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https://github.com/ggml-org/llama.cpp.git
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1 Commits
dev-metal
...
0cc4m/vulk
| Author | SHA1 | Date | |
|---|---|---|---|
|
037397792a |
@@ -341,6 +341,9 @@ set(GGML_PUBLIC_HEADERS
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include/gguf.h)
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set_target_properties(ggml PROPERTIES PUBLIC_HEADER "${GGML_PUBLIC_HEADERS}")
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#if (GGML_METAL)
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# set_target_properties(ggml PROPERTIES RESOURCE "${CMAKE_CURRENT_SOURCE_DIR}/src/ggml-metal.metal")
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#endif()
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install(TARGETS ggml LIBRARY PUBLIC_HEADER)
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install(TARGETS ggml-base LIBRARY)
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@@ -24,119 +24,62 @@ if (GGML_METAL_NDEBUG)
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endif()
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set(METALLIB_COMMON "${CMAKE_CURRENT_SOURCE_DIR}/../ggml-common.h")
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set(METALLIB_KERNELS_COMMON "${CMAKE_CURRENT_SOURCE_DIR}/kernels/common.h")
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set(METALLIB_KERNELS_DEQUANTIZE "${CMAKE_CURRENT_SOURCE_DIR}/kernels/dequantize.h")
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set(METALLIB_KERNELS_QUANTIZE "${CMAKE_CURRENT_SOURCE_DIR}/kernels/quantize.h")
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set(METALLIB_KERNEL_SOURCES
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kernels/fa.metal
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kernels/mul_mv.metal
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kernels/mul_mm.metal
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kernels/quantize.metal
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kernels/softmax.metal
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kernels/norm.metal
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kernels/unary.metal
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kernels/binbcast.metal
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kernels/reduce.metal
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kernels/tri.metal
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kernels/ssm.metal
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kernels/wkv.metal
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kernels/gated_delta_net.metal
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kernels/solve_tri.metal
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kernels/rope.metal
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kernels/conv.metal
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kernels/upscale.metal
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kernels/argsort.metal
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kernels/pool.metal
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kernels/misc.metal
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)
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if (GGML_METAL_EMBED_LIBRARY)
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enable_language(ASM)
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add_compile_definitions(GGML_METAL_EMBED_LIBRARY)
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set(METALLIB_IMPL "${CMAKE_CURRENT_SOURCE_DIR}/ggml-metal-impl.h")
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set(METALLIB_SOURCE "${CMAKE_CURRENT_SOURCE_DIR}/ggml-metal.metal")
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set(METALLIB_IMPL "${CMAKE_CURRENT_SOURCE_DIR}/ggml-metal-impl.h")
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file(MAKE_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}/autogenerated")
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set(METALLIB_EMBED_ASM_FILES "")
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foreach(src ${METALLIB_KERNEL_SOURCES})
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get_filename_component(kind ${src} NAME_WE)
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# symbol names must be valid C identifiers ('-' is not allowed)
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string(REPLACE "-" "_" kind_sym ${kind})
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# merge ggml-common.h and ggml-metal.metal into a single file
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set(METALLIB_EMBED_ASM "${CMAKE_CURRENT_BINARY_DIR}/autogenerated/ggml-metal-embed.s")
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set(METALLIB_SOURCE_EMBED "${CMAKE_CURRENT_BINARY_DIR}/autogenerated/ggml-metal-embed.metal")
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set(METALLIB_SOURCE_EMBED_TMP "${CMAKE_CURRENT_BINARY_DIR}/autogenerated/ggml-metal-embed.metal.tmp")
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set(SRC "${CMAKE_CURRENT_SOURCE_DIR}/kernels/${kind}.metal")
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set(EMBED "${CMAKE_CURRENT_BINARY_DIR}/autogenerated/ggml-metal-embed-${kind}.metal")
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set(ASM "${CMAKE_CURRENT_BINARY_DIR}/autogenerated/ggml-metal-embed-${kind}.s")
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add_custom_command(
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OUTPUT "${METALLIB_EMBED_ASM}"
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COMMAND echo "Embedding Metal library"
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COMMAND sed -e "/__embed_ggml-common.h__/r ${METALLIB_COMMON}" -e "/__embed_ggml-common.h__/d" < "${METALLIB_SOURCE}" > "${METALLIB_SOURCE_EMBED_TMP}"
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COMMAND sed -e "/\#include \"ggml-metal-impl.h\"/r ${METALLIB_IMPL}" -e "/\#include \"ggml-metal-impl.h\"/d" < "${METALLIB_SOURCE_EMBED_TMP}" > "${METALLIB_SOURCE_EMBED}"
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COMMAND echo ".section __DATA,__ggml_metallib" > "${METALLIB_EMBED_ASM}"
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COMMAND echo ".globl _ggml_metallib_start" >> "${METALLIB_EMBED_ASM}"
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COMMAND echo "_ggml_metallib_start:" >> "${METALLIB_EMBED_ASM}"
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COMMAND echo .incbin "\"${METALLIB_SOURCE_EMBED}\"" >> "${METALLIB_EMBED_ASM}"
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COMMAND echo ".globl _ggml_metallib_end" >> "${METALLIB_EMBED_ASM}"
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COMMAND echo "_ggml_metallib_end:" >> "${METALLIB_EMBED_ASM}"
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DEPENDS ../ggml-common.h ggml-metal.metal ggml-metal-impl.h
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COMMENT "Generate assembly for embedded Metal library"
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VERBATIM
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)
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# only prepend headers that this source actually includes
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set(HEADERS_FOR_SRC ${METALLIB_KERNELS_COMMON})
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file(STRINGS ${SRC} _has_dequantize REGEX "#include \"dequantize\\.h\"")
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file(STRINGS ${SRC} _has_quantize REGEX "#include \"quantize\\.h\"")
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if(_has_dequantize)
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list(APPEND HEADERS_FOR_SRC ${METALLIB_KERNELS_DEQUANTIZE})
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endif()
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if(_has_quantize)
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list(APPEND HEADERS_FOR_SRC ${METALLIB_KERNELS_QUANTIZE})
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endif()
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add_custom_command(
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OUTPUT "${ASM}"
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# Step 1: concatenate shared headers + this kernel source
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COMMAND cat ${HEADERS_FOR_SRC} ${SRC} > "${EMBED}.tmp1"
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# Step 2: remove internal #include and #pragma once
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COMMAND sed -e "/\#include \"common.h\"/d" -e "/\#include \"dequantize.h\"/d" -e "/\#include \"quantize.h\"/d" -e "/\#pragma once/d" < "${EMBED}.tmp1" > "${EMBED}.tmp2"
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# Step 3: inline ggml-common.h (replacing __embed_ggml-common.h__ sentinel)
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COMMAND sed -e "/__embed_ggml-common.h__/r ${METALLIB_COMMON}" -e "/__embed_ggml-common.h__/d" < "${EMBED}.tmp2" > "${EMBED}.tmp3"
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# Step 4: inline ggml-metal-impl.h
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COMMAND sed -e "/\#include \"ggml-metal-impl.h\"/r ${METALLIB_IMPL}" -e "/\#include \"ggml-metal-impl.h\"/d" < "${EMBED}.tmp3" > "${EMBED}"
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# Step 5: emit an asm chunk with kind-specific start/end symbols
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# note: '-' is illegal in C symbols, so we use kind_sym; the macOS
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# section name is limited to 16 chars so we keep it shared
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# across kinds (__ggml_metallib) and only vary the global symbols.
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COMMAND echo ".section __DATA,__ggml_metallib" > "${ASM}"
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COMMAND echo ".globl _ggml_metallib_${kind_sym}_start" >> "${ASM}"
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COMMAND echo "_ggml_metallib_${kind_sym}_start:" >> "${ASM}"
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COMMAND echo .incbin "\"${EMBED}\"" >> "${ASM}"
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COMMAND echo ".globl _ggml_metallib_${kind_sym}_end" >> "${ASM}"
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COMMAND echo "_ggml_metallib_${kind_sym}_end:" >> "${ASM}"
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DEPENDS ../ggml-common.h ggml-metal-impl.h
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kernels/common.h kernels/dequantize.h kernels/quantize.h
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kernels/${kind}.metal
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COMMENT "Generate embedded Metal library for ${kind}"
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VERBATIM
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)
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list(APPEND METALLIB_EMBED_ASM_FILES "${ASM}")
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endforeach()
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target_sources(ggml-metal PRIVATE ${METALLIB_EMBED_ASM_FILES})
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target_sources(ggml-metal PRIVATE "${METALLIB_EMBED_ASM}")
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else()
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# copy header files to bin directory
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# copy metal files to bin directory
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configure_file(../ggml-common.h ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-common.h COPYONLY)
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configure_file(ggml-metal.metal ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal COPYONLY)
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configure_file(ggml-metal-impl.h ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal-impl.h COPYONLY)
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file(MAKE_DIRECTORY "${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/kernels")
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configure_file(kernels/common.h ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/kernels/common.h COPYONLY)
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configure_file(kernels/dequantize.h ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/kernels/dequantize.h COPYONLY)
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configure_file(kernels/quantize.h ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/kernels/quantize.h COPYONLY)
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foreach(src ${METALLIB_KERNEL_SOURCES})
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configure_file(${src} ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/${src} COPYONLY)
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endforeach()
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if (GGML_METAL_SHADER_DEBUG)
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# note: disabling fast math is needed in order to pass tests/test-backend-ops
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# custom command to do the following:
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# xcrun -sdk macosx metal -fno-fast-math -c ggml-metal.metal -o ggml-metal.air
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# xcrun -sdk macosx metallib ggml-metal.air -o default.metallib
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#
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# note: this is the only way I found to disable fast-math in Metal. it's ugly, but at least it works
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# disabling fast math is needed in order to pass tests/test-backend-ops
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# note: adding -fno-inline fixes the tests when using MTL_SHADER_VALIDATION=1
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# note: unfortunately, we have to call it default.metallib instead of ggml.metallib
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# ref: https://github.com/ggml-org/whisper.cpp/issues/1720
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# note: adding -g causes segmentation fault during compile
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#set(XC_FLAGS -fno-fast-math -fno-inline -g)
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set(XC_FLAGS -fno-fast-math -fno-inline)
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else()
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set(XC_FLAGS -O3)
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endif()
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# Append macOS metal versioning flags
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if (GGML_METAL_MACOSX_VERSION_MIN)
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message(STATUS "Adding -mmacosx-version-min=${GGML_METAL_MACOSX_VERSION_MIN} flag to metal compilation")
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list (APPEND XC_FLAGS -mmacosx-version-min=${GGML_METAL_MACOSX_VERSION_MIN})
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@@ -147,46 +90,35 @@ else()
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list (APPEND XC_FLAGS -std=${GGML_METAL_STD})
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endif()
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# Compile each kernel source to .air, then link into default.metallib
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set(AIR_FILES "")
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foreach(src ${METALLIB_KERNEL_SOURCES})
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get_filename_component(name ${src} NAME_WE)
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set(AIR "${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/${name}.air")
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list(APPEND AIR_FILES ${AIR})
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add_custom_command(
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OUTPUT ${AIR}
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COMMAND xcrun -sdk macosx metal ${XC_FLAGS} -I ${CMAKE_RUNTIME_OUTPUT_DIRECTORY} -c ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/${src} -o ${AIR}
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DEPENDS ${src} kernels/common.h kernels/dequantize.h kernels/quantize.h ${METALLIB_COMMON} ggml-metal-impl.h
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COMMENT "Compiling ${src}"
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VERBATIM
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)
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endforeach()
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add_custom_command(
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OUTPUT ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
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COMMAND xcrun -sdk macosx metallib ${AIR_FILES} -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
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COMMAND xcrun -sdk macosx metal ${XC_FLAGS} -c ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal -o - |
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xcrun -sdk macosx metallib - -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
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COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-common.h
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COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal-impl.h
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COMMAND rm -rf ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/kernels
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DEPENDS ${AIR_FILES}
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COMMENT "Linking Metal kernels into default.metallib"
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)
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COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal
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DEPENDS ggml-metal.metal ${METALLIB_COMMON}
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COMMENT "Compiling Metal kernels"
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)
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# FIXME: only add to the ggml-metal target?
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add_custom_target(
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ggml-metal-lib ALL
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DEPENDS ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
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)
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)
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endif() # GGML_METAL_EMBED_LIBRARY
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if (NOT GGML_METAL_EMBED_LIBRARY)
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install(
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DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/kernels/
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DESTINATION ${CMAKE_INSTALL_BINDIR}/kernels
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FILES_MATCHING PATTERN "*.metal" PATTERN "*.h"
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)
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FILES src/ggml-metal/ggml-metal.metal
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PERMISSIONS
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OWNER_READ
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OWNER_WRITE
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GROUP_READ
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WORLD_READ
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DESTINATION ${CMAKE_INSTALL_BINDIR})
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|
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install(
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FILES ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
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DESTINATION ${CMAKE_INSTALL_BINDIR}
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)
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install(
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FILES ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
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DESTINATION ${CMAKE_INSTALL_BINDIR}
|
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)
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endif()
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@@ -94,63 +94,8 @@ int ggml_metal_pipeline_max_theads_per_threadgroup(struct ggml_metal_pipeline_wi
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return pipeline.pipeline->obj.maxTotalThreadsPerThreadgroup;
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}
|
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|
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//
|
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// MTLLibrary collection (one library per op-source, compiled separately)
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//
|
||||
|
||||
// Single source of truth for the per-kind metal libraries. The order here
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||||
// defines the enum values and every per-kind table below, so adding a library
|
||||
// is a one-line change here (plus adding its source to CMakeLists.txt).
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||||
// X(suffix, name): name is both the kernels/<name>.metal basename and the
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// ggml_metallib_<name>_{start,end} embed-symbol stem.
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#define GGML_METAL_LIBS \
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X(FA, fa) \
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X(MUL_MV, mul_mv) \
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X(MUL_MM, mul_mm) \
|
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X(QUANTIZE, quantize) \
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X(SOFTMAX, softmax) \
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X(NORM, norm) \
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X(UNARY, unary) \
|
||||
X(BINBCAST, binbcast) \
|
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X(REDUCE, reduce) \
|
||||
X(TRI, tri) \
|
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X(SSM, ssm) \
|
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X(WKV, wkv) \
|
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X(GATED_DELTA_NET, gated_delta_net)\
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X(SOLVE_TRI, solve_tri) \
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X(ROPE, rope) \
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X(CONV, conv) \
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X(UPSCALE, upscale) \
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X(ARGSORT, argsort) \
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X(POOL, pool) \
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X(MISC, misc)
|
||||
|
||||
enum ggml_metal_lib_kind {
|
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#define X(e, s) GGML_METAL_LIB_##e,
|
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GGML_METAL_LIBS
|
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#undef X
|
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GGML_METAL_LIB_COUNT,
|
||||
};
|
||||
|
||||
static const char * const k_lib_names[GGML_METAL_LIB_COUNT] = {
|
||||
#define X(e, s) [GGML_METAL_LIB_##e] = #s,
|
||||
GGML_METAL_LIBS
|
||||
#undef X
|
||||
};
|
||||
|
||||
struct ggml_metal_library {
|
||||
// Per-kind compiled libraries. When single_library is true, the whole library
|
||||
// (e.g. a pre-compiled default.metallib or a from-source build) lives at
|
||||
// objs[0] and the remaining slots are nil.
|
||||
id<MTLLibrary> objs[GGML_METAL_LIB_COUNT];
|
||||
bool single_library; // true: combined library at objs[0]; false: per-kind libs in objs[*]
|
||||
|
||||
// Routing table: kernel function name -> objs[] index, populated from each
|
||||
// compiled library's -[MTLLibrary functionNames]. The actual compiled
|
||||
// libraries are the single source of truth for which library owns a kernel,
|
||||
// so adding kernels later requires no manual routing maintenance.
|
||||
// nil in single_library mode (everything resolves to objs[0]).
|
||||
NSMutableDictionary<NSString *, NSNumber *> * fn_to_lib;
|
||||
id<MTLLibrary> obj;
|
||||
|
||||
ggml_metal_device_t dev;
|
||||
ggml_metal_pipelines_t pipelines; // cache of compiled pipelines
|
||||
@@ -158,376 +103,160 @@ struct ggml_metal_library {
|
||||
NSLock * lock;
|
||||
};
|
||||
|
||||
// Build the fn_to_lib routing table by querying each compiled library's public
|
||||
// function names. Call once after all per-kind libraries have been compiled.
|
||||
static void ggml_metal_library_build_index(ggml_metal_library_t lib) {
|
||||
@autoreleasepool {
|
||||
NSMutableDictionary<NSString *, NSNumber *> * index = [[NSMutableDictionary alloc] init];
|
||||
for (int kind = 0; kind < GGML_METAL_LIB_COUNT; ++kind) {
|
||||
for (NSString * fname in [lib->objs[kind] functionNames]) {
|
||||
index[fname] = @(kind);
|
||||
}
|
||||
}
|
||||
lib->fn_to_lib = index;
|
||||
}
|
||||
}
|
||||
ggml_metal_library_t ggml_metal_library_init(ggml_metal_device_t dev) {
|
||||
id<MTLLibrary> library = nil;
|
||||
id<MTLDevice> device = ggml_metal_device_get_obj(dev);
|
||||
|
||||
// Parse a `#include "name"` line. Returns the quoted name in *include_name on
|
||||
// success. Whitespace-tolerant; ignores `#include <...>` (system headers).
|
||||
static bool ggml_metal_library_parse_quoted_include(NSString * line, NSString ** include_name) {
|
||||
NSScanner * scanner = [NSScanner scannerWithString:line];
|
||||
scanner.charactersToBeSkipped = [NSCharacterSet whitespaceCharacterSet];
|
||||
// load library
|
||||
//
|
||||
// - first check if the library is embedded
|
||||
// - then check if the library is in the bundle
|
||||
// - if not found, load the source and compile it
|
||||
// - if that fails, return NULL
|
||||
//
|
||||
// TODO: move to a function
|
||||
{
|
||||
const int64_t t_start = ggml_time_us();
|
||||
|
||||
if (![scanner scanString:@"#" intoString:NULL] ||
|
||||
![scanner scanString:@"include" intoString:NULL] ||
|
||||
![scanner scanString:@"\"" intoString:NULL]) {
|
||||
return false;
|
||||
}
|
||||
NSError * error = nil;
|
||||
NSString * src = nil;
|
||||
|
||||
NSString * name = nil;
|
||||
if (![scanner scanUpToString:@"\"" intoString:&name]) {
|
||||
return false;
|
||||
}
|
||||
#if GGML_METAL_EMBED_LIBRARY
|
||||
GGML_LOG_INFO("%s: using embedded metal library\n", __func__);
|
||||
|
||||
if (include_name) {
|
||||
*include_name = name;
|
||||
}
|
||||
return true;
|
||||
}
|
||||
extern const char ggml_metallib_start[];
|
||||
extern const char ggml_metallib_end[];
|
||||
|
||||
// Recursively inline `#include "name"` directives. System includes (<...>),
|
||||
// `#if/#else/#endif`, and other preprocessor lines are passed through to the
|
||||
// Metal compiler unchanged. `#pragma once` is dropped since `seen` already
|
||||
// guards against double-inclusion.
|
||||
static bool ggml_metal_library_flatten_file(NSMutableString * dst, NSString * path,
|
||||
NSArray<NSString *> * search_paths,
|
||||
NSMutableSet<NSString *> * seen, NSError ** error) {
|
||||
NSString * key = [path stringByStandardizingPath];
|
||||
if ([seen containsObject:key]) {
|
||||
return true;
|
||||
}
|
||||
[seen addObject:key];
|
||||
src = [[NSString alloc] initWithBytes:ggml_metallib_start length:(ggml_metallib_end-ggml_metallib_start) encoding:NSUTF8StringEncoding];
|
||||
#else
|
||||
|
||||
NSString * src = [NSString stringWithContentsOfFile:path encoding:NSUTF8StringEncoding error:error];
|
||||
if (!src) {
|
||||
return false;
|
||||
}
|
||||
#ifdef SWIFT_PACKAGE
|
||||
NSBundle * bundle = SWIFTPM_MODULE_BUNDLE;
|
||||
#else
|
||||
NSBundle * bundle = [NSBundle bundleForClass:[GGMLMetalClass class]];
|
||||
#endif
|
||||
|
||||
NSFileManager * fm = [NSFileManager defaultManager];
|
||||
for (NSString * line in [src componentsSeparatedByString:@"\n"]) {
|
||||
NSString * trimmed = [line stringByTrimmingCharactersInSet:[NSCharacterSet whitespaceCharacterSet]];
|
||||
if ([trimmed isEqualToString:@"#pragma once"]) {
|
||||
continue;
|
||||
}
|
||||
NSString * path_lib = [bundle pathForResource:@"default" ofType:@"metallib"];
|
||||
if (path_lib == nil) {
|
||||
// Try to find the resource in the directory where the current binary located.
|
||||
NSString * bin_cur = [[NSProcessInfo processInfo] arguments][0];
|
||||
NSString * bin_dir = [bin_cur stringByDeletingLastPathComponent];
|
||||
|
||||
NSString * include_name = nil;
|
||||
if (ggml_metal_library_parse_quoted_include(line, &include_name)) {
|
||||
NSString * resolved = nil;
|
||||
for (NSString * dir in search_paths) {
|
||||
NSString * candidate = [dir stringByAppendingPathComponent:include_name];
|
||||
if ([fm isReadableFileAtPath:candidate]) {
|
||||
resolved = candidate;
|
||||
break;
|
||||
NSString * path_lib_default = [NSString pathWithComponents:@[bin_dir, @"default.metallib"]];
|
||||
if ([[NSFileManager defaultManager] isReadableFileAtPath:path_lib_default]) {
|
||||
GGML_LOG_INFO("%s: found '%s'\n", __func__, [path_lib_default UTF8String]);
|
||||
|
||||
NSDictionary * atts = [[NSFileManager defaultManager] attributesOfItemAtPath:path_lib_default error:&error];
|
||||
if (atts && atts[NSFileType] == NSFileTypeSymbolicLink) {
|
||||
// Optionally, if this is a symlink, try to resolve it.
|
||||
path_lib_default = [[NSFileManager defaultManager] destinationOfSymbolicLinkAtPath:path_lib_default error:&error];
|
||||
if (path_lib_default && [path_lib_default length] > 0 && ![[path_lib_default substringToIndex:1] isEqualToString:@"/"]) {
|
||||
// It is a relative path, adding the binary directory as directory prefix.
|
||||
path_lib_default = [NSString pathWithComponents:@[bin_dir, path_lib_default]];
|
||||
}
|
||||
if (!path_lib_default || ![[NSFileManager defaultManager] isReadableFileAtPath:path_lib_default]) {
|
||||
// Link to the resource could not be resolved.
|
||||
path_lib_default = nil;
|
||||
} else {
|
||||
GGML_LOG_INFO("%s: symlink resolved '%s'\n", __func__, [path_lib_default UTF8String]);
|
||||
}
|
||||
}
|
||||
} else {
|
||||
// The resource couldn't be found in the binary's directory.
|
||||
path_lib_default = nil;
|
||||
}
|
||||
if (!resolved) {
|
||||
if (error) {
|
||||
NSString * msg = [NSString stringWithFormat:@"could not resolve include \"%@\" from '%@'", include_name, path];
|
||||
*error = [NSError errorWithDomain:@"ggml-metal-source-flatten" code:1
|
||||
userInfo:@{NSLocalizedDescriptionKey: msg}];
|
||||
}
|
||||
return false;
|
||||
}
|
||||
if (!ggml_metal_library_flatten_file(dst, resolved, search_paths, seen, error)) {
|
||||
return false;
|
||||
}
|
||||
continue;
|
||||
|
||||
path_lib = path_lib_default;
|
||||
}
|
||||
|
||||
[dst appendString:line];
|
||||
[dst appendString:@"\n"];
|
||||
}
|
||||
if (path_lib != nil) {
|
||||
// pre-compiled library found
|
||||
NSURL * libURL = [NSURL fileURLWithPath:path_lib];
|
||||
GGML_LOG_INFO("%s: loading '%s'\n", __func__, [path_lib UTF8String]);
|
||||
|
||||
return true;
|
||||
}
|
||||
library = [device newLibraryWithURL:libURL error:&error];
|
||||
if (error) {
|
||||
GGML_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
|
||||
return nil;
|
||||
}
|
||||
} else {
|
||||
GGML_LOG_INFO("%s: default.metallib not found, loading from source\n", __func__);
|
||||
|
||||
static NSString * ggml_metal_library_flatten_source(NSString * path_source, NSError ** error) {
|
||||
// Search paths cover both runtime layout (build/bin/kernels + build/bin)
|
||||
// and source-tree layout (ggml/src/ggml-metal/kernels + ggml/src/ggml-metal + ggml/src).
|
||||
NSString * path_kernels = [path_source stringByDeletingLastPathComponent];
|
||||
NSString * path_base = [path_kernels stringByDeletingLastPathComponent];
|
||||
NSArray<NSString *> * search_paths = @[
|
||||
path_kernels,
|
||||
path_base,
|
||||
[path_base stringByDeletingLastPathComponent],
|
||||
];
|
||||
NSString * path_source;
|
||||
NSString * path_resource = [[NSProcessInfo processInfo].environment objectForKey:@"GGML_METAL_PATH_RESOURCES"];
|
||||
|
||||
NSMutableString * src = [[NSMutableString alloc] init];
|
||||
NSMutableSet<NSString *> * seen = [NSMutableSet set];
|
||||
GGML_LOG_INFO("%s: GGML_METAL_PATH_RESOURCES = %s\n", __func__, path_resource ? [path_resource UTF8String] : "nil");
|
||||
|
||||
if (!ggml_metal_library_flatten_file(src, path_source, search_paths, seen, error)) {
|
||||
[src release];
|
||||
return nil;
|
||||
}
|
||||
return src;
|
||||
}
|
||||
|
||||
// Compile all per-kind libraries in parallel. `source_for_kind` returns the MSL
|
||||
// source for a kind (the helper takes ownership and releases it), or nil with
|
||||
// *err set on failure. On success the objs[] slots are populated and the routing
|
||||
// index is built; on any failure every error is logged and false is returned
|
||||
// (the caller is responsible for freeing `res`).
|
||||
static bool ggml_metal_library_compile_all(
|
||||
ggml_metal_library_t res,
|
||||
id<MTLDevice> device,
|
||||
NSDictionary * prep,
|
||||
NSString * (^source_for_kind)(int kind, NSError ** err),
|
||||
const char * origin) {
|
||||
const int64_t t_start = ggml_time_us();
|
||||
|
||||
int64_t * t_per_lib = calloc(GGML_METAL_LIB_COUNT, sizeof(int64_t));
|
||||
NSError ** err_per_lib = calloc(GGML_METAL_LIB_COUNT, sizeof(NSError *));
|
||||
__block atomic_bool any_failure = false;
|
||||
|
||||
dispatch_group_t group = dispatch_group_create();
|
||||
dispatch_queue_t queue = dispatch_get_global_queue(QOS_CLASS_USER_INITIATED, 0);
|
||||
|
||||
for (int kind = 0; kind < GGML_METAL_LIB_COUNT; ++kind) {
|
||||
dispatch_group_async(group, queue, ^{
|
||||
|
||||
const int64_t t0 = ggml_time_us();
|
||||
|
||||
NSError * error = nil;
|
||||
|
||||
NSString * src = source_for_kind(kind, &error);
|
||||
if (!src) {
|
||||
err_per_lib[kind] = [error retain];
|
||||
atomic_store(&any_failure, true);
|
||||
return;
|
||||
if (path_resource) {
|
||||
path_source = [path_resource stringByAppendingPathComponent:@"ggml-metal.metal"];
|
||||
} else {
|
||||
path_source = [bundle pathForResource:@"ggml-metal" ofType:@"metal"];
|
||||
}
|
||||
|
||||
id<MTLLibrary> lib = nil;
|
||||
if (path_source == nil) {
|
||||
GGML_LOG_WARN("%s: error: could not use bundle path to find ggml-metal.metal, falling back to trying cwd\n", __func__);
|
||||
path_source = @"ggml-metal.metal";
|
||||
}
|
||||
|
||||
GGML_LOG_INFO("%s: loading '%s'\n", __func__, [path_source UTF8String]);
|
||||
|
||||
src = [NSString stringWithContentsOfFile:path_source encoding:NSUTF8StringEncoding error:&error];
|
||||
if (error) {
|
||||
GGML_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
|
||||
return nil;
|
||||
}
|
||||
}
|
||||
#endif
|
||||
|
||||
if (!library) {
|
||||
@autoreleasepool {
|
||||
// dictionary of preprocessor macros
|
||||
NSMutableDictionary * prep = [NSMutableDictionary dictionary];
|
||||
|
||||
if (ggml_metal_device_get_props(dev)->has_bfloat) {
|
||||
[prep setObject:@"1" forKey:@"GGML_METAL_HAS_BF16"];
|
||||
}
|
||||
|
||||
if (ggml_metal_device_get_props(dev)->has_tensor) {
|
||||
[prep setObject:@"1" forKey:@"GGML_METAL_HAS_TENSOR"];
|
||||
}
|
||||
|
||||
#if GGML_METAL_EMBED_LIBRARY
|
||||
[prep setObject:@"1" forKey:@"GGML_METAL_EMBED_LIBRARY"];
|
||||
#endif
|
||||
|
||||
MTLCompileOptions * options = [MTLCompileOptions new];
|
||||
options.preprocessorMacros = prep;
|
||||
|
||||
lib = [device newLibraryWithSource:src options:options error:&error];
|
||||
//[options setFastMathEnabled:false];
|
||||
|
||||
[options release];
|
||||
|
||||
// retain the error before the autorelease pool drains it
|
||||
if (!lib) {
|
||||
err_per_lib[kind] = [error retain];
|
||||
library = [device newLibraryWithSource:src options:options error:&error];
|
||||
if (error) {
|
||||
GGML_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
|
||||
return nil;
|
||||
}
|
||||
}
|
||||
|
||||
[src release];
|
||||
|
||||
t_per_lib[kind] = ggml_time_us() - t0;
|
||||
|
||||
if (!lib) {
|
||||
atomic_store(&any_failure, true);
|
||||
return;
|
||||
}
|
||||
|
||||
res->objs[kind] = lib;
|
||||
});
|
||||
}
|
||||
dispatch_group_wait(group, DISPATCH_TIME_FOREVER);
|
||||
dispatch_release(group);
|
||||
|
||||
const bool ok = !atomic_load(&any_failure);
|
||||
|
||||
if (ok) {
|
||||
const int64_t t_total = ggml_time_us() - t_start;
|
||||
int64_t t_max = 0;
|
||||
for (int kind = 0; kind < GGML_METAL_LIB_COUNT; ++kind) {
|
||||
GGML_LOG_DEBUG("%s: compiled '%s' library in %.3f sec\n",
|
||||
__func__, k_lib_names[kind], t_per_lib[kind] / 1e6);
|
||||
if (t_per_lib[kind] > t_max) t_max = t_per_lib[kind];
|
||||
}
|
||||
GGML_LOG_INFO("%s: loaded %d libraries from %s in %.3f sec (max single = %.3f sec)\n",
|
||||
__func__, GGML_METAL_LIB_COUNT, origin, t_total / 1e6, t_max / 1e6);
|
||||
|
||||
ggml_metal_library_build_index(res);
|
||||
} else {
|
||||
for (int kind = 0; kind < GGML_METAL_LIB_COUNT; ++kind) {
|
||||
if (err_per_lib[kind]) {
|
||||
GGML_LOG_ERROR("%s: failed to build '%s' library: %s\n", __func__,
|
||||
k_lib_names[kind], [[err_per_lib[kind] description] UTF8String]);
|
||||
[err_per_lib[kind] release];
|
||||
#if !__has_feature(objc_arc)
|
||||
[options release];
|
||||
#endif
|
||||
}
|
||||
}
|
||||
|
||||
#if GGML_METAL_EMBED_LIBRARY
|
||||
[src release];
|
||||
#endif // GGML_METAL_EMBED_LIBRARY
|
||||
|
||||
GGML_LOG_INFO("%s: loaded in %.3f sec\n", __func__, (ggml_time_us() - t_start) / 1e6);
|
||||
}
|
||||
|
||||
free(err_per_lib);
|
||||
free(t_per_lib);
|
||||
|
||||
return ok;
|
||||
}
|
||||
|
||||
ggml_metal_library_t ggml_metal_library_init(ggml_metal_device_t dev) {
|
||||
id<MTLDevice> device = ggml_metal_device_get_obj(dev);
|
||||
|
||||
ggml_metal_library_t res = calloc(1, sizeof(struct ggml_metal_library));
|
||||
|
||||
res->obj = library;
|
||||
res->dev = dev;
|
||||
res->pipelines = ggml_metal_pipelines_init();
|
||||
res->lock = [NSLock new];
|
||||
|
||||
// shared MTLCompileOptions preprocessor macros (matches the build-time defines)
|
||||
NSMutableDictionary * prep = [NSMutableDictionary dictionary];
|
||||
if (ggml_metal_device_get_props(dev)->has_bfloat) {
|
||||
[prep setObject:@"1" forKey:@"GGML_METAL_HAS_BF16"];
|
||||
}
|
||||
if (ggml_metal_device_get_props(dev)->has_tensor) {
|
||||
[prep setObject:@"1" forKey:@"GGML_METAL_HAS_TENSOR"];
|
||||
}
|
||||
#if GGML_METAL_EMBED_LIBRARY
|
||||
[prep setObject:@"1" forKey:@"GGML_METAL_EMBED_LIBRARY"];
|
||||
#endif
|
||||
|
||||
#if GGML_METAL_EMBED_LIBRARY
|
||||
GGML_LOG_INFO("%s: using embedded metal library\n", __func__);
|
||||
|
||||
// start/end symbols emitted by CMake (see CMakeLists.txt), one pair per kind
|
||||
#define X(e, s) extern const char ggml_metallib_##s##_start[]; extern const char ggml_metallib_##s##_end[];
|
||||
GGML_METAL_LIBS
|
||||
#undef X
|
||||
|
||||
static const char * const lib_start[GGML_METAL_LIB_COUNT] = {
|
||||
#define X(e, s) [GGML_METAL_LIB_##e] = ggml_metallib_##s##_start,
|
||||
GGML_METAL_LIBS
|
||||
#undef X
|
||||
};
|
||||
static const char * const lib_end[GGML_METAL_LIB_COUNT] = {
|
||||
#define X(e, s) [GGML_METAL_LIB_##e] = ggml_metallib_##s##_end,
|
||||
GGML_METAL_LIBS
|
||||
#undef X
|
||||
};
|
||||
|
||||
const bool ok = ggml_metal_library_compile_all(res, device, prep,
|
||||
^NSString * (int kind, NSError ** err) {
|
||||
(void) err;
|
||||
return [[NSString alloc] initWithBytes:lib_start[kind]
|
||||
length:(lib_end[kind] - lib_start[kind])
|
||||
encoding:NSUTF8StringEncoding];
|
||||
}, "embedded data");
|
||||
|
||||
if (!ok) {
|
||||
ggml_metal_library_free(res);
|
||||
return NULL;
|
||||
}
|
||||
|
||||
return res;
|
||||
#else
|
||||
#ifdef SWIFT_PACKAGE
|
||||
NSBundle * bundle = SWIFTPM_MODULE_BUNDLE;
|
||||
#else
|
||||
NSBundle * bundle = [NSBundle bundleForClass:[GGMLMetalClass class]];
|
||||
#endif
|
||||
|
||||
const int64_t t_start = ggml_time_us();
|
||||
|
||||
NSError * error = nil;
|
||||
NSString * path_lib = [bundle pathForResource:@"default" ofType:@"metallib"];
|
||||
if (path_lib == nil) {
|
||||
// Try to find the resource in the directory where the current binary located.
|
||||
NSString * bin_cur = [[NSProcessInfo processInfo] arguments][0];
|
||||
NSString * bin_dir = [bin_cur stringByDeletingLastPathComponent];
|
||||
|
||||
NSString * path_lib_default = [NSString pathWithComponents:@[bin_dir, @"default.metallib"]];
|
||||
if ([[NSFileManager defaultManager] isReadableFileAtPath:path_lib_default]) {
|
||||
GGML_LOG_INFO("%s: found '%s'\n", __func__, [path_lib_default UTF8String]);
|
||||
|
||||
NSDictionary * atts = [[NSFileManager defaultManager] attributesOfItemAtPath:path_lib_default error:&error];
|
||||
if (atts && atts[NSFileType] == NSFileTypeSymbolicLink) {
|
||||
// Optionally, if this is a symlink, try to resolve it.
|
||||
path_lib_default = [[NSFileManager defaultManager] destinationOfSymbolicLinkAtPath:path_lib_default error:&error];
|
||||
if (path_lib_default && [path_lib_default length] > 0 && ![[path_lib_default substringToIndex:1] isEqualToString:@"/"]) {
|
||||
// It is a relative path, adding the binary directory as directory prefix.
|
||||
path_lib_default = [NSString pathWithComponents:@[bin_dir, path_lib_default]];
|
||||
}
|
||||
if (!path_lib_default || ![[NSFileManager defaultManager] isReadableFileAtPath:path_lib_default]) {
|
||||
// Link to the resource could not be resolved.
|
||||
path_lib_default = nil;
|
||||
} else {
|
||||
GGML_LOG_INFO("%s: symlink resolved '%s'\n", __func__, [path_lib_default UTF8String]);
|
||||
}
|
||||
}
|
||||
} else {
|
||||
// The resource couldn't be found in the binary's directory.
|
||||
path_lib_default = nil;
|
||||
}
|
||||
|
||||
path_lib = path_lib_default;
|
||||
}
|
||||
|
||||
if (path_lib != nil) {
|
||||
// pre-compiled library found: a single combined default.metallib
|
||||
NSURL * libURL = [NSURL fileURLWithPath:path_lib];
|
||||
GGML_LOG_INFO("%s: loading '%s'\n", __func__, [path_lib UTF8String]);
|
||||
|
||||
res->objs[0] = [device newLibraryWithURL:libURL error:&error];
|
||||
res->single_library = true;
|
||||
if (!res->objs[0]) {
|
||||
GGML_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
|
||||
ggml_metal_library_free(res);
|
||||
return NULL;
|
||||
}
|
||||
|
||||
GGML_LOG_INFO("%s: loaded in %.3f sec\n", __func__, (ggml_time_us() - t_start) / 1e6);
|
||||
return res;
|
||||
}
|
||||
|
||||
// no pre-compiled metallib: fall back to compiling each kernel source separately
|
||||
GGML_LOG_INFO("%s: default.metallib not found, loading kernel sources\n", __func__);
|
||||
|
||||
NSString * path_resource = [[NSProcessInfo processInfo].environment objectForKey:@"GGML_METAL_PATH_RESOURCES"];
|
||||
if (path_resource) {
|
||||
GGML_LOG_INFO("%s: GGML_METAL_PATH_RESOURCES = %s\n", __func__, [path_resource UTF8String]);
|
||||
}
|
||||
|
||||
// resolve each kind's source path up front (file lookup/logging stays on the calling thread)
|
||||
NSString ** path_per_kind = calloc(GGML_METAL_LIB_COUNT, sizeof(NSString *));
|
||||
for (int kind = 0; kind < GGML_METAL_LIB_COUNT; ++kind) {
|
||||
NSString * rel = [NSString stringWithFormat:@"kernels/%s.metal", k_lib_names[kind]];
|
||||
|
||||
NSString * path_source = nil;
|
||||
if (path_resource) {
|
||||
path_source = [path_resource stringByAppendingPathComponent:rel];
|
||||
} else {
|
||||
NSString * stem = [NSString stringWithFormat:@"kernels/%s", k_lib_names[kind]];
|
||||
path_source = [bundle pathForResource:stem ofType:@"metal"];
|
||||
}
|
||||
|
||||
if (path_source == nil || ![[NSFileManager defaultManager] isReadableFileAtPath:path_source]) {
|
||||
GGML_LOG_WARN("%s: could not locate %s in bundle, falling back to cwd\n", __func__, [rel UTF8String]);
|
||||
path_source = rel;
|
||||
}
|
||||
|
||||
GGML_LOG_DEBUG("%s: loading '%s'\n", __func__, [path_source UTF8String]);
|
||||
|
||||
path_per_kind[kind] = [path_source retain];
|
||||
}
|
||||
|
||||
const bool ok = ggml_metal_library_compile_all(res, device, prep,
|
||||
^NSString * (int kind, NSError ** err) {
|
||||
return ggml_metal_library_flatten_source(path_per_kind[kind], err);
|
||||
}, "source");
|
||||
|
||||
for (int kind = 0; kind < GGML_METAL_LIB_COUNT; ++kind) {
|
||||
[path_per_kind[kind] release];
|
||||
}
|
||||
free(path_per_kind);
|
||||
|
||||
if (!ok) {
|
||||
ggml_metal_library_free(res);
|
||||
return NULL;
|
||||
}
|
||||
|
||||
return res;
|
||||
#endif
|
||||
}
|
||||
|
||||
ggml_metal_library_t ggml_metal_library_init_from_source(ggml_metal_device_t dev, const char * source, bool verbose) {
|
||||
@@ -589,11 +318,10 @@ ggml_metal_library_t ggml_metal_library_init_from_source(ggml_metal_device_t dev
|
||||
return NULL;
|
||||
}
|
||||
|
||||
res->objs[0] = library;
|
||||
res->single_library = true;
|
||||
res->dev = dev;
|
||||
res->pipelines = ggml_metal_pipelines_init();
|
||||
res->lock = [NSLock new];
|
||||
res->obj = library;
|
||||
res->dev = dev;
|
||||
res->pipelines = ggml_metal_pipelines_init();
|
||||
res->lock = [NSLock new];
|
||||
|
||||
return res;
|
||||
}
|
||||
@@ -603,14 +331,8 @@ void ggml_metal_library_free(ggml_metal_library_t lib) {
|
||||
return;
|
||||
}
|
||||
|
||||
for (int kind = 0; kind < GGML_METAL_LIB_COUNT; ++kind) {
|
||||
if (lib->objs[kind]) {
|
||||
[lib->objs[kind] release];
|
||||
}
|
||||
}
|
||||
|
||||
if (lib->fn_to_lib) {
|
||||
[lib->fn_to_lib release];
|
||||
if (lib->obj) {
|
||||
[lib->obj release];
|
||||
}
|
||||
|
||||
ggml_metal_pipelines_free(lib->pipelines);
|
||||
@@ -671,28 +393,11 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_compile_pipeline(ggml_
|
||||
|
||||
GGML_LOG_DEBUG("%s: compiling pipeline: base = '%s', name = '%s'\n", __func__, base, name);
|
||||
|
||||
// route to the library that actually defines this kernel; fn_to_lib is
|
||||
// built from -[MTLLibrary functionNames] so it's always in sync
|
||||
int lib_idx = 0;
|
||||
if (!lib->single_library) {
|
||||
NSNumber * idx = lib->fn_to_lib[base_func];
|
||||
if (!idx) {
|
||||
[lib->lock unlock];
|
||||
|
||||
GGML_LOG_ERROR("%s: kernel not found in any metal library: base = '%s', name = '%s'\n", __func__, base, name);
|
||||
|
||||
return res;
|
||||
}
|
||||
lib_idx = [idx intValue];
|
||||
}
|
||||
|
||||
id<MTLLibrary> mtl_lib = lib->objs[lib_idx];
|
||||
|
||||
id<MTLFunction> mtl_function;
|
||||
if (!cv) {
|
||||
mtl_function = [mtl_lib newFunctionWithName:base_func];
|
||||
mtl_function = [lib->obj newFunctionWithName:base_func];
|
||||
} else {
|
||||
mtl_function = [mtl_lib newFunctionWithName:base_func constantValues:cv->obj error:&error];
|
||||
mtl_function = [lib->obj newFunctionWithName:base_func constantValues:cv->obj error:&error];
|
||||
}
|
||||
if (!mtl_function) {
|
||||
[lib->lock unlock];
|
||||
|
||||
10754
ggml/src/ggml-metal/ggml-metal.metal
Normal file
10754
ggml/src/ggml-metal/ggml-metal.metal
Normal file
File diff suppressed because it is too large
Load Diff
@@ -1,232 +0,0 @@
|
||||
#include "common.h"
|
||||
|
||||
// bitonic sort implementation following the CUDA kernels as reference
|
||||
typedef void (argsort_t)(
|
||||
constant ggml_metal_kargs_argsort & args,
|
||||
device const char * src0,
|
||||
device int32_t * dst,
|
||||
threadgroup int32_t * shmem_i32 [[threadgroup(0)]],
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort3 tpitg[[thread_position_in_threadgroup]],
|
||||
ushort3 ntg[[threads_per_threadgroup]]);
|
||||
|
||||
template<ggml_sort_order order>
|
||||
kernel void kernel_argsort_f32_i32(
|
||||
constant ggml_metal_kargs_argsort & args,
|
||||
device const char * src0,
|
||||
device int32_t * dst,
|
||||
threadgroup int32_t * shmem_i32 [[threadgroup(0)]],
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort3 tpitg[[thread_position_in_threadgroup]],
|
||||
ushort3 ntg[[threads_per_threadgroup]]) {
|
||||
// bitonic sort
|
||||
const int col = tpitg[0];
|
||||
const int ib = tgpig[0] / args.ne01;
|
||||
|
||||
const int i00 = ib*ntg.x;
|
||||
const int i01 = tgpig[0] % args.ne01;
|
||||
const int i02 = tgpig[1];
|
||||
const int i03 = tgpig[2];
|
||||
|
||||
device const float * src0_row = (device const float *) (src0 + args.nb01*i01 + args.nb02*i02 + args.nb03*i03);
|
||||
|
||||
// initialize indices
|
||||
shmem_i32[col] = i00 + col;
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
for (int k = 2; k <= ntg.x; k *= 2) {
|
||||
for (int j = k / 2; j > 0; j /= 2) {
|
||||
int ixj = col ^ j;
|
||||
if (ixj > col) {
|
||||
if ((col & k) == 0) {
|
||||
if (shmem_i32[col] >= args.ne00 ||
|
||||
(shmem_i32[ixj] < args.ne00 && (order == GGML_SORT_ORDER_ASC ?
|
||||
src0_row[shmem_i32[col]] > src0_row[shmem_i32[ixj]] :
|
||||
src0_row[shmem_i32[col]] < src0_row[shmem_i32[ixj]]))
|
||||
) {
|
||||
SWAP(shmem_i32[col], shmem_i32[ixj]);
|
||||
}
|
||||
} else {
|
||||
if (shmem_i32[ixj] >= args.ne00 ||
|
||||
(shmem_i32[col] < args.ne00 && (order == GGML_SORT_ORDER_ASC ?
