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QuantizedElemwiseMulOpShape accesses out of bound elements #18034
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Thanks to reporting the issue. We will fix soon. |
leezu
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Apr 14, 2020
As per #17968, require C++17 compatible compiler. For cuda code, use C++14 mode introduced in Cuda 9. C++17 support for Cuda will be available in Cuda 11. Switching to C++17 requires modernizing the toolchain, which exposed a number of technical debt issues in the codebase. All blocking issues are fixed as part of this PR. See the full list below. This PR contains the following specific changes: Switch CI pipeline to use gcc7 on Ubuntu and CentOS Switch CD pipeline to CentOS 7 with https://www.softwarecollections.org/en/scls/rhscl/devtoolset-7/ This enables us to build with gcc7 C++17 compiler while keeping a relatively old glibc requirement for distribution. Simplify ARM Edge builds Switch to standard Ubuntu / Debian cross-compilation toolchain for ARMv7, ARMv8 Switch to https://toolchains.bootlin.com/ toolchain for ARMv6 (the Debian ARMv6 toolchain is for ARMv4 + ARMv5 + ARMv6, but we wish to only target ARMv6 and make use of ARMv6 features) Remove reliance on dockcross for cross compilation. Simplify Jetson build Use standard Ubuntu / Debian cross-compilation toolchain for ARMv8 Upgrade to Cuda 10 and Jetpack 4.3 Simplify build setup Simplify QEMU ARM virtualization test setup on CI Remove complex "Virtual Machine in Docker" logic and run a QEMU based Docker container instead based on arm32v7/ubuntu Fix out of bounds vector accesses in SoftmaxGradOpType MKLDNNFCBackward Fix use of non-standard rand_r function (which is not available on anymore on newer Android toolchains and shouldn't be use in any case). Fix reproducibility of RNN with Dropout Fix reproducibility of DGL Graph Sampling Operators Update tests for Android Edge build to NDK19. The previously used standalone toolchain is obsolete. Those Dockerfiles that required refactoring as part of the effort were refactored based on the following consideration Maximize the use of system dependencies provided by the distribution instead of manually installing dependencies from source or from third party vendors. This reduces the complexity of the installation process and essentially pins the dependency versions, increasing CI stability. Further, Dockerfile build speed is improved. To facilitate this, use recent distribution versions. We still ensure backwards compatibility via CentOS7 based build and test stages Minimize the number of layers in the Dockerfile. Don't have 5 different script files executed, each calling apt-get update and install, but just execute once. Speeds up the build and reduces image size. Keep each Dockerfile simple and tailored to a purpose, instead of running 20 scripts to install dependencies for every thinkable scenario, which is unmaintainable. Some more small changes: Remove outdated references to Cuda 7 and Cuda 8 in various files. Remove C++03 support in mshadow Disable broken tests NumpyBooleanAssignForwardCPU #17990 test_init.test_rsp_const_init #17988 quantized_elemwise_mul #18034 List of squashed commits * cpp standard * Remove leftover files of Cuda 7 and Cuda 8 support * thrust 1.9.8 for clang10 * compiler warnings * Disable broken test_init.test_rsp_const_init * Disable tests invoking NumpyBooleanAssignForwardCPU * Fix out of bounds access in SoftmaxGradOpType * Use CentOS 7 for staticbuilds CentOS 7 fullfills the requirements for PEP 599 manylinux-2014 and provides a C++17 toolchain. * Fix MKLDNNFCBackward * Update edge toolchain * Support platforms without rand_r * Cleanup random.h * Greatly simplify qemu setup * Remove unused functions in Jenkins_steps.groovy * Skip quantized_elemwise_mul due QuantizedElemwiseMulOpShape bug * Fix R package installation #18042 * Fix centos ccache * Fix GPU Makefile staticbuild on CentOS7 * CentOS7 NCCL * CentOS7 staticbuild fix link with libculibos
cc @bgawrych |
Ping @bgawrych @pengzhao-intel Let's try to fix this and reenable the tests. Thanks for your help |
Thanks @bgawrych |
AntiZpvoh
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Jul 6, 2020
