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Summary: Checking assertExportImport for all of the generated test jit tests. Pull Request resolved: pytorch#10982 Differential Revision: D9636935 Pulled By: eellison fbshipit-source-id: f3f1ce77d454848098f2ac7e0fa18bf8564890be
…rch#11380) Summary: cc SsnL Pull Request resolved: pytorch#11380 Reviewed By: ezyang Differential Revision: D9705877 Pulled By: zou3519 fbshipit-source-id: 02470c25236f57fa02f4ac9d7ed63d38a6355db2
Summary: Pull Request resolved: pytorch#11167 Narrow the Blob API as preparation for merging Blob/IValue - get rid of templated IsType and Operator::InputIsType / OutputIsType - Use 'using' instead of 'typedef' for DestroyCall (just for readability) Reviewed By: ezyang Differential Revision: D9623916 fbshipit-source-id: 952f0b0cf5a525094b02e8d2798dd57a56a9e1d8
Summary: Pull Request resolved: pytorch#11238 - when moving an IValue, free the old value instead of keeping it allocated - making classes final - moving std::string - making ConstantList const Reviewed By: ezyang Differential Revision: D9644700 fbshipit-source-id: ab7228368e4f00f664ba54e1242b0307d91c5e7e
Summary: Pull Request resolved: pytorch#11258 The two intrusive_ptr constructors in Tensor can be combined into one implementation that does both, moving and copying. Reviewed By: ezyang Differential Revision: D9652088 fbshipit-source-id: 5efca02654ba305c99c20bbeb83551469d17a51d
Summary: Pull Request resolved: pytorch#11260 This is needed to make something like this work: intrusive_ptr<TensorImpl, UndefinedTensorImpl> a = make_intrusive<SparseTensorImpl>(...); Reviewed By: ezyang Differential Revision: D9652089 fbshipit-source-id: 19c65e98460ccb27bc69e36d7e558cb9d6e67615
Summary: Pull Request resolved: pytorch#11294 The Tensor(ptr, retain) constructor is error prone and circumvents the intrusive_ptr safety. This diff removes that and pushes the responsibility to callers. Step by step, manual refcounting can be pushed back and possibly eliminated in the end. Reviewed By: ezyang Differential Revision: D9663476 fbshipit-source-id: 7f010e5e47b137a9575960201c5bf5d552c5c2f5
Summary: I'm 80% sure that this fixes the math bug. But I can't repro locally so I don't know. Pull Request resolved: pytorch#11472 Differential Revision: D9755328 Pulled By: SsnL fbshipit-source-id: 130be664d3c6ceee3c0c166c1a86fc9ec3b79d74
…ytorch#11109) Summary: Normalizing by the world size before the reduction is less likely to cause overflow in FP16 training. Pull Request resolved: pytorch#11109 Differential Revision: D9594708 Pulled By: myleott fbshipit-source-id: 93ab53cb782ee1cbe1264e529b333490a0940338
Summary: The controller you requested could not be found. found there are some issues when using comparison operators for half types when certain THC header are included. I was able to reproduce and added a test. I also fix the issue by adding the proper definitions to avoid this issue. Reported in pytorch#10301 (comment) Related: pytorch/tutorials#292 soumith fmassa Pull Request resolved: pytorch#11395 Differential Revision: D9725102 Pulled By: goldsborough fbshipit-source-id: 630425829046bbebea3409bb792a9d62c91f41ad
Summary: NCCL1 uses `int` as its numerical type for fields like `count`, which makes broadcasting tensors larger than `2 << 31 - 1` impossible, and raises opaque error `invalid arguments`. NCCL2 greatly increase the limit on many platforms by using `size_t`. This patch statically detects this type, and raises properly if the broadcast tensor exceeds the limit. No test because I don't think our test suite should broadcast big tensors. Pull Request resolved: pytorch#11466 Differential Revision: D9754753 Pulled By: SsnL fbshipit-source-id: 73506450cae047e06b5b225b39efdb42d5d26685
…11288) Summary: Many constructors like `torch.zeros` or `torch.randn` didn't support size tracing correctly which is fixed by this pass. Same issue has been fixed in legacy tensor constructors. Additionally, new tensor constructors, which do not participate in tracing (most notably `torch.tensor`, `torch.as_tensor` and `torch.from_numpy`) raise a warning when they are used. Finally, entering a traceable operation disables the tracing in its body. This is needed because zdevito Pull Request resolved: pytorch#11288 Reviewed By: ezyang Differential Revision: D9751183 Pulled By: apaszke fbshipit-source-id: 51444a39d76a3e164adc396c432fd5ee3c8d5f7f