|
||||
src0_row[shmem_i32[col]] < src0_row[shmem_i32[ixj]] :
|
||||
src0_row[shmem_i32[col]] > src0_row[shmem_i32[ixj]]))
|
||||
) {
|
||||
SWAP(shmem_i32[col], shmem_i32[ixj]);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
}
|
||||
}
|
||||
|
||||
const int64_t i0 = ib*args.top_k;
|
||||
|
||||
// copy the result to dst without the padding
|
||||
if (i0 + col < args.ne0 && col < args.top_k) {
|
||||
dst += i0 + args.ne0*i01 + args.ne0*args.ne1*i02 + args.ne0*args.ne1*args.ne2*i03;
|
||||
|
||||
dst[col] = shmem_i32[col];
|
||||
}
|
||||
}
|
||||
|
||||
template [[host_name("kernel_argsort_f32_i32_asc")]] kernel argsort_t kernel_argsort_f32_i32<GGML_SORT_ORDER_ASC>;
|
||||
template [[host_name("kernel_argsort_f32_i32_desc")]] kernel argsort_t kernel_argsort_f32_i32<GGML_SORT_ORDER_DESC>;
|
||||
|
||||
typedef void (argsort_merge_t)(
|
||||
constant ggml_metal_kargs_argsort_merge & args,
|
||||
device const char * src0,
|
||||
device const int32_t * tmp,
|
||||
device int32_t * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort3 tpitg[[thread_position_in_threadgroup]],
|
||||
ushort3 ntg[[threads_per_threadgroup]]);
|
||||
|
||||
template<ggml_sort_order order>
|
||||
kernel void kernel_argsort_merge_f32_i32(
|
||||
constant ggml_metal_kargs_argsort_merge & args,
|
||||
device const char * src0,
|
||||
device const int32_t * tmp,
|
||||
device int32_t * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort3 tpitg[[thread_position_in_threadgroup]],
|
||||
ushort3 ntg[[threads_per_threadgroup]]) {
|
||||
|
||||
const int im = tgpig[0] / args.ne01;
|
||||
const int i01 = tgpig[0] % args.ne01;
|
||||
const int i02 = tgpig[1];
|
||||
const int i03 = tgpig[2];
|
||||
|
||||
const int start = im * (2 * args.len);
|
||||
|
||||
const int len0 = MIN(args.len, MAX(0, args.ne0 - (int)(start)));
|
||||
const int len1 = MIN(args.len, MAX(0, args.ne0 - (int)(start + args.len)));
|
||||
|
||||
const int total = len0 + len1;
|
||||
|
||||
device const int32_t * tmp0 = tmp + start
|
||||
+ i01*args.ne0
|
||||
+ i02*args.ne0*args.ne01
|
||||
+ i03*args.ne0*args.ne01*args.ne02;
|
||||
|
||||
device const int32_t * tmp1 = tmp0 + args.len;
|
||||
|
||||
dst += start
|
||||
+ i01*args.top_k
|
||||
+ i02*args.top_k*args.ne01
|
||||
+ i03*args.top_k*args.ne01*args.ne02;
|
||||
|
||||
device const float * src0_row = (device const float *)(src0
|
||||
+ args.nb01*i01
|
||||
+ args.nb02*i02
|
||||
+ args.nb03*i03);
|
||||
|
||||
if (total == 0) {
|
||||
return;
|
||||
}
|
||||
|
||||
const int chunk = (total + ntg.x - 1) / ntg.x;
|
||||
|
||||
const int k0 = tpitg.x * chunk;
|
||||
const int k1 = MIN(MIN(k0 + chunk, total), args.top_k);
|
||||
|
||||
if (k0 >= args.top_k) {
|
||||
return;
|
||||
}
|
||||
|
||||
if (k0 >= total) {
|
||||
return;
|
||||
}
|
||||
|
||||
int low = k0 > len1 ? k0 - len1 : 0;
|
||||
int high = MIN(k0, len0);
|
||||
|
||||
// binary-search partition (i, j) such that i + j = k
|
||||
while (low < high) {
|
||||
const int mid = (low + high) >> 1;
|
||||
|
||||
const int32_t idx0 = tmp0[mid];
|
||||
const int32_t idx1 = tmp1[k0 - mid - 1];
|
||||
|
||||
const float val0 = src0_row[idx0];
|
||||
const float val1 = src0_row[idx1];
|
||||
|
||||
bool take_left;
|
||||
if (order == GGML_SORT_ORDER_ASC) {
|
||||
take_left = (val0 <= val1);
|
||||
} else {
|
||||
take_left = (val0 >= val1);
|
||||
}
|
||||
|
||||
if (take_left) {
|
||||
low = mid + 1;
|
||||
} else {
|
||||
high = mid;
|
||||
}
|
||||
}
|
||||
|
||||
int i = low;
|
||||
int j = k0 - i;
|
||||
|
||||
// keep the merge fronts into registers
|
||||
int32_t idx0 = 0;
|
||||
float val0 = 0.0f;
|
||||
if (i < len0) {
|
||||
idx0 = tmp0[i];
|
||||
val0 = src0_row[idx0];
|
||||
}
|
||||
|
||||
int32_t idx1 = 0;
|
||||
float val1 = 0.0f;
|
||||
if (j < len1) {
|
||||
idx1 = tmp1[j];
|
||||
val1 = src0_row[idx1];
|
||||
}
|
||||
|
||||
for (int k = k0; k < k1; ++k) {
|
||||
int32_t out_idx;
|
||||
|
||||
if (i >= len0) {
|
||||
while (k < k1) {
|
||||
dst[k++] = tmp1[j++];
|
||||
}
|
||||
break;
|
||||
} else if (j >= len1) {
|
||||
while (k < k1) {
|
||||
dst[k++] = tmp0[i++];
|
||||
}
|
||||
break;
|
||||
} else {
|
||||
bool take_left;
|
||||
|
||||
if (order == GGML_SORT_ORDER_ASC) {
|
||||
take_left = (val0 <= val1);
|
||||
} else {
|
||||
take_left = (val0 >= val1);
|
||||
}
|
||||
|
||||
if (take_left) {
|
||||
out_idx = idx0;
|
||||
++i;
|
||||
if (i < len0) {
|
||||
idx0 = tmp0[i];
|
||||
val0 = src0_row[idx0];
|
||||
}
|
||||
} else {
|
||||
out_idx = idx1;
|
||||
++j;
|
||||
if (j < len1) {
|
||||
idx1 = tmp1[j];
|
||||
val1 = src0_row[idx1];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
dst[k] = out_idx;
|
||||
}
|
||||
}
|
||||
|
||||
template [[host_name("kernel_argsort_merge_f32_i32_asc")]] kernel argsort_merge_t kernel_argsort_merge_f32_i32<GGML_SORT_ORDER_ASC>;
|
||||
template [[host_name("kernel_argsort_merge_f32_i32_desc")]] kernel argsort_merge_t kernel_argsort_merge_f32_i32<GGML_SORT_ORDER_DESC>;
|
||||
@@ -1,226 +0,0 @@
|
||||
#include "common.h"
|
||||
|
||||
// OP: 0 - add, 1 - sub, 2 - mul, 3 - div
|
||||
constant short FC_bin_op [[function_constant(FC_BIN + 0)]];
|
||||
constant short FC_bin_f [[function_constant(FC_BIN + 1)]];
|
||||
constant bool FC_bin_rb [[function_constant(FC_BIN + 2)]];
|
||||
constant bool FC_bin_cb [[function_constant(FC_BIN + 3)]];
|
||||
|
||||
template <typename T0, typename T1, typename T>
|
||||
kernel void kernel_bin_fuse_impl(
|
||||
constant ggml_metal_kargs_bin & args,
|
||||
device const char * src0,
|
||||
device const char * src1,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort3 tpitg[[thread_position_in_threadgroup]],
|
||||
ushort3 ntg[[threads_per_threadgroup]]) {
|
||||
#define FC_OP FC_bin_op
|
||||
#define FC_F FC_bin_f
|
||||
#define FC_RB FC_bin_rb
|
||||
#define FC_CB FC_bin_cb
|
||||
|
||||
if (FC_RB) {
|
||||
// row broadcast
|
||||
const uint i0 = tgpig.y*args.ne00 + tgpig.x;
|
||||
const uint i1 = FC_CB ? tgpig.x%args.ne10 : tgpig.x;
|
||||
|
||||
device const T0 * src0_row = (device const T0 *) (src0);
|
||||
device T * dst_row = (device T *) (dst);
|
||||
|
||||
if (FC_F == 1) {
|
||||
device const T1 * src1_row = (device const T1 *) (src1 + args.o1[0]);
|
||||
|
||||
if (FC_OP == 0) {
|
||||
dst_row[i0] = src0_row[i0] + src1_row[i1];
|
||||
}
|
||||
|
||||
if (FC_OP == 1) {
|
||||
dst_row[i0] = src0_row[i0] - src1_row[i1];
|
||||
}
|
||||
|
||||
if (FC_OP == 2) {
|
||||
dst_row[i0] = src0_row[i0] * src1_row[i1];
|
||||
}
|
||||
|
||||
if (FC_OP == 3) {
|
||||
dst_row[i0] = src0_row[i0] / src1_row[i1];
|
||||
}
|
||||
} else {
|
||||
T0 res = src0_row[i0];
|
||||
|
||||
if (FC_OP == 0) {
|
||||
FOR_UNROLL (short j = 0; j < FC_F; ++j) {
|
||||
res += ((device const T1 *) (src1 + args.o1[j]))[i1];
|
||||
}
|
||||
}
|
||||
|
||||
if (FC_OP == 1) {
|
||||
FOR_UNROLL (short j = 0; j < FC_F; ++j) {
|
||||
res -= ((device const T1 *) (src1 + args.o1[j]))[i1];
|
||||
}
|
||||
}
|
||||
|
||||
if (FC_OP == 2) {
|
||||
FOR_UNROLL (short j = 0; j < FC_F; ++j) {
|
||||
res *= ((device const T1 *) (src1 + args.o1[j]))[i1];
|
||||
}
|
||||
}
|
||||
|
||||
if (FC_OP == 3) {
|
||||
FOR_UNROLL (short j = 0; j < FC_F; ++j) {
|
||||
res /= ((device const T1 *) (src1 + args.o1[j]))[i1];
|
||||
}
|
||||
}
|
||||
|
||||
dst_row[i0] = res;
|
||||
}
|
||||
} else {
|
||||
const int i03 = tgpig.z;
|
||||
const int i02 = tgpig.y;
|
||||
const int i01 = tgpig.x;
|
||||
|
||||
if (i01 >= args.ne01) {
|
||||
return;
|
||||
}
|
||||
|
||||
const int i13 = i03%args.ne13;
|
||||
const int i12 = i02%args.ne12;
|
||||
const int i11 = i01%args.ne11;
|
||||
|
||||
device const T0 * src0_ptr = (device const T0 *) (src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + args.offs);
|
||||
device T * dst_ptr = (device T *) (dst + i03*args.nb3 + i02*args.nb2 + i01*args.nb1 + args.offs);
|
||||
|
||||
if (FC_F == 1) {
|
||||
device const T1 * src1_ptr = (device const T1 *) (src1 + args.o1[0] + i13*args.nb13 + i12*args.nb12 + i11*args.nb11);
|
||||
|
||||
for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
|
||||
const int i10 = FC_CB ? i0%args.ne10 : i0;
|
||||
|
||||
if (FC_OP == 0) {
|
||||
dst_ptr[i0] = src0_ptr[i0] + src1_ptr[i10];
|
||||
}
|
||||
|
||||
if (FC_OP == 1) {
|
||||
dst_ptr[i0] = src0_ptr[i0] - src1_ptr[i10];
|
||||
}
|
||||
|
||||
if (FC_OP == 2) {
|
||||
dst_ptr[i0] = src0_ptr[i0] * src1_ptr[i10];
|
||||
}
|
||||
|
||||
if (FC_OP == 3) {
|
||||
dst_ptr[i0] = src0_ptr[i0] / src1_ptr[i10];
|
||||
}
|
||||
}
|
||||
} else {
|
||||
device const T1 * src1_ptr[8];
|
||||
FOR_UNROLL (short j = 0; j < FC_F; ++j) {
|
||||
src1_ptr[j] = (device const T1 *) (src1 + args.o1[j] + i13*args.nb13 + i12*args.nb12 + i11*args.nb11);
|
||||
}
|
||||
|
||||
for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
|
||||
const int i10 = FC_CB ? i0%args.ne10 : i0;
|
||||
|
||||
T res = src0_ptr[i0];
|
||||
|
||||
if (FC_OP == 0) {
|
||||
FOR_UNROLL (short j = 0; j < FC_F; ++j) {
|
||||
res += src1_ptr[j][i10];
|
||||
}
|
||||
}
|
||||
|
||||
if (FC_OP == 1) {
|
||||
FOR_UNROLL (short j = 0; j < FC_F; ++j) {
|
||||
res -= src1_ptr[j][i10];
|
||||
}
|
||||
}
|
||||
|
||||
if (FC_OP == 2) {
|
||||
FOR_UNROLL (short j = 0; j < FC_F; ++j) {
|
||||
res *= src1_ptr[j][i10];
|
||||
}
|
||||
}
|
||||
|
||||
if (FC_OP == 3) {
|
||||
FOR_UNROLL (short j = 0; j < FC_F; ++j) {
|
||||
res /= src1_ptr[j][i10];
|
||||
}
|
||||
}
|
||||
|
||||
dst_ptr[i0] = res;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#undef FC_OP
|
||||
#undef FC_F
|
||||
#undef FC_RB
|
||||
#undef FC_CB
|
||||
}
|
||||
|
||||
typedef decltype(kernel_bin_fuse_impl<float, float, float>) kernel_bin_fuse_t;
|
||||
|
||||
template [[host_name("kernel_bin_fuse_f32_f32_f32")]] kernel kernel_bin_fuse_t kernel_bin_fuse_impl<float, float, float>;
|
||||
template [[host_name("kernel_bin_fuse_f32_f32_f32_4")]] kernel kernel_bin_fuse_t kernel_bin_fuse_impl<float4, float4, float4>;
|
||||
|
||||
kernel void kernel_add_id(
|
||||
constant ggml_metal_kargs_add_id & args,
|
||||
device const char * src0,
|
||||
device const char * src1,
|
||||
device const char * src2,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort3 tpitg[[thread_position_in_threadgroup]],
|
||||
ushort3 ntg[[threads_per_threadgroup]]) {
|
||||
const int i1 = tgpig.x;
|
||||
const int i2 = tgpig.y;
|
||||
|
||||
const int i11 = *((device const int32_t *) (src2 + i1*sizeof(int32_t) + i2*args.nb21));
|
||||
|
||||
const size_t nb1 = args.ne0 * sizeof(float);
|
||||
const size_t nb2 = args.ne1 * nb1;
|
||||
|
||||
device float * dst_row = (device float *)((device char *)dst + i1*nb1 + i2*nb2);
|
||||
device const float * src0_row = (device const float *)((device char *)src0 + i1*args.nb01 + i2*args.nb02);
|
||||
device const float * src1_row = (device const float *)((device char *)src1 + i11*args.nb11);
|
||||
|
||||
for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
|
||||
dst_row[i0] = src0_row[i0] + src1_row[i0];
|
||||
}
|
||||
}
|
||||
|
||||
template<typename T>
|
||||
kernel void kernel_repeat(
|
||||
constant ggml_metal_kargs_repeat & args,
|
||||
device const char * src0,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort3 tpitg[[thread_position_in_threadgroup]],
|
||||
ushort3 ntg[[threads_per_threadgroup]]) {
|
||||
const int i3 = tgpig.z;
|
||||
const int i2 = tgpig.y;
|
||||
const int i1 = tgpig.x;
|
||||
|
||||
const int i03 = i3%args.ne03;
|
||||
const int i02 = i2%args.ne02;
|
||||
const int i01 = i1%args.ne01;
|
||||
|
||||
device const char * src0_ptr = src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01;
|
||||
device char * dst_ptr = dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1;
|
||||
|
||||
for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
|
||||
const int i00 = i0%args.ne00;
|
||||
*((device T *)(dst_ptr + i0*args.nb0)) = *((device T *)(src0_ptr + i00*args.nb00));
|
||||
}
|
||||
}
|
||||
|
||||
typedef decltype(kernel_repeat<float>) kernel_repeat_t;
|
||||
|
||||
template [[host_name("kernel_repeat_f32")]] kernel kernel_repeat_t kernel_repeat<float>;
|
||||
template [[host_name("kernel_repeat_f16")]] kernel kernel_repeat_t kernel_repeat<half>;
|
||||
#if defined(GGML_METAL_HAS_BF16)
|
||||
template [[host_name("kernel_repeat_bf16")]] kernel kernel_repeat_t kernel_repeat<bfloat>;
|
||||
#endif
|
||||
template [[host_name("kernel_repeat_i32")]] kernel kernel_repeat_t kernel_repeat<int>;
|
||||
template [[host_name("kernel_repeat_i16")]] kernel kernel_repeat_t kernel_repeat<short>;
|
||||
@@ -1,126 +0,0 @@
|
||||
#pragma once
|
||||
|
||||
#include "ggml-metal-impl.h"
|
||||
|
||||
#include <metal_stdlib>
|
||||
|
||||
#ifdef GGML_METAL_HAS_TENSOR
|
||||
#include <metal_tensor>
|
||||
|
||||
#include <MetalPerformancePrimitives/MetalPerformancePrimitives.h>
|
||||
#endif
|
||||
|
||||
using namespace metal;
|
||||
|
||||
#define MAX(x, y) ((x) > (y) ? (x) : (y))
|
||||
#define MIN(x, y) ((x) < (y) ? (x) : (y))
|
||||
#define SWAP(x, y) { auto tmp = (x); (x) = (y); (y) = tmp; }
|
||||
|
||||
#define PAD2(x, n) (((x) + (n) - 1) & ~((n) - 1))
|
||||
|
||||
#define FOR_UNROLL(x) _Pragma("clang loop unroll(full)") for (x)
|
||||
|
||||
#define N_SIMDWIDTH 32 // assuming SIMD group size is 32
|
||||
|
||||
// ref: https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf
|
||||
//
|
||||
// cmd:
|
||||
// .../usr/bin/metal -dM -E -c ggml/src/ggml-metal/kernels/<src>.metal
|
||||
// .../usr/bin/metal -dM -E -c -target air64-apple-ios14.0 ggml/src/ggml-metal/kernels/<src>.metal
|
||||
//
|
||||
#if __METAL_VERSION__ < 310 && defined(GGML_METAL_HAS_BF16)
|
||||
#undef GGML_METAL_HAS_BF16
|
||||
#endif
|
||||
|
||||
#if defined(GGML_METAL_HAS_BF16)
|
||||
typedef matrix<bfloat, 4, 4> bfloat4x4;
|
||||
typedef matrix<bfloat, 2, 4> bfloat2x4;
|
||||
#endif
|
||||
|
||||
constexpr constant static float kvalues_iq4nl_f[16] = {
|
||||
-127.f, -104.f, -83.f, -65.f, -49.f, -35.f, -22.f, -10.f, 1.f, 13.f, 25.f, 38.f, 53.f, 69.f, 89.f, 113.f
|
||||
};
|
||||
|
||||
constexpr constant static float kvalues_mxfp4_f[16] = {
|
||||
0, .5f, 1.f, 1.5f, 2.f, 3.f, 4.f, 6.f, -0, -.5f, -1.f, -1.5f, -2.f, -3.f, -4.f, -6.f
|
||||
};
|
||||
|
||||
static inline int best_index_int8(int n, constant float * val, float x) {
|
||||
if (x <= val[0]) return 0;
|
||||
if (x >= val[n-1]) return n-1;
|
||||
int ml = 0, mu = n-1;
|
||||
while (mu-ml > 1) {
|
||||
int mav = (ml+mu)/2;
|
||||
if (x < val[mav]) mu = mav; else ml = mav;
|
||||
}
|
||||
return x - val[mu-1] < val[mu] - x ? mu-1 : mu;
|
||||
}
|
||||
|
||||
static inline float e8m0_to_fp32(uint8_t x) {
|
||||
uint32_t bits;
|
||||
|
||||
if (x == 0) {
|
||||
bits = 0x00400000;
|
||||
} else {
|
||||
bits = (uint32_t) x << 23;
|
||||
}
|
||||
|
||||
return as_type<float>(bits);
|
||||
}
|
||||
|
||||
static inline float dot(float x, float y) {
|
||||
return x*y;
|
||||
}
|
||||
|
||||
static inline float sum(float x) {
|
||||
return x;
|
||||
}
|
||||
|
||||
static inline float sum(float4 x) {
|
||||
return x[0] + x[1] + x[2] + x[3];
|
||||
}
|
||||
|
||||
enum ggml_sort_order {
|
||||
GGML_SORT_ORDER_ASC,
|
||||
GGML_SORT_ORDER_DESC,
|
||||
};
|
||||
|
||||
constant float GELU_COEF_A = 0.044715f;
|
||||
constant float GELU_QUICK_COEF = -1.702f;
|
||||
constant float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f;
|
||||
constant float SQRT_2_INV = 0.70710678118654752440084436210484f;
|
||||
|
||||
// based on Abramowitz and Stegun formula 7.1.26 or similar Hastings' approximation
|
||||
// ref: https://www.johndcook.com/blog/python_erf/
|
||||
constant float p_erf = 0.3275911f;
|
||||
constant float a1_erf = 0.254829592f;
|
||||
constant float a2_erf = -0.284496736f;
|
||||
constant float a3_erf = 1.421413741f;
|
||||
constant float a4_erf = -1.453152027f;
|
||||
constant float a5_erf = 1.061405429f;
|
||||
|
||||
template<typename T>
|
||||
inline T erf_approx(T x) {
|
||||
T sign_x = sign(x);
|
||||
x = fabs(x);
|
||||
T t = 1.0f / (1.0f + p_erf * x);
|
||||
T y = 1.0f - (((((a5_erf * t + a4_erf) * t) + a3_erf) * t + a2_erf) * t + a1_erf) * t * exp(-x * x);
|
||||
return sign_x * y;
|
||||
}
|
||||
|
||||
template<typename T> T elu_approx(T x);
|
||||
|
||||
template<> inline float elu_approx<float>(float x) {
|
||||
return (x > 0.f) ? x : (exp(x) - 1);
|
||||
}
|
||||
|
||||
template<> inline float4 elu_approx<float4>(float4 x) {
|
||||
float4 res;
|
||||
|
||||
res[0] = (x[0] > 0.0f) ? x[0] : (exp(x[0]) - 1.0f);
|
||||
res[1] = (x[1] > 0.0f) ? x[1] : (exp(x[1]) - 1.0f);
|
||||
res[2] = (x[2] > 0.0f) ? x[2] : (exp(x[2]) - 1.0f);
|
||||
res[3] = (x[3] > 0.0f) ? x[3] : (exp(x[3]) - 1.0f);
|
||||
|
||||
return res;
|
||||
}
|
||||
@@ -1,485 +0,0 @@
|
||||
#include "common.h"
|
||||
|
||||
typedef void (im2col_t)(
|
||||
constant ggml_metal_kargs_im2col & args,
|
||||
device const float * x,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tgpg[[threadgroups_per_grid]],
|
||||
uint3 tpitg[[thread_position_in_threadgroup]],
|
||||
uint3 ntg[[threads_per_threadgroup]]);
|
||||
|
||||
template <typename T>
|
||||
kernel void kernel_im2col(
|
||||
constant ggml_metal_kargs_im2col & args,
|
||||
device const float * x,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tgpg[[threadgroups_per_grid]],
|
||||
uint3 tpitg[[thread_position_in_threadgroup]],
|
||||
uint3 ntg[[threads_per_threadgroup]]) {
|
||||
// const int64_t IC = tgpg[0];
|
||||
const int64_t OH = tgpg[1];
|
||||
const int64_t OW = tgpg[2];
|
||||
|
||||
const int64_t KH = ntg[1];
|
||||
const int64_t KW = ntg[2];
|
||||
|
||||
int64_t in = tpitg[0];
|
||||
const int64_t ikh = tpitg[1];
|
||||
const int64_t ikw = tpitg[2];
|
||||
|
||||
const int64_t iic = tgpig[0];
|
||||
const int64_t ioh = tgpig[1];
|
||||
const int64_t iow = tgpig[2];
|
||||
|
||||
const int64_t iiw = iow*args.s0 + ikw*args.d0 - args.p0;
|
||||
const int64_t iih = ioh*args.s1 + ikh*args.d1 - args.p1;
|
||||
|
||||
int64_t offset_dst = (in*OH*OW + ioh*OW + iow)*args.CHW + (iic*(KH*KW) + ikh*KW + ikw);
|
||||
|
||||
device T * pdst = (device T *) (dst);
|
||||
|
||||
if (iih < 0 || iih >= args.IH || iiw < 0 || iiw >= args.IW) {
|
||||
while (in < args.N) {
|
||||
pdst[offset_dst] = 0.0f;
|
||||
offset_dst += ntg[0]*args.CHW*OH*OW;
|
||||
|
||||
in += ntg[0];
|
||||
}
|
||||
} else {
|
||||
int64_t offset_src = in*args.ofs0 + iic*args.ofs1 + iih*args.IW + iiw;
|
||||
|
||||
while (in < args.N) {
|
||||
pdst[offset_dst] = x[offset_src];
|
||||
|
||||
offset_dst += ntg[0]*args.CHW*OH*OW;
|
||||
offset_src += ntg[0]*args.ofs0;
|
||||
|
||||
in += ntg[0];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template [[host_name("kernel_im2col_f32")]] kernel im2col_t kernel_im2col<float>;
|
||||
template [[host_name("kernel_im2col_f16")]] kernel im2col_t kernel_im2col<half>;
|
||||
|
||||
// TODO: optimize
|
||||
typedef void (im2col_ext_t)(
|
||||
constant ggml_metal_kargs_im2col & args,
|
||||
device const float * x,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tgpg[[threadgroups_per_grid]],
|
||||
uint3 tpitg[[thread_position_in_threadgroup]],
|
||||
uint3 ntg[[threads_per_threadgroup]]);
|
||||
|
||||
template <typename T>
|
||||
kernel void kernel_im2col_ext(
|
||||
constant ggml_metal_kargs_im2col & args,
|
||||
device const float * x,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tgpg[[threadgroups_per_grid]], // tgpg[0] = D x IC x KH x KW, CHW = IC x KH x KW
|
||||
uint3 tpitg[[thread_position_in_threadgroup]],
|
||||
uint3 ntg[[threads_per_threadgroup]]) { // [M, 1, 1]
|
||||
const int64_t KHW = (int64_t)args.KHW;
|
||||
|
||||
const int64_t d = tgpig[0] / args.CHW;
|
||||
const int64_t chw = tgpig[0] % args.CHW;
|
||||
const int64_t tgpig_0 = chw / KHW; // 0 ~ (IC - 1)
|
||||
const int64_t HW = tgpig[0] % KHW;
|
||||
|
||||
const int64_t tpitg_0 = (d * ntg[0]) + tpitg[0];
|
||||
if (tpitg_0 >= args.N) {
|
||||
return;
|
||||
}
|
||||
|
||||
const int64_t tpitg_1 = HW / args.KW;
|
||||
const int64_t tpitg_2 = HW % args.KW;
|
||||
|
||||
const int64_t iiw = tgpig[2] * args.s0 + tpitg_2 * args.d0 - args.p0;
|
||||
const int64_t iih = tgpig[1] * args.s1 + tpitg_1 * args.d1 - args.p1;
|
||||
|
||||
const int64_t offset_dst =
|
||||
(tpitg_0 * tgpg[1] * tgpg[2] + tgpig[1] * tgpg[2] + tgpig[2]) * args.CHW +
|
||||
(tgpig_0 * KHW + tpitg_1 * args.KW + tpitg_2);
|
||||
|
||||
device T * pdst = (device T *) (dst);
|
||||
|
||||
if (iih < 0 || iih >= args.IH || iiw < 0 || iiw >= args.IW) {
|
||||
pdst[offset_dst] = 0.0f;
|
||||
} else {
|
||||
const int64_t offset_src = tpitg_0 * args.ofs0 + tgpig_0 * args.ofs1;
|
||||
pdst[offset_dst] = x[offset_src + iih * args.IW + iiw];
|
||||
}
|
||||
}
|
||||
|
||||
template [[host_name("kernel_im2col_ext_f32")]] kernel im2col_ext_t kernel_im2col_ext<float>;
|
||||
template [[host_name("kernel_im2col_ext_f16")]] kernel im2col_ext_t kernel_im2col_ext<half>;
|
||||
|
||||
template <typename TK>
|
||||
kernel void kernel_conv_2d(
|
||||
constant ggml_metal_kargs_conv_2d & args,
|
||||
device const char * weights,
|
||||
device const char * src,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tgpg[[threadgroups_per_grid]],
|
||||
uint3 tpitg[[thread_position_in_threadgroup]],
|
||||
uint3 ntg[[threads_per_threadgroup]]) {
|
||||
|
||||
const uint threads_per_tg = ntg.x * ntg.y * ntg.z;
|
||||
const uint tg_index = (tgpig.z * tgpg.y + tgpig.y) * tgpg.x + tgpig.x;
|
||||
const uint local_thread = tpitg.z * (ntg.x * ntg.y) + tpitg.y * ntg.x + tpitg.x;
|
||||
const uint thread_index = tg_index * threads_per_tg + local_thread;
|
||||
const uint64_t total_threads = (uint64_t) threads_per_tg * tgpg.x * tgpg.y * tgpg.z;
|
||||
const uint64_t total_outputs = (uint64_t) args.N * args.OC * args.OH * args.OW;
|
||||
|
||||
for (uint64_t index = thread_index; index < total_outputs; index += total_threads) {
|
||||
uint64_t tmp = index;
|
||||
|
||||
const int32_t ow = tmp % args.OW; tmp /= args.OW;
|
||||
const int32_t oh = tmp % args.OH; tmp /= args.OH;
|
||||
const int32_t oc = tmp % args.OC; tmp /= args.OC;
|
||||
const int32_t n = tmp;
|
||||
|
||||
float acc = 0.0f;
|
||||
|
||||
const int32_t base_x = ow*args.s0 - args.p0;
|
||||
const int32_t base_y = oh*args.s1 - args.p1;
|
||||
|
||||
int32_t ky_start = 0;
|
||||
if (base_y < 0) {
|
||||
ky_start = (-base_y + args.d1 - 1)/args.d1;
|
||||
}
|
||||
int32_t ky_end = args.KH;
|
||||
const int32_t y_max = args.IH - 1 - base_y;
|
||||
if (y_max < 0) {
|
||||
ky_end = ky_start;
|
||||
} else if (base_y + (args.KH - 1)*args.d1 >= args.IH) {
|
||||
ky_end = min(ky_end, y_max/args.d1 + 1);
|
||||
}
|
||||
|
||||
int32_t kx_start = 0;
|
||||
if (base_x < 0) {
|
||||
kx_start = (-base_x + args.d0 - 1)/args.d0;
|
||||
}
|
||||
int32_t kx_end = args.KW;
|
||||
const int32_t x_max = args.IW - 1 - base_x;
|
||||
if (x_max < 0) {
|
||||
kx_end = kx_start;
|
||||
} else if (base_x + (args.KW - 1)*args.d0 >= args.IW) {
|
||||
kx_end = min(kx_end, x_max/args.d0 + 1);
|
||||
}
|
||||
|
||||
if (ky_start < ky_end && kx_start < kx_end) {
|
||||
const uint64_t src_base_n = (uint64_t) n * args.nb13;
|
||||
const uint64_t w_base_oc = (uint64_t) oc * args.nb03;
|
||||
|
||||
for (int32_t ic = 0; ic < args.IC; ++ic) {
|
||||
const uint64_t src_base_nc = src_base_n + (uint64_t) ic * args.nb12;
|
||||
const uint64_t w_base_ocic = w_base_oc + (uint64_t) ic * args.nb02;
|
||||
|
||||
for (int32_t ky = ky_start; ky < ky_end; ++ky) {
|
||||
const int32_t iy = base_y + ky*args.d1;
|
||||
const uint64_t src_base_row = src_base_nc + (uint64_t) iy * args.nb11;
|
||||
const uint64_t w_base_row = w_base_ocic + (uint64_t) ky * args.nb01;
|
||||
|
||||
for (int32_t kx = kx_start; kx < kx_end; ++kx) {
|
||||
const int32_t ix = base_x + kx*args.d0;
|
||||
const uint64_t src_offs = src_base_row + (uint64_t) ix * args.nb10;
|
||||
const uint64_t w_offs = w_base_row + (uint64_t) kx * args.nb00;
|
||||
|
||||
const float x = *(device const float *)(src + src_offs);
|
||||
const float w = (float) (*(device const TK *)(weights + w_offs));
|
||||
|
||||
acc += x * w;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
const uint64_t dst_offs =
|
||||
(uint64_t) n * args.nb3 +
|
||||
(uint64_t) oc * args.nb2 +
|
||||
(uint64_t) oh * args.nb1 +
|
||||
(uint64_t) ow * args.nb0;
|
||||
|
||||
*(device float *)(dst + dst_offs) = acc;
|
||||
}
|
||||
}
|
||||
|
||||
template [[host_name("kernel_conv_2d_f32_f32")]]
|
||||
kernel void kernel_conv_2d<float>(
|
||||
constant ggml_metal_kargs_conv_2d & args,
|
||||
device const char * weights,
|
||||
device const char * src,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tgpg[[threadgroups_per_grid]],
|
||||
uint3 tpitg[[thread_position_in_threadgroup]],
|
||||
uint3 ntg[[threads_per_threadgroup]]);
|
||||
|
||||
template [[host_name("kernel_conv_2d_f16_f32")]]
|
||||
kernel void kernel_conv_2d<half>(
|
||||
constant ggml_metal_kargs_conv_2d & args,
|
||||
device const char * weights,
|
||||
device const char * src,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tgpg[[threadgroups_per_grid]],
|
||||
uint3 tpitg[[thread_position_in_threadgroup]],
|
||||
uint3 ntg[[threads_per_threadgroup]]);
|
||||
|
||||
typedef void (conv_transpose_1d_t)(
|
||||
constant ggml_metal_kargs_conv_transpose_1d & args,
|
||||
device const float * src0,
|
||||
device const float * src1,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tgpg[[threadgroups_per_grid]]);
|
||||
|
||||
template <typename T>
|
||||
kernel void kernel_conv_transpose_1d(
|
||||
constant ggml_metal_kargs_conv_transpose_1d & args,
|
||||
device const T * src0,
|
||||
device const float * src1,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tgpg[[threadgroups_per_grid]]) {
|
||||
|
||||
// For output position j on the time axis, only input positions
|
||||
// i such that i*s0 <= j < i*s0 + K
|
||||
// contribute -- i.e. i in [ceil((j - K + 1)/s0), floor(j/s0)]
|
||||
// intersected with [0, IL-1]. That's at most ceil(K/s0) values
|
||||
// (typically 2 for stride==K/2 transposed convs).
|
||||
const int32_t j = tgpig[0];
|
||||
const int32_t s0 = args.s0;
|
||||
const int32_t K = args.K;
|
||||
const int32_t IL = args.IL;
|
||||
|
||||
int32_t i_min;
|
||||
{
|
||||
int32_t a = j - K + 1;
|
||||
i_min = a <= 0 ? 0 : (a + s0 - 1) / s0; // ceil(a/s0) for a>0
|
||||
}
|
||||
int32_t i_max = j / s0;
|
||||
if (i_max > IL - 1) i_max = IL - 1;
|
||||
|
||||
float v = 0.0f;
|
||||
if (i_min <= i_max) {
|
||||
for (int64_t c = 0; c < args.IC; c++) {
|
||||
const int32_t kernel_offset = c * tgpg[1] * K + K * tgpig[1];
|
||||
const int32_t input_offset = c * IL;
|
||||
|
||||
for (int32_t i = i_min; i <= i_max; i++) {
|
||||
v += float(src0[kernel_offset + j - i * s0]) * src1[input_offset + i];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
device float * dst_ptr = (device float *) (dst + tgpig[0] * args.nb0 + tgpig[1] * args.nb1);
|
||||
|
||||
dst_ptr[0] = v;
|
||||
}
|
||||
|
||||
template [[host_name("kernel_conv_transpose_1d_f32_f32")]]
|
||||
kernel void kernel_conv_transpose_1d<float>(
|
||||
constant ggml_metal_kargs_conv_transpose_1d & args,
|
||||
device const float * src0,
|
||||
device const float * src1,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tgpg[[threadgroups_per_grid]]);
|
||||
|
||||
template [[host_name("kernel_conv_transpose_1d_f16_f32")]]
|
||||
kernel void kernel_conv_transpose_1d<half>(
|
||||
constant ggml_metal_kargs_conv_transpose_1d & args,
|
||||
device const half * src0,
|
||||
device const float * src1,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tgpg[[threadgroups_per_grid]]);
|
||||
|
||||
|
||||
typedef void (conv_transpose_2d_t)(
|
||||
constant ggml_metal_kargs_conv_transpose_2d & args,
|
||||
device const float * src0,
|
||||
device const float * src1,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tgpg[[threadgroups_per_grid]]);
|
||||
|
||||
template <typename T>
|
||||
kernel void kernel_conv_transpose_2d(
|
||||
constant ggml_metal_kargs_conv_transpose_2d & args,
|
||||
device const T * src0,
|
||||
device const float * src1,
|
||||
device char * dst,
|
||||
threadgroup float * shared_sum [[threadgroup(0)]],
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tpitg[[thread_position_in_threadgroup]],
|
||||
uint3 ntg[[threads_per_threadgroup]]) {
|
||||
|
||||
const int64_t out_x = tgpig[0];
|
||||
const int64_t out_y = tgpig[1];
|
||||
const int64_t out_c = tgpig[2];
|
||||
|
||||
const int64_t kw = tpitg[0];
|
||||
const int64_t kh = tpitg[1];
|
||||
|
||||
float v = 0.0f;
|
||||
|
||||
for (int64_t in_c = 0; in_c < args.IC; in_c++) {
|
||||
int64_t in_y = out_y - kh;
|
||||
|
||||
if (in_y < 0 || in_y % args.s0) continue;
|
||||
|
||||
in_y /= args.s0;
|
||||
|
||||
if (in_y >= args.IH) continue;
|
||||
|
||||
int64_t in_x = out_x - kw;
|
||||
|
||||
if (in_x < 0 || in_x % args.s0) continue;
|
||||
|
||||
in_x /= args.s0;
|
||||
|
||||
if (in_x >= args.IW) continue;
|
||||
|
||||
const int64_t input_idx = (args.IW * args.IH) * in_c + (args.IW) * in_y + in_x;
|
||||
const int64_t kernel_idx = (args.KH * args.KW * args.OC) * in_c + (args.KH * args.KW) * out_c + (args.KW) * kh + kw;
|
||||
|
||||
v += (float)src0[kernel_idx] * src1[input_idx];
|
||||
}
|
||||
|
||||
const uint tid = tpitg.y * ntg.x + tpitg.x;
|
||||
shared_sum[tid] = v;
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
if (tid == 0) {
|
||||
float total = 0.0f;
|
||||
const uint num_threads = ntg.x * ntg.y;
|
||||
for (uint i = 0; i < num_threads; i++) {
|
||||
total += shared_sum[i];
|
||||
}
|
||||
|
||||
device float * dst_ptr = (device float *) (dst + out_x*args.nb0 + out_y * args.nb1 + out_c*args.nb2);
|
||||
dst_ptr[0] = total;
|
||||
}
|
||||
}
|
||||
|
||||
template [[host_name("kernel_conv_transpose_2d_f32_f32")]]
|
||||
kernel void kernel_conv_transpose_2d<float>(
|
||||
constant ggml_metal_kargs_conv_transpose_2d & args,
|
||||
device const float * src0,
|
||||
device const float * src1,
|
||||
device char * dst,
|
||||
threadgroup float * shared_sum [[threadgroup(0)]],
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tpitg[[thread_position_in_threadgroup]],
|
||||
uint3 ntg[[threads_per_threadgroup]]);
|
||||
|
||||
template [[host_name("kernel_conv_transpose_2d_f16_f32")]]
|
||||
kernel void kernel_conv_transpose_2d<half>(
|
||||
constant ggml_metal_kargs_conv_transpose_2d & args,
|
||||
device const half * src0,
|
||||
device const float * src1,
|
||||
device char * dst,
|
||||
threadgroup float * shared_sum [[threadgroup(0)]],
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tpitg[[thread_position_in_threadgroup]],
|
||||
uint3 ntg[[threads_per_threadgroup]]);
|
||||
|
||||
template <typename T>
|
||||
kernel void kernel_conv_3d(
|
||||
constant ggml_metal_kargs_conv_3d & args,
|
||||
device const char * src0, // Weights [IC * OC, KD, KH, KW]
|
||||
device const char * src1, // Inputs [IC * N, ID, IH, IW]
|
||||
device char * dst, // Outputs [OC * N, OD, OH, OW]
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tpitg[[thread_position_in_threadgroup]]) {
|
||||
|
||||
// 1. Un-flatten the spatial dimension from Grid X
|
||||
int64_t spatial_idx = tgpig.x * 32 + tpitg.x;
|
||||
|
||||
if (spatial_idx >= args.OW * args.OH * args.OD) {
|
||||
return; // Thread falls outside the spatial volume
|
||||
}
|
||||
|
||||
int64_t od = spatial_idx / (args.OW * args.OH);
|
||||
int64_t oh = (spatial_idx / args.OW) % args.OH;
|
||||
int64_t ow = spatial_idx % args.OW;
|
||||
|
||||
// 2. Map Y to Channels, Z to Batch
|
||||
int64_t oc = tgpig.y;
|
||||
int64_t batch_idx = tgpig.z;
|
||||
|
||||
// 3. Calculate anchor coordinates in the Input volume
|
||||
int64_t i_w_base = ow * args.s0 - args.p0;
|
||||
int64_t i_h_base = oh * args.s1 - args.p1;
|
||||
int64_t i_d_base = od * args.s2 - args.p2;
|
||||
|
||||
float sum = 0.0f;
|
||||
|
||||
// 4. Gather Loop (Iterate over Input Channels -> Depth -> Height -> Width)
|
||||
for (int64_t ic = 0; ic < args.IC; ++ic) {
|
||||
|
||||
// ggml packs batch and channel together in the 4th dimension
|
||||
int64_t src_cn_idx = batch_idx * args.IC + ic;
|
||||
int64_t w_cn_idx = oc * args.IC + ic;
|
||||
|
||||
for (int64_t kz = 0; kz < args.KD; ++kz) {
|
||||
int64_t id = i_d_base + kz * args.d2;
|
||||
if (id < 0 || id >= args.ID) continue; // Boundary check (Padding)
|
||||
|
||||
for (int64_t ky = 0; ky < args.KH; ++ky) {
|
||||
int64_t ih = i_h_base + ky * args.d1;
|
||||
if (ih < 0 || ih >= args.IH) continue;
|
||||
|
||||
for (int64_t kx = 0; kx < args.KW; ++kx) {
|
||||
int64_t iw = i_w_base + kx * args.d0;
|
||||
if (iw < 0 || iw >= args.IW) continue;
|
||||
|
||||
// Convert multi-dimensional coordinates to flat byte offsets
|
||||
int64_t w_idx = kx*args.nb00 + ky*args.nb01 + kz*args.nb02 + w_cn_idx*args.nb03;
|
||||
int64_t i_idx = iw*args.nb10 + ih*args.nb11 + id*args.nb12 + src_cn_idx*args.nb13;
|
||||
|
||||
// Dereference memory and cast weights to f32 if they were f16
|
||||
float w_val = (float)*(device const T*)((device const char*)src0 + w_idx);
|
||||
float i_val = *(device const float*)((device const char*)src1 + i_idx);
|
||||
|
||||
sum += w_val * i_val;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// 5. Write the accumulated value out to RAM
|
||||
int64_t dst_cn_idx = batch_idx * args.OC + oc;
|
||||
int64_t d_idx = ow*args.nb0 + oh*args.nb1 + od*args.nb2 + dst_cn_idx*args.nb3;
|
||||
|
||||
*(device float*)(dst + d_idx) = sum;
|
||||
}
|
||||
|
||||
// Explicit instantiations so the JIT compiler can find them by name
|
||||
template [[host_name("kernel_conv_3d_f32_f32")]]
|
||||
kernel void kernel_conv_3d<float>(
|
||||
constant ggml_metal_kargs_conv_3d & args,
|
||||
device const char * src0,
|
||||
device const char * src1,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tpitg[[thread_position_in_threadgroup]]);
|
||||
|
||||
// Explicit instantiation for f16 weights
|
||||
template [[host_name("kernel_conv_3d_f16_f32")]]
|
||||
kernel void kernel_conv_3d<half>(
|
||||
constant ggml_metal_kargs_conv_3d & args,
|
||||
device const char * src0,
|
||||
device const char * src1,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tpitg[[thread_position_in_threadgroup]]);
|
||||
@@ -1,686 +0,0 @@
|
||||
#pragma once
|
||||
|
||||
#include "common.h"
|
||||
|
||||
#define GGML_COMMON_DECL_METAL
|
||||
#define GGML_COMMON_IMPL_METAL
|
||||
#if defined(GGML_METAL_EMBED_LIBRARY)
|
||||
__embed_ggml-common.h__
|
||||
#else
|
||||
#include "ggml-common.h"
|
||||
#endif
|
||||
|
||||
#define QK_NL 16 // shared by mul_mm and get_rows_q instantiations
|
||||
|
||||
// NOTE: this is not dequantizing - we are simply fitting the template
|
||||
template <typename type4x4>
|
||||
void dequantize_f32(device const float4x4 * src, short il, thread type4x4 & reg) {
|
||||
reg = (type4x4)(*src);
|
||||
}
|
||||
|
||||
template <typename type4>
|
||||
void dequantize_f32_t4(device const float4 * src, short il, thread type4 & reg) {
|
||||
reg = (type4)(*src);
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_f16(device const half4x4 * src, short il, thread type4x4 & reg) {
|
||||
reg = (type4x4)(*src);
|
||||
}
|
||||
|
||||
template <typename type4>
|
||||
void dequantize_f16_t4(device const half4 * src, short il, thread type4 & reg) {
|
||||
reg = (type4)(*(src));
|
||||
}
|
||||
|
||||
#if defined(GGML_METAL_HAS_BF16)
|
||||
template <typename type4x4>
|
||||
void dequantize_bf16(device const bfloat4x4 * src, short il, thread type4x4 & reg) {
|
||||
reg = (type4x4)(*src);
|
||||
}
|
||||
|
||||
template <typename type4>
|
||||
void dequantize_bf16_t4(device const bfloat4 * src, short il, thread type4 & reg) {
|
||||
reg = (type4)(*(src));
|
||||
}
|
||||
#endif
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_q1_0(device const block_q1_0 * xb, short il, thread type4x4 & reg) {
|
||||
device const uint8_t * qs = xb->qs;
|
||||
const float d = xb->d;
|
||||
const float neg_d = -d;
|
||||
|
||||
const int byte_offset = il * 2; // il*16 bits = il*2 bytes
|
||||
const uint8_t b0 = qs[byte_offset];
|
||||
const uint8_t b1 = qs[byte_offset + 1];
|
||||
|
||||
float4x4 reg_f;
|
||||
|
||||
reg_f[0][0] = select(neg_d, d, bool(b0 & 0x01));
|
||||
reg_f[0][1] = select(neg_d, d, bool(b0 & 0x02));
|
||||
reg_f[0][2] = select(neg_d, d, bool(b0 & 0x04));
|
||||
reg_f[0][3] = select(neg_d, d, bool(b0 & 0x08));
|
||||
reg_f[1][0] = select(neg_d, d, bool(b0 & 0x10));
|
||||
reg_f[1][1] = select(neg_d, d, bool(b0 & 0x20));
|
||||
reg_f[1][2] = select(neg_d, d, bool(b0 & 0x40));
|
||||
reg_f[1][3] = select(neg_d, d, bool(b0 & 0x80));
|
||||
|
||||
reg_f[2][0] = select(neg_d, d, bool(b1 & 0x01));
|
||||
reg_f[2][1] = select(neg_d, d, bool(b1 & 0x02));
|
||||
reg_f[2][2] = select(neg_d, d, bool(b1 & 0x04));
|
||||
reg_f[2][3] = select(neg_d, d, bool(b1 & 0x08));
|
||||
reg_f[3][0] = select(neg_d, d, bool(b1 & 0x10));
|
||||
reg_f[3][1] = select(neg_d, d, bool(b1 & 0x20));
|
||||
reg_f[3][2] = select(neg_d, d, bool(b1 & 0x40));
|
||||
reg_f[3][3] = select(neg_d, d, bool(b1 & 0x80));
|
||||
|
||||
reg = (type4x4) reg_f;
|
||||
}
|
||||
|
||||
template <typename type4>
|
||||
void dequantize_q1_0_t4(device const block_q1_0 * xb, short il, thread type4 & reg) {
|
||||
const float d = xb->d;
|
||||
const float neg_d = -d;
|
||||
const int base = il * 4;
|
||||
const uint8_t byte = xb->qs[base / 8];
|
||||
const int s = base % 8;
|
||||
|
||||
float4 reg_f;
|
||||
reg_f[0] = select(neg_d, d, bool((byte >> (s )) & 1));
|
||||
reg_f[1] = select(neg_d, d, bool((byte >> (s + 1)) & 1));
|
||||
reg_f[2] = select(neg_d, d, bool((byte >> (s + 2)) & 1));
|
||||
reg_f[3] = select(neg_d, d, bool((byte >> (s + 3)) & 1));
|
||||
|
||||
reg = (type4) reg_f;
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_q4_0(device const block_q4_0 * xb, short il, thread type4x4 & reg) {
|
||||
device const uint16_t * qs = ((device const uint16_t *)xb + 1);
|
||||
const float d1 = il ? (xb->d / 16.h) : xb->d;
|
||||
const float d2 = d1 / 256.f;
|
||||
const float md = -8.h * xb->d;
|
||||
const ushort mask0 = il ? 0x00F0 : 0x000F;
|
||||
const ushort mask1 = mask0 << 8;
|
||||
|
||||
float4x4 reg_f;
|
||||
|
||||
for (int i = 0; i < 8; i++) {
|
||||
reg_f[i/2][2*(i%2) + 0] = d1 * (qs[i] & mask0) + md;
|
||||
reg_f[i/2][2*(i%2) + 1] = d2 * (qs[i] & mask1) + md;
|
||||
}
|
||||
|
||||
reg = (type4x4) reg_f;
|
||||
}
|
||||
|
||||
template <typename type4>
|
||||
void dequantize_q4_0_t4(device const block_q4_0 * xb, short il, thread type4 & reg) {
|
||||
device const uint16_t * qs = ((device const uint16_t *)xb + 1);
|
||||
const float d1 = (il/4) ? (xb->d / 16.h) : xb->d;
|
||||
const float d2 = d1 / 256.f;
|
||||
const float md = -8.h * xb->d;
|
||||
const ushort mask0 = (il/4) ? 0x00F0 : 0x000F;
|
||||
const ushort mask1 = mask0 << 8;
|
||||
|
||||
for (int i = 0; i < 2; i++) {
|
||||
reg[2*i + 0] = d1 * (qs[2*(il%4) + i] & mask0) + md;
|
||||
reg[2*i + 1] = d2 * (qs[2*(il%4) + i] & mask1) + md;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_q4_1(device const block_q4_1 * xb, short il, thread type4x4 & reg) {
|
||||
device const uint16_t * qs = ((device const uint16_t *)xb + 2);
|
||||
const float d1 = il ? (xb->d / 16.h) : xb->d;
|
||||
const float d2 = d1 / 256.f;
|
||||
const float m = xb->m;
|
||||
const ushort mask0 = il ? 0x00F0 : 0x000F;
|
||||
const ushort mask1 = mask0 << 8;
|
||||
|
||||
float4x4 reg_f;
|
||||
|
||||
for (int i = 0; i < 8; i++) {
|
||||
reg_f[i/2][2*(i%2) + 0] = ((qs[i] & mask0) * d1) + m;
|
||||
reg_f[i/2][2*(i%2) + 1] = ((qs[i] & mask1) * d2) + m;
|
||||
}
|
||||
|
||||
reg = (type4x4) reg_f;
|
||||
}
|
||||
|
||||
template <typename type4>
|
||||
void dequantize_q4_1_t4(device const block_q4_1 * xb, short il, thread type4 & reg) {
|
||||
device const uint16_t * qs = ((device const uint16_t *)xb + 2);
|
||||
const float d1 = (il/4) ? (xb->d / 16.h) : xb->d;
|
||||
const float d2 = d1 / 256.f;
|
||||
const float m = xb->m;
|
||||
const ushort mask0 = (il/4) ? 0x00F0 : 0x000F;
|
||||
const ushort mask1 = mask0 << 8;
|
||||
|
||||
for (int i = 0; i < 2; i++) {
|
||||
reg[2*i + 0] = d1 * (qs[2*(il%4) + i] & mask0) + m;
|
||||
reg[2*i + 1] = d2 * (qs[2*(il%4) + i] & mask1) + m;
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_q5_0(device const block_q5_0 * xb, short il, thread type4x4 & reg) {
|
||||
device const uint16_t * qs = ((device const uint16_t *)xb + 3);
|
||||
const float d = xb->d;
|
||||
const float md = -16.h * xb->d;
|
||||
const ushort mask = il ? 0x00F0 : 0x000F;
|
||||
|
||||
const uint32_t qh = *((device const uint32_t *)xb->qh);
|
||||
|
||||
const int x_mv = il ? 4 : 0;
|
||||
|
||||
const int gh_mv = il ? 12 : 0;
|
||||
const int gh_bk = il ? 0 : 4;
|
||||
|
||||
float4x4 reg_f;
|
||||
|
||||
for (int i = 0; i < 8; i++) {
|
||||
// extract the 5-th bits for x0 and x1
|
||||
const uint8_t xh_0 = ((qh >> (gh_mv + 2*i )) << gh_bk) & 0x10;
|
||||
const uint8_t xh_1 = ((qh >> (gh_mv + 2*i+1)) << gh_bk) & 0x10;
|
||||
|
||||
// combine the 4-bits from qs with the 5th bit
|
||||
const int32_t x0 = ((((qs[i] ) & mask) >> x_mv) | xh_0);
|
||||
const int32_t x1 = ((((qs[i] >> 8) & mask) >> x_mv) | xh_1);
|
||||
|
||||
reg_f[i/2][2*(i%2) + 0] = d * x0 + md;
|
||||
reg_f[i/2][2*(i%2) + 1] = d * x1 + md;
|
||||
}
|
||||
|
||||
reg = (type4x4) reg_f;
|
||||
}
|
||||
|
||||
template <typename type4>
|
||||
void dequantize_q5_0_t4(device const block_q5_0 * xb, short il, thread type4 & reg) {
|
||||
device const uint16_t * qs = ((device const uint16_t *)xb + 3);
|
||||
const float d = xb->d;
|
||||
const float md = -16.h * xb->d;
|
||||
const ushort mask = (il/4) ? 0x00F0 : 0x000F;
|
||||
|
||||
const uint32_t qh = *((device const uint32_t *)xb->qh);
|
||||
|
||||
const int x_mv = (il/4) ? 4 : 0;
|
||||
|
||||
const int gh_mv = (il/4) ? 12 : 0;
|
||||
const int gh_bk = (il/4) ? 0 : 4;
|
||||
|
||||
for (int ii = 0; ii < 2; ii++) {
|
||||
int i = 2*(il%4) + ii;
|
||||
|
||||
// extract the 5-th bits for x0 and x1
|
||||
const uint8_t xh_0 = ((qh >> (gh_mv + 2*i )) << gh_bk) & 0x10;
|
||||
const uint8_t xh_1 = ((qh >> (gh_mv + 2*i+1)) << gh_bk) & 0x10;
|
||||
|
||||
// combine the 4-bits from qs with the 5th bit
|
||||
const int32_t x0 = ((((qs[i] ) & mask) >> x_mv) | xh_0);
|
||||
const int32_t x1 = ((((qs[i] >> 8) & mask) >> x_mv) | xh_1);
|
||||
|
||||
reg[2*ii + 0] = d * x0 + md;
|
||||
reg[2*ii + 1] = d * x1 + md;
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_q5_1(device const block_q5_1 * xb, short il, thread type4x4 & reg) {
|
||||
device const uint16_t * qs = ((device const uint16_t *)xb + 4);
|
||||
const float d = xb->d;
|
||||
const float m = xb->m;
|
||||
const ushort mask = il ? 0x00F0 : 0x000F;
|
||||
|
||||
const uint32_t qh = *((device const uint32_t *)xb->qh);
|
||||
|
||||
const int x_mv = il ? 4 : 0;
|
||||
|
||||
const int gh_mv = il ? 12 : 0;
|
||||
const int gh_bk = il ? 0 : 4;
|
||||
|
||||
float4x4 reg_f;
|
||||
|
||||
for (int i = 0; i < 8; i++) {
|
||||
// extract the 5-th bits for x0 and x1
|
||||
const uint8_t xh_0 = ((qh >> (gh_mv + 2*i )) << gh_bk) & 0x10;
|
||||
const uint8_t xh_1 = ((qh >> (gh_mv + 2*i+1)) << gh_bk) & 0x10;
|
||||
|
||||
// combine the 4-bits from qs with the 5th bit
|
||||
const int32_t x0 = ((((qs[i] ) & mask) >> x_mv) | xh_0);
|
||||
const int32_t x1 = ((((qs[i] >> 8) & mask) >> x_mv) | xh_1);
|
||||
|
||||
reg_f[i/2][2*(i%2) + 0] = d * x0 + m;
|
||||
reg_f[i/2][2*(i%2) + 1] = d * x1 + m;
|
||||
}
|
||||
|
||||
reg = (type4x4) reg_f;
|
||||
}
|
||||
|
||||
template <typename type4>
|
||||
void dequantize_q5_1_t4(device const block_q5_1 * xb, short il, thread type4 & reg) {
|
||||
device const uint16_t * qs = ((device const uint16_t *)xb + 4);
|
||||
const float d = xb->d;
|
||||
const float m = xb->m;
|
||||
const ushort mask = (il/4) ? 0x00F0 : 0x000F;
|
||||
|
||||
const uint32_t qh = *((device const uint32_t *)xb->qh);
|
||||
|
||||
const int x_mv = (il/4) ? 4 : 0;
|
||||
|
||||
const int gh_mv = (il/4) ? 12 : 0;
|
||||
const int gh_bk = (il/4) ? 0 : 4;
|
||||
|
||||
for (int ii = 0; ii < 2; ii++) {
|
||||
int i = 2*(il%4) + ii;
|
||||
|
||||
// extract the 5-th bits for x0 and x1
|
||||
const uint8_t xh_0 = ((qh >> (gh_mv + 2*i )) << gh_bk) & 0x10;
|
||||
const uint8_t xh_1 = ((qh >> (gh_mv + 2*i+1)) << gh_bk) & 0x10;
|
||||
|
||||
// combine the 4-bits from qs with the 5th bit
|
||||
const int32_t x0 = ((((qs[i] ) & mask) >> x_mv) | xh_0);
|
||||
const int32_t x1 = ((((qs[i] >> 8) & mask) >> x_mv) | xh_1);
|
||||
|
||||
reg[2*ii + 0] = d * x0 + m;
|
||||
reg[2*ii + 1] = d * x1 + m;
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_q8_0(device const block_q8_0 *xb, short il, thread type4x4 & reg) {
|
||||
device const int8_t * qs = ((device const int8_t *)xb->qs);
|
||||
const float d = xb->d;
|
||||
|
||||
float4x4 reg_f;
|
||||
|
||||
for (int i = 0; i < 16; i++) {
|
||||
reg_f[i/4][i%4] = (qs[i + 16*il] * d);
|
||||
}
|
||||
|
||||
reg = (type4x4) reg_f;
|
||||
}
|
||||
|
||||
template <typename type4>
|
||||
void dequantize_q8_0_t4(device const block_q8_0 *xb, short il, thread type4 & reg) {
|
||||
device const int8_t * qs = ((device const int8_t *)xb->qs);
|
||||
const float d = xb->d;
|
||||
|
||||
for (int i = 0; i < 4; i++) {
|
||||
reg[i] = (qs[4*(il%4) + i + 16*(il/4)] * d);
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_mxfp4(device const block_mxfp4 * xb, short il, thread type4x4 & reg) {
|
||||
device const uint8_t * q2 = (device const uint8_t *)xb->qs;
|
||||
|
||||
const float d = e8m0_to_fp32(xb->e);
|
||||
const uint8_t shr = il >= 1 ? 4 : 0;
|
||||
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
reg[i][0] = d * kvalues_mxfp4_f[(q2[4*i + 0] >> shr) & 0x0F];
|
||||
reg[i][1] = d * kvalues_mxfp4_f[(q2[4*i + 1] >> shr) & 0x0F];
|
||||
reg[i][2] = d * kvalues_mxfp4_f[(q2[4*i + 2] >> shr) & 0x0F];
|
||||
reg[i][3] = d * kvalues_mxfp4_f[(q2[4*i + 3] >> shr) & 0x0F];
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4>
|
||||
void dequantize_mxfp4_t4(device const block_mxfp4 * xb, short il, thread type4 & reg) {
|
||||
device const uint8_t * q2 = (device const uint8_t *)xb->qs;
|
||||
|
||||
const float d = e8m0_to_fp32(xb->e);
|
||||
const short il4 = il%4;
|
||||
|
||||
const uint8_t shr = il >= 4 ? 4 : 0;
|
||||
|
||||
reg[0] = d * kvalues_mxfp4_f[(q2[4*il4 + 0] >> shr) & 0x0F];
|
||||
reg[1] = d * kvalues_mxfp4_f[(q2[4*il4 + 1] >> shr) & 0x0F];
|
||||
reg[2] = d * kvalues_mxfp4_f[(q2[4*il4 + 2] >> shr) & 0x0F];
|
||||
reg[3] = d * kvalues_mxfp4_f[(q2[4*il4 + 3] >> shr) & 0x0F];
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_q2_K(device const block_q2_K *xb, short il, thread type4x4 & reg) {
|
||||
const float d = xb->d;
|
||||
const float min = xb->dmin;
|
||||
device const uint8_t * q = (device const uint8_t *)xb->qs;
|
||||
float dl, ml;
|
||||
uint8_t sc = xb->scales[il];
|
||||
|
||||
q = q + 32*(il/8) + 16*(il&1);
|
||||
il = (il/2)%4;
|
||||
|
||||
half coef = il>1 ? (il>2 ? 1/64.h : 1/16.h) : (il>0 ? 1/4.h : 1.h);
|
||||
uchar mask = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3);
|
||||
dl = d * (sc & 0xF) * coef, ml = min * (sc >> 4);
|
||||
for (int i = 0; i < 16; ++i) {
|
||||
reg[i/4][i%4] = dl * (q[i] & mask) - ml;
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_q3_K(device const block_q3_K *xb, short il, thread type4x4 & reg) {
|
||||
const half d_all = xb->d;
|
||||
device const uint8_t * q = (device const uint8_t *)xb->qs;
|
||||
device const uint8_t * h = (device const uint8_t *)xb->hmask;
|
||||
device const int8_t * scales = (device const int8_t *)xb->scales;
|
||||
|
||||
q = q + 32 * (il/8) + 16 * (il&1);
|
||||
h = h + 16 * (il&1);
|
||||
uint8_t m = 1 << (il/2);
|
||||
uint16_t kmask1 = (il/4)>1 ? ((il/4)>2 ? 192 : 48) : \
|
||||
((il/4)>0 ? 12 : 3);
|
||||
uint16_t kmask2 = il/8 ? 0xF0 : 0x0F;
|
||||
uint16_t scale_2 = scales[il%8], scale_1 = scales[8 + il%4];
|
||||
int16_t dl_int = (il/4)&1 ? (scale_2&kmask2) | ((scale_1&kmask1) << 2)
|
||||
: (scale_2&kmask2) | ((scale_1&kmask1) << 4);
|
||||
float dl = il<8 ? d_all * (dl_int - 32.f) : d_all * (dl_int / 16.f - 32.f);
|
||||
const float ml = 4.f * dl;
|
||||
|
||||
il = (il/2) & 3;
|
||||
const half coef = il>1 ? (il>2 ? 1/64.h : 1/16.h) : (il>0 ? 1/4.h : 1.h);
|
||||
const uint8_t mask = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3);
|
||||
dl *= coef;
|
||||
|
||||
for (int i = 0; i < 16; ++i) {
|
||||
reg[i/4][i%4] = dl * (q[i] & mask) - (h[i] & m ? 0 : ml);
|
||||
}
|
||||
}
|
||||
|
||||
static inline uchar2 get_scale_min_k4_just2(int j, int k, device const uchar * q) {
|
||||
return j < 4 ? uchar2{uchar(q[j+0+k] & 63), uchar(q[j+4+k] & 63)}
|
||||
: uchar2{uchar((q[j+4+k] & 0xF) | ((q[j-4+k] & 0xc0) >> 2)), uchar((q[j+4+k] >> 4) | ((q[j-0+k] & 0xc0) >> 2))};
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_q4_K(device const block_q4_K * xb, short il, thread type4x4 & reg) {
|
||||
device const uchar * q = xb->qs;
|
||||
|
||||
short is = (il/4) * 2;
|
||||
q = q + (il/4) * 32 + 16 * (il&1);
|
||||
il = il & 3;
|
||||
const uchar2 sc = get_scale_min_k4_just2(is, il/2, xb->scales);
|
||||
const float d = il < 2 ? xb->d : xb->d / 16.h;
|
||||
const float min = xb->dmin;
|
||||
const float dl = d * sc[0];
|
||||
const float ml = min * sc[1];
|
||||
|
||||
const ushort mask = il < 2 ? 0x0F : 0xF0;
|
||||
for (int i = 0; i < 16; ++i) {
|
||||
reg[i/4][i%4] = dl * (q[i] & mask) - ml;
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_q5_K(device const block_q5_K *xb, short il, thread type4x4 & reg) {
|
||||
device const uint8_t * q = xb->qs;
|
||||
device const uint8_t * qh = xb->qh;
|
||||
|
||||
short is = (il/4) * 2;
|
||||
q = q + 32 * (il/4) + 16 * (il&1);
|
||||
qh = qh + 16 * (il&1);
|
||||
uint8_t ul = 1 << (il/2);
|
||||
il = il & 3;
|
||||
const uchar2 sc = get_scale_min_k4_just2(is, il/2, xb->scales);
|
||||
const float d = il < 2 ? xb->d : xb->d / 16.f;
|
||||
const float min = xb->dmin;
|
||||
const float dl = d * sc[0];
|
||||
const float ml = min * sc[1];
|
||||
|
||||
const ushort mask = il<2 ? 0x0F : 0xF0;
|
||||
const float qh_val = il<2 ? 16.f : 256.f;
|
||||
for (int i = 0; i < 16; ++i) {
|
||||
reg[i/4][i%4] = dl * ((q[i] & mask) + (qh[i] & ul ? qh_val : 0)) - ml;
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_q6_K(device const block_q6_K *xb, short il, thread type4x4 & reg) {
|
||||
const half d_all = xb->d;
|
||||
device const uint16_t * ql = (device const uint16_t *)xb->ql;
|
||||
device const uint16_t * qh = (device const uint16_t *)xb->qh;
|
||||
device const int8_t * scales = (device const int8_t *)xb->scales;
|
||||
|
||||
ql = ql + 32*(il/8) + 16*((il/2)&1) + 8*(il&1);
|
||||
qh = qh + 16*(il/8) + 8*(il&1);
|
||||
float sc = scales[(il%2) + 2 * ((il/2))];
|
||||
il = (il/2) & 3;
|
||||
|
||||
const uint32_t kmask1 = il>1 ? (il>2 ? 0xC0C0C0C0 : 0x30303030) : (il>0 ? 0x0C0C0C0C : 0x03030303);
|
||||
const uint32_t kmask2 = il>1 ? 0xF0F0F0F0 : 0x0F0F0F0F;
|
||||
const float ml = d_all * sc * 32.f;
|
||||
const float dl0 = d_all * sc;
|
||||
const float dl1 = dl0 / 256.f;
|
||||
const float dl2 = dl0 / (256.f * 256.f);
|
||||
const float dl3 = dl0 / (256.f * 256.f * 256.f);
|
||||
const uint8_t shr_h = il>2 ? 2 : 0;
|
||||
const uint8_t shl_h = il>1 ? 0 : (il>0 ? 2 : 4);
|
||||
const uint8_t shr_l = il>1 ? 4 : 0;
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
const uint32_t low = (ql[2*i] | (uint32_t)(ql[2*i+1] << 16)) & kmask2;
|
||||
const uint32_t high = (qh[2*i] | (uint32_t)(qh[2*i+1] << 16)) & kmask1;
|
||||
const uint32_t q = ((high << shl_h) >> shr_h) | (low >> shr_l);
|
||||
reg[i][0] = dl0 * ((half)(q & 0xFF)) - ml;
|
||||
reg[i][1] = dl1 * ((float)(q & 0xFF00)) - ml;
|
||||
reg[i][2] = dl2 * ((float)(q & 0xFF0000)) - ml;
|
||||
reg[i][3] = dl3 * ((float)(q & 0xFF000000)) - ml;
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_iq2_xxs(device const block_iq2_xxs * xb, short il, thread type4x4 & reg) {
|
||||
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
|
||||
const float d = xb->d;
|
||||
const int ib32 = il/2;
|
||||
il = il%2;
|
||||
// il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
|
||||
// each block of 32 needs 2 uint32_t's for the quants & scale, so 4 uint16_t's.
|
||||
device const uint16_t * q2 = xb->qs + 4*ib32;
|
||||
const uint32_t aux32_g = q2[0] | (q2[1] << 16);
|
||||
const uint32_t aux32_s = q2[2] | (q2[3] << 16);
|
||||
thread const uint8_t * aux8 = (thread const uint8_t *)&aux32_g;
|
||||
const float dl = d * (0.5f + (aux32_s >> 28)) * 0.25f;
|
||||
constant uint8_t * grid = (constant uint8_t *)(iq2xxs_grid + aux8[2*il+0]);
|
||||
uint8_t signs = ksigns_iq2xs[(aux32_s >> 14*il) & 127];
|
||||
for (int i = 0; i < 8; ++i) {
|
||||
reg[i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f);
|
||||
}
|
||||
grid = (constant uint8_t *)(iq2xxs_grid + aux8[2*il+1]);
|
||||
signs = ksigns_iq2xs[(aux32_s >> (14*il+7)) & 127];
|
||||
for (int i = 0; i < 8; ++i) {
|
||||
reg[2+i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f);
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_iq2_xs(device const block_iq2_xs * xb, short il, thread type4x4 & reg) {
|
||||
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
|
||||
const float d = xb->d;
|
||||
const int ib32 = il/2;
|
||||
il = il%2;
|
||||
// il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
|
||||
device const uint16_t * q2 = xb->qs + 4*ib32;
|
||||
const float dl = d * (0.5f + ((xb->scales[ib32] >> 4*il) & 0xf)) * 0.25f;
|
||||
constant uint8_t * grid = (constant uint8_t *)(iq2xs_grid + (q2[2*il+0] & 511));
|
||||
uint8_t signs = ksigns_iq2xs[q2[2*il+0] >> 9];
|
||||
for (int i = 0; i < 8; ++i) {
|
||||
reg[i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f);
|
||||
}
|
||||
grid = (constant uint8_t *)(iq2xs_grid + (q2[2*il+1] & 511));
|
||||
signs = ksigns_iq2xs[q2[2*il+1] >> 9];
|
||||
for (int i = 0; i < 8; ++i) {
|
||||
reg[2+i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f);
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_iq3_xxs(device const block_iq3_xxs * xb, short il, thread type4x4 & reg) {
|
||||
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
|
||||
const float d = xb->d;
|
||||
const int ib32 = il/2;
|
||||
il = il%2;
|
||||
// il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
|
||||
device const uint8_t * q3 = xb->qs + 8*ib32;
|
||||
device const uint16_t * gas = (device const uint16_t *)(xb->qs + QK_K/4) + 2*ib32;
|
||||
const uint32_t aux32 = gas[0] | (gas[1] << 16);
|
||||
const float dl = d * (0.5f + (aux32 >> 28)) * 0.5f;
|
||||
constant uint8_t * grid1 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+0]);
|
||||
constant uint8_t * grid2 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+1]);
|
||||
uint8_t signs = ksigns_iq2xs[(aux32 >> 14*il) & 127];
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
reg[0][i] = dl * grid1[i] * (signs & kmask_iq2xs[i+0] ? -1.f : 1.f);
|
||||
reg[1][i] = dl * grid2[i] * (signs & kmask_iq2xs[i+4] ? -1.f : 1.f);
|
||||
}
|
||||
grid1 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+2]);
|
||||
grid2 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+3]);
|
||||
signs = ksigns_iq2xs[(aux32 >> (14*il+7)) & 127];
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
reg[2][i] = dl * grid1[i] * (signs & kmask_iq2xs[i+0] ? -1.f : 1.f);
|
||||
reg[3][i] = dl * grid2[i] * (signs & kmask_iq2xs[i+4] ? -1.f : 1.f);
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_iq3_s(device const block_iq3_s * xb, short il, thread type4x4 & reg) {
|
||||
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
|
||||
const float d = xb->d;
|
||||
const int ib32 = il/2;
|
||||
il = il%2;
|
||||
// il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
|
||||
device const uint8_t * qs = xb->qs + 8*ib32;
|
||||
device const uint8_t * signs = xb->signs + 4*ib32 + 2*il;
|
||||
const uint8_t qh = xb->qh[ib32] >> 4*il;
|
||||
const float dl = d * (1 + 2*((xb->scales[ib32/2] >> 4*(ib32%2)) & 0xf));
|
||||
constant uint8_t * grid1 = (constant uint8_t *)(iq3s_grid + (qs[4*il+0] | ((qh << 8) & 256)));
|
||||
constant uint8_t * grid2 = (constant uint8_t *)(iq3s_grid + (qs[4*il+1] | ((qh << 7) & 256)));
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
reg[0][i] = dl * grid1[i] * select(1, -1, signs[0] & kmask_iq2xs[i+0]);
|
||||
reg[1][i] = dl * grid2[i] * select(1, -1, signs[0] & kmask_iq2xs[i+4]);
|
||||
}
|
||||
grid1 = (constant uint8_t *)(iq3s_grid + (qs[4*il+2] | ((qh << 6) & 256)));
|
||||
grid2 = (constant uint8_t *)(iq3s_grid + (qs[4*il+3] | ((qh << 5) & 256)));
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
reg[2][i] = dl * grid1[i] * select(1, -1, signs[1] & kmask_iq2xs[i+0]);
|
||||
reg[3][i] = dl * grid2[i] * select(1, -1, signs[1] & kmask_iq2xs[i+4]);
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_iq2_s(device const block_iq2_s * xb, short il, thread type4x4 & reg) {
|
||||
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
|
||||
const float d = xb->d;
|
||||
const int ib32 = il/2;
|
||||
il = il%2;
|
||||
// il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
|
||||
device const uint8_t * qs = xb->qs + 4*ib32 + 2*il;
|
||||
device const uint8_t * signs = qs + QK_K/8;
|
||||
const uint8_t qh = xb->qh[ib32] >> 4*il;
|
||||
const float dl = d * (0.5f + ((xb->scales[ib32] >> 4*il) & 0xf)) * 0.25f;
|
||||
constant uint8_t * grid1 = (constant uint8_t *)(iq2s_grid + (qs[0] | ((qh << 8) & 0x300)));
|
||||
constant uint8_t * grid2 = (constant uint8_t *)(iq2s_grid + (qs[1] | ((qh << 6) & 0x300)));
|
||||
for (int i = 0; i < 8; ++i) {
|
||||
reg[i/4+0][i%4] = dl * grid1[i] * select(1, -1, signs[0] & kmask_iq2xs[i]);
|
||||
reg[i/4+2][i%4] = dl * grid2[i] * select(1, -1, signs[1] & kmask_iq2xs[i]);
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_iq1_s(device const block_iq1_s * xb, short il, thread type4x4 & reg) {
|
||||
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
|
||||
const int ib32 = il/2;
|
||||
il = il%2;
|
||||
const float d = xb->d;
|
||||
device const uint8_t * qs = xb->qs + 4*ib32 + 2*il;
|
||||
device const uint16_t * qh = xb->qh;
|
||||
const float dl = d * (2*((qh[ib32] >> 12) & 7) + 1);
|
||||
const float ml = dl * (qh[ib32] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA);
|
||||
const uint16_t h = qh[ib32] >> 6*il;
|
||||
constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((h << 8) & 0x700)));
|
||||
constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((h << 5) & 0x700)));
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
reg[0][i] = dl * (grid1[i] & 0xf) + ml;
|
||||
reg[1][i] = dl * (grid1[i] >> 4) + ml;
|
||||
reg[2][i] = dl * (grid2[i] & 0xf) + ml;
|
||||
reg[3][i] = dl * (grid2[i] >> 4) + ml;
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_iq1_m(device const block_iq1_m * xb, short il, thread type4x4 & reg) {
|
||||
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
|
||||
const int ib32 = il/2;
|
||||
il = il%2;
|
||||
device const uint16_t * sc = (device const uint16_t *)xb->scales;
|
||||
|
||||
iq1m_scale_t scale;
|
||||
scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
|
||||
const float d = scale.f16;
|
||||
|
||||
device const uint8_t * qs = xb->qs + 4*ib32 + 2*il;
|
||||
device const uint8_t * qh = xb->qh + 2*ib32 + il;
|
||||
|
||||
const float dl = d * (2*((sc[ib32/2] >> (6*(ib32%2)+3*il)) & 7) + 1);
|
||||
const float ml1 = dl * (qh[0] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA);
|
||||
const float ml2 = dl * (qh[0] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA);
|
||||
constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((qh[0] << 8) & 0x700)));
|
||||
constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((qh[0] << 4) & 0x700)));
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
reg[0][i] = dl * (grid1[i] & 0xf) + ml1;
|
||||
reg[1][i] = dl * (grid1[i] >> 4) + ml1;
|
||||
reg[2][i] = dl * (grid2[i] & 0xf) + ml2;
|
||||
reg[3][i] = dl * (grid2[i] >> 4) + ml2;
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_iq4_nl(device const block_iq4_nl * xb, short il, thread type4x4 & reg) {
|
||||
device const uint16_t * q4 = (device const uint16_t *)xb->qs;
|
||||
const float d = xb->d;
|
||||
uint32_t aux32;
|
||||
thread const uint8_t * q8 = (thread const uint8_t *)&aux32;
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
aux32 = ((q4[2*i] | (q4[2*i+1] << 16)) >> 4*il) & 0x0f0f0f0f;
|
||||
reg[i][0] = d * kvalues_iq4nl_f[q8[0]];
|
||||
reg[i][1] = d * kvalues_iq4nl_f[q8[1]];
|
||||
reg[i][2] = d * kvalues_iq4nl_f[q8[2]];
|
||||
reg[i][3] = d * kvalues_iq4nl_f[q8[3]];
|
||||
}
|
||||
}
|
||||
|
||||
template <typename type4>
|
||||
void dequantize_iq4_nl_t4(device const block_iq4_nl * xb, short il, thread type4 & reg) {
|
||||
device const uint16_t * q4 = (device const uint16_t *)xb->qs;
|
||||
const float d = xb->d;
|
||||
uint32_t aux32;
|
||||
thread const uint8_t * q8 = (thread const uint8_t *)&aux32;
|
||||
aux32 = ((q4[2*(il%4)] | (q4[2*(il%4)+1] << 16)) >> 4*(il/4)) & 0x0f0f0f0f;
|
||||
reg[0] = d * kvalues_iq4nl_f[q8[0]];
|
||||
reg[1] = d * kvalues_iq4nl_f[q8[1]];
|
||||
reg[2] = d * kvalues_iq4nl_f[q8[2]];
|
||||
reg[3] = d * kvalues_iq4nl_f[q8[3]];
|
||||
}
|
||||
|
||||
template <typename type4x4>
|
||||
void dequantize_iq4_xs(device const block_iq4_xs * xb, short il, thread type4x4 & reg) {
|
||||
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
|
||||
const int ib32 = il/2;
|
||||
il = il%2;
|
||||
// il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
|
||||
device const uint32_t * q4 = (device const uint32_t *)xb->qs + 4*ib32;
|
||||
const int ls = ((xb->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((xb->scales_h >> 2*ib32) & 3) << 4);
|
||||
const float d = (float)xb->d * (ls - 32);
|
||||
uint32_t aux32;
|
||||
thread const uint8_t * q8 = (thread const uint8_t *)&aux32;
|
||||
for (int i = 0; i < 4; ++i) {
|
||||
aux32 = (q4[i] >> 4*il) & 0x0f0f0f0f;
|
||||
reg[i][0] = d * kvalues_iq4nl_f[q8[0]];
|
||||
reg[i][1] = d * kvalues_iq4nl_f[q8[1]];
|
||||
reg[i][2] = d * kvalues_iq4nl_f[q8[2]];
|
||||
reg[i][3] = d * kvalues_iq4nl_f[q8[3]];
|
||||
}
|
||||
}
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,250 +0,0 @@
|
||||
#include "common.h"
|
||||
|
||||
constant short FC_gated_delta_net_ne20 [[function_constant(FC_GATED_DELTA_NET + 0)]];
|
||||
constant short FC_gated_delta_net_ne30 [[function_constant(FC_GATED_DELTA_NET + 1)]];
|
||||
constant short FC_gated_delta_net_K [[function_constant(FC_GATED_DELTA_NET + 2)]];
|
||||
|
||||
#if 1
|
||||
template<short NSG>
|
||||
kernel void kernel_gated_delta_net_impl(
|
||||
constant ggml_metal_kargs_gated_delta_net & args,
|
||||
device const char * q,
|
||||
device const char * k,
|
||||
device const char * v,
|
||||
device const char * g,
|
||||
device const char * b,
|
||||
device const char * s,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tpitg[[thread_position_in_threadgroup]],
|
||||
uint3 ntg[[threads_per_threadgroup]]) {
|
||||
#define S_v FC_gated_delta_net_ne20
|
||||
#define G FC_gated_delta_net_ne30
|
||||
#define K FC_gated_delta_net_K
|
||||
|
||||
const uint tx = tpitg.x;
|
||||
const uint ty = tpitg.y;
|
||||
|
||||
const uint i23 = tgpig.z; // B (n_seqs)
|
||||
const uint i21 = tgpig.y; // H (head)
|
||||
const uint i20 = tgpig.x*NSG + ty; // row within S_v
|
||||
|
||||
const uint i01 = i21 % args.ne01;
|
||||
const uint i11 = i21 % args.ne11;
|
||||
|
||||
const float scale = 1.0f / sqrt((float)S_v);
|
||||
|
||||
// input state layout [S_v, S_v, H, n_seqs] (s0 only): per-seq stride is H*D.
