Commit 6fe3627a authored by Chao Liu's avatar Chao Liu Committed by GitHub
Browse files

Composable kernel init integration v3 (#1097)

* Squashed 'src/composable_kernel/' content from commit f6edda61

git-subtree-dir: src/composable_kernel
git-subtree-split: f6edda61

* add solver ConvIgemmFwdV6r1DlopsNchwKcyxNkhw; rename static ck source files

* Squashed 'src/composable_kernel/' changes from f6edda61..5781adf5

5781adf5 Update develop (#5) (#6)
97e6d514 Merge pull request #4 from ROCmSoftwarePlatform/separate_online_compile
7b1ec41e refactor
49c33aae refactor
54b3e73d rename

git-subtree-dir: src/composable_kernel
git-subtree-split: 5781adf5



* fix

* refactor

* remove online compilation from CK

* refactor

* fix

* add ctest

* add c-style pointer cast

* vector/scalar pointer cast use c-style pointer cast instead of reinterpret_cast

* fix clang warning suppression

* tidy

* suppress cppcheck

* fix enum issue

* revert chagnes to hip build

* fix kernel filename

* update CK build script

* rename

* rename

* make innner product compatiable on gfx900

* Update src/include/miopen/solver/ck_utility_common.hpp
Co-authored-by: default avatarJD <Jehandad.Khan@amd.com>

* compiler parameter use stream

* use int instead of index_t in kernel wrapper

* DynamicBuffer, StaticBuffer, amd_buffer_load support customized value for invalid element

