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36 Commits

Author SHA1 Message Date
1ab1027954 Updated mma_sm80.h to avoid perf penalty due to reinterpret_cast<>. (#100)
- 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
2020-06-15 10:47:01 -07:00
86931fef85 CUTLASS 2.2 (#96)
Adds support for NVIDIA Ampere Architecture features. CUDA 11 Toolkit recommended.
2020-06-08 16:17:35 -07:00
e33d90b361 update tools/library/CMakeLists to require python 3.6 according to #70 (#82)
#70 only updates the documentation. This commit reflects this bump in python version to the CMake configuration as well.
2020-04-08 10:54:36 -07:00
96dab34ad9 CUTLASS 2.1 (#83)
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
2020-04-07 13:51:25 -07:00
7c0cd26d13 Need Python 3.6 to use enum.auto() (#70) 2019-11-22 09:39:12 -08:00
45ecbc885b Removed redundant conjugation operations from matrix_traits. (#65) 2019-11-20 11:27:13 -08:00
8aca98f9a7 Improved formatting, clarity, and content of several documents. (#64)
* Improved formatting, clarity, and content of several documents.
2019-11-20 10:42:15 -08:00
f4d9c8f755 Clang GPU compilation requires explicit CUDACC version flags (#63) 2019-11-20 09:52:11 -08:00
fb335f6a5f CUTLASS 2.0 (#62)
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.
2019-11-19 16:55:34 -08:00
b5cab177a9 Performance enhancement for Volta Tensor Cores TN layout (#53)
* Fixed performance defect with indirect access to pointer array for Volta TensorCores TN arrangement.

* Updated patch version and changelog.

* Updated patch version and changelog.

* Added link to changelog in readme.

* Fixed markdown link
2019-07-10 10:54:12 -07:00
eb41735933 Merge pull request #47 from Artem-B/cutlass-1.3-clang
Make CUTLASS compileable with Clang.
2019-05-13 10:52:45 -07:00
fb8b3a98b7 Addressed code review comments. 2019-05-10 10:24:52 -07:00
d9d357877f Added missing file (#48) 2019-05-09 14:07:52 -07:00
e18292db46 Make CUTLASS compileable with Clang.
Requires a recent clang build (r359248 or newer).

Enable compilation with clang with these options:
cmake -DCUDA_COMPILER=clang -DCMAKE_CXX_COMPILER=/path/to/clang++
2019-05-02 11:00:22 -07:00
fe3438a3c1 cutlass 1.3.1 (#46)
CUTLASS 1.3.1 patch resolves failing text with NVRTC.
2019-04-19 16:54:52 -07:00
877bdcace6 Cutlass 1.3 Release (#42)
CUTLASS 1.3 Release
- Efficient GEMM kernel targeting Volta Tensor Cores via mma.sync instruction added in CUDA 10.1.
2019-03-20 10:49:17 -07:00
19a9d64e3c Removed patch version from README.
Removed patch version from README.
2018-12-19 15:20:43 -08:00
80e6f7c860 Merge pull request #38 from NVIDIA/resolve_maxwell
Resolved issue for incorrect SGEMM on Maxwell architecture.
2018-12-19 15:17:41 -08:00
822b0952cd Resolved issue for incorrect SGEMM on Maxwell architecture. 2018-12-19 15:07:16 -08:00
ed2ed4d667 Merge pull request #33 from NVIDIA/cutlass_1.2
CUTLASS 1.2
2018-10-26 14:59:50 -07:00
4db423c40f Minor edit to CHANGELOG. 2018-10-26 14:58:31 -07:00
b2bc0d3b79 Updating Doxygen docs 2018-10-26 14:54:58 -07:00
74df0331f2 CUTLASS 1.2 2018-10-26 14:38:46 -07:00
2332df492e Merge pull request #30 from NVIDIA/fix_utilities_example
Fixed cutlass_utilities example.
2018-09-29 15:09:18 -07:00
cfe4b933ef CUDA 9 lacks host-side conversions from float=>half. Instead, we must reinterpret_cast<> from cutlass::half_t => half. 2018-09-29 15:04:20 -07:00
6877595a5e Merge pull request #28 from NVIDIA/cutlass_1.1
Fixed typeo
2018-09-28 12:59:49 -07:00
69e3709da4 Fixed typeo
Fixed typeo
2018-09-28 12:59:20 -07:00
d419094c28 Merge pull request #26 from NVIDIA/cutlass_1.1
Clarification to README
2018-09-21 11:44:47 -07:00
1a7ac522f8 Clarification to README 2018-09-20 11:04:03 -07:00
bf6eec53eb Merge pull request #25 from NVIDIA/cutlass_1.1
Updated CUTLASS.md
2018-09-19 21:33:04 -07:00
206e38dac5 Updated copyright of CUTLASS.md 2018-09-19 21:31:12 -07:00
d85f6a1cec Merge pull request #24 from NVIDIA/cutlass_1.1
Cutlass 1.1
2018-09-19 21:16:53 -07:00
0826572c4c Reduced range of random values to avoid bit-level inconsistencies for large matrices. 2018-09-19 21:11:48 -07:00
77d1e0ca81 Updated README and CHANGELOG. 2018-09-19 20:42:51 -07:00
d7137f9c0a Updated doxygen 2018-09-19 14:02:08 -07:00
461f417b9d Checkpointing CUTLASS 1.1 release. 2018-09-18 16:58:03 -07:00
5196 changed files with 676761 additions and 148622 deletions

3
.gitmodules vendored
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@ -1,3 +0,0 @@
[submodule "tools/external/googletest"]
path = tools/external/googletest
url = https://github.com/google/googletest.git

127
CHANGELOG.md Normal file
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@ -0,0 +1,127 @@
# NVIDIA CUTLASS Changelog
# CUTLASS 2.x
## [2.2.0](https://github.com/NVIDIA/cutlass/releases/tag/v2.2.0) (2020-06-08)
* [NVIDIA Ampere Architecture features](https://devblogs.nvidia.com/nvidia-ampere-architecture-in-depth/)
* Fast Tensor Core operations:
* Maximum performance via [`mma.sync`](https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#warp-level-matrix-instructions-mma-and-friends)
* Tensor Float 32, BFloat16, and double-precision data types
* Mixed integer data types (int8, int4, bin1)
* Asynchronous copy for deep software pipelines via [`cp.async`](https://docs.nvidia.com/cuda/parallel-thread-execution)
* Described in [GTC 2020 Webinar (SR 21745)](https://developer.nvidia.com/gtc/2020/video/s21745) (free registration required)
* Features:
* SDK examples showing GEMM fused with bias+relu and fused GEMM+GEMM
* Complex-valued GEMMs targeting NVIDIA Ampere Tensor Cores in double-precision and Tensor Float 32
* Gaussian complex GEMMs using 3m complex multiply algorithm
* Universal GEMM kernel supporting two batch modes and two algorithms for parallel reductions
* Policy updates:
* [CUDA 11 Toolkit](https://developer.nvidia.com/cuda-toolkit) needed to enable NVIDIA Ampere Architecture features
* Disabled F16C by default for compatibility - enable on cmake command line with `-DCUTLASS_ENABLE_F16C=ON`
## [2.1.0](https://github.com/NVIDIA/cutlass/releases/tag/v2.1.0) (2020-04-06)
* BLAS-style host-side API added to [CUTLASS Library](/media/docs/quickstart.md#cutlass-library)
* API to launch compiled kernel instances for GEMM and planar complex GEMM
* Planar Complex GEMM kernels targeting Volta and Turing Tensor Cores
* Computes complex matrix products on matrices stored as disjoint real and imaginary parts
* [SDK Examples of Planar Complex GEMMs](/examples/10_planar_complex/planar_complex.cu)
* Minor enhancements and bug fixes
## [2.0.0](https://github.com/NVIDIA/cutlass/releases/tag/v2.0.0) (2019-11-19)
* 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](/media/docs/quickstart.md)
* [Documentation](/README.md#documentation)
* [Utilities](/media/docs/utilities.md)
* [CUTLASS Profiler](/media/docs/profiler.md)
* 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: a host compiler supporting C++11 or greater is required.**
# CUTLASS 1.x
## [1.3.2](https://github.com/NVIDIA/cutlass/releases/tag/v1.3.2) (2019-07-09)
* Performance improvement for Volta Tensor Cores TN and TT layouts.
## [1.3.1](https://github.com/NVIDIA/cutlass/releases/tag/v1.3.1) (2019-04-09)
* Corrected NVRTC unit tests.
## [1.3.0](https://github.com/NVIDIA/cutlass/releases/tag/v1.3.0) (2019-03-20)
* Efficient GEMM kernel targeting Volta Tensor Cores via `mma.sync` instruction added in CUDA 10.1.
## [1.2.0](https://github.com/NVIDIA/cutlass/releases/tag/v1.2.0) (2018-10-26)
* Parallelized reductions across threadblocks ("Split-K")
* Improved IGEMM performance
* Batched strided WMMA GEMMs
## [1.1.0](https://github.com/NVIDIA/cutlass/releases/tag/v1.1.0) (2018-09-19)
* Turing Features
* WMMA GEMM targeting TensorCores - INT8, INT4, 1-bit
* Batched Strided GEMM
* Threadblock rasterization strategies
* Improved performance for adverse problem sizes and data layouts
* Extended CUTLASS Core comonents
* Tensor views support arbitrary matrix and tensor layouts
* Zip iterators for structuring multiple data streams
* Enhanced CUTLASS utilities
* Reference code for tensor operations in host and device code
* Added HostMatrix<> for simplified matrix creation
* Examples
* Basic GEMM, tensor views, CUTLASS utilities, batched GEMM, WMMA GEMM
## [1.0.1](https://github.com/NVIDIA/cutlass/releases/tag/v1.0.1) (2018-06-11)
* Intra-threadblock reduction added for small threadblock tile sizes
* sgemm_64x128x16, sgemm_128x128x16, sgemm_128x64x16, sgemm_128x32x16, sgemm_64x64x16, sgemm_64x32x16
* igemm_32x32x128
* GEMM _K_ residue handled during prologue prior to mainloop
* Replaced Google Test copy with submodule. Use `git submodule init --recursive --update`
## [1.0.0](https://github.com/NVIDIA/cutlass/commit/2028ebe120aab22bfd0b2baf8902d4c9627eb33f) (2018-05-16)
* Substantial rewrite to accommodate new architecture
* Kernels: SGEMM, DGEMM, IGEMM, HGEMM, WMMA GEMM
* Unit and performance tests
## [0.0.1](https://github.com/NVIDIA/cutlass/commit/d08ba8ac46e2fa3f745e070c390182edb56b2e91) (2017-12-04)
* Initial release
## Copyright
Copyright (c) 2017-2020, NVIDIA CORPORATION. All rights reserved.
```
Redistribution and use in source and binary forms, with or without modification, are permitted
provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this list of
conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice, this list of
conditions and the following disclaimer in the documentation and/or other materials
provided with the distribution.
* Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
to endorse or promote products derived from this software without specific prior written
permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
```

490
CMakeLists.txt Normal file → Executable file
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@ -1,4 +1,4 @@
# Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
# Copyright (c) 2017-2020, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification, are permitted
# provided that the following conditions are met:
@ -20,48 +20,120 @@
# STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
cmake_minimum_required(VERSION 3.3.0)
cmake_minimum_required(VERSION 3.12.4 FATAL_ERROR)
set(CUTLASS_LANGUAGES CXX)
# CMake 3.9.0 has native support for CUDA without the need of the CUDA package. Use it!
if(WIN32 AND NOT ${CMAKE_VERSION} VERSION_LESS "3.9.0")
list(APPEND CUTLASS_LANGUAGES CUDA)
set(CUTLASS_NATIVE_CUDA TRUE)
macro(cutlass_add_executable)
add_executable(${ARGN})
endmacro()
if(cutlass_LOADED)
# If CUTLASS has been previously fetched and loaded, don't do it again.
return()
else()
# FindCUDA fails to detect VS 2017 due to a changed directory format of the toolkits.
# For this configuration we need CMake >= 3.9.0 to use the native CUDA support.
if (WIN32 AND MSVC_VERSION GREATER 1800)
message(FATAL_ERROR "Please upgrade CMake to version >= 3.9.0 to support Visual Studio 2017 or higher")
endif()
# Fall back to the FindCUDA version to create an executable with CUDA files
macro(cutlass_add_executable)
cuda_add_executable(${ARGN})
endmacro()
set(cutlass_LOADED ON)
set(CUTLASS_DIR ${CMAKE_CURRENT_SOURCE_DIR} CACHE PATH "CUTLASS Repository Directory")
endif()
project(CUTLASS ${CUTLASS_LANGUAGES})
message(STATUS "CMake Version: ${CMAKE_VERSION}")
project(CUTLASS VERSION 2.2.0 LANGUAGES CXX)
include(${CMAKE_CURRENT_SOURCE_DIR}/CUDA.cmake)
find_package(Doxygen QUIET)
#
# CUTLASS 2.x requires C++11
#
set(CMAKE_CXX_STANDARD 11)
set(CMAKE_CXX_STANDARD_REQUIRED ON)
set(CMAKE_CXX_EXTENSIONS OFF)
if(CUTLASS_NATIVE_CUDA)
set(CMAKE_CUDA_STANDARD 11)
set(CMAKE_CUDA_STANDARD_REQUIRED ON)
else()
list(APPEND CUTLASS_CUDA_NVCC_FLAGS --std=c++11)
endif()
if(CMAKE_INSTALL_PREFIX_INITIALIZED_TO_DEFAULT)
set(CMAKE_INSTALL_PREFIX install CACHE PATH "Default installation location." FORCE)
endif()
message(STATUS "Default Install Location: ${CMAKE_INSTALL_PREFIX}")
set(CUTLASS_ENABLE_HEADERS_ONLY OFF CACHE BOOL "Enable only the header library")
if(CUTLASS_ENABLE_HEADERS_ONLY)
set(CUTLASS_ENABLE_EXAMPLES_INIT OFF)
set(CUTLASS_ENABLE_TOOLS_INIT OFF)
else()
set(CUTLASS_ENABLE_EXAMPLES_INIT ON)
set(CUTLASS_ENABLE_TOOLS_INIT ON)
endif()
set(CUTLASS_ENABLE_EXAMPLES ${CUTLASS_ENABLE_EXAMPLES_INIT} CACHE BOOL "Enable CUTLASS Examples")
set(CUTLASS_ENABLE_TOOLS ${CUTLASS_ENABLE_TOOLS_INIT} CACHE BOOL "Enable CUTLASS Tools")
if(${CMAKE_PROJECT_NAME} STREQUAL ${PROJECT_NAME})
set(CUTLASS_ENABLE_TESTS_INIT ${CUTLASS_ENABLE_TOOLS_INIT})
else()
set(CUTLASS_ENABLE_TESTS_INIT OFF)
endif()
set(CUTLASS_ENABLE_TESTS ${CUTLASS_ENABLE_TESTS_INIT} CACHE BOOL "Enable CUTLASS Tests")
if (CUTLASS_ENABLE_TESTS)
include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/googletest.cmake)
endif()
set(CUTLASS_NVCC_ARCHS_SUPPORTED "")
if (NOT CUDA_VERSION VERSION_LESS 7.5)
list(APPEND CUTLASS_NVCC_ARCHS_SUPPORTED 53)
endif()
if (NOT CUDA_VERSION VERSION_LESS 8.0)
list(APPEND CUTLASS_NVCC_ARCHS_SUPPORTED 60 61)
endif()
if (NOT CUDA_VERSION VERSION_LESS 9.0)
list(APPEND CUTLASS_NVCC_ARCHS_SUPPORTED 70)
endif()
if (NOT CUDA_VERSION VERSION_LESS 9.2)
list(APPEND CUTLASS_NVCC_ARCHS_SUPPORTED 72)
endif()
if (NOT CUDA_VERSION VERSION_LESS 10.0)
list(APPEND CUTLASS_NVCC_ARCHS_SUPPORTED 75)
endif()
if (NOT CUDA_VERSION VERSION_LESS 11.0)
list(APPEND CUTLASS_NVCC_ARCHS_SUPPORTED 80)
endif()
set(CUTLASS_NVCC_ARCHS ${CUTLASS_NVCC_ARCHS_SUPPORTED} CACHE STRING "The SM architectures requested.")
set(CUTLASS_NVCC_ARCHS_ENABLED ${CUTLASS_NVCC_ARCHS} CACHE STRING "The SM architectures to build code for.")
# Special policy introduced in CMake 3.13
if (POLICY CMP0076)
cmake_policy(SET CMP0076 NEW)
endif()
# check if the configuration is supported
if( NOT CMAKE_SIZEOF_VOID_P EQUAL 8 )
message(FATAL_ERROR "CUTLASS requires a 64-bit compiler!")
endif()
find_package(CUDA)
find_package(Doxygen QUIET)
include(GNUInstallDirs)
link_directories(${CUDA_TOOLKIT_ROOT_DIR}/lib64/stubs)
###################################################################################################
#
# Configure CMake variables
#
###################################################################################################
message(STATUS "CUDA Compilation Architectures: ${CUTLASS_NVCC_ARCHS_ENABLED}")
# By default we want to build in Release mode to ensure that we're getting best performance
if (NOT (CMAKE_BUILD_TYPE OR CONFIGURATION_TYPES))
# By default we want to build in Release mode to ensure that we're getting best performance.
set(CMAKE_BUILD_TYPE Release CACHE STRING "Choose build level" FORCE)
# We do support Debug or Release builds
set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Debug" "Release")
set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Debug" "RelWithDebInfo" "Release")
endif()
set(CMAKE_POSITION_INDEPENDENT_CODE ON)
set(CUTLASS_LIBRARY_DEBUG_POSTFIX ".debug" CACHE STRING "Default postfix value for debug libraries")
if(WIN32)
# On Windows we link against the shared (DLL) runtime. Change gtest settings to match this.
set(gtest_force_shared_crt ON CACHE BOOL "Use shared (DLL) run-time lib even when Google Test is built as static lib" FORCE)
@ -69,96 +141,281 @@ endif()
if (WIN32)
# Enable more warnings and treat as errors
string(APPEND NVCC_FLAGS " -Xcompiler /W3 -Xcompiler /WX")
list(APPEND CUTLASS_CUDA_NVCC_FLAGS -Xcompiler=/W3 -Xcompiler=/WX)
# Disable warning on Unicode characters
list(APPEND CUTLASS_CUDA_NVCC_FLAGS -Xcompiler=/wd4819)
# Disable excess x86 floating point precision that can lead to results being labeled incorrectly
string(APPEND NVCC_FLAGS " -Xcompiler /fp:strict")
# Verbose option
if (${CUTLASS_NVCC_VERBOSE})
string(APPEND NVCC_FLAGS " -v")
endif()
list(APPEND CUTLASS_CUDA_NVCC_FLAGS -Xcompiler=/fp:strict)
endif(WIN32)
# Configure CUDA options
set(CUTLASS_NVCC_ARCHS "50;60;61;70" CACHE STRING "The SM architectures to build code for.")
set(CUTLASS_NVCC_KEEP OFF CACHE BOOL "Keep intermediate files generated by NVCC.")
foreach(ARCH ${CUTLASS_NVCC_ARCHS})
string(APPEND NVCC_FLAGS " -gencode arch=compute_${ARCH},code=sm_${ARCH}")
endforeach()
if (CUTLASS_NVCC_KEEP)
string(APPEND NVCC_FLAGS " -keep")
if (${CUTLASS_NVCC_VERBOSE})
list(APPEND CUTLASS_CUDA_NVCC_FLAGS -v)
endif()
if (WIN32 AND CUTLASS_NATIVE_CUDA)
string(APPEND NVCC_FLAGS_RELEASE " -lineinfo")
set(CUTLASS_NVCC_EMBED_CUBIN ON CACHE BOOL "Embed compiled CUDA kernel binaries into executables.")
set(CUTLASS_NVCC_EMBED_PTX ON CACHE BOOL "Embed compiled PTX into executables.")
set(CUTLASS_NVCC_KEEP OFF CACHE BOOL "Keep intermediate files generated by NVCC.")
set(CUTLASS_ENABLE_F16C OFF CACHE BOOL "Enable F16C x86 extensions in host code.")
#
# CUTLASS generator cmake configuration
#
set(CUTLASS_LIBRARY_OPERATIONS "all" CACHE STRING "Comma delimited list of operation name filters. Default '' means all operations are enabled.")
set(CUTLASS_LIBRARY_KERNELS "" CACHE STRING "Comma delimited list of kernel name filters. If unspecified, only the largest tile size is enabled. If 'all' is specified, all kernels are enabled.")
# Test Levels L0, L1, L2
set(CUTLASS_TEST_LEVEL "0" CACHE STRING "Level of tests to compile.")
set_property(CACHE CUTLASS_TEST_LEVEL PROPERTY STRINGS 0 1 2)
list(APPEND CUTLASS_CUDA_NVCC_FLAGS -DCUTLASS_TEST_LEVEL=${CUTLASS_TEST_LEVEL})
#
# CUDA 10.1 introduces "mma" in PTX performing collective matrix multiply operations.
#
if (CUDA_VERSION VERSION_LESS 10.1)
set(CUTLASS_ENABLE_TENSOR_CORE_MMA_DEFAULT OFF)
else()
string(APPEND NVCC_FLAGS " -lineinfo")
set(CUTLASS_ENABLE_TENSOR_CORE_MMA_DEFAULT ON)
endif()
if (UNIX)
string(APPEND NVCC_FLAGS " -Xcompiler -Wconversion")
set(CUTLASS_ENABLE_TENSOR_CORE_MMA ${CUTLASS_ENABLE_TENSOR_CORE_MMA_DEFAULT} CACHE BOOL
"Enable PTX mma instruction for collective matrix multiply operations.")
#
# NOTE: running with asan and CUDA requires the following environment variable:
#
# ASAN_OPTIONS=protect_shadow_gap=0:replace_intrin=0:detect_leaks=0
#
# without the above environment setting, an error like the following may be generated:
#
# *** Error: Could not detect active GPU device ID [out of memory]
# ...
# ==9149==ERROR: LeakSanitizer: detected memory leaks
# ...
#
if(ENABLE_ASAN) # https://github.com/google/sanitizers/wiki/AddressSanitizer
list(APPEND CUTLASS_CUDA_NVCC_FLAGS --compiler-options=-fsanitize=address --compiler-options=-fno-omit-frame-pointer)
string(APPEND CMAKE_EXE_LINKER_FLAGS " -fsanitize=address")
endif()
string(APPEND NVCC_FLAGS_DEBUG " -g")
string(APPEND NVCC_FLAGS_RELEASE " -O3")
###################################################################################################
#
# Configure CUDA build options
#
###################################################################################################
# define NDEBUG for release mode to disable assertions
string(APPEND NVCC_FLAGS_RELEASE " -DNDEBUG")
if (CUTLASS_NATIVE_CUDA)
set(CMAKE_CUDA_FLAGS "${NVCC_FLAGS}")
set(CMAKE_CUDA_FLAGS_DEBUG "${NVCC_FLAGS_DEBUG}")
set(CMAKE_CUDA_FLAGS_RELEASE "${NVCC_FLAGS_RELEASE}")
else()
set(CUDA_NVCC_FLAGS ${NVCC_FLAGS})
set(CUDA_NVCC_FLAGS_DEBUG ${NVCC_FLAGS_DEBUG})
set(CUDA_NVCC_FLAGS_RELEASE ${NVCC_FLAGS_RELEASE})
if(CUTLASS_NVCC_EMBED_PTX)
list(APPEND CUTLASS_CUDA_CLANG_FLAGS --cuda-include-ptx=all)
endif()
if (CUTLASS_ENABLE_TENSOR_CORE_MMA)
list(APPEND CUTLASS_CUDA_FLAGS -DCUTLASS_ENABLE_TENSOR_CORE_MMA=1)
endif()
if (NOT MSVC AND CUTLASS_NVCC_KEEP)
# MSVC flow handles caching already, but for other generators we handle it here.
set(CUTLASS_NVCC_KEEP_DIR ${CMAKE_CURRENT_BINARY_DIR}/tmp CACHE PATH "Location to store NVCC scratch files")
file(MAKE_DIRECTORY ${CUTLASS_NVCC_KEEP_DIR})
list(APPEND CUTLASS_CUDA_NVCC_FLAGS --keep) # --keep-dir may not work with nvcc for some directories.
list(APPEND CUTLASS_CUDA_CLANG_FLAGS -save-temps=${CUTLASS_NVCC_KEEP_DIR})
endif()
if (CUTLASS_ENABLE_F16C AND NOT CMAKE_CROSSCOMPILING)
list(APPEND CUTLASS_CUDA_FLAGS -DCUTLASS_ENABLE_F16C=1)
if ((CMAKE_CXX_COMPILER_ID MATCHES "GNU") OR (CMAKE_CXX_COMPILER_ID MATCHES "Clang"))
list(APPEND CUTLASS_CUDA_NVCC_FLAGS -Xcompiler=-mf16c)
elseif((CMAKE_CXX_COMPILER_ID MATCHES "MSVC"))
list(APPEND CUTLASS_CUDA_NVCC_FLAGS -Xcompiler=/arch:AVX2)
endif()
endif()
list(APPEND CUTLASS_CUDA_NVCC_FLAGS $<$<BOOL:${UNIX}>:-Xcompiler=-Wconversion>)
list(APPEND CUTLASS_CUDA_NVCC_FLAGS $<$<BOOL:${UNIX}>:-Xcompiler=-fno-strict-aliasing>)
# Don't leak lineinfo in release builds
if (NOT CMAKE_BUILD_TYPE MATCHES "Release")
list(APPEND CUTLASS_CUDA_CLANG_FLAGS -gmlt)
list(APPEND CUTLASS_CUDA_NVCC_FLAGS -lineinfo)
endif()
if(CUDA_COMPILER MATCHES "[Cc]lang")
if( NOT CMAKE_CXX_COMPILER_ID MATCHES "Clang" )
message(FATAL_ERROR "Clang CUDA compilation requires Clang CXX compilation. Currently CMAKE_CXX_COMPILER is ${CMAKE_CXX_COMPILER_ID}" )
endif()
if (CMAKE_CXX_COMPILER_VERSION VERSION_LESS 7.0)
message(FATAL_ERROR "Clang 7.0+ required for GPU compilation")
endif()
list(APPEND CUTLASS_CUDA_CLANG_FLAGS --cuda-path=${CUDA_TOOLKIT_ROOT_DIR})
list(APPEND CUTLASS_CUDA_CLANG_FLAGS -mllvm -pragma-unroll-threshold=100000)
list(APPEND CUTLASS_CUDA_CLANG_FLAGS -mllvm -unroll-threshold=5000)
list(APPEND CUTLASS_CUDA_CLANG_FLAGS -Wno-unused-command-line-argument)
string(REPLACE "." ";" CUDA_VERSION_PARTS ${CMAKE_CUDA_COMPILER_VERSION})
list(GET CUDA_VERSION_PARTS 0 CUDA_VERSION_MAJOR)
list(GET CUDA_VERSION_PARTS 1 CUDA_VERSION_MINOR)
list(APPEND CUTLASS_CUDA_CLANG_FLAGS -D__CUDACC_VER_MAJOR__=${CUDA_VERSION_MAJOR} -D__CUDACC_VER_MINOR__=${CUDA_VERSION_MINOR})
# needed for libcublasLt.so in case it's installed in the same location as libcudart.so
# dynamic linker can find it if linker sets RPATH (forced by --disable-new-tags)
# Otherwise linker uses RUNPATH and that does not propagate to loaded libs.
list(APPEND CUTLASS_CUDA_CLANG_FLAGS -Wl,--disable-new-dtags)
link_libraries(nvidia::cudart)
endif()
function(cutlass_apply_cuda_gencode_flags TARGET)
set(NVCC_FLAGS)
set(CLANG_FLAGS)
foreach(ARCH ${CUTLASS_NVCC_ARCHS_ENABLED})
list(APPEND CLANG_FLAGS --cuda-gpu-arch=sm_${ARCH})
set(CODES)
if(CUTLASS_NVCC_EMBED_CUBIN)
list(APPEND CODES sm_${ARCH})
endif()
if(CUTLASS_NVCC_EMBED_PTX)
list(APPEND CODES compute_${ARCH})
endif()
list(JOIN CODES "," CODES_STR)
list(APPEND NVCC_FLAGS -gencode=arch=compute_${ARCH},code=[${CODES_STR}])
endforeach()
if (CUDA_COMPILER MATCHES "[Cc]lang")
target_compile_options(
${TARGET}
PRIVATE
$<$<COMPILE_LANGUAGE:CXX>:${CLANG_FLAGS}>
)
else()
target_compile_options(
${TARGET}
PRIVATE
$<$<COMPILE_LANGUAGE:CUDA>:${NVCC_FLAGS}>
)
endif()
endfunction()
function(cutlass_apply_standard_compile_options TARGET)
if(CUDA_COMPILER MATCHES "[Cc]lang")
set(CUDA_COMPILE_LANGUAGE CXX)
set(_FLAGS ${CUTLASS_CUDA_FLAGS} ${CUTLASS_CUDA_CLANG_FLAGS})
set(_FLAGS_RELEASE ${CUTLASS_CUDA_FLAGS_RELEASE} ${CUTLASS_CUDA_CLANG_FLAGS_RELEASE})
set(_FLAGS_RELWITHDEBINFO ${CUTLASS_CUDA_FLAGS_RELWITHDEBINFO} ${CUTLASS_CUDA_CLANG_FLAGS_RELWITHDEBINFO})
set(_FLAGS_DEBUG ${CUTLASS_CUDA_FLAGS_DEBUG} ${CUTLASS_CUDA_CLANG_FLAGS_DEBUG})
else()
set(CUDA_COMPILE_LANGUAGE CUDA)
set(_FLAGS ${CUTLASS_CUDA_FLAGS} ${CUTLASS_CUDA_NVCC_FLAGS})
set(_FLAGS_RELEASE ${CUTLASS_CUDA_FLAGS_RELEASE} ${CUTLASS_CUDA_NVCC_FLAGS_RELEASE})
set(_FLAGS_RELWITHDEBINFO ${CUTLASS_CUDA_FLAGS_RELWITHDEBINFO} ${CUTLASS_CUDA_NVCC_FLAGS_RELWITHDEBINFO})
set(_FLAGS_DEBUG ${CUTLASS_CUDA_FLAGS_DEBUG} ${CUTLASS_CUDA_NVCC_FLAGS_DEBUG})
endif()
target_compile_options(
${TARGET}
PRIVATE
$<$<COMPILE_LANGUAGE:${CUDA_COMPILE_LANGUAGE}>:${_FLAGS}>
$<$<COMPILE_LANGUAGE:${CUDA_COMPILE_LANGUAGE}>:$<$<CONFIG:RELEASE>:${_FLAGS_RELEASE}>>
$<$<COMPILE_LANGUAGE:${CUDA_COMPILE_LANGUAGE}>:$<$<CONFIG:RELWITHDEBINFO>:${_FLAGS_RELWITHDEBINFO}>>
$<$<COMPILE_LANGUAGE:${CUDA_COMPILE_LANGUAGE}>:$<$<CONFIG:DEBUG>:${_FLAGS_DEBUG}>>
)
endfunction()
#
# The following items should eventually be pushed into cutlass/CMakeLists.txt
#
# GLOB for CUTLASS header files. Should we use a static list instead?
file(GLOB CUTLASS_GEMM RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} cutlass/gemm/*.h)
file(GLOB CUTLASS_UTIL RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} cutlass/util/*.h)
file(GLOB CUTLASS_DEVICE RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} cutlass/device/*.h)
file(GLOB CUTLASS_CORE RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} cutlass/*.h)
file(GLOB_RECURSE CUTLASS_INCLUDE RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} include/cutlass/*.h)
file(GLOB_RECURSE CUTLASS_CUTLASS RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}/include include/cutlass/*.h)
file(GLOB_RECURSE CUTLASS_NVRTC RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}/test test/unit/nvrtc/kernel/*.h)
source_group("cutlass\\gemm" FILES ${CUTLASS_GEMM})
source_group("cutlass\\util" FILES ${CUTLASS_UTIL})
source_group("cutlass\\device" FILES ${CUTLASS_DEVICE})
source_group("cutlass" FILES ${CUTLASS_CORE})
###################################################################################################
#
# Define build targets
#
###################################################################################################
source_group(TREE ${CMAKE_CURRENT_SOURCE_DIR}/include REGULAR_EXPRESSION ".*\.h")
add_library(CUTLASS INTERFACE)
include_directories("${CMAKE_CURRENT_SOURCE_DIR}")
target_sources(CUTLASS INTERFACE
${CUTLASS_GEMM}
${CUTLASS_UTIL}
${CUTLASS_DEVICE}
${CUTLASS_CORE}
)
add_library(nvidia::cutlass::cutlass ALIAS CUTLASS)
set_target_properties(CUTLASS PROPERTIES EXPORT_NAME cutlass)
target_include_directories(CUTLASS INTERFACE ${CMAKE_CURRENT_SOURCE_DIR})
set(CUTLASS_INCLUDE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/include CACHE PATH "CUTLASS Header Library")
set(CUTLASS_GENERATOR_DIR ${CMAKE_CURRENT_SOURCE_DIR}/tools/library/)
# The following utility directory is needed even if the tools build is disabled, so it exists here.
set(CUTLASS_TOOLS_UTIL_INCLUDE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/tools/util/include CACHE INTERNAL "")
include_directories(${CUTLASS_INCLUDE_DIR})
target_compile_features(CUTLASS INTERFACE cxx_std_11)
if (NOT DEFINED CUTLASS_REVISION)
find_package(Git QUIET)
execute_process(
COMMAND ${GIT_EXECUTABLE} rev-parse --short HEAD
RESULT_VARIABLE CUTLASS_REVISION_RESULT
OUTPUT_VARIABLE CUTLASS_REVISION
OUTPUT_STRIP_TRAILING_WHITESPACE
)
if (CUTLASS_REVISION_RESULT)
message(STATUS "CUTLASS Revision: Unable to detect, Git returned code ${CUTLASS_REVISION_RESULT}.")
else()
message(STATUS "CUTLASS Revision: ${CUTLASS_REVISION}")
endif()
endif()
configure_file(
${CMAKE_CURRENT_SOURCE_DIR}/cmake/version.h.in
${CMAKE_CURRENT_BINARY_DIR}/include/cutlass/version.h
@ONLY)
target_include_directories(
CUTLASS
INTERFACE
$<INSTALL_INTERFACE:include>
$<BUILD_INTERFACE:${CUTLASS_INCLUDE_DIR}>
$<BUILD_INTERFACE:${CMAKE_CURRENT_BINARY_DIR}/include>
$<BUILD_INTERFACE:${CUDA_TOOLKIT_ROOT_DIR}/include>
)
install(
DIRECTORY
${CUTLASS_INCLUDE_DIR}/
${CMAKE_CURRENT_BINARY_DIR}/include/
DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}
)
install(
TARGETS CUTLASS
EXPORT NvidiaCutlass
PUBLIC_HEADER DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}
)
################################################################################
# Create a custom target to ensure that the CUTLASS sources are visible in an IDE
add_custom_target(cutlass_ide SOURCES
${CUTLASS_GEMM}
${CUTLASS_UTIL}
${CUTLASS_DEVICE}
${CUTLASS_CORE}
)
# Doxygen is available. Generate documentation
if (DOXYGEN_FOUND)
# DOT is available. Enable graph generation in the documentation
if (DOXYGEN_DOT_EXECUTABLE)
set(CUTLASS_ENABLE_DOXYGEN_DOT ON CACHE BOOL "Use dot to generate graphs in the doxygen documentation.")
set(CUTLASS_ENABLE_DOXYGEN_DOT ON CACHE BOOL "Use dot to generate graphs in the doxygen documentation.")
else()
set(CUTLASS_ENABLE_DOXYGEN_DOT OFF CACHE BOOL "Use dot to generate graphs in the doxygen documentation." FORCE)
set(CUTLASS_ENABLE_DOXYGEN_DOT OFF CACHE BOOL "Use dot to generate graphs in the doxygen documentation." FORCE)
endif()
if (CUTLASS_ENABLE_DOXYGEN_DOT)
@ -177,6 +434,55 @@ if (DOXYGEN_FOUND)
)
endif()
if(NOT WIN32)
# Add common library search paths so executables and libraries can load and run
# without LD_LIBRARY_PATH being set.
link_libraries(
"-Wl,-rpath,'$ORIGIN'"
"-Wl,-rpath,'$ORIGIN/../lib64'"
"-Wl,-rpath,'$ORIGIN/../lib'"
"-Wl,-rpath,'${CUDA_TOOLKIT_ROOT_DIR}/lib64'"
"-Wl,-rpath,'${CUDA_TOOLKIT_ROOT_DIR}/lib'"
)
endif()
#add_subdirectory(examples/gemm)
add_subdirectory(tools)
################################################################################
include(${CMAKE_CURRENT_SOURCE_DIR}/cuBLAS.cmake)
if (CUTLASS_ENABLE_CUBLAS)
target_compile_definitions(CUTLASS INTERFACE CUTLASS_ENABLE_CUBLAS=1)
endif()
################################################################################
if(CUTLASS_ENABLE_TOOLS)
add_subdirectory(tools)
endif()
if(CUTLASS_ENABLE_EXAMPLES)
add_subdirectory(examples)
endif()
if(CUTLASS_ENABLE_TESTS)
include(CTest)
enable_testing()
add_subdirectory(test)
endif()
################################################################################
install(
FILES ${CMAKE_CURRENT_SOURCE_DIR}/cmake/NvidiaCutlassConfig.cmake
DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/
)
install(
EXPORT NvidiaCutlass
NAMESPACE nvidia::cutlass::
DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/
FILE NvidiaCutlassTargets.cmake
)
################################################################################
include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/NvidiaCutlassPackageConfig.cmake)

57
CONTRIBUTORS.md Normal file
View File

@ -0,0 +1,57 @@
![ALT](/media/images/gemm-hierarchy-with-epilogue-no-labels.png "CUTLASS")
[README](/README.md#documentation) > **Contributors**
# CUTLASS Developers and Contributors
This is the official list of CUTLASS developers and contributors.
## DEVELOPERS
Andrew Kerr
Haicheng Wu
Manish Gupta
Dustyn Blasig
Pradeep Ramani
Naila Farooqui
Piotr Majcher
Paul Springer
Jin Wang
Scott Yokim
Markus Hohnerbach
Aditya Atluri
David Tanner
## CONTRIBUTORS
Timothy Costa
Julien Demouth
Brian Fahs
Michael Goldfarb
Mostafa Hagog
Fei Hu
Alan Kaatz
Tina Li
Timmy Liu
Duane Merrill
Kevin Siu
Markus Tavenrath
John Tran
Vicki Wang
Junkai Wu
Fung Xie
Albert Xu
Jack Yang
Xiuxia Zhang
Nick Zhao
## ACKNOWLEDGEMENTS
Girish Bharambe
Cris Cecka
Luke Durant
Olivier Giroux
Stephen Jones
Rishkul Kulkarni
Bryce Lelbach
Joel McCormack
Kyrylo Perelygin

349
CUDA.cmake Normal file
View File

@ -0,0 +1,349 @@
# Copyright (c) 2017-2020, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification, are permitted
# provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright notice, this list of
# conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright notice, this list of
# conditions and the following disclaimer in the documentation and/or other materials
# provided with the distribution.
# * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
# to endorse or promote products derived from this software without specific prior written
# permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
# IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
# FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
# STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
if(CUDA_COMPILER MATCHES "[Cc]lang")
set(CUTLASS_NATIVE_CUDA_INIT ON)
elseif(CMAKE_VERSION VERSION_LESS 3.12.4)
set(CUTLASS_NATIVE_CUDA_INIT OFF)
else()
set(CUTLASS_NATIVE_CUDA_INIT ON)
endif()
set(CUTLASS_NATIVE_CUDA ${CUTLASS_NATIVE_CUDA_INIT} CACHE BOOL "Utilize the CMake native CUDA flow")
if(NOT DEFINED ENV{CUDACXX} AND NOT DEFINED ENV{CUDA_BIN_PATH} AND DEFINED ENV{CUDA_PATH})
# For backward compatibility, allow use of CUDA_PATH.
set(ENV{CUDACXX} $ENV{CUDA_PATH}/bin/nvcc)
endif()
if(CUTLASS_NATIVE_CUDA)
enable_language(CUDA)
if(NOT CUDA_VERSION)
set(CUDA_VERSION ${CMAKE_CUDA_COMPILER_VERSION})
endif()
if(NOT CUDA_TOOLKIT_ROOT_DIR)
get_filename_component(CUDA_TOOLKIT_ROOT_DIR "${CMAKE_CUDA_COMPILER}/../.." ABSOLUTE)
endif()
else()
find_package(CUDA REQUIRED)
# We workaround missing variables with the native flow by also finding the CUDA toolkit the old way.
if(NOT CMAKE_CUDA_COMPILER_VERSION)
set(CMAKE_CUDA_COMPILER_VERSION ${CUDA_VERSION})
endif()
endif()
if (CUDA_VERSION VERSION_LESS 9.2)
message(FATAL_ERROR "CUDA 9.2+ Required, Found ${CUDA_VERSION}.")
endif()
if(NOT CUTLASS_NATIVE_CUDA OR CUDA_COMPILER MATCHES "[Cc]lang")
set(CMAKE_CUDA_COMPILER ${CUDA_TOOLKIT_ROOT_DIR}/bin/nvcc)
message(STATUS "CUDA Compiler: ${CMAKE_CUDA_COMPILER}")
endif()
find_library(
CUDART_LIBRARY cudart
PATHS
${CUDA_TOOLKIT_ROOT_DIR}
PATH_SUFFIXES
lib/x64
lib64
lib
NO_DEFAULT_PATH
# We aren't going to search any system paths. We want to find the runtime
# in the CUDA toolkit we're building against.
)
if(NOT TARGET cudart AND CUDART_LIBRARY)
message(STATUS "CUDART: ${CUDART_LIBRARY}")
if(WIN32)
add_library(cudart STATIC IMPORTED GLOBAL)
# Even though we're linking against a .dll, in Windows you statically link against
# the .lib file found under lib/x64. The .dll will be loaded at runtime automatically
# from the PATH search.
else()
add_library(cudart SHARED IMPORTED GLOBAL)
endif()
add_library(nvidia::cudart ALIAS cudart)
set_property(
TARGET cudart
PROPERTY IMPORTED_LOCATION
${CUDART_LIBRARY}
)
elseif(TARGET cudart)
message(STATUS "CUDART: Already Found")
else()
message(STATUS "CUDART: Not Found")
endif()
find_library(
CUDA_DRIVER_LIBRARY cuda
PATHS
${CUDA_TOOLKIT_ROOT_DIR}
PATH_SUFFIXES
lib/x64
lib64
lib
lib64/stubs
lib/stubs
NO_DEFAULT_PATH
# We aren't going to search any system paths. We want to find the runtime
# in the CUDA toolkit we're building against.
)
if(NOT TARGET cuda_driver AND CUDA_DRIVER_LIBRARY)
message(STATUS "CUDA Driver: ${CUDA_DRIVER_LIBRARY}")
if(WIN32)
add_library(cuda_driver STATIC IMPORTED GLOBAL)
# Even though we're linking against a .dll, in Windows you statically link against
# the .lib file found under lib/x64. The .dll will be loaded at runtime automatically
# from the PATH search.
else()
add_library(cuda_driver SHARED IMPORTED GLOBAL)
endif()
add_library(nvidia::cuda_driver ALIAS cuda_driver)
set_property(
TARGET cuda_driver
PROPERTY IMPORTED_LOCATION
${CUDA_DRIVER_LIBRARY}
)
elseif(TARGET cuda_driver)
message(STATUS "CUDA Driver: Already Found")
else()
message(STATUS "CUDA Driver: Not Found")
endif()
find_library(
NVRTC_LIBRARY nvrtc
PATHS
${CUDA_TOOLKIT_ROOT_DIR}
PATH_SUFFIXES
lib/x64
lib64
lib
NO_DEFAULT_PATH
# We aren't going to search any system paths. We want to find the runtime
# in the CUDA toolkit we're building against.
)
if(NOT TARGET nvrtc AND NVRTC_LIBRARY)
message(STATUS "NVRTC: ${NVRTC_LIBRARY}")
if(WIN32)
add_library(nvrtc STATIC IMPORTED GLOBAL)
# Even though we're linking against a .dll, in Windows you statically link against
# the .lib file found under lib/x64. The .dll will be loaded at runtime automatically
# from the PATH search.
else()
add_library(nvrtc SHARED IMPORTED GLOBAL)
endif()
add_library(nvidia::nvrtc ALIAS nvrtc)
set_property(
TARGET nvrtc
PROPERTY IMPORTED_LOCATION
${NVRTC_LIBRARY}
)
elseif(TARGET nvrtc)
message(STATUS "NVRTC: Already Found")
else()
message(STATUS "NVRTC: Not Found")
endif()
include_directories(SYSTEM ${CUDA_INCLUDE_DIRS})
# Some platforms (e.g. Visual Studio) don't add the CUDA include directories to the system include
# paths by default, so we add it explicitly here.
function(cutlass_correct_source_file_language_property)
if(CUDA_COMPILER MATCHES "clang")
foreach(File ${ARGN})
if(File MATCHES ".*\.cu$")
set_source_files_properties(${File} PROPERTIES LANGUAGE CXX)
endif()
endforeach()
endif()
endfunction()
set(CUTLASS_UNITY_BUILD_ENABLED OFF CACHE BOOL "Enable combined source compilation")
set(CUTLASS_UNITY_BUILD_BATCH_SIZE 16 CACHE STRING "Batch size for unified source files")
function(cutlass_unify_source_files TARGET_ARGS_VAR)
set(options)
set(oneValueArgs BATCH_SOURCES BATCH_SIZE)
set(multiValueArgs)
cmake_parse_arguments(_ "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
if (NOT DEFINED TARGET_ARGS_VAR)
message(FATAL_ERROR "TARGET_ARGS_VAR parameter is required")
endif()
if (__BATCH_SOURCES AND NOT DEFINED __BATCH_SIZE)
set(__BATCH_SIZE ${CUTLASS_UNITY_BUILD_BATCH_SIZE})
endif()
if (CUTLASS_UNITY_BUILD_ENABLED AND DEFINED __BATCH_SIZE AND __BATCH_SIZE GREATER 1)
set(CUDA_FILE_ARGS)
set(TARGET_SOURCE_ARGS)
foreach(ARG ${__UNPARSED_ARGUMENTS})
if(${ARG} MATCHES ".*\.cu$")
list(APPEND CUDA_FILE_ARGS ${ARG})
else()
list(APPEND TARGET_SOURCE_ARGS ${ARG})
endif()
endforeach()
list(LENGTH CUDA_FILE_ARGS NUM_CUDA_FILE_ARGS)
while(NUM_CUDA_FILE_ARGS GREATER 0)
list(SUBLIST CUDA_FILE_ARGS 0 ${__BATCH_SIZE} CUDA_FILE_BATCH)
string(SHA256 CUDA_FILE_BATCH_HASH "${CUDA_FILE_BATCH}")
string(SUBSTRING ${CUDA_FILE_BATCH_HASH} 0 12 CUDA_FILE_BATCH_HASH)
set(BATCH_FILE ${CMAKE_CURRENT_BINARY_DIR}/${NAME}.unity.${CUDA_FILE_BATCH_HASH}.cu)
message(STATUS "Generating ${BATCH_FILE}")
file(WRITE ${BATCH_FILE} "// Unity File - Auto Generated!\n")
foreach(CUDA_FILE ${CUDA_FILE_BATCH})
get_filename_component(CUDA_FILE_ABS_PATH ${CUDA_FILE} ABSOLUTE)
file(APPEND ${BATCH_FILE} "#include \"${CUDA_FILE_ABS_PATH}\"\n")
endforeach()
list(APPEND TARGET_SOURCE_ARGS ${BATCH_FILE})
if (NUM_CUDA_FILE_ARGS LESS_EQUAL __BATCH_SIZE)
break()
endif()
list(SUBLIST CUDA_FILE_ARGS ${__BATCH_SIZE} -1 CUDA_FILE_ARGS)
list(LENGTH CUDA_FILE_ARGS NUM_CUDA_FILE_ARGS)
endwhile()
else()
set(TARGET_SOURCE_ARGS ${__UNPARSED_ARGUMENTS})
endif()
set(${TARGET_ARGS_VAR} ${TARGET_SOURCE_ARGS} PARENT_SCOPE)
endfunction()
function(cutlass_add_library NAME)
set(options)
set(oneValueArgs EXPORT_NAME)
set(multiValueArgs)
cmake_parse_arguments(_ "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
cutlass_unify_source_files(TARGET_SOURCE_ARGS ${__UNPARSED_ARGUMENTS})
if(CUTLASS_NATIVE_CUDA OR CUDA_COMPILER MATCHES "clang")
cutlass_correct_source_file_language_property(${TARGET_SOURCE_ARGS})
add_library(${NAME} ${TARGET_SOURCE_ARGS})
else()
set(CUDA_LINK_LIBRARIES_KEYWORD PRIVATE)
cuda_add_library(${NAME} ${TARGET_SOURCE_ARGS})
endif()
cutlass_apply_standard_compile_options(${NAME})
cutlass_apply_cuda_gencode_flags(${NAME})
target_compile_features(
${NAME}
INTERFACE
cxx_std_11
)
if(__EXPORT_NAME)
add_library(nvidia::cutlass::${__EXPORT_NAME} ALIAS ${NAME})
set_target_properties(${NAME} PROPERTIES EXPORT_NAME ${__EXPORT_NAME})
endif()
endfunction()
function(cutlass_add_executable NAME)
set(options)
set(oneValueArgs)
set(multiValueArgs)
cmake_parse_arguments(_ "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
cutlass_unify_source_files(TARGET_SOURCE_ARGS ${__UNPARSED_ARGUMENTS})
if(CUTLASS_NATIVE_CUDA OR CUDA_COMPILER MATCHES "clang")
cutlass_correct_source_file_language_property(${TARGET_SOURCE_ARGS})
add_executable(${NAME} ${TARGET_SOURCE_ARGS})
else()
set(CUDA_LINK_LIBRARIES_KEYWORD PRIVATE)
cuda_add_executable(${NAME} ${TARGET_SOURCE_ARGS})
endif()
cutlass_apply_standard_compile_options(${NAME})
cutlass_apply_cuda_gencode_flags(${NAME})
target_compile_features(
${NAME}
INTERFACE
cxx_std_11
)
endfunction()
function(cutlass_target_sources NAME)
set(options)
set(oneValueArgs)
set(multiValueArgs)
cmake_parse_arguments(_ "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
cutlass_unify_source_files(TARGET_SOURCE_ARGS ${__UNPARSED_ARGUMENTS})
cutlass_correct_source_file_language_property(${TARGET_SOURCE_ARGS})
target_sources(${NAME} ${TARGET_SOURCE_ARGS})
endfunction()

