[WIP] [DO NOT MERGE] A simple test to showcase VaryingShape
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Krovatkin
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Mar 27, 2020
Summary: Pull Request resolved: pytorch#35454 Differential Revision: D20665160 Pulled By: Krovatkin fbshipit-source-id: e04cbe92b2ee5a3288f3c4e5c83533bfea85bf85
Krovatkin
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Nov 3, 2020
Summary: Pull Request resolved: pytorch#46966 These tests had false positives in TSAN for modifying thread local variables: ``` WARNING: ThreadSanitizer: data race (pid=5364) Write of size 8 at 0x7b2c0004ff70 by thread T2: #0 free <null> (libtools_build_sanitizers_tsan-py.so+0xde6ad) #1 __GI__dl_deallocate_tls Previous write of size 1 at 0x7b2c0004ff71 by thread T3: #0 at::GradMode::set_enabled(bool) caffe2/aten/src/ATen/core/grad_mode.cpp:20 (libcaffe2_ATen-core.so+0x40e013) #1 torch::autograd::set_grad_enabled(_object*, _object*) caffe2/torch/csrc/autograd/init.cpp:143 (libcaffe2__C_impl_cuda.so+0x115ef0e) #2 _PyMethodDef_RawFastCallKeywords Thread T3 (tid=5385, finished) created by main thread at: #0 pthread_create <null> (libtools_build_sanitizers_tsan-py.so+0xc5a86) #1 PyThread_start_new_thread ``` ghstack-source-id: 115330433 Test Plan: waitforbuildbot Reviewed By: mrshenli Differential Revision: D24584411 fbshipit-source-id: e35f704dfcb7b161a13a4902beaf8b1e41ccd596
Krovatkin
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Nov 18, 2020
) Summary: `torch.inverse` now works for complex inputs on GPU. Opening a new PR here. The previous PR was merged and reverted due to a bug in tests marked with `slowTest`. Previous PR pytorch#45034 Ref. pytorch#33152 Pull Request resolved: pytorch#47595 Reviewed By: navahgar Differential Revision: D24840955 Pulled By: anjali411 fbshipit-source-id: ec49fffdc4b3cb4ae7507270fa24e127be14f59b
Krovatkin
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Dec 3, 2020
Summary: Relanding pytorch#46862 There was an issue with the simultaneous merge of two slightly conflicting PRs. This PR adds `torch.lu_solve` for complex inputs both on CPU and GPU. Pull Request resolved: pytorch#48028 Reviewed By: linbinyu Differential Revision: D25003700 Pulled By: zou3519 fbshipit-source-id: 24cd1babe9ccdbaa4e2ed23f08a9153d40d0f0cd
Krovatkin
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May 25, 2021
Summary: added more statistic info for static runtime Test Plan: caffe2/benchmarks/static_runtime:static_runtime_cpptest Expected output example: Static runtime ms per iter: 0.939483. Iters per second: 1064.41 Node #0: 0.195671 ms/iter, %wide_offset.1 : Tensor = aten::add(%wide.1, %self._mu, %4) Node #1: 0.169457 ms/iter, %wide_normalized.1 : Tensor = aten::mul(%wide_offset.1, %self._sigma) Node #2: 0.118218 ms/iter, %wide_preproc.1 : Tensor = aten::clamp(%wide_normalized.1, %5, %6) Node #3: 0.038814 ms/iter, %user_emb_t.1 : Tensor = aten::transpose(%user_emb.1, %4, %7) Node #4: 0.0860747 ms/iter, %dp_unflatten.1 : Tensor = aten::bmm(%ad_emb_packed.1, %user_emb_t.1) Node pytorch#5: 0.0102666 ms/iter, %31 : Tensor = static_runtime::flatten_copy(%dp_unflatten.1, %4, %8) Node pytorch#6: 0.000476333 ms/iter, %19 : Tensor[] = prim::ListConstruct(%31, %wide_preproc.1) Node pytorch#7: 0.0707332 ms/iter, %input.1 : Tensor = aten::cat(%19, %4) Node pytorch#8: 0.123695 ms/iter, %fc1.1 : Tensor = aten::addmm(%self._fc_b, %input.1, %29, %4, %4) Node pytorch#9: 0.0309244 ms/iter, %23 : Tensor = aten::sigmoid(%fc1.1) Node pytorch#10: 0.0046297 ms/iter, %24 : (Tensor) = prim::TupleConstruct(%23) Time per node type: 0.195671 ms. 23.0483%. aten::add (1 nodes) 0.169457 ms. 19.9605%. aten::mul (1 nodes, out variant) 0.123695 ms. 14.5702%. aten::addmm (1 nodes, out variant) 0.118218 ms. 13.925%. aten::clamp (1 nodes, out variant) 0.0860747 ms. 10.1388%. aten::bmm (1 nodes, out variant) 0.0707332 ms. 8.33175%. aten::cat (1 nodes, out variant) 0.038814 ms. 4.57195%. aten::transpose (1 nodes) 0.0309244 ms. 3.64263%. aten::sigmoid (1 nodes, out variant) 0.0102666 ms. 1.20932%. static_runtime::flatten_copy (1 nodes, out variant) 0.0046297 ms. 0.545338%. prim::TupleConstruct (1 nodes, out variant) 0.000476333 ms. 0.0561079%. prim::ListConstruct (1 nodes, out variant) 0.848959 ms. in Total StaticRuntime setup time: 0.018925 ms Memory allocation time: 0.019808 ms Memory deallocation time: 0.0120445 ms Outputs deallocation time: 0.0864947 ms Total memory managed: 19328 bytes Total number of reused tensors: 3 Total number of 'out' variant nodes/total number of nodes: 9/11 (81.8182%) Reviewed By: hlu1 Differential Revision: D28553029 fbshipit-source-id: 55e7eab50b4b475ae219896100bdf4f6678875a4
Krovatkin
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Jul 14, 2021
Summary: Pull Request resolved: pytorch#60987 We were seeing deadlocks as follows during shutdown: ``` Thread 1 (LWP 2432101): #0 0x00007efca470190b in __pause_nocancel () from /lib64/libc.so.6 #1 0x00007efca49de485 in __pthread_mutex_lock_full () from /lib64/libpthread.so.0 #2 0x00007ef91d4c42c6 in __cuda_CallJitEntryPoint () from /lib64/libnvidia-ptxjitcompiler.so.1 #3 0x00007efc651ac8f1 in ?? () from /lib64/libcuda.so #4 0x00007efc651aee03 in ?? () from /lib64/libcuda.so pytorch#5 0x00007efc64f76b84 in ?? () from /lib64/libcuda.so pytorch#6 0x00007efc64f77f5d in ?? () from /lib64/libcuda.so pytorch#7 0x00007efc64eac858 in ?? () from /lib64/libcuda.so pytorch#8 0x00007efc64eacfbc in ?? () from /lib64/libcuda.so pytorch#9 0x00007efc7810a924 in ?? () from /usr/local/cuda/lib64/libcublas.so.11 pytorch#10 0x00007efc780fa2be in ?? () from /usr/local/cuda/lib64/libcublas.so.11 pytorch#11 0x00007efc78111044 in ?? () from /usr/local/cuda/lib64/libcublas.so.11 pytorch#12 0x00007efc7811580a in ?? () from /usr/local/cuda/lib64/libcublas.so.11 pytorch#13 0x00007efc78115aa4 in ?? () from /usr/local/cuda/lib64/libcublas.so.11 pytorch#14 0x00007efc781079ec in ?? () from /usr/local/cuda/lib64/libcublas.so.11 pytorch#15 0x00007efc780e6a7a in ?? () from /usr/local/cuda/lib64/libcublas.so.11 pytorch#16 0x00007efc7811cfa5 in ?? () from /usr/local/cuda/lib64/libcublas.so.11 pytorch#17 0x00007efc777ea98c in ?? () from /usr/local/cuda/lib64/libcublas.so.11 pytorch#18 0x00007efc777ebd80 in ?? () from /usr/local/cuda/lib64/libcublas.so.11 pytorch#19 0x00007efc777ea2c9 in ?? () from /usr/local/cuda/lib64/libcublas.so.11 pytorch#20 0x00007efc778c2e2d in cublasDestroy_v2 () from /usr/local/cuda/lib64/libcublas.so.11 pytorch#21 0x00007efc51a3fb56 in std::_Sp_counted_ptr_inplace<at::cuda::(anonymous namespace)::DeviceThreadHandlePool<cublasContext*, &at::cuda::(anonymous namespace)::createCublasHandle, &at::cuda::(anonymous namespace)::destroyCublasHandle>, std::allocator<at::cuda::(anonymous namespace)::DeviceThreadHandlePool<cublasContext*, &at::cuda::(anonymous namespace)::createCublasHandle, &at::cuda::(anonymous namespace)::destroyCublasHandle> >, (__gnu_cxx::_Lock_policy)2>::_M_dispose() () from /data/users/pritam/pytorch/torch/lib/libtorch_cuda.so pytorch#22 0x00007efc51a3fc5f in std::shared_ptr<at::cuda::(anonymous namespace)::DeviceThreadHandlePool<cublasContext*, &at::cuda::(anonymous namespace)::createCublasHandle, &at::cuda::(anonymous namespace)::destroyCublasHandle> >::~shared_ptr() () from /data/users/pritam/pytorch/torch/lib/libtorch_cuda.so pytorch#23 0x00007efca4648b0c in __run_exit_handlers () from /lib64/libc.so.6 pytorch#24 0x00007efca4648c40 in exit () from /lib64/libc.so.6 pytorch#25 0x0000558c8852e5f9 in Py_Exit (sts=0) at /tmp/build/80754af9/python_1614362349910/work/Python/pylifecycle.c:2292 pytorch#26 0x0000558c8852e6a7 in handle_system_exit () at /tmp/build/80754af9/python_1614362349910/work/Python/pythonrun.c:636 pytorch#27 0x0000558c8852e742 in PyErr_PrintEx (set_sys_last_vars=<optimized out>, set_sys_last_vars=<optimized out>) at /tmp/build/80754af9/python_1614362349910/work/Python/pythonrun.c:646 pytorch#28 0x0000558c88540dd6 in PyRun_SimpleStringFlags (command=0x7efca4dc9050 "from multiprocessing.spawn import spawn_main; spawn_main(tracker_fd=9, pipe_handle=13)\n", flags=0x7ffe3a986110) at /tmp/build/80754af9/python_1614362349910/work/Python/pythonrun.c:457 pytorch#29 0x0000558c88540ead in pymain_run_command (cf=0x7ffe3a986110, command=<optimized out>) at /tmp/build/80754af9/python_1614362349910/work/Modules/main.c:420 pytorch#30 pymain_run_python (pymain=0x7ffe3a986220) at /tmp/build/80754af9/python_1614362349910/work/Modules/main.c:2907 pytorch#31 pymain_main (pymain=0x7ffe3a986220) at /tmp/build/80754af9/python_1614362349910/work/Modules/main.c:3460 pytorch#32 0x0000558c8854122c in _Py_UnixMain (argc=<optimized out>, argv=<optimized out>) at /tmp/build/80754af9/python_1614362349910/work/Modules/main.c:3495 pytorch#33 0x00007efca4632493 in __libc_start_main () from /lib64/libc.so.6 pytorch#34 0x0000558c884e5e90 in _start () at ../sysdeps/x86_64/elf/start.S:103 ``` This was likely caused due to a static singleton that wasn't leaky. Following the guidance in https://isocpp.org/wiki/faq/ctors#construct-on-first-use-v2 to use a leaky singleton instead. ghstack-source-id: 132847448 Test Plan: Verified locally. Reviewed By: malfet Differential Revision: D29468866 fbshipit-source-id: 89250594c5cd2643417b1da584c658b742dc5a5c
Krovatkin
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Jul 22, 2021
Summary: Pull Request resolved: pytorch#61588 As part of debugging pytorch#60290, we discovered the following deadlock: ``` Thread 79 (Thread 0x7f52ff7fe700 (LWP 205437)): #0 pthread_cond_timedwait@GLIBC_2.3.2 () at ../sysdeps/unix/sysv/linux/x86_64/pthread_cond_timedwait.S:225 #1 0x0000564880199152 in PyCOND_TIMEDWAIT (cond=0x564880346080 <gil_cond>, mut=0x564880346100 <gil_mutex>, us=5000) at /home/builder/ktietz/cos6/ci_cos6/python_1622833237666/work/Python/condvar.h:103 #2 take_gil (tstate=0x7f5254005ef0) at /home/builder/ktietz/cos6/ci_cos6/python_1622833237666/work/Python/ceval_gil.h:224 #3 0x0000564880217b62 in PyEval_AcquireThread (tstate=0x7f5254005ef0) at /home/builder/ktietz/cos6/ci_cos6/python_1622833237666/work/Python/ceval.c:278 #4 0x00007f557d54aabd in pybind11::gil_scoped_acquire::gil_scoped_acquire() () from /opt/conda/lib/python3.6/site-packages/torch/lib/libtorch_python.so pytorch#5 0x00007f557da7792f in (anonymous namespace)::concrete_decref_fn(c10::impl::PyInterpreter const*, _object*) () from /opt/conda/lib/python3.6/site-packages/torch/lib/libtorch_python.so pytorch#6 0x00007f5560dadba6 in c10::TensorImpl::release_resources() () from /opt/conda/lib/python3.6/site-packages/torch/lib/libc10.so pytorch#7 0x00007f5574c885bc in std::_Sp_counted_ptr_inplace<torch::distributed::autograd::DistAutogradContext, std::allocator<torch::distributed::autograd::DistAutogradContext>, (__gnu_cxx::_Lock_policy)2>::_M_dispose() () from /opt/conda/lib/python3.6/site-packages/torch/lib/libtorch_cpu.so pytorch#8 0x00007f5574c815e9 in std::__detail::_Hashtable_alloc<std::allocator<std::__detail::_Hash_node<std::pair<long const, std::shared_ptr<torch::distributed::autograd::DistAutogradContext> >, false> > >::_M_deallocate_node(std::__detail::_Hash_node<std::pair<long const, std::shared_ptr<torch::distributed::autograd::DistAutogradContext> >, false>*) [clone .isra.325] () from /opt/conda/lib/python3.6/site-packages/torch/lib/libtorch_cpu.so pytorch#9 0x00007f5574c81bf1 in torch::distributed::autograd::DistAutogradContainer::eraseContextIdAndReset(torch::distributed::autograd::DistAutogradContainer::ContextsShard&, long) () from /opt/conda/lib/python3.6/site-packages/torch/lib/libtorch_cpu.so pytorch#10 0x00007f5574c86e83 in torch::distributed::autograd::DistAutogradContainer::releaseContextIfPresent(long) () from /opt/conda/lib/python3.6/site-packages/torch/lib/libtorch_cpu.so pytorch#11 0x00007f5574cc6395 in torch::distributed::rpc::RequestCallbackNoPython::processCleanupAutogradContextReq(torch::distributed::rpc::RpcCommandBase&) const () from /opt/conda/lib/python3.6/site-packages/torch/lib/libtorch_cpu.so pytorch#12 0x00007f5574cccf15 in torch::distributed::rpc::RequestCallbackNoPython::processRpc(torch::distributed::rpc::RpcCommandBase&, torch::distributed::rpc::MessageType