* refactor

* refactor

* change cmakelist

* change ck common utility

* fix
Co-authored-by: default avatarJD <Jehandad.Khan@amd.com>
parents
---
Language: Cpp
AccessModifierOffset: 0
AlignAfterOpenBracket: Align
AlignConsecutiveAssignments: true
AlignConsecutiveDeclarations: false
AlignEscapedNewlinesLeft: true
AlignOperands: true
AlignTrailingComments: true
AllowAllParametersOfDeclarationOnNextLine: true
AllowShortBlocksOnASingleLine: true
AllowShortCaseLabelsOnASingleLine: true
AllowShortFunctionsOnASingleLine: All
AllowShortIfStatementsOnASingleLine: false
AllowShortLoopsOnASingleLine: false
AlwaysBreakAfterDefinitionReturnType: None
AlwaysBreakAfterReturnType: None
AlwaysBreakBeforeMultilineStrings: false
AlwaysBreakTemplateDeclarations: true
BinPackArguments: false
BinPackParameters: false
BraceWrapping:
AfterClass: true
AfterControlStatement: true
AfterEnum: true
AfterFunction: true
AfterNamespace: false
AfterObjCDeclaration: true
AfterStruct: true
AfterUnion: true
BeforeCatch: true
BeforeElse: true
IndentBraces: false
BreakBeforeBinaryOperators: None
BreakBeforeBraces: Custom
BreakBeforeTernaryOperators: true
BreakConstructorInitializersBeforeComma: false
ColumnLimit: 100
CommentPragmas: '^ IWYU pragma:'
ConstructorInitializerAllOnOneLineOrOnePerLine: true
ConstructorInitializerIndentWidth: 4
ContinuationIndentWidth: 4
Cpp11BracedListStyle: true
DerivePointerAlignment: false
DisableFormat: false
ExperimentalAutoDetectBinPacking: false
ForEachMacros: [ foreach, Q_FOREACH, BOOST_FOREACH ]
IncludeCategories:
- Regex: '^"(llvm|llvm-c|clang|clang-c)/'
Priority: 2
- Regex: '^(<|"(gtest|isl|json)/)'
Priority: 3
- Regex: '.*'
Priority: 1
IndentCaseLabels: false
IndentWidth: 4
IndentWrappedFunctionNames: false
KeepEmptyLinesAtTheStartOfBlocks: true
MacroBlockBegin: ''
MacroBlockEnd: ''
MaxEmptyLinesToKeep: 1
NamespaceIndentation: None
ObjCBlockIndentWidth: 2
ObjCSpaceAfterProperty: false
ObjCSpaceBeforeProtocolList: true
PenaltyBreakBeforeFirstCallParameter: 19
PenaltyBreakComment: 300
PenaltyBreakFirstLessLess: 120
PenaltyBreakString: 1000
PenaltyExcessCharacter: 1000000
PenaltyReturnTypeOnItsOwnLine: 60
PointerAlignment: Left
ReflowComments: true
SortIncludes: false
SpaceAfterCStyleCast: false
# SpaceAfterTemplateKeyword: true
SpaceBeforeAssignmentOperators: true
SpaceBeforeParens: Never
SpaceInEmptyParentheses: false
SpacesBeforeTrailingComments: 1
SpacesInAngles: false
SpacesInContainerLiterals: true
SpacesInCStyleCastParentheses: false
SpacesInParentheses: false
SpacesInSquareBrackets: false
Standard: Cpp11
TabWidth: 8
UseTab: Never
...
CheckOptions:
- key: bugprone-reserved-identifier.AllowedIdentifiers
value: '__HIP_PLATFORM_HCC__;__HIP_ROCclr__'
cmake_minimum_required(VERSION 3.5)
project(composable_kernel)
list(APPEND CMAKE_MODULE_PATH "${PROJECT_SOURCE_DIR}/cmake")
include(CheckCXXCompilerFlag)
## C++
enable_language(CXX)
set(CMAKE_CXX_STANDARD 17)
set(CMAKE_CXX_STANDARD_REQUIRED ON)
set(CMAKE_CXX_EXTENSIONS OFF)
message("CMAKE_CXX_COMPILER_ID: ${CMAKE_CXX_COMPILER_ID}")
## OpenMP
if(CMAKE_CXX_COMPILER_ID MATCHES "Clang")
# workaround issue hipcc in rocm3.5 cannot find openmp
set(OpenMP_CXX "${CMAKE_CXX_COMPILER}")
set(OpenMP_CXX_FLAGS "-fopenmp=libomp -Wno-unused-command-line-argument")
set(OpenMP_CXX_LIB_NAMES "libomp" "libgomp" "libiomp5")
set(OpenMP_libomp_LIBRARY ${OpenMP_CXX_LIB_NAMES})
set(OpenMP_libgomp_LIBRARY ${OpenMP_CXX_LIB_NAMES})
set(OpenMP_libiomp5_LIBRARY ${OpenMP_CXX_LIB_NAMES})
else()
find_package(OpenMP REQUIRED)
endif()
message("OpenMP_CXX_LIB_NAMES: ${OpenMP_CXX_LIB_NAMES}")
message("OpenMP_gomp_LIBRARY: ${OpenMP_gomp_LIBRARY}")
message("OpenMP_pthread_LIBRARY: ${OpenMP_pthread_LIBRARY}")
message("OpenMP_CXX_FLAGS: ${OpenMP_CXX_FLAGS}")
set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
link_libraries(${OpenMP_gomp_LIBRARY})
link_libraries(${OpenMP_pthread_LIBRARY})
## HIP
find_package(HIP REQUIRED)
message(STATUS "Build with HIP ${hip_VERSION}")
## half
#find_path(HALF_INCLUDE_DIR half.hpp)
message("HALF_INCLUDE_DIR: ${HALF_INCLUDE_DIR}")
# CMAKE_CXX_FLAGS
SET(BUILD_DEV ON CACHE BOOL "BUILD_DEV")
if(BUILD_DEV)
string(APPEND CMAKE_CXX_FLAGS " -Werror -Weverything")
endif()
message("CMAKE_CXX_FLAGS: ${CMAKE_CXX_FLAGS}")
## tidy
include(EnableCompilerWarnings)
set(MIOPEN_TIDY_ERRORS ERRORS * -readability-inconsistent-declaration-parameter-name)
if(CMAKE_CXX_COMPILER MATCHES ".*hcc" OR CMAKE_CXX_COMPILER MATCHES ".*clang\\+\\+")
set(MIOPEN_TIDY_CHECKS -modernize-use-override -readability-non-const-parameter)
# Enable tidy on hip
elseif(MIOPEN_BACKEND STREQUAL "HIP" OR MIOPEN_BACKEND STREQUAL "HIPNOGPU")
set(MIOPEN_TIDY_ERRORS ALL)
endif()
include(ClangTidy)
enable_clang_tidy(
CHECKS
*
-abseil-*
-android-cloexec-fopen
# Yea we shouldn't be using rand()
-cert-msc30-c
-bugprone-exception-escape
-bugprone-macro-parentheses
-cert-env33-c
-cert-msc32-c
-cert-msc50-cpp
-cert-msc51-cpp
-cert-dcl37-c
-cert-dcl51-cpp
-clang-analyzer-alpha.core.CastToStruct
-clang-analyzer-optin.performance.Padding
-clang-diagnostic-deprecated-declarations
-clang-diagnostic-extern-c-compat
-clang-diagnostic-unused-command-line-argument
-cppcoreguidelines-avoid-c-arrays
-cppcoreguidelines-avoid-magic-numbers
-cppcoreguidelines-explicit-virtual-functions
-cppcoreguidelines-init-variables
-cppcoreguidelines-macro-usage
-cppcoreguidelines-non-private-member-variables-in-classes
-cppcoreguidelines-pro-bounds-array-to-pointer-decay
-cppcoreguidelines-pro-bounds-constant-array-index
-cppcoreguidelines-pro-bounds-pointer-arithmetic
-cppcoreguidelines-pro-type-member-init
-cppcoreguidelines-pro-type-reinterpret-cast
-cppcoreguidelines-pro-type-union-access
-cppcoreguidelines-pro-type-vararg
-cppcoreguidelines-special-member-functions
-fuchsia-*
-google-explicit-constructor
-google-readability-braces-around-statements
-google-readability-todo
-google-runtime-int
-google-runtime-references
-hicpp-vararg
-hicpp-braces-around-statements
-hicpp-explicit-conversions
-hicpp-named-parameter
-hicpp-no-array-decay
# We really shouldn't use bitwise operators with signed integers, but
# opencl leaves us no choice
-hicpp-avoid-c-arrays
-hicpp-signed-bitwise
-hicpp-special-member-functions
-hicpp-uppercase-literal-suffix
-hicpp-use-auto
-hicpp-use-equals-default
-hicpp-use-override
-llvm-header-guard
-llvm-include-order
#-llvmlibc-*
-llvmlibc-restrict-system-libc-headers
-llvmlibc-callee-namespace
-llvmlibc-implementation-in-namespace
-llvm-else-after-return
-llvm-qualified-auto
-misc-misplaced-const
-misc-non-private-member-variables-in-classes
-misc-no-recursion
-modernize-avoid-bind
-modernize-avoid-c-arrays
-modernize-pass-by-value
-modernize-use-auto
-modernize-use-default-member-init
-modernize-use-equals-default
-modernize-use-trailing-return-type
-modernize-use-transparent-functors
-performance-unnecessary-value-param
-readability-braces-around-statements
-readability-else-after-return
# we are not ready to use it, but very useful
-readability-function-cognitive-complexity
-readability-isolate-declaration
-readability-magic-numbers
-readability-named-parameter
-readability-uppercase-literal-suffix
-readability-convert-member-functions-to-static
-readability-qualified-auto
-readability-redundant-string-init
# too many narrowing conversions in our code
-bugprone-narrowing-conversions
-cppcoreguidelines-narrowing-conversions
-altera-struct-pack-align
-cppcoreguidelines-prefer-member-initializer
${MIOPEN_TIDY_CHECKS}
${MIOPEN_TIDY_ERRORS}
HEADER_FILTER
"\.hpp$"
EXTRA_ARGS
-DMIOPEN_USE_CLANG_TIDY
)
include(CppCheck)
enable_cppcheck(
CHECKS
warning
style
performance
portability
SUPPRESS
ConfigurationNotChecked
constStatement
duplicateCondition
noExplicitConstructor
passedByValue
preprocessorErrorDirective
shadowVariable
unusedFunction
unusedPrivateFunction
unusedStructMember
unmatchedSuppression
FORCE
SOURCES
host/host_tensor/src
host/driver_offline/src
composable_kernel/src/kernel_wrapper
INCLUDE
host/host_tensor/include
host/solver/include
host/driver_offline/include
composable_kernel/include/*
${CMAKE_CURRENT_SOURCE_DIR}/include
${CMAKE_CURRENT_BINARY_DIR}/include
DEFINE
CPPCHECK=1
__linux__=1
)
add_subdirectory(host)
# How to build and run
# Docker
```
docker run \
-it \
--rm \
--privileged \
--group-add sudo \
-w /root/workspace \
-v ${PATH_TO_LOCAL_WORKSPACE}:/root/workspace \
rocm/tensorflow:rocm4.2-tf2.4-dev \
/bin/bash
```
# Install Boost for online compilation
https://www.boost.org/doc/libs/1_66_0/more/getting_started/unix-variants.html#easy-build-and-install
# Build
Add path of Boost
```
export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
```
```
mkdir build && cd build
```
cmake cmd. Need to Specify target ID, example below is gfx908
```
cmake \
-D CMAKE_BUILD_TYPE=Release \
-D CMAKE_CXX_FLAGS="-DCK_AMD_GPU_GFX908 -O3 --amdgpu-target=gfx908 -mllvm --amdgpu-spill-vgpr-to-agpr=0 -gline-tables-only -save-temps=$PWD" \
-D HIP_ONLINE_COMPILER_FLAGS="-DCK_AMD_GPU_GFX908" \
-D CMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc \
-D CMAKE_PREFIX_PATH=/opt/rocm \
-D CMAKE_VERBOSE_MAKEFILE:BOOL=ON \
..
```
Build drivers: \
``conv_fwd_driver_offline`` is (offline compilation) driver for forward convolution, \
``conv_bwd_driver_offline`` is (offline compilation) driver for backward-data convolution \
``conv_fwd_driver_online`` is (online compilation) driver for forward convolution
```
make -j conv_fwd_driver_offline
make -j conv_bwd_driver_offline
make -j conv_fwd_driver_online
```
# Run
* layout: 0 = NCHW; 1 = NHWC
* algo: algorithm
* verify: 0 = no verification; 1 = do verification
* init: 0 ~ 5. initialization method
* log: 0 = no log; 1 = do log
* repeat: number of time kernel being launched
```
######################################################## layout algo verify init log repeat N__ K___ C___ Y X Hi_ Wi__ Strides Dilations LeftPads RightPads
./host/driver_offline/conv_fwd_driver_offline 0 4 0 0 0 1 128 256 192 3 3 71 71 2 2 1 1 1 1 1 1
./host/driver_offline/conv_fwd_driver_offline 0 4 0 0 0 1 256 1024 256 3 3 14 14 1 1 1 1 1 1 1 1
./host/driver_offline/conv_fwd_driver_offline 1 5 0 0 0 1 128 256 192 3 3 71 71 2 2 1 1 1 1 1 1
./host/driver_offline/conv_fwd_driver_offline 1 5 0 0 0 1 256 1024 256 3 3 14 14 1 1 1 1 1 1 1 1
./host/driver_offline/conv_bwd_driver_offline 1 5 0 0 0 1 256 256 1024 3 3 14 14 1 1 1 1 1 1 1 1
```
# Result
Forward convoltuion, FP16, NCHW
```
./host/driver_offline/conv_fwd_driver_offline 0 4 0 0 0 1 128 256 192 3 3 71 71 2 2 1 1 1 1 1 1
layout: 0
in: dim 4, lengths {128, 192, 71, 71}, strides {967872, 5041, 71, 1}
wei: dim 4, lengths {256, 192, 3, 3}, strides {1728, 9, 3, 1}
out: dim 4, lengths {128, 256, 36, 36}, strides {331776, 1296, 36, 1}
InLeftPads size 2, {1, 1, }
InRightPads size 2, {1, 1, }
ConvStrides size 2, {2, 2, }
ConvDilations size 2, {1, 1, }
device_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw
a_k0_m_k1_grid_desc{216, 256, 8}
b_k0_n_k1_grid_desc{216, 165888, 8}
c_m_n_grid_desc{ 256, 165888}
launch_and_time_kernel: grid_dim {1296, 1, 1}, block_dim {256, 1, 1}
Warm up
Start running 1 times...
Average time : 1.4155 ms, 103.686 TFlop/s
```
Forward convoltuion, FP16, NCHW
```
./host/driver_offline/conv_fwd_driver_offline 0 4 0 0 0 1 256 1024 256 3 3 14 14 1 1 1 1 1 1 1 1
layout: 0
in: dim 4, lengths {256, 256, 14, 14}, strides {50176, 196, 14, 1}
wei: dim 4, lengths {1024, 256, 3, 3}, strides {2304, 9, 3, 1}
out: dim 4, lengths {256, 1024, 14, 14}, strides {200704, 196, 14, 1}
InLeftPads size 2, {1, 1, }
InRightPads size 2, {1, 1, }
ConvStrides size 2, {1, 1, }
ConvDilations size 2, {1, 1, }
device_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw
a_k0_m_k1_grid_desc{288, 1024, 8}
b_k0_n_k1_grid_desc{288, 50176, 8}
c_m_n_grid_desc{ 1024, 50176}
launch_and_time_kernel: grid_dim {1568, 1, 1}, block_dim {256, 1, 1}
Warm up
Start running 1 times...
Average time : 2.21357 ms, 106.959 TFlop/s
```
Forward convolution, FP16, NHWC
```
./host/driver_offline/conv_fwd_driver_offline 1 5 0 0 0 1 128 256 192 3 3 71 71 2 2 1 1 1 1 1 1
layout: 1
in: dim 4, lengths {128, 71, 71, 192}, strides {967872, 13632, 192, 1}
wei: dim 4, lengths {256, 3, 3, 192}, strides {1728, 576, 192, 1}
out: dim 4, lengths {128, 36, 36, 256}, strides {331776, 9216, 256, 1}
InLeftPads size 2, {1, 1, }
InRightPads size 2, {1, 1, }
ConvStrides size 2, {2, 2, }
ConvDilations size 2, {1, 1, }
device_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk
a_k0_m_k1_grid_desc{216, 165888, 8}
b_k0_n_k1_grid_desc{216, 256, 8}
c_m_n_grid_desc{ 165888, 256}
launch_and_time_kernel: grid_dim {1296, 1, 1}, block_dim {256, 1, 1}
Warm up
Start running 1 times...
Average time : 1.12014 ms, 131.025 TFlop/s
```
Forward convolution, FP16, NHWC
```
./host/driver_offline/conv_fwd_driver_offline 1 5 0 0 0 1 256 1024 256 3 3 14 14 1 1 1 1 1 1 1 1
layout: 1
in: dim 4, lengths {256, 14, 14, 256}, strides {50176, 3584, 256, 1}
wei: dim 4, lengths {1024, 3, 3, 256}, strides {2304, 768, 256, 1}
out: dim 4, lengths {256, 14, 14, 1024}, strides {200704, 14336, 1024, 1}
InLeftPads size 2, {1, 1, }
InRightPads size 2, {1, 1, }
ConvStrides size 2, {1, 1, }
ConvDilations size 2, {1, 1, }
device_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk
a_k0_m_k1_grid_desc{288, 50176, 8}
b_k0_n_k1_grid_desc{288, 1024, 8}
c_m_n_grid_desc{ 50176, 1024}
launch_and_time_kernel: grid_dim {1568, 1, 1}, block_dim {256, 1, 1}
Warm up
Start running 1 times...
Average time : 1.86877 ms, 126.693 TFlop/s
```
Backward data convolution, FP16, NHWC
```
./host/driver_offline/conv_bwd_driver_offline 1 1 0 3 0 1 256 256 1024 3 3 14 14 1 1 1 1 1 1 1 1
layout: 1
in: dim 4, lengths {256, 14, 14, 1024}, strides {200704, 14336, 1024, 1}
wei: dim 4, lengths {256, 3, 3, 1024}, strides {9216, 3072, 1024, 1}
out: dim 4, lengths {256, 14, 14, 256}, strides {50176, 3584, 256, 1}
InLeftPads size 2, {1, 1, }
InRightPads size 2, {1, 1, }
ConvStrides size 2, {1, 1, }
ConvDilations size 2, {1, 1, }
device_convolution_backward_data_implicit_gemm_v4r1r2_xdlops_nhwc_kyxc_nhwk
a_k0_m_k1_grid_desc{288, 50176, 8}
b_k0_n_k1_grid_desc{288, 1024, 8}
c_m_n_grid_desc{ 50176, 1024}
launch_and_time_kernel: grid_dim {1568, 1, 1}, block_dim {256, 1, 1}
Warm up
Start running 1 times...
Average time : 2.22461 ms, 106.428 TFlop/s
```
################################################################################
#
# MIT License
#
# Copyright (c) 2017 Advanced Micro Devices, Inc.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#
################################################################################
if(NOT TARGET analyze)
add_custom_target(analyze)
endif()
function(mark_as_analyzer)
add_dependencies(analyze ${ARGN})
endfunction()
################################################################################
#
# MIT License
#
# Copyright (c) 2017 Advanced Micro Devices, Inc.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#
################################################################################
include(CMakeParseArguments)
include(Analyzers)
get_filename_component(CLANG_TIDY_EXE_HINT "${CMAKE_CXX_COMPILER}" PATH)
find_program(CLANG_TIDY_EXE
NAMES
clang-tidy
clang-tidy-5.0
clang-tidy-4.0
clang-tidy-3.9
clang-tidy-3.8
clang-tidy-3.7
clang-tidy-3.6
clang-tidy-3.5
HINTS
${CLANG_TIDY_EXE_HINT}
PATH_SUFFIXES
compiler/bin
PATHS
/opt/rocm/llvm/bin
/opt/rocm/hcc
/usr/local/opt/llvm/bin
)
function(find_clang_tidy_version VAR)
execute_process(COMMAND ${CLANG_TIDY_EXE} -version OUTPUT_VARIABLE VERSION_OUTPUT)
separate_arguments(VERSION_OUTPUT_LIST UNIX_COMMAND "${VERSION_OUTPUT}")
list(FIND VERSION_OUTPUT_LIST "version" VERSION_INDEX)
if(VERSION_INDEX GREATER 0)
math(EXPR VERSION_INDEX "${VERSION_INDEX} + 1")
list(GET VERSION_OUTPUT_LIST ${VERSION_INDEX} VERSION)
set(${VAR} ${VERSION} PARENT_SCOPE)
else()
set(${VAR} "0.0" PARENT_SCOPE)
endif()
endfunction()
if( NOT CLANG_TIDY_EXE )
message( STATUS "Clang tidy not found" )
set(CLANG_TIDY_VERSION "0.0")
else()
find_clang_tidy_version(CLANG_TIDY_VERSION)
message( STATUS "Clang tidy found: ${CLANG_TIDY_VERSION}")
endif()
set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
set(CLANG_TIDY_FIXIT_DIR ${CMAKE_BINARY_DIR}/fixits)
file(MAKE_DIRECTORY ${CLANG_TIDY_FIXIT_DIR})
set_property(DIRECTORY APPEND PROPERTY ADDITIONAL_MAKE_CLEAN_FILES ${CLANG_TIDY_FIXIT_DIR})
macro(enable_clang_tidy)
set(options ANALYZE_TEMPORARY_DTORS ALL)
set(oneValueArgs HEADER_FILTER)
set(multiValueArgs CHECKS ERRORS EXTRA_ARGS)
cmake_parse_arguments(PARSE "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
string(REPLACE ";" "," CLANG_TIDY_CHECKS "${PARSE_CHECKS}")
string(REPLACE ";" "," CLANG_TIDY_ERRORS "${PARSE_ERRORS}")
set(CLANG_TIDY_EXTRA_ARGS)
foreach(ARG ${PARSE_EXTRA_ARGS})
list(APPEND CLANG_TIDY_EXTRA_ARGS "-extra-arg=${ARG}")
endforeach()
set(CLANG_TIDY_ALL)
if(PARSE_ALL)
set(CLANG_TIDY_ALL ALL)
endif()
message(STATUS "Clang tidy checks: ${CLANG_TIDY_CHECKS}")
if (${PARSE_ANALYZE_TEMPORARY_DTORS})
set(CLANG_TIDY_ANALYZE_TEMPORARY_DTORS "-analyze-temporary-dtors")
endif()
if (${CLANG_TIDY_VERSION} VERSION_LESS "3.9.0")
set(CLANG_TIDY_ERRORS_ARG "")
else()
set(CLANG_TIDY_ERRORS_ARG "-warnings-as-errors='${CLANG_TIDY_ERRORS}'")
endif()
if (${CLANG_TIDY_VERSION} VERSION_LESS "3.9.0")
set(CLANG_TIDY_QUIET_ARG "")
else()
set(CLANG_TIDY_QUIET_ARG "-quiet")
endif()
if(PARSE_HEADER_FILTER)
string(REPLACE "$" "$$" CLANG_TIDY_HEADER_FILTER "${PARSE_HEADER_FILTER}")
else()
set(CLANG_TIDY_HEADER_FILTER ".*")
endif()
set(CLANG_TIDY_COMMAND
${CLANG_TIDY_EXE}
${CLANG_TIDY_QUIET_ARG}
-p ${CMAKE_BINARY_DIR}
-checks='${CLANG_TIDY_CHECKS}'
${CLANG_TIDY_ERRORS_ARG}
${CLANG_TIDY_EXTRA_ARGS}
${CLANG_TIDY_ANALYZE_TEMPORARY_DTORS}
-header-filter='${CLANG_TIDY_HEADER_FILTER}'
)
add_custom_target(tidy ${CLANG_TIDY_ALL})
mark_as_analyzer(tidy)
add_custom_target(tidy-base)
add_custom_target(tidy-make-fixit-dir COMMAND ${CMAKE_COMMAND} -E make_directory ${CLANG_TIDY_FIXIT_DIR})
add_custom_target(tidy-rm-fixit-dir COMMAND ${CMAKE_COMMAND} -E remove_directory ${CLANG_TIDY_FIXIT_DIR})
add_dependencies(tidy-make-fixit-dir tidy-rm-fixit-dir)
add_dependencies(tidy-base tidy-make-fixit-dir)
endmacro()
function(clang_tidy_check TARGET)
get_target_property(SOURCES ${TARGET} SOURCES)
# TODO: Use generator expressions instead
# COMMAND ${CLANG_TIDY_COMMAND} $<TARGET_PROPERTY:${TARGET},SOURCES>
# COMMAND ${CLANG_TIDY_COMMAND} $<JOIN:$<TARGET_PROPERTY:${TARGET},SOURCES>, >
foreach(SOURCE ${SOURCES})
if((NOT "${SOURCE}" MATCHES "(h|hpp|hxx)$") AND (NOT "${SOURCE}" MATCHES "TARGET_OBJECTS"))
string(MAKE_C_IDENTIFIER "${SOURCE}" tidy_file)
set(tidy_target tidy-target-${TARGET}-${tidy_file})
add_custom_target(${tidy_target}
# for some targets clang-tidy not able to get information from .clang-tidy
DEPENDS ${SOURCE}
COMMAND ${CLANG_TIDY_COMMAND} "-config=\{CheckOptions: \[\{key: bugprone-reserved-identifier.AllowedIdentifiers,value: __HIP_PLATFORM_HCC__\; __HIP_ROCclr__\}\]\}" ${SOURCE} "-export-fixes=${CLANG_TIDY_FIXIT_DIR}/${TARGET}-${tidy_file}.yaml"
WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
COMMENT "clang-tidy: Running clang-tidy on target ${SOURCE}..."
)
add_dependencies(${tidy_target} ${TARGET})
add_dependencies(${tidy_target} tidy-base)
add_dependencies(tidy ${tidy_target})
endif()
endforeach()
endfunction()
################################################################################
#
# MIT License
#
# Copyright (c) 2017 Advanced Micro Devices, Inc.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#
################################################################################
include(CMakeParseArguments)
include(ProcessorCount)
include(Analyzers)
find_program(CPPCHECK_EXE
NAMES
cppcheck
PATHS
/opt/rocm/bin
)
ProcessorCount(CPPCHECK_JOBS)
set(CPPCHECK_BUILD_DIR ${CMAKE_BINARY_DIR}/cppcheck-build)
file(MAKE_DIRECTORY ${CPPCHECK_BUILD_DIR})
set_property(DIRECTORY APPEND PROPERTY ADDITIONAL_MAKE_CLEAN_FILES ${CPPCHECK_BUILD_DIR})
macro(enable_cppcheck)
set(options FORCE)
set(oneValueArgs)
set(multiValueArgs CHECKS SUPPRESS DEFINE UNDEFINE INCLUDE SOURCES)
cmake_parse_arguments(PARSE "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
string(REPLACE ";" "," CPPCHECK_CHECKS "${PARSE_CHECKS}")
string(REPLACE ";" "\n" CPPCHECK_SUPPRESS "${PARSE_SUPPRESS};*:/usr/*")
file(WRITE ${CMAKE_BINARY_DIR}/cppcheck-supressions "${CPPCHECK_SUPPRESS}")
set(CPPCHECK_DEFINES)
foreach(DEF ${PARSE_DEFINE})
set(CPPCHECK_DEFINES "${CPPCHECK_DEFINES} -D${DEF}")
endforeach()
set(CPPCHECK_UNDEFINES)
foreach(DEF ${PARSE_UNDEFINE})
set(CPPCHECK_UNDEFINES "${CPPCHECK_UNDEFINES} -U${DEF}")
endforeach()
set(CPPCHECK_INCLUDES)
foreach(INC ${PARSE_INCLUDE})
set(CPPCHECK_INCLUDES "${CPPCHECK_INCLUDES} -I${INC}")
endforeach()
# set(CPPCHECK_FORCE)
set(CPPCHECK_FORCE "--project=${CMAKE_BINARY_DIR}/compile_commands.json")
if(PARSE_FORCE)
set(CPPCHECK_FORCE --force)
endif()
set(SOURCES)
set(GLOBS)
foreach(SOURCE ${PARSE_SOURCES})
get_filename_component(ABS_SOURCE ${SOURCE} ABSOLUTE)
if(EXISTS ${ABS_SOURCE})
if(IS_DIRECTORY ${ABS_SOURCE})
set(GLOBS "${GLOBS} ${ABS_SOURCE}/*.cpp ${ABS_SOURCE}/*.hpp ${ABS_SOURCE}/*.cxx ${ABS_SOURCE}/*.c ${ABS_SOURCE}/*.h")
else()
set(SOURCES "${SOURCES} ${ABS_SOURCE}")
endif()
else()
set(GLOBS "${GLOBS} ${ABS_SOURCE}")
endif()
endforeach()
file(WRITE ${CMAKE_BINARY_DIR}/cppcheck.cmake "
file(GLOB_RECURSE GSRCS ${GLOBS})
set(CPPCHECK_COMMAND
${CPPCHECK_EXE}
-q
# -v
# --report-progress
${CPPCHECK_FORCE}
--cppcheck-build-dir=${CPPCHECK_BUILD_DIR}
--platform=native
--template=gcc
--error-exitcode=1
-j ${CPPCHECK_JOBS}
${CPPCHECK_DEFINES}
${CPPCHECK_UNDEFINES}
${CPPCHECK_INCLUDES}
--enable=${CPPCHECK_CHECKS}
--inline-suppr
--suppressions-list=${CMAKE_BINARY_DIR}/cppcheck-supressions
${SOURCES} \${GSRCS}
)
string(REPLACE \";\" \" \" CPPCHECK_SHOW_COMMAND \"\${CPPCHECK_COMMAND}\")
message(\"\${CPPCHECK_SHOW_COMMAND}\")
execute_process(
COMMAND \${CPPCHECK_COMMAND}
WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
RESULT_VARIABLE RESULT
)
if(NOT RESULT EQUAL 0)
message(FATAL_ERROR \"Cppcheck failed\")
endif()
")
add_custom_target(cppcheck
COMMAND ${CMAKE_COMMAND} -P ${CMAKE_BINARY_DIR}/cppcheck.cmake
WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
COMMENT "cppcheck: Running cppcheck..."
)
mark_as_analyzer(cppcheck)
endmacro()
################################################################################
#
# MIT License
#
# Copyright (c) 2017 Advanced Micro Devices, Inc.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#
################################################################################
include(CMakeParseArguments)
include(MainDoc)
find_program(DOXYGEN_EXECUTABLE NAMES doxygen
PATH_SUFFIXES bin
DOC "Doxygen documentation generator"
)
mark_as_advanced(DOXYGEN_EXECUTABLE)
find_path(DOT_EXECUTABLE NAMES dot
PATH_SUFFIXES bin
DOC "Graphviz"
)
mark_as_advanced(DOT_EXECUTABLE)
set(DOXYGEN_ARGS
ABBREVIATE_BRIEF
ALIASES
ALLEXTERNALS
ALLOW_UNICODE_NAMES
ALPHABETICAL_INDEX
ALWAYS_DETAILED_SEC
AUTOLINK_SUPPORT
BINARY_TOC
BRIEF_MEMBER_DESC
BUILTIN_STL_SUPPORT
CALLER_GRAPH
CALL_GRAPH
CASE_SENSE_NAMES
CHM_FILE
CHM_INDEX_ENCODING
CITE_BIB_FILES
CLANG_ASSISTED_PARSING
CLANG_OPTIONS
CLASS_DIAGRAMS
CLASS_GRAPH
COLLABORATION_GRAPH
COLS_IN_ALPHA_INDEX
COMPACT_LATEX
COMPACT_RTF
CPP_CLI_SUPPORT
CREATE_SUBDIRS
DIAFILE_DIRS
DIA_PATH