View File

@ -32,7 +32,7 @@ DOXYFILE_ENCODING = UTF-8
# title of most generated pages and in a few other places.
# The default value is: My Project.
PROJECT_NAME = "Cutlass"
PROJECT_NAME = "CUTLASS"
# The PROJECT_NUMBER tag can be used to enter a project or revision number. This
# could be handy for archiving the generated documentation or if some version
@ -51,14 +51,14 @@ PROJECT_BRIEF = "CUDA Templates for Linear Algebra Subroutines and Solv
# and the maximum width should not exceed 200 pixels. Doxygen will copy the logo
# to the output directory.
PROJECT_LOGO =
PROJECT_LOGO = media/images/cutlass-logo-small.png
# The OUTPUT_DIRECTORY tag is used to specify the (relative or absolute) path
# into which the generated documentation will be written. If a relative path is
# entered, it will be relative to the location where doxygen was started. If
# left blank the current directory will be used.
OUTPUT_DIRECTORY = docs
OUTPUT_DIRECTORY = doxygen
# If the CREATE_SUBDIRS tag is set to YES, then doxygen will create 4096 sub-
# directories (in 2 levels) under the output directory of each output format and
@ -206,7 +206,7 @@ SEPARATE_MEMBER_PAGES = NO
# uses this value to replace tabs by spaces in code fragments.
# Minimum value: 1, maximum value: 16, default value: 4.
TAB_SIZE = 4
TAB_SIZE = 2
# This tag can be used to specify a number of aliases that act as commands in
# the documentation. An alias has the form:
@ -297,7 +297,7 @@ AUTOLINK_SUPPORT = YES
# diagrams that involve STL classes more complete and accurate.
# The default value is: NO.
BUILTIN_STL_SUPPORT = NO
BUILTIN_STL_SUPPORT = YES
# If you use Microsoft's C++/CLI language, you should set this option to YES to
# enable parsing support.
@ -734,7 +734,9 @@ WARN_LOGFILE =
# spaces.
# Note: If this tag is empty the current directory is searched.
INPUT = cutlass
INPUT = include/cutlass tools/util/include/cutlass/ tools/library/include/cutlass/
INPUT += media/docs/doxygen_mainpage.md
# This tag can be used to specify the character encoding of the source files
# that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses
@ -870,7 +872,7 @@ FILTER_SOURCE_PATTERNS =
# (index.html). This can be useful if you have a project on for instance GitHub
# and want to reuse the introduction page also for the doxygen output.
USE_MDFILE_AS_MAINPAGE =
USE_MDFILE_AS_MAINPAGE = media/docs/doxygen_mainpage.md
#---------------------------------------------------------------------------
# Configuration options related to source browsing
@ -999,7 +1001,7 @@ GENERATE_HTML = YES
# The default directory is: html.
# This tag requires that the tag GENERATE_HTML is set to YES.
HTML_OUTPUT = generated-html
HTML_OUTPUT =
# The HTML_FILE_EXTENSION tag can be used to specify the file extension for each
# generated HTML page (for example: .htm, .php, .asp).
@ -1080,7 +1082,7 @@ HTML_EXTRA_FILES =
# Minimum value: 0, maximum value: 359, default value: 220.
# This tag requires that the tag GENERATE_HTML is set to YES.
HTML_COLORSTYLE_HUE = 82
HTML_COLORSTYLE_HUE = 100
# The HTML_COLORSTYLE_SAT tag controls the purity (or saturation) of the colors
# in the HTML output. For a value of 0 the output will use grayscales only. A
@ -1088,7 +1090,7 @@ HTML_COLORSTYLE_HUE = 82
# Minimum value: 0, maximum value: 255, default value: 100.
# This tag requires that the tag GENERATE_HTML is set to YES.
HTML_COLORSTYLE_SAT = 100
HTML_COLORSTYLE_SAT = 50
# The HTML_COLORSTYLE_GAMMA tag controls the gamma correction applied to the
# luminance component of the colors in the HTML output. Values below 100
@ -1107,7 +1109,7 @@ HTML_COLORSTYLE_GAMMA = 80
# The default value is: YES.
# This tag requires that the tag GENERATE_HTML is set to YES.
HTML_TIMESTAMP = YES
HTML_TIMESTAMP = NO
# If the HTML_DYNAMIC_SECTIONS tag is set to YES then the generated HTML
# documentation will contain sections that can be hidden and shown after the

View File

@ -1,23 +0,0 @@
Copyright (c) 2017, NVIDIA CORPORATION. All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
* Neither the name of the NVIDIA CORPORATION nor the
names of its contributors may be used to endorse or promote products
derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

23
LICENSE.txt Normal file
View File

@ -0,0 +1,23 @@
Copyright (c) 2017 - 2020, NVIDIA CORPORATION. All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
* Neither the name of the NVIDIA CORPORATION nor the
names of its contributors may be used to endorse or promote products
derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

