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Type Error in torch.compile with mixed input types · Issue #141559 · pytorch/pytorch · GitHub
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app-name="issues-react" initial-path="/pytorch/pytorch/issues/141559" style="display: block; min-height: calc(100vh - 64px);" data-attempted-ssr="true" data-ssr="true" data-lazy="false" data-alternate="false" data-data-router-enabled="false" > <script type="application/json" data-target="react-app.embeddedData">{"payload":{"preloaded_records":{},"preloadedQueries":[{"queryId":"98fbce80c1210854fef52742786b67c3","queryName":"IssueViewerViewQuery","variables":{"id":"repository","number":141559,"owner":"pytorch","repo":"pytorch"},"result":{"data":{"repository":{"isOwnerEnterpriseManaged":false,"issue":{"id":"I_kwDOA-j9z86gmRCZ","updatedAt":"2024-12-13T10:14:48Z","title":"Type Error in torch.compile with mixed input 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types","url":"https://github.com/pytorch/pytorch/issues/141559","viewerCanUpdateNext":false,"issueType":null,"state":"CLOSED","stateReason":"COMPLETED","duplicateOf":null,"linkedPullRequests":{"nodes":[]},"subIssuesSummary":{"total":0,"completed":0},"__isLabelable":"Issue","labels":{"edges":[{"node":{"id":"LA_kwDOA-j9z88AAAABFAyhcQ","color":"f9d0c4","name":"module: inductor","nameHTML":"module: inductor","description":"","url":"https://github.com/pytorch/pytorch/labels/module%3A%20inductor","__typename":"Label"},"cursor":"MQ"},{"node":{"id":"LA_kwDOA-j9z88AAAABcI0cvQ","color":"0B2400","name":"oncall: cpu inductor","nameHTML":"oncall: cpu inductor","description":"CPU Inductor issues for Intel team to triage","url":"https://github.com/pytorch/pytorch/labels/oncall%3A%20cpu%20inductor","__typename":"Label"},"cursor":"Mg"},{"node":{"id":"LA_kwDOA-j9z87_xjCX","color":"6385A2","name":"oncall: pt2","nameHTML":"oncall: 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Describe the bug\n\nUsing `torch.compile` with a function that operates on boolean tensors derived from comparisons of input tensors with differing underlying data types results in a type error. The error occurs during compilation and suggests incompatible data types. The issue is only present in `backend=\"inductor\"` and happens for any valid options for the `dynamic` argument.\r\n\r\nMinimal Reproducible Example:\r\n```python\r\nimport torch\r\n\r\ndef func(a, b):\r\n c = (a \u003e 1) \u0026 (b \u003e 1) \r\n return c\r\n\r\na = torch.ones((10), dtype=torch.int64)\r\nb = torch.ones((10), dtype=torch.uint8)\r\nfunc_compiled = torch.compile(func)\r\nresult = func_compiled(a, b)\r\n```\r\n\r\nExpected Behavior:\r\nThe merge function should compile and execute, returning a tensor of boolean values, just like calling the function without the usage of `torch.compile`.\r\n\r\nCurrent Workaround:\r\nCasting `b` to `int64` before execution. \n\n### Error logs\n\n```bash\r\nOutput:\r\n/tmp/torchinductor/jp/cjpgr7kzmov5mnel42mv52btpe66vldze7kyo4fd44atcyfhmeg4.cpp: In function ‘void kernel(const int64_t*, const uint8_t*, bool*)’:\r\n/tmp/torchinductor/jp/cjpgr7kzmov5mnel42mv52btpe66vldze7kyo4fd44atcyfhmeg4.cpp:18:30: error: no match for ‘operator\u0026’ (operand types are ‘at::vec::CPU_CAPABILITY::VecMask\u003clong int, 2\u003e’ and ‘at::vec::CPU_CAPABILITY::VecMask\u003cunsigned char, 1\u003e’)\r\n 18 | auto tmp8 = tmp3 \u0026 tmp7;\r\n | ~~~~ ^ ~~~~\r\n | | |\r\n | | VecMask\u003cunsigned char,1\u003e\r\n | VecMask\u003clong int,2\u003e\r\n```\n\n### Versions\n\nPyTorch version: 2.5.1+cu124\r\nIs debug build: False\r\nCUDA used to build PyTorch: 12.4\r\nROCM used to build PyTorch: N/A\r\n\r\nOS: Linux Mint 20.3 (x86_64)\r\nGCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0\r\nClang version: Could not collect\r\nCMake version: version 3.24.1\r\nLibc version: glibc-2.31\r\n\r\nPython version: 3.10.14 (main, May 6 2024, 19:42:50) [GCC 11.2.0] (64-bit runtime)\r\nPython platform: Linux-5.4.0-156-generic-x86_64-with-glibc2.31\r\nIs CUDA available: True\r\nCUDA runtime version: 12.4.131\r\nCUDA_MODULE_LOADING set to: LAZY\r\nGPU models and configuration: GPU 0: NVIDIA GeForce RTX 3060 Ti\r\nNvidia driver version: 555.42.06\r\ncuDNN version: Probably one of the following:\r\n/usr/lib/x86_64-linux-gnu/libcudnn.so.9.5.1\r\n/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.5.1\r\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.5.1\r\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.5.1\r\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.5.1\r\n/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.5.1\r\n/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.5.1\r\n/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.5.1\r\nHIP runtime version: N/A\r\nMIOpen runtime version: N/A\r\nIs XNNPACK available: True\r\n\r\nCPU:\r\nArchitektur: x86_64\r\nCPU Operationsmodus: 32-bit, 64-bit\r\nByte-Reihenfolge: Little Endian\r\nAdressgrößen: 48 bits physical, 48 bits virtual\r\nCPU(s): 16\r\nListe der Online-CPU(s): 0-15\r\nThread(s) pro Kern: 2\r\nKern(e) pro Socket: 8\r\nSockel: 1\r\nNUMA-Knoten: 1\r\nAnbieterkennung: AuthenticAMD\r\nProzessorfamilie: 25\r\nModell: 33\r\nModellname: AMD Ryzen 7 5800 8-Core Processor\r\nStepping: 0\r\nFrequenzanhebung: aktiviert\r\nCPU MHz: 2954.127\r\nMaximale Taktfrequenz der CPU: 3400,0000\r\nMinimale Taktfrequenz der CPU: 2200,0000\r\nBogoMIPS: 6787.22\r\nVirtualisierung: AMD-V\r\nL1d Cache: 256 KiB\r\nL1i Cache: 256 KiB\r\nL2 Cache: 4 MiB\r\nL3 Cache: 32 MiB\r\nNUMA-Knoten0 CPU(s): 0-15\r\nVulnerability Itlb multihit: Not affected\r\nVulnerability L1tf: Not affected\r\nVulnerability Mds: Not affected\r\nVulnerability Meltdown: Not affected\r\nVulnerability Mmio stale data: Not affected\r\nVulnerability Retbleed: Not affected\r\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\r\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\r\nVulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected\r\nVulnerability Srbds: Not affected\r\nVulnerability Tsx async abort: Not affected\r\nMarkierungen: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca\r\n\r\nVersions of relevant libraries:\r\n[pip3] flake8==7.0.0\r\n[pip3] mypy==1.8.0\r\n[pip3] mypy-extensions==1.0.0\r\n[pip3] numpy==2.1.3\r\n[pip3] numpydoc==1.5.0\r\n[pip3] nvidia-cublas-cu12==12.4.5.8\r\n[pip3] nvidia-cuda-cupti-cu12==12.4.127\r\n[pip3] nvidia-cuda-nvrtc-cu12==12.4.127\r\n[pip3] nvidia-cuda-runtime-cu12==12.4.127\r\n[pip3] nvidia-cudnn-cu12==9.1.0.70\r\n[pip3] nvidia-cufft-cu12==11.2.1.3\r\n[pip3] nvidia-curand-cu12==10.3.5.147\r\n[pip3] nvidia-cusolver-cu12==11.6.1.9\r\n[pip3] nvidia-cusparse-cu12==12.3.1.170\r\n[pip3] nvidia-nccl-cu12==2.21.5\r\n[pip3] nvidia-nvjitlink-cu12==12.4.127\r\n[pip3] nvidia-nvtx-cu12==12.4.127\r\n[pip3] onnx==1.16.1\r\n[pip3] open_clip_torch==2.29.0\r\n[pip3] torch==2.5.1\r\n[pip3] torchaudio==2.3.0\r\n[pip3] torchmetrics==0.10.2\r\n[pip3] torchvision==0.20.1\r\n[pip3] triton==3.1.0\r\n[conda] _anaconda_depends 