DIRECTORY_GRAPH
DISABLE_INDEX
DISTRIBUTE_GROUP_DOC
DOCBOOK_OUTPUT
DOCBOOK_PROGRAMLISTING
DOCSET_BUNDLE_ID
DOCSET_FEEDNAME
DOCSET_PUBLISHER_ID
DOCSET_PUBLISHER_NAME
DOTFILE_DIRS
DOT_CLEANUP
DOT_FONTNAME
DOT_FONTPATH
DOT_FONTSIZE
DOT_GRAPH_MAX_NODES
DOT_IMAGE_FORMAT
DOT_MULTI_TARGETS
DOT_NUM_THREADS
# DOT_PATH
DOT_TRANSPARENT
DOXYFILE_ENCODING
ECLIPSE_DOC_ID
ENABLED_SECTIONS
ENABLE_PREPROCESSING
ENUM_VALUES_PER_LINE
EXAMPLE_PATH
EXAMPLE_PATTERNS
EXAMPLE_RECURSIVE
EXCLUDE
EXCLUDE_PATTERNS
EXCLUDE_SYMBOLS
EXCLUDE_SYMLINKS
EXPAND_AS_DEFINED
EXPAND_ONLY_PREDEF
EXTENSION_MAPPING
EXTERNAL_GROUPS
EXTERNAL_PAGES
EXTERNAL_SEARCH
EXTERNAL_SEARCH_ID
EXTRACT_ALL
EXTRACT_ANON_NSPACES
EXTRACT_LOCAL_CLASSES
EXTRACT_LOCAL_METHODS
EXTRACT_PACKAGE
EXTRACT_PRIVATE
EXTRACT_STATIC
EXTRA_PACKAGES
EXTRA_SEARCH_MAPPINGS
EXT_LINKS_IN_WINDOW
FILE_PATTERNS
FILE_VERSION_FILTER
FILTER_PATTERNS
FILTER_SOURCE_FILES
FILTER_SOURCE_PATTERNS
FORCE_LOCAL_INCLUDES
FORMULA_FONTSIZE
FORMULA_TRANSPARENT
FULL_PATH_NAMES
GENERATE_AUTOGEN_DEF
GENERATE_BUGLIST
GENERATE_CHI
GENERATE_DEPRECATEDLIST
GENERATE_DOCBOOK
GENERATE_DOCSET
GENERATE_ECLIPSEHELP
GENERATE_HTML
GENERATE_HTMLHELP
GENERATE_LATEX
GENERATE_LEGEND
GENERATE_MAN
GENERATE_PERLMOD
GENERATE_QHP
GENERATE_RTF
GENERATE_TAGFILE
GENERATE_TESTLIST
GENERATE_TODOLIST
GENERATE_TREEVIEW
GENERATE_XML
GRAPHICAL_HIERARCHY
GROUP_GRAPHS
GROUP_NESTED_COMPOUNDS
# HAVE_DOT
HHC_LOCATION
HIDE_COMPOUND_REFERENCE
HIDE_FRIEND_COMPOUNDS
HIDE_IN_BODY_DOCS
HIDE_SCOPE_NAMES
HIDE_UNDOC_CLASSES
HIDE_UNDOC_MEMBERS
HIDE_UNDOC_RELATIONS
HTML_COLORSTYLE_GAMMA
HTML_COLORSTYLE_HUE
HTML_COLORSTYLE_SAT
HTML_DYNAMIC_SECTIONS
HTML_EXTRA_FILES
HTML_EXTRA_STYLESHEET
HTML_FILE_EXTENSION
HTML_FOOTER
HTML_HEADER
HTML_INDEX_NUM_ENTRIES
HTML_OUTPUT
HTML_STYLESHEET
HTML_TIMESTAMP
IDL_PROPERTY_SUPPORT
IGNORE_PREFIX
IMAGE_PATH
INCLUDED_BY_GRAPH
INCLUDE_FILE_PATTERNS
INCLUDE_GRAPH
INCLUDE_PATH
INHERIT_DOCS
INLINE_GROUPED_CLASSES
INLINE_INFO
INLINE_INHERITED_MEMB
INLINE_SIMPLE_STRUCTS
INLINE_SOURCES
INPUT
INPUT_ENCODING
INPUT_FILTER
INTERACTIVE_SVG
INTERNAL_DOCS
JAVADOC_AUTOBRIEF
LATEX_BATCHMODE
LATEX_BIB_STYLE
LATEX_CMD_NAME
LATEX_EXTRA_FILES
LATEX_EXTRA_STYLESHEET
LATEX_FOOTER
LATEX_HEADER
LATEX_HIDE_INDICES
LATEX_OUTPUT
LATEX_SOURCE_CODE
LATEX_TIMESTAMP
LAYOUT_FILE
LOOKUP_CACHE_SIZE
MACRO_EXPANSION
MAKEINDEX_CMD_NAME
MAN_EXTENSION
MAN_LINKS
MAN_OUTPUT
MAN_SUBDIR
MARKDOWN_SUPPORT
MATHJAX_CODEFILE
MATHJAX_EXTENSIONS
MATHJAX_FORMAT
MATHJAX_RELPATH
MAX_DOT_GRAPH_DEPTH
MAX_INITIALIZER_LINES
MSCFILE_DIRS
MSCGEN_PATH
MULTILINE_CPP_IS_BRIEF
OPTIMIZE_FOR_FORTRAN
OPTIMIZE_OUTPUT_FOR_C
OPTIMIZE_OUTPUT_JAVA
OPTIMIZE_OUTPUT_VHDL
OUTPUT_DIRECTORY
OUTPUT_LANGUAGE
PAPER_TYPE
PDF_HYPERLINKS
PERLMOD_LATEX
PERLMOD_MAKEVAR_PREFIX
PERLMOD_PRETTY
PERL_PATH
PLANTUML_CFG_FILE
PLANTUML_INCLUDE_PATH
PLANTUML_JAR_PATH
PREDEFINED
PROJECT_BRIEF
PROJECT_LOGO
PROJECT_NAME
PROJECT_NUMBER
QCH_FILE
QHG_LOCATION
QHP_CUST_FILTER_ATTRS
QHP_CUST_FILTER_NAME
QHP_NAMESPACE
QHP_SECT_FILTER_ATTRS
QHP_VIRTUAL_FOLDER
QT_AUTOBRIEF
QUIET
RECURSIVE
REFERENCED_BY_RELATION
REFERENCES_LINK_SOURCE
REFERENCES_RELATION
REPEAT_BRIEF
RTF_EXTENSIONS_FILE
RTF_HYPERLINKS
RTF_OUTPUT
RTF_SOURCE_CODE
RTF_STYLESHEET_FILE
SEARCHDATA_FILE
SEARCHENGINE
SEARCHENGINE_URL
SEARCH_INCLUDES
SEPARATE_MEMBER_PAGES
SERVER_BASED_SEARCH
SHORT_NAMES
SHOW_FILES
SHOW_GROUPED_MEMB_INC
SHOW_INCLUDE_FILES
SHOW_NAMESPACES
SHOW_USED_FILES
SIP_SUPPORT
SKIP_FUNCTION_MACROS
SORT_BRIEF_DOCS
SORT_BY_SCOPE_NAME
SORT_GROUP_NAMES
SORT_MEMBERS_CTORS_1ST
SORT_MEMBER_DOCS
SOURCE_BROWSER
SOURCE_TOOLTIPS
STRICT_PROTO_MATCHING
STRIP_CODE_COMMENTS
STRIP_FROM_INC_PATH
STRIP_FROM_PATH
SUBGROUPING
TAB_SIZE
TAGFILES
TCL_SUBST
TEMPLATE_RELATIONS
TOC_EXPAND
TOC_INCLUDE_HEADINGS
TREEVIEW_WIDTH
TYPEDEF_HIDES_STRUCT
UML_LIMIT_NUM_FIELDS
UML_LOOK
USE_HTAGS
USE_MATHJAX
USE_MDFILE_AS_MAINPAGE
USE_PDFLATEX
VERBATIM_HEADERS
WARNINGS
WARN_AS_ERROR
WARN_FORMAT
WARN_IF_DOC_ERROR
WARN_IF_UNDOCUMENTED
WARN_LOGFILE
WARN_NO_PARAMDOC
XML_OUTPUT
XML_PROGRAMLISTING
)
set(DOXYGEN_CONFIG_FILE "${CMAKE_CURRENT_BINARY_DIR}/doxygen/doxygen.conf" CACHE PATH "Path to generated doxygen configuration file")
function(add_doxygen_doc)
set(options)
set(oneValueArgs)
set(multiValueArgs DEPENDS ${DOXYGEN_ARGS})
cmake_parse_arguments(PARSE "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
file(WRITE ${DOXYGEN_CONFIG_FILE} "# Auto-generated doxygen configuration file\n")
foreach(ARG ${DOXYGEN_ARGS})
if(PARSE_${ARG})
string(REPLACE ";" " " ARG_VALUE ${PARSE_${ARG}})
file(APPEND ${DOXYGEN_CONFIG_FILE} "\n${ARG} = ${ARG_VALUE}\n")
endif()
endforeach()
if(PARSE_OUTPUT_DIRECTORY)
if(NOT EXISTS ${PARSE_OUTPUT_DIRECTORY})
file(MAKE_DIRECTORY ${PARSE_OUTPUT_DIRECTORY})
endif()
endif()
if(DOT_EXECUTABLE)
file(APPEND ${DOXYGEN_CONFIG_FILE} "\nDOT_PATH = \"${DOT_EXECUTABLE}\"\n")
file(APPEND ${DOXYGEN_CONFIG_FILE} "\nHAVE_DOT = YES\n")
else()
file(APPEND ${DOXYGEN_CONFIG_FILE} "\nHAVE_DOT = NO\n")
endif()
add_custom_target(doxygen
${DOXYGEN_EXECUTABLE} ${DOXYGEN_CONFIG_FILE}
WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
COMMENT "Building documentation with doxygen"
)
if(PARSE_OUTPUT_DIRECTORY)
clean_doc_output(${PARSE_OUTPUT_DIRECTORY})
endif()
mark_as_doc(doxygen)
if(PARSE_DEPENDS)
add_dependencies(doxygen ${PARSE_DEPENDS})
endif()
endfunction()
################################################################################
#
# MIT License
#
# Copyright (c) 2017 Advanced Micro Devices, Inc.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#
################################################################################
# - Enable warning all for gcc/clang or use /W4 for visual studio
## Strict warning level
if (MSVC)
# Use the highest warning level for visual studio.
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /w")
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} /w")
# set(CMAKE_CXX_WARNING_LEVEL 4)
# if (CMAKE_CXX_FLAGS MATCHES "/W[0-4]")
# string(REGEX REPLACE "/W[0-4]" "/W4" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
# else ()
# set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /W4")
# endif ()
# set(CMAKE_C_WARNING_LEVEL 4)
# if (CMAKE_C_FLAGS MATCHES "/W[0-4]")
# string(REGEX REPLACE "/W[0-4]" "/W4" CMAKE_C_FLAGS "${CMAKE_C_FLAGS}")
# else ()
# set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} /W4")
# endif ()
else()
foreach(COMPILER C CXX)
set(CMAKE_COMPILER_WARNINGS)
# use -Wall for gcc and clang
list(APPEND CMAKE_COMPILER_WARNINGS
-Wall
-Wextra
-Wcomment
-Wendif-labels
-Wformat
-Winit-self
-Wreturn-type
-Wsequence-point
# Shadow is broken on gcc when using lambdas
# -Wshadow
-Wswitch
-Wtrigraphs
-Wundef
-Wuninitialized
-Wunreachable-code
-Wunused
-Wno-sign-compare
-Wno-extra-semi-stmt
)
if (CMAKE_${COMPILER}_COMPILER_ID MATCHES "Clang")
list(APPEND CMAKE_COMPILER_WARNINGS
-Weverything
-Wno-c++98-compat
-Wno-c++98-compat-pedantic
-Wno-conversion
-Wno-double-promotion
-Wno-exit-time-destructors
-Wno-extra-semi
-Wno-float-conversion
-Wno-gnu-anonymous-struct
-Wno-gnu-zero-variadic-macro-arguments
-Wno-missing-prototypes
-Wno-nested-anon-types
-Wno-padded
-Wno-return-std-move-in-c++11
-Wno-shorten-64-to-32
-Wno-sign-conversion
-Wno-unknown-warning-option
-Wno-unused-command-line-argument
-Wno-weak-vtables
-Wno-covered-switch-default
)
else()
if (CMAKE_${COMPILER}_COMPILER_ID MATCHES "GNU" AND ${COMPILER} MATCHES "CXX")
# cmake 3.5.2 does not support >=.
if(NOT CMAKE_CXX_COMPILER_VERSION VERSION_LESS "6.1")
list(APPEND CMAKE_COMPILER_WARNINGS
-Wno-ignored-attributes)
endif()
endif()
list(APPEND CMAKE_COMPILER_WARNINGS
-Wno-missing-field-initializers
-Wno-deprecated-declarations
)
endif()
add_definitions(${CMAKE_COMPILER_WARNINGS})
endforeach()
endif ()
#ifndef CK_GRIDWISE_OPERATION_KERNEL_WRAPPER
#define CK_GRIDWISE_OPERATION_KERNEL_WRAPPER
template <typename GridwiseOp, typename... Xs>
__global__ void
#if CK_USE_LAUNCH_BOUNDS
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif
run_gridwise_operation(Xs... xs)
{
GridwiseOp{}.Run(xs...);
}
#endif
#ifndef CK_TRANSFORM_BACKWARD_DATA_CONVOLUTION_INTO_GEMM_V4R1_NHWC_KYXC_NHWK_HPP
#define CK_TRANSFORM_BACKWARD_DATA_CONVOLUTION_INTO_GEMM_V4R1_NHWC_KYXC_NHWK_HPP
#include "common_header.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
namespace ck {
// Number of GEMMs = YTilda * XTilda
// GemmM = C
// GemmN = N * HTildaSlice * WTildaSlice
// GemmK = K * YDotSlice * XDotSlice
template <typename... Wei,
typename... In,
typename... Out,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads,
index_t IYTildaValue,
index_t IXTildaValue,
index_t GemmK1Value>
__host__ __device__ constexpr auto
transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
const TensorDescriptor<Wei...>& wei_k_y_x_c_grid_desc,
const TensorDescriptor<Out...>& out_n_ho_wo_k_grid_desc,
const TensorDescriptor<In...>& in_n_hi_wi_c_grid_desc,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads,
Number<IYTildaValue>,
Number<IXTildaValue>,
Number<GemmK1Value>)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto GemmK1 = Number<GemmK1Value>{};
constexpr auto IYTilda = Number<IYTildaValue>{};
constexpr auto IXTilda = Number<IXTildaValue>{};
const auto N = in_n_hi_wi_c_grid_desc.GetLength(I0);
const auto C = in_n_hi_wi_c_grid_desc.GetLength(I3);
const auto K = out_n_ho_wo_k_grid_desc.GetLength(I3);
const auto Hi = in_n_hi_wi_c_grid_desc.GetLength(I1);
const auto Wi = in_n_hi_wi_c_grid_desc.GetLength(I2);
const auto Ho = out_n_ho_wo_k_grid_desc.GetLength(I1);
const auto Wo = out_n_ho_wo_k_grid_desc.GetLength(I2);
const auto Y = wei_k_y_x_c_grid_desc.GetLength(I1);
const auto X = wei_k_y_x_c_grid_desc.GetLength(I2);
const auto ConvStrideH = conv_strides[I0];
const auto ConvStrideW = conv_strides[I1];
const auto ConvDilationH = conv_dilations[I0];
const auto ConvDilationW = conv_dilations[I1];
const auto InLeftPadH = in_left_pads[I0];
const auto InLeftPadW = in_left_pads[I1];
const auto InRightPadH = in_right_pads[I0];
const auto InRightPadW = in_right_pads[I1];
const auto GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
const auto GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
const auto YTilda = ConvStrideH / GcdStrideDilationH;
const auto XTilda = ConvStrideW / GcdStrideDilationW;
const auto YDot = math::integer_divide_ceil(Y, YTilda);
const auto XDot = math::integer_divide_ceil(X, XTilda);
const auto HTilda = Ho + math::integer_divide_ceil(ConvDilationH * (Y - I1), ConvStrideH);
const auto WTilda = Wo + math::integer_divide_ceil(ConvDilationW * (X - I1), ConvStrideW);
// only work on HTilda and WTilda that contribute to non-padding area of input tensor
const auto IHTildaSliceBegin = math::integer_divide_floor(
math::max(I0, InLeftPadH - ConvDilationH * (YTilda - I1)), ConvStrideH);
const auto IWTildaSliceBegin = math::integer_divide_floor(
math::max(I0, InLeftPadW - ConvDilationW * (XTilda - I1)), ConvStrideW);
const auto IHTildaSliceEnd =
math::min(HTilda, math::integer_divide_ceil(InLeftPadH + Hi - I1, ConvStrideH) + I1);
const auto IWTildaSliceEnd =
math::min(WTilda, math::integer_divide_ceil(InLeftPadW + Wi - I1, ConvStrideW) + I1);
const auto HTildaSlice = IHTildaSliceEnd - IHTildaSliceBegin;
const auto WTildaSlice = IWTildaSliceEnd - IWTildaSliceBegin;
// GemmK is different for each GEMM
const auto YDotSlice = math::integer_divide_ceil(Y - IYTilda, YTilda);
const auto XDotSlice = math::integer_divide_ceil(X - IXTilda, XTilda);
const auto K1 = GemmK1;
const auto K0 = K / K1;
// weight tensor
const auto wei_k_ydot_ytilda_xdot_xtilda_c_grid_desc = transform_tensor_descriptor(
wei_k_y_x_c_grid_desc,
make_tuple(make_pass_through_transform(K),
make_embed_transform(make_tuple(YDot, YTilda),
