ROCm/composable_kernel
1
0
Fork 0
mirror of https://github.com/ROCm/composable_kernel.git synced 2026-05-11 17:00:18 +00:00
Code Issues Packages Projects Releases Wiki Activity

Merge pull request #16 from ROCmSoftwarePlatform/develop

Browse Source 
Merge develop into master
This commit is contained in:
Chao Liu
2021-08-18 11:22:34 -05:00
committed by GitHub
parent 5781adf5cf b62bf8c3f8
commit 31b403526e
151 changed files with 5778 additions and 16953 deletions
Download Patch File Download Diff File Expand all files Collapse all files

3
.clang-tidy Normal file
View File

@@ -0,0 +1,3 @@
CheckOptions:
- key: bugprone-reserved-identifier.AllowedIdentifiers
value: '__HIP_PLATFORM_HCC__;__HIP_ROCclr__'

164
CMakeLists.txt
View File

@@ -1,10 +1,9 @@
cmake_minimum_required(VERSION 2.8.3)
project(modular_convolution)
cmake_minimum_required(VERSION 3.5)
project(composable_kernel)
list(APPEND CMAKE_MODULE_PATH "${PROJECT_SOURCE_DIR}/cmake")
include(TargetFlags)
include(AddKernels)
include(CheckCXXCompilerFlag)
## C++
enable_language(CXX)
@@ -39,4 +38,161 @@ link_libraries(${OpenMP_pthread_LIBRARY})
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)

10
README.md
View File

@@ -78,7 +78,7 @@ InLeftPads size 2, {1, 1, }
InRightPads size 2, {1, 1, }
ConvStrides size 2, {2, 2, }
ConvDilations size 2, {1, 1, }
device_dynamic_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw
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}
@@ -100,7 +100,7 @@ InLeftPads size 2, {1, 1, }
InRightPads size 2, {1, 1, }
ConvStrides size 2, {1, 1, }
ConvDilations size 2, {1, 1, }
device_dynamic_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw
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}
@@ -122,7 +122,7 @@ InLeftPads size 2, {1, 1, }
InRightPads size 2, {1, 1, }
ConvStrides size 2, {2, 2, }
ConvDilations size 2, {1, 1, }
device_dynamic_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk
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}
@@ -144,7 +144,7 @@ InLeftPads size 2, {1, 1, }
InRightPads size 2, {1, 1, }
ConvStrides size 2, {1, 1, }
ConvDilations size 2, {1, 1, }
device_dynamic_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk
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}
@@ -166,7 +166,7 @@ InLeftPads size 2, {1, 1, }
InRightPads size 2, {1, 1, }
ConvStrides size 2, {1, 1, }
ConvDilations size 2, {1, 1, }
device_dynamic_convolution_backward_data_implicit_gemm_v4r1r2_xdlops_nhwc_kyxc_nhwk
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}

40
cmake/AddKernels.cmake
View File

@@ -1,40 +0,0 @@
function(add_kernels SRC_DIR KERNEL_FILES)
set(INIT_KERNELS_LIST)
set(KERNELS_DECLS)
foreach(KERNEL_FILE ${KERNEL_FILES})
if("${CMAKE_VERSION}" VERSION_LESS 3.0)
configure_file(${KERNEL_FILE} ${KERNEL_FILE}.delete)
else()
set_property(DIRECTORY APPEND PROPERTY CMAKE_CONFIGURE_DEPENDS ${KERNEL_FILE})
endif()
get_filename_component(BASE_NAME ${KERNEL_FILE} NAME_WE)
string(TOUPPER "${BASE_NAME}" KEY_NAME)
string(MAKE_C_IDENTIFIER "${KEY_NAME}" VAR_NAME)
string(APPEND KERNELS_DECLS "extern const size_t APP_KERNEL_${VAR_NAME}_SIZE;\n")
string(APPEND KERNELS_DECLS "extern const unsigned char APP_KERNEL_${VAR_NAME}[];\n")
list(APPEND INIT_KERNELS_LIST " { \"${KEY_NAME}\", std::string(reinterpret_cast<const char*>(APP_KERNEL_${VAR_NAME}), APP_KERNEL_${VAR_NAME}_SIZE) }")
endforeach()
string(REPLACE ";" ",\n" INIT_KERNELS "${INIT_KERNELS_LIST}")
configure_file(${SRC_DIR}/kernel.cpp.in ${PROJECT_BINARY_DIR}/kernel.cpp)
endfunction()
function(add_kernel_includes SRC_DIR KERNEL_FILES)
set(INIT_KERNELS_LIST)
foreach(KERNEL_FILE ${KERNEL_FILES})
if("${CMAKE_VERSION}" VERSION_LESS 3.0)
configure_file(${KERNEL_FILE} ${KERNEL_FILE}.delete)
else()
set_property(DIRECTORY APPEND PROPERTY CMAKE_CONFIGURE_DEPENDS ${KERNEL_FILE})
endif()
get_filename_component(BASE_NAME ${KERNEL_FILE} NAME_WE)
get_filename_component(FILE_NAME ${KERNEL_FILE} NAME)
string(TOUPPER "${BASE_NAME}" KEY_NAME)
string(MAKE_C_IDENTIFIER "${KEY_NAME}" VAR_NAME)
list(APPEND INIT_KERNELS_LIST " { \"${FILE_NAME}\", std::string(reinterpret_cast<const char*>(${VAR_NAME}), ${VAR_NAME}_SIZE) }")
endforeach()
string(REPLACE ";" ",\n" INIT_KERNELS "${INIT_KERNELS_LIST}")
configure_file(${SRC_DIR}/kernel_includes.cpp.in ${PROJECT_BINARY_DIR}/kernel_includes.cpp)
endfunction()

8
host/online_compile/addkernels/CMakeLists.txt → cmake/Analyzers.cmake
View File

@@ -24,7 +24,11 @@
#
################################################################################
set(ADD_KERNELS_SOURCE include_inliner.cpp addkernels.cpp)
if(NOT TARGET analyze)
add_custom_target(analyze)
endif()
add_executable(addkernels EXCLUDE_FROM_ALL ${ADD_KERNELS_SOURCE})
function(mark_as_analyzer)
add_dependencies(analyze ${ARGN})
endfunction()

162
cmake/ClangTidy.cmake Normal file
View File

@@ -0,0 +1,162 @@
################################################################################
#
# 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()

130
cmake/CppCheck.cmake Normal file
View File

@@ -0,0 +1,130 @@
################################################################################
#
# 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()

355
cmake/DoxygenDoc.cmake Normal file
View File

@@ -0,0 +1,355 @@
################################################################################
#
# 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()

110
cmake/EnableCompilerWarnings.cmake Normal file
View File

@@ -0,0 +1,110 @@
################################################################################
#
# 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 ()

50
cmake/TargetFlags.cmake
View File

@@ -1,50 +0,0 @@
function(get_target_property2 VAR TARGET PROPERTY)
get_target_property(_pflags ${TARGET} ${PROPERTY})
if(_pflags)
set(${VAR} ${_pflags} PARENT_SCOPE)
else()
set(${VAR} "" PARENT_SCOPE)
endif()
endfunction()
macro(append_flags FLAGS TARGET PROPERTY PREFIX)
get_target_property2(_pflags ${TARGET} ${PROPERTY})
foreach(FLAG ${_pflags})
if(TARGET ${FLAG})
target_flags(_pflags2 ${FLAG})
string(APPEND ${FLAGS} " ${_pflags2}")
else()
string(APPEND ${FLAGS} " ${PREFIX}${FLAG}")
endif()
endforeach()
endmacro()
macro(append_link_flags FLAGS TARGET PROPERTY)
get_target_property2(_pflags ${TARGET} ${PROPERTY})
foreach(FLAG ${_pflags})
if(TARGET ${FLAG})
target_flags(_pflags2 ${FLAG})
string(APPEND ${FLAGS} " ${_pflags2}")
elseif(FLAG MATCHES "^-.*")
string(APPEND ${FLAGS} " ${FLAG}")
elseif(EXISTS ${FLAG})
string(APPEND ${FLAGS} " ${FLAG}")
else()
string(APPEND ${FLAGS} " -l${FLAG}")
endif()
endforeach()
endmacro()
function(target_flags FLAGS TARGET)
set(_flags)
append_flags(_flags ${TARGET} "INTERFACE_COMPILE_OPTIONS" "")
append_flags(_flags ${TARGET} "INTERFACE_COMPILE_DEFINITIONS" "-D")
append_flags(_flags ${TARGET} "INTERFACE_INCLUDE_DIRECTORIES" "-isystem ")
append_flags(_flags ${TARGET} "INTERFACE_LINK_DIRECTORIES" "-L ")
append_flags(_flags ${TARGET} "INTERFACE_LINK_OPTIONS" "")
append_link_flags(_flags ${TARGET} "INTERFACE_LINK_LIBRARIES" "")
# message("_flags: ${_flags}")
set(${FLAGS} ${_flags} PARENT_SCOPE)
endfunction()

72
composable_kernel/include/problem_transform/transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk.hpp
View File

@@ -2,8 +2,8 @@
#define CK_TRANSFORM_BACKWARD_DATA_CONVOLUTION_INTO_GEMM_V4R1_NHWC_KYXC_NHWK_HPP
#include "common_header.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
namespace ck {
@@ -23,9 +23,9 @@ template <typename... Wei,
index_t GemmK1Value>
__host__ __device__ constexpr auto
transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
const DynamicTensorDescriptor<Wei...>& wei_k_y_x_c_grid_desc,
const DynamicTensorDescriptor<Out...>& out_n_ho_wo_k_grid_desc,
const DynamicTensorDescriptor<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 TensorDescriptor<In...>& in_n_hi_wi_c_grid_desc,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
@@ -102,7 +102,7 @@ transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
const auto K0 = K / K1;
// weight tensor
const auto wei_k_ydot_ytilda_xdot_xtilda_c_grid_desc = transform_dynamic_tensor_descriptor(
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),
@@ -114,28 +114,28 @@ transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
const auto wei_k0_k1_ydotslice_xdotslice_c_grid_desc =
transform_dynamic_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>{}));
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_dynamic_tensor_descriptor(
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),
@@ -143,7 +143,7 @@ transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
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_dynamic_tensor_descriptor(
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),
@@ -154,7 +154,7 @@ transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
// output tensor
// this add padding check
const auto out_n_hop_wop_k_grid_desc = transform_dynamic_tensor_descriptor(
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),
@@ -163,7 +163,7 @@ transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
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_dynamic_tensor_descriptor(
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),
@@ -175,7 +175,7 @@ transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
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_dynamic_tensor_descriptor(
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),
@@ -197,7 +197,7 @@ transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
Sequence<5, 6>{}));
#if 1
const auto out_gemmk0_gemmn_gemmk1_grid_desc = transform_dynamic_tensor_descriptor(
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)),
@@ -205,7 +205,7 @@ transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
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_dynamic_tensor_descriptor(
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)),
@@ -215,7 +215,7 @@ transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
#endif
// input tensor
const auto in_n_hip_wip_c_grid_desc = transform_dynamic_tensor_descriptor(
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),
@@ -224,7 +224,7 @@ transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
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_dynamic_tensor_descriptor(
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),
@@ -235,7 +235,7 @@ transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
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_dynamic_tensor_descriptor(
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),
@@ -256,7 +256,7 @@ transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk(
Sequence<2>{},
Sequence<3>{}));
const auto in_gemmm_gemmn_grid_desc = transform_dynamic_tensor_descriptor(
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))),

72
composable_kernel/include/problem_transform/transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk.hpp
View File

@@ -2,8 +2,8 @@
#define CK_TRANSFORM_BACKWARD_DATA_CONVOLUTION_INTO_GEMM_V4R1R2_NHWC_KYXC_NHWK_HPP
#include "common_header.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
namespace ck {
@@ -26,9 +26,9 @@ template <typename... Wei,
index_t GemmK1Value>
__host__ __device__ constexpr auto
transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
const DynamicTensorDescriptor<Out...>& out_n_ho_wo_k_grid_desc,
const DynamicTensorDescriptor<Wei...>& wei_k_y_x_c_grid_desc,
const DynamicTensorDescriptor<In...>& in_n_hi_wi_c_grid_desc,
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,
@@ -106,7 +106,7 @@ transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
// A: output tensor
// this add padding check
const auto out_n_hop_wop_k_grid_desc = transform_dynamic_tensor_descriptor(
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),
@@ -115,7 +115,7 @@ transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
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_dynamic_tensor_descriptor(
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),
@@ -127,7 +127,7 @@ transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
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_dynamic_tensor_descriptor(
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),
@@ -149,7 +149,7 @@ transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
Sequence<5, 6>{}));
#if 1
const auto out_gemmk0_gemmm_gemmk1_grid_desc = transform_dynamic_tensor_descriptor(
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)),
@@ -157,7 +157,7 @@ transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
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_dynamic_tensor_descriptor(
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)),
@@ -167,7 +167,7 @@ transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
#endif
// B: weight tensor
const auto wei_k_ydot_ytilda_xdot_xtilda_c_grid_desc = transform_dynamic_tensor_descriptor(
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),
@@ -179,28 +179,28 @@ transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
const auto wei_k0_k1_ydotslice_xdotslice_c_grid_desc =
transform_dynamic_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>{}));
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_dynamic_tensor_descriptor(
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),
@@ -208,7 +208,7 @@ transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
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_dynamic_tensor_descriptor(
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),
@@ -218,7 +218,7 @@ transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
#endif
// C: input tensor
const auto in_n_hip_wip_c_grid_desc = transform_dynamic_tensor_descriptor(
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),
@@ -227,7 +227,7 @@ transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
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_dynamic_tensor_descriptor(
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),
@@ -238,7 +238,7 @@ transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
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_dynamic_tensor_descriptor(
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),
@@ -259,7 +259,7 @@ transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk(
Sequence<2>{},
Sequence<3>{}));
const auto in_gemmm_gemmn_grid_desc = transform_dynamic_tensor_descriptor(
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)),

80
composable_kernel/include/problem_transform/transform_forward_convolution_into_gemm_v4r4_nchw_kcyx_nkhw.hpp
View File

@@ -2,8 +2,8 @@
#define CK_TRANSFORM_FORWARD_CONVOLUTION_INTO_GEMM_V4R4_NCHW_KCYX_NKHW_HPP
#include "common_header.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
namespace ck {
@@ -18,9 +18,9 @@ template <typename... Wei,
typename InLeftPads,
typename InRightPads>
__host__ __device__ constexpr auto transform_forward_convolution_into_gemm_v4r4_nchw_kcyx_nkhw_pad(
const DynamicTensorDescriptor<Wei...>& wei_k_c_y_x_global_desc,
const DynamicTensorDescriptor<In...>& in_n_c_hi_wi_global_desc,
const DynamicTensorDescriptor<Out...>& out_n_k_ho_wo_global_desc,
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,
@@ -57,14 +57,14 @@ __host__ __device__ constexpr auto transform_forward_convolution_into_gemm_v4r4_
const auto InRightPadW = in_right_pads[I1];
// weight tensor
const auto wei_gemmk_gemmm_global_desc = transform_dynamic_tensor_descriptor(
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(K, C * Y * X)),
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_dynamic_tensor_descriptor(
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),
@@ -73,7 +73,7 @@ __host__ __device__ constexpr auto transform_forward_convolution_into_gemm_v4r4_
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_dynamic_tensor_descriptor(
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),
@@ -83,15 +83,15 @@ __host__ __device__ constexpr auto transform_forward_convolution_into_gemm_v4r4_
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4, 5>{}));
const auto in_gemmk_gemmn_global_desc =
transform_dynamic_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>{}));
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_dynamic_tensor_descriptor(
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(N, K, Ho * Wo)),
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>{}));
@@ -109,9 +109,9 @@ template <typename... Wei,
typename InRightPads>
__host__ __device__ constexpr auto
transform_forward_convolution_into_gemm_v4r4_nchw_kcyx_nkhw_no_pad(
const DynamicTensorDescriptor<Wei...>& wei_k_c_y_x_global_desc,
const DynamicTensorDescriptor<In...>& in_n_c_hi_wi_global_desc,
const DynamicTensorDescriptor<Out...>& out_n_k_ho_wo_global_desc,
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,
@@ -126,9 +126,6 @@ transform_forward_convolution_into_gemm_v4r4_nchw_kcyx_nkhw_no_pad(
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);
@@ -150,14 +147,14 @@ transform_forward_convolution_into_gemm_v4r4_nchw_kcyx_nkhw_no_pad(
assert(InLeftPadH == 0 && InLeftPadW == 0 && InRightPadH == 0 && InRightPadW == 0);
// weight tensor
const auto wei_gemmk_gemmm_global_desc = transform_dynamic_tensor_descriptor(
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(K, C * Y * X)),
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_dynamic_tensor_descriptor(
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),
@@ -167,15 +164,15 @@ transform_forward_convolution_into_gemm_v4r4_nchw_kcyx_nkhw_no_pad(
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4, 5>{}));
const auto in_gemmk_gemmn_global_desc =
transform_dynamic_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>{}));
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_dynamic_tensor_descriptor(
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(N, K, Ho * Wo)),
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>{}));
@@ -192,9 +189,9 @@ template <typename... Wei,
typename InLeftPads,
typename InRightPads>
__host__ __device__ constexpr auto transform_forward_convolution_into_gemm_v4r4_nchw_kcyx_nkhw_1x1(
const DynamicTensorDescriptor<Wei...>& wei_k_c_y_x_global_desc,
const DynamicTensorDescriptor<In...>& in_n_c_hi_wi_global_desc,
const DynamicTensorDescriptor<Out...>& out_n_k_ho_wo_global_desc,
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,
@@ -209,9 +206,6 @@ __host__ __device__ constexpr auto transform_forward_convolution_into_gemm_v4r4_
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);
@@ -235,22 +229,22 @@ __host__ __device__ constexpr auto transform_forward_convolution_into_gemm_v4r4_
InRightPadW == 0);
// weight tensor
const auto wei_gemmk_gemmm_global_desc = transform_dynamic_tensor_descriptor(
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(K, C)),
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_dynamic_tensor_descriptor(
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_dynamic_tensor_descriptor(
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(N, K, Ho * Wo)),
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>{}));

53
composable_kernel/include/problem_transform/transform_forward_convolution_into_gemm_v4r4_nhwc_kyxc_nhwk.hpp
View File

@@ -2,8 +2,8 @@
#define CK_TRANSFORM_FORWARD_CONVOLUTION_INTO_GEMM_V4R4_NHWC_KYXC_NHWK_HPP
#include "common_header.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
namespace ck {
@@ -18,9 +18,9 @@ template <typename... Wei,
typename InLeftPads,
typename InRightPads>
__host__ __device__ constexpr auto transform_forward_convolution_into_gemm_v4r4_nhwc_kyxc_nhwk_pad(
const DynamicTensorDescriptor<Wei...>& wei_k_y_x_c_grid_desc,
const DynamicTensorDescriptor<In...>& in_n_hi_wi_c_grid_desc,
const DynamicTensorDescriptor<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 TensorDescriptor<Out...>& out_n_ho_wo_k_grid_desc,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
@@ -57,14 +57,14 @@ __host__ __device__ constexpr auto transform_forward_convolution_into_gemm_v4r4_
const auto InRightPadW = in_right_pads[I1];
// weight tensor
const auto wei_gemmk_gemmm_grid_desc = transform_dynamic_tensor_descriptor(
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(K, Y * X * C)),
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_dynamic_tensor_descriptor(
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),
@@ -73,7 +73,7 @@ __host__ __device__ constexpr auto transform_forward_convolution_into_gemm_v4r4_
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_dynamic_tensor_descriptor(
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)),
@@ -83,15 +83,15 @@ __host__ __device__ constexpr auto transform_forward_convolution_into_gemm_v4r4_
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
const auto in_gemmk_gemmn_grid_desc =
transform_dynamic_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>{}));
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_dynamic_tensor_descriptor(
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(N * Ho * Wo, K)),
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>{}));
@@ -108,9 +108,9 @@ template <typename... Wei,
typename InLeftPads,
typename InRightPads>
__host__ __device__ constexpr auto transform_forward_convolution_into_gemm_v4r4_nhwc_kyxc_nhwk_1x1(
const DynamicTensorDescriptor<Wei...>& wei_k_y_x_c_grid_desc,
const DynamicTensorDescriptor<In...>& in_n_hi_wi_c_grid_desc,
const DynamicTensorDescriptor<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 TensorDescriptor<Out...>& out_n_ho_wo_k_grid_desc,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
@@ -125,9 +125,6 @@ __host__ __device__ constexpr auto transform_forward_convolution_into_gemm_v4r4_
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);
@@ -151,22 +148,22 @@ __host__ __device__ constexpr auto transform_forward_convolution_into_gemm_v4r4_
InRightPadW == 0);
// weight tensor
const auto wei_gemmk_gemmm_grid_desc = transform_dynamic_tensor_descriptor(
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(K, C)),
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_dynamic_tensor_descriptor(
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(N * Ho * Wo, C)),
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_dynamic_tensor_descriptor(
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(N * Ho * Wo, K)),
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>{}));

56
composable_kernel/include/problem_transform/transform_forward_convolution_into_gemm_v4r4r2_nchw_kcyx_nkhw.hpp
View File

@@ -2,8 +2,8 @@
#define CK_TRANSFORM_FORWARD_CONVOLUTION_INTO_GEMM_V4R4R2_NCHW_KCYX_NKHW_HPP
#include "common_header.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
namespace ck {
@@ -20,9 +20,9 @@ template <typename... Wei,
index_t GemmK1Value>
__host__ __device__ constexpr auto
transform_forward_convolution_into_gemm_v4r4r2_nchw_kcyx_nkhw_pad(
const DynamicTensorDescriptor<Wei...>& wei_k_c_y_x_grid_desc,
const DynamicTensorDescriptor<In...>& in_n_c_hi_wi_grid_desc,
const DynamicTensorDescriptor<Out...>& out_n_k_ho_wo_grid_desc,
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,
@@ -67,21 +67,21 @@ transform_forward_convolution_into_gemm_v4r4r2_nchw_kcyx_nkhw_pad(
const auto GemmK0 = GemmK / GemmK1;
// weight tensor
const auto wei_gemmk_gemmm_grid_desc = transform_dynamic_tensor_descriptor(
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(K, C * Y * X)),
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_dynamic_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>{}));
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_dynamic_tensor_descriptor(
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),
@@ -90,7 +90,7 @@ transform_forward_convolution_into_gemm_v4r4r2_nchw_kcyx_nkhw_pad(
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_dynamic_tensor_descriptor(
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),
@@ -100,22 +100,22 @@ transform_forward_convolution_into_gemm_v4r4r2_nchw_kcyx_nkhw_pad(
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4, 5>{}));
const auto in_gemmk_gemmn_grid_desc =
transform_dynamic_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>{}));
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_dynamic_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>{}));
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_dynamic_tensor_descriptor(
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(N, K, Ho * Wo)),
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>{}));

56
composable_kernel/include/problem_transform/transform_forward_convolution_into_gemm_v4r4r2_nhwc_kyxc_nhwk.hpp
View File

@@ -2,8 +2,8 @@
#define CK_TRANSFORM_FORWARD_CONVOLUTION_INTO_GEMM_V4R4R2_NHWC_KYXC_NHWK_HPP
#include "common_header.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
namespace ck {
@@ -20,9 +20,9 @@ template <typename... Wei,
index_t GemmK1Value>
__host__ __device__ constexpr auto
transform_forward_convolution_into_gemm_v4r4r2_nhwc_kyxc_nhwk_pad(
const DynamicTensorDescriptor<Wei...>& wei_k_y_x_c_grid_desc,
const DynamicTensorDescriptor<In...>& in_n_hi_wi_c_grid_desc,
const DynamicTensorDescriptor<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 TensorDescriptor<Out...>& out_n_ho_wo_k_grid_desc,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
@@ -67,21 +67,21 @@ transform_forward_convolution_into_gemm_v4r4r2_nhwc_kyxc_nhwk_pad(
const auto GemmK0 = GemmK / GemmK1;
// weight tensor
const auto wei_gemmk_gemmm_grid_desc = transform_dynamic_tensor_descriptor(
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(K, Y * X * C)),
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_dynamic_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>{}));
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_dynamic_tensor_descriptor(
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),
@@ -90,7 +90,7 @@ transform_forward_convolution_into_gemm_v4r4r2_nhwc_kyxc_nhwk_pad(
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_dynamic_tensor_descriptor(
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)),
@@ -100,22 +100,22 @@ transform_forward_convolution_into_gemm_v4r4r2_nhwc_kyxc_nhwk_pad(
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
const auto in_gemmk_gemmn_grid_desc =
transform_dynamic_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>{}));
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_dynamic_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>{}));
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_dynamic_tensor_descriptor(
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(N * Ho * Wo, K)),
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>{}));

56
composable_kernel/include/problem_transform/transform_forward_convolution_into_gemm_v4r4r4_nhwc_kyxc_nhwk.hpp
View File

@@ -2,8 +2,8 @@
#define CK_TRANSFORM_FORWARD_CONVOLUTION_INTO_GEMM_V4R4R4_NHWC_KYXC_NHWK_HPP
#include "common_header.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
namespace ck {
@@ -23,9 +23,9 @@ template <typename... In,
index_t GemmK1Value>
__host__ __device__ constexpr auto
transform_forward_convolution_into_gemm_v4r4r4_nhwc_kyxc_nhwk_pad(
const DynamicTensorDescriptor<In...>& in_n_hi_wi_c_grid_desc,
const DynamicTensorDescriptor<Wei...>& wei_k_y_x_c_grid_desc,
const DynamicTensorDescriptor<Out...>& out_n_ho_wo_k_grid_desc,
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,
@@ -70,7 +70,7 @@ transform_forward_convolution_into_gemm_v4r4r4_nhwc_kyxc_nhwk_pad(
const auto GemmK0 = GemmK / GemmK1;
// A: input tensor
const auto in_n_hip_wip_c_grid_desc = transform_dynamic_tensor_descriptor(
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),
@@ -79,7 +79,7 @@ transform_forward_convolution_into_gemm_v4r4r4_nhwc_kyxc_nhwk_pad(
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_dynamic_tensor_descriptor(
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)),
@@ -89,36 +89,36 @@ transform_forward_convolution_into_gemm_v4r4r4_nhwc_kyxc_nhwk_pad(
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
const auto in_gemmk_gemmm_grid_desc =
transform_dynamic_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>{}));
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_dynamic_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>{}));
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_dynamic_tensor_descriptor(
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(K, Y * X * C)),
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_dynamic_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>{}));
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_dynamic_tensor_descriptor(
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(N * Ho * Wo, K)),
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>{}));

46
composable_kernel/include/problem_transform/transform_forward_convolution_into_gemm_v6r1_nchw_kcyx_nkhw.hpp
View File

@@ -2,8 +2,8 @@
#define CK_TRANSFORM_FORWARD_CONVOLUTION_INTO_CONTRACTION_V6R1_NCHW_KCYX_NKHW_HPP
#include "common_header.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
namespace ck {
@@ -24,9 +24,9 @@ template <typename... Wei,
typename C0Type>
__host__ __device__ constexpr auto
transform_forward_convolution_into_contraction_v6r1_nchw_kcyx_nkhw_pad(
const DynamicTensorDescriptor<Wei...>& wei_k_c_y_x_grid_desc,
const DynamicTensorDescriptor<In...>& in_n_c_hi_wi_grid_desc,
const DynamicTensorDescriptor<Out...>& out_n_k_ho_wo_grid_desc,
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,
@@ -68,15 +68,15 @@ transform_forward_convolution_into_contraction_v6r1_nchw_kcyx_nkhw_pad(
const auto C1 = C / C0;
// weight tensor
const auto wei_gk0_gm0_gm1_gk1_grid_desc = transform_dynamic_tensor_descriptor(
make_dynamic_naive_tensor_descriptor_packed_v2(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>{}));
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_dynamic_tensor_descriptor(
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),
@@ -85,7 +85,7 @@ transform_forward_convolution_into_contraction_v6r1_nchw_kcyx_nkhw_pad(
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_dynamic_tensor_descriptor(
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)),
@@ -94,7 +94,7 @@ transform_forward_convolution_into_contraction_v6r1_nchw_kcyx_nkhw_pad(
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_dynamic_tensor_descriptor(
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),
@@ -105,17 +105,17 @@ transform_forward_convolution_into_contraction_v6r1_nchw_kcyx_nkhw_pad(
// output tensor
const auto out_n_k_howo_grid_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(N, K, Ho * Wo));
make_naive_tensor_descriptor_packed(make_tuple(N, K, Ho * Wo));
const auto out_n0_n1_1_k_howo_grid_desc = transform_dynamic_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_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_dynamic_tensor_descriptor(
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),

2
composable_kernel/include/tensor_description/cluster_descriptor.hpp
View File

@@ -8,7 +8,7 @@ namespace ck {
template <typename Lengths,
typename ArrangeOrder = typename arithmetic_sequence_gen<0, Lengths::Size(), 1>::type>
__host__ __device__ constexpr auto make_cluster_descriptor_v2(
__host__ __device__ constexpr auto make_cluster_descriptor(
const Lengths& lengths,
ArrangeOrder order = typename arithmetic_sequence_gen<0, Lengths::Size(), 1>::type{})
{

222
composable_kernel/include/tensor_description/dynamic_multi_index_transform.hpp → composable_kernel/include/tensor_description/multi_index_transform.hpp
View File

@@ -1,5 +1,5 @@
#ifndef CK_DYNAMIC_MULTI_INDEX_TRANSFORM_HPP
#define CK_DYNAMIC_MULTI_INDEX_TRANSFORM_HPP
#ifndef CK_MULTI_INDEX_TRANSFORM_HPP
#define CK_MULTI_INDEX_TRANSFORM_HPP
#include "common_header.hpp"
#include "multi_index.hpp"
@@ -7,7 +7,7 @@
namespace ck {
template <typename LowLength>
struct DynamicPassThrough
struct PassThrough
{
using LowerIndex = MultiIndex<1>;
using UpperIndex = MultiIndex<1>;
@@ -16,9 +16,9 @@ struct DynamicPassThrough
UpLengths up_lengths_;
__host__ __device__ constexpr DynamicPassThrough() = default;
__host__ __device__ constexpr PassThrough() = default;
__host__ __device__ constexpr DynamicPassThrough(const LowLength& low_length)
__host__ __device__ constexpr PassThrough(const LowLength& low_length)
: up_lengths_{make_tuple(low_length)}
{
}
@@ -82,33 +82,36 @@ struct DynamicPassThrough
__host__ __device__ void Print() const
{
printf("{");
printf("DynamicPassThrough, ");
printf("PassThrough, ");
printf("up_lengths_");
print_multi_index(up_lengths_);
printf("}");
}
};
template <typename LowLength, typename LeftPad, typename RightPad, bool SkipIsValidCheck = false>
struct DynamicPad
template <typename LowLength,
typename LeftPadLength,
typename RightPadLength,
bool SkipIsValidCheck = false>
struct Pad
{
using LowerIndex = MultiIndex<1>;
using UpperIndex = MultiIndex<1>;
using UpLengths = decltype(make_tuple(LowLength{} + LeftPad{} + RightPad{}));
using UpLengths = decltype(make_tuple(LowLength{} + LeftPadLength{} + RightPadLength{}));
UpLengths up_lengths_;
LeftPad left_pad_;
RightPad right_pad_;
LeftPadLength left_pad_length_;
RightPadLength right_pad_length_;
__host__ __device__ constexpr DynamicPad() = default;
__host__ __device__ constexpr Pad() = default;
__host__ __device__ constexpr DynamicPad(const LowLength& low_length,
const LeftPad& left_pad,
const RightPad& right_pad)
: up_lengths_{make_tuple(low_length + left_pad + right_pad)},
left_pad_{left_pad},
right_pad_{right_pad}
__host__ __device__ constexpr Pad(const LowLength& low_length,
const LeftPadLength& left_pad_length,
const RightPadLength& right_pad_length)
: up_lengths_{make_tuple(low_length + left_pad_length + right_pad_length)},
left_pad_length_{left_pad_length},
right_pad_length_{right_pad_length}
{
}
@@ -125,7 +128,7 @@ struct DynamicPad
static_assert(LowIdx::Size() == 1 && UpIdx::Size() == 1,
"wrong! inconsistent # of dimension");
idx_low(Number<0>{}) = idx_up[Number<0>{}] - left_pad_;
idx_low(Number<0>{}) = idx_up[Number<0>{}] - left_pad_length_;
}
template <typename LowIdxDiff,
@@ -161,45 +164,46 @@ struct DynamicPad
__host__ __device__ constexpr bool
IsValidUpperIndexMappedToValidLowerIndex(const UpIdx& idx_up) const
{
return SkipIsValidCheck || ((idx_up[Number<0>{}] >= left_pad_) &&
(idx_up[Number<0>{}] < up_lengths_[Number<0>{}] - right_pad_));
return SkipIsValidCheck ||
((idx_up[Number<0>{}] >= left_pad_length_) &&
(idx_up[Number<0>{}] < up_lengths_[Number<0>{}] - right_pad_length_));
}
__host__ __device__ static constexpr bool IsKnownAtCompileTime()
{
return is_known_at_compile_time<UpLengths>::value &&
is_known_at_compile_time<LeftPad>::value &&
is_known_at_compile_time<RightPad>::value;
is_known_at_compile_time<LeftPadLength>::value &&
is_known_at_compile_time<RightPadLength>::value;
}
__host__ __device__ void Print() const
{
printf("{");
printf("DynamicPad, ");
printf("Pad, ");
printf("up_lengths_");
print_multi_index(up_lengths_);
printf("left_pad_ %d", index_t{left_pad_});
printf("right_pad_ %d", index_t{right_pad_});
printf("left_pad_length %d", index_t{left_pad_length_});
printf("right_pad_length %d", index_t{right_pad_length_});
printf("}");
}
};
template <typename LowLength, typename LeftPad, bool SkipIsValidCheck = false>
struct DynamicLeftPad
template <typename LowLength, typename LeftPadLength, bool SkipIsValidCheck = false>
struct LeftPad
{
using LowerIndex = MultiIndex<1>;
using UpperIndex = MultiIndex<1>;
using UpLengths = decltype(make_tuple(LowLength{} + LeftPad{}));
using UpLengths = decltype(make_tuple(LowLength{} + LeftPadLength{}));
UpLengths up_lengths_;
LeftPad left_pad_;
LeftPadLength left_pad_length_;
__host__ __device__ constexpr DynamicLeftPad() = default;
__host__ __device__ constexpr LeftPad() = default;
__host__ __device__ constexpr DynamicLeftPad(const LowLength& low_length,
const LeftPad& left_pad)
: up_lengths_{make_tuple(low_length + left_pad)}, left_pad_{left_pad}
__host__ __device__ constexpr LeftPad(const LowLength& low_length,
const LeftPadLength& left_pad_length)
: up_lengths_{make_tuple(low_length + left_pad_length)}, left_pad_length_{left_pad_length}
{
}
@@ -216,7 +220,7 @@ struct DynamicLeftPad
static_assert(LowIdx::Size() == 1 && UpIdx::Size() == 1,
"wrong! inconsistent # of dimension");
idx_low(Number<0>{}) = idx_up[Number<0>{}] - left_pad_;
idx_low(Number<0>{}) = idx_up[Number<0>{}] - left_pad_length_;
}
template <typename LowIdxDiff,
@@ -252,45 +256,45 @@ struct DynamicLeftPad
__host__ __device__ constexpr bool
IsValidUpperIndexMappedToValidLowerIndex(const UpIdx& idx_up) const
{
return SkipIsValidCheck || (idx_up[Number<0>{}] >= left_pad_);
return SkipIsValidCheck || (idx_up[Number<0>{}] >= left_pad_length_);
}
__host__ __device__ static constexpr bool IsKnownAtCompileTime()
{
return is_known_at_compile_time<UpLengths>::value &&
is_known_at_compile_time<LeftPad>::value;
is_known_at_compile_time<LeftPadLength>::value;
}
__host__ __device__ void Print() const
{
printf("{");
printf("DynamicLeftPad, ");
printf("LeftPad, ");
printf("up_lengths_");
print_multi_index(up_lengths_);
printf("left_pad_ %d", index_t{left_pad_});
printf("left_pad_length_ %d", index_t{left_pad_length_});
printf("}");
}
};
template <typename LowLength, typename RightPad, bool SkipIsValidCheck = false>
struct DynamicRightPad
template <typename LowLength, typename RightPadLength, bool SkipIsValidCheck = false>
struct RightPad
{
using LowerIndex = MultiIndex<1>;
using UpperIndex = MultiIndex<1>;
using UpLengths = decltype(make_tuple(LowLength{} + RightPad{}));
using UpLengths = decltype(make_tuple(LowLength{} + RightPadLength{}));
UpLengths up_lengths_;
LowLength low_length_;
RightPad right_pad_;
RightPadLength right_pad_length_;
__host__ __device__ constexpr DynamicRightPad() = default;
__host__ __device__ constexpr RightPad() = default;
__host__ __device__ constexpr DynamicRightPad(const LowLength& low_length,
const RightPad& right_pad)
: up_lengths_{make_tuple(low_length + right_pad)},
__host__ __device__ constexpr RightPad(const LowLength& low_length,
const RightPadLength& right_pad_length)
: up_lengths_{make_tuple(low_length + right_pad_length)},
low_length_{low_length},
right_pad_{right_pad}
right_pad_length_{right_pad_length}
{
}
@@ -350,17 +354,17 @@ struct DynamicRightPad
{
return is_known_at_compile_time<UpLengths>::value &&
is_known_at_compile_time<LowLength>::value &&
is_known_at_compile_time<RightPad>::value;
is_known_at_compile_time<RightPadLength>::value;
}
__host__ __device__ void Print() const
{
printf("{");
printf("DynamicRightPad, ");
printf("RightPad, ");
printf("up_lengths_");
print_multi_index(up_lengths_);
printf("low_length_ %d", index_t{low_length_});
printf("left_pad_ %d", index_t{right_pad_});
printf("left_pad_length_ %d", index_t{right_pad_length_});
printf("}");
}
};
@@ -373,8 +377,8 @@ struct DynamicRightPad
// at compile-time
template <typename UpLengths,
typename Coefficients,
typename std::enable_if<UpLengths::Size() == Coefficients::Size(), bool>::type = false>
struct DynamicEmbed
typename enable_if<UpLengths::Size() == Coefficients::Size(), bool>::type = false>
struct Embed
{
static constexpr index_t NDimUp = UpLengths::Size();
@@ -384,10 +388,10 @@ struct DynamicEmbed
UpLengths up_lengths_;
Coefficients coefficients_;
__host__ __device__ constexpr DynamicEmbed() = default;
__host__ __device__ constexpr Embed() = default;
__host__ __device__ constexpr DynamicEmbed(const UpLengths& up_lengths,
const Coefficients& coefficients)
__host__ __device__ constexpr Embed(const UpLengths& up_lengths,
const Coefficients& coefficients)
: up_lengths_{up_lengths}, coefficients_{coefficients}
{
}
@@ -458,7 +462,7 @@ struct DynamicEmbed
__host__ __device__ void Print() const
{
printf("{");
printf("DynamicEmbed, ");
printf("Embed, ");
printf("up_lengths_ ");
print_multi_index(up_lengths_);
printf("coefficients_ ");
@@ -470,30 +474,30 @@ struct DynamicEmbed
// Implementation of "Merge" transformation primitive that uses regular to do lowering of
// multi-index and use carry-and-borrow check to do lowering of multi-index delta
template <typename LowLengths>
struct DynamicMerge_v1_carry_check
struct Merge_v1_carry_check
{
static constexpr index_t NDimLow = LowLengths::Size();
using LowerIndex = MultiIndex<NDimLow>;
using UpperIndex = MultiIndex<1>;
using LowLengthsScan = decltype(
container_reverse_exclusive_scan(LowLengths{}, math::multiplies_v2{}, Number<1>{}));
using LowLengthsScan =
decltype(container_reverse_exclusive_scan(LowLengths{}, math::multiplies{}, Number<1>{}));
using UpLengths =
decltype(make_tuple(container_reduce(LowLengths{}, math::multiplies_v2{}, Number<1>{})));
decltype(make_tuple(container_reduce(LowLengths{}, math::multiplies{}, Number<1>{})));
LowLengths low_lengths_;
LowLengthsScan low_lengths_scan_;
UpLengths up_lengths_;
__host__ __device__ constexpr DynamicMerge_v1_carry_check() = default;
__host__ __device__ constexpr Merge_v1_carry_check() = default;
__host__ __device__ constexpr DynamicMerge_v1_carry_check(const LowLengths& low_lengths)
__host__ __device__ constexpr Merge_v1_carry_check(const LowLengths& low_lengths)
: low_lengths_{low_lengths},
low_lengths_scan_{
container_reverse_exclusive_scan(low_lengths, math::multiplies_v2{}, Number<1>{})},
up_lengths_{make_tuple(container_reduce(low_lengths, math::multiplies_v2{}, Number<1>{}))}
container_reverse_exclusive_scan(low_lengths, math::multiplies{}, Number<1>{})},
up_lengths_{make_tuple(container_reduce(low_lengths, math::multiplies{}, Number<1>{}))}
{
static_assert(LowerIndex::Size() == NDimLow, "wrong!");
}
@@ -555,7 +559,7 @@ struct DynamicMerge_v1_carry_check
LowerIndex idx_low_length_minus_idx_diff_low_const;
LowerIndex idx_low_length_plus_idx_diff_low_const;
#if !CK_HACK_DYNAMIC_MERGE_CALCULATE_IDX_DIFF_LOW_CONST_USE_AMD_GCN_READ_FIRST_LANE
#if !CK_HACK_MERGE_CALCULATE_IDX_DIFF_LOW_CONST_USE_AMD_GCN_READ_FIRST_LANE
index_t tmp = idx_diff_up[Number<0>{}];
static_for<0, NDimLow - 1, 1>{}([&](auto i) {
@@ -698,7 +702,7 @@ struct DynamicMerge_v1_carry_check
LowerIndex idx_low_length_minus_idx_diff_low_const;
LowerIndex idx_low_length_plus_idx_diff_low_const;
#if !CK_HACK_DYNAMIC_MERGE_CALCULATE_IDX_DIFF_LOW_CONST_USE_AMD_GCN_READ_FIRST_LANE
#if !CK_HACK_MERGE_CALCULATE_IDX_DIFF_LOW_CONST_USE_AMD_GCN_READ_FIRST_LANE
index_t tmp = idx_diff_up[Number<0>{}];
static_for<0, NDimLow - 1, 1>{}([&](auto i) {
@@ -838,7 +842,7 @@ struct DynamicMerge_v1_carry_check
// very expensive.
LowerIndex idx_diff_low_const;
#if !CK_HACK_DYNAMIC_MERGE_CALCULATE_IDX_DIFF_LOW_CONST_USE_AMD_GCN_READ_FIRST_LANE
#if !CK_HACK_MERGE_CALCULATE_IDX_DIFF_LOW_CONST_USE_AMD_GCN_READ_FIRST_LANE
index_t tmp = idx_diff_up[Number<0>{}];
static_for<0, NDimLow - 1, 1>{}([&](auto i) {
@@ -981,7 +985,7 @@ struct DynamicMerge_v1_carry_check
__host__ __device__ void Print() const
{
printf("{");
printf("DynamicMerge_v1_carry_check, ");
printf("Merge_v1_carry_check, ");
printf("low_lengths_ ");
print_multi_index(low_lengths_);
printf("low_lengths_scan_ ");
@@ -1025,7 +1029,7 @@ struct lambda_merge_generate_MagicDivision_calculate_magic_shift
// 5. When upper-index is int32_t type (when index_t is int32_t), its value need to be
// non-negative.
template <typename LowLengths>
struct DynamicMerge_v2_magic_division
struct Merge_v2_magic_division
{
static constexpr index_t NDimLow = LowLengths::Size();
@@ -1033,7 +1037,7 @@ struct DynamicMerge_v2_magic_division
using UpperIndex = MultiIndex<1>;
using UpLengths =
decltype(make_tuple(container_reduce(LowLengths{}, math::multiplies_v2{}, Number<1>{})));
decltype(make_tuple(container_reduce(LowLengths{}, math::multiplies{}, Number<1>{})));
using LowLengthsMagicDivisorMultipiler = decltype(
generate_tuple(lambda_merge_generate_MagicDivision_calculate_magic_multiplier<LowLengths>{},
@@ -1048,9 +1052,9 @@ struct DynamicMerge_v2_magic_division
LowLengthsMagicDivisorShift low_lengths_magic_divisor_shift_;
UpLengths up_lengths_;
__host__ __device__ constexpr DynamicMerge_v2_magic_division() = default;
__host__ __device__ constexpr Merge_v2_magic_division() = default;
__host__ __device__ constexpr DynamicMerge_v2_magic_division(const LowLengths& low_lengths)
__host__ __device__ constexpr Merge_v2_magic_division(const LowLengths& low_lengths)
: low_lengths_{low_lengths},
low_lengths_magic_divisor_multiplier_{generate_tuple(
[&](auto i) { return MagicDivision::CalculateMagicMultiplier(low_lengths[i]); },
@@ -1058,7 +1062,7 @@ struct DynamicMerge_v2_magic_division
low_lengths_magic_divisor_shift_{generate_tuple(
[&](auto i) { return MagicDivision::CalculateMagicShift(low_lengths[i]); },
Number<NDimLow>{})},
up_lengths_{make_tuple(container_reduce(low_lengths, math::multiplies_v2{}, Number<1>{}))}
up_lengths_{make_tuple(container_reduce(low_lengths, math::multiplies{}, Number<1>{}))}
{
static_assert(LowerIndex::Size() == NDimLow, "wrong!");
}
@@ -1151,7 +1155,7 @@ struct DynamicMerge_v2_magic_division
__host__ __device__ void Print() const
{
printf("{");
printf("DynamicMerge_v2_magic_division, ");
printf("Merge_v2_magic_division, ");
printf("low_lengths_ ");
print_multi_index(low_lengths_);
printf("low_lengths_magic_divisor_multiplier_ ");
@@ -1177,18 +1181,18 @@ struct DynamicMerge_v2_magic_division
// 5. When upper-index is int32_t type (when index_t is int32_t), its value need to be
// non-negative.
template <typename LowLengths>
struct DynamicMerge_v2r2_magic_division
struct Merge_v2r2_magic_division
{
static constexpr index_t NDimLow = LowLengths::Size();
using LowerIndex = MultiIndex<NDimLow>;
using UpperIndex = MultiIndex<1>;
using LowLengthsScan = decltype(
container_reverse_exclusive_scan(LowLengths{}, math::multiplies_v2{}, Number<1>{}));
using LowLengthsScan =
decltype(container_reverse_exclusive_scan(LowLengths{}, math::multiplies{}, Number<1>{}));
using UpLengths =
decltype(make_tuple(container_reduce(LowLengths{}, math::multiplies_v2{}, Number<1>{})));
decltype(make_tuple(container_reduce(LowLengths{}, math::multiplies{}, Number<1>{})));
using LowLengthsScanMagicDivisorMultipiler = decltype(generate_tuple(
lambda_merge_generate_MagicDivision_calculate_magic_multiplier<LowLengthsScan>{},
@@ -1204,19 +1208,19 @@ struct DynamicMerge_v2r2_magic_division
LowLengthsScanMagicDivisorShift low_lengths_scan_magic_divisor_shift_;
UpLengths up_lengths_;
__host__ __device__ constexpr DynamicMerge_v2r2_magic_division() = default;
__host__ __device__ constexpr Merge_v2r2_magic_division() = default;
__host__ __device__ constexpr DynamicMerge_v2r2_magic_division(const LowLengths& low_lengths)
__host__ __device__ constexpr Merge_v2r2_magic_division(const LowLengths& low_lengths)
: low_lengths_{low_lengths},
low_lengths_scan_{
container_reverse_exclusive_scan(low_lengths, math::multiplies_v2{}, Number<1>{})},
container_reverse_exclusive_scan(low_lengths, math::multiplies{}, Number<1>{})},
low_lengths_scan_magic_divisor_multiplier_{generate_tuple(
[&](auto i) { return MagicDivision::CalculateMagicMultiplier(low_lengths_scan_[i]); },
Number<NDimLow>{})},
low_lengths_scan_magic_divisor_shift_{generate_tuple(
[&](auto i) { return MagicDivision::CalculateMagicShift(low_lengths_scan_[i]); },
Number<NDimLow>{})},
up_lengths_{make_tuple(container_reduce(low_lengths, math::multiplies_v2{}, Number<1>{}))}
up_lengths_{make_tuple(container_reduce(low_lengths, math::multiplies{}, Number<1>{}))}
{
static_assert(LowerIndex::Size() == NDimLow, "wrong!");
}
@@ -1308,7 +1312,7 @@ struct DynamicMerge_v2r2_magic_division
__host__ __device__ void Print() const
{
printf("{");
printf("DynamicMerge_v2r2_magic_division, ");
printf("Merge_v2r2_magic_division, ");
printf("low_lengths_ ");
print_multi_index(low_lengths_);
printf("low_lengths_scan ");
@@ -1324,7 +1328,7 @@ struct DynamicMerge_v2r2_magic_division
};
template <typename UpLengths, bool Use24BitIntegerCalculation>
struct DynamicUnMerge
struct UnMerge
{
static constexpr index_t NDimUp = UpLengths::Size();
@@ -1332,17 +1336,17 @@ struct DynamicUnMerge
using UpperIndex = MultiIndex<NDimUp>;
using UpLengthsScan =
decltype(container_reverse_exclusive_scan(UpLengths{}, math::multiplies_v2{}, Number<1>{}));
decltype(container_reverse_exclusive_scan(UpLengths{}, math::multiplies{}, Number<1>{}));
UpLengths up_lengths_;
UpLengthsScan up_lengths_scan_;
__host__ __device__ constexpr DynamicUnMerge() = default;
__host__ __device__ constexpr UnMerge() = default;
__host__ __device__ constexpr DynamicUnMerge(const UpLengths& up_lengths)
__host__ __device__ constexpr UnMerge(const UpLengths& up_lengths)
: up_lengths_{up_lengths},
up_lengths_scan_{
container_reverse_exclusive_scan(up_lengths, math::multiplies_v2{}, Number<1>{})}
container_reverse_exclusive_scan(up_lengths, math::multiplies{}, Number<1>{})}
{
}
@@ -1414,7 +1418,7 @@ struct DynamicUnMerge
__host__ __device__ void Print() const
{
printf("{");
printf("DynamicUnMerge, ");
printf("UnMerge, ");
printf("up_lengths_");
print_multi_index(up_lengths_);
printf("up_lengths_scan_");
@@ -1424,13 +1428,13 @@ struct DynamicUnMerge
};
template <typename LowerIndex>
struct DynamicFreeze
struct Freeze
{
LowerIndex low_idx_;
__host__ __device__ constexpr DynamicFreeze() = default;
__host__ __device__ constexpr Freeze() = default;
__host__ __device__ constexpr DynamicFreeze(const LowerIndex& low_idx) : low_idx_{low_idx} {}
__host__ __device__ constexpr Freeze(const LowerIndex& low_idx) : low_idx_{low_idx} {}
__host__ __device__ static constexpr index_t GetNumOfLowerDimension() { return 1; }
@@ -1483,22 +1487,22 @@ struct DynamicFreeze
__host__ __device__ void Print() const
{
printf("DynamicFreeze");
printf("Freeze");
printf("low_idx_ %d", index_t{low_idx_});
}
};
// Insert a dangling upper dimension without lower dimension
template <typename UpperLength>
struct DynamicInsert
struct Insert
{
using UpLengths = decltype(make_tuple(UpperLength{}));
UpLengths up_lengths_;
__host__ __device__ constexpr DynamicInsert() = default;
__host__ __device__ constexpr Insert() = default;
__host__ __device__ constexpr DynamicInsert(const UpperLength& up_length)
__host__ __device__ constexpr Insert(const UpperLength& up_length)
: up_lengths_{make_tuple(up_length)}
{
}
@@ -1550,13 +1554,13 @@ struct DynamicInsert
__host__ __device__ void Print() const
{
printf("DynamicInsert");
printf("Insert");
print_multi_index(up_lengths_);
}
};
template <typename VectorSize, typename UpLength>
struct DynamicVectorize
struct Vectorize
{
using LowerIndex = MultiIndex<1>;
using UpperIndex = MultiIndex<1>;
@@ -1566,10 +1570,10 @@ struct DynamicVectorize
UpLengths up_lengths_;
VectorSize vector_size_;
__host__ __device__ constexpr DynamicVectorize() = default;
__host__ __device__ constexpr Vectorize() = default;
__host__ __device__ constexpr DynamicVectorize(const VectorSize& vector_size,
const UpLength& up_length)
__host__ __device__ constexpr Vectorize(const VectorSize& vector_size,
const UpLength& up_length)
: vector_size_{vector_size}, up_lengths_{make_tuple(up_length)}
{
}
@@ -1633,7 +1637,7 @@ struct DynamicVectorize
__host__ __device__ void Print() const
{
printf("{");
printf("DynamicVectorize, ");
printf("Vectorize, ");
printf("up_lengths_");
print_multi_index(up_lengths_);
printf("}");
@@ -1641,7 +1645,7 @@ struct DynamicVectorize
};
template <typename LowLength, typename SliceBegin, typename SliceEnd>
struct DynamicSlice
struct Slice
{
using LowerIndex = MultiIndex<1>;
using UpperIndex = MultiIndex<1>;
@@ -1652,11 +1656,11 @@ struct DynamicSlice
SliceBegin slice_begin_;
SliceEnd slice_end_;
__host__ __device__ constexpr DynamicSlice() = default;
__host__ __device__ constexpr Slice() = default;
__host__ __device__ constexpr DynamicSlice(const LowLength&,
const SliceBegin& slice_begin,
const SliceEnd& slice_end)
__host__ __device__ constexpr Slice(const LowLength&,
const SliceBegin& slice_begin,
const SliceEnd& slice_end)
: up_lengths_{make_tuple(slice_end - slice_begin)},
slice_begin_{slice_begin},
slice_end_{slice_end}
@@ -1724,7 +1728,7 @@ struct DynamicSlice
__host__ __device__ void Print() const
{
printf("{");
printf("DynamicSlice, ");
printf("Slice, ");
printf("up_lengths_");
print_multi_index(up_lengths_);
printf("slice_begin_ %d", index_t{slice_begin_});

43
composable_kernel/include/tensor_description/dynamic_multi_index_transform_helper.hpp → composable_kernel/include/tensor_description/multi_index_transform_helper.hpp
View File

@@ -1,15 +1,15 @@
#ifndef CK_DYNAMIC_MULTI_INDEX_TRANSFORM_HELPER_HPP
#define CK_DYNAMIC_MULTI_INDEX_TRANSFORM_HELPER_HPP
#ifndef CK_MULTI_INDEX_TRANSFORM_HELPER_HPP
#define CK_MULTI_INDEX_TRANSFORM_HELPER_HPP
#include "common_header.hpp"
#include "dynamic_multi_index_transform.hpp"
#include "multi_index_transform.hpp"
namespace ck {
template <typename LowLength>
__host__ __device__ constexpr auto make_pass_through_transform(const LowLength& low_length)
{
return DynamicPassThrough<LowLength>{low_length};
return PassThrough<LowLength>{low_length};
}
template <typename LowLength, typename LeftPad, typename RightPad, bool SkipIsValidCheck = false>
@@ -19,47 +19,46 @@ make_pad_transform(const LowLength& low_length,
const RightPad& right_pad,
integral_constant<bool, SkipIsValidCheck> = integral_constant<bool, false>{})
{
return DynamicPad<LowLength, LeftPad, RightPad, SkipIsValidCheck>{
low_length, left_pad, right_pad};
return Pad<LowLength, LeftPad, RightPad, SkipIsValidCheck>{low_length, left_pad, right_pad};
}
template <typename LowLength, typename LeftPad, bool SkipIsValidCheck = false>
template <typename LowLength, typename LeftPadLength, bool SkipIsValidCheck = false>
__host__ __device__ constexpr auto make_left_pad_transform(
const LowLength& low_length,
const LeftPad& left_pad,
const LeftPadLength& left_pad,
integral_constant<bool, SkipIsValidCheck> = integral_constant<bool, false>{})
{
return DynamicLeftPad<LowLength, LeftPad, SkipIsValidCheck>{low_length, left_pad};
return LeftPad<LowLength, LeftPadLength, SkipIsValidCheck>{low_length, left_pad};
}
template <typename LowLength, typename RightPad, bool SkipIsValidCheck>
template <typename LowLength, typename RightPadLength, bool SkipIsValidCheck>
__host__ __device__ constexpr auto make_right_pad_transform(
const LowLength& low_length,
const RightPad& right_pad,
const RightPadLength& right_pad,
integral_constant<bool, SkipIsValidCheck> = integral_constant<bool, false>{})
{
return DynamicRightPad<LowLength, RightPad, SkipIsValidCheck>{low_length, right_pad};
return RightPad<LowLength, RightPadLength, SkipIsValidCheck>{low_length, right_pad};
}
template <typename UpLengths,
typename Coefficients,
typename std::enable_if<UpLengths::Size() == Coefficients::Size(), bool>::type = false>
typename enable_if<UpLengths::Size() == Coefficients::Size(), bool>::type = false>
__host__ __device__ constexpr auto make_embed_transform(const UpLengths& up_lengths,
const Coefficients& coefficients)
{
return DynamicEmbed<UpLengths, Coefficients>{up_lengths, coefficients};
return Embed<UpLengths, Coefficients>{up_lengths, coefficients};
}
template <typename LowLengths>
__host__ __device__ constexpr auto make_merge_transform(const LowLengths& low_lengths)
{
#if !CK_EXPERIMENTAL_MERGE_USE_MAGIC_DIVISION
return DynamicMerge_v1_carry_check<LowLengths>{low_lengths};
return Merge_v1_carry_check<LowLengths>{low_lengths};
#else
#if 1
return DynamicMerge_v2_magic_division<LowLengths>{low_lengths};
return Merge_v2_magic_division<LowLengths>{low_lengths};
#else
return DynamicMerge_v2r2_magic_division<LowLengths>{low_lengths};
return Merge_v2r2_magic_division<LowLengths>{low_lengths};
#endif
#endif
}
@@ -68,7 +67,7 @@ template <typename LowLengths>
__host__ __device__ constexpr auto
make_merge_transform_v2_magic_division(const LowLengths& low_lengths)
{
return DynamicMerge_v2_magic_division<LowLengths>{low_lengths};
return Merge_v2_magic_division<LowLengths>{low_lengths};
}
template <typename UpLengths, bool Use24BitIntegerCalculation = false>
@@ -76,13 +75,13 @@ __host__ __device__ constexpr auto make_unmerge_transform(
const UpLengths& up_lengths,
integral_constant<bool, Use24BitIntegerCalculation> = integral_constant<bool, false>{})
{
return DynamicUnMerge<UpLengths, Use24BitIntegerCalculation>{up_lengths};
return UnMerge<UpLengths, Use24BitIntegerCalculation>{up_lengths};
}
template <typename LowerIndex>
__host__ __device__ constexpr auto make_freeze_transform(const LowerIndex& low_idx)
{
return DynamicFreeze<LowerIndex>{low_idx};
return Freeze<LowerIndex>{low_idx};
}
template <typename LowLength, typename SliceBegin, typename SliceEnd>
@@ -90,14 +89,14 @@ __host__ __device__ constexpr auto make_slice_transform(const LowLength& low_len
const SliceBegin& slice_begin,
const SliceEnd& slice_end)
{
return DynamicSlice<LowLength, SliceBegin, SliceEnd>{low_length, slice_begin, slice_end};
return Slice<LowLength, SliceBegin, SliceEnd>{low_length, slice_begin, slice_end};
}
template <typename VectorSize, typename UpLength>
__host__ __device__ constexpr auto make_vectorize_transform(const VectorSize& vector_size,
const UpLength& up_length)
{
return DynamicVectorize<VectorSize, UpLength>{vector_size, up_length};
return Vectorize<VectorSize, UpLength>{vector_size, up_length};
}
} // namespace ck

10
composable_kernel/include/tensor_description/tensor_adaptor.hpp
View File

@@ -2,8 +2,8 @@
#define CK_TENSOR_ADAPTOR_HPP
#include "common_header.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
namespace ck {
@@ -64,7 +64,7 @@ struct TensorAdaptor
Number<ndim_top_>{});
// TODO: make container_reduce support tuple of Number and index_t
return container_reduce(lengths, math::multiplies_v2{}, Number<1>{});
return container_reduce(lengths, math::multiplies{}, Number<1>{});
}
template <index_t IDim>
@@ -454,9 +454,7 @@ __host__ __device__ constexpr auto make_single_stage_tensor_adaptor(const Transf
remove_cv_t<decltype(top_dim_hidden_ids)>>{transforms};
}
template <typename X,
typename... Xs,
typename std::enable_if<sizeof...(Xs) >= 2, bool>::type = false>
template <typename X, typename... Xs, typename enable_if<sizeof...(Xs) >= 2, bool>::type = false>
__host__ __device__ constexpr auto chain_tensor_adaptors(const X& x, const Xs&... xs)
{
return chain_tensor_adaptors(x, chain_tensor_adaptors(xs...));

107
composable_kernel/include/tensor_description/dynamic_tensor_descriptor.hpp → composable_kernel/include/tensor_description/tensor_descriptor.hpp
View File

@@ -1,16 +1,16 @@
#ifndef CK_DYNAMIC_TENSOR_DESCRIPTOR_HPP
#define CK_DYNAMIC_TENSOR_DESCRIPTOR_HPP
#ifndef CK_TENSOR_DESCRIPTOR_HPP
#define CK_TENSOR_DESCRIPTOR_HPP
#include "common_header.hpp"
#include "dynamic_multi_index_transform.hpp"
#include "multi_index_transform.hpp"
namespace ck {
template <index_t NDimHidden, typename VisibleDimensionIds>
struct DynamicTensorCoordinate;
struct TensorCoordinate;
template <index_t NTransform, index_t NDimVisible, typename UpdateLowerIndexHack>
struct DynamicTensorCoordinateIterator;
struct TensorCoordinateStep;
// Transforms: Tuple<transforms...>
// LowerDimensionIdss : Tuple<Sequence<...>, ...>
@@ -21,7 +21,7 @@ template <typename Transforms,
typename UpperDimensionIdss,
typename VisibleDimensionIds,
typename ElementSpaceSize>
struct DynamicTensorDescriptor
struct TensorDescriptor
{
// TODO make these private
__host__ __device__ static constexpr index_t GetNumOfTransform() { return Transforms::Size(); }
@@ -69,7 +69,7 @@ struct DynamicTensorDescriptor
Number<ndim_visible_>{});
// TODO: make container_reduce support tuple of Number and index_t
return container_reduce(lengths, math::multiplies_v2{}, Number<1>{});
return container_reduce(lengths, math::multiplies{}, Number<1>{});
}
template <index_t IDim>
@@ -105,16 +105,16 @@ struct DynamicTensorDescriptor
using VisibleIndex = MultiIndex<ndim_visible_>;
using HiddenIndex = MultiIndex<ndim_hidden_>;
using Coordinate = DynamicTensorCoordinate<ndim_hidden_, VisibleDimensionIds>;
using Coordinate = TensorCoordinate<ndim_hidden_, VisibleDimensionIds>;
// may be index_t or Number<>
using ElementSize = remove_cv_t<decltype(InitializeElementSize(Transforms{}))>;
public:
__host__ __device__ constexpr DynamicTensorDescriptor() = default;
__host__ __device__ constexpr TensorDescriptor() = default;
__host__ __device__ constexpr DynamicTensorDescriptor(const Transforms& transforms,
ElementSpaceSize element_space_size)
__host__ __device__ constexpr TensorDescriptor(const Transforms& transforms,
ElementSpaceSize element_space_size)
: transforms_{transforms},
element_size_{InitializeElementSize(transforms)},
element_space_size_{element_space_size}
@@ -159,7 +159,7 @@ struct DynamicTensorDescriptor
{
static_assert(Idx::Size() == GetNumOfDimension(), "wrong! inconsistent # of dimension");
return make_dynamic_tensor_coordinate(*this, idx).GetOffset();
return make_tensor_coordinate(*this, idx).GetOffset();
}
// TODO make these private
@@ -196,7 +196,7 @@ struct DynamicTensorDescriptor
__host__ __device__ void Print() const
{
printf("{");
printf("DynamicTensorDescriptor, ");
printf("TensorDescriptor, ");
static_for<0, ntransform_, 1>{}([&](auto i) {
printf("transforms: ");
transforms_[i].Print();
@@ -217,7 +217,7 @@ struct DynamicTensorDescriptor
};
template <index_t NDimHidden, typename VisibleDimensionIds>
struct DynamicTensorCoordinate
struct TensorCoordinate
{
// TODO make these private
static constexpr index_t ndim_visible_ = VisibleDimensionIds::Size();
@@ -226,9 +226,9 @@ struct DynamicTensorCoordinate
using VisibleIndex = MultiIndex<ndim_visible_>;
public:
__host__ __device__ constexpr DynamicTensorCoordinate() = default;
__host__ __device__ constexpr TensorCoordinate() = default;
__host__ __device__ constexpr DynamicTensorCoordinate(const HiddenIndex& idx_hidden)
__host__ __device__ constexpr TensorCoordinate(const HiddenIndex& idx_hidden)
: idx_hidden_{idx_hidden}
{
}
@@ -252,16 +252,16 @@ struct DynamicTensorCoordinate
};
template <index_t NTransform, index_t NDimVisible, typename UpdateLowerIndexHack>
struct DynamicTensorCoordinateIterator
struct TensorCoordinateStep
{
// TODO make these private
using VisibleIndex = MultiIndex<NDimVisible>;
public:
__host__ __device__ constexpr DynamicTensorCoordinateIterator() = default;
__host__ __device__ constexpr TensorCoordinateStep() = default;
__host__ __device__ constexpr DynamicTensorCoordinateIterator(
const VisibleIndex& idx_diff_visible, const MultiIndex<NTransform>& do_transforms)
__host__ __device__ constexpr TensorCoordinateStep(const VisibleIndex& idx_diff_visible,
const MultiIndex<NTransform>& do_transforms)
: idx_diff_visible_{idx_diff_visible}, do_transforms_{do_transforms}
{
}
@@ -283,7 +283,7 @@ struct DynamicTensorCoordinateIterator
// TODO: How to fix this? It uses an struct instead of lambda because lambda
// doesn't have constructor, and to put it outside the scope where it is used
// (transform_dynamic_tensor_descriptor) because template cannot be defined inside a function
// (transform_tensor_descriptor) because template cannot be defined inside a function
// template
template <typename NewTransforms>
struct lambda_get_up_dim_num
@@ -301,10 +301,10 @@ template <typename OldTensorDescriptor,
typename NewLowerDimensionOldVisibleIdss,
typename NewUpperDimensionNewVisibleIdss>
__host__ __device__ constexpr auto
transform_dynamic_tensor_descriptor(const OldTensorDescriptor& old_tensor_desc,
const NewTransforms& new_transforms,
NewLowerDimensionOldVisibleIdss,
NewUpperDimensionNewVisibleIdss)
transform_tensor_descriptor(const OldTensorDescriptor& old_tensor_desc,
const NewTransforms& new_transforms,
NewLowerDimensionOldVisibleIdss,
NewUpperDimensionNewVisibleIdss)
{
// sanity check
{
@@ -376,17 +376,17 @@ transform_dynamic_tensor_descriptor(const OldTensorDescriptor& old_tensor_desc,
const auto element_space_size = old_tensor_desc.GetElementSpaceSize();
return DynamicTensorDescriptor<remove_cv_t<decltype(all_transforms)>,
remove_cv_t<decltype(all_low_dim_hidden_idss)>,
remove_cv_t<decltype(all_up_dim_hidden_idss)>,
remove_cv_t<decltype(new_visible_dim_hidden_ids)>,
remove_cv_t<decltype(element_space_size)>>{all_transforms,
element_space_size};
return TensorDescriptor<remove_cv_t<decltype(all_transforms)>,
remove_cv_t<decltype(all_low_dim_hidden_idss)>,
remove_cv_t<decltype(all_up_dim_hidden_idss)>,
remove_cv_t<decltype(new_visible_dim_hidden_ids)>,
remove_cv_t<decltype(element_space_size)>>{all_transforms,
element_space_size};
}
template <typename TensorDesc, typename VisibleIndex>
__host__ __device__ constexpr auto make_dynamic_tensor_coordinate(const TensorDesc& tensor_desc,
const VisibleIndex& idx_visible)
__host__ __device__ constexpr auto make_tensor_coordinate(const TensorDesc& tensor_desc,
const VisibleIndex& idx_visible)
{
static_assert(TensorDesc::GetNumOfDimension() == VisibleIndex::Size(),
"wrong! # of dimension inconsistent");
@@ -416,14 +416,15 @@ __host__ __device__ constexpr auto make_dynamic_tensor_coordinate(const TensorDe
set_container_subset(idx_hidden, dims_low, idx_low);
});
return DynamicTensorCoordinate<ndim_hidden, decltype(visible_dim_ids)>{idx_hidden};
return TensorCoordinate<ndim_hidden, decltype(visible_dim_ids)>{idx_hidden};
}
// UpdateLowerIndexHack: Sequence<...>
// HACK: control UpdateLowerIndex
template <typename TensorDesc, typename VisibleIndex, typename UpdateLowerIndexHack>
__host__ __device__ constexpr auto make_dynamic_tensor_coordinate_iterator(
const TensorDesc&, const VisibleIndex& idx_diff_visible, UpdateLowerIndexHack)
__host__ __device__ constexpr auto make_tensor_coordinate_step(const TensorDesc&,
const VisibleIndex& idx_diff_visible,
UpdateLowerIndexHack)
{
static_assert(TensorDesc::GetNumOfDimension() == VisibleIndex::Size(),
"wrong! # of dimension inconsistent");
@@ -470,23 +471,24 @@ __host__ __device__ constexpr auto make_dynamic_tensor_coordinate_iterator(
set_container_subset(is_non_zero_diff, dims_low, non_zero_diff_pick_low);
});
return DynamicTensorCoordinateIterator<ntransform, ndim_visible, UpdateLowerIndexHack>{
idx_diff_visible, do_transforms};
return TensorCoordinateStep<ntransform, ndim_visible, UpdateLowerIndexHack>{idx_diff_visible,
do_transforms};
}
template <typename TensorDesc, typename VisibleIndex>
__host__ __device__ constexpr auto
make_dynamic_tensor_coordinate_iterator(const TensorDesc&, const VisibleIndex& idx_diff_visible)
__host__ __device__ constexpr auto make_tensor_coordinate_step(const TensorDesc&,
const VisibleIndex& idx_diff_visible)
{
constexpr index_t ntransform = TensorDesc::GetNumOfTransform();
return make_dynamic_tensor_coordinate_iterator(
return make_tensor_coordinate_step(
TensorDesc{}, idx_diff_visible, typename uniform_sequence_gen<ntransform, 0>::type{});
}
template <typename TensorDesc, typename TensorCoord, typename TensorCoordIterator>
__host__ __device__ constexpr void move_dynamic_tensor_coordinate(
const TensorDesc& tensor_desc, TensorCoord& coord, const TensorCoordIterator& coord_iterator)
template <typename TensorDesc, typename TensorCoord, typename TensorCoordStep>
__host__ __device__ constexpr void move_tensor_coordinate(const TensorDesc& tensor_desc,
TensorCoord& coord,
const TensorCoordStep& coord_step)
{
constexpr index_t ndim_hidden = TensorDesc::GetNumOfHiddenDimension();
constexpr index_t ntransform = TensorDesc::GetNumOfTransform();
@@ -495,9 +497,8 @@ __host__ __device__ constexpr void move_dynamic_tensor_coordinate(
auto idx_diff_hidden = make_zero_multi_index<ndim_hidden>();
// initialize visible index diff
set_container_subset(idx_diff_hidden,
TensorDesc::GetVisibleDimensionIds(),
coord_iterator.GetVisibleIndexDiff());
set_container_subset(
idx_diff_hidden, TensorDesc::GetVisibleDimensionIds(), coord_step.GetVisibleIndexDiff());
// this is what needs to be updated
auto& idx_hidden = coord.GetHiddenIndex();
@@ -506,13 +507,13 @@ __host__ __device__ constexpr void move_dynamic_tensor_coordinate(
auto idx_hidden_pick_visible =
get_container_subset(idx_hidden, TensorDesc::GetVisibleDimensionIds());
idx_hidden_pick_visible += coord_iterator.GetIndexDiff();
idx_hidden_pick_visible += coord_step.GetIndexDiff();
set_container_subset(idx_hidden, TensorDesc::GetVisibleDimensionIds(), idx_hidden_pick_visible);
// update rest of hidden index
static_for<ntransform - 1, -1, -1>{}([&](auto itran) {
if(coord_iterator.do_transforms_[itran])
if(coord_step.do_transforms_[itran])
{
const auto& tran = tensor_desc.GetTransforms().At(itran);
constexpr auto dims_low = TensorDesc::GetLowerDimensionIdss().At(itran);
@@ -524,8 +525,8 @@ __host__ __device__ constexpr void move_dynamic_tensor_coordinate(
MultiIndex<dims_low.Size()> idx_diff_low;
// HACK: control UpdateLowerIndex for DynamicMerge using hack
constexpr index_t Hack = decltype(coord_iterator.update_lower_index_hack_)::At(itran);
// HACK: control UpdateLowerIndex for Merge using hack
constexpr index_t Hack = decltype(coord_step.update_lower_index_hack_)::At(itran);
tran.UpdateLowerIndex(idx_diff_low, idx_diff_up, idx_low, idx_up_new, Number<Hack>{});
@@ -585,11 +586,11 @@ __host__ __device__ constexpr bool coordinate_has_valid_offset(const TensorDesc&
}
template <typename TensorDesc>
using DynamicTensorCoordinate_t = decltype(make_dynamic_tensor_coordinate(
using TensorCoordinate_t = decltype(make_tensor_coordinate(
TensorDesc{}, MultiIndex<remove_cv_t<remove_reference_t<TensorDesc>>::GetNumOfDimension()>{}));
template <typename TensorDesc>
using DynamicTensorCoordinateIterator_t = decltype(make_dynamic_tensor_coordinate_iterator(
using TensorCoordinateStep_t = decltype(make_tensor_coordinate_step(
TensorDesc{}, MultiIndex<remove_cv_t<remove_reference_t<TensorDesc>>::GetNumOfDimension()>{}));
} // namespace ck

49
composable_kernel/include/tensor_description/dynamic_tensor_descriptor_helper.hpp → composable_kernel/include/tensor_description/tensor_descriptor_helper.hpp
View File

@@ -1,9 +1,9 @@
#ifndef CK_DYNAMIC_TENSOR_DESCRIPTOR_HELPER_HPP
#define CK_DYNAMIC_TENSOR_DESCRIPTOR_HELPER_HPP
#ifndef CK_TENSOR_DESCRIPTOR_HELPER_HPP
#define CK_TENSOR_DESCRIPTOR_HELPER_HPP
#include "common_header.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_multi_index_transform_helper.hpp"
#include "tensor_descriptor.hpp"
#include "multi_index_transform_helper.hpp"
namespace ck {
@@ -37,10 +37,9 @@ __host__ __device__ constexpr auto calculate_element_space_size_impl(const Lengt
template <typename... Lengths,
typename... Strides,
typename std::enable_if<sizeof...(Lengths) == sizeof...(Strides), bool>::type = false>
__host__ __device__ constexpr auto
make_dynamic_naive_tensor_descriptor_v2(const Tuple<Lengths...>& lengths,
const Tuple<Strides...>& strides)
typename enable_if<sizeof...(Lengths) == sizeof...(Strides), bool>::type = false>
__host__ __device__ constexpr auto make_naive_tensor_descriptor(const Tuple<Lengths...>& lengths,
const Tuple<Strides...>& strides)
{
constexpr index_t N = sizeof...(Lengths);
@@ -75,12 +74,12 @@ make_dynamic_naive_tensor_descriptor_v2(const Tuple<Lengths...>& lengths,
calculate_element_space_size_impl(lengths, strides, Number<0>{}, Number<1>{});
#endif
return DynamicTensorDescriptor<remove_cv_t<decltype(transforms)>,
remove_cv_t<decltype(low_dim_hidden_idss)>,
remove_cv_t<decltype(up_dim_hidden_idss)>,
remove_cv_t<decltype(visible_dim_hidden_ids)>,
remove_cv_t<decltype(element_space_size)>>{transforms,
element_space_size};
return TensorDescriptor<remove_cv_t<decltype(transforms)>,
remove_cv_t<decltype(low_dim_hidden_idss)>,
remove_cv_t<decltype(up_dim_hidden_idss)>,
remove_cv_t<decltype(visible_dim_hidden_ids)>,
remove_cv_t<decltype(element_space_size)>>{transforms,
element_space_size};
}
// Lengths... can be:
@@ -88,7 +87,7 @@ make_dynamic_naive_tensor_descriptor_v2(const Tuple<Lengths...>& lengths,
// 2) Number<>, which is known at compile-time
template <typename... Lengths>
__host__ __device__ constexpr auto
make_dynamic_naive_tensor_descriptor_packed_v2(const Tuple<Lengths...>& lengths)
make_naive_tensor_descriptor_packed(const Tuple<Lengths...>& lengths)
{
constexpr index_t N = sizeof...(Lengths);
@@ -101,19 +100,19 @@ make_dynamic_naive_tensor_descriptor_packed_v2(const Tuple<Lengths...>& lengths)
constexpr auto visible_dim_hidden_ids = typename arithmetic_sequence_gen<1, N + 1, 1>::type{};
const auto element_space_size = container_reduce(lengths, math::multiplies_v2{}, Number<1>{});
const auto element_space_size = container_reduce(lengths, math::multiplies{}, Number<1>{});
return DynamicTensorDescriptor<remove_cv_t<decltype(transforms)>,
remove_cv_t<decltype(low_dim_hidden_idss)>,
remove_cv_t<decltype(up_dim_hidden_idss)>,
remove_cv_t<decltype(visible_dim_hidden_ids)>,
remove_cv_t<decltype(element_space_size)>>{transforms,
element_space_size};
return TensorDescriptor<remove_cv_t<decltype(transforms)>,
remove_cv_t<decltype(low_dim_hidden_idss)>,
remove_cv_t<decltype(up_dim_hidden_idss)>,
remove_cv_t<decltype(visible_dim_hidden_ids)>,
remove_cv_t<decltype(element_space_size)>>{transforms,
element_space_size};
}
template <typename... Lengths, typename Align>
__host__ __device__ constexpr auto
make_dynamic_naive_tensor_descriptor_aligned_v2(const Tuple<Lengths...>& lengths, Align align)
make_naive_tensor_descriptor_aligned(const Tuple<Lengths...>& lengths, Align align)
{
constexpr auto I1 = Number<1>{};
@@ -134,7 +133,7 @@ make_dynamic_naive_tensor_descriptor_aligned_v2(const Tuple<Lengths...>& lengths
else
{
return container_reduce(lengths,
math::multiplies_v2{},
math::multiplies{},
Number<stride_n_minus_2>{},
i + I1,
Number<N - 1>{},
@@ -143,7 +142,7 @@ make_dynamic_naive_tensor_descriptor_aligned_v2(const Tuple<Lengths...>& lengths
},
Number<N>{});
return make_dynamic_naive_tensor_descriptor_v2(lengths, strides);
return make_naive_tensor_descriptor(lengths, strides);
}
} // namespace ck

90
composable_kernel/include/tensor_operation/blockwise_gemm_dlops_v2r2.hpp
View File

@@ -3,7 +3,7 @@
#include "common_header.hpp"
#include "tensor_adaptor.hpp"
#include "threadwise_dynamic_tensor_slice_transfer.hpp"
#include "threadwise_tensor_slice_transfer.hpp"
#include "threadwise_contraction_dlops.hpp"
namespace ck {
@@ -22,24 +22,24 @@ namespace ck {
// 2. CThreadBuffer is StaticBuffer
// Also assume:
// M0 = N0 = 2. It will do 2x2 pipelined read and fma (ABBA optimization)
template <index_t BlockSize,
typename FloatA,
typename FloatB,
typename FloatC,
typename AKMBlockDesc,
typename BKNBlockDesc,
index_t M1PerThreadM11,
index_t N1PerThreadN11,
index_t KPerThread,
index_t M1N1ThreadClusterM100,
index_t M1N1ThreadClusterN100,
index_t M1N1ThreadClusterM101,
index_t M1N1ThreadClusterN101,
index_t AThreadCopyScalarPerVector_M11,
index_t BThreadCopyScalarPerVector_N11,
typename std::enable_if<AKMBlockDesc::IsKnownAtCompileTime() &&
BKNBlockDesc::IsKnownAtCompileTime(),
bool>::type = false>
template <
index_t BlockSize,
typename FloatA,
typename FloatB,
typename FloatC,
typename AKMBlockDesc,
typename BKNBlockDesc,
index_t M1PerThreadM11,
index_t N1PerThreadN11,
index_t KPerThread,
index_t M1N1ThreadClusterM100,
index_t M1N1ThreadClusterN100,
index_t M1N1ThreadClusterM101,
index_t M1N1ThreadClusterN101,
index_t AThreadCopyScalarPerVector_M11,
index_t BThreadCopyScalarPerVector_N11,
typename enable_if<AKMBlockDesc::IsKnownAtCompileTime() && BKNBlockDesc::IsKnownAtCompileTime(),
bool>::type = false>
struct BlockwiseGemmDlops_km_kn_m0m1n0n1_v2r2_pipeline_2x2
{
using AIndex = MultiIndex<3>;
@@ -71,9 +71,9 @@ struct BlockwiseGemmDlops_km_kn_m0m1n0n1_v2r2_pipeline_2x2
static constexpr index_t N0 = N / N1;
__host__ __device__ static constexpr auto
MakeAKM0M1BlockDescriptor(const AKMBlockDesc& a_k_m_block_desc)
MakeAKM0M1BlockDescriptor(const AKMBlockDesc& /* a_k_m_block_desc */)
{
const auto a_k_m0_m1_block_desc = transform_dynamic_tensor_descriptor(
const auto a_k_m0_m1_block_desc = transform_tensor_descriptor(
AKMBlockDesc{},
make_tuple(make_pass_through_transform(Number<K>{}),
make_unmerge_transform(make_tuple(Number<M0>{}, Number<M1>{}))),
@@ -84,9 +84,9 @@ struct BlockwiseGemmDlops_km_kn_m0m1n0n1_v2r2_pipeline_2x2
}
__host__ __device__ static constexpr auto
MakeBKN0N1BlockDescriptor(const BKNBlockDesc& b_k_n_block_desc)
MakeBKN0N1BlockDescriptor(const BKNBlockDesc& /* b_k_n_block_desc */)
{
const auto b_k_n0_n1_block_desc = transform_dynamic_tensor_descriptor(
const auto b_k_n0_n1_block_desc = transform_tensor_descriptor(
BKNBlockDesc{},
make_tuple(make_pass_through_transform(Number<K>{}),
make_unmerge_transform(make_tuple(Number<N0>{}, Number<N1>{}))),
@@ -194,7 +194,7 @@ struct BlockwiseGemmDlops_km_kn_m0m1n0n1_v2r2_pipeline_2x2
typename ABlockBuffer,
typename BBlockBuffer,
typename CThreadBuffer>
__device__ void Run(const CM0M1N0N1ThreadDesc& c_m0_m1_n0_n1_thread_desc,
__device__ void Run(const CM0M1N0N1ThreadDesc& /* c_m0_m1_n0_n1_thread_desc */,
const ABlockBuffer& a_block_buf,
const BBlockBuffer& b_block_buf,
CThreadBuffer& c_thread_buf) const
@@ -357,34 +357,32 @@ struct BlockwiseGemmDlops_km_kn_m0m1n0n1_v2r2_pipeline_2x2
private:
// A[K, M0, M1]
static constexpr auto a_k_m0_m1_thread_desc_ = make_dynamic_naive_tensor_descriptor_packed_v2(
static constexpr auto a_k_m0_m1_thread_desc_ = make_naive_tensor_descriptor_packed(
make_tuple(Number<KPerThread>{}, Number<M0>{}, Number<M1PerThreadM11>{}));
// B[K, N0, N1]
static constexpr auto b_k_n0_n1_thread_desc_ = make_dynamic_naive_tensor_descriptor_packed_v2(
static constexpr auto b_k_n0_n1_thread_desc_ = make_naive_tensor_descriptor_packed(
make_tuple(Number<KPerThread>{}, Number<N0>{}, Number<N1PerThreadN11>{}));
using AThreadCopy =
ThreadwiseDynamicTensorSliceTransfer_v4<FloatA,
FloatA,
decltype(a_k_m0_m1_block_desc_),
decltype(a_k_m0_m1_thread_desc_),
Sequence<KPerThread, 1, M1PerThreadM11>,
Sequence<0, 1, 2>,
2,
AThreadCopyScalarPerVector_M11,
1>;
using AThreadCopy = ThreadwiseTensorSliceTransfer_v4<FloatA,
FloatA,
decltype(a_k_m0_m1_block_desc_),
decltype(a_k_m0_m1_thread_desc_),
Sequence<KPerThread, 1, M1PerThreadM11>,
Sequence<0, 1, 2>,
2,
AThreadCopyScalarPerVector_M11,
1>;
using BThreadCopy =
ThreadwiseDynamicTensorSliceTransfer_v4<FloatB,
FloatB,
decltype(b_k_n0_n1_block_desc_),
decltype(b_k_n0_n1_thread_desc_),
Sequence<KPerThread, 1, N1PerThreadN11>,
Sequence<0, 1, 2>,
2,
BThreadCopyScalarPerVector_N11,
1>;
using BThreadCopy = ThreadwiseTensorSliceTransfer_v4<FloatB,
FloatB,
decltype(b_k_n0_n1_block_desc_),
decltype(b_k_n0_n1_thread_desc_),
Sequence<KPerThread, 1, N1PerThreadN11>,
Sequence<0, 1, 2>,
2,
BThreadCopyScalarPerVector_N11,
1>;
CIndex c_thread_origin_data_idx_;

20
composable_kernel/include/tensor_operation/blockwise_gemm_dlops_v2r3.hpp
View File

