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Summary: Pull Request resolved: pytorch#40171 It checks that all of the bdims in BatchedTensorImpl are sorted in order of ascending `level`. Test Plan: - Check that nothing breaks in `./build/bin/vmap_test` Differential Revision: D22102077 Pulled By: zou3519 fbshipit-source-id: 094b7abc6c65208437f0f51a0d0083091912decc
Summary: Pull Request resolved: pytorch#40172 This PR introduces the initial vmap frontend API. It has the following limitations that we can resolve in the future: - the inputs must be a flat list of tensors - the outputs must be a flat list of tensors - in_dims = 0 (so we always vmap over dim 0 of input tensors) - out_dims = 0 (so the returned tensors have their vmap dim appear at dim 0) - Coverage limited to operations that have batching rules implemented (torch.mul, torch.sum, torch.expand). There are some other semantic limitations (like not being able to handle mutation, aside from pytorch operations that perform mutation) that will be documented in the future. I wanted to introduce the API before adding a slow fallback for the coverage so that we can test future batching rules (and coverage) via the python API to avoid verbosity in C++-land. The way vmap works is that `vmap(func)(inputs)` wraps all Tensor inputs to be batched in BatchedTensors, sends those into func, and then unwraps the output BatchedTensors. Operations on BatchedTensors perform the batched operations that the user is asking for. When performing nested vmaps, each nested vmap adds a batch dimension upon entry and removes a batch dimension on exit. Coming up in the near future: - Support for non-zero in_dims and out_dims - docstring for vmap - slow fallback for operators that do not have a batching rule implemented. Test Plan: - `pytest test/test_vmap.py -v` Differential Revision: D22102076 Pulled By: zou3519 fbshipit-source-id: b119f0a8a3a3b1717c92dbbd180dfb1618295563
Summary: Partial support for slicing of Sequential containers. - works around missing Sequential slice functionality by converting to tuple - only supports iteration of resulting tuple values, not direct call() on the sliced sequential Pull Request resolved: pytorch#40445 Differential Revision: D22192469 Pulled By: wconstab fbshipit-source-id: 61c85deda2d58f6e3bea2f1fa1d5d5dde568b9b5
…36786) Summary: Should close pytorch#35810. I decided to keep sparse handling on the Python side for clarity, although it could be moved to the C++ side (into `_amp_non_finite_check_and_unscale_`) without much trouble. For non-fp16 sparse grads the logic is simple (call `_amp_non_finite_check_and_unscale_` on `grad._values()`) instead of `grad` itself. At least I hope it's that easy. For fp16 sparse grads, it's tricker. Sparse tensors can be uncoalesced. From the [Note](https://pytorch.org/docs/master/sparse.html#torch.sparse.FloatTensor): > Our sparse tensor format permits uncoalesced sparse tensors, where there may be duplicate coordinates in the indices; in this case, the interpretation is that the value at that index is the sum of all duplicate value entries. An uncoalesced scaled fp16 grad may have values at duplicate coordinates that are all finite but large, such that adding them to make the coalesced version WOULD cause overflows.** If I checked `_values()` on the uncoalesced version, it might not report overflows, but I think it should. So, if the grad is sparse, fp16, and uncoalesced, I still call `_amp_non_finite_check_and_unscale_` to unscale `grad._values()` in-place, but I also double-check the coalesced version by calling a second `_amp_non_finite_check_and_unscale_` on `grad.coalesce()._values()`. `coalesce()` is out-of-place, so this call doesn't redundantly affect `grad._values()`, but it does have the power to populate the same `found_inf` tensor. The `is_coalesced()` check and `coalesce()` probably aren't great for performance, but if someone needs a giant embedding table in FP16, they're better than nothing and memorywise, they'll only create a copy of nnz gradient values+indices, which is still way better than changing the whole table to FP32. An `unscale` variant with liberty to create unscaled grads out-of-place, and replace `param.grad` instead of writing through it, could get away with just one `_amp_non_finite_check_and_unscale_`. It could say `coalesced = grad.coalesced()`, do only the stronger `_amp_non_finite_check_and_unscale_` on `coalesced._values()`, and set `param.grad = coalesced`. I could even avoid replacing `param.grad` itself by going one level deeper and setting `param.grad`'s indices and values to `coalesced`'s, but that seems brittle and still isn't truly "in place". ** you could whiteboard an uncoalesced fp32 grad with the same property, but fp32's range is big enough that I don't think it's realistic. Pull Request resolved: pytorch#36786 Reviewed By: ezyang Differential Revision: D22202832 Pulled By: ngimel fbshipit-source-id: b70961a4b6fc3a4c1882f65e7f34874066435735
…ytorch#40146) Summary: Currently, even if USE_OPENMP is turned off, ATEN_THEADING can still use OpenMP. This commit fixes it. Pull Request resolved: pytorch#40146 Reviewed By: ezyang Differential Revision: D22208758 Pulled By: pbelevich fbshipit-source-id: 0866c9bb9b3b5b99d586aed176eb0fbe177efa4a
…ch#40494) Summary: Pull Request resolved: pytorch#40494 Resubmit the diff because D22124313 (pytorch@1ec4337) was reverted due to CI test failures Added the int8_gen_quant_params.cc to CMakeList.txt to fix the CI failures Test Plan: buck test caffe2/caffe2/quantization/server: Reviewed By: hx89 Differential Revision: D22204244 fbshipit-source-id: a2c8b668f199cc5b0c5894086f554f7c459b1ad7
…ytorch#40115) Summary: Pull Request resolved: pytorch#40115 Closes pytorch#37790 Closes pytorch#37944 A user may wish to run DDP's forward + backwards step under a non-default CUDA stream such as those created by `with torch.cuda.Stream(stream)`. In this case, the user should be responsible for synchronizing events on this stream with other streams used in the program (per the documentation at https://pytorch.org/docs/stable/notes/cuda.html#cuda-semantics), but currently DDP has a bug which causes DDP under non-default streams to fail. If a user does the following: ``` model = DDP(...) loss = model(inptut).sum() loss.backward() grad = model.module.weight.grad() average = dist.all_reduce(grad) ``` There is a chance that `average` and `grad` will not be equal. This is because the CUDA kernels corresponding to the `all_reduce` call may run before `loss.backward()`'s kernels are finished. Specifically, in DDP we copy the allreduced gradients back to the model parameter gradients in an autograd engine callback, but this callback runs on the default stream. Note that this can also be fixed by the application synchronizing on the current stream, although this should not be expected, since the application is not using the current stream at all. This PR fixes the issue by passing the current stream into DDP's callback. Tested by adding a UT `test_DistributedDataParallel_non_default_stream` that fails without this PR ghstack-source-id: 106481208 Differential Revision: D22073353 fbshipit-source-id: 70da9b44e5f546ff8b6d8c42022ecc846dff033e
Summary: Pull Request resolved: pytorch#40506 Test Plan: Imported from OSS Differential Revision: D22208965 Pulled By: mrshenli fbshipit-source-id: 7d27b60e2c09e641b4eeb1c89d9f9917c4e72e52
Summary: BC NOTE: This change makes it so modules saved with torch.jit.save in PyTorch 1.6 can be loaded by previous versions of PyTorch unless they use torch.div or (soon) torch.full. It also lets tensors saved using torch.save be loaded by previous versions. So this is the opposite of BC-breaking, but I'm using that label to highlight this issue since we don't have a "BC-improving" label. PR NOTE: When an operator's semantics change in PyTorch we want to do two things: 1) Preserve the semantics of older serialized Torchscript programs that use the operator 2) Ensure the new semantics are respected Historically, this meant writing a Versioned Symbol that would remap older versions of the operator into current PyTorch code (1), and bumping the produced file format version (2). Unfortunately, bumping the produced file format version is a nuclear option for ensuring semantics are respected, since it also prevents older versions of PyTorch from loading anything (even tensors!) from newer versions. Dynamic versioning addresses the nuclear consequences of bumping the produced file format version by only bumping it when necessary. That is, when an operator with changed semantics is detected in the serialized Torchscript. This will prevent Torchscript programs that use the changed operator from loading on earlier versions of PyTorch, as desired, but will have no impact on programs that don't use the changed operator. Note that this change is only applicable when using torch.jit.save and torch.jit.load. torch.save pickles the given object using pickle (by default), which saves a function's Python directly. No new tests for this behavior are added since the existing tests for versioned division in test_save_load already validate that models with div are loaded correctly at version 4. Pull Request resolved: pytorch#40279 Reviewed By: dzhulgakov Differential Revision: D22168291 Pulled By: mruberry fbshipit-source-id: e71d6380e727e25123c7eedf6d80e5d7f1fe9f95
Summary: Partially fixes pytorch#38911 Pull Request resolved: pytorch#39681 Differential Revision: D22161342 Pulled By: mrshenli fbshipit-source-id: 60295077159b02087823e93bb6ebac9d70adea0a
Summary: Pull Request resolved: pytorch#40483 Reviewed By: ezyang Differential Revision: D22213696 Pulled By: ngimel fbshipit-source-id: 0321eee8fcaf144b20a5182aa76f98d505c65400
Summary: PyTorch should stop polluting global namespace with symbols such as `ERROR` `WARNING` and `INFO`. Since `logging_is_not_google_glog.h` is a C++ header, define severity levels in namespace and add `GLOG_` prefix to match an unshortened glog severity levels. Change `LOG` and `LOG_IF` macros to use prefix + namespaced severity levels. Closes pytorch#40083 Pull Request resolved: pytorch#40491 Test Plan: CI Reviewed By: ezyang Differential Revision: D22210925 Pulled By: malfet fbshipit-source-id: 0ec1181a53baa8bca2f526f245e398582304aeab
