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Summary: This should not be there since logging does not depend on protobuf. Pull Request resolved: pytorch#11814 Reviewed By: ezyang Differential Revision: D9923819 Pulled By: Yangqing fbshipit-source-id: 4d4edaea1a2e317f5db6e92c35d58c85dd35c5fb
…ntext_base.h (pytorch#11818) Summary: Pull Request resolved: pytorch#11818 To do this, I have to move the static context registry into ATen/core. I take the opportunity to convert it into an unordered_map. Reviewed By: Yangqing Differential Revision: D9924348 fbshipit-source-id: 8d92b9e8b4246ce608eba24ecef7ad5f8b9b6582
Summary: Adds some pretty-printing capability to the IR graph to make debugging easier/more human readable, see `torch/csrc/jit/test_jit.cpp:925` and onwards for example outputs. Results aren't perfect yet but it's a start. Pull Request resolved: pytorch#10319 Reviewed By: zdevito Differential Revision: D9558402 Pulled By: driazati fbshipit-source-id: 1d61c02818daa4c9bdca36d1477d1734cfc7d043
Summary: Currently one of our GPU perf tests `test_gpu_speed_mnist` reports NaN after this commit (pytorch#8018), and we didn't have the logic in place to raise error when this happens. This PR fixes the problem and will also update the baseline properly even if its previous value is NaN. Pull Request resolved: pytorch#11588 Differential Revision: D9831798 Pulled By: yf225 fbshipit-source-id: b95eee38d69b3b8273f48b8ac7b7e0e79cf756ed
Summary: Pull Request resolved: pytorch#11704 Add plan name to the logging in RunPlan Reviewed By: Tianshu-Bao Differential Revision: D9802416 fbshipit-source-id: 45c359dba0a5d992e303b3cdcf34624881a631d8
…or_impl.h from context_base.h Differential Revision: D9924348 Original commit changeset: 8d92b9e8b424 fbshipit-source-id: 0d1792804d7387023af3a9c29477f1da6f40044a
Summary: - fixes pytorch#9534 Pull Request resolved: pytorch#10185 Differential Revision: D9141222 Pulled By: weiyangfb fbshipit-source-id: bb652e42cc15917019df080d6bce2926b18f3476
Summary: Pull Request resolved: pytorch#11845 The device pointer will be used by cudaPointerGetAttributes, which handles nullptr already. So this check is not necessary. https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__UNIFIED.html#group__CUDART__UNIFIED_1gd89830e17d399c064a2f3c3fa8bb4390 Reviewed By: salexspb Differential Revision: D9929828 fbshipit-source-id: d862f7e5590998ffafe9bfc7754b0f83d2ae4af4
Summary: Probably not needed, but fwiw. Pull Request resolved: pytorch#11820 Reviewed By: orionr Differential Revision: D9924953 Pulled By: Yangqing fbshipit-source-id: 4d340e3d4f4dadc50fb68bed9572b8e1e54b5f6d
Summary: might fix some binary build issues Pull Request resolved: pytorch#11812 Reviewed By: ezyang Differential Revision: D9927309 Pulled By: soumith fbshipit-source-id: 9ed6c2c6fedc2a1cffbf52bc0a795135d4239800
Summary: Pull Request resolved: pytorch#11779 Pull Request resolved: pytorch#11731 This Predictor provides threadsafe interface and also cleans-up activations after each run. So in multi-model setup activation space doesn't explode Reviewed By: highker Differential Revision: D9842374 fbshipit-source-id: bfe253ae5fc813e73a347c5147ff6b58d50781ea
Summary: currently grad assignment for half type fails with a misleading RuntimeError ``` RuntimeError: torch.cuda.sparse.HalfTensor is not enabled. ``` Pull Request resolved: pytorch#11781 Differential Revision: D9931884 Pulled By: soumith fbshipit-source-id: 03e946c3833d1339a99585c9aa2dbb670f8bf459
Summary: Operator level proto conversion between (new) torch proto and (old) caffe2 proto. Pull Request resolved: pytorch#11621 Reviewed By: BIT-silence Differential Revision: D9892422 Pulled By: houseroad fbshipit-source-id: 01a55ec0a09479876a27082d90fc970723f4d431
Summary: I noticed I was including `torch/nn/pimpl.h` in the optimizer library just to access `TORCH_ARG`, even though that file includes a lot of irrelevant code. Let's save some re-compilation time by refactoring this macro into a separate logical file. #small-wins ebetica ezyang apaszke Pull Request resolved: pytorch#11787 Differential Revision: D9924447 Pulled By: goldsborough fbshipit-source-id: 5acd4ba559ffb2a3e97277e74bb731d7b1074dcf
Summary: To illustrate the benefits of this commit, I'll use the time/iter I got from one of the JIT benchmarks on my machine. | Run | Time | |----------------------------------------------|-------------------------| | No profiler | 45ms | | With profiler | 56ms | | Use `clock_gettime` instead of `std::chrono` | 48ms | | Touch all pages on block allocation | 48ms (less jitter) | | Use `const char*` instead of `std::string` | 47ms (even less jitter) | Pull Request resolved: pytorch#11773 Differential Revision: D9886858 Pulled By: apaszke fbshipit-source-id: 58f926f09e95df0b11ec687763a72b06b66991d0
Summary: This PR adds empty sparse tensor tests to `test_sparse.py`, and also fix various places in internal code to make the tests pass. **[NOTE] API CHANGE:** - `coalesce` on sparse tensor will always be performed out-of-place now (meaning the original tensor will never be affected) Pull Request resolved: pytorch#11228 Differential Revision: D9930449 Pulled By: yf225 fbshipit-source-id: 7c62439b216a6badf7938a10741c358ff18a556d
Summary: A missing environment variable raised a missing key error. Now it raises a more descriptive error of the actual problem, for example: ValueError: Error initializing torch.distributed using env:// rendezvous: environment variable WORLD_SIZE expected, but not set Pull Request resolved: pytorch#11782 Differential Revision: D9888962 Pulled By: pietern fbshipit-source-id: 5947e7a7bf7aa45f13bbd7b5e997529f26cc92d6
Summary: Followup to the [first refactor](pytorch#11350). Increase coverage of tests Pull Request resolved: pytorch#11811 Reviewed By: houseroad Differential Revision: D9923074 Pulled By: ajyu fbshipit-source-id: 0f899bb9e9a75bf7ed939e06cc9b028daa7f6bd9
Summary: Otherwise, some macro doesn't have the definition. Pull Request resolved: pytorch#11864 Reviewed By: BIT-silence Differential Revision: D9943327 Pulled By: houseroad fbshipit-source-id: 53e1bfc7a6b832f249f169b75a8fc15cdab63bf4
Summary: Pull Request resolved: pytorch#11821 Differential Revision: D9948292 Pulled By: SsnL fbshipit-source-id: 01c21c129423c0f7844b403e665a8fe021a9c820
…orch#11875) Summary: Pull Request resolved: pytorch#11875 Seems like the refactor to predictor_config dropped some functionality that is now blocking other teams rFBS2b30208263c14ce7039f27c618a3b232bf11ee33 is the change that was missed hoping to land this quickly :) Reviewed By: jonmorton Differential Revision: D9948324 fbshipit-source-id: 1628f7c51c06319fa7ca5dc9d59799135bb82c5f
Summary: + pytorch#10236 : torch.bernoulli's out kwarg is broken fixed in moving `bernoulli_out` to ATen + pytorch#9917 : BUG torch.bernoulli(p.expand(shape)) is broken fixed in moving all `bernoulli` ops in ATen to use the modern apply utils methods + pytorch#10357 : torch.bernoulli inconsistent gpu/cpu results fixed by adding CUDA asserts In order to use `curand_uniform4`, I made some changes to `CUDAApplyUtils.cuh`. Specifically, I introduced an optional template parameter `int step` to the `CUDA_tensor_applyN` methods, representing that we want to process `step` values at each time for each of the `N` tensors. The calling convention for `step = 1` (default) isn't changed. But if `step > 1`, the given lambda `op` must take in `int n` as its first argument, representing the number of valid values, because there may not be full `step` values at the boundary. E.g., here is what the `bernoulli(self, p_tensor)` call look like: ```cpp // The template argument `4` below indicates that we want to operate on four // element at each time. See NOTE [ CUDA_tensor_applyN helpers ] for details. at::cuda::CUDA_tensor_apply2<scalar_t, prob_t, 4>( ret, p, [seeds] __device__( int n, scalar_t& v1, scalar_t& v2, scalar_t& v3, scalar_t& v4, const prob_t& p1, const prob_t& p2, const prob_t& p3, const prob_t& p4) { curandStatePhilox4_32_10_t state; curand_init( seeds.first, blockIdx.x * blockDim.x + threadIdx.x, seeds.second, &state); float4 rand = curand_uniform4(&state); switch (n) { case 4: { assert(0 <= p4 && p4 <= 1); v4 = static_cast<scalar_t>(rand.w <= p4); } case 3: { assert(0 <= p3 && p3 <= 1); v3 = static_cast<scalar_t>(rand.z <= p3); } case 2: { assert(0 <= p2 && p2 <= 1); v2 = static_cast<scalar_t>(rand.y <= p2); } case 1: { assert(0 <= p1 && p1 <= 1); v1 = static_cast<scalar_t>(rand.x <= p1); } } } ); ``` Benchmarking on `torch.rand(200, 300, 400)` 20 times, each time with 20 loops: post patch ``` ➜ ~ numactl --cpunodebind 1 --membind 1 -- taskset -c 12,13,14,15,16,17,18,19,20,21,22,23 env CUDA_LAUNCH_BLOCKING=1 python bern.py torch.bernoulli(x) 6.841588497161865 +- 0.05413117632269859 torch.bernoulli(xc) 0.05963418632745743 +- 0.0008014909108169377 x.bernoulli_() 0.4024486541748047 +- 0.0021550932433456182 xc.bernoulli_() 0.02167394384741783 +- 2.3818030967959203e-05 ``` pre-patch ``` ➜ ~ numactl --cpunodebind 1 --membind 1 -- taskset -c 12,13,14,15,16,17,18,19,20,21,22,23 env CUDA_LAUNCH_BLOCKING=1 python bern.py torch.bernoulli(x) 12.394511222839355 +- 0.0966421514749527 torch.bernoulli(xc) 0.08970972150564194 +- 0.0038722590543329716 x.bernoulli_() 1.654480218887329 +- 0.02364428900182247 xc.bernoulli_() 0.058352887630462646 +- 0.003094920190051198 ``` Pull Request resolved: pytorch#10273 Differential Revision: D9831294 Pulled By: SsnL fbshipit-source-id: 65e0655a36b90d5278b675d35cb5327751604088
Summary: Signed-off-by: Edward Z. Yang <[email protected]> Pull Request resolved: pytorch#11819 Differential Revision: D9928730 Pulled By: ezyang fbshipit-source-id: 3140b6ef168586558f04fa8ee90f6f2169605d7d
… from tensor_impl.h from context_base.h" Summary: Original commit changeset: 0d1792804d73 Reviewed By: Yangqing Differential Revision: D9940725 fbshipit-source-id: 540a8ac7afcfe56a6b63abc6ed297c9434320998
Summary: Pull Request resolved: pytorch#11816 The file isn't in the std:: namespace, so is_same must be qualified. Reviewed By: smessmer Differential Revision: D9923774 fbshipit-source-id: 126532e27f08b5616ca46be1293d5d837920f588
Summary: Pull Request resolved: pytorch#11862 Reviewed By: Yangqing Differential Revision: D9942428 fbshipit-source-id: dea03f5ba0e621a047aa50bc4aa97acc834d2a39
…11866) Summary: This fixes the numerical problem in log_softmax cpu code when inputs are big but their differences are small. Pull Request resolved: pytorch#11866 Differential Revision: D9946799 Pulled By: soumith fbshipit-source-id: 11fe8d92b91ef6b7a66f33fbce37ec2f0f0929be
Summary: The ATen interface was changed. Pull Request resolved: pytorch#11840 Reviewed By: BIT-silence Differential Revision: D9932452 Pulled By: houseroad fbshipit-source-id: dd2040fcaa0f6052e5856ee19823cf3064124585
Summary: Following through on warning that indexing 0-dim tensor would be an error in PyTorch 0.5 and to use `item()` instead Pull Request resolved: pytorch#11679 Reviewed By: soumith Differential Revision: D9833570 Pulled By: driazati fbshipit-source-id: ac19f811fa7320d30b7f60cf66b596d6de684d86
Summary: Two improvements to C++ extensions: 1. In verbose mode, show the ninja build output (the exact compile commands, very useful) 2. When raising an error, don't show the `CalledProcessError` that shows ninja failing, only show the `RuntimeError` with the captured stdout soumith fmassa ezyang Pull Request resolved: pytorch#11724 Differential Revision: D9922459 Pulled By: goldsborough fbshipit-source-id: 5b319bf24348eabfe5f4c55d6d8e799b9abe523a
Summary: This PR enables BUILD_TEST for Caffe2 on windows. Pull Request resolved: pytorch#11802 Reviewed By: orionr Differential Revision: D9951223 Pulled By: mingzhe09088 fbshipit-source-id: 7cdc1626b999daadeae482bd569eebdbd53eb6d4
Summary: Pull Request resolved: pytorch#11710 Added a test to check that output and gradient values are correctly calculated wehn combine_spatial_bn is true on data parallel model Reviewed By: enosair Differential Revision: D9833660 fbshipit-source-id: 14d29fbebefa9dc303ffae06f9899ea4bde23025
…h#11855) Summary: The doc of PyThreadState [1] emphasizes that interp is its only public member. Use PyEval_GetFrame() instead. [1] https://docs.python.org/3/c-api/init.html#c.PyThreadState Pull Request resolved: pytorch#11855 Differential Revision: D9954430 Pulled By: ezyang fbshipit-source-id: 92da6781e45e2bcb5e3a37b162fa40e49d823215
Summary: Original commit changeset: bfe253ae5fc8 Apparently Ads push process detected some regression which normal canaries don't show. https://fb.facebook.com/groups/1274424122598505/permalink/2597819483592289/ Reviewed By: highker, Prowindy Differential Revision: D9952807 fbshipit-source-id: 1a3ea249c3b1e2618220c61f3d51468824b6ef10
Summary: Pull Request resolved: pytorch#11378 Differential Revision: D9943578 Pulled By: zou3519 fbshipit-source-id: fb9e4303e844d5e2515acce7869bcbe11526ab56
Summary: Pull Request resolved: pytorch#11844 Reviewed By: colesbury, SsnL Differential Revision: D9929055 Pulled By: pietern fbshipit-source-id: 3a34a179cb80f495f18aa926c0f9513924737d8e
Summary: Fixes pytorch#11518 Upstream PR submitted at https://gitlab.kitware.com/cmake/cmake/merge_requests/2400 On some embedded platforms, the NVIDIA driver is verbose logging unexpected output to stdout. One example is Drive PX2, where we see something like this whenever a CUDA program is run: ``` nvrm_gpu: Bug 200215060 workaround enabled. ``` This patch does a regex on the output of the architecture detection program to only capture architecture patterns. It's more robust than before, but not fool-proof. Pull Request resolved: pytorch#11851 Differential Revision: D9968362 Pulled By: soumith fbshipit-source-id: b7952a87132ab05c724b287b76de263f1f671a0e