const&, std::vector<c10::Stream, std::allocator<c10::Stream> >) const () from /opt/conda/lib/python3.6/site-packages/torch/lib/libtorch_cpu.so Thread 72 (Thread 0x7f53077fe700 (LWP 205412)): #0 __lll_lock_wait () at ../sysdeps/unix/sysv/linux/x86_64/lowlevellock.S:135 #1 0x00007f55bc62adbd in __GI___pthread_mutex_lock (mutex=0x564884396440) at ../nptl/pthread_mutex_lock.c:80 #2 0x00007f5574c82a2f in torch::distributed::autograd::DistAutogradContainer::retrieveContext(long) () from /opt/conda/lib/python3.6/site-packages/torch/lib/libtorch_cpu.so #3 0x00007f557de9bb2f in pybind11::cpp_function::initialize<torch::distributed::autograd::(anonymous namespace)::dist_autograd_init(_object*, _object*)::{lambda(long)pytorch#11}, pybind11::dict, long, pybind11::name, pybind11::scope, pybind11::sibling, char [931], pybind11::arg>(torch::distributed::autograd::(anonymous namespace)::dist_autograd_init(_object*, _object*)::{lambda(long)pytorch#11}&&, pybind11::dict (*)(long), pybind11::name const&, pybind11::scope const&, pybind11::sibling const&, char const (&) [931], pybind11::arg const&)::{lambda(pybind11::detail::function_call&)#3}::_FUN(pybind11::detail::function_call) () from /opt/conda/lib/python3.6/site-packages/torch/lib/libtorch_python.so ``` Basically Thread 72, holds GIL and tries to acquire the lock for DistAutogradContainer to perform a lookup on a map. On the other hand, Thread 79 holds the lock on DistAutogradContainer to remove a Tensor and as part of TensorImpl destructor, concrete_decref_fn is called which waits for GIL. As a result, we have a deadlock. To fix this issue, I've ensured we release GIL when we call `retrieveContext` and acquire it later when needed. ghstack-source-id: 133493659 Test Plan: waitforbuildbot Reviewed By: mrshenli Differential Revision: D29682624 fbshipit-source-id: f68a1fb39040ca0447a26e456a97bce64af6b79c
Krovatkin
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Aug 9, 2021
Summary: Pull Request resolved: pytorch#61983 Trial #2. The previous PR (pytorch#61498) was reverted because this caused a failure in `pytorch_linux_backward_compatibility_check_test`. Fixed that now by adding to the exception list in `check_backward_compatibility.py`. Test Plan: Imported from OSS Reviewed By: eellison Differential Revision: D29828830 Pulled By: navahgar fbshipit-source-id: 947a7b1622ff6e3e575c051b8f34a789e105bcee
Krovatkin
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Aug 25, 2021
…ytorch#63339) Summary: Pull Request resolved: pytorch#63339 # Context https://fb.workplace.com/groups/pytorch.dev/permalink/900474523864362/?comment_id=901125403799274&reply_comment_id=905023386742809 ##### WHAT IS A STACK TRACE? A stack trace (also called stack backtrace or stack traceback) is a report of the active stack frames at a certain point in time during the execution of a program. Typically when an exception is thrown, one would expect to see the code (file:line) that threw the exception, and every intermediate frame up to and including the main function. We are enabling android stack trace to help debugging on android devices. Test Plan: ## Steps to test ``` buck build fbsource//xplat/caffe2/mode/aibench_pytorch_android -c pt.enable_qpl=0 -c pt.has_backtraces=1 fbsource//xplat/caffe2/fb/lite_predictor:lite_predictorAndroid#android-x86_64 one_world android emulator android-28 adb push ~/fbsource/buck-out/gen/xplat/caffe2/fb/lite_predictor/lite_predictorAndroid#android-x86_64 /data/local/tmp cd /data/local/tmp ./lite_predictorAndroid#android-x86_64 ./lite_predictorAndroid#android-x86_64 --model ./detect.bc --input_dims "1,3,192,192" --input_type float --warmup 20 --iter 5 --report_pep true ``` ## See how model file is not found stack traces is: ### before ``` ./lite_predictorAndroid#android-x86_64 --model ./detect.bc --input_dims "1,3,192,192" --input_type float --warmup 20 --iter 5 --report_pep true Run with 2 threads Run with 2 threads Loading model... terminating with uncaught exception of type c10::Error: open file failed, file path: ./detect.bc Exception raised from RAIIFile at xplat/caffe2/caffe2/serialize/file_adapter.cc:13 (most recent call first): (no backtrace available) Aborted ``` ### after ``` 134|generic_x86_64:/data/local/tmp $ ./lite_predictorAndroid#android-x86_64 --model ./detect.bc --input_dims "1,3,192,192" --input_type float --warmup 20 --iter 5 --report_pep true Run with 2 threads Run with 2 threads Loading model... terminating with uncaught exception of type c10::Error: open file failed, file path: ./detect.bc Exception raised from RAIIFile at xplat/caffe2/caffe2/serialize/file_adapter.cc:13 (most recent call first): frame #0 c10::get_backtrace(unsigned long, unsigned long, bool)[0x59494274f10e] frame #1 [0x5949427b1eee] frame #2 [0x5949427b1eb2] frame #3 [0x5949427b1cdc] frame #4 std::__ndk1::function<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> > ()>::operator()() const[0x5949427afc34] frame pytorch#5 c10::Error::Error(c10::SourceLocation, std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >)[0x5949427b05b1] frame pytorch#6 c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> > const&)[0x5949427aca5f] frame pytorch#7 caffe2::serialize::FileAdapter::RAIIFile::RAIIFile(std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> > const&)[0x5949426b37b2] frame pytorch#8 caffe2::serialize::FileAdapter::FileAdapter(std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> > const&)[0x5949426b3903] frame pytorch#9 torch::jit::_load_for_mobile(std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> > const&, c10::optional<c10::Device>, std::__ndk1::unordered_map<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >, std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> >, std::__ndk1::hash<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> > >, std::__ndk1::equal_to<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> > >, std::__ndk1::allocator<std::__ndk1::pair<std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> > const, std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> > > > >&)[0x5949422737bd] frame pytorch#10 