[WIP] Kernel IR Refactoring #249
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Summary: Adding tensor symbolic for opset 9 Pull Request resolved: pytorch#41872 Reviewed By: houseroad Differential Revision: D22968426 Pulled By: bzinodev fbshipit-source-id: 70e1afc7397e38039e2030e550fd72f09bac7c7c
…er Tensor Kernels (CPU and GPU) (pytorch#42384) Summary: Pull Request resolved: pytorch#42384 In this diff, the original backward pass implementation is sped up by merging the 3 iterations computing dX, dScale, and dZeroPoint separately. In this case, a native loop is directly used on a byte-wise level (referenced by `strides`). In the benchmark test on the operators, for an input of shape `3x3x256x256`, we have observed the following improvement in performance: - original python operator: 1021037 microseconds - original learnable kernel: 407576 microseconds - optimized learnable kernel: 102584 microseconds - original non-backprop kernel: 139806 microseconds **Speedup from python operator**: ~10x **Speedup from original learnable kernel**: ~4x **Speedup from non-backprop kernel**: ~1.2x Test Plan: To assert correctness of the new kernel, on a devvm, enter the command `buck test //caffe2/test:quantization -- learnable_backward_per_tensor` To benchmark the operators, on a devvm, enter the command 1. Set the kernel size to 3x3x256x256 or a reasonable input size. 2. Run `buck test //caffe2/benchmarks/operator_benchmark/pt:quantization_test` 3. The relevant outputs are as follows: (CPU) ``` # Benchmarking PyTorch: FakeQuantizePerTensorOpBenchmark # Mode: Eager # Name: FakeQuantizePerTensorOpBenchmark_N3_C3_H256_W256_nbits4_cpu_op_typepy_module # Input: N: 3, C: 3, H: 256, W: 256, device: cpu, op_type: py_module Backward Execution Time (us) : 1021036.957 # Benchmarking PyTorch: FakeQuantizePerTensorOpBenchmark # Mode: Eager # Name: FakeQuantizePerTensorOpBenchmark_N3_C3_H256_W256_nbits4_cpu_op_typelearnable_kernel # Input: N: 3, C: 3, H: 256, W: 256, device: cpu, op_type: learnable_kernel Backward Execution Time (us) : 102583.693 # Benchmarking PyTorch: FakeQuantizePerTensorOpBenchmark # Mode: Eager # Name: FakeQuantizePerTensorOpBenchmark_N3_C3_H256_W256_nbits4_cpu_op_typeoriginal_kernel # Input: N: 3, C: 3, H: 256, W: 256, device: cpu, op_type: original_kernel Backward Execution Time (us) : 139806.086 ``` (GPU) ``` # Benchmarking PyTorch: FakeQuantizePerChannelOpBenchmark # Mode: Eager # Name: FakeQuantizePerChannelOpBenchmark_N3_C3_H256_W256_cuda_op_typepy_module # Input: N: 3, C: 3, H: 256, W: 256, device: cuda, op_type: py_module Backward Execution Time (us) : 6548.350 # Benchmarking PyTorch: FakeQuantizePerChannelOpBenchmark # Mode: Eager # Name: FakeQuantizePerChannelOpBenchmark_N3_C3_H256_W256_cuda_op_typelearnable_kernel # Input: N: 3, C: 3, H: 256, W: 256, device: cuda, op_type: learnable_kernel Backward Execution Time (us) : 1340.724 # Benchmarking PyTorch: FakeQuantizePerChannelOpBenchmark # Mode: Eager # Name: FakeQuantizePerChannelOpBenchmark_N3_C3_H256_W256_cuda_op_typeoriginal_kernel # Input: N: 3, C: 3, H: 256, W: 256, device: cuda, op_type: original_kernel Backward Execution Time (us) : 656.863 ``` Reviewed By: vkuzo Differential Revision: D22875998 fbshipit-source-id: cfcd62c327bb622270a783d2cbe97f00508c4a16
Summary: in `_jit_pass_onnx`, symbolic functions are called for each node for conversion. However, there are nodes that cannot be converted without additional context. For example, the number of outputs from split (and whether it is static or dynamic) is unknown until the point where it is unpacked by listUnpack node. This pass does a preprocess, and prepares the nodes such that enough context can be received by the symbolic function. * After preprocessing, `_jit_pass_onnx` should have enough context to produce valid ONNX nodes, instead of half baked nodes that replies on fixes from later postpasses. * `_jit_pass_onnx_peephole` should be a pass that does ONNX specific optimizations instead of ONNX specific fixes. * Producing more valid ONNX nodes in `_jit_pass_onnx` enables better utilization of the ONNX shape inference pytorch#40628. Pull Request resolved: pytorch#41832 Reviewed By: ZolotukhinM Differential Revision: D22968334 Pulled By: bzinodev fbshipit-source-id: 8226f03c5b29968e8197d242ca8e620c6e1d42a5