make_tuple(ConvStrideH / GcdStrideDilationH, I1)),
make_embed_transform(make_tuple(XDot, XTilda),
make_tuple(ConvStrideW / GcdStrideDilationW, I1)),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
const auto wei_k0_k1_ydotslice_xdotslice_c_grid_desc =
transform_tensor_descriptor(wei_k_ydot_ytilda_xdot_xtilda_c_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(K0, K1)),
make_slice_transform(YDot, I0, YDotSlice),
make_slice_transform(XDot, I0, XDotSlice),
make_freeze_transform(IYTilda),
make_freeze_transform(IXTilda),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{},
Sequence<1>{},
Sequence<3>{},
Sequence<2>{},
Sequence<4>{},
Sequence<5>{}),
make_tuple(Sequence<0, 1>{},
Sequence<2>{},
Sequence<3>{},
Sequence<>{},
Sequence<>{},
Sequence<4>{}));
#if 1
const auto wei_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
wei_k0_k1_ydotslice_xdotslice_c_grid_desc,
make_tuple(make_merge_transform(make_tuple(YDotSlice, XDotSlice, K0)),
make_pass_through_transform(C),
make_pass_through_transform(K1)),
make_tuple(Sequence<2, 3, 0>{}, Sequence<4>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
#else
const auto wei_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
wei_k0_k1_ydotslice_xdotslice_c_grid_desc,
make_tuple(make_merge_transform(make_tuple(K0, YDotSlice, XDotSlice)),
make_pass_through_transform(C),
make_pass_through_transform(K1)),
make_tuple(Sequence<0, 2, 3>{}, Sequence<4>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
#endif
// output tensor
// this add padding check
const auto out_n_hop_wop_k_grid_desc = transform_tensor_descriptor(
out_n_ho_wo_k_grid_desc,
make_tuple(make_pass_through_transform(N),
make_pad_transform(Ho, I0, I0),
make_pad_transform(Wo, I0, I0),
make_pass_through_transform(K)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
const auto out_n_ydot_htilda_xdot_wtilda_k_grid_desc = transform_tensor_descriptor(
out_n_hop_wop_k_grid_desc,
make_tuple(make_pass_through_transform(N),
make_embed_transform(make_tuple(YDot, HTilda),
make_tuple(-ConvDilationH / GcdStrideDilationH, I1)),
make_embed_transform(make_tuple(XDot, WTilda),
make_tuple(-ConvDilationW / GcdStrideDilationW, I1)),
make_pass_through_transform(K)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
const auto out_n_ydotslice_htildaslice_xdotslice_wtildaslice_k0_k1_grid_desc =
transform_tensor_descriptor(
out_n_ydot_htilda_xdot_wtilda_k_grid_desc,
make_tuple(make_pass_through_transform(N),
make_slice_transform(YDot, I0, YDotSlice),
make_slice_transform(HTilda, IHTildaSliceBegin, HTildaSlice),
make_slice_transform(XDot, I0, XDotSlice),
make_slice_transform(WTilda, IWTildaSliceBegin, WTildaSlice),
make_unmerge_transform(make_tuple(K0, K1))),
make_tuple(Sequence<0>{},
Sequence<1>{},
Sequence<2>{},
Sequence<3>{},
Sequence<4>{},
Sequence<5>{}),
make_tuple(Sequence<0>{},
Sequence<1>{},
Sequence<2>{},
Sequence<3>{},
Sequence<4>{},
Sequence<5, 6>{}));
#if 1
const auto out_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
out_n_ydotslice_htildaslice_xdotslice_wtildaslice_k0_k1_grid_desc,
make_tuple(make_merge_transform(make_tuple(YDotSlice, XDotSlice, K0)),
make_merge_transform(make_tuple(N, HTildaSlice, WTildaSlice)),
make_pass_through_transform(K1)),
make_tuple(Sequence<1, 3, 5>{}, Sequence<0, 2, 4>{}, Sequence<6>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
#else
const auto out_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
out_n_ydotslice_htildaslice_xdotslice_wtildaslice_k0_k1_grid_desc,
make_tuple(make_merge_transform(make_tuple(K0, YDotSlice, XDotSlice)),
make_merge_transform(make_tuple(N, HTildaSlice, WTildaSlice)),
make_pass_through_transform(K1)),
make_tuple(Sequence<5, 1, 3>{}, Sequence<0, 2, 4>{}, Sequence<6>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
#endif
// input tensor
const auto in_n_hip_wip_c_grid_desc = transform_tensor_descriptor(
in_n_hi_wi_c_grid_desc,
make_tuple(make_pass_through_transform(N),
make_pad_transform(Hi, InLeftPadH, InRightPadH),
make_pad_transform(Wi, InLeftPadW, InRightPadW),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
const auto in_n_ytilda_htilda_xtilda_wtilda_c_grid_desc = transform_tensor_descriptor(
in_n_hip_wip_c_grid_desc,
make_tuple(make_pass_through_transform(N),
make_embed_transform(make_tuple(YTilda, HTilda),
make_tuple(ConvDilationH, ConvStrideH)),
make_embed_transform(make_tuple(XTilda, WTilda),
make_tuple(ConvDilationW, ConvStrideW)),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
const auto in_n_htildaslice_wtildaslice_c_grid_desc = transform_tensor_descriptor(
in_n_ytilda_htilda_xtilda_wtilda_c_grid_desc,
make_tuple(make_pass_through_transform(N),
make_freeze_transform(IYTilda),
make_slice_transform(HTilda, IHTildaSliceBegin, HTildaSlice),
make_freeze_transform(IXTilda),
make_slice_transform(WTilda, IWTildaSliceBegin, WTildaSlice),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{},
Sequence<1>{},
Sequence<2>{},
Sequence<3>{},
Sequence<4>{},
Sequence<5>{}),
make_tuple(Sequence<0>{},
Sequence<>{},
Sequence<1>{},
Sequence<>{},
Sequence<2>{},
Sequence<3>{}));
const auto in_gemmm_gemmn_grid_desc = transform_tensor_descriptor(
in_n_htildaslice_wtildaslice_c_grid_desc,
make_tuple(make_pass_through_transform(C),
make_merge_transform(make_tuple(N, HTildaSlice, WTildaSlice))),
make_tuple(Sequence<3>{}, Sequence<0, 1, 2>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
return make_tuple(wei_gemmk0_gemmm_gemmk1_grid_desc,
out_gemmk0_gemmn_gemmk1_grid_desc,
in_gemmm_gemmn_grid_desc);
}
} // namespace ck
#endif
#ifndef CK_TRANSFORM_BACKWARD_DATA_CONVOLUTION_INTO_GEMM_V4R1R2_NHWC_KYXC_NHWK_HPP
#define CK_TRANSFORM_BACKWARD_DATA_CONVOLUTION_INTO_GEMM_V4R1R2_NHWC_KYXC_NHWK_HPP
#include "common_header.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
namespace ck {
// A: out
// B: wei
// C: in
// Number of GEMMs = YTilda * XTilda
// GemmM = N * HTildaSlice * WTildaSlice
// GemmN = C
// GemmK = K * YDotSlice * XDotSlice
template <typename... Wei,
typename... In,
typename... Out,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads,
index_t IYTildaValue,
index_t IXTildaValue,
index_t GemmK1Value>
__host__ __device__ constexpr auto
transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
const TensorDescriptor<Out...>& out_n_ho_wo_k_grid_desc,
const TensorDescriptor<Wei...>& wei_k_y_x_c_grid_desc,
const TensorDescriptor<In...>& in_n_hi_wi_c_grid_desc,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads,
Number<IYTildaValue>,
Number<IXTildaValue>,
Number<GemmK1Value>)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto GemmK1 = Number<GemmK1Value>{};
constexpr auto IYTilda = Number<IYTildaValue>{};
constexpr auto IXTilda = Number<IXTildaValue>{};
const auto N = in_n_hi_wi_c_grid_desc.GetLength(I0);
const auto C = in_n_hi_wi_c_grid_desc.GetLength(I3);
const auto K = out_n_ho_wo_k_grid_desc.GetLength(I3);
const auto Hi = in_n_hi_wi_c_grid_desc.GetLength(I1);
const auto Wi = in_n_hi_wi_c_grid_desc.GetLength(I2);
const auto Ho = out_n_ho_wo_k_grid_desc.GetLength(I1);
const auto Wo = out_n_ho_wo_k_grid_desc.GetLength(I2);
const auto Y = wei_k_y_x_c_grid_desc.GetLength(I1);
const auto X = wei_k_y_x_c_grid_desc.GetLength(I2);
const auto ConvStrideH = conv_strides[I0];
const auto ConvStrideW = conv_strides[I1];
const auto ConvDilationH = conv_dilations[I0];
const auto ConvDilationW = conv_dilations[I1];
const auto InLeftPadH = in_left_pads[I0];
const auto InLeftPadW = in_left_pads[I1];
const auto InRightPadH = in_right_pads[I0];
const auto InRightPadW = in_right_pads[I1];
const auto GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
const auto GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
const auto YTilda = ConvStrideH / GcdStrideDilationH;
const auto XTilda = ConvStrideW / GcdStrideDilationW;
const auto YDot = math::integer_divide_ceil(Y, YTilda);
const auto XDot = math::integer_divide_ceil(X, XTilda);
const auto HTilda = Ho + math::integer_divide_ceil(ConvDilationH * (Y - I1), ConvStrideH);
const auto WTilda = Wo + math::integer_divide_ceil(ConvDilationW * (X - I1), ConvStrideW);
// only work on HTilda and WTilda that contribute to non-padding area of input tensor
const auto IHTildaSliceBegin = math::integer_divide_floor(
math::max(I0, InLeftPadH - ConvDilationH * (YTilda - I1)), ConvStrideH);
const auto IWTildaSliceBegin = math::integer_divide_floor(
math::max(I0, InLeftPadW - ConvDilationW * (XTilda - I1)), ConvStrideW);
const auto IHTildaSliceEnd =
math::min(HTilda, math::integer_divide_ceil(InLeftPadH + Hi - I1, ConvStrideH) + I1);
const auto IWTildaSliceEnd =
math::min(WTilda, math::integer_divide_ceil(InLeftPadW + Wi - I1, ConvStrideW) + I1);
const auto HTildaSlice = IHTildaSliceEnd - IHTildaSliceBegin;
const auto WTildaSlice = IWTildaSliceEnd - IWTildaSliceBegin;
// GemmK is different for each GEMM
const auto YDotSlice = math::integer_divide_ceil(Y - IYTilda, YTilda);
const auto XDotSlice = math::integer_divide_ceil(X - IXTilda, XTilda);
const auto K1 = GemmK1;
const auto K0 = K / K1;
// A: output tensor
// this add padding check
const auto out_n_hop_wop_k_grid_desc = transform_tensor_descriptor(
out_n_ho_wo_k_grid_desc,
make_tuple(make_pass_through_transform(N),
make_pad_transform(Ho, I0, I0),
make_pad_transform(Wo, I0, I0),
make_pass_through_transform(K)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
const auto out_n_ydot_htilda_xdot_wtilda_k_grid_desc = transform_tensor_descriptor(
out_n_hop_wop_k_grid_desc,
make_tuple(make_pass_through_transform(N),
make_embed_transform(make_tuple(YDot, HTilda),
make_tuple(-ConvDilationH / GcdStrideDilationH, I1)),
make_embed_transform(make_tuple(XDot, WTilda),
make_tuple(-ConvDilationW / GcdStrideDilationW, I1)),
make_pass_through_transform(K)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
const auto out_n_ydotslice_htildaslice_xdotslice_wtildaslice_k0_k1_grid_desc =
transform_tensor_descriptor(
out_n_ydot_htilda_xdot_wtilda_k_grid_desc,
make_tuple(make_pass_through_transform(N),
make_slice_transform(YDot, I0, YDotSlice),
make_slice_transform(HTilda, IHTildaSliceBegin, HTildaSlice),
make_slice_transform(XDot, I0, XDotSlice),
make_slice_transform(WTilda, IWTildaSliceBegin, WTildaSlice),
make_unmerge_transform(make_tuple(K0, K1))),
make_tuple(Sequence<0>{},
Sequence<1>{},
Sequence<2>{},
Sequence<3>{},
Sequence<4>{},
Sequence<5>{}),
make_tuple(Sequence<0>{},
Sequence<1>{},
Sequence<2>{},
Sequence<3>{},
Sequence<4>{},
Sequence<5, 6>{}));
#if 1
const auto out_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
out_n_ydotslice_htildaslice_xdotslice_wtildaslice_k0_k1_grid_desc,
make_tuple(make_merge_transform(make_tuple(YDotSlice, XDotSlice, K0)),
make_merge_transform(make_tuple(N, HTildaSlice, WTildaSlice)),
make_pass_through_transform(K1)),
make_tuple(Sequence<1, 3, 5>{}, Sequence<0, 2, 4>{}, Sequence<6>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
#else
const auto out_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
out_n_ydotslice_htildaslice_xdotslice_wtildaslice_k0_k1_grid_desc,
make_tuple(make_merge_transform(make_tuple(K0, YDotSlice, XDotSlice)),
make_merge_transform(make_tuple(N, HTildaSlice, WTildaSlice)),
make_pass_through_transform(K1)),
make_tuple(Sequence<5, 1, 3>{}, Sequence<0, 2, 4>{}, Sequence<6>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
#endif
// B: weight tensor
const auto wei_k_ydot_ytilda_xdot_xtilda_c_grid_desc = transform_tensor_descriptor(
wei_k_y_x_c_grid_desc,
make_tuple(make_pass_through_transform(K),
make_embed_transform(make_tuple(YDot, YTilda),
make_tuple(ConvStrideH / GcdStrideDilationH, I1)),
make_embed_transform(make_tuple(XDot, XTilda),