torch::jit::_load_for_mobile(std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> > const&, c10::optional<c10::Device>)[0x594942273769] frame pytorch#11 benchmark(std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> > const&, int, std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> > const&, std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> > const&, std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> > const&, bool, int, int, int, bool, int, bool, int, double, bool, bool, bool, std::__ndk1::basic_string<char, std::__ndk1::char_traits<char>, std::__ndk1::allocator<char> > const&)[0x59494189b21d] frame pytorch#12 main[0x594941882aff] frame pytorch#13 __libc_init[0x7b699d08578d] ``` ### what we get for os:linux ``` (base) [[email protected] /data/users/pavithran/fbsource] ./buck-out/gen/xplat/caffe2/fb/lite_predictor/lite_predictor --model ./detect.bc --input_dims "1,3,192,192" --input_type float --warmup 20 --iter 5 --report_pep true Run with 24 threads Run with 24 threads Loading model... terminate called after throwing an instance of 'c10::Error' what(): open file failed, file path: ./detect.bc Exception raised from RAIIFile at xplat/caffe2/caffe2/serialize/file_adapter.cc:13 (most recent call first): frame #0: ./buck-out/gen/xplat/caffe2/fb/lite_predictor/lite_predictor() [0x20cb7fe] frame #1: ./buck-out/gen/xplat/caffe2/fb/lite_predictor/lite_predictor() [0x20cb6c6] frame #2: std::function<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > ()>::operator()() const + 0x54 (0x20ca4e4 in ./buck-out/gen/xplat/caffe2/fb/lite_predictor/lite_predictor) frame #3: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x57 (0x20ca9a7 in ./buck-out/gen/xplat/caffe2/fb/lite_predictor/lite_predictor) frame #4: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) + 0x7a (0x20c823a in ./buck-out/gen/xplat/caffe2/fb/lite_predictor/lite_predictor) frame pytorch#5: caffe2::serialize::FileAdapter::RAIIFile::RAIIFile(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) + 0x96 (0x206f3d6 in ./buck-out/gen/xplat/caffe2/fb/lite_predictor/lite_predictor) frame pytorch#6: caffe2::serialize::FileAdapter::FileAdapter(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) + 0x42 (0x206f502 in ./buck-out/gen/xplat/caffe2/fb/lite_predictor/lite_predictor) frame pytorch#7: torch::jit::_load_for_mobile(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, c10::optional<c10::Device>, std::unordered_map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::hash<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::equal_to<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<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> > > > >&) + 0x30 (0x1be826c in ./buck-out/gen/xplat/caffe2/fb/lite_predictor/lite_predictor) frame pytorch#8: torch::jit::_load_for_mobile(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, c10::optional<c10::Device>) + 0x35 (0x1be8214 in ./buck-out/gen/xplat/caffe2/fb/lite_predictor/lite_predictor) frame pytorch#9: benchmark(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, int, 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> > const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, bool, int, int, int, bool, int, bool, int, double, bool, bool, bool, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) + 0x16d (0x12093ad in ./buck-out/gen/xplat/caffe2/fb/lite_predictor/lite_predictor) frame pytorch#10: main + 0x25c (0x11f933c in ./buck-out/gen/xplat/caffe2/fb/lite_predictor/lite_predictor) frame pytorch#11: __libc_start_main + 0x105 (0x7fc7b9f2ed95 in /usr/local/fbcode/platform009/lib/libc.so.6) frame pytorch#12: _start + 0x2a (0x11f902a in ./buck-out/gen/xplat/caffe2/fb/lite_predictor/lite_predictor) Aborted (core dumped) ```` Reviewed By: dhruvbird Differential Revision: D30135947 fbshipit-source-id: f50c634ef4545843305cad4b4a14a8776b1aec76
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…4332) Summary: Pull Request resolved: pytorch#64332 With this diff, if a compiler bug occurs (unlikely, I know!) we'll be able to get a c++ stacktrace leading to the exception, rather than just a terse message. E.g., ``` RuntimeError: UNSUPPORTED DTYPE Exception raised from compilation_error at ../torch/csrc/jit/tensorexpr/exceptions.h:32 (most recent call first): frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x6b (0x7f966659b2eb in /fsx/users/bertrand/c\ onda/envs/pytorch/lib/python3.8/site-packages/torch/lib/libc10.so) frame #1: <unknown function> + 0x376f099 (0x7f966a195099 in /fsx/users/bertrand/conda/envs/pytorch/lib/python3.8/site-packages/torch/lib/libtorch_cuda.so) frame #2: <unknown function> + 0x3763bf5 (0x7f966a189bf5 in /fsx/users/bertrand/conda/envs/pytorch/lib/python3.8/site-packages/torch/lib/libtorch_cuda.so) frame #3: torch::jit::tensorexpr::CudaCodeGen::Initialize() + 0xdd8 (0x7f966a193368 in /fsx/users/bertrand/conda/envs/pytorch/lib/python3.8/site-packages/torch/lib/libtorch_cuda\ .so) ``` Test Plan: Imported from OSS Reviewed By: huiguoo Differential Revision: D30745610 Pulled By: bertmaher fbshipit-source-id: a1cfaa7364ef4120de834e9cbe57ced1d082ab4e
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Summary: Pull Request resolved: pytorch#66009 Fixes ``` test_trace_c10_ops (jit.test_tracer.TestTracer) ... third-party-buck/platform009/build/eigen/include/Eigen/src/Core/Block.h:374:24: runtime error: applying non-zero offset 4 to null pointer #0 0x7f5228f72227 in Eigen::internal::BlockImpl_dense<Eigen::Map<Eigen::Array<float, -1, -1, 1, -1, -1>, 0, Eigen::Stride<0, 0> >, -1, -1, false, true>::BlockImpl_dense(Eigen::Map<Eigen::Array<float, -1, -1, 1, -1, -1>, 0, Eigen::Stride<0, 0> >&, long, long, long, long) third-party-buck/platform009/build/eigen/include/Eigen/src/Core/Block.h:374 #1 0x7f5228f7212c in Eigen::BlockImpl<Eigen::Map<Eigen::Array<float, -1, -1, 1, -1, -1>, 0, Eigen::Stride<0, 0> >, -1, -1, false, Eigen::Dense>::BlockImpl(Eigen::Map<Eigen::Array<float, -1, -1, 1, -1, -1>, 0, Eigen::Stride<0, 0> >&, long, long, long, long) third-party-buck/platform009/build/eigen/include/Eigen/src/Core/Block.h:166 #2 0x7f5228f720dc in