2024.02 py310_mkl_1 \r\n[conda] blas 1.0 mkl \r\n[conda] cuda-cudart 11.8.89 0 nvidia\r\n[conda] cuda-cupti 11.8.87 0 nvidia\r\n[conda] cuda-libraries 11.8.0 0 nvidia\r\n[conda] cuda-nvrtc 11.8.89 0 nvidia\r\n[conda] cuda-nvtx 11.8.86 0 nvidia\r\n[conda] cuda-runtime 11.8.0 0 nvidia\r\n[conda] ffmpeg 4.3 hf484d3e_0 pytorch\r\n[conda] libcublas 11.11.3.6 0 nvidia\r\n[conda] libcufft 10.9.0.58 0 nvidia\r\n[conda] libcurand 10.3.5.147 0 nvidia\r\n[conda] libcusolver 11.4.1.48 0 nvidia\r\n[conda] libcusparse 11.7.5.86 0 nvidia\r\n[conda] libjpeg-turbo 2.0.0 h9bf148f_0 pytorch\r\n[conda] mkl 2023.1.0 h213fc3f_46344 \r\n[conda] mkl-service 2.4.0 py310h5eee18b_1 \r\n[conda] mkl_fft 1.3.8 py310h5eee18b_0 \r\n[conda] mkl_random 1.2.4 py310hdb19cb5_0 \r\n[conda] numpy 2.1.3 pypi_0 pypi\r\n[conda] numpydoc 1.5.0 py310h06a4308_0 \r\n[conda] nvidia-cublas-cu12 12.4.5.8 pypi_0 pypi\r\n[conda] nvidia-cuda-cupti-cu12 12.4.127 pypi_0 pypi\r\n[conda] nvidia-cuda-nvrtc-cu12 12.4.127 pypi_0 pypi\r\n[conda] nvidia-cuda-runtime-cu12 12.4.127 pypi_0 pypi\r\n[conda] nvidia-cudnn-cu12 9.1.0.70 pypi_0 pypi\r\n[conda] nvidia-cufft-cu12 11.2.1.3 pypi_0 pypi\r\n[conda] nvidia-curand-cu12 10.3.5.147 pypi_0 pypi\r\n[conda] nvidia-cusolver-cu12 11.6.1.9 pypi_0 pypi\r\n[conda] nvidia-cusparse-cu12 12.3.1.170 pypi_0 pypi\r\n[conda] nvidia-nccl-cu12 2.21.5 pypi_0 pypi\r\n[conda] nvidia-nvjitlink-cu12 12.4.127 pypi_0 pypi\r\n[conda] nvidia-nvtx-cu12 12.4.127 pypi_0 pypi\r\n[conda] open-clip-torch 2.29.0 pypi_0 pypi\r\n[conda] pytorch-cuda 11.8 h7e8668a_5 pytorch\r\n[conda] pytorch-mutex 1.0 cuda pytorch\r\n[conda] torch 2.5.1 pypi_0 pypi\r\n[conda] torchaudio 2.3.0 py310_cu118 pytorch\r\n[conda] torchmetrics 0.10.2 pypi_0 pypi\r\n[conda] torchvision 0.20.1 pypi_0 pypi\r\n[conda] triton 3.1.0 pypi_0 pypi\n\ncc @chauhang @penguinwu @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desertfire @aakhundov","bodyHTML":"\u003ch3 dir=\"auto\"\u003e🐛 Describe the bug\u003c/h3\u003e\n\u003cp dir=\"auto\"\u003eUsing \u003ccode class=\"notranslate\"\u003etorch.compile\u003c/code\u003e with a function that operates on boolean tensors derived from comparisons of input tensors with differing underlying data types results in a type error. The error occurs during compilation and suggests incompatible data types. The issue is only present in \u003ccode class=\"notranslate\"\u003ebackend=\"inductor\"\u003c/code\u003e and happens for any valid options for the \u003ccode class=\"notranslate\"\u003edynamic\u003c/code\u003e argument.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eMinimal Reproducible Example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"import torch\n\ndef func(a, b):\n c = (a \u0026gt; 1) \u0026amp; (b \u0026gt; 1) \n return c\n\na = torch.ones((10), dtype=torch.int64)\nb = torch.ones((10), dtype=torch.uint8)\nfunc_compiled = torch.compile(func)\nresult = func_compiled(a, b)\"\u003e\u003cpre class=\"notranslate\"\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e\n\n\u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003efunc\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eb\u003c/span\u003e):\n \u003cspan class=\"pl-s1\"\u003ec\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e (\u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e) \u003cspan class=\"pl-c1\"\u003e\u0026amp;\u003c/span\u003e (\u003cspan class=\"pl-s1\"\u003eb\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e) \n \u003cspan class=\"pl-k\"\u003ereturn\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ec\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eones\u003c/span\u003e((\u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e), \u003cspan class=\"pl-s1\"\u003edtype\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eint64\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003eb\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eones\u003c/span\u003e((\u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e), \u003cspan class=\"pl-s1\"\u003edtype\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003euint8\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003efunc_compiled\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ecompile\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003efunc\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003eresult\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003efunc_compiled\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ea\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eb\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eExpected Behavior:\u003cbr\u003e\nThe merge function should compile and execute, returning a tensor of boolean values, just like calling the function without the usage of \u003ccode class=\"notranslate\"\u003etorch.compile\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eCurrent Workaround:\u003cbr\u003e\nCasting \u003ccode class=\"notranslate\"\u003eb\u003c/code\u003e to \u003ccode class=\"notranslate\"\u003eint64\u003c/code\u003e before execution.\u003c/p\u003e\n\u003ch3 dir=\"auto\"\u003eError logs\u003c/h3\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"Output:\n/tmp/torchinductor/jp/cjpgr7kzmov5mnel42mv52btpe66vldze7kyo4fd44atcyfhmeg4.cpp: In function ‘void kernel(const int64_t*, const uint8_t*, bool*)’:\n/tmp/torchinductor/jp/cjpgr7kzmov5mnel42mv52btpe66vldze7kyo4fd44atcyfhmeg4.cpp:18:30: error: no match for ‘operator\u0026amp;’ (operand types are ‘at::vec::CPU_CAPABILITY::VecMask\u0026lt;long int, 2\u0026gt;’ and ‘at::vec::CPU_CAPABILITY::VecMask\u0026lt;unsigned char, 1\u0026gt;’)\n 18 | auto tmp8 = tmp3 \u0026amp; tmp7;\n | ~~~~ ^ ~~~~\n | | |\n | | VecMask\u0026lt;unsigned char,1\u0026gt;\n | VecMask\u0026lt;long int,2\u0026gt;\"\u003e\u003cpre class=\"notranslate\"\u003eOutput:\n/tmp/torchinductor/jp/cjpgr7kzmov5mnel42mv52btpe66vldze7kyo4fd44atcyfhmeg4.cpp: In \u003cspan class=\"pl-k\"\u003efunction\u003c/span\u003e \u003cspan class=\"pl-en\"\u003e‘void\u003c/span\u003e kernel(const int64_t\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e, const uint8_t\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e, bool\u003cspan class=\"pl-k\"\u003e*\u003c/span\u003e)’:\n/tmp/torchinductor/jp/cjpgr7kzmov5mnel42mv52btpe66vldze7kyo4fd44atcyfhmeg4.cpp:18:30: error: no match \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e ‘operator\u003cspan class=\"pl-k\"\u003e\u0026amp;\u003c/span\u003e’ (operand types are ‘at::vec::CPU_CAPABILITY::VecMask\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003elong int, \u003cspan class=\"pl-k\"\u003e2\u0026gt;\u003c/span\u003e’ and ‘at::vec::CPU_CAPABILITY::VecMask\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eunsigned char, \u003cspan class=\"pl-k\"\u003e1\u0026gt;\u003c/span\u003e’)\n 18 \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e auto tmp8 = tmp3 \u003cspan class=\"pl-k\"\u003e\u0026amp;\u003c/span\u003e tmp7\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e~~~ ^ \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e~~~\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e VecMask\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eunsigned char,\u003cspan class=\"pl-k\"\u003e1\u0026gt;\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e VecMask\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003elong int,\u003cspan class=\"pl-k\"\u003e2\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003ch3 dir=\"auto\"\u003eVersions\u003c/h3\u003e\n\u003cp dir=\"auto\"\u003ePyTorch version: 2.5.1+cu124\u003cbr\u003e\nIs debug build: False\u003cbr\u003e\nCUDA used to build PyTorch: 12.4\u003cbr\u003e\nROCM used to build PyTorch: N/A\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eOS: Linux Mint 20.3 (x86_64)\u003cbr\u003e\nGCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0\u003cbr\u003e\nClang version: Could not collect\u003cbr\u003e\nCMake version: version 3.24.1\u003cbr\u003e\nLibc version: glibc-2.31\u003c/p\u003e\n\u003cp dir=\"auto\"\u003ePython version: 3.10.14 (main, May 6 2024, 19:42:50) [GCC 11.2.0] (64-bit runtime)\u003cbr\u003e\nPython platform: Linux-5.4.0-156-generic-x86_64-with-glibc2.31\u003cbr\u003e\nIs CUDA available: True\u003cbr\u003e\nCUDA runtime version: 12.4.131\u003cbr\u003e\nCUDA_MODULE_LOADING set to: LAZY\u003cbr\u003e\nGPU models and configuration: GPU 0: NVIDIA GeForce RTX 3060 Ti\u003cbr\u003e\nNvidia driver version: 555.42.06\u003cbr\u003e\ncuDNN version: Probably one of the following:\u003cbr\u003e\n/usr/lib/x86_64-linux-gnu/libcudnn.so.9.5.1\u003cbr\u003e\n/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.5.1\u003cbr\u003e\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.5.1\u003cbr\u003e\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.5.1\u003cbr\u003e\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.5.1\u003cbr\u003e\n/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.5.1\u003cbr\u003e\n/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.5.1\u003cbr\u003e\n/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.5.1\u003cbr\u003e\nHIP runtime version: N/A\u003cbr\u003e\nMIOpen runtime version: N/A\u003cbr\u003e\nIs XNNPACK available: True\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eCPU:\u003cbr\u003e\nArchitektur: x86_64\u003cbr\u003e\nCPU Operationsmodus: 32-bit, 64-bit\u003cbr\u003e\nByte-Reihenfolge: Little Endian\u003cbr\u003e\nAdressgrößen: 48 bits physical, 48 bits virtual\u003cbr\u003e\nCPU(s): 16\u003cbr\u003e\nListe der Online-CPU(s): 0-15\u003cbr\u003e\nThread(s) pro Kern: 2\u003cbr\u003e\nKern(e) pro Socket: 8\u003cbr\u003e\nSockel: 1\u003cbr\u003e\nNUMA-Knoten: 1\u003cbr\u003e\nAnbieterkennung: AuthenticAMD\u003cbr\u003e\nProzessorfamilie: 25\u003cbr\u003e\nModell: 33\u003cbr\u003e\nModellname: AMD Ryzen 7 5800 8-Core Processor\u003cbr\u003e\nStepping: 0\u003cbr\u003e\nFrequenzanhebung: aktiviert\u003cbr\u003e\nCPU MHz: 2954.127\u003cbr\u003e\nMaximale Taktfrequenz der CPU: 3400,0000\u003cbr\u003e\nMinimale Taktfrequenz der CPU: 2200,0000\u003cbr\u003e\nBogoMIPS: 6787.22\u003cbr\u003e\nVirtualisierung: AMD-V\u003cbr\u003e\nL1d Cache: 256 KiB\u003cbr\u003e\nL1i Cache: 256 KiB\u003cbr\u003e\nL2 Cache: 4 MiB\u003cbr\u003e\nL3 Cache: 32 MiB\u003cbr\u003e\nNUMA-Knoten0 CPU(s): 0-15\u003cbr\u003e\nVulnerability Itlb multihit: Not affected\u003cbr\u003e\nVulnerability L1tf: Not affected\u003cbr\u003e\nVulnerability Mds: Not affected\u003cbr\u003e\nVulnerability Meltdown: Not affected\u003cbr\u003e\nVulnerability Mmio stale data: Not affected\u003cbr\u003e\nVulnerability Retbleed: Not affected\u003cbr\u003e\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\u003cbr\u003e\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\u003cbr\u003e\nVulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected\u003cbr\u003e\nVulnerability Srbds: Not affected\u003cbr\u003e\nVulnerability Tsx async abort: Not affected\u003cbr\u003e\nMarkierungen: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eVersions of relevant libraries:\u003cbr\u003e\n[pip3] flake8==7.0.0\u003cbr\u003e\n[pip3] mypy==1.8.0\u003cbr\u003e\n[pip3] mypy-extensions==1.0.0\u003cbr\u003e\n[pip3] numpy==2.1.3\u003cbr\u003e\n[pip3] numpydoc==1.5.0\u003cbr\u003e\n[pip3] nvidia-cublas-cu12==12.4.5.8\u003cbr\u003e\n[pip3] nvidia-cuda-cupti-cu12==12.4.127\u003cbr\u003e\n[pip3] nvidia-cuda-nvrtc-cu12==12.4.127\u003cbr\u003e\n[pip3] nvidia-cuda-runtime-cu12==12.4.127\u003cbr\u003e\n[pip3] nvidia-cudnn-cu12==9.1.0.70\u003cbr\u003e\n[pip3] nvidia-cufft-cu12==11.2.1.3\u003cbr\u003e\n[pip3] nvidia-curand-cu12==10.3.5.147\u003cbr\u003e\n[pip3] nvidia-cusolver-cu12==11.6.1.9\u003cbr\u003e\n[pip3] nvidia-cusparse-cu12==12.3.1.170\u003cbr\u003e\n[pip3] nvidia-nccl-cu12==2.21.5\u003cbr\u003e\n[pip3] nvidia-nvjitlink-cu12==12.4.127\u003cbr\u003e\n[pip3] nvidia-nvtx-cu12==12.4.127\u003cbr\u003e\n[pip3] onnx==1.16.1\u003cbr\u003e\n[pip3] open_clip_torch==2.29.0\u003cbr\u003e\n[pip3] torch==2.5.1\u003cbr\u003e\n[pip3] torchaudio==2.3.0\u003cbr\u003e\n[pip3] torchmetrics==0.10.2\u003cbr\u003e\n[pip3] torchvision==0.20.1\u003cbr\u003e\n[pip3] triton==3.1.0\u003cbr\u003e\n[conda] _anaconda_depends 2024.02 py310_mkl_1\u003cbr\u003e\n[conda] blas 1.0 mkl\u003cbr\u003e\n[conda] cuda-cudart 11.8.89 0 nvidia\u003cbr\u003e\n[conda] cuda-cupti 11.8.87 0 nvidia\u003cbr\u003e\n[conda] cuda-libraries 11.8.0 0 nvidia\u003cbr\u003e\n[conda] cuda-nvrtc 11.8.89 0 nvidia\u003cbr\u003e\n[conda] cuda-nvtx 11.8.86 0 nvidia\u003cbr\u003e\n[conda] cuda-runtime 11.8.0 0 nvidia\u003cbr\u003e\n[conda] ffmpeg 4.3 hf484d3e_0 pytorch\u003cbr\u003e\n[conda] libcublas 11.11.3.6 0 nvidia\u003cbr\u003e\n[conda] libcufft 10.9.0.58 0 nvidia\u003cbr\u003e\n[conda] libcurand 10.3.5.147 0 nvidia\u003cbr\u003e\n[conda] libcusolver 11.4.1.48 0 nvidia\u003cbr\u003e\n[conda] libcusparse 11.7.5.86 0 nvidia\u003cbr\u003e\n[conda] libjpeg-turbo 2.0.0 h9bf148f_0 pytorch\u003cbr\u003e\n[conda] mkl 2023.1.0 h213fc3f_46344\u003cbr\u003e\n[conda] mkl-service 2.4.0 py310h5eee18b_1\u003cbr\u003e\n[conda] mkl_fft 1.3.8 py310h5eee18b_0\u003cbr\u003e\n[conda] mkl_random 1.2.4 py310hdb19cb5_0\u003cbr\u003e\n[conda] numpy 2.1.3 pypi_0 pypi\u003cbr\u003e\n[conda] numpydoc 1.5.0 py310h06a4308_0\u003cbr\u003e\n[conda] nvidia-cublas-cu12 12.4.5.8 pypi_0 pypi\u003cbr\u003e\n[conda] nvidia-cuda-cupti-cu12 12.4.127 pypi_0 pypi\u003cbr\u003e\n[conda] nvidia-cuda-nvrtc-cu12 12.4.127 pypi_0 pypi\u003cbr\u003e\n[conda] nvidia-cuda-runtime-cu12 12.4.127 pypi_0 pypi\u003cbr\u003e\n[conda] nvidia-cudnn-cu12 9.1.0.70 pypi_0 pypi\u003cbr\u003e\n[conda] nvidia-cufft-cu12 11.2.1.3 pypi_0 pypi\u003cbr\u003e\n[conda] nvidia-curand-cu12 10.3.5.147 pypi_0 pypi\u003cbr\u003e\n[conda] nvidia-cusolver-cu12 11.6.1.9 pypi_0 pypi\u003cbr\u003e\n[conda] nvidia-cusparse-cu12 12.3.1.170 pypi_0 pypi\u003cbr\u003e\n[conda] nvidia-nccl-cu12 2.21.5 pypi_0 pypi\u003cbr\u003e\n[conda] nvidia-nvjitlink-cu12 12.4.127 pypi_0 