@@ -3,7 +3,7 @@
#include "common_header.hpp"
#include "tensor_adaptor.hpp"
#include "threadwise_dynamic_tensor_slice_transfer_v2.hpp"
#include "threadwise_tensor_slice_transfer_v2.hpp"
#include "threadwise_contraction_dlops.hpp"
namespace ck {
@@ -38,9 +38,9 @@ template <index_t BlockSize,
// BM10BN10ThreadClusterBN101, ...>
index_t AThreadCopyScalarPerVector_BM11,
index_t BThreadCopyScalarPerVector_BN11,
typename std::enable_if<ABlockDesc_BK0_BM_BK1::IsKnownAtCompileTime() &&
BBlockDesc_BK0_BN_BK1::IsKnownAtCompileTime(),
bool>::type = false>
typename enable_if<ABlockDesc_BK0_BM_BK1::IsKnownAtCompileTime() &&
BBlockDesc_BK0_BN_BK1::IsKnownAtCompileTime(),
bool>::type = false>
struct BlockwiseGemmDlops_A_BK0_BM_BK1_B_BK0_BN_BK1_C_BM0_BM1_BN0_BN1_pipeline_BM0_2_BN0_2
{
using AIndex = MultiIndex<3>;
@@ -75,7 +75,7 @@ struct BlockwiseGemmDlops_A_BK0_BM_BK1_B_BK0_BN_BK1_C_BM0_BM1_BN0_BN1_pipeline_B
__host__ __device__ static constexpr auto
MakeABlockDescriptor_BK0_BM0_BM1_BK1(const ABlockDesc_BK0_BM_BK1& a_block_desc_bk0_bm_bk1)
{
const auto a_block_bk0_bm0_bm1_bk1 = transform_dynamic_tensor_descriptor(
const auto a_block_bk0_bm0_bm1_bk1 = transform_tensor_descriptor(
a_block_desc_bk0_bm_bk1,
make_tuple(make_pass_through_transform(Number<BK0>{}),
make_unmerge_transform(make_tuple(Number<BM0>{}, Number<BM1>{})),
@@ -89,7 +89,7 @@ struct BlockwiseGemmDlops_A_BK0_BM_BK1_B_BK0_BN_BK1_C_BM0_BM1_BN0_BN1_pipeline_B
__host__ __device__ static constexpr auto
MakeBBlockDescriptor_BK0_BN0_BN1_BK1(const BBlockDesc_BK0_BN_BK1& b_block_desc_bk0_bn_bk1)
{
const auto b_block_desc_bk0_bn0_bn1_bk1 = transform_dynamic_tensor_descriptor(
const auto b_block_desc_bk0_bn0_bn1_bk1 = transform_tensor_descriptor(
b_block_desc_bk0_bn_bk1,
make_tuple(make_pass_through_transform(Number<BK0>{}),
make_unmerge_transform(make_tuple(Number<BN0>{}, Number<BN1>{})),
@@ -372,15 +372,15 @@ struct BlockwiseGemmDlops_A_BK0_BM_BK1_B_BK0_BN_BK1_C_BM0_BM1_BN0_BN1_pipeline_B
private:
// A[BK0, BM0, BM1, BK1]
static constexpr auto a_thread_desc_bk0_bm0_bm1_bk1_ =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(
make_naive_tensor_descriptor_packed(make_tuple(
Number<BK0PerThread>{}, Number<BM0>{}, Number<BM1PerThreadBM11>{}, Number<BK1>{}));
// B[BK0, BN0, BN1, BK1]
static constexpr auto b_thread_desc_bk0_bn0_bn1_bk1_ =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(
make_naive_tensor_descriptor_packed(make_tuple(
Number<BK0PerThread>{}, Number<BN0>{}, Number<BN1PerThreadBN11>{}, Number<BK1>{}));
using AThreadCopy = ThreadwiseDynamicTensorSliceTransfer_v4r1<
using AThreadCopy = ThreadwiseTensorSliceTransfer_v4r1<
FloatA,
FloatA,
decltype(a_block_desc_bk0_bm0_bm1_bk1_),
@@ -390,7 +390,7 @@ struct BlockwiseGemmDlops_A_BK0_BM_BK1_B_BK0_BN_BK1_C_BM0_BM1_BN0_BN1_pipeline_B
Sequence<1, 1, BM1PerThreadBM11, BK1>, // SrcVectorTensorLengths
Sequence<0, 1, 2, 3>>; // SrcVectorTensorContiguousDimOrder
using BThreadCopy = ThreadwiseDynamicTensorSliceTransfer_v4r1<
using BThreadCopy = ThreadwiseTensorSliceTransfer_v4r1<
FloatB,
FloatB,
decltype(b_block_desc_bk0_bn0_bn1_bk1_),

31
composable_kernel/include/tensor_operation/blockwise_gemm_dlops_v3.hpp
View File

@@ -31,25 +31,24 @@ struct BlockwiseGemmDlops_km_kn_m0m1n0n1_v3
// HACK: fix this @Jing Zhang
static constexpr index_t KPerThreadSubC = 4;
static constexpr auto a_thread_mtx_ = make_dynamic_naive_tensor_descriptor_packed_v2(
static constexpr auto a_thread_mtx_ = make_naive_tensor_descriptor_packed(
make_tuple(Number<EPerThreadLoop>{}, Number<KPerThreadSubC>{}));
static constexpr auto b_thread_mtx_ = make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(
static constexpr auto b_thread_mtx_ = make_naive_tensor_descriptor_packed(make_tuple(
Number<EPerThreadLoop>{}, Number<1>{}, Number<HPerThread>{}, Number<WPerThread>{}));
static constexpr auto c_thread_mtx_ = make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(
static constexpr auto c_thread_mtx_ = make_naive_tensor_descriptor_packed(make_tuple(
Number<KPerThreadSubC>{}, Number<1>{}, Number<HPerThread>{}, Number<WPerThread>{}));
using AThreadCopy =
ThreadwiseDynamicTensorSliceTransfer_v4<FloatA,
FloatA,
BlockMatrixA,
decltype(a_thread_mtx_),
Sequence<EPerThreadLoop, KPerThreadSubC>,
Sequence<0, 1>,
1,
ThreadGemmADataPerRead_K,
1>;
using AThreadCopy = ThreadwiseTensorSliceTransfer_v4<FloatA,
FloatA,
BlockMatrixA,
decltype(a_thread_mtx_),
Sequence<EPerThreadLoop, KPerThreadSubC>,
Sequence<0, 1>,
1,
ThreadGemmADataPerRead_K,
1>;
__device__ BlockwiseGemmDlops_km_kn_m0m1n0n1_v3()
: c_thread_begin_mtx_idx_{GetBeginOfThreadMatrixC(get_thread_local_1d_id())},
@@ -69,7 +68,6 @@ struct BlockwiseGemmDlops_km_kn_m0m1n0n1_v3
"wrong! K dimension not consistent\n");
constexpr index_t K = BlockMatrixA{}.GetLength(I1); // A is transposed
constexpr index_t N = BlockMatrixB{}.GetLength(I1);
constexpr index_t H = BlockMatrixB{}.GetLength(I2);
constexpr index_t W = BlockMatrixB{}.GetLength(I3);
@@ -121,9 +119,6 @@ struct BlockwiseGemmDlops_km_kn_m0m1n0n1_v3
"wrong! inconsistent type");
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto a_block_mtx = BlockMatrixA{};
@@ -138,7 +133,7 @@ struct BlockwiseGemmDlops_km_kn_m0m1n0n1_v3
static_assert(WPerThread % WoPerThreadSubC == 0, "");
// thread A buffer for GEMM
StaticBuffer<AddressSpaceEnum_t::Vgpr, FloatA, a_thread_mtx_.GetElementSpaceSize()>
StaticBuffer<AddressSpaceEnum_t::Vgpr, FloatA, a_thread_mtx_.GetElementSpaceSize(), true>
a_thread_buf;
constexpr auto threadwise_gemm = ThreadwiseGemmDlops_km_kn_mn_v3<FloatA,

102
composable_kernel/include/tensor_operation/blockwise_gemm_xdlops.hpp
View File

@@ -2,7 +2,7 @@
#define CK_BLOCKWISE_GEMM_XDLOPS_HPP
#include "common_header.hpp"
#include "threadwise_dynamic_tensor_slice_transfer.hpp"
#include "threadwise_tensor_slice_transfer.hpp"
#include "xdlops_gemm.hpp"
namespace ck {
@@ -52,7 +52,6 @@ struct BlockwiseGemmXdlops_km_kn_m0m1m2n_v1
const index_t waveId = thread_id / WaveSize;
const index_t laneId = thread_id % WaveSize;
const index_t waveId_m = waveId / NWaves;
const index_t waveId_n = waveId % NWaves;
if constexpr(xdlops_gemm.IsKReduction)
{
@@ -73,7 +72,6 @@ struct BlockwiseGemmXdlops_km_kn_m0m1m2n_v1
const index_t thread_id = get_thread_local_1d_id();
const index_t waveId = thread_id / WaveSize;
const index_t laneId = thread_id % WaveSize;
const index_t waveId_m = waveId / NWaves;
const index_t waveId_n = waveId % NWaves;
if constexpr(xdlops_gemm.IsKReduction)
@@ -193,35 +191,35 @@ struct BlockwiseGemmXdlops_km_kn_m0m1m2n_v1
private:
// A[K, M]
static constexpr auto a_thread_desc_ = make_dynamic_naive_tensor_descriptor_packed_v2(
make_tuple(I1, Number<MRepeat>{}, I1, Number<K1>{}));
static constexpr auto a_thread_desc_ =
make_naive_tensor_descriptor_packed(make_tuple(I1, Number<MRepeat>{}, I1, Number<K1>{}));
// B[K, N]
static constexpr auto b_thread_desc_ = make_dynamic_naive_tensor_descriptor_packed_v2(
make_tuple(I1, Number<NRepeat>{}, I1, Number<K1>{}));
static constexpr auto b_thread_desc_ =
make_naive_tensor_descriptor_packed(make_tuple(I1, Number<NRepeat>{}, I1, Number<K1>{}));
static constexpr auto c_thread_desc_ = make_dynamic_naive_tensor_descriptor_packed_v2(
make_tuple(Number<MRepeat>{}, Number<NRepeat>{}));
static constexpr auto c_thread_desc_ =
make_naive_tensor_descriptor_packed(make_tuple(Number<MRepeat>{}, Number<NRepeat>{}));
using AThreadCopy = ThreadwiseDynamicTensorSliceTransfer_v4<FloatAB,
FloatAB,
ABlockDesc,
decltype(a_thread_desc_),
Sequence<1, MRepeat, 1, K1>,
Sequence<0, 1, 2, 3>,
3,
K1,
1>;
using AThreadCopy = ThreadwiseTensorSliceTransfer_v4<FloatAB,
FloatAB,
ABlockDesc,
decltype(a_thread_desc_),
Sequence<1, MRepeat, 1, K1>,
Sequence<0, 1, 2, 3>,
3,
K1,
1>;
using BThreadCopy = ThreadwiseDynamicTensorSliceTransfer_v4<FloatAB,
FloatAB,
BBlockDesc,
decltype(b_thread_desc_),
Sequence<1, NRepeat, 1, K1>,
Sequence<0, 1, 2, 3>,
3,
K1,
1>;
using BThreadCopy = ThreadwiseTensorSliceTransfer_v4<FloatAB,
FloatAB,
BBlockDesc,
decltype(b_thread_desc_),
Sequence<1, NRepeat, 1, K1>,
Sequence<0, 1, 2, 3>,
3,
K1,
1>;
AThreadCopy a_thread_copy_;
BThreadCopy b_thread_copy_;
@@ -272,7 +270,6 @@ struct BlockwiseGemmXdlops_km_kn_m0m1m2n_v1_2x2pipeline
const index_t waveId = thread_id / WaveSize;
const index_t laneId = thread_id % WaveSize;
const index_t waveId_m = waveId / NWaves;
const index_t waveId_n = waveId % NWaves;
if constexpr(xdlops_gemm.IsKReduction)
{
@@ -293,7 +290,6 @@ struct BlockwiseGemmXdlops_km_kn_m0m1m2n_v1_2x2pipeline
const index_t thread_id = get_thread_local_1d_id();
const index_t waveId = thread_id / WaveSize;
const index_t laneId = thread_id % WaveSize;
const index_t waveId_m = waveId / NWaves;
const index_t waveId_n = waveId % NWaves;
if constexpr(xdlops_gemm.IsKReduction)
@@ -490,35 +486,35 @@ struct BlockwiseGemmXdlops_km_kn_m0m1m2n_v1_2x2pipeline
private:
// A[K, M]
static constexpr auto a_thread_desc_ = make_dynamic_naive_tensor_descriptor_packed_v2(
make_tuple(I1, Number<MRepeat>{}, I1, Number<K1>{}));
static constexpr auto a_thread_desc_ =
make_naive_tensor_descriptor_packed(make_tuple(I1, Number<MRepeat>{}, I1, Number<K1>{}));
// B[K, N]
static constexpr auto b_thread_desc_ = make_dynamic_naive_tensor_descriptor_packed_v2(
make_tuple(I1, Number<NRepeat>{}, I1, Number<K1>{}));
static constexpr auto b_thread_desc_ =
make_naive_tensor_descriptor_packed(make_tuple(I1, Number<NRepeat>{}, I1, Number<K1>{}));
static constexpr auto c_thread_desc_ = make_dynamic_naive_tensor_descriptor_packed_v2(
make_tuple(Number<MRepeat>{}, Number<NRepeat>{}));
static constexpr auto c_thread_desc_ =
make_naive_tensor_descriptor_packed(make_tuple(Number<MRepeat>{}, Number<NRepeat>{}));
using AThreadCopy = ThreadwiseDynamicTensorSliceTransfer_v4<FloatAB,
FloatAB,
ABlockDesc,
decltype(a_thread_desc_),
Sequence<1, 1, 1, K1>,
Sequence<0, 1, 2, 3>,
3,
1, // K1,
1>;
using AThreadCopy = ThreadwiseTensorSliceTransfer_v4<FloatAB,
FloatAB,
ABlockDesc,
decltype(a_thread_desc_),
Sequence<1, 1, 1, K1>,
Sequence<0, 1, 2, 3>,
3,
1, // K1,
1>;
using BThreadCopy = ThreadwiseDynamicTensorSliceTransfer_v4<FloatAB,
FloatAB,
BBlockDesc,
decltype(b_thread_desc_),
Sequence<1, 1, 1, K1>,
Sequence<0, 1, 2, 3>,
3,
1, // K1,
1>;
using BThreadCopy = ThreadwiseTensorSliceTransfer_v4<FloatAB,
FloatAB,
BBlockDesc,
decltype(b_thread_desc_),
Sequence<1, 1, 1, K1>,
Sequence<0, 1, 2, 3>,
3,
1, // K1,
1>;
AThreadCopy a_thread_copy_;
BThreadCopy b_thread_copy_;

75
composable_kernel/include/tensor_operation/blockwise_dynamic_tensor_slice_transfer.hpp → composable_kernel/include/tensor_operation/blockwise_tensor_slice_transfer.hpp
View File

@@ -1,18 +1,18 @@
#ifndef CK_BLOCKWISE_DYNAMIC_TENSOR_SLICE_TRANSFER_HPP
#define CK_BLOCKWISE_DYNAMIC_TENSOR_SLICE_TRANSFER_HPP
#ifndef CK_BLOCKWISE_TENSOR_SLICE_TRANSFER_HPP
#define CK_BLOCKWISE_TENSOR_SLICE_TRANSFER_HPP
#include "common_header.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "cluster_descriptor.hpp"
#include "threadwise_dynamic_tensor_slice_transfer.hpp"
#include "threadwise_tensor_slice_transfer.hpp"
namespace ck {
// this version does following things to avoid scratch memory issue
// 1. Use StaticallyIndexedArray instead of C array for thread buffer
// 2. ThreadwiseDynamicTensorSliceTransfer_v3 does not keep reference to tensor descriptor
// 3. ThreadwiseDynamicTensorSliceTransfer_v3::Run() does not construct new tensor coordinate
// 2. ThreadwiseTensorSliceTransfer_v3 does not keep reference to tensor descriptor
// 3. ThreadwiseTensorSliceTransfer_v3::Run() does not construct new tensor coordinate
template <index_t BlockSize,
InMemoryDataOperationEnum_t DstInMemOp,
typename BlockSliceLengths,
@@ -33,16 +33,16 @@ template <index_t BlockSize,
index_t DstScalarStrideInVector,
bool ThreadTransferSrcResetCoordinateAfterRun,
bool ThreadTransferDstResetCoordinateAfterRun>
struct BlockwiseDynamicTensorSliceTransfer_v4
struct BlockwiseTensorSliceTransfer_v4
{
static constexpr index_t nDim = remove_reference_t<SrcDesc>::GetNumOfDimension();
using Index = MultiIndex<nDim>;
__device__ constexpr BlockwiseDynamicTensorSliceTransfer_v4(const SrcDesc& src_desc,
const Index& src_block_slice_origin,
const DstDesc& dst_desc,
const Index& dst_block_slice_origin)
__device__ constexpr BlockwiseTensorSliceTransfer_v4(const SrcDesc& src_desc,
const Index& src_block_slice_origin,
const DstDesc& dst_desc,
const Index& dst_block_slice_origin)
: threadwise_transfer_(
src_desc, make_zero_multi_index<nDim>(), dst_desc, make_zero_multi_index<nDim>())
@@ -77,15 +77,14 @@ struct BlockwiseDynamicTensorSliceTransfer_v4
}
}
template <typename SrcBuffer, typename SrcIteratorHacks>
__device__ void RunRead(const SrcDesc& src_desc,
const SrcBuffer& src_buf,
const SrcIteratorHacks& src_iterator_hacks)
template <typename SrcBuffer, typename SrcStepHacks>
__device__ void
RunRead(const SrcDesc& src_desc, const SrcBuffer& src_buf, const SrcStepHacks& src_step_hacks)
{
if(BlockSize == thread_cluster_desc_.GetElementSize() or
get_thread_local_1d_id() < thread_cluster_desc_.GetElementSize())
{
threadwise_transfer_.RunRead(src_desc, src_buf, src_iterator_hacks);
threadwise_transfer_.RunRead(src_desc, src_buf, src_step_hacks);
}
}
@@ -118,18 +117,18 @@ struct BlockwiseDynamicTensorSliceTransfer_v4
}
}
// SrcMoveSliceWindowIteratorHack to control index calculation move slice window
template <typename SrcMoveSliceWindowIteratorHack>
// SrcMoveSliceWindowStepHack to control index calculation move slice window
template <typename SrcMoveSliceWindowStepHack>
__device__ void
MoveSrcSliceWindow(const SrcDesc& src_desc,
const Index& step,
const SrcMoveSliceWindowIteratorHack& src_move_slice_window_iterator_hack)
const SrcMoveSliceWindowStepHack& src_move_slice_window_step_hack)
{
if(BlockSize == thread_cluster_desc_.GetElementSize() or
get_thread_local_1d_id() < thread_cluster_desc_.GetElementSize())
{
threadwise_transfer_.MoveSrcSliceWindow(
src_desc, step, src_move_slice_window_iterator_hack);
src_desc, step, src_move_slice_window_step_hack);
}
}
@@ -144,25 +143,25 @@ struct BlockwiseDynamicTensorSliceTransfer_v4
private:
static constexpr auto thread_cluster_desc_ =
make_cluster_descriptor_v2(ThreadClusterLengths{}, ThreadClusterArrangeOrder{});
make_cluster_descriptor(ThreadClusterLengths{}, ThreadClusterArrangeOrder{});
using ThreadwiseTransfer =
ThreadwiseDynamicTensorSliceTransfer_v3<ThreadSliceLengths,
DstInMemOp,
SrcData,
DstData,
SrcDesc,
DstDesc,
SrcDimAccessOrder,
DstDimAccessOrder,
SrcVectorDim,
DstVectorDim,
SrcScalarPerVector,
DstScalarPerVector,
SrcScalarStrideInVector,
DstScalarStrideInVector,
ThreadTransferSrcResetCoordinateAfterRun,
ThreadTransferDstResetCoordinateAfterRun>;
ThreadwiseTensorSliceTransfer_v3<ThreadSliceLengths,
DstInMemOp,
SrcData,
DstData,
SrcDesc,
DstDesc,
SrcDimAccessOrder,
DstDimAccessOrder,
SrcVectorDim,
DstVectorDim,
SrcScalarPerVector,
DstScalarPerVector,
SrcScalarStrideInVector,
DstScalarStrideInVector,
ThreadTransferSrcResetCoordinateAfterRun,
ThreadTransferDstResetCoordinateAfterRun>;
ThreadwiseTransfer threadwise_transfer_;
};

72
composable_kernel/include/tensor_operation/blockwise_dynamic_tensor_slice_transfer_v2.hpp → composable_kernel/include/tensor_operation/blockwise_tensor_slice_transfer_v2.hpp
View File

@@ -1,18 +1,18 @@
#ifndef CK_BLOCKWISE_DYNAMIC_TENSOR_SLICE_TRANSFER_V2_HPP
#define CK_BLOCKWISE_DYNAMIC_TENSOR_SLICE_TRANSFER_V2_HPP
#ifndef CK_BLOCKWISE_TENSOR_SLICE_TRANSFER_V2_HPP
#define CK_BLOCKWISE_TENSOR_SLICE_TRANSFER_V2_HPP
#include "common_header.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "cluster_descriptor.hpp"
#include "threadwise_dynamic_tensor_slice_transfer_v2.hpp"
#include "threadwise_tensor_slice_transfer_v2.hpp"
namespace ck {
// this version does following things to avoid scratch memory issue
// 1. Use StaticallyIndexedArray instead of C array for thread buffer
// 2. ThreadwiseDynamicTensorSliceTransfer_v3 does not keep reference to tensor descriptor
// 3. ThreadwiseDynamicTensorSliceTransfer_v3::Run() does not construct new tensor coordinate
// 2. ThreadwiseTensorSliceTransfer_v3 does not keep reference to tensor descriptor
// 3. ThreadwiseTensorSliceTransfer_v3::Run() does not construct new tensor coordinate
template <index_t BlockSize,
InMemoryDataOperationEnum_t DstInMemOp,
typename BlockSliceLengths,
@@ -31,17 +31,16 @@ template <index_t BlockSize,
typename DstVectorTensorContiguousDimOrder,
bool ThreadTransferSrcResetCoordinateAfterRun,
bool ThreadTransferDstResetCoordinateAfterRun>
struct BlockwiseDynamicTensorSliceTransfer_v4r1
struct BlockwiseTensorSliceTransfer_v4r1
{
static constexpr index_t nDim = remove_reference_t<SrcDesc>::GetNumOfDimension();
using Index = MultiIndex<nDim>;
__device__ constexpr BlockwiseDynamicTensorSliceTransfer_v4r1(
const SrcDesc& src_desc,
const Index& src_block_slice_origin,
const DstDesc& dst_desc,
const Index& dst_block_slice_origin)
__device__ constexpr BlockwiseTensorSliceTransfer_v4r1(const SrcDesc& src_desc,
const Index& src_block_slice_origin,
const DstDesc& dst_desc,
const Index& dst_block_slice_origin)
: threadwise_transfer_(
src_desc, make_zero_multi_index<nDim>(), dst_desc, make_zero_multi_index<nDim>())
@@ -76,15 +75,14 @@ struct BlockwiseDynamicTensorSliceTransfer_v4r1
}
}
template <typename SrcBuffer, typename SrcIteratorHacks>
__device__ void RunRead(const SrcDesc& src_desc,
const SrcBuffer& src_buf,
const SrcIteratorHacks& src_iterator_hacks)
template <typename SrcBuffer, typename SrcStepHacks>
__device__ void
RunRead(const SrcDesc& src_desc, const SrcBuffer& src_buf, const SrcStepHacks& src_step_hacks)
{
if(BlockSize == thread_cluster_desc_.GetElementSize() or
get_thread_local_1d_id() < thread_cluster_desc_.GetElementSize())
{
threadwise_transfer_.RunRead(src_desc, src_buf, src_iterator_hacks);
threadwise_transfer_.RunRead(src_desc, src_buf, src_step_hacks);
}
}
@@ -107,18 +105,18 @@ struct BlockwiseDynamicTensorSliceTransfer_v4r1
}
}
// SrcMoveSliceWindowIteratorHack to control index calculation move slice window
template <typename SrcMoveSliceWindowIteratorHack>
// SrcMoveSliceWindowStepHack to control index calculation move slice window
template <typename SrcMoveSliceWindowStepHack>
__device__ void
MoveSrcSliceWindow(const SrcDesc& src_desc,
const Index& step,
const SrcMoveSliceWindowIteratorHack& src_move_slice_window_iterator_hack)
const SrcMoveSliceWindowStepHack& src_move_slice_window_step_hack)
{
if(BlockSize == thread_cluster_desc_.GetElementSize() or
get_thread_local_1d_id() < thread_cluster_desc_.GetElementSize())
{
threadwise_transfer_.MoveSrcSliceWindow(
src_desc, step, src_move_slice_window_iterator_hack);
src_desc, step, src_move_slice_window_step_hack);
}
}
@@ -133,23 +131,23 @@ struct BlockwiseDynamicTensorSliceTransfer_v4r1
private:
static constexpr auto thread_cluster_desc_ =
make_cluster_descriptor_v2(ThreadClusterLengths{}, ThreadClusterArrangeOrder{});
make_cluster_descriptor(ThreadClusterLengths{}, ThreadClusterArrangeOrder{});
using ThreadwiseTransfer =
ThreadwiseDynamicTensorSliceTransfer_v3r1<ThreadSliceLengths,
DstInMemOp,
SrcData,
DstData,
SrcDesc,
DstDesc,
SrcDimAccessOrder,
DstDimAccessOrder,
SrcVectorTensorLengths,
DstVectorTensorLengths,
SrcVectorTensorContiguousDimOrder,
DstVectorTensorContiguousDimOrder,
ThreadTransferSrcResetCoordinateAfterRun,
ThreadTransferDstResetCoordinateAfterRun>;
ThreadwiseTensorSliceTransfer_v3r1<ThreadSliceLengths,
DstInMemOp,
SrcData,
DstData,
SrcDesc,
DstDesc,
SrcDimAccessOrder,
DstDimAccessOrder,
SrcVectorTensorLengths,
DstVectorTensorLengths,
SrcVectorTensorContiguousDimOrder,
DstVectorTensorContiguousDimOrder,
ThreadTransferSrcResetCoordinateAfterRun,
ThreadTransferDstResetCoordinateAfterRun>;
ThreadwiseTransfer threadwise_transfer_;
};

125
composable_kernel/include/tensor_operation/gridwise_dynamic_contraction_dlops_v1r2.hpp → composable_kernel/include/tensor_operation/gridwise_contraction_dlops_v1r2.hpp
View File

@@ -1,14 +1,14 @@
#ifndef CK_GRIDWISE_DYNAMIC_CONTRACTION_DLOPS_V1R2_HPP
#define CK_GRIDWISE_DYNAMIC_CONTRACTION_DLOPS_V1R2_HPP
#ifndef CK_GRIDWISE_CONTRACTION_DLOPS_V1R2_HPP
#define CK_GRIDWISE_CONTRACTION_DLOPS_V1R2_HPP
#include "common_header.hpp"
#include "dynamic_multi_index_transform_helper.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "multi_index_transform_helper.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "blockwise_gemm_dlops_v2r3.hpp"
#include "blockwise_dynamic_tensor_slice_transfer_v2.hpp"
#include "threadwise_dynamic_tensor_slice_transfer.hpp"
#include "threadwise_dynamic_tensor_slice_set.hpp"
#include "blockwise_tensor_slice_transfer_v2.hpp"
#include "threadwise_tensor_slice_transfer.hpp"
#include "threadwise_tensor_slice_set.hpp"
namespace ck {
@@ -25,7 +25,7 @@ __global__ void
#if CK_USE_LAUNCH_BOUNDS
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif
kernel_dynamic_contraction_dlops_v1r2(
kernel_contraction_dlops_v1r2(
const FloatAB* __restrict__ p_a_grid,
const FloatAB* __restrict__ p_b_grid,
FloatC* __restrict__ p_c_grid,
@@ -84,12 +84,12 @@ template <index_t BlockSize,
typename CThreadTransferSrcDstAccessOrder,
index_t CThreadTransferSrcDstVectorDim,
index_t CThreadTransferDstScalarPerVector,
typename AGridIteratorHacks,
typename BGridIteratorHacks,
typename CGridIteratorHacks,
typename AGridMoveSliceWindowIteratorHacks,
typename BGridMoveSliceWindowIteratorHacks>
struct GridwiseDynamicContractionDlops_A_GK0_GM0_GM1_GK1_B_GK0_GN0_GN1_GK1_C_GM0_GM1_GN0_GN1
typename AGridStepHacks,
typename BGridStepHacks,
typename CGridStepHacks,
typename AGridMoveSliceWindowStepHacks,
typename BGridMoveSliceWindowStepHacks>
struct GridwiseContractionDlops_A_GK0_GM0_GM1_GK1_B_GK0_GN0_GN1_GK1_C_GM0_GM1_GN0_GN1
{
static constexpr auto I0 = Number<0>{};
static constexpr auto I1 = Number<1>{};
@@ -110,17 +110,15 @@ struct GridwiseDynamicContractionDlops_A_GK0_GM0_GM1_GK1_B_GK0_GN0_GN1_GK1_C_GM0
// A matrix in LDS memory, dst of blockwise copy
// be careful of LDS alignment
constexpr auto a_block_desc_gk0_gm0_gm10_gm11_gk1 =
make_dynamic_naive_tensor_descriptor_aligned_v2(
make_tuple(Number<GK0PerBlock>{}, GM0, I1, Number<GM1PerBlockGM11>{}, GK1),
max_lds_align);
constexpr auto a_block_desc_gk0_gm0_gm10_gm11_gk1 = make_naive_tensor_descriptor_aligned(
make_tuple(Number<GK0PerBlock>{}, GM0, I1, Number<GM1PerBlockGM11>{}, GK1),
max_lds_align);
// B matrix in LDS memory, dst of blockwise copy
// be careful of LDS alignment
constexpr auto b_block_desc_gk0_gn0_gn10_gn11_gk1 =
make_dynamic_naive_tensor_descriptor_aligned_v2(
make_tuple(Number<GK0PerBlock>{}, GN0, I1, Number<GN1PerBlockGN11>{}, GK1),
max_lds_align);
constexpr auto b_block_desc_gk0_gn0_gn10_gn11_gk1 = make_naive_tensor_descriptor_aligned(
make_tuple(Number<GK0PerBlock>{}, GN0, I1, Number<GN1PerBlockGN11>{}, GK1),
max_lds_align);
// LDS allocation for A and B: be careful of alignment
constexpr auto a_block_aligned_space_size = math::integer_least_multiple(
@@ -201,7 +199,7 @@ struct GridwiseDynamicContractionDlops_A_GK0_GM0_GM1_GK1_B_GK0_GN0_GN1_GK1_C_GM0
const auto GM11 = Number<GM1PerBlockGM11>{};
const auto GM10 = GM1 / GM11;
const auto a_grid_desc_gk0_gm0_gm10_gm11_gk1 = transform_dynamic_tensor_descriptor(
const auto a_grid_desc_gk0_gm0_gm10_gm11_gk1 = transform_tensor_descriptor(
a_grid_desc_gk0_gm0_gm1_gk1,
make_tuple(make_pass_through_transform(GK0),
make_pass_through_transform(GM0),
@@ -222,7 +220,7 @@ struct GridwiseDynamicContractionDlops_A_GK0_GM0_GM1_GK1_B_GK0_GN0_GN1_GK1_C_GM0
const auto GN11 = Number<GN1PerBlockGN11>{};
const auto GN10 = GN1 / GN11;
const auto b_grid_desc_gk0_gn0_gn10_gn11_gk1 = transform_dynamic_tensor_descriptor(
const auto b_grid_desc_gk0_gn0_gn10_gn11_gk1 = transform_tensor_descriptor(
b_grid_desc_gk0_gn0_gn1_gk1,
make_tuple(make_pass_through_transform(GK0),
make_pass_through_transform(GN0),
@@ -250,16 +248,16 @@ struct GridwiseDynamicContractionDlops_A_GK0_GM0_GM1_GK1_B_GK0_GN0_GN1_GK1_C_GM0
constexpr auto BN = GN0 * GN11;
constexpr auto BM1 =
Number<container_reduce(BM10BN10ThreadClusterBM10Xs{}, math::multiplies_v2{}, I1) *
Number<container_reduce(BM10BN10ThreadClusterBM10Xs{}, math::multiplies{}, I1) *
BM1PerThreadBM11>{};
constexpr auto BN1 =
Number<container_reduce(BM10BN10ThreadClusterBN10Xs{}, math::multiplies_v2{}, I1) *
Number<container_reduce(BM10BN10ThreadClusterBN10Xs{}, math::multiplies{}, I1) *
BN1PerThreadBN11>{};
constexpr auto BM0 = BM / BM1;
constexpr auto BN0 = BN / BN1;
const auto c_gm0_gm10_gm11_gn0_gn10_gn11_grid_desc = transform_dynamic_tensor_descriptor(
const auto c_gm0_gm10_gm11_gn0_gn10_gn11_grid_desc = transform_tensor_descriptor(
c_grid_desc_gm0_gm1_gn0_gn1,
make_tuple(make_pass_through_transform(GM0),
make_unmerge_transform(make_tuple(GM10, GM11)),
@@ -268,7 +266,7 @@ struct GridwiseDynamicContractionDlops_A_GK0_GM0_GM1_GK1_B_GK0_GN0_GN1_GK1_C_GM0
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3>{}, Sequence<4, 5>{}));
const auto c_gm10_bm_gn10_bn_grid_desc = transform_dynamic_tensor_descriptor(
const auto c_gm10_bm_gn10_bn_grid_desc = transform_tensor_descriptor(
c_gm0_gm10_gm11_gn0_gn10_gn11_grid_desc,
make_tuple(make_pass_through_transform(GM10),
make_merge_transform(make_tuple(GM0, GM11)),
@@ -277,7 +275,7 @@ struct GridwiseDynamicContractionDlops_A_GK0_GM0_GM1_GK1_B_GK0_GN0_GN1_GK1_C_GM0
make_tuple(Sequence<1>{}, Sequence<0, 2>{}, Sequence<4>{}, Sequence<3, 5>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
const auto c_grid_desc_gm10_bm0_bm1_gn10_bn0_bn1 = transform_dynamic_tensor_descriptor(
const auto c_grid_desc_gm10_bm0_bm1_gn10_bn0_bn1 = transform_tensor_descriptor(
c_gm10_bm_gn10_bn_grid_desc,
make_tuple(make_pass_through_transform(GM10),
make_unmerge_transform(make_tuple(BM0, BM1)),
@@ -356,26 +354,24 @@ struct GridwiseDynamicContractionDlops_A_GK0_GM0_GM1_GK1_B_GK0_GN0_GN1_GK1_C_GM0
// A matrix in LDS memory, dst of blockwise copy
// be careful of LDS alignment
constexpr auto a_block_desc_gk0_gm0_gm10_gm11_gk1 =
make_dynamic_naive_tensor_descriptor_aligned_v2(
make_tuple(Number<GK0PerBlock>{}, GM0, I1, Number<GM1PerBlockGM11>{}, GK1),
max_lds_align);
constexpr auto a_block_desc_gk0_gm0_gm10_gm11_gk1 = make_naive_tensor_descriptor_aligned(
make_tuple(Number<GK0PerBlock>{}, GM0, I1, Number<GM1PerBlockGM11>{}, GK1),
max_lds_align);
// B matrix in LDS memory, dst of blockwise copy
// be careful of LDS alignment
constexpr auto b_block_desc_gk0_gn0_gn10_gn11_gk1 =
make_dynamic_naive_tensor_descriptor_aligned_v2(
make_tuple(Number<GK0PerBlock>{}, GN0, I1, Number<GN1PerBlockGN11>{}, GK1),
max_lds_align);
constexpr auto b_block_desc_gk0_gn0_gn10_gn11_gk1 = make_naive_tensor_descriptor_aligned(
make_tuple(Number<GK0PerBlock>{}, GN0, I1, Number<GN1PerBlockGN11>{}, GK1),
max_lds_align);
// A matrix in LDS memory for blockwise GEMM
// be careful of LDS alignment
constexpr auto a_block_desc_gk0_bm_gk1 = make_dynamic_naive_tensor_descriptor_aligned_v2(
constexpr auto a_block_desc_gk0_bm_gk1 = make_naive_tensor_descriptor_aligned(
make_tuple(Number<GK0PerBlock>{}, GM0 * Number<GM1PerBlockGM11>{}, GK1), max_lds_align);
// B matrix in LDS memory for blockwise GEMM
// be careful of LDS alignment
constexpr auto b_block_desc_gk0_bn_gk1 = make_dynamic_naive_tensor_descriptor_aligned_v2(
constexpr auto b_block_desc_gk0_bn_gk1 = make_naive_tensor_descriptor_aligned(
make_tuple(Number<GK0PerBlock>{}, GN0 * Number<GN1PerBlockGN11>{}, GK1), max_lds_align);
static_assert(a_block_desc_gk0_gm0_gm10_gm11_gk1.GetElementSpaceSize() ==
@@ -385,7 +381,7 @@ struct GridwiseDynamicContractionDlops_A_GK0_GM0_GM1_GK1_B_GK0_GN0_GN1_GK1_C_GM0
"wrong!");
// A matrix blockwise copy
auto a_blockwise_copy = BlockwiseDynamicTensorSliceTransfer_v4r1<
auto a_blockwise_copy = BlockwiseTensorSliceTransfer_v4r1<
BlockSize,
InMemoryDataOperationEnum_t::Set,
Sequence<GK0PerBlock, GM0, 1, GM1PerBlockGM11, GK1.value>,
@@ -409,7 +405,7 @@ struct GridwiseDynamicContractionDlops_A_GK0_GM0_GM1_GK1_B_GK0_GN0_GN1_GK1_C_GM0
make_multi_index(0, 0, 0, 0, 0));
// B matrix blockwise copy
auto b_blockwise_copy = BlockwiseDynamicTensorSliceTransfer_v4r1<
auto b_blockwise_copy = BlockwiseTensorSliceTransfer_v4r1<
BlockSize,
InMemoryDataOperationEnum_t::Set,
Sequence<GK0PerBlock, GN0, 1, GN1PerBlockGN11, GK1.value>,
@@ -457,9 +453,8 @@ struct GridwiseDynamicContractionDlops_A_GK0_GM0_GM1_GK1_B_GK0_GN0_GN1_GK1_C_GM0
constexpr auto c_thread_tensor_lengths_bm0_bm1_bn0_bn1 =
decltype(blockwise_gemm)::GetCThreadTensorLengths_BM0_BM1_BN0_BN1();
constexpr auto c_thread_desc_bm0_bm1_bn0_bn1 =
make_dynamic_naive_tensor_descriptor_packed_v2(
sequence_to_tuple_of_number(c_thread_tensor_lengths_bm0_bm1_bn0_bn1));
constexpr auto c_thread_desc_bm0_bm1_bn0_bn1 = make_naive_tensor_descriptor_packed(
sequence_to_tuple_of_number(c_thread_tensor_lengths_bm0_bm1_bn0_bn1));
// LDS allocation for A and B: be careful of alignment
constexpr auto a_block_aligned_space_size = math::integer_least_multiple(
@@ -475,9 +470,9 @@ struct GridwiseDynamicContractionDlops_A_GK0_GM0_GM1_GK1_B_GK0_GN0_GN1_GK1_C_GM0
auto c_thread_buf = make_static_buffer<AddressSpaceEnum_t::Vgpr, FloatAcc>(
c_thread_desc_bm0_bm1_bn0_bn1.GetElementSpaceSize());
ThreadwiseDynamicTensorSliceSet_v1<FloatAcc,
decltype(c_thread_desc_bm0_bm1_bn0_bn1),
decltype(c_thread_tensor_lengths_bm0_bm1_bn0_bn1)>{}
ThreadwiseTensorSliceSet_v1<FloatAcc,
decltype(c_thread_desc_bm0_bm1_bn0_bn1),
decltype(c_thread_tensor_lengths_bm0_bm1_bn0_bn1)>{}
.Run(c_thread_desc_bm0_bm1_bn0_bn1,
make_tuple(I0, I0, I0, I0),
c_thread_buf,
@@ -501,9 +496,9 @@ struct GridwiseDynamicContractionDlops_A_GK0_GM0_GM1_GK1_B_GK0_GN0_GN1_GK1_C_GM0
// LDS double buffer: preload data into LDS
{
a_blockwise_copy.RunRead(
a_grid_desc_gk0_gm0_gm10_gm11_gk1, a_global_buf, AGridIteratorHacks{});
a_grid_desc_gk0_gm0_gm10_gm11_gk1, a_global_buf, AGridStepHacks{});
b_blockwise_copy.RunRead(
b_grid_desc_gk0_gn0_gn10_gn11_gk1, b_global_buf, BGridIteratorHacks{});
b_grid_desc_gk0_gn0_gn10_gn11_gk1, b_global_buf, BGridStepHacks{});
a_blockwise_copy.RunWrite(a_block_desc_gk0_gm0_gm10_gm11_gk1, a_block_even_buf);
b_blockwise_copy.RunWrite(b_block_desc_gk0_gn0_gn10_gn11_gk1, b_block_even_buf);
@@ -520,18 +515,18 @@ struct GridwiseDynamicContractionDlops_A_GK0_GM0_GM1_GK1_B_GK0_GN0_GN1_GK1_C_GM0
// even iteration
a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc_gk0_gm0_gm10_gm11_gk1,
a_block_slice_copy_step,
AGridMoveSliceWindowIteratorHacks{});
AGridMoveSliceWindowStepHacks{});
b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc_gk0_gn0_gn10_gn11_gk1,
b_block_slice_copy_step,
BGridMoveSliceWindowIteratorHacks{});
BGridMoveSliceWindowStepHacks{});
__syncthreads();
// LDS doubel buffer: load next data from device mem
a_blockwise_copy.RunRead(
a_grid_desc_gk0_gm0_gm10_gm11_gk1, a_global_buf, AGridIteratorHacks{});
a_grid_desc_gk0_gm0_gm10_gm11_gk1, a_global_buf, AGridStepHacks{});
b_blockwise_copy.RunRead(
b_grid_desc_gk0_gn0_gn10_gn11_gk1, b_global_buf, BGridIteratorHacks{});
b_grid_desc_gk0_gn0_gn10_gn11_gk1, b_global_buf, BGridStepHacks{});
// LDS double buffer: GEMM on current data
blockwise_gemm.Run(c_thread_desc_bm0_bm1_bn0_bn1,
@@ -546,18 +541,18 @@ struct GridwiseDynamicContractionDlops_A_GK0_GM0_GM1_GK1_B_GK0_GN0_GN1_GK1_C_GM0
// odd iteration
a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc_gk0_gm0_gm10_gm11_gk1,
a_block_slice_copy_step,
AGridMoveSliceWindowIteratorHacks{});
AGridMoveSliceWindowStepHacks{});
b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc_gk0_gn0_gn10_gn11_gk1,
b_block_slice_copy_step,
BGridMoveSliceWindowIteratorHacks{});
BGridMoveSliceWindowStepHacks{});
__syncthreads();
// LDS doubel buffer: load next data from device mem
a_blockwise_copy.RunRead(
a_grid_desc_gk0_gm0_gm10_gm11_gk1, a_global_buf, AGridIteratorHacks{});
a_grid_desc_gk0_gm0_gm10_gm11_gk1, a_global_buf, AGridStepHacks{});
b_blockwise_copy.RunRead(
b_grid_desc_gk0_gn0_gn10_gn11_gk1, b_global_buf, BGridIteratorHacks{});
b_grid_desc_gk0_gn0_gn10_gn11_gk1, b_global_buf, BGridStepHacks{});
// LDS double buffer: GEMM on current data
blockwise_gemm.Run(
@@ -576,18 +571,18 @@ struct GridwiseDynamicContractionDlops_A_GK0_GM0_GM1_GK1_B_GK0_GN0_GN1_GK1_C_GM0
{
a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc_gk0_gm0_gm10_gm11_gk1,
a_block_slice_copy_step,
AGridMoveSliceWindowIteratorHacks{});
AGridMoveSliceWindowStepHacks{});
b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc_gk0_gn0_gn10_gn11_gk1,
b_block_slice_copy_step,
BGridMoveSliceWindowIteratorHacks{});
BGridMoveSliceWindowStepHacks{});
__syncthreads();
// LDS double buffer: load last data from device mem
a_blockwise_copy.RunRead(
a_grid_desc_gk0_gm0_gm10_gm11_gk1, a_global_buf, AGridIteratorHacks{});
a_grid_desc_gk0_gm0_gm10_gm11_gk1, a_global_buf, AGridStepHacks{});
b_blockwise_copy.RunRead(
b_grid_desc_gk0_gn0_gn10_gn11_gk1, b_global_buf, BGridIteratorHacks{});
b_grid_desc_gk0_gn0_gn10_gn11_gk1, b_global_buf, BGridStepHacks{});
// LDS double buffer: GEMM on 2nd-last data
blockwise_gemm.Run(
@@ -615,7 +610,7 @@ struct GridwiseDynamicContractionDlops_A_GK0_GM0_GM1_GK1_B_GK0_GN0_GN1_GK1_C_GM0
// output: register to global memory
{
constexpr auto c_thread_desc_gm10_bm0_bm1_gn10_bn0_bn1 =
make_dynamic_naive_tensor_descriptor_packed_v2(
make_naive_tensor_descriptor_packed(
make_tuple(I1,
Number<c_thread_tensor_lengths_bm0_bm1_bn0_bn1[I0]>{},
Number<c_thread_tensor_lengths_bm0_bm1_bn0_bn1[I1]>{},
@@ -627,7 +622,7 @@ struct GridwiseDynamicContractionDlops_A_GK0_GM0_GM1_GK1_B_GK0_GN0_GN1_GK1_C_GM0
blockwise_gemm.CalculateCThreadOriginOnBlock_BM0_BM1_BN0_BN1(
get_thread_local_1d_id());
ThreadwiseDynamicTensorSliceTransfer_v1r3<
ThreadwiseTensorSliceTransfer_v1r3<
FloatAcc,
FloatC,
decltype(c_thread_desc_gm10_bm0_bm1_gn10_bn0_bn1),
@@ -655,7 +650,7 @@ struct GridwiseDynamicContractionDlops_A_GK0_GM0_GM1_GK1_B_GK0_GN0_GN1_GK1_C_GM0
c_thread_buf,
c_grid_desc_gm10_bm0_bm1_gn10_bn0_bn1,
c_grid_buf,
CGridIteratorHacks{});
CGridStepHacks{});
}
}
};

255
composable_kernel/include/tensor_operation/gridwise_dynamic_gemm_dlops_v1r2.hpp → composable_kernel/include/tensor_operation/gridwise_gemm_dlops_v1r2.hpp
View File

@@ -1,14 +1,14 @@
#ifndef CK_GRIDWISE_DYNAMIC_GEMM_DLOPS_V1R2_HPP
#define CK_GRIDWISE_DYNAMIC_GEMM_DLOPS_V1R2_HPP
#ifndef CK_GRIDWISE_GEMM_DLOPS_V1R2_HPP
#define CK_GRIDWISE_GEMM_DLOPS_V1R2_HPP
#include "common_header.hpp"
#include "dynamic_multi_index_transform_helper.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "multi_index_transform_helper.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "blockwise_gemm_dlops_v2r2.hpp"
#include "blockwise_dynamic_tensor_slice_transfer.hpp"
#include "threadwise_dynamic_tensor_slice_transfer.hpp"
#include "threadwise_dynamic_tensor_slice_set.hpp"
#include "blockwise_tensor_slice_transfer.hpp"
#include "threadwise_tensor_slice_transfer.hpp"
#include "threadwise_tensor_slice_set.hpp"
namespace ck {
@@ -26,7 +26,7 @@ __global__ void
#if CK_USE_LAUNCH_BOUNDS
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif
kernel_dynamic_gemm_dlops_v1r2(
kernel_gemm_dlops_v1r2(
const FloatAB* __restrict__ p_a_grid,
const FloatAB* __restrict__ p_b_grid,
FloatC* __restrict__ p_c_grid,
@@ -68,28 +68,27 @@ __global__ void
#if CK_USE_LAUNCH_BOUNDS
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif
kernel_dynamic_gemm_dlops_v1r2(
const FloatAB* __restrict__ p_a_grid,
const FloatAB* __restrict__ p_b_grid,
FloatC* __restrict__ p_c_grid,
const void CONSTANT* p_a_k_m0_m1_grid_desc,
const void CONSTANT* p_b_k_n0_n1_grid_desc,
const void CONSTANT* p_c_m0_m10_m11_n0_n10_n11_grid_desc,
const void CONSTANT* p_c_blockid_to_m0_n0_block_cluster_adaptor)
kernel_gemm_dlops_v1r2(const FloatAB* __restrict__ p_a_grid,
const FloatAB* __restrict__ p_b_grid,
FloatC* __restrict__ p_c_grid,
const void CONSTANT* p_a_k_m0_m1_grid_desc,
const void CONSTANT* p_b_k_n0_n1_grid_desc,
const void CONSTANT* p_c_m0_m10_m11_n0_n10_n11_grid_desc,
const void CONSTANT* p_c_blockid_to_m0_n0_block_cluster_adaptor)
{
// first cast void CONSTANT void* to void*
// second cast void* to Desc*
// the copy constructor of tensor descriptor doesn't take address_space(4)
const auto a_k_m0_m1_grid_desc =
*reinterpret_cast<const AKM0M1GridDesc*>((const void*)p_a_k_m0_m1_grid_desc);
const auto b_k_n0_n1_grid_desc =
*reinterpret_cast<const BKN0N1GridDesc*>((const void*)p_b_k_n0_n1_grid_desc);
const auto a_k_m0_m1_grid_desc = *reinterpret_cast<const AKM0M1GridDesc*>(
cast_pointer_to_generic_address_space(p_a_k_m0_m1_grid_desc));
const auto b_k_n0_n1_grid_desc = *reinterpret_cast<const BKN0N1GridDesc*>(
cast_pointer_to_generic_address_space(p_b_k_n0_n1_grid_desc));
const auto c_m0_m10_m11_n0_n10_n11_grid_desc =
*reinterpret_cast<const CM0M10M11N0N10N11GridDesc*>(
(const void*)p_c_m0_m10_m11_n0_n10_n11_grid_desc);
cast_pointer_to_generic_address_space(p_c_m0_m10_m11_n0_n10_n11_grid_desc));
const auto c_blockid_to_m0_n0_block_cluster_adaptor =
*reinterpret_cast<const CBlockIdToM0N0BlockClusterAdaptor*>(
(const void*)p_c_blockid_to_m0_n0_block_cluster_adaptor);
cast_pointer_to_generic_address_space(p_c_blockid_to_m0_n0_block_cluster_adaptor));
constexpr index_t shared_block_size =
GridwiseGemm::GetSharedMemoryNumberOfByte() / sizeof(FloatAB);
@@ -146,12 +145,12 @@ template <index_t BlockSize,
typename CThreadTransferSrcDstAccessOrder,
index_t CThreadTransferSrcDstVectorDim,
index_t CThreadTransferDstScalarPerVector,
typename AGridIteratorHacks,
typename BGridIteratorHacks,
typename CGridIteratorHacks,
typename AGridMoveSliceWindowIteratorHacks,
typename BGridMoveSliceWindowIteratorHacks>
struct GridwiseDynamicGemmDlops_km_kn_mn_v1r2
typename AGridStepHacks,
typename BGridStepHacks,
typename CGridStepHacks,
typename AGridMoveSliceWindowStepHacks,
typename BGridMoveSliceWindowStepHacks>
struct GridwiseGemmDlops_km_kn_mn_v1r2
{
static constexpr auto I0 = Number<0>{};
static constexpr auto I1 = Number<1>{};
@@ -167,12 +166,12 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v1r2
// A matrix in LDS memory, dst of blockwise copy
// be careful of LDS alignment
constexpr auto a_k_m_block_desc = make_dynamic_naive_tensor_descriptor_aligned_v2(
constexpr auto a_k_m_block_desc = make_naive_tensor_descriptor_aligned(
make_tuple(Number<KPerBlock>{}, Number<MPerBlockM1>{}), max_lds_align);
// B matrix in LDS memory, dst of blockwise copy
// be careful of LDS alignment
constexpr auto b_k_n_block_desc = make_dynamic_naive_tensor_descriptor_aligned_v2(
constexpr auto b_k_n_block_desc = make_naive_tensor_descriptor_aligned(
make_tuple(Number<KPerBlock>{}, Number<NPerBlockN1>{}), max_lds_align);
// LDS allocation for A and B: be careful of alignment
@@ -230,7 +229,7 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v1r2
const auto M1 = Number<MPerBlockM1>{};
const auto M0 = M / M1;
const auto a_k_m0_m1_grid_desc = transform_dynamic_tensor_descriptor(
const auto a_k_m0_m1_grid_desc = transform_tensor_descriptor(
a_k_m_grid_desc,
make_tuple(make_pass_through_transform(K), make_unmerge_transform(make_tuple(M0, M1))),
make_tuple(Sequence<0>{}, Sequence<1>{}),
@@ -248,7 +247,7 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v1r2
const auto N1 = Number<NPerBlockN1>{};
const auto N0 = N / N1;
const auto b_k_n0_n1_grid_desc = transform_dynamic_tensor_descriptor(
const auto b_k_n0_n1_grid_desc = transform_tensor_descriptor(
b_k_n_grid_desc,
make_tuple(make_pass_through_transform(K), make_unmerge_transform(make_tuple(N0, N1))),
make_tuple(Sequence<0>{}, Sequence<1>{}),
@@ -277,7 +276,7 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v1r2
constexpr auto M10 = M1 / M11;
constexpr auto N10 = N1 / N11;
const auto c_m0_m10_m11_n0_n10_n11_grid_desc = transform_dynamic_tensor_descriptor(
const auto c_m0_m10_m11_n0_n10_n11_grid_desc = transform_tensor_descriptor(
c_m_n_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(M0, M10, M11)),
make_unmerge_transform(make_tuple(N0, N10, N11))),
@@ -352,75 +351,75 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v1r2
// A matrix in LDS memory, dst of blockwise copy
// be careful of LDS alignment
constexpr auto a_k_m_block_desc = make_dynamic_naive_tensor_descriptor_aligned_v2(
constexpr auto a_k_m_block_desc = make_naive_tensor_descriptor_aligned(
make_tuple(Number<KPerBlock>{}, Number<MPerBlockM1>{}), max_lds_align);
// B matrix in LDS memory, dst of blockwise copy
// be careful of LDS alignment
constexpr auto b_k_n_block_desc = make_dynamic_naive_tensor_descriptor_aligned_v2(
constexpr auto b_k_n_block_desc = make_naive_tensor_descriptor_aligned(
make_tuple(Number<KPerBlock>{}, Number<NPerBlockN1>{}), max_lds_align);
// A matrix in LDS memory, dst of blockwise copy
// be careful of LDS alignment
constexpr auto a_k_m0_m1_block_desc = make_dynamic_naive_tensor_descriptor_aligned_v2(
constexpr auto a_k_m0_m1_block_desc = make_naive_tensor_descriptor_aligned(
make_tuple(Number<KPerBlock>{}, I1, Number<MPerBlockM1>{}), max_lds_align);
// B matrix in LDS memory, dst of blockwise copy
// be careful of LDS alignment
constexpr auto b_k_n0_n1_block_desc = make_dynamic_naive_tensor_descriptor_aligned_v2(
constexpr auto b_k_n0_n1_block_desc = make_naive_tensor_descriptor_aligned(
make_tuple(Number<KPerBlock>{}, I1, Number<NPerBlockN1>{}), max_lds_align);
// A matrix blockwise copy
auto a_blockwise_copy =
BlockwiseDynamicTensorSliceTransfer_v4<BlockSize,
InMemoryDataOperationEnum_t::Set,
Sequence<KPerBlock, 1, MPerBlockM1>,
ABlockTransferThreadSliceLengths_K_M0_M1,
ABlockTransferThreadClusterLengths_K_M0_M1,
ABlockTransferThreadClusterArrangeOrder,
FloatAB,
FloatAB,
decltype(a_k_m0_m1_grid_desc),
decltype(a_k_m0_m1_block_desc),
ABlockTransferSrcAccessOrder,
Sequence<0, 1, 2>,
ABlockTransferSrcVectorDim,
2,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_M1,
1,
1,
AThreadTransferSrcResetCoordinateAfterRun,
true>(a_k_m0_m1_grid_desc,
make_multi_index(0, im0, 0),
a_k_m0_m1_block_desc,
make_multi_index(0, 0, 0));
BlockwiseTensorSliceTransfer_v4<BlockSize,
InMemoryDataOperationEnum_t::Set,
Sequence<KPerBlock, 1, MPerBlockM1>,
ABlockTransferThreadSliceLengths_K_M0_M1,
ABlockTransferThreadClusterLengths_K_M0_M1,
ABlockTransferThreadClusterArrangeOrder,
FloatAB,
FloatAB,
decltype(a_k_m0_m1_grid_desc),
decltype(a_k_m0_m1_block_desc),
ABlockTransferSrcAccessOrder,
Sequence<0, 1, 2>,
ABlockTransferSrcVectorDim,
2,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_M1,
1,
1,
AThreadTransferSrcResetCoordinateAfterRun,
true>(a_k_m0_m1_grid_desc,
make_multi_index(0, im0, 0),
a_k_m0_m1_block_desc,
make_multi_index(0, 0, 0));
// B matrix blockwise copy
auto b_blockwise_copy =
BlockwiseDynamicTensorSliceTransfer_v4<BlockSize,
InMemoryDataOperationEnum_t::Set,
Sequence<KPerBlock, 1, NPerBlockN1>,
BBlockTransferThreadSliceLengths_K_N0_N1,
BBlockTransferThreadClusterLengths_K_N0_N1,
BBlockTransferThreadClusterArrangeOrder,
FloatAB,
FloatAB,
decltype(b_k_n0_n1_grid_desc),
decltype(b_k_n0_n1_block_desc),
BBlockTransferSrcAccessOrder,
Sequence<0, 1, 2>,
BBlockTransferSrcVectorDim,
2,
BBlockTransferSrcScalarPerVector,
BBlockTransferDstScalarPerVector_N1,
1,
1,
BThreadTransferSrcResetCoordinateAfterRun,
true>(b_k_n0_n1_grid_desc,
make_multi_index(0, in0, 0),
b_k_n0_n1_block_desc,
make_multi_index(0, 0, 0));
BlockwiseTensorSliceTransfer_v4<BlockSize,
InMemoryDataOperationEnum_t::Set,
Sequence<KPerBlock, 1, NPerBlockN1>,
BBlockTransferThreadSliceLengths_K_N0_N1,
BBlockTransferThreadClusterLengths_K_N0_N1,
BBlockTransferThreadClusterArrangeOrder,
FloatAB,
FloatAB,
decltype(b_k_n0_n1_grid_desc),
decltype(b_k_n0_n1_block_desc),
BBlockTransferSrcAccessOrder,
Sequence<0, 1, 2>,
BBlockTransferSrcVectorDim,
2,
BBlockTransferSrcScalarPerVector,
BBlockTransferDstScalarPerVector_N1,
1,
1,
BThreadTransferSrcResetCoordinateAfterRun,
true>(b_k_n0_n1_grid_desc,
make_multi_index(0, in0, 0),
b_k_n0_n1_block_desc,
make_multi_index(0, 0, 0));
// GEMM definition
// c_mtx += transpose(a_mtx) * b_mtx
@@ -447,9 +446,8 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v1r2
constexpr auto c_m10_m11_n10_n11_thread_tensor_lengths =
decltype(blockwise_gemm)::GetCM0M1N0N1ThreadTensorLengths();
constexpr auto c_m10_m11_n10_n11_thread_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(
sequence_to_tuple_of_number(c_m10_m11_n10_n11_thread_tensor_lengths));
constexpr auto c_m10_m11_n10_n11_thread_desc = make_naive_tensor_descriptor_packed(
sequence_to_tuple_of_number(c_m10_m11_n10_n11_thread_tensor_lengths));
// LDS allocation for A and B: be careful of alignment
constexpr auto a_block_aligned_space_size =
@@ -465,9 +463,9 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v1r2
auto c_thread_buf = make_static_buffer<AddressSpaceEnum_t::Vgpr, FloatAcc>(
c_m10_m11_n10_n11_thread_desc.GetElementSpaceSize());
ThreadwiseDynamicTensorSliceSet_v1<FloatAcc,
decltype(c_m10_m11_n10_n11_thread_desc),
decltype(c_m10_m11_n10_n11_thread_tensor_lengths)>{}
ThreadwiseTensorSliceSet_v1<FloatAcc,
decltype(c_m10_m11_n10_n11_thread_desc),
decltype(c_m10_m11_n10_n11_thread_tensor_lengths)>{}
.Run(c_m10_m11_n10_n11_thread_desc,
make_tuple(I0, I0, I0, I0),
c_thread_buf,
@@ -477,15 +475,15 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v1r2
constexpr auto b_block_slice_copy_step = make_multi_index(KPerBlock, 0, 0);
// hack to control index calculation when iterating over A and B matrix for threadwise copy
constexpr auto a_k_m0_m1_global_iterator_hacks = AGridIteratorHacks{};
constexpr auto b_k_n0_n1_global_iterator_hacks = BGridIteratorHacks{};
constexpr auto a_k_m0_m1_global_step_hacks = AGridStepHacks{};
constexpr auto b_k_n0_n1_global_step_hacks = BGridStepHacks{};
// hack to control index calculation when move slice window for A and B matrix for
// threadwise copy
constexpr auto a_k_m0_m1_global_move_slice_window_iterator_hack =
AGridMoveSliceWindowIteratorHacks{};
constexpr auto b_k_n0_n1_global_move_slice_window_iterator_hack =
BGridMoveSliceWindowIteratorHacks{};
constexpr auto a_k_m0_m1_global_move_slice_window_step_hack =
AGridMoveSliceWindowStepHacks{};
constexpr auto b_k_n0_n1_global_move_slice_window_step_hack =
BGridMoveSliceWindowStepHacks{};
auto a_block_even_buf = make_dynamic_buffer<AddressSpaceEnum_t::Lds>(
p_a_block_double, a_k_m0_m1_block_desc.GetElementSpaceSize());
@@ -502,9 +500,9 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v1r2
// LDS double buffer: preload data into LDS
{
a_blockwise_copy.RunRead(
a_k_m0_m1_grid_desc, a_global_buf, a_k_m0_m1_global_iterator_hacks);
a_k_m0_m1_grid_desc, a_global_buf, a_k_m0_m1_global_step_hacks);
b_blockwise_copy.RunRead(
b_k_n0_n1_grid_desc, b_global_buf, b_k_n0_n1_global_iterator_hacks);
b_k_n0_n1_grid_desc, b_global_buf, b_k_n0_n1_global_step_hacks);
a_blockwise_copy.RunWrite(a_k_m0_m1_block_desc, a_block_even_buf);
b_blockwise_copy.RunWrite(b_k_n0_n1_block_desc, b_block_even_buf);
@@ -519,22 +517,20 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v1r2
do
{
// even iteration
a_blockwise_copy.MoveSrcSliceWindow(
a_k_m0_m1_grid_desc,
a_block_slice_copy_step,
a_k_m0_m1_global_move_slice_window_iterator_hack);
b_blockwise_copy.MoveSrcSliceWindow(
b_k_n0_n1_grid_desc,
b_block_slice_copy_step,
b_k_n0_n1_global_move_slice_window_iterator_hack);
a_blockwise_copy.MoveSrcSliceWindow(a_k_m0_m1_grid_desc,
a_block_slice_copy_step,
a_k_m0_m1_global_move_slice_window_step_hack);
b_blockwise_copy.MoveSrcSliceWindow(b_k_n0_n1_grid_desc,
b_block_slice_copy_step,
b_k_n0_n1_global_move_slice_window_step_hack);
__syncthreads();
// LDS doubel buffer: load next data from device mem
a_blockwise_copy.RunRead(
a_k_m0_m1_grid_desc, a_global_buf, a_k_m0_m1_global_iterator_hacks);
a_k_m0_m1_grid_desc, a_global_buf, a_k_m0_m1_global_step_hacks);
b_blockwise_copy.RunRead(
b_k_n0_n1_grid_desc, b_global_buf, b_k_n0_n1_global_iterator_hacks);
b_k_n0_n1_grid_desc, b_global_buf, b_k_n0_n1_global_step_hacks);
// LDS double buffer: GEMM on current data
blockwise_gemm.Run(c_m10_m11_n10_n11_thread_desc,
@@ -547,22 +543,20 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v1r2
b_blockwise_copy.RunWrite(b_k_n0_n1_block_desc, b_block_odd_buf);
// odd iteration
a_blockwise_copy.MoveSrcSliceWindow(
a_k_m0_m1_grid_desc,
a_block_slice_copy_step,
a_k_m0_m1_global_move_slice_window_iterator_hack);
b_blockwise_copy.MoveSrcSliceWindow(
b_k_n0_n1_grid_desc,
b_block_slice_copy_step,
b_k_n0_n1_global_move_slice_window_iterator_hack);
a_blockwise_copy.MoveSrcSliceWindow(a_k_m0_m1_grid_desc,
a_block_slice_copy_step,
a_k_m0_m1_global_move_slice_window_step_hack);
b_blockwise_copy.MoveSrcSliceWindow(b_k_n0_n1_grid_desc,
b_block_slice_copy_step,
b_k_n0_n1_global_move_slice_window_step_hack);
__syncthreads();
// LDS doubel buffer: load next data from device mem
a_blockwise_copy.RunRead(
a_k_m0_m1_grid_desc, a_global_buf, a_k_m0_m1_global_iterator_hacks);
a_k_m0_m1_grid_desc, a_global_buf, a_k_m0_m1_global_step_hacks);
b_blockwise_copy.RunRead(
b_k_n0_n1_grid_desc, b_global_buf, b_k_n0_n1_global_iterator_hacks);
b_k_n0_n1_grid_desc, b_global_buf, b_k_n0_n1_global_step_hacks);
// LDS double buffer: GEMM on current data
blockwise_gemm.Run(
@@ -581,18 +575,18 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v1r2
{
a_blockwise_copy.MoveSrcSliceWindow(a_k_m0_m1_grid_desc,
a_block_slice_copy_step,
a_k_m0_m1_global_move_slice_window_iterator_hack);
a_k_m0_m1_global_move_slice_window_step_hack);
b_blockwise_copy.MoveSrcSliceWindow(b_k_n0_n1_grid_desc,
b_block_slice_copy_step,
b_k_n0_n1_global_move_slice_window_iterator_hack);
b_k_n0_n1_global_move_slice_window_step_hack);
__syncthreads();
// LDS double buffer: load last data from device mem
a_blockwise_copy.RunRead(
a_k_m0_m1_grid_desc, a_global_buf, a_k_m0_m1_global_iterator_hacks);
a_k_m0_m1_grid_desc, a_global_buf, a_k_m0_m1_global_step_hacks);
b_blockwise_copy.RunRead(
b_k_n0_n1_grid_desc, b_global_buf, b_k_n0_n1_global_iterator_hacks);
b_k_n0_n1_grid_desc, b_global_buf, b_k_n0_n1_global_step_hacks);
// LDS double buffer: GEMM on 2nd-last data
blockwise_gemm.Run(
@@ -619,19 +613,8 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v1r2
// output: register to global memory
{
constexpr index_t M11 =
M1PerThreadM111 * M11N11ThreadClusterM1100 * M11N11ThreadClusterM1101;
constexpr index_t N11 =
N1PerThreadN111 * M11N11ThreadClusterN1100 * M11N11ThreadClusterN1101;
constexpr index_t M10 = MPerBlockM1 / M11;
constexpr index_t N10 = NPerBlockN1 / N11;
constexpr index_t M111 = M1PerThreadM111;
constexpr index_t N111 = N1PerThreadN111;
constexpr auto c_m0_m10_m11_n0_n10_n11_thread_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(
make_naive_tensor_descriptor_packed(
make_tuple(I1,
Number<c_m10_m11_n10_n11_thread_tensor_lengths[I0]>{},
Number<c_m10_m11_n10_n11_thread_tensor_lengths[I1]>{},
@@ -642,7 +625,7 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v1r2
const auto c_m10_m11_n10_n11_thread_origin_idx_on_block =
blockwise_gemm.CalculateCM0M1N0N1ThreadOriginOnBlock(get_thread_local_1d_id());
ThreadwiseDynamicTensorSliceTransfer_v1r3<
ThreadwiseTensorSliceTransfer_v1r3<
FloatAcc,
FloatC,
decltype(c_m0_m10_m11_n0_n10_n11_thread_desc),
@@ -670,7 +653,7 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v1r2
c_thread_buf,
c_m0_m10_m11_n0_n10_n11_grid_desc,
c_grid_buf,
CGridIteratorHacks{});
CGridStepHacks{});
}
}
};

177
composable_kernel/include/tensor_operation/gridwise_dynamic_gemm_dlops_v1r3.hpp → composable_kernel/include/tensor_operation/gridwise_gemm_dlops_v1r3.hpp
View File

@@ -1,14 +1,14 @@
#ifndef CK_GRIDWISE_DYNAMIC_GEMM_V1R3_HPP
#define CK_GRIDWISE_DYNAMIC_GEMM_V1R3_HPP
#ifndef CK_GRIDWISE_GEMM_V1R3_HPP
#define CK_GRIDWISE_GEMM_V1R3_HPP
#include "common_header.hpp"
#include "dynamic_multi_index_transform_helper.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "multi_index_transform_helper.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "blockwise_gemm_dlops_v2r3.hpp"
#include "blockwise_dynamic_tensor_slice_transfer_v2.hpp"
#include "threadwise_dynamic_tensor_slice_transfer_v2.hpp"
#include "threadwise_dynamic_tensor_slice_set.hpp"
#include "blockwise_tensor_slice_transfer_v2.hpp"
#include "threadwise_tensor_slice_transfer_v2.hpp"
#include "threadwise_tensor_slice_set.hpp"
namespace ck {
@@ -26,7 +26,7 @@ __global__ void
#if CK_USE_LAUNCH_BOUNDS
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif
kernel_dynamic_gemm_dlops_v1r3(
kernel_gemm_dlops_v1r3(
const FloatAB* __restrict__ p_a_grid,
const FloatAB* __restrict__ p_b_grid,
FloatC* __restrict__ p_c_grid,
@@ -68,28 +68,27 @@ __global__ void
#if CK_USE_LAUNCH_BOUNDS
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif
kernel_dynamic_gemm_dlops_v1r3(
const FloatAB* __restrict__ p_a_grid,
const FloatAB* __restrict__ p_b_grid,
FloatC* __restrict__ p_c_grid,
const void CONSTANT* p_a_k0_m0_m1_k1_grid_desc,
const void CONSTANT* p_b_k0_n0_n1_k1_grid_desc,
const void CONSTANT* p_c_m0_m10_m11_n0_n10_n11_grid_desc,
const void CONSTANT* p_c_blockid_to_m0_n0_block_cluster_adaptor)
kernel_gemm_dlops_v1r3(const FloatAB* __restrict__ p_a_grid,
const FloatAB* __restrict__ p_b_grid,
FloatC* __restrict__ p_c_grid,
const void CONSTANT* p_a_k0_m0_m1_k1_grid_desc,
const void CONSTANT* p_b_k0_n0_n1_k1_grid_desc,
const void CONSTANT* p_c_m0_m10_m11_n0_n10_n11_grid_desc,
const void CONSTANT* p_c_blockid_to_m0_n0_block_cluster_adaptor)
{
// first cast void CONSTANT void* to void*
// second cast void* to Desc*
// the copy constructor of tensor descriptor doesn't take address_space(4)
const auto a_k0_m0_m1_k1_grid_desc =
*reinterpret_cast<const AK0M0M1K1GridDesc*>((const void*)p_a_k0_m0_m1_k1_grid_desc);
const auto b_k0_n0_n1_k1_grid_desc =
*reinterpret_cast<const BK0N0N1K1GridDesc*>((const void*)p_b_k0_n0_n1_k1_grid_desc);
const auto a_k0_m0_m1_k1_grid_desc = *reinterpret_cast<const AK0M0M1K1GridDesc*>(
cast_pointer_to_generic_address_space(p_a_k0_m0_m1_k1_grid_desc));
const auto b_k0_n0_n1_k1_grid_desc = *reinterpret_cast<const BK0N0N1K1GridDesc*>(
cast_pointer_to_generic_address_space(p_b_k0_n0_n1_k1_grid_desc));
const auto c_m0_m10_m11_n0_n10_n11_grid_desc =
*reinterpret_cast<const CM0M10M11N0N10N11GridDesc*>(
(const void*)p_c_m0_m10_m11_n0_n10_n11_grid_desc);
cast_pointer_to_generic_address_space(p_c_m0_m10_m11_n0_n10_n11_grid_desc));
const auto c_blockid_to_m0_n0_block_cluster_adaptor =
*reinterpret_cast<const CBlockIdToM0N0BlockClusterAdaptor*>(
(const void*)p_c_blockid_to_m0_n0_block_cluster_adaptor);
cast_pointer_to_generic_address_space(p_c_blockid_to_m0_n0_block_cluster_adaptor));
constexpr index_t shared_block_size =
GridwiseGemm::GetSharedMemoryNumberOfByte() / sizeof(FloatAB);
@@ -142,12 +141,12 @@ template <index_t BlockSize,
typename CThreadTransferSrcDstAccessOrder,
index_t CThreadTransferSrcDstVectorDim,
index_t CThreadTransferDstScalarPerVector,
typename AGridIteratorHacks,
typename BGridIteratorHacks,
typename CGridIteratorHacks,
typename AGridMoveSliceWindowIteratorHacks,
typename BGridMoveSliceWindowIteratorHacks>
struct GridwiseDynamicGemmDlops_km_kn_mn_v1r3
typename AGridStepHacks,
typename BGridStepHacks,
typename CGridStepHacks,
typename AGridMoveSliceWindowStepHacks,
typename BGridMoveSliceWindowStepHacks>
struct GridwiseGemmDlops_km_kn_mn_v1r3
{
static constexpr auto I0 = Number<0>{};
static constexpr auto I1 = Number<1>{};
@@ -164,12 +163,12 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v1r3
// TODO: check alignment
// A matrix in LDS memory, dst of blockwise copy
constexpr auto a_k_m_block_desc = make_dynamic_naive_tensor_descriptor_aligned_v2(
constexpr auto a_k_m_block_desc = make_naive_tensor_descriptor_aligned(
make_tuple(Number<KPerBlock>{}, Number<MPerBlockM1>{}, K1), max_lds_align);
// TODO: check alignment
// B matrix in LDS memory, dst of blockwise copy
constexpr auto b_k_n_block_desc = make_dynamic_naive_tensor_descriptor_aligned_v2(
constexpr auto b_k_n_block_desc = make_naive_tensor_descriptor_aligned(
make_tuple(Number<KPerBlock>{}, Number<NPerBlockN1>{}, K1), max_lds_align);
// TODO: check alignment
@@ -191,12 +190,12 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v1r3
const auto M = a_k0_m_k1_grid_desc.GetLength(I1);
const auto N = b_k0_n_k1_grid_desc.GetLength(I1);
const auto K0 = a_k0_m_k1_grid_desc.GetLength(I0);
const auto K1 = a_k0_m_k1_grid_desc.GetLength(I2);
// TODO: also check validity of all components (blockwise-copy, threadwise-copy, etc)
return (M == c_m_n_grid_desc.GetLength(I0) && N == c_m_n_grid_desc.GetLength(I1) &&
K0 == b_k0_n_k1_grid_desc.GetLength(I0) &&
K1 == a_k0_m_k1_grid_desc.GetLength(I2) &&
K1 == b_k0_n_k1_grid_desc.GetLength(I2)) &&
(M % MPerBlockM1 == 0 && N % NPerBlockN1 == 0 && K0 % KPerBlock == 0);
}
@@ -231,13 +230,13 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v1r3
const auto M1 = Number<MPerBlockM1>{};
const auto M0 = M / M1;
const auto a_k0_m0_m1_k1_grid_desc = transform_dynamic_tensor_descriptor(
a_k0_m_k1_grid_desc,
make_tuple(make_pass_through_transform(K0),
make_unmerge_transform(make_tuple(M0, M1)),
make_pass_through_transform(K1)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3>{}));
const auto a_k0_m0_m1_k1_grid_desc =
transform_tensor_descriptor(a_k0_m_k1_grid_desc,
make_tuple(make_pass_through_transform(K0),
make_unmerge_transform(make_tuple(M0, M1)),
make_pass_through_transform(K1)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3>{}));
return a_k0_m0_m1_k1_grid_desc;
}
@@ -251,13 +250,13 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v1r3
const auto N1 = Number<NPerBlockN1>{};
const auto N0 = N / N1;
const auto b_k0_n0_n1_k1_grid_desc = transform_dynamic_tensor_descriptor(
b_k0_n_k1_grid_desc,
make_tuple(make_pass_through_transform(K0),
make_unmerge_transform(make_tuple(N0, N1)),
make_pass_through_transform(K1)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3>{}));
const auto b_k0_n0_n1_k1_grid_desc =
transform_tensor_descriptor(b_k0_n_k1_grid_desc,
make_tuple(make_pass_through_transform(K0),
make_unmerge_transform(make_tuple(N0, N1)),
make_pass_through_transform(K1)),
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3>{}));
return b_k0_n0_n1_k1_grid_desc;
}
@@ -275,16 +274,16 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v1r3
const auto N0 = N / N1;
constexpr auto M11 =
Number<container_reduce(M11N11ThreadClusterM110Xs{}, math::multiplies_v2{}, I1) *
Number<container_reduce(M11N11ThreadClusterM110Xs{}, math::multiplies{}, I1) *
M1PerThreadM111>{};
constexpr auto N11 =
Number<container_reduce(M11N11ThreadClusterN110Xs{}, math::multiplies_v2{}, I1) *
Number<container_reduce(M11N11ThreadClusterN110Xs{}, math::multiplies{}, I1) *
N1PerThreadN111>{};
constexpr auto M10 = M1 / M11;
constexpr auto N10 = N1 / N11;
const auto c_m0_m10_m11_n0_n10_n11_grid_desc = transform_dynamic_tensor_descriptor(
const auto c_m0_m10_m11_n0_n10_n11_grid_desc = transform_tensor_descriptor(
c_m_n_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(M0, M10, M11)),
make_unmerge_transform(make_tuple(N0, N10, N11))),
@@ -355,23 +354,23 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v1r3
// TODO: check alignment
// A matrix in LDS memory, dst of blockwise copy
// be careful of LDS alignment
constexpr auto a_k0_m0_m1_k1_block_desc = make_dynamic_naive_tensor_descriptor_aligned_v2(
constexpr auto a_k0_m0_m1_k1_block_desc = make_naive_tensor_descriptor_aligned(
make_tuple(Number<KPerBlock>{}, I1, Number<MPerBlockM1>{}, K1), max_lds_align);
// TODO: check alignment
// B matrix in LDS memory, dst of blockwise copy
// be careful of LDS alignment
constexpr auto b_k0_n0_n1_k1_block_desc = make_dynamic_naive_tensor_descriptor_aligned_v2(
constexpr auto b_k0_n0_n1_k1_block_desc = make_naive_tensor_descriptor_aligned(
make_tuple(Number<KPerBlock>{}, I1, Number<NPerBlockN1>{}, K1), max_lds_align);
// TODO: check alignment
// A matrix in LDS memory, for blockwise GEMM
constexpr auto a_k0_m_k1_block_desc = make_dynamic_naive_tensor_descriptor_aligned_v2(
constexpr auto a_k0_m_k1_block_desc = make_naive_tensor_descriptor_aligned(
make_tuple(Number<KPerBlock>{}, Number<MPerBlockM1>{}, K1), max_lds_align);
// TODO: check alignment
// B matrix in LDS memory, for blockwise GEMM
constexpr auto b_k0_n_k1_block_desc = make_dynamic_naive_tensor_descriptor_aligned_v2(
constexpr auto b_k0_n_k1_block_desc = make_naive_tensor_descriptor_aligned(
make_tuple(Number<KPerBlock>{}, Number<NPerBlockN1>{}, K1), max_lds_align);
static_assert(a_k0_m0_m1_k1_block_desc.GetElementSpaceSize() ==
@@ -381,7 +380,7 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v1r3
"wrong!");
// A matrix blockwise copy
auto a_blockwise_copy = BlockwiseDynamicTensorSliceTransfer_v4r1<
auto a_blockwise_copy = BlockwiseTensorSliceTransfer_v4r1<
BlockSize,
InMemoryDataOperationEnum_t::Set,
Sequence<KPerBlock, 1, MPerBlockM1, K1.value>,
@@ -405,7 +404,7 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v1r3
make_multi_index(0, 0, 0, 0));
// B matrix blockwise copy
auto b_blockwise_copy = BlockwiseDynamicTensorSliceTransfer_v4r1<
auto b_blockwise_copy = BlockwiseTensorSliceTransfer_v4r1<
BlockSize,
InMemoryDataOperationEnum_t::Set,
Sequence<KPerBlock, 1, NPerBlockN1, K1.value>,
@@ -453,9 +452,8 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v1r3
constexpr auto c_m10_m11_n10_n11_thread_tensor_lengths =
decltype(blockwise_gemm)::GetCThreadTensorLengths_BM0_BM1_BN0_BN1();
constexpr auto c_m10_m11_n10_n11_thread_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(
sequence_to_tuple_of_number(c_m10_m11_n10_n11_thread_tensor_lengths));
constexpr auto c_m10_m11_n10_n11_thread_desc = make_naive_tensor_descriptor_packed(
sequence_to_tuple_of_number(c_m10_m11_n10_n11_thread_tensor_lengths));
// LDS allocation for A and B: be careful of alignment
constexpr auto a_block_aligned_space_size = math::integer_least_multiple(
@@ -471,9 +469,9 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v1r3
auto c_thread_buf = make_static_buffer<AddressSpaceEnum_t::Vgpr, FloatAcc>(
c_m10_m11_n10_n11_thread_desc.GetElementSpaceSize());
ThreadwiseDynamicTensorSliceSet_v1<FloatAcc,
decltype(c_m10_m11_n10_n11_thread_desc),
decltype(c_m10_m11_n10_n11_thread_tensor_lengths)>{}
ThreadwiseTensorSliceSet_v1<FloatAcc,
decltype(c_m10_m11_n10_n11_thread_desc),
decltype(c_m10_m11_n10_n11_thread_tensor_lengths)>{}
.Run(c_m10_m11_n10_n11_thread_desc,
make_tuple(I0, I0, I0, I0),
c_thread_buf,
@@ -496,8 +494,8 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v1r3
// LDS double buffer: preload data into LDS
{
a_blockwise_copy.RunRead(a_k0_m0_m1_k1_grid_desc, a_global_buf, AGridIteratorHacks{});
b_blockwise_copy.RunRead(b_k0_n0_n1_k1_grid_desc, b_global_buf, BGridIteratorHacks{});
a_blockwise_copy.RunRead(a_k0_m0_m1_k1_grid_desc, a_global_buf, AGridStepHacks{});
b_blockwise_copy.RunRead(b_k0_n0_n1_k1_grid_desc, b_global_buf, BGridStepHacks{});
a_blockwise_copy.RunWrite(a_k0_m0_m1_k1_block_desc, a_block_even_buf);
b_blockwise_copy.RunWrite(b_k0_n0_n1_k1_block_desc, b_block_even_buf);
@@ -516,18 +514,16 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v1r3
// even iteration
a_blockwise_copy.MoveSrcSliceWindow(a_k0_m0_m1_k1_grid_desc,
a_block_slice_copy_step,
AGridMoveSliceWindowIteratorHacks{});
AGridMoveSliceWindowStepHacks{});
b_blockwise_copy.MoveSrcSliceWindow(b_k0_n0_n1_k1_grid_desc,
b_block_slice_copy_step,
BGridMoveSliceWindowIteratorHacks{});
BGridMoveSliceWindowStepHacks{});
__syncthreads();
// LDS doubel buffer: load next data from device mem
a_blockwise_copy.RunRead(
a_k0_m0_m1_k1_grid_desc, a_global_buf, AGridIteratorHacks{});
b_blockwise_copy.RunRead(
b_k0_n0_n1_k1_grid_desc, b_global_buf, BGridIteratorHacks{});
a_blockwise_copy.RunRead(a_k0_m0_m1_k1_grid_desc, a_global_buf, AGridStepHacks{});
b_blockwise_copy.RunRead(b_k0_n0_n1_k1_grid_desc, b_global_buf, BGridStepHacks{});
// LDS double buffer: GEMM on current data
blockwise_gemm.Run(c_m10_m11_n10_n11_thread_desc,
@@ -542,18 +538,16 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v1r3
// odd iteration
a_blockwise_copy.MoveSrcSliceWindow(a_k0_m0_m1_k1_grid_desc,
a_block_slice_copy_step,
AGridMoveSliceWindowIteratorHacks{});
AGridMoveSliceWindowStepHacks{});
b_blockwise_copy.MoveSrcSliceWindow(b_k0_n0_n1_k1_grid_desc,
b_block_slice_copy_step,
BGridMoveSliceWindowIteratorHacks{});
BGridMoveSliceWindowStepHacks{});
__syncthreads();
// LDS doubel buffer: load next data from device mem
a_blockwise_copy.RunRead(
a_k0_m0_m1_k1_grid_desc, a_global_buf, AGridIteratorHacks{});
b_blockwise_copy.RunRead(
b_k0_n0_n1_k1_grid_desc, b_global_buf, BGridIteratorHacks{});
a_blockwise_copy.RunRead(a_k0_m0_m1_k1_grid_desc, a_global_buf, AGridStepHacks{});
b_blockwise_copy.RunRead(b_k0_n0_n1_k1_grid_desc, b_global_buf, BGridStepHacks{});
// LDS double buffer: GEMM on current data
blockwise_gemm.Run(
@@ -570,18 +564,16 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v1r3
// LDS double buffer: tail
if constexpr(HasDoubleTailKBlockLoop) // if has 2 iteration left
{
a_blockwise_copy.MoveSrcSliceWindow(a_k0_m0_m1_k1_grid_desc,
a_block_slice_copy_step,
AGridMoveSliceWindowIteratorHacks{});
b_blockwise_copy.MoveSrcSliceWindow(b_k0_n0_n1_k1_grid_desc,
b_block_slice_copy_step,
BGridMoveSliceWindowIteratorHacks{});
a_blockwise_copy.MoveSrcSliceWindow(
a_k0_m0_m1_k1_grid_desc, a_block_slice_copy_step, AGridMoveSliceWindowStepHacks{});
b_blockwise_copy.MoveSrcSliceWindow(
b_k0_n0_n1_k1_grid_desc, b_block_slice_copy_step, BGridMoveSliceWindowStepHacks{});
__syncthreads();
// LDS double buffer: load last data from device mem
a_blockwise_copy.RunRead(a_k0_m0_m1_k1_grid_desc, a_global_buf, AGridIteratorHacks{});
b_blockwise_copy.RunRead(b_k0_n0_n1_k1_grid_desc, b_global_buf, BGridIteratorHacks{});
a_blockwise_copy.RunRead(a_k0_m0_m1_k1_grid_desc, a_global_buf, AGridStepHacks{});
b_blockwise_copy.RunRead(b_k0_n0_n1_k1_grid_desc, b_global_buf, BGridStepHacks{});
// LDS double buffer: GEMM on 2nd-last data
blockwise_gemm.Run(
@@ -608,21 +600,8 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v1r3
// output: register to global memory
{
constexpr auto M11 =
Number<container_reduce(M11N11ThreadClusterM110Xs{}, math::multiplies_v2{}, I1) *
M1PerThreadM111>{};
constexpr auto N11 =
Number<container_reduce(M11N11ThreadClusterN110Xs{}, math::multiplies_v2{}, I1) *
N1PerThreadN111>{};
constexpr index_t M10 = MPerBlockM1 / M11;
constexpr index_t N10 = NPerBlockN1 / N11;
constexpr index_t M111 = M1PerThreadM111;
constexpr index_t N111 = N1PerThreadN111;
constexpr auto c_m0_m10_m11_n0_n10_n11_thread_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(
make_naive_tensor_descriptor_packed(
make_tuple(I1,
Number<c_m10_m11_n10_n11_thread_tensor_lengths[I0]>{},
Number<c_m10_m11_n10_n11_thread_tensor_lengths[I1]>{},
@@ -634,7 +613,7 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v1r3
blockwise_gemm.CalculateCThreadOriginOnBlock_BM0_BM1_BN0_BN1(
get_thread_local_1d_id());
ThreadwiseDynamicTensorSliceTransfer_v1r3<
ThreadwiseTensorSliceTransfer_v1r3<
FloatAcc,
FloatC,
decltype(c_m0_m10_m11_n0_n10_n11_thread_desc),
@@ -662,7 +641,7 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v1r3
c_thread_buf,
c_m0_m10_m11_n0_n10_n11_grid_desc,
c_grid_buf,
CGridIteratorHacks{});
CGridStepHacks{});
}
}
};