Eigen::Block<Eigen::Map<Eigen::Array<float, -1, -1, 1, -1, -1>, 0, Eigen::Stride<0, 0> >, -1, -1, false>::Block(Eigen::Map<Eigen::Array<float, -1, -1, 1, -1, -1>, 0, Eigen::Stride<0, 0> >&, long, long, long, long) third-party-buck/platform009/build/eigen/include/Eigen/src/Core/Block.h:142 #3 0x7f5229b0e059 in Eigen::DenseBase<Eigen::Map<Eigen::Array<float, -1, -1, 1, -1, -1>, 0, Eigen::Stride<0, 0> > >::FixedBlockXpr<internal::get_fixed_value<int>::value, internal::get_fixed_value<long>::value>::Type Eigen::DenseBase<Eigen::Map<Eigen::Array<float, -1, -1, 1, -1, -1>, 0, Eigen::Stride<0, 0> > >::block<int, long>(long, long, int, long) third-party-buck/platform009/build/eigen/include/Eigen/src/Core/../plugins/BlockMethods.h:98 #4 0x7f5229b0c5ca in caffe2::GenerateProposalsOp<caffe2::CPUContext>::RunOnDevice() caffe2/caffe2/operators/generate_proposals_op.cc:348 ``` Also cleans up some data type and const issues around the area. Test Plan: Sandcastle Reviewed By: xush6528 Differential Revision: D31343046 fbshipit-source-id: fd9096c8e47a0aad529c72fd313f64ca98dcb80b
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Summary: Pull Request resolved: pytorch#66060 Fixes ``` testTumHistoryAdditionalLaser (caffe2.caffe2.fb.layers.tests.tum_history_test.TestTumHistory) ... caffe2/caffe2/operators/concat_split_op.h:363:74: runtime error: applying non-zero offset 8 to null pointer #0 0x7f8f39d29795 in caffe2::ConcatOp<caffe2::CPUContext>::RunOnDevice() caffe2/caffe2/operators/concat_split_op.h:363 #1 0x7f8f39c4978d in caffe2::Operator<caffe2::CPUContext>::Run(int) caffe2/caffe2/core/operator.h:987 #2 0x7f8f381fe9c9 in caffe2::SimpleNet::Run() caffe2/caffe2/core/net_simple.cc:67 #3 0x7f8f38ee488e in caffe2::Workspace::RunNet(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) caffe2/caffe2/core/workspace.cc:289 ``` Test Plan: Sandcastle Reviewed By: dzhulgakov, xush6528 Differential Revision: D31366205 fbshipit-source-id: 566aa519677c9d371189e4b1f81d595732861efc
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Summary: Pull Request resolved: pytorch/pytorch-canary#2 Pull Request resolved: pytorch#66881 Adds `static_runtime::fused_equally_split` operator and removes `is_fused` logic from original operator. Modifies `FuseUnpackListV2` to map `fb::equally_split` to this new operator. Test Plan: ``` adityapillai@5960 /data/sandcastle/boxes/fbsource/fbcode 1m 13s ❯ buck test //caffe2/benchmarks/static_runtime/fb:test_fb_operators ``` and sandcastle strange_what_could_go_wrong Reviewed By: mikeiovine Differential Revision: D31742293 fbshipit-source-id: 60b35589c8817719b005d49811f575b6590d1c39
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This makes the rocm jobs run on master-only. We've been battling queue times for a few months now (pytorch#73039). So far we have tried or investigated: 1. Moving distributed builds to master 2. Moving distributed builds to periodic 3. Only running rocm on a specific set of paths 4. Running multiple jobs on a single rocm host. Unfortunately, we haven't been able to reduce queuing times to good levels. As a result, ROCm jobs are the "weightiest" job in PR CI, with an average TTS of 3.3h (see https://hud.pytorch.org/metrics, panel name "Job time-to-signal, all branches"). There are two things we haven't tried so far: 1. Running "smoke tests" only on PR 2. Switching rocm builds to master Since #2 is easiest let's give it a try. For now, the policy would be the same as what we do for other capacity-constrained configurations (Win and Mac)—run on master only, but revert if there is a breakage introduced. [skip ci] Pull Request resolved: pytorch#77989 Approved by: https://github.com/malfet, https://github.com/janeyx99
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…78136) This prevents `import torch` accidentally crash on machines with no metal devices Should prevent crashes reported in pytorch#77662 (comment) and https://github.com/pytorch/functorch/runs/6560056366?check_suite_focus=true Backtrace to the crash: ``` (lldb) bt * thread #1, stop reason = signal SIGSTOP * frame #0: 0x00007fff7202be57 libobjc.A.dylib`objc_msgSend + 23 frame #1: 0x000000010fd9f524 libtorch_cpu.dylib`at::mps::HeapAllocator::MPSHeapAllocatorImpl::MPSHeapAllocatorImpl() + 436 frame #2: 0x000000010fda011d libtorch_cpu.dylib`_GLOBAL__sub_I_MPSAllocator.mm + 125 frame #3: 0x000000010ada81e3 dyld`ImageLoaderMachO::doModInitFunctions(ImageLoader::LinkContext const&) + 535 frame #4: 0x000000010ada85ee dyld`ImageLoaderMachO::doInitialization(ImageLoader::LinkContext const&) + 40(lldb) up frame #1: 0x000000010fd9f524 libtorch_cpu.dylib`at::mps::HeapAllocator::MPSHeapAllocatorImpl::MPSHeapAllocatorImpl() + 436 libtorch_cpu.dylib`at::mps::HeapAllocator::MPSHeapAllocatorImpl::MPSHeapAllocatorImpl: -> 0x10fd9f524 <+436>: movq %rax, 0x1b0(%rbx) 0x10fd9f52b <+443>: movw $0x0, 0x1b8(%rbx) 0x10fd9f534 <+452>: addq $0x8, %rsp 0x10fd9f538 <+456>: popq %rbx (lldb) disassemble ... 0x10fd9f514 <+420>: movq 0xf19ad15(%rip), %rsi ; "maxBufferLength" 0x10fd9f51b <+427>: movq %r14, %rdi 0x10fd9f51e <+430>: callq *0xeaa326c(%rip) ; (void *)0x00007fff7202be40: objc_msgSend ``` which corresponds to `[m_device maxBufferLength]` call, where `m_device` is not initialized in https://github.com/pytorch/pytorch/blob/2ae3c59e4bcb8e6e75b4a942cacc2d338c88e609/aten/src/ATen/mps/MPSAllocator.h#L171 Pull Request resolved: pytorch#78136 Approved by: https://github.com/seemethere