pypi\u003cbr\u003e\n[conda] nvidia-nvtx-cu12 12.4.127 pypi_0 pypi\u003cbr\u003e\n[conda] open-clip-torch 2.29.0 pypi_0 pypi\u003cbr\u003e\n[conda] pytorch-cuda 11.8 h7e8668a_5 pytorch\u003cbr\u003e\n[conda] pytorch-mutex 1.0 cuda pytorch\u003cbr\u003e\n[conda] torch 2.5.1 pypi_0 pypi\u003cbr\u003e\n[conda] torchaudio 2.3.0 py310_cu118 pytorch\u003cbr\u003e\n[conda] torchmetrics 0.10.2 pypi_0 pypi\u003cbr\u003e\n[conda] torchvision 0.20.1 pypi_0 pypi\u003cbr\u003e\n[conda] triton 3.1.0 pypi_0 pypi\u003c/p\u003e\n\u003cp dir=\"auto\"\u003ecc \u003ca class=\"user-mention notranslate\" data-hovercard-type=\"user\" data-hovercard-url=\"/users/chauhang/hovercard\" data-octo-click=\"hovercard-link-click\" data-octo-dimensions=\"link_type:self\" href=\"https://github.com/chauhang\"\u003e@chauhang\u003c/a\u003e \u003ca class=\"user-mention notranslate\" data-hovercard-type=\"user\" data-hovercard-url=\"/users/penguinwu/hovercard\" data-octo-click=\"hovercard-link-click\" data-octo-dimensions=\"link_type:self\" href=\"https://github.com/penguinwu\"\u003e@penguinwu\u003c/a\u003e \u003ca class=\"user-mention notranslate\" data-hovercard-type=\"user\" data-hovercard-url=\"/users/voznesenskym/hovercard\" data-octo-click=\"hovercard-link-click\" data-octo-dimensions=\"link_type:self\" href=\"https://github.com/voznesenskym\"\u003e@voznesenskym\u003c/a\u003e \u003ca class=\"user-mention notranslate\" data-hovercard-type=\"user\" data-hovercard-url=\"/users/EikanWang/hovercard\" data-octo-click=\"hovercard-link-click\" data-octo-dimensions=\"link_type:self\" href=\"https://github.com/EikanWang\"\u003e@EikanWang\u003c/a\u003e \u003ca class=\"user-mention notranslate\" data-hovercard-type=\"user\" data-hovercard-url=\"/users/jgong5/hovercard\" data-octo-click=\"hovercard-link-click\" data-octo-dimensions=\"link_type:self\" href=\"https://github.com/jgong5\"\u003e@jgong5\u003c/a\u003e \u003ca class=\"user-mention notranslate\" data-hovercard-type=\"user\" data-hovercard-url=\"/users/Guobing-Chen/hovercard\" data-octo-click=\"hovercard-link-click\" data-octo-dimensions=\"link_type:self\" href=\"https://github.com/Guobing-Chen\"\u003e@Guobing-Chen\u003c/a\u003e \u003ca class=\"user-mention notranslate\" data-hovercard-type=\"user\" data-hovercard-url=\"/users/XiaobingSuper/hovercard\" data-octo-click=\"hovercard-link-click\" data-octo-dimensions=\"link_type:self\" 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for the review! I've just noticed that on some of our nodes (running the exact same environment) the reproducer runs without issue, but on other nodes it causes the compilation issue. I'll do my best to narrow the problem down further.\r\n\r\nEdit: So far it only seems to happen on AMD CPUs. Intel CPUs seem to work fine.","bodyHTML":"\u003cp dir=\"auto\"\u003eThanks for the review! I've just noticed that on some of our nodes (running the exact same environment) the reproducer runs without issue, but on other nodes it causes the compilation issue. I'll do my best to narrow the problem down further.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eEdit: So far it only seems to happen on AMD CPUs. Intel CPUs seem to work fine.\u003c/p\u003e","bodyVersion":"fe83e64c1a491f00196e41fd26569ab3e6be9a0925c0192d8a37a357d529c762","viewerCanUpdate":false,"url":"https://github.com/pytorch/pytorch/issues/141559#issuecomment-2503372857","createdAt":"2024-11-27T09:32:37Z","authorAssociation":"NONE","viewerCanDelete":false,"viewerCanMinimize":false,"viewerCanReport":false,"viewerCanReportToMaintainer":false,"viewerCanBlockFromOrg":false,"viewerCanUnblockFromOrg":false,"isHidden":false,"minimizedReason":null,"showSpammyBadge":false,"createdViaEmail":false,"authorToRepoOwnerSponsorship":null,"repository":{"id":"MDEwOlJlcG9zaXRvcnk2NTYwMDk3NQ==","name":"pytorch","owner":{"__typename":"Organization","id":"MDEyOk9yZ2FuaXphdGlvbjIxMDAzNzEw","login":"pytorch","url":"https://github.com/pytorch"},"isPrivate":false,"slashCommandsEnabled":false,"nameWithOwner":"pytorch/pytorch","databaseId":65600975},"__isComment":"IssueComment","viewerCanReadUserContentEdits":true,"lastEditedAt":"2024-11-27T11:13:18Z","lastUserContentEdit":{"editor":{"__typename":"User","url":"https://github.com/Tripton","login":"Tripton","id":"MDQ6VXNlcjgxMDU0NDU="},"id":"UCE_lALOA-j9z86VNnA5zlY2UJ4"},"__isReactable":"IssueComment","reactionGroups":[{"content":"THUMBS_UP","viewerHasReacted":false,"reactors":{"totalCount":0,"nodes":[]}},{"content":"THUMBS_DOWN","viewerHasReacted":false,"reactors":{"totalCount":0,"nodes":[]}},{"content":"LAUGH","viewerHasReacted":false,"reactors":{"totalCount":0,"nodes":[]}},{"content":"HOORAY","viewerHasReacted":false,"reactors":{"totalCount":0,"nodes":[]}},{"content":"CONFUSED","viewerHasReacted":false,"reactors":{"totalCount":0,"nodes":[]}},{"content":"HEART","viewerHasReacted":false,"reactors":{"totalCount":0,"nodes":[]}},{"content":"ROCKET","viewerHasReacted":false,"reactors":{"totalCount":0,"nodes":[]}},{"content":"EYES","viewerHasReacted":false,"reactors":{"totalCount":0,"nodes":[]}}],"__isNode":"IssueComment"},"cursor":"Y3Vyc29yOnYyOpPPAAABk2z3JwgAqjI1MDMzNzI4NTc="},{"node":{"__typename":"LabeledEvent","__isIssueTimelineItems":"LabeledEvent","__isTimelineEvent":"LabeledEvent","databaseId":15530036172,"createdAt":"2024-12-04T17:24:55Z","actor":{"__typename":"User","login":"eellison","id":"MDQ6VXNlcjExNDc3OTc0","__isActor":"User","avatarUrl":"https://avatars.githubusercontent.com/u/11477974?s=64\u0026v=4"},"label":{"id":"LA_kwDOA-j9z88AAAABcI0cvQ","nameHTML":"oncall: cpu inductor","name":"oncall: cpu inductor","color":"0B2400","description":"CPU Inductor issues for Intel team to triage"},"__isNode":"LabeledEvent","id":"LE_lADOA-j9z86gmRCZzwAAAAOdqYvM"},"cursor":"Y3Vyc29yOnYyOpPPAAABk5K0ElgBqzE1NTMwMDM2MTcy"},{"node":{"__typename":"AssignedEvent","__isIssueTimelineItems":"AssignedEvent","__isTimelineEvent":"AssignedEvent","databaseId":15535304637,"createdAt":"2024-12-05T03:56:25Z","actor":{"__typename":"User","login":"leslie-fang-intel","id":"MDQ6VXNlcjUzODQxNDcy","__isActor":"User","avatarUrl":"https://avatars.githubusercontent.com/u/53841472?s=64\u0026v=4"},"assignee":{"__typename":"User","id":"MDQ6VXNlcjUzODQxNDcy","__isNode":"User","login":"leslie-fang-intel"},"__isNode":"AssignedEvent","id":"AE_lADOA-j9z86gmRCZzwAAAAOd-e-9"},"cursor":"Y3Vyc29yOnYyOpPPAAABk5T2OigBqzE1NTM1MzA0NjM3"},{"node":{"__typename":"IssueComment","__isIssueTimelineItems":"IssueComment","databaseId":2519035070,"viewerDidAuthor":false,"issue":{"author":{"__typename":"User","login":"Tripton","id":"MDQ6VXNlcjgxMDU0NDU="},"id":"I_kwDOA-j9z86gmRCZ","number":141559,"locked":false,"databaseId":2694385817},"author":{"__typename":"User","login":"leslie-fang-intel","avatarUrl":"https://avatars.githubusercontent.com/u/53841472?v=4","id":"MDQ6VXNlcjUzODQxNDcy"},"id":"IC_kwDOA-j9z86WJWy-","body":"Thanks for reporting the issue. Should be fixed by https://github.com/pytorch/pytorch/pull/142103","bodyHTML":"\u003cp