* Treat negative zero as zero in the sparse gemm compressor
Signed-off-by: Tyler Michael Smith <tyler@neuralmagic.com>
* format
Signed-off-by: Tyler Michael Smith <tyler@neuralmagic.com>
* Apply patch
Signed-off-by: Tyler Michael Smith <tyler@neuralmagic.com>
* sm90_sparse_gemm_compressor.hpp
* test/unit/transform/CMakeLists.txt
* test/unit/transform/device/sm90_sparse_gemm_compressor_legacy.hpp
* include/cutlass/numeric_types.h
---------
Signed-off-by: Tyler Michael Smith <tyler@neuralmagic.com>
Co-authored-by: Haicheng Wu <haichengw@nvidia.com>
Co-authored-by: Haicheng Wu <57973641+hwu36@users.noreply.github.com>
CUTLASS 2.0
Substantially refactored for
- Better performance, particularly for native Turing Tensor Cores
- Robust and durable templates spanning the design space
- Encapsulated functionality embodying modern C++11 programming techniques
- Optimized containers and data types for efficient, generic, portable device code
Updates to:
- Quick start guide
- Documentation
- Utilities
- CUTLASS Profiler
Native Turing Tensor Cores
- Efficient GEMM kernels targeting Turing Tensor Cores
- Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands
Coverage of existing CUTLASS functionality:
- GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs
- Volta Tensor Cores through native mma.sync and through WMMA API
- Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions
- Batched GEMM operations
- Complex-valued GEMMs
Note: this commit and all that follow require a host compiler supporting C++11 or greater.