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… of libtorch_python (pytorch#78028) Summary: This moves torch::class_<WorkerInfo> into `rpc_agent.cpp` so it gets registered in libtorch instead of libtorch_python. This is intermediate work to getting torch::deploy to load an unmodified copy of libtorch. Current RPC is incompatible due to duplicate registrations. ``` unknown file: Failure C++ exception with description "Exception Caught inside torch::deploy embedded library: Custom class with name __torch__.torch.classes.dist_rpc.WorkerInfo is already registered. Ensure that registration with torch::class_ is only called once. Exception raised from registerCustomClass at ../aten/src/ATen/core/custom_class.cpp:61 (most recent call first): frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x3e (0x7f3bd9adb92e in /home/tristanr/venvs/multipy/lib/python3.8/site-packages/torch/lib/libc10.so) frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::string const&) + 0x5c (0x7f3bd9ab7068 in /home/tristanr/venvs/multipy/lib/python3.8/site-packages/torch/lib/libc10.so) frame #2: torch::registerCustomClass(std::shared_ptr<c10::ClassType>) + 0x110 (0x7f3bc2258980 in /home/tristanr/venvs/multipy/lib/python3.8/site-packages/torch/lib/libtorch_cpu.so) frame #3: torch::detail::class_base::class_base(std::string const&, std::string const&, std::string, std::type_info const&, std::type_info const&) + 0x3b9 (0x7f3bc225a419 in /home/tristanr/venvs/multipy/lib/python3.8/site-packages/torch/lib/libtorch_cpu.so) frame #4: [0x7f3ba45cfea1] frame pytorch#5: <unknown function> + 0x1b5334 (0x5652bdab9334 in ./test_deploy) frame pytorch#6: <unknown function> + 0x1b4f3e (0x5652bdab8f3e in ./test_deploy) frame pytorch#7: <unknown function> + 0x1b519b (0x5652bdab919b in ./test_deploy) frame pytorch#8: loadSearchFile(char const*) + 0x23e (0x7f3ba62f37f8 in /tmp/torch_deploy9ATEFg) frame pytorch#9: deploy_set_self + 0x51 (0x7f3ba62f38f9 in /tmp/torch_deploy9ATEFg) frame pytorch#10: torch::deploy::Interpreter::Interpreter(torch::deploy::InterpreterManager*, std::shared_ptr<torch::deploy::Environment>) + 0x274 (0x5652bdaaa790 in ./test_deploy) frame pytorch#11: void __gnu_cxx::new_allocator<torch::deploy::Interpreter>::construct<torch::deploy::Interpreter, torch::deploy::InterpreterManager*, std::shared_ptr<torch::deploy::Environment>&>(torch::deploy::Interpreter*, torch::deploy::InterpreterManager*&&, std::shared_ptr<torch::deploy::Environment>&) + 0x81 (0x5652bdaaf58b in ./test_deploy) frame pytorch#12: void std::allocator_traits<std::allocator<torch::deploy::Interpreter> >::construct<torch::deploy::Interpreter, torch::deploy::InterpreterManager*, std::shared_ptr<torch::deploy::Environment>&>(std::allocator<torch::deploy::Interpreter>&, torch::deploy::Interpreter*, torch::deploy::InterpreterManager*&&, std::shared_ptr<torch::deploy::Environment>&) + 0x4a (0x5652bdaae320 in ./test_deploy) frame pytorch#13: void std::vector<torch::deploy::Interpreter, std::allocator<torch::deploy::Interpreter> >::_M_realloc_insert<torch::deploy::InterpreterManager*, std::shared_ptr<torch::deploy::Environment>&>(__gnu_cxx::__normal_iterator<torch::deploy::Interpreter*, std::vector<torch::deploy::Interpreter, std::allocator<torch::deploy::Interpreter> > >, torch::deploy::InterpreterManager*&&, std::shared_ptr<torch::deploy::Environment>&) + 0xee (0x5652bdaae4a0 in ./test_deploy) frame pytorch#14: void std::vector<torch::deploy::Interpreter, std::allocator<torch::deploy::Interpreter> >::emplace_back<torch::deploy::InterpreterManager*, std::shared_ptr<torch::deploy::Environment>&>(torch::deploy::InterpreterManager*&&, std::shared_ptr<torch::deploy::Environment>&) + 0xb6 (0x5652bdaad258 in ./test_deploy) frame pytorch#15: torch::deploy::InterpreterManager::InterpreterManager(unsigned long, std::shared_ptr<torch::deploy::Environment>) + 0x123 (0x5652bdaa83b1 in ./test_deploy) frame pytorch#16: TorchpyTest_InitTwice_Test::TestBody() + 0x65 (0x5652bda075a9 in ./test_deploy) frame pytorch#17: void testing::internal::HandleSehExceptionsInMethodIfSupported<testing::Test, void>(testing::Test*, void (testing::Test::*)(), char const*) + 0x65 (0x5652bda944b7 in ./test_deploy) frame pytorch#18: void testing::internal::HandleExceptionsInMethodIfSupported<testing::Test, void>(testing::Test*, void (testing::Test::*)(), char const*) + 0x5a (0x5652bda8cfe7 in ./test_deploy) frame pytorch#19: testing::Test::Run() + 0x100 (0x5652bda68622 in ./test_deploy) frame pytorch#20: testing::TestInfo::Run() + 0x10f (0x5652bda68fb3 in ./test_deploy) frame pytorch#21: testing::TestSuite::Run() + 0x121 (0x5652bda6980d in ./test_deploy) frame pytorch#22: testing::internal::UnitTestImpl::RunAllTests() + 0x38e (0x5652bda756e6 in ./test_deploy) frame pytorch#23: bool testing::internal::HandleSehExceptionsInMethodIfSupported<testing::internal::UnitTestImpl, bool>(testing::internal::UnitTestImpl*, bool (testing::internal::UnitTestImpl::*)(), char const*) + 0x65 (0x5652bda9586b in ./test_deploy) frame pytorch#24: bool testing::internal::HandleExceptionsInMethodIfSupported<testing::internal::UnitTestImpl, bool>(testing::internal::UnitTestImpl*, bool (testing::internal::UnitTestImpl::*)(), char const*) + 0x5a (0x5652bda8e0f7 in ./test_deploy) frame pytorch#25: testing::UnitTest::Run() + 0xc9 (0x5652bda73fd1 in ./test_deploy) frame pytorch#26: RUN_ALL_TESTS() + 0x11 (0x5652bda169fa in ./test_deploy) frame pytorch#27: main + 0x27 (0x5652bda10ce2 in ./test_deploy) frame pytorch#28: <unknown function> + 0x2d310 (0x7f3bc0431310 in /usr/lib/libc.so.6) frame pytorch#29: __libc_start_main + 0x81 (0x7f3bc04313c1 in /usr/lib/libc.so.6) frame pytorch#30: _start + 0x25 (0x5652bda063b5 in ./test_deploy) ``` Test Plan: CI Differential Revision: D36564258 Pull Request resolved: pytorch#78028 Approved by: https://github.com/rohan-varma
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… to conform with non-quantized countertpart filenames Summary: Names of analogous files in quantized directory (previously snake case) were inconsistent with their non-quantized filename counterparts (pascal case). This is the first of a series of PRs that changes all files in quantized (and sub-directories) dir to have pascal case. `aten/src/ATen/native/quantized/qconv_unpack.cpp` has not been renamed yet because (for reasons currently unknown) after making the name change, `import torch` produces the below error (`qlinear_unpack.cpp` renaming also seems to fail some phabricator CI tests for similar reasons). We suspect that these may be undefined errors and will revisit naming these files in a future PR. ``` terminate called after throwing an instance of 'c10::Error' what(): Type c10::intrusive_ptr<ConvPackedParamsBase<2> > could not be converted to any of the known