Summary: pytorch/pytorch.github.io#68 (comment) Pull Request resolved: pytorch#11894 Differential Revision: D9973695 Pulled By: soumith fbshipit-source-id: 1f74b12487ec39f4e88b527dcdfca0742e689c15
Summary: Pull Request resolved: pytorch#11583 Reviewed By: SsnL Differential Revision: D9792800 fbshipit-source-id: 9af46d577911ff38647790169df66aa5d0379dd9
Summary: Pull Request resolved: pytorch#11893 This is needed to run binaries compiled with CUDA support on on CPU-only machines. Reviewed By: teng-li Differential Revision: D9972872 fbshipit-source-id: 7e4107925b3cd4d2fcf84ae532e800ab65f4b563
Summary: Largely unused and hinders current development Pull Request resolved: pytorch#11823 Differential Revision: D9925094 Pulled By: cpuhrsch fbshipit-source-id: c797f62180e2128f9a567b0c57c8347957470ea5
Summary: This patch adds fused forward and backward for clamp to the jit. This is one item of pytorch#11118 . If it's OK, I'd be happy to also add some more of pytorch#11118 . The patch depends on pytorch#11150 , which I merged into master as a base. I'll rebase it when that or pytorch#10981 is merged. This is first serious jit patch, thank you, ngimel and the others for their guidance. All errors are my own. Pull Request resolved: pytorch#11574 Differential Revision: D9943090 Pulled By: apaszke fbshipit-source-id: c40954b8c28c374baab8d3bd89acc9250580dc67
Summary: Python never closes shared library it `dlopen`s. This means that calling `load` or `load_inline` (i.e. building a JIT C++ extension) with the same C++ extension name twice in the same Python process will never re-load the library, even if the compiled source code and the underlying shared library have changed. The only way to circumvent this is to create a new library and load it under a new module name. I fix this, of course, by introducing a layer of indirection. Loading a JIT C++ extension now goes through an `ExtensionVersioner`, which hashes the contents of the source files as well as build flags, and if this hash changed, bumps an internal version stored for each module name. A bump in the version will result in the ninja file being edited and a new shared library and effectively a new C++ extension to be compiled. For this the version name is appended as `_v<version>` to the extension name for all versions greater zero. One caveat is that if you were to update your code many times and always re-load it in the same process, you may end up with quite a lot of shared library objects in your extension's folder under `/tmp`. I imagine this isn't too bad, since extensions are typically small and there isn't really a good way for us to garbage collect old libraries, since we don't know what still has handles to them. Fixes pytorch#11398 CC The controller you requested could not be found. ezyang gchanan soumith fmassa Pull Request resolved: pytorch#11725 Differential Revision: D9948244 Pulled By: goldsborough fbshipit-source-id: 695bbdc1f1597c5e4306a45cd8ba46f15c941383
Summary: Currently, norm function only supports vector norm. This PR extends vector norm to matrix norm. Pull Request resolved: pytorch#11261 Reviewed By: li-roy Differential Revision: D9652379 Pulled By: yya007 fbshipit-source-id: 519b3fb80b563c17c56a24675c7b0e46bf5a3a1c
Summary: This PR contains changes for: 1. Performance enhancements for group conv using MIOpen 2. Performance enhancements by removing unnecessary computations while running pooling through MIOpen 3. Added check for bwdData comptutation while running MIOpen convGradient operator 4. Fix in MIOpen poolingGradient operator to compute window size for global pooling case 5. Minor code cleanup in MIOpen spatial batch norm operator Differential Revision: D9979050 Pulled By: bddppq fbshipit-source-id: fabc7a44a2f9ca0307d99564d1ce8fe1de9a6fbb
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@pytorchbot retest this please |
lcskrishna
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May 15, 2023
When tensor is resized, reference array to it's sizes may become invalid. Make a copy in advance.
<details>
<summary>ASAN report</summary>
```
=================================================================
==1115867==ERROR: AddressSanitizer: heap-use-after-free on address 0x61000013d790 at pc 0x03ff8e7da360 bp 0x03fff53c83a0 sp 0x03fff53c8390
READ of size 8 at 0x61000013d790 thread T0
#0 0x3ff8e7da35f in c10::SymInt::is_heap_allocated() const /home/user/pytorch/c10/core/SymInt.h:154
ROCm#1 0x3ff8e7da35f in c10::SymInt::maybe_as_int() const /home/user/pytorch/c10/core/SymInt.h:215
ROCm#2 0x3ff8e7d0a6d in c10::SymInt::sym_eq(c10::SymInt const&) const /home/user/pytorch/c10/core/SymInt.cpp:69
ROCm#3 0x3ff7a9ab0bd in c10::SymInt::operator==(c10::SymInt const&) const /home/user/pytorch/c10/core/SymInt.h:177
ROCm#4 0x3ff7a9aaedd in bool std::__equal<false>::equal<c10::SymInt const*, c10::SymInt const*>(c10::SymInt const*, c10::SymInt const*, c10::SymInt const*) /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-
v11/bits/stl_algobase.h:1162
ROCm#5 0x3ff7a9aae4b in bool std::__equal_aux1<c10::SymInt const*, c10::SymInt const*>(c10::SymInt const*, c10::SymInt const*, c10::SymInt const*) /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/bits/
stl_algobase.h:1211
ROCm#6 0x3ff7a9aae05 in bool std::__equal_aux<c10::SymInt const*, c10::SymInt const*>(c10::SymInt const*, c10::SymInt const*, c10::SymInt const*) /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/bits/s
tl_algobase.h:1219
ROCm#7 0x3ff7a9aad97 in bool std::equal<c10::SymInt const*, c10::SymInt const*>(c10::SymInt const*, c10::SymInt const*, c10::SymInt const*) /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/bits/stl_alg
obase.h:1556
ROCm#8 0x3ff4b23c771 in c10::ArrayRef<c10::SymInt>::equals(c10::ArrayRef<c10::SymInt>) const /home/user/pytorch/c10/util/ArrayRef.h:188
ROCm#9 0x3ff4cb91bc1 in bool c10::operator!=<c10::SymInt>(c10::ArrayRef<c10::SymInt>, c10::ArrayRef<c10::SymInt>) /home/user/pytorch/c10/util/ArrayRef.h:341
ROCm#10 0x3ff6d1b57ff in torch::ADInplaceOrView::resize_(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>) /home/user/pytorch/torch/csrc/autograd/Variab
leTypeManual.cpp:408
ROCm#11 0x3ff6d1e59c7 in c10::impl::detail::WrapFunctionIntoFunctor_<c10::CompileTimeFunctionPointer<at::Tensor const& (c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c1
0::MemoryFormat>), &torch::ADInplaceOrView::resize_>, at::Tensor const&, c10::guts::typelist::typelist<c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>
> >::operator()(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>) /home/user/pytorch/aten/src/ATen/core/boxing/impl/WrapFunctionIntoFunctor.h:13
ROCm#12 0x3ff6d1e59c7 in c10::impl::wrap_kernel_functor_unboxed_<c10::impl::detail::WrapFunctionIntoFunctor_<c10::CompileTimeFunctionPointer<at::Tensor const& (c10::DispatchKeySet, at::Tensor const&, c10:
:ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>), &torch::ADInplaceOrView::resize_>, at::Tensor const&, c10::guts::typelist::typelist<c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::Sy
mInt>, c10::optional<c10::MemoryFormat> > >, at::Tensor const& (c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>)>::call(c10::OperatorKernel*, c10::Disp
atchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>) /home/user/pytorch/aten/src/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h:480
ROCm#13 0x3ff51ca5129 in at::Tensor const& c10::callUnboxedKernelFunction<at::Tensor const&, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat> >(void*, c10::OperatorKernel*,
c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>&&, c10::optional<c10::MemoryFormat>&&) /home/user/pytorch/aten/src/ATen/core/boxing/KernelFunction_impl.h:50
ROCm#14 0x3ff51ca6e8f in at::Tensor const& c10::KernelFunction::call<at::Tensor const&, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat> >(c10::OperatorHandle const&, c10::D
ispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>) const /home/user/pytorch/aten/src/ATen/core/boxing/KernelFunction_impl.h:90
ROCm#15 0x3ff51ca6e8f in at::Tensor const& c10::Dispatcher::redispatch<at::Tensor const&, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat> >(c10::TypedOperatorHandle<at::Ten
sor const& (at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>)> const&, c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>)
const /home/user/pytorch/aten/src/ATen/core/dispatch/Dispatcher.h:656
ROCm#16 0x3ff5182006b in c10::TypedOperatorHandle<at::Tensor const& (at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>)>::redispatch(c10::DispatchKeySet, at::Tensor const&, c
10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>) const /home/user/pytorch/aten/src/ATen/core/dispatch/Dispatcher.h:492
ROCm#17 0x3ff5182006b in at::_ops::resize_::redispatch(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>) aten/src/ATen/Operators_4.cpp:2144
ROCm#18 0x3ff6d1d5e07 in at::redispatch::resize__symint(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>) aten/src/ATen/RedispatchFunctions.h:2847
ROCm#19 0x3ff6d1bbb67 in torch::autograd::VariableType::(anonymous namespace)::resize_(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>) /home/user/pyto
rch/torch/csrc/autograd/VariableTypeManual.cpp:243
ROCm#20 0x3ff6d1bd197 in c10::impl::detail::WrapFunctionIntoFunctor_<c10::CompileTimeFunctionPointer<at::Tensor const& (c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c1
0::MemoryFormat>), &torch::autograd::VariableType::(anonymous namespace)::resize_>, at::Tensor const&, c10::guts::typelist::typelist<c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10
::optional<c10::MemoryFormat> > >::operator()(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>) /home/user/pytorch/aten/src/ATen/core/boxing/impl/WrapFu
nctionIntoFunctor.h:13
ROCm#21 0x3ff6d1bd197 in c10::impl::wrap_kernel_functor_unboxed_<c10::impl::detail::WrapFunctionIntoFunctor_<c10::CompileTimeFunctionPointer<at::Tensor const& (c10::DispatchKeySet, at::Tensor const&, c10:
:ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>), &torch::autograd::VariableType::(anonymous namespace)::resize_>, at::Tensor const&, c10::guts::typelist::typelist<c10::DispatchKeySet, at::Tensor
const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat> > >, at::Tensor const& (c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>)>::call(c
10::OperatorKernel*, c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>) /home/user/pytorch/aten/src/ATen/core/boxing/impl/make_boxed_from_unboxed_functor
.h:480
ROCm#22 0x3ff51ca5129 in at::Tensor const& c10::callUnboxedKernelFunction<at::Tensor const&, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat> >(void*, c10::OperatorKernel*,
c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>&&, c10::optional<c10::MemoryFormat>&&) /home/user/pytorch/aten/src/ATen/core/boxing/KernelFunction_impl.h:50
ROCm#23 0x3ff5181ead1 in at::Tensor const& c10::KernelFunction::call<at::Tensor const&, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat> >(c10::OperatorHandle const&, c10::D
ispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>) const /home/user/pytorch/aten/src/ATen/core/boxing/KernelFunction_impl.h:90
ROCm#24 0x3ff5181ead1 in at::Tensor const& c10::Dispatcher::call<at::Tensor const&, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat> >(c10::TypedOperatorHandle<at::Tensor co
nst& (at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>)> const&, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>) const /home/user/pytorch/at
en/src/ATen/core/dispatch/Dispatcher.h:639
ROCm#25 0x3ff5181ead1 in c10::TypedOperatorHandle<at::Tensor const& (at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>)>::call(at::Tensor const&, c10::ArrayRef<c10::SymInt>,
c10::optional<c10::MemoryFormat>) const /home/user/pytorch/aten/src/ATen/core/dispatch/Dispatcher.h:487
ROCm#26 0x3ff5181ead1 in at::_ops::resize_::call(at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>) aten/src/ATen/Operators_4.cpp:2137
ROCm#27 0x3ff79b44fcf in at::Tensor::resize__symint(c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>) const aten/src/ATen/core/TensorBody.h:2452
ROCm#28 0x3ff79a802db in torch::autograd::THPVariable_resize_(_object*, _object*, _object*)::$_0::operator()(at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>) const /home/us
er/pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:13417
ROCm#29 0x3ff7999f1eb in torch::autograd::THPVariable_resize_(_object*, _object*, _object*) /home/user/pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:13419
ROCm#30 0x3ffa2c9b009 in method_vectorcall_VARARGS_KEYWORDS Objects/descrobject.c:344
ROCm#31 0x3ffa2df00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#32 0x3ffa2df013d in PyObject_Vectorcall Include/cpython/abstract.h:123
ROCm#33 0x3ffa2e05447 in call_function Python/ceval.c:5891
ROCm#34 0x3ffa2dff7d7 in _PyEval_EvalFrameDefault Python/ceval.c:4198
ROCm#35 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#36 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#37 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#38 0x3ffa2c8ab15 in PyVectorcall_Call Objects/call.c:255
ROCm#39 0x3ffa2c8ac65 in _PyObject_Call Objects/call.c:290
ROCm#40 0x3ffa2c8ada9 in PyObject_Call Objects/call.c:317
ROCm#41 0x3ffa2e059c7 in do_call_core Python/ceval.c:5943
ROCm#42 0x3ffa2dffd39 in _PyEval_EvalFrameDefault Python/ceval.c:4277
ROCm#43 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#44 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#45 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#46 0x3ffa2c8ab15 in PyVectorcall_Call Objects/call.c:255
ROCm#47 0x3ffa2c8ac65 in _PyObject_Call Objects/call.c:290
ROCm#48 0x3ffa2c8ada9 in PyObject_Call Objects/call.c:317
