Supported ROCm and NVIDIA CUDA functions

Here is a list of the ROCm and NVIDIA CUDA functions supported by hipBLASLt:

• ROCm

  • AMD sparse MFMA matrix core support

  • Mixed-precision computation support:

    • FP16 input/output, FP32 matrix core accumulate

    • BFLOAT16 input/output, FP32 matrix core accumulate

    • INT8 input/output, INT32 matrix core accumulate

    • INT8 input, FP16 output, INT32 matrix core accumulate

    • FP8 input, FP32 output, FP32 matrix core accumulate

    • BF8 input, FP32 output, FP32 matrix core accumulate

  • Matrix pruning and compression functionalities

  • Auto-tuning functionality (see hipsparseLtMatmulSearch())

  • Batched sparse GEMM support:

    • Single sparse matrix/multiple dense matrices (broadcast)

    • Multiple sparse and dense matrices

    • Batched bias vector

  • Activation function fuse in SpMM kernel support:

    • ReLU

    • ClippedReLU (ReLU with upper bound and threshold setting)

    • GeLU

    • GeLU scaling (implied enable GeLU)

    • Abs

    • LeakyReLU

    • Sigmoid

    • Tanh

  • Ongoing feature development

    • Add support for mixed-precision computation:

      • FP8 input/output, FP32 matrix core accumulate

      • BF8 input/output, FP32 matrix core accumulate

      • Add kernel selection and generator, used to provide the appropriate solution for the specific problem

• CUDA

  • Support for CUDA cuSPARSELt v0.6.3