330
README.md
View File

@ -1,10 +1,10 @@
![ALT](/media/images/gemm-hierarchy-with-epilogue-no-labels.png "Complete CUDA GEMM decomposition")
# CUTLASS 1.0
# CUTLASS 2.2
_CUTLASS 1.0.1 - June 2018_
_CUTLASS 2.2 - June 2020_
CUTLASS 1.0 is a collection of CUDA C++ template abstractions for implementing
CUTLASS is a collection of CUDA C++ template abstractions for implementing
high-performance matrix-multiplication (GEMM) at all levels and scales within CUDA.
It incorporates strategies for hierarchical decomposition and data movement similar
to those used to implement cuBLAS. CUTLASS decomposes these "moving parts" into
@ -16,20 +16,46 @@ and applications.
To support a wide variety of applications, CUTLASS provides extensive support for
mixed-precision computations, providing specialized data-movement and
multiply-accumulate abstractions for 8-bit integer, half-precision floating
point (FP16), single-precision floating point (FP32), and double-precision floating
point (FP64) types. Furthermore, CUTLASS demonstrates CUDA's WMMA API for targeting
the programmable, high-throughput _Tensor Cores_ provided by NVIDIA's Volta architecture
and beyond.
multiply-accumulate abstractions for half-precision floating
point (FP16), BFloat16 (BF16), Tensor Float 32 (TF32),
single-precision floating point (FP32), double-precision floating
point (FP64) types, integer data types (4b and 8b), and binary data types (1b).
CUTLASS 1.0 has changed substantially from our preview release described in
the [CUTLASS Parallel For All](https://devblogs.nvidia.com/parallelforall/cutlass-linear-algebra-cuda)
post. We have decomposed the structure of the GEMM computation into deeper, structured
primitives for loading data, computing predicate masks, streaming data at each level of
the GEMM hierarchy, and updating the output matrix.
Furthermore, CUTLASS demonstrates warp-synchronous matrix multiply operations
targeting the programmable, high-throughput _Tensor Cores_ implemented by
NVIDIA's Volta, Turing, and Ampere architectures.
CUTLASS 1.0 is described in the [Doxygen documentation](https://nvidia.github.io/cutlass)
and our talk at the [GPU Technology Conference 2018](http://on-demand.gputechconf.com/gtc/2018/presentation/s8854-cutlass-software-primitives-for-dense-linear-algebra-at-all-levels-and-scales-within-cuda.pdf).
See the [Quick Start Guide](/media/docs/quickstart.md) to get started quickly.
See the [functionality listing](media/docs/functionality.md) for the list of operations
supported at each level of the execution model hierarchy.
# What's New in CUTLASS 2.2
CUTLASS 2.2 is a significant update to CUTLASS adding:
- Coverage of [NVIDIA Ampere Architecture features](https://devblogs.nvidia.com/nvidia-ampere-architecture-in-depth/)
- Tensor Core-accelerated GEMMs targeting Tensor Float 32, BFloat16, and double-precision data types
- Deep software pipelines using asynchronous copy
- Described in [GTC 2020 Webinar (SR 21745)](https://developer.nvidia.com/gtc/2020/video/s21745)
- Intended to be compiled with [CUDA 11 Toolkit](https://developer.nvidia.com/cuda-toolkit)
# What's New in CUTLASS 2.1
CUTLASS 2.1 is a minor update to CUTLASS 2.0 adding:
- [Planar complex GEMM kernels](/examples/10_planar_complex/planar_complex.cu) targeting Volta and Turing Tensor Cores
- BLAS-style API to launch kernels compiled into the [CUTLASS Library](/media/docs/quickstart.md#cutlass-library)
# What's New in CUTLASS 2.0
CUTLASS 2.0 is a substantial refactoring from the previous version, intended to offer:
- Better performance over 1.x, particularly for kernels targeting Turing Tensor Cores
- Robust and durable templates that reliably span the design space
- Encapsulated functionality that may be reusable in other contexts
**See the [CHANGELOG](CHANGELOG.md) for more details.**
# Performance
@ -38,164 +64,256 @@ and our talk at the [GPU Technology Conference 2018](http://on-demand.gputechcon
CUTLASS primitives are very efficient. When used to construct device-wide GEMM kernels,
they exhibit performance comparable to cuBLAS for scalar GEMM
computations. The above figure shows CUTLASS performance relative to cuBLAS
for large matrix dimensions (M=10240, N=K=4096) running on an NVIDIA Titan V GPU
when compiled with CUDA 9.2.
for large matrix dimensions on an NVIDIA GeForce 2080 Ti, an NVIDIA A100, and an NVIDIA TitanV
using CUDA 11.0 Toolkit. Tensor Core operations are implemented using CUDA's
[mma instruction](https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#warp-level-matrix-instructions-mma).
# Compatibility
CUTLASS requires CUDA 9 and performs best with [CUDA 9.2 Toolkit](ttps://developer.nvidia.com/cuda-toolkit) or later.
CUTLASS requires a C++11 host compiler and
performs best when built with the [CUDA 11.0 Toolkit](https://developer.nvidia.com/cuda-toolkit).
It is compatible with CUDA 9.2, CUDA 10.0, CUDA 10.1, and CUDA 10.2.
We have tested the following environments.
|**Operating System** | **Compiler** |
|-----------------|----------|
| Windows 10 | Microsoft Visual Studio 2015|
| | Microsoft Visual Studio 2017|
| Ubuntu 14.04 | GCC 4.8.2 |
| Ubuntu 16.04 | GCC 5.4.0 |
| Ubuntu 18.04 | GCC 7.5.0 |
Additionally, CUTLASS may be built with clang.
See [these instructions](media/docs/quickstart.md#clang) for more details.
CUTLASS runs successfully on the following NVIDIA GPUs, and it is expected to be efficient on
any Maxwell-, Pascal-, or Volta-architecture NVIDIA GPU.
any Maxwell-, Pascal-, Volta-, Turing-, or NVIDIA Ampere- architecture NVIDIA GPU.
|**GPU**|
|---|
|NVIDIA GeForce 1080|
|NVIDIA TitanXP|
|NVIDIA Tesla P100|
|NVIDIA Tesla V100|
|NVIDIA TitanV|
|**GPU**|**CUDA Compute Capability**|**Minimum CUDA Toolkit**|**CUDA Toolkit Enabling Native Tensor Cores**|
|---|---|---|---|
|NVIDIA Tesla P100|6.0|9.2| |
|NVIDIA GeForce 1080|6.1|9.2| |
|NVIDIA TitanXP|6.1|9.2| |
|NVIDIA Tesla V100|7.0|9.2|10.1|
|NVIDIA TitanV|7.0|9.2|10.1|
|NVIDIA GeForce RTX 2080 TI, 2080, 2070|7.5|10.0|10.2|
|NVIDIA Tesla T4|7.5|10.0|10.2|
|NVIDIA A100|8.0|11.0|11.0|
# Documentation
CUTLASS 2.2 is described in the following documents and the accompanying
[Doxygen documentation](https://nvidia.github.io/cutlass).
- [Quick Start Guide](/media/docs/quickstart.md) - build and run CUTLASS
- [Functionality](/media/docs/functionality.md) - summarizes functionality available in CUTLASS
- [Efficient GEMM in CUDA](media/docs/efficient_gemm.md) - describes how GEMM kernels may be implemented efficiently in CUDA
- [GEMM API](media/docs/gemm_api.md) - describes the CUTLASS GEMM model and C++ template concepts
- [Code Organization](media/docs/code_organization.md) - describes the organization and contents of the CUTLASS project
- [Terminology](media/docs/terminology.md) - describes terms used in the code
- [Programming Guidelines](media/docs/programming_guidelines.md) - guidelines for writing efficient modern CUDA C++
- [Fundamental types](media/docs/fundamental_types.md) - describes basic C++ classes used in CUTLASS to represent numeric quantities and arrays
- [Layouts](media/docs/layout.md) - describes layouts of matrices and tensors in memory
- [Tile Iterators](media/docs/tile_iterator_concept.md) - describes C++ concepts for iterating over tiles of matrices in memory
- [CUTLASS Profiler](media/docs/profiler.md) - command-line driven profiling application
- [CUTLASS Utilities](media/docs/utilities.md) - additional templates used to facilate rapid development
We have also described the structure of an efficient GEMM in our talk at the
[GPU Technology Conference 2018](http://on-demand.gputechconf.com/gtc/2018/presentation/s8854-cutlass-software-primitives-for-dense-linear-algebra-at-all-levels-and-scales-within-cuda.pdf).
# Building CUTLASS
CUTLASS is a header-only template library and does not need to be built to be used by other
projects. However, we distribute extensive unit tests and utility programs to demonstrate
CUTLASS. These instructions are for building those test programs.
projects. Client applications should target CUTLASS's `include/` directory in their include
paths.
CUTLASS's unit tests depend on Google Test which exists as a git submodule. You can fetch
submodules as follows.
CUTLASS unit tests, examples, and utilities can be build with CMake starting version 3.12.
Make sure the `CUDACXX` environment variable points to NVCC in the CUDA Toolkit installed
on your system.
```
$ git submodule update --init --recursive
$ export CUDACXX=${CUDA_INSTALL_PATH}/bin/nvcc
```
CUTLASS can be build with CMake starting version 3.10. By default CUTLASS will build kernels
for CUDA architecture versions 5.0, 6.0, 6.1 and 7.0. To reduce compile time you can specify
Create a build directory within the CUTLASS project, then run CMake. By default CUTLASS will build kernels
for CUDA architecture versions 5.0, 6.0, 6.1, 7.0, 7.5, and 8.0. To reduce compile time you can specify
the architectures to build CUTLASS for by changing the CMake configuration setting
`CUTLASS_NVCC_ARCHS`.
Create a build directory within the CUTLASS project, then run CMake once.
```
$ mkdir build && cd build
$ cmake ..
$ cmake .. -DCUTLASS_NVCC_ARCHS=75 # compiles for NVIDIA's Turing GPU architecture
```
Compile the CUTLASS project by running Make. Include the -j argument to compile sources in
parallel and speed up the build process.
From the `build/` directory, compile and run the CUTLASS unit tests by building the target `test_unit` with make.
The unit tests are organized as several binaries mirroring the top-level namespaces of CUTLASS,
and they may be executed in parallel via make's `-j` command line argument.
```
$ make -j12
...
$
```
Verify CUTLASS has been built correctly by running the unit tests from the build/ directory.
```
$ ./tools/test/unit/cutlass_unit_test
$ make test_unit -j
...
...
...
[----------] Global test environment tear-down
[==========] 481 tests from 24 test cases ran. (5954 ms total)
[ PASSED ] 481 tests.
[==========] 946 tests from 57 test cases ran. (10812 ms total)
[ PASSED ] 946 tests.
```
All tests should pass, though the exact number of tests may vary over time.
All tests should pass on supported platforms, though the exact number of tests may vary over time.
# Project Structure
CUTLASS is arranged as a header-only library with several example test programs
that demonstrate instantiating a GEMM task within a CUDA kernel. The Doxygen documentation
provides a complete list of files, classes, and template concepts defined in the CUTLASS
project. A brief summary is described below.
CUTLASS is arranged as a header-only library along with Utilities, Tools, Examples, and unit tests.
[Doxygen documentation](https://nvidia.github.io/cutlass) provides a complete list of files, classes,
and template concepts defined in the CUTLASS project.
The CUTLASS library is defined in the cutlass/ directory and consists of CUDA C++ template
classes and other definitions for implementing efficient GPU GEMM kernels. A set of core
classes and templates define basic primitives that are then applied to compute GEMM via
templates in the cutlass/gemm directory.
A detailed explanation of the source code organization may be found in the
[CUTLASS documentation](media/docs/code_organization.md), but several main components are summarized below.
## CUTLASS Template Library
```
cutlass/
gemm/
util/
<core API components>
include/ # client applications should target this directory in their build's include paths
cutlass/ # CUDA Templates for Linear Algebra Subroutines and Solvers - headers only
arch/ # direct exposure of architecture features (including instruction-level GEMMs)
gemm/ # code specialized for general matrix product computations
layout/ # layout definitions for matrices, tensors, and other mathematical objects in memory
platform/ # CUDA-capable Standard Library components
reduction/ # bandwidth-limited reduction kernels that do not fit the "gemm" model
transform/ # code specialized for layout, type, and domain transformations
* # core vocabulary types, containers, and basic numeric operations
```
Several tools and test programs are also distributed with the CUTLASS library. They are
contained in the following directories.
### CUTLASS SDK Examples
[CUTLASS SDK examples](/examples) apply CUTLASS templates to implement basic computations.
```
examples/
00_basic_gemm/ # launches a basic GEMM with single precision inputs and outputs
01_cutlass_utilities/ # demonstrates CUTLASS Utilities for allocating and initializing tensors
02_dump_reg_smem/ # debugging utilities for printing register and shared memory contents
03_visualize_layout/ # utility for visualizing all layout functions in CUTLASS
04_tile_iterator/ # example demonstrating an iterator over tiles in memory
05_batched_gemm/ # example demonstrating CUTLASS's batched strided GEMM operation
06_splitK_gemm/ # exmaple demonstrating CUTLASS's Split-K parallel reduction kernel
07_volta_tensorop_gemm/ # example demonstrating mixed precision GEMM using Volta Tensor Cores
08_turing_tensorop_gemm/ # example demonstrating integer GEMM using Turing Tensor Cores
10_planar_complex/ # example demonstrating planar complex GEMM kernels
11_planar_complex_array/ # example demonstrating planar complex kernels with batch-specific problem sizes
12_gemm_bias_relu/ # example demonstrating GEMM fused with bias and relu
13_fused_two_gemms/ # example demonstrating two GEMms fused in one kernel
```
### Tools
```
tools/
test/
unit/
core/
gemm/
perf/
util/
<utilities>
library/ # CUTLASS Instance Library - contains instantiations of all supported CUTLASS templates
include/
cutlass/
library/
profiler/ # CUTLASS Profiler - command-line utility for executing operations in the
# CUTLASS Library
util/ # CUTLASS Utilities - contains numerous helper classes for
include/ # manging tensors in device memory, reference
cutlass/ # implementations for GEMM, random initialization
util/ # of tensors, and I/O.
```
### Test
The `test/unit/` directory consist of unit tests implemented with Google Test that demonstrate
basic usage of Core API components and complete tests of the CUTLASS GEMM computations.
Instructions for building and running the Unit tests are described in the [Quickstart guide](media/docs/quickstart.md).
# Performance Profiling
The `test/perf/` directory contains a command-line utility for launching each of the GEMM kernels.
Its usage is shown below.
Program usage:
The `tools/profiler/` directory contains a command-line utility for launching each of the GEMM kernels.
It can be built as follows:
```
cutlass_perf_test [options]
--help
--append=<true|false*> If true, appends output to existing CSV file. If false, overwrites.
--alpha=<alpha> Value for alpha to be used in GEMM experiments
--beta=<beta> Value for beta to be used in GEMM experiments
--dist=<distribution> Describes the random distribution of each of the input matrix operands.
--execution_mode=<mode> Specifies execution mode: profile, verify, single
--output=<filename.csv> Writes summary of profiling to specified .csv file
--iterations=<timing iterations> maximum number of iterations to execute when profiling
--m=<height>[:max height[:step]] Height of GEMM problem (number of rows of C). May specify a range with optional step size.
--n=<width>[:max width[:step]] Width of GEMM problem (number of columns of C). May specify a range with optional step size.
--k=<depth>[:max depth[:step]] Size of inner dimension of A and B. May specify a range with optional step size.
--kernels=<{s|d|h|i|wmma_}gemm_{nn,nt,tn,tt}> Select GEMM datatype and layout to use for tests
--peak=<bool> If true, only reports peak performance per kernel after profiling specified problem space.
--save_workspace={*never,incorrect,always} Specifies when to save the GEMM inputs and results to the filesystem.
--seed=<seed> Random seed used by the random number generator in initializing input matrices.
--tags=<column:tag,...> Inserts leading columns in output table and uniform values for each column.
Example usage:
# Runs one problem size for all kernels
$ ./tools/test/perf/cutlass_perf_test --m=10240 --n=1024 --k=1024
# Varies GEMM K dimension for SGEMM and IGEMM with column-major multiplicands
$ ./tools/test/perf/cutlass_perf_test --m=10240 --n=4096 --k=1024:8192:128 --kernels=sgemm_nn,igemm_nn
$ make cutlass_profiler -j
```
To limit compilation time, only one tile size is instantiated for each data type, math instruction, and layout.
To instantiate all, set the following environment variable when running CMake from an empty `build/` directory.
```
$ cmake .. -DCUTLASS_NVCC_ARCHS=75 -DCUTLASS_LIBRARY_KERNELS=all
...
$ make cutlass_profiler -j
```
Example command line for profiling SGEMM kernels is as follows:
```
$ ./tools/profiler/cutlass_profiler --kernels=sgemm --m=3456 --n=4096 --k=4096
=============================
Problem ID: 1
Provider: CUTLASS
OperationKind: gemm
Operation: cutlass_simt_sgemm_128x128_8x2_nn_align1
Status: Success
Verification: ON
Disposition: Passed
cuBLAS: Passed
Arguments: --m=3456 --n=4096 --k=4096 --A=f32:column --B=f32:column --C=f32:column --alpha=1 --beta=0 --split_k_slices=1 \
--batch_count=1 --op_class=simt --accum=f32 --cta_m=128 --cta_n=128 --cta_k=8 --stages=2 --warps_m=4 \
--warps_n=2 --warps_k=1 --inst_m=1 --inst_n=1 --inst_k=1 --min_cc=50 --max_cc=1024
Bytes: 180355072 bytes
FLOPs: 115992428544 flops
Runtime: 6.73655 ms
Memory: 24.934 GiB/s
Math: 17218.4 GFLOP/s
```
[Further details about the CUTLASS Profiler are described here.](media/docs/profiler.md)
# About
CUTLASS is released by NVIDIA Corporation as Open Source software under the
3-clause "New" BSD license.
CUTLASS is released by NVIDIA Corporation as Open Source software under the
[3-clause "New" BSD license](LICENSE.txt).
# Contributors
The official list of CUTLASS developers and contributors is available here: [CONTRIBUTORS](CONTRIBUTORS.md).
# Copyright
Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
Copyright (c) 2017-2020, NVIDIA CORPORATION. All rights reserved.
```
Redistribution and use in source and binary forms, with or without modification, are permitted

View File

@ -13,8 +13,8 @@ function(FILE_TO_C_STRING FILENAME VARIABLE_NAME OUTPUT_STRING ZERO_TERMINATED)
set(${OUTPUT_STRING} "${HEX_OUTPUT}" PARENT_SCOPE)
endfunction()
message("Create header file for ${FILE_IN}")
message("Create header file for ${FILE_OUT}")
# message("Create header file for ${FILE_IN}")
# message("Create header file for ${FILE_OUT}")
file_to_c_string(${FILE_IN} ${VARIABLE_NAME} OUTPUT_STRING ZERO_TERMINATED)
set(RESULT "#pragma once\n")

View File

@ -1,47 +0,0 @@
# NVIDIA CUTLASS Changelog
## [1.0.1](https://github.com/NVIDIA/cutlass/releases/tag/v1.0.1) (2018-06-11)
* Intra-threadblock reduction added for small threadblock tile sizes
* sgemm_64x128x16, sgemm_128x128x16, sgemm_128x64x16, sgemm_128x32x16, sgemm_64x64x16, sgemm_64x32x16
* igemm_32x32x128
* GEMM _K_ residue handled during prologue prior to mainloop
* Replaced Google Test copy with submodule. Use `git submodule init`
## [1.0.0](https://github.com/NVIDIA/cutlass/commit/2028ebe120aab22bfd0b2baf8902d4c9627eb33f) (2018-05-16)
* Substantial rewrite to accommodate new architecture
* Kernels: SGEMM, DGEMM, IGEMM, HGEMM, WMMA GEMM
* Unit and performance tests
## [0.0.1](https://github.com/NVIDIA/cutlass/commit/d08ba8ac46e2fa3f745e070c390182edb56b2e91) (2017-12-04)
* Initial release
## Copyright
Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
```
Redistribution and use in source and binary forms, with or without modification, are permitted
provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this list of
conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice, this list of
conditions and the following disclaimer in the documentation and/or other materials
provided with the distribution.
* Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
to endorse or promote products derived from this software without specific prior written
permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
```

View File

@ -1,17 +0,0 @@
#!/bin/bash
set -e
function formatFiles {
for f in `find "$1" -type f -name "*.$2"` ; do
COMMAND="clang-format -i $f"
echo $COMMAND
$COMMAND
done
}
formatFiles "cutlass" "h"
formatFiles "tools/test" "h"
formatFiles "tools/test" "cpp"
formatFiles "tools/util" "h"

View File

@ -0,0 +1,7 @@
get_filename_component(NvidiaCutlass_CMAKE_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH)
include(CMakeFindDependencyMacro)
if(NOT TARGET nvidia::cutlass::CUTLASS)
include("${NvidiaCutlass_CMAKE_DIR}/NvidiaCutlassTargets.cmake")
endif()

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@ -0,0 +1,14 @@
set(CPACK_PACKAGE_NAME NvidiaCutlass)
set(CPACK_PACKAGE_VENDOR NVIDIA)
set(CPACK_PACKAGE_CONTACT info@nvidia.com)
set(CPACK_PACKAGE_DESCRIPTION_SUMMARY "CUTLASS CUDA C++ Template Linear Algebra Library")
set(CPACK_PACKAGE_INSTALL_DIRECTORY ${CPACK_PACKAGE_NAME})
set(CPACK_PACKAGE_VERSION_MAJOR ${PROJECT_VERSION_MAJOR})
set(CPACK_PACKAGE_VERSION_MINOR ${PROJECT_VERSION_MINOR})
set(CPACK_PACKAGE_VERSION_PATCH ${PROJECT_VERSION_PATCH})
set(CPACK_VERBATIM_VARIABLES YES)
# set(CPACK_PACKAGE_DESCRIPTION_FILE ${CMAKE_CURRENT_LIST_DIR}/Description.txt)
# set(CPACK_RESOURCE_FILE_WELCOME ${CMAKE_CURRENT_LIST_DIR}/Welcome.txt)
# set(CPACK_RESOURCE_FILE_LICENSE ${CMAKE_CURRENT_LIST_DIR}/License.txt)
# set(CPACK_RESOURCE_FILE_README ${CMAKE_CURRENT_LIST_DIR}/Readme.txt)
include(CPack)

23
cmake/googletest.cmake Normal file
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include(FetchContent)
set(GOOGLETEST_DIR "" CACHE STRING "Location of local GoogleTest repo to build against")
if(GOOGLETEST_DIR)
set(FETCHCONTENT_SOURCE_DIR_GOOGLETEST ${GOOGLETEST_DIR} CACHE STRING "GoogleTest source directory override")
endif()
FetchContent_Declare(
googletest
GIT_REPOSITORY https://github.com/google/googletest.git
GIT_TAG 0fe9660
)
FetchContent_GetProperties(googletest)
if(NOT googletest_POPULATED)
FetchContent_Populate(googletest)
if (MSVC)
set(gtest_force_shared_crt ON CACHE BOOL "" FORCE)
endif()
add_subdirectory(${googletest_SOURCE_DIR} ${googletest_BINARY_DIR} EXCLUDE_FROM_ALL)
endif()

43
cmake/nop.cu Normal file
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/***************************************************************************************************
* Copyright (c) 2017-2020, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Basic CUDA file for testing compiler flags.
*/
__device__ int inner()
{
return -1;
}
__global__ void test()
{
inner();
}
int main()
{
test<<<1,1>>>();
return 0;
}

38
cmake/version.h.in Normal file
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#include <cstdint>
#include <string>
#define CUTLASS_MAJOR @CUTLASS_VERSION_MAJOR@
#define CUTLASS_MINOR @CUTLASS_VERSION_MINOR@
#define CUTLASS_PATCH @CUTLASS_VERSION_PATCH@
#define CUTLASS_BUILD @CUTLASS_VERSION_BUILD@
#define CUTLASS_VERSION ((CUTLASS_MAJOR)*100 + (CUTLASS_MINOR)*10 + CUTLASS_PATCH)
namespace cutlass {
inline uint32_t getVersion() {
return CUTLASS_VERSION;
}
inline uint32_t getVersionMajor() {
return CUTLASS_MAJOR;
}
inline uint32_t getVersionMinor() {
return CUTLASS_MINOR;
}
inline uint32_t getVersionPatch() {
return CUTLASS_PATCH;
}
inline uint32_t getVersionBuild() {
return CUTLASS_BUILD + 0;
}
inline std::string getVersionString() {
std::string version = "@CUTLASS_VERSION@";
if (getVersionBuild()) {
version += "." + std::to_string(getVersionBuild());
}
return version;
}
inline std::string getGitRevision() {
return "@CUTLASS_REVISION@";
}
} // namespace cutlass

125
cuBLAS.cmake Normal file
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message(STATUS "Configuring cublas ...")
if((DEFINED CUTLASS_ENABLE_CUBLAS AND NOT CUTLASS_ENABLE_CUBLAS) OR
(DEFINED CUBLAS_ENABLED AND NOT CUBLAS_ENABLED))
# Don't add cuBLAS if it's defined and false, assume it's not found.
set(CUBLAS_FOUND OFF)
message(STATUS "cuBLAS Disabled.")
elseif(NOT TARGET cublas)
find_path(
_CUBLAS_INCLUDE_DIR
NAMES cublas.h
HINTS
${CUBLAS_INCLUDE_PATH}
ENV CUBLAS_INCLUDE_PATH
${CUBLAS_PATH}
ENV CUBLAS_PATH
${CUDA_TOOLKIT_ROOT_DIR}
PATH_SUFFIXES
include
)
find_library(
_CUBLAS_LIBRARY
NAMES cublas
HINTS
${CUBLAS_LIBRARY_PATH}
ENV CUBLAS_LIBRARY_PATH
${_CUBLAS_INCLUDE_DIR}/..
${CUBLAS_PATH}
ENV CUBLAS_PATH
${CUDA_TOOLKIT_ROOT_DIR}
PATH_SUFFIXES
lib64
lib/x64
lib
)
if(_CUBLAS_INCLUDE_DIR AND _CUBLAS_LIBRARY)
message(STATUS "cuBLAS: ${_CUBLAS_LIBRARY}")
message(STATUS "cuBLAS: ${_CUBLAS_INCLUDE_DIR}")
set(CUBLAS_FOUND ON CACHE INTERNAL "cublas Library Found")
set(CUBLAS_LIBRARY ${_CUBLAS_LIBRARY})
set(CUBLAS_INCLUDE_DIR ${_CUBLAS_INCLUDE_DIR})
else()
message(STATUS "cublas not found.")
set(CUBLAS_FOUND OFF CACHE INTERNAL "cublas Library Found")
endif()
endif()
set(CUTLASS_ENABLE_CUBLAS ${CUBLAS_FOUND} CACHE BOOL "Enable CUTLASS to build with cuBLAS library.")
if(CUTLASS_ENABLE_CUBLAS AND NOT CUBLAS_FOUND)
message(FATAL_ERROR "CUTLASS_ENABLE_CUBLAS enabled but cuBLAS library could not be found.")
endif()
if(CUTLASS_ENABLE_CUBLAS AND NOT TARGET cublas)
if(WIN32)
add_library(cublas STATIC IMPORTED GLOBAL)
else()
add_library(cublas SHARED IMPORTED GLOBAL)
endif()
add_library(nvidia::cublas ALIAS cublas)
set_property(
TARGET cublas
PROPERTY IMPORTED_LOCATION
${CUBLAS_LIBRARY})
target_include_directories(
cublas
INTERFACE
$<INSTALL_INTERFACE:include>
$<BUILD_INTERFACE:${CUBLAS_INCLUDE_DIR}>)
find_library(
_CUBLASLT_LIBRARY
NAMES cublasLt
HINTS
${CUBLAS_LIBRARY_PATH}
ENV CUBLAS_LIBRARY_PATH
${_CUBLAS_INCLUDE_DIR}/..
${CUBLAS_PATH}
ENV CUBLAS_PATH
${CUDA_TOOLKIT_ROOT_DIR}
PATH_SUFFIXES
lib64
lib/x64
lib
)
if(_CUBLASLT_LIBRARY AND NOT TARGET cublasLt)
if(WIN32)
add_library(cublasLt STATIC IMPORTED GLOBAL)
else()
add_library(cublasLt SHARED IMPORTED GLOBAL)
endif()
set_property(
TARGET cublasLt
PROPERTY IMPORTED_LOCATION
${_CUBLASLT_LIBRARY})
add_library(nvidia::cublasLt ALIAS cublasLt)
target_link_libraries(cublas INTERFACE cublasLt)
endif()
endif()
message(STATUS "Configuring cuBLAS ... done.")

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@ -1,102 +0,0 @@
/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*!
\file
\brief Defines conversion operations among Fragments of different base type.
*/
#pragma once
#include <cutlass/fragment.h>
namespace cutlass {
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename InputFragment_, typename OutputFragment_>
struct Convert {};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename InputScalar_, typename OutputScalar_, int kScalars_>
struct Convert<Fragment<InputScalar_, kScalars_>, Fragment<OutputScalar_, kScalars_> > {
/// The input fragment.
typedef Fragment<InputScalar_, kScalars_> InputFragment;
/// The output fragment.
typedef Fragment<OutputScalar_, kScalars_> OutputFragment;
/// Ctor.
CUTLASS_DEVICE Convert() {}
/// Transform a fragment.
CUTLASS_DEVICE void transform(InputFragment const& src, OutputFragment& dst) {
transform(src, 0, dst);
}
/// Transform a fragment.
template <typename Fragment_>
CUTLASS_DEVICE void transform(Fragment_ const& src, int offset, OutputFragment& dst) {
for (int i = 0; i < kScalars_; ++i) {
dst[i] = static_cast<OutputScalar_>(src[i + offset]);
}
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename Fragment_>
struct Copy {
/// The input fragment.
typedef Fragment_ InputFragment;
/// The output fragment.
typedef Fragment_ OutputFragment;
/// Ctor.
CUTLASS_DEVICE Copy() {}
/// Transform a fragment.
CUTLASS_DEVICE void transform(Fragment_ const& src, Fragment_& dst) { transform(src, 0, dst); }
/// Transform a fragment.
template <typename InputFragment_>
CUTLASS_DEVICE void transform(InputFragment_ const& src, int offset, Fragment_& dst) {
if (sizeof(typename Fragment_::Element) == 8) {
uint64_t const* src_ptr = reinterpret_cast<uint64_t const*>(&src[offset]);
uint64_t* dst_ptr = reinterpret_cast<uint64_t*>(&dst[0]);
for (int i = 0; i < sizeof(Fragment_) / 8; ++i) {
dst_ptr[i] = src_ptr[i];
}
} else {
uint32_t const* src_ptr = reinterpret_cast<uint32_t const*>(&src[offset]);
uint32_t* dst_ptr = reinterpret_cast<uint32_t*>(&dst[0]);
for (int i = 0; i < sizeof(Fragment_) / 4; ++i) {
dst_ptr[i] = src_ptr[i];
}
}
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace cutlass

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@ -1,287 +0,0 @@
/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief A Coord is a coordinate of arbitrary rank into a tensor or matrix
*/
#pragma once
#include <cutlass/cutlass.h>
namespace cutlass {
////////////////////////////////////////////////////////////////////////////////////////////////////
/// Describes identity elements
struct Identity {
/// Enumeration describing identity elements. Value assignments are significant.
/// Feel free to add or multiply by these, respectively.
enum Kind { Additive = 0, Multiplicative = 1 };
};
////////////////////////////////////////////////////////////////////////////////////////////////////
/// Statically-sized array specifying Coords within a tensor
template <int N_>
struct Coord {
//
// Type and constant definitions
//
static int const N = N_;
//
// Data members
//
/// Indices
int idx[N];
//
// Methods
//
/// Default ctor initializes uniformly
CUTLASS_HOST_DEVICE
Coord(int value = 0) {
for (int i = 0; i < N; ++i) {
idx[i] = value;
}
}
/// Constructs from an array of integers
CUTLASS_HOST_DEVICE
Coord(int _idx[]) {
for (int i = 0; i < N; ++i) {
idx[i] = _idx[i];
}
}
/// Element-wise addition
CUTLASS_HOST_DEVICE
Coord operator+(Coord const& b) const {
Coord c;
for (int i = 0; i < N; ++i) {
c.idx[i] = idx[i] + b.idx[i];
}
return c;
}
/// Element-wise subtraction
CUTLASS_HOST_DEVICE
Coord operator-(Coord const& b) const {
Coord c;
for (int i = 0; i < N; ++i) {
c.idx[i] = idx[i] - b.idx[i];
}
return c;
}
/// Element-wise multiplication
CUTLASS_HOST_DEVICE
Coord operator*(Coord const& b) const {
Coord c;
for (int i = 0; i < N; ++i) {
c.idx[i] = idx[i] * b.idx[i];
}
return c;
}
/// Element-wise division
CUTLASS_HOST_DEVICE
Coord operator/(Coord const& b) const {
Coord c;
for (int i = 0; i < N; ++i) {
c.idx[i] = idx[i] / b.idx[i];
}
return c;
}
/// In-place addition
CUTLASS_HOST_DEVICE
Coord& operator+=(Coord const& b) {
for (int i = 0; i < N; ++i) {
idx[i] += b.idx[i];
}
return *this;
}
/// In-place subtraction
CUTLASS_HOST_DEVICE
Coord& operator-=(Coord const& b) {
for (int i = 0; i < N; ++i) {
idx[i] -= b.idx[i];
}
return *this;
}
/// In-place multiplication
CUTLASS_HOST_DEVICE
Coord& operator*=(Coord const& b) {
for (int i = 0; i < N; ++i) {
idx[i] *= b.idx[i];
}
return *this;
}
/// In-place division
CUTLASS_HOST_DEVICE
Coord& operator/=(Coord const& b) {
for (int i = 0; i < N; ++i) {
idx[i] /= b.idx[i];
}
return *this;
}
/// Member access operator
CUTLASS_HOST_DEVICE int& operator[](int dim) { return idx[dim]; }
/// Member access operator
CUTLASS_HOST_DEVICE int const& operator[](int dim) const { return idx[dim]; }
/// Computes the dot product of two Coord instances
template <typename T>
CUTLASS_HOST_DEVICE T dot(Coord const& b, T sum) const {
for (int i = 0; i < N; ++i) {
sum += idx[i] * b.idx[i];
}
return sum;
}
/// Computes the dot product of two Coord instances
template <typename T>
CUTLASS_HOST_DEVICE T dot(Coord const& b) const {
T sum = T(0);
for (int i = 0; i < N; ++i) {
sum += idx[i] * b.idx[i];
}
return sum;
}
/// Gets the index of a given Coord element
template <int Dim>
CUTLASS_HOST_DEVICE int& at() {
return idx[Dim];
}
/// Access via index; may limit unrolling potential
CUTLASS_HOST_DEVICE
int& at(int dim) { return idx[dim]; }
/// Gets the index of a given Coord element
template <int Dim>
CUTLASS_HOST_DEVICE int const& at() const {
return idx[Dim];
}
/// Access via index; may limit unrolling potential
CUTLASS_HOST_DEVICE
int const& at(int dim) const { return idx[dim]; }
/// Determines if two Coord<> objects are equal
CUTLASS_HOST_DEVICE
bool operator==(Coord<N> const& b) const {
bool equal = true;
for (int i = 0; equal && i < N; ++i) {
equal = (idx[i] == b.idx[i]);
}
return equal;
}
/// Not equal
CUTLASS_HOST_DEVICE
bool operator!=(Coord<N> const& b) const { return !(*this == b); }
/// Clamps a coordinate to a range specified by maximum and minimum values
CUTLASS_HOST_DEVICE
Coord& clamp(Coord<N> const& max, Coord<N> const& min = Coord<N>()) {
for (int i = 0; i < N; ++i) {
idx[i] = __NV_STD_MAX(__NV_STD_MIN(idx[i], max.idx[i]), min.idx[i]);
}
return *this;
}
/// Returns the product of all elements
CUTLASS_HOST_DEVICE
int count() const {
int product = idx[0];
for (int i = 1; i < N; ++i) {
product *= idx[i];
}
return product;
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
/// Helper to make a 2-element coordinate
CUTLASS_HOST_DEVICE
Coord<1> make_Coord(int _0) {
int values[1] = {_0};
return Coord<1>(values);
}
/// Helper to make a 2-element coordinate
CUTLASS_HOST_DEVICE
Coord<2> make_Coord(int _0, int _1) {
int values[2] = {_0, _1};
return Coord<2>(values);
}
/// Helper to make a 3-element coordinate
CUTLASS_HOST_DEVICE
Coord<3> make_Coord(int _0, int _1, int _2) {
int values[3] = {_0, _1, _2};
return Coord<3>(values);
}
/// Helper to make a 4-element coordinate
CUTLASS_HOST_DEVICE
Coord<4> make_Coord(int _0, int _1, int _2, int _3) {
int values[4] = {_0, _1, _2, _3};
return Coord<4>(values);
}
////////////////////////////////////////////////////////////////////////////////////////////////////
/// Getter
CUTLASS_HOST_DEVICE
Coord<2> get_Coord_hw(Coord<3> const& coord) { return make_Coord(coord[1], coord[2]); }
/// Getter
CUTLASS_HOST_DEVICE
Coord<2> get_Coord_hw(Coord<4> const& coord) { return make_Coord(coord[1], coord[2]); }
/// Getter
CUTLASS_HOST_DEVICE
Coord<3> get_Coord_hwc(Coord<4> const& coord) { return make_Coord(coord[1], coord[2], coord[3]); }
/// Getter
CUTLASS_HOST_DEVICE
Coord<3> get_Coord_dhw(Coord<4> const& coord) { return make_Coord(coord[0], coord[1], coord[2]); }
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace cutlass

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@ -1,44 +0,0 @@
/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
#pragma once
/*! \file
\brief Helpers for printing cutlass/core objects
*/
#pragma once
#include <iosfwd>
#include <typeinfo>
#include <cutlass/coord.h>
template <int Rank>
std::ostream& operator<<(std::ostream& out, cutlass::Coord<Rank> const& coord) {
for (int i = 0; i < Rank; ++i) {
out << (i ? ", " : "") << coord.idx[i];
}
return out;
}

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@ -1,73 +0,0 @@
/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Basic include for CUTLASS macros
*/
#pragma once
////////////////////////////////////////////////////////////////////////////////////////////////////
#define CUTLASS_MAJOR 1
#define CUTLASS_MINOR 0
#define CUTLASS_PATCH 1
#define CUTLASS_VERSION ((CUTLASS_MAJOR)*100 + (CUTLASS_MINOR)*10 + CUTLASS_PATCH)
#ifdef __NVCC__
#define CUTLASS_HOST_DEVICE __forceinline__ __device__ __host__
#define CUTLASS_DEVICE __forceinline__ __device__
#elif defined(__CUDACC_RTC__)
#define CUTLASS_HOST_DEVICE __forceinline__ __device__
#define CUTLASS_DEVICE __forceinline__ __device__
#else
#define CUTLASS_HOST_DEVICE
// CUTLASS_DEVICE is an error if not compiling device code
#endif
// CUTLASS_PRAGMA_UNROLL inserts a CUTLASS_PRAGMA_UNROLL if supported by the compiler
#if defined(__CUDA_ARCH__)
#if defined(_MSC_VER)
#define CUTLASS_PRAGMA_UNROLL __pragma("unroll")
#define CUTLASS_PRAGMA_NO_UNROLL __pragma("unroll 1")
#else
#define CUTLASS_PRAGMA_UNROLL _Pragma("unroll")
#define CUTLASS_PRAGMA_NO_UNROLL _Pragma("unroll 1")
#endif
#else
#define CUTLASS_PRAGMA_UNROLL
#define CUTLASS_PRAGMA_NO_UNROLL
#endif
#define CUTLASS_ASSERT(x) assert(x)
namespace cutlass {
/// NVIDIA GPU Warp size
static const int kWarpSize = 32;
} // namespace cutlass
////////////////////////////////////////////////////////////////////////////////////////////////////

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@ -1,278 +0,0 @@
/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Defines Fragment, a statically-sized array for storing parts of matrices within a
thread's registers.
*/
#pragma once
#include <assert.h>
#include <cutlass/shape.h>
#include <cutlass/util/cutlass_math.h>
#include <cutlass/vector.h>
namespace cutlass {
///////////////////////////////////////////////////////////////////////////////////////////////////
/*!@defgroup fragment_concept Fragment Concept
@{
\ref fragment_concept is a statically sized array for storing parts of tiles held by individual CUDA
threads.
@par \ref fragment_concept
Types satisfying \ref fragment_concept define the following members
- <b>Element</b> - type of each access held within the fragment
- <b>kElements</b> - number of elements stored by the fragment
- <b>clear()</b> - overwrites the fragment storage with zeros
- <b>Element & operator[](int i)</b> - by-reference access of the ith element
- <b>Element const & operator[](int i) const</b> - const by-reference access of the ith element
@}
*/
///////////////////////////////////////////////////////////////////////////////////////////////////
/*!@defgroup fragment_iterator_concept Fragment Iterator Concept
@{
\ref fragment_iterator_concept provides structured access to the elements within a fragment with an
optional bitcast to the desired access type
@par \ref fragment_iterator_concept
Types satisfying \ref fragment_iterator_concept define the following members
- <b>AccessType& operator[](int i)</b> - provides access to the ith element of the fragment
- <b>AccessType& at(int d, int h, int w, int c)</b> - applies \ref layout_concept to fragment and
provides access to element at (d, h, w, c)
@}
*/
////////////////////////////////////////////////////////////////////////////////////////////////////
template <int kAlignment_>
struct StorageType {
typedef uint64_t Type;
};
template <>
struct StorageType<4> {
typedef uint32_t Type;
};
template <>
struct StorageType<2> {
typedef uint16_t Type;
};
template <>
struct StorageType<1> {
typedef uint8_t Type;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
/**
* @brief A template defining \ref fragment_concept
* @concept{fragment_concept}
*/
template <typename Element_, int kElements_, size_t kAlignment_ = 16>
struct Fragment : public AlignedStruct<kAlignment_> {
/// Make sure the alignment makes sense wrt the size of elements.
static_assert(kAlignment_ == 16 || kAlignment_ >= sizeof(Element_), "Alignment is too small");
/// Alignment must be a power of two
static_assert(is_pow2<kAlignment_>::value, "Alignment must be a power of two");
/// This class.
typedef Fragment<Element_, kElements_> This_;
/// The element.
typedef Element_ Element;
/// The number of elements.
static int const kElements = kElements_;
/// Clear a fragment.
CUTLASS_DEVICE void clear() {
// Avoid element-wise access for sub 32b element type
if (kAlignment_ >= 8 && (kElements * sizeof(Element)) % 8 == 0) {
uint64_t* ptr = reinterpret_cast<uint64_t*>(storage);
for (int i = 0; i < (kElements * sizeof(Element)) / 8; ++i) {
ptr[i] = uint64_t(0);
}
} else if (kAlignment_ >= 4 && (kElements * sizeof(Element)) % 4 == 0) {
uint32_t* ptr = reinterpret_cast<uint32_t*>(storage);
for (int i = 0; i < (kElements * sizeof(Element)) / 4; ++i) {
ptr[i] = uint32_t(0);
}
} else if (kAlignment_ >= 2 && (kElements * sizeof(Element)) % 2 == 0) {
uint16_t* ptr = reinterpret_cast<uint16_t*>(storage);
for (int i = 0; i < (kElements * sizeof(Element)) / 2; ++i) {
ptr[i] = uint16_t(0);
}
} else {
for (int i = 0; i < kElements; ++i) {
storage[i] = 0;
}
}
}
/// The accessor.
CUTLASS_DEVICE Element& operator[](int i) {
assert(i < kElements_);
return reinterpret_cast<Element*>(storage)[i];
}
/// The accessor.
CUTLASS_DEVICE Element const& operator[](int i) const {
assert(i < kElements_);
return reinterpret_cast<Element const*>(storage)[i];
}
private:
/// Storage type to use for Elements
typedef typename StorageType<kAlignment_>::Type StorageType;
/// Number of elements in the storage
static int const kStorageCount =
(sizeof(Element_) * kElements_ + sizeof(StorageType) - 1) / sizeof(StorageType);
/// The storage.
StorageType storage[kStorageCount];
/// Ensure that there's enough storage for all elements
static_assert(sizeof(StorageType) <= kAlignment_, "StorageType is too big for given alignment");
};
////////////////////////////////////////////////////////////////////////////////////////////////////
/**
* @brief A template defining \ref fragment_iterator_concept
* @concept{fragment_iterator_concept}
*/
template <typename Fragment_, typename Iterations_, typename AccessType_>
struct FragmentIterator {
/// This class.
typedef FragmentIterator<Fragment_, Iterations_, AccessType_> This_;
/// The fragment.
typedef Fragment_ Fragment;
/// The number of iterations.
typedef Iterations_ Iterations;
/// The access type.
typedef AccessType_ AccessType;
/// The element.
typedef typename Fragment::Element Element;
/// The number of elements per access.
static int const kElementsPerAccess = (int)(sizeof(AccessType) / sizeof(Element));
/// The shape of the the fragment.
typedef typename ShapeMul<Iterations, Shape<1, 1, 1, kElementsPerAccess> >::Shape FragmentShape;
/// The linear strides for iterations.
typedef typename ShapeStrides<FragmentShape, kElementsPerAccess>::Shape Strides;
/// Ctor.
template <typename OtherFragment_>
CUTLASS_DEVICE FragmentIterator(OtherFragment_& fragment, int offset = 0)
: pointer(reinterpret_cast<Element*>(&fragment[offset])) {
static_assert(OtherFragment_::kElements >= Fragment::kElements, "");
}
/// The accessor.
CUTLASS_DEVICE AccessType const& at(int d, int h, int w, int c = 0) const {
int const imm = ComputeOffsetFromStrides<Strides>::get(d, h, w, c);
return reinterpret_cast<AccessType const&>(pointer[imm]);
}
/// The accessor.
CUTLASS_DEVICE AccessType& at(int d, int h, int w, int c = 0) {
int const imm = ComputeOffsetFromStrides<Strides>::get(d, h, w, c);
return reinterpret_cast<AccessType&>(pointer[imm]);
}
/// The accessor.
CUTLASS_DEVICE AccessType const& operator[](int i) const {
return reinterpret_cast<AccessType const&>(pointer[i * kElementsPerAccess]);
}
/// The accessor.
CUTLASS_DEVICE AccessType& operator[](int i) {
return reinterpret_cast<AccessType&>(pointer[i * kElementsPerAccess]);
}
/// Is the iterator valid?
CUTLASS_DEVICE bool valid(int d, int h, int w, int c) const { return true; }
/// The pointer.
Element* pointer;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename Fragment_, typename Iterations_, typename AccessType_>
struct FragmentConstIterator {
/// This class.
typedef FragmentIterator<Fragment_, Iterations_, AccessType_> This_;
/// The fragment.
typedef Fragment_ Fragment;
/// The number of iterations.
typedef Iterations_ Iterations;
/// The access type.
typedef AccessType_ AccessType;
/// The element.
typedef typename Fragment::Element Element;
/// The number of elements per access.
static int const kElementsPerAccess = (int)(sizeof(AccessType) / sizeof(Element));
/// The shape of the the fragment.
typedef typename ShapeMul<Iterations, Shape<1, 1, 1, kElementsPerAccess> >::Shape FragmentShape;
/// The linear strides for iterations.
typedef typename ShapeStrides<FragmentShape, kElementsPerAccess>::Shape IterationsStrides;
/// Ctor.
template <typename OtherFragment_>
CUTLASS_DEVICE FragmentConstIterator(OtherFragment_& fragment, int offset = 0)
: pointer(reinterpret_cast<Element const*>(&fragment[offset])) {
static_assert(OtherFragment_::kElements >= Fragment::kElements, "");
}
/// Create from non-constant FragmentIterator
CUTLASS_DEVICE FragmentConstIterator(
FragmentIterator<Fragment_, Iterations_, AccessType_> const& rhs_)
: pointer(reinterpret_cast<Element const*>(rhs_.offset)) {}
/// The accessor.
CUTLASS_DEVICE AccessType const& at(int d, int h, int w, int c = 0) const {
int const imm = ComputeOffsetFromStrides<IterationsStrides>::get(d, h, w, c);
return reinterpret_cast<AccessType const&>(pointer[imm]);
}
/// The accessor.
CUTLASS_DEVICE AccessType const& operator[](int i) const {
return reinterpret_cast<AccessType const&>(pointer[i * kElementsPerAccess]);
}
/// Is the iterator valid?
CUTLASS_DEVICE bool valid(int d, int h, int w, int c) const { return true; }
/// The pointer.
Element const* pointer;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace cutlass

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/***************************************************************************************************
* Copyright (c) 2017, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Defines accessors for loading and storing fragments to memory efficiently.
*/
#pragma once
#include <cutlass/load_store.h>
#include <cutlass/vector.h>
namespace cutlass {
////////////////////////////////////////////////////////////////////////////////////////////////////
template <IteratorFragment::Kind kIteratorFragment,
int kAccessSize,
typename Scalar_,
MemorySpace::Kind Memory_,
typename FragmentElement_,
int kStride>
struct FragmentLoad {};
template <int kAccessSize,
typename Scalar_,
MemorySpace::Kind Memory_,
typename FragmentElement_,
int kStride>
struct FragmentLoad<IteratorFragment::kWmmaMatrix,
kAccessSize,
Scalar_,
Memory_,
FragmentElement_,
kStride> {
/// The output type.
typedef FragmentElement_ AccessType;
/// The load function.
static CUTLASS_DEVICE void load(AccessType& value, Scalar_ const* pointer, int offset) {
value.load(&pointer[offset], kStride);
}
};
template <int kAccessSize,
typename Scalar_,
MemorySpace::Kind Memory_,
typename FragmentElement_,
int kStride>
struct FragmentLoad<IteratorFragment::kScalar,
kAccessSize,
Scalar_,
Memory_,
FragmentElement_,
kStride> {
/// The output type.
typedef typename Vectorize<Scalar_, kAccessSize>::Type AccessType;
/// The load function.
static CUTLASS_DEVICE void load(AccessType& value, Scalar_ const* pointer, int offset) {
Load<Scalar_, kAccessSize, Memory_>::load(value, pointer, offset);
}
};
template <IteratorFragment::Kind kIteratorFragment,
int kAccessSize,
typename Scalar_,
MemorySpace::Kind Memory_,
typename FragmentElement_,
int kStride>
struct FragmentStore {};
template <int kAccessSize,
typename Scalar_,
MemorySpace::Kind Memory_,
typename FragmentElement_,
int kStride>
struct FragmentStore<IteratorFragment::kWmmaMatrix,
kAccessSize,
Scalar_,
Memory_,
FragmentElement_,
kStride> {
/// The input type.
typedef FragmentElement_ AccessType;
/// The store function.
static CUTLASS_DEVICE void store(AccessType const& value, Scalar_* pointer, int offset) {
value.store(&pointer[offset], kStride);
}
};
template <int kAccessSize,
typename Scalar_,
MemorySpace::Kind Memory_,
typename FragmentElement_,
int kStride>
struct FragmentStore<IteratorFragment::kScalar,
kAccessSize,
Scalar_,
Memory_,
FragmentElement_,
kStride> {
/// The input type.
typedef typename Vectorize<Scalar_, kAccessSize>::Type AccessType;
/// The store function.
static CUTLASS_DEVICE void store(AccessType const& value, Scalar_* pointer, int offset) {
Store<Scalar_, kAccessSize, Memory_>::store(value, pointer, offset);
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
} /// namespace cutlass

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/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Defines multiply-add operations on fragments within a thread.
*/
#pragma once
#include <cutlass/fragment.h>
namespace cutlass {
namespace gemm {
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename Scalar_>
struct FragmentMultiplyAdd {
/// The shape of the instruction.
typedef Shape<1, 1, 1, 1> InstructionShape;
/// The type for A.
typedef Scalar_ ScalarA;
/// The type for B.
typedef Scalar_ ScalarB;
/// The type for C and D.
typedef Scalar_ ScalarC;
/// Ctor.
CUTLASS_DEVICE FragmentMultiplyAdd() {}
/// Multiply : d = a*b.
template <typename FragmentB_, typename FragmentCd_>
CUTLASS_DEVICE void multiply(Scalar_ a, FragmentB_ const& b, FragmentCd_& d) {
int const kReduction = FragmentB_::kElements / FragmentCd_::kElements;
for (int j = 0; j < FragmentCd_::kElements; ++j) {
d[j] = a * b[j * kReduction + 0];
for (int k = 1; k < kReduction; ++k) {
d[j] += a * b[j * kReduction + k];
}
}
}
/// Multiply : d = a*b + c.
template <typename FragmentB_, typename FragmentCd_>
CUTLASS_DEVICE void multiply_add(Scalar_ a,
FragmentB_ const& b,
FragmentCd_ const& c,
FragmentCd_& d) {
int const kReduction = FragmentB_::kElements / FragmentCd_::kElements;
for (int j = 0; j < FragmentCd_::kElements; ++j) {
d[j] = a * b[j * kReduction + 0] + c[j];
for (int k = 1; k < kReduction; ++k) {
d[j] += a * b[j * kReduction + k];
}
}
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
#if !defined(__CUDACC_RTC__) || defined(CUTLASS_NVRTC_HAS_FP16)
template <>
struct FragmentMultiplyAdd<half> {
/// The shape of the instruction.
typedef Shape<1, 1, 2, 1> InstructionShape;
/// The type for A.
typedef half ScalarA;
/// The type for B.
typedef half ScalarB;
/// The type for C and D.
typedef half ScalarC;
/// Ctor.
CUTLASS_DEVICE FragmentMultiplyAdd() {}
/// Multiply : d = a*b.
template <typename FragmentB_, typename FragmentCd_>
CUTLASS_DEVICE void multiply(half a, FragmentB_ const& b, FragmentCd_& d) {
#if defined(__CUDACC__) && __CUDA_ARCH__ >= 530
// Assemble a half2 from a.
__half2 const a_half2 = __half2half2(a);
// The input.
__half2 const* b_half2 = reinterpret_cast<__half2 const*>(&b[0]);
// The output.
__half2* d_half2 = reinterpret_cast<__half2*>(&d[0]);
int const kReduction = FragmentB_::kElements / FragmentCd_::kElements;
for (int j = 0; j < FragmentCd_::kElements / 2; ++j) {
d_half2[j] = __hmul2(a_half2, b_half2[j * kReduction + 0]);
for (int k = 1; k < kReduction; ++k) {
d_half2[j] = __hfma2(a_half2, b_half2[j * kReduction + k], d_half2[j]);
}
}
#endif
}
/// Multiply : d = a*b + c.
template <typename FragmentB_, typename FragmentCd_>
CUTLASS_DEVICE void multiply_add(half a,
FragmentB_ const& b,
FragmentCd_ const& c,
FragmentCd_& d) {
#if defined(__CUDACC__) && __CUDA_ARCH__ >= 530
// Assemble a half2 from a.
__half2 const a_half2 = __half2half2(a);
// The inputs.
__half2 const* b_half2 = reinterpret_cast<__half2 const*>(&b[0]);
__half2 const* c_half2 = reinterpret_cast<__half2 const*>(&c[0]);
// The output.
__half2* d_half2 = reinterpret_cast<__half2*>(&d[0]);
int const kReduction = (FragmentB_::kElements / FragmentCd_::kElements);
for (int j = 0; j < FragmentCd_::kElements / 2; ++j) {
d_half2[j] = __hfma2(a_half2, b_half2[j * kReduction + 0], c_half2[j]);
for (int k = 1; k < kReduction; ++k) {
d_half2[j] = __hfma2(a_half2, b_half2[j * kReduction + k], d_half2[j]);
}
}
#endif
}
};
#endif
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace gemm
} // namespace cutlass

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/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Defines abstractions for efficiently clearing accumulator tiles.
*/
#pragma once
#include <cutlass/vector.h>
namespace cutlass {
namespace gemm {
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename Scalar_, int kLanes_ = 1>
struct ClearAccumulators {
/// The shared storage.
struct SharedStorage {};
/// Ctor.
CUTLASS_DEVICE ClearAccumulators() {}
/// Ctor.
CUTLASS_DEVICE ClearAccumulators(SharedStorage& shared_storage) {}
/// Clear the fragment.
template <typename Fragment_>
CUTLASS_DEVICE void clear(Fragment_& fragment) {
fragment.clear();
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace gemm
} // namespace cutlass

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/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Defines structural traits of double-precision GEMM.
*/
#pragma once
#include <cutlass/gemm/gemm.h>
#include <cutlass/gemm/gemm_epilogue.h>
#include <cutlass/gemm/gemm_epilogue_traits.h>
#include <cutlass/gemm/gemm_global_tile.h>
#include <cutlass/gemm/gemm_shared_tile.h>
#include <cutlass/gemm/gemm_traits.h>
#include <cutlass/gemm/thread_multiply_add.h>
namespace cutlass {
namespace gemm {
////////////////////////////////////////////////////////////////////////////////////////////////////
template <
/// The tile size for the GEMM KxNxM.
typename OutputTile_,
/// The number of accumulators per thread.
typename AccumulatorsPerThread_,
/// The number of scalars per LDG for A.
int kScalarsPerLdgA_ = 1,
/// The number of scalars per LDG for B.
int kScalarsPerLdgB_ = 1>
struct DgemmConfig
: public GemmConfig<
/// The scalar type for A.
double,
/// The scalar type for B.
double,
/// The scalar type for C.
double,
/// The scalar type for D.
double,
/// The tile size for the GEMM KxNxM.
OutputTile_,
/// The functor to do the math in the main loop.
ThreadMultiplyAdd<AccumulatorsPerThread_, Shape<1, 4, 8>, double, double, double>,
/// The number of scalars per LDG for A.
kScalarsPerLdgA_,
/// The number of scalars per STS for A.
kScalarsPerLdgA_,
/// The number of scalars per LDS for A.
2,
/// The number of scalars per LDG for B.
kScalarsPerLdgB_,
/// The number of scalars per STS for B.
kScalarsPerLdgB_,
/// The number of scalars per LDS for B.
2,
/// The number of scalars per LDG for C and STG for D.
1,
/// The number of scalars per STS for D.
2,
/// The number of scalars per LDS for D.
1,
/// The number of stages in shared memory.
2> {};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <
/// The layout for A.
MatrixLayout::Kind kLayoutA_,
/// The layout for B.
MatrixLayout::Kind kLayoutB_,
/// The output tile.
typename OutputTile_ = Shape<8, 64, 128>,
/// The functor to use in the epilogue.
typename EpilogueFunctor_ = LinearScaling<double>,
/// The number of accumulators per thread.
typename AccumulatorsPerThread_ = Shape<8, 8, 8>,
/// The number of doubles loaded in one LDG for A.
int kScalarsPerLdgA_ = 1,
/// The number of doubles loaded in one LDG for B.
int kScalarsPerLdgB_ = 1,
/// The index.
typename Index_ = int,
/// The DGEMM config.
typename GemmConfig_ =
DgemmConfig<OutputTile_, AccumulatorsPerThread_, kScalarsPerLdgA_, kScalarsPerLdgB_>,
/// The traits class for the epilogue.
typename GemmEpilogueTraits_ =
SimplifiedGemmEpilogueTraits<GemmConfig_, EpilogueFunctor_, Index_> >
struct DgemmTraits : public SimplifiedGemmTraits<
// The layout for A.
kLayoutA_,
// The layout for B.
kLayoutB_,
// The config.
GemmConfig_,
// The epilogue.
GemmEpilogue<GemmEpilogueTraits_>,
// The index.
Index_> {};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace gemm
} // namespace cutlass

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/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Implements a software-pipelined efficient GEMM.
*/
#pragma once
#if !defined(__CUDACC_RTC__)
#include <cuda.h>
#endif
#include <cutlass/coord.h>
#include <cutlass/util/platform.h>
namespace cutlass {
namespace gemm {
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename Gemm_>
__global__ /*__launch_bounds__(Gemm_::kThreads)*/ void gemm_kernel(typename Gemm_::Params params) {
// Declare shared memory.
__shared__ typename Gemm_::SharedStorage shared_storage;
// Construct the GEMM object.
Gemm_ gemm(params, shared_storage);
// Run GEMM.
gemm.multiply_add();
}
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename Scalar_, typename Index_ = int>
struct GemmDesc {
/// The dimensions of the GEMM.
Index_ m, n, k;
/// The alpha/beta scaling values.
Scalar_ alpha, beta;
/// The source matrix A.
void const* d_a;
/// The stride for A.
Index_ lda;
/// The source matrix B.
void const* d_b;
/// The stride for B.
Index_ ldb;
/// The source matrix C.
void const* d_c;
/// The stride for C.
Index_ ldc;
/// The destination matrix D.
void* d_d;
/// The stride for D.
