access2rohit edited a comment on issue #17331: URL: https://github.com/apache/incubator-mxnet/issues/17331#issuecomment-656499453
Currently Large Tensor Support work on all operators implemented in MXNet and MKLDNN also supports int64. CUDA kernels written inside MXNET both generic(cpu/gpu) and specific(gpu only) support large tensors depending on device memory. BLAS and LAPACK libs were not considered while defining the scope of the project. Currently following BLAS and LAPACK implementations are supported inside MXNet openBLAS (Default) MKL ATLAS Apple Accelerate upon investigation openBLAS needs to be built with specific flag to support int64_t signatures and MKL will support long long int signatures(in which case reinterpret_cast<>() is needed for casting pointers as int64_t is treated as long int* as opposed to long long int* in MKL). Additionally LAPACK and BLAS wrappers need to be updated from int -> int64_t. Initially openBLAS can be supported since it is used by default and in pypi wheels as well. Thus not, breaking any default behaviour of customer. Users attempting to use Large Tensor with other BLAS and LAPACK implementations won't face issues as long as they don't use large tensors. Additional error messages will be added in case Large Tensor is used BLAS implementation is not openBLAS until that BLAS library is made to work with large tensor support of MXNet. NOTE: currently openBLAS works correctly with smaller inputs(within range of int32) but will truncate parameters passed with higher values and hence will result in either SIGSEGV(mostly) or garbage values being found(will eventually cause SIGSEGV in a bigger script) @sandeep-krishnamurthy @leezu @szha @zheng-da ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