|
||||
// state is stored transposed: M[i20][is] = S[is][i20], so row i20 is contiguous
|
||||
const uint state_in_base = (i23*args.ne21 + i21)*S_v*S_v + i20*S_v;
|
||||
device const float * s_ptr = (device const float *) (s) + state_in_base;
|
||||
|
||||
float ls[NSG];
|
||||
|
||||
FOR_UNROLL (short j = 0; j < NSG; j++) {
|
||||
const short is = tx*NSG + j;
|
||||
ls[j] = s_ptr[is];
|
||||
}
|
||||
|
||||
device float * dst_attn = (device float *) (dst) + (i23*args.ne22*args.ne21 + i21)*S_v + i20;
|
||||
|
||||
device const float * q_ptr = (device const float *) (q + i23*args.nb03 + i01*args.nb01);
|
||||
device const float * k_ptr = (device const float *) (k + i23*args.nb13 + i11*args.nb11);
|
||||
device const float * v_ptr = (device const float *) (v + i23*args.nb23 + i21*args.nb21);
|
||||
|
||||
device const float * b_ptr = (device const float *) (b) + (i23*args.ne22*args.ne21 + i21);
|
||||
device const float * g_ptr = (device const float *) (g) + (i23*args.ne22*args.ne21 + i21)*G;
|
||||
|
||||
// snapshot slot mapping: slot 0 = most recent state, slot s = s tokens back.
|
||||
// When n_tokens < K, only slots 0..n_tokens-1 are written; older slots are caller-owned.
|
||||
|
||||
// output state base offset: after attention scores
|
||||
const uint attn_size = args.ne22 * args.ne21 * S_v * args.ne23;
|
||||
// output state per-slot size: S_v * S_v * H * n_seqs
|
||||
const uint state_size_per_snap = S_v * S_v * args.ne21 * args.ne23;
|
||||
// per-(seq,head) offset within a slot
|
||||
const uint state_out_base = (i23*args.ne21 + i21)*S_v*S_v + i20*S_v;
|
||||
|
||||
for (short t = 0; t < args.ne22; t++) {
|
||||
float s_k = 0.0f;
|
||||
|
||||
if (G == 1) {
|
||||
const float g_exp = exp(g_ptr[0]);
|
||||
|
||||
FOR_UNROLL (short j = 0; j < NSG; j++) {
|
||||
const short is = tx*NSG + j;
|
||||
ls[j] *= g_exp;
|
||||
|
||||
s_k += ls[j]*k_ptr[is];
|
||||
}
|
||||
} else {
|
||||
// KDA
|
||||
FOR_UNROLL (short j = 0; j < NSG; j++) {
|
||||
const short is = tx*NSG + j;
|
||||
ls[j] *= exp(g_ptr[is]);
|
||||
|
||||
s_k += ls[j]*k_ptr[is];
|
||||
}
|
||||
}
|
||||
|
||||
s_k = simd_sum(s_k);
|
||||
|
||||
const float d = (v_ptr[i20] - s_k)*b_ptr[0];
|
||||
|
||||
float y = 0.0f;
|
||||
|
||||
FOR_UNROLL (short j = 0; j < NSG; j++) {
|
||||
const short is = tx*NSG + j;
|
||||
ls[j] += k_ptr[is]*d;
|
||||
|
||||
y += ls[j]*q_ptr[is];
|
||||
}
|
||||
|
||||
y = simd_sum(y);
|
||||
|
||||
if (tx == 0) {
|
||||
dst_attn[t*args.ne21*S_v] = y*scale;
|
||||
}
|
||||
|
||||
q_ptr += args.ns02;
|
||||
k_ptr += args.ns12;
|
||||
v_ptr += args.ns22;
|
||||
|
||||
b_ptr += args.ne21;
|
||||
g_ptr += args.ne21*G;
|
||||
|
||||
if (K > 1) {
|
||||
const int target_slot = (int)args.ne22 - 1 - (int)t;
|
||||
if (target_slot >= 0 && target_slot < (int)K) {
|
||||
device float * dst_state = (device float *) (dst) + attn_size + (uint)target_slot * state_size_per_snap + state_out_base;
|
||||
FOR_UNROLL (short j = 0; j < NSG; j++) {
|
||||
const short is = tx*NSG + j;
|
||||
dst_state[is] = ls[j];
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (K == 1) {
|
||||
device float * dst_state = (device float *) (dst) + attn_size + state_out_base;
|
||||
FOR_UNROLL (short j = 0; j < NSG; j++) {
|
||||
const short is = tx*NSG + j;
|
||||
dst_state[is] = ls[j];
|
||||
}
|
||||
}
|
||||
|
||||
#undef S_v
|
||||
#undef G
|
||||
#undef K
|
||||
}
|
||||
|
||||
typedef decltype(kernel_gated_delta_net_impl<4>) kernel_gated_delta_net_t;
|
||||
|
||||
template [[host_name("kernel_gated_delta_net_f32_1")]] kernel kernel_gated_delta_net_t kernel_gated_delta_net_impl<1>;
|
||||
template [[host_name("kernel_gated_delta_net_f32_2")]] kernel kernel_gated_delta_net_t kernel_gated_delta_net_impl<2>;
|
||||
template [[host_name("kernel_gated_delta_net_f32_4")]] kernel kernel_gated_delta_net_t kernel_gated_delta_net_impl<4>;
|
||||
|
||||
#else
|
||||
// a simplified version of the above
|
||||
// no performance improvement, so keep the above version for now
|
||||
|
||||
template<typename T, short NSG>
|
||||
kernel void kernel_gated_delta_net_impl(
|
||||
constant ggml_metal_kargs_gated_delta_net & args,
|
||||
device const char * q,
|
||||
device const char * k,
|
||||
device const char * v,
|
||||
device const char * g,
|
||||
device const char * b,
|
||||
device const char * s,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tpitg[[thread_position_in_threadgroup]],
|
||||
uint3 ntg[[threads_per_threadgroup]]) {
|
||||
#define S_v FC_gated_delta_net_ne20
|
||||
#define G FC_gated_delta_net_ne30
|
||||
|
||||
const uint tx = tpitg.x;
|
||||
const uint ty = tpitg.y;
|
||||
|
||||
const uint i23 = tgpig.z; // B
|
||||
const uint i21 = tgpig.y; // H
|
||||
const uint i20 = tgpig.x*NSG + ty;
|
||||
|
||||
const uint i01 = i21 % args.ne01;
|
||||
const uint i11 = i21 % args.ne11;
|
||||
|
||||
const float scale = 1.0f / sqrt((float)S_v);
|
||||
|
||||
device const float * s_ptr = (device const float *) (s) + (i23*args.ne21 + i21)*S_v*S_v + i20;
|
||||
|
||||
float lsf[NSG];
|
||||
|
||||
FOR_UNROLL (short j = 0; j < NSG; j++) {
|
||||
const short is = tx*NSG + j;
|
||||
lsf[j] = s_ptr[is*S_v];
|
||||
}
|
||||
|
||||
thread T * ls = (thread T *) (lsf);
|
||||
|
||||
device float * dst_attn = (device float *) (dst) + (i23*args.ne22*args.ne21 + i21)*S_v + i20;
|
||||
|
||||
device const float * q_ptr = (device const float *) (q + i23*args.nb03 + i01*args.nb01);
|
||||
device const float * k_ptr = (device const float *) (k + i23*args.nb13 + i11*args.nb11);
|
||||
device const float * v_ptr = (device const float *) (v + i23*args.nb23 + i21*args.nb21);
|
||||
|
||||
device const float * b_ptr = (device const float *) (b) + (i23*args.ne22*args.ne21 + i21);
|
||||
device const float * g_ptr = (device const float *) (g) + (i23*args.ne22*args.ne21 + i21)*G;
|
||||
|
||||
for (short t = 0; t < args.ne22; t++) {
|
||||
device const T * qt_ptr = (device const T *) (q_ptr);
|
||||
device const T * kt_ptr = (device const T *) (k_ptr);
|
||||
device const T * gt_ptr = (device const T *) (g_ptr);
|
||||
|
||||
if (G == 1) {
|
||||
*ls *= exp(g_ptr[0]);
|
||||
} else {
|
||||
// KDA
|
||||
*ls *= exp(gt_ptr[tx]);
|
||||
}
|
||||
|
||||
const float s_k = simd_sum(dot(*ls, kt_ptr[tx]));
|
||||
|
||||
const float d = (v_ptr[i20] - s_k)*b_ptr[0];
|
||||
|
||||
*ls += kt_ptr[tx]*d;
|
||||
|
||||
const float y = simd_sum(dot(*ls, qt_ptr[tx]));
|
||||
|
||||
if (tx == 0) {
|
||||
*dst_attn = y*scale;
|
||||
}
|
||||
|
||||
q_ptr += args.ns02;
|
||||
k_ptr += args.ns12;
|
||||
v_ptr += args.ns22;
|
||||
|
||||
b_ptr += args.ne21;
|
||||
g_ptr += args.ne21*G;
|
||||
|
||||
dst_attn += args.ne21*S_v;
|
||||
}
|
||||
|
||||
device float * dst_state = (device float *) (dst) + args.ne23*args.ne22*args.ne21*S_v + (i23*args.ne21 + i21)*S_v*S_v + i20;
|
||||
device T * dstt_state = (device T *) (dst_state);
|
||||
|
||||
FOR_UNROLL (short j = 0; j < NSG; j++) {
|
||||
const short is = tx*NSG + j;
|
||||
dst_state[is*S_v] = lsf[j];
|
||||
}
|
||||
|
||||
#undef S_v
|
||||
#undef G
|
||||
}
|
||||
|
||||
typedef decltype(kernel_gated_delta_net_impl<float4, 4>) kernel_gated_delta_net_t;
|
||||
|
||||
template [[host_name("kernel_gated_delta_net_f32_1")]] kernel kernel_gated_delta_net_t kernel_gated_delta_net_impl<float, 1>;
|
||||
template [[host_name("kernel_gated_delta_net_f32_2")]] kernel kernel_gated_delta_net_t kernel_gated_delta_net_impl<float2, 2>;
|
||||
template [[host_name("kernel_gated_delta_net_f32_4")]] kernel kernel_gated_delta_net_t kernel_gated_delta_net_impl<float4, 4>;
|
||||
#endif
|
||||
@@ -1,347 +0,0 @@
|
||||
#include "common.h"
|
||||
|
||||
kernel void kernel_argmax_f32(
|
||||
constant ggml_metal_kargs_argmax & args,
|
||||
device const char * src0,
|
||||
device char * dst,
|
||||
threadgroup char * shmem [[threadgroup(0)]],
|
||||
uint tgpig[[threadgroup_position_in_grid]],
|
||||
uint tpitg[[thread_position_in_threadgroup]],
|
||||
uint sgitg[[simdgroup_index_in_threadgroup]],
|
||||
uint tiisg[[thread_index_in_simdgroup]],
|
||||
uint ntg[[threads_per_threadgroup]]) {
|
||||
device const float * x_row = (device const float *) ((device const char *) src0 + tgpig * args.nb01);
|
||||
|
||||
float lmax = -INFINITY;
|
||||
int32_t larg = -1;
|
||||
|
||||
for (int i00 = tpitg; i00 < args.ne00; i00 += ntg) {
|
||||
if (x_row[i00] > lmax) {
|
||||
lmax = x_row[i00];
|
||||
larg = i00;
|
||||
}
|
||||
}
|
||||
|
||||
// find the argmax value in the block
|
||||
float max_val = simd_max(lmax);
|
||||
int32_t arg_val = simd_max(select(-1, larg, lmax == max_val));
|
||||
|
||||
device int32_t * dst_i32 = (device int32_t *) dst;
|
||||
|
||||
threadgroup float * shared_maxval = (threadgroup float *) shmem;
|
||||
threadgroup int32_t * shared_argmax = (threadgroup int32_t *) shmem + N_SIMDWIDTH;
|
||||
|
||||
if (ntg > N_SIMDWIDTH) {
|
||||
if (sgitg == 0) {
|
||||
shared_maxval[tiisg] = -INFINITY;
|
||||
shared_argmax[tiisg] = -1;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
if (tiisg == 0) {
|
||||
shared_maxval[sgitg] = max_val;
|
||||
shared_argmax[sgitg] = arg_val;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
max_val = shared_maxval[tiisg];
|
||||
arg_val = shared_argmax[tiisg];
|
||||
|
||||
float max_val_reduced = simd_max(max_val);
|
||||
int32_t arg_val_reduced = simd_max(select(-1, arg_val, max_val == max_val_reduced));
|
||||
|
||||
dst_i32[tgpig] = arg_val_reduced;
|
||||
|
||||
return;
|
||||
}
|
||||
|
||||
dst_i32[tgpig] = arg_val;
|
||||
}
|
||||
|
||||
kernel void kernel_diag_f32(
|
||||
constant ggml_metal_kargs_diag & args,
|
||||
device const char * src0,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort tiitg[[thread_index_in_threadgroup]]) {
|
||||
constexpr short NW = N_SIMDWIDTH;
|
||||
|
||||
const int32_t i3 = tgpig.z;
|
||||
const int32_t i2 = tgpig.y;
|
||||
const int32_t i1 = tgpig.x;
|
||||
|
||||
device const float * src0_ptr = (device const float *)(src0 + i2*args.nb02 + i3*args.nb03);
|
||||
device float * dst_ptr = (device float *)(dst + i1*args.nb01 + i2*args.nb2 + i3*args.nb3);
|
||||
|
||||
for (int i0 = tiitg; i0 < args.ne0; i0 += NW) {
|
||||
dst_ptr[i0] = i0 == i1 ? src0_ptr[i0] : 0.0f;
|
||||
}
|
||||
}
|
||||
|
||||
kernel void kernel_roll_f32(
|
||||
constant ggml_metal_kargs_roll & args,
|
||||
device const char * src0,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tpitg[[thread_position_in_threadgroup]],
|
||||
uint3 ntg[[threads_per_threadgroup]]) {
|
||||
|
||||
const int64_t i3 = tgpig.z;
|
||||
const int64_t i2 = tgpig.y;
|
||||
const int64_t i1 = tgpig.x;
|
||||
|
||||
device const float * src0_ptr = (device const float *) src0;
|
||||
device float * dst_ptr = (device float *) dst;
|
||||
|
||||
for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
|
||||
// apply shifts and wrap around
|
||||
int64_t i00 = i0 - args.s0;
|
||||
int64_t i01 = i1 - args.s1;
|
||||
int64_t i02 = i2 - args.s2;
|
||||
int64_t i03 = i3 - args.s3;
|
||||
|
||||
if (i00 < 0) { i00 += args.ne00; } else if (i00 >= args.ne00) { i00 -= args.ne00; }
|
||||
if (i01 < 0) { i01 += args.ne01; } else if (i01 >= args.ne01) { i01 -= args.ne01; }
|
||||
if (i02 < 0) { i02 += args.ne02; } else if (i02 >= args.ne02) { i02 -= args.ne02; }
|
||||
if (i03 < 0) { i03 += args.ne03; } else if (i03 >= args.ne03) { i03 -= args.ne03; }
|
||||
|
||||
int64_t src_idx = i03*args.ne02*args.ne01*args.ne00 + i02*args.ne01*args.ne00 + i01*args.ne00 + i00;
|
||||
int64_t dst_idx = i3 *args.ne2 *args.ne1 *args.ne0 + i2 *args.ne1 *args.ne0 + i1 *args.ne0 + i0;
|
||||
|
||||
dst_ptr[dst_idx] = src0_ptr[src_idx];
|
||||
}
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
kernel void kernel_pad_impl(
|
||||
constant ggml_metal_kargs_pad & args,
|
||||
device const char * src0,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tpitg[[thread_position_in_threadgroup]],
|
||||
uint3 ntg[[threads_per_threadgroup]]) {
|
||||
const int32_t i3 = tgpig.z;
|
||||
const int32_t i2 = tgpig.y;
|
||||
const int32_t k0 = tgpig.x/args.ne1;
|
||||
const int32_t i1 = tgpig.x - k0*args.ne1;
|
||||
|
||||
const int32_t i03 = i3;
|
||||
const int32_t i02 = i2;
|
||||
const int32_t i01 = i1;
|
||||
|
||||
device const T * src0_ptr = (device const T *) (src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01);
|
||||
device T * dst_ptr = (device T *) (dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1);
|
||||
|
||||
for (int32_t l0 = 0; l0 < 1024; l0 += ntg.x) {
|
||||
const int32_t i0 = k0*1024 + tpitg.x + l0;
|
||||
if (i0 >= args.ne0) {
|
||||
break;
|
||||
}
|
||||
|
||||
if (i0 < args.ne00 && i1 < args.ne01 && i2 < args.ne02 && i3 < args.ne03) {
|
||||
dst_ptr[i0] = src0_ptr[i0];
|
||||
} else {
|
||||
dst_ptr[i0] = 0.0f;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
typedef decltype(kernel_pad_impl<float>) kernel_pad_t;
|
||||
|
||||
template [[host_name("kernel_pad_f32")]] kernel kernel_pad_t kernel_pad_impl<float>;
|
||||
template [[host_name("kernel_pad_f32_4")]] kernel kernel_pad_t kernel_pad_impl<float4>;
|
||||
|
||||
// TODO: this is slow - optimize
|
||||
kernel void kernel_pad_reflect_1d_f32(
|
||||
constant ggml_metal_kargs_pad_reflect_1d & args,
|
||||
device const char * src0,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tgpg[[threadgroups_per_grid]],
|
||||
uint3 tpitg[[thread_position_in_threadgroup]],
|
||||
uint3 ntg[[threads_per_threadgroup]]) {
|
||||
|
||||
const int64_t i3 = tgpig.z;
|
||||
const int64_t i2 = tgpig.y;
|
||||
const int64_t i1 = tgpig.x;
|
||||
|
||||
const int64_t i03 = i3;
|
||||
const int64_t i02 = i2;
|
||||
const int64_t i01 = i1;
|
||||
|
||||
device const float * src0_ptr = (device const float *) (src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01);
|
||||
device float * dst_ptr = (device float *) (dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1);
|
||||
|
||||
if (i1 < args.ne01 && i2 < args.ne02 && i3 < args.ne03) {
|
||||
for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
|
||||
if (i0 < args.p0) {
|
||||
dst_ptr[i0] = src0_ptr[args.p0 - i0];
|
||||
} else if (i0 < args.ne0 - args.p1) {
|
||||
dst_ptr[i0] = src0_ptr[i0 - args.p0];
|
||||
} else {
|
||||
dst_ptr[i0] = src0_ptr[(args.ne0 - args.p1 - args.p0) - (args.p1 + 1 - (args.ne0 - i0)) - 1];
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
kernel void kernel_arange_f32(
|
||||
constant ggml_metal_kargs_arange & args,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tpitg[[thread_position_in_threadgroup]],
|
||||
uint3 ntg[[threads_per_threadgroup]]) {
|
||||
|
||||
device float * dst_ptr = (device float *) dst;
|
||||
|
||||
for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
|
||||
dst_ptr[i0] = args.start + args.step * i0;
|
||||
}
|
||||
}
|
||||
|
||||
kernel void kernel_timestep_embedding_f32(
|
||||
constant ggml_metal_kargs_timestep_embedding & args,
|
||||
device const char * src0,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tpitg[[thread_position_in_threadgroup]],
|
||||
uint3 ntg[[threads_per_threadgroup]]) {
|
||||
|
||||
int i = tgpig.x;
|
||||
device float * embed_data = (device float *)(dst + i*args.nb1);
|
||||
|
||||
int half_ = args.dim / 2;
|
||||
for (int j = tpitg.x; j < half_; j += ntg.x) {
|
||||
float timestep = ((device float *)src0)[i];
|
||||
float freq = (float)exp(-log((float)args.max_period) * j / half_);
|
||||
float arg = timestep * freq;
|
||||
embed_data[j ] = cos(arg);
|
||||
embed_data[j + half_] = sin(arg);
|
||||
}
|
||||
|
||||
if (args.dim % 2 != 0 && tpitg.x == 0) {
|
||||
embed_data[2 * half_] = 0.f;
|
||||
}
|
||||
}
|
||||
|
||||
kernel void kernel_opt_step_adamw_f32(
|
||||
constant ggml_metal_kargs_opt_step_adamw & args,
|
||||
device float * x,
|
||||
device const float * g,
|
||||
device float * g_m,
|
||||
device float * g_v,
|
||||
device const float * pars,
|
||||
uint gid[[thread_position_in_grid]]) {
|
||||
|
||||
if (gid >= args.np) {
|
||||
return;
|
||||
}
|
||||
|
||||
const float alpha = pars[0];
|
||||
const float beta1 = pars[1];
|
||||
const float beta2 = pars[2];
|
||||
const float eps = pars[3];
|
||||
const float wd = pars[4];
|
||||
const float beta1h = pars[5];
|
||||
const float beta2h = pars[6];
|
||||
|
||||
const float gi = g[gid];
|
||||
const float gmi = g_m[gid] * beta1 + gi * (1.0f - beta1);
|
||||
const float gvi = g_v[gid] * beta2 + gi * gi * (1.0f - beta2);
|
||||
|
||||
g_m[gid] = gmi;
|
||||
g_v[gid] = gvi;
|
||||
|
||||
const float mh = gmi * beta1h;
|
||||
const float vh = sqrt(gvi * beta2h) + eps;
|
||||
|
||||
x[gid] = x[gid] * (1.0f - alpha * wd) - alpha * mh / vh;
|
||||
}
|
||||
|
||||
kernel void kernel_opt_step_sgd_f32(
|
||||
constant ggml_metal_kargs_opt_step_sgd & args,
|
||||
device float * x,
|
||||
device const float * g,
|
||||
device const float * pars,
|
||||
uint gid[[thread_position_in_grid]]) {
|
||||
|
||||
if (gid >= args.np) {
|
||||
return;
|
||||
}
|
||||
|
||||
x[gid] = x[gid] * (1.0f - pars[0] * pars[1]) - pars[0] * g[gid];
|
||||
}
|
||||
|
||||
template<typename T>
|
||||
kernel void kernel_memset(
|
||||
constant ggml_metal_kargs_memset & args,
|
||||
device T * dst,
|
||||
uint tpig[[thread_position_in_grid]]) {
|
||||
dst[tpig] = args.val;
|
||||
}
|
||||
|
||||
typedef decltype(kernel_memset<int64_t>) kernel_memset_t;
|
||||
|
||||
template [[host_name("kernel_memset_i64")]] kernel kernel_memset_t kernel_memset<int64_t>;
|
||||
|
||||
constant short FC_count_equal_nsg [[function_constant(FC_COUNT_EQUAL + 0)]];
|
||||
|
||||
template<typename T>
|
||||
kernel void kernel_count_equal(
|
||||
constant ggml_metal_kargs_count_equal & args,
|
||||
device const char * src0,
|
||||
device const char * src1,
|
||||
device atomic_int * dst,
|
||||
threadgroup int32_t * shmem_i32 [[threadgroup(0)]],
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort3 tpitg[[thread_position_in_threadgroup]],
|
||||
ushort sgitg[[simdgroup_index_in_threadgroup]],
|
||||
ushort tiisg[[thread_index_in_simdgroup]],
|
||||
ushort3 ntg[[threads_per_threadgroup]]) {
|
||||
const short NSG = FC_count_equal_nsg;
|
||||
|
||||
const int i3 = tgpig.z;
|
||||
const int i2 = tgpig.y;
|
||||
const int i1 = tgpig.x;
|
||||
|
||||
if (i3 >= args.ne03 || i2 >= args.ne02 || i1 >= args.ne01) {
|
||||
return;
|
||||
}
|
||||
|
||||
int sum = 0;
|
||||
|
||||
device const char * base0 = src0 + i1*args.nb01 + i2*args.nb02 + i3*args.nb03;
|
||||
device const char * base1 = src1 + i1*args.nb11 + i2*args.nb12 + i3*args.nb13;
|
||||
|
||||
for (int64_t i0 = tpitg.x; i0 < args.ne00; i0 += ntg.x) {
|
||||
const T v0 = *(device const T *)(base0 + i0*args.nb00);
|
||||
const T v1 = *(device const T *)(base1 + i0*args.nb10);
|
||||
sum += (v0 == v1);
|
||||
}
|
||||
|
||||
sum = simd_sum(sum);
|
||||
|
||||
if (tiisg == 0) {
|
||||
shmem_i32[sgitg] = sum;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
if (sgitg == 0) {
|
||||
float v = 0.0f;
|
||||
if (tpitg.x < NSG) {
|
||||
v = shmem_i32[tpitg.x];
|
||||
}
|
||||
|
||||
float total = simd_sum(v);
|
||||
if (tpitg.x == 0) {
|
||||
atomic_fetch_add_explicit(dst, (int32_t) total, memory_order_relaxed);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
typedef decltype(kernel_count_equal<int32_t>) kernel_count_equal_t;
|
||||
|
||||
template [[host_name("kernel_count_equal_i32")]] kernel kernel_count_equal_t kernel_count_equal<int32_t>;
|
||||
@@ -1,838 +0,0 @@
|
||||
#include "common.h"
|
||||
#include "dequantize.h"
|
||||
|
||||
constant bool FC_mul_mm_bc_inp [[function_constant(FC_MUL_MM + 0)]];
|
||||
constant bool FC_mul_mm_bc_out [[function_constant(FC_MUL_MM + 1)]];
|
||||
constant short FC_mul_mm_ne12 [[function_constant(FC_MUL_MM + 2)]];
|
||||
constant short FC_mul_mm_ne13 [[function_constant(FC_MUL_MM + 3)]];
|
||||
constant short FC_mul_mm_r2 [[function_constant(FC_MUL_MM + 4)]];
|
||||
constant short FC_mul_mm_r3 [[function_constant(FC_MUL_MM + 5)]];
|
||||
|
||||
// each block_q contains 16*nl weights
|
||||
#ifdef GGML_METAL_HAS_TENSOR
|
||||
template<
|
||||
typename SA, typename SA_4x4, typename SA_8x8,
|
||||
typename SB, typename SB_2x4, typename SB_8x8,
|
||||
typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread SA_4x4 &),
|
||||
typename T0, typename T0_4x4, typename T1, typename T1_2x4>
|
||||
kernel void kernel_mul_mm(
|
||||
constant ggml_metal_kargs_mul_mm & args,
|
||||
device const char * srcA,
|
||||
device const char * srcB,
|
||||
device char * dst,
|
||||
threadgroup char * shmem [[threadgroup(0)]],
|
||||
uint3 tgpig [[threadgroup_position_in_grid]],
|
||||
ushort tiitg [[thread_index_in_threadgroup]],
|
||||
ushort sgitg [[simdgroup_index_in_threadgroup]]) {
|
||||
(void) sgitg;
|
||||
|
||||
// Matrix dimensions: A(M,K) x B(K,N) -> C(M,N)
|
||||
const int K = args.ne00;
|
||||
const int M = args.ne0;
|
||||
const int N = args.ne1;
|
||||
|
||||
// Batch dimension handling
|
||||
const int im = tgpig.z;
|
||||
const int i12 = im % FC_mul_mm_ne12;
|
||||
const int i13 = im / FC_mul_mm_ne12;
|
||||
|
||||
// Batch offsets for srcA and srcB
|
||||
const uint64_t offset0 = (i12/FC_mul_mm_r2)*args.nb02 + (i13/FC_mul_mm_r3)*args.nb03;
|
||||
|
||||
// Tile dimensions
|
||||
constexpr int NRB = SZ_SIMDGROUP * N_MM_BLOCK_X * N_MM_SIMD_GROUP_X;
|
||||
constexpr int NRA = SZ_SIMDGROUP * N_MM_BLOCK_Y * N_MM_SIMD_GROUP_Y;
|
||||
|
||||
// Tile offsets in output matrix
|
||||
const int ra = tgpig.y * NRA;
|
||||
const int rb = tgpig.x * NRB;
|
||||
|
||||
// Threadgroup memory for dequantized A tile only
|
||||
threadgroup SA * sa = (threadgroup SA *)(shmem);
|
||||
|
||||
// Work-item count for A loading
|
||||
constexpr int A_WORK_ITEMS = NRA * N_MM_NK;
|
||||
constexpr int NUM_THREADS = N_SIMDWIDTH * N_MM_SIMD_GROUP_X * N_MM_SIMD_GROUP_Y;
|
||||
|
||||
// tA wraps threadgroup memory
|
||||
auto tA = tensor(sa, dextents<int32_t, 2>(N_MM_NK_TOTAL, NRA));
|
||||
|
||||
// tB wraps device memory directly
|
||||
device T1 * ptrB = (device T1 *)(srcB + args.nb12*i12 + args.nb13*i13);
|
||||
const int strideB = args.nb11 / sizeof(T1);
|
||||
auto tB = tensor(ptrB, dextents<int32_t, 2>(K, N), array<int, 2>({1, strideB}));
|
||||
|
||||
// Configure matmul operation
|
||||
mpp::tensor_ops::matmul2d<
|
||||
mpp::tensor_ops::matmul2d_descriptor(
|
||||
NRB, NRA, N_MM_NK_TOTAL, false, true, true,
|
||||
mpp::tensor_ops::matmul2d_descriptor::mode::multiply_accumulate),
|
||||
execution_simdgroups<N_MM_SIMD_GROUP_X * N_MM_SIMD_GROUP_Y>> mm;
|
||||
|
||||
auto cT = mm.get_destination_cooperative_tensor<decltype(tB), decltype(tA), float>();
|
||||
|
||||
// Accumulate partial results over K dimension
|
||||
for (int loop_k = 0; loop_k < K; loop_k += N_MM_NK_TOTAL) {
|
||||
// === PHASE 1: Dequantization of A into threadgroup memory ===
|
||||
for (int work = tiitg; work < A_WORK_ITEMS; work += NUM_THREADS) {
|
||||
const int row = work / N_MM_NK;
|
||||
const int k_chunk = work % N_MM_NK;
|
||||
const int k_pos = loop_k + k_chunk * 16;
|
||||
const short k_base = k_chunk * 16;
|
||||
|
||||
// Bounds check: skip device read if row is out of matrix bounds
|
||||
if (ra + row < M) {
|
||||
if (is_same<T0_4x4, block_q>::value && FC_mul_mm_bc_inp) {
|
||||
// Element-wise reads when K is not aligned (nb01 not aligned for half4x4/float4x4).
|
||||
// MSL spec Table 2.5: half4x4 requires 8-byte alignment. When K is odd,
|
||||
// nb01 = K*2 is not 8-byte aligned, so odd-row pointers are misaligned.
|
||||
// Mirrors the legacy kernel's existing guard.
|
||||
device const T0 * row_ptr = (device const T0 *)(srcA + args.nb01 * (ra + row) + offset0);
|
||||
|
||||
FOR_UNROLL (short i = 0; i < 16; i++) {
|
||||
sa[row * N_MM_NK_TOTAL + (k_base + i)] = (k_pos + i < K) ? (SA) row_ptr[k_pos + i] : (SA)0;
|
||||
}
|
||||
} else {
|
||||
const int block_idx = k_pos / (16 * nl);
|
||||
const short il = (k_pos / 16) % nl;
|
||||
|
||||
device const block_q * row_ptr = (device const block_q *)(srcA + args.nb01 * (ra + row) + offset0);
|
||||
|
||||
SA_4x4 temp_a;
|
||||
dequantize_func(row_ptr + block_idx, il, temp_a);
|
||||
|
||||
FOR_UNROLL (short i = 0; i < 16; i++) {
|
||||
// Zero-pad A for K positions beyond valid range (handles partial K iterations)
|
||||
sa[row * N_MM_NK_TOTAL + (k_base + i)] = (k_pos + i < K) ? temp_a[i/4][i%4] : (SA)0;
|
||||
}
|
||||
}
|
||||
} else {
|
||||
// Zero-pad rows beyond matrix bounds
|
||||
FOR_UNROLL (short i = 0; i < 16; i++) {
|
||||
sa[row * N_MM_NK_TOTAL + (k_base + i)] = (SA)0;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
// === PHASE 2: Tensor matmul ===
|
||||
auto mA = tA.slice(0, 0);
|
||||
auto mB = tB.slice(loop_k, rb);
|
||||
|
||||
mm.run(mB, mA, cT);
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
}
|
||||
|
||||
// Store result tile to output matrix (with batch offset)
|
||||
// cT.store handles bounds checking via tD's extents (M, N)
|
||||
device float * dstBatch = (device float *)dst + im * N * M;
|
||||
|
||||
auto tD = tensor(dstBatch, dextents<int32_t, 2>(M, N), array<int, 2>({1, M}));
|
||||
cT.store(tD.slice(ra, rb));
|
||||
}
|
||||
|
||||
#else
|
||||
|
||||
template<
|
||||
typename S0, typename S0_4x4, typename S0_8x8,
|
||||
typename S1, typename S1_2x4, typename S1_8x8,
|
||||
typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread S0_4x4 &),
|
||||
typename T0, typename T0_4x4, typename T1, typename T1_2x4>
|
||||
kernel void kernel_mul_mm(
|
||||
constant ggml_metal_kargs_mul_mm & args,
|
||||
device const char * src0,
|
||||
device const char * src1,
|
||||
device char * dst,
|
||||
threadgroup char * shmem [[threadgroup(0)]],
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort tiitg[[thread_index_in_threadgroup]],
|
||||
ushort sgitg[[simdgroup_index_in_threadgroup]]) {
|
||||
|
||||
threadgroup S0 * sa = (threadgroup S0 *)(shmem);
|
||||
threadgroup S1 * sb = (threadgroup S1 *)(shmem + 4096);
|
||||
|
||||
constexpr int NR0 = 64;
|
||||
constexpr int NR1 = 32;
|
||||
|
||||
constexpr int NK = 32;
|
||||
constexpr int NL0 = NK/16;
|
||||
constexpr int NL1 = NK/8;
|
||||
|
||||
const int im = tgpig.z;
|
||||
const int r0 = tgpig.y*NR0;
|
||||
const int r1 = tgpig.x*NR1;
|
||||
|
||||
// if this block is of 64x32 shape or smaller
|
||||
const short nr0 = (args.ne0 - r0 < NR0) ? (args.ne0 - r0) : NR0;
|
||||
const short nr1 = (args.ne1 - r1 < NR1) ? (args.ne1 - r1) : NR1;
|
||||
|
||||
// a thread shouldn't load data outside of the matrix
|
||||
const short lr0 = ((short)tiitg/NL0) < nr0 ? ((short)tiitg/NL0) : nr0 - 1; // 0 .. 63
|
||||
const short lr1 = ((short)tiitg/NL1) < nr1 ? ((short)tiitg/NL1) : nr1 - 1; // 0 .. 31
|
||||
|
||||
const short il0 = (tiitg % NL0);
|
||||
|
||||
short il = il0;
|
||||
|
||||
const int i12 = im % FC_mul_mm_ne12;
|
||||
const int i13 = im / FC_mul_mm_ne12;
|
||||
|
||||
const uint64_t offset0 = (i12/FC_mul_mm_r2)*args.nb02 + (i13/FC_mul_mm_r3)*args.nb03;
|
||||
const short offset1 = il0/nl;
|
||||
|
||||
device const block_q * x = (device const block_q *)(src0 + args.nb01*(r0 + lr0) + offset0) + offset1;
|
||||
|
||||
const short iy = 8*(tiitg % NL1);
|
||||
|
||||
device const T1 * y = (device const T1 *)(src1
|
||||
+ args.nb13*i13
|
||||
+ args.nb12*i12
|
||||
+ args.nb11*(r1 + lr1)
|
||||
+ args.nb10*iy);
|
||||
|
||||
S0_8x8 ma[4];
|
||||
S1_8x8 mb[2];
|
||||
|
||||
simdgroup_float8x8 mc[8];
|
||||
|
||||
for (short i = 0; i < 8; i++){
|
||||
mc[i] = make_filled_simdgroup_matrix<float, 8>(0.f);
|
||||
}
|
||||
|
||||
for (int loop_k = 0; loop_k < args.ne00; loop_k += NK) {
|
||||
// load data and store to threadgroup memory
|
||||
if (is_same<T0_4x4, block_q>::value && FC_mul_mm_bc_inp) {
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
// no need for dequantization
|
||||
for (short i = 0; i < 16; i++) {
|
||||
const short sx = 2*il0 + i/8;
|
||||
const short sy = (tiitg/NL0)/8;
|
||||
|
||||
//const short lx = i%8;
|
||||
//const short ly = (tiitg/NL0)%8;
|
||||
const short lx = (tiitg/NL0)%8;
|
||||
const short ly = i%8;
|
||||
|
||||
const short ib = 8*sx + sy;
|
||||
|
||||
*(sa + 64*ib + 8*ly + lx) = loop_k + 16*il + i < args.ne00 ? *((device T0 *) x + i) : 0;
|
||||
}
|
||||
} else {
|
||||
S0_4x4 temp_a;
|
||||
dequantize_func(x, il, temp_a);
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
FOR_UNROLL (short i = 0; i < 16; i++) {
|
||||
const short sx = 2*il0 + i/8;
|
||||
const short sy = (tiitg/NL0)/8;
|
||||
|
||||
//const short lx = i%8;
|
||||
//const short ly = (tiitg/NL0)%8;
|
||||
const short lx = (tiitg/NL0)%8;
|
||||
const short ly = i%8;
|
||||
|
||||
const short ib = 8*sx + sy;
|
||||
|
||||
// NOTE: this is massively slower.. WTF?
|
||||
//sa[64*ib + 8*ly + lx] = temp_a[i/4][i%4];
|
||||
|
||||
*(sa + 64*ib + 8*ly + lx) = temp_a[i/4][i%4];
|
||||
}
|
||||
}
|
||||
|
||||
if (FC_mul_mm_bc_inp) {
|
||||
for (short i = 0; i < 8; ++i) {
|
||||
const short sx = (tiitg%NL1);
|
||||
const short sy = (tiitg/NL1)/8;
|
||||
|
||||
const short lx = i;
|
||||
const short ly = (tiitg/NL1)%8;
|
||||
//const short lx = (tiitg/NL1)%8;
|
||||
//const short ly = i;
|
||||
|
||||
const short ib = 4*sx + sy;
|
||||
|
||||
*(sb + 64*ib + 8*ly + lx) = loop_k + iy + i < args.ne00 ? (S1) *((device T1 *) y + i) : 0;
|
||||
}
|
||||
} else {
|
||||
const short sx = (tiitg%NL1);
|
||||
const short sy = (tiitg/NL1)/8;
|
||||
|
||||
//const short dx = sx;
|
||||
//const short dy = sy;
|
||||
|
||||
const short ly = (tiitg/NL1)%8;
|
||||
|
||||
const short ib = 4*sx + sy;
|
||||
|
||||
*(threadgroup S1_2x4 *)(sb + 64*ib + 8*ly) = (S1_2x4)(*((device T1_2x4 *) y));
|
||||
}
|
||||
|
||||
il = (il + 2 < nl) ? il + 2 : il % 2;
|
||||
x = (il < 2) ? x + (2 + nl - 1)/nl : x;
|
||||
|
||||
y += NK;
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
// load matrices from threadgroup memory and conduct outer products
|
||||
threadgroup const S0 * lsma = (sa + 4*64*(sgitg%2));
|
||||
threadgroup const S1 * lsmb = (sb + 2*64*(sgitg/2));
|
||||
|
||||
FOR_UNROLL (short ik = 0; ik < NK/8; ik++) {
|
||||
simdgroup_barrier(mem_flags::mem_none);
|
||||
|
||||
FOR_UNROLL (short i = 0; i < 4; i++) {
|
||||
simdgroup_load(ma[i], lsma + 64*i, 8, 0, false);
|
||||
}
|
||||
|
||||
simdgroup_barrier(mem_flags::mem_none);
|
||||
|
||||
FOR_UNROLL (short i = 0; i < 2; i++) {
|
||||
simdgroup_load(mb[i], lsmb + 64*i, 8, 0, false);
|
||||
}
|
||||
|
||||
simdgroup_barrier(mem_flags::mem_none);
|
||||
|
||||
FOR_UNROLL (short i = 0; i < 8; i++){
|
||||
simdgroup_multiply_accumulate(mc[i], mb[i/4], ma[i%4], mc[i]);
|
||||
}
|
||||
|
||||
lsma += 8*64;
|
||||
lsmb += 4*64;
|
||||
}
|
||||
}
|
||||
|
||||
if (!FC_mul_mm_bc_out || (r0 + NR0 <= args.ne0 && r1 + NR1 <= args.ne1)) {
|
||||
// if no bounds checks on the output are needed, we can directly write to device memory
|
||||
device float * C = (device float *) dst +
|
||||
(r0 + 32*(sgitg & 1)) + \
|
||||
(r1 + 16*(sgitg >> 1)) * args.ne0 + im*args.ne1*args.ne0;
|
||||
|
||||
for (short i = 0; i < 8; i++) {
|
||||
simdgroup_store(mc[i], C + 8*(i%4) + 8*args.ne0*(i/4), args.ne0, 0, false);
|
||||
}
|
||||
} else {
|
||||
// block is smaller than 64x32, we should avoid writing data outside of the matrix
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
threadgroup float * temp_str = ((threadgroup float *) shmem) + 32*(sgitg&1) + (16*(sgitg >> 1))*NR0;
|
||||
|
||||
for (short i = 0; i < 8; i++) {
|
||||
simdgroup_store(mc[i], temp_str + 8*(i%4) + 8*NR0*(i/4), NR0, 0, false);
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
if (sgitg == 0) {
|
||||
for (int j = tiitg; j < nr1; j += NR1) {
|
||||
device float * D = (device float *) dst + r0 + (r1 + j)*args.ne0 + im*args.ne1*args.ne0;
|
||||
device float4 * D4 = (device float4 *) D;
|
||||
|
||||
threadgroup float * C = temp_str + (j*NR0);
|
||||
threadgroup float4 * C4 = (threadgroup float4 *) C;
|
||||
|
||||
int i = 0;
|
||||
for (; i < nr0/4; i++) {
|
||||
*(D4 + i) = *(C4 + i);
|
||||
}
|
||||
|
||||
i *= 4;
|
||||
for (; i < nr0; i++) {
|
||||
*(D + i) = *(C + i);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#endif // GGML_METAL_HAS_TENSOR
|
||||
|
||||
template<short ne20> // n_expert_used
|
||||
kernel void kernel_mul_mm_id_map0(
|
||||
constant ggml_metal_kargs_mul_mm_id_map0 & args,
|
||||
device const char * src2,
|
||||
device char * htpe,
|
||||
device char * hids,
|
||||
threadgroup char * shmem [[threadgroup(0)]],
|
||||
ushort tpitg[[thread_position_in_threadgroup]],
|
||||
ushort ntg[[threads_per_threadgroup]]) {
|
||||
const short ide = tpitg; // expert id
|
||||
|
||||
uint32_t n_all = 0;
|
||||
|
||||
device int32_t * ids_i32 = (device int32_t *) hids + ide*args.ne21;
|
||||
|
||||
for (int i21 = 0; i21 < args.ne21; i21 += ntg) { // n_tokens
|
||||
if (i21 + tpitg < args.ne21) {
|
||||
device const int32_t * src2_i32 = (device const int32_t *) (src2 + (i21 + tpitg)*args.nb21);
|
||||
|
||||
threadgroup uint16_t * sids = (threadgroup uint16_t *) shmem + tpitg*ne20;
|
||||
|
||||
#pragma unroll(ne20)
|
||||
for (short i20 = 0; i20 < ne20; i20++) {
|
||||
sids[i20] = src2_i32[i20];
|
||||
}
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
for (short t = 0; t < ntg; t++) {
|
||||
if (i21 + t >= args.ne21) {
|
||||
break;
|
||||
}
|
||||
|
||||
threadgroup const uint16_t * sids = (threadgroup const uint16_t *) shmem + t*ne20;
|
||||
|
||||
short sel = 0;
|
||||
#pragma unroll(ne20)
|
||||
for (short i20 = 0; i20 < ne20; i20++) {
|
||||
sel += (sids[i20] == ide)*(i20 + 1);
|
||||
}
|
||||
|
||||
ids_i32[n_all] = (i21 + t)*ne20 + sel - 1;
|
||||
|
||||
n_all += sel > 0;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
}
|
||||
|
||||
device uint32_t * tpe_u32 = (device uint32_t *) (htpe);
|
||||
tpe_u32[ide] = n_all;
|
||||
}
|
||||
|
||||
typedef decltype(kernel_mul_mm_id_map0<1>) kernel_mul_mm_id_map0_t;
|
||||
|
||||
template [[host_name("kernel_mul_mm_id_map0_ne20_1" )]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<1>;
|
||||
template [[host_name("kernel_mul_mm_id_map0_ne20_2" )]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<2>;
|
||||
template [[host_name("kernel_mul_mm_id_map0_ne20_4" )]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<4>;
|
||||
template [[host_name("kernel_mul_mm_id_map0_ne20_5" )]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<5>;
|
||||
template [[host_name("kernel_mul_mm_id_map0_ne20_6" )]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<6>;
|
||||
template [[host_name("kernel_mul_mm_id_map0_ne20_8" )]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<8>;
|
||||
template [[host_name("kernel_mul_mm_id_map0_ne20_10")]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<10>;
|
||||
template [[host_name("kernel_mul_mm_id_map0_ne20_16")]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<16>;
|
||||
template [[host_name("kernel_mul_mm_id_map0_ne20_22")]] kernel kernel_mul_mm_id_map0_t kernel_mul_mm_id_map0<22>;
|
||||
|
||||
template<typename S0, typename S0_4x4, typename S0_8x8, typename S1, typename S1_2x4, typename S1_8x8, typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread S0_4x4 &), typename T0, typename T0_4x4, typename T1, typename T1_2x4>
|
||||
kernel void kernel_mul_mm_id(
|
||||
constant ggml_metal_kargs_mul_mm_id & args,
|
||||
device const char * src0,
|
||||
device const char * src1,
|
||||
device const char * htpe,
|
||||
device const char * hids,
|
||||
device char * dst,
|
||||
threadgroup char * shmem [[threadgroup(0)]],
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort tiitg[[thread_index_in_threadgroup]],
|
||||
ushort tiisg[[thread_index_in_simdgroup]],
|
||||
ushort sgitg[[simdgroup_index_in_threadgroup]]) {
|
||||
threadgroup S0 * sa = (threadgroup S0 *)(shmem);
|
||||
threadgroup S1 * sb = (threadgroup S1 *)(shmem + 4096);
|
||||
|
||||
#ifdef GGML_METAL_HAS_TENSOR
|
||||
threadgroup float * sc = (threadgroup float *)(shmem);
|
||||
#endif
|
||||
|
||||
constexpr int NR0 = 64;
|
||||
constexpr int NR1 = 32;
|
||||
|
||||
constexpr int NK = 32;
|
||||
constexpr int NL0 = NK/16;
|
||||
constexpr int NL1 = NK/8;
|
||||
|
||||
const int im = tgpig.z; // expert
|
||||
const int r0 = tgpig.y*NR0;
|
||||
const int r1 = tgpig.x*NR1;
|
||||
|
||||
device const uint32_t * tpe_u32 = (device const uint32_t *) (htpe);
|
||||
device const int32_t * ids_i32 = (device const int32_t *) (hids);
|
||||
|
||||
const int32_t neh1 = tpe_u32[im];
|
||||
|
||||
if (r1 >= neh1) {
|
||||
return;
|
||||
}
|
||||
|
||||
// if this block is of 64x32 shape or smaller
|
||||
const short nr0 = (args.ne0 - r0 < NR0) ? (args.ne0 - r0) : NR0;
|
||||
const short nr1 = ( neh1 - r1 < NR1) ? ( neh1 - r1) : NR1;
|
||||
|
||||
// a thread shouldn't load data outside of the matrix
|
||||
const short lr0 = ((short)tiitg/NL0) < nr0 ? ((short)tiitg/NL0) : nr0 - 1; // 0 .. 63
|
||||
const short lr1 = ((short)tiitg/NL1) < nr1 ? ((short)tiitg/NL1) : nr1 - 1; // 0 .. 31
|
||||
|
||||
const short il0 = (tiitg % NL0);
|
||||
|
||||
short il = il0;
|
||||
|
||||
const int id = ids_i32[im*args.ne21 + r1 + lr1];
|
||||
|
||||
const short i11 = (id % args.ne20) % args.ne11;
|
||||
const short i12 = (id / args.ne20);
|
||||
const short i13 = 0;
|
||||
|
||||
const uint64_t offset0 = im*args.nb02 + i13*args.nb03;
|
||||
const short offset1 = il0/nl;
|
||||
|
||||
device const block_q * x = (device const block_q *)(src0 + args.nb01*(r0 + lr0) + offset0) + offset1;
|
||||
|
||||
const short iy = 8*(tiitg % NL1);
|
||||
|
||||
device const T1 * y = (device const T1 *)(src1
|
||||
+ args.nb13*i13
|
||||
+ args.nb12*i12
|
||||
+ args.nb11*i11
|
||||
+ args.nb10*iy);
|
||||
|
||||
#ifndef GGML_METAL_HAS_TENSOR
|
||||
S0_8x8 ma[4];
|
||||
S1_8x8 mb[2];
|
||||
|
||||
simdgroup_float8x8 mc[8];
|
||||
|
||||
for (short i = 0; i < 8; i++){
|
||||
mc[i] = make_filled_simdgroup_matrix<float, 8>(0.f);
|
||||
}
|
||||
#else
|
||||
auto tA = tensor<threadgroup S0, dextents<int32_t, 2>, tensor_inline>(sa, dextents<int32_t, 2>(NK, NR0));
|
||||
auto tB = tensor<threadgroup S1, dextents<int32_t, 2>, tensor_inline>(sb, dextents<int32_t, 2>(NR1, NK ));
|
||||
|
||||
mpp::tensor_ops::matmul2d<
|
||||
mpp::tensor_ops::matmul2d_descriptor(NR1, NR0, NK, false, true, false, mpp::tensor_ops::matmul2d_descriptor::mode::multiply_accumulate),
|
||||
execution_simdgroups<4>> mm;
|
||||
|
||||
auto cT = mm.get_destination_cooperative_tensor<decltype(tA), decltype(tB), float>();
|
||||
#endif
|
||||
|
||||
for (int loop_k = 0; loop_k < args.ne00; loop_k += NK) {
|
||||
#ifndef GGML_METAL_HAS_TENSOR
|
||||
// load data and store to threadgroup memory
|
||||
if (is_same<T0_4x4, block_q>::value && FC_mul_mm_bc_inp) {
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
// no need for dequantization
|
||||
for (short i = 0; i < 16; i++) {
|
||||
const short sx = 2*il0 + i/8;
|
||||
const short sy = (tiitg/NL0)/8;
|
||||
|
||||
//const short lx = i%8;
|
||||
//const short ly = (tiitg/NL0)%8;
|
||||
const short lx = (tiitg/NL0)%8;
|
||||
const short ly = i%8;
|
||||
|
||||
const short ib = 8*sx + sy;
|
||||
|
||||
*(sa + 64*ib + 8*ly + lx) = loop_k + 16*il + i < args.ne00 ? (S0) *((device T0 *) x + i) : (S0) 0;
|
||||
}
|
||||
} else {
|
||||
S0_4x4 temp_a;
|
||||
dequantize_func(x, il, temp_a);
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
FOR_UNROLL (short i = 0; i < 16; i++) {
|
||||
const short sx = 2*il0 + i/8;
|
||||
const short sy = (tiitg/NL0)/8;
|
||||
|
||||
//const short lx = i%8;
|
||||
//const short ly = (tiitg/NL0)%8;
|
||||
const short lx = (tiitg/NL0)%8;
|
||||
const short ly = i%8;
|
||||
|
||||
const short ib = 8*sx + sy;
|
||||
|
||||
// NOTE: this is massively slower.. WTF?