dir=\"auto\"\u003eThanks for reporting the issue. Should be fixed by \u003ca class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"2719250766\" data-permission-text=\"Title is private\" data-url=\"https://github.com/pytorch/pytorch/issues/142103\" data-hovercard-type=\"pull_request\" data-hovercard-url=\"/pytorch/pytorch/pull/142103/hovercard\" href=\"https://github.com/pytorch/pytorch/pull/142103\"\u003e#142103\u003c/a\u003e\u003c/p\u003e","bodyVersion":"a4954325393f1cdae72b90b9311289f9dadfc31c96bf53c506fb210670547413","viewerCanUpdate":false,"url":"https://github.com/pytorch/pytorch/issues/141559#issuecomment-2519035070","createdAt":"2024-12-05T03:56:46Z","authorAssociation":"COLLABORATOR","viewerCanDelete":false,"viewerCanMinimize":false,"viewerCanReport":false,"viewerCanReportToMaintainer":false,"viewerCanBlockFromOrg":false,"viewerCanUnblockFromOrg":false,"isHidden":false,"minimizedReason":null,"showSpammyBadge":false,"createdViaEmail":false,"authorToRepoOwnerSponsorship":null,"repository":{"id":"MDEwOlJlcG9zaXRvcnk2NTYwMDk3NQ==","name":"pytorch","owner":{"__typename":"Organization","id":"MDEyOk9yZ2FuaXphdGlvbjIxMDAzNzEw","login":"pytorch","url":"https://github.com/pytorch"},"isPrivate":false,"slashCommandsEnabled":false,"nameWithOwner":"pytorch/pytorch","databaseId":65600975},"__isComment":"IssueComment","viewerCanReadUserContentEdits":true,"lastEditedAt":null,"lastUserContentEdit":null,"__isReactable":"IssueComment","reactionGroups":[{"content":"THUMBS_UP","viewerHasReacted":false,"reactors":{"totalCount":0,"nodes":[]}},{"content":"THUMBS_DOWN","viewerHasReacted":false,"reactors":{"totalCount":0,"nodes":[]}},{"content":"LAUGH","viewerHasReacted":false,"reactors":{"totalCount":0,"nodes":[]}},{"content":"HOORAY","viewerHasReacted":false,"reactors":{"totalCount":1,"nodes":[{"__typename":"User","login":"Tripton","__isNode":"User","id":"MDQ6VXNlcjgxMDU0NDU="}]}},{"content":"CONFUSED","viewerHasReacted":false,"reactors":{"totalCount":0,"nodes":[]}},{"content":"HEART","viewerHasReacted":false,"reactors":{"totalCount":0,"nodes":[]}},{"content":"ROCKET","viewerHasReacted":false,"reactors":{"totalCount":0,"nodes":[]}},{"content":"EYES","viewerHasReacted":false,"reactors":{"totalCount":0,"nodes":[]}}],"__isNode":"IssueComment"},"cursor":"Y3Vyc29yOnYyOpPPAAABk5T2jDAAqjI1MTkwMzUwNzA="},{"node":{"__typename":"CrossReferencedEvent","__isIssueTimelineItems":"CrossReferencedEvent","__isTimelineEvent":"CrossReferencedEvent","databaseId":1779177671,"createdAt":"2024-12-05T03:57:56Z","actor":{"__typename":"User","login":"leslie-fang-intel","id":"MDQ6VXNlcjUzODQxNDcy","__isActor":"User","avatarUrl":"https://avatars.githubusercontent.com/u/53841472?s=64\u0026v=4"},"source":{"__typename":"PullRequest","__isNode":"PullRequest","id":"PR_kwDOA-j9z86EHvYM"},"willCloseTarget":false,"referencedAt":"2024-12-05T03:57:56Z","target":{"__typename":"Issue","repository":{"id":"MDEwOlJlcG9zaXRvcnk2NTYwMDk3NQ=="},"__isNode":"Issue","id":"I_kwDOA-j9z86gmRCZ"},"innerSource":{"__typename":"PullRequest","__isReferencedSubject":"PullRequest","id":"PR_kwDOA-j9z86EHvYM","pullTitleHTML":"[Inductor][CPP] Fix Mask Dtype mismatch","url":"https://github.com/pytorch/pytorch/pull/142103","number":142103,"state":"CLOSED","isDraft":false,"isInMergeQueue":false,"repository":{"id":"MDEwOlJlcG9zaXRvcnk2NTYwMDk3NQ==","name":"pytorch","isPrivate":false,"owner":{"__typename":"Organization","login":"pytorch","id":"MDEyOk9yZ2FuaXphdGlvbjIxMDAzNzEw"}},"__isNode":"PullRequest"},"__isNode":"CrossReferencedEvent","id":"CRE_kwDOA-j9z85qDBjH"},"cursor":"Y3Vyc29yOnYyOpPPAAABk5T3naACqjE3NzkxNzc2NzE="},{"node":{"__typename":"ReferencedEvent","__isIssueTimelineItems":"ReferencedEvent","__isTimelineEvent":"ReferencedEvent","databaseId":15627172453,"createdAt":"2024-12-12T01:21:35Z","actor":{"__typename":"User","login":"pytorchmergebot","id":"U_kgDOBdPDPA","__isActor":"User","avatarUrl":"https://avatars.githubusercontent.com/u/97764156?s=64\u0026u=088b606990ce89d2c126e1762165677ecad185e1\u0026v=4"},"willCloseSubject":false,"subject":{"__typename":"Issue","__isNode":"Issue","id":"I_kwDOA-j9z86gmRCZ"},"commit":{"message":"[Inductor][CPP] Fix Mask Dtype mismatch (#142103)\n\n**Summary**\nFix issue: https://github.com/pytorch/pytorch/issues/141559. The `vec_mask` store data type doesn't aligned when doing `bitwise_and`.\n\nPull Request resolved: https://github.com/pytorch/pytorch/pull/142103\nApproved by: https://github.com/jgong5","messageHeadlineHTML":"[Inductor][CPP] Fix Mask Dtype mismatch (\u003ca class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"2719250766\" data-permission-text=\"Title is private\" data-url=\"https://github.com/pytorch/pytorch/issues/142103\" data-hovercard-type=\"pull_request\" data-hovercard-url=\"/pytorch/pytorch/pull/142103/hovercard\" href=\"https://github.com/pytorch/pytorch/pull/142103\"\u003e#142103\u003c/a\u003e)","messageBodyHTML":"**Summary**\nFix issue: \u003ca class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"2694385817\" data-permission-text=\"Title is private\" data-url=\"https://github.com/pytorch/pytorch/issues/141559\" data-hovercard-type=\"issue\" data-hovercard-url=\"/pytorch/pytorch/issues/141559/hovercard\" href=\"https://github.com/pytorch/pytorch/issues/141559\"\u003e#141559\u003c/a\u003e. The `vec_mask` store data type doesn't aligned when doing `bitwise_and`.\n\nPull Request \u003cspan class=\"issue-keyword tooltipped tooltipped-se\" aria-label=\"This commit closes pull request #142103.\"\u003eresolved\u003c/span\u003e: \u003ca class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"2719250766\" data-permission-text=\"Title is private\" data-url=\"https://github.com/pytorch/pytorch/issues/142103\" data-hovercard-type=\"pull_request\" data-hovercard-url=\"/pytorch/pytorch/pull/142103/hovercard\" href=\"https://github.com/pytorch/pytorch/pull/142103\"\u003e#142103\u003c/a\u003e\nApproved by: \u003ca href=\"https://github.com/jgong5\"\u003ehttps://github.com/jgong5\u003c/a\u003e","url":"https://github.com/pytorch/pytorch/commit/06075d3d1807e86c83b69baba559f1d21bba58b2","abbreviatedOid":"06075d3","signature":null,"verificationStatus":"UNSIGNED","hasSignature":false,"repository":{"name":"pytorch","owner":{"__typename":"Organization","login":"pytorch","id":"MDEyOk9yZ2FuaXphdGlvbjIxMDAzNzEw"},"defaultBranch":"main","id":"MDEwOlJlcG9zaXRvcnk2NTYwMDk3NQ=="},"id":"C_kwDOA-j9z9oAKDA2MDc1ZDNkMTgwN2U4NmM4M2I2OWJhYmE1NTlmMWQyMWJiYTU4YjI"},"__isNode":"ReferencedEvent","id":"REFE_lADOA-j9z86gmRCZzwAAAAOjc7pl"},"cursor":"Y3Vyc29yOnYyOpPPAAABk7h0_RgBqzE1NjI3MTcyNDUz"},{"node":{"__typename":"IssueComment","__isIssueTimelineItems":"IssueComment","databaseId":2537540477,"viewerDidAuthor":false,"issue":{"author":{"__typename":"User","login":"Tripton","id":"MDQ6VXNlcjgxMDU0NDU="},"id":"I_kwDOA-j9z86gmRCZ","number":141559,"locked":false,"databaseId":2694385817},"author":{"__typename":"User","login":"leslie-fang-intel","avatarUrl":"https://avatars.githubusercontent.com/u/53841472?v=4","id":"MDQ6VXNlcjUzODQxNDcy"},"id":"IC_kwDOA-j9z86XP8t9","body":"https://github.com/pytorch/pytorch/pull/142103 has landed to fix this issue. @Tripton please help to verify.","bodyHTML":"\u003cp dir=\"auto\"\u003e\u003ca class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"2719250766\" data-permission-text=\"Title is private\" data-url=\"https://github.com/pytorch/pytorch/issues/142103\" data-hovercard-type=\"pull_request\" data-hovercard-url=\"/pytorch/pytorch/pull/142103/hovercard\" href=\"https://github.com/pytorch/pytorch/pull/142103\"\u003e#142103\u003c/a\u003e has landed to fix this issue. \u003ca class=\"user-mention notranslate\" data-hovercard-type=\"user\" data-hovercard-url=\"/users/Tripton/hovercard\" data-octo-click=\"hovercard-link-click\" data-octo-dimensions=\"link_type:self\" href=\"https://github.com/Tripton\"\u003e@Tripton\u003c/a\u003e please help to verify.