As per apache#17968, require C++17 compatible compiler. For cuda code, use C++14 mode introduced in Cuda 9. C++17 support for Cuda will be available in Cuda 11. Switching to C++17 requires modernizing the toolchain, which exposed a number of technical debt issues in the codebase. All blocking issues are fixed as part of this PR. See the full list below. This PR contains the following specific changes: Switch CI pipeline to use gcc7 on Ubuntu and CentOS Switch CD pipeline to CentOS 7 with https://www.softwarecollections.org/en/scls/rhscl/devtoolset-7/ This enables us to build with gcc7 C++17 compiler while keeping a relatively old glibc requirement for distribution. Simplify ARM Edge builds Switch to standard Ubuntu / Debian cross-compilation toolchain for ARMv7, ARMv8 Switch to https://toolchains.bootlin.com/ toolchain for ARMv6 (the Debian ARMv6 toolchain is for ARMv4 + ARMv5 + ARMv6, but we wish to only target ARMv6 and make use of ARMv6 features) Remove reliance on dockcross for cross compilation. Simplify Jetson build Use standard Ubuntu / Debian cross-compilation toolchain for ARMv8 Upgrade to Cuda 10 and Jetpack 4.3 Simplify build setup Simplify QEMU ARM virtualization test setup on CI Remove complex "Virtual Machine in Docker" logic and run a QEMU based Docker container instead based on arm32v7/ubuntu Fix out of bounds vector accesses in SoftmaxGradOpType MKLDNNFCBackward Fix use of non-standard rand_r function (which is not available on anymore on newer Android toolchains and shouldn't be use in any case). Fix reproducibility of RNN with Dropout Fix reproducibility of DGL Graph Sampling Operators Update tests for Android Edge build to NDK19. The previously used standalone toolchain is obsolete. Those Dockerfiles that required refactoring as part of the effort were refactored based on the following consideration Maximize the use of system dependencies provided by the distribution instead of manually installing dependencies from source or from third party vendors. This reduces the complexity of the installation process and essentially pins the dependency versions, increasing CI stability. Further, Dockerfile build speed is improved. To facilitate this, use recent distribution versions. We still ensure backwards compatibility via CentOS7 based build and test stages Minimize the number of layers in the Dockerfile. Don't have 5 different script files executed, each calling apt-get update and install, but just execute once. Speeds up the build and reduces image size. Keep each Dockerfile simple and tailored to a purpose, instead of running 20 scripts to install dependencies for every thinkable scenario, which is unmaintainable. Some more small changes: Remove outdated references to Cuda 7 and Cuda 8 in various files. Remove C++03 support in mshadow Disable broken tests NumpyBooleanAssignForwardCPU apache#17990 test_init.test_rsp_const_init apache#17988 quantized_elemwise_mul apache#18034 List of squashed commits * cpp standard * Remove leftover files of Cuda 7 and Cuda 8 support * thrust 1.9.8 for clang10 * compiler warnings * Disable broken test_init.test_rsp_const_init * Disable tests invoking NumpyBooleanAssignForwardCPU * Fix out of bounds access in SoftmaxGradOpType * Use CentOS 7 for staticbuilds CentOS 7 fullfills the requirements for PEP 599 manylinux-2014 and provides a C++17 toolchain. * Fix MKLDNNFCBackward * Update edge toolchain * Support platforms without rand_r * Cleanup random.h * Greatly simplify qemu setup * Remove unused functions in Jenkins_steps.groovy * Skip quantized_elemwise_mul due QuantizedElemwiseMulOpShape bug * Fix R package installation apache#18042 * Fix centos ccache * Fix GPU Makefile staticbuild on CentOS7 * CentOS7 NCCL * CentOS7 staticbuild fix link with libculibos
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Description
MKLDNN
QuantizedElemwiseMulOpShape
accesses out of bound elements in line 64 duringquantized_elemwise_mul
test.https://github.com/apache/incubator-mxnet/blob/ab4f7f6a7335e88034edcf61402aec170cdca5fd/src/operator/quantization/quantized_elemwise_mul.cc#L63-L65
Preventing that invalid access by adding
out_attrs->size() > quantized_elemwise_mul::kOutMin
to the if condition, leads to errors ininfer_shape_pass
.CI with updated toolchain (ie #17984) catches the bug. See #17987 for instructions to reproduce: use recent gcc and build in debug build
CC: @xinyu-intel
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