|
||||
//sa[64*ib + 8*ly + lx] = temp_a[i/4][i%4];
|
||||
|
||||
*(sa + 64*ib + 8*ly + lx) = temp_a[i/4][i%4];
|
||||
}
|
||||
}
|
||||
|
||||
if (FC_mul_mm_bc_inp) {
|
||||
for (short i = 0; i < 8; ++i) {
|
||||
const short sx = (tiitg%NL1);
|
||||
const short sy = (tiitg/NL1)/8;
|
||||
|
||||
const short lx = i;
|
||||
const short ly = (tiitg/NL1)%8;
|
||||
//const short lx = (tiitg/NL1)%8;
|
||||
//const short ly = i;
|
||||
|
||||
const short ib = 4*sx + sy;
|
||||
|
||||
*(sb + 64*ib + 8*ly + lx) = loop_k + iy + i < args.ne00 ? (S1) *((device T1 *) y + i) : 0;
|
||||
}
|
||||
} else {
|
||||
const short sx = (tiitg%NL1);
|
||||
const short sy = (tiitg/NL1)/8;
|
||||
|
||||
//const short dx = sx;
|
||||
//const short dy = sy;
|
||||
|
||||
const short ly = (tiitg/NL1)%8;
|
||||
|
||||
const short ib = 4*sx + sy;
|
||||
|
||||
*(threadgroup S1_2x4 *)(sb + 64*ib + 8*ly) = (S1_2x4)(*((device T1_2x4 *) y));
|
||||
}
|
||||
#else
|
||||
// load data and store to threadgroup memory
|
||||
if (is_same<T0_4x4, block_q>::value && FC_mul_mm_bc_inp) {
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
// no need for dequantization
|
||||
for (short i = 0; i < 16; i++) {
|
||||
const short sx = 2*il0 + i/8;
|
||||
const short sy = (tiitg/NL0)/8;
|
||||
|
||||
const short lx = i%8;
|
||||
const short ly = (tiitg/NL0)%8;
|
||||
//const short lx = (tiitg/NL0)%8;
|
||||
//const short ly = i%8;
|
||||
|
||||
*(sa + NK*(8*sy + ly) + 8*sx + lx) = loop_k + 16*il + i < args.ne00 ? *((device T0 *) x + i) : 0;
|
||||
}
|
||||
} else {
|
||||
S0_4x4 temp_a;
|
||||
dequantize_func(x, il, temp_a);
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
FOR_UNROLL (short i = 0; i < 16; i++) {
|
||||
const short sx = 2*il0 + i/8;
|
||||
const short sy = (tiitg/NL0)/8;
|
||||
|
||||
const short lx = i%8;
|
||||
const short ly = (tiitg/NL0)%8;
|
||||
//const short lx = (tiitg/NL0)%8;
|
||||
//const short ly = i%8;
|
||||
|
||||
*(sa + NK*(8*sy + ly) + 8*sx + lx) = temp_a[i/4][i%4];
|
||||
}
|
||||
}
|
||||
|
||||
if (FC_mul_mm_bc_inp) {
|
||||
for (short i = 0; i < 8; ++i) {
|
||||
const short sx = (tiitg%NL1);
|
||||
const short sy = (tiitg/NL1)/8;
|
||||
|
||||
const short lx = i;
|
||||
const short ly = (tiitg/NL1)%8;
|
||||
//const short lx = (tiitg/NL1)%8;
|
||||
//const short ly = i;
|
||||
|
||||
*(sb + NK*(8*sy + ly) + 8*sx + lx) = loop_k + iy + i < args.ne00 ? (S1) *((device T1 *) y + i) : 0;
|
||||
}
|
||||
} else {
|
||||
const short sx = (tiitg%NL1);
|
||||
const short sy = (tiitg/NL1)/8;
|
||||
|
||||
//const short lx = i;
|
||||
const short ly = (tiitg/NL1)%8;
|
||||
//const short lx = (tiitg/NL1)%8;
|
||||
//const short ly = i;
|
||||
|
||||
*(threadgroup S1_2x4 *)(sb + NK*(8*sy + ly) + 8*sx) = (S1_2x4)(*((device T1_2x4 *) y));
|
||||
}
|
||||
#endif
|
||||
|
||||
il = (il + 2 < nl) ? il + 2 : il % 2;
|
||||
x = (il < 2) ? x + (2 + nl - 1)/nl : x;
|
||||
|
||||
y += NK;
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
#ifndef GGML_METAL_HAS_TENSOR
|
||||
// load matrices from threadgroup memory and conduct outer products
|
||||
threadgroup const S0 * lsma = (sa + 4*64*(sgitg%2));
|
||||
threadgroup const S1 * lsmb = (sb + 2*64*(sgitg/2));
|
||||
|
||||
FOR_UNROLL (short ik = 0; ik < NK/8; ik++) {
|
||||
simdgroup_barrier(mem_flags::mem_none);
|
||||
|
||||
FOR_UNROLL (short i = 0; i < 4; i++) {
|
||||
simdgroup_load(ma[i], lsma + 64*i, 8, 0, false);
|
||||
}
|
||||
|
||||
simdgroup_barrier(mem_flags::mem_none);
|
||||
|
||||
FOR_UNROLL (short i = 0; i < 2; i++) {
|
||||
simdgroup_load(mb[i], lsmb + 64*i, 8, 0, false);
|
||||
}
|
||||
|
||||
simdgroup_barrier(mem_flags::mem_none);
|
||||
|
||||
FOR_UNROLL (short i = 0; i < 8; i++){
|
||||
simdgroup_multiply_accumulate(mc[i], mb[i/4], ma[i%4], mc[i]);
|
||||
}
|
||||
|
||||
lsma += 8*64;
|
||||
lsmb += 4*64;
|
||||
}
|
||||
#else
|
||||
auto sA = tA.slice(0, 0);
|
||||
auto sB = tB.slice(0, 0);
|
||||
|
||||
mm.run(sB, sA, cT);
|
||||
#endif
|
||||
}
|
||||
|
||||
// block is smaller than 64x32, we should avoid writing data outside of the matrix
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
#ifdef GGML_METAL_HAS_TENSOR
|
||||
auto tC = tensor<threadgroup float, dextents<int32_t, 2>, tensor_inline>(sc, dextents<int32_t, 2>(NR0, NR1));
|
||||
cT.store(tC);
|
||||
#else
|
||||
threadgroup float * temp_str = ((threadgroup float *) shmem) + 32*(sgitg&1) + (16*(sgitg >> 1))*NR0;
|
||||
|
||||
for (short i = 0; i < 8; i++) {
|
||||
simdgroup_store(mc[i], temp_str + 8*(i%4) + 8*NR0*(i/4), NR0, 0, false);
|
||||
}
|
||||
#endif
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
for (short j = sgitg; j < nr1; j += 4) {
|
||||
const int id = ids_i32[im*args.ne21 + r1 + j];
|
||||
|
||||
const short ide = id % args.ne20;
|
||||
const short idt = id / args.ne20;
|
||||
|
||||
device float * D = (device float *) dst + r0 + ide*args.ne0 + idt*args.ne1*args.ne0;
|
||||
device float4 * D4 = (device float4 *) D;
|
||||
|
||||
threadgroup float * C = (threadgroup float *) shmem + j*NR0;
|
||||
threadgroup float4 * C4 = (threadgroup float4 *) C;
|
||||
|
||||
int i = tiisg;
|
||||
for (; i < nr0/4; i += 32) {
|
||||
*(D4 + i) = *(C4 + i);
|
||||
}
|
||||
|
||||
i = (4*(nr0/4)) + tiisg;
|
||||
for (; i < nr0; i += 32) {
|
||||
*(D + i) = *(C + i);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
//
|
||||
// matrix-matrix multiplication
|
||||
//
|
||||
|
||||
typedef decltype(kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, float4x4, 1, dequantize_f32, float, float4x4, float, float2x4>) mul_mm_t;
|
||||
|
||||
template [[host_name("kernel_mul_mm_f32_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, float4x4, 1, dequantize_f32, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_f16_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, half4x4, 1, dequantize_f16, half, half4x4, float, float2x4>;
|
||||
#if defined(GGML_METAL_HAS_BF16)
|
||||
template [[host_name("kernel_mul_mm_bf16_f32")]] kernel mul_mm_t kernel_mul_mm<bfloat, bfloat4x4, simdgroup_bfloat8x8, bfloat, bfloat2x4, simdgroup_bfloat8x8, bfloat4x4, 1, dequantize_bf16, bfloat, bfloat4x4, float, float2x4>;
|
||||
#endif
|
||||
template [[host_name("kernel_mul_mm_q1_0_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q1_0, 8, dequantize_q1_0, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_q4_0_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q4_0, 2, dequantize_q4_0, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_q4_1_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q4_1, 2, dequantize_q4_1, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_q5_0_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q5_0, 2, dequantize_q5_0, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_q5_1_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q5_1, 2, dequantize_q5_1, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_q8_0_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q8_0, 2, dequantize_q8_0, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_mxfp4_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_mxfp4, 2, dequantize_mxfp4, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_q2_K_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q2_K, QK_NL, dequantize_q2_K, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_q3_K_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q3_K, QK_NL, dequantize_q3_K, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_q4_K_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q4_K, QK_NL, dequantize_q4_K, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_q5_K_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q5_K, QK_NL, dequantize_q5_K, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_q6_K_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q6_K, QK_NL, dequantize_q6_K, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_iq2_xxs_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq2_xxs, QK_NL, dequantize_iq2_xxs, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_iq2_xs_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq2_xs, QK_NL, dequantize_iq2_xs, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_iq3_xxs_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq3_xxs, QK_NL, dequantize_iq3_xxs, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_iq3_s_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq3_s, QK_NL, dequantize_iq3_s, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_iq2_s_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq2_s, QK_NL, dequantize_iq2_s, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_iq1_s_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq1_s, QK_NL, dequantize_iq1_s, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_iq1_m_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq1_m, QK_NL, dequantize_iq1_m, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_iq4_nl_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq4_nl, 2, dequantize_iq4_nl, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_iq4_xs_f32")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq4_xs, QK_NL, dequantize_iq4_xs, float, float4x4, float, float2x4>;
|
||||
|
||||
template [[host_name("kernel_mul_mm_f32_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, float4x4, 1, dequantize_f32, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_f16_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, half4x4, 1, dequantize_f16, half, half4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_q1_0_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q1_0, 8, dequantize_q1_0, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_q4_0_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q4_0, 2, dequantize_q4_0, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_q4_1_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q4_1, 2, dequantize_q4_1, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_q5_0_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q5_0, 2, dequantize_q5_0, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_q5_1_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q5_1, 2, dequantize_q5_1, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_q8_0_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q8_0, 2, dequantize_q8_0, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_mxfp4_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_mxfp4, 2, dequantize_mxfp4, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_q2_K_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q2_K, QK_NL, dequantize_q2_K, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_q3_K_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q3_K, QK_NL, dequantize_q3_K, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_q4_K_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q4_K, QK_NL, dequantize_q4_K, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_q5_K_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q5_K, QK_NL, dequantize_q5_K, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_q6_K_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q6_K, QK_NL, dequantize_q6_K, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_iq2_xxs_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq2_xxs, QK_NL, dequantize_iq2_xxs, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_iq2_xs_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq2_xs, QK_NL, dequantize_iq2_xs, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_iq3_xxs_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq3_xxs, QK_NL, dequantize_iq3_xxs, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_iq3_s_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq3_s, QK_NL, dequantize_iq3_s, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_iq2_s_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq2_s, QK_NL, dequantize_iq2_s, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_iq1_s_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq1_s, QK_NL, dequantize_iq1_s, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_iq1_m_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq1_m, QK_NL, dequantize_iq1_m, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_iq4_nl_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq4_nl, 2, dequantize_iq4_nl, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_iq4_xs_f16")]] kernel mul_mm_t kernel_mul_mm<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq4_xs, QK_NL, dequantize_iq4_xs, float, float4x4, half, half2x4>;
|
||||
|
||||
//
|
||||
// indirect matrix-matrix multiplication
|
||||
//
|
||||
|
||||
typedef decltype(kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, float4x4, 1, dequantize_f32, float, float4x4, float, float2x4>) mul_mm_id;
|
||||
|
||||
template [[host_name("kernel_mul_mm_id_f32_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, float4x4, 1, dequantize_f32, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_f16_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, half4x4, 1, dequantize_f16, half, half4x4, float, float2x4>;
|
||||
#if defined(GGML_METAL_HAS_BF16)
|
||||
template [[host_name("kernel_mul_mm_id_bf16_f32")]] kernel mul_mm_id kernel_mul_mm_id<bfloat, bfloat4x4, simdgroup_bfloat8x8, bfloat, bfloat2x4, simdgroup_bfloat8x8, bfloat4x4, 1, dequantize_bf16, bfloat, bfloat4x4, float, float2x4>;
|
||||
#endif
|
||||
template [[host_name("kernel_mul_mm_id_q1_0_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q1_0, 8, dequantize_q1_0, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_q4_0_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q4_0, 2, dequantize_q4_0, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_q4_1_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q4_1, 2, dequantize_q4_1, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_q5_0_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q5_0, 2, dequantize_q5_0, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_q5_1_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q5_1, 2, dequantize_q5_1, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_q8_0_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q8_0, 2, dequantize_q8_0, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_mxfp4_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_mxfp4, 2, dequantize_mxfp4, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_q2_K_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q2_K, QK_NL, dequantize_q2_K, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_q3_K_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q3_K, QK_NL, dequantize_q3_K, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_q4_K_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q4_K, QK_NL, dequantize_q4_K, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_q5_K_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q5_K, QK_NL, dequantize_q5_K, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_q6_K_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q6_K, QK_NL, dequantize_q6_K, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_iq2_xxs_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq2_xxs, QK_NL, dequantize_iq2_xxs, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_iq2_xs_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq2_xs, QK_NL, dequantize_iq2_xs, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_iq3_xxs_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq3_xxs, QK_NL, dequantize_iq3_xxs, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_iq3_s_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq3_s, QK_NL, dequantize_iq3_s, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_iq2_s_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq2_s, QK_NL, dequantize_iq2_s, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_iq1_s_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq1_s, QK_NL, dequantize_iq1_s, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_iq1_m_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq1_m, QK_NL, dequantize_iq1_m, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_iq4_nl_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq4_nl, 2, dequantize_iq4_nl, float, float4x4, float, float2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_iq4_xs_f32")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq4_xs, QK_NL, dequantize_iq4_xs, float, float4x4, float, float2x4>;
|
||||
|
||||
template [[host_name("kernel_mul_mm_id_f32_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, float4x4, 1, dequantize_f32, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_f16_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, half4x4, 1, dequantize_f16, half, half4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_q1_0_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q1_0, 8, dequantize_q1_0, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_q4_0_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q4_0, 2, dequantize_q4_0, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_q4_1_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q4_1, 2, dequantize_q4_1, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_q5_0_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q5_0, 2, dequantize_q5_0, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_q5_1_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q5_1, 2, dequantize_q5_1, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_q8_0_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q8_0, 2, dequantize_q8_0, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_mxfp4_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_mxfp4, 2, dequantize_mxfp4, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_q2_K_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q2_K, QK_NL, dequantize_q2_K, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_q3_K_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q3_K, QK_NL, dequantize_q3_K, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_q4_K_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q4_K, QK_NL, dequantize_q4_K, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_q5_K_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q5_K, QK_NL, dequantize_q5_K, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_q6_K_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_q6_K, QK_NL, dequantize_q6_K, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_iq2_xxs_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq2_xxs, QK_NL, dequantize_iq2_xxs, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_iq2_xs_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq2_xs, QK_NL, dequantize_iq2_xs, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_iq3_xxs_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq3_xxs, QK_NL, dequantize_iq3_xxs, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_iq3_s_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq3_s, QK_NL, dequantize_iq3_s, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_iq2_s_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq2_s, QK_NL, dequantize_iq2_s, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_iq1_s_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq1_s, QK_NL, dequantize_iq1_s, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_iq1_m_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq1_m, QK_NL, dequantize_iq1_m, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_iq4_nl_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq4_nl, 2, dequantize_iq4_nl, float, float4x4, half, half2x4>;
|
||||
template [[host_name("kernel_mul_mm_id_iq4_xs_f16")]] kernel mul_mm_id kernel_mul_mm_id<half, half4x4, simdgroup_half8x8, half, half2x4, simdgroup_half8x8, block_iq4_xs, QK_NL, dequantize_iq4_xs, float, float4x4, half, half2x4>;
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,308 +0,0 @@
|
||||
#include "common.h"
|
||||
|
||||
// F == 1 : norm (no fuse)
|
||||
// F == 2 : norm + mul
|
||||
// F == 3 : norm + mul + add
|
||||
template <typename T, short F>
|
||||
kernel void kernel_norm_fuse_impl(
|
||||
constant ggml_metal_kargs_norm & args,
|
||||
device const char * src0,
|
||||
device const char * src1_0,
|
||||
device const char * src1_1,
|
||||
device char * dst,
|
||||
threadgroup float * shmem_f32 [[threadgroup(0)]],
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort3 tpitg[[thread_position_in_threadgroup]],
|
||||
ushort sgitg[[simdgroup_index_in_threadgroup]],
|
||||
ushort tiisg[[thread_index_in_simdgroup]],
|
||||
ushort3 ntg[[threads_per_threadgroup]]) {
|
||||
if (sgitg == 0) {
|
||||
shmem_f32[tiisg] = 0.0f;
|
||||
}
|
||||
|
||||
const int i01 = tgpig.x;
|
||||
const int i02 = tgpig.y;
|
||||
const int i03 = tgpig.z;
|
||||
|
||||
device const T * x = (device const T *) (src0 + i03*args.nbf3[0] + i02*args.nbf2[0] + i01*args.nbf1[0]);
|
||||
|
||||
device const T * f0 = (device const T *) (src1_0 + (i03%args.nef3[1])*args.nbf3[1] + (i02%args.nef2[1])*args.nbf2[1] + (i01%args.nef1[1])*args.nbf1[1]);
|
||||
device const T * f1 = (device const T *) (src1_1 + (i03%args.nef3[2])*args.nbf3[2] + (i02%args.nef2[2])*args.nbf2[2] + (i01%args.nef1[2])*args.nbf1[2]);
|
||||
|
||||
T sumft(0.0f);
|
||||
|
||||
float sumf = 0.0f;
|
||||
|
||||
for (int i00 = tpitg.x; i00 < args.ne00_t; i00 += ntg.x) {
|
||||
sumft += x[i00];
|
||||
}
|
||||
sumf = dot(sumft, T(1.0f));
|
||||
sumf = simd_sum(sumf);
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
if (tiisg == 0) {
|
||||
shmem_f32[sgitg] = sumf;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
sumf = shmem_f32[tiisg];
|
||||
sumf = simd_sum(sumf);
|
||||
|
||||
const float mean = sumf/args.ne00;
|
||||
|
||||
device T * y = (device T *) (dst + i03*args.nb3 + i02*args.nb2 + i01*args.nb1);
|
||||
|
||||
sumf = 0.0f;
|
||||
for (int i00 = tpitg.x; i00 < args.ne00_t; i00 += ntg.x) {
|
||||
y[i00] = x[i00] - mean;
|
||||
sumf += dot(y[i00], y[i00]);
|
||||
}
|
||||
sumf = simd_sum(sumf);
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
if (tiisg == 0) {
|
||||
shmem_f32[sgitg] = sumf;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
sumf = shmem_f32[tiisg];
|
||||
sumf = simd_sum(sumf);
|
||||
|
||||
const float variance = sumf/args.ne00;
|
||||
|
||||
const float scale = 1.0f/sqrt(variance + args.eps);
|
||||
for (int i00 = tpitg.x; i00 < args.ne00_t; i00 += ntg.x) {
|
||||
if (F == 1) {
|
||||
y[i00] = (y[i00]*scale);
|
||||
}
|
||||
if (F == 2) {
|
||||
y[i00] = (y[i00]*scale)*f0[i00];
|
||||
}
|
||||
if (F == 3) {
|
||||
y[i00] = (y[i00]*scale)*f0[i00] + f1[i00];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
typedef decltype(kernel_norm_fuse_impl<float4, 1>) kernel_norm_fuse_t;
|
||||
|
||||
template [[host_name("kernel_norm_f32")]] kernel kernel_norm_fuse_t kernel_norm_fuse_impl<float, 1>;
|
||||
template [[host_name("kernel_norm_mul_f32")]] kernel kernel_norm_fuse_t kernel_norm_fuse_impl<float, 2>;
|
||||
template [[host_name("kernel_norm_mul_add_f32")]] kernel kernel_norm_fuse_t kernel_norm_fuse_impl<float, 3>;
|
||||
|
||||
template [[host_name("kernel_norm_f32_4")]] kernel kernel_norm_fuse_t kernel_norm_fuse_impl<float4, 1>;
|
||||
template [[host_name("kernel_norm_mul_f32_4")]] kernel kernel_norm_fuse_t kernel_norm_fuse_impl<float4, 2>;
|
||||
template [[host_name("kernel_norm_mul_add_f32_4")]] kernel kernel_norm_fuse_t kernel_norm_fuse_impl<float4, 3>;
|
||||
|
||||
// F == 1 : rms_norm (no fuse)
|
||||
// F == 2 : rms_norm + mul
|
||||
// F == 3 : rms_norm + mul + add
|
||||
template <typename T, short F>
|
||||
kernel void kernel_rms_norm_fuse_impl(
|
||||
constant ggml_metal_kargs_norm & args,
|
||||
device const char * src0,
|
||||
device const char * src1_0,
|
||||
device const char * src1_1,
|
||||
device char * dst,
|
||||
threadgroup float * shmem_f32 [[threadgroup(0)]],
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort3 tpitg[[thread_position_in_threadgroup]],
|
||||
ushort sgitg[[simdgroup_index_in_threadgroup]],
|
||||
ushort tiisg[[thread_index_in_simdgroup]],
|
||||
ushort3 ntg[[threads_per_threadgroup]]) {
|
||||
if (sgitg == 0) {
|
||||
shmem_f32[tiisg] = 0.0f;
|
||||
}
|
||||
|
||||
const int i01 = tgpig.x;
|
||||
const int i02 = tgpig.y;
|
||||
const int i03 = tgpig.z;
|
||||
|
||||
device const T * x = (device const T *) (src0 + i03*args.nbf3[0] + i02*args.nbf2[0] + i01*args.nbf1[0]);
|
||||
|
||||
device const T * f0 = (device const T *) (src1_0 + (i03%args.nef3[1])*args.nbf3[1] + (i02%args.nef2[1])*args.nbf2[1] + (i01%args.nef1[1])*args.nbf1[1]);
|
||||
device const T * f1 = (device const T *) (src1_1 + (i03%args.nef3[2])*args.nbf3[2] + (i02%args.nef2[2])*args.nbf2[2] + (i01%args.nef1[2])*args.nbf1[2]);
|
||||
|
||||
float sumf = 0.0f;
|
||||
|
||||
// parallel sum
|
||||
for (int i00 = tpitg.x; i00 < args.ne00_t; i00 += ntg.x) {
|
||||
sumf += dot(x[i00], x[i00]);
|
||||
}
|
||||
sumf = simd_sum(sumf);
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
if (tiisg == 0) {
|
||||
shmem_f32[sgitg] = sumf;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
sumf = shmem_f32[tiisg];
|
||||
sumf = simd_sum(sumf);
|
||||
|
||||
const float mean = sumf/args.ne00;
|
||||
const float scale = 1.0f/sqrt(mean + args.eps);
|
||||
|
||||
device T * y = (device T *) (dst + i03*args.nb3 + i02*args.nb2 + i01*args.nb1);
|
||||
for (int i00 = tpitg.x; i00 < args.ne00_t; i00 += ntg.x) {
|
||||
if (F == 1) {
|
||||
y[i00] = (x[i00]*scale);
|
||||
}
|
||||
if (F == 2) {
|
||||
y[i00] = (x[i00]*scale)*f0[i00];
|
||||
}
|
||||
if (F == 3) {
|
||||
y[i00] = (x[i00]*scale)*f0[i00] + f1[i00];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
typedef decltype(kernel_rms_norm_fuse_impl<float4, 1>) kernel_rms_norm_fuse_t;
|
||||
|
||||
template [[host_name("kernel_rms_norm_f32")]] kernel kernel_rms_norm_fuse_t kernel_rms_norm_fuse_impl<float, 1>;
|
||||
template [[host_name("kernel_rms_norm_mul_f32")]] kernel kernel_rms_norm_fuse_t kernel_rms_norm_fuse_impl<float, 2>;
|
||||
template [[host_name("kernel_rms_norm_mul_add_f32")]] kernel kernel_rms_norm_fuse_t kernel_rms_norm_fuse_impl<float, 3>;
|
||||
|
||||
template [[host_name("kernel_rms_norm_f32_4")]] kernel kernel_rms_norm_fuse_t kernel_rms_norm_fuse_impl<float4, 1>;
|
||||
template [[host_name("kernel_rms_norm_mul_f32_4")]] kernel kernel_rms_norm_fuse_t kernel_rms_norm_fuse_impl<float4, 2>;
|
||||
template [[host_name("kernel_rms_norm_mul_add_f32_4")]] kernel kernel_rms_norm_fuse_t kernel_rms_norm_fuse_impl<float4, 3>;
|
||||
|
||||
template <typename T0, typename T>
|
||||
kernel void kernel_l2_norm_impl(
|
||||
constant ggml_metal_kargs_l2_norm & args,
|
||||
device const char * src0,
|
||||
device char * dst,
|
||||
threadgroup float * shmem_f32 [[threadgroup(0)]],
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort3 tpitg[[thread_position_in_threadgroup]],
|
||||
ushort sgitg[[simdgroup_index_in_threadgroup]],
|
||||
ushort tiisg[[thread_index_in_simdgroup]],
|
||||
ushort3 ntg[[threads_per_threadgroup]]) {
|
||||
const int i03 = tgpig.z;
|
||||
const int i02 = tgpig.y;
|
||||
const int i01 = tgpig.x;
|
||||
|
||||
if (sgitg == 0) {
|
||||
shmem_f32[tiisg] = 0.0f;
|
||||
}
|
||||
|
||||
device const T0 * x = (device const T0 *) (src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01);
|
||||
device T * y = (device T *) (dst + i03*args.nb3 + i02*args.nb2 + i01*args.nb1);
|
||||
|
||||
float sumf = 0.0f;
|
||||
|
||||
// parallel sum
|
||||
for (int i00 = tpitg.x; i00 < args.ne00; i00 += ntg.x) {
|
||||
sumf += dot(x[i00], x[i00]);
|
||||
}
|
||||
sumf = simd_sum(sumf);
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
if (tiisg == 0) {
|
||||
shmem_f32[sgitg] = sumf;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
sumf = shmem_f32[tiisg];
|
||||
sumf = simd_sum(sumf);
|
||||
|
||||
const float scale = 1.0f/max(sqrt(sumf), args.eps);
|
||||
|
||||
for (int i00 = tpitg.x; i00 < args.ne00; i00 += ntg.x) {
|
||||
y[i00] = x[i00] * scale;
|
||||
}
|
||||
}
|
||||
|
||||
typedef decltype(kernel_l2_norm_impl<float, float>) kernel_l2_norm_t;
|
||||
|
||||
template [[host_name("kernel_l2_norm_f32_f32")]] kernel kernel_l2_norm_t kernel_l2_norm_impl<float, float>;
|
||||
template [[host_name("kernel_l2_norm_f32_f32_4")]] kernel kernel_l2_norm_t kernel_l2_norm_impl<float4, float4>;
|
||||
|
||||
kernel void kernel_group_norm_f32(
|
||||
constant ggml_metal_kargs_group_norm & args,
|
||||
device const float * src0,
|
||||
device float * dst,
|
||||
threadgroup float * buf [[threadgroup(0)]],
|
||||
uint tgpig[[threadgroup_position_in_grid]],
|
||||
uint tpitg[[thread_position_in_threadgroup]],
|
||||
uint sgitg[[simdgroup_index_in_threadgroup]],
|
||||
uint tiisg[[thread_index_in_simdgroup]],
|
||||
uint ntg[[threads_per_threadgroup]]) {
|
||||
const int64_t ne = args.ne00*args.ne01*args.ne02;
|
||||
const int64_t gs = args.ne00*args.ne01*((args.ne02 + args.ngrp - 1) / args.ngrp);
|
||||
|
||||
int start = tgpig * gs;
|
||||
int end = start + gs;
|
||||
|
||||
start += tpitg;
|
||||
|
||||
if (end >= ne) {
|
||||
end = ne;
|
||||
}
|
||||
|
||||
float tmp = 0.0f; // partial sum for thread in warp
|
||||
|
||||
for (int j = start; j < end; j += ntg) {
|
||||
tmp += src0[j];
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
tmp = simd_sum(tmp);
|
||||
if (ntg > N_SIMDWIDTH) {
|
||||
if (sgitg == 0) {
|
||||
buf[tiisg] = 0.0f;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
if (tiisg == 0) {
|
||||
buf[sgitg] = tmp;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
tmp = buf[tiisg];
|
||||
tmp = simd_sum(tmp);
|
||||
}
|
||||
|
||||
const float mean = tmp / gs;
|
||||
tmp = 0.0f;
|
||||
|
||||
for (int j = start; j < end; j += ntg) {
|
||||
float xi = src0[j] - mean;
|
||||
dst[j] = xi;
|
||||
tmp += xi * xi;
|
||||
}
|
||||
|
||||
tmp = simd_sum(tmp);
|
||||
if (ntg > N_SIMDWIDTH) {
|
||||
if (sgitg == 0) {
|
||||
buf[tiisg] = 0.0f;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
if (tiisg == 0) {
|
||||
buf[sgitg] = tmp;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
tmp = buf[tiisg];
|
||||
tmp = simd_sum(tmp);
|
||||
}
|
||||
|
||||
const float variance = tmp / gs;
|
||||
const float scale = 1.0f/sqrt(variance + args.eps);
|
||||
for (int j = start; j < end; j += ntg) {
|
||||
dst[j] *= scale;
|
||||
}
|
||||
}
|
||||
@@ -1,148 +0,0 @@
|
||||
#include "common.h"
|
||||
|
||||
kernel void kernel_pool_2d_max_f32(
|
||||
constant ggml_metal_kargs_pool_2d & args,
|
||||
device const float * src0,
|
||||
device float * dst,
|
||||
uint gid[[thread_position_in_grid]]) {
|
||||
|
||||
if (gid >= args.np) {
|
||||
return;
|
||||
}
|
||||
|
||||
const int idx = gid;
|
||||
const int I_HW = args.IH * args.IW;
|
||||
const int O_HW = args.OH * args.OW;
|
||||
const int nc = idx / O_HW;
|
||||
const int cur_oh = idx % O_HW / args.OW;
|
||||
const int cur_ow = idx % O_HW % args.OW;
|
||||
|
||||
device const float * i_ptr = src0 + nc * I_HW;
|
||||
device float * o_ptr = dst + nc * O_HW;
|
||||
|
||||
const int start_h = cur_oh * args.s1 - args.p1;
|
||||
const int bh = MAX(0, start_h);
|
||||
const int eh = MIN(args.IH, start_h + args.k1);
|
||||
const int start_w = cur_ow * args.s0 - args.p0;
|
||||
const int bw = MAX(0, start_w);
|
||||
const int ew = MIN(args.IW, start_w + args.k0);
|
||||
|
||||
float res = -INFINITY;
|
||||
|
||||
for (int i = bh; i < eh; i += 1) {
|
||||
for (int j = bw; j < ew; j += 1) {
|
||||
res = MAX(res, i_ptr[i * args.IW + j]);
|
||||
}
|
||||
}
|
||||
|
||||
o_ptr[cur_oh * args.OW + cur_ow] = res;
|
||||
}
|
||||
|
||||
kernel void kernel_pool_2d_avg_f32(
|
||||
constant ggml_metal_kargs_pool_2d & args,
|
||||
device const float * src0,
|
||||
device float * dst,
|
||||
uint gid[[thread_position_in_grid]]) {
|
||||
|
||||
if (gid >= args.np) {
|
||||
return;
|
||||
}
|
||||
|
||||
const int idx = gid;
|
||||
const int I_HW = args.IH * args.IW;
|
||||
const int O_HW = args.OH * args.OW;
|
||||
const int nc = idx / O_HW;
|
||||
const int cur_oh = idx % O_HW / args.OW;
|
||||
const int cur_ow = idx % O_HW % args.OW;
|
||||
|
||||
device const float * i_ptr = src0 + nc * I_HW;
|
||||
device float * o_ptr = dst + nc * O_HW;
|
||||
|
||||
const int start_h = cur_oh * args.s1 - args.p1;
|
||||
const int bh = MAX(0, start_h);
|
||||
const int eh = MIN(args.IH, start_h + args.k1);
|
||||
const int start_w = cur_ow * args.s0 - args.p0;
|
||||
const int bw = MAX(0, start_w);
|
||||
const int ew = MIN(args.IW, start_w + args.k0);
|
||||
// const float scale = 1. / ((eh - bh) * (ew - bw));
|
||||
const float scale = 1. / (args.k0 * args.k1);
|
||||
|
||||
float res = 0;
|
||||
|
||||
for (int i = bh; i < eh; i += 1) {
|
||||
for (int j = bw; j < ew; j += 1) {
|
||||
float cur = i_ptr[i * args.IW + j];
|
||||
res += cur * scale;
|
||||
}
|
||||
}
|
||||
|
||||
o_ptr[cur_oh * args.OW + cur_ow] = res;
|
||||
}
|
||||
|
||||
|
||||
kernel void kernel_pool_1d_max_f32(
|
||||
constant ggml_metal_kargs_pool_1d & args,
|
||||
device const float * src,
|
||||
device float * dst,
|
||||
uint gid [[thread_position_in_grid]]
|
||||
) {
|
||||
|
||||
if (gid >= args.np) {
|
||||
return;
|
||||
}
|
||||
|
||||
const int ow = (int)gid % args.OW;
|
||||
const int row = (int)gid / args.OW;
|
||||
|
||||
const int base = ow * args.s0 - args.p0;
|
||||
|
||||
float acc = -INFINITY;
|
||||
|
||||
const int src_off = row * args.IW;
|
||||
const int dst_off = row * args.OW;
|
||||
|
||||
for (int ki = 0; ki < args.k0; ++ki) {
|
||||
int j = base + ki;
|
||||
if (j < 0 || j >= args.IW){
|
||||
continue;
|
||||
}
|
||||
float v = src[src_off + j];
|
||||
acc = max(acc, v);
|
||||
}
|
||||
|
||||
dst[dst_off + ow] = acc;
|
||||
}
|
||||
|
||||
kernel void kernel_pool_1d_avg_f32(
|
||||
constant ggml_metal_kargs_pool_1d & args,
|
||||
device const float * src,
|
||||
device float * dst,
|
||||
uint gid [[thread_position_in_grid]]
|
||||
) {
|
||||
|
||||
if (gid >= args.np) {
|
||||
return;
|
||||
}
|
||||
|
||||
const int ow = (int)gid % args.OW;
|
||||
const int row = (int)gid / args.OW;
|
||||
|
||||
const int base = ow * args.s0 - args.p0;
|
||||
|
||||
float acc = 0.0f;
|
||||
int cnt = 0;
|
||||
|
||||
const int src_off = row * args.IW;
|
||||
const int dst_off = row * args.OW;
|
||||
|
||||
for (int ki = 0; ki < args.k0; ++ki) {
|
||||
const int j = base + ki;
|
||||
if (j < 0 || j >= args.IW) {
|
||||
continue;
|
||||
}
|
||||
acc += src[src_off + j];
|
||||
cnt += 1;
|
||||
}
|
||||
|
||||
dst[dst_off + ow] = (cnt > 0) ? (acc / (float)cnt) : 0.0f;
|
||||
}
|
||||
@@ -1,213 +0,0 @@
|
||||
#pragma once
|
||||
|
||||
#include "common.h"
|
||||
|
||||
void quantize_q1_0(device const float * src, device block_q1_0 & dst) {
|
||||
float sum_abs = 0.0f;
|
||||
for (int j = 0; j < QK1_0; j++) {
|
||||
sum_abs += fabs(src[j]);
|
||||
}
|
||||
dst.d = sum_abs / QK1_0;
|
||||
|
||||
for (int j = 0; j < QK1_0 / 8; j++) {
|
||||
dst.qs[j] = 0;
|
||||
}
|
||||
for (int j = 0; j < QK1_0; j++) {
|
||||
if (src[j] >= 0.0f) {
|
||||
dst.qs[j / 8] |= (1 << (j % 8));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void quantize_q4_0(device const float * src, device block_q4_0 & dst) {
|
||||
#pragma METAL fp math_mode(safe)
|
||||
float amax = 0.0f; // absolute max
|
||||
float max = 0.0f;
|
||||
|
||||
for (int j = 0; j < QK4_0; j++) {
|
||||
const float v = src[j];
|
||||
if (amax < fabs(v)) {
|
||||
amax = fabs(v);
|
||||
max = v;
|
||||
}
|
||||
}
|
||||
|
||||
const float d = max / -8;
|
||||
const float id = d ? 1.0f/d : 0.0f;
|
||||
|
||||
dst.d = d;
|
||||
|
||||
for (int j = 0; j < QK4_0/2; ++j) {
|
||||
const float x0 = src[0 + j]*id;
|
||||
const float x1 = src[QK4_0/2 + j]*id;
|
||||
|
||||
const uint8_t xi0 = MIN(15, (int8_t)(x0 + 8.5f));
|
||||
const uint8_t xi1 = MIN(15, (int8_t)(x1 + 8.5f));
|
||||
|
||||
dst.qs[j] = xi0;
|
||||
dst.qs[j] |= xi1 << 4;
|
||||
}
|
||||
}
|
||||
|
||||
void quantize_q4_1(device const float * src, device block_q4_1 & dst) {
|
||||
#pragma METAL fp math_mode(safe)
|
||||
float min = FLT_MAX;
|
||||
float max = -FLT_MAX;
|
||||
|
||||
for (int j = 0; j < QK4_1; j++) {
|
||||
const float v = src[j];
|
||||
if (min > v) min = v;
|
||||
if (max < v) max = v;
|
||||
}
|
||||
|
||||
const float d = (max - min) / ((1 << 4) - 1);
|
||||
const float id = d ? 1.0f/d : 0.0f;
|
||||
|
||||
dst.d = d;
|
||||
dst.m = min;
|
||||
|
||||
for (int j = 0; j < QK4_1/2; ++j) {
|
||||
const float x0 = (src[0 + j] - min)*id;
|
||||
const float x1 = (src[QK4_1/2 + j] - min)*id;
|
||||
|
||||
const uint8_t xi0 = MIN(15, (int8_t)(x0 + 0.5f));
|
||||
const uint8_t xi1 = MIN(15, (int8_t)(x1 + 0.5f));
|
||||
|
||||
dst.qs[j] = xi0;
|
||||
dst.qs[j] |= xi1 << 4;
|
||||
}
|
||||
}
|
||||
|
||||
void quantize_q5_0(device const float * src, device block_q5_0 & dst) {
|
||||
#pragma METAL fp math_mode(safe)
|
||||
float amax = 0.0f; // absolute max
|
||||
float max = 0.0f;
|
||||
|
||||
for (int j = 0; j < QK5_0; j++) {
|
||||
const float v = src[j];
|
||||
if (amax < fabs(v)) {
|
||||
amax = fabs(v);
|
||||
max = v;
|
||||
}
|
||||
}
|
||||
|
||||
const float d = max / -16;
|
||||
const float id = d ? 1.0f/d : 0.0f;
|
||||
|
||||
dst.d = d;
|
||||
|
||||
uint32_t qh = 0;
|
||||
for (int j = 0; j < QK5_0/2; ++j) {
|
||||
const float x0 = src[0 + j]*id;
|
||||
const float x1 = src[QK5_0/2 + j]*id;
|
||||
|
||||
const uint8_t xi0 = MIN(31, (int8_t)(x0 + 16.5f));
|
||||
const uint8_t xi1 = MIN(31, (int8_t)(x1 + 16.5f));
|
||||
|
||||
dst.qs[j] = (xi0 & 0xf) | ((xi1 & 0xf) << 4);
|
||||
qh |= ((xi0 & 0x10u) >> 4) << (j + 0);
|
||||
qh |= ((xi1 & 0x10u) >> 4) << (j + QK5_0/2);
|
||||
}
|
||||
|
||||
thread const uint8_t * qh8 = (thread const uint8_t *)&qh;
|
||||
|
||||
for (int j = 0; j < 4; ++j) {
|
||||
dst.qh[j] = qh8[j];
|
||||
}
|
||||
}
|
||||
|
||||
void quantize_q5_1(device const float * src, device block_q5_1 & dst) {
|
||||
#pragma METAL fp math_mode(safe)
|
||||
float max = src[0];
|
||||
float min = src[0];
|
||||
|
||||
for (int j = 1; j < QK5_1; j++) {
|
||||
const float v = src[j];
|
||||
min = v < min ? v : min;
|
||||
max = v > max ? v : max;
|
||||
}
|
||||
|
||||
const float d = (max - min) / 31;
|
||||
const float id = d ? 1.0f/d : 0.0f;
|
||||
|
||||
dst.d = d;
|
||||
dst.m = min;
|
||||
|
||||
uint32_t qh = 0;
|
||||
for (int j = 0; j < QK5_1/2; ++j) {
|
||||
const float x0 = (src[0 + j] - min)*id;
|
||||
const float x1 = (src[QK5_1/2 + j] - min)*id;
|
||||
|
||||
const uint8_t xi0 = (uint8_t)(x0 + 0.5f);
|
||||
const uint8_t xi1 = (uint8_t)(x1 + 0.5f);
|
||||
|
||||
dst.qs[j] = (xi0 & 0xf) | ((xi1 & 0xf) << 4);
|
||||
qh |= ((xi0 & 0x10u) >> 4) << (j + 0);
|
||||
qh |= ((xi1 & 0x10u) >> 4) << (j + QK5_1/2);
|
||||
}
|
||||
|
||||
thread const uint8_t * qh8 = (thread const uint8_t *)&qh;
|
||||
|
||||
for (int j = 0; j < 4; ++j) {
|
||||
dst.qh[j] = qh8[j];
|
||||
}
|
||||
}
|
||||
|
||||
void quantize_q8_0(device const float * src, device block_q8_0 & dst) {
|
||||
#pragma METAL fp math_mode(safe)
|
||||
float amax = 0.0f; // absolute max
|
||||
|
||||
for (int j = 0; j < QK8_0; j++) {
|
||||
const float v = src[j];
|
||||
amax = MAX(amax, fabs(v));
|
||||
}
|
||||
|
||||
const float d = amax / ((1 << 7) - 1);
|
||||
const float id = d ? 1.0f/d : 0.0f;
|
||||
|
||||
dst.d = d;
|
||||
|
||||
for (int j = 0; j < QK8_0; ++j) {
|
||||
const float x0 = src[j]*id;
|
||||
|
||||
dst.qs[j] = round(x0);
|
||||
}
|
||||
}
|
||||
|
||||
void quantize_iq4_nl(device const float * src, device block_iq4_nl & dst) {
|
||||
#pragma METAL fp math_mode(safe)
|
||||
float amax = 0.0f; // absolute max
|
||||
float max = 0.0f;
|
||||
|
||||
for (int j = 0; j < QK4_NL; j++) {
|
||||
const float v = src[j];
|
||||
if (amax < fabs(v)) {
|
||||
amax = fabs(v);
|
||||
max = v;
|
||||
}
|
||||
}
|
||||
|
||||
const float d = max / kvalues_iq4nl_f[0];
|
||||
const float id = d ? 1.0f/d : 0.0f;
|
||||
|
||||
float sumqx = 0, sumq2 = 0;
|
||||
for (int j = 0; j < QK4_NL/2; ++j) {
|
||||
const float x0 = src[0 + j]*id;
|
||||
const float x1 = src[QK4_NL/2 + j]*id;
|
||||
|
||||
const uint8_t xi0 = best_index_int8(16, kvalues_iq4nl_f, x0);
|
||||
const uint8_t xi1 = best_index_int8(16, kvalues_iq4nl_f, x1);
|
||||
|
||||
dst.qs[j] = xi0 | (xi1 << 4);
|
||||
|
||||
const float v0 = kvalues_iq4nl_f[xi0];
|
||||
const float v1 = kvalues_iq4nl_f[xi1];
|
||||
const float w0 = src[0 + j]*src[0 + j];
|
||||
const float w1 = src[QK4_NL/2 + j]*src[QK4_NL/2 + j];
|
||||
sumqx += w0*v0*src[j] + w1*v1*src[QK4_NL/2 + j];
|
||||
sumq2 += w0*v0*v0 + w1*v1*v1;
|
||||
|
||||
}
|
||||
|
||||
dst.d = sumq2 > 0 ? sumqx/sumq2 : d;
|
||||
}
|
||||
@@ -1,389 +0,0 @@
|
||||
#include "common.h"
|
||||
#include "dequantize.h"
|
||||
#include "quantize.h"
|
||||
|
||||
template<typename T0, typename T1>
|
||||
kernel void kernel_cpy_t_t(
|
||||
constant ggml_metal_kargs_cpy & args,
|
||||
device const char * src0,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort3 tpitg[[thread_position_in_threadgroup]],
|
||||
ushort3 ntg[[threads_per_threadgroup]]) {
|
||||
const int32_t i03 = tgpig[2];
|
||||
const int32_t i02 = tgpig[1];
|
||||
const int32_t i01 = ntg[1] == 1 ? tgpig[0]%args.ne01 : tgpig[0]*ntg[1] + tpitg.y;
|
||||
const int32_t iw0 = ntg[1] == 1 ? tgpig[0]/args.ne01 : 0;
|
||||
|
||||
if (i01 >= args.ne01) {
|
||||
return;
|
||||
}
|
||||
|
||||
const int64_t n = i03*args.ne02*args.ne01*args.ne00 + i02*args.ne01*args.ne00 + i01*args.ne00;
|
||||
|
||||
const int32_t i3 = n/(args.ne2*args.ne1*args.ne0);
|
||||
const int32_t i2 = (n - i3*args.ne2*args.ne1*args.ne0)/(args.ne1*args.ne0);
|
||||
const int32_t i1 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0)/args.ne0;
|
||||
const int32_t i0 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0 - i1*args.ne0);
|
||||
|
||||
device T1 * dst_data = (device T1 *) (dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0);
|
||||
|
||||
for (int32_t i00 = iw0*ntg[0] + tpitg.x; i00 < args.ne00;) {
|
||||
device const T0 * src = (device T0 *)(src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + i00*args.nb00);
|
||||
dst_data[i00] = (T1) src[0];
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
typedef decltype(kernel_cpy_t_t<float, float>) kernel_cpy_t;
|
||||
|
||||
template [[host_name("kernel_cpy_f32_f32")]] kernel kernel_cpy_t kernel_cpy_t_t<float, float>;
|
||||
template [[host_name("kernel_cpy_f32_f16")]] kernel kernel_cpy_t kernel_cpy_t_t<float, half>;
|
||||
template [[host_name("kernel_cpy_f32_i32")]] kernel kernel_cpy_t kernel_cpy_t_t<float, int32_t>;
|
||||
template [[host_name("kernel_cpy_i32_f32")]] kernel kernel_cpy_t kernel_cpy_t_t<int32_t, float>;
|
||||
template [[host_name("kernel_cpy_i32_i32")]] kernel kernel_cpy_t kernel_cpy_t_t<int32_t, int32_t>;
|
||||
#if defined(GGML_METAL_HAS_BF16)
|
||||
template [[host_name("kernel_cpy_f32_bf16")]] kernel kernel_cpy_t kernel_cpy_t_t<float, bfloat>;
|
||||
#endif
|
||||
template [[host_name("kernel_cpy_f16_f32")]] kernel kernel_cpy_t kernel_cpy_t_t<half, float>;
|
||||
template [[host_name("kernel_cpy_f16_f16")]] kernel kernel_cpy_t kernel_cpy_t_t<half, half>;
|
||||
#if defined(GGML_METAL_HAS_BF16)
|
||||
template [[host_name("kernel_cpy_bf16_f32")]] kernel kernel_cpy_t kernel_cpy_t_t<bfloat, float>;
|
||||
template [[host_name("kernel_cpy_bf16_bf16")]] kernel kernel_cpy_t kernel_cpy_t_t<bfloat, bfloat>;
|
||||
#endif
|
||||
|
||||
template<short QK,
|
||||
typename block_q,
|
||||
void (*quantize_func)(device const float *, device block_q &)>
|
||||
kernel void kernel_cpy_f32_q(
|
||||
constant ggml_metal_kargs_cpy & args,
|
||||
device const char * src0,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort3 tpitg[[thread_position_in_threadgroup]],
|
||||
ushort3 ntg[[threads_per_threadgroup]]) {
|
||||
const int32_t i03 = tgpig[2];
|
||||
const int32_t i02 = tgpig[1];
|
||||
const int32_t i01 = ntg[1] == 1 ? tgpig[0]%args.ne01 : tgpig[0]*ntg[1] + tpitg.y;
|
||||
const int32_t iw0 = ntg[1] == 1 ? tgpig[0]/args.ne01 : 0;
|
||||
|
||||
if (i01 >= args.ne01) {
|
||||
return;
|
||||
}
|
||||
|
||||
const int64_t n = i03*args.ne02*args.ne01*args.ne00 + i02*args.ne01*args.ne00 + i01*args.ne00;
|
||||
|
||||
const int32_t i3 = n / (args.ne2*args.ne1*args.ne0);
|
||||
const int32_t i2 = (n - i3*args.ne2*args.ne1*args.ne0) / (args.ne1*args.ne0);
|
||||
const int32_t i1 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0) / args.ne0;
|
||||
const int32_t i0 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0 - i1*args.ne0)/QK;
|
||||
|
||||
device block_q * dst_data = (device block_q *)(dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0);
|
||||
|
||||
for (int32_t i00 = iw0*ntg[0] + tpitg.x; i00 < args.nk0;) {
|
||||
device const float * src = (device const float *)(src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + (i00*QK)*args.nb00);
|
||||
|
||||
quantize_func(src, dst_data[i00]);
|
||||
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
typedef decltype(kernel_cpy_f32_q<QK8_0, block_q8_0, quantize_q8_0>) cpy_f_q_t;
|
||||
|
||||
template [[host_name("kernel_cpy_f32_q8_0")]] kernel cpy_f_q_t kernel_cpy_f32_q<QK8_0, block_q8_0, quantize_q8_0>;
|
||||
template [[host_name("kernel_cpy_f32_q1_0")]] kernel cpy_f_q_t kernel_cpy_f32_q<QK1_0, block_q1_0, quantize_q1_0>;
|
||||
template [[host_name("kernel_cpy_f32_q4_0")]] kernel cpy_f_q_t kernel_cpy_f32_q<QK4_0, block_q4_0, quantize_q4_0>;
|
||||
template [[host_name("kernel_cpy_f32_q4_1")]] kernel cpy_f_q_t kernel_cpy_f32_q<QK4_1, block_q4_1, quantize_q4_1>;
|
||||
template [[host_name("kernel_cpy_f32_q5_0")]] kernel cpy_f_q_t kernel_cpy_f32_q<QK5_0, block_q5_0, quantize_q5_0>;
|
||||
template [[host_name("kernel_cpy_f32_q5_1")]] kernel cpy_f_q_t kernel_cpy_f32_q<QK5_1, block_q5_1, quantize_q5_1>;
|
||||
template [[host_name("kernel_cpy_f32_iq4_nl")]] kernel cpy_f_q_t kernel_cpy_f32_q<QK4_NL, block_iq4_nl, quantize_iq4_nl>;
|
||||
|
||||
template<typename T4x4, typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread T4x4 &)>
|
||||
kernel void kernel_cpy_q_f32(
|
||||
constant ggml_metal_kargs_cpy & args,
|
||||
device const char * src0,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort3 tpitg[[thread_position_in_threadgroup]],
|
||||
ushort3 ntg[[threads_per_threadgroup]]) {
|
||||
const int32_t i03 = tgpig[2];
|
||||
const int32_t i02 = tgpig[1];
|
||||
const int32_t i01 = ntg[1] == 1 ? tgpig[0]%args.ne01 : tgpig[0]*ntg[1] + tpitg.y;
|
||||
const int32_t iw0 = ntg[1] == 1 ? tgpig[0]/args.ne01 : 0;
|
||||
|
||||
if (i01 >= args.ne01) {
|
||||
return;
|
||||
}
|
||||
|
||||
const int64_t n = i03*args.ne02*args.ne01*args.ne00 + i02*args.ne01*args.ne00 + i01*args.ne00;
|
||||
|
||||
const int32_t i3 = n/(args.ne2*args.ne1*args.ne0);
|
||||
const int32_t i2 = (n - i3*args.ne2*args.ne1*args.ne0)/(args.ne1*args.ne0);
|
||||
const int32_t i1 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0)/args.ne0;
|
||||
const int32_t i0 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0 - i1*args.ne0);
|
||||
|
||||
device const block_q * src_data = (device const block_q *)(src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01);
|
||||
device T4x4 * dst_data = (device T4x4 *)(dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0);
|
||||
|
||||
for (int32_t i00 = iw0*ntg[0] + tpitg.x; i00 < args.nk0;) {
|
||||
T4x4 temp;
|
||||
dequantize_func(src_data + i00/nl, i00%nl, temp);
|
||||
dst_data[i00] = temp;
|
||||
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
typedef decltype(kernel_cpy_q_f32<float4x4, block_q4_0, 2, dequantize_q4_0>) cpy_q_f_t;
|
||||
|
||||
template [[host_name("kernel_cpy_q1_0_f32")]] kernel cpy_q_f_t kernel_cpy_q_f32<float4x4, block_q1_0, 8, dequantize_q1_0>;
|
||||
template [[host_name("kernel_cpy_q4_0_f32")]] kernel cpy_q_f_t kernel_cpy_q_f32<float4x4, block_q4_0, 2, dequantize_q4_0>;
|
||||
template [[host_name("kernel_cpy_q4_1_f32")]] kernel cpy_q_f_t kernel_cpy_q_f32<float4x4, block_q4_1, 2, dequantize_q4_1>;
|
||||
template [[host_name("kernel_cpy_q5_0_f32")]] kernel cpy_q_f_t kernel_cpy_q_f32<float4x4, block_q5_0, 2, dequantize_q5_0>;
|
||||
template [[host_name("kernel_cpy_q5_1_f32")]] kernel cpy_q_f_t kernel_cpy_q_f32<float4x4, block_q5_1, 2, dequantize_q5_1>;
|
||||
template [[host_name("kernel_cpy_q8_0_f32")]] kernel cpy_q_f_t kernel_cpy_q_f32<float4x4, block_q8_0, 2, dequantize_q8_0>;
|
||||
|
||||
template [[host_name("kernel_cpy_q1_0_f16")]] kernel cpy_q_f_t kernel_cpy_q_f32<half4x4, block_q1_0, 8, dequantize_q1_0>;
|
||||
template [[host_name("kernel_cpy_q4_0_f16")]] kernel cpy_q_f_t kernel_cpy_q_f32<half4x4, block_q4_0, 2, dequantize_q4_0>;
|
||||
template [[host_name("kernel_cpy_q4_1_f16")]] kernel cpy_q_f_t kernel_cpy_q_f32<half4x4, block_q4_1, 2, dequantize_q4_1>;
|
||||
template [[host_name("kernel_cpy_q5_0_f16")]] kernel cpy_q_f_t kernel_cpy_q_f32<half4x4, block_q5_0, 2, dequantize_q5_0>;
|
||||
template [[host_name("kernel_cpy_q5_1_f16")]] kernel cpy_q_f_t kernel_cpy_q_f32<half4x4, block_q5_1, 2, dequantize_q5_1>;
|
||||
template [[host_name("kernel_cpy_q8_0_f16")]] kernel cpy_q_f_t kernel_cpy_q_f32<half4x4, block_q8_0, 2, dequantize_q8_0>;
|
||||
|
||||
template<typename T>
|
||||
kernel void kernel_concat(
|
||||
constant ggml_metal_kargs_concat & args,
|
||||
device const char * src0,
|
||||
device const char * src1,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort3 tpitg[[thread_position_in_threadgroup]],
|
||||
ushort3 ntg[[threads_per_threadgroup]]) {
|
||||
|
||||
const int i3 = tgpig.z;
|
||||
const int i2 = tgpig.y;
|
||||
const int i1 = ntg.y == 1 ? tgpig.x : tgpig.x*ntg.y + tpitg.y;
|
||||
|
||||
if (i1 >= args.ne1) {
|
||||
return;
|
||||
}
|
||||
|
||||
int o[4] = {0, 0, 0, 0};
|
||||
o[args.dim] = args.dim == 0 ? args.ne00 : (args.dim == 1 ? args.ne01 : (args.dim == 2 ? args.ne02 : args.ne03));
|
||||
|
||||
for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
|
||||
device const T * x;
|
||||
|
||||
if (i0 < args.ne00 && i1 < args.ne01 && i2 < args.ne02 && i3 < args.ne03) {
|
||||
x = (device const T *)(src0 + (i3 )*args.nb03 + (i2 )*args.nb02 + (i1 )*args.nb01 + (i0 )*args.nb00);
|
||||
} else {
|
||||
x = (device const T *)(src1 + (i3 - o[3])*args.nb13 + (i2 - o[2])*args.nb12 + (i1 - o[1])*args.nb11 + (i0 - o[0])*args.nb10);
|
||||
}
|
||||
|
||||
device T * y = (device T *)(dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0);
|
||||
|
||||
*y = *x;
|
||||
}
|
||||
}
|
||||
|
||||
typedef decltype(kernel_concat<float>) kernel_concat_t;
|
||||
|
||||
template [[host_name("kernel_concat_f32")]] kernel kernel_concat_t kernel_concat<float>;
|
||||
template [[host_name("kernel_concat_f16")]] kernel kernel_concat_t kernel_concat<half>;
|
||||
#if defined(GGML_METAL_HAS_BF16)
|
||||
template [[host_name("kernel_concat_bf16")]] kernel kernel_concat_t kernel_concat<bfloat>;
|
||||
#endif
|
||||
template [[host_name("kernel_concat_i8")]] kernel kernel_concat_t kernel_concat<char>;
|
||||
template [[host_name("kernel_concat_i16")]] kernel kernel_concat_t kernel_concat<short>;
|
||||
template [[host_name("kernel_concat_i32")]] kernel kernel_concat_t kernel_concat<int>;
|
||||
template [[host_name("kernel_concat_i64")]] kernel kernel_concat_t kernel_concat<long>;
|
||||
|
||||
template<typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread float4x4 &)>
|
||||
kernel void kernel_get_rows_q(
|
||||
constant ggml_metal_kargs_get_rows & args,
|
||||
device const void * src0,
|
||||
device const void * src1,
|
||||
device void * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort tiitg[[thread_index_in_threadgroup]],
|
||||
ushort3 ntg [[threads_per_threadgroup]]) {
|
||||
const int32_t iw0 = tgpig.x/args.ne10;
|
||||
const int32_t i10 = tgpig.x%args.ne10;
|
||||
const int32_t i11 = tgpig.y;
|
||||
const int32_t i12 = tgpig.z;
|
||||
|
||||
const int32_t r = ((const device int32_t *) ((const device char *) src1 + i12*args.nb12 + i11*args.nb11 + i10*args.nb10))[0];
|
||||
|
||||
const int32_t i02 = i11;
|
||||
const int32_t i03 = i12;
|
||||
|
||||
auto psrc = (device const block_q *) ((const device char *) src0 + i03*args.nb03 + i02*args.nb02 + r*args.nb01);
|
||||
auto pdst = (device float4x4 *) (( device char *) dst + i12*args.nb3 + i11*args.nb2 + i10*args.nb1);
|
||||
|
||||
for (int ind = iw0*ntg.x + tiitg; ind < args.ne00t;) {
|
||||
float4x4 temp;
|
||||
dequantize_func(psrc + ind/nl, ind%nl, temp);
|
||||
pdst[ind] = temp;
|
||||
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
template<typename T0, typename T>
|
||||
kernel void kernel_get_rows_f(
|
||||
constant ggml_metal_kargs_get_rows & args,
|
||||
device const void * src0,
|
||||
device const void * src1,
|
||||
device void * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort tiitg[[thread_index_in_threadgroup]],
|
||||
ushort3 ntg [[threads_per_threadgroup]]) {
|
||||
const int32_t iw0 = tgpig.x/args.ne10;
|
||||
const int32_t i10 = tgpig.x%args.ne10;
|
||||
const int32_t i11 = tgpig.y;
|
||||
const int32_t i12 = tgpig.z;
|
||||
|
||||
const int32_t r = ((const device int32_t *) ((const device char *) src1 + i12*args.nb12 + i11*args.nb11 + i10*args.nb10))[0];
|
||||
|
||||
const int32_t i02 = i11;
|
||||
const int32_t i03 = i12;
|
||||
|
||||
auto psrc = (const device T0 *) ((const device char *) src0 + i03*args.nb03 + i02*args.nb02 + r*args.nb01);
|
||||
auto pdst = ( device T *) (( device char *) dst + i12*args.nb3 + i11*args.nb2 + i10*args.nb1);
|
||||
|
||||
for (int ind = iw0*ntg.x + tiitg; ind < args.ne00t;) {
|
||||
pdst[ind] = psrc[ind];
|
||||
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
template<typename TI, typename block_q, void (*quantize_func)(device const float *, device block_q &)>
|
||||
kernel void kernel_set_rows_q32(
|
||||
constant ggml_metal_kargs_set_rows & args,
|
||||
device const void * src0,
|
||||
device const void * src1,
|
||||
device float * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint tiitg[[thread_index_in_threadgroup]],
|
||||
uint3 tptg [[threads_per_threadgroup]]) {
|
||||
const int32_t i03 = tgpig.z;
|
||||
const int32_t i02 = tgpig.y;
|
||||
|
||||
const int32_t i12 = i03%args.ne12;
|
||||
const int32_t i11 = i02%args.ne11;
|
||||
|
||||
const int32_t i01 = tgpig.x*tptg.y + tiitg/tptg.x;
|
||||
if (i01 >= args.ne01) {
|
||||
return;
|
||||
}
|
||||
|
||||
const int32_t i10 = i01;
|
||||
const TI i1 = ((const device TI *) ((const device char *) src1 + i10*args.nb10 + i11*args.nb11 + i12*args.nb12))[0];
|
||||
|
||||
device block_q * dst_row = ( device block_q *) (( device char *) dst + i1*args.nb1 + i02*args.nb2 + i03*args.nb3);
|
||||
const device float * src_row = (const device float *) ((const device char *) src0 + i01*args.nb01 + i02*args.nb02 + i03*args.nb03);
|
||||
|
||||
for (int ind = tiitg%tptg.x; ind < args.nk0; ind += tptg.x) {
|
||||
quantize_func(src_row + 32*ind, dst_row[ind]);
|
||||
}
|
||||
}
|
||||
|
||||
template<typename T, typename TI>
|
||||
kernel void kernel_set_rows_f(
|
||||
constant ggml_metal_kargs_set_rows & args,
|
||||
device const void * src0,
|
||||
device const void * src1,
|
||||
device float * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint tiitg[[thread_index_in_threadgroup]],
|
||||
uint3 tptg [[threads_per_threadgroup]]) {
|
||||
const int32_t i03 = tgpig.z;
|
||||
const int32_t i02 = tgpig.y;
|
||||
|
||||
const int32_t i12 = i03%args.ne12;
|
||||
const int32_t i11 = i02%args.ne11;
|
||||
|
||||
const int32_t i01 = tgpig.x*tptg.y + tiitg/tptg.x;
|
||||
if (i01 >= args.ne01) {
|
||||
return;
|
||||
}
|
||||
|
||||
const int32_t i10 = i01;
|
||||
const TI i1 = ((const device TI *) ((const device char *) src1 + i10*args.nb10 + i11*args.nb11 + i12*args.nb12))[0];
|
||||
|
||||
device T * dst_row = ( device T *) (( device char *) dst + i1*args.nb1 + i02*args.nb2 + i03*args.nb3);
|
||||
const device float * src_row = (const device float *) ((const device char *) src0 + i01*args.nb01 + i02*args.nb02 + i03*args.nb03);
|
||||
|
||||
for (int ind = tiitg%tptg.x; ind < args.nk0; ind += tptg.x) {
|
||||
dst_row[ind] = (T) src_row[ind];
|
||||
}
|
||||
}
|
||||
|
||||
//
|
||||
// get rows
|
||||
//
|
||||
|
||||
typedef decltype(kernel_get_rows_f<float, float>) get_rows_f_t;
|
||||
|
||||
template [[host_name("kernel_get_rows_f32")]] kernel get_rows_f_t kernel_get_rows_f<float, float>;
|
||||
template [[host_name("kernel_get_rows_f16")]] kernel get_rows_f_t kernel_get_rows_f<half, float>;
|
||||
template [[host_name("kernel_get_rows_i32")]] kernel get_rows_f_t kernel_get_rows_f<int32_t, int32_t>;
|
||||
#if defined(GGML_METAL_HAS_BF16)
|
||||
template [[host_name("kernel_get_rows_bf16")]] kernel get_rows_f_t kernel_get_rows_f<bfloat, float>;
|
||||
#endif
|
||||
|
||||
typedef decltype(kernel_get_rows_q<block_q4_0, 2, dequantize_q4_0>) get_rows_q_t;
|
||||
|
||||
template [[host_name("kernel_get_rows_q1_0")]] kernel get_rows_q_t kernel_get_rows_q<block_q1_0, 8, dequantize_q1_0>;
|
||||
template [[host_name("kernel_get_rows_q4_0")]] kernel get_rows_q_t kernel_get_rows_q<block_q4_0, 2, dequantize_q4_0>;
|
||||
template [[host_name("kernel_get_rows_q4_1")]] kernel get_rows_q_t kernel_get_rows_q<block_q4_1, 2, dequantize_q4_1>;
|
||||
template [[host_name("kernel_get_rows_q5_0")]] kernel get_rows_q_t kernel_get_rows_q<block_q5_0, 2, dequantize_q5_0>;
|
||||
template [[host_name("kernel_get_rows_q5_1")]] kernel get_rows_q_t kernel_get_rows_q<block_q5_1, 2, dequantize_q5_1>;
|
||||
template [[host_name("kernel_get_rows_q8_0")]] kernel get_rows_q_t kernel_get_rows_q<block_q8_0, 2, dequantize_q8_0>;
|
||||
template [[host_name("kernel_get_rows_mxfp4")]] kernel get_rows_q_t kernel_get_rows_q<block_mxfp4, 2, dequantize_mxfp4>;
|
||||
template [[host_name("kernel_get_rows_q2_K")]] kernel get_rows_q_t kernel_get_rows_q<block_q2_K, QK_NL, dequantize_q2_K>;
|
||||
template [[host_name("kernel_get_rows_q3_K")]] kernel get_rows_q_t kernel_get_rows_q<block_q3_K, QK_NL, dequantize_q3_K>;
|
||||
template [[host_name("kernel_get_rows_q4_K")]] kernel get_rows_q_t kernel_get_rows_q<block_q4_K, QK_NL, dequantize_q4_K>;
|
||||
template [[host_name("kernel_get_rows_q5_K")]] kernel get_rows_q_t kernel_get_rows_q<block_q5_K, QK_NL, dequantize_q5_K>;
|
||||
template [[host_name("kernel_get_rows_q6_K")]] kernel get_rows_q_t kernel_get_rows_q<block_q6_K, QK_NL, dequantize_q6_K>;
|
||||
template [[host_name("kernel_get_rows_iq2_xxs")]] kernel get_rows_q_t kernel_get_rows_q<block_iq2_xxs, QK_NL, dequantize_iq2_xxs>;
|
||||
template [[host_name("kernel_get_rows_iq2_xs")]] kernel get_rows_q_t kernel_get_rows_q<block_iq2_xs, QK_NL, dequantize_iq2_xs>;
|
||||
template [[host_name("kernel_get_rows_iq3_xxs")]] kernel get_rows_q_t kernel_get_rows_q<block_iq3_xxs, QK_NL, dequantize_iq3_xxs>;
|
||||
template [[host_name("kernel_get_rows_iq3_s")]] kernel get_rows_q_t kernel_get_rows_q<block_iq3_s, QK_NL, dequantize_iq3_s>;
|
||||
template [[host_name("kernel_get_rows_iq2_s")]] kernel get_rows_q_t kernel_get_rows_q<block_iq2_s, QK_NL, dequantize_iq2_s>;
|
||||
template [[host_name("kernel_get_rows_iq1_s")]] kernel get_rows_q_t kernel_get_rows_q<block_iq1_s, QK_NL, dequantize_iq1_s>;
|
||||
template [[host_name("kernel_get_rows_iq1_m")]] kernel get_rows_q_t kernel_get_rows_q<block_iq1_m, QK_NL, dequantize_iq1_m>;
|
||||
template [[host_name("kernel_get_rows_iq4_nl")]] kernel get_rows_q_t kernel_get_rows_q<block_iq4_nl, 2, dequantize_iq4_nl>;
|
||||
template [[host_name("kernel_get_rows_iq4_xs")]] kernel get_rows_q_t kernel_get_rows_q<block_iq4_xs, QK_NL, dequantize_iq4_xs>;
|
||||
|
||||
//
|
||||
// set rows
|
||||
//
|
||||
|
||||
typedef decltype(kernel_set_rows_f<float, int64_t>) set_rows_f_t;
|
||||
|
||||
template [[host_name("kernel_set_rows_f32_i64")]] kernel set_rows_f_t kernel_set_rows_f<float, int64_t>;
|
||||
template [[host_name("kernel_set_rows_f32_i32")]] kernel set_rows_f_t kernel_set_rows_f<float, int32_t>;
|
||||
template [[host_name("kernel_set_rows_f16_i64")]] kernel set_rows_f_t kernel_set_rows_f<half, int64_t>;
|
||||
template [[host_name("kernel_set_rows_f16_i32")]] kernel set_rows_f_t kernel_set_rows_f<half, int32_t>;
|
||||
#if defined(GGML_METAL_HAS_BF16)
|
||||
template [[host_name("kernel_set_rows_bf16_i64")]] kernel set_rows_f_t kernel_set_rows_f<bfloat, int64_t>;
|
||||
template [[host_name("kernel_set_rows_bf16_i32")]] kernel set_rows_f_t kernel_set_rows_f<bfloat, int32_t>;
|
||||
#endif
|
||||
|
||||
typedef decltype(kernel_set_rows_q32<int64_t, block_q8_0, quantize_q8_0>) set_rows_q32_t;
|
||||
|
||||
template [[host_name("kernel_set_rows_q8_0_i64")]] kernel set_rows_q32_t kernel_set_rows_q32<int64_t, block_q8_0, quantize_q8_0>;
|
||||
template [[host_name("kernel_set_rows_q8_0_i32")]] kernel set_rows_q32_t kernel_set_rows_q32<int32_t, block_q8_0, quantize_q8_0>;
|
||||
template [[host_name("kernel_set_rows_q4_0_i64")]] kernel set_rows_q32_t kernel_set_rows_q32<int64_t, block_q4_0, quantize_q4_0>;
|
||||
template [[host_name("kernel_set_rows_q4_0_i32")]] kernel set_rows_q32_t kernel_set_rows_q32<int32_t, block_q4_0, quantize_q4_0>;
|
||||
template [[host_name("kernel_set_rows_q4_1_i64")]] kernel set_rows_q32_t kernel_set_rows_q32<int64_t, block_q4_1, quantize_q4_1>;
|
||||
template [[host_name("kernel_set_rows_q4_1_i32")]] kernel set_rows_q32_t kernel_set_rows_q32<int32_t, block_q4_1, quantize_q4_1>;
|
||||
template [[host_name("kernel_set_rows_q5_0_i64")]] kernel set_rows_q32_t kernel_set_rows_q32<int64_t, block_q5_0, quantize_q5_0>;
|
||||
template [[host_name("kernel_set_rows_q5_0_i32")]] kernel set_rows_q32_t kernel_set_rows_q32<int32_t, block_q5_0, quantize_q5_0>;
|
||||
template [[host_name("kernel_set_rows_q5_1_i64")]] kernel set_rows_q32_t kernel_set_rows_q32<int64_t, block_q5_1, quantize_q5_1>;
|
||||
template [[host_name("kernel_set_rows_q5_1_i32")]] kernel set_rows_q32_t kernel_set_rows_q32<int32_t, block_q5_1, quantize_q5_1>;
|
||||
template [[host_name("kernel_set_rows_iq4_nl_i64")]] kernel set_rows_q32_t kernel_set_rows_q32<int64_t, block_iq4_nl, quantize_iq4_nl>;
|
||||
template [[host_name("kernel_set_rows_iq4_nl_i32")]] kernel set_rows_q32_t kernel_set_rows_q32<int32_t, block_iq4_nl, quantize_iq4_nl>;
|
||||
@@ -1,228 +0,0 @@
|
||||
#include "common.h"
|
||||
|
||||
kernel void kernel_op_sum_f32(
|
||||
constant ggml_metal_kargs_sum & args,
|
||||
device const float * src0,
|
||||
device float * dst,
|
||||
threadgroup float * shmem_f32 [[threadgroup(0)]],
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort3 tpitg[[thread_position_in_threadgroup]],
|
||||
ushort sgitg[[simdgroup_index_in_threadgroup]],
|
||||
ushort tiisg[[thread_index_in_simdgroup]],
|
||||
ushort3 ntg[[threads_per_threadgroup]]) {
|
||||
|
||||
if (args.np == 0) {
|
||||
return;
|
||||
}
|
||||
|
||||
// TODO: become function constant
|
||||
const uint nsg = (ntg.x + 31) / 32;
|
||||
|
||||
float sumf = 0;
|
||||
|
||||
for (uint64_t i0 = tpitg.x; i0 < args.np; i0 += ntg.x) {
|
||||
sumf += src0[i0];
|
||||
}
|
||||
|
||||
sumf = simd_sum(sumf);
|
||||
|
||||
if (tiisg == 0) {
|
||||
shmem_f32[sgitg] = sumf;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
float total = 0;
|
||||
|
||||
if (sgitg == 0) {
|
||||
float v = 0;
|
||||
|
||||
if (tpitg.x < nsg) {
|
||||
v = shmem_f32[tpitg.x];
|
||||
}
|
||||
|
||||
total = simd_sum(v);
|
||||
|
||||
if (tpitg.x == 0) {
|
||||
dst[0] = total;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
constant short FC_sum_rows_op [[function_constant(FC_SUM_ROWS + 0)]];
|
||||
|
||||
template <typename T0, typename T>
|
||||
kernel void kernel_sum_rows_impl(
|
||||
constant ggml_metal_kargs_sum_rows & args,
|
||||
device const char * src0,
|
||||
device char * dst,
|
||||
threadgroup char * shmem [[threadgroup(0)]],
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort3 tpitg[[thread_position_in_threadgroup]],
|
||||
ushort sgitg[[simdgroup_index_in_threadgroup]],
|
||||
ushort tiisg[[thread_index_in_simdgroup]],
|
||||
ushort3 ntg[[threads_per_threadgroup]]) {
|
||||
#define FC_OP FC_sum_rows_op
|
||||
|
||||
const int i3 = tgpig.z;
|
||||
const int i2 = tgpig.y;
|
||||
const int i1 = tgpig.x;
|
||||
|
||||
threadgroup T0 * shmem_t = (threadgroup T0 *) shmem;
|
||||
|
||||
if (sgitg == 0) {
|
||||
shmem_t[tiisg] = 0.0f;
|
||||
}
|
||||
|
||||
device const T0 * src_row = (device const T0 *) (src0 + i1*args.nb01 + i2*args.nb02 + i3*args.nb03);
|
||||
device T * dst_row = (device T *) (dst + i1*args.nb1 + i2*args.nb2 + i3*args.nb3);
|
||||
|
||||
T0 sumf = T0(0.0f);
|
||||
|
||||
for (int64_t i0 = tpitg.x; i0 < args.ne00; i0 += ntg.x) {
|
||||
sumf += src_row[i0];
|
||||
}
|
||||
|
||||
sumf = simd_sum(sumf);
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
if (tiisg == 0) {
|
||||
shmem_t[sgitg] = sumf;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
sumf = shmem_t[tiisg];
|
||||
sumf = simd_sum(sumf);
|
||||
|
||||
if (tpitg.x == 0) {
|
||||
if (FC_OP == OP_SUM_ROWS_NUM_MEAN) {
|
||||
if (is_same<float4, T0>::value) {
|
||||
dst_row[0] = sum(sumf) / (4*args.ne00);
|
||||
} else {
|
||||
dst_row[0] = sum(sumf) / args.ne00;
|
||||
}
|
||||
} else {
|
||||
dst_row[0] = sum(sumf);
|
||||
}
|
||||
}
|
||||
|
||||
#undef FC_OP
|
||||
}
|
||||
|
||||
typedef decltype(kernel_sum_rows_impl<float, float>) kernel_sum_rows_t;
|
||||
|
||||
template [[host_name("kernel_sum_rows_f32_f32")]] kernel kernel_sum_rows_t kernel_sum_rows_impl<float, float>;
|
||||
template [[host_name("kernel_sum_rows_f32_f32_4")]] kernel kernel_sum_rows_t kernel_sum_rows_impl<float4, float>;
|
||||
|
||||
template<typename T>
|
||||
kernel void kernel_cumsum_blk(
|
||||
constant ggml_metal_kargs_cumsum_blk & args,
|
||||
device const char * src0,
|
||||
device char * tmp,
|
||||
device char * dst,
|
||||
threadgroup char * shmem [[threadgroup(0)]],
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort3 tpitg[[thread_position_in_threadgroup]],
|
||||
ushort sgitg[[simdgroup_index_in_threadgroup]],
|
||||
ushort tiisg[[thread_index_in_simdgroup]],
|
||||
ushort3 ntg[[threads_per_threadgroup]]) {
|
||||
const int ib = tgpig[0]/args.ne01;
|
||||
|
||||
const int i00 = ib*ntg.x;
|
||||
const int i01 = tgpig[0]%args.ne01;
|
||||
const int i02 = tgpig[1];
|
||||
const int i03 = tgpig[2];
|
||||
|
||||
device const float * src0_row = (device const float *) (src0 +
|
||||
args.nb01*i01 +
|
||||
args.nb02*i02 +
|
||||
args.nb03*i03);
|
||||
|
||||
threadgroup float * shmem_f32 = (threadgroup float *) shmem;
|
||||
|
||||
float v = 0.0f;
|
||||
|
||||
if (i00 + tpitg.x < args.ne00) {
|
||||
v = src0_row[i00 + tpitg.x];
|
||||
}
|
||||
|
||||
float s = simd_prefix_inclusive_sum(v);
|
||||
|
||||
if (tiisg == N_SIMDWIDTH - 1) {
|
||||
shmem_f32[sgitg] = s;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
if (sgitg == 0) {
|
||||
shmem_f32[tiisg] = simd_prefix_exclusive_sum(shmem_f32[tiisg]);
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
s += shmem_f32[sgitg];
|
||||
|
||||
device float * dst_row = (device float *) dst +
|
||||
args.ne00*i01 +
|
||||
args.ne00*args.ne01*i02 +
|
||||
args.ne00*args.ne01*args.ne02*i03;
|
||||
|
||||
if (i00 + tpitg.x < args.ne00) {
|
||||
dst_row[i00 + tpitg.x] = s;
|
||||
}
|
||||
|
||||
if (args.outb && tpitg.x == ntg.x - 1) {
|
||||
device float * tmp_row = (device float *) tmp +
|
||||
args.net0*i01 +
|
||||
args.net0*args.net1*i02 +
|
||||
args.net0*args.net1*args.net2*i03;
|
||||
|
||||
tmp_row[ib] = s;
|
||||
}
|
||||
}
|
||||
|
||||
typedef decltype(kernel_cumsum_blk<float>) kernel_cumsum_blk_t;
|
||||
|
||||
template [[host_name("kernel_cumsum_blk_f32")]] kernel kernel_cumsum_blk_t kernel_cumsum_blk<float>;
|
||||
|
||||
template<typename T>
|
||||
kernel void kernel_cumsum_add(
|
||||
constant ggml_metal_kargs_cumsum_add & args,
|
||||
device const char * tmp,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort3 tpitg[[thread_position_in_threadgroup]],
|
||||
ushort sgitg[[simdgroup_index_in_threadgroup]],
|
||||
ushort tiisg[[thread_index_in_simdgroup]],
|
||||
ushort3 ntg[[threads_per_threadgroup]]) {
|
||||
const int ib = tgpig[0]/args.ne01;
|
||||
|
||||
if (ib == 0) {
|
||||
return;
|
||||
}
|
||||
|
||||
const int i00 = ib*ntg.x;
|
||||
const int i01 = tgpig[0]%args.ne01;
|
||||
const int i02 = tgpig[1];
|
||||
const int i03 = tgpig[2];
|
||||
|
||||
device const float * tmp_row = (device const float *) (tmp +
|
||||
args.nbt1*i01 +
|
||||
args.nbt2*i02 +
|
||||
args.nbt3*i03);
|
||||
|
||||
device float * dst_row = (device float *) dst +
|
||||
args.ne00*i01 +
|
||||
args.ne00*args.ne01*i02 +
|
||||
args.ne00*args.ne01*args.ne02*i03;
|
||||
|
||||
if (i00 + tpitg.x < args.ne00) {
|
||||
dst_row[i00 + tpitg.x] += tmp_row[ib - 1];
|
||||
}
|
||||
}
|
||||
|
||||
typedef decltype(kernel_cumsum_add<float>) kernel_cumsum_add_t;
|
||||
|
||||
template [[host_name("kernel_cumsum_add_f32")]] kernel kernel_cumsum_add_t kernel_cumsum_add<float>;
|
||||
@@ -1,318 +0,0 @@
|
||||
#include "common.h"
|
||||
|
||||
constant bool FC_rope_is_imrope [[function_constant(FC_ROPE + 0)]];
|
||||
constant bool FC_rope_is_back [[function_constant(FC_ROPE + 1)]];
|
||||
|
||||
static float rope_yarn_ramp(const float low, const float high, const int i0) {
|
||||
const float y = (i0 / 2 - low) / max(0.001f, high - low);
|
||||
return 1.0f - min(1.0f, max(0.0f, y));
|
||||
}
|
||||
|
||||
// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn
|
||||
// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng.