Summary: This PR is stacked on pytorch#10610, and only adds changes in one file `.jenkins/pytorch/test.sh`, where we now build the custom op tests and run them. I'd also like to take this PR to discuss whether the [`TorchConfig.cmake`](https://github.com/pytorch/pytorch/blob/master/cmake/TorchConfig.cmake.in) I made is robust enough (we will also see in the CI) orionr Yangqing dzhulgakov what do you think? Also ezyang for CI changes Pull Request resolved: pytorch#10611 Differential Revision: D9597627 Pulled By: goldsborough fbshipit-source-id: f5af8164c076894f448cef7e5b356a6b3159f8b3
…orms we care about. (pytorch#11394) Summary: While the use of memcpy as part of the byte swapping sequence looks funky, all major compilers recognize and optimize this pattern reliably, resulting in essentially optimal code generation. For example, decodeUInt32LE goes from this on iOS arm64: > ldrb w8, [x0, ROCm#3] > ldrb w9, [x0, ROCm#2] > bfi w8, w9, ROCm#8, ROCm#8 > ldrb w9, [x0, #1] > bfi w8, w9, ROCm#16, ROCm#8 > ldrb w9, [x0] > bfi w8, w9, ROCm#24, ROCm#8 > mov x0, x8 > ret To this: > ldr w8, [x0] > rev w0, w8 > ret Pull Request resolved: pytorch#11394 Reviewed By: SsnL Differential Revision: D9728659 Pulled By: resistor fbshipit-source-id: 9afbd4adfad1d1fb7b01f1179e6707ee21fa726f
Summary: Pull Request resolved: pytorch#10974 Pull Request resolved: pytorch#10291 This new operator will do the following: Given a LENGTHS vector and n_splits, output a "split" LENGTHS vector where: 1. Each length in input vector is split into n_splits values (thus output vector should have LENGTHS.size(0) * n_splits elements) 2. The new lengths in output should be evenly split, and if the length is not divisible by n_splits, then order new values in descending order. (e.g. n_splits = 3, length = 5 -> 2 2 1) 3. If n_splits > some element in the array, its split elements will contain 0s. (e.g. n_splits = 3, length = 2 - > 1 1 0) Reviewed By: bddppq, chocjy Differential Revision: D9013119 fbshipit-source-id: 82bf3371ec08c41fc3379177f0007afc142e0d84
Summary: There's a bunch of legacy code where people are explicitly instantiating Variable, and these call-sites have thus far been untraceable (appearing as prim::Constant nodes with the tensor value at the time of tracing). This makes it so that the new variable inherits the traced Value* from the tensor it's being constructed from Pull Request resolved: pytorch#11463 Differential Revision: D9756529 Pulled By: jamesr66a fbshipit-source-id: da99c6a7621957a305f2699ec9cb9def69b1b2d7
…orch#11411) Summary: Pull Request resolved: pytorch#11411 Simple fix Reviewed By: goldsborough Differential Revision: D9730371 fbshipit-source-id: f841327c01faa13cfb6b7fc6e279b8fc50fad1db
Summary: Skip torch tests as well when NO_TEST=1 environment variable is set. Also remove the separate ATen code path for not being built with Caffe2, since it will always be built with Caffe2. cc The controller you requested could not be found. Pull Request resolved: pytorch#11415 Reviewed By: soumith Differential Revision: D9758179 Pulled By: orionr fbshipit-source-id: e3e3327364fccdc57a703aeaad8c4f30452973fb
Summary: Add flags for LMDB and LevelDB, default `OFF`. These can be enabled with ``` USE_LMDB=1 USE_LEVELDB=1 python setup.py build_deps ``` Also add a flag to build Caffe2 ops, which is default `ON`. Disable with ``` NO_CAFFE2_OPS=1 python setup.py build_deps ``` cc Yangqing soumith pjh5 mingzhe09088 Pull Request resolved: pytorch#11462 Reviewed By: soumith Differential Revision: D9758156 Pulled By: orionr fbshipit-source-id: 95fd206d72fdf44df54fc5d0aeab598bff900c63
Summary: ebetica soumith ezyang Pull Request resolved: pytorch#11469 Differential Revision: D9757547 Pulled By: goldsborough fbshipit-source-id: a95673abe949bb81d716dbc03c5c3e2a11cc15d3
Summary: Document the `Functional` module in the C++ API. ebetica ezyang soumith Pull Request resolved: pytorch#11460 Differential Revision: D9757555 Pulled By: goldsborough fbshipit-source-id: 15f8bf6d60bd26f3f4e69fb8e414e186e3c220ee
Summary: Pull Request resolved: pytorch#11497 Differential Revision: D9762014 Pulled By: gchanan fbshipit-source-id: 996419cc5e86d000af953d030ff361adafb921ad
Summary: Pull Request resolved: pytorch#11508 Differential Revision: D9764380 Pulled By: goldsborough fbshipit-source-id: 3abb9c04f46137be833ea26d67734741e14f8010
Summary: The old `torch.distributed` will go to `torch.distributed.deprecated` The old DDP will go to `torch.nn.parallel.deprecated` Now `torch.nn.parallel.DDP` will use c10d DDP Now `torch.distributed` will use C10d frontend API Pull Request resolved: pytorch#11405 Reviewed By: pietern Differential Revision: D9733733 Pulled By: teng-li fbshipit-source-id: d6a3f3e73f8d3a7fcb1f4baef53c78063b8cbb08