make_tuple(ConvStrideW / GcdStrideDilationW, I1)),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
const auto wei_k0_k1_ydotslice_xdotslice_c_grid_desc =
transform_tensor_descriptor(wei_k_ydot_ytilda_xdot_xtilda_c_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(K0, K1)),
make_slice_transform(YDot, I0, YDotSlice),
make_slice_transform(XDot, I0, XDotSlice),
make_freeze_transform(IYTilda),
make_freeze_transform(IXTilda),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{},
Sequence<1>{},
Sequence<3>{},
Sequence<2>{},
Sequence<4>{},
Sequence<5>{}),
make_tuple(Sequence<0, 1>{},
Sequence<2>{},
Sequence<3>{},
Sequence<>{},
Sequence<>{},
Sequence<4>{}));
#if 1
const auto wei_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
wei_k0_k1_ydotslice_xdotslice_c_grid_desc,
make_tuple(make_merge_transform(make_tuple(YDotSlice, XDotSlice, K0)),
make_pass_through_transform(C),
make_pass_through_transform(K1)),
make_tuple(Sequence<2, 3, 0>{}, Sequence<4>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
#else
const auto wei_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
wei_k0_k1_ydotslice_xdotslice_c_grid_desc,
make_tuple(make_merge_transform(make_tuple(K0, YDotSlice, XDotSlice)),
make_pass_through_transform(C),
make_pass_through_transform(K1)),
make_tuple(Sequence<0, 2, 3>{}, Sequence<4>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
#endif
// C: input tensor
const auto in_n_hip_wip_c_grid_desc = transform_tensor_descriptor(
in_n_hi_wi_c_grid_desc,
make_tuple(make_pass_through_transform(N),
make_pad_transform(Hi, InLeftPadH, InRightPadH),
make_pad_transform(Wi, InLeftPadW, InRightPadW),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
const auto in_n_ytilda_htilda_xtilda_wtilda_c_grid_desc = transform_tensor_descriptor(
in_n_hip_wip_c_grid_desc,
make_tuple(make_pass_through_transform(N),
make_embed_transform(make_tuple(YTilda, HTilda),
make_tuple(ConvDilationH, ConvStrideH)),
make_embed_transform(make_tuple(XTilda, WTilda),
make_tuple(ConvDilationW, ConvStrideW)),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
const auto in_n_htildaslice_wtildaslice_c_grid_desc = transform_tensor_descriptor(
in_n_ytilda_htilda_xtilda_wtilda_c_grid_desc,
make_tuple(make_pass_through_transform(N),
make_freeze_transform(IYTilda),
make_slice_transform(HTilda, IHTildaSliceBegin, HTildaSlice),
make_freeze_transform(IXTilda),
make_slice_transform(WTilda, IWTildaSliceBegin, WTildaSlice),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{},
Sequence<1>{},
Sequence<2>{},
Sequence<3>{},
Sequence<4>{},
Sequence<5>{}),
make_tuple(Sequence<0>{},
Sequence<>{},
Sequence<1>{},
Sequence<>{},
Sequence<2>{},
Sequence<3>{}));
const auto in_gemmm_gemmn_grid_desc = transform_tensor_descriptor(
in_n_htildaslice_wtildaslice_c_grid_desc,
make_tuple(make_merge_transform(make_tuple(N, HTildaSlice, WTildaSlice)),
make_pass_through_transform(C)),
make_tuple(Sequence<0, 1, 2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
return make_tuple(out_gemmk0_gemmm_gemmk1_grid_desc,
wei_gemmk0_gemmn_gemmk1_grid_desc,
in_gemmm_gemmn_grid_desc);
}
} // namespace ck
#endif
#ifndef CK_TRANSFORM_FORWARD_CONVOLUTION_INTO_GEMM_V4R4_NCHW_KCYX_NKHW_HPP
#define CK_TRANSFORM_FORWARD_CONVOLUTION_INTO_GEMM_V4R4_NCHW_KCYX_NKHW_HPP
#include "common_header.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
namespace ck {
// GemmM = K
// GemmN = N * Ho * Wo
// GemmK = C * Y * X
template <typename... Wei,
typename... In,
typename... Out,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
__host__ __device__ constexpr auto transform_forward_convolution_into_gemm_v4r4_nchw_kcyx_nkhw_pad(
const TensorDescriptor<Wei...>& wei_k_c_y_x_global_desc,
const TensorDescriptor<In...>& in_n_c_hi_wi_global_desc,
const TensorDescriptor<Out...>& out_n_k_ho_wo_global_desc,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
const auto N = in_n_c_hi_wi_global_desc.GetLength(I0);
const auto C = in_n_c_hi_wi_global_desc.GetLength(I1);
const auto K = out_n_k_ho_wo_global_desc.GetLength(I1);
const auto Hi = in_n_c_hi_wi_global_desc.GetLength(I2);
const auto Wi = in_n_c_hi_wi_global_desc.GetLength(I3);
const auto Ho = out_n_k_ho_wo_global_desc.GetLength(I2);
const auto Wo = out_n_k_ho_wo_global_desc.GetLength(I3);
const auto Y = wei_k_c_y_x_global_desc.GetLength(I2);
const auto X = wei_k_c_y_x_global_desc.GetLength(I3);
const auto ConvStrideH = conv_strides[I0];
const auto ConvStrideW = conv_strides[I1];
const auto ConvDilationH = conv_dilations[I0];
const auto ConvDilationW = conv_dilations[I1];
const auto InLeftPadH = in_left_pads[I0];
const auto InLeftPadW = in_left_pads[I1];
const auto InRightPadH = in_right_pads[I0];
const auto InRightPadW = in_right_pads[I1];
// weight tensor
const auto wei_gemmk_gemmm_global_desc = transform_tensor_descriptor(
make_naive_tensor_descriptor_packed(make_tuple(K, C * Y * X)),
make_tuple(make_pass_through_transform(K), make_pass_through_transform(C * Y * X)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<1>{}, Sequence<0>{}));
// input tensor
const auto in_n_c_hip_wip_global_desc = transform_tensor_descriptor(
in_n_c_hi_wi_global_desc,
make_tuple(make_pass_through_transform(N),
make_pass_through_transform(C),
make_pad_transform(Hi, InLeftPadH, InRightPadH),
make_pad_transform(Wi, InLeftPadW, InRightPadW)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
const auto in_n_c_y_ho_x_wo_global_desc = transform_tensor_descriptor(
in_n_c_hip_wip_global_desc,
make_tuple(make_pass_through_transform(N),
make_pass_through_transform(C),
make_embed_transform(make_tuple(Y, Ho), make_tuple(ConvDilationH, ConvStrideH)),
make_embed_transform(make_tuple(X, Wo), make_tuple(ConvDilationW, ConvStrideW))),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4, 5>{}));
const auto in_gemmk_gemmn_global_desc =
transform_tensor_descriptor(in_n_c_y_ho_x_wo_global_desc,
make_tuple(make_merge_transform(make_tuple(C, Y, X)),
make_merge_transform(make_tuple(N, Ho, Wo))),
make_tuple(Sequence<1, 2, 4>{}, Sequence<0, 3, 5>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
// output tensor
const auto out_gemmm_gemmn_global_desc = transform_tensor_descriptor(
make_naive_tensor_descriptor_packed(make_tuple(N, K, Ho * Wo)),
make_tuple(make_pass_through_transform(K), make_merge_transform(make_tuple(N, Ho * Wo))),
make_tuple(Sequence<1>{}, Sequence<0, 2>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
return make_tuple(
wei_gemmk_gemmm_global_desc, in_gemmk_gemmn_global_desc, out_gemmm_gemmn_global_desc);
}
template <typename... Wei,
typename... In,
typename... Out,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
__host__ __device__ constexpr auto
transform_forward_convolution_into_gemm_v4r4_nchw_kcyx_nkhw_no_pad(
const TensorDescriptor<Wei...>& wei_k_c_y_x_global_desc,
const TensorDescriptor<In...>& in_n_c_hi_wi_global_desc,
const TensorDescriptor<Out...>& out_n_k_ho_wo_global_desc,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
const auto N = in_n_c_hi_wi_global_desc.GetLength(I0);
const auto C = in_n_c_hi_wi_global_desc.GetLength(I1);
const auto K = out_n_k_ho_wo_global_desc.GetLength(I1);
const auto Ho = out_n_k_ho_wo_global_desc.GetLength(I2);
const auto Wo = out_n_k_ho_wo_global_desc.GetLength(I3);
const auto Y = wei_k_c_y_x_global_desc.GetLength(I2);
const auto X = wei_k_c_y_x_global_desc.GetLength(I3);
const auto ConvStrideH = conv_strides[I0];
const auto ConvStrideW = conv_strides[I1];
const auto ConvDilationH = conv_dilations[I0];
const auto ConvDilationW = conv_dilations[I1];
const auto InLeftPadH = in_left_pads[I0];
const auto InLeftPadW = in_left_pads[I1];
const auto InRightPadH = in_right_pads[I0];
const auto InRightPadW = in_right_pads[I1];
assert(InLeftPadH == 0 && InLeftPadW == 0 && InRightPadH == 0 && InRightPadW == 0);
// weight tensor
const auto wei_gemmk_gemmm_global_desc = transform_tensor_descriptor(
make_naive_tensor_descriptor_packed(make_tuple(K, C * Y * X)),
make_tuple(make_pass_through_transform(K), make_pass_through_transform(C * Y * X)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<1>{}, Sequence<0>{}));
// input tensor
const auto in_n_c_y_ho_x_wo_global_desc = transform_tensor_descriptor(
in_n_c_hi_wi_global_desc,
make_tuple(make_pass_through_transform(N),
make_pass_through_transform(C),
make_embed_transform(make_tuple(Y, Ho), make_tuple(ConvDilationH, ConvStrideH)),
make_embed_transform(make_tuple(X, Wo), make_tuple(ConvDilationW, ConvStrideW))),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4, 5>{}));
const auto in_gemmk_gemmn_global_desc =
transform_tensor_descriptor(in_n_c_y_ho_x_wo_global_desc,
make_tuple(make_merge_transform(make_tuple(C, Y, X)),
make_merge_transform(make_tuple(N, Ho, Wo))),
make_tuple(Sequence<1, 2, 4>{}, Sequence<0, 3, 5>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
// output tensor
const auto out_gemmm_gemmn_global_desc = transform_tensor_descriptor(
make_naive_tensor_descriptor_packed(make_tuple(N, K, Ho * Wo)),
make_tuple(make_pass_through_transform(K), make_merge_transform(make_tuple(N, Ho * Wo))),
make_tuple(Sequence<1>{}, Sequence<0, 2>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
return make_tuple(
wei_gemmk_gemmm_global_desc, in_gemmk_gemmn_global_desc, out_gemmm_gemmn_global_desc);
}
template <typename... Wei,
typename... In,
typename... Out,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
__host__ __device__ constexpr auto transform_forward_convolution_into_gemm_v4r4_nchw_kcyx_nkhw_1x1(
const TensorDescriptor<Wei...>& wei_k_c_y_x_global_desc,
const TensorDescriptor<In...>& in_n_c_hi_wi_global_desc,
const TensorDescriptor<Out...>& out_n_k_ho_wo_global_desc,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
const auto N = in_n_c_hi_wi_global_desc.GetLength(I0);
const auto C = in_n_c_hi_wi_global_desc.GetLength(I1);
const auto K = out_n_k_ho_wo_global_desc.GetLength(I1);
const auto Ho = out_n_k_ho_wo_global_desc.GetLength(I2);
const auto Wo = out_n_k_ho_wo_global_desc.GetLength(I3);
const auto Y = wei_k_c_y_x_global_desc.GetLength(I2);
const auto X = wei_k_c_y_x_global_desc.GetLength(I3);
const auto ConvStrideH = conv_strides[I0];
const auto ConvStrideW = conv_strides[I1];
const auto ConvDilationH = conv_dilations[I0];
const auto ConvDilationW = conv_dilations[I1];
const auto InLeftPadH = in_left_pads[I0];
const auto InLeftPadW = in_left_pads[I1];
const auto InRightPadH = in_right_pads[I0];
const auto InRightPadW = in_right_pads[I1];
assert(Y == 1 && X == 1 && ConvStrideH == 1 && ConvStrideW == 1 && ConvDilationH == 1 &&
ConvDilationW == 1 && InLeftPadH == 0 && InLeftPadW == 0 && InRightPadH == 0 &&
InRightPadW == 0);
// weight tensor
const auto wei_gemmk_gemmm_global_desc = transform_tensor_descriptor(
make_naive_tensor_descriptor_packed(make_tuple(K, C)),
make_tuple(make_pass_through_transform(K), make_pass_through_transform(C)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<1>{}, Sequence<0>{}));
// input tensor
const auto in_gemmk_gemmn_global_desc = transform_tensor_descriptor(
in_n_c_hi_wi_global_desc,
make_tuple(make_pass_through_transform(C), make_merge_transform(make_tuple(N, Ho, Wo))),
make_tuple(Sequence<1>{}, Sequence<0, 2, 3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
// output tensor
const auto out_gemmm_gemmn_global_desc = transform_tensor_descriptor(
make_naive_tensor_descriptor_packed(make_tuple(N, K, Ho * Wo)),
make_tuple(make_pass_through_transform(K), make_merge_transform(make_tuple(N, Ho * Wo))),
make_tuple(Sequence<1>{}, Sequence<0, 2>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
return make_tuple(
wei_gemmk_gemmm_global_desc, in_gemmk_gemmn_global_desc, out_gemmm_gemmn_global_desc);
}
} // namespace ck
#endif
