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CHANGELOG.md

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Changelog for hipBLASLt

Full documentation for hipBLASLt is available at rocm.docs.amd.com/projects/hipBLASLt.

hipBLASLt 0.10.0 for ROCm 6.3.0

Added

  • Support the V2 CPP extension API for backward compatibility
  • Support for data type Int8 in with Int8 out
  • Support for data type FP32/FP64 for gfx110x
  • Add the Extension API hipblaslt_ext::matmulIsTuned
  • Output atol and rtol for hipblaslt-bench validation
  • Output the bench command for hipblaslt CPP ext API path if HIPBLASLT_LOG_MASK=32 is set
  • Support odd sizes for FP8/BF8 GEMM

Changed

  • Reorganize and add more sample code
  • Add a dependency with the hipblas-common package and remove the dependency with the hipblas package

Optimized

  • Support fused kernel for HIPBLASLT_MATMUL_DESC_AMAX_D_POINTER for FP8/BF8 data type
  • Improve the library loading time
  • Improve the overall performance of first returned solution

Upcoming changes

  • The V1 CPP extension API will be deprecated in a future release of hipBLASLt

hipBLASLt 0.8.0

Added

  • Extension APIs:
    • hipblasltExtAMaxWithScale
  • GemmTuning extension parameter to set wgm by user
  • Support HIPBLASLT_MATMUL_DESC_AMAX_D_POINTER for the FP8/BF8 data types
  • Support for FP8/BF8 input, FP32/FP16/BF16/F8/BF8 output (only for the gfx94x architectures)
  • Support HIPBLASLT_MATMUL_DESC_COMPUTE_INPUT_TYPE_A_EXT and HIPBLASLT_MATMUL_DESC_COMPUTE_INPUT_TYPE_B_EXT for FP16 input data type to use FP8/BF8 mfma
  • Support for the gfx110x architecture

Optimized

  • Improve the library loading time

hipBLASLt 0.7.0

Additions

  • Extension APIs:
    • hipblasltExtSoftmax
    • hipblasltExtLayerNorm
    • hipblasltExtAMax
  • GemmTuning extension parameter to set split-k by user
  • Support for mixed-precision datatype: FP16/FP8 in with FP16 out
  • Add CMake support for documentation

Deprecations

  • algoGetHeuristic() ext API for GroupGemm will be deprecated in a future release of hipBLASLt

hipBLASLt 0.6.0

Additions

  • New UserArguments variable for GroupedGemm
  • Support for datatype: FP16 in with FP32 out
  • Support for datatype: Int8 in Int32 out
  • Support for gfx94x platform
  • Support for FP8/BF8 datatype (only for gfx94x platform)
  • Support scalar A,B,C,D for FP8/BF8 datatype
  • Added samples

Changes

  • Replaced hipblasDatatype_t with hipDataType
  • Replaced hipblasLtComputeType_t with hipblasComputeType_t

Removals

  • Deprecated HIPBLASLT_MATMUL_DESC_D_SCALE_VECTOR_POINTER

hipBLASLt 0.3.0

Additions

  • Added getAllAlgos extension APIs
  • TensileLite support for new epilogues: gradient gelu, gradient D, gradient A/B, aux
  • Added a sample package that includes three sample apps
  • Added a new C++ GEMM class in the hipBLASLt extension

Changes

  • Refactored GroupGemm APIs as C++ class in the hipBLASLt extension
  • Changed the scaleD vector enum to HIPBLASLT_MATMUL_DESC_D_SCALE_VECTOR_POINTER

Fixes

  • Enabled norm check validation for CI

Optimizations

  • GSU kernel: wider memory, PGR N
  • Updated logic yaml to improve some FP16 NN sizes
  • GroupGemm support for GSU kernel
  • Added grouped GEMM tuning for aldebaran

hipBLASLt 0.2.0

Additions

  • Added CI tests for TensileLite
  • Initialized extension group GEMM APIs (FP16 only)
  • Added a group GEMM sample app: example_hipblaslt_groupedgemm

Fixes

  • Fixed incorrect results for the ScaleD kernel

Optimizations

  • Tuned equality sizes for the HHS data type
  • Reduced host-side overhead for hipblasLtMatmul()
  • Removed unused kernel arguments
  • Schedule values setup before first s_waitcnt
  • Refactored TensileLite host codes
  • Optimized build time

hipBLASLt 0.1.0

Additions

  • Enabled hipBLASLt APIs
  • Support for gfx90a
  • Support for problem type: FP32, FP16, BF16
  • Support activation: relu, gelu
  • Support for bias vectors
  • Integrated with TensileLite kernel generator
  • Added Gtest: hipblaslt-test
  • Added the full function tool hipblaslt-bench
  • Added the sample app example_hipblaslt_preference

Optimizations

  • gridBase solution search algorithm for untuned size
  • Tuned 10k sizes for each problem type