181
composable_kernel/include/tensor_operation/gridwise_dynamic_gemm_dlops_v2.hpp → composable_kernel/include/tensor_operation/gridwise_gemm_dlops_v2.hpp
View File

@@ -1,12 +1,12 @@
#ifndef CK_GRIDWISE_DYNAMIC_GEMM_V2_HPP
#define CK_GRIDWISE_DYNAMIC_GEMM_V2_HPP
#ifndef CK_GRIDWISE_GEMM_V2_HPP
#define CK_GRIDWISE_GEMM_V2_HPP
#include "common_header.hpp"
#include "dynamic_multi_index_transform_helper.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "blockwise_dynamic_tensor_slice_transfer.hpp"
#include "threadwise_dynamic_tensor_slice_transfer.hpp"
#include "multi_index_transform_helper.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "blockwise_tensor_slice_transfer.hpp"
#include "threadwise_tensor_slice_transfer.hpp"
#include "blockwise_gemm_dlops_v3.hpp"
namespace ck {
@@ -42,12 +42,12 @@ template <index_t BlockSize,
typename CThreadTransferSrcDstAccessOrder,
index_t CThreadTransferSrcDstVectorDim,
index_t CThreadTransferDstScalarPerVector,
typename AGlobalIteratorHacks,
typename BGlobalIteratorHacks,
typename CGlobalIteratorHacks,
typename AGlobalMoveSliceWindowIteratorHacks,
typename BGlobalMoveSliceWindowIteratorHacks>
struct GridwiseDynamicGemmDlops_km_kn_mn_v3
typename AGlobalStepHacks,
typename BGlobalStepHacks,
typename CGlobalStepHacks,
typename AGlobalMoveSliceWindowStepHacks,
typename BGlobalMoveSliceWindowStepHacks>
struct GridwiseGemmDlops_km_kn_mn_v3
{
__host__ __device__ static constexpr index_t GetSharedMemoryNumberOfByte()
{
@@ -58,7 +58,7 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v3
// A matrix in LDS memory, dst of blockwise copy
// be careful of LDS alignment
constexpr auto a_e_k_desc = make_dynamic_naive_tensor_descriptor_aligned_v2(
constexpr auto a_e_k_desc = make_naive_tensor_descriptor_aligned(
make_tuple(Number<E>{}, Number<KPerBlock>{}), max_lds_align);
// LDS allocation for A and B: be careful of alignment
@@ -102,7 +102,6 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v3
// divide block work by [M, N]
#if 0
const auto k_block_work_num = K / Number<KPerBlock>{};
const auto ho_block_work_num = Ho / Number<HoPerBlock>{};
const auto wo_block_work_num = Wo / Number<WoPerBlock>{};
const auto hwo_block_work_num = ho_block_work_num * wo_block_work_num;
@@ -114,7 +113,6 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v3
const index_t wo_block_work_id = hwo_block_work_id - ho_block_work_id * wo_block_work_num;
#else
// Hack: this force result into SGPR
const index_t k_block_work_num = __builtin_amdgcn_readfirstlane(K / KPerBlock);
const index_t ho_block_work_num = __builtin_amdgcn_readfirstlane(Ho / HoPerBlock);
const index_t wo_block_work_num = __builtin_amdgcn_readfirstlane(Wo / WoPerBlock);
const index_t hwo_block_work_num = ho_block_work_num * wo_block_work_num;
@@ -134,23 +132,21 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v3
// A matrix in LDS memory, dst of blockwise copy
// be careful of LDS alignment
constexpr auto a_e_k_block_desc = make_dynamic_naive_tensor_descriptor_aligned_v2(
constexpr auto a_e_k_block_desc = make_naive_tensor_descriptor_aligned(
make_tuple(Number<EPerBlock>{}, Number<KPerBlock>{}), max_lds_align);
constexpr auto a_e_k_desc = make_dynamic_naive_tensor_descriptor_aligned_v2(
constexpr auto a_e_k_desc = make_naive_tensor_descriptor_aligned(
make_tuple(Number<E>{}, Number<KPerBlock>{}), max_lds_align);
// B matrix in LDS memory, dst of blockwise copy
// be careful of LDS alignment
constexpr auto b_e_n_ho_wo_block_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(
Number<EPerBlock>{}, Number<1>{}, Number<HoPerBlock>{}, Number<WoPerBlock>{}));
constexpr auto b_e_n_ho_wo_block_desc = make_naive_tensor_descriptor_packed(make_tuple(
Number<EPerBlock>{}, Number<1>{}, Number<HoPerBlock>{}, Number<WoPerBlock>{}));
// c_thread_mtx definition: this is a mess
// TODO:: more elegent way of defining c_thread_mtx
constexpr auto c_k_n_ho_wo_thread_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(
Number<KPerThread>{}, Number<1>{}, Number<HoPerThread>{}, Number<WoPerThread>{}));
constexpr auto c_k_n_ho_wo_thread_desc = make_naive_tensor_descriptor_packed(make_tuple(
Number<KPerThread>{}, Number<1>{}, Number<HoPerThread>{}, Number<WoPerThread>{}));
auto blockwise_gemm =
BlockwiseGemmDlops_km_kn_m0m1n0n1_v3<BlockSize,
@@ -184,47 +180,46 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v3
// A matrix blockwise copy
auto a_blockwise_copy =
BlockwiseDynamicTensorSliceTransfer_v4<BlockSize,
InMemoryDataOperationEnum_t::Set,
Sequence<E, KPerBlock>,
ABlockTransferThreadSliceLengths_E_K,
ABlockTransferThreadClusterLengths_E_K,
ABlockTransferThreadClusterArrangeOrder,
FloatAB,
FloatAB,
decltype(a_e_k_global_desc),
decltype(a_e_k_desc),
ABlockTransferSrcAccessOrder,
Sequence<0, 1>,
ABlockTransferSrcVectorDim,
1,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_K,
1,
1,
AThreadTransferSrcResetCoordinateAfterRun,
true>(
a_e_k_global_desc,
make_multi_index(0, k_block_data_on_global),
a_e_k_desc,
make_multi_index(0, 0));
BlockwiseTensorSliceTransfer_v4<BlockSize,
InMemoryDataOperationEnum_t::Set,
Sequence<E, KPerBlock>,
ABlockTransferThreadSliceLengths_E_K,
ABlockTransferThreadClusterLengths_E_K,
ABlockTransferThreadClusterArrangeOrder,
FloatAB,
FloatAB,
decltype(a_e_k_global_desc),
decltype(a_e_k_desc),
ABlockTransferSrcAccessOrder,
Sequence<0, 1>,
ABlockTransferSrcVectorDim,
1,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_K,
1,
1,
AThreadTransferSrcResetCoordinateAfterRun,
true>(a_e_k_global_desc,
make_multi_index(0, k_block_data_on_global),
a_e_k_desc,
make_multi_index(0, 0));
constexpr auto b_e_n_ho_wo_thread_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(
Number<EPerBlock>{}, Number<1>{}, Number<HoPerThread>{}, Number<WoPerThread>{}));
constexpr auto b_e_n_ho_wo_thread_desc = make_naive_tensor_descriptor_packed(make_tuple(
Number<EPerBlock>{}, Number<1>{}, Number<HoPerThread>{}, Number<WoPerThread>{}));
auto b_threadwise_transfer = ThreadwiseDynamicTensorSliceTransfer_v2<
FloatAB,
FloatAB,
decltype(b_e_n_ho_wo_global_desc),
decltype(b_e_n_ho_wo_thread_desc),
Sequence<EPerBlock, 1, HoPerThread, WoPerThread>,
BBlockTransferSrcAccessOrder,
BBlockTransferSrcVectorDim,
BBlockTransferSrcScalarPerVector,
1,
true>(b_e_n_ho_wo_global_desc,
make_multi_index(0, 0, ho_thread_data_on_global, wo_thread_data_on_global));
auto b_threadwise_transfer =
ThreadwiseTensorSliceTransfer_v2<FloatAB,
FloatAB,
decltype(b_e_n_ho_wo_global_desc),
decltype(b_e_n_ho_wo_thread_desc),
Sequence<EPerBlock, 1, HoPerThread, WoPerThread>,
BBlockTransferSrcAccessOrder,
BBlockTransferSrcVectorDim,
BBlockTransferSrcScalarPerVector,
1,
true>(
b_e_n_ho_wo_global_desc,
make_multi_index(0, 0, ho_thread_data_on_global, wo_thread_data_on_global));
auto a_block_buf = make_dynamic_buffer<AddressSpaceEnum_t::Lds>(
p_shared_block, a_e_k_desc.GetElementSpaceSize());
@@ -232,44 +227,45 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v3
// register allocation for output
StaticBuffer<AddressSpaceEnum_t::Vgpr,
FloatAcc,
c_k_n_ho_wo_thread_desc.GetElementSpaceSize()>
c_k_n_ho_wo_thread_desc.GetElementSpaceSize(),
true>
c_thread_buf;
// initialize output thread tensor
ThreadwiseDynamicTensorSliceSet_v1<FloatAcc,
decltype(c_k_n_ho_wo_thread_desc),
Sequence<KPerThread, 1, HoPerThread, WoPerThread>>{}
ThreadwiseTensorSliceSet_v1<FloatAcc,
decltype(c_k_n_ho_wo_thread_desc),
Sequence<KPerThread, 1, HoPerThread, WoPerThread>>{}
.Run(c_k_n_ho_wo_thread_desc, make_tuple(I0, I0, I0, I0), c_thread_buf, FloatAcc{0});
constexpr auto b_thread_slice_copy_step = make_multi_index(EPerBlock, 0, 0, 0);
// hack to control index calculation when iterating over A and B matrix for threadwise copy
constexpr auto a_e_k_global_iterator_hacks = AGlobalIteratorHacks{};
constexpr auto b_e_n_ho_wo_global_iterator_hacks = BGlobalIteratorHacks{};
constexpr auto a_e_k_global_step_hacks = AGlobalStepHacks{};
constexpr auto b_e_n_ho_wo_global_step_hacks = BGlobalStepHacks{};
// hack to control index calculation when move slice window for A and B matrix for
// threadwise copy
constexpr auto a_e_k_global_move_slice_window_iterator_hack =
AGlobalMoveSliceWindowIteratorHacks{};
constexpr auto b_e_n_ho_wo_global_move_slice_window_iterator_hack =
BGlobalMoveSliceWindowIteratorHacks{};
constexpr auto a_e_k_global_move_slice_window_step_hack = AGlobalMoveSliceWindowStepHacks{};
constexpr auto b_e_n_ho_wo_global_move_slice_window_step_hack =
BGlobalMoveSliceWindowStepHacks{};
// double regsiter buffer for b
StaticBuffer<AddressSpaceEnum_t::Vgpr,
FloatAB,
b_e_n_ho_wo_thread_desc.GetElementSpaceSize()>
b_e_n_ho_wo_thread_desc.GetElementSpaceSize(),
true>
b_thread_even_buf, b_thread_odd_buf;
// LDS double buffer: preload data
{
a_blockwise_copy.RunRead(a_e_k_global_desc, a_global_buf, a_e_k_global_iterator_hacks);
a_blockwise_copy.RunRead(a_e_k_global_desc, a_global_buf, a_e_k_global_step_hacks);
b_threadwise_transfer.Run(b_e_n_ho_wo_global_desc,
b_global_buf,
b_e_n_ho_wo_thread_desc,
make_tuple(I0, I0, I0, I0),
b_thread_even_buf,
b_e_n_ho_wo_global_iterator_hacks);
b_e_n_ho_wo_global_step_hacks);
a_blockwise_copy.RunWrite(a_e_k_desc, a_block_buf);
}
@@ -293,7 +289,7 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v3
b_e_n_ho_wo_thread_desc,
make_tuple(I0, I0, I0, I0),
b_thread_odd_buf,
b_e_n_ho_wo_global_iterator_hacks);
b_e_n_ho_wo_global_step_hacks);
// LDS double buffer: GEMM on current data
// TODO: @Zhang Jing: blockwise gemm should be able to move slice window
@@ -309,7 +305,7 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v3
b_e_n_ho_wo_thread_desc,
make_tuple(I0, I0, I0, I0),
b_thread_even_buf,
b_e_n_ho_wo_global_iterator_hacks);
b_e_n_ho_wo_global_step_hacks);
// LDS double buffer: GEMM on current data
blockwise_gemm.Run(a_block_buf, b_thread_odd_buf, c_thread_buf);
@@ -332,7 +328,7 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v3
b_e_n_ho_wo_thread_desc,
make_tuple(I0, I0, I0, I0),
b_thread_odd_buf,
b_e_n_ho_wo_global_iterator_hacks);
b_e_n_ho_wo_global_step_hacks);
// LDS double buffer: GEMM on 2nd-last data
blockwise_gemm.Run(a_block_buf, b_thread_even_buf, c_thread_buf);
@@ -351,23 +347,22 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v3
// output: register to global memory
{
// hack to control index calculation when iterating over c_k_n_ho_wo_global tensor
constexpr auto c_k_n_ho_wo_global_tensor_iterator_hacks = CGlobalIteratorHacks{};
constexpr auto c_k_n_ho_wo_global_tensor_step_hacks = CGlobalStepHacks{};
const index_t k_thread_data_on_global =
k_block_data_on_global + k_thread_id * KPerThread;
ThreadwiseDynamicTensorSliceTransfer_v1r3<
FloatAcc,
FloatC,
decltype(c_k_n_ho_wo_thread_desc),
decltype(c_k_n_ho_wo_global_desc),
Sequence<KPerThread, 1, HoPerThread, WoPerThread>,
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
CGlobalMemoryDataOperation,
1,
true>(
ThreadwiseTensorSliceTransfer_v1r3<FloatAcc,
FloatC,
decltype(c_k_n_ho_wo_thread_desc),
decltype(c_k_n_ho_wo_global_desc),
Sequence<KPerThread, 1, HoPerThread, WoPerThread>,
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
CGlobalMemoryDataOperation,
1,
true>(
c_k_n_ho_wo_global_desc,
make_multi_index(
k_thread_data_on_global, 0, ho_thread_data_on_global, wo_thread_data_on_global))
@@ -376,7 +371,7 @@ struct GridwiseDynamicGemmDlops_km_kn_mn_v3
c_thread_buf,
c_k_n_ho_wo_global_desc,
c_global_buf,
c_k_n_ho_wo_global_tensor_iterator_hacks);
c_k_n_ho_wo_global_tensor_step_hacks);
}
}

278
composable_kernel/include/tensor_operation/gridwise_dynamic_gemm_xdlops_v2r3.hpp → composable_kernel/include/tensor_operation/gridwise_gemm_xdlops_v2r3.hpp
View File

@@ -1,14 +1,14 @@
#ifndef CK_GRIDWISE_DYNAMIC_GEMM_XDLOPS_V2R3_HPP
#define CK_GRIDWISE_DYNAMIC_GEMM_XDLOPS_V2R3_HPP
#ifndef CK_GRIDWISE_GEMM_XDLOPS_V2R3_HPP
#define CK_GRIDWISE_GEMM_XDLOPS_V2R3_HPP
#include "common_header.hpp"
#include "dynamic_multi_index_transform_helper.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "multi_index_transform_helper.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "blockwise_gemm_xdlops.hpp"
#include "blockwise_dynamic_tensor_slice_transfer.hpp"
#include "threadwise_dynamic_tensor_slice_transfer.hpp"
#include "threadwise_dynamic_tensor_slice_set.hpp"
#include "blockwise_tensor_slice_transfer.hpp"
#include "threadwise_tensor_slice_transfer.hpp"
#include "threadwise_tensor_slice_set.hpp"
namespace ck {
@@ -24,13 +24,13 @@ __global__ void
#if CK_USE_LAUNCH_BOUNDS
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif
kernel_dynamic_gemm_xdlops_v2r3(const FloatAB* __restrict__ p_a_grid,
const FloatAB* __restrict__ p_b_grid,
FloatC* __restrict__ p_c_grid,
const AK0MK1GridDesc a_k0_m_k1_grid_desc,
const BK0NK1GridDesc b_k0_n_k1_grid_desc,
const CM0M1M2NGridDesc c_m0_m1_m2_n_grid_desc,
const CBlockClusterAdaptor c_block_cluster_adaptor)
kernel_gemm_xdlops_v2r3(const FloatAB* __restrict__ p_a_grid,
const FloatAB* __restrict__ p_b_grid,
FloatC* __restrict__ p_c_grid,
const AK0MK1GridDesc a_k0_m_k1_grid_desc,
const BK0NK1GridDesc b_k0_n_k1_grid_desc,
const CM0M1M2NGridDesc c_m0_m1_m2_n_grid_desc,
const CBlockClusterAdaptor c_block_cluster_adaptor)
{
constexpr index_t shared_block_size =
GridwiseGemm::GetSharedMemoryNumberOfByte() / sizeof(FloatAB);
@@ -58,25 +58,25 @@ __global__ void
#if CK_USE_LAUNCH_BOUNDS
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif
kernel_dynamic_gemm_xdlops_v2r3(const FloatAB* __restrict__ p_a_grid,
const FloatAB* __restrict__ p_b_grid,
FloatC* __restrict__ p_c_grid,
const void CONSTANT* p_a_k0_m_k1_grid_desc,
const void CONSTANT* p_b_k0_n_k1_grid_desc,
const void CONSTANT* p_c_m0_m1_m2_n_grid_desc,
const void CONSTANT* p_c_block_cluster_adaptor)
kernel_gemm_xdlops_v2r3(const FloatAB* __restrict__ p_a_grid,
const FloatAB* __restrict__ p_b_grid,
FloatC* __restrict__ p_c_grid,
const void CONSTANT* p_a_k0_m_k1_grid_desc,
const void CONSTANT* p_b_k0_n_k1_grid_desc,
const void CONSTANT* p_c_m0_m1_m2_n_grid_desc,
const void CONSTANT* p_c_block_cluster_adaptor)
{
constexpr index_t shared_block_size =
GridwiseGemm::GetSharedMemoryNumberOfByte() / sizeof(FloatAB);
const auto a_k0_m_k1_grid_desc =
*reinterpret_cast<const AK0MK1GridDesc*>((const void*)p_a_k0_m_k1_grid_desc);
const auto b_k0_n_k1_grid_desc =
*reinterpret_cast<const BK0NK1GridDesc*>((const void*)p_b_k0_n_k1_grid_desc);
const auto c_m0_m1_m2_n_grid_desc =
*reinterpret_cast<const CM0M1M2NGridDesc*>((const void*)p_c_m0_m1_m2_n_grid_desc);
const auto c_block_cluster_adaptor =
*reinterpret_cast<const CBlockClusterAdaptor*>((const void*)p_c_block_cluster_adaptor);
const auto a_k0_m_k1_grid_desc = *reinterpret_cast<const AK0MK1GridDesc*>(
cast_pointer_to_generic_address_space(p_a_k0_m_k1_grid_desc));
const auto b_k0_n_k1_grid_desc = *reinterpret_cast<const BK0NK1GridDesc*>(
cast_pointer_to_generic_address_space(p_b_k0_n_k1_grid_desc));
const auto c_m0_m1_m2_n_grid_desc = *reinterpret_cast<const CM0M1M2NGridDesc*>(
cast_pointer_to_generic_address_space(p_c_m0_m1_m2_n_grid_desc));
const auto c_block_cluster_adaptor = *reinterpret_cast<const CBlockClusterAdaptor*>(
cast_pointer_to_generic_address_space(p_c_block_cluster_adaptor));
__shared__ FloatAB p_shared_block[shared_block_size];
@@ -126,13 +126,13 @@ template <index_t BlockSize,
typename CThreadTransferSrcDstAccessOrder,
index_t CThreadTransferSrcDstVectorDim,
index_t CThreadTransferDstScalarPerVector,
typename AGridIteratorHacks,
typename BGridIteratorHacks,
typename CGridIteratorHacks,
typename AGridMoveSliceWindowIteratorHacks,
typename BGridMoveSliceWindowIteratorHacks,
typename AGridStepHacks,
typename BGridStepHacks,
typename CGridStepHacks,
typename AGridMoveSliceWindowStepHacks,
typename BGridMoveSliceWindowStepHacks,
bool CAccessOrderMRepeatNRepeat>
struct GridwiseDynamicGemm_k0mk1_k0nk1_mn_xdlops_v2r3
struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3
{
static constexpr auto I0 = Number<0>{};
static constexpr auto I1 = Number<1>{};
@@ -148,12 +148,12 @@ struct GridwiseDynamicGemm_k0mk1_k0nk1_mn_xdlops_v2r3
// A matrix in LDS memory, dst of blockwise copy
// be careful of LDS alignment
constexpr auto a_k0_m_k1_block_desc = make_dynamic_naive_tensor_descriptor_aligned_v2(
constexpr auto a_k0_m_k1_block_desc = make_naive_tensor_descriptor_aligned(
make_tuple(Number<KPerBlock>{}, Number<MPerBlock>{}, K1), max_lds_align);
// B matrix in LDS memory, dst of blockwise copy
// be careful of LDS alignment
constexpr auto b_k0_n_k1_block_desc = make_dynamic_naive_tensor_descriptor_aligned_v2(
constexpr auto b_k0_n_k1_block_desc = make_naive_tensor_descriptor_aligned(
make_tuple(Number<KPerBlock>{}, Number<NPerBlock>{}, K1), max_lds_align);
// LDS allocation for A and B: be careful of alignment
@@ -203,9 +203,6 @@ struct GridwiseDynamicGemm_k0mk1_k0nk1_mn_xdlops_v2r3
__host__ __device__ static constexpr auto
MakeCM0M1M2NGridDescriptor(const CMNGridDesc& c_m_n_grid_desc)
{
const auto M = c_m_n_grid_desc.GetLength(I0);
const auto N = c_m_n_grid_desc.GetLength(I1);
constexpr auto xdlops_gemm = XdlopsGemm<FloatAB, MPerWave, NPerWave, K1>{};
constexpr auto CLayout = xdlops_gemm.GetCLayout();
@@ -217,10 +214,9 @@ struct GridwiseDynamicGemm_k0mk1_k0nk1_mn_xdlops_v2r3
constexpr index_t MWaves = MPerBlock / (MPerWave * MRepeat);
constexpr index_t NWaves = NPerBlock / (NPerWave * NRepeat);
constexpr auto N0 = Number<CLayout.N1()>{};
constexpr auto N1 = Number<CLayout.N0()>{};
const auto c_m0_m1_m2_n_grid_desc = transform_dynamic_tensor_descriptor(
const auto c_m0_m1_m2_n_grid_desc = transform_tensor_descriptor(
c_m_n_grid_desc,
make_tuple(make_unmerge_transform(make_tuple(MRepeat, MWaves, M0, M1, M2)),
make_unmerge_transform(make_tuple(NRepeat, NWaves, N1))),
@@ -269,11 +265,6 @@ struct GridwiseDynamicGemm_k0mk1_k0nk1_mn_xdlops_v2r3
const CM0M1M2NGridDesc& c_m0_m1_m2_n_grid_desc,
const CBlockClusterAdaptor& c_block_cluster_adaptor)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
const auto a_grid_buf = make_dynamic_buffer<AddressSpaceEnum_t::Global>(
p_a_grid, a_k0_m_k1_grid_desc.GetElementSpaceSize());
const auto b_grid_buf = make_dynamic_buffer<AddressSpaceEnum_t::Global>(
@@ -282,8 +273,6 @@ struct GridwiseDynamicGemm_k0mk1_k0nk1_mn_xdlops_v2r3
p_c_grid, c_m0_m1_m2_n_grid_desc.GetElementSpaceSize());
const auto K0 = a_k0_m_k1_grid_desc.GetLength(I0);
const auto M = a_k0_m_k1_grid_desc.GetLength(I1);
const auto N = b_k0_n_k1_grid_desc.GetLength(I1);
// divide block work by [M, N]
const auto block_work_idx =
@@ -301,67 +290,65 @@ struct GridwiseDynamicGemm_k0mk1_k0nk1_mn_xdlops_v2r3
// A matrix in LDS memory, dst of blockwise copy
// be careful of LDS alignment
constexpr auto a_k0_m_k1_block_desc = make_dynamic_naive_tensor_descriptor_aligned_v2(
constexpr auto a_k0_m_k1_block_desc = make_naive_tensor_descriptor_aligned(
make_tuple(Number<KPerBlock>{}, Number<MPerBlock>{}, K1), max_lds_align);
// B matrix in LDS memory, dst of blockwise copy
// be careful of LDS alignment
constexpr auto b_k0_n_k1_block_desc = make_dynamic_naive_tensor_descriptor_aligned_v2(
constexpr auto b_k0_n_k1_block_desc = make_naive_tensor_descriptor_aligned(
make_tuple(Number<KPerBlock>{}, Number<NPerBlock>{}, K1), max_lds_align);
// A matrix blockwise copy
auto a_blockwise_copy =
BlockwiseDynamicTensorSliceTransfer_v4<BlockSize,
InMemoryDataOperationEnum_t::Set,
Sequence<KPerBlock, MPerBlock, K1>,
ABlockTransferThreadSliceLengths_K0_M_K1,
ABlockTransferThreadClusterLengths_K0_M_K1,
ABlockTransferThreadClusterArrangeOrder,
FloatAB,
FloatAB,
decltype(a_k0_m_k1_grid_desc),
decltype(a_k0_m_k1_block_desc),
ABlockTransferSrcAccessOrder,
Sequence<1, 0, 2>,
ABlockTransferSrcVectorDim,
2,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_K1,
1,
1,
AThreadTransferSrcResetCoordinateAfterRun,
true>(
a_k0_m_k1_grid_desc,
make_multi_index(0, m_block_data_idx_on_grid, 0),
a_k0_m_k1_block_desc,
make_multi_index(0, 0, 0));
BlockwiseTensorSliceTransfer_v4<BlockSize,
InMemoryDataOperationEnum_t::Set,
Sequence<KPerBlock, MPerBlock, K1>,
ABlockTransferThreadSliceLengths_K0_M_K1,
ABlockTransferThreadClusterLengths_K0_M_K1,
ABlockTransferThreadClusterArrangeOrder,
FloatAB,
FloatAB,
decltype(a_k0_m_k1_grid_desc),
decltype(a_k0_m_k1_block_desc),
ABlockTransferSrcAccessOrder,
Sequence<1, 0, 2>,
ABlockTransferSrcVectorDim,
2,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_K1,
1,
1,
AThreadTransferSrcResetCoordinateAfterRun,
true>(a_k0_m_k1_grid_desc,
make_multi_index(0, m_block_data_idx_on_grid, 0),
a_k0_m_k1_block_desc,
make_multi_index(0, 0, 0));
// B matrix blockwise copy
auto b_blockwise_copy =
BlockwiseDynamicTensorSliceTransfer_v4<BlockSize,
InMemoryDataOperationEnum_t::Set,
Sequence<KPerBlock, NPerBlock, K1>,
BBlockTransferThreadSliceLengths_K0_N_K1,
BBlockTransferThreadClusterLengths_K0_N_K1,
BBlockTransferThreadClusterArrangeOrder,
FloatAB,
FloatAB,
decltype(b_k0_n_k1_grid_desc),
decltype(b_k0_n_k1_block_desc),
BBlockTransferSrcAccessOrder,
Sequence<1, 0, 2>,
BBlockTransferSrcVectorDim,
2,
BBlockTransferSrcScalarPerVector,
BBlockTransferDstScalarPerVector_K1,
1,
1,
BThreadTransferSrcResetCoordinateAfterRun,
true>(
b_k0_n_k1_grid_desc,
make_multi_index(0, n_block_data_idx_on_grid, 0),
b_k0_n_k1_block_desc,
make_multi_index(0, 0, 0));
BlockwiseTensorSliceTransfer_v4<BlockSize,
InMemoryDataOperationEnum_t::Set,
Sequence<KPerBlock, NPerBlock, K1>,
BBlockTransferThreadSliceLengths_K0_N_K1,
BBlockTransferThreadClusterLengths_K0_N_K1,
BBlockTransferThreadClusterArrangeOrder,
FloatAB,
FloatAB,
decltype(b_k0_n_k1_grid_desc),
decltype(b_k0_n_k1_block_desc),
BBlockTransferSrcAccessOrder,
Sequence<1, 0, 2>,
BBlockTransferSrcVectorDim,
2,
BBlockTransferSrcScalarPerVector,
BBlockTransferDstScalarPerVector_K1,
1,
1,
BThreadTransferSrcResetCoordinateAfterRun,
true>(b_k0_n_k1_grid_desc,
make_multi_index(0, n_block_data_idx_on_grid, 0),
b_k0_n_k1_block_desc,
make_multi_index(0, 0, 0));
// GEMM definition
// c_mtx += transpose(a_mtx) * b_mtx
@@ -375,7 +362,7 @@ struct GridwiseDynamicGemm_k0mk1_k0nk1_mn_xdlops_v2r3
NPerBlock % (NPerWave * NRepeat) == 0,
"wrong!");
constexpr auto a_k0_m0_m1_k1_block_desc = transform_dynamic_tensor_descriptor(
constexpr auto a_k0_m0_m1_k1_block_desc = transform_tensor_descriptor(
a_k0_m_k1_block_desc,
make_tuple(make_pass_through_transform(Number<KPerBlock>{}),
make_unmerge_transform(
@@ -384,7 +371,7 @@ struct GridwiseDynamicGemm_k0mk1_k0nk1_mn_xdlops_v2r3
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3>{}));
constexpr auto b_k0_n0_n1_k1_block_desc = transform_dynamic_tensor_descriptor(
constexpr auto b_k0_n0_n1_k1_block_desc = transform_tensor_descriptor(
b_k0_n_k1_block_desc,
make_tuple(make_pass_through_transform(Number<KPerBlock>{}),
make_unmerge_transform(
@@ -410,21 +397,19 @@ struct GridwiseDynamicGemm_k0mk1_k0nk1_mn_xdlops_v2r3
static_assert(NumBlks == 1 && NumXdlops == 1, "K Reduction Mfma only");
constexpr auto c_mr_nr_blk_desc = make_dynamic_naive_tensor_descriptor_packed_v2(
make_tuple(Number<MRepeat>{}, Number<NRepeat>{}));
constexpr auto c_mr_nr_blk_desc =
make_naive_tensor_descriptor_packed(make_tuple(Number<MRepeat>{}, Number<NRepeat>{}));
StaticBuffer<AddressSpaceEnum_t::Vgpr,
vector_type<FloatAcc, BlkSize>,
c_mr_nr_blk_desc.GetElementSpaceSize()>
c_mr_nr_blk_desc.GetElementSpaceSize(),
true>
c_thread_buf;
// LDS allocation for A and B: be careful of alignment
constexpr auto a_block_space_size =
math::integer_least_multiple(a_k0_m_k1_block_desc.GetElementSpaceSize(), max_lds_align);
constexpr auto b_block_space_size =
math::integer_least_multiple(b_k0_n_k1_block_desc.GetElementSpaceSize(), max_lds_align);
FloatAB* p_a_block = p_shared_block;
FloatAB* p_b_block = p_shared_block + a_block_space_size;
@@ -432,15 +417,13 @@ struct GridwiseDynamicGemm_k0mk1_k0nk1_mn_xdlops_v2r3
constexpr auto b_block_slice_copy_step = make_multi_index(KPerBlock, 0, 0);
// hack to control index calculation when iterating over A and B matrix for threadwise copy
constexpr auto a_k0_m_k1_grid_iterator_hacks = AGridIteratorHacks{};
constexpr auto b_k0_n_k1_grid_iterator_hacks = BGridIteratorHacks{};
constexpr auto a_k0_m_k1_grid_step_hacks = AGridStepHacks{};
constexpr auto b_k0_n_k1_grid_step_hacks = BGridStepHacks{};
// hack to control index calculation when move slice window for A and B matrix for
// threadwise copy
constexpr auto a_k0_m_k1_grid_move_slice_window_iterator_hack =
AGridMoveSliceWindowIteratorHacks{};
constexpr auto b_k0_n_k1_grid_move_slice_window_iterator_hack =
BGridMoveSliceWindowIteratorHacks{};
constexpr auto a_k0_m_k1_grid_move_slice_window_step_hack = AGridMoveSliceWindowStepHacks{};
constexpr auto b_k0_n_k1_grid_move_slice_window_step_hack = BGridMoveSliceWindowStepHacks{};
auto a_block_buf = make_dynamic_buffer<AddressSpaceEnum_t::Lds>(
p_a_block, a_k0_m_k1_block_desc.GetElementSpaceSize());
@@ -449,10 +432,8 @@ struct GridwiseDynamicGemm_k0mk1_k0nk1_mn_xdlops_v2r3
// preload data into LDS
{
a_blockwise_copy.RunRead(
a_k0_m_k1_grid_desc, a_grid_buf, a_k0_m_k1_grid_iterator_hacks);
b_blockwise_copy.RunRead(
b_k0_n_k1_grid_desc, b_grid_buf, b_k0_n_k1_grid_iterator_hacks);
a_blockwise_copy.RunRead(a_k0_m_k1_grid_desc, a_grid_buf, a_k0_m_k1_grid_step_hacks);
b_blockwise_copy.RunRead(b_k0_n_k1_grid_desc, b_grid_buf, b_k0_n_k1_grid_step_hacks);
a_blockwise_copy.RunWrite(a_k0_m_k1_block_desc, a_block_buf);
b_blockwise_copy.RunWrite(b_k0_n_k1_block_desc, b_block_buf);
@@ -465,18 +446,16 @@ struct GridwiseDynamicGemm_k0mk1_k0nk1_mn_xdlops_v2r3
{
a_blockwise_copy.MoveSrcSliceWindow(a_k0_m_k1_grid_desc,
a_block_slice_copy_step,
a_k0_m_k1_grid_move_slice_window_iterator_hack);
a_k0_m_k1_grid_move_slice_window_step_hack);
b_blockwise_copy.MoveSrcSliceWindow(b_k0_n_k1_grid_desc,
b_block_slice_copy_step,
b_k0_n_k1_grid_move_slice_window_iterator_hack);
b_k0_n_k1_grid_move_slice_window_step_hack);
a_blockwise_copy.RunRead(
a_k0_m_k1_grid_desc, a_grid_buf, a_k0_m_k1_grid_iterator_hacks);
a_blockwise_copy.RunRead(a_k0_m_k1_grid_desc, a_grid_buf, a_k0_m_k1_grid_step_hacks);
block_sync_lds();
b_blockwise_copy.RunRead(
b_k0_n_k1_grid_desc, b_grid_buf, b_k0_n_k1_grid_iterator_hacks);
b_blockwise_copy.RunRead(b_k0_n_k1_grid_desc, b_grid_buf, b_k0_n_k1_grid_step_hacks);
blockwise_gemm.Run(a_block_buf, b_block_buf, c_thread_buf);
@@ -506,7 +485,7 @@ struct GridwiseDynamicGemm_k0mk1_k0nk1_mn_xdlops_v2r3
constexpr index_t N1 = CLayout.N0();
constexpr auto c_m0_m1_m2_n_thread_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(Number<MRepeat>{},
make_naive_tensor_descriptor_packed(make_tuple(Number<MRepeat>{},
Number<NRepeat>{},
Number<1>{},
Number<1>{},
@@ -515,7 +494,7 @@ struct GridwiseDynamicGemm_k0mk1_k0nk1_mn_xdlops_v2r3
Number<M2>{},
Number<1>{}));
StaticBuffer<AddressSpaceEnum_t::Vgpr, FloatC, c_m0_m1_m2_n_thread_desc.GetElementSpaceSize()>
StaticBuffer<AddressSpaceEnum_t::Vgpr, FloatC, c_m0_m1_m2_n_thread_desc.GetElementSpaceSize(), true>
c_blk_buf_;
static_for<0, MRepeat, 1>{}([&](auto mr_i) {
@@ -542,12 +521,12 @@ struct GridwiseDynamicGemm_k0mk1_k0nk1_mn_xdlops_v2r3
const index_t n_thread_data_on_grid =
n_block_data_idx_on_grid + c_thread_mtx_on_block[I1];
constexpr auto c_m0_m1_m2_n_grid_tensor_iterator_hacks = CGridIteratorHacks{};
constexpr auto c_m0_m1_m2_n_grid_tensor_step_hacks = CGridStepHacks{};
constexpr index_t MWaves = MPerBlock / (MPerWave * MRepeat);
constexpr index_t NWaves = NPerBlock / (NPerWave * NRepeat);
ThreadwiseDynamicTensorSliceTransfer_v1r3<
ThreadwiseTensorSliceTransfer_v1r3<
FloatC,
FloatC,
decltype(c_m0_m1_m2_n_thread_desc),
@@ -573,7 +552,7 @@ struct GridwiseDynamicGemm_k0mk1_k0nk1_mn_xdlops_v2r3
c_blk_buf_,
c_m0_m1_m2_n_grid_desc,
c_grid_buf,
c_m0_m1_m2_n_grid_tensor_iterator_hacks);
c_m0_m1_m2_n_grid_tensor_step_hacks);
}
#else
{
@@ -581,11 +560,8 @@ struct GridwiseDynamicGemm_k0mk1_k0nk1_mn_xdlops_v2r3
constexpr index_t M1 = CLayout.N1();
constexpr index_t M2 = CLayout.M0();
constexpr auto c_m0_m1_m2_n_thread_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(
I1, I1, I1, I1, Number<M0>{}, Number<1>{}, Number<M2>{}, Number<1>{}));
StaticBuffer<AddressSpaceEnum_t::Vgpr, FloatC, BlkSize> c_blk_buf_;
constexpr auto c_m0_m1_m2_n_thread_desc = make_naive_tensor_descriptor_packed(
make_tuple(I1, I1, I1, I1, Number<M0>{}, Number<1>{}, Number<M2>{}, Number<1>{}));
// calculate origin of thread output tensor on global memory
// blockwise GEMM c matrix starting index
@@ -598,20 +574,20 @@ struct GridwiseDynamicGemm_k0mk1_k0nk1_mn_xdlops_v2r3
const index_t n_thread_data_on_grid =
n_block_data_idx_on_grid + c_thread_mtx_on_block[I1];
constexpr auto c_m0_m1_m2_n_grid_tensor_iterator_hacks = CGridIteratorHacks{};
constexpr auto c_m0_m1_m2_n_grid_tensor_step_hacks = CGridStepHacks{};
auto c_thread_copy =
ThreadwiseDynamicTensorSliceTransfer_v1r3<FloatC,
FloatC,
decltype(c_m0_m1_m2_n_thread_desc),
decltype(c_m0_m1_m2_n_grid_desc),
Sequence<1, 1, 1, 1, M0, 1, M2, 1>,
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
CGlobalMemoryDataOperation,
1,
true>{
ThreadwiseTensorSliceTransfer_v1r3<FloatC,
FloatC,
decltype(c_m0_m1_m2_n_thread_desc),
decltype(c_m0_m1_m2_n_grid_desc),
Sequence<1, 1, 1, 1, M0, 1, M2, 1>,
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
CGlobalMemoryDataOperation,
1,
true>{
c_m0_m1_m2_n_grid_desc,
make_multi_index(0,
0,
@@ -629,7 +605,7 @@ struct GridwiseDynamicGemm_k0mk1_k0nk1_mn_xdlops_v2r3
c_thread_buf[Number<blk_off>{}].template AsType<FloatAcc>(),
c_m0_m1_m2_n_grid_desc,
c_grid_buf,
c_m0_m1_m2_n_grid_tensor_iterator_hacks);
c_m0_m1_m2_n_grid_tensor_step_hacks);
return c_thread_idx_;
};
@@ -644,7 +620,7 @@ struct GridwiseDynamicGemm_k0mk1_k0nk1_mn_xdlops_v2r3
c_thread_buf[Number<blk_off>{}].template AsType<FloatAcc>(),
c_m0_m1_m2_n_grid_desc,
c_grid_buf,
c_m0_m1_m2_n_grid_tensor_iterator_hacks);
c_m0_m1_m2_n_grid_tensor_step_hacks);
};
auto nrepeat_plus_copy = [&](auto c_thread_idx_) {
@@ -657,7 +633,7 @@ struct GridwiseDynamicGemm_k0mk1_k0nk1_mn_xdlops_v2r3
c_thread_buf[Number<blk_off>{}].template AsType<FloatAcc>(),
c_m0_m1_m2_n_grid_desc,
c_grid_buf,
c_m0_m1_m2_n_grid_tensor_iterator_hacks);
c_m0_m1_m2_n_grid_tensor_step_hacks);
};
auto mrepeat_minus_copy = [&](auto c_thread_idx_) {
@@ -670,7 +646,7 @@ struct GridwiseDynamicGemm_k0mk1_k0nk1_mn_xdlops_v2r3
c_thread_buf[Number<blk_off>{}].template AsType<FloatAcc>(),
c_m0_m1_m2_n_grid_desc,
c_grid_buf,
c_m0_m1_m2_n_grid_tensor_iterator_hacks);
c_m0_m1_m2_n_grid_tensor_step_hacks);
};
auto nrepeat_minus_copy = [&](auto c_thread_idx_) {
@@ -683,7 +659,7 @@ struct GridwiseDynamicGemm_k0mk1_k0nk1_mn_xdlops_v2r3
c_thread_buf[Number<blk_off>{}].template AsType<FloatAcc>(),
c_m0_m1_m2_n_grid_desc,
c_grid_buf,
c_m0_m1_m2_n_grid_tensor_iterator_hacks);
c_m0_m1_m2_n_grid_tensor_step_hacks);
};
static_assert((MRepeat == 4 && NRepeat == 4) or (MRepeat == 4 && NRepeat == 2) or

25
composable_kernel/include/tensor_operation/threadwise_contraction_dlops.hpp
View File

@@ -21,10 +21,10 @@ template <typename FloatA,
typename TKLengths,
typename TMLengths,
typename TNLengths,
typename std::enable_if<AThreadDesc_TK0_TM0_TM1_TK1::IsKnownAtCompileTime() &&
BThreadDesc_TK0_TN0_TN1_TK1::IsKnownAtCompileTime() &&
CThreadDesc_TM0_TM1_TN0_TN1::IsKnownAtCompileTime(),
bool>::type = false>
typename enable_if<AThreadDesc_TK0_TM0_TM1_TK1::IsKnownAtCompileTime() &&
BThreadDesc_TK0_TN0_TN1_TK1::IsKnownAtCompileTime() &&
CThreadDesc_TM0_TM1_TN0_TN1::IsKnownAtCompileTime(),
bool>::type = false>
struct ThreadwiseGemmDlops_km0m1_kn0n1_m0m1n0n1
{
__device__ constexpr ThreadwiseGemmDlops_km0m1_kn0n1_m0m1n0n1()
@@ -97,10 +97,9 @@ struct ThreadwiseGemmDlops_km0m1_kn0n1_m0m1n0n1
CThreadDesc_TM0_TM1_TN0_TN1{}.CalculateOffset(
c_origin_idx + make_multi_index(tm0, tm1, tn0, tn1));
amd_inner_product_dlop<FloatA, FloatB, FloatC>(
a_buf[Number<a_offset>{}],
b_buf[Number<b_offset>{}],
c_buf(Number<c_offset>{}));
inner_product<FloatA, FloatB, FloatC>(a_buf[Number<a_offset>{}],
b_buf[Number<b_offset>{}],
c_buf(Number<c_offset>{}));
});
});
});
@@ -124,10 +123,10 @@ template <typename FloatA,
typename TKLengths,
typename TMLengths,
typename TNLengths,
typename std::enable_if<AThreadDesc_TK0_TM0_TM1_TK1::IsKnownAtCompileTime() &&
BThreadDesc_TK0_TN0_TN1_TK1::IsKnownAtCompileTime() &&
CThreadDesc_TM0_TM1_TN0_TN1::IsKnownAtCompileTime(),
bool>::type = false>
typename enable_if<AThreadDesc_TK0_TM0_TM1_TK1::IsKnownAtCompileTime() &&
BThreadDesc_TK0_TN0_TN1_TK1::IsKnownAtCompileTime() &&
CThreadDesc_TM0_TM1_TN0_TN1::IsKnownAtCompileTime(),
bool>::type = false>
struct ThreadwiseContractionDlops_A_TK0_TM0_TM1_TK1_B_TK0_TN0_TN1_TK1_C_TM0_TM1_TN0_TN1
{
__device__ constexpr ThreadwiseContractionDlops_A_TK0_TM0_TM1_TK1_B_TK0_TN0_TN1_TK1_C_TM0_TM1_TN0_TN1()
@@ -214,7 +213,7 @@ struct ThreadwiseContractionDlops_A_TK0_TM0_TM1_TK1_B_TK0_TN0_TN1_TK1_C_TM0_TM1_
CThreadDesc_TM0_TM1_TN0_TN1{}.CalculateOffset(
c_origin_idx + make_multi_index(tm0, tm1, tn0, tn1));
amd_inner_product_dlop<a_vector_t, b_vector_t, FloatC>(
inner_product<a_vector_t, b_vector_t, FloatC>(
a_vec.template AsType<a_vector_t>()[I0],
b_vec.template AsType<b_vector_t>()[I0],
c_buf(Number<c_offset>{}));

8
composable_kernel/include/tensor_operation/threadwise_gemm_dlops_v3.hpp
View File

@@ -19,9 +19,9 @@ template <typename FloatA,
typename CDesc,
index_t H,
index_t W,
typename std::enable_if<ADesc::IsKnownAtCompileTime() && BDesc::IsKnownAtCompileTime() &&
CDesc::IsKnownAtCompileTime(),
bool>::type = false>
typename enable_if<ADesc::IsKnownAtCompileTime() && BDesc::IsKnownAtCompileTime() &&
CDesc::IsKnownAtCompileTime(),
bool>::type = false>
struct ThreadwiseGemmDlops_km_kn_mn_v3
{
template <typename ABuffer,
@@ -57,8 +57,6 @@ struct ThreadwiseGemmDlops_km_kn_mn_v3
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto E = ADesc{}.GetLength(I0);
constexpr auto K = ADesc{}.GetLength(I1);

16
composable_kernel/include/tensor_operation/threadwise_dynamic_tensor_slice_set.hpp → composable_kernel/include/tensor_operation/threadwise_tensor_slice_set.hpp
View File

@@ -1,9 +1,9 @@
#ifndef CK_THREADWISE_DYNAMIC_TENSOR_SET_HPP
#define CK_THREADWISE_DYNAMIC_TENSOR_SET_HPP
#ifndef CK_THREADWISE_TENSOR_SET_HPP
#define CK_THREADWISE_TENSOR_SET_HPP
#include "common_header.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
namespace ck {
@@ -11,12 +11,12 @@ namespace ck {
// 1. Desc is known at compile-time
// 2. Buffer is StaticBuffer
// 3. OriginIdx is known at compile-time
// 4. use #-iterator
// 4. use #-step
template <typename Data,
typename Desc,
typename SliceLengths,
typename std::enable_if<Desc::IsKnownAtCompileTime(), bool>::type = false>
struct ThreadwiseDynamicTensorSliceSet_v1
typename enable_if<Desc::IsKnownAtCompileTime(), bool>::type = false>
struct ThreadwiseTensorSliceSet_v1
{
static constexpr index_t nDim = SliceLengths::Size();
@@ -40,7 +40,7 @@ struct ThreadwiseDynamicTensorSliceSet_v1
constexpr auto origin_idx = to_multi_index(OriginIdx{});
static_ford<SliceLengths>{}([&](auto access_idx) {
constexpr auto coord = make_dynamic_tensor_coordinate(desc, origin_idx + access_idx);
constexpr auto coord = make_tensor_coordinate(desc, origin_idx + access_idx);
constexpr bool is_valid =
coordinate_has_valid_offset_assuming_visible_index_is_valid(desc, coord);

342
composable_kernel/include/tensor_operation/threadwise_dynamic_tensor_slice_transfer.hpp → composable_kernel/include/tensor_operation/threadwise_tensor_slice_transfer.hpp
View File

@@ -1,9 +1,9 @@
#ifndef CK_THREADWISE_DYNAMIC_TENSOR_SLICE_TRANSFER_HPP
#define CK_THREADWISE_DYNAMIC_TENSOR_SLICE_TRANSFER_HPP
#ifndef CK_THREADWISE_TENSOR_SLICE_TRANSFER_HPP
#define CK_THREADWISE_TENSOR_SLICE_TRANSFER_HPP
#include "common_header.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
namespace ck {
@@ -57,20 +57,20 @@ template <typename SrcData,
InMemoryDataOperationEnum_t DstInMemOp,
index_t DstScalarStrideInVector,
bool DstResetCoordinateAfterRun,
typename std::enable_if<SrcDesc::IsKnownAtCompileTime(), bool>::type = false>
struct ThreadwiseDynamicTensorSliceTransfer_v1r3
typename enable_if<SrcDesc::IsKnownAtCompileTime(), bool>::type = false>
struct ThreadwiseTensorSliceTransfer_v1r3
{
static constexpr index_t nDim = SliceLengths::Size();
using Index = MultiIndex<nDim>;
using DstCoord = decltype(make_dynamic_tensor_coordinate(DstDesc{}, Index{}));
using DstCoord = decltype(make_tensor_coordinate(DstDesc{}, Index{}));
using DstCoordIterator = decltype(make_dynamic_tensor_coordinate_iterator(DstDesc{}, Index{}));
using DstCoordStep = decltype(make_tensor_coordinate_step(DstDesc{}, Index{}));
__device__ constexpr ThreadwiseDynamicTensorSliceTransfer_v1r3(
const DstDesc& dst_desc, const Index& dst_slice_origin_idx)
: dst_coord_(make_dynamic_tensor_coordinate(dst_desc, dst_slice_origin_idx))
__device__ constexpr ThreadwiseTensorSliceTransfer_v1r3(const DstDesc& dst_desc,
const Index& dst_slice_origin_idx)
: dst_coord_(make_tensor_coordinate(dst_desc, dst_slice_origin_idx))
{
static_assert(SrcDesc::IsKnownAtCompileTime(),
"wrong! SrcDesc need to known at compile-time");
@@ -78,19 +78,19 @@ struct ThreadwiseDynamicTensorSliceTransfer_v1r3
__device__ void SetDstSliceOrigin(const DstDesc& dst_desc, const Index& dst_slice_origin_idx)
{
dst_coord_ = make_dynamic_tensor_coordinate(dst_desc, dst_slice_origin_idx);
dst_coord_ = make_tensor_coordinate(dst_desc, dst_slice_origin_idx);
}
template <typename SrcSliceOriginIdx,
typename SrcBuffer,
typename DstBuffer,
typename DstIteratorHacks>
typename DstStepHacks>
__device__ void Run(const SrcDesc&,
const SrcSliceOriginIdx&,
const SrcBuffer& src_buf,
const DstDesc& dst_desc,
DstBuffer& dst_buf,
const DstIteratorHacks& dst_iterator_hacks)
const DstStepHacks& dst_step_hacks)
{
static_assert(SrcDesc::IsKnownAtCompileTime(),
"wrong! SrcDesc need to known at compile-time");
@@ -127,31 +127,31 @@ struct ThreadwiseDynamicTensorSliceTransfer_v1r3
constexpr auto ordered_access_lengths =
container_reorder_given_new2old(access_lengths, dim_access_order);
// make forward iterators
const auto dst_forward_iterators = generate_tuple(
// make forward steps
const auto dst_forward_steps = generate_tuple(
[&](auto i) {
Index forward_step;
Index forward_step_idx;
static_for<0, nDim, 1>{}([&](auto j) {
forward_step(j) = (i.value == j.value) ? dst_scalar_per_access[i] : 0;
forward_step_idx(j) = (i.value == j.value) ? dst_scalar_per_access[i] : 0;
});
return make_dynamic_tensor_coordinate_iterator(
dst_desc, forward_step, dst_iterator_hacks[I0][i]);
return make_tensor_coordinate_step(
dst_desc, forward_step_idx, dst_step_hacks[I0][i]);
},
Number<nDim>{});
// make backward iterators
const auto dst_backward_iterators = generate_tuple(
// make backward steps
const auto dst_backward_steps = generate_tuple(
[&](auto i) {
Index backward_step;
Index backward_step_idx;
static_for<0, nDim, 1>{}([&](auto j) {
backward_step(j) = (i.value == j.value) ? -dst_scalar_per_access[i] : 0;
backward_step_idx(j) = (i.value == j.value) ? -dst_scalar_per_access[i] : 0;
});
return make_dynamic_tensor_coordinate_iterator(
dst_desc, backward_step, dst_iterator_hacks[I1][i]);
return make_tensor_coordinate_step(
dst_desc, backward_step_idx, dst_step_hacks[I1][i]);
},
Number<nDim>{});
@@ -235,13 +235,13 @@ struct ThreadwiseDynamicTensorSliceTransfer_v1r3
{
if constexpr(forward_sweep[i])
{
move_dynamic_tensor_coordinate(
dst_desc, dst_coord_, dst_forward_iterators[dim_access_order[i]]);
move_tensor_coordinate(
dst_desc, dst_coord_, dst_forward_steps[dim_access_order[i]]);
}
else
{
move_dynamic_tensor_coordinate(
dst_desc, dst_coord_, dst_backward_iterators[dim_access_order[i]]);
move_tensor_coordinate(
dst_desc, dst_coord_, dst_backward_steps[dim_access_order[i]]);
}
}
});
@@ -250,10 +250,10 @@ struct ThreadwiseDynamicTensorSliceTransfer_v1r3
// move dst coordinate back to slice origin (or not)
if constexpr(DstResetCoordinateAfterRun)
{
const auto dst_reset_iterator =
make_dynamic_tensor_coordinate_iterator(dst_desc, GetDstCoordinateResetStep());
const auto dst_reset_step =
make_tensor_coordinate_step(dst_desc, GetDstCoordinateResetStep());
move_dynamic_tensor_coordinate(dst_desc, dst_coord_, dst_reset_iterator);
move_tensor_coordinate(dst_desc, dst_coord_, dst_reset_step);
}
}
@@ -268,11 +268,11 @@ struct ThreadwiseDynamicTensorSliceTransfer_v1r3
constexpr auto zeros = typename uniform_sequence_gen<ntransform_dst, 0>::type{};
constexpr auto dst_iterator_hacks =
constexpr auto dst_step_hacks =
make_tuple(generate_tuple([&](auto) { return zeros; }, Number<nDim>{}),
generate_tuple([&](auto) { return zeros; }, Number<nDim>{}));
Run(SrcDesc{}, SrcSliceOriginIdx{}, src_buf, dst_desc, dst_buf, dst_iterator_hacks);
Run(SrcDesc{}, SrcSliceOriginIdx{}, src_buf, dst_desc, dst_buf, dst_step_hacks);
}
__device__ static constexpr auto GetDstCoordinateResetStep()
@@ -345,10 +345,9 @@ struct ThreadwiseDynamicTensorSliceTransfer_v1r3
: dst_slice_origin_step_idx + GetDstCoordinateResetStep();
// is it OK to construct a new step every time?
const auto adjusted_step =
make_dynamic_tensor_coordinate_iterator(dst_desc, adjusted_step_idx);
const auto adjusted_step = make_tensor_coordinate_step(dst_desc, adjusted_step_idx);
move_dynamic_tensor_coordinate(dst_desc, dst_coord_, adjusted_step);
move_tensor_coordinate(dst_desc, dst_coord_, adjusted_step);
}
private:
@@ -374,20 +373,20 @@ template <typename SrcData,
index_t SrcScalarPerVector,
index_t SrcScalarStrideInVector,
bool SrcResetCoordinateAfterRun,
typename std::enable_if<DstDesc::IsKnownAtCompileTime(), bool>::type = false>
struct ThreadwiseDynamicTensorSliceTransfer_v2
typename enable_if<DstDesc::IsKnownAtCompileTime(), bool>::type = false>
struct ThreadwiseTensorSliceTransfer_v2
{
static constexpr index_t nDim = SliceLengths::Size();
using Index = MultiIndex<nDim>;
using SrcCoord = decltype(make_dynamic_tensor_coordinate(SrcDesc{}, Index{}));
using SrcCoord = decltype(make_tensor_coordinate(SrcDesc{}, Index{}));
using SrcCoordIterator = decltype(make_dynamic_tensor_coordinate_iterator(SrcDesc{}, Index{}));
using SrcCoordStep = decltype(make_tensor_coordinate_step(SrcDesc{}, Index{}));
__device__ constexpr ThreadwiseDynamicTensorSliceTransfer_v2(const SrcDesc& src_desc,
const Index& src_slice_origin_idx)
: src_coord_(make_dynamic_tensor_coordinate(src_desc, src_slice_origin_idx))
__device__ constexpr ThreadwiseTensorSliceTransfer_v2(const SrcDesc& src_desc,
const Index& src_slice_origin_idx)
: src_coord_(make_tensor_coordinate(src_desc, src_slice_origin_idx))
{
static_assert(DstDesc::IsKnownAtCompileTime(),
"wrong! SrcDesc need to known at compile-time");
@@ -395,19 +394,19 @@ struct ThreadwiseDynamicTensorSliceTransfer_v2
__device__ void SetDstSliceOrigin(const SrcDesc& src_desc, const Index& src_slice_origin_idx)
{
src_coord_ = make_dynamic_tensor_coordinate(src_desc, src_slice_origin_idx);
src_coord_ = make_tensor_coordinate(src_desc, src_slice_origin_idx);
}
template <typename SrcBuffer,
typename DstBuffer,
typename DstSliceOriginIdx,
typename SrcIteratorHacks>
typename SrcStepHacks>
__device__ void Run(const SrcDesc& src_desc,
const SrcBuffer& src_buf,
const DstDesc&,
const DstSliceOriginIdx&,
DstBuffer& dst_buf,
const SrcIteratorHacks& src_iterator_hacks)
const SrcStepHacks& src_step_hacks)
{
static_assert(DstDesc::IsKnownAtCompileTime(),
"wrong! DstDesc need to known at compile-time");
@@ -442,31 +441,31 @@ struct ThreadwiseDynamicTensorSliceTransfer_v2
constexpr auto ordered_access_lengths =
container_reorder_given_new2old(access_lengths, dim_access_order);
// make forward iterators
const auto src_forward_iterators = generate_tuple(
// make forward steps
const auto src_forward_steps = generate_tuple(
[&](auto i) {
Index forward_step;
Index forward_step_idx;
static_for<0, nDim, 1>{}([&](auto j) {
forward_step(j) = (i.value == j.value) ? src_scalar_per_access[i] : 0;
forward_step_idx(j) = (i.value == j.value) ? src_scalar_per_access[i] : 0;
});
return make_dynamic_tensor_coordinate_iterator(
src_desc, forward_step, src_iterator_hacks[I0][i]);
return make_tensor_coordinate_step(
src_desc, forward_step_idx, src_step_hacks[I0][i]);
},
Number<nDim>{});
// make backward iterators
const auto src_backward_iterators = generate_tuple(
// make backward steps
const auto src_backward_steps = generate_tuple(
[&](auto i) {
Index backward_step;
Index backward_step_idx;
static_for<0, nDim, 1>{}([&](auto j) {
backward_step(j) = (i.value == j.value) ? -src_scalar_per_access[i] : 0;
backward_step_idx(j) = (i.value == j.value) ? -src_scalar_per_access[i] : 0;
});
return make_dynamic_tensor_coordinate_iterator(
src_desc, backward_step, src_iterator_hacks[I1][i]);
return make_tensor_coordinate_step(
src_desc, backward_step_idx, src_step_hacks[I1][i]);
},
Number<nDim>{});
@@ -548,13 +547,13 @@ struct ThreadwiseDynamicTensorSliceTransfer_v2
{
if constexpr(forward_sweep[i])
{
move_dynamic_tensor_coordinate(
src_desc, src_coord_, src_forward_iterators[dim_access_order[i]]);
move_tensor_coordinate(
src_desc, src_coord_, src_forward_steps[dim_access_order[i]]);
}
else
{
move_dynamic_tensor_coordinate(
src_desc, src_coord_, src_backward_iterators[dim_access_order[i]]);
move_tensor_coordinate(
src_desc, src_coord_, src_backward_steps[dim_access_order[i]]);
}
}
});
@@ -563,10 +562,10 @@ struct ThreadwiseDynamicTensorSliceTransfer_v2
// move src coordinate back to slice origin (or not)
if constexpr(SrcResetCoordinateAfterRun)
{
const auto src_reset_iterator =
make_dynamic_tensor_coordinate_iterator(src_desc, GetSrcCoordinateResetStep());
const auto src_reset_step =
make_tensor_coordinate_step(src_desc, GetSrcCoordinateResetStep());
move_dynamic_tensor_coordinate(src_desc, src_coord_, src_reset_iterator);
move_tensor_coordinate(src_desc, src_coord_, src_reset_step);
}
}
@@ -581,11 +580,11 @@ struct ThreadwiseDynamicTensorSliceTransfer_v2
constexpr auto zeros = typename uniform_sequence_gen<ntransform_src, 0>::type{};
constexpr auto src_iterator_hacks =
constexpr auto src_step_hacks =
make_tuple(generate_tuple([&](auto) { return zeros; }, Number<nDim>{}),
generate_tuple([&](auto) { return zeros; }, Number<nDim>{}));
Run(src_desc, src_buf, DstDesc{}, DstSliceOriginIdx{}, dst_buf, src_iterator_hacks);
Run(src_desc, src_buf, DstDesc{}, DstSliceOriginIdx{}, dst_buf, src_step_hacks);
}
__device__ static constexpr auto GetSrcCoordinateResetStep()
@@ -658,10 +657,9 @@ struct ThreadwiseDynamicTensorSliceTransfer_v2
: src_slice_origin_step_idx + GetSrcCoordinateResetStep();
// is it OK to construct a new step every time?
const auto adjusted_step =
make_dynamic_tensor_coordinate_iterator(src_desc, adjusted_step_idx);
const auto adjusted_step = make_tensor_coordinate_step(src_desc, adjusted_step_idx);
move_dynamic_tensor_coordinate(src_desc, src_coord_, adjusted_step);
move_tensor_coordinate(src_desc, src_coord_, adjusted_step);
}
private:
@@ -693,23 +691,23 @@ template <typename SliceLengths,
bool DstResetCoordinateAfterRun> // control whether to move back dst coordinate after each
// RunWrite(), will be fused with MoveDstSliceWindow to
// save addr computation
struct ThreadwiseDynamicTensorSliceTransfer_v3
struct ThreadwiseTensorSliceTransfer_v3
{
static constexpr index_t nDim = SliceLengths::Size();
using Index = MultiIndex<nDim>;
using SrcCoord = decltype(make_dynamic_tensor_coordinate(SrcDesc{}, Index{}));
using DstCoord = decltype(make_dynamic_tensor_coordinate(DstDesc{}, Index{}));
using SrcCoord = decltype(make_tensor_coordinate(SrcDesc{}, Index{}));
using DstCoord = decltype(make_tensor_coordinate(DstDesc{}, Index{}));
using SrcCoordIterator = decltype(make_dynamic_tensor_coordinate_iterator(SrcDesc{}, Index{}));
using DstCoordIterator = decltype(make_dynamic_tensor_coordinate_iterator(DstDesc{}, Index{}));
using SrcCoordStep = decltype(make_tensor_coordinate_step(SrcDesc{}, Index{}));
using DstCoordStep = decltype(make_tensor_coordinate_step(DstDesc{}, Index{}));
__device__ constexpr ThreadwiseDynamicTensorSliceTransfer_v3(const SrcDesc& src_desc,
const Index& src_slice_origin,
const DstDesc& dst_desc,
const Index& dst_slice_origin)
: src_coord_(make_dynamic_tensor_coordinate(src_desc, src_slice_origin)),
dst_coord_(make_dynamic_tensor_coordinate(dst_desc, dst_slice_origin))
__device__ constexpr ThreadwiseTensorSliceTransfer_v3(const SrcDesc& src_desc,
const Index& src_slice_origin,
const DstDesc& dst_desc,
const Index& dst_slice_origin)
: src_coord_(make_tensor_coordinate(src_desc, src_slice_origin)),
dst_coord_(make_tensor_coordinate(dst_desc, dst_slice_origin))
{
// TODO: fix this
static_assert(is_same<SrcData, DstData>::value,
@@ -718,18 +716,17 @@ struct ThreadwiseDynamicTensorSliceTransfer_v3
__device__ void SetSrcSliceOrigin(const SrcDesc& src_desc, const Index& src_slice_origin_idx)
{
src_coord_ = make_dynamic_tensor_coordinate(src_desc, src_slice_origin_idx);
src_coord_ = make_tensor_coordinate(src_desc, src_slice_origin_idx);
}
__device__ void SetDstSliceOrigin(const DstDesc& dst_desc, const Index& dst_slice_origin_idx)
{
dst_coord_ = make_dynamic_tensor_coordinate(dst_desc, dst_slice_origin_idx);
dst_coord_ = make_tensor_coordinate(dst_desc, dst_slice_origin_idx);
}
template <typename SrcBuffer, typename SrcIteratorHacks>
__device__ void RunRead(const SrcDesc& src_desc,
const SrcBuffer& src_buf,
const SrcIteratorHacks& src_iterator_hacks)
template <typename SrcBuffer, typename SrcStepHacks>
__device__ void
RunRead(const SrcDesc& src_desc, const SrcBuffer& src_buf, const SrcStepHacks& src_step_hacks)
{
static_assert(SrcBuffer::GetAddressSpace() == AddressSpaceEnum_t::Global or
SrcBuffer::GetAddressSpace() == AddressSpaceEnum_t::Lds,
@@ -757,31 +754,31 @@ struct ThreadwiseDynamicTensorSliceTransfer_v3
constexpr auto ordered_src_access_lengths =
container_reorder_given_new2old(src_access_lengths, src_dim_access_order);
// make forward iterators
const auto src_forward_iterators = generate_tuple(
// make forward steps
const auto src_forward_steps = generate_tuple(
[&](auto i) {
Index forward_step;
Index forward_step_idx;
static_for<0, nDim, 1>{}([&](auto j) {
forward_step(j) = (i.value == j.value) ? src_scalar_per_access[i] : 0;
forward_step_idx(j) = (i.value == j.value) ? src_scalar_per_access[i] : 0;
});
return make_dynamic_tensor_coordinate_iterator(
src_desc, forward_step, src_iterator_hacks[I0][i]);
return make_tensor_coordinate_step(
src_desc, forward_step_idx, src_step_hacks[I0][i]);
},
Number<nDim>{});
// make backward iterators
const auto src_backward_iterators = generate_tuple(
// make backward steps
const auto src_backward_steps = generate_tuple(
[&](auto i) {
Index backward_step;
Index backward_step_idx;
static_for<0, nDim, 1>{}([&](auto j) {
backward_step(j) = (i.value == j.value) ? -src_scalar_per_access[i] : 0;
backward_step_idx(j) = (i.value == j.value) ? -src_scalar_per_access[i] : 0;
});
return make_dynamic_tensor_coordinate_iterator(
src_desc, backward_step, src_iterator_hacks[I1][i]);
return make_tensor_coordinate_step(
src_desc, backward_step_idx, src_step_hacks[I1][i]);
},
Number<nDim>{});
@@ -862,13 +859,13 @@ struct ThreadwiseDynamicTensorSliceTransfer_v3
{
if constexpr(forward_sweep[i])
{
move_dynamic_tensor_coordinate(
src_desc, src_coord_, src_forward_iterators[src_dim_access_order[i]]);
move_tensor_coordinate(
src_desc, src_coord_, src_forward_steps[src_dim_access_order[i]]);
}
else
{
move_dynamic_tensor_coordinate(
src_desc, src_coord_, src_backward_iterators[src_dim_access_order[i]]);
move_tensor_coordinate(
src_desc, src_coord_, src_backward_steps[src_dim_access_order[i]]);
}
}
});
@@ -877,17 +874,16 @@ struct ThreadwiseDynamicTensorSliceTransfer_v3
// move src coordinate back to slice origin (or not)
if constexpr(SrcResetCoordinateAfterRun)
{
const auto src_reset_iterator =
make_dynamic_tensor_coordinate_iterator(src_desc, GetSrcCoordinateResetStep());
const auto src_reset_step =
make_tensor_coordinate_step(src_desc, GetSrcCoordinateResetStep());
move_dynamic_tensor_coordinate(src_desc, src_coord_, src_reset_iterator);
move_tensor_coordinate(src_desc, src_coord_, src_reset_step);
}
}
template <typename DstBuffer, typename DstIteratorHacks>
__device__ void RunWrite(const DstDesc& dst_desc,
DstBuffer& dst_buf,
const DstIteratorHacks& dst_iterator_hacks)
template <typename DstBuffer, typename DstStepHacks>
__device__ void
RunWrite(const DstDesc& dst_desc, DstBuffer& dst_buf, const DstStepHacks& dst_step_hacks)
{
static_assert(DstBuffer::GetAddressSpace() == AddressSpaceEnum_t::Global or
DstBuffer::GetAddressSpace() == AddressSpaceEnum_t::Lds,
@@ -915,35 +911,31 @@ struct ThreadwiseDynamicTensorSliceTransfer_v3
constexpr auto ordered_dst_access_lengths =
container_reorder_given_new2old(dst_access_lengths, dst_dim_access_order);
// make forward iterators
const auto dst_forward_iterators = generate_tuple(
// make forward steps
const auto dst_forward_steps = generate_tuple(
[&](auto i) {
Index forward_step;
Index forward_step_idx;
static_for<0, nDim, 1>{}([&](auto j) {
forward_step(j) = (i.value == j.value) ? dst_scalar_per_access[i] : 0;
forward_step_idx(j) = (i.value == j.value) ? dst_scalar_per_access[i] : 0;
});
const auto forward_iterator = make_dynamic_tensor_coordinate_iterator(
dst_desc, forward_step, dst_iterator_hacks[I0][i]);
return forward_iterator;
return make_tensor_coordinate_step(
dst_desc, forward_step_idx, dst_step_hacks[I0][i]);
},
Number<nDim>{});
// make backward iterators
const auto dst_backward_iterators = generate_tuple(
// make backward steps
const auto dst_backward_steps = generate_tuple(
[&](auto i) {
Index backward_step;
Index backward_step_idx;
static_for<0, nDim, 1>{}([&](auto j) {
backward_step(j) = (i.value == j.value) ? -dst_scalar_per_access[i] : 0;
backward_step_idx(j) = (i.value == j.value) ? -dst_scalar_per_access[i] : 0;
});
const auto backward_iterator = make_dynamic_tensor_coordinate_iterator(
dst_desc, backward_step, dst_iterator_hacks[I1][i]);
return backward_iterator;
return make_tensor_coordinate_step(
dst_desc, backward_step_idx, dst_step_hacks[I1][i]);
},
Number<nDim>{});
@@ -1026,13 +1018,13 @@ struct ThreadwiseDynamicTensorSliceTransfer_v3
{
if constexpr(forward_sweep[i])
{
move_dynamic_tensor_coordinate(
dst_desc, dst_coord_, dst_forward_iterators[dst_dim_access_order[i]]);
move_tensor_coordinate(
dst_desc, dst_coord_, dst_forward_steps[dst_dim_access_order[i]]);
}
else
{
move_dynamic_tensor_coordinate(
dst_desc, dst_coord_, dst_backward_iterators[dst_dim_access_order[i]]);
move_tensor_coordinate(
dst_desc, dst_coord_, dst_backward_steps[dst_dim_access_order[i]]);
}
}
});
@@ -1041,10 +1033,10 @@ struct ThreadwiseDynamicTensorSliceTransfer_v3
// move dst coordinate back to slice origin (or not)
if constexpr(DstResetCoordinateAfterRun)
{
const auto dst_reset_iterator =
make_dynamic_tensor_coordinate_iterator(dst_desc, GetDstCoordinateResetStep());
const auto dst_reset_step =
make_tensor_coordinate_step(dst_desc, GetDstCoordinateResetStep());
move_dynamic_tensor_coordinate(dst_desc, dst_coord_, dst_reset_iterator);
move_tensor_coordinate(dst_desc, dst_coord_, dst_reset_step);
}
}
@@ -1055,11 +1047,11 @@ struct ThreadwiseDynamicTensorSliceTransfer_v3
constexpr auto zeros = typename uniform_sequence_gen<ntransform_src, 0>::type{};
constexpr auto src_iterator_hacks =
constexpr auto src_step_hacks =
make_tuple(generate_tuple([&](auto) { return zeros; }, Number<nDim>{}),
generate_tuple([&](auto) { return zeros; }, Number<nDim>{}));
RunRead(src_desc, src_buf, src_iterator_hacks);
RunRead(src_desc, src_buf, src_step_hacks);
}
template <typename DstBuffer>
@@ -1069,11 +1061,11 @@ struct ThreadwiseDynamicTensorSliceTransfer_v3
constexpr auto zeros = typename uniform_sequence_gen<ntransform_dst, 0>::type{};
constexpr auto dst_iterator_hacks =
constexpr auto dst_step_hacks =
make_tuple(generate_tuple([&](auto) { return zeros; }, Number<nDim>{}),
generate_tuple([&](auto) { return zeros; }, Number<nDim>{}));
RunWrite(dst_desc, dst_buf, dst_iterator_hacks);
RunWrite(dst_desc, dst_buf, dst_step_hacks);
}
__device__ static constexpr auto GetSrcCoordinateResetStep()
@@ -1206,18 +1198,17 @@ struct ThreadwiseDynamicTensorSliceTransfer_v3
: src_slice_origin_step_idx + GetSrcCoordinateResetStep();
// is it OK to construct a new step every time?
const auto adjusted_step =
make_dynamic_tensor_coordinate_iterator(src_desc, adjusted_step_idx);
const auto adjusted_step = make_tensor_coordinate_step(src_desc, adjusted_step_idx);
move_dynamic_tensor_coordinate(src_desc, src_coord_, adjusted_step);
move_tensor_coordinate(src_desc, src_coord_, adjusted_step);
}
// src_slice_origin_step_idx need to be known at compile-time, for performance reason
template <typename SrcMoveSliceWindowIteratorHack>
template <typename SrcMoveSliceWindowStepHack>
__device__ void
MoveSrcSliceWindow(const SrcDesc& src_desc,
const Index& src_slice_origin_step_idx,
const SrcMoveSliceWindowIteratorHack& src_move_slice_window_iterator_hack)
const SrcMoveSliceWindowStepHack& src_move_slice_window_step_hack)
{
// if src coord was not reset by RunRead(), then need to adjust the step here
const auto adjusted_step_idx =
@@ -1225,10 +1216,10 @@ struct ThreadwiseDynamicTensorSliceTransfer_v3
: src_slice_origin_step_idx + GetSrcCoordinateResetStep();
// is it OK to construct a new step every time?
const auto adjusted_step = make_dynamic_tensor_coordinate_iterator(
src_desc, adjusted_step_idx, src_move_slice_window_iterator_hack);
const auto adjusted_step = make_tensor_coordinate_step(
src_desc, adjusted_step_idx, src_move_slice_window_step_hack);
move_dynamic_tensor_coordinate(src_desc, src_coord_, adjusted_step);
move_tensor_coordinate(src_desc, src_coord_, adjusted_step);
}
// dst_slice_origin_step_idx need to be known at compile-time, for performance reason
__device__ void MoveDstSliceWindow(const DstDesc& dst_desc,
@@ -1240,19 +1231,18 @@ struct ThreadwiseDynamicTensorSliceTransfer_v3
: dst_slice_origin_step_idx + GetDstCoordinateResetStep();
// is it OK to construct a new step every time?
const auto adjusted_step =
make_dynamic_tensor_coordinate_iterator(dst_desc, adjusted_step_idx);
const auto adjusted_step = make_tensor_coordinate_step(dst_desc, adjusted_step_idx);
move_dynamic_tensor_coordinate(dst_desc, dst_coord_, adjusted_step);
move_tensor_coordinate(dst_desc, dst_coord_, adjusted_step);
}
private:
static constexpr auto buffer_desc_ =
make_dynamic_naive_tensor_descriptor_packed_v2(sequence_to_tuple_of_number(SliceLengths{}));
make_naive_tensor_descriptor_packed(sequence_to_tuple_of_number(SliceLengths{}));
static constexpr auto buffer_size_ = buffer_desc_.GetElementSpaceSize();
StaticBuffer<AddressSpaceEnum_t::Vgpr, SrcData, buffer_size_> buffer_;
StaticBuffer<AddressSpaceEnum_t::Vgpr, SrcData, buffer_size_, true> buffer_;
SrcCoord src_coord_;
DstCoord dst_coord_;
@@ -1264,37 +1254,36 @@ struct ThreadwiseDynamicTensorSliceTransfer_v3
// 2. SrcBuffer is DynamicBuffer
// 3. src_ref_idx is known at run-time
// 4. SrcRefToOriginDisplacement is known at compile-time
// 5. use #-iterator
// 5. use #-step
// 2. dst:
// 1. DstDesc is known at compile-time
// 2. DstBuffer is StaticBuffer
// 3. DstOriginIdx is known at compile-time
// 4. use direct address calculation
// 3. vector access on src
template <
typename SrcData,
typename DstData,
typename SrcDesc,
typename DstDesc,
typename SliceLengths,
typename DimAccessOrder,
index_t SrcVectorDim,
index_t SrcScalarPerVector,
index_t SrcScalarStrideInVector,
typename std::enable_if<SrcDesc::IsKnownAtCompileTime() && DstDesc::IsKnownAtCompileTime(),
bool>::type = false>
struct ThreadwiseDynamicTensorSliceTransfer_v4
template <typename SrcData,
typename DstData,
typename SrcDesc,
typename DstDesc,
typename SliceLengths,
typename DimAccessOrder,
index_t SrcVectorDim,
index_t SrcScalarPerVector,
index_t SrcScalarStrideInVector,
typename enable_if<SrcDesc::IsKnownAtCompileTime() && DstDesc::IsKnownAtCompileTime(),
bool>::type = false>
struct ThreadwiseTensorSliceTransfer_v4
{
static constexpr index_t nDim = SliceLengths::Size();
using Index = MultiIndex<nDim>;
using SrcCoord = decltype(make_dynamic_tensor_coordinate(SrcDesc{}, Index{}));
using SrcCoord = decltype(make_tensor_coordinate(SrcDesc{}, Index{}));
using SrcCoordIterator = decltype(make_dynamic_tensor_coordinate_iterator(SrcDesc{}, Index{}));
using SrcCoordStep = decltype(make_tensor_coordinate_step(SrcDesc{}, Index{}));
__device__ constexpr ThreadwiseDynamicTensorSliceTransfer_v4(const Index& src_ref_idx)
: src_ref_coord_(make_dynamic_tensor_coordinate(SrcDesc{}, src_ref_idx))
__device__ constexpr ThreadwiseTensorSliceTransfer_v4(const Index& src_ref_idx)
: src_ref_coord_(make_tensor_coordinate(SrcDesc{}, src_ref_idx))
{
static_assert(SrcDesc::IsKnownAtCompileTime() && DstDesc::IsKnownAtCompileTime(),
"wrong! SrcDesc and DstDesc need to known at compile-time");
@@ -1390,13 +1379,12 @@ struct ThreadwiseDynamicTensorSliceTransfer_v4
constexpr auto src_ref_to_data_disp_idx =
src_ref_to_origin_disp_idx + data_to_origin_disp_idx;
constexpr auto src_ref_to_data_disp_coord_iterator =
make_dynamic_tensor_coordinate_iterator(src_desc, src_ref_to_data_disp_idx);
constexpr auto src_ref_to_data_disp_coord_step =
make_tensor_coordinate_step(src_desc, src_ref_to_data_disp_idx);
auto src_data_coord = src_ref_coord_;
move_dynamic_tensor_coordinate(
src_desc, src_data_coord, src_ref_to_data_disp_coord_iterator);
move_tensor_coordinate(src_desc, src_data_coord, src_ref_to_data_disp_coord_step);
vector_type_maker_t<SrcData, SrcScalarPerVector> src_tmp_vector;
@@ -1435,10 +1423,10 @@ struct ThreadwiseDynamicTensorSliceTransfer_v4
{
constexpr auto src_desc = SrcDesc{};
const auto src_slice_move_step_iter = make_dynamic_tensor_coordinate_iterator(
src_desc, to_multi_index(src_slice_move_step_idx));
const auto src_slice_move_step_iter =
make_tensor_coordinate_step(src_desc, to_multi_index(src_slice_move_step_idx));
move_dynamic_tensor_coordinate(SrcDesc{}, src_ref_coord_, src_slice_move_step_iter);
move_tensor_coordinate(SrcDesc{}, src_ref_coord_, src_slice_move_step_iter);
}
private:

230
composable_kernel/include/tensor_operation/threadwise_dynamic_tensor_slice_transfer_v2.hpp → composable_kernel/include/tensor_operation/threadwise_tensor_slice_transfer_v2.hpp
View File