|
||||
static void rope_yarn(
|
||||
float theta_extrap, float freq_scale, float corr_dims[2], int i0, float ext_factor, float mscale,
|
||||
thread float * cos_theta, thread float * sin_theta) {
|
||||
// Get n-d rotational scaling corrected for extrapolation
|
||||
float theta_interp = freq_scale * theta_extrap;
|
||||
float theta = theta_interp;
|
||||
if (ext_factor != 0.0f) {
|
||||
float ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], i0) * ext_factor;
|
||||
theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
|
||||
|
||||
// Get n-d magnitude scaling corrected for interpolation
|
||||
mscale *= 1.0f + 0.1f * log(1.0f / freq_scale);
|
||||
}
|
||||
*cos_theta = cos(theta) * mscale;
|
||||
*sin_theta = sin(theta) * mscale;
|
||||
if (FC_rope_is_back) {
|
||||
*sin_theta *= -1.0f;
|
||||
}
|
||||
}
|
||||
|
||||
// Apparently solving `n_rot = 2pi * x * base^((2 * max_pos_emb) / n_dims)` for x, we get
|
||||
// `corr_fac(n_rot) = n_dims * log(max_pos_emb / (n_rot * 2pi)) / (2 * log(base))`
|
||||
static float rope_yarn_corr_factor(int n_dims, int n_ctx_orig, float n_rot, float base) {
|
||||
return n_dims * log(n_ctx_orig / (n_rot * 2 * M_PI_F)) / (2 * log(base));
|
||||
}
|
||||
|
||||
static void rope_yarn_corr_dims(
|
||||
int n_dims, int n_ctx_orig, float freq_base, float beta_fast, float beta_slow, float dims[2]
|
||||
) {
|
||||
// start and end correction dims
|
||||
dims[0] = max(0.0f, floor(rope_yarn_corr_factor(n_dims, n_ctx_orig, beta_fast, freq_base)));
|
||||
dims[1] = min(n_dims - 1.0f, ceil(rope_yarn_corr_factor(n_dims, n_ctx_orig, beta_slow, freq_base)));
|
||||
}
|
||||
|
||||
template<typename T>
|
||||
kernel void kernel_rope_norm(
|
||||
constant ggml_metal_kargs_rope & args,
|
||||
device const char * src0,
|
||||
device const char * src1,
|
||||
device const char * src2,
|
||||
device char * dst,
|
||||
ushort tiitg[[thread_index_in_threadgroup]],
|
||||
ushort3 tptg [[threads_per_threadgroup]],
|
||||
uint3 tgpig[[threadgroup_position_in_grid]]) {
|
||||
const int i3 = tgpig[2];
|
||||
const int i2 = tgpig[1];
|
||||
const int i1 = tgpig[0];
|
||||
|
||||
float corr_dims[2];
|
||||
rope_yarn_corr_dims(args.n_dims, args.n_ctx_orig, args.freq_base, args.beta_fast, args.beta_slow, corr_dims);
|
||||
|
||||
device const int32_t * pos = (device const int32_t *) src1;
|
||||
|
||||
const float theta_base = (float) pos[i2];
|
||||
const float inv_ndims = -1.f/args.n_dims;
|
||||
|
||||
float cos_theta;
|
||||
float sin_theta;
|
||||
|
||||
for (int i0 = 2*tiitg; i0 < args.ne0; i0 += 2*tptg.x) {
|
||||
if (i0 < args.n_dims) {
|
||||
const int ic = i0/2;
|
||||
|
||||
const float theta = theta_base * pow(args.freq_base, inv_ndims*i0);
|
||||
|
||||
const float freq_factor = args.src2 ? ((device const float *) src2)[ic] : 1.0f;
|
||||
|
||||
rope_yarn(theta/freq_factor, args.freq_scale, corr_dims, i0, args.ext_factor, args.attn_factor, &cos_theta, &sin_theta);
|
||||
|
||||
device const T * const src = (device T *)(src0 + i3*args.nb03 + i2*args.nb02 + i1*args.nb01 + i0*args.nb00);
|
||||
device T * dst_data = (device T *)( dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0);
|
||||
|
||||
const float x0 = src[0];
|
||||
const float x1 = src[1];
|
||||
|
||||
dst_data[0] = x0*cos_theta - x1*sin_theta;
|
||||
dst_data[1] = x0*sin_theta + x1*cos_theta;
|
||||
} else {
|
||||
device const T * const src = (device T *)(src0 + i3*args.nb03 + i2*args.nb02 + i1*args.nb01 + i0*args.nb00);
|
||||
device T * dst_data = (device T *)( dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0);
|
||||
|
||||
dst_data[0] = src[0];
|
||||
dst_data[1] = src[1];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template<typename T>
|
||||
kernel void kernel_rope_neox(
|
||||
constant ggml_metal_kargs_rope & args,
|
||||
device const char * src0,
|
||||
device const char * src1,
|
||||
device const char * src2,
|
||||
device char * dst,
|
||||
ushort tiitg[[thread_index_in_threadgroup]],
|
||||
ushort3 tptg [[threads_per_threadgroup]],
|
||||
uint3 tgpig[[threadgroup_position_in_grid]]) {
|
||||
const int i3 = tgpig[2];
|
||||
const int i2 = tgpig[1];
|
||||
const int i1 = tgpig[0];
|
||||
|
||||
float corr_dims[2];
|
||||
rope_yarn_corr_dims(args.n_dims, args.n_ctx_orig, args.freq_base, args.beta_fast, args.beta_slow, corr_dims);
|
||||
|
||||
device const int32_t * pos = (device const int32_t *) src1;
|
||||
|
||||
const float theta_base = (float) pos[i2];
|
||||
const float inv_ndims = -1.f/args.n_dims;
|
||||
|
||||
float cos_theta;
|
||||
float sin_theta;
|
||||
|
||||
for (int i0 = 2*tiitg; i0 < args.ne0; i0 += 2*tptg.x) {
|
||||
if (i0 < args.n_dims) {
|
||||
const int ic = i0/2;
|
||||
|
||||
const float theta = theta_base * pow(args.freq_base, inv_ndims*i0);
|
||||
|
||||
const float freq_factor = args.src2 ? ((device const float *) src2)[ic] : 1.0f;
|
||||
|
||||
rope_yarn(theta/freq_factor, args.freq_scale, corr_dims, i0, args.ext_factor, args.attn_factor, &cos_theta, &sin_theta);
|
||||
|
||||
device const T * const src = (device T *)(src0 + i3*args.nb03 + i2*args.nb02 + i1*args.nb01 + ic*args.nb00);
|
||||
device T * dst_data = (device T *)( dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + ic*args.nb0);
|
||||
|
||||
const float x0 = src[0];
|
||||
const float x1 = src[args.n_dims/2];
|
||||
|
||||
dst_data[0] = x0*cos_theta - x1*sin_theta;
|
||||
dst_data[args.n_dims/2] = x0*sin_theta + x1*cos_theta;
|
||||
} else {
|
||||
device const T * const src = (device T *)(src0 + i3*args.nb03 + i2*args.nb02 + i1*args.nb01 + i0*args.nb00);
|
||||
device T * dst_data = (device T *)( dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0);
|
||||
|
||||
dst_data[0] = src[0];
|
||||
dst_data[1] = src[1];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template<typename T>
|
||||
kernel void kernel_rope_multi(
|
||||
constant ggml_metal_kargs_rope & args,
|
||||
device const char * src0,
|
||||
device const char * src1,
|
||||
device const char * src2,
|
||||
device char * dst,
|
||||
ushort tiitg[[thread_index_in_threadgroup]],
|
||||
ushort3 tptg [[threads_per_threadgroup]],
|
||||
uint3 tgpig[[threadgroup_position_in_grid]]) {
|
||||
const int i3 = tgpig[2];
|
||||
const int i2 = tgpig[1];
|
||||
const int i1 = tgpig[0];
|
||||
|
||||
float corr_dims[2];
|
||||
rope_yarn_corr_dims(args.n_dims, args.n_ctx_orig, args.freq_base, args.beta_fast, args.beta_slow, corr_dims);
|
||||
|
||||
device const int32_t * pos = (device const int32_t *) src1;
|
||||
|
||||
const float inv_ndims = -1.f/args.n_dims;
|
||||
|
||||
float cos_theta;
|
||||
float sin_theta;
|
||||
|
||||
for (int i0 = 2*tiitg; i0 < args.ne0; i0 += 2*tptg.x) {
|
||||
if (i0 < args.n_dims) {
|
||||
const int ic = i0/2;
|
||||
|
||||
// mrope theta calculations
|
||||
// note: the rest is the same as kernel_rope_neox
|
||||
const int sect_dims = args.sect_0 + args.sect_1 + args.sect_2 + args.sect_3;
|
||||
const int sec_w01 = args.sect_0 + args.sect_1; // end of section 1
|
||||
const int sec_w012 = args.sect_0 + args.sect_1 + args.sect_2; // end of section 2
|
||||
const int sector = ic % sect_dims;
|
||||
|
||||
float theta_base;
|
||||
if (FC_rope_is_imrope) {
|
||||
if (sector % 3 == 1 && sector < 3 * args.sect_1) { // h
|
||||
theta_base = (float) pos[i2 + args.ne02 * 1];
|
||||
} else if (sector % 3 == 2 && sector < 3 * args.sect_2) { // w
|
||||
theta_base = (float) pos[i2 + args.ne02 * 2];
|
||||
} else if (sector % 3 == 0 && sector < 3 * args.sect_0) { // t
|
||||
theta_base = (float) pos[i2 + args.ne02 * 0];
|
||||
} else { // e
|
||||
theta_base = (float) pos[i2 + args.ne02 * 3];
|
||||
}
|
||||
} else {
|
||||
if (sector < args.sect_0) {
|
||||
theta_base = (float) pos[i2];
|
||||
} else if (sector < sec_w01) {
|
||||
theta_base = (float) pos[i2 + args.ne02 * 1];
|
||||
} else if (sector < sec_w012) {
|
||||
theta_base = (float) pos[i2 + args.ne02 * 2];
|
||||
} else {
|
||||
theta_base = (float) pos[i2 + args.ne02 * 3];
|
||||
}
|
||||
}
|
||||
// end of mrope
|
||||
|
||||
const float theta = theta_base * pow(args.freq_base, inv_ndims*i0);
|
||||
|
||||
const float freq_factor = args.src2 ? ((device const float *) src2)[ic] : 1.0f;
|
||||
|
||||
rope_yarn(theta/freq_factor, args.freq_scale, corr_dims, i0, args.ext_factor, args.attn_factor, &cos_theta, &sin_theta);
|
||||
|
||||
device const T * const src = (device T *)(src0 + i3*args.nb03 + i2*args.nb02 + i1*args.nb01 + ic*args.nb00);
|
||||
device T * dst_data = (device T *)( dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + ic*args.nb0);
|
||||
|
||||
const float x0 = src[0];
|
||||
const float x1 = src[args.n_dims/2];
|
||||
|
||||
dst_data[0] = x0*cos_theta - x1*sin_theta;
|
||||
dst_data[args.n_dims/2] = x0*sin_theta + x1*cos_theta;
|
||||
} else {
|
||||
device const T * const src = (device T *)(src0 + i3*args.nb03 + i2*args.nb02 + i1*args.nb01 + i0*args.nb00);
|
||||
device T * dst_data = (device T *)( dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0);
|
||||
|
||||
dst_data[0] = src[0];
|
||||
dst_data[1] = src[1];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template<typename T>
|
||||
kernel void kernel_rope_vision(
|
||||
constant ggml_metal_kargs_rope & args,
|
||||
device const char * src0,
|
||||
device const char * src1,
|
||||
device const char * src2,
|
||||
device char * dst,
|
||||
ushort tiitg[[thread_index_in_threadgroup]],
|
||||
ushort3 tptg [[threads_per_threadgroup]],
|
||||
uint3 tgpig[[threadgroup_position_in_grid]]) {
|
||||
const int i3 = tgpig[2];
|
||||
const int i2 = tgpig[1];
|
||||
const int i1 = tgpig[0];
|
||||
|
||||
float corr_dims[2];
|
||||
rope_yarn_corr_dims(args.n_dims, args.n_ctx_orig, args.freq_base, args.beta_fast, args.beta_slow, corr_dims);
|
||||
|
||||
device const int32_t * pos = (device const int32_t *) src1;
|
||||
|
||||
const float inv_ndims = -1.f/args.n_dims;
|
||||
|
||||
float cos_theta;
|
||||
float sin_theta;
|
||||
|
||||
for (int i0 = 2*tiitg; i0 < args.ne0; i0 += 2*tptg.x) {
|
||||
if (i0 < 2*args.n_dims) { // different from kernel_rope_multi
|
||||
const int ic = i0/2;
|
||||
|
||||
// mrope theta calculations (only support 2 dimensions)
|
||||
const int sect_dims = args.sect_0 + args.sect_1;
|
||||
const int sector = ic % sect_dims;
|
||||
|
||||
float p;
|
||||
float theta_base;
|
||||
if (sector < args.sect_1) {
|
||||
p = (float) sector;
|
||||
theta_base = (float) pos[i2];
|
||||
} else {
|
||||
p = (float) sector - args.sect_0;
|
||||
theta_base = (float) pos[i2 + args.ne02];
|
||||
}
|
||||
|
||||
const float theta = theta_base * pow(args.freq_base, 2.0f * inv_ndims * p);
|
||||
// end of mrope
|
||||
|
||||
const float freq_factor = args.src2 ? ((device const float *) src2)[ic] : 1.0f;
|
||||
|
||||
rope_yarn(theta/freq_factor, args.freq_scale, corr_dims, i0, args.ext_factor, args.attn_factor, &cos_theta, &sin_theta);
|
||||
|
||||
device const T * const src = (device T *)(src0 + i3*args.nb03 + i2*args.nb02 + i1*args.nb01 + ic*args.nb00);
|
||||
device T * dst_data = (device T *)( dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + ic*args.nb0);
|
||||
|
||||
const float x0 = src[0];
|
||||
const float x1 = src[args.n_dims]; // different from kernel_rope_multi
|
||||
|
||||
dst_data[0] = x0*cos_theta - x1*sin_theta;
|
||||
dst_data[args.n_dims] = x0*sin_theta + x1*cos_theta; // different from kernel_rope_multi
|
||||
} else {
|
||||
device const T * const src = (device T *)(src0 + i3*args.nb03 + i2*args.nb02 + i1*args.nb01 + i0*args.nb00);
|
||||
device T * dst_data = (device T *)( dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0);
|
||||
|
||||
dst_data[0] = src[0];
|
||||
dst_data[1] = src[1];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
typedef decltype(kernel_rope_norm<float>) kernel_rope_norm_t;
|
||||
typedef decltype(kernel_rope_neox<float>) kernel_rope_neox_t;
|
||||
typedef decltype(kernel_rope_multi<float>) kernel_rope_multi_t;
|
||||
typedef decltype(kernel_rope_vision<float>) kernel_rope_vision_t;
|
||||
|
||||
template [[host_name("kernel_rope_norm_f32")]] kernel kernel_rope_norm_t kernel_rope_norm<float>;
|
||||
template [[host_name("kernel_rope_norm_f16")]] kernel kernel_rope_norm_t kernel_rope_norm<half>;
|
||||
|
||||
template [[host_name("kernel_rope_neox_f32")]] kernel kernel_rope_neox_t kernel_rope_neox<float>;
|
||||
template [[host_name("kernel_rope_neox_f16")]] kernel kernel_rope_neox_t kernel_rope_neox<half>;
|
||||
|
||||
template [[host_name("kernel_rope_multi_f32")]] kernel kernel_rope_multi_t kernel_rope_multi<float>;
|
||||
template [[host_name("kernel_rope_multi_f16")]] kernel kernel_rope_multi_t kernel_rope_multi<half>;
|
||||
|
||||
template [[host_name("kernel_rope_vision_f32")]] kernel kernel_rope_vision_t kernel_rope_vision<float>;
|
||||
template [[host_name("kernel_rope_vision_f16")]] kernel kernel_rope_vision_t kernel_rope_vision<half>;
|
||||
@@ -1,223 +0,0 @@
|
||||
#include "common.h"
|
||||
|
||||
template<typename T>
|
||||
kernel void kernel_soft_max(
|
||||
constant ggml_metal_kargs_soft_max & args,
|
||||
device const char * src0,
|
||||
device const char * src1,
|
||||
device const char * src2,
|
||||
device char * dst,
|
||||
threadgroup float * buf [[threadgroup(0)]],
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tpitg[[thread_position_in_threadgroup]],
|
||||
uint sgitg[[simdgroup_index_in_threadgroup]],
|
||||
uint tiisg[[thread_index_in_simdgroup]],
|
||||
uint3 tptg[[threads_per_threadgroup]]) {
|
||||
const int32_t i03 = tgpig.z;
|
||||
const int32_t i02 = tgpig.y;
|
||||
const int32_t i01 = tgpig.x;
|
||||
|
||||
const int32_t i13 = i03%args.ne13;
|
||||
const int32_t i12 = i02%args.ne12;
|
||||
const int32_t i11 = i01;
|
||||
|
||||
device const float * psrc0 = (device const float *) (src0 + i01*args.nb01 + i02*args.nb02 + i03*args.nb03);
|
||||
device const T * pmask = src1 != src0 ? (device const T * ) (src1 + i11*args.nb11 + i12*args.nb12 + i13*args.nb13) : nullptr;
|
||||
device const float * psrc2 = src2 != src0 ? (device const float *) (src2) : nullptr;
|
||||
device float * pdst = (device float *) (dst + i01*args.nb1 + i02*args.nb2 + i03*args.nb3);
|
||||
|
||||
float slope = 1.0f;
|
||||
|
||||
// ALiBi
|
||||
if (args.max_bias > 0.0f) {
|
||||
const int32_t h = i02;
|
||||
|
||||
const float base = h < args.n_head_log2 ? args.m0 : args.m1;
|
||||
const int exp = h < args.n_head_log2 ? h + 1 : 2*(h - args.n_head_log2) + 1;
|
||||
|
||||
slope = pow(base, exp);
|
||||
}
|
||||
|
||||
// parallel max
|
||||
float lmax = psrc2 ? psrc2[i02] : -INFINITY;
|
||||
|
||||
for (int i00 = tpitg.x; i00 < args.ne00; i00 += tptg.x) {
|
||||
lmax = MAX(lmax, psrc0[i00]*args.scale + (pmask ? slope*pmask[i00] : 0.0f));
|
||||
}
|
||||
|
||||
// find the max value in the block
|
||||
float max_val = simd_max(lmax);
|
||||
if (tptg.x > N_SIMDWIDTH) {
|
||||
if (sgitg == 0) {
|
||||
buf[tiisg] = -INFINITY;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
if (tiisg == 0) {
|
||||
buf[sgitg] = max_val;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
max_val = buf[tiisg];
|
||||
max_val = simd_max(max_val);
|
||||
}
|
||||
|
||||
// parallel sum
|
||||
float lsum = 0.0f;
|
||||
for (int i00 = tpitg.x; i00 < args.ne00; i00 += tptg.x) {
|
||||
const float exp_psrc0 = exp((psrc0[i00]*args.scale + (pmask ? slope*pmask[i00] : 0.0f)) - max_val);
|
||||
lsum += exp_psrc0;
|
||||
pdst[i00] = exp_psrc0;
|
||||
}
|
||||
|
||||
// This barrier fixes a failing test
|
||||
// ref: https://github.com/ggml-org/ggml/pull/621#discussion_r1425156335
|
||||
threadgroup_barrier(mem_flags::mem_none);
|
||||
|
||||
float sum = simd_sum(lsum);
|
||||
|
||||
if (tptg.x > N_SIMDWIDTH) {
|
||||
if (sgitg == 0) {
|
||||
buf[tiisg] = 0.0f;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
if (tiisg == 0) {
|
||||
buf[sgitg] = sum;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
sum = buf[tiisg];
|
||||
sum = simd_sum(sum);
|
||||
}
|
||||
|
||||
if (psrc2) {
|
||||
sum += exp(psrc2[i02] - max_val);
|
||||
}
|
||||
|
||||
const float inv_sum = 1.0f/sum;
|
||||
|
||||
for (int i00 = tpitg.x; i00 < args.ne00; i00 += tptg.x) {
|
||||
pdst[i00] *= inv_sum;
|
||||
}
|
||||
}
|
||||
|
||||
template<typename T>
|
||||
kernel void kernel_soft_max_4(
|
||||
constant ggml_metal_kargs_soft_max & args,
|
||||
device const char * src0,
|
||||
device const char * src1,
|
||||
device const char * src2,
|
||||
device char * dst,
|
||||
threadgroup float * buf [[threadgroup(0)]],
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tpitg[[thread_position_in_threadgroup]],
|
||||
uint sgitg[[simdgroup_index_in_threadgroup]],
|
||||
uint tiisg[[thread_index_in_simdgroup]],
|
||||
uint3 tptg[[threads_per_threadgroup]]) {
|
||||
const int32_t i03 = tgpig.z;
|
||||
const int32_t i02 = tgpig.y;
|
||||
const int32_t i01 = tgpig.x;
|
||||
|
||||
const int32_t i13 = i03%args.ne13;
|
||||
const int32_t i12 = i02%args.ne12;
|
||||
const int32_t i11 = i01;
|
||||
|
||||
device const float4 * psrc4 = (device const float4 *) (src0 + i01*args.nb01 + i02*args.nb02 + i03*args.nb03);
|
||||
device const T * pmask = src1 != src0 ? (device const T * ) (src1 + i11*args.nb11 + i12*args.nb12 + i13*args.nb13) : nullptr;
|
||||
device const float * psrc2 = src2 != src0 ? (device const float * ) (src2) : nullptr;
|
||||
device float4 * pdst4 = (device float4 *) (dst + i01*args.nb1 + i02*args.nb2 + i03*args.nb3);
|
||||
|
||||
float slope = 1.0f;
|
||||
|
||||
if (args.max_bias > 0.0f) {
|
||||
const int32_t h = i02;
|
||||
|
||||
const float base = h < args.n_head_log2 ? args.m0 : args.m1;
|
||||
const int exp = h < args.n_head_log2 ? h + 1 : 2*(h - args.n_head_log2) + 1;
|
||||
|
||||
slope = pow(base, exp);
|
||||
}
|
||||
|
||||
// parallel max
|
||||
float4 lmax4 = psrc2 ? psrc2[i02] : -INFINITY;
|
||||
|
||||
for (int i00 = tpitg.x; i00 < args.ne00/4; i00 += tptg.x) {
|
||||
lmax4 = fmax(lmax4, psrc4[i00]*args.scale + (float4)((pmask ? slope*pmask[i00] : 0.0f)));
|
||||
}
|
||||
|
||||
const float lmax = MAX(MAX(lmax4[0], lmax4[1]), MAX(lmax4[2], lmax4[3]));
|
||||
|
||||
float max_val = simd_max(lmax);
|
||||
if (tptg.x > N_SIMDWIDTH) {
|
||||
if (sgitg == 0) {
|
||||
buf[tiisg] = -INFINITY;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
if (tiisg == 0) {
|
||||
buf[sgitg] = max_val;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
max_val = buf[tiisg];
|
||||
max_val = simd_max(max_val);
|
||||
}
|
||||
|
||||
// parallel sum
|
||||
float4 lsum4 = 0.0f;
|
||||
for (int i00 = tpitg.x; i00 < args.ne00/4; i00 += tptg.x) {
|
||||
const float4 exp_psrc4 = exp((psrc4[i00]*args.scale + (float4)((pmask ? slope*pmask[i00] : 0.0f))) - max_val);
|
||||
lsum4 += exp_psrc4;
|
||||
pdst4[i00] = exp_psrc4;
|
||||
}
|
||||
|
||||
const float lsum = lsum4[0] + lsum4[1] + lsum4[2] + lsum4[3];
|
||||
|
||||
// This barrier fixes a failing test
|
||||
// ref: https://github.com/ggml-org/ggml/pull/621#discussion_r1425156335
|
||||
threadgroup_barrier(mem_flags::mem_none);
|
||||
|
||||
float sum = simd_sum(lsum);
|
||||
|
||||
if (tptg.x > N_SIMDWIDTH) {
|
||||
if (sgitg == 0) {
|
||||
buf[tiisg] = 0.0f;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
if (tiisg == 0) {
|
||||
buf[sgitg] = sum;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
sum = buf[tiisg];
|
||||
sum = simd_sum(sum);
|
||||
}
|
||||
|
||||
if (psrc2) {
|
||||
sum += exp(psrc2[i02] - max_val);
|
||||
}
|
||||
|
||||
const float inv_sum = 1.0f/sum;
|
||||
|
||||
for (int i00 = tpitg.x; i00 < args.ne00/4; i00 += tptg.x) {
|
||||
pdst4[i00] *= inv_sum;
|
||||
}
|
||||
}
|
||||
|
||||
typedef decltype(kernel_soft_max<float>) kernel_soft_max_t;
|
||||
typedef decltype(kernel_soft_max_4<float4>) kernel_soft_max_4_t;
|
||||
|
||||
template [[host_name("kernel_soft_max_f16")]] kernel kernel_soft_max_t kernel_soft_max<half>;
|
||||
template [[host_name("kernel_soft_max_f32")]] kernel kernel_soft_max_t kernel_soft_max<float>;
|
||||
template [[host_name("kernel_soft_max_f16_4")]] kernel kernel_soft_max_4_t kernel_soft_max_4<half4>;
|
||||
template [[host_name("kernel_soft_max_f32_4")]] kernel kernel_soft_max_4_t kernel_soft_max_4<float4>;
|
||||
@@ -1,75 +0,0 @@
|
||||
#include "common.h"
|
||||
|
||||
constant short FC_solve_tri_nsg [[function_constant(FC_SOLVE_TRI + 0)]];
|
||||
constant short FC_solve_tri_n [[function_constant(FC_SOLVE_TRI + 1)]];
|
||||
constant short FC_solve_tri_k [[function_constant(FC_SOLVE_TRI + 2)]];
|
||||
|
||||
kernel void kernel_solve_tri_f32(
|
||||
constant ggml_metal_kargs_solve_tri & args,
|
||||
device const char * src0,
|
||||
device const char * src1,
|
||||
device char * dst,
|
||||
threadgroup char * shmem [[threadgroup(0)]],
|
||||
ushort3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort sgitg[[simdgroup_index_in_threadgroup]],
|
||||
ushort tiisg[[thread_index_in_simdgroup]],
|
||||
ushort3 ntg[[threads_per_threadgroup]]) {
|
||||
constexpr short NW = N_SIMDWIDTH;
|
||||
|
||||
const short NSG = FC_solve_tri_nsg;
|
||||
const short N = FC_solve_tri_n;
|
||||
const short K = FC_solve_tri_k;
|
||||
const short NP = PAD2(N, NW);
|
||||
|
||||
const int32_t i03 = tgpig.z;
|
||||
const int32_t i02 = tgpig.y;
|
||||
const int32_t i01 = tgpig.x*NSG + sgitg;
|
||||
|
||||
threadgroup float * sh0 = (threadgroup float *) shmem;
|
||||
|
||||
device const float * src0_ptr = (device const float *)(src0 + i02 * args.nb02 + i03 * args.nb03) + sgitg*N;
|
||||
device const float * src1_ptr = (device const float *)(src1 + i02 * args.nb12 + i03 * args.nb13) + i01;
|
||||
device float * dst_ptr = (device float *)(dst + i02 * args.nb2 + i03 * args.nb3) + i01;
|
||||
|
||||
for (short rr = 0; rr < N; rr += NSG) {
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
{
|
||||
threadgroup float * sh0_cur = sh0 + sgitg*NP;
|
||||
|
||||
for (short t = 0; t*NW < N; ++t) {
|
||||
const short idx = t*NW + tiisg;
|
||||
sh0_cur[idx] = src0_ptr[idx];
|
||||
}
|
||||
|
||||
src0_ptr += NSG*N;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
if (i01 >= args.ne10) {
|
||||
continue;
|
||||
}
|
||||
|
||||
for (short ir = 0; ir < NSG && rr + ir < N; ++ir) {
|
||||
const short r = rr + ir;
|
||||
|
||||
threadgroup float * sh0_cur = sh0 + ir*NP;
|
||||
|
||||
float sum = 0.0f;
|
||||
|
||||
for (short t = 0; t*NW < r; ++t) {
|
||||
const short idx = t*NW + tiisg;
|
||||
sum += sh0_cur[idx] * dst_ptr[idx*K] * (idx < r);
|
||||
}
|
||||
|
||||
sum = simd_sum(sum);
|
||||
|
||||
if (tiisg == 0) {
|
||||
const float diag = sh0_cur[r];
|
||||
|
||||
dst_ptr[r*K] = (src1_ptr[r*K] - sum) / diag;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,279 +0,0 @@
|
||||
#include "common.h"
|
||||
|
||||
// ref: ggml.c:ggml_compute_forward_ssm_conv_f32
|
||||
kernel void kernel_ssm_conv_f32_f32(
|
||||
constant ggml_metal_kargs_ssm_conv & args,
|
||||
device const void * src0,
|
||||
device const void * src1,
|
||||
device float * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tpitg[[thread_position_in_threadgroup]],
|
||||
uint3 ntg[[threads_per_threadgroup]]) {
|
||||
const int64_t ir = tgpig.x;
|
||||
const int64_t i2 = tgpig.y;
|
||||
const int64_t i3 = tgpig.z;
|
||||
|
||||
const int64_t nc = args.ne10;
|
||||
//const int64_t ncs = args.ne00;
|
||||
//const int64_t nr = args.ne01;
|
||||
//const int64_t n_t = args.ne1;
|
||||
//const int64_t n_s = args.ne2;
|
||||
|
||||
device const float * s = (device const float *) ((device const char *) src0 + ir*args.nb01 + i2*args.nb00 + i3*args.nb02);
|
||||
device const float * c = (device const float *) ((device const char *) src1 + ir*args.nb11);
|
||||
device float * x = (device float *) ((device char *) dst + ir*args.nb0 + i2*args.nb1 + i3*args.nb2);
|
||||
|
||||
float sumf = 0.0f;
|
||||
|
||||
for (int64_t i0 = 0; i0 < nc; ++i0) {
|
||||
sumf += s[i0] * c[i0];
|
||||
}
|
||||
|
||||
x[0] = sumf;
|
||||
}
|
||||
|
||||
kernel void kernel_ssm_conv_f32_f32_4(
|
||||
constant ggml_metal_kargs_ssm_conv & args,
|
||||
device const void * src0,
|
||||
device const void * src1,
|
||||
device float * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tpitg[[thread_position_in_threadgroup]],
|
||||
uint3 ntg[[threads_per_threadgroup]]) {
|
||||
const int64_t ir = tgpig.x;
|
||||
const int64_t i2 = tgpig.y;
|
||||
const int64_t i3 = tgpig.z;
|
||||
|
||||
const int64_t nc = args.ne10;
|
||||
//const int64_t ncs = args.ne00;
|
||||
//const int64_t nr = args.ne01;
|
||||
//const int64_t n_t = args.ne1;
|
||||
//const int64_t n_s = args.ne2;
|
||||
|
||||
device const float4 * s = (device const float4 *) ((device const char *) src0 + ir*args.nb01 + i2*args.nb00 + i3*args.nb02);
|
||||
device const float4 * c = (device const float4 *) ((device const char *) src1 + ir*args.nb11);
|
||||
device float * x = (device float *) ((device char *) dst + ir*args.nb0 + i2*args.nb1 + i3*args.nb2);
|
||||
|
||||
float sumf = 0.0f;
|
||||
|
||||
for (int64_t i0 = 0; i0 < nc/4; ++i0) {
|
||||
sumf += dot(s[i0], c[i0]);
|
||||
}
|
||||
|
||||
x[0] = sumf;
|
||||
}
|
||||
|
||||
constant short FC_ssm_conv_bs [[function_constant(FC_SSM_CONV + 0)]];
|
||||
|
||||
// Batched version: each threadgroup processes multiple tokens for better efficiency
|
||||
// Thread layout: each thread handles one token, threadgroup covers BATCH_SIZE tokens
|
||||
kernel void kernel_ssm_conv_f32_f32_batched(
|
||||
constant ggml_metal_kargs_ssm_conv & args,
|
||||
device const void * src0,
|
||||
device const void * src1,
|
||||
device float * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tpitg[[thread_position_in_threadgroup]],
|
||||
uint3 ntg[[threads_per_threadgroup]]) {
|
||||
// tgpig.x = row index (ir)
|
||||
// tgpig.y = batch of tokens (i2_base / BATCH_SIZE)
|
||||
// tgpig.z = sequence index (i3)
|
||||
// tpitg.x = thread within batch (0..BATCH_SIZE-1)
|
||||
const short BATCH_SIZE = FC_ssm_conv_bs;
|
||||
|
||||
const int64_t ir = tgpig.x;
|
||||
const int64_t i2_base = tgpig.y * BATCH_SIZE;
|
||||
const int64_t i3 = tgpig.z;
|
||||
const int64_t i2_off = tpitg.x;
|
||||
const int64_t i2 = i2_base + i2_off;
|
||||
|
||||
const int64_t nc = args.ne10; // conv kernel size (typically 4)
|
||||
const int64_t n_t = args.ne1; // number of tokens
|
||||
|
||||
// Bounds check for partial batches at the end
|
||||
if (i2 >= n_t) {
|
||||
return;
|
||||
}
|
||||
|
||||
// Load conv weights (shared across all tokens for this row)
|
||||
device const float * c = (device const float *) ((device const char *) src1 + ir*args.nb11);
|
||||
|
||||
// Load source for this specific token
|
||||
device const float * s = (device const float *) ((device const char *) src0 + ir*args.nb01 + i2*args.nb00 + i3*args.nb02);
|
||||
|
||||
// Output location for this token
|
||||
device float * x = (device float *) ((device char *) dst + ir*args.nb0 + i2*args.nb1 + i3*args.nb2);
|
||||
|
||||
float sumf = 0.0f;
|
||||
for (int64_t i0 = 0; i0 < nc; ++i0) {
|
||||
sumf += s[i0] * c[i0];
|
||||
}
|
||||
|
||||
x[0] = sumf;
|
||||
}
|
||||
|
||||
kernel void kernel_ssm_conv_f32_f32_batched_4(
|
||||
constant ggml_metal_kargs_ssm_conv & args,
|
||||
device const void * src0,
|
||||
device const void * src1,
|
||||
device float * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tpitg[[thread_position_in_threadgroup]],
|
||||
uint3 ntg[[threads_per_threadgroup]]) {
|
||||
// tgpig.x = row index (ir)
|
||||
// tgpig.y = batch of tokens (i2_base / BATCH_SIZE)
|
||||
// tgpig.z = sequence index (i3)
|
||||
// tpitg.x = thread within batch (0..BATCH_SIZE-1)
|
||||
const short BATCH_SIZE = FC_ssm_conv_bs;
|
||||
|
||||
const int64_t ir = tgpig.x;
|
||||
const int64_t i2_base = tgpig.y * BATCH_SIZE;
|
||||
const int64_t i3 = tgpig.z;
|
||||
const int64_t i2_off = tpitg.x;
|
||||
const int64_t i2 = i2_base + i2_off;
|
||||
|
||||
const int64_t nc = args.ne10; // conv kernel size (typically 4)
|
||||
const int64_t n_t = args.ne1; // number of tokens
|
||||
|
||||
// Bounds check for partial batches at the end
|
||||
if (i2 >= n_t) {
|
||||
return;
|
||||
}
|
||||
|
||||
// Load conv weights (shared across all tokens for this row)
|
||||
device const float4 * c = (device const float4 *) ((device const char *) src1 + ir*args.nb11);
|
||||
|
||||
// Load source for this specific token
|
||||
device const float4 * s = (device const float4 *) ((device const char *) src0 + ir*args.nb01 + i2*args.nb00 + i3*args.nb02);
|
||||
|
||||
// Output location for this token
|
||||
device float * x = (device float *) ((device char *) dst + ir*args.nb0 + i2*args.nb1 + i3*args.nb2);
|
||||
|
||||
float sumf = 0.0f;
|
||||
for (int64_t i0 = 0; i0 < nc/4; ++i0) {
|
||||
sumf += dot(s[i0], c[i0]);
|
||||
}
|
||||
|
||||
x[0] = sumf;
|
||||
}
|
||||
|
||||
// ref: ggml.c:ggml_compute_forward_ssm_scan_f32, Mamba-2 part
|
||||
// Optimized version: reduces redundant memory loads by having one thread load shared values
|
||||
kernel void kernel_ssm_scan_f32(
|
||||
constant ggml_metal_kargs_ssm_scan & args,
|
||||
device const void * src0,
|
||||
device const void * src1,
|
||||
device const void * src2,
|
||||
device const void * src3,
|
||||
device const void * src4,
|
||||
device const void * src5,
|
||||
device const void * src6,
|
||||
device float * dst,
|
||||
threadgroup float * shared [[threadgroup(0)]],
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort3 tpitg[[thread_position_in_threadgroup]],
|
||||
ushort sgitg[[simdgroup_index_in_threadgroup]],
|
||||
ushort tiisg[[thread_index_in_simdgroup]],
|
||||
ushort sgptg[[simdgroups_per_threadgroup]],
|
||||
uint3 tgpg[[threadgroups_per_grid]]) {
|
||||
constexpr short NW = N_SIMDWIDTH;
|
||||
|
||||
// Shared memory layout:
|
||||
// [0..sgptg*NW-1]: partial sums for reduction (existing)
|
||||
// [sgptg*NW..sgptg*NW+sgptg-1]: pre-computed x_dt values for each token in batch
|
||||
// [sgptg*NW+sgptg..sgptg*NW+2*sgptg-1]: pre-computed dA values for each token in batch
|
||||
threadgroup float * shared_sums = shared;
|
||||
threadgroup float * shared_x_dt = shared + sgptg * NW;
|
||||
threadgroup float * shared_dA = shared + sgptg * NW + sgptg;
|
||||
|
||||
shared_sums[tpitg.x] = 0.0f;
|
||||
|
||||
const int32_t i0 = tpitg.x;
|
||||
const int32_t i1 = tgpig.x;
|
||||
const int32_t ir = tgpig.y; // current head
|
||||
const int32_t i3 = tgpig.z; // current seq
|
||||
|
||||
const int32_t nc = args.d_state;
|
||||
const int32_t nr = args.d_inner;
|
||||
const int32_t nh = args.n_head;
|
||||
const int32_t ng = args.n_group;
|
||||
const int32_t n_t = args.n_seq_tokens;
|
||||
|
||||
const int32_t s_off = args.s_off;
|
||||
|
||||
device const int32_t * ids = (device const int32_t *) src6;
|
||||
|
||||
device const float * s0_buff = (device const float *) ((device const char *) src0 + ir*args.nb02 + ids[i3]*args.nb03);
|
||||
device float * s_buff = (device float *) ((device char *) dst + ir*args.nb02 + i3*args.nb03 + s_off);
|
||||
|
||||
const int32_t i = i0 + i1*nc;
|
||||
const int32_t g = ir / (nh / ng); // repeat_interleave
|
||||
|
||||
float s0 = s0_buff[i];
|
||||
float s = 0.0f;
|
||||
|
||||
device const float * A = (device const float *) ((device const char *) src3 + ir*args.nb31); // {ne30, nh}
|
||||
|
||||
const float A0 = A[i0%args.ne30];
|
||||
|
||||
device const float * x = (device const float *)((device const char *) src1 + i1*args.nb10 + ir*args.nb11 + i3*args.nb13); // {dim, nh, nt, ns}
|
||||
device const float * dt = (device const float *)((device const char *) src2 + ir*args.nb20 + i3*args.nb22); // {nh, nt, ns}
|
||||
device const float * B = (device const float *)((device const char *) src4 + g*args.nb41 + i3*args.nb43); // {d_state, ng, nt, ns}
|
||||
device const float * C = (device const float *)((device const char *) src5 + g*args.nb51 + i3*args.nb53); // {d_state, ng, nt, ns}
|
||||
|
||||
device float * y = dst + (i1 + ir*(nr) + i3*(n_t*nh*nr)); // {dim, nh, nt, ns}
|
||||
|
||||
for (int i2 = 0; i2 < n_t; i2 += sgptg) {
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
// Pre-compute x_dt and dA for this batch of tokens
|
||||
// Only first sgptg threads do the loads and expensive math
|
||||
if (i0 < sgptg && i2 + i0 < n_t) {
|
||||
// ns12 and ns21 are element strides (nb12/nb10, nb21/nb20)
|
||||
device const float * x_t = x + i0 * args.ns12;
|
||||
device const float * dt_t = dt + i0 * args.ns21;
|
||||
|
||||
const float dt0 = dt_t[0];
|
||||
const float dtsp = dt0 <= 20.0f ? log(1.0f + exp(dt0)) : dt0;
|
||||
shared_x_dt[i0] = x_t[0] * dtsp;
|
||||
shared_dA[i0] = dtsp; // Store dtsp, compute exp(dtsp * A0) per-thread since A0 varies
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
for (int t = 0; t < sgptg && i2 + t < n_t; t++) {
|
||||
const float x_dt = shared_x_dt[t];
|
||||
const float dA = exp(shared_dA[t] * A0);
|
||||
|
||||
s = (s0 * dA) + (B[i0] * x_dt);
|
||||
|
||||
const float sumf = simd_sum(s * C[i0]);
|
||||
|
||||
if (tiisg == 0) {
|
||||
shared_sums[t*NW + sgitg] = sumf;
|
||||
}
|
||||
|
||||
// recurse
|
||||
s0 = s;
|
||||
|
||||
B += args.ns42;
|
||||
C += args.ns52;
|
||||
}
|
||||
|
||||
// Advance pointers for next batch
|
||||
x += sgptg * args.ns12;
|
||||
dt += sgptg * args.ns21;
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
const float sumf = simd_sum(shared_sums[sgitg*NW + tiisg]);
|
||||
|
||||
if (tiisg == 0 && i2 + sgitg < n_t) {
|
||||
y[sgitg*nh*nr] = sumf;
|
||||
}
|
||||
|
||||
y += sgptg*nh*nr;
|
||||
}
|
||||
|
||||
s_buff[i] = s;
|
||||
}
|
||||
@@ -1,69 +0,0 @@
|
||||
#include "common.h"
|
||||
|
||||
template<uint32_t ttype>
|
||||
bool _ggml_vec_tri_cmp(const int i, const int r);
|
||||
|
||||
template<>
|
||||
bool _ggml_vec_tri_cmp</* GGML_TRI_TYPE_LOWER */ 3>(const int i, const int r) {
|
||||
return i < r;
|
||||
}
|
||||
|
||||
template<>
|
||||
bool _ggml_vec_tri_cmp</* GGML_TRI_TYPE_LOWER_DIAG */ 2>(const int i, const int r) {
|
||||
return i <= r;
|
||||
}
|
||||
|
||||
template<>
|
||||
bool _ggml_vec_tri_cmp</* GGML_TRI_TYPE_UPPER */ 1>(const int i, const int r) {
|
||||
return i > r;
|
||||
}
|
||||
|
||||
template<>
|
||||
bool _ggml_vec_tri_cmp</* GGML_TRI_TYPE_UPPER_DIAG */ 0>(const int i, const int r) {
|
||||
return i >= r;
|
||||
}
|
||||
|
||||
template<typename T, int ttype>
|
||||
kernel void kernel_tri(
|
||||
constant ggml_metal_kargs_tri & args,
|
||||
device const char * src0,
|
||||
device const char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort3 tpitg[[thread_position_in_threadgroup]],
|
||||
ushort3 ntg[[threads_per_threadgroup]]) {
|
||||
const int i3 = tgpig.z;
|
||||
const int i2 = tgpig.y;
|
||||
const int i1 = tgpig.x;
|
||||
|
||||
if (i3 >= args.ne03 || i2 >= args.ne02 || i1 >= args.ne01) {
|
||||
return;
|
||||
}
|
||||
|
||||
device const T * src_row = (device const T *) ((device const char *) src0 + i1*args.nb01 + i2*args.nb02 + i3*args.nb03);
|
||||
device T * dst_row = (device T *) ((device char *) dst + i1*args.nb1 + i2*args.nb2 + i3*args.nb3);
|
||||
|
||||
// Each thread is a single element of the row if ne00 < max threads per
|
||||
// threadgroup, so this will loop once for each index that this thread is
|
||||
// responsible for
|
||||
for (int64_t i0 = tpitg.x; i0 < args.ne00; i0 += ntg.x) {
|
||||
// Use the comparison as a mask for branchless
|
||||
dst_row[i0] = static_cast<T>(_ggml_vec_tri_cmp<ttype>(i0, i1)) * src_row[i0];
|
||||
}
|
||||
}
|
||||
|
||||
typedef decltype(kernel_tri<float, 0>) kernel_tri_t;
|
||||
|
||||
template [[host_name("kernel_tri_f32_0")]] kernel kernel_tri_t kernel_tri<float, 0>;
|
||||
template [[host_name("kernel_tri_f32_1")]] kernel kernel_tri_t kernel_tri<float, 1>;
|
||||
template [[host_name("kernel_tri_f32_2")]] kernel kernel_tri_t kernel_tri<float, 2>;
|
||||
template [[host_name("kernel_tri_f32_3")]] kernel kernel_tri_t kernel_tri<float, 3>;
|
||||
template [[host_name("kernel_tri_f16_0")]] kernel kernel_tri_t kernel_tri<half, 0>;
|
||||
template [[host_name("kernel_tri_f16_1")]] kernel kernel_tri_t kernel_tri<half, 1>;
|
||||
template [[host_name("kernel_tri_f16_2")]] kernel kernel_tri_t kernel_tri<half, 2>;
|
||||
template [[host_name("kernel_tri_f16_3")]] kernel kernel_tri_t kernel_tri<half, 3>;
|
||||
#if defined(GGML_METAL_HAS_BF16)
|
||||
template [[host_name("kernel_tri_bf16_0")]] kernel kernel_tri_t kernel_tri<bfloat, 0>;
|
||||
template [[host_name("kernel_tri_bf16_1")]] kernel kernel_tri_t kernel_tri<bfloat, 1>;
|
||||
template [[host_name("kernel_tri_bf16_2")]] kernel kernel_tri_t kernel_tri<bfloat, 2>;
|
||||
template [[host_name("kernel_tri_bf16_3")]] kernel kernel_tri_t kernel_tri<bfloat, 3>;
|
||||
#endif
|
||||
@@ -1,360 +0,0 @@
|
||||
#include "common.h"
|
||||
|
||||
constant short FC_unary_op [[function_constant(FC_UNARY + 0)]];
|
||||
constant bool FC_unary_cnt[[function_constant(FC_UNARY + 1)]];
|
||||
|
||||
template <typename T0, typename T, typename TC>
|
||||
kernel void kernel_unary_impl(
|
||||
constant ggml_metal_kargs_unary & args,
|
||||
device const char * src0,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort3 tpitg[[thread_position_in_threadgroup]],
|
||||
ushort3 ntg[[threads_per_threadgroup]]) {
|
||||
#define FC_OP FC_unary_op
|
||||
#define FC_CNT FC_unary_cnt
|
||||
|
||||
device const T0 * src0_ptr;
|
||||
device T * dst_ptr;
|
||||
|
||||
int i0;
|
||||
|
||||
if (FC_CNT) {
|
||||
i0 = tgpig.x;
|
||||
|
||||
src0_ptr = (device const T0 *) (src0);
|
||||
dst_ptr = (device T *) (dst);
|
||||
} else {
|
||||
const int i03 = tgpig.z;
|
||||
const int i02 = tgpig.y;
|
||||
const int k0 = tgpig.x/args.ne01;
|
||||
const int i01 = tgpig.x - k0*args.ne01;
|
||||
|
||||
i0 = k0*ntg.x + tpitg.x;
|
||||
|
||||
src0_ptr = (device const T0 *) (src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01);
|
||||
dst_ptr = (device T *) (dst + i03*args.nb3 + i02*args.nb2 + i01*args.nb1 );
|
||||
}
|
||||
|
||||
{
|
||||
//threadgroup_barrier(mem_flags::mem_none);
|
||||
|
||||
if (!FC_CNT) {
|
||||
if (i0 >= args.ne0) {
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
const TC x = (TC) src0_ptr[i0];
|
||||
|
||||
if (FC_OP == OP_UNARY_NUM_SCALE) {
|
||||
dst_ptr[i0] = (T) (args.scale * x + args.bias);
|
||||
}
|
||||
|
||||
if (FC_OP == OP_UNARY_NUM_FILL) {
|
||||
dst_ptr[i0] = (T) args.val;
|
||||
}
|
||||
|
||||
if (FC_OP == OP_UNARY_NUM_CLAMP) {
|
||||
dst_ptr[i0] = (T) clamp(x, args.min, args.max);
|
||||
}
|
||||
|
||||
if (FC_OP == OP_UNARY_NUM_SQR) {
|
||||
dst_ptr[i0] = (T) (x * x);
|
||||
}
|
||||
|
||||
if (FC_OP == OP_UNARY_NUM_SQRT) {
|
||||
dst_ptr[i0] = (T) sqrt(x);
|
||||
}
|
||||
|
||||
if (FC_OP == OP_UNARY_NUM_SIN) {
|
||||
dst_ptr[i0] = (T) sin(x);
|
||||
}
|
||||
|
||||
if (FC_OP == OP_UNARY_NUM_COS) {
|
||||
dst_ptr[i0] = (T) cos(x);
|
||||
}
|
||||
|
||||
if (FC_OP == OP_UNARY_NUM_LOG) {
|
||||
dst_ptr[i0] = (T) log(x);
|
||||
}
|
||||
|
||||
if (FC_OP == OP_UNARY_NUM_LEAKY_RELU) {
|
||||
dst_ptr[i0] = (T) (TC(x > 0)*x + TC(x <= 0)*(x * args.slope));
|
||||
}
|
||||
|
||||
if (FC_OP == OP_UNARY_NUM_TANH) {
|
||||
dst_ptr[i0] = (T) precise::tanh(x);
|
||||
}
|
||||
|
||||
if (FC_OP == OP_UNARY_NUM_RELU) {
|
||||
dst_ptr[i0] = (T) fmax(0, x);
|
||||
}
|
||||
|
||||
if (FC_OP == OP_UNARY_NUM_SIGMOID) {
|
||||
dst_ptr[i0] = (T) (1 / (1 + exp(-x)));
|
||||
}
|
||||
|
||||
if (FC_OP == OP_UNARY_NUM_GELU) {
|
||||
dst_ptr[i0] = (T) (0.5*x*(1 + precise::tanh(SQRT_2_OVER_PI*x*(1 + GELU_COEF_A*x*x))));
|
||||
}
|
||||
|
||||
if (FC_OP == OP_UNARY_NUM_GELU_ERF) {
|
||||
dst_ptr[i0] = (T) (0.5*x*(1 + erf_approx(SQRT_2_INV*x)));
|
||||
}
|
||||
|
||||
if (FC_OP == OP_UNARY_NUM_GELU_QUICK) {
|
||||
dst_ptr[i0] = (T) (x * (1/(1 + exp(GELU_QUICK_COEF*x))));
|
||||
}
|
||||
|
||||
if (FC_OP == OP_UNARY_NUM_SILU) {
|
||||
dst_ptr[i0] = (T) (x / (1 + exp(-x)));
|
||||
}
|
||||
|
||||
if (FC_OP == OP_UNARY_NUM_ELU) {
|
||||
dst_ptr[i0] = (T) elu_approx(x);
|
||||
}
|
||||
|
||||
if (FC_OP == OP_UNARY_NUM_NEG) {
|
||||
dst_ptr[i0] = (T) -x;
|
||||
}
|
||||
|
||||
if (FC_OP == OP_UNARY_NUM_ABS) {
|
||||
dst_ptr[i0] = (T) fabs(x);
|
||||
}
|
||||
|
||||
if (FC_OP == OP_UNARY_NUM_SGN) {
|
||||
dst_ptr[i0] = T(x > 0) - T(x < 0);
|
||||
}
|
||||
|
||||
if (FC_OP == OP_UNARY_NUM_STEP) {
|
||||
dst_ptr[i0] = T(x > 0);
|
||||
}
|
||||
|
||||
if (FC_OP == OP_UNARY_NUM_HARDSWISH) {
|
||||
dst_ptr[i0] = (T) (x * fmax(0, fmin(1, x/6 + 0.5)));
|
||||
}
|
||||
|
||||
if (FC_OP == OP_UNARY_NUM_HARDSIGMOID) {
|
||||
dst_ptr[i0] = (T) fmax(0, fmin(1, x/6 + 0.5));
|
||||
}
|
||||
|
||||
if (FC_OP == OP_UNARY_NUM_EXP) {
|
||||
dst_ptr[i0] = (T) exp(x);
|
||||
}
|
||||
|
||||
if (FC_OP == OP_UNARY_NUM_SOFTPLUS) {
|
||||
dst_ptr[i0] = (T) select(log(1 + exp(x)), x, x > 20);
|
||||
}
|
||||
|
||||
if (FC_OP == OP_UNARY_NUM_EXPM1) {
|
||||
// TODO: precise implementation
|
||||
dst_ptr[i0] = (T) (exp(x) - 1);
|
||||
}
|
||||
|
||||
if (FC_OP == OP_UNARY_NUM_FLOOR) {
|
||||
dst_ptr[i0] = (T) floor(x);
|
||||
}
|
||||
|
||||
if (FC_OP == OP_UNARY_NUM_CEIL) {
|
||||
dst_ptr[i0] = (T) ceil(x);
|
||||
}
|
||||
|
||||
if (FC_OP == OP_UNARY_NUM_ROUND) {
|
||||
dst_ptr[i0] = (T) round(x);
|
||||
}
|
||||
|
||||
if (FC_OP == OP_UNARY_NUM_TRUNC) {
|
||||
dst_ptr[i0] = (T) trunc(x);
|
||||
}
|
||||
|
||||
if (FC_OP == OP_UNARY_NUM_XIELU) {
|
||||
const TC xi = x;
|
||||
const TC gate = TC(xi > TC(0.0f));
|
||||
const TC clamped = fmin(xi, TC(args.val));
|
||||
const TC y_pos = TC(args.scale) * xi * xi + TC(args.bias) * xi;
|
||||
const TC y_neg = (exp(clamped) - TC(1.0f) - xi) * TC(args.slope) + TC(args.bias) * xi;
|
||||
dst_ptr[i0] = (T) (gate * y_pos + (TC(1.0f) - gate) * y_neg);
|
||||
}
|
||||
}
|
||||
|
||||
#undef FC_OP
|
||||
#undef FC_CNT
|
||||
}
|
||||
|
||||
typedef decltype(kernel_unary_impl<float, float, float>) kernel_unary_t;
|
||||
|
||||
template [[host_name("kernel_unary_f32_f32")]] kernel kernel_unary_t kernel_unary_impl<float, float, float>;
|
||||
template [[host_name("kernel_unary_f32_f32_4")]] kernel kernel_unary_t kernel_unary_impl<float4, float4, float4>;
|
||||
template [[host_name("kernel_unary_f16_f16")]] kernel kernel_unary_t kernel_unary_impl<half, half, float>;
|
||||
template [[host_name("kernel_unary_f16_f16_4")]] kernel kernel_unary_t kernel_unary_impl<half4, half4, float4>;
|
||||
|
||||
template<typename T>
|
||||
kernel void kernel_reglu(
|
||||
constant ggml_metal_kargs_glu & args,
|
||||
device const char * src0,
|
||||
device const char * src1,
|
||||
device char * dst,
|
||||
uint tgpig[[threadgroup_position_in_grid]],
|
||||
uint tpitg[[thread_position_in_threadgroup]],
|
||||
uint ntg[[threads_per_threadgroup]]) {
|
||||
device const T * src0_row = (device const T *) ((device const char *) src0 + tgpig*args.nb01) + args.i00;
|
||||
device const T * src1_row = (device const T *) ((device const char *) src1 + tgpig*args.nb11) + args.i10;
|
||||
device T * dst_row = (device T *) ((device char *) dst + tgpig*args.nb1);
|
||||
|
||||
for (int i0 = tpitg; i0 < args.ne0; i0 += ntg) {
|
||||
const float x0 = src0_row[i0];
|
||||
const float x1 = src1_row[i0];
|
||||
|
||||
dst_row[i0] = (T)(x0*x1*(x0 > 0.0f));
|
||||
}
|
||||
}
|
||||
|
||||
typedef decltype(kernel_reglu<float>) kernel_reglu_t;
|
||||
|
||||
template [[host_name("kernel_reglu_f32")]] kernel kernel_reglu_t kernel_reglu<float>;
|
||||
template [[host_name("kernel_reglu_f16")]] kernel kernel_reglu_t kernel_reglu<half>;
|
||||
|
||||
template<typename T>
|
||||
kernel void kernel_geglu(
|
||||
constant ggml_metal_kargs_glu & args,
|
||||
device const char * src0,
|
||||
device const char * src1,
|
||||
device char * dst,
|
||||
uint tgpig[[threadgroup_position_in_grid]],
|
||||
uint tpitg[[thread_position_in_threadgroup]],
|
||||
uint ntg[[threads_per_threadgroup]]) {
|
||||
device const T * src0_row = (device const T *) ((device const char *) src0 + tgpig*args.nb01) + args.i00;
|
||||
device const T * src1_row = (device const T *) ((device const char *) src1 + tgpig*args.nb11) + args.i10;
|
||||
device T * dst_row = (device T *) ((device char *) dst + tgpig*args.nb1);
|
||||
|
||||
for (int i0 = tpitg; i0 < args.ne0; i0 += ntg) {
|
||||
const float x0 = src0_row[i0];
|
||||
const float x1 = src1_row[i0];
|
||||
|
||||
const float gelu = 0.5f*x0*(1.0f + precise::tanh(SQRT_2_OVER_PI*x0*(1.0f + GELU_COEF_A*x0*x0)));
|
||||
|
||||
dst_row[i0] = (T)(gelu*x1);
|
||||
}
|
||||
}
|
||||
|
||||
typedef decltype(kernel_geglu<float>) kernel_geglu_t;
|
||||
|
||||
template [[host_name("kernel_geglu_f32")]] kernel kernel_geglu_t kernel_geglu<float>;
|
||||
template [[host_name("kernel_geglu_f16")]] kernel kernel_geglu_t kernel_geglu<half>;
|
||||
|
||||
template<typename T>
|
||||
kernel void kernel_swiglu(
|
||||
constant ggml_metal_kargs_glu & args,
|
||||
device const char * src0,
|
||||
device const char * src1,
|