types. Exception raised from operator() at ../aten/src/ATen/core/jit_type.h:1735 (most recent call first): frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x55 (0x7f26745c0c65 in /data/users/dzdang/pytorch/torch/lib/libc10.so) frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) + 0xb1 (0x7f26745bdcd1 in /data/users/dzdang/pytorch/torch/lib/libc10.so) frame #2: <unknown function> + 0x1494e24 (0x7f2663b14e24 in /data/users/dzdang/pytorch/torch/lib/libtorch_cpu.so) frame #3: <unknown function> + 0xfed0bc (0x7f266366d0bc in /data/users/dzdang/pytorch/torch/lib/libtorch_cpu.so) frame #4: c10::detail::infer_schema::make_function_schema(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >&&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >&&, c10::ArrayRef<c10::detail::infer_schema::ArgumentDef>, c10::ArrayRef<c10::detail::infer_schema::ArgumentDef>) + 0x5a (0x7f266366d71a in /data/users/dzdang/pytorch/torch/lib/libtorch_cpu.so) frame pytorch#5: c10::detail::infer_schema::make_function_schema(c10::ArrayRef<c10::detail::infer_schema::ArgumentDef>, c10::ArrayRef<c10::detail::infer_schema::ArgumentDef>) + 0x7b (0x7f266366e06b in /data/users/dzdang/pytorch/torch/lib/libtorch_cpu.so) frame pytorch#6: <unknown function> + 0x1493f32 (0x7f2663b13f32 in /data/users/dzdang/pytorch/torch/lib/libtorch_cpu.so) frame pytorch#7: <unknown function> + 0xe227dd (0x7f26634a27dd in /data/users/dzdang/pytorch/torch/lib/libtorch_cpu.so) frame pytorch#8: <unknown function> + 0x14e0a (0x7f268c934e0a in /lib64/ld-linux-x86-64.so.2) ..........................truncated............. ``` Test Plan: ``` python test/test_quantization.py ``` Pull Request resolved: pytorch#77037 Approved by: https://github.com/jerryzh168
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…se method overloads" Pull Request resolved: pytorch#79819 Approved by: https://github.com/mruberry
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…ops to use method overloads"" This reverts commit f3665dd. Reverted pytorch#79819 on behalf of https://github.com/malfet due to land raced with softshrink refs
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…ytorch#81031) Re-attempting after original PR pytorch#79596 was reverted due to causing ROCm build failures Pull Request resolved: pytorch#81031 Approved by: https://github.com/jeffdaily, https://github.com/malfet
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### Summary: This PR implements PTQ for APoT FakeQuant. It runs models (Resnet-18 pre-trained model, ImageNet dataset) to compare accuracy metrics for different qconfig settings of uniform vs. APoT quantized activation and weight. According to the collected accuracy stats, model #2 (uniform activation and APoT weight) appears to have a slight improvement in accuracy compared to model #1 (uniform activation and uniform weight) for 8-bit and significant improvement for 4-bit (see "Accuracy Stats" section below). ### Test Plan: Run models with: `python test/quantization/core/experimental/fx_graph_mode_apot.py` ### Accuracy Stats: 8-bit (Uniform int8, APoT b = 8 k = 2) **Model #1:** Uniform activation, uniform weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 64.43% (Top-1), 85.62% (Top-5) **Model #2:** Uniform activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 64.51% (Top-1), 85.78% (Top-5) **Model #3:** APoT activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 64.32% (Top-1), 85.78% (Top-5) 4-bit (Uniform int4, APoT b = 4 k = 2) **Model #1:** Uniform activation, uniform weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 45.63% (Top-1), 71.96% (Top-5) **Model #2:** Uniform activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 64.24% (Top-1), 85.56% (Top-5) **Model #3:** APoT activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 45.40% (Top-1), 76.21% (Top-5) **Full Precision model (FX Graph Mode quantized)** Evaluation accuracy on test dataset: 69.76% (Top-1), 89.08% (Top-5) **Eager mode quantized model** Evaluation accuracy on test dataset: 69.49% (Top-1), 88.90% (Top-5) Pull Request resolved: pytorch#81040 Approved by: https://github.com/jerryzh168
Krovatkin
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Aug 11, 2022
Hi! I was playing with libfuzzer and found bug when loading a model from file via `torch::jit::load` function. There is an unhandled exception in caffe2/serialize when calling a `stoull` function on unsanitized version string. The bug can be reproduced with `aot_model_compiler` binary: ``` aot_model_compiler --model=crash-stoull --model_name=name --model_version=1 --input_dims='1,3,224,224;2,2' --input_types='float;float' ``` Crash file is provided in [crash.zip](https://github.com/pytorch/pytorch/files/8701504/crash.zip). gdb output: ``` Temporary breakpoint 1, main (argc=6, argv=0x7ffcd160f9f8) at /pytorch_master/binaries/aot_model_compiler.cc:87 87 "Run NNC AOT compiler for pytorch model. Example usage:\n" (gdb) c Continuing. terminate called after throwing an instance of 'std::invalid_argument' what(): stoull Program received signal SIGABRT, Aborted. __GI_raise (sig=sig@entry=6) at ../sysdeps/unix/sysv/linux/raise.c:50 50 ../sysdeps/unix/sysv/linux/raise.c: No such file or directory. (gdb) bt #0 __GI_raise (sig=sig@entry=6) at ../sysdeps/unix/sysv/linux/raise.c:50 #1 0x00007fa637f16859 in __GI_abort () at abort.c:79 #2 0x00007fa6381c1911 in ?? () from /lib/x86_64-linux-gnu/libstdc++.so.6 #3 0x00007fa6381cd38c in ?? () from /lib/x86_64-linux-gnu/libstdc++.so.6 #4 0x00007fa6381cd3f7 in std::terminate() () from /lib/x86_64-linux-gnu/libstdc++.so.6 pytorch#5 0x00007fa6381cd6a9 in __cxa_throw () from /lib/x86_64-linux-gnu/libstdc++.so.6 pytorch#6 0x00007fa6381c42ce in std::__throw_invalid_argument(char const*) () from /lib/x86_64-linux-gnu/libstdc++.so.6 pytorch#7 0x000000000247d567 in __gnu_cxx::__stoa<unsigned long long, unsigned long long, char, int> (__str=0x7ffcd160f228 "ZZ", __idx=0x0, __base=10, __convf=<optimized out>, __name=<optimized out>) at /usr/bin/../lib/gcc/x86_64-linux-gnu/10/../../../../include/c++/10/ext/string_conversions.h:83 pytorch#8 std::__cxx11::stoull (__str="ZZ", __idx=0x0, __base=10) at /usr/bin/../lib/gcc/x86_64-linux-gnu/10/../../../../include/c++/10/bits/basic_string.h:6577 pytorch#9 caffe2::serialize::PyTorchStreamReader::init (this=this@entry=0x8c11ce0) at /pytorch_master/caffe2/serialize/inline_container.cc:145 pytorch#10 0x000000000247d9c7 in caffe2::serialize::PyTorchStreamReader::PyTorchStreamReader (this=0x8c11ce0, in=std::shared_ptr<class caffe2::serialize::ReadAdapterInterface> (empty) = {...