#ifndef CK_TRANSFORM_FORWARD_CONVOLUTION_INTO_GEMM_V4R4_NHWC_KYXC_NHWK_HPP
#define CK_TRANSFORM_FORWARD_CONVOLUTION_INTO_GEMM_V4R4_NHWC_KYXC_NHWK_HPP
#include "common_header.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
namespace ck {
// GemmM = K
// GemmN = N * Ho * Wo
// GemmK = C * Y * X
template <typename... Wei,
typename... In,
typename... Out,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
__host__ __device__ constexpr auto transform_forward_convolution_into_gemm_v4r4_nhwc_kyxc_nhwk_pad(
const TensorDescriptor<Wei...>& wei_k_y_x_c_grid_desc,
const TensorDescriptor<In...>& in_n_hi_wi_c_grid_desc,
const TensorDescriptor<Out...>& out_n_ho_wo_k_grid_desc,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
const auto N = in_n_hi_wi_c_grid_desc.GetLength(I0);
const auto C = in_n_hi_wi_c_grid_desc.GetLength(I3);
const auto K = out_n_ho_wo_k_grid_desc.GetLength(I3);
const auto Hi = in_n_hi_wi_c_grid_desc.GetLength(I1);
const auto Wi = in_n_hi_wi_c_grid_desc.GetLength(I2);
const auto Ho = out_n_ho_wo_k_grid_desc.GetLength(I1);
const auto Wo = out_n_ho_wo_k_grid_desc.GetLength(I2);
const auto Y = wei_k_y_x_c_grid_desc.GetLength(I1);
const auto X = wei_k_y_x_c_grid_desc.GetLength(I2);
const auto ConvStrideH = conv_strides[I0];
const auto ConvStrideW = conv_strides[I1];
const auto ConvDilationH = conv_dilations[I0];
const auto ConvDilationW = conv_dilations[I1];
const auto InLeftPadH = in_left_pads[I0];
const auto InLeftPadW = in_left_pads[I1];
const auto InRightPadH = in_right_pads[I0];
const auto InRightPadW = in_right_pads[I1];
// weight tensor
const auto wei_gemmk_gemmm_grid_desc = transform_tensor_descriptor(
make_naive_tensor_descriptor_packed(make_tuple(K, Y * X * C)),
make_tuple(make_pass_through_transform(K), make_pass_through_transform(Y * X * C)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<1>{}, Sequence<0>{}));
// input tensor
const auto in_n_hip_wip_c_grid_desc = transform_tensor_descriptor(
in_n_hi_wi_c_grid_desc,
make_tuple(make_pass_through_transform(N),
make_pad_transform(Hi, InLeftPadH, InRightPadH),
make_pad_transform(Wi, InLeftPadW, InRightPadW),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
const auto in_n_y_ho_x_wo_c_grid_desc = transform_tensor_descriptor(
in_n_hip_wip_c_grid_desc,
make_tuple(make_pass_through_transform(N),
make_embed_transform(make_tuple(Y, Ho), make_tuple(ConvDilationH, ConvStrideH)),
make_embed_transform(make_tuple(X, Wo), make_tuple(ConvDilationW, ConvStrideW)),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
const auto in_gemmk_gemmn_grid_desc =
transform_tensor_descriptor(in_n_y_ho_x_wo_c_grid_desc,
make_tuple(make_merge_transform(make_tuple(Y, X, C)),
make_merge_transform(make_tuple(N, Ho, Wo))),
make_tuple(Sequence<1, 3, 5>{}, Sequence<0, 2, 4>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
// output tensor
const auto out_gemmm_gemmn_grid_desc = transform_tensor_descriptor(
make_naive_tensor_descriptor_packed(make_tuple(N * Ho * Wo, K)),
make_tuple(make_pass_through_transform(N * Ho * Wo), make_pass_through_transform(K)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<1>{}, Sequence<0>{}));
return make_tuple(
wei_gemmk_gemmm_grid_desc, in_gemmk_gemmn_grid_desc, out_gemmm_gemmn_grid_desc);
}
template <typename... Wei,
typename... In,
typename... Out,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
__host__ __device__ constexpr auto transform_forward_convolution_into_gemm_v4r4_nhwc_kyxc_nhwk_1x1(
const TensorDescriptor<Wei...>& wei_k_y_x_c_grid_desc,
const TensorDescriptor<In...>& in_n_hi_wi_c_grid_desc,
const TensorDescriptor<Out...>& out_n_ho_wo_k_grid_desc,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
const auto N = in_n_hi_wi_c_grid_desc.GetLength(I0);
const auto C = in_n_hi_wi_c_grid_desc.GetLength(I3);
const auto K = out_n_ho_wo_k_grid_desc.GetLength(I3);
const auto Ho = out_n_ho_wo_k_grid_desc.GetLength(I1);
const auto Wo = out_n_ho_wo_k_grid_desc.GetLength(I2);
const auto Y = wei_k_y_x_c_grid_desc.GetLength(I1);
const auto X = wei_k_y_x_c_grid_desc.GetLength(I2);
const auto ConvStrideH = conv_strides[I0];
const auto ConvStrideW = conv_strides[I1];
const auto ConvDilationH = conv_dilations[I0];
const auto ConvDilationW = conv_dilations[I1];
const auto InLeftPadH = in_left_pads[I0];
const auto InLeftPadW = in_left_pads[I1];
const auto InRightPadH = in_right_pads[I0];
const auto InRightPadW = in_right_pads[I1];
assert(Y == 1 && X == 1 && ConvStrideH == 1 && ConvStrideW == 1 && ConvDilationH == 1 &&
ConvDilationW == 1 && InLeftPadH == 0 && InLeftPadW == 0 && InRightPadH == 0 &&
InRightPadW == 0);
// weight tensor
const auto wei_gemmk_gemmm_grid_desc = transform_tensor_descriptor(
make_naive_tensor_descriptor_packed(make_tuple(K, C)),
make_tuple(make_pass_through_transform(K), make_pass_through_transform(C)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<1>{}, Sequence<0>{}));
// input tensor
const auto in_gemmk_gemmn_grid_desc = transform_tensor_descriptor(
make_naive_tensor_descriptor_packed(make_tuple(N * Ho * Wo, C)),
make_tuple(make_pass_through_transform(N * Ho * Wo), make_pass_through_transform(C)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<1>{}, Sequence<0>{}));
// output tensor
const auto out_gemmm_gemmn_grid_desc = transform_tensor_descriptor(
make_naive_tensor_descriptor_packed(make_tuple(N * Ho * Wo, K)),
make_tuple(make_pass_through_transform(N * Ho * Wo), make_pass_through_transform(K)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<1>{}, Sequence<0>{}));
return make_tuple(
wei_gemmk_gemmm_grid_desc, in_gemmk_gemmn_grid_desc, out_gemmm_gemmn_grid_desc);
}
} // namespace ck
#endif
#ifndef CK_TRANSFORM_FORWARD_CONVOLUTION_INTO_GEMM_V4R4R2_NCHW_KCYX_NKHW_HPP
#define CK_TRANSFORM_FORWARD_CONVOLUTION_INTO_GEMM_V4R4R2_NCHW_KCYX_NKHW_HPP
#include "common_header.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
namespace ck {
// GemmM = K
// GemmN = N * Ho * Wo
// GemmK = C * Y * X
template <typename... Wei,
typename... In,
typename... Out,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads,
index_t GemmK1Value>
__host__ __device__ constexpr auto
transform_forward_convolution_into_gemm_v4r4r2_nchw_kcyx_nkhw_pad(
const TensorDescriptor<Wei...>& wei_k_c_y_x_grid_desc,
const TensorDescriptor<In...>& in_n_c_hi_wi_grid_desc,
const TensorDescriptor<Out...>& out_n_k_ho_wo_grid_desc,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads,
Number<GemmK1Value>)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto GemmK1 = Number<GemmK1Value>{};
const auto N = in_n_c_hi_wi_grid_desc.GetLength(I0);
const auto C = in_n_c_hi_wi_grid_desc.GetLength(I1);
const auto K = out_n_k_ho_wo_grid_desc.GetLength(I1);
const auto Hi = in_n_c_hi_wi_grid_desc.GetLength(I2);
const auto Wi = in_n_c_hi_wi_grid_desc.GetLength(I3);
const auto Ho = out_n_k_ho_wo_grid_desc.GetLength(I2);
const auto Wo = out_n_k_ho_wo_grid_desc.GetLength(I3);
const auto Y = wei_k_c_y_x_grid_desc.GetLength(I2);
const auto X = wei_k_c_y_x_grid_desc.GetLength(I3);
const auto ConvStrideH = conv_strides[I0];
const auto ConvStrideW = conv_strides[I1];
const auto ConvDilationH = conv_dilations[I0];
const auto ConvDilationW = conv_dilations[I1];
const auto InLeftPadH = in_left_pads[I0];
const auto InLeftPadW = in_left_pads[I1];
const auto InRightPadH = in_right_pads[I0];
const auto InRightPadW = in_right_pads[I1];
const auto GemmM = K;
const auto GemmN = N * Ho * Wo;
const auto GemmK = C * Y * X;
const auto GemmK0 = GemmK / GemmK1;
// weight tensor
const auto wei_gemmk_gemmm_grid_desc = transform_tensor_descriptor(
make_naive_tensor_descriptor_packed(make_tuple(K, C * Y * X)),
make_tuple(make_pass_through_transform(K), make_pass_through_transform(C * Y * X)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<1>{}, Sequence<0>{}));
const auto wei_gemmk0_gemmm_gemmk1_grid_desc =
transform_tensor_descriptor(wei_gemmk_gemmm_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1)),
make_pass_through_transform(GemmM)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
// input tensor
const auto in_n_c_hip_wip_grid_desc = transform_tensor_descriptor(
in_n_c_hi_wi_grid_desc,
make_tuple(make_pass_through_transform(N),
make_pass_through_transform(C),
make_pad_transform(Hi, InLeftPadH, InRightPadH),
make_pad_transform(Wi, InLeftPadW, InRightPadW)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
const auto in_n_c_y_ho_x_wo_grid_desc = transform_tensor_descriptor(
in_n_c_hip_wip_grid_desc,
make_tuple(make_pass_through_transform(N),
make_pass_through_transform(C),
make_embed_transform(make_tuple(Y, Ho), make_tuple(ConvDilationH, ConvStrideH)),
make_embed_transform(make_tuple(X, Wo), make_tuple(ConvDilationW, ConvStrideW))),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4, 5>{}));
const auto in_gemmk_gemmn_grid_desc =
transform_tensor_descriptor(in_n_c_y_ho_x_wo_grid_desc,
make_tuple(make_merge_transform(make_tuple(C, Y, X)),
make_merge_transform(make_tuple(N, Ho, Wo))),
make_tuple(Sequence<1, 2, 4>{}, Sequence<0, 3, 5>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
const auto in_gemmk0_gemmn_gemmk1_grid_desc =
transform_tensor_descriptor(in_gemmk_gemmn_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1)),
make_pass_through_transform(GemmN)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
// output tensor
const auto out_gemmm_gemmn_grid_desc = transform_tensor_descriptor(
make_naive_tensor_descriptor_packed(make_tuple(N, K, Ho * Wo)),
make_tuple(make_pass_through_transform(K), make_merge_transform(make_tuple(N, Ho * Wo))),
make_tuple(Sequence<1>{}, Sequence<0, 2>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
return make_tuple(wei_gemmk0_gemmm_gemmk1_grid_desc,
in_gemmk0_gemmn_gemmk1_grid_desc,
out_gemmm_gemmn_grid_desc);
}
} // namespace ck
#endif
#ifndef CK_TRANSFORM_FORWARD_CONVOLUTION_INTO_GEMM_V4R4R2_NHWC_KYXC_NHWK_HPP
#define CK_TRANSFORM_FORWARD_CONVOLUTION_INTO_GEMM_V4R4R2_NHWC_KYXC_NHWK_HPP
#include "common_header.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
namespace ck {
// GemmM = K
// GemmN = N * Ho * Wo
// GemmK = C * Y * X
template <typename... Wei,
typename... In,
typename... Out,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads,
index_t GemmK1Value>
__host__ __device__ constexpr auto
transform_forward_convolution_into_gemm_v4r4r2_nhwc_kyxc_nhwk_pad(
const TensorDescriptor<Wei...>& wei_k_y_x_c_grid_desc,
const TensorDescriptor<In...>& in_n_hi_wi_c_grid_desc,
const TensorDescriptor<Out...>& out_n_ho_wo_k_grid_desc,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads,
Number<GemmK1Value>)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto GemmK1 = Number<GemmK1Value>{};
const auto N = in_n_hi_wi_c_grid_desc.GetLength(I0);
const auto C = in_n_hi_wi_c_grid_desc.GetLength(I3);
const auto K = out_n_ho_wo_k_grid_desc.GetLength(I3);
const auto Hi = in_n_hi_wi_c_grid_desc.GetLength(I1);
const auto Wi = in_n_hi_wi_c_grid_desc.GetLength(I2);
const auto Ho = out_n_ho_wo_k_grid_desc.GetLength(I1);
const auto Wo = out_n_ho_wo_k_grid_desc.GetLength(I2);
const auto Y = wei_k_y_x_c_grid_desc.GetLength(I1);
const auto X = wei_k_y_x_c_grid_desc.GetLength(I2);
const auto ConvStrideH = conv_strides[I0];
const auto ConvStrideW = conv_strides[I1];
const auto ConvDilationH = conv_dilations[I0];
const auto ConvDilationW = conv_dilations[I1];
const auto InLeftPadH = in_left_pads[I0];
const auto InLeftPadW = in_left_pads[I1];
const auto InRightPadH = in_right_pads[I0];
const auto InRightPadW = in_right_pads[I1];
const auto GemmM = K;
const auto GemmN = N * Ho * Wo;
const auto GemmK = C * Y * X;
const auto GemmK0 = GemmK / GemmK1;
// weight tensor
const auto wei_gemmk_gemmm_grid_desc = transform_tensor_descriptor(