||||
device char * dst,
|
||||
uint tgpig[[threadgroup_position_in_grid]],
|
||||
uint tpitg[[thread_position_in_threadgroup]],
|
||||
uint ntg[[threads_per_threadgroup]]) {
|
||||
device const T * src0_row = (device const T *) ((device const char *) src0 + tgpig*args.nb01) + args.i00;
|
||||
device const T * src1_row = (device const T *) ((device const char *) src1 + tgpig*args.nb11) + args.i10;
|
||||
device T * dst_row = (device T *) ((device char *) dst + tgpig*args.nb1);
|
||||
|
||||
for (int i0 = tpitg; i0 < args.ne0; i0 += ntg) {
|
||||
const float x0 = src0_row[i0];
|
||||
const float x1 = src1_row[i0];
|
||||
|
||||
const float silu = x0 / (1.0f + exp(-x0));
|
||||
|
||||
dst_row[i0] = (T)(silu*x1);
|
||||
}
|
||||
}
|
||||
|
||||
typedef decltype(kernel_swiglu<float>) kernel_swiglu_t;
|
||||
|
||||
template [[host_name("kernel_swiglu_f32")]] kernel kernel_swiglu_t kernel_swiglu<float>;
|
||||
template [[host_name("kernel_swiglu_f16")]] kernel kernel_swiglu_t kernel_swiglu<half>;
|
||||
|
||||
template<typename T>
|
||||
kernel void kernel_swiglu_oai(
|
||||
constant ggml_metal_kargs_glu & args,
|
||||
device const char * src0,
|
||||
device const char * src1,
|
||||
device char * dst,
|
||||
uint tgpig[[threadgroup_position_in_grid]],
|
||||
uint tpitg[[thread_position_in_threadgroup]],
|
||||
uint ntg[[threads_per_threadgroup]]) {
|
||||
device const T * src0_row = (device const T *) ((device const char *) src0 + tgpig*args.nb01) + args.i00;
|
||||
device const T * src1_row = (device const T *) ((device const char *) src1 + tgpig*args.nb11) + args.i10;
|
||||
device T * dst_row = (device T *) ((device char *) dst + tgpig*args.nb1);
|
||||
|
||||
for (int i0 = tpitg; i0 < args.ne0; i0 += ntg) {
|
||||
float x0 = src0_row[i0];
|
||||
float x1 = src1_row[i0];
|
||||
|
||||
x0 = min(x0, args.limit);
|
||||
x1 = max(min(x1, args.limit), -args.limit);
|
||||
|
||||
float out_glu = x0 / (1.0f + exp(-x0 * args.alpha));
|
||||
out_glu = out_glu * (1.0f + x1);
|
||||
|
||||
dst_row[i0] = (T)out_glu;
|
||||
}
|
||||
}
|
||||
|
||||
typedef decltype(kernel_swiglu_oai<float>) kernel_swiglu_oai_t;
|
||||
|
||||
template [[host_name("kernel_swiglu_oai_f32")]] kernel kernel_swiglu_oai_t kernel_swiglu_oai<float>;
|
||||
template [[host_name("kernel_swiglu_oai_f16")]] kernel kernel_swiglu_oai_t kernel_swiglu_oai<half>;
|
||||
|
||||
template<typename T>
|
||||
kernel void kernel_geglu_erf(
|
||||
constant ggml_metal_kargs_glu & args,
|
||||
device const char * src0,
|
||||
device const char * src1,
|
||||
device char * dst,
|
||||
uint tgpig[[threadgroup_position_in_grid]],
|
||||
uint tpitg[[thread_position_in_threadgroup]],
|
||||
uint ntg[[threads_per_threadgroup]]) {
|
||||
device const T * src0_row = (device const T *) ((device const char *) src0 + tgpig*args.nb01) + args.i00;
|
||||
device const T * src1_row = (device const T *) ((device const char *) src1 + tgpig*args.nb11) + args.i10;
|
||||
device T * dst_row = (device T *) ((device char *) dst + tgpig*args.nb1);
|
||||
|
||||
for (int i0 = tpitg; i0 < args.ne0; i0 += ntg) {
|
||||
const float x0 = src0_row[i0];
|
||||
const float x1 = src1_row[i0];
|
||||
|
||||
const float gelu_erf = 0.5f*x0*(1.0f+erf_approx<float>(x0*SQRT_2_INV));
|
||||
|
||||
dst_row[i0] = (T)(gelu_erf*x1);
|
||||
}
|
||||
}
|
||||
|
||||
typedef decltype(kernel_geglu_erf<float>) kernel_geglu_erf_t;
|
||||
|
||||
template [[host_name("kernel_geglu_erf_f32")]] kernel kernel_geglu_erf_t kernel_geglu_erf<float>;
|
||||
template [[host_name("kernel_geglu_erf_f16")]] kernel kernel_geglu_erf_t kernel_geglu_erf<half>;
|
||||
|
||||
template<typename T>
|
||||
kernel void kernel_geglu_quick(
|
||||
constant ggml_metal_kargs_glu & args,
|
||||
device const char * src0,
|
||||
device const char * src1,
|
||||
device char * dst,
|
||||
uint tgpig[[threadgroup_position_in_grid]],
|
||||
uint tpitg[[thread_position_in_threadgroup]],
|
||||
uint ntg[[threads_per_threadgroup]]) {
|
||||
device const T * src0_row = (device const T *) ((device const char *) src0 + tgpig*args.nb01) + args.i00;
|
||||
device const T * src1_row = (device const T *) ((device const char *) src1 + tgpig*args.nb11) + args.i10;
|
||||
device T * dst_row = (device T *) ((device char *) dst + tgpig*args.nb1);
|
||||
|
||||
for (int i0 = tpitg; i0 < args.ne0; i0 += ntg) {
|
||||
const float x0 = src0_row[i0];
|
||||
const float x1 = src1_row[i0];
|
||||
|
||||
const float gelu_quick = x0*(1.0f/(1.0f+exp(GELU_QUICK_COEF*x0)));
|
||||
|
||||
dst_row[i0] = (T)(gelu_quick*x1);
|
||||
}
|
||||
}
|
||||
|
||||
typedef decltype(kernel_geglu_quick<float>) kernel_geglu_quick_t;
|
||||
|
||||
template [[host_name("kernel_geglu_quick_f32")]] kernel kernel_geglu_quick_t kernel_geglu_quick<float>;
|
||||
template [[host_name("kernel_geglu_quick_f16")]] kernel kernel_geglu_quick_t kernel_geglu_quick<half>;
|
||||
@@ -1,179 +0,0 @@
|
||||
#include "common.h"
|
||||
|
||||
constant bool FC_upscale_aa [[function_constant(FC_UPSCALE + 0)]];
|
||||
|
||||
kernel void kernel_upscale_nearest_f32(
|
||||
constant ggml_metal_kargs_upscale & args,
|
||||
device const char * src0,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tpitg[[thread_position_in_threadgroup]],
|
||||
uint3 ntg[[threads_per_threadgroup]]) {
|
||||
|
||||
const int64_t i3 = tgpig.z;
|
||||
const int64_t i2 = tgpig.y;
|
||||
const int64_t i1 = tgpig.x;
|
||||
|
||||
const int64_t i03 = i3/args.sf3;
|
||||
const int64_t i02 = i2/args.sf2;
|
||||
const int64_t i01 = i1/args.sf1;
|
||||
|
||||
for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
|
||||
const int64_t i00 = i0/args.sf0;
|
||||
|
||||
device const float * src0_ptr = (device const float *) (src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + i00*args.nb00);
|
||||
device float * dst_ptr = (device float *) (dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0);
|
||||
|
||||
dst_ptr[0] = src0_ptr[0];
|
||||
}
|
||||
}
|
||||
|
||||
static inline float bilinear_tri(float x) {
|
||||
return MAX(0.0f, 1.0f - fabs(x));
|
||||
}
|
||||
|
||||
kernel void kernel_upscale_bilinear_f32(
|
||||
constant ggml_metal_kargs_upscale & args,
|
||||
device const char * src0,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tpitg[[thread_position_in_threadgroup]],
|
||||
uint3 ntg[[threads_per_threadgroup]]) {
|
||||
|
||||
const int64_t i3 = tgpig.z;
|
||||
const int64_t i2 = tgpig.y;
|
||||
const int64_t i1 = tgpig.x;
|
||||
|
||||
const int64_t i03 = i3 / args.sf3;
|
||||
const int64_t i02 = i2 / args.sf2;
|
||||
|
||||
const float f01 = ((float)i1 + args.poffs) / args.sf1 - args.poffs;
|
||||
const int64_t i01 = MAX(0, MIN(args.ne01 - 1, (int64_t)floor(f01)));
|
||||
const int64_t i01p = MAX(0, MIN(args.ne01 - 1, i01 + 1));
|
||||
const float fd1 = MAX(0.0f, MIN(1.0f, f01 - (float)i01));
|
||||
|
||||
src0 += i03*args.nb03 + i02*args.nb02;
|
||||
|
||||
device float * dst_ptr = (device float *)(dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1);
|
||||
|
||||
if (FC_upscale_aa) {
|
||||
const float support0 = MAX(1.0f, 1.0f / args.sf0);
|
||||
const float invscale0 = 1.0f / support0;
|
||||
const float support1 = MAX(1.0f, 1.0f / args.sf1);
|
||||
const float invscale1 = 1.0f / support1;
|
||||
|
||||
for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
|
||||
const float f00 = ((float)i0 + args.poffs) / args.sf0 - args.poffs;
|
||||
|
||||
int64_t x_min = MAX((int64_t)0, (int64_t)floor(f00 - support0 + args.poffs));
|
||||
int64_t x_max = MIN(args.ne00, (int64_t)ceil (f00 + support0 + args.poffs));
|
||||
|
||||
int64_t y_min = MAX((int64_t)0, (int64_t)floor(f01 - support1 + args.poffs));
|
||||
int64_t y_max = MIN(args.ne01, (int64_t)ceil (f01 + support1 + args.poffs));
|
||||
|
||||
float sum = 0.0f;
|
||||
float wsum = 0.0f;
|
||||
|
||||
for (int64_t sy = y_min; sy < y_max; ++sy) {
|
||||
const float wy = MAX(0.0f, 1.0f - fabs((float)sy - f01) * invscale1);
|
||||
for (int64_t sx = x_min; sx < x_max; ++sx) {
|
||||
const float wx = MAX(0.0f, 1.0f - fabs((float)sx - f00) * invscale0);
|
||||
const float w = wx * wy;
|
||||
device const float * src_ptr = (device const float *)(src0 + sy*args.nb01 + sx*args.nb00);
|
||||
sum += (*src_ptr) * w;
|
||||
wsum += w;
|
||||
}
|
||||
}
|
||||
|
||||
const float v = (wsum > 0.0f) ? (sum / wsum) : 0.0f;
|
||||
dst_ptr[i0] = v;
|
||||
}
|
||||
} else {
|
||||
for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
|
||||
const float f00 = ((float)i0 + args.poffs) / args.sf0 - args.poffs;
|
||||
const int64_t i00 = MAX(0, MIN(args.ne00 - 1, (int64_t)floor(f00)));
|
||||
const int64_t i00p = MAX(0, MIN(args.ne00 - 1, i00 + 1));
|
||||
const float fd0 = MAX(0.0f, MIN(1.0f, f00 - (float)i00));
|
||||
|
||||
device const float * src00 = (device const float *)(src0 + i01*args.nb01 + i00*args.nb00);
|
||||
device const float * src10 = (device const float *)(src0 + i01*args.nb01 + i00p*args.nb00);
|
||||
device const float * src01 = (device const float *)(src0 + i01p*args.nb01 + i00*args.nb00);
|
||||
device const float * src11 = (device const float *)(src0 + i01p*args.nb01 + i00p*args.nb00);
|
||||
|
||||
const float v =
|
||||
(*src00) * (1.0f - fd0) * (1.0f - fd1) +
|
||||
(*src10) * fd0 * (1.0f - fd1) +
|
||||
(*src01) * (1.0f - fd0) * fd1 +
|
||||
(*src11) * fd0 * fd1;
|
||||
|
||||
dst_ptr[i0] = v;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
static inline float bicubic_weight1(float x) {
|
||||
const float a = -0.75f;
|
||||
return ((a + 2) * x - (a + 3)) * x * x + 1;
|
||||
}
|
||||
|
||||
static inline float bicubic_weight2(float x) {
|
||||
const float a = -0.75f;
|
||||
return ((a * x - 5 * a) * x + 8 * a) * x - 4 * a;
|
||||
}
|
||||
|
||||
kernel void kernel_upscale_bicubic_f32(
|
||||
constant ggml_metal_kargs_upscale & args,
|
||||
device const char * src0,
|
||||
device char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tpitg[[thread_position_in_threadgroup]],
|
||||
uint3 ntg[[threads_per_threadgroup]]) {
|
||||
|
||||
const int64_t i3 = tgpig.z;
|
||||
const int64_t i2 = tgpig.y;
|
||||
const int64_t i1 = tgpig.x;
|
||||
|
||||
const int64_t i03 = i3 / args.sf3;
|
||||
const int64_t i02 = i2 / args.sf2;
|
||||
|
||||
const float f01 = ((float)i1 + args.poffs) / args.sf1 - args.poffs;
|
||||
const int64_t i01 = (int64_t)floor(f01);
|
||||
const float fd1 = f01 - (float)i01;
|
||||
|
||||
const float w_y0 = bicubic_weight2(fd1 + 1.0f);
|
||||
const float w_y1 = bicubic_weight1(fd1);
|
||||
const float w_y2 = bicubic_weight1(1.0f - fd1);
|
||||
const float w_y3 = bicubic_weight2(2.0f - fd1);
|
||||
|
||||
const device const char * src_slice = src0 + i03 * args.nb03 + i02 * args.nb02;
|
||||
|
||||
device float * dst_ptr = (device float *)(dst + i3 * args.nb3 + i2 * args.nb2 + i1 * args.nb1);
|
||||
|
||||
for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
|
||||
const float f00 = ((float)i0 + args.poffs) / args.sf0 - args.poffs;
|
||||
const int64_t i00 = (int64_t)floor(f00);
|
||||
const float fd0 = f00 - (float)i00;
|
||||
|
||||
const float w_x0 = bicubic_weight2(fd0 + 1.0f);
|
||||
const float w_x1 = bicubic_weight1(fd0);
|
||||
const float w_x2 = bicubic_weight1(1.0f - fd0);
|
||||
const float w_x3 = bicubic_weight2(2.0f - fd0);
|
||||
|
||||
float sum = 0.0f;
|
||||
|
||||
for (int dy = -1; dy <= 2; ++dy) {
|
||||
const int64_t iy = MAX(0, MIN(args.ne01 - 1, i01 + dy));
|
||||
const float wy = (dy == -1) ? w_y0 : (dy == 0) ? w_y1 : (dy == 1) ? w_y2 : w_y3;
|
||||
|
||||
for (int dx = -1; dx <= 2; ++dx) {
|
||||
const int64_t ix = MAX(0, MIN(args.ne00 - 1, i00 + dx));
|
||||
const float wx = (dx == -1) ? w_x0 : (dx == 0) ? w_x1 : (dx == 1) ? w_x2 : w_x3;
|
||||
|
||||
device const float * src_ptr = (device const float *)(src_slice + iy * args.nb01 + ix * args.nb00);
|
||||
sum += (*src_ptr) * wx * wy;
|
||||
}
|
||||
}
|
||||
|
||||
dst_ptr[i0] = sum;
|
||||
}
|
||||
}
|
||||
@@ -1,179 +0,0 @@
|
||||
#include "common.h"
|
||||
|
||||
kernel void kernel_rwkv_wkv6_f32(
|
||||
device const float * k,
|
||||
device const float * v,
|
||||
device const float * r,
|
||||
device const float * tf,
|
||||
device const float * td,
|
||||
device const float * state_in,
|
||||
device float * dst,
|
||||
constant uint & B,
|
||||
constant uint & T,
|
||||
constant uint & C,
|
||||
constant uint & H,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tpitg[[thread_position_in_threadgroup]],
|
||||
uint3 ntg[[threads_per_threadgroup]]) {
|
||||
|
||||
const uint head_size = 64; // TODO: support head_size = 128
|
||||
const uint batch_id = tgpig.x / H;
|
||||
const uint head_id = tgpig.x % H;
|
||||
const uint tid = tpitg.x;
|
||||
|
||||
if (batch_id >= B || head_id >= H) {
|
||||
return;
|
||||
}
|
||||
|
||||
const uint state_size = C * head_size;
|
||||
const uint n_seq_tokens = T / B;
|
||||
|
||||
threadgroup float _k[head_size];
|
||||
threadgroup float _r[head_size];
|
||||
threadgroup float _tf[head_size];
|
||||
threadgroup float _td[head_size];
|
||||
|
||||
float state[head_size];
|
||||
|
||||
for (uint i = 0; i < head_size; i++) {
|
||||
state[i] = state_in[batch_id * state_size + head_id * head_size * head_size
|
||||
+ i * head_size + tid];
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
_tf[tid] = tf[head_id * head_size + tid];
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
const uint start_t = batch_id * n_seq_tokens * C + head_id * head_size + tid;
|
||||
const uint end_t = (batch_id + 1) * n_seq_tokens * C + head_id * head_size + tid;
|
||||
|
||||
for (uint t = start_t; t < end_t; t += C) {
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
_k[tid] = k[t];
|
||||
_r[tid] = r[t];
|
||||
_td[tid] = td[t];
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
const float v_val = v[t];
|
||||
float y = 0.0;
|
||||
|
||||
for (uint j = 0; j < head_size; j += 4) {
|
||||
float4 k_vec = float4(_k[j], _k[j+1], _k[j+2], _k[j+3]);
|
||||
float4 r_vec = float4(_r[j], _r[j+1], _r[j+2], _r[j+3]);
|
||||
float4 tf_vec = float4(_tf[j], _tf[j+1], _tf[j+2], _tf[j+3]);
|
||||
float4 td_vec = float4(_td[j], _td[j+1], _td[j+2], _td[j+3]);
|
||||
float4 s_vec = float4(state[j], state[j+1], state[j+2], state[j+3]);
|
||||
|
||||
float4 kv = k_vec * v_val;
|
||||
|
||||
float4 temp = tf_vec * kv + s_vec;
|
||||
y += dot(r_vec, temp);
|
||||
|
||||
s_vec = s_vec * td_vec + kv;
|
||||
state[j] = s_vec[0];
|
||||
state[j+1] = s_vec[1];
|
||||
state[j+2] = s_vec[2];
|
||||
state[j+3] = s_vec[3];
|
||||
}
|
||||
|
||||
dst[t] = y;
|
||||
}
|
||||
|
||||
for (uint i = 0; i < head_size; i++) {
|
||||
dst[T * C + batch_id * state_size + head_id * head_size * head_size
|
||||
+ i * head_size + tid] = state[i];
|
||||
}
|
||||
}
|
||||
|
||||
kernel void kernel_rwkv_wkv7_f32(
|
||||
device const float * r,
|
||||
device const float * w,
|
||||
device const float * k,
|
||||
device const float * v,
|
||||
device const float * a,
|
||||
device const float * b,
|
||||
device const float * state_in,
|
||||
device float * dst,
|
||||
constant uint & B,
|
||||
constant uint & T,
|
||||
constant uint & C,
|
||||
constant uint & H,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tpitg[[thread_position_in_threadgroup]],
|
||||
uint3 ntg[[threads_per_threadgroup]]) {
|
||||
|
||||
const uint head_size = 64; // TODO: support head_size = 128
|
||||
const uint batch_id = tgpig.x / H;
|
||||
const uint head_id = tgpig.x % H;
|
||||
const uint tid = tpitg.x;
|
||||
|
||||
if (batch_id >= B || head_id >= H) {
|
||||
return;
|
||||
}
|
||||
|
||||
const uint state_size = C * head_size;
|
||||
const uint n_seq_tokens = T / B;
|
||||
|
||||
threadgroup float _r[head_size];
|
||||
threadgroup float _w[head_size];
|
||||
threadgroup float _k[head_size];
|
||||
threadgroup float _a[head_size];
|
||||
threadgroup float _b[head_size];
|
||||
|
||||
float state[head_size];
|
||||
|
||||
for (uint i = 0; i < head_size; i++) {
|
||||
state[i] = state_in[batch_id * state_size + head_id * head_size * head_size
|
||||
+ tid * head_size + i];
|
||||
}
|
||||
|
||||
const uint start_t = batch_id * n_seq_tokens * C + head_id * head_size + tid;
|
||||
const uint end_t = (batch_id + 1) * n_seq_tokens * C + head_id * head_size + tid;
|
||||
|
||||
for (uint t = start_t; t < end_t; t += C) {
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
_r[tid] = r[t];
|
||||
_w[tid] = w[t];
|
||||
_k[tid] = k[t];
|
||||
_a[tid] = a[t];
|
||||
_b[tid] = b[t];
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
const float v_val = v[t];
|
||||
float y = 0.0, sa = 0.0;
|
||||
|
||||
float4 sa_vec(0.0);
|
||||
|
||||
for (uint j = 0; j < head_size; j += 4) {
|
||||
float4 a_vec = float4(_a[j], _a[j+1], _a[j+2], _a[j+3]);
|
||||
float4 s_vec = float4(state[j], state[j+1], state[j+2], state[j+3]);
|
||||
sa_vec += a_vec * s_vec;
|
||||
}
|
||||
sa = sa_vec[0] + sa_vec[1] + sa_vec[2] + sa_vec[3];
|
||||
|
||||
for (uint j = 0; j < head_size; j += 4) {
|
||||
float4 r_vec = float4(_r[j], _r[j+1], _r[j+2], _r[j+3]);
|
||||
float4 w_vec = float4(_w[j], _w[j+1], _w[j+2], _w[j+3]);
|
||||
float4 k_vec = float4(_k[j], _k[j+1], _k[j+2], _k[j+3]);
|
||||
float4 b_vec = float4(_b[j], _b[j+1], _b[j+2], _b[j+3]);
|
||||
float4 s_vec = float4(state[j], state[j+1], state[j+2], state[j+3]);
|
||||
|
||||
float4 kv = k_vec * v_val;
|
||||
|
||||
s_vec = s_vec * w_vec + kv + sa * b_vec;
|
||||
y += dot(s_vec, r_vec);
|
||||
|
||||
state[j] = s_vec[0];
|
||||
state[j+1] = s_vec[1];
|
||||
state[j+2] = s_vec[2];
|
||||
state[j+3] = s_vec[3];
|
||||
}
|
||||
|
||||
dst[t] = y;
|
||||
}
|
||||
|
||||
for (uint i = 0; i < head_size; i++) {
|
||||
dst[T * C + batch_id * state_size + head_id * head_size * head_size
|
||||
+ tid * head_size + i] = state[i];
|
||||
}
|
||||
}
|
||||
@@ -60,7 +60,19 @@ if (Vulkan_FOUND)
|
||||
message(STATUS "Vulkan found")
|
||||
|
||||
ggml_add_backend_library(ggml-vulkan
|
||||
ggml-vulkan.cpp
|
||||
ggml-vulkan-instance.cpp
|
||||
ggml-vulkan-shaders.cpp
|
||||
ggml-vulkan-buffers.cpp
|
||||
ggml-vulkan-pipelines.cpp
|
||||
ggml-vulkan-matmul.cpp
|
||||
ggml-vulkan-flash-attn.cpp
|
||||
ggml-vulkan-operators.cpp
|
||||
ggml-vulkan-graph.cpp
|
||||
ggml-vulkan-backend.cpp
|
||||
ggml-vulkan-debug.cpp
|
||||
ggml-vulkan-types.h
|
||||
ggml-vulkan-push-constants.h
|
||||
ggml-vulkan-common.h
|
||||
../../include/ggml-vulkan.h
|
||||
)
|
||||
|
||||
|
||||
1867
ggml/src/ggml-vulkan/ggml-vulkan-backend.cpp
Normal file
1867
ggml/src/ggml-vulkan/ggml-vulkan-backend.cpp
Normal file
File diff suppressed because it is too large
Load Diff
783
ggml/src/ggml-vulkan/ggml-vulkan-buffers.cpp
Normal file
783
ggml/src/ggml-vulkan/ggml-vulkan-buffers.cpp
Normal file
@@ -0,0 +1,783 @@
|
||||
#include "ggml-vulkan-common.h"
|
||||
|
||||
ggml_backend_buffer_type_i ggml_backend_vk_buffer_type_interface = {
|
||||
/* .get_name = */ ggml_backend_vk_buffer_type_name,
|
||||
/* .alloc_buffer = */ ggml_backend_vk_buffer_type_alloc_buffer,
|
||||
/* .get_alignment = */ ggml_backend_vk_buffer_type_get_alignment,
|
||||
/* .get_max_size = */ ggml_backend_vk_buffer_type_get_max_size,
|
||||
/* .get_alloc_size = */ ggml_backend_vk_buffer_type_get_alloc_size,
|
||||
/* .is_host = */ NULL,
|
||||
};
|
||||
|
||||
static std::vector<uint32_t> ggml_vk_find_memory_properties(const vk::PhysicalDeviceMemoryProperties* mem_props, vk::MemoryRequirements* mem_req, vk::MemoryPropertyFlags flags) {
|
||||
std::vector<uint32_t> indices;
|
||||
|
||||
for (uint32_t i = 0; i < mem_props->memoryTypeCount; ++i) {
|
||||
vk::MemoryType memory_type = mem_props->memoryTypes[i];
|
||||
if ((mem_req->memoryTypeBits & ((uint64_t)1 << i)) &&
|
||||
(flags & memory_type.propertyFlags) == flags &&
|
||||
mem_props->memoryHeaps[memory_type.heapIndex].size >= mem_req->size) {
|
||||
indices.push_back(i);
|
||||
}
|
||||
}
|
||||
return indices;
|
||||
}
|
||||
|
||||
static vk_buffer ggml_vk_create_buffer(vk_device& device, size_t size, const std::initializer_list<vk::MemoryPropertyFlags> & req_flags_list,
|
||||
void *import_ptr = nullptr) {
|
||||
VK_LOG_DEBUG("ggml_vk_create_buffer(" << device->name << ", " << size << ", " << to_string(req_flags_list.begin()[0]) << ", " << to_string(req_flags_list.begin()[req_flags_list.size()-1]) << ")");
|
||||
if (size > device->max_buffer_size) {
|
||||
throw vk::OutOfDeviceMemoryError("Requested buffer size exceeds device buffer size limit");
|
||||
}
|
||||
|
||||
vk_buffer buf = std::make_shared<vk_buffer_struct>();
|
||||
|
||||
if (size == 0) {
|
||||
buf->size = 0;
|
||||
return buf;
|
||||
}
|
||||
|
||||
vk::BufferUsageFlags usage_flags = vk::BufferUsageFlagBits::eStorageBuffer | vk::BufferUsageFlagBits::eTransferSrc | vk::BufferUsageFlagBits::eTransferDst;
|
||||
vk::MemoryAllocateFlags mem_flags {};
|
||||
if (device->buffer_device_address) {
|
||||
usage_flags |= vk::BufferUsageFlagBits::eShaderDeviceAddress;
|
||||
mem_flags |= vk::MemoryAllocateFlagBits::eDeviceAddress;
|
||||
}
|
||||
|
||||
vk::BufferCreateInfo buffer_create_info{
|
||||
vk::BufferCreateFlags(),
|
||||
size,
|
||||
usage_flags,
|
||||
vk::SharingMode::eExclusive,
|
||||
0,
|
||||
nullptr,
|
||||
};
|
||||
|
||||
vk::ExternalMemoryBufferCreateInfo external_memory_bci;
|
||||
if (import_ptr) {
|
||||
external_memory_bci.handleTypes = vk::ExternalMemoryHandleTypeFlagBits::eHostAllocationEXT;
|
||||
buffer_create_info.setPNext(&external_memory_bci);
|
||||
}
|
||||
|
||||
buf->buffer = device->device.createBuffer(buffer_create_info);
|
||||
|
||||
vk::MemoryRequirements mem_req = device->device.getBufferMemoryRequirements(buf->buffer);
|
||||
|
||||
vk::PhysicalDeviceMemoryProperties mem_props = device->physical_device.getMemoryProperties();
|
||||
|
||||
const vk::MemoryPriorityAllocateInfoEXT mem_priority_info { 1.0f };
|
||||
|
||||
vk::MemoryAllocateFlagsInfo mem_flags_info { mem_flags };
|
||||
|
||||
if (device->memory_priority) {
|
||||
mem_flags_info.setPNext(&mem_priority_info);
|
||||
}
|
||||
|
||||
if (import_ptr) {
|
||||
vk::MemoryHostPointerPropertiesEXT host_pointer_props;
|
||||
try {
|
||||
host_pointer_props = device->device.getMemoryHostPointerPropertiesEXT(vk::ExternalMemoryHandleTypeFlagBits::eHostAllocationEXT, import_ptr);
|
||||
} catch (vk::SystemError& e) {
|
||||
GGML_LOG_WARN("ggml_vulkan: Failed getMemoryHostPointerPropertiesEXT (%s)\n", e.what());
|
||||
device->device.destroyBuffer(buf->buffer);
|
||||
return {};
|
||||
}
|
||||
vk::PhysicalDeviceMemoryProperties mem_props = device->physical_device.getMemoryProperties();
|
||||
|
||||
uint32_t memory_type_idx;
|
||||
vk::MemoryPropertyFlags property_flags = *req_flags_list.begin();
|
||||
for (memory_type_idx = 0; memory_type_idx < 32; ++memory_type_idx) {
|
||||
if (!(host_pointer_props.memoryTypeBits & (1u << memory_type_idx))) {
|
||||
continue;
|
||||
}
|
||||
if (!(mem_req.memoryTypeBits & (1u << memory_type_idx))) {
|
||||
continue;
|
||||
}
|
||||
|
||||
vk::MemoryType memory_type = mem_props.memoryTypes[memory_type_idx];
|
||||
// check for visible+coherent+cached. Other flags (e.g. devicelocal) are allowed
|
||||
if ((memory_type.propertyFlags & property_flags) == property_flags) {
|
||||
property_flags = memory_type.propertyFlags;
|
||||
break;
|
||||
}
|
||||
}
|
||||
if (memory_type_idx == 32) {
|
||||
GGML_LOG_WARN("ggml_vulkan: Memory type for host allocation not found\n");
|
||||
device->device.destroyBuffer(buf->buffer);
|
||||
return {};
|
||||
}
|
||||
|
||||
buf->memory_property_flags = mem_props.memoryTypes[memory_type_idx].propertyFlags;
|
||||
try {
|
||||
vk::ImportMemoryHostPointerInfoEXT import_info;
|
||||
import_info.handleType = vk::ExternalMemoryHandleTypeFlagBits::eHostAllocationEXT;
|
||||
import_info.pHostPointer = import_ptr;
|
||||
import_info.setPNext(&mem_flags_info);
|
||||
buf->device_memory = device->device.allocateMemory({ size, memory_type_idx, &import_info });
|
||||
} catch (const vk::SystemError& e) {
|
||||
}
|
||||
} else {
|
||||
for (auto it = req_flags_list.begin(); it != req_flags_list.end(); it++) {
|
||||
const auto & req_flags = *it;
|
||||
|
||||
const std::vector<uint32_t> memory_type_indices = ggml_vk_find_memory_properties(&mem_props, &mem_req, req_flags);
|
||||
|
||||
if (memory_type_indices.empty()) {
|
||||
continue;
|
||||
}
|
||||
|
||||
bool done = false;
|
||||
|
||||
for (auto mtype_it = memory_type_indices.begin(); mtype_it != memory_type_indices.end(); mtype_it++) {
|
||||
try {
|
||||
buf->device_memory = device->device.allocateMemory({ mem_req.size, *mtype_it, &mem_flags_info });
|
||||
buf->memory_property_flags = mem_props.memoryTypes[*mtype_it].propertyFlags;
|
||||
done = true;
|
||||
break;
|
||||
} catch (const vk::SystemError& e) {
|
||||
// loop and retry
|
||||
// during last attempt throw the exception
|
||||
if (it + 1 == req_flags_list.end() && mtype_it + 1 == memory_type_indices.end()) {
|
||||
device->device.destroyBuffer(buf->buffer);
|
||||
throw e;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (done) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (!buf->device_memory) {
|
||||
device->device.destroyBuffer(buf->buffer);
|
||||
throw vk::OutOfDeviceMemoryError("No suitable memory type found");
|
||||
}
|
||||
|
||||
buf->ptr = nullptr;
|
||||
|
||||
if (import_ptr) {
|
||||
buf->ptr = import_ptr;
|
||||
} else {
|
||||
if (buf->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) {
|
||||
buf->ptr = device->device.mapMemory(buf->device_memory, 0, VK_WHOLE_SIZE);
|
||||
}
|
||||
}
|
||||
|
||||
device->device.bindBufferMemory(buf->buffer, buf->device_memory, 0);
|
||||
|
||||
buf->device = device;
|
||||
buf->size = size;
|
||||
|
||||
if (device->buffer_device_address) {
|
||||
const vk::BufferDeviceAddressInfo addressInfo(buf->buffer);
|
||||
buf->bda_addr = device->device.getBufferAddress(addressInfo);
|
||||
}
|
||||
|
||||
device->memory_logger->log_allocation(buf, size);
|
||||
|
||||
return buf;
|
||||
}
|
||||
|
||||
vk_buffer ggml_vk_create_buffer_check(vk_device& device, size_t size, vk::MemoryPropertyFlags req_flags, vk::MemoryPropertyFlags fallback_flags) {
|
||||
try {
|
||||
return ggml_vk_create_buffer(device, size, {req_flags, fallback_flags});
|
||||
} catch (const vk::SystemError& e) {
|
||||
std::cerr << "ggml_vulkan: Memory allocation of size " << size << " failed." << std::endl;
|
||||
std::cerr << "ggml_vulkan: " << e.what() << std::endl;
|
||||
throw e;
|
||||
}
|
||||
}
|
||||
|
||||
vk_buffer ggml_vk_create_buffer_device(vk_device& device, size_t size) {
|
||||
vk_buffer buf;
|
||||
try {
|
||||
if (device->prefer_host_memory) {
|
||||
buf = ggml_vk_create_buffer(device, size, {vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent,
|
||||
vk::MemoryPropertyFlagBits::eDeviceLocal});
|
||||
} else if (device->uma) {
|
||||
// On UMA, prefer host-visible memory so direct tensor borrowing works.
|
||||
// If unavailable, fall back to device-local memory.
|
||||
buf = ggml_vk_create_buffer(device, size, {vk::MemoryPropertyFlagBits::eDeviceLocal | vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent,
|
||||
vk::MemoryPropertyFlagBits::eDeviceLocal,
|
||||
vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent});
|
||||
} else if (device->disable_host_visible_vidmem) {
|
||||
if (device->allow_sysmem_fallback) {
|
||||
buf = ggml_vk_create_buffer(device, size, {vk::MemoryPropertyFlagBits::eDeviceLocal,
|
||||
vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent});
|
||||
} else {
|
||||
buf = ggml_vk_create_buffer(device, size, {vk::MemoryPropertyFlagBits::eDeviceLocal});
|
||||
}
|
||||
} else {
|
||||
// use rebar if available, otherwise fallback to device only visible memory
|
||||
if (device->allow_sysmem_fallback) {
|
||||
buf = ggml_vk_create_buffer(device, size, {vk::MemoryPropertyFlagBits::eDeviceLocal | vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent,
|
||||
vk::MemoryPropertyFlagBits::eDeviceLocal,
|
||||
vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent});
|
||||
} else {
|
||||
buf = ggml_vk_create_buffer(device, size, {vk::MemoryPropertyFlagBits::eDeviceLocal | vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent,
|
||||
vk::MemoryPropertyFlagBits::eDeviceLocal});
|
||||
}
|
||||
}
|
||||
} catch (const vk::SystemError& e) {
|
||||
std::cerr << "ggml_vulkan: Device memory allocation of size " << size << " failed." << std::endl;
|
||||
std::cerr << "ggml_vulkan: " << e.what() << std::endl;
|
||||
throw e;
|
||||
}
|
||||
|
||||
return buf;
|
||||
}
|
||||
|
||||
void ggml_vk_destroy_buffer(vk_buffer& buf) {
|
||||
if (buf == nullptr) {
|
||||
return;
|
||||
}
|
||||
|
||||
if (buf->device != nullptr) {
|
||||
buf->device->memory_logger->log_deallocation(buf);
|
||||
}
|
||||
|
||||
buf.reset();
|
||||
}
|
||||
|
||||
void * ggml_vk_host_malloc(vk_device& device, size_t size) {
|
||||
VK_LOG_MEMORY("ggml_vk_host_malloc(" << size << ")");
|
||||
vk_buffer buf = ggml_vk_create_buffer(device, size,
|
||||
{vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached,
|
||||
vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent});
|
||||
|
||||
if(!(buf->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible)) {
|
||||
fprintf(stderr, "WARNING: failed to allocate %.2f MB of pinned memory\n",
|
||||
size/1024.0/1024.0);
|
||||
device->device.freeMemory(buf->device_memory);
|
||||
device->device.destroyBuffer(buf->buffer);
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
std::lock_guard<std::shared_mutex> guard(device->pinned_memory_mutex);
|
||||
device->pinned_memory.push_back(std::make_tuple(buf->ptr, size, buf));
|
||||
|
||||
return buf->ptr;
|
||||
}
|
||||
|
||||
void ggml_vk_host_free(vk_device& device, void* ptr) {
|
||||
if (ptr == nullptr) {
|
||||
return;
|
||||
}
|
||||
VK_LOG_MEMORY("ggml_vk_host_free(" << ptr << ")");
|
||||
std::lock_guard<std::shared_mutex> guard(device->pinned_memory_mutex);
|
||||
|
||||
vk_buffer buf;
|
||||
size_t index;
|
||||
for (size_t i = 0; i < device->pinned_memory.size(); i++) {
|
||||
const uint8_t* addr = (const uint8_t*) std::get<0>(device->pinned_memory[i]);
|
||||
const uint8_t* endr = addr + std::get<1>(device->pinned_memory[i]);
|
||||
if (ptr >= addr && ptr < endr) {
|
||||
buf = std::get<2>(device->pinned_memory[i]);
|
||||
index = i;
|
||||
break;
|
||||
}
|
||||
}
|
||||
if (buf == nullptr) {
|
||||
fprintf(stderr, "WARNING: failed to free pinned memory: memory not in map\n");
|
||||
return;
|
||||
}
|
||||
|
||||
ggml_vk_destroy_buffer(buf);
|
||||
|
||||
device->pinned_memory.erase(device->pinned_memory.begin() + index);
|
||||
}
|
||||
|
||||
void ggml_vk_host_get(const vk_device& device, const void * ptr, vk_buffer& buf, size_t& buf_offset) {
|
||||
std::shared_lock<std::shared_mutex> guard(device->pinned_memory_mutex);
|
||||
buf = nullptr;
|
||||
buf_offset = 0;
|
||||
for (size_t i = 0; i < device->pinned_memory.size(); i++) {
|
||||
const uint8_t* addr = (const uint8_t*) std::get<0>(device->pinned_memory[i]);
|
||||
const uint8_t* endr = addr + std::get<1>(device->pinned_memory[i]);
|
||||
if (ptr >= addr && ptr < endr) {
|
||||
buf = std::get<2>(device->pinned_memory[i]);
|
||||
buf_offset = ((const uint8_t *)ptr) - addr;
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void ggml_vk_ensure_sync_staging_buffer(vk_device& device, size_t size) {
|
||||
if (device->sync_staging == nullptr || device->sync_staging->size < size) {
|
||||
VK_LOG_MEMORY("ggml_vk_ensure_sync_staging_buffer(" << size << ")");
|
||||
ggml_vk_destroy_buffer(device->sync_staging);
|
||||
device->sync_staging = ggml_vk_create_buffer_check(device, size,
|
||||
vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached,
|
||||
vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
|
||||
}
|
||||
}
|
||||
|
||||
void ggml_vk_ensure_sync_staging_buffer(ggml_backend_vk_context * ctx, size_t size) {
|
||||
if (ctx->sync_staging == nullptr || ctx->sync_staging->size < size) {
|
||||
VK_LOG_MEMORY("ggml_vk_ensure_sync_staging_buffer(" << size << ")");
|
||||
ggml_vk_destroy_buffer(ctx->sync_staging);
|
||||
ctx->sync_staging = ggml_vk_create_buffer_check(ctx->device, size,
|
||||
vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached,
|
||||
vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
|
||||
}
|
||||
}
|
||||
|
||||
static void ggml_vk_buffer_write_nc_async(ggml_backend_vk_context * ctx, vk_context& subctx, vk_buffer& dst, size_t offset, const ggml_tensor * tensor, bool sync_staging = false) {
|
||||
VK_LOG_DEBUG("ggml_vk_buffer_write_nc_async(" << tensor << ")");
|
||||
GGML_ASSERT(!ggml_is_contiguous(tensor));
|
||||
// Buffer is already mapped
|
||||
if(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) {
|
||||
std::cerr << "ggml_vulkan: buffer_write_nc_async dst buffer is host_visible. Use synchronous write." << std::endl;
|
||||
GGML_ABORT("fatal error");
|
||||
}
|
||||
// Check if src is pinned memory
|
||||
vk_buffer buf = nullptr;
|
||||
size_t buf_offset = 0;
|
||||
ggml_vk_host_get(ctx->device, tensor->data, buf, buf_offset);
|
||||
|
||||
const uint64_t ne0 = tensor->ne[0];
|
||||
const uint64_t ne1 = tensor->ne[1];
|
||||
const uint64_t ne2 = tensor->ne[2];
|
||||
const uint64_t ne3 = tensor->ne[3];
|
||||
const uint64_t nb0 = tensor->nb[0];
|
||||
const uint64_t nb1 = tensor->nb[1];
|
||||
const uint64_t nb2 = tensor->nb[2];
|
||||
const uint64_t nb3 = tensor->nb[3];
|
||||
const ggml_type type = tensor->type;
|
||||
const uint64_t ts = ggml_type_size(type);
|
||||
const uint64_t bs = ggml_blck_size(type);
|
||||
|
||||
const uint64_t dstnb0 = ts;
|
||||
const uint64_t dstnb1 = dstnb0*(ne0/bs);
|
||||
const uint64_t dstnb2 = dstnb1*ne1;
|
||||
const uint64_t dstnb3 = dstnb2*ne2;
|
||||
|
||||
const uint64_t ne = ggml_nelements(tensor);
|
||||
|
||||
if (buf != nullptr) {
|
||||
// Memory is pinned, use as staging buffer
|
||||
std::vector<vk::BufferCopy> slices;
|
||||
|
||||
for (uint64_t i3 = 0; i3 < ne3; i3++) {
|
||||
for (uint64_t i2 = 0; i2 < ne2; i2++) {
|
||||
// Find longest contiguous slice
|
||||
if (ne1*nb1 == dstnb2) {
|
||||
slices.push_back({ buf_offset + i3*nb3 + i2*nb2, offset + i3*dstnb3 + i2*dstnb2, dstnb2 });
|
||||
} else {
|
||||
for (uint64_t i1 = 0; i1 < ne1; i1++) {
|
||||
if (ne0*nb0/bs == dstnb1) {
|
||||
slices.push_back({ buf_offset + i3*nb3 + i2*nb2 + i1*nb1, offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1, dstnb1 });
|
||||
} else {
|
||||
const uint64_t s_off = buf_offset + i3*nb3 + i2*nb2 + i1*nb1;
|
||||
const uint64_t d_off = offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1;
|
||||
for (uint64_t i0 = 0; i0 < ne0; i0++) {
|
||||
slices.push_back({ s_off + i0*nb0, d_off + i0*dstnb0, dstnb0 });
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
ggml_vk_sync_buffers(ctx, subctx);
|
||||
subctx->s->buffer->buf.copyBuffer(buf->buffer, dst->buffer, slices);
|
||||
return;
|
||||
}
|
||||
|
||||
if (!sync_staging) {
|
||||
GGML_ABORT("Asynchronous write to non-pinned memory not supported");
|
||||
}
|
||||
|
||||
// Staging buffer required
|
||||
vk_buffer& staging = ctx->device->sync_staging;
|
||||
const uint64_t copy_size = ts*ne/bs;
|
||||
ggml_vk_ensure_sync_staging_buffer(ctx->device, copy_size);
|
||||
VkBufferCopy buf_copy{ 0, offset, copy_size };
|
||||
|
||||
ggml_vk_sync_buffers(ctx, subctx);
|
||||
vkCmdCopyBuffer(subctx->s->buffer->buf, (VkBuffer)staging->buffer, (VkBuffer)dst->buffer, 1, &buf_copy);
|
||||
|
||||
for (uint64_t i3 = 0; i3 < ne3; i3++) {
|
||||
for (uint64_t i2 = 0; i2 < ne2; i2++) {
|
||||
// Find longest contiguous slice
|
||||
if (ne1*nb1 == dstnb2) {
|
||||
deferred_memcpy((uint8_t *)staging->ptr + i3*dstnb3 + i2*dstnb2, (const uint8_t *) tensor->data + buf_offset + i3*nb3 + i2*nb2, dstnb2, &subctx->in_memcpys);
|
||||
} else {
|
||||
for (uint64_t i1 = 0; i1 < ne1; i1++) {
|
||||
if (ne0*nb0/bs == dstnb1) {
|
||||
deferred_memcpy((uint8_t *)staging->ptr + i3*dstnb3 + i2*dstnb2 + i1*dstnb1, (const uint8_t *) tensor->data + buf_offset + i3*nb3 + i2*nb2 + i1*nb1, dstnb1, &subctx->in_memcpys);
|
||||
} else {
|
||||
const uint64_t s_off = buf_offset + i3*nb3 + i2*nb2 + i1*nb1;
|
||||
const uint64_t d_off = i3*dstnb3 + i2*dstnb2 + i1*dstnb1;
|
||||
for (uint64_t i0 = 0; i0 < ne0; i0++) {
|
||||
deferred_memcpy((uint8_t *)staging->ptr + d_off + i0*dstnb0, (const uint8_t *) tensor->data + s_off + i0*nb0, dstnb0, &subctx->in_memcpys);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
bool ggml_vk_buffer_write_2d_async(vk_context subctx, vk_buffer& dst, size_t offset, const void * src, size_t spitch, size_t dpitch, size_t width, size_t height, bool sync_staging) {
|
||||
VK_LOG_DEBUG("ggml_vk_buffer_write_2d_async(" << width << ", " << height << ")");
|
||||
// Check if src is pinned memory
|
||||
vk_buffer buf = nullptr;
|
||||
size_t buf_offset = 0;
|
||||
ggml_vk_host_get(dst->device, src, buf, buf_offset);
|
||||
|
||||
if (buf != nullptr) {
|
||||
// Memory is pinned, use as staging buffer
|
||||
std::vector<vk::BufferCopy> slices(1);
|
||||
if (width == spitch && width == dpitch) {
|
||||
// Only do single write if stride is equal
|
||||
slices[0].srcOffset = buf_offset;
|
||||
slices[0].dstOffset = offset;
|
||||
slices[0].size = width * height;
|
||||
} else {
|
||||
slices.resize(height);
|
||||
for (size_t i = 0; i < height; i++) {
|
||||
slices[i].srcOffset = buf_offset + i * spitch;
|
||||
slices[i].dstOffset = offset + i * dpitch;
|
||||
slices[i].size = width;
|
||||
}
|
||||
}
|
||||
|
||||
ggml_vk_sync_buffers(nullptr, subctx);
|
||||
subctx->s->buffer->buf.copyBuffer(buf->buffer, dst->buffer, slices);
|
||||
return true;
|
||||
}
|
||||
VK_LOG_DEBUG("STAGING");
|
||||
|
||||
if (!sync_staging) {
|
||||
// copy was not handled caller needs to fall back
|
||||
return false;
|
||||
}
|
||||
|
||||
// Staging buffer required
|
||||
const size_t staging_size = width * height;
|
||||
ggml_vk_ensure_sync_staging_buffer(dst->device, staging_size);
|
||||
|
||||
vk_buffer& staging_buffer = dst->device->sync_staging;
|
||||
|
||||
std::vector<vk::BufferCopy> slices(1);
|
||||
if (width == dpitch) {
|
||||
slices[0].srcOffset = 0;
|
||||
slices[0].dstOffset = offset;
|
||||
slices[0].size = staging_size;
|
||||
} else {
|
||||
slices.resize(height);
|
||||
for (size_t i = 0; i < height; i++) {
|
||||
slices[i].srcOffset = i * width;
|
||||
slices[i].dstOffset = offset + i * dpitch;
|
||||
slices[i].size = width;
|
||||
}
|
||||
}
|
||||
|
||||
ggml_vk_sync_buffers(nullptr, subctx);
|
||||
subctx->s->buffer->buf.copyBuffer((VkBuffer)staging_buffer->buffer, (VkBuffer)dst->buffer, slices);
|
||||
|
||||
if (width == spitch) {
|
||||
deferred_memcpy((uint8_t *)staging_buffer->ptr, src, staging_size, &subctx->in_memcpys);
|
||||
} else {
|
||||
for (size_t i = 0; i < height; i++) {
|
||||
deferred_memcpy((uint8_t *)staging_buffer->ptr + i * width, (const uint8_t *) src + i * spitch, width, &subctx->in_memcpys);
|
||||
}
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
bool ggml_vk_buffer_write_async(vk_context subctx, vk_buffer& dst, size_t offset, const void * src, size_t size, bool sync_staging) {
|
||||
VK_LOG_DEBUG("ggml_vk_buffer_write_async(" << size << ")");
|
||||
return ggml_vk_buffer_write_2d_async(subctx, dst, offset, src, size, size, size, 1, sync_staging);
|
||||
}
|
||||
|
||||
void ggml_vk_buffer_write_2d(vk_buffer& dst, size_t offset, const void * src, size_t spitch, size_t dpitch, size_t width, size_t height) {
|
||||
VK_LOG_DEBUG("ggml_vk_buffer_write_2d(" << width << ", " << height << ")");
|
||||
// Buffer is already mapped
|
||||
if(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) {
|
||||
GGML_ASSERT(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostCoherent);
|
||||
|
||||
if (width == spitch && width == dpitch) {
|
||||
memcpy((uint8_t *)dst->ptr + offset, src, width * height);
|
||||
} else {
|
||||
for (size_t i = 0; i < height; i++) {
|
||||
memcpy((uint8_t *)dst->ptr + offset + i * dpitch, (const uint8_t *) src + i * spitch, width);
|
||||
}
|
||||
}
|
||||
} else {
|
||||
std::lock_guard<std::recursive_mutex> guard(dst->device->mutex);
|
||||
|
||||
vk_context subctx = ggml_vk_create_temporary_context(dst->device->transfer_queue.cmd_pool);
|
||||
ggml_vk_ctx_begin(dst->device, subctx);
|
||||
bool ret = ggml_vk_buffer_write_2d_async(subctx, dst, offset, src, spitch, dpitch, width, height, true);
|
||||
GGML_ASSERT(ret);
|
||||
ggml_vk_ctx_end(subctx);
|
||||
|
||||
for (auto& cpy : subctx->in_memcpys) {
|
||||
memcpy(cpy.dst, cpy.src, cpy.n);
|
||||
}
|
||||
|
||||
for (auto& mset : subctx->memsets) {
|
||||
memset(mset.dst, mset.val, mset.n);
|
||||
}
|
||||
|
||||
ggml_vk_submit(subctx, dst->device->fence);
|
||||
VK_CHECK(dst->device->device.waitForFences({ dst->device->fence }, true, UINT64_MAX), "vk_buffer_write_2d waitForFences");
|
||||
dst->device->device.resetFences({ dst->device->fence });
|
||||
ggml_vk_queue_command_pools_cleanup(dst->device);
|
||||
}
|
||||
}
|
||||
|
||||
void ggml_vk_buffer_write(vk_buffer& dst, size_t offset, const void * src, size_t size) {
|
||||
VK_LOG_DEBUG("ggml_vk_buffer_write(" << size << ")");
|
||||
ggml_vk_buffer_write_2d(dst, offset, src, size, size, size, 1);
|
||||
}
|
||||
|
||||
bool ggml_vk_buffer_read_2d_async(vk_context subctx, vk_buffer& src, size_t offset, void * dst, size_t spitch, size_t dpitch, size_t width, size_t height, bool sync_staging) {
|
||||
VK_LOG_DEBUG("ggml_vk_buffer_read_2d_async(offset=" << offset << ", width=" << width << ", height=" << height << ")");
|
||||
GGML_ASSERT(width > 0);
|
||||
GGML_ASSERT(height > 0);
|
||||
GGML_ASSERT(src != nullptr);
|
||||
|
||||
// TODO: staging_offset is not used
|
||||
|
||||
// Check if dst is pinned memory
|
||||
vk_buffer buf = nullptr;
|
||||
size_t buf_offset = 0;
|
||||
ggml_vk_host_get(src->device, dst, buf, buf_offset);
|
||||
|
||||
std::vector<vk::BufferCopy> slices(1);
|
||||
if (width == spitch && width == dpitch) {
|
||||
// Only do single write if stride is equal
|
||||
slices[0].srcOffset = offset;
|
||||
slices[0].dstOffset = buf_offset;
|
||||
slices[0].size = width * height;
|
||||
} else {
|
||||
slices.resize(height);
|
||||
for (size_t i = 0; i < height; i++) {
|
||||
slices[i].srcOffset = offset + i * spitch;
|
||||
slices[i].dstOffset = buf_offset + i * dpitch;
|
||||
slices[i].size = width;
|
||||
}
|
||||
}
|
||||
|
||||
if (buf != nullptr) {
|
||||
// Memory is pinned, use as staging buffer
|
||||
ggml_vk_sync_buffers(nullptr, subctx);
|
||||
subctx->s->buffer->buf.copyBuffer(src->buffer, buf->buffer, slices);
|
||||
|
||||
return true;
|
||||
}
|
||||
VK_LOG_DEBUG("STAGING");
|
||||
|
||||
if (!sync_staging) {
|
||||
// copy was not handled caller needs to fall back
|
||||
return false;
|
||||
}
|
||||
|
||||
// Fall back to staging buffer
|
||||
const size_t staging_size = width * height;
|
||||
ggml_vk_ensure_sync_staging_buffer(src->device, staging_size);
|
||||
|
||||
vk_buffer& staging_buffer = src->device->sync_staging;
|
||||
|
||||
std::vector<vk::BufferCopy> staging_slices(1);
|
||||
if (width == spitch) {
|
||||
staging_slices[0].srcOffset = offset;
|
||||
staging_slices[0].dstOffset = 0;
|
||||
staging_slices[0].size = staging_size;
|
||||
} else {
|
||||
staging_slices.resize(height);
|
||||
for (size_t i = 0; i < height; i++) {
|
||||
staging_slices[i].srcOffset = offset + i * spitch;
|
||||
staging_slices[i].dstOffset = i * width;
|
||||
staging_slices[i].size = width;
|
||||
}
|
||||
}
|
||||
|
||||
ggml_vk_sync_buffers(nullptr, subctx);
|
||||
subctx->s->buffer->buf.copyBuffer(src->buffer, staging_buffer->buffer, staging_slices);
|
||||
|
||||
if (width == dpitch) {
|
||||
deferred_memcpy(dst, staging_buffer->ptr, staging_size, &subctx->out_memcpys);
|
||||
} else {
|
||||
for (size_t i = 0; i < height; i++) {
|
||||
deferred_memcpy((uint8_t *) dst + i * dpitch, (const uint8_t *) staging_buffer->ptr + i * width, width, &subctx->out_memcpys);
|
||||
}
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
static bool ggml_vk_buffer_read_async(vk_context subctx, vk_buffer& src, size_t offset, void * dst, size_t size, bool sync_staging = false) {
|
||||
return ggml_vk_buffer_read_2d_async(subctx, src, offset, dst, size, size, size, 1, sync_staging);
|
||||
}
|
||||
|
||||
void ggml_vk_buffer_read_2d(vk_buffer& src, size_t offset, void * dst, size_t spitch, size_t dpitch, size_t width, size_t height) {
|
||||
VK_LOG_DEBUG("ggml_vk_buffer_read_2d(" << src->buffer << ", " << offset << ", " << width << ", " << height << ")");
|
||||
|
||||
// If the device is not an UMA device the memory is host-accessible through rebar. While writing
|
||||
// through PCIe is sufficient fast reading back data from PCIe is slower than going through
|
||||
// the HW device to host copy path.