Index_ ldd;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename GemmTraits_>
struct Gemm {
/// This class.
typedef Gemm<GemmTraits_> This_;
/// The traits.
typedef GemmTraits_ Traits;
/// The shared storage.
typedef typename Traits::SharedStorage SharedStorage;
/// The scalar for A.
typedef typename Traits::ScalarA ScalarA;
/// The scalar for B.
typedef typename Traits::ScalarB ScalarB;
/// The scalar in the epilogue.
typedef typename Traits::Epilogue::Scalar ScalarEpilogue;
/// The scalar for C.
typedef typename Traits::Epilogue::ScalarC ScalarC;
/// The scalar for D.
typedef typename Traits::Epilogue::ScalarD ScalarD;
/// The index.
typedef typename Traits::Index Index;
/// The number of threads.
static int const kThreads = Traits::GemmConfig::kThreads;
/// The params.
struct Params : public Traits::Params {
CUTLASS_HOST_DEVICE int initialize(Index m,
Index n,
Index k,
ScalarEpilogue alpha,
ScalarA const* d_a,
Index lda,
ScalarB const* d_b,
Index ldb,
ScalarEpilogue beta,
ScalarC const* d_c,
Index ldc,
ScalarD* d_d,
Index ldd) {
GemmDesc<ScalarEpilogue, Index> desc;
desc.m = m;
desc.n = n;
desc.k = k;
desc.alpha = alpha;
desc.beta = beta;
desc.d_a = reinterpret_cast<void const*>(d_a);
desc.lda = lda;
desc.d_b = reinterpret_cast<void const*>(d_b);
desc.ldb = ldb;
desc.d_c = reinterpret_cast<void const*>(d_c);
desc.ldc = ldc;
desc.d_d = reinterpret_cast<void*>(d_d);
desc.ldd = ldd;
return Traits::Params::initialize(desc);
}
};
#if !defined(__CUDACC_RTC__)
/// Launch the kernel.
static __host__ cudaError_t launch(Params const& params,
cudaStream_t stream = cudaStreamDefault) {
// Setup the grid.
dim3 grid;
grid.x = (params.m + Traits::OutputTile::kW - 1) / Traits::OutputTile::kW;
grid.y = (params.n + Traits::OutputTile::kH - 1) / Traits::OutputTile::kH;
// The number of threads.
dim3 block;
block.x = kThreads;
// Launch the kernel.
void const* params_ = reinterpret_cast<void const*>(&params);
return cudaLaunchKernel(reinterpret_cast<void*>(&gemm_kernel<This_>),
grid,
block,
const_cast<void**>(&params_),
0,
stream);
}
/// Launch the kernel.
static __host__ cudaError_t launch(CUfunction kernel,
Params const& params,
CUstream stream = CU_STREAM_LEGACY) {
// Setup the grid.
dim3 grid;
grid.x = (params.m + Traits::OutputTile::kW - 1) / Traits::OutputTile::kW;
grid.y = (params.n + Traits::OutputTile::kH - 1) / Traits::OutputTile::kH;
// The number of threads.
dim3 block;
block.x = kThreads;
// Launch the kernel.
void* params_[] = {const_cast<void*>(reinterpret_cast<void const*>(&params))};
// return cudaLaunchKernel(reinterpret_cast<void*>(&gemm_kernel<This_>), grid, block,
// const_cast<void**>(&params_), 0, stream);
CUresult result = cuLaunchKernel(
kernel, grid.x, grid.y, grid.z, block.x, block.y, block.z, 0, stream, params_, 0);
if (result != CUDA_SUCCESS) {
return cudaErrorLaunchFailure;
}
return cudaSuccess;
}
#endif
/// Ctor.
CUTLASS_DEVICE Gemm(Params const& params_, SharedStorage& shared_storage_)
: params(params_), shared_storage(shared_storage_) {}
/// Consume a single iteration of the loop.
template <bool kIsLastIteration>
CUTLASS_DEVICE void consume_tile(typename Traits::GlobalLoadStream& global_stream,
typename Traits::SharedLoadStream& shared_load_stream,
typename Traits::MultiplyAdd::Accumulators& accumulators,
Index outer_k) {
// If that's the last "load iteration" update the predicates.
if (!kIsLastIteration) {
global_stream.move_to_residue<false>(outer_k);
}
// Load data for the next iteration of the main loop.
if (!kIsLastIteration) {
global_stream.copy();
}
// The unrolling steps for the main loop.
int const kUnrollingSteps =
Traits::MultiplyAdd::AccumulatorsPerWarp::kD / Traits::MultiplyAdd::InstructionShape::kD;
CUTLASS_PRAGMA_UNROLL
for (int step = 0; step < kUnrollingSteps - 1; ++step) {
// Trigger the copy from shared memory for the next A/B values.
shared_load_stream.copy(step + 1);
// Make sure the values are available for the current iteration to do the multiply-add.
shared_load_stream.commit(step);
// Do the math on the fragments of the current iteration.
typename Traits::MultiplyAdd multiply_add;
multiply_add.multiply_add(shared_load_stream.fragment_a(step),
shared_load_stream.fragment_b(step),
accumulators,
accumulators);
}
// Make sure the data from shared memory has been entirely consumed.
Traits::shared_load_fence(true);
// Commit the data in shared memory for A/B.
if (!kIsLastIteration) {
global_stream.commit();
}
// Make sure the data is in shared memory.
Traits::shared_store_fence(true);
// Trigger the loads for the next iteration (if needed).
if (!kIsLastIteration) {
// Move to the next stage for the load (if it makes sense).
shared_load_stream.inc_stage();
// Trigger the copy from shared memory for the next loop iteration.
shared_load_stream.copy(0);
}
// Make sure the values are available for the current iteration to do the multiply-add.
shared_load_stream.commit(kUnrollingSteps - 1);
// Do the math on the fragments of the current iteration.
typename Traits::MultiplyAdd multiply_add;
multiply_add.multiply_add(shared_load_stream.fragment_a(kUnrollingSteps - 1),
shared_load_stream.fragment_b(kUnrollingSteps - 1),
accumulators,
accumulators);
}
/// Do the GEMM.
CUTLASS_DEVICE void multiply_add() {
// Swizzle the IDs of the block (to enable better cache behavior).
typename Traits::BlockSwizzle block_swizzle;
dim3 block = block_swizzle.swizzle();
// Scale the id.
block.x *= Traits::OutputTile::kW;
block.y *= Traits::OutputTile::kH;
// We may want to use shared memory to clear the registers.
typedef typename Traits::ClearAccumulators ClearAccumulators;
// The streams to read A/B from global memory to shared memory.
typename Traits::GlobalLoadStream global_stream(params, shared_storage, block);
// Create the accumulator clear.
ClearAccumulators clear(shared_storage.main_loop.clear);
// By how much we unroll the main loop.
Index const kUnroll = static_cast<Index>(Traits::OutputTile::kD);
// If we do not have enough steps in the main loop, trigger the residue code.
global_stream.move_to_residue<true>(params.k);
// Fetch the fragments for A and B from global memory.
global_stream.copy();
// Copy the elements to shared memory (after transformation if needed).
global_stream.commit();
// Make sure the data is in shared memory.
Traits::shared_store_fence(false);
// Rollback to the beginning of the GEMM-K dimension. It may have no impact.
global_stream.rollback();
// The unrolling steps for the main loop.
int const kUnrollingSteps =
Traits::MultiplyAdd::AccumulatorsPerWarp::kD / Traits::MultiplyAdd::InstructionShape::kD;
// Make sure we have at least 2 unrolling steps or our pipeling is not going to work.
static_assert(kUnrollingSteps >= 2, "The pipelining assumes at least two steps");
// The stream of data from shared memory to fragments.
typename Traits::SharedLoadStream shared_load_stream(params, shared_storage);
// Trigger the copy from shared memory for the 1st stream.
shared_load_stream.copy(0);
// Allocate the accumulators.
typename Traits::MultiplyAdd::Accumulators accumulators;
// Clear the accumulators.
clear.clear(accumulators);
// The loop index.
Index outer_k = params.k - kUnroll;
// Enter the main loop and iterate.
for (; outer_k > 0; outer_k -= kUnroll) {
consume_tile<false>(global_stream, shared_load_stream, accumulators, outer_k);
}
// Residual loop.
for (; outer_k > -kUnroll; outer_k -= kUnroll) {
consume_tile<true>(global_stream, shared_load_stream, accumulators, outer_k);
}
// Epilogue.
typedef typename Traits::Epilogue Epilogue;
Epilogue epilogue(params.epilogue, shared_storage.epilogue, params.m, params.n);
epilogue.epilogue(cutlass::make_Coord(0, block.y, block.x), accumulators);
}
/// The params.
Params const& params;
/// The shared storage.
SharedStorage& shared_storage;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace gemm
} // namespace cutlass

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@ -1,231 +0,0 @@
/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Implements the epilogue phase of the GEMM kernel that efficiently updates global memory
with
the computed matrix product.
*/
#pragma once
#include <cutlass/convert.h>
#include <cutlass/coord.h>
#include <cutlass/fragment.h>
namespace cutlass {
namespace gemm {
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename T>
CUTLASS_DEVICE bool is_zero(T x) {
return x == T(0);
}
#if !defined(__CUDACC_RTC__) || defined(CUTLASS_NVRTC_HAS_FP16)
CUTLASS_DEVICE bool is_zero(half x) { return reinterpret_cast<int16_t&>(x) == int16_t(0); }
#endif
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename GemmEpilogueTraits_>
struct GemmEpilogue {
/// The traits class.
typedef GemmEpilogueTraits_ Traits;
/// The params.
typedef typename Traits::Params Params;
/// The shared storage.
typedef typename Traits::SharedStorage SharedStorage;
/// The output tile.
typedef typename Traits::OutputTile OutputTile;
/// The number of iterations.
typedef typename Traits::Iterations Iterations;
/// The accumulators.
typedef typename Traits::Accumulators Accumulators;
/// The scalar.
typedef typename Traits::Scalar Scalar;
/// The functor in charge of the math.
typedef typename Traits::Functor Functor;
/// We do not support 3D or 4D shapes.
static_assert(Iterations::kD == 1 && Iterations::kC == 1, "Unsupported 3D/4D shapes");
/// The iterator for C in global memory.
typedef typename Traits::GlobalLoadIteratorC GlobalLoadIteratorC;
/// The transformer for C.
typedef typename Traits::GlobalTransformerC GlobalTransformerC;
/// The transformer for D.
typedef typename Traits::GlobalTransformerD GlobalTransformerD;
/// The iterator for D in global memory.
typedef typename Traits::GlobalStoreIteratorD GlobalStoreIteratorD;
/// The iterator to store D in shared memory.
typedef typename Traits::SharedStoreIteratorD SharedStoreIteratorD;
/// The shared store transformer for D.
typedef typename Traits::SharedStoreTransformerD SharedStoreTransformerD;
/// The iterator to load D in shared memory.
typedef typename Traits::SharedLoadIteratorD SharedLoadIteratorD;
/// The shared load transformer for D.
typedef Copy<typename SharedLoadIteratorD::Fragment> SharedLoadTransformerD;
/// The index.
typedef typename Traits::Index Index;
/// The scalar for C.
typedef typename GlobalLoadIteratorC::Scalar ScalarC;
/// The scalar for D.
typedef typename GlobalStoreIteratorD::Scalar ScalarD;
/// Ctor.
CUTLASS_DEVICE GemmEpilogue(Params const& params_,
SharedStorage& shared_storage_,
Index m_,
Index n_)
: params(params_), shared_storage(shared_storage_), m(m_), n(n_) {}
/// Execute the epilogue.
CUTLASS_DEVICE void epilogue(Coord<3> const& block, Accumulators& accumulators) {
if (is_zero(params.functor.beta)) {
epilogue_with_or_without_beta<true>(block, accumulators);
} else {
epilogue_with_or_without_beta<false>(block, accumulators);
}
}
template <bool kBetaIsZero_>
CUTLASS_DEVICE void epilogue_with_or_without_beta(Coord<3> const& block,
Accumulators& accumulators) {
// The problem size.
Coord<3> const bounds = cutlass::make_Coord(0, n, m);
// The functor.
Functor functor(params.functor);
// The C fragment.
typename GlobalLoadIteratorC::Fragment fragment_c;
// The transformed C fragment.
typename GlobalTransformerC::OutputFragment transformed_c;
CUTLASS_PRAGMA_UNROLL
for (int h = 0; h < Iterations::kH; ++h) {
// Compute pointer and predicate offsets for C and D global iterators.
int const pointer_offset =
((params.iterator_d.inc_h * (GlobalStoreIteratorD::Iterations::kH - 1) +
params.iterator_d.inc_advance) *
Iterations::kW +
params.stride_h) *
h;
int const predicate_offset =
((params.iterator_d.predicate_inc_h * (GlobalStoreIteratorD::Iterations::kH - 1) +
params.iterator_d.predicate_inc_advance) *
Iterations::kW +
Traits::Delta::kH) *
h;
// The iterator to load the elements of the C matrix.
GlobalLoadIteratorC global_load_iterator(
params.iterator_c, bounds, block, pointer_offset, predicate_offset);
// The transformer for C.
GlobalTransformerC transformer_c;
// The transformer for D.
GlobalTransformerD transformer_d;
// The iterator to store into the D matrix.
GlobalStoreIteratorD global_store_iterator(
params.iterator_d, bounds, block, pointer_offset, predicate_offset);
// The transformer to transform before storing to shared memory.
SharedStoreTransformerD shared_store_transformer;
typename SharedStoreTransformerD::OutputFragment shared_store_transformed_d;
// The iterator to store to shared memory.
SharedStoreIteratorD shared_store_iterator(params.shared_store_iterator_d,
shared_storage.shared_stream.store);
// The iterator to load from shared memory. TODO: Use a stream.
SharedLoadIteratorD shared_load_iterator(params.shared_load_iterator_d,
shared_storage.shared_stream.load);
CUTLASS_PRAGMA_UNROLL
for (int w = 0; w < Iterations::kW; ++w) {
// Load the C matrix into fragment.
if (!kBetaIsZero_) {
iterator_load(global_load_iterator, fragment_c);
}
// Make sure we can write to shared memory.
shared_load_fence();
// Copy the accumulators to shared memory.
int const offset = (h * Iterations::kW + w) * SharedStoreIteratorD::Fragment::kElements;
shared_store_transformer.transform(accumulators, offset, shared_store_transformed_d);
shared_iterator_store(shared_store_iterator, shared_store_transformed_d);
// Make sure the data is in shared memory.
shared_store_fence();
// Copy the accumulators back to registers from shared memory.
typename SharedLoadIteratorD::Fragment fetched_d;
shared_iterator_load(shared_load_iterator, fetched_d);
// Do the math.
typename GlobalTransformerD::InputFragment fragment_d;
if (kBetaIsZero_) {
functor.evaluate(fetched_d, fragment_d);
} else {
// Transform C fragment.
transformer_c.transform(fragment_c, transformed_c);
// Do the math.
functor.evaluate(fetched_d, transformed_c, fragment_d);
}
// Transform D fragment.
typename GlobalTransformerD::OutputFragment transformed_d;
transformer_d.transform(fragment_d, transformed_d);
// Copy the results to global memory.
iterator_store(global_store_iterator, transformed_d);
}
}
}
/// The memory fence for shared loads.
CUTLASS_DEVICE void shared_load_fence() { __syncthreads(); }
/// The memory fence for shared stores.
CUTLASS_DEVICE void shared_store_fence() { __syncthreads(); }
/// The params.
Params const& params;
/// The shared storage.
SharedStorage& shared_storage;
/// The dimensions of the GEMM.
Index m, n;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace gemm
} // namespace cutlass

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@ -1,331 +0,0 @@
/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Defines structural properties of the GEMM epilogue.
*/
#pragma once
#include <cutlass/convert.h>
#include <cutlass/coord.h>
#include <cutlass/gemm/gemm_global_stream.h>
#include <cutlass/gemm/gemm_shared_stream.h>
#include <cutlass/gemm/linear_scaling.h>
#include <cutlass/reshape_tile.h>
#include <cutlass/tile_iterator.h>
namespace cutlass {
namespace gemm {
////////////////////////////////////////////////////////////////////////////////////////////////////
template <
/// The output tile.
typename OutputTile_,
/// The accumulators.
typename Accumulators_,
/// The iterator to load C from global memory.
typename GlobalLoadIteratorC_,
/// The transformer for C.
typename GlobalTransformerC_,
/// The transformer for D.
typename GlobalTransformerD_,
/// The iterator to store D to global memory.
typename GlobalStoreIteratorD_,
/// The iterator to store D to shared memory.
typename SharedStoreIteratorD_,
/// The shared store transformer for D.
typename SharedStoreTransformerD_,
/// The iterator to load D from shared memory.
typename SharedLoadIteratorD_,
/// The number of iterations in the epilogue.
typename Iterations_,
/// The iterations strides.
typename Delta_,
/// The functor to be used in the epilogue.
typename Functor_,
/// The index.
typename Index_ = int>
struct GemmEpilogueTraits {
//
/// The output tile.
typedef OutputTile_ OutputTile;
/// The number of iterations.
/// The accumulators.
typedef Accumulators_ Accumulators;
/// The iterator for C in global memory.
typedef GlobalLoadIteratorC_ GlobalLoadIteratorC;
/// The transformer for C.
typedef GlobalTransformerC_ GlobalTransformerC;
/// The transformer for D.
typedef GlobalTransformerD_ GlobalTransformerD;
/// The iterator for D in global memory.
typedef GlobalStoreIteratorD_ GlobalStoreIteratorD;
/// The iterator to store D in shared memory.
typedef SharedStoreIteratorD_ SharedStoreIteratorD;
/// The shared store transformer for D.
typedef SharedStoreTransformerD_ SharedStoreTransformerD;
/// The iterator to store D in shared memory.
typedef SharedLoadIteratorD_ SharedLoadIteratorD;
/// typedef typename GemmConfig::EpilogueIterations Iterations;
typedef Iterations_ Iterations;
/// The iterations strides.
typedef Delta_ Delta;
/// The functor in charge of the math.
typedef Functor_ Functor;
/// The index.
typedef Index_ Index;
/// We do not support 3D or 4D shapes.
static_assert(Iterations::kD == 1 && Iterations::kC == 1, "Unsupported 3D/4D shapes");
/// The scalar.
typedef typename Functor::Scalar Scalar;
/// The scalar for C.
typedef typename GlobalLoadIteratorC::Scalar ScalarC;
/// The scalar for D.
typedef typename GlobalStoreIteratorD::Scalar ScalarD;
/// The params.
struct Params {
/// The strides for H and W in the different iterations of the epilogue.
Index stride_h, stride_w;
/// The params for the C iterator.
typename GlobalLoadIteratorC::Params iterator_c;
/// The params for the D global iterator.
typename GlobalStoreIteratorD::Params iterator_d;
/// The params for the D shared store iterator.
typename SharedStoreIteratorD::Params shared_store_iterator_d;
/// The params for the D shared load iterator.
typename SharedLoadIteratorD::Params shared_load_iterator_d;
/// The functor params.
typename Functor::Params functor;
/// Setup the params.
template <typename GemmDesc_>
CUTLASS_HOST_DEVICE int initialize(GemmDesc_ const& desc) {
// The parameters for the functor.
int error_code = functor.initialize(desc);
if (error_code) {
return error_code;
}
// At the end of the H iteration, we jump over a number of columns.
this->stride_h = desc.ldd * Delta::kH;
// Nothing to do here.
this->stride_w = 0;
// Setup the params for the global memory iterator for C.
error_code = iterator_c.initialize(
reinterpret_cast<ScalarC const*>(desc.d_c), desc.ldc, desc.n, stride_w, Delta::kW);
if (error_code) {
return error_code;
}
// Setup the params for the global memory iterator for D.
return iterator_d.initialize(
reinterpret_cast<ScalarD*>(desc.d_d), desc.ldd, desc.n, stride_w, Delta::kW);
}
};
/// The shared memory storage to exchange data.
union StreamSharedStorage {
// The storage for the store iterator.
typename SharedStoreIteratorD::SharedStorage store;
// The storage for the store iterator.
typename SharedLoadIteratorD::SharedStorage load;
};
/// The shared memory to swizzle the data in the epilogue.
struct SharedStorage {
// The storage for the shared stream D.
StreamSharedStorage shared_stream;
};
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename GemmConfig_, typename EpilogueFunctor_, typename Index_ = int>
struct GemmEpilogueTraitsHelper {
/// The scalar.
typedef typename EpilogueFunctor_::Scalar Scalar;
/// The output tile.
typedef typename GemmConfig_::OutputTile OutputTile;
/// The number of iterations in the epilogue.
typedef Shape<1,
GemmConfig_::MultiplyAdd::AccumulatorsPerThread::kH /
GemmConfig_::kAccumulatorsPerLdsB,
GemmConfig_::kAccumulatorsPerLdsB>
Iterations;
// The iteration strides in the H/W dimension.
typedef Shape<0,
GemmConfig_::kAccumulatorsPerLdsB*(
GemmConfig_::Warps::kH* GemmConfig_::MultiplyAdd::ThreadsPerWarp::kH - 1),
0>
Delta;
/// The functor to do the math in the epilogue.
typedef EpilogueFunctor_ Functor;
/// The traits class to build the iterator to store to shared memory for D.
typedef GemmSharedStoreTileDTraits<
// The pointer is float.
typename Functor::Scalar,
// The output tile size.
typename GemmConfig_::OutputTile,
// The number of warps.
typename GemmConfig_::Warps,
// The number of threads per warp.
typename GemmConfig_::MultiplyAdd::ThreadsPerWarp,
// The number of scalars per STS.
GemmConfig_::kScalarsPerStsD,
// The skew -- 128 / sizeof(ScalarD) / kScalarsPerStsD is the number of threads involved in
// a single STS. We divide by 2 as our objective is to add a skew to the odd threads to
// avoid bank conflicts between odd and even threads.
128 / sizeof(typename GemmConfig_::ScalarD) / GemmConfig_::kScalarsPerStsD / 2 *
GemmConfig_::kScalarsPerStsD>
SharedStoreTileTraits;
/// The iterator to store D to shared memory.
typedef TileStoreIterator<SharedStoreTileTraits,
typename SharedStoreTileTraits::Scalar,
IteratorAdvance::kH,
MemorySpace::kShared>
SharedStoreIteratorD;
/// The shared store transformer for D.
typedef Copy<typename SharedStoreIteratorD::Fragment> SharedStoreTransformerD;
/// The traits class to build the iterator to load from shared memory for D.
typedef GemmSharedLoadTileDTraits<
// The pointer is float.
typename Functor::Scalar,
// The output tile size.
typename GemmConfig_::OutputTile,
// The number of warps.
typename GemmConfig_::Warps,
// The number of threads per warp.
typename GemmConfig_::MultiplyAdd::ThreadsPerWarp,
// The number of columns of the output tile written by iteration.
GemmConfig_::OutputTile::kH / ShapeCount<Iterations>::kCount,
// The number of scalars per LDS.
GemmConfig_::kScalarsPerLdsD,
// The skew.
SharedStoreTileTraits::kSkew>
SharedLoadTileTraits;
/// The iterator to load D from shared memory.
typedef TileLoadIterator<SharedLoadTileTraits,
typename SharedLoadTileTraits::Scalar,
IteratorAdvance::kH,
MemorySpace::kShared>
SharedLoadIteratorD;
/// The traits class to build the iterator to load data from global memory for C^N.
typedef GemmGlobalTileCdTraits<
// The pointer is float const.
typename GemmConfig_::ScalarC const,
// The tile has size (N / Iterations)xM in GEMM's terminology.
Shape<1,
GemmConfig_::OutputTile::kH / ShapeCount<Iterations>::kCount,
GemmConfig_::OutputTile::kW>,
// The threads are distributed as warps x 32 (the traits may reorganize).
Shape<1, ShapeCount<typename GemmConfig_::Warps>::kCount, GemmConfig_::kWarpSize>,
// How many elements do we jump over at each iteration?
Iterations::kW,
// The number of scalars per LDG (LDG.32 or LDG.128, etc).
GemmConfig_::kScalarsPerLdgC>
GlobalLoadTileTraits;
/// The iterator to load C.
typedef GemmGlobalIteratorCd<GlobalLoadTileTraits, Index_> GlobalLoadIteratorC;
/// The transformer for C.
typedef Copy<typename GlobalLoadIteratorC::Fragment> GlobalTransformerC;
/// The traits class to build the iterator to store data to global memory for D^N.
typedef GemmGlobalTileCdTraits<
// The pointer is float.
typename GemmConfig_::ScalarD,
// The tile has size (N / Iterations)xM in GEMM's terminology.
Shape<1,
GemmConfig_::OutputTile::kH / ShapeCount<Iterations>::kCount,
GemmConfig_::OutputTile::kW>,
// The threads are distributed as warps x 32 (the traits may reorganize).
Shape<1, ShapeCount<typename GemmConfig_::Warps>::kCount, GemmConfig_::kWarpSize>,
// How many elements do we jump over at each iteration?
Iterations::kW,
// The number of scalars per LDG (LDG.32 or LDG.128, etc).
GemmConfig_::kScalarsPerStgD>
GlobalStoreTileTraits;
/// The iterator to store D.
typedef GemmGlobalIteratorCd<GlobalStoreTileTraits, Index_> GlobalStoreIteratorD;
/// The transformer for D.
typedef Copy<typename GlobalStoreIteratorD::Fragment> GlobalTransformerD;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <
/// The GEMM config.
typename GemmConfig_,
/// The epilogue functor to do the math in the epilogue.
typename EpilogueFunctor_,
/// The index.
typename Index_ = int,
/// The helper to create the traits class.
typename Helper_ = GemmEpilogueTraitsHelper<GemmConfig_, EpilogueFunctor_, Index_> >
struct SimplifiedGemmEpilogueTraits : public GemmEpilogueTraits<
// The output tile.
typename GemmConfig_::OutputTile,
// The accumulators.
typename GemmConfig_::Accumulators,
// The global iterator for C.
typename Helper_::GlobalLoadIteratorC,
// The transformer for C.
typename Helper_::GlobalTransformerC,
// The transformer for D.
typename Helper_::GlobalTransformerD,
// The global iterator for D.
typename Helper_::GlobalStoreIteratorD,
// The iterator to store D to shared memory.
typename Helper_::SharedStoreIteratorD,
// The shared store transformer for D.
typename Helper_::SharedStoreTransformerD,
// The iterator to load D from shared memory.
typename Helper_::SharedLoadIteratorD,
// The number of iterations.
typename Helper_::Iterations,
// The strides between iterations.
typename Helper_::Delta,
// The functor to be used in the epilogue.
EpilogueFunctor_,
// The index.
Index_> {};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace gemm
} // namespace cutlass

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@ -1,182 +0,0 @@
/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Implements efficient loading of the thread block-level tile from global memory and
storing
to shared memory.
*/
#pragma once
#include <cutlass/convert.h>
#include <cutlass/gemm/gemm_global_tile.h>
#include <cutlass/iterator_access.h>
namespace cutlass {
namespace gemm {
////////////////////////////////////////////////////////////////////////////////////////////////////
template <
/// The load iterator.
typename LoadIterator_,
/// The store iterator to copy to shared memory.
typename StoreIterator_,
/// The transformer to be applied after the data has been copied from global memory.
typename Transformer_>
struct GlobalLoadStreamBase {
/// The load iterator.
typedef LoadIterator_ LoadIterator;
/// The transformer.
typedef Transformer_ Transformer;
/// The store iterator to write to shared memory.
typedef StoreIterator_ StoreIterator;
/// The fragment that is copied from shared memory.
typedef typename LoadIterator::Fragment FetchedFragment;
/// The fragment that is obtained after the transformation by the transformer.
typedef typename Transformer::OutputFragment TransformedFragment;
/// Make sure the fragments match.
static_assert((platform::is_same<FetchedFragment, typename Transformer::InputFragment>::value),
"");
/// The output fragment.
typedef TransformedFragment Fragment;
/// Make sure the transformed fragment is the same as the store fragment.
static_assert((platform::is_same<TransformedFragment, typename StoreIterator::Fragment>::value),
"");
/// The layout.
static MatrixLayout::Kind const kLayout = LoadIterator::kLayout;
/// The scalar type of the iterator.
typedef typename LoadIterator::Scalar Scalar;
/// The pointer.
typedef typename LoadIterator::Pointer Pointer;
/// The index.
typedef typename LoadIterator::Index Index;
/// The params.
struct Params {
// The load iterator.
typename LoadIterator::Params load_iterator;
// The store iterator.
typename StoreIterator::Params store_iterator;
/// Setup the params.
template <typename GemmDesc_>
CUTLASS_HOST_DEVICE int initialize(GemmDesc_ const& desc, Pointer pointer, Index ld) {
int error_code = load_iterator.initialize(desc, pointer, ld);
if (error_code) {
return error_code;
}
return store_iterator.initialize();
}
};
/// The amount of storage in shared memory needed to store the tile.
typedef typename StoreIterator::SharedStorage SharedStoreStorage;
/// The storage in shared memory needed by that stream.
union SharedStorage {
// The load iterator.
typename LoadIterator::SharedStorage load_iterator;
// The store iterator.
SharedStoreStorage store_iterator;
};
/// Ctor.
CUTLASS_DEVICE GlobalLoadStreamBase(Params const& params,
SharedStorage& shared_storage,
Coord<3> const bounds,
Coord<3> const& block)
: load_iterator(params.load_iterator, bounds, block),
transformer(),
store_iterator(params.store_iterator, shared_storage.store_iterator)
{
fetched_fragment.clear();
}
/// Load the data from shared memory to the fetch fragment.
CUTLASS_DEVICE void copy() { iterator_load(load_iterator, fetched_fragment); }
/// Commit the data.
CUTLASS_DEVICE void commit() {
transformer.transform(fetched_fragment, transformed_fragment);
iterator_store(store_iterator, transformed_fragment);
store_iterator.inc_stage();
}
/// Move to the beginning of the residue code. That's a new code path in CUTLASS 1.0.1.
CUTLASS_DEVICE void move_to_residue(Index k) { load_iterator.move_to_residue(k); }
/// Execute the residue code.
CUTLASS_DEVICE void residue(Index k, bool skip_clear = false) {
load_iterator.residue(k);
if (!skip_clear) {
fetched_fragment.clear();
}
}
/// Rollback to the beginning of the GEMM-k dimension.
CUTLASS_DEVICE void rollback() { load_iterator.rollback(); }
/// The iterator.
LoadIterator load_iterator;
/// The fragment to fetch from shared memory.
FetchedFragment fetched_fragment;
/// The transformer.
Transformer transformer;
/// The fragment to convert the data after it has been fetched from shared memory.
TransformedFragment transformed_fragment;
/// The store iterator.
StoreIterator store_iterator;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <
/// The load iterator.
typename LoadIterator_,
/// The store iterator to copy to shared memory.
typename StoreIterator_,
/// The transformer to be applied after the data has been copied from global memory.
typename Transformer_ = Copy<typename LoadIterator_::Fragment> >
struct GlobalLoadStream : public GlobalLoadStreamBase<LoadIterator_, StoreIterator_, Transformer_> {
/// The base class.
typedef GlobalLoadStreamBase<LoadIterator_, StoreIterator_, Transformer_> Base;
/// Ctor.
CUTLASS_DEVICE GlobalLoadStream(typename Base::Params const& params,
typename Base::SharedStorage& shared_storage,
Coord<3> const& bounds,
Coord<3> const& block)
: Base(params, shared_storage, bounds, block) {}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace gemm
} // namespace cutlass

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@ -1,541 +0,0 @@
/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Defines iterators for efficiently loading and storing to global memory.
*/
#pragma once
#include <cutlass/coord.h>
#include <cutlass/util/platform.h>
#include <cutlass/gemm/gemm_operand.h>
#include <cutlass/matrix_traits.h>
#include <cutlass/predicate_vector.h>
#include <cutlass/reshape_tile.h>
#include <cutlass/tile_iterator.h>
namespace cutlass {
namespace gemm {
////////////////////////////////////////////////////////////////////////////////////////////////////
// The following functor reshapes a tile of threads to match a tile of data. The idea is that when
// the user wants to build the iterator traits, he/she may want to specify the tile independently
// from the number of scalars loaded/stored per instruction. For example, in the row-major version
// with a tile of size 128x8 - the user may want to that the iterator works with 32x8 threads if
// each thread loads 1 scalar per LDG. If the user changes to 4 scalars per LDG, then the tile of
// threads has to change. The code below detects that and correct the code automatically - it is
// a helper when the user does not specify the right configuration.
template <typename Tile_, typename Threads_, bool = (Tile_::kW < Threads_::kW)>
struct ReshapeThreads {
typedef Threads_ Threads;
};
template <typename Tile_, typename Threads_>
struct ReshapeThreads<Tile_, Threads_, true> {
typedef Shape<Threads_::kD, Threads_::kH * Threads_::kW / Tile_::kW, Tile_::kW, 1> Threads;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <GemmOperand::Kind kOperand_,
MatrixLayout::Kind kLayout_,
typename Scalar_,
typename Tile_,
typename Threads_,
int kAccessSize_>
struct GemmGlobalTileTraits {
/// Identity of the operand
static GemmOperand::Kind const kOperand = kOperand_;
/// The layout.
static MatrixLayout::Kind const kLayout = kLayout_;
/// The scalar.
typedef typename platform::remove_const<Scalar_>::type Scalar;
/// The pointer.
typedef Scalar_* Pointer;
/// The number of scalars per LDG/STG.
static int const kAccessSize = kAccessSize_;
/// The memory space.
static MemorySpace::Kind const kMemorySpace = MemorySpace::kGlobal;
/// The tile shape
typedef typename ReshapeTile<Tile_, kAccessSize_>::Tile Tile;
/// The threads shape
typedef typename ReshapeThreads<Tile, Threads_>::Threads Threads;
/// The relative offset between two elements in the H/W dimension in adjacent threads.
typedef Shape<1, 1, Tile::kC> ThreadsDelta;
/// The strides in each dimension between different loads/stores.
typedef Shape<0, Threads::kH, Threads::kW * kAccessSize> Delta;
/// Strides for immediate offset computation
typedef Shape<0, 0, Threads::kW * ThreadsDelta::kW, kAccessSize> ImmediateOffsetStrides;
/// The number of iterations needed to load/store the tile.
typedef Shape<1, Tile::kH / Threads::kH, Tile::kW / Threads::kW, Tile::kC / kAccessSize>
Iterations;
typedef GemmMultiplicandTraits<Tile, kOperand, kLayout> MultiplicandTraits;
/// Computes the thread offset in (H, W) based on thread ID
struct ThreadOffset {
CUTLASS_HOST_DEVICE
Coord<4> operator()() const {
int thread_offset_h = threadIdx.x / Threads::kW * ThreadsDelta::kH;
int thread_offset_w = threadIdx.x % Threads::kW * ThreadsDelta::kW;
return make_Coord(0, thread_offset_h, thread_offset_w, 0);
}
};
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename Scalar_, typename Tile_, typename Threads_, int kStrideH_, int kAccessSize_>
struct GemmGlobalTileCdTraits : public GemmGlobalTileTraits<GemmOperand::kC,
MatrixLayout::kColumnMajor,
Scalar_,
Tile_,
Threads_,
kAccessSize_> {
/// The base class.
typedef GemmGlobalTileTraits<GemmOperand::kC,
MatrixLayout::kColumnMajor,
Scalar_,
Tile_,
Threads_,
kAccessSize_>
Base;
/// The stride in the H dimension.
static int const kStrideH = kStrideH_;
/// Override the strides in each dimension between different loads/stores.
typedef Shape<0, 0, Base::Delta::kW, Base::Delta::kC> Delta;
typedef typename Base::Iterations Iterations;
typedef typename Base::Threads Threads;
typedef typename Base::ThreadsDelta ThreadsDelta;
typedef typename Base::ImmediateOffsetStrides ImmediateOffsetStrides;
/// Computes the thread offset in (H, W) based on thread ID
struct ThreadOffset {
CUTLASS_HOST_DEVICE
Coord<4> operator()() const {
int thread_offset_h = threadIdx.x / Threads::kW * kStrideH * Iterations::kH;
int thread_offset_w = threadIdx.x % Threads::kW * ThreadsDelta::kW;
return make_Coord(0, thread_offset_h, thread_offset_w, 0);
}
};
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename TileTraits_, typename Index_ = int>
struct GemmGlobalIteratorAb
: public TileLoadIterator<TileTraits_,
typename TileTraits_::Scalar,
TileTraits_::MultiplicandTraits::kKstrided ? IteratorAdvance::kH
: IteratorAdvance::kW,
MemorySpace::kGlobal,
Index_> {
/// This class.
typedef GemmGlobalIteratorAb<TileTraits_, Index_> This_; /// The base class.
typedef TileLoadIterator<TileTraits_,
typename TileTraits_::Scalar,
TileTraits_::MultiplicandTraits::kKstrided ? IteratorAdvance::kH
: IteratorAdvance::kW,
MemorySpace::kGlobal,
Index_>
Base;
/// The layout.
static MatrixLayout::Kind const kLayout = TileTraits_::kLayout;
/// Fragment type loaded by the iterator
typedef typename Base::Fragment Fragment;
/// The scalar.
typedef typename TileTraits_::Scalar Scalar;
/// The threads.
typedef typename TileTraits_::Threads Threads;
/// The index.
typedef Index_ Index;
/// The thread offset
typedef typename TileTraits_::ThreadOffset ThreadOffset;
/// Specifies in which dimension post-increment accesses advance.
static IteratorAdvance::Kind const kAdvance = Base::kAdvance;
typedef cutlass::PredicateVector<ShapeCount<typename Base::Iterations>::kCount> PredicateVector;
/// Iterator parameters type
typedef typename Base::Params BaseParams;
struct Params : public BaseParams {
/// Initializes params to load a strip-mined tile, given pointer and stride_h.
template <typename GemmDesc_>
CUTLASS_HOST_DEVICE int initialize(GemmDesc_ const& desc, Scalar const* ptr, Index stride_h) {
Index inc_d = 0;
Index inc_advance = 0;
// Move by some columns for each iteration in the H dimension.
Index inc_h = Base::Delta::kH * stride_h;
// Move by some more columns in the number of iterations if the D dimension is > 1.
if (Base::Delta::kD > 0) {
inc_d = Base::Delta::kD * stride_h - (Base::Iterations::kH - 1) * inc_h;
}
// Move to the beginning of the next iteration.
if (kAdvance == IteratorAdvance::kH && Base::Delta::kD > 0) {
inc_advance = inc_d;
} else if (kAdvance == IteratorAdvance::kH) {
inc_advance = inc_h;
} else if (Base::Delta::kD > 0) {
inc_advance = (Base::Iterations::kW + 0) * ShapeCount<typename Base::Delta>::kWc -
(Base::Iterations::kH - 1) * inc_h -
(Base::Iterations::kD - 1) * Base::Delta::kD * stride_h;
} else {
inc_advance = (Base::Iterations::kW + 0) * ShapeCount<typename Base::Delta>::kWc -
(Base::Iterations::kH - 1) * inc_h;
}
// The dimensions of the tile.
int const kH = TileTraits_::Tile::kH;
int const kW = TileTraits_::Tile::kW * TileTraits_::kAccessSize;
// Move to the residue.
Index const kBlock = kAdvance == IteratorAdvance::kH ? kH : kW;
// The jump in the gemm-k dimension.
Index const stride = kAdvance == IteratorAdvance::kH ? stride_h : 1;
// Compute the offset to the residue and how to "come" back.
Index const kResidue = desc.k % kBlock;
if (kResidue > 0) {
move_to_residue_offset = (desc.k - kResidue) * stride;
} else {
move_to_residue_offset = (desc.k - kBlock) * stride;
}
Base::Params::initialize(ptr, 0, stride_h, 1, inc_d, inc_h, 0, inc_advance);
return 0;
}
// The extra offset to control moving to the residue.
Index move_to_residue_offset;
};
/// Ctor.
CUTLASS_DEVICE GemmGlobalIteratorAb(Params const& _params,
const Coord<3>& bounds,
const Coord<3>& block,
ThreadOffset thread_offset_func = ThreadOffset())
: params(_params) {
thread_offset = thread_offset_func();
// The column.
Index block_h = thread_offset[1];
// The contiguous dimension.
Index block_w = thread_offset[2];
// Add the blocks indices.
if (kAdvance == IteratorAdvance::kH) {
block_h += block[1];
block_w += block[2];
} else {
block_h += block[2];
block_w += block[1];
}
// Setup the pointer.
params.pointer += (block_h * params.stride_h + block_w);
// Initialize predicates
initialize_predicates(bounds, make_Coord(0, block_h, block_w));
}
/// The accessor.
CUTLASS_DEVICE void get(typename Base::AccessType& value, int d, int h, int w, int c) const {
int const imm =
ComputeOffsetFromStrides<typename Base::ImmediateOffsetStrides>::get(0, 0, w, c);
Load<Scalar, TileTraits_::kAccessSize, MemorySpace::kGlobal>::load(value, params.pointer, imm);
}
/// Increment the pointer in the H dimension.
CUTLASS_DEVICE void inc_h() { params.pointer += params.inc_h; }
/// Increment the pointer in the D dimension.
CUTLASS_DEVICE void inc_d() { params.pointer += params.inc_d; }
/// Increment the pointer to move to the next iteration.
CUTLASS_DEVICE void inc_advance() { params.pointer += params.inc_advance; }
/// Initialize the predicates.
CUTLASS_DEVICE void initialize_predicates(const Coord<3>& bounds, const Coord<3>& block) {
// Setup the masks to control loads.
predicates.fill(0);
int bounds_h, bounds_w;
if (kAdvance == IteratorAdvance::kH) {
bounds_w = bounds[2] - block[2];
bounds_h = bounds[1];
} else {
bounds_w = bounds[1];
bounds_h = bounds[2] - block[1];
}
// Fill in the bits of the predicate vector.
for (int d = 0; d < Base::Iterations::kD; ++d) {
for (int h = 0; h < Base::Iterations::kH; ++h) {
for (int w = 0; w < Base::Iterations::kW; ++w) {
for (int c = 0; c < Base::Iterations::kC; ++c) {
bool flag = w * Base::Delta::kW < bounds_w;
if (kAdvance == IteratorAdvance::kH) {
flag = flag && (h * Base::Delta::kH + d * Base::Delta::kD) < bounds_h;
} else {
flag = flag && (h * Base::Delta::kH) < bounds_h;
}
int const bit = ComputeOffsetFromShape<typename Base::Iterations>::get(d, h, w, c);
predicates.set(bit, flag);
}
}
}
}
}
/// Move to residue portion.
CUTLASS_DEVICE void move_to_residue(Index k) {
// Store the pointer and the predicates.
stored_pointer = params.pointer;
stored_predicates = predicates;
// Move the pointer to the residue.
params.pointer += params.move_to_residue_offset;
// The dimensions of the tile.
int const kH = TileTraits_::Tile::kH;
int const kW = TileTraits_::Tile::kW * TileTraits_::kAccessSize;
// The unrolling factor.
int const kUnroll = kAdvance == IteratorAdvance::kH ? kH : kW;
// Clear the predicates for the residue. TODO: We can do something smarter.
int const kResidue = (int)(k % (Index)kUnroll);
if (kResidue > 0) {
residue(kResidue);
}
}
/// That's the residue! Update the predicates.
CUTLASS_DEVICE void residue(Index k) {
// The coordinates of the thread.
Index block_h = thread_offset[1];
// The contiguous dimension.
Index block_w = thread_offset[2];
// Update the predicate vector.
for (int d = 0; d < Base::Iterations::kD; ++d) {
for (int h = 0; h < Base::Iterations::kH; ++h) {
for (int w = 0; w < Base::Iterations::kW; ++w) {
for (int c = 0; c < Base::Iterations::kC; ++c) {
Index offset = 0;
if (kAdvance == IteratorAdvance::kH) {
offset += block_h + h * Base::Delta::kH + d * Base::Delta::kD;
} else {
offset += block_w + w * Base::Delta::kW;
}
int const bit = ComputeOffsetFromShape<typename Base::Iterations>::get(d, h, w, c);
if (offset >= k) {
predicates.set(bit, false);
}
}
}
}
}
}
/// Rollback to beginning of first tile and initialize predicates.
CUTLASS_DEVICE void rollback() {
params.pointer = stored_pointer;
predicates = stored_predicates;
}
/// Is the iterator valid?
CUTLASS_DEVICE bool valid(int d, int h, int w, int c) const {
int const bit = ComputeOffsetFromShape<typename Base::Iterations>::get(d, h, w, c);
return predicates[bit];
}
/// Offset of an individual lane from the start of the tile
Coord<4> thread_offset;
/// The parameters
Params params;
/// The pointer.
typename Base::Scalar const* stored_pointer;
/// The predicates.
PredicateVector predicates, stored_predicates;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename TileTraits_, typename Index_ = int>
struct GemmGlobalIteratorCd : public TileIteratorBase<TileTraits_,
typename TileTraits_::Scalar,
IteratorAdvance::kH,
MemorySpace::kGlobal,
Index_> {
/// This class.
typedef GemmGlobalIteratorCd<TileTraits_, Index_> This_;
/// The base class.
typedef TileIteratorBase<TileTraits_,
typename TileTraits_::Scalar,
IteratorAdvance::kH,
MemorySpace::kGlobal,
Index_>
Base;
/// The layout.
static MatrixLayout::Kind const kLayout = TileTraits_::kLayout;
/// The scalar.
typedef typename TileTraits_::Scalar Scalar;
/// The pointer.
typedef typename TileTraits_::Pointer Pointer;
/// The threads.
typedef typename TileTraits_::Threads Threads;
/// The index.
typedef Index_ Index;
/// The thread offset
typedef typename TileTraits_::ThreadOffset ThreadOffset;
/// The params.
struct Params {
/// The pointer.
Pointer pointer;
/// The stride in the H dimension to setup the thread in the block.
Index stride_h;
/// The strides to increment the pointer.
Index inc_advance, inc_h;
/// The strides to increment the predicate offset
Index predicate_inc_advance, predicate_inc_h;
/// The column offset to compute the predicate for the columns.
Index predicate_offset;
/// Setup the params.
CUTLASS_HOST_DEVICE int initialize(
Pointer pointer, Index ld, Index bound, Index epilogue_stride_w, Index epilogue_delta_w) {
// The pointer.
this->pointer = pointer;
// Each column of the matrix.
stride_h = TileTraits_::ThreadsDelta::kH * ld;
// Each thread output 1 column per iteration. The stride between columns is given by the
// number of scalars that are loaded per LDS for B.
inc_h = ld * TileTraits_::kStrideH;
inc_advance =
(ld - ld * TileTraits_::kStrideH * (Base::Iterations::kH - 1)) + epilogue_stride_w;
predicate_offset = bound;
predicate_inc_h = TileTraits_::kStrideH;
predicate_inc_advance =
-((TileTraits_::kStrideH * (Base::Iterations::kH - 1) - 1) + epilogue_delta_w);
return 0;
}
};
Params params;
/// Offset of an individual lane from the start of the tile
Coord<4> thread_offset;
/// Ctor.
CUTLASS_DEVICE GemmGlobalIteratorCd() {}
/// Ctor.
CUTLASS_DEVICE GemmGlobalIteratorCd(Params const& params,
const Coord<3>& bounds,
const Coord<3>& block,
int offset = 0,
int pred_offset = 0,
ThreadOffset thread_offset_func = ThreadOffset())
: params(params) {
thread_offset = thread_offset_func();
// Each warp works on a different column of the tile.
int const h = thread_offset[1] + block[1];
// Each lane writes a different element.
int const w = thread_offset[2] + block[2];
// Setup the pointer.
this->params.pointer += ((h * params.stride_h + w) + offset);
// Prepare the vector of predicates.
for (int i = 0; i < Base::Iterations::kW; ++i) {
predicates.set(i, w + i * Base::Delta::kW < bounds[2]);
}
this->params.predicate_offset -= (h + pred_offset);
}
/// The accessor.
CUTLASS_DEVICE void get(typename Base::AccessType& value, int d, int h, int w, int c) const {
int const imm =
ComputeOffsetFromStrides<typename Base::ImmediateOffsetStrides>::get(0, 0, w, c);
Load<Scalar, TileTraits_::kAccessSize, MemorySpace::kGlobal>::load(value, params.pointer, imm);
}
/// Increment the pointer in the C dimension.
CUTLASS_DEVICE void inc_c() {}
/// Increment the pointer in the W dimension.
CUTLASS_DEVICE void inc_w() {}
/// Increment the pointer in the H dimension.
CUTLASS_DEVICE void inc_h() {
params.pointer += params.inc_h;
params.predicate_offset -= params.predicate_inc_h;
}
/// Increment the pointer in the D dimension.
CUTLASS_DEVICE void inc_d() {}
/// Increment the pointer to move to the next iteration.
CUTLASS_DEVICE void inc_advance() {
params.pointer += params.inc_advance;
this->params.predicate_offset -= params.predicate_inc_advance;
}
/// The accessor.
CUTLASS_DEVICE void set(typename Base::AccessType const& value, int d, int h, int w, int c) {
int const imm =
ComputeOffsetFromStrides<typename Base::ImmediateOffsetStrides>::get(0, 0, w, c);
Store<Scalar, TileTraits_::kAccessSize, MemorySpace::kGlobal>::store(
value, params.pointer, imm);
}
/// Test the validity of the iterator.
CUTLASS_DEVICE bool valid(int d, int h, int w, int c) const {
return predicates.at(w) && params.predicate_offset > 0;
}
/// The predicates for the row.
cutlass::PredicateVector<Base::Iterations::kW> predicates;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace gemm
} // namespace cutlass

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@ -1,141 +0,0 @@
/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Defines constant expressions for mapping GEMM problem size and strides onto pitch-linear
memory.
*/
#pragma once
#include <cutlass/matrix_traits.h>
#include <cutlass/reshape_tile.h>
#include <cutlass/util/platform.h>
namespace cutlass {
namespace gemm {
////////////////////////////////////////////////////////////////////////////////////////////////////
/// Helper to describe attributes of GEMM matrix operands
template <GemmOperand::Kind kOperand_, MatrixLayout::Kind kLayout_>
struct GemmOperandTraitsAb {
static const bool Congruous =
(kOperand_ == GemmOperand::kA ^ kLayout_ == MatrixLayout::kRowMajor);
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename GemmOperand::Kind kOperand_, typename Tile_>
struct GetExtent;
template <typename Tile_>
struct GetExtent<GemmOperand::kA, Tile_> {
static const int kExtent = Tile_::kW;
};
template <typename Tile_>
struct GetExtent<GemmOperand::kB, Tile_> {
static const int kExtent = Tile_::kH;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
/// Determines the shape of a multiplicand tile in terms of strided (H) and contiguous (W)
/// dimensions
template <typename ThreadBlockTile_, GemmOperand::Kind Usage, MatrixLayout::Kind Layout>
struct GemmMultiplicandTraits {
// Only defined for A or B
static_assert(Usage == GemmOperand::kA || Usage == GemmOperand::kB,
"MultiplicandTileShape defined only for A or B operands.");
/// Shape of GEMM thread block tile (K, N, M)
typedef ThreadBlockTile_ ThreadBlockTile;
/// Identifies multiplicand
static GemmOperand::Kind const kUsage = Usage;
/// Layout of tile
static MatrixLayout::Kind const kLayout = Layout;
// True if K is the strided dimension
static bool const kKstrided = (kUsage == GemmOperand::kA ^ kLayout == MatrixLayout::kRowMajor);
/// Map the ThreadBlockShape onto (kH, kW) dimensions for A and B operand
typedef typename platform::conditional<
kKstrided,
Shape<1, ThreadBlockTile::kD, GetExtent<Usage, ThreadBlockTile>::kExtent>,
Shape<1, GetExtent<Usage, ThreadBlockTile>::kExtent, ThreadBlockTile::kD> >::type Shape;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
/// Project's a coordinate (K, N, M) onto inner and outer dimensions defined for each
/// operand.
template <GemmOperand::Kind operand, bool Kstrided = true>
struct ProjectOperand;
/// Project A operand - (0, K, M)
template <bool Kstrided>
struct ProjectOperand<GemmOperand::kA, Kstrided> {
CUTLASS_HOST_DEVICE
static Coord<3> project(Coord<3> const &coord) {
if (Kstrided) {
return make_Coord(0, coord[0], coord[2]);
} else {
return make_Coord(0, coord[2], coord[0]);
}
}
};
/// Project B operand - (0, K, N)
template <bool Kstrided>
struct ProjectOperand<GemmOperand::kB, Kstrided> {
CUTLASS_HOST_DEVICE
static Coord<3> project(Coord<3> const &coord) {
if (Kstrided) {
return make_Coord(0, coord[0], coord[1]);
} else {
return make_Coord(0, coord[1], coord[0]);
}
}
};
/// Project C operand - (0, N, M)
template <>
struct ProjectOperand<GemmOperand::kC, true> {
CUTLASS_HOST_DEVICE
static Coord<3> project(Coord<3> const &coord) { return make_Coord(0, coord[1], coord[2]); }
};
/// Project D operand - (0, N, M)
template <>
struct ProjectOperand<GemmOperand::kD, true> {
CUTLASS_HOST_DEVICE
static Coord<3> project(Coord<3> const &coord) { return make_Coord(0, coord[1], coord[2]); }
};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace gemm
} // namespace cutlass

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@ -1,113 +0,0 @@
/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Defines abstractions for managing loading and storing fragments to shared memory in the
efficient GEMM pipeline.
*/
#pragma once
#include <cutlass/gemm/gemm_shared_tile.h>
namespace cutlass {
namespace gemm {
////////////////////////////////////////////////////////////////////////////////////////////////////
template <
/// The load iterator.
typename Iterator_,
/// The transformer to be applied after the data has been copied from shared memory.
typename Transformer_ = Copy<typename Iterator_::Fragment> >
struct SharedLoadStream {
/// The load iterator.
typedef Iterator_ Iterator;
/// The transformer.
typedef Transformer_ Transformer;
/// The fragment that is copied from shared memory.
typedef typename Iterator::Fragment FetchedFragment;
/// The fragment that is obtained after the transformation by the transformer.
typedef typename Transformer::OutputFragment TransformedFragment;
/// Make sure the fragments match.
static_assert((platform::is_same<FetchedFragment, typename Transformer::InputFragment>::value),
"");
/// The output fragment.
typedef TransformedFragment Fragment;
/// The params.
struct Params {
/// The iterator params.
typename Iterator::Params iterator;
/// Setup the params.
CUTLASS_HOST_DEVICE int initialize() { return iterator.initialize(); }
};
/// The storage in shared memory needed by that stream.
typedef typename Iterator::Storage SharedStorage;
/// Ctor.
CUTLASS_DEVICE SharedLoadStream() {}
/// Ctor.
CUTLASS_DEVICE SharedLoadStream(Params const &params, SharedStorage &shared_storage) {
this->initialize(params, shared_storage);
}
/// Initialize the stream.
CUTLASS_DEVICE void initialize(Params const &params, SharedStorage &shared_storage) {
// The iterator.
iterator = Iterator(params.iterator, shared_storage);
// The transformer.
transformer = Transformer();
}
/// Load the data from shared memory to the fetch fragment.
CUTLASS_DEVICE void copy(FetchedFragment &fetched) { shared_iterator_load(iterator, fetched); }
/// Load the data from shared memory to the fetch fragment.
CUTLASS_DEVICE void copy(int d, FetchedFragment &fetched) {
shared_iterator_load(iterator, fetched, d);
}
/// Commit the data.
CUTLASS_DEVICE void commit(FetchedFragment &fetched, TransformedFragment &transformed) {
transformer.transform(fetched, transformed);
}
/// Increment the stage.
CUTLASS_DEVICE void inc_stage() { iterator.inc_stage(); }
/// The iterator.
Iterator iterator;
/// The transformer.
Transformer transformer;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace gemm
} // namespace cutlass

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@ -1,417 +0,0 @@
/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Defines iterators for efficiently loading and storing tiles to and from shared memory.
*/
#pragma once
#include <cutlass/gemm/gemm_operand.h>
namespace cutlass {
namespace gemm {
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename Scalar_, typename Tile_, typename Threads_, int kScalarsPerSts_>
struct GemmSharedStoreTileAbTraits {
/// The scalar.
typedef typename platform::remove_const<Scalar_>::type Scalar;
/// The pointer.
typedef Scalar_* Pointer;
/// The tile.
typedef typename ReshapeTile<Tile_, kScalarsPerSts_>::Tile Tile;
/// The threads.
typedef Threads_ Threads;
/// The strides to compute the base position of the thread.
typedef Shape<0, ShapeCount<Tile>::kWc, Tile::kC, kScalarsPerSts_> ThreadsStrides;
/// The skew.
static int const kSkew = 0;
/// The number of scalars per LDG/STG.
static int const kAccessSize = kScalarsPerSts_;
/// The memory space.
static MemorySpace::Kind const kMemorySpace = MemorySpace::kShared;
/// The number of iterations needed to load/store the tile.
typedef Shape<1,
Tile::kH / Threads::kH,
Tile::kW / Threads::kW,
Tile::kC / Threads::kC / kAccessSize>
Iterations;
/// The strides in each dimension between different loads/stores.
typedef Shape<0, Threads::kH * ShapeCount<Tile>::kWc, Threads::kW * kAccessSize> Delta;
/// The strides in each dimension between different loads/stores.
typedef Shape<0, Threads::kH * ShapeCount<Tile>::kWc, Threads::kW * kAccessSize>
ImmediateOffsetStrides;
struct ThreadOffset {
CUTLASS_HOST_DEVICE
Coord<4> operator()() const {
int offset = ComputeThreadOffsetFromStrides<Threads, ThreadsStrides>::get();
return make_Coord(0, 0, offset, 0);
}
};
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename Scalar_, typename Tile_, typename Threads_, int kScalarsPerSts_, int kSkew_>
struct GemmSharedStoreWithSkewTileAbTraits {
/// The scalar.
typedef typename platform::remove_const<Scalar_>::type Scalar;
/// The pointer.
typedef Scalar_* Pointer;
/// The tile without skews.
typedef typename ReshapeTile<Tile_, kScalarsPerSts_>::Tile TileWithoutSkew;
/// The tile.
typedef typename ReshapeTile<Shape<Tile_::kD, Tile_::kH, Tile_::kW + kSkew_>,
kScalarsPerSts_>::Tile Tile;
/// The threads.
typedef Threads_ Threads;
/// The skew.
static int const kSkew = kSkew_;
/// The number of scalars per STS.
static int const kAccessSize = kScalarsPerSts_;
/// The memory space.
static MemorySpace::Kind const kMemorySpace = MemorySpace::kShared;
/// The number of iterations needed to load/store the tile.
typedef Shape<1, TileWithoutSkew::kH / Threads::kW, TileWithoutSkew::kW / Threads::kH> Iterations;
/// The strides in each dimension between different loads/stores.
typedef Shape<0, ShapeCount<Tile>::kWc, Threads::kH * kAccessSize> Delta;
/// The strides in each dimension between different loads/stores.
typedef Shape<0, ShapeCount<Tile>::kWc, Threads::kH * kAccessSize> ImmediateOffsetStrides;
struct ThreadOffset {
CUTLASS_HOST_DEVICE Coord<4> operator()() const {