…on (pytorch#40241) Summary: Pull Request resolved: pytorch#40241 We abort incomplete NCCL Communicators in the ProcessGroupNCCL destructor, otherwise pending NCCL communciators may block other CUDA ops. Closes: pytorch#32231 ghstack-source-id: 106469423 Test Plan: CI/Sandcastle Reviewed By: jiayisuse Differential Revision: D22103662 fbshipit-source-id: 1f6f88b56bd7a5e9ca5a41698995a76e60e8ad9f
…torch#40404) Summary: Pull Request resolved: pytorch#40404 Adds docs to the finish function in ProcessGroup::Work. It's better to have some documentation around these functions since we have some PR's with API-changes/optimizations for these work-level functions here and in the subclasses. ghstack-source-id: 106381736 Test Plan: CI (Docs change only) Differential Revision: D22174891 fbshipit-source-id: 7901ea3b35caf6f69f37178ca574104d3412de28
…ytorch#40405) Summary: Pull Request resolved: pytorch#40405 This adds a finishAndThrow function that completes the work object, sets an exception if one is provided by the user, and throws an exception (if it is already set or passed by the caller). This is now done by grabbing the lock just once and simplifies the wait functions in ProcessGroupGloo. ghstack-source-id: 106516114 Test Plan: CI Differential Revision: D22174890 fbshipit-source-id: ea74702216c4328187c8d193bf39e1fea43847f6
Summary: Addresses pytorch#40485. Pull Request resolved: pytorch#40486 Differential Revision: D22217493 Pulled By: malfet fbshipit-source-id: 6654c3b53e8af063b508f91728e58262ffbab053
Summary: pytorch#24697 VitalyFedyunin glaringlee Test script: ```Python import timeit setup_ones = """ import torch a = torch.ones(({n}, {n}), dtype={dtype}) b = torch.ones(({n}, {n}), dtype={dtype}) """ for n, t in [(1000, 10000), (2000, 10000)]: for dtype in ('torch.bool', 'torch.int', 'torch.long', 'torch.bfloat16', 'torch.float', 'torch.double'): #for dtype in ('torch.bool', 'torch.int', 'torch.long', 'torch.float', 'torch.double'): print('torch.ones(({n}, {n})) equal for {t} times {dtype}'.format(n=n, t=t, dtype=dtype)) print(timeit.timeit(stmt='torch.equal(a, b)', setup=setup_ones.format(n=n, dtype=dtype), number=t)) setup_rand = """ import torch a = torch.rand(({n}, {n}), dtype={dtype}) b = a.clone() """ for n, t in [(1000, 10000), (2000, 10000)]: for dtype in ('torch.float', 'torch.double'): print('torch.rand(({n}, {n})) for {t} times {dtype}'.format(n=n, t=t, dtype=dtype)) print(timeit.timeit(stmt='torch.equal(a, b)', setup=setup_rand.format(n=n, dtype=dtype), number=t)) setup_non_contiguous = """ import torch a = torch.rand(({n}, {n}), dtype={dtype}) a2 = a[:, 500:] a3 = a2.clone() torch.equal(a2, a3) """ for n, t in [(1000, 10000), (2000, 10000)]: for dtype in ('torch.float', 'torch.double'): print('non_contiguous torch.rand(({n}, {n})) for {t} times {dtype}'.format(n=n, t=t, dtype=dtype)) print(timeit.timeit(stmt='torch.equal(a2, a3)', setup=setup_non_contiguous.format(n=n, dtype=dtype), number=t)) setup_not_equal = """ import torch a = torch.rand(({n}, {n}), dtype={dtype}) b = torch.rand(({n}, {n}), dtype={dtype}) torch.equal(a, b) """ for n, t in [(1000, 10000), (2000, 10000)]: for dtype in ('torch.float', 'torch.double'): print('not equal torch.rand(({n}, {n})) for {t} times {dtype}'.format(n=n, t=t, dtype=dtype)) print(timeit.timeit(stmt='torch.equal(a, b)', setup=setup_not_equal.format(n=n, dtype=dtype), number=t)) ``` TH ``` torch.ones((1000, 1000)) equal for 10000 times torch.bool 1.8391206220258027 torch.ones((1000, 1000)) equal for 10000 times torch.int 1.8877864250680432 torch.ones((1000, 1000)) equal for 10000 times torch.long 1.938108820002526 torch.ones((1000, 1000)) equal for 10000 times torch.bfloat16 3.184849138953723 torch.ones((1000, 1000)) equal for 10000 times torch.float 1.8825413499725983 torch.ones((1000, 1000)) equal for 10000 times torch.double 2.7266416549682617 torch.ones((2000, 2000)) equal for 10000 times torch.bool 7.227149627986364 torch.ones((2000, 2000)) equal for 10000 times torch.int 7.76215292501729 torch.ones((2000, 2000)) equal for 10000 times torch.long 9.631909006042406 torch.ones((2000, 2000)) equal for 10000 times torch.bfloat16 8.097328286035918 torch.ones((2000, 2000)) equal for 10000 times torch.float 5.5739822529722005 torch.ones((2000, 2000)) equal for 10000 times torch.double 8.444009944912978 torch.rand((1000, 1000)) for 10000 times torch.float 1.168096570065245 torch.rand((1000, 1000)) for 10000 times torch.double 1.6577326939441264 torch.rand((2000, 2000)) for 10000 times torch.float 5.49395391496364 torch.rand((2000, 2000)) for 10000 times torch.double 8.507486199960113 non_contiguous torch.rand((1000, 1000)) for 10000 times torch.float 6.074504268006422 non_contiguous torch.rand((1000, 1000)) for 10000 times torch.double 6.1426916810451075 non_contiguous torch.rand((2000, 2000)) for 10000 times torch.float 37.501055537955835 non_contiguous torch.rand((2000, 2000)) for 10000 times torch.double 44.6880351039581 not equal torch.rand((1000, 1000)) for 10000 times torch.float 0.029356416082009673 not equal torch.rand((1000, 1000)) for 10000 times torch.double 0.025421109050512314 not equal torch.rand((2000, 2000)) for 10000 times torch.float 0.026333761983551085 not equal torch.rand((2000, 2000)) for 10000 times torch.double 0.02748022007290274 ``` ATen ``` torch.ones((1000, 1000)) equal for 10000 times torch.bool 0.7961567062884569 torch.ones((1000, 1000)) equal for 10000 times torch.int 0.49172434909269214 torch.ones((1000, 1000)) equal for 10000 times torch.long 0.9459248608909547 torch.ones((1000, 1000)) equal for 10000 times torch.bfloat16 2.0877483217045665 torch.ones((1000, 1000)) equal for 10000 times torch.float 0.606857153121382 torch.ones((1000, 1000)) equal for 10000 times torch.double 1.1388208279386163 torch.ones((2000, 2000)) equal for 10000 times torch.bool 2.0329296849668026 torch.ones((2000, 2000)) equal for 10000 times torch.int 3.534358019940555 torch.ones((2000, 2000)) equal for 10000 times torch.long 8.19841272290796 torch.ones((2000, 2000)) equal for 10000 times torch.bfloat16 6.595649406313896 torch.ones((2000, 2000)) equal for 10000 times torch.float 4.193911510054022 torch.ones((2000, 2000)) equal for 10000 times torch.double 7.931309659034014 torch.rand((1000, 1000)) for 10000 times torch.float 0.8877940969541669 torch.rand((1000, 1000)) for 10000 times torch.double 1.4142901846207678 torch.rand((2000, 2000)) for 10000 times torch.float 4.010025603231043 torch.rand((2000, 2000)) for 10000 times torch.double 8.126411964651197 non_contiguous torch.rand((1000, 1000)) for 10000 times torch.float 0.602473056409508 non_contiguous torch.rand((1000, 1000)) for 10000 times torch.double 0.6784545010887086 non_contiguous torch.rand((2000, 2000)) for 10000 times torch.float 3.0991827426478267 non_contiguous torch.rand((2000, 2000)) for 10000 times torch.double 5.719010795000941 not equal torch.rand((1000, 1000)) for 10000 times torch.float 0.046060710679739714 not equal torch.rand((1000, 1000)) for 10000 times torch.double 0.036034489050507545 not equal torch.rand((2000, 2000)) for 10000 times torch.float 0.03686975734308362 not equal torch.rand((2000, 2000)) for 10000 times torch.double 0.04189508780837059 ``` Pull Request resolved: pytorch#33286 Differential Revision: D22211962 Pulled By: glaringlee fbshipit-source-id: a5c48f328432c1996f28e19bc75cb495fb689f6b
Summary: Update the following feature classifications in docs to align with the changes: 1. [High Level Autograd APIs](https://pytorch.org/docs/stable/autograd.html#functional-higher-level-api): Beta (was experimental) 2. [Eager Mode Quantization](https://pytorch.org/docs/stable/quantization.html): Beta (was experimental) 3. [Named Tensors](https://pytorch.org/docs/stable/named_tensor.html): Prototype (was experimental) 4. [TorchScript/RPC](https://pytorch.org/docs/stable/rpc.html#rpc): Prototype (was experimental) 5. [Channels Last Memory Layout](https://pytorch.org/docs/stable/tensor_attributes.html#torch-memory-format): Beta (was experimental) 6. [Custom C++ Classes](https://pytorch.org/docs/stable/cpp_index.html): Beta (was experimental) 7. [Torch.Sparse](https://pytorch.org/docs/stable/sparse.html): Beta (was experimental) Pull Request resolved: pytorch#39966 Differential Revision: D22213217 Pulled By: jlin27 fbshipit-source-id: dc49337cbc7026ed8dcac506fc60029dc3add854
Summary: Pull Request resolved: pytorch#40424 dictConstruct should preserve the inputs order Test Plan: Imported from OSS Differential Revision: D22202690 Pulled By: wanchaol fbshipit-source-id: c313b531b7fa49e6f3486396d61bfc5d6400cd01
…9601) Summary: Pull Request resolved: pytorch#39601 Test Plan: Imported from OSS Reviewed By: jamesr66a Differential Revision: D22202689 Pulled By: wanchaol fbshipit-source-id: 5271eb3d8fdcda3d730a085aa555b43c35d14876
…rch#40522) Summary: Pull Request resolved: pytorch#40522 Differential Revision: D22215685 Pulled By: AshkanAliabadi fbshipit-source-id: 78c103c4f7ad21e78069dc86a8ee47aebc9aa73e
…rch#40520) Summary: Pull Request resolved: pytorch#40520 Differential Revision: D22215614 Pulled By: AshkanAliabadi fbshipit-source-id: 5e41a3a69522cbfe1cc4ac76a0d1f3e90a58528d
Summary: Pull Request resolved: pytorch#40525 Move `USE_CUDNN` define under `USE_CUDA` guard, add `cuda/shared/cudnn.cpp` to filelist if either USE_ROCM or USE_CUDNN is set. This is a prep change for PyTorch CUDA src filelist unification change. Test Plan: CI Differential Revision: D22214899 fbshipit-source-id: b71b32fc603783b41cdef0e7fab2cc9cbe750a4e
…ytorch#40516) Summary: Pull Request resolved: pytorch#40516 Differential Revision: D22215554 Pulled By: AshkanAliabadi fbshipit-source-id: f779cf6e08cf344b87071c2ffc9b3f7cf4659085
…#40451) Summary: Fixes pytorchgh-40287 The `int -> bool` conversion takes higher precedence than `int -> IntArrayRef`. So, calling `std(0)` in C++ would select the `std(unbiased=False)` overload instead. Pull Request resolved: pytorch#40451 Differential Revision: D22217926 Pulled By: ezyang fbshipit-source-id: 7520792fab5ab6665bddd03b6f57444c6c729af4