Summary: Pull Request resolved: pytorch#42692 Test Plan: Imported from OSS Reviewed By: mruberry Differential Revision: D22986112 Pulled By: bertmaher fbshipit-source-id: 52ec3389535c8b276858bef8c470a59aeba4946f
Summary: [5/N] Implement Enum JIT support Implement Enum class iteration Add aten.ne for EnumType Supported: Enum-typed function arguments using Enum type and comparing them Support getting name/value attrs of enums Using Enum value as constant Support Enum-typed return values Support iterating through Enum class (enum value list) TODO: Support serialization and deserialization Pull Request resolved: pytorch#42661 Reviewed By: SplitInfinity Differential Revision: D22977364 Pulled By: gmagogsfm fbshipit-source-id: 1a0216f91d296119e34cc292791f9aef1095b5a8
…del loading script Summary: Put user embedding before ads embedding in blobReorder, for flash verification reason. Test Plan: ``` buck run mode/opt-clang -c python.package_style=inplace sigrid/predictor/scripts:enable_large_model_loading -- --model_path_src="/home/$USER/models/" --model_path_dst="/home/$USER/models_modified/" --model_file_name="182560549_0.predictor" ``` https://www.internalfb.com/intern/anp/view/?id=320921 to check blobsOrder Reviewed By: yinghai Differential Revision: D22964332 fbshipit-source-id: 78b4861476a3c889a5ff62492939f717c307a8d2
Summary: This PR canonicalizes our (current) pattern for adding aliases to PyTorch. That pattern is: - Copy the original functions native_functions.yaml entry, but replace the original function's name with their own. - Implement the corresponding functions and have them redispatch to the original function. - Add docstrings to the new functions that reference the original function. - Update the alias_map in torch/csrc/jit/passes/normalize_ops.cpp. - Update the op_alias_mappings in torch/testing/_internal/jit_utils.py. - Add a test validating the alias's behavior is the same as the original function's. An alternative pattern would be to use Python and C++ language features to alias ops directly. For example in Python: ``` torch.absolute = torch.abs ``` Let the pattern in this PR be the "native function" pattern, and the alternative pattern be the "language pattern." There are pros/cons to both approaches: **Pros of the "Language Pattern"** - torch.absolute is torch.abs. - no (or very little) overhead for calling the alias. - no native_functions.yaml redundancy or possibility of "drift" between the original function's entries and the alias's. **Cons of the "Language Pattern"** - requires manually adding doc entries - requires updating Python alias and C++ alias lists - requires hand writing alias methods on Tensor (technically this should require a C++ test to validate) - no single list of all PyTorch ops -- have to check native_functions.yaml and one of the separate alias lists **Pros of the "Native Function" pattern** - alias declarations stay in native_functions.yaml - doc entries are written as normal **Cons of the "Native Function" pattern** - aliases redispatch to the original functions - torch.absolute is not torch.abs (requires writing test to validate behavior) - possibility of drift between original's and alias's native_functions.yaml entries While either approach is reasonable, I suggest the "native function" pattern since it preserves "native_functions.yaml" as a source of truth and minimizes the number of alias lists that need to be maintained. In the future, entries in native_functions.yaml may support an "alias" argument and replace whatever pattern we choose now. Ops that are likely to use aliasing are: - div (divide, true_divide) - mul (multiply) - bucketize (digitize) - cat (concatenate) - clamp (clip) - conj (conjugate) - rad2deg (degrees) - trunc (fix) - neg (negative) - deg2rad (radians) - round (rint) - acos (arccos) - acosh (arcosh) - asin (arcsin) - asinh (arcsinh) - atan (arctan) - atan2 (arctan2) - atanh (arctanh) - bartlett_window (bartlett) - hamming_window (hamming) - hann_window (hanning) - bitwise_not (invert) - gt (greater) - ge (greater_equal) - lt (less) - le (less_equal) - ne (not_equal) - ger (outer) Pull Request resolved: pytorch#42586 Reviewed By: ngimel Differential Revision: D22991086 Pulled By: mruberry fbshipit-source-id: d6ac96512d095b261ed2f304d7dddd38cf45e7b0