…ibuted doc (pytorch#11450) Summary: This is the new documentation for c10d release, and it also deprecates the old torch.distributed document. This PR depends on pytorch#11405 and should only be landed after pytorch#11405 is landed Pull Request resolved: pytorch#11450 Differential Revision: D9765504 Pulled By: teng-li fbshipit-source-id: 48f38b27b8c270baf389f8e478ea226b9ecc63db
Summary: Pull Request resolved: pytorch#11418 Several improvements that aim to make the APIs more straightforward to use - Get rid of helper methods subgraph and nonTerminal . Users now should create a NNMatchGraph directly via graph's createNode and createEdge API - Get rid of operatorSubgraph helper method - invertGraphTraversal flag applies to both the match graph and the scanned graph. This allows user to create match graph in the same direction as the scanned graph, thus reduce confusion. - additional parameters of matchNode (count, includeInSubgraph, nonTerminal) are removed from the constructors and moved into setter methods. (We no longer enforce that MatchNode is immutable but this helps improve code clarity). - Tests are updated to reflect the changes Follow up changes: - Possibly clean up the tests further. This change aims to minimally modify the unit tests. - Help a validity check that enforce the current limitation of the match graph (single source node), and throws if the match graph does not satisfy the criteria. - Have the single source node be detected automatically and callers just need to pass in the matchGraph instead of the source node reference. Differential Revision: D9732565 fbshipit-source-id: ae8320e2bc89b867f6bb4b1c1aad635f4b219fa1
…1358) Summary: [Here's a list](https://gist.github.com/apaszke/f0821840bdcc67a977832dc58acc1b85) of ops that are in `register_aten_ops.cpp`, but aren't supported in shape prop. Everything else should work now. Pull Request resolved: pytorch#11358 Differential Revision: D9753693 Pulled By: apaszke fbshipit-source-id: efeae0126ce16cb56b8797fc5246405588bcae3c
Summary: This enabled `torch.einsum` both in tracing and in script mode. It's used all over Pyro at the moment, and is needed for any use of the JIT in there. Fixes pytorch#11157. zdevito fritzo neerajprad Pull Request resolved: pytorch#11506 Differential Revision: D9764787 Pulled By: apaszke fbshipit-source-id: 9b5251b9e7c5897034602bd07ff67b425d33326c
Summary: This adds a `.expand` method for distributions that is akin to the `torch.Tensor.expand` method for tensors. It returns a new distribution instance with batch dimensions expanded to the desired `batch_shape`. Since this calls `torch.Tensor.expand` on the distribution's parameters, it does not allocate new memory for the expanded distribution instance's parameters. e.g. ```python >>> d = dist.Normal(torch.zeros(100, 1), torch.ones(100, 1)) >>> d.sample().shape torch.Size([100, 1]) >>> d.expand([100, 10]).sample().shape torch.Size([100, 10]) ``` We have already been using the `.expand` method in Pyro in our [patch](https://github.com/uber/pyro/blob/dev/pyro/distributions/torch.py#L10) of `torch.distributions`. We use this in our models to enable dynamic broadcasting. This has also been requested by a few users on the distributions slack, and we believe will be useful to the larger community. Note that currently, there is no convenient and efficient way to expand distribution instances: - Many distributions use `TransformedDistribution` (or wrap over another distribution instance. e.g. `OneHotCategorical` uses a `Categorical` instance) under the hood, or have lazy parameters. This makes it difficult to collect all the relevant parameters, broadcast them and construct new instances. - In the few cases where this is even possible, the resulting implementation would be inefficient since we will go through a lot of broadcasting and args validation logic in `__init__.py` that can be avoided. The `.expand` method allows for a safe and efficient way to expand distribution instances. Additionally, this bypasses `__init__.py` (using `__new__` and populating relevant attributes) since we do not need to do any broadcasting or args validation (which was already done when the instance was first created). This can result in significant savings as compared to constructing new instances via `__init__` (that said, the `sample` and `log_prob` methods will probably be the rate determining steps in many applications). e.g. ```python >>> a = dist.Bernoulli(torch.ones([10000, 1]), validate_args=True) >>> %timeit a.expand([10000, 100]) 15.2 µs ± 224 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) >>> %timeit dist.Bernoulli(torch.ones([10000, 100]), validate_args=True) 11.8 ms ± 153 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) ``` cc. fritzo, apaszke, vishwakftw, alicanb Pull Request resolved: pytorch#11341 Differential Revision: D9728485 Pulled By: soumith fbshipit-source-id: 3b94c23bc6a43ee704389e6287aa83d1e278d52f