ROCm#49 0x3ffa2e059c7 in do_call_core Python/ceval.c:5943
ROCm#50 0x3ffa2dffd39 in _PyEval_EvalFrameDefault Python/ceval.c:4277
ROCm#51 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#52 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#53 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#54 0x3ffa2df00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#55 0x3ffa2df013d in PyObject_Vectorcall Include/cpython/abstract.h:123
ROCm#56 0x3ffa2e05447 in call_function Python/ceval.c:5891
ROCm#57 0x3ffa2dff7d7 in _PyEval_EvalFrameDefault Python/ceval.c:4198
ROCm#58 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#59 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#60 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#61 0x3ffa2c8e941 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#62 0x3ffa2c8eddd in method_vectorcall Objects/classobject.c:53
ROCm#63 0x3ffa2df00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#64 0x3ffa2df013d in PyObject_Vectorcall Include/cpython/abstract.h:123
ROCm#65 0x3ffa2e05447 in call_function Python/ceval.c:5891
ROCm#66 0x3ffa2dff905 in _PyEval_EvalFrameDefault Python/ceval.c:4213
ROCm#67 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#68 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#69 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#70 0x3ffa2df00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#71 0x3ffa2df013d in PyObject_Vectorcall Include/cpython/abstract.h:123
ROCm#72 0x3ffa2e05447 in call_function Python/ceval.c:5891
ROCm#73 0x3ffa2dff7d7 in _PyEval_EvalFrameDefault Python/ceval.c:4198
ROCm#74 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#75 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#76 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#77 0x3ffa2c8e941 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#78 0x3ffa2c8eddd in method_vectorcall Objects/classobject.c:53
ROCm#79 0x3ffa2df00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#80 0x3ffa2df013d in PyObject_Vectorcall Include/cpython/abstract.h:123
ROCm#81 0x3ffa2e05447 in call_function Python/ceval.c:5891
ROCm#82 0x3ffa2dffa57 in _PyEval_EvalFrameDefault Python/ceval.c:4231
ROCm#83 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#84 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#85 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#86 0x3ffa2c8e941 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#87 0x3ffa2c8eddd in method_vectorcall Objects/classobject.c:53
ROCm#88 0x3ffa2df00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#89 0x3ffa2df013d in PyObject_Vectorcall Include/cpython/abstract.h:123
ROCm#90 0x3ffa2e05447 in call_function Python/ceval.c:5891
ROCm#91 0x3ffa2dffa57 in _PyEval_EvalFrameDefault Python/ceval.c:4231
ROCm#92 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#93 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#94 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#95 0x3ffa2c8e941 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#96 0x3ffa2c8eddd in method_vectorcall Objects/classobject.c:53
ROCm#97 0x3ffa2c8ab9b in PyVectorcall_Call Objects/call.c:267
ROCm#98 0x3ffa2c8ac65 in _PyObject_Call Objects/call.c:290
ROCm#99 0x3ffa2c8ada9 in PyObject_Call Objects/call.c:317
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ROCm#106 0x3ffa2c8b271 in _PyObject_Call_Prepend Objects/call.c:431
ROCm#107 0x3ffa2d3f307 in slot_tp_call Objects/typeobject.c:7494
ROCm#108 0x3ffa2c8a933 in _PyObject_MakeTpCall Objects/call.c:215
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ROCm#122 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
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ROCm#124 0x3ffa2c8ac65 in _PyObject_Call Objects/call.c:290
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ROCm#220 0x3ffa2c8b271 in _PyObject_Call_Prepend Objects/call.c:431
ROCm#221 0x3ffa2d3f307 in slot_tp_call Objects/typeobject.c:7494
ROCm#222 0x3ffa2c8a933 in _PyObject_MakeTpCall Objects/call.c:215
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ROCm#224 0x3ffa2df013d in PyObject_Vectorcall Include/cpython/abstract.h:123
ROCm#225 0x3ffa2e05447 in call_function Python/ceval.c:5891
ROCm#226 0x3ffa2dffa57 in _PyEval_EvalFrameDefault Python/ceval.c:4231
ROCm#227 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#228 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#229 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#230 0x3ffa2c8ab15 in PyVectorcall_Call Objects/call.c:255
ROCm#231 0x3ffa2c8ac65 in _PyObject_Call Objects/call.c:290
ROCm#232 0x3ffa2c8ada9 in PyObject_Call Objects/call.c:317
ROCm#233 0x3ffa2e059c7 in do_call_core Python/ceval.c:5943
ROCm#234 0x3ffa2dffd39 in _PyEval_EvalFrameDefault Python/ceval.c:4277
ROCm#235 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#236 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#237 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#238 0x3ffa2df00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#239 0x3ffa2df013d in PyObject_Vectorcall Include/cpython/abstract.h:123
ROCm#240 0x3ffa2e05447 in call_function Python/ceval.c:5891
ROCm#241 0x3ffa2dff779 in _PyEval_EvalFrameDefault Python/ceval.c:4181
ROCm#242 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#243 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#244 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#245 0x3ffa2c8e941 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#246 0x3ffa2c8eddd in method_vectorcall Objects/classobject.c:53
ROCm#247 0x3ffa2df00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#248 0x3ffa2df013d in PyObject_Vectorcall Include/cpython/abstract.h:123
ROCm#249 0x3ffa2e05447 in call_function Python/ceval.c:5891
ROCm#250 0x3ffa2dff779 in _PyEval_EvalFrameDefault Python/ceval.c:4181
ROCm#251 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#252 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#253 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#254 0x3ffa2c8a695 in _PyObject_FastCallDictTstate Objects/call.c:153
ROCm#255 0x3ffa2c8b271 in _PyObject_Call_Prepend Objects/call.c:431
ROCm#256 0x3ffa2d3f307 in slot_tp_call Objects/typeobject.c:7494
ROCm#257 0x3ffa2c8a933 in _PyObject_MakeTpCall Objects/call.c:215
0x61000013d790 is located 80 bytes inside of 192-byte region [0x61000013d740,0x61000013d800)
freed by thread T0 here:
#0 0x3ffa3237de5 in operator delete(void*) /var/tmp/portage/sys-devel/gcc-11.3.1_p20230303/work/gcc-11-20230303/libsanitizer/asan/asan_new_delete.cpp:160
ROCm#1 0x3ff8e7e3221 in c10::TensorImpl::~TensorImpl() /home/user/pytorch/c10/core/TensorImpl.cpp:75
previously allocated by thread T0 here:
#0 0x3ffa323734f in operator new(unsigned long) /var/tmp/portage/sys-devel/gcc-11.3.1_p20230303/work/gcc-11-20230303/libsanitizer/asan/asan_new_delete.cpp:99
ROCm#1 0x3ff4aeeb3d1 in c10::intrusive_ptr<c10::TensorImpl, c10::detail::intrusive_target_default_null_type<c10::TensorImpl> > c10::intrusive_ptr<c10::TensorImpl, c10::detail::intrusive_target_default_nul
l_type<c10::TensorImpl> >::make<c10::intrusive_ptr<c10::StorageImpl, c10::detail::intrusive_target_default_null_type<c10::StorageImpl> >, c10::DispatchKeySet&, caffe2::TypeMeta&>(c10::intrusive_ptr<c10::S
torageImpl, c10::detail::intrusive_target_default_null_type<c10::StorageImpl> >&&, c10::DispatchKeySet&, caffe2::TypeMeta&) /home/user/pytorch/c10/util/intrusive_ptr.h:498
ROCm#2 0x3ff76f79e17 (/home/user/pytorch/build/lib.linux-s390x-cpython-310/torch/lib/libtorch_cpu.so+0x2fb79e17)
SUMMARY: AddressSanitizer: heap-use-after-free /home/user/pytorch/c10/core/SymInt.h:154 in c10::SymInt::is_heap_allocated() const
Shadow bytes around the buggy address:
0x100c2000027aa0: fa fa fa fa fa fa fa fa fd fd fd fd fd fd fd fd
0x100c2000027ab0: fd fd fd fd fd fd fd fd fd fd fd fd fd fd fd fd
0x100c2000027ac0: fa fa fa fa fa fa fa fa fd fd fd fd fd fd fd fd
0x100c2000027ad0: fd fd fd fd fd fd fd fd fd fd fd fd fd fd fd fd
0x100c2000027ae0: fa fa fa fa fa fa fa fa fd fd fd fd fd fd fd fd
=>0x100c2000027af0: fd fd[fd]fd fd fd fd fd fd fd fd fd fd fd fd fd
0x100c2000027b00: fa fa fa fa fa fa fa fa 00 00 00 00 00 00 00 00
0x100c2000027b10: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
0x100c2000027b20: fa fa fa fa fa fa fa fa 00 00 00 00 00 00 00 00
0x100c2000027b30: 00 00 00 00 04 fa fa fa fa fa fa fa fa fa fa fa
0x100c2000027b40: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
Shadow byte legend (one shadow byte represents 8 application bytes):
Addressable: 00
Partially addressable: 01 02 03 04 05 06 07
Heap left redzone: fa
Freed heap region: fd
Stack left redzone: f1
Stack mid redzone: f2
Stack right redzone: f3
Stack after return: f5
Stack use after scope: f8
Global redzone: f9
Global init order: f6
Poisoned by user: f7
Container overflow: fc
Array cookie: ac
Intra object redzone: bb
ASan internal: fe
Left alloca redzone: ca
Right alloca redzone: cb
Shadow gap: cc
==1115867==ABORTING
```
</details>
<details>
<summary>Additional backtraces (not full)</summary>
Memory deallocation:
```
#0 operator delete (ptr=0x61000013d740) at /var/tmp/portage/sys-devel/gcc-11.3.1_p20230303/work/gcc-11-20230303/libsanitizer/asan/asan_new_delete.cpp:160
ROCm#1 0x000003ffa77e3222 in c10::TensorImpl::~TensorImpl (this=0x61000013d740) at /home/user/pytorch/c10/core/TensorImpl.cpp:75
ROCm#2 0x000003ff63e76e8c in c10::intrusive_ptr<c10::TensorImpl, c10::UndefinedTensorImpl>::reset_ (this=0x3ffd7ec8230) at /home/user/pytorch/c10/util/intrusive_ptr.h:291
ROCm#3 0x000003ff63e76910 in c10::intrusive_ptr<c10::TensorImpl, c10::UndefinedTensorImpl>::~intrusive_ptr (this=0x3ffd7ec8230) at /home/user/pytorch/c10/util/intrusive_ptr.h:370
ROCm#4 0x000003ff63e67240 in at::TensorBase::~TensorBase (this=0x3ffd7ec8230) at /home/user/pytorch/aten/src/ATen/core/TensorBase.h:80
ROCm#5 0x000003ff63e85ee0 in at::Tensor::~Tensor (this=0x3ffd7ec8230) at aten/src/ATen/core/TensorBody.h:90
ROCm#6 0x000003ff63f67304 in resize__functionalization (dispatchKeySet=..., self=..., size=..., memory_format=...) at /home/user/pytorch/aten/src/ATen/FunctionalizeFallbackKernel.cpp:173
ROCm#7 0x000003ff63f89258 in c10::impl::detail::WrapFunctionIntoFunctor_<c10::CompileTimeFunctionPointer<at::Tensor const& (c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>, c10::optional<c10::MemoryFormat>), &(resize__functionalization(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>, c10::optional<c10::MemoryFormat>))>, at::Tensor const&, c10::guts::typelist::typelist<c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>, c10::optional<c10::MemoryFormat> > >::operator()(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>, c10::optional<c10::MemoryFormat>) (
this=0x6030000390a0, args=..., args=..., args=..., args=...) at /home/user/pytorch/aten/src/ATen/core/boxing/impl/WrapFunctionIntoFunctor.h:13
ROCm#8 c10::impl::wrap_kernel_functor_unboxed_<c10::impl::detail::WrapFunctionIntoFunctor_<c10::CompileTimeFunctionPointer<at::Tensor const& (c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>, c10::optional<c10::MemoryFormat>), &(resize__functionalization(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>, c10::optional<c10::MemoryFormat>))>, at::Tensor const&, c10::guts::typelist::typelist<c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>, c10::optional<c10::MemoryFormat> > >, at::Tensor const& (c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>, c10::optional<c10::MemoryFormat>)>::call(c10::OperatorKernel*, c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>, c10::optional<c10::MemoryFormat>) (functor=0x6030000390a0, dispatchKeySet=..., args=..., args=...,
args=...) at /home/user/pytorch/aten/src/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h:480
ROCm#9 0x000003ff6aca560a in c10::callUnboxedKernelFunction<at::Tensor const&, at::Tensor const&, c10::ArrayRef<long>, c10::optional<c10::MemoryFormat> > (
unboxed_kernel_func=0x3ff63f88a80 <c10::impl::wrap_kernel_functor_unboxed_<c10::impl::detail::WrapFunctionIntoFunctor_<c10::CompileTimeFunctionPointer<at::Tensor const& (c10::DispatchKeySet, at::Tenso
r const&, c10::ArrayRef<long>, c10::optional<c10::MemoryFormat>), &(resize__functionalization(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>, c10::optional<c10::MemoryFormat>))>, at::Tensor const&, c10::guts::typelist::typelist<c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>, c10::optional<c10::MemoryFormat> > >, at::Tensor const& (c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>, c10::optional<c10::MemoryFormat>)>::call(c10::OperatorKernel*, c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<long>, c10::optional<c10::MemoryFormat>)>, functor=0x6030000390a0,
dispatchKeySet=..., args=..., args=..., args=...) at /home/user/pytorch/aten/src/ATen/core/boxing/KernelFunction_impl.h:50
ROCm#10 0x000003ff6aca715c in c10::KernelFunction::call<at::Tensor const&, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat> > (this=0x6210005e1b28, opHandle=...,
dispatchKeySet=..., args=..., args=..., args=...) at /home/user/pytorch/aten/src/ATen/core/boxing/KernelFunction_impl.h:96
ROCm#11 c10::Dispatcher::redispatch<at::Tensor const&, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat> >(c10::TypedOperatorHandle<at::Tensor const& (at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>)> const&, c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>) const (
this=0x3ff919400e0 <c10::Dispatcher::realSingleton()::_singleton>, op=..., currentDispatchKeySet=..., args=..., args=..., args=...) at /home/user/pytorch/aten/src/ATen/core/dispatch/Dispatcher.h:656
ROCm#12 0x000003ff6a82006c in c10::TypedOperatorHandle<at::Tensor const& (at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>)>::redispatch(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>) const (