int offset = ComputeThreadOffsetFromStrides<Threads, ThreadsStrides>::get();
return make_Coord(0, 0, offset, 0);
}
};
protected:
/// The strides to compute the base position of the thread.
typedef Shape<0, kScalarsPerSts_, ShapeCount<Tile>::kHwc / Threads::kW> ThreadsStrides;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename Scalar_,
typename OutputTile_,
typename Warps_,
typename ThreadsPerWarp_,
typename InstructionShape_,
int kStages_,
int kScalarsPerLds_,
int kSkew_ = 0>
struct GemmSharedLoadTileATraits {
static GemmOperand::Kind const kOperand = GemmOperand::kA;
/// The scalar.
typedef typename platform::remove_const<Scalar_>::type Scalar;
/// The pointer.
typedef Scalar_* Pointer;
/// The tile without skew.
typedef Shape<kStages_,
OutputTile_::kD / InstructionShape_::kD,
GetExtent<kOperand, OutputTile_>::kExtent * InstructionShape_::kD>
TileWithoutSkew_;
/// The tile with skew.
typedef Shape<kStages_, TileWithoutSkew_::kH, TileWithoutSkew_::kW + kSkew_> TileWithSkew;
/// The tile without skew after reshaping.
typedef typename ReshapeTile<TileWithoutSkew_, kScalarsPerLds_>::Tile TileWithoutSkew;
/// The tile.
typedef typename ReshapeTile<TileWithSkew, kScalarsPerLds_>::Tile Tile;
/// The number of warps.
typedef Warps_ Warps;
/// The threads in a warp.
typedef ThreadsPerWarp_ ThreadsPerWarp;
/// The number of scalars per LDG/STG.
// static int const kScalarsPerLds = kScalarsPerLds_;
static int const kAccessSize = kScalarsPerLds_;
/// The skew.
static int const kSkew = kSkew_;
/// The memory space.
static MemorySpace::Kind const kMemorySpace = MemorySpace::kShared;
/// The number of warps.
static int const kWarps = GetExtent<kOperand, Warps>::kExtent;
/// The number of threads in one dimension of the warp.
static int const kThreadsPerWarp = GetExtent<kOperand, ThreadsPerWarp>::kExtent;
/// The number of iterations needed to load/store the tile.
typedef Shape<1, 1, TileWithoutSkew::kW / kWarps / kThreadsPerWarp /* / kScalarsPerLds*/>
Iterations;
/// The strides in each dimension between different loads/stores.
typedef Shape<TileWithSkew::kW * Warps::kD, 0, kWarps * kThreadsPerWarp * kAccessSize, 0>
ImmediateOffsetStrides;
typedef Shape<TileWithSkew::kW * Warps::kD, 0, kWarps * kThreadsPerWarp * kAccessSize, 0> Delta;
/// Computes the thread offset in (H, W) based on thread ID
struct ThreadOffset {
CUTLASS_HOST_DEVICE Coord<4> operator()() const {
// Extract the warp.
int const warp = threadIdx.x / kWarpSize;
// Extract the slice.
int const slice = warp / (Warps::kH * Warps::kW);
// Compute the row offset for each warp.
int const warp_row = warp % Warps::kW;
// Compute the row offset for each thread.
int const lane_row = (threadIdx.x & 0x0e) / 2;
// The offset.
int const offset =
slice * Tile::kW * Tile::kC + (warp_row * ThreadsPerWarp::kW + lane_row) * kAccessSize;
// Embed the offset in a 4D coordinate vector.
return make_Coord(0, 0, offset, 0);
}
};
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename Scalar_,
typename OutputTile_,
typename Warps_,
typename ThreadsPerWarp_,
typename InstructionShape_,
int kStages_,
int kScalarsPerLds_,
int kSkew_ = 0>
struct GemmSharedLoadTileBTraits {
static GemmOperand::Kind const kOperand = GemmOperand::kB;
/// The scalar.
typedef typename platform::remove_const<Scalar_>::type Scalar;
/// The pointer.
typedef Scalar_* Pointer;
/// The tile without skew.
typedef Shape<kStages_,
OutputTile_::kD / InstructionShape_::kD,
GetExtent<kOperand, OutputTile_>::kExtent * InstructionShape_::kD>
TileWithoutSkew_;
/// The tile with skew.
typedef Shape<kStages_, TileWithoutSkew_::kH, TileWithoutSkew_::kW + kSkew_> TileWithSkew;
/// The tile without skew after reshaping.
typedef typename ReshapeTile<TileWithoutSkew_, kScalarsPerLds_>::Tile TileWithoutSkew;
/// The tile.
typedef typename ReshapeTile<TileWithSkew, kScalarsPerLds_>::Tile Tile;
/// The number of warps.
typedef Warps_ Warps;
/// The threads in a warp.
typedef ThreadsPerWarp_ ThreadsPerWarp;
/// The number of scalars per LDG/STG.
static int const kAccessSize = kScalarsPerLds_;
/// The skew.
static int const kSkew = kSkew_;
/// The memory space.
static MemorySpace::Kind const kMemorySpace = MemorySpace::kShared;
/// The number of warps.
static int const kWarps = GetExtent<kOperand, Warps>::kExtent;
/// The number of threads in one dimension of the warp.
static int const kThreadsPerWarp = GetExtent<kOperand, ThreadsPerWarp>::kExtent;
/// The number of iterations needed to load/store the tile.
typedef Shape<1, 1, TileWithoutSkew::kW / kWarps / kThreadsPerWarp /* / kAccessSize*/> Iterations;
/// The strides in each dimension between different loads/stores.
typedef Shape<TileWithSkew::kW * Warps::kD, 0, kWarps * kThreadsPerWarp * kAccessSize, 0>
ImmediateOffsetStrides;
typedef Shape<TileWithSkew::kW * Warps::kD, 0, kWarps * kThreadsPerWarp * kAccessSize, 0> Delta;
/// Computes the thread offset in (H, W) based on thread ID
struct ThreadOffset {
CUTLASS_HOST_DEVICE Coord<4> operator()() const {
// Extract the warp.
int const warp = threadIdx.x / kWarpSize;
// Extract the slice.
int const slice = warp / (Warps::kH * Warps::kW);
// The warp in the slice.
int const warp_in_slice = warp % (Warps::kH * Warps::kW);
// Compute the row offset for each warp.
int const warp_col = warp_in_slice / Warps::kW;
// Compute the row offset for each thread.
int const lane_col = (threadIdx.x & 0x10) / 8 + (threadIdx.x & 0x01);
// The offset.
int const offset =
slice * Tile::kW * Tile::kC + (warp_col * ThreadsPerWarp::kH + lane_col) * kAccessSize;
// Embed the offset in a 4D coordinate.
return make_Coord(0, 0, offset, 0);
}
};
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename Scalar_,
typename OutputTile_,
typename Warps_,
typename ThreadsPerWarp_,
int kScalarsPerSts_,
int kSkew_ = 0>
struct GemmSharedStoreTileDTraits {
/// The scalar.
typedef typename platform::remove_const<Scalar_>::type Scalar;
/// The pointer.
typedef Scalar_* Pointer;
/// The dimension of the output tile.
typedef OutputTile_ OutputTile;
/// The warps in the tile.
typedef Warps_ Warps;
/// The threads in the warps.
typedef ThreadsPerWarp_ ThreadsPerWarp;
/// The number of scalars per LDG/STG.
static int const kAccessSize = kScalarsPerSts_;
/// The skew.
static int const kSkew = kSkew_;
/// The memory space.
static MemorySpace::Kind const kMemorySpace = MemorySpace::kShared;
/// The number of scalars per thread.
static int const kScalarsPerThread = OutputTile_::kW / Warps::kW / ThreadsPerWarp::kW;
/// The number of threads.
static int const kThreads = ShapeCount<Warps>::kCount * kWarpSize;
/// The number of scalars per row. We build a tile with 2 rows (to avoid bank conflicts).
static int const kScalarsPerRow = kThreads / 2 * kScalarsPerThread + kSkew;
/// The tile.
typedef Shape<1, 2, kScalarsPerRow / kAccessSize, kAccessSize> Tile;
/// The number of iterations needed to store the tile.
typedef Shape<1, 1, kScalarsPerThread / kAccessSize> Iterations;
/// The strides in each dimension between different loads/stores.
typedef Shape<0, 0, Warps::kW * ThreadsPerWarp::kW * kAccessSize> Delta;
/// The strides in each dimension between different loads/stores.
typedef Shape<0, 0, Warps::kW * ThreadsPerWarp::kW * kAccessSize> ImmediateOffsetStrides;
/// Computes the thread offset in (H, W) based on thread ID
struct ThreadOffset {
CUTLASS_HOST_DEVICE Coord<4> operator()() const {
// The warp.
int const warp = threadIdx.x / kWarpSize;
// The position of the warp in the 2D tile.
int const warp_row = warp % Warps::kW;
int const warp_col = warp / Warps::kW;
// We assume that the elements are distributed in a warps as 4 columns of 8 elements. The
// columns are stored in threads col0=[0, 2, 4, 6, 8, 10, 12, 14], col1=[1, 3, 5, 7, .., 15],
// col2=[16, 18, 20, ..., 30] and col3=[17, 19, ..., 31].
int hi_halfwarp_offset = ((threadIdx.x >> 4) & 0x1) * OutputTile::kW;
int lo_halfwarp_offset = ((threadIdx.x >> 1) & 0x7) + ThreadsPerWarp::kW * warp_row;
// Odd threads go to the second half of shared memory.
int const row = threadIdx.x & 0x01;
int col = warp_col * (ThreadsPerWarp::kH / 2) * OutputTile::kW +
lo_halfwarp_offset * kAccessSize + hi_halfwarp_offset;
// Embed the offset in a 4D coords.
return make_Coord(0, 0, row * kScalarsPerRow + col, 0);
}
};
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename Scalar_,
typename OutputTile_,
typename Warps_,
typename ThreadsPerWarp_,
int kTileH_,
int kScalarsPerLds_,
int kSkew_ = 0>
struct GemmSharedLoadTileDTraits {
/// The scalar.
typedef typename platform::remove_const<Scalar_>::type Scalar;
/// The pointer.
typedef Scalar_* Pointer;
/// The dimension of the output tile.
typedef OutputTile_ OutputTile;
/// The warps in the tile.
typedef Warps_ Warps;
/// The threads in the warps.
typedef ThreadsPerWarp_ ThreadsPerWarp;
/// The number of scalars per LDG/STG.
static int const kAccessSize = kScalarsPerLds_;
/// The skew.
static int const kSkew = kSkew_;
/// The memory space.
static MemorySpace::Kind const kMemorySpace = MemorySpace::kShared;
/// The number of scalars per thread.
static int const kScalarsPerThread = OutputTile_::kW / Warps::kW / ThreadsPerWarp::kW;
/// The number of threads.
static int const kThreads = ShapeCount<Warps>::kCount * kWarpSize;
/// The number of scalars per row. We build a tile with 2 rows (to avoid bank conflicts).
static int const kScalarsPerRow = kThreads / 2 * kScalarsPerThread + kSkew;
/// The tile. We have 2 rows of scalars. We use those two rows to make sure we do not have bank
/// conflicts in the epilogue.
typedef Shape<1, 2, kScalarsPerRow / kAccessSize, kAccessSize> Tile;
// Compute the number of iterations per warp in the Tile::kH dimension.
static int const kIterationsInHPerWarp = kTileH_ / ShapeCount<Warps>::kCount;
// As explained above, the shared memory tile is composed of 2 rows and each rows is made of
// kScalarsPerRow. A warp is expected to read from the 1st row, then move to the 2nd row and go
// back to the 1st row. To model that scheme we define the Iterations shape as Shape<X, 2, ...>.
// However, in some cases, we have only 1 iteration per warp. In that case, we must define the
// shape as Shape<1, 1, ...>. The following code does that except that we hijack the kH dimension
// to keep the number of elements to reduce for split-K.
static int const kIterationsH = kIterationsInHPerWarp == 1 ? 1 : 2;
// As soon as we know kIterationsH, it is trivial to compute kIterationsD:
static int const kIterationsD = kIterationsInHPerWarp / kIterationsH;
// If we have split-K enabled, we have to jump over the elements from the "odd/even" column of
// threads to grab the other elements.
static int const kSplitK = OutputTile::kW * ThreadsPerWarp::kH / 2 * Warps::kH;
/// The number of iterations needed to store the tile.
typedef Shape<kIterationsD, kIterationsH, OutputTile::kW / kWarpSize / kAccessSize, Warps::kD>
Iterations;
/// The strides in each dimension between different loads/stores.
typedef Shape<OutputTile::kW, kScalarsPerRow, kWarpSize * kAccessSize, kSplitK>
ImmediateOffsetStrides;
/// The strides in each dimension between different loads/stores.
typedef Shape<OutputTile::kW, kScalarsPerRow, kWarpSize * kAccessSize, kSplitK> Delta;
/// Computes the thread offset in (H, W) based on thread ID
struct ThreadOffset {
CUTLASS_HOST_DEVICE Coord<4> operator()() const {
// Each warp works on a different column.
int const h = threadIdx.x / kWarpSize;
// Compute the row.
int const w = (threadIdx.x & (kWarpSize - 1)) * kAccessSize;
int offset = 0;
if (Iterations::kH == 1) {
int const row = h & 0x1;
int const col = h / 2;
offset = row * ShapeCount<Tile>::kWc + col * OutputTile::kW * Iterations::kD + w;
} else {
offset = h * OutputTile::kW * Iterations::kD + w;
}
return make_Coord(0, 0, offset, 0);
}
};
};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace gemm
} // namespace cutlass

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@ -1,818 +0,0 @@
/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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**************************************************************************************************/
/*! \file
\brief Defines structural properties of complete GEMM computation.
*/
#pragma once
#include <cutlass/convert.h>
#include <cutlass/gemm/clear_accumulators.h>
#include <cutlass/gemm/gemm_global_stream.h>
#include <cutlass/gemm/gemm_operand.h>
#include <cutlass/gemm/gemm_shared_stream.h>
#include <cutlass/gemm/identity_block_swizzle.h>
#include <cutlass/matrix_traits.h>
#include <cutlass/reshape_tile.h>
#include <cutlass/tile_iterator.h>
namespace cutlass {
namespace gemm {
////////////////////////////////////////////////////////////////////////////////////////////////////
template <
/// The scalar type for A.
typename ScalarA_,
/// The scalar type for B.
typename ScalarB_,
/// The scalar type for C.
typename ScalarC_,
/// The scalar type for D.
typename ScalarD_,
/// The output tile size for the GEMM KxNxM.
typename OutputTile_,
/// The functor to do the math.
typename MultiplyAdd_,
/// The number of scalars per LDG for A.
int kScalarsPerLdgA_,
/// The number of scalars per STS for A.
int kScalarsPerStsA_,
/// The number of scalars per LDG for A.
int kScalarsPerLdsA_,
/// The number of scalars per LDG for B.
int kScalarsPerLdgB_,
/// The number of scalars per STS for B.
int kScalarsPerStsB_,
/// The number of scalars per LDS for B.
int kScalarsPerLdsB_,
/// The number of scalars per LDG for C and STG for D.
int kScalarsPerLdgCAndStgD_,
/// The number of scalars per STS for D.
int kScalarsPerStsD_,
/// The number of scalars per LDS for D.
int kScalarsPerLdsD_,
/// The number of stages in shared memory to do single/double/triple-buffering.
int kStages_,
/// Do we do the residue in the prologue?
bool kResidueInPrologue_ = false>
struct GemmConfig {
//
/// The scalar for A.
typedef ScalarA_ ScalarA;
/// The scalar for B.
typedef ScalarB_ ScalarB;
/// The scalar for C.
typedef ScalarC_ ScalarC;
/// The scalar for D.
typedef ScalarD_ ScalarD;
/// The tile.
typedef OutputTile_ OutputTile;
/// The functor to do D = A*B + C.
typedef MultiplyAdd_ MultiplyAdd;
/// The shape of the instruction.
typedef typename MultiplyAdd::InstructionShape InstructionShape;
/// The number of accumulators per warp.
typedef typename MultiplyAdd::AccumulatorsPerWarp AccumulatorsPerWarp;
/// The accumulators.
typedef typename MultiplyAdd::Accumulators Accumulators;
/// The number of warps.
typedef typename ShapeDiv<OutputTile, AccumulatorsPerWarp>::Shape Warps;
/// The default warp size (32 threads per warp).
static int const kWarpSize = cutlass::kWarpSize;
/// The numnber of threads.
static int const kThreads = ShapeCount<Warps>::kCount * kWarpSize;
/// The number of scalars per LDG/STS/LDS for A.
static int const kScalarsPerLdgA = kScalarsPerLdgA_;
static int const kScalarsPerStsA = kScalarsPerStsA_;
static int const kScalarsPerLdsA = kScalarsPerLdsA_;
/// The number of scalars per LDG/STS/LDS for B.
static int const kScalarsPerLdgB = kScalarsPerLdgB_;
static int const kScalarsPerStsB = kScalarsPerStsB_;
static int const kScalarsPerLdsB = kScalarsPerLdsB_;
/// The number of scalars per LDG for C.
static int const kScalarsPerLdgC = kScalarsPerLdgCAndStgD_;
/// The number of scalars per STS/LDS/STG for D.
static int const kScalarsPerStgD = kScalarsPerLdgCAndStgD_;
static int const kScalarsPerStsD = kScalarsPerStsD_;
static int const kScalarsPerLdsD = kScalarsPerLdsD_;
/// The number of accumulators that are going to be fed from one LDS A/B.
static int const kAccumulatorsPerLdsA = kScalarsPerLdsA / InstructionShape::kD;
static int const kAccumulatorsPerLdsB = kScalarsPerLdsB / InstructionShape::kD;
/// The number of stages in shared memory to implement double, triple, more-buffering.
static int const kStages = kStages_;
/// Do we do the residue in the prologue?
static bool const kResidueInPrologue = kResidueInPrologue_;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <enum MatrixLayout::Kind, typename GemmConfig_>
struct GemmTileTraitsHelperA {};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename GemmConfig_>
struct GemmTileTraitsHelperA<MatrixLayout::kColumnMajor, GemmConfig_> {
/// The layout.
static MatrixLayout::Kind const kLayout = MatrixLayout::kColumnMajor;
/// The input scalar.
typedef typename GemmConfig_::ScalarA Scalar;
/// The scalar stored in shared memory.
typedef typename GemmConfig_::MultiplyAdd::ScalarA MultiplyAddScalar;
/// The traits class to build the iterator to load data from global memory for A^N.
typedef GemmGlobalTileTraits<
// That's A.
GemmOperand::kA,
// A is column-major.
MatrixLayout::kColumnMajor,
// The pointer is float const.
Scalar const,
// The tile has size KxM in GEMM's terminology.
Shape<1, GemmConfig_::OutputTile::kD, GemmConfig_::OutputTile::kW>,
// The threads are distributed as warps x 32 (the traits may reorganize).
Shape<1, ShapeCount<typename GemmConfig_::Warps>::kCount, GemmConfig_::kWarpSize>,
// The number of scalars per LDG (LDG.32 or LDG.128, etc).
GemmConfig_::kScalarsPerLdgA>
GlobalTileTraits;
/// The traits class to build the iterator to store data to shared memory for A^N.
typedef GemmSharedStoreTileAbTraits<
// The pointer is float.
MultiplyAddScalar,
// The tile has size KxM in GEMM's terminology.
Shape<GemmConfig_::kStages,
GemmConfig_::OutputTile::kD / GemmConfig_::InstructionShape::kD,
GemmConfig_::OutputTile::kW * GemmConfig_::InstructionShape::kD>,
// The threads are distributed as warps x 32 (the traits may reorganize).
typename GlobalTileTraits::Threads,
// The number of scalars per STS (STS.32 or STS.128, etc).
GemmConfig_::kScalarsPerStsA>
SharedStoreTileTraits;
/// The traits class to build the iterator to load from shared memory for A^N.
typedef GemmSharedLoadTileATraits<
// The pointer is float const.
MultiplyAddScalar const,
// The output tile size.
typename GemmConfig_::OutputTile,
// The number of warps.
typename GemmConfig_::Warps,
// The number of threads per warp.
typename GemmConfig_::MultiplyAdd::ThreadsPerWarp,
// The shape of the FMA instruction.
typename GemmConfig_::InstructionShape,
// The number of stages.
GemmConfig_::kStages,
// The number of scalars per LDS.
GemmConfig_::kScalarsPerLdsA,
// The skew.
0>
SharedLoadTileTraits;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename GemmConfig_>
struct GemmTileTraitsHelperA<MatrixLayout::kRowMajor, GemmConfig_> {
/// The layout.
static MatrixLayout::Kind const kLayout = MatrixLayout::kRowMajor;
/// The input scalar.
typedef typename GemmConfig_::ScalarA Scalar;
/// The scalar stored in shared memory.
typedef typename GemmConfig_::MultiplyAdd::ScalarA MultiplyAddScalar;
/// The traits class to build the iterator to load data from global memory for A^T.
typedef GemmGlobalTileTraits<
// That's A.
GemmOperand::kA,
// A is row-major.
MatrixLayout::kRowMajor,
// The pointer is float const.
Scalar const,
// The tile has size MxK in GEMM's terminology.
Shape<1, GemmConfig_::OutputTile::kW, GemmConfig_::OutputTile::kD>,
// The threads are distributed as (threads / K) x K (the traits may reorganize).
Shape<1, GemmConfig_::kThreads / GemmConfig_::OutputTile::kD, GemmConfig_::OutputTile::kD>,
// The number of scalars per LDG (LDG.32 or LDG.128, etc).
GemmConfig_::kScalarsPerLdgA>
GlobalTileTraits;
/// The number of scalars in 4B.
static int const kScalarsIn4B = sizeof(MultiplyAddScalar) > 4 ? 1 : 4 / sizeof(MultiplyAddScalar);
/// The skew for A.
static int const kSkewA = 128 / sizeof(MultiplyAddScalar) / GemmConfig_::kScalarsPerStsA /
GlobalTileTraits::Threads::kW * kScalarsIn4B;
/// The traits class to build the iterator to store data to shared memory for A^T.
typedef GemmSharedStoreWithSkewTileAbTraits <
// The pointer is float.
MultiplyAddScalar,
// The tile has size KxM in GEMM's terminology.
Shape<GemmConfig_::kStages,
GemmConfig_::OutputTile::kD / GemmConfig_::InstructionShape::kD,
GemmConfig_::OutputTile::kW * GemmConfig_::InstructionShape::kD>,
// The threads are distributed as (threads / K) x K (the traits may reorganize).
typename GlobalTileTraits::Threads,
// The number of scalars per STS.
GemmConfig_::kScalarsPerStsA,
// The skew to avoid bank conflicts added in the tile W dimension.
kSkewA<GemmConfig_::kScalarsPerLdsA ? GemmConfig_::kScalarsPerLdsA : kSkewA>
SharedStoreTileTraits;
/// The traits class to build the iterator to load from shared memory for A^T.
typedef GemmSharedLoadTileATraits<
// The pointer is float const.
MultiplyAddScalar const,
// The output tile size.
typename GemmConfig_::OutputTile,
// The number of warps.
typename GemmConfig_::Warps,
// The number of threads per warp.
typename GemmConfig_::MultiplyAdd::ThreadsPerWarp,
// The shape of the FMA instruction.
typename GemmConfig_::InstructionShape,
// The number of stages.
GemmConfig_::kStages,
// The number of scalars per LDS.
GemmConfig_::kScalarsPerLdsA,
// The skew.
SharedStoreTileTraits::kSkew>
SharedLoadTileTraits;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <enum MatrixLayout::Kind, typename GemmConfig_>
struct GemmTileTraitsHelperB {};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename GemmConfig_>
struct GemmTileTraitsHelperB<MatrixLayout::kColumnMajor, GemmConfig_> {
/// The layout.
static MatrixLayout::Kind const kLayout = MatrixLayout::kColumnMajor;
/// The input scalar.
typedef typename GemmConfig_::ScalarB Scalar;
/// The scalar stored in shared memory.
typedef typename GemmConfig_::MultiplyAdd::ScalarB MultiplyAddScalar;
/// The traits class to build the iterator to load data from global memory for B^N.
typedef GemmGlobalTileTraits<
// That's B.
GemmOperand::kB,
// B is column-major.
MatrixLayout::kColumnMajor,
// The pointer is float const.
Scalar const,
// The tile has size MxK in GEMM's terminology.
Shape<1, GemmConfig_::OutputTile::kH, GemmConfig_::OutputTile::kD>,
// The threads are distributed as (threads / K) x K (the traits may reorganize).
Shape<1, GemmConfig_::kThreads / GemmConfig_::OutputTile::kD, GemmConfig_::OutputTile::kD>,
// The number of scalars per LDG (LDG.32 or LDG.128, etc).
GemmConfig_::kScalarsPerLdgB>
GlobalTileTraits;
/// The number of scalars in 4B.
static int const kScalarsIn4B = sizeof(MultiplyAddScalar) > 4 ? 1 : 4 / sizeof(MultiplyAddScalar);
/// The skew for B.
static int const kSkewB = 128 / sizeof(MultiplyAddScalar) / GemmConfig_::kScalarsPerStsB /
GlobalTileTraits::Threads::kW * kScalarsIn4B;
/// The traits class to build the iterator to store data to shared memory for B^N.
typedef GemmSharedStoreWithSkewTileAbTraits <
// The pointer is float.
MultiplyAddScalar,
// The tile has size KxN in GEMM's terminology.
Shape<GemmConfig_::kStages,
GemmConfig_::OutputTile::kD / GemmConfig_::InstructionShape::kD,
GemmConfig_::OutputTile::kH * GemmConfig_::InstructionShape::kD>,
// The threads are distributed as (threads / K) x K (the traits may reorganize).
typename GlobalTileTraits::Threads,
// The number of scalars per STS.
GemmConfig_::kScalarsPerStsB,
// The skew to avoid bank conflicts added in the tile W dimension.
kSkewB<GemmConfig_::kScalarsPerLdsB ? GemmConfig_::kScalarsPerLdsB : kSkewB>
SharedStoreTileTraits;
/// The traits class to build the iterator to load from shared memory for B^N.
typedef GemmSharedLoadTileBTraits<
// The pointer is float const.
MultiplyAddScalar const,
// The output tile size.
typename GemmConfig_::OutputTile,
// The number of warps.
typename GemmConfig_::Warps,
// The number of threads per warp.
typename GemmConfig_::MultiplyAdd::ThreadsPerWarp,
// The shape of the FMA instruction.
typename GemmConfig_::InstructionShape,
// The number of stages.
GemmConfig_::kStages,
// The number of scalars per LDS.
GemmConfig_::kScalarsPerLdsB,
// The skew.
SharedStoreTileTraits::kSkew>
SharedLoadTileTraits;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename GemmConfig_>
struct GemmTileTraitsHelperB<MatrixLayout::kRowMajor, GemmConfig_> {
/// The layout.
static MatrixLayout::Kind const kLayout = MatrixLayout::kRowMajor;
/// The input scalar.
typedef typename GemmConfig_::ScalarB Scalar;
/// The scalar stored in shared memory.
typedef typename GemmConfig_::MultiplyAdd::ScalarB MultiplyAddScalar;
/// The traits class to build the iterator to load data from global memory for B^T.
typedef GemmGlobalTileTraits<
// That's B.
GemmOperand::kB,
// B is row-major.
MatrixLayout::kRowMajor,
// The pointer is float const.
Scalar const,
// The tile has size KxN in GEMM's terminology.
Shape<1, GemmConfig_::OutputTile::kD, GemmConfig_::OutputTile::kH>,
// The threads are distributed as warps x 32 (the traits may reorganize).
Shape<1, ShapeCount<typename GemmConfig_::Warps>::kCount, GemmConfig_::kWarpSize>,
// The number of scalars per LDG (LDG.32 or LDG.128, etc).
GemmConfig_::kScalarsPerLdgB>
GlobalTileTraits;
/// The traits class to build the iterator to store data to shared memory for B^T.
typedef GemmSharedStoreTileAbTraits<
// The pointer is float.
MultiplyAddScalar,
// The tile has size KxN in GEMM's terminology.
Shape<GemmConfig_::kStages,
GemmConfig_::OutputTile::kD / GemmConfig_::InstructionShape::kD,
GemmConfig_::OutputTile::kH * GemmConfig_::InstructionShape::kD>,
// The threads are distributed as warps x 32 (the traits may reorganize).
typename GlobalTileTraits::Threads,
// The number of scalars per STS (STS.32 or STS.128, etc).
GemmConfig_::kScalarsPerStsB>
SharedStoreTileTraits;
/// The traits class to build the iterator to load from shared memory for B^T.
typedef GemmSharedLoadTileBTraits<
// The pointer is float const.
MultiplyAddScalar const,
// The output tile size.
typename GemmConfig_::OutputTile,
// The number of warps.
typename GemmConfig_::Warps,
// The number of threads per warp.
typename GemmConfig_::MultiplyAdd::ThreadsPerWarp,
// The shape of the FMA instruction.
typename GemmConfig_::InstructionShape,
// The number of stages.
GemmConfig_::kStages,
// The number of scalars per LDS.
GemmConfig_::kScalarsPerLdsB,
// The skew.
0>
SharedLoadTileTraits;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename GemmTraits_, bool kResidueInPrologue_ = GemmTraits_::kResidueInPrologue>
struct GemmResidue {
/// Move to residue portion.
template <bool kIsPrologue>
static CUTLASS_DEVICE void move_to_residue(typename GemmTraits_::GlobalLoadStreamA& stream_a,
typename GemmTraits_::GlobalLoadStreamB& stream_b,
typename GemmTraits_::Index k) {
// The new code path in CUTLASS 1.0.1: We treat the residue in the prologue so we can have
// complete main loops after that. It helps simplify the logic in the main loop.
if (kIsPrologue) {
stream_a.move_to_residue(k);
stream_b.move_to_residue(k);
}
}
/// Rollback to beginning of first tile and initialize predicates.
static CUTLASS_DEVICE void rollback(typename GemmTraits_::GlobalLoadStreamA& stream_a,
typename GemmTraits_::GlobalLoadStreamB& stream_b) {
stream_a.rollback();
stream_b.rollback();
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename GemmTraits_>
struct GemmResidue<GemmTraits_, false> {
/// Move to residue portion.
template <bool kIsPrologue>
static CUTLASS_DEVICE void move_to_residue(typename GemmTraits_::GlobalLoadStreamA& stream_a,
typename GemmTraits_::GlobalLoadStreamB& stream_b,
typename GemmTraits_::Index k) {
// The index.
typedef typename GemmTraits_::Index Index;
// By how much we unroll the main loop.
Index const kUnroll = static_cast<Index>(GemmTraits_::OutputTile::kD);
// Call the residue code. That's the same path as CUTLASS 1.0.0.
if (kIsPrologue && k < kUnroll) {
stream_a.residue(k, true);
stream_b.residue(k, true);
} else if (k <= kUnroll) {
stream_a.residue(k, false);
stream_b.residue(k, false);
}
}
/// Rollback to beginning of first tile and initialize predicates.
static CUTLASS_DEVICE void rollback(typename GemmTraits_::GlobalLoadStreamA& stream_a,
typename GemmTraits_::GlobalLoadStreamB& stream_b) {}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <
/// The GEMM configuration.
typename GemmConfig_,
/// The stream to load A from global memory to shared memory.
typename GlobalLoadStreamA_,
/// The stream to load B from global memory to shared memory.
typename GlobalLoadStreamB_,
/// The stream to load A from shared memory.
typename SharedLoadStreamA_,
/// The stream to load B from shared memory.
typename SharedLoadStreamB_,
/// The epilogue.
typename Epilogue_,
/// The block swizzle to reorganize the grid.
typename BlockSwizzle_ = IdentityBlockSwizzle,
/// The index.
typename Index_ = int,
/// The tool used to clear accumulators.
typename ClearAccumulators_ = ClearAccumulators<typename GemmConfig_::Accumulators::Scalar> >
struct GemmTraits {
/// This class.
typedef GemmTraits<GemmConfig_,
GlobalLoadStreamA_,
GlobalLoadStreamB_,
SharedLoadStreamA_,
SharedLoadStreamB_,
Epilogue_,
BlockSwizzle_,
Index_,
ClearAccumulators_>
This_;
/// The configuration.
typedef GemmConfig_ GemmConfig;
/// The output tile.
typedef typename GemmConfig::OutputTile OutputTile;
/// Is the residue treated in the prologue?
static bool const kResidueInPrologue = GemmConfig::kResidueInPrologue;
/// The stream to load A from global memory to shared memory.
typedef GlobalLoadStreamA_ GlobalLoadStreamA;
/// The layout of A.
static MatrixLayout::Kind const kLayoutA = GlobalLoadStreamA::kLayout;
/// The scalar for A.
typedef typename GlobalLoadStreamA_::Scalar ScalarA;
/// The stream to load B from global memory to shared memory.
typedef GlobalLoadStreamB_ GlobalLoadStreamB;
/// The layout of B.
static MatrixLayout::Kind const kLayoutB = GlobalLoadStreamB::kLayout;
/// The scalar for B.
typedef typename GlobalLoadStreamB_::Scalar ScalarB;
/// The iterator for A to load from shared memory.
typedef SharedLoadStreamA_ SharedLoadStreamA;
/// The iterator for B to load from shared memory.
typedef SharedLoadStreamB_ SharedLoadStreamB;
/// The multiply-add functor.
typedef typename GemmConfig::MultiplyAdd MultiplyAdd;
/// The epilogue.
typedef Epilogue_ Epilogue;
/// The scalars in the epilogue.
typedef typename Epilogue::ScalarC ScalarC;
typedef typename Epilogue::ScalarD ScalarD;
/// The block swizzle to reorganize the grid.
typedef BlockSwizzle_ BlockSwizzle;
/// The index.
typedef Index_ Index;
/// Clear the accumulators.
typedef ClearAccumulators_ ClearAccumulators;
/// The params.
struct Params {
/// The dimensions of the GEMM.
Index m, n, k;
/// The params for the A stream.
typename GlobalLoadStreamA::Params global_stream_a;
/// The params for the B stream.
typename GlobalLoadStreamB::Params global_stream_b;
/// The params for the A stream from shared memory.
typename SharedLoadStreamA::Params shared_stream_a;
/// The params for the B stream from shared memory.
typename SharedLoadStreamB::Params shared_stream_b;
/// The params for the epilogue.
typename Epilogue::Params epilogue;
/// Initialize the parameters.
template <typename GemmDesc_>
CUTLASS_HOST_DEVICE int initialize(GemmDesc_ const& desc) {
// Set the problem size.
this->m = desc.m;
this->n = desc.n;
this->k = desc.k;
// Initialize the iterator for A.
int error_code =
global_stream_a.initialize(desc, reinterpret_cast<ScalarA const*>(desc.d_a), desc.lda);
if (error_code) {
return error_code;
}
// Initialize the iterator for B.
error_code =
global_stream_b.initialize(desc, reinterpret_cast<ScalarB const*>(desc.d_b), desc.ldb);
if (error_code) {
return error_code;
}
// The epilogue.
return epilogue.initialize(desc);
}
};
// The storage for A.
template <typename GlobalLoadStream_, typename SharedLoadStream_>
union StreamSharedStorage {
// The storage needed by the global stream.
typename GlobalLoadStream_::SharedStorage global;
// The storage needed by the shared stream.
typename SharedLoadStream_::SharedStorage shared;
};
// The storage for the main loop + prologue.
struct MainLoopSharedStorage {
// The storage to shuffle the A matrix in shared memory.
StreamSharedStorage<GlobalLoadStreamA, SharedLoadStreamA> stream_a;
// The storage to shuffle the B matrix in shared memory.
StreamSharedStorage<GlobalLoadStreamB, SharedLoadStreamB> stream_b;
// The storage to clear the accumulators if needed.
typename ClearAccumulators::SharedStorage clear;
};
/// The storage in shared memory.
union SharedStorage {
// The storage for the main loop.
MainLoopSharedStorage main_loop;
// The storage for the epilogue.
typename Epilogue::SharedStorage epilogue;
};
/// Assemble the global load streams for A/B.
struct GlobalLoadStream {
/// Ctor.
CUTLASS_DEVICE GlobalLoadStream(Params const& params,
SharedStorage& shared_storage,
dim3 const& block)
: stream_a(params.global_stream_a,
shared_storage.main_loop.stream_a.global,
cutlass::make_Coord(0, params.k, params.m),
cutlass::make_Coord(0, 0, block.x)),
stream_b(params.global_stream_b,
shared_storage.main_loop.stream_b.global,
cutlass::make_Coord(0, params.k, params.n),
make_Coord(0, 0, block.y)) {}
/// Trigger the copies from shared memory to registers.
CUTLASS_DEVICE void copy() {
stream_a.copy();
stream_b.copy();
}
/// Commit the data.
CUTLASS_DEVICE void commit() {
stream_a.commit();
stream_b.commit();
}
/// Move to residue portion.
template <bool kIsPrologue>
CUTLASS_DEVICE void move_to_residue(Index k) {
GemmResidue<This_>::move_to_residue<kIsPrologue>(stream_a, stream_b, k);
}
/// Rollback to beginning of first tile and initialize predicates.
CUTLASS_DEVICE void rollback() { GemmResidue<This_>::rollback(stream_a, stream_b); }
/// The stream for A.
GlobalLoadStreamA stream_a;
/// The stream for B.
GlobalLoadStreamB stream_b;
};
/// Assemble the shared load stream for A/B.
struct SharedLoadStream {
/// Ctor.
CUTLASS_DEVICE SharedLoadStream(Params const& params, SharedStorage& shared_storage) {
stream_a.initialize(params.shared_stream_a, shared_storage.main_loop.stream_a.shared);
stream_b.initialize(params.shared_stream_b, shared_storage.main_loop.stream_b.shared);
}
/// Trigger the copies from shared memory to registers.
CUTLASS_DEVICE void copy(int step) {
stream_a.copy(step, fetched_a[step % 2]);
stream_b.copy(step, fetched_b[step % 2]);
}
/// Commit the data.
CUTLASS_DEVICE void commit(int step) {
stream_a.commit(fetched_a[step % 2], transformed_a[step % 2]);
stream_b.commit(fetched_b[step % 2], transformed_b[step % 2]);
}
/// The fragment A.
CUTLASS_DEVICE typename SharedLoadStreamA::Fragment const& fragment_a(int step) const {
return transformed_a[step % 2];
}
/// The fragment B.
CUTLASS_DEVICE typename SharedLoadStreamB::Fragment const& fragment_b(int step) const {
return transformed_b[step % 2];
}
/// Increment the stage.
CUTLASS_DEVICE void inc_stage() {
stream_a.inc_stage();
stream_b.inc_stage();
}
/// The stream for A.
SharedLoadStreamA stream_a;
/// The fragments to fetch A.
typename SharedLoadStreamA::FetchedFragment fetched_a[2];
/// The fragments to transform A.
typename SharedLoadStreamA::TransformedFragment transformed_a[2];
/// The stream for B.
SharedLoadStreamB stream_b;
/// The fragments to fetch B.
typename SharedLoadStreamB::FetchedFragment fetched_b[2];
/// The fragments to transform B.
typename SharedLoadStreamB::TransformedFragment transformed_b[2];
};
/// The memory fence for shared loads.
static CUTLASS_DEVICE void shared_load_fence(bool in_loop) {
if (SharedLoadStreamA::Iterator::kRequiresLoadFence ||
SharedLoadStreamB::Iterator::kRequiresLoadFence) {
__syncthreads();
}
}
/// The memory fence for shared stores.
static CUTLASS_DEVICE void shared_store_fence(bool in_loop) { __syncthreads(); }
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename GemmTileTraitsHelperA_, typename GemmTileTraitsHelperB_, typename Index_>
struct SimplifiedGemmTraitsHelper {
/// The global iterator to load A from global memory.
typedef GemmGlobalIteratorAb<typename GemmTileTraitsHelperA_::GlobalTileTraits, Index_>
GlobalLoadIteratorA;
/// The data converter for A before storing to shared memory.
typedef Copy<typename GlobalLoadIteratorA::Fragment> GlobalTransformerA;
/// The iterator to store A to shared memory.
typedef TileStoreIterator<typename GemmTileTraitsHelperA_::SharedStoreTileTraits,
typename GemmTileTraitsHelperA_::SharedStoreTileTraits::Scalar,
IteratorAdvance::kH,
MemorySpace::kShared>
SharedStoreIteratorA;
/// The stream to load A from global memory to shared memory.
typedef GlobalLoadStream<GlobalLoadIteratorA, SharedStoreIteratorA, GlobalTransformerA>
GlobalLoadStreamA;
/// The global iterator to load B from global memory.
typedef GemmGlobalIteratorAb<typename GemmTileTraitsHelperB_::GlobalTileTraits, Index_>
GlobalLoadIteratorB;
/// The data converter for B before storing to shared memory.
typedef Copy<typename GlobalLoadIteratorB::Fragment> GlobalTransformerB;
/// The iterator to store B to shared memory.
typedef TileStoreIterator<typename GemmTileTraitsHelperB_::SharedStoreTileTraits,
typename GemmTileTraitsHelperB_::SharedStoreTileTraits::Scalar,
IteratorAdvance::kH,
MemorySpace::kShared>
SharedStoreIteratorB;
/// The stream to load B from global memory to shared memory.
typedef GlobalLoadStream<GlobalLoadIteratorB, SharedStoreIteratorB, GlobalTransformerB>
GlobalLoadStreamB;
/// The iterator to load A from shared memory.
typedef TileLoadIterator<typename GemmTileTraitsHelperA_::SharedLoadTileTraits,
typename GemmTileTraitsHelperA_::Scalar,
IteratorAdvance::kH,
MemorySpace::kShared>
SharedLoadIteratorA;
/// The stream to load A from shared memory.
typedef SharedLoadStream<SharedLoadIteratorA> SharedLoadStreamA;
/// The iterator to load B from shared memory.
typedef TileLoadIterator<typename GemmTileTraitsHelperB_::SharedLoadTileTraits,
typename GemmTileTraitsHelperB_::Scalar,
IteratorAdvance::kH,
MemorySpace::kShared>
SharedLoadIteratorB;
/// The stream to load B from shared memory.
typedef SharedLoadStream<SharedLoadIteratorB> SharedLoadStreamB;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <
/// The layout for A.
MatrixLayout::Kind kLayoutA_,
/// The layout for B.
MatrixLayout::Kind kLayoutB_,
/// The config for the GEMM.
typename GemmConfig_,
/// The epilogue.
typename Epilogue_,
/// The index.
typename Index_ = int,
// The configuration for the A matrix.
typename GemmTileTraitsHelperA_ = GemmTileTraitsHelperA<kLayoutA_, GemmConfig_>,
// The configuration for the B matrix.
typename GemmTileTraitsHelperB_ = GemmTileTraitsHelperB<kLayoutB_, GemmConfig_>,
// The helper class to create the streams and iterators.
typename Helper_ =
SimplifiedGemmTraitsHelper<GemmTileTraitsHelperA_, GemmTileTraitsHelperB_, Index_> >
struct SimplifiedGemmTraits : public GemmTraits<
// The config.
GemmConfig_,
// The stream to load A from global memory to shared memory.
typename Helper_::GlobalLoadStreamA,
// The stream to load B from global memory to shared memory.
typename Helper_::GlobalLoadStreamB,
// The stream to load A from shared memory.
typename Helper_::SharedLoadStreamA,
// The stream to load B from shared memory.
typename Helper_::SharedLoadStreamB,
// The epilogue.
Epilogue_,
// The block swizzle to reorganize the grid.
IdentityBlockSwizzle,
// The index.
Index_,
// The tool used to clear accumulators.
ClearAccumulators<typename GemmConfig_::Accumulators::Element> > {
};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace gemm
} // namespace cutlass

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/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Tile traits used to construct global tile iterator for HGEMM. This is intended to
partition the thread block-level tile into 2D subtiles loaded by the threads and facilitate
memory accesses larger than 16 bits.
*/
#pragma once
#include <cutlass/coord.h>
#include <cutlass/gemm/gemm_global_tile.h>
#include <cutlass/matrix_traits.h>
#include <cutlass/reshape_tile.h>
namespace cutlass {
namespace gemm {
////////////////////////////////////////////////////////////////////////////////////////////////////
template <GemmOperand::Kind kOperand_,
MatrixLayout::Kind kLayout_,
typename Scalar_,
typename Tile_,
typename Threads_,
int kAccessSize_>
struct HgemmCrosswiseGlobalTileTraits : public GemmGlobalTileTraits<
// Which GEMM operand?
kOperand_,
// The layout.
kLayout_,
// The scalar.
Scalar_,
// The tile.
Tile_,
// The threads.
Threads_,
// The number of scalars per LDG/STG.
kAccessSize_> {
/// The base class.
typedef GemmGlobalTileTraits<kOperand_, kLayout_, Scalar_, Tile_, Threads_, kAccessSize_> Base;
/// The threads.
typedef typename Base::Threads Threads;
/// The threads strides.
typedef Shape<1, 2, Base::Tile::kC> ThreadsDelta;
/// The strides in each dimension between different loads/stores.
typedef Shape<Base::Threads::kH * 2, 1, Base::Threads::kW, Base::kAccessSize> Delta;
/// The number of iterations needed to load/store the tile.
typedef Shape<Base::Tile::kH / Base::Threads::kH / 2,
2,
Base::Tile::kW / Base::Threads::kW,
Base::Tile::kC / Base::kAccessSize>
Iterations;
/// Computes the thread offset in (H, W) based on thread ID
struct ThreadOffset {
CUTLASS_HOST_DEVICE
Coord<4> operator()() const {
int thread_offset_h = threadIdx.x / Threads::kW * ThreadsDelta::kH;
int thread_offset_w = threadIdx.x % Threads::kW * ThreadsDelta::kW;
return make_Coord(0, thread_offset_h, thread_offset_w, 0);
}
};
};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace gemm
} // namespace cutlass

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/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Specialization implementing multiply-add operation on half-precision floating point
fragments.
*/
#pragma once
#include <cutlass/fragment.h>
#include <cutlass/gemm/thread_multiply_add.h>
namespace cutlass {
namespace gemm {
////////////////////////////////////////////////////////////////////////////////////////////////////
/// Template performing matrix multiply-add operation within a thread
template <typename AccumulatorsPerThread_, typename ThreadsPerWarp_>
struct ThreadMultiplyAdd<AccumulatorsPerThread_, ThreadsPerWarp_, half, half, half> {
/// The shape of the instruction.
typedef Shape<1, 1, 2, 1> InstructionShape;
/// The number of accumulators per thread.
typedef AccumulatorsPerThread_ AccumulatorsPerThread;
/// The number of threads per warp.
typedef ThreadsPerWarp_ ThreadsPerWarp;
/// The number of accumulators per warp.
typedef typename ShapeMul<AccumulatorsPerThread, ThreadsPerWarp>::Shape AccumulatorsPerWarp;
/// The type for A.
typedef half ScalarA;
/// The fragment for A.
typedef Fragment<ScalarA, AccumulatorsPerThread::kW> FragmentA;
/// The type for B.
typedef half ScalarB;
/// The fragment for B.
typedef Fragment<ScalarB, AccumulatorsPerThread::kH> FragmentB;
/// The type for C and D.
typedef half ScalarC;
/// The accumulators.
typedef Fragment<half, AccumulatorsPerThread::kH * AccumulatorsPerThread::kW> Accumulators;
/// Make sure there's an even number of elements in both dimensions.
static_assert(AccumulatorsPerThread::kH % 2 == 0, "Invalid size");
static_assert(AccumulatorsPerThread::kW % 2 == 0, "Invalid size");
/// Ctor.
CUTLASS_DEVICE ThreadMultiplyAdd() {}
/// Multiply : d = a*b + c.
CUTLASS_DEVICE void multiply_add(FragmentA const& a,
FragmentB const& b,
Accumulators const& c,
Accumulators& d) {
#if defined(__CUDACC__) && __CUDA_ARCH__ >= 530
// The inputs.
__half2 const* a_half2 = reinterpret_cast<__half2 const*>(&a[0]);
__half2 const* b_half2 = reinterpret_cast<__half2 const*>(&b[0]);
__half2 const* c_half2 = reinterpret_cast<__half2 const*>(&c[0]);
// The output.
__half2* d_half2 = reinterpret_cast<__half2*>(&d[0]);
for (int j = 0; j < AccumulatorsPerThread::kH / 2; ++j) {
for (int i = 0; i < AccumulatorsPerThread::kW / 2; ++i) {
// The offsets in the output fragment.
int const k0 = (2 * j + 0) * (AccumulatorsPerThread::kW / 2) + i;
int const k1 = (2 * j + 1) * (AccumulatorsPerThread::kW / 2) + i;
// Compute the product a[i] * b[j].H0_H0.
d_half2[k0] = __hfma2(a_half2[i], __low2half2(b_half2[j]), c_half2[k0]);
// Compute the product a[i] * b[j].H1_H1.
d_half2[k1] = __hfma2(a_half2[i], __high2half2(b_half2[j]), c_half2[k1]);
}
}
#endif
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace gemm
} // namespace cutlass

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/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Transposes a tile of 16b elements. Used by HGEMM to construct a K-strided layout in
shared memory for multiplicands.
*/
#pragma once
#include <cuda_fp16.h>
#include <cutlass/fragment.h>
namespace cutlass {
namespace gemm {
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename GlobalIterator_>
struct HgemmSwizzle {
/// The global iterator.
typedef GlobalIterator_ GlobalIterator;
/// The source fragment.
typedef typename GlobalIterator::Fragment Fragment;
/// The shape of the source fragment.
typedef typename GlobalIterator::FragmentShape FragmentShape;
/// The input fragment.
typedef Fragment InputFragment;
/// The output fragment.
typedef Fragment OutputFragment;
/// The src/dst must be half fragments.
static_assert((platform::is_same<typename Fragment::Element, half>::value), "Works on half");
/// The number of elements must be a multiple of 2.
static_assert(FragmentShape::kH == 2 && ShapeCount<FragmentShape>::kWc == 2, "Not multiple of 2");
/// Ctor.
CUTLASS_DEVICE HgemmSwizzle() {}
/// Transform a fragment.
CUTLASS_DEVICE void transform(Fragment const& src, Fragment& dst) {
// Expose src/dst as int arrays.
int const* src_int = reinterpret_cast<int const*>(&src[0]);
int* dst_int = reinterpret_cast<int*>(&dst[0]);
// Transpose the data.
for (int d = 0; d < FragmentShape::kD; ++d) {
// The indices to read two consecutive "rows".
int const i0 = 2 * d + 0;
int const i1 = 2 * d + 1;
int a0 = src_int[i0];
int a1 = src_int[i1];
int b0, b1;
asm volatile("prmt.b32 %0, %1, %2, 0x5410;" : "=r"(b0) : "r"(a0), "r"(a1));
asm volatile("prmt.b32 %0, %1, %2, 0x7632;" : "=r"(b1) : "r"(a0), "r"(a1));
// The indices to store with "strides".
int const j0 = 0 * (ShapeCount<FragmentShape>::kDhw / 2) + d;
int const j1 = 1 * (ShapeCount<FragmentShape>::kDhw / 2) + d;
dst_int[j0] = b0;
dst_int[j1] = b1;
}
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace gemm
} // namespace cutlass

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@ -1,397 +0,0 @@
/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Defies structural properties of half-precision GEMM computation.
*/
#pragma once
#include <cutlass/convert.h>
#include <cutlass/reshape_tile.h>
#include <cutlass/gemm/gemm.h>
#include <cutlass/gemm/gemm_epilogue.h>
#include <cutlass/gemm/gemm_epilogue_traits.h>
#include <cutlass/gemm/gemm_global_tile.h>
#include <cutlass/gemm/gemm_shared_tile.h>
#include <cutlass/gemm/gemm_traits.h>
#include <cutlass/gemm/hgemm_global_tile.h>
#include <cutlass/gemm/hgemm_multiply_add.h>
#include <cutlass/gemm/hgemm_swizzle.h>
namespace cutlass {
namespace gemm {
////////////////////////////////////////////////////////////////////////////////////////////////////
template <
/// The tile size for the GEMM KxNxM.
typename OutputTile_,
/// The number of accumulators per thread.
typename AccumulatorsPerThread_,
/// The number of scalars per LDG for A.
int kScalarsPerLdgA_ = 2,
/// The number of scalars per LDG for B.
int kScalarsPerLdgB_ = 2>
struct HgemmConfig
: public GemmConfig<
/// The scalar type for A.
half,
/// The scalar type for B.
half,
/// The scalar type for C.
half,
/// The scalar type for D.
half,
/// The tile size for the GEMM KxNxM.
OutputTile_,
/// The functor to do the math in the main loop.
ThreadMultiplyAdd<AccumulatorsPerThread_, Shape<1, 4, 8>, half, half, half>,
/// The number of scalars per LDG for A.
kScalarsPerLdgA_,
/// The number of scalars per STS for A.
kScalarsPerLdgA_,
/// The number of scalars per LDS for A.
8,
/// The number of scalars per LDG for B.
kScalarsPerLdgB_,
/// The number of scalars per STS for B.
kScalarsPerLdgB_,
/// The number of scalars per LDS for B.
8,
/// The number of scalars per LDG for C and STG for D.
2,
/// The number of scalars per STS for D.
8,
/// The number of scalars per LDS for D.
2,
/// The number of stages in shared memory.
2> {};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <enum MatrixLayout::Kind kLayout_, typename Iterator_>
struct HgemmTransformerA {};
template <typename Iterator_>
struct HgemmTransformerA<MatrixLayout::kColumnMajor, Iterator_> {
typedef Convert<typename Iterator_::Fragment, typename Iterator_::Fragment> Transformer;
};
template <typename Iterator_>
struct HgemmTransformerA<MatrixLayout::kRowMajor, Iterator_> {
typedef HgemmSwizzle<Iterator_> Transformer;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <enum MatrixLayout::Kind kLayout_, typename Iterator_>
struct HgemmTransformerB {};
template <typename Iterator_>
struct HgemmTransformerB<MatrixLayout::kRowMajor, Iterator_> {
typedef Convert<typename Iterator_::Fragment, typename Iterator_::Fragment> Transformer;
};
template <typename Iterator_>
struct HgemmTransformerB<MatrixLayout::kColumnMajor, Iterator_> {
typedef HgemmSwizzle<Iterator_> Transformer;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <enum MatrixLayout::Kind kLayout_, typename GemmConfig_>
struct HgemmTileTraitsHelperA : public GemmTileTraitsHelperA<kLayout_, GemmConfig_> {};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename GemmConfig_>
struct HgemmTileTraitsHelperA<MatrixLayout::kRowMajor, GemmConfig_>
: public GemmTileTraitsHelperA<MatrixLayout::kRowMajor, GemmConfig_> {
/// The base config.
typedef GemmTileTraitsHelperA<MatrixLayout::kRowMajor, GemmConfig_> Base;
/// The traits class to build the iterator to load data from global memory for A^T.
typedef HgemmCrosswiseGlobalTileTraits<
GemmOperand::kA,
// The layout.
MatrixLayout::kRowMajor,
// The pointer.
half const,
// The tile has size MxK in GEMM's terminology.
Shape<1, GemmConfig_::OutputTile::kW, GemmConfig_::OutputTile::kD>,
// The threads are distributed as (threads / K ) x K (the traits may reorganize).
Shape<1, GemmConfig_::kThreads / GemmConfig_::OutputTile::kD, GemmConfig_::OutputTile::kD>,
// The number of scalars per LDG (LDG.32 or LDG.128, etc)
GemmConfig_::kScalarsPerLdgA>
GlobalTileTraits;
/// The skew.
static int const kSkewA = 128 / sizeof(half) / GlobalTileTraits::Threads::kW / 2;
/// The traits class to build the iterator to store data to shared memory for A^T.
typedef GemmSharedStoreWithSkewTileAbTraits <
// The pointer.
half,
// The tile has size KxM in GEMM's terminology.
Shape<GemmConfig_::kStages,
GemmConfig_::OutputTile::kD / GemmConfig_::InstructionShape::kD,
GemmConfig_::OutputTile::kW * GemmConfig_::InstructionShape::kD>,
// The threads are distributed as warps x 32(the traits may reorganize).
typename GlobalTileTraits::Threads,
// The number of scalars per STS (STS.32 or STS.128, etc).
2,
// The skew to avoid bank conflicts added in the tile W dimension.
kSkewA<GemmConfig_::kScalarsPerLdsA ? GemmConfig_::kScalarsPerLdsA : kSkewA>
SharedStoreTileTraits;
/// The traits class to build the iterator to load from shared memory for A^T.
typedef GemmSharedLoadTileATraits<
// The pointer.
half const,
// The output tile size.
typename GemmConfig_::OutputTile,
// The number of warps.
typename GemmConfig_::Warps,
// The number of threads per warp.
typename GemmConfig_::MultiplyAdd::ThreadsPerWarp,
// The shape of the FMA instruction.
typename GemmConfig_::InstructionShape,
// The number of stages.
GemmConfig_::kStages,
// The number of scalars per LDS.
8,
// The skew.
SharedStoreTileTraits::kSkew>
SharedLoadTileTraits;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <enum MatrixLayout::Kind kLayout_, typename GemmConfig_>
struct HgemmTileTraitsHelperB : public GemmTileTraitsHelperB<kLayout_, GemmConfig_> {};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename GemmConfig_>
struct HgemmTileTraitsHelperB<MatrixLayout::kColumnMajor, GemmConfig_>
: public GemmTileTraitsHelperB<MatrixLayout::kColumnMajor, GemmConfig_> {
/// The base config.
typedef GemmTileTraitsHelperB<MatrixLayout::kColumnMajor, GemmConfig_> Base;
/// The traits class to build the iterator to load data from global memory for B^N.
typedef HgemmCrosswiseGlobalTileTraits<
GemmOperand::kB,
// The layout.
MatrixLayout::kColumnMajor,
// The pointer.
half const,
// The tile has size KxN in GEMM's terminology.
Shape<1, GemmConfig_::OutputTile::kH, GemmConfig_::OutputTile::kD>,
// The threads are distributed as (threads / K) x K (the traits may reorganize).
Shape<1, GemmConfig_::kThreads / GemmConfig_::OutputTile::kD, GemmConfig_::OutputTile::kD>,
// The number of scalars per LDG (LDG.32 or LDG.128, etc)
GemmConfig_::kScalarsPerLdgB>
GlobalTileTraits;
/// The skew for B.
static int const kSkewB = 128 / sizeof(half) / GlobalTileTraits::Threads::kW / 2;
/// The traits class to build the iterator to store data to shared memory for B^N.
typedef GemmSharedStoreWithSkewTileAbTraits <
// The pointer.
half,
// The tile has size KxN in GEMM's terminology.
Shape<GemmConfig_::kStages,
GemmConfig_::OutputTile::kD / GemmConfig_::InstructionShape::kD,
GemmConfig_::OutputTile::kH * GemmConfig_::InstructionShape::kD>,
// The threads are distributed as (threads / K) x K (the traits may reorganize).
typename GlobalTileTraits::Threads,
// The number of scalars per STS (STS.32 or STS.128, etc).
2,
// The skew to avoid bank conflicts added in the tile W dimension.
kSkewB<GemmConfig_::kScalarsPerLdsB ? GemmConfig_::kScalarsPerLdsB : kSkewB>
SharedStoreTileTraits;
/// The traits class to build the iterator to load from shared memory for B^N.
typedef GemmSharedLoadTileBTraits<
// The pointer.
half const,
// The output tile size.
typename GemmConfig_::OutputTile,
// The number of warps.
typename GemmConfig_::Warps,
// The number of threads per warp.
typename GemmConfig_::MultiplyAdd::ThreadsPerWarp,
// The shape of the FMA instruction.
typename GemmConfig_::InstructionShape,
// The number of stages.
GemmConfig_::kStages,
// The number of scalars per LDS.
8,
// The skew.
SharedStoreTileTraits::kSkew>
SharedLoadTileTraits;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <
/// The layout for A.
MatrixLayout::Kind kLayoutA_,
/// The layout for B.
MatrixLayout::Kind kLayoutB_,
/// The output tile.
typename OutputTile_,
/// The functor to do the math in the epilogue.
typename EpilogueFunctor_,
/// The number of accumulators per thread.
typename AccumulatorsPerThread_ = Shape<8, 8, 16>,
/// The number of halfs loaded in one LDG for A.
int kScalarsPerLdgA_ = 2,
/// The number of halfs loaded in one LDG for B.
int kScalarsPerLdgB_ = 2,
/// The index.
typename Index_ = int>
struct HgemmTraitsHelper {
/// The HGEMM config.
typedef HgemmConfig<OutputTile_, AccumulatorsPerThread_, kScalarsPerLdgA_, kScalarsPerLdgB_>
GemmConfig;
/// The GEMM config for A.
typedef HgemmTileTraitsHelperA<kLayoutA_, GemmConfig> GemmTileTraitsHelperA;
/// The GEMM config for B.
typedef HgemmTileTraitsHelperB<kLayoutB_, GemmConfig> GemmTileTraitsHelperB;
/// The iterator to load A from global memory.
typedef GemmGlobalIteratorAb<typename GemmTileTraitsHelperA::GlobalTileTraits, Index_>
GlobalLoadIteratorA;
/// The default transformer for A.
typedef typename HgemmTransformerA<GemmTileTraitsHelperA::kLayout,
GlobalLoadIteratorA>::Transformer GlobalTransformerA;
/// The iterator to store A to shared memory.
typedef TileStoreIterator<typename GemmTileTraitsHelperA::SharedStoreTileTraits,
typename GemmTileTraitsHelperA::SharedStoreTileTraits::Scalar,
IteratorAdvance::kH,
MemorySpace::kShared>
SharedStoreIteratorA;
/// The stream to load A from global memory to shared memory.
typedef GlobalLoadStream<GlobalLoadIteratorA, SharedStoreIteratorA, GlobalTransformerA>
GlobalLoadStreamA;
/// The iterator to load B from global memory.