…h#40526) Summary: Pull Request resolved: pytorch#40526 Differential Revision: D22215600 Pulled By: AshkanAliabadi fbshipit-source-id: 6ff0c17d17f118b64ae34c0007b705c7127f07ef
Summary: Pull Request resolved: pytorch#40495 As part of debugging flaky ddp_under_dist_autograd tests, I realized we were running into the following deadlock. 1) Rank 0 would go into DDP construction, hold GIL and wait for broadcast in DDP construction. 2) Rank 3 is a little slower and performs an RRef fetch call before the DDP construction. 3) The RRef fetch call is done on Rank 0 and tries to acquire GIL. 4) We now have a deadlock since Rank 0 is waiting for Rank 3 to enter the collective and Rank 3 is waiting for Rank 0 to release GIL. ghstack-source-id: 106534442 Test Plan: 1) Ran ddp_under_dist_autograd 500 times. 2) waitforbuildbot Differential Revision: D22205180 fbshipit-source-id: 6afd55342e801b9edb9591ff25158a244a8ea66a
…#39516) Summary: Fixes pytorch#38716, fixes pytorch#37234 This algorithm does the summation along a single axis with multiple "levels" of accumulator, each of which is designed to hold the sum of an order of magnitude more values than the previous. e.g. if there are 2^16 elements, the first level will hold the sum of 2^4 elements, and so on in increasing powers of 2: 2^4, 2^8, 2^12 and finally 2^16. This limits the differences in magnitude of the partial results being added together, and so we don't lose accuracy as the axis length increases. WIP to write a vectorized version. Pull Request resolved: pytorch#39516 Reviewed By: ezyang Differential Revision: D22106251 Pulled By: ngimel fbshipit-source-id: b56de4773292439dbda62b91f44ff37715850ae9
Summary: Add `int8_gen_quant_params.cc` added by pytorch#40494 to bazel build rules Pull Request resolved: pytorch#40536 Reviewed By: mruberry Differential Revision: D22219595 Pulled By: malfet fbshipit-source-id: 2875a0b9c55bad2b052a898661b96eab490f6451
Summary: These were changes that had to be made in the `release/1.6` branch in order to get backups to work. They should be brought to the master branch. Pull Request resolved: pytorch#40515 Differential Revision: D22221308 Pulled By: seemethere fbshipit-source-id: 24e2a0196a8e775fe324a383c8f0c681118b741b
…ytorch#40883) Summary: There's is a TODO tracked in pytorch#40882 Pull Request resolved: pytorch#40883 Reviewed By: pbelevich Differential Revision: D22346087 Pulled By: ailzhang fbshipit-source-id: b4789ca3a10f6a72c6e77276bde45633eb6cf545
Summary: Add documentation for dynamic quantized modules Pull Request resolved: pytorch#40896 Differential Revision: D22395955 Pulled By: z-a-f fbshipit-source-id: cdc956d1509a0901bc24b73b6ca68a1b65e00cc2
Summary: Pull Request resolved: pytorch#40903 This PR continues the work of pytorch#38467 - decoupling Autograd and Trace for manually registered ops. ghstack-source-id: 107158638 Test Plan: CI Differential Revision: D22354804 fbshipit-source-id: f5ea45ade2850296c62707a2a4449d7d67a9f5b5
Summary: Pull Request resolved: pytorch#41004 Tracing has been moved into separate files. Now we can disable it by not compiling the source files for xplat mobile build. ghstack-source-id: 107158627 Test Plan: CI + build size bot Reviewed By: iseeyuan Differential Revision: D22372615 fbshipit-source-id: bf2e2249e401295ff63020a292df119b188fb966
…tation (pytorch#41025) Summary: Bundle of small edits to fix formatting. Pull Request resolved: pytorch#41025 Differential Revision: D22398364 Pulled By: mruberry fbshipit-source-id: 8d484cb52a1cf4a8eb1f64914574250c9fd5043d
Summary: Pull Request resolved: pytorch#40625 Test Plan: Continuous integration. Reviewed By: suo Differential Revision: D22259289 fbshipit-source-id: 76cb097dd06a636004fc780b17cb20f27d3821de
…0864) Summary: Have basic reduction fusion working, and have improved code generator to approach performance of eager mode reductions. Coming soon will be pointwise-reduction fusions in a way that should prevent the possibility of hitting regressions. Also working on performant softmax kernels in the code generator which may be our next fusion target. Pull Request resolved: pytorch#40864 Reviewed By: ngimel Differential Revision: D22392877 Pulled By: soumith fbshipit-source-id: 457448a807d628b1035f6d90bc0abe8a87bf8447
Summary: Closes pytorch#40560 This adds the equation for the weighted mean to `CrossEntropyLoss`'s docs and the `reduction` argument for `CrossEntropyLoss` and `NLLLoss` no longer describes a non-weighted mean of the outputs. Pull Request resolved: pytorch#40991 Differential Revision: D22395805 Pulled By: ezyang fbshipit-source-id: a623b6dd2aab17220fe0bf706bd9b62d6ba531fd
…ction methods. (pytorch#40962) Summary: Follow up to pytorch#36447 . Update for pytorch#33389. Also removes unused `unordered_map` include from the CPP file. Pull Request resolved: pytorch#40962 Differential Revision: D22376253 Pulled By: ngimel fbshipit-source-id: 4e7432190e9a847321aec6d6f6634056fa69bdb8
Summary: This trick should have no effect on performance, but it reduces size of kernels using the template by 10% For example, sizeof(BinaryMulDivKernel.cu.o) compiled by CUDA-10.1 toolchain for sm_75 before the change was 4.2Mb, after 3.8Mb Pull Request resolved: pytorch#40992 Differential Revision: D22398733 Pulled By: malfet fbshipit-source-id: 6576f4da00dc5fc2575b2313577f52c6571d5e6f
Summary: Pull Request resolved: pytorch#40856 Add a new activation function - Mish: A Self Regularized Non-Monotonic Neural Activation Function https://arxiv.org/abs/1908.08681 Test Plan: buck test //caffe2/caffe2/python/operator_test:elementwise_ops_test -- 'test_mish' {F242275183} Differential Revision: D22158035 fbshipit-source-id: 459c1dd0ac5b515913fc09b5f4cd13dcf095af31
Summary: Pull Request resolved: pytorch#40795 Test Plan: Imported from OSS Reviewed By: suo Differential Revision: D22314215 Pulled By: jamesr66a fbshipit-source-id: a2fb5c6804d4014f8e437c6858a7be8cd3efb380
Summary: Fixes pytorch#24557 ASV benchmark: ``` import torch sizes = [ (10**6,), (1000, 1000), (10, 10), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10), ] class EqualTrue: params = range(len(sizes)) def setup(self, n): dims = sizes[n] self.a = torch.rand(dims, device='cuda') self.b = self.a.clone() def time_equal(self, n): torch.equal(self.a, self.b) class EqualFalse: params = range(len(sizes)) def setup(self, n): dims = sizes[n] self.a = torch.rand(dims, device='cuda') self.b = torch.rand(dims, device='cuda') def time_equal(self, n): torch.equal(self.a, self.b) ``` Old results: ``` [ 75.00%] ··· equal.EqualFalse.time_equal [ 75.00%] ··· ======== ============ param1 -------- ------------ 0 67.7±7μs 1 74.0±2μs 2 24.4±0.1μs 3 135±0.2μs ======== ============ [100.00%] ··· equal.EqualTrue.time_equal [100.00%] ··· ======== ============ param1 -------- ------------ 0 59.8±0.2μs 1 59.9±0.3μs 2 25.0±0.5μs 3 136±0.2μs ======== ============ ``` New results: ``` [ 75.00%] ··· equal.EqualFalse.time_equal [ 75.00%] ··· ======== ============ param1 -------- ------------ 0 44.4±0.2μs 1 44.5±0.4μs 2 31.3±0.3μs 3 96.6±0.5μs ======== ============ [100.00%] ··· equal.EqualTrue.time_equal [100.00%] ··· ======== ============ param1 -------- ------------ 0 44.2±0.2μs 1 44.6±0.2μs 2 30.8±0.3μs 3 97.3±0.2μs ======== ============ ``` Pull Request resolved: pytorch#36483 Differential Revision: D21451829 Pulled By: VitalyFedyunin fbshipit-source-id: 033e8060192c54f139310aeafe8ba784bab94ded
Summary: Original commit changeset: 46c59d849fa8 The original commit is breaking DPER3 release pipeline with the following failures: https://www.internalfb.com/intern/chronos/jobinstance?jobinstanceid=9007207344413239&smc=chronos_gp_admin_client&offset=0 ``` Child workflow f 202599639 failed with error: c10::Error: [enforce fail at operator.cc:76] blob != nullptr. op Save: Encountered a non-existing input blob: feature_preproc/feature_sparse_to_dense/default_float_value ``` https://www.internalfb.com/intern/chronos/jobinstance?jobinstanceid=9007207344855973&smc=chronos_gp_admin_client&offset=0 ``` Child workflow f 202629391 failed with error: c10::Error: [enforce fail at operator.cc:76] blob != nullptr. op Save: Encountered a non-existing input blob: tum_preproc/inductive/feature_sparse_to_dense/default_float_value ``` Related UBN tasks: T69529846, T68986110 Test Plan: Build a DPER3 package on top of this commit, and check that DPER3 release test `model_deliverability_test` is passing. Differential Revision: D22396317 fbshipit-source-id: 92d5b30cc146c005d6159a8d5bfe8973e2c546dd
Summary: Pull Request resolved: pytorch#40938 already accepted in pytorch#40645 Test Plan: Imported from OSS Reviewed By: jamesr66a, Krovatkin Differential Revision: D22394675 Pulled By: eellison fbshipit-source-id: 1e9dbb24a4cb564d9a68280d2166329ca9fb0425
Summary: Pull Request resolved: pytorch#40939 Previously, when we would do shape analysis by running the op with representative inputs, we would always set the grad property to false. This led to a wrong static analysis when we would create differentiable subgraphs, and propagate shapes without also propagating requires_grad, and then uninline them. Test Plan: Imported from OSS Differential Revision: D22394676 Pulled By: eellison fbshipit-source-id: 254e6e9f964b40d160befe0e125abe1b7aa2bd5e
Summary: Most time-consuming tests in test_nn (taking about half the time) were gradgradchecks on Conv3d. Reduce their sizes, and, most importantly, run gradgradcheck single-threaded, because that cuts the time of conv3d tests by an order of magnitude, and barely affects other tests. These changes bring test_nn time down from 1200 s to ~550 s on my machine. Pull Request resolved: pytorch#40999 Differential Revision: D22396896 Pulled By: ngimel fbshipit-source-id: 3b247caceb65d64be54499de1a55de377fdf9506