this=0x3ff919a07e0 <at::_ops::resize_::redispatch(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>)::op>, currentDispatchKeySet=..., args=...,
args=..., args=...) at /home/user/pytorch/aten/src/ATen/core/dispatch/Dispatcher.h:492
ROCm#13 at::_ops::resize_::redispatch (dispatchKeySet=..., self=..., size=..., memory_format=...) at /home/user/pytorch/build/aten/src/ATen/Operators_4.cpp:2144
ROCm#14 0x000003ff861d5e08 in at::redispatch::resize__symint (dispatchKeySet=..., self=..., size=..., memory_format=...) at aten/src/ATen/RedispatchFunctions.h:2847
ROCm#15 0x000003ff861b579e in torch::ADInplaceOrView::resize_ (ks=..., self=..., size=..., optional_memory_format=...) at /home/user/pytorch/torch/csrc/autograd/VariableTypeManual.cpp:401
```
Memory access:
```
#0 c10::SymInt::maybe_as_int (this=0x61000013d790) at /home/user/pytorch/c10/core/SymInt.h:215
ROCm#1 0x000003ff734d0a6e in c10::SymInt::sym_eq (this=0x61000013d790, sci=...) at /home/user/pytorch/c10/core/SymInt.cpp:69
ROCm#2 0x000003ff5f6ab0be in c10::SymInt::operator== (this=0x61000013d790, o=...) at /home/user/pytorch/c10/core/SymInt.h:177
ROCm#3 0x000003ff5f6aaede in std::__equal<false>::equal<c10::SymInt const*, c10::SymInt const*> (__first1=0x61000013d790, __last1=0x61000013d7a0, __first2=0x602000015c30)
at /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/bits/stl_algobase.h:1162
ROCm#4 0x000003ff5f6aae4c in std::__equal_aux1<c10::SymInt const*, c10::SymInt const*> (__first1=0x61000013d790, __last1=0x61000013d7a0, __first2=0x602000015c30)
at /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/bits/stl_algobase.h:1211
ROCm#5 0x000003ff5f6aae06 in std::__equal_aux<c10::SymInt const*, c10::SymInt const*> (__first1=0x61000013d790, __last1=0x61000013d7a0, __first2=0x602000015c30)
at /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/bits/stl_algobase.h:1219
ROCm#6 0x000003ff5f6aad98 in std::equal<c10::SymInt const*, c10::SymInt const*> (__first1=0x61000013d790, __last1=0x61000013d7a0, __first2=0x602000015c30)
at /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/bits/stl_algobase.h:1556
ROCm#7 0x000003ff2ff3c772 in c10::ArrayRef<c10::SymInt>::equals (this=0x3ffed7c9900, RHS=...) at /home/user/pytorch/c10/util/ArrayRef.h:188
ROCm#8 0x000003ff31891bc2 in c10::operator!=<c10::SymInt> (a1=..., a2=...) at /home/user/pytorch/c10/util/ArrayRef.h:341
ROCm#9 0x000003ff51eb5800 in torch::ADInplaceOrView::resize_ (ks=..., self=..., size=..., optional_memory_format=...) at /home/user/pytorch/torch/csrc/autograd/VariableTypeManual.cpp:408
ROCm#10 0x000003ff51ee59c8 in c10::impl::detail::WrapFunctionIntoFunctor_<c10::CompileTimeFunctionPointer<at::Tensor const& (c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c
10::MemoryFormat>), &torch::ADInplaceOrView::resize_>, at::Tensor const&, c10::guts::typelist::typelist<c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>
> >::operator()(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>) (this=0x6030007dca40, args=..., args=..., args=..., args=...)
at /home/user/pytorch/aten/src/ATen/core/boxing/impl/WrapFunctionIntoFunctor.h:13
ROCm#11 c10::impl::wrap_kernel_functor_unboxed_<c10::impl::detail::WrapFunctionIntoFunctor_<c10::CompileTimeFunctionPointer<at::Tensor const& (c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt
>, c10::optional<c10::MemoryFormat>), &torch::ADInplaceOrView::resize_>, at::Tensor const&, c10::guts::typelist::typelist<c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<
c10::MemoryFormat> > >, at::Tensor const& (c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>)>::call(c10::OperatorKernel*, c10::DispatchKeySet, at::Tenso
r const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>) (functor=0x6030007dca40, dispatchKeySet=..., args=..., args=..., args=...)
at /home/user/pytorch/aten/src/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h:480
ROCm#12 0x000003ff369a512a in c10::callUnboxedKernelFunction<at::Tensor const&, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat> > (
unboxed_kernel_func=0x3ff51ee51f0 <c10::impl::wrap_kernel_functor_unboxed_<c10::impl::detail::WrapFunctionIntoFunctor_<c10::CompileTimeFunctionPointer<at::Tensor const& (c10::DispatchKeySet, at::Tenso
r const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>), &torch::ADInplaceOrView::resize_>, at::Tensor const&, c10::guts::typelist::typelist<c10::DispatchKeySet, at::Tensor const&, c10::Ar
rayRef<c10::SymInt>, c10::optional<c10::MemoryFormat> > >, at::Tensor const& (c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>)>::call(c10::OperatorKern
el*, c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>)>, functor=0x6030007dca40, dispatchKeySet=..., args=..., args=..., args=...)
at /home/user/pytorch/aten/src/ATen/core/boxing/KernelFunction_impl.h:50
ROCm#13 0x000003ff369a6e90 in c10::KernelFunction::call<at::Tensor const&, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat> > (this=0x6210005e1bc8, opHandle=...,
dispatchKeySet=..., args=..., args=..., args=...) at /home/user/pytorch/aten/src/ATen/core/boxing/KernelFunction_impl.h:90
ROCm#14 c10::Dispatcher::redispatch<at::Tensor const&, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat> >(c10::TypedOperatorHandle<at::Tensor const& (at::Tensor const&, c10::Arr
ayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>)> const&, c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>) const (
this=0x3ff5d6400e0 <c10::Dispatcher::realSingleton()::_singleton>, op=..., currentDispatchKeySet=..., args=..., args=..., args=...) at /home/user/pytorch/aten/src/ATen/core/dispatch/Dispatcher.h:656
ROCm#15 0x000003ff3652006c in c10::TypedOperatorHandle<at::Tensor const& (at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>)>::redispatch(c10::DispatchKeySet, at::Tensor const&,
c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>) const (
this=0x3ff5d6a07e0 <at::_ops::resize_::redispatch(c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>)::op>, currentDispatchKeySet=..., args=...,
args=..., args=...) at /home/user/pytorch/aten/src/ATen/core/dispatch/Dispatcher.h:492
ROCm#16 at::_ops::resize_::redispatch (dispatchKeySet=..., self=..., size=..., memory_format=...) at /home/user/pytorch/build/aten/src/ATen/Operators_4.cpp:2144
ROCm#17 0x000003ff51ed5e08 in at::redispatch::resize__symint (dispatchKeySet=..., self=..., size=..., memory_format=...) at aten/src/ATen/RedispatchFunctions.h:2847
ROCm#18 0x000003ff51ebbb68 in torch::autograd::VariableType::(anonymous namespace)::resize_ (ks=..., self=..., size=..., optional_memory_format=...)
at /home/user/pytorch/torch/csrc/autograd/VariableTypeManual.cpp:243
```
</details>
Pull Request resolved: pytorch#101064
Approved by: https://github.com/Skylion007, https://github.com/albanD
alugorey
pushed a commit
to alugorey/pytorch
that referenced
this pull request
May 17, 2023
arguments() returns vector member of object returned by schema() call.
When object returned by schema() call is destroyed, the vector is deallocated as well,
it's lifetime isn't extended.
This issue detected while running `pytest -v test/mobile/test_lite_script_type.py -k test_nest_typing_namedtuple_custom_classtype` with ASAN.
<details>
<summary>ASAN output</summary>
```
==1134126==ERROR: AddressSanitizer: heap-use-after-free on address 0x60d0005a5790 at pc 0x03ff844488d8 bp 0x03fff584afe8 sp 0x03fff584afd8
READ of size 8 at 0x60d0005a5790 thread T0
#0 0x3ff844488d7 in __gnu_cxx::__normal_iterator<c10::Argument const*, std::vector<c10::Argument, std::allocator<c10::Argument> > >::__normal_iterator(c10::Argument const* const&) /usr/lib/gcc/s390x-i
bm-linux-gnu/11/include/g++-v11/bits/stl_iterator.h:1028
#1 0x3ff8444293f in std::vector<c10::Argument, std::allocator<c10::Argument> >::begin() const /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/bits/stl_vector.h:821
#2 0x3ff84d807d1 in torch::jit::toPyObject(c10::IValue) /home/user/pytorch/torch/csrc/jit/python/pybind_utils.cpp:617
ROCm#3 0x3ff84d80305 in torch::jit::toPyObject(c10::IValue) /home/user/pytorch/torch/csrc/jit/python/pybind_utils.cpp:604
ROCm#4 0x3ff84856871 in pybind11::detail::type_caster<c10::IValue, void>::cast(c10::IValue, pybind11::return_value_policy, pybind11::handle) /home/user/pytorch/torch/csrc/jit/python/pybind.h:138
ROCm#5 0x3ff85318191 in pybind11::cpp_function::initialize<torch::jit::initJitScriptBindings(_object*)::$_45, c10::IValue, torch::jit::mobile::Module&, pybind11::tuple const&, pybind11::name, pybind11::is
_method, pybind11::sibling, pybind11::arg>(torch::jit::initJitScriptBindings(_object*)::$_45&&, c10::IValue (*)(torch::jit::mobile::Module&, pybind11::tuple const&), pybind11::name const&, pybind11::is_me
thod const&, pybind11::sibling const&, pybind11::arg const&)::{lambda(pybind11::detail::function_call&)#1}::operator()(pybind11::detail::function_call&) const /home/user/pytorch/cmake/../third_party/pybin
d11/include/pybind11/pybind11.h:249
ROCm#6 0x3ff85317cfd in pybind11::cpp_function::initialize<torch::jit::initJitScriptBindings(_object*)::$_45, c10::IValue, torch::jit::mobile::Module&, pybind11::tuple const&, pybind11::name, pybind11::is
_method, pybind11::sibling, pybind11::arg>(torch::jit::initJitScriptBindings(_object*)::$_45&&, c10::IValue (*)(torch::jit::mobile::Module&, pybind11::tuple const&), pybind11::name const&, pybind11::is_me
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freed by thread T0 here:
#0 0x3ffab537de5 in operator delete(void*) /var/tmp/portage/sys-devel/gcc-11.3.1_p20230303/work/gcc-11-20230303/libsanitizer/asan/asan_new_delete.cpp:160
#1 0x3ff55984fdb in __gnu_cxx::new_allocator<std::_Sp_counted_ptr_inplace<c10::FunctionSchema, std::allocator<c10::FunctionSchema>, (__gnu_cxx::_Lock_policy)2> >::deallocate(std::_Sp_counted_ptr_inplace<c10::FunctionSchema, std::allocator<c10::FunctionSchema>, (__gnu_cxx::_Lock_policy)2>*, unsigned long) /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/ext/new_allocator.h:145
previously allocated by thread T0 here:
#0 0x3ffab53734f in operator new(unsigned long) /var/tmp/portage/sys-devel/gcc-11.3.1_p20230303/work/gcc-11-20230303/libsanitizer/asan/asan_new_delete.cpp:99
#1 0x3ff5598443f in __gnu_cxx::new_allocator<std::_Sp_counted_ptr_inplace<c10::FunctionSchema, std::allocator<c10::FunctionSchema>, (__gnu_cxx::_Lock_policy)2> >::allocate(unsigned long, void const*) /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/ext/new_allocator.h:127
#2 0x3fff5849ecf ([stack]+0xb2ecf)
SUMMARY: AddressSanitizer: heap-use-after-free /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/bits/stl_iterator.h:1028 in __gnu_cxx::__normal_iterator<c10::Argument const*, std::vector<c10::Argument, std::allocator<c10::Argument> > >::__normal_iterator(c10::Argument const* const&)
Shadow bytes around the buggy address:
0x100c1a000b4aa0: fd fd fd fd fd fd fd fd fd fd fd fa fa fa fa fa
0x100c1a000b4ab0: fa fa fa fa fd fd fd fd fd fd fd fd fd fd fd fd
0x100c1a000b4ac0: fd fd fd fd fd fa fa fa fa fa fa fa fa fa fd fd
0x100c1a000b4ad0: fd fd fd fd fd fd fd fd fd fd fd fd fd fd fd fa
0x100c1a000b4ae0: fa fa fa fa fa fa fa fa fd fd fd fd fd fd fd fd
=>0x100c1a000b4af0: fd fd[fd]fd fd fd fd fd fd fa fa fa fa fa fa fa
0x100c1a000b4b00: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
0x100c1a000b4b10: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
0x100c1a000b4b20: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
0x100c1a000b4b30: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
0x100c1a000b4b40: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
Shadow byte legend (one shadow byte represents 8 application bytes):
Addressable: 00
Partially addressable: 01 02 03 04 05 06 07
Heap left redzone: fa
Freed heap region: fd
Stack left redzone: f1
Stack mid redzone: f2
Stack right redzone: f3
Stack after return: f5
Stack use after scope: f8
Global redzone: f9
Global init order: f6
Poisoned by user: f7
Container overflow: fc
Array cookie: ac
Intra object redzone: bb
ASan internal: fe
Left alloca redzone: ca
Right alloca redzone: cb
Shadow gap: cc
==1134126==ABORTING
```
Additional backtraces (not full):
Allocation:
```
#0 __memset_z196 () at ../sysdeps/s390/memset-z900.S:144
#1 0x000003ff96f3072a in __asan::Allocator::Allocate (this=this@entry=0x3ff97041eb8 <__asan::instance>, size=size@entry=136, alignment=8, alignment@entry=0, stack=<optimized out>,
stack@entry=0x3ffdbb45d78, alloc_type=<optimized out>, can_fill=true) at /var/tmp/portage/sys-devel/gcc-11.3.1_p20230303/work/gcc-11-20230303/libsanitizer/asan/asan_allocator.cpp:599
#2 0x000003ff96f2c088 in __asan::asan_memalign (alignment=alignment@entry=0, size=size@entry=136, stack=stack@entry=0x3ffdbb45d78, alloc_type=alloc_type@entry=__asan::FROM_NEW)
at /var/tmp/portage/sys-devel/gcc-11.3.1_p20230303/work/gcc-11-20230303/libsanitizer/asan/asan_allocator.cpp:1039
ROCm#3 0x000003ff96fb73b0 in operator new (size=136) at /var/tmp/portage/sys-devel/gcc-11.3.1_p20230303/work/gcc-11-20230303/libsanitizer/asan/asan_new_delete.cpp:99
ROCm#4 0x000003ff41404440 in __gnu_cxx::new_allocator<std::_Sp_counted_ptr_inplace<c10::FunctionSchema, std::allocator<c10::FunctionSchema>, (__gnu_cxx::_Lock_policy)2> >::allocate (this=0x3ffdbb468c0,
__n=1) at /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/ext/new_allocator.h:127
ROCm#5 0x000003ff414042a0 in std::allocator_traits<std::allocator<std::_Sp_counted_ptr_inplace<c10::FunctionSchema, std::allocator<c10::FunctionSchema>, (__gnu_cxx::_Lock_policy)2> > >::allocate (__a=...,
__n=1) at /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/bits/alloc_traits.h:464
ROCm#6 0x000003ff41403b66 in std::__allocate_guarded<std::allocator<std::_Sp_counted_ptr_inplace<c10::FunctionSchema, std::allocator<c10::FunctionSchema>, (__gnu_cxx::_Lock_policy)2> > > (__a=...)