typedef GemmGlobalIteratorAb<typename GemmTileTraitsHelperB::GlobalTileTraits, Index_>
GlobalLoadIteratorB;
// The default transformer for B.
typedef typename HgemmTransformerB<GemmTileTraitsHelperB::kLayout,
GlobalLoadIteratorB>::Transformer GlobalTransformerB;
/// The iterator to store B to shared memory.
typedef TileStoreIterator<typename GemmTileTraitsHelperB::SharedStoreTileTraits,
typename GemmTileTraitsHelperB::SharedStoreTileTraits::Scalar,
IteratorAdvance::kH,
MemorySpace::kShared>
SharedStoreIteratorB;
/// The stream to load B from global memory to shared memory.
typedef GlobalLoadStream<GlobalLoadIteratorB, SharedStoreIteratorB, GlobalTransformerB>
GlobalLoadStreamB;
/// The iterator to load A from shared memory
typedef TileLoadIterator<typename GemmTileTraitsHelperA::SharedLoadTileTraits,
typename GemmTileTraitsHelperA::SharedLoadTileTraits::Scalar,
IteratorAdvance::kH,
MemorySpace::kShared>
SharedLoadIteratorA;
/// The stream to load A from shared memory.
typedef SharedLoadStream<SharedLoadIteratorA> SharedLoadStreamA;
/// The iterator to load B from shared memory.
typedef TileLoadIterator<typename GemmTileTraitsHelperB::SharedLoadTileTraits,
typename GemmTileTraitsHelperB::SharedLoadTileTraits::Scalar,
IteratorAdvance::kH,
MemorySpace::kShared>
SharedLoadIteratorB;
/// The stream to load B from shared memory.
typedef SharedLoadStream<SharedLoadIteratorB> SharedLoadStreamB;
/// The functor to do the multiply-add in the main loop.
typedef typename GemmConfig::MultiplyAdd MultiplyAdd;
/// The object to clear accumulators.
typedef ClearAccumulators<typename MultiplyAdd::ScalarC> ClearAccumulators;
/// The traits class for the epilogue.
typedef SimplifiedGemmEpilogueTraits<GemmConfig, EpilogueFunctor_, Index_> GemmEpilogueTraits;
/// The epilogue.
typedef GemmEpilogue<GemmEpilogueTraits> Epilogue;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <
/// The layout for A.
MatrixLayout::Kind kLayoutA_,
/// The layout for B.
MatrixLayout::Kind kLayoutB_,
/// The output tile.
typename OutputTile_ = Shape<8, 128, 128>,
/// The functor to do the math in the epilogue.
typename EpilogueFunctor_ = LinearScaling<half>,
/// The number of accumulators per thread.
typename AccumulatorsPerThread_ = Shape<8, 8, 16>,
/// The number of halfs loaded in one LDG for A.
int kScalarsPerLdgA_ = 2,
/// The number of halfs loaded in one LDG for B.
int kScalarsPerLdgB_ = 2,
/// The index.
typename Index_ = int,
/// The helper class.
typename Helper_ = HgemmTraitsHelper<kLayoutA_,
kLayoutB_,
OutputTile_,
EpilogueFunctor_,
AccumulatorsPerThread_,
kScalarsPerLdgA_,
kScalarsPerLdgB_,
Index_> >
struct HgemmTraits : public GemmTraits<
// The config.
typename Helper_::GemmConfig,
// The stream to load A from global memory to shared memory.
typename Helper_::GlobalLoadStreamA,
// The stream to load B from global memory to shared memory.
typename Helper_::GlobalLoadStreamB,
// The stream to load A from shared memory.
typename Helper_::SharedLoadStreamA,
// The stream to load B from shared memory.
typename Helper_::SharedLoadStreamB,
// The epilogue.
typename Helper_::Epilogue,
// The block swizzle to reorganize the grid.
IdentityBlockSwizzle,
// The index.
Index_,
// The tool used to clear accumulators.
typename Helper_::ClearAccumulators> {};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace gemm
} // namespace cutlass

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@ -1,48 +0,0 @@
/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Defies functors for mapping blockIdx to partitions of the GEMM computation.
Currently, we only implement an identity mapping.
*/
#pragma once
namespace cutlass {
namespace gemm {
////////////////////////////////////////////////////////////////////////////////////////////////////
struct IdentityBlockSwizzle {
/// Ctor.
CUTLASS_DEVICE IdentityBlockSwizzle() {}
/// Swizzle the block index.
CUTLASS_DEVICE dim3 swizzle() { return blockIdx; }
};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace gemm
} // namespace cutlass

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@ -1,320 +0,0 @@
/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Defines the epilogue phase of the GEMM computation for IGEMM, supporting integer and
floating-point output matrix formats.
*/
#pragma once
#include <cutlass/convert.h>
#include <cutlass/fragment.h>
#include <cutlass/gemm/gemm_global_stream.h>
#include <cutlass/gemm/gemm_shared_stream.h>
#include <cutlass/gemm/igemm_global_tile.h>
#include <cutlass/reshape_tile.h>
#include <cutlass/tile_iterator.h>
namespace cutlass {
namespace gemm {
////////////////////////////////////////////////////////////////////////////////////////////////////
template <int kElements_>
struct IgemmFloatToInt8Converter {
/// The input fragment.
typedef Fragment<float, kElements_> InputFragment;
/// The output fragment.
typedef Fragment<int8_t, kElements_> OutputFragment;
// We are packing 4 floats into int32 registers so we need kElements to be multiple of 4.
static_assert(kElements_ % 4 == 0, "kElements must be multiple of 4");
/// Ctor.
CUTLASS_DEVICE IgemmFloatToInt8Converter() {}
/// Transform a fragment.
CUTLASS_DEVICE void transform(InputFragment const& src, OutputFragment& dst) {
transform(src, 0, dst);
}
/// Transform a fragment.
template <typename Fragment_>
CUTLASS_DEVICE void transform(Fragment_ const& src, int offset, OutputFragment& dst) {
// The inputs.
float4 const* src_f4 = reinterpret_cast<float4 const*>(&src[0]);
// The outputs.
int* dst_int = reinterpret_cast<int*>(&dst[0]);
// Iterate over the floats and pack them together to produce ints.
for (int i = 0; i < kElements_ / 4; ++i) {
// Read the float4.
float4 f4 = src_f4[i];
// Clamp the 4 elements of the floats to the [-128, +127] range.
float x = fmaxf(-128.f, fminf(127.f, f4.x));
float y = fmaxf(-128.f, fminf(127.f, f4.y));
float z = fmaxf(-128.f, fminf(127.f, f4.z));
float w = fmaxf(-128.f, fminf(127.f, f4.w));
// Convert to integers.
int ix = (int)x;
int iy = (int)y;
int iz = (int)z;
int iw = (int)w;
// Extract the lower bytes to build an int32 with 4 int8.
asm volatile("prmt.b32 %0, %0, %1, 0x1140;" : "+r"(ix) : "r"(iy));
asm volatile("prmt.b32 %0, %0, %1, 0x1140;" : "+r"(iz) : "r"(iw));
asm volatile("prmt.b32 %0, %0, %1, 0x5410;" : "+r"(ix) : "r"(iz));
// Store the int.
dst_int[i] = ix;
}
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename InputScalar_, typename OutputFragment_>
struct IgemmGlobalStoreTransformer {
typedef Convert<Fragment<InputScalar_, OutputFragment_::kElements>, OutputFragment_> Transformer;
};
template <int kElements_>
struct IgemmGlobalStoreTransformer<float, Fragment<int8_t, kElements_> > {
typedef IgemmFloatToInt8Converter<kElements_> Transformer;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <int kElements_>
struct IgemmInt8ToFloatConverter {
/// The input fragment.
typedef Fragment<int8_t, kElements_> InputFragment;
/// The output fragment.
typedef Fragment<float, kElements_> OutputFragment;
// We are unpacking 4 int8s from int32.
static_assert(kElements_ % 4 == 0, "kElements must be multiple of 4");
/// Ctor.
CUTLASS_DEVICE IgemmInt8ToFloatConverter() {}
/// Transform a fragment.
CUTLASS_DEVICE void transform(InputFragment const& src, OutputFragment& dst) {
transform(src, 0, dst);
}
/// Transform a fragment.
template <typename Fragment_>
CUTLASS_DEVICE void transform(Fragment_ const& src, int offset, OutputFragment& dst) {
// The inputs.
int const* src_int = reinterpret_cast<int const*>(&src[0]);
// The outputs.
float4* dst_f4 = reinterpret_cast<float4*>(&dst[0]);
// Iterate over the int8 and unpack them together to produce floats.
for (int i = 0; i < kElements_ / 4; ++i) {
// Read the int.
int ix, iy, iz, iw = src_int[i];
// Extract the 4 bytes.
asm volatile("prmt.b32 %0, 0x0, %1, 0x4440;" : "=r"(ix) : "r"(iw));
asm volatile("prmt.b32 %0, 0x0, %1, 0x4441;" : "=r"(iy) : "r"(iw));
asm volatile("prmt.b32 %0, 0x0, %1, 0x4442;" : "=r"(iz) : "r"(iw));
asm volatile("prmt.b32 %0, 0x0, %1, 0x4443;" : "=r"(iw) : "r"(iw));
// The floats.
float fx, fy, fz, fw;
// Convert to floats (make sure we generate I2F.F32.S8).
asm volatile("cvt.rn.f32.s8 %0, %1;" : "=f"(fx) : "r"(ix));
asm volatile("cvt.rn.f32.s8 %0, %1;" : "=f"(fy) : "r"(iy));
asm volatile("cvt.rn.f32.s8 %0, %1;" : "=f"(fz) : "r"(iz));
asm volatile("cvt.rn.f32.s8 %0, %1;" : "=f"(fw) : "r"(iw));
// Store the float4.
dst_f4[i] = make_float4(fx, fy, fz, fw);
}
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename InputFragment_, typename OutputScalar_>
struct IgemmGlobalLoadTransformer {
typedef Convert<InputFragment_, Fragment<OutputScalar_, InputFragment_::kElements> > Transformer;
};
template <int kElements_>
struct IgemmGlobalLoadTransformer<Fragment<int8_t, kElements_>, float> {
typedef IgemmInt8ToFloatConverter<kElements_> Transformer;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename InputScalar_, typename OutputFragment_>
struct IgemmSharedStoreTransformer {
typedef Convert<Fragment<InputScalar_, OutputFragment_::kElements>, OutputFragment_> Transformer;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename IgemmConfig_, typename EpilogueFunctor_, typename Index_>
struct IgemmEpilogueTraitsHelper
: public GemmEpilogueTraitsHelper<IgemmConfig_, EpilogueFunctor_, Index_> {
/// The base class.
typedef GemmEpilogueTraitsHelper<IgemmConfig_, EpilogueFunctor_, Index_> Base;
/// The config.
typedef IgemmConfig_ IgemmConfig;
/// The scalar type of the epilogue.
typedef typename Base::Scalar Scalar;
/// The iterations.
typedef typename Base::Iterations Iterations;
/// The iterations strides.
typedef typename Base::Delta Delta;
/// The traits class for the iterator.
typedef typename Base::GlobalLoadTileTraits GlobalLoadTileTraits;
/// The iterator to store to shared memory.
typedef GemmGlobalIteratorCd<GlobalLoadTileTraits> GlobalLoadIteratorC;
/// The fragment that needs to be produced by the load iterator.
typedef typename GlobalLoadIteratorC::Fragment GlobalFragmentC;
/// The transformer from loaded data to math fragment.
typedef
typename IgemmGlobalLoadTransformer<GlobalFragmentC, Scalar>::Transformer GlobalTransformerC;
/// The traits class for the iterator.
typedef typename Base::GlobalStoreTileTraits GlobalStoreTileTraits;
/// The iterator to store to shared memory.
typedef GemmGlobalIteratorCd<GlobalStoreTileTraits> GlobalStoreIteratorD;
/// The fragment that needs to be passed to that store iterator.
typedef typename GlobalStoreIteratorD::Fragment GlobalFragmentD;
/// The transformer from accumulators to shared memory fragments.
typedef
typename IgemmGlobalStoreTransformer<Scalar, GlobalFragmentD>::Transformer GlobalTransformerD;
/// The traits class for the shared iterator to store D to shared memory.
typedef typename Base::SharedStoreTileTraits SharedStoreTileTraits;
/// The shared iterator to store D to shared memory.
typedef TileStoreIterator<SharedStoreTileTraits,
typename SharedStoreTileTraits::Scalar,
IteratorAdvance::kH,
MemorySpace::kGlobal>
SharedStoreIteratorD;
/// The fragment that needs to be passed to that store iterator.
typedef typename SharedStoreIteratorD::Fragment SharedStoreFragmentD;
/// The transformer from accumulators to shared memory fragments.
typedef typename IgemmSharedStoreTransformer<typename IgemmConfig::Accumulators::Element,
SharedStoreFragmentD>::Transformer
SharedStoreTransformerD;
/// The traits class for the shared iterator to load D from shared memory.
typedef typename Base::SharedLoadTileTraits SharedLoadTileTraits;
/// The shared iterator to load D from shared memory.
typedef TileLoadIterator<SharedLoadTileTraits,
typename SharedLoadTileTraits::Scalar,
IteratorAdvance::kH,
MemorySpace::kShared>
SharedLoadIteratorD;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <
/// The config.
typename IgemmConfig_,
/// The functor to do the math in the epilogue.
typename EpilogueFunctor_,
/// The index.
typename Index_ = int,
/// The helper class to assemble the traits.
typename Helper_ = IgemmEpilogueTraitsHelper<IgemmConfig_, EpilogueFunctor_, Index_> >
struct IgemmEpilogueTraits : public GemmEpilogueTraits<
// The output tile.
typename IgemmConfig_::OutputTile,
// The accumulators.
typename IgemmConfig_::Accumulators,
// The global iterator for C.
typename Helper_::GlobalLoadIteratorC,
// The transformer for C.
typename Helper_::GlobalTransformerC,
// The transformer for D.
typename Helper_::GlobalTransformerD,
// The global iterator for D.
typename Helper_::GlobalStoreIteratorD,
// The iterator to store D to shared memory.
typename Helper_::SharedStoreIteratorD,
// The shared store transformer for D.
typename Helper_::SharedStoreTransformerD,
// The iterator to load D from shared memory.
typename Helper_::SharedLoadIteratorD,
// The iterations.
typename Helper_::Iterations,
// The strides between iterations.
typename Helper_::Delta,
// The functor to be used in the epilogue.
EpilogueFunctor_,
// The index.
Index_> {
/// Do we output in int8?
static bool const kInt8Output =
platform::is_same<typename IgemmConfig_::ScalarC, int8_t>::value != 0;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename GemmEpilogueTraits_, bool = GemmEpilogueTraits_::kInt8Output>
struct IgemmEpilogue : public GemmEpilogue<GemmEpilogueTraits_> {
/// The base class.
typedef GemmEpilogue<GemmEpilogueTraits_> Base;
/// Ctor.
CUTLASS_DEVICE IgemmEpilogue(typename Base::Params const& params_,
typename Base::SharedStorage& shared_storage_,
typename Base::Index m_,
typename Base::Index n_)
: Base(params_, shared_storage_, m_, n_) {}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename GemmEpilogueTraits_>
struct IgemmEpilogue<GemmEpilogueTraits_, true> : public GemmEpilogue<GemmEpilogueTraits_> {
/// The base class.
typedef GemmEpilogue<GemmEpilogueTraits_> Base;
/// Ctor.
CUTLASS_DEVICE IgemmEpilogue(typename Base::Params const& params_,
typename Base::SharedStorage& shared_storage_,
typename Base::Index m_,
typename Base::Index n_)
: Base(params_, shared_storage_, m_, n_) {}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace gemm
} // namespace cutlass

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/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Implements tile iterators to partition the thread block tile into 2D subtiles and
efficiently load each. Applies permute transformation to construct 'interleaved K-strided'
data layout in which 4-element dot products from the same K index are arranged in consecutive
locations within shared memory.
Supports efficient loads from shared memory to target the DP4A instruction.
*/
#pragma once
#include <cutlass/coord.h>
#include <cutlass/gemm/gemm_global_tile.h>
#include <cutlass/matrix_traits.h>
namespace cutlass {
namespace gemm {
////////////////////////////////////////////////////////////////////////////////////////////////////
template <GemmOperand::Kind kOperand_,
MatrixLayout::Kind kLayout_,
typename Scalar_,
typename Tile_,
typename Threads_,
int kAccessSize_>
struct IgemmGlobalTileTraits : public GemmGlobalTileTraits<
// Which GEMM operand?
kOperand_,
// The layout.
kLayout_,
// The scalar.
Scalar_,
// The tile.
Tile_,
// The threads.
Threads_,
// The number of scalars per LDG/STG.
kAccessSize_> {
/// The base class.
typedef GemmGlobalTileTraits<kOperand_, kLayout_, Scalar_, Tile_, Threads_, kAccessSize_> Base;
/// The threads.
typedef typename Base::Threads Threads;
/// The strides in each dimension between different loads/stores.
typedef Shape<Base::Threads::kH * 4, 1, Base::Threads::kW, Base::kAccessSize> Delta;
/// The number of iterations needed to load/store the tile.
typedef Shape<Base::Tile::kH / Base::Threads::kH / 4,
4,
Base::Tile::kW / Base::Threads::kW,
Base::Tile::kC / Base::kAccessSize>
Iterations;
/// Computes the thread offset in (H, W) based on thread ID
struct ThreadOffset {
CUTLASS_HOST_DEVICE
Coord<4> operator()() const {
int thread_offset_h = threadIdx.x / Threads::kW * ThreadsDelta::kH;
int thread_offset_w = threadIdx.x % Threads::kW * ThreadsDelta::kW;
return make_Coord(0, thread_offset_h, thread_offset_w, 0);
}
};
public:
/// The threads strides.
typedef Shape<1, 4, Base::Tile::kC> ThreadsDelta;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
/// Deprecated. Please use IgemmGlobalTileTraits instead.
template <GemmOperand::Kind kOperand_,
MatrixLayout::Kind kLayout_,
typename Scalar_,
typename Tile_,
typename Threads_,
int kAccessSize_>
struct IgemmContiguousGlobalTileTraits
: public IgemmGlobalTileTraits<kOperand_, kLayout_, Scalar_, Tile_, Threads_, kAccessSize_> {};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename TileTraits_, typename Index_ = int>
struct IgemmGlobalIteratorAb : public GemmGlobalIteratorAb<TileTraits_, Index_> {
/// The base class.
typedef GemmGlobalIteratorAb<TileTraits_, Index_> Base;
/// The functor to compute the thread offset.
typedef typename TileTraits_::ThreadOffset ThreadOffset;
/// Constructor.
CUTLASS_DEVICE IgemmGlobalIteratorAb(typename Base::Params const& _params,
const Coord<3>& bounds,
const Coord<3>& block,
ThreadOffset thread_offset_func = ThreadOffset())
: Base(_params, bounds, block, thread_offset_func), in_residue_(false), mask_(0xffffffff) {
// The number of elements read in a single iteration.
int const kBlock = TileTraits_::Tile::kW * TileTraits_::kAccessSize;
// The residue.
int const kResidue = (int)(bounds[1] % kBlock);
// Compute the number of elements that are valid.
int const left = kResidue - Base::thread_offset[2];
if (left > 0 && left < 4) {
mask_ = (1u << (8 * left)) - 1u;
}
}
/// The accessor.
CUTLASS_DEVICE void get(typename Base::AccessType& value, int d, int h, int w, int c) const {
Base::get(value, d, h, w, c);
if (in_residue_) {
reinterpret_cast<uint32_t&>(value) &= mask_;
}
}
/// Move to residue portion.
CUTLASS_DEVICE void move_to_residue(typename Base::Index k) {
Base::move_to_residue(k);
in_residue_ = true;
}
/// Move back to the beginning of the first tile.
CUTLASS_DEVICE void rollback() {
Base::rollback();
in_residue_ = false;
}
/// Are we in the residue?
bool in_residue_;
/// The mask to clean up the values.
uint32_t mask_;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace gemm
} // namespace cutlass

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/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Implements matrix multiply accumulate operation of 8-bit integer data using DP4A
instruction.
*/
#pragma once
#include <cutlass/fragment.h>
#include <cutlass/gemm/thread_multiply_add.h>
namespace cutlass {
namespace gemm {
////////////////////////////////////////////////////////////////////////////////////////////////////
/// Template performing matrix multiply-add operation within a thread
template <typename AccumulatorsPerThread_, typename ThreadsPerWarp_>
struct ThreadMultiplyAdd<AccumulatorsPerThread_, ThreadsPerWarp_, int8_t, int8_t, int> {
/// The shape of the instruction.
typedef Shape<4, 1, 1> InstructionShape;
/// The number of accumulators per thread.
typedef AccumulatorsPerThread_ AccumulatorsPerThread;
/// The number of threads per warp.
typedef ThreadsPerWarp_ ThreadsPerWarp;
/// The number of accumulators per warp.
typedef typename ShapeMul<AccumulatorsPerThread, ThreadsPerWarp>::Shape AccumulatorsPerWarp;
/// The type for A.
typedef int8_t ScalarA;
/// The fragment for A.
typedef Fragment<ScalarA, AccumulatorsPerThread::kW * 4> FragmentA;
/// The type for B.
typedef int8_t ScalarB;
/// The fragment for B.
typedef Fragment<ScalarB, AccumulatorsPerThread::kH * 4> FragmentB;
/// The type for C and D.
typedef int ScalarC;
/// The accumulators.
typedef Fragment<ScalarC, AccumulatorsPerThread::kH * AccumulatorsPerThread::kW> Accumulators;
/// Ctor.
CUTLASS_DEVICE ThreadMultiplyAdd() {}
/// Multiply : d = a*b + c.
CUTLASS_DEVICE void multiply_add(FragmentA const& a,
FragmentB const& b,
Accumulators const& c,
Accumulators& d) {
// The inputs.
int const* a_int = reinterpret_cast<int const*>(&a[0]);
int const* b_int = reinterpret_cast<int const*>(&b[0]);
for (int j = 0; j < AccumulatorsPerThread::kH; ++j) {
for (int i = 0; i < AccumulatorsPerThread::kW; ++i) {
asm volatile("dp4a.s32.s32 %0, %1, %2, %3;"
: "=r"(d[j * AccumulatorsPerThread::kW + i])
: "r"(a_int[i]), "r"(b_int[j]), "r"(c[j * AccumulatorsPerThread::kW + i]));
}
}
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace gemm
} // namespace cutlass

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@ -1,115 +0,0 @@
/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Transposes a fragment of data containing packed 8-bit integer elements.
*/
#pragma once
#include <cutlass/fragment.h>
namespace cutlass {
namespace gemm {
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename GlobalIterator_>
struct IgemmSwizzle {
/// The global iterator.
typedef GlobalIterator_ GlobalIterator;
/// The source fragment.
typedef typename GlobalIterator::Fragment Fragment;
/// The shape of the source fragment.
typedef typename GlobalIterator::FragmentShape FragmentShape;
/// The source fragment.
typedef Fragment InputFragment;
/// The destination fragment.
typedef Fragment OutputFragment;
/// The src/dst must be int8 fragments.
static_assert((platform::is_same<typename Fragment::Element, int8_t>::value), "Works on int8");
/// The number of elements must be a multiple of 4.
static_assert(FragmentShape::kH % 4 == 0 && ShapeCount<FragmentShape>::kWc % 4 == 0,
"Not multiple of 4");
/// Ctor.
CUTLASS_DEVICE IgemmSwizzle() {}
/// Transform a fragment.
CUTLASS_DEVICE void transform(Fragment const& src, Fragment& dst) {
// Expose src/dst as int arrays.
int const* src_int = reinterpret_cast<int const*>(&src[0]);
int* dst_int = reinterpret_cast<int*>(&dst[0]);
// Transpose the data.
for (int d = 0; d < FragmentShape::kD; ++d) {
for (int h = 0; h < FragmentShape::kH / 4; ++h) {
for (int w = 0; w < ShapeCount<FragmentShape>::kWc / 4; ++w) {
int const i0 = d * (ShapeCount<FragmentShape>::kHwc / 4) +
(4 * h + 0) * (ShapeCount<FragmentShape>::kWc / 4) + w;
int const i1 = d * (ShapeCount<FragmentShape>::kHwc / 4) +
(4 * h + 1) * (ShapeCount<FragmentShape>::kWc / 4) + w;
int const i2 = d * (ShapeCount<FragmentShape>::kHwc / 4) +
(4 * h + 2) * (ShapeCount<FragmentShape>::kWc / 4) + w;
int const i3 = d * (ShapeCount<FragmentShape>::kHwc / 4) +
(4 * h + 3) * (ShapeCount<FragmentShape>::kWc / 4) + w;
int a0 = src_int[i0];
int a1 = src_int[i1];
int a2 = src_int[i2];
int a3 = src_int[i3];
int b0, b1, b2, b3, c0;
asm volatile("prmt.b32 %0, %1, %2, 0x0040;" : "=r"(b0) : "r"(a0), "r"(a1));
asm volatile("prmt.b32 %0, %1, %2, 0x0040;" : "=r"(c0) : "r"(a2), "r"(a3));
asm volatile("prmt.b32 %0, %1, %2, 0x5410;" : "=r"(b0) : "r"(b0), "r"(c0));
asm volatile("prmt.b32 %0, %1, %2, 0x0051;" : "=r"(b1) : "r"(a0), "r"(a1));
asm volatile("prmt.b32 %0, %1, %2, 0x0051;" : "=r"(c0) : "r"(a2), "r"(a3));
asm volatile("prmt.b32 %0, %1, %2, 0x5410;" : "=r"(b1) : "r"(b1), "r"(c0));
asm volatile("prmt.b32 %0, %1, %2, 0x0062;" : "=r"(b2) : "r"(a0), "r"(a1));
asm volatile("prmt.b32 %0, %1, %2, 0x0062;" : "=r"(c0) : "r"(a2), "r"(a3));
asm volatile("prmt.b32 %0, %1, %2, 0x5410;" : "=r"(b2) : "r"(b2), "r"(c0));
asm volatile("prmt.b32 %0, %1, %2, 0x0073;" : "=r"(b3) : "r"(a0), "r"(a1));
asm volatile("prmt.b32 %0, %1, %2, 0x0073;" : "=r"(c0) : "r"(a2), "r"(a3));
asm volatile("prmt.b32 %0, %1, %2, 0x5410;" : "=r"(b3) : "r"(b3), "r"(c0));
dst_int[i0] = b0;
dst_int[i1] = b1;
dst_int[i2] = b2;
dst_int[i3] = b3;
}
}
}
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace gemm
} // namespace cutlass

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@ -1,539 +0,0 @@
/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Defies structural properties of mixed-precision integer GEMM. Multiplicands are assumed
to be packed 8bit integers, accumulators are assumed to be 32b signed integers, and output
formats vary.
*/
#pragma once
#include <cutlass/convert.h>
#include <cutlass/gemm/gemm.h>
#include <cutlass/gemm/gemm_epilogue.h>
#include <cutlass/gemm/gemm_epilogue_traits.h>
#include <cutlass/gemm/gemm_global_tile.h>
#include <cutlass/gemm/gemm_shared_tile.h>
#include <cutlass/gemm/gemm_traits.h>
#include <cutlass/gemm/igemm_epilogue.h>
#include <cutlass/gemm/igemm_global_tile.h>
#include <cutlass/gemm/igemm_multiply_add.h>
#include <cutlass/gemm/igemm_swizzle.h>
#include <cutlass/reshape_tile.h>
namespace cutlass {
namespace gemm {
////////////////////////////////////////////////////////////////////////////////////////////////////
template <
/// The tile size for the GEMM KxNxM.
typename OutputTile_,
/// The output type.
typename ScalarD_,
/// The number of accumulators per thread.
typename AccumulatorsPerThread_>
struct IgemmConfig
: public GemmConfig<
/// The scalar type for A.
int8_t,
/// The scalar type for B.
int8_t,
/// The scalar type for C.
ScalarD_,
/// The scalar type for D.
ScalarD_,
/// The tile size for the GEMM KxNxM.
OutputTile_,
/// The functor to do the math in the main loop.
ThreadMultiplyAdd<AccumulatorsPerThread_, Shape<1, 4, 8>, int8_t, int8_t, int>,
/// The number of scalars per LDG for A.
4,
/// The number of scalars per STS for A.
4,
/// The number of scalars per LDS for A.
16,
/// The number of scalars per LDG for B.
4,
/// The number of scalars per STS for B.
4,
/// The number of scalars per LDS for B.
16,
/// The number of scalars per LDG for C and STG for D.
1,
/// The number of scalars per STS for D.
4,
/// The number of scalars per LDS for D.
1,
/// The number of stages in shared memory.
2,
/// Enable the code path that deals with the residue in epilogue.
true> {};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename OutputTile_, typename AccumulatorsPerThread_>
struct IgemmConfig<OutputTile_, int8_t, AccumulatorsPerThread_>
: public GemmConfig<
/// The scalar type for A.
int8_t,
/// The scalar type for B.
int8_t,
/// The scalar type for C.
int8_t,
/// The scalar type for D.
int8_t,
/// The tile size for the GEMM KxNxM.
OutputTile_,
/// The functor to do the math in the main loop.
ThreadMultiplyAdd<AccumulatorsPerThread_, Shape<1, 4, 8>, int8_t, int8_t, int>,
/// The number of scalars per LDG for A.
4,
/// The number of scalars per STS for A.
4,
/// The number of scalars per LDS for A.
16,
/// The number of scalars per LDG for B.
4,
/// The number of scalars per STS for B.
4,
/// The number of scalars per LDS for B.
16,
/// The number of scalars per LDG for C and STG for D.
4,
/// The number of scalars per STS for D.
4,
/// The number of scalars per LDS for D.
4,
/// The number of stages in shared memory.
2,
/// Enable the code path that deals with the residue in epilogue.
true> {};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <enum MatrixLayout::Kind kLayout_, typename GemmConfig_, typename Index_>
struct IgemmTileTraitsHelperA : public GemmTileTraitsHelperA<kLayout_, GemmConfig_> {};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename GemmConfig_, typename Index_>
struct IgemmTileTraitsHelperA<MatrixLayout::kColumnMajor, GemmConfig_, Index_>
: public GemmTileTraitsHelperA<MatrixLayout::kColumnMajor, GemmConfig_> {
/// The base config.
typedef GemmTileTraitsHelperA<MatrixLayout::kColumnMajor, GemmConfig_> Base;
/// The number of scalars per LDG/STS/LDS for A.
static int const kScalarsPerStsA = 16;
/// The traits class to build the iterator to load data from global memory for A^N.
typedef IgemmGlobalTileTraits<
GemmOperand::kA,
// The layout.
MatrixLayout::kColumnMajor,
// The pointer is float const.
int8_t const,
// The tile has size KxM in GEMM's terminology.
Shape<1, GemmConfig_::OutputTile::kD, GemmConfig_::OutputTile::kW>,
// The threads are distributed as warps x 32 (the traits may reorganize).
Shape<1, ShapeCount<typename GemmConfig_::Warps>::kCount, GemmConfig_::kWarpSize>,
// The number of scalars per LDG (LDG.32 or LDG.128, etc).
GemmConfig_::kScalarsPerLdgA>
GlobalTileTraits;
// The iterator.
typedef GemmGlobalIteratorAb<GlobalTileTraits, Index_> GlobalLoadIterator;
/// The traits class to build the iterator to store data to shared memory for A^N.
typedef GemmSharedStoreTileAbTraits<
// The pointer is float.
int8_t,
// The tile has size KxM in GEMM's terminology.
Shape<GemmConfig_::kStages, GemmConfig_::OutputTile::kD / 4, GemmConfig_::OutputTile::kW * 4>,
// The threads are distributed as warps x 32 (the traits may reorganize).
typename GlobalTileTraits::Threads,
// The number of scalars per STS (STS.32 or STS.128, etc).
kScalarsPerStsA>
SharedStoreTileTraits;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename GemmConfig_, typename Index_>
struct IgemmTileTraitsHelperA<MatrixLayout::kRowMajor, GemmConfig_, Index_> {
/// The layout.
static MatrixLayout::Kind const kLayout = MatrixLayout::kRowMajor;
/// The input scalar.
typedef int8_t Scalar;
/// The scalar stored in shared memory.
typedef int8_t MultiplyAddScalar;
/// The number of scalars per LDG/STS/LDS for A.
static int const kScalarsPerStsA = 16;
/// The traits class to build the iterator to load data from global memory for A^T.
typedef IgemmGlobalTileTraits<
GemmOperand::kA,
// The layout.
MatrixLayout::kRowMajor,
// The pointer is float const.
int8_t const,
// The tile has size NxK in GEMM's terminology.
Shape<1, GemmConfig_::OutputTile::kW, GemmConfig_::OutputTile::kD>,
// The threads are distributed as warps x 32 (the traits may reorganize).
Shape<1, ShapeCount<typename GemmConfig_::Warps>::kCount, GemmConfig_::kWarpSize>,
// The number of scalars per LDG (LDG.32 or LDG.128, etc).
GemmConfig_::kScalarsPerLdgA>
GlobalTileTraits;
// The iterator.
typedef IgemmGlobalIteratorAb<GlobalTileTraits, Index_> GlobalLoadIterator;
/// The traits class to build the iterator to store data to shared memory for A^N.
typedef GemmSharedStoreWithSkewTileAbTraits<
// The pointer is int8.
int8_t,
// The tile has size KxN in GEMM's terminology.
Shape<GemmConfig_::kStages, GemmConfig_::OutputTile::kD / 4, GemmConfig_::OutputTile::kW * 4>,
// The threads are distributed as (threads / K) x K (the traits may reorganize).
typename GlobalTileTraits::Threads,
// The number of scalars per STS.
kScalarsPerStsA,
// The skew to avoid bank conflicts added in the tile W dimension.
16>
SharedStoreTileTraits;
/// The traits class to build the iterator to load from shared memory for A^N.
typedef GemmSharedLoadTileATraits<
// The pointer is float const.
int8_t const,
// The output tile size.
typename GemmConfig_::OutputTile,
// The number of warps.
typename GemmConfig_::Warps,
// The number of threads per warp.
typename GemmConfig_::MultiplyAdd::ThreadsPerWarp,
// The shape of the FMA instruction.
typename GemmConfig_::InstructionShape,
// The number of stages.
GemmConfig_::kStages,
// The number of scalars per LDS.
16,
// The skew.
SharedStoreTileTraits::kSkew>
SharedLoadTileTraits;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <enum MatrixLayout::Kind kLayout_, typename GemmConfig_, typename Index_>
struct IgemmTileTraitsHelperB : public GemmTileTraitsHelperB<kLayout_, GemmConfig_> {};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename GemmConfig_, typename Index_>
struct IgemmTileTraitsHelperB<MatrixLayout::kColumnMajor, GemmConfig_, Index_> {
/// The layout.
static MatrixLayout::Kind const kLayout = MatrixLayout::kColumnMajor;
/// The input scalar.
typedef int8_t Scalar;
/// The scalar stored in shared memory.
typedef int8_t MultiplyAddScalar;
/// The number of scalars per LDG/STS/LDS for B.
static int const kScalarsPerStsB = 16;
/// The traits class to build the iterator to load data from global memory for B^T.
typedef IgemmGlobalTileTraits<
GemmOperand::kB,
// The layout.
MatrixLayout::kColumnMajor,
// The pointer is float const.
int8_t const,
// The tile has size NxK in GEMM's terminology.
Shape<1, GemmConfig_::OutputTile::kH, GemmConfig_::OutputTile::kD>,
// The threads are distributed as warps x 32 (the traits may reorganize).
Shape<1, ShapeCount<typename GemmConfig_::Warps>::kCount, GemmConfig_::kWarpSize>,
// The number of scalars per LDG (LDG.32 or LDG.128, etc).
GemmConfig_::kScalarsPerLdgB>
GlobalTileTraits;
// The iterator.
typedef IgemmGlobalIteratorAb<GlobalTileTraits, Index_> GlobalLoadIterator;
/// The traits class to build the iterator to store data to shared memory for B^N.
typedef GemmSharedStoreWithSkewTileAbTraits<
// The pointer is int8.
int8_t,
// The tile has size KxN in GEMM's terminology.
Shape<GemmConfig_::kStages, GemmConfig_::OutputTile::kD / 4, GemmConfig_::OutputTile::kH * 4>,
// The threads are distributed as (threads / K) x K (the traits may reorganize).
typename GlobalTileTraits::Threads,
// The number of scalars per STS.
kScalarsPerStsB,
// The skew to avoid bank conflicts added in the tile W dimension.
16>
SharedStoreTileTraits;
/// The traits class to build the iterator to load from shared memory for B^N.
typedef GemmSharedLoadTileBTraits<
// The pointer is float const.
int8_t const,
// The output tile size.
typename GemmConfig_::OutputTile,
// The number of warps.
typename GemmConfig_::Warps,
// The number of threads per warp.
typename GemmConfig_::MultiplyAdd::ThreadsPerWarp,
// The shape of the FMA instruction.
typename GemmConfig_::InstructionShape,
// The number of stages.
GemmConfig_::kStages,
// The number of scalars per LDS.
16,
// The skew.
SharedStoreTileTraits::kSkew>
SharedLoadTileTraits;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename GemmConfig_, typename Index_>
struct IgemmTileTraitsHelperB<MatrixLayout::kRowMajor, GemmConfig_, Index_>
: public GemmTileTraitsHelperB<MatrixLayout::kRowMajor, GemmConfig_> {
/// The base config.
typedef GemmTileTraitsHelperB<MatrixLayout::kRowMajor, GemmConfig_> Base;
/// The number of scalars per LDG/STS/LDS for B.
static int const kScalarsPerStsB = 16;
/// The traits class to build the iterator to load data from global memory for B^T.
typedef IgemmGlobalTileTraits<
GemmOperand::kB,
// The layout.
MatrixLayout::kRowMajor,
// The pointer is float const.
int8_t const,
// The tile has size KxM in GEMM's terminology.
Shape<1, GemmConfig_::OutputTile::kD, GemmConfig_::OutputTile::kH>,
// The threads are distributed as warps x 32 (the traits may reorganize).
Shape<1, ShapeCount<typename GemmConfig_::Warps>::kCount, GemmConfig_::kWarpSize>,
// The number of scalars per LDG (LDG.32 or LDG.128, etc).
GemmConfig_::kScalarsPerLdgB>
GlobalTileTraits;
// The iterator.
typedef GemmGlobalIteratorAb<GlobalTileTraits, Index_> GlobalLoadIterator;
/// The traits class to build the iterator to store data to shared memory for B^N.
typedef GemmSharedStoreTileAbTraits<
// The pointer is float.
int8_t,
// The tile has size KxM in GEMM's terminology.
Shape<GemmConfig_::kStages, GemmConfig_::OutputTile::kD / 4, GemmConfig_::OutputTile::kH * 4>,
// The threads are distributed as warps x 32 (the traits may reorganize).
typename GlobalTileTraits::Threads,
// The number of scalars per STS (STS.32 or STS.128, etc).
kScalarsPerStsB>
SharedStoreTileTraits;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <enum MatrixLayout::Kind kLayout_, typename Iterator_>
struct IgemmTransformerA {};
template <typename Iterator_>
struct IgemmTransformerA<MatrixLayout::kRowMajor, Iterator_> {
typedef Copy<typename Iterator_::Fragment> Transformer;
};
template <typename Iterator_>
struct IgemmTransformerA<MatrixLayout::kColumnMajor, Iterator_> {
typedef IgemmSwizzle<Iterator_> Transformer;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <enum MatrixLayout::Kind kLayout_, typename Iterator_>
struct IgemmTransformerB {};
template <typename Iterator_>
struct IgemmTransformerB<MatrixLayout::kColumnMajor, Iterator_> {
typedef Copy<typename Iterator_::Fragment> Transformer;
};
template <typename Iterator_>
struct IgemmTransformerB<MatrixLayout::kRowMajor, Iterator_> {
typedef IgemmSwizzle<Iterator_> Transformer;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <
/// The layout for A.
MatrixLayout::Kind kLayoutA_,
/// The layout for B.
MatrixLayout::Kind kLayoutB_,
/// The output tile.
typename OutputTile_,
/// The output type.
typename ScalarD_,
/// The functor to do the math in the epilogue.
typename EpilogueFunctor_,
/// The number of accumulators per thread.
typename AccumulatorsPerThread_ = Shape<32, 8, 8>,
/// The index.
typename Index_ = int>
struct IgemmTraitsHelper {
/// The IGEMM config.
typedef IgemmConfig<OutputTile_, ScalarD_, AccumulatorsPerThread_> GemmConfig;
/// The GEMM config for A.
typedef IgemmTileTraitsHelperA<kLayoutA_, GemmConfig, Index_> GemmTileTraitsHelperA;
/// The GEMM config for B.
typedef IgemmTileTraitsHelperB<kLayoutB_, GemmConfig, Index_> GemmTileTraitsHelperB;
/// The iterator to load A from global memory.
typedef typename GemmTileTraitsHelperA::GlobalLoadIterator GlobalLoadIteratorA;
/// The default transformer for A.
typedef typename IgemmTransformerA<GemmTileTraitsHelperA::kLayout,
GlobalLoadIteratorA>::Transformer GlobalTransformerA;
/// The iterator to store A to shared memory.
typedef TileStoreIterator<typename GemmTileTraitsHelperA::SharedStoreTileTraits,
typename GemmTileTraitsHelperA::SharedStoreTileTraits::Scalar,
IteratorAdvance::kH,
MemorySpace::kShared>
SharedStoreIteratorA;
/// The stream to load A from global memory to shared memory.
typedef GlobalLoadStream<GlobalLoadIteratorA, SharedStoreIteratorA, GlobalTransformerA>
GlobalLoadStreamA;
/// The iterator to load B from global memory.
typedef typename GemmTileTraitsHelperB::GlobalLoadIterator GlobalLoadIteratorB;
// The default transformer for B.
typedef typename IgemmTransformerB<GemmTileTraitsHelperB::kLayout,
GlobalLoadIteratorB>::Transformer GlobalTransformerB;
/// The iterator to store B to shared memory.
typedef TileStoreIterator<typename GemmTileTraitsHelperB::SharedStoreTileTraits,
typename GemmTileTraitsHelperB::SharedStoreTileTraits::Scalar,
IteratorAdvance::kH,
MemorySpace::kShared>
SharedStoreIteratorB;
/// The stream to load B from global memory to shared memory.
typedef GlobalLoadStream<GlobalLoadIteratorB, SharedStoreIteratorB, GlobalTransformerB>
GlobalLoadStreamB;
/// The iterator to load A from shared memory.
typedef TileLoadIterator<typename GemmTileTraitsHelperA::SharedLoadTileTraits,
typename GemmTileTraitsHelperA::SharedLoadTileTraits::Scalar,
IteratorAdvance::kH,
MemorySpace::kShared>
SharedLoadIteratorA;
/// The stream to load A from shared memory.
typedef SharedLoadStream<SharedLoadIteratorA, Copy<typename SharedLoadIteratorA::Fragment> >
SharedLoadStreamA;
/// The iterator to load B from shared memory.
typedef TileLoadIterator<typename GemmTileTraitsHelperB::SharedLoadTileTraits,
typename GemmTileTraitsHelperB::SharedLoadTileTraits::Scalar,
IteratorAdvance::kH,
MemorySpace::kShared>
SharedLoadIteratorB;
/// The stream to load B from shared memory.
typedef SharedLoadStream<SharedLoadIteratorB, Copy<typename SharedLoadIteratorB::Fragment> >
SharedLoadStreamB;
/// The multiply-add functor.
typedef typename GemmConfig::MultiplyAdd MultiplyAdd;
/// The object to clear accumulators.
typedef ClearAccumulators<typename MultiplyAdd::ScalarC> ClearAccumulators;
/// The epilogue.
typedef IgemmEpilogue<IgemmEpilogueTraits<GemmConfig, EpilogueFunctor_> > Epilogue;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename ScalarD_>
struct IgemmEpilogueScalar {
typedef float Scalar;
};
template <>
struct IgemmEpilogueScalar<int> {
typedef int Scalar;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <
/// The layout for A.
MatrixLayout::Kind kLayoutA_,
/// The layout for B.
MatrixLayout::Kind kLayoutB_,
/// The output tile.
typename OutputTile_ = Shape<32, 128, 128>,
/// The output type.
typename ScalarD_ = int,
/// The functor to do the math in the epilogue.
typename EpilogueFunctor_ = LinearScaling<typename IgemmEpilogueScalar<ScalarD_>::Scalar>,
/// The number of accumulators per thread.
typename AccumulatorsPerThread_ = Shape<32, 8, 8>,
/// The index.
typename Index_ = int,
/// The helper class.
typename Helper_ = IgemmTraitsHelper<kLayoutA_,
kLayoutB_,
OutputTile_,
ScalarD_,
EpilogueFunctor_,
AccumulatorsPerThread_,
Index_> >
struct IgemmTraits : public GemmTraits<
// The config.
typename Helper_::GemmConfig,
// The stream to load A from global memory to shared memory.
typename Helper_::GlobalLoadStreamA,
// The stream to load B from global memory to shared memory.
typename Helper_::GlobalLoadStreamB,
// The stream to load A from shared memory.
typename Helper_::SharedLoadStreamA,
// The stream to load B from shared memory.
typename Helper_::SharedLoadStreamB,
// The epilogue.
typename Helper_::Epilogue,
// The block swizzle to reorganize the grid.
IdentityBlockSwizzle,
// The index.
Index_,
// The tool used to clear accumulators.
typename Helper_::ClearAccumulators> {};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace gemm
} // namespace cutlass

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@ -1,85 +0,0 @@
/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Implements the BLAS linear scaling function alpha*AB + beta*C
*/
#pragma once
#include <cutlass/fragment_multiply_add.h>
namespace cutlass {
namespace gemm {
////////////////////////////////////////////////////////////////////////////////////////////////////
/// Functor to compute linear combination of fragments
template <typename Scalar_, typename FragmentMultiplyAdd_ = FragmentMultiplyAdd<Scalar_> >
struct LinearScaling {
// The scalar.
typedef Scalar_ Scalar;
// The adapater.
typedef FragmentMultiplyAdd_ FragmentMultiplyAdd;
/// The parameters.
struct Params {
/// The alpha/beta scaling params.
Scalar alpha, beta;
/// Initialize the parameters.
template <typename GemmDesc_>
CUTLASS_HOST_DEVICE int initialize(GemmDesc_ const& desc) {
alpha = desc.alpha;
beta = desc.beta;
return 0;
}
};
/// Ctor.
CUTLASS_DEVICE LinearScaling(Params const& params) : alpha(params.alpha), beta(params.beta) {}
/// Evaluate the functor.
template <typename FragmentA_, typename FragmentB_>
CUTLASS_DEVICE void evaluate(FragmentA_ const& accum, FragmentB_& output) {
FragmentMultiplyAdd mad;
mad.multiply(alpha, accum, output);
}
/// Evaluate the functor.
template <typename FragmentA_, typename FragmentB_>
CUTLASS_DEVICE void evaluate(FragmentA_ const& accum, FragmentB_ const& old, FragmentB_& output) {
FragmentMultiplyAdd mad;
FragmentB_ tmp;
mad.multiply(beta, old, tmp);
mad.multiply_add(alpha, accum, tmp, output);
}
/// The alpha/beta scaling factors.
Scalar alpha, beta;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace gemm
} // namespace cutlass

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/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Defies structural properties of single-precision GEMM.
*/
#pragma once
#include <cutlass/gemm/gemm.h>
#include <cutlass/gemm/gemm_epilogue.h>
#include <cutlass/gemm/gemm_epilogue_traits.h>
#include <cutlass/gemm/gemm_global_tile.h>
#include <cutlass/gemm/gemm_shared_tile.h>
#include <cutlass/gemm/gemm_traits.h>
#include <cutlass/gemm/thread_multiply_add.h>
namespace cutlass {
namespace gemm {
////////////////////////////////////////////////////////////////////////////////////////////////////
template <
/// The tile size for the GEMM KxNxM.
typename OutputTile_,
/// The number of accumulators per thread.
typename AccumulatorsPerThread_,
/// The number of scalars per LDG for A.
int kScalarsPerLdgA_ = 1,
/// The number of scalars per LDG for B.
int kScalarsPerLdgB_ = 1>
struct SgemmConfig
: public GemmConfig<
/// The scalar type for A.
float,
/// The scalar type for B.
float,
/// The scalar type for C.
float,
/// The scalar type for D.
float,
/// The tile size for the GEMM KxNxM.
OutputTile_,
/// The functor to do the math in the main loop.
ThreadMultiplyAdd<AccumulatorsPerThread_, Shape<1, 4, 8>, float, float, float>,
/// The number of scalars per LDG for A.
kScalarsPerLdgA_,
/// The number of scalars per STS for A.
kScalarsPerLdgA_,
/// The number of scalars per LDS for A.
4,
/// The number of scalars per LDG for B.
kScalarsPerLdgB_,
/// The number of scalars per STS for B.
kScalarsPerLdgB_,
/// The number of scalars per LDS for B.
4,
/// The number of scalars per LDG for C and STG for D.
1,
/// The number of scalars per STS for D.
4,
/// The number of scalars per LDS for D.
1,
/// The number of stages in shared memory.
2> {};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <
/// The layout for A.
MatrixLayout::Kind kLayoutA_,
/// The layout for B.
MatrixLayout::Kind kLayoutB_,
/// The output tile.
typename OutputTile_ = Shape<8, 128, 128>,
/// The functor to use in the epilogue.
typename EpilogueFunctor_ = LinearScaling<float>,
/// The number of accumulators per thread.
typename AccumulatorsPerThread_ = Shape<8, 8, 8>,
/// The number of floats loaded in one LDG for A.
int kScalarsPerLdgA_ = 1,
/// The number of floats loaded in one LDG for B.
int kScalarsPerLdgB_ = 1,
/// The index.
typename Index_ = int,
/// The SGEMM config.
typename GemmConfig_ =
SgemmConfig<OutputTile_, AccumulatorsPerThread_, kScalarsPerLdgA_, kScalarsPerLdgB_>,
/// The traits class for the epilogue.
typename GemmEpilogueTraits_ =
SimplifiedGemmEpilogueTraits<GemmConfig_, EpilogueFunctor_, Index_> >
struct SgemmTraits : public SimplifiedGemmTraits<
// The layout for A.
kLayoutA_,
// The layout for B.
kLayoutB_,
// The config.
GemmConfig_,
// The epilogue.
GemmEpilogue<GemmEpilogueTraits_>,
// The index.
Index_> {};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace gemm
} // namespace cutlass

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@ -1,84 +0,0 @@
/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Template implementing matrix multiply-add operations on fragments.
*/
#pragma once
#include <cutlass/fragment.h>
namespace cutlass {
namespace gemm {
////////////////////////////////////////////////////////////////////////////////////////////////////
/// Template performing matrix multiply-add operation within a thread
template <typename AccumulatorsPerThread_,
typename ThreadsPerWarp_,
typename ScalarA_,
typename ScalarB_,
typename ScalarC_>
struct ThreadMultiplyAdd {
/// The shape of the instruction.
typedef Shape<1, 1, 1, 1> InstructionShape;
/// The number of accumulators per thread.
typedef AccumulatorsPerThread_ AccumulatorsPerThread;
/// The number of threads per warp.
typedef ThreadsPerWarp_ ThreadsPerWarp;
/// The number of accumulators per warp.
typedef typename ShapeMul<AccumulatorsPerThread, ThreadsPerWarp>::Shape AccumulatorsPerWarp;
/// The type for A.
typedef ScalarA_ ScalarA;
/// The fragment for A.
typedef Fragment<ScalarA, AccumulatorsPerThread::kW> FragmentA;
/// The type for B.
typedef ScalarB_ ScalarB;
/// The fragment for B.
typedef Fragment<ScalarB, AccumulatorsPerThread::kH> FragmentB;
/// The type for C and D.
typedef ScalarC_ ScalarC;
/// The accumulators.
typedef Fragment<ScalarC, AccumulatorsPerThread::kH * AccumulatorsPerThread::kW, 16> Accumulators;
/// Ctor.
CUTLASS_DEVICE ThreadMultiplyAdd() {}
/// Multiply : d = a*b + c.
CUTLASS_DEVICE void multiply_add(FragmentA const& a,
FragmentB const& b,
Accumulators const& c,
Accumulators& d) {
for (int j = 0; j < AccumulatorsPerThread::kH; ++j) {
for (int i = 0; i < AccumulatorsPerThread::kW; ++i) {
d[j * AccumulatorsPerThread::kW + i] = a[i] * b[j] + c[j * AccumulatorsPerThread::kW + i];
}
}
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace gemm
} // namespace cutlass

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/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Defines structural properties of WMMA GEMM's epilogue phase.
*/
#pragma once
#include <cutlass/wmma_matrix.h>
#ifdef CUTLASS_USE_WMMA_API
#include <cutlass/convert.h>
#include <cutlass/coord.h>
#include <cutlass/gemm/gemm_global_stream.h>
#include <cutlass/gemm/gemm_shared_stream.h>
#include <cutlass/gemm/linear_scaling.h>
#include <cutlass/gemm/wmma_gemm_global_tile.h>
#include <cutlass/gemm/wmma_gemm_shared_tile.h>
#include <cutlass/reshape_tile.h>
#include <cutlass/tile_iterator.h>
namespace cutlass {
namespace gemm {
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename GemmConfig_, typename EpilogueFunctor_, typename Index_ = int>
struct WmmaGemmEpilogueTraitsHelper {
/// The scalar.
typedef typename EpilogueFunctor_::Scalar Scalar;
/// The output tile.
typedef typename GemmConfig_::OutputTile OutputTile;
/// The number of WMMAs in the H dimension.
static int const kWmmasPerH =
GemmConfig_::AccumulatorsPerWarp::kH / GemmConfig_::InstructionShape::kH;
/// The number of iterations in the epilogue. That's the number of "horizontal" WMMAs.
typedef Shape<1, 1, kWmmasPerH> Iterations;
// The iteration strides in the H/W dimension.
typedef Shape<0, 0, 0> Delta;
/// The functor to do the math in the epilogue.
typedef EpilogueFunctor_ Functor;
/// The traits class to build the iterator to store to shared memory for D.
typedef WmmaGemmSharedStoreTileDTraits<
// The output layout.
MatrixLayout::kColumnMajor,
// The pointer is float.
typename Functor::Scalar,
// The output tile size.
typename GemmConfig_::OutputTile,
// The number of warps.
typename GemmConfig_::Warps,
// The shape of the instruction.
typename GemmConfig_::InstructionShape>
SharedStoreTileTraits;
typedef WmmaMatrix<GemmOperand::kC,
MatrixLayout::kColumnMajor,
Scalar,
typename GemmConfig_::InstructionShape>
WmmaMatrix;
/// The iterator to store D to shared memory.
typedef TileStoreIterator<SharedStoreTileTraits,
typename SharedStoreTileTraits::Scalar,
IteratorAdvance::kH,
MemorySpace::kShared,
Index_,
WmmaMatrix,
IteratorFragment::kWmmaMatrix>
SharedStoreIteratorD;
/// The shared store transformer for D.
typedef Copy<typename SharedStoreIteratorD::Fragment> SharedStoreTransformerD;
/// The traits class to build the iterator to load from shared memory for D.
typedef WmmaGemmSharedLoadTileDTraits<
// The pointer.
typename Functor::Scalar,
// The tile size.
typename SharedStoreIteratorD::Tile,
// The number of threads.
Shape<1, ShapeCount<typename GemmConfig_::Warps>::kCount, GemmConfig_::kWarpSize>,
// The number of scalars per LDS.
GemmConfig_::kScalarsPerLdsD>
SharedLoadTileTraits;
/// The iterator to load D from shared memory.
typedef TileLoadIterator<SharedLoadTileTraits,
typename SharedLoadTileTraits::Scalar,
IteratorAdvance::kH,
MemorySpace::kShared>
SharedLoadIteratorD;
/// The traits class to build the iterator to load data from global memory for C^N.
typedef WmmaGemmGlobalIteratorCdTraits<
// The pointer is float const.
typename GemmConfig_::ScalarC const,
// The tile has size (N / Iterations)xM in GEMM's terminology.
Shape<1,
GemmConfig_::OutputTile::kH / ShapeCount<Iterations>::kCount,
GemmConfig_::OutputTile::kW>,
// The threads are distributed as warps x 32 (the traits may reorganize).
Shape<1, ShapeCount<typename GemmConfig_::Warps>::kCount, GemmConfig_::kWarpSize>,
// The number of scalars per LDG (LDG.32 or LDG.128, etc).
GemmConfig_::kScalarsPerLdgC>
GlobalLoadTileTraits;
/// The iterator to load C.
typedef WmmaGemmGlobalIteratorCd<GlobalLoadTileTraits, Index_> GlobalLoadIteratorC;
/// The transformer for C.
typedef Copy<typename GlobalLoadIteratorC::Fragment> GlobalTransformerC;
/// The traits class to build the iterator to store data to global memory for D^N.
typedef WmmaGemmGlobalIteratorCdTraits<
// The pointer is float.
typename GemmConfig_::ScalarD,
// The tile has size (N / Iterations)xM in GEMM's terminology.
Shape<1,
GemmConfig_::OutputTile::kH / ShapeCount<Iterations>::kCount,
GemmConfig_::OutputTile::kW>,
// The threads are distributed as warps x 32 (the traits may reorganize).
Shape<1, ShapeCount<typename GemmConfig_::Warps>::kCount, GemmConfig_::kWarpSize>,
// The number of scalars per LDG (LDG.32 or LDG.128, etc).
GemmConfig_::kScalarsPerStgD>
GlobalStoreTileTraits;
/// The iterator to store D.
typedef WmmaGemmGlobalIteratorCd<GlobalStoreTileTraits, Index_> GlobalStoreIteratorD;
/// The transformer for D.
typedef Copy<typename GlobalStoreIteratorD::Fragment> GlobalTransformerD;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace gemm
} // namespace cutlass
#endif // defined CUTLASS_USE_WMMA_API

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/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Defines tile iterator traits for loading thread block-level tile from global memory.
*/
#pragma once
#include <cutlass/gemm/gemm_global_tile.h>
namespace cutlass {
namespace gemm {
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename Scalar_, typename Tile_, typename Threads_, int kAccessSize_>
struct WmmaGemmGlobalIteratorCdTraits : public GemmGlobalTileTraits<GemmOperand::kC,
MatrixLayout::kColumnMajor,
Scalar_,
Tile_,
Threads_,
kAccessSize_> {
/// The base class.
typedef GemmGlobalTileTraits<GemmOperand::kC,
MatrixLayout::kColumnMajor,
Scalar_,
Tile_,
Threads_,
kAccessSize_>
Base;
/// Override the strides in each dimension between different loads/stores.
typedef Shape<0, 0, Base::Delta::kW, Base::Delta::kC> Delta;
/// Computes the thread offset in (H, W) based on thread ID
struct ThreadOffset {
CUTLASS_HOST_DEVICE
Coord<4> operator()() const {
int thread_offset_h = threadIdx.x / Base::Threads::kW;
int thread_offset_w = threadIdx.x % Base::Threads::kW * Base::ThreadsDelta::kW;
return make_Coord(0, thread_offset_h, thread_offset_w, 0);
}
};
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename TileTraits_, typename Index_ = int>
struct WmmaGemmGlobalIteratorCd : public TileIteratorBase<TileTraits_,
typename TileTraits_::Scalar,
IteratorAdvance::kH,
MemorySpace::kGlobal,
Index_> {
/// This class.
typedef WmmaGemmGlobalIteratorCd<TileTraits_, Index_> This_;
/// The traits.
typedef TileTraits_ Traits;
/// The base class.
typedef TileIteratorBase<Traits,
typename TileTraits_::Scalar,
IteratorAdvance::kH,
MemorySpace::kGlobal,
Index_>
Base;
/// Override the strides in each dimension between different loads/stores.
typedef Shape<0, 0, Base::Delta::kW, Base::Delta::kC> ImmediateOffsetStrides;
/// The layout.
static MatrixLayout::Kind const kLayout = TileTraits_::kLayout;
/// The scalar.
typedef typename TileTraits_::Scalar Scalar;
/// The pointer.
typedef typename TileTraits_::Pointer Pointer;
/// The threads.
typedef typename TileTraits_::Threads Threads;
/// The index.
typedef Index_ Index;
/// The thread offset functor.
typedef typename TileTraits_::ThreadOffset ThreadOffset;
/// The params.
struct Params {
/// The pointer.
Pointer pointer;
/// The stride in the H dimension to setup the thread in the block.
Index stride_h;
/// The strides to increment the pointer.
Index inc_h, inc_advance;
/// The column offset to compute the predicate for the columns.
Index predicate_offset;
/// The strides to increment the predicate offset.
Index predicate_inc_h, predicate_inc_advance;
/// Setup the params.
CUTLASS_HOST_DEVICE int initialize(
Pointer pointer, Index ld, Index n, Index epilogue_stride_w, Index epilogue_delta_w) {
// The pointer.
this->pointer = pointer;
// Setup the base stride. One "group of threads" per column.
stride_h = ld;
// Each thread output 1 column per iteration. .
inc_h = ld * TileTraits_::Threads::kH;
inc_advance = inc_h + epilogue_stride_w;
predicate_offset = n;
predicate_inc_h = TileTraits_::Threads::kH;
predicate_inc_advance = predicate_inc_h + epilogue_delta_w;
// It worked.
return 0;
}
};
Params params;
Coord<4> thread_offset;
/// Ctor.
CUTLASS_DEVICE WmmaGemmGlobalIteratorCd() {}
/// Ctor.
CUTLASS_DEVICE WmmaGemmGlobalIteratorCd(Params const& params,
const Coord<3>& bounds,
const Coord<3>& block,
int const pointer_offset = 0,
int const pred_offset = 0,
ThreadOffset thread_offset_func = ThreadOffset())
: params(params) {
thread_offset = thread_offset_func();