|
||||
if(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible && src->device->uma) {
|
||||
GGML_ASSERT(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostCoherent);
|
||||
|
||||
std::lock_guard<std::recursive_mutex> guard(src->device->mutex);
|
||||
vk_context subctx = ggml_vk_create_temporary_context(src->device->compute_queue.cmd_pool);
|
||||
ggml_vk_ctx_begin(src->device, subctx);
|
||||
subctx->s->buffer->buf.pipelineBarrier(
|
||||
vk::PipelineStageFlagBits::eComputeShader | vk::PipelineStageFlagBits::eTransfer,
|
||||
vk::PipelineStageFlagBits::eHost,
|
||||
{},
|
||||
{ { vk::AccessFlagBits::eShaderWrite | vk::AccessFlagBits::eTransferWrite,
|
||||
vk::AccessFlagBits::eHostRead } },
|
||||
{}, {});
|
||||
ggml_vk_ctx_end(subctx);
|
||||
ggml_vk_submit(subctx, src->device->fence);
|
||||
VK_CHECK(src->device->device.waitForFences({ src->device->fence }, true, UINT64_MAX),
|
||||
"vk_buffer_read_2d uma waitForFences");
|
||||
src->device->device.resetFences({ src->device->fence });
|
||||
ggml_vk_queue_command_pools_cleanup(src->device);
|
||||
|
||||
if (width == spitch && width == dpitch) {
|
||||
memcpy(dst, (const uint8_t *) src->ptr + offset, width * height);
|
||||
} else {
|
||||
for (size_t i = 0; i < height; i++) {
|
||||
memcpy((uint8_t *) dst + i * dpitch, (const uint8_t *) src->ptr + offset + i * spitch, width);
|
||||
}
|
||||
}
|
||||
} else {
|
||||
std::lock_guard<std::recursive_mutex> guard(src->device->mutex);
|
||||
|
||||
vk_context subctx = ggml_vk_create_temporary_context(src->device->transfer_queue.cmd_pool);
|
||||
ggml_vk_ctx_begin(src->device, subctx);
|
||||
bool ret = ggml_vk_buffer_read_2d_async(subctx, src, offset, dst, spitch, dpitch, width, height, true);
|
||||
GGML_ASSERT(ret);
|
||||
ggml_vk_ctx_end(subctx);
|
||||
|
||||
ggml_vk_submit(subctx, src->device->fence);
|
||||
VK_CHECK(src->device->device.waitForFences({ src->device->fence }, true, UINT64_MAX), "vk_buffer_read_2d waitForFences");
|
||||
src->device->device.resetFences({ src->device->fence });
|
||||
ggml_vk_queue_command_pools_cleanup(src->device);
|
||||
|
||||
for (auto& cpy : subctx->out_memcpys) {
|
||||
memcpy(cpy.dst, cpy.src, cpy.n);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void ggml_vk_buffer_read(vk_buffer& src, size_t offset, void * dst, size_t size) {
|
||||
VK_LOG_DEBUG("ggml_vk_buffer_read(" << src->buffer << ", " << offset << ", " << size << ")");
|
||||
ggml_vk_buffer_read_2d(src, offset, dst, size, size, size, 1);
|
||||
}
|
||||
|
||||
void ggml_vk_buffer_copy_async(vk_context& ctx, vk_buffer& dst, size_t dst_offset, vk_buffer& src, size_t src_offset, size_t size) {
|
||||
VK_LOG_DEBUG("ggml_vk_buffer_copy_async(" << size << ")");
|
||||
// Make sure both buffers are on same device
|
||||
GGML_ASSERT(src->device == dst->device);
|
||||
|
||||
VkBufferCopy bc{ src_offset, dst_offset, size };
|
||||
|
||||
vkCmdCopyBuffer(ctx->s->buffer->buf, (VkBuffer)src->buffer, (VkBuffer)dst->buffer, 1, &bc);
|
||||
}
|
||||
|
||||
void ggml_vk_buffer_copy(vk_buffer& dst, size_t dst_offset, vk_buffer& src, size_t src_offset, size_t size) {
|
||||
if (src->device == dst->device) {
|
||||
std::lock_guard<std::recursive_mutex> guard(src->device->mutex);
|
||||
VK_LOG_DEBUG("ggml_vk_buffer_copy(SINGLE_DEVICE, " << size << ")");
|
||||
// Copy within the device
|
||||
vk_context subctx = ggml_vk_create_temporary_context(src->device->transfer_queue.cmd_pool);
|
||||
ggml_vk_ctx_begin(src->device, subctx);
|
||||
ggml_vk_buffer_copy_async(subctx, dst, dst_offset, src, src_offset, size);
|
||||
ggml_vk_ctx_end(subctx);
|
||||
ggml_vk_submit(subctx, src->device->fence);
|
||||
VK_CHECK(src->device->device.waitForFences({ src->device->fence }, true, UINT64_MAX), "vk_buffer_copy waitForFences");
|
||||
src->device->device.resetFences({ src->device->fence });
|
||||
ggml_vk_queue_command_pools_cleanup(src->device);
|
||||
} else {
|
||||
VK_LOG_DEBUG("ggml_vk_buffer_copy(MULTI_DEVICE, " << size << ")");
|
||||
// Copy device to device
|
||||
ggml_vk_ensure_sync_staging_buffer(src->device, size);
|
||||
|
||||
// Copy to src staging buffer
|
||||
ggml_vk_buffer_copy(src->device->sync_staging, 0, src, src_offset, size);
|
||||
// Copy to dst buffer
|
||||
ggml_vk_buffer_write(dst, dst_offset, src->device->sync_staging->ptr, size);
|
||||
}
|
||||
}
|
||||
|
||||
void ggml_vk_buffer_memset_async(vk_context& ctx, vk_buffer& dst, size_t offset, uint32_t c, size_t size) {
|
||||
VK_LOG_DEBUG("ggml_vk_buffer_memset_async(" << offset << ", " << c << ", " << size << ")");
|
||||
|
||||
if (dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible &&
|
||||
dst->device->uma) {
|
||||
deferred_memset((uint8_t*)dst->ptr + offset, c, size, &ctx->memsets);
|
||||
return;
|
||||
}
|
||||
|
||||
// Fall back to GPU fillBuffer for non-UMA or non-host-visible buffers
|
||||
ctx->s->buffer->buf.fillBuffer(dst->buffer, offset, size, c);
|
||||
}
|
||||
|
||||
void ggml_vk_buffer_memset(vk_buffer& dst, size_t offset, uint32_t c, size_t size) {
|
||||
VK_LOG_DEBUG("ggml_vk_buffer_memset(" << offset << ", " << c << ", " << size << ")");
|
||||
|
||||
if (dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible &&
|
||||
dst->device->uma) {
|
||||
memset((uint8_t*)dst->ptr + offset, c, size);
|
||||
return;
|
||||
}
|
||||
|
||||
std::lock_guard<std::recursive_mutex> guard(dst->device->mutex);
|
||||
vk_context subctx = ggml_vk_create_temporary_context(dst->device->transfer_queue.cmd_pool);
|
||||
ggml_vk_ctx_begin(dst->device, subctx);
|
||||
subctx->s->buffer->buf.fillBuffer(dst->buffer, offset, size, c);
|
||||
ggml_vk_ctx_end(subctx);
|
||||
|
||||
ggml_vk_submit(subctx, dst->device->fence);
|
||||
VK_CHECK(dst->device->device.waitForFences({ dst->device->fence }, true, UINT64_MAX), "vk_memset waitForFences");
|
||||
dst->device->device.resetFences({ dst->device->fence });
|
||||
ggml_vk_queue_command_pools_cleanup(dst->device);
|
||||
}
|
||||
|
||||
ggml_backend_buffer_i ggml_backend_vk_buffer_interface = {
|
||||
/* .free_buffer = */ ggml_backend_vk_buffer_free_buffer,
|
||||
/* .get_base = */ ggml_backend_vk_buffer_get_base,
|
||||
/* .init_tensor = */ ggml_backend_vk_buffer_init_tensor,
|
||||
/* .memset_tensor = */ ggml_backend_vk_buffer_memset_tensor,
|
||||
/* .set_tensor = */ ggml_backend_vk_buffer_set_tensor,
|
||||
/* .get_tensor = */ ggml_backend_vk_buffer_get_tensor,
|
||||
/* .set_tensor_2d = */ ggml_backend_vk_buffer_set_tensor_2d,
|
||||
/* .get_tensor_2d = */ ggml_backend_vk_buffer_get_tensor_2d,
|
||||
/* .cpy_tensor = */ ggml_backend_vk_buffer_cpy_tensor,
|
||||
/* .clear = */ ggml_backend_vk_buffer_clear,
|
||||
/* .reset = */ NULL,
|
||||
};
|
||||
|
||||
vk_buffer ggml_vk_buffer_from_host_ptr(vk_device & device, void * ptr, size_t size) {
|
||||
if (!device->external_memory_host) {
|
||||
return {};
|
||||
}
|
||||
|
||||
uintptr_t uptr = reinterpret_cast<uintptr_t>(ptr);
|
||||
if (uptr & (device->min_imported_host_pointer_alignment - 1)) {
|
||||
return {};
|
||||
}
|
||||
if (size & (device->min_imported_host_pointer_alignment - 1)) {
|
||||
return {};
|
||||
}
|
||||
|
||||
const vk::MemoryPropertyFlags property_flags = vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached;
|
||||
|
||||
vk_buffer buf {};
|
||||
try {
|
||||
buf = ggml_vk_create_buffer(device, size, { property_flags }, ptr);
|
||||
} catch (vk::SystemError& e) {
|
||||
GGML_LOG_WARN("ggml_vulkan: Failed ggml_vk_create_buffer (%s)\n", e.what());
|
||||
}
|
||||
|
||||
return buf;
|
||||
}
|
||||
|
||||
228
ggml/src/ggml-vulkan/ggml-vulkan-common.h
Normal file
228
ggml/src/ggml-vulkan/ggml-vulkan-common.h
Normal file
@@ -0,0 +1,228 @@
|
||||
#pragma once
|
||||
#include "ggml-vulkan-push-constants.h"
|
||||
|
||||
extern std::mutex queue_mutex;
|
||||
extern ggml_backend_buffer_type_i ggml_backend_vk_buffer_type_interface;
|
||||
extern bool vk_memory_logger_enabled;
|
||||
extern bool vk_perf_logger_enabled;
|
||||
extern bool vk_perf_logger_concurrent;
|
||||
extern bool vk_enable_sync_logger;
|
||||
extern uint32_t vk_perf_logger_frequency;
|
||||
extern std::string vk_pipeline_stats_filter;
|
||||
extern void * const vk_ptr_base;
|
||||
extern vk_instance_t vk_instance;
|
||||
extern ggml_backend_buffer_i ggml_backend_vk_buffer_interface;
|
||||
|
||||
uint64_t vk_tensor_offset(const ggml_tensor * tensor);
|
||||
uint32_t get_misalign_bytes(const ggml_backend_vk_context * ctx, const ggml_tensor * t);
|
||||
void ggml_vk_wait_for_fence(ggml_backend_vk_context * ctx);
|
||||
void ggml_vk_destroy_pipeline(vk::Device& device, vk_pipeline& pipeline);
|
||||
void ggml_pipeline_request_descriptor_sets(ggml_backend_vk_context *ctx, vk_pipeline& pipeline, uint32_t n);
|
||||
void ggml_pipeline_allocate_descriptor_sets(ggml_backend_vk_context * ctx);
|
||||
void ggml_vk_submit(vk_context& ctx, vk::Fence fence);
|
||||
uint32_t ggml_vk_find_queue_family_index(std::vector<vk::QueueFamilyProperties>& queue_family_props, const vk::QueueFlags& required, const vk::QueueFlags& avoid, int32_t compute_index, uint32_t min_num_queues);
|
||||
void ggml_vk_create_queue(vk_device& device, vk_queue& q, uint32_t queue_family_index, uint32_t queue_index, vk::PipelineStageFlags&& stage_flags, bool transfer_only);
|
||||
vk_context ggml_vk_create_context(ggml_backend_vk_context * ctx, vk_command_pool& p);
|
||||
vk_context ggml_vk_create_temporary_context(vk_command_pool& p);
|
||||
void ggml_vk_command_pool_cleanup(vk_device& device, vk_command_pool& p);
|
||||
void ggml_vk_queue_command_pools_cleanup(vk_device& device);
|
||||
vk_buffer ggml_vk_create_buffer_check(vk_device& device, size_t size, vk::MemoryPropertyFlags req_flags, vk::MemoryPropertyFlags fallback_flags = vk::MemoryPropertyFlags(0));
|
||||
vk_buffer ggml_vk_create_buffer_device(vk_device& device, size_t size);
|
||||
void ggml_vk_destroy_buffer(vk_buffer& buf);
|
||||
vk_subbuffer ggml_vk_subbuffer(const ggml_backend_vk_context* ctx, const vk_buffer& buf, size_t offset = 0);
|
||||
void ggml_vk_sync_buffers(ggml_backend_vk_context* ctx, vk_context& subctx);
|
||||
void ggml_vk_set_event(vk_context& ctx, vk::Event& event);
|
||||
void ggml_vk_wait_events(vk_context& ctx, std::vector<vk::Event>&& events);
|
||||
vk_fa_tuning_params get_fa_tuning_params(const vk_device& device, uint32_t hsk, uint32_t hsv, uint32_t n_rows, uint32_t n_kv, ggml_type k_type, ggml_type v_type, bool f32acc);
|
||||
vk_fa_pipeline_state get_fa_pipeline_state(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool aligned, bool f32acc, bool use_mask, bool use_mask_opt, bool use_logit_softcap, ggml_type k_type, ggml_type v_type);
|
||||
uint32_t get_subgroup_size(const std::string &pipeline_name, const vk_device_architecture &arch);
|
||||
void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested = nullptr);
|
||||
vk_device ggml_vk_get_device(size_t idx);
|
||||
DispatchLoaderDynamic & ggml_vk_default_dispatcher();
|
||||
void ggml_vk_instance_init();
|
||||
void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx);
|
||||
vk_pipeline ggml_vk_get_to_fp16(ggml_backend_vk_context * ctx, ggml_type type);
|
||||
void * ggml_vk_host_malloc(vk_device& device, size_t size);
|
||||
void ggml_vk_host_free(vk_device& device, void* ptr);
|
||||
void ggml_vk_host_get(const vk_device& device, const void * ptr, vk_buffer& buf, size_t& buf_offset);
|
||||
vk_subbuffer ggml_vk_tensor_subbuffer( const ggml_backend_vk_context * ctx, const ggml_tensor * tensor, bool allow_misalign = false);
|
||||
void ggml_vk_ctx_end(vk_context& ctx);
|
||||
void ggml_vk_ctx_begin(vk_device& device, vk_context& subctx);
|
||||
vk_context ggml_vk_get_compute_ctx(ggml_backend_vk_context * ctx);
|
||||
bool ggml_vk_submit_transfer_ctx(ggml_backend_vk_context * ctx);
|
||||
size_t ggml_vk_align_size(size_t width, size_t align);
|
||||
void deferred_memcpy(void * dst, const void * src, size_t size, std::vector<vk_staging_memcpy>* memcpys = nullptr);
|
||||
void deferred_memset(void * dst, uint32_t val, size_t size, std::vector<vk_staging_memset>* memsets = nullptr);
|
||||
void ggml_vk_ensure_sync_staging_buffer(vk_device& device, size_t size);
|
||||
void ggml_vk_ensure_sync_staging_buffer(ggml_backend_vk_context * ctx, size_t size);
|
||||
bool ggml_vk_buffer_write_2d_async(vk_context subctx, vk_buffer& dst, size_t offset, const void * src, size_t spitch, size_t dpitch, size_t width, size_t height, bool sync_staging = false);
|
||||
bool ggml_vk_buffer_write_async(vk_context subctx, vk_buffer& dst, size_t offset, const void * src, size_t size, bool sync_staging = false);
|
||||
void ggml_vk_buffer_write_2d(vk_buffer& dst, size_t offset, const void * src, size_t spitch, size_t dpitch, size_t width, size_t height);
|
||||
void ggml_vk_buffer_write(vk_buffer& dst, size_t offset, const void * src, size_t size);
|
||||
bool ggml_vk_buffer_read_2d_async(vk_context subctx, vk_buffer& src, size_t offset, void * dst, size_t spitch, size_t dpitch, size_t width, size_t height, bool sync_staging = false);
|
||||
void ggml_vk_buffer_read_2d(vk_buffer& src, size_t offset, void * dst, size_t spitch, size_t dpitch, size_t width, size_t height);
|
||||
void ggml_vk_buffer_read(vk_buffer& src, size_t offset, void * dst, size_t size);
|
||||
void ggml_vk_buffer_copy_async(vk_context& ctx, vk_buffer& dst, size_t dst_offset, vk_buffer& src, size_t src_offset, size_t size);
|
||||
void ggml_vk_buffer_copy(vk_buffer& dst, size_t dst_offset, vk_buffer& src, size_t src_offset, size_t size);
|
||||
void ggml_vk_buffer_memset_async(vk_context& ctx, vk_buffer& dst, size_t offset, uint32_t c, size_t size);
|
||||
void ggml_vk_buffer_memset(vk_buffer& dst, size_t offset, uint32_t c, size_t size);
|
||||
void ggml_vk_matmul( ggml_backend_vk_context * ctx, vk_context& subctx, vk_pipeline& pipeline, vk_subbuffer&& a, vk_subbuffer&& b, vk_subbuffer&& d, vk_subbuffer&& split_k_buffer, uint32_t m, uint32_t n, uint32_t k, uint32_t stride_a, uint32_t stride_b, uint32_t stride_d, uint32_t batch_stride_a, uint32_t batch_stride_b, uint32_t batch_stride_d, uint32_t split_k, uint32_t batch, uint32_t ne02, uint32_t ne12, uint32_t broadcast2, uint32_t broadcast3, uint32_t padded_n);
|
||||
bool ggml_vk_dim01_contiguous(const ggml_tensor * tensor);
|
||||
vk_pipeline ggml_vk_get_cpy_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src, const ggml_tensor * dst, ggml_type to);
|
||||
void ggml_vk_cpy_to_contiguous(ggml_backend_vk_context * ctx, vk_context& subctx, vk_pipeline pipeline, const ggml_tensor * tensor, const vk_subbuffer & in, const vk_subbuffer & out);
|
||||
vk_pipeline ggml_vk_get_quantize_pipeline(ggml_backend_vk_context * ctx, ggml_type type);
|
||||
void ggml_vk_quantize_q8_1(ggml_backend_vk_context * ctx, vk_context& subctx, const vk_subbuffer & in, const vk_subbuffer & out, uint32_t ne);
|
||||
bool ggml_vk_can_use_fwht(const ggml_backend_vk_context * ctx, const ggml_tensor * src1, const ggml_tensor * dst);
|
||||
void ggml_vk_fwht(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src, ggml_tensor * dst);
|
||||
void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context& subctx, const struct ggml_cgraph * cgraph, int node_idx);
|
||||
bool ggml_vk_use_mul_mat_vec_id(const struct ggml_cgraph * cgraph, int node_idx);
|
||||
void ggml_vk_mul_mat_id(ggml_backend_vk_context * ctx, vk_context& subctx, const struct ggml_cgraph * cgraph, int node_idx);
|
||||
bool ggml_vk_flash_attn_scalar_shmem_support(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool f32acc, ggml_type k_type, ggml_type v_type);
|
||||
bool ggml_vk_flash_attn_coopmat_shmem_support(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool f32acc, ggml_type k_type = GGML_TYPE_F16);
|
||||
void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * q, const ggml_tensor * k, const ggml_tensor * v, const ggml_tensor * mask, const ggml_tensor * sinks, ggml_tensor * dst);
|
||||
void ggml_vk_get_rows(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
|
||||
void ggml_vk_acc(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
|
||||
void ggml_vk_multi_add(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_cgraph * cgraph, int node_idx);
|
||||
void ggml_vk_add(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
|
||||
void ggml_vk_sub(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
|
||||
void ggml_vk_mul(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
|
||||
void ggml_vk_div(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
|
||||
void ggml_vk_add_id(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst);
|
||||
void ggml_vk_rwkv_wkv6(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst);
|
||||
void ggml_vk_rwkv_wkv7(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst);
|
||||
void ggml_vk_gated_delta_net(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst);
|
||||
void ggml_vk_ssm_scan(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst);
|
||||
void ggml_vk_ssm_conv(ggml_backend_vk_context * ctx, vk_context& subctx, const struct ggml_cgraph * cgraph, int node_idx);
|
||||
void ggml_vk_opt_step_adamw(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst);
|
||||
void ggml_vk_opt_step_sgd(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst);
|
||||
void ggml_vk_concat(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
|
||||
void ggml_vk_upscale(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst);
|
||||
void ggml_vk_scale(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst);
|
||||
void ggml_vk_sqr(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst);
|
||||
void ggml_vk_sqrt(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst);
|
||||
void ggml_vk_add1(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
|
||||
void ggml_vk_arange(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst);
|
||||
void ggml_vk_fill(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst);
|
||||
void ggml_vk_sin(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst);
|
||||
void ggml_vk_cos(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst);
|
||||
void ggml_vk_log(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst);
|
||||
void ggml_vk_tri(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst);
|
||||
void ggml_vk_diag(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst);
|
||||
void ggml_vk_clamp(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst);
|
||||
void ggml_vk_pad(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst);
|
||||
void ggml_vk_roll(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst);
|
||||
void ggml_vk_repeat(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst);
|
||||
void ggml_vk_repeat_back(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst);
|
||||
void ggml_vk_cpy(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst);
|
||||
void ggml_vk_set_rows(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
|
||||
void ggml_vk_silu_back(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
|
||||
void ggml_vk_norm(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst);
|
||||
void ggml_vk_group_norm(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst);
|
||||
uint32_t ggml_vk_rms_partials_size(ggml_backend_vk_context * ctx, const ggml_tensor *node);
|
||||
void ggml_vk_rms_norm(ggml_backend_vk_context * ctx, vk_context& subctx, const struct ggml_cgraph * cgraph, int node_idx, float * op_params);
|
||||
void ggml_vk_rms_norm_back(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
|
||||
void ggml_vk_l2_norm(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst);
|
||||
void ggml_vk_unary(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst);
|
||||
void ggml_vk_xielu(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst);
|
||||
void ggml_vk_glu(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
|
||||
void ggml_vk_diag_mask_inf(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst);
|
||||
void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst);
|
||||
void ggml_vk_soft_max_back(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
|
||||
void ggml_vk_topk_moe(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_cgraph * cgraph, int node_idx);
|
||||
void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_cgraph * cgraph, int node_idx, bool backprop);
|
||||
void ggml_vk_argsort(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst);
|
||||
void ggml_vk_topk(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst);
|
||||
void ggml_vk_sum(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst);
|
||||
void ggml_vk_sum_rows(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst);
|
||||
void ggml_vk_mean(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst);
|
||||
void ggml_vk_cumsum(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst);
|
||||
void ggml_vk_argmax(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst);
|
||||
void ggml_vk_count_equal(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
|
||||
void ggml_vk_solve_tri(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
|
||||
void ggml_vk_im2col(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
|
||||
void ggml_vk_im2col_3d(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
|
||||
void ggml_vk_timestep_embedding(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst);
|
||||
void ggml_vk_conv_transpose_1d(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
|
||||
void ggml_vk_col2im_1d(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst);
|
||||
void ggml_vk_snake_dispatch_fused(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_cgraph * cgraph, int node_idx);
|
||||
void ggml_vk_pool_2d(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst);
|
||||
void ggml_vk_conv_2d(ggml_backend_vk_context * ctx, vk_context & subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
|
||||
void ggml_vk_conv_2d_dw(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
|
||||
void ggml_vk_leaky_relu(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst);
|
||||
void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx, vk_context subctx);
|
||||
bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgraph, int node_idx, ggml_tensor *node_begin, int node_idx_begin, bool last_node, bool almost_ready, bool submit);
|
||||
void ggml_vk_compute_forward(ggml_backend_vk_context* ctx, ggml_cgraph * cgraph, ggml_tensor* tensor, int tensor_idx, bool almost_ready);
|
||||
void ggml_vk_graph_cleanup(ggml_backend_vk_context * ctx);
|
||||
void ggml_vk_cleanup(ggml_backend_vk_context * ctx);
|
||||
int ggml_vk_get_device_count();
|
||||
void ggml_vk_get_device_description(int device, char * description, size_t description_size);
|
||||
bool ggml_backend_buffer_is_vk(ggml_backend_buffer_t buffer);
|
||||
void ggml_backend_vk_buffer_free_buffer(ggml_backend_buffer_t buffer);
|
||||
void * ggml_backend_vk_buffer_get_base(ggml_backend_buffer_t buffer);
|
||||
enum ggml_status ggml_backend_vk_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor);
|
||||
void ggml_backend_vk_buffer_memset_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, uint8_t value, size_t offset, size_t size);
|
||||
void ggml_backend_vk_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size);
|
||||
void ggml_backend_vk_buffer_set_tensor_2d(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data);
|
||||
void ggml_backend_vk_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size);
|
||||
void ggml_backend_vk_buffer_get_tensor_2d(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data);
|
||||
bool ggml_backend_vk_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst);
|
||||
void ggml_backend_vk_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value);
|
||||
const char * ggml_backend_vk_buffer_type_name(ggml_backend_buffer_type_t buft);
|
||||
ggml_backend_buffer_t ggml_backend_vk_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size);
|
||||
size_t ggml_backend_vk_buffer_type_get_alignment(ggml_backend_buffer_type_t buft);
|
||||
size_t ggml_backend_vk_buffer_type_get_max_size(ggml_backend_buffer_type_t buft);
|
||||
size_t ggml_backend_vk_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor);
|
||||
void ggml_backend_vk_free(ggml_backend_t backend);
|
||||
void ggml_vk_synchronize(ggml_backend_vk_context * ctx);
|
||||
bool ggml_vk_is_empty(ggml_tensor * node);
|
||||
bool ggml_vk_can_fuse(const ggml_backend_vk_context * ctx, const struct ggml_cgraph * cgraph, int node_idx, std::initializer_list<enum ggml_op> ops);
|
||||
bool ggml_vk_can_fuse_ssm_conv(const ggml_backend_vk_context * ctx, const struct ggml_cgraph * cgraph, int node_idx, int num_extra);
|
||||
bool ggml_vk_can_fuse_topk_moe(ggml_backend_vk_context * ctx, const struct ggml_cgraph * cgraph, int node_idx, topk_moe_mode mode);
|
||||
bool ggml_vk_can_fuse_rope_set_rows(ggml_backend_vk_context * ctx, const struct ggml_cgraph * cgraph, int node_idx);
|
||||
bool ggml_vk_can_fuse_snake(ggml_backend_vk_context * ctx, const struct ggml_cgraph * cgraph, int node_idx);
|
||||
bool ggml_vk_tensors_overlap(const ggml_tensor * a, const ggml_tensor * b, bool elementwise);
|
||||
bool ggml_vk_can_fuse_rms_norm_mul_rope(ggml_backend_vk_context * ctx, const struct ggml_cgraph * cgraph, int node_idx);
|
||||
uint32_t ggml_vk_fuse_multi_add(ggml_backend_vk_context * ctx, const struct ggml_cgraph * cgraph, int node_idx);
|
||||
void ggml_vk_graph_optimize(ggml_backend_t backend, struct ggml_cgraph * graph);
|
||||
int64_t ggml_vk_get_op_batch_size(const ggml_tensor * op);
|
||||
vk_buffer ggml_vk_buffer_from_host_ptr(vk_device & device, void * ptr, size_t size);
|
||||
ggml_backend_reg_t ggml_backend_vk_reg();
|
||||
bool ggml_vk_instance_layer_settings_available();
|
||||
bool ggml_vk_instance_portability_enumeration_ext_available(const std::vector<vk::ExtensionProperties>& instance_extensions);
|
||||
bool ggml_vk_instance_debug_utils_ext_available(const std::vector<vk::ExtensionProperties> & instance_extensions);
|
||||
bool ggml_vk_device_is_supported(const vk::PhysicalDevice & vkdev);
|
||||
bool ggml_vk_khr_cooperative_matrix_support(const vk::PhysicalDeviceProperties& props, const vk::PhysicalDeviceDriverProperties& driver_props, vk_device_architecture arch);
|
||||
uint32_t ggml_vk_intel_shader_core_count(const vk::PhysicalDevice& vkdev);
|
||||
|
||||
template <typename T>
|
||||
static void ggml_vk_dispatch_pipeline(ggml_backend_vk_context* ctx, vk_context& subctx, vk_pipeline& pipeline, std::initializer_list<vk::DescriptorBufferInfo> const& descriptor_buffer_infos, const T &push_constants, std::array<uint32_t, 3> elements) {
|
||||
const uint32_t wg0 = CEIL_DIV(elements[0], pipeline->wg_denoms[0]);
|
||||
const uint32_t wg1 = CEIL_DIV(elements[1], pipeline->wg_denoms[1]);
|
||||
const uint32_t wg2 = CEIL_DIV(elements[2], pipeline->wg_denoms[2]);
|
||||
VK_LOG_DEBUG("ggml_vk_dispatch_pipeline(" << pipeline->name << ", {";
|
||||
for (auto& buffer : descriptor_buffer_infos) {
|
||||
std::cerr << "(" << buffer.buffer << ", " << buffer.offset << ", " << buffer.range << "), ";
|
||||
}
|
||||
std::cerr << "}, (" << wg0 << "," << wg1 << "," << wg2 << "))");
|
||||
GGML_ASSERT(wg0 <= ctx->device->properties.limits.maxComputeWorkGroupCount[0] &&
|
||||
wg1 <= ctx->device->properties.limits.maxComputeWorkGroupCount[1] &&
|
||||
wg2 <= ctx->device->properties.limits.maxComputeWorkGroupCount[2]);
|
||||
GGML_ASSERT(ctx->descriptor_set_idx < ctx->descriptor_sets.size());
|
||||
GGML_ASSERT(descriptor_buffer_infos.size() <= MAX_PARAMETER_COUNT);
|
||||
GGML_ASSERT(pipeline->parameter_count == descriptor_buffer_infos.size());
|
||||
GGML_ASSERT(pipeline->push_constant_size == push_constant_size(push_constants));
|
||||
|
||||
vk::DescriptorSet& descriptor_set = ctx->descriptor_sets[ctx->descriptor_set_idx++];
|
||||
vk::WriteDescriptorSet write_descriptor_set{ descriptor_set, 0, 0, pipeline->parameter_count, vk::DescriptorType::eStorageBuffer, nullptr, descriptor_buffer_infos.begin() };
|
||||
ctx->device->device.updateDescriptorSets({ write_descriptor_set }, {});
|
||||
|
||||
subctx->s->buffer->buf.pushConstants(pipeline->layout, vk::ShaderStageFlagBits::eCompute, 0, push_constant_size(push_constants), push_constant_data(push_constants));
|
||||
subctx->s->buffer->buf.bindPipeline(vk::PipelineBindPoint::eCompute, pipeline->pipeline);
|
||||
subctx->s->buffer->buf.bindDescriptorSets(vk::PipelineBindPoint::eCompute,
|
||||
pipeline->layout,
|
||||
0,
|
||||
{ descriptor_set },
|
||||
{});
|
||||
subctx->s->buffer->buf.dispatch(wg0, wg1, wg2);
|
||||
}
|
||||
|
||||
1608
ggml/src/ggml-vulkan/ggml-vulkan-debug.cpp
Normal file
1608
ggml/src/ggml-vulkan/ggml-vulkan-debug.cpp
Normal file
File diff suppressed because it is too large
Load Diff
293
ggml/src/ggml-vulkan/ggml-vulkan-flash-attn.cpp
Normal file
293
ggml/src/ggml-vulkan/ggml-vulkan-flash-attn.cpp
Normal file
@@ -0,0 +1,293 @@
|
||||
#include "ggml-vulkan-common.h"
|
||||
|
||||
void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * q, const ggml_tensor * k, const ggml_tensor * v, const ggml_tensor * mask, const ggml_tensor * sinks, ggml_tensor * dst) {
|
||||
VK_LOG_DEBUG("ggml_vk_flash_attn((" << q << ", name=" << q->name << ", type=" << q->type << ", ne0=" << q->ne[0] << ", ne1=" << q->ne[1] << ", ne2=" << q->ne[2] << ", ne3=" << q->ne[3] << ", nb0=" << q->nb[0] << ", nb1=" << q->nb[1] << ", nb2=" << q->nb[2] << ", nb3=" << q->nb[3];
|
||||
std::cerr << "), (" << k << ", name=" << k->name << ", type=" << k->type << ", ne0=" << k->ne[0] << ", ne1=" << k->ne[1] << ", ne2=" << k->ne[2] << ", ne3=" << k->ne[3] << ", nb0=" << k->nb[0] << ", nb1=" << k->nb[1] << ", nb2=" << k->nb[2] << ", nb3=" << k->nb[3];
|
||||
std::cerr << "), (" << v << ", name=" << v->name << ", type=" << v->type << ", ne0=" << v->ne[0] << ", ne1=" << v->ne[1] << ", ne2=" << v->ne[2] << ", ne3=" << v->ne[3] << ", nb0=" << v->nb[0] << ", nb1=" << v->nb[1] << ", nb2=" << v->nb[2] << ", nb3=" << v->nb[3];
|
||||
std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3];
|
||||
if (sinks) {
|
||||
std::cerr << "), (" << sinks << ", name=" << sinks->name << ", type=" << sinks->type << ", ne0=" << sinks->ne[0] << ", ne1=" << sinks->ne[1] << ", ne2=" << sinks->ne[2] << ", ne3=" << sinks->ne[3] << ", nb0=" << sinks->nb[0] << ", nb1=" << sinks->nb[1] << ", nb2=" << sinks->nb[2] << ", nb3=" << sinks->nb[3];
|
||||
}
|
||||
std::cerr << "))");
|
||||
|
||||
GGML_TENSOR_LOCALS(int64_t, neq, q, ne)
|
||||
GGML_TENSOR_LOCALS(size_t, nbq, q, nb)
|
||||
GGML_TENSOR_LOCALS(int64_t, nek, k, ne)
|
||||
GGML_TENSOR_LOCALS(size_t, nbk, k, nb)
|
||||
GGML_TENSOR_LOCALS(int64_t, nev, v, ne)
|
||||
GGML_TENSOR_LOCALS(size_t, nbv, v, nb)
|
||||
GGML_TENSOR_LOCALS(int64_t, ne, dst, ne)
|
||||
GGML_TENSOR_LOCALS(size_t, nb, dst, nb)
|
||||
|
||||
const uint32_t nem0 = mask ? mask->ne[0] : 0;
|
||||
const uint32_t nem1 = mask ? mask->ne[1] : 0;
|
||||
const uint32_t nem2 = mask ? mask->ne[2] : 0;
|
||||
const uint32_t nem3 = mask ? mask->ne[3] : 0;
|
||||
|
||||
const uint32_t HSK = nek0;
|
||||
const uint32_t HSV = nev0;
|
||||
uint32_t N = neq1;
|
||||
const uint32_t KV = nek1;
|
||||
|
||||
GGML_ASSERT(ne0 == HSV);
|
||||
GGML_ASSERT(ne2 == N);
|
||||
|
||||
// input tensor rows must be contiguous
|
||||
GGML_ASSERT(nbq0 == ggml_type_size(q->type));
|
||||
GGML_ASSERT(nbk0 == ggml_type_size(k->type));
|
||||
GGML_ASSERT(nbv0 == ggml_type_size(v->type));
|
||||
|
||||
GGML_ASSERT(neq0 == HSK);
|
||||
|
||||
GGML_ASSERT(neq1 == N);
|
||||
|
||||
GGML_ASSERT(nev1 == nek1);
|
||||
|
||||
// dst cannot be transposed or permuted
|
||||
GGML_ASSERT(nb0 == sizeof(float));
|
||||
GGML_ASSERT(nb0 <= nb1);
|
||||
GGML_ASSERT(nb1 <= nb2);
|
||||
GGML_ASSERT(nb2 <= nb3);
|
||||
|
||||
assert(dst->type == GGML_TYPE_F32);
|
||||
assert(q->type == GGML_TYPE_F32);
|
||||
uint32_t gqa_ratio = 1;
|
||||
uint32_t qk_ratio = neq2 / nek2;
|
||||
uint32_t workgroups_x = (uint32_t)neq1;
|
||||
uint32_t workgroups_y = (uint32_t)neq2;
|
||||
uint32_t workgroups_z = (uint32_t)neq3;
|
||||
|
||||
const bool f32acc = !ctx->device->fp16 || dst->op_params[3] == GGML_PREC_F32 || k->type == GGML_TYPE_BF16;
|
||||
|
||||
// For scalar/coopmat1 FA, we can use the "large" size to accommodate qga.
|
||||
// For coopmat2 FA, we always use the small size (which is still pretty large for gqa).
|
||||
vk_fa_tuning_params tuning_params = get_fa_tuning_params(ctx->device, HSK, HSV, 512, KV, k->type, v->type, f32acc);
|
||||
const uint32_t max_gqa = std::min(tuning_params.block_rows, 32u);
|
||||
|
||||
if (N <= 8 && qk_ratio > 1 && qk_ratio <= max_gqa &&
|
||||
qk_ratio * nek2 == neq2 && nek2 == nev2 && nem2 <= 1) {
|
||||
// grouped query attention - make the N dimension equal to gqa_ratio, reduce
|
||||
// workgroups proportionally in y dimension. The shader will detect gqa_ratio > 1
|
||||
// and change addressing calculations to index Q's dimension 2.
|
||||
gqa_ratio = qk_ratio;
|
||||
N = gqa_ratio;
|
||||
workgroups_y /= gqa_ratio;
|
||||
}
|
||||
|
||||
tuning_params = get_fa_tuning_params(ctx->device, HSK, HSV, N, KV, k->type, v->type, f32acc);
|
||||
|
||||
const uint32_t q_stride = (uint32_t)(nbq1 / ggml_type_size(q->type));
|
||||
uint32_t k_stride = (uint32_t)(nbk1 / ggml_type_size(k->type));
|
||||
uint32_t v_stride = (uint32_t)(nbv1 / ggml_type_size(v->type));
|
||||
|
||||
// For F32, the shader treats it as a block of size 4 (for vec4 loads)
|
||||
if (k->type == GGML_TYPE_F32) {
|
||||
k_stride /= 4;
|
||||
}
|
||||
if (v->type == GGML_TYPE_F32) {
|
||||
v_stride /= 4;
|
||||
}
|
||||
|
||||
const uint32_t alignment = tuning_params.block_cols;
|
||||
bool aligned = (KV % alignment) == 0 &&
|
||||
// the "aligned" shader variant will forcibly align strides, for performance
|
||||
(q_stride & 7) == 0 && (k_stride & 7) == 0 && (v_stride & 7) == 0;
|
||||
|
||||
// Need to use the coopmat2 variant that clamps loads when HSK/HSV aren't sufficiently aligned.
|
||||
if (((HSK | HSV) % 16) != 0 && tuning_params.path == FA_COOPMAT2) {
|
||||
aligned = false;
|
||||
}
|
||||
|
||||
float scale = 1.0f;
|
||||
float max_bias = 0.0f;
|
||||
float logit_softcap = 0.0f;
|
||||
|
||||
memcpy(&scale, (const float *) dst->op_params + 0, sizeof(float));
|
||||
memcpy(&max_bias, (const float *) dst->op_params + 1, sizeof(float));
|
||||
memcpy(&logit_softcap, (const float *) dst->op_params + 2, sizeof(float));
|
||||
|
||||
if (logit_softcap != 0) {
|
||||
scale /= logit_softcap;
|
||||
}
|
||||
|
||||
// Only use mask opt when the mask is fairly large. This hasn't been tuned extensively.
|
||||
bool use_mask_opt = mask && nem1 >= 32 && nem0 * nem1 > 32768 && nem0 >= tuning_params.block_cols * 16;
|
||||
vk_fa_pipeline_state fa_pipeline_state = get_fa_pipeline_state(ctx->device, tuning_params, HSK, HSV, aligned, f32acc,
|
||||
mask != nullptr, use_mask_opt, logit_softcap != 0, k->type, v->type);
|
||||
|
||||
vk_pipeline pipeline = nullptr;
|
||||
|
||||
{
|
||||
std::lock_guard<std::mutex> guard(ctx->device->compile_mutex);
|
||||
auto &pipelines = ctx->device->pipeline_flash_attn_f32_f16;
|
||||
auto it = pipelines.find(fa_pipeline_state);
|
||||
if (it != pipelines.end()) {
|
||||
pipeline = it->second;
|
||||
} else {
|
||||
pipelines[fa_pipeline_state] = pipeline = std::make_shared<vk_pipeline_struct>();
|
||||
}
|
||||
}
|
||||
|
||||
assert(pipeline);
|
||||
// Compile early to initialize wg_denoms.
|
||||
ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);
|
||||
|
||||
uint32_t split_kv = KV;
|
||||
uint32_t split_k = 1;
|
||||
|
||||
// Intel Alchemist prefers more workgroups
|
||||
const uint32_t shader_core_count_multiplier = (ctx->device->vendor_id == VK_VENDOR_ID_INTEL && ctx->device->architecture != INTEL_XE2) ? 2 : 1;
|
||||
|
||||
// Use a placeholder core count if one isn't available. split_k is a big help for perf.
|
||||
const uint32_t shader_core_count = ctx->device->shader_core_count ? ctx->device->shader_core_count * shader_core_count_multiplier : 16;
|
||||
|
||||
const uint32_t Br = fa_pipeline_state.Br;
|
||||
const uint32_t Bc = fa_pipeline_state.Bc;
|
||||
|
||||
GGML_ASSERT(Br == pipeline->wg_denoms[0]);
|
||||
const uint32_t Tr = CEIL_DIV(N, Br);
|
||||
|
||||
// Try to use split_k when KV is large enough to be worth the overhead.
|
||||
if (gqa_ratio > 1 && workgroups_x <= Br) {
|
||||
split_k = shader_core_count * 2 / (workgroups_x * workgroups_y * workgroups_z);
|
||||
} else if (gqa_ratio <= 1) {
|
||||
uint32_t total_wgs_no_split = Tr * workgroups_y * workgroups_z;
|
||||
if (total_wgs_no_split < shader_core_count * 2) {
|
||||
split_k = shader_core_count * 2 / total_wgs_no_split;
|
||||
}
|
||||
}
|
||||
|
||||
if (split_k > 1) {
|
||||
// Try to evenly split KV into split_k chunks, but it needs to be a multiple
|
||||
// of "align", so recompute split_k based on that.
|
||||
split_kv = ROUNDUP_POW2(std::max(1u, KV / split_k), alignment);
|
||||
split_k = CEIL_DIV(KV, split_kv);
|
||||
}
|
||||
|
||||
// Reserve space for split_k temporaries. For each split x batch, we need to store the O matrix (D x ne1)
|
||||
// and the per-row m and L values (ne1 rows). We store all the matrices first, followed by the rows.
|
||||
// For matrices, the order is (inner to outer) [HSV, ne1, k, ne2, ne3].
|
||||
// For L/M, the order is (inner to outer) [ne1, k, ne2, ne3].
|
||||
const uint64_t split_k_size = split_k > 1 ? (HSV * ne1 * sizeof(float) + ne1 * sizeof(float) * 2) * split_k * ne2 * ne3 : 0;
|
||||
if (split_k_size > ctx->device->properties.limits.maxStorageBufferRange) {
|
||||
GGML_ABORT("Requested preallocation size is too large");
|
||||
}
|
||||
if (ctx->prealloc_size_split_k < split_k_size) {
|
||||
ctx->prealloc_size_split_k = split_k_size;
|
||||
ggml_vk_preallocate_buffers(ctx, subctx);
|
||||
}
|
||||
|
||||
const uint32_t mask_opt_num_dwords = CEIL_DIV(nem0, 16 * Bc);
|
||||
const uint64_t mask_opt_size = sizeof(uint32_t) * mask_opt_num_dwords * CEIL_DIV(nem1, Br) * nem2 * nem3;
|
||||
|
||||
vk_pipeline pipeline_fa_mask_opt = nullptr;
|
||||
if (use_mask_opt) {
|
||||
{
|
||||
std::lock_guard<std::mutex> guard(ctx->device->compile_mutex);
|
||||
auto &pipelines = ctx->device->pipeline_fa_mask_opt;
|
||||
auto it = pipelines.find({Br, Bc});
|
||||
if (it != pipelines.end()) {
|
||||
pipeline_fa_mask_opt = it->second;
|
||||
} else {
|
||||
pipelines[{Br, Bc}] = pipeline_fa_mask_opt = std::make_shared<vk_pipeline_struct>();
|
||||
}
|
||||
}
|
||||
assert(pipeline_fa_mask_opt);
|
||||
ggml_pipeline_request_descriptor_sets(ctx, pipeline_fa_mask_opt, 1);
|
||||
|
||||
if (ctx->prealloc_size_y < mask_opt_size) {
|
||||
ctx->prealloc_size_y = mask_opt_size;
|
||||
ggml_vk_preallocate_buffers(ctx, subctx);
|
||||
}
|
||||
if (ctx->prealloc_y_need_sync) {
|
||||
ggml_vk_sync_buffers(ctx, subctx);
|
||||
}
|
||||
}
|
||||
|
||||
const uint32_t n_head_kv = neq2;
|
||||
const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv));
|
||||
const float m0 = powf(2.0f, -(max_bias ) / n_head_log2);
|
||||
const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
|
||||
|
||||
vk_subbuffer q_buf = ggml_vk_tensor_subbuffer(ctx, q);
|
||||
vk_subbuffer k_buf = ggml_vk_tensor_subbuffer(ctx, k);
|
||||
vk_subbuffer v_buf = ggml_vk_tensor_subbuffer(ctx, v);
|
||||
vk_subbuffer dst_buf = ggml_vk_tensor_subbuffer(ctx, dst);
|
||||
vk_subbuffer mask_buf = mask ? ggml_vk_tensor_subbuffer(ctx, mask) : q_buf;
|
||||
vk_subbuffer sinks_buf = sinks ? ggml_vk_tensor_subbuffer(ctx, sinks) : q_buf;
|
||||
vk_subbuffer mask_opt_buf = use_mask_opt ? ggml_vk_subbuffer(ctx, ctx->prealloc_y, 0) : q_buf;
|
||||
|
||||
uint32_t mask_n_head_log2 = ((sinks != nullptr) << 24) | n_head_log2;
|
||||
|
||||
if (use_mask_opt)
|
||||
{
|
||||
const vk_op_flash_attn_mask_opt_push_constants opt_pc = {
|
||||
nem0,
|
||||
nem1,
|
||||
nem2,
|
||||
(uint32_t)(mask->nb[1] / sizeof(ggml_fp16_t)),
|
||||
(uint32_t)(mask->nb[2] / sizeof(ggml_fp16_t)),
|
||||
(uint32_t)(mask->nb[3] / sizeof(ggml_fp16_t)),
|
||||
mask_opt_num_dwords,
|
||||
mask_opt_num_dwords * CEIL_DIV(nem1, Br),
|
||||
mask_opt_num_dwords * CEIL_DIV(nem1, Br) * nem2,
|
||||
};
|
||||
|
||||
ggml_vk_dispatch_pipeline(ctx, subctx, pipeline_fa_mask_opt,
|
||||
{ mask_buf, mask_opt_buf }, opt_pc,
|
||||
{ mask_opt_num_dwords, CEIL_DIV(nem1, Br), nem2 * nem3 });
|
||||
ggml_vk_sync_buffers(ctx, subctx);
|
||||
}
|
||||
|
||||
const vk_flash_attn_push_constants pc = { N, KV,
|
||||
(uint32_t)ne1, (uint32_t)ne2, (uint32_t)ne3,
|
||||
(uint32_t)neq2, (uint32_t)neq3,
|
||||
(uint32_t)nek2, (uint32_t)nek3,
|
||||
(uint32_t)nev2, (uint32_t)nev3,
|
||||
nem1, nem2, nem3,
|
||||
q_stride, (uint32_t)nbq2, (uint32_t)nbq3,
|
||||
k_stride, (uint32_t)nbk2, (uint32_t)nbk3,
|
||||
v_stride, (uint32_t)nbv2, (uint32_t)nbv3,
|
||||
scale, max_bias, logit_softcap,
|
||||
mask_n_head_log2, m0, m1,
|
||||
gqa_ratio, split_kv, split_k };
|
||||
|
||||
if (split_k > 1) {
|
||||
ggml_pipeline_request_descriptor_sets(ctx, ctx->device->pipeline_flash_attn_split_k_reduce, 1);
|
||||
|
||||
if (ctx->prealloc_split_k_need_sync) {
|
||||
ggml_vk_sync_buffers(ctx, subctx);
|
||||
}
|
||||
|
||||
// We reuse workgroups_x to mean the number of splits, so we need to
|
||||
// cancel out the divide by wg_denoms[0].
|
||||
uint32_t dispatch_x;
|
||||
if (gqa_ratio > 1) {
|
||||
workgroups_x *= pipeline->wg_denoms[0];
|
||||
dispatch_x = split_k * workgroups_x;
|
||||
} else {
|
||||
dispatch_x = Tr * split_k * pipeline->wg_denoms[0];
|
||||
}
|
||||
|
||||
vk_subbuffer split_k_buf = ggml_vk_subbuffer(ctx, ctx->prealloc_split_k, 0);
|
||||
ggml_vk_dispatch_pipeline(ctx, subctx, pipeline,
|
||||
{q_buf, k_buf, v_buf, mask_buf, sinks_buf, split_k_buf, mask_opt_buf},
|
||||
pc, { dispatch_x, workgroups_y, workgroups_z });
|
||||
|
||||
ggml_vk_sync_buffers(ctx, subctx);
|
||||
const vk_op_flash_attn_split_k_reduce_push_constants pc2 = { HSV, (uint32_t)ne1, (uint32_t)ne2, (uint32_t)ne3, split_k, (sinks != nullptr) };
|
||||
ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_flash_attn_split_k_reduce,
|
||||
{split_k_buf, sinks_buf, dst_buf},
|
||||
pc2, { (uint32_t)ne1, HSV, (uint32_t)(ne2 * ne3) });
|
||||
ctx->prealloc_split_k_need_sync = true;
|
||||
} else {
|
||||
if (gqa_ratio > 1) {
|
||||
// When using gqa, we want one actual workgroup per batch, so cancel out wg_denoms
|
||||
workgroups_x *= pipeline->wg_denoms[0];
|
||||
}
|
||||
ggml_vk_dispatch_pipeline(ctx, subctx, pipeline,
|
||||
{q_buf, k_buf, v_buf, mask_buf, sinks_buf, dst_buf, mask_opt_buf},
|
||||
pc, { workgroups_x, workgroups_y, workgroups_z });
|
||||
}
|
||||
}
|
||||
|
||||
1610
ggml/src/ggml-vulkan/ggml-vulkan-graph.cpp
Normal file
1610
ggml/src/ggml-vulkan/ggml-vulkan-graph.cpp
Normal file
File diff suppressed because it is too large
Load Diff
1618
ggml/src/ggml-vulkan/ggml-vulkan-instance.cpp
Normal file
1618
ggml/src/ggml-vulkan/ggml-vulkan-instance.cpp
Normal file
File diff suppressed because it is too large
Load Diff
2207
ggml/src/ggml-vulkan/ggml-vulkan-matmul.cpp
Normal file
2207
ggml/src/ggml-vulkan/ggml-vulkan-matmul.cpp
Normal file
File diff suppressed because it is too large
Load Diff
2893
ggml/src/ggml-vulkan/ggml-vulkan-operators.cpp
Normal file
2893
ggml/src/ggml-vulkan/ggml-vulkan-operators.cpp
Normal file
File diff suppressed because it is too large
Load Diff
512
ggml/src/ggml-vulkan/ggml-vulkan-pipelines.cpp
Normal file
512
ggml/src/ggml-vulkan/ggml-vulkan-pipelines.cpp
Normal file
@@ -0,0 +1,512 @@
|
||||
#include "ggml-vulkan-common.h"
|
||||
|
||||
std::mutex queue_mutex;
|
||||
|
||||
void * const vk_ptr_base = (void *)(uintptr_t) 0x1000; // NOLINT
|
||||
|
||||
uint64_t vk_tensor_offset(const ggml_tensor * tensor) {
|
||||
if (tensor->view_src) {
|
||||
return (uint8_t *) tensor->view_src->data - (uint8_t *) vk_ptr_base;
|
||||
}
|
||||
return (uint8_t *) tensor->data - (uint8_t *) vk_ptr_base;
|
||||
}
|
||||
|
||||
uint32_t get_misalign_bytes(const ggml_backend_vk_context * ctx, const ggml_tensor * t)
|
||||
{
|
||||
return ((vk_tensor_offset(t) + t->view_offs) & (ctx->device->properties.limits.minStorageBufferOffsetAlignment - 1));;
|
||||
}
|
||||
|
||||
static VkDeviceSize ggml_vk_get_max_buffer_range(const ggml_backend_vk_context * ctx, const vk_buffer &buf, const VkDeviceSize offset) {
|
||||
const VkDeviceSize range = std::min(VkDeviceSize{buf->size - offset},
|
||||
VkDeviceSize{ctx->device->properties.limits.maxStorageBufferRange});
|
||||
return range;
|
||||
}
|
||||
|
||||
void ggml_vk_wait_for_fence(ggml_backend_vk_context * ctx) {
|
||||
// Use waitForFences while most of the graph executes. Hopefully the CPU can sleep
|
||||
// during this wait.
|
||||
if (ctx->almost_ready_fence_pending) {
|
||||
VK_CHECK(ctx->device->device.waitForFences({ ctx->almost_ready_fence }, true, UINT64_MAX), "almost_ready_fence");
|
||||
ctx->device->device.resetFences({ ctx->almost_ready_fence });
|
||||
ctx->almost_ready_fence_pending = false;
|
||||
}
|
||||
|
||||
// Spin (w/pause) waiting for the graph to finish executing.