Summary: Moves TensorImpl to core. Renames UndefinedTensor to UndefinedTensorImpl and moves to core. Pull Request resolved: pytorch#11441 Differential Revision: D9736620 Pulled By: gchanan fbshipit-source-id: 0322ae3b903e338de253b35a0d74a9d3e219204b
Summary: fixes pytorch#11762 Pull Request resolved: pytorch#11768 Differential Revision: D9884185 Pulled By: soumith fbshipit-source-id: 2a0c3e1f5a4fb4833ae6e9fc791abcf45f7fbea2
Summary: I have no idea how to run distributed tests locally so I'll let CI do this. Hopefully everything still works with `IntEnum`. cc mcarilli Pull Request resolved: pytorch#11715 Reviewed By: pietern Differential Revision: D9889646 Pulled By: SsnL fbshipit-source-id: 1e2a487cb6fe0bd4cc67501c9d72a295c35693e2
… test_sparse Differential Revision: D9755189 Original commit changeset: e9d36f437db1 fbshipit-source-id: 8b99edf626418a953a8bd786847a6e0174a3a14d
Summary: More functionality to prep nomnigraph for scheduler implementations Reviewed By: duc0 Differential Revision: D9794686 fbshipit-source-id: b460859d8ff965d0049b2a696bd8d2f5c97f3f86
Summary: Pull Request resolved: pytorch#11805 Some of our headers in Caffe2 pollute the macro namespace with things like MAX, MIN, CHECK, so I renamed these in places where this is a problem. This patch courtesy of gchanan, extracted out of pytorch#11721 Reviewed By: Yangqing Differential Revision: D9917757 fbshipit-source-id: 17fc692ca04b208dcb8ae00731ed60e393284f7c
Summary: For example, outputs of control blocks often have Dynamic type, and when we try to export them to ONNX we get an invalid proto, since `elem_type` is not populated on the TypeInfoProto. This makes it so at least we can get past the checker, since having a dynamic typed output from a control block should still be semantically valid Pull Request resolved: pytorch#11810 Differential Revision: D9922754 Pulled By: jamesr66a fbshipit-source-id: 5c66113cc302a9d9b8b9f5a8605473d3c6ad5af1
Summary: The PR fixes pytorch#10873 The context is aten::add and aten::sub ST overloads don't have alpha, so onnx symbolic does not match. Pull Request resolved: pytorch#10972 Reviewed By: jamesr66a Differential Revision: D9724224 Pulled By: wanchaol fbshipit-source-id: eb5d1b09fa8f1604b288f4a62b8d1f0bc66611af
Summary: cc apaszke Pull Request resolved: pytorch#11799 Differential Revision: D9922745 Pulled By: orionr fbshipit-source-id: b88724b7c2919aabc00d98658e8e563233e01c85
Differential Revision: D9919120 Pulled By: goldsborough fbshipit-source-id: bf14cbe4ab79524495957cb749828046af864aab
Summary: Pull Request resolved: pytorch#11402 - Simplify move constructor/assignment - Make more things noexcept Reviewed By: ezyang Differential Revision: D9728631 fbshipit-source-id: 92562e30ea1e4d05ca857665a02b0ca66b0739e3
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@pytorchbot retest this please |
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@pytorchbot retest this please |
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@pytorchbot retest this please |
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@pytorchbot retest this please |
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@pytorchbot retest this please |
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@pytorchbot retest this please |
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@pytorchbot retest this please |
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@pytorchbot retest this please |
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Everything but windows passed this time. @pytorchbot retest this please |
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Finally! |
lcskrishna
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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:
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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
