Tp, _Alloc>::reference = long int&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]: Assertion '__n < this->size()' failed. ``` The relevant check is https://github.com/gcc-mirror/gcc/blob/releases/gcc-11.1.0/libstdc++-v3/include/bits/stl_vector.h#L1045, which checks the vector index. The issue can be reproduced by exporting Mask R-CNN or similar ones. For example, ```Python import io import torch import torchvision as tv model = tv.models.detection.maskrcnn_resnet50_fpn(pretrained=False) x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)] with io.BytesIO() as f: torch.onnx.export(model, x, f, opset_version=11) ``` (extracted from [onnxoptimizer tests](https://github.com/onnx/optimizer/blob/master/onnxoptimizer/test/optimizer_test.py)) Tested environment: Arch Linux x86_64 with pytorch and torchvisoin installed from [the official repo](https://github.com/archlinux/svntogit-community/blob/packages/python-pytorch/trunk/PKGBUILD) and [AUR](https://aur.archlinux.org/cgit/aur.git/tree/PKGBUILD?h=python-torchvision), respectively. Pull Request resolved: #72989 Test Plan: automation Reviewed By: seemethere Differential Revision: D34347596 Pulled By: seemethere fbshipit-source-id: 3d33eee5654dfae23d12247f1e74e6d6bc9fdd9d
cyyever
pushed a commit
to cyyever/pytorch_private
that referenced
this pull request
Mar 3, 2022
Before this change, exporting a model to ONNX involving Slice crashes at `axes[i]` in line 153 if C++ assertions are enabled: ``` /usr/include/c++/11.1.0/bits/stl_vector.h:1045: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = long int; _Alloc = std::allocator<long int>; std::vector<_Tp, _Alloc>::reference = long int&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]: Assertion '__n < this->size()' failed. ``` The relevant check is https://github.com/gcc-mirror/gcc/blob/releases/gcc-11.1.0/libstdc++-v3/include/bits/stl_vector.h#L1045, which checks the vector index. The issue can be reproduced by exporting Mask R-CNN or similar ones. For example, ```Python import io import torch import torchvision as tv model = tv.models.detection.maskrcnn_resnet50_fpn(pretrained=False) x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)] with io.BytesIO() as f: torch.onnx.export(model, x, f, opset_version=11) ``` (extracted from [onnxoptimizer tests](https://github.com/onnx/optimizer/blob/master/onnxoptimizer/test/optimizer_test.py)) Tested environment: Arch Linux x86_64 with pytorch and torchvisoin installed from [the official repo](https://github.com/archlinux/svntogit-community/blob/packages/python-pytorch/trunk/PKGBUILD) and [AUR](https://aur.archlinux.org/cgit/aur.git/tree/PKGBUILD?h=python-torchvision), respectively. Pull Request resolved: pytorch/pytorch#72989
cyyever
pushed a commit
to cyyever/pytorch_private
that referenced
this pull request
Mar 3, 2022
Before this change, exporting a model to ONNX involving Slice crashes at `axes[i]` in line 153 if C++ assertions are enabled: ``` /usr/include/c++/11.1.0/bits/stl_vector.h:1045: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = long int; _Alloc = std::allocator<long int>; std::vector<_Tp, _Alloc>::reference = long int&; std::vector<_Tp, _Alloc>::size_type = long unsigned int]: Assertion '__n < this->size()' failed. ``` The relevant check is https://github.com/gcc-mirror/gcc/blob/releases/gcc-11.1.0/libstdc++-v3/include/bits/stl_vector.h#L1045, which checks the vector index. The issue can be reproduced by exporting Mask R-CNN or similar ones. For example, ```Python import io import torch import torchvision as tv model = tv.models.detection.maskrcnn_resnet50_fpn(pretrained=False) x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)] with io.BytesIO() as f: torch.onnx.export(model, x, f, opset_version=11) ``` (extracted from [onnxoptimizer tests](https://github.com/onnx/optimizer/blob/master/onnxoptimizer/test/optimizer_test.py)) Tested environment: Arch Linux x86_64 with pytorch and torchvisoin installed from [the official repo](https://github.com/archlinux/svntogit-community/blob/packages/python-pytorch/trunk/PKGBUILD) and [AUR](https://aur.archlinux.org/cgit/aur.git/tree/PKGBUILD?h=python-torchvision), respectively. Pull Request resolved: pytorch/pytorch#72989
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Stack from ghstack:
Before this change, exporting a model to ONNX involving Slice crashes at
axes[i]in line 153 if C++ assertions are enabled:The relevant check is https://github.com/gcc-mirror/gcc/blob/releases/gcc-11.1.0/libstdc++-v3/include/bits/stl_vector.h#L1045, which checks the vector index.
The issue can be reproduced by exporting Mask R-CNN or similar ones. For example,
(extracted from onnxoptimizer tests)
Tested environment: Arch Linux x86_64 with pytorch and torchvisoin installed from the official repo and AUR, respectively.