- adds missing commas
- adjusts misaligned usage of CUTLASS_DEVICE between
template declaration and specializations
Signed-off-by: Janusz Lisiecki <jlisiecki@nvidia.com>
* Fixed template struct/class mismatch
* Use platform implementation instead of std::abs and std::conditional during nvrtc compilation
* Use platform implementation instead of std::abs and std::conditional during nvrtc compilation
* Revert absolute_value() usage
* Fix separate compilation `-dc`
- when cutlass is included in multiple compilation units
compiled with `-dc` OOB_NAN_F16x8 device constant is
instantiated multiple times causing
Multiple definition of '_ZN7cutlass4arch13OOB_NAN_F16x8E' error
This PR makes this variable a local constant as it is not
modified during runtime
Signed-off-by: Janusz Lisiecki <jlisiecki@nvidia.com>
* Fix
Signed-off-by: Janusz Lisiecki <jlisiecki@nvidia.com>
* Test GH
Signed-off-by: Janusz Lisiecki <jlisiecki@nvidia.com>
* Revert test GH
Signed-off-by: Janusz Lisiecki <jlisiecki@nvidia.com>
* Removed trivial copy constructors on parameter classes to enable device-side launch of CUTLASS kernels
* Added SFINAE to the `TensorRef(NonConstTensorRef const&)` constructor to avoid making it a copy-constructor for device code
* std => platform
* fix affine2
* really fix affine2
Co-authored-by: Haicheng Wu <haichengw@nvidia.com>
* Fix the build of cutlass/gemm/device/gemm_array.h and add a demo for GemmArray
* Add a reference to GemmArray to the docs
Co-authored-by: Ivan Komarov <dfyz@yandex-team.ru>
* Add epilogue functor for residual block fusion
* Do not run split-k tests when ActivationOp is not Identity
* explain TestSplitK param
* return early
* Support half precision sigmoid activation
* introduce a vectorized variant using fast_tanh
* move the math to fast_math.h
* fixed compile
* .raw() -> .to_half()
Co-authored-by: Haicheng Wu <haichengw@nvidia.com>
* Support half precision sigmoid activation
* introduce a vectorized variant using fast_tanh
* refactored sigmoid using the new interface
* refactored gelu
* add silu activation
* add hardswish
* remove sigmoid for now
* add description to silu and hardswish, and other doc update
* Do not ignore Round
* use constant N
* Set isHeavy = true in sigmoid and silu epilogue
CUTLASS 2.7
Mainloop fusion for GEMM: summation over A or B
Strided DGRAD (optimized iterators)
Half-precision GELU_taylor activation functions
Use these when accumulation and epilogue compute types are all cutlass::half_t
Tuning and bug fixes to fused GEMM + GEMM example
Support for smaller than 128b aligned Convolutions: see examples
Caching of results to accelerate Convolution unit tests
Can be enabled or disabled by running cmake .. -DCUTLASS_TEST_ENABLE_CACHED_RESULTS=OFF
Corrections and bug fixes reported by the CUTLASS community
Thank you for filing these issues!
authored-by: Haicheng Wu haichengw@nvidia.com, Manish Gupta manigupta@nvidia.com, Dustyn Blasig dblasig@nvidia.com, Andrew Kerr akerr@nvidia.com
CUTLASS 2.3 adds GEMMs targeting Sparse Tensor Cores on the NVIDIA Ampere Architecture, fast SGEMM, and small matrix classes, bug fixes, and performance enhancements.
- Updated mma_sm80.h to avoid perf penalty due to reinterpret_cast<>.
- Enhancement to CUTLASS Utility Library's HostTensorPlanarComplex template to support copy-in and copy-out
- Added test_examples target to build and test all CUTLASS examples
- Minor edits to documentation to point to GTC 2020 webinar
CUTLASS 2.1 contributes:
- BLAS-style host-side API added to CUTLASS Library
- Planar Complex GEMM kernels targeting Volta and Turing Tensor Cores
- Minor enhancements and bug fixes
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.