@@ -1,9 +1,9 @@
#ifndef CK_THREADWISE_DYNAMIC_TENSOR_SLICE_TRANSFER_V2_HPP
#define CK_THREADWISE_DYNAMIC_TENSOR_SLICE_TRANSFER_V2_HPP
#ifndef CK_THREADWISE_TENSOR_SLICE_TRANSFER_V2_HPP
#define CK_THREADWISE_TENSOR_SLICE_TRANSFER_V2_HPP
#include "common_header.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
namespace ck {
@@ -30,7 +30,7 @@ template <typename SliceLengths,
bool DstResetCoordinateAfterRun> // control whether to move back dst coordinate after each
// RunWrite(), will be fused with MoveDstSliceWindow to
// save addr computation
struct ThreadwiseDynamicTensorSliceTransfer_v3r1
struct ThreadwiseTensorSliceTransfer_v3r1
{
static constexpr auto I0 = Number<0>{};
static constexpr auto I1 = Number<1>{};
@@ -38,18 +38,18 @@ struct ThreadwiseDynamicTensorSliceTransfer_v3r1
static constexpr index_t nDim = SliceLengths::Size();
using Index = MultiIndex<nDim>;
using SrcCoord = decltype(make_dynamic_tensor_coordinate(SrcDesc{}, Index{}));
using DstCoord = decltype(make_dynamic_tensor_coordinate(DstDesc{}, Index{}));
using SrcCoord = decltype(make_tensor_coordinate(SrcDesc{}, Index{}));
using DstCoord = decltype(make_tensor_coordinate(DstDesc{}, Index{}));
using SrcCoordIterator = decltype(make_dynamic_tensor_coordinate_iterator(SrcDesc{}, Index{}));
using DstCoordIterator = decltype(make_dynamic_tensor_coordinate_iterator(DstDesc{}, Index{}));
using SrcCoordStep = decltype(make_tensor_coordinate_step(SrcDesc{}, Index{}));
using DstCoordStep = decltype(make_tensor_coordinate_step(DstDesc{}, Index{}));
__device__ constexpr ThreadwiseDynamicTensorSliceTransfer_v3r1(const SrcDesc& src_desc,
const Index& src_slice_origin,
const DstDesc& dst_desc,
const Index& dst_slice_origin)
: src_coord_(make_dynamic_tensor_coordinate(src_desc, src_slice_origin)),
dst_coord_(make_dynamic_tensor_coordinate(dst_desc, dst_slice_origin))
__device__ constexpr ThreadwiseTensorSliceTransfer_v3r1(const SrcDesc& src_desc,
const Index& src_slice_origin,
const DstDesc& dst_desc,
const Index& dst_slice_origin)
: src_coord_(make_tensor_coordinate(src_desc, src_slice_origin)),
dst_coord_(make_tensor_coordinate(dst_desc, dst_slice_origin))
{
// TODO: fix this
static_assert(is_same<SrcData, DstData>::value,
@@ -64,18 +64,17 @@ struct ThreadwiseDynamicTensorSliceTransfer_v3r1
__device__ void SetSrcSliceOrigin(const SrcDesc& src_desc, const Index& src_slice_origin_idx)
{
src_coord_ = make_dynamic_tensor_coordinate(src_desc, src_slice_origin_idx);
src_coord_ = make_tensor_coordinate(src_desc, src_slice_origin_idx);
}
__device__ void SetDstSliceOrigin(const DstDesc& dst_desc, const Index& dst_slice_origin_idx)
{
dst_coord_ = make_dynamic_tensor_coordinate(dst_desc, dst_slice_origin_idx);
dst_coord_ = make_tensor_coordinate(dst_desc, dst_slice_origin_idx);
}
template <typename SrcBuffer, typename SrcIteratorHacks>
__device__ void RunRead(const SrcDesc& src_desc,
const SrcBuffer& src_buf,
const SrcIteratorHacks& src_iterator_hacks)
template <typename SrcBuffer, typename SrcStepHacks>
__device__ void
RunRead(const SrcDesc& src_desc, const SrcBuffer& src_buf, const SrcStepHacks& src_step_hacks)
{
static_assert(SrcBuffer::GetAddressSpace() == AddressSpaceEnum_t::Global or
SrcBuffer::GetAddressSpace() == AddressSpaceEnum_t::Lds,
@@ -92,13 +91,13 @@ struct ThreadwiseDynamicTensorSliceTransfer_v3r1
container_reverse_exclusive_scan(
container_reorder_given_new2old(src_vector_tensor_lengths,
SrcVectorTensorContiguousDimOrder{}),
math::multiplies_v2{},
math::multiplies{},
I1),
SrcVectorTensorContiguousDimOrder{});
constexpr auto src_vector_desc = make_dynamic_naive_tensor_descriptor_v2(
sequence_to_tuple_of_number(src_vector_tensor_lengths),
sequence_to_tuple_of_number(src_vector_tensor_strides));
constexpr auto src_vector_desc =
make_naive_tensor_descriptor(sequence_to_tuple_of_number(src_vector_tensor_lengths),
sequence_to_tuple_of_number(src_vector_tensor_strides));
// access order and lengths
constexpr auto src_access_lengths = SliceLengths{} / src_vector_tensor_lengths;
@@ -108,31 +107,31 @@ struct ThreadwiseDynamicTensorSliceTransfer_v3r1
constexpr auto ordered_src_access_lengths =
container_reorder_given_new2old(src_access_lengths, src_dim_access_order);
// make forward iterators
const auto src_forward_iterators = generate_tuple(
// make forward steps
const auto src_forward_steps = generate_tuple(
[&](auto i) {
Index forward_step;
Index forward_step_idx;
static_for<0, nDim, 1>{}([&](auto j) {
forward_step(j) = (i.value == j.value) ? src_vector_tensor_lengths[i] : 0;
forward_step_idx(j) = (i.value == j.value) ? src_vector_tensor_lengths[i] : 0;
});
return make_dynamic_tensor_coordinate_iterator(
src_desc, forward_step, src_iterator_hacks[I0][i]);
return make_tensor_coordinate_step(
src_desc, forward_step_idx, src_step_hacks[I0][i]);
},
Number<nDim>{});
// make backward iterators
const auto src_backward_iterators = generate_tuple(
// make backward steps
const auto src_backward_steps = generate_tuple(
[&](auto i) {
Index backward_step;
Index backward_step_idx;
static_for<0, nDim, 1>{}([&](auto j) {
backward_step(j) = (i.value == j.value) ? -src_vector_tensor_lengths[i] : 0;
backward_step_idx(j) = (i.value == j.value) ? -src_vector_tensor_lengths[i] : 0;
});
return make_dynamic_tensor_coordinate_iterator(
src_desc, backward_step, src_iterator_hacks[I1][i]);
return make_tensor_coordinate_step(
src_desc, backward_step_idx, src_step_hacks[I1][i]);
},
Number<nDim>{});
@@ -219,13 +218,13 @@ struct ThreadwiseDynamicTensorSliceTransfer_v3r1
{
if constexpr(forward_sweep[i])
{
move_dynamic_tensor_coordinate(
src_desc, src_coord_, src_forward_iterators[src_dim_access_order[i]]);
move_tensor_coordinate(
src_desc, src_coord_, src_forward_steps[src_dim_access_order[i]]);
}
else
{
move_dynamic_tensor_coordinate(
src_desc, src_coord_, src_backward_iterators[src_dim_access_order[i]]);
move_tensor_coordinate(
src_desc, src_coord_, src_backward_steps[src_dim_access_order[i]]);
}
}
});
@@ -234,17 +233,16 @@ struct ThreadwiseDynamicTensorSliceTransfer_v3r1
// move src coordinate back to slice origin (or not)
if constexpr(SrcResetCoordinateAfterRun)
{
const auto src_reset_iterator =
make_dynamic_tensor_coordinate_iterator(src_desc, GetSrcCoordinateResetStep());
const auto src_reset_step =
make_tensor_coordinate_step(src_desc, GetSrcCoordinateResetStep());
move_dynamic_tensor_coordinate(src_desc, src_coord_, src_reset_iterator);
move_tensor_coordinate(src_desc, src_coord_, src_reset_step);
}
}
template <typename DstBuffer, typename DstIteratorHacks>
__device__ void RunWrite(const DstDesc& dst_desc,
DstBuffer& dst_buf,
const DstIteratorHacks& dst_iterator_hacks)
template <typename DstBuffer, typename DstStepHacks>
__device__ void
RunWrite(const DstDesc& dst_desc, DstBuffer& dst_buf, const DstStepHacks& dst_step_hacks)
{
static_assert(DstBuffer::GetAddressSpace() == AddressSpaceEnum_t::Global or
DstBuffer::GetAddressSpace() == AddressSpaceEnum_t::Lds,
@@ -261,13 +259,13 @@ struct ThreadwiseDynamicTensorSliceTransfer_v3r1
container_reverse_exclusive_scan(
container_reorder_given_new2old(dst_vector_tensor_lengths,
DstVectorTensorContiguousDimOrder{}),
math::multiplies_v2{},
math::multiplies{},
I1),
DstVectorTensorContiguousDimOrder{});
constexpr auto dst_vector_desc = make_dynamic_naive_tensor_descriptor_v2(
sequence_to_tuple_of_number(dst_vector_tensor_lengths),
sequence_to_tuple_of_number(dst_vector_tensor_strides));
constexpr auto dst_vector_desc =
make_naive_tensor_descriptor(sequence_to_tuple_of_number(dst_vector_tensor_lengths),
sequence_to_tuple_of_number(dst_vector_tensor_strides));
// dst access order and lengths
constexpr auto dst_access_lengths = SliceLengths{} / dst_vector_tensor_lengths;
@@ -277,35 +275,31 @@ struct ThreadwiseDynamicTensorSliceTransfer_v3r1
constexpr auto ordered_dst_access_lengths =
container_reorder_given_new2old(dst_access_lengths, dst_dim_access_order);
// make forward iterators
const auto dst_forward_iterators = generate_tuple(
// make forward steps
const auto dst_forward_steps = generate_tuple(
[&](auto i) {
Index forward_step;
Index forward_step_idx;
static_for<0, nDim, 1>{}([&](auto j) {
forward_step(j) = (i.value == j.value) ? dst_vector_tensor_lengths[i] : 0;
forward_step_idx(j) = (i.value == j.value) ? dst_vector_tensor_lengths[i] : 0;
});
const auto forward_iterator = make_dynamic_tensor_coordinate_iterator(
dst_desc, forward_step, dst_iterator_hacks[I0][i]);
return forward_iterator;
return make_tensor_coordinate_step(
dst_desc, forward_step_idx, dst_step_hacks[I0][i]);
},
Number<nDim>{});
// make backward iterators
const auto dst_backward_iterators = generate_tuple(
// make backward steps
const auto dst_backward_steps = generate_tuple(
[&](auto i) {
Index backward_step;
Index backward_step_idx;
static_for<0, nDim, 1>{}([&](auto j) {
backward_step(j) = (i.value == j.value) ? -dst_vector_tensor_lengths[i] : 0;
backward_step_idx(j) = (i.value == j.value) ? -dst_vector_tensor_lengths[i] : 0;
});
const auto backward_iterator = make_dynamic_tensor_coordinate_iterator(
dst_desc, backward_step, dst_iterator_hacks[I1][i]);
return backward_iterator;
return make_tensor_coordinate_step(
dst_desc, backward_step_idx, dst_step_hacks[I1][i]);
},
Number<nDim>{});
@@ -394,13 +388,13 @@ struct ThreadwiseDynamicTensorSliceTransfer_v3r1
{
if constexpr(forward_sweep[i])
{
move_dynamic_tensor_coordinate(
dst_desc, dst_coord_, dst_forward_iterators[dst_dim_access_order[i]]);
move_tensor_coordinate(
dst_desc, dst_coord_, dst_forward_steps[dst_dim_access_order[i]]);
}
else
{
move_dynamic_tensor_coordinate(
dst_desc, dst_coord_, dst_backward_iterators[dst_dim_access_order[i]]);
move_tensor_coordinate(
dst_desc, dst_coord_, dst_backward_steps[dst_dim_access_order[i]]);
}
}
});
@@ -409,10 +403,10 @@ struct ThreadwiseDynamicTensorSliceTransfer_v3r1
// move dst coordinate back to slice origin (or not)
if constexpr(DstResetCoordinateAfterRun)
{
const auto dst_reset_iterator =
make_dynamic_tensor_coordinate_iterator(dst_desc, GetDstCoordinateResetStep());
const auto dst_reset_step =
make_tensor_coordinate_step(dst_desc, GetDstCoordinateResetStep());
move_dynamic_tensor_coordinate(dst_desc, dst_coord_, dst_reset_iterator);
move_tensor_coordinate(dst_desc, dst_coord_, dst_reset_step);
}
}
@@ -423,11 +417,11 @@ struct ThreadwiseDynamicTensorSliceTransfer_v3r1
constexpr auto zeros = typename uniform_sequence_gen<ntransform_src, 0>::type{};
constexpr auto src_iterator_hacks =
constexpr auto src_step_hacks =
make_tuple(generate_tuple([&](auto) { return zeros; }, Number<nDim>{}),
generate_tuple([&](auto) { return zeros; }, Number<nDim>{}));
RunRead(src_desc, src_buf, src_iterator_hacks);
RunRead(src_desc, src_buf, src_step_hacks);
}
template <typename DstBuffer>
@@ -437,11 +431,11 @@ struct ThreadwiseDynamicTensorSliceTransfer_v3r1
constexpr auto zeros = typename uniform_sequence_gen<ntransform_dst, 0>::type{};
constexpr auto dst_iterator_hacks =
constexpr auto dst_step_hacks =
make_tuple(generate_tuple([&](auto) { return zeros; }, Number<nDim>{}),
generate_tuple([&](auto) { return zeros; }, Number<nDim>{}));
RunWrite(dst_desc, dst_buf, dst_iterator_hacks);
RunWrite(dst_desc, dst_buf, dst_step_hacks);
}
__device__ static constexpr auto GetSrcCoordinateResetStep()
@@ -564,18 +558,17 @@ struct ThreadwiseDynamicTensorSliceTransfer_v3r1
: src_slice_origin_step_idx + GetSrcCoordinateResetStep();
// is it OK to construct a new step every time?
const auto adjusted_step =
make_dynamic_tensor_coordinate_iterator(src_desc, adjusted_step_idx);
const auto adjusted_step = make_tensor_coordinate_step(src_desc, adjusted_step_idx);
move_dynamic_tensor_coordinate(src_desc, src_coord_, adjusted_step);
move_tensor_coordinate(src_desc, src_coord_, adjusted_step);
}
// src_slice_origin_step_idx need to be known at compile-time, for performance reason
template <typename SrcMoveSliceWindowIteratorHack>
template <typename SrcMoveSliceWindowStepHack>
__device__ void
MoveSrcSliceWindow(const SrcDesc& src_desc,
const Index& src_slice_origin_step_idx,
const SrcMoveSliceWindowIteratorHack& src_move_slice_window_iterator_hack)
const SrcMoveSliceWindowStepHack& src_move_slice_window_step_hack)
{
// if src coord was not reset by RunRead(), then need to adjust the step here
const auto adjusted_step_idx =
@@ -583,10 +576,10 @@ struct ThreadwiseDynamicTensorSliceTransfer_v3r1
: src_slice_origin_step_idx + GetSrcCoordinateResetStep();
// is it OK to construct a new step every time?
const auto adjusted_step = make_dynamic_tensor_coordinate_iterator(
src_desc, adjusted_step_idx, src_move_slice_window_iterator_hack);
const auto adjusted_step = make_tensor_coordinate_step(
src_desc, adjusted_step_idx, src_move_slice_window_step_hack);
move_dynamic_tensor_coordinate(src_desc, src_coord_, adjusted_step);
move_tensor_coordinate(src_desc, src_coord_, adjusted_step);
}
// dst_slice_origin_step_idx need to be known at compile-time, for performance reason
__device__ void MoveDstSliceWindow(const DstDesc& dst_desc,
@@ -598,19 +591,18 @@ struct ThreadwiseDynamicTensorSliceTransfer_v3r1
: dst_slice_origin_step_idx + GetDstCoordinateResetStep();
// is it OK to construct a new step every time?
const auto adjusted_step =
make_dynamic_tensor_coordinate_iterator(dst_desc, adjusted_step_idx);
const auto adjusted_step = make_tensor_coordinate_step(dst_desc, adjusted_step_idx);
move_dynamic_tensor_coordinate(dst_desc, dst_coord_, adjusted_step);
move_tensor_coordinate(dst_desc, dst_coord_, adjusted_step);
}
private:
static constexpr auto buffer_desc_ =
make_dynamic_naive_tensor_descriptor_packed_v2(sequence_to_tuple_of_number(SliceLengths{}));
make_naive_tensor_descriptor_packed(sequence_to_tuple_of_number(SliceLengths{}));
static constexpr auto buffer_size_ = buffer_desc_.GetElementSpaceSize();
StaticBuffer<AddressSpaceEnum_t::Vgpr, SrcData, buffer_size_> buffer_;
StaticBuffer<AddressSpaceEnum_t::Vgpr, SrcData, buffer_size_, true> buffer_;
SrcCoord src_coord_;
DstCoord dst_coord_;
@@ -622,25 +614,24 @@ struct ThreadwiseDynamicTensorSliceTransfer_v3r1
// 2. SrcBuffer is DynamicBuffer
// 3. src_ref_idx is known at run-time
// 4. SrcRefToOriginDisplacement is known at compile-time
// 5. use #-iterator
// 5. use #-step
// 2. dst:
// 1. DstDesc is known at compile-time
// 2. DstBuffer is StaticBuffer
// 3. DstOriginIdx is known at compile-time
// 4. use direct address calculation
// 3. vector access on src
template <
typename SrcData,
typename DstData,
typename SrcDesc,
typename DstDesc,
typename SliceLengths,
typename DimAccessOrder,
typename SrcVectorTensorLengths,
typename SrcVectorTensorContiguousDimOrder,
typename std::enable_if<SrcDesc::IsKnownAtCompileTime() && DstDesc::IsKnownAtCompileTime(),
bool>::type = false>
struct ThreadwiseDynamicTensorSliceTransfer_v4r1
template <typename SrcData,
typename DstData,
typename SrcDesc,
typename DstDesc,
typename SliceLengths,
typename DimAccessOrder,
typename SrcVectorTensorLengths,
typename SrcVectorTensorContiguousDimOrder,
typename enable_if<SrcDesc::IsKnownAtCompileTime() && DstDesc::IsKnownAtCompileTime(),
bool>::type = false>
struct ThreadwiseTensorSliceTransfer_v4r1
{
static constexpr auto I0 = Number<0>{};
static constexpr auto I1 = Number<1>{};
@@ -649,12 +640,12 @@ struct ThreadwiseDynamicTensorSliceTransfer_v4r1
using Index = MultiIndex<nDim>;
using SrcCoord = decltype(make_dynamic_tensor_coordinate(SrcDesc{}, Index{}));
using SrcCoord = decltype(make_tensor_coordinate(SrcDesc{}, Index{}));
using SrcCoordIterator = decltype(make_dynamic_tensor_coordinate_iterator(SrcDesc{}, Index{}));
using SrcCoordStep = decltype(make_tensor_coordinate_step(SrcDesc{}, Index{}));
__device__ constexpr ThreadwiseDynamicTensorSliceTransfer_v4r1(const Index& src_ref_idx)
: src_ref_coord_(make_dynamic_tensor_coordinate(SrcDesc{}, src_ref_idx))
__device__ constexpr ThreadwiseTensorSliceTransfer_v4r1(const Index& src_ref_idx)
: src_ref_coord_(make_tensor_coordinate(SrcDesc{}, src_ref_idx))
{
static_assert(SrcDesc::IsKnownAtCompileTime() && DstDesc::IsKnownAtCompileTime(),
"wrong! SrcDesc and DstDesc need to known at compile-time");
@@ -708,13 +699,13 @@ struct ThreadwiseDynamicTensorSliceTransfer_v4r1
container_reverse_exclusive_scan(
container_reorder_given_new2old(src_vector_tensor_lengths,
SrcVectorTensorContiguousDimOrder{}),
math::multiplies_v2{},
math::multiplies{},
I1),
SrcVectorTensorContiguousDimOrder{});
constexpr auto src_vector_desc = make_dynamic_naive_tensor_descriptor_v2(
sequence_to_tuple_of_number(src_vector_tensor_lengths),
sequence_to_tuple_of_number(src_vector_tensor_strides));
constexpr auto src_vector_desc =
make_naive_tensor_descriptor(sequence_to_tuple_of_number(src_vector_tensor_lengths),
sequence_to_tuple_of_number(src_vector_tensor_strides));
// access order and lengths
constexpr auto access_lengths = SliceLengths{} / src_vector_tensor_lengths;
@@ -734,13 +725,12 @@ struct ThreadwiseDynamicTensorSliceTransfer_v4r1
constexpr auto src_ref_to_data_disp_idx =
src_ref_to_origin_disp_idx + data_to_origin_disp_idx;
constexpr auto src_ref_to_data_disp_coord_iterator =
make_dynamic_tensor_coordinate_iterator(src_desc, src_ref_to_data_disp_idx);
constexpr auto src_ref_to_data_disp_coord_step =
make_tensor_coordinate_step(src_desc, src_ref_to_data_disp_idx);
auto src_data_coord = src_ref_coord_;
move_dynamic_tensor_coordinate(
src_desc, src_data_coord, src_ref_to_data_disp_coord_iterator);
move_tensor_coordinate(src_desc, src_data_coord, src_ref_to_data_disp_coord_step);
vector_type_maker_t<SrcData, src_vector_desc.GetElementSpaceSize()> src_vector;
@@ -775,10 +765,10 @@ struct ThreadwiseDynamicTensorSliceTransfer_v4r1
{
constexpr auto src_desc = SrcDesc{};
const auto src_slice_move_step_iter = make_dynamic_tensor_coordinate_iterator(
src_desc, to_multi_index(src_slice_move_step_idx));
const auto src_slice_move_step_iter =
make_tensor_coordinate_step(src_desc, to_multi_index(src_slice_move_step_idx));
move_dynamic_tensor_coordinate(SrcDesc{}, src_ref_coord_, src_slice_move_step_iter);
move_tensor_coordinate(SrcDesc{}, src_ref_coord_, src_slice_move_step_iter);
}
private:

20
composable_kernel/include/tensor_operation/xdlops_gemm.hpp
View File

@@ -350,8 +350,8 @@ struct mfma_info<mfma_instr::mfma_f32_32x32x2bf16>
class FloatC>
__device__ FloatC run(const FloatA* a, const FloatB* b, FloatC reg_c) const
{
const auto p_a = reinterpret_cast<const ushort2_t*>(a);
const auto p_b = reinterpret_cast<const ushort2_t*>(b);
const auto p_a = c_style_pointer_cast<const ushort2_t*>(a);
const auto p_b = c_style_pointer_cast<const ushort2_t*>(b);
return intrin_mfma_f32_32x32x2bf16<MPerXdlops, NPerXdlops, AStride, BStride>::run(
p_a, p_b, reg_c);
@@ -384,8 +384,8 @@ struct mfma_info<mfma_instr::mfma_f32_32x32x4bf16>
class FloatC>
__device__ FloatC run(const FloatA* a, const FloatB* b, FloatC reg_c) const
{
const auto p_a = reinterpret_cast<const ushort2_t*>(a);
const auto p_b = reinterpret_cast<const ushort2_t*>(b);
const auto p_a = c_style_pointer_cast<const ushort2_t*>(a);
const auto p_b = c_style_pointer_cast<const ushort2_t*>(b);
return intrin_mfma_f32_32x32x4bf16(p_a, p_b, reg_c);
}
@@ -417,8 +417,8 @@ struct mfma_info<mfma_instr::mfma_f32_16x16x8bf16>
class FloatC>
__device__ FloatC run(const FloatA* a, const FloatB* b, FloatC reg_c) const
{
const auto p_a = reinterpret_cast<const ushort2_t*>(a);
const auto p_b = reinterpret_cast<const ushort2_t*>(b);
const auto p_a = c_style_pointer_cast<const ushort2_t*>(a);
const auto p_b = c_style_pointer_cast<const ushort2_t*>(b);
return intrin_mfma_f32_16x16x8bf16(p_a, p_b, reg_c);
}
@@ -450,8 +450,8 @@ struct mfma_info<mfma_instr::mfma_f32_16x16x2bf16>
class FloatC>
__device__ FloatC run(const FloatA* a, const FloatB* b, FloatC reg_c) const
{
const auto p_a = reinterpret_cast<const ushort2_t*>(a);
const auto p_b = reinterpret_cast<const ushort2_t*>(b);
const auto p_a = c_style_pointer_cast<const ushort2_t*>(a);
const auto p_b = c_style_pointer_cast<const ushort2_t*>(b);
return intrin_mfma_f32_16x16x2bf16<MPerXdlops, NPerXdlops>(p_a, p_b, reg_c);
}
@@ -483,8 +483,8 @@ struct mfma_info<mfma_instr::mfma_f32_4x4x2bf16>
class FloatC>
__device__ FloatC run(const FloatA* a, const FloatB* b, FloatC reg_c) const
{
const auto p_a = reinterpret_cast<const ushort2_t*>(a);
const auto p_b = reinterpret_cast<const ushort2_t*>(b);
const auto p_a = c_style_pointer_cast<const ushort2_t*>(a);
const auto p_b = c_style_pointer_cast<const ushort2_t*>(b);
return intrin_mfma_f32_4x4x2bf16<MPerXdlops, NPerXdlops>::run(p_a, p_b, reg_c);
}

44
composable_kernel/include/utility/amd_address_space.hpp Normal file
View File

@@ -0,0 +1,44 @@
#ifndef CK_AMD_ADDRESS_SPACE_HPP
#define CK_AMD_ADDRESS_SPACE_HPP
#include "config.hpp"
#include "c_style_pointer_cast.hpp"
// Address Space for AMDGCN
// https://llvm.org/docs/AMDGPUUsage.html#address-space
namespace ck {
enum AddressSpaceEnum_t
{
Generic,
Global,
Lds,
Sgpr,
Vgpr,
};
template <typename T>
__device__ T* cast_pointer_to_generic_address_space(T CONSTANT* p)
{
// cast a pointer in "Constant" address space (4) to "Generic" address space (0)
// only c-style pointer cast seems be able to be compiled
#pragma clang diagnostic push
#pragma clang diagnostic ignored "-Wold-style-cast"
return (T*)p; // NOLINT(old-style-cast)
#pragma clang diagnostic pop
}
template <typename T>
__host__ __device__ T CONSTANT* cast_pointer_to_constant_address_space(T* p)
{
// cast a pointer in "Generic" address space (0) to "Constant" address space (4)
// only c-style pointer cast seems be able to be compiled
#pragma clang diagnostic push
#pragma clang diagnostic ignored "-Wold-style-cast"
return (T CONSTANT*)p; // NOLINT(old-style-cast)
#pragma clang diagnostic pop
}
} // namespace ck
#endif

109
composable_kernel/include/utility/amd_buffer_addressing_v2.hpp → composable_kernel/include/utility/amd_buffer_addressing.hpp
View File

@@ -1,34 +1,34 @@
#ifndef CK_AMD_BUFFER_ADDRESSING_V2_HPP
#define CK_AMD_BUFFER_ADDRESSING_V2_HPP
#ifndef CK_AMD_BUFFER_ADDRESSING_HPP
#define CK_AMD_BUFFER_ADDRESSING_HPP
#include "data_type.hpp"
namespace ck {
template <typename T>
union BufferResource_v2
union BufferResource
{
// 128 bit SGPRs to supply buffer resource in buffer instructions
// https://rocm-documentation.readthedocs.io/en/latest/GCN_ISA_Manuals/testdocbook.html#vector-memory-buffer-instructions
int32x4_t data;
int32x4_t content;
StaticallyIndexedArray<T*, 2> address;
StaticallyIndexedArray<int32_t, 4> range;
StaticallyIndexedArray<int32_t, 4> config;
};
template <typename T>
__device__ int32x4_t make_wave_buffer_resource(T* p_wave, index_t data_space_size)
__device__ int32x4_t make_wave_buffer_resource(T* p_wave, index_t element_space_size)
{
BufferResource_v2<T> wave_buffer_resource;
BufferResource<T> wave_buffer_resource;
// wavewise base address (64 bit)
wave_buffer_resource.address(Number<0>{}) = const_cast<remove_cv_t<T>*>(p_wave);
// wavewise range (32 bit)
wave_buffer_resource.range(Number<2>{}) = data_space_size * sizeof(T);
wave_buffer_resource.range(Number<2>{}) = element_space_size * sizeof(T);
// wavewise setting (32 bit)
wave_buffer_resource.config(Number<3>{}) = CK_BUFFER_RESOURCE_3RD_DWORD;
return wave_buffer_resource.data;
return wave_buffer_resource.content;
}
// load
@@ -204,10 +204,9 @@ llvm_amdgcn_raw_buffer_store_fp32x4(float4_t vdata,
index_t glc_slc) __asm("llvm.amdgcn.raw.buffer.store.v4f32");
template <typename T, index_t N>
__device__ typename vector_type<T, N>::type
amd_buffer_load_impl_v2(int32x4_t src_wave_buffer_resource,
index_t src_thread_addr_offset,
index_t src_wave_addr_offset)
__device__ typename vector_type<T, N>::type amd_buffer_load_impl(int32x4_t src_wave_buffer_resource,
index_t src_thread_addr_offset,
index_t src_wave_addr_offset)
{
static_assert(
(is_same<T, float>::value && (N == 1 || N == 2 || N == 4 || N == 8)) ||
@@ -412,10 +411,10 @@ amd_buffer_load_impl_v2(int32x4_t src_wave_buffer_resource,
}
template <typename T, index_t N>
__device__ void amd_buffer_store_impl_v2(const typename vector_type<T, N>::type src_thread_data,
int32x4_t dst_wave_buffer_resource,
index_t dst_thread_addr_offset,
index_t dst_wave_addr_offset)
__device__ void amd_buffer_store_impl(const typename vector_type<T, N>::type src_thread_data,
int32x4_t dst_wave_buffer_resource,
index_t dst_thread_addr_offset,
index_t dst_wave_addr_offset)
{
static_assert(
(is_same<T, float>::value && (N == 1 || N == 2 || N == 4)) ||
@@ -584,67 +583,95 @@ __device__ void amd_buffer_store_impl_v2(const typename vector_type<T, N>::type
// buffer_load requires:
// 1) p_src_wave must be in global memory space
// 2) p_src_wave to be a wavewise pointer.
// 2) p_src_wave must be a wavewise pointer.
// It is user's responsibility to make sure that is true.
template <typename T, index_t N>
__device__ typename vector_type_maker<T, N>::type::type
amd_buffer_load_v2(const T* p_src_wave,
index_t src_thread_data_offset,
bool src_thread_data_valid,
index_t src_element_space)
amd_buffer_load_invalid_element_return_return_zero(const T* p_src_wave,
index_t src_thread_element_offset,
bool src_thread_element_valid,
index_t src_element_space_size)
{
const int32x4_t src_wave_buffer_resource =
make_wave_buffer_resource(p_src_wave, src_element_space);
make_wave_buffer_resource(p_src_wave, src_element_space_size);
index_t src_thread_addr_offset = src_thread_data_offset * sizeof(T);
index_t src_thread_addr_offset = src_thread_element_offset * sizeof(T);
using vector_t = typename vector_type_maker<T, N>::type::type;
using scalar_t = typename scalar_type<vector_t>::type;
using vector_t = typename vector_type_maker<T, N>::type::type;
using scalar_t = typename scalar_type<vector_t>::type;
constexpr index_t vector_size = scalar_type<vector_t>::vector_size;
#if CK_EXPERIMENTAL_USE_BUFFER_LOAD_OOB_CHECK_OFFSET_TRICK
uint32_t src_addr_shift = src_thread_data_valid ? 0 : 0x7fffffff;
uint32_t src_addr_shift = src_thread_element_valid ? 0 : 0x7fffffff;
return amd_buffer_load_impl_v2<scalar_t, vector_size>(
return amd_buffer_load_impl<scalar_t, vector_size>(
src_wave_buffer_resource, src_addr_shift + src_thread_addr_offset, 0);
#else
vector_t tmp = amd_buffer_load_impl_v2<scalar_t, vector_size>(
vector_t tmp = amd_buffer_load_impl<scalar_t, vector_size>(
src_wave_buffer_resource, src_thread_addr_offset, 0);
return src_thread_data_valid ? tmp : vector_t(0);
return src_thread_element_valid ? tmp : vector_t(0);
#endif
}
// buffer_load requires:
// 1) p_src_wave must be in global memory space
// 2) p_src_wave must be a wavewise pointer.
// It is user's responsibility to make sure that is true.
template <typename T, index_t N>
__device__ typename vector_type_maker<T, N>::type::type
amd_buffer_load_invalid_element_return_customized_value(const T* p_src_wave,
index_t src_thread_element_offset,
bool src_thread_element_valid,
index_t src_element_space_size,
T customized_value)
{
const int32x4_t src_wave_buffer_resource =
make_wave_buffer_resource(p_src_wave, src_element_space_size);
index_t src_thread_addr_offset = src_thread_element_offset * sizeof(T);
using vector_t = typename vector_type_maker<T, N>::type::type;
using scalar_t = typename scalar_type<vector_t>::type;
constexpr index_t vector_size = scalar_type<vector_t>::vector_size;
vector_t tmp = amd_buffer_load_impl<scalar_t, vector_size>(
src_wave_buffer_resource, src_thread_addr_offset, 0);
return src_thread_element_valid ? tmp : vector_t(customized_value);
}
// buffer_store requires:
// 1) p_dst_wave must be global memory
// 2) p_dst_wave to be a wavewise pointer.
// It is user's responsibility to make sure that is true.
template <typename T, index_t N>
__device__ void
amd_buffer_store_v2(const typename vector_type_maker<T, N>::type::type src_thread_data,
T* p_dst_wave,
const index_t dst_thread_data_offset,
const bool dst_thread_data_valid,
const index_t dst_element_space)
__device__ void amd_buffer_store(const typename vector_type_maker<T, N>::type::type src_thread_data,
T* p_dst_wave,
const index_t dst_thread_element_offset,
const bool dst_thread_element_valid,
const index_t dst_element_space_size)
{
const int32x4_t dst_wave_buffer_resource =
make_wave_buffer_resource(p_dst_wave, dst_element_space);
make_wave_buffer_resource(p_dst_wave, dst_element_space_size);
index_t dst_thread_addr_offset = dst_thread_data_offset * sizeof(T);
index_t dst_thread_addr_offset = dst_thread_element_offset * sizeof(T);
using vector_t = typename vector_type_maker<T, N>::type::type;
using scalar_t = typename scalar_type<vector_t>::type;
constexpr index_t vector_size = scalar_type<vector_t>::vector_size;
#if CK_EXPERIMENTAL_USE_BUFFER_STORE_OOB_CHECK_OFFSET_TRICK
uint32_t dst_addr_shift = dst_thread_data_valid ? 0 : 0x7fffffff;
uint32_t dst_addr_shift = dst_thread_element_valid ? 0 : 0x7fffffff;
amd_buffer_store_impl_v2<scalar_t, vector_size>(
amd_buffer_store_impl<scalar_t, vector_size>(
src_thread_data, dst_wave_buffer_resource, dst_addr_shift + dst_thread_addr_offset, 0);
#else
if(dst_thread_data_valid)
if(dst_thread_element_valid)
{
amd_buffer_store_impl_v2<scalar_t, vector_size>(
amd_buffer_store_impl<scalar_t, vector_size>(
src_thread_data, dst_wave_buffer_resource, dst_thread_addr_offset, 0);
}
#endif

188
composable_kernel/include/utility/amd_dlop.hpp
View File

@@ -1,188 +0,0 @@
#ifndef CK_AMD_DLOP_HPP
#define CK_AMD_DLOP_HPP
#include "data_type.hpp"
namespace ck {
template <typename TA, typename TB, typename TC>
__device__ void amd_inner_product_dlop(const TA& a, const TB& b, TC& c);
template <>
__device__ void
amd_inner_product_dlop<float, float, float>(const float& a, const float& b, float& c)
{
#if CK_USE_AMD_DLOP_INLINE_ASM
asm volatile("\n \
v_fmac_f32 %0, %1, %2 \n \
"
: "=v"(c)
: "v"(a), "v"(b), "0"(c));
#else
c += a * b;
#endif
}
template <>
__device__ void
amd_inner_product_dlop<float2_t, float2_t, float>(const float2_t& a, const float2_t& b, float& c)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
amd_inner_product_dlop(vector_type<float, 2>{a}.AsType<float>()[I0],
vector_type<float, 2>{b}.AsType<float>()[I0],
c);
amd_inner_product_dlop(vector_type<float, 2>{a}.AsType<float>()[I1],
vector_type<float, 2>{b}.AsType<float>()[I1],
c);
}
template <>
__device__ void
amd_inner_product_dlop<float4_t, float4_t, float>(const float4_t& a, const float4_t& b, float& c)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
amd_inner_product_dlop(vector_type<float, 4>{a}.AsType<float>()[I0],
vector_type<float, 4>{b}.AsType<float>()[I0],
c);
amd_inner_product_dlop(vector_type<float, 4>{a}.AsType<float>()[I1],
vector_type<float, 4>{b}.AsType<float>()[I1],
c);
amd_inner_product_dlop(vector_type<float, 4>{a}.AsType<float>()[I2],
vector_type<float, 4>{b}.AsType<float>()[I2],
c);
amd_inner_product_dlop(vector_type<float, 4>{a}.AsType<float>()[I3],
vector_type<float, 4>{b}.AsType<float>()[I3],
c);
}
#if CK_USE_AMD_DLOP
template <>
__device__ void
amd_inner_product_dlop<half2_t, half2_t, float>(const half2_t& a, const half2_t& b, float& c)
{
#if CK_USE_AMD_DLOP_INLINE_ASM
asm volatile("\n \
v_dot2_f32_f16 %0, %1, %2, %0\n \
"
: "=v"(c)
: "v"(a), "v"(b), "0"(c));
#else
c = __builtin_amdgcn_sdot2(a, b, c, false);
#endif
}
template <>
__device__ void
amd_inner_product_dlop<half4_t, half4_t, float>(const half4_t& a, const half4_t& b, float& c)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
amd_inner_product_dlop(vector_type<half_t, 4>{a}.AsType<half2_t>()[I0],
vector_type<half_t, 4>{b}.AsType<half2_t>()[I0],
c);
amd_inner_product_dlop(vector_type<half_t, 4>{a}.AsType<half2_t>()[I1],
vector_type<half_t, 4>{b}.AsType<half2_t>()[I1],
c);
}
template <>
__device__ void
amd_inner_product_dlop<half8_t, half8_t, float>(const half8_t& a, const half8_t& b, float& c)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
amd_inner_product_dlop(vector_type<half_t, 8>{a}.AsType<half2_t>()[I0],
vector_type<half_t, 8>{b}.AsType<half2_t>()[I0],
c);
amd_inner_product_dlop(vector_type<half_t, 8>{a}.AsType<half2_t>()[I1],
vector_type<half_t, 8>{b}.AsType<half2_t>()[I1],
c);
amd_inner_product_dlop(vector_type<half_t, 8>{a}.AsType<half2_t>()[I2],
vector_type<half_t, 8>{b}.AsType<half2_t>()[I2],
c);
amd_inner_product_dlop(vector_type<half_t, 8>{a}.AsType<half2_t>()[I3],
vector_type<half_t, 8>{b}.AsType<half2_t>()[I3],
c);
}
template <>
__device__ void amd_inner_product_dlop<int8x4_t, int8x4_t, int32_t>(const int8x4_t& a,
const int8x4_t& b,
int32_t& c)
{
#if CK_USE_AMD_DLOP_INLINE_ASM
asm volatile("\n \
v_dot4_i32_i8 %0, %1, %2, %0\n \
"
: "=v"(c)
: "v"(as_type<int32_t>(a)), "v"(as_type<int32_t>(b)), "0"(c));
#else
c = __builtin_amdgcn_sdot4(as_type<int32_t>(a), as_type<int32_t>(b), c, false);
#endif
}
template <>
__device__ void amd_inner_product_dlop<int8x8_t, int8x8_t, int32_t>(const int8x8_t& a,
const int8x8_t& b,
int32_t& c)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
amd_inner_product_dlop(vector_type<int8_t, 8>{a}.AsType<int8x4_t>()[I0],
vector_type<int8_t, 8>{b}.AsType<int8x4_t>()[I0],
c);
amd_inner_product_dlop(vector_type<int8_t, 8>{a}.AsType<int8x4_t>()[I1],
vector_type<int8_t, 8>{b}.AsType<int8x4_t>()[I1],
c);
}
template <>
__device__ void amd_inner_product_dlop<int8x16_t, int8x16_t, int32_t>(const int8x16_t& a,
const int8x16_t& b,
int32_t& c)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
amd_inner_product_dlop(vector_type<int8_t, 16>{a}.AsType<int8x4_t>()[I0],
vector_type<int8_t, 16>{b}.AsType<int8x4_t>()[I0],
c);
amd_inner_product_dlop(vector_type<int8_t, 16>{a}.AsType<int8x4_t>()[I1],
vector_type<int8_t, 16>{b}.AsType<int8x4_t>()[I1],
c);
amd_inner_product_dlop(vector_type<int8_t, 16>{a}.AsType<int8x4_t>()[I2],
vector_type<int8_t, 16>{b}.AsType<int8x4_t>()[I2],
c);
amd_inner_product_dlop(vector_type<int8_t, 16>{a}.AsType<int8x4_t>()[I3],
vector_type<int8_t, 16>{b}.AsType<int8x4_t>()[I3],
c);
}
#endif // CK_USE_AMD_DLOP
} // namespace ck
#endif

39
composable_kernel/include/utility/amd_inline_asm.hpp
View File

@@ -2,6 +2,9 @@
#define CK_AMD_INLINE_ASM_HPP
#include "data_type.hpp"
#include "c_style_pointer_cast.hpp"
// TODO: deprecate all amd_assembly_outer_product_xxx
namespace ck {
@@ -53,9 +56,9 @@ __device__ void
amd_assembly_outer_product_1x2(half4_t a, half4_t b0, half4_t b1, float& c0, float& c1)
{
// TODO remove pointer casting
const half2_t* p_a_half2 = reinterpret_cast<const half2_t*>(&a);
const half2_t* p_b0_half2 = reinterpret_cast<const half2_t*>(&b0);
const half2_t* p_b1_half2 = reinterpret_cast<const half2_t*>(&b1);
const half2_t* p_a_half2 = c_style_pointer_cast<const half2_t*>(&a);
const half2_t* p_b0_half2 = c_style_pointer_cast<const half2_t*>(&b0);
const half2_t* p_b1_half2 = c_style_pointer_cast<const half2_t*>(&b1);
// do dot2 two times
asm volatile("\n \
@@ -114,11 +117,11 @@ __device__ void amd_assembly_outer_product_1x4(half4_t a,
float& c3)
{
// TODO remove pointer casting
const half2_t* p_a_half2 = reinterpret_cast<const half2_t*>(&a);
const half2_t* p_b0_half2 = reinterpret_cast<const half2_t*>(&b0);
const half2_t* p_b1_half2 = reinterpret_cast<const half2_t*>(&b1);
const half2_t* p_b2_half2 = reinterpret_cast<const half2_t*>(&b2);
const half2_t* p_b3_half2 = reinterpret_cast<const half2_t*>(&b3);
const half2_t* p_a_half2 = c_style_pointer_cast<const half2_t*>(&a);
const half2_t* p_b0_half2 = c_style_pointer_cast<const half2_t*>(&b0);
const half2_t* p_b1_half2 = c_style_pointer_cast<const half2_t*>(&b1);
const half2_t* p_b2_half2 = c_style_pointer_cast<const half2_t*>(&b2);
const half2_t* p_b3_half2 = c_style_pointer_cast<const half2_t*>(&b3);
// do dot2 two times
asm volatile("\n \
@@ -160,11 +163,11 @@ __device__ void amd_assembly_outer_product_1x4(half8_t a,
{
// TODO remove pointer casting
const half4_t* p_a_half4 = reinterpret_cast<const half4_t*>(&a);
const half4_t* p_b0_half4 = reinterpret_cast<const half4_t*>(&b0);
const half4_t* p_b1_half4 = reinterpret_cast<const half4_t*>(&b1);
const half4_t* p_b2_half4 = reinterpret_cast<const half4_t*>(&b2);
const half4_t* p_b3_half4 = reinterpret_cast<const half4_t*>(&b3);
const half4_t* p_a_half4 = c_style_pointer_cast<const half4_t*>(&a);
const half4_t* p_b0_half4 = c_style_pointer_cast<const half4_t*>(&b0);
const half4_t* p_b1_half4 = c_style_pointer_cast<const half4_t*>(&b1);
const half4_t* p_b2_half4 = c_style_pointer_cast<const half4_t*>(&b2);
const half4_t* p_b3_half4 = c_style_pointer_cast<const half4_t*>(&b3);
amd_assembly_outer_product_1x4(
p_a_half4[0], p_b0_half4[0], p_b1_half4[0], p_b2_half4[0], p_b3_half4[0], c0, c1, c2, c3);
@@ -184,11 +187,11 @@ __device__ void amd_assembly_outer_product_1x4(half16_t a,
float& c3)
{
// TODO remove pointer casting
const half8_t* p_a_half8 = reinterpret_cast<const half8_t*>(&a);
const half8_t* p_b0_half8 = reinterpret_cast<const half8_t*>(&b0);
const half8_t* p_b1_half8 = reinterpret_cast<const half8_t*>(&b1);
const half8_t* p_b2_half8 = reinterpret_cast<const half8_t*>(&b2);
const half8_t* p_b3_half8 = reinterpret_cast<const half8_t*>(&b3);
const half8_t* p_a_half8 = c_style_pointer_cast<const half8_t*>(&a);
const half8_t* p_b0_half8 = c_style_pointer_cast<const half8_t*>(&b0);
const half8_t* p_b1_half8 = c_style_pointer_cast<const half8_t*>(&b1);
const half8_t* p_b2_half8 = c_style_pointer_cast<const half8_t*>(&b2);
const half8_t* p_b3_half8 = c_style_pointer_cast<const half8_t*>(&b3);
amd_assembly_outer_product_1x4(
p_a_half8[0], p_b0_half8[0], p_b1_half8[0], p_b2_half8[0], p_b3_half8[0], c0, c1, c2, c3);

22
composable_kernel/include/utility/c_style_pointer_cast.hpp Normal file
View File

@@ -0,0 +1,22 @@
#ifndef CK_C_STYLE_POINTER_CAST_HPP
#define CK_C_STYLE_POINTER_CAST_HPP
#include "type.hpp"
#include "enable_if.hpp"
namespace ck {
template <typename PY,
typename PX,
typename enable_if<is_pointer_v<PY> && is_pointer_v<PX>, bool>::type = false>
__host__ __device__ PY c_style_pointer_cast(PX p_x)
{
#pragma clang diagnostic push
#pragma clang diagnostic ignored "-Wold-style-cast"
#pragma clang diagnostic ignored "-Wcast-align"
return (PY)p_x; // NOLINT(old-style-cast, cast-align)
#pragma clang diagnostic pop
}
} // namespace ck
#endif

17
composable_kernel/include/utility/common_header.hpp
View File

@@ -7,13 +7,14 @@
#include "statically_indexed_array.hpp"
#include "container_element_picker.hpp"
#include "multi_index.hpp"
#include "data_type_enum.hpp"
#include "data_type.hpp"
#include "data_type_helper.hpp"
#include "data_type_enum.hpp"
#include "data_type_enum_helper.hpp"
#include "functional.hpp"
#include "functional2.hpp"
#include "functional3.hpp"
#include "functional4.hpp"
#include "enable_if.hpp"
#include "integral_constant.hpp"
#include "math.hpp"
#include "number.hpp"
@@ -23,21 +24,21 @@
#include "tuple.hpp"
#include "tuple_helper.hpp"
#include "type.hpp"
#include "utility.hpp"
#include "magic_division.hpp"
#include "amd_buffer_addressing_v2.hpp"
#include "utility.hpp"
#include "c_style_pointer_cast.hpp"
#include "amd_address_space.hpp"
#include "amd_buffer_addressing.hpp"
#include "static_buffer.hpp"
#include "dynamic_buffer.hpp"
#include "inner_product.hpp"
// TODO: remove this
#if CK_USE_AMD_INLINE_ASM
#include "amd_inline_asm.hpp"
#endif
#if CK_USE_AMD_DLOP
#include "amd_dlop.hpp"
#endif
#if CK_USE_AMD_XDLOPS
#include "amd_xdlops.hpp"
#endif

42
composable_kernel/include/utility/config.hpp
View File

@@ -7,19 +7,14 @@
#endif
#include "bfloat16_dev.hpp"
// address space for kernel parameter
// "Constant" address space for kernel parameter
#define CONSTANT __attribute__((address_space(4)))
// GPU target
// should enable one and only one GPU target
#if !(defined(CK_AMD_GPU_GFX803) || defined(CK_AMD_GPU_GFX900) || defined(CK_AMD_GPU_GFX906) || \
defined(CK_AMD_GPU_GFX908) || defined(CK_AMD_GPU_GFX90A) || defined(CK_AMD_GPU_GFX1030))
#error Need to define a single GPU target
#endif
// HIP version
#ifndef CK_HIP_VERSION_FLAT
#define CK_HIP_VERSION_FLAT 0
#error Need to define (only) one GPU target
#endif
// launch bounds
@@ -38,6 +33,16 @@
#define CK_BUFFER_RESOURCE_3RD_DWORD 0x31014000
#endif
// FMA instruction
#if defined(CK_AMD_GPU_GFX803) || defined(CK_AMD_GPU_GFX900)
#define CK_USE_AMD_V_MAC_F32
#elif defined(CK_AMD_GPU_GFX906) || defined(CK_AMD_GPU_GFX908) || defined(CK_AMD_GPU_GFX90a) || \
defined(CK_AMD_GPU_GFX1030)
#define CK_USE_AMD_V_FMAC_F32
#define CK_USE_AMD_V_DOT2_F32_F16
#define CK_USE_AMD_V_DOT4_I32_I8
#endif
// multi index
#define CK_USE_DYNAMICALLY_INDEXED_MULTI_INDEX 0
@@ -46,13 +51,9 @@
#define CK_USE_AMD_INLINE_ASM 1
#endif
// AMD DLOPS
#ifndef CK_USE_AMD_DLOP
#define CK_USE_AMD_DLOP 1
#endif
#ifndef CK_USE_AMD_DLOP_INLINE_ASM
#define CK_USE_AMD_DLOP_INLINE_ASM 1
// AMD inner product (DLOP)
#ifndef CK_USE_AMD_INNER_PRODUCT_INLINE_ASM
#define CK_USE_AMD_INNER_PRODUCT_INLINE_ASM 1
#endif
// AMD buffer addressing
@@ -99,8 +100,8 @@
// hack for forcing register to keep idx_diff_low_const in SGPR. idx_diff_low_const must be
// thread-invariant, otherwise it's a bug
// TODO: separate index calculation into "compile-time", "global", "block", "wave", "thread"
#ifndef CK_HACK_DYNAMIC_MERGE_CALCULATE_IDX_DIFF_LOW_CONST_USE_AMD_GCN_READ_FIRST_LANE
#define CK_HACK_DYNAMIC_MERGE_CALCULATE_IDX_DIFF_LOW_CONST_USE_AMD_GCN_READ_FIRST_LANE 0
#ifndef CK_HACK_MERGE_CALCULATE_IDX_DIFF_LOW_CONST_USE_AMD_GCN_READ_FIRST_LANE
#define CK_HACK_MERGE_CALCULATE_IDX_DIFF_LOW_CONST_USE_AMD_GCN_READ_FIRST_LANE 0
#endif
// workaround for compiler crash when compiling recursive lambda
@@ -120,15 +121,6 @@
namespace ck {
enum AddressSpaceEnum_t
{
Generic,
Global,
Lds,
Sgpr,
Vgpr
};
enum InMemoryDataOperationEnum_t
{
Set,

5
composable_kernel/include/utility/data_type_enum.hpp
View File

@@ -3,8 +3,7 @@
namespace ck {
// this enumerate should be synchronized with include/miopen.h
typedef enum
enum DataTypeEnum_t
{
Half = 0,
Float = 1,
@@ -14,7 +13,7 @@ typedef enum
BFloat16 = 5,
Double = 6,
Unknown = 100,
} DataTypeEnum_t;
};
} // namespace ck
#endif

4
composable_kernel/include/utility/data_type_helper.hpp → composable_kernel/include/utility/data_type_enum_helper.hpp
View File

@@ -1,5 +1,5 @@
#ifndef CK_DATA_TYPE_HELPER_HPP
#define CK_DATA_TYPE_HELPER_HPP
#ifndef CK_DATA_TYPE_ENUM_HELPER_HPP
#define CK_DATA_TYPE_ENUM_HELPER_HPP
#include "data_type.hpp"
#include "data_type_enum.hpp"

134
composable_kernel/include/utility/dynamic_buffer.hpp
View File

@@ -1,38 +1,49 @@
#ifndef CK_DYNAMIC_BUFFER_HPP
#define CK_DYNAMIC_BUFFER_HPP
#ifndef CK_BUFFER_HPP
#define CK_BUFFER_HPP
#include "amd_buffer_addressing.hpp"
#include "c_style_pointer_cast.hpp"
#include "enable_if.hpp"
namespace ck {
#include "amd_buffer_addressing_v2.hpp"
template <AddressSpaceEnum_t BufferAddressSpace, typename T, typename ElementSpaceSize>
template <AddressSpaceEnum_t BufferAddressSpace,
typename T,
typename ElementSpaceSize,
bool InvalidElementUseNumericalZeroValue>
struct DynamicBuffer
{
using type = T;
T* p_data_;
ElementSpaceSize element_space_size_;
T invalid_element_value_ = T{0};
__host__ __device__ constexpr DynamicBuffer(T* p_data, ElementSpaceSize element_space_size)
: p_data_{p_data}, element_space_size_{element_space_size}
{
}
__host__ __device__ constexpr DynamicBuffer(T* p_data,
ElementSpaceSize element_space_size,
T invalid_element_value)
: p_data_{p_data},
element_space_size_{element_space_size},
invalid_element_value_{invalid_element_value}
{
}
__host__ __device__ static constexpr AddressSpaceEnum_t GetAddressSpace()
{
return BufferAddressSpace;
}
__host__ __device__ constexpr const T& operator[](index_t i) const { return p_data_[i]; }
__host__ __device__ constexpr T& operator()(index_t i) { return p_data_[i]; }
template <typename X,
typename std::enable_if<
typename enable_if<
is_same<typename scalar_type<remove_cv_t<remove_reference_t<X>>>::type,
typename scalar_type<remove_cv_t<remove_reference_t<T>>>::type>::value,
bool>::type = false>
__host__ __device__ constexpr auto Get(index_t i, bool is_valid_offset) const
__host__ __device__ constexpr auto Get(index_t i, bool is_valid_element) const
{
// X contains multiple T
constexpr index_t scalar_per_t_vector =
@@ -44,29 +55,50 @@ struct DynamicBuffer
static_assert(scalar_per_x_vector % scalar_per_t_vector == 0,
"wrong! X need to be multiple T");
constexpr index_t t_per_x = scalar_per_x_vector / scalar_per_t_vector;
if constexpr(GetAddressSpace() == AddressSpaceEnum_t::Global)
{
#if CK_USE_AMD_BUFFER_ADDRESSING
return amd_buffer_load_v2<remove_cv_t<remove_reference_t<T>>, t_per_x>(
p_data_, i, is_valid_offset, element_space_size_);
bool constexpr use_amd_buffer_addressing = true;
#else
return is_valid_offset ? *reinterpret_cast<const X*>(&p_data_[i]) : X{0};
bool constexpr use_amd_buffer_addressing = false;
#endif
if constexpr(GetAddressSpace() == AddressSpaceEnum_t::Global && use_amd_buffer_addressing)
{
constexpr index_t t_per_x = scalar_per_x_vector / scalar_per_t_vector;
if constexpr(InvalidElementUseNumericalZeroValue)
{
return amd_buffer_load_invalid_element_return_return_zero<
remove_cv_t<remove_reference_t<T>>,
t_per_x>(p_data_, i, is_valid_element, element_space_size_);
}
else
{
return amd_buffer_load_invalid_element_return_customized_value<
remove_cv_t<remove_reference_t<T>>,
t_per_x>(
p_data_, i, is_valid_element, element_space_size_, invalid_element_value_);
}
}
else
{
return is_valid_offset ? *reinterpret_cast<const X*>(&p_data_[i]) : X{0};
if constexpr(InvalidElementUseNumericalZeroValue)
{
return is_valid_element ? *c_style_pointer_cast<const X*>(&p_data_[i]) : X{0};
}
else
{
return is_valid_element ? *c_style_pointer_cast<const X*>(&p_data_[i])
: X{invalid_element_value_};
}
}
}
template <typename X,
typename std::enable_if<
typename enable_if<
is_same<typename scalar_type<remove_cv_t<remove_reference_t<X>>>::type,
typename scalar_type<remove_cv_t<remove_reference_t<T>>>::type>::value,
bool>::type = false>
__host__ __device__ void Set(index_t i, bool is_valid_offset, const X& x)
__host__ __device__ void Set(index_t i, bool is_valid_element, const X& x)
{
// X contains multiple T
constexpr index_t scalar_per_t_vector =
@@ -78,26 +110,26 @@ struct DynamicBuffer
static_assert(scalar_per_x_vector % scalar_per_t_vector == 0,
"wrong! X need to be multiple T");
constexpr index_t t_per_x = scalar_per_x_vector / scalar_per_t_vector;
if constexpr(GetAddressSpace() == AddressSpaceEnum_t::Global)
{
#if CK_USE_AMD_BUFFER_ADDRESSING
amd_buffer_store_v2<remove_cv_t<remove_reference_t<T>>, t_per_x>(
x, p_data_, i, is_valid_offset, element_space_size_);
constexpr index_t t_per_x = scalar_per_x_vector / scalar_per_t_vector;
amd_buffer_store<remove_cv_t<remove_reference_t<T>>, t_per_x>(
x, p_data_, i, is_valid_element, element_space_size_);
#else
if(is_valid_offset)
if(is_valid_element)
{
*reinterpret_cast<X*>(&p_data_[i]) = x;
*c_style_pointer_cast<X*>(&p_data_[i]) = x;
}
#endif
}
else if constexpr(GetAddressSpace() == AddressSpaceEnum_t::Lds)
{
if(is_valid_offset)
if(is_valid_element)
{
#if !CK_WORKAROUND_SWDEV_XXXXXX_INT8_DS_WRITE_ISSUE
*reinterpret_cast<X*>(&p_data_[i]) = x;
*c_style_pointer_cast<X*>(&p_data_[i]) = x;
#else
// HACK: compiler would lower IR "store<i8, 16> address_space(3)" into
// inefficient
@@ -128,24 +160,24 @@ struct DynamicBuffer
{
// HACK: cast pointer of x is bad
// TODO: remove this after compiler fix
*reinterpret_cast<int8_t*>(&p_data_[i]) =
*reinterpret_cast<const int8_t*>(&x);
*c_style_pointer_cast<int8_t*>(&p_data_[i]) =
*c_style_pointer_cast<const int8_t*>(&x);
}
else if constexpr(is_same<remove_cv_t<remove_reference_t<T>>, int8_t>::value &&
is_same<remove_cv_t<remove_reference_t<X>>, int8x2_t>::value)
{
// HACK: cast pointer of x is bad
// TODO: remove this after compiler fix
*reinterpret_cast<int16_t*>(&p_data_[i]) =
*reinterpret_cast<const int16_t*>(&x);
*c_style_pointer_cast<int16_t*>(&p_data_[i]) =
*c_style_pointer_cast<const int16_t*>(&x);
}
else if constexpr(is_same<remove_cv_t<remove_reference_t<T>>, int8_t>::value &&
is_same<remove_cv_t<remove_reference_t<X>>, int8x4_t>::value)
{
// HACK: cast pointer of x is bad
// TODO: remove this after compiler fix
*reinterpret_cast<int32_t*>(&p_data_[i]) =
*reinterpret_cast<const int32_t*>(&x);
*c_style_pointer_cast<int32_t*>(&p_data_[i]) =
*c_style_pointer_cast<const int32_t*>(&x);
}
else if constexpr(is_same<remove_cv_t<remove_reference_t<T>>,
int8x4_t>::value &&
@@ -153,8 +185,8 @@ struct DynamicBuffer
{
// HACK: cast pointer of x is bad
// TODO: remove this after compiler fix
*reinterpret_cast<int32_t*>(&p_data_[i]) =
*reinterpret_cast<const int32_t*>(&x);
*c_style_pointer_cast<int32_t*>(&p_data_[i]) =
*c_style_pointer_cast<const int32_t*>(&x);
}
else if constexpr(is_same<remove_cv_t<remove_reference_t<T>>,
int8x8_t>::value &&
@@ -162,8 +194,8 @@ struct DynamicBuffer
{
// HACK: cast pointer of x is bad
// TODO: remove this after compiler fix
*reinterpret_cast<int32x2_t*>(&p_data_[i]) =
*reinterpret_cast<const int32x2_t*>(&x);
*c_style_pointer_cast<int32x2_t*>(&p_data_[i]) =
*c_style_pointer_cast<const int32x2_t*>(&x);
}
else if constexpr(is_same<remove_cv_t<remove_reference_t<T>>,
int8x16_t>::value &&
@@ -171,22 +203,22 @@ struct DynamicBuffer
{
// HACK: cast pointer of x is bad
// TODO: remove this after compiler fix
*reinterpret_cast<int32x4_t*>(&p_data_[i]) =
*reinterpret_cast<const int32x4_t*>(&x);
*c_style_pointer_cast<int32x4_t*>(&p_data_[i]) =
*c_style_pointer_cast<const int32x4_t*>(&x);
}
}
else
{
*reinterpret_cast<X*>(&p_data_[i]) = x;
*c_style_pointer_cast<X*>(&p_data_[i]) = x;
}
#endif
}
}
else
{
if(is_valid_offset)
if(is_valid_element)
{
*reinterpret_cast<X*>(&p_data_[i]) = x;
*c_style_pointer_cast<X*>(&p_data_[i]) = x;
}
}
}
@@ -196,12 +228,18 @@ struct DynamicBuffer
__host__ __device__ static constexpr bool IsDynamicBuffer() { return true; }
};
template <AddressSpaceEnum_t BufferAddressSpace = AddressSpaceEnum_t::Generic,
typename T,
typename ElementSpaceSize>
template <AddressSpaceEnum_t BufferAddressSpace, typename T, typename ElementSpaceSize>
__host__ __device__ constexpr auto make_dynamic_buffer(T* p, ElementSpaceSize element_space_size)
{
return DynamicBuffer<BufferAddressSpace, T, ElementSpaceSize>{p, element_space_size};
return DynamicBuffer<BufferAddressSpace, T, ElementSpaceSize, true>{p, element_space_size};
}
template <AddressSpaceEnum_t BufferAddressSpace, typename T, typename ElementSpaceSize>
__host__ __device__ constexpr auto
make_dynamic_buffer(T* p, ElementSpaceSize element_space_size, T invalid_element_value)
{
return DynamicBuffer<BufferAddressSpace, T, ElementSpaceSize, false>{
p, element_space_size, invalid_element_value};
}
} // namespace ck

13
composable_kernel/include/utility/enable_if.hpp Normal file
View File

@@ -0,0 +1,13 @@
#ifndef CK_ENABLE_IF_HPP
#define CK_ENABLE_IF_HPP
namespace ck {
template <bool B, typename T = void>
using enable_if = std::enable_if<B, T>;
template <bool B, typename T = void>
using enable_if_t = typename std::enable_if<B, T>::type;
} // namespace ck
#endif

207
composable_kernel/include/utility/inner_product.hpp Normal file
View File

@@ -0,0 +1,207 @@
#ifndef CK_INNER_PRODUCT_HPP
#define CK_INNER_PRODUCT_HPP
#include "data_type.hpp"
namespace ck {
template <typename TA, typename TB, typename TC>
__device__ void inner_product(const TA& a, const TB& b, TC& c);
template <>
__device__ void inner_product<float, float, float>(const float& a, const float& b, float& c)
{
#if CK_USE_AMD_INNER_PRODUCT_INLINE_ASM && defined(CK_USE_AMD_V_MAC_F32)
asm volatile("\n \
v_mac_f32 %0, %1, %2 \n \
"
: "=v"(c)
: "v"(a), "v"(b), "0"(c));
#elif CK_USE_AMD_INNER_PRODUCT_INLINE_ASM && defined(CK_USE_AMD_V_FMAC_F32)
asm volatile("\n \
v_fmac_f32 %0, %1, %2 \n \
"
: "=v"(c)
: "v"(a), "v"(b), "0"(c));
#else
c += a * b;
#endif
}
template <>
__device__ void
inner_product<float2_t, float2_t, float>(const float2_t& a, const float2_t& b, float& c)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
inner_product(vector_type<float, 2>{a}.AsType<float>()[I0],
vector_type<float, 2>{b}.AsType<float>()[I0],
c);
inner_product(vector_type<float, 2>{a}.AsType<float>()[I1],
vector_type<float, 2>{b}.AsType<float>()[I1],
c);
}
template <>
__device__ void
inner_product<float4_t, float4_t, float>(const float4_t& a, const float4_t& b, float& c)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
inner_product(vector_type<float, 4>{a}.AsType<float>()[I0],
vector_type<float, 4>{b}.AsType<float>()[I0],
c);
inner_product(vector_type<float, 4>{a}.AsType<float>()[I1],
vector_type<float, 4>{b}.AsType<float>()[I1],
c);
inner_product(vector_type<float, 4>{a}.AsType<float>()[I2],
vector_type<float, 4>{b}.AsType<float>()[I2],
c);
inner_product(vector_type<float, 4>{a}.AsType<float>()[I3],
vector_type<float, 4>{b}.AsType<float>()[I3],
c);
}
template <>
__device__ void inner_product<half2_t, half2_t, float>(const half2_t& a, const half2_t& b, float& c)
{
#if defined(CK_USE_AMD_V_DOT2_F32_F16)
#if CK_USE_AMD_INNER_PRODUCT_INLINE_ASM
asm volatile("\n \
v_dot2_f32_f16 %0, %1, %2, %0\n \
"
: "=v"(c)
: "v"(a), "v"(b), "0"(c));
#else
c = __builtin_amdgcn_sdot2(a, b, c, false);
#endif
#else
const auto convert = type_convert<int32_t>{};
const vector_type<half_t, 2> a_vector{a};
const vector_type<half_t, 2> b_vector{b};
static_for<0, 2, 1>{}([&](auto i) {
c += convert(a_vector.AsType<half_t>()[i]) * convert(b_vector.AsType<half_t>()[i]);
});
#endif
}
template <>
__device__ void inner_product<half4_t, half4_t, float>(const half4_t& a, const half4_t& b, float& c)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
inner_product(vector_type<half_t, 4>{a}.AsType<half2_t>()[I0],
vector_type<half_t, 4>{b}.AsType<half2_t>()[I0],
c);
inner_product(vector_type<half_t, 4>{a}.AsType<half2_t>()[I1],
vector_type<half_t, 4>{b}.AsType<half2_t>()[I1],
c);
}
template <>
__device__ void inner_product<half8_t, half8_t, float>(const half8_t& a, const half8_t& b, float& c)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
inner_product(vector_type<half_t, 8>{a}.AsType<half2_t>()[I0],
vector_type<half_t, 8>{b}.AsType<half2_t>()[I0],
c);
inner_product(vector_type<half_t, 8>{a}.AsType<half2_t>()[I1],
vector_type<half_t, 8>{b}.AsType<half2_t>()[I1],
c);
inner_product(vector_type<half_t, 8>{a}.AsType<half2_t>()[I2],
vector_type<half_t, 8>{b}.AsType<half2_t>()[I2],
c);
inner_product(vector_type<half_t, 8>{a}.AsType<half2_t>()[I3],
vector_type<half_t, 8>{b}.AsType<half2_t>()[I3],
c);
}
template <>
__device__ void
inner_product<int8x4_t, int8x4_t, int32_t>(const int8x4_t& a, const int8x4_t& b, int32_t& c)
{
#if defined(CK_USE_DOT4_I32_I8)
#if CK_USE_AMD_INNER_PRODUCT_INLINE_ASM
asm volatile("\n \
v_dot4_i32_i8 %0, %1, %2, %0\n \
"
: "=v"(c)
: "v"(as_type<int32_t>(a)), "v"(as_type<int32_t>(b)), "0"(c));
#else
c = __builtin_amdgcn_sdot4(as_type<int32_t>(a), as_type<int32_t>(b), c, false);
#endif
#else
const auto convert = type_convert<int32_t>{};
const vector_type<int8_t, 4> a_vector{a};
const vector_type<int8_t, 4> b_vector{b};
static_for<0, 4, 1>{}([&](auto i) {
c += convert(a_vector.AsType<int8_t>()[i]) * convert(b_vector.AsType<int8_t>()[i]);
});
#endif
}
template <>
__device__ void
inner_product<int8x8_t, int8x8_t, int32_t>(const int8x8_t& a, const int8x8_t& b, int32_t& c)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
inner_product(vector_type<int8_t, 8>{a}.AsType<int8x4_t>()[I0],
vector_type<int8_t, 8>{b}.AsType<int8x4_t>()[I0],
c);
inner_product(vector_type<int8_t, 8>{a}.AsType<int8x4_t>()[I1],
vector_type<int8_t, 8>{b}.AsType<int8x4_t>()[I1],
c);
}
template <>
__device__ void
inner_product<int8x16_t, int8x16_t, int32_t>(const int8x16_t& a, const int8x16_t& b, int32_t& c)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
inner_product(vector_type<int8_t, 16>{a}.AsType<int8x4_t>()[I0],
vector_type<int8_t, 16>{b}.AsType<int8x4_t>()[I0],
c);
inner_product(vector_type<int8_t, 16>{a}.AsType<int8x4_t>()[I1],
vector_type<int8_t, 16>{b}.AsType<int8x4_t>()[I1],
c);
inner_product(vector_type<int8_t, 16>{a}.AsType<int8x4_t>()[I2],
vector_type<int8_t, 16>{b}.AsType<int8x4_t>()[I2],
c);
inner_product(vector_type<int8_t, 16>{a}.AsType<int8x4_t>()[I3],
vector_type<int8_t, 16>{b}.AsType<int8x4_t>()[I3],
c);
}
} // namespace ck
#endif

15
composable_kernel/include/utility/math.hpp
View File

@@ -5,6 +5,7 @@
#include "integral_constant.hpp"
#include "number.hpp"
#include "type.hpp"
#include "enable_if.hpp"
namespace ck {
namespace math {
@@ -27,13 +28,7 @@ struct minus
__host__ __device__ constexpr T operator()(T a, T b) const { return a - b; }
};
template <typename T>
struct multiplies
{
__host__ __device__ constexpr T operator()(T a, T b) const { return a * b; }
};
struct multiplies_v2
{
template <typename A, typename B>
__host__ __device__ constexpr auto operator()(const A& a, const B& b) const
@@ -184,9 +179,7 @@ __host__ __device__ constexpr auto gcd(Number<X>, Number<Y>)
return Number<r>{};
}
template <typename X,
typename... Ys,
typename std::enable_if<sizeof...(Ys) >= 2, bool>::type = false>
template <typename X, typename... Ys, typename enable_if<sizeof...(Ys) >= 2, bool>::type = false>
__host__ __device__ constexpr auto gcd(X x, Ys... ys)
{
return gcd(x, gcd(ys...));
@@ -199,9 +192,7 @@ __host__ __device__ constexpr auto lcm(X x, Y y)
return (x * y) / gcd(x, y);
}
template <typename X,
typename... Ys,
typename std::enable_if<sizeof...(Ys) >= 2, bool>::type = false>
template <typename X, typename... Ys, typename enable_if<sizeof...(Ys) >= 2, bool>::type = false>
__host__ __device__ constexpr auto lcm(X x, Ys... ys)
{
return lcm(x, lcm(ys...));

48
composable_kernel/include/utility/print.hpp
View File

@@ -11,59 +11,11 @@ namespace ck {
template <typename T>
__host__ __device__ void print_array(const char* s, T a)
{
using data_type = decltype(a.At(Number<0>{}));
constexpr index_t nsize = a.Size();
#if 0
if constexpr(is_same<data_type, uint32_t>{})
{
printf("%s size %u, {", s, nsize);
static_for<0, nsize, 1>{}([&a](auto i) constexpr { printf("%u, ", uint32_t{a[i]}); });
printf("}\n");
}
else if constexpr(is_same<data_type, int32_t>{})
{
printf("%s size %d, {", s, nsize);
static_for<0, nsize, 1>{}([&a](auto i) constexpr { printf("%d, ", int32_t{a[i]}); });
printf("}\n");
}
else if constexpr(is_same<data_type, bool>{})
{
printf("%s size %d, {", s, nsize);
static_for<0, nsize, 1>{}([&a](auto i) constexpr { printf("%d, ", bool{a[i]}); });
printf("}\n");
}
#else
printf("%s size %d, {", s, nsize);
static_for<0, nsize, 1>{}([&a](auto i) constexpr { printf("%d, ", int32_t{a[i]}); });
printf("}\n");
#endif
}
template <typename T>
__host__ __device__ void print_array_v2(const char* s, T a)
{
using data_type = decltype(a.At(Number<0>{}));
constexpr index_t nsize = a.Size();
#if 0
if constexpr(is_same<data_type, uint32_t>{})
{
printf("%s size %u, {", s, nsize);
static_for<0, nsize, 1>{}([&a](auto i) constexpr { printf("[%u] %u, ", i.value, a[i]); });
printf("}\n");
}
else if constexpr(is_same<data_type, int32_t>{})
{
printf("%s size %d, {", s, nsize);
static_for<0, nsize, 1>{}([&a](auto i) constexpr { printf("[%d] %d, ", i.value, a[i]); });
printf("}\n");
}
#else
printf("%s size %d, {", s, nsize);
static_for<0, nsize, 1>{}([&a](auto i) constexpr { printf("[%d] %d, ", i.value, a[i]); });
printf("}\n");
#endif
}
} // namespace ck

2
composable_kernel/include/utility/sequence.hpp
View File

@@ -685,8 +685,6 @@ __host__ __device__ constexpr auto operator+(Number<Y>, Sequence<Xs...>)
template <index_t Y, index_t... Xs>
__host__ __device__ constexpr auto operator-(Number<Y>, Sequence<Xs...>)
{
constexpr auto seq_x = Sequence<Xs...>{};
return Sequence<(Y - Xs)...>{};
}

46
composable_kernel/include/utility/static_buffer.hpp
View File

@@ -5,30 +5,66 @@
namespace ck {
template <AddressSpaceEnum_t BufferAddressSpace, typename T, index_t N>
template <AddressSpaceEnum_t BufferAddressSpace,
typename T,
index_t N,
bool InvalidElementUseNumericalZeroValue>
struct StaticBuffer : public StaticallyIndexedArray<T, N>
{
using type = T;
using base = StaticallyIndexedArray<T, N>;
T invalid_element_value_ = T{0};
__host__ __device__ constexpr StaticBuffer() : base{} {}
__host__ __device__ constexpr StaticBuffer(T invalid_element_value)
: base{}, invalid_element_value_{invalid_element_value}
{
}
__host__ __device__ static constexpr AddressSpaceEnum_t GetAddressSpace()
{
return BufferAddressSpace;
}
template <index_t I>
__host__ __device__ constexpr auto Get(Number<I> i, bool is_valid_element) const
{
if constexpr(InvalidElementUseNumericalZeroValue)
{
return is_valid_element ? At(i) : T{0};
}
else
{
return is_valid_element ? At(i) : invalid_element_value_;
}
}
template <index_t I>
__host__ __device__ void Set(Number<I> i, bool is_valid_element, const T& x)
{
if(is_valid_element)
{
At(i) = x;
}
}
__host__ __device__ static constexpr bool IsStaticBuffer() { return true; }
__host__ __device__ static constexpr bool IsDynamicBuffer() { return false; }
};
template <AddressSpaceEnum_t BufferAddressSpace = AddressSpaceEnum_t::Generic,
typename T,
index_t N>
template <AddressSpaceEnum_t BufferAddressSpace, typename T, index_t N>
__host__ __device__ constexpr auto make_static_buffer(Number<N>)
{
return StaticBuffer<BufferAddressSpace, T, N>{};
return StaticBuffer<BufferAddressSpace, T, N, true>{};
}
template <AddressSpaceEnum_t BufferAddressSpace, typename T, index_t N>
__host__ __device__ constexpr auto make_static_buffer(Number<N>, T invalid_element_value)
{
return StaticBuffer<BufferAddressSpace, T, N, false>{invalid_element_value};
}
} // namespace ck

29
composable_kernel/include/utility/tuple.hpp
View File

@@ -4,6 +4,7 @@
#include "integral_constant.hpp"
#include "sequence.hpp"
#include "type.hpp"
#include "enable_if.hpp"
namespace ck {
@@ -20,10 +21,9 @@ struct TupleElement
{
__host__ __device__ constexpr TupleElement() = default;
template <
typename T,
typename std::enable_if<!is_same<remove_reference_t<remove_cv_t<T>>, TupleElement>::value,
bool>::type = false>
template <typename T,
typename enable_if<!is_same<remove_reference_t<remove_cv_t<T>>, TupleElement>::value,
bool>::type = false>
__host__ __device__ constexpr TupleElement(T&& v) : mData(std::forward<T>(v))
{
}
@@ -58,17 +58,16 @@ struct TupleImpl<Sequence<Is...>, Xs...> : TupleElement<TupleElementKey<Is>, Xs>
{
__host__ __device__ constexpr TupleImpl() = default;
template <
typename Y,
typename std::enable_if<sizeof...(Is) == 1 && sizeof...(Xs) == 1 &&
!is_same<remove_reference_t<remove_cv_t<Y>>, TupleImpl>::value,
bool>::type = false>
template <typename Y,
typename enable_if<sizeof...(Is) == 1 && sizeof...(Xs) == 1 &&
!is_same<remove_reference_t<remove_cv_t<Y>>, TupleImpl>::value,
bool>::type = false>
__host__ __device__ constexpr TupleImpl(Y&& y)
: TupleElement<TupleElementKey<Is>, Xs>(std::forward<Y>(y))...
{
}
template <typename... Ys, typename std::enable_if<sizeof...(Ys) >= 2, bool>::type = false>
template <typename... Ys, typename enable_if<sizeof...(Ys) >= 2, bool>::type = false>
__host__ __device__ constexpr TupleImpl(Ys&&... ys)
: TupleElement<TupleElementKey<Is>, Xs>(std::forward<Ys>(ys))...
{
@@ -102,16 +101,16 @@ struct Tuple : detail::TupleImpl<typename arithmetic_sequence_gen<0, sizeof...(X
__host__ __device__ constexpr Tuple() = default;
template <typename Y,
typename std::enable_if<
sizeof...(Xs) == 1 && !is_same<remove_reference_t<remove_cv_t<Y>>, Tuple>::value,
bool>::type = false>
typename enable_if<sizeof...(Xs) == 1 &&
!is_same<remove_reference_t<remove_cv_t<Y>>, Tuple>::value,
bool>::type = false>
__host__ __device__ constexpr Tuple(Y&& y) : base(std::forward<Y>(y))
{
}
template <typename... Ys,
typename std::enable_if<sizeof...(Ys) == sizeof...(Xs) && sizeof...(Ys) >= 2,
bool>::type = false>
typename enable_if<sizeof...(Ys) == sizeof...(Xs) && sizeof...(Ys) >= 2, bool>::type =
false>
__host__ __device__ constexpr Tuple(Ys&&... ys) : base(std::forward<Ys>(ys)...)
{
}

10
composable_kernel/include/utility/type.hpp
View File

@@ -2,6 +2,7 @@
#define CK_TYPE_HPP
#include "integral_constant.hpp"
#include "enable_if.hpp"
namespace ck {
@@ -22,10 +23,7 @@ template <typename T>
using remove_cv_t = typename std::remove_cv<T>::type;
template <typename T>
constexpr std::remove_reference_t<T>&& move(T&& t) noexcept
{
return static_cast<typename std::remove_reference<T>::type&&>(t);
}
inline constexpr bool is_pointer_v = std::is_pointer<T>::value;
template <typename T>
struct is_known_at_compile_time;
@@ -42,9 +40,7 @@ struct is_known_at_compile_time<integral_constant<T, X>>
static constexpr bool value = true;
};
template <typename Y,
typename X,
typename std::enable_if<sizeof(X) == sizeof(Y), bool>::type = false>
template <typename Y, typename X, typename enable_if<sizeof(X) == sizeof(Y), bool>::type = false>
__host__ __device__ constexpr Y as_type(X x)
{
union AsType

370
composable_kernel/src/kernel_wrapper/convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw.cpp Normal file
View File

@@ -0,0 +1,370 @@
#include "common_header.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "gridwise_gemm_dlops_v1r2.hpp"
#include "transform_forward_convolution_into_gemm_v4r4_nchw_kcyx_nkhw.hpp"
using namespace ck;
constexpr DataTypeEnum_t ABDataTypeEnum = static_cast<DataTypeEnum_t>(CK_PARAM_ABDataTypeEnum);
constexpr DataTypeEnum_t AccDataTypeEnum = static_cast<DataTypeEnum_t>(CK_PARAM_AccDataTypeEnum);
constexpr DataTypeEnum_t CDataTypeEnum = static_cast<DataTypeEnum_t>(CK_PARAM_CDataTypeEnum);
using FloatAB = typename get_datatype_from_enum<ABDataTypeEnum>::type;
using FloatAcc = typename get_datatype_from_enum<AccDataTypeEnum>::type;
using FloatC = typename get_datatype_from_enum<CDataTypeEnum>::type;
constexpr index_t BlockSize = CK_PARAM_BlockSize;
constexpr index_t MPerBlock = CK_PARAM_MPerBlock;
constexpr index_t NPerBlock = CK_PARAM_NPerBlock;
constexpr index_t KPerBlock = CK_PARAM_KPerBlock;
constexpr index_t M1PerThread = CK_PARAM_M1PerThread;
constexpr index_t N1PerThread = CK_PARAM_N1PerThread;
constexpr index_t KPerThread = CK_PARAM_KPerThread;
constexpr index_t M1N1ThreadClusterM10 = CK_PARAM_M1N1ThreadClusterM10;
constexpr index_t M1N1ThreadClusterN10 = CK_PARAM_M1N1ThreadClusterN10;
constexpr index_t M1N1ThreadClusterM11 = CK_PARAM_M1N1ThreadClusterM11;
constexpr index_t M1N1ThreadClusterN11 = CK_PARAM_M1N1ThreadClusterN11;
using ABlockTransferThreadSliceLengths_K_M0_M1 =
Sequence<CK_PARAM_ABlockTransferThreadSliceLengths_K_M0_M1>;
using ABlockTransferThreadClusterLengths_K_M0_M1 =
Sequence<CK_PARAM_ABlockTransferThreadClusterLengths_K_M0_M1>;
using ABlockTransferThreadClusterArrangeOrder =
Sequence<CK_PARAM_ABlockTransferThreadClusterArrangeOrder>;
using ABlockTransferSrcAccessOrder = Sequence<CK_PARAM_ABlockTransferSrcAccessOrder>;
constexpr index_t ABlockTransferSrcVectorDim = CK_PARAM_ABlockTransferSrcVectorDim;
constexpr index_t ABlockTransferSrcScalarPerVector = CK_PARAM_ABlockTransferSrcScalarPerVector;
constexpr index_t ABlockTransferDstScalarPerVector_M1 =
CK_PARAM_ABlockTransferDstScalarPerVector_M1;
constexpr bool AThreadTransferSrcResetCoordinateAfterRun =
static_cast<bool>(CK_PARAM_AThreadTransferSrcResetCoordinateAfterRun);
using BBlockTransferThreadSliceLengths_K_N0_N1 =
Sequence<CK_PARAM_BBlockTransferThreadSliceLengths_K_N0_N1>;
using BBlockTransferThreadClusterLengths_K_N0_N1 =
Sequence<CK_PARAM_BBlockTransferThreadClusterLengths_K_N0_N1>;
using BBlockTransferThreadClusterArrangeOrder =
Sequence<CK_PARAM_BBlockTransferThreadClusterArrangeOrder>;
using BBlockTransferSrcAccessOrder = Sequence<CK_PARAM_BBlockTransferSrcAccessOrder>;
constexpr index_t BBlockTransferSrcVectorDim = CK_PARAM_BBlockTransferSrcVectorDim;
constexpr index_t BBlockTransferSrcScalarPerVector = CK_PARAM_BBlockTransferSrcScalarPerVector;
constexpr index_t BBlockTransferDstScalarPerVector_N1 =
CK_PARAM_BBlockTransferDstScalarPerVector_N1;
constexpr bool BThreadTransferSrcResetCoordinateAfterRun =
static_cast<bool>(CK_PARAM_BThreadTransferSrcResetCoordinateAfterRun);
using CThreadTransferSrcDstAccessOrder = Sequence<CK_PARAM_CThreadTransferSrcDstAccessOrder>;
constexpr index_t CThreadTransferSrcDstVectorDim = CK_PARAM_CThreadTransferSrcDstVectorDim;
constexpr index_t CThreadTransferDstScalarPerVector = CK_PARAM_CThreadTransferDstScalarPerVector;
constexpr bool HasMainKBlockLoop = static_cast<bool>(CK_PARAM_HAS_MAIN_KBLOCK_LOOP);
constexpr bool HasDoubleTailKBlockLoop = static_cast<bool>(CK_PARAM_HAS_DOUBLE_TAIL_KBLOCK_LOOP);
extern "C" __global__ void convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw_prepare(
int n,
int c,
int hi,
int wi,
int k,
int y,
int x,
int convStrideH,
int convStrideW,
int convDilationY,
int convDilationX,
int leftPadH,
int leftPadW,
int rightPadH,
int rightPadW,
void* p_a_k_m0_m1_grid_desc,
void* p_b_k_n0_n1_grid_desc,
void* p_c_m0_m10_m11_n0_n10_n11_grid_desc,
void* p_c_blockid_to_m0_n0_block_cluster_adaptor)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
const index_t ho = (hi + leftPadH + rightPadH - convDilationY * (y - 1) - 1) / convStrideH + 1;
const index_t wo = (wi + leftPadW + rightPadW - convDilationX * (x - 1) - 1) / convStrideW + 1;
const auto in_n_c_hi_wi_desc = make_naive_tensor_descriptor_packed(make_tuple(n, c, hi, wi));
const auto wei_k_c_y_x_desc = make_naive_tensor_descriptor_packed(make_tuple(k, c, y, x));
const auto out_n_k_ho_wo_desc = make_naive_tensor_descriptor_packed(make_tuple(n, k, ho, wo));
const auto descs = transform_forward_convolution_into_gemm_v4r4_nchw_kcyx_nkhw_pad(
wei_k_c_y_x_desc,
in_n_c_hi_wi_desc,
out_n_k_ho_wo_desc,
make_tuple(convStrideH, convStrideW),
make_tuple(convDilationY, convDilationX),
make_tuple(leftPadH, leftPadW),
make_tuple(rightPadH, rightPadW));
const auto a_k_m_grid_desc = descs[I0];
const auto b_k_n_grid_desc = descs[I1];
const auto c_m_n_grid_desc = descs[I2];
using AKMGridDesc = decltype(a_k_m_grid_desc);
using BKNGridDesc = decltype(b_k_n_grid_desc);
using CMNGridDesc = decltype(c_m_n_grid_desc);
using AGridStepHacks = decltype(make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{})));
using BGridStepHacks =
decltype(make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{})));
using CGridStepHacks = decltype(make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{})));
using AGridMoveSliceWindowStepHacks = Sequence<0, 0, 0, 0, 0>;
using BGridMoveSliceWindowStepHacks = Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0>;
using GridwiseGemm =
GridwiseGemmDlops_km_kn_mn_v1r2<BlockSize,
FloatAB,
FloatAcc,
FloatC,
InMemoryDataOperationEnum_t::Set, /* ToDo tunable */
AKMGridDesc,
BKNGridDesc,
CMNGridDesc,
MPerBlock,
NPerBlock,
KPerBlock,
M1PerThread,
N1PerThread,
KPerThread,
M1N1ThreadClusterM10,
M1N1ThreadClusterN10,
M1N1ThreadClusterM11,
M1N1ThreadClusterN11,
ABlockTransferThreadSliceLengths_K_M0_M1,
ABlockTransferThreadClusterLengths_K_M0_M1,
ABlockTransferThreadClusterArrangeOrder,
ABlockTransferSrcAccessOrder,
ABlockTransferSrcVectorDim,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_M1,
AThreadTransferSrcResetCoordinateAfterRun,
BBlockTransferThreadSliceLengths_K_N0_N1,
BBlockTransferThreadClusterLengths_K_N0_N1,
BBlockTransferThreadClusterArrangeOrder,
BBlockTransferSrcAccessOrder,
BBlockTransferSrcVectorDim,
BBlockTransferSrcScalarPerVector,
BBlockTransferDstScalarPerVector_N1,
BThreadTransferSrcResetCoordinateAfterRun,
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
AGridStepHacks,
BGridStepHacks,
CGridStepHacks,
AGridMoveSliceWindowStepHacks,
BGridMoveSliceWindowStepHacks>;
auto a_k_m0_m1_grid_desc = GridwiseGemm::MakeAKM0M1GridDescriptor(a_k_m_grid_desc);
auto b_k_n0_n1_grid_desc = GridwiseGemm::MakeBKN0N1GridDescriptor(b_k_n_grid_desc);
auto c_m0_m10_m11_n0_n10_n11_grid_desc =
GridwiseGemm::MakeCM0M10M11N0N10N11GridDescriptor(c_m_n_grid_desc);
auto c_blockid_to_m0_n0_block_cluster_adaptor =
GridwiseGemm::MakeCBlockIdToM0N0BlockClusterAdaptor(c_m_n_grid_desc);
if(hipThreadIdx_x == 0)
{
*static_cast<decltype(a_k_m0_m1_grid_desc)*>(p_a_k_m0_m1_grid_desc) = a_k_m0_m1_grid_desc;
*static_cast<decltype(b_k_n0_n1_grid_desc)*>(p_b_k_n0_n1_grid_desc) = b_k_n0_n1_grid_desc;
*static_cast<decltype(c_m0_m10_m11_n0_n10_n11_grid_desc)*>(
p_c_m0_m10_m11_n0_n10_n11_grid_desc) = c_m0_m10_m11_n0_n10_n11_grid_desc;
*static_cast<decltype(c_blockid_to_m0_n0_block_cluster_adaptor)*>(
p_c_blockid_to_m0_n0_block_cluster_adaptor) = c_blockid_to_m0_n0_block_cluster_adaptor;
};
};
extern "C" __global__ void
#if CK_USE_LAUNCH_BOUNDS
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif
convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw(
const FloatAB* __restrict__ p_a_grid,
const FloatAB* __restrict__ p_b_grid,
FloatC* __restrict__ p_c_grid,
const void CONSTANT* p_a_k_m0_m1_grid_desc,
const void CONSTANT* p_b_k_n0_n1_grid_desc,
const void CONSTANT* p_c_m0_m10_m11_n0_n10_n11_grid_desc,
const void CONSTANT* p_c_blockid_to_m0_n0_block_cluster_adaptor)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto in_n_c_hi_wi_desc =
make_naive_tensor_descriptor_packed(make_tuple(256, 256, 28, 28));
constexpr auto wei_k_c_y_x_desc =
make_naive_tensor_descriptor_packed(make_tuple(256, 256, 3, 3));
constexpr auto out_n_k_ho_wo_desc =
make_naive_tensor_descriptor_packed(make_tuple(256, 256, 28, 28));
constexpr auto descs =
transform_forward_convolution_into_gemm_v4r4_nchw_kcyx_nkhw_pad(wei_k_c_y_x_desc,
in_n_c_hi_wi_desc,
out_n_k_ho_wo_desc,
make_tuple(1, 1),
make_tuple(1, 1),
make_tuple(1, 1),
make_tuple(1, 1));
constexpr auto a_k_m_grid_desc = descs[I0];
constexpr auto b_k_n_grid_desc = descs[I1];
constexpr auto c_m_n_grid_desc = descs[I2];
using AKMGridDesc = decltype(a_k_m_grid_desc);
using BKNGridDesc = decltype(b_k_n_grid_desc);
using CMNGridDesc = decltype(c_m_n_grid_desc);
using AGridStepHacks = decltype(make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{})));
using BGridStepHacks =
decltype(make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{})));
using CGridStepHacks = decltype(make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{})));
using AGridMoveSliceWindowStepHacks = Sequence<0, 0, 0, 0, 0>;
using BGridMoveSliceWindowStepHacks = Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0>;
using GridwiseGemm =
GridwiseGemmDlops_km_kn_mn_v1r2<BlockSize,
FloatAB,
FloatAcc,
FloatC,
InMemoryDataOperationEnum_t::Set, /* ToDo tunable */
AKMGridDesc,
BKNGridDesc,
CMNGridDesc,
MPerBlock,
NPerBlock,
KPerBlock,
M1PerThread,
N1PerThread,
KPerThread,
M1N1ThreadClusterM10,
M1N1ThreadClusterN10,
M1N1ThreadClusterM11,
M1N1ThreadClusterN11,
ABlockTransferThreadSliceLengths_K_M0_M1,
ABlockTransferThreadClusterLengths_K_M0_M1,
ABlockTransferThreadClusterArrangeOrder,
ABlockTransferSrcAccessOrder,
ABlockTransferSrcVectorDim,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_M1,
AThreadTransferSrcResetCoordinateAfterRun,
BBlockTransferThreadSliceLengths_K_N0_N1,
BBlockTransferThreadClusterLengths_K_N0_N1,
BBlockTransferThreadClusterArrangeOrder,
BBlockTransferSrcAccessOrder,
BBlockTransferSrcVectorDim,
BBlockTransferSrcScalarPerVector,
BBlockTransferDstScalarPerVector_N1,
BThreadTransferSrcResetCoordinateAfterRun,
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
AGridStepHacks,
BGridStepHacks,
CGridStepHacks,
AGridMoveSliceWindowStepHacks,
BGridMoveSliceWindowStepHacks>;
constexpr auto a_k_m0_m1_grid_desc_tmp =
GridwiseGemm::MakeAKM0M1GridDescriptor(a_k_m_grid_desc);
constexpr auto b_k_n0_n1_grid_desc_tmp =
GridwiseGemm::MakeBKN0N1GridDescriptor(b_k_n_grid_desc);
constexpr auto c_m0_m10_m11_n0_n10_n11_grid_desc_tmp =
GridwiseGemm::MakeCM0M10M11N0N10N11GridDescriptor(c_m_n_grid_desc);
constexpr auto c_blockid_to_m0_n0_block_cluster_adaptor_tmp =
GridwiseGemm::MakeCBlockIdToM0N0BlockClusterAdaptor(c_m_n_grid_desc);
using AKM0M1GridDesc = decltype(a_k_m0_m1_grid_desc_tmp);
using BKN0N1GridDesc = decltype(b_k_n0_n1_grid_desc_tmp);
using CM0M10M11N0N10N11GridDesc = decltype(c_m0_m10_m11_n0_n10_n11_grid_desc_tmp);
using CBlockIdToM0N0BlockClusterAdaptor =
decltype(c_blockid_to_m0_n0_block_cluster_adaptor_tmp);
const auto a_k_m0_m1_grid_desc =
*reinterpret_cast<const AKM0M1GridDesc*>((const void*)p_a_k_m0_m1_grid_desc);
const auto b_k_n0_n1_grid_desc =
*reinterpret_cast<const BKN0N1GridDesc*>((const void*)p_b_k_n0_n1_grid_desc);
const auto c_m0_m10_m11_n0_n10_n11_grid_desc =
*reinterpret_cast<const CM0M10M11N0N10N11GridDesc*>(
(const void*)p_c_m0_m10_m11_n0_n10_n11_grid_desc);
const auto c_blockid_to_m0_n0_block_cluster_adaptor =
*reinterpret_cast<const CBlockIdToM0N0BlockClusterAdaptor*>(
(const void*)p_c_blockid_to_m0_n0_block_cluster_adaptor);
constexpr index_t shared_block_size =
GridwiseGemm::GetSharedMemoryNumberOfByte() / sizeof(FloatAB);
__shared__ FloatAB p_shared_block[shared_block_size];
GridwiseGemm::Run(p_a_grid,
p_b_grid,
p_c_grid,
p_shared_block,
a_k_m0_m1_grid_desc,
b_k_n0_n1_grid_desc,
c_m0_m10_m11_n0_n10_n11_grid_desc,
c_blockid_to_m0_n0_block_cluster_adaptor,
integral_constant<bool, HasMainKBlockLoop>{},
integral_constant<bool, HasDoubleTailKBlockLoop>{});
};