\u003c/p\u003e","bodyVersion":"0c668f43f72f89551d71ea74dfb81654f96aca2c87b208910171c75cd785bcd5","viewerCanUpdate":false,"url":"https://github.com/pytorch/pytorch/issues/141559#issuecomment-2537540477","createdAt":"2024-12-12T01:23:01Z","authorAssociation":"COLLABORATOR","viewerCanDelete":false,"viewerCanMinimize":false,"viewerCanReport":false,"viewerCanReportToMaintainer":false,"viewerCanBlockFromOrg":false,"viewerCanUnblockFromOrg":false,"isHidden":false,"minimizedReason":null,"showSpammyBadge":false,"createdViaEmail":false,"authorToRepoOwnerSponsorship":null,"repository":{"id":"MDEwOlJlcG9zaXRvcnk2NTYwMDk3NQ==","name":"pytorch","owner":{"__typename":"Organization","id":"MDEyOk9yZ2FuaXphdGlvbjIxMDAzNzEw","login":"pytorch","url":"https://github.com/pytorch"},"isPrivate":false,"slashCommandsEnabled":false,"nameWithOwner":"pytorch/pytorch","databaseId":65600975},"__isComment":"IssueComment","viewerCanReadUserContentEdits":true,"lastEditedAt":null,"lastUserContentEdit":null,"__isReactable":"IssueComment","reactionGroups":[{"content":"THUMBS_UP","viewerHasReacted":false,"reactors":{"totalCount":0,"nodes":[]}},{"content":"THUMBS_DOWN","viewerHasReacted":false,"reactors":{"totalCount":0,"nodes":[]}},{"content":"LAUGH","viewerHasReacted":false,"reactors":{"totalCount":0,"nodes":[]}},{"content":"HOORAY","viewerHasReacted":false,"reactors":{"totalCount":0,"nodes":[]}},{"content":"CONFUSED","viewerHasReacted":false,"reactors":{"totalCount":0,"nodes":[]}},{"content":"HEART","viewerHasReacted":false,"reactors":{"totalCount":0,"nodes":[]}},{"content":"ROCKET","viewerHasReacted":false,"reactors":{"totalCount":0,"nodes":[]}},{"content":"EYES","viewerHasReacted":false,"reactors":{"totalCount":0,"nodes":[]}}],"__isNode":"IssueComment"},"cursor":"Y3Vyc29yOnYyOpPPAAABk7h2TQgAqjI1Mzc1NDA0Nzc="},{"node":{"__typename":"IssueComment","__isIssueTimelineItems":"IssueComment","databaseId":2541087603,"viewerDidAuthor":false,"issue":{"author":{"__typename":"User","login":"Tripton","id":"MDQ6VXNlcjgxMDU0NDU="},"id":"I_kwDOA-j9z86gmRCZ","number":141559,"locked":false,"databaseId":2694385817},"author":{"__typename":"User","login":"Tripton","avatarUrl":"https://avatars.githubusercontent.com/u/8105445?v=4","id":"MDQ6VXNlcjgxMDU0NDU="},"id":"IC_kwDOA-j9z86Xdetz","body":"Thanks, the latest nightly works just fine! Thank you for your help!","bodyHTML":"\u003cp dir=\"auto\"\u003eThanks, the latest nightly works just fine! Thank you for your help!\u003c/p\u003e","bodyVersion":"ffa7d12ea3bc36e12e8655b88dbbaa59fc0443462d0ec295a6c131b864ad7413","viewerCanUpdate":false,"url":"https://github.com/pytorch/pytorch/issues/141559#issuecomment-2541087603","createdAt":"2024-12-13T10:14:48Z","authorAssociation":"NONE","viewerCanDelete":false,"viewerCanMinimize":false,"viewerCanReport":false,"viewerCanReportToMaintainer":false,"viewerCanBlockFromOrg":false,"viewerCanUnblockFromOrg":false,"isHidden":false,"minimizedReason":null,"showSpammyBadge":false,"createdViaEmail":false,"authorToRepoOwnerSponsorship":null,"repository":{"id":"MDEwOlJlcG9zaXRvcnk2NTYwMDk3NQ==","name":"pytorch","owner":{"__typename":"Organization","id":"MDEyOk9yZ2FuaXphdGlvbjIxMDAzNzEw","login":"pytorch","url":"https://github.com/pytorch"},"isPrivate":false,"slashCommandsEnabled":false,"nameWithOwner":"pytorch/pytorch","databaseId":65600975},"__isComment":"IssueComment","viewerCanReadUserContentEdits":true,"lastEditedAt":null,"lastUserContentEdit":null,"__isReactable":"IssueComment","reactionGroups":[{"content":"THUMBS_UP","viewerHasReacted":false,"reactors":{"totalCount":0,"nodes":[]}},{"content":"THUMBS_DOWN","viewerHasReacted":false,"reactors":{"totalCount":0,"nodes":[]}},{"content":"LAUGH","viewerHasReacted":false,"reactors":{"totalCount":0,"nodes":[]}},{"content":"HOORAY","viewerHasReacted":false,"reactors":{"totalCount":0,"nodes":[]}},{"content":"CONFUSED","viewerHasReacted":false,"reactors":{"totalCount":0,"nodes":[]}},{"content":"HEART","viewerHasReacted":false,"reactors":{"totalCount":0,"nodes":[]}},{"content":"ROCKET","viewerHasReacted":false,"reactors":{"totalCount":0,"nodes":[]}},{"content":"EYES","viewerHasReacted":false,"reactors":{"totalCount":0,"nodes":[]}}],"__isNode":"IssueComment"},"cursor":"Y3Vyc29yOnYyOpPPAAABk7-DhcAAqjI1NDEwODc2MDM="},{"node":{"__typename":"ClosedEvent","__isIssueTimelineItems":"ClosedEvent","__isTimelineEvent":"ClosedEvent","databaseId":15646690635,"createdAt":"2024-12-13T10:14:48Z","actor":{"__typename":"User","login":"Tripton","id":"MDQ6VXNlcjgxMDU0NDU=","__isActor":"User","avatarUrl":"https://avatars.githubusercontent.com/u/8105445?s=64\u0026v=4"},"stateReason":"COMPLETED","duplicateOf":null,"closingProjectItemStatus":null,"closer":null,"__isNode":"ClosedEvent","id":"CE_lADOA-j9z86gmRCZzwAAAAOknY1L"},"cursor":"Y3Vyc29yOnYyOpPPAAABk7-DhcABqzE1NjQ2NjkwNjM1"},{"node":{"__typename":"ReferencedEvent","__isIssueTimelineItems":"ReferencedEvent","__isTimelineEvent":"ReferencedEvent","databaseId":15657280222,"createdAt":"2024-12-14T13:34:08Z","actor":{"__typename":"User","login":"bluenote10","id":"MDQ6VXNlcjM2MjA3MDM=","__isActor":"User","avatarUrl":"https://avatars.githubusercontent.com/u/3620703?s=64\u0026u=0cea2c41b33d670e29cdf29ff7afefae4bccb1ba\u0026v=4"},"willCloseSubject":false,"subject":{"__typename":"Issue","__isNode":"Issue","id":"I_kwDOA-j9z86gmRCZ"},"commit":{"message":"[Inductor][CPP] Fix Mask Dtype mismatch (#142103)\n\n**Summary**\nFix issue: https://github.com/pytorch/pytorch/issues/141559. The `vec_mask` store data type doesn't aligned when doing `bitwise_and`.