…pytorch#4787) Summary: Pull Request resolved: pytorch/glow#4787 Resurrect ONNX as a backend through onnxifiGlow (was killed as part of D16215878). Then look for the `use_glow_aot` argument in the Onnxifi op. If it's there and true, then we override whatever `backend_id` is set and use the ONNX backend. Reviewed By: yinghai, rdzhabarov Differential Revision: D22762123 fbshipit-source-id: abb4c3458261f8b7eeae3016dda5359fa85672f0
Summary: Fixes issues in pytorch#41704 and pytorch#41705 Pull Request resolved: pytorch#42590 Reviewed By: ailzhang Differential Revision: D22977357 Pulled By: malfet fbshipit-source-id: ab61b964cfdf8bd2b469f4ff8f6486a76bc697de
Summary: Pull Request resolved: pytorch#42194 Test Plan: Imported from OSS Reviewed By: AshkanAliabadi Differential Revision: D22803036 Pulled By: IvanKobzarev fbshipit-source-id: 2f402541aecf887d78f650bf05d758a0e403bc4d
Summary:
If argumenets in set_target_properties are not separated by whitespace, cmake raises a warning:
```
CMake Warning (dev) at cmake/public/cuda.cmake:269:
Syntax Warning in cmake code at column 54
Argument not separated from preceding token by whitespace.
```
Fixes #{issue number}
Pull Request resolved: pytorch#42707
Reviewed By: ailzhang
Differential Revision: D22988055
Pulled By: malfet
fbshipit-source-id: c3744f23b383d603788cd36f89a8286a46b6c00f
Summary: Pull Request resolved: pytorch#42383 Test Plan - Updated existing tests to run for complex dtypes as well. Also added tests for `torch.addmm`, `torch.badmm` Test Plan: Imported from OSS Reviewed By: ezyang Differential Revision: D22960339 Pulled By: anjali411 fbshipit-source-id: 0805f21caaa40f6e671cefb65cef83a980328b7d
Summary: This PR adds the `torch.linalg` namespace as part of our continued effort to be more compatible with NumPy. The namespace is tested by adding a single function, `torch.linalg.outer`, and testing it in a new test suite, test_linalg.py. It follows the same pattern that pytorch#41911, which added the `torch.fft` namespace, did. Future PRs will likely: - add more functions to torch.linalg - expand the testing done in test_linalg.py, including legacy functions, like torch.ger - deprecate existing linalg functions outside of `torch.linalg` in preference to the new namespace Pull Request resolved: pytorch#42664 Reviewed By: ngimel Differential Revision: D22991019 Pulled By: mruberry fbshipit-source-id: 39258d9b116a916817b3588f160b141f956e5d0b
Summary: Essentially, replace `-Wl,--whole-archive,$<TARGET_FILE:FOO>` with `-Wl,--whole-archive,\"$<TARGET_FILE:FOO>\"` as TARGET_FILE might return path containing whitespaces Fixes pytorch#42657 Pull Request resolved: pytorch#42718 Reviewed By: ezyang Differential Revision: D22993568 Pulled By: malfet fbshipit-source-id: de878b17d20e35b51dd350f20d079c8b879f70b5
Summary: Allow passing scale and bias to fake fp16 layernorm. Test Plan: net_runner. Now matches glow's fused layernorm. Reviewed By: hyuen Differential Revision: D22952646 fbshipit-source-id: cf9ad055b14f9d0167016a18a6b6e26449cb4de8
Summary: Awhile back when commonizing the Let and LetStmt nodes, I ended up removing both and adding a separate VarBinding section the Block. At the time I couldn't find a counter example, but I found it today: Local Vars and Allocations dependencies may go in either direction and so we need to support interleaving of those statements. So, I've removed all the VarBinding logic and reimplemented Let statements. ZolotukhinM I think you get to say "I told you so". No new tests, existing tests should cover this. Pull Request resolved: pytorch#42634 Reviewed By: mruberry Differential Revision: D22969771 Pulled By: nickgg fbshipit-source-id: a46c5193357902d0f59bf30ab103fe123b1503f1
Summary: I noticed that `TensorIteratorDynamicCasting.h` defines a helper meta-function `CPPTypeToScalarType` which does exactly the same thing as the `c10::CppTypeToScalarType` meta-function I added in pytorchgh-40927. No need for two identical definitions. Pull Request resolved: pytorch#42640 Reviewed By: malfet Differential Revision: D22969708 Pulled By: ezyang fbshipit-source-id: 8303c7f4a75ae248f393a4811ae9d2bcacab44ff