make_naive_tensor_descriptor_packed(make_tuple(K, Y * X * C)),
make_tuple(make_pass_through_transform(K), make_pass_through_transform(Y * X * C)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<1>{}, Sequence<0>{}));
const auto wei_gemmk0_gemmm_gemmk1_grid_desc =
transform_tensor_descriptor(wei_gemmk_gemmm_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1)),
make_pass_through_transform(GemmM)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
// input tensor
const auto in_n_hip_wip_c_grid_desc = transform_tensor_descriptor(
in_n_hi_wi_c_grid_desc,
make_tuple(make_pass_through_transform(N),
make_pad_transform(Hi, InLeftPadH, InRightPadH),
make_pad_transform(Wi, InLeftPadW, InRightPadW),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
const auto in_n_y_ho_x_wo_c_grid_desc = transform_tensor_descriptor(
in_n_hip_wip_c_grid_desc,
make_tuple(make_pass_through_transform(N),
make_embed_transform(make_tuple(Y, Ho), make_tuple(ConvDilationH, ConvStrideH)),
make_embed_transform(make_tuple(X, Wo), make_tuple(ConvDilationW, ConvStrideW)),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
const auto in_gemmk_gemmn_grid_desc =
transform_tensor_descriptor(in_n_y_ho_x_wo_c_grid_desc,
make_tuple(make_merge_transform(make_tuple(Y, X, C)),
make_merge_transform(make_tuple(N, Ho, Wo))),
make_tuple(Sequence<1, 3, 5>{}, Sequence<0, 2, 4>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
const auto in_gemmk0_gemmn_gemmk1_grid_desc =
transform_tensor_descriptor(in_gemmk_gemmn_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1)),
make_pass_through_transform(GemmN)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
// output tensor
const auto out_gemmm_gemmn_grid_desc = transform_tensor_descriptor(
make_naive_tensor_descriptor_packed(make_tuple(N * Ho * Wo, K)),
make_tuple(make_pass_through_transform(N * Ho * Wo), make_pass_through_transform(K)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<1>{}, Sequence<0>{}));
return make_tuple(wei_gemmk0_gemmm_gemmk1_grid_desc,
in_gemmk0_gemmn_gemmk1_grid_desc,
out_gemmm_gemmn_grid_desc);
}
} // namespace ck
#endif
#ifndef CK_TRANSFORM_FORWARD_CONVOLUTION_INTO_GEMM_V4R4R4_NHWC_KYXC_NHWK_HPP
#define CK_TRANSFORM_FORWARD_CONVOLUTION_INTO_GEMM_V4R4R4_NHWC_KYXC_NHWK_HPP
#include "common_header.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
namespace ck {
// A: in
// B: wei
// C: out
// GemmM = N * Ho * Wo
// GemmN = K
// GemmK = Y * X * C
template <typename... In,
typename... Wei,
typename... Out,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads,
index_t GemmK1Value>
__host__ __device__ constexpr auto
transform_forward_convolution_into_gemm_v4r4r4_nhwc_kyxc_nhwk_pad(
const TensorDescriptor<In...>& in_n_hi_wi_c_grid_desc,
const TensorDescriptor<Wei...>& wei_k_y_x_c_grid_desc,
const TensorDescriptor<Out...>& out_n_ho_wo_k_grid_desc,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads,
Number<GemmK1Value>)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto GemmK1 = Number<GemmK1Value>{};
const auto N = in_n_hi_wi_c_grid_desc.GetLength(I0);
const auto C = in_n_hi_wi_c_grid_desc.GetLength(I3);
const auto K = out_n_ho_wo_k_grid_desc.GetLength(I3);
const auto Hi = in_n_hi_wi_c_grid_desc.GetLength(I1);
const auto Wi = in_n_hi_wi_c_grid_desc.GetLength(I2);
const auto Ho = out_n_ho_wo_k_grid_desc.GetLength(I1);
const auto Wo = out_n_ho_wo_k_grid_desc.GetLength(I2);
const auto Y = wei_k_y_x_c_grid_desc.GetLength(I1);
const auto X = wei_k_y_x_c_grid_desc.GetLength(I2);
const auto ConvStrideH = conv_strides[I0];
const auto ConvStrideW = conv_strides[I1];
const auto ConvDilationH = conv_dilations[I0];
const auto ConvDilationW = conv_dilations[I1];
const auto InLeftPadH = in_left_pads[I0];
const auto InLeftPadW = in_left_pads[I1];
const auto InRightPadH = in_right_pads[I0];
const auto InRightPadW = in_right_pads[I1];
const auto GemmM = N * Ho * Wo;
const auto GemmN = K;
const auto GemmK = Y * X * C;
const auto GemmK0 = GemmK / GemmK1;
// A: input tensor
const auto in_n_hip_wip_c_grid_desc = transform_tensor_descriptor(
in_n_hi_wi_c_grid_desc,
make_tuple(make_pass_through_transform(N),
make_pad_transform(Hi, InLeftPadH, InRightPadH),
make_pad_transform(Wi, InLeftPadW, InRightPadW),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
const auto in_n_y_ho_x_wo_c_grid_desc = transform_tensor_descriptor(
in_n_hip_wip_c_grid_desc,
make_tuple(make_pass_through_transform(N),
make_embed_transform(make_tuple(Y, Ho), make_tuple(ConvDilationH, ConvStrideH)),
make_embed_transform(make_tuple(X, Wo), make_tuple(ConvDilationW, ConvStrideW)),
make_pass_through_transform(C)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
const auto in_gemmk_gemmm_grid_desc =
transform_tensor_descriptor(in_n_y_ho_x_wo_c_grid_desc,
make_tuple(make_merge_transform(make_tuple(Y, X, C)),
make_merge_transform(make_tuple(N, Ho, Wo))),
make_tuple(Sequence<1, 3, 5>{}, Sequence<0, 2, 4>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
const auto in_gemmk0_gemmm_gemmk1_grid_desc =
transform_tensor_descriptor(in_gemmk_gemmm_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1)),
make_pass_through_transform(GemmM)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
// B: weight tensor
const auto wei_gemmk_gemmn_grid_desc = transform_tensor_descriptor(
make_naive_tensor_descriptor_packed(make_tuple(K, Y * X * C)),
make_tuple(make_pass_through_transform(K), make_pass_through_transform(Y * X * C)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<1>{}, Sequence<0>{}));
const auto wei_gemmk0_gemmn_gemmk1_grid_desc =
transform_tensor_descriptor(wei_gemmk_gemmn_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1)),
make_pass_through_transform(GemmN)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
// C: output tensor
const auto out_gemmm_gemmn_grid_desc = transform_tensor_descriptor(
make_naive_tensor_descriptor_packed(make_tuple(N * Ho * Wo, K)),
make_tuple(make_pass_through_transform(N * Ho * Wo), make_pass_through_transform(K)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
return make_tuple(in_gemmk0_gemmm_gemmk1_grid_desc,
wei_gemmk0_gemmn_gemmk1_grid_desc,
out_gemmm_gemmn_grid_desc);
}
} // namespace ck
#endif
#ifndef CK_TRANSFORM_FORWARD_CONVOLUTION_INTO_CONTRACTION_V6R1_NCHW_KCYX_NKHW_HPP
#define CK_TRANSFORM_FORWARD_CONVOLUTION_INTO_CONTRACTION_V6R1_NCHW_KCYX_NKHW_HPP
#include "common_header.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
namespace ck {
// GemmM0 = 1
// GemmM1 = K
// GemmN0 = N0
// GemmN1 = (N / N0) * Ho * Wo
// GemmK0 = (C / C0) * Y * X
// GemmK1 = C0
template <typename... Wei,
typename... In,
typename... Out,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads,
typename N0Type,
typename C0Type>
__host__ __device__ constexpr auto
transform_forward_convolution_into_contraction_v6r1_nchw_kcyx_nkhw_pad(
const TensorDescriptor<Wei...>& wei_k_c_y_x_grid_desc,
const TensorDescriptor<In...>& in_n_c_hi_wi_grid_desc,
const TensorDescriptor<Out...>& out_n_k_ho_wo_grid_desc,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads,
const N0Type& N0,
const C0Type& C0)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
const auto N = in_n_c_hi_wi_grid_desc.GetLength(I0);
const auto C = in_n_c_hi_wi_grid_desc.GetLength(I1);
const auto K = out_n_k_ho_wo_grid_desc.GetLength(I1);
const auto Hi = in_n_c_hi_wi_grid_desc.GetLength(I2);
const auto Wi = in_n_c_hi_wi_grid_desc.GetLength(I3);
const auto Ho = out_n_k_ho_wo_grid_desc.GetLength(I2);
const auto Wo = out_n_k_ho_wo_grid_desc.GetLength(I3);
const auto Y = wei_k_c_y_x_grid_desc.GetLength(I2);
const auto X = wei_k_c_y_x_grid_desc.GetLength(I3);
const auto ConvStrideH = conv_strides[I0];
const auto ConvStrideW = conv_strides[I1];
const auto ConvDilationH = conv_dilations[I0];
const auto ConvDilationW = conv_dilations[I1];
const auto InLeftPadH = in_left_pads[I0];
const auto InLeftPadW = in_left_pads[I1];
const auto InRightPadH = in_right_pads[I0];
const auto InRightPadW = in_right_pads[I1];
const auto N1 = N / N0;
const auto C1 = C / C0;
// weight tensor
const auto wei_gk0_gm0_gm1_gk1_grid_desc =
transform_tensor_descriptor(make_naive_tensor_descriptor_packed(make_tuple(K, C * Y * X)),
make_tuple(make_unmerge_transform(make_tuple(I1, K)),
make_unmerge_transform(make_tuple(C0, C1 * Y * X))),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<1, 2>{}, Sequence<3, 0>{}));
// input tensor
const auto in_n_c_hip_wip_grid_desc = transform_tensor_descriptor(
in_n_c_hi_wi_grid_desc,
make_tuple(make_pass_through_transform(N),
make_pass_through_transform(C),
make_pad_transform(Hi, InLeftPadH, InRightPadH),
make_pad_transform(Wi, InLeftPadW, InRightPadW)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
const auto in_n0_n1_c0_c1_y_ho_x_wo_grid_desc = transform_tensor_descriptor(
in_n_c_hip_wip_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(N0, N1)),
make_unmerge_transform(make_tuple(C0, C1)),
make_embed_transform(make_tuple(Y, Ho), make_tuple(ConvDilationH, ConvStrideH)),
make_embed_transform(make_tuple(X, Wo), make_tuple(ConvDilationW, ConvStrideW))),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0, 1>{}, Sequence<2, 3>{}, Sequence<4, 5>{}, Sequence<6, 7>{}));
const auto in_gk0_gn0_gn1_gk1_grid_desc = transform_tensor_descriptor(
in_n0_n1_c0_c1_y_ho_x_wo_grid_desc,
make_tuple(make_merge_transform(make_tuple(C1, Y, X)),
make_pass_through_transform(N0),
make_merge_transform(make_tuple(N1, Ho, Wo)),
make_pass_through_transform(C0)),
make_tuple(Sequence<3, 4, 6>{}, Sequence<0>{}, Sequence<1, 5, 7>{}, Sequence<2>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
// output tensor
const auto out_n_k_howo_grid_desc =
make_naive_tensor_descriptor_packed(make_tuple(N, K, Ho * Wo));
const auto out_n0_n1_1_k_howo_grid_desc =
transform_tensor_descriptor(out_n_k_howo_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(N0, N1)),
make_unmerge_transform(make_tuple(I1, K)),
make_pass_through_transform(Ho * Wo)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
make_tuple(Sequence<0, 1>{}, Sequence<2, 3>{}, Sequence<4>{}));
const auto out_gm0_gm1_gn0_gn1_grid_desc = transform_tensor_descriptor(
out_n0_n1_1_k_howo_grid_desc,
make_tuple(make_pass_through_transform(I1),
make_pass_through_transform(K),
make_pass_through_transform(N0),
make_merge_transform_v2_magic_division(make_tuple(N1, Ho * Wo))),
make_tuple(Sequence<2>{}, Sequence<3>{}, Sequence<0>{}, Sequence<1, 4>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
return make_tuple(
wei_gk0_gm0_gm1_gk1_grid_desc, in_gk0_gn0_gn1_gk1_grid_desc, out_gm0_gm1_gn0_gn1_grid_desc);
}
} // namespace ck
#endif
#ifndef CK_CLUSTER_DESCRIPTOR_HPP
#define CK_CLUSTER_DESCRIPTOR_HPP
#include "common_header.hpp"
#include "tensor_adaptor.hpp"
namespace ck {
template <typename Lengths,
typename ArrangeOrder = typename arithmetic_sequence_gen<0, Lengths::Size(), 1>::type>
__host__ __device__ constexpr auto make_cluster_descriptor(
const Lengths& lengths,
ArrangeOrder order = typename arithmetic_sequence_gen<0, Lengths::Size(), 1>::type{})
{
constexpr index_t ndim_low = Lengths::Size();
const auto reordered_lengths = container_reorder_given_new2old(lengths, order);
const auto low_lengths = generate_tuple(
[&](auto idim_low) { return reordered_lengths[idim_low]; }, Number<ndim_low>{});
const auto transform = make_merge_transform(low_lengths);
constexpr auto low_dim_old_top_ids = ArrangeOrder{};
constexpr auto up_dim_new_top_ids = Sequence<0>{};
return make_single_stage_tensor_adaptor(
make_tuple(transform), make_tuple(low_dim_old_top_ids), make_tuple(up_dim_new_top_ids));
}
} // namespace ck
#endif
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