I have downloaded the libtorch CPU-only version from the website and unzipped it.
Inside my .cpp application which uses libtorch, I write (I am using intel-mkl for other parts of the application, and I wish libtorch uses this as well):
omp_set_num_threads(64);
mkl_set_num_threads(64);
I then check:
std::cout << "torch::get_num_threads() returns: " << torch::get_num_threads() << std::endl;
std::cout << "omp_get_max_threads() returns: " << omp_get_max_threads() << std::endl;
std::cout << "mkl_get_max_threads() returns: " << mkl_get_max_threads() << std::endl;
These all return 64.
(yes, I do have so many cores, I am on a HPC machine with 128 cores per node and I am launching 2 MPI processes per node).
I then perform std::complex<double> matrix-matrix multiplications via torch::matmul() function calls.
These multiplications, for me, seem to be slow.
How can I check that:
Thank you!
EDIT:
$ nm -a --demangle libtorch_cpu.so | grep 'zgemv'
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$ nm -a --demangle libtorch_cpu.so | grep 'zgemm'
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Broadly speaking, there are a few different ways of doing this at different levels of complexity. You can:
libtorch links to intel-mkl using ldd libtorch or a similar command line.intel-mkl routines you're interested in are referenced in libtorch, using a command like nm -a --demangle <some path to libtorch.*.so> | grep 'mkl'.torch::matmul() at runtime, using a tool like VTune, oprofile, or perf.(1) is an extremely basic sanity check that won't tell you much other than that the specific libtorch library you're using is at least going to try to pull some symbols in from intel-mkl. It can help with debugging whether you're using the version of libtorch that you expected and that that version of the library is able to traverse the system's library path to find the version of intel-mkl that it should be. This is rarely an issue, but is easy and quick to check, and you don't want to spend longer than you have to debugging what could turn out to be, for example, an incorrect installation of MKL. (2) will tell you a little bit more about which MKL routines your copy of libtorch is referencing statically - although notably not what codepaths it takes for those to end up being called at runtime.
(3) is almost certainly what you want, and will answer in much greater depth why those calls seem slow. I'm guessing that if you're using MKL, VTune might also be available (for AMD, consider the similar tool μProf). If you're on a system with Intel CPUs, VTune will also have detailed knowledge of those microarchitectures built into it, provided that the version of VTune you're using was released after the processor you're running on (again, likely). A VTune callstack report can tell you what paths in your code are calling down to libtorch and in turn which if any of those end up in particular MKL routines, and then which of those are taking up a lot of time and why. VTune thread analysis may also tell you if your OMP/MKL/torch threads have well-balanced datasets to work on, whether other threads you're not thinking about are taking up time or getting them unscheduled, and so on. Similar analyses are available with other tools but may just be more time consuming to put together and visualize.
There are also trickier, more time consuming things you can do to hook your own profiling and logging into assembled code like what's in MKL. It's unlikely based on what's been stated so far that this is what you need, but is a possible option of last resort if existing tools are not doing what you need. If you strongly believe you need techniques like this and it will be worth your effort, A study of Binary Instrumentation techniques by Soumyakant Priyadarshan, Intel's PIN dynamic binary instrumentation or similar may be good resources.