Summary: Pull Request resolved: pytorch#42195 Test Plan: Imported from OSS Reviewed By: AshkanAliabadi Differential Revision: D22803035 Pulled By: IvanKobzarev fbshipit-source-id: d7bf256437eccb5c421a7fd0aa8ec23a8fec0470
Summary: Just fixed a typo in test/test_sparse.py Pull Request resolved: pytorch#42731 Reviewed By: ezyang Differential Revision: D22999930 Pulled By: mrshenli fbshipit-source-id: 1b5b21d7cb274bd172fb541b2761f727ba06302c
Summary: Pull Request resolved: pytorch#42611 **Summary** This commit modifies the Python frontend to ignore static functions on Torchscript classes when compiling them. They are currently included along with methods, which causes the first argument of the staticfunction to be unconditionally inferred to be of the type of the class it belongs to (regardless of how it is annotated or whether it is annotated at all). This can lead to compilation errors depending on how that argument is used in the body of the function. Static functions are instead imported and scripted as if they were standalone functions. **Test Plan** This commit augments the unit test for static methods in `test_class_types.py` to test that static functions can call each other and the class constructor. **Fixes** This commit fixes pytorch#39308. Test Plan: Imported from OSS Reviewed By: ZolotukhinM Differential Revision: D22958163 Pulled By: SplitInfinity fbshipit-source-id: 45c3c372792299e6e5288e1dbb727291e977a2af
Summary: Pull Request resolved: pytorch#42633 Test Plan: Imported from OSS Reviewed By: ezyang Differential Revision: D22994332 Pulled By: glaringlee fbshipit-source-id: 873abdf887d135fb05bde560d695e2e8c992c946
Summary: 22x speedup over the code this replaces. Tested on ResNet18 on a devvm using CPU only, using default parameters for HistogramObserver (i.e. 2048 bins). Pull Request resolved: pytorch#41041 Test Plan: To run the test against the reference (old) implementation, you can use `python test/test_quantization.py TestRecordHistogramObserver.test_histogram_observer_against_reference`. To run the benchmark, while in the folder `benchmarks/operator_benchmark`, you can use `python -m benchmark_all_quantized_test --operators HistogramObserverCalculateQparams`. Benchmark results before speedup: ``` # ---------------------------------------- # PyTorch/Caffe2 Operator Micro-benchmarks # ---------------------------------------- # Tag : short # Benchmarking PyTorch: HistogramObserverCalculateQparams # Mode: Eager # Name: HistogramObserverCalculateQparams_C3_M512_N512_dtypetorch.quint8_cpu_qschemetorch.per_tensor_affine # Input: C: 3, M: 512, N: 512, dtype: torch.quint8, device: cpu, qscheme: torch.per_tensor_affine Forward Execution Time (us) : 185818.566 # Benchmarking PyTorch: HistogramObserverCalculateQparams # Mode: Eager # Name: HistogramObserverCalculateQparams_C3_M512_N512_dtypetorch.quint8_cpu_qschemetorch.per_tensor_symmetric # Input: C: 3, M: 512, N: 512, dtype: torch.quint8, device: cpu, qscheme: torch.per_tensor_symmetric Forward Execution Time (us) : 165325.916 ``` Benchmark results after speedup: ``` # ---------------------------------------- # PyTorch/Caffe2 Operator Micro-benchmarks # ---------------------------------------- # Tag : short # Benchmarking PyTorch: HistogramObserverCalculateQparams # Mode: Eager # Name: HistogramObserverCalculateQparams_C3_M512_N512_dtypetorch.quint8_cpu_qschemetorch.per_tensor_affine # Input: C: 3, M: 512, N: 512, dtype: torch.quint8, device: cpu, qscheme: torch.per_tensor_affine Forward Execution Time (us) : 12242.241 # Benchmarking PyTorch: HistogramObserverCalculateQparams # Mode: Eager # Name: HistogramObserverCalculateQparams_C3_M512_N512_dtypetorch.quint8_cpu_qschemetorch.per_tensor_symmetric # Input: C: 3, M: 512, N: 512, dtype: torch.quint8, device: cpu, qscheme: torch.per_tensor_symmetric Forward Execution Time (us) : 12655.354 ``` Reviewed By: raghuramank100 Differential Revision: D22400755 Pulled By: durumu fbshipit-source-id: 639ac796a554710a33c8a930c1feae95a1148718