at /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/bits/allocated_ptr.h:98
ROCm#7 0x000003ff4140372a in std::__shared_count<(__gnu_cxx::_Lock_policy)2>::__shared_count<c10::FunctionSchema, std::allocator<c10::FunctionSchema>, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::vector<c10::Argument, std::allocator<c10::Argument> >, std::vector<c10::Argument, std::allocator<c10::Argument> > > (this=0x3ffdbb47888, __p=@0x3ffdbb47880: 0x0, __a=..., __args=..., __args=..., __args=..., __args=...)
at /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/bits/shared_ptr_base.h:648
ROCm#8 0x000003ff41403328 in std::__shared_ptr<c10::FunctionSchema, (__gnu_cxx::_Lock_policy)2>::__shared_ptr<std::allocator<c10::FunctionSchema>, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::vector<c10::Argument, std::allocator<c10::Argument> >, std::vector<c10::Argument, std::allocator<c10::Argument> > > (this=0x3ffdbb47880, __tag=..., __args=..., __args=..., __args=..., __args=...) at /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/bits/shared_ptr_base.h:1342
ROCm#9 0x000003ff41402f06 in std::shared_ptr<c10::FunctionSchema>::shared_ptr<std::allocator<c10::FunctionSchema>, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::vector<c10::Argument, std::allocator<c10::Argument> >, std::vector<c10::Argument, std::allocator<c10::Argument> > > (
this=0x3ffdbb47880, __tag=..., __args=..., __args=..., __args=..., __args=...) at /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/bits/shared_ptr.h:409
ROCm#10 0x000003ff41402b6e in std::allocate_shared<c10::FunctionSchema, std::allocator<c10::FunctionSchema>, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::vector<c10::Argument, std::allocator<c10::Argument> >, std::vector<c10::Argument, std::allocator<c10::Argument> > > (__a=...,
__args=..., __args=..., __args=..., __args=...) at /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/bits/shared_ptr.h:862
ROCm#11 0x000003ff4140215c in std::make_shared<c10::FunctionSchema, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::vector<c10::Argument, std::allocator<c10::Argument> >, std::vector<c10::Argument, std::allocator<c10::Argument> > > (__args=..., __args=..., __args=..., __args=...)
at /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/bits/shared_ptr.h:878
ROCm#12 0x000003ff413d180c in c10::TupleType::createWithSpec<c10::basic_string_view<char> > (qualName=..., field_names=std::vector of length 1, capacity 1 = {...},
field_types=std::vector of length 1, capacity 1 = {...}, field_defaults=std::vector of length 0, capacity 0) at /home/user/pytorch/aten/src/ATen/core/type.cpp:769
ROCm#13 0x000003ff413b9ca6 in c10::TupleType::createNamed (qualName=..., field_names=std::vector of length 1, capacity 1 = {...}, field_types=std::vector of length 1, capacity 1 = {...})
at /home/user/pytorch/aten/src/ATen/core/type.cpp:725
ROCm#14 0x000003ff4115fbac in c10::ivalue::TupleTypeFactory<c10::TupleType>::fallback (type=...) at /home/user/pytorch/aten/src/ATen/core/dynamic_type.cpp:383
ROCm#15 0x000003ff708217fe in c10::ivalue::Tuple::type<c10::TupleType> (this=0x6080004b8520) at /home/user/pytorch/aten/src/ATen/core/ivalue_inl.h:781
ROCm#16 0x000003ff70800740 in torch::jit::toPyObject (ivalue=...) at /home/user/pytorch/torch/csrc/jit/python/pybind_utils.cpp:613
ROCm#17 0x000003ff70800306 in torch::jit::toPyObject (ivalue=...) at /home/user/pytorch/torch/csrc/jit/python/pybind_utils.cpp:604
ROCm#18 0x000003ff702d6872 in pybind11::detail::type_caster<c10::IValue, void>::cast (src=...) at /home/user/pytorch/torch/csrc/jit/python/pybind.h:138
ROCm#19 0x000003ff70d98192 in pybind11::cpp_function::initialize<torch::jit::initJitScriptBindings(_object*)::$_45, c10::IValue, torch::jit::mobile::Module&, pybind11::tuple const&, pybind11::name, pybind11::is_method, pybind11::sibling, pybind11::arg>(torch::jit::initJitScriptBindings(_object*)::$_45&&, c10::IValue (*)(torch::jit::mobile::Module&, pybind11::tuple const&), pybind11::name const&, pybind11::is_method const&, pybind11::sibling const&, pybind11::arg const&)::{lambda(pybind11::detail::function_call&)#1}::operator()(pybind11::detail::function_call&) const (this=0x3ffdbb4ca20, call=...)
at /home/user/pytorch/cmake/../third_party/pybind11/include/pybind11/pybind11.h:249
ROCm#20 0x000003ff70d97cfe in pybind11::cpp_function::initialize<torch::jit::initJitScriptBindings(_object*)::$_45, c10::IValue, torch::jit::mobile::Module&, pybind11::tuple const&, pybind11::name, pybind11::is_method, pybind11::sibling, pybind11::arg>(torch::jit::initJitScriptBindings(_object*)::$_45&&, c10::IValue (*)(torch::jit::mobile::Module&, pybind11::tuple const&), pybind11::name const&, pybind11::is_method const&, pybind11::sibling const&, pybind11::arg const&)::{lambda(pybind11::detail::function_call&)#1}::__invoke(pybind11::detail::function_call&) (call=...)
at /home/user/pytorch/cmake/../third_party/pybind11/include/pybind11/pybind11.h:224
ROCm#21 0x000003ff6e9652ea in pybind11::cpp_function::dispatcher (self=<PyCapsule at remote 0x3ff83e27720>,
args_in=(<torch._C.LiteScriptModule at remote 0x3ff811844b0>, (<Tensor at remote 0x3ff814efb00>,)), kwargs_in=0x0) at /home/user/pytorch/cmake/../third_party/pybind11/include/pybind11/pybind11.h:929
```
Deallocation:
```
#0 operator delete (ptr=0x60d0005a5740) at /var/tmp/portage/sys-devel/gcc-11.3.1_p20230303/work/gcc-11-20230303/libsanitizer/asan/asan_new_delete.cpp:160
#1 0x000003ff44904fdc in __gnu_cxx::new_allocator<std::_Sp_counted_ptr_inplace<c10::FunctionSchema, std::allocator<c10::FunctionSchema>, (__gnu_cxx::_Lock_policy)2> >::deallocate (this=0x3ffc5dc8020,
__p=0x60d0005a5740, __t=1) at /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/ext/new_allocator.h:145
#2 0x000003ff44904fa8 in std::allocator_traits<std::allocator<std::_Sp_counted_ptr_inplace<c10::FunctionSchema, std::allocator<c10::FunctionSchema>, (__gnu_cxx::_Lock_policy)2> > >::deallocate (
__a=..., __p=0x60d0005a5740, __n=1) at /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/bits/alloc_traits.h:496
ROCm#3 0x000003ff449041f2 in std::__allocated_ptr<std::allocator<std::_Sp_counted_ptr_inplace<c10::FunctionSchema, std::allocator<c10::FunctionSchema>, (__gnu_cxx::_Lock_policy)2> > >::~__allocated_ptr (
this=0x3ffc5dc8030) at /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/bits/allocated_ptr.h:74
ROCm#4 0x000003ff44904888 in std::_Sp_counted_ptr_inplace<c10::FunctionSchema, std::allocator<c10::FunctionSchema>, (__gnu_cxx::_Lock_policy)2>::_M_destroy (this=0x60d0005a5740)
at /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/bits/shared_ptr_base.h:538
ROCm#5 0x000003ff43895a62 in std::_Sp_counted_base<(__gnu_cxx::_Lock_policy)2>::_M_release (this=0x60d0005a5740) at /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/bits/shared_ptr_base.h:184
ROCm#6 0x000003ff43895420 in std::__shared_count<(__gnu_cxx::_Lock_policy)2>::~__shared_count (this=0x611000c40648) at /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/bits/shared_ptr_base.h:705
ROCm#7 0x000003ff4466e7f4 in std::__shared_ptr<c10::FunctionSchema, (__gnu_cxx::_Lock_policy)2>::~__shared_ptr (this=0x611000c40640)
at /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/bits/shared_ptr_base.h:1154
ROCm#8 0x000003ff4466d820 in std::shared_ptr<c10::FunctionSchema>::~shared_ptr (this=0x611000c40640) at /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/bits/shared_ptr.h:122
ROCm#9 0x000003ff448d82f6 in c10::TupleType::~TupleType (this=0x611000c40580) at /home/user/pytorch/aten/src/ATen/core/jit_type.h:1142
ROCm#10 0x000003ff448d8346 in c10::TupleType::~TupleType (this=0x611000c40580) at /home/user/pytorch/aten/src/ATen/core/jit_type.h:1142
ROCm#11 0x000003ff731296a4 in std::_Sp_counted_ptr<c10::TupleType*, (__gnu_cxx::_Lock_policy)2>::_M_dispose (this=0x603000c43ae0)
at /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/bits/shared_ptr_base.h:348
ROCm#12 0x000003ff71eaf666 in std::_Sp_counted_base<(__gnu_cxx::_Lock_policy)2>::_M_release (this=0x603000c43ae0) at /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/bits/shared_ptr_base.h:168
ROCm#13 0x000003ff71eaf330 in std::__shared_count<(__gnu_cxx::_Lock_policy)2>::~__shared_count (this=0x3ffc5dc9368) at /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/bits/shared_ptr_base.h:705
ROCm#14 0x000003ff73129ee4 in std::__shared_ptr<c10::TupleType, (__gnu_cxx::_Lock_policy)2>::~__shared_ptr (this=0x3ffc5dc9360)
at /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/bits/shared_ptr_base.h:1154
ROCm#15 0x000003ff73122390 in std::shared_ptr<c10::TupleType>::~shared_ptr (this=0x3ffc5dc9360) at /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/bits/shared_ptr.h:122
ROCm#16 0x000003ff73d00788 in torch::jit::toPyObject (ivalue=...) at /home/user/pytorch/torch/csrc/jit/python/pybind_utils.cpp:613
ROCm#17 0x000003ff73d00306 in torch::jit::toPyObject (ivalue=...) at /home/user/pytorch/torch/csrc/jit/python/pybind_utils.cpp:604
```
</details>
Pull Request resolved: pytorch#101400
Approved by: https://github.com/zou3519
lcskrishna
pushed a commit
to lcskrishna/pytorch
that referenced
this pull request
May 29, 2023
3 disabled functions are attempting out of bounds reads. Disable them until sleef library is fixed.