// Each warp works on a different column of the tile.
int const h = thread_offset[1] + block[1];
// Each lane writes a different element.
int const w = thread_offset[2] + block[2];
// Setup the pointer.
this->params.pointer += ((h * params.stride_h + w) + pointer_offset);
// Prepare the vector of predicates.
for (int i = 0; i < Base::Iterations::kW; ++i) {
predicates.set(i, w + i * Base::Delta::kW < bounds[2]);
}
this->params.predicate_offset -= (h + pred_offset);
}
/// The accessor.
CUTLASS_DEVICE void get(typename Base::AccessType& value, int d, int h, int w, int c) const {
int const imm =
ComputeOffsetFromStrides<typename Base::ImmediateOffsetStrides>::get(0, 0, w, c);
Load<Scalar, TileTraits_::kAccessSize, MemorySpace::kGlobal>::load(value, params.pointer, imm);
}
/// Increment the pointer in the C dimension.
CUTLASS_DEVICE void inc_c() {}
/// Increment the pointer in the W dimension.
CUTLASS_DEVICE void inc_w() {}
/// Increment the pointer in the H dimension.
CUTLASS_DEVICE void inc_h() {
params.pointer += params.inc_h;
params.predicate_offset -= params.predicate_inc_h;
}
/// Increment the pointer in the D dimension.
CUTLASS_DEVICE void inc_d() {}
/// Increment the pointer to move to the next iteration.
CUTLASS_DEVICE void inc_advance() {
params.pointer += params.inc_advance;
params.predicate_offset -= params.predicate_inc_advance;
}
/// The accessor.
CUTLASS_DEVICE void set(typename Base::AccessType const& value, int d, int h, int w, int c) {
int const imm =
ComputeOffsetFromStrides<typename Base::ImmediateOffsetStrides>::get(d, h, w, 0);
Store<Scalar, TileTraits_::kAccessSize, MemorySpace::kGlobal>::store(
value, params.pointer, imm);
}
/// Test the predicate.
CUTLASS_DEVICE bool valid(int d, int h, int w, int c) const {
return predicates.at(w) && params.predicate_offset > 0;
}
/// The predicates for the row.
cutlass::PredicateVector<Base::Iterations::kW> predicates;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace gemm
} // namespace cutlass

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/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Implements warp-level matrix multiply-accumulate operation using CUDA WMMA API.
*/
#pragma once
#include <cutlass/wmma_matrix.h>
#ifdef CUTLASS_USE_WMMA_API
#include <cutlass/fragment.h>
namespace cutlass {
namespace gemm {
////////////////////////////////////////////////////////////////////////////////////////////////////
template <MatrixLayout::Kind kLayoutA_,
typename ScalarA_,
MatrixLayout::Kind kLayoutB_,
typename ScalarB_,
MatrixLayout::Kind kLayoutC_,
typename ScalarC_,
typename AccumulatorsPerWarp_,
typename InstructionShape_>
struct WmmaGemmMultiplyAdd {
/// The shape of the instruction.
typedef InstructionShape_ InstructionShape;
/// The number of threads per warp. That's a dummy configuration.
typedef Shape<1, InstructionShape_::kH, InstructionShape_::kW> ThreadsPerWarp;
/// The dimensions.
typedef AccumulatorsPerWarp_ AccumulatorsPerWarp;
/// The type for A.
typedef ScalarA_ ScalarA;
/// The type for B.
typedef ScalarB_ ScalarB;
/// The type for C and D.
typedef ScalarC_ ScalarC;
/// The number of iterations.
typedef typename ShapeDiv<AccumulatorsPerWarp, InstructionShape>::Shape Iterations;
/// The element for A.
typedef WmmaMatrix<GemmOperand::kA, kLayoutA_, ScalarA, InstructionShape> ElementA;
/// The fragment for A.
typedef Fragment<ElementA, Iterations::kW> FragmentA;
/// The element for B.
typedef WmmaMatrix<GemmOperand::kB, kLayoutB_, ScalarB, InstructionShape> ElementB;
/// The fragment for B.
typedef Fragment<ElementB, Iterations::kH> FragmentB;
/// The element for C.
typedef WmmaMatrix<GemmOperand::kC, kLayoutC_, ScalarC, InstructionShape> ElementC;
/// The fragment for C.
typedef Fragment<ElementC, Iterations::kH * Iterations::kW> Accumulators;
/// Ctor.
CUTLASS_DEVICE WmmaGemmMultiplyAdd() {}
/// Multiply : d = a*b.
CUTLASS_DEVICE void multiply_add(FragmentA const& a,
FragmentB const& b,
Accumulators const& c,
Accumulators& d) {
for (int j = 0; j < Iterations::kH; ++j) {
for (int i = 0; i < Iterations::kW; ++i) {
// The input elements.
ElementA const& elt_a = a[i];
ElementB const& elt_b = b[j];
ElementC const& elt_c = c[j * Iterations::kW + i];
// The output element.
ElementC& elt_d = d[j * Iterations::kW + i];
// The wmma instruction.
nvcuda::wmma::mma_sync(elt_d, elt_a, elt_b, elt_c);
}
}
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace gemm
} // namespace cutlass
#endif // defined CUTLASS_USE_WMMA_API

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@ -1,240 +0,0 @@
/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Defines iterator traits for efficiently loading and storing fragment to and from shared
memory, specialized for WMMA GEMM.
*/
#pragma once
#include <cutlass/wmma_matrix.h>
#ifdef CUTLASS_USE_WMMA_API
#include <cutlass/gemm/gemm_operand.h>
#include <cutlass/reshape_tile.h>
namespace cutlass {
namespace gemm {
template <class>
struct Debug {};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <MatrixLayout::Kind kLayout_,
typename Scalar_,
typename Tile_,
typename Warps_,
int kWarpStride_,
typename Iterations_,
typename Delta_,
typename WmmaShape_>
struct WmmaGemmSharedLoadTileATraits {
/// The operand.
static GemmOperand::Kind const kOperand = GemmOperand::kA;
/// The layout.
static MatrixLayout::Kind const kLayout = kLayout_;
/// The scalar.
typedef Scalar_ Scalar;
/// The pointer.
typedef Scalar const* Pointer;
/// The access size
static int const kAccessSize = 1;
/// The tile with skew.
typedef Tile_ Tile;
/// The number of warps.
typedef Warps_ Warps;
/// The warps strides.
static int const kWarpStride = kWarpStride_;
/// The number of iterations.
typedef Iterations_ Iterations;
/// The strides between iterations.
typedef Delta_ Delta;
/// The strides between iterations.
typedef Delta_ ImmediateOffsetStrides;
/// The shape of the WMMA instruction.
typedef WmmaShape_ WmmaShape;
/// The memory space.
static MemorySpace::Kind const kMemorySpace = MemorySpace::kShared;
/// ThreadOffset
struct ThreadOffset {
CUTLASS_HOST_DEVICE
Coord<4> operator()() const {
// The warp id.
int const warp = threadIdx.x / kWarpSize;
// The offset.
int const offset = warp % Warps::kW * kWarpStride;
return make_Coord(0, 0, offset, 0);
}
};
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <MatrixLayout::Kind kLayout_,
typename Scalar_,
typename Tile_,
typename Warps_,
int kWarpStride_,
typename Iterations_,
typename Delta_,
typename WmmaShape_>
struct WmmaGemmSharedLoadTileBTraits {
/// The operand.
static GemmOperand::Kind const kOperand = GemmOperand::kB;
/// The layout.
static MatrixLayout::Kind const kLayout = kLayout_;
/// The scalar.
typedef Scalar_ Scalar;
/// The pointer.
typedef Scalar const* Pointer;
/// The access size
static int const kAccessSize = 1;
/// The tile with skew.
typedef Tile_ Tile;
/// The number of warps.
typedef Warps_ Warps;
/// The warps strides.
static int const kWarpStride = kWarpStride_;
/// The number of iterations.
typedef Iterations_ Iterations;
/// The strides between iterations.
typedef Delta_ Delta;
/// The strides between iterations.
typedef Delta_ ImmediateOffsetStrides;
/// The shape of the WMMA instruction.
typedef WmmaShape_ WmmaShape;
/// The memory space.
static MemorySpace::Kind const kMemorySpace = MemorySpace::kShared;
/// ThreadOffset
struct ThreadOffset {
CUTLASS_HOST_DEVICE
Coord<4> operator()() const {
// The warp id.
int const warp = threadIdx.x / kWarpSize;
// The offset.
int const offset = warp / Warps::kW * kWarpStride;
return make_Coord(0, 0, offset, 0);
}
};
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <MatrixLayout::Kind kLayout_,
typename Scalar_,
typename OutputTile_,
typename Warps_,
typename WmmaShape_,
int kSkew_ = 0>
struct WmmaGemmSharedStoreTileDTraits {
/// The operand.
static GemmOperand::Kind const kOperand = GemmOperand::kC;
/// The layout.
static MatrixLayout::Kind const kLayout = kLayout_;
/// The scalar.
typedef Scalar_ Scalar;
// The access size
static int const kAccessSize = 1;
/// The pointer.
typedef Scalar* Pointer;
/// The number of warps.
typedef Warps_ Warps;
/// The shape of the WMMA instruction.
typedef WmmaShape_ WmmaShape;
/// The skew.
static int const kSkew = kSkew_;
/// The memory space.
static MemorySpace::Kind const kMemorySpace = MemorySpace::kShared;
/// The tile with skew.
typedef Shape<1, Warps_::kH * WmmaShape_::kH, OutputTile_::kW + kSkew_> Tile;
/// The number of iterations needed to store the tile.
typedef Shape<1, 1, OutputTile_::kW / Warps::kW / WmmaShape_::kW> Iterations;
/// The strides in each dimension between different loads/stores.
typedef Shape<0, 0, Warps::kW * WmmaShape_::kW, 0> Delta;
/// The strides in each dimension between different loads/stores.
typedef Shape<0, 0, Warps::kW * WmmaShape_::kW, 0> ImmediateOffsetStrides;
/// ThreadOffset
struct ThreadOffset {
CUTLASS_HOST_DEVICE
Coord<4> operator()() const {
// The warp id.
int const warp = threadIdx.x / kWarpSize;
// The starting column.
int const h = warp / Warps::kW * WmmaShape::kH;
// The w.
int const w = warp % Warps::kW * WmmaShape::kW;
// The offset.
int const offset = h * Tile::kW + w;
return make_Coord(0, 0, offset, 0);
}
};
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename Scalar_, typename Tile_, typename Threads_, int kScalarsPerLds_>
struct WmmaGemmSharedLoadTileDTraits {
/// The scalar.
typedef Scalar_ Scalar;
/// The pointer.
typedef Scalar const* Pointer;
/// The access size
static int const kAccessSize = kScalarsPerLds_;
/// The tile.
typedef typename ReshapeTile<Tile_, kScalarsPerLds_>::Tile Tile;
/// The threads.
typedef typename ReshapeThreads<Tile, Threads_>::Threads Threads;
/// The threads strides.
typedef Shape<1, Tile::kW * Tile::kC, Tile::kC> ThreadsStrides;
/// The memory space.
static MemorySpace::Kind const kMemorySpace = MemorySpace::kShared;
/// The strides in each dimension between different loads/stores.
typedef Shape<0, Threads::kH * ShapeCount<Tile>::kWc, Threads::kW * kScalarsPerLds_> Delta;
/// The strides in each dimension between different loads/stores.
typedef Shape<0, Threads::kH * ShapeCount<Tile>::kWc, Threads::kW * kScalarsPerLds_>
ImmediateOffsetStrides;
/// The number of iterations needed to load/store the tile.
typedef Shape<1, Tile::kH / Threads::kH, Tile::kW / Threads::kW, Tile::kC / kScalarsPerLds_>
Iterations;
/// ThreadOffset
struct ThreadOffset {
CUTLASS_HOST_DEVICE
Coord<4> operator()() const {
// The offset.
int const offset = ComputeThreadOffsetFromStrides<Threads, ThreadsStrides>::get();
return make_Coord(0, 0, offset, 0);
}
};
};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace gemm
} // namespace cutlass
#endif // defined CUTLASS_USE_WMMA_API

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@ -1,574 +0,0 @@
/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Defies structural properties of GEMM targeting WMMA API in CUDA.
*/
#pragma once
#include <cutlass/wmma_matrix.h>
#ifdef CUTLASS_USE_WMMA_API
#include <cutlass/convert.h>
#include <cutlass/gemm/gemm.h>
#include <cutlass/gemm/gemm_epilogue.h>
#include <cutlass/gemm/gemm_epilogue_traits.h>
#include <cutlass/gemm/gemm_global_tile.h>
#include <cutlass/gemm/gemm_shared_tile.h>
#include <cutlass/gemm/gemm_traits.h>
#include <cutlass/gemm/wmma_gemm_epilogue_traits.h>
#include <cutlass/gemm/wmma_gemm_global_tile.h>
#include <cutlass/gemm/wmma_gemm_multiply_add.h>
namespace cutlass {
namespace gemm {
////////////////////////////////////////////////////////////////////////////////////////////////////
template <
/// The layout for A.
MatrixLayout::Kind kLayoutA_,
/// The layout for B.
MatrixLayout::Kind kLayoutB_,
/// The tile size for the GEMM KxNxM.
typename OutputTile_,
/// The output type.
typename ScalarC_,
/// The accumulator type.
typename Accumulator_,
/// The number of accumulators per warp.
typename AccumulatorsPerWarp_,
/// The shape of the WMMA instruction.
typename InstructionShape_,
/// The number of scalars per LDG for A.
int kScalarsPerLdgA_,
/// The number of scalars per LDG for B.
int kScalarsPerLdgB_>
struct WmmaGemmConfig : public GemmConfig<
/// The scalar type for A.
half,
/// The scalar type for B.
half,
/// The scalar type for C.
ScalarC_,
/// The scalar type for D.
ScalarC_,
/// The tile size for the GEMM KxNxM.
OutputTile_,
/// The functor to do the math in the main loop.
WmmaGemmMultiplyAdd<kLayoutA_,
half,
kLayoutB_,
half,
MatrixLayout::kColumnMajor,
Accumulator_,
AccumulatorsPerWarp_,
InstructionShape_>,
/// The number of scalars per LDG for A.
kScalarsPerLdgA_,
/// The number of scalars per STS for A.
kScalarsPerLdgA_,
/// The number of scalars per LDS for A.
8,
/// The number of scalars per LDG for B.
kScalarsPerLdgB_,
/// The number of scalars per STS for B.
kScalarsPerLdgB_,
/// The number of scalars per LDS for B.
8,
/// The number of scalars per LDG for C and STG for D.
16 / sizeof(ScalarC_),
/// The number of scalars per STS for D.
16 / sizeof(ScalarC_),
/// The number of scalars per LDS for D.
16 / sizeof(ScalarC_),
/// The number of stages in shared memory.
1> {};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <enum MatrixLayout::Kind kLayout_, typename GemmConfig_>
struct WmmaGemmTileTraitsHelperA {};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename GemmConfig_>
struct WmmaGemmTileTraitsHelperA<MatrixLayout::kColumnMajor, GemmConfig_>
: public GemmTileTraitsHelperA<MatrixLayout::kColumnMajor, GemmConfig_> {
/// The base config.
typedef GemmTileTraitsHelperA<MatrixLayout::kColumnMajor, GemmConfig_> Base;
/// The skew.
static int const kSkew = 16 / sizeof(typename Base::MultiplyAddScalar);
/// The shared tile size.
typedef Shape<GemmConfig_::kStages,
GemmConfig_::OutputTile::kD,
GemmConfig_::OutputTile::kW + kSkew>
Tile;
/// WMMA matrix
typedef WmmaMatrix<GemmOperand::kA,
MatrixLayout::kColumnMajor,
typename Base::MultiplyAddScalar,
typename GemmConfig_::InstructionShape>
WmmaMatrix;
/// The traits class to build the iterator to store data to shared memory for A^N.
typedef GemmSharedStoreTileAbTraits<
// The pointer.
typename Base::MultiplyAddScalar,
// The tile has size KxM in GEMM's terminology.
Tile,
// The threads are distributed as warps x 32 (the traits may reorganize).
typename Base::GlobalTileTraits::Threads,
// The number of scalars per STS (STS.32 or STS.128, etc).
GemmConfig_::kScalarsPerStsA>
SharedStoreTileTraits;
/// The number of elements loaded in one LDG.
static int const kScalarsPerW = GemmConfig_::InstructionShape::kW * GemmConfig_::Warps::kW;
/// The number of scalars loaded per iteration.
static int const kScalarsPerIteration = Tile::kW * GemmConfig_::InstructionShape::kD;
/// The traits class to build the iterator to load from shared memory for A.
typedef WmmaGemmSharedLoadTileATraits<
// The layout of the matrix.
MatrixLayout::kColumnMajor,
// The pointer.
typename Base::MultiplyAddScalar,
// The output tile size.
Tile,
// The number of warps.
typename GemmConfig_::Warps,
// The strides between warps.
GemmConfig_::InstructionShape::kW,
// The number of iterations to load the data.
Shape<1, 1, GemmConfig_::OutputTile::kW / kScalarsPerW>,
// The stride between iterations.
Shape<kScalarsPerIteration, 0, kScalarsPerW, 0>,
// The shape of the instruction.
typename GemmConfig_::InstructionShape>
SharedLoadTileTraits;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename GemmConfig_>
struct WmmaGemmTileTraitsHelperA<MatrixLayout::kRowMajor, GemmConfig_> {
/// The layout.
static MatrixLayout::Kind const kLayout = MatrixLayout::kRowMajor;
/// The input scalar.
typedef typename GemmConfig_::ScalarA Scalar;
/// The scalar stored in shared memory.
typedef typename GemmConfig_::MultiplyAdd::ScalarA MultiplyAddScalar;
/// WMMA matrix
typedef WmmaMatrix<GemmOperand::kA,
MatrixLayout::kRowMajor,
MultiplyAddScalar,
typename GemmConfig_::InstructionShape>
WmmaMatrix;
/// The traits class to build the iterator to load data from global memory for A^T.
typedef GemmGlobalTileTraits<
// That's A.
GemmOperand::kA,
// A is row-major.
MatrixLayout::kRowMajor,
// The pointer is float const.
Scalar const,
// The tile has size KxM in GEMM's terminology.
Shape<1, GemmConfig_::OutputTile::kW, GemmConfig_::OutputTile::kD>,
// The threads are distributed as warps x 32 (the traits may reorganize).
Shape<1, GemmConfig_::kThreads / GemmConfig_::OutputTile::kD, GemmConfig_::OutputTile::kD>,
// The number of scalars per LDG (LDG.32 or LDG.128, etc).
GemmConfig_::kScalarsPerLdgA>
GlobalTileTraits;
/// The skew.
static int const kSkew = 16 / sizeof(MultiplyAddScalar);
/// The tile.
typedef Shape<GemmConfig_::kStages,
GemmConfig_::OutputTile::kW,
GemmConfig_::OutputTile::kD + kSkew>
Tile;
/// The traits class to build the iterator to store data to shared memory for A^N.
typedef GemmSharedStoreTileAbTraits<
// The pointer.
MultiplyAddScalar,
// The tile has size KxM in GEMM's terminology.
Tile,
// The threads are distributed as warps x 32 (the traits may reorganize).
typename GlobalTileTraits::Threads,
// The number of scalars per STS (STS.32 or STS.128, etc).
GemmConfig_::kScalarsPerStsA>
SharedStoreTileTraits;
/// The number of elements loaded in one LDG.
static int const kScalarsPerW = GemmConfig_::InstructionShape::kW * GemmConfig_::Warps::kW;
/// The traits class to build the iterator to load from shared memory for A.
typedef WmmaGemmSharedLoadTileATraits<
// The layout of the matrix.
MatrixLayout::kRowMajor,
// The pointer.
MultiplyAddScalar,
// The tile in shared memory.
Tile,
// The number of warps.
typename GemmConfig_::Warps,
// The strides between warps.
GemmConfig_::InstructionShape::kW * Tile::kW,
// The number of iterations to load the data.
Shape<1, 1, GemmConfig_::OutputTile::kW / kScalarsPerW>,
// The stride between iterations.
Shape<GemmConfig_::InstructionShape::kD, 0, kScalarsPerW * Tile::kW>,
// The shape of the instruction.
typename GemmConfig_::InstructionShape>
SharedLoadTileTraits;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <enum MatrixLayout::Kind kLayout_, typename GemmConfig_>
struct WmmaGemmTileTraitsHelperB {};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename GemmConfig_>
struct WmmaGemmTileTraitsHelperB<MatrixLayout::kRowMajor, GemmConfig_>
: public GemmTileTraitsHelperB<MatrixLayout::kRowMajor, GemmConfig_> {
/// The base config.
typedef GemmTileTraitsHelperB<MatrixLayout::kRowMajor, GemmConfig_> Base;
/// The skew.
static int const kSkew = 16 / sizeof(typename Base::MultiplyAddScalar);
/// The shared tile size.
typedef Shape<GemmConfig_::kStages,
GemmConfig_::OutputTile::kD,
GemmConfig_::OutputTile::kH + kSkew>
Tile;
/// WMMA matrix
typedef WmmaMatrix<GemmOperand::kB,
MatrixLayout::kRowMajor,
typename Base::MultiplyAddScalar,
typename GemmConfig_::InstructionShape>
WmmaMatrix;
/// The traits class to build the iterator to store data to shared memory for B^T.
typedef GemmSharedStoreTileAbTraits<
// The pointer.
typename Base::MultiplyAddScalar,
// The tile has size KxM in GEMM's terminology.
Tile,
// The threads are distributed as warps x 32 (the traits may reorganize).
typename Base::GlobalTileTraits::Threads,
// The number of scalars per STS (STS.32 or STS.128, etc).
GemmConfig_::kScalarsPerStsB>
SharedStoreTileTraits;
/// The number of elements loaded in one LDG.
static int const kScalarsPerW = GemmConfig_::InstructionShape::kH * GemmConfig_::Warps::kH;
/// The number of scalars loaded per iteration.
static int const kScalarsPerIteration = Tile::kW * GemmConfig_::InstructionShape::kD;
/// The traits class to build the iterator to load from shared memory for B.
typedef WmmaGemmSharedLoadTileBTraits<
// The layout of the matrix.
MatrixLayout::kRowMajor,
// The pointer.
typename Base::MultiplyAddScalar,
// The output tile size.
Tile,
// The number of warps.
typename GemmConfig_::Warps,
// The strides between warps.
GemmConfig_::InstructionShape::kH,
// The number of iterations to load the data.
Shape<1, 1, GemmConfig_::OutputTile::kH / kScalarsPerW>,
// The stride between iterations.
Shape<kScalarsPerIteration, 0, kScalarsPerW, 0>,
// The shape of the instruction.
typename GemmConfig_::InstructionShape>
SharedLoadTileTraits;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename GemmConfig_>
struct WmmaGemmTileTraitsHelperB<MatrixLayout::kColumnMajor, GemmConfig_> {
/// The layout.
static MatrixLayout::Kind const kLayout = MatrixLayout::kColumnMajor;
/// The input scalar.
typedef typename GemmConfig_::ScalarB Scalar;
/// The scalar stored in shared memory.
typedef typename GemmConfig_::MultiplyAdd::ScalarB MultiplyAddScalar;
/// WMMA matrix
typedef WmmaMatrix<GemmOperand::kB,
MatrixLayout::kColumnMajor,
MultiplyAddScalar,
typename GemmConfig_::InstructionShape>
WmmaMatrix;
/// The traits class to build the iterator to load data from global memory for B^N.
typedef GemmGlobalTileTraits<
// That's B.
GemmOperand::kB,
// A is row-major.
MatrixLayout::kColumnMajor,
// The pointer is float const.
Scalar const,
// The tile has size KxM in GEMM's terminology.
Shape<1, GemmConfig_::OutputTile::kH, GemmConfig_::OutputTile::kD>,
// The threads are distributed as warps x 32 (the traits may reorganize).
Shape<1, GemmConfig_::kThreads / GemmConfig_::OutputTile::kD, GemmConfig_::OutputTile::kD>,
// The number of scalars per LDG (LDG.32 or LDG.128, etc).
GemmConfig_::kScalarsPerLdgB>
GlobalTileTraits;
/// The skew.
static int const kSkew = 16 / sizeof(MultiplyAddScalar);
/// The tile.
typedef Shape<GemmConfig_::kStages,
GemmConfig_::OutputTile::kH,
GemmConfig_::OutputTile::kD + kSkew>
Tile;
/// The traits class to build the iterator to store data to shared memory for B^N.
typedef GemmSharedStoreTileAbTraits<
// The pointer.
MultiplyAddScalar,
// The tile has size KxM in GEMM's terminology.
Tile,
// The threads are distributed as warps x 32 (the traits may reorganize).
typename GlobalTileTraits::Threads,
// The number of scalars per STS (STS.32 or STS.128, etc).
GemmConfig_::kScalarsPerStsB>
SharedStoreTileTraits;
/// The number of elements loaded in one LDG.
static int const kScalarsPerW = GemmConfig_::InstructionShape::kH * GemmConfig_::Warps::kH;
/// The traits class to build the iterator to load from shared memory for B.
typedef WmmaGemmSharedLoadTileBTraits<
// The layout of the matrix.
MatrixLayout::kColumnMajor,
// The pointer.
MultiplyAddScalar,
// The tile in shared memory.
Tile,
// The number of warps.
typename GemmConfig_::Warps,
// The strides between warps.
GemmConfig_::InstructionShape::kH * Tile::kW,
// The number of iterations to load the data.
Shape<1, 1, GemmConfig_::OutputTile::kH / kScalarsPerW>,
// The stride between iterations.
Shape<GemmConfig_::InstructionShape::kD, 0, kScalarsPerW * Tile::kW>,
// The shape of the instruction.
typename GemmConfig_::InstructionShape>
SharedLoadTileTraits;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <
/// The layout for A.
MatrixLayout::Kind kLayoutA_,
/// The layout for B.
MatrixLayout::Kind kLayoutB_,
/// The output tile.
typename OutputTile_,
/// The output type.
typename ScalarC_,
/// The accumulator type.
typename Accumulator_,
/// The functor to do the math in the epilogue.
typename EpilogueFunctor_,
/// The number of accumulators per warp.
typename AccumulatorsPerWarp_,
/// The shape of the WMMA instruction.
typename InstructionShape_,
/// The number of halfs loaded in one LDG for A.
int kScalarsPerLdgA_,
/// The number of halfs loaded in one LDG for B.
int kScalarsPerLdgB_,
/// The index.
typename Index_>
struct WmmaGemmTraitsHelper {
/// The WMMA GEMM config.
typedef WmmaGemmConfig<kLayoutA_,
kLayoutB_,
OutputTile_,
ScalarC_,
Accumulator_,
AccumulatorsPerWarp_,
InstructionShape_,
kScalarsPerLdgA_,
kScalarsPerLdgB_>
GemmConfig;
/// The GEMM config for A.
typedef WmmaGemmTileTraitsHelperA<kLayoutA_, GemmConfig> GemmTileTraitsHelperA;
/// The GEMM config for B.
typedef WmmaGemmTileTraitsHelperB<kLayoutB_, GemmConfig> GemmTileTraitsHelperB;
/// The iterator to load A from global memory.
typedef GemmGlobalIteratorAb<typename GemmTileTraitsHelperA::GlobalTileTraits, Index_>
GlobalLoadIteratorA;
/// The default transformer for A.
typedef Copy<typename GlobalLoadIteratorA::Fragment> GlobalTransformerA;
/// The iterator to store A to shared memory.
typedef TileStoreIterator<typename GemmTileTraitsHelperA::SharedStoreTileTraits,
typename GemmTileTraitsHelperA::SharedStoreTileTraits::Scalar,
IteratorAdvance::kH,
MemorySpace::kShared>
SharedStoreIteratorA;
/// The stream to load A from global memory to shared memory.
typedef GlobalLoadStream<GlobalLoadIteratorA, SharedStoreIteratorA, GlobalTransformerA>
GlobalLoadStreamA;
/// The iterator to load B from global memory.
typedef GemmGlobalIteratorAb<typename GemmTileTraitsHelperB::GlobalTileTraits, Index_>
GlobalLoadIteratorB;
// The default transformer for B.
typedef Copy<typename GlobalLoadIteratorB::Fragment> GlobalTransformerB;
/// The iterator to store B to shared memory.
typedef TileStoreIterator<typename GemmTileTraitsHelperB::SharedStoreTileTraits,
typename GemmTileTraitsHelperB::SharedStoreTileTraits::Scalar,
IteratorAdvance::kH,
MemorySpace::kShared>
SharedStoreIteratorB;
/// The stream to load B from global memory to shared memory.
typedef GlobalLoadStream<GlobalLoadIteratorB, SharedStoreIteratorB, GlobalTransformerB>
GlobalLoadStreamB;
/// The iterator to load A from shared memory.
typedef TileLoadIterator<typename GemmTileTraitsHelperA::SharedLoadTileTraits,
typename GemmTileTraitsHelperA::SharedLoadTileTraits::Scalar,
IteratorAdvance::kH,
MemorySpace::kShared,
Index_,
typename GemmTileTraitsHelperA::WmmaMatrix,
IteratorFragment::kWmmaMatrix>
SharedLoadIteratorA;
/// The stream to load A from shared memory.
typedef SharedLoadStream<SharedLoadIteratorA> SharedLoadStreamA;
/// The iterator to load B from shared memory.
typedef TileLoadIterator<typename GemmTileTraitsHelperB::SharedLoadTileTraits,
typename GemmTileTraitsHelperB::SharedLoadTileTraits::Scalar,
IteratorAdvance::kH,
MemorySpace::kShared,
Index_,
typename GemmTileTraitsHelperB::WmmaMatrix,
IteratorFragment::kWmmaMatrix>
SharedLoadIteratorB;
/// The stream to load B from shared memory.
typedef SharedLoadStream<SharedLoadIteratorB> SharedLoadStreamB;
/// The functor to do the multiply-add in the main loop.
typedef typename GemmConfig::MultiplyAdd MultiplyAdd;
/// The object to clear accumulators.
typedef ClearAccumulators<typename MultiplyAdd::ScalarC> ClearAccumulators;
/// The helper to create the epilogue traits.
typedef WmmaGemmEpilogueTraitsHelper<GemmConfig, EpilogueFunctor_, Index_> EpilogueTraitsHelper;
/// The traits class for the epilogue.
typedef SimplifiedGemmEpilogueTraits<GemmConfig, EpilogueFunctor_, Index_, EpilogueTraitsHelper>
GemmEpilogueTraits;
/// The epilogue.
typedef GemmEpilogue<GemmEpilogueTraits> Epilogue;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename OutputTile_, typename DefaultShape_ = Shape<64, 32, 64> >
struct WmmaGemmAccumulatorsPerWarp {
typedef typename ShapeMin<OutputTile_, DefaultShape_>::Shape Shape;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <
/// The layout for A.
MatrixLayout::Kind kLayoutA_,
/// The layout for B.
MatrixLayout::Kind kLayoutB_,
/// The tile size for the GEMM KxNxM.
typename OutputTile_ = Shape<64, 128, 128>,
/// The output type.
typename ScalarC_ = float,
/// The functor to do the math in the epilogue.
typename EpilogueFunctor_ = LinearScaling<ScalarC_>,
/// The accumulator type.
typename Accumulator_ = ScalarC_,
/// The number of accumulators per warp.
typename AccumulatorsPerWarp_ = typename WmmaGemmAccumulatorsPerWarp<OutputTile_>::Shape,
/// The shape of the WMMA instruction.
typename InstructionShape_ = Shape<16, 16, 16>,
/// The number of scalars per LDG for A.
int kScalarsPerLdgA_ = 8,
/// The number of scalars per LDG for B.
int kScalarsPerLdgB_ = 8,
/// The index.
typename Index_ = int,
/// The helper class.
typename Helper_ = WmmaGemmTraitsHelper<kLayoutA_,
kLayoutB_,
OutputTile_,
ScalarC_,
Accumulator_,
EpilogueFunctor_,
AccumulatorsPerWarp_,
InstructionShape_,
kScalarsPerLdgA_,
kScalarsPerLdgB_,
Index_> >
struct WmmaGemmTraits : public GemmTraits<
// The config.
typename Helper_::GemmConfig,
// The stream to load A from global memory to shared memory.
typename Helper_::GlobalLoadStreamA,
// The stream to load B from global memory to shared memory.
typename Helper_::GlobalLoadStreamB,
// The stream to load A from shared memory.
typename Helper_::SharedLoadStreamA,
// The stream to load B from shared memory.
typename Helper_::SharedLoadStreamB,
// The epilogue.
typename Helper_::Epilogue,
// The block swizzle to reorganize the grid.
IdentityBlockSwizzle,
// The index.
Index_,
// The tool used to clear accumulators.
typename Helper_::ClearAccumulators> {};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace gemm
} // namespace cutlass
#endif // defined CUTLASS_USE_WMMA_API

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@ -1,318 +0,0 @@
/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Free functions for loading and storing to implementations of tile iteartor concepts.
*/
#pragma once
#include <cutlass/fragment_load_store.h>
#include <cutlass/load_store.h>
#include <cutlass/predicate_vector.h>
#include <cutlass/shape.h>
namespace cutlass {
///////////////////////////////////////////////////////////////////////////////////////////////////
/// Loads a fragment from an input iterator
template <typename InputIterator, typename Fragment>
CUTLASS_HOST_DEVICE void iterator_load(InputIterator &iterator, Fragment &fragment) {
typename InputIterator::FragmentIterator frag_iterator(fragment);
for (int d = 0; d < InputIterator::Iterations::kD; ++d) {
for (int h = 0; h < InputIterator::Iterations::kH; ++h) {
for (int w = 0; w < InputIterator::Iterations::kW; ++w) {
for (int c = 0; c < InputIterator::Iterations::kC; ++c) {
if (iterator.valid(d, h, w, c)) {
iterator.get(reinterpret_cast<typename InputIterator::AccessType &>(
frag_iterator.at(d, h, w, c)),
d,
h,
w,
c);
}
}
if (w < InputIterator::Iterations::kW - 1) {
iterator.inc_w();
}
}
if (h < InputIterator::Iterations::kH - 1) {
iterator.inc_h();
}
}
if (d < InputIterator::Iterations::kD - 1) {
iterator.inc_d();
}
}
iterator.inc_advance();
}
/// Loads a fragment from a shared memory input iterator
template <typename InputIterator, typename Fragment>
CUTLASS_DEVICE void shared_iterator_load(InputIterator &iterator, Fragment &fragment) {
typename InputIterator::FragmentIterator frag_iterator(fragment);
for (int d = 0; d < InputIterator::Iterations::kD; ++d) {
for (int h = 0; h < InputIterator::Iterations::kH; ++h) {
for (int w = 0; w < InputIterator::Iterations::kW; ++w) {
for (int c = 0; c < InputIterator::Iterations::kC; ++c) {
int const offset =
ComputeOffsetFromStrides<typename InputIterator::ImmediateOffsetStrides>::get(
d, h, w, c);
FragmentLoad<InputIterator::kIteratorFragment,
InputIterator::Tile::kC,
typename InputIterator::Scalar,
InputIterator::kMemorySpace,
typename InputIterator::FragmentElement,
InputIterator::Tile::kW>::load(frag_iterator.at(d, h, w, c),
iterator.data(),
offset);
}
}
}
}
}
/// Loads a fragment from a shared memory input iterator
template <typename InputIterator, typename Fragment>
CUTLASS_DEVICE void shared_iterator_load(InputIterator &iterator, Fragment &fragment, int d) {
typename InputIterator::FragmentIterator frag_iterator(fragment);
for (int h = 0; h < InputIterator::Iterations::kH; ++h) {
for (int w = 0; w < InputIterator::Iterations::kW; ++w) {
for (int c = 0; c < InputIterator::Iterations::kC; ++c) {
int const offset =
ComputeOffsetFromStrides<typename InputIterator::ImmediateOffsetStrides>::get(
d, h, w, c);
FragmentLoad<InputIterator::kIteratorFragment,
InputIterator::Tile::kC,
typename InputIterator::Scalar,
InputIterator::kMemorySpace,
typename InputIterator::FragmentElement,
InputIterator::Tile::kW>::load(frag_iterator.at(0, h, w, c),
iterator.data(),
offset);
}
}
}
}
/// Loads a fragment from an input iterator, masked by a predicate iterator
template <typename InputIterator, typename Fragment, typename ConstPredicateAdapter>
CUTLASS_HOST_DEVICE void iterator_load_post_increment(InputIterator &iterator,
Fragment &fragment,
typename InputIterator::Index offset,
ConstPredicateAdapter predicate_adapter) {
for (int d = 0; d < InputIterator::Iterations::kD; ++d, iterator.inc_d()) {
for (int h = 0; h < InputIterator::Iterations::kH; ++h, iterator.inc_h()) {
for (int w = 0; w < InputIterator::Iterations::kW; ++w, iterator.inc_w()) {
if (predicate_adapter.at(d, h, w, 0)) {
int idx = InputIterator::Tile::kC *
(w + InputIterator::Iterations::kW * (h + InputIterator::Iterations::kH * d));
Load<typename Fragment::Element, InputIterator::Tile::kC, InputIterator::kMemorySpace>::
load(reinterpret_cast<typename InputIterator::AccessType &>(fragment[idx]),
iterator.data(),
offset);
}
}
}
}
}
/// Loads a fragment from an input iterator
template <typename InputIterator, typename Fragment>
CUTLASS_HOST_DEVICE void iterator_load_post_increment(InputIterator &iterator,
Fragment &fragment,
typename InputIterator::Index offset = 0) {
TrivialPredicateTileAdapter pred;
iterator_load_post_increment(iterator, fragment, offset, pred);
}
/// Loads a fragment from an input iterator
template <typename InputIterator, typename Fragment, typename ConstPredicateAdapter>
CUTLASS_HOST_DEVICE void iterator_load_post_increment(InputIterator &iterator,
Fragment &fragment,
ConstPredicateAdapter pred_it) {
iterator_load_post_increment(iterator, fragment, 0, pred_it);
}
template <typename InputIterator, typename Fragment, typename ConstPredicateAdapter>
CUTLASS_HOST_DEVICE void iterator_load(InputIterator const &_iterator,
Fragment &fragment,
typename InputIterator::Index offset,
ConstPredicateAdapter predicate_adapter) {
InputIterator iterator(_iterator);
iterator_load_post_increment(iterator, fragment, offset, predicate_adapter);
}
/// Loads a fragment from an input iterator
template <typename InputIterator, typename Fragment>
CUTLASS_HOST_DEVICE void iterator_load(InputIterator const &iterator,
Fragment &fragment,
typename InputIterator::Index offset = 0) {
TrivialPredicateTileAdapter pred;
iterator_load(iterator, fragment, offset, pred);
}
/// Loads a fragment from an input iterator
template <typename InputIterator, typename Fragment, typename ConstPredicateAdapter>
CUTLASS_HOST_DEVICE void iterator_load(InputIterator const &iterator,
Fragment &fragment,
ConstPredicateAdapter pred_it) {
iterator_load(iterator, fragment, 0, pred_it);
}
///////////////////////////////////////////////////////////////////////////////////////////////////
/// Stores a fragment to an output iterator
template <typename OutputIterator, typename Fragment>
CUTLASS_HOST_DEVICE void iterator_store(OutputIterator &iterator, Fragment &fragment) {
typename OutputIterator::FragmentIterator frag_iterator(fragment);
for (int d = 0; d < OutputIterator::Iterations::kD; ++d) {
for (int h = 0; h < OutputIterator::Iterations::kH; ++h) {
for (int w = 0; w < OutputIterator::Iterations::kW; ++w) {
if (iterator.valid(d, h, w, 0)) {
iterator.set(reinterpret_cast<typename OutputIterator::AccessType const &>(
frag_iterator.at(d, h, w, 0)),
d,
h,
w,
0);
}
if (w < OutputIterator::Iterations::kW - 1) {
iterator.inc_w();
}
}
if (h < OutputIterator::Iterations::kH - 1) {
iterator.inc_h();
}
}
if (d < OutputIterator::Iterations::kD - 1) {
iterator.inc_d();
}
}
iterator.inc_advance();
}
/// Stores a fragment to a shared memory output iterator
template <typename OutputIterator, typename Fragment>
CUTLASS_DEVICE void shared_iterator_store(OutputIterator &iterator, Fragment const &fragment) {
typename OutputIterator::FragmentConstIterator frag_iterator(fragment);
for (int d = 0; d < OutputIterator::Iterations::kD; ++d) {
for (int h = 0; h < OutputIterator::Iterations::kH; ++h) {
for (int w = 0; w < OutputIterator::Iterations::kW; ++w) {
for (int c = 0; c < OutputIterator::Iterations::kC; ++c) {
int const offset =
ComputeOffsetFromStrides<typename OutputIterator::ImmediateOffsetStrides>::get(
d, h, w, c);
FragmentStore<OutputIterator::kIteratorFragment,
OutputIterator::Tile::kC,
typename OutputIterator::Scalar,
OutputIterator::kMemorySpace,
typename OutputIterator::FragmentElement,
OutputIterator::Tile::kW>::store(frag_iterator.at(d, h, w, c),
iterator.data(),
offset);
}
}
}
}
}
////////////////////////////////////////////////////////////////////////////////////////////////////
/// Stores a fragment to an output iterator, masked by a predicate iterator
template <typename OutputIterator, typename Fragment, typename ConstPredicateAdapter>
CUTLASS_HOST_DEVICE void iterator_store_post_increment(OutputIterator &iterator,
Fragment const &fragment,
typename OutputIterator::Index offset,
ConstPredicateAdapter predicate_adapter) {
for (int d = 0; d < OutputIterator::Iterations::kD; ++d, iterator.inc_d()) {
for (int h = 0; h < OutputIterator::Iterations::kH; ++h, iterator.inc_h()) {
for (int w = 0; w < OutputIterator::Iterations::kW; ++w, iterator.inc_w()) {
if (predicate_adapter.at(d, h, w, 0)) {
int idx = OutputIterator::Tile::kC *
(w + OutputIterator::Iterations::kW * (h + OutputIterator::Iterations::kH * d));
Store<typename Fragment::Element,
OutputIterator::Tile::kC,
OutputIterator::kMemorySpace>::
store(reinterpret_cast<typename OutputIterator::AccessType const &>(fragment[idx]),
iterator.data(),
offset);
}
}
}
}
}
/// Stores a fragment to an output iterator
template <typename OutputIterator, typename Fragment>
CUTLASS_HOST_DEVICE void iterator_store_post_increment(OutputIterator &iterator,
Fragment const &fragment,
typename OutputIterator::Index offset = 0) {
TrivialPredicateTileAdapter pred;
iterator_store_post_increment(iterator, fragment, offset, pred);
}
/// Stores a fragment to an output iterator
template <typename OutputIterator, typename Fragment, typename ConstPredicateAdapter>
CUTLASS_HOST_DEVICE void iterator_store_post_increment(OutputIterator &iterator,
Fragment const &fragment,
ConstPredicateAdapter pred_it) {
iterator_store_post_increment(iterator, fragment, 0, pred_it);
}
/// Stores a fragment to an output iterator, masked by a predicate iterator
template <typename OutputIterator, typename Fragment, typename ConstPredicateAdapter>
CUTLASS_HOST_DEVICE void iterator_store(OutputIterator const &_iterator,
Fragment const &fragment,
typename OutputIterator::Index offset,
ConstPredicateAdapter predicate_adapter) {
OutputIterator iterator(_iterator);
iterator_store_post_increment(iterator, fragment, offset, predicate_adapter);
}
/// Stores a fragment to an output iterator
template <typename OutputIterator, typename Fragment>
CUTLASS_HOST_DEVICE void iterator_store(OutputIterator const &iterator,
Fragment const &fragment,
typename OutputIterator::Index offset = 0) {
TrivialPredicateTileAdapter pred;
iterator_store(iterator, fragment, offset, pred);
}
/// Stores a fragment to an output iterator
template <typename OutputIterator, typename Fragment, typename ConstPredicateAdapter>
CUTLASS_HOST_DEVICE void iterator_store(OutputIterator const &iterator,
Fragment const &fragment,
ConstPredicateAdapter pred_it) {
iterator_store(iterator, fragment, 0, pred_it);
}
///////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace cutlass

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@ -1,222 +0,0 @@
/***************************************************************************************************
* Copyright (c) 2017, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Defines abstractions for efficiently loading and storing vectors to memory.
*/
#pragma once
#include <cutlass/vector.h>
namespace cutlass {
////////////////////////////////////////////////////////////////////////////////////////////////////
/**
* @brief Enum to specify which memory space data resides in.
*/
struct MemorySpace {
enum Kind {
kGeneric, // Data accessed through pointer dereferencing
kShared, // Data resides in shared memory
kGlobal // Data resides in global memory
};
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename Scalar_,
int Lanes_,
MemorySpace::Kind Memory_,
bool = (Lanes_ > 1),
size_t = (sizeof(Scalar_) * Lanes_)>
struct Load {
/// The output type.
typedef typename Vectorize<Scalar_, Lanes_>::Type AccessType;
/// The load function.
static CUTLASS_DEVICE void load(AccessType& dst, Scalar_ const* pointer, int offset) {
dst = reinterpret_cast<AccessType const*>(&pointer[offset])[0];
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename Scalar_, int Lanes_, MemorySpace::Kind Memory_>
struct Load<Scalar_, Lanes_, Memory_, true, 4> {
/// The output type.
typedef typename Vectorize<Scalar_, Lanes_>::Type AccessType;
/// The store function.
static CUTLASS_DEVICE void load(AccessType& dst, Scalar_ const* pointer, int offset) {
dst.registers[0] = reinterpret_cast<uint32_t const*>(&pointer[offset])[0];
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename Scalar_, int Lanes_, MemorySpace::Kind Memory_>
struct Load<Scalar_, Lanes_, Memory_, true, 8> {
/// The output type.
typedef typename Vectorize<Scalar_, Lanes_>::Type AccessType;
/// The store function.
static CUTLASS_DEVICE void load(AccessType& dst, Scalar_ const* pointer, int offset) {
uint2 tmp = reinterpret_cast<uint2 const*>(&pointer[offset])[0];
dst.registers[0] = tmp.x;
dst.registers[1] = tmp.y;
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <MemorySpace::Kind Memory_>
struct Load<double, 2, Memory_, true, 16> {
/// The output type.
typedef typename Vectorize<double, 2>::Type AccessType;
/// The store function.
static CUTLASS_DEVICE void load(AccessType& dst, double const* pointer, int offset) {
double2 tmp = reinterpret_cast<double2 const*>(&pointer[offset])[0];
dst[0] = tmp.x;
dst[1] = tmp.y;
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
#if defined(__CUDACC_VERSION_MAJOR) && __CUDACC_VERSION_MAJOR < 10
// WAR bug in NVCC where the upper and lower half of the register end up being the same
template <MemorySpace::Kind Memory_>
struct Load<half, 8, Memory_, true, 16> {
/// The output type.
typedef typename Vectorize<half, 8>::Type AccessType;
/// The store function.
static CUTLASS_DEVICE void load(AccessType& dst, half const* pointer, int offset) {
int2 tmp = reinterpret_cast<int2 const*>(&pointer[offset])[0];
dst.registers[0] = tmp.x;
dst.registers[1] = tmp.y;
tmp = reinterpret_cast<int2 const*>(&pointer[offset + 4])[0];
dst.registers[2] = tmp.x;
dst.registers[3] = tmp.y;
}
};
#endif
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename Scalar_, int Lanes_, MemorySpace::Kind Memory_>
struct Load<Scalar_, Lanes_, Memory_, true, 16> {
/// The output type.
typedef typename Vectorize<Scalar_, Lanes_>::Type AccessType;
/// The store function.
static CUTLASS_DEVICE void load(AccessType& dst, Scalar_ const* pointer, int offset) {
uint4 tmp = reinterpret_cast<uint4 const*>(&pointer[offset])[0];
dst.registers[0] = tmp.x;
dst.registers[1] = tmp.y;
dst.registers[2] = tmp.z;
dst.registers[3] = tmp.w;
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename Scalar_,
int Lanes_,
MemorySpace::Kind Memory_,
bool = (Lanes_ > 1),
size_t = (sizeof(Scalar_) * Lanes_)>
struct Store {
/// The output type.
typedef typename Vectorize<Scalar_, Lanes_>::Type AccessType;
/// The store function.
static CUTLASS_DEVICE void store(AccessType const& src, Scalar_* pointer, int offset) {
pointer[offset] = src;
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename Scalar_, int Lanes_, MemorySpace::Kind Memory_>
struct Store<Scalar_, Lanes_, Memory_, true, 4> {
/// The output type.
typedef typename Vectorize<Scalar_, Lanes_>::Type AccessType;
/// The store function.
static CUTLASS_DEVICE void store(AccessType const& src, Scalar_* pointer, int offset) {
uint32_t* addr = reinterpret_cast<uint32_t*>(&pointer[offset]);
addr[0] = src.registers[0];
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename Scalar_, int Lanes_, MemorySpace::Kind Memory_>
struct Store<Scalar_, Lanes_, Memory_, true, 8> {
/// The output type.
typedef typename Vectorize<Scalar_, Lanes_>::Type AccessType;
/// The store function.
static CUTLASS_DEVICE void store(AccessType const& src, Scalar_* pointer, int offset) {
uint2* addr = reinterpret_cast<uint2*>(&pointer[offset]);
addr[0] = make_uint2(src.registers[0], src.registers[1]);
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <MemorySpace::Kind Memory_>
struct Store<double, 2, Memory_, true, 16> {
/// The output type.
typedef typename Vectorize<double, 2>::Type AccessType;
/// The store function.
static CUTLASS_DEVICE void store(AccessType const& src, double* pointer, int offset) {
double2* addr = reinterpret_cast<double2*>(&pointer[offset]);
addr[0] = make_double2(src[0], src[1]);
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename Scalar_, int Lanes_, MemorySpace::Kind Memory_>
struct Store<Scalar_, Lanes_, Memory_, true, 16> {
/// The output type.
typedef typename Vectorize<Scalar_, Lanes_>::Type AccessType;
/// The store function.
static CUTLASS_DEVICE void store(AccessType const& src, Scalar_* pointer, int offset) {
uint4* addr = reinterpret_cast<uint4*>(&pointer[offset]);
addr[0] = make_uint4(src.registers[0], src.registers[1], src.registers[2], src.registers[3]);
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace cutlass

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@ -1,58 +0,0 @@
/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Defines a type for restructuring a tile.
*/
#pragma once
#include <cutlass/shape.h>
namespace cutlass {
////////////////////////////////////////////////////////////////////////////////////////////////////
// The following functor reshapes a tile of data. The goal is to have at least kAccessSize in
// the inner-most dimension. If the user respects that constraint, there is nothing to be done. If
// that's not the case, this functor will correct that and "extract" the right number of elements
// from the next dimension.
template <typename Tile_, int kAccessSize_, bool = (Tile_::kC < kAccessSize_)>
struct ReshapeTile {
typedef Tile_ Tile;
};
template <typename Tile_, int kAccessSize_>
struct ReshapeTile<Tile_, kAccessSize_, true> {
// Make sure the W dimension of the tile is large enough.
static_assert(Tile_::kW >= kAccessSize_, "The W dimension is too small");
// Make sure the dimension can be divided by the number of scalars.
static_assert(Tile_::kW % kAccessSize_ == 0, "Not supported");
// Collapse the W dimension.
typedef Shape<Tile_::kD, Tile_::kH, Tile_::kW / kAccessSize_, kAccessSize_> Tile;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace cutlass

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@ -1,305 +0,0 @@
/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Defines Shape implementing the Layout concept for representing a 4D hypercube of objects.
*/
#pragma once
#include <cutlass/cutlass.h>
namespace cutlass {
////////////////////////////////////////////////////////////////////////////////////////////////////
/*!@defgroup layout_concept Layout Concept
* @{
* @par Implementations of \ref layout_concept are used to describe a cube with DxHxW elements and C
scalars per element.
A HxW slice of a cube is called an image and a cube consists of D images.
*
* @par Notations
* Let Layout be an implementation of the \ref layout_concept.
*
* @par Valid Expressions
* - <b>Layout::D</b> specifies the depth of a cube
* - <b>Layout::H</b> specifies the height of a cube
* - <b>Layout::W</b> specifies the height of a cube
* - <b>Layout::C</b> specifies the number of channels of each element in a cube
* - <b>Layout::W_c</b> specifies the number of scalars of each row in one image of a cube.
* - <b>Layout::H_w</b> specifies the number of elements in an image slice.
* - <b>Layout::H_w_c</b>_specifies the number of scalars in an image slice.
* - <b>Layout::D_h_w</b> specifies the number of elements in a cube.
* - <b>Layout::D_h_w_c</b> specifies the number of scalars in a cube.
* - <b>Layout::Strides</b> is a \ref layout_concept specifying the strides.
* @}
*/
/**
* @brief A Shape implementing \ref layout_concept describing the dimensions of a cube.
* @concept{layout_concept}
*/
template <int kD_ = 1, int kH_ = 1, int kW_ = 1, int kC_ = 1>
struct Shape {
/// The depth of the cube.
static int const kD = kD_;
/// The height of the cube.
static int const kH = kH_;
/// The width of the cube.
static int const kW = kW_;
/// The number of scalars per element.
static int const kC = kC_;
};
/**
* @brief Compute derived counted of a \ref layout_concept based class
*/
template <typename Shape>
struct ShapeCount {
/// The number of elements per row.
static int const kWc = Shape::kW * Shape::kC;
/// The number of pixels per image.
static int const kHw = Shape::kH * Shape::kW;
/// The number of elements per image.
static int const kHwc = Shape::kH * kWc;
/// The number of pixels per cube.
static int const kDhw = Shape::kD * kHw;
/// The number of elements in the 4D space.
static int const kDhwc = Shape::kD * kHwc;
/// The number of elements in the 4D space.
static int const kCount = kDhwc;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename A_, int kScale_>
struct ShapeScale {
typedef Shape<A_::kD * kScale_, A_::kH * kScale_, A_::kW * kScale_, A_::kC * kScale_> Shape;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename A_, typename B_>
struct ShapeAdd {
typedef Shape<A_::kD + B_::kD, A_::kH + B_::kH, A_::kW + B_::kW, A_::kC + B_::kC> Shape;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename A_, typename B_>
struct ShapeSub {
typedef Shape<A_::kD - B_::kD, A_::kH - B_::kH, A_::kW - B_::kW, A_::kC - B_::kC> Shape;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename A_, typename B_>
struct ShapeMul {
typedef Shape<A_::kD * B_::kD, A_::kH * B_::kH, A_::kW * B_::kW, A_::kC * B_::kC> Shape;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename A_, typename B_>
struct ShapeDiv {
typedef Shape<A_::kD / B_::kD, A_::kH / B_::kH, A_::kW / B_::kW, A_::kC / B_::kC> Shape;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename A_, typename B_>
struct ShapeMax {
typedef Shape<(A_::kD > B_::kD ? A_::kD : B_::kD),
(A_::kH > B_::kH ? A_::kH : B_::kH),
(A_::kW > B_::kW ? A_::kW : B_::kW),
(A_::kC > B_::kC ? A_::kC : B_::kC)>
Shape;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename A_, typename B_>
struct ShapeMin {
typedef Shape<(A_::kD < B_::kD ? A_::kD : B_::kD),
(A_::kH < B_::kH ? A_::kH : B_::kH),
(A_::kW < B_::kW ? A_::kW : B_::kW),
(A_::kC < B_::kC ? A_::kC : B_::kC)>
Shape;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename Shape_, int kElementsPerAccess>
struct ShapeStrides {
typedef Shape<Shape_::kH * Shape_::kW * Shape_::kC,
Shape_::kW * Shape_::kC,
Shape_::kC,
kElementsPerAccess>
Shape;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
/**
* @brief Compute the offset for the given coordinates in a cube
* @tparam A \ref layout_concept where each dimension of the cube specifies the corresponding stride.
*/
template <typename Shape_>
struct ComputeOffsetFromShape {
static CUTLASS_DEVICE int get(int d, int h, int w, int c) {
// clang-format off
return d * Shape_::kH * Shape_::kW * Shape_::kC +
h * Shape_::kW * Shape_::kC +
w * Shape_::kC +
c;
// clang-format on
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
/**
* @brief Compute the offset for the given coordinates in a cube with a depth of 1
* @tparam kSh Elements in the H dimension
* @tparam kSw Elements in the W dimension
* @tparam kSc Separation between two elements in "elements"
*/
template <int kSh_, int kSw_, int kSc_>
struct ComputeOffsetFromShape<Shape<1, kSh_, kSw_, kSc_> > {
static CUTLASS_DEVICE int get(int d, int h, int w, int c) {
return h * kSw_ * kSc_ + w * kSc_ + c;
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
/**
* @brief Compute the offset for the given coordinates in a cube with one channel and a depth of 1
* @tparam kSh Elements in the H dimension
* @tparam kSw Elements in the W dimension
*/
template <int kSh_, int kSw_>
struct ComputeOffsetFromShape<Shape<1, kSh_, kSw_, 1> > {
static CUTLASS_DEVICE int get(int d, int h, int w, int c) { return h * kSw_ + w; }
};
////////////////////////////////////////////////////////////////////////////////////////////////////
/**
* @brief Compute the offset for the given coordinates in a cube
* @tparam A \ref layout_concept where each dimension of the cube specifies the corresponding stride.
*/
template <typename Strides_>
struct ComputeOffsetFromStrides {
static CUTLASS_DEVICE int get(int d, int h, int w, int c) {
return d * Strides_::kD + h * Strides_::kH + w * Strides_::kW + c * Strides_::kC;
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
/**
* @brief Compute the offset for the given coordinates in a cube with a depth of 1
* @tparam S_h Stride in the H dimension in scalars
* @tparam S_w Stride in the W dimension in scalars
* @tparam S_c Stride between two scalars.
*/
template <int S_h_, int S_w_, int S_c_>
struct ComputeOffsetFromStrides<Shape<1, S_h_, S_w_, S_c_> > {
static CUTLASS_DEVICE int get(int d, int h, int w, int c) {
return h * S_h_ + w * S_w_ + c * S_c_;
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
/**
* @brief Compute the offset for the given coordinates in a cube with one channel and a depth of 1
* @tparam S_h Stride in the H dimension in scalars
* @tparam S_w Stride in the W dimension in scalars
*/
template <int S_h_, int S_w_>
struct ComputeOffsetFromStrides<Shape<1, S_h_, S_w_, 1> > {
static CUTLASS_DEVICE int get(int d, int h, int w, int c) { return h * S_h_ + w * S_w_; }
};
////////////////////////////////////////////////////////////////////////////////////////////////////
/**
* @brief Decompose threadId.x into coordinate of a cube whose dimensions are specified by Threads_.
* Afterwards compute the offset of those coordinates using Strides_
* @tparam Threads_ The dimension of the cube the threadIdx.x value is mapped on
* @tparam Strides_ The strides to use when compute the offsets based on the coordinates of the cube.
*/
template <typename Threads_, typename Strides_>
struct ComputeThreadOffsetFromStrides {
static CUTLASS_DEVICE int get() {
// Decompose the thread index.
int c = threadIdx.x % Threads_::kC;
int w = threadIdx.x / Threads_::kC % Threads_::kW;
int h = threadIdx.x / Threads_::kC / Threads_::kW % Threads_::kH;
int d = threadIdx.x / Threads_::kC / Threads_::kW / Threads_::kH;
// Compute the offset.
return d * Strides_::kD + h * Strides_::kH + w * Strides_::kW + c * Strides_::kC;
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
/**
*@brief Specialization for D=1
*/
template <int T_h_, int T_w_, int T_c_, int S_h_, int S_w_, int S_c_>
struct ComputeThreadOffsetFromStrides<Shape<1, T_h_, T_w_, T_c_>, Shape<1, S_h_, S_w_, S_c_> > {
static CUTLASS_DEVICE int get() {
// Decompose the thread index.
int c = threadIdx.x % T_c_;
int w = threadIdx.x / T_c_ % T_w_;
int h = threadIdx.x / T_c_ / T_w_ % T_h_;
// Compute the offset.
return h * S_h_ + w * S_w_ + c * S_c_;
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
/**
*@brief Specialization for D=1 and C=1
*/
template <int T_h_, int T_w_, int S_h_, int S_w_>
struct ComputeThreadOffsetFromStrides<Shape<1, T_h_, T_w_, 1>, Shape<1, S_h_, S_w_, 1> > {
static CUTLASS_DEVICE int get() {
// Decompose the thread index.
int w = threadIdx.x % T_w_;
int h = threadIdx.x / T_w_;
// Compute the offset.
return h * S_h_ + w * S_w_;
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace cutlass

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@ -1,151 +0,0 @@