…ytorch#42669) Summary: cc rohan-varma Fixes pytorch#41362 pytorch#39708 # Description NCCL doesn't support `BAND, BOR, BXOR`. Since the [current mapping](https://github.com/pytorch/pytorch/blob/0642d17efc73041e5209e3be265d9a39892e8908/torch/lib/c10d/ProcessGroupNCCL.cpp#L39) doesn't contain any of the mentioned bitwise operator, a default value of `ncclSum` is used instead. This PR should provide the expected behaviour where a runtime exception is thrown. # Notes - The way I'm throwing exceptions is derived from [ProcessGroupGloo.cpp](https://github.com/pytorch/pytorch/blob/0642d17efc73041e5209e3be265d9a39892e8908/torch/lib/c10d/ProcessGroupGloo.cpp#L101) Pull Request resolved: pytorch#42669 Reviewed By: ezyang Differential Revision: D22996295 Pulled By: rohan-varma fbshipit-source-id: 83a9fedf11050d2890f9f05ebcedf53be0fc3516
Summary: Add Python type annotations for the `caffe2.distributed.python` module. Test Plan: Will check sandcastle results. Reviewed By: jeffdunn Differential Revision: D22994012 fbshipit-source-id: 30565cc41dd05b5fbc639ae994dfe2ddd9e56cb1
Summary: This is an automated pull request to update the first-party submodule for [pytorch/FBGEMM](https://github.com/pytorch/FBGEMM). New submodule commit: pytorch/FBGEMM@a989b99 Pull Request resolved: pytorch#42713 Test Plan: Ensure that CI jobs succeed on GitHub before landing. Reviewed By: amylittleyang Differential Revision: D22990108 Pulled By: jspark1105 fbshipit-source-id: 3252a0f5ad9546221ef2fe908ce6b896252e1887
Summary: Pull Request resolved: pytorch#42756 Similar to ELU, CELU was also broken in the quantized benchmark, fixing. Test Plan: ``` cd benchmarks/operator_benchmark python -m pt.qactivation_test ``` Imported from OSS Reviewed By: jerryzh168 Differential Revision: D23010863 fbshipit-source-id: 203e63f9cff760af6809f6f345b0d222dc1e9e1b
Summary: Pull Request resolved: pytorch#42694 The old implementation allowed calling SmallVector constructor and operator= for any type without restrictions, but then failed with a compiler error when the type wasn't a collection. Instead, we should only use it if Container follows a container concept and just not match the constructor otherwise. This fixes an issue kimishpatel was running into. ghstack-source-id: 109370513 Test Plan: unit tests Reviewed By: kimishpatel, ezyang Differential Revision: D22983020 fbshipit-source-id: c31264f5c393762d822f3d64dd2a8e3279d8da44
Summary: Fixes ROCm build on OSS master. Pull Request resolved: pytorch#42759 Reviewed By: ngimel Differential Revision: D23011560 Pulled By: mruberry fbshipit-source-id: 3339ecbd5a0ca47aede6f7c3f84739af1ac820d5
Summary: As titled. Test Plan: ``` buck test caffe2/caffe2/python/operator_test:torch_integration_test -- test_percentile ``` Reviewed By: yf225 Differential Revision: D22999896 fbshipit-source-id: 2e3686cb893dff1518d533cb3d78c92eb2a6efa5
Summary: This diff adds FakeQuantizeWithBackward. This works the same way as the regular FakeQuantize module, allowing QAT to occur in the forward pass, except it has an additional quantize_backward parameter. When quantize_backward is enabled, the gradients are fake quantized as well (dynamically, using hard-coded values). This allows the user to see whether there would be a significant loss of accuracy if the gradients were quantized in their model. Pull Request resolved: pytorch#40532 Test Plan: The relevant test for this can be run using `python test/test_quantization.py TestQATBackward.test_forward_and_backward` Reviewed By: supriyar Differential Revision: D22217029 Pulled By: durumu fbshipit-source-id: 7055a2cdafcf022f1ea11c3442721ae146d2b3f2
Summary: Pull Request resolved: pytorch#42745 Test Plan: Imported from OSS Reviewed By: izdeby Differential Revision: D23056382 Pulled By: anjali411 fbshipit-source-id: c97f15e057095f78069844dbe0299c14104d2fce
…ytorch#43067) Summary: Since OpenMP is not available on some platforms, or might be disabled by user, set default `ATEN_THREADING` based on USE_OPENMP and USE_TBB options Fixes pytorch#43036 Pull Request resolved: pytorch#43067 Reviewed By: houseroad Differential Revision: D23138856 Pulled By: malfet fbshipit-source-id: cc8f9ee59a5559baeb3f19bf461abbc08043b71c
Summary:
Fixes #{issue number}
Pull Request resolved: pytorch#43047
Reviewed By: ezyang
Differential Revision: D23134326
Pulled By: ailzhang