<details>
<summary>ASAN report</summary>
```
=================================================================
==2030580==ERROR: AddressSanitizer: global-buffer-overflow on address 0x03ff70f54570 at pc 0x03ff6704e960 bp 0x03ffce128940 sp 0x03ffce128930
READ of size 4 at 0x03ff70f54570 thread T0
#0 0x3ff6704e95f in vgather_vf_p_vi2 /home/user/pytorch/third_party/sleef/src/arch/helpers390x_128.h:129
ROCm#1 0x3ff6704e95f in rempif /home/user/pytorch/third_party/sleef/src/libm/sleefsimdsp.c:550
ROCm#2 0x3ff6704e95f in Sleef_cosf4_u10vxe2 /home/user/pytorch/third_party/sleef/src/libm/sleefsimdsp.c:1021
ROCm#3 0x3ff67029cfb in Sleef_cosf4_u10 /home/user/pytorch/build/sleef/src/libm/disps390x_128.c:182
ROCm#4 0x3ff55d21941 in at::vec::ZVECTOR::Vectorized<float, void> at::vec::ZVECTOR::Vectorized<float, void>::mapSleef<float __vector(4) const (*)(float __vector(4)), double __vector(2) const (*)(double __
vector(2)), float, 0>(float __vector(4) const (*)(float __vector(4)), double __vector(2) const (*)(double __vector(2))) const /home/user/pytorch/aten/src/ATen/cpu/vec/vec256/zarch/vec256_zarch.h:991
ROCm#5 0x3ff5689ad01 in at::vec::ZVECTOR::Vectorized<float, void>::cos() const /home/user/pytorch/aten/src/ATen/cpu/vec/vec256/zarch/vec256_zarch.h:1074
ROCm#6 0x3ff5685df97 in at::vml::ZVECTOR::vcos<float>(float*, float const*, long)::{lambda(at::vec::ZVECTOR::Vectorized<float, void>)ROCm#1}::operator()(at::vec::ZVECTOR::Vectorized<float, void>) const /home/
user/pytorch/aten/src/ATen/cpu/vml.h:71
ROCm#7 0x3ff5689b691 in void at::vec::map<float, at::vml::ZVECTOR::vcos<float>(float*, float const*, long)::{lambda(at::vec::ZVECTOR::Vectorized<float, void>)ROCm#1}, 0>(at::vml::ZVECTOR::vcos<float>(float*,
float const*, long)::{lambda(at::vec::ZVECTOR::Vectorized<float, void>)ROCm#1} const&, float*, float const*, long) /home/user/pytorch/aten/src/ATen/cpu/vec/functional_base.h:239
ROCm#8 0x3ff5685e0df in void at::vml::ZVECTOR::vcos<float>(float*, float const*, long) /home/user/pytorch/aten/src/ATen/cpu/vml.h:71
ROCm#9 0x3ff563fdde3 in operator() /home/user/pytorch/aten/src/ATen/native/cpu/UnaryOpsKernel.cpp:770
ROCm#10 0x3ff5648e4a3 in operator() /home/user/pytorch/aten/src/ATen/TensorIterator.h:406
ROCm#11 0x3ff5663cae1 in callback_fn<at::TensorIteratorBase::loop_2d_from_1d<at::native::ZVECTOR::cos_kernel(at::TensorIteratorBase&)::<lambda()>::<lambda()>::<lambda(char**, const int64_t*, int64_t)> >(c
onst at::native::ZVECTOR::cos_kernel(at::TensorIteratorBase&)::<lambda()>::<lambda()>::<lambda(char**, const int64_t*, int64_t)>&)::<lambda(char**, const int64_t*, int64_t, int64_t)> > /home/user/pytorch/
c10/util/FunctionRef.h:43
ROCm#12 0x3ff4d45a933 in c10::function_ref<void (char**, long const*, long, long)>::operator()(char**, long const*, long, long) const /home/user/pytorch/c10/util/FunctionRef.h:64
ROCm#13 0x3ff4d455133 in at::internal::serial_for_each(c10::ArrayRef<long>, c10::ArrayRef<long>, char**, unsigned long, c10::function_ref<void (char**, long const*, long, long)>, at::Range) /home/user/pyt
orch/aten/src/ATen/TensorIteratorInternal.h:52
ROCm#14 0x3ff4d43b703 in at::TensorIteratorBase::serial_for_each(c10::function_ref<void (char**, long const*, long, long)>, at::Range) const /home/user/pytorch/aten/src/ATen/TensorIterator.cpp:777
ROCm#15 0x3ff4d43ab59 in at::TensorIteratorBase::for_each(c10::function_ref<void (char**, long const*, long, long)>, long) /home/user/pytorch/aten/src/ATen/TensorIterator.cpp:749
ROCm#16 0x3ff5648e851 in for_each<at::native::ZVECTOR::cos_kernel(at::TensorIteratorBase&)::<lambda()>::<lambda()>::<lambda(char**, const int64_t*, int64_t)> > /home/user/pytorch/aten/src/ATen/TensorItera
tor.h:421
ROCm#17 0x3ff563fe5f9 in operator() /home/user/pytorch/aten/src/ATen/native/cpu/UnaryOpsKernel.cpp:770
ROCm#18 0x3ff56400915 in operator() /home/user/pytorch/aten/src/ATen/native/cpu/UnaryOpsKernel.cpp:770
ROCm#19 0x3ff56400f1d in at::native::ZVECTOR::cos_kernel(at::TensorIteratorBase&) /home/user/pytorch/aten/src/ATen/native/cpu/UnaryOpsKernel.cpp:770
ROCm#20 0x3ff4f303007 in void at::native::DispatchStub<void (*)(at::TensorIteratorBase&), at::native::cos_stub>::operator()<at::native::structured_cos_out&>(c10::DeviceType, at::native::structured_cos_out
&) /home/user/pytorch/aten/src/ATen/native/DispatchStub.h:158
ROCm#21 0x3ff4f2edb3f in at::native::structured_cos_out::impl(at::Tensor const&, at::Tensor const&) /home/user/pytorch/aten/src/ATen/native/UnaryOps.cpp:330
ROCm#22 0x3ff526ef739 in wrapper_CPU_cos /home/user/pytorch/build/aten/src/ATen/RegisterCPU.cpp:4307
ROCm#23 0x3ff52c651d9 in operator() /home/user/pytorch/aten/src/ATen/core/boxing/impl/WrapFunctionIntoFunctor.h:13
ROCm#24 0x3ff52c651d9 in call /home/user/pytorch/aten/src/ATen/core/boxing/impl/make_boxed_from_unboxed_functor.h:463
ROCm#25 0x3ff5076df2f in at::Tensor c10::callUnboxedKernelFunction<at::Tensor, at::Tensor const&>(void*, c10::OperatorKernel*, c10::DispatchKeySet, at::Tensor const&) /home/user/pytorch/aten/src/ATen/core
/boxing/KernelFunction_impl.h:50
ROCm#26 0x3ff5009a93f in at::Tensor c10::KernelFunction::call<at::Tensor, at::Tensor const&>(c10::OperatorHandle const&, c10::DispatchKeySet, at::Tensor const&) const /home/user/pytorch/aten/src/ATen/core
/boxing/KernelFunction_impl.h:103
ROCm#27 0x3ff5009a93f in at::Tensor c10::Dispatcher::call<at::Tensor, at::Tensor const&>(c10::TypedOperatorHandle<at::Tensor (at::Tensor const&)> const&, at::Tensor const&) const /home/user/pytorch/aten/s
rc/ATen/core/dispatch/Dispatcher.h:639
ROCm#28 0x3ff5009a93f in c10::TypedOperatorHandle<at::Tensor (at::Tensor const&)>::call(at::Tensor const&) const /home/user/pytorch/aten/src/ATen/core/dispatch/Dispatcher.h:487
ROCm#29 0x3ff5009a93f in at::_ops::cos::call(at::Tensor const&) /home/user/pytorch/build/aten/src/ATen/Operators_0.cpp:2215
ROCm#30 0x3ff7d813741 in at::Tensor::cos() const /home/user/pytorch/build/aten/src/ATen/core/TensorBody.h:2107
ROCm#31 0x3ff7dc0f2b7 in operator() /home/user/pytorch/torch/csrc/autograd/generated/python_torch_functions_2.cpp:2953
ROCm#32 0x3ff7dc0faf7 in THPVariable_cos /home/user/pytorch/torch/csrc/autograd/generated/python_torch_functions_2.cpp:2955
ROCm#33 0x3ffa5ef5ae1 in cfunction_call Objects/methodobject.c:543
ROCm#34 0x3ffa5e843f3 in _PyObject_Call Objects/call.c:305
ROCm#35 0x3ffa5e84483 in PyObject_Call Objects/call.c:317
ROCm#36 0x3ffa5feb50d in do_call_core Python/ceval.c:5915
ROCm#37 0x3ffa5fe6019 in _PyEval_EvalFrameDefault Python/ceval.c:4277
ROCm#38 0x3ffa5fd7aed in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#39 0x3ffa5fe8ba9 in _PyEval_Vector Python/ceval.c:5065
ROCm#40 0x3ffa5e8459b in _PyFunction_Vectorcall Objects/call.c:342
ROCm#41 0x3ffa5e841fb in PyVectorcall_Call Objects/call.c:255
ROCm#42 0x3ffa5e84347 in _PyObject_Call Objects/call.c:290
ROCm#43 0x3ffa5e84483 in PyObject_Call Objects/call.c:317
ROCm#44 0x3ff7f87a393 in torch::impl::dispatch::PythonKernelHolder::operator()(c10::OperatorHandle const&, c10::DispatchKeySet, std::vector<c10::IValue, std::allocator<c10::IValue> >*) /home/user/pytorch/
torch/csrc/utils/python_dispatch.cpp:175
ROCm#45 0x3ff7f8871a7 in c10::BoxedKernel::makeFromFunctor<torch::impl::dispatch::PythonKernelHolder>(std::unique_ptr<torch::impl::dispatch::PythonKernelHolder, std::default_delete<torch::impl::dispatch::
PythonKernelHolder> >)::{lambda(c10::OperatorKernel*, c10::OperatorHandle const&, c10::DispatchKeySet, std::vector<c10::IValue, std::allocator<c10::IValue> >*)ROCm#1}::operator()(c10::OperatorKernel*, c10::Op
eratorHandle const&, c10::DispatchKeySet, std::vector<c10::IValue, std::allocator<c10::IValue> >*) const /home/user/pytorch/aten/src/ATen/core/boxing/BoxedKernel_impl.h:87
ROCm#46 0x3ff7f887261 in c10::BoxedKernel::makeFromFunctor<torch::impl::dispatch::PythonKernelHolder>(std::unique_ptr<torch::impl::dispatch::PythonKernelHolder, std::default_delete<torch::impl::dispatch::
PythonKernelHolder> >)::{lambda(c10::OperatorKernel*, c10::OperatorHandle const&, c10::DispatchKeySet, std::vector<c10::IValue, std::allocator<c10::IValue> >*)ROCm#1}::_FUN(c10::OperatorKernel*, c10::Operator
Handle const&, c10::DispatchKeySet, std::vector<c10::IValue, std::allocator<c10::IValue> >*) /home/user/pytorch/aten/src/ATen/core/boxing/BoxedKernel_impl.h:86
ROCm#47 0x3ff7e0d10ab in c10::BoxedKernel::callBoxed(c10::OperatorHandle const&, c10::DispatchKeySet, std::vector<c10::IValue, std::allocator<c10::IValue> >*) const /home/user/pytorch/aten/src/ATen/core/b
oxing/BoxedKernel_impl.h:41
ROCm#48 0x3ff7e0d1459 in c10::KernelFunction::callBoxed(c10::OperatorHandle const&, c10::DispatchKeySet, std::vector<c10::IValue, std::allocator<c10::IValue> >*) const /home/user/pytorch/aten/src/ATen/cor
e/boxing/KernelFunction_impl.h:43
ROCm#49 0x3ff7f876421 in c10::Dispatcher::callBoxed(c10::OperatorHandle const&, std::vector<c10::IValue, std::allocator<c10::IValue> >*) const /home/user/pytorch/aten/src/ATen/core/dispatch/Dispatcher.h:6
91
ROCm#50 0x3ff4d22bcdd in c10::OperatorHandle::callBoxed(std::vector<c10::IValue, std::allocator<c10::IValue> >*) const /home/user/pytorch/aten/src/ATen/core/dispatch/Dispatcher.h:417
ROCm#51 0x3ff65a092d5 in c10::OperatorHandle::callBoxed(std::vector<c10::IValue, std::allocator<c10::IValue> >&) const /home/user/pytorch/aten/src/ATen/core/dispatch/Dispatcher.h:421
ROCm#52 0x3ff65a05641 in operator() /home/user/pytorch/torch/csrc/jit/runtime/register_c10_ops.cpp:15
ROCm#53 0x3ff65a08cb5 in __invoke_impl<void, torch::jit::(anonymous namespace)::createOperatorFromC10(const c10::OperatorHandle&)::<lambda(torch::jit::Stack&)>&, std::vector<c10::IValue, std::allocator<c1
0::IValue> >&> /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/bits/invoke.h:61
ROCm#54 0x3ff65a0897b in __invoke_r<void, torch::jit::(anonymous namespace)::createOperatorFromC10(const c10::OperatorHandle&)::<lambda(torch::jit::Stack&)>&, std::vector<c10::IValue, std::allocator<c10::
IValue> >&> /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/bits/invoke.h:111
ROCm#55 0x3ff65a084e1 in _M_invoke /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/bits/std_function.h:290
ROCm#56 0x3ff7eb2cb21 in std::function<void (std::vector<c10::IValue, std::allocator<c10::IValue> >&)>::operator()(std::vector<c10::IValue, std::allocator<c10::IValue> >&) const /usr/lib/gcc/s390x-ibm-lin
ux-gnu/11/include/g++-v11/bits/std_function.h:590
ROCm#57 0x3ff7eb1b659 in torch::jit::Operation::operator()(std::vector<c10::IValue, std::allocator<c10::IValue> >&) /home/user/pytorch/aten/src/ATen/core/stack.h:41