\n\nPull Request resolved: https://github.com/pytorch/pytorch/pull/142103\nApproved by: https://github.com/jgong5","messageHeadlineHTML":"[Inductor][CPP] Fix Mask Dtype mismatch (\u003ca class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"2719250766\" data-permission-text=\"Title is private\" data-url=\"https://github.com/pytorch/pytorch/issues/142103\" data-hovercard-type=\"pull_request\" data-hovercard-url=\"/pytorch/pytorch/pull/142103/hovercard\" href=\"https://github.com/pytorch/pytorch/pull/142103\"\u003epytorch#142103\u003c/a\u003e)","messageBodyHTML":"**Summary**\nFix issue: \u003ca class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"2694385817\" data-permission-text=\"Title is private\" data-url=\"https://github.com/pytorch/pytorch/issues/141559\" data-hovercard-type=\"issue\" data-hovercard-url=\"/pytorch/pytorch/issues/141559/hovercard\" href=\"https://github.com/pytorch/pytorch/issues/141559\"\u003epytorch#141559\u003c/a\u003e. The `vec_mask` store data type doesn't aligned when doing `bitwise_and`.\n\nPull Request \u003cspan class=\"issue-keyword tooltipped tooltipped-se\" aria-label=\"This commit closes pull request #142103.\"\u003eresolved\u003c/span\u003e: \u003ca class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"2719250766\" data-permission-text=\"Title is private\" data-url=\"https://github.com/pytorch/pytorch/issues/142103\" data-hovercard-type=\"pull_request\" data-hovercard-url=\"/pytorch/pytorch/pull/142103/hovercard\" href=\"https://github.com/pytorch/pytorch/pull/142103\"\u003epytorch#142103\u003c/a\u003e\nApproved by: \u003ca 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IssueViewer-module__issueViewerContainer--flht4"><!--$--><div class="Box-sc-g0xbh4-0 ehzXEi"><div aria-label="Header" role="region" data-testid="issue-header"><div class="Box-sc-g0xbh4-0 fHrHav HeaderViewer-module__headerContainer--p0Eo1 "><div class="Box-sc-g0xbh4-0 krPEL" data-component="TitleArea" data-size-variant="medium"><h1 class="Box-sc-g0xbh4-0 clYxDg prc-Heading-Heading-6CmGO" data-component="PH_Title" data-hidden="false" style="--custom-font-size:26px,26px,var(--text-title-size-large, 32px),var(--text-title-size-large, 32px);--custom-line-height:1;--custom-font-weight:normal"><bdi class="Box-sc-g0xbh4-0 lhNOUb markdown-title" data-testid="issue-title">Type Error in torch.compile with mixed input types</bdi> <span class="Box-sc-g0xbh4-0 YJa-Di">#141559</span></h1></div><div class="Box-sc-g0xbh4-0 kuCWTy"><div class="Box-sc-g0xbh4-0 vcYPI"><div class="Box-sc-g0xbh4-0 cQpCwc"><div class="Box-sc-g0xbh4-0 lkyTpy"><a type="button" href="/login?return_to=" target="_blank" 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prc-Link-Link-85e08" href="https://github.com/pytorch/pytorch/issues/141559#issue-2694385817" data-testid="issue-body-header-link"><relative-time sx="[object Object]" class="sc-aXZVg gcWyXp">on <!-- -->Nov 26, 2024</relative-time></a></div></div><div class="Box-sc-g0xbh4-0 kgTqOk ActivityHeader-module__narrowViewportWrapper--Hjl75"><div class="Box-sc-g0xbh4-0 izrTon ActivityHeader-module__edits--LwHTE"></div><div class="Box-sc-g0xbh4-0 kpoUe"><div class="Box-sc-g0xbh4-0 ezcJRX"></div><div class="Box-sc-g0xbh4-0 dnyPuu"><button data-component="IconButton" type="button" aria-label="Issue body actions" aria-haspopup="true" aria-expanded="false" tabindex="0" class="Box-sc-g0xbh4-0 ffkqe prc-Button-ButtonBase-c50BI prc-Button-IconButton-szpyj" data-loading="false" data-no-visuals="true" data-size="small" data-variant="invisible" aria-describedby=":R15bd5db:-loading-announcement" id=":R15bd5db:"><svg aria-hidden="true" focusable="false" class="octicon octicon-kebab-horizontal" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" style="display:inline-block;user-select:none;vertical-align:text-bottom;overflow:visible"><path d="M8 9a1.5 1.5 0 1 0 0-3 1.5 1.5 0 0 0 0 3ZM1.5 9a1.5 1.5 0 1 0 0-3 1.5 1.5 0 0 0 0 3Zm13 0a1.5 1.5 0 1 0 0-3 1.5 1.5 0 0 0 0 3Z"></path></svg></button></div></div></div></div></div><div class="IssueBodyViewer-module__IssueBody--MXyFt"><div data-testid="markdown-body" data-team-hovercards-enabled="true" class="markdown-body" data-turbolinks="false"><div class="Box-sc-g0xbh4-0 markdown-body NewMarkdownViewer-module__safe-html-box--cRsz0"><h3 dir="auto">🐛 Describe the bug</h3> <p dir="auto">Using <code class="notranslate">torch.compile</code> with a function that operates on boolean tensors derived from comparisons of input tensors with differing underlying data types results in a type error. The error occurs during compilation and suggests incompatible data types. The issue is only present in <code class="notranslate">backend="inductor"</code> and happens for any valid options for the <code class="notranslate">dynamic</code> argument.</p> <p dir="auto">Minimal Reproducible Example:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="import torch def func(a, b): c = (a > 1) & (b > 1) return c a = torch.ones((10), dtype=torch.int64) b = torch.ones((10), dtype=torch.uint8) func_compiled = torch.compile(func) result = func_compiled(a, b)"><pre class="notranslate"><span class="pl-k">import</span> <span class="pl-s1">torch</span> <span class="pl-k">def</span> <span class="pl-en">func</span>(<span class="pl-s1">a</span>, <span class="pl-s1">b</span>): <span class="pl-s1">c</span> <span class="pl-c1">=</span> (<span class="pl-s1">a</span> <span class="pl-c1">></span> <span class="pl-c1">1</span>) <span class="pl-c1">&</span> (<span class="pl-s1">b</span> <span class="pl-c1">></span> <span class="pl-c1">1</span>) <span class="pl-k">return</span> <span class="pl-s1">c</span> <span class="pl-s1">a</span> <span class="pl-c1">=</span> <span class="pl-s1">torch</span>.<span class="pl-c1">ones</span>((<span class="pl-c1">10</span>), <span class="pl-s1">dtype</span><span class="pl-c1">=</span><span class="pl-s1">torch</span>.<span class="pl-c1">int64</span>) <span class="pl-s1">b</span> <span class="pl-c1">=</span> <span class="pl-s1">torch</span>.<span class="pl-c1">ones</span>((<span class="pl-c1">10</span>), <span class="pl-s1">dtype</span><span class="pl-c1">=</span><span class="pl-s1">torch</span>.<span class="pl-c1">uint8</span>) <span class="pl-s1">func_compiled</span> <span class="pl-c1">=</span> <span class="pl-s1">torch</span>.<span class="pl-c1">compile</span>(<span class="pl-s1">func</span>) <span class="pl-s1">result</span> <span class="pl-c1">=</span> <span class="pl-en">func_compiled</span>(<span class="pl-s1">a</span>, <span class="pl-s1">b</span>)</pre></div> <p dir="auto">Expected Behavior:<br> The merge function should compile and execute, returning a tensor of boolean values, just like calling the function without the usage of <code class="notranslate">torch.compile</code>.</p> <p dir="auto">Current Workaround:<br> Casting <code class="notranslate">b</code> to <code class="notranslate">int64</code> before execution.