fbshipit-source-id: 5fcbc23755daa8a28f9b03af6aeb3ea0603b5c9a
Summary: LLVM builds took a large amount of time and bogged down docker builds in general. Since we build it the same for everything let's just copy it from a pre-built image instead of building it from source every time. Builds are defined in pytorch/builder#491 Signed-off-by: Eli Uriegas <eliuriegas@fb.com> Pull Request resolved: pytorch#43038 Reviewed By: malfet Differential Revision: D23119513 Pulled By: seemethere fbshipit-source-id: f44324439d45d97065246caad07c848e261a1ab6
Summary: Pull Request resolved: pytorch#43028 There was a bug where we always tried to grab the `__name__` attribute of the function passed in by the user. Not all Callables have the `__name__` attribute, an example being a Callable produced by functools.partial. This PR modifies the error-checking code to use `repr` if `__name__` is not available. Furthermore, it moves the "get the name of this function" functionality to the actual error sites as an optimization so we don't spend time trying to compute `__repr__` for the Callable if there is no error. Test Plan: - `pytest test/test_vmap.py -v`, added new tests. Reviewed By: yf225 Differential Revision: D23130235 Pulled By: zou3519 fbshipit-source-id: 937f3640cc4d759bf6fa38b600161f5387a54dcf
Summary: Pull Request resolved: pytorch#43059 This PR implements batching rules for some unary ops. In particular, it implements the batching rules for the unary ops that take a single tensor as input (and nothing else). The batching rule for a unary op is: (1) grab the physical tensor straight out of the BatchedTensor (2) call the unary op (3) rewrap the physical tensor in a BatchedTensor Test Plan: - new tests `pytest test/test_vmap.py -v -k "Operators"` Reviewed By: ezyang Differential Revision: D23132277 Pulled By: zou3519 fbshipit-source-id: 24b9d7535338207531d767155cdefd2c373ada77
…h#43122) Summary: This PR: - Adds a method variant to movedim - Fixes the movedim docs so it will actually appear in the documentation - Fixes three view doc links which were broken Pull Request resolved: pytorch#43122 Reviewed By: ngimel Differential Revision: D23166222 Pulled By: mruberry fbshipit-source-id: 14971585072bbc04b5366d4cc146574839e79cdb
Summary: Closes pytorchgh-42982 Pull Request resolved: pytorch#43108 Reviewed By: malfet Differential Revision: D23167560 Pulled By: ezyang fbshipit-source-id: 0d660ca686ada2347bf440c6349551d1539f99ef
Summary: Pull Request resolved: pytorch#43093 without this it's hard to tell which module is going wrong Test Plan: ``` > TypeError: > 'numpy.int64' object in attribute 'Linear.in_features' is not a valid constant. > Valid constants are: > 1. a nn.ModuleList > 2. a value of type {bool, float, int, str, NoneType, torch.device, torch.layout, torch.dtype} > 3. a list or tuple of (2) ``` Reviewed By: eellison Differential Revision: D23148516 fbshipit-source-id: b86296cdeb7b47c9fd69b5cfa479914c58ef02e6
…pytorch#42511) Summary: Pull Request resolved: pytorch#42511 DistEngine currently only has a single thread to execute GPU to CPU continuations as part of the backward pass. This would be a significant performance bottleneck in cases where we have such continuations and would like to execute these using all CPU cores. To alleviate this in this PR, we have the single thread in DistEngine only dequeue work from the global queue, but then hand off execution of that work to the c10 threadpool where we call "execute_graph_task_until_ready_queue_empty". For more context please see: pytorch#40255 (comment). ghstack-source-id: 109997718 Test Plan: waitforbuildbot Reviewed By: albanD Differential Revision: D22917579 fbshipit-source-id: c634b6c97f3051f071fd7b994333e6ecb8c54155
Summary: Pull Request resolved: pytorch#42257 Reviewed By: gchanan Differential Revision: D23109328 Pulled By: ezyang fbshipit-source-id: dacd438395fedd1050ad3ffb81327bbb746c776c
Summary: Pull Request resolved: pytorch#42956 In preparation for observer perf improvement, cleans up the micro benchmarks: * disable CUDA for histogram observers (it's too slow) * add larger shapes for better representation of real workloads Test Plan: ``` cd benchmarks/operator_benchmark python -m pt.qobserver_test ``` Imported from OSS Reviewed By: supriyar Differential Revision: D23093996 fbshipit-source-id: 5dc477c9bd5490d79d85ff8537270cd25aca221a