Summary: Pull Request resolved: pytorch#40717 `in_dims` specifies which dimension of the input tensors should be vmapped over. One can also specify `None` as an `in_dim` for a particular input to indicate that we do not map over said input. We implement `in_dims` by creating a BatchedTensor with BatchDim equal to said `in_dim`. Most of this PR is error checking. `in_dims` must satisfy the following: - `in_dim` can be either an int or a Tuple[Optional[int]]. If it is an int, we use it to mean the `in_dim` for every input. - If `in_dims` is not-None at some index `idx`, then the input at index `idx` MUST be a tensor (vmap can only map over tensors). jax supports something more generalized: their `in_dims` can match the structure of the `inputs` to the function (i.e., it is a nested python data structure matching the data structure of `inputs` specifying where in `inputs` the Tensors to be mapped are and what their map dims should be). We don't have the infrastruture yet so we only support `int` or a flat tuple for `in_dims`. Test Plan: - `pytest test/test_vmap.py -v` Differential Revision: D22397914 Pulled By: zou3519 fbshipit-source-id: 56d2e14be8b6024e4cde2729eff384da305b4ea3
Summary: Closes pytorch#40784 Pull Request resolved: pytorch#41038 Differential Revision: D22404273 Pulled By: malfet fbshipit-source-id: 8df05f948f069ac95591d523222faa1327429e71
Summary: I ran `make linkcheck` using `sphinx.builders.linkcheck` on the documentation and noticed a few links weren't using HTTPS so I quickly updated them all. Pull Request resolved: pytorch#40878 Differential Revision: D22404647 Pulled By: ngimel fbshipit-source-id: 9c9756db59197304023fddc28f252314f6cf4af3
Summary: In issue pytorch#36997 the user encountered a non-meaningful error message when trying to export the model to ONNX. The Pad operator in opset 9 requires the list of paddings to be constant. This PR tries to improve the error message given to the user when this is not the case. Pull Request resolved: pytorch#39651 Reviewed By: hl475 Differential Revision: D21992262 Pulled By: houseroad fbshipit-source-id: b817111c2a40deba85e4c6cdb874c1713312dba1
Summary: Fix export of full_like when fill_value is of type torch._C.Value. This PR fixes a bug when exporting GPT2DoubleHeadsModel huggingface/transformers#4950 Pull Request resolved: pytorch#40063 Reviewed By: hl475 Differential Revision: D22398353 Pulled By: houseroad fbshipit-source-id: 6980a61211fe571c2e4a57716970f474851d811e
Summary: This PR adds support for the torch `view_as` operator. Pull Request resolved: pytorch#40496 Reviewed By: hl475 Differential Revision: D22398318 Pulled By: houseroad fbshipit-source-id: f92057f9067a201b707aa9b8fc4ad34643dd5fa3
Summary: It's a known gcc-5.4 bug that enum class is not hasheable by default, so `std::unordered_map` needs 3rd explicit parameters to compute hash from the type. Should fix regression caused by pytorch#40864 Pull Request resolved: pytorch#41055 Differential Revision: D22405478 Pulled By: malfet fbshipit-source-id: f4bd36bebdc1ad0251ebd1e6cefba866e6605fe6
Summary: Forgot to add this to pytorch#41055 Pull Request resolved: pytorch#41063 Differential Revision: D22407451 Pulled By: malfet fbshipit-source-id: 6f06653b165cc4817d134657f87caf643182832a
Summary: Pull Request resolved: pytorch#41023 Remove Logger in get_matching_activations since it's not used. ghstack-source-id: 107237046 Test Plan: buck test mode/dev caffe2/test:quantization -- 'test_compare_weights_lstm_dynamic' buck test mode/dev caffe2/test:quantization -- 'test_compare_model_stub_lstm_dynamic' buck test mode/dev caffe2/test:quantization -- 'test_compare_model_outputs_lstm_dynamic' buck test mode/dev caffe2/test:quantization -- 'test_compare_weights_conv_static' buck test mode/dev caffe2/test:quantization -- 'test_compare_weights_linear_static' buck test mode/dev caffe2/test:quantization -- 'test_compare_weights_linear_dynamic' buck test mode/dev caffe2/test:quantization -- 'test_compare_model_stub_conv_static' buck test mode/dev caffe2/test:quantization -- 'test_compare_model_stub_linear_static' buck test mode/dev caffe2/test:quantization -- 'test_compare_model_stub_submodule_static' buck test mode/dev caffe2/test:quantization -- 'test_compare_model_stub_functional_static' buck test mode/dev caffe2/test:quantization -- 'test_compare_model_stub_linear_dynamic' buck test mode/dev caffe2/test:quantization -- 'test_compare_model_outputs_conv_static' buck test mode/dev caffe2/test:quantization -- 'test_compare_model_outputs_linear_static' buck test mode/dev caffe2/test:quantization -- 'test_compare_model_outputs_functional_static' buck test mode/dev caffe2/test:quantization -- 'test_compare_model_outputs_linear_dynamic' Differential Revision: D22394957 fbshipit-source-id: 7d59e0f35e9f4c304b8487460d48236ee6e5a872
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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
#1 0x3ff8e7da35f in c10::SymInt::maybe_as_int() const /home/user/pytorch/c10/core/SymInt.h:215
csarofeen#2 0x3ff8e7d0a6d in c10::SymInt::sym_eq(c10::SymInt const&) const /home/user/pytorch/c10/core/SymInt.cpp:69
csarofeen#3 0x3ff7a9ab0bd in c10::SymInt::operator==(c10::SymInt const&) const /home/user/pytorch/c10/core/SymInt.h:177
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#8 0x3ff4b23c771 in c10::ArrayRef<c10::SymInt>::equals(c10::ArrayRef<c10::SymInt>) const /home/user/pytorch/c10/util/ArrayRef.h:188
csarofeen#9 0x3ff4cb91bc1 in bool c10::operator!=<c10::SymInt>(c10::ArrayRef<c10::SymInt>, c10::ArrayRef<c10::SymInt>) /home/user/pytorch/c10/util/ArrayRef.h:341
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#21 0x3ff6d1bd197 in c10::impl::wrap_kernel_functor_unboxed_<c10::impl::detail::WrapFunctionIntoFunctor_<c10::CompileTimeFunctionPointer<at::Tensor const& (c10::DispatchKeySet, at::Tensor const&, c10:
:ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>), &torch::autograd::VariableType::(anonymous namespace)::resize_>, at::Tensor const&, c10::guts::typelist::typelist<c10::DispatchKeySet, at::Tensor
const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat> > >, at::Tensor const& (c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>)>::call(c
10::OperatorKernel*, c10::DispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>) /home/user/pytorch/aten/src/ATen/core/boxing/impl/make_boxed_from_unboxed_functor
.h:480
csarofeen#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
csarofeen#23 0x3ff5181ead1 in at::Tensor const& c10::KernelFunction::call<at::Tensor const&, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat> >(c10::OperatorHandle const&, c10::D
ispatchKeySet, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>) const /home/user/pytorch/aten/src/ATen/core/boxing/KernelFunction_impl.h:90
csarofeen#24 0x3ff5181ead1 in at::Tensor const& c10::Dispatcher::call<at::Tensor const&, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat> >(c10::TypedOperatorHandle<at::Tensor co
nst& (at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>)> const&, at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>) const /home/user/pytorch/at
en/src/ATen/core/dispatch/Dispatcher.h:639
csarofeen#25 0x3ff5181ead1 in c10::TypedOperatorHandle<at::Tensor const& (at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>)>::call(at::Tensor const&, c10::ArrayRef<c10::SymInt>,
c10::optional<c10::MemoryFormat>) const /home/user/pytorch/aten/src/ATen/core/dispatch/Dispatcher.h:487
csarofeen#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
csarofeen#27 0x3ff79b44fcf in at::Tensor::resize__symint(c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>) const aten/src/ATen/core/TensorBody.h:2452
csarofeen#28 0x3ff79a802db in torch::autograd::THPVariable_resize_(_object*, _object*, _object*)::$_0::operator()(at::Tensor const&, c10::ArrayRef<c10::SymInt>, c10::optional<c10::MemoryFormat>) const /home/us
er/pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:13417
csarofeen#29 0x3ff7999f1eb in torch::autograd::THPVariable_resize_(_object*, _object*, _object*) /home/user/pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:13419
csarofeen#30 0x3ffa2c9b009 in method_vectorcall_VARARGS_KEYWORDS Objects/descrobject.c:344
csarofeen#31 0x3ffa2df00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#32 0x3ffa2df013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#33 0x3ffa2e05447 in call_function Python/ceval.c:5891
csarofeen#34 0x3ffa2dff7d7 in _PyEval_EvalFrameDefault Python/ceval.c:4198
csarofeen#35 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#36 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#37 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#38 0x3ffa2c8ab15 in PyVectorcall_Call Objects/call.c:255
csarofeen#39 0x3ffa2c8ac65 in _PyObject_Call Objects/call.c:290
csarofeen#40 0x3ffa2c8ada9 in PyObject_Call Objects/call.c:317
csarofeen#41 0x3ffa2e059c7 in do_call_core Python/ceval.c:5943
csarofeen#42 0x3ffa2dffd39 in _PyEval_EvalFrameDefault Python/ceval.c:4277
csarofeen#43 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#44 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#45 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#46 0x3ffa2c8ab15 in PyVectorcall_Call Objects/call.c:255
csarofeen#47 0x3ffa2c8ac65 in _PyObject_Call Objects/call.c:290
csarofeen#48 0x3ffa2c8ada9 in PyObject_Call Objects/call.c:317
csarofeen#49 0x3ffa2e059c7 in do_call_core Python/ceval.c:5943
csarofeen#50 0x3ffa2dffd39 in _PyEval_EvalFrameDefault Python/ceval.c:4277
csarofeen#51 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#52 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#53 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#54 0x3ffa2df00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#55 0x3ffa2df013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#56 0x3ffa2e05447 in call_function Python/ceval.c:5891
csarofeen#57 0x3ffa2dff7d7 in _PyEval_EvalFrameDefault Python/ceval.c:4198
csarofeen#58 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#59 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#60 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#61 0x3ffa2c8e941 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#62 0x3ffa2c8eddd in method_vectorcall Objects/classobject.c:53