358
composable_kernel/src/kernel_wrapper/convolution_forward_implicit_gemm_v4r4_xdlops_nchw_kcyx_nkhw.cpp Normal file
View File

@@ -0,0 +1,358 @@
#include "common_header.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "gridwise_gemm_xdlops_v2r3.hpp"
#include "transform_forward_convolution_into_gemm_v4r4r2_nchw_kcyx_nkhw.hpp"
using namespace ck;
constexpr DataTypeEnum_t ABDataTypeEnum = static_cast<DataTypeEnum_t>(CK_PARAM_ABDataTypeEnum);
constexpr DataTypeEnum_t AccDataTypeEnum = static_cast<DataTypeEnum_t>(CK_PARAM_AccDataTypeEnum);
constexpr DataTypeEnum_t CDataTypeEnum = static_cast<DataTypeEnum_t>(CK_PARAM_CDataTypeEnum);
using FloatAB = typename get_datatype_from_enum<ABDataTypeEnum>::type;
using FloatAcc = typename get_datatype_from_enum<AccDataTypeEnum>::type;
using FloatC = typename get_datatype_from_enum<CDataTypeEnum>::type;
constexpr index_t BlockSize = CK_PARAM_BlockSize;
constexpr index_t MPerBlock = CK_PARAM_MPerBlock;
constexpr index_t NPerBlock = CK_PARAM_NPerBlock;
constexpr index_t KPerBlock = CK_PARAM_KPerBlock;
constexpr index_t MPerWave = CK_PARAM_MPerWave;
constexpr index_t NPerWave = CK_PARAM_NPerWave;
constexpr index_t MRepeat = CK_PARAM_MRepeat;
constexpr index_t NRepeat = CK_PARAM_NRepeat;
constexpr index_t K1 = CK_PARAM_K1;
using ABlockTransferThreadSliceLengths_K0_M_K1 =
Sequence<CK_PARAM_ABlockTransferThreadSliceLengths_K0_M_K1>;
using ABlockTransferThreadClusterLengths_K0_M_K1 =
Sequence<CK_PARAM_ABlockTransferThreadClusterLengths_K0_M_K1>;
using ABlockTransferThreadClusterArrangeOrder =
Sequence<CK_PARAM_ABlockTransferThreadClusterArrangeOrder>;
using ABlockTransferSrcAccessOrder = Sequence<CK_PARAM_ABlockTransferSrcAccessOrder>;
constexpr index_t ABlockTransferSrcVectorDim = CK_PARAM_ABlockTransferSrcVectorDim;
constexpr index_t ABlockTransferSrcScalarPerVector = CK_PARAM_ABlockTransferSrcScalarPerVector;
constexpr index_t ABlockTransferDstScalarPerVector_K1 =
CK_PARAM_ABlockTransferDstScalarPerVector_K1;
constexpr bool AThreadTransferSrcResetCoordinateAfterRun =
static_cast<bool>(CK_PARAM_AThreadTransferSrcResetCoordinateAfterRun);
using BBlockTransferThreadSliceLengths_K0_N_K1 =
Sequence<CK_PARAM_BBlockTransferThreadSliceLengths_K0_N_K1>;
using BBlockTransferThreadClusterLengths_K0_N_K1 =
Sequence<CK_PARAM_BBlockTransferThreadClusterLengths_K0_N_K1>;
using BBlockTransferThreadClusterArrangeOrder =
Sequence<CK_PARAM_BBlockTransferThreadClusterArrangeOrder>;
using BBlockTransferSrcAccessOrder = Sequence<CK_PARAM_BBlockTransferSrcAccessOrder>;
constexpr index_t BBlockTransferSrcVectorDim = CK_PARAM_BBlockTransferSrcVectorDim;
constexpr index_t BBlockTransferSrcScalarPerVector = CK_PARAM_BBlockTransferSrcScalarPerVector;
constexpr index_t BBlockTransferDstScalarPerVector_K1 =
CK_PARAM_BBlockTransferDstScalarPerVector_K1;
constexpr bool BThreadTransferSrcResetCoordinateAfterRun =
static_cast<bool>(CK_PARAM_BThreadTransferSrcResetCoordinateAfterRun);
using CThreadTransferSrcDstAccessOrder = Sequence<CK_PARAM_CThreadTransferSrcDstAccessOrder>;
constexpr index_t CThreadTransferSrcDstVectorDim = CK_PARAM_CThreadTransferSrcDstVectorDim;
constexpr index_t CThreadTransferDstScalarPerVector = CK_PARAM_CThreadTransferDstScalarPerVector;
extern "C" __global__ void convolution_forward_implicit_gemm_v4r4_xdlops_nchw_kcyx_nkhw_prepare(
int n,
int c,
int hi,
int wi,
int k,
int y,
int x,
int convStrideH,
int convStrideW,
int convDilationY,
int convDilationX,
int leftPadH,
int leftPadW,
int rightPadH,
int rightPadW,
void* p_a_k0_m_k1_grid_desc,
void* p_b_k0_n_k1_grid_desc,
void* p_c_m0_m1_m2_n_grid_desc,
void* p_c_blockid_to_m0_n0_block_cluster_adaptor)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
const index_t ho = (hi + leftPadH + rightPadH - convDilationY * (y - 1) - 1) / convStrideH + 1;
const index_t wo = (wi + leftPadW + rightPadW - convDilationX * (x - 1) - 1) / convStrideW + 1;
const auto in_n_c_hi_wi_desc = make_naive_tensor_descriptor_packed(make_tuple(n, c, hi, wi));
const auto wei_k_c_y_x_desc = make_naive_tensor_descriptor_packed(make_tuple(k, c, y, x));
const auto out_n_k_ho_wo_desc = make_naive_tensor_descriptor_packed(make_tuple(n, k, ho, wo));
const auto descs = transform_forward_convolution_into_gemm_v4r4r2_nchw_kcyx_nkhw_pad(
wei_k_c_y_x_desc,
in_n_c_hi_wi_desc,
out_n_k_ho_wo_desc,
make_tuple(convStrideH, convStrideW),
make_tuple(convDilationY, convDilationX),
make_tuple(leftPadH, leftPadW),
make_tuple(rightPadH, rightPadW),
Number<K1>{});
const auto a_k0_m_k1_grid_desc = descs[I0];
const auto b_k0_n_k1_grid_desc = descs[I1];
const auto c_m_n_grid_desc = descs[I2];
using AK0MK1GridDesc = decltype(a_k0_m_k1_grid_desc);
using BK0NK1GridDesc = decltype(b_k0_n_k1_grid_desc);
using CMNGridDesc = decltype(c_m_n_grid_desc);
using AGridStepHacks = decltype(make_tuple(
make_tuple(Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}),
make_tuple(
Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{})));
using BGridStepHacks =
decltype(make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{})));
using CGridStepHacks = decltype(make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{})));
using AGridMoveSliceWindowStepHacks = Sequence<0, 0, 0, 0, 0>;
using BGridMoveSliceWindowStepHacks = Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0>;
using GridwiseGemm =
GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3<BlockSize,
FloatAB,
FloatAcc,
FloatC,
InMemoryDataOperationEnum_t::Set,
AK0MK1GridDesc,
BK0NK1GridDesc,
CMNGridDesc,
MPerBlock,
NPerBlock,
KPerBlock,
MPerWave,
NPerWave,
K1,
MRepeat,
NRepeat,
ABlockTransferThreadSliceLengths_K0_M_K1,
ABlockTransferThreadClusterLengths_K0_M_K1,
ABlockTransferThreadClusterArrangeOrder,
ABlockTransferSrcAccessOrder,
ABlockTransferSrcVectorDim,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_K1,
AThreadTransferSrcResetCoordinateAfterRun,
BBlockTransferThreadSliceLengths_K0_N_K1,
BBlockTransferThreadClusterLengths_K0_N_K1,
BBlockTransferThreadClusterArrangeOrder,
BBlockTransferSrcAccessOrder,
BBlockTransferSrcVectorDim,
BBlockTransferSrcScalarPerVector,
BBlockTransferDstScalarPerVector_K1,
BThreadTransferSrcResetCoordinateAfterRun,
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
AGridStepHacks,
BGridStepHacks,
CGridStepHacks,
AGridMoveSliceWindowStepHacks,
BGridMoveSliceWindowStepHacks,
false>;
auto c_m0_m1_m2_n_grid_desc = GridwiseGemm::MakeCM0M1M2NGridDescriptor(c_m_n_grid_desc);
auto c_blockid_to_m0_n0_block_cluster_adaptor =
GridwiseGemm::MakeCBlockClusterAdaptor(c_m_n_grid_desc);
if(hipThreadIdx_x == 0)
{
*static_cast<remove_cv_t<decltype(a_k0_m_k1_grid_desc)>*>(p_a_k0_m_k1_grid_desc) =
a_k0_m_k1_grid_desc;
*static_cast<remove_cv_t<decltype(b_k0_n_k1_grid_desc)>*>(p_b_k0_n_k1_grid_desc) =
b_k0_n_k1_grid_desc;
*static_cast<decltype(c_m0_m1_m2_n_grid_desc)*>(p_c_m0_m1_m2_n_grid_desc) =
c_m0_m1_m2_n_grid_desc;
*static_cast<decltype(c_blockid_to_m0_n0_block_cluster_adaptor)*>(
p_c_blockid_to_m0_n0_block_cluster_adaptor) = c_blockid_to_m0_n0_block_cluster_adaptor;
}
};
extern "C" __global__ void
#if CK_USE_LAUNCH_BOUNDS
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif
convolution_forward_implicit_gemm_v4r4_xdlops_nchw_kcyx_nkhw(
const FloatAB* __restrict__ p_a_grid,
const FloatAB* __restrict__ p_b_grid,
FloatC* __restrict__ p_c_grid,
const void CONSTANT* p_a_k0_m_k1_grid_desc,
const void CONSTANT* p_b_k0_n_k1_grid_desc,
const void CONSTANT* p_c_m0_m1_m2_n_grid_desc,
const void CONSTANT* p_c_blockid_to_m0_n0_block_cluster_adaptor)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto in_n_c_hi_wi_desc =
make_naive_tensor_descriptor_packed(make_tuple(256, 256, 28, 28));
constexpr auto wei_k_c_y_x_desc =
make_naive_tensor_descriptor_packed(make_tuple(256, 256, 3, 3));
constexpr auto out_n_k_ho_wo_desc =
make_naive_tensor_descriptor_packed(make_tuple(256, 256, 28, 28));
constexpr auto descs =
transform_forward_convolution_into_gemm_v4r4r2_nchw_kcyx_nkhw_pad(wei_k_c_y_x_desc,
in_n_c_hi_wi_desc,
out_n_k_ho_wo_desc,
make_tuple(1, 1),
make_tuple(1, 1),
make_tuple(1, 1),
make_tuple(1, 1),
Number<K1>{});
constexpr auto a_k0_m_k1_grid_desc_tmp = descs[I0];
constexpr auto b_k0_n_k1_grid_desc_tmp = descs[I1];
constexpr auto c_m_n_grid_desc = descs[I2];
using AGridStepHacks = decltype(make_tuple(
make_tuple(Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}),
make_tuple(
Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{})));
using BGridStepHacks =
decltype(make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{})));
using CGridStepHacks = decltype(make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{})));
using AGridMoveSliceWindowStepHacks = Sequence<0, 0, 0, 0, 0>;
using BGridMoveSliceWindowStepHacks = Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0>;
using AK0MK1GridDesc = decltype(a_k0_m_k1_grid_desc_tmp);
using BK0NK1GridDesc = decltype(b_k0_n_k1_grid_desc_tmp);
using CMNGridDesc = decltype(c_m_n_grid_desc);
using GridwiseGemm =
GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3<BlockSize,
FloatAB,
FloatAcc,
FloatC,
InMemoryDataOperationEnum_t::Set,
AK0MK1GridDesc,
BK0NK1GridDesc,
CMNGridDesc,
MPerBlock,
NPerBlock,
KPerBlock,
MPerWave,
NPerWave,
K1,
MRepeat,
NRepeat,
ABlockTransferThreadSliceLengths_K0_M_K1,
ABlockTransferThreadClusterLengths_K0_M_K1,
ABlockTransferThreadClusterArrangeOrder,
ABlockTransferSrcAccessOrder,
ABlockTransferSrcVectorDim,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_K1,
AThreadTransferSrcResetCoordinateAfterRun,
BBlockTransferThreadSliceLengths_K0_N_K1,
BBlockTransferThreadClusterLengths_K0_N_K1,
BBlockTransferThreadClusterArrangeOrder,
BBlockTransferSrcAccessOrder,
BBlockTransferSrcVectorDim,
BBlockTransferSrcScalarPerVector,
BBlockTransferDstScalarPerVector_K1,
BThreadTransferSrcResetCoordinateAfterRun,
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
AGridStepHacks,
BGridStepHacks,
CGridStepHacks,
AGridMoveSliceWindowStepHacks,
BGridMoveSliceWindowStepHacks,
false>;
constexpr auto c_m0_m1_m2_n_grid_desc_tmp =
GridwiseGemm::MakeCM0M1M2NGridDescriptor(c_m_n_grid_desc);
constexpr auto c_blockid_to_m0_n0_block_cluster_adaptor_tmp =
GridwiseGemm::MakeCBlockClusterAdaptor(c_m_n_grid_desc);
using CM0M1M2NGridDesc = decltype(c_m0_m1_m2_n_grid_desc_tmp);
using CBlockIdToM0N0BlockClusterAdaptor =
decltype(c_blockid_to_m0_n0_block_cluster_adaptor_tmp);
const auto a_k0_m_k1_grid_desc =
*reinterpret_cast<const AK0MK1GridDesc*>((const void*)p_a_k0_m_k1_grid_desc);
const auto b_k0_n_k1_grid_desc =
*reinterpret_cast<const BK0NK1GridDesc*>((const void*)p_b_k0_n_k1_grid_desc);
const auto c_m0_m1_m2_n_grid_desc =
*reinterpret_cast<const CM0M1M2NGridDesc*>((const void*)p_c_m0_m1_m2_n_grid_desc);
const auto c_blockid_to_m0_n0_block_cluster_adaptor =
*reinterpret_cast<const CBlockIdToM0N0BlockClusterAdaptor*>(
(const void*)p_c_blockid_to_m0_n0_block_cluster_adaptor);
constexpr index_t shared_block_size =
GridwiseGemm::GetSharedMemoryNumberOfByte() / sizeof(FloatAB);
__shared__ FloatAB p_shared_block[shared_block_size];
GridwiseGemm::Run(p_a_grid,
p_b_grid,
p_c_grid,
p_shared_block,
a_k0_m_k1_grid_desc,
b_k0_n_k1_grid_desc,
c_m0_m1_m2_n_grid_desc,
c_blockid_to_m0_n0_block_cluster_adaptor);
};

357
composable_kernel/src/kernel_wrapper/convolution_forward_implicit_gemm_v4r4_xdlops_nhwc_kyxc_nhwk.cpp Normal file
View File

@@ -0,0 +1,357 @@
#include "common_header.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "gridwise_gemm_xdlops_v2r3.hpp"
#include "transform_forward_convolution_into_gemm_v4r4r4_nhwc_kyxc_nhwk.hpp"
using namespace ck;
constexpr DataTypeEnum_t ABDataTypeEnum = static_cast<DataTypeEnum_t>(CK_PARAM_ABDataTypeEnum);
constexpr DataTypeEnum_t AccDataTypeEnum = static_cast<DataTypeEnum_t>(CK_PARAM_AccDataTypeEnum);
constexpr DataTypeEnum_t CDataTypeEnum = static_cast<DataTypeEnum_t>(CK_PARAM_CDataTypeEnum);
using FloatAB = typename get_datatype_from_enum<ABDataTypeEnum>::type;
using FloatAcc = typename get_datatype_from_enum<AccDataTypeEnum>::type;
using FloatC = typename get_datatype_from_enum<CDataTypeEnum>::type;
constexpr index_t BlockSize = CK_PARAM_BlockSize;
constexpr index_t MPerBlock = CK_PARAM_MPerBlock;
constexpr index_t NPerBlock = CK_PARAM_NPerBlock;
constexpr index_t KPerBlock = CK_PARAM_KPerBlock;
constexpr index_t MPerWave = CK_PARAM_MPerWave;
constexpr index_t NPerWave = CK_PARAM_NPerWave;
constexpr index_t MRepeat = CK_PARAM_MRepeat;
constexpr index_t NRepeat = CK_PARAM_NRepeat;
constexpr index_t K1 = CK_PARAM_K1;
using ABlockTransferThreadSliceLengths_K0_M_K1 =
Sequence<CK_PARAM_ABlockTransferThreadSliceLengths_K0_M_K1>;
using ABlockTransferThreadClusterLengths_K0_M_K1 =
Sequence<CK_PARAM_ABlockTransferThreadClusterLengths_K0_M_K1>;
using ABlockTransferThreadClusterArrangeOrder =
Sequence<CK_PARAM_ABlockTransferThreadClusterArrangeOrder>;
using ABlockTransferSrcAccessOrder = Sequence<CK_PARAM_ABlockTransferSrcAccessOrder>;
constexpr index_t ABlockTransferSrcVectorDim = CK_PARAM_ABlockTransferSrcVectorDim;
constexpr index_t ABlockTransferSrcScalarPerVector = CK_PARAM_ABlockTransferSrcScalarPerVector;
constexpr index_t ABlockTransferDstScalarPerVector_K1 =
CK_PARAM_ABlockTransferDstScalarPerVector_K1;
constexpr bool AThreadTransferSrcResetCoordinateAfterRun =
static_cast<bool>(CK_PARAM_AThreadTransferSrcResetCoordinateAfterRun);
using BBlockTransferThreadSliceLengths_K0_N_K1 =
Sequence<CK_PARAM_BBlockTransferThreadSliceLengths_K0_N_K1>;
using BBlockTransferThreadClusterLengths_K0_N_K1 =
Sequence<CK_PARAM_BBlockTransferThreadClusterLengths_K0_N_K1>;
using BBlockTransferThreadClusterArrangeOrder =
Sequence<CK_PARAM_BBlockTransferThreadClusterArrangeOrder>;
using BBlockTransferSrcAccessOrder = Sequence<CK_PARAM_BBlockTransferSrcAccessOrder>;
constexpr index_t BBlockTransferSrcVectorDim = CK_PARAM_BBlockTransferSrcVectorDim;
constexpr index_t BBlockTransferSrcScalarPerVector = CK_PARAM_BBlockTransferSrcScalarPerVector;
constexpr index_t BBlockTransferDstScalarPerVector_K1 =
CK_PARAM_BBlockTransferDstScalarPerVector_K1;
constexpr bool BThreadTransferSrcResetCoordinateAfterRun =
static_cast<bool>(CK_PARAM_BThreadTransferSrcResetCoordinateAfterRun);
using CThreadTransferSrcDstAccessOrder = Sequence<CK_PARAM_CThreadTransferSrcDstAccessOrder>;
constexpr index_t CThreadTransferSrcDstVectorDim = CK_PARAM_CThreadTransferSrcDstVectorDim;
constexpr index_t CThreadTransferDstScalarPerVector = CK_PARAM_CThreadTransferDstScalarPerVector;
extern "C" __global__ void convolution_forward_implicit_gemm_v4r4_xdlops_nhwc_kyxc_nhwk_prepare(
int n,
int hi,
int wi,
int c,
int k,
int y,
int x,
int convStrideH,
int convStrideW,
int convDilationY,
int convDilationX,
int leftPadH,
int leftPadW,
int rightPadH,
int rightPadW,
void* p_a_k0_m_k1_grid_desc,
void* p_b_k0_n_k1_grid_desc,
void* p_c_m0_m1_m2_n_grid_desc,
void* p_c_blockid_to_m0_n0_block_cluster_adaptor)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
const index_t ho = (hi + leftPadH + rightPadH - convDilationY * (y - 1) - 1) / convStrideH + 1;
const index_t wo = (wi + leftPadW + rightPadW - convDilationX * (x - 1) - 1) / convStrideW + 1;
const auto in_n_hi_wi_c_desc = make_naive_tensor_descriptor_packed(make_tuple(n, hi, wi, c));
const auto wei_k_y_x_c_desc = make_naive_tensor_descriptor_packed(make_tuple(k, y, x, c));
const auto out_n_ho_wo_k_desc = make_naive_tensor_descriptor_packed(make_tuple(n, ho, wo, k));
const auto descs = transform_forward_convolution_into_gemm_v4r4r4_nhwc_kyxc_nhwk_pad(
in_n_hi_wi_c_desc,
wei_k_y_x_c_desc,
out_n_ho_wo_k_desc,
make_tuple(convStrideH, convStrideW),
make_tuple(convDilationY, convDilationX),
make_tuple(leftPadH, leftPadW),
make_tuple(rightPadH, rightPadW),
Number<K1>{});
const auto a_k0_m_k1_grid_desc = descs[I0];
const auto b_k0_n_k1_grid_desc = descs[I1];
const auto c_m_n_grid_desc = descs[I2];
using AK0MK1GridDesc = decltype(a_k0_m_k1_grid_desc);
using BK0NK1GridDesc = decltype(b_k0_n_k1_grid_desc);
using CMNGridDesc = decltype(c_m_n_grid_desc);
using BGridStepHacks = decltype(make_tuple(
make_tuple(Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}),
make_tuple(
Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{})));
using AGridStepHacks =
decltype(make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{})));
using CGridStepHacks = decltype(make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{})));
using AGridMoveSliceWindowStepHacks = Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0>;
using BGridMoveSliceWindowStepHacks = Sequence<0, 0, 0, 0, 0>;
using GridwiseGemm =
GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3<BlockSize,
FloatAB,
FloatAcc,
FloatC,
InMemoryDataOperationEnum_t::Set,
AK0MK1GridDesc,
BK0NK1GridDesc,
CMNGridDesc,
MPerBlock,
NPerBlock,
KPerBlock,
MPerWave,
NPerWave,
K1,
MRepeat,
NRepeat,
ABlockTransferThreadSliceLengths_K0_M_K1,
ABlockTransferThreadClusterLengths_K0_M_K1,
ABlockTransferThreadClusterArrangeOrder,
ABlockTransferSrcAccessOrder,
ABlockTransferSrcVectorDim,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_K1,
AThreadTransferSrcResetCoordinateAfterRun,
BBlockTransferThreadSliceLengths_K0_N_K1,
BBlockTransferThreadClusterLengths_K0_N_K1,
BBlockTransferThreadClusterArrangeOrder,
BBlockTransferSrcAccessOrder,
BBlockTransferSrcVectorDim,
BBlockTransferSrcScalarPerVector,
BBlockTransferDstScalarPerVector_K1,
BThreadTransferSrcResetCoordinateAfterRun,
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
AGridStepHacks,
BGridStepHacks,
CGridStepHacks,
AGridMoveSliceWindowStepHacks,
BGridMoveSliceWindowStepHacks,
false>;
auto c_m0_m1_m2_n_grid_desc = GridwiseGemm::MakeCM0M1M2NGridDescriptor(c_m_n_grid_desc);
auto c_blockid_to_m0_n0_block_cluster_adaptor =
GridwiseGemm::MakeCBlockClusterAdaptor(c_m_n_grid_desc);
if(hipThreadIdx_x == 0)
{
*static_cast<remove_cv_t<decltype(a_k0_m_k1_grid_desc)>*>(p_a_k0_m_k1_grid_desc) =
a_k0_m_k1_grid_desc;
*static_cast<remove_cv_t<decltype(b_k0_n_k1_grid_desc)>*>(p_b_k0_n_k1_grid_desc) =
b_k0_n_k1_grid_desc;
*static_cast<decltype(c_m0_m1_m2_n_grid_desc)*>(p_c_m0_m1_m2_n_grid_desc) =
c_m0_m1_m2_n_grid_desc;
*static_cast<decltype(c_blockid_to_m0_n0_block_cluster_adaptor)*>(
p_c_blockid_to_m0_n0_block_cluster_adaptor) = c_blockid_to_m0_n0_block_cluster_adaptor;
}
};
extern "C" __global__ void
#if CK_USE_LAUNCH_BOUNDS
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif
convolution_forward_implicit_gemm_v4r4_xdlops_nhwc_kyxc_nhwk(
const FloatAB* __restrict__ p_a_grid,
const FloatAB* __restrict__ p_b_grid,
FloatC* __restrict__ p_c_grid,
const void CONSTANT* p_a_k0_m_k1_grid_desc,
const void CONSTANT* p_b_k0_n_k1_grid_desc,
const void CONSTANT* p_c_m0_m1_m2_n_grid_desc,
const void CONSTANT* p_c_blockid_to_m0_n0_block_cluster_adaptor)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto in_n_hi_wi_c_desc =
make_naive_tensor_descriptor_packed(make_tuple(256, 28, 28, 256));
constexpr auto wei_k_y_x_c_desc =
make_naive_tensor_descriptor_packed(make_tuple(256, 3, 3, 256));
constexpr auto out_n_ho_wo_k_desc =
make_naive_tensor_descriptor_packed(make_tuple(256, 28, 28, 256));
constexpr auto descs =
transform_forward_convolution_into_gemm_v4r4r4_nhwc_kyxc_nhwk_pad(in_n_hi_wi_c_desc,
wei_k_y_x_c_desc,
out_n_ho_wo_k_desc,
make_tuple(1, 1),
make_tuple(1, 1),
make_tuple(1, 1),
make_tuple(1, 1),
Number<K1>{});
constexpr auto a_k0_m_k1_grid_desc_tmp = descs[I0];
constexpr auto b_k0_n_k1_grid_desc_tmp = descs[I1];
constexpr auto c_m_n_grid_desc = descs[I2];
using AK0MK1GridDesc = decltype(a_k0_m_k1_grid_desc_tmp);
using BK0NK1GridDesc = decltype(b_k0_n_k1_grid_desc_tmp);
using CMNGridDesc = decltype(c_m_n_grid_desc);
using BGridStepHacks = decltype(make_tuple(
make_tuple(Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}),
make_tuple(
Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{})));
using AGridStepHacks =
decltype(make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{})));
using CGridStepHacks = decltype(make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{})));
using AGridMoveSliceWindowStepHacks = Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0>;
using BGridMoveSliceWindowStepHacks = Sequence<0, 0, 0, 0, 0>;
using GridwiseGemm =
GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3<BlockSize,
FloatAB,
FloatAcc,
FloatC,
InMemoryDataOperationEnum_t::Set,
AK0MK1GridDesc,
BK0NK1GridDesc,
CMNGridDesc,
MPerBlock,
NPerBlock,
KPerBlock,
MPerWave,
NPerWave,
K1,
MRepeat,
NRepeat,
ABlockTransferThreadSliceLengths_K0_M_K1,
ABlockTransferThreadClusterLengths_K0_M_K1,
ABlockTransferThreadClusterArrangeOrder,
ABlockTransferSrcAccessOrder,
ABlockTransferSrcVectorDim,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_K1,
AThreadTransferSrcResetCoordinateAfterRun,
BBlockTransferThreadSliceLengths_K0_N_K1,
BBlockTransferThreadClusterLengths_K0_N_K1,
BBlockTransferThreadClusterArrangeOrder,
BBlockTransferSrcAccessOrder,
BBlockTransferSrcVectorDim,
BBlockTransferSrcScalarPerVector,
BBlockTransferDstScalarPerVector_K1,
BThreadTransferSrcResetCoordinateAfterRun,
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
AGridStepHacks,
BGridStepHacks,
CGridStepHacks,
AGridMoveSliceWindowStepHacks,
BGridMoveSliceWindowStepHacks,
false>;
constexpr auto c_m0_m1_m2_n_grid_desc_tmp =
GridwiseGemm::MakeCM0M1M2NGridDescriptor(c_m_n_grid_desc);
constexpr auto c_blockid_to_m0_n0_block_cluster_adaptor_tmp =
GridwiseGemm::MakeCBlockClusterAdaptor(c_m_n_grid_desc);
using CM0M1M2NGridDesc = decltype(c_m0_m1_m2_n_grid_desc_tmp);
using CBlockIdToM0N0BlockClusterAdaptor =
decltype(c_blockid_to_m0_n0_block_cluster_adaptor_tmp);
const auto a_k0_m_k1_grid_desc =
*reinterpret_cast<const AK0MK1GridDesc*>((const void*)p_a_k0_m_k1_grid_desc);
const auto b_k0_n_k1_grid_desc =
*reinterpret_cast<const BK0NK1GridDesc*>((const void*)p_b_k0_n_k1_grid_desc);
const auto c_m0_m1_m2_n_grid_desc =
*reinterpret_cast<const CM0M1M2NGridDesc*>((const void*)p_c_m0_m1_m2_n_grid_desc);
const auto c_blockid_to_m0_n0_block_cluster_adaptor =
*reinterpret_cast<const CBlockIdToM0N0BlockClusterAdaptor*>(
(const void*)p_c_blockid_to_m0_n0_block_cluster_adaptor);
constexpr index_t shared_block_size =
GridwiseGemm::GetSharedMemoryNumberOfByte() / sizeof(FloatAB);
__shared__ FloatAB p_shared_block[shared_block_size];
GridwiseGemm::Run(p_a_grid,
p_b_grid,
p_c_grid,
p_shared_block,
a_k0_m_k1_grid_desc,
b_k0_n_k1_grid_desc,
c_m0_m1_m2_n_grid_desc,
c_blockid_to_m0_n0_block_cluster_adaptor);
};

115
composable_kernel/src/kernel_wrapper/dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw.cpp → composable_kernel/src/kernel_wrapper/convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw.cpp
View File

@@ -1,7 +1,7 @@
#include "common_header.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "gridwise_dynamic_contraction_dlops_v1r2.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "gridwise_contraction_dlops_v1r2.hpp"
#include "transform_forward_convolution_into_gemm_v6r1_nchw_kcyx_nkhw.hpp"
using namespace ck;
@@ -62,23 +62,39 @@ constexpr bool HasMainKBlockLoop = static_cast<bool>(CK_PARAM_HasMainKBloc
constexpr bool HasDoubleTailKBlockLoop = static_cast<bool>(CK_PARAM_HasDoubleTailKBlockLoop);
extern "C" __global__ void
dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw_prepare(index_t N,
index_t C,
index_t Hi,
index_t Wi,
index_t K,
index_t Y,
index_t X,
index_t ConvStrideH,
index_t ConvStrideW,
index_t ConvDilationH,
index_t ConvDilationW,
index_t InLeftPadH,
index_t InLeftPadW,
index_t InRightPadH,
index_t InRightPadW,
void* p_desc_tuple)
convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw_prepare(int N_,
int C_,
int Hi_,
int Wi_,
int K_,
int Y_,
int X_,
int ConvStrideH_,
int ConvStrideW_,
int ConvDilationH_,
int ConvDilationW_,
int InLeftPadH_,
int InLeftPadW_,
int InRightPadH_,
int InRightPadW_,
void* p_desc_tuple)
{
index_t N = static_cast<index_t>(N_);
index_t C = static_cast<index_t>(C_);
index_t Hi = static_cast<index_t>(Hi_);
index_t Wi = static_cast<index_t>(Wi_);
index_t K = static_cast<index_t>(K_);
index_t Y = static_cast<index_t>(Y_);
index_t X = static_cast<index_t>(X_);
index_t ConvStrideH = static_cast<index_t>(ConvStrideH_);
index_t ConvStrideW = static_cast<index_t>(ConvStrideW_);
index_t ConvDilationH = static_cast<index_t>(ConvDilationH_);
index_t ConvDilationW = static_cast<index_t>(ConvDilationW_);
index_t InLeftPadH = static_cast<index_t>(InLeftPadH_);
index_t InLeftPadW = static_cast<index_t>(InLeftPadW_);
index_t InRightPadH = static_cast<index_t>(InRightPadH_);
index_t InRightPadW = static_cast<index_t>(InRightPadW_);
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
@@ -88,12 +104,9 @@ dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw_prepare(inde
const index_t Wo =
(Wi + InLeftPadW + InRightPadW - ConvDilationW * (X - 1) - 1) / ConvStrideW + 1;
const auto in_n_c_hi_wi_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(N, C, Hi, Wi));
const auto wei_k_c_y_x_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(K, C, Y, X));
const auto out_n_k_ho_wo_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(N, K, Ho, Wo));
const auto in_n_c_hi_wi_desc = make_naive_tensor_descriptor_packed(make_tuple(N, C, Hi, Wi));
const auto wei_k_c_y_x_desc = make_naive_tensor_descriptor_packed(make_tuple(K, C, Y, X));
const auto out_n_k_ho_wo_desc = make_naive_tensor_descriptor_packed(make_tuple(N, K, Ho, Wo));
const auto descs = transform_forward_convolution_into_contraction_v6r1_nchw_kcyx_nkhw_pad(
wei_k_c_y_x_desc,
@@ -114,7 +127,7 @@ dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw_prepare(inde
using BGridDesc_GK0_GN0_GN1_GK1 = decltype(b_grid_desc_gk0_gn0_gn1_gk1);
using CGridDesc_GM0_GM1_GN0_GN1 = decltype(c_grid_desc_gm0_gm1_gn0_gn1);
using AGridIteratorHacks =
using AGridStepHacks =
decltype(make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0>{}, // 0+: GK0
Sequence<0, 0, 0, 0, 0, 0, 0>{}, // 1+: GM0
Sequence<0, 0, 0, 0, 0, 0, 0>{}, // 2+: GM10
@@ -126,7 +139,7 @@ dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw_prepare(inde
Sequence<0, 0, 0, 0, 0, 0, 0>{}, // 3-: GM11
Sequence<0, 0, 0, 0, 0, 0, 0>{}))); // 4-: GK1
using BGridIteratorHacks = decltype(make_tuple(
using BGridStepHacks = decltype(make_tuple(
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0>{}, // 0+: GK0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0>{}, // 1+: GN0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0>{}, // 2+: GN10
@@ -138,7 +151,7 @@ dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw_prepare(inde
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0>{}, // 3-: GN11
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}))); // 4-: GK1
using CGridIteratorHacks = decltype(make_tuple(
using CGridStepHacks = decltype(make_tuple(
make_tuple(
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0+: GM10
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0>{}, // 1+: BM0
@@ -154,13 +167,13 @@ dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw_prepare(inde
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0>{}, // 4-: BN0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0>{}))); // 5-: GN1
using AGridMoveSliceWindowIteratorHacks = Sequence<0, 0, 0, 0, 0, 0, 0>;
using AGridMoveSliceWindowStepHacks = Sequence<0, 0, 0, 0, 0, 0, 0>;
using BGridMoveSliceWindowIteratorHacks =
using BGridMoveSliceWindowStepHacks =
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0>;
using GridwiseContraction =
GridwiseDynamicContractionDlops_A_GK0_GM0_GM1_GK1_B_GK0_GN0_GN1_GK1_C_GM0_GM1_GN0_GN1<
GridwiseContractionDlops_A_GK0_GM0_GM1_GK1_B_GK0_GN0_GN1_GK1_C_GM0_GM1_GN0_GN1<
BlockSize,
FloatAB,
FloatAcc,
@@ -194,11 +207,11 @@ dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw_prepare(inde
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
AGridIteratorHacks,
BGridIteratorHacks,
CGridIteratorHacks,
AGridMoveSliceWindowIteratorHacks,
BGridMoveSliceWindowIteratorHacks>;
AGridStepHacks,
BGridStepHacks,
CGridStepHacks,
AGridMoveSliceWindowStepHacks,
BGridMoveSliceWindowStepHacks>;
if(get_block_1d_id() == 0 && get_thread_local_1d_id() == 0)
{
@@ -220,7 +233,7 @@ extern "C" __global__ void
#if CK_USE_LAUNCH_BOUNDS
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif
dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw(
convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw(
const FloatAB* __restrict__ p_a_grid,
const FloatAB* __restrict__ p_b_grid,
FloatC* __restrict__ p_c_grid,
@@ -232,11 +245,11 @@ extern "C" __global__ void
constexpr auto I3 = Number<3>{};
constexpr auto in_n_c_hi_wi_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(256, 256, 28, 28));
make_naive_tensor_descriptor_packed(make_tuple(256, 256, 28, 28));
constexpr auto wei_k_c_y_x_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(256, 256, 3, 3));
make_naive_tensor_descriptor_packed(make_tuple(256, 256, 3, 3));
constexpr auto out_n_k_ho_wo_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(256, 256, 28, 28));
make_naive_tensor_descriptor_packed(make_tuple(256, 256, 28, 28));
constexpr auto descs =
transform_forward_convolution_into_contraction_v6r1_nchw_kcyx_nkhw_pad(wei_k_c_y_x_desc,
@@ -257,7 +270,7 @@ extern "C" __global__ void
using BGridDesc_GK0_GN0_GN1_GK1 = decltype(b_grid_desc_gk0_gn0_gn1_gk1);
using CGridDesc_GM0_GM1_GN0_GN1 = decltype(c_grid_desc_gm0_gm1_gn0_gn1);
using AGridIteratorHacks =
using AGridStepHacks =
decltype(make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0>{}, // 0+: GK0
Sequence<0, 0, 0, 0, 0, 0, 0>{}, // 1+: GM0
Sequence<0, 0, 0, 0, 0, 0, 0>{}, // 2+: GM10
@@ -269,7 +282,7 @@ extern "C" __global__ void
Sequence<0, 0, 0, 0, 0, 0, 0>{}, // 3-: GM11
Sequence<0, 0, 0, 0, 0, 0, 0>{}))); // 4-: GK1
using BGridIteratorHacks = decltype(make_tuple(
using BGridStepHacks = decltype(make_tuple(
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0>{}, // 0+: GK0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0>{}, // 1+: GN0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0>{}, // 2+: GN10
@@ -281,7 +294,7 @@ extern "C" __global__ void
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0>{}, // 3-: GN11
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}))); // 4-: GK1
using CGridIteratorHacks = decltype(make_tuple(
using CGridStepHacks = decltype(make_tuple(
make_tuple(
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0+: GM10
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0>{}, // 1+: BM0
@@ -297,13 +310,13 @@ extern "C" __global__ void
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0>{}, // 4-: BN0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0>{}))); // 5-: GN1
using AGridMoveSliceWindowIteratorHacks = Sequence<0, 0, 0, 0, 0, 0, 0>;
using AGridMoveSliceWindowStepHacks = Sequence<0, 0, 0, 0, 0, 0, 0>;
using BGridMoveSliceWindowIteratorHacks =
using BGridMoveSliceWindowStepHacks =
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0>;
using GridwiseContraction =
GridwiseDynamicContractionDlops_A_GK0_GM0_GM1_GK1_B_GK0_GN0_GN1_GK1_C_GM0_GM1_GN0_GN1<
GridwiseContractionDlops_A_GK0_GM0_GM1_GK1_B_GK0_GN0_GN1_GK1_C_GM0_GM1_GN0_GN1<
BlockSize,
FloatAB,
FloatAcc,
@@ -337,11 +350,11 @@ extern "C" __global__ void
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
AGridIteratorHacks,
BGridIteratorHacks,
CGridIteratorHacks,
AGridMoveSliceWindowIteratorHacks,
BGridMoveSliceWindowIteratorHacks>;
AGridStepHacks,
BGridStepHacks,
CGridStepHacks,
AGridMoveSliceWindowStepHacks,
BGridMoveSliceWindowStepHacks>;
using AGridDesc_GK0_GM0_GM10_GM11_GK1 =
decltype(GridwiseContraction::MakeAGridDescriptor_GK0_GM0_GM10_GM11_GK1(

374
composable_kernel/src/kernel_wrapper/dynamic_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw.cpp
View File

@@ -1,374 +0,0 @@
#include "common_header.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "gridwise_dynamic_gemm_dlops_v1r2.hpp"
#include "transform_forward_convolution_into_gemm_v4r4_nchw_kcyx_nkhw.hpp"
using namespace ck;
constexpr DataTypeEnum_t ABDataTypeEnum = static_cast<DataTypeEnum_t>(CK_PARAM_ABDataTypeEnum);
constexpr DataTypeEnum_t AccDataTypeEnum = static_cast<DataTypeEnum_t>(CK_PARAM_AccDataTypeEnum);
constexpr DataTypeEnum_t CDataTypeEnum = static_cast<DataTypeEnum_t>(CK_PARAM_CDataTypeEnum);
using FloatAB = typename get_datatype_from_enum<ABDataTypeEnum>::type;
using FloatAcc = typename get_datatype_from_enum<AccDataTypeEnum>::type;
using FloatC = typename get_datatype_from_enum<CDataTypeEnum>::type;
constexpr index_t BlockSize = CK_PARAM_BlockSize;
constexpr index_t MPerBlock = CK_PARAM_MPerBlock;
constexpr index_t NPerBlock = CK_PARAM_NPerBlock;
constexpr index_t KPerBlock = CK_PARAM_KPerBlock;
constexpr index_t M1PerThread = CK_PARAM_M1PerThread;
constexpr index_t N1PerThread = CK_PARAM_N1PerThread;
constexpr index_t KPerThread = CK_PARAM_KPerThread;
constexpr index_t M1N1ThreadClusterM10 = CK_PARAM_M1N1ThreadClusterM10;
constexpr index_t M1N1ThreadClusterN10 = CK_PARAM_M1N1ThreadClusterN10;
constexpr index_t M1N1ThreadClusterM11 = CK_PARAM_M1N1ThreadClusterM11;
constexpr index_t M1N1ThreadClusterN11 = CK_PARAM_M1N1ThreadClusterN11;
using ABlockTransferThreadSliceLengths_K_M0_M1 =
Sequence<CK_PARAM_ABlockTransferThreadSliceLengths_K_M0_M1>;
using ABlockTransferThreadClusterLengths_K_M0_M1 =
Sequence<CK_PARAM_ABlockTransferThreadClusterLengths_K_M0_M1>;
using ABlockTransferThreadClusterArrangeOrder =
Sequence<CK_PARAM_ABlockTransferThreadClusterArrangeOrder>;
using ABlockTransferSrcAccessOrder = Sequence<CK_PARAM_ABlockTransferSrcAccessOrder>;
constexpr index_t ABlockTransferSrcVectorDim = CK_PARAM_ABlockTransferSrcVectorDim;
constexpr index_t ABlockTransferSrcScalarPerVector = CK_PARAM_ABlockTransferSrcScalarPerVector;
constexpr index_t ABlockTransferDstScalarPerVector_M1 =
CK_PARAM_ABlockTransferDstScalarPerVector_M1;
constexpr bool AThreadTransferSrcResetCoordinateAfterRun =
static_cast<bool>(CK_PARAM_AThreadTransferSrcResetCoordinateAfterRun);
using BBlockTransferThreadSliceLengths_K_N0_N1 =
Sequence<CK_PARAM_BBlockTransferThreadSliceLengths_K_N0_N1>;
using BBlockTransferThreadClusterLengths_K_N0_N1 =
Sequence<CK_PARAM_BBlockTransferThreadClusterLengths_K_N0_N1>;
using BBlockTransferThreadClusterArrangeOrder =
Sequence<CK_PARAM_BBlockTransferThreadClusterArrangeOrder>;
using BBlockTransferSrcAccessOrder = Sequence<CK_PARAM_BBlockTransferSrcAccessOrder>;
constexpr index_t BBlockTransferSrcVectorDim = CK_PARAM_BBlockTransferSrcVectorDim;
constexpr index_t BBlockTransferSrcScalarPerVector = CK_PARAM_BBlockTransferSrcScalarPerVector;
constexpr index_t BBlockTransferDstScalarPerVector_N1 =
CK_PARAM_BBlockTransferDstScalarPerVector_N1;
constexpr bool BThreadTransferSrcResetCoordinateAfterRun =
static_cast<bool>(CK_PARAM_BThreadTransferSrcResetCoordinateAfterRun);
using CThreadTransferSrcDstAccessOrder = Sequence<CK_PARAM_CThreadTransferSrcDstAccessOrder>;
constexpr index_t CThreadTransferSrcDstVectorDim = CK_PARAM_CThreadTransferSrcDstVectorDim;
constexpr index_t CThreadTransferDstScalarPerVector = CK_PARAM_CThreadTransferDstScalarPerVector;
constexpr bool HasMainKBlockLoop = static_cast<bool>(CK_PARAM_HAS_MAIN_KBLOCK_LOOP);
constexpr bool HasDoubleTailKBlockLoop = static_cast<bool>(CK_PARAM_HAS_DOUBLE_TAIL_KBLOCK_LOOP);
extern "C" __global__ void
dynamic_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw_prepare(
int n,
int c,
int hi,
int wi,
int k,
int y,
int x,
int convStrideH,
int convStrideW,
int convDilationY,
int convDilationX,
int leftPadH,
int leftPadW,
int rightPadH,
int rightPadW,
void* p_a_k_m0_m1_grid_desc,
void* p_b_k_n0_n1_grid_desc,
void* p_c_m0_m10_m11_n0_n10_n11_grid_desc,
void* p_c_blockid_to_m0_n0_block_cluster_adaptor)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
const index_t ho = (hi + leftPadH + rightPadH - convDilationY * (y - 1) - 1) / convStrideH + 1;
const index_t wo = (wi + leftPadW + rightPadW - convDilationX * (x - 1) - 1) / convStrideW + 1;
const auto in_n_c_hi_wi_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(n, c, hi, wi));
const auto wei_k_c_y_x_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(k, c, y, x));
const auto out_n_k_ho_wo_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(n, k, ho, wo));
const auto descs = transform_forward_convolution_into_gemm_v4r4_nchw_kcyx_nkhw_pad(
wei_k_c_y_x_desc,
in_n_c_hi_wi_desc,
out_n_k_ho_wo_desc,
make_tuple(convStrideH, convStrideW),
make_tuple(convDilationY, convDilationX),
make_tuple(leftPadH, leftPadW),
make_tuple(rightPadH, rightPadW));
const auto a_k_m_grid_desc = descs[I0];
const auto b_k_n_grid_desc = descs[I1];
const auto c_m_n_grid_desc = descs[I2];
using AKMGridDesc = decltype(a_k_m_grid_desc);
using BKNGridDesc = decltype(b_k_n_grid_desc);
using CMNGridDesc = decltype(c_m_n_grid_desc);
using AGridIteratorHacks = decltype(make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{})));
using BGridIteratorHacks =
decltype(make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{})));
using CGridIteratorHacks = decltype(make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{})));
using AGridMoveSliceWindowIteratorHacks = Sequence<0, 0, 0, 0, 0>;
using BGridMoveSliceWindowIteratorHacks = Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0>;
using GridwiseGemm =
GridwiseDynamicGemmDlops_km_kn_mn_v1r2<BlockSize,
FloatAB,
FloatAcc,
FloatC,
InMemoryDataOperationEnum_t::Set, /* ToDo tunable */
AKMGridDesc,
BKNGridDesc,
CMNGridDesc,
MPerBlock,
NPerBlock,
KPerBlock,
M1PerThread,
N1PerThread,
KPerThread,
M1N1ThreadClusterM10,
M1N1ThreadClusterN10,
M1N1ThreadClusterM11,
M1N1ThreadClusterN11,
ABlockTransferThreadSliceLengths_K_M0_M1,
ABlockTransferThreadClusterLengths_K_M0_M1,
ABlockTransferThreadClusterArrangeOrder,
ABlockTransferSrcAccessOrder,
ABlockTransferSrcVectorDim,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_M1,
AThreadTransferSrcResetCoordinateAfterRun,
BBlockTransferThreadSliceLengths_K_N0_N1,
BBlockTransferThreadClusterLengths_K_N0_N1,
BBlockTransferThreadClusterArrangeOrder,
BBlockTransferSrcAccessOrder,
BBlockTransferSrcVectorDim,
BBlockTransferSrcScalarPerVector,
BBlockTransferDstScalarPerVector_N1,
BThreadTransferSrcResetCoordinateAfterRun,
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
AGridIteratorHacks,
BGridIteratorHacks,
CGridIteratorHacks,
AGridMoveSliceWindowIteratorHacks,
BGridMoveSliceWindowIteratorHacks>;
auto a_k_m0_m1_grid_desc = GridwiseGemm::MakeAKM0M1GridDescriptor(a_k_m_grid_desc);
auto b_k_n0_n1_grid_desc = GridwiseGemm::MakeBKN0N1GridDescriptor(b_k_n_grid_desc);
auto c_m0_m10_m11_n0_n10_n11_grid_desc =
GridwiseGemm::MakeCM0M10M11N0N10N11GridDescriptor(c_m_n_grid_desc);
auto c_blockid_to_m0_n0_block_cluster_adaptor =
GridwiseGemm::MakeCBlockIdToM0N0BlockClusterAdaptor(c_m_n_grid_desc);
if(hipThreadIdx_x == 0)
{
*static_cast<decltype(a_k_m0_m1_grid_desc)*>(p_a_k_m0_m1_grid_desc) = a_k_m0_m1_grid_desc;
*static_cast<decltype(b_k_n0_n1_grid_desc)*>(p_b_k_n0_n1_grid_desc) = b_k_n0_n1_grid_desc;
*static_cast<decltype(c_m0_m10_m11_n0_n10_n11_grid_desc)*>(
p_c_m0_m10_m11_n0_n10_n11_grid_desc) = c_m0_m10_m11_n0_n10_n11_grid_desc;
*static_cast<decltype(c_blockid_to_m0_n0_block_cluster_adaptor)*>(
p_c_blockid_to_m0_n0_block_cluster_adaptor) = c_blockid_to_m0_n0_block_cluster_adaptor;
};
};
extern "C" __global__ void
#if CK_USE_LAUNCH_BOUNDS
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif
dynamic_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw(
const FloatAB* __restrict__ p_a_grid,
const FloatAB* __restrict__ p_b_grid,
FloatC* __restrict__ p_c_grid,
const void CONSTANT* p_a_k_m0_m1_grid_desc,
const void CONSTANT* p_b_k_n0_n1_grid_desc,
const void CONSTANT* p_c_m0_m10_m11_n0_n10_n11_grid_desc,
const void CONSTANT* p_c_blockid_to_m0_n0_block_cluster_adaptor)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto in_n_c_hi_wi_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(256, 256, 28, 28));
constexpr auto wei_k_c_y_x_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(256, 256, 3, 3));
constexpr auto out_n_k_ho_wo_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(256, 256, 28, 28));
constexpr auto descs =
transform_forward_convolution_into_gemm_v4r4_nchw_kcyx_nkhw_pad(wei_k_c_y_x_desc,
in_n_c_hi_wi_desc,
out_n_k_ho_wo_desc,
make_tuple(1, 1),
make_tuple(1, 1),
make_tuple(1, 1),
make_tuple(1, 1));
constexpr auto a_k_m_grid_desc = descs[I0];
constexpr auto b_k_n_grid_desc = descs[I1];
constexpr auto c_m_n_grid_desc = descs[I2];
using AKMGridDesc = decltype(a_k_m_grid_desc);
using BKNGridDesc = decltype(b_k_n_grid_desc);
using CMNGridDesc = decltype(c_m_n_grid_desc);
using AGridIteratorHacks = decltype(make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{})));
using BGridIteratorHacks =
decltype(make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{})));
using CGridIteratorHacks = decltype(make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{})));
using AGridMoveSliceWindowIteratorHacks = Sequence<0, 0, 0, 0, 0>;
using BGridMoveSliceWindowIteratorHacks = Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0>;
using GridwiseGemm =
GridwiseDynamicGemmDlops_km_kn_mn_v1r2<BlockSize,
FloatAB,
FloatAcc,
FloatC,
InMemoryDataOperationEnum_t::Set, /* ToDo tunable */
AKMGridDesc,
BKNGridDesc,
CMNGridDesc,
MPerBlock,
NPerBlock,
KPerBlock,
M1PerThread,
N1PerThread,
KPerThread,
M1N1ThreadClusterM10,
M1N1ThreadClusterN10,
M1N1ThreadClusterM11,
M1N1ThreadClusterN11,
ABlockTransferThreadSliceLengths_K_M0_M1,
ABlockTransferThreadClusterLengths_K_M0_M1,
ABlockTransferThreadClusterArrangeOrder,
ABlockTransferSrcAccessOrder,
ABlockTransferSrcVectorDim,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_M1,
AThreadTransferSrcResetCoordinateAfterRun,
BBlockTransferThreadSliceLengths_K_N0_N1,
BBlockTransferThreadClusterLengths_K_N0_N1,
BBlockTransferThreadClusterArrangeOrder,
BBlockTransferSrcAccessOrder,
BBlockTransferSrcVectorDim,
BBlockTransferSrcScalarPerVector,
BBlockTransferDstScalarPerVector_N1,
BThreadTransferSrcResetCoordinateAfterRun,
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
AGridIteratorHacks,
BGridIteratorHacks,
CGridIteratorHacks,
AGridMoveSliceWindowIteratorHacks,
BGridMoveSliceWindowIteratorHacks>;
constexpr auto a_k_m0_m1_grid_desc_tmp =
GridwiseGemm::MakeAKM0M1GridDescriptor(a_k_m_grid_desc);
constexpr auto b_k_n0_n1_grid_desc_tmp =
GridwiseGemm::MakeBKN0N1GridDescriptor(b_k_n_grid_desc);
constexpr auto c_m0_m10_m11_n0_n10_n11_grid_desc_tmp =
GridwiseGemm::MakeCM0M10M11N0N10N11GridDescriptor(c_m_n_grid_desc);
constexpr auto c_blockid_to_m0_n0_block_cluster_adaptor_tmp =
GridwiseGemm::MakeCBlockIdToM0N0BlockClusterAdaptor(c_m_n_grid_desc);
using AKM0M1GridDesc = decltype(a_k_m0_m1_grid_desc_tmp);
using BKN0N1GridDesc = decltype(b_k_n0_n1_grid_desc_tmp);
using CM0M10M11N0N10N11GridDesc = decltype(c_m0_m10_m11_n0_n10_n11_grid_desc_tmp);
using CBlockIdToM0N0BlockClusterAdaptor =
decltype(c_blockid_to_m0_n0_block_cluster_adaptor_tmp);
const auto a_k_m0_m1_grid_desc =
*reinterpret_cast<const AKM0M1GridDesc*>((const void*)p_a_k_m0_m1_grid_desc);
const auto b_k_n0_n1_grid_desc =
*reinterpret_cast<const BKN0N1GridDesc*>((const void*)p_b_k_n0_n1_grid_desc);
const auto c_m0_m10_m11_n0_n10_n11_grid_desc =
*reinterpret_cast<const CM0M10M11N0N10N11GridDesc*>(
(const void*)p_c_m0_m10_m11_n0_n10_n11_grid_desc);
const auto c_blockid_to_m0_n0_block_cluster_adaptor =
*reinterpret_cast<const CBlockIdToM0N0BlockClusterAdaptor*>(
(const void*)p_c_blockid_to_m0_n0_block_cluster_adaptor);
constexpr index_t shared_block_size =
GridwiseGemm::GetSharedMemoryNumberOfByte() / sizeof(FloatAB);
__shared__ FloatAB p_shared_block[shared_block_size];
GridwiseGemm::Run(p_a_grid,
p_b_grid,
p_c_grid,
p_shared_block,
a_k_m0_m1_grid_desc,
b_k_n0_n1_grid_desc,
c_m0_m10_m11_n0_n10_n11_grid_desc,
c_blockid_to_m0_n0_block_cluster_adaptor,
integral_constant<bool, HasMainKBlockLoop>{},
integral_constant<bool, HasDoubleTailKBlockLoop>{});
};

362
composable_kernel/src/kernel_wrapper/dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nchw_kcyx_nkhw.cpp
View File

@@ -1,362 +0,0 @@
#include "common_header.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "gridwise_dynamic_gemm_xdlops_v2r3.hpp"
#include "transform_forward_convolution_into_gemm_v4r4r2_nchw_kcyx_nkhw.hpp"
using namespace ck;
constexpr DataTypeEnum_t ABDataTypeEnum = static_cast<DataTypeEnum_t>(CK_PARAM_ABDataTypeEnum);
constexpr DataTypeEnum_t AccDataTypeEnum = static_cast<DataTypeEnum_t>(CK_PARAM_AccDataTypeEnum);
constexpr DataTypeEnum_t CDataTypeEnum = static_cast<DataTypeEnum_t>(CK_PARAM_CDataTypeEnum);
using FloatAB = typename get_datatype_from_enum<ABDataTypeEnum>::type;
using FloatAcc = typename get_datatype_from_enum<AccDataTypeEnum>::type;
using FloatC = typename get_datatype_from_enum<CDataTypeEnum>::type;
constexpr index_t BlockSize = CK_PARAM_BlockSize;
constexpr index_t MPerBlock = CK_PARAM_MPerBlock;
constexpr index_t NPerBlock = CK_PARAM_NPerBlock;
constexpr index_t KPerBlock = CK_PARAM_KPerBlock;
constexpr index_t MPerWave = CK_PARAM_MPerWave;
constexpr index_t NPerWave = CK_PARAM_NPerWave;
constexpr index_t MRepeat = CK_PARAM_MRepeat;
constexpr index_t NRepeat = CK_PARAM_NRepeat;
constexpr index_t K1 = CK_PARAM_K1;
using ABlockTransferThreadSliceLengths_K0_M_K1 =
Sequence<CK_PARAM_ABlockTransferThreadSliceLengths_K0_M_K1>;
using ABlockTransferThreadClusterLengths_K0_M_K1 =
Sequence<CK_PARAM_ABlockTransferThreadClusterLengths_K0_M_K1>;
using ABlockTransferThreadClusterArrangeOrder =
Sequence<CK_PARAM_ABlockTransferThreadClusterArrangeOrder>;
using ABlockTransferSrcAccessOrder = Sequence<CK_PARAM_ABlockTransferSrcAccessOrder>;
constexpr index_t ABlockTransferSrcVectorDim = CK_PARAM_ABlockTransferSrcVectorDim;
constexpr index_t ABlockTransferSrcScalarPerVector = CK_PARAM_ABlockTransferSrcScalarPerVector;
constexpr index_t ABlockTransferDstScalarPerVector_K1 =
CK_PARAM_ABlockTransferDstScalarPerVector_K1;
constexpr bool AThreadTransferSrcResetCoordinateAfterRun =
static_cast<bool>(CK_PARAM_AThreadTransferSrcResetCoordinateAfterRun);
using BBlockTransferThreadSliceLengths_K0_N_K1 =
Sequence<CK_PARAM_BBlockTransferThreadSliceLengths_K0_N_K1>;
using BBlockTransferThreadClusterLengths_K0_N_K1 =
Sequence<CK_PARAM_BBlockTransferThreadClusterLengths_K0_N_K1>;
using BBlockTransferThreadClusterArrangeOrder =
Sequence<CK_PARAM_BBlockTransferThreadClusterArrangeOrder>;
using BBlockTransferSrcAccessOrder = Sequence<CK_PARAM_BBlockTransferSrcAccessOrder>;
constexpr index_t BBlockTransferSrcVectorDim = CK_PARAM_BBlockTransferSrcVectorDim;
constexpr index_t BBlockTransferSrcScalarPerVector = CK_PARAM_BBlockTransferSrcScalarPerVector;
constexpr index_t BBlockTransferDstScalarPerVector_K1 =
CK_PARAM_BBlockTransferDstScalarPerVector_K1;
constexpr bool BThreadTransferSrcResetCoordinateAfterRun =
static_cast<bool>(CK_PARAM_BThreadTransferSrcResetCoordinateAfterRun);
using CThreadTransferSrcDstAccessOrder = Sequence<CK_PARAM_CThreadTransferSrcDstAccessOrder>;
constexpr index_t CThreadTransferSrcDstVectorDim = CK_PARAM_CThreadTransferSrcDstVectorDim;
constexpr index_t CThreadTransferDstScalarPerVector = CK_PARAM_CThreadTransferDstScalarPerVector;
extern "C" __global__ void
dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nchw_kcyx_nkhw_prepare(
int n,
int c,
int hi,
int wi,
int k,
int y,
int x,
int convStrideH,
int convStrideW,
int convDilationY,
int convDilationX,
int leftPadH,
int leftPadW,
int rightPadH,
int rightPadW,
void* p_a_k0_m_k1_grid_desc,
void* p_b_k0_n_k1_grid_desc,
void* p_c_m0_m1_m2_n_grid_desc,
void* p_c_blockid_to_m0_n0_block_cluster_adaptor)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
const index_t ho = (hi + leftPadH + rightPadH - convDilationY * (y - 1) - 1) / convStrideH + 1;
const index_t wo = (wi + leftPadW + rightPadW - convDilationX * (x - 1) - 1) / convStrideW + 1;
const auto in_n_c_hi_wi_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(n, c, hi, wi));
const auto wei_k_c_y_x_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(k, c, y, x));
const auto out_n_k_ho_wo_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(n, k, ho, wo));
const auto descs = transform_forward_convolution_into_gemm_v4r4r2_nchw_kcyx_nkhw_pad(
wei_k_c_y_x_desc,
in_n_c_hi_wi_desc,
out_n_k_ho_wo_desc,
make_tuple(convStrideH, convStrideW),
make_tuple(convDilationY, convDilationX),
make_tuple(leftPadH, leftPadW),
make_tuple(rightPadH, rightPadW),
Number<K1>{});
const auto a_k0_m_k1_grid_desc = descs[I0];
const auto b_k0_n_k1_grid_desc = descs[I1];
const auto c_m_n_grid_desc = descs[I2];
using AK0MK1GridDesc = decltype(a_k0_m_k1_grid_desc);
using BK0NK1GridDesc = decltype(b_k0_n_k1_grid_desc);
using CMNGridDesc = decltype(c_m_n_grid_desc);
using AGridIteratorHacks = decltype(make_tuple(
make_tuple(Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}),
make_tuple(
Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{})));
using BGridIteratorHacks =
decltype(make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{})));
using CGridIteratorHacks = decltype(make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{})));
using AGridMoveSliceWindowIteratorHacks = Sequence<0, 0, 0, 0, 0>;
using BGridMoveSliceWindowIteratorHacks = Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0>;
using GridwiseGemm =
GridwiseDynamicGemm_k0mk1_k0nk1_mn_xdlops_v2r3<BlockSize,
FloatAB,
FloatAcc,
FloatC,
InMemoryDataOperationEnum_t::Set,
AK0MK1GridDesc,
BK0NK1GridDesc,
CMNGridDesc,
MPerBlock,
NPerBlock,
KPerBlock,
MPerWave,
NPerWave,
K1,
MRepeat,
NRepeat,
ABlockTransferThreadSliceLengths_K0_M_K1,
ABlockTransferThreadClusterLengths_K0_M_K1,
ABlockTransferThreadClusterArrangeOrder,
ABlockTransferSrcAccessOrder,
ABlockTransferSrcVectorDim,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_K1,
AThreadTransferSrcResetCoordinateAfterRun,
BBlockTransferThreadSliceLengths_K0_N_K1,
BBlockTransferThreadClusterLengths_K0_N_K1,
BBlockTransferThreadClusterArrangeOrder,
BBlockTransferSrcAccessOrder,
BBlockTransferSrcVectorDim,
BBlockTransferSrcScalarPerVector,
BBlockTransferDstScalarPerVector_K1,
BThreadTransferSrcResetCoordinateAfterRun,
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
AGridIteratorHacks,
BGridIteratorHacks,
CGridIteratorHacks,
AGridMoveSliceWindowIteratorHacks,
BGridMoveSliceWindowIteratorHacks,
false>;
auto c_m0_m1_m2_n_grid_desc = GridwiseGemm::MakeCM0M1M2NGridDescriptor(c_m_n_grid_desc);
auto c_blockid_to_m0_n0_block_cluster_adaptor =
GridwiseGemm::MakeCBlockClusterAdaptor(c_m_n_grid_desc);
if(hipThreadIdx_x == 0)
{
*static_cast<remove_cv_t<decltype(a_k0_m_k1_grid_desc)>*>(p_a_k0_m_k1_grid_desc) =
a_k0_m_k1_grid_desc;
*static_cast<remove_cv_t<decltype(b_k0_n_k1_grid_desc)>*>(p_b_k0_n_k1_grid_desc) =
b_k0_n_k1_grid_desc;
*static_cast<decltype(c_m0_m1_m2_n_grid_desc)*>(p_c_m0_m1_m2_n_grid_desc) =
c_m0_m1_m2_n_grid_desc;
*static_cast<decltype(c_blockid_to_m0_n0_block_cluster_adaptor)*>(
p_c_blockid_to_m0_n0_block_cluster_adaptor) = c_blockid_to_m0_n0_block_cluster_adaptor;
}
};
extern "C" __global__ void
#if CK_USE_LAUNCH_BOUNDS
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif
dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nchw_kcyx_nkhw(
const FloatAB* __restrict__ p_a_grid,
const FloatAB* __restrict__ p_b_grid,
FloatC* __restrict__ p_c_grid,
const void CONSTANT* p_a_k0_m_k1_grid_desc,
const void CONSTANT* p_b_k0_n_k1_grid_desc,
const void CONSTANT* p_c_m0_m1_m2_n_grid_desc,
const void CONSTANT* p_c_blockid_to_m0_n0_block_cluster_adaptor)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto in_n_c_hi_wi_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(256, 256, 28, 28));
constexpr auto wei_k_c_y_x_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(256, 256, 3, 3));
constexpr auto out_n_k_ho_wo_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(256, 256, 28, 28));
constexpr auto descs =
transform_forward_convolution_into_gemm_v4r4r2_nchw_kcyx_nkhw_pad(wei_k_c_y_x_desc,
in_n_c_hi_wi_desc,
out_n_k_ho_wo_desc,
make_tuple(1, 1),
make_tuple(1, 1),
make_tuple(1, 1),
make_tuple(1, 1),
Number<K1>{});
constexpr auto a_k0_m_k1_grid_desc_tmp = descs[I0];
constexpr auto b_k0_n_k1_grid_desc_tmp = descs[I1];
constexpr auto c_m_n_grid_desc = descs[I2];
using AGridIteratorHacks = decltype(make_tuple(
make_tuple(Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}),
make_tuple(
Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{})));
using BGridIteratorHacks =
decltype(make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{})));
using CGridIteratorHacks = decltype(make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{})));
using AGridMoveSliceWindowIteratorHacks = Sequence<0, 0, 0, 0, 0>;
using BGridMoveSliceWindowIteratorHacks = Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0>;
using AK0MK1GridDesc = decltype(a_k0_m_k1_grid_desc_tmp);
using BK0NK1GridDesc = decltype(b_k0_n_k1_grid_desc_tmp);
using CMNGridDesc = decltype(c_m_n_grid_desc);
using GridwiseGemm =
GridwiseDynamicGemm_k0mk1_k0nk1_mn_xdlops_v2r3<BlockSize,
FloatAB,
FloatAcc,
FloatC,
InMemoryDataOperationEnum_t::Set,
AK0MK1GridDesc,
BK0NK1GridDesc,
CMNGridDesc,
MPerBlock,
NPerBlock,
KPerBlock,
MPerWave,
NPerWave,
K1,
MRepeat,
NRepeat,
ABlockTransferThreadSliceLengths_K0_M_K1,
ABlockTransferThreadClusterLengths_K0_M_K1,
ABlockTransferThreadClusterArrangeOrder,
ABlockTransferSrcAccessOrder,
ABlockTransferSrcVectorDim,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_K1,
AThreadTransferSrcResetCoordinateAfterRun,
BBlockTransferThreadSliceLengths_K0_N_K1,
BBlockTransferThreadClusterLengths_K0_N_K1,
BBlockTransferThreadClusterArrangeOrder,
BBlockTransferSrcAccessOrder,
BBlockTransferSrcVectorDim,
BBlockTransferSrcScalarPerVector,
BBlockTransferDstScalarPerVector_K1,
BThreadTransferSrcResetCoordinateAfterRun,
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
AGridIteratorHacks,
BGridIteratorHacks,
CGridIteratorHacks,
AGridMoveSliceWindowIteratorHacks,
BGridMoveSliceWindowIteratorHacks,
false>;
constexpr auto c_m0_m1_m2_n_grid_desc_tmp =
GridwiseGemm::MakeCM0M1M2NGridDescriptor(c_m_n_grid_desc);
constexpr auto c_blockid_to_m0_n0_block_cluster_adaptor_tmp =
GridwiseGemm::MakeCBlockClusterAdaptor(c_m_n_grid_desc);
using CM0M1M2NGridDesc = decltype(c_m0_m1_m2_n_grid_desc_tmp);
using CBlockIdToM0N0BlockClusterAdaptor =
decltype(c_blockid_to_m0_n0_block_cluster_adaptor_tmp);
const auto a_k0_m_k1_grid_desc =
*reinterpret_cast<const AK0MK1GridDesc*>((const void*)p_a_k0_m_k1_grid_desc);
const auto b_k0_n_k1_grid_desc =
*reinterpret_cast<const BK0NK1GridDesc*>((const void*)p_b_k0_n_k1_grid_desc);
const auto c_m0_m1_m2_n_grid_desc =
*reinterpret_cast<const CM0M1M2NGridDesc*>((const void*)p_c_m0_m1_m2_n_grid_desc);
const auto c_blockid_to_m0_n0_block_cluster_adaptor =
*reinterpret_cast<const CBlockIdToM0N0BlockClusterAdaptor*>(
(const void*)p_c_blockid_to_m0_n0_block_cluster_adaptor);
constexpr index_t shared_block_size =
GridwiseGemm::GetSharedMemoryNumberOfByte() / sizeof(FloatAB);
__shared__ FloatAB p_shared_block[shared_block_size];
GridwiseGemm::Run(p_a_grid,
p_b_grid,
p_c_grid,
p_shared_block,
a_k0_m_k1_grid_desc,
b_k0_n_k1_grid_desc,
c_m0_m1_m2_n_grid_desc,
c_blockid_to_m0_n0_block_cluster_adaptor);
};

362
composable_kernel/src/kernel_wrapper/dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nhwc_kyxc_nhwk.cpp
View File

@@ -1,362 +0,0 @@
#include "common_header.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "gridwise_dynamic_gemm_xdlops_v2r3.hpp"
#include "transform_forward_convolution_into_gemm_v4r4r4_nhwc_kyxc_nhwk.hpp"
using namespace ck;
constexpr DataTypeEnum_t ABDataTypeEnum = static_cast<DataTypeEnum_t>(CK_PARAM_ABDataTypeEnum);
constexpr DataTypeEnum_t AccDataTypeEnum = static_cast<DataTypeEnum_t>(CK_PARAM_AccDataTypeEnum);
constexpr DataTypeEnum_t CDataTypeEnum = static_cast<DataTypeEnum_t>(CK_PARAM_CDataTypeEnum);
using FloatAB = typename get_datatype_from_enum<ABDataTypeEnum>::type;
using FloatAcc = typename get_datatype_from_enum<AccDataTypeEnum>::type;
using FloatC = typename get_datatype_from_enum<CDataTypeEnum>::type;
constexpr index_t BlockSize = CK_PARAM_BlockSize;
constexpr index_t MPerBlock = CK_PARAM_MPerBlock;
constexpr index_t NPerBlock = CK_PARAM_NPerBlock;
constexpr index_t KPerBlock = CK_PARAM_KPerBlock;
constexpr index_t MPerWave = CK_PARAM_MPerWave;
constexpr index_t NPerWave = CK_PARAM_NPerWave;
constexpr index_t MRepeat = CK_PARAM_MRepeat;
constexpr index_t NRepeat = CK_PARAM_NRepeat;
constexpr index_t K1 = CK_PARAM_K1;
using ABlockTransferThreadSliceLengths_K0_M_K1 =
Sequence<CK_PARAM_ABlockTransferThreadSliceLengths_K0_M_K1>;
using ABlockTransferThreadClusterLengths_K0_M_K1 =
Sequence<CK_PARAM_ABlockTransferThreadClusterLengths_K0_M_K1>;
using ABlockTransferThreadClusterArrangeOrder =
Sequence<CK_PARAM_ABlockTransferThreadClusterArrangeOrder>;
using ABlockTransferSrcAccessOrder = Sequence<CK_PARAM_ABlockTransferSrcAccessOrder>;
constexpr index_t ABlockTransferSrcVectorDim = CK_PARAM_ABlockTransferSrcVectorDim;
constexpr index_t ABlockTransferSrcScalarPerVector = CK_PARAM_ABlockTransferSrcScalarPerVector;
constexpr index_t ABlockTransferDstScalarPerVector_K1 =
CK_PARAM_ABlockTransferDstScalarPerVector_K1;
constexpr bool AThreadTransferSrcResetCoordinateAfterRun =
static_cast<bool>(CK_PARAM_AThreadTransferSrcResetCoordinateAfterRun);
using BBlockTransferThreadSliceLengths_K0_N_K1 =
Sequence<CK_PARAM_BBlockTransferThreadSliceLengths_K0_N_K1>;
using BBlockTransferThreadClusterLengths_K0_N_K1 =
Sequence<CK_PARAM_BBlockTransferThreadClusterLengths_K0_N_K1>;
using BBlockTransferThreadClusterArrangeOrder =
Sequence<CK_PARAM_BBlockTransferThreadClusterArrangeOrder>;
using BBlockTransferSrcAccessOrder = Sequence<CK_PARAM_BBlockTransferSrcAccessOrder>;
constexpr index_t BBlockTransferSrcVectorDim = CK_PARAM_BBlockTransferSrcVectorDim;
constexpr index_t BBlockTransferSrcScalarPerVector = CK_PARAM_BBlockTransferSrcScalarPerVector;
constexpr index_t BBlockTransferDstScalarPerVector_K1 =
CK_PARAM_BBlockTransferDstScalarPerVector_K1;
constexpr bool BThreadTransferSrcResetCoordinateAfterRun =
static_cast<bool>(CK_PARAM_BThreadTransferSrcResetCoordinateAfterRun);
using CThreadTransferSrcDstAccessOrder = Sequence<CK_PARAM_CThreadTransferSrcDstAccessOrder>;
constexpr index_t CThreadTransferSrcDstVectorDim = CK_PARAM_CThreadTransferSrcDstVectorDim;
constexpr index_t CThreadTransferDstScalarPerVector = CK_PARAM_CThreadTransferDstScalarPerVector;
extern "C" __global__ void
dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nhwc_kyxc_nhwk_prepare(
int n,
int hi,
int wi,
int c,
int k,
int y,
int x,
int convStrideH,
int convStrideW,
int convDilationY,
int convDilationX,
int leftPadH,
int leftPadW,
int rightPadH,
int rightPadW,
void* p_a_k0_m_k1_grid_desc,
void* p_b_k0_n_k1_grid_desc,
void* p_c_m0_m1_m2_n_grid_desc,
void* p_c_blockid_to_m0_n0_block_cluster_adaptor)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
const index_t ho = (hi + leftPadH + rightPadH - convDilationY * (y - 1) - 1) / convStrideH + 1;
const index_t wo = (wi + leftPadW + rightPadW - convDilationX * (x - 1) - 1) / convStrideW + 1;
const auto in_n_hi_wi_c_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(n, hi, wi, c));
const auto wei_k_y_x_c_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(k, y, x, c));
const auto out_n_ho_wo_k_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(n, ho, wo, k));
const auto descs = transform_forward_convolution_into_gemm_v4r4r4_nhwc_kyxc_nhwk_pad(
in_n_hi_wi_c_desc,
wei_k_y_x_c_desc,
out_n_ho_wo_k_desc,
make_tuple(convStrideH, convStrideW),
make_tuple(convDilationY, convDilationX),
make_tuple(leftPadH, leftPadW),
make_tuple(rightPadH, rightPadW),
Number<K1>{});
const auto a_k0_m_k1_grid_desc = descs[I0];
const auto b_k0_n_k1_grid_desc = descs[I1];
const auto c_m_n_grid_desc = descs[I2];
using AK0MK1GridDesc = decltype(a_k0_m_k1_grid_desc);
using BK0NK1GridDesc = decltype(b_k0_n_k1_grid_desc);
using CMNGridDesc = decltype(c_m_n_grid_desc);
using BGridIteratorHacks = decltype(make_tuple(
make_tuple(Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}),
make_tuple(
Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{})));
using AGridIteratorHacks =
decltype(make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{})));
using CGridIteratorHacks = decltype(make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{})));
using AGridMoveSliceWindowIteratorHacks = Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0>;
using BGridMoveSliceWindowIteratorHacks = Sequence<0, 0, 0, 0, 0>;
using GridwiseGemm =
GridwiseDynamicGemm_k0mk1_k0nk1_mn_xdlops_v2r3<BlockSize,
FloatAB,
FloatAcc,
FloatC,
InMemoryDataOperationEnum_t::Set,
AK0MK1GridDesc,
BK0NK1GridDesc,
CMNGridDesc,
MPerBlock,
NPerBlock,
KPerBlock,
MPerWave,
NPerWave,
K1,
MRepeat,
NRepeat,
ABlockTransferThreadSliceLengths_K0_M_K1,
ABlockTransferThreadClusterLengths_K0_M_K1,
ABlockTransferThreadClusterArrangeOrder,
ABlockTransferSrcAccessOrder,
ABlockTransferSrcVectorDim,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_K1,
AThreadTransferSrcResetCoordinateAfterRun,
BBlockTransferThreadSliceLengths_K0_N_K1,
BBlockTransferThreadClusterLengths_K0_N_K1,
BBlockTransferThreadClusterArrangeOrder,
BBlockTransferSrcAccessOrder,
BBlockTransferSrcVectorDim,
BBlockTransferSrcScalarPerVector,
BBlockTransferDstScalarPerVector_K1,
BThreadTransferSrcResetCoordinateAfterRun,
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
AGridIteratorHacks,
BGridIteratorHacks,
CGridIteratorHacks,
AGridMoveSliceWindowIteratorHacks,
BGridMoveSliceWindowIteratorHacks,
false>;
auto c_m0_m1_m2_n_grid_desc = GridwiseGemm::MakeCM0M1M2NGridDescriptor(c_m_n_grid_desc);
auto c_blockid_to_m0_n0_block_cluster_adaptor =
GridwiseGemm::MakeCBlockClusterAdaptor(c_m_n_grid_desc);
if(hipThreadIdx_x == 0)
{
*static_cast<remove_cv_t<decltype(a_k0_m_k1_grid_desc)>*>(p_a_k0_m_k1_grid_desc) =
a_k0_m_k1_grid_desc;
*static_cast<remove_cv_t<decltype(b_k0_n_k1_grid_desc)>*>(p_b_k0_n_k1_grid_desc) =
b_k0_n_k1_grid_desc;
*static_cast<decltype(c_m0_m1_m2_n_grid_desc)*>(p_c_m0_m1_m2_n_grid_desc) =
c_m0_m1_m2_n_grid_desc;
*static_cast<decltype(c_blockid_to_m0_n0_block_cluster_adaptor)*>(
p_c_blockid_to_m0_n0_block_cluster_adaptor) = c_blockid_to_m0_n0_block_cluster_adaptor;
}
};
extern "C" __global__ void
#if CK_USE_LAUNCH_BOUNDS
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif
dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nhwc_kyxc_nhwk(
const FloatAB* __restrict__ p_a_grid,
const FloatAB* __restrict__ p_b_grid,
FloatC* __restrict__ p_c_grid,
const void CONSTANT* p_a_k0_m_k1_grid_desc,
const void CONSTANT* p_b_k0_n_k1_grid_desc,
const void CONSTANT* p_c_m0_m1_m2_n_grid_desc,
const void CONSTANT* p_c_blockid_to_m0_n0_block_cluster_adaptor)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto in_n_hi_wi_c_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(256, 28, 28, 256));
constexpr auto wei_k_y_x_c_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(256, 3, 3, 256));
constexpr auto out_n_ho_wo_k_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(256, 28, 28, 256));
constexpr auto descs =
transform_forward_convolution_into_gemm_v4r4r4_nhwc_kyxc_nhwk_pad(in_n_hi_wi_c_desc,
wei_k_y_x_c_desc,
out_n_ho_wo_k_desc,
make_tuple(1, 1),
make_tuple(1, 1),
make_tuple(1, 1),
make_tuple(1, 1),
Number<K1>{});
constexpr auto a_k0_m_k1_grid_desc_tmp = descs[I0];
constexpr auto b_k0_n_k1_grid_desc_tmp = descs[I1];
constexpr auto c_m_n_grid_desc = descs[I2];
using AK0MK1GridDesc = decltype(a_k0_m_k1_grid_desc_tmp);
using BK0NK1GridDesc = decltype(b_k0_n_k1_grid_desc_tmp);
using CMNGridDesc = decltype(c_m_n_grid_desc);
using BGridIteratorHacks = decltype(make_tuple(
make_tuple(Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}),
make_tuple(
Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{})));
using AGridIteratorHacks =
decltype(make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{})));
using CGridIteratorHacks = decltype(make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{}),
make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{})));
using AGridMoveSliceWindowIteratorHacks = Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0>;
using BGridMoveSliceWindowIteratorHacks = Sequence<0, 0, 0, 0, 0>;
using GridwiseGemm =
GridwiseDynamicGemm_k0mk1_k0nk1_mn_xdlops_v2r3<BlockSize,
FloatAB,
FloatAcc,
FloatC,
InMemoryDataOperationEnum_t::Set,
AK0MK1GridDesc,
BK0NK1GridDesc,
CMNGridDesc,
MPerBlock,
NPerBlock,
KPerBlock,
MPerWave,
NPerWave,
K1,
MRepeat,
NRepeat,
ABlockTransferThreadSliceLengths_K0_M_K1,
ABlockTransferThreadClusterLengths_K0_M_K1,
ABlockTransferThreadClusterArrangeOrder,
ABlockTransferSrcAccessOrder,
ABlockTransferSrcVectorDim,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_K1,
AThreadTransferSrcResetCoordinateAfterRun,
BBlockTransferThreadSliceLengths_K0_N_K1,
BBlockTransferThreadClusterLengths_K0_N_K1,
BBlockTransferThreadClusterArrangeOrder,
BBlockTransferSrcAccessOrder,
BBlockTransferSrcVectorDim,
BBlockTransferSrcScalarPerVector,
BBlockTransferDstScalarPerVector_K1,
BThreadTransferSrcResetCoordinateAfterRun,
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
AGridIteratorHacks,
BGridIteratorHacks,
CGridIteratorHacks,
AGridMoveSliceWindowIteratorHacks,
BGridMoveSliceWindowIteratorHacks,
false>;
constexpr auto c_m0_m1_m2_n_grid_desc_tmp =
GridwiseGemm::MakeCM0M1M2NGridDescriptor(c_m_n_grid_desc);
constexpr auto c_blockid_to_m0_n0_block_cluster_adaptor_tmp =
GridwiseGemm::MakeCBlockClusterAdaptor(c_m_n_grid_desc);
using CM0M1M2NGridDesc = decltype(c_m0_m1_m2_n_grid_desc_tmp);
using CBlockIdToM0N0BlockClusterAdaptor =
decltype(c_blockid_to_m0_n0_block_cluster_adaptor_tmp);
const auto a_k0_m_k1_grid_desc =
*reinterpret_cast<const AK0MK1GridDesc*>((const void*)p_a_k0_m_k1_grid_desc);
const auto b_k0_n_k1_grid_desc =
*reinterpret_cast<const BK0NK1GridDesc*>((const void*)p_b_k0_n_k1_grid_desc);
const auto c_m0_m1_m2_n_grid_desc =
*reinterpret_cast<const CM0M1M2NGridDesc*>((const void*)p_c_m0_m1_m2_n_grid_desc);
const auto c_blockid_to_m0_n0_block_cluster_adaptor =
*reinterpret_cast<const CBlockIdToM0N0BlockClusterAdaptor*>(
(const void*)p_c_blockid_to_m0_n0_block_cluster_adaptor);
constexpr index_t shared_block_size =
GridwiseGemm::GetSharedMemoryNumberOfByte() / sizeof(FloatAB);
__shared__ FloatAB p_shared_block[shared_block_size];
GridwiseGemm::Run(p_a_grid,
p_b_grid,
p_c_grid,
p_shared_block,
a_k0_m_k1_grid_desc,
b_k0_n_k1_grid_desc,
c_m0_m1_m2_n_grid_desc,
c_blockid_to_m0_n0_block_cluster_adaptor);
};

5670
external/half/include/half.hpp vendored
View File

File diff suppressed because it is too large Load Diff

2
host/CMakeLists.txt
View File

@@ -1,4 +1,2 @@
add_subdirectory(host_tensor)
add_subdirectory(online_compile)
add_subdirectory(driver_offline)
add_subdirectory(driver_online)