Summary: Pull Request resolved: pytorch#43149 This value doesn't change, making it a buffer to only pay the cost of creating a tensor once. Test Plan: Imported from OSS Reviewed By: jerryzh168 Differential Revision: D23170428 fbshipit-source-id: 6b963951a573efcc5b5a57649c814590b448dd72
…#43150) Summary: Pull Request resolved: pytorch#43150 The current logic was expensive because it created tensors on CUDA. Switching to clamp since it can work without needing to create tensors. Test Plan: benchmarks Imported from OSS Reviewed By: jerryzh168 Differential Revision: D23170427 fbshipit-source-id: 6fe3a728e737aca9f6c2c4d518c6376738577e21
…ch#43151) Summary: Pull Request resolved: pytorch#43151 Using `torch.all` instead of `torch.sum` and length check. It's unclear whether the increase in perf (~5% for small inputs) is real, but should be a net benefit, especially for larger channel inputs. Test Plan: Imported from OSS Reviewed By: jerryzh168 Differential Revision: D23170426 fbshipit-source-id: ee5c25eb93cee1430661128ac9458a9c525df8e5
Summary: Pull Request resolved: pytorch#43164 Test Plan: Imported from OSS Reviewed By: mruberry Differential Revision: D23175392 Pulled By: gchanan fbshipit-source-id: 0d2d918fdf4a94361cdc3344bf1bc89dd0286ace
…a dimension with shape > 1 (pytorch#38476) Summary: The ONNX spec for the Squeeze operator: > Remove single-dimensional entries from the shape of a tensor. Takes a parameter axes with a list of axes to squeeze. If axes is not provided, all the single dimensions will be removed from the shape. If an axis is selected with shape entry not equal to one, an error is raised. Currently, as explained in issue pytorch#36796, it is possible to export such a model to ONNX, and this results in an exception from ONNX runtime. Fixes pytorch#36796. Pull Request resolved: pytorch#38476 Reviewed By: hl475 Differential Revision: D22158024 Pulled By: houseroad fbshipit-source-id: bed625f3c626eabcbfb2ea83ec2f992963defa19
Summary: fixes pytorch#41340 Unfortunately, I still can not get a K80 to verify the fix, but it should be working. Pull Request resolved: pytorch#41824 Reviewed By: mruberry Differential Revision: D23172775 Pulled By: ngimel fbshipit-source-id: aa6af96fe74e3bb07982c006cb35ecc7f18181bc
Summary: small cleanup of dead code Pull Request resolved: pytorch#43148 Reviewed By: mruberry Differential Revision: D23175571 Pulled By: ngimel fbshipit-source-id: b1b0ae9864d373c75666b95c589d090a9ca791b2
Summary: VC++14.27 fails to compile mkl-dnn, see oneapi-src/oneDNN#812 Pull Request resolved: pytorch#43184 Reviewed By: glaringlee Differential Revision: D23181803 Pulled By: malfet fbshipit-source-id: 9861c6243673c775374d77d2f51b45a42791b475
Summary: Had a bunch of merged commits that shouldn't have been there, reverted them to prevent conflicts. Lots of new features, highlights listed below. **Overall:** - Enables pointwise fusion, single (but N-D) broadcast -- pointwise fusion, single (but N-D) broadcast -- pointwise -- single (but N-D) reduction fusion. **Integration:** - Separate "magic scheduler" logic that takes a fusion and generates code generator schedule - Reduction fusion scheduling with heuristics closely matching eagermode (unrolling supported, but no vectorize support) - 2-Stage caching mechanism, one on contiguity, device, type, and operations, the other one is input size->reduction heuristic **Code Generation:** - More generic support in code generation for computeAt - Full rework of loop nest generation and Indexing to more generically handle broadcast operations - Code generator has automatic kernel launch configuration (including automatic allocation of grid reduction buffers) - Symbolic (runtime) tilling on grid/block dimensions is supported - Simplified index generation based on user-defined input contiguity - Automatic broadcast support (similar to numpy/pytorch semantics) - Support for compile time constant shared memory buffers - Parallelized broadcast support (i.e. block reduction -> block broadcast support) Pull Request resolved: pytorch#43129 Reviewed By: mrshenli Differential Revision: D23162207 Pulled By: soumith fbshipit-source-id: 16deee4074c64de877eed7c271d6a359927111b2