csarofeen#63 0x3ffa2df00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#64 0x3ffa2df013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#65 0x3ffa2e05447 in call_function Python/ceval.c:5891
csarofeen#66 0x3ffa2dff905 in _PyEval_EvalFrameDefault Python/ceval.c:4213
csarofeen#67 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#68 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#69 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#70 0x3ffa2df00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#71 0x3ffa2df013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#72 0x3ffa2e05447 in call_function Python/ceval.c:5891
csarofeen#73 0x3ffa2dff7d7 in _PyEval_EvalFrameDefault Python/ceval.c:4198
csarofeen#74 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#75 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#76 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#77 0x3ffa2c8e941 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#78 0x3ffa2c8eddd in method_vectorcall Objects/classobject.c:53
csarofeen#79 0x3ffa2df00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#80 0x3ffa2df013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#81 0x3ffa2e05447 in call_function Python/ceval.c:5891
csarofeen#82 0x3ffa2dffa57 in _PyEval_EvalFrameDefault Python/ceval.c:4231
csarofeen#83 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#84 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#85 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#86 0x3ffa2c8e941 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#87 0x3ffa2c8eddd in method_vectorcall Objects/classobject.c:53
csarofeen#88 0x3ffa2df00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#89 0x3ffa2df013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#90 0x3ffa2e05447 in call_function Python/ceval.c:5891
csarofeen#91 0x3ffa2dffa57 in _PyEval_EvalFrameDefault Python/ceval.c:4231
csarofeen#92 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#93 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#94 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#95 0x3ffa2c8e941 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#96 0x3ffa2c8eddd in method_vectorcall Objects/classobject.c:53
csarofeen#97 0x3ffa2c8ab9b in PyVectorcall_Call Objects/call.c:267
csarofeen#98 0x3ffa2c8ac65 in _PyObject_Call Objects/call.c:290
csarofeen#99 0x3ffa2c8ada9 in PyObject_Call Objects/call.c:317
csarofeen#100 0x3ffa2e059c7 in do_call_core Python/ceval.c:5943
csarofeen#101 0x3ffa2dffd39 in _PyEval_EvalFrameDefault Python/ceval.c:4277
csarofeen#102 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#103 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#104 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#105 0x3ffa2c8a695 in _PyObject_FastCallDictTstate Objects/call.c:153
csarofeen#106 0x3ffa2c8b271 in _PyObject_Call_Prepend Objects/call.c:431
csarofeen#107 0x3ffa2d3f307 in slot_tp_call Objects/typeobject.c:7494
csarofeen#108 0x3ffa2c8a933 in _PyObject_MakeTpCall Objects/call.c:215
csarofeen#109 0x3ffa2df0081 in _PyObject_VectorcallTstate Include/cpython/abstract.h:112
csarofeen#110 0x3ffa2df013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#111 0x3ffa2e05447 in call_function Python/ceval.c:5891
csarofeen#112 0x3ffa2dffa57 in _PyEval_EvalFrameDefault Python/ceval.c:4231
csarofeen#113 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#114 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#115 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#116 0x3ffa2df00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#117 0x3ffa2df013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#118 0x3ffa2e05447 in call_function Python/ceval.c:5891
csarofeen#119 0x3ffa2dff7d7 in _PyEval_EvalFrameDefault Python/ceval.c:4198
csarofeen#120 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#121 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#122 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#123 0x3ffa2c8ab15 in PyVectorcall_Call Objects/call.c:255
csarofeen#124 0x3ffa2c8ac65 in _PyObject_Call Objects/call.c:290
csarofeen#125 0x3ffa2c8ada9 in PyObject_Call Objects/call.c:317
csarofeen#126 0x3ffa2e059c7 in do_call_core Python/ceval.c:5943
csarofeen#127 0x3ffa2dffd39 in _PyEval_EvalFrameDefault Python/ceval.c:4277
csarofeen#128 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#129 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#130 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#131 0x3ffa2df00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#132 0x3ffa2df013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#133 0x3ffa2e05447 in call_function Python/ceval.c:5891
csarofeen#134 0x3ffa2dff779 in _PyEval_EvalFrameDefault Python/ceval.c:4181
csarofeen#135 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#136 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#137 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#138 0x3ffa2c8e941 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#139 0x3ffa2c8eddd in method_vectorcall Objects/classobject.c:53
csarofeen#140 0x3ffa2df00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#141 0x3ffa2df013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#142 0x3ffa2e05447 in call_function Python/ceval.c:5891
csarofeen#143 0x3ffa2dff779 in _PyEval_EvalFrameDefault Python/ceval.c:4181
csarofeen#144 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#145 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#146 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#147 0x3ffa2c8a695 in _PyObject_FastCallDictTstate Objects/call.c:153
csarofeen#148 0x3ffa2c8b271 in _PyObject_Call_Prepend Objects/call.c:431
csarofeen#149 0x3ffa2d3f307 in slot_tp_call Objects/typeobject.c:7494
csarofeen#150 0x3ffa2c8ad17 in _PyObject_Call Objects/call.c:305
csarofeen#151 0x3ffa2c8ada9 in PyObject_Call Objects/call.c:317
csarofeen#152 0x3ffa2e059c7 in do_call_core Python/ceval.c:5943
csarofeen#153 0x3ffa2dffd39 in _PyEval_EvalFrameDefault Python/ceval.c:4277
csarofeen#154 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#155 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#156 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#157 0x3ffa2df00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#158 0x3ffa2df013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#159 0x3ffa2e05447 in call_function Python/ceval.c:5891
csarofeen#160 0x3ffa2dff905 in _PyEval_EvalFrameDefault Python/ceval.c:4213
csarofeen#161 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#162 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#163 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#164 0x3ffa2c8e941 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#165 0x3ffa2c8eddd in method_vectorcall Objects/classobject.c:53
csarofeen#166 0x3ffa2df00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#167 0x3ffa2df013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#168 0x3ffa2e05447 in call_function Python/ceval.c:5891
csarofeen#169 0x3ffa2dffa57 in _PyEval_EvalFrameDefault Python/ceval.c:4231
csarofeen#170 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#171 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#172 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#173 0x3ffa2c8ab15 in PyVectorcall_Call Objects/call.c:255
csarofeen#174 0x3ffa2c8ac65 in _PyObject_Call Objects/call.c:290
csarofeen#175 0x3ffa2c8ada9 in PyObject_Call Objects/call.c:317
csarofeen#176 0x3ffa2e059c7 in do_call_core Python/ceval.c:5943
csarofeen#177 0x3ffa2dffd39 in _PyEval_EvalFrameDefault Python/ceval.c:4277
csarofeen#178 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#179 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#180 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#181 0x3ffa2df00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#182 0x3ffa2df013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#183 0x3ffa2e05447 in call_function Python/ceval.c:5891
csarofeen#184 0x3ffa2dff905 in _PyEval_EvalFrameDefault Python/ceval.c:4213
csarofeen#185 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#186 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#187 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#188 0x3ffa2df00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#189 0x3ffa2df013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#190 0x3ffa2e05447 in call_function Python/ceval.c:5891
csarofeen#191 0x3ffa2dffa57 in _PyEval_EvalFrameDefault Python/ceval.c:4231
csarofeen#192 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#193 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#194 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#195 0x3ffa2c8ab15 in PyVectorcall_Call Objects/call.c:255
csarofeen#196 0x3ffa2c8ac65 in _PyObject_Call Objects/call.c:290
csarofeen#197 0x3ffa2c8ada9 in PyObject_Call Objects/call.c:317
csarofeen#198 0x3ffa2e059c7 in do_call_core Python/ceval.c:5943
csarofeen#199 0x3ffa2dffd39 in _PyEval_EvalFrameDefault Python/ceval.c:4277
csarofeen#200 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#201 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#202 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#203 0x3ffa2df00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#204 0x3ffa2df013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#205 0x3ffa2e05447 in call_function Python/ceval.c:5891
csarofeen#206 0x3ffa2dff779 in _PyEval_EvalFrameDefault Python/ceval.c:4181
csarofeen#207 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#208 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#209 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#210 0x3ffa2c8e941 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#211 0x3ffa2c8eddd in method_vectorcall Objects/classobject.c:53
csarofeen#212 0x3ffa2df00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#213 0x3ffa2df013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#214 0x3ffa2e05447 in call_function Python/ceval.c:5891
csarofeen#215 0x3ffa2dff779 in _PyEval_EvalFrameDefault Python/ceval.c:4181