/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Defines a structure containing strides, bounds, and a pointer to tensor data.
*/
#pragma once
#include <typeinfo>
#include <cutlass/coord.h>
#include <cutlass/cutlass.h>
#include <cutlass/vector.h>
namespace cutlass {
////////////////////////////////////////////////////////////////////////////////////////////////////
/// Structure modeling a pointer and stride into a tensor
template <typename Storage_, int Rank_>
class TensorRef {
public:
/// Data type of individual access
typedef Storage_ Storage;
/// Rank of tensor
static int const Rank = Rank_;
private:
//
// Data members
//
/// Pointer to storage element
Storage* ptr_;
/// Stride information
Coord<Rank> stride_;
public:
//
// Methods
//
/// Default ctor
CUTLASS_HOST_DEVICE
TensorRef() : ptr_(nullptr) {}
/// Constructs from a pointer, size, and stride
CUTLASS_HOST_DEVICE
TensorRef(Storage* ptr, Coord<Rank> stride) : ptr_(ptr), stride_(stride) {}
/// Updates the pointer, stride, and location within a TensorRef
CUTLASS_HOST_DEVICE
void reset(Storage* ptr = nullptr, Coord<Rank> stride = Coord<Rank>(0)) {
ptr_ = ptr;
stride_ = stride;
}
/// Conversion function
template <typename T>
TensorRef<T, Rank> convert() {
Coord<Rank> converted_stride;
for (int i = 0; i < Rank - 1; ++i) {
converted_stride[i] = stride_[i] * Extent<Storage>::kValue / Extent<T>::kValue;
}
converted_stride[Rank - 1] = stride_[Rank - 1];
return TensorRef<T, Rank>(reinterpret_cast<T*>(ptr_), converted_stride);
}
/// Returns true if the TensorRef may be safely accessed
CUTLASS_HOST_DEVICE
bool good() const { return ptr_ != nullptr; }
/// Returns the pointer to referenced data
CUTLASS_HOST_DEVICE
Storage* data() const { return ptr_; }
/// Returns the stride of the tensor
CUTLASS_HOST_DEVICE
Coord<Rank> const& stride() const { return stride_; }
/// Returns the stride of the tensor in the given dimension
CUTLASS_HOST_DEVICE
int const& stride(int dim) const { return stride_.at(dim); }
/// Returns the maximum stride element as the 'leading dimension'
CUTLASS_HOST_DEVICE
int leading_dim() const { return __NV_STD_MAX(stride_[1], stride_[2]); }
/// Computes the offset of an index from the origin of the tensor
CUTLASS_HOST_DEVICE
long long offset(Coord<Rank> const& coord) const {
return stride_.template dot<long long>(coord);
}
/// Returns a reference to the element at a given Coord
CUTLASS_HOST_DEVICE
Storage& at(Coord<Rank> const& coord) const { return ptr_[offset(coord)]; }
/// Element-wise accessor
Storage& operator[](Coord<Rank> const& coord) const { return at(coord); }
/// Returns a reference to the element at a given Coord
CUTLASS_HOST_DEVICE
Storage& at(int idx) const { return ptr_[idx]; }
/// Element-wise accessor
Storage& operator[](int idx) const { return at(idx); }
/// Adds an offset to the pointer
CUTLASS_HOST_DEVICE
TensorRef& advance(Coord<Rank> const& b) {
ptr_ += offset(b);
return *this;
}
/// Returns a TensorRef offset by a given amount
CUTLASS_HOST_DEVICE
TensorRef operator+(Coord<Rank> const& b) const { return TensorRef(ptr_ + offset(b), stride_); }
/// Returns a TensorRef offset by a given amount
CUTLASS_HOST_DEVICE
TensorRef operator-(Coord<Rank> const& b) const { return TensorRef(ptr_ - offset(b), stride_); }
};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace cutlass

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/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Defines a structure containing strides and a pointer to tensor data.
*/
#pragma once
#include <cmath>
#include <cutlass/cutlass.h>
#include <cutlass/tensor_ref.h>
namespace cutlass {
////////////////////////////////////////////////////////////////////////////////////////////////////
/// Host-side reference implementation of tensor operations
template <typename T>
class TensorView : public TensorRef<T, 4> {
public:
/// Reference and stride
typedef TensorRef<T, 4> Base;
/// Reference and stride
typedef Base TensorRef_t;
/// Reference to constant type
typedef TensorRef<T const, 4> ConstTensorRef_t;
/// Rank of tensor
static int const Rank = TensorRef_t::Rank;
/// Type used to compute the offset of an element to the base of a tensor
typedef int Offset_t;
/// Coordinate into tensor
typedef Coord<Rank> Coord_t;
private:
//
// Data members
//
/// Pointer to pitch-linear memory
TensorRef_t ref_;
/// Dimensions of coordinate (independent of stride)
Coord_t size_;
public:
//
// Device and Host Methods
//
/// Default constructor
CUTLASS_HOST_DEVICE
TensorView() {}
/// Constructs a Tensor_view from a TensorRef and size
CUTLASS_HOST_DEVICE
TensorView(TensorRef_t const& _ref, Coord_t const& _size) : Base(_ref), size_(_size) {}
/// Returns true if the Tensor_view is bound to some memory
CUTLASS_HOST_DEVICE
bool good() const { return ref().good(); }
/// Returns a pointer to data
CUTLASS_HOST_DEVICE
T* data() const { return ref().data(); }
/// Updates the reference and size of a Tensor_view object
CUTLASS_HOST_DEVICE
void reset(TensorRef_t const& _ref = TensorRef_t(0), Coord_t const& _size = Coord_t()) {
Base::operator=(_ref);
size_ = _size;
}
/// Accesses the tensor reference pointing to data
CUTLASS_HOST_DEVICE
TensorRef_t& ref() { return *this; }
///
CUTLASS_HOST_DEVICE
ConstTensorRef_t const_ref() { return ConstTensorRef_t(data(), stride()); }
/// Accesses the tensor reference pointing to data
CUTLASS_HOST_DEVICE
TensorRef_t const& ref() const { return *this; }
/// Accesses the size
CUTLASS_HOST_DEVICE
Coord_t const& size() const { return size_; }
/// Accesses the size
CUTLASS_HOST_DEVICE
int size(int dim) const { return size_.at(dim); }
/// Accesses the stride
CUTLASS_HOST_DEVICE
Coord_t const& stride() const { return ref().stride(); }
/// Accesses the stride
CUTLASS_HOST_DEVICE
int const& stride(int dim) const { return ref().stride(dim); }
/// Assigns the Tensor_view
CUTLASS_HOST_DEVICE
TensorView& operator=(TensorView const& _tensor) {
Base::operator=(_tensor._ref);
size_ = _tensor.size_;
return *this;
}
/// Returns the index of an element
CUTLASS_HOST_DEVICE
Offset_t offset(Coord_t const& coord) const { return ref().offset(coord); }
/// Determines whether a location is within a tensor
CUTLASS_HOST_DEVICE
bool contains(Coord_t const& coord) const {
for (int dim = 0; dim < Rank; ++dim) {
if (coord.at(dim) >= size_.at(dim)) {
return false;
}
}
return true;
}
/// Element-wise accessor
CUTLASS_HOST_DEVICE
T& at(Coord_t const& coord) const { return ref().at(coord); }
/// Element-wise accessor
T& operator[](Coord<Rank> const& coord) const { return at(coord); }
/// Element-wise accessor
CUTLASS_HOST_DEVICE
T& at(Offset_t idx) const { return ref().at(idx); }
/// Returns a Tensor_view given location and size quantities
CUTLASS_HOST_DEVICE
TensorView<T> subview(Coord_t const& location, Coord_t size) const {
return TensorView<T>(ref() + location, size.clamp(size_ - location));
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace cutlass

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@ -1,899 +0,0 @@
/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Defines the Tile Traits concept and iterators for loading and storing to tiles
efficiently.
*/
#pragma once
#include <cutlass/fragment.h>
#include <cutlass/load_store.h>
#include <cutlass/predicate_vector.h>
#include <cutlass/vector.h>
namespace cutlass {
///////////////////////////////////////////////////////////////////////////////////////////////////
/*!@defgroup tile_traits_concept Tile Traits Concept
@{
\ref tile_traits_concept is a type definining the shape of a tile and the distribution of accesses
by individual entities, either threads or other.
@par Tile Traits Concept
Types satisfying \ref tile_traits_concept define the following members
- <b>Tile</b> - a type satisfying \ref layout_concept describing the dimensions of the tile
- <b>Delta</b> - a type satisfying \ref layout_concept describing the increments between accesses
along each dimension
- <b>Iterations</b> - a type satisfying \ref layout_concept describing the number of accesses
along each dimension
- <b>Offset</b> - the type of a <i>functor</i> computing the offset of each participating entity
as a Coord<4>.
@}
*/
///////////////////////////////////////////////////////////////////////////////////////////////////
/// Specifies dimension in which post-increment accesses advance
struct IteratorAdvance {
enum Kind { kD, kH, kW };
};
/// Specifies whether iterator storage fragment consists of Scalar values or WMMA matrix
struct IteratorFragment {
enum Kind { kScalar, kWmmaMatrix };
};
///////////////////////////////////////////////////////////////////////////////////////////////////
/**
* @brief A template defining \ref tile_traits_concept
* @concept{tile_traits_concept}
*/
template <typename Tile_,
typename Delta_,
typename Iterations_,
typename ThreadOffset_,
int kAccessSize>
struct TileTraits {
/// Shape of the tile
typedef Tile_ Tile;
/// Number of steps between accesses along each dimension
typedef Delta_ Delta;
/// Number of accesses performed
typedef Iterations_ Iterations;
/// Functor that returns the logical coordinate of each entity's initial offset in the tile
typedef ThreadOffset_ ThreadOffset;
};
///////////////////////////////////////////////////////////////////////////////////////////////////
/// Iterator for accessing a stripmined tile in memory
template <typename Traits_,
typename Scalar_,
IteratorAdvance::Kind Advance_ = IteratorAdvance::kH,
MemorySpace::Kind MemorySpace = MemorySpace::kGeneric,
typename Index_ = int,
typename FragmentElement_ = Scalar_,
IteratorFragment::Kind IteratorFragment_ = IteratorFragment::kScalar,
typename Skew_ = Shape<0, 0, 0, 0> >
struct TileIteratorBase {
/// concept TileTraits
typedef Traits_ Traits;
/// Scalar element
typedef Scalar_ Scalar;
/// Fragment element
typedef FragmentElement_ FragmentElement;
/// Specifies dimension in which post-increment accesses advance.
static IteratorAdvance::Kind const kAdvance = Advance_;
/// Specifies iterator storage fragment type (Scalar or WmmaMatrix)
static IteratorFragment::Kind const kIteratorFragment = IteratorFragment_;
/// Source or destination memory space
static MemorySpace::Kind const kMemorySpace = MemorySpace;
/// Index type
typedef Index_ Index;
/// Skew quantity
typedef Skew_ Skew;
/// Tile shape
typedef typename Traits::Tile Tile;
/// Distance along each dimension
typedef typename Traits::Delta Delta;
/// The strides in each dimension between different loads/stores.
typedef typename Traits::ImmediateOffsetStrides ImmediateOffsetStrides;
/// Iterations
typedef typename Traits::Iterations Iterations;
/// Thread offset
typedef typename Traits::ThreadOffset ThreadOffset;
/// The number of scalars accessed per load/store.
static int const kAccessSize = Tile::kC;
/// The elements loaded/store by one instruction.
typedef typename Vectorize<FragmentElement, kAccessSize>::Type AccessType;
/// The size of storage needed per fragment
static int const kFragmentSize =
(kIteratorFragment == IteratorFragment::kWmmaMatrix ? 16 : sizeof(AccessType));
/// The storage.
typedef Fragment<Scalar, ShapeCount<Tile>::kCount, kFragmentSize> Storage;
/// The fragment.
typedef Fragment<FragmentElement, ShapeCount<Iterations>::kCount * kAccessSize> Fragment;
/// The fragment iterator.
typedef FragmentIterator<Fragment, Iterations, AccessType> FragmentIterator;
/// The fragment const iterator.
typedef FragmentConstIterator<Fragment, Iterations, AccessType> FragmentConstIterator;
/// The shape of the fragment.
typedef typename FragmentIterator::FragmentShape FragmentShape;
/// Default predicate mask type
typedef PredicateVector<ShapeCount<Iterations>::kCount> PredicateVector;
//
// Params struct
//
/// Parameters to the iterator
struct Params {
Index stride_d;
Index stride_h;
Index stride_w;
Index inc_d;
Index inc_h;
Index inc_w;
Index inc_advance;
/// Initializes params
CUTLASS_HOST_DEVICE
int initialize(Index _stride_d,
Index _stride_h,
Index _stride_w,
Index _inc_d,
Index _inc_h,
Index _inc_w,
Index _inc_advance) {
stride_d = _stride_d;
stride_h = _stride_h;
stride_w = _stride_w;
inc_d = _inc_d;
inc_h = _inc_h;
inc_w = _inc_w;
inc_advance = _inc_advance;
return 0;
}
CUTLASS_HOST_DEVICE
int initialize(Index _stride_d, Index _stride_h, Index _stride_w) {
stride_d = _stride_d;
stride_h = _stride_h;
stride_w = _stride_w;
inc_w = stride_w * Delta::kW;
inc_h = stride_h * Delta::kH - stride_w * Delta::kW * (Iterations::kW - 1);
if (kAdvance == IteratorAdvance::kH) {
// Advance in the H dimension.
inc_d = 0;
} else if (kAdvance == IteratorAdvance::kW) {
// Advance in the W dimension.
inc_d = stride_w * Tile::kW - stride_h * Tile::kH;
} else {
// Advance in the D dimension.
inc_d = stride_d;
}
inc_advance = 0;
return 0;
}
CUTLASS_HOST_DEVICE int initialize() {
stride_d = 0;
stride_h = 0;
stride_w = 1;
inc_d = inc_h = inc_w = inc_advance = 0;
return 0;
}
};
/// Is the iterator valid?
CUTLASS_DEVICE bool valid(int d, int h, int w, int c) const { return true; }
//
// Static function members
//
/// Initializes a predicate vector
template <typename PredicateIterator>
CUTLASS_DEVICE static void initialize_predicates(PredicateIterator predicate_it,
Coord<3> const &bounds,
Coord<3> const &offset = make_Coord(0, 0, 0)) {
for (int d = 0; d < Iterations::kD; ++d) {
bool enable_d = (d * Delta::kD + offset[0] < bounds[0]);
for (int h = 0; h < Iterations::kH; ++h) {
bool enable_h = (h * Delta::kH + offset[1] < bounds[1]);
for (int w = 0; w < Iterations::kW; ++w) {
bool enable_w = (w * Tile::kC * Delta::kW + offset[2] < bounds[2]);
predicate_it.set(d, h, w, 0, enable_d && enable_h && enable_w);
}
}
}
}
};
///////////////////////////////////////////////////////////////////////////////////////////////////
/*!@defgroup tile_load_iterator_concept Tile Load Iterator Concept
@{
\ref tile_load_iterator_concept enables loading a tile from addressable memory into a fragment
@par Tile Load Iterator Concept
Types satisfying \ref tile_load_iterator_concept define the following members
- <b>PredicateVector</b> - a \ref predicate_vector_concept with sufficient predicate storage for
each access implied by the tile traits
- <b>Fragment</b> - the destination fragment type satisfying \ref fragment_concept
- <b>initialize_predicates(pred_it, bounds, block_offset)</b> - function initializing a predicate
vector according to externally specified bounds
- <b>load_post_increment(fragment, pred_it)</b> - a method that loads a fragment and increments
the iterator to the next tile, guarded by a \ref predicate_iterator_concept
- <b>load_post_increment(fragment)</b> - a method that loads a fragment and increments the
iterator to the next tile
- <b>load(fragment, pred_it)</b> - a const method that loads a fragment, guarded by a \ref
predicate_iterator_concept
- <b>load(fragment)</b> - a method that loads a fragment
@}
*/
///////////////////////////////////////////////////////////////////////////////////////////////////
/**
* @brief An iterator implementing \ref tile_load_iterator_concept for loading a tile from memory
* @concept{tile_load_iterator_concept}
*/
template <typename Traits_,
typename Scalar_,
IteratorAdvance::Kind Advance_ = IteratorAdvance::kH,
MemorySpace::Kind MemorySpace = MemorySpace::kGeneric,
typename Index_ = int,
typename FragmentElement_ = Scalar_,
IteratorFragment::Kind IteratorFragment_ = IteratorFragment::kScalar,
typename Skew_ = Shape<0, 0, 0, 0> >
struct TileLoadIterator : public TileIteratorBase<Traits_,
Scalar_,
Advance_,
MemorySpace,
Index_,
FragmentElement_,
IteratorFragment_,
Skew_> {
/// Base class
typedef TileIteratorBase<Traits_,
Scalar_,
Advance_,
MemorySpace,
Index_,
FragmentElement_,
IteratorFragment_,
Skew_>
Base;
/// concept TileTraits
typedef typename Base::Traits Traits;
/// Scalar element
typedef typename Base::Scalar Scalar;
/// Fragment element
typedef typename Base::FragmentElement FragmentElement;
/// Specifies in which dimension post-increment accesses advance.
static IteratorAdvance::Kind const kAdvance = Base::kAdvance;
/// Specifies type of iterator fragment storage (Salar or WmmaMatrix)
static IteratorFragment::Kind const kIteratorFragment = Base::kIteratorFragment;
/// Source or destination memory space
static MemorySpace::Kind const kMemorySpace = Base::kMemorySpace;
/// Index type
typedef typename Base::Index Index;
/// Skew quantity
typedef typename Base::Skew Skew;
/// Tile shape
typedef typename Base::Tile Tile;
/// Delta
typedef typename Base::Delta Delta;
/// Iterations
typedef typename Base::Iterations Iterations;
/// ThreadOffset functor
typedef typename Base::ThreadOffset ThreadOffset;
/// Fragment type
typedef typename Base::FragmentShape FragmentShape;
/// Memory access type
typedef typename Base::AccessType AccessType;
/// Fragment definition
typedef typename Base::Fragment Fragment;
/// Fragment iterator definition
typedef typename Base::FragmentIterator FragmentIterator;
/// Fragment const iterator definition
typedef typename Base::FragmentConstIterator FragmentConstIterator;
/// Default predicate mask type
typedef typename Base::PredicateVector PredicateVector;
/// Storage object that may be loaded from
typedef typename Base::Storage SharedStorage;
/// IteratorBase parameters
typedef typename Base::Params BaseParams;
/// Do we require a fence?
enum { kRequiresLoadFence = Tile::kD == 1 };
/// The pointer type
typedef Scalar const *Pointer;
/// Parameters
struct Params : public BaseParams {
/// Pointer to memory
Scalar const *pointer;
/// Initialize params to access storage object
CUTLASS_HOST_DEVICE
int initialize(SharedStorage const &storage) {
pointer = &storage[0];
return 0;
}
/// Initializes params to access a raw pointer
CUTLASS_HOST_DEVICE
int initialize(Scalar const *ptr, Index stride_d, Index stride_h, Index stride_w) {
Base::Params::initialize(stride_d, stride_h, stride_w);
pointer = ptr;
return 0;
}
/// Initializes params
CUTLASS_HOST_DEVICE
int initialize(Scalar const *ptr,
Index _stride_d,
Index _stride_h,
Index _stride_w,
Index _inc_d,
Index _inc_h,
Index _inc_w,
Index _inc_advance) {
pointer = ptr;
Base::Params::initialize(
_stride_d, _stride_h, _stride_w, _inc_d, _inc_h, _inc_w, _inc_advance);
return 0;
}
// Initializes params to default values
CUTLASS_HOST_DEVICE
int initialize() { return Base::Params::initialize(); }
};
//
// Data members
//
/// Parameters structure
Params params;
/// Offset of an individual lane from the start of the tile
Coord<4> thread_offset;
/// Stage argument enables wrapping after some number of tiles have been loaded.
int stage;
//
// Static member functions
//
/// Initializes a predicate vector
template <typename PredicateIterator>
CUTLASS_HOST_DEVICE void initialize_predicates(PredicateIterator predicate_it,
Coord<3> const &bounds,
Coord<3> const &block_offset = make_Coord(0,
0,
0)) {
Base::initialize_predicates(
predicate_it,
bounds,
block_offset + make_Coord(0, thread_offset[1], thread_offset[2] * Tile::kC));
}
//
// Methods
//
/// Default constructor
CUTLASS_HOST_DEVICE
TileLoadIterator() {}
/// Constructs a tile load iterator
CUTLASS_HOST_DEVICE
TileLoadIterator(Params const &_params,
Coord<3> const &block_offset = make_Coord(0, 0, 0),
ThreadOffset thread_offset_func = ThreadOffset())
: params(_params), stage(0) {
thread_offset = thread_offset_func();
Index block_offset_h = 0;
Index block_offset_w = 0;
if (kAdvance == IteratorAdvance::kH) {
block_offset_h = block_offset[1];
block_offset_w = block_offset[2];
} else {
block_offset_h = block_offset[2];
block_offset_w = block_offset[1];
}
params.pointer += block_offset[0] * params.stride_d +
(block_offset_h + thread_offset[1]) * params.stride_h +
(block_offset_w + thread_offset[2] * Tile::kC) / Tile::kC * params.stride_w;
}
/// Constructs a tile load iterator
CUTLASS_HOST_DEVICE
TileLoadIterator(Params const &,
SharedStorage &shared_storage,
Coord<3> const &block_offset = make_Coord(0, 0, 0),
ThreadOffset thread_offset_func = ThreadOffset())
: stage(0) {
int const offset = thread_offset_func()[2];
params.pointer = &shared_storage[offset];
}
/// Returns the current pointer
CUTLASS_HOST_DEVICE
Scalar const *data() const { return params.pointer; }
/// The accessor.
CUTLASS_DEVICE void get(AccessType &value, int d, int h, int w, int c) const {
int const imm =
ComputeOffsetFromStrides<typename Base::ImmediateOffsetStrides>::get(d, h, w, c);
Load<Scalar, Base::kAccessSize, kMemorySpace>::load(value, params.pointer, imm);
}
/// Increment in the D dimension
CUTLASS_HOST_DEVICE void inc_d() { params.pointer += params.inc_d; }
/// Increment in the H dimension
CUTLASS_HOST_DEVICE void inc_h() { params.pointer += params.inc_h; }
/// Increment in the W dimension
CUTLASS_HOST_DEVICE void inc_w() { params.pointer += params.inc_w; }
/// Increment in the next dimension
CUTLASS_HOST_DEVICE void inc_advance() { params.pointer += params.inc_advance; }
/// Increment the stage.
CUTLASS_DEVICE void inc_stage() {
if (Tile::kD > 1) {
int const kStageSize = Tile::kH * Tile::kW * Tile::kC;
if (stage == Tile::kD - 1) {
params.pointer -= (Tile::kD - 1) * kStageSize;
stage = 0;
} else {
params.pointer += kStageSize;
stage = stage + 1;
}
}
}
public:
/// Loads a fragment and advances the iterator to the next tile.
template <typename Fragment, typename PredicateIterator>
CUTLASS_HOST_DEVICE void load_post_increment(Fragment &fragment, PredicateIterator pred_it) {
FragmentIterator frag_iterator(fragment);
for (int d = 0; d < Iterations::kD; ++d) {
for (int h = 0; h < Iterations::kH; ++h) {
for (int w = 0; w < Iterations::kW; ++w, ++pred_it) {
if (*pred_it) {
Load<typename Fragment::Element, Tile::kC, kMemorySpace>::load(
reinterpret_cast<AccessType &>(frag_iterator.at(d, h, w, 0)), data(), 0);
}
if (w < Iterations::kW - 1) {
inc_w();
}
}
if (h < Iterations::kH - 1) {
inc_h();
}
}
if (d < Iterations::kD - 1) {
inc_d();
}
}
inc_advance();
}
/// Loads a fragment and advances the iterator to the next tile.
template <typename Fragment>
CUTLASS_HOST_DEVICE void load_post_increment(Fragment &fragment) {
typename PredicateVector::TrivialIterator pred_it;
load_post_increment(fragment, pred_it);
}
/// Loads a fragment without advancing the iterator..
template <typename Fragment, typename PredicateIterator>
CUTLASS_HOST_DEVICE void load(Fragment &fragment, PredicateIterator pred_it) const {
TileLoadIterator _load_it(*this);
_load_it.load_post_increment(fragment, pred_it);
}
/// Loads a fragment without advancing the iterator..
template <typename Fragment>
CUTLASS_HOST_DEVICE void load(Fragment &fragment) const {
typename PredicateVector::TrivialIterator pred_it;
load(fragment, pred_it);
}
};
///////////////////////////////////////////////////////////////////////////////////////////////////
/*!@defgroup tile_store_iterator_concept Tile Store Iterator Concept
@{
\ref tile_store_iterator_concept enables storing a tile to addressable memory
@par Tile Store Iterator Concept
Types satisfying \ref tile_load_iterator_concept define the following members
- <b>PredicateVector</b> - a \ref predicate_vector_concept with sufficient predicate storage for
each access implied by the tile traits
- <b>Fragment</b> - the destination fragment type satisfying \ref fragment_concept
- <b>initialize_predicates(pred_it, bounds, block_offset)</b> - function initializing a predicate
vector according to externally specified bounds
- <b>store_post_increment(fragment, pred_it)</b> - a method that stores a fragment and increments
the iterator to the next tile, guarded by a \ref predicate_iterator_concept
- <b>store_post_increment(fragment)</b> - a method that stores a fragment and increments the
iterator to the next tile
- <b>store(fragment, pred_it)</b> - a const method that stores a fragment, guarded by a \ref
predicate_iterator_concept
- <b>store(fragment)</b> - a method that loads a fragment
@}
*/
///////////////////////////////////////////////////////////////////////////////////////////////////
/**
* @brief An iterator implementing \ref tile_store_iterator_concept for storing a tile to memory
* @concept{tile_store_iterator_concept}
*/
template <typename Traits_,
typename Scalar_,
IteratorAdvance::Kind Advance_ = IteratorAdvance::kH,
MemorySpace::Kind MemorySpace = MemorySpace::kGeneric,
typename Index_ = int,
typename FragmentElement_ = Scalar_,
IteratorFragment::Kind IteratorFragment_ = IteratorFragment::kScalar,
typename Skew_ = Shape<0, 0, 0, 0> >
struct TileStoreIterator : public TileIteratorBase<Traits_,
Scalar_,
Advance_,
MemorySpace,
Index_,
FragmentElement_,
IteratorFragment_,
Skew_> {
/// Base class
typedef TileIteratorBase<Traits_,
Scalar_,
Advance_,
MemorySpace,
Index_,
FragmentElement_,
IteratorFragment_,
Skew_>
Base;
/// concept TileTraits
typedef typename Base::Traits Traits;
/// Scalar element
typedef typename Base::Scalar Scalar;
/// Fragment element
typedef typename Base::FragmentElement FragmentElement;
/// Specifies in which dimension post-increment accesses advance.
static IteratorAdvance::Kind const kAdvance = Base::kAdvance;
/// Specifies type of iterator fragment storage (Salar or WmmaMatrix)
static IteratorFragment::Kind const kIteratorFragment = Base::kIteratorFragment;
/// Source or destination memory space
static MemorySpace::Kind const kMemorySpace = Base::kMemorySpace;
/// Index type
typedef typename Base::Index Index;
/// Skew quantity
typedef typename Base::Skew Skew;
/// Tile shape
typedef typename Base::Tile Tile;
/// Delta
typedef typename Base::Delta Delta;
/// Iterations
typedef typename Base::Iterations Iterations;
/// ThreadOffset functor
typedef typename Base::ThreadOffset ThreadOffset;
/// Fragment type
typedef typename Base::FragmentShape FragmentShape;
/// Memory access type
typedef typename Base::AccessType AccessType;
/// Fragment definition
typedef typename Base::Fragment Fragment;
/// Fragment iterator definition
typedef typename Base::FragmentIterator FragmentIterator;
/// Fragment const iterator definition
typedef typename Base::FragmentConstIterator FragmentConstIterator;
/// Default predicate mask type
typedef typename Base::PredicateVector PredicateVector;
/// Storage object which may be stored to
typedef typename Base::Storage SharedStorage;
/// IteratorBase parameters
typedef typename Base::Params BaseParams;
/// Parameters
struct Params : public BaseParams {
/// Pointer to memory
Scalar *pointer;
/// Initialize params to access storage object
CUTLASS_HOST_DEVICE
int initialize(SharedStorage &storage) {
pointer = &storage[0];
return 0;
}
/// Initializes params to access a raw pointer
CUTLASS_HOST_DEVICE
int initialize(Scalar *ptr, Index stride_d, Index stride_h, Index stride_w) {
Base::Params::initialize(stride_d, stride_h, stride_w);
pointer = ptr;
return 0;
}
/// Initializes params
CUTLASS_HOST_DEVICE
int initialize(Scalar *ptr,
Index _stride_d,
Index _stride_h,
Index _stride_w,
Index _inc_d,
Index _inc_h,
Index _inc_w,
Index _inc_advance) {
pointer = ptr;
Base::Params::initialize(
_stride_d, _stride_h, _stride_w, _inc_d, _inc_h, _inc_w, _inc_advance);
return 0;
}
/// Initializes params to default values
CUTLASS_HOST_DEVICE
int initialize() { return Base::Params::initialize(); }
};
//
// Data members
//
/// Parameters structure
Params params;
/// Offset of an individual lane from the start of the tile
Coord<4> thread_offset;
/// The stage.
int stage;
//
// Static member functions
//
/// Initializes a predicate vector
template <typename PredicateIterator>
CUTLASS_HOST_DEVICE void initialize_predicates(PredicateIterator predicate_it,
Coord<3> const &bounds,
Coord<3> const &block_offset = make_Coord(0,
0,
0)) {
Base::initialize_predicates(
predicate_it,
bounds,
block_offset + make_Coord(0, thread_offset[1], thread_offset[2] * Tile::kC));
}
//
// Methods
//
/// Default constructor
CUTLASS_HOST_DEVICE
TileStoreIterator() {}
/// Constructs a tile store iterator
CUTLASS_HOST_DEVICE
TileStoreIterator(Params const &_params,
Coord<3> const &block_offset = make_Coord(0, 0, 0),
ThreadOffset thread_offset_func = ThreadOffset())
: params(_params), stage(0) {
thread_offset = thread_offset_func();
params.pointer += block_offset[0] * params.stride_d +
(block_offset[1] + thread_offset[1]) * params.stride_h +
(block_offset[2] + thread_offset[2] * Tile::kC) / Tile::kC * params.stride_w;
}
/// Constructs a tile store iterator
CUTLASS_HOST_DEVICE
TileStoreIterator(Params const &,
SharedStorage &shared_storage,
Coord<3> const &block_offset = make_Coord(0, 0, 0),
ThreadOffset thread_offset_func = ThreadOffset())
: stage(0) {
int const offset = thread_offset_func()[2];
params.pointer = &shared_storage[offset];
}
/// Returns the current pointer
CUTLASS_HOST_DEVICE
Scalar *data() const { return params.pointer; }
/// Increment in the D dimension
CUTLASS_HOST_DEVICE void inc_d() { params.pointer += params.inc_d; }
/// Increment in the H dimension
CUTLASS_HOST_DEVICE void inc_h() { params.pointer += params.inc_h; }
/// Increment in the W dimension
CUTLASS_HOST_DEVICE void inc_w() { params.pointer += params.inc_w; }
/// Increment in the next dimension
CUTLASS_HOST_DEVICE void inc_advance() {}
/// Increment the stage.
CUTLASS_DEVICE void inc_stage() {
if (Tile::kD > 1) {
int const kStageSize = Tile::kH * Tile::kW * Tile::kC;
if (stage == Tile::kD - 1) {
params.pointer -= (Tile::kD - 1) * kStageSize;
stage = 0;
} else {
params.pointer += kStageSize;
stage = stage + 1;
}
}
}
/// The accessor.
CUTLASS_DEVICE void set(AccessType const &value, int d, int h, int w, int c) {
int const imm =
ComputeOffsetFromStrides<typename Base::ImmediateOffsetStrides>::get(d, h, w, c);
Store<Scalar, Base::kAccessSize, kMemorySpace>::store(value, params.pointer, imm);
}
public:
/// Stores a fragment and advances to the next tile.
template <typename Fragment, typename PredicateIterator>
CUTLASS_HOST_DEVICE void store_post_increment(Fragment &fragment, PredicateIterator pred_it) {
FragmentIterator frag_iterator(fragment);
for (int d = 0; d < Iterations::kD; ++d) {
for (int h = 0; h < Iterations::kH; ++h) {
for (int w = 0; w < Iterations::kW; ++w, ++pred_it) {
if (*pred_it) {
Store<typename Fragment::Element, Tile::kC, kMemorySpace>::store(
reinterpret_cast<AccessType &>(frag_iterator.at(d, h, w, 0)), data(), 0);
}
if (w < Iterations::kW - 1) {
inc_w();
}
}
if (h < Iterations::kH - 1) {
inc_h();
}
}
if (d < Iterations::kD - 1) {
inc_d();
}
}
inc_advance();
}
/// Stores a fragment and advances to the next tile.
template <typename Fragment>
CUTLASS_HOST_DEVICE void store_post_increment(Fragment &fragment) {
typename PredicateVector::TrivialIterator pred_it;
store_post_increment(fragment, pred_it);
}
/// Stores a fragment without advancing the iterator.
template <typename Fragment, typename PredicateIterator>
CUTLASS_HOST_DEVICE void store(Fragment &fragment, PredicateIterator pred_it) const {
TileStoreIterator _store_it(*this);
_store_it.store_post_increment(fragment, pred_it);
}
/// Stores a fragment without advancing the iterator.
template <typename Fragment>
CUTLASS_HOST_DEVICE void store(Fragment &fragment) const {
typename PredicateVector::TrivialIterator pred_it;
store(fragment, pred_it);
}
};
}

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@ -1,238 +0,0 @@
/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Defines tile traits for several tile partitioning arrangements of threads expected to
achieve efficient streaming performance.
*/
#pragma once
#include <cutlass/tile_iterator.h>
namespace cutlass {
///////////////////////////////////////////////////////////////////////////////////////////////////
/// Basic thread offset function computed from a thread shape
template <typename ThreadShape>
struct TiledThreadOffset {
/// Computes the logical coordinate from thread shape
CUTLASS_HOST_DEVICE
Coord<4> operator()() const {
Coord<4> thread_offset;
int index = threadIdx.x;
thread_offset[3] = (index % ThreadShape::kC);
index = (index / ThreadShape::kC);
thread_offset[2] = (index % ThreadShape::kW);
index = (index / ThreadShape::kW);
thread_offset[1] = (index % ThreadShape::kH);
index = (index / ThreadShape::kH);
thread_offset[0] = index;
return thread_offset;
}
};
///////////////////////////////////////////////////////////////////////////////////////////////////
/// Tiling in which the number of threads is greater than the
/// contiguous dimension of the tile.
template <typename Tile_, int Threads>
struct TileTraitsStrideMajor {
/// Shape of tile
typedef Tile_ Tile;
/// Number of participating threads
static int const kThreads = Threads;
// Static assertions
static_assert(!(ShapeCount<Tile>::kDhw % kThreads),
"Tiling undefined if elements not divisible by threads.");
static_assert(Tile::kW <= kThreads,
"This specialization assumes there are more threads than the contiguous dimension "
"of the tile.");
/// Shape of threads
typedef Shape<1, kThreads / Tile::kW, Tile::kW, 1> ThreadShape;
/// Delta along each dimension
typedef Shape<1, ThreadShape::kH, 1, 1> Delta;
/// Number of iterations
typedef Shape<1, Tile::kH / ThreadShape::kH, 1, 1> Iterations;
/// Computes the initial offset
typedef TiledThreadOffset<ThreadShape> ThreadOffset;
};
///////////////////////////////////////////////////////////////////////////////////////////////////
/// Tiling in which the number of threads is fewer than the tile size
/// in the contiguous dimension.
template <typename Tile_, int Threads>
struct TileTraitsContiguousMajor {
/// Shape of tile
typedef Tile_ Tile;
/// Number of participating threads
static int const kThreads = Threads;
// Static assertions
static_assert(Tile::kW >= kThreads,
"This specialization assumes there are more threads than the contiguous dimension "
"of the tile.");
static_assert(!(ShapeCount<Tile>::kDhw % kThreads),
"Tiling undefined if elements not divisible by threads.");
static_assert(!(Tile::kW % kThreads),
"The contiguous size of the tile must be divisible by the number of threads.");
/// Thread shape
typedef Shape<1, 1, kThreads> ThreadShape;
/// Delta between each thread's access
typedef Shape<1, 1, kThreads> Delta;
/// Number of iterations
typedef Shape<1, Tile::kH, Tile::kW / kThreads> Iterations;
/// Computes the initial offset
typedef TiledThreadOffset<ThreadShape> ThreadOffset;
};
///////////////////////////////////////////////////////////////////////////////////////////////////
/// Tiling in which warps rake across the contiguous dimension
template <typename Tile_, int Threads>
struct TileTraitsWarpRake {
/// Shape of tile
typedef Tile_ Tile;
/// Number of participating threads
static int const kThreads = Threads;
/// Hard-coded warp size
static int const kWarpSize = 32;
/// Number of participating warps
static int const kWarpCount = kThreads / kWarpSize;
// Static assertions
static_assert(!(ShapeCount<Tile>::kDhw % kThreads),
"Tiling undefined if elements not divisible by threads.");
static_assert(!(kThreads % kWarpSize), "Number of threads must be divisible by the warp size.");
static_assert(!(Tile::kW % kWarpSize), "Contiguous dimension must be divisible by the warp size");
/// Warps strip-mined across strided dimension
static int const kWarpsStrided = __NV_STD_MIN(kWarpCount, Tile::kH);
/// Warps stripmined contiguous dimension
static int const kWarpsContiguous = kWarpCount / kWarpsStrided;
/// Arrangement of threads
typedef Shape<1, kWarpsStrided, kWarpsContiguous * kWarpSize> ThreadShape;
/// The same warp rakes along the contiguous dimension
typedef Shape<1, kWarpsStrided, kWarpSize> Delta;
/// Number of iterations
typedef Shape<1, Tile::kH / Delta::kH, Tile::kW / ThreadShape::kW> Iterations;
/// Computes the thread offset in (H, W) based on thread ID
struct ThreadOffset {
/// Basic thread offset function computed from a thread shape
CUTLASS_HOST_DEVICE
Coord<4> operator()() const {
int tid = threadIdx.x;
int warp = (tid / kWarpSize);
int lane = (tid % kWarpSize);
static int const kWarpSpanContiguous = kWarpSize * Iterations::kW;
int warp_w = (warp % kWarpsContiguous);
int warp_h = (warp / kWarpsContiguous);
return make_Coord(0, warp_h, lane + kWarpSpanContiguous * warp_w, 0);
}
};
};
///////////////////////////////////////////////////////////////////////////////////////////////////
/// Chooses 'best' shape to enable warp raking along contiguous dimension if possible.
template <typename Tile_, int Threads>
struct TileTraitsStandard {
/// Shape of tile
typedef Tile_ Tile;
/// Number of participating threads
static int const kThreads = Threads;
/// Hard-coded warp size
static int const kWarpSize = 32;
/// Number of participating warps
static int const kWarpCount = kThreads / kWarpSize;
// Static assertions
static_assert(!(ShapeCount<Tile>::kDhw % kThreads),
"Tiling undefined if elements not divisible by threads.");
/// Choose the stride-major contiguous tiling if the contiguous dimension is
/// smaller than the warp size. Otherwise, if it is divisible by the warp size,
/// choose the warp rake arrangement.
typedef typename platform::conditional <
Tile::kW<kWarpSize,
TileTraitsStrideMajor<Tile, Threads>,
typename platform::conditional<!(Tile::kW % kWarpSize),
TileTraitsWarpRake<Tile, Threads>,
TileTraitsContiguousMajor<Tile, Threads> >::type>::
type Traits;
/// Delta between accesses
typedef typename Traits::Delta Delta;
/// Delta between each thread's access
/// TODO MTA this is wrong for sure, but Delta is used for stride computation at the moment
typedef Delta ImmediateOffsetStrides;
/// Number of accesses
typedef typename Traits::Iterations Iterations;
/// Thread offset functor
typedef typename Traits::ThreadOffset ThreadOffset;
};
///////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace cutlass

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/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
#pragma once
/**
* \file
* \brief Math utilities
*/
#include <cutlass/util/platform.h>
namespace cutlass {
/******************************************************************************
* Static math utilities
******************************************************************************/
/**
* Statically determine if N is a power-of-two
*/
template <int N>
struct is_pow2 : platform::integral_constant<bool, (N & (N - 1)) == 0> {};
/**
* Statically determine log2(N), rounded down
*/
template <int N, int CurrentVal = N, int Count = 0>
struct log2_down {
/// Static logarithm value
enum { value = log2_down<N, (CurrentVal >> 1), Count + 1>::value };
};
// Base case
template <int N, int Count>
struct log2_down<N, 1, Count> {
enum { value = Count };
};
/**
* Statically determine log2(N), rounded up
*/
template <int N, int CurrentVal = N, int Count = 0>
struct log2_up {
/// Static logarithm value
enum { value = log2_up<N, (CurrentVal >> 1), Count + 1>::value };
};
// Base case
template <int N, int Count>
struct log2_up<N, 1, Count> {
enum { value = ((1 << Count) < N) ? Count + 1 : Count };
};
/**
* Statically estimate sqrt(N) to the nearest power-of-two
*/
template <int N>
struct sqrt_est {
enum { value = 1 << (log2_up<N>::value / 2) };
};
/**
* For performing a constant-division with a compile-time assertion that the
* Divisor evenly-divides the Dividend.
*/
template <int Dividend, int Divisor>
struct divide_assert {
enum { value = Dividend / Divisor };
static_assert((Dividend % Divisor == 0), "Not an even multiple");
};
/******************************************************************************
* Rounding
******************************************************************************/
/**
* Round dividend up to the nearest multiple of divisor
*/
template <typename dividend_t, typename divisor_t>
CUTLASS_HOST_DEVICE dividend_t round_nearest(dividend_t dividend, divisor_t divisor) {
return ((dividend + divisor - 1) / divisor) * divisor;
}
/**
* Greatest common divisor
*/
template <typename value_t>
CUTLASS_HOST_DEVICE value_t gcd(value_t a, value_t b) {
for (;;) {
if (a == 0) return b;
b %= a;
if (b == 0) return a;
a %= b;
}
}
/**
* Least common multiple
*/
template <typename value_t>
CUTLASS_HOST_DEVICE value_t lcm(value_t a, value_t b) {
value_t temp = gcd(a, b);
return temp ? (a / temp * b) : 0;
}
} // namespace cutlass

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/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
#pragma once
/**
* \file
* \brief Debugging and logging functionality
*/
#include <stdio.h>
namespace cutlass {
/******************************************************************************
* Debug and logging macros
******************************************************************************/
/**
* Formats and prints the given message to stdout
*/
#if !defined(CUDA_LOG)
#if !defined(__CUDA_ARCH__)
#define CUDA_LOG(format, ...) printf(format, __VA_ARGS__)
#else
#define CUDA_LOG(format, ...) \
printf("[block (%d,%d,%d), thread (%d,%d,%d)]: " format, \
blockIdx.x, \
blockIdx.y, \
blockIdx.z, \
threadIdx.x, \
threadIdx.y, \
threadIdx.z, \
__VA_ARGS__);
#endif
#endif
/**
* Formats and prints the given message to stdout only if DEBUG is defined
*/
#if !defined(CUDA_LOG_DEBUG)
#ifdef DEBUG
#define CUDA_LOG_DEBUG(format, ...) CUDA_LOG(format, __VA_ARGS__)
#else
#define CUDA_LOG_DEBUG(format, ...)
#endif
#endif
/**
* \brief The corresponding error message is printed to \p stderr (or \p stdout in device code)
* along with the supplied source context.
*
* \return The CUDA error.
*/
__host__ CUTLASS_DEVICE cudaError_t cuda_perror_impl(cudaError_t error,
const char* filename,
int line) {
(void)filename;
(void)line;
if (error) {
#if !defined(__CUDA_ARCH__)
fprintf(
stderr, "CUDA error %d [%s, %d]: %s\n", error, filename, line, cudaGetErrorString(error));
fflush(stderr);
#else
printf("CUDA error %d [%s, %d]\n", error, filename, line);
#endif
}
return error;
}
/**
* \brief Perror macro
*/
#ifndef CUDA_PERROR
#define CUDA_PERROR(e) cuda_perror_impl((cudaError_t)(e), __FILE__, __LINE__)
#endif
/**
* \brief Perror macro with exit
*/
#ifndef CUDA_PERROR_EXIT
#define CUDA_PERROR_EXIT(e) \
if (cuda_perror_impl((cudaError_t)(e), __FILE__, __LINE__)) { \
exit(1); \
}
#endif
/**
* \brief Perror macro only if DEBUG is defined
*/
#ifndef CUDA_PERROR_DEBUG
#ifdef DEBUG
#define CUDA_PERROR_DEBUG(e) CUDA_PERROR(e)
#else
#define CUDA_PERROR_DEBUG(e) (e)
#endif
#endif
} // namespace cutlass

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/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Defines a 1D vector of elements held in the registers of each thread.
*/
#pragma once
#if !defined(__CUDACC_RTC__) || defined(CUTLASS_NVRTC_HAS_FP16)
#include <cuda_fp16.h>
#endif
#include <cutlass/util/platform.h>
namespace cutlass {
////////////////////////////////////////////////////////////////////////////////////////////////////
template <size_t kAlignment_>
struct AlignedStruct {};
template <>
struct __align__(1) AlignedStruct<1>{};
template <>
struct __align__(2) AlignedStruct<2>{};
template <>
struct __align__(4) AlignedStruct<4>{};
template <>
struct __align__(8) AlignedStruct<8>{};
template <>
struct __align__(16) AlignedStruct<16>{};
template <>
struct __align__(32) AlignedStruct<32>{};
template <>
struct __align__(64) AlignedStruct<64>{};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename Scalar_, int kLanes_>
union Vector {
/// The scalar type.
typedef Scalar_ Scalar;
/// The number of elements in the vector.
enum { kLanes = kLanes_ };
/// The size of the vector.
enum { kVectorSize = kLanes * (int)sizeof(Scalar) };
/// The number of registers needed to store the vector.
enum { kRegisters = kVectorSize < 4 ? 1 : kVectorSize / 4 };
// Make sure that the vector type makes sense.
static_assert(kVectorSize <= 16, "Vector type is too large");
/// The aligned storage to make sure we have good alignment.
AlignedStruct<kVectorSize> aligned_;
/// The associated array of scalars.
Scalar scalars[kLanes];
/// The data in registers.
uint32_t registers[kRegisters];
/// Accessor to the ith lane.
CUTLASS_DEVICE Scalar const& operator[](uint32_t i) const { return scalars[i]; }
/// Accessor to the ith lane.
CUTLASS_DEVICE Scalar& operator[](uint32_t i) { return scalars[i]; }
};
////////////////////////////////////////////////////////////////////////////////////////////////////
#if !defined(__CUDACC_RTC__) || defined(CUTLASS_NVRTC_HAS_FP16)
template <int kLanes_>
union Vector<half, kLanes_> {
/// The scalar type.
typedef half Scalar;
/// The number of elements in the vector.
enum { kLanes = kLanes_ };
/// The size of the vector.
enum { kVectorSize = kLanes * (int)sizeof(Scalar) };
/// The number of registers needed to store the vector.
enum { kRegisters = kVectorSize < 4 ? 1 : kVectorSize / 4 };
// Make sure that the vector type makes sense.
static_assert(kVectorSize <= size_t(16), "Vector type is too large");
/// The aligned storage to make sure we have good alignment.
AlignedStruct<kVectorSize> aligned_;
/// The associated array of scalars.
uint16_t scalars[kLanes];
/// The data in registers.
uint32_t registers[kRegisters];
/// Accessor to the ith lane.
CUTLASS_DEVICE Scalar const& operator[](uint32_t i) const {
return reinterpret_cast<Scalar const&>(scalars[i]);
}
/// Accessor to the ith lane.
CUTLASS_DEVICE Scalar& operator[](uint32_t i) { return reinterpret_cast<Scalar&>(scalars[i]); }
};
#endif
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename Scalar_>
CUTLASS_DEVICE void make_zero(Scalar_& x) {
x = Scalar_(0);
}
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename Element_, int kLanes_ = 1>
struct Vectorize {
typedef Vector<Element_, kLanes_> Type;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename Element_>
struct Vectorize<Element_, 1> {
typedef Element_ Type;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename Scalar_, int kLanes_>
CUTLASS_DEVICE void make_zero(Vector<Scalar_, kLanes_>& vec) {
for (int i = 0; i < Vector<Scalar_, kLanes_>::kRegisters; ++i) {
vec.registers[i] = 0;
}
}
////////////////////////////////////////////////////////////////////////////////////////////////////
//
// cutlass::Extent similar to std::extent but applicable to CUTLASS types
//
/// Returns the extent of a scalar or vector
template <typename T>
struct Extent {
static size_t const kValue = 1;
};
/// Returns the number of lanes of a vector if need be
template <typename T, int Lanes>
struct Extent<Vector<T, Lanes> > {
static size_t const kValue = Lanes;
};
/// Returns the number of lanes of a vector if need be
template <typename T, int Lanes>
struct Extent<Vector<T, Lanes> const> {
static size_t const kValue = Lanes;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
/// Traits describing properties of vectors and scalar-as-vectors
template <typename T>
struct VectorTraits {
/// Scalar type
typedef T Scalar;
/// Number of lanes of vector
static int const kLanes = 1;
/// True if the type is actually a cutlass::Vector, otherwise false
static bool const IsVector = false;
/// Type that is always a vector
typedef Vector<T, 1> Vector;
};
/// Partial specialization for actual cutlass::Vector
template <typename T, int Lanes>
struct VectorTraits<Vector<T, Lanes> > {
/// Scalar type
typedef T Scalar;
/// Number of lanes of vector
static int const kLanes = Lanes;
/// Type is actually a cutlass::Vector
static bool const IsVector = true;
/// Type that is always a Vector
typedef Vector<T, Lanes> Vector;
};
/// Partial specialization for actual cutlass::Vector
template <typename T, int Lanes>
struct VectorTraits<Vector<T, Lanes> const> {
/// Scalar type
typedef T Scalar;
/// Number of lanes of vector
static int const kLanes = Lanes;
/// Type is actually a cutlass::Vector
static bool const IsVector = true;
/// Type that is always a Vector
typedef Vector<T, Lanes> Vector;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace cutlass

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/***************************************************************************************************
* Copyright (c) 2017-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Abstractions for loading and storing matrices using the CUDA WMMA API.
*/
#pragma once
#if defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 700)
// Dependent header files should use the following macro to guard all code using
// nvcuda::wmma:: to enable compilation for CUDA Compute Capabilities < sm_70.
// Earlier shader models not support Tensor Cores.
#define CUTLASS_USE_WMMA_API
#include "stdio.h"
#include <crt/mma.h>
#include <cutlass/fragment.h>
#include <cutlass/load_store.h>
#include <cutlass/matrix_traits.h>
#include <cutlass/shape.h>
#include <cutlass/vector.h>
namespace cutlass {
////////////////////////////////////////////////////////////////////////////////////////////////////
/// Statically maps cutlass::MatrixLayout => nvcuda::wmma layout tags
template <MatrixLayout::Kind kLayout_>
struct WmmaLayout {
typedef nvcuda::wmma::col_major Layout;
};
/// Statically maps cutlass::MatrixLayout => nvcuda::wmma layout tags
template <>
struct WmmaLayout<MatrixLayout::kRowMajor> {
typedef nvcuda::wmma::row_major Layout;
};
////////////////////////////////////////////////////////////////////////////////////////////////////
/// Adapter to nvcuda::wmma fragment load and store operations
template <GemmOperand::Kind kOperand_,
MatrixLayout::Kind kLayout_,
typename Scalar_,
typename WmmaShape_>
struct WmmaMatrix {};
////////////////////////////////////////////////////////////////////////////////////////////////////
/// Adapter to nvcuda::wmma fragment accessors for A operand
template <MatrixLayout::Kind kLayout_, typename Scalar_, typename WmmaShape_>
struct WmmaMatrix<GemmOperand::kA, kLayout_, Scalar_, WmmaShape_>
: public nvcuda::wmma::fragment<
/// The nvcuda::wmma operand name.
nvcuda::wmma::matrix_a,
/// The dimensions.
WmmaShape_::kW,
WmmaShape_::kH,
WmmaShape_::kD,
/// The scalar.
Scalar_,
/// The layout.
typename WmmaLayout<kLayout_>::Layout> {
/// This type.
typedef WmmaMatrix<GemmOperand::kA, kLayout_, Scalar_, WmmaShape_> This_;
/// Fill-in the element.
CUTLASS_DEVICE This_& operator=(Scalar_ const& x) {
nvcuda::wmma::fill_fragment(*this, x);
return *this;
}
/// Load from memory.
CUTLASS_DEVICE void load(Scalar_ const* pointer, int const stride) {
nvcuda::wmma::load_matrix_sync(*this, pointer, stride);
}
/// Store to memory.
CUTLASS_DEVICE void store(Scalar_* pointer, int const stride) const {
nvcuda::wmma::store_matrix_sync(pointer, *this, stride);
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
/// Adapter to nvcuda::wmma fragment accessors for B operand
template <MatrixLayout::Kind kLayout_, typename Scalar_, typename WmmaShape_>
struct WmmaMatrix<GemmOperand::kB, kLayout_, Scalar_, WmmaShape_>
: public nvcuda::wmma::fragment<
/// The nvcuda::wmma operand name.
nvcuda::wmma::matrix_b,
/// The dimensions.
WmmaShape_::kW,
WmmaShape_::kH,
WmmaShape_::kD,
/// The scalar.
Scalar_,
/// The layout.
typename WmmaLayout<kLayout_>::Layout> {
/// This type.
typedef WmmaMatrix<GemmOperand::kB, kLayout_, Scalar_, WmmaShape_> This_;
/// Fill-in the element.
CUTLASS_DEVICE This_& operator=(Scalar_ const& x) {
nvcuda::wmma::fill_fragment(*this, x);
return *this;
}
/// Load from memory.
CUTLASS_DEVICE void load(Scalar_ const* pointer, int const stride) {
nvcuda::wmma::load_matrix_sync(*this, pointer, stride);
}
/// Store to memory.
CUTLASS_DEVICE void store(Scalar_* pointer, int const stride) const {
nvcuda::wmma::store_matrix_sync(pointer, *this, stride);
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
/// Adapter to nvcuda::wmma fragment accessors for C operand
template <MatrixLayout::Kind kLayout_, typename Scalar_, typename WmmaShape_>
struct WmmaMatrix<GemmOperand::kC, kLayout_, Scalar_, WmmaShape_>
: public nvcuda::wmma::fragment<
/// The nvcuda::wmma operand name.
nvcuda::wmma::accumulator,
/// The dimensions.
WmmaShape_::kW,
WmmaShape_::kH,
WmmaShape_::kD,
/// The scalar.
Scalar_> {
/// This type.
typedef WmmaMatrix<GemmOperand::kC, kLayout_, Scalar_, WmmaShape_> This_;
/// The layout.
static MatrixLayout::Kind const kLayout = kLayout_;
/// Fill-in the element.
CUTLASS_DEVICE This_& operator=(Scalar_ const& x) {
nvcuda::wmma::fill_fragment(*this, x);
return *this;
}
/// Load from memory.
CUTLASS_DEVICE void load(Scalar_ const* pointer, int const stride) {
bool const kIsRowMajor = kLayout == MatrixLayout::kRowMajor;
nvcuda::wmma::load_matrix_sync(
*this,
pointer,
stride,
kIsRowMajor ? nvcuda::wmma::mem_row_major : nvcuda::wmma::mem_col_major);
}
/// Store to memory.
CUTLASS_DEVICE void store(Scalar_* pointer, int const stride) const {
bool const kIsRowMajor = kLayout == MatrixLayout::kRowMajor;
nvcuda::wmma::store_matrix_sync(
pointer,
*this,
stride,
kIsRowMajor ? nvcuda::wmma::mem_row_major : nvcuda::wmma::mem_col_major);
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace cutlass
#endif // defined CUTLASS_USE_WMMA_API

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<a href="#nested-classes">Classes</a> &#124;
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<div class="title">aligned_buffer.h File Reference</div> </div>
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<div class="contents">
<p>AlignedBuffer is a container for trivially copyable elements suitable for use in unions and shared memory.
<a href="#details">More...</a></p>
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<p>Templates exposing architecture support for multiply-add operations.
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<div class="textblock"><code>#include &quot;<a class="el" href="array_8h_source.html">cutlass/array.h</a>&quot;</code><br />
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<p>Matrix multiply.
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<div class="textblock"><code>#include &quot;<a class="el" href="arch_2mma_8h_source.html">cutlass/arch/mma.h</a>&quot;</code><br />
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<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Matrix multiply-add operation. <a href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_011_00_011_01_4_00_011_00_01float_004bb3fd76ca2af7b3210676fa9644d95b.html#details">More...</a><br /></td></tr>
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<p>Matrix multiply.
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<div class="textblock"><code>#include &lt;cuda_fp16.h&gt;</code><br />
<code>#include &quot;<a class="el" href="arch_2mma_8h_source.html">cutlass/arch/mma.h</a>&quot;</code><br />
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<div class="textblock"><code>#include &quot;<a class="el" href="layout_2matrix_8h_source.html">cutlass/layout/matrix.h</a>&quot;</code><br />
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<p>Statically sized array of elements that accommodates all CUTLASS-supported numeric types and is safe to use in a union.
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<div class="textblock"><code>#include &quot;<a class="el" href="cutlass_8h_source.html">cutlass/cutlass.h</a>&quot;</code><br />
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<p>Implements a software-pipelined efficient batched reduction. D = alpha * Reduction(A) + beta * C.
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<div class="textblock"><code>#include &lt;cuda.h&gt;</code><br />
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="structcutlass_1_1reduction_1_1BatchedReduction.html">cutlass::reduction::BatchedReduction&lt; BatchedReductionTraits_ &gt;</a></td></tr>
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<tr class="memitem:a9665e8f438a7b290d6e2eb640d93045f"><td class="memTemplParams" colspan="2">template&lt;typename batched_reduction_ &gt; </td></tr>
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