}) at /pytorch_master/caffe2/serialize/inline_container.cc:88 pytorch#11 0x00000000035b7ba4 in __gnu_cxx::new_allocator<caffe2::serialize::PyTorchStreamReader>::construct<caffe2::serialize::PyTorchStreamReader, std::shared_ptr<caffe2::serialize::ReadAdapterInterface> > ( __p=0x2, __args=..., this=<optimized out>) at /usr/bin/../lib/gcc/x86_64-linux-gnu/10/../../../../include/c++/10/ext/new_allocator.h:150 pytorch#12 std::allocator_traits<std::allocator<caffe2::serialize::PyTorchStreamReader> >::construct<caffe2::serialize::PyTorchStreamReader, std::shared_ptr<caffe2::serialize::ReadAdapterInterface> > (__a=..., __p=0x2, __p@entry=0x8c11ce0, __args=...) at /usr/bin/../lib/gcc/x86_64-linux-gnu/10/../../../../include/c++/10/bits/alloc_traits.h:512 pytorch#13 0x00000000035b1988 in std::_Sp_counted_ptr_inplace<caffe2::serialize::PyTorchStreamReader, std::allocator<caffe2::serialize::PyTorchStreamReader>, (__gnu_cxx::_Lock_policy)2>::_Sp_counted_ptr_inplace<std::shared_ptr<caffe2::serialize::ReadAdapterInterface> > (this=0x8c11cd0, __a=..., __args=...) at /usr/bin/../lib/gcc/x86_64-linux-gnu/10/../../../../include/c++/10/bits/shared_ptr_base.h:551 pytorch#14 std::__shared_count<(__gnu_cxx::_Lock_policy)2>::__shared_count<caffe2::serialize::PyTorchStreamReader, std::allocator<caffe2::serialize::PyTorchStreamReader>, std::shared_ptr<caffe2::serialize::ReadAdapterInterface> > (this=0x7ffcd160f3a8, __p=@0x7ffcd160f3a0: 0x10, __args=..., __a=...) at /usr/bin/../lib/gcc/x86_64-linux-gnu/10/../../../../include/c++/10/bits/shared_ptr_base.h:683 pytorch#15 std::__shared_ptr<caffe2::serialize::PyTorchStreamReader, (__gnu_cxx::_Lock_policy)2>::__shared_ptr<std::allocator<caffe2::serialize::PyTorchStreamReader>, std::shared_ptr<caffe2::serialize::ReadAdapterInterface> > (this=0x7ffcd160f3a0, __args=..., __tag=...) at /usr/bin/../lib/gcc/x86_64-linux-gnu/10/../../../../include/c++/10/bits/shared_ptr_base.h:1371 pytorch#16 std::shared_ptr<caffe2::serialize::PyTorchStreamReader>::shared_ptr<std::allocator<caffe2::serialize::PyTorchStreamReader>, std::shared_ptr<caffe2::serialize::ReadAdapterInterface> > (this=0x7ffcd160f3a0, __args=..., __tag=...) at /usr/bin/../lib/gcc/x86_64-linux-gnu/10/../../../../include/c++/10/bits/shared_ptr.h:408 pytorch#17 std::allocate_shared<caffe2::serialize::PyTorchStreamReader, std::allocator<caffe2::serialize::PyTorchStreamReader>, std::shared_ptr<caffe2::serialize::ReadAdapterInterface> > (__args=..., __a=...) at /usr/bin/../lib/gcc/x86_64-linux-gnu/10/../../../../include/c++/10/bits/shared_ptr.h:859 pytorch#18 std::make_shared<caffe2::serialize::PyTorchStreamReader, std::shared_ptr<caffe2::serialize::ReadAdapterInterface> > (__args=...) at /usr/bin/../lib/gcc/x86_64-linux-gnu/10/../../../../include/c++/10/bits/shared_ptr.h:875 pytorch#19 torch::jit::load (rai=std::shared_ptr<class caffe2::serialize::ReadAdapterInterface> (empty) = {...}, device=device@entry=..., Python Exception <class 'gdb.error'> No type named std::__detail::_Hash_node<struct std::pair<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> > >, true>.: extra_files=std::unordered_map with 0 elements) at /pytorch_master/torch/csrc/jit/serialization/import.cpp:474 pytorch#20 0x00000000035b1ef6 in torch::jit::load (filename="crash-stoull", device=device@entry=..., Python Exception <class 'gdb.error'> No type named std::__detail::_Hash_node<struct std::pair<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> > >, true>.: extra_files=std::unordered_map with 0 elements) at /pytorch_master/torch/csrc/jit/serialization/import.cpp:444 pytorch#21 0x00000000035b1d22 in torch::jit::load (filename="", device=device@entry=...) at /pytorch_master/torch/csrc/jit/serialization/import.cpp:424 pytorch#22 0x00000000008f9be3 in main (argc=1, argv=0x7ffcd160f9f8) at /pytorch_master/binaries/aot_model_compiler.cc:128 ``` Pull Request resolved: pytorch#77557 Approved by: https://github.com/Gamrix
Krovatkin
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Aug 12, 2022
### Summary: This PR implements QAT for APoT FakeQuant. It runs QAT with FX graph mode quantized models (Resnet-18 pre-trained model, full ImageNet dataset) to compare accuracy metrics for different qconfig settings of uniform vs. APoT quantized activation and weight. It also refactors the APoT PTQ module `apot_fx_graph_mode_ptq.py` (previously `fx_graph_mode_apot.py`) such that shared helper functions between PTQ and QAT are in a separate file `quantization_util.py`. Model #2 (uniformly quantized activation, APoT quantized weight) shows comparable accuracy compared to model #1 (uniformly quantized activation, APoT quantized weight) for 8-bit and significant accuracy improvement for 4-bit (see "Accuracy Stats" section below). ### Test Plan: Run QAT models with: `python test/quantization/core/experimental/apot_qat.py` Run PTQ models with: `python test/quantization/core/experimental/apot_ptq.py` ### Accuracy Stats 8-bit (Uniform int8, APoT b = 8 k = 2) Model #1: Uniform activation, uniform weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 69.67% (Top-1), 89.04% (Top-5) Model #2: Uniform activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 69.72% (Top-1), 89.06% (Top-5) 4-bit (Uniform int4, APoT b = 4 k = 2) Model #1: Uniform activation, uniform weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 46.85% (Top-1), 72.85% (Top-5) Model #2: Uniform activation, APoT weight (FX Graph Mode quantized) Evaluation accuracy on test dataset: 66.45% (Top-1), 86.23% (Top-5) Pull Request resolved: pytorch#83282 Approved by: https://github.com/jerryzh168
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