csarofeen#216 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#217 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#218 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#219 0x3ffa2c8a695 in _PyObject_FastCallDictTstate Objects/call.c:153
csarofeen#220 0x3ffa2c8b271 in _PyObject_Call_Prepend Objects/call.c:431
csarofeen#221 0x3ffa2d3f307 in slot_tp_call Objects/typeobject.c:7494
csarofeen#222 0x3ffa2c8a933 in _PyObject_MakeTpCall Objects/call.c:215
csarofeen#223 0x3ffa2df0081 in _PyObject_VectorcallTstate Include/cpython/abstract.h:112
csarofeen#224 0x3ffa2df013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#225 0x3ffa2e05447 in call_function Python/ceval.c:5891
csarofeen#226 0x3ffa2dffa57 in _PyEval_EvalFrameDefault Python/ceval.c:4231
csarofeen#227 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#228 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#229 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#230 0x3ffa2c8ab15 in PyVectorcall_Call Objects/call.c:255
csarofeen#231 0x3ffa2c8ac65 in _PyObject_Call Objects/call.c:290
csarofeen#232 0x3ffa2c8ada9 in PyObject_Call Objects/call.c:317
csarofeen#233 0x3ffa2e059c7 in do_call_core Python/ceval.c:5943
csarofeen#234 0x3ffa2dffd39 in _PyEval_EvalFrameDefault Python/ceval.c:4277
csarofeen#235 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#236 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#237 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#238 0x3ffa2df00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#239 0x3ffa2df013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#240 0x3ffa2e05447 in call_function Python/ceval.c:5891
csarofeen#241 0x3ffa2dff779 in _PyEval_EvalFrameDefault Python/ceval.c:4181
csarofeen#242 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#243 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#244 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#245 0x3ffa2c8e941 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#246 0x3ffa2c8eddd in method_vectorcall Objects/classobject.c:53
csarofeen#247 0x3ffa2df00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#248 0x3ffa2df013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#249 0x3ffa2e05447 in call_function Python/ceval.c:5891
csarofeen#250 0x3ffa2dff779 in _PyEval_EvalFrameDefault Python/ceval.c:4181
csarofeen#251 0x3ffa2df052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#252 0x3ffa2e02b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#253 0x3ffa2c8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#254 0x3ffa2c8a695 in _PyObject_FastCallDictTstate Objects/call.c:153
csarofeen#255 0x3ffa2c8b271 in _PyObject_Call_Prepend Objects/call.c:431
csarofeen#256 0x3ffa2d3f307 in slot_tp_call Objects/typeobject.c:7494
csarofeen#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
#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
#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
csarofeen#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
#1 0x000003ffa77e3222 in c10::TensorImpl::~TensorImpl (this=0x61000013d740) at /home/user/pytorch/c10/core/TensorImpl.cpp:75
csarofeen#2 0x000003ff63e76e8c in c10::intrusive_ptr<c10::TensorImpl, c10::UndefinedTensorImpl>::reset_ (this=0x3ffd7ec8230) at /home/user/pytorch/c10/util/intrusive_ptr.h:291
csarofeen#3 0x000003ff63e76910 in c10::intrusive_ptr<c10::TensorImpl, c10::UndefinedTensorImpl>::~intrusive_ptr (this=0x3ffd7ec8230) at /home/user/pytorch/c10/util/intrusive_ptr.h:370
csarofeen#4 0x000003ff63e67240 in at::TensorBase::~TensorBase (this=0x3ffd7ec8230) at /home/user/pytorch/aten/src/ATen/core/TensorBase.h:80
csarofeen#5 0x000003ff63e85ee0 in at::Tensor::~Tensor (this=0x3ffd7ec8230) at aten/src/ATen/core/TensorBody.h:90
csarofeen#6 0x000003ff63f67304 in resize__functionalization (dispatchKeySet=..., self=..., size=..., memory_format=...) at /home/user/pytorch/aten/src/ATen/FunctionalizeFallbackKernel.cpp:173
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#13 at::_ops::resize_::redispatch (dispatchKeySet=..., self=..., size=..., memory_format=...) at /home/user/pytorch/build/aten/src/ATen/Operators_4.cpp:2144
csarofeen#14 0x000003ff861d5e08 in at::redispatch::resize__symint (dispatchKeySet=..., self=..., size=..., memory_format=...) at aten/src/ATen/RedispatchFunctions.h:2847
csarofeen#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
#1 0x000003ff734d0a6e in c10::SymInt::sym_eq (this=0x61000013d790, sci=...) at /home/user/pytorch/c10/core/SymInt.cpp:69
csarofeen#2 0x000003ff5f6ab0be in c10::SymInt::operator== (this=0x61000013d790, o=...) at /home/user/pytorch/c10/core/SymInt.h:177
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#7 0x000003ff2ff3c772 in c10::ArrayRef<c10::SymInt>::equals (this=0x3ffed7c9900, RHS=...) at /home/user/pytorch/c10/util/ArrayRef.h:188
csarofeen#8 0x000003ff31891bc2 in c10::operator!=<c10::SymInt> (a1=..., a2=...) at /home/user/pytorch/c10/util/ArrayRef.h:341
csarofeen#9 0x000003ff51eb5800 in torch::ADInplaceOrView::resize_ (ks=..., self=..., size=..., optional_memory_format=...) at /home/user/pytorch/torch/csrc/autograd/VariableTypeManual.cpp:408
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#16 at::_ops::resize_::redispatch (dispatchKeySet=..., self=..., size=..., memory_format=...) at /home/user/pytorch/build/aten/src/ATen/Operators_4.cpp:2144
csarofeen#17 0x000003ff51ed5e08 in at::redispatch::resize__symint (dispatchKeySet=..., self=..., size=..., memory_format=...) at aten/src/ATen/RedispatchFunctions.h:2847
csarofeen#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
ftxj
pushed a commit
to ftxj/pytorch
that referenced
this pull request
May 25, 2023
arguments() returns vector member of object returned by schema() call.
When object returned by schema() call is destroyed, the vector is deallocated as well,
it's lifetime isn't extended.
This issue detected while running `pytest -v test/mobile/test_lite_script_type.py -k test_nest_typing_namedtuple_custom_classtype` with ASAN.
<details>
<summary>ASAN output</summary>
```
==1134126==ERROR: AddressSanitizer: heap-use-after-free on address 0x60d0005a5790 at pc 0x03ff844488d8 bp 0x03fff584afe8 sp 0x03fff584afd8
READ of size 8 at 0x60d0005a5790 thread T0
#0 0x3ff844488d7 in __gnu_cxx::__normal_iterator<c10::Argument const*, std::vector<c10::Argument, std::allocator<c10::Argument> > >::__normal_iterator(c10::Argument const* const&) /usr/lib/gcc/s390x-i
bm-linux-gnu/11/include/g++-v11/bits/stl_iterator.h:1028
#1 0x3ff8444293f in std::vector<c10::Argument, std::allocator<c10::Argument> >::begin() const /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/bits/stl_vector.h:821
csarofeen#2 0x3ff84d807d1 in torch::jit::toPyObject(c10::IValue) /home/user/pytorch/torch/csrc/jit/python/pybind_utils.cpp:617
csarofeen#3 0x3ff84d80305 in torch::jit::toPyObject(c10::IValue) /home/user/pytorch/torch/csrc/jit/python/pybind_utils.cpp:604
csarofeen#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
csarofeen#5 0x3ff85318191 in pybind11::cpp_function::initialize<torch::jit::initJitScriptBindings(_object*)::$_45, c10::IValue, torch::jit::mobile::Module&, pybind11::tuple const&, pybind11::name, pybind11::is
_method, pybind11::sibling, pybind11::arg>(torch::jit::initJitScriptBindings(_object*)::$_45&&, c10::IValue (*)(torch::jit::mobile::Module&, pybind11::tuple const&), pybind11::name const&, pybind11::is_me
thod const&, pybind11::sibling const&, pybind11::arg const&)::{lambda(pybind11::detail::function_call&)#1}::operator()(pybind11::detail::function_call&) const /home/user/pytorch/cmake/../third_party/pybin
d11/include/pybind11/pybind11.h:249
csarofeen#6 0x3ff85317cfd in pybind11::cpp_function::initialize<torch::jit::initJitScriptBindings(_object*)::$_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&)#1}::__invoke(pybind11::detail::function_call&) /home/user/pytorch/cmake/../third_party/pybind11/incl
ude/pybind11/pybind11.h:224
csarofeen#7 0x3ff82ee52e9 in pybind11::cpp_function::dispatcher(_object*, _object*, _object*) /home/user/pytorch/cmake/../third_party/pybind11/include/pybind11/pybind11.h:929
csarofeen#8 0x3ffab002903 in cfunction_call Objects/methodobject.c:543
csarofeen#9 0x3ffaaf8a933 in _PyObject_MakeTpCall Objects/call.c:215
csarofeen#10 0x3ffaaf8e919 in _PyObject_VectorcallTstate Include/cpython/abstract.h:112
csarofeen#11 0x3ffaaf8eddd in method_vectorcall Objects/classobject.c:53
csarofeen#12 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#13 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#14 0x3ffab105447 in call_function Python/ceval.c:5891
csarofeen#15 0x3ffab0ff779 in _PyEval_EvalFrameDefault Python/ceval.c:4181
csarofeen#16 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#17 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#18 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#19 0x3ffaaf8a615 in _PyObject_FastCallDictTstate Objects/call.c:142
csarofeen#20 0x3ffaaf8b271 in _PyObject_Call_Prepend Objects/call.c:431
csarofeen#21 0x3ffab03f307 in slot_tp_call Objects/typeobject.c:7494
csarofeen#22 0x3ffaaf8a933 in _PyObject_MakeTpCall Objects/call.c:215
csarofeen#23 0x3ffab0f0081 in _PyObject_VectorcallTstate Include/cpython/abstract.h:112
csarofeen#24 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#25 0x3ffab105447 in call_function Python/ceval.c:5891
csarofeen#26 0x3ffab0ff905 in _PyEval_EvalFrameDefault Python/ceval.c:4213
csarofeen#27 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#28 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#29 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#30 0x3ffaaf8e941 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#31 0x3ffaaf8eddd in method_vectorcall Objects/classobject.c:53
csarofeen#32 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#33 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#34 0x3ffab105447 in call_function Python/ceval.c:5891
csarofeen#35 0x3ffab0ff905 in _PyEval_EvalFrameDefault Python/ceval.c:4213
csarofeen#36 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#37 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#38 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#39 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#40 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#41 0x3ffab105447 in call_function Python/ceval.c:5891