</p> <h3 dir="auto">Error logs</h3> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="Output: /tmp/torchinductor/jp/cjpgr7kzmov5mnel42mv52btpe66vldze7kyo4fd44atcyfhmeg4.cpp: In function ‘void kernel(const int64_t*, const uint8_t*, bool*)’: /tmp/torchinductor/jp/cjpgr7kzmov5mnel42mv52btpe66vldze7kyo4fd44atcyfhmeg4.cpp:18:30: error: no match for ‘operator&’ (operand types are ‘at::vec::CPU_CAPABILITY::VecMask<long int, 2>’ and ‘at::vec::CPU_CAPABILITY::VecMask<unsigned char, 1>’) 18 | auto tmp8 = tmp3 & tmp7; | ~~~~ ^ ~~~~ | | | | | VecMask<unsigned char,1> | VecMask<long int,2>"><pre class="notranslate">Output: /tmp/torchinductor/jp/cjpgr7kzmov5mnel42mv52btpe66vldze7kyo4fd44atcyfhmeg4.cpp: In <span class="pl-k">function</span> <span class="pl-en">‘void</span> kernel(const int64_t<span class="pl-k">*</span>, const uint8_t<span class="pl-k">*</span>, bool<span class="pl-k">*</span>)’: /tmp/torchinductor/jp/cjpgr7kzmov5mnel42mv52btpe66vldze7kyo4fd44atcyfhmeg4.cpp:18:30: error: no match <span class="pl-k">for</span> ‘operator<span class="pl-k">&</span>’ (operand types are ‘at::vec::CPU_CAPABILITY::VecMask<span class="pl-k"><</span>long int, <span class="pl-k">2></span>’ and ‘at::vec::CPU_CAPABILITY::VecMask<span class="pl-k"><</span>unsigned char, <span class="pl-k">1></span>’) 18 <span class="pl-k">|</span> auto tmp8 = tmp3 <span class="pl-k">&</span> tmp7<span class="pl-k">;</span> <span class="pl-k">|</span> <span class="pl-k">~</span>~~~ ^ <span class="pl-k">~</span>~~~ <span class="pl-k">|</span> <span class="pl-k">|</span> <span class="pl-k">|</span> <span class="pl-k">|</span> <span class="pl-k">|</span> VecMask<span class="pl-k"><</span>unsigned char,<span class="pl-k">1></span> <span class="pl-k">|</span> VecMask<span class="pl-k"><</span>long int,<span class="pl-k">2></span></pre></div> <h3 dir="auto">Versions</h3> <p dir="auto">PyTorch version: 2.5.1+cu124<br> Is debug build: False<br> CUDA used to build PyTorch: 12.4<br> ROCM used to build PyTorch: N/A</p> <p dir="auto">OS: Linux Mint 20.3 (x86_64)<br> GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0<br> Clang version: Could not collect<br> CMake version: version 3.24.1<br> Libc version: glibc-2.31</p> <p dir="auto">Python version: 3.10.14 (main, May 6 2024, 19:42:50) [GCC 11.2.0] (64-bit runtime)<br> Python platform: Linux-5.4.0-156-generic-x86_64-with-glibc2.31<br> Is CUDA available: True<br> CUDA runtime version: 12.4.131<br> CUDA_MODULE_LOADING set to: LAZY<br> GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3060 Ti<br> Nvidia driver version: 555.42.06<br> cuDNN version: Probably one of the following:<br> /usr/lib/x86_64-linux-gnu/libcudnn.so.9.5.1<br> /usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.5.1<br> /usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.5.1<br> /usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.5.1<br> /usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.5.1<br> /usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.5.1<br> /usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.5.1<br> /usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.5.1<br> HIP runtime version: N/A<br> MIOpen runtime version: N/A<br> Is XNNPACK available: True</p> <p dir="auto">CPU:<br> Architektur: x86_64<br> CPU Operationsmodus: 32-bit, 64-bit<br> Byte-Reihenfolge: Little Endian<br> Adressgrößen: 48 bits physical, 48 bits virtual<br> CPU(s): 16<br> Liste der Online-CPU(s): 0-15<br> Thread(s) pro Kern: 2<br> Kern(e) pro Socket: 8<br> Sockel: 1<br> NUMA-Knoten: 1<br> Anbieterkennung: AuthenticAMD<br> Prozessorfamilie: 25<br> Modell: 33<br> Modellname: AMD Ryzen 7 5800 8-Core Processor<br> Stepping: 0<br> Frequenzanhebung: aktiviert<br> CPU MHz: 2954.127<br> Maximale Taktfrequenz der CPU: 3400,0000<br> Minimale Taktfrequenz der CPU: 2200,0000<br> BogoMIPS: 6787.22<br> Virtualisierung: AMD-V<br> L1d Cache: 256 KiB<br> L1i Cache: 256 KiB<br> L2 Cache: 4 MiB<br> L3 Cache: 32 MiB<br> NUMA-Knoten0 CPU(s): 0-15<br> Vulnerability Itlb multihit: Not affected<br> Vulnerability L1tf: Not affected<br> Vulnerability Mds: Not affected<br> Vulnerability Meltdown: Not affected<br> Vulnerability Mmio stale data: Not affected<br> Vulnerability Retbleed: Not affected<br> Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp<br> Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization<br> Vulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected<br> Vulnerability Srbds: Not affected<br> Vulnerability Tsx async abort: Not affected<br> Markierungen: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca</p> <p dir="auto">Versions of relevant libraries:<br> [pip3] flake8==7.0.0<br> [pip3] mypy==1.8.0<br> [pip3] mypy-extensions==1.0.0<br> [pip3] numpy==2.1.3<br> [pip3] numpydoc==1.5.0<br> [pip3] nvidia-cublas-cu12==12.4.5.8<br> [pip3] nvidia-cuda-cupti-cu12==12.4.127<br> [pip3] nvidia-cuda-nvrtc-cu12==12.4.127<br> [pip3] nvidia-cuda-runtime-cu12==12.4.127<br> [pip3] nvidia-cudnn-cu12==9.1.0.70<br> [pip3] nvidia-cufft-cu12==11.2.1.3<br> [pip3] nvidia-curand-cu12==10.3.5.147<br> [pip3] nvidia-cusolver-cu12==11.6.1.9<br> [pip3] nvidia-cusparse-cu12==12.3.1.170<br> [pip3] nvidia-nccl-cu12==2.21.5<br> [pip3] nvidia-nvjitlink-cu12==12.4.127<br> [pip3] nvidia-nvtx-cu12==12.4.127<br> [pip3] onnx==1.16.1<br> [pip3] open_clip_torch==2.29.0<br> [pip3] torch==2.5.1<br> [pip3] torchaudio==2.3.0<br> [pip3] torchmetrics==0.10.2<br> [pip3] torchvision==0.20.1<br> [pip3] triton==3.1.0<br> [conda] _anaconda_depends 2024.02 py310_mkl_1<br> [conda] blas 1.0 mkl<br> [conda] cuda-cudart 11.8.89 0 nvidia<br> [conda] cuda-cupti 11.8.87 0 nvidia<br> [conda] cuda-libraries 11.8.0 0 nvidia<br> [conda] cuda-nvrtc 11.8.89 0 nvidia<br> [conda] cuda-nvtx 11.8.86 0 nvidia<br> [conda] cuda-runtime 11.8.0 0 nvidia<br> [conda] ffmpeg 4.3 hf484d3e_0 pytorch<br> [conda] libcublas 11.11.3.6 0 nvidia<br> [conda] libcufft 10.9.0.58 0 nvidia<br> [conda] libcurand 10.3.5.147 0 nvidia<br> [conda] libcusolver 11.4.1.48 0 nvidia<br> [conda] libcusparse 11.7.5.86 0 nvidia<br> [conda] libjpeg-turbo 2.0.0 h9bf148f_0 pytorch<br> [conda] mkl 2023.1.0 h213fc3f_46344<br> [conda] mkl-service 2.4.0 py310h5eee18b_1<br> [conda] mkl_fft 1.3.8 py310h5eee18b_0<br> [conda] mkl_random 1.2.4 py310hdb19cb5_0<br> [conda] numpy 2.1.3 pypi_0 pypi<br> [conda] numpydoc 1.5.0 py310h06a4308_0<br> [conda] nvidia-cublas-cu12 12.4.5.8 pypi_0 pypi<br> [conda] nvidia-cuda-cupti-cu12 12.4.127 pypi_0 pypi<br> [conda] nvidia-cuda-nvrtc-cu12 12.4.127 pypi_0 pypi<br> [conda] nvidia-cuda-runtime-cu12 12.4.127 pypi_0 pypi<br> [conda] nvidia-cudnn-cu12 9.1.0.70 pypi_0 pypi<br> [conda] nvidia-cufft-cu12 11.2.1.3 pypi_0 pypi<br> [conda] nvidia-curand-cu12 10.3.5.147 pypi_0 pypi<br> [conda] nvidia-cusolver-cu12 11.6.1.9 pypi_0 pypi<br> [conda] nvidia-cusparse-cu12 12.3.1.170 pypi_0 pypi<br> [conda] nvidia-nccl-cu12 2.21.5 pypi_0 pypi<br> [conda] nvidia-nvjitlink-cu12 12.4.127 pypi_0 pypi<br> [conda] nvidia-nvtx-cu12 12.4.127 pypi_0 pypi<br> [conda] open-clip-torch 2.29.0 pypi_0 pypi<br> [conda] pytorch-cuda 11.8 h7e8668a_5 pytorch<br> [conda] pytorch-mutex 1.0 cuda pytorch<br> [conda] torch 2.5.1 pypi_0 pypi<br> [conda] torchaudio 2.3.0 py310_cu118 pytorch<br> [conda] torchmetrics 0.10.2 pypi_0 pypi<br> [conda] torchvision 0.20.1 pypi_0 pypi<br> [conda] triton 3.1.0 pypi_0 pypi</p> <p dir="auto">cc <a class="user-mention notranslate" data-hovercard-type="user" data-hovercard-url="/users/chauhang/hovercard" data-octo-click="hovercard-link-click" 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