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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
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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>,
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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
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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
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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
ROCm#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
ROCm#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*)::$_45&&, c10::IValue (*)(torch::jit::mobile::Module&, pybind11::tuple const&), pybind11::name const&, pybind11::is_me
thod const&, pybind11::sibling const&, pybind11::arg const&)::{lambda(pybind11::detail::function_call&)ROCm#1}::operator()(pybind11::detail::function_call&) const /home/user/pytorch/cmake/../third_party/pybin
d11/include/pybind11/pybind11.h:249
ROCm#6 0x3ff85317cfd in pybind11::cpp_function::initialize<torch::jit::initJitScriptBindings(_object*)::$_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
thod const&, pybind11::sibling const&, pybind11::arg const&)::{lambda(pybind11::detail::function_call&)ROCm#1}::__invoke(pybind11::detail::function_call&) /home/user/pytorch/cmake/../third_party/pybind11/incl
ude/pybind11/pybind11.h:224
ROCm#7 0x3ff82ee52e9 in pybind11::cpp_function::dispatcher(_object*, _object*, _object*) /home/user/pytorch/cmake/../third_party/pybind11/include/pybind11/pybind11.h:929
ROCm#8 0x3ffab002903 in cfunction_call Objects/methodobject.c:543
ROCm#9 0x3ffaaf8a933 in _PyObject_MakeTpCall Objects/call.c:215
ROCm#10 0x3ffaaf8e919 in _PyObject_VectorcallTstate Include/cpython/abstract.h:112
ROCm#11 0x3ffaaf8eddd in method_vectorcall Objects/classobject.c:53
ROCm#12 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#13 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
ROCm#14 0x3ffab105447 in call_function Python/ceval.c:5891
ROCm#15 0x3ffab0ff779 in _PyEval_EvalFrameDefault Python/ceval.c:4181
ROCm#16 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#17 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#18 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#19 0x3ffaaf8a615 in _PyObject_FastCallDictTstate Objects/call.c:142
ROCm#20 0x3ffaaf8b271 in _PyObject_Call_Prepend Objects/call.c:431
ROCm#21 0x3ffab03f307 in slot_tp_call Objects/typeobject.c:7494
ROCm#22 0x3ffaaf8a933 in _PyObject_MakeTpCall Objects/call.c:215
ROCm#23 0x3ffab0f0081 in _PyObject_VectorcallTstate Include/cpython/abstract.h:112
ROCm#24 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
ROCm#25 0x3ffab105447 in call_function Python/ceval.c:5891
ROCm#26 0x3ffab0ff905 in _PyEval_EvalFrameDefault Python/ceval.c:4213
ROCm#27 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#28 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#29 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#30 0x3ffaaf8e941 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#31 0x3ffaaf8eddd in method_vectorcall Objects/classobject.c:53
ROCm#32 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#33 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
ROCm#34 0x3ffab105447 in call_function Python/ceval.c:5891
ROCm#35 0x3ffab0ff905 in _PyEval_EvalFrameDefault Python/ceval.c:4213
ROCm#36 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#37 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#38 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#39 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#40 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
ROCm#41 0x3ffab105447 in call_function Python/ceval.c:5891
ROCm#42 0x3ffab0ff7d7 in _PyEval_EvalFrameDefault Python/ceval.c:4198
ROCm#43 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#44 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#45 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#46 0x3ffaaf8e941 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#47 0x3ffaaf8eddd in method_vectorcall Objects/classobject.c:53
ROCm#48 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#49 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
ROCm#50 0x3ffab105447 in call_function Python/ceval.c:5891
ROCm#51 0x3ffab0ffa57 in _PyEval_EvalFrameDefault Python/ceval.c:4231