csarofeen#42 0x3ffab0ff7d7 in _PyEval_EvalFrameDefault Python/ceval.c:4198
csarofeen#43 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#44 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#45 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#46 0x3ffaaf8e941 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#47 0x3ffaaf8eddd in method_vectorcall Objects/classobject.c:53
csarofeen#48 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#49 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#50 0x3ffab105447 in call_function Python/ceval.c:5891
csarofeen#51 0x3ffab0ffa57 in _PyEval_EvalFrameDefault Python/ceval.c:4231
csarofeen#52 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#53 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#54 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#55 0x3ffaaf8e941 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#56 0x3ffaaf8eddd in method_vectorcall Objects/classobject.c:53
csarofeen#57 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#58 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#59 0x3ffab105447 in call_function Python/ceval.c:5891
csarofeen#60 0x3ffab0ffa57 in _PyEval_EvalFrameDefault Python/ceval.c:4231
csarofeen#61 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#62 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#63 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#64 0x3ffaaf8e941 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#65 0x3ffaaf8eddd in method_vectorcall Objects/classobject.c:53
csarofeen#66 0x3ffaaf8ab9b in PyVectorcall_Call Objects/call.c:267
csarofeen#67 0x3ffaaf8ac65 in _PyObject_Call Objects/call.c:290
csarofeen#68 0x3ffaaf8ada9 in PyObject_Call Objects/call.c:317
csarofeen#69 0x3ffab1059c7 in do_call_core Python/ceval.c:5943
csarofeen#70 0x3ffab0ffd39 in _PyEval_EvalFrameDefault Python/ceval.c:4277
csarofeen#71 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#72 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#73 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#74 0x3ffaaf8a695 in _PyObject_FastCallDictTstate Objects/call.c:153
csarofeen#75 0x3ffaaf8b271 in _PyObject_Call_Prepend Objects/call.c:431
csarofeen#76 0x3ffab03f307 in slot_tp_call Objects/typeobject.c:7494
csarofeen#77 0x3ffaaf8a933 in _PyObject_MakeTpCall Objects/call.c:215
csarofeen#78 0x3ffab0f0081 in _PyObject_VectorcallTstate Include/cpython/abstract.h:112
csarofeen#79 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#80 0x3ffab105447 in call_function Python/ceval.c:5891
csarofeen#81 0x3ffab0ffa57 in _PyEval_EvalFrameDefault Python/ceval.c:4231
csarofeen#82 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#83 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#84 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#85 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#86 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#87 0x3ffab105447 in call_function Python/ceval.c:5891
csarofeen#88 0x3ffab0ff7d7 in _PyEval_EvalFrameDefault Python/ceval.c:4198
csarofeen#89 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#90 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#91 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#92 0x3ffaaf8ab15 in PyVectorcall_Call Objects/call.c:255
csarofeen#93 0x3ffaaf8ac65 in _PyObject_Call Objects/call.c:290
csarofeen#94 0x3ffaaf8ada9 in PyObject_Call Objects/call.c:317
csarofeen#95 0x3ffab1059c7 in do_call_core Python/ceval.c:5943
csarofeen#96 0x3ffab0ffd39 in _PyEval_EvalFrameDefault Python/ceval.c:4277
csarofeen#97 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#98 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#99 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#100 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#101 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#102 0x3ffab105447 in call_function Python/ceval.c:5891
csarofeen#103 0x3ffab0ff779 in _PyEval_EvalFrameDefault Python/ceval.c:4181
csarofeen#104 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#105 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#106 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#107 0x3ffaaf8e941 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#108 0x3ffaaf8eddd in method_vectorcall Objects/classobject.c:53
csarofeen#109 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#110 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#111 0x3ffab105447 in call_function Python/ceval.c:5891
csarofeen#112 0x3ffab0ff779 in _PyEval_EvalFrameDefault Python/ceval.c:4181
csarofeen#113 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#114 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#115 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#116 0x3ffaaf8a695 in _PyObject_FastCallDictTstate Objects/call.c:153
csarofeen#117 0x3ffaaf8b271 in _PyObject_Call_Prepend Objects/call.c:431
csarofeen#118 0x3ffab03f307 in slot_tp_call Objects/typeobject.c:7494
csarofeen#119 0x3ffaaf8ad17 in _PyObject_Call Objects/call.c:305
csarofeen#120 0x3ffaaf8ada9 in PyObject_Call Objects/call.c:317
csarofeen#121 0x3ffab1059c7 in do_call_core Python/ceval.c:5943
csarofeen#122 0x3ffab0ffd39 in _PyEval_EvalFrameDefault Python/ceval.c:4277
csarofeen#123 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#124 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#125 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#126 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#127 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#128 0x3ffab105447 in call_function Python/ceval.c:5891
csarofeen#129 0x3ffab0ff905 in _PyEval_EvalFrameDefault Python/ceval.c:4213
csarofeen#130 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#131 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#132 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#133 0x3ffaaf8e941 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#134 0x3ffaaf8eddd in method_vectorcall Objects/classobject.c:53
csarofeen#135 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#136 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#137 0x3ffab105447 in call_function Python/ceval.c:5891
csarofeen#138 0x3ffab0ffa57 in _PyEval_EvalFrameDefault Python/ceval.c:4231
csarofeen#139 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#140 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#141 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#142 0x3ffaaf8ab15 in PyVectorcall_Call Objects/call.c:255
csarofeen#143 0x3ffaaf8ac65 in _PyObject_Call Objects/call.c:290
csarofeen#144 0x3ffaaf8ada9 in PyObject_Call Objects/call.c:317
csarofeen#145 0x3ffab1059c7 in do_call_core Python/ceval.c:5943
csarofeen#146 0x3ffab0ffd39 in _PyEval_EvalFrameDefault Python/ceval.c:4277
csarofeen#147 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#148 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#149 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#150 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#151 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#152 0x3ffab105447 in call_function Python/ceval.c:5891
csarofeen#153 0x3ffab0ff905 in _PyEval_EvalFrameDefault Python/ceval.c:4213
csarofeen#154 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#155 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#156 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#157 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#158 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#159 0x3ffab105447 in call_function Python/ceval.c:5891
csarofeen#160 0x3ffab0ffa57 in _PyEval_EvalFrameDefault Python/ceval.c:4231
csarofeen#161 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#162 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#163 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#164 0x3ffaaf8ab15 in PyVectorcall_Call Objects/call.c:255
csarofeen#165 0x3ffaaf8ac65 in _PyObject_Call Objects/call.c:290
csarofeen#166 0x3ffaaf8ada9 in PyObject_Call Objects/call.c:317
csarofeen#167 0x3ffab1059c7 in do_call_core Python/ceval.c:5943
csarofeen#168 0x3ffab0ffd39 in _PyEval_EvalFrameDefault Python/ceval.c:4277
csarofeen#169 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#170 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#171 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#172 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#173 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#174 0x3ffab105447 in call_function Python/ceval.c:5891
csarofeen#175 0x3ffab0ff779 in _PyEval_EvalFrameDefault Python/ceval.c:4181
csarofeen#176 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#177 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#178 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#179 0x3ffaaf8e941 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#180 0x3ffaaf8eddd in method_vectorcall Objects/classobject.c:53
csarofeen#181 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#182 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#183 0x3ffab105447 in call_function Python/ceval.c:5891
csarofeen#184 0x3ffab0ff779 in _PyEval_EvalFrameDefault Python/ceval.c:4181
csarofeen#185 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#186 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#187 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#188 0x3ffaaf8a695 in _PyObject_FastCallDictTstate Objects/call.c:153
csarofeen#189 0x3ffaaf8b271 in _PyObject_Call_Prepend Objects/call.c:431
csarofeen#190 0x3ffab03f307 in slot_tp_call Objects/typeobject.c:7494
csarofeen#191 0x3ffaaf8a933 in _PyObject_MakeTpCall Objects/call.c:215
csarofeen#192 0x3ffab0f0081 in _PyObject_VectorcallTstate Include/cpython/abstract.h:112
csarofeen#193 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#194 0x3ffab105447 in call_function Python/ceval.c:5891
csarofeen#195 0x3ffab0ffa57 in _PyEval_EvalFrameDefault Python/ceval.c:4231