5
host/driver_offline/CMakeLists.txt
View File

@@ -9,11 +9,10 @@ include_directories(BEFORE
${PROJECT_SOURCE_DIR}/composable_kernel/include/problem_transform
${PROJECT_SOURCE_DIR}/composable_kernel/include/driver
${PROJECT_SOURCE_DIR}/external/rocm/include
${PROJECT_SOURCE_DIR}/external/half/include
)
set(CONV_FWD_DRIVER_OFFLINE_SOURCE conv_fwd_driver_offline.cpp)
set(CONV_BWD_DRIVER_OFFLINE_SOURCE conv_bwd_driver_offline.cpp)
set(CONV_FWD_DRIVER_OFFLINE_SOURCE src/conv_fwd_driver_offline.cpp)
set(CONV_BWD_DRIVER_OFFLINE_SOURCE src/conv_bwd_driver_offline.cpp)
add_executable(conv_fwd_driver_offline ${CONV_FWD_DRIVER_OFFLINE_SOURCE})
add_executable(conv_bwd_driver_offline ${CONV_BWD_DRIVER_OFFLINE_SOURCE})

55
host/driver_offline/include/device_dynamic_convolution_backward_data_implicit_gemm_v4r1_xdlops_nhwc_kyxc_nhwk.hpp → host/driver_offline/include/device_convolution_backward_data_implicit_gemm_v4r1_xdlops_nhwc_kyxc_nhwk.hpp
View File

@@ -2,7 +2,7 @@
#include "device.hpp"
#include "host_tensor.hpp"
#include "transform_backward_data_convolution_into_gemm_v4r1_nhwc_kyxc_nhwk.hpp"
#include "driver_dynamic_gemm_xdlops_v2r3.hpp"
#include "driver_gemm_xdlops_v2r3.hpp"
template <typename TInWei,
typename TAcc,
@@ -14,7 +14,7 @@ template <typename TInWei,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
void device_dynamic_convolution_backward_data_implicit_gemm_v4r1_xdlops_nhwc_kyxc_nhwk(
void device_convolution_backward_data_implicit_gemm_v4r1_xdlops_nhwc_kyxc_nhwk(
const InLengths& in_n_hi_wi_c_lengths,
const WeiLengths& wei_k_y_x_c_lengths,
const OutLengths& out_n_ho_wo_k_lengths,
@@ -35,11 +35,6 @@ void device_dynamic_convolution_backward_data_implicit_gemm_v4r1_xdlops_nhwc_kyx
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto I4 = Number<4>{};
constexpr auto I5 = Number<5>{};
constexpr auto I6 = Number<6>{};
constexpr auto I7 = Number<7>{};
constexpr auto I8 = Number<8>{};
DeviceMem in_n_hi_wi_c_device_buf(sizeof(TInWei) * in_n_hi_wi_c.mDesc.GetElementSpace());
DeviceMem wei_k_y_x_c_device_buf(sizeof(TInWei) * wei_k_y_x_c.mDesc.GetElementSpace());
@@ -49,12 +44,9 @@ void device_dynamic_convolution_backward_data_implicit_gemm_v4r1_xdlops_nhwc_kyx
wei_k_y_x_c_device_buf.ToDevice(wei_k_y_x_c.mData.data());
out_n_ho_wo_k_device_buf.ToDevice(out_n_ho_wo_k.mData.data());
const auto in_n_hi_wi_c_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(in_n_hi_wi_c_lengths);
const auto wei_k_y_x_c_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(wei_k_y_x_c_lengths);
const auto out_n_ho_wo_k_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(out_n_ho_wo_k_lengths);
const auto in_n_hi_wi_c_desc = make_naive_tensor_descriptor_packed(in_n_hi_wi_c_lengths);
const auto wei_k_y_x_c_desc = make_naive_tensor_descriptor_packed(wei_k_y_x_c_lengths);
const auto out_n_ho_wo_k_desc = make_naive_tensor_descriptor_packed(out_n_ho_wo_k_lengths);
#if 1
// [M, N, K0, K1] = [128, 128, 4, 4] for fp32
@@ -215,7 +207,7 @@ void device_dynamic_convolution_backward_data_implicit_gemm_v4r1_xdlops_nhwc_kyx
const auto in_gemmm_gemmn_grid_desc = descs[I2];
// HACK: hacks that control index calculation when iterating over A, B, C matrix
constexpr auto wei_gemmk0_gemmm_gemmk1_grid_iterator_hacks =
constexpr auto wei_gemmk0_gemmm_gemmk1_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{}, // 0+: gemmk0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1+: gemmm
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}), // 2+: gemmk1
@@ -223,7 +215,7 @@ void device_dynamic_convolution_backward_data_implicit_gemm_v4r1_xdlops_nhwc_kyx
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1-: Gemmm
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 2-: Gemmk1
constexpr auto out_gemmk0_gemmn_gemmk1_grid_iterator_hacks = make_tuple(
constexpr auto out_gemmk0_gemmn_gemmk1_grid_step_hacks = make_tuple(
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{}, // 0+: gemmk0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0>{}, // 1+: gemmn
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}), // 2+: gemmk1
@@ -231,7 +223,7 @@ void device_dynamic_convolution_backward_data_implicit_gemm_v4r1_xdlops_nhwc_kyx
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0>{}, // 1-: gemmn
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 2-: gemmk1
constexpr auto in_m0_m1_m2_n_grid_iterator_hacks = make_tuple(
constexpr auto in_m0_m1_m2_n_grid_step_hacks = make_tuple(
make_tuple(
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0+: MRepeat
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{}, // 1+: NRepeat
@@ -251,15 +243,15 @@ void device_dynamic_convolution_backward_data_implicit_gemm_v4r1_xdlops_nhwc_kyx
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 6-: M2
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{})); // 7-: N1
constexpr auto wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_iterator_hacks =
constexpr auto wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks =
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{};
constexpr auto out_gemmk0_gemmn_gemmk1_grid_move_slice_window_iterator_hacks =
constexpr auto out_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks =
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0>{};
for(index_t i = 0; i < 5; ++i)
{
float ave_time = driver_dynamic_gemm_xdlops_v2r3<
float ave_time = driver_gemm_xdlops_v2r3<
BlockSize,
TInWei,
TAcc,
@@ -295,11 +287,11 @@ void device_dynamic_convolution_backward_data_implicit_gemm_v4r1_xdlops_nhwc_kyx
Sequence<1, 3, 7, 0, 2, 4, 5, 6>,
6,
GemmCThreadTransferDstScalarPerVector,
decltype(wei_gemmk0_gemmm_gemmk1_grid_iterator_hacks),
decltype(out_gemmk0_gemmn_gemmk1_grid_iterator_hacks),
decltype(in_m0_m1_m2_n_grid_iterator_hacks),
decltype(wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_iterator_hacks),
decltype(out_gemmk0_gemmn_gemmk1_grid_move_slice_window_iterator_hacks),
decltype(wei_gemmk0_gemmm_gemmk1_grid_step_hacks),
decltype(out_gemmk0_gemmn_gemmk1_grid_step_hacks),
decltype(in_m0_m1_m2_n_grid_step_hacks),
decltype(wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks),
decltype(out_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks),
false // CAccessOrderMRepeatNRepeat
>(static_cast<TInWei*>(wei_k_y_x_c_device_buf.GetDeviceBuffer()),
static_cast<TOut*>(out_n_ho_wo_k_device_buf.GetDeviceBuffer()),
@@ -307,11 +299,11 @@ void device_dynamic_convolution_backward_data_implicit_gemm_v4r1_xdlops_nhwc_kyx
wei_gemmk0_gemmm_gemmk1_grid_desc,
out_gemmk0_gemmn_gemmk1_grid_desc,
in_gemmm_gemmn_grid_desc,
wei_gemmk0_gemmm_gemmk1_grid_iterator_hacks,
out_gemmk0_gemmn_gemmk1_grid_iterator_hacks,
in_m0_m1_m2_n_grid_iterator_hacks,
wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_iterator_hacks,
out_gemmk0_gemmn_gemmk1_grid_move_slice_window_iterator_hacks,
wei_gemmk0_gemmm_gemmk1_grid_step_hacks,
out_gemmk0_gemmn_gemmk1_grid_step_hacks,
in_m0_m1_m2_n_grid_step_hacks,
wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks,
out_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks,
nrepeat);
{
@@ -319,16 +311,13 @@ void device_dynamic_convolution_backward_data_implicit_gemm_v4r1_xdlops_nhwc_kyx
const auto K = out_n_ho_wo_k_lengths[I3];
const auto C = wei_k_y_x_c_lengths[I3];
const auto Hi = in_n_hi_wi_c_lengths[I1];
const auto Wi = in_n_hi_wi_c_lengths[I2];
const auto Ho = out_n_ho_wo_k_lengths[I1];
const auto Wo = out_n_ho_wo_k_lengths[I2];
const auto Y = wei_k_y_x_c_lengths[I1];
const auto X = wei_k_y_x_c_lengths[I2];
float perf = (float)(std::size_t(2) * N * K * Ho * Wo * C * Y * X) /
float perf = static_cast<float>((std::size_t(2) * N * K * Ho * Wo * C * Y * X)) /
(std::size_t(1000) * 1000 * 1000) / ave_time;
std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s"

55
host/driver_offline/include/device_dynamic_convolution_backward_data_implicit_gemm_v4r1r2_xdlops_nhwc_kyxc_nhwk.hpp → host/driver_offline/include/device_convolution_backward_data_implicit_gemm_v4r1r2_xdlops_nhwc_kyxc_nhwk.hpp
View File

@@ -2,7 +2,7 @@
#include "device.hpp"
#include "host_tensor.hpp"
#include "transform_backward_data_convolution_into_gemm_v4r1r2_nhwc_kyxc_nhwk.hpp"
#include "driver_dynamic_gemm_xdlops_v2r3.hpp"
#include "driver_gemm_xdlops_v2r3.hpp"
template <typename TInWei,
typename TAcc,
@@ -14,7 +14,7 @@ template <typename TInWei,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
void device_dynamic_convolution_backward_data_implicit_gemm_v4r1r2_xdlops_nhwc_kyxc_nhwk(
void device_convolution_backward_data_implicit_gemm_v4r1r2_xdlops_nhwc_kyxc_nhwk(
const InLengths& in_n_hi_wi_c_lengths,
const WeiLengths& wei_k_y_x_c_lengths,
const OutLengths& out_n_ho_wo_k_lengths,
@@ -35,11 +35,6 @@ void device_dynamic_convolution_backward_data_implicit_gemm_v4r1r2_xdlops_nhwc_k
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto I4 = Number<4>{};
constexpr auto I5 = Number<5>{};
constexpr auto I6 = Number<6>{};
constexpr auto I7 = Number<7>{};
constexpr auto I8 = Number<8>{};
DeviceMem in_n_hi_wi_c_device_buf(sizeof(TInWei) * in_n_hi_wi_c.mDesc.GetElementSpace());
DeviceMem wei_k_y_x_c_device_buf(sizeof(TInWei) * wei_k_y_x_c.mDesc.GetElementSpace());
@@ -49,12 +44,9 @@ void device_dynamic_convolution_backward_data_implicit_gemm_v4r1r2_xdlops_nhwc_k
wei_k_y_x_c_device_buf.ToDevice(wei_k_y_x_c.mData.data());
out_n_ho_wo_k_device_buf.ToDevice(out_n_ho_wo_k.mData.data());
const auto in_n_hi_wi_c_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(in_n_hi_wi_c_lengths);
const auto wei_k_y_x_c_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(wei_k_y_x_c_lengths);
const auto out_n_ho_wo_k_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(out_n_ho_wo_k_lengths);
const auto in_n_hi_wi_c_desc = make_naive_tensor_descriptor_packed(in_n_hi_wi_c_lengths);
const auto wei_k_y_x_c_desc = make_naive_tensor_descriptor_packed(wei_k_y_x_c_lengths);
const auto out_n_ho_wo_k_desc = make_naive_tensor_descriptor_packed(out_n_ho_wo_k_lengths);
#if 0
// [M, N, K0, K1] = [256, 128, 4, 4] for fp32
@@ -187,7 +179,7 @@ void device_dynamic_convolution_backward_data_implicit_gemm_v4r1r2_xdlops_nhwc_k
const auto in_gemmm_gemmn_grid_desc = descs[I2];
// HACK: hacks that control index calculation when iterating over A, B, C matrix
constexpr auto out_gemmk0_gemmm_gemmk1_grid_iterator_hacks = make_tuple(
constexpr auto out_gemmk0_gemmm_gemmk1_grid_step_hacks = make_tuple(
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{}, // 0+: gemmk0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0>{}, // 1+: gemmm
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}), // 2+: gemmk1
@@ -195,7 +187,7 @@ void device_dynamic_convolution_backward_data_implicit_gemm_v4r1r2_xdlops_nhwc_k
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0>{}, // 1-: gemmm
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 2-: gemmk1
constexpr auto wei_gemmk0_gemmn_gemmk1_grid_iterator_hacks =
constexpr auto wei_gemmk0_gemmn_gemmk1_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{}, // 0+: gemmk0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1+: gemmn
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}), // 2+: gemmk1
@@ -203,7 +195,7 @@ void device_dynamic_convolution_backward_data_implicit_gemm_v4r1r2_xdlops_nhwc_k
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1-: Gemmn
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 2-: Gemmk1
constexpr auto in_m0_m1_m2_n_grid_iterator_hacks = make_tuple(
constexpr auto in_m0_m1_m2_n_grid_step_hacks = make_tuple(
make_tuple(
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{}, // 0+: MRepeat
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 1+: NRepeat
@@ -223,15 +215,15 @@ void device_dynamic_convolution_backward_data_implicit_gemm_v4r1r2_xdlops_nhwc_k
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{}, // 6-: M2
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 7-: N1
constexpr auto out_gemmk0_gemmm_gemmk1_grid_move_slice_window_iterator_hacks =
constexpr auto out_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks =
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0>{};
constexpr auto wei_gemmk0_gemmn_gemmk1_grid_move_slice_window_iterator_hacks =
constexpr auto wei_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks =
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{};
for(index_t i = 0; i < 5; ++i)
{
float ave_time = driver_dynamic_gemm_xdlops_v2r3<
float ave_time = driver_gemm_xdlops_v2r3<
BlockSize,
TInWei,
TAcc,
@@ -271,11 +263,11 @@ void device_dynamic_convolution_backward_data_implicit_gemm_v4r1r2_xdlops_nhwc_k
#endif
7,
GemmCThreadTransferDstScalarPerVector,
decltype(out_gemmk0_gemmm_gemmk1_grid_iterator_hacks),
decltype(wei_gemmk0_gemmn_gemmk1_grid_iterator_hacks),
decltype(in_m0_m1_m2_n_grid_iterator_hacks),
decltype(out_gemmk0_gemmm_gemmk1_grid_move_slice_window_iterator_hacks),
decltype(wei_gemmk0_gemmn_gemmk1_grid_move_slice_window_iterator_hacks),
decltype(out_gemmk0_gemmm_gemmk1_grid_step_hacks),
decltype(wei_gemmk0_gemmn_gemmk1_grid_step_hacks),
decltype(in_m0_m1_m2_n_grid_step_hacks),
decltype(out_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks),
decltype(wei_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks),
true // CAccessOrderMRepeatNRepeat
>(static_cast<TOut*>(out_n_ho_wo_k_device_buf.GetDeviceBuffer()),
static_cast<TInWei*>(wei_k_y_x_c_device_buf.GetDeviceBuffer()),
@@ -283,11 +275,11 @@ void device_dynamic_convolution_backward_data_implicit_gemm_v4r1r2_xdlops_nhwc_k
out_gemmk0_gemmm_gemmk1_grid_desc,
wei_gemmk0_gemmn_gemmk1_grid_desc,
in_gemmm_gemmn_grid_desc,
out_gemmk0_gemmm_gemmk1_grid_iterator_hacks,
wei_gemmk0_gemmn_gemmk1_grid_iterator_hacks,
in_m0_m1_m2_n_grid_iterator_hacks,
out_gemmk0_gemmm_gemmk1_grid_move_slice_window_iterator_hacks,
wei_gemmk0_gemmn_gemmk1_grid_move_slice_window_iterator_hacks,
out_gemmk0_gemmm_gemmk1_grid_step_hacks,
wei_gemmk0_gemmn_gemmk1_grid_step_hacks,
in_m0_m1_m2_n_grid_step_hacks,
out_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks,
wei_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks,
nrepeat);
{
@@ -295,16 +287,13 @@ void device_dynamic_convolution_backward_data_implicit_gemm_v4r1r2_xdlops_nhwc_k
const auto K = out_n_ho_wo_k_lengths[I3];
const auto C = wei_k_y_x_c_lengths[I3];
const auto Hi = in_n_hi_wi_c_lengths[I1];
const auto Wi = in_n_hi_wi_c_lengths[I2];
const auto Ho = out_n_ho_wo_k_lengths[I1];
const auto Wo = out_n_ho_wo_k_lengths[I2];
const auto Y = wei_k_y_x_c_lengths[I1];
const auto X = wei_k_y_x_c_lengths[I2];
float perf = (float)(std::size_t(2) * N * K * Ho * Wo * C * Y * X) /
float perf = static_cast<float>((std::size_t(2) * N * K * Ho * Wo * C * Y * X)) /
(std::size_t(1000) * 1000 * 1000) / ave_time;
std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s"

55
host/driver_offline/include/device_dynamic_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw.hpp → host/driver_offline/include/device_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw.hpp
View File

@@ -2,7 +2,7 @@
#include "device.hpp"
#include "host_tensor.hpp"
#include "transform_forward_convolution_into_gemm_v4r4_nchw_kcyx_nkhw.hpp"
#include "driver_dynamic_gemm_dlops_v1r2.hpp"
#include "driver_gemm_dlops_v1r2.hpp"
template <typename TInWei,
typename TAcc,
@@ -14,7 +14,7 @@ template <typename TInWei,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
void device_dynamic_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw(
void device_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw(
const InLengths& in_n_c_hi_wi_lengths,
const WeiLengths& wei_k_c_y_x_lengths,
const OutLengths& out_n_k_ho_wo_lengths,
@@ -34,12 +34,6 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw(
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto I4 = Number<4>{};
constexpr auto I5 = Number<5>{};
constexpr auto I6 = Number<6>{};
constexpr auto I7 = Number<7>{};
constexpr auto I8 = Number<8>{};
DeviceMem in_n_c_hi_wi_device_buf(sizeof(TInWei) * in_n_c_hi_wi.mDesc.GetElementSpace());
DeviceMem wei_k_c_y_x_device_buf(sizeof(TInWei) * wei_k_c_y_x.mDesc.GetElementSpace());
@@ -49,12 +43,9 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw(
wei_k_c_y_x_device_buf.ToDevice(wei_k_c_y_x.mData.data());
out_n_k_ho_wo_device_buf.ToDevice(out_n_k_ho_wo.mData.data());
const auto in_n_c_hi_wi_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(in_n_c_hi_wi_lengths);
const auto wei_k_c_y_x_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(wei_k_c_y_x_lengths);
const auto out_n_k_ho_wo_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(out_n_k_ho_wo_lengths);
const auto in_n_c_hi_wi_desc = make_naive_tensor_descriptor_packed(in_n_c_hi_wi_lengths);
const auto wei_k_c_y_x_desc = make_naive_tensor_descriptor_packed(wei_k_c_y_x_lengths);
const auto out_n_k_ho_wo_desc = make_naive_tensor_descriptor_packed(out_n_k_ho_wo_lengths);
#if 1
// cdata = 64, BlockSize = 256, 128x128x8
@@ -98,7 +89,7 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw(
in_right_pads);
// HACK: hacks that control index calculation when iterating over A, B, C matrix
constexpr auto wei_gemmk_gemmm0_gemmn1_grid_iterator_hacks =
constexpr auto wei_gemmk_gemmm0_gemmn1_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
@@ -108,7 +99,7 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw(
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{}));
constexpr auto in_gemmk_gemmn0_gemmn1_grid_iterator_hacks =
constexpr auto in_gemmk_gemmn0_gemmn1_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{}),
@@ -116,7 +107,7 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw(
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{}));
constexpr auto out_gemmm0_gemmm10_gemmm11_gemmn0_gemmn10_gemmn11_grid_iterator_hacks =
constexpr auto out_gemmm0_gemmm10_gemmm11_gemmn0_gemmn10_gemmn11_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
@@ -130,10 +121,10 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw(
Sequence<0, 0, 2, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{}));
constexpr auto wei_gemmk_gemmm0_gemmm1_grid_move_slice_window_iterator_hacks =
constexpr auto wei_gemmk_gemmm0_gemmm1_grid_move_slice_window_step_hacks =
Sequence<0, 0, 0, 0, 0>{};
constexpr auto in_gemmk_gemmn0_gemmn1_grid_move_slice_window_iterator_hacks =
constexpr auto in_gemmk_gemmn0_gemmn1_grid_move_slice_window_step_hacks =
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0>{};
const auto wei_gemmk_gemmm_grid_desc = descs[I0];
@@ -142,7 +133,7 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw(
for(index_t i = 0; i < 5; ++i)
{
float ave_time = driver_dynamic_gemm_dlops_v1r2<
float ave_time = driver_gemm_dlops_v1r2<
BlockSize,
TInWei,
TAcc,
@@ -180,26 +171,26 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw(
Sequence<3, 4, 5, 0, 1, 2>, // CThreadTransferSrcDstAccessOrder
5, // CThreadTransferSrcDstVectorDim
GemmCThreadTransferDstScalarPerVector_N11,
decltype(wei_gemmk_gemmm0_gemmn1_grid_iterator_hacks),
decltype(in_gemmk_gemmn0_gemmn1_grid_iterator_hacks),
decltype(out_gemmm0_gemmm10_gemmm11_gemmn0_gemmn10_gemmn11_grid_iterator_hacks),
decltype(wei_gemmk_gemmm0_gemmm1_grid_move_slice_window_iterator_hacks),
decltype(in_gemmk_gemmn0_gemmn1_grid_move_slice_window_iterator_hacks)>(
decltype(wei_gemmk_gemmm0_gemmn1_grid_step_hacks),
decltype(in_gemmk_gemmn0_gemmn1_grid_step_hacks),
decltype(out_gemmm0_gemmm10_gemmm11_gemmn0_gemmn10_gemmn11_grid_step_hacks),
decltype(wei_gemmk_gemmm0_gemmm1_grid_move_slice_window_step_hacks),
decltype(in_gemmk_gemmn0_gemmn1_grid_move_slice_window_step_hacks)>(
static_cast<TInWei*>(wei_k_c_y_x_device_buf.GetDeviceBuffer()),
static_cast<TInWei*>(in_n_c_hi_wi_device_buf.GetDeviceBuffer()),
static_cast<TOut*>(out_n_k_ho_wo_device_buf.GetDeviceBuffer()),
wei_gemmk_gemmm_grid_desc,
in_gemmk_gemmn_grid_desc,
out_gemmm_gemmn_grid_desc,
wei_gemmk_gemmm0_gemmn1_grid_iterator_hacks,
in_gemmk_gemmn0_gemmn1_grid_iterator_hacks,
out_gemmm0_gemmm10_gemmm11_gemmn0_gemmn10_gemmn11_grid_iterator_hacks,
wei_gemmk_gemmm0_gemmm1_grid_move_slice_window_iterator_hacks,
in_gemmk_gemmn0_gemmn1_grid_move_slice_window_iterator_hacks,
wei_gemmk_gemmm0_gemmn1_grid_step_hacks,
in_gemmk_gemmn0_gemmn1_grid_step_hacks,
out_gemmm0_gemmm10_gemmm11_gemmn0_gemmn10_gemmn11_grid_step_hacks,
wei_gemmk_gemmm0_gemmm1_grid_move_slice_window_step_hacks,
in_gemmk_gemmn0_gemmn1_grid_move_slice_window_step_hacks,
nrepeat);
float perf = (float)calculate_convolution_flops(
in_n_c_hi_wi_desc, wei_k_c_y_x_desc, out_n_k_ho_wo_desc) /
float perf = static_cast<float>(calculate_convolution_flops(
in_n_c_hi_wi_desc, wei_k_c_y_x_desc, out_n_k_ho_wo_desc)) /
(std::size_t(1000) * 1000 * 1000) / ave_time;
std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s" << std::endl;

17
host/driver_offline/include/device_dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nchw_kcyx_nkhw.hpp → host/driver_offline/include/device_convolution_forward_implicit_gemm_v4r4_xdlops_nchw_kcyx_nkhw.hpp
View File

@@ -1,7 +1,7 @@
#include <unistd.h>
#include "device.hpp"
#include "host_tensor.hpp"
#include "driver_dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nchw_kcyx_nkhw.hpp"
#include "driver_convolution_forward_implicit_gemm_v4r4_xdlops_nchw_kcyx_nkhw.hpp"
template <typename TInWei,
typename TAcc,
@@ -13,7 +13,7 @@ template <typename TInWei,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
void device_dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nchw_kcyx_nkhw(
void device_convolution_forward_implicit_gemm_v4r4_xdlops_nchw_kcyx_nkhw(
const InLengths& in_n_c_hi_wi_lengths,
const WeiLengths& wei_k_c_y_x_lengths,
const OutLengths& out_n_k_ho_wo_lengths,
@@ -48,12 +48,9 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nchw_kcyx_nkhw
wei_k_c_y_x_device_buf.ToDevice(wei_k_c_y_x.mData.data());
out_n_k_ho_wo_device_buf.ToDevice(out_n_k_ho_wo.mData.data());
const auto in_n_c_hi_wi_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(in_n_c_hi_wi_lengths);
const auto wei_k_c_y_x_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(wei_k_c_y_x_lengths);
const auto out_n_k_ho_wo_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(out_n_k_ho_wo_lengths);
const auto in_n_c_hi_wi_desc = make_naive_tensor_descriptor_packed(in_n_c_hi_wi_lengths);
const auto wei_k_c_y_x_desc = make_naive_tensor_descriptor_packed(wei_k_c_y_x_lengths);
const auto out_n_k_ho_wo_desc = make_naive_tensor_descriptor_packed(out_n_k_ho_wo_lengths);
#if 0
constexpr index_t BlockSize = 256;
@@ -212,9 +209,9 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nchw_kcyx_nkhw
for(index_t i = 0; i < 5; ++i)
{
#if 0
float ave_time = launch_kernel_dynamic_gemm_xdlops_v1
float ave_time = launch_kernel_gemm_xdlops_v1
#else
float ave_time = launch_kernel_dynamic_gemm_xdlops_v2
float ave_time = launch_kernel_gemm_xdlops_v2
#endif
<BlockSize,
TInWei,

57
host/driver_offline/include/device_dynamic_convolution_forward_implicit_gemm_v4r4r2_dlops_nhwc_kyxc_nhwk.hpp → host/driver_offline/include/device_convolution_forward_implicit_gemm_v4r4r2_dlops_nhwc_kyxc_nhwk.hpp
View File

@@ -2,7 +2,7 @@
#include "device.hpp"
#include "host_tensor.hpp"
#include "transform_forward_convolution_into_gemm_v4r4r4_nhwc_kyxc_nhwk.hpp"
#include "driver_dynamic_gemm_dlops_v1r3.hpp"
#include "driver_gemm_dlops_v1r3.hpp"
template <typename TInWei,
typename TAcc,
@@ -14,7 +14,7 @@ template <typename TInWei,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
void device_dynamic_convolution_forward_implicit_gemm_v4r4r2_dlops_nhwc_kyxc_nhwk(
void device_convolution_forward_implicit_gemm_v4r4r2_dlops_nhwc_kyxc_nhwk(
const InLengths& in_n_hi_wi_c_lengths,
const WeiLengths& wei_k_y_x_c_lengths,
const OutLengths& out_n_ho_wo_k_lengths,
@@ -35,11 +35,6 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r2_dlops_nhwc_kyxc_nhw
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto I4 = Number<4>{};
constexpr auto I5 = Number<5>{};
constexpr auto I6 = Number<6>{};
constexpr auto I7 = Number<7>{};
constexpr auto I8 = Number<8>{};
DeviceMem in_n_hi_wi_c_device_buf(sizeof(TInWei) * in_n_hi_wi_c.mDesc.GetElementSpace());
DeviceMem wei_k_y_x_c_device_buf(sizeof(TInWei) * wei_k_y_x_c.mDesc.GetElementSpace());
@@ -49,14 +44,11 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r2_dlops_nhwc_kyxc_nhw
wei_k_y_x_c_device_buf.ToDevice(wei_k_y_x_c.mData.data());
out_n_ho_wo_k_device_buf.ToDevice(out_n_ho_wo_k.mData.data());
const auto in_n_hi_wi_c_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(in_n_hi_wi_c_lengths);
const auto wei_k_y_x_c_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(wei_k_y_x_c_lengths);
const auto out_n_ho_wo_k_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(out_n_ho_wo_k_lengths);
const auto in_n_hi_wi_c_desc = make_naive_tensor_descriptor_packed(in_n_hi_wi_c_lengths);
const auto wei_k_y_x_c_desc = make_naive_tensor_descriptor_packed(wei_k_y_x_c_lengths);
const auto out_n_ho_wo_k_desc = make_naive_tensor_descriptor_packed(out_n_ho_wo_k_lengths);
#if 1
#if 0
// [M, N, K0, K1] = [128, 128, 8, 1] for fp32
// cdata = 64, BlockSize = 256
constexpr index_t BlockSize = 256;
@@ -163,7 +155,7 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r2_dlops_nhwc_kyxc_nhw
const auto out_gemmm_gemmn_grid_desc = descs[I2];
// HACK: hacks that control index calculation when iterating over A, B, C matrix
constexpr auto in_gemmk0_gemmm0_gemmm1_gemmk1_grid_iterator_hacks = make_tuple(
constexpr auto in_gemmk0_gemmm0_gemmm1_gemmk1_grid_step_hacks = make_tuple(
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0>{}, // 0+: GemmK0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0>{}, // 1+: GemmM0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0>{}, // 2+: GemmM1
@@ -173,7 +165,7 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r2_dlops_nhwc_kyxc_nhw
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0>{}, // 3-: GemmM1
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0>{})); // 3-: GemmK1
constexpr auto wei_gemmk0_gemmn0_gemmn1_gemmk1_grid_iterator_hacks =
constexpr auto wei_gemmk0_gemmn0_gemmn1_gemmk1_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0>{}, // 0+: GemmK0
Sequence<0, 0, 0, 0, 0, 0, 0, 0>{}, // 1+: GemmN0
Sequence<0, 0, 0, 0, 0, 0, 0, 0>{}, // 2+: GemmN1
@@ -183,7 +175,7 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r2_dlops_nhwc_kyxc_nhw
Sequence<0, 0, 0, 0, 0, 0, 0, 0>{}, // 2-: GemmN1
Sequence<0, 0, 0, 0, 0, 0, 0, 0>{})); // 3-: GemmK1
constexpr auto out_gemmm0_gemmm10_gemmm11_gemmn0_gemmn10_gemmn11_grid_iterator_hacks =
constexpr auto out_gemmm0_gemmm10_gemmm11_gemmn0_gemmn10_gemmn11_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{}, // 0+: GemmM0
Sequence<0, 0, 0, 0, 0>{}, // 1+: GemmM10
Sequence<0, 0, 0, 0, 0>{}, // 2+: GemmM11
@@ -197,15 +189,15 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r2_dlops_nhwc_kyxc_nhw
Sequence<0, 0, 0, 0, 0>{}, // 4-: GemmN10
Sequence<0, 0, 0, 0, 0>{})); // 5-: GemmN11
constexpr auto in_gemmk0_gemmm0_gemmm1_gemmk1_grid_move_slice_window_iterator_hacks =
constexpr auto in_gemmk0_gemmm0_gemmm1_gemmk1_grid_move_slice_window_step_hacks =
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0>{};
constexpr auto wei_gemmk0_gemmn0_gemmn1_gemmk1_grid_move_slice_window_iterator_hacks =
constexpr auto wei_gemmk0_gemmn0_gemmn1_gemmk1_grid_move_slice_window_step_hacks =
Sequence<0, 0, 0, 0, 0, 0, 0, 0>{};
for(index_t i = 0; i < 5; ++i)
{
float ave_time = driver_dynamic_gemm_dlops_v1r3<
float ave_time = driver_gemm_dlops_v1r3<
BlockSize,
TInWei,
TAcc,
@@ -239,22 +231,22 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r2_dlops_nhwc_kyxc_nhw
Sequence<0, 1, 2, 3, 4, 5>, // CThreadTransferSrcDstAccessOrder
5, // CThreadTransferSrcDstVectorDim
GemmCThreadTransferDstScalarPerVector_N11,
decltype(in_gemmk0_gemmm0_gemmm1_gemmk1_grid_iterator_hacks),
decltype(wei_gemmk0_gemmn0_gemmn1_gemmk1_grid_iterator_hacks),
decltype(out_gemmm0_gemmm10_gemmm11_gemmn0_gemmn10_gemmn11_grid_iterator_hacks),
decltype(in_gemmk0_gemmm0_gemmm1_gemmk1_grid_move_slice_window_iterator_hacks),
decltype(wei_gemmk0_gemmn0_gemmn1_gemmk1_grid_move_slice_window_iterator_hacks)>(
decltype(in_gemmk0_gemmm0_gemmm1_gemmk1_grid_step_hacks),
decltype(wei_gemmk0_gemmn0_gemmn1_gemmk1_grid_step_hacks),
decltype(out_gemmm0_gemmm10_gemmm11_gemmn0_gemmn10_gemmn11_grid_step_hacks),
decltype(in_gemmk0_gemmm0_gemmm1_gemmk1_grid_move_slice_window_step_hacks),
decltype(wei_gemmk0_gemmn0_gemmn1_gemmk1_grid_move_slice_window_step_hacks)>(
static_cast<TInWei*>(in_n_hi_wi_c_device_buf.GetDeviceBuffer()),
static_cast<TInWei*>(wei_k_y_x_c_device_buf.GetDeviceBuffer()),
static_cast<TOut*>(out_n_ho_wo_k_device_buf.GetDeviceBuffer()),
in_gemmk0_gemmm_gemmk1_grid_desc,
wei_gemmk0_gemmn_gemmk1_grid_desc,
out_gemmm_gemmn_grid_desc,
in_gemmk0_gemmm0_gemmm1_gemmk1_grid_iterator_hacks,
wei_gemmk0_gemmn0_gemmn1_gemmk1_grid_iterator_hacks,
out_gemmm0_gemmm10_gemmm11_gemmn0_gemmn10_gemmn11_grid_iterator_hacks,
in_gemmk0_gemmm0_gemmm1_gemmk1_grid_move_slice_window_iterator_hacks,
wei_gemmk0_gemmn0_gemmn1_gemmk1_grid_move_slice_window_iterator_hacks,
in_gemmk0_gemmm0_gemmm1_gemmk1_grid_step_hacks,
wei_gemmk0_gemmn0_gemmn1_gemmk1_grid_step_hacks,
out_gemmm0_gemmm10_gemmm11_gemmn0_gemmn10_gemmn11_grid_step_hacks,
in_gemmk0_gemmm0_gemmm1_gemmk1_grid_move_slice_window_step_hacks,
wei_gemmk0_gemmn0_gemmn1_gemmk1_grid_move_slice_window_step_hacks,
nrepeat);
{
@@ -262,16 +254,13 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r2_dlops_nhwc_kyxc_nhw
const auto K = out_n_ho_wo_k_lengths[I3];
const auto C = wei_k_y_x_c_lengths[I3];
const auto Hi = in_n_hi_wi_c_lengths[I1];
const auto Wi = in_n_hi_wi_c_lengths[I2];
const auto Ho = out_n_ho_wo_k_lengths[I1];
const auto Wo = out_n_ho_wo_k_lengths[I2];
const auto Y = wei_k_y_x_c_lengths[I1];
const auto X = wei_k_y_x_c_lengths[I2];
float perf = (float)(std::size_t(2) * N * K * Ho * Wo * C * Y * X) /
float perf = static_cast<float>(std::size_t(2) * N * K * Ho * Wo * C * Y * X) /
(std::size_t(1000) * 1000 * 1000) / ave_time;
std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s"

55
host/driver_offline/include/device_dynamic_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw.hpp → host/driver_offline/include/device_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw.hpp
View File

@@ -2,7 +2,7 @@
#include "device.hpp"
#include "host_tensor.hpp"
#include "transform_forward_convolution_into_gemm_v4r4r2_nchw_kcyx_nkhw.hpp"
#include "driver_dynamic_gemm_xdlops_v2r3.hpp"
#include "driver_gemm_xdlops_v2r3.hpp"
template <typename TInWei,
typename TAcc,
@@ -14,7 +14,7 @@ template <typename TInWei,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
void device_dynamic_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw(
void device_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw(
const InLengths& in_n_c_hi_wi_lengths,
const WeiLengths& wei_k_c_y_x_lengths,
const OutLengths& out_n_k_ho_wo_lengths,
@@ -34,12 +34,6 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nk
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto I4 = Number<4>{};
constexpr auto I5 = Number<5>{};
constexpr auto I6 = Number<6>{};
constexpr auto I7 = Number<7>{};
constexpr auto I8 = Number<8>{};
DeviceMem in_n_c_hi_wi_device_buf(sizeof(TInWei) * in_n_c_hi_wi.mDesc.GetElementSpace());
DeviceMem wei_k_c_y_x_device_buf(sizeof(TInWei) * wei_k_c_y_x.mDesc.GetElementSpace());
@@ -49,12 +43,9 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nk
wei_k_c_y_x_device_buf.ToDevice(wei_k_c_y_x.mData.data());
out_n_k_ho_wo_device_buf.ToDevice(out_n_k_ho_wo.mData.data());
const auto in_n_c_hi_wi_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(in_n_c_hi_wi_lengths);
const auto wei_k_c_y_x_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(wei_k_c_y_x_lengths);
const auto out_n_k_ho_wo_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(out_n_k_ho_wo_lengths);
const auto in_n_c_hi_wi_desc = make_naive_tensor_descriptor_packed(in_n_c_hi_wi_lengths);
const auto wei_k_c_y_x_desc = make_naive_tensor_descriptor_packed(wei_k_c_y_x_lengths);
const auto out_n_k_ho_wo_desc = make_naive_tensor_descriptor_packed(out_n_k_ho_wo_lengths);
#if 1
// [M, N, K0, K1] = [256, 128, 4, 8] for fp16
@@ -101,12 +92,12 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nk
const auto out_gemmm_gemmn_grid_desc = descs[I2];
// HACK: hacks that control index calculation when iterating over A, B, C matrix
constexpr auto wei_gemmk0_gemmm_gemmk1_grid_iterator_hacks = make_tuple(
constexpr auto wei_gemmk0_gemmm_gemmk1_grid_step_hacks = make_tuple(
make_tuple(Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}),
make_tuple(
Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}));
constexpr auto in_gemmk0_gemmn_gemmk1_grid_iterator_hacks =
constexpr auto in_gemmk0_gemmn_gemmk1_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{}),
@@ -114,7 +105,7 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nk
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{}));
constexpr auto out_m0_m1_m2_n_grid_iterator_hacks =
constexpr auto out_m0_m1_m2_n_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
@@ -132,15 +123,15 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nk
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{}));
constexpr auto wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_iterator_hacks =
constexpr auto wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks =
Sequence<0, 0, 0, 0, 0>{};
constexpr auto in_gemmk0_gemmn_gemmk1_grid_move_slice_window_iterator_hacks =
constexpr auto in_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks =
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0>{};
for(index_t i = 0; i < 5; ++i)
{
float ave_time = driver_dynamic_gemm_xdlops_v2r3<
float ave_time = driver_gemm_xdlops_v2r3<
BlockSize,
TInWei,
TAcc,
@@ -176,26 +167,26 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nk
Sequence<3, 0, 1, 2, 7, 5, 4, 6>,
7,
GemmCThreadTransferDstScalarPerVector,
decltype(wei_gemmk0_gemmm_gemmk1_grid_iterator_hacks),
decltype(in_gemmk0_gemmn_gemmk1_grid_iterator_hacks),
decltype(out_m0_m1_m2_n_grid_iterator_hacks),
decltype(wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_iterator_hacks),
decltype(in_gemmk0_gemmn_gemmk1_grid_move_slice_window_iterator_hacks),
decltype(wei_gemmk0_gemmm_gemmk1_grid_step_hacks),
decltype(in_gemmk0_gemmn_gemmk1_grid_step_hacks),
decltype(out_m0_m1_m2_n_grid_step_hacks),
decltype(wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks),
decltype(in_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks),
false>(static_cast<TInWei*>(wei_k_c_y_x_device_buf.GetDeviceBuffer()),
static_cast<TInWei*>(in_n_c_hi_wi_device_buf.GetDeviceBuffer()),
static_cast<TOut*>(out_n_k_ho_wo_device_buf.GetDeviceBuffer()),
wei_gemmk0_gemmm_gemmk1_grid_desc,
in_gemmk0_gemmn_gemmk1_grid_desc,
out_gemmm_gemmn_grid_desc,
wei_gemmk0_gemmm_gemmk1_grid_iterator_hacks,
in_gemmk0_gemmn_gemmk1_grid_iterator_hacks,
out_m0_m1_m2_n_grid_iterator_hacks,
wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_iterator_hacks,
in_gemmk0_gemmn_gemmk1_grid_move_slice_window_iterator_hacks,
wei_gemmk0_gemmm_gemmk1_grid_step_hacks,
in_gemmk0_gemmn_gemmk1_grid_step_hacks,
out_m0_m1_m2_n_grid_step_hacks,
wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks,
in_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks,
nrepeat);
float perf = (float)calculate_convolution_flops(
in_n_c_hi_wi_desc, wei_k_c_y_x_desc, out_n_k_ho_wo_desc) /
float perf = static_cast<float>(calculate_convolution_flops(
in_n_c_hi_wi_desc, wei_k_c_y_x_desc, out_n_k_ho_wo_desc)) /
(std::size_t(1000) * 1000 * 1000) / ave_time;
std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s" << std::endl;

53
host/driver_offline/include/device_dynamic_convolution_forward_implicit_gemm_v4r4r2_xdlops_nhwc_kyxc_nhwk.hpp → host/driver_offline/include/device_convolution_forward_implicit_gemm_v4r4r2_xdlops_nhwc_kyxc_nhwk.hpp
View File

@@ -2,7 +2,7 @@
#include "device.hpp"
#include "host_tensor.hpp"
#include "transform_forward_convolution_into_gemm_v4r4r2_nhwc_kyxc_nhwk.hpp"
#include "driver_dynamic_gemm_xdlops_v2r2.hpp"
#include "driver_gemm_xdlops_v2r2.hpp"
template <typename TInWei,
typename TAcc,
@@ -14,7 +14,7 @@ template <typename TInWei,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
void device_dynamic_convolution_forward_implicit_gemm_v4r4r2_xdlops_nhwc_kyxc_nhwk(
void device_convolution_forward_implicit_gemm_v4r4r2_xdlops_nhwc_kyxc_nhwk(
const InLengths& in_n_hi_wi_c_lengths,
const WeiLengths& wei_k_y_x_c_lengths,
const OutLengths& out_n_ho_wo_k_lengths,
@@ -35,11 +35,6 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r2_xdlops_nhwc_kyxc_nh
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto I4 = Number<4>{};
constexpr auto I5 = Number<5>{};
constexpr auto I6 = Number<6>{};
constexpr auto I7 = Number<7>{};
constexpr auto I8 = Number<8>{};
DeviceMem in_n_hi_wi_c_device_buf(sizeof(TInWei) * in_n_hi_wi_c.mDesc.GetElementSpace());
DeviceMem wei_k_y_x_c_device_buf(sizeof(TInWei) * wei_k_y_x_c.mDesc.GetElementSpace());
@@ -49,12 +44,9 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r2_xdlops_nhwc_kyxc_nh
wei_k_y_x_c_device_buf.ToDevice(wei_k_y_x_c.mData.data());
out_n_ho_wo_k_device_buf.ToDevice(out_n_ho_wo_k.mData.data());
const auto in_n_hi_wi_c_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(in_n_hi_wi_c_lengths);
const auto wei_k_y_x_c_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(wei_k_y_x_c_lengths);
const auto out_n_ho_wo_k_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(out_n_ho_wo_k_lengths);
const auto in_n_hi_wi_c_desc = make_naive_tensor_descriptor_packed(in_n_hi_wi_c_lengths);
const auto wei_k_y_x_c_desc = make_naive_tensor_descriptor_packed(wei_k_y_x_c_lengths);
const auto out_n_ho_wo_k_desc = make_naive_tensor_descriptor_packed(out_n_ho_wo_k_lengths);
#if 1
// [M, N, K0, K1] = [256, 128, 4, 4] for fp32
@@ -129,12 +121,12 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r2_xdlops_nhwc_kyxc_nh
const auto out_gemmm_gemmn_grid_desc = descs[I2];
// HACK: hacks that control index calculation when iterating over A, B, C matrix
constexpr auto wei_gemmk0_gemmm_gemmk1_grid_iterator_hacks = make_tuple(
constexpr auto wei_gemmk0_gemmm_gemmk1_grid_step_hacks = make_tuple(
make_tuple(Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}),
make_tuple(
Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}));
constexpr auto in_gemmk0_gemmn_gemmk1_grid_iterator_hacks =
constexpr auto in_gemmk0_gemmn_gemmk1_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{}),
@@ -142,7 +134,7 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r2_xdlops_nhwc_kyxc_nh
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{}));
constexpr auto out_m0_m1_m2_n_grid_iterator_hacks =
constexpr auto out_m0_m1_m2_n_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
@@ -152,15 +144,15 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r2_xdlops_nhwc_kyxc_nh
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{}));
constexpr auto wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_iterator_hacks =
constexpr auto wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks =
Sequence<0, 0, 0, 0, 0>{};
constexpr auto in_gemmk0_gemmn_gemmk1_grid_move_slice_window_iterator_hacks =
constexpr auto in_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks =
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0>{};
for(index_t i = 0; i < 5; ++i)
{
float ave_time = driver_dynamic_gemm_xdlops_v2r2<
float ave_time = driver_gemm_xdlops_v2r2<
BlockSize,
TInWei,
TAcc,
@@ -195,22 +187,22 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r2_xdlops_nhwc_kyxc_nh
Sequence<2, 3, 0, 1>,
2,
GemmCThreadTransferDstScalarPerVector,
decltype(wei_gemmk0_gemmm_gemmk1_grid_iterator_hacks),
decltype(in_gemmk0_gemmn_gemmk1_grid_iterator_hacks),
decltype(out_m0_m1_m2_n_grid_iterator_hacks),
decltype(wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_iterator_hacks),
decltype(in_gemmk0_gemmn_gemmk1_grid_move_slice_window_iterator_hacks)>(
decltype(wei_gemmk0_gemmm_gemmk1_grid_step_hacks),
decltype(in_gemmk0_gemmn_gemmk1_grid_step_hacks),
decltype(out_m0_m1_m2_n_grid_step_hacks),
decltype(wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks),
decltype(in_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks)>(
static_cast<TInWei*>(wei_k_y_x_c_device_buf.GetDeviceBuffer()),
static_cast<TInWei*>(in_n_hi_wi_c_device_buf.GetDeviceBuffer()),
static_cast<TOut*>(out_n_ho_wo_k_device_buf.GetDeviceBuffer()),
wei_gemmk0_gemmm_gemmk1_grid_desc,
in_gemmk0_gemmn_gemmk1_grid_desc,
out_gemmm_gemmn_grid_desc,
wei_gemmk0_gemmm_gemmk1_grid_iterator_hacks,
in_gemmk0_gemmn_gemmk1_grid_iterator_hacks,
out_m0_m1_m2_n_grid_iterator_hacks,
wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_iterator_hacks,
in_gemmk0_gemmn_gemmk1_grid_move_slice_window_iterator_hacks,
wei_gemmk0_gemmm_gemmk1_grid_step_hacks,
in_gemmk0_gemmn_gemmk1_grid_step_hacks,
out_m0_m1_m2_n_grid_step_hacks,
wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks,
in_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks,
nrepeat);
{
@@ -218,9 +210,6 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r2_xdlops_nhwc_kyxc_nh
const auto K = out_n_ho_wo_k_lengths[I3];
const auto C = wei_k_y_x_c_lengths[I3];
const auto Hi = in_n_hi_wi_c_lengths[I1];
const auto Wi = in_n_hi_wi_c_lengths[I2];
const auto Ho = out_n_ho_wo_k_lengths[I1];
const auto Wo = out_n_ho_wo_k_lengths[I2];

45
host/driver_offline/include/device_dynamic_convolution_forward_implicit_gemm_v4r4r3_xdlops_nhwc_kyxc_nhwk.hpp → host/driver_offline/include/device_convolution_forward_implicit_gemm_v4r4r3_xdlops_nhwc_kyxc_nhwk.hpp
View File

@@ -2,7 +2,7 @@
#include "device.hpp"
#include "host_tensor.hpp"
#include "transform_forward_convolution_into_gemm_v4r4r2_nhwc_kyxc_nhwk.hpp"
#include "driver_dynamic_gemm_xdlops_v2r3.hpp"
#include "driver_gemm_xdlops_v2r3.hpp"
template <typename TInWei,
typename TAcc,
@@ -14,7 +14,7 @@ template <typename TInWei,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
void device_dynamic_convolution_forward_implicit_gemm_v4r4r3_xdlops_nhwc_kyxc_nhwk(
void device_convolution_forward_implicit_gemm_v4r4r3_xdlops_nhwc_kyxc_nhwk(
const InLengths& in_n_hi_wi_c_lengths,
const WeiLengths& wei_k_y_x_c_lengths,
const OutLengths& out_n_ho_wo_k_lengths,
@@ -49,12 +49,9 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r3_xdlops_nhwc_kyxc_nh
wei_k_y_x_c_device_buf.ToDevice(wei_k_y_x_c.mData.data());
out_n_ho_wo_k_device_buf.ToDevice(out_n_ho_wo_k.mData.data());
const auto in_n_hi_wi_c_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(in_n_hi_wi_c_lengths);
const auto wei_k_y_x_c_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(wei_k_y_x_c_lengths);
const auto out_n_ho_wo_k_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(out_n_ho_wo_k_lengths);
const auto in_n_hi_wi_c_desc = make_naive_tensor_descriptor_packed(in_n_hi_wi_c_lengths);
const auto wei_k_y_x_c_desc = make_naive_tensor_descriptor_packed(wei_k_y_x_c_lengths);
const auto out_n_ho_wo_k_desc = make_naive_tensor_descriptor_packed(out_n_ho_wo_k_lengths);
#if 1
// [M, N, K0, K1] = [256, 128, 4, 4] for fp32
@@ -185,12 +182,12 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r3_xdlops_nhwc_kyxc_nh
const auto out_gemmm_gemmn_grid_desc = descs[I2];
// HACK: hacks that control index calculation when iterating over A, B, C matrix
constexpr auto wei_gemmk0_gemmm_gemmk1_grid_iterator_hacks = make_tuple(
constexpr auto wei_gemmk0_gemmm_gemmk1_grid_step_hacks = make_tuple(
make_tuple(Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}),
make_tuple(
Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}));
constexpr auto in_gemmk0_gemmn_gemmk1_grid_iterator_hacks =
constexpr auto in_gemmk0_gemmn_gemmk1_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{}),
@@ -198,7 +195,7 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r3_xdlops_nhwc_kyxc_nh
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{}));
constexpr auto out_m0_m1_m2_n_grid_iterator_hacks =
constexpr auto out_m0_m1_m2_n_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 1, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
@@ -216,15 +213,15 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r3_xdlops_nhwc_kyxc_nh
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 2, 0, 0>{}));
constexpr auto wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_iterator_hacks =
constexpr auto wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks =
Sequence<0, 0, 0, 0, 0>{};
constexpr auto in_gemmk0_gemmn_gemmk1_grid_move_slice_window_iterator_hacks =
constexpr auto in_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks =
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0>{};
for(index_t i = 0; i < 5; ++i)
{
float ave_time = driver_dynamic_gemm_xdlops_v2r3<
float ave_time = driver_gemm_xdlops_v2r3<
BlockSize,
TInWei,
TAcc,
@@ -259,11 +256,11 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r3_xdlops_nhwc_kyxc_nh
Sequence<2, 3, 0, 1, 7, 5, 4, 6>,
6,
GemmCThreadTransferDstScalarPerVector,
decltype(wei_gemmk0_gemmm_gemmk1_grid_iterator_hacks),
decltype(in_gemmk0_gemmn_gemmk1_grid_iterator_hacks),
decltype(out_m0_m1_m2_n_grid_iterator_hacks),
decltype(wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_iterator_hacks),
decltype(in_gemmk0_gemmn_gemmk1_grid_move_slice_window_iterator_hacks),
decltype(wei_gemmk0_gemmm_gemmk1_grid_step_hacks),
decltype(in_gemmk0_gemmn_gemmk1_grid_step_hacks),
decltype(out_m0_m1_m2_n_grid_step_hacks),
decltype(wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks),
decltype(in_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks),
false // CAccessOrderMRepeatNRepeat
>(static_cast<TInWei*>(wei_k_y_x_c_device_buf.GetDeviceBuffer()),
static_cast<TInWei*>(in_n_hi_wi_c_device_buf.GetDeviceBuffer()),
@@ -271,11 +268,11 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r3_xdlops_nhwc_kyxc_nh
wei_gemmk0_gemmm_gemmk1_grid_desc,
in_gemmk0_gemmn_gemmk1_grid_desc,
out_gemmm_gemmn_grid_desc,
wei_gemmk0_gemmm_gemmk1_grid_iterator_hacks,
in_gemmk0_gemmn_gemmk1_grid_iterator_hacks,
out_m0_m1_m2_n_grid_iterator_hacks,
wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_iterator_hacks,
in_gemmk0_gemmn_gemmk1_grid_move_slice_window_iterator_hacks,
wei_gemmk0_gemmm_gemmk1_grid_step_hacks,
in_gemmk0_gemmn_gemmk1_grid_step_hacks,
out_m0_m1_m2_n_grid_step_hacks,
wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks,
in_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks,
nrepeat);
{

55
host/driver_offline/include/device_dynamic_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk.hpp → host/driver_offline/include/device_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk.hpp
View File

@@ -2,7 +2,7 @@
#include "device.hpp"
#include "host_tensor.hpp"
#include "transform_forward_convolution_into_gemm_v4r4r4_nhwc_kyxc_nhwk.hpp"
#include "driver_dynamic_gemm_xdlops_v2r3.hpp"
#include "driver_gemm_xdlops_v2r3.hpp"
template <typename TInWei,
typename TAcc,
@@ -14,7 +14,7 @@ template <typename TInWei,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
void device_dynamic_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk(
void device_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk(
const InLengths& in_n_hi_wi_c_lengths,
const WeiLengths& wei_k_y_x_c_lengths,
const OutLengths& out_n_ho_wo_k_lengths,
@@ -35,11 +35,6 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nh
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto I4 = Number<4>{};
constexpr auto I5 = Number<5>{};
constexpr auto I6 = Number<6>{};
constexpr auto I7 = Number<7>{};
constexpr auto I8 = Number<8>{};
DeviceMem in_n_hi_wi_c_device_buf(sizeof(TInWei) * in_n_hi_wi_c.mDesc.GetElementSpace());
DeviceMem wei_k_y_x_c_device_buf(sizeof(TInWei) * wei_k_y_x_c.mDesc.GetElementSpace());
@@ -49,12 +44,9 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nh
wei_k_y_x_c_device_buf.ToDevice(wei_k_y_x_c.mData.data());
out_n_ho_wo_k_device_buf.ToDevice(out_n_ho_wo_k.mData.data());
const auto in_n_hi_wi_c_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(in_n_hi_wi_c_lengths);
const auto wei_k_y_x_c_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(wei_k_y_x_c_lengths);
const auto out_n_ho_wo_k_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(out_n_ho_wo_k_lengths);
const auto in_n_hi_wi_c_desc = make_naive_tensor_descriptor_packed(in_n_hi_wi_c_lengths);
const auto wei_k_y_x_c_desc = make_naive_tensor_descriptor_packed(wei_k_y_x_c_lengths);
const auto out_n_ho_wo_k_desc = make_naive_tensor_descriptor_packed(out_n_ho_wo_k_lengths);
#if 0
// [M, N, K0, K1] = [256, 128, 4, 4] for fp32
@@ -241,7 +233,7 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nh
const auto out_gemmm_gemmn_grid_desc = descs[I2];
// HACK: hacks that control index calculation when iterating over A, B, C matrix
constexpr auto in_gemmk0_gemmm_gemmk1_grid_iterator_hacks =
constexpr auto in_gemmk0_gemmm_gemmk1_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{}, // 0+: GemmK0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{}, // 1+: GemmM
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{}), // 2+: GemmK1
@@ -249,7 +241,7 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nh
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{}, // 1-: GemmM
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{})); // 2-: GemmK1
constexpr auto wei_gemmk0_gemmn_gemmk1_grid_iterator_hacks =
constexpr auto wei_gemmk0_gemmn_gemmk1_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{}, // 0+: GemmK0
Sequence<0, 0, 0, 0, 0>{}, // 1+: GemmN
Sequence<0, 0, 0, 0, 0>{}), // 2+: GemmK1
@@ -257,7 +249,7 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nh
Sequence<0, 0, 0, 0, 0>{}, // 1-: GemmN
Sequence<0, 0, 0, 0, 0>{})); // 2-: GemmK1
constexpr auto out_m0_m1_m2_n_grid_iterator_hacks =
constexpr auto out_m0_m1_m2_n_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{}, // 0+: MRepeat
Sequence<0, 0, 0, 0, 0>{}, // 1+: NRepeat
Sequence<0, 0, 0, 0, 0>{}, // 2+: MWaves
@@ -275,15 +267,15 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nh
Sequence<0, 0, 0, 0, 0>{}, // 6-: M2
Sequence<0, 0, 0, 0, 0>{})); // 7-: N1
constexpr auto in_gemmk0_gemmm_gemmk1_grid_move_slice_window_iterator_hacks =
constexpr auto in_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks =
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0>{};
constexpr auto wei_gemmk0_gemmn_gemmk1_grid_move_slice_window_iterator_hacks =
constexpr auto wei_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks =
Sequence<0, 0, 0, 0, 0>{};
for(index_t i = 0; i < 5; ++i)
{
float ave_time = driver_dynamic_gemm_xdlops_v2r3<
float ave_time = driver_gemm_xdlops_v2r3<
BlockSize,
TInWei,
TAcc,
@@ -319,11 +311,11 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nh
Sequence<2, 3, 0, 1, 7, 5, 4, 6>,
7,
GemmCThreadTransferDstScalarPerVector,
decltype(in_gemmk0_gemmm_gemmk1_grid_iterator_hacks),
decltype(wei_gemmk0_gemmn_gemmk1_grid_iterator_hacks),
decltype(out_m0_m1_m2_n_grid_iterator_hacks),
decltype(in_gemmk0_gemmm_gemmk1_grid_move_slice_window_iterator_hacks),
decltype(wei_gemmk0_gemmn_gemmk1_grid_move_slice_window_iterator_hacks),
decltype(in_gemmk0_gemmm_gemmk1_grid_step_hacks),
decltype(wei_gemmk0_gemmn_gemmk1_grid_step_hacks),
decltype(out_m0_m1_m2_n_grid_step_hacks),
decltype(in_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks),
decltype(wei_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks),
false // CAccessOrderMRepeatNRepeat
>(static_cast<TInWei*>(in_n_hi_wi_c_device_buf.GetDeviceBuffer()),
static_cast<TInWei*>(wei_k_y_x_c_device_buf.GetDeviceBuffer()),
@@ -331,11 +323,11 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nh
in_gemmk0_gemmm_gemmk1_grid_desc,
wei_gemmk0_gemmn_gemmk1_grid_desc,
out_gemmm_gemmn_grid_desc,
in_gemmk0_gemmm_gemmk1_grid_iterator_hacks,
wei_gemmk0_gemmn_gemmk1_grid_iterator_hacks,
out_m0_m1_m2_n_grid_iterator_hacks,
in_gemmk0_gemmm_gemmk1_grid_move_slice_window_iterator_hacks,
wei_gemmk0_gemmn_gemmk1_grid_move_slice_window_iterator_hacks,
in_gemmk0_gemmm_gemmk1_grid_step_hacks,
wei_gemmk0_gemmn_gemmk1_grid_step_hacks,
out_m0_m1_m2_n_grid_step_hacks,
in_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks,
wei_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks,
nrepeat);
{
@@ -343,16 +335,13 @@ void device_dynamic_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nh
const auto K = out_n_ho_wo_k_lengths[I3];
const auto C = wei_k_y_x_c_lengths[I3];
const auto Hi = in_n_hi_wi_c_lengths[I1];
const auto Wi = in_n_hi_wi_c_lengths[I2];
const auto Ho = out_n_ho_wo_k_lengths[I1];
const auto Wo = out_n_ho_wo_k_lengths[I2];
const auto Y = wei_k_y_x_c_lengths[I1];
const auto X = wei_k_y_x_c_lengths[I2];
float perf = (float)(std::size_t(2) * N * K * Ho * Wo * C * Y * X) /
float perf = static_cast<float>((std::size_t(2) * N * K * Ho * Wo * C * Y * X)) /
(std::size_t(1000) * 1000 * 1000) / ave_time;
std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s"

16
host/driver_offline/include/device_dynamic_convolution_forward_implicit_gemm_v5r1_dlops_nchw_kcyx_nkhw.hpp → host/driver_offline/include/device_convolution_forward_implicit_gemm_v5r1_dlops_nchw_kcyx_nkhw.hpp
View File

@@ -1,8 +1,8 @@
#include <unistd.h>
#include "device.hpp"
#include "host_tensor.hpp"
#include "driver_dynamic_convolution_forward_implicit_gemm_v5r1_dlops_nchw_kcyx_nkhw.hpp"
#include "driver_dynamic_convolution_forward_implicit_gemm_v5r1_dlops_nchw_kcyx_nkhw_outpad.hpp"
#include "driver_convolution_forward_implicit_gemm_v5r1_dlops_nchw_kcyx_nkhw.hpp"
#include "driver_convolution_forward_implicit_gemm_v5r1_dlops_nchw_kcyx_nkhw_outpad.hpp"
template <typename TInWei,
ck::index_t InWeiVectorSize,
@@ -15,7 +15,7 @@ template <typename TInWei,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
void device_dynamic_convolution_forward_implicit_gemm_v5r1_dlops_nchw_kcyx_nkhw(
void device_convolution_forward_implicit_gemm_v5r1_dlops_nchw_kcyx_nkhw(
const InLengths& in_n_c_hi_wi_lengths,
const WeiLengths& wei_k_c_y_x_lengths,
const OutLengths& out_n_k_ho_wo_lengths,
@@ -26,7 +26,7 @@ void device_dynamic_convolution_forward_implicit_gemm_v5r1_dlops_nchw_kcyx_nkhw(
const Tensor<TInWei>& in_n_c_hi_wi,
const Tensor<TInWei>& wei_k_c_y_x,
Tensor<TOut>& out_n_k_ho_wo,
ck::index_t nrepeat)
ck::index_t /* nrepeat */)
{
using namespace ck;
@@ -85,12 +85,10 @@ void device_dynamic_convolution_forward_implicit_gemm_v5r1_dlops_nchw_kcyx_nkhw(
in_n_c0_hi_wi_c1_device_buf.ToDevice(in_n_c0_hi_wi_c1.mData.data());
wei_k_c0_y_x_c1_device_buf.ToDevice(wei_k_c0_y_x_c1.mData.data());
const auto in_n_c0_hi_wi_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(N, C0, Hi, Wi));
const auto wei_k_c0_y_x_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(K, C0, Y, X));
const auto in_n_c0_hi_wi_desc = make_naive_tensor_descriptor_packed(make_tuple(N, C0, Hi, Wi));
const auto wei_k_c0_y_x_desc = make_naive_tensor_descriptor_packed(make_tuple(K, C0, Y, X));
const auto out_n_k0_ho_wo_k1_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(N, K0, Ho, Wo, K1));
make_naive_tensor_descriptor_packed(make_tuple(N, K0, Ho, Wo, K1));
#if 1
// cdata = 64, BlockSize = 64, 16x8x32x4

49
host/driver_offline/include/device_dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw.hpp → host/driver_offline/include/device_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw.hpp
View File

@@ -3,7 +3,7 @@
#include "device.hpp"
#include "host_tensor.hpp"
#include "transform_forward_convolution_into_gemm_v6r1_nchw_kcyx_nkhw.hpp"
#include "driver_dynamic_contraction_dlops_v1r2.hpp"
#include "driver_contraction_dlops_v1r2.hpp"
template <typename TInWei,
typename TAcc,
@@ -15,7 +15,7 @@ template <typename TInWei,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
void device_dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw(
void device_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw(
const InLengths& in_n_c_hi_wi_lengths,
const WeiLengths& wei_k_c_y_x_lengths,
const OutLengths& out_n_k_ho_wo_lengths,
@@ -44,12 +44,9 @@ void device_dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw(
wei_k_c_y_x_device_buf.ToDevice(wei_k_c_y_x.mData.data());
out_n_k_ho_wo_device_buf.ToDevice(out_n_k_ho_wo.mData.data());
const auto in_desc_n_c_hi_wi =
make_dynamic_naive_tensor_descriptor_packed_v2(in_n_c_hi_wi_lengths);
const auto wei_desc_k_c_y_x =
make_dynamic_naive_tensor_descriptor_packed_v2(wei_k_c_y_x_lengths);
const auto out_desc_n_k_ho_wo =
make_dynamic_naive_tensor_descriptor_packed_v2(out_n_k_ho_wo_lengths);
const auto in_desc_n_c_hi_wi = make_naive_tensor_descriptor_packed(in_n_c_hi_wi_lengths);
const auto wei_desc_k_c_y_x = make_naive_tensor_descriptor_packed(wei_k_c_y_x_lengths);
const auto out_desc_n_k_ho_wo = make_naive_tensor_descriptor_packed(out_n_k_ho_wo_lengths);
#if 1
// [8, 1, 128, 1] * [8, 4, 32, 1] = [1, 128, 4, 32] for fp32
@@ -133,7 +130,7 @@ void device_dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw(
const auto out_grid_desc_gm0_gm1_gn0_gn1 = descs[I2];
// HACK: hacks that control index calculation when iterating over A, B, C matrix
constexpr auto wei_grid_iterator_hacks =
constexpr auto wei_grid_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0>{}, // 0+: GK0
Sequence<0, 0, 0, 0, 0, 0, 0>{}, // 1+: GM0
Sequence<0, 0, 0, 0, 0, 0, 0>{}, // 2+: GM10
@@ -145,7 +142,7 @@ void device_dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw(
Sequence<0, 0, 0, 0, 0, 0, 0>{}, // 3-: GM11
Sequence<0, 0, 0, 0, 0, 0, 0>{})); // 4-: GK1
constexpr auto in_grid_iterator_hacks = make_tuple(
constexpr auto in_grid_step_hacks = make_tuple(
make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0>{}, // 0+: GK0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0>{}, // 1+: GN0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0>{}, // 2+: GN10
@@ -157,7 +154,7 @@ void device_dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw(
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0>{}, // 3-: GN11
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 4-: GK1
constexpr auto out_grid_iterator_hacks = make_tuple(
constexpr auto out_grid_step_hacks = make_tuple(
make_tuple(
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}, // 0+: GM10
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0>{}, // 1+: BM0
@@ -173,14 +170,14 @@ void device_dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw(
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0>{}, // 4-: BN0
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0>{})); // 5-: GN1
constexpr auto wei_grid_move_slice_window_iterator_hacks = Sequence<0, 0, 0, 0, 0, 0, 0>{};
constexpr auto wei_grid_move_slice_window_step_hacks = Sequence<0, 0, 0, 0, 0, 0, 0>{};
constexpr auto in_grid_move_slice_window_iterator_hacks =
constexpr auto in_grid_move_slice_window_step_hacks =
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0>{};
for(index_t i = 0; i < 5; ++i)
{
float ave_time = driver_dynamic_contraction_dlops_v1r2<
float ave_time = driver_contraction_dlops_v1r2<
BlockSize,
TInWei,
TAcc,
@@ -214,26 +211,26 @@ void device_dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw(
Sequence<3, 4, 5, 0, 1, 2>, // CThreadTransferSrcDstAccessOrder
5, // CThreadTransferSrcDstVectorDim
CThreadTransferDstScalarPerVector_BN1,
decltype(wei_grid_iterator_hacks),
decltype(in_grid_iterator_hacks),
decltype(out_grid_iterator_hacks),
decltype(wei_grid_move_slice_window_iterator_hacks),
decltype(in_grid_move_slice_window_iterator_hacks)>(
decltype(wei_grid_step_hacks),
decltype(in_grid_step_hacks),
decltype(out_grid_step_hacks),
decltype(wei_grid_move_slice_window_step_hacks),
decltype(in_grid_move_slice_window_step_hacks)>(
static_cast<TInWei*>(wei_k_c_y_x_device_buf.GetDeviceBuffer()),
static_cast<TInWei*>(in_n_c_hi_wi_device_buf.GetDeviceBuffer()),
static_cast<TOut*>(out_n_k_ho_wo_device_buf.GetDeviceBuffer()),
wei_grid_desc_gk0_gm0_gm1_gk1,
in_grid_desc_gk0_gn0_gn1_gk1,
out_grid_desc_gm0_gm1_gn0_gn1,
wei_grid_iterator_hacks,
in_grid_iterator_hacks,
out_grid_iterator_hacks,
wei_grid_move_slice_window_iterator_hacks,
in_grid_move_slice_window_iterator_hacks,
wei_grid_step_hacks,
in_grid_step_hacks,
out_grid_step_hacks,
wei_grid_move_slice_window_step_hacks,
in_grid_move_slice_window_step_hacks,
nrepeat);
float perf = (float)calculate_convolution_flops(
in_desc_n_c_hi_wi, wei_desc_k_c_y_x, out_desc_n_k_ho_wo) /
float perf = static_cast<float>(calculate_convolution_flops(
in_desc_n_c_hi_wi, wei_desc_k_c_y_x, out_desc_n_k_ho_wo)) /
(std::size_t(1000) * 1000 * 1000) / ave_time;
std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s" << std::endl;

70
host/driver_offline/include/driver_dynamic_contraction_dlops_v1r2.hpp → host/driver_offline/include/driver_contraction_dlops_v1r2.hpp
View File

@@ -1,10 +1,10 @@
#ifndef DRIVER_DYNAMIC_CONTRACTION_DLOPS_V1R2_HPP
#define DRIVER_DYNAMIC_CONTRACTION_DLOPS_V1R2_HPP
#ifndef DRIVER_CONTRACTION_DLOPS_V1R2_HPP
#define DRIVER_CONTRACTION_DLOPS_V1R2_HPP
#include "common_header.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "gridwise_dynamic_contraction_dlops_v1r2.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "gridwise_contraction_dlops_v1r2.hpp"
template <ck::index_t BlockSize,
typename FloatAB,
@@ -39,24 +39,24 @@ template <ck::index_t BlockSize,
typename CThreadTransferSrcDstAccessOrder,
ck::index_t CThreadTransferSrcDstVectorDim,
ck::index_t CThreadTransferDstScalarPerVector,
typename AGridIteratorHacks,
typename BGridIteratorHacks,
typename CGridIteratorHacks,
typename AGridMoveSliceWindowIteratorHacks,
typename BGridMoveSliceWindowIteratorHacks>
typename AGridStepHacks,
typename BGridStepHacks,
typename CGridStepHacks,
typename AGridMoveSliceWindowStepHacks,
typename BGridMoveSliceWindowStepHacks>
__host__ float
driver_dynamic_contraction_dlops_v1r2(const FloatAB* p_a_grid,
const FloatAB* p_b_grid,
FloatC* p_c_grid,
const AGridDesc_GK0_GM0_GM1_GK1& a_grid_desc_gk0_gm0_gm1_gk1,
const BGridDesc_GK0_GN0_GN1_GK1& b_grid_desc_gk0_gn0_gn1_gk1,
const CGridDesc_GM0_GM1_GN0_GN1& c_grid_desc_gm0_gm1_gn0_gn1,
AGridIteratorHacks,
BGridIteratorHacks,
CGridIteratorHacks,
AGridMoveSliceWindowIteratorHacks,
BGridMoveSliceWindowIteratorHacks,
ck::index_t nrepeat)
driver_contraction_dlops_v1r2(const FloatAB* p_a_grid,
const FloatAB* p_b_grid,
FloatC* p_c_grid,
const AGridDesc_GK0_GM0_GM1_GK1& a_grid_desc_gk0_gm0_gm1_gk1,
const BGridDesc_GK0_GN0_GN1_GK1& b_grid_desc_gk0_gn0_gn1_gk1,
const CGridDesc_GM0_GM1_GN0_GN1& c_grid_desc_gm0_gm1_gn0_gn1,
AGridStepHacks,
BGridStepHacks,
CGridStepHacks,
AGridMoveSliceWindowStepHacks,
BGridMoveSliceWindowStepHacks,
ck::index_t nrepeat)
{
using namespace ck;
@@ -70,7 +70,7 @@ driver_dynamic_contraction_dlops_v1r2(const FloatAB* p_a_grid,
// GEMM
using GridwiseContraction =
GridwiseDynamicContractionDlops_A_GK0_GM0_GM1_GK1_B_GK0_GN0_GN1_GK1_C_GM0_GM1_GN0_GN1<
GridwiseContractionDlops_A_GK0_GM0_GM1_GK1_B_GK0_GN0_GN1_GK1_C_GM0_GM1_GN0_GN1<
BlockSize,
FloatAB,
FloatAcc,
@@ -104,11 +104,11 @@ driver_dynamic_contraction_dlops_v1r2(const FloatAB* p_a_grid,
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
AGridIteratorHacks,
BGridIteratorHacks,
CGridIteratorHacks,
AGridMoveSliceWindowIteratorHacks,
BGridMoveSliceWindowIteratorHacks>;
AGridStepHacks,
BGridStepHacks,
CGridStepHacks,
AGridMoveSliceWindowStepHacks,
BGridMoveSliceWindowStepHacks>;
const auto GK0 = a_grid_desc_gk0_gm0_gm1_gk1.GetLength(I0);
@@ -116,7 +116,7 @@ driver_dynamic_contraction_dlops_v1r2(const FloatAB* p_a_grid,
a_grid_desc_gk0_gm0_gm1_gk1, b_grid_desc_gk0_gn0_gn1_gk1, c_grid_desc_gm0_gm1_gn0_gn1))
{
throw std::runtime_error("wrong! "
"GridwiseDynamicContraction_A_GK0_GM0_GM1_GK1_B_GK0_GN0_GN1_GK1_C_"
"GridwiseContraction_A_GK0_GM0_GM1_GK1_B_GK0_GN0_GN1_GK1_C_"
"GM0_GM1_GN0_GN1 has invalid setting");
}
@@ -178,7 +178,7 @@ driver_dynamic_contraction_dlops_v1r2(const FloatAB* p_a_grid,
if(has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel = kernel_dynamic_contraction_dlops_v1r2<
const auto kernel = kernel_contraction_dlops_v1r2<
GridwiseContraction,
FloatAB,
FloatC,
@@ -194,7 +194,6 @@ driver_dynamic_contraction_dlops_v1r2(const FloatAB* p_a_grid,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
@@ -205,7 +204,7 @@ driver_dynamic_contraction_dlops_v1r2(const FloatAB* p_a_grid,
}
else if(has_main_k_block_loop && !has_double_tail_k_block_loop)
{
const auto kernel = kernel_dynamic_contraction_dlops_v1r2<
const auto kernel = kernel_contraction_dlops_v1r2<
GridwiseContraction,
FloatAB,
FloatC,
@@ -221,7 +220,6 @@ driver_dynamic_contraction_dlops_v1r2(const FloatAB* p_a_grid,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
@@ -232,7 +230,7 @@ driver_dynamic_contraction_dlops_v1r2(const FloatAB* p_a_grid,
}
else if(!has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel = kernel_dynamic_contraction_dlops_v1r2<
const auto kernel = kernel_contraction_dlops_v1r2<
GridwiseContraction,
FloatAB,
FloatC,
@@ -248,7 +246,6 @@ driver_dynamic_contraction_dlops_v1r2(const FloatAB* p_a_grid,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
@@ -259,7 +256,7 @@ driver_dynamic_contraction_dlops_v1r2(const FloatAB* p_a_grid,
}
else
{
const auto kernel = kernel_dynamic_contraction_dlops_v1r2<
const auto kernel = kernel_contraction_dlops_v1r2<
GridwiseContraction,
FloatAB,
FloatC,
@@ -275,7 +272,6 @@ driver_dynamic_contraction_dlops_v1r2(const FloatAB* p_a_grid,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,

65
host/driver_offline/include/driver_dynamic_convolution_forward_implicit_gemm_v5r1_dlops_nchw_kcyx_nkhw.hpp → host/driver_offline/include/driver_convolution_forward_implicit_gemm_v5r1_dlops_nchw_kcyx_nkhw.hpp
View File

@@ -1,10 +1,10 @@
#ifndef DRIVER_DYNAMIC_CONVOLUTION_FORWARD_IMPLICIT_GEMM_V5R1_NCHW_KCYX_NKHW_HPP
#define DRIVER_DYNAMIC_CONVOLUTION_FORWARD_IMPLICIT_GEMM_V5R1_NCHW_KCYX_NKHW_HPP
#ifndef DRIVER_CONVOLUTION_FORWARD_IMPLICIT_GEMM_V5R1_NCHW_KCYX_NKHW_HPP
#define DRIVER_CONVOLUTION_FORWARD_IMPLICIT_GEMM_V5R1_NCHW_KCYX_NKHW_HPP
#include "common_header.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "gridwise_dynamic_gemm_dlops_v2.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "gridwise_gemm_dlops_v2.hpp"
#include "gridwise_operation_wrapper.hpp"
template <ck::index_t BlockSize,
@@ -34,9 +34,9 @@ struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nchw_kcyx_nkhw_pad
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
__host__ void Run(const ck::DynamicTensorDescriptor<Wei...>& wei_k_c_y_x_global_desc,
const ck::DynamicTensorDescriptor<In...>& in_n_c_hi_wi_global_desc,
const ck::DynamicTensorDescriptor<Out...>& out_n_k0_ho_wo_k1_global_desc,
__host__ void Run(const ck::TensorDescriptor<Wei...>& wei_k_c_y_x_global_desc,
const ck::TensorDescriptor<In...>& in_n_c_hi_wi_global_desc,
const ck::TensorDescriptor<Out...>& out_n_k0_ho_wo_k1_global_desc,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
@@ -82,14 +82,14 @@ struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nchw_kcyx_nkhw_pad
const auto InRightPadW = in_right_pads[I1];
// weight tensor
const auto wei_e_k_global_desc = transform_dynamic_tensor_descriptor(
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(K, C * Y * X)),
const auto wei_e_k_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_dynamic_tensor_descriptor(
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),
@@ -98,7 +98,7 @@ struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nchw_kcyx_nkhw_pad
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_dynamic_tensor_descriptor(
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),
@@ -108,7 +108,7 @@ struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nchw_kcyx_nkhw_pad
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_e_n_ho_wo_global_desc = transform_dynamic_tensor_descriptor(
const auto in_e_n_ho_wo_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_pass_through_transform(N),
@@ -118,8 +118,8 @@ struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nchw_kcyx_nkhw_pad
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
// output tensor
const auto out_k_n_ho_wo_global_desc = transform_dynamic_tensor_descriptor(
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(N, K0, Ho, Wo, K1)),
const auto out_k_n_ho_wo_global_desc = transform_tensor_descriptor(
make_naive_tensor_descriptor_packed(make_tuple(N, K0, Ho, Wo, K1)),
make_tuple(make_merge_transform(make_tuple(K0, K1)),
make_pass_through_transform(N),
make_pass_through_transform(Ho),
@@ -136,13 +136,13 @@ struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nchw_kcyx_nkhw_pad
}
// hack to control index calculation when iterating over a_k_m_global tensor
constexpr auto a_e_k_global_iterator_hacks =
constexpr auto a_e_k_global_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0>{}, Sequence<0, 0, 0>{}),
make_tuple(Sequence<0, 0, 0>{}, Sequence<0, 0, 0>{}));
constexpr auto a_e_k_global_move_slice_window_iterator_hack = Sequence<0, 0, 0>{};
constexpr auto a_e_k_global_move_slice_window_step_hack = Sequence<0, 0, 0>{};
constexpr auto b_e_n_ho_wo_global_iterator_hacks =
constexpr auto b_e_n_ho_wo_global_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
@@ -152,12 +152,12 @@ struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nchw_kcyx_nkhw_pad
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}));
constexpr auto b_e_n_ho_wo_global_move_slice_window_iterator_hack =
constexpr auto b_e_n_ho_wo_global_move_slice_window_step_hack =
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{};
// hack to control index calculation when iterating over c_m0_m1_n0_n1_global tensor
// hack for NKHW format
constexpr auto c_k_n_ho_wo_global_tensor_iterator_hacks =
constexpr auto c_k_n_ho_wo_global_tensor_step_hacks =
make_tuple(make_tuple(Sequence<0, 1, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
@@ -169,7 +169,7 @@ struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nchw_kcyx_nkhw_pad
#if 1
// GEMM
using gridwise_gemm = GridwiseDynamicGemmDlops_km_kn_mn_v3<
using gridwise_gemm = GridwiseGemmDlops_km_kn_mn_v3<
BlockSize,
FloatAB,
FloatAcc,
@@ -202,11 +202,11 @@ struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nchw_kcyx_nkhw_pad
Sequence<0, 2, 3, 1>,
0,
CThreadTransferDstScalarPerVector_W,
decltype(a_e_k_global_iterator_hacks),
decltype(b_e_n_ho_wo_global_iterator_hacks),
decltype(c_k_n_ho_wo_global_tensor_iterator_hacks),
decltype(a_e_k_global_move_slice_window_iterator_hack),
decltype(b_e_n_ho_wo_global_move_slice_window_iterator_hack)>;
decltype(a_e_k_global_step_hacks),
decltype(b_e_n_ho_wo_global_step_hacks),
decltype(c_k_n_ho_wo_global_tensor_step_hacks),
decltype(a_e_k_global_move_slice_window_step_hack),
decltype(b_e_n_ho_wo_global_move_slice_window_step_hack)>;
const auto GridSize = (K / KPerBlock) * (Ho / HoPerBlock) * (Wo / WoPerBlock) * N;
@@ -244,7 +244,6 @@ struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nchw_kcyx_nkhw_pad
dim3(GridSize),
dim3(BlockSize),
0,
0,
wei_e_k_global_desc,
p_wei_global,
in_e_n_ho_wo_global_desc,
@@ -270,7 +269,6 @@ struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nchw_kcyx_nkhw_pad
dim3(GridSize),
dim3(BlockSize),
0,
0,
wei_e_k_global_desc,
p_wei_global,
in_e_n_ho_wo_global_desc,
@@ -296,7 +294,6 @@ struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nchw_kcyx_nkhw_pad
dim3(GridSize),
dim3(BlockSize),
0,
0,
wei_e_k_global_desc,
p_wei_global,
in_e_n_ho_wo_global_desc,
@@ -322,7 +319,6 @@ struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nchw_kcyx_nkhw_pad
dim3(GridSize),
dim3(BlockSize),
0,
0,
wei_e_k_global_desc,
p_wei_global,
in_e_n_ho_wo_global_desc,
@@ -338,10 +334,11 @@ struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nchw_kcyx_nkhw_pad
float ave_time = timer.GetElapsedTime() / nrepeat;
float perf = (float)calculate_convolution_flops(in_n_c_hi_wi_global_desc,
wei_k_c_y_x_global_desc,
out_n_k0_ho_wo_k1_global_desc) /
(std::size_t(1000) * 1000 * 1000) / ave_time;
float perf =
static_cast<float>(calculate_convolution_flops(in_n_c_hi_wi_global_desc,
wei_k_c_y_x_global_desc,
out_n_k0_ho_wo_k1_global_desc)) /
(std::size_t(1000) * 1000 * 1000) / ave_time;
std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s"
<< std::endl;

65
host/driver_offline/include/driver_dynamic_convolution_forward_implicit_gemm_v5r1_dlops_nchw_kcyx_nkhw_outpad.hpp → host/driver_offline/include/driver_convolution_forward_implicit_gemm_v5r1_dlops_nchw_kcyx_nkhw_outpad.hpp
View File

