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cutlass/examples/CMakeLists.txt

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CUTLASS 2.5
2021-02-26 22:58:26 +08:00
# Copyright (c) 2017-2021, NVIDIA CORPORATION. All rights reserved.
Checkpointing CUTLASS 1.1 release.
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#
# 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,
Cutlass 2.6 Update 1 (#301) * cutlass 2.6 update * remove debug prints
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# STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
Checkpointing CUTLASS 1.1 release.
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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.
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set(CUTLASS_EXAMPLES_COMMON_SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/common)
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.
2019-03-21 01:49:17 +08:00
CUTLASS 2.4 (Implicit GEMM convolution) (#147) CUTLASS 2.4 (Implicit GEMM Convolution) Co-authored-by: Manish Gupta <manigupta@nvidia.com>, Haicheng Wu <haichengw@nvidia.com>, Dustyn Blasig <dblasig@nvidia.com>, Andrew Kerr <akerr@nvidia.com>
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add_custom_target(cutlass_examples)
add_custom_target(test_examples)
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
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function(cutlass_example_add_executable NAME)
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.
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set(options)
CUTLASS 2.6 (#298) CUTLASS 2.6
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set(oneValueArgs DISABLE_TESTS)
CUTLASS 2.4 (Implicit GEMM convolution) (#147) CUTLASS 2.4 (Implicit GEMM Convolution) Co-authored-by: Manish Gupta <manigupta@nvidia.com>, Haicheng Wu <haichengw@nvidia.com>, Dustyn Blasig <dblasig@nvidia.com>, Andrew Kerr <akerr@nvidia.com>
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set(multiValueArgs DEPENDS DEPENDEES TEST_COMMAND_OPTIONS)
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.
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cmake_parse_arguments(_ "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
CUTLASS 2.6 (#298) CUTLASS 2.6
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if (NOT DEFINED __DISABLE_TESTS)
set(__DISABLE_TESTS OFF)
endif()
CUTLASS 2.1 (#83) CUTLASS 2.1 contributes: - BLAS-style host-side API added to CUTLASS Library - Planar Complex GEMM kernels targeting Volta and Turing Tensor Cores - Minor enhancements and bug fixes
2020-04-08 04:51:25 +08:00
cutlass_add_executable(${NAME} ${__UNPARSED_ARGUMENTS})
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.
2019-11-20 08:55:34 +08:00
CUTLASS 2.4 (Implicit GEMM convolution) (#147) CUTLASS 2.4 (Implicit GEMM Convolution) Co-authored-by: Manish Gupta <manigupta@nvidia.com>, Haicheng Wu <haichengw@nvidia.com>, Dustyn Blasig <dblasig@nvidia.com>, Andrew Kerr <akerr@nvidia.com>
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add_dependencies(cutlass_examples ${NAME})
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.
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target_link_libraries(
${NAME}
PRIVATE
CUTLASS
cutlass_tools_util_includes
)
target_include_directories(
${NAME}
PRIVATE
${CUTLASS_EXAMPLES_COMMON_SOURCE_DIR}
)
CUTLASS 2.4 (Implicit GEMM convolution) (#147) CUTLASS 2.4 (Implicit GEMM Convolution) Co-authored-by: Manish Gupta <manigupta@nvidia.com>, Haicheng Wu <haichengw@nvidia.com>, Dustyn Blasig <dblasig@nvidia.com>, Andrew Kerr <akerr@nvidia.com>
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install(
TARGETS ${NAME}
RUNTIME DESTINATION ${CMAKE_INSTALL_BINDIR}
Updated mma_sm80.h to avoid perf penalty due to reinterpret_cast<>. (#100) - Updated mma_sm80.h to avoid perf penalty due to reinterpret_cast<>. - Enhancement to CUTLASS Utility Library's HostTensorPlanarComplex template to support copy-in and copy-out - Added test_examples target to build and test all CUTLASS examples - Minor edits to documentation to point to GTC 2020 webinar
2020-06-16 01:47:01 +08:00
)
CUTLASS 2.4 (Implicit GEMM convolution) (#147) CUTLASS 2.4 (Implicit GEMM Convolution) Co-authored-by: Manish Gupta <manigupta@nvidia.com>, Haicheng Wu <haichengw@nvidia.com>, Dustyn Blasig <dblasig@nvidia.com>, Andrew Kerr <akerr@nvidia.com>
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cutlass_add_executable_tests(
test_examples_${NAME} ${NAME}
DEPENDS ${__DEPENDS}
DEPENDEES test_examples ${__DEPENDEES}
TEST_COMMAND_OPTIONS ${__TEST_COMMAND_OPTIONS}
DISABLE_EXECUTABLE_INSTALL_RULE
CUTLASS 2.6 (#298) CUTLASS 2.6
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DISABLE_TESTS ${__DISABLE_TESTS}
CUTLASS 2.4 (Implicit GEMM convolution) (#147) CUTLASS 2.4 (Implicit GEMM Convolution) Co-authored-by: Manish Gupta <manigupta@nvidia.com>, Haicheng Wu <haichengw@nvidia.com>, Dustyn Blasig <dblasig@nvidia.com>, Andrew Kerr <akerr@nvidia.com>
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)
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.