Summary: Pull Request resolved: pytorch#43181 att Test Plan: ``` buck test caffe2/caffe2/opt:bound_shape_inference_test ``` Reviewed By: ChunliF Differential Revision: D23097145 fbshipit-source-id: 3e4506308446f28fbeb01dcac97dce70c0443975
Summary: Fixes pytorch#39968 tested with `TORCH_CUDA_ARCH_LIST='3.5 5.2 6.0 6.1 7.0 7.5 8.0+PTX'`, before this PR, it was failing, and with this PR, the build succeed. With `TORCH_CUDA_ARCH_LIST='7.0 7.5 8.0+PTX'`, `libtorch_cuda.so` with symbols changes from 2.9GB -> 2.2GB cc: ptrblck mcarilli jjsjann123 Pull Request resolved: pytorch#43074 Reviewed By: mrshenli Differential Revision: D23176095 Pulled By: malfet fbshipit-source-id: 7b3e6d049fc080e519f21e80df05ef68e7bea57e
jjsjann123
pushed a commit
that referenced
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Jun 8, 2022
…e_fx and prepare_qat_fx (#249) (pytorch#77608) Summary: X-link: facebookresearch/d2go#249 X-link: https://github.com/fairinternal/ClassyVision/pull/104 X-link: pytorch/benchmark#916 X-link: facebookresearch/ClassyVision#791 X-link: facebookresearch/mobile-vision#68 FX Graph Mode Quantization needs to know whether an fx node is a floating point Tensor before it can decide whether to insert observer/fake_quantize module or not, since we only insert observer/fake_quantize module for floating point Tensors. Currently we have some hacks to support this by defining some rules like NON_OBSERVABLE_ARG_DICT (https://github.com/pytorch/pytorch/blob/master/torch/ao/quantization/fx/utils.py#L496), but this approach is fragile and we do not plan to maintain it long term in the pytorch code base. As we discussed in the design review, we'd need to ask users to provide sample args and sample keyword args so that we can infer the type in a more robust way. This PR starts with changing the prepare_fx and prepare_qat_fx api to require user to either provide example arguments thrugh example_inputs, Note this api doesn't support kwargs, kwargs can make pytorch#76496 (comment) (comment) simpler, but it will be rare, and even then we can still workaround with positional arguments, also torch.jit.trace(https://pytorch.org/docs/stable/generated/torch.jit.trace.html) and ShapeProp: https://github.com/pytorch/pytorch/blob/master/torch/fx/passes/shape_prop.py#L140 just have single positional args, we'll just use a single example_inputs argument for now. If needed, we can extend the api with an optional example_kwargs. e.g. in case when there are a lot of arguments for forward and it makes more sense to pass the arguments by keyword BC-breaking Note: Before: ```python m = resnet18(...) m = prepare_fx(m, qconfig_dict) # or m = prepare_qat_fx(m, qconfig_dict) ``` After: ```python m = resnet18(...) m = prepare_fx(m, qconfig_dict, example_inputs=(torch.randn(1, 3, 224, 224),)) # or m = prepare_qat_fx(m, qconfig_dict, example_inputs=(torch.randn(1, 3, 224, 224),)) ``` Test Plan: python test/test_quantization.py TestQuantizeFx python test/test_quantization.py TestQuantizeFxOps python test/test_quantization.py TestQuantizeFxModels Imported from OSS **Static Docs Preview: classyvision** |[Full Site](https://our.intern.facebook.com/intern/staticdocs/eph/D35984526/V30/classyvision/)| |**Modified Pages**| Reviewed By: vkuzo, andrewor14 Differential Revision: D35984526 Pull Request resolved: pytorch#77608 Approved by: https://github.com/dzdang
ftxj
pushed a commit
to ftxj/pytorch
that referenced
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May 25, 2023
When tensor is resized, reference array to it's sizes may become invalid. Make a copy in advance.
<details>
<summary>ASAN report</summary>
```
=================================================================
==1115867==ERROR: AddressSanitizer: heap-use-after-free on address 0x61000013d790 at pc 0x03ff8e7da360 bp 0x03fff53c83a0 sp 0x03fff53c8390
READ of size 8 at 0x61000013d790 thread T0
#0 0x3ff8e7da35f in c10::SymInt::is_heap_allocated() const /home/user/pytorch/c10/core/SymInt.h:154
#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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