csarofeen#196 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#197 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#198 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#199 0x3ffaaf8ab15 in PyVectorcall_Call Objects/call.c:255
csarofeen#200 0x3ffaaf8ac65 in _PyObject_Call Objects/call.c:290
csarofeen#201 0x3ffaaf8ada9 in PyObject_Call Objects/call.c:317
csarofeen#202 0x3ffab1059c7 in do_call_core Python/ceval.c:5943
csarofeen#203 0x3ffab0ffd39 in _PyEval_EvalFrameDefault Python/ceval.c:4277
csarofeen#204 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#205 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#206 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#207 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#208 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#209 0x3ffab105447 in call_function Python/ceval.c:5891
csarofeen#210 0x3ffab0ff779 in _PyEval_EvalFrameDefault Python/ceval.c:4181
csarofeen#211 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#212 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#213 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#214 0x3ffaaf8e941 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#215 0x3ffaaf8eddd in method_vectorcall Objects/classobject.c:53
csarofeen#216 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#216 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#217 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#218 0x3ffab105447 in call_function Python/ceval.c:5891
csarofeen#219 0x3ffab0ff779 in _PyEval_EvalFrameDefault Python/ceval.c:4181
csarofeen#220 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#221 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#222 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#223 0x3ffaaf8a695 in _PyObject_FastCallDictTstate Objects/call.c:153
csarofeen#224 0x3ffaaf8b271 in _PyObject_Call_Prepend Objects/call.c:431
csarofeen#225 0x3ffab03f307 in slot_tp_call Objects/typeobject.c:7494
csarofeen#226 0x3ffaaf8a933 in _PyObject_MakeTpCall Objects/call.c:215
csarofeen#227 0x3ffab0f0081 in _PyObject_VectorcallTstate Include/cpython/abstract.h:112
csarofeen#228 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#229 0x3ffab105447 in call_function Python/ceval.c:5891
csarofeen#230 0x3ffab0ffa57 in _PyEval_EvalFrameDefault Python/ceval.c:4231
csarofeen#231 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#232 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#233 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#234 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#235 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#236 0x3ffab105447 in call_function Python/ceval.c:5891
csarofeen#237 0x3ffab0ff905 in _PyEval_EvalFrameDefault Python/ceval.c:4213
csarofeen#238 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#239 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#240 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#241 0x3ffab0f00a9 in _PyObject_VectorcallTstate Include/cpython/abstract.h:114
csarofeen#242 0x3ffab0f013d in PyObject_Vectorcall Include/cpython/abstract.h:123
csarofeen#243 0x3ffab105447 in call_function Python/ceval.c:5891
csarofeen#244 0x3ffab0ff905 in _PyEval_EvalFrameDefault Python/ceval.c:4213
csarofeen#245 0x3ffab0f052b in _PyEval_EvalFrame Include/internal/pycore_ceval.h:46
csarofeen#246 0x3ffab102b67 in _PyEval_Vector Python/ceval.c:5065
csarofeen#247 0x3ffaaf8aec1 in _PyFunction_Vectorcall Objects/call.c:342
csarofeen#248 0x3ffaaf8ab15 in PyVectorcall_Call Objects/call.c:255
csarofeen#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
#1 0x3ff55984fdb in __gnu_cxx::new_allocator<std::_Sp_counted_ptr_inplace<c10::FunctionSchema, std::allocator<c10::FunctionSchema>, (__gnu_cxx::_Lock_policy)2> >::deallocate(std::_Sp_counted_ptr_inplace<c10::FunctionSchema, std::allocator<c10::FunctionSchema>, (__gnu_cxx::_Lock_policy)2>*, unsigned long) /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/ext/new_allocator.h:145
previously allocated by thread T0 here:
#0 0x3ffab53734f in operator new(unsigned long) /var/tmp/portage/sys-devel/gcc-11.3.1_p20230303/work/gcc-11-20230303/libsanitizer/asan/asan_new_delete.cpp:99
#1 0x3ff5598443f in __gnu_cxx::new_allocator<std::_Sp_counted_ptr_inplace<c10::FunctionSchema, std::allocator<c10::FunctionSchema>, (__gnu_cxx::_Lock_policy)2> >::allocate(unsigned long, void const*) /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/ext/new_allocator.h:127
csarofeen#2 0x3fff5849ecf ([stack]+0xb2ecf)
SUMMARY: AddressSanitizer: heap-use-after-free /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/bits/stl_iterator.h:1028 in __gnu_cxx::__normal_iterator<c10::Argument const*, std::vector<c10::Argument, std::allocator<c10::Argument> > >::__normal_iterator(c10::Argument const* const&)
Shadow bytes around the buggy address:
0x100c1a000b4aa0: fd fd fd fd fd fd fd fd fd fd fd fa fa fa fa fa
0x100c1a000b4ab0: fa fa fa fa fd fd fd fd fd fd fd fd fd fd fd fd
0x100c1a000b4ac0: fd fd fd fd fd fa fa fa fa fa fa fa fa fa fd fd
0x100c1a000b4ad0: fd fd fd fd fd fd fd fd fd fd fd fd fd fd fd fa
0x100c1a000b4ae0: fa fa fa fa fa fa fa fa fd fd fd fd fd fd fd fd
=>0x100c1a000b4af0: fd fd[fd]fd fd fd fd fd fd fa fa fa fa fa fa fa
0x100c1a000b4b00: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
0x100c1a000b4b10: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
0x100c1a000b4b20: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
0x100c1a000b4b30: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
0x100c1a000b4b40: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
Shadow byte legend (one shadow byte represents 8 application bytes):
Addressable: 00
Partially addressable: 01 02 03 04 05 06 07
Heap left redzone: fa
Freed heap region: fd
Stack left redzone: f1
Stack mid redzone: f2
Stack right redzone: f3
Stack after return: f5
Stack use after scope: f8
Global redzone: f9
Global init order: f6
Poisoned by user: f7
Container overflow: fc
Array cookie: ac
Intra object redzone: bb
ASan internal: fe
Left alloca redzone: ca
Right alloca redzone: cb
Shadow gap: cc
==1134126==ABORTING
```
Additional backtraces (not full):
Allocation:
```
#0 __memset_z196 () at ../sysdeps/s390/memset-z900.S:144
#1 0x000003ff96f3072a in __asan::Allocator::Allocate (this=this@entry=0x3ff97041eb8 <__asan::instance>, size=size@entry=136, alignment=8, alignment@entry=0, stack=<optimized out>,
stack@entry=0x3ffdbb45d78, alloc_type=<optimized out>, can_fill=true) at /var/tmp/portage/sys-devel/gcc-11.3.1_p20230303/work/gcc-11-20230303/libsanitizer/asan/asan_allocator.cpp:599
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#14 0x000003ff4115fbac in c10::ivalue::TupleTypeFactory<c10::TupleType>::fallback (type=...) at /home/user/pytorch/aten/src/ATen/core/dynamic_type.cpp:383
csarofeen#15 0x000003ff708217fe in c10::ivalue::Tuple::type<c10::TupleType> (this=0x6080004b8520) at /home/user/pytorch/aten/src/ATen/core/ivalue_inl.h:781
csarofeen#16 0x000003ff70800740 in torch::jit::toPyObject (ivalue=...) at /home/user/pytorch/torch/csrc/jit/python/pybind_utils.cpp:613
csarofeen#17 0x000003ff70800306 in torch::jit::toPyObject (ivalue=...) at /home/user/pytorch/torch/csrc/jit/python/pybind_utils.cpp:604
csarofeen#18 0x000003ff702d6872 in pybind11::detail::type_caster<c10::IValue, void>::cast (src=...) at /home/user/pytorch/torch/csrc/jit/python/pybind.h:138
csarofeen#19 0x000003ff70d98192 in pybind11::cpp_function::initialize<torch::jit::initJitScriptBindings(_object*)::$_45, c10::IValue, torch::jit::mobile::Module&, pybind11::tuple const&, pybind11::name, pybind11::is_method, pybind11::sibling, pybind11::arg>(torch::jit::initJitScriptBindings(_object*)::$_45&&, c10::IValue (*)(torch::jit::mobile::Module&, pybind11::tuple const&), pybind11::name const&, pybind11::is_method const&, pybind11::sibling const&, pybind11::arg const&)::{lambda(pybind11::detail::function_call&)#1}::operator()(pybind11::detail::function_call&) const (this=0x3ffdbb4ca20, call=...)
at /home/user/pytorch/cmake/../third_party/pybind11/include/pybind11/pybind11.h:249
csarofeen#20 0x000003ff70d97cfe in pybind11::cpp_function::initialize<torch::jit::initJitScriptBindings(_object*)::$_45, c10::IValue, torch::jit::mobile::Module&, pybind11::tuple const&, pybind11::name, pybind11::is_method, pybind11::sibling, pybind11::arg>(torch::jit::initJitScriptBindings(_object*)::$_45&&, c10::IValue (*)(torch::jit::mobile::Module&, pybind11::tuple const&), pybind11::name const&, pybind11::is_method const&, pybind11::sibling const&, pybind11::arg const&)::{lambda(pybind11::detail::function_call&)#1}::__invoke(pybind11::detail::function_call&) (call=...)
at /home/user/pytorch/cmake/../third_party/pybind11/include/pybind11/pybind11.h:224
csarofeen#21 0x000003ff6e9652ea in pybind11::cpp_function::dispatcher (self=<PyCapsule at remote 0x3ff83e27720>,
args_in=(<torch._C.LiteScriptModule at remote 0x3ff811844b0>, (<Tensor at remote 0x3ff814efb00>,)), kwargs_in=0x0) at /home/user/pytorch/cmake/../third_party/pybind11/include/pybind11/pybind11.h:929
```
Deallocation:
```
#0 operator delete (ptr=0x60d0005a5740) at /var/tmp/portage/sys-devel/gcc-11.3.1_p20230303/work/gcc-11-20230303/libsanitizer/asan/asan_new_delete.cpp:160
#1 0x000003ff44904fdc in __gnu_cxx::new_allocator<std::_Sp_counted_ptr_inplace<c10::FunctionSchema, std::allocator<c10::FunctionSchema>, (__gnu_cxx::_Lock_policy)2> >::deallocate (this=0x3ffc5dc8020,
__p=0x60d0005a5740, __t=1) at /usr/lib/gcc/s390x-ibm-linux-gnu/11/include/g++-v11/ext/new_allocator.h:145
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#9 0x000003ff448d82f6 in c10::TupleType::~TupleType (this=0x611000c40580) at /home/user/pytorch/aten/src/ATen/core/jit_type.h:1142
csarofeen#10 0x000003ff448d8346 in c10::TupleType::~TupleType (this=0x611000c40580) at /home/user/pytorch/aten/src/ATen/core/jit_type.h:1142
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#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
csarofeen#16 0x000003ff73d00788 in torch::jit::toPyObject (ivalue=...) at /home/user/pytorch/torch/csrc/jit/python/pybind_utils.cpp:613
csarofeen#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
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