ROCm#58 0x3ff7eb08449 in torch::jit::invokeOperatorFromPython(std::vector<std::shared_ptr<torch::jit::Operator>, std::allocator<std::shared_ptr<torch::jit::Operator> > > const&, pybind11::args, pybind11::
kwargs const&, c10::optional<c10::DispatchKey>) /home/user/pytorch/torch/csrc/jit/python/pybind_utils.cpp:764
ROCm#59 0x3ff7eb09d85 in torch::jit::_get_operation_for_overload_or_packet(std::vector<std::shared_ptr<torch::jit::Operator>, std::allocator<std::shared_ptr<torch::jit::Operator> > > const&, c10::Symbol,
pybind11::args, pybind11::kwargs const&, bool, c10::optional<c10::DispatchKey>) /home/user/pytorch/torch/csrc/jit/python/pybind_utils.cpp:829
ROCm#60 0x3ff7e573eb9 in operator() /home/user/pytorch/torch/csrc/jit/python/init.cpp:1549
ROCm#61 0x3ff7e6728dd in call_impl<pybind11::object, torch::jit::initJITBindings(PyObject*)::<lambda(const string&, const string&)>::<lambda(pybind11::args, pybind11::kwargs)>&, 0, 1, pybind11::detail::vo
id_type> /home/user/pytorch/third_party/pybind11/include/pybind11/cast.h:1439
ROCm#62 0x3ff7e64312f in call<pybind11::object, pybind11::detail::void_type, torch::jit::initJITBindings(PyObject*)::<lambda(const string&, const string&)>::<lambda(pybind11::args, pybind11::kwargs)>&> /h
ome/user/pytorch/third_party/pybind11/include/pybind11/cast.h:1408
ROCm#63 0x3ff7e5da259 in operator() /home/user/pytorch/third_party/pybind11/include/pybind11/pybind11.h:249
ROCm#64 0x3ff7e5da441 in _FUN /home/user/pytorch/third_party/pybind11/include/pybind11/pybind11.h:224
ROCm#65 0x3ff7d317a1f in pybind11::cpp_function::dispatcher(_object*, _object*, _object*) /home/user/pytorch/third_party/pybind11/include/pybind11/pybind11.h:929
ROCm#66 0x3ffa5ef5ae1 in cfunction_call Objects/methodobject.c:543
ROCm#67 0x3ffa5e843f3 in _PyObject_Call Objects/call.c:305
ROCm#68 0x3ffa5e84483 in PyObject_Call Objects/call.c:317
ROCm#69 0x3ffa5feb50d in do_call_core Python/ceval.c:5915
ROCm#70 0x3ffa5fe6019 in _PyEval_EvalFrameDefault Python/ceval.c:4277
ROCm#71 0x3ffa5fd7aed in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#72 0x3ffa5fe8ba9 in _PyEval_Vector Python/ceval.c:5065
ROCm#73 0x3ffa5e8459b in _PyFunction_Vectorcall Objects/call.c:342
ROCm#74 0x3ffa5e83d1f in _PyObject_FastCallDictTstate Objects/call.c:142
ROCm#75 0x3ffa5e84937 in _PyObject_Call_Prepend Objects/call.c:431
ROCm#76 0x3ffa5f2f577 in slot_tp_call Objects/typeobject.c:7494
ROCm#77 0x3ffa5e843f3 in _PyObject_Call Objects/call.c:305
ROCm#78 0x3ffa5e84483 in PyObject_Call Objects/call.c:317
ROCm#79 0x3ffa5feb7cf in do_call_core Python/ceval.c:5943
ROCm#80 0x3ffa5fe6019 in _PyEval_EvalFrameDefault Python/ceval.c:4277
ROCm#81 0x3ffa5fd7aed in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#82 0x3ffa5fe8ba9 in _PyEval_Vector Python/ceval.c:5065
ROCm#83 0x3ffa5e8459b in _PyFunction_Vectorcall Objects/call.c:342
ROCm#84 0x3ffa5fd76a3 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#85 0x3ffa5fd772f in PyObject_Vectorcall Include/cpython/abstract.h:123
ROCm#86 0x3ffa5feb289 in call_function Python/ceval.c:5891
ROCm#87 0x3ffa5fe5c3b in _PyEval_EvalFrameDefault Python/ceval.c:4213
ROCm#88 0x3ffa5fd7aed in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#89 0x3ffa5fe8ba9 in _PyEval_Vector Python/ceval.c:5065
ROCm#90 0x3ffa5e8459b in _PyFunction_Vectorcall Objects/call.c:342
ROCm#91 0x3ffa5e841fb in PyVectorcall_Call Objects/call.c:255
ROCm#92 0x3ffa5e84347 in _PyObject_Call Objects/call.c:290
ROCm#93 0x3ffa5e84483 in PyObject_Call Objects/call.c:317
ROCm#94 0x3ffa5feb7cf in do_call_core Python/ceval.c:5943
ROCm#95 0x3ffa5fe6019 in _PyEval_EvalFrameDefault Python/ceval.c:4277
ROCm#96 0x3ffa5fd7aed in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#97 0x3ffa5fe8ba9 in _PyEval_Vector Python/ceval.c:5065
ROCm#98 0x3ffa5e8459b in _PyFunction_Vectorcall Objects/call.c:342
ROCm#99 0x3ffa5e841fb in PyVectorcall_Call Objects/call.c:255
ROCm#100 0x3ffa5e84347 in _PyObject_Call Objects/call.c:290
ROCm#101 0x3ffa5e84483 in PyObject_Call Objects/call.c:317
ROCm#102 0x3ff7f87a393 in torch::impl::dispatch::PythonKernelHolder::operator()(c10::OperatorHandle const&, c10::DispatchKeySet, std::vector<c10::IValue, std::allocator<c10::IValue> >*) /home/user/pytorch
/torch/csrc/utils/python_dispatch.cpp:175
ROCm#103 0x3ff7f8871a7 in c10::BoxedKernel::makeFromFunctor<torch::impl::dispatch::PythonKernelHolder>(std::unique_ptr<torch::impl::dispatch::PythonKernelHolder, std::default_delete<torch::impl::dispatch:
:PythonKernelHolder> >)::{lambda(c10::OperatorKernel*, c10::OperatorHandle const&, c10::DispatchKeySet, std::vector<c10::IValue, std::allocator<c10::IValue> >*)ROCm#1}::operator()(c10::OperatorKernel*, c10::O
peratorHandle const&, c10::DispatchKeySet, std::vector<c10::IValue, std::allocator<c10::IValue> >*) const /home/user/pytorch/aten/src/ATen/core/boxing/BoxedKernel_impl.h:87
ROCm#104 0x3ff7f887261 in c10::BoxedKernel::makeFromFunctor<torch::impl::dispatch::PythonKernelHolder>(std::unique_ptr<torch::impl::dispatch::PythonKernelHolder, std::default_delete<torch::impl::dispatch:
:PythonKernelHolder> >)::{lambda(c10::OperatorKernel*, c10::OperatorHandle const&, c10::DispatchKeySet, std::vector<c10::IValue, std::allocator<c10::IValue> >*)ROCm#1}::_FUN(c10::OperatorKernel*, c10::Operato
rHandle const&, c10::DispatchKeySet, std::vector<c10::IValue, std::allocator<c10::IValue> >*) /home/user/pytorch/aten/src/ATen/core/boxing/BoxedKernel_impl.h:86
ROCm#105 0x3ff7e0d10ab in c10::BoxedKernel::callBoxed(c10::OperatorHandle const&, c10::DispatchKeySet, std::vector<c10::IValue, std::allocator<c10::IValue> >*) const /home/user/pytorch/aten/src/ATen/core/
boxing/BoxedKernel_impl.h:41
ROCm#106 0x3ff7e0d1459 in c10::KernelFunction::callBoxed(c10::OperatorHandle const&, c10::DispatchKeySet, std::vector<c10::IValue, std::allocator<c10::IValue> >*) const /home/user/pytorch/aten/src/ATen/co
re/boxing/KernelFunction_impl.h:43
ROCm#107 0x3ff7f876421 in c10::Dispatcher::callBoxed(c10::OperatorHandle const&, std::vector<c10::IValue, std::allocator<c10::IValue> >*) const /home/user/pytorch/aten/src/ATen/core/dispatch/Dispatcher.h:
691
ROCm#108 0x3ff4d22bcdd in c10::OperatorHandle::callBoxed(std::vector<c10::IValue, std::allocator<c10::IValue> >*) const /home/user/pytorch/aten/src/ATen/core/dispatch/Dispatcher.h:417
ROCm#109 0x3ff65a092d5 in c10::OperatorHandle::callBoxed(std::vector<c10::IValue, std::allocator<c10::IValue> >&) const /home/user/pytorch/aten/src/ATen/core/dispatch/Dispatcher.h:421
ROCm#110 0x3ff65a05641 in operator() /home/user/pytorch/torch/csrc/jit/runtime/register_c10_ops.cpp:15
ROCm#111 0x3ff65a08cb5 in __invoke_impl<void, torch::jit::(anonymous namespace)::createOperatorFromC10(const c10::OperatorHandle&)::<lambda(torch::jit::Stack&)>&, std::vector<c10::IValue, std::allocator<c
10::IValue> >&> /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/bits/invoke.h:61
ROCm#112 0x3ff65a0897b in __invoke_r<void, torch::jit::(anonymous namespace)::createOperatorFromC10(const c10::OperatorHandle&)::<lambda(torch::jit::Stack&)>&, std::vector<c10::IValue, std::allocator<c10:
:IValue> >&> /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/bits/invoke.h:111
ROCm#113 0x3ff65a084e1 in _M_invoke /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/bits/std_function.h:290
ROCm#114 0x3ff7eb2cb21 in std::function<void (std::vector<c10::IValue, std::allocator<c10::IValue> >&)>::operator()(std::vector<c10::IValue, std::allocator<c10::IValue> >&) const /usr/lib/gcc/s390x-ibm-li
nux-gnu/11/include/g++-v11/bits/std_function.h:590
ROCm#115 0x3ff7eb1b659 in torch::jit::Operation::operator()(std::vector<c10::IValue, std::allocator<c10::IValue> >&) /home/user/pytorch/aten/src/ATen/core/stack.h:41
ROCm#116 0x3ff7eb08449 in torch::jit::invokeOperatorFromPython(std::vector<std::shared_ptr<torch::jit::Operator>, std::allocator<std::shared_ptr<torch::jit::Operator> > > const&, pybind11::args, pybind11:
:kwargs const&, c10::optional<c10::DispatchKey>) /home/user/pytorch/torch/csrc/jit/python/pybind_utils.cpp:764
ROCm#117 0x3ff7eb09d85 in torch::jit::_get_operation_for_overload_or_packet(std::vector<std::shared_ptr<torch::jit::Operator>, std::allocator<std::shared_ptr<torch::jit::Operator> > > const&, c10::Symbol,
pybind11::args, pybind11::kwargs const&, bool, c10::optional<c10::DispatchKey>) /home/user/pytorch/torch/csrc/jit/python/pybind_utils.cpp:829
ROCm#118 0x3ff7e573eb9 in operator() /home/user/pytorch/torch/csrc/jit/python/init.cpp:1549
ROCm#119 0x3ff7e6728dd in call_impl<pybind11::object, torch::jit::initJITBindings(PyObject*)::<lambda(const string&, const string&)>::<lambda(pybind11::args, pybind11::kwargs)>&, 0, 1, pybind11::detail::v
oid_type> /home/user/pytorch/third_party/pybind11/include/pybind11/cast.h:1439
ROCm#120 0x3ff7e64312f in call<pybind11::object, pybind11::detail::void_type, torch::jit::initJITBindings(PyObject*)::<lambda(const string&, const string&)>::<lambda(pybind11::args, pybind11::kwargs)>&> /
home/user/pytorch/third_party/pybind11/include/pybind11/cast.h:1408
ROCm#121 0x3ff7e5da259 in operator() /home/user/pytorch/third_party/pybind11/include/pybind11/pybind11.h:249
ROCm#122 0x3ff7e5da441 in _FUN /home/user/pytorch/third_party/pybind11/include/pybind11/pybind11.h:224
ROCm#123 0x3ff7d317a1f in pybind11::cpp_function::dispatcher(_object*, _object*, _object*) /home/user/pytorch/third_party/pybind11/include/pybind11/pybind11.h:929
ROCm#124 0x3ffa5ef5ae1 in cfunction_call Objects/methodobject.c:543
ROCm#125 0x3ffa5e843f3 in _PyObject_Call Objects/call.c:305
ROCm#126 0x3ffa5e84483 in PyObject_Call Objects/call.c:317
ROCm#127 0x3ffa5feb50d in do_call_core Python/ceval.c:5915
ROCm#128 0x3ffa5fe6019 in _PyEval_EvalFrameDefault Python/ceval.c:4277
ROCm#129 0x3ffa5fd7aed in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#130 0x3ffa5fe8ba9 in _PyEval_Vector Python/ceval.c:5065
ROCm#131 0x3ffa5e8459b in _PyFunction_Vectorcall Objects/call.c:342
ROCm#132 0x3ffa5e83d1f in _PyObject_FastCallDictTstate Objects/call.c:142
ROCm#133 0x3ffa5e84937 in _PyObject_Call_Prepend Objects/call.c:431
ROCm#134 0x3ffa5f2f577 in slot_tp_call Objects/typeobject.c:7494
ROCm#135 0x3ffa5e843f3 in _PyObject_Call Objects/call.c:305
ROCm#136 0x3ffa5e84483 in PyObject_Call Objects/call.c:317
ROCm#137 0x3ffa5feb7cf in do_call_core Python/ceval.c:5943
ROCm#138 0x3ffa5fe6019 in _PyEval_EvalFrameDefault Python/ceval.c:4277
ROCm#139 0x3ffa5fd7aed in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#140 0x3ffa5fe8ba9 in _PyEval_Vector Python/ceval.c:5065
ROCm#141 0x3ffa5e8459b in _PyFunction_Vectorcall Objects/call.c:342