ROCm#52 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#53 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#54 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#55 0x3ffaaf8e941 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#56 0x3ffaaf8eddd in method_vectorcall Objects/classobject.c:53
ROCm#57 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#58 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
ROCm#59 0x3ffab105447 in call_function Python/ceval.c:5891
ROCm#60 0x3ffab0ffa57 in _PyEval_EvalFrameDefault Python/ceval.c:4231
ROCm#61 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#62 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#63 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#64 0x3ffaaf8e941 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#65 0x3ffaaf8eddd in method_vectorcall Objects/classobject.c:53
ROCm#66 0x3ffaaf8ab9b in PyVectorcall_Call Objects/call.c:267
ROCm#67 0x3ffaaf8ac65 in _PyObject_Call Objects/call.c:290
ROCm#68 0x3ffaaf8ada9 in PyObject_Call Objects/call.c:317
ROCm#69 0x3ffab1059c7 in do_call_core Python/ceval.c:5943
ROCm#70 0x3ffab0ffd39 in _PyEval_EvalFrameDefault Python/ceval.c:4277
ROCm#71 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#72 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#73 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#74 0x3ffaaf8a695 in _PyObject_FastCallDictTstate Objects/call.c:153
ROCm#75 0x3ffaaf8b271 in _PyObject_Call_Prepend Objects/call.c:431
ROCm#76 0x3ffab03f307 in slot_tp_call Objects/typeobject.c:7494
ROCm#77 0x3ffaaf8a933 in _PyObject_MakeTpCall Objects/call.c:215
ROCm#78 0x3ffab0f0081 in _PyObject_VectorcallTstate Include/cpython/abstract.h:112
ROCm#79 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
ROCm#80 0x3ffab105447 in call_function Python/ceval.c:5891
ROCm#81 0x3ffab0ffa57 in _PyEval_EvalFrameDefault Python/ceval.c:4231
ROCm#82 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#83 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#84 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#85 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#86 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
ROCm#87 0x3ffab105447 in call_function Python/ceval.c:5891
ROCm#88 0x3ffab0ff7d7 in _PyEval_EvalFrameDefault Python/ceval.c:4198
ROCm#89 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#90 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#91 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#92 0x3ffaaf8ab15 in PyVectorcall_Call Objects/call.c:255
ROCm#93 0x3ffaaf8ac65 in _PyObject_Call Objects/call.c:290
ROCm#94 0x3ffaaf8ada9 in PyObject_Call Objects/call.c:317
ROCm#95 0x3ffab1059c7 in do_call_core Python/ceval.c:5943
ROCm#96 0x3ffab0ffd39 in _PyEval_EvalFrameDefault Python/ceval.c:4277
ROCm#97 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#98 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#99 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#100 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#101 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
ROCm#102 0x3ffab105447 in call_function Python/ceval.c:5891
ROCm#103 0x3ffab0ff779 in _PyEval_EvalFrameDefault Python/ceval.c:4181
ROCm#104 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#105 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#106 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#107 0x3ffaaf8e941 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#108 0x3ffaaf8eddd in method_vectorcall Objects/classobject.c:53
ROCm#109 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#110 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
ROCm#111 0x3ffab105447 in call_function Python/ceval.c:5891
ROCm#112 0x3ffab0ff779 in _PyEval_EvalFrameDefault Python/ceval.c:4181
ROCm#113 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#114 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#115 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#116 0x3ffaaf8a695 in _PyObject_FastCallDictTstate Objects/call.c:153
ROCm#117 0x3ffaaf8b271 in _PyObject_Call_Prepend Objects/call.c:431
ROCm#118 0x3ffab03f307 in slot_tp_call Objects/typeobject.c:7494
ROCm#119 0x3ffaaf8ad17 in _PyObject_Call Objects/call.c:305
ROCm#120 0x3ffaaf8ada9 in PyObject_Call Objects/call.c:317
ROCm#121 0x3ffab1059c7 in do_call_core Python/ceval.c:5943
ROCm#122 0x3ffab0ffd39 in _PyEval_EvalFrameDefault Python/ceval.c:4277
ROCm#123 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#124 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#125 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#126 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#127 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