2019-11-20 08:55:34 +08:00
CUTLASS 2.4 (Implicit GEMM convolution) (#147) CUTLASS 2.4 (Implicit GEMM Convolution) Co-authored-by: Manish Gupta <manigupta@nvidia.com>, Haicheng Wu <haichengw@nvidia.com>, Dustyn Blasig <dblasig@nvidia.com>, Andrew Kerr <akerr@nvidia.com>
2020-11-20 13:25:25 +08:00
endfunction()
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.
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foreach(EXAMPLE
00_basic_gemm
01_cutlass_utilities
02_dump_reg_shmem
03_visualize_layout
04_tile_iterator
05_batched_gemm
06_splitK_gemm
07_volta_tensorop_gemm
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
2020-04-08 04:51:25 +08:00
08_turing_tensorop_gemm
CUTLASS 2.4 (Implicit GEMM convolution) (#147) CUTLASS 2.4 (Implicit GEMM Convolution) Co-authored-by: Manish Gupta <manigupta@nvidia.com>, Haicheng Wu <haichengw@nvidia.com>, Dustyn Blasig <dblasig@nvidia.com>, Andrew Kerr <akerr@nvidia.com>
2020-11-20 13:25:25 +08:00
09_turing_tensorop_conv2dfprop
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
2020-04-08 04:51:25 +08:00
10_planar_complex
11_planar_complex_array
CUTLASS 2.2 (#96) Adds support for NVIDIA Ampere Architecture features. CUDA 11 Toolkit recommended.
2020-06-09 07:17:35 +08:00
12_gemm_bias_relu
CUTLASS 2.5
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13_two_tensor_op_fusion
CUTLASS 2.3 initial commit (#134) CUTLASS 2.3 adds GEMMs targeting Sparse Tensor Cores on the NVIDIA Ampere Architecture, fast SGEMM, and small matrix classes, bug fixes, and performance enhancements.
2020-09-24 05:00:58 +08:00
14_ampere_tf32_tensorop_gemm
15_ampere_sparse_tensorop_gemm
CUTLASS 2.5
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16_ampere_tensorop_conv2dfprop
17_fprop_per_channel_bias
CUTLASS 2.6 (#298) CUTLASS 2.6
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18_ampere_fp64_tensorop_affine2_gemm
19_tensorop_canonical
20_simt_canonical
21_quaternion_gemm
22_quaternion_conv
CUTLASS 2.4 (Implicit GEMM convolution) (#147) CUTLASS 2.4 (Implicit GEMM Convolution) Co-authored-by: Manish Gupta <manigupta@nvidia.com>, Haicheng Wu <haichengw@nvidia.com>, Dustyn Blasig <dblasig@nvidia.com>, Andrew Kerr <akerr@nvidia.com>
2020-11-20 13:25:25 +08:00
)
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.
2019-11-20 08:55:34 +08:00
add_subdirectory(${EXAMPLE})
endforeach()
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