@@ -1,10 +1,10 @@
#ifndef DRIVER_DYNAMIC_CONVOLUTION_FORWARD_IMPLICIT_GEMM_V5R1_DLOPS_NCHW_KCYX_NKHW_OUTPAD_HPP
#define DRIVER_DYNAMIC_CONVOLUTION_FORWARD_IMPLICIT_GEMM_V5R1_DLOPS_NCHW_KCYX_NKHW_OUTPAD_HPP
#ifndef DRIVER_CONVOLUTION_FORWARD_IMPLICIT_GEMM_V5R1_DLOPS_NCHW_KCYX_NKHW_OUTPAD_HPP
#define DRIVER_CONVOLUTION_FORWARD_IMPLICIT_GEMM_V5R1_DLOPS_NCHW_KCYX_NKHW_OUTPAD_HPP
#include "common_header.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "gridwise_dynamic_gemm_dlops_v2.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "gridwise_gemm_dlops_v2.hpp"
#include "gridwise_operation_wrapper.hpp"
template <ck::index_t BlockSize,
@@ -34,9 +34,9 @@ struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nchw_kcyx_nkhw_outp
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
__host__ void Run(const ck::DynamicTensorDescriptor<Wei...>& wei_k_c_y_x_global_desc,
const ck::DynamicTensorDescriptor<In...>& in_n_c_hi_wi_global_desc,
const ck::DynamicTensorDescriptor<Out...>& out_n_k0_ho_wo_k1_global_desc,
__host__ void Run(const ck::TensorDescriptor<Wei...>& wei_k_c_y_x_global_desc,
const ck::TensorDescriptor<In...>& in_n_c_hi_wi_global_desc,
const ck::TensorDescriptor<Out...>& out_n_k0_ho_wo_k1_global_desc,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
@@ -93,14 +93,14 @@ struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nchw_kcyx_nkhw_outp
<< std::endl;
// weight tensor
const auto wei_e_k_global_desc = transform_dynamic_tensor_descriptor(
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(K, C * Y * X)),
const auto wei_e_k_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_dynamic_tensor_descriptor(
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),
@@ -109,7 +109,7 @@ struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nchw_kcyx_nkhw_outp
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_dynamic_tensor_descriptor(
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),
@@ -119,7 +119,7 @@ struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nchw_kcyx_nkhw_outp
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_e_n_ho_wo_global_desc = transform_dynamic_tensor_descriptor(
const auto in_e_n_ho_wo_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_pass_through_transform(N),
@@ -129,8 +129,8 @@ struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nchw_kcyx_nkhw_outp
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
// output tensor
const auto out_k_n_hop_wop_global_desc = transform_dynamic_tensor_descriptor(
make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(N, K0, Ho, Wo, K1)),
const auto out_k_n_hop_wop_global_desc = transform_tensor_descriptor(
make_naive_tensor_descriptor_packed(make_tuple(N, K0, Ho, Wo, K1)),
make_tuple(make_merge_transform(make_tuple(K0, K1)),
make_pass_through_transform(N),
make_pad_transform(Ho, 0, OutRightPadH),
@@ -149,13 +149,13 @@ struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nchw_kcyx_nkhw_outp
}
// hack to control index calculation when iterating over a_k_m_global tensor
constexpr auto a_e_k_global_iterator_hacks =
constexpr auto a_e_k_global_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0>{}, Sequence<0, 0, 0>{}),
make_tuple(Sequence<0, 0, 0>{}, Sequence<0, 0, 0>{}));
constexpr auto a_e_k_global_move_slice_window_iterator_hack = Sequence<0, 0, 0>{};
constexpr auto a_e_k_global_move_slice_window_step_hack = Sequence<0, 0, 0>{};
constexpr auto b_e_n_ho_wo_global_iterator_hacks =
constexpr auto b_e_n_ho_wo_global_step_hacks =
make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
@@ -165,12 +165,12 @@ struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nchw_kcyx_nkhw_outp
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}));
constexpr auto b_e_n_ho_wo_global_move_slice_window_iterator_hack =
constexpr auto b_e_n_ho_wo_global_move_slice_window_step_hack =
Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{};
// hack to control index calculation when iterating over c_m0_m1_n0_n1_global tensor
// hack for NKHW format
constexpr auto c_k_n_ho_wo_global_tensor_iterator_hacks =
constexpr auto c_k_n_ho_wo_global_tensor_step_hacks =
make_tuple(make_tuple(Sequence<0, 1, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
Sequence<0, 0, 0, 0, 0>{},
@@ -181,7 +181,7 @@ struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nchw_kcyx_nkhw_outp
Sequence<0, 0, 0, 0, 0>{}));
// GEMM
using gridwise_gemm = GridwiseDynamicGemmDlops_km_kn_mn_v3<
using gridwise_gemm = GridwiseGemmDlops_km_kn_mn_v3<
BlockSize,
FloatAB,
FloatAcc,
@@ -214,11 +214,11 @@ struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nchw_kcyx_nkhw_outp
Sequence<0, 2, 3, 1>,
0,
CThreadTransferDstScalarPerVector_W,
decltype(a_e_k_global_iterator_hacks),
decltype(b_e_n_ho_wo_global_iterator_hacks),
decltype(c_k_n_ho_wo_global_tensor_iterator_hacks),
decltype(a_e_k_global_move_slice_window_iterator_hack),
decltype(b_e_n_ho_wo_global_move_slice_window_iterator_hack)>;
decltype(a_e_k_global_step_hacks),
decltype(b_e_n_ho_wo_global_step_hacks),
decltype(c_k_n_ho_wo_global_tensor_step_hacks),
decltype(a_e_k_global_move_slice_window_step_hack),
decltype(b_e_n_ho_wo_global_move_slice_window_step_hack)>;
const auto GridSize = (K / KPerBlock) * (Hop / HoPerBlock) * (Wop / WoPerBlock) * N;
@@ -257,7 +257,6 @@ struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nchw_kcyx_nkhw_outp
dim3(GridSize),
dim3(BlockSize),
0,
0,
wei_e_k_global_desc,
p_wei_global,
in_e_n_ho_wo_global_desc,
@@ -284,7 +283,6 @@ struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nchw_kcyx_nkhw_outp
dim3(GridSize),
dim3(BlockSize),
0,
0,
wei_e_k_global_desc,
p_wei_global,
in_e_n_ho_wo_global_desc,
@@ -311,7 +309,6 @@ struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nchw_kcyx_nkhw_outp
dim3(GridSize),
dim3(BlockSize),
0,
0,
wei_e_k_global_desc,
p_wei_global,
in_e_n_ho_wo_global_desc,
@@ -338,7 +335,6 @@ struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nchw_kcyx_nkhw_outp
dim3(GridSize),
dim3(BlockSize),
0,
0,
wei_e_k_global_desc,
p_wei_global,
in_e_n_ho_wo_global_desc,
@@ -354,10 +350,11 @@ struct DriverDynamicConvolutionForwardImplicitGemmDlops_v5r1_nchw_kcyx_nkhw_outp
float ave_time = timer.GetElapsedTime() / nrepeat;
float perf = (float)calculate_convolution_flops(in_n_c_hi_wi_global_desc,
wei_k_c_y_x_global_desc,
out_n_k0_ho_wo_k1_global_desc) /
(std::size_t(1000) * 1000 * 1000) / ave_time;
float perf =
static_cast<float>(calculate_convolution_flops(in_n_c_hi_wi_global_desc,
wei_k_c_y_x_global_desc,
out_n_k0_ho_wo_k1_global_desc)) /
(std::size_t(1000) * 1000 * 1000) / ave_time;
std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s"
<< std::endl;

415
host/driver_offline/include/driver_dynamic_gemm_dlops_v1r2.hpp
View File

@@ -1,415 +0,0 @@
#ifndef DRIVER_DYNAMIC_GEMM_DLOPS_V1R2
#define DRIVER_DYNAMIC_GEMM_DLOPS_V1R2
#include "common_header.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "gridwise_dynamic_gemm_dlops_v1r2.hpp"
template <ck::index_t BlockSize,
typename FloatAB,
typename FloatAcc,
typename FloatC,
ck::InMemoryDataOperationEnum_t CGlobalMemoryDataOperation,
typename AKMGridDesc,
typename BKNGridDesc,
typename CMNGridDesc,
ck::index_t MPerBlock,
ck::index_t NPerBlock,
ck::index_t KPerBlock,
ck::index_t M1PerThread,
ck::index_t N1PerThread,
ck::index_t KPerThread,
ck::index_t M1N1ThreadClusterM10,
ck::index_t M1N1ThreadClusterN10,
ck::index_t M1N1ThreadClusterM11,
ck::index_t M1N1ThreadClusterN11,
typename ABlockTransferThreadSliceLengths_K_M0_M1,
typename ABlockTransferThreadClusterLengths_K_M0_M1,
typename ABlockTransferThreadClusterArrangeOrder,
typename ABlockTransferSrcAccessOrder,
ck::index_t ABlockTransferSrcVectorDim,
ck::index_t ABlockTransferSrcScalarPerVector,
ck::index_t ABlockTransferDstScalarPerVector_M1,
bool AThreadTransferSrcResetCoordinateAfterRun,
typename BBlockTransferThreadSliceLengths_K_N0_N1,
typename BBlockTransferThreadClusterLengths_K_N0_N1,
typename BBlockTransferThreadClusterArrangeOrder,
typename BBlockTransferSrcAccessOrder,
ck::index_t BBlockTransferSrcVectorDim,
ck::index_t BBlockTransferSrcScalarPerVector,
ck::index_t BBlockTransferDstScalarPerVector_N1,
bool BThreadTransferSrcResetCoordinateAfterRun,
typename CThreadTransferSrcDstAccessOrder,
ck::index_t CThreadTransferSrcDstVectorDim,
ck::index_t CThreadTransferDstScalarPerVector,
typename AGridIteratorHacks,
typename BGridIteratorHacks,
typename CGridIteratorHacks,
typename AGridMoveSliceWindowIteratorHacks,
typename BGridMoveSliceWindowIteratorHacks>
__host__ float driver_dynamic_gemm_dlops_v1r2(const FloatAB* p_a_grid,
const FloatAB* p_b_grid,
FloatC* p_c_grid,
const AKMGridDesc& a_k_m_grid_desc,
const BKNGridDesc& b_k_n_grid_desc,
const CMNGridDesc& c_m_n_grid_desc,
AGridIteratorHacks,
BGridIteratorHacks,
CGridIteratorHacks,
AGridMoveSliceWindowIteratorHacks,
BGridMoveSliceWindowIteratorHacks,
ck::index_t nrepeat)
{
using namespace ck;
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto I4 = Number<4>{};
constexpr auto I5 = Number<5>{};
// GEMM
using GridwiseGemm =
GridwiseDynamicGemmDlops_km_kn_mn_v1r2<BlockSize,
FloatAB,
FloatAcc,
FloatC,
CGlobalMemoryDataOperation,
AKMGridDesc,
BKNGridDesc,
CMNGridDesc,
MPerBlock,
NPerBlock,
KPerBlock,
M1PerThread,
N1PerThread,
KPerThread,
M1N1ThreadClusterM10,
M1N1ThreadClusterN10,
M1N1ThreadClusterM11,
M1N1ThreadClusterN11,
ABlockTransferThreadSliceLengths_K_M0_M1,
ABlockTransferThreadClusterLengths_K_M0_M1,
ABlockTransferThreadClusterArrangeOrder,
ABlockTransferSrcAccessOrder,
ABlockTransferSrcVectorDim,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_M1,
AThreadTransferSrcResetCoordinateAfterRun,
BBlockTransferThreadSliceLengths_K_N0_N1,
BBlockTransferThreadClusterLengths_K_N0_N1,
BBlockTransferThreadClusterArrangeOrder,
BBlockTransferSrcAccessOrder,
BBlockTransferSrcVectorDim,
BBlockTransferSrcScalarPerVector,
BBlockTransferDstScalarPerVector_N1,
BThreadTransferSrcResetCoordinateAfterRun,
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
AGridIteratorHacks,
BGridIteratorHacks,
CGridIteratorHacks,
AGridMoveSliceWindowIteratorHacks,
BGridMoveSliceWindowIteratorHacks>;
const auto M = a_k_m_grid_desc.GetLength(I1);
const auto N = b_k_n_grid_desc.GetLength(I1);
const auto K = a_k_m_grid_desc.GetLength(I0);
if(!GridwiseGemm::CheckValidity(a_k_m_grid_desc, b_k_n_grid_desc, c_m_n_grid_desc))
{
throw std::runtime_error(
"wrong! GridwiseDynamicGemmDlops_km_kn_mn_v1r2 has invalid setting");
}
const auto a_k_m0_m1_grid_desc = GridwiseGemm::MakeAKM0M1GridDescriptor(a_k_m_grid_desc);
const auto b_k_n0_n1_grid_desc = GridwiseGemm::MakeBKN0N1GridDescriptor(b_k_n_grid_desc);
using AKM0M1GridDesc = decltype(a_k_m0_m1_grid_desc);
using BKN0N1GridDesc = decltype(b_k_n0_n1_grid_desc);
// c_m0_m10_m11_n0_n10_n11_grid_desc
const auto c_m0_m10_m11_n0_n10_n11_grid_desc =
GridwiseGemm::MakeCM0M10M11N0N10N11GridDescriptor(c_m_n_grid_desc);
using CM0M10M11N0N10N11GridDesc = decltype(c_m0_m10_m11_n0_n10_n11_grid_desc);
// c_blockid_to_m0_n0_block_cluster_adaptor
const auto c_blockid_to_m0_n0_block_cluster_adaptor =
GridwiseGemm::MakeCBlockIdToM0N0BlockClusterAdaptor(c_m_n_grid_desc);
using CBlockIdToM0N0BlockClusterAdaptor = decltype(c_blockid_to_m0_n0_block_cluster_adaptor);
const index_t grid_size = GridwiseGemm::CalculateGridSize(M, N);
const bool has_main_k_block_loop = GridwiseGemm::CalculateHasMainKBlockLoop(K);
const bool has_double_tail_k_block_loop = GridwiseGemm::CalculateHasDoubleTailKBlockLoop(K);
{
std::cout << "a_k_m0_m1_grid_desc{" << a_k_m0_m1_grid_desc.GetLength(I0) << ", "
<< a_k_m0_m1_grid_desc.GetLength(I1) << ", " << a_k_m0_m1_grid_desc.GetLength(I2)
<< "}" << std::endl;
std::cout << "b_k_n0_n1_grid_desc{" << b_k_n0_n1_grid_desc.GetLength(I0) << ", "
<< b_k_n0_n1_grid_desc.GetLength(I1) << ", " << b_k_n0_n1_grid_desc.GetLength(I2)
<< "}" << std::endl;
std::cout << "c_m0_m10_m11_n0_n10_n11_grid_desc{ "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I0) << ", "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I1) << ", "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I2) << ", "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I3) << ", "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I4) << ", "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I5) << "}" << std::endl;
}
#if CK_EXPERIMENTAL_PASS_TENSOR_DESCRIPTOR_BY_VALUE
float ave_time = 0;
if(has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel =
kernel_dynamic_gemm_dlops_v1r2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AKM0M1GridDesc>,
remove_reference_t<BKN0N1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
true,
true>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_k_m0_m1_grid_desc,
b_k_n0_n1_grid_desc,
c_m0_m10_m11_n0_n10_n11_grid_desc,
c_blockid_to_m0_n0_block_cluster_adaptor);
}
else if(has_main_k_block_loop && !has_double_tail_k_block_loop)
{
const auto kernel =
kernel_dynamic_gemm_dlops_v1r2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AKM0M1GridDesc>,
remove_reference_t<BKN0N1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
true,
false>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_k_m0_m1_grid_desc,
b_k_n0_n1_grid_desc,
c_m0_m10_m11_n0_n10_n11_grid_desc,
c_blockid_to_m0_n0_block_cluster_adaptor);
}
else if(!has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel =
kernel_dynamic_gemm_dlops_v1r2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AKM0M1GridDesc>,
remove_reference_t<BKN0N1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
false,
true>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_k_m0_m1_grid_desc,
b_k_n0_n1_grid_desc,
c_m0_m10_m11_n0_n10_n11_grid_desc,
c_blockid_to_m0_n0_block_cluster_adaptor);
}
else
{
const auto kernel =
kernel_dynamic_gemm_dlops_v1r2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AKM0M1GridDesc>,
remove_reference_t<BKN0N1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
false,
false>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_k_m0_m1_grid_desc,
b_k_n0_n1_grid_desc,
c_m0_m10_m11_n0_n10_n11_grid_desc,
c_blockid_to_m0_n0_block_cluster_adaptor);
}
return ave_time;
#elif CK_EXPERIMENTAL_PASS_TENSOR_DESCRIPTOR_BY_VOID_POINTER
DeviceMem a_k_m0_m1_grid_desc_dev_buf(sizeof(AKM0M1GridDesc));
DeviceMem b_k_n0_n1_grid_desc_dev_buf(sizeof(BKN0N1GridDesc));
DeviceMem c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf(sizeof(CM0M10M11N0N10N11GridDesc));
DeviceMem c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf(
sizeof(CBlockIdToM0N0BlockClusterAdaptor));
a_k_m0_m1_grid_desc_dev_buf.ToDevice(&a_k_m0_m1_grid_desc);
b_k_n0_n1_grid_desc_dev_buf.ToDevice(&b_k_n0_n1_grid_desc);
c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf.ToDevice(&c_m0_m10_m11_n0_n10_n11_grid_desc);
c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf.ToDevice(
&c_blockid_to_m0_n0_block_cluster_adaptor);
float ave_time = 0;
if(has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel =
kernel_dynamic_gemm_dlops_v1r2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AKM0M1GridDesc>,
remove_reference_t<BKN0N1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
true,
true>;
ave_time = launch_and_time_kernel(
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
(void CONSTANT*)a_k_m0_m1_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)b_k_n0_n1_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf.GetDeviceBuffer());
}
else if(has_main_k_block_loop && !has_double_tail_k_block_loop)
{
const auto kernel =
kernel_dynamic_gemm_dlops_v1r2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AKM0M1GridDesc>,
remove_reference_t<BKN0N1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
true,
false>;
ave_time = launch_and_time_kernel(
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
(void CONSTANT*)a_k_m0_m1_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)b_k_n0_n1_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf.GetDeviceBuffer());
}
else if(!has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel =
kernel_dynamic_gemm_dlops_v1r2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AKM0M1GridDesc>,
remove_reference_t<BKN0N1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
false,
true>;
ave_time = launch_and_time_kernel(
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
(void CONSTANT*)a_k_m0_m1_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)b_k_n0_n1_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf.GetDeviceBuffer());
}
else
{
const auto kernel =
kernel_dynamic_gemm_dlops_v1r2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AKM0M1GridDesc>,
remove_reference_t<BKN0N1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
false,
false>;
ave_time = launch_and_time_kernel(
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
(void CONSTANT*)a_k_m0_m1_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)b_k_n0_n1_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf.GetDeviceBuffer());
}
return ave_time;
#endif
}
#endif

411
host/driver_offline/include/driver_dynamic_gemm_dlops_v1r3.hpp
View File

@@ -1,411 +0,0 @@
#ifndef DRIVER_DYNAMIC_GEMM_DLOPS_V1R3
#define DRIVER_DYNAMIC_GEMM_DLOPS_V1R3
#include "common_header.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "gridwise_dynamic_gemm_dlops_v1r3.hpp"
template <ck::index_t BlockSize,
typename FloatAB,
typename FloatAcc,
typename FloatC,
ck::InMemoryDataOperationEnum_t CGlobalMemoryDataOperation,
typename AK0MK1GridDesc,
typename BK0NK1GridDesc,
typename CMNGridDesc,
ck::index_t MPerBlock,
ck::index_t NPerBlock,
ck::index_t KPerBlock,
ck::index_t M1PerThread,
ck::index_t N1PerThread,
ck::index_t KPerThread,
typename M1N1ThreadClusterM1Xs,
typename M1N1ThreadClusterN1Xs,
typename ABlockTransferThreadSliceLengths_K0_M0_M1_K1,
typename ABlockTransferThreadClusterLengths_K0_M0_M1_K1,
typename ABlockTransferThreadClusterArrangeOrder,
typename ABlockTransferSrcAccessOrder,
typename ABlockTransferSrcVectorTensorLengths_K0_M0_M1_K1,
typename ABlockTransferSrcVectorTensorContiguousDimOrder,
typename ABlockTransferDstVectorTensorLengths_K0_M0_M1_K1,
typename BBlockTransferThreadSliceLengths_K0_N0_N1_K1,
typename BBlockTransferThreadClusterLengths_K0_N0_N1_K1,
typename BBlockTransferThreadClusterArrangeOrder,
typename BBlockTransferSrcAccessOrder,
typename BBlockTransferSrcVectorTensorLengths_K0_N0_N1_K1,
typename BBlockTransferSrcVectorTensorContiguousDimOrder,
typename BBlockTransferDstVectorTensorLengths_K0_N0_N1_K1,
typename CThreadTransferSrcDstAccessOrder,
ck::index_t CThreadTransferSrcDstVectorDim,
ck::index_t CThreadTransferDstScalarPerVector,
typename AGridIteratorHacks,
typename BGridIteratorHacks,
typename CGridIteratorHacks,
typename AGridMoveSliceWindowIteratorHacks,
typename BGridMoveSliceWindowIteratorHacks>
__host__ float driver_dynamic_gemm_dlops_v1r3(const FloatAB* p_a_grid,
const FloatAB* p_b_grid,
FloatC* p_c_grid,
const AK0MK1GridDesc& a_k0_m_k1_grid_desc,
const BK0NK1GridDesc& b_k0_n_k1_grid_desc,
const CMNGridDesc& c_m_n_grid_desc,
AGridIteratorHacks,
BGridIteratorHacks,
CGridIteratorHacks,
AGridMoveSliceWindowIteratorHacks,
BGridMoveSliceWindowIteratorHacks,
ck::index_t nrepeat)
{
using namespace ck;
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto I4 = Number<4>{};
constexpr auto I5 = Number<5>{};
// GEMM
using GridwiseGemm =
GridwiseDynamicGemmDlops_km_kn_mn_v1r3<BlockSize,
FloatAB,
FloatAcc,
FloatC,
CGlobalMemoryDataOperation,
AK0MK1GridDesc,
BK0NK1GridDesc,
CMNGridDesc,
MPerBlock,
NPerBlock,
KPerBlock,
M1PerThread,
N1PerThread,
KPerThread,
M1N1ThreadClusterM1Xs,
M1N1ThreadClusterN1Xs,
ABlockTransferThreadSliceLengths_K0_M0_M1_K1,
ABlockTransferThreadClusterLengths_K0_M0_M1_K1,
ABlockTransferThreadClusterArrangeOrder,
ABlockTransferSrcAccessOrder,
ABlockTransferSrcVectorTensorLengths_K0_M0_M1_K1,
ABlockTransferSrcVectorTensorContiguousDimOrder,
ABlockTransferDstVectorTensorLengths_K0_M0_M1_K1,
BBlockTransferThreadSliceLengths_K0_N0_N1_K1,
BBlockTransferThreadClusterLengths_K0_N0_N1_K1,
BBlockTransferThreadClusterArrangeOrder,
BBlockTransferSrcAccessOrder,
BBlockTransferSrcVectorTensorLengths_K0_N0_N1_K1,
BBlockTransferSrcVectorTensorContiguousDimOrder,
BBlockTransferDstVectorTensorLengths_K0_N0_N1_K1,
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
AGridIteratorHacks,
BGridIteratorHacks,
CGridIteratorHacks,
AGridMoveSliceWindowIteratorHacks,
BGridMoveSliceWindowIteratorHacks>;
const auto M = a_k0_m_k1_grid_desc.GetLength(I1);
const auto N = b_k0_n_k1_grid_desc.GetLength(I1);
const auto K0 = a_k0_m_k1_grid_desc.GetLength(I0);
if(!GridwiseGemm::CheckValidity(a_k0_m_k1_grid_desc, b_k0_n_k1_grid_desc, c_m_n_grid_desc))
{
throw std::runtime_error(
"wrong! GridwiseDynamicGemmDlops_km_kn_mn_v1r3 has invalid setting");
}
const auto a_k0_m0_m1_k1_grid_desc =
GridwiseGemm::MakeAK0M0M1K1GridDescriptor(a_k0_m_k1_grid_desc);
const auto b_k0_n0_n1_k1_grid_desc =
GridwiseGemm::MakeBK0N0N1K1GridDescriptor(b_k0_n_k1_grid_desc);
using AK0M0M1K1GridDesc = decltype(a_k0_m0_m1_k1_grid_desc);
using BK0N0N1K1GridDesc = decltype(b_k0_n0_n1_k1_grid_desc);
// c_m0_m10_m11_n0_n10_n11_grid_desc
const auto c_m0_m10_m11_n0_n10_n11_grid_desc =
GridwiseGemm::MakeCM0M10M11N0N10N11GridDescriptor(c_m_n_grid_desc);
using CM0M10M11N0N10N11GridDesc = decltype(c_m0_m10_m11_n0_n10_n11_grid_desc);
// c_blockid_to_m0_n0_block_cluster_adaptor
const auto c_blockid_to_m0_n0_block_cluster_adaptor =
GridwiseGemm::MakeCBlockIdToM0N0BlockClusterAdaptor(c_m_n_grid_desc);
using CBlockIdToM0N0BlockClusterAdaptor = decltype(c_blockid_to_m0_n0_block_cluster_adaptor);
const index_t grid_size = GridwiseGemm::CalculateGridSize(M, N);
const bool has_main_k_block_loop = GridwiseGemm::CalculateHasMainKBlockLoop(K0);
const bool has_double_tail_k_block_loop = GridwiseGemm::CalculateHasDoubleTailKBlockLoop(K0);
{
std::cout << "a_k0_m0_m1_k1_grid_desc{" << a_k0_m0_m1_k1_grid_desc.GetLength(I0) << ", "
<< a_k0_m0_m1_k1_grid_desc.GetLength(I1) << ", "
<< a_k0_m0_m1_k1_grid_desc.GetLength(I2) << ", "
<< a_k0_m0_m1_k1_grid_desc.GetLength(I3) << "}" << std::endl;
std::cout << "b_k0_n0_n1_k1_grid_desc{" << b_k0_n0_n1_k1_grid_desc.GetLength(I0) << ", "
<< b_k0_n0_n1_k1_grid_desc.GetLength(I1) << ", "
<< b_k0_n0_n1_k1_grid_desc.GetLength(I2) << ", "
<< b_k0_n0_n1_k1_grid_desc.GetLength(I3) << "}" << std::endl;
std::cout << "c_m0_m10_m11_n0_n10_n11_grid_desc{ "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I0) << ", "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I1) << ", "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I2) << ", "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I3) << ", "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I4) << ", "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I5) << "}" << std::endl;
}
#if CK_EXPERIMENTAL_PASS_TENSOR_DESCRIPTOR_BY_VALUE
float ave_time = 0;
if(has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel =
kernel_dynamic_gemm_dlops_v1r3<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AK0M0M1K1GridDesc>,
remove_reference_t<BK0N0N1K1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
true,
true>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_k0_m0_m1_k1_grid_desc,
b_k0_n0_n1_k1_grid_desc,
c_m0_m10_m11_n0_n10_n11_grid_desc,
c_blockid_to_m0_n0_block_cluster_adaptor);
}
else if(has_main_k_block_loop && !has_double_tail_k_block_loop)
{
const auto kernel =
kernel_dynamic_gemm_dlops_v1r3<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AK0M0M1K1GridDesc>,
remove_reference_t<BK0N0N1K1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
true,
false>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_k0_m0_m1_k1_grid_desc,
b_k0_n0_n1_k1_grid_desc,
c_m0_m10_m11_n0_n10_n11_grid_desc,
c_blockid_to_m0_n0_block_cluster_adaptor);
}
else if(!has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel =
kernel_dynamic_gemm_dlops_v1r3<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AK0M0M1K1GridDesc>,
remove_reference_t<BK0N0N1K1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
false,
true>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_k0_m0_m1_k1_grid_desc,
b_k0_n0_n1_k1_grid_desc,
c_m0_m10_m11_n0_n10_n11_grid_desc,
c_blockid_to_m0_n0_block_cluster_adaptor);
}
else
{
const auto kernel =
kernel_dynamic_gemm_dlops_v1r3<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AK0M0M1K1GridDesc>,
remove_reference_t<BK0N0N1K1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
false,
false>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_k0_m0_m1_k1_grid_desc,
b_k0_n0_n1_k1_grid_desc,
c_m0_m10_m11_n0_n10_n11_grid_desc,
c_blockid_to_m0_n0_block_cluster_adaptor);
}
return ave_time;
#elif CK_EXPERIMENTAL_PASS_TENSOR_DESCRIPTOR_BY_VOID_POINTER
DeviceMem a_k0_m0_m1_k1_grid_desc_dev_buf(sizeof(AK0M0M1K1GridDesc));
DeviceMem b_k0_n0_n1_k1_grid_desc_dev_buf(sizeof(BK0N0N1K1GridDesc));
DeviceMem c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf(sizeof(CM0M10M11N0N10N11GridDesc));
DeviceMem c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf(
sizeof(CBlockIdToM0N0BlockClusterAdaptor));
a_k0_m0_m1_k1_grid_desc_dev_buf.ToDevice(&a_k0_m0_m1_k1_grid_desc);
b_k0_n0_n1_k1_grid_desc_dev_buf.ToDevice(&b_k0_n0_n1_k1_grid_desc);
c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf.ToDevice(&c_m0_m10_m11_n0_n10_n11_grid_desc);
c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf.ToDevice(
&c_blockid_to_m0_n0_block_cluster_adaptor);
float ave_time = 0;
if(has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel =
kernel_dynamic_gemm_dlops_v1r3<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AK0M0M1K1GridDesc>,
remove_reference_t<BK0N0N1K1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
true,
true>;
ave_time = launch_and_time_kernel(
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
(void CONSTANT*)a_k0_m0_m1_k1_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)b_k0_n0_n1_k1_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf.GetDeviceBuffer());
}
else if(has_main_k_block_loop && !has_double_tail_k_block_loop)
{
const auto kernel =
kernel_dynamic_gemm_dlops_v1r3<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AK0M0M1K1GridDesc>,
remove_reference_t<BK0N0N1K1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
true,
false>;
ave_time = launch_and_time_kernel(
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
(void CONSTANT*)a_k0_m0_m1_k1_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)b_k0_n0_n1_k1_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf.GetDeviceBuffer());
}
else if(!has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel =
kernel_dynamic_gemm_dlops_v1r3<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AK0M0M1K1GridDesc>,
remove_reference_t<BK0N0N1K1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
false,
true>;
ave_time = launch_and_time_kernel(
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
(void CONSTANT*)a_k0_m0_m1_k1_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)b_k0_n0_n1_k1_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf.GetDeviceBuffer());
}
else
{
const auto kernel =
kernel_dynamic_gemm_dlops_v1r3<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AK0M0M1K1GridDesc>,
remove_reference_t<BK0N0N1K1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
false,
false>;
ave_time = launch_and_time_kernel(
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
(void CONSTANT*)a_k0_m0_m1_k1_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)b_k0_n0_n1_k1_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf.GetDeviceBuffer());
}
return ave_time;
#endif
}
#endif

196
host/driver_offline/include/driver_dynamic_gemm_xdlops_v2r3.hpp
View File

@@ -1,196 +0,0 @@
#ifndef DRIVER_DYNAMIC_GEMM_XDLOPS_V2R3
#define DRIVER_DYNAMIC_GEMM_XDLOPS_V2R3
#include "common_header.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "gridwise_dynamic_gemm_xdlops_v2r3.hpp"
template <ck::index_t BlockSize,
typename FloatAB,
typename FloatAcc,
typename FloatC,
ck::InMemoryDataOperationEnum_t CGlobalMemoryDataOperation,
typename AK0MK1GridDesc,
typename BK0NK1GridDesc,
typename CMNGridDesc,
ck::index_t MPerBlock,
ck::index_t NPerBlock,
ck::index_t KPerBlock,
ck::index_t MPerWave,
ck::index_t NPerWave,
ck::index_t K1,
ck::index_t MRepeat,
ck::index_t NRepeat,
typename ABlockTransferThreadSliceLengths_K0_M_K1,
typename ABlockTransferThreadClusterLengths_K0_M_K1,
typename ABlockTransferThreadClusterArrangeOrder,
typename ABlockTransferSrcAccessOrder,
ck::index_t ABlockTransferSrcVectorDim,
ck::index_t ABlockTransferSrcScalarPerVector,
ck::index_t ABlockTransferDstScalarPerVector_K1,
bool AThreadTransferSrcResetCoordinateAfterRun,
typename BBlockTransferThreadSliceLengths_K0_N_K1,
typename BBlockTransferThreadClusterLengths_K0_N_K1,
typename BBlockTransferThreadClusterArrangeOrder,
typename BBlockTransferSrcAccessOrder,
ck::index_t BBlockTransferSrcVectorDim,
ck::index_t BBlockTransferSrcScalarPerVector,
ck::index_t BBlockTransferDstScalarPerVector_K1,
bool BThreadTransferSrcResetCoordinateAfterRun,
typename CThreadTransferSrcDstAccessOrder,
ck::index_t CThreadTransferSrcDstVectorDim,
ck::index_t CThreadTransferDstScalarPerVector,
typename AGridIteratorHacks,
typename BGridIteratorHacks,
typename CGridIteratorHacks,
typename AGridMoveSliceWindowIteratorHacks,
typename BGridMoveSliceWindowIteratorHacks,
bool CAccessOrderMRepeatNRepeat>
__host__ float driver_dynamic_gemm_xdlops_v2r3(const FloatAB* p_a_grid,
const FloatAB* p_b_grid,
FloatC* p_c_grid,
const AK0MK1GridDesc& a_k0_m_k1_grid_desc,
const BK0NK1GridDesc& b_k0_n_k1_grid_desc,
const CMNGridDesc& c_m_n_grid_desc,
AGridIteratorHacks,
BGridIteratorHacks,
CGridIteratorHacks,
AGridMoveSliceWindowIteratorHacks,
BGridMoveSliceWindowIteratorHacks,
ck::index_t nrepeat)
{
using namespace ck;
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto I4 = Number<4>{};
constexpr auto I5 = Number<5>{};
using GridwiseGemm =
GridwiseDynamicGemm_k0mk1_k0nk1_mn_xdlops_v2r3<BlockSize,
FloatAB,
FloatAcc,
FloatC,
CGlobalMemoryDataOperation,
AK0MK1GridDesc,
BK0NK1GridDesc,
CMNGridDesc,
MPerBlock,
NPerBlock,
KPerBlock,
MPerWave,
NPerWave,
K1,
MRepeat,
NRepeat,
ABlockTransferThreadSliceLengths_K0_M_K1,
ABlockTransferThreadClusterLengths_K0_M_K1,
ABlockTransferThreadClusterArrangeOrder,
ABlockTransferSrcAccessOrder,
ABlockTransferSrcVectorDim,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_K1,
AThreadTransferSrcResetCoordinateAfterRun,
BBlockTransferThreadSliceLengths_K0_N_K1,
BBlockTransferThreadClusterLengths_K0_N_K1,
BBlockTransferThreadClusterArrangeOrder,
BBlockTransferSrcAccessOrder,
BBlockTransferSrcVectorDim,
BBlockTransferSrcScalarPerVector,
BBlockTransferDstScalarPerVector_K1,
BThreadTransferSrcResetCoordinateAfterRun,
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
AGridIteratorHacks,
BGridIteratorHacks,
CGridIteratorHacks,
AGridMoveSliceWindowIteratorHacks,
BGridMoveSliceWindowIteratorHacks,
CAccessOrderMRepeatNRepeat>;
{
std::cout << "a_k0_m_k1_grid_desc{" << a_k0_m_k1_grid_desc.GetLength(I0) << ", "
<< a_k0_m_k1_grid_desc.GetLength(I1) << ", " << a_k0_m_k1_grid_desc.GetLength(I2)
<< "}" << std::endl;
std::cout << "b_k0_n_k1_grid_desc{" << b_k0_n_k1_grid_desc.GetLength(I0) << ", "
<< b_k0_n_k1_grid_desc.GetLength(I1) << ", " << b_k0_n_k1_grid_desc.GetLength(I2)
<< "}" << std::endl;
std::cout << "c_m_n_grid_desc{ " << c_m_n_grid_desc.GetLength(I0) << ", "
<< c_m_n_grid_desc.GetLength(I1) << "}" << std::endl;
}
if(!GridwiseGemm::CheckValidity(a_k0_m_k1_grid_desc, b_k0_n_k1_grid_desc, c_m_n_grid_desc))
{
throw std::runtime_error(
"wrong! GridwiseDynamicGemm_km_kn_m0m1n0n1_xdlops_v2r3 has invalid setting");
}
const auto c_m0_m1_m2_n_grid_desc = GridwiseGemm::MakeCM0M1M2NGridDescriptor(c_m_n_grid_desc);
using CM0M1M2NGridDesc = decltype(c_m0_m1_m2_n_grid_desc);
const auto c_block_cluster_adaptor = GridwiseGemm::MakeCBlockClusterAdaptor(c_m_n_grid_desc);
using CBlockClusterAdaptor = decltype(c_block_cluster_adaptor);
const index_t grid_size = GridwiseGemm::CalculateGridSize(c_m_n_grid_desc);
const auto kernel = kernel_dynamic_gemm_xdlops_v2r3<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AK0MK1GridDesc>,
remove_reference_t<BK0NK1GridDesc>,
remove_reference_t<CM0M1M2NGridDesc>,
remove_reference_t<CBlockClusterAdaptor>>;
#if CK_EXPERIMENTAL_PASS_TENSOR_DESCRIPTOR_BY_VALUE
float ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_k0_m_k1_grid_desc,
b_k0_n_k1_grid_desc,
c_m0_m1_m2_n_grid_desc,
c_block_cluster_adaptor);
#elif CK_EXPERIMENTAL_PASS_TENSOR_DESCRIPTOR_BY_VOID_POINTER
DeviceMem a_k0_m_k1_grid_desc_dev_buf(sizeof(AK0MK1GridDesc));
DeviceMem b_k0_n_k1_grid_desc_dev_buf(sizeof(BK0NK1GridDesc));
DeviceMem c_m0_m1_m2_n_grid_desc_dev_buf(sizeof(CM0M1M2NGridDesc));
DeviceMem c_block_cluster_adaptor_dev_buf(sizeof(CBlockClusterAdaptor));
a_k0_m_k1_grid_desc_dev_buf.ToDevice(&a_k0_m_k1_grid_desc);
b_k0_n_k1_grid_desc_dev_buf.ToDevice(&b_k0_n_k1_grid_desc);
c_m0_m1_m2_n_grid_desc_dev_buf.ToDevice(&c_m0_m1_m2_n_grid_desc);
c_block_cluster_adaptor_dev_buf.ToDevice(&c_block_cluster_adaptor);
float ave_time =
launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
0,
p_a_grid,
p_b_grid,
p_c_grid,
(void CONSTANT*)a_k0_m_k1_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)b_k0_n_k1_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)c_m0_m1_m2_n_grid_desc_dev_buf.GetDeviceBuffer(),
(void CONSTANT*)c_block_cluster_adaptor_dev_buf.GetDeviceBuffer());
#endif
return ave_time;
}
#endif

413
host/driver_offline/include/driver_gemm_dlops_v1r2.hpp Normal file
View File

@@ -0,0 +1,413 @@
#ifndef DRIVER_GEMM_DLOPS_V1R2
#define DRIVER_GEMM_DLOPS_V1R2
#include "common_header.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "gridwise_gemm_dlops_v1r2.hpp"
template <ck::index_t BlockSize,
typename FloatAB,
typename FloatAcc,
typename FloatC,
ck::InMemoryDataOperationEnum_t CGlobalMemoryDataOperation,
typename AKMGridDesc,
typename BKNGridDesc,
typename CMNGridDesc,
ck::index_t MPerBlock,
ck::index_t NPerBlock,
ck::index_t KPerBlock,
ck::index_t M1PerThread,
ck::index_t N1PerThread,
ck::index_t KPerThread,
ck::index_t M1N1ThreadClusterM10,
ck::index_t M1N1ThreadClusterN10,
ck::index_t M1N1ThreadClusterM11,
ck::index_t M1N1ThreadClusterN11,
typename ABlockTransferThreadSliceLengths_K_M0_M1,
typename ABlockTransferThreadClusterLengths_K_M0_M1,
typename ABlockTransferThreadClusterArrangeOrder,
typename ABlockTransferSrcAccessOrder,
ck::index_t ABlockTransferSrcVectorDim,
ck::index_t ABlockTransferSrcScalarPerVector,
ck::index_t ABlockTransferDstScalarPerVector_M1,
bool AThreadTransferSrcResetCoordinateAfterRun,
typename BBlockTransferThreadSliceLengths_K_N0_N1,
typename BBlockTransferThreadClusterLengths_K_N0_N1,
typename BBlockTransferThreadClusterArrangeOrder,
typename BBlockTransferSrcAccessOrder,
ck::index_t BBlockTransferSrcVectorDim,
ck::index_t BBlockTransferSrcScalarPerVector,
ck::index_t BBlockTransferDstScalarPerVector_N1,
bool BThreadTransferSrcResetCoordinateAfterRun,
typename CThreadTransferSrcDstAccessOrder,
ck::index_t CThreadTransferSrcDstVectorDim,
ck::index_t CThreadTransferDstScalarPerVector,
typename AGridStepHacks,
typename BGridStepHacks,
typename CGridStepHacks,
typename AGridMoveSliceWindowStepHacks,
typename BGridMoveSliceWindowStepHacks>
__host__ float driver_gemm_dlops_v1r2(const FloatAB* p_a_grid,
const FloatAB* p_b_grid,
FloatC* p_c_grid,
const AKMGridDesc& a_k_m_grid_desc,
const BKNGridDesc& b_k_n_grid_desc,
const CMNGridDesc& c_m_n_grid_desc,
AGridStepHacks,
BGridStepHacks,
CGridStepHacks,
AGridMoveSliceWindowStepHacks,
BGridMoveSliceWindowStepHacks,
ck::index_t nrepeat)
{
using namespace ck;
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto I4 = Number<4>{};
constexpr auto I5 = Number<5>{};
// GEMM
using GridwiseGemm = GridwiseGemmDlops_km_kn_mn_v1r2<BlockSize,
FloatAB,
FloatAcc,
FloatC,
CGlobalMemoryDataOperation,
AKMGridDesc,
BKNGridDesc,
CMNGridDesc,
MPerBlock,
NPerBlock,
KPerBlock,
M1PerThread,
N1PerThread,
KPerThread,
M1N1ThreadClusterM10,
M1N1ThreadClusterN10,
M1N1ThreadClusterM11,
M1N1ThreadClusterN11,
ABlockTransferThreadSliceLengths_K_M0_M1,
ABlockTransferThreadClusterLengths_K_M0_M1,
ABlockTransferThreadClusterArrangeOrder,
ABlockTransferSrcAccessOrder,
ABlockTransferSrcVectorDim,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_M1,
AThreadTransferSrcResetCoordinateAfterRun,
BBlockTransferThreadSliceLengths_K_N0_N1,
BBlockTransferThreadClusterLengths_K_N0_N1,
BBlockTransferThreadClusterArrangeOrder,
BBlockTransferSrcAccessOrder,
BBlockTransferSrcVectorDim,
BBlockTransferSrcScalarPerVector,
BBlockTransferDstScalarPerVector_N1,
BThreadTransferSrcResetCoordinateAfterRun,
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
AGridStepHacks,
BGridStepHacks,
CGridStepHacks,
AGridMoveSliceWindowStepHacks,
BGridMoveSliceWindowStepHacks>;
const auto M = a_k_m_grid_desc.GetLength(I1);
const auto N = b_k_n_grid_desc.GetLength(I1);
const auto K = a_k_m_grid_desc.GetLength(I0);
if(!GridwiseGemm::CheckValidity(a_k_m_grid_desc, b_k_n_grid_desc, c_m_n_grid_desc))
{
throw std::runtime_error("wrong! GridwiseGemmDlops_km_kn_mn_v1r2 has invalid setting");
}
const auto a_k_m0_m1_grid_desc = GridwiseGemm::MakeAKM0M1GridDescriptor(a_k_m_grid_desc);
const auto b_k_n0_n1_grid_desc = GridwiseGemm::MakeBKN0N1GridDescriptor(b_k_n_grid_desc);
using AKM0M1GridDesc = decltype(a_k_m0_m1_grid_desc);
using BKN0N1GridDesc = decltype(b_k_n0_n1_grid_desc);
// c_m0_m10_m11_n0_n10_n11_grid_desc
const auto c_m0_m10_m11_n0_n10_n11_grid_desc =
GridwiseGemm::MakeCM0M10M11N0N10N11GridDescriptor(c_m_n_grid_desc);
using CM0M10M11N0N10N11GridDesc = decltype(c_m0_m10_m11_n0_n10_n11_grid_desc);
// c_blockid_to_m0_n0_block_cluster_adaptor
const auto c_blockid_to_m0_n0_block_cluster_adaptor =
GridwiseGemm::MakeCBlockIdToM0N0BlockClusterAdaptor(c_m_n_grid_desc);
using CBlockIdToM0N0BlockClusterAdaptor = decltype(c_blockid_to_m0_n0_block_cluster_adaptor);
const index_t grid_size = GridwiseGemm::CalculateGridSize(M, N);
const bool has_main_k_block_loop = GridwiseGemm::CalculateHasMainKBlockLoop(K);
const bool has_double_tail_k_block_loop = GridwiseGemm::CalculateHasDoubleTailKBlockLoop(K);
{
std::cout << "a_k_m0_m1_grid_desc{" << a_k_m0_m1_grid_desc.GetLength(I0) << ", "
<< a_k_m0_m1_grid_desc.GetLength(I1) << ", " << a_k_m0_m1_grid_desc.GetLength(I2)
<< "}" << std::endl;
std::cout << "b_k_n0_n1_grid_desc{" << b_k_n0_n1_grid_desc.GetLength(I0) << ", "
<< b_k_n0_n1_grid_desc.GetLength(I1) << ", " << b_k_n0_n1_grid_desc.GetLength(I2)
<< "}" << std::endl;
std::cout << "c_m0_m10_m11_n0_n10_n11_grid_desc{ "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I0) << ", "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I1) << ", "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I2) << ", "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I3) << ", "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I4) << ", "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I5) << "}" << std::endl;
}
#if CK_EXPERIMENTAL_PASS_TENSOR_DESCRIPTOR_BY_VALUE
float ave_time = 0;
if(has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel =
kernel_gemm_dlops_v1r2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AKM0M1GridDesc>,
remove_reference_t<BKN0N1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
true,
true>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_k_m0_m1_grid_desc,
b_k_n0_n1_grid_desc,
c_m0_m10_m11_n0_n10_n11_grid_desc,
c_blockid_to_m0_n0_block_cluster_adaptor);
}
else if(has_main_k_block_loop && !has_double_tail_k_block_loop)
{
const auto kernel =
kernel_gemm_dlops_v1r2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AKM0M1GridDesc>,
remove_reference_t<BKN0N1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
true,
false>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_k_m0_m1_grid_desc,
b_k_n0_n1_grid_desc,
c_m0_m10_m11_n0_n10_n11_grid_desc,
c_blockid_to_m0_n0_block_cluster_adaptor);
}
else if(!has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel =
kernel_gemm_dlops_v1r2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AKM0M1GridDesc>,
remove_reference_t<BKN0N1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
false,
true>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_k_m0_m1_grid_desc,
b_k_n0_n1_grid_desc,
c_m0_m10_m11_n0_n10_n11_grid_desc,
c_blockid_to_m0_n0_block_cluster_adaptor);
}
else
{
const auto kernel =
kernel_gemm_dlops_v1r2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AKM0M1GridDesc>,
remove_reference_t<BKN0N1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
false,
false>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_k_m0_m1_grid_desc,
b_k_n0_n1_grid_desc,
c_m0_m10_m11_n0_n10_n11_grid_desc,
c_blockid_to_m0_n0_block_cluster_adaptor);
}
return ave_time;
#elif CK_EXPERIMENTAL_PASS_TENSOR_DESCRIPTOR_BY_VOID_POINTER
DeviceMem a_k_m0_m1_grid_desc_dev_buf(sizeof(AKM0M1GridDesc));
DeviceMem b_k_n0_n1_grid_desc_dev_buf(sizeof(BKN0N1GridDesc));
DeviceMem c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf(sizeof(CM0M10M11N0N10N11GridDesc));
DeviceMem c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf(
sizeof(CBlockIdToM0N0BlockClusterAdaptor));
a_k_m0_m1_grid_desc_dev_buf.ToDevice(&a_k_m0_m1_grid_desc);
b_k_n0_n1_grid_desc_dev_buf.ToDevice(&b_k_n0_n1_grid_desc);
c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf.ToDevice(&c_m0_m10_m11_n0_n10_n11_grid_desc);
c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf.ToDevice(
&c_blockid_to_m0_n0_block_cluster_adaptor);
float ave_time = 0;
if(has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel =
kernel_gemm_dlops_v1r2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AKM0M1GridDesc>,
remove_reference_t<BKN0N1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
true,
true>;
ave_time = launch_and_time_kernel(
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
cast_pointer_to_constant_address_space(a_k_m0_m1_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(b_k_n0_n1_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf.GetDeviceBuffer()));
}
else if(has_main_k_block_loop && !has_double_tail_k_block_loop)
{
const auto kernel =
kernel_gemm_dlops_v1r2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AKM0M1GridDesc>,
remove_reference_t<BKN0N1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
true,
false>;
ave_time = launch_and_time_kernel(
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
cast_pointer_to_constant_address_space(a_k_m0_m1_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(b_k_n0_n1_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf.GetDeviceBuffer()));
}
else if(!has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel =
kernel_gemm_dlops_v1r2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AKM0M1GridDesc>,
remove_reference_t<BKN0N1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
false,
true>;
ave_time = launch_and_time_kernel(
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
cast_pointer_to_constant_address_space(a_k_m0_m1_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(b_k_n0_n1_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf.GetDeviceBuffer()));
}
else
{
const auto kernel =
kernel_gemm_dlops_v1r2<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AKM0M1GridDesc>,
remove_reference_t<BKN0N1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
false,
false>;
ave_time = launch_and_time_kernel(
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
cast_pointer_to_constant_address_space(a_k_m0_m1_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(b_k_n0_n1_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf.GetDeviceBuffer()));
}
return ave_time;
#endif
}
#endif

418
host/driver_offline/include/driver_gemm_dlops_v1r3.hpp Normal file
View File

@@ -0,0 +1,418 @@
#ifndef DRIVER_GEMM_DLOPS_V1R3
#define DRIVER_GEMM_DLOPS_V1R3
#include "common_header.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "gridwise_gemm_dlops_v1r3.hpp"
template <ck::index_t BlockSize,
typename FloatAB,
typename FloatAcc,
typename FloatC,
ck::InMemoryDataOperationEnum_t CGlobalMemoryDataOperation,
typename AK0MK1GridDesc,
typename BK0NK1GridDesc,
typename CMNGridDesc,
ck::index_t MPerBlock,
ck::index_t NPerBlock,
ck::index_t KPerBlock,
ck::index_t M1PerThread,
ck::index_t N1PerThread,
ck::index_t KPerThread,
typename M1N1ThreadClusterM1Xs,
typename M1N1ThreadClusterN1Xs,
typename ABlockTransferThreadSliceLengths_K0_M0_M1_K1,
typename ABlockTransferThreadClusterLengths_K0_M0_M1_K1,
typename ABlockTransferThreadClusterArrangeOrder,
typename ABlockTransferSrcAccessOrder,
typename ABlockTransferSrcVectorTensorLengths_K0_M0_M1_K1,
typename ABlockTransferSrcVectorTensorContiguousDimOrder,
typename ABlockTransferDstVectorTensorLengths_K0_M0_M1_K1,
typename BBlockTransferThreadSliceLengths_K0_N0_N1_K1,
typename BBlockTransferThreadClusterLengths_K0_N0_N1_K1,
typename BBlockTransferThreadClusterArrangeOrder,
typename BBlockTransferSrcAccessOrder,
typename BBlockTransferSrcVectorTensorLengths_K0_N0_N1_K1,
typename BBlockTransferSrcVectorTensorContiguousDimOrder,
typename BBlockTransferDstVectorTensorLengths_K0_N0_N1_K1,
typename CThreadTransferSrcDstAccessOrder,
ck::index_t CThreadTransferSrcDstVectorDim,
ck::index_t CThreadTransferDstScalarPerVector,
typename AGridStepHacks,
typename BGridStepHacks,
typename CGridStepHacks,
typename AGridMoveSliceWindowStepHacks,
typename BGridMoveSliceWindowStepHacks>
__host__ float driver_gemm_dlops_v1r3(const FloatAB* p_a_grid,
const FloatAB* p_b_grid,
FloatC* p_c_grid,
const AK0MK1GridDesc& a_k0_m_k1_grid_desc,
const BK0NK1GridDesc& b_k0_n_k1_grid_desc,
const CMNGridDesc& c_m_n_grid_desc,
AGridStepHacks,
BGridStepHacks,
CGridStepHacks,
AGridMoveSliceWindowStepHacks,
BGridMoveSliceWindowStepHacks,
ck::index_t nrepeat)
{
using namespace ck;
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto I4 = Number<4>{};
constexpr auto I5 = Number<5>{};
// GEMM
using GridwiseGemm =
GridwiseGemmDlops_km_kn_mn_v1r3<BlockSize,
FloatAB,
FloatAcc,
FloatC,
CGlobalMemoryDataOperation,
AK0MK1GridDesc,
BK0NK1GridDesc,
CMNGridDesc,
MPerBlock,
NPerBlock,
KPerBlock,
M1PerThread,
N1PerThread,
KPerThread,
M1N1ThreadClusterM1Xs,
M1N1ThreadClusterN1Xs,
ABlockTransferThreadSliceLengths_K0_M0_M1_K1,
ABlockTransferThreadClusterLengths_K0_M0_M1_K1,
ABlockTransferThreadClusterArrangeOrder,
ABlockTransferSrcAccessOrder,
ABlockTransferSrcVectorTensorLengths_K0_M0_M1_K1,
ABlockTransferSrcVectorTensorContiguousDimOrder,
ABlockTransferDstVectorTensorLengths_K0_M0_M1_K1,
BBlockTransferThreadSliceLengths_K0_N0_N1_K1,
BBlockTransferThreadClusterLengths_K0_N0_N1_K1,
BBlockTransferThreadClusterArrangeOrder,
BBlockTransferSrcAccessOrder,
BBlockTransferSrcVectorTensorLengths_K0_N0_N1_K1,
BBlockTransferSrcVectorTensorContiguousDimOrder,
BBlockTransferDstVectorTensorLengths_K0_N0_N1_K1,
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
AGridStepHacks,
BGridStepHacks,
CGridStepHacks,
AGridMoveSliceWindowStepHacks,
BGridMoveSliceWindowStepHacks>;
const auto M = a_k0_m_k1_grid_desc.GetLength(I1);
const auto N = b_k0_n_k1_grid_desc.GetLength(I1);
const auto K0 = a_k0_m_k1_grid_desc.GetLength(I0);
if(!GridwiseGemm::CheckValidity(a_k0_m_k1_grid_desc, b_k0_n_k1_grid_desc, c_m_n_grid_desc))
{
throw std::runtime_error("wrong! GridwiseGemmDlops_km_kn_mn_v1r3 has invalid setting");
}
const auto a_k0_m0_m1_k1_grid_desc =
GridwiseGemm::MakeAK0M0M1K1GridDescriptor(a_k0_m_k1_grid_desc);
const auto b_k0_n0_n1_k1_grid_desc =
GridwiseGemm::MakeBK0N0N1K1GridDescriptor(b_k0_n_k1_grid_desc);
using AK0M0M1K1GridDesc = decltype(a_k0_m0_m1_k1_grid_desc);
using BK0N0N1K1GridDesc = decltype(b_k0_n0_n1_k1_grid_desc);
// c_m0_m10_m11_n0_n10_n11_grid_desc
const auto c_m0_m10_m11_n0_n10_n11_grid_desc =
GridwiseGemm::MakeCM0M10M11N0N10N11GridDescriptor(c_m_n_grid_desc);
using CM0M10M11N0N10N11GridDesc = decltype(c_m0_m10_m11_n0_n10_n11_grid_desc);
// c_blockid_to_m0_n0_block_cluster_adaptor
const auto c_blockid_to_m0_n0_block_cluster_adaptor =
GridwiseGemm::MakeCBlockIdToM0N0BlockClusterAdaptor(c_m_n_grid_desc);
using CBlockIdToM0N0BlockClusterAdaptor = decltype(c_blockid_to_m0_n0_block_cluster_adaptor);
const index_t grid_size = GridwiseGemm::CalculateGridSize(M, N);
const bool has_main_k_block_loop = GridwiseGemm::CalculateHasMainKBlockLoop(K0);
const bool has_double_tail_k_block_loop = GridwiseGemm::CalculateHasDoubleTailKBlockLoop(K0);
{
std::cout << "a_k0_m0_m1_k1_grid_desc{" << a_k0_m0_m1_k1_grid_desc.GetLength(I0) << ", "
<< a_k0_m0_m1_k1_grid_desc.GetLength(I1) << ", "
<< a_k0_m0_m1_k1_grid_desc.GetLength(I2) << ", "
<< a_k0_m0_m1_k1_grid_desc.GetLength(I3) << "}" << std::endl;
std::cout << "b_k0_n0_n1_k1_grid_desc{" << b_k0_n0_n1_k1_grid_desc.GetLength(I0) << ", "
<< b_k0_n0_n1_k1_grid_desc.GetLength(I1) << ", "
<< b_k0_n0_n1_k1_grid_desc.GetLength(I2) << ", "
<< b_k0_n0_n1_k1_grid_desc.GetLength(I3) << "}" << std::endl;
std::cout << "c_m0_m10_m11_n0_n10_n11_grid_desc{ "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I0) << ", "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I1) << ", "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I2) << ", "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I3) << ", "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I4) << ", "
<< c_m0_m10_m11_n0_n10_n11_grid_desc.GetLength(I5) << "}" << std::endl;
}
#if CK_EXPERIMENTAL_PASS_TENSOR_DESCRIPTOR_BY_VALUE
float ave_time = 0;
if(has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel =
kernel_gemm_dlops_v1r3<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AK0M0M1K1GridDesc>,
remove_reference_t<BK0N0N1K1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
true,
true>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_k0_m0_m1_k1_grid_desc,
b_k0_n0_n1_k1_grid_desc,
c_m0_m10_m11_n0_n10_n11_grid_desc,
c_blockid_to_m0_n0_block_cluster_adaptor);
}
else if(has_main_k_block_loop && !has_double_tail_k_block_loop)
{
const auto kernel =
kernel_gemm_dlops_v1r3<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AK0M0M1K1GridDesc>,
remove_reference_t<BK0N0N1K1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
true,
false>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_k0_m0_m1_k1_grid_desc,
b_k0_n0_n1_k1_grid_desc,
c_m0_m10_m11_n0_n10_n11_grid_desc,
c_blockid_to_m0_n0_block_cluster_adaptor);
}
else if(!has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel =
kernel_gemm_dlops_v1r3<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AK0M0M1K1GridDesc>,
remove_reference_t<BK0N0N1K1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
false,
true>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_k0_m0_m1_k1_grid_desc,
b_k0_n0_n1_k1_grid_desc,
c_m0_m10_m11_n0_n10_n11_grid_desc,
c_blockid_to_m0_n0_block_cluster_adaptor);
}
else
{
const auto kernel =
kernel_gemm_dlops_v1r3<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AK0M0M1K1GridDesc>,
remove_reference_t<BK0N0N1K1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
false,
false>;
ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_k0_m0_m1_k1_grid_desc,
b_k0_n0_n1_k1_grid_desc,
c_m0_m10_m11_n0_n10_n11_grid_desc,
c_blockid_to_m0_n0_block_cluster_adaptor);
}
return ave_time;
#elif CK_EXPERIMENTAL_PASS_TENSOR_DESCRIPTOR_BY_VOID_POINTER
DeviceMem a_k0_m0_m1_k1_grid_desc_dev_buf(sizeof(AK0M0M1K1GridDesc));
DeviceMem b_k0_n0_n1_k1_grid_desc_dev_buf(sizeof(BK0N0N1K1GridDesc));
DeviceMem c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf(sizeof(CM0M10M11N0N10N11GridDesc));
DeviceMem c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf(
sizeof(CBlockIdToM0N0BlockClusterAdaptor));
a_k0_m0_m1_k1_grid_desc_dev_buf.ToDevice(&a_k0_m0_m1_k1_grid_desc);
b_k0_n0_n1_k1_grid_desc_dev_buf.ToDevice(&b_k0_n0_n1_k1_grid_desc);
c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf.ToDevice(&c_m0_m10_m11_n0_n10_n11_grid_desc);
c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf.ToDevice(
&c_blockid_to_m0_n0_block_cluster_adaptor);
float ave_time = 0;
if(has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel =
kernel_gemm_dlops_v1r3<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AK0M0M1K1GridDesc>,
remove_reference_t<BK0N0N1K1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
true,
true>;
ave_time = launch_and_time_kernel(
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
cast_pointer_to_constant_address_space(
a_k0_m0_m1_k1_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
b_k0_n0_n1_k1_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf.GetDeviceBuffer()));
}
else if(has_main_k_block_loop && !has_double_tail_k_block_loop)
{
const auto kernel =
kernel_gemm_dlops_v1r3<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AK0M0M1K1GridDesc>,
remove_reference_t<BK0N0N1K1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
true,
false>;
ave_time = launch_and_time_kernel(
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
cast_pointer_to_constant_address_space(
a_k0_m0_m1_k1_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
b_k0_n0_n1_k1_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf.GetDeviceBuffer()));
}
else if(!has_main_k_block_loop && has_double_tail_k_block_loop)
{
const auto kernel =
kernel_gemm_dlops_v1r3<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AK0M0M1K1GridDesc>,
remove_reference_t<BK0N0N1K1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
false,
true>;
ave_time = launch_and_time_kernel(
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
cast_pointer_to_constant_address_space(
a_k0_m0_m1_k1_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
b_k0_n0_n1_k1_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf.GetDeviceBuffer()));
}
else
{
const auto kernel =
kernel_gemm_dlops_v1r3<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AK0M0M1K1GridDesc>,
remove_reference_t<BK0N0N1K1GridDesc>,
remove_reference_t<CM0M10M11N0N10N11GridDesc>,
remove_reference_t<CBlockIdToM0N0BlockClusterAdaptor>,
false,
false>;
ave_time = launch_and_time_kernel(
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
cast_pointer_to_constant_address_space(
a_k0_m0_m1_k1_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
b_k0_n0_n1_k1_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(
c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf.GetDeviceBuffer()));
}
return ave_time;
#endif
}
#endif

191
host/driver_offline/include/driver_gemm_xdlops_v2r3.hpp Normal file
View File

@@ -0,0 +1,191 @@
#ifndef DRIVER_GEMM_XDLOPS_V2R3
#define DRIVER_GEMM_XDLOPS_V2R3
#include "common_header.hpp"
#include "tensor_descriptor.hpp"
#include "tensor_descriptor_helper.hpp"
#include "gridwise_gemm_xdlops_v2r3.hpp"
template <ck::index_t BlockSize,
typename FloatAB,
typename FloatAcc,
typename FloatC,
ck::InMemoryDataOperationEnum_t CGlobalMemoryDataOperation,
typename AK0MK1GridDesc,
typename BK0NK1GridDesc,
typename CMNGridDesc,
ck::index_t MPerBlock,
ck::index_t NPerBlock,
ck::index_t KPerBlock,
ck::index_t MPerWave,
ck::index_t NPerWave,
ck::index_t K1,
ck::index_t MRepeat,
ck::index_t NRepeat,
typename ABlockTransferThreadSliceLengths_K0_M_K1,
typename ABlockTransferThreadClusterLengths_K0_M_K1,
typename ABlockTransferThreadClusterArrangeOrder,
typename ABlockTransferSrcAccessOrder,
ck::index_t ABlockTransferSrcVectorDim,
ck::index_t ABlockTransferSrcScalarPerVector,
ck::index_t ABlockTransferDstScalarPerVector_K1,
bool AThreadTransferSrcResetCoordinateAfterRun,
typename BBlockTransferThreadSliceLengths_K0_N_K1,
typename BBlockTransferThreadClusterLengths_K0_N_K1,
typename BBlockTransferThreadClusterArrangeOrder,
typename BBlockTransferSrcAccessOrder,
ck::index_t BBlockTransferSrcVectorDim,
ck::index_t BBlockTransferSrcScalarPerVector,
ck::index_t BBlockTransferDstScalarPerVector_K1,
bool BThreadTransferSrcResetCoordinateAfterRun,
typename CThreadTransferSrcDstAccessOrder,
ck::index_t CThreadTransferSrcDstVectorDim,
ck::index_t CThreadTransferDstScalarPerVector,
typename AGridStepHacks,
typename BGridStepHacks,
typename CGridStepHacks,
typename AGridMoveSliceWindowStepHacks,
typename BGridMoveSliceWindowStepHacks,
bool CAccessOrderMRepeatNRepeat>
__host__ float driver_gemm_xdlops_v2r3(const FloatAB* p_a_grid,
const FloatAB* p_b_grid,
FloatC* p_c_grid,
const AK0MK1GridDesc& a_k0_m_k1_grid_desc,
const BK0NK1GridDesc& b_k0_n_k1_grid_desc,
const CMNGridDesc& c_m_n_grid_desc,
AGridStepHacks,
BGridStepHacks,
CGridStepHacks,
AGridMoveSliceWindowStepHacks,
BGridMoveSliceWindowStepHacks,
ck::index_t nrepeat)
{
using namespace ck;
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
using GridwiseGemm =
GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3<BlockSize,
FloatAB,
FloatAcc,
FloatC,
CGlobalMemoryDataOperation,
AK0MK1GridDesc,
BK0NK1GridDesc,
CMNGridDesc,
MPerBlock,
NPerBlock,
KPerBlock,
MPerWave,
NPerWave,
K1,
MRepeat,
NRepeat,
ABlockTransferThreadSliceLengths_K0_M_K1,
ABlockTransferThreadClusterLengths_K0_M_K1,
ABlockTransferThreadClusterArrangeOrder,
ABlockTransferSrcAccessOrder,
ABlockTransferSrcVectorDim,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_K1,
AThreadTransferSrcResetCoordinateAfterRun,
BBlockTransferThreadSliceLengths_K0_N_K1,
BBlockTransferThreadClusterLengths_K0_N_K1,
BBlockTransferThreadClusterArrangeOrder,
BBlockTransferSrcAccessOrder,
BBlockTransferSrcVectorDim,
BBlockTransferSrcScalarPerVector,
BBlockTransferDstScalarPerVector_K1,
BThreadTransferSrcResetCoordinateAfterRun,
CThreadTransferSrcDstAccessOrder,
CThreadTransferSrcDstVectorDim,
CThreadTransferDstScalarPerVector,
AGridStepHacks,
BGridStepHacks,
CGridStepHacks,
AGridMoveSliceWindowStepHacks,
BGridMoveSliceWindowStepHacks,
CAccessOrderMRepeatNRepeat>;
{
std::cout << "a_k0_m_k1_grid_desc{" << a_k0_m_k1_grid_desc.GetLength(I0) << ", "
<< a_k0_m_k1_grid_desc.GetLength(I1) << ", " << a_k0_m_k1_grid_desc.GetLength(I2)
<< "}" << std::endl;
std::cout << "b_k0_n_k1_grid_desc{" << b_k0_n_k1_grid_desc.GetLength(I0) << ", "
<< b_k0_n_k1_grid_desc.GetLength(I1) << ", " << b_k0_n_k1_grid_desc.GetLength(I2)
<< "}" << std::endl;
std::cout << "c_m_n_grid_desc{ " << c_m_n_grid_desc.GetLength(I0) << ", "
<< c_m_n_grid_desc.GetLength(I1) << "}" << std::endl;
}
if(!GridwiseGemm::CheckValidity(a_k0_m_k1_grid_desc, b_k0_n_k1_grid_desc, c_m_n_grid_desc))
{
throw std::runtime_error(
"wrong! GridwiseGemm_km_kn_m0m1n0n1_xdlops_v2r3 has invalid setting");
}
const auto c_m0_m1_m2_n_grid_desc = GridwiseGemm::MakeCM0M1M2NGridDescriptor(c_m_n_grid_desc);
using CM0M1M2NGridDesc = decltype(c_m0_m1_m2_n_grid_desc);
const auto c_block_cluster_adaptor = GridwiseGemm::MakeCBlockClusterAdaptor(c_m_n_grid_desc);
using CBlockClusterAdaptor = decltype(c_block_cluster_adaptor);
const index_t grid_size = GridwiseGemm::CalculateGridSize(c_m_n_grid_desc);
const auto kernel = kernel_gemm_xdlops_v2r3<GridwiseGemm,
FloatAB,
FloatC,
remove_reference_t<AK0MK1GridDesc>,
remove_reference_t<BK0NK1GridDesc>,
remove_reference_t<CM0M1M2NGridDesc>,
remove_reference_t<CBlockClusterAdaptor>>;
#if CK_EXPERIMENTAL_PASS_TENSOR_DESCRIPTOR_BY_VALUE
float ave_time = launch_and_time_kernel(kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
a_k0_m_k1_grid_desc,
b_k0_n_k1_grid_desc,
c_m0_m1_m2_n_grid_desc,
c_block_cluster_adaptor);
#elif CK_EXPERIMENTAL_PASS_TENSOR_DESCRIPTOR_BY_VOID_POINTER
DeviceMem a_k0_m_k1_grid_desc_dev_buf(sizeof(AK0MK1GridDesc));
DeviceMem b_k0_n_k1_grid_desc_dev_buf(sizeof(BK0NK1GridDesc));
DeviceMem c_m0_m1_m2_n_grid_desc_dev_buf(sizeof(CM0M1M2NGridDesc));
DeviceMem c_block_cluster_adaptor_dev_buf(sizeof(CBlockClusterAdaptor));
a_k0_m_k1_grid_desc_dev_buf.ToDevice(&a_k0_m_k1_grid_desc);
b_k0_n_k1_grid_desc_dev_buf.ToDevice(&b_k0_n_k1_grid_desc);
c_m0_m1_m2_n_grid_desc_dev_buf.ToDevice(&c_m0_m1_m2_n_grid_desc);
c_block_cluster_adaptor_dev_buf.ToDevice(&c_block_cluster_adaptor);
float ave_time = launch_and_time_kernel(
kernel,
nrepeat,
dim3(grid_size),
dim3(BlockSize),
0,
p_a_grid,
p_b_grid,
p_c_grid,
cast_pointer_to_constant_address_space(a_k0_m_k1_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(b_k0_n_k1_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(c_m0_m1_m2_n_grid_desc_dev_buf.GetDeviceBuffer()),
cast_pointer_to_constant_address_space(c_block_cluster_adaptor_dev_buf.GetDeviceBuffer()));
#endif
return ave_time;
}
#endif

178
host/driver_offline/conv_bwd_driver_offline.cpp → host/driver_offline/src/conv_bwd_driver_offline.cpp
View File

@@ -12,10 +12,10 @@
#include "conv_common.hpp"
#include "host_conv_bwd_data.hpp"
#include "device_tensor.hpp"
#include "device_dynamic_convolution_backward_data_implicit_gemm_v4r1_xdlops_nhwc_kyxc_nhwk.hpp"
#include "device_dynamic_convolution_backward_data_implicit_gemm_v4r1r2_xdlops_nhwc_kyxc_nhwk.hpp"
#include "device_convolution_backward_data_implicit_gemm_v4r1_xdlops_nhwc_kyxc_nhwk.hpp"
#include "device_convolution_backward_data_implicit_gemm_v4r1r2_xdlops_nhwc_kyxc_nhwk.hpp"
#define USE_DYNAMIC_MODE 1
#define USE_MODE 1
#define USE_CONV_BWD_V4R1_XDL_NHWC 1
#define USE_CONV_BWD_V4R1R2_XDL_NHWC 1
@@ -37,7 +37,7 @@ int main(int argc, char* argv[])
constexpr auto I5 = Number<5>{};
constexpr auto I6 = Number<6>{};
#if USE_DYNAMIC_MODE
#if USE_MODE
// dynamic mode
if(argc != 22)
{
@@ -46,29 +46,29 @@ int main(int argc, char* argv[])
exit(1);
}
const ConvTensorLayout layout = static_cast<ConvTensorLayout>(atoi(argv[1]));
const ConvBackwardDataAlgo algo = static_cast<ConvBackwardDataAlgo>(atoi(argv[2]));
const bool do_verification = atoi(argv[3]);
const int init_method = atoi(argv[4]);
const bool do_log = atoi(argv[5]);
const int nrepeat = atoi(argv[6]);
const ConvTensorLayout layout = static_cast<ConvTensorLayout>(std::stoi(argv[1]));
const ConvBackwardDataAlgo algo = static_cast<ConvBackwardDataAlgo>(std::stoi(argv[2]));
const bool do_verification = std::stoi(argv[3]);
const int init_method = std::stoi(argv[4]);
const bool do_log = std::stoi(argv[5]);
const int nrepeat = std::stoi(argv[6]);
const index_t N = atoi(argv[7]);
const index_t K = atoi(argv[8]);
const index_t C = atoi(argv[9]);
const index_t Y = atoi(argv[10]);
const index_t X = atoi(argv[11]);
const index_t Hi = atoi(argv[12]);
const index_t Wi = atoi(argv[13]);
const index_t N = std::stoi(argv[7]);
const index_t K = std::stoi(argv[8]);
const index_t C = std::stoi(argv[9]);
const index_t Y = std::stoi(argv[10]);
const index_t X = std::stoi(argv[11]);
const index_t Hi = std::stoi(argv[12]);
const index_t Wi = std::stoi(argv[13]);
const index_t conv_stride_h = atoi(argv[14]);
const index_t conv_stride_w = atoi(argv[15]);
const index_t conv_dilation_h = atoi(argv[16]);
const index_t conv_dilation_w = atoi(argv[17]);
const index_t in_left_pad_h = atoi(argv[18]);
const index_t in_left_pad_w = atoi(argv[19]);
const index_t in_right_pad_h = atoi(argv[20]);
const index_t in_right_pad_w = atoi(argv[21]);
const index_t conv_stride_h = std::stoi(argv[14]);
const index_t conv_stride_w = std::stoi(argv[15]);
const index_t conv_dilation_h = std::stoi(argv[16]);
const index_t conv_dilation_w = std::stoi(argv[17]);
const index_t in_left_pad_h = std::stoi(argv[18]);
const index_t in_left_pad_w = std::stoi(argv[19]);
const index_t in_right_pad_h = std::stoi(argv[20]);
const index_t in_right_pad_w = std::stoi(argv[21]);
const index_t YEff = (Y - 1) * conv_dilation_h + 1;
const index_t XEff = (X - 1) * conv_dilation_w + 1;
@@ -83,12 +83,12 @@ int main(int argc, char* argv[])
exit(1);
}
const ConvTensorLayout layout = static_cast<ConvTensorLayout>(atoi(argv[1]));
const ConvBackwardDataAlgo algo = static_cast<ConvBackwardDataAlgo>(atoi(argv[2]));
const bool do_verification = atoi(argv[3]);
const int init_method = atoi(argv[4]);
const bool do_log = atoi(argv[5]);
const int nrepeat = atoi(argv[6]);
const ConvTensorLayout layout = static_cast<ConvTensorLayout>(std::stoi(argv[1]));
const ConvBackwardDataAlgo algo = static_cast<ConvBackwardDataAlgo>(std::stoi(argv[2]));
const bool do_verification = std::stoi(argv[3]);
const int init_method = std::stoi(argv[4]);
const bool do_log = std::stoi(argv[5]);
const int nrepeat = std::stoi(argv[6]);
constexpr index_t N = 128;
constexpr index_t C = 192;
@@ -115,23 +115,19 @@ int main(int argc, char* argv[])
#endif
#if 0
constexpr index_t in_vector_size = 1;
using in_data_t = float;
using acc_data_t = float;
using out_data_t = float;
#elif 1
constexpr index_t in_vector_size = 1;
using in_data_t = half_t;
using acc_data_t = float;
using out_data_t = half_t;
using in_data_t = half_t;
using acc_data_t = float;
using out_data_t = half_t;
#endif
std::vector<std::size_t> in_lengths_host(4), wei_lengths_host(4), out_lengths_host(4);
switch(layout)
if(layout == ConvTensorLayout::NCHW)
{
case ConvTensorLayout::NCHW:
// NCHW
in_lengths_host[0] = static_cast<std::size_t>(N);
in_lengths_host[1] = static_cast<std::size_t>(C);
in_lengths_host[2] = static_cast<std::size_t>(Hi);
@@ -144,9 +140,9 @@ int main(int argc, char* argv[])
out_lengths_host[1] = static_cast<std::size_t>(K);
out_lengths_host[2] = static_cast<std::size_t>(Ho);
out_lengths_host[3] = static_cast<std::size_t>(Wo);
break;
case ConvTensorLayout::NHWC:
// NHWC
}
else if(layout == ConvTensorLayout::NHWC)
{
in_lengths_host[0] = static_cast<std::size_t>(N);
in_lengths_host[1] = static_cast<std::size_t>(Hi);
in_lengths_host[2] = static_cast<std::size_t>(Wi);
@@ -159,8 +155,10 @@ int main(int argc, char* argv[])
out_lengths_host[1] = static_cast<std::size_t>(Ho);
out_lengths_host[2] = static_cast<std::size_t>(Wo);
out_lengths_host[3] = static_cast<std::size_t>(K);
break;
default: throw std::runtime_error("wrong! not implemented");
}
else
{
throw std::runtime_error("wrong! not implemented");
}
Tensor<in_data_t> in_host(in_lengths_host);
@@ -213,40 +211,8 @@ int main(int argc, char* argv[])
wei.GenerateTensorValue(gen_wei, num_thread);
}
auto f_make_for_device_nchw = [&]() {
#if USE_DYNAMIC_MODE
const auto in_lengths_dev = make_tuple(N, C, Hi, Wi);
const auto wei_lengths_dev = make_tuple(K, C, Y, X);
const auto out_lengths_dev = make_tuple(N, K, Ho, Wo);
const auto conv_strides_dev = make_tuple(conv_stride_h, conv_stride_w);
const auto conv_dilations_dev = make_tuple(conv_dilation_h, conv_dilation_w);
const auto in_left_pads_dev = make_tuple(in_left_pad_h, in_left_pad_w);
const auto in_right_pads_dev = make_tuple(in_right_pad_h, in_right_pad_w);
#else
const auto in_lengths_dev =
make_tuple(Number<N>{}, Number<C>{}, Number<Hi>{}, Number<Wi>{});
const auto wei_lengths_dev = make_tuple(Number<K>{}, Number<C>{}, Number<Y>{}, Number<X>{});
const auto out_lengths_dev =
make_tuple(Number<N>{}, Number<K>{}, Number<Ho>{}, Number<Wo>{});
const auto conv_strides_dev = make_tuple(Number<conv_stride_h>{}, Number<conv_stride_w>{});
const auto conv_dilations_dev =
make_tuple(Number<conv_dilation_h>{}, Number<conv_dilation_w>{});
const auto in_left_pads_dev = make_tuple(Number<in_left_pad_h>{}, Number<in_left_pad_w>{});
const auto in_right_pads_dev =
make_tuple(Number<in_right_pad_h>{}, Number<in_right_pad_w>{});
#endif
return make_tuple(in_lengths_dev,
wei_lengths_dev,
out_lengths_dev,
conv_strides_dev,
conv_dilations_dev,
in_left_pads_dev,
in_right_pads_dev);
};
auto f_make_for_device_nhwc = [&]() {
#if USE_DYNAMIC_MODE
#if USE_MODE
const auto in_lengths_dev = make_tuple(N, Hi, Wi, C);
const auto wei_lengths_dev = make_tuple(K, Y, X, C);
const auto out_lengths_dev = make_tuple(N, Ho, Wo, K);
@@ -277,8 +243,6 @@ int main(int argc, char* argv[])
in_right_pads_dev);
};
const auto nhwc_desc = f_make_for_device_nhwc();
#if USE_CONV_BWD_V4R1_XDL_NHWC
if(algo == ConvBackwardDataAlgo::V4R1XDLNHWC)
{
@@ -289,20 +253,20 @@ int main(int argc, char* argv[])
const auto tmp = f_make_for_device_nhwc();
device_dynamic_convolution_backward_data_implicit_gemm_v4r1_xdlops_nhwc_kyxc_nhwk<
in_data_t,
acc_data_t,
out_data_t>(tmp[I0],
tmp[I1],
tmp[I2],
tmp[I3],
tmp[I4],
tmp[I5],
tmp[I6],
in_device,
wei,
out,
nrepeat);
device_convolution_backward_data_implicit_gemm_v4r1_xdlops_nhwc_kyxc_nhwk<in_data_t,
acc_data_t,
out_data_t>(
tmp[I0],
tmp[I1],
tmp[I2],
tmp[I3],
tmp[I4],
tmp[I5],
tmp[I6],
in_device,
wei,
out,
nrepeat);
}
#endif
@@ -316,20 +280,20 @@ int main(int argc, char* argv[])
const auto tmp = f_make_for_device_nhwc();
device_dynamic_convolution_backward_data_implicit_gemm_v4r1r2_xdlops_nhwc_kyxc_nhwk<
in_data_t,
acc_data_t,
out_data_t>(tmp[I0],
tmp[I1],
tmp[I2],
tmp[I3],
tmp[I4],
tmp[I5],
tmp[I6],
in_device,
wei,
out,
nrepeat);
device_convolution_backward_data_implicit_gemm_v4r1r2_xdlops_nhwc_kyxc_nhwk<in_data_t,
acc_data_t,
out_data_t>(
tmp[I0],
tmp[I1],
tmp[I2],
tmp[I3],
tmp[I4],
tmp[I5],
tmp[I6],
in_device,
wei,
out,
nrepeat);
}
#endif

216
host/driver_offline/conv_fwd_driver_offline.cpp → host/driver_offline/src/conv_fwd_driver_offline.cpp
View File

@@ -12,17 +12,17 @@
#include "conv_common.hpp"
#include "host_conv.hpp"
#include "device_tensor.hpp"
#include "device_dynamic_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw.hpp"
#include "device_dynamic_convolution_forward_implicit_gemm_v4r4r2_dlops_nhwc_kyxc_nhwk.hpp"
#include "device_dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw.hpp"
#include "device_dynamic_convolution_forward_implicit_gemm_v5r1_dlops_nchw_kcyx_nkhw.hpp"
#include "device_dynamic_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw.hpp"
#include "device_dynamic_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk.hpp"
#include "device_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw.hpp"
#include "device_convolution_forward_implicit_gemm_v4r4r2_dlops_nhwc_kyxc_nhwk.hpp"
#include "device_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw.hpp"
#include "device_convolution_forward_implicit_gemm_v5r1_dlops_nchw_kcyx_nkhw.hpp"
#include "device_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw.hpp"
#include "device_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk.hpp"
#define USE_DYNAMIC_MODE 1
#define USE_MODE 1
#define USE_CONV_FWD_V4R4_NCHW 1
#define USE_CONV_FWD_V4R4R2_NHWC 1
#define USE_CONV_FWD_V6R1_NCHW 1
#define USE_CONV_FWD_V6R1_NCHW 0
#define USE_CONV_FWD_V5R1_NCHW 0
#define USE_CONV_FWD_V4R4R2_XDL_NCHW 0
#define USE_CONV_FWD_V4R4R4_XDL_NHWC 0
@@ -49,7 +49,7 @@ int main(int argc, char* argv[])
constexpr auto I5 = Number<5>{};
constexpr auto I6 = Number<6>{};
#if USE_DYNAMIC_MODE
#if USE_MODE
// dynamic mode
if(argc != 22)
{
@@ -58,29 +58,29 @@ int main(int argc, char* argv[])
exit(1);
}
const ConvTensorLayout layout = static_cast<ConvTensorLayout>(atoi(argv[1]));
const ConvForwardAlgo algo = static_cast<ConvForwardAlgo>(atoi(argv[2]));
const bool do_verification = atoi(argv[3]);
const int init_method = atoi(argv[4]);
const bool do_log = atoi(argv[5]);
const int nrepeat = atoi(argv[6]);
const ConvTensorLayout layout = static_cast<ConvTensorLayout>(std::stoi(argv[1]));
const ConvForwardAlgo algo = static_cast<ConvForwardAlgo>(std::stoi(argv[2]));
const bool do_verification = std::stoi(argv[3]);
const int init_method = std::stoi(argv[4]);
const bool do_log = std::stoi(argv[5]);
const int nrepeat = std::stoi(argv[6]);
const index_t N = atoi(argv[7]);
const index_t K = atoi(argv[8]);
const index_t C = atoi(argv[9]);
const index_t Y = atoi(argv[10]);
const index_t X = atoi(argv[11]);
const index_t Hi = atoi(argv[12]);
const index_t Wi = atoi(argv[13]);
const index_t N = std::stoi(argv[7]);
const index_t K = std::stoi(argv[8]);
const index_t C = std::stoi(argv[9]);
const index_t Y = std::stoi(argv[10]);
const index_t X = std::stoi(argv[11]);
const index_t Hi = std::stoi(argv[12]);
const index_t Wi = std::stoi(argv[13]);
const index_t conv_stride_h = atoi(argv[14]);
const index_t conv_stride_w = atoi(argv[15]);
const index_t conv_dilation_h = atoi(argv[16]);
const index_t conv_dilation_w = atoi(argv[17]);
const index_t in_left_pad_h = atoi(argv[18]);
const index_t in_left_pad_w = atoi(argv[19]);
const index_t in_right_pad_h = atoi(argv[20]);
const index_t in_right_pad_w = atoi(argv[21]);
const index_t conv_stride_h = std::stoi(argv[14]);
const index_t conv_stride_w = std::stoi(argv[15]);
const index_t conv_dilation_h = std::stoi(argv[16]);
const index_t conv_dilation_w = std::stoi(argv[17]);
const index_t in_left_pad_h = std::stoi(argv[18]);
const index_t in_left_pad_w = std::stoi(argv[19]);
const index_t in_right_pad_h = std::stoi(argv[20]);
const index_t in_right_pad_w = std::stoi(argv[21]);
const index_t YEff = (Y - 1) * conv_dilation_h + 1;
const index_t XEff = (X - 1) * conv_dilation_w + 1;
@@ -95,12 +95,12 @@ int main(int argc, char* argv[])
exit(1);
}
const ConvTensorLayout layout = static_cast<ConvTensorLayout>(atoi(argv[1]));
const ConvForwardAlgo algo = static_cast<ConvForwardAlgo>(atoi(argv[2]));
const bool do_verification = atoi(argv[3]);
const int init_method = atoi(argv[4]);
const bool do_log = atoi(argv[5]);
const int nrepeat = atoi(argv[6]);
const ConvTensorLayout layout = static_cast<ConvTensorLayout>(std::stoi(argv[1]));
const ConvForwardAlgo algo = static_cast<ConvForwardAlgo>(std::stoi(argv[2]));
const bool do_verification = std::stoi(argv[3]);
const int init_method = std::stoi(argv[4]);
const bool do_log = std::stoi(argv[5]);
const int nrepeat = std::stoi(argv[6]);
constexpr index_t N = 128;
constexpr index_t C = 192;
@@ -142,10 +142,8 @@ int main(int argc, char* argv[])
std::vector<std::size_t> in_lengths_host(4), wei_lengths_host(4), out_lengths_host(4);
switch(layout)
if(layout == ConvTensorLayout::NCHW)
{
case ConvTensorLayout::NCHW:
// NCHW
in_lengths_host[0] = static_cast<std::size_t>(N);
in_lengths_host[1] = static_cast<std::size_t>(C);
in_lengths_host[2] = static_cast<std::size_t>(Hi);
@@ -158,9 +156,9 @@ int main(int argc, char* argv[])
out_lengths_host[1] = static_cast<std::size_t>(K);
out_lengths_host[2] = static_cast<std::size_t>(Ho);
out_lengths_host[3] = static_cast<std::size_t>(Wo);
break;
case ConvTensorLayout::NHWC:
// NHWC
}
else if(layout == ConvTensorLayout::NHWC)
{
in_lengths_host[0] = static_cast<std::size_t>(N);
in_lengths_host[1] = static_cast<std::size_t>(Hi);
in_lengths_host[2] = static_cast<std::size_t>(Wi);
@@ -173,8 +171,10 @@ int main(int argc, char* argv[])
out_lengths_host[1] = static_cast<std::size_t>(Ho);
out_lengths_host[2] = static_cast<std::size_t>(Wo);
out_lengths_host[3] = static_cast<std::size_t>(K);
break;
default: throw std::runtime_error("wrong! not implemented");
}
else
{
std::runtime_error("wrong! not implemented");
}
Tensor<in_data_t> in(in_lengths_host);
@@ -228,7 +228,7 @@ int main(int argc, char* argv[])
}
auto f_make_for_device_nchw = [&]() {
#if USE_DYNAMIC_MODE
#if USE_MODE
const auto in_lengths_dev = make_tuple(N, C, Hi, Wi);
const auto wei_lengths_dev = make_tuple(K, C, Y, X);
const auto out_lengths_dev = make_tuple(N, K, Ho, Wo);
@@ -260,7 +260,7 @@ int main(int argc, char* argv[])
};
auto f_make_for_device_nhwc = [&]() {
#if USE_DYNAMIC_MODE
#if USE_MODE
const auto in_lengths_dev = make_tuple(N, Hi, Wi, C);
const auto wei_lengths_dev = make_tuple(K, Y, X, C);
const auto out_lengths_dev = make_tuple(N, Ho, Wo, K);
@@ -301,20 +301,19 @@ int main(int argc, char* argv[])
const auto tmp = f_make_for_device_nchw();
device_dynamic_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw<in_data_t,
acc_data_t,
out_data_t>(
tmp[I0],
tmp[I1],
tmp[I2],
tmp[I3],
tmp[I4],
tmp[I5],
tmp[I6],
in,
wei,
out_device,
nrepeat);
device_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw<in_data_t,
acc_data_t,
out_data_t>(tmp[I0],
tmp[I1],
tmp[I2],
tmp[I3],
tmp[I4],
tmp[I5],
tmp[I6],
in,
wei,
out_device,
nrepeat);
}
#endif
@@ -328,20 +327,19 @@ int main(int argc, char* argv[])
const auto tmp = f_make_for_device_nhwc();
device_dynamic_convolution_forward_implicit_gemm_v4r4r2_dlops_nhwc_kyxc_nhwk<in_data_t,
acc_data_t,
out_data_t>(
tmp[I0],
tmp[I1],
tmp[I2],
tmp[I3],
tmp[I4],
tmp[I5],
tmp[I6],
in,
wei,
out_device,
nrepeat);
device_convolution_forward_implicit_gemm_v4r4r2_dlops_nhwc_kyxc_nhwk<in_data_t,
acc_data_t,
out_data_t>(tmp[I0],
tmp[I1],
tmp[I2],
tmp[I3],
tmp[I4],
tmp[I5],
tmp[I6],
in,
wei,
out_device,
nrepeat);
}
#endif
@@ -355,20 +353,19 @@ int main(int argc, char* argv[])
const auto tmp = f_make_for_device_nchw();
device_dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw<in_data_t,
acc_data_t,
out_data_t>(
tmp[I0],
tmp[I1],
tmp[I2],
tmp[I3],
tmp[I4],
tmp[I5],
tmp[I6],
in,
wei,
out_device,
nrepeat);
device_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw<in_data_t,
acc_data_t,
out_data_t>(tmp[I0],
tmp[I1],
tmp[I2],
tmp[I3],
tmp[I4],
tmp[I5],
tmp[I6],
in,
wei,
out_device,
nrepeat);
}
#endif
@@ -382,21 +379,20 @@ int main(int argc, char* argv[])
const auto tmp = f_make_for_device_nchw();
device_dynamic_convolution_forward_implicit_gemm_v5r1_dlops_nchw_kcyx_nkhw<in_data_t,
16,
acc_data_t,
out_data_t>(
tmp[I0],
tmp[I1],
tmp[I2],
tmp[I3],
tmp[I4],
tmp[I5],
tmp[I6],
in,
wei,
out_device,
nrepeat);
device_convolution_forward_implicit_gemm_v5r1_dlops_nchw_kcyx_nkhw<in_data_t,
16,
acc_data_t,
out_data_t>(tmp[I0],
tmp[I1],
tmp[I2],
tmp[I3],
tmp[I4],
tmp[I5],
tmp[I6],
in,
wei,
out_device,
nrepeat);
}
#endif
@@ -410,9 +406,9 @@ int main(int argc, char* argv[])
const auto tmp = f_make_for_device_nchw();
device_dynamic_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw<in_data_t,
acc_data_t,
out_data_t>(
device_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw<in_data_t,
acc_data_t,
out_data_t>(
tmp[I0],
tmp[I1],
tmp[I2],
@@ -437,9 +433,9 @@ int main(int argc, char* argv[])
const auto tmp = f_make_for_device_nhwc();
device_dynamic_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk<in_data_t,
acc_data_t,
out_data_t>(
device_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk<in_data_t,
acc_data_t,
out_data_t>(
tmp[I0],
tmp[I1],
tmp[I2],
@@ -467,7 +463,6 @@ int main(int argc, char* argv[])
check_error(out_host, out_device);
#if 0
if(do_log)
{
LogRangeAsType<float>(std::cout << "in : ", in.mData, ",") << std::endl;
@@ -475,6 +470,5 @@ int main(int argc, char* argv[])
LogRangeAsType<float>(std::cout << "out_host : ", out_host.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "out_device: ", out_device.mData, ",") << std::endl;
}
#endif
}
}