ROCm#142 0x3ffa5e87d2b in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#143 0x3ffa5e882dd in method_vectorcall Objects/classobject.c:83
ROCm#144 0x3ffa5e836d3 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#145 0x3ffa5e84b6f in _PyObject_CallFunctionVa Objects/call.c:485
ROCm#146 0x3ffa5e84f2d in callmethod Objects/call.c:557
ROCm#147 0x3ffa5e85039 in PyObject_CallMethod Objects/call.c:577
ROCm#148 0x3ff7f7efa05 in torch::handle_torch_function_no_python_arg_parser(c10::ArrayRef<pybind11::handle>, _object*, _object*, char const*, _object*, char const*, torch::TorchFunctionName) /home/user/py
torch/torch/csrc/utils/python_arg_parser.cpp:338
ROCm#149 0x3ff7eb09b67 in torch::jit::_get_operation_for_overload_or_packet(std::vector<std::shared_ptr<torch::jit::Operator>, std::allocator<std::shared_ptr<torch::jit::Operator> > > const&, c10::Symbol,
pybind11::args, pybind11::kwargs const&, bool, c10::optional<c10::DispatchKey>) /home/user/pytorch/torch/csrc/jit/python/pybind_utils.cpp:827
ROCm#150 0x3ff7e573eb9 in operator() /home/user/pytorch/torch/csrc/jit/python/init.cpp:1549
ROCm#151 0x3ff7e6728dd in call_impl<pybind11::object, torch::jit::initJITBindings(PyObject*)::<lambda(const string&, const string&)>::<lambda(pybind11::args, pybind11::kwargs)>&, 0, 1, pybind11::detail::v
oid_type> /home/user/pytorch/third_party/pybind11/include/pybind11/cast.h:1439
ROCm#152 0x3ff7e64312f in call<pybind11::object, pybind11::detail::void_type, torch::jit::initJITBindings(PyObject*)::<lambda(const string&, const string&)>::<lambda(pybind11::args, pybind11::kwargs)>&> /
home/user/pytorch/third_party/pybind11/include/pybind11/cast.h:1408
ROCm#153 0x3ff7e5da259 in operator() /home/user/pytorch/third_party/pybind11/include/pybind11/pybind11.h:249
ROCm#154 0x3ff7e5da441 in _FUN /home/user/pytorch/third_party/pybind11/include/pybind11/pybind11.h:224
ROCm#155 0x3ff7d317a1f in pybind11::cpp_function::dispatcher(_object*, _object*, _object*) /home/user/pytorch/third_party/pybind11/include/pybind11/pybind11.h:929
ROCm#156 0x3ffa5ef5ae1 in cfunction_call Objects/methodobject.c:543
ROCm#157 0x3ffa5e843f3 in _PyObject_Call Objects/call.c:305
ROCm#158 0x3ffa5e84483 in PyObject_Call Objects/call.c:317
ROCm#159 0x3ffa5feb50d in do_call_core Python/ceval.c:5915
ROCm#160 0x3ffa5fe6019 in _PyEval_EvalFrameDefault Python/ceval.c:4277
ROCm#161 0x3ffa5fd7aed in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#162 0x3ffa5fe8ba9 in _PyEval_Vector Python/ceval.c:5065
ROCm#163 0x3ffa5e8459b in _PyFunction_Vectorcall Objects/call.c:342
ROCm#164 0x3ffa5e83d1f in _PyObject_FastCallDictTstate Objects/call.c:142
ROCm#165 0x3ffa5e84937 in _PyObject_Call_Prepend Objects/call.c:431
ROCm#166 0x3ffa5f2f577 in slot_tp_call Objects/typeobject.c:7494
ROCm#167 0x3ffa5e84027 in _PyObject_MakeTpCall Objects/call.c:215
ROCm#168 0x3ffa5fd767b in _PyObject_VectorcallTstate Include/cpython/abstract.h:112
ROCm#169 0x3ffa5fd772f in PyObject_Vectorcall Include/cpython/abstract.h:123
ROCm#170 0x3ffa5feb289 in call_function Python/ceval.c:5891
ROCm#171 0x3ffa5fe5ad1 in _PyEval_EvalFrameDefault Python/ceval.c:4181
ROCm#172 0x3ffa5fd7aed in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#173 0x3ffa5fe8ba9 in _PyEval_Vector Python/ceval.c:5065
ROCm#174 0x3ffa5e8459b in _PyFunction_Vectorcall Objects/call.c:342
ROCm#175 0x3ffa5fd76a3 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#176 0x3ffa5fd772f in PyObject_Vectorcall Include/cpython/abstract.h:123
ROCm#177 0x3ffa5feb289 in call_function Python/ceval.c:5891
ROCm#178 0x3ffa5fe5c3b in _PyEval_EvalFrameDefault Python/ceval.c:4213
ROCm#179 0x3ffa5fd7aed in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#180 0x3ffa5fe8ba9 in _PyEval_Vector Python/ceval.c:5065
ROCm#181 0x3ffa5e8459b in _PyFunction_Vectorcall Objects/call.c:342
ROCm#182 0x3ffa5e8427f in PyVectorcall_Call Objects/call.c:267
ROCm#183 0x3ffa5e84347 in _PyObject_Call Objects/call.c:290
ROCm#184 0x3ffa5e84483 in PyObject_Call Objects/call.c:317
ROCm#185 0x3ffa5feb7cf in do_call_core Python/ceval.c:5943
ROCm#186 0x3ffa5fe6019 in _PyEval_EvalFrameDefault Python/ceval.c:4277
ROCm#187 0x3ffa5fd7aed in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#188 0x3ffa5fe8ba9 in _PyEval_Vector Python/ceval.c:5065
ROCm#189 0x3ffa5e8459b in _PyFunction_Vectorcall Objects/call.c:342
ROCm#190 0x3ffa5e841fb in PyVectorcall_Call Objects/call.c:255
ROCm#191 0x3ffa5e84347 in _PyObject_Call Objects/call.c:290
ROCm#192 0x3ffa5e84483 in PyObject_Call Objects/call.c:317
ROCm#193 0x3ffa5feb7cf in do_call_core Python/ceval.c:5943
ROCm#194 0x3ffa5fe6019 in _PyEval_EvalFrameDefault Python/ceval.c:4277
ROCm#195 0x3ffa5fd7aed in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#196 0x3ffa5fe8ba9 in _PyEval_Vector Python/ceval.c:5065
ROCm#197 0x3ffa5e8459b in _PyFunction_Vectorcall Objects/call.c:342
ROCm#198 0x3ffa5e841fb in PyVectorcall_Call Objects/call.c:255
ROCm#199 0x3ffa5e84347 in _PyObject_Call Objects/call.c:290
ROCm#200 0x3ffa5e84483 in PyObject_Call Objects/call.c:317
ROCm#201 0x3ffa5feb7cf in do_call_core Python/ceval.c:5943
ROCm#202 0x3ffa5fe6019 in _PyEval_EvalFrameDefault Python/ceval.c:4277
ROCm#203 0x3ffa5fd7aed in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#204 0x3ffa5fe8ba9 in _PyEval_Vector Python/ceval.c:5065
ROCm#205 0x3ffa5e8459b in _PyFunction_Vectorcall Objects/call.c:342
ROCm#206 0x3ffa5e841fb in PyVectorcall_Call Objects/call.c:255
ROCm#207 0x3ffa5e84347 in _PyObject_Call Objects/call.c:290
ROCm#208 0x3ffa5e84483 in PyObject_Call Objects/call.c:317
ROCm#209 0x3ffa5feb7cf in do_call_core Python/ceval.c:5943
ROCm#210 0x3ffa5fe6019 in _PyEval_EvalFrameDefault Python/ceval.c:4277
ROCm#211 0x3ffa5fd7aed in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#212 0x3ffa5fe8ba9 in _PyEval_Vector Python/ceval.c:5065
ROCm#213 0x3ffa5e8459b in _PyFunction_Vectorcall Objects/call.c:342
ROCm#214 0x3ffa5e83d1f in _PyObject_FastCallDictTstate Objects/call.c:142
ROCm#215 0x3ffa5e84937 in _PyObject_Call_Prepend Objects/call.c:431
ROCm#216 0x3ffa5f2f577 in slot_tp_call Objects/typeobject.c:7494
ROCm#217 0x3ffa5e843f3 in _PyObject_Call Objects/call.c:305
ROCm#218 0x3ffa5e84483 in PyObject_Call Objects/call.c:317
ROCm#219 0x3ffa5feb7cf in do_call_core Python/ceval.c:5943
ROCm#220 0x3ffa5fe6019 in _PyEval_EvalFrameDefault Python/ceval.c:4277
ROCm#221 0x3ffa5fd7aed in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#222 0x3ffa5fe8ba9 in _PyEval_Vector Python/ceval.c:5065
ROCm#223 0x3ffa5e8459b in _PyFunction_Vectorcall Objects/call.c:342
ROCm#224 0x3ffa5fd76a3 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#225 0x3ffa5fd772f in PyObject_Vectorcall Include/cpython/abstract.h:123
ROCm#226 0x3ffa5feb289 in call_function Python/ceval.c:5891
ROCm#227 0x3ffa5fe5b21 in _PyEval_EvalFrameDefault Python/ceval.c:4198
ROCm#228 0x3ffa5fd7aed in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#229 0x3ffa5fe8ba9 in _PyEval_Vector Python/ceval.c:5065
ROCm#230 0x3ffa5e8459b in _PyFunction_Vectorcall Objects/call.c:342
ROCm#231 0x3ffa5e8427f in PyVectorcall_Call Objects/call.c:267
ROCm#232 0x3ffa5e84347 in _PyObject_Call Objects/call.c:290
ROCm#233 0x3ffa5e84483 in PyObject_Call Objects/call.c:317
ROCm#234 0x3ffa5feb7cf in do_call_core Python/ceval.c:5943
ROCm#235 0x3ffa5fe6019 in _PyEval_EvalFrameDefault Python/ceval.c:4277
ROCm#236 0x3ffa5fd7aed in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#237 0x3ffa5fe8ba9 in _PyEval_Vector Python/ceval.c:5065
ROCm#238 0x3ffa5e8459b in _PyFunction_Vectorcall Objects/call.c:342
ROCm#239 0x3ffa5e8427f in PyVectorcall_Call Objects/call.c:267
ROCm#240 0x3ffa5e84347 in _PyObject_Call Objects/call.c:290
ROCm#241 0x3ffa5e84483 in PyObject_Call Objects/call.c:317
ROCm#242 0x3ffa5feb7cf in do_call_core Python/ceval.c:5943
ROCm#243 0x3ffa5fe6019 in _PyEval_EvalFrameDefault Python/ceval.c:4277
ROCm#244 0x3ffa5fd7aed in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#245 0x3ffa5fe8ba9 in _PyEval_Vector Python/ceval.c:5065
ROCm#246 0x3ffa5e8459b in _PyFunction_Vectorcall Objects/call.c:342
ROCm#247 0x3ffa5e8427f in PyVectorcall_Call Objects/call.c:267
ROCm#248 0x3ffa5e84347 in _PyObject_Call Objects/call.c:290
ROCm#249 0x3ffa5e84483 in PyObject_Call Objects/call.c:317
ROCm#250 0x3ffa5feb7cf in do_call_core Python/ceval.c:5943
ROCm#251 0x3ffa5fe6019 in _PyEval_EvalFrameDefault Python/ceval.c:4277
ROCm#252 0x3ffa5fd7aed in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#253 0x3ffa5fe8ba9 in _PyEval_Vector Python/ceval.c:5065
ROCm#254 0x3ffa5e8459b in _PyFunction_Vectorcall Objects/call.c:342
ROCm#255 0x3ffa5e8427f in PyVectorcall_Call Objects/call.c:267
0x03ff70f54570 is located 0 bytes to the right of global variable 'Sleef_rempitabsp' defined in '/home/user/pytorch/third_party/sleef/src/libm/rempitab.c:986:34' (0x3ff70f53f00) of size 1648
SUMMARY: AddressSanitizer: global-buffer-overflow /home/user/pytorch/third_party/sleef/src/arch/helpers390x_128.h:129 in vgather_vf_p_vi2
Shadow bytes around the buggy address:
0x10007fee1ea850: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
0x10007fee1ea860: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
0x10007fee1ea870: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
0x10007fee1ea880: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
0x10007fee1ea890: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
=>0x10007fee1ea8a0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00[f9]f9
0x10007fee1ea8b0: f9 f9 f9 f9 00 00 00 00 00 00 00 00 00 00 00 00
0x10007fee1ea8c0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
0x10007fee1ea8d0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
0x10007fee1ea8e0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
0x10007fee1ea8f0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
Shadow byte legend (one shadow byte represents 8 application bytes):
Addressable: 00
Partially addressable: 01 02 03 04 05 06 07
Heap left redzone: fa
Freed heap region: fd
Stack left redzone: f1
Stack mid redzone: f2
Stack right redzone: f3
Stack after return: f5
Stack use after scope: f8
Global redzone: f9
Global init order: f6
Poisoned by user: f7
Container overflow: fc
Array cookie: ac
Intra object redzone: bb
ASan internal: fe
Left alloca redzone: ca
Right alloca redzone: cb
Shadow gap: cc
==2030580==ABORTING
```
</details>
It reproduces when running `pytest -v test/test_ops.py -k test_python_ref__refs_cos_cpu_bfloat16` under address sanitizer on s390x.
See also: shibatch/sleef#464
Pull Request resolved: pytorch#102266
Approved by: https://github.com/malfet
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This is via the ROCm/pytorch:upstream branch.