|
||||
vk::Result result;
|
||||
while ((result = ctx->device->device.getFenceStatus(ctx->fence)) != vk::Result::eSuccess) {
|
||||
if (result != vk::Result::eNotReady) {
|
||||
fprintf(stderr, "ggml_vulkan: error %s at %s:%d\n", to_string(result).c_str(), __FILE__, __LINE__);
|
||||
exit(1);
|
||||
}
|
||||
for (uint32_t i = 0; i < 100; ++i) {
|
||||
YIELD();
|
||||
YIELD();
|
||||
YIELD();
|
||||
YIELD();
|
||||
YIELD();
|
||||
YIELD();
|
||||
YIELD();
|
||||
YIELD();
|
||||
YIELD();
|
||||
YIELD();
|
||||
}
|
||||
}
|
||||
ctx->device->device.resetFences({ ctx->fence });
|
||||
}
|
||||
|
||||
void ggml_pipeline_request_descriptor_sets(ggml_backend_vk_context *ctx, vk_pipeline& pipeline, uint32_t n) {
|
||||
VK_LOG_DEBUG("ggml_pipeline_request_descriptor_sets(" << pipeline->name << ", " << n << ")");
|
||||
ctx->pipeline_descriptor_set_requirements += n;
|
||||
if (!pipeline->compiled) {
|
||||
ggml_vk_load_shaders(ctx->device, pipeline);
|
||||
}
|
||||
ggml_pipeline_allocate_descriptor_sets(ctx);
|
||||
}
|
||||
|
||||
void ggml_pipeline_allocate_descriptor_sets(ggml_backend_vk_context * ctx) {
|
||||
|
||||
if (ctx->descriptor_sets.size() >= ctx->pipeline_descriptor_set_requirements) {
|
||||
// Enough descriptors are available
|
||||
return;
|
||||
}
|
||||
|
||||
vk_device& device = ctx->device;
|
||||
|
||||
// Grow by 50% to avoid frequent allocations
|
||||
uint32_t needed = std::max(3 * ctx->descriptor_sets.size() / 2, size_t{ctx->pipeline_descriptor_set_requirements});
|
||||
uint32_t to_alloc = needed - ctx->descriptor_sets.size();
|
||||
uint32_t pool_remaining = VK_DEVICE_DESCRIPTOR_POOL_SIZE - ctx->descriptor_sets.size() % VK_DEVICE_DESCRIPTOR_POOL_SIZE;
|
||||
uint32_t pool_idx = ctx->descriptor_sets.size() / VK_DEVICE_DESCRIPTOR_POOL_SIZE;
|
||||
|
||||
while (to_alloc > 0) {
|
||||
const uint32_t alloc_count = std::min(pool_remaining, to_alloc);
|
||||
to_alloc -= alloc_count;
|
||||
pool_remaining = VK_DEVICE_DESCRIPTOR_POOL_SIZE;
|
||||
|
||||
if (pool_idx >= ctx->descriptor_pools.size()) {
|
||||
vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, MAX_PARAMETER_COUNT * VK_DEVICE_DESCRIPTOR_POOL_SIZE);
|
||||
vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, VK_DEVICE_DESCRIPTOR_POOL_SIZE, descriptor_pool_size);
|
||||
ctx->descriptor_pools.push_back(device->device.createDescriptorPool(descriptor_pool_create_info));
|
||||
}
|
||||
|
||||
std::vector<vk::DescriptorSetLayout> layouts(alloc_count);
|
||||
for (uint32_t i = 0; i < alloc_count; i++) {
|
||||
layouts[i] = device->dsl;
|
||||
}
|
||||
vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(ctx->descriptor_pools[pool_idx], alloc_count, layouts.data());
|
||||
std::vector<vk::DescriptorSet> sets = device->device.allocateDescriptorSets(descriptor_set_alloc_info);
|
||||
ctx->descriptor_sets.insert(ctx->descriptor_sets.end(), sets.begin(), sets.end());
|
||||
|
||||
pool_idx++;
|
||||
}
|
||||
}
|
||||
|
||||
static vk_command_buffer* ggml_vk_create_cmd_buffer(vk_device& device, vk_command_pool& p) {
|
||||
VK_LOG_DEBUG("ggml_vk_create_cmd_buffer()");
|
||||
vk::CommandBufferAllocateInfo command_buffer_alloc_info(
|
||||
p.pool,
|
||||
vk::CommandBufferLevel::ePrimary,
|
||||
1);
|
||||
const std::vector<vk::CommandBuffer> cmd_buffers = device->device.allocateCommandBuffers(command_buffer_alloc_info);
|
||||
p.cmd_buffers.push_back({ cmd_buffers.front(), 0, true });
|
||||
return &p.cmd_buffers[p.cmd_buffers.size()-1];
|
||||
}
|
||||
|
||||
void ggml_vk_submit(vk_context& ctx, vk::Fence fence) {
|
||||
if (ctx->seqs.empty()) {
|
||||
if (fence) {
|
||||
std::lock_guard<std::mutex> guard(queue_mutex);
|
||||
ctx->p->q->queue.submit({}, fence);
|
||||
}
|
||||
return;
|
||||
}
|
||||
VK_LOG_DEBUG("ggml_vk_submit(" << ctx << ", " << fence << ")");
|
||||
|
||||
std::vector<std::vector<uint64_t>> tl_wait_vals;
|
||||
std::vector<std::vector<uint64_t>> tl_signal_vals;
|
||||
std::vector<std::vector<vk::Semaphore>> tl_wait_semaphores;
|
||||
std::vector<std::vector<vk::Semaphore>> tl_signal_semaphores;
|
||||
std::vector<vk::TimelineSemaphoreSubmitInfo> tl_submit_infos;
|
||||
std::vector<vk::SubmitInfo> submit_infos;
|
||||
int idx = -1;
|
||||
std::vector<std::vector<vk::PipelineStageFlags>> stage_flags;
|
||||
|
||||
size_t reserve = 0;
|
||||
|
||||
for (const auto& sequence : ctx->seqs) {
|
||||
reserve += sequence.size();
|
||||
}
|
||||
|
||||
// Pre-reserve vectors to prevent reallocation, which invalidates pointers
|
||||
tl_wait_semaphores.reserve(reserve);
|
||||
tl_wait_vals.reserve(reserve);
|
||||
tl_signal_semaphores.reserve(reserve);
|
||||
tl_signal_vals.reserve(reserve);
|
||||
tl_submit_infos.reserve(reserve);
|
||||
submit_infos.reserve(reserve);
|
||||
stage_flags.reserve(reserve);
|
||||
|
||||
for (const auto& sequence : ctx->seqs) {
|
||||
for (const auto& submission : sequence) {
|
||||
stage_flags.push_back({});
|
||||
idx++;
|
||||
tl_wait_vals.push_back({});
|
||||
tl_wait_semaphores.push_back({});
|
||||
tl_signal_vals.push_back({});
|
||||
tl_signal_semaphores.push_back({});
|
||||
for (size_t i = 0; i < submission.wait_semaphores.size(); i++) {
|
||||
stage_flags[idx].push_back(ctx->p->q->stage_flags);
|
||||
tl_wait_vals[idx].push_back(submission.wait_semaphores[i].value);
|
||||
tl_wait_semaphores[idx].push_back(submission.wait_semaphores[i].s);
|
||||
}
|
||||
for (size_t i = 0; i < submission.signal_semaphores.size(); i++) {
|
||||
tl_signal_vals[idx].push_back(submission.signal_semaphores[i].value);
|
||||
tl_signal_semaphores[idx].push_back(submission.signal_semaphores[i].s);
|
||||
}
|
||||
tl_submit_infos.push_back({
|
||||
(uint32_t) submission.wait_semaphores.size(),
|
||||
tl_wait_vals[idx].data(),
|
||||
(uint32_t) submission.signal_semaphores.size(),
|
||||
tl_signal_vals[idx].data(),
|
||||
});
|
||||
tl_submit_infos[idx].sType = vk::StructureType::eTimelineSemaphoreSubmitInfo;
|
||||
tl_submit_infos[idx].pNext = nullptr;
|
||||
vk::SubmitInfo si{
|
||||
(uint32_t) submission.wait_semaphores.size(),
|
||||
tl_wait_semaphores[idx].data(),
|
||||
stage_flags[idx].data(),
|
||||
1,
|
||||
&submission.buffer->buf,
|
||||
(uint32_t) submission.signal_semaphores.size(),
|
||||
tl_signal_semaphores[idx].data(),
|
||||
};
|
||||
si.setPNext(&tl_submit_infos[idx]);
|
||||
submit_infos.push_back(si);
|
||||
}
|
||||
}
|
||||
|
||||
std::lock_guard<std::mutex> guard(queue_mutex);
|
||||
ctx->p->q->queue.submit(submit_infos, fence);
|
||||
|
||||
ctx->seqs.clear();
|
||||
}
|
||||
|
||||
uint32_t ggml_vk_find_queue_family_index(std::vector<vk::QueueFamilyProperties>& queue_family_props, const vk::QueueFlags& required, const vk::QueueFlags& avoid, int32_t compute_index, uint32_t min_num_queues) {
|
||||
VK_LOG_DEBUG("ggml_vk_find_queue_family_index()");
|
||||
const uint32_t qfsize = queue_family_props.size();
|
||||
|
||||
// Try with avoid preferences first
|
||||
for (uint32_t i = 0; i < qfsize; i++) {
|
||||
if (queue_family_props[i].queueCount >= min_num_queues && (compute_index < 0 || i != (uint32_t) compute_index) && queue_family_props[i].queueFlags & required && !(queue_family_props[i].queueFlags & avoid)) {
|
||||
return i;
|
||||
}
|
||||
}
|
||||
|
||||
// Fall back to only required
|
||||
for (size_t i = 0; i < qfsize; i++) {
|
||||
if (queue_family_props[i].queueCount >= min_num_queues && (compute_index < 0 || i != (uint32_t) compute_index) && queue_family_props[i].queueFlags & required) {
|
||||
return i;
|
||||
}
|
||||
}
|
||||
|
||||
// Fall back to reusing compute queue
|
||||
for (size_t i = 0; i < qfsize; i++) {
|
||||
if (queue_family_props[i].queueCount >= min_num_queues && queue_family_props[i].queueFlags & required) {
|
||||
return i;
|
||||
}
|
||||
}
|
||||
|
||||
// Fall back to ignoring min_num_queries
|
||||
for (size_t i = 0; i < qfsize; i++) {
|
||||
if (queue_family_props[i].queueFlags & required) {
|
||||
return i;
|
||||
}
|
||||
}
|
||||
|
||||
// All commands that are allowed on a queue that supports transfer operations are also allowed on a queue that supports either graphics or compute operations.
|
||||
// Thus, if the capabilities of a queue family include VK_QUEUE_GRAPHICS_BIT or VK_QUEUE_COMPUTE_BIT, then reporting the VK_QUEUE_TRANSFER_BIT capability separately for that queue family is optional.
|
||||
if (compute_index >= 0) {
|
||||
return compute_index;
|
||||
}
|
||||
|
||||
std::cerr << "ggml_vulkan: No suitable queue family index found." << std::endl;
|
||||
|
||||
for(auto &q_family : queue_family_props) {
|
||||
std::cerr << "Queue number: " + std::to_string(q_family.queueCount) << " flags: " + to_string(q_family.queueFlags) << std::endl;
|
||||
}
|
||||
abort();
|
||||
}
|
||||
|
||||
void ggml_vk_create_queue(vk_device& device, vk_queue& q, uint32_t queue_family_index, uint32_t queue_index, vk::PipelineStageFlags&& stage_flags, bool transfer_only) {
|
||||
VK_LOG_DEBUG("ggml_vk_create_queue()");
|
||||
std::lock_guard<std::recursive_mutex> guard(device->mutex);
|
||||
|
||||
q.queue_family_index = queue_family_index;
|
||||
q.transfer_only = transfer_only;
|
||||
|
||||
q.cmd_pool.init(device, &q);
|
||||
|
||||
q.queue = device->device.getQueue(queue_family_index, queue_index);
|
||||
|
||||
q.stage_flags = stage_flags;
|
||||
}
|
||||
|
||||
vk_context ggml_vk_create_context(ggml_backend_vk_context * ctx, vk_command_pool& p) {
|
||||
vk_context result = std::make_shared<vk_context_struct>();
|
||||
VK_LOG_DEBUG("ggml_vk_create_context(" << result << ")");
|
||||
ctx->gc.contexts.emplace_back(result);
|
||||
result->p = &p;
|
||||
return result;
|
||||
}
|
||||
|
||||
vk_context ggml_vk_create_temporary_context(vk_command_pool& p) {
|
||||
vk_context result = std::make_shared<vk_context_struct>();
|
||||
VK_LOG_DEBUG("ggml_vk_create_temporary_context(" << result << ")");
|
||||
result->p = &p;
|
||||
return result;
|
||||
}
|
||||
|
||||
static vk_semaphore * ggml_vk_create_binary_semaphore(ggml_backend_vk_context * ctx) {
|
||||
VK_LOG_DEBUG("ggml_vk_create_timeline_semaphore()");
|
||||
vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eBinary, 0 };
|
||||
vk::SemaphoreCreateInfo ci{};
|
||||
ci.setPNext(&tci);
|
||||
vk::Semaphore semaphore = ctx->device->device.createSemaphore(ci);
|
||||
ctx->gc.semaphores.push_back({ semaphore, 0 });
|
||||
return &ctx->gc.semaphores[ctx->gc.semaphores.size() - 1];
|
||||
}
|
||||
|
||||
static vk_semaphore * ggml_vk_create_timeline_semaphore(ggml_backend_vk_context * ctx) {
|
||||
VK_LOG_DEBUG("ggml_vk_create_timeline_semaphore()");
|
||||
if (ctx->semaphore_idx >= ctx->gc.tl_semaphores.size()) {
|
||||
vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eTimeline, 0 };
|
||||
vk::SemaphoreCreateInfo ci{};
|
||||
ci.setPNext(&tci);
|
||||
vk::Semaphore semaphore = ctx->device->device.createSemaphore(ci);
|
||||
ctx->gc.tl_semaphores.push_back({ semaphore, 0 });
|
||||
}
|
||||
return &ctx->gc.tl_semaphores[ctx->semaphore_idx++];
|
||||
}
|
||||
|
||||
static vk::Event ggml_vk_create_event(ggml_backend_vk_context * ctx) {
|
||||
if (ctx->event_idx >= ctx->gc.events.size()) {
|
||||
ctx->gc.events.push_back(ctx->device->device.createEvent({}));
|
||||
}
|
||||
return ctx->gc.events[ctx->event_idx++];
|
||||
}
|
||||
|
||||
void ggml_vk_command_pool_cleanup(vk_device& device, vk_command_pool& p) {
|
||||
VK_LOG_DEBUG("ggml_vk_command_pool_cleanup()");
|
||||
|
||||
// Requires command buffers to be done
|
||||
device->device.resetCommandPool(p.pool);
|
||||
// Don't clear the command buffers and mark them as not in use.
|
||||
// This allows us to reuse them
|
||||
for (auto& cmd_buffer : p.cmd_buffers) {
|
||||
cmd_buffer.in_use = false;
|
||||
}
|
||||
}
|
||||
|
||||
void ggml_vk_queue_command_pools_cleanup(vk_device& device) {
|
||||
VK_LOG_DEBUG("ggml_vk_queue_command_pools_cleanup()");
|
||||
|
||||
// Arbitrary frequency to cleanup/reuse command buffers
|
||||
static constexpr uint32_t cleanup_frequency = 10;
|
||||
|
||||
if (device->compute_queue.cmd_pool.buffers_in_use() >= cleanup_frequency) {
|
||||
ggml_vk_command_pool_cleanup(device, device->compute_queue.cmd_pool);
|
||||
}
|
||||
if (device->transfer_queue.cmd_pool.buffers_in_use() >= cleanup_frequency) {
|
||||
ggml_vk_command_pool_cleanup(device, device->transfer_queue.cmd_pool);
|
||||
}
|
||||
}
|
||||
|
||||
vk_subbuffer ggml_vk_subbuffer(const ggml_backend_vk_context* ctx, const vk_buffer& buf, size_t offset) {
|
||||
return { buf, offset, ggml_vk_get_max_buffer_range(ctx, buf, offset) };
|
||||
}
|
||||
|
||||
void ggml_vk_sync_buffers(ggml_backend_vk_context* ctx, vk_context& subctx) {
|
||||
VK_LOG_DEBUG("ggml_vk_sync_buffers()");
|
||||
|
||||
const bool transfer_queue = subctx->p->q->transfer_only;
|
||||
|
||||
if (ctx) {
|
||||
ctx->prealloc_x_need_sync = ctx->prealloc_y_need_sync = ctx->prealloc_split_k_need_sync = false;
|
||||
}
|
||||
|
||||
subctx->s->buffer->buf.pipelineBarrier(
|
||||
subctx->p->q->stage_flags,
|
||||
subctx->p->q->stage_flags,
|
||||
{},
|
||||
{ {
|
||||
{ !transfer_queue ? (vk::AccessFlagBits::eShaderRead | vk::AccessFlagBits::eShaderWrite | vk::AccessFlagBits::eTransferRead | vk::AccessFlagBits::eTransferWrite) : (vk::AccessFlagBits::eTransferRead | vk::AccessFlagBits::eTransferWrite) },
|
||||
{ !transfer_queue ? (vk::AccessFlagBits::eShaderRead | vk::AccessFlagBits::eShaderWrite | vk::AccessFlagBits::eTransferRead | vk::AccessFlagBits::eTransferWrite) : (vk::AccessFlagBits::eTransferRead | vk::AccessFlagBits::eTransferWrite) }
|
||||
} },
|
||||
{},
|
||||
{}
|
||||
);
|
||||
}
|
||||
|
||||
static void ggml_vk_reset_event(vk_context& ctx, vk::Event& event) {
|
||||
VK_LOG_DEBUG("ggml_vk_set_event()");
|
||||
|
||||
ctx->s->buffer->buf.resetEvent(
|
||||
event,
|
||||
ctx->p->q->stage_flags
|
||||
);
|
||||
}
|
||||
|
||||
void ggml_vk_set_event(vk_context& ctx, vk::Event& event) {
|
||||
VK_LOG_DEBUG("ggml_vk_set_event()");
|
||||
|
||||
ctx->s->buffer->buf.setEvent(
|
||||
event,
|
||||
ctx->p->q->stage_flags
|
||||
);
|
||||
}
|
||||
|
||||
void ggml_vk_wait_events(vk_context& ctx, std::vector<vk::Event>&& events) {
|
||||
VK_LOG_DEBUG("ggml_vk_wait_events()");
|
||||
if (events.empty()) {
|
||||
return;
|
||||
}
|
||||
|
||||
ctx->s->buffer->buf.waitEvents(
|
||||
events,
|
||||
ctx->p->q->stage_flags,
|
||||
ctx->p->q->stage_flags,
|
||||
{},
|
||||
{},
|
||||
{}
|
||||
);
|
||||
}
|
||||
|
||||
vk_subbuffer ggml_vk_tensor_subbuffer(
|
||||
const ggml_backend_vk_context * ctx, const ggml_tensor * tensor, bool allow_misalign) {
|
||||
|
||||
vk_buffer buffer = nullptr;
|
||||
size_t offset = 0;
|
||||
if (ctx->device->uma) {
|
||||
ggml_vk_host_get(ctx->device, tensor->data, buffer, offset);
|
||||
}
|
||||
if (!buffer) {
|
||||
auto buf_ctx = (ggml_backend_vk_buffer_context *)tensor->buffer->context;
|
||||
buffer = buf_ctx->dev_buffer;
|
||||
offset = vk_tensor_offset(tensor) + tensor->view_offs;
|
||||
}
|
||||
GGML_ASSERT(buffer != nullptr);
|
||||
|
||||
size_t size = ggml_nbytes(tensor);
|
||||
|
||||
size_t misalign_bytes = offset & (ctx->device->properties.limits.minStorageBufferOffsetAlignment - 1);
|
||||
// The shader must support misaligned offsets when indexing into the buffer
|
||||
GGML_ASSERT(allow_misalign || misalign_bytes == 0);
|
||||
offset &= ~misalign_bytes;
|
||||
size += misalign_bytes;
|
||||
|
||||
return vk_subbuffer{buffer, offset, size};
|
||||
}
|
||||
|
||||
static vk_command_buffer* ggml_vk_get_or_create_cmd_buffer(vk_device& device, vk_command_pool& pool) {
|
||||
for (auto& cmd_buffer : pool.cmd_buffers) {
|
||||
if (!cmd_buffer.in_use) {
|
||||
cmd_buffer.use_counter++;
|
||||
cmd_buffer.in_use = true;
|
||||
return &cmd_buffer;
|
||||
}
|
||||
}
|
||||
return ggml_vk_create_cmd_buffer(device, pool);
|
||||
}
|
||||
|
||||
static vk_submission ggml_vk_begin_submission(vk_device& device, vk_command_pool& p, bool one_time = true) {
|
||||
vk_submission s;
|
||||
s.buffer = ggml_vk_get_or_create_cmd_buffer(device, p);
|
||||
if (one_time) {
|
||||
s.buffer->buf.begin({ vk::CommandBufferUsageFlagBits::eOneTimeSubmit });
|
||||
} else {
|
||||
s.buffer->buf.begin({ vk::CommandBufferUsageFlags{} });
|
||||
}
|
||||
|
||||
return s;
|
||||
}
|
||||
|
||||
void ggml_vk_ctx_end(vk_context& ctx) {
|
||||
VK_LOG_DEBUG("ggml_vk_ctx_end(" << ctx << ", " << ctx->seqs.size() << ")");
|
||||
if (ctx->s == nullptr) {
|
||||
return;
|
||||
}
|
||||
|
||||
ctx->s->buffer->buf.end();
|
||||
ctx->s = nullptr;
|
||||
}
|
||||
|
||||
void ggml_vk_ctx_begin(vk_device& device, vk_context& subctx) {
|
||||
VK_LOG_DEBUG("ggml_vk_ctx_begin(" << device->name << ")");
|
||||
if (subctx->s != nullptr) {
|
||||
ggml_vk_ctx_end(subctx);
|
||||
}
|
||||
|
||||
subctx->seqs.push_back({ ggml_vk_begin_submission(device, *subctx->p) });
|
||||
subctx->s = subctx->seqs[subctx->seqs.size() - 1].data();
|
||||
}
|
||||
|
||||
vk_context ggml_vk_get_compute_ctx(ggml_backend_vk_context * ctx) {
|
||||
vk_context result;
|
||||
if (!ctx->compute_ctx.expired()) {
|
||||
result = ctx->compute_ctx.lock();
|
||||
} else {
|
||||
result = ggml_vk_create_context(ctx, ctx->compute_cmd_pool);
|
||||
|
||||
ctx->compute_ctx = result;
|
||||
ggml_vk_ctx_begin(ctx->device, result);
|
||||
}
|
||||
|
||||
if (ctx->device->async_use_transfer_queue && ctx->transfer_semaphore_last_submitted < ctx->transfer_semaphore.value) {
|
||||
result->s->wait_semaphores.push_back(ctx->transfer_semaphore);
|
||||
ctx->transfer_semaphore_last_submitted = ctx->transfer_semaphore.value;
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
bool ggml_vk_submit_transfer_ctx(ggml_backend_vk_context * ctx) {
|
||||
if (!ctx->device->async_use_transfer_queue || ctx->transfer_ctx.expired()) {
|
||||
return false;
|
||||
}
|
||||
|
||||
vk_context cpy_ctx = ctx->transfer_ctx.lock();
|
||||
ggml_vk_ctx_end(cpy_ctx);
|
||||
|
||||
for (auto& cpy : cpy_ctx->in_memcpys) {
|
||||
memcpy(cpy.dst, cpy.src, cpy.n);
|
||||
}
|
||||
|
||||
ctx->transfer_semaphore.value++;
|
||||
cpy_ctx->seqs.back().back().signal_semaphores.push_back(ctx->transfer_semaphore);
|
||||
|
||||
ggml_vk_submit(cpy_ctx, {});
|
||||
ctx->transfer_ctx.reset();
|
||||
return true;
|
||||
}
|
||||
|
||||
size_t ggml_vk_align_size(size_t width, size_t align) {
|
||||
VK_LOG_DEBUG("ggml_vk_align_size(" << width << ", " << align << ")");
|
||||
return CEIL_DIV(width, align) * align;
|
||||
}
|
||||
|
||||
void deferred_memcpy(void * dst, const void * src, size_t size, std::vector<vk_staging_memcpy>* memcpys) {
|
||||
if (memcpys == nullptr) {
|
||||
memcpy(dst, src, size);
|
||||
} else {
|
||||
memcpys->emplace_back(dst, src, size);
|
||||
}
|
||||
}
|
||||
|
||||
void deferred_memset(void * dst, uint32_t val, size_t size, std::vector<vk_staging_memset>* memsets) {
|
||||
if (memsets == nullptr) {
|
||||
memset(dst, val, size);
|
||||
} else {
|
||||
memsets->emplace_back(dst, val, size);
|
||||
}
|
||||
}
|
||||
|
||||
872
ggml/src/ggml-vulkan/ggml-vulkan-push-constants.h
Normal file
872
ggml/src/ggml-vulkan/ggml-vulkan-push-constants.h
Normal file
@@ -0,0 +1,872 @@
|
||||
#pragma once
|
||||
#include "ggml-vulkan-types.h"
|
||||
|
||||
uint32_t get_misalign_bytes(const ggml_backend_vk_context * ctx, const ggml_tensor * t);
|
||||
|
||||
struct vk_mat_mat_push_constants {
|
||||
uint32_t M; uint32_t N; uint32_t K;
|
||||
uint32_t stride_a; uint32_t stride_b; uint32_t stride_d;
|
||||
uint32_t batch_stride_a; uint32_t batch_stride_b; uint32_t batch_stride_d;
|
||||
uint32_t base_work_group_z; uint32_t num_batches;
|
||||
uint32_t k_split;
|
||||
uint32_t ne02; uint32_t ne12; uint32_t broadcast2; uint32_t broadcast3;
|
||||
uint32_t padded_N;
|
||||
};
|
||||
|
||||
struct vk_mat_vec_push_constants {
|
||||
uint32_t ncols;
|
||||
uint32_t stride_a;
|
||||
uint32_t stride_b;
|
||||
uint32_t stride_d;
|
||||
uint32_t batch_stride_a;
|
||||
uint32_t batch_stride_b;
|
||||
uint32_t batch_stride_d;
|
||||
uint32_t fusion_flags;
|
||||
uint32_t base_work_group_y;
|
||||
uint32_t ne02;
|
||||
uint32_t ne12;
|
||||
uint32_t broadcast2;
|
||||
uint32_t broadcast3;
|
||||
};
|
||||
|
||||
struct vk_mat_vec_p021_push_constants {
|
||||
uint32_t ncols_x;
|
||||
uint32_t nrows_x;
|
||||
uint32_t nchannels_x;
|
||||
uint32_t nchannels_y;
|
||||
uint32_t b_offset;
|
||||
uint32_t d_offset;
|
||||
uint32_t fusion_flags;
|
||||
};
|
||||
|
||||
struct vk_mat_vec_nc_push_constants {
|
||||
uint32_t ncols_x;
|
||||
uint32_t nrows_x;
|
||||
uint32_t row_stride_x;
|
||||
uint32_t channel_stride_x;
|
||||
uint32_t channel_stride_y;
|
||||
uint32_t channel_x_divisor;
|
||||
uint32_t ne12;
|
||||
uint32_t b_offset;
|
||||
uint32_t d_offset;
|
||||
uint32_t nb03;
|
||||
uint32_t nb13;
|
||||
uint32_t nb23;
|
||||
uint32_t fusion_flags;
|
||||
};
|
||||
|
||||
struct vk_mat_mat_id_push_constants {
|
||||
uint32_t M; uint32_t N; uint32_t K;
|
||||
uint32_t stride_a; uint32_t stride_b; uint32_t stride_d;
|
||||
uint32_t batch_stride_a; uint32_t batch_stride_b; uint32_t batch_stride_d;
|
||||
uint32_t nei0; uint32_t nei1; uint32_t nbi1; uint32_t ne11;
|
||||
uint32_t padded_N;
|
||||
};
|
||||
|
||||
struct vk_mat_vec_id_push_constants {
|
||||
uint32_t ncols;
|
||||
uint32_t stride_a;
|
||||
uint32_t stride_b;
|
||||
uint32_t stride_d;
|
||||
uint32_t batch_stride_a;
|
||||
uint32_t batch_stride_b;
|
||||
uint32_t batch_stride_d;
|
||||
uint32_t fusion_flags;
|
||||
uint32_t nei0;
|
||||
uint32_t ne11;
|
||||
uint32_t expert_i1;
|
||||
uint32_t nbi1;
|
||||
};
|
||||
|
||||
struct vk_flash_attn_push_constants {
|
||||
uint32_t N;
|
||||
uint32_t KV;
|
||||
|
||||
uint32_t ne1;
|
||||
uint32_t ne2;
|
||||
uint32_t ne3;
|
||||
|
||||
uint32_t neq2;
|
||||
uint32_t neq3;
|
||||
uint32_t nek2;
|
||||
uint32_t nek3;
|
||||
uint32_t nev2;
|
||||
uint32_t nev3;
|
||||
uint32_t nem1;
|
||||
uint32_t nem2;
|
||||
uint32_t nem3;
|
||||
|
||||
uint32_t nb01;
|
||||
uint32_t nb02;
|
||||
uint32_t nb03;
|
||||
uint32_t nb11;
|
||||
uint32_t nb12;
|
||||
uint32_t nb13;
|
||||
uint32_t nb21;
|
||||
uint32_t nb22;
|
||||
uint32_t nb23;
|
||||
|
||||
float scale;
|
||||
float max_bias;
|
||||
float logit_softcap;
|
||||
|
||||
uint32_t mask_n_head_log2;
|
||||
float m0;
|
||||
float m1;
|
||||
|
||||
uint32_t gqa_ratio;
|
||||
uint32_t split_kv;
|
||||
uint32_t k_num;
|
||||
};
|
||||
|
||||
static_assert(sizeof(vk_flash_attn_push_constants) <= 128, "sizeof(vk_flash_attn_push_constants) must be <= 128");
|
||||
|
||||
struct vk_op_push_constants {
|
||||
uint32_t KX;
|
||||
uint32_t KY;
|
||||
float param1;
|
||||
float param2;
|
||||
float param3;
|
||||
float param4;
|
||||
};
|
||||
|
||||
struct vk_op_fwht_push_constants {
|
||||
uint32_t n_rows;
|
||||
uint32_t src_offset;
|
||||
uint32_t dst_offset;
|
||||
float scale;
|
||||
};
|
||||
|
||||
struct vk_op_count_experts_push_constants {
|
||||
uint32_t ne00;
|
||||
uint32_t ne01;
|
||||
uint32_t nb00;
|
||||
uint32_t nb01;
|
||||
uint32_t a_offset;
|
||||
};
|
||||
|
||||
struct vk_op_glu_push_constants {
|
||||
uint32_t N;
|
||||
uint32_t ne00;
|
||||
uint32_t ne20;
|
||||
uint32_t mode; // 0: default, 1: swapped, 2: split
|
||||
float alpha; // for swiglu_oai
|
||||
float limit;
|
||||
uint32_t nb00;
|
||||
uint32_t nb01;
|
||||
uint32_t nb02;
|
||||
uint32_t nb03;
|
||||
uint32_t nb10;
|
||||
uint32_t nb11;
|
||||
uint32_t nb12;
|
||||
uint32_t nb13;
|
||||
uint32_t nb20;
|
||||
uint32_t nb21;
|
||||
uint32_t nb22;
|
||||
uint32_t nb23;
|
||||
uint32_t ne21;
|
||||
uint32_t ne22;
|
||||
uint32_t misalign_offsets;
|
||||
uint32_t ne2_012mp; uint32_t ne2_012L;
|
||||
uint32_t ne2_01mp; uint32_t ne2_01L;
|
||||
uint32_t ne2_0mp; uint32_t ne2_0L;
|
||||
};
|
||||
|
||||
static_assert(sizeof(vk_op_glu_push_constants) <= 128, "sizeof(vk_op_glu_push_constants) must be <= 128");
|
||||
|
||||
struct vk_op_unary_push_constants {
|
||||
uint32_t ne;
|
||||
uint32_t ne00; uint32_t ne01; uint32_t ne02; uint32_t ne03; uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03;
|
||||
uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; uint32_t nb10; uint32_t nb11; uint32_t nb12; uint32_t nb13;
|
||||
uint32_t misalign_offsets;
|
||||
float param1; float param2; float param3; float param4;
|
||||
uint32_t ne0_012mp; uint32_t ne0_01mp; uint32_t ne0_0mp; uint32_t ne0_Ls;
|
||||
uint32_t ne1_012mp; uint32_t ne1_01mp; uint32_t ne1_0mp; uint32_t ne1_Ls;
|
||||
};
|
||||
|
||||
static_assert(sizeof(vk_op_unary_push_constants) <= 128, "sizeof(vk_op_unary_push_constants) must be <= 128");
|
||||
|
||||
static vk_op_unary_push_constants vk_op_unary_push_constants_init(const ggml_tensor * src0, const ggml_tensor * dst, int64_t ne = 0) {
|
||||
GGML_ASSERT(ne != 0 || (ggml_nelements(src0) == ggml_nelements(dst)));
|
||||
ne = ne != 0 ? ne : ggml_nelements(dst);
|
||||
GGML_ASSERT(ne <= (int64_t)std::numeric_limits<uint32_t>::max());
|
||||
|
||||
vk_op_unary_push_constants p{};
|
||||
p.ne = (uint32_t)ne;
|
||||
|
||||
size_t src0_tsize = ggml_type_size(src0->type);
|
||||
p.ne00 = (uint32_t)src0->ne[0];
|
||||
p.ne01 = (uint32_t)src0->ne[1];
|
||||
p.ne02 = (uint32_t)src0->ne[2];
|
||||
p.ne03 = (uint32_t)src0->ne[3];
|
||||
p.nb00 = (uint32_t)(src0->nb[0] / src0_tsize);
|
||||
p.nb01 = (uint32_t)(src0->nb[1] / src0_tsize);
|
||||
p.nb02 = (uint32_t)(src0->nb[2] / src0_tsize);
|
||||
p.nb03 = (uint32_t)(src0->nb[3] / src0_tsize);
|
||||
|
||||
size_t dst_tsize = ggml_type_size(dst->type);
|
||||
p.ne10 = (uint32_t)dst->ne[0];
|
||||
p.ne11 = (uint32_t)dst->ne[1];
|
||||
p.ne12 = (uint32_t)dst->ne[2];
|
||||
p.ne13 = (uint32_t)dst->ne[3];
|
||||
p.nb10 = (uint32_t)(dst->nb[0] / dst_tsize);
|
||||
p.nb11 = (uint32_t)(dst->nb[1] / dst_tsize);
|
||||
p.nb12 = (uint32_t)(dst->nb[2] / dst_tsize);
|
||||
p.nb13 = (uint32_t)(dst->nb[3] / dst_tsize);
|
||||
|
||||
return p; // offsets are initialized later in ggml_vk_op
|
||||
}
|
||||
|
||||
struct vk_op_pad_push_constants {
|
||||
uint32_t ne;
|
||||
uint32_t ne00; uint32_t ne01; uint32_t ne02; uint32_t ne03; uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03;
|
||||
uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; uint32_t nb10; uint32_t nb11; uint32_t nb12; uint32_t nb13;
|
||||
uint32_t misalign_offsets;
|
||||
uint32_t circular;
|
||||
|
||||
uint32_t lp0; uint32_t rp0;
|
||||
uint32_t lp1; uint32_t rp1;
|
||||
uint32_t lp2; uint32_t rp2;
|
||||
uint32_t lp3; uint32_t rp3;
|
||||
};
|
||||
|
||||
static vk_op_pad_push_constants vk_op_pad_push_constants_init(const ggml_tensor * src0, const ggml_tensor * dst) {
|
||||
int64_t ne = ggml_nelements(dst);
|
||||
GGML_ASSERT(ne <= (int64_t)std::numeric_limits<uint32_t>::max());
|
||||
|
||||
vk_op_pad_push_constants p{};
|
||||
p.ne = (uint32_t)ne;
|
||||
|
||||
size_t src0_tsize = ggml_type_size(src0->type);
|
||||
p.ne00 = (uint32_t)src0->ne[0];
|
||||
p.ne01 = (uint32_t)src0->ne[1];
|
||||
p.ne02 = (uint32_t)src0->ne[2];
|
||||
p.ne03 = (uint32_t)src0->ne[3];
|
||||
p.nb00 = (uint32_t)(src0->nb[0] / src0_tsize);
|
||||
p.nb01 = (uint32_t)(src0->nb[1] / src0_tsize);
|
||||
p.nb02 = (uint32_t)(src0->nb[2] / src0_tsize);
|
||||
p.nb03 = (uint32_t)(src0->nb[3] / src0_tsize);
|
||||
|
||||
size_t dst_tsize = ggml_type_size(dst->type);
|
||||
p.ne10 = (uint32_t)dst->ne[0];
|
||||
p.ne11 = (uint32_t)dst->ne[1];
|
||||
p.ne12 = (uint32_t)dst->ne[2];
|
||||
p.ne13 = (uint32_t)dst->ne[3];
|
||||
p.nb10 = (uint32_t)(dst->nb[0] / dst_tsize);
|
||||
p.nb11 = (uint32_t)(dst->nb[1] / dst_tsize);
|
||||
p.nb12 = (uint32_t)(dst->nb[2] / dst_tsize);
|
||||
p.nb13 = (uint32_t)(dst->nb[3] / dst_tsize);
|
||||
|
||||
p.lp0 = dst->op_params[0];
|
||||
p.rp0 = dst->op_params[1];
|
||||
p.lp1 = dst->op_params[2];
|
||||
p.rp1 = dst->op_params[3];
|
||||
p.lp2 = dst->op_params[4];
|
||||
p.rp2 = dst->op_params[5];
|
||||
p.lp3 = dst->op_params[6];
|
||||
p.rp3 = dst->op_params[7];
|
||||
p.circular = dst->op_params[8];
|
||||
|
||||
return p; // fastdiv values and offsets are initialized later in ggml_vk_op
|
||||
}
|
||||
|
||||
static void init_fastdiv_values(uint32_t d, uint32_t &mp, uint32_t &L)
|
||||
{
|
||||
// compute L = ceil(log2(d));
|
||||
L = 0;
|
||||
while (L < 32 && (uint32_t{1} << L) < d) {
|
||||
L++;
|
||||
}
|
||||
|
||||
mp = (uint32_t)((uint64_t{1} << 32) * ((uint64_t{1} << L) - d) / d + 1);
|
||||
}
|
||||
|
||||
static uint32_t pack_fastdiv_L(uint32_t L0, uint32_t L1, uint32_t L2) {
|
||||
return L0 | (L1 << 8) | (L2 << 16);
|
||||
}
|
||||
|
||||
template <typename T> void init_pushconst_fastdiv(T &p) {
|
||||
GGML_UNUSED(p);
|
||||
static_assert(!std::is_const<T>::value, "unexpected type");
|
||||
}
|
||||
|
||||
template <> inline void init_pushconst_fastdiv(vk_op_unary_push_constants &p) {
|
||||
// Compute magic values to divide by these six numbers.
|
||||
uint32_t ne0_012L;
|
||||
uint32_t ne0_01L;
|
||||
uint32_t ne0_0L;
|
||||
uint32_t ne1_012L;
|
||||
uint32_t ne1_01L;
|
||||
uint32_t ne1_0L;
|
||||
|
||||
init_fastdiv_values(p.ne02*p.ne01*p.ne00, p.ne0_012mp, ne0_012L);
|
||||
init_fastdiv_values(p.ne01*p.ne00, p.ne0_01mp, ne0_01L);
|
||||
init_fastdiv_values(p.ne00, p.ne0_0mp, ne0_0L);
|
||||
init_fastdiv_values(p.ne12*p.ne11*p.ne10, p.ne1_012mp, ne1_012L);
|
||||
init_fastdiv_values(p.ne11*p.ne10, p.ne1_01mp, ne1_01L);
|
||||
init_fastdiv_values(p.ne10, p.ne1_0mp, ne1_0L);
|
||||
|
||||
p.ne0_Ls = pack_fastdiv_L(ne0_012L, ne0_01L, ne0_0L);
|
||||
p.ne1_Ls = pack_fastdiv_L(ne1_012L, ne1_01L, ne1_0L);
|
||||
}
|
||||
|
||||
template <> inline void init_pushconst_fastdiv(vk_op_glu_push_constants &p) {
|
||||
// GLU linearizes over dst, then uses dst coordinates for src0/src1.
|
||||
init_fastdiv_values(p.ne22*p.ne21*p.ne20, p.ne2_012mp, p.ne2_012L);
|
||||
init_fastdiv_values(p.ne21*p.ne20, p.ne2_01mp, p.ne2_01L);
|
||||
init_fastdiv_values(p.ne20, p.ne2_0mp, p.ne2_0L);
|
||||
}
|
||||
|
||||
struct vk_op_binary_push_constants {
|
||||
uint32_t ne;
|
||||
uint32_t ne00; uint32_t ne01; uint32_t ne02; uint32_t ne03; uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03;
|
||||
uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; uint32_t nb10; uint32_t nb11; uint32_t nb12; uint32_t nb13;
|
||||
uint32_t ne20; uint32_t ne21; uint32_t ne22; uint32_t ne23; uint32_t nb20; uint32_t nb21; uint32_t nb22; uint32_t nb23;
|
||||
uint32_t misalign_offsets;
|
||||
float param1; float param2; int32_t param3;
|
||||
};
|
||||
|
||||
struct vk_op_multi_add_push_constants {
|
||||
// shape for dst
|
||||
uint32_t ne20; uint32_t ne21; uint32_t ne22; uint32_t ne23;
|
||||
|
||||
// strides for srcs+dst
|
||||
uint32_t nb[MAX_PARAMETER_COUNT][4];
|
||||
|
||||
uint32_t rms_partials;
|
||||
};
|
||||
|
||||
static_assert(MAX_PARAMETER_COUNT == 12);
|
||||
|
||||
static_assert(sizeof(vk_op_multi_add_push_constants) <= 256);
|
||||
|
||||
struct vk_op_topk_moe_push_constants {
|
||||
uint32_t n_rows;
|
||||
uint32_t n_experts_push;
|
||||
uint32_t n_expert_used;
|
||||
float clamp_min;
|
||||
float clamp_max;
|
||||
uint32_t gating_func;
|
||||
uint32_t has_bias;
|
||||
uint32_t with_norm;
|
||||
float output_scale;
|
||||
float output_bias;
|
||||
};
|
||||
|
||||
struct vk_op_add_id_push_constants {
|
||||
uint32_t ne0;
|
||||
uint32_t ne1;
|
||||
uint32_t s01;
|
||||
uint32_t s02;
|
||||
uint32_t s11;
|
||||
uint32_t s21;
|
||||
};
|
||||
|
||||
struct vk_op_diag_mask_push_constants {
|
||||
uint32_t ncols;
|
||||
uint32_t rows_per_channel;
|
||||
int32_t n_past;
|
||||
};
|
||||
|
||||
struct vk_op_rope_push_constants {
|
||||
uint32_t rope_mode;
|
||||
uint32_t nrows;
|
||||
uint32_t n_dims;
|
||||
float freq_scale;
|
||||
float freq_base;
|
||||
float ext_factor;
|
||||
float attn_factor;
|
||||
float corr_dims[2];
|
||||
float theta_scale;
|
||||
uint32_t has_ff;
|
||||
int32_t sections[4];
|
||||
uint32_t is_imrope;
|
||||
uint32_t is_back;
|
||||
uint32_t set_rows_stride;
|
||||
uint32_t ne00;
|
||||
uint32_t ne01;
|
||||
uint32_t ne02;
|
||||
uint32_t nb01;
|
||||
uint32_t nb02;
|
||||
uint32_t nb03;
|
||||
uint32_t nb11;
|
||||
uint32_t nb12;
|
||||
uint32_t nb13;
|
||||
uint32_t a_offset;
|
||||
uint32_t d_offset;
|
||||
};
|
||||
|
||||
static_assert(sizeof(vk_op_rope_push_constants) <= 128, "sizeof(vk_op_rope_push_constants) must be <= 128");
|
||||
|
||||
struct vk_op_rms_norm_mul_rope_push_constants {
|
||||
vk_op_binary_push_constants bin;
|
||||
vk_op_rope_push_constants rope;
|
||||
};
|
||||
|
||||
struct vk_op_soft_max_push_constants {
|
||||
uint32_t KX;
|
||||
uint32_t KY;
|
||||
uint32_t ne00;
|
||||
uint32_t ne01;
|
||||
uint32_t ne02;
|
||||
uint32_t ne12;
|
||||
uint32_t ne13;
|
||||
uint32_t nb11;
|
||||
uint32_t nb12;
|
||||
uint32_t nb13;
|
||||
float scale;
|
||||
float max_bias;
|
||||
float m0;
|
||||
float m1;
|
||||
uint32_t n_head_log2;
|
||||
uint32_t nrows_x;
|
||||
uint32_t has_sinks;
|
||||
};
|
||||
|
||||
struct vk_op_argsort_push_constants {
|
||||
uint32_t ncols;
|
||||
uint32_t ncols_padded;
|
||||
uint32_t ncols_padded_log2;
|
||||
uint32_t nrows;
|
||||
uint32_t order;
|
||||
uint32_t outer_start;
|
||||
uint32_t outer_end;
|
||||
uint32_t inner_start;
|
||||
uint32_t inner_end;
|
||||
};
|
||||
|
||||
struct vk_op_topk_push_constants {
|
||||
uint32_t orig_ncols;
|
||||
uint32_t ncols_input;
|
||||
uint32_t ncols_output;
|
||||
uint32_t k;
|
||||
uint32_t nrows;
|
||||
uint32_t first_pass;
|
||||
uint32_t last_pass;
|
||||
};
|
||||
|
||||
struct vk_op_im2col_push_constants {
|
||||
uint64_t dst_addr;
|
||||
uint32_t batch_offset; uint32_t offset_delta;
|
||||
uint32_t IC;
|
||||
uint32_t IW; uint32_t IH;
|
||||
uint32_t OW; uint32_t OH;
|
||||
uint32_t KW; uint32_t KH;
|
||||
uint32_t OH_batch;
|
||||
uint32_t CHW;
|
||||
int32_t s0; int32_t s1;
|
||||
int32_t p0; int32_t p1;
|
||||
int32_t d0; int32_t d1;
|
||||
uint32_t batch_IC;
|
||||
};
|
||||
|
||||
struct vk_op_im2col_3d_push_constants {
|
||||
uint64_t dst_addr;
|
||||
uint32_t nb10;
|
||||
uint32_t nb11;
|
||||
uint32_t nb12;
|
||||
uint32_t nb13;
|
||||
uint32_t s0;
|
||||
uint32_t s1;
|
||||
uint32_t s2;
|
||||
uint32_t p0;
|
||||
uint32_t p1;
|
||||
uint32_t p2;
|
||||
uint32_t d0;
|
||||
uint32_t d1;
|
||||
uint32_t d2;
|
||||
uint32_t IW;
|
||||
uint32_t IH;
|
||||
uint32_t ID;
|
||||
uint32_t IC;
|
||||
uint32_t KW;
|
||||
uint32_t OH;
|
||||
uint32_t KD_KH_KW;
|
||||
uint32_t KH_KW;
|
||||
uint32_t IC_KD_KH_KW;
|
||||
uint32_t N_OD_OH;
|
||||
uint32_t OD_OH;
|
||||
uint32_t OD_OH_OW_IC_KD_KH_KW;
|
||||
uint32_t OH_OW_IC_KD_KH_KW;
|
||||
uint32_t OW_IC_KD_KH_KW;
|
||||
uint32_t misalign_offsets;
|
||||
};
|
||||
|
||||
struct vk_op_timestep_embedding_push_constants {
|
||||
uint32_t nb1;
|
||||
uint32_t dim;
|
||||
uint32_t max_period;
|
||||
};
|
||||
|
||||
struct vk_op_col2im_1d_push_constants {
|
||||
uint32_t T_out;
|
||||
uint32_t OC;
|
||||
uint32_t K_OC;
|
||||
uint32_t T_in;
|
||||
uint32_t K;
|
||||
int32_t stride;
|
||||
int32_t p0;
|
||||
};
|
||||
|
||||
struct vk_op_conv_transpose_1d_push_constants {
|
||||
uint32_t Cout;
|
||||
uint32_t Cin;
|
||||
uint32_t K;
|
||||
uint32_t L;
|
||||
uint32_t KL;
|
||||
|
||||
uint32_t nb01;
|
||||
uint32_t nb02;
|
||||
uint32_t nb11;
|
||||
uint32_t nb1;
|
||||
|
||||
int32_t s0;
|
||||
};
|
||||
|
||||
struct vk_op_snake_push_constants {
|
||||
uint32_t ne0;
|
||||
uint32_t ne1;
|
||||
};
|
||||
|
||||
struct vk_op_pool2d_push_constants {
|
||||
uint32_t IW; uint32_t IH;
|
||||
uint32_t OW; uint32_t OH;
|
||||
uint32_t OC;
|
||||
uint32_t pelements;
|
||||
uint32_t op;
|
||||
int32_t k0; int32_t k1;
|
||||
int32_t s0; int32_t s1;
|
||||
int32_t p0; int32_t p1;
|
||||
};
|
||||
|
||||
struct vk_op_rwkv_wkv6_push_constants {
|
||||
uint32_t B;
|
||||
uint32_t T;
|
||||
uint32_t C;
|
||||
uint32_t H;
|
||||
};
|
||||
|
||||
struct vk_op_rwkv_wkv7_push_constants {
|
||||
uint32_t B;
|
||||
uint32_t T;
|
||||
uint32_t C;
|
||||
uint32_t H;
|
||||
};
|
||||
|
||||
struct vk_op_gated_delta_net_push_constants {
|
||||
uint32_t H;
|
||||
uint32_t n_tokens;
|
||||
uint32_t n_seqs;
|
||||
uint32_t s_off;
|
||||
uint32_t sq1, sq2, sq3;
|
||||
uint32_t sv1, sv2, sv3;
|
||||
uint32_t sb1, sb2, sb3;
|
||||
uint32_t neq1, rq3;
|
||||
float scale;
|
||||
uint32_t K;
|
||||
};
|
||||
|
||||
struct vk_op_ssm_scan_push_constants {
|
||||
uint32_t nb02, nb03, nb12, nb13;
|
||||
uint32_t nb21, nb22, nb31;
|
||||
uint32_t nb42, nb43, nb52, nb53;
|
||||
uint32_t s_off;
|
||||
uint32_t n_head, d_head, n_group, n_tok;
|
||||
};
|
||||
|
||||
struct vk_op_ssm_conv_push_constants {
|
||||
uint32_t nb01, nb02;
|
||||
uint32_t nb11;
|
||||
uint32_t dst_nb0, dst_nb1, dst_nb2;
|
||||
uint32_t nc, ncs, nr, n_t, n_s;
|
||||
};
|
||||
|
||||
struct vk_op_conv2d_push_constants {
|
||||
uint32_t Cout;
|
||||
uint32_t Cin;
|
||||
uint32_t N;
|
||||
|
||||
uint32_t W;
|
||||
uint32_t H;
|
||||
uint32_t OW;
|
||||
uint32_t OH;
|
||||
|
||||
uint32_t nb01;
|
||||
uint32_t nb02;
|
||||
uint32_t nb03;
|
||||
|
||||
uint32_t nb11;
|
||||
uint32_t nb12;
|
||||
uint32_t nb13;
|
||||
|
||||
uint32_t nb1;
|
||||
uint32_t nb2;
|
||||
uint32_t nb3;
|
||||
|
||||
// init_fastdiv_values constants for dividing by OW, OW*OH
|
||||
uint32_t OWmp; uint32_t OWL;
|
||||
uint32_t OWOHmp; uint32_t OWOHL;
|
||||
};
|
||||
|
||||
template <> inline void init_pushconst_fastdiv(vk_op_conv2d_push_constants &p) {
|
||||
// Compute magic values to divide by OW, OW*OH
|
||||
init_fastdiv_values(p.OW, p.OWmp, p.OWL);
|
||||
init_fastdiv_values(p.OW*p.OH, p.OWOHmp, p.OWOHL);
|
||||
}
|
||||
|
||||
struct vk_op_conv2d_dw_push_constants {
|
||||
uint32_t ne;
|
||||
uint32_t batches;
|
||||
uint32_t channels;
|
||||
uint32_t dst_w;
|
||||
uint32_t dst_h;
|
||||
uint32_t src_w;
|
||||
uint32_t src_h;
|
||||
uint32_t knl_w;
|
||||
uint32_t knl_h;
|
||||
int32_t stride_x;
|
||||
int32_t stride_y;
|
||||
int32_t pad_x;
|
||||
int32_t pad_y;
|
||||
int32_t dilation_x;
|
||||
int32_t dilation_y;
|
||||
};
|
||||
|
||||
struct vk_op_upscale_push_constants {
|
||||
uint32_t ne; uint32_t a_offset; uint32_t d_offset;
|
||||
uint32_t ne00; uint32_t ne01;
|
||||
uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03;
|
||||
uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13;
|
||||
float sf0; float sf1; float sf2; float sf3;
|
||||
float pixel_offset;
|
||||
};
|
||||
|
||||
struct vk_op_sum_rows_push_constants
|
||||
{
|
||||
uint32_t n_cols;
|
||||
uint32_t ne01, ne02;
|
||||
uint32_t nb01, nb02, nb03;
|
||||
uint32_t nb11, nb12, nb13;
|
||||
float weight;
|
||||
uint32_t misalign_offsets;
|
||||
uint32_t ne0_12mp, ne0_12L;
|
||||
uint32_t ne0_1mp, ne0_1L;
|
||||
};
|
||||
|
||||
static vk_op_sum_rows_push_constants vk_op_sum_rows_push_constants_init(const ggml_tensor * src, const ggml_tensor * dst, int64_t n_cols) {
|
||||
uint32_t type_size = (uint32_t)ggml_type_size(src->type);
|
||||
vk_op_sum_rows_push_constants p = {};
|
||||
p.n_cols = (uint32_t)n_cols;
|
||||
p.ne01 = (uint32_t)src->ne[1];
|
||||
p.ne02 = (uint32_t)src->ne[2];
|
||||
p.nb01 = (uint32_t)src->nb[1] / type_size;
|
||||
p.nb02 = (uint32_t)src->nb[2] / type_size;
|
||||
p.nb03 = (uint32_t)src->nb[3] / type_size;
|
||||
p.nb11 = (uint32_t)dst->nb[1] / type_size;
|
||||
p.nb12 = (uint32_t)dst->nb[2] / type_size;
|
||||
p.nb13 = (uint32_t)dst->nb[3] / type_size;
|
||||
p.weight = 1.0f;
|
||||
return p;
|
||||
}
|
||||
|
||||
template <> inline void init_pushconst_fastdiv(vk_op_sum_rows_push_constants &p) {
|
||||
init_fastdiv_values(p.ne01*p.ne02, p.ne0_12mp, p.ne0_12L);
|
||||
init_fastdiv_values(p.ne01, p.ne0_1mp, p.ne0_1L);
|
||||
}
|
||||
|
||||
struct vk_quantize_q8_1_push_constants {
|
||||
uint32_t ne;
|
||||
uint32_t num_blocks;
|
||||
};
|
||||
|
||||
struct vk_op_flash_attn_split_k_reduce_push_constants {
|
||||
uint32_t D;
|
||||
uint32_t ne1;
|
||||
uint32_t ne2;
|
||||
uint32_t ne3;
|
||||
uint32_t k_num;
|
||||
uint32_t sinks;
|
||||
};
|
||||
|
||||
struct vk_op_flash_attn_mask_opt_push_constants {
|
||||
uint32_t nem0;
|
||||
uint32_t nem1;
|
||||
uint32_t nem2;
|
||||
uint32_t nbm1;
|
||||
uint32_t nbm2;
|
||||
uint32_t nbm3;
|
||||
uint32_t nbd1;
|
||||
uint32_t nbd2;
|
||||
uint32_t nbd3;
|
||||
};
|
||||
|
||||
template <typename T> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, T &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) {
|
||||
GGML_UNUSED(p);
|
||||
GGML_UNUSED(src0);
|
||||
GGML_UNUSED(src1);
|
||||
GGML_UNUSED(src2);
|
||||
GGML_UNUSED(src3);
|
||||
GGML_UNUSED(dst);
|
||||
static_assert(!std::is_const<T>::value, "unexpected type");
|
||||
GGML_ASSERT(!src0 || get_misalign_bytes(ctx, src0) == 0);
|
||||
GGML_ASSERT(!src1 || get_misalign_bytes(ctx, src1) == 0);
|
||||
GGML_ASSERT(!src2 || get_misalign_bytes(ctx, src2) == 0);
|
||||
GGML_ASSERT(!src3 || get_misalign_bytes(ctx, src3) == 0);
|
||||
GGML_ASSERT(!dst || get_misalign_bytes(ctx, dst) == 0);
|
||||
}
|
||||
|
||||
template <> inline void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_mat_vec_p021_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) {
|
||||
const uint32_t b_offset = get_misalign_bytes(ctx, src1) / ggml_type_size(src1->type);
|
||||
const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type);
|
||||
|
||||
p.b_offset = b_offset;
|
||||
p.d_offset = d_offset;
|
||||
|
||||
GGML_UNUSED(src0);
|
||||
GGML_UNUSED(src2);
|
||||
GGML_UNUSED(src3);
|
||||
}
|
||||
|
||||
template <> inline void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_mat_vec_nc_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) {
|
||||
const uint32_t b_offset = get_misalign_bytes(ctx, src1) / ggml_type_size(src1->type);
|
||||
const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type);
|
||||
|
||||
p.b_offset = b_offset;
|
||||
p.d_offset = d_offset;
|
||||
|
||||
GGML_UNUSED(src0);
|
||||
GGML_UNUSED(src2);
|
||||
GGML_UNUSED(src3);
|
||||
}
|
||||
|
||||
template <> inline void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_fwht_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) {
|
||||
p.src_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type);
|
||||
p.dst_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type);
|
||||
|
||||
GGML_UNUSED(src1);
|
||||
GGML_UNUSED(src2);
|
||||
GGML_UNUSED(src3);
|
||||
}
|
||||
|
||||
template <typename T> size_t push_constant_size(const T &t) {
|
||||
static_assert(std::is_class<T>::value, "T must be a struct/class");
|
||||
GGML_UNUSED(t);
|
||||
return sizeof(T);
|
||||
}
|
||||
|
||||
template <typename T> size_t push_constant_size(const std::vector<T> &t) {
|
||||
GGML_UNUSED(t);
|
||||
return sizeof(T) * t.size();
|
||||
}
|
||||
|
||||
template <typename T, uint32_t N> size_t push_constant_size(const std::array<T, N> &t) {
|
||||
GGML_UNUSED(t);
|
||||
return sizeof(T) * N;
|
||||
}
|
||||
|
||||
template <typename T> const T *push_constant_data(const T &t) {
|
||||
static_assert(std::is_class<T>::value, "T must be a struct/class");
|
||||
return &t;
|
||||
}
|
||||
|
||||
template <typename T> const T *push_constant_data(const std::vector<T> &t) {
|
||||
return t.data();
|
||||
}
|
||||
|
||||
template <typename T, uint32_t N> const T *push_constant_data(const std::array<T, N> &t) {
|
||||
return t.data();
|
||||
}
|
||||
|
||||
template <> inline void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_unary_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) {
|
||||
const uint32_t a_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type);
|
||||
const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type);
|
||||
|
||||
p.misalign_offsets = (a_offset << 16) | d_offset;
|
||||
|
||||
GGML_UNUSED(src1);
|
||||
GGML_UNUSED(src2);
|
||||
GGML_UNUSED(src3);
|
||||
}
|
||||
|
||||
template <> inline void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_glu_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) {
|
||||
const uint32_t a_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type);
|
||||
const uint32_t b_offset = src1 ? get_misalign_bytes(ctx, src1) / ggml_type_size(src1->type) : a_offset;
|
||||
const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type);
|
||||
|
||||
GGML_ASSERT(a_offset < (1u << 8));
|
||||
GGML_ASSERT(b_offset < (1u << 8));
|
||||
GGML_ASSERT(d_offset < (1u << 8));
|
||||
|
||||
p.misalign_offsets = (a_offset << 16) | (b_offset << 8) | d_offset;
|
||||
|
||||
GGML_UNUSED(src2);
|
||||
GGML_UNUSED(src3);
|
||||
}
|
||||
|
||||
template <> inline void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_sum_rows_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) {
|
||||
const uint32_t a_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type);
|
||||
const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type);
|
||||
|
||||
p.misalign_offsets = (a_offset << 16) | d_offset;
|
||||
|
||||
GGML_UNUSED(src1);
|
||||
GGML_UNUSED(src2);
|
||||
GGML_UNUSED(src3);
|
||||
}
|
||||
|
||||
template <> inline void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_pad_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) {
|
||||
const uint32_t a_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type);
|
||||
const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type);
|
||||
|
||||
p.misalign_offsets = (a_offset << 16) | d_offset;
|
||||
|
||||
GGML_UNUSED(src1);
|
||||
GGML_UNUSED(src2);
|
||||
GGML_UNUSED(src3);
|
||||
}
|
||||
|
||||
template <> inline void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_im2col_3d_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) {
|
||||
const uint32_t a_offset = get_misalign_bytes(ctx, src1) / ggml_type_size(src1->type);
|
||||
const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type);
|
||||
|
||||
p.misalign_offsets = (a_offset << 16) | d_offset;
|
||||
|
||||
GGML_UNUSED(src0);
|
||||
GGML_UNUSED(src2);
|
||||
GGML_UNUSED(src3);
|
||||
}
|
||||
|
||||
template <> inline void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_binary_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) {
|
||||
const uint32_t a_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type);
|
||||
const uint32_t b_offset = get_misalign_bytes(ctx, src1) / ggml_type_size(src1->type);
|
||||
const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type);
|
||||
|
||||
GGML_ASSERT(dst->op != GGML_OP_GET_ROWS || (a_offset == 0 && b_offset == 0 && d_offset == 0));
|
||||
|
||||
p.misalign_offsets = (a_offset << 16) | (b_offset << 8) | d_offset;
|
||||
|
||||
GGML_UNUSED(src2);
|
||||
GGML_UNUSED(src3);
|
||||
}
|
||||
|
||||
template <> inline void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_upscale_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) {
|
||||
const uint32_t a_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type);
|
||||
const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type);
|
||||
|
||||
p.a_offset = a_offset;
|
||||
p.d_offset = d_offset;
|
||||
|
||||
GGML_UNUSED(src1);
|
||||
GGML_UNUSED(src2);
|
||||
GGML_UNUSED(src3);
|
||||
}
|
||||
|
||||
template <> inline void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_rope_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) {
|
||||
p.a_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type);
|
||||
p.d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type);
|
||||
|
||||
GGML_UNUSED(src1);
|
||||
GGML_UNUSED(src2);
|
||||
GGML_UNUSED(src3);
|
||||
}
|
||||
|
||||
2704
ggml/src/ggml-vulkan/ggml-vulkan-shaders.cpp
Normal file
2704
ggml/src/ggml-vulkan/ggml-vulkan-shaders.cpp
Normal file
File diff suppressed because it is too large
Load Diff
1341
ggml/src/ggml-vulkan/ggml-vulkan-types.h
Normal file
1341
ggml/src/ggml-vulkan/ggml-vulkan-types.h
Normal file
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user