22
host/driver_online/CMakeLists.txt
View File

@@ -1,22 +0,0 @@
include_directories(BEFORE
include
${PROJECT_BINARY_DIR}/host/online_compile/include
${PROJECT_SOURCE_DIR}/host/online_compile/include
${PROJECT_SOURCE_DIR}/host/host_tensor/include
${PROJECT_SOURCE_DIR}/host/solver/include
${PROJECT_SOURCE_DIR}/composable_kernel/include
${PROJECT_SOURCE_DIR}/composable_kernel/include/utility
${PROJECT_SOURCE_DIR}/composable_kernel/include/tensor_description
${PROJECT_SOURCE_DIR}/composable_kernel/include/tensor_operation
${PROJECT_SOURCE_DIR}/composable_kernel/include/problem_transform
${PROJECT_SOURCE_DIR}/composable_kernel/include/driver
${PROJECT_SOURCE_DIR}/external/rocm/include
${PROJECT_SOURCE_DIR}/external/half/include
)
set(CONV_FWD_DRIVER_ONLINE_SOURCE conv_fwd_driver_online.cpp)
add_executable(conv_fwd_driver_online ${CONV_FWD_DRIVER_ONLINE_SOURCE})
target_link_libraries(conv_fwd_driver_online PRIVATE host_tensor)
target_link_libraries(conv_fwd_driver_online PRIVATE online_compile)

453
host/driver_online/conv_fwd_driver_online.cpp
View File

@@ -1,453 +0,0 @@
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include <stdlib.h>
#include <half.hpp>
#include "config.hpp"
#include "print.hpp"
#include "device.hpp"
#include "host_tensor.hpp"
#include "host_tensor_generator.hpp"
#include "conv_common.hpp"
#include "host_conv.hpp"
#include "device_tensor.hpp"
#include "handle.hpp"
#include "hipCheck.hpp"
#include "online_device_dynamic_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw.hpp"
#include "online_device_dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw.hpp"
#include "online_device_dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nchw_kcyx_nkhw.hpp"
#include "online_device_dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nhwc_kyxc_nhwk.hpp"
#define USE_CONV_FWD_V4R4_NCHW 1
#define USE_CONV_FWD_V6R1_NCHW 1
#define USE_CONV_FWD_V4R4_XDLOPS_NCHW 1
#define USE_CONV_FWD_V4R4_XDLOPS_NHWC 1
enum ConvForwardAlgo
{
V4R4NCHW, // 0
V6R1NCHW, // 1
V4R4XDLNCHW, // 2
V4R4XDLNHWC // 3
};
int main(int argc, char* argv[])
{
using namespace ck;
using namespace ck_driver;
using size_t = std::size_t;
hipStream_t stream;
online_compile::Handle* handle;
MY_HIP_CHECK(hipStreamCreate(&stream));
handle = new online_compile::Handle(stream);
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto I4 = Number<4>{};
constexpr auto I5 = Number<5>{};
constexpr auto I6 = Number<6>{};
if(argc != 22)
{
printf("arg1 to 5: layout, algo, do_verification, init_method, do_log, nrepeat\n");
printf("rest: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, RightPx\n");
exit(1);
}
const ConvTensorLayout layout = static_cast<ConvTensorLayout>(atoi(argv[1]));
const ConvForwardAlgo algo = static_cast<ConvForwardAlgo>(atoi(argv[2]));
const bool do_verification = atoi(argv[3]);
const int init_method = atoi(argv[4]);
const bool do_log = atoi(argv[5]);
const int nrepeat = atoi(argv[6]);
const index_t N = atoi(argv[7]);
const index_t K = atoi(argv[8]);
const index_t C = atoi(argv[9]);
const index_t Y = atoi(argv[10]);
const index_t X = atoi(argv[11]);
const index_t Hi = atoi(argv[12]);
const index_t Wi = atoi(argv[13]);
const index_t conv_stride_h = atoi(argv[14]);
const index_t conv_stride_w = atoi(argv[15]);
const index_t conv_dilation_h = atoi(argv[16]);
const index_t conv_dilation_w = atoi(argv[17]);
const index_t in_left_pad_h = atoi(argv[18]);
const index_t in_left_pad_w = atoi(argv[19]);
const index_t in_right_pad_h = atoi(argv[20]);
const index_t in_right_pad_w = atoi(argv[21]);
const index_t YEff = (Y - 1) * conv_dilation_h + 1;
const index_t XEff = (X - 1) * conv_dilation_w + 1;
const index_t Ho = (Hi + in_left_pad_h + in_right_pad_h - YEff) / conv_stride_h + 1;
const index_t Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + 1;
#if 1
using in_data_t = float;
using acc_data_t = float;
using out_data_t = float;
#elif 0
using in_data_t = half_t;
using acc_data_t = float;
using out_data_t = half_t;
#elif 1
using in_data_t = int8_t;
using acc_data_t = int32_t;
using out_data_t = int8_t;
#endif
std::vector<std::size_t> in_lengths_host(4), wei_lengths_host(4), out_lengths_host(4);
switch(layout)
{
case ConvTensorLayout::NCHW:
// NCHW
in_lengths_host[0] = static_cast<std::size_t>(N);
in_lengths_host[1] = static_cast<std::size_t>(C);
in_lengths_host[2] = static_cast<std::size_t>(Hi);
in_lengths_host[3] = static_cast<std::size_t>(Wi);
wei_lengths_host[0] = static_cast<std::size_t>(K);
wei_lengths_host[1] = static_cast<std::size_t>(C);
wei_lengths_host[2] = static_cast<std::size_t>(Y);
wei_lengths_host[3] = static_cast<std::size_t>(X);
out_lengths_host[0] = static_cast<std::size_t>(N);
out_lengths_host[1] = static_cast<std::size_t>(K);
out_lengths_host[2] = static_cast<std::size_t>(Ho);
out_lengths_host[3] = static_cast<std::size_t>(Wo);
break;
case ConvTensorLayout::NHWC:
// NHWC
in_lengths_host[0] = static_cast<std::size_t>(N);
in_lengths_host[1] = static_cast<std::size_t>(Hi);
in_lengths_host[2] = static_cast<std::size_t>(Wi);
in_lengths_host[3] = static_cast<std::size_t>(C);
wei_lengths_host[0] = static_cast<std::size_t>(K);
wei_lengths_host[1] = static_cast<std::size_t>(Y);
wei_lengths_host[2] = static_cast<std::size_t>(X);
wei_lengths_host[3] = static_cast<std::size_t>(C);
out_lengths_host[0] = static_cast<std::size_t>(N);
out_lengths_host[1] = static_cast<std::size_t>(Ho);
out_lengths_host[2] = static_cast<std::size_t>(Wo);
out_lengths_host[3] = static_cast<std::size_t>(K);
break;
default: throw std::runtime_error("wrong! not implemented");
}
Tensor<in_data_t> in(in_lengths_host);
Tensor<in_data_t> wei(wei_lengths_host);
Tensor<out_data_t> out_host(out_lengths_host);
Tensor<out_data_t> out_device(out_lengths_host);
std::cout << "layout: " << layout << std::endl;
ostream_HostTensorDescriptor(in.mDesc, std::cout << "in: ");
ostream_HostTensorDescriptor(wei.mDesc, std::cout << "wei: ");
ostream_HostTensorDescriptor(out_host.mDesc, std::cout << "out: ");
print_array("InLeftPads", make_tuple(in_left_pad_h, in_left_pad_w));
print_array("InRightPads", make_tuple(in_right_pad_h, in_right_pad_w));
print_array("ConvStrides", make_tuple(conv_stride_h, conv_stride_w));
print_array("ConvDilations", make_tuple(conv_dilation_h, conv_dilation_w));
std::size_t num_thread = std::thread::hardware_concurrency();
switch(init_method)
{
case 0:
// no initialization
break;
case 1:
in.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
wei.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
break;
case 2:
in.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
wei.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
break;
case 3:
in.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
wei.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
break;
case 4:
in.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
wei.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
break;
case 5:
in.GenerateTensorValue(GeneratorTensor_3<float>{0.0, 1.0}, num_thread);
wei.GenerateTensorValue(GeneratorTensor_3<float>{-0.5, 0.5}, num_thread);
break;
default:
in.GenerateTensorValue(GeneratorTensor_2{1, 5}, num_thread);
auto gen_wei = [](auto... is) {
return GeneratorTensor_2{1, 5}(is...) * GeneratorTensor_Checkboard{}(is...);
};
wei.GenerateTensorValue(gen_wei, num_thread);
}
auto f_make_for_device_nchw = [&]() {
const auto in_lengths_dev = make_tuple(N, C, Hi, Wi);
const auto wei_lengths_dev = make_tuple(K, C, Y, X);
const auto out_lengths_dev = make_tuple(N, K, Ho, Wo);
return make_tuple(in_lengths_dev, wei_lengths_dev, out_lengths_dev);
};
auto f_make_for_device_nhwc = [&]() {
const auto in_lengths_dev = make_tuple(N, Hi, Wi, C);
const auto wei_lengths_dev = make_tuple(K, Y, X, C);
const auto out_lengths_dev = make_tuple(N, Ho, Wo, K);
return make_tuple(in_lengths_dev, wei_lengths_dev, out_lengths_dev);
};
const auto conv_strides = make_tuple(conv_stride_h, conv_stride_w);
const auto conv_dilations = make_tuple(conv_dilation_h, conv_dilation_w);
const auto in_left_pads = make_tuple(in_left_pad_h, in_left_pad_w);
const auto in_right_pads = make_tuple(in_right_pad_h, in_right_pad_w);
#if USE_CONV_FWD_V4R4_NCHW
if(algo == ConvForwardAlgo::V4R4NCHW)
{
if(layout != ConvTensorLayout::NCHW)
{
throw std::runtime_error("wrong! layout");
}
const auto tmp = f_make_for_device_nchw();
tunable_dyn_conv_fwd_v4r4_dlops_nchw_kcyx_nkhw* tunable =
&default_tunable_dyn_conv_fwd_v4r4_dlops_nchw_kcyx_nkhw;
online_device_dynamic_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw<
in_data_t,
acc_data_t,
out_data_t>(handle,
tmp[I0],
tmp[I1],
tmp[I2],
conv_strides,
conv_dilations,
in_left_pads,
in_right_pads,
in,
wei,
out_device,
tunable,
nrepeat);
}
#endif
#if USE_CONV_FWD_V6R1_NCHW
if(algo == ConvForwardAlgo::V6R1NCHW)
{
if(layout != ConvTensorLayout::NCHW)
{
throw std::runtime_error("wrong! layout");
}
const auto tmp = f_make_for_device_nchw();
#if 1
const CompileParameterConvIgemmFwdV6r1DlopsNchwKcyxNkhw compile_param = {
get_datatype_enum_from_type<in_data_t>::value,
get_datatype_enum_from_type<acc_data_t>::value,
get_datatype_enum_from_type<out_data_t>::value,
256,
4,
1,
128,
32,
8,
4,
4,
1,
{8, 2},
{8, 2},
{4, 1, 1, 1, 1},
{2, 1, 1, 128, 1},
{4, 1, 1, 1, 1},
{1, 1, 1, 1, 1},
{1, 4, 1, 1, 1},
{8, 1, 1, 32, 1},
{1, 1, 1, 1, 1},
{1, 1, 1, 1, 1},
4,
true,
true};
#elif 0
const CompileParameterConvIgemmFwdV6r1DlopsNchwKcyxNkhw compile_param = {
get_datatype_enum_from_type<in_data_t>::value,
get_datatype_enum_from_type<acc_data_t>::value,
get_datatype_enum_from_type<out_data_t>::value,
256,
4,
2,
128,
32,
8,
4,
4,
1,
{8, 2},
{8, 2},
{4, 1, 1, 1, 2},
{2, 1, 1, 128, 1},
{4, 1, 1, 1, 1},
{1, 1, 1, 1, 1},
{1, 4, 1, 1, 2},
{8, 1, 1, 32, 1},
{1, 1, 1, 1, 1},
{1, 1, 1, 1, 1},
4,
true,
true};
#elif 1
const CompileParameterConvIgemmFwdV6r1DlopsNchwKcyxNkhw compile_param = {
get_datatype_enum_from_type<in_data_t>::value,
get_datatype_enum_from_type<acc_data_t>::value,
get_datatype_enum_from_type<out_data_t>::value,
256,
4,
4,
128,
32,
8,
4,
4,
1,
{8, 2},
{8, 2},
{4, 1, 1, 1, 4},
{2, 1, 1, 128, 1},
{4, 1, 1, 1, 1},
{1, 1, 1, 1, 1},
{1, 4, 1, 1, 4},
{8, 1, 1, 32, 1},
{1, 1, 1, 1, 1},
{1, 1, 1, 1, 1},
4,
true,
true};
#endif
online_device_dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw<
in_data_t,
acc_data_t,
out_data_t>(handle,
tmp[I0],
tmp[I1],
tmp[I2],
conv_strides,
conv_dilations,
in_left_pads,
in_right_pads,
in,
wei,
out_device,
compile_param,
nrepeat);
}
#endif
#if USE_CONV_FWD_V4R4_XDLOPS_NCHW
if(algo == ConvForwardAlgo::V4R4XDLNCHW)
{
if(layout != ConvTensorLayout::NCHW)
{
throw std::runtime_error("wrong! layout");
}
const auto tmp = f_make_for_device_nchw();
tunable_dyn_conv_fwd_v4r4_xdlops_nchw_kcyx_nkhw* tunable =
&default_tunable_dyn_conv_fwd_v4r4_xdlops_nchw_kcyx_nkhw;
online_device_dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nchw_kcyx_nkhw<
in_data_t,
acc_data_t,
out_data_t>(handle,
tmp[I0],
tmp[I1],
tmp[I2],
conv_strides,
conv_dilations,
in_left_pads,
in_right_pads,
in,
wei,
out_device,
tunable,
nrepeat);
}
#endif
#if USE_CONV_FWD_V4R4_XDLOPS_NHWC
if(algo == ConvForwardAlgo::V4R4XDLNHWC)
{
if(layout != ConvTensorLayout::NHWC)
{
throw std::runtime_error("wrong! layout");
}
const auto tmp = f_make_for_device_nhwc();
tunable_dyn_conv_fwd_v4r4_xdlops_nhwc_kyxc_nhwk* tunable =
&default_tunable_dyn_conv_fwd_v4r4_xdlops_nhwc_kyxc_nhwk;
online_device_dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nhwc_kyxc_nhwk<
in_data_t,
acc_data_t,
out_data_t>(handle,
tmp[I0],
tmp[I1],
tmp[I2],
conv_strides,
conv_dilations,
in_left_pads,
in_right_pads,
in,
wei,
out_device,
tunable,
nrepeat);
}
#endif
if(do_verification)
{
host_direct_convolution(in,
wei,
out_host,
make_tuple(conv_stride_h, conv_stride_w),
make_tuple(conv_dilation_h, conv_dilation_w),
make_tuple(in_left_pad_h, in_left_pad_w),
make_tuple(in_right_pad_h, in_right_pad_w),
layout);
check_error(out_host, out_device);
#if 0
if(do_log)
{
LogRangeAsType<float>(std::cout << "in : ", in.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "wei: ", wei.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "out_host : ", out_host.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "out_device: ", out_device.mData, ",") << std::endl;
}
#endif
}
delete handle;
MY_HIP_CHECK(hipStreamDestroy(stream));
}

395
host/driver_online/include/online_device_dynamic_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw.hpp
View File

@@ -1,395 +0,0 @@
#pragma once
#include "device.hpp"
#include "host_tensor.hpp"
#include "handle.hpp"
#include "online_driver_common.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "transform_forward_convolution_into_gemm_v4r4_nchw_kcyx_nkhw.hpp"
#include "conv_tunable_fwd_v4r4_dlops_nchw_kcyx_nkhw.hpp"
namespace detail_dyn_conv_fwd_v4r4_nchw_kcyx_nkhw {
template <typename TInWei, typename TAcc, typename TOut>
static std::string get_network_config_string_from_types()
{
using namespace ck;
std::string out;
out += std::to_string(get_datatype_enum_from_type<TInWei>::value) + "_" +
std::to_string(get_datatype_enum_from_type<TAcc>::value) + "_" +
std::to_string(get_datatype_enum_from_type<TOut>::value);
return (out);
};
static std::string
get_network_config_string_from_tunable(const tunable_dyn_conv_fwd_v4r4_dlops_nchw_kcyx_nkhw* pt)
{
std::string out("TUN_");
out += std::to_string(pt->BlockSize) + "_";
out += std::to_string(pt->MPerBlock) + "x" + std::to_string(pt->NPerBlock) + "x" +
std::to_string(pt->KPerBlock) + "_";
out += std::to_string(pt->M1PerThread) + "x" + std::to_string(pt->N1PerThread) + "x" +
std::to_string(pt->KPerThread) + "_";
out += std::to_string(pt->M1N1ThreadClusterM10) + "x" +
std::to_string(pt->M1N1ThreadClusterN10) + "x" +
std::to_string(pt->M1N1ThreadClusterM11) + "x" +
std::to_string(pt->M1N1ThreadClusterN11) + "_";
out += std::to_string(pt->ABlockTransferThreadSliceLengths_K_M0_M1[0]) + "x" +
std::to_string(pt->ABlockTransferThreadSliceLengths_K_M0_M1[1]) + "x" +
std::to_string(pt->ABlockTransferThreadSliceLengths_K_M0_M1[2]) + "_";
out += std::to_string(pt->ABlockTransferThreadClusterLengths_K_M0_M1[0]) + "x" +
std::to_string(pt->ABlockTransferThreadClusterLengths_K_M0_M1[1]) + "x" +
std::to_string(pt->ABlockTransferThreadClusterLengths_K_M0_M1[2]) + "_";
out += std::to_string(pt->ABlockTransferThreadClusterArrangeOrder[0]) + "x" +
std::to_string(pt->ABlockTransferThreadClusterArrangeOrder[1]) + "x" +
std::to_string(pt->ABlockTransferThreadClusterArrangeOrder[2]) + "_";
out += std::to_string(pt->ABlockTransferSrcAccessOrder[0]) + "x" +
std::to_string(pt->ABlockTransferSrcAccessOrder[1]) + "x" +
std::to_string(pt->ABlockTransferSrcAccessOrder[2]) + "_";
out += std::to_string(pt->ABlockTransferSrcVectorDim) + "_";
out += std::to_string(pt->ABlockTransferSrcScalarPerVector) + "_";
out += std::to_string(pt->ABlockTransferDstScalarPerVector_M1) + "_";
out += std::to_string(pt->AThreadTransferSrcResetCoordinateAfterRun) + "_";
out += std::to_string(pt->BBlockTransferThreadSliceLengths_K_N0_N1[0]) + "x" +
std::to_string(pt->BBlockTransferThreadSliceLengths_K_N0_N1[1]) + "x" +
std::to_string(pt->BBlockTransferThreadSliceLengths_K_N0_N1[2]) + "_";
out += std::to_string(pt->BBlockTransferThreadClusterLengths_K_N0_N1[0]) + "x" +
std::to_string(pt->BBlockTransferThreadClusterLengths_K_N0_N1[1]) + "x" +
std::to_string(pt->BBlockTransferThreadClusterLengths_K_N0_N1[2]) + "_";
out += std::to_string(pt->BBlockTransferThreadClusterArrangeOrder[0]) + "x" +
std::to_string(pt->BBlockTransferThreadClusterArrangeOrder[1]) + "x" +
std::to_string(pt->BBlockTransferThreadClusterArrangeOrder[2]) + "_";
out += std::to_string(pt->BBlockTransferSrcAccessOrder[0]) + "x" +
std::to_string(pt->BBlockTransferSrcAccessOrder[1]) + "x" +
std::to_string(pt->BBlockTransferSrcAccessOrder[2]) + "_";
out += std::to_string(pt->BBlockTransferSrcVectorDim) + "_";
out += std::to_string(pt->BBlockTransferSrcScalarPerVector) + "_";
out += std::to_string(pt->BBlockTransferDstScalarPerVector_N1) + "_";
out += std::to_string(pt->BThreadTransferSrcResetCoordinateAfterRun) + "_";
out += std::to_string(pt->CThreadTransferSrcDstAccessOrder[0]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[1]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[2]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[3]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[4]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[5]) + "_";
out += std::to_string(pt->CThreadTransferSrcDstVectorDim) + "_";
out += std::to_string(pt->CThreadTransferDstScalarPerVector);
return (out);
};
template <typename TInWei, typename TAcc, typename TOut>
static std::string get_definition_string_from_types()
{
using namespace ck;
std::string out;
out +=
" -DCK_PARAM_ABDataTypeEnum=" + std::to_string(get_datatype_enum_from_type<TInWei>::value) +
" -DCK_PARAM_AccDataTypeEnum=" + std::to_string(get_datatype_enum_from_type<TAcc>::value) +
" -DCK_PARAM_CDataTypeEnum=" + std::to_string(get_datatype_enum_from_type<TOut>::value);
return (out);
};
static std::string
get_definition_string_from_tunable(const tunable_dyn_conv_fwd_v4r4_dlops_nchw_kcyx_nkhw* pt)
{
std::string out;
out += " -DCK_PARAM_BlockSize=" + std::to_string(pt->BlockSize);
out += " -DCK_PARAM_MPerBlock=" + std::to_string(pt->MPerBlock) +
" -DCK_PARAM_NPerBlock=" + std::to_string(pt->NPerBlock) +
" -DCK_PARAM_KPerBlock=" + std::to_string(pt->KPerBlock);
out += " -DCK_PARAM_M1PerThread=" + std::to_string(pt->M1PerThread) +
" -DCK_PARAM_N1PerThread=" + std::to_string(pt->N1PerThread) +
" -DCK_PARAM_KPerThread=" + std::to_string(pt->KPerThread);
out += " -DCK_PARAM_M1N1ThreadClusterM10=" + std::to_string(pt->M1N1ThreadClusterM10) +
" -DCK_PARAM_M1N1ThreadClusterN10=" + std::to_string(pt->M1N1ThreadClusterN10) +
" -DCK_PARAM_M1N1ThreadClusterM11=" + std::to_string(pt->M1N1ThreadClusterM11) +
" -DCK_PARAM_M1N1ThreadClusterN11=" + std::to_string(pt->M1N1ThreadClusterN11);
out += " -DCK_PARAM_ABlockTransferThreadSliceLengths_K_M0_M1=" +
std::to_string(pt->ABlockTransferThreadSliceLengths_K_M0_M1[0]) + "," +
std::to_string(pt->ABlockTransferThreadSliceLengths_K_M0_M1[1]) + "," +
std::to_string(pt->ABlockTransferThreadSliceLengths_K_M0_M1[2]);
out += " -DCK_PARAM_ABlockTransferThreadClusterLengths_K_M0_M1=" +
std::to_string(pt->ABlockTransferThreadClusterLengths_K_M0_M1[0]) + "," +
std::to_string(pt->ABlockTransferThreadClusterLengths_K_M0_M1[1]) + "," +
std::to_string(pt->ABlockTransferThreadClusterLengths_K_M0_M1[2]);
out += " -DCK_PARAM_ABlockTransferThreadClusterArrangeOrder=" +
std::to_string(pt->ABlockTransferThreadClusterArrangeOrder[0]) + "," +
std::to_string(pt->ABlockTransferThreadClusterArrangeOrder[1]) + "," +
std::to_string(pt->ABlockTransferThreadClusterArrangeOrder[2]);
out += " -DCK_PARAM_ABlockTransferSrcAccessOrder=" +
std::to_string(pt->ABlockTransferSrcAccessOrder[0]) + "," +
std::to_string(pt->ABlockTransferSrcAccessOrder[1]) + "," +
std::to_string(pt->ABlockTransferSrcAccessOrder[2]);
out +=
" -DCK_PARAM_ABlockTransferSrcVectorDim=" + std::to_string(pt->ABlockTransferSrcVectorDim);
out += " -DCK_PARAM_ABlockTransferSrcScalarPerVector=" +
std::to_string(pt->ABlockTransferSrcScalarPerVector);
out += " -DCK_PARAM_ABlockTransferDstScalarPerVector_M1=" +
std::to_string(pt->ABlockTransferDstScalarPerVector_M1);
out += " -DCK_PARAM_AThreadTransferSrcResetCoordinateAfterRun=" +
std::to_string(pt->AThreadTransferSrcResetCoordinateAfterRun);
out += " -DCK_PARAM_BBlockTransferThreadSliceLengths_K_N0_N1=" +
std::to_string(pt->BBlockTransferThreadSliceLengths_K_N0_N1[0]) + "," +
std::to_string(pt->BBlockTransferThreadSliceLengths_K_N0_N1[1]) + "," +
std::to_string(pt->BBlockTransferThreadSliceLengths_K_N0_N1[2]);
out += " -DCK_PARAM_BBlockTransferThreadClusterLengths_K_N0_N1=" +
std::to_string(pt->BBlockTransferThreadClusterLengths_K_N0_N1[0]) + "," +
std::to_string(pt->BBlockTransferThreadClusterLengths_K_N0_N1[1]) + "," +
std::to_string(pt->BBlockTransferThreadClusterLengths_K_N0_N1[2]);
out += " -DCK_PARAM_BBlockTransferThreadClusterArrangeOrder=" +
std::to_string(pt->BBlockTransferThreadClusterArrangeOrder[0]) + "," +
std::to_string(pt->BBlockTransferThreadClusterArrangeOrder[1]) + "," +
std::to_string(pt->BBlockTransferThreadClusterArrangeOrder[2]);
out += " -DCK_PARAM_BBlockTransferSrcAccessOrder=" +
std::to_string(pt->BBlockTransferSrcAccessOrder[0]) + "," +
std::to_string(pt->BBlockTransferSrcAccessOrder[1]) + "," +
std::to_string(pt->BBlockTransferSrcAccessOrder[2]);
out +=
" -DCK_PARAM_BBlockTransferSrcVectorDim=" + std::to_string(pt->BBlockTransferSrcVectorDim);
out += " -DCK_PARAM_BBlockTransferSrcScalarPerVector=" +
std::to_string(pt->BBlockTransferSrcScalarPerVector);
out += " -DCK_PARAM_BBlockTransferDstScalarPerVector_N1=" +
std::to_string(pt->BBlockTransferDstScalarPerVector_N1);
out += " -DCK_PARAM_BThreadTransferSrcResetCoordinateAfterRun=" +
std::to_string(pt->BThreadTransferSrcResetCoordinateAfterRun);
out += " -DCK_PARAM_CThreadTransferSrcDstAccessOrder=" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[0]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[1]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[2]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[3]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[4]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[5]);
out += " -DCK_PARAM_CThreadTransferSrcDstVectorDim=" +
std::to_string(pt->CThreadTransferSrcDstVectorDim);
out += " -DCK_PARAM_CThreadTransferDstScalarPerVector=" +
std::to_string(pt->CThreadTransferDstScalarPerVector);
return (out);
};
} // namespace detail_dyn_conv_fwd_v4r4_nchw_kcyx_nkhw
template <typename TInWei,
typename TAcc,
typename TOut,
typename InLengths,
typename WeiLengths,
typename OutLengths,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
void online_device_dynamic_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw(
online_compile::Handle* handle,
const InLengths& in_n_c_hi_wi_lengths,
const WeiLengths& wei_k_c_y_x_lengths,
const OutLengths& out_n_k_ho_wo_lengths,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads,
const Tensor<TInWei>& in_n_c_hi_wi,
const Tensor<TInWei>& wei_k_c_y_x,
Tensor<TOut>& out_n_k_ho_wo,
const tunable_dyn_conv_fwd_v4r4_dlops_nchw_kcyx_nkhw* tunable,
ck::index_t nrepeat)
{
using namespace ck;
using namespace ck_driver;
using namespace detail_dyn_conv_fwd_v4r4_nchw_kcyx_nkhw;
using size_t = std::size_t;
/////////////////////////////////////////////////////////////////////////////////////////////////////////////
// The follow codes are only used for computing the grid_size, hasMainKBlockLoop,
// hasDoubleTailKBlockLoop
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
const auto in_n_c_hi_wi_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(in_n_c_hi_wi_lengths);
const auto wei_k_c_y_x_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(wei_k_c_y_x_lengths);
const auto out_n_k_ho_wo_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(out_n_k_ho_wo_lengths);
const auto descs =
transform_forward_convolution_into_gemm_v4r4_nchw_kcyx_nkhw_pad(wei_k_c_y_x_desc,
in_n_c_hi_wi_desc,
out_n_k_ho_wo_desc,
conv_strides,
conv_dilations,
in_left_pads,
in_right_pads);
const auto a_k_m_grid_desc = descs[I0];
const auto c_m_n_grid_desc = descs[I2];
const auto M = c_m_n_grid_desc.GetLength(I0);
const auto N = c_m_n_grid_desc.GetLength(I1);
const auto K = a_k_m_grid_desc.GetLength(I0);
const index_t grid_size = (M / tunable->MPerBlock) * (N / tunable->NPerBlock);
const bool hasMainKBlockLoop = ((K + tunable->KPerBlock) / (2 * tunable->KPerBlock) > 1);
const bool hasDoubleTailKBlockLoop = ((K / tunable->KPerBlock) % 2 == 0);
/////////////////////////////////////////////////////////////////////////////////////////////////////////////
// these buffers are usually provided by the user application
DeviceMem in_n_c_hi_wi_dev_buf(sizeof(TInWei) * in_n_c_hi_wi.mDesc.GetElementSpace());
DeviceMem wei_k_c_y_x_dev_buf(sizeof(TInWei) * wei_k_c_y_x.mDesc.GetElementSpace());
DeviceMem out_n_k_ho_wo_dev_buf(sizeof(TOut) * out_n_k_ho_wo.mDesc.GetElementSpace());
in_n_c_hi_wi_dev_buf.ToDevice(in_n_c_hi_wi.mData.data());
wei_k_c_y_x_dev_buf.ToDevice(wei_k_c_y_x.mData.data());
out_n_k_ho_wo_dev_buf.ToDevice(out_n_k_ho_wo.mData.data());
// these are workspace buffers that should be expressed to the user by the corresponding
// workspace API
DeviceMem workspace_buf(4096);
void* a_k_m0_m1_grid_desc_dev_buf = workspace_buf.GetDeviceBuffer();
void* b_k_n0_n1_grid_desc_dev_buf =
static_cast<void*>(static_cast<unsigned char*>(workspace_buf.GetDeviceBuffer()) + 1024);
void* c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf =
static_cast<void*>(static_cast<unsigned char*>(workspace_buf.GetDeviceBuffer()) + 2048);
void* c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf =
static_cast<void*>(static_cast<unsigned char*>(workspace_buf.GetDeviceBuffer()) + 3072);
const std::vector<size_t> vld = {static_cast<size_t>(tunable->BlockSize), 1, 1};
const std::vector<size_t> vgd1 = {static_cast<size_t>(tunable->BlockSize), 1, 1};
const std::vector<size_t> vgd2 = {static_cast<size_t>(grid_size * tunable->BlockSize), 1, 1};
std::string program_name =
"dynamic_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw.cpp";
std::string algo_name = "implicit_gemm_conv_fwd_v4r4_dlops_nchw";
std::string param = " -std=c++17 ";
std::string network_config;
param += get_definition_string_from_types<TInWei, TAcc, TOut>() + " " +
get_definition_string_from_tunable(tunable) +
" -DCK_PARAM_HAS_MAIN_KBLOCK_LOOP=" + std::to_string(hasMainKBlockLoop) +
" -DCK_PARAM_HAS_DOUBLE_TAIL_KBLOCK_LOOP=" + std::to_string(hasDoubleTailKBlockLoop);
network_config = get_network_config_string_from_types<TInWei, TAcc, TOut>() + "_" +
get_network_config_string_from_tunable(tunable) + "_" +
std::to_string(hasMainKBlockLoop) + "_" +
std::to_string(hasDoubleTailKBlockLoop);
std::vector<float> kernel1_times;
std::vector<float> kernel2_times;
for(index_t i = 0; i < nrepeat; ++i)
{
KernelTimer timer1, timer2;
std::string kernel_name;
kernel_name = "dynamic_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw_prepare";
auto network_config_1 = network_config + "_1";
timer1.Start();
handle->AddKernel(algo_name, network_config_1, program_name, kernel_name, vld, vgd1, param)(
static_cast<index_t>(in_n_c_hi_wi_lengths[I0]),
static_cast<index_t>(in_n_c_hi_wi_lengths[I1]),
static_cast<index_t>(in_n_c_hi_wi_lengths[I2]),
static_cast<index_t>(in_n_c_hi_wi_lengths[I3]),
static_cast<index_t>(wei_k_c_y_x_lengths[I0]),
static_cast<index_t>(wei_k_c_y_x_lengths[I2]),
static_cast<index_t>(wei_k_c_y_x_lengths[I3]),
conv_strides[I0],
conv_strides[I1],
conv_dilations[I0],
conv_dilations[I1],
in_left_pads[I0],
in_left_pads[I1],
in_right_pads[I0],
in_right_pads[I1],
a_k_m0_m1_grid_desc_dev_buf,
b_k_n0_n1_grid_desc_dev_buf,
c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf,
c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf);
timer1.End();
kernel_name = "dynamic_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw";
auto network_config_2 = network_config + "_2";
timer2.Start();
handle->AddKernel(algo_name, network_config_2, program_name, kernel_name, vld, vgd2, param)(
reinterpret_cast<const TInWei*>(wei_k_c_y_x_dev_buf.GetDeviceBuffer()),
reinterpret_cast<const TInWei*>(in_n_c_hi_wi_dev_buf.GetDeviceBuffer()),
reinterpret_cast<TOut*>(out_n_k_ho_wo_dev_buf.GetDeviceBuffer()),
(const void*)(a_k_m0_m1_grid_desc_dev_buf),
(const void*)(b_k_n0_n1_grid_desc_dev_buf),
(const void*)(c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf),
(const void*)(c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf));
timer2.End();
kernel1_times.push_back(timer1.GetElapsedTime());
kernel2_times.push_back(timer2.GetElapsedTime());
}
{
auto ave_time1 =
std::accumulate(
std::next(kernel1_times.begin()), kernel1_times.end(), 0., std::plus<float>{}) /
(nrepeat - 1);
auto ave_time2 =
std::accumulate(
std::next(kernel2_times.begin()), kernel2_times.end(), 0., std::plus<float>{}) /
(nrepeat - 1);
const auto N = in_n_c_hi_wi_lengths[I0];
const auto C = in_n_c_hi_wi_lengths[I1];
const auto K = out_n_k_ho_wo_lengths[I1];
const auto Ho = out_n_k_ho_wo_lengths[I2];
const auto Wo = out_n_k_ho_wo_lengths[I3];
const auto Y = wei_k_c_y_x_lengths[I2];
const auto X = wei_k_c_y_x_lengths[I3];
float perf = (float)(std::size_t(2) * N * K * Ho * Wo * C * Y * X) /
(std::size_t(1000) * 1000 * 1000) / (ave_time1 + ave_time2);
std::cout << "Average time : " << ave_time1 + ave_time2 << " ms(" << ave_time1 << ", "
<< ave_time2 << "), " << perf << " TFlop/s" << std::endl;
};
// copy result back to host
out_n_k_ho_wo_dev_buf.FromDevice(out_n_k_ho_wo.mData.data());
}

386
host/driver_online/include/online_device_dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nchw_kcyx_nkhw.hpp
View File

@@ -1,386 +0,0 @@
#include "device.hpp"
#include "host_tensor.hpp"
#include "handle.hpp"
#include "online_driver_common.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "conv_tunable_fwd_v4r4_xdlops_nchw_kcyx_nkhw.hpp"
namespace detail_dyn_conv_fwd_v4r4_xdlops_nchw_kcyx_nkhw {
template <typename TInWei, typename TAcc, typename TOut>
static std::string get_network_config_string_from_types()
{
using namespace ck;
std::string out;
out += std::to_string(get_datatype_enum_from_type<TInWei>::value) + "_" +
std::to_string(get_datatype_enum_from_type<TAcc>::value) + "_" +
std::to_string(get_datatype_enum_from_type<TOut>::value);
return (out);
};
static std::string
get_network_config_string_from_tunable(const tunable_dyn_conv_fwd_v4r4_xdlops_nchw_kcyx_nkhw* pt)
{
std::string out("TUN_");
out += std::to_string(pt->BlockSize) + "_";
out += std::to_string(pt->MPerBlock) + "x" + std::to_string(pt->NPerBlock) + "x" +
std::to_string(pt->KPerBlock) + "_";
out += std::to_string(pt->MPerWave) + "x" + std::to_string(pt->NPerWave) + "x" +
std::to_string(pt->MRepeat) + "x" + std::to_string(pt->NRepeat) + "x" +
std::to_string(pt->K1) + "_";
out += std::to_string(pt->ABlockTransferThreadSliceLengths_K0_M_K1[0]) + "x" +
std::to_string(pt->ABlockTransferThreadSliceLengths_K0_M_K1[1]) + "x" +
std::to_string(pt->ABlockTransferThreadSliceLengths_K0_M_K1[2]) + "_";
out += std::to_string(pt->ABlockTransferThreadClusterLengths_K0_M_K1[0]) + "x" +
std::to_string(pt->ABlockTransferThreadClusterLengths_K0_M_K1[1]) + "x" +
std::to_string(pt->ABlockTransferThreadClusterLengths_K0_M_K1[2]) + "_";
out += std::to_string(pt->ABlockTransferThreadClusterArrangeOrder[0]) + "x" +
std::to_string(pt->ABlockTransferThreadClusterArrangeOrder[1]) + "x" +
std::to_string(pt->ABlockTransferThreadClusterArrangeOrder[2]) + "_";
out += std::to_string(pt->ABlockTransferSrcAccessOrder[0]) + "x" +
std::to_string(pt->ABlockTransferSrcAccessOrder[1]) + "x" +
std::to_string(pt->ABlockTransferSrcAccessOrder[2]) + "_";
out += std::to_string(pt->ABlockTransferSrcVectorDim) + "_";
out += std::to_string(pt->ABlockTransferSrcScalarPerVector) + "_";
out += std::to_string(pt->ABlockTransferDstScalarPerVector_K1) + "_";
out += std::to_string(pt->AThreadTransferSrcResetCoordinateAfterRun) + "_";
out += std::to_string(pt->BBlockTransferThreadSliceLengths_K0_N_K1[0]) + "x" +
std::to_string(pt->BBlockTransferThreadSliceLengths_K0_N_K1[1]) + "x" +
std::to_string(pt->BBlockTransferThreadSliceLengths_K0_N_K1[2]) + "_";
out += std::to_string(pt->BBlockTransferThreadClusterLengths_K0_N_K1[0]) + "x" +
std::to_string(pt->BBlockTransferThreadClusterLengths_K0_N_K1[1]) + "x" +
std::to_string(pt->BBlockTransferThreadClusterLengths_K0_N_K1[2]) + "_";
out += std::to_string(pt->BBlockTransferThreadClusterArrangeOrder[0]) + "x" +
std::to_string(pt->BBlockTransferThreadClusterArrangeOrder[1]) + "x" +
std::to_string(pt->BBlockTransferThreadClusterArrangeOrder[2]) + "_";
out += std::to_string(pt->BBlockTransferSrcAccessOrder[0]) + "x" +
std::to_string(pt->BBlockTransferSrcAccessOrder[1]) + "x" +
std::to_string(pt->BBlockTransferSrcAccessOrder[2]) + "_";
out += std::to_string(pt->BBlockTransferSrcVectorDim) + "_";
out += std::to_string(pt->BBlockTransferSrcScalarPerVector) + "_";
out += std::to_string(pt->BBlockTransferDstScalarPerVector_K1) + "_";
out += std::to_string(pt->BThreadTransferSrcResetCoordinateAfterRun) + "_";
out += std::to_string(pt->CThreadTransferSrcDstAccessOrder[0]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[1]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[2]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[3]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[4]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[5]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[6]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[7]) + "_";
out += std::to_string(pt->CThreadTransferSrcDstVectorDim) + "_";
out += std::to_string(pt->CThreadTransferDstScalarPerVector);
return (out);
};
template <typename TInWei, typename TAcc, typename TOut>
static std::string get_definition_string_from_types()
{
using namespace ck;
std::string out;
out +=
" -DCK_PARAM_ABDataTypeEnum=" + std::to_string(get_datatype_enum_from_type<TInWei>::value) +
" -DCK_PARAM_AccDataTypeEnum=" + std::to_string(get_datatype_enum_from_type<TAcc>::value) +
" -DCK_PARAM_CDataTypeEnum=" + std::to_string(get_datatype_enum_from_type<TOut>::value);
return (out);
};
static std::string
get_definition_string_from_tunable(const tunable_dyn_conv_fwd_v4r4_xdlops_nchw_kcyx_nkhw* pt)
{
std::string out;
out += " -DCK_PARAM_BlockSize=" + std::to_string(pt->BlockSize);
out += " -DCK_PARAM_MPerBlock=" + std::to_string(pt->MPerBlock) +
" -DCK_PARAM_NPerBlock=" + std::to_string(pt->NPerBlock) +
" -DCK_PARAM_KPerBlock=" + std::to_string(pt->KPerBlock);
out += " -DCK_PARAM_MPerWave=" + std::to_string(pt->MPerWave) +
" -DCK_PARAM_NPerWave=" + std::to_string(pt->NPerWave) +
" -DCK_PARAM_K1=" + std::to_string(pt->K1) +
" -DCK_PARAM_MRepeat=" + std::to_string(pt->MRepeat) +
" -DCK_PARAM_NRepeat=" + std::to_string(pt->NRepeat);
out += " -DCK_PARAM_ABlockTransferThreadSliceLengths_K0_M_K1=" +
std::to_string(pt->ABlockTransferThreadSliceLengths_K0_M_K1[0]) + "," +
std::to_string(pt->ABlockTransferThreadSliceLengths_K0_M_K1[1]) + "," +
std::to_string(pt->ABlockTransferThreadSliceLengths_K0_M_K1[2]);
out += " -DCK_PARAM_ABlockTransferThreadClusterLengths_K0_M_K1=" +
std::to_string(pt->ABlockTransferThreadClusterLengths_K0_M_K1[0]) + "," +
std::to_string(pt->ABlockTransferThreadClusterLengths_K0_M_K1[1]) + "," +
std::to_string(pt->ABlockTransferThreadClusterLengths_K0_M_K1[2]);
out += " -DCK_PARAM_ABlockTransferThreadClusterArrangeOrder=" +
std::to_string(pt->ABlockTransferThreadClusterArrangeOrder[0]) + "," +
std::to_string(pt->ABlockTransferThreadClusterArrangeOrder[1]) + "," +
std::to_string(pt->ABlockTransferThreadClusterArrangeOrder[2]);
out += " -DCK_PARAM_ABlockTransferSrcAccessOrder=" +
std::to_string(pt->ABlockTransferSrcAccessOrder[0]) + "," +
std::to_string(pt->ABlockTransferSrcAccessOrder[1]) + "," +
std::to_string(pt->ABlockTransferSrcAccessOrder[2]);
out +=
" -DCK_PARAM_ABlockTransferSrcVectorDim=" + std::to_string(pt->ABlockTransferSrcVectorDim);
out += " -DCK_PARAM_ABlockTransferSrcScalarPerVector=" +
std::to_string(pt->ABlockTransferSrcScalarPerVector);
out += " -DCK_PARAM_ABlockTransferDstScalarPerVector_K1=" +
std::to_string(pt->ABlockTransferDstScalarPerVector_K1);
out += " -DCK_PARAM_AThreadTransferSrcResetCoordinateAfterRun=" +
std::to_string(pt->AThreadTransferSrcResetCoordinateAfterRun);
out += " -DCK_PARAM_BBlockTransferThreadSliceLengths_K0_N_K1=" +
std::to_string(pt->BBlockTransferThreadSliceLengths_K0_N_K1[0]) + "," +
std::to_string(pt->BBlockTransferThreadSliceLengths_K0_N_K1[1]) + "," +
std::to_string(pt->BBlockTransferThreadSliceLengths_K0_N_K1[2]);
out += " -DCK_PARAM_BBlockTransferThreadClusterLengths_K0_N_K1=" +
std::to_string(pt->BBlockTransferThreadClusterLengths_K0_N_K1[0]) + "," +
std::to_string(pt->BBlockTransferThreadClusterLengths_K0_N_K1[1]) + "," +
std::to_string(pt->BBlockTransferThreadClusterLengths_K0_N_K1[2]);
out += " -DCK_PARAM_BBlockTransferThreadClusterArrangeOrder=" +
std::to_string(pt->BBlockTransferThreadClusterArrangeOrder[0]) + "," +
std::to_string(pt->BBlockTransferThreadClusterArrangeOrder[1]) + "," +
std::to_string(pt->BBlockTransferThreadClusterArrangeOrder[2]);
out += " -DCK_PARAM_BBlockTransferSrcAccessOrder=" +
std::to_string(pt->BBlockTransferSrcAccessOrder[0]) + "," +
std::to_string(pt->BBlockTransferSrcAccessOrder[1]) + "," +
std::to_string(pt->BBlockTransferSrcAccessOrder[2]);
out +=
" -DCK_PARAM_BBlockTransferSrcVectorDim=" + std::to_string(pt->BBlockTransferSrcVectorDim);
out += " -DCK_PARAM_BBlockTransferSrcScalarPerVector=" +
std::to_string(pt->BBlockTransferSrcScalarPerVector);
out += " -DCK_PARAM_BBlockTransferDstScalarPerVector_K1=" +
std::to_string(pt->BBlockTransferDstScalarPerVector_K1);
out += " -DCK_PARAM_BThreadTransferSrcResetCoordinateAfterRun=" +
std::to_string(pt->BThreadTransferSrcResetCoordinateAfterRun);
out += " -DCK_PARAM_CThreadTransferSrcDstAccessOrder=" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[0]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[1]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[2]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[3]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[4]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[5]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[6]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[7]);
out += " -DCK_PARAM_CThreadTransferSrcDstVectorDim=" +
std::to_string(pt->CThreadTransferSrcDstVectorDim);
out += " -DCK_PARAM_CThreadTransferDstScalarPerVector=" +
std::to_string(pt->CThreadTransferDstScalarPerVector);
return (out);
};
} // namespace detail_dyn_conv_fwd_v4r4_xdlops_nchw_kcyx_nkhw
template <typename TInWei,
typename TAcc,
typename TOut,
typename InLengths,
typename WeiLengths,
typename OutLengths,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
void online_device_dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nchw_kcyx_nkhw(
online_compile::Handle* handle,
const InLengths& in_n_c_hi_wi_lengths,
const WeiLengths& wei_k_c_y_x_lengths,
const OutLengths& out_n_k_ho_wo_lengths,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads,
const Tensor<TInWei>& in_n_c_hi_wi,
const Tensor<TInWei>& wei_k_c_y_x,
Tensor<TOut>& out_n_k_ho_wo,
const tunable_dyn_conv_fwd_v4r4_xdlops_nchw_kcyx_nkhw* tunable,
ck::index_t nrepeat)
{
using namespace ck;
using namespace ck_driver;
using namespace detail_dyn_conv_fwd_v4r4_xdlops_nchw_kcyx_nkhw;
using size_t = std::size_t;
/////////////////////////////////////////////////////////////////////////////////////////////////////////////
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
const auto in_n_c_hi_wi_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(in_n_c_hi_wi_lengths);
const auto wei_k_c_y_x_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(wei_k_c_y_x_lengths);
const auto out_n_k_ho_wo_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(out_n_k_ho_wo_lengths);
const auto n = in_n_c_hi_wi_desc.GetLength(I0);
const auto c = in_n_c_hi_wi_desc.GetLength(I1);
const auto hi = in_n_c_hi_wi_desc.GetLength(I2);
const auto wi = in_n_c_hi_wi_desc.GetLength(I3);
const auto k = wei_k_c_y_x_desc.GetLength(I0);
const auto y = wei_k_c_y_x_desc.GetLength(I2);
const auto x = wei_k_c_y_x_desc.GetLength(I3);
const auto ho = out_n_k_ho_wo_desc.GetLength(I2);
const auto wo = out_n_k_ho_wo_desc.GetLength(I3);
const auto M = k;
const auto N = n * ho * wo;
const auto K = c * y * x;
const auto K0 = K / tunable->K1;
const index_t grid_size = (M / tunable->MPerBlock) * (N / tunable->NPerBlock);
/////////////////////////////////////////////////////////////////////////////////////////////////////////////
// these buffers are usually provided by the user application
DeviceMem in_n_c_hi_wi_dev_buf(sizeof(TInWei) * in_n_c_hi_wi.mDesc.GetElementSpace());
DeviceMem wei_k_c_y_x_dev_buf(sizeof(TInWei) * wei_k_c_y_x.mDesc.GetElementSpace());
DeviceMem out_n_k_ho_wo_dev_buf(sizeof(TOut) * out_n_k_ho_wo.mDesc.GetElementSpace());
in_n_c_hi_wi_dev_buf.ToDevice(in_n_c_hi_wi.mData.data());
wei_k_c_y_x_dev_buf.ToDevice(wei_k_c_y_x.mData.data());
out_n_k_ho_wo_dev_buf.ToDevice(out_n_k_ho_wo.mData.data());
// these are workspace buffers that should be expressed to the user by the corresponding
// workspace API
DeviceMem workspace_buf(4096);
void* a_k_m0_m1_grid_desc_dev_buf = workspace_buf.GetDeviceBuffer();
void* b_k_n0_n1_grid_desc_dev_buf =
static_cast<void*>(static_cast<unsigned char*>(workspace_buf.GetDeviceBuffer()) + 1024);
void* c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf =
static_cast<void*>(static_cast<unsigned char*>(workspace_buf.GetDeviceBuffer()) + 2048);
void* c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf =
static_cast<void*>(static_cast<unsigned char*>(workspace_buf.GetDeviceBuffer()) + 3072);
const std::vector<size_t> vld = {static_cast<size_t>(tunable->BlockSize), 1, 1};
const std::vector<size_t> vgd1 = {static_cast<size_t>(tunable->BlockSize), 1, 1};
const std::vector<size_t> vgd2 = {static_cast<size_t>(grid_size * tunable->BlockSize), 1, 1};
std::string program_name =
"dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nchw_kcyx_nkhw.cpp";
std::string algo_name = "implicit_gemm_conv_fwd_v4r4_xdlops_nchw";
std::string param = " -std=c++17 ";
std::string network_config;
param += get_definition_string_from_types<TInWei, TAcc, TOut>() + " " + " -DCK_USE_AMD_XDLOPS" +
get_definition_string_from_tunable(tunable);
network_config = get_network_config_string_from_types<TInWei, TAcc, TOut>() + "_" +
get_network_config_string_from_tunable(tunable);
std::vector<float> kernel1_times;
std::vector<float> kernel2_times;
for(index_t i = 0; i < nrepeat; ++i)
{
KernelTimer timer1, timer2;
std::string kernel_name;
kernel_name =
"dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nchw_kcyx_nkhw_prepare";
auto network_config_1 = network_config + "_1";
timer1.Start();
handle->AddKernel(algo_name, network_config_1, program_name, kernel_name, vld, vgd1, param)(
static_cast<index_t>(in_n_c_hi_wi_lengths[I0]),
static_cast<index_t>(in_n_c_hi_wi_lengths[I1]),
static_cast<index_t>(in_n_c_hi_wi_lengths[I2]),
static_cast<index_t>(in_n_c_hi_wi_lengths[I3]),
static_cast<index_t>(wei_k_c_y_x_lengths[I0]),
static_cast<index_t>(wei_k_c_y_x_lengths[I2]),
static_cast<index_t>(wei_k_c_y_x_lengths[I3]),
conv_strides[I0],
conv_strides[I1],
conv_dilations[I0],
conv_dilations[I1],
in_left_pads[I0],
in_left_pads[I1],
in_right_pads[I0],
in_right_pads[I1],
a_k_m0_m1_grid_desc_dev_buf,
b_k_n0_n1_grid_desc_dev_buf,
c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf,
c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf);
timer1.End();
kernel_name = "dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nchw_kcyx_nkhw";
auto network_config_2 = network_config + "_2";
timer2.Start();
handle->AddKernel(algo_name, network_config_2, program_name, kernel_name, vld, vgd2, param)(
reinterpret_cast<const TInWei*>(wei_k_c_y_x_dev_buf.GetDeviceBuffer()),
reinterpret_cast<const TInWei*>(in_n_c_hi_wi_dev_buf.GetDeviceBuffer()),
reinterpret_cast<TOut*>(out_n_k_ho_wo_dev_buf.GetDeviceBuffer()),
(const void*)(a_k_m0_m1_grid_desc_dev_buf),
(const void*)(b_k_n0_n1_grid_desc_dev_buf),
(const void*)(c_m0_m10_m11_n0_n10_n11_grid_desc_dev_buf),
(const void*)(c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf));
timer2.End();
kernel1_times.push_back(timer1.GetElapsedTime());
kernel2_times.push_back(timer2.GetElapsedTime());
}
{
auto ave_time1 =
std::accumulate(
std::next(kernel1_times.begin()), kernel1_times.end(), 0., std::plus<float>{}) /
(nrepeat - 1);
auto ave_time2 =
std::accumulate(
std::next(kernel2_times.begin()), kernel2_times.end(), 0., std::plus<float>{}) /
(nrepeat - 1);
const auto N = in_n_c_hi_wi_lengths[I0];
const auto C = in_n_c_hi_wi_lengths[I1];
const auto K = out_n_k_ho_wo_lengths[I1];
const auto Ho = out_n_k_ho_wo_lengths[I2];
const auto Wo = out_n_k_ho_wo_lengths[I3];
const auto Y = wei_k_c_y_x_lengths[I2];
const auto X = wei_k_c_y_x_lengths[I3];
float perf = (float)(std::size_t(2) * N * K * Ho * Wo * C * Y * X) /
(std::size_t(1000) * 1000 * 1000) / (ave_time1 + ave_time2);
std::cout << "Average time : " << ave_time1 + ave_time2 << " ms(" << ave_time1 << ", "
<< ave_time2 << "), " << perf << " TFlop/s" << std::endl;
};
// copy result back to host
out_n_k_ho_wo_dev_buf.FromDevice(out_n_k_ho_wo.mData.data());
}

389
host/driver_online/include/online_device_dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nhwc_kyxc_nhwk.hpp
View File

@@ -1,389 +0,0 @@
#include "device.hpp"
#include "host_tensor.hpp"
#include "handle.hpp"
#include "online_driver_common.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "transform_forward_convolution_into_gemm_v4r4r4_nhwc_kyxc_nhwk.hpp"
#include "conv_tunable_fwd_v4r4_xdlops_nhwc_kyxc_nhwk.hpp"
namespace detail_dyn_conv_fwd_v4r4_xdlops_nhwc_kyxc_nhwk {
template <typename TInWei, typename TAcc, typename TOut>
static std::string get_network_config_string_from_types()
{
using namespace ck;
std::string out;
out += std::to_string(get_datatype_enum_from_type<TInWei>::value) + "_" +
std::to_string(get_datatype_enum_from_type<TAcc>::value) + "_" +
std::to_string(get_datatype_enum_from_type<TOut>::value);
return (out);
};
static std::string
get_network_config_string_from_tunable(const tunable_dyn_conv_fwd_v4r4_xdlops_nhwc_kyxc_nhwk* pt)
{
std::string out("TUN_");
out += std::to_string(pt->BlockSize) + "_";
out += std::to_string(pt->MPerBlock) + "x" + std::to_string(pt->NPerBlock) + "x" +
std::to_string(pt->KPerBlock) + "_";
out += std::to_string(pt->MPerWave) + "x" + std::to_string(pt->NPerWave) + "x" +
std::to_string(pt->MRepeat) + "x" + std::to_string(pt->NRepeat) + "x" +
std::to_string(pt->K1) + "_";
out += std::to_string(pt->ABlockTransferThreadSliceLengths_K0_M_K1[0]) + "x" +
std::to_string(pt->ABlockTransferThreadSliceLengths_K0_M_K1[1]) + "x" +
std::to_string(pt->ABlockTransferThreadSliceLengths_K0_M_K1[2]) + "_";
out += std::to_string(pt->ABlockTransferThreadClusterLengths_K0_M_K1[0]) + "x" +
std::to_string(pt->ABlockTransferThreadClusterLengths_K0_M_K1[1]) + "x" +
std::to_string(pt->ABlockTransferThreadClusterLengths_K0_M_K1[2]) + "_";
out += std::to_string(pt->ABlockTransferThreadClusterArrangeOrder[0]) + "x" +
std::to_string(pt->ABlockTransferThreadClusterArrangeOrder[1]) + "x" +
std::to_string(pt->ABlockTransferThreadClusterArrangeOrder[2]) + "_";
out += std::to_string(pt->ABlockTransferSrcAccessOrder[0]) + "x" +
std::to_string(pt->ABlockTransferSrcAccessOrder[1]) + "x" +
std::to_string(pt->ABlockTransferSrcAccessOrder[2]) + "_";
out += std::to_string(pt->ABlockTransferSrcVectorDim) + "_";
out += std::to_string(pt->ABlockTransferSrcScalarPerVector) + "_";
out += std::to_string(pt->ABlockTransferDstScalarPerVector_K1) + "_";
out += std::to_string(pt->AThreadTransferSrcResetCoordinateAfterRun) + "_";
out += std::to_string(pt->BBlockTransferThreadSliceLengths_K0_N_K1[0]) + "x" +
std::to_string(pt->BBlockTransferThreadSliceLengths_K0_N_K1[1]) + "x" +
std::to_string(pt->BBlockTransferThreadSliceLengths_K0_N_K1[2]) + "_";
out += std::to_string(pt->BBlockTransferThreadClusterLengths_K0_N_K1[0]) + "x" +
std::to_string(pt->BBlockTransferThreadClusterLengths_K0_N_K1[1]) + "x" +
std::to_string(pt->BBlockTransferThreadClusterLengths_K0_N_K1[2]) + "_";
out += std::to_string(pt->BBlockTransferThreadClusterArrangeOrder[0]) + "x" +
std::to_string(pt->BBlockTransferThreadClusterArrangeOrder[1]) + "x" +
std::to_string(pt->BBlockTransferThreadClusterArrangeOrder[2]) + "_";
out += std::to_string(pt->BBlockTransferSrcAccessOrder[0]) + "x" +
std::to_string(pt->BBlockTransferSrcAccessOrder[1]) + "x" +
std::to_string(pt->BBlockTransferSrcAccessOrder[2]) + "_";
out += std::to_string(pt->BBlockTransferSrcVectorDim) + "_";
out += std::to_string(pt->BBlockTransferSrcScalarPerVector) + "_";
out += std::to_string(pt->BBlockTransferDstScalarPerVector_K1) + "_";
out += std::to_string(pt->BThreadTransferSrcResetCoordinateAfterRun) + "_";
out += std::to_string(pt->CThreadTransferSrcDstAccessOrder[0]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[1]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[2]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[3]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[4]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[5]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[6]) + "x" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[7]) + "_";
out += std::to_string(pt->CThreadTransferSrcDstVectorDim) + "_";
out += std::to_string(pt->CThreadTransferDstScalarPerVector);
return (out);
};
template <typename TInWei, typename TAcc, typename TOut>
static std::string get_definition_string_from_types()
{
using namespace ck;
std::string out;
out +=
" -DCK_PARAM_ABDataTypeEnum=" + std::to_string(get_datatype_enum_from_type<TInWei>::value) +
" -DCK_PARAM_AccDataTypeEnum=" + std::to_string(get_datatype_enum_from_type<TAcc>::value) +
" -DCK_PARAM_CDataTypeEnum=" + std::to_string(get_datatype_enum_from_type<TOut>::value);
return (out);
};
static std::string
get_definition_string_from_tunable(const tunable_dyn_conv_fwd_v4r4_xdlops_nhwc_kyxc_nhwk* pt)
{
std::string out;
out += " -DCK_PARAM_BlockSize=" + std::to_string(pt->BlockSize);
out += " -DCK_PARAM_MPerBlock=" + std::to_string(pt->MPerBlock) +
" -DCK_PARAM_NPerBlock=" + std::to_string(pt->NPerBlock) +
" -DCK_PARAM_KPerBlock=" + std::to_string(pt->KPerBlock);
out += " -DCK_PARAM_MPerWave=" + std::to_string(pt->MPerWave) +
" -DCK_PARAM_NPerWave=" + std::to_string(pt->NPerWave) +
" -DCK_PARAM_K1=" + std::to_string(pt->K1) +
" -DCK_PARAM_MRepeat=" + std::to_string(pt->MRepeat) +
" -DCK_PARAM_NRepeat=" + std::to_string(pt->NRepeat);
out += " -DCK_PARAM_ABlockTransferThreadSliceLengths_K0_M_K1=" +
std::to_string(pt->ABlockTransferThreadSliceLengths_K0_M_K1[0]) + "," +
std::to_string(pt->ABlockTransferThreadSliceLengths_K0_M_K1[1]) + "," +
std::to_string(pt->ABlockTransferThreadSliceLengths_K0_M_K1[2]);
out += " -DCK_PARAM_ABlockTransferThreadClusterLengths_K0_M_K1=" +
std::to_string(pt->ABlockTransferThreadClusterLengths_K0_M_K1[0]) + "," +
std::to_string(pt->ABlockTransferThreadClusterLengths_K0_M_K1[1]) + "," +
std::to_string(pt->ABlockTransferThreadClusterLengths_K0_M_K1[2]);
out += " -DCK_PARAM_ABlockTransferThreadClusterArrangeOrder=" +
std::to_string(pt->ABlockTransferThreadClusterArrangeOrder[0]) + "," +
std::to_string(pt->ABlockTransferThreadClusterArrangeOrder[1]) + "," +
std::to_string(pt->ABlockTransferThreadClusterArrangeOrder[2]);
out += " -DCK_PARAM_ABlockTransferSrcAccessOrder=" +
std::to_string(pt->ABlockTransferSrcAccessOrder[0]) + "," +
std::to_string(pt->ABlockTransferSrcAccessOrder[1]) + "," +
std::to_string(pt->ABlockTransferSrcAccessOrder[2]);
out +=
" -DCK_PARAM_ABlockTransferSrcVectorDim=" + std::to_string(pt->ABlockTransferSrcVectorDim);
out += " -DCK_PARAM_ABlockTransferSrcScalarPerVector=" +
std::to_string(pt->ABlockTransferSrcScalarPerVector);
out += " -DCK_PARAM_ABlockTransferDstScalarPerVector_K1=" +
std::to_string(pt->ABlockTransferDstScalarPerVector_K1);
out += " -DCK_PARAM_AThreadTransferSrcResetCoordinateAfterRun=" +
std::to_string(pt->AThreadTransferSrcResetCoordinateAfterRun);
out += " -DCK_PARAM_BBlockTransferThreadSliceLengths_K0_N_K1=" +
std::to_string(pt->BBlockTransferThreadSliceLengths_K0_N_K1[0]) + "," +
std::to_string(pt->BBlockTransferThreadSliceLengths_K0_N_K1[1]) + "," +
std::to_string(pt->BBlockTransferThreadSliceLengths_K0_N_K1[2]);
out += " -DCK_PARAM_BBlockTransferThreadClusterLengths_K0_N_K1=" +
std::to_string(pt->BBlockTransferThreadClusterLengths_K0_N_K1[0]) + "," +
std::to_string(pt->BBlockTransferThreadClusterLengths_K0_N_K1[1]) + "," +
std::to_string(pt->BBlockTransferThreadClusterLengths_K0_N_K1[2]);
out += " -DCK_PARAM_BBlockTransferThreadClusterArrangeOrder=" +
std::to_string(pt->BBlockTransferThreadClusterArrangeOrder[0]) + "," +
std::to_string(pt->BBlockTransferThreadClusterArrangeOrder[1]) + "," +
std::to_string(pt->BBlockTransferThreadClusterArrangeOrder[2]);
out += " -DCK_PARAM_BBlockTransferSrcAccessOrder=" +
std::to_string(pt->BBlockTransferSrcAccessOrder[0]) + "," +
std::to_string(pt->BBlockTransferSrcAccessOrder[1]) + "," +
std::to_string(pt->BBlockTransferSrcAccessOrder[2]);
out +=
" -DCK_PARAM_BBlockTransferSrcVectorDim=" + std::to_string(pt->BBlockTransferSrcVectorDim);
out += " -DCK_PARAM_BBlockTransferSrcScalarPerVector=" +
std::to_string(pt->BBlockTransferSrcScalarPerVector);
out += " -DCK_PARAM_BBlockTransferDstScalarPerVector_K1=" +
std::to_string(pt->BBlockTransferDstScalarPerVector_K1);
out += " -DCK_PARAM_BThreadTransferSrcResetCoordinateAfterRun=" +
std::to_string(pt->BThreadTransferSrcResetCoordinateAfterRun);
out += " -DCK_PARAM_CThreadTransferSrcDstAccessOrder=" +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[0]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[1]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[2]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[3]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[4]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[5]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[6]) + "," +
std::to_string(pt->CThreadTransferSrcDstAccessOrder[7]);
out += " -DCK_PARAM_CThreadTransferSrcDstVectorDim=" +
std::to_string(pt->CThreadTransferSrcDstVectorDim);
out += " -DCK_PARAM_CThreadTransferDstScalarPerVector=" +
std::to_string(pt->CThreadTransferDstScalarPerVector);
return (out);
};
} // namespace detail_dyn_conv_fwd_v4r4_xdlops_nhwc_kyxc_nhwk
template <typename TInWei,
typename TAcc,
typename TOut,
typename InLengths,
typename WeiLengths,
typename OutLengths,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
void online_device_dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nhwc_kyxc_nhwk(
online_compile::Handle* handle,
const InLengths& in_n_hi_wi_c_lengths,
const WeiLengths& wei_k_y_x_c_lengths,
const OutLengths& out_n_ho_wo_k_lengths,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads,
const Tensor<TInWei>& in_n_hi_wi_c,
const Tensor<TInWei>& wei_k_y_x_c,
Tensor<TOut>& out_n_ho_wo_k,
const tunable_dyn_conv_fwd_v4r4_xdlops_nhwc_kyxc_nhwk* tunable,
ck::index_t nrepeat)
{
using namespace ck;
using namespace detail_dyn_conv_fwd_v4r4_xdlops_nhwc_kyxc_nhwk;
using size_t = std::size_t;
/////////////////////////////////////////////////////////////////////////////////////////////////////////////
// The follow codes are only used for computing the grid_size, hasMainKBlockLoop,
// hasDoubleTailKBlockLoop
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
const auto in_n_hi_wi_c_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(in_n_hi_wi_c_lengths);
const auto wei_k_y_x_c_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(wei_k_y_x_c_lengths);
const auto out_n_ho_wo_k_desc =
make_dynamic_naive_tensor_descriptor_packed_v2(out_n_ho_wo_k_lengths);
const auto n = in_n_hi_wi_c_desc.GetLength(I0);
const auto hi = in_n_hi_wi_c_desc.GetLength(I1);
const auto wi = in_n_hi_wi_c_desc.GetLength(I2);
const auto c = in_n_hi_wi_c_desc.GetLength(I3);
const auto k = wei_k_y_x_c_desc.GetLength(I0);
const auto y = wei_k_y_x_c_desc.GetLength(I1);
const auto x = wei_k_y_x_c_desc.GetLength(I2);
const auto ho = out_n_ho_wo_k_desc.GetLength(I1);
const auto wo = out_n_ho_wo_k_desc.GetLength(I2);
const auto M = k;
const auto N = n * ho * wo;
const auto K = c * y * x;
const auto K0 = K / tunable->K1;
const index_t grid_size = (M / tunable->MPerBlock) * (N / tunable->NPerBlock);
// these buffers are usually provided by the user application
DeviceMem in_n_hi_wi_c_dev_buf(sizeof(TInWei) * in_n_hi_wi_c.mDesc.GetElementSpace());
DeviceMem wei_k_y_x_c_dev_buf(sizeof(TInWei) * wei_k_y_x_c.mDesc.GetElementSpace());
DeviceMem out_n_ho_wo_k_dev_buf(sizeof(TOut) * out_n_ho_wo_k.mDesc.GetElementSpace());
in_n_hi_wi_c_dev_buf.ToDevice(in_n_hi_wi_c.mData.data());
wei_k_y_x_c_dev_buf.ToDevice(wei_k_y_x_c.mData.data());
out_n_ho_wo_k_dev_buf.ToDevice(out_n_ho_wo_k.mData.data());
// these are workspace buffers that should be expressed to the user by the corresponding
// workspace API
DeviceMem workspace_buf(4096);
void* a_k0_m_k1_grid_desc_dev_buf = workspace_buf.GetDeviceBuffer();
void* b_k0_n_k1_grid_desc_dev_buf =
static_cast<void*>(static_cast<unsigned char*>(workspace_buf.GetDeviceBuffer()) + 1024);
void* c_m0_m1_m2_n_grid_desc_dev_buf =
static_cast<void*>(static_cast<unsigned char*>(workspace_buf.GetDeviceBuffer()) + 2048);
void* c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf =
static_cast<void*>(static_cast<unsigned char*>(workspace_buf.GetDeviceBuffer()) + 3072);
const std::vector<size_t> vld = {static_cast<size_t>(tunable->BlockSize), 1, 1};
const std::vector<size_t> vgd1 = {static_cast<size_t>(tunable->BlockSize), 1, 1};
const std::vector<size_t> vgd2 = {static_cast<size_t>(grid_size * tunable->BlockSize), 1, 1};
std::string program_name =
"dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nhwc_kyxc_nhwk.cpp";
std::string algo_name = "implicit_gemm_conv_fwd_v4r4_xdlops_nhwc";
std::string param = " -std=c++17 ";
std::string network_config;
param += get_definition_string_from_types<TInWei, TAcc, TOut>() + " -DCK_USE_AMD_XDLOPS ";
param += get_definition_string_from_tunable(tunable);
network_config = get_network_config_string_from_types<TInWei, TAcc, TOut>() + "_" +
get_network_config_string_from_tunable(tunable);
std::vector<float> kernel1_times;
std::vector<float> kernel2_times;
for(index_t i = 0; i < nrepeat; ++i)
{
KernelTimer timer1, timer2;
std::string kernel_name;
kernel_name =
"dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nhwc_kyxc_nhwk_prepare";
auto network_config_1 = network_config + "_1";
timer1.Start();
handle->AddKernel(algo_name, network_config_1, program_name, kernel_name, vld, vgd1, param)(
static_cast<index_t>(in_n_hi_wi_c_lengths[I0]),
static_cast<index_t>(in_n_hi_wi_c_lengths[I1]),
static_cast<index_t>(in_n_hi_wi_c_lengths[I2]),
static_cast<index_t>(in_n_hi_wi_c_lengths[I3]),
static_cast<index_t>(wei_k_y_x_c_lengths[I0]),
static_cast<index_t>(wei_k_y_x_c_lengths[I1]),
static_cast<index_t>(wei_k_y_x_c_lengths[I2]),
conv_strides[I0],
conv_strides[I1],
conv_dilations[I0],
conv_dilations[I1],
in_left_pads[I0],
in_left_pads[I1],
in_right_pads[I0],
in_right_pads[I1],
a_k0_m_k1_grid_desc_dev_buf,
b_k0_n_k1_grid_desc_dev_buf,
c_m0_m1_m2_n_grid_desc_dev_buf,
c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf);
timer1.End();
kernel_name = "dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nhwc_kyxc_nhwk";
auto network_config_2 = network_config + "_2";
timer2.Start();
handle->AddKernel(algo_name, network_config_2, program_name, kernel_name, vld, vgd2, param)(
reinterpret_cast<const TInWei*>(in_n_hi_wi_c_dev_buf.GetDeviceBuffer()),
reinterpret_cast<const TInWei*>(wei_k_y_x_c_dev_buf.GetDeviceBuffer()),
reinterpret_cast<TOut*>(out_n_ho_wo_k_dev_buf.GetDeviceBuffer()),
(const void*)(a_k0_m_k1_grid_desc_dev_buf),
(const void*)(b_k0_n_k1_grid_desc_dev_buf),
(const void*)(c_m0_m1_m2_n_grid_desc_dev_buf),
(const void*)(c_blockid_to_m0_n0_block_cluster_adaptor_dev_buf));
timer2.End();
kernel1_times.push_back(timer1.GetElapsedTime());
kernel2_times.push_back(timer2.GetElapsedTime());
}
{
auto ave_time1 =
std::accumulate(
std::next(kernel1_times.begin()), kernel1_times.end(), 0., std::plus<float>{}) /
(nrepeat - 1);
auto ave_time2 =
std::accumulate(
std::next(kernel2_times.begin()), kernel2_times.end(), 0., std::plus<float>{}) /
(nrepeat - 1);
const auto N = in_n_hi_wi_c_lengths[I0];
const auto C = in_n_hi_wi_c_lengths[I3];
const auto Ho = out_n_ho_wo_k_lengths[I1];
const auto Wo = out_n_ho_wo_k_lengths[I2];
const auto K = out_n_ho_wo_k_lengths[I3];
const auto Y = wei_k_y_x_c_lengths[I1];
const auto X = wei_k_y_x_c_lengths[I2];
float perf = (float)(std::size_t(2) * N * K * Ho * Wo * C * Y * X) /
(std::size_t(1000) * 1000 * 1000) / ave_time2;
std::cout << "Average time : " << ave_time1 + ave_time2 << " ms(" << ave_time1 << ", "
<< ave_time2 << "), " << perf << " TFlop/s" << std::endl;
};
// copy result back to host
out_n_ho_wo_k_dev_buf.FromDevice(out_n_ho_wo_k.mData.data());
}

182
host/driver_online/include/online_device_dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw.hpp
View File

@@ -1,182 +0,0 @@
#pragma once
#include "device.hpp"
#include "host_tensor.hpp"
#include "handle.hpp"
#include "online_driver_common.hpp"
#include "convolution_problem_descriptor.hpp"
#include "dynamic_tensor_descriptor.hpp"
#include "dynamic_tensor_descriptor_helper.hpp"
#include "transform_forward_convolution_into_gemm_v6r1_nchw_kcyx_nkhw.hpp"
#include "conv_igemm_fwd_v6r1_dlops_nchw_kcyx_nkhw.hpp"
template <typename TInWei,
typename TAcc,
typename TOut,
typename InLengths,
typename WeiLengths,
typename OutLengths,
typename ConvStrides,
typename ConvDilations,
typename InLeftPads,
typename InRightPads>
void online_device_dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw(
online_compile::Handle* handle,
const InLengths& in_n_c_hi_wi_lengths,
const WeiLengths& wei_k_c_y_x_lengths,
const OutLengths& out_n_k_ho_wo_lengths,
const ConvStrides& conv_strides,
const ConvDilations& conv_dilations,
const InLeftPads& in_left_pads,
const InRightPads& in_right_pads,
const Tensor<TInWei>& in_n_c_hi_wi,
const Tensor<TInWei>& wei_k_c_y_x,
Tensor<TOut>& out_n_k_ho_wo,
const ck_driver::CompileParameterConvIgemmFwdV6r1DlopsNchwKcyxNkhw& compile_param,
ck::index_t nrepeat)
{
using namespace ck;
using namespace ck_driver;
using size_t = std::size_t;
std::cout << __func__ << std::endl;
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
ConvolutionProblemDescriptor conv_problem_desc{in_n_c_hi_wi_lengths[I0],
out_n_k_ho_wo_lengths[I1],
in_n_c_hi_wi_lengths[I1],
wei_k_c_y_x_lengths[I2],
wei_k_c_y_x_lengths[I3],
in_n_c_hi_wi_lengths[I2],
in_n_c_hi_wi_lengths[I3],
out_n_k_ho_wo_lengths[I2],
out_n_k_ho_wo_lengths[I3],
conv_strides[I0],
conv_strides[I1],
conv_dilations[I0],
conv_dilations[I1],
in_left_pads[I0],
in_left_pads[I1],
in_right_pads[I0],
in_right_pads[I1],
get_datatype_enum_from_type<TInWei>::value,
get_datatype_enum_from_type<TInWei>::value,
get_datatype_enum_from_type<TOut>::value};
if(!ConvIgemmFwdV6r1DlopsNchwKcyxNkhw::IsValidCompileParameter(conv_problem_desc,
compile_param))
{
throw std::runtime_error("wrong! IsValidCompileParameter fail");
}
DeviceMem in_n_c_hi_wi_dev_buf(sizeof(TInWei) * in_n_c_hi_wi.mDesc.GetElementSpace());
DeviceMem wei_k_c_y_x_dev_buf(sizeof(TInWei) * wei_k_c_y_x.mDesc.GetElementSpace());
DeviceMem out_n_k_ho_wo_dev_buf(sizeof(TOut) * out_n_k_ho_wo.mDesc.GetElementSpace());
in_n_c_hi_wi_dev_buf.ToDevice(in_n_c_hi_wi.mData.data());
wei_k_c_y_x_dev_buf.ToDevice(wei_k_c_y_x.mData.data());
out_n_k_ho_wo_dev_buf.ToDevice(out_n_k_ho_wo.mData.data());
// workspace is used for save transformed tensor descritpors created by prepare kernel
DeviceMem workspace_dev_buf(
ConvIgemmFwdV6r1DlopsNchwKcyxNkhw::GetWorkSpaceSize(conv_problem_desc, compile_param));
const auto block_size = std::size_t(
ConvIgemmFwdV6r1DlopsNchwKcyxNkhw::GetBlockSize(conv_problem_desc, compile_param));
const auto grid_size = std::size_t(
ConvIgemmFwdV6r1DlopsNchwKcyxNkhw::GetGridSize(conv_problem_desc, compile_param));
const std::vector<size_t> vld1 = {1, 1, 1};
const std::vector<size_t> vgd1 = {1, 1, 1};
const std::vector<size_t> vld2 = {static_cast<size_t>(block_size), 1, 1};
const std::vector<size_t> vgd2 = {static_cast<size_t>(grid_size * block_size), 1, 1};
std::string program_name =
"dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw.cpp";
std::string algo_name = "implicit_gemm_conv_fwd_v6r1_dlops_nchw";
std::string compile_param_string = get_ck_hip_online_compile_common_flag() + compile_param.GetCompileParameterString();
std::string network_config = compile_param_string;
std::vector<float> kernel1_times;
std::vector<float> kernel2_times;
for(index_t i = 0; i < nrepeat + 1; ++i)
{
KernelTimer timer1, timer2;
std::string kernel_name;
kernel_name = "dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw_prepare";
auto network_config_1 = network_config + "_1";
timer1.Start();
handle->AddKernel(algo_name,
network_config_1,
program_name,
kernel_name,
vld1,
vgd1,
compile_param_string)(static_cast<index_t>(in_n_c_hi_wi_lengths[I0]),
static_cast<index_t>(in_n_c_hi_wi_lengths[I1]),
static_cast<index_t>(in_n_c_hi_wi_lengths[I2]),
static_cast<index_t>(in_n_c_hi_wi_lengths[I3]),
static_cast<index_t>(wei_k_c_y_x_lengths[I0]),
static_cast<index_t>(wei_k_c_y_x_lengths[I2]),
static_cast<index_t>(wei_k_c_y_x_lengths[I3]),
conv_strides[I0],
conv_strides[I1],
conv_dilations[I0],
conv_dilations[I1],
in_left_pads[I0],
in_left_pads[I1],
in_right_pads[I0],
in_right_pads[I1],
(void*)(workspace_dev_buf.GetDeviceBuffer()));
timer1.End();
kernel_name = "dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw";
auto network_config_2 = network_config + "_2";
timer2.Start();
handle->AddKernel(algo_name,
network_config_2,
program_name,
kernel_name,
vld2,
vgd2,
compile_param_string)(
reinterpret_cast<const TInWei*>(wei_k_c_y_x_dev_buf.GetDeviceBuffer()),
reinterpret_cast<const TInWei*>(in_n_c_hi_wi_dev_buf.GetDeviceBuffer()),
reinterpret_cast<TOut*>(out_n_k_ho_wo_dev_buf.GetDeviceBuffer()),
(const void*)(workspace_dev_buf.GetDeviceBuffer()));
timer2.End();
kernel1_times.push_back(timer1.GetElapsedTime());
kernel2_times.push_back(timer2.GetElapsedTime());
}
{
auto ave_time1 =
std::accumulate(
std::next(kernel1_times.begin()), kernel1_times.end(), 0., std::plus<float>{}) /
nrepeat;
auto ave_time2 =
std::accumulate(
std::next(kernel2_times.begin()), kernel2_times.end(), 0., std::plus<float>{}) /
nrepeat;
float perf = (float)(conv_problem_desc.CalculateFlop()) /
(std::size_t(1000) * 1000 * 1000) / (ave_time1 + ave_time2);
std::cout << "Average time : " << ave_time1 + ave_time2 << " ms(" << ave_time1 << ", "
<< ave_time2 << "), " << perf << " TFlop/s" << std::endl;
};
// copy result back to host
out_n_k_ho_wo_dev_buf.FromDevice(out_n_k_ho_wo.mData.data());
}

2
host/host_tensor/CMakeLists.txt
View File

@@ -10,6 +10,8 @@ set(HOST_TENSOR_SOURCE
## the library target
add_library(host_tensor SHARED ${HOST_TENSOR_SOURCE})
target_include_directories(host_tensor SYSTEM PUBLIC $<BUILD_INTERFACE:${HALF_INCLUDE_DIR}>)
target_link_libraries(host_tensor PRIVATE hip::device)
target_link_libraries(host_tensor INTERFACE hip::host)

10
host/host_tensor/include/conv_common.hpp
View File

@@ -1,7 +1,7 @@
#ifndef CONV_COMMON_HPP
#define CONV_COMMON_HPP
#include "dynamic_tensor_descriptor.hpp"
#include "tensor_descriptor.hpp"
enum ConvTensorLayout
{
@@ -19,8 +19,8 @@ template <typename... InDesc,
typename LeftPads,
typename RightPads>
constexpr auto get_convolution_output_default_4d_tensor_descriptor(
const ck::DynamicTensorDescriptor<InDesc...>& in_desc,
const ck::DynamicTensorDescriptor<WeiDesc...>& wei_desc,
const ck::TensorDescriptor<InDesc...>& in_desc,
const ck::TensorDescriptor<WeiDesc...>& wei_desc,
const ConvStrides& conv_strides,
const ConvDilations conv_dilations,
const LeftPads& left_pads,
@@ -57,12 +57,12 @@ constexpr auto get_convolution_output_default_4d_tensor_descriptor(
const auto Ho = (Hi + LeftPadH + RightPadH - YEff) / conv_strides[I0] + I1;
const auto Wo = (Wi + LeftPadW + RightPadW - XEff) / conv_strides[I1] + I1;
return make_dynamic_naive_tensor_descriptor_packed_v2(make_tuple(N, K, Ho, Wo));
return make_naive_tensor_descriptor_packed(make_tuple(N, K, Ho, Wo));
}
template <class InDesc, class WeiDesc, class OutDesc>
constexpr std::size_t
calculate_convolution_flops(const InDesc& in_desc, const WeiDesc& wei_desc, const OutDesc& out_desc)
calculate_convolution_flops(const InDesc&, const WeiDesc& wei_desc, const OutDesc& out_desc)
{
using namespace ck;

20
host/host_tensor/include/device.hpp
View File

@@ -34,24 +34,16 @@ struct KernelTimer
using device_stream_t = hipStream_t;
template <typename... Args, typename F>
void launch_kernel(F kernel,
dim3 grid_dim,
dim3 block_dim,
std::size_t lds_byte,
hipStream_t stream_id,
Args... args)
void launch_kernel(F kernel, dim3 grid_dim, dim3 block_dim, std::size_t lds_byte, Args... args)
{
hipStream_t stream_id = nullptr;
hipLaunchKernelGGL(kernel, grid_dim, block_dim, lds_byte, stream_id, args...);
}
template <typename... Args, typename F>
float launch_and_time_kernel(F kernel,
int nrepeat,
dim3 grid_dim,
dim3 block_dim,
std::size_t lds_byte,
hipStream_t stream_id,
Args... args)
float launch_and_time_kernel(
F kernel, int nrepeat, dim3 grid_dim, dim3 block_dim, std::size_t lds_byte, Args... args)
{
KernelTimer timer;
@@ -66,6 +58,8 @@ float launch_and_time_kernel(F kernel,
printf("Warm up\n");
hipStream_t stream_id = nullptr;
// warm up
hipLaunchKernelGGL(kernel, grid_dim, block_dim, lds_byte, stream_id, args...);

Some files were not shown because too many files have changed in this diff Show More