ROCm#128 0x3ffab105447 in call_function Python/ceval.c:5891
ROCm#129 0x3ffab0ff905 in _PyEval_EvalFrameDefault Python/ceval.c:4213
ROCm#130 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#131 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#132 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#133 0x3ffaaf8e941 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#134 0x3ffaaf8eddd in method_vectorcall Objects/classobject.c:53
ROCm#135 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#136 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
ROCm#137 0x3ffab105447 in call_function Python/ceval.c:5891
ROCm#138 0x3ffab0ffa57 in _PyEval_EvalFrameDefault Python/ceval.c:4231
ROCm#139 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#140 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#141 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#142 0x3ffaaf8ab15 in PyVectorcall_Call Objects/call.c:255
ROCm#143 0x3ffaaf8ac65 in _PyObject_Call Objects/call.c:290
ROCm#144 0x3ffaaf8ada9 in PyObject_Call Objects/call.c:317
ROCm#145 0x3ffab1059c7 in do_call_core Python/ceval.c:5943
ROCm#146 0x3ffab0ffd39 in _PyEval_EvalFrameDefault Python/ceval.c:4277
ROCm#147 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#148 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#149 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#150 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#151 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
ROCm#152 0x3ffab105447 in call_function Python/ceval.c:5891
ROCm#153 0x3ffab0ff905 in _PyEval_EvalFrameDefault Python/ceval.c:4213
ROCm#154 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#155 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#156 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#157 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#158 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
ROCm#159 0x3ffab105447 in call_function Python/ceval.c:5891
ROCm#160 0x3ffab0ffa57 in _PyEval_EvalFrameDefault Python/ceval.c:4231
ROCm#161 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#162 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#163 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#164 0x3ffaaf8ab15 in PyVectorcall_Call Objects/call.c:255
ROCm#165 0x3ffaaf8ac65 in _PyObject_Call Objects/call.c:290
ROCm#166 0x3ffaaf8ada9 in PyObject_Call Objects/call.c:317
ROCm#167 0x3ffab1059c7 in do_call_core Python/ceval.c:5943
ROCm#168 0x3ffab0ffd39 in _PyEval_EvalFrameDefault Python/ceval.c:4277
ROCm#169 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#170 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#171 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#172 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#173 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
ROCm#174 0x3ffab105447 in call_function Python/ceval.c:5891
ROCm#175 0x3ffab0ff779 in _PyEval_EvalFrameDefault Python/ceval.c:4181
ROCm#176 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#177 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#178 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#179 0x3ffaaf8e941 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#180 0x3ffaaf8eddd in method_vectorcall Objects/classobject.c:53
ROCm#181 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#182 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
ROCm#183 0x3ffab105447 in call_function Python/ceval.c:5891
ROCm#184 0x3ffab0ff779 in _PyEval_EvalFrameDefault Python/ceval.c:4181
ROCm#185 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#186 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#187 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#188 0x3ffaaf8a695 in _PyObject_FastCallDictTstate Objects/call.c:153
ROCm#189 0x3ffaaf8b271 in _PyObject_Call_Prepend Objects/call.c:431
ROCm#190 0x3ffab03f307 in slot_tp_call Objects/typeobject.c:7494
ROCm#191 0x3ffaaf8a933 in _PyObject_MakeTpCall Objects/call.c:215
ROCm#192 0x3ffab0f0081 in _PyObject_VectorcallTstate Include/cpython/abstract.h:112
ROCm#193 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
ROCm#194 0x3ffab105447 in call_function Python/ceval.c:5891
ROCm#195 0x3ffab0ffa57 in _PyEval_EvalFrameDefault Python/ceval.c:4231
ROCm#196 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#197 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#198 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#199 0x3ffaaf8ab15 in PyVectorcall_Call Objects/call.c:255
ROCm#200 0x3ffaaf8ac65 in _PyObject_Call Objects/call.c:290
ROCm#201 0x3ffaaf8ada9 in PyObject_Call Objects/call.c:317
ROCm#202 0x3ffab1059c7 in do_call_core Python/ceval.c:5943
ROCm#203 0x3ffab0ffd39 in _PyEval_EvalFrameDefault Python/ceval.c:4277
ROCm#204 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#205 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#206 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#207 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#208 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
ROCm#209 0x3ffab105447 in call_function Python/ceval.c:5891
ROCm#210 0x3ffab0ff779 in _PyEval_EvalFrameDefault Python/ceval.c:4181
ROCm#211 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#212 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#213 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#214 0x3ffaaf8e941 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#215 0x3ffaaf8eddd in method_vectorcall Objects/classobject.c:53
ROCm#216 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#216 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#217 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
ROCm#218 0x3ffab105447 in call_function Python/ceval.c:5891
ROCm#219 0x3ffab0ff779 in _PyEval_EvalFrameDefault Python/ceval.c:4181
ROCm#220 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#221 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#222 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#223 0x3ffaaf8a695 in _PyObject_FastCallDictTstate Objects/call.c:153
ROCm#224 0x3ffaaf8b271 in _PyObject_Call_Prepend Objects/call.c:431
ROCm#225 0x3ffab03f307 in slot_tp_call Objects/typeobject.c:7494
ROCm#226 0x3ffaaf8a933 in _PyObject_MakeTpCall Objects/call.c:215
ROCm#227 0x3ffab0f0081 in _PyObject_VectorcallTstate Include/cpython/abstract.h:112
ROCm#228 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
ROCm#229 0x3ffab105447 in call_function Python/ceval.c:5891
ROCm#230 0x3ffab0ffa57 in _PyEval_EvalFrameDefault Python/ceval.c:4231
ROCm#231 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#232 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#233 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#234 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#235 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
ROCm#236 0x3ffab105447 in call_function Python/ceval.c:5891
ROCm#237 0x3ffab0ff905 in _PyEval_EvalFrameDefault Python/ceval.c:4213
ROCm#238 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#239 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#240 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#241 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
ROCm#242 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
ROCm#243 0x3ffab105447 in call_function Python/ceval.c:5891
ROCm#244 0x3ffab0ff905 in _PyEval_EvalFrameDefault Python/ceval.c:4213
ROCm#245 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
ROCm#246 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
ROCm#247 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
ROCm#248 0x3ffaaf8ab15 in PyVectorcall_Call Objects/call.c:255
ROCm#249 0x3ffaaf8ac65 in _PyObject_Call Objects/call.c:290
0x60d0005a5790 is located 80 bytes inside of 136-byte region [0x60d0005a5740,0x60d0005a57c8)
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
ROCm#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
ROCm#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
ROCm#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
ROCm#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
ROCm#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*)::$_45&&, c10::IValue (*)(torch::jit::mobile::Module&, pybind11::tuple const&), pybind11::name const&, pybind11::is_method const&, pybind11::sibling const&, pybind11::arg const&)::{lambda(pybind11::detail::function_call&)ROCm#1}::operator()(pybind11::detail::function_call&) const (this=0x3ffdbb4ca20, call=...)
at /home/user/pytorch/cmake/../third_party/pybind11/include/pybind11/pybind11.h:249
ROCm#20 0x000003ff70d97cfe in pybind11::cpp_function::initialize<torch::jit::initJitScriptBindings(_object*)::$_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&